From dfe7bd168d9bcf8c53f993f459ab473d893457b0 Mon Sep 17 00:00:00 2001
From: Joseph Batchik <joseph.batchik@cloudera.com>
Date: Mon, 3 Aug 2015 11:17:38 -0700
Subject: [PATCH 001/633] [SPARK-9511] [SQL] Fixed Table Name Parsing

The issue was that the tokenizer was parsing "1one" into the numeric 1 using the code on line 110. I added another case to accept strings that start with a number and then have a letter somewhere else in it as well.

Author: Joseph Batchik <joseph.batchik@cloudera.com>

Closes #7844 from JDrit/parse_error and squashes the following commits:

b8ca12f [Joseph Batchik] fixed parsing issue by adding another case
---
 .../spark/sql/catalyst/AbstractSparkSQLParser.scala    |  2 ++
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala     | 10 ++++++++++
 2 files changed, 12 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala
index d494ae7b71d16..5898a5f93f381 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/AbstractSparkSQLParser.scala
@@ -104,6 +104,8 @@ class SqlLexical extends StdLexical {
   override lazy val token: Parser[Token] =
     ( identChar ~ (identChar | digit).* ^^
       { case first ~ rest => processIdent((first :: rest).mkString) }
+    | digit.* ~ identChar ~ (identChar | digit).* ^^
+      { case first ~ middle ~ rest => processIdent((first ++ (middle :: rest)).mkString) }
     | rep1(digit) ~ ('.' ~> digit.*).? ^^ {
         case i ~ None => NumericLit(i.mkString)
         case i ~ Some(d) => FloatLit(i.mkString + "." + d.mkString)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index bbadc202a4f06..f1abae0720058 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1604,4 +1604,14 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     checkAnswer(df.select(-df("i")),
       Row(new CalendarInterval(-(12 * 3 - 3), -(7L * MICROS_PER_WEEK + 123))))
   }
+
+  test("SPARK-9511: error with table starting with number") {
+    val df = sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
+      .toDF("num", "str")
+    df.registerTempTable("1one")
+
+    checkAnswer(sqlContext.sql("select count(num) from 1one"), Row(10))
+
+    sqlContext.dropTempTable("1one")
+  }
 }

From 7a9d09f0bb472a1671d3457e1f7108f4c2eb4121 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 3 Aug 2015 11:22:02 -0700
Subject: [PATCH 002/633] [SQL][minor] Simplify
 UnsafeRow.calculateBitSetWidthInBytes.

Author: Reynold Xin <rxin@databricks.com>

Closes #7897 from rxin/calculateBitSetWidthInBytes and squashes the following commits:

2e73b3a [Reynold Xin] [SQL][minor] Simplify UnsafeRow.calculateBitSetWidthInBytes.
---
 .../spark/sql/catalyst/expressions/UnsafeRow.java      |  2 +-
 .../scala/org/apache/spark/sql/UnsafeRowSuite.scala    | 10 ++++++++++
 2 files changed, 11 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index f4230cfaba375..e6750fce4fa80 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -59,7 +59,7 @@ public final class UnsafeRow extends MutableRow {
   //////////////////////////////////////////////////////////////////////////////
 
   public static int calculateBitSetWidthInBytes(int numFields) {
-    return ((numFields / 64) + (numFields % 64 == 0 ? 0 : 1)) * 8;
+    return ((numFields + 63)/ 64) * 8;
   }
 
   /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
index c5faaa663e749..89bad1bfdab0a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
@@ -28,6 +28,16 @@ import org.apache.spark.unsafe.memory.MemoryAllocator
 import org.apache.spark.unsafe.types.UTF8String
 
 class UnsafeRowSuite extends SparkFunSuite {
+
+  test("bitset width calculation") {
+    assert(UnsafeRow.calculateBitSetWidthInBytes(0) === 0)
+    assert(UnsafeRow.calculateBitSetWidthInBytes(1) === 8)
+    assert(UnsafeRow.calculateBitSetWidthInBytes(32) === 8)
+    assert(UnsafeRow.calculateBitSetWidthInBytes(64) === 8)
+    assert(UnsafeRow.calculateBitSetWidthInBytes(65) === 16)
+    assert(UnsafeRow.calculateBitSetWidthInBytes(128) === 16)
+  }
+
   test("writeToStream") {
     val row = InternalRow.apply(UTF8String.fromString("hello"), UTF8String.fromString("world"), 123)
     val arrayBackedUnsafeRow: UnsafeRow =

From 703e44bff19f4c394f6f9bff1ce9152cdc68c51e Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 3 Aug 2015 12:06:58 -0700
Subject: [PATCH 003/633] [SPARK-9554] [SQL] Enables in-memory partition
 pruning by default

Author: Cheng Lian <lian@databricks.com>

Closes #7895 from liancheng/spark-9554/enable-in-memory-partition-pruning and squashes the following commits:

67c403e [Cheng Lian] Enables in-memory partition pruning by default
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 387960c4b482b..41ba1c7fe0574 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -200,7 +200,7 @@ private[spark] object SQLConf {
 
   val IN_MEMORY_PARTITION_PRUNING =
     booleanConf("spark.sql.inMemoryColumnarStorage.partitionPruning",
-      defaultValue = Some(false),
+      defaultValue = Some(true),
       doc = "When true, enable partition pruning for in-memory columnar tables.",
       isPublic = false)
 

From ff9169a002f1b75231fd25b7d04157a912503038 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Mon, 3 Aug 2015 12:17:46 -0700
Subject: [PATCH 004/633] [SPARK-5133] [ML] Added featureImportance to
 RandomForestClassifier and Regressor

Added featureImportance to RandomForestClassifier and Regressor.

This follows the scikit-learn implementation here: [https://github.com/scikit-learn/scikit-learn/blob/a95203b249c1cf392f86d001ad999e29b2392739/sklearn/tree/_tree.pyx#L3341]

CC: yanboliang  Would you mind taking a look?  Thanks!

Author: Joseph K. Bradley <joseph@databricks.com>
Author: Feynman Liang <fliang@databricks.com>

Closes #7838 from jkbradley/dt-feature-importance and squashes the following commits:

72a167a [Joseph K. Bradley] fixed unit test
86cea5f [Joseph K. Bradley] Modified RF featuresImportances to return Vector instead of Map
5aa74f0 [Joseph K. Bradley] finally fixed unit test for real
33df5db [Joseph K. Bradley] fix unit test
42a2d3b [Joseph K. Bradley] fix unit test
fe94e72 [Joseph K. Bradley] modified feature importance unit tests
cc693ee [Feynman Liang] Add classifier tests
79a6f87 [Feynman Liang] Compare dense vectors in test
21d01fc [Feynman Liang] Added failing SKLearn test
ac0b254 [Joseph K. Bradley] Added featureImportance to RandomForestClassifier/Regressor.  Need to add unit tests
---
 .../RandomForestClassifier.scala              |  30 ++++-
 .../ml/regression/RandomForestRegressor.scala |  33 +++++-
 .../scala/org/apache/spark/ml/tree/Node.scala |  19 +++-
 .../spark/ml/tree/impl/RandomForest.scala     |  92 +++++++++++++++
 .../org/apache/spark/ml/tree/treeModels.scala |   6 +
 .../JavaRandomForestClassifierSuite.java      |   2 +
 .../JavaRandomForestRegressorSuite.java       |   2 +
 .../RandomForestClassifierSuite.scala         |  31 ++++-
 .../org/apache/spark/ml/impl/TreeTests.scala  |  18 +++
 .../RandomForestRegressorSuite.scala          |  27 ++++-
 .../ml/tree/impl/RandomForestSuite.scala      | 107 ++++++++++++++++++
 11 files changed, 351 insertions(+), 16 deletions(-)
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 56e80cc8fe6e1..b59826a59499a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -95,7 +95,8 @@ final class RandomForestClassifier(override val uid: String)
     val trees =
       RandomForest.run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed)
         .map(_.asInstanceOf[DecisionTreeClassificationModel])
-    new RandomForestClassificationModel(trees, numClasses)
+    val numFeatures = oldDataset.first().features.size
+    new RandomForestClassificationModel(trees, numFeatures, numClasses)
   }
 
   override def copy(extra: ParamMap): RandomForestClassifier = defaultCopy(extra)
@@ -118,11 +119,13 @@ object RandomForestClassifier {
  * features.
  * @param _trees  Decision trees in the ensemble.
  *               Warning: These have null parents.
+ * @param numFeatures  Number of features used by this model
  */
 @Experimental
 final class RandomForestClassificationModel private[ml] (
     override val uid: String,
     private val _trees: Array[DecisionTreeClassificationModel],
+    val numFeatures: Int,
     override val numClasses: Int)
   extends ProbabilisticClassificationModel[Vector, RandomForestClassificationModel]
   with TreeEnsembleModel with Serializable {
@@ -133,8 +136,8 @@ final class RandomForestClassificationModel private[ml] (
    * Construct a random forest classification model, with all trees weighted equally.
    * @param trees  Component trees
    */
-  def this(trees: Array[DecisionTreeClassificationModel], numClasses: Int) =
-    this(Identifiable.randomUID("rfc"), trees, numClasses)
+  def this(trees: Array[DecisionTreeClassificationModel], numFeatures: Int, numClasses: Int) =
+    this(Identifiable.randomUID("rfc"), trees, numFeatures, numClasses)
 
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
 
@@ -182,13 +185,30 @@ final class RandomForestClassificationModel private[ml] (
   }
 
   override def copy(extra: ParamMap): RandomForestClassificationModel = {
-    copyValues(new RandomForestClassificationModel(uid, _trees, numClasses), extra)
+    copyValues(new RandomForestClassificationModel(uid, _trees, numFeatures, numClasses), extra)
   }
 
   override def toString: String = {
     s"RandomForestClassificationModel with $numTrees trees"
   }
 
+  /**
+   * Estimate of the importance of each feature.
+   *
+   * This generalizes the idea of "Gini" importance to other losses,
+   * following the explanation of Gini importance from "Random Forests" documentation
+   * by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.
+   *
+   * This feature importance is calculated as follows:
+   *  - Average over trees:
+   *     - importance(feature j) = sum (over nodes which split on feature j) of the gain,
+   *       where gain is scaled by the number of instances passing through node
+   *     - Normalize importances for tree based on total number of training instances used
+   *       to build tree.
+   *  - Normalize feature importance vector to sum to 1.
+   */
+  lazy val featureImportances: Vector = RandomForest.featureImportances(trees, numFeatures)
+
   /** (private[ml]) Convert to a model in the old API */
   private[ml] def toOld: OldRandomForestModel = {
     new OldRandomForestModel(OldAlgo.Classification, _trees.map(_.toOld))
@@ -210,6 +230,6 @@ private[ml] object RandomForestClassificationModel {
       DecisionTreeClassificationModel.fromOld(tree, null, categoricalFeatures)
     }
     val uid = if (parent != null) parent.uid else Identifiable.randomUID("rfc")
-    new RandomForestClassificationModel(uid, newTrees, numClasses)
+    new RandomForestClassificationModel(uid, newTrees, -1, numClasses)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 17fb1ad5e15d4..1ee43c8725732 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -30,7 +30,7 @@ import org.apache.spark.mllib.tree.model.{RandomForestModel => OldRandomForestMo
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.DoubleType
+
 
 /**
  * :: Experimental ::
@@ -87,7 +87,8 @@ final class RandomForestRegressor(override val uid: String)
     val trees =
       RandomForest.run(oldDataset, strategy, getNumTrees, getFeatureSubsetStrategy, getSeed)
         .map(_.asInstanceOf[DecisionTreeRegressionModel])
-    new RandomForestRegressionModel(trees)
+    val numFeatures = oldDataset.first().features.size
+    new RandomForestRegressionModel(trees, numFeatures)
   }
 
   override def copy(extra: ParamMap): RandomForestRegressor = defaultCopy(extra)
@@ -108,11 +109,13 @@ object RandomForestRegressor {
  * [[http://en.wikipedia.org/wiki/Random_forest  Random Forest]] model for regression.
  * It supports both continuous and categorical features.
  * @param _trees  Decision trees in the ensemble.
+ * @param numFeatures  Number of features used by this model
  */
 @Experimental
 final class RandomForestRegressionModel private[ml] (
     override val uid: String,
-    private val _trees: Array[DecisionTreeRegressionModel])
+    private val _trees: Array[DecisionTreeRegressionModel],
+    val numFeatures: Int)
   extends PredictionModel[Vector, RandomForestRegressionModel]
   with TreeEnsembleModel with Serializable {
 
@@ -122,7 +125,8 @@ final class RandomForestRegressionModel private[ml] (
    * Construct a random forest regression model, with all trees weighted equally.
    * @param trees  Component trees
    */
-  def this(trees: Array[DecisionTreeRegressionModel]) = this(Identifiable.randomUID("rfr"), trees)
+  def this(trees: Array[DecisionTreeRegressionModel], numFeatures: Int) =
+    this(Identifiable.randomUID("rfr"), trees, numFeatures)
 
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
 
@@ -147,13 +151,30 @@ final class RandomForestRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): RandomForestRegressionModel = {
-    copyValues(new RandomForestRegressionModel(uid, _trees), extra)
+    copyValues(new RandomForestRegressionModel(uid, _trees, numFeatures), extra)
   }
 
   override def toString: String = {
     s"RandomForestRegressionModel with $numTrees trees"
   }
 
+  /**
+   * Estimate of the importance of each feature.
+   *
+   * This generalizes the idea of "Gini" importance to other losses,
+   * following the explanation of Gini importance from "Random Forests" documentation
+   * by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.
+   *
+   * This feature importance is calculated as follows:
+   *  - Average over trees:
+   *     - importance(feature j) = sum (over nodes which split on feature j) of the gain,
+   *       where gain is scaled by the number of instances passing through node
+   *     - Normalize importances for tree based on total number of training instances used
+   *       to build tree.
+   *  - Normalize feature importance vector to sum to 1.
+   */
+  lazy val featureImportances: Vector = RandomForest.featureImportances(trees, numFeatures)
+
   /** (private[ml]) Convert to a model in the old API */
   private[ml] def toOld: OldRandomForestModel = {
     new OldRandomForestModel(OldAlgo.Regression, _trees.map(_.toOld))
@@ -173,6 +194,6 @@ private[ml] object RandomForestRegressionModel {
       // parent for each tree is null since there is no good way to set this.
       DecisionTreeRegressionModel.fromOld(tree, null, categoricalFeatures)
     }
-    new RandomForestRegressionModel(parent.uid, newTrees)
+    new RandomForestRegressionModel(parent.uid, newTrees, -1)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
index 8879352a600a9..cd24931293903 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
@@ -44,7 +44,7 @@ sealed abstract class Node extends Serializable {
    * and probabilities.
    * For classification, the array of class counts must be normalized to a probability distribution.
    */
-  private[tree] def impurityStats: ImpurityCalculator
+  private[ml] def impurityStats: ImpurityCalculator
 
   /** Recursive prediction helper method */
   private[ml] def predictImpl(features: Vector): LeafNode
@@ -72,6 +72,12 @@ sealed abstract class Node extends Serializable {
    * @param id  Node ID using old format IDs
    */
   private[ml] def toOld(id: Int): OldNode
+
+  /**
+   * Trace down the tree, and return the largest feature index used in any split.
+   * @return  Max feature index used in a split, or -1 if there are no splits (single leaf node).
+   */
+  private[ml] def maxSplitFeatureIndex(): Int
 }
 
 private[ml] object Node {
@@ -109,7 +115,7 @@ private[ml] object Node {
 final class LeafNode private[ml] (
     override val prediction: Double,
     override val impurity: Double,
-    override val impurityStats: ImpurityCalculator) extends Node {
+    override private[ml] val impurityStats: ImpurityCalculator) extends Node {
 
   override def toString: String =
     s"LeafNode(prediction = $prediction, impurity = $impurity)"
@@ -129,6 +135,8 @@ final class LeafNode private[ml] (
     new OldNode(id, new OldPredict(prediction, prob = impurityStats.prob(prediction)),
       impurity, isLeaf = true, None, None, None, None)
   }
+
+  override private[ml] def maxSplitFeatureIndex(): Int = -1
 }
 
 /**
@@ -150,7 +158,7 @@ final class InternalNode private[ml] (
     val leftChild: Node,
     val rightChild: Node,
     val split: Split,
-    override val impurityStats: ImpurityCalculator) extends Node {
+    override private[ml] val impurityStats: ImpurityCalculator) extends Node {
 
   override def toString: String = {
     s"InternalNode(prediction = $prediction, impurity = $impurity, split = $split)"
@@ -190,6 +198,11 @@ final class InternalNode private[ml] (
         new OldPredict(leftChild.prediction, prob = 0.0),
         new OldPredict(rightChild.prediction, prob = 0.0))))
   }
+
+  override private[ml] def maxSplitFeatureIndex(): Int = {
+    math.max(split.featureIndex,
+      math.max(leftChild.maxSplitFeatureIndex(), rightChild.maxSplitFeatureIndex()))
+  }
 }
 
 private object InternalNode {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
index a8b90d9d266a1..4ac51a475474a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/impl/RandomForest.scala
@@ -26,6 +26,7 @@ import org.apache.spark.Logging
 import org.apache.spark.ml.classification.DecisionTreeClassificationModel
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
 import org.apache.spark.ml.tree._
+import org.apache.spark.mllib.linalg.{Vectors, Vector}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.impl.{BaggedPoint, DTStatsAggregator, DecisionTreeMetadata,
@@ -34,6 +35,7 @@ import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
 import org.apache.spark.mllib.tree.model.ImpurityStats
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
+import org.apache.spark.util.collection.OpenHashMap
 import org.apache.spark.util.random.{SamplingUtils, XORShiftRandom}
 
 
@@ -1113,4 +1115,94 @@ private[ml] object RandomForest extends Logging {
     }
   }
 
+  /**
+   * Given a Random Forest model, compute the importance of each feature.
+   * This generalizes the idea of "Gini" importance to other losses,
+   * following the explanation of Gini importance from "Random Forests" documentation
+   * by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.
+   *
+   * This feature importance is calculated as follows:
+   *  - Average over trees:
+   *     - importance(feature j) = sum (over nodes which split on feature j) of the gain,
+   *       where gain is scaled by the number of instances passing through node
+   *     - Normalize importances for tree based on total number of training instances used
+   *       to build tree.
+   *  - Normalize feature importance vector to sum to 1.
+   *
+   * Note: This should not be used with Gradient-Boosted Trees.  It only makes sense for
+   *       independently trained trees.
+   * @param trees  Unweighted forest of trees
+   * @param numFeatures  Number of features in model (even if not all are explicitly used by
+   *                     the model).
+   *                     If -1, then numFeatures is set based on the max feature index in all trees.
+   * @return  Feature importance values, of length numFeatures.
+   */
+  private[ml] def featureImportances(trees: Array[DecisionTreeModel], numFeatures: Int): Vector = {
+    val totalImportances = new OpenHashMap[Int, Double]()
+    trees.foreach { tree =>
+      // Aggregate feature importance vector for this tree
+      val importances = new OpenHashMap[Int, Double]()
+      computeFeatureImportance(tree.rootNode, importances)
+      // Normalize importance vector for this tree, and add it to total.
+      // TODO: In the future, also support normalizing by tree.rootNode.impurityStats.count?
+      val treeNorm = importances.map(_._2).sum
+      if (treeNorm != 0) {
+        importances.foreach { case (idx, impt) =>
+          val normImpt = impt / treeNorm
+          totalImportances.changeValue(idx, normImpt, _ + normImpt)
+        }
+      }
+    }
+    // Normalize importances
+    normalizeMapValues(totalImportances)
+    // Construct vector
+    val d = if (numFeatures != -1) {
+      numFeatures
+    } else {
+      // Find max feature index used in trees
+      val maxFeatureIndex = trees.map(_.maxSplitFeatureIndex()).max
+      maxFeatureIndex + 1
+    }
+    if (d == 0) {
+      assert(totalImportances.size == 0, s"Unknown error in computing RandomForest feature" +
+        s" importance: No splits in forest, but some non-zero importances.")
+    }
+    val (indices, values) = totalImportances.iterator.toSeq.sortBy(_._1).unzip
+    Vectors.sparse(d, indices.toArray, values.toArray)
+  }
+
+  /**
+   * Recursive method for computing feature importances for one tree.
+   * This walks down the tree, adding to the importance of 1 feature at each node.
+   * @param node  Current node in recursion
+   * @param importances  Aggregate feature importances, modified by this method
+   */
+  private[impl] def computeFeatureImportance(
+      node: Node,
+      importances: OpenHashMap[Int, Double]): Unit = {
+    node match {
+      case n: InternalNode =>
+        val feature = n.split.featureIndex
+        val scaledGain = n.gain * n.impurityStats.count
+        importances.changeValue(feature, scaledGain, _ + scaledGain)
+        computeFeatureImportance(n.leftChild, importances)
+        computeFeatureImportance(n.rightChild, importances)
+      case n: LeafNode =>
+        // do nothing
+    }
+  }
+
+  /**
+   * Normalize the values of this map to sum to 1, in place.
+   * If all values are 0, this method does nothing.
+   * @param map  Map with non-negative values.
+   */
+  private[impl] def normalizeMapValues(map: OpenHashMap[Int, Double]): Unit = {
+    val total = map.map(_._2).sum
+    if (total != 0) {
+      val keys = map.iterator.map(_._1).toArray
+      keys.foreach { key => map.changeValue(key, 0.0, _ / total) }
+    }
+  }
+
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
index 22873909c33fa..b77191156f68f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeModels.scala
@@ -53,6 +53,12 @@ private[ml] trait DecisionTreeModel {
     val header = toString + "\n"
     header + rootNode.subtreeToString(2)
   }
+
+  /**
+   * Trace down the tree, and return the largest feature index used in any split.
+   * @return  Max feature index used in a split, or -1 if there are no splits (single leaf node).
+   */
+  private[ml] def maxSplitFeatureIndex(): Int = rootNode.maxSplitFeatureIndex()
 }
 
 /**
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
index 32d0b3856b7e2..a66a1e12927be 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaRandomForestClassifierSuite.java
@@ -29,6 +29,7 @@
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.impl.TreeTests;
 import org.apache.spark.mllib.classification.LogisticRegressionSuite;
+import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.DataFrame;
 
@@ -85,6 +86,7 @@ public void runDT() {
     model.toDebugString();
     model.trees();
     model.treeWeights();
+    Vector importances = model.featureImportances();
 
     /*
     // TODO: Add test once save/load are implemented.  SPARK-6725
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
index e306ebadfe7cf..a00ce5e249c34 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaRandomForestRegressorSuite.java
@@ -29,6 +29,7 @@
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.mllib.classification.LogisticRegressionSuite;
 import org.apache.spark.ml.impl.TreeTests;
+import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.DataFrame;
 
@@ -85,6 +86,7 @@ public void runDT() {
     model.toDebugString();
     model.trees();
     model.treeWeights();
+    Vector importances = model.featureImportances();
 
     /*
     // TODO: Add test once save/load are implemented.   SPARK-6725
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
index edf848b21a905..6ca4b5aa5fde8 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
@@ -67,7 +67,7 @@ class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
   test("params") {
     ParamsSuite.checkParams(new RandomForestClassifier)
     val model = new RandomForestClassificationModel("rfc",
-      Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0, null), 2)), 2)
+      Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0, null), 2)), 2, 2)
     ParamsSuite.checkParams(model)
   }
 
@@ -149,6 +149,35 @@ class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
     }
   }
 
+  /////////////////////////////////////////////////////////////////////////////
+  // Tests of feature importance
+  /////////////////////////////////////////////////////////////////////////////
+  test("Feature importance with toy data") {
+    val numClasses = 2
+    val rf = new RandomForestClassifier()
+      .setImpurity("Gini")
+      .setMaxDepth(3)
+      .setNumTrees(3)
+      .setFeatureSubsetStrategy("all")
+      .setSubsamplingRate(1.0)
+      .setSeed(123)
+
+    // In this data, feature 1 is very important.
+    val data: RDD[LabeledPoint] = sc.parallelize(Seq(
+      new LabeledPoint(0, Vectors.dense(1, 0, 0, 0, 1)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 1, 0)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 0, 0)),
+      new LabeledPoint(0, Vectors.dense(1, 0, 0, 0, 0)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 0, 0))
+    ))
+    val categoricalFeatures = Map.empty[Int, Int]
+    val df: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses)
+
+    val importances = rf.fit(df).featureImportances
+    val mostImportantFeature = importances.argmax
+    assert(mostImportantFeature === 1)
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////
diff --git a/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala
index 778abcba22c10..460849c79f04f 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/impl/TreeTests.scala
@@ -124,4 +124,22 @@ private[ml] object TreeTests extends SparkFunSuite {
         "checkEqual failed since the two tree ensembles were not identical")
     }
   }
+
+  /**
+   * Helper method for constructing a tree for testing.
+   * Given left, right children, construct a parent node.
+   * @param split  Split for parent node
+   * @return  Parent node with children attached
+   */
+  def buildParentNode(left: Node, right: Node, split: Split): Node = {
+    val leftImp = left.impurityStats
+    val rightImp = right.impurityStats
+    val parentImp = leftImp.copy.add(rightImp)
+    val leftWeight = leftImp.count / parentImp.count.toDouble
+    val rightWeight = rightImp.count / parentImp.count.toDouble
+    val gain = parentImp.calculate() -
+      (leftWeight * leftImp.calculate() + rightWeight * rightImp.calculate())
+    val pred = parentImp.predict
+    new InternalNode(pred, parentImp.calculate(), gain, left, right, split, parentImp)
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
index b24ecaa57c89b..992ce9562434e 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
+import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
@@ -26,7 +27,6 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
 
-
 /**
  * Test suite for [[RandomForestRegressor]].
  */
@@ -71,6 +71,31 @@ class RandomForestRegressorSuite extends SparkFunSuite with MLlibTestSparkContex
     regressionTestWithContinuousFeatures(rf)
   }
 
+  test("Feature importance with toy data") {
+    val rf = new RandomForestRegressor()
+      .setImpurity("variance")
+      .setMaxDepth(3)
+      .setNumTrees(3)
+      .setFeatureSubsetStrategy("all")
+      .setSubsamplingRate(1.0)
+      .setSeed(123)
+
+    // In this data, feature 1 is very important.
+    val data: RDD[LabeledPoint] = sc.parallelize(Seq(
+      new LabeledPoint(0, Vectors.dense(1, 0, 0, 0, 1)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 1, 0)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 0, 0)),
+      new LabeledPoint(0, Vectors.dense(1, 0, 0, 0, 0)),
+      new LabeledPoint(1, Vectors.dense(1, 1, 0, 0, 0))
+    ))
+    val categoricalFeatures = Map.empty[Int, Int]
+    val df: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, 0)
+
+    val importances = rf.fit(df).featureImportances
+    val mostImportantFeature = importances.argmax
+    assert(mostImportantFeature === 1)
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala
new file mode 100644
index 0000000000000..dc852795c7f62
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala
@@ -0,0 +1,107 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.tree.impl
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.classification.DecisionTreeClassificationModel
+import org.apache.spark.ml.impl.TreeTests
+import org.apache.spark.ml.tree.{ContinuousSplit, DecisionTreeModel, LeafNode, Node}
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.tree.impurity.GiniCalculator
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.util.collection.OpenHashMap
+
+/**
+ * Test suite for [[RandomForest]].
+ */
+class RandomForestSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  import RandomForestSuite.mapToVec
+
+  test("computeFeatureImportance, featureImportances") {
+    /* Build tree for testing, with this structure:
+          grandParent
+      left2       parent
+                left  right
+     */
+    val leftImp = new GiniCalculator(Array(3.0, 2.0, 1.0))
+    val left = new LeafNode(0.0, leftImp.calculate(), leftImp)
+
+    val rightImp = new GiniCalculator(Array(1.0, 2.0, 5.0))
+    val right = new LeafNode(2.0, rightImp.calculate(), rightImp)
+
+    val parent = TreeTests.buildParentNode(left, right, new ContinuousSplit(0, 0.5))
+    val parentImp = parent.impurityStats
+
+    val left2Imp = new GiniCalculator(Array(1.0, 6.0, 1.0))
+    val left2 = new LeafNode(0.0, left2Imp.calculate(), left2Imp)
+
+    val grandParent = TreeTests.buildParentNode(left2, parent, new ContinuousSplit(1, 1.0))
+    val grandImp = grandParent.impurityStats
+
+    // Test feature importance computed at different subtrees.
+    def testNode(node: Node, expected: Map[Int, Double]): Unit = {
+      val map = new OpenHashMap[Int, Double]()
+      RandomForest.computeFeatureImportance(node, map)
+      assert(mapToVec(map.toMap) ~== mapToVec(expected) relTol 0.01)
+    }
+
+    // Leaf node
+    testNode(left, Map.empty[Int, Double])
+
+    // Internal node with 2 leaf children
+    val feature0importance = parentImp.calculate() * parentImp.count -
+      (leftImp.calculate() * leftImp.count + rightImp.calculate() * rightImp.count)
+    testNode(parent, Map(0 -> feature0importance))
+
+    // Full tree
+    val feature1importance = grandImp.calculate() * grandImp.count -
+      (left2Imp.calculate() * left2Imp.count + parentImp.calculate() * parentImp.count)
+    testNode(grandParent, Map(0 -> feature0importance, 1 -> feature1importance))
+
+    // Forest consisting of (full tree) + (internal node with 2 leafs)
+    val trees = Array(parent, grandParent).map { root =>
+      new DecisionTreeClassificationModel(root, numClasses = 3).asInstanceOf[DecisionTreeModel]
+    }
+    val importances: Vector = RandomForest.featureImportances(trees, 2)
+    val tree2norm = feature0importance + feature1importance
+    val expected = Vectors.dense((1.0 + feature0importance / tree2norm) / 2.0,
+      (feature1importance / tree2norm) / 2.0)
+    assert(importances ~== expected relTol 0.01)
+  }
+
+  test("normalizeMapValues") {
+    val map = new OpenHashMap[Int, Double]()
+    map(0) = 1.0
+    map(2) = 2.0
+    RandomForest.normalizeMapValues(map)
+    val expected = Map(0 -> 1.0 / 3.0, 2 -> 2.0 / 3.0)
+    assert(mapToVec(map.toMap) ~== mapToVec(expected) relTol 0.01)
+  }
+
+}
+
+private object RandomForestSuite {
+
+  def mapToVec(map: Map[Int, Double]): Vector = {
+    val size = (map.keys.toSeq :+ 0).max + 1
+    val (indices, values) = map.toSeq.sortBy(_._1).unzip
+    Vectors.sparse(size, indices.toArray, values.toArray)
+  }
+}

From ba1c4e138de2ea84b55def4eed2bd363e60aea4d Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Mon, 3 Aug 2015 12:53:44 -0700
Subject: [PATCH 005/633] [SPARK-9558][DOCS]Update docs to follow the increase
 of memory defaults.

Now the memory defaults of master and slave in Standalone mode and History Server is 1g, not 512m. So let's update docs.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #7896 from sarutak/update-doc-for-daemon-memory and squashes the following commits:

a77626c [Kousuke Saruta] Fix docs to follow the update of increase of memory defaults
---
 conf/spark-env.sh.template | 1 +
 docs/monitoring.md         | 2 +-
 docs/spark-standalone.md   | 2 +-
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/conf/spark-env.sh.template b/conf/spark-env.sh.template
index 192d3ae091134..c05fe381a36a7 100755
--- a/conf/spark-env.sh.template
+++ b/conf/spark-env.sh.template
@@ -38,6 +38,7 @@
 # - SPARK_WORKER_INSTANCES, to set the number of worker processes per node
 # - SPARK_WORKER_DIR, to set the working directory of worker processes
 # - SPARK_WORKER_OPTS, to set config properties only for the worker (e.g. "-Dx=y")
+# - SPARK_DAEMON_MEMORY, to allocate to the master, worker and history server themselves (default: 1g).
 # - SPARK_HISTORY_OPTS, to set config properties only for the history server (e.g. "-Dx=y")
 # - SPARK_SHUFFLE_OPTS, to set config properties only for the external shuffle service (e.g. "-Dx=y")
 # - SPARK_DAEMON_JAVA_OPTS, to set config properties for all daemons (e.g. "-Dx=y")
diff --git a/docs/monitoring.md b/docs/monitoring.md
index bcf885fe4e681..cedceb2958023 100644
--- a/docs/monitoring.md
+++ b/docs/monitoring.md
@@ -48,7 +48,7 @@ follows:
   <tr><th style="width:21%">Environment Variable</th><th>Meaning</th></tr>
   <tr>
     <td><code>SPARK_DAEMON_MEMORY</code></td>
-    <td>Memory to allocate to the history server (default: 512m).</td>
+    <td>Memory to allocate to the history server (default: 1g).</td>
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_JAVA_OPTS</code></td>
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index 4f71fbc086cd0..2fe9ec3542b28 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -152,7 +152,7 @@ You can optionally configure the cluster further by setting environment variable
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_MEMORY</code></td>
-    <td>Memory to allocate to the Spark master and worker daemons themselves (default: 512m).</td>
+    <td>Memory to allocate to the Spark master and worker daemons themselves (default: 1g).</td>
   </tr>
   <tr>
     <td><code>SPARK_DAEMON_JAVA_OPTS</code></td>

From 8ca287ebbd58985a568341b08040d0efa9d3641a Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 3 Aug 2015 13:58:00 -0700
Subject: [PATCH 006/633] [SPARK-9191] [ML] [Doc] Add ml.PCA user guide and
 code examples

Add ml.PCA user guide document and code examples for Scala/Java/Python.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7522 from yanboliang/ml-pca-md and squashes the following commits:

60dec05 [Yanbo Liang] address comments
f992abe [Yanbo Liang] Add ml.PCA doc and examples
---
 docs/ml-features.md | 86 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 86 insertions(+)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 54068debe2159..fa0ad1f00ab12 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -461,6 +461,92 @@ for binarized_feature, in binarizedFeatures.collect():
 </div>
 </div>
 
+## PCA
+
+[PCA](http://en.wikipedia.org/wiki/Principal_component_analysis) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. A [PCA](api/scala/index.html#org.apache.spark.ml.feature.PCA) class trains a model to project vectors to a low-dimensional space using PCA. The example below shows how to project 5-dimensional feature vectors into 3-dimensional principal components.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+See the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.feature.PCA) for API details.
+{% highlight scala %}
+import org.apache.spark.ml.feature.PCA
+import org.apache.spark.mllib.linalg.Vectors
+
+val data = Array(
+  Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))),
+  Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
+  Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
+)
+val df = sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features")
+val pca = new PCA()
+  .setInputCol("features")
+  .setOutputCol("pcaFeatures")
+  .setK(3)
+  .fit(df)
+val pcaDF = pca.transform(df)
+val result = pcaDF.select("pcaFeatures")
+result.show()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+See the [Java API documentation](api/java/org/apache/spark/ml/feature/PCA.html) for API details.
+{% highlight java %}
+import com.google.common.collect.Lists;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.feature.PCA
+import org.apache.spark.ml.feature.PCAModel
+import org.apache.spark.mllib.linalg.VectorUDT;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+JavaSparkContext jsc = ...
+SQLContext jsql = ...
+JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+  RowFactory.create(Vectors.sparse(5, new int[]{1, 3}, new double[]{1.0, 7.0})),
+  RowFactory.create(Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0)),
+  RowFactory.create(Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0))
+));
+StructType schema = new StructType(new StructField[] {
+  new StructField("features", new VectorUDT(), false, Metadata.empty()),
+});
+DataFrame df = jsql.createDataFrame(data, schema);
+PCAModel pca = new PCA()
+  .setInputCol("features")
+  .setOutputCol("pcaFeatures")
+  .setK(3)
+  .fit(df);
+DataFrame result = pca.transform(df).select("pcaFeatures");
+result.show();
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+See the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.feature.PCA) for API details.
+{% highlight python %}
+from pyspark.ml.feature import PCA
+from pyspark.mllib.linalg import Vectors
+
+data = [(Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),),
+  (Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),),
+  (Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)]
+df = sqlContext.createDataFrame(data,["features"])
+pca = PCA(k=3, inputCol="features", outputCol="pcaFeatures")
+model = pca.fit(df)
+result = model.transform(df).select("pcaFeatures")
+result.show(truncate=False)
+{% endhighlight %}
+</div>
+</div>
+
 ## PolynomialExpansion
 
 [Polynomial expansion](http://en.wikipedia.org/wiki/Polynomial_expansion) is the process of expanding your features into a polynomial space, which is formulated by an n-degree combination of original dimensions. A [PolynomialExpansion](api/scala/index.html#org.apache.spark.ml.feature.PolynomialExpansion) class provides this functionality.  The example below shows how to expand your features into a 3-degree polynomial space.

From e4765a46833baff1dd7465c4cf50e947de7e8f21 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 3 Aug 2015 13:59:35 -0700
Subject: [PATCH 007/633] [SPARK-9544] [MLLIB] add Python API for RFormula

Add Python API for RFormula. Similar to other feature transformers in Python. This is just a thin wrapper over the Scala implementation. ericl MechCoder

Author: Xiangrui Meng <meng@databricks.com>

Closes #7879 from mengxr/SPARK-9544 and squashes the following commits:

3d5ff03 [Xiangrui Meng] add an doctest for . and -
5e969a5 [Xiangrui Meng] fix pydoc
1cd41f8 [Xiangrui Meng] organize imports
3c18b10 [Xiangrui Meng] add Python API for RFormula
---
 .../apache/spark/ml/feature/RFormula.scala    | 21 ++---
 python/pyspark/ml/feature.py                  | 85 ++++++++++++++++++-
 2 files changed, 91 insertions(+), 15 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index d1726917e4517..d5360c9217ea9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -19,16 +19,14 @@ package org.apache.spark.ml.feature
 
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
-import scala.util.parsing.combinator.RegexParsers
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.ml.{Estimator, Model, Transformer, Pipeline, PipelineModel, PipelineStage}
+import org.apache.spark.ml.{Estimator, Model, Pipeline, PipelineModel, PipelineStage, Transformer}
 import org.apache.spark.ml.param.{Param, ParamMap}
 import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol}
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.linalg.VectorUDT
 import org.apache.spark.sql.DataFrame
-import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
 
 /**
@@ -63,31 +61,26 @@ class RFormula(override val uid: String) extends Estimator[RFormulaModel] with R
    */
   val formula: Param[String] = new Param(this, "formula", "R model formula")
 
-  private var parsedFormula: Option[ParsedRFormula] = None
-
   /**
    * Sets the formula to use for this transformer. Must be called before use.
    * @group setParam
    * @param value an R formula in string form (e.g. "y ~ x + z")
    */
-  def setFormula(value: String): this.type = {
-    parsedFormula = Some(RFormulaParser.parse(value))
-    set(formula, value)
-    this
-  }
+  def setFormula(value: String): this.type = set(formula, value)
 
   /** @group getParam */
   def getFormula: String = $(formula)
 
   /** Whether the formula specifies fitting an intercept. */
   private[ml] def hasIntercept: Boolean = {
-    require(parsedFormula.isDefined, "Must call setFormula() first.")
-    parsedFormula.get.hasIntercept
+    require(isDefined(formula), "Formula must be defined first.")
+    RFormulaParser.parse($(formula)).hasIntercept
   }
 
   override def fit(dataset: DataFrame): RFormulaModel = {
-    require(parsedFormula.isDefined, "Must call setFormula() first.")
-    val resolvedFormula = parsedFormula.get.resolve(dataset.schema)
+    require(isDefined(formula), "Formula must be defined first.")
+    val parsedFormula = RFormulaParser.parse($(formula))
+    val resolvedFormula = parsedFormula.resolve(dataset.schema)
     // StringType terms and terms representing interactions need to be encoded before assembly.
     // TODO(ekl) add support for feature interactions
     val encoderStages = ArrayBuffer[PipelineStage]()
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 015e7a9d4900a..3f04c41ac5ab6 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -24,7 +24,7 @@
 __all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer', 'OneHotEncoder',
            'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', 'StandardScalerModel',
            'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer',
-           'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel']
+           'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
 
 
 @inherit_doc
@@ -1110,6 +1110,89 @@ class PCAModel(JavaModel):
     """
 
 
+@inherit_doc
+class RFormula(JavaEstimator, HasFeaturesCol, HasLabelCol):
+    """
+    .. note:: Experimental
+
+    Implements the transforms required for fitting a dataset against an
+    R model formula. Currently we support a limited subset of the R
+    operators, including '~', '+', '-', and '.'. Also see the R formula
+    docs:
+    http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
+
+    >>> df = sqlContext.createDataFrame([
+    ...     (1.0, 1.0, "a"),
+    ...     (0.0, 2.0, "b"),
+    ...     (0.0, 0.0, "a")
+    ... ], ["y", "x", "s"])
+    >>> rf = RFormula(formula="y ~ x + s")
+    >>> rf.fit(df).transform(df).show()
+    +---+---+---+---------+-----+
+    |  y|  x|  s| features|label|
+    +---+---+---+---------+-----+
+    |1.0|1.0|  a|[1.0,1.0]|  1.0|
+    |0.0|2.0|  b|[2.0,0.0]|  0.0|
+    |0.0|0.0|  a|[0.0,1.0]|  0.0|
+    +---+---+---+---------+-----+
+    ...
+    >>> rf.fit(df, {rf.formula: "y ~ . - s"}).transform(df).show()
+    +---+---+---+--------+-----+
+    |  y|  x|  s|features|label|
+    +---+---+---+--------+-----+
+    |1.0|1.0|  a|   [1.0]|  1.0|
+    |0.0|2.0|  b|   [2.0]|  0.0|
+    |0.0|0.0|  a|   [0.0]|  0.0|
+    +---+---+---+--------+-----+
+    ...
+    """
+
+    # a placeholder to make it appear in the generated doc
+    formula = Param(Params._dummy(), "formula", "R model formula")
+
+    @keyword_only
+    def __init__(self, formula=None, featuresCol="features", labelCol="label"):
+        """
+        __init__(self, formula=None, featuresCol="features", labelCol="label")
+        """
+        super(RFormula, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RFormula", self.uid)
+        self.formula = Param(self, "formula", "R model formula")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, formula=None, featuresCol="features", labelCol="label"):
+        """
+        setParams(self, formula=None, featuresCol="features", labelCol="label")
+        Sets params for RFormula.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setFormula(self, value):
+        """
+        Sets the value of :py:attr:`formula`.
+        """
+        self._paramMap[self.formula] = value
+        return self
+
+    def getFormula(self):
+        """
+        Gets the value of :py:attr:`formula`.
+        """
+        return self.getOrDefault(self.formula)
+
+    def _create_model(self, java_model):
+        return RFormulaModel(java_model)
+
+
+class RFormulaModel(JavaModel):
+    """
+    Model fitted by :py:class:`RFormula`.
+    """
+
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext

From 702aa9d7fb16c98a50e046edfd76b8a7861d0391 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 3 Aug 2015 14:22:07 -0700
Subject: [PATCH 008/633] [SPARK-8735] [SQL] Expose memory usage for shuffles,
 joins and aggregations

This patch exposes the memory used by internal data structures on the SparkUI. This tracks memory used by all spilling operations and SQL operators backed by Tungsten, e.g. `BroadcastHashJoin`, `ExternalSort`, `GeneratedAggregate` etc. The metric exposed is "peak execution memory", which broadly refers to the peak in-memory sizes of each of these data structure.

A separate patch will extend this by linking the new information to the SQL operators themselves.

<img width="950" alt="screen shot 2015-07-29 at 7 43 17 pm" src="https://cloud.githubusercontent.com/assets/2133137/8974776/b90fc980-362a-11e5-9e2b-842da75b1641.png">
<img width="802" alt="screen shot 2015-07-29 at 7 43 05 pm" src="https://cloud.githubusercontent.com/assets/2133137/8974777/baa76492-362a-11e5-9b77-e364a6a6b64e.png">

<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/7770)
<!-- Reviewable:end -->

Author: Andrew Or <andrew@databricks.com>

Closes #7770 from andrewor14/expose-memory-metrics and squashes the following commits:

9abecb9 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
f5b0d68 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
d7df332 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
8eefbc5 [Andrew Or] Fix non-failing tests
9de2a12 [Andrew Or] Fix tests due to another logical merge conflict
876bfa4 [Andrew Or] Fix failing test after logical merge conflict
361a359 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
40b4802 [Andrew Or] Fix style?
d0fef87 [Andrew Or] Fix tests?
b3b92f6 [Andrew Or] Address comments
0625d73 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
c00a197 [Andrew Or] Fix potential NPEs
10da1cd [Andrew Or] Fix compile
17f4c2d [Andrew Or] Fix compile?
a87b4d0 [Andrew Or] Fix compile?
d70874d [Andrew Or] Fix test compile + address comments
2840b7d [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
6aa2f7a [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
b889a68 [Andrew Or] Minor changes: comments, spacing, style
663a303 [Andrew Or] UnsafeShuffleWriter: update peak memory before close
d090a94 [Andrew Or] Fix style
2480d84 [Andrew Or] Expand test coverage
5f1235b [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
1ecf678 [Andrew Or] Minor changes: comments, style, unused imports
0b6926c [Andrew Or] Oops
111a05e [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
a7a39a5 [Andrew Or] Strengthen presence check for accumulator
a919eb7 [Andrew Or] Add tests for unsafe shuffle writer
23c845d [Andrew Or] Add tests for SQL operators
a757550 [Andrew Or] Address comments
b5c51c1 [Andrew Or] Re-enable test in JavaAPISuite
5107691 [Andrew Or] Add tests for internal accumulators
59231e4 [Andrew Or] Fix tests
9528d09 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
5b5e6f3 [Andrew Or] Add peak execution memory to summary table + tooltip
92b4b6b [Andrew Or] Display peak execution memory on the UI
eee5437 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
d9b9015 [Andrew Or] Track execution memory in unsafe shuffles
770ee54 [Andrew Or] Track execution memory in broadcast joins
9c605a4 [Andrew Or] Track execution memory in GeneratedAggregate
9e824f2 [Andrew Or] Add back execution memory tracking for *ExternalSort
4ef4cb1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into expose-memory-metrics
e6c3e2f [Andrew Or] Move internal accumulators creation to Stage
a417592 [Andrew Or] Expose memory metrics in UnsafeExternalSorter
3c4f042 [Andrew Or] Track memory usage in ExternalAppendOnlyMap / ExternalSorter
bd7ab3f [Andrew Or] Add internal accumulators to TaskContext
---
 .../unsafe/UnsafeShuffleExternalSorter.java   |  27 ++-
 .../shuffle/unsafe/UnsafeShuffleWriter.java   |  38 +++-
 .../spark/unsafe/map/BytesToBytesMap.java     |   8 +-
 .../unsafe/sort/UnsafeExternalSorter.java     |  29 ++-
 .../org/apache/spark/ui/static/webui.css      |   2 +-
 .../scala/org/apache/spark/Accumulators.scala |  60 +++++-
 .../scala/org/apache/spark/Aggregator.scala   |  24 +--
 .../scala/org/apache/spark/TaskContext.scala  |  13 +-
 .../org/apache/spark/TaskContextImpl.scala    |   8 +
 .../org/apache/spark/rdd/CoGroupedRDD.scala   |   9 +-
 .../spark/scheduler/AccumulableInfo.scala     |   9 +-
 .../apache/spark/scheduler/DAGScheduler.scala |  28 ++-
 .../apache/spark/scheduler/ResultTask.scala   |   6 +-
 .../spark/scheduler/ShuffleMapTask.scala      |  10 +-
 .../org/apache/spark/scheduler/Stage.scala    |  16 ++
 .../org/apache/spark/scheduler/Task.scala     |  18 +-
 .../shuffle/hash/HashShuffleReader.scala      |   8 +-
 .../scala/org/apache/spark/ui/ToolTips.scala  |   7 +
 .../org/apache/spark/ui/jobs/StagePage.scala  | 140 +++++++++----
 .../spark/ui/jobs/TaskDetailsClassNames.scala |   1 +
 .../collection/ExternalAppendOnlyMap.scala    |  13 +-
 .../util/collection/ExternalSorter.scala      |  20 +-
 .../java/org/apache/spark/JavaAPISuite.java   |   3 +-
 .../unsafe/UnsafeShuffleWriterSuite.java      |  54 +++++
 .../map/AbstractBytesToBytesMapSuite.java     |  39 ++++
 .../sort/UnsafeExternalSorterSuite.java       |  46 +++++
 .../org/apache/spark/AccumulatorSuite.scala   | 193 +++++++++++++++++-
 .../org/apache/spark/CacheManagerSuite.scala  |  10 +-
 .../org/apache/spark/rdd/PipedRDDSuite.scala  |   2 +-
 .../org/apache/spark/scheduler/FakeTask.scala |   6 +-
 .../scheduler/NotSerializableFakeTask.scala   |   2 +-
 .../spark/scheduler/TaskContextSuite.scala    |   7 +-
 .../spark/scheduler/TaskSetManagerSuite.scala |   2 +-
 .../shuffle/hash/HashShuffleReaderSuite.scala |   2 +-
 .../ShuffleBlockFetcherIteratorSuite.scala    |   8 +-
 .../org/apache/spark/ui/StagePageSuite.scala  |  76 +++++++
 .../ExternalAppendOnlyMapSuite.scala          |  15 ++
 .../util/collection/ExternalSorterSuite.scala |  14 +-
 .../execution/UnsafeExternalRowSorter.java    |   7 +
 .../UnsafeFixedWidthAggregationMap.java       |   8 +
 .../sql/execution/GeneratedAggregate.scala    |  11 +-
 .../execution/joins/BroadcastHashJoin.scala   |  10 +-
 .../joins/BroadcastHashOuterJoin.scala        |   8 +
 .../joins/BroadcastLeftSemiJoinHash.scala     |  10 +-
 .../sql/execution/joins/HashedRelation.scala  |  22 +-
 .../org/apache/spark/sql/execution/sort.scala |  12 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  60 ++++--
 .../sql/execution/TungstenSortSuite.scala     |  12 ++
 .../UnsafeFixedWidthAggregationMapSuite.scala |   3 +-
 .../UnsafeKVExternalSorterSuite.scala         |   3 +-
 .../execution/joins/BroadcastJoinSuite.scala  |  94 +++++++++
 51 files changed, 1070 insertions(+), 163 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala

diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index 1aa6ba4201261..bf4eaa59ff589 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -20,6 +20,7 @@
 import java.io.File;
 import java.io.IOException;
 import java.util.LinkedList;
+import javax.annotation.Nullable;
 
 import scala.Tuple2;
 
@@ -86,9 +87,12 @@ final class UnsafeShuffleExternalSorter {
 
   private final LinkedList<SpillInfo> spills = new LinkedList<SpillInfo>();
 
+  /** Peak memory used by this sorter so far, in bytes. **/
+  private long peakMemoryUsedBytes;
+
   // These variables are reset after spilling:
-  private UnsafeShuffleInMemorySorter sorter;
-  private MemoryBlock currentPage = null;
+  @Nullable private UnsafeShuffleInMemorySorter sorter;
+  @Nullable private MemoryBlock currentPage = null;
   private long currentPagePosition = -1;
   private long freeSpaceInCurrentPage = 0;
 
@@ -106,6 +110,7 @@ public UnsafeShuffleExternalSorter(
     this.blockManager = blockManager;
     this.taskContext = taskContext;
     this.initialSize = initialSize;
+    this.peakMemoryUsedBytes = initialSize;
     this.numPartitions = numPartitions;
     // Use getSizeAsKb (not bytes) to maintain backwards compatibility if no units are provided
     this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
@@ -279,10 +284,26 @@ private long getMemoryUsage() {
     for (MemoryBlock page : allocatedPages) {
       totalPageSize += page.size();
     }
-    return sorter.getMemoryUsage() + totalPageSize;
+    return ((sorter == null) ? 0 : sorter.getMemoryUsage()) + totalPageSize;
+  }
+
+  private void updatePeakMemoryUsed() {
+    long mem = getMemoryUsage();
+    if (mem > peakMemoryUsedBytes) {
+      peakMemoryUsedBytes = mem;
+    }
+  }
+
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  long getPeakMemoryUsedBytes() {
+    updatePeakMemoryUsed();
+    return peakMemoryUsedBytes;
   }
 
   private long freeMemory() {
+    updatePeakMemoryUsed();
     long memoryFreed = 0;
     for (MemoryBlock block : allocatedPages) {
       memoryManager.freePage(block);
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
index d47d6fc9c2ac4..6e2eeb37c86f1 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
@@ -27,6 +27,7 @@
 import scala.collection.JavaConversions;
 import scala.reflect.ClassTag;
 import scala.reflect.ClassTag$;
+import scala.collection.immutable.Map;
 
 import com.google.common.annotations.VisibleForTesting;
 import com.google.common.io.ByteStreams;
@@ -78,8 +79,9 @@ public class UnsafeShuffleWriter<K, V> extends ShuffleWriter<K, V> {
   private final SparkConf sparkConf;
   private final boolean transferToEnabled;
 
-  private MapStatus mapStatus = null;
-  private UnsafeShuffleExternalSorter sorter = null;
+  @Nullable private MapStatus mapStatus;
+  @Nullable private UnsafeShuffleExternalSorter sorter;
+  private long peakMemoryUsedBytes = 0;
 
   /** Subclass of ByteArrayOutputStream that exposes `buf` directly. */
   private static final class MyByteArrayOutputStream extends ByteArrayOutputStream {
@@ -131,9 +133,28 @@ public UnsafeShuffleWriter(
 
   @VisibleForTesting
   public int maxRecordSizeBytes() {
+    assert(sorter != null);
     return sorter.maxRecordSizeBytes;
   }
 
+  private void updatePeakMemoryUsed() {
+    // sorter can be null if this writer is closed
+    if (sorter != null) {
+      long mem = sorter.getPeakMemoryUsedBytes();
+      if (mem > peakMemoryUsedBytes) {
+        peakMemoryUsedBytes = mem;
+      }
+    }
+  }
+
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  public long getPeakMemoryUsedBytes() {
+    updatePeakMemoryUsed();
+    return peakMemoryUsedBytes;
+  }
+
   /**
    * This convenience method should only be called in test code.
    */
@@ -144,7 +165,7 @@ public void write(Iterator<Product2<K, V>> records) throws IOException {
 
   @Override
   public void write(scala.collection.Iterator<Product2<K, V>> records) throws IOException {
-    // Keep track of success so we know if we ecountered an exception
+    // Keep track of success so we know if we encountered an exception
     // We do this rather than a standard try/catch/re-throw to handle
     // generic throwables.
     boolean success = false;
@@ -189,6 +210,8 @@ private void open() throws IOException {
 
   @VisibleForTesting
   void closeAndWriteOutput() throws IOException {
+    assert(sorter != null);
+    updatePeakMemoryUsed();
     serBuffer = null;
     serOutputStream = null;
     final SpillInfo[] spills = sorter.closeAndGetSpills();
@@ -209,6 +232,7 @@ void closeAndWriteOutput() throws IOException {
 
   @VisibleForTesting
   void insertRecordIntoSorter(Product2<K, V> record) throws IOException {
+    assert(sorter != null);
     final K key = record._1();
     final int partitionId = partitioner.getPartition(key);
     serBuffer.reset();
@@ -431,6 +455,14 @@ private long[] mergeSpillsWithTransferTo(SpillInfo[] spills, File outputFile) th
   @Override
   public Option<MapStatus> stop(boolean success) {
     try {
+      // Update task metrics from accumulators (null in UnsafeShuffleWriterSuite)
+      Map<String, Accumulator<Object>> internalAccumulators =
+        taskContext.internalMetricsToAccumulators();
+      if (internalAccumulators != null) {
+        internalAccumulators.apply(InternalAccumulator.PEAK_EXECUTION_MEMORY())
+          .add(getPeakMemoryUsedBytes());
+      }
+
       if (stopping) {
         return Option.apply(null);
       } else {
diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 01a66084e918e..20347433e16b2 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -505,7 +505,7 @@ public boolean putNewKey(
       // Here, we'll copy the data into our data pages. Because we only store a relative offset from
       // the key address instead of storing the absolute address of the value, the key and value
       // must be stored in the same memory page.
-      // (8 byte key length) (key) (8 byte value length) (value)
+      // (8 byte key length) (key) (value)
       final long requiredSize = 8 + keyLengthBytes + valueLengthBytes;
 
       // --- Figure out where to insert the new record ---------------------------------------------
@@ -655,7 +655,10 @@ public long getPageSizeBytes() {
     return pageSizeBytes;
   }
 
-  /** Returns the total amount of memory, in bytes, consumed by this map's managed structures. */
+  /**
+   * Returns the total amount of memory, in bytes, consumed by this map's managed structures.
+   * Note that this is also the peak memory used by this map, since the map is append-only.
+   */
   public long getTotalMemoryConsumption() {
     long totalDataPagesSize = 0L;
     for (MemoryBlock dataPage : dataPages) {
@@ -674,7 +677,6 @@ public long getTimeSpentResizingNs() {
     return timeSpentResizingNs;
   }
 
-
   /**
    * Returns the average number of probes per key lookup.
    */
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index b984301cbbf2b..bf5f965a9d8dc 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -70,13 +70,14 @@ public final class UnsafeExternalSorter {
   private final LinkedList<UnsafeSorterSpillWriter> spillWriters = new LinkedList<>();
 
   // These variables are reset after spilling:
-  private UnsafeInMemorySorter inMemSorter;
+  @Nullable private UnsafeInMemorySorter inMemSorter;
   // Whether the in-mem sorter is created internally, or passed in from outside.
   // If it is passed in from outside, we shouldn't release the in-mem sorter's memory.
   private boolean isInMemSorterExternal = false;
   private MemoryBlock currentPage = null;
   private long currentPagePosition = -1;
   private long freeSpaceInCurrentPage = 0;
+  private long peakMemoryUsedBytes = 0;
 
   public static UnsafeExternalSorter createWithExistingInMemorySorter(
       TaskMemoryManager taskMemoryManager,
@@ -183,6 +184,7 @@ public void closeCurrentPage() {
    * Sort and spill the current records in response to memory pressure.
    */
   public void spill() throws IOException {
+    assert(inMemSorter != null);
     logger.info("Thread {} spilling sort data of {} to disk ({} {} so far)",
       Thread.currentThread().getId(),
       Utils.bytesToString(getMemoryUsage()),
@@ -219,7 +221,22 @@ private long getMemoryUsage() {
     for (MemoryBlock page : allocatedPages) {
       totalPageSize += page.size();
     }
-    return inMemSorter.getMemoryUsage() + totalPageSize;
+    return ((inMemSorter == null) ? 0 : inMemSorter.getMemoryUsage()) + totalPageSize;
+  }
+
+  private void updatePeakMemoryUsed() {
+    long mem = getMemoryUsage();
+    if (mem > peakMemoryUsedBytes) {
+      peakMemoryUsedBytes = mem;
+    }
+  }
+
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  public long getPeakMemoryUsedBytes() {
+    updatePeakMemoryUsed();
+    return peakMemoryUsedBytes;
   }
 
   @VisibleForTesting
@@ -233,6 +250,7 @@ public int getNumberOfAllocatedPages() {
    * @return the number of bytes freed.
    */
   public long freeMemory() {
+    updatePeakMemoryUsed();
     long memoryFreed = 0;
     for (MemoryBlock block : allocatedPages) {
       taskMemoryManager.freePage(block);
@@ -277,7 +295,8 @@ public void deleteSpillFiles() {
    * @return true if the record can be inserted without requiring more allocations, false otherwise.
    */
   private boolean haveSpaceForRecord(int requiredSpace) {
-    assert (requiredSpace > 0);
+    assert(requiredSpace > 0);
+    assert(inMemSorter != null);
     return (inMemSorter.hasSpaceForAnotherRecord() && (requiredSpace <= freeSpaceInCurrentPage));
   }
 
@@ -290,6 +309,7 @@ private boolean haveSpaceForRecord(int requiredSpace) {
    *                      the record size.
    */
   private void allocateSpaceForRecord(int requiredSpace) throws IOException {
+    assert(inMemSorter != null);
     // TODO: merge these steps to first calculate total memory requirements for this insert,
     // then try to acquire; no point in acquiring sort buffer only to spill due to no space in the
     // data page.
@@ -350,6 +370,7 @@ public void insertRecord(
     if (!haveSpaceForRecord(totalSpaceRequired)) {
       allocateSpaceForRecord(totalSpaceRequired);
     }
+    assert(inMemSorter != null);
 
     final long recordAddress =
       taskMemoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
@@ -382,6 +403,7 @@ public void insertKVRecord(
     if (!haveSpaceForRecord(totalSpaceRequired)) {
       allocateSpaceForRecord(totalSpaceRequired);
     }
+    assert(inMemSorter != null);
 
     final long recordAddress =
       taskMemoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
@@ -405,6 +427,7 @@ public void insertKVRecord(
   }
 
   public UnsafeSorterIterator getSortedIterator() throws IOException {
+    assert(inMemSorter != null);
     final UnsafeSorterIterator inMemoryIterator = inMemSorter.getSortedIterator();
     int numIteratorsToMerge = spillWriters.size() + (inMemoryIterator.hasNext() ? 1 : 0);
     if (spillWriters.isEmpty()) {
diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css
index b1cef47042247..648cd1b104802 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/webui.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css
@@ -207,7 +207,7 @@ span.additional-metric-title {
 /* Hide all additional metrics by default. This is done here rather than using JavaScript to
  * avoid slow page loads for stage pages with large numbers (e.g., thousands) of tasks. */
 .scheduler_delay, .deserialization_time, .fetch_wait_time, .shuffle_read_remote,
-.serialization_time, .getting_result_time {
+.serialization_time, .getting_result_time, .peak_execution_memory {
   display: none;
 }
 
diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index eb75f26718e19..b6a0119c696fd 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -152,8 +152,14 @@ class Accumulable[R, T] private[spark] (
     in.defaultReadObject()
     value_ = zero
     deserialized = true
-    val taskContext = TaskContext.get()
-    taskContext.registerAccumulator(this)
+    // Automatically register the accumulator when it is deserialized with the task closure.
+    // Note that internal accumulators are deserialized before the TaskContext is created and
+    // are registered in the TaskContext constructor.
+    if (!isInternal) {
+      val taskContext = TaskContext.get()
+      assume(taskContext != null, "Task context was null when deserializing user accumulators")
+      taskContext.registerAccumulator(this)
+    }
   }
 
   override def toString: String = if (value_ == null) "null" else value_.toString
@@ -248,10 +254,20 @@ GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Serializa
  * @param param helper object defining how to add elements of type `T`
  * @tparam T result type
  */
-class Accumulator[T](@transient initialValue: T, param: AccumulatorParam[T], name: Option[String])
-  extends Accumulable[T, T](initialValue, param, name) {
+class Accumulator[T] private[spark] (
+    @transient initialValue: T,
+    param: AccumulatorParam[T],
+    name: Option[String],
+    internal: Boolean)
+  extends Accumulable[T, T](initialValue, param, name, internal) {
+
+  def this(initialValue: T, param: AccumulatorParam[T], name: Option[String]) = {
+    this(initialValue, param, name, false)
+  }
 
-  def this(initialValue: T, param: AccumulatorParam[T]) = this(initialValue, param, None)
+  def this(initialValue: T, param: AccumulatorParam[T]) = {
+    this(initialValue, param, None, false)
+  }
 }
 
 /**
@@ -342,3 +358,37 @@ private[spark] object Accumulators extends Logging {
   }
 
 }
+
+private[spark] object InternalAccumulator {
+  val PEAK_EXECUTION_MEMORY = "peakExecutionMemory"
+  val TEST_ACCUMULATOR = "testAccumulator"
+
+  // For testing only.
+  // This needs to be a def since we don't want to reuse the same accumulator across stages.
+  private def maybeTestAccumulator: Option[Accumulator[Long]] = {
+    if (sys.props.contains("spark.testing")) {
+      Some(new Accumulator(
+        0L, AccumulatorParam.LongAccumulatorParam, Some(TEST_ACCUMULATOR), internal = true))
+    } else {
+      None
+    }
+  }
+
+  /**
+   * Accumulators for tracking internal metrics.
+   *
+   * These accumulators are created with the stage such that all tasks in the stage will
+   * add to the same set of accumulators. We do this to report the distribution of accumulator
+   * values across all tasks within each stage.
+   */
+  def create(): Seq[Accumulator[Long]] = {
+    Seq(
+      // Execution memory refers to the memory used by internal data structures created
+      // during shuffles, aggregations and joins. The value of this accumulator should be
+      // approximately the sum of the peak sizes across all such data structures created
+      // in this task. For SQL jobs, this only tracks all unsafe operators and ExternalSort.
+      new Accumulator(
+        0L, AccumulatorParam.LongAccumulatorParam, Some(PEAK_EXECUTION_MEMORY), internal = true)
+    ) ++ maybeTestAccumulator.toSeq
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/Aggregator.scala b/core/src/main/scala/org/apache/spark/Aggregator.scala
index ceeb58075d345..289aab9bd9e51 100644
--- a/core/src/main/scala/org/apache/spark/Aggregator.scala
+++ b/core/src/main/scala/org/apache/spark/Aggregator.scala
@@ -58,12 +58,7 @@ case class Aggregator[K, V, C] (
     } else {
       val combiners = new ExternalAppendOnlyMap[K, V, C](createCombiner, mergeValue, mergeCombiners)
       combiners.insertAll(iter)
-      // Update task metrics if context is not null
-      // TODO: Make context non optional in a future release
-      Option(context).foreach { c =>
-        c.taskMetrics.incMemoryBytesSpilled(combiners.memoryBytesSpilled)
-        c.taskMetrics.incDiskBytesSpilled(combiners.diskBytesSpilled)
-      }
+      updateMetrics(context, combiners)
       combiners.iterator
     }
   }
@@ -89,13 +84,18 @@ case class Aggregator[K, V, C] (
     } else {
       val combiners = new ExternalAppendOnlyMap[K, C, C](identity, mergeCombiners, mergeCombiners)
       combiners.insertAll(iter)
-      // Update task metrics if context is not null
-      // TODO: Make context non-optional in a future release
-      Option(context).foreach { c =>
-        c.taskMetrics.incMemoryBytesSpilled(combiners.memoryBytesSpilled)
-        c.taskMetrics.incDiskBytesSpilled(combiners.diskBytesSpilled)
-      }
+      updateMetrics(context, combiners)
       combiners.iterator
     }
   }
+
+  /** Update task metrics after populating the external map. */
+  private def updateMetrics(context: TaskContext, map: ExternalAppendOnlyMap[_, _, _]): Unit = {
+    Option(context).foreach { c =>
+      c.taskMetrics().incMemoryBytesSpilled(map.memoryBytesSpilled)
+      c.taskMetrics().incDiskBytesSpilled(map.diskBytesSpilled)
+      c.internalMetricsToAccumulators(
+        InternalAccumulator.PEAK_EXECUTION_MEMORY).add(map.peakMemoryUsedBytes)
+    }
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/TaskContext.scala b/core/src/main/scala/org/apache/spark/TaskContext.scala
index 5d2c551d58514..63cca80b2d734 100644
--- a/core/src/main/scala/org/apache/spark/TaskContext.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContext.scala
@@ -61,12 +61,12 @@ object TaskContext {
   protected[spark] def unset(): Unit = taskContext.remove()
 
   /**
-   * Return an empty task context that is not actually used.
-   * Internal use only.
+   * An empty task context that does not represent an actual task.
    */
-  private[spark] def empty(): TaskContext = {
-    new TaskContextImpl(0, 0, 0, 0, null, null)
+  private[spark] def empty(): TaskContextImpl = {
+    new TaskContextImpl(0, 0, 0, 0, null, null, Seq.empty)
   }
+
 }
 
 
@@ -187,4 +187,9 @@ abstract class TaskContext extends Serializable {
    * accumulator id and the value of the Map is the latest accumulator local value.
    */
   private[spark] def collectAccumulators(): Map[Long, Any]
+
+  /**
+   * Accumulators for tracking internal metrics indexed by the name.
+   */
+  private[spark] val internalMetricsToAccumulators: Map[String, Accumulator[Long]]
 }
diff --git a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
index 9ee168ae016f8..5df94c6d3a103 100644
--- a/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
+++ b/core/src/main/scala/org/apache/spark/TaskContextImpl.scala
@@ -32,6 +32,7 @@ private[spark] class TaskContextImpl(
     override val attemptNumber: Int,
     override val taskMemoryManager: TaskMemoryManager,
     @transient private val metricsSystem: MetricsSystem,
+    internalAccumulators: Seq[Accumulator[Long]],
     val runningLocally: Boolean = false,
     val taskMetrics: TaskMetrics = TaskMetrics.empty)
   extends TaskContext
@@ -114,4 +115,11 @@ private[spark] class TaskContextImpl(
   private[spark] override def collectAccumulators(): Map[Long, Any] = synchronized {
     accumulators.mapValues(_.localValue).toMap
   }
+
+  private[spark] override val internalMetricsToAccumulators: Map[String, Accumulator[Long]] = {
+    // Explicitly register internal accumulators here because these are
+    // not captured in the task closure and are already deserialized
+    internalAccumulators.foreach(registerAccumulator)
+    internalAccumulators.map { a => (a.name.get, a) }.toMap
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
index 130b58882d8ee..9c617fc719cb5 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
@@ -23,8 +23,7 @@ import java.io.{IOException, ObjectOutputStream}
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.{InterruptibleIterator, Partition, Partitioner, SparkEnv, TaskContext}
-import org.apache.spark.{Dependency, OneToOneDependency, ShuffleDependency}
+import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.util.collection.{ExternalAppendOnlyMap, AppendOnlyMap, CompactBuffer}
 import org.apache.spark.util.Utils
@@ -169,8 +168,10 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part:
       for ((it, depNum) <- rddIterators) {
         map.insertAll(it.map(pair => (pair._1, new CoGroupValue(pair._2, depNum))))
       }
-      context.taskMetrics.incMemoryBytesSpilled(map.memoryBytesSpilled)
-      context.taskMetrics.incDiskBytesSpilled(map.diskBytesSpilled)
+      context.taskMetrics().incMemoryBytesSpilled(map.memoryBytesSpilled)
+      context.taskMetrics().incDiskBytesSpilled(map.diskBytesSpilled)
+      context.internalMetricsToAccumulators(
+        InternalAccumulator.PEAK_EXECUTION_MEMORY).add(map.peakMemoryUsedBytes)
       new InterruptibleIterator(context,
         map.iterator.asInstanceOf[Iterator[(K, Array[Iterable[_]])]])
     }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
index e0edd7d4ae968..11d123eec43ca 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
@@ -24,11 +24,12 @@ import org.apache.spark.annotation.DeveloperApi
  * Information about an [[org.apache.spark.Accumulable]] modified during a task or stage.
  */
 @DeveloperApi
-class AccumulableInfo (
+class AccumulableInfo private[spark] (
     val id: Long,
     val name: String,
     val update: Option[String], // represents a partial update within a task
-    val value: String) {
+    val value: String,
+    val internal: Boolean) {
 
   override def equals(other: Any): Boolean = other match {
     case acc: AccumulableInfo =>
@@ -40,10 +41,10 @@ class AccumulableInfo (
 
 object AccumulableInfo {
   def apply(id: Long, name: String, update: Option[String], value: String): AccumulableInfo = {
-    new AccumulableInfo(id, name, update, value)
+    new AccumulableInfo(id, name, update, value, internal = false)
   }
 
   def apply(id: Long, name: String, value: String): AccumulableInfo = {
-    new AccumulableInfo(id, name, None, value)
+    new AccumulableInfo(id, name, None, value, internal = false)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index c4fa277c21254..bb489c6b6e98f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -773,16 +773,26 @@ class DAGScheduler(
     stage.pendingTasks.clear()
 
     // First figure out the indexes of partition ids to compute.
-    val partitionsToCompute: Seq[Int] = {
+    val (allPartitions: Seq[Int], partitionsToCompute: Seq[Int]) = {
       stage match {
         case stage: ShuffleMapStage =>
-          (0 until stage.numPartitions).filter(id => stage.outputLocs(id).isEmpty)
+          val allPartitions = 0 until stage.numPartitions
+          val filteredPartitions = allPartitions.filter { id => stage.outputLocs(id).isEmpty }
+          (allPartitions, filteredPartitions)
         case stage: ResultStage =>
           val job = stage.resultOfJob.get
-          (0 until job.numPartitions).filter(id => !job.finished(id))
+          val allPartitions = 0 until job.numPartitions
+          val filteredPartitions = allPartitions.filter { id => !job.finished(id) }
+          (allPartitions, filteredPartitions)
       }
     }
 
+    // Reset internal accumulators only if this stage is not partially submitted
+    // Otherwise, we may override existing accumulator values from some tasks
+    if (allPartitions == partitionsToCompute) {
+      stage.resetInternalAccumulators()
+    }
+
     val properties = jobIdToActiveJob.get(stage.firstJobId).map(_.properties).orNull
 
     runningStages += stage
@@ -852,7 +862,8 @@ class DAGScheduler(
           partitionsToCompute.map { id =>
             val locs = taskIdToLocations(id)
             val part = stage.rdd.partitions(id)
-            new ShuffleMapTask(stage.id, stage.latestInfo.attemptId, taskBinary, part, locs)
+            new ShuffleMapTask(stage.id, stage.latestInfo.attemptId,
+              taskBinary, part, locs, stage.internalAccumulators)
           }
 
         case stage: ResultStage =>
@@ -861,7 +872,8 @@ class DAGScheduler(
             val p: Int = job.partitions(id)
             val part = stage.rdd.partitions(p)
             val locs = taskIdToLocations(id)
-            new ResultTask(stage.id, stage.latestInfo.attemptId, taskBinary, part, locs, id)
+            new ResultTask(stage.id, stage.latestInfo.attemptId,
+              taskBinary, part, locs, id, stage.internalAccumulators)
           }
       }
     } catch {
@@ -916,9 +928,11 @@ class DAGScheduler(
           // To avoid UI cruft, ignore cases where value wasn't updated
           if (acc.name.isDefined && partialValue != acc.zero) {
             val name = acc.name.get
-            stage.latestInfo.accumulables(id) = AccumulableInfo(id, name, s"${acc.value}")
+            val value = s"${acc.value}"
+            stage.latestInfo.accumulables(id) =
+              new AccumulableInfo(id, name, None, value, acc.isInternal)
             event.taskInfo.accumulables +=
-              AccumulableInfo(id, name, Some(s"$partialValue"), s"${acc.value}")
+              new AccumulableInfo(id, name, Some(s"$partialValue"), value, acc.isInternal)
           }
         }
       } catch {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
index 9c2606e278c54..c4dc080e2b22b 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
@@ -45,8 +45,10 @@ private[spark] class ResultTask[T, U](
     taskBinary: Broadcast[Array[Byte]],
     partition: Partition,
     @transient locs: Seq[TaskLocation],
-    val outputId: Int)
-  extends Task[U](stageId, stageAttemptId, partition.index) with Serializable {
+    val outputId: Int,
+    internalAccumulators: Seq[Accumulator[Long]])
+  extends Task[U](stageId, stageAttemptId, partition.index, internalAccumulators)
+  with Serializable {
 
   @transient private[this] val preferredLocs: Seq[TaskLocation] = {
     if (locs == null) Nil else locs.toSet.toSeq
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
index 14c8c00961487..f478f9982afef 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala
@@ -43,12 +43,14 @@ private[spark] class ShuffleMapTask(
     stageAttemptId: Int,
     taskBinary: Broadcast[Array[Byte]],
     partition: Partition,
-    @transient private var locs: Seq[TaskLocation])
-  extends Task[MapStatus](stageId, stageAttemptId, partition.index) with Logging {
+    @transient private var locs: Seq[TaskLocation],
+    internalAccumulators: Seq[Accumulator[Long]])
+  extends Task[MapStatus](stageId, stageAttemptId, partition.index, internalAccumulators)
+  with Logging {
 
   /** A constructor used only in test suites. This does not require passing in an RDD. */
   def this(partitionId: Int) {
-    this(0, 0, null, new Partition { override def index: Int = 0 }, null)
+    this(0, 0, null, new Partition { override def index: Int = 0 }, null, null)
   }
 
   @transient private val preferredLocs: Seq[TaskLocation] = {
@@ -69,7 +71,7 @@ private[spark] class ShuffleMapTask(
       val manager = SparkEnv.get.shuffleManager
       writer = manager.getWriter[Any, Any](dep.shuffleHandle, partitionId, context)
       writer.write(rdd.iterator(partition, context).asInstanceOf[Iterator[_ <: Product2[Any, Any]]])
-      return writer.stop(success = true).get
+      writer.stop(success = true).get
     } catch {
       case e: Exception =>
         try {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index 40a333a3e06b2..de05ee256dbfc 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -68,6 +68,22 @@ private[spark] abstract class Stage(
   val name = callSite.shortForm
   val details = callSite.longForm
 
+  private var _internalAccumulators: Seq[Accumulator[Long]] = Seq.empty
+
+  /** Internal accumulators shared across all tasks in this stage. */
+  def internalAccumulators: Seq[Accumulator[Long]] = _internalAccumulators
+
+  /**
+   * Re-initialize the internal accumulators associated with this stage.
+   *
+   * This is called every time the stage is submitted, *except* when a subset of tasks
+   * belonging to this stage has already finished. Otherwise, reinitializing the internal
+   * accumulators here again will override partial values from the finished tasks.
+   */
+  def resetInternalAccumulators(): Unit = {
+    _internalAccumulators = InternalAccumulator.create()
+  }
+
   /**
    * Pointer to the [StageInfo] object for the most recent attempt. This needs to be initialized
    * here, before any attempts have actually been created, because the DAGScheduler uses this
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Task.scala b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
index 1978305cfefbd..9edf9f048f9fd 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Task.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Task.scala
@@ -23,7 +23,7 @@ import java.nio.ByteBuffer
 import scala.collection.mutable.HashMap
 
 import org.apache.spark.metrics.MetricsSystem
-import org.apache.spark.{SparkEnv, TaskContextImpl, TaskContext}
+import org.apache.spark.{Accumulator, SparkEnv, TaskContextImpl, TaskContext}
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.serializer.SerializerInstance
 import org.apache.spark.unsafe.memory.TaskMemoryManager
@@ -47,7 +47,8 @@ import org.apache.spark.util.Utils
 private[spark] abstract class Task[T](
     val stageId: Int,
     val stageAttemptId: Int,
-    var partitionId: Int) extends Serializable {
+    val partitionId: Int,
+    internalAccumulators: Seq[Accumulator[Long]]) extends Serializable {
 
   /**
    * The key of the Map is the accumulator id and the value of the Map is the latest accumulator
@@ -68,12 +69,13 @@ private[spark] abstract class Task[T](
     metricsSystem: MetricsSystem)
   : (T, AccumulatorUpdates) = {
     context = new TaskContextImpl(
-      stageId = stageId,
-      partitionId = partitionId,
-      taskAttemptId = taskAttemptId,
-      attemptNumber = attemptNumber,
-      taskMemoryManager = taskMemoryManager,
-      metricsSystem = metricsSystem,
+      stageId,
+      partitionId,
+      taskAttemptId,
+      attemptNumber,
+      taskMemoryManager,
+      metricsSystem,
+      internalAccumulators,
       runningLocally = false)
     TaskContext.setTaskContext(context)
     context.taskMetrics.setHostname(Utils.localHostName())
diff --git a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
index de79fa56f017b..0c8f08f0f3b1b 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/hash/HashShuffleReader.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.shuffle.hash
 
-import org.apache.spark.{InterruptibleIterator, Logging, MapOutputTracker, SparkEnv, TaskContext}
+import org.apache.spark._
 import org.apache.spark.serializer.Serializer
 import org.apache.spark.shuffle.{BaseShuffleHandle, ShuffleReader}
 import org.apache.spark.storage.{BlockManager, ShuffleBlockFetcherIterator}
@@ -100,8 +100,10 @@ private[spark] class HashShuffleReader[K, C](
         // the ExternalSorter won't spill to disk.
         val sorter = new ExternalSorter[K, C, C](ordering = Some(keyOrd), serializer = Some(ser))
         sorter.insertAll(aggregatedIter)
-        context.taskMetrics.incMemoryBytesSpilled(sorter.memoryBytesSpilled)
-        context.taskMetrics.incDiskBytesSpilled(sorter.diskBytesSpilled)
+        context.taskMetrics().incMemoryBytesSpilled(sorter.memoryBytesSpilled)
+        context.taskMetrics().incDiskBytesSpilled(sorter.diskBytesSpilled)
+        context.internalMetricsToAccumulators(
+          InternalAccumulator.PEAK_EXECUTION_MEMORY).add(sorter.peakMemoryUsedBytes)
         sorter.iterator
       case None =>
         aggregatedIter
diff --git a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
index e2d25e36365fa..cb122eaed83d1 100644
--- a/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
+++ b/core/src/main/scala/org/apache/spark/ui/ToolTips.scala
@@ -62,6 +62,13 @@ private[spark] object ToolTips {
     """Time that the executor spent paused for Java garbage collection while the task was
        running."""
 
+  val PEAK_EXECUTION_MEMORY =
+    """Execution memory refers to the memory used by internal data structures created during
+       shuffles, aggregations and joins when Tungsten is enabled. The value of this accumulator
+       should be approximately the sum of the peak sizes across all such data structures created
+       in this task. For SQL jobs, this only tracks all unsafe operators, broadcast joins, and
+       external sort."""
+
   val JOB_TIMELINE =
     """Shows when jobs started and ended and when executors joined or left. Drag to scroll.
        Click Enable Zooming and use mouse wheel to zoom in/out."""
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index cf04b5e59239b..3954c3d1ef894 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -26,6 +26,7 @@ import scala.xml.{Elem, Node, Unparsed}
 
 import org.apache.commons.lang3.StringEscapeUtils
 
+import org.apache.spark.{InternalAccumulator, SparkConf}
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler.{AccumulableInfo, TaskInfo}
 import org.apache.spark.ui._
@@ -67,6 +68,8 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
   // if we find that it's okay.
   private val MAX_TIMELINE_TASKS = parent.conf.getInt("spark.ui.timeline.tasks.maximum", 1000)
 
+  private val displayPeakExecutionMemory =
+    parent.conf.getOption("spark.sql.unsafe.enabled").exists(_.toBoolean)
 
   def render(request: HttpServletRequest): Seq[Node] = {
     progressListener.synchronized {
@@ -114,10 +117,11 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
 
       val stageData = stageDataOption.get
       val tasks = stageData.taskData.values.toSeq.sortBy(_.taskInfo.launchTime)
-
       val numCompleted = tasks.count(_.taskInfo.finished)
-      val accumulables = progressListener.stageIdToData((stageId, stageAttemptId)).accumulables
-      val hasAccumulators = accumulables.size > 0
+
+      val allAccumulables = progressListener.stageIdToData((stageId, stageAttemptId)).accumulables
+      val externalAccumulables = allAccumulables.values.filter { acc => !acc.internal }
+      val hasAccumulators = externalAccumulables.size > 0
 
       val summary =
         <div>
@@ -221,6 +225,15 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
                   <span class="additional-metric-title">Getting Result Time</span>
                 </span>
               </li>
+              {if (displayPeakExecutionMemory) {
+                <li>
+                  <span data-toggle="tooltip"
+                        title={ToolTips.PEAK_EXECUTION_MEMORY} data-placement="right">
+                    <input type="checkbox" name={TaskDetailsClassNames.PEAK_EXECUTION_MEMORY}/>
+                    <span class="additional-metric-title">Peak Execution Memory</span>
+                  </span>
+                </li>
+              }}
             </ul>
           </div>
         </div>
@@ -241,11 +254,12 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
       val accumulableTable = UIUtils.listingTable(
         accumulableHeaders,
         accumulableRow,
-        accumulables.values.toSeq)
+        externalAccumulables.toSeq)
 
       val currentTime = System.currentTimeMillis()
       val (taskTable, taskTableHTML) = try {
         val _taskTable = new TaskPagedTable(
+          parent.conf,
           UIUtils.prependBaseUri(parent.basePath) +
             s"/stages/stage?id=${stageId}&attempt=${stageAttemptId}",
           tasks,
@@ -294,12 +308,14 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
         else {
           def getDistributionQuantiles(data: Seq[Double]): IndexedSeq[Double] =
             Distribution(data).get.getQuantiles()
-
           def getFormattedTimeQuantiles(times: Seq[Double]): Seq[Node] = {
             getDistributionQuantiles(times).map { millis =>
               <td>{UIUtils.formatDuration(millis.toLong)}</td>
             }
           }
+          def getFormattedSizeQuantiles(data: Seq[Double]): Seq[Elem] = {
+            getDistributionQuantiles(data).map(d => <td>{Utils.bytesToString(d.toLong)}</td>)
+          }
 
           val deserializationTimes = validTasks.map { case TaskUIData(_, metrics, _) =>
             metrics.get.executorDeserializeTime.toDouble
@@ -349,6 +365,23 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
               </span>
             </td> +:
             getFormattedTimeQuantiles(gettingResultTimes)
+
+          val peakExecutionMemory = validTasks.map { case TaskUIData(info, _, _) =>
+            info.accumulables
+              .find { acc => acc.name == InternalAccumulator.PEAK_EXECUTION_MEMORY }
+              .map { acc => acc.value.toLong }
+              .getOrElse(0L)
+              .toDouble
+          }
+          val peakExecutionMemoryQuantiles = {
+            <td>
+              <span data-toggle="tooltip"
+                    title={ToolTips.PEAK_EXECUTION_MEMORY} data-placement="right">
+                Peak Execution Memory
+              </span>
+            </td> +: getFormattedSizeQuantiles(peakExecutionMemory)
+          }
+
           // The scheduler delay includes the network delay to send the task to the worker
           // machine and to send back the result (but not the time to fetch the task result,
           // if it needed to be fetched from the block manager on the worker).
@@ -359,10 +392,6 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
             title={ToolTips.SCHEDULER_DELAY} data-placement="right">Scheduler Delay</span></td>
           val schedulerDelayQuantiles = schedulerDelayTitle +:
             getFormattedTimeQuantiles(schedulerDelays)
-
-          def getFormattedSizeQuantiles(data: Seq[Double]): Seq[Elem] =
-            getDistributionQuantiles(data).map(d => <td>{Utils.bytesToString(d.toLong)}</td>)
-
           def getFormattedSizeQuantilesWithRecords(data: Seq[Double], records: Seq[Double])
             : Seq[Elem] = {
             val recordDist = getDistributionQuantiles(records).iterator
@@ -466,6 +495,13 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
               {serializationQuantiles}
             </tr>,
             <tr class={TaskDetailsClassNames.GETTING_RESULT_TIME}>{gettingResultQuantiles}</tr>,
+            if (displayPeakExecutionMemory) {
+              <tr class={TaskDetailsClassNames.PEAK_EXECUTION_MEMORY}>
+                {peakExecutionMemoryQuantiles}
+              </tr>
+            } else {
+              Nil
+            },
             if (stageData.hasInput) <tr>{inputQuantiles}</tr> else Nil,
             if (stageData.hasOutput) <tr>{outputQuantiles}</tr> else Nil,
             if (stageData.hasShuffleRead) {
@@ -499,7 +535,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
       val executorTable = new ExecutorTable(stageId, stageAttemptId, parent)
 
       val maybeAccumulableTable: Seq[Node] =
-        if (accumulables.size > 0) { <h4>Accumulators</h4> ++ accumulableTable } else Seq()
+        if (hasAccumulators) { <h4>Accumulators</h4> ++ accumulableTable } else Seq()
 
       val content =
         summary ++
@@ -750,29 +786,30 @@ private[ui] case class TaskTableRowBytesSpilledData(
  * Contains all data that needs for sorting and generating HTML. Using this one rather than
  * TaskUIData to avoid creating duplicate contents during sorting the data.
  */
-private[ui] case class TaskTableRowData(
-    index: Int,
-    taskId: Long,
-    attempt: Int,
-    speculative: Boolean,
-    status: String,
-    taskLocality: String,
-    executorIdAndHost: String,
-    launchTime: Long,
-    duration: Long,
-    formatDuration: String,
-    schedulerDelay: Long,
-    taskDeserializationTime: Long,
-    gcTime: Long,
-    serializationTime: Long,
-    gettingResultTime: Long,
-    accumulators: Option[String], // HTML
-    input: Option[TaskTableRowInputData],
-    output: Option[TaskTableRowOutputData],
-    shuffleRead: Option[TaskTableRowShuffleReadData],
-    shuffleWrite: Option[TaskTableRowShuffleWriteData],
-    bytesSpilled: Option[TaskTableRowBytesSpilledData],
-    error: String)
+private[ui] class TaskTableRowData(
+    val index: Int,
+    val taskId: Long,
+    val attempt: Int,
+    val speculative: Boolean,
+    val status: String,
+    val taskLocality: String,
+    val executorIdAndHost: String,
+    val launchTime: Long,
+    val duration: Long,
+    val formatDuration: String,
+    val schedulerDelay: Long,
+    val taskDeserializationTime: Long,
+    val gcTime: Long,
+    val serializationTime: Long,
+    val gettingResultTime: Long,
+    val peakExecutionMemoryUsed: Long,
+    val accumulators: Option[String], // HTML
+    val input: Option[TaskTableRowInputData],
+    val output: Option[TaskTableRowOutputData],
+    val shuffleRead: Option[TaskTableRowShuffleReadData],
+    val shuffleWrite: Option[TaskTableRowShuffleWriteData],
+    val bytesSpilled: Option[TaskTableRowBytesSpilledData],
+    val error: String)
 
 private[ui] class TaskDataSource(
     tasks: Seq[TaskUIData],
@@ -816,10 +853,15 @@ private[ui] class TaskDataSource(
     val serializationTime = metrics.map(_.resultSerializationTime).getOrElse(0L)
     val gettingResultTime = getGettingResultTime(info, currentTime)
 
-    val maybeAccumulators = info.accumulables
-    val accumulatorsReadable = maybeAccumulators.map { acc =>
+    val (taskInternalAccumulables, taskExternalAccumulables) =
+      info.accumulables.partition(_.internal)
+    val externalAccumulableReadable = taskExternalAccumulables.map { acc =>
       StringEscapeUtils.escapeHtml4(s"${acc.name}: ${acc.update.get}")
     }
+    val peakExecutionMemoryUsed = taskInternalAccumulables
+      .find { acc => acc.name == InternalAccumulator.PEAK_EXECUTION_MEMORY }
+      .map { acc => acc.value.toLong }
+      .getOrElse(0L)
 
     val maybeInput = metrics.flatMap(_.inputMetrics)
     val inputSortable = maybeInput.map(_.bytesRead).getOrElse(0L)
@@ -923,7 +965,7 @@ private[ui] class TaskDataSource(
         None
       }
 
-    TaskTableRowData(
+    new TaskTableRowData(
       info.index,
       info.taskId,
       info.attempt,
@@ -939,7 +981,8 @@ private[ui] class TaskDataSource(
       gcTime,
       serializationTime,
       gettingResultTime,
-      if (hasAccumulators) Some(accumulatorsReadable.mkString("<br/>")) else None,
+      peakExecutionMemoryUsed,
+      if (hasAccumulators) Some(externalAccumulableReadable.mkString("<br/>")) else None,
       input,
       output,
       shuffleRead,
@@ -1006,6 +1049,10 @@ private[ui] class TaskDataSource(
         override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
           Ordering.Long.compare(x.gettingResultTime, y.gettingResultTime)
       }
+      case "Peak Execution Memory" => new Ordering[TaskTableRowData] {
+        override def compare(x: TaskTableRowData, y: TaskTableRowData): Int =
+          Ordering.Long.compare(x.peakExecutionMemoryUsed, y.peakExecutionMemoryUsed)
+      }
       case "Accumulators" =>
         if (hasAccumulators) {
           new Ordering[TaskTableRowData] {
@@ -1132,6 +1179,7 @@ private[ui] class TaskDataSource(
 }
 
 private[ui] class TaskPagedTable(
+    conf: SparkConf,
     basePath: String,
     data: Seq[TaskUIData],
     hasAccumulators: Boolean,
@@ -1143,7 +1191,11 @@ private[ui] class TaskPagedTable(
     currentTime: Long,
     pageSize: Int,
     sortColumn: String,
-    desc: Boolean) extends PagedTable[TaskTableRowData]{
+    desc: Boolean) extends PagedTable[TaskTableRowData] {
+
+  // We only track peak memory used for unsafe operators
+  private val displayPeakExecutionMemory =
+    conf.getOption("spark.sql.unsafe.enabled").exists(_.toBoolean)
 
   override def tableId: String = ""
 
@@ -1195,6 +1247,13 @@ private[ui] class TaskPagedTable(
         ("GC Time", ""),
         ("Result Serialization Time", TaskDetailsClassNames.RESULT_SERIALIZATION_TIME),
         ("Getting Result Time", TaskDetailsClassNames.GETTING_RESULT_TIME)) ++
+        {
+          if (displayPeakExecutionMemory) {
+            Seq(("Peak Execution Memory", TaskDetailsClassNames.PEAK_EXECUTION_MEMORY))
+          } else {
+            Nil
+          }
+        } ++
         {if (hasAccumulators) Seq(("Accumulators", "")) else Nil} ++
         {if (hasInput) Seq(("Input Size / Records", "")) else Nil} ++
         {if (hasOutput) Seq(("Output Size / Records", "")) else Nil} ++
@@ -1271,6 +1330,11 @@ private[ui] class TaskPagedTable(
       <td class={TaskDetailsClassNames.GETTING_RESULT_TIME}>
         {UIUtils.formatDuration(task.gettingResultTime)}
       </td>
+      {if (displayPeakExecutionMemory) {
+        <td class={TaskDetailsClassNames.PEAK_EXECUTION_MEMORY}>
+          {Utils.bytesToString(task.peakExecutionMemoryUsed)}
+        </td>
+      }}
       {if (task.accumulators.nonEmpty) {
         <td>{Unparsed(task.accumulators.get)}</td>
       }}
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala b/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala
index 9bf67db8acde1..d2dfc5a32915c 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/TaskDetailsClassNames.scala
@@ -31,4 +31,5 @@ private[spark] object TaskDetailsClassNames {
   val SHUFFLE_READ_REMOTE_SIZE = "shuffle_read_remote"
   val RESULT_SERIALIZATION_TIME = "serialization_time"
   val GETTING_RESULT_TIME = "getting_result_time"
+  val PEAK_EXECUTION_MEMORY = "peak_execution_memory"
 }
diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
index d166037351c31..f929b12606f0a 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalAppendOnlyMap.scala
@@ -89,6 +89,7 @@ class ExternalAppendOnlyMap[K, V, C](
 
   // Number of bytes spilled in total
   private var _diskBytesSpilled = 0L
+  def diskBytesSpilled: Long = _diskBytesSpilled
 
   // Use getSizeAsKb (not bytes) to maintain backwards compatibility if no units are provided
   private val fileBufferSize =
@@ -97,6 +98,10 @@ class ExternalAppendOnlyMap[K, V, C](
   // Write metrics for current spill
   private var curWriteMetrics: ShuffleWriteMetrics = _
 
+  // Peak size of the in-memory map observed so far, in bytes
+  private var _peakMemoryUsedBytes: Long = 0L
+  def peakMemoryUsedBytes: Long = _peakMemoryUsedBytes
+
   private val keyComparator = new HashComparator[K]
   private val ser = serializer.newInstance()
 
@@ -126,7 +131,11 @@ class ExternalAppendOnlyMap[K, V, C](
 
     while (entries.hasNext) {
       curEntry = entries.next()
-      if (maybeSpill(currentMap, currentMap.estimateSize())) {
+      val estimatedSize = currentMap.estimateSize()
+      if (estimatedSize > _peakMemoryUsedBytes) {
+        _peakMemoryUsedBytes = estimatedSize
+      }
+      if (maybeSpill(currentMap, estimatedSize)) {
         currentMap = new SizeTrackingAppendOnlyMap[K, C]
       }
       currentMap.changeValue(curEntry._1, update)
@@ -207,8 +216,6 @@ class ExternalAppendOnlyMap[K, V, C](
     spilledMaps.append(new DiskMapIterator(file, blockId, batchSizes))
   }
 
-  def diskBytesSpilled: Long = _diskBytesSpilled
-
   /**
    * Return an iterator that merges the in-memory map with the spilled maps.
    * If no spill has occurred, simply return the in-memory map's iterator.
diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
index ba7ec834d622d..19287edbaf166 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
@@ -152,6 +152,9 @@ private[spark] class ExternalSorter[K, V, C](
   private var _diskBytesSpilled = 0L
   def diskBytesSpilled: Long = _diskBytesSpilled
 
+  // Peak size of the in-memory data structure observed so far, in bytes
+  private var _peakMemoryUsedBytes: Long = 0L
+  def peakMemoryUsedBytes: Long = _peakMemoryUsedBytes
 
   // A comparator for keys K that orders them within a partition to allow aggregation or sorting.
   // Can be a partial ordering by hash code if a total ordering is not provided through by the
@@ -224,15 +227,22 @@ private[spark] class ExternalSorter[K, V, C](
       return
     }
 
+    var estimatedSize = 0L
     if (usingMap) {
-      if (maybeSpill(map, map.estimateSize())) {
+      estimatedSize = map.estimateSize()
+      if (maybeSpill(map, estimatedSize)) {
         map = new PartitionedAppendOnlyMap[K, C]
       }
     } else {
-      if (maybeSpill(buffer, buffer.estimateSize())) {
+      estimatedSize = buffer.estimateSize()
+      if (maybeSpill(buffer, estimatedSize)) {
         buffer = newBuffer()
       }
     }
+
+    if (estimatedSize > _peakMemoryUsedBytes) {
+      _peakMemoryUsedBytes = estimatedSize
+    }
   }
 
   /**
@@ -684,8 +694,10 @@ private[spark] class ExternalSorter[K, V, C](
       }
     }
 
-    context.taskMetrics.incMemoryBytesSpilled(memoryBytesSpilled)
-    context.taskMetrics.incDiskBytesSpilled(diskBytesSpilled)
+    context.taskMetrics().incMemoryBytesSpilled(memoryBytesSpilled)
+    context.taskMetrics().incDiskBytesSpilled(diskBytesSpilled)
+    context.internalMetricsToAccumulators(
+      InternalAccumulator.PEAK_EXECUTION_MEMORY).add(peakMemoryUsedBytes)
 
     lengths
   }
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index e948ca33471a4..ffe4b4baffb2a 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -51,7 +51,6 @@
 
 import org.apache.spark.api.java.*;
 import org.apache.spark.api.java.function.*;
-import org.apache.spark.executor.TaskMetrics;
 import org.apache.spark.input.PortableDataStream;
 import org.apache.spark.partial.BoundedDouble;
 import org.apache.spark.partial.PartialResult;
@@ -1011,7 +1010,7 @@ public void persist() {
   @Test
   public void iterator() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 2);
-    TaskContext context = new TaskContextImpl(0, 0, 0L, 0, null, null, false, new TaskMetrics());
+    TaskContext context = TaskContext$.MODULE$.empty();
     Assert.assertEquals(1, rdd.iterator(rdd.partitions().get(0), context).next().intValue());
   }
 
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
index 04fc09b323dbb..98c32bbc298d7 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
@@ -190,6 +190,7 @@ public Tuple2<TempShuffleBlockId, File> answer(
       });
 
     when(taskContext.taskMetrics()).thenReturn(taskMetrics);
+    when(taskContext.internalMetricsToAccumulators()).thenReturn(null);
 
     when(shuffleDep.serializer()).thenReturn(Option.<Serializer>apply(serializer));
     when(shuffleDep.partitioner()).thenReturn(hashPartitioner);
@@ -542,4 +543,57 @@ public void spillFilesAreDeletedWhenStoppingAfterError() throws IOException {
     writer.stop(false);
     assertSpillFilesWereCleanedUp();
   }
+
+  @Test
+  public void testPeakMemoryUsed() throws Exception {
+    final long recordLengthBytes = 8;
+    final long pageSizeBytes = 256;
+    final long numRecordsPerPage = pageSizeBytes / recordLengthBytes;
+    final SparkConf conf = new SparkConf().set("spark.buffer.pageSize", pageSizeBytes + "b");
+    final UnsafeShuffleWriter<Object, Object> writer =
+      new UnsafeShuffleWriter<Object, Object>(
+        blockManager,
+        shuffleBlockResolver,
+        taskMemoryManager,
+        shuffleMemoryManager,
+        new UnsafeShuffleHandle<Object, Object>(0, 1, shuffleDep),
+        0, // map id
+        taskContext,
+        conf);
+
+    // Peak memory should be monotonically increasing. More specifically, every time
+    // we allocate a new page it should increase by exactly the size of the page.
+    long previousPeakMemory = writer.getPeakMemoryUsedBytes();
+    long newPeakMemory;
+    try {
+      for (int i = 0; i < numRecordsPerPage * 10; i++) {
+        writer.insertRecordIntoSorter(new Tuple2<Object, Object>(1, 1));
+        newPeakMemory = writer.getPeakMemoryUsedBytes();
+        if (i % numRecordsPerPage == 0) {
+          // We allocated a new page for this record, so peak memory should change
+          assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
+        } else {
+          assertEquals(previousPeakMemory, newPeakMemory);
+        }
+        previousPeakMemory = newPeakMemory;
+      }
+
+      // Spilling should not change peak memory
+      writer.forceSorterToSpill();
+      newPeakMemory = writer.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+      for (int i = 0; i < numRecordsPerPage; i++) {
+        writer.insertRecordIntoSorter(new Tuple2<Object, Object>(1, 1));
+      }
+      newPeakMemory = writer.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+
+      // Closing the writer should not change peak memory
+      writer.closeAndWriteOutput();
+      newPeakMemory = writer.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+    } finally {
+      writer.stop(false);
+    }
+  }
 }
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index dbb7c662d7871..0e23a64fb74bb 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -25,6 +25,7 @@
 import org.mockito.invocation.InvocationOnMock;
 import org.mockito.stubbing.Answer;
 import static org.hamcrest.Matchers.greaterThan;
+import static org.junit.Assert.*;
 import static org.mockito.AdditionalMatchers.geq;
 import static org.mockito.Mockito.*;
 
@@ -495,4 +496,42 @@ public void resizingLargeMap() {
     map.growAndRehash();
     map.free();
   }
+
+  @Test
+  public void testTotalMemoryConsumption() {
+    final long recordLengthBytes = 24;
+    final long pageSizeBytes = 256 + 8; // 8 bytes for end-of-page marker
+    final long numRecordsPerPage = (pageSizeBytes - 8) / recordLengthBytes;
+    final BytesToBytesMap map = new BytesToBytesMap(
+      taskMemoryManager, shuffleMemoryManager, 1024, pageSizeBytes);
+
+    // Since BytesToBytesMap is append-only, we expect the total memory consumption to be
+    // monotonically increasing. More specifically, every time we allocate a new page it
+    // should increase by exactly the size of the page. In this regard, the memory usage
+    // at any given time is also the peak memory used.
+    long previousMemory = map.getTotalMemoryConsumption();
+    long newMemory;
+    try {
+      for (long i = 0; i < numRecordsPerPage * 10; i++) {
+        final long[] value = new long[]{i};
+        map.lookup(value, PlatformDependent.LONG_ARRAY_OFFSET, 8).putNewKey(
+          value,
+          PlatformDependent.LONG_ARRAY_OFFSET,
+          8,
+          value,
+          PlatformDependent.LONG_ARRAY_OFFSET,
+          8);
+        newMemory = map.getTotalMemoryConsumption();
+        if (i % numRecordsPerPage == 0) {
+          // We allocated a new page for this record, so peak memory should change
+          assertEquals(previousMemory + pageSizeBytes, newMemory);
+        } else {
+          assertEquals(previousMemory, newMemory);
+        }
+        previousMemory = newMemory;
+      }
+    } finally {
+      map.free();
+    }
+  }
 }
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index 52fa8bcd57e79..c11949d57a0ea 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -247,4 +247,50 @@ public void testFillingPage() throws Exception {
     assertSpillFilesWereCleanedUp();
   }
 
+  @Test
+  public void testPeakMemoryUsed() throws Exception {
+    final long recordLengthBytes = 8;
+    final long pageSizeBytes = 256;
+    final long numRecordsPerPage = pageSizeBytes / recordLengthBytes;
+    final UnsafeExternalSorter sorter = UnsafeExternalSorter.create(
+      taskMemoryManager,
+      shuffleMemoryManager,
+      blockManager,
+      taskContext,
+      recordComparator,
+      prefixComparator,
+      1024,
+      pageSizeBytes);
+
+    // Peak memory should be monotonically increasing. More specifically, every time
+    // we allocate a new page it should increase by exactly the size of the page.
+    long previousPeakMemory = sorter.getPeakMemoryUsedBytes();
+    long newPeakMemory;
+    try {
+      for (int i = 0; i < numRecordsPerPage * 10; i++) {
+        insertNumber(sorter, i);
+        newPeakMemory = sorter.getPeakMemoryUsedBytes();
+        if (i % numRecordsPerPage == 0) {
+          // We allocated a new page for this record, so peak memory should change
+          assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
+        } else {
+          assertEquals(previousPeakMemory, newPeakMemory);
+        }
+        previousPeakMemory = newPeakMemory;
+      }
+
+      // Spilling should not change peak memory
+      sorter.spill();
+      newPeakMemory = sorter.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+      for (int i = 0; i < numRecordsPerPage; i++) {
+        insertNumber(sorter, i);
+      }
+      newPeakMemory = sorter.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+    } finally {
+      sorter.freeMemory();
+    }
+  }
+
 }
diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
index e942d6579b2fd..48f549575f4d1 100644
--- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
@@ -18,13 +18,17 @@
 package org.apache.spark
 
 import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
 import scala.ref.WeakReference
 
 import org.scalatest.Matchers
+import org.scalatest.exceptions.TestFailedException
 
+import org.apache.spark.scheduler._
 
-class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContext {
 
+class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContext {
+  import InternalAccumulator._
 
   implicit def setAccum[A]: AccumulableParam[mutable.Set[A], A] =
     new AccumulableParam[mutable.Set[A], A] {
@@ -155,4 +159,191 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
     assert(!Accumulators.originals.get(accId).isDefined)
   }
 
+  test("internal accumulators in TaskContext") {
+    val accums = InternalAccumulator.create()
+    val taskContext = new TaskContextImpl(0, 0, 0, 0, null, null, accums)
+    val internalMetricsToAccums = taskContext.internalMetricsToAccumulators
+    val collectedInternalAccums = taskContext.collectInternalAccumulators()
+    val collectedAccums = taskContext.collectAccumulators()
+    assert(internalMetricsToAccums.size > 0)
+    assert(internalMetricsToAccums.values.forall(_.isInternal))
+    assert(internalMetricsToAccums.contains(TEST_ACCUMULATOR))
+    val testAccum = internalMetricsToAccums(TEST_ACCUMULATOR)
+    assert(collectedInternalAccums.size === internalMetricsToAccums.size)
+    assert(collectedInternalAccums.size === collectedAccums.size)
+    assert(collectedInternalAccums.contains(testAccum.id))
+    assert(collectedAccums.contains(testAccum.id))
+  }
+
+  test("internal accumulators in a stage") {
+    val listener = new SaveInfoListener
+    val numPartitions = 10
+    sc = new SparkContext("local", "test")
+    sc.addSparkListener(listener)
+    // Have each task add 1 to the internal accumulator
+    sc.parallelize(1 to 100, numPartitions).mapPartitions { iter =>
+      TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
+      iter
+    }.count()
+    val stageInfos = listener.getCompletedStageInfos
+    val taskInfos = listener.getCompletedTaskInfos
+    assert(stageInfos.size === 1)
+    assert(taskInfos.size === numPartitions)
+    // The accumulator values should be merged in the stage
+    val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
+    assert(stageAccum.value.toLong === numPartitions)
+    // The accumulator should be updated locally on each task
+    val taskAccumValues = taskInfos.map { taskInfo =>
+      val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
+      assert(taskAccum.update.isDefined)
+      assert(taskAccum.update.get.toLong === 1)
+      taskAccum.value.toLong
+    }
+    // Each task should keep track of the partial value on the way, i.e. 1, 2, ... numPartitions
+    assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
+  }
+
+  test("internal accumulators in multiple stages") {
+    val listener = new SaveInfoListener
+    val numPartitions = 10
+    sc = new SparkContext("local", "test")
+    sc.addSparkListener(listener)
+    // Each stage creates its own set of internal accumulators so the
+    // values for the same metric should not be mixed up across stages
+    sc.parallelize(1 to 100, numPartitions)
+      .map { i => (i, i) }
+      .mapPartitions { iter =>
+        TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
+        iter
+      }
+      .reduceByKey { case (x, y) => x + y }
+      .mapPartitions { iter =>
+        TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 10
+        iter
+      }
+      .repartition(numPartitions * 2)
+      .mapPartitions { iter =>
+        TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 100
+        iter
+      }
+      .count()
+    // We ran 3 stages, and the accumulator values should be distinct
+    val stageInfos = listener.getCompletedStageInfos
+    assert(stageInfos.size === 3)
+    val firstStageAccum = findAccumulableInfo(stageInfos(0).accumulables.values, TEST_ACCUMULATOR)
+    val secondStageAccum = findAccumulableInfo(stageInfos(1).accumulables.values, TEST_ACCUMULATOR)
+    val thirdStageAccum = findAccumulableInfo(stageInfos(2).accumulables.values, TEST_ACCUMULATOR)
+    assert(firstStageAccum.value.toLong === numPartitions)
+    assert(secondStageAccum.value.toLong === numPartitions * 10)
+    assert(thirdStageAccum.value.toLong === numPartitions * 2 * 100)
+  }
+
+  test("internal accumulators in fully resubmitted stages") {
+    testInternalAccumulatorsWithFailedTasks((i: Int) => true) // fail all tasks
+  }
+
+  test("internal accumulators in partially resubmitted stages") {
+    testInternalAccumulatorsWithFailedTasks((i: Int) => i % 2 == 0) // fail a subset
+  }
+
+  /**
+   * Return the accumulable info that matches the specified name.
+   */
+  private def findAccumulableInfo(
+      accums: Iterable[AccumulableInfo],
+      name: String): AccumulableInfo = {
+    accums.find { a => a.name == name }.getOrElse {
+      throw new TestFailedException(s"internal accumulator '$name' not found", 0)
+    }
+  }
+
+  /**
+   * Test whether internal accumulators are merged properly if some tasks fail.
+   */
+  private def testInternalAccumulatorsWithFailedTasks(failCondition: (Int => Boolean)): Unit = {
+    val listener = new SaveInfoListener
+    val numPartitions = 10
+    val numFailedPartitions = (0 until numPartitions).count(failCondition)
+    // This says use 1 core and retry tasks up to 2 times
+    sc = new SparkContext("local[1, 2]", "test")
+    sc.addSparkListener(listener)
+    sc.parallelize(1 to 100, numPartitions).mapPartitionsWithIndex { case (i, iter) =>
+      val taskContext = TaskContext.get()
+      taskContext.internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
+      // Fail the first attempts of a subset of the tasks
+      if (failCondition(i) && taskContext.attemptNumber() == 0) {
+        throw new Exception("Failing a task intentionally.")
+      }
+      iter
+    }.count()
+    val stageInfos = listener.getCompletedStageInfos
+    val taskInfos = listener.getCompletedTaskInfos
+    assert(stageInfos.size === 1)
+    assert(taskInfos.size === numPartitions + numFailedPartitions)
+    val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
+    // We should not double count values in the merged accumulator
+    assert(stageAccum.value.toLong === numPartitions)
+    val taskAccumValues = taskInfos.flatMap { taskInfo =>
+      if (!taskInfo.failed) {
+        // If a task succeeded, its update value should always be 1
+        val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
+        assert(taskAccum.update.isDefined)
+        assert(taskAccum.update.get.toLong === 1)
+        Some(taskAccum.value.toLong)
+      } else {
+        // If a task failed, we should not get its accumulator values
+        assert(taskInfo.accumulables.isEmpty)
+        None
+      }
+    }
+    assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
+  }
+
+}
+
+private[spark] object AccumulatorSuite {
+
+  /**
+   * Run one or more Spark jobs and verify that the peak execution memory accumulator
+   * is updated afterwards.
+   */
+  def verifyPeakExecutionMemorySet(
+      sc: SparkContext,
+      testName: String)(testBody: => Unit): Unit = {
+    val listener = new SaveInfoListener
+    sc.addSparkListener(listener)
+    // Verify that the accumulator does not already exist
+    sc.parallelize(1 to 10).count()
+    val accums = listener.getCompletedStageInfos.flatMap(_.accumulables.values)
+    assert(!accums.exists(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY))
+    testBody
+    // Verify that peak execution memory is updated
+    val accum = listener.getCompletedStageInfos
+      .flatMap(_.accumulables.values)
+      .find(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY)
+      .getOrElse {
+        throw new TestFailedException(
+          s"peak execution memory accumulator not set in '$testName'", 0)
+      }
+    assert(accum.value.toLong > 0)
+  }
+}
+
+/**
+ * A simple listener that keeps track of the TaskInfos and StageInfos of all completed jobs.
+ */
+private class SaveInfoListener extends SparkListener {
+  private val completedStageInfos: ArrayBuffer[StageInfo] = new ArrayBuffer[StageInfo]
+  private val completedTaskInfos: ArrayBuffer[TaskInfo] = new ArrayBuffer[TaskInfo]
+
+  def getCompletedStageInfos: Seq[StageInfo] = completedStageInfos.toArray.toSeq
+  def getCompletedTaskInfos: Seq[TaskInfo] = completedTaskInfos.toArray.toSeq
+
+  override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = {
+    completedStageInfos += stageCompleted.stageInfo
+  }
+
+  override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = {
+    completedTaskInfos += taskEnd.taskInfo
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
index 618a5fb24710f..cb8bd04e496a7 100644
--- a/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CacheManagerSuite.scala
@@ -21,7 +21,7 @@ import org.mockito.Mockito._
 import org.scalatest.BeforeAndAfter
 import org.scalatest.mock.MockitoSugar
 
-import org.apache.spark.executor.DataReadMethod
+import org.apache.spark.executor.{DataReadMethod, TaskMetrics}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage._
 
@@ -65,7 +65,7 @@ class CacheManagerSuite extends SparkFunSuite with LocalSparkContext with Before
     // in blockManager.put is a losing battle. You have been warned.
     blockManager = sc.env.blockManager
     cacheManager = sc.env.cacheManager
-    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val context = TaskContext.empty()
     val computeValue = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
     val getValue = blockManager.get(RDDBlockId(rdd.id, split.index))
     assert(computeValue.toList === List(1, 2, 3, 4))
@@ -77,7 +77,7 @@ class CacheManagerSuite extends SparkFunSuite with LocalSparkContext with Before
     val result = new BlockResult(Array(5, 6, 7).iterator, DataReadMethod.Memory, 12)
     when(blockManager.get(RDDBlockId(0, 0))).thenReturn(Some(result))
 
-    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val context = TaskContext.empty()
     val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
     assert(value.toList === List(5, 6, 7))
   }
@@ -86,14 +86,14 @@ class CacheManagerSuite extends SparkFunSuite with LocalSparkContext with Before
     // Local computation should not persist the resulting value, so don't expect a put().
     when(blockManager.get(RDDBlockId(0, 0))).thenReturn(None)
 
-    val context = new TaskContextImpl(0, 0, 0, 0, null, null, true)
+    val context = new TaskContextImpl(0, 0, 0, 0, null, null, Seq.empty, runningLocally = true)
     val value = cacheManager.getOrCompute(rdd, split, context, StorageLevel.MEMORY_ONLY)
     assert(value.toList === List(1, 2, 3, 4))
   }
 
   test("verify task metrics updated correctly") {
     cacheManager = sc.env.cacheManager
-    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val context = TaskContext.empty()
     cacheManager.getOrCompute(rdd3, split, context, StorageLevel.MEMORY_ONLY)
     assert(context.taskMetrics.updatedBlocks.getOrElse(Seq()).size === 2)
   }
diff --git a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
index 3e8816a4c65be..5f73ec8675966 100644
--- a/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/PipedRDDSuite.scala
@@ -175,7 +175,7 @@ class PipedRDDSuite extends SparkFunSuite with SharedSparkContext {
       }
       val hadoopPart1 = generateFakeHadoopPartition()
       val pipedRdd = new PipedRDD(nums, "printenv " + varName)
-      val tContext = new TaskContextImpl(0, 0, 0, 0, null, null)
+      val tContext = TaskContext.empty()
       val rddIter = pipedRdd.compute(hadoopPart1, tContext)
       val arr = rddIter.toArray
       assert(arr(0) == "/some/path")
diff --git a/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala b/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala
index b3ca150195a5f..f7e16af9d3a92 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/FakeTask.scala
@@ -19,9 +19,11 @@ package org.apache.spark.scheduler
 
 import org.apache.spark.TaskContext
 
-class FakeTask(stageId: Int, prefLocs: Seq[TaskLocation] = Nil) extends Task[Int](stageId, 0, 0) {
+class FakeTask(
+    stageId: Int,
+    prefLocs: Seq[TaskLocation] = Nil)
+  extends Task[Int](stageId, 0, 0, Seq.empty) {
   override def runTask(context: TaskContext): Int = 0
-
   override def preferredLocations: Seq[TaskLocation] = prefLocs
 }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala b/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala
index 383855caefa2f..f33324792495b 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/NotSerializableFakeTask.scala
@@ -25,7 +25,7 @@ import org.apache.spark.TaskContext
  * A Task implementation that fails to serialize.
  */
 private[spark] class NotSerializableFakeTask(myId: Int, stageId: Int)
-  extends Task[Array[Byte]](stageId, 0, 0) {
+  extends Task[Array[Byte]](stageId, 0, 0, Seq.empty) {
 
   override def runTask(context: TaskContext): Array[Byte] = Array.empty[Byte]
   override def preferredLocations: Seq[TaskLocation] = Seq[TaskLocation]()
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
index 9201d1e1f328b..450ab7b9fe92b 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala
@@ -57,8 +57,9 @@ class TaskContextSuite extends SparkFunSuite with BeforeAndAfter with LocalSpark
     }
     val closureSerializer = SparkEnv.get.closureSerializer.newInstance()
     val func = (c: TaskContext, i: Iterator[String]) => i.next()
-    val task = new ResultTask[String, String](0, 0,
-      sc.broadcast(closureSerializer.serialize((rdd, func)).array), rdd.partitions(0), Seq(), 0)
+    val taskBinary = sc.broadcast(closureSerializer.serialize((rdd, func)).array)
+    val task = new ResultTask[String, String](
+      0, 0, taskBinary, rdd.partitions(0), Seq.empty, 0, Seq.empty)
     intercept[RuntimeException] {
       task.run(0, 0, null)
     }
@@ -66,7 +67,7 @@ class TaskContextSuite extends SparkFunSuite with BeforeAndAfter with LocalSpark
   }
 
   test("all TaskCompletionListeners should be called even if some fail") {
-    val context = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val context = TaskContext.empty()
     val listener = mock(classOf[TaskCompletionListener])
     context.addTaskCompletionListener(_ => throw new Exception("blah"))
     context.addTaskCompletionListener(listener)
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index 3abb99c4b2b54..f7cc4bb61d574 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -136,7 +136,7 @@ class FakeTaskScheduler(sc: SparkContext, liveExecutors: (String, String)* /* ex
 /**
  * A Task implementation that results in a large serialized task.
  */
-class LargeTask(stageId: Int) extends Task[Array[Byte]](stageId, 0, 0) {
+class LargeTask(stageId: Int) extends Task[Array[Byte]](stageId, 0, 0, Seq.empty) {
   val randomBuffer = new Array[Byte](TaskSetManager.TASK_SIZE_TO_WARN_KB * 1024)
   val random = new Random(0)
   random.nextBytes(randomBuffer)
diff --git a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
index db718ecabbdb9..05b3afef5b839 100644
--- a/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
+++ b/core/src/test/scala/org/apache/spark/shuffle/hash/HashShuffleReaderSuite.scala
@@ -138,7 +138,7 @@ class HashShuffleReaderSuite extends SparkFunSuite with LocalSparkContext {
       shuffleHandle,
       reduceId,
       reduceId + 1,
-      new TaskContextImpl(0, 0, 0, 0, null, null),
+      TaskContext.empty(),
       blockManager,
       mapOutputTracker)
 
diff --git a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
index cf8bd8ae69625..828153bdbfc44 100644
--- a/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/ShuffleBlockFetcherIteratorSuite.scala
@@ -29,7 +29,7 @@ import org.mockito.invocation.InvocationOnMock
 import org.mockito.stubbing.Answer
 import org.scalatest.PrivateMethodTester
 
-import org.apache.spark.{SparkFunSuite, TaskContextImpl}
+import org.apache.spark.{SparkFunSuite, TaskContext}
 import org.apache.spark.network._
 import org.apache.spark.network.buffer.ManagedBuffer
 import org.apache.spark.network.shuffle.BlockFetchingListener
@@ -95,7 +95,7 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
     )
 
     val iterator = new ShuffleBlockFetcherIterator(
-      new TaskContextImpl(0, 0, 0, 0, null, null),
+      TaskContext.empty(),
       transfer,
       blockManager,
       blocksByAddress,
@@ -165,7 +165,7 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
       (remoteBmId, blocks.keys.map(blockId => (blockId, 1.asInstanceOf[Long])).toSeq))
 
-    val taskContext = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val taskContext = TaskContext.empty()
     val iterator = new ShuffleBlockFetcherIterator(
       taskContext,
       transfer,
@@ -227,7 +227,7 @@ class ShuffleBlockFetcherIteratorSuite extends SparkFunSuite with PrivateMethodT
     val blocksByAddress = Seq[(BlockManagerId, Seq[(BlockId, Long)])](
       (remoteBmId, blocks.keys.map(blockId => (blockId, 1.asInstanceOf[Long])).toSeq))
 
-    val taskContext = new TaskContextImpl(0, 0, 0, 0, null, null)
+    val taskContext = TaskContext.empty()
     val iterator = new ShuffleBlockFetcherIterator(
       taskContext,
       transfer,
diff --git a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
new file mode 100644
index 0000000000000..98f9314f31dff
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ui
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.Node
+
+import org.mockito.Mockito.{mock, when, RETURNS_SMART_NULLS}
+
+import org.apache.spark.{LocalSparkContext, SparkConf, SparkFunSuite, Success}
+import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.scheduler._
+import org.apache.spark.ui.jobs.{JobProgressListener, StagePage, StagesTab}
+import org.apache.spark.ui.scope.RDDOperationGraphListener
+
+class StagePageSuite extends SparkFunSuite with LocalSparkContext {
+
+  test("peak execution memory only displayed if unsafe is enabled") {
+    val unsafeConf = "spark.sql.unsafe.enabled"
+    val conf = new SparkConf().set(unsafeConf, "true")
+    val html = renderStagePage(conf).toString().toLowerCase
+    val targetString = "peak execution memory"
+    assert(html.contains(targetString))
+    // Disable unsafe and make sure it's not there
+    val conf2 = new SparkConf().set(unsafeConf, "false")
+    val html2 = renderStagePage(conf2).toString().toLowerCase
+    assert(!html2.contains(targetString))
+  }
+
+  /**
+   * Render a stage page started with the given conf and return the HTML.
+   * This also runs a dummy stage to populate the page with useful content.
+   */
+  private def renderStagePage(conf: SparkConf): Seq[Node] = {
+    val jobListener = new JobProgressListener(conf)
+    val graphListener = new RDDOperationGraphListener(conf)
+    val tab = mock(classOf[StagesTab], RETURNS_SMART_NULLS)
+    val request = mock(classOf[HttpServletRequest])
+    when(tab.conf).thenReturn(conf)
+    when(tab.progressListener).thenReturn(jobListener)
+    when(tab.operationGraphListener).thenReturn(graphListener)
+    when(tab.appName).thenReturn("testing")
+    when(tab.headerTabs).thenReturn(Seq.empty)
+    when(request.getParameter("id")).thenReturn("0")
+    when(request.getParameter("attempt")).thenReturn("0")
+    val page = new StagePage(tab)
+
+    // Simulate a stage in job progress listener
+    val stageInfo = new StageInfo(0, 0, "dummy", 1, Seq.empty, Seq.empty, "details")
+    val taskInfo = new TaskInfo(0, 0, 0, 0, "0", "localhost", TaskLocality.ANY, false)
+    jobListener.onStageSubmitted(SparkListenerStageSubmitted(stageInfo))
+    jobListener.onTaskStart(SparkListenerTaskStart(0, 0, taskInfo))
+    taskInfo.markSuccessful()
+    jobListener.onTaskEnd(
+      SparkListenerTaskEnd(0, 0, "result", Success, taskInfo, TaskMetrics.empty))
+    jobListener.onStageCompleted(SparkListenerStageCompleted(stageInfo))
+    page.render(request)
+  }
+
+}
diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
index 9c362f0de7076..12e9bafcc92c1 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalAppendOnlyMapSuite.scala
@@ -399,4 +399,19 @@ class ExternalAppendOnlyMapSuite extends SparkFunSuite with LocalSparkContext {
     sc.stop()
   }
 
+  test("external aggregation updates peak execution memory") {
+    val conf = createSparkConf(loadDefaults = false)
+      .set("spark.shuffle.memoryFraction", "0.001")
+      .set("spark.shuffle.manager", "hash") // make sure we're not also using ExternalSorter
+    sc = new SparkContext("local", "test", conf)
+    // No spilling
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "external map without spilling") {
+      sc.parallelize(1 to 10, 2).map { i => (i, i) }.reduceByKey(_ + _).count()
+    }
+    // With spilling
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "external map with spilling") {
+      sc.parallelize(1 to 1000 * 1000, 2).map { i => (i, i) }.reduceByKey(_ + _).count()
+    }
+  }
+
 }
diff --git a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala
index 986cd8623d145..bdb0f4d507a7e 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala
@@ -692,7 +692,7 @@ class ExternalSorterSuite extends SparkFunSuite with LocalSparkContext {
     sortWithoutBreakingSortingContracts(createSparkConf(true, false))
   }
 
-  def sortWithoutBreakingSortingContracts(conf: SparkConf) {
+  private def sortWithoutBreakingSortingContracts(conf: SparkConf) {
     conf.set("spark.shuffle.memoryFraction", "0.01")
     conf.set("spark.shuffle.manager", "sort")
     sc = new SparkContext("local-cluster[1,1,1024]", "test", conf)
@@ -743,5 +743,15 @@ class ExternalSorterSuite extends SparkFunSuite with LocalSparkContext {
     }
 
     sorter2.stop()
- }
+  }
+
+  test("sorting updates peak execution memory") {
+    val conf = createSparkConf(loadDefaults = false, kryo = false)
+      .set("spark.shuffle.manager", "sort")
+    sc = new SparkContext("local", "test", conf)
+    // Avoid aggregating here to make sure we're not also using ExternalAppendOnlyMap
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "external sorter") {
+      sc.parallelize(1 to 1000, 2).repartition(100).count()
+    }
+  }
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index 5e4c6232c9471..193906d24790e 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -106,6 +106,13 @@ void spill() throws IOException {
     sorter.spill();
   }
 
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  public long getPeakMemoryUsage() {
+    return sorter.getPeakMemoryUsedBytes();
+  }
+
   private void cleanupResources() {
     sorter.freeMemory();
   }
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 9e2c9334a7bee..43d06ce9bdfa3 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -208,6 +208,14 @@ public void close() {
     };
   }
 
+  /**
+   * The memory used by this map's managed structures, in bytes.
+   * Note that this is also the peak memory used by this map, since the map is append-only.
+   */
+  public long getMemoryUsage() {
+    return map.getTotalMemoryConsumption();
+  }
+
   /**
    * Free the memory associated with this map. This is idempotent and can be called multiple times.
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
index cd87b8deba0c2..bf4905dc1eef9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution
 
 import java.io.IOException
 
-import org.apache.spark.{SparkEnv, TaskContext}
+import org.apache.spark.{InternalAccumulator, SparkEnv, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -263,11 +263,12 @@ case class GeneratedAggregate(
         assert(iter.hasNext, "There should be at least one row for this path")
         log.info("Using Unsafe-based aggregator")
         val pageSizeBytes = SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
+        val taskContext = TaskContext.get()
         val aggregationMap = new UnsafeFixedWidthAggregationMap(
           newAggregationBuffer(EmptyRow),
           aggregationBufferSchema,
           groupKeySchema,
-          TaskContext.get.taskMemoryManager(),
+          taskContext.taskMemoryManager(),
           SparkEnv.get.shuffleMemoryManager,
           1024 * 16, // initial capacity
           pageSizeBytes,
@@ -284,6 +285,10 @@ case class GeneratedAggregate(
           updateProjection.target(aggregationBuffer)(joinedRow(aggregationBuffer, currentRow))
         }
 
+        // Record memory used in the process
+        taskContext.internalMetricsToAccumulators(
+          InternalAccumulator.PEAK_EXECUTION_MEMORY).add(aggregationMap.getMemoryUsage)
+
         new Iterator[InternalRow] {
           private[this] val mapIterator = aggregationMap.iterator()
           private[this] val resultProjection = resultProjectionBuilder()
@@ -300,7 +305,7 @@ case class GeneratedAggregate(
               } else {
                 // This is the last element in the iterator, so let's free the buffer. Before we do,
                 // though, we need to make a defensive copy of the result so that we don't return an
-                // object that might contain dangling pointers to the freed memory
+                // object that might contain dangling pointers to the freed memory.
                 val resultCopy = result.copy()
                 aggregationMap.free()
                 resultCopy
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
index 624efc1b1d734..e73e2523a777f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.joins
 import scala.concurrent._
 import scala.concurrent.duration._
 
+import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -70,7 +71,14 @@ case class BroadcastHashJoin(
     val broadcastRelation = Await.result(broadcastFuture, timeout)
 
     streamedPlan.execute().mapPartitions { streamedIter =>
-      hashJoin(streamedIter, broadcastRelation.value)
+      val hashedRelation = broadcastRelation.value
+      hashedRelation match {
+        case unsafe: UnsafeHashedRelation =>
+          TaskContext.get().internalMetricsToAccumulators(
+            InternalAccumulator.PEAK_EXECUTION_MEMORY).add(unsafe.getUnsafeSize)
+        case _ =>
+      }
+      hashJoin(streamedIter, hashedRelation)
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index 309716a0efcc0..c35e439cc9deb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.joins
 import scala.concurrent._
 import scala.concurrent.duration._
 
+import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -75,6 +76,13 @@ case class BroadcastHashOuterJoin(
       val hashTable = broadcastRelation.value
       val keyGenerator = streamedKeyGenerator
 
+      hashTable match {
+        case unsafe: UnsafeHashedRelation =>
+          TaskContext.get().internalMetricsToAccumulators(
+            InternalAccumulator.PEAK_EXECUTION_MEMORY).add(unsafe.getUnsafeSize)
+        case _ =>
+      }
+
       joinType match {
         case LeftOuter =>
           streamedIter.flatMap(currentRow => {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
index a60593911f94f..5bd06fbdca605 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution.joins
 
+import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -51,7 +52,14 @@ case class BroadcastLeftSemiJoinHash(
       val broadcastedRelation = sparkContext.broadcast(hashRelation)
 
       left.execute().mapPartitions { streamIter =>
-        hashSemiJoin(streamIter, broadcastedRelation.value)
+        val hashedRelation = broadcastedRelation.value
+        hashedRelation match {
+          case unsafe: UnsafeHashedRelation =>
+            TaskContext.get().internalMetricsToAccumulators(
+              InternalAccumulator.PEAK_EXECUTION_MEMORY).add(unsafe.getUnsafeSize)
+          case _ =>
+        }
+        hashSemiJoin(streamIter, hashedRelation)
       }
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index cc8bbfd2f8943..58b4236f7b5b5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -183,8 +183,27 @@ private[joins] final class UnsafeHashedRelation(
   private[joins] def this() = this(null)  // Needed for serialization
 
   // Use BytesToBytesMap in executor for better performance (it's created when deserialization)
+  // This is used in broadcast joins and distributed mode only
   @transient private[this] var binaryMap: BytesToBytesMap = _
 
+  /**
+   * Return the size of the unsafe map on the executors.
+   *
+   * For broadcast joins, this hashed relation is bigger on the driver because it is
+   * represented as a Java hash map there. While serializing the map to the executors,
+   * however, we rehash the contents in a binary map to reduce the memory footprint on
+   * the executors.
+   *
+   * For non-broadcast joins or in local mode, return 0.
+   */
+  def getUnsafeSize: Long = {
+    if (binaryMap != null) {
+      binaryMap.getTotalMemoryConsumption
+    } else {
+      0
+    }
+  }
+
   override def get(key: InternalRow): Seq[InternalRow] = {
     val unsafeKey = key.asInstanceOf[UnsafeRow]
 
@@ -214,7 +233,7 @@ private[joins] final class UnsafeHashedRelation(
       }
 
     } else {
-      // Use the JavaHashMap in Local mode or ShuffleHashJoin
+      // Use the Java HashMap in local mode or for non-broadcast joins (e.g. ShuffleHashJoin)
       hashTable.get(unsafeKey)
     }
   }
@@ -316,6 +335,7 @@ private[joins] object UnsafeHashedRelation {
       keyGenerator: UnsafeProjection,
       sizeEstimate: Int): HashedRelation = {
 
+    // Use a Java hash table here because unsafe maps expect fixed size records
     val hashTable = new JavaHashMap[UnsafeRow, CompactBuffer[UnsafeRow]](sizeEstimate)
 
     // Create a mapping of buildKeys -> rows
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index 92cf328c76cbc..3192b6ebe9075 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
@@ -76,6 +77,11 @@ case class ExternalSort(
       val sorter = new ExternalSorter[InternalRow, Null, InternalRow](ordering = Some(ordering))
       sorter.insertAll(iterator.map(r => (r.copy(), null)))
       val baseIterator = sorter.iterator.map(_._1)
+      val context = TaskContext.get()
+      context.taskMetrics().incDiskBytesSpilled(sorter.diskBytesSpilled)
+      context.taskMetrics().incMemoryBytesSpilled(sorter.memoryBytesSpilled)
+      context.internalMetricsToAccumulators(
+        InternalAccumulator.PEAK_EXECUTION_MEMORY).add(sorter.peakMemoryUsedBytes)
       // TODO(marmbrus): The complex type signature below thwarts inference for no reason.
       CompletionIterator[InternalRow, Iterator[InternalRow]](baseIterator, sorter.stop())
     }, preservesPartitioning = true)
@@ -137,7 +143,11 @@ case class TungstenSort(
       if (testSpillFrequency > 0) {
         sorter.setTestSpillFrequency(testSpillFrequency)
       }
-      sorter.sort(iter.asInstanceOf[Iterator[UnsafeRow]])
+      val sortedIterator = sorter.sort(iter.asInstanceOf[Iterator[UnsafeRow]])
+      val taskContext = TaskContext.get()
+      taskContext.internalMetricsToAccumulators(
+        InternalAccumulator.PEAK_EXECUTION_MEMORY).add(sorter.getPeakMemoryUsage)
+      sortedIterator
     }, preservesPartitioning = true)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index f1abae0720058..29dfcf2575227 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -21,6 +21,7 @@ import java.sql.Timestamp
 
 import org.scalatest.BeforeAndAfterAll
 
+import org.apache.spark.AccumulatorSuite
 import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.errors.DialectException
@@ -258,6 +259,23 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     }
   }
 
+  private def testCodeGen(sqlText: String, expectedResults: Seq[Row]): Unit = {
+    val df = sql(sqlText)
+    // First, check if we have GeneratedAggregate.
+    val hasGeneratedAgg = df.queryExecution.executedPlan
+      .collect { case _: GeneratedAggregate | _: aggregate.Aggregate => true }
+      .nonEmpty
+    if (!hasGeneratedAgg) {
+      fail(
+        s"""
+           |Codegen is enabled, but query $sqlText does not have GeneratedAggregate in the plan.
+           |${df.queryExecution.simpleString}
+         """.stripMargin)
+    }
+    // Then, check results.
+    checkAnswer(df, expectedResults)
+  }
+
   test("aggregation with codegen") {
     val originalValue = sqlContext.conf.codegenEnabled
     sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
@@ -267,26 +285,6 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       .unionAll(sqlContext.table("testData"))
       .registerTempTable("testData3x")
 
-    def testCodeGen(sqlText: String, expectedResults: Seq[Row]): Unit = {
-      val df = sql(sqlText)
-      // First, check if we have GeneratedAggregate.
-      var hasGeneratedAgg = false
-      df.queryExecution.executedPlan.foreach {
-        case generatedAgg: GeneratedAggregate => hasGeneratedAgg = true
-        case newAggregate: aggregate.Aggregate => hasGeneratedAgg = true
-        case _ =>
-      }
-      if (!hasGeneratedAgg) {
-        fail(
-          s"""
-             |Codegen is enabled, but query $sqlText does not have GeneratedAggregate in the plan.
-             |${df.queryExecution.simpleString}
-           """.stripMargin)
-      }
-      // Then, check results.
-      checkAnswer(df, expectedResults)
-    }
-
     try {
       // Just to group rows.
       testCodeGen(
@@ -1605,6 +1603,28 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       Row(new CalendarInterval(-(12 * 3 - 3), -(7L * MICROS_PER_WEEK + 123))))
   }
 
+  test("aggregation with codegen updates peak execution memory") {
+    withSQLConf(
+        (SQLConf.CODEGEN_ENABLED.key, "true"),
+        (SQLConf.USE_SQL_AGGREGATE2.key, "false")) {
+      val sc = sqlContext.sparkContext
+      AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "aggregation with codegen") {
+        testCodeGen(
+          "SELECT key, count(value) FROM testData GROUP BY key",
+          (1 to 100).map(i => Row(i, 1)))
+      }
+    }
+  }
+
+  test("external sorting updates peak execution memory") {
+    withSQLConf((SQLConf.EXTERNAL_SORT.key, "true")) {
+      val sc = sqlContext.sparkContext
+      AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "external sort") {
+        sortTest()
+      }
+    }
+  }
+
   test("SPARK-9511: error with table starting with number") {
     val df = sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
       .toDF("num", "str")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
index c7949848513cf..88bce0e319f9e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
@@ -21,6 +21,7 @@ import scala.util.Random
 
 import org.scalatest.BeforeAndAfterAll
 
+import org.apache.spark.AccumulatorSuite
 import org.apache.spark.sql.{RandomDataGenerator, Row, SQLConf}
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.test.TestSQLContext
@@ -59,6 +60,17 @@ class TungstenSortSuite extends SparkPlanTest with BeforeAndAfterAll {
     )
   }
 
+  test("sorting updates peak execution memory") {
+    val sc = TestSQLContext.sparkContext
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "unsafe external sort") {
+      checkThatPlansAgree(
+        (1 to 100).map(v => Tuple1(v)).toDF("a"),
+        (child: SparkPlan) => TungstenSort('a.asc :: Nil, true, child),
+        (child: SparkPlan) => Sort('a.asc :: Nil, global = true, child),
+        sortAnswers = false)
+    }
+  }
+
   // Test sorting on different data types
   for (
     dataType <- DataTypeTestUtils.atomicTypes ++ Set(NullType);
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index 7c591f6143b9e..ef827b0fe9b5b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -69,7 +69,8 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
         taskAttemptId = Random.nextInt(10000),
         attemptNumber = 0,
         taskMemoryManager = taskMemoryManager,
-        metricsSystem = null))
+        metricsSystem = null,
+        internalAccumulators = Seq.empty))
 
       try {
         f
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
index 0282b25b9dd50..601a5a07ad002 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
@@ -76,7 +76,8 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite {
         taskAttemptId = 98456,
         attemptNumber = 0,
         taskMemoryManager = taskMemMgr,
-        metricsSystem = null))
+        metricsSystem = null,
+        internalAccumulators = Seq.empty))
 
       // Create the data converters
       val kExternalConverter = CatalystTypeConverters.createToCatalystConverter(keySchema)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
new file mode 100644
index 0000000000000..0554e11d252ba
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
@@ -0,0 +1,94 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+// TODO: uncomment the test here! It is currently failing due to
+// bad interaction with org.apache.spark.sql.test.TestSQLContext.
+
+// scalastyle:off
+//package org.apache.spark.sql.execution.joins
+//
+//import scala.reflect.ClassTag
+//
+//import org.scalatest.BeforeAndAfterAll
+//
+//import org.apache.spark.{AccumulatorSuite, SparkConf, SparkContext}
+//import org.apache.spark.sql.functions._
+//import org.apache.spark.sql.{SQLConf, SQLContext, QueryTest}
+//
+///**
+// * Test various broadcast join operators with unsafe enabled.
+// *
+// * This needs to be its own suite because [[org.apache.spark.sql.test.TestSQLContext]] runs
+// * in local mode, but for tests in this suite we need to run Spark in local-cluster mode.
+// * In particular, the use of [[org.apache.spark.unsafe.map.BytesToBytesMap]] in
+// * [[org.apache.spark.sql.execution.joins.UnsafeHashedRelation]] is not triggered without
+// * serializing the hashed relation, which does not happen in local mode.
+// */
+//class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
+//  private var sc: SparkContext = null
+//  private var sqlContext: SQLContext = null
+//
+//  /**
+//   * Create a new [[SQLContext]] running in local-cluster mode with unsafe and codegen enabled.
+//   */
+//  override def beforeAll(): Unit = {
+//    super.beforeAll()
+//    val conf = new SparkConf()
+//      .setMaster("local-cluster[2,1,1024]")
+//      .setAppName("testing")
+//    sc = new SparkContext(conf)
+//    sqlContext = new SQLContext(sc)
+//    sqlContext.setConf(SQLConf.UNSAFE_ENABLED, true)
+//    sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
+//  }
+//
+//  override def afterAll(): Unit = {
+//    sc.stop()
+//    sc = null
+//    sqlContext = null
+//  }
+//
+//  /**
+//   * Test whether the specified broadcast join updates the peak execution memory accumulator.
+//   */
+//  private def testBroadcastJoin[T: ClassTag](name: String, joinType: String): Unit = {
+//    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, name) {
+//      val df1 = sqlContext.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
+//      val df2 = sqlContext.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
+//      // Comparison at the end is for broadcast left semi join
+//      val joinExpression = df1("key") === df2("key") && df1("value") > df2("value")
+//      val df3 = df1.join(broadcast(df2), joinExpression, joinType)
+//      val plan = df3.queryExecution.executedPlan
+//      assert(plan.collect { case p: T => p }.size === 1)
+//      plan.executeCollect()
+//    }
+//  }
+//
+//  test("unsafe broadcast hash join updates peak execution memory") {
+//    testBroadcastJoin[BroadcastHashJoin]("unsafe broadcast hash join", "inner")
+//  }
+//
+//  test("unsafe broadcast hash outer join updates peak execution memory") {
+//    testBroadcastJoin[BroadcastHashOuterJoin]("unsafe broadcast hash outer join", "left_outer")
+//  }
+//
+//  test("unsafe broadcast left semi join updates peak execution memory") {
+//    testBroadcastJoin[BroadcastLeftSemiJoinHash]("unsafe broadcast left semi join", "leftsemi")
+//  }
+//
+//}
+// scalastyle:on

From b2e4b85d2db0320e9cbfaf5a5542f749f1f11cf4 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 3 Aug 2015 14:51:15 -0700
Subject: [PATCH 009/633] Revert "[SPARK-9372] [SQL] Filter nulls in join keys"

This reverts commit 687c8c37150f4c93f8e57d86bb56321a4891286b.
---
 .../catalyst/expressions/nullFunctions.scala  |  48 +---
 .../sql/catalyst/optimizer/Optimizer.scala    |  64 ++---
 .../plans/logical/basicOperators.scala        |  32 +--
 .../expressions/ExpressionEvalHelper.scala    |   4 +-
 .../expressions/MathFunctionsSuite.scala      |   3 +-
 .../expressions/NullFunctionsSuite.scala      |  49 +---
 .../spark/sql/DataFrameNaFunctions.scala      |   2 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |   6 -
 .../org/apache/spark/sql/SQLContext.scala     |   5 +-
 .../extendedOperatorOptimizations.scala       | 160 ------------
 .../optimizer/FilterNullsInJoinKeySuite.scala | 236 ------------------
 11 files changed, 37 insertions(+), 572 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala
 delete mode 100644 sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
index d58c4756938c7..287718fab7f0d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
@@ -210,58 +210,14 @@ case class IsNotNull(child: Expression) extends UnaryExpression with Predicate {
   }
 }
 
-/**
- * A predicate that is evaluated to be true if there are at least `n` null values.
- */
-case class AtLeastNNulls(n: Int, children: Seq[Expression]) extends Predicate {
-  override def nullable: Boolean = false
-  override def foldable: Boolean = children.forall(_.foldable)
-  override def toString: String = s"AtLeastNNulls($n, ${children.mkString(",")})"
-
-  private[this] val childrenArray = children.toArray
-
-  override def eval(input: InternalRow): Boolean = {
-    var numNulls = 0
-    var i = 0
-    while (i < childrenArray.length && numNulls < n) {
-      val evalC = childrenArray(i).eval(input)
-      if (evalC == null) {
-        numNulls += 1
-      }
-      i += 1
-    }
-    numNulls >= n
-  }
-
-  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val numNulls = ctx.freshName("numNulls")
-    val code = children.map { e =>
-      val eval = e.gen(ctx)
-      s"""
-        if ($numNulls < $n) {
-          ${eval.code}
-          if (${eval.isNull}) {
-            $numNulls += 1;
-          }
-        }
-      """
-    }.mkString("\n")
-    s"""
-      int $numNulls = 0;
-      $code
-      boolean ${ev.isNull} = false;
-      boolean ${ev.primitive} = $numNulls >= $n;
-     """
-  }
-}
 
 /**
  * A predicate that is evaluated to be true if there are at least `n` non-null and non-NaN values.
  */
-case class AtLeastNNonNullNans(n: Int, children: Seq[Expression]) extends Predicate {
+case class AtLeastNNonNulls(n: Int, children: Seq[Expression]) extends Predicate {
   override def nullable: Boolean = false
   override def foldable: Boolean = children.forall(_.foldable)
-  override def toString: String = s"AtLeastNNonNullNans($n, ${children.mkString(",")})"
+  override def toString: String = s"AtLeastNNulls(n, ${children.mkString(",")})"
 
   private[this] val childrenArray = children.toArray
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index e4b6294dc7b8e..29d706dcb39a7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -31,14 +31,8 @@ import org.apache.spark.sql.types._
 
 abstract class Optimizer extends RuleExecutor[LogicalPlan]
 
-class DefaultOptimizer extends Optimizer {
-
-  /**
-   * Override to provide additional rules for the "Operator Optimizations" batch.
-   */
-  val extendedOperatorOptimizationRules: Seq[Rule[LogicalPlan]] = Nil
-
-  lazy val batches =
+object DefaultOptimizer extends Optimizer {
+  val batches =
     // SubQueries are only needed for analysis and can be removed before execution.
     Batch("Remove SubQueries", FixedPoint(100),
       EliminateSubQueries) ::
@@ -47,27 +41,26 @@ class DefaultOptimizer extends Optimizer {
       RemoveLiteralFromGroupExpressions) ::
     Batch("Operator Optimizations", FixedPoint(100),
       // Operator push down
-      SetOperationPushDown ::
-      SamplePushDown ::
-      PushPredicateThroughJoin ::
-      PushPredicateThroughProject ::
-      PushPredicateThroughGenerate ::
-      ColumnPruning ::
+      SetOperationPushDown,
+      SamplePushDown,
+      PushPredicateThroughJoin,
+      PushPredicateThroughProject,
+      PushPredicateThroughGenerate,
+      ColumnPruning,
       // Operator combine
-      ProjectCollapsing ::
-      CombineFilters ::
-      CombineLimits ::
+      ProjectCollapsing,
+      CombineFilters,
+      CombineLimits,
       // Constant folding
-      NullPropagation ::
-      OptimizeIn ::
-      ConstantFolding ::
-      LikeSimplification ::
-      BooleanSimplification ::
-      RemovePositive ::
-      SimplifyFilters ::
-      SimplifyCasts ::
-      SimplifyCaseConversionExpressions ::
-      extendedOperatorOptimizationRules.toList : _*) ::
+      NullPropagation,
+      OptimizeIn,
+      ConstantFolding,
+      LikeSimplification,
+      BooleanSimplification,
+      RemovePositive,
+      SimplifyFilters,
+      SimplifyCasts,
+      SimplifyCaseConversionExpressions) ::
     Batch("Decimal Optimizations", FixedPoint(100),
       DecimalAggregates) ::
     Batch("LocalRelation", FixedPoint(100),
@@ -229,18 +222,12 @@ object ColumnPruning extends Rule[LogicalPlan] {
   }
 
   /** Applies a projection only when the child is producing unnecessary attributes */
-  private def prunedChild(c: LogicalPlan, allReferences: AttributeSet) = {
+  private def prunedChild(c: LogicalPlan, allReferences: AttributeSet) =
     if ((c.outputSet -- allReferences.filter(c.outputSet.contains)).nonEmpty) {
-      // We need to preserve the nullability of c's output.
-      // So, we first create a outputMap and if a reference is from the output of
-      // c, we use that output attribute from c.
-      val outputMap = AttributeMap(c.output.map(attr => (attr, attr)))
-      val projectList = allReferences.filter(outputMap.contains).map(outputMap).toSeq
-      Project(projectList, c)
+      Project(allReferences.filter(c.outputSet.contains).toSeq, c)
     } else {
       c
     }
-  }
 }
 
 /**
@@ -530,13 +517,6 @@ object BooleanSimplification extends Rule[LogicalPlan] with PredicateHelper {
  */
 object CombineFilters extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-    case Filter(Not(AtLeastNNulls(1, e1)), Filter(Not(AtLeastNNulls(1, e2)), grandChild)) =>
-      // If we are combining two expressions Not(AtLeastNNulls(1, e1)) and
-      // Not(AtLeastNNulls(1, e2))
-      // (this is used to make sure there is no null in the result of e1 and e2 and
-      // they are added by FilterNullsInJoinKey optimziation rule), we can
-      // just create a Not(AtLeastNNulls(1, (e1 ++ e2).distinct)).
-      Filter(Not(AtLeastNNulls(1, (e1 ++ e2).distinct)), grandChild)
     case ff @ Filter(fc, nf @ Filter(nc, grandChild)) => Filter(And(nc, fc), grandChild)
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 54b5f49772664..aacfc86ab0e49 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -86,37 +86,7 @@ case class Generate(
 }
 
 case class Filter(condition: Expression, child: LogicalPlan) extends UnaryNode {
-  /**
-   * Indicates if `atLeastNNulls` is used to check if atLeastNNulls.children
-   * have at least one null value and atLeastNNulls.children are all attributes.
-   */
-  private def isAtLeastOneNullOutputAttributes(atLeastNNulls: AtLeastNNulls): Boolean = {
-    val expressions = atLeastNNulls.children
-    val n = atLeastNNulls.n
-    if (n != 1) {
-      // AtLeastNNulls is not used to check if atLeastNNulls.children have
-      // at least one null value.
-      false
-    } else {
-      // AtLeastNNulls is used to check if atLeastNNulls.children have
-      // at least one null value. We need to make sure all atLeastNNulls.children
-      // are attributes.
-      expressions.forall(_.isInstanceOf[Attribute])
-    }
-  }
-
-  override def output: Seq[Attribute] = condition match {
-    case Not(a: AtLeastNNulls) if isAtLeastOneNullOutputAttributes(a) =>
-      // The condition is used to make sure that there is no null value in
-      // a.children.
-      val nonNullableAttributes = AttributeSet(a.children.asInstanceOf[Seq[Attribute]])
-      child.output.map {
-        case attr if nonNullableAttributes.contains(attr) =>
-          attr.withNullability(false)
-        case attr => attr
-      }
-    case _ => child.output
-  }
+  override def output: Seq[Attribute] = child.output
 }
 
 case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index 3e55151298741..a41185b4d8754 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -31,8 +31,6 @@ import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
 trait ExpressionEvalHelper {
   self: SparkFunSuite =>
 
-  protected val defaultOptimizer = new DefaultOptimizer
-
   protected def create_row(values: Any*): InternalRow = {
     InternalRow.fromSeq(values.map(CatalystTypeConverters.convertToCatalyst))
   }
@@ -188,7 +186,7 @@ trait ExpressionEvalHelper {
       expected: Any,
       inputRow: InternalRow = EmptyRow): Unit = {
     val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation)
-    val optimizedPlan = defaultOptimizer.execute(plan)
+    val optimizedPlan = DefaultOptimizer.execute(plan)
     checkEvaluationWithoutCodegen(optimizedPlan.expressions.head, expected, inputRow)
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 649a5b44dc036..9fcb548af6bbb 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -23,6 +23,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.{GenerateProjection, GenerateMutableProjection}
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
 import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
 import org.apache.spark.sql.types._
 
@@ -148,7 +149,7 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     expression: Expression,
     inputRow: InternalRow = EmptyRow): Unit = {
     val plan = Project(Alias(expression, s"Optimized($expression)")() :: Nil, OneRowRelation)
-    val optimizedPlan = defaultOptimizer.execute(plan)
+    val optimizedPlan = DefaultOptimizer.execute(plan)
     checkNaNWithoutCodegen(optimizedPlan.expressions.head, inputRow)
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
index bf197124d8dbc..ace6c15dc8418 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/NullFunctionsSuite.scala
@@ -77,7 +77,7 @@ class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     }
   }
 
-  test("AtLeastNNonNullNans") {
+  test("AtLeastNNonNulls") {
     val mix = Seq(Literal("x"),
       Literal.create(null, StringType),
       Literal.create(null, DoubleType),
@@ -96,46 +96,11 @@ class NullFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       Literal(Float.MaxValue),
       Literal(false))
 
-    checkEvaluation(AtLeastNNonNullNans(0, mix), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(2, mix), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(3, mix), false, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(0, nanOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(3, nanOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(4, nanOnly), false, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(0, nullOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(3, nullOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNonNullNans(4, nullOnly), false, EmptyRow)
-  }
-
-  test("AtLeastNNull") {
-    val mix = Seq(Literal("x"),
-      Literal.create(null, StringType),
-      Literal.create(null, DoubleType),
-      Literal(Double.NaN),
-      Literal(5f))
-
-    val nanOnly = Seq(Literal("x"),
-      Literal(10.0),
-      Literal(Float.NaN),
-      Literal(math.log(-2)),
-      Literal(Double.MaxValue))
-
-    val nullOnly = Seq(Literal("x"),
-      Literal.create(null, DoubleType),
-      Literal.create(null, DecimalType.USER_DEFAULT),
-      Literal(Float.MaxValue),
-      Literal(false))
-
-    checkEvaluation(AtLeastNNulls(0, mix), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(1, mix), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(2, mix), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(3, mix), false, EmptyRow)
-    checkEvaluation(AtLeastNNulls(0, nanOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(1, nanOnly), false, EmptyRow)
-    checkEvaluation(AtLeastNNulls(2, nanOnly), false, EmptyRow)
-    checkEvaluation(AtLeastNNulls(0, nullOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(1, nullOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(2, nullOnly), true, EmptyRow)
-    checkEvaluation(AtLeastNNulls(3, nullOnly), false, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(2, mix), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(3, mix), false, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(3, nanOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(4, nanOnly), false, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(3, nullOnly), true, EmptyRow)
+    checkEvaluation(AtLeastNNonNulls(4, nullOnly), false, EmptyRow)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
index ea85f0657a726..a4fd4cf3b330b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
@@ -122,7 +122,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
   def drop(minNonNulls: Int, cols: Seq[String]): DataFrame = {
     // Filtering condition:
     // only keep the row if it has at least `minNonNulls` non-null and non-NaN values.
-    val predicate = AtLeastNNonNullNans(minNonNulls, cols.map(name => df.resolve(name)))
+    val predicate = AtLeastNNonNulls(minNonNulls, cols.map(name => df.resolve(name)))
     df.filter(Column(predicate))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 41ba1c7fe0574..f836122b3e0e4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -413,10 +413,6 @@ private[spark] object SQLConf {
     "spark.sql.useSerializer2",
     defaultValue = Some(true), isPublic = false)
 
-  val ADVANCED_SQL_OPTIMIZATION = booleanConf(
-    "spark.sql.advancedOptimization",
-    defaultValue = Some(true), isPublic = false)
-
   object Deprecated {
     val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
   }
@@ -488,8 +484,6 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2)
 
-  private[spark] def advancedSqlOptimizations: Boolean = getConf(ADVANCED_SQL_OPTIMIZATION)
-
   private[spark] def autoBroadcastJoinThreshold: Int = getConf(AUTO_BROADCASTJOIN_THRESHOLD)
 
   private[spark] def defaultSizeInBytes: Long =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 31e2b508d485e..dbb2a09846548 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -41,7 +41,6 @@ import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.apache.spark.sql.catalyst.{InternalRow, ParserDialect, _}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.datasources._
-import org.apache.spark.sql.optimizer.FilterNullsInJoinKey
 import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -157,9 +156,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     }
 
   @transient
-  protected[sql] lazy val optimizer: Optimizer = new DefaultOptimizer {
-    override val extendedOperatorOptimizationRules = FilterNullsInJoinKey(self) :: Nil
-  }
+  protected[sql] lazy val optimizer: Optimizer = DefaultOptimizer
 
   @transient
   protected[sql] val ddlParser = new DDLParser(sqlParser.parse(_))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala b/sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala
deleted file mode 100644
index 5a4dde5756964..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/optimizer/extendedOperatorOptimizations.scala
+++ /dev/null
@@ -1,160 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.optimizer
-
-import org.apache.spark.sql.SQLContext
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
-import org.apache.spark.sql.catalyst.plans.{Inner, LeftOuter, RightOuter, LeftSemi}
-import org.apache.spark.sql.catalyst.plans.logical.{Project, Filter, Join, LogicalPlan}
-import org.apache.spark.sql.catalyst.rules.Rule
-
-/**
- * An optimization rule used to insert Filters to filter out rows whose equal join keys
- * have at least one null values. For this kind of rows, they will not contribute to
- * the join results of equal joins because a null does not equal another null. We can
- * filter them out before shuffling join input rows. For example, we have two tables
- *
- * table1(key String, value Int)
- * "str1"|1
- * null  |2
- *
- * table2(key String, value Int)
- * "str1"|3
- * null  |4
- *
- * For a inner equal join, the result will be
- * "str1"|1|"str1"|3
- *
- * those two rows having null as the value of key will not contribute to the result.
- * So, we can filter them out early.
- *
- * This optimization rule can be disabled by setting spark.sql.advancedOptimization to false.
- *
- */
-case class FilterNullsInJoinKey(
-    sqlContext: SQLContext)
-  extends Rule[LogicalPlan] {
-
-  /**
-   * Checks if we need to add a Filter operator. We will add a Filter when
-   * there is any attribute in `keys` whose corresponding attribute of `keys`
-   * in `plan.output` is still nullable (`nullable` field is `true`).
-   */
-  private def needsFilter(keys: Seq[Expression], plan: LogicalPlan): Boolean = {
-    val keyAttributeSet = AttributeSet(keys.filter(_.isInstanceOf[Attribute]))
-    plan.output.filter(keyAttributeSet.contains).exists(_.nullable)
-  }
-
-  /**
-   * Adds a Filter operator to make sure that every attribute in `keys` is non-nullable.
-   */
-  private def addFilterIfNecessary(
-      keys: Seq[Expression],
-      child: LogicalPlan): LogicalPlan = {
-    // We get all attributes from keys.
-    val attributes = keys.filter(_.isInstanceOf[Attribute])
-
-    // Then, we create a Filter to make sure these attributes are non-nullable.
-    val filter =
-      if (attributes.nonEmpty) {
-        Filter(Not(AtLeastNNulls(1, attributes)), child)
-      } else {
-        child
-      }
-
-    filter
-  }
-
-  /**
-   * We reconstruct the join condition.
-   */
-  private def reconstructJoinCondition(
-      leftKeys: Seq[Expression],
-      rightKeys: Seq[Expression],
-      otherPredicate: Option[Expression]): Expression = {
-    // First, we rewrite the equal condition part. When we extract those keys,
-    // we use splitConjunctivePredicates. So, it is safe to use .reduce(And).
-    val rewrittenEqualJoinCondition = leftKeys.zip(rightKeys).map {
-      case (l, r) => EqualTo(l, r)
-    }.reduce(And)
-
-    // Then, we add otherPredicate. When we extract those equal condition part,
-    // we use splitConjunctivePredicates. So, it is safe to use
-    // And(rewrittenEqualJoinCondition, c).
-    val rewrittenJoinCondition = otherPredicate
-      .map(c => And(rewrittenEqualJoinCondition, c))
-      .getOrElse(rewrittenEqualJoinCondition)
-
-    rewrittenJoinCondition
-  }
-
-  def apply(plan: LogicalPlan): LogicalPlan = {
-    if (!sqlContext.conf.advancedSqlOptimizations) {
-      plan
-    } else {
-      plan transform {
-        case join: Join => join match {
-          // For a inner join having equal join condition part, we can add filters
-          // to both sides of the join operator.
-          case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
-            if needsFilter(leftKeys, left) || needsFilter(rightKeys, right) =>
-            val withLeftFilter = addFilterIfNecessary(leftKeys, left)
-            val withRightFilter = addFilterIfNecessary(rightKeys, right)
-            val rewrittenJoinCondition =
-              reconstructJoinCondition(leftKeys, rightKeys, condition)
-
-            Join(withLeftFilter, withRightFilter, Inner, Some(rewrittenJoinCondition))
-
-          // For a left outer join having equal join condition part, we can add a filter
-          // to the right side of the join operator.
-          case ExtractEquiJoinKeys(LeftOuter, leftKeys, rightKeys, condition, left, right)
-            if needsFilter(rightKeys, right) =>
-            val withRightFilter = addFilterIfNecessary(rightKeys, right)
-            val rewrittenJoinCondition =
-              reconstructJoinCondition(leftKeys, rightKeys, condition)
-
-            Join(left, withRightFilter, LeftOuter, Some(rewrittenJoinCondition))
-
-          // For a right outer join having equal join condition part, we can add a filter
-          // to the left side of the join operator.
-          case ExtractEquiJoinKeys(RightOuter, leftKeys, rightKeys, condition, left, right)
-            if needsFilter(leftKeys, left) =>
-            val withLeftFilter = addFilterIfNecessary(leftKeys, left)
-            val rewrittenJoinCondition =
-              reconstructJoinCondition(leftKeys, rightKeys, condition)
-
-            Join(withLeftFilter, right, RightOuter, Some(rewrittenJoinCondition))
-
-          // For a left semi join having equal join condition part, we can add filters
-          // to both sides of the join operator.
-          case ExtractEquiJoinKeys(LeftSemi, leftKeys, rightKeys, condition, left, right)
-            if needsFilter(leftKeys, left) || needsFilter(rightKeys, right) =>
-            val withLeftFilter = addFilterIfNecessary(leftKeys, left)
-            val withRightFilter = addFilterIfNecessary(rightKeys, right)
-            val rewrittenJoinCondition =
-              reconstructJoinCondition(leftKeys, rightKeys, condition)
-
-            Join(withLeftFilter, withRightFilter, LeftSemi, Some(rewrittenJoinCondition))
-
-          case other => other
-        }
-      }
-    }
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala
deleted file mode 100644
index f98e4acafbf2c..0000000000000
--- a/sql/core/src/test/scala/org/apache/spark/sql/optimizer/FilterNullsInJoinKeySuite.scala
+++ /dev/null
@@ -1,236 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.optimizer
-
-import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
-import org.apache.spark.sql.catalyst.dsl.plans._
-import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.catalyst.expressions.{Not, AtLeastNNulls}
-import org.apache.spark.sql.catalyst.optimizer._
-import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.catalyst.plans.logical.{Filter, LocalRelation, LogicalPlan}
-import org.apache.spark.sql.catalyst.rules.RuleExecutor
-import org.apache.spark.sql.test.TestSQLContext
-
-/** This is the test suite for FilterNullsInJoinKey optimization rule. */
-class FilterNullsInJoinKeySuite extends PlanTest {
-
-  // We add predicate pushdown rules at here to make sure we do not
-  // create redundant Filter operators. Also, because the attribute ordering of
-  // the Project operator added by ColumnPruning may be not deterministic
-  // (the ordering may depend on the testing environment),
-  // we first construct the plan with expected Filter operators and then
-  // run the optimizer to add the the Project for column pruning.
-  object Optimize extends RuleExecutor[LogicalPlan] {
-    val batches =
-      Batch("Subqueries", Once,
-        EliminateSubQueries) ::
-      Batch("Operator Optimizations", FixedPoint(100),
-        FilterNullsInJoinKey(TestSQLContext), // This is the rule we test in this suite.
-        CombineFilters,
-        PushPredicateThroughProject,
-        BooleanSimplification,
-        PushPredicateThroughJoin,
-        PushPredicateThroughGenerate,
-        ColumnPruning,
-        ProjectCollapsing) :: Nil
-  }
-
-  val leftRelation = LocalRelation('a.int, 'b.int, 'c.int, 'd.int)
-
-  val rightRelation = LocalRelation('e.int, 'f.int, 'g.int, 'h.int)
-
-  test("inner join") {
-    val joinCondition =
-      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, Inner, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    // For an inner join, FilterNullsInJoinKey add filter to both side.
-    val correctLeft =
-      leftRelation
-        .where(!(AtLeastNNulls(1, 'a.expr :: Nil)))
-
-    val correctRight =
-      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
-
-    val correctAnswer =
-      correctLeft
-        .join(correctRight, Inner, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
-  }
-
-  test("make sure we do not keep adding filters") {
-    val thirdRelation = LocalRelation('i.int, 'j.int, 'k.int, 'l.int)
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, Inner, Some('a === 'e))
-        .join(thirdRelation, Inner, Some('b === 'i && 'a === 'j))
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-    val conditions = optimized.collect {
-      case Filter(condition @ Not(AtLeastNNulls(1, exprs)), _) => exprs
-    }
-
-    // Make sure that we have three Not(AtLeastNNulls(1, exprs)) for those three tables.
-    assert(conditions.length === 3)
-
-    // Make sure attribtues are indeed a, b, e, i, and j.
-    assert(
-      conditions.flatMap(exprs => exprs).toSet ===
-        joinedPlan.select('a, 'b, 'e, 'i, 'j).analyze.output.toSet)
-  }
-
-  test("inner join (partially optimized)") {
-    val joinCondition =
-      ('a + 2 === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, Inner, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    // We cannot extract attribute from the left join key.
-    val correctRight =
-      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
-
-    val correctAnswer =
-      leftRelation
-        .join(correctRight, Inner, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
-  }
-
-  test("inner join (not optimized)") {
-    val nonOptimizedJoinConditions =
-      Some('c - 100 + 'd === 'g + 1 - 'h) ::
-        Some('d > 'h || 'c === 'g) ::
-        Some('d + 'g + 'c > 'd - 'h) :: Nil
-
-    nonOptimizedJoinConditions.foreach { joinCondition =>
-      val joinedPlan =
-        leftRelation
-          .join(rightRelation.select('f, 'g, 'h), Inner, joinCondition)
-          .select('a, 'c, 'f, 'd, 'h, 'g)
-
-      val optimized = Optimize.execute(joinedPlan.analyze)
-
-      comparePlans(optimized, Optimize.execute(joinedPlan.analyze))
-    }
-  }
-
-  test("left outer join") {
-    val joinCondition =
-      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, LeftOuter, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    // For a left outer join, FilterNullsInJoinKey add filter to the right side.
-    val correctRight =
-      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
-
-    val correctAnswer =
-      leftRelation
-        .join(correctRight, LeftOuter, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
-  }
-
-  test("right outer join") {
-    val joinCondition =
-      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, RightOuter, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    // For a right outer join, FilterNullsInJoinKey add filter to the left side.
-    val correctLeft =
-      leftRelation
-        .where(!(AtLeastNNulls(1, 'a.expr :: Nil)))
-
-    val correctAnswer =
-      correctLeft
-        .join(rightRelation, RightOuter, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-
-    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
-  }
-
-  test("full outer join") {
-    val joinCondition =
-      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, FullOuter, Some(joinCondition))
-        .select('a, 'f, 'd, 'h)
-
-    // FilterNullsInJoinKey does not fire for a full outer join.
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    comparePlans(optimized, Optimize.execute(joinedPlan.analyze))
-  }
-
-  test("left semi join") {
-    val joinCondition =
-      ('a === 'e && 'b + 1 === 'f) && ('d > 'h || 'd === 'g)
-
-    val joinedPlan =
-      leftRelation
-        .join(rightRelation, LeftSemi, Some(joinCondition))
-        .select('a, 'd)
-
-    val optimized = Optimize.execute(joinedPlan.analyze)
-
-    // For a left semi join, FilterNullsInJoinKey add filter to both side.
-    val correctLeft =
-      leftRelation
-        .where(!(AtLeastNNulls(1, 'a.expr :: Nil)))
-
-    val correctRight =
-      rightRelation.where(!(AtLeastNNulls(1, 'e.expr :: 'f.expr :: Nil)))
-
-    val correctAnswer =
-      correctLeft
-        .join(correctRight, LeftSemi, Some(joinCondition))
-        .select('a, 'd)
-
-    comparePlans(optimized, Optimize.execute(correctAnswer.analyze))
-  }
-}

From a2409d1c8e8ddec04b529ac6f6a12b5993f0eeda Mon Sep 17 00:00:00 2001
From: Steve Loughran <stevel@hortonworks.com>
Date: Mon, 3 Aug 2015 15:24:34 -0700
Subject: [PATCH 010/633] [SPARK-8064] [SQL] Build against Hive 1.2.1

Cherry picked the parts of the initial SPARK-8064 WiP branch needed to get sql/hive to compile against hive 1.2.1. That's the ASF release packaged under org.apache.hive, not any fork.

Tests not run yet: that's what the machines are for

Author: Steve Loughran <stevel@hortonworks.com>
Author: Cheng Lian <lian@databricks.com>
Author: Michael Armbrust <michael@databricks.com>
Author: Patrick Wendell <patrick@databricks.com>

Closes #7191 from steveloughran/stevel/feature/SPARK-8064-hive-1.2-002 and squashes the following commits:

7556d85 [Cheng Lian] Updates .q files and corresponding golden files
ef4af62 [Steve Loughran] Merge commit '6a92bb09f46a04d6cd8c41bdba3ecb727ebb9030' into stevel/feature/SPARK-8064-hive-1.2-002
6a92bb0 [Cheng Lian] Overrides HiveConf time vars
dcbb391 [Cheng Lian] Adds com.twitter:parquet-hadoop-bundle:1.6.0 for Hive Parquet SerDe
0bbe475 [Steve Loughran] SPARK-8064 scalastyle rejects the standard Hadoop ASF license header...
fdf759b [Steve Loughran] SPARK-8064 classpath dependency suite to be in sync with shading in final (?) hive-exec spark
7a6c727 [Steve Loughran] SPARK-8064 switch to second staging repo of the spark-hive artifacts. This one has the protobuf-shaded hive-exec jar
376c003 [Steve Loughran] SPARK-8064 purge duplicate protobuf declaration
2c74697 [Steve Loughran] SPARK-8064 switch to the protobuf shaded hive-exec jar with tests to chase it down
cc44020 [Steve Loughran] SPARK-8064 remove hadoop.version from runtest.py, as profile will fix that automatically.
6901fa9 [Steve Loughran] SPARK-8064 explicit protobuf import
da310dc [Michael Armbrust] Fixes for Hive tests.
a775a75 [Steve Loughran] SPARK-8064 cherry-pick-incomplete
7404f34 [Patrick Wendell] Add spark-hive staging repo
832c164 [Steve Loughran] SPARK-8064 try to supress compiler warnings on Complex.java pasted-thrift-code
312c0d4 [Steve Loughran] SPARK-8064  maven/ivy dependency purge; calcite declaration needed
fa5ae7b [Steve Loughran] HIVE-8064 fix up hive-thriftserver dependencies and cut back on evicted references in the hive- packages; this keeps mvn and ivy resolution compatible, as the reconciliation policy is "by hand"
c188048 [Steve Loughran] SPARK-8064 manage the Hive depencencies to that -things that aren't needed are excluded -sql/hive built with ivy is in sync with the maven reconciliation policy, rather than latest-first
4c8be8d [Cheng Lian] WIP: Partial fix for Thrift server and CLI tests
314eb3c [Steve Loughran] SPARK-8064 deprecation warning  noise in one of the tests
17b0341 [Steve Loughran] SPARK-8064 IDE-hinted cleanups of Complex.java to reduce compiler warnings. It's all autogenerated code, so still ugly.
d029b92 [Steve Loughran] SPARK-8064 rely on unescaping to have already taken place, so go straight to map of serde options
23eca7e [Steve Loughran] HIVE-8064 handle raw and escaped property tokens
54d9b06 [Steve Loughran] SPARK-8064 fix compilation regression surfacing from rebase
0b12d5f [Steve Loughran] HIVE-8064 use subset of hive complex type whose types deserialize
fce73b6 [Steve Loughran] SPARK-8064 poms rely implicitly on the version of kryo chill provides
fd3aa5d [Steve Loughran] SPARK-8064 version of hive to d/l from ivy is 1.2.1
dc73ece [Steve Loughran] SPARK-8064 revert to master's determinstic pushdown strategy
d3c1e4a [Steve Loughran] SPARK-8064 purge UnionType
051cc21 [Steve Loughran] SPARK-8064 switch to an unshaded version of hive-exec-core, which must have been built with Kryo 2.21. This currently looks for a (locally built) version 1.2.1.spark
6684c60 [Steve Loughran] SPARK-8064 ignore RTE raised in blocking process.exitValue() call
e6121e5 [Steve Loughran] SPARK-8064 address review comments
aa43dc6 [Steve Loughran] SPARK-8064  more robust teardown on JavaMetastoreDatasourcesSuite
f2bff01 [Steve Loughran] SPARK-8064 better takeup of asynchronously caught error text
8b1ef38 [Steve Loughran] SPARK-8064: on failures executing spark-submit in HiveSparkSubmitSuite, print command line and all logged output.
5a9ce6b [Steve Loughran] SPARK-8064 add explicit reason for kv split failure, rather than array OOB. *does not address the issue*
642b63a [Steve Loughran] SPARK-8064 reinstate something cut briefly during rebasing
97194dc [Steve Loughran] SPARK-8064 add extra logging to the YarnClusterSuite classpath test. There should be no reason why this is failing on jenkins, but as it is (and presumably its CP-related), improve the logging including any exception raised.
335357f [Steve Loughran] SPARK-8064 fail fast on thrive process spawning tests on exit codes and/or error string patterns seen in log.
3ed872f [Steve Loughran] SPARK-8064 rename field double to  dbl
bca55e5 [Steve Loughran] SPARK-8064 missed one of the `date` escapes
41d6479 [Steve Loughran] SPARK-8064 wrap tests with withTable() calls to avoid table-exists exceptions
2bc29a4 [Steve Loughran] SPARK-8064 ParquetSuites to escape `date` field name
1ab9bc4 [Steve Loughran] SPARK-8064 TestHive to use sered2.thrift.test.Complex
bf3a249 [Steve Loughran] SPARK-8064: more resubmit than fix; tighten startup timeout to 60s. Still no obvious reason why jersey server code in spark-assembly isn't being picked up -it hasn't been shaded
c829b8f [Steve Loughran] SPARK-8064: reinstate yarn-rm-server dependencies to hive-exec to ensure that jersey server is on classpath on hadoop versions < 2.6
0b0f738 [Steve Loughran] SPARK-8064: thrift server startup to fail fast on any exception in the main thread
13abaf1 [Steve Loughran] SPARK-8064 Hive compatibilty tests sin sync with explain/show output from Hive 1.2.1
d14d5ea [Steve Loughran] SPARK-8064: DATE is now a predicate; you can't use it as a field in select ops
26eef1c [Steve Loughran] SPARK-8064: HIVE-9039 renamed TOK_UNION => TOK_UNIONALL while adding TOK_UNIONDISTINCT
3d64523 [Steve Loughran] SPARK-8064 improve diagns on uknown token; fix scalastyle failure
d0360f6 [Steve Loughran] SPARK-8064: delicate merge in of the branch vanzin/hive-1.1
1126e5a [Steve Loughran] SPARK-8064: name of unrecognized file format wasn't appearing in error text
8cb09c4 [Steve Loughran] SPARK-8064: test resilience/assertion improvements. Independent of the rest of the work; can be backported to earlier versions
dec12cb [Steve Loughran] SPARK-8064: when a CLI suite test fails include the full output text in the raised exception; this ensures that the stdout/stderr is included in jenkins reports, so it becomes possible to diagnose the cause.
463a670 [Steve Loughran] SPARK-8064 run-tests.py adds a hadoop-2.6 profile, and changes info messages to say "w/Hive 1.2.1" in console output
2531099 [Steve Loughran] SPARK-8064 successful attempt to get rid of pentaho as a transitive dependency of hive-exec
1d59100 [Steve Loughran] SPARK-8064 (unsuccessful) attempt to get rid of pentaho as a transitive dependency of hive-exec
75733fc [Steve Loughran] SPARK-8064 change thrift binary startup message to "Starting ThriftBinaryCLIService on port"
3ebc279 [Steve Loughran] SPARK-8064 move strings used to check for http/bin thrift services up into constants
c80979d [Steve Loughran] SPARK-8064: SparkSQLCLIDriver drops remote mode support. CLISuite Tests pass instead of timing out: undetected regression?
27e8370 [Steve Loughran] SPARK-8064 fix some style & IDE warnings
00e50d6 [Steve Loughran] SPARK-8064 stop excluding hive shims from dependency (commented out , for now)
cb4f142 [Steve Loughran] SPARK-8054 cut pentaho dependency from calcite
f7aa9cb [Steve Loughran] SPARK-8064 everything compiles with some commenting and moving of classes into a hive package
6c310b4 [Steve Loughran] SPARK-8064 subclass  Hive ServerOptionsProcessor to make it public again
f61a675 [Steve Loughran] SPARK-8064 thrift server switched to Hive 1.2.1, though it doesn't compile everywhere
4890b9d [Steve Loughran] SPARK-8064, build against Hive 1.2.1
---
 core/pom.xml                                  |   20 -
 dev/run-tests.py                              |    7 +-
 pom.xml                                       |  654 +++++++++-
 sbin/spark-daemon.sh                          |    2 +-
 sql/catalyst/pom.xml                          |    1 -
 .../parquet/ParquetCompatibilityTest.scala    |   13 +-
 sql/hive-thriftserver/pom.xml                 |   22 +-
 .../HiveServerServerOptionsProcessor.scala    |   37 +
 .../hive/thriftserver/HiveThriftServer2.scala |   27 +-
 .../SparkExecuteStatementOperation.scala      |    9 +-
 .../hive/thriftserver/SparkSQLCLIDriver.scala |   56 +-
 .../thriftserver/SparkSQLCLIService.scala     |   13 +-
 .../thriftserver/SparkSQLSessionManager.scala |   11 +-
 .../sql/hive/thriftserver/CliSuite.scala      |   75 +-
 .../HiveThriftServer2Suites.scala             |   40 +-
 .../execution/HiveCompatibilitySuite.scala    |   29 +-
 sql/hive/pom.xml                              |   92 +-
 .../apache/spark/sql/hive/HiveContext.scala   |  114 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |    5 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    |   97 +-
 .../org/apache/spark/sql/hive/HiveShim.scala  |   15 +-
 .../sql/hive/client/ClientInterface.scala     |    4 +
 .../spark/sql/hive/client/ClientWrapper.scala |    5 +-
 .../spark/sql/hive/client/HiveShim.scala      |    2 +-
 .../hive/client/IsolatedClientLoader.scala    |    2 +-
 .../spark/sql/hive/client/package.scala       |    2 +-
 .../hive/execution/InsertIntoHiveTable.scala  |    2 +-
 .../hive/execution/ScriptTransformation.scala |    6 +-
 .../org/apache/spark/sql/hive/hiveUDFs.scala  |    2 +-
 .../spark/sql/hive/hiveWriterContainers.scala |    2 +-
 .../spark/sql/hive/orc/OrcFilters.scala       |    6 +-
 .../apache/spark/sql/hive/test/TestHive.scala |   36 +-
 .../apache/spark/sql/hive/test/Complex.java   | 1139 +++++++++++++++++
 .../hive/JavaMetastoreDataSourcesSuite.java   |    6 +-
 ...perator-0-ee7f6a60a9792041b85b18cda56429bf |    1 +
 ..._string-1-db089ff46f9826c7883198adacdfad59 |    6 +-
 ...tar_by-5-41d474f5e6d7c61c36f74b4bec4e9e44} |    0
 ...e_alter-3-2a91d52719cf4552ebeb867204552a26 |    2 +-
 ...b_table-4-b585371b624cbab2616a49f553a870a0 |    2 +-
 ...limited-1-2a91d52719cf4552ebeb867204552a26 |    2 +-
 ...e_serde-1-2a91d52719cf4552ebeb867204552a26 |    2 +-
 ...nctions-0-45a7762c39f1b0f26f076220e2764043 |   21 +
 ...perties-1-be4adb893c7f946ebd76a648ce3cc1ae |    2 +-
 ...date_add-1-efb60fcbd6d78ad35257fb1ec39ace2 |    4 +-
 ...ate_sub-1-7efeb74367835ade71e5e42b22f8ced4 |    4 +-
 ...atediff-1-34ae7a68b13c2bc9a89f61acf2edd4c5 |    2 +-
 ...udf_day-0-c4c503756384ff1220222d84fd25e756 |    2 +-
 .../udf_day-1-87168babe1110fe4c38269843414ca4 |   11 +-
 ...ofmonth-0-7b2caf942528656555cf19c261a18502 |    2 +-
 ...ofmonth-1-ca24d07102ad264d79ff30c64a73a7e8 |   11 +-
 .../udf_if-0-b7ffa85b5785cccef2af1b285348cc2c |    2 +-
 .../udf_if-1-30cf7f51f92b5684e556deff3032d49a |    2 +-
 .../udf_if-1-b7ffa85b5785cccef2af1b285348cc2c |    2 +-
 .../udf_if-2-30cf7f51f92b5684e556deff3032d49a |    2 +-
 ..._minute-0-9a38997c1f41f4afe00faa0abc471aee |    2 +-
 ..._minute-1-16995573ac4f4a1b047ad6ee88699e48 |    8 +-
 ...f_month-0-9a38997c1f41f4afe00faa0abc471aee |    2 +-
 ...f_month-1-16995573ac4f4a1b047ad6ee88699e48 |    8 +-
 ...udf_std-1-6759bde0e50a3607b7c3fd5a93cbd027 |    2 +-
 ..._stddev-1-18e1d598820013453fad45852e1a303d |    2 +-
 ...union3-0-99620f72f0282904846a596ca5b3e46c} |    0
 ...union3-2-90ca96ea59fd45cf0af8c020ae77c908} |    0
 ...union3-3-72b149ccaef751bcfe55d5ca37cb5fd7} |    0
 .../clientpositive/parenthesis_star_by.q      |    2 +-
 .../src/test/queries/clientpositive/union3.q  |   11 +-
 .../sql/hive/ClasspathDependenciesSuite.scala |  110 ++
 .../spark/sql/hive/HiveSparkSubmitSuite.scala |   29 +-
 .../sql/hive/InsertIntoHiveTableSuite.scala   |    7 +-
 .../hive/ParquetHiveCompatibilitySuite.scala  |    9 +
 .../spark/sql/hive/StatisticsSuite.scala      |    3 +
 .../spark/sql/hive/client/VersionsSuite.scala |    6 +-
 .../sql/hive/execution/HiveQuerySuite.scala   |   89 +-
 .../sql/hive/execution/PruningSuite.scala     |    8 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |  140 +-
 .../hive/orc/OrcHadoopFsRelationSuite.scala   |    8 +-
 .../hive/orc/OrcPartitionDiscoverySuite.scala |    3 +-
 .../apache/spark/sql/hive/parquetSuites.scala |  327 ++---
 yarn/pom.xml                                  |   10 -
 .../spark/deploy/yarn/YarnClusterSuite.scala  |   24 +-
 79 files changed, 2861 insertions(+), 584 deletions(-)
 create mode 100644 sql/hive-thriftserver/src/main/scala/org/apache/hive/service/server/HiveServerServerOptionsProcessor.scala
 create mode 100644 sql/hive/src/test/java/org/apache/spark/sql/hive/test/Complex.java
 create mode 100644 sql/hive/src/test/resources/golden/! operator-0-ee7f6a60a9792041b85b18cda56429bf
 rename sql/hive/src/test/resources/golden/{parenthesis_star_by-5-6888c7f7894910538d82eefa23443189 => parenthesis_star_by-5-41d474f5e6d7c61c36f74b4bec4e9e44} (100%)
 rename sql/hive/src/test/resources/golden/{union3-0-6a8a35102de1b0b88c6721a704eb174d => union3-0-99620f72f0282904846a596ca5b3e46c} (100%)
 rename sql/hive/src/test/resources/golden/{union3-2-2a1dcd937f117f1955a169592b96d5f9 => union3-2-90ca96ea59fd45cf0af8c020ae77c908} (100%)
 rename sql/hive/src/test/resources/golden/{union3-3-8fc63f8edb2969a63cd4485f1867ba97 => union3-3-72b149ccaef751bcfe55d5ca37cb5fd7} (100%)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/hive/ClasspathDependenciesSuite.scala

diff --git a/core/pom.xml b/core/pom.xml
index 202678779150b..0e53a79fd2235 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -46,30 +46,10 @@
     <dependency>
       <groupId>com.twitter</groupId>
       <artifactId>chill_${scala.binary.version}</artifactId>
-      <exclusions>
-        <exclusion>
-          <groupId>org.ow2.asm</groupId>
-          <artifactId>asm</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.ow2.asm</groupId>
-          <artifactId>asm-commons</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
     <dependency>
       <groupId>com.twitter</groupId>
       <artifactId>chill-java</artifactId>
-      <exclusions>
-        <exclusion>
-          <groupId>org.ow2.asm</groupId>
-          <artifactId>asm</artifactId>
-        </exclusion>
-        <exclusion>
-          <groupId>org.ow2.asm</groupId>
-          <artifactId>asm-commons</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
     <dependency>
       <groupId>org.apache.hadoop</groupId>
diff --git a/dev/run-tests.py b/dev/run-tests.py
index b6d181418f027..d1852b95bb292 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -273,6 +273,7 @@ def get_hadoop_profiles(hadoop_version):
         "hadoop2.0": ["-Phadoop-1", "-Dhadoop.version=2.0.0-mr1-cdh4.1.1"],
         "hadoop2.2": ["-Pyarn", "-Phadoop-2.2"],
         "hadoop2.3": ["-Pyarn", "-Phadoop-2.3", "-Dhadoop.version=2.3.0"],
+        "hadoop2.6": ["-Pyarn", "-Phadoop-2.6"],
     }
 
     if hadoop_version in sbt_maven_hadoop_profiles:
@@ -289,7 +290,7 @@ def build_spark_maven(hadoop_version):
     mvn_goals = ["clean", "package", "-DskipTests"]
     profiles_and_goals = build_profiles + mvn_goals
 
-    print("[info] Building Spark (w/Hive 0.13.1) using Maven with these arguments: ",
+    print("[info] Building Spark (w/Hive 1.2.1) using Maven with these arguments: ",
           " ".join(profiles_and_goals))
 
     exec_maven(profiles_and_goals)
@@ -305,14 +306,14 @@ def build_spark_sbt(hadoop_version):
                  "streaming-kinesis-asl-assembly/assembly"]
     profiles_and_goals = build_profiles + sbt_goals
 
-    print("[info] Building Spark (w/Hive 0.13.1) using SBT with these arguments: ",
+    print("[info] Building Spark (w/Hive 1.2.1) using SBT with these arguments: ",
           " ".join(profiles_and_goals))
 
     exec_sbt(profiles_and_goals)
 
 
 def build_apache_spark(build_tool, hadoop_version):
-    """Will build Spark against Hive v0.13.1 given the passed in build tool (either `sbt` or
+    """Will build Spark against Hive v1.2.1 given the passed in build tool (either `sbt` or
     `maven`). Defaults to using `sbt`."""
 
     set_title_and_block("Building Spark", "BLOCK_BUILD")
diff --git a/pom.xml b/pom.xml
index be0dac953abf7..a958cec867eae 100644
--- a/pom.xml
+++ b/pom.xml
@@ -134,11 +134,12 @@
     <curator.version>2.4.0</curator.version>
     <hive.group>org.spark-project.hive</hive.group>
     <!-- Version used in Maven Hive dependency -->
-    <hive.version>0.13.1a</hive.version>
+    <hive.version>1.2.1.spark</hive.version>
     <!-- Version used for internal directory structure -->
-    <hive.version.short>0.13.1</hive.version.short>
+    <hive.version.short>1.2.1</hive.version.short>
     <derby.version>10.10.1.1</derby.version>
     <parquet.version>1.7.0</parquet.version>
+    <hive.parquet.version>1.6.0</hive.parquet.version>
     <jblas.version>1.2.4</jblas.version>
     <jetty.version>8.1.14.v20131031</jetty.version>
     <orbit.version>3.0.0.v201112011016</orbit.version>
@@ -151,7 +152,10 @@
     <jets3t.version>0.7.1</jets3t.version>
     <aws.java.sdk.version>1.9.16</aws.java.sdk.version>
     <aws.kinesis.client.version>1.2.1</aws.kinesis.client.version>
+    <!--  org.apache.httpcomponents/httpclient-->
     <commons.httpclient.version>4.3.2</commons.httpclient.version>
+    <!--  commons-httpclient/commons-httpclient-->
+    <httpclient.classic.version>3.1</httpclient.classic.version>
     <commons.math3.version>3.4.1</commons.math3.version>
     <scala.version>2.10.4</scala.version>
     <scala.binary.version>2.10</scala.binary.version>
@@ -161,6 +165,23 @@
     <fasterxml.jackson.version>2.4.4</fasterxml.jackson.version>
     <snappy.version>1.1.1.7</snappy.version>
     <netlib.java.version>1.1.2</netlib.java.version>
+    <calcite.version>1.2.0-incubating</calcite.version>
+    <commons-codec.version>1.10</commons-codec.version>
+    <!-- org.apache.commons/commons-lang/-->
+    <commons-lang2.version>2.6</commons-lang2.version>
+    <!-- org.apache.commons/commons-lang3/-->
+    <commons-lang3.version>3.3.2</commons-lang3.version>
+    <datanucleus-core.version>3.2.10</datanucleus-core.version>
+    <janino.version>2.7.8</janino.version>
+    <jersey.version>1.9</jersey.version>
+    <joda.version>2.5</joda.version>
+    <jodd.version>3.5.2</jodd.version>
+    <jsr305.version>1.3.9</jsr305.version>
+    <libthrift.version>0.9.2</libthrift.version>
+
+    <!-- For maven shade plugin (see SPARK-8819) -->
+    <create.dependency.reduced.pom>false</create.dependency.reduced.pom>
+
     <test.java.home>${java.home}</test.java.home>
 
     <!--
@@ -188,7 +209,6 @@
     <MaxPermGen>512m</MaxPermGen>
     <CodeCacheSize>512m</CodeCacheSize>
   </properties>
-
   <repositories>
     <repository>
       <id>central</id>
@@ -246,6 +266,14 @@
         <enabled>false</enabled>
       </snapshots>
     </repository>
+    <repository>
+      <id>spark-hive-staging</id>
+      <name>Staging Repo for Hive 1.2.1 (Spark Version)</name>
+      <url>https://oss.sonatype.org/content/repositories/orgspark-project-1113</url>
+      <releases>
+        <enabled>true</enabled>
+      </releases>
+    </repository>
     <repository>
       <id>mapr-repo</id>
       <name>MapR Repository</name>
@@ -257,12 +285,13 @@
         <enabled>false</enabled>
       </snapshots>
     </repository>
+    <!-- returning unauthorized on some operations -->
     <repository>
       <id>spring-releases</id>
       <name>Spring Release Repository</name>
       <url>https://repo.spring.io/libs-release</url>
       <releases>
-        <enabled>true</enabled>
+        <enabled>false</enabled>
       </releases>
       <snapshots>
         <enabled>false</enabled>
@@ -402,12 +431,17 @@
       <dependency>
         <groupId>org.apache.commons</groupId>
         <artifactId>commons-lang3</artifactId>
-        <version>3.3.2</version>
+        <version>${commons-lang3.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.commons</groupId>
+        <artifactId>commons-lang</artifactId>
+        <version>${commons-lang2.version}</version>
       </dependency>
       <dependency>
         <groupId>commons-codec</groupId>
         <artifactId>commons-codec</artifactId>
-        <version>1.10</version>
+        <version>${commons-codec.version}</version>
       </dependency>
       <dependency>
         <groupId>org.apache.commons</groupId>
@@ -422,7 +456,12 @@
       <dependency>
         <groupId>com.google.code.findbugs</groupId>
         <artifactId>jsr305</artifactId>
-        <version>1.3.9</version>
+        <version>${jsr305.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>commons-httpclient</groupId>
+        <artifactId>commons-httpclient</artifactId>
+        <version>${httpclient.classic.version}</version>
       </dependency>
       <dependency>
         <groupId>org.apache.httpcomponents</groupId>
@@ -439,6 +478,16 @@
         <artifactId>selenium-java</artifactId>
         <version>2.42.2</version>
         <scope>test</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>com.google.guava</groupId>
+            <artifactId>guava</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>io.netty</groupId>
+            <artifactId>netty</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
       <!-- Added for selenium only, and should match its dependent version: -->
       <dependency>
@@ -624,15 +673,26 @@
       <dependency>
         <groupId>com.sun.jersey</groupId>
         <artifactId>jersey-server</artifactId>
-        <version>1.9</version>
+        <version>${jersey.version}</version>
         <scope>${hadoop.deps.scope}</scope>
       </dependency>
       <dependency>
         <groupId>com.sun.jersey</groupId>
         <artifactId>jersey-core</artifactId>
-        <version>1.9</version>
+        <version>${jersey.version}</version>
         <scope>${hadoop.deps.scope}</scope>
       </dependency>
+      <dependency>
+        <groupId>com.sun.jersey</groupId>
+        <artifactId>jersey-json</artifactId>
+        <version>${jersey.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>stax</groupId>
+            <artifactId>stax-api</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
       <dependency>
         <groupId>org.scala-lang</groupId>
         <artifactId>scala-compiler</artifactId>
@@ -1022,58 +1082,499 @@
         <artifactId>hive-beeline</artifactId>
         <version>${hive.version}</version>
         <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-exec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-jdbc</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-metastore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-service</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
       <dependency>
         <groupId>${hive.group}</groupId>
         <artifactId>hive-cli</artifactId>
         <version>${hive.version}</version>
         <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-exec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-jdbc</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-metastore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-serde</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-service</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
       <dependency>
         <groupId>${hive.group}</groupId>
-        <artifactId>hive-exec</artifactId>
+        <artifactId>hive-common</artifactId>
         <version>${hive.version}</version>
         <scope>${hive.deps.scope}</scope>
         <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.ant</groupId>
+            <artifactId>ant</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.zookeeper</groupId>
+            <artifactId>zookeeper</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>commons-logging</groupId>
             <artifactId>commons-logging</artifactId>
           </exclusion>
+        </exclusions>
+      </dependency>
+
+      <dependency>
+        <groupId>${hive.group}</groupId>
+        <artifactId>hive-exec</artifactId>
+<!--
+        <classifier>core</classifier>
+-->
+        <version>${hive.version}</version>
+        <scope>${hive.deps.scope}</scope>
+        <exclusions>
+
+          <!-- pull this in when needed; the explicit definition culls the surplis-->
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-metastore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-ant</artifactId>
+          </exclusion>
+          <!-- break the loop -->
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>spark-client</artifactId>
+          </exclusion>
+
+          <!-- excluded dependencies & transitive.
+           Some may be needed to be explicitly included-->
+          <exclusion>
+            <groupId>ant</groupId>
+            <artifactId>ant</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.ant</groupId>
+            <artifactId>ant</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>com.esotericsoftware.kryo</groupId>
             <artifactId>kryo</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>commons-codec</groupId>
+            <artifactId>commons-codec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-httpclient</groupId>
+            <artifactId>commons-httpclient</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>org.apache.avro</groupId>
             <artifactId>avro-mapred</artifactId>
           </exclusion>
+          <!--  this is needed and must be explicitly included later-->
+          <exclusion>
+            <groupId>org.apache.calcite</groupId>
+            <artifactId>calcite-core</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>apache-curator</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-client</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-framework</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libfb303</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.zookeeper</groupId>
+            <artifactId>zookeeper</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
         </exclusions>
       </dependency>
       <dependency>
         <groupId>${hive.group}</groupId>
         <artifactId>hive-jdbc</artifactId>
         <version>${hive.version}</version>
-        <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-metastore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-serde</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-service</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpclient</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpcore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-framework</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libfb303</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.zookeeper</groupId>
+            <artifactId>zookeeper</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-logging</groupId>
+            <artifactId>commons-logging</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
+
       <dependency>
         <groupId>${hive.group}</groupId>
         <artifactId>hive-metastore</artifactId>
         <version>${hive.version}</version>
         <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-serde</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libfb303</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.google.guava</groupId>
+            <artifactId>guava</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+        </exclusions>
       </dependency>
+
       <dependency>
         <groupId>${hive.group}</groupId>
         <artifactId>hive-serde</artifactId>
         <version>${hive.version}</version>
         <scope>${hive.deps.scope}</scope>
         <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-codec</groupId>
+            <artifactId>commons-codec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.google.code.findbugs</groupId>
+            <artifactId>jsr305</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.avro</groupId>
+            <artifactId>avro</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libfb303</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>commons-logging</groupId>
             <artifactId>commons-logging</artifactId>
           </exclusion>
+        </exclusions>
+      </dependency>
+
+      <dependency>
+        <groupId>${hive.group}</groupId>
+        <artifactId>hive-service</artifactId>
+        <version>${hive.version}</version>
+        <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-common</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-exec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-metastore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>${hive.group}</groupId>
+            <artifactId>hive-shims</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>commons-codec</groupId>
+            <artifactId>commons-codec</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-framework</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-recipes</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libfb303</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
+
+      <!-- hive shims pulls in hive 0.23 and a transitive dependency of the Hadoop version
+        Hive was built against. This dependency cuts out the YARN/hadoop dependency, which
+        is needed by Hive to submit work to a YARN cluster.-->
+      <dependency>
+        <groupId>${hive.group}</groupId>
+        <artifactId>hive-shims</artifactId>
+        <version>${hive.version}</version>
+        <scope>${hive.deps.scope}</scope>
+        <exclusions>
+          <exclusion>
+            <groupId>com.google.guava</groupId>
+            <artifactId>guava</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.hadoop</groupId>
+            <artifactId>hadoop-yarn-server-resourcemanager</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.curator</groupId>
+            <artifactId>curator-framework</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.thrift</groupId>
+            <artifactId>libthrift</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.zookeeper</groupId>
+            <artifactId>zookeeper</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-log4j12</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>log4j</groupId>
+            <artifactId>log4j</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>commons-logging</groupId>
-            <artifactId>commons-logging-api</artifactId>
+            <artifactId>commons-logging</artifactId>
           </exclusion>
         </exclusions>
       </dependency>
@@ -1095,6 +1596,12 @@
         <version>${parquet.version}</version>
         <scope>${parquet.test.deps.scope}</scope>
       </dependency>
+      <dependency>
+        <groupId>com.twitter</groupId>
+        <artifactId>parquet-hadoop-bundle</artifactId>
+        <version>${hive.parquet.version}</version>
+        <scope>runtime</scope>
+      </dependency>
       <dependency>
         <groupId>org.apache.flume</groupId>
         <artifactId>flume-ng-core</artifactId>
@@ -1135,6 +1642,125 @@
           </exclusion>
         </exclusions>
       </dependency>
+      <dependency>
+        <groupId>org.apache.calcite</groupId>
+        <artifactId>calcite-core</artifactId>
+        <version>${calcite.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-annotations</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-core</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-databind</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.google.guava</groupId>
+            <artifactId>guava</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.google.code.findbugs</groupId>
+            <artifactId>jsr305</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.codehaus.janino</groupId>
+            <artifactId>janino</artifactId>
+          </exclusion>
+          <!-- hsqldb interferes with the use of derby as the default db
+            in hive's use of datanucleus.
+          -->
+          <exclusion>
+            <groupId>org.hsqldb</groupId>
+            <artifactId>hsqldb</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.pentaho</groupId>
+            <artifactId>pentaho-aggdesigner-algorithm</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.calcite</groupId>
+        <artifactId>calcite-avatica</artifactId>
+        <version>${calcite.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-annotations</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-core</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>com.fasterxml.jackson.core</groupId>
+            <artifactId>jackson-databind</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
+      <dependency>
+        <groupId>org.codehaus.janino</groupId>
+        <artifactId>janino</artifactId>
+        <version>${janino.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>joda-time</groupId>
+        <artifactId>joda-time</artifactId>
+        <version>${joda.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>org.jodd</groupId>
+        <artifactId>jodd-core</artifactId>
+        <version>${jodd.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>org.datanucleus</groupId>
+        <artifactId>datanucleus-core</artifactId>
+        <version>${datanucleus-core.version}</version>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.thrift</groupId>
+        <artifactId>libthrift</artifactId>
+        <version>${libthrift.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpclient</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpcore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
+      <dependency>
+        <groupId>org.apache.thrift</groupId>
+        <artifactId>libfb303</artifactId>
+        <version>${libthrift.version}</version>
+        <exclusions>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpclient</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.apache.httpcomponents</groupId>
+            <artifactId>httpcore</artifactId>
+          </exclusion>
+          <exclusion>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+          </exclusion>
+        </exclusions>
+      </dependency>
     </dependencies>
   </dependencyManagement>
 
@@ -1271,6 +1897,8 @@
               <spark.ui.showConsoleProgress>false</spark.ui.showConsoleProgress>
               <spark.driver.allowMultipleContexts>true</spark.driver.allowMultipleContexts>
               <spark.unsafe.exceptionOnMemoryLeak>true</spark.unsafe.exceptionOnMemoryLeak>
+              <!-- Needed by sql/hive tests. -->
+              <test.src.tables>src</test.src.tables>
             </systemProperties>
             <failIfNoTests>false</failIfNoTests>
           </configuration>
@@ -1305,6 +1933,8 @@
               <spark.ui.showConsoleProgress>false</spark.ui.showConsoleProgress>
               <spark.driver.allowMultipleContexts>true</spark.driver.allowMultipleContexts>
               <spark.unsafe.exceptionOnMemoryLeak>true</spark.unsafe.exceptionOnMemoryLeak>
+              <!-- Needed by sql/hive tests. -->
+              <test.src.tables>__not_used__</test.src.tables>
             </systemProperties>
           </configuration>
           <executions>
diff --git a/sbin/spark-daemon.sh b/sbin/spark-daemon.sh
index de762acc8fa0e..0fbe795822fbf 100755
--- a/sbin/spark-daemon.sh
+++ b/sbin/spark-daemon.sh
@@ -29,7 +29,7 @@
 #   SPARK_NICENESS The scheduling priority for daemons. Defaults to 0.
 ##
 
-usage="Usage: spark-daemon.sh [--config <conf-dir>] (start|stop|status) <spark-command> <spark-instance-number> <args...>"
+usage="Usage: spark-daemon.sh [--config <conf-dir>] (start|stop|submit|status) <spark-command> <spark-instance-number> <args...>"
 
 # if no args specified, show usage
 if [ $# -le 1 ]; then
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index f4b1cc3a4ffe7..75ab575dfde83 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -66,7 +66,6 @@
     <dependency>
       <groupId>org.codehaus.janino</groupId>
       <artifactId>janino</artifactId>
-      <version>2.7.8</version>
     </dependency>
   </dependencies>
   <build>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
index b4cdfd9e98f6f..57478931cd509 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
@@ -31,6 +31,14 @@ import org.apache.spark.util.Utils
 abstract class ParquetCompatibilityTest extends QueryTest with ParquetTest with BeforeAndAfterAll {
   protected var parquetStore: File = _
 
+  /**
+   * Optional path to a staging subdirectory which may be created during query processing
+   * (Hive does this).
+   * Parquet files under this directory will be ignored in [[readParquetSchema()]]
+   * @return an optional staging directory to ignore when scanning for parquet files.
+   */
+  protected def stagingDir: Option[String] = None
+
   override protected def beforeAll(): Unit = {
     parquetStore = Utils.createTempDir(namePrefix = "parquet-compat_")
     parquetStore.delete()
@@ -43,7 +51,10 @@ abstract class ParquetCompatibilityTest extends QueryTest with ParquetTest with
   def readParquetSchema(path: String): MessageType = {
     val fsPath = new Path(path)
     val fs = fsPath.getFileSystem(configuration)
-    val parquetFiles = fs.listStatus(fsPath).toSeq.filterNot(_.getPath.getName.startsWith("_"))
+    val parquetFiles = fs.listStatus(fsPath).toSeq.filterNot { status =>
+      status.getPath.getName.startsWith("_") ||
+        stagingDir.map(status.getPath.getName.startsWith).getOrElse(false)
+    }
     val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
     footers.head.getParquetMetadata.getFileMetaData.getSchema
   }
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
index 73e6ccdb1eaf8..2dfbcb2425a37 100644
--- a/sql/hive-thriftserver/pom.xml
+++ b/sql/hive-thriftserver/pom.xml
@@ -60,21 +60,31 @@
       <groupId>${hive.group}</groupId>
       <artifactId>hive-jdbc</artifactId>
     </dependency>
+    <dependency>
+      <groupId>${hive.group}</groupId>
+      <artifactId>hive-service</artifactId>
+    </dependency>
     <dependency>
       <groupId>${hive.group}</groupId>
       <artifactId>hive-beeline</artifactId>
     </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-core</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-json</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-server</artifactId>
+    </dependency>
     <!-- Added for selenium: -->
     <dependency>
       <groupId>org.seleniumhq.selenium</groupId>
       <artifactId>selenium-java</artifactId>
       <scope>test</scope>
-      <exclusions>
-        <exclusion>
-          <groupId>io.netty</groupId>
-          <artifactId>netty</artifactId>
-        </exclusion>
-      </exclusions>
     </dependency>
   </dependencies>
   <build>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/hive/service/server/HiveServerServerOptionsProcessor.scala b/sql/hive-thriftserver/src/main/scala/org/apache/hive/service/server/HiveServerServerOptionsProcessor.scala
new file mode 100644
index 0000000000000..2228f651e2387
--- /dev/null
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/hive/service/server/HiveServerServerOptionsProcessor.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.hive.service.server
+
+import org.apache.hive.service.server.HiveServer2.{StartOptionExecutor, ServerOptionsProcessor}
+
+/**
+ * Class to upgrade a package-private class to public, and
+ * implement a `process()` operation consistent with
+ * the behavior of older Hive versions
+ * @param serverName name of the hive server
+ */
+private[apache] class HiveServerServerOptionsProcessor(serverName: String)
+    extends ServerOptionsProcessor(serverName) {
+
+  def process(args: Array[String]): Boolean = {
+    // A parse failure automatically triggers a system exit
+    val response = super.parse(args)
+    val executor = response.getServerOptionsExecutor()
+    // return true if the parsed option was to start the service
+    executor.isInstanceOf[StartOptionExecutor]
+  }
+}
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
index b7db80d93f852..9c047347cb58d 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
@@ -17,6 +17,9 @@
 
 package org.apache.spark.sql.hive.thriftserver
 
+import java.util.Locale
+import java.util.concurrent.atomic.AtomicBoolean
+
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
@@ -24,7 +27,7 @@ import org.apache.commons.logging.LogFactory
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hive.service.cli.thrift.{ThriftBinaryCLIService, ThriftHttpCLIService}
-import org.apache.hive.service.server.{HiveServer2, ServerOptionsProcessor}
+import org.apache.hive.service.server.{HiveServerServerOptionsProcessor, HiveServer2}
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationEnd, SparkListenerJobStart}
@@ -65,7 +68,7 @@ object HiveThriftServer2 extends Logging {
   }
 
   def main(args: Array[String]) {
-    val optionsProcessor = new ServerOptionsProcessor("HiveThriftServer2")
+    val optionsProcessor = new HiveServerServerOptionsProcessor("HiveThriftServer2")
     if (!optionsProcessor.process(args)) {
       System.exit(-1)
     }
@@ -241,9 +244,12 @@ object HiveThriftServer2 extends Logging {
 private[hive] class HiveThriftServer2(hiveContext: HiveContext)
   extends HiveServer2
   with ReflectedCompositeService {
+  // state is tracked internally so that the server only attempts to shut down if it successfully
+  // started, and then once only.
+  private val started = new AtomicBoolean(false)
 
   override def init(hiveConf: HiveConf) {
-    val sparkSqlCliService = new SparkSQLCLIService(hiveContext)
+    val sparkSqlCliService = new SparkSQLCLIService(this, hiveContext)
     setSuperField(this, "cliService", sparkSqlCliService)
     addService(sparkSqlCliService)
 
@@ -259,8 +265,19 @@ private[hive] class HiveThriftServer2(hiveContext: HiveContext)
   }
 
   private def isHTTPTransportMode(hiveConf: HiveConf): Boolean = {
-    val transportMode: String = hiveConf.getVar(ConfVars.HIVE_SERVER2_TRANSPORT_MODE)
-    transportMode.equalsIgnoreCase("http")
+    val transportMode = hiveConf.getVar(ConfVars.HIVE_SERVER2_TRANSPORT_MODE)
+    transportMode.toLowerCase(Locale.ENGLISH).equals("http")
+  }
+
+
+  override def start(): Unit = {
+    super.start()
+    started.set(true)
   }
 
+  override def stop(): Unit = {
+    if (started.getAndSet(false)) {
+       super.stop()
+    }
+  }
 }
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
index e8758887ff3a2..833bf62d47d07 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
@@ -32,8 +32,7 @@ import org.apache.hive.service.cli._
 import org.apache.hadoop.hive.ql.metadata.Hive
 import org.apache.hadoop.hive.ql.metadata.HiveException
 import org.apache.hadoop.hive.ql.session.SessionState
-import org.apache.hadoop.hive.shims.ShimLoader
-import org.apache.hadoop.security.UserGroupInformation
+import org.apache.hadoop.hive.shims.Utils
 import org.apache.hive.service.cli.operation.ExecuteStatementOperation
 import org.apache.hive.service.cli.session.HiveSession
 
@@ -146,7 +145,7 @@ private[hive] class SparkExecuteStatementOperation(
     } else {
       val parentSessionState = SessionState.get()
       val hiveConf = getConfigForOperation()
-      val sparkServiceUGI = ShimLoader.getHadoopShims.getUGIForConf(hiveConf)
+      val sparkServiceUGI = Utils.getUGI()
       val sessionHive = getCurrentHive()
       val currentSqlSession = hiveContext.currentSession
 
@@ -174,7 +173,7 @@ private[hive] class SparkExecuteStatementOperation(
           }
 
           try {
-            ShimLoader.getHadoopShims().doAs(sparkServiceUGI, doAsAction)
+            sparkServiceUGI.doAs(doAsAction)
           } catch {
             case e: Exception =>
               setOperationException(new HiveSQLException(e))
@@ -201,7 +200,7 @@ private[hive] class SparkExecuteStatementOperation(
     }
   }
 
-  private def runInternal(): Unit = {
+  override def runInternal(): Unit = {
     statementId = UUID.randomUUID().toString
     logInfo(s"Running query '$statement' with $statementId")
     setState(OperationState.RUNNING)
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
index f66a17b20915f..d3886142b388d 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -20,9 +20,10 @@ package org.apache.spark.sql.hive.thriftserver
 import scala.collection.JavaConversions._
 
 import java.io._
-import java.util.{ArrayList => JArrayList}
+import java.util.{ArrayList => JArrayList, Locale}
 
-import jline.{ConsoleReader, History}
+import jline.console.ConsoleReader
+import jline.console.history.FileHistory
 
 import org.apache.commons.lang3.StringUtils
 import org.apache.commons.logging.LogFactory
@@ -40,6 +41,10 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.util.Utils
 
+/**
+ * This code doesn't support remote connections in Hive 1.2+, as the underlying CliDriver
+ * has dropped its support.
+ */
 private[hive] object SparkSQLCLIDriver extends Logging {
   private var prompt = "spark-sql"
   private var continuedPrompt = "".padTo(prompt.length, ' ')
@@ -111,16 +116,9 @@ private[hive] object SparkSQLCLIDriver extends Logging {
     // Clean up after we exit
     Utils.addShutdownHook { () => SparkSQLEnv.stop() }
 
+    val remoteMode = isRemoteMode(sessionState)
     // "-h" option has been passed, so connect to Hive thrift server.
-    if (sessionState.getHost != null) {
-      sessionState.connect()
-      if (sessionState.isRemoteMode) {
-        prompt = s"[${sessionState.getHost}:${sessionState.getPort}]" + prompt
-        continuedPrompt = "".padTo(prompt.length, ' ')
-      }
-    }
-
-    if (!sessionState.isRemoteMode) {
+    if (!remoteMode) {
       // Hadoop-20 and above - we need to augment classpath using hiveconf
       // components.
       // See also: code in ExecDriver.java
@@ -131,6 +129,9 @@ private[hive] object SparkSQLCLIDriver extends Logging {
       }
       conf.setClassLoader(loader)
       Thread.currentThread().setContextClassLoader(loader)
+    } else {
+      // Hive 1.2 + not supported in CLI
+      throw new RuntimeException("Remote operations not supported")
     }
 
     val cli = new SparkSQLCLIDriver
@@ -171,14 +172,14 @@ private[hive] object SparkSQLCLIDriver extends Logging {
     val reader = new ConsoleReader()
     reader.setBellEnabled(false)
     // reader.setDebug(new PrintWriter(new FileWriter("writer.debug", true)))
-    CliDriver.getCommandCompletor.foreach((e) => reader.addCompletor(e))
+    CliDriver.getCommandCompleter.foreach((e) => reader.addCompleter(e))
 
     val historyDirectory = System.getProperty("user.home")
 
     try {
       if (new File(historyDirectory).exists()) {
         val historyFile = historyDirectory + File.separator + ".hivehistory"
-        reader.setHistory(new History(new File(historyFile)))
+        reader.setHistory(new FileHistory(new File(historyFile)))
       } else {
         logWarning("WARNING: Directory for Hive history file: " + historyDirectory +
                            " does not exist.   History will not be available during this session.")
@@ -190,10 +191,14 @@ private[hive] object SparkSQLCLIDriver extends Logging {
         logWarning(e.getMessage)
     }
 
+    // TODO: missing
+/*
     val clientTransportTSocketField = classOf[CliSessionState].getDeclaredField("transport")
     clientTransportTSocketField.setAccessible(true)
 
     transport = clientTransportTSocketField.get(sessionState).asInstanceOf[TSocket]
+*/
+    transport = null
 
     var ret = 0
     var prefix = ""
@@ -230,6 +235,13 @@ private[hive] object SparkSQLCLIDriver extends Logging {
 
     System.exit(ret)
   }
+
+
+  def isRemoteMode(state: CliSessionState): Boolean = {
+    //    sessionState.isRemoteMode
+    state.isHiveServerQuery
+  }
+
 }
 
 private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
@@ -239,25 +251,33 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
 
   private val console = new SessionState.LogHelper(LOG)
 
+  private val isRemoteMode = {
+    SparkSQLCLIDriver.isRemoteMode(sessionState)
+  }
+
   private val conf: Configuration =
     if (sessionState != null) sessionState.getConf else new Configuration()
 
   // Force initializing SparkSQLEnv. This is put here but not object SparkSQLCliDriver
   // because the Hive unit tests do not go through the main() code path.
-  if (!sessionState.isRemoteMode) {
+  if (!isRemoteMode) {
     SparkSQLEnv.init()
+  } else {
+    // Hive 1.2 + not supported in CLI
+    throw new RuntimeException("Remote operations not supported")
   }
 
   override def processCmd(cmd: String): Int = {
     val cmd_trimmed: String = cmd.trim()
+    val cmd_lower = cmd_trimmed.toLowerCase(Locale.ENGLISH)
     val tokens: Array[String] = cmd_trimmed.split("\\s+")
     val cmd_1: String = cmd_trimmed.substring(tokens(0).length()).trim()
-    if (cmd_trimmed.toLowerCase.equals("quit") ||
-      cmd_trimmed.toLowerCase.equals("exit") ||
-      tokens(0).equalsIgnoreCase("source") ||
+    if (cmd_lower.equals("quit") ||
+      cmd_lower.equals("exit") ||
+      tokens(0).toLowerCase(Locale.ENGLISH).equals("source") ||
       cmd_trimmed.startsWith("!") ||
       tokens(0).toLowerCase.equals("list") ||
-      sessionState.isRemoteMode) {
+      isRemoteMode) {
       val start = System.currentTimeMillis()
       super.processCmd(cmd)
       val end = System.currentTimeMillis()
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
index 41f647d5f8c5a..644165acf70a7 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
@@ -23,11 +23,12 @@ import javax.security.auth.login.LoginException
 
 import org.apache.commons.logging.Log
 import org.apache.hadoop.hive.conf.HiveConf
-import org.apache.hadoop.hive.shims.ShimLoader
+import org.apache.hadoop.hive.shims.Utils
 import org.apache.hadoop.security.UserGroupInformation
 import org.apache.hive.service.Service.STATE
 import org.apache.hive.service.auth.HiveAuthFactory
 import org.apache.hive.service.cli._
+import org.apache.hive.service.server.HiveServer2
 import org.apache.hive.service.{AbstractService, Service, ServiceException}
 
 import org.apache.spark.sql.hive.HiveContext
@@ -35,22 +36,22 @@ import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
 
 import scala.collection.JavaConversions._
 
-private[hive] class SparkSQLCLIService(hiveContext: HiveContext)
-  extends CLIService
+private[hive] class SparkSQLCLIService(hiveServer: HiveServer2, hiveContext: HiveContext)
+  extends CLIService(hiveServer)
   with ReflectedCompositeService {
 
   override def init(hiveConf: HiveConf) {
     setSuperField(this, "hiveConf", hiveConf)
 
-    val sparkSqlSessionManager = new SparkSQLSessionManager(hiveContext)
+    val sparkSqlSessionManager = new SparkSQLSessionManager(hiveServer, hiveContext)
     setSuperField(this, "sessionManager", sparkSqlSessionManager)
     addService(sparkSqlSessionManager)
     var sparkServiceUGI: UserGroupInformation = null
 
-    if (ShimLoader.getHadoopShims.isSecurityEnabled) {
+    if (UserGroupInformation.isSecurityEnabled) {
       try {
         HiveAuthFactory.loginFromKeytab(hiveConf)
-        sparkServiceUGI = ShimLoader.getHadoopShims.getUGIForConf(hiveConf)
+        sparkServiceUGI = Utils.getUGI()
         setSuperField(this, "serviceUGI", sparkServiceUGI)
       } catch {
         case e @ (_: IOException | _: LoginException) =>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
index 2d5ee68002286..92ac0ec3fca29 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLSessionManager.scala
@@ -25,14 +25,15 @@ import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hive.service.cli.SessionHandle
 import org.apache.hive.service.cli.session.SessionManager
 import org.apache.hive.service.cli.thrift.TProtocolVersion
+import org.apache.hive.service.server.HiveServer2
 
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
 import org.apache.spark.sql.hive.thriftserver.server.SparkSQLOperationManager
 
 
-private[hive] class SparkSQLSessionManager(hiveContext: HiveContext)
-  extends SessionManager
+private[hive] class SparkSQLSessionManager(hiveServer: HiveServer2, hiveContext: HiveContext)
+  extends SessionManager(hiveServer)
   with ReflectedCompositeService {
 
   private lazy val sparkSqlOperationManager = new SparkSQLOperationManager(hiveContext)
@@ -55,12 +56,14 @@ private[hive] class SparkSQLSessionManager(hiveContext: HiveContext)
       protocol: TProtocolVersion,
       username: String,
       passwd: String,
+      ipAddress: String,
       sessionConf: java.util.Map[String, String],
       withImpersonation: Boolean,
       delegationToken: String): SessionHandle = {
     hiveContext.openSession()
-    val sessionHandle = super.openSession(
-      protocol, username, passwd, sessionConf, withImpersonation, delegationToken)
+    val sessionHandle =
+      super.openSession(protocol, username, passwd, ipAddress, sessionConf, withImpersonation,
+          delegationToken)
     val session = super.getSession(sessionHandle)
     HiveThriftServer2.listener.onSessionCreated(
       session.getIpAddress, sessionHandle.getSessionId.toString, session.getUsername)
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
index df80d04b40801..121b3e077f71f 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
@@ -23,6 +23,7 @@ import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.duration._
 import scala.concurrent.{Await, Promise}
 import scala.sys.process.{Process, ProcessLogger}
+import scala.util.Failure
 
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.scalatest.BeforeAndAfter
@@ -37,31 +38,46 @@ import org.apache.spark.util.Utils
 class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
   val warehousePath = Utils.createTempDir()
   val metastorePath = Utils.createTempDir()
+  val scratchDirPath = Utils.createTempDir()
 
   before {
-      warehousePath.delete()
-      metastorePath.delete()
+    warehousePath.delete()
+    metastorePath.delete()
+    scratchDirPath.delete()
   }
 
   after {
-      warehousePath.delete()
-      metastorePath.delete()
+    warehousePath.delete()
+    metastorePath.delete()
+    scratchDirPath.delete()
   }
 
+  /**
+   * Run a CLI operation and expect all the queries and expected answers to be returned.
+   * @param timeout maximum time for the commands to complete
+   * @param extraArgs any extra arguments
+   * @param errorResponses a sequence of strings whose presence in the stdout of the forked process
+   *                       is taken as an immediate error condition. That is: if a line beginning
+   *                       with one of these strings is found, fail the test immediately.
+   *                       The default value is `Seq("Error:")`
+   *
+   * @param queriesAndExpectedAnswers one or more tupes of query + answer
+   */
   def runCliWithin(
       timeout: FiniteDuration,
-      extraArgs: Seq[String] = Seq.empty)(
+      extraArgs: Seq[String] = Seq.empty,
+      errorResponses: Seq[String] = Seq("Error:"))(
       queriesAndExpectedAnswers: (String, String)*): Unit = {
 
     val (queries, expectedAnswers) = queriesAndExpectedAnswers.unzip
-    val cliScript = "../../bin/spark-sql".split("/").mkString(File.separator)
-
     val command = {
+      val cliScript = "../../bin/spark-sql".split("/").mkString(File.separator)
       val jdbcUrl = s"jdbc:derby:;databaseName=$metastorePath;create=true"
       s"""$cliScript
          |  --master local
          |  --hiveconf ${ConfVars.METASTORECONNECTURLKEY}=$jdbcUrl
          |  --hiveconf ${ConfVars.METASTOREWAREHOUSE}=$warehousePath
+         |  --hiveconf ${ConfVars.SCRATCHDIR}=$scratchDirPath
        """.stripMargin.split("\\s+").toSeq ++ extraArgs
     }
 
@@ -81,6 +97,12 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
         if (next == expectedAnswers.size) {
           foundAllExpectedAnswers.trySuccess(())
         }
+      } else {
+        errorResponses.foreach( r => {
+          if (line.startsWith(r)) {
+            foundAllExpectedAnswers.tryFailure(
+              new RuntimeException(s"Failed with error line '$line'"))
+          }})
       }
     }
 
@@ -88,16 +110,44 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
     val process = (Process(command, None) #< queryStream).run(
       ProcessLogger(captureOutput("stdout"), captureOutput("stderr")))
 
+    // catch the output value
+    class exitCodeCatcher extends Runnable {
+      var exitValue = 0
+
+      override def run(): Unit = {
+        try {
+          exitValue = process.exitValue()
+        } catch {
+          case rte: RuntimeException =>
+            // ignored as it will get triggered when the process gets destroyed
+            logDebug("Ignoring exception while waiting for exit code", rte)
+        }
+        if (exitValue != 0) {
+          // process exited: fail fast
+          foundAllExpectedAnswers.tryFailure(
+            new RuntimeException(s"Failed with exit code $exitValue"))
+        }
+      }
+    }
+    // spin off the code catche thread. No attempt is made to kill this
+    // as it will exit once the launched process terminates.
+    val codeCatcherThread = new Thread(new exitCodeCatcher())
+    codeCatcherThread.start()
+
     try {
-      Await.result(foundAllExpectedAnswers.future, timeout)
+      Await.ready(foundAllExpectedAnswers.future, timeout)
+      foundAllExpectedAnswers.future.value match {
+        case Some(Failure(t)) => throw t
+        case _ =>
+      }
     } catch { case cause: Throwable =>
-      logError(
+      val message =
         s"""
            |=======================
            |CliSuite failure output
            |=======================
            |Spark SQL CLI command line: ${command.mkString(" ")}
-           |
+           |Exception: $cause
            |Executed query $next "${queries(next)}",
            |But failed to capture expected output "${expectedAnswers(next)}" within $timeout.
            |
@@ -105,8 +155,9 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
            |===========================
            |End CliSuite failure output
            |===========================
-         """.stripMargin, cause)
-      throw cause
+         """.stripMargin
+      logError(message, cause)
+      fail(message, cause)
     } finally {
       process.destroy()
     }
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
index 39b31523e07cb..8374629b5d45a 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
@@ -19,7 +19,6 @@ package org.apache.spark.sql.hive.thriftserver
 
 import java.io.File
 import java.net.URL
-import java.nio.charset.StandardCharsets
 import java.sql.{Date, DriverManager, SQLException, Statement}
 
 import scala.collection.mutable.ArrayBuffer
@@ -492,7 +491,7 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
         new File(s"$tempLog4jConf/log4j.properties"),
         UTF_8)
 
-      tempLog4jConf + File.pathSeparator + sys.props("java.class.path")
+      tempLog4jConf // + File.pathSeparator + sys.props("java.class.path")
     }
 
     s"""$startScript
@@ -508,6 +507,20 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
      """.stripMargin.split("\\s+").toSeq
   }
 
+  /**
+   * String to scan for when looking for the the thrift binary endpoint running.
+   * This can change across Hive versions.
+   */
+  val THRIFT_BINARY_SERVICE_LIVE = "Starting ThriftBinaryCLIService on port"
+
+  /**
+   * String to scan for when looking for the the thrift HTTP endpoint running.
+   * This can change across Hive versions.
+   */
+  val THRIFT_HTTP_SERVICE_LIVE = "Started ThriftHttpCLIService in http"
+
+  val SERVER_STARTUP_TIMEOUT = 1.minute
+
   private def startThriftServer(port: Int, attempt: Int) = {
     warehousePath = Utils.createTempDir()
     warehousePath.delete()
@@ -545,23 +558,26 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
 
     // Ensures that the following "tail" command won't fail.
     logPath.createNewFile()
+    val successLines = Seq(THRIFT_BINARY_SERVICE_LIVE, THRIFT_HTTP_SERVICE_LIVE)
+    val failureLines = Seq("HiveServer2 is stopped", "Exception in thread", "Error:")
     logTailingProcess =
       // Using "-n +0" to make sure all lines in the log file are checked.
       Process(s"/usr/bin/env tail -n +0 -f ${logPath.getCanonicalPath}").run(ProcessLogger(
         (line: String) => {
           diagnosisBuffer += line
-
-          if (line.contains("ThriftBinaryCLIService listening on") ||
-              line.contains("Started ThriftHttpCLIService in http")) {
-            serverStarted.trySuccess(())
-          } else if (line.contains("HiveServer2 is stopped")) {
-            // This log line appears when the server fails to start and terminates gracefully (e.g.
-            // because of port contention).
-            serverStarted.tryFailure(new RuntimeException("Failed to start HiveThriftServer2"))
-          }
+          successLines.foreach(r => {
+            if (line.contains(r)) {
+              serverStarted.trySuccess(())
+            }
+          })
+          failureLines.foreach(r => {
+            if (line.contains(r)) {
+              serverStarted.tryFailure(new RuntimeException(s"Failed with output '$line'"))
+            }
+          })
         }))
 
-    Await.result(serverStarted.future, 2.minute)
+    Await.result(serverStarted.future, SERVER_STARTUP_TIMEOUT)
   }
 
   private def stopThriftServer(): Unit = {
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index 53d5b22b527b2..c46a4a4b0be54 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -267,7 +267,34 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     "date_udf",
 
     // Unlike Hive, we do support log base in (0, 1.0], therefore disable this
-    "udf7"
+    "udf7",
+
+    // Trivial changes to DDL output
+    "compute_stats_empty_table",
+    "compute_stats_long",
+    "create_view_translate",
+    "show_create_table_serde",
+    "show_tblproperties",
+
+    // Odd changes to output
+    "merge4",
+
+    // Thift is broken...
+    "inputddl8",
+
+    // Hive changed ordering of ddl:
+    "varchar_union1",
+
+    // Parser changes in Hive 1.2
+    "input25",
+    "input26",
+
+    // Uses invalid table name
+    "innerjoin",
+
+    // classpath problems
+    "compute_stats.*",
+    "udf_bitmap_.*"
   )
 
   /**
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index b00f320318be0..be1607476e254 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -36,6 +36,11 @@
   </properties>
 
   <dependencies>
+    <!-- Added for Hive Parquet SerDe -->
+    <dependency>
+      <groupId>com.twitter</groupId>
+      <artifactId>parquet-hadoop-bundle</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-core_${scala.binary.version}</artifactId>
@@ -53,32 +58,42 @@
       <artifactId>spark-sql_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
     </dependency>
+<!--
     <dependency>
-      <groupId>${hive.group}</groupId>
-      <artifactId>hive-metastore</artifactId>
+      <groupId>com.google.guava</groupId>
+      <artifactId>guava</artifactId>
     </dependency>
     <dependency>
-      <groupId>commons-httpclient</groupId>
-      <artifactId>commons-httpclient</artifactId>
-      <version>3.1</version>
+      <groupId>com.google.protobuf</groupId>
+      <artifactId>protobuf-java</artifactId>
+      <version>${protobuf.version}</version>
     </dependency>
     <dependency>
       <groupId>${hive.group}</groupId>
-      <artifactId>hive-exec</artifactId>
-    </dependency>
-    <dependency>
-      <groupId>org.apache.httpcomponents</groupId>
-      <artifactId>httpclient</artifactId>
-      <version>${commons.httpclient.version}</version>
+      <artifactId>hive-common</artifactId>
     </dependency>
+-->
     <dependency>
-      <groupId>org.codehaus.jackson</groupId>
-      <artifactId>jackson-mapper-asl</artifactId>
+      <groupId>${hive.group}</groupId>
+      <artifactId>hive-exec</artifactId>
+<!--
+      <classifier>core</classifier>
+-->
     </dependency>
     <dependency>
       <groupId>${hive.group}</groupId>
-      <artifactId>hive-serde</artifactId>
+      <artifactId>hive-metastore</artifactId>
     </dependency>
+    <!--
+        <dependency>
+          <groupId>${hive.group}</groupId>
+          <artifactId>hive-serde</artifactId>
+        </dependency>
+        <dependency>
+          <groupId>${hive.group}</groupId>
+          <artifactId>hive-shims</artifactId>
+        </dependency>
+    -->
     <!-- hive-serde already depends on avro, but this brings in customized config of avro deps from parent -->
     <dependency>
       <groupId>org.apache.avro</groupId>
@@ -91,6 +106,55 @@
       <artifactId>avro-mapred</artifactId>
       <classifier>${avro.mapred.classifier}</classifier>
     </dependency>
+    <dependency>
+      <groupId>commons-httpclient</groupId>
+      <artifactId>commons-httpclient</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.calcite</groupId>
+      <artifactId>calcite-avatica</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.calcite</groupId>
+      <artifactId>calcite-core</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.httpcomponents</groupId>
+      <artifactId>httpclient</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.codehaus.jackson</groupId>
+      <artifactId>jackson-mapper-asl</artifactId>
+    </dependency>
+    <!-- transitive dependencies of hive-exec-core doesn't declare -->
+    <dependency>
+      <groupId>commons-codec</groupId>
+      <artifactId>commons-codec</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>joda-time</groupId>
+      <artifactId>joda-time</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.jodd</groupId>
+      <artifactId>jodd-core</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>com.google.code.findbugs</groupId>
+      <artifactId>jsr305</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.datanucleus</groupId>
+      <artifactId>datanucleus-core</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.thrift</groupId>
+      <artifactId>libthrift</artifactId>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.thrift</groupId>
+      <artifactId>libfb303</artifactId>
+    </dependency>
     <dependency>
       <groupId>org.scalacheck</groupId>
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 110f51a305861..567d7fa12ff14 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -20,15 +20,18 @@ package org.apache.spark.sql.hive
 import java.io.File
 import java.net.{URL, URLClassLoader}
 import java.sql.Timestamp
+import java.util.concurrent.TimeUnit
 
 import scala.collection.JavaConversions._
 import scala.collection.mutable.HashMap
 import scala.language.implicitConversions
+import scala.concurrent.duration._
 
 import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.hive.common.StatsSetupConst
 import org.apache.hadoop.hive.common.`type`.HiveDecimal
 import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.ql.metadata.Table
 import org.apache.hadoop.hive.ql.parse.VariableSubstitution
 import org.apache.hadoop.hive.ql.session.SessionState
@@ -164,6 +167,16 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
   }
   SessionState.setCurrentSessionState(executionHive.state)
 
+  /**
+   * Overrides default Hive configurations to avoid breaking changes to Spark SQL users.
+   *  - allow SQL11 keywords to be used as identifiers
+   */
+  private[sql] def defaultOverides() = {
+    setConf(ConfVars.HIVE_SUPPORT_SQL11_RESERVED_KEYWORDS.varname, "false")
+  }
+
+  defaultOverides()
+
   /**
    * The copy of the Hive client that is used to retrieve metadata from the Hive MetaStore.
    * The version of the Hive client that is used here must match the metastore that is configured
@@ -252,6 +265,10 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
   }
 
   protected[sql] override def parseSql(sql: String): LogicalPlan = {
+    var state = SessionState.get()
+    if (state == null) {
+      SessionState.setCurrentSessionState(tlSession.get().asInstanceOf[SQLSession].sessionState)
+    }
     super.parseSql(substitutor.substitute(hiveconf, sql))
   }
 
@@ -298,10 +315,21 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
         // Can we use fs.getContentSummary in future?
         // Seems fs.getContentSummary returns wrong table size on Jenkins. So we use
         // countFileSize to count the table size.
+        val stagingDir = metadataHive.getConf(HiveConf.ConfVars.STAGINGDIR.varname,
+          HiveConf.ConfVars.STAGINGDIR.defaultStrVal)
+
         def calculateTableSize(fs: FileSystem, path: Path): Long = {
           val fileStatus = fs.getFileStatus(path)
           val size = if (fileStatus.isDir) {
-            fs.listStatus(path).map(status => calculateTableSize(fs, status.getPath)).sum
+            fs.listStatus(path)
+              .map { status =>
+                if (!status.getPath().getName().startsWith(stagingDir)) {
+                  calculateTableSize(fs, status.getPath)
+                } else {
+                  0L
+                }
+              }
+              .sum
           } else {
             fileStatus.getLen
           }
@@ -398,7 +426,58 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
   }
 
   /** Overridden by child classes that need to set configuration before the client init. */
-  protected def configure(): Map[String, String] = Map.empty
+  protected def configure(): Map[String, String] = {
+    // Hive 0.14.0 introduces timeout operations in HiveConf, and changes default values of a bunch
+    // of time `ConfVar`s by adding time suffixes (`s`, `ms`, and `d` etc.).  This breaks backwards-
+    // compatibility when users are trying to connecting to a Hive metastore of lower version,
+    // because these options are expected to be integral values in lower versions of Hive.
+    //
+    // Here we enumerate all time `ConfVar`s and convert their values to numeric strings according
+    // to their output time units.
+    Seq(
+      ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_CLIENT_SOCKET_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_CLIENT_SOCKET_LIFETIME -> TimeUnit.SECONDS,
+      ConfVars.HMSHANDLERINTERVAL -> TimeUnit.MILLISECONDS,
+      ConfVars.METASTORE_EVENT_DB_LISTENER_TTL -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_EVENT_CLEAN_FREQ -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_EVENT_EXPIRY_DURATION -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_AGGREGATE_STATS_CACHE_TTL -> TimeUnit.SECONDS,
+      ConfVars.METASTORE_AGGREGATE_STATS_CACHE_MAX_WRITER_WAIT -> TimeUnit.MILLISECONDS,
+      ConfVars.METASTORE_AGGREGATE_STATS_CACHE_MAX_READER_WAIT -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVES_AUTO_PROGRESS_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_LOG_INCREMENTAL_PLAN_PROGRESS_INTERVAL -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_STATS_JDBC_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_STATS_RETRIES_WAIT -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_LOCK_SLEEP_BETWEEN_RETRIES -> TimeUnit.SECONDS,
+      ConfVars.HIVE_ZOOKEEPER_SESSION_TIMEOUT -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_ZOOKEEPER_CONNECTION_BASESLEEPTIME -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_TXN_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_COMPACTOR_WORKER_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_COMPACTOR_CHECK_INTERVAL -> TimeUnit.SECONDS,
+      ConfVars.HIVE_COMPACTOR_CLEANER_RUN_INTERVAL -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_HTTP_MAX_IDLE_TIME -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_HTTP_WORKER_KEEPALIVE_TIME -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_HTTP_COOKIE_MAX_AGE -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_LOGIN_BEBACKOFF_SLOT_LENGTH -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_LOGIN_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_THRIFT_WORKER_KEEPALIVE_TIME -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_ASYNC_EXEC_SHUTDOWN_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_ASYNC_EXEC_KEEPALIVE_TIME -> TimeUnit.SECONDS,
+      ConfVars.HIVE_SERVER2_LONG_POLLING_TIMEOUT -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_SESSION_CHECK_INTERVAL -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_IDLE_SESSION_TIMEOUT -> TimeUnit.MILLISECONDS,
+      ConfVars.HIVE_SERVER2_IDLE_OPERATION_TIMEOUT -> TimeUnit.MILLISECONDS,
+      ConfVars.SERVER_READ_SOCKET_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.HIVE_LOCALIZE_RESOURCE_WAIT_INTERVAL -> TimeUnit.MILLISECONDS,
+      ConfVars.SPARK_CLIENT_FUTURE_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.SPARK_JOB_MONITOR_TIMEOUT -> TimeUnit.SECONDS,
+      ConfVars.SPARK_RPC_CLIENT_CONNECT_TIMEOUT -> TimeUnit.MILLISECONDS,
+      ConfVars.SPARK_RPC_CLIENT_HANDSHAKE_TIMEOUT -> TimeUnit.MILLISECONDS
+    ).map { case (confVar, unit) =>
+      confVar.varname -> hiveconf.getTimeVar(confVar, unit).toString
+    }.toMap
+  }
 
   protected[hive] class SQLSession extends super.SQLSession {
     protected[sql] override lazy val conf: SQLConf = new SQLConf {
@@ -515,19 +594,23 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
 
 private[hive] object HiveContext {
   /** The version of hive used internally by Spark SQL. */
-  val hiveExecutionVersion: String = "0.13.1"
+  val hiveExecutionVersion: String = "1.2.1"
 
   val HIVE_METASTORE_VERSION: String = "spark.sql.hive.metastore.version"
   val HIVE_METASTORE_JARS = stringConf("spark.sql.hive.metastore.jars",
     defaultValue = Some("builtin"),
-    doc = "Location of the jars that should be used to instantiate the HiveMetastoreClient. This" +
-      " property can be one of three options: " +
-      "1. \"builtin\" Use Hive 0.13.1, which is bundled with the Spark assembly jar when " +
-      "<code>-Phive</code> is enabled. When this option is chosen, " +
-      "spark.sql.hive.metastore.version must be either <code>0.13.1</code> or not defined. " +
-      "2. \"maven\" Use Hive jars of specified version downloaded from Maven repositories." +
-      "3. A classpath in the standard format for both Hive and Hadoop.")
-
+    doc = s"""
+      | Location of the jars that should be used to instantiate the HiveMetastoreClient.
+      | This property can be one of three options: "
+      | 1. "builtin"
+      |   Use Hive ${hiveExecutionVersion}, which is bundled with the Spark assembly jar when
+      |   <code>-Phive</code> is enabled. When this option is chosen,
+      |   <code>spark.sql.hive.metastore.version</code> must be either
+      |   <code>${hiveExecutionVersion}</code> or not defined.
+      | 2. "maven"
+      |   Use Hive jars of specified version downloaded from Maven repositories.
+      | 3. A classpath in the standard format for both Hive and Hadoop.
+    """.stripMargin)
   val CONVERT_METASTORE_PARQUET = booleanConf("spark.sql.hive.convertMetastoreParquet",
     defaultValue = Some(true),
     doc = "When set to false, Spark SQL will use the Hive SerDe for parquet tables instead of " +
@@ -566,17 +649,18 @@ private[hive] object HiveContext {
   /** Constructs a configuration for hive, where the metastore is located in a temp directory. */
   def newTemporaryConfiguration(): Map[String, String] = {
     val tempDir = Utils.createTempDir()
-    val localMetastore = new File(tempDir, "metastore").getAbsolutePath
+    val localMetastore = new File(tempDir, "metastore")
     val propMap: HashMap[String, String] = HashMap()
     // We have to mask all properties in hive-site.xml that relates to metastore data source
     // as we used a local metastore here.
     HiveConf.ConfVars.values().foreach { confvar =>
       if (confvar.varname.contains("datanucleus") || confvar.varname.contains("jdo")) {
-        propMap.put(confvar.varname, confvar.defaultVal)
+        propMap.put(confvar.varname, confvar.getDefaultExpr())
       }
     }
-    propMap.put("javax.jdo.option.ConnectionURL",
-      s"jdbc:derby:;databaseName=$localMetastore;create=true")
+    propMap.put(HiveConf.ConfVars.METASTOREWAREHOUSE.varname, localMetastore.toURI.toString)
+    propMap.put(HiveConf.ConfVars.METASTORECONNECTURLKEY.varname,
+      s"jdbc:derby:;databaseName=${localMetastore.getAbsolutePath};create=true")
     propMap.put("datanucleus.rdbms.datastoreAdapterClassName",
       "org.datanucleus.store.rdbms.adapter.DerbyAdapter")
     propMap.toMap
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index a8c9b4fa71b99..16c186627f6cc 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -649,11 +649,12 @@ private[hive] case class MetastoreRelation
     table.outputFormat.foreach(sd.setOutputFormat)
 
     val serdeInfo = new org.apache.hadoop.hive.metastore.api.SerDeInfo
-    sd.setSerdeInfo(serdeInfo)
     table.serde.foreach(serdeInfo.setSerializationLib)
+    sd.setSerdeInfo(serdeInfo)
+
     val serdeParameters = new java.util.HashMap[String, String]()
-    serdeInfo.setParameters(serdeParameters)
     table.serdeProperties.foreach { case (k, v) => serdeParameters.put(k, v) }
+    serdeInfo.setParameters(serdeParameters)
 
     new Table(tTable)
   }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index e6df64d2642bc..e2fdfc6163a00 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.hive
 
 import java.sql.Date
+import java.util.Locale
 
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.serde.serdeConstants
@@ -80,6 +81,7 @@ private[hive] object HiveQl extends Logging {
     "TOK_ALTERDATABASE_PROPERTIES",
     "TOK_ALTERINDEX_PROPERTIES",
     "TOK_ALTERINDEX_REBUILD",
+    "TOK_ALTERTABLE",
     "TOK_ALTERTABLE_ADDCOLS",
     "TOK_ALTERTABLE_ADDPARTS",
     "TOK_ALTERTABLE_ALTERPARTS",
@@ -94,6 +96,7 @@ private[hive] object HiveQl extends Logging {
     "TOK_ALTERTABLE_SKEWED",
     "TOK_ALTERTABLE_TOUCH",
     "TOK_ALTERTABLE_UNARCHIVE",
+    "TOK_ALTERVIEW",
     "TOK_ALTERVIEW_ADDPARTS",
     "TOK_ALTERVIEW_AS",
     "TOK_ALTERVIEW_DROPPARTS",
@@ -248,7 +251,7 @@ private[hive] object HiveQl extends Logging {
      * Otherwise, there will be Null pointer exception,
      * when retrieving properties form HiveConf.
      */
-    val hContext = new Context(hiveConf)
+    val hContext = new Context(SessionState.get().getConf())
     val node = ParseUtils.findRootNonNullToken((new ParseDriver).parse(sql, hContext))
     hContext.clear()
     node
@@ -577,12 +580,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             "TOK_TABLESKEWED", // Skewed by
             "TOK_TABLEROWFORMAT",
             "TOK_TABLESERIALIZER",
-            "TOK_FILEFORMAT_GENERIC", // For file formats not natively supported by Hive.
-            "TOK_TBLSEQUENCEFILE", // Stored as SequenceFile
-            "TOK_TBLTEXTFILE", // Stored as TextFile
-            "TOK_TBLRCFILE", // Stored as RCFile
-            "TOK_TBLORCFILE", // Stored as ORC File
-            "TOK_TBLPARQUETFILE", // Stored as PARQUET
+            "TOK_FILEFORMAT_GENERIC",
             "TOK_TABLEFILEFORMAT", // User-provided InputFormat and OutputFormat
             "TOK_STORAGEHANDLER", // Storage handler
             "TOK_TABLELOCATION",
@@ -706,36 +704,51 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             tableDesc = tableDesc.copy(serdeProperties = tableDesc.serdeProperties ++ serdeParams)
           }
         case Token("TOK_FILEFORMAT_GENERIC", child :: Nil) =>
-          throw new SemanticException(
-            "Unrecognized file format in STORED AS clause:${child.getText}")
+          child.getText().toLowerCase(Locale.ENGLISH) match {
+            case "orc" =>
+              tableDesc = tableDesc.copy(
+                inputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat"),
+                outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat"))
+              if (tableDesc.serde.isEmpty) {
+                tableDesc = tableDesc.copy(
+                  serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde"))
+              }
 
-        case Token("TOK_TBLRCFILE", Nil) =>
-          tableDesc = tableDesc.copy(
-            inputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat"),
-            outputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat"))
-          if (tableDesc.serde.isEmpty) {
-            tableDesc = tableDesc.copy(
-              serde = Option("org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe"))
-          }
+            case "parquet" =>
+              tableDesc = tableDesc.copy(
+                inputFormat =
+                  Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"),
+                outputFormat =
+                  Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"))
+              if (tableDesc.serde.isEmpty) {
+                tableDesc = tableDesc.copy(
+                  serde = Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe"))
+              }
 
-        case Token("TOK_TBLORCFILE", Nil) =>
-          tableDesc = tableDesc.copy(
-            inputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat"),
-            outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat"))
-          if (tableDesc.serde.isEmpty) {
-            tableDesc = tableDesc.copy(
-              serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde"))
-          }
+            case "rcfile" =>
+              tableDesc = tableDesc.copy(
+                inputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat"),
+                outputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat"))
+              if (tableDesc.serde.isEmpty) {
+                tableDesc = tableDesc.copy(
+                  serde = Option("org.apache.hadoop.hive.serde2.columnar.LazyBinaryColumnarSerDe"))
+              }
 
-        case Token("TOK_TBLPARQUETFILE", Nil) =>
-          tableDesc = tableDesc.copy(
-            inputFormat =
-              Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"),
-            outputFormat =
-              Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"))
-          if (tableDesc.serde.isEmpty) {
-            tableDesc = tableDesc.copy(
-              serde = Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe"))
+            case "textfile" =>
+              tableDesc = tableDesc.copy(
+                inputFormat =
+                  Option("org.apache.hadoop.mapred.TextInputFormat"),
+                outputFormat =
+                  Option("org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat"))
+
+            case "sequencefile" =>
+              tableDesc = tableDesc.copy(
+                inputFormat = Option("org.apache.hadoop.mapred.SequenceFileInputFormat"),
+                outputFormat = Option("org.apache.hadoop.mapred.SequenceFileOutputFormat"))
+
+            case _ =>
+              throw new SemanticException(
+                s"Unrecognized file format in STORED AS clause: ${child.getText}")
           }
 
         case Token("TOK_TABLESERIALIZER",
@@ -751,7 +764,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
         case Token("TOK_TABLEPROPERTIES", list :: Nil) =>
           tableDesc = tableDesc.copy(properties = tableDesc.properties ++ getProperties(list))
-        case list @ Token("TOK_TABLEFILEFORMAT", _) =>
+        case list @ Token("TOK_TABLEFILEFORMAT", children) =>
           tableDesc = tableDesc.copy(
             inputFormat =
               Option(BaseSemanticAnalyzer.unescapeSQLString(list.getChild(0).getText)),
@@ -889,7 +902,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
                 Token("TOK_TABLEPROPLIST", propsClause) :: Nil) :: Nil) :: Nil =>
                 val serdeProps = propsClause.map {
                   case Token("TOK_TABLEPROPERTY", Token(name, Nil) :: Token(value, Nil) :: Nil) =>
-                    (name, value)
+                    (BaseSemanticAnalyzer.unescapeSQLString(name),
+                      BaseSemanticAnalyzer.unescapeSQLString(value))
                 }
                 (Nil, Some(BaseSemanticAnalyzer.unescapeSQLString(serdeClass)), serdeProps)
 
@@ -1037,10 +1051,11 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       // return With plan if there is CTE
       cteRelations.map(With(query, _)).getOrElse(query)
 
-    case Token("TOK_UNION", left :: right :: Nil) => Union(nodeToPlan(left), nodeToPlan(right))
+    // HIVE-9039 renamed TOK_UNION => TOK_UNIONALL while adding TOK_UNIONDISTINCT
+    case Token("TOK_UNIONALL", left :: right :: Nil) => Union(nodeToPlan(left), nodeToPlan(right))
 
     case a: ASTNode =>
-      throw new NotImplementedError(s"No parse rules for:\n ${dumpTree(a).toString} ")
+      throw new NotImplementedError(s"No parse rules for $node:\n ${dumpTree(a).toString} ")
   }
 
   val allJoinTokens = "(TOK_.*JOIN)".r
@@ -1251,7 +1266,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       InsertIntoTable(UnresolvedRelation(tableIdent, None), partitionKeys, query, overwrite, true)
 
     case a: ASTNode =>
-      throw new NotImplementedError(s"No parse rules for:\n ${dumpTree(a).toString} ")
+      throw new NotImplementedError(s"No parse rules for ${a.getName}:" +
+          s"\n ${dumpTree(a).toString} ")
   }
 
   protected def selExprNodeToExpr(node: Node): Option[Expression] = node match {
@@ -1274,7 +1290,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
     case Token("TOK_HINTLIST", _) => None
 
     case a: ASTNode =>
-      throw new NotImplementedError(s"No parse rules for:\n ${dumpTree(a).toString} ")
+      throw new NotImplementedError(s"No parse rules for ${a.getName }:" +
+          s"\n ${dumpTree(a).toString } ")
   }
 
   protected val escapedIdentifier = "`([^`]+)`".r
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
index a357bb39ca7fd..267074f3ad102 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
@@ -20,6 +20,8 @@ package org.apache.spark.sql.hive
 import java.io.{InputStream, OutputStream}
 import java.rmi.server.UID
 
+import org.apache.avro.Schema
+
 /* Implicit conversions */
 import scala.collection.JavaConversions._
 import scala.language.implicitConversions
@@ -33,7 +35,7 @@ import org.apache.hadoop.fs.Path
 import org.apache.hadoop.hive.ql.exec.{UDF, Utilities}
 import org.apache.hadoop.hive.ql.plan.{FileSinkDesc, TableDesc}
 import org.apache.hadoop.hive.serde2.ColumnProjectionUtils
-import org.apache.hadoop.hive.serde2.avro.AvroGenericRecordWritable
+import org.apache.hadoop.hive.serde2.avro.{AvroGenericRecordWritable, AvroSerdeUtils}
 import org.apache.hadoop.hive.serde2.objectinspector.primitive.HiveDecimalObjectInspector
 import org.apache.hadoop.io.Writable
 
@@ -82,10 +84,19 @@ private[hive] object HiveShim {
    * Bug introduced in hive-0.13. AvroGenericRecordWritable has a member recordReaderID that
    * is needed to initialize before serialization.
    */
-  def prepareWritable(w: Writable): Writable = {
+  def prepareWritable(w: Writable, serDeProps: Seq[(String, String)]): Writable = {
     w match {
       case w: AvroGenericRecordWritable =>
         w.setRecordReaderID(new UID())
+        // In Hive 1.1, the record's schema may need to be initialized manually or a NPE will
+        // be thrown.
+        if (w.getFileSchema() == null) {
+          serDeProps
+            .find(_._1 == AvroSerdeUtils.AvroTableProperties.SCHEMA_LITERAL.getPropName())
+            .foreach { kv =>
+              w.setFileSchema(new Schema.Parser().parse(kv._2))
+            }
+        }
       case _ =>
     }
     w
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
index d834b4e83e043..a82e152dcda2c 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
@@ -87,6 +87,10 @@ private[hive] case class HiveTable(
  * shared classes.
  */
 private[hive] trait ClientInterface {
+
+  /** Returns the configuration for the given key in the current session. */
+  def getConf(key: String, defaultValue: String): String
+
   /**
    * Runs a HiveQL command using Hive, returning the results as a list of strings.  Each row will
    * result in one string.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 6e0912da5862d..dc372be0e5a37 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -38,7 +38,6 @@ import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.execution.QueryExecutionException
 import org.apache.spark.util.{CircularBuffer, Utils}
 
-
 /**
  * A class that wraps the HiveClient and converts its responses to externally visible classes.
  * Note that this class is typically loaded with an internal classloader for each instantiation,
@@ -115,6 +114,10 @@ private[hive] class ClientWrapper(
   /** Returns the configuration for the current session. */
   def conf: HiveConf = SessionState.get().getConf
 
+  override def getConf(key: String, defaultValue: String): String = {
+    conf.get(key, defaultValue)
+  }
+
   // TODO: should be a def?s
   // When we create this val client, the HiveConf of it (conf) is the one associated with state.
   @GuardedBy("this")
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index 956997e5f9dce..6e826ce552204 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -512,7 +512,7 @@ private[client] class Shim_v1_2 extends Shim_v1_1 {
       listBucketingEnabled: Boolean): Unit = {
     loadDynamicPartitionsMethod.invoke(hive, loadPath, tableName, partSpec, replace: JBoolean,
       numDP: JInteger, holdDDLTime: JBoolean, listBucketingEnabled: JBoolean, JBoolean.FALSE,
-      0: JLong)
+      0L: JLong)
   }
 
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
index 97fb98199991b..f58bc7d7a0af4 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -55,7 +55,7 @@ private[hive] object IsolatedClientLoader {
     case "14" | "0.14" | "0.14.0" => hive.v14
     case "1.0" | "1.0.0" => hive.v1_0
     case "1.1" | "1.1.0" => hive.v1_1
-    case "1.2" | "1.2.0" => hive.v1_2
+    case "1.2" | "1.2.0" | "1.2.1" => hive.v1_2
   }
 
   private def downloadVersion(version: HiveVersion, ivyPath: Option[String]): Seq[URL] = {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
index b48082fe4b363..0503691a44249 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
@@ -56,7 +56,7 @@ package object client {
         "net.hydromatic:linq4j",
         "net.hydromatic:quidem"))
 
-    case object v1_2 extends HiveVersion("1.2.0",
+    case object v1_2 extends HiveVersion("1.2.1",
       exclusions = Seq("eigenbase:eigenbase-properties",
         "org.apache.curator:*",
         "org.pentaho:pentaho-aggdesigner-algorithm",
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 40a6a32156687..12c667e6e92da 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -129,7 +129,7 @@ case class InsertIntoHiveTable(
     // instances within the closure, since Serializer is not serializable while TableDesc is.
     val tableDesc = table.tableDesc
     val tableLocation = table.hiveQlTable.getDataLocation
-    val tmpLocation = hiveContext.getExternalTmpPath(tableLocation.toUri)
+    val tmpLocation = hiveContext.getExternalTmpPath(tableLocation)
     val fileSinkConf = new FileSinkDesc(tmpLocation.toString, tableDesc, false)
     val isCompressed = sc.hiveconf.getBoolean(
       ConfVars.COMPRESSRESULT.varname, ConfVars.COMPRESSRESULT.defaultBoolVal)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index 7e3342cc84c0e..fbb86406f40cb 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -247,7 +247,7 @@ private class ScriptTransformationWriterThread(
         } else {
           val writable = inputSerde.serialize(
             row.asInstanceOf[GenericInternalRow].values, inputSoi)
-          prepareWritable(writable).write(dataOutputStream)
+          prepareWritable(writable, ioschema.outputSerdeProps).write(dataOutputStream)
         }
       }
       outputStream.close()
@@ -345,9 +345,7 @@ case class HiveScriptIOSchema (
 
     val columnTypesNames = columnTypes.map(_.toTypeInfo.getTypeName()).mkString(",")
 
-    var propsMap = serdeProps.map(kv => {
-      (kv._1.split("'")(1), kv._2.split("'")(1))
-    }).toMap + (serdeConstants.LIST_COLUMNS -> columns.mkString(","))
+    var propsMap = serdeProps.toMap + (serdeConstants.LIST_COLUMNS -> columns.mkString(","))
     propsMap = propsMap + (serdeConstants.LIST_COLUMN_TYPES -> columnTypesNames)
 
     val properties = new Properties()
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index abe5c69003130..8a86a87368f29 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -249,7 +249,7 @@ private[spark] object ResolveHiveWindowFunction extends Rule[LogicalPlan] {
           // Get the class of this function.
           // In Hive 0.12, there is no windowFunctionInfo.getFunctionClass. So, we use
           // windowFunctionInfo.getfInfo().getFunctionClass for both Hive 0.13 and Hive 0.13.1.
-          val functionClass = windowFunctionInfo.getfInfo().getFunctionClass
+          val functionClass = windowFunctionInfo.getFunctionClass()
           val newChildren =
             // Rank(), DENSE_RANK(), CUME_DIST(), and PERCENT_RANK() do not take explicit
             // input parameters and requires implicit parameters, which
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 8850e060d2a73..684ea1d137b49 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -171,7 +171,7 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
   import SparkHiveDynamicPartitionWriterContainer._
 
   private val defaultPartName = jobConf.get(
-    ConfVars.DEFAULTPARTITIONNAME.varname, ConfVars.DEFAULTPARTITIONNAME.defaultVal)
+    ConfVars.DEFAULTPARTITIONNAME.varname, ConfVars.DEFAULTPARTITIONNAME.defaultStrVal)
 
   @transient private var writers: mutable.HashMap[String, FileSinkOperator.RecordWriter] = _
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
index ddd5d24717add..86142e5d66f37 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.hive.orc
 
 import org.apache.hadoop.hive.common.`type`.{HiveChar, HiveDecimal, HiveVarchar}
-import org.apache.hadoop.hive.ql.io.sarg.SearchArgument
+import org.apache.hadoop.hive.ql.io.sarg.{SearchArgumentFactory, SearchArgument}
 import org.apache.hadoop.hive.ql.io.sarg.SearchArgument.Builder
 import org.apache.hadoop.hive.serde2.io.DateWritable
 
@@ -33,13 +33,13 @@ import org.apache.spark.sql.sources._
 private[orc] object OrcFilters extends Logging {
   def createFilter(expr: Array[Filter]): Option[SearchArgument] = {
     expr.reduceOption(And).flatMap { conjunction =>
-      val builder = SearchArgument.FACTORY.newBuilder()
+      val builder = SearchArgumentFactory.newBuilder()
       buildSearchArgument(conjunction, builder).map(_.build())
     }
   }
 
   private def buildSearchArgument(expression: Filter, builder: Builder): Option[Builder] = {
-    def newBuilder = SearchArgument.FACTORY.newBuilder()
+    def newBuilder = SearchArgumentFactory.newBuilder()
 
     def isSearchableLiteral(value: Any): Boolean = value match {
       // These are types recognized by the `SearchArgumentImpl.BuilderImpl.boxLiteral()` method.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 7bbdef90cd6b9..8d0bf46e8fad7 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -20,29 +20,25 @@ package org.apache.spark.sql.hive.test
 import java.io.File
 import java.util.{Set => JavaSet}
 
-import org.apache.hadoop.hive.conf.HiveConf
+import scala.collection.mutable
+import scala.language.implicitConversions
+
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.ql.exec.FunctionRegistry
 import org.apache.hadoop.hive.ql.io.avro.{AvroContainerInputFormat, AvroContainerOutputFormat}
-import org.apache.hadoop.hive.ql.metadata.Table
-import org.apache.hadoop.hive.ql.parse.VariableSubstitution
 import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe
 import org.apache.hadoop.hive.serde2.avro.AvroSerDe
 
-import org.apache.spark.sql.catalyst.CatalystConf
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.CacheTableCommand
 import org.apache.spark.sql.hive._
 import org.apache.spark.sql.hive.execution.HiveNativeCommand
-import org.apache.spark.sql.SQLConf
 import org.apache.spark.util.Utils
 import org.apache.spark.{SparkConf, SparkContext}
 
-import scala.collection.mutable
-import scala.language.implicitConversions
-
 /* Implicit conversions */
 import scala.collection.JavaConversions._
 
@@ -83,15 +79,25 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
 
   hiveconf.set("hive.plan.serialization.format", "javaXML")
 
-  lazy val warehousePath = Utils.createTempDir()
+  lazy val warehousePath = Utils.createTempDir(namePrefix = "warehouse-")
+
+  lazy val scratchDirPath = {
+    val dir = Utils.createTempDir(namePrefix = "scratch-")
+    dir.delete()
+    dir
+  }
 
   private lazy val temporaryConfig = newTemporaryConfiguration()
 
   /** Sets up the system initially or after a RESET command */
-  protected override def configure(): Map[String, String] =
-    temporaryConfig ++ Map(
-      ConfVars.METASTOREWAREHOUSE.varname -> warehousePath.toString,
-      ConfVars.METASTORE_INTEGER_JDO_PUSHDOWN.varname -> "true")
+  protected override def configure(): Map[String, String] = {
+    super.configure() ++ temporaryConfig ++ Map(
+      ConfVars.METASTOREWAREHOUSE.varname -> warehousePath.toURI.toString,
+      ConfVars.METASTORE_INTEGER_JDO_PUSHDOWN.varname -> "true",
+      ConfVars.SCRATCHDIR.varname -> scratchDirPath.toURI.toString,
+      ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY.varname -> "1"
+    )
+  }
 
   val testTempDir = Utils.createTempDir()
 
@@ -244,7 +250,6 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
     }),
     TestTable("src_thrift", () => {
       import org.apache.hadoop.hive.serde2.thrift.ThriftDeserializer
-      import org.apache.hadoop.hive.serde2.thrift.test.Complex
       import org.apache.hadoop.mapred.{SequenceFileInputFormat, SequenceFileOutputFormat}
       import org.apache.thrift.protocol.TBinaryProtocol
 
@@ -253,7 +258,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
          |CREATE TABLE src_thrift(fake INT)
          |ROW FORMAT SERDE '${classOf[ThriftDeserializer].getName}'
          |WITH SERDEPROPERTIES(
-         |  'serialization.class'='${classOf[Complex].getName}',
+         |  'serialization.class'='org.apache.spark.sql.hive.test.Complex',
          |  'serialization.format'='${classOf[TBinaryProtocol].getName}'
          |)
          |STORED AS
@@ -437,6 +442,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
         case (k, v) =>
           metadataHive.runSqlHive(s"SET $k=$v")
       }
+      defaultOverides()
 
       runSqlHive("USE default")
 
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/test/Complex.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/test/Complex.java
new file mode 100644
index 0000000000000..e010112bb9327
--- /dev/null
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/test/Complex.java
@@ -0,0 +1,1139 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.sql.hive.test;
+
+import org.apache.commons.lang.builder.HashCodeBuilder;
+import org.apache.thrift.scheme.IScheme;
+import org.apache.thrift.scheme.SchemeFactory;
+import org.apache.thrift.scheme.StandardScheme;
+
+import org.apache.hadoop.hive.serde2.thrift.test.IntString;
+import org.apache.thrift.scheme.TupleScheme;
+import org.apache.thrift.protocol.TTupleProtocol;
+import org.apache.thrift.EncodingUtils;
+import java.util.List;
+import java.util.ArrayList;
+import java.util.Map;
+import java.util.HashMap;
+import java.util.EnumMap;
+import java.util.EnumSet;
+import java.util.Collections;
+import java.util.BitSet;
+
+/**
+ * This is a fork of Hive 0.13's org/apache/hadoop/hive/serde2/thrift/test/Complex.java, which
+ * does not contain union fields that are not supported by Spark SQL.
+ */
+
+@SuppressWarnings({"ALL", "unchecked"})
+public class Complex implements org.apache.thrift.TBase<Complex, Complex._Fields>, java.io.Serializable, Cloneable {
+  private static final org.apache.thrift.protocol.TStruct STRUCT_DESC = new org.apache.thrift.protocol.TStruct("Complex");
+
+  private static final org.apache.thrift.protocol.TField AINT_FIELD_DESC = new org.apache.thrift.protocol.TField("aint", org.apache.thrift.protocol.TType.I32, (short)1);
+  private static final org.apache.thrift.protocol.TField A_STRING_FIELD_DESC = new org.apache.thrift.protocol.TField("aString", org.apache.thrift.protocol.TType.STRING, (short)2);
+  private static final org.apache.thrift.protocol.TField LINT_FIELD_DESC = new org.apache.thrift.protocol.TField("lint", org.apache.thrift.protocol.TType.LIST, (short)3);
+  private static final org.apache.thrift.protocol.TField L_STRING_FIELD_DESC = new org.apache.thrift.protocol.TField("lString", org.apache.thrift.protocol.TType.LIST, (short)4);
+  private static final org.apache.thrift.protocol.TField LINT_STRING_FIELD_DESC = new org.apache.thrift.protocol.TField("lintString", org.apache.thrift.protocol.TType.LIST, (short)5);
+  private static final org.apache.thrift.protocol.TField M_STRING_STRING_FIELD_DESC = new org.apache.thrift.protocol.TField("mStringString", org.apache.thrift.protocol.TType.MAP, (short)6);
+
+  private static final Map<Class<? extends IScheme>, SchemeFactory> schemes = new HashMap<Class<? extends IScheme>, SchemeFactory>();
+  static {
+    schemes.put(StandardScheme.class, new ComplexStandardSchemeFactory());
+    schemes.put(TupleScheme.class, new ComplexTupleSchemeFactory());
+  }
+
+  private int aint; // required
+  private String aString; // required
+  private List<Integer> lint; // required
+  private List<String> lString; // required
+  private List<IntString> lintString; // required
+  private Map<String,String> mStringString; // required
+
+  /** The set of fields this struct contains, along with convenience methods for finding and manipulating them. */
+  public enum _Fields implements org.apache.thrift.TFieldIdEnum {
+    AINT((short)1, "aint"),
+    A_STRING((short)2, "aString"),
+    LINT((short)3, "lint"),
+    L_STRING((short)4, "lString"),
+    LINT_STRING((short)5, "lintString"),
+    M_STRING_STRING((short)6, "mStringString");
+
+    private static final Map<String, _Fields> byName = new HashMap<String, _Fields>();
+
+    static {
+      for (_Fields field : EnumSet.allOf(_Fields.class)) {
+        byName.put(field.getFieldName(), field);
+      }
+    }
+
+    /**
+     * Find the _Fields constant that matches fieldId, or null if its not found.
+     */
+    public static _Fields findByThriftId(int fieldId) {
+      switch(fieldId) {
+        case 1: // AINT
+          return AINT;
+        case 2: // A_STRING
+          return A_STRING;
+        case 3: // LINT
+          return LINT;
+        case 4: // L_STRING
+          return L_STRING;
+        case 5: // LINT_STRING
+          return LINT_STRING;
+        case 6: // M_STRING_STRING
+          return M_STRING_STRING;
+        default:
+          return null;
+      }
+    }
+
+    /**
+     * Find the _Fields constant that matches fieldId, throwing an exception
+     * if it is not found.
+     */
+    public static _Fields findByThriftIdOrThrow(int fieldId) {
+      _Fields fields = findByThriftId(fieldId);
+      if (fields == null) throw new IllegalArgumentException("Field " + fieldId + " doesn't exist!");
+      return fields;
+    }
+
+    /**
+     * Find the _Fields constant that matches name, or null if its not found.
+     */
+    public static _Fields findByName(String name) {
+      return byName.get(name);
+    }
+
+    private final short _thriftId;
+    private final String _fieldName;
+
+    _Fields(short thriftId, String fieldName) {
+      _thriftId = thriftId;
+      _fieldName = fieldName;
+    }
+
+    public short getThriftFieldId() {
+      return _thriftId;
+    }
+
+    public String getFieldName() {
+      return _fieldName;
+    }
+  }
+
+  // isset id assignments
+  private static final int __AINT_ISSET_ID = 0;
+  private byte __isset_bitfield = 0;
+  public static final Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> metaDataMap;
+  static {
+    Map<_Fields, org.apache.thrift.meta_data.FieldMetaData> tmpMap = new EnumMap<_Fields, org.apache.thrift.meta_data.FieldMetaData>(_Fields.class);
+    tmpMap.put(_Fields.AINT, new org.apache.thrift.meta_data.FieldMetaData("aint", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32)));
+    tmpMap.put(_Fields.A_STRING, new org.apache.thrift.meta_data.FieldMetaData("aString", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING)));
+    tmpMap.put(_Fields.LINT, new org.apache.thrift.meta_data.FieldMetaData("lint", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST,
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.I32))));
+    tmpMap.put(_Fields.L_STRING, new org.apache.thrift.meta_data.FieldMetaData("lString", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST,
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING))));
+    tmpMap.put(_Fields.LINT_STRING, new org.apache.thrift.meta_data.FieldMetaData("lintString", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.ListMetaData(org.apache.thrift.protocol.TType.LIST,
+            new org.apache.thrift.meta_data.StructMetaData(org.apache.thrift.protocol.TType.STRUCT, IntString.class))));
+    tmpMap.put(_Fields.M_STRING_STRING, new org.apache.thrift.meta_data.FieldMetaData("mStringString", org.apache.thrift.TFieldRequirementType.DEFAULT,
+        new org.apache.thrift.meta_data.MapMetaData(org.apache.thrift.protocol.TType.MAP,
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING),
+            new org.apache.thrift.meta_data.FieldValueMetaData(org.apache.thrift.protocol.TType.STRING))));
+    metaDataMap = Collections.unmodifiableMap(tmpMap);
+    org.apache.thrift.meta_data.FieldMetaData.addStructMetaDataMap(Complex.class, metaDataMap);
+  }
+
+  public Complex() {
+  }
+
+  public Complex(
+    int aint,
+    String aString,
+    List<Integer> lint,
+    List<String> lString,
+    List<IntString> lintString,
+    Map<String,String> mStringString)
+  {
+    this();
+    this.aint = aint;
+    setAintIsSet(true);
+    this.aString = aString;
+    this.lint = lint;
+    this.lString = lString;
+    this.lintString = lintString;
+    this.mStringString = mStringString;
+  }
+
+  /**
+   * Performs a deep copy on <i>other</i>.
+   */
+  public Complex(Complex other) {
+    __isset_bitfield = other.__isset_bitfield;
+    this.aint = other.aint;
+    if (other.isSetAString()) {
+      this.aString = other.aString;
+    }
+    if (other.isSetLint()) {
+      List<Integer> __this__lint = new ArrayList<Integer>();
+      for (Integer other_element : other.lint) {
+        __this__lint.add(other_element);
+      }
+      this.lint = __this__lint;
+    }
+    if (other.isSetLString()) {
+      List<String> __this__lString = new ArrayList<String>();
+      for (String other_element : other.lString) {
+        __this__lString.add(other_element);
+      }
+      this.lString = __this__lString;
+    }
+    if (other.isSetLintString()) {
+      List<IntString> __this__lintString = new ArrayList<IntString>();
+      for (IntString other_element : other.lintString) {
+        __this__lintString.add(new IntString(other_element));
+      }
+      this.lintString = __this__lintString;
+    }
+    if (other.isSetMStringString()) {
+      Map<String,String> __this__mStringString = new HashMap<String,String>();
+      for (Map.Entry<String, String> other_element : other.mStringString.entrySet()) {
+
+        String other_element_key = other_element.getKey();
+        String other_element_value = other_element.getValue();
+
+        String __this__mStringString_copy_key = other_element_key;
+
+        String __this__mStringString_copy_value = other_element_value;
+
+        __this__mStringString.put(__this__mStringString_copy_key, __this__mStringString_copy_value);
+      }
+      this.mStringString = __this__mStringString;
+    }
+  }
+
+  public Complex deepCopy() {
+    return new Complex(this);
+  }
+
+  @Override
+  public void clear() {
+    setAintIsSet(false);
+    this.aint = 0;
+    this.aString = null;
+    this.lint = null;
+    this.lString = null;
+    this.lintString = null;
+    this.mStringString = null;
+  }
+
+  public int getAint() {
+    return this.aint;
+  }
+
+  public void setAint(int aint) {
+    this.aint = aint;
+    setAintIsSet(true);
+  }
+
+  public void unsetAint() {
+    __isset_bitfield = EncodingUtils.clearBit(__isset_bitfield, __AINT_ISSET_ID);
+  }
+
+  /** Returns true if field aint is set (has been assigned a value) and false otherwise */
+  public boolean isSetAint() {
+    return EncodingUtils.testBit(__isset_bitfield, __AINT_ISSET_ID);
+  }
+
+  public void setAintIsSet(boolean value) {
+    __isset_bitfield = EncodingUtils.setBit(__isset_bitfield, __AINT_ISSET_ID, value);
+  }
+
+  public String getAString() {
+    return this.aString;
+  }
+
+  public void setAString(String aString) {
+    this.aString = aString;
+  }
+
+  public void unsetAString() {
+    this.aString = null;
+  }
+
+  /** Returns true if field aString is set (has been assigned a value) and false otherwise */
+  public boolean isSetAString() {
+    return this.aString != null;
+  }
+
+  public void setAStringIsSet(boolean value) {
+    if (!value) {
+      this.aString = null;
+    }
+  }
+
+  public int getLintSize() {
+    return (this.lint == null) ? 0 : this.lint.size();
+  }
+
+  public java.util.Iterator<Integer> getLintIterator() {
+    return (this.lint == null) ? null : this.lint.iterator();
+  }
+
+  public void addToLint(int elem) {
+    if (this.lint == null) {
+      this.lint = new ArrayList<>();
+    }
+    this.lint.add(elem);
+  }
+
+  public List<Integer> getLint() {
+    return this.lint;
+  }
+
+  public void setLint(List<Integer> lint) {
+    this.lint = lint;
+  }
+
+  public void unsetLint() {
+    this.lint = null;
+  }
+
+  /** Returns true if field lint is set (has been assigned a value) and false otherwise */
+  public boolean isSetLint() {
+    return this.lint != null;
+  }
+
+  public void setLintIsSet(boolean value) {
+    if (!value) {
+      this.lint = null;
+    }
+  }
+
+  public int getLStringSize() {
+    return (this.lString == null) ? 0 : this.lString.size();
+  }
+
+  public java.util.Iterator<String> getLStringIterator() {
+    return (this.lString == null) ? null : this.lString.iterator();
+  }
+
+  public void addToLString(String elem) {
+    if (this.lString == null) {
+      this.lString = new ArrayList<String>();
+    }
+    this.lString.add(elem);
+  }
+
+  public List<String> getLString() {
+    return this.lString;
+  }
+
+  public void setLString(List<String> lString) {
+    this.lString = lString;
+  }
+
+  public void unsetLString() {
+    this.lString = null;
+  }
+
+  /** Returns true if field lString is set (has been assigned a value) and false otherwise */
+  public boolean isSetLString() {
+    return this.lString != null;
+  }
+
+  public void setLStringIsSet(boolean value) {
+    if (!value) {
+      this.lString = null;
+    }
+  }
+
+  public int getLintStringSize() {
+    return (this.lintString == null) ? 0 : this.lintString.size();
+  }
+
+  public java.util.Iterator<IntString> getLintStringIterator() {
+    return (this.lintString == null) ? null : this.lintString.iterator();
+  }
+
+  public void addToLintString(IntString elem) {
+    if (this.lintString == null) {
+      this.lintString = new ArrayList<>();
+    }
+    this.lintString.add(elem);
+  }
+
+  public List<IntString> getLintString() {
+    return this.lintString;
+  }
+
+  public void setLintString(List<IntString> lintString) {
+    this.lintString = lintString;
+  }
+
+  public void unsetLintString() {
+    this.lintString = null;
+  }
+
+  /** Returns true if field lintString is set (has been assigned a value) and false otherwise */
+  public boolean isSetLintString() {
+    return this.lintString != null;
+  }
+
+  public void setLintStringIsSet(boolean value) {
+    if (!value) {
+      this.lintString = null;
+    }
+  }
+
+  public int getMStringStringSize() {
+    return (this.mStringString == null) ? 0 : this.mStringString.size();
+  }
+
+  public void putToMStringString(String key, String val) {
+    if (this.mStringString == null) {
+      this.mStringString = new HashMap<String,String>();
+    }
+    this.mStringString.put(key, val);
+  }
+
+  public Map<String,String> getMStringString() {
+    return this.mStringString;
+  }
+
+  public void setMStringString(Map<String,String> mStringString) {
+    this.mStringString = mStringString;
+  }
+
+  public void unsetMStringString() {
+    this.mStringString = null;
+  }
+
+  /** Returns true if field mStringString is set (has been assigned a value) and false otherwise */
+  public boolean isSetMStringString() {
+    return this.mStringString != null;
+  }
+
+  public void setMStringStringIsSet(boolean value) {
+    if (!value) {
+      this.mStringString = null;
+    }
+  }
+
+  public void setFieldValue(_Fields field, Object value) {
+    switch (field) {
+    case AINT:
+      if (value == null) {
+        unsetAint();
+      } else {
+        setAint((Integer)value);
+      }
+      break;
+
+    case A_STRING:
+      if (value == null) {
+        unsetAString();
+      } else {
+        setAString((String)value);
+      }
+      break;
+
+    case LINT:
+      if (value == null) {
+        unsetLint();
+      } else {
+        setLint((List<Integer>)value);
+      }
+      break;
+
+    case L_STRING:
+      if (value == null) {
+        unsetLString();
+      } else {
+        setLString((List<String>)value);
+      }
+      break;
+
+    case LINT_STRING:
+      if (value == null) {
+        unsetLintString();
+      } else {
+        setLintString((List<IntString>)value);
+      }
+      break;
+
+    case M_STRING_STRING:
+      if (value == null) {
+        unsetMStringString();
+      } else {
+        setMStringString((Map<String,String>)value);
+      }
+      break;
+
+    }
+  }
+
+  public Object getFieldValue(_Fields field) {
+    switch (field) {
+    case AINT:
+      return Integer.valueOf(getAint());
+
+    case A_STRING:
+      return getAString();
+
+    case LINT:
+      return getLint();
+
+    case L_STRING:
+      return getLString();
+
+    case LINT_STRING:
+      return getLintString();
+
+    case M_STRING_STRING:
+      return getMStringString();
+
+    }
+    throw new IllegalStateException();
+  }
+
+  /** Returns true if field corresponding to fieldID is set (has been assigned a value) and false otherwise */
+  public boolean isSet(_Fields field) {
+    if (field == null) {
+      throw new IllegalArgumentException();
+    }
+
+    switch (field) {
+    case AINT:
+      return isSetAint();
+    case A_STRING:
+      return isSetAString();
+    case LINT:
+      return isSetLint();
+    case L_STRING:
+      return isSetLString();
+    case LINT_STRING:
+      return isSetLintString();
+    case M_STRING_STRING:
+      return isSetMStringString();
+    }
+    throw new IllegalStateException();
+  }
+
+  @Override
+  public boolean equals(Object that) {
+    if (that == null)
+      return false;
+    if (that instanceof Complex)
+      return this.equals((Complex)that);
+    return false;
+  }
+
+  public boolean equals(Complex that) {
+    if (that == null)
+      return false;
+
+    boolean this_present_aint = true;
+    boolean that_present_aint = true;
+    if (this_present_aint || that_present_aint) {
+      if (!(this_present_aint && that_present_aint))
+        return false;
+      if (this.aint != that.aint)
+        return false;
+    }
+
+    boolean this_present_aString = true && this.isSetAString();
+    boolean that_present_aString = true && that.isSetAString();
+    if (this_present_aString || that_present_aString) {
+      if (!(this_present_aString && that_present_aString))
+        return false;
+      if (!this.aString.equals(that.aString))
+        return false;
+    }
+
+    boolean this_present_lint = true && this.isSetLint();
+    boolean that_present_lint = true && that.isSetLint();
+    if (this_present_lint || that_present_lint) {
+      if (!(this_present_lint && that_present_lint))
+        return false;
+      if (!this.lint.equals(that.lint))
+        return false;
+    }
+
+    boolean this_present_lString = true && this.isSetLString();
+    boolean that_present_lString = true && that.isSetLString();
+    if (this_present_lString || that_present_lString) {
+      if (!(this_present_lString && that_present_lString))
+        return false;
+      if (!this.lString.equals(that.lString))
+        return false;
+    }
+
+    boolean this_present_lintString = true && this.isSetLintString();
+    boolean that_present_lintString = true && that.isSetLintString();
+    if (this_present_lintString || that_present_lintString) {
+      if (!(this_present_lintString && that_present_lintString))
+        return false;
+      if (!this.lintString.equals(that.lintString))
+        return false;
+    }
+
+    boolean this_present_mStringString = true && this.isSetMStringString();
+    boolean that_present_mStringString = true && that.isSetMStringString();
+    if (this_present_mStringString || that_present_mStringString) {
+      if (!(this_present_mStringString && that_present_mStringString))
+        return false;
+      if (!this.mStringString.equals(that.mStringString))
+        return false;
+    }
+
+    return true;
+  }
+
+  @Override
+  public int hashCode() {
+    HashCodeBuilder builder = new HashCodeBuilder();
+
+    boolean present_aint = true;
+    builder.append(present_aint);
+    if (present_aint)
+      builder.append(aint);
+
+    boolean present_aString = true && (isSetAString());
+    builder.append(present_aString);
+    if (present_aString)
+      builder.append(aString);
+
+    boolean present_lint = true && (isSetLint());
+    builder.append(present_lint);
+    if (present_lint)
+      builder.append(lint);
+
+    boolean present_lString = true && (isSetLString());
+    builder.append(present_lString);
+    if (present_lString)
+      builder.append(lString);
+
+    boolean present_lintString = true && (isSetLintString());
+    builder.append(present_lintString);
+    if (present_lintString)
+      builder.append(lintString);
+
+    boolean present_mStringString = true && (isSetMStringString());
+    builder.append(present_mStringString);
+    if (present_mStringString)
+      builder.append(mStringString);
+
+    return builder.toHashCode();
+  }
+
+  public int compareTo(Complex other) {
+    if (!getClass().equals(other.getClass())) {
+      return getClass().getName().compareTo(other.getClass().getName());
+    }
+
+    int lastComparison = 0;
+    Complex typedOther = (Complex)other;
+
+    lastComparison = Boolean.valueOf(isSetAint()).compareTo(typedOther.isSetAint());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetAint()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.aint, typedOther.aint);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetAString()).compareTo(typedOther.isSetAString());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetAString()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.aString, typedOther.aString);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetLint()).compareTo(typedOther.isSetLint());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetLint()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.lint, typedOther.lint);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetLString()).compareTo(typedOther.isSetLString());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetLString()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.lString, typedOther.lString);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetLintString()).compareTo(typedOther.isSetLintString());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetLintString()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.lintString, typedOther.lintString);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    lastComparison = Boolean.valueOf(isSetMStringString()).compareTo(typedOther.isSetMStringString());
+    if (lastComparison != 0) {
+      return lastComparison;
+    }
+    if (isSetMStringString()) {
+      lastComparison = org.apache.thrift.TBaseHelper.compareTo(this.mStringString, typedOther.mStringString);
+      if (lastComparison != 0) {
+        return lastComparison;
+      }
+    }
+    return 0;
+  }
+
+  public _Fields fieldForId(int fieldId) {
+    return _Fields.findByThriftId(fieldId);
+  }
+
+  public void read(org.apache.thrift.protocol.TProtocol iprot) throws org.apache.thrift.TException {
+    schemes.get(iprot.getScheme()).getScheme().read(iprot, this);
+  }
+
+  public void write(org.apache.thrift.protocol.TProtocol oprot) throws org.apache.thrift.TException {
+    schemes.get(oprot.getScheme()).getScheme().write(oprot, this);
+  }
+
+  @Override
+  public String toString() {
+    StringBuilder sb = new StringBuilder("Complex(");
+    boolean first = true;
+
+    sb.append("aint:");
+    sb.append(this.aint);
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("aString:");
+    if (this.aString == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.aString);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("lint:");
+    if (this.lint == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.lint);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("lString:");
+    if (this.lString == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.lString);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("lintString:");
+    if (this.lintString == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.lintString);
+    }
+    first = false;
+    if (!first) sb.append(", ");
+    sb.append("mStringString:");
+    if (this.mStringString == null) {
+      sb.append("null");
+    } else {
+      sb.append(this.mStringString);
+    }
+    first = false;
+    sb.append(")");
+    return sb.toString();
+  }
+
+  public void validate() throws org.apache.thrift.TException {
+    // check for required fields
+    // check for sub-struct validity
+  }
+
+  private void writeObject(java.io.ObjectOutputStream out) throws java.io.IOException {
+    try {
+      write(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(out)));
+    } catch (org.apache.thrift.TException te) {
+      throw new java.io.IOException(te);
+    }
+  }
+
+  private void readObject(java.io.ObjectInputStream in) throws java.io.IOException, ClassNotFoundException {
+    try {
+      // it doesn't seem like you should have to do this, but java serialization is wacky, and doesn't call the default constructor.
+      __isset_bitfield = 0;
+      read(new org.apache.thrift.protocol.TCompactProtocol(new org.apache.thrift.transport.TIOStreamTransport(in)));
+    } catch (org.apache.thrift.TException te) {
+      throw new java.io.IOException(te);
+    }
+  }
+
+  private static class ComplexStandardSchemeFactory implements SchemeFactory {
+    public ComplexStandardScheme getScheme() {
+      return new ComplexStandardScheme();
+    }
+  }
+
+  private static class ComplexStandardScheme extends StandardScheme<Complex> {
+
+    public void read(org.apache.thrift.protocol.TProtocol iprot, Complex struct) throws org.apache.thrift.TException {
+      org.apache.thrift.protocol.TField schemeField;
+      iprot.readStructBegin();
+      while (true)
+      {
+        schemeField = iprot.readFieldBegin();
+        if (schemeField.type == org.apache.thrift.protocol.TType.STOP) {
+          break;
+        }
+        switch (schemeField.id) {
+          case 1: // AINT
+            if (schemeField.type == org.apache.thrift.protocol.TType.I32) {
+              struct.aint = iprot.readI32();
+              struct.setAintIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 2: // A_STRING
+            if (schemeField.type == org.apache.thrift.protocol.TType.STRING) {
+              struct.aString = iprot.readString();
+              struct.setAStringIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 3: // LINT
+            if (schemeField.type == org.apache.thrift.protocol.TType.LIST) {
+              {
+                org.apache.thrift.protocol.TList _list0 = iprot.readListBegin();
+                struct.lint = new ArrayList<Integer>(_list0.size);
+                for (int _i1 = 0; _i1 < _list0.size; ++_i1)
+                {
+                  int _elem2; // required
+                  _elem2 = iprot.readI32();
+                  struct.lint.add(_elem2);
+                }
+                iprot.readListEnd();
+              }
+              struct.setLintIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 4: // L_STRING
+            if (schemeField.type == org.apache.thrift.protocol.TType.LIST) {
+              {
+                org.apache.thrift.protocol.TList _list3 = iprot.readListBegin();
+                struct.lString = new ArrayList<String>(_list3.size);
+                for (int _i4 = 0; _i4 < _list3.size; ++_i4)
+                {
+                  String _elem5; // required
+                  _elem5 = iprot.readString();
+                  struct.lString.add(_elem5);
+                }
+                iprot.readListEnd();
+              }
+              struct.setLStringIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 5: // LINT_STRING
+            if (schemeField.type == org.apache.thrift.protocol.TType.LIST) {
+              {
+                org.apache.thrift.protocol.TList _list6 = iprot.readListBegin();
+                struct.lintString = new ArrayList<IntString>(_list6.size);
+                for (int _i7 = 0; _i7 < _list6.size; ++_i7)
+                {
+                  IntString _elem8; // required
+                  _elem8 = new IntString();
+                  _elem8.read(iprot);
+                  struct.lintString.add(_elem8);
+                }
+                iprot.readListEnd();
+              }
+              struct.setLintStringIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          case 6: // M_STRING_STRING
+            if (schemeField.type == org.apache.thrift.protocol.TType.MAP) {
+              {
+                org.apache.thrift.protocol.TMap _map9 = iprot.readMapBegin();
+                struct.mStringString = new HashMap<String,String>(2*_map9.size);
+                for (int _i10 = 0; _i10 < _map9.size; ++_i10)
+                {
+                  String _key11; // required
+                  String _val12; // required
+                  _key11 = iprot.readString();
+                  _val12 = iprot.readString();
+                  struct.mStringString.put(_key11, _val12);
+                }
+                iprot.readMapEnd();
+              }
+              struct.setMStringStringIsSet(true);
+            } else {
+              org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+            }
+            break;
+          default:
+            org.apache.thrift.protocol.TProtocolUtil.skip(iprot, schemeField.type);
+        }
+        iprot.readFieldEnd();
+      }
+      iprot.readStructEnd();
+      struct.validate();
+    }
+
+    public void write(org.apache.thrift.protocol.TProtocol oprot, Complex struct) throws org.apache.thrift.TException {
+      struct.validate();
+
+      oprot.writeStructBegin(STRUCT_DESC);
+      oprot.writeFieldBegin(AINT_FIELD_DESC);
+      oprot.writeI32(struct.aint);
+      oprot.writeFieldEnd();
+      if (struct.aString != null) {
+        oprot.writeFieldBegin(A_STRING_FIELD_DESC);
+        oprot.writeString(struct.aString);
+        oprot.writeFieldEnd();
+      }
+      if (struct.lint != null) {
+        oprot.writeFieldBegin(LINT_FIELD_DESC);
+        {
+          oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.I32, struct.lint.size()));
+          for (int _iter13 : struct.lint)
+          {
+            oprot.writeI32(_iter13);
+          }
+          oprot.writeListEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      if (struct.lString != null) {
+        oprot.writeFieldBegin(L_STRING_FIELD_DESC);
+        {
+          oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRING, struct.lString.size()));
+          for (String _iter14 : struct.lString)
+          {
+            oprot.writeString(_iter14);
+          }
+          oprot.writeListEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      if (struct.lintString != null) {
+        oprot.writeFieldBegin(LINT_STRING_FIELD_DESC);
+        {
+          oprot.writeListBegin(new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRUCT, struct.lintString.size()));
+          for (IntString _iter15 : struct.lintString)
+          {
+            _iter15.write(oprot);
+          }
+          oprot.writeListEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      if (struct.mStringString != null) {
+        oprot.writeFieldBegin(M_STRING_STRING_FIELD_DESC);
+        {
+          oprot.writeMapBegin(new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.STRING, org.apache.thrift.protocol.TType.STRING, struct.mStringString.size()));
+          for (Map.Entry<String, String> _iter16 : struct.mStringString.entrySet())
+          {
+            oprot.writeString(_iter16.getKey());
+            oprot.writeString(_iter16.getValue());
+          }
+          oprot.writeMapEnd();
+        }
+        oprot.writeFieldEnd();
+      }
+      oprot.writeFieldStop();
+      oprot.writeStructEnd();
+    }
+
+  }
+
+  private static class ComplexTupleSchemeFactory implements SchemeFactory {
+    public ComplexTupleScheme getScheme() {
+      return new ComplexTupleScheme();
+    }
+  }
+
+  private static class ComplexTupleScheme extends TupleScheme<Complex> {
+
+    @Override
+    public void write(org.apache.thrift.protocol.TProtocol prot, Complex struct) throws org.apache.thrift.TException {
+      TTupleProtocol oprot = (TTupleProtocol) prot;
+      BitSet optionals = new BitSet();
+      if (struct.isSetAint()) {
+        optionals.set(0);
+      }
+      if (struct.isSetAString()) {
+        optionals.set(1);
+      }
+      if (struct.isSetLint()) {
+        optionals.set(2);
+      }
+      if (struct.isSetLString()) {
+        optionals.set(3);
+      }
+      if (struct.isSetLintString()) {
+        optionals.set(4);
+      }
+      if (struct.isSetMStringString()) {
+        optionals.set(5);
+      }
+      oprot.writeBitSet(optionals, 6);
+      if (struct.isSetAint()) {
+        oprot.writeI32(struct.aint);
+      }
+      if (struct.isSetAString()) {
+        oprot.writeString(struct.aString);
+      }
+      if (struct.isSetLint()) {
+        {
+          oprot.writeI32(struct.lint.size());
+          for (int _iter17 : struct.lint)
+          {
+            oprot.writeI32(_iter17);
+          }
+        }
+      }
+      if (struct.isSetLString()) {
+        {
+          oprot.writeI32(struct.lString.size());
+          for (String _iter18 : struct.lString)
+          {
+            oprot.writeString(_iter18);
+          }
+        }
+      }
+      if (struct.isSetLintString()) {
+        {
+          oprot.writeI32(struct.lintString.size());
+          for (IntString _iter19 : struct.lintString)
+          {
+            _iter19.write(oprot);
+          }
+        }
+      }
+      if (struct.isSetMStringString()) {
+        {
+          oprot.writeI32(struct.mStringString.size());
+          for (Map.Entry<String, String> _iter20 : struct.mStringString.entrySet())
+          {
+            oprot.writeString(_iter20.getKey());
+            oprot.writeString(_iter20.getValue());
+          }
+        }
+      }
+    }
+
+    @Override
+    public void read(org.apache.thrift.protocol.TProtocol prot, Complex struct) throws org.apache.thrift.TException {
+      TTupleProtocol iprot = (TTupleProtocol) prot;
+      BitSet incoming = iprot.readBitSet(6);
+      if (incoming.get(0)) {
+        struct.aint = iprot.readI32();
+        struct.setAintIsSet(true);
+      }
+      if (incoming.get(1)) {
+        struct.aString = iprot.readString();
+        struct.setAStringIsSet(true);
+      }
+      if (incoming.get(2)) {
+        {
+          org.apache.thrift.protocol.TList _list21 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.I32, iprot.readI32());
+          struct.lint = new ArrayList<Integer>(_list21.size);
+          for (int _i22 = 0; _i22 < _list21.size; ++_i22)
+          {
+            int _elem23; // required
+            _elem23 = iprot.readI32();
+            struct.lint.add(_elem23);
+          }
+        }
+        struct.setLintIsSet(true);
+      }
+      if (incoming.get(3)) {
+        {
+          org.apache.thrift.protocol.TList _list24 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRING, iprot.readI32());
+          struct.lString = new ArrayList<String>(_list24.size);
+          for (int _i25 = 0; _i25 < _list24.size; ++_i25)
+          {
+            String _elem26; // required
+            _elem26 = iprot.readString();
+            struct.lString.add(_elem26);
+          }
+        }
+        struct.setLStringIsSet(true);
+      }
+      if (incoming.get(4)) {
+        {
+          org.apache.thrift.protocol.TList _list27 = new org.apache.thrift.protocol.TList(org.apache.thrift.protocol.TType.STRUCT, iprot.readI32());
+          struct.lintString = new ArrayList<IntString>(_list27.size);
+          for (int _i28 = 0; _i28 < _list27.size; ++_i28)
+          {
+            IntString _elem29; // required
+            _elem29 = new IntString();
+            _elem29.read(iprot);
+            struct.lintString.add(_elem29);
+          }
+        }
+        struct.setLintStringIsSet(true);
+      }
+      if (incoming.get(5)) {
+        {
+          org.apache.thrift.protocol.TMap _map30 = new org.apache.thrift.protocol.TMap(org.apache.thrift.protocol.TType.STRING, org.apache.thrift.protocol.TType.STRING, iprot.readI32());
+          struct.mStringString = new HashMap<String,String>(2*_map30.size);
+          for (int _i31 = 0; _i31 < _map30.size; ++_i31)
+          {
+            String _key32; // required
+            String _val33; // required
+            _key32 = iprot.readString();
+            _val33 = iprot.readString();
+            struct.mStringString.put(_key32, _val33);
+          }
+        }
+        struct.setMStringStringIsSet(true);
+      }
+    }
+  }
+
+}
+
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
index 64d1ce92931eb..15c2c3deb0d83 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
@@ -90,8 +90,10 @@ public void setUp() throws IOException {
   @After
   public void tearDown() throws IOException {
     // Clean up tables.
-    sqlContext.sql("DROP TABLE IF EXISTS javaSavedTable");
-    sqlContext.sql("DROP TABLE IF EXISTS externalTable");
+    if (sqlContext != null) {
+      sqlContext.sql("DROP TABLE IF EXISTS javaSavedTable");
+      sqlContext.sql("DROP TABLE IF EXISTS externalTable");
+    }
   }
 
   @Test
diff --git a/sql/hive/src/test/resources/golden/! operator-0-ee7f6a60a9792041b85b18cda56429bf b/sql/hive/src/test/resources/golden/! operator-0-ee7f6a60a9792041b85b18cda56429bf
new file mode 100644
index 0000000000000..d00491fd7e5bb
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/! operator-0-ee7f6a60a9792041b85b18cda56429bf	
@@ -0,0 +1 @@
+1
diff --git a/sql/hive/src/test/resources/golden/convert_enum_to_string-1-db089ff46f9826c7883198adacdfad59 b/sql/hive/src/test/resources/golden/convert_enum_to_string-1-db089ff46f9826c7883198adacdfad59
index d35bf9093ca9c..2383bef940973 100644
--- a/sql/hive/src/test/resources/golden/convert_enum_to_string-1-db089ff46f9826c7883198adacdfad59
+++ b/sql/hive/src/test/resources/golden/convert_enum_to_string-1-db089ff46f9826c7883198adacdfad59
@@ -15,9 +15,9 @@ my_enum_structlist_map	map<string,array<struct<my_string:string,my_enum:string,o
 my_stringlist       	array<string>       	from deserializer   
 my_structlist       	array<struct<my_string:string,my_enum:string,optionals:struct<>>>	from deserializer   
 my_enumlist         	array<string>       	from deserializer   
-my_stringset        	struct<>            	from deserializer   
-my_enumset          	struct<>            	from deserializer   
-my_structset        	struct<>            	from deserializer   
+my_stringset        	array<string>       	from deserializer   
+my_enumset          	array<string>       	from deserializer   
+my_structset        	array<struct<my_string:string,my_enum:string,optionals:struct<>>>	from deserializer   
 optionals           	struct<>            	from deserializer   
 b                   	string              	                    
 	 	 
diff --git a/sql/hive/src/test/resources/golden/parenthesis_star_by-5-6888c7f7894910538d82eefa23443189 b/sql/hive/src/test/resources/golden/parenthesis_star_by-5-41d474f5e6d7c61c36f74b4bec4e9e44
similarity index 100%
rename from sql/hive/src/test/resources/golden/parenthesis_star_by-5-6888c7f7894910538d82eefa23443189
rename to sql/hive/src/test/resources/golden/parenthesis_star_by-5-41d474f5e6d7c61c36f74b4bec4e9e44
diff --git a/sql/hive/src/test/resources/golden/show_create_table_alter-3-2a91d52719cf4552ebeb867204552a26 b/sql/hive/src/test/resources/golden/show_create_table_alter-3-2a91d52719cf4552ebeb867204552a26
index 501bb6ab32f25..7bb2c0ab43984 100644
--- a/sql/hive/src/test/resources/golden/show_create_table_alter-3-2a91d52719cf4552ebeb867204552a26
+++ b/sql/hive/src/test/resources/golden/show_create_table_alter-3-2a91d52719cf4552ebeb867204552a26
@@ -1,4 +1,4 @@
-CREATE  TABLE `tmp_showcrt1`(
+CREATE TABLE `tmp_showcrt1`(
   `key` smallint, 
   `value` float)
 COMMENT 'temporary table'
diff --git a/sql/hive/src/test/resources/golden/show_create_table_db_table-4-b585371b624cbab2616a49f553a870a0 b/sql/hive/src/test/resources/golden/show_create_table_db_table-4-b585371b624cbab2616a49f553a870a0
index 90f8415a1c6be..3cc1a57ee3a47 100644
--- a/sql/hive/src/test/resources/golden/show_create_table_db_table-4-b585371b624cbab2616a49f553a870a0
+++ b/sql/hive/src/test/resources/golden/show_create_table_db_table-4-b585371b624cbab2616a49f553a870a0
@@ -1,4 +1,4 @@
-CREATE  TABLE `tmp_feng.tmp_showcrt`(
+CREATE TABLE `tmp_feng.tmp_showcrt`(
   `key` string, 
   `value` int)
 ROW FORMAT SERDE 
diff --git a/sql/hive/src/test/resources/golden/show_create_table_delimited-1-2a91d52719cf4552ebeb867204552a26 b/sql/hive/src/test/resources/golden/show_create_table_delimited-1-2a91d52719cf4552ebeb867204552a26
index 4ee22e5230316..b51c71a71f91c 100644
--- a/sql/hive/src/test/resources/golden/show_create_table_delimited-1-2a91d52719cf4552ebeb867204552a26
+++ b/sql/hive/src/test/resources/golden/show_create_table_delimited-1-2a91d52719cf4552ebeb867204552a26
@@ -1,4 +1,4 @@
-CREATE  TABLE `tmp_showcrt1`(
+CREATE TABLE `tmp_showcrt1`(
   `key` int, 
   `value` string, 
   `newvalue` bigint)
diff --git a/sql/hive/src/test/resources/golden/show_create_table_serde-1-2a91d52719cf4552ebeb867204552a26 b/sql/hive/src/test/resources/golden/show_create_table_serde-1-2a91d52719cf4552ebeb867204552a26
index 6fda2570b53f1..29189e1d860a4 100644
--- a/sql/hive/src/test/resources/golden/show_create_table_serde-1-2a91d52719cf4552ebeb867204552a26
+++ b/sql/hive/src/test/resources/golden/show_create_table_serde-1-2a91d52719cf4552ebeb867204552a26
@@ -1,4 +1,4 @@
-CREATE  TABLE `tmp_showcrt1`(
+CREATE TABLE `tmp_showcrt1`(
   `key` int, 
   `value` string, 
   `newvalue` bigint)
diff --git a/sql/hive/src/test/resources/golden/show_functions-0-45a7762c39f1b0f26f076220e2764043 b/sql/hive/src/test/resources/golden/show_functions-0-45a7762c39f1b0f26f076220e2764043
index 3049cd6243ad8..1b283db3e7744 100644
--- a/sql/hive/src/test/resources/golden/show_functions-0-45a7762c39f1b0f26f076220e2764043
+++ b/sql/hive/src/test/resources/golden/show_functions-0-45a7762c39f1b0f26f076220e2764043
@@ -17,6 +17,7 @@
 ^
 abs
 acos
+add_months
 and
 array
 array_contains
@@ -29,6 +30,7 @@ base64
 between
 bin
 case
+cbrt
 ceil
 ceiling
 coalesce
@@ -47,7 +49,11 @@ covar_samp
 create_union
 cume_dist
 current_database
+current_date
+current_timestamp
+current_user
 date_add
+date_format
 date_sub
 datediff
 day
@@ -65,6 +71,7 @@ ewah_bitmap_empty
 ewah_bitmap_or
 exp
 explode
+factorial
 field
 find_in_set
 first_value
@@ -73,6 +80,7 @@ format_number
 from_unixtime
 from_utc_timestamp
 get_json_object
+greatest
 hash
 hex
 histogram_numeric
@@ -81,6 +89,7 @@ if
 in
 in_file
 index
+initcap
 inline
 instr
 isnotnull
@@ -88,10 +97,13 @@ isnull
 java_method
 json_tuple
 lag
+last_day
 last_value
 lcase
 lead
+least
 length
+levenshtein
 like
 ln
 locate
@@ -109,11 +121,15 @@ max
 min
 minute
 month
+months_between
 named_struct
 negative
+next_day
 ngrams
 noop
+noopstreaming
 noopwithmap
+noopwithmapstreaming
 not
 ntile
 nvl
@@ -147,10 +163,14 @@ rpad
 rtrim
 second
 sentences
+shiftleft
+shiftright
+shiftrightunsigned
 sign
 sin
 size
 sort_array
+soundex
 space
 split
 sqrt
@@ -170,6 +190,7 @@ to_unix_timestamp
 to_utc_timestamp
 translate
 trim
+trunc
 ucase
 unbase64
 unhex
diff --git a/sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae b/sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae
index 0f6cc6f44f1f7..fdf701f962800 100644
--- a/sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae
+++ b/sql/hive/src/test/resources/golden/show_tblproperties-1-be4adb893c7f946ebd76a648ce3cc1ae
@@ -1 +1 @@
-Table tmpfoo does not have property: bar	 
+Table default.tmpfoo does not have property: bar	 
diff --git a/sql/hive/src/test/resources/golden/udf_date_add-1-efb60fcbd6d78ad35257fb1ec39ace2 b/sql/hive/src/test/resources/golden/udf_date_add-1-efb60fcbd6d78ad35257fb1ec39ace2
index 3c91e138d7bd5..d8ec084f0b2b0 100644
--- a/sql/hive/src/test/resources/golden/udf_date_add-1-efb60fcbd6d78ad35257fb1ec39ace2
+++ b/sql/hive/src/test/resources/golden/udf_date_add-1-efb60fcbd6d78ad35257fb1ec39ace2
@@ -1,5 +1,5 @@
 date_add(start_date, num_days) - Returns the date that is num_days after start_date.
 start_date is a string in the format 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'. num_days is a number. The time part of start_date is ignored.
 Example:
-   > SELECT date_add('2009-30-07', 1) FROM src LIMIT 1;
-  '2009-31-07'
+   > SELECT date_add('2009-07-30', 1) FROM src LIMIT 1;
+  '2009-07-31'
diff --git a/sql/hive/src/test/resources/golden/udf_date_sub-1-7efeb74367835ade71e5e42b22f8ced4 b/sql/hive/src/test/resources/golden/udf_date_sub-1-7efeb74367835ade71e5e42b22f8ced4
index 29d663f35c586..169c500036255 100644
--- a/sql/hive/src/test/resources/golden/udf_date_sub-1-7efeb74367835ade71e5e42b22f8ced4
+++ b/sql/hive/src/test/resources/golden/udf_date_sub-1-7efeb74367835ade71e5e42b22f8ced4
@@ -1,5 +1,5 @@
 date_sub(start_date, num_days) - Returns the date that is num_days before start_date.
 start_date is a string in the format 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'. num_days is a number. The time part of start_date is ignored.
 Example:
-   > SELECT date_sub('2009-30-07', 1) FROM src LIMIT 1;
-  '2009-29-07'
+   > SELECT date_sub('2009-07-30', 1) FROM src LIMIT 1;
+  '2009-07-29'
diff --git a/sql/hive/src/test/resources/golden/udf_datediff-1-34ae7a68b13c2bc9a89f61acf2edd4c5 b/sql/hive/src/test/resources/golden/udf_datediff-1-34ae7a68b13c2bc9a89f61acf2edd4c5
index 7ccaee7ad3bd4..42197f7ad3e51 100644
--- a/sql/hive/src/test/resources/golden/udf_datediff-1-34ae7a68b13c2bc9a89f61acf2edd4c5
+++ b/sql/hive/src/test/resources/golden/udf_datediff-1-34ae7a68b13c2bc9a89f61acf2edd4c5
@@ -1,5 +1,5 @@
 datediff(date1, date2) - Returns the number of days between date1 and date2
 date1 and date2 are strings in the format 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'. The time parts are ignored.If date1 is earlier than date2, the result is negative.
 Example:
-   > SELECT datediff('2009-30-07', '2009-31-07') FROM src LIMIT 1;
+   > SELECT datediff('2009-07-30', '2009-07-31') FROM src LIMIT 1;
   1
diff --git a/sql/hive/src/test/resources/golden/udf_day-0-c4c503756384ff1220222d84fd25e756 b/sql/hive/src/test/resources/golden/udf_day-0-c4c503756384ff1220222d84fd25e756
index d4017178b4e6b..09703d10eab7a 100644
--- a/sql/hive/src/test/resources/golden/udf_day-0-c4c503756384ff1220222d84fd25e756
+++ b/sql/hive/src/test/resources/golden/udf_day-0-c4c503756384ff1220222d84fd25e756
@@ -1 +1 @@
-day(date) - Returns the date of the month of date
+day(param) - Returns the day of the month of date/timestamp, or day component of interval
diff --git a/sql/hive/src/test/resources/golden/udf_day-1-87168babe1110fe4c38269843414ca4 b/sql/hive/src/test/resources/golden/udf_day-1-87168babe1110fe4c38269843414ca4
index 6135aafa50860..7c0ec1dc3be59 100644
--- a/sql/hive/src/test/resources/golden/udf_day-1-87168babe1110fe4c38269843414ca4
+++ b/sql/hive/src/test/resources/golden/udf_day-1-87168babe1110fe4c38269843414ca4
@@ -1,6 +1,9 @@
-day(date) - Returns the date of the month of date
+day(param) - Returns the day of the month of date/timestamp, or day component of interval
 Synonyms: dayofmonth
-date is a string in the format of 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'.
-Example:
-   > SELECT day('2009-30-07', 1) FROM src LIMIT 1;
+param can be one of:
+1. A string in the format of 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'.
+2. A date value
+3. A timestamp value
+4. A day-time interval valueExample:
+   > SELECT day('2009-07-30') FROM src LIMIT 1;
   30
diff --git a/sql/hive/src/test/resources/golden/udf_dayofmonth-0-7b2caf942528656555cf19c261a18502 b/sql/hive/src/test/resources/golden/udf_dayofmonth-0-7b2caf942528656555cf19c261a18502
index 47a7018d9d5ac..c37eb0ec2e969 100644
--- a/sql/hive/src/test/resources/golden/udf_dayofmonth-0-7b2caf942528656555cf19c261a18502
+++ b/sql/hive/src/test/resources/golden/udf_dayofmonth-0-7b2caf942528656555cf19c261a18502
@@ -1 +1 @@
-dayofmonth(date) - Returns the date of the month of date
+dayofmonth(param) - Returns the day of the month of date/timestamp, or day component of interval
diff --git a/sql/hive/src/test/resources/golden/udf_dayofmonth-1-ca24d07102ad264d79ff30c64a73a7e8 b/sql/hive/src/test/resources/golden/udf_dayofmonth-1-ca24d07102ad264d79ff30c64a73a7e8
index d9490e20a3b6d..9e931f649914b 100644
--- a/sql/hive/src/test/resources/golden/udf_dayofmonth-1-ca24d07102ad264d79ff30c64a73a7e8
+++ b/sql/hive/src/test/resources/golden/udf_dayofmonth-1-ca24d07102ad264d79ff30c64a73a7e8
@@ -1,6 +1,9 @@
-dayofmonth(date) - Returns the date of the month of date
+dayofmonth(param) - Returns the day of the month of date/timestamp, or day component of interval
 Synonyms: day
-date is a string in the format of 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'.
-Example:
-   > SELECT dayofmonth('2009-30-07', 1) FROM src LIMIT 1;
+param can be one of:
+1. A string in the format of 'yyyy-MM-dd HH:mm:ss' or 'yyyy-MM-dd'.
+2. A date value
+3. A timestamp value
+4. A day-time interval valueExample:
+   > SELECT dayofmonth('2009-07-30') FROM src LIMIT 1;
   30
diff --git a/sql/hive/src/test/resources/golden/udf_if-0-b7ffa85b5785cccef2af1b285348cc2c b/sql/hive/src/test/resources/golden/udf_if-0-b7ffa85b5785cccef2af1b285348cc2c
index 2cf0d9d61882e..ce583fe81ff68 100644
--- a/sql/hive/src/test/resources/golden/udf_if-0-b7ffa85b5785cccef2af1b285348cc2c
+++ b/sql/hive/src/test/resources/golden/udf_if-0-b7ffa85b5785cccef2af1b285348cc2c
@@ -1 +1 @@
-There is no documentation for function 'if'
+IF(expr1,expr2,expr3) - If expr1 is TRUE (expr1 <> 0 and expr1 <> NULL) then IF() returns expr2; otherwise it returns expr3. IF() returns a numeric or string value, depending on the context in which it is used.
diff --git a/sql/hive/src/test/resources/golden/udf_if-1-30cf7f51f92b5684e556deff3032d49a b/sql/hive/src/test/resources/golden/udf_if-1-30cf7f51f92b5684e556deff3032d49a
index 2cf0d9d61882e..ce583fe81ff68 100644
--- a/sql/hive/src/test/resources/golden/udf_if-1-30cf7f51f92b5684e556deff3032d49a
+++ b/sql/hive/src/test/resources/golden/udf_if-1-30cf7f51f92b5684e556deff3032d49a
@@ -1 +1 @@
-There is no documentation for function 'if'
+IF(expr1,expr2,expr3) - If expr1 is TRUE (expr1 <> 0 and expr1 <> NULL) then IF() returns expr2; otherwise it returns expr3. IF() returns a numeric or string value, depending on the context in which it is used.
diff --git a/sql/hive/src/test/resources/golden/udf_if-1-b7ffa85b5785cccef2af1b285348cc2c b/sql/hive/src/test/resources/golden/udf_if-1-b7ffa85b5785cccef2af1b285348cc2c
index 2cf0d9d61882e..ce583fe81ff68 100644
--- a/sql/hive/src/test/resources/golden/udf_if-1-b7ffa85b5785cccef2af1b285348cc2c
+++ b/sql/hive/src/test/resources/golden/udf_if-1-b7ffa85b5785cccef2af1b285348cc2c
@@ -1 +1 @@
-There is no documentation for function 'if'
+IF(expr1,expr2,expr3) - If expr1 is TRUE (expr1 <> 0 and expr1 <> NULL) then IF() returns expr2; otherwise it returns expr3. IF() returns a numeric or string value, depending on the context in which it is used.
diff --git a/sql/hive/src/test/resources/golden/udf_if-2-30cf7f51f92b5684e556deff3032d49a b/sql/hive/src/test/resources/golden/udf_if-2-30cf7f51f92b5684e556deff3032d49a
index 2cf0d9d61882e..ce583fe81ff68 100644
--- a/sql/hive/src/test/resources/golden/udf_if-2-30cf7f51f92b5684e556deff3032d49a
+++ b/sql/hive/src/test/resources/golden/udf_if-2-30cf7f51f92b5684e556deff3032d49a
@@ -1 +1 @@
-There is no documentation for function 'if'
+IF(expr1,expr2,expr3) - If expr1 is TRUE (expr1 <> 0 and expr1 <> NULL) then IF() returns expr2; otherwise it returns expr3. IF() returns a numeric or string value, depending on the context in which it is used.
diff --git a/sql/hive/src/test/resources/golden/udf_minute-0-9a38997c1f41f4afe00faa0abc471aee b/sql/hive/src/test/resources/golden/udf_minute-0-9a38997c1f41f4afe00faa0abc471aee
index 231e4f382566d..06650592f8d3c 100644
--- a/sql/hive/src/test/resources/golden/udf_minute-0-9a38997c1f41f4afe00faa0abc471aee
+++ b/sql/hive/src/test/resources/golden/udf_minute-0-9a38997c1f41f4afe00faa0abc471aee
@@ -1 +1 @@
-minute(date) - Returns the minute of date
+minute(param) - Returns the minute component of the string/timestamp/interval
diff --git a/sql/hive/src/test/resources/golden/udf_minute-1-16995573ac4f4a1b047ad6ee88699e48 b/sql/hive/src/test/resources/golden/udf_minute-1-16995573ac4f4a1b047ad6ee88699e48
index ea842ea174ae4..08ddc19b84d82 100644
--- a/sql/hive/src/test/resources/golden/udf_minute-1-16995573ac4f4a1b047ad6ee88699e48
+++ b/sql/hive/src/test/resources/golden/udf_minute-1-16995573ac4f4a1b047ad6ee88699e48
@@ -1,6 +1,8 @@
-minute(date) - Returns the minute of date
-date is a string in the format of 'yyyy-MM-dd HH:mm:ss' or 'HH:mm:ss'.
-Example:
+minute(param) - Returns the minute component of the string/timestamp/interval
+param can be one of:
+1. A string in the format of 'yyyy-MM-dd HH:mm:ss' or 'HH:mm:ss'.
+2. A timestamp value
+3. A day-time interval valueExample:
    > SELECT minute('2009-07-30 12:58:59') FROM src LIMIT 1;
   58
   > SELECT minute('12:58:59') FROM src LIMIT 1;
diff --git a/sql/hive/src/test/resources/golden/udf_month-0-9a38997c1f41f4afe00faa0abc471aee b/sql/hive/src/test/resources/golden/udf_month-0-9a38997c1f41f4afe00faa0abc471aee
index 231e4f382566d..06650592f8d3c 100644
--- a/sql/hive/src/test/resources/golden/udf_month-0-9a38997c1f41f4afe00faa0abc471aee
+++ b/sql/hive/src/test/resources/golden/udf_month-0-9a38997c1f41f4afe00faa0abc471aee
@@ -1 +1 @@
-minute(date) - Returns the minute of date
+minute(param) - Returns the minute component of the string/timestamp/interval
diff --git a/sql/hive/src/test/resources/golden/udf_month-1-16995573ac4f4a1b047ad6ee88699e48 b/sql/hive/src/test/resources/golden/udf_month-1-16995573ac4f4a1b047ad6ee88699e48
index ea842ea174ae4..08ddc19b84d82 100644
--- a/sql/hive/src/test/resources/golden/udf_month-1-16995573ac4f4a1b047ad6ee88699e48
+++ b/sql/hive/src/test/resources/golden/udf_month-1-16995573ac4f4a1b047ad6ee88699e48
@@ -1,6 +1,8 @@
-minute(date) - Returns the minute of date
-date is a string in the format of 'yyyy-MM-dd HH:mm:ss' or 'HH:mm:ss'.
-Example:
+minute(param) - Returns the minute component of the string/timestamp/interval
+param can be one of:
+1. A string in the format of 'yyyy-MM-dd HH:mm:ss' or 'HH:mm:ss'.
+2. A timestamp value
+3. A day-time interval valueExample:
    > SELECT minute('2009-07-30 12:58:59') FROM src LIMIT 1;
   58
   > SELECT minute('12:58:59') FROM src LIMIT 1;
diff --git a/sql/hive/src/test/resources/golden/udf_std-1-6759bde0e50a3607b7c3fd5a93cbd027 b/sql/hive/src/test/resources/golden/udf_std-1-6759bde0e50a3607b7c3fd5a93cbd027
index d54ebfbd6fb1a..a529b107ff216 100644
--- a/sql/hive/src/test/resources/golden/udf_std-1-6759bde0e50a3607b7c3fd5a93cbd027
+++ b/sql/hive/src/test/resources/golden/udf_std-1-6759bde0e50a3607b7c3fd5a93cbd027
@@ -1,2 +1,2 @@
 std(x) - Returns the standard deviation of a set of numbers
-Synonyms: stddev_pop, stddev
+Synonyms: stddev, stddev_pop
diff --git a/sql/hive/src/test/resources/golden/udf_stddev-1-18e1d598820013453fad45852e1a303d b/sql/hive/src/test/resources/golden/udf_stddev-1-18e1d598820013453fad45852e1a303d
index 5f674788180e8..ac3176a382547 100644
--- a/sql/hive/src/test/resources/golden/udf_stddev-1-18e1d598820013453fad45852e1a303d
+++ b/sql/hive/src/test/resources/golden/udf_stddev-1-18e1d598820013453fad45852e1a303d
@@ -1,2 +1,2 @@
 stddev(x) - Returns the standard deviation of a set of numbers
-Synonyms: stddev_pop, std
+Synonyms: std, stddev_pop
diff --git a/sql/hive/src/test/resources/golden/union3-0-6a8a35102de1b0b88c6721a704eb174d b/sql/hive/src/test/resources/golden/union3-0-99620f72f0282904846a596ca5b3e46c
similarity index 100%
rename from sql/hive/src/test/resources/golden/union3-0-6a8a35102de1b0b88c6721a704eb174d
rename to sql/hive/src/test/resources/golden/union3-0-99620f72f0282904846a596ca5b3e46c
diff --git a/sql/hive/src/test/resources/golden/union3-2-2a1dcd937f117f1955a169592b96d5f9 b/sql/hive/src/test/resources/golden/union3-2-90ca96ea59fd45cf0af8c020ae77c908
similarity index 100%
rename from sql/hive/src/test/resources/golden/union3-2-2a1dcd937f117f1955a169592b96d5f9
rename to sql/hive/src/test/resources/golden/union3-2-90ca96ea59fd45cf0af8c020ae77c908
diff --git a/sql/hive/src/test/resources/golden/union3-3-8fc63f8edb2969a63cd4485f1867ba97 b/sql/hive/src/test/resources/golden/union3-3-72b149ccaef751bcfe55d5ca37cb5fd7
similarity index 100%
rename from sql/hive/src/test/resources/golden/union3-3-8fc63f8edb2969a63cd4485f1867ba97
rename to sql/hive/src/test/resources/golden/union3-3-72b149ccaef751bcfe55d5ca37cb5fd7
diff --git a/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/parenthesis_star_by.q b/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/parenthesis_star_by.q
index 9e036c1a91d3b..e911fbf2d2c5c 100644
--- a/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/parenthesis_star_by.q
+++ b/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/parenthesis_star_by.q
@@ -5,6 +5,6 @@ SELECT * FROM (SELECT key, value FROM src DISTRIBUTE BY key, value)t ORDER BY ke
 
 
 SELECT key, value FROM src CLUSTER BY (key, value);
-SELECT key, value FROM src ORDER BY (key ASC, value ASC);
+SELECT key, value FROM src ORDER BY key ASC, value ASC;
 SELECT key, value FROM src SORT BY (key, value);
 SELECT * FROM (SELECT key, value FROM src DISTRIBUTE BY (key, value))t ORDER BY key, value;
diff --git a/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/union3.q b/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/union3.q
index b26a2e2799f7a..a989800cbf851 100644
--- a/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/union3.q
+++ b/sql/hive/src/test/resources/ql/src/test/queries/clientpositive/union3.q
@@ -1,42 +1,41 @@
+-- SORT_QUERY_RESULTS
 
 explain
 SELECT *
 FROM (
   SELECT 1 AS id
   FROM (SELECT * FROM src LIMIT 1) s1
-  CLUSTER BY id
   UNION ALL
   SELECT 2 AS id
   FROM (SELECT * FROM src LIMIT 1) s1
-  CLUSTER BY id
   UNION ALL
   SELECT 3 AS id
   FROM (SELECT * FROM src LIMIT 1) s2
   UNION ALL
   SELECT 4 AS id
   FROM (SELECT * FROM src LIMIT 1) s2
+  CLUSTER BY id
 ) a;
 
 
 
 CREATE TABLE union_out (id int);
 
-insert overwrite table union_out 
+insert overwrite table union_out
 SELECT *
 FROM (
   SELECT 1 AS id
   FROM (SELECT * FROM src LIMIT 1) s1
-  CLUSTER BY id
   UNION ALL
   SELECT 2 AS id
   FROM (SELECT * FROM src LIMIT 1) s1
-  CLUSTER BY id
   UNION ALL
   SELECT 3 AS id
   FROM (SELECT * FROM src LIMIT 1) s2
   UNION ALL
   SELECT 4 AS id
   FROM (SELECT * FROM src LIMIT 1) s2
+  CLUSTER BY id
 ) a;
 
-select * from union_out cluster by id;
+select * from union_out;
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ClasspathDependenciesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ClasspathDependenciesSuite.scala
new file mode 100644
index 0000000000000..34b2edb44b033
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ClasspathDependenciesSuite.scala
@@ -0,0 +1,110 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive
+
+import java.net.URL
+
+import org.apache.spark.SparkFunSuite
+
+/**
+ * Verify that some classes load and that others are not found on the classpath.
+ *
+ *
+ * This is used to detect classpath and shading conflict, especially between
+ * Spark's required Kryo version and that which can be found in some Hive versions.
+ */
+class ClasspathDependenciesSuite extends SparkFunSuite {
+  private val classloader = this.getClass.getClassLoader
+
+  private def assertLoads(classname: String): Unit = {
+    val resourceURL: URL = Option(findResource(classname)).getOrElse {
+      fail(s"Class $classname not found as ${resourceName(classname)}")
+    }
+
+    logInfo(s"Class $classname at $resourceURL")
+    classloader.loadClass(classname)
+  }
+
+  private def assertLoads(classes: String*): Unit = {
+    classes.foreach(assertLoads)
+  }
+
+  private def findResource(classname: String): URL = {
+    val resource = resourceName(classname)
+    classloader.getResource(resource)
+  }
+
+  private def resourceName(classname: String): String = {
+    classname.replace(".", "/") + ".class"
+  }
+
+  private def assertClassNotFound(classname: String): Unit = {
+    Option(findResource(classname)).foreach { resourceURL =>
+      fail(s"Class $classname found at $resourceURL")
+    }
+
+    intercept[ClassNotFoundException] {
+      classloader.loadClass(classname)
+    }
+  }
+
+  private def assertClassNotFound(classes: String*): Unit = {
+    classes.foreach(assertClassNotFound)
+  }
+
+  private val KRYO = "com.esotericsoftware.kryo.Kryo"
+
+  private val SPARK_HIVE = "org.apache.hive."
+  private val SPARK_SHADED = "org.spark-project.hive.shaded."
+
+  test("shaded Protobuf") {
+    assertLoads(SPARK_SHADED + "com.google.protobuf.ServiceException")
+  }
+
+  test("hive-common") {
+    assertLoads("org.apache.hadoop.hive.conf.HiveConf")
+  }
+
+  test("hive-exec") {
+    assertLoads("org.apache.hadoop.hive.ql.CommandNeedRetryException")
+  }
+
+  private val STD_INSTANTIATOR = "org.objenesis.strategy.StdInstantiatorStrategy"
+
+  test("unshaded kryo") {
+    assertLoads(KRYO, STD_INSTANTIATOR)
+  }
+
+  test("Forbidden Dependencies") {
+    assertClassNotFound(
+      SPARK_HIVE + KRYO,
+      SPARK_SHADED + KRYO,
+      "org.apache.hive." + KRYO,
+      "com.esotericsoftware.shaded." + STD_INSTANTIATOR,
+      SPARK_HIVE + "com.esotericsoftware.shaded." + STD_INSTANTIATOR,
+      "org.apache.hive.com.esotericsoftware.shaded." + STD_INSTANTIATOR
+    )
+  }
+
+  test("parquet-hadoop-bundle") {
+    assertLoads(
+      "parquet.hadoop.ParquetOutputFormat",
+      "parquet.hadoop.ParquetInputFormat"
+    )
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index 72b35959a491b..b8d41065d3f02 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -19,8 +19,11 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
+import scala.collection.mutable.ArrayBuffer
 import scala.sys.process.{ProcessLogger, Process}
 
+import org.scalatest.exceptions.TestFailedDueToTimeoutException
+
 import org.apache.spark._
 import org.apache.spark.sql.hive.test.{TestHive, TestHiveContext}
 import org.apache.spark.util.{ResetSystemProperties, Utils}
@@ -84,23 +87,39 @@ class HiveSparkSubmitSuite
   // This is copied from org.apache.spark.deploy.SparkSubmitSuite
   private def runSparkSubmit(args: Seq[String]): Unit = {
     val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!"))
+    val history = ArrayBuffer.empty[String]
+    val commands = Seq("./bin/spark-submit") ++ args
+    val commandLine = commands.mkString("'", "' '", "'")
     val process = Process(
-      Seq("./bin/spark-submit") ++ args,
+      commands,
       new File(sparkHome),
       "SPARK_TESTING" -> "1",
       "SPARK_HOME" -> sparkHome
     ).run(ProcessLogger(
       // scalastyle:off println
-      (line: String) => { println(s"out> $line") },
-      (line: String) => { println(s"err> $line") }
+      (line: String) => { println(s"stdout> $line"); history += s"out> $line"},
+      (line: String) => { println(s"stderr> $line"); history += s"err> $line" }
       // scalastyle:on println
     ))
 
     try {
-      val exitCode = failAfter(180 seconds) { process.exitValue() }
+      val exitCode = failAfter(180.seconds) { process.exitValue() }
       if (exitCode != 0) {
-        fail(s"Process returned with exit code $exitCode. See the log4j logs for more detail.")
+        // include logs in output. Note that logging is async and may not have completed
+        // at the time this exception is raised
+        Thread.sleep(1000)
+        val historyLog = history.mkString("\n")
+        fail(s"$commandLine returned with exit code $exitCode." +
+            s" See the log4j logs for more detail." +
+            s"\n$historyLog")
       }
+    } catch {
+      case to: TestFailedDueToTimeoutException =>
+        val historyLog = history.mkString("\n")
+        fail(s"Timeout of $commandLine" +
+            s" See the log4j logs for more detail." +
+            s"\n$historyLog", to)
+        case t: Throwable => throw t
     } finally {
       // Ensure we still kill the process in case it timed out
       process.destroy()
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
index 508695919e9a7..d33e81227db88 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
+import org.apache.hadoop.hive.conf.HiveConf
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.execution.QueryExecutionException
@@ -113,6 +114,8 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
   test("SPARK-4203:random partition directory order") {
     sql("CREATE TABLE tmp_table (key int, value string)")
     val tmpDir = Utils.createTempDir()
+    val stagingDir = new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR)
+
     sql(
       s"""
          |CREATE TABLE table_with_partition(c1 string)
@@ -145,7 +148,7 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
       """.stripMargin)
     def listFolders(path: File, acc: List[String]): List[List[String]] = {
       val dir = path.listFiles()
-      val folders = dir.filter(_.isDirectory).toList
+      val folders = dir.filter { e => e.isDirectory && !e.getName().startsWith(stagingDir) }.toList
       if (folders.isEmpty) {
         List(acc.reverse)
       } else {
@@ -158,7 +161,7 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
       "p1=a"::"p2=b"::"p3=c"::"p4=c"::"p5=1"::Nil ,
       "p1=a"::"p2=b"::"p3=c"::"p4=c"::"p5=4"::Nil
     )
-    assert(listFolders(tmpDir, List()).sortBy(_.toString()) == expected.sortBy(_.toString))
+    assert(listFolders(tmpDir, List()).sortBy(_.toString()) === expected.sortBy(_.toString))
     sql("DROP TABLE table_with_partition")
     sql("DROP TABLE tmp_table")
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index bb5f1febe9ad4..f00d3754c364a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive
 
+import org.apache.hadoop.hive.conf.HiveConf
+
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.parquet.ParquetCompatibilityTest
 import org.apache.spark.sql.{Row, SQLConf, SQLContext}
@@ -26,6 +28,13 @@ class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
 
   override val sqlContext: SQLContext = TestHive
 
+  /**
+   * Set the staging directory (and hence path to ignore Parquet files under)
+   * to that set by [[HiveConf.ConfVars.STAGINGDIR]].
+   */
+  override val stagingDir: Option[String] =
+    Some(new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR))
+
   override protected def beforeAll(): Unit = {
     super.beforeAll()
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
index bc72b0172a467..e4fec7e2c8a2a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
@@ -54,6 +54,9 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
       }
     }
 
+    // Ensure session state is initialized.
+    ctx.parseSql("use default")
+
     assertAnalyzeCommand(
       "ANALYZE TABLE Table1 COMPUTE STATISTICS",
       classOf[HiveNativeCommand])
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
index 3eb127e23d486..f0bb77092c0cf 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.hive.client
 
 import java.io.File
 
+import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.sql.catalyst.expressions.{NamedExpression, Literal, AttributeReference, EqualTo}
 import org.apache.spark.sql.catalyst.util.quietly
@@ -48,7 +49,9 @@ class VersionsSuite extends SparkFunSuite with Logging {
   }
 
   test("success sanity check") {
-    val badClient = IsolatedClientLoader.forVersion("13", buildConf(), ivyPath).client
+    val badClient = IsolatedClientLoader.forVersion(HiveContext.hiveExecutionVersion,
+      buildConf(),
+      ivyPath).client
     val db = new HiveDatabase("default", "")
     badClient.createDatabase(db)
   }
@@ -91,6 +94,7 @@ class VersionsSuite extends SparkFunSuite with Logging {
   versions.foreach { version =>
     test(s"$version: create client") {
       client = null
+      System.gc() // Hack to avoid SEGV on some JVM versions.
       client = IsolatedClientLoader.forVersion(version, buildConf(), ivyPath).client
     }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 11a843becce69..a7cfac51cc097 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -52,14 +52,6 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
     TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
     // Add Locale setting
     Locale.setDefault(Locale.US)
-    sql(s"ADD JAR ${TestHive.getHiveFile("TestUDTF.jar").getCanonicalPath()}")
-    // The function source code can be found at:
-    // https://cwiki.apache.org/confluence/display/Hive/DeveloperGuide+UDTF
-    sql(
-      """
-        |CREATE TEMPORARY FUNCTION udtf_count2
-        |AS 'org.apache.spark.sql.hive.execution.GenericUDTFCount2'
-      """.stripMargin)
   }
 
   override def afterAll() {
@@ -69,15 +61,6 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
     sql("DROP TEMPORARY FUNCTION udtf_count2")
   }
 
-  createQueryTest("Test UDTF.close in Lateral Views",
-     """
-       |SELECT key, cc
-       |FROM src LATERAL VIEW udtf_count2(value) dd AS cc
-     """.stripMargin, false) // false mean we have to keep the temp function in registry
-
-  createQueryTest("Test UDTF.close in SELECT",
-     "SELECT udtf_count2(a) FROM (SELECT 1 AS a FROM src LIMIT 3) table", false)
-
   test("SPARK-4908: concurrent hive native commands") {
     (1 to 100).par.map { _ =>
       sql("USE default")
@@ -176,8 +159,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
   createQueryTest("! operator",
     """
       |SELECT a FROM (
-      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 2 AS a FROM src LIMIT 1) table
+      |  SELECT 1 AS a UNION ALL SELECT 2 AS a) t
       |WHERE !(a>1)
     """.stripMargin)
 
@@ -229,71 +211,6 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
     |FROM src LIMIT 1;
   """.stripMargin)
 
-  createQueryTest("count distinct 0 values",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 'a' AS a FROM src LIMIT 0) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 1 value strings",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 'a' AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 'b' AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 1 value",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 1 AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 2 values",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 2 AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 2 values including null",
-    """
-      |SELECT COUNT(DISTINCT a, 1) FROM (
-      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT null AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 1 value + null",
-  """
-    |SELECT COUNT(DISTINCT a) FROM (
-    |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-    |  SELECT 1 AS a FROM src LIMIT 1 UNION ALL
-    |  SELECT null AS a FROM src LIMIT 1) table
-  """.stripMargin)
-
-  createQueryTest("count distinct 1 value long",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 1L AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 2 values long",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 2L AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
-  createQueryTest("count distinct 1 value + null long",
-    """
-      |SELECT COUNT(DISTINCT a) FROM (
-      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT 1L AS a FROM src LIMIT 1 UNION ALL
-      |  SELECT null AS a FROM src LIMIT 1) table
-    """.stripMargin)
-
   createQueryTest("null case",
     "SELECT case when(true) then 1 else null end FROM src LIMIT 1")
 
@@ -674,7 +591,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
     sql(
       """
         |SELECT a FROM (
-        |  SELECT 1 AS a FROM src LIMIT 1 ) table
+        |  SELECT 1 AS a FROM src LIMIT 1 ) t
         |WHERE abs(20141202) is not null
       """.stripMargin).collect()
   }
@@ -987,7 +904,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
         .zip(parts)
         .map { case (k, v) =>
           if (v == "NULL") {
-            s"$k=${ConfVars.DEFAULTPARTITIONNAME.defaultVal}"
+            s"$k=${ConfVars.DEFAULTPARTITIONNAME.defaultStrVal}"
           } else {
             s"$k=$v"
           }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
index e83a7dc77e329..3bf8f3ac20480 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
@@ -82,16 +82,16 @@ class PruningSuite extends HiveComparisonTest with BeforeAndAfter {
     Seq.empty)
 
   createPruningTest("Column pruning - non-trivial top project with aliases",
-    "SELECT c1 * 2 AS double FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 LIMIT 3",
-    Seq("double"),
+    "SELECT c1 * 2 AS dbl FROM (SELECT key AS c1 FROM src WHERE key > 10) t1 LIMIT 3",
+    Seq("dbl"),
     Seq("key"),
     Seq.empty)
 
   // Partition pruning tests
 
   createPruningTest("Partition pruning - non-partitioned, non-trivial project",
-    "SELECT key * 2 AS double FROM src WHERE value IS NOT NULL",
-    Seq("double"),
+    "SELECT key * 2 AS dbl FROM src WHERE value IS NOT NULL",
+    Seq("dbl"),
     Seq("key", "value"),
     Seq.empty)
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index c4923d83e48f3..95c1da6e9796c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -67,6 +67,25 @@ class MyDialect extends DefaultParserDialect
 class SQLQuerySuite extends QueryTest with SQLTestUtils {
   override def sqlContext: SQLContext = TestHive
 
+  test("UDTF") {
+    sql(s"ADD JAR ${TestHive.getHiveFile("TestUDTF.jar").getCanonicalPath()}")
+    // The function source code can be found at:
+    // https://cwiki.apache.org/confluence/display/Hive/DeveloperGuide+UDTF
+    sql(
+      """
+        |CREATE TEMPORARY FUNCTION udtf_count2
+        |AS 'org.apache.spark.sql.hive.execution.GenericUDTFCount2'
+      """.stripMargin)
+
+    checkAnswer(
+      sql("SELECT key, cc FROM src LATERAL VIEW udtf_count2(value) dd AS cc"),
+      Row(97, 500) :: Row(97, 500) :: Nil)
+
+    checkAnswer(
+      sql("SELECT udtf_count2(a) FROM (SELECT 1 AS a FROM src LIMIT 3) t"),
+      Row(3) :: Row(3) :: Nil)
+  }
+
   test("SPARK-6835: udtf in lateral view") {
     val df = Seq((1, 1)).toDF("c1", "c2")
     df.registerTempTable("table1")
@@ -264,47 +283,51 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
 
     setConf(HiveContext.CONVERT_CTAS, true)
 
-    sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
-    sql("CREATE TABLE IF NOT EXISTS ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
-    var message = intercept[AnalysisException] {
+    try {
       sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
-    }.getMessage
-    assert(message.contains("ctas1 already exists"))
-    checkRelation("ctas1", true)
-    sql("DROP TABLE ctas1")
-
-    // Specifying database name for query can be converted to data source write path
-    // is not allowed right now.
-    message = intercept[AnalysisException] {
-      sql("CREATE TABLE default.ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
-    }.getMessage
-    assert(
-      message.contains("Cannot specify database name in a CTAS statement"),
-      "When spark.sql.hive.convertCTAS is true, we should not allow " +
-      "database name specified.")
-
-    sql("CREATE TABLE ctas1 stored as textfile AS SELECT key k, value FROM src ORDER BY k, value")
-    checkRelation("ctas1", true)
-    sql("DROP TABLE ctas1")
-
-    sql(
-      "CREATE TABLE ctas1 stored as sequencefile AS SELECT key k, value FROM src ORDER BY k, value")
-    checkRelation("ctas1", true)
-    sql("DROP TABLE ctas1")
-
-    sql("CREATE TABLE ctas1 stored as rcfile AS SELECT key k, value FROM src ORDER BY k, value")
-    checkRelation("ctas1", false)
-    sql("DROP TABLE ctas1")
-
-    sql("CREATE TABLE ctas1 stored as orc AS SELECT key k, value FROM src ORDER BY k, value")
-    checkRelation("ctas1", false)
-    sql("DROP TABLE ctas1")
-
-    sql("CREATE TABLE ctas1 stored as parquet AS SELECT key k, value FROM src ORDER BY k, value")
-    checkRelation("ctas1", false)
-    sql("DROP TABLE ctas1")
-
-    setConf(HiveContext.CONVERT_CTAS, originalConf)
+      sql("CREATE TABLE IF NOT EXISTS ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
+      var message = intercept[AnalysisException] {
+        sql("CREATE TABLE ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
+      }.getMessage
+      assert(message.contains("ctas1 already exists"))
+      checkRelation("ctas1", true)
+      sql("DROP TABLE ctas1")
+
+      // Specifying database name for query can be converted to data source write path
+      // is not allowed right now.
+      message = intercept[AnalysisException] {
+        sql("CREATE TABLE default.ctas1 AS SELECT key k, value FROM src ORDER BY k, value")
+      }.getMessage
+      assert(
+        message.contains("Cannot specify database name in a CTAS statement"),
+        "When spark.sql.hive.convertCTAS is true, we should not allow " +
+            "database name specified.")
+
+      sql("CREATE TABLE ctas1 stored as textfile" +
+          " AS SELECT key k, value FROM src ORDER BY k, value")
+      checkRelation("ctas1", true)
+      sql("DROP TABLE ctas1")
+
+      sql("CREATE TABLE ctas1 stored as sequencefile" +
+            " AS SELECT key k, value FROM src ORDER BY k, value")
+      checkRelation("ctas1", true)
+      sql("DROP TABLE ctas1")
+
+      sql("CREATE TABLE ctas1 stored as rcfile AS SELECT key k, value FROM src ORDER BY k, value")
+      checkRelation("ctas1", false)
+      sql("DROP TABLE ctas1")
+
+      sql("CREATE TABLE ctas1 stored as orc AS SELECT key k, value FROM src ORDER BY k, value")
+      checkRelation("ctas1", false)
+      sql("DROP TABLE ctas1")
+
+      sql("CREATE TABLE ctas1 stored as parquet AS SELECT key k, value FROM src ORDER BY k, value")
+      checkRelation("ctas1", false)
+      sql("DROP TABLE ctas1")
+    } finally {
+      setConf(HiveContext.CONVERT_CTAS, originalConf)
+      sql("DROP TABLE IF EXISTS ctas1")
+    }
   }
 
   test("SQL Dialect Switching") {
@@ -670,22 +693,25 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
     val originalConf = convertCTAS
     setConf(HiveContext.CONVERT_CTAS, false)
 
-    sql("CREATE TABLE explodeTest (key bigInt)")
-    table("explodeTest").queryExecution.analyzed match {
-      case metastoreRelation: MetastoreRelation => // OK
-      case _ =>
-        fail("To correctly test the fix of SPARK-5875, explodeTest should be a MetastoreRelation")
-    }
+    try {
+      sql("CREATE TABLE explodeTest (key bigInt)")
+      table("explodeTest").queryExecution.analyzed match {
+        case metastoreRelation: MetastoreRelation => // OK
+        case _ =>
+          fail("To correctly test the fix of SPARK-5875, explodeTest should be a MetastoreRelation")
+      }
 
-    sql(s"INSERT OVERWRITE TABLE explodeTest SELECT explode(a) AS val FROM data")
-    checkAnswer(
-      sql("SELECT key from explodeTest"),
-      (1 to 5).flatMap(i => Row(i) :: Row(i + 1) :: Nil)
-    )
+      sql(s"INSERT OVERWRITE TABLE explodeTest SELECT explode(a) AS val FROM data")
+      checkAnswer(
+        sql("SELECT key from explodeTest"),
+        (1 to 5).flatMap(i => Row(i) :: Row(i + 1) :: Nil)
+      )
 
-    sql("DROP TABLE explodeTest")
-    dropTempTable("data")
-    setConf(HiveContext.CONVERT_CTAS, originalConf)
+      sql("DROP TABLE explodeTest")
+      dropTempTable("data")
+    } finally {
+      setConf(HiveContext.CONVERT_CTAS, originalConf)
+    }
   }
 
   test("sanity test for SPARK-6618") {
@@ -1058,12 +1084,12 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
   test("SPARK-8588 HiveTypeCoercion.inConversion fires too early") {
     val df =
       TestHive.createDataFrame(Seq((1, "2014-01-01"), (2, "2015-01-01"), (3, "2016-01-01")))
-    df.toDF("id", "date").registerTempTable("test_SPARK8588")
+    df.toDF("id", "datef").registerTempTable("test_SPARK8588")
     checkAnswer(
       TestHive.sql(
         """
-          |select id, concat(year(date))
-          |from test_SPARK8588 where concat(year(date), ' year') in ('2015 year', '2014 year')
+          |select id, concat(year(datef))
+          |from test_SPARK8588 where concat(year(datef), ' year') in ('2015 year', '2014 year')
         """.stripMargin),
       Row(1, "2014") :: Row(2, "2015") :: Nil
     )
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
index af3f468aaa5e9..deec0048d24b8 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
@@ -48,11 +48,9 @@ class OrcHadoopFsRelationSuite extends HadoopFsRelationTest {
         StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
 
       checkQueries(
-        load(
-          source = dataSourceName,
-          options = Map(
-            "path" -> file.getCanonicalPath,
-            "dataSchema" -> dataSchemaWithPartition.json)))
+        read.options(Map(
+          "path" -> file.getCanonicalPath,
+          "dataSchema" -> dataSchemaWithPartition.json)).format(dataSourceName).load())
     }
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
index d463e8fd626f9..a46ca9a2c9706 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
@@ -31,7 +31,6 @@ import org.scalatest.BeforeAndAfterAll
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
-
 // The data where the partitioning key exists only in the directory structure.
 case class OrcParData(intField: Int, stringField: String)
 
@@ -40,7 +39,7 @@ case class OrcParDataWithKey(intField: Int, pi: Int, stringField: String, ps: St
 
 // TODO This test suite duplicates ParquetPartitionDiscoverySuite a lot
 class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll {
-  val defaultPartitionName = ConfVars.DEFAULTPARTITIONNAME.defaultVal
+  val defaultPartitionName = ConfVars.DEFAULTPARTITIONNAME.defaultStrVal
 
   def withTempDir(f: File => Unit): Unit = {
     val dir = Utils.createTempDir().getCanonicalFile
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
index f56fb96c52d37..c4bc60086f6e1 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
@@ -60,7 +60,14 @@ case class ParquetDataWithKeyAndComplexTypes(
 class ParquetMetastoreSuite extends ParquetPartitioningTest {
   override def beforeAll(): Unit = {
     super.beforeAll()
-
+    dropTables("partitioned_parquet",
+      "partitioned_parquet_with_key",
+      "partitioned_parquet_with_complextypes",
+      "partitioned_parquet_with_key_and_complextypes",
+      "normal_parquet",
+      "jt",
+      "jt_array",
+      "test_parquet")
     sql(s"""
       create external table partitioned_parquet
       (
@@ -172,14 +179,14 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
   }
 
   override def afterAll(): Unit = {
-    sql("DROP TABLE partitioned_parquet")
-    sql("DROP TABLE partitioned_parquet_with_key")
-    sql("DROP TABLE partitioned_parquet_with_complextypes")
-    sql("DROP TABLE partitioned_parquet_with_key_and_complextypes")
-    sql("DROP TABLE normal_parquet")
-    sql("DROP TABLE IF EXISTS jt")
-    sql("DROP TABLE IF EXISTS jt_array")
-    sql("DROP TABLE IF EXISTS test_parquet")
+    dropTables("partitioned_parquet",
+      "partitioned_parquet_with_key",
+      "partitioned_parquet_with_complextypes",
+      "partitioned_parquet_with_key_and_complextypes",
+      "normal_parquet",
+      "jt",
+      "jt_array",
+       "test_parquet")
     setConf(HiveContext.CONVERT_METASTORE_PARQUET, false)
   }
 
@@ -203,6 +210,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
   }
 
   test("insert into an empty parquet table") {
+    dropTables("test_insert_parquet")
     sql(
       """
         |create table test_insert_parquet
@@ -228,7 +236,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
       sql(s"SELECT intField, stringField FROM test_insert_parquet WHERE intField > 2"),
       Row(3, "str3") :: Row(4, "str4") :: Nil
     )
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
+    dropTables("test_insert_parquet")
 
     // Create it again.
     sql(
@@ -255,118 +263,118 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
       sql(s"SELECT intField, stringField FROM test_insert_parquet"),
       (1 to 10).map(i => Row(i, s"str$i")) ++ (1 to 4).map(i => Row(i, s"str$i"))
     )
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
+    dropTables("test_insert_parquet")
   }
 
   test("scan a parquet table created through a CTAS statement") {
-    sql(
-      """
-        |create table test_parquet_ctas ROW FORMAT
-        |SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-        |STORED AS
-        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
-        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-        |AS select * from jt
-      """.stripMargin)
+    withTable("test_parquet_ctas") {
+      sql(
+        """
+          |create table test_parquet_ctas ROW FORMAT
+          |SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+          |STORED AS
+          |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+          |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+          |AS select * from jt
+        """.stripMargin)
 
-    checkAnswer(
-      sql(s"SELECT a, b FROM test_parquet_ctas WHERE a = 1"),
-      Seq(Row(1, "str1"))
-    )
+      checkAnswer(
+        sql(s"SELECT a, b FROM test_parquet_ctas WHERE a = 1"),
+        Seq(Row(1, "str1"))
+      )
 
-    table("test_parquet_ctas").queryExecution.optimizedPlan match {
-      case LogicalRelation(_: ParquetRelation) => // OK
-      case _ => fail(
-        "test_parquet_ctas should be converted to " +
-          s"${classOf[ParquetRelation].getCanonicalName}")
+      table("test_parquet_ctas").queryExecution.optimizedPlan match {
+        case LogicalRelation(_: ParquetRelation) => // OK
+        case _ => fail(
+          "test_parquet_ctas should be converted to " +
+              s"${classOf[ParquetRelation].getCanonicalName }")
+      }
     }
-
-    sql("DROP TABLE IF EXISTS test_parquet_ctas")
   }
 
   test("MetastoreRelation in InsertIntoTable will be converted") {
-    sql(
-      """
-        |create table test_insert_parquet
-        |(
-        |  intField INT
-        |)
-        |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-        |STORED AS
-        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
-        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-      """.stripMargin)
+    withTable("test_insert_parquet") {
+      sql(
+        """
+          |create table test_insert_parquet
+          |(
+          |  intField INT
+          |)
+          |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+          |STORED AS
+          |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+          |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+        """.stripMargin)
+
+      val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt")
+      df.queryExecution.executedPlan match {
+        case ExecutedCommand(InsertIntoHadoopFsRelation(_: ParquetRelation, _, _)) => // OK
+        case o => fail("test_insert_parquet should be converted to a " +
+          s"${classOf[ParquetRelation].getCanonicalName} and " +
+          s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan. " +
+          s"However, found a ${o.toString} ")
+      }
 
-    val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt")
-    df.queryExecution.executedPlan match {
-      case ExecutedCommand(InsertIntoHadoopFsRelation(_: ParquetRelation, _, _)) => // OK
-      case o => fail("test_insert_parquet should be converted to a " +
-        s"${classOf[ParquetRelation].getCanonicalName} and " +
-        s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan. " +
-        s"However, found a ${o.toString} ")
+      checkAnswer(
+        sql("SELECT intField FROM test_insert_parquet WHERE test_insert_parquet.intField > 5"),
+        sql("SELECT a FROM jt WHERE jt.a > 5").collect()
+      )
     }
-
-    checkAnswer(
-      sql("SELECT intField FROM test_insert_parquet WHERE test_insert_parquet.intField > 5"),
-      sql("SELECT a FROM jt WHERE jt.a > 5").collect()
-    )
-
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
   }
 
   test("MetastoreRelation in InsertIntoHiveTable will be converted") {
-    sql(
-      """
-        |create table test_insert_parquet
-        |(
-        |  int_array array<int>
-        |)
-        |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-        |STORED AS
-        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
-        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-      """.stripMargin)
+    withTable("test_insert_parquet") {
+      sql(
+        """
+          |create table test_insert_parquet
+          |(
+          |  int_array array<int>
+          |)
+          |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+          |STORED AS
+          |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+          |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+        """.stripMargin)
+
+      val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt_array")
+      df.queryExecution.executedPlan match {
+        case ExecutedCommand(InsertIntoHadoopFsRelation(r: ParquetRelation, _, _)) => // OK
+        case o => fail("test_insert_parquet should be converted to a " +
+          s"${classOf[ParquetRelation].getCanonicalName} and " +
+          s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan." +
+          s"However, found a ${o.toString} ")
+      }
 
-    val df = sql("INSERT INTO TABLE test_insert_parquet SELECT a FROM jt_array")
-    df.queryExecution.executedPlan match {
-      case ExecutedCommand(InsertIntoHadoopFsRelation(r: ParquetRelation, _, _)) => // OK
-      case o => fail("test_insert_parquet should be converted to a " +
-        s"${classOf[ParquetRelation].getCanonicalName} and " +
-        s"${classOf[InsertIntoDataSource].getCanonicalName} is expcted as the SparkPlan." +
-        s"However, found a ${o.toString} ")
+      checkAnswer(
+        sql("SELECT int_array FROM test_insert_parquet"),
+        sql("SELECT a FROM jt_array").collect()
+      )
     }
-
-    checkAnswer(
-      sql("SELECT int_array FROM test_insert_parquet"),
-      sql("SELECT a FROM jt_array").collect()
-    )
-
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
   }
 
   test("SPARK-6450 regression test") {
-    sql(
-      """CREATE TABLE IF NOT EXISTS ms_convert (key INT)
-        |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
-        |STORED AS
-        |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
-        |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
-      """.stripMargin)
+    withTable("ms_convert") {
+      sql(
+        """CREATE TABLE IF NOT EXISTS ms_convert (key INT)
+          |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
+          |STORED AS
+          |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
+          |  OUTPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
+        """.stripMargin)
+
+      // This shouldn't throw AnalysisException
+      val analyzed = sql(
+        """SELECT key FROM ms_convert
+          |UNION ALL
+          |SELECT key FROM ms_convert
+        """.stripMargin).queryExecution.analyzed
 
-    // This shouldn't throw AnalysisException
-    val analyzed = sql(
-      """SELECT key FROM ms_convert
-        |UNION ALL
-        |SELECT key FROM ms_convert
-      """.stripMargin).queryExecution.analyzed
-
-    assertResult(2) {
-      analyzed.collect {
-        case r @ LogicalRelation(_: ParquetRelation) => r
-      }.size
+      assertResult(2) {
+        analyzed.collect {
+          case r@LogicalRelation(_: ParquetRelation) => r
+        }.size
+      }
     }
-
-    sql("DROP TABLE ms_convert")
   }
 
   def collectParquetRelation(df: DataFrame): ParquetRelation = {
@@ -379,42 +387,42 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
   }
 
   test("SPARK-7749: non-partitioned metastore Parquet table lookup should use cached relation") {
-    sql(
-      s"""CREATE TABLE nonPartitioned (
-         |  key INT,
-         |  value STRING
-         |)
-         |STORED AS PARQUET
-       """.stripMargin)
-
-    // First lookup fills the cache
-    val r1 = collectParquetRelation(table("nonPartitioned"))
-    // Second lookup should reuse the cache
-    val r2 = collectParquetRelation(table("nonPartitioned"))
-    // They should be the same instance
-    assert(r1 eq r2)
-
-    sql("DROP TABLE nonPartitioned")
+    withTable("nonPartitioned") {
+      sql(
+        s"""CREATE TABLE nonPartitioned (
+           |  key INT,
+           |  value STRING
+           |)
+           |STORED AS PARQUET
+         """.stripMargin)
+
+      // First lookup fills the cache
+      val r1 = collectParquetRelation(table("nonPartitioned"))
+      // Second lookup should reuse the cache
+      val r2 = collectParquetRelation(table("nonPartitioned"))
+      // They should be the same instance
+      assert(r1 eq r2)
+    }
   }
 
   test("SPARK-7749: partitioned metastore Parquet table lookup should use cached relation") {
-    sql(
-      s"""CREATE TABLE partitioned (
-         |  key INT,
-         |  value STRING
-         |)
-         |PARTITIONED BY (part INT)
-         |STORED AS PARQUET
+    withTable("partitioned") {
+      sql(
+        s"""CREATE TABLE partitioned (
+           | key INT,
+           | value STRING
+           |)
+           |PARTITIONED BY (part INT)
+           |STORED AS PARQUET
        """.stripMargin)
 
-    // First lookup fills the cache
-    val r1 = collectParquetRelation(table("partitioned"))
-    // Second lookup should reuse the cache
-    val r2 = collectParquetRelation(table("partitioned"))
-    // They should be the same instance
-    assert(r1 eq r2)
-
-    sql("DROP TABLE partitioned")
+      // First lookup fills the cache
+      val r1 = collectParquetRelation(table("partitioned"))
+      // Second lookup should reuse the cache
+      val r2 = collectParquetRelation(table("partitioned"))
+      // They should be the same instance
+      assert(r1 eq r2)
+    }
   }
 
   test("Caching converted data source Parquet Relations") {
@@ -430,8 +438,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
       }
     }
 
-    sql("DROP TABLE IF EXISTS test_insert_parquet")
-    sql("DROP TABLE IF EXISTS test_parquet_partitioned_cache_test")
+    dropTables("test_insert_parquet", "test_parquet_partitioned_cache_test")
 
     sql(
       """
@@ -479,7 +486,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
         |  intField INT,
         |  stringField STRING
         |)
-        |PARTITIONED BY (date string)
+        |PARTITIONED BY (`date` string)
         |ROW FORMAT SERDE 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
         |STORED AS
         |  INPUTFORMAT 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
@@ -491,7 +498,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
     sql(
       """
         |INSERT INTO TABLE test_parquet_partitioned_cache_test
-        |PARTITION (date='2015-04-01')
+        |PARTITION (`date`='2015-04-01')
         |select a, b from jt
       """.stripMargin)
     // Right now, insert into a partitioned Parquet is not supported in data source Parquet.
@@ -500,7 +507,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
     sql(
       """
         |INSERT INTO TABLE test_parquet_partitioned_cache_test
-        |PARTITION (date='2015-04-02')
+        |PARTITION (`date`='2015-04-02')
         |select a, b from jt
       """.stripMargin)
     assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) === null)
@@ -510,7 +517,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
     checkCached(tableIdentifier)
     // Make sure we can read the data.
     checkAnswer(
-      sql("select STRINGField, date, intField from test_parquet_partitioned_cache_test"),
+      sql("select STRINGField, `date`, intField from test_parquet_partitioned_cache_test"),
       sql(
         """
           |select b, '2015-04-01', a FROM jt
@@ -521,8 +528,7 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
     invalidateTable("test_parquet_partitioned_cache_test")
     assert(catalog.cachedDataSourceTables.getIfPresent(tableIdentifier) === null)
 
-    sql("DROP TABLE test_insert_parquet")
-    sql("DROP TABLE test_parquet_partitioned_cache_test")
+    dropTables("test_insert_parquet", "test_parquet_partitioned_cache_test")
   }
 }
 
@@ -532,6 +538,11 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
 class ParquetSourceSuite extends ParquetPartitioningTest {
   override def beforeAll(): Unit = {
     super.beforeAll()
+    dropTables("partitioned_parquet",
+      "partitioned_parquet_with_key",
+      "partitioned_parquet_with_complextypes",
+      "partitioned_parquet_with_key_and_complextypes",
+      "normal_parquet")
 
     sql( s"""
       create temporary table partitioned_parquet
@@ -635,22 +646,22 @@ class ParquetSourceSuite extends ParquetPartitioningTest {
           StructField("a", arrayType1, nullable = true) :: Nil)
     assert(df.schema === expectedSchema1)
 
-    df.write.format("parquet").saveAsTable("alwaysNullable")
+    withTable("alwaysNullable") {
+      df.write.format("parquet").saveAsTable("alwaysNullable")
 
-    val mapType2 = MapType(IntegerType, IntegerType, valueContainsNull = true)
-    val arrayType2 = ArrayType(IntegerType, containsNull = true)
-    val expectedSchema2 =
-      StructType(
-        StructField("m", mapType2, nullable = true) ::
-          StructField("a", arrayType2, nullable = true) :: Nil)
+      val mapType2 = MapType(IntegerType, IntegerType, valueContainsNull = true)
+      val arrayType2 = ArrayType(IntegerType, containsNull = true)
+      val expectedSchema2 =
+        StructType(
+          StructField("m", mapType2, nullable = true) ::
+              StructField("a", arrayType2, nullable = true) :: Nil)
 
-    assert(table("alwaysNullable").schema === expectedSchema2)
-
-    checkAnswer(
-      sql("SELECT m, a FROM alwaysNullable"),
-      Row(Map(2 -> 3), Seq(4, 5, 6)))
+      assert(table("alwaysNullable").schema === expectedSchema2)
 
-    sql("DROP TABLE alwaysNullable")
+      checkAnswer(
+        sql("SELECT m, a FROM alwaysNullable"),
+        Row(Map(2 -> 3), Seq(4, 5, 6)))
+    }
   }
 
   test("Aggregation attribute names can't contain special chars \" ,;{}()\\n\\t=\"") {
@@ -738,6 +749,16 @@ abstract class ParquetPartitioningTest extends QueryTest with SQLTestUtils with
     partitionedTableDirWithKeyAndComplexTypes.delete()
   }
 
+  /**
+   * Drop named tables if they exist
+   * @param tableNames tables to drop
+   */
+  def dropTables(tableNames: String*): Unit = {
+    tableNames.foreach { name =>
+      sql(s"DROP TABLE IF EXISTS $name")
+    }
+  }
+
   Seq(
     "partitioned_parquet",
     "partitioned_parquet_with_key",
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 2aeed98285aa8..49360c48256ea 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -30,7 +30,6 @@
   <name>Spark Project YARN</name>
   <properties>
     <sbt.project.name>yarn</sbt.project.name>
-    <jersey.version>1.9</jersey.version>
   </properties>
 
   <dependencies>
@@ -125,25 +124,16 @@
      <dependency>
        <groupId>com.sun.jersey</groupId>
        <artifactId>jersey-core</artifactId>
-       <version>${jersey.version}</version>
        <scope>test</scope>
      </dependency>
      <dependency>
        <groupId>com.sun.jersey</groupId>
        <artifactId>jersey-json</artifactId>
-       <version>${jersey.version}</version>
        <scope>test</scope>
-       <exclusions>
-        <exclusion>
-          <groupId>stax</groupId>
-          <artifactId>stax-api</artifactId>
-        </exclusion>
-       </exclusions>
      </dependency>
      <dependency>
        <groupId>com.sun.jersey</groupId>
        <artifactId>jersey-server</artifactId>
-       <version>${jersey.version}</version>
        <scope>test</scope>
      </dependency>
   </dependencies>
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index 547863d9a0739..eb6e1fd370620 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -384,19 +384,29 @@ private object YarnClusterDriver extends Logging with Matchers {
 
 }
 
-private object YarnClasspathTest {
+private object YarnClasspathTest extends Logging {
+
+  var exitCode = 0
+
+  def error(m: String, ex: Throwable = null): Unit = {
+    logError(m, ex)
+    // scalastyle:off println
+    System.out.println(m)
+    if (ex != null) {
+      ex.printStackTrace(System.out)
+    }
+    // scalastyle:on println
+  }
 
   def main(args: Array[String]): Unit = {
     if (args.length != 2) {
-      // scalastyle:off println
-      System.err.println(
+      error(
         s"""
         |Invalid command line: ${args.mkString(" ")}
         |
         |Usage: YarnClasspathTest [driver result file] [executor result file]
         """.stripMargin)
       // scalastyle:on println
-      System.exit(1)
     }
 
     readResource(args(0))
@@ -406,6 +416,7 @@ private object YarnClasspathTest {
     } finally {
       sc.stop()
     }
+    System.exit(exitCode)
   }
 
   private def readResource(resultPath: String): Unit = {
@@ -415,6 +426,11 @@ private object YarnClasspathTest {
       val resource = ccl.getResourceAsStream("test.resource")
       val bytes = ByteStreams.toByteArray(resource)
       result = new String(bytes, 0, bytes.length, UTF_8)
+    } catch {
+      case t: Throwable =>
+        error(s"loading test.resource to $resultPath", t)
+        // set the exit code if not yet set
+        exitCode = 2
     } finally {
       Files.write(result, new File(resultPath), UTF_8)
     }

From 13675c742a71cbdc8324701c3694775ce1dd5c62 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Mon, 3 Aug 2015 16:44:25 -0700
Subject: [PATCH 011/633] [SPARK-8874] [ML] Add missing methods in Word2Vec

Add missing methods

1. getVectors
2. findSynonyms

to W2Vec scala and python API

mengxr

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7263 from MechCoder/missing_methods_w2vec and squashes the following commits:

149d5ca [MechCoder] minor doc
69d91b7 [MechCoder] [SPARK-8874] [ML] Add missing methods in Word2Vec
---
 .../apache/spark/ml/feature/Word2Vec.scala    | 38 +++++++++++-
 .../spark/ml/feature/Word2VecSuite.scala      | 62 +++++++++++++++++++
 2 files changed, 99 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index 6ea6590956300..b4f46cef798dd 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -18,15 +18,17 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.Experimental
+import org.apache.spark.SparkContext
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
 import org.apache.spark.mllib.feature
-import org.apache.spark.mllib.linalg.{VectorUDT, Vectors}
+import org.apache.spark.mllib.linalg.{VectorUDT, Vector, Vectors}
 import org.apache.spark.mllib.linalg.BLAS._
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.types._
 
 /**
@@ -146,6 +148,40 @@ class Word2VecModel private[ml] (
     wordVectors: feature.Word2VecModel)
   extends Model[Word2VecModel] with Word2VecBase {
 
+
+  /**
+   * Returns a dataframe with two fields, "word" and "vector", with "word" being a String and
+   * and the vector the DenseVector that it is mapped to.
+   */
+  val getVectors: DataFrame = {
+    val sc = SparkContext.getOrCreate()
+    val sqlContext = SQLContext.getOrCreate(sc)
+    import sqlContext.implicits._
+    val wordVec = wordVectors.getVectors.mapValues(vec => Vectors.dense(vec.map(_.toDouble)))
+    sc.parallelize(wordVec.toSeq).toDF("word", "vector")
+  }
+
+  /**
+   * Find "num" number of words closest in similarity to the given word.
+   * Returns a dataframe with the words and the cosine similarities between the
+   * synonyms and the given word.
+   */
+  def findSynonyms(word: String, num: Int): DataFrame = {
+    findSynonyms(wordVectors.transform(word), num)
+  }
+
+  /**
+   * Find "num" number of words closest to similarity to the given vector representation
+   * of the word. Returns a dataframe with the words and the cosine similarities between the
+   * synonyms and the given word vector.
+   */
+  def findSynonyms(word: Vector, num: Int): DataFrame = {
+    val sc = SparkContext.getOrCreate()
+    val sqlContext = SQLContext.getOrCreate(sc)
+    import sqlContext.implicits._
+    sc.parallelize(wordVectors.findSynonyms(word, num)).toDF("word", "similarity")
+  }
+
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
index aa6ce533fd885..adcda0e623b25 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
@@ -67,5 +67,67 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext {
         assert(vector1 ~== vector2 absTol 1E-5, "Transformed vector is different with expected.")
     }
   }
+
+  test("getVectors") {
+
+    val sqlContext = new SQLContext(sc)
+    import sqlContext.implicits._
+
+    val sentence = "a b " * 100 + "a c " * 10
+    val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" "))
+
+    val codes = Map(
+      "a" -> Array(-0.2811822295188904, -0.6356269121170044, -0.3020961284637451),
+      "b" -> Array(1.0309048891067505, -1.29472815990448, 0.22276712954044342),
+      "c" -> Array(-0.08456747233867645, 0.5137411952018738, 0.11731560528278351)
+    )
+    val expectedVectors = codes.toSeq.sortBy(_._1).map { case (w, v) => Vectors.dense(v) }
+
+    val docDF = doc.zip(doc).toDF("text", "alsotext")
+
+    val model = new Word2Vec()
+      .setVectorSize(3)
+      .setInputCol("text")
+      .setOutputCol("result")
+      .setSeed(42L)
+      .fit(docDF)
+
+    val realVectors = model.getVectors.sort("word").select("vector").map {
+      case Row(v: Vector) => v
+    }.collect()
+
+    realVectors.zip(expectedVectors).foreach {
+      case (real, expected) =>
+        assert(real ~== expected absTol 1E-5, "Actual vector is different from expected.")
+    }
+  }
+
+  test("findSynonyms") {
+
+    val sqlContext = new SQLContext(sc)
+    import sqlContext.implicits._
+
+    val sentence = "a b " * 100 + "a c " * 10
+    val doc = sc.parallelize(Seq(sentence, sentence)).map(line => line.split(" "))
+    val docDF = doc.zip(doc).toDF("text", "alsotext")
+
+    val model = new Word2Vec()
+      .setVectorSize(3)
+      .setInputCol("text")
+      .setOutputCol("result")
+      .setSeed(42L)
+      .fit(docDF)
+
+    val expectedSimilarity = Array(0.2789285076917586, -0.6336972059851644)
+    val (synonyms, similarity) = model.findSynonyms("a", 2).map {
+      case Row(w: String, sim: Double) => (w, sim)
+    }.collect().unzip
+
+    assert(synonyms.toArray === Array("b", "c"))
+    expectedSimilarity.zip(similarity).map {
+      case (expected, actual) => assert(math.abs((expected - actual) / expected) < 1E-5)
+    }
+
+  }
 }
 

From 7abaaad5b169520fbf7299808b2bafde089a16a2 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Mon, 3 Aug 2015 17:00:59 -0700
Subject: [PATCH 012/633] Add a prerequisites section for building docs

This puts all the install commands that need to be run in one section instead of being spread over many paragraphs

cc rxin

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #7912 from shivaram/docs-setup-readme and squashes the following commits:

cf7a204 [Shivaram Venkataraman] Add a prerequisites section for building docs
---
 docs/README.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/docs/README.md b/docs/README.md
index d7652e921f7df..50209896f986c 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -8,6 +8,16 @@ Read on to learn more about viewing documentation in plain text (i.e., markdown)
 documentation yourself. Why build it yourself? So that you have the docs that corresponds to
 whichever version of Spark you currently have checked out of revision control.
 
+## Prerequisites
+The Spark documenation build uses a number of tools to build HTML docs and API docs in Scala, Python
+and R. To get started you can run the following commands
+
+    $ sudo gem install jekyll
+    $ sudo gem install jekyll-redirect-from
+    $ sudo pip install Pygments
+    $ Rscript -e 'install.packages(c("knitr", "devtools"), repos="http://cran.stat.ucla.edu/")'
+
+
 ## Generating the Documentation HTML
 
 We include the Spark documentation as part of the source (as opposed to using a hosted wiki, such as

From b79b4f5f2251ed7efeec1f4b26e45a8ea6b85a6a Mon Sep 17 00:00:00 2001
From: Matthew Brandyberry <mbrandy@us.ibm.com>
Date: Mon, 3 Aug 2015 17:36:56 -0700
Subject: [PATCH 013/633] [SPARK-9483] Fix UTF8String.getPrefix for big-endian.

Previous code assumed little-endian.

Author: Matthew Brandyberry <mbrandy@us.ibm.com>

Closes #7902 from mtbrandy/SPARK-9483 and squashes the following commits:

ec31df8 [Matthew Brandyberry] [SPARK-9483] Changes from review comments.
17d54c6 [Matthew Brandyberry] [SPARK-9483] Fix UTF8String.getPrefix for big-endian.
---
 .../apache/spark/unsafe/types/UTF8String.java | 40 ++++++++++++++-----
 1 file changed, 30 insertions(+), 10 deletions(-)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index f6c9b87778f8f..d80bd57bd2048 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -20,6 +20,7 @@
 import javax.annotation.Nonnull;
 import java.io.Serializable;
 import java.io.UnsupportedEncodingException;
+import java.nio.ByteOrder;
 import java.util.Arrays;
 
 import org.apache.spark.unsafe.PlatformDependent;
@@ -53,6 +54,8 @@ public final class UTF8String implements Comparable<UTF8String>, Serializable {
     5, 5, 5, 5,
     6, 6};
 
+  private static ByteOrder byteOrder = ByteOrder.nativeOrder();
+
   public static final UTF8String EMPTY_UTF8 = UTF8String.fromString("");
 
   /**
@@ -175,18 +178,35 @@ public long getPrefix() {
     // If size is greater than 4, assume we have at least 8 bytes of data to fetch.
     // After getting the data, we use a mask to mask out data that is not part of the string.
     long p;
-    if (numBytes >= 8) {
-      p = PlatformDependent.UNSAFE.getLong(base, offset);
-    } else  if (numBytes > 4) {
-      p = PlatformDependent.UNSAFE.getLong(base, offset);
-      p = p & ((1L << numBytes * 8) - 1);
-    } else if (numBytes > 0) {
-      p = (long) PlatformDependent.UNSAFE.getInt(base, offset);
-      p = p & ((1L << numBytes * 8) - 1);
+    long mask = 0;
+    if (byteOrder == ByteOrder.LITTLE_ENDIAN) {
+      if (numBytes >= 8) {
+        p = PlatformDependent.UNSAFE.getLong(base, offset);
+      } else if (numBytes > 4) {
+        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        mask = (1L << (8 - numBytes) * 8) - 1;
+      } else if (numBytes > 0) {
+        p = (long) PlatformDependent.UNSAFE.getInt(base, offset);
+        mask = (1L << (8 - numBytes) * 8) - 1;
+      } else {
+        p = 0;
+      }
+      p = java.lang.Long.reverseBytes(p);
     } else {
-      p = 0;
+      // byteOrder == ByteOrder.BIG_ENDIAN
+      if (numBytes >= 8) {
+        p = PlatformDependent.UNSAFE.getLong(base, offset);
+      } else if (numBytes > 4) {
+        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        mask = (1L << (8 - numBytes) * 8) - 1;
+      } else if (numBytes > 0) {
+        p = ((long) PlatformDependent.UNSAFE.getInt(base, offset)) << 32;
+        mask = (1L << (8 - numBytes) * 8) - 1;
+      } else {
+        p = 0;
+      }
     }
-    p = java.lang.Long.reverseBytes(p);
+    p &= ~mask;
     return p;
   }
 

From 1633d0a2612d94151f620c919425026150e69ae1 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Mon, 3 Aug 2015 17:42:03 -0700
Subject: [PATCH 014/633] [SPARK-9263] Added flags to exclude dependencies when
 using --packages

While the functionality is there to exclude packages, there are no flags that allow users to exclude dependencies, in case of dependency conflicts. We should provide users with a flag to add dependency exclusions in case the packages are not resolved properly (or not available due to licensing).

The flag I added was --packages-exclude, but I'm open on renaming it. I also added property flags in case people would like to use a conf file to provide dependencies, which is possible if there is a long list of dependencies or exclusions.

cc andrewor14 vanzin pwendell

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #7599 from brkyvz/packages-exclusions and squashes the following commits:

636f410 [Burak Yavuz] addressed nits
6e54ede [Burak Yavuz] is this the culprit
b5e508e [Burak Yavuz] Merge branch 'master' of github.com:apache/spark into packages-exclusions
154f5db [Burak Yavuz] addressed initial comments
1536d7a [Burak Yavuz] Added flags to exclude packages using --packages-exclude
---
 .../org/apache/spark/deploy/SparkSubmit.scala | 29 +++++++++---------
 .../spark/deploy/SparkSubmitArguments.scala   | 11 +++++++
 .../spark/deploy/SparkSubmitUtilsSuite.scala  | 30 +++++++++++++++++++
 .../launcher/SparkSubmitOptionParser.java     |  2 ++
 4 files changed, 57 insertions(+), 15 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 0b39ee8fe3ba0..31185c8e77def 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -24,6 +24,7 @@ import java.security.PrivilegedExceptionAction
 
 import scala.collection.mutable.{ArrayBuffer, HashMap, Map}
 
+import org.apache.commons.lang3.StringUtils
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.security.UserGroupInformation
 import org.apache.ivy.Ivy
@@ -37,6 +38,7 @@ import org.apache.ivy.core.settings.IvySettings
 import org.apache.ivy.plugins.matcher.GlobPatternMatcher
 import org.apache.ivy.plugins.repository.file.FileRepository
 import org.apache.ivy.plugins.resolver.{FileSystemResolver, ChainResolver, IBiblioResolver}
+
 import org.apache.spark.api.r.RUtils
 import org.apache.spark.SPARK_VERSION
 import org.apache.spark.deploy.rest._
@@ -275,21 +277,18 @@ object SparkSubmit {
 
     // Resolve maven dependencies if there are any and add classpath to jars. Add them to py-files
     // too for packages that include Python code
-    val resolvedMavenCoordinates =
-      SparkSubmitUtils.resolveMavenCoordinates(
-        args.packages, Option(args.repositories), Option(args.ivyRepoPath))
-    if (!resolvedMavenCoordinates.trim.isEmpty) {
-      if (args.jars == null || args.jars.trim.isEmpty) {
-        args.jars = resolvedMavenCoordinates
+    val exclusions: Seq[String] =
+      if (!StringUtils.isBlank(args.packagesExclusions)) {
+        args.packagesExclusions.split(",")
       } else {
-        args.jars += s",$resolvedMavenCoordinates"
+        Nil
       }
+    val resolvedMavenCoordinates = SparkSubmitUtils.resolveMavenCoordinates(args.packages,
+      Some(args.repositories), Some(args.ivyRepoPath), exclusions = exclusions)
+    if (!StringUtils.isBlank(resolvedMavenCoordinates)) {
+      args.jars = mergeFileLists(args.jars, resolvedMavenCoordinates)
       if (args.isPython) {
-        if (args.pyFiles == null || args.pyFiles.trim.isEmpty) {
-          args.pyFiles = resolvedMavenCoordinates
-        } else {
-          args.pyFiles += s",$resolvedMavenCoordinates"
-        }
+        args.pyFiles = mergeFileLists(args.pyFiles, resolvedMavenCoordinates)
       }
     }
 
@@ -736,7 +735,7 @@ object SparkSubmit {
    * no files, into a single comma-separated string.
    */
   private def mergeFileLists(lists: String*): String = {
-    val merged = lists.filter(_ != null)
+    val merged = lists.filterNot(StringUtils.isBlank)
                       .flatMap(_.split(","))
                       .mkString(",")
     if (merged == "") null else merged
@@ -938,7 +937,7 @@ private[spark] object SparkSubmitUtils {
         // are supplied to spark-submit
         val alternateIvyCache = ivyPath.getOrElse("")
         val packagesDirectory: File =
-          if (alternateIvyCache.trim.isEmpty) {
+          if (alternateIvyCache == null || alternateIvyCache.trim.isEmpty) {
             new File(ivySettings.getDefaultIvyUserDir, "jars")
           } else {
             ivySettings.setDefaultIvyUserDir(new File(alternateIvyCache))
@@ -1010,7 +1009,7 @@ private[spark] object SparkSubmitUtils {
     }
   }
 
-  private def createExclusion(
+  private[deploy] def createExclusion(
       coords: String,
       ivySettings: IvySettings,
       ivyConfName: String): ExcludeRule = {
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index b3710073e330c..44852ce4e84ac 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -59,6 +59,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
   var packages: String = null
   var repositories: String = null
   var ivyRepoPath: String = null
+  var packagesExclusions: String = null
   var verbose: Boolean = false
   var isPython: Boolean = false
   var pyFiles: String = null
@@ -172,6 +173,9 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
     name = Option(name).orElse(sparkProperties.get("spark.app.name")).orNull
     jars = Option(jars).orElse(sparkProperties.get("spark.jars")).orNull
     ivyRepoPath = sparkProperties.get("spark.jars.ivy").orNull
+    packages = Option(packages).orElse(sparkProperties.get("spark.jars.packages")).orNull
+    packagesExclusions = Option(packagesExclusions)
+      .orElse(sparkProperties.get("spark.jars.excludes")).orNull
     deployMode = Option(deployMode).orElse(env.get("DEPLOY_MODE")).orNull
     numExecutors = Option(numExecutors)
       .getOrElse(sparkProperties.get("spark.executor.instances").orNull)
@@ -299,6 +303,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
     |  childArgs               [${childArgs.mkString(" ")}]
     |  jars                    $jars
     |  packages                $packages
+    |  packagesExclusions      $packagesExclusions
     |  repositories            $repositories
     |  verbose                 $verbose
     |
@@ -391,6 +396,9 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
       case PACKAGES =>
         packages = value
 
+      case PACKAGES_EXCLUDE =>
+        packagesExclusions = value
+
       case REPOSITORIES =>
         repositories = value
 
@@ -482,6 +490,9 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
         |                              maven repo, then maven central and any additional remote
         |                              repositories given by --repositories. The format for the
         |                              coordinates should be groupId:artifactId:version.
+        |  --exclude-packages          Comma-separated list of groupId:artifactId, to exclude while
+        |                              resolving the dependencies provided in --packages to avoid
+        |                              dependency conflicts.
         |  --repositories              Comma-separated list of additional remote repositories to
         |                              search for the maven coordinates given with --packages.
         |  --py-files PY_FILES         Comma-separated list of .zip, .egg, or .py files to place
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
index 01ece1a10f46d..63c346c1b8908 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitUtilsSuite.scala
@@ -95,6 +95,25 @@ class SparkSubmitUtilsSuite extends SparkFunSuite with BeforeAndAfterAll {
     assert(md.getDependencies.length === 2)
   }
 
+  test("excludes works correctly") {
+    val md = SparkSubmitUtils.getModuleDescriptor
+    val excludes = Seq("a:b", "c:d")
+    excludes.foreach { e =>
+      md.addExcludeRule(SparkSubmitUtils.createExclusion(e + ":*", new IvySettings, "default"))
+    }
+    val rules = md.getAllExcludeRules
+    assert(rules.length === 2)
+    val rule1 = rules(0).getId.getModuleId
+    assert(rule1.getOrganisation === "a")
+    assert(rule1.getName === "b")
+    val rule2 = rules(1).getId.getModuleId
+    assert(rule2.getOrganisation === "c")
+    assert(rule2.getName === "d")
+    intercept[IllegalArgumentException] {
+      SparkSubmitUtils.createExclusion("e:f:g:h", new IvySettings, "default")
+    }
+  }
+
   test("ivy path works correctly") {
     val md = SparkSubmitUtils.getModuleDescriptor
     val artifacts = for (i <- 0 until 3) yield new MDArtifact(md, s"jar-$i", "jar", "jar")
@@ -168,4 +187,15 @@ class SparkSubmitUtilsSuite extends SparkFunSuite with BeforeAndAfterAll {
       assert(files.indexOf(main.artifactId) >= 0, "Did not return artifact")
     }
   }
+
+  test("exclude dependencies end to end") {
+    val main = new MavenCoordinate("my.great.lib", "mylib", "0.1")
+    val dep = "my.great.dep:mydep:0.5"
+    IvyTestUtils.withRepository(main, Some(dep), None) { repo =>
+      val files = SparkSubmitUtils.resolveMavenCoordinates(main.toString,
+        Some(repo), None, Seq("my.great.dep:mydep"), isTest = true)
+      assert(files.indexOf(main.artifactId) >= 0, "Did not return artifact")
+      assert(files.indexOf("my.great.dep") < 0, "Returned excluded artifact")
+    }
+  }
 }
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
index b88bba883ac65..5779eb3fc0f78 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
@@ -51,6 +51,7 @@ class SparkSubmitOptionParser {
   protected final String MASTER = "--master";
   protected final String NAME = "--name";
   protected final String PACKAGES = "--packages";
+  protected final String PACKAGES_EXCLUDE = "--exclude-packages";
   protected final String PROPERTIES_FILE = "--properties-file";
   protected final String PROXY_USER = "--proxy-user";
   protected final String PY_FILES = "--py-files";
@@ -105,6 +106,7 @@ class SparkSubmitOptionParser {
     { NAME },
     { NUM_EXECUTORS },
     { PACKAGES },
+    { PACKAGES_EXCLUDE },
     { PRINCIPAL },
     { PROPERTIES_FILE },
     { PROXY_USER },

From 3b0e44490aebfba30afc147e4a34a63439d985c6 Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Mon, 3 Aug 2015 18:20:40 -0700
Subject: [PATCH 015/633] [SPARK-8416] highlight and topping the executor
 threads in thread dumping page

https://issues.apache.org/jira/browse/SPARK-8416

To facilitate debugging, I made this patch with three changes:

* render the executor-thread and non executor-thread entries with different background colors

* put the executor threads on the top of the list

* sort the threads alphabetically

Author: CodingCat <zhunansjtu@gmail.com>

Closes #7808 from CodingCat/SPARK-8416 and squashes the following commits:

34fc708 [CodingCat] fix className
d7b79dd [CodingCat] lowercase threadName
d032882 [CodingCat] sort alphabetically and change the css class name
f0513b1 [CodingCat] change the color & group threads by name
2da6e06 [CodingCat] small fix
3fc9f36 [CodingCat] define classes in webui.css
8ee125e [CodingCat] highlight and put on top the executor threads in thread dumping page
---
 .../org/apache/spark/ui/static/webui.css      |  8 +++++++
 .../ui/exec/ExecutorThreadDumpPage.scala      | 24 ++++++++++++++++---
 2 files changed, 29 insertions(+), 3 deletions(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/webui.css b/core/src/main/resources/org/apache/spark/ui/static/webui.css
index 648cd1b104802..04f3070d25b4a 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/webui.css
+++ b/core/src/main/resources/org/apache/spark/ui/static/webui.css
@@ -224,3 +224,11 @@ span.additional-metric-title {
 a.expandbutton {
   cursor: pointer;
 }
+
+.executor-thread {
+  background: #E6E6E6;
+}
+
+.non-executor-thread {
+  background: #FAFAFA;
+}
\ No newline at end of file
diff --git a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorThreadDumpPage.scala b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorThreadDumpPage.scala
index f0ae95bb8c812..b0a2cb4aa4d4b 100644
--- a/core/src/main/scala/org/apache/spark/ui/exec/ExecutorThreadDumpPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/exec/ExecutorThreadDumpPage.scala
@@ -49,11 +49,29 @@ private[ui] class ExecutorThreadDumpPage(parent: ExecutorsTab) extends WebUIPage
     val maybeThreadDump = sc.get.getExecutorThreadDump(executorId)
 
     val content = maybeThreadDump.map { threadDump =>
-      val dumpRows = threadDump.map { thread =>
+      val dumpRows = threadDump.sortWith {
+        case (threadTrace1, threadTrace2) => {
+          val v1 = if (threadTrace1.threadName.contains("Executor task launch")) 1 else 0
+          val v2 = if (threadTrace2.threadName.contains("Executor task launch")) 1 else 0
+          if (v1 == v2) {
+            threadTrace1.threadName.toLowerCase < threadTrace2.threadName.toLowerCase
+          } else {
+            v1 > v2
+          }
+        }
+      }.map { thread =>
+        val threadName = thread.threadName
+        val className = "accordion-heading " + {
+          if (threadName.contains("Executor task launch")) {
+            "executor-thread"
+          } else {
+            "non-executor-thread"
+          }
+        }
         <div class="accordion-group">
-          <div class="accordion-heading" onclick="$(this).next().toggleClass('hidden')">
+          <div class={className} onclick="$(this).next().toggleClass('hidden')">
             <a class="accordion-toggle">
-              Thread {thread.threadId}: {thread.threadName} ({thread.threadState})
+              Thread {thread.threadId}: {threadName} ({thread.threadState})
             </a>
           </div>
           <div class="accordion-body hidden">

From 5eb89f67e323dcf9fa3d5b30f9b5cb8f10ca1e8c Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 3 Aug 2015 18:47:02 -0700
Subject: [PATCH 016/633] [SPARK-9577][SQL] Surface concrete iterator types in
 various sort classes.

We often return abstract iterator types in various sort-related classes (e.g. UnsafeKVExternalSorter). It is actually better to return a more concrete type, so the callsite uses that type and JIT can inline the iterator calls.

Author: Reynold Xin <rxin@databricks.com>

Closes #7911 from rxin/surface-concrete-type and squashes the following commits:

0422add [Reynold Xin] [SPARK-9577][SQL] Surface concrete iterator types in various sort classes.
---
 .../unsafe/sort/UnsafeExternalSorter.java     |   2 +-
 .../unsafe/sort/UnsafeInMemorySorter.java     |   6 +-
 .../sql/execution/UnsafeKVExternalSorter.java | 112 +++++++++---------
 .../UnsafeHybridAggregationIterator.scala     |  30 +----
 4 files changed, 65 insertions(+), 85 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index bf5f965a9d8dc..dec7fcfa0ddc1 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -428,7 +428,7 @@ public void insertKVRecord(
 
   public UnsafeSorterIterator getSortedIterator() throws IOException {
     assert(inMemSorter != null);
-    final UnsafeSorterIterator inMemoryIterator = inMemSorter.getSortedIterator();
+    final UnsafeInMemorySorter.SortedIterator inMemoryIterator = inMemSorter.getSortedIterator();
     int numIteratorsToMerge = spillWriters.size() + (inMemoryIterator.hasNext() ? 1 : 0);
     if (spillWriters.isEmpty()) {
       return inMemoryIterator;
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
index 3131465391900..1e4b8a116e11a 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
@@ -133,7 +133,7 @@ public void insertRecord(long recordPointer, long keyPrefix) {
     pointerArrayInsertPosition++;
   }
 
-  private static final class SortedIterator extends UnsafeSorterIterator {
+  public static final class SortedIterator extends UnsafeSorterIterator {
 
     private final TaskMemoryManager memoryManager;
     private final int sortBufferInsertPosition;
@@ -144,7 +144,7 @@ private static final class SortedIterator extends UnsafeSorterIterator {
     private long keyPrefix;
     private int recordLength;
 
-    SortedIterator(
+    private SortedIterator(
         TaskMemoryManager memoryManager,
         int sortBufferInsertPosition,
         long[] sortBuffer) {
@@ -186,7 +186,7 @@ public void loadNext() {
    * Return an iterator over record pointers in sorted order. For efficiency, all calls to
    * {@code next()} will return the same mutable object.
    */
-  public UnsafeSorterIterator getSortedIterator() {
+  public SortedIterator getSortedIterator() {
     sorter.sort(pointerArray, 0, pointerArrayInsertPosition / 2, sortComparator);
     return new SortedIterator(memoryManager, pointerArrayInsertPosition, pointerArray);
   }
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index f6b017686306b..312ec8ea0dd9d 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -134,7 +134,7 @@ public void insertKV(UnsafeRow key, UnsafeRow value) throws IOException {
       value.getBaseObject(), value.getBaseOffset(), value.getSizeInBytes(), prefix);
   }
 
-  public KVIterator<UnsafeRow, UnsafeRow> sortedIterator() throws IOException {
+  public KVSorterIterator sortedIterator() throws IOException {
     try {
       final UnsafeSorterIterator underlying = sorter.getSortedIterator();
       if (!underlying.hasNext()) {
@@ -142,58 +142,7 @@ public KVIterator<UnsafeRow, UnsafeRow> sortedIterator() throws IOException {
         // here in order to prevent memory leaks.
         cleanupResources();
       }
-
-      return new KVIterator<UnsafeRow, UnsafeRow>() {
-        private UnsafeRow key = new UnsafeRow();
-        private UnsafeRow value = new UnsafeRow();
-        private int numKeyFields = keySchema.size();
-        private int numValueFields = valueSchema.size();
-
-        @Override
-        public boolean next() throws IOException {
-          try {
-            if (underlying.hasNext()) {
-              underlying.loadNext();
-
-              Object baseObj = underlying.getBaseObject();
-              long recordOffset = underlying.getBaseOffset();
-              int recordLen = underlying.getRecordLength();
-
-              // Note that recordLen = keyLen + valueLen + 4 bytes (for the keyLen itself)
-              int keyLen = PlatformDependent.UNSAFE.getInt(baseObj, recordOffset);
-              int valueLen = recordLen - keyLen - 4;
-
-              key.pointTo(baseObj, recordOffset + 4, numKeyFields, keyLen);
-              value.pointTo(baseObj, recordOffset + 4 + keyLen, numValueFields, valueLen);
-
-              return true;
-            } else {
-              key = null;
-              value = null;
-              cleanupResources();
-              return false;
-            }
-          } catch (IOException e) {
-            cleanupResources();
-            throw e;
-          }
-        }
-
-        @Override
-        public UnsafeRow getKey() {
-          return key;
-        }
-
-        @Override
-        public UnsafeRow getValue() {
-          return value;
-        }
-
-        @Override
-        public void close() {
-          cleanupResources();
-        }
-      };
+      return new KVSorterIterator(underlying);
     } catch (IOException e) {
       cleanupResources();
       throw e;
@@ -233,4 +182,61 @@ public int compare(Object baseObj1, long baseOff1, Object baseObj2, long baseOff
       return ordering.compare(row1, row2);
     }
   }
+
+  public class KVSorterIterator extends KVIterator<UnsafeRow, UnsafeRow> {
+    private UnsafeRow key = new UnsafeRow();
+    private UnsafeRow value = new UnsafeRow();
+    private final int numKeyFields = keySchema.size();
+    private final int numValueFields = valueSchema.size();
+    private final UnsafeSorterIterator underlying;
+
+    private KVSorterIterator(UnsafeSorterIterator underlying) {
+      this.underlying = underlying;
+    }
+
+    @Override
+    public boolean next() throws IOException {
+      try {
+        if (underlying.hasNext()) {
+          underlying.loadNext();
+
+          Object baseObj = underlying.getBaseObject();
+          long recordOffset = underlying.getBaseOffset();
+          int recordLen = underlying.getRecordLength();
+
+          // Note that recordLen = keyLen + valueLen + 4 bytes (for the keyLen itself)
+          int keyLen = PlatformDependent.UNSAFE.getInt(baseObj, recordOffset);
+          int valueLen = recordLen - keyLen - 4;
+
+          key.pointTo(baseObj, recordOffset + 4, numKeyFields, keyLen);
+          value.pointTo(baseObj, recordOffset + 4 + keyLen, numValueFields, valueLen);
+
+          return true;
+        } else {
+          key = null;
+          value = null;
+          cleanupResources();
+          return false;
+        }
+      } catch (IOException e) {
+        cleanupResources();
+        throw e;
+      }
+    }
+
+    @Override
+    public UnsafeRow getKey() {
+      return key;
+    }
+
+    @Override
+    public UnsafeRow getValue() {
+      return value;
+    }
+
+    @Override
+    public void close() {
+      cleanupResources();
+    }
+  };
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
index 37d34eb7ccf09..b465787fe8cbd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
@@ -17,12 +17,12 @@
 
 package org.apache.spark.sql.execution.aggregate
 
-import org.apache.spark.sql.execution.{UnsafeKeyValueSorter, UnsafeFixedWidthAggregationMap}
 import org.apache.spark.unsafe.KVIterator
 import org.apache.spark.{SparkEnv, TaskContext}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.execution.{UnsafeKVExternalSorter, UnsafeFixedWidthAggregationMap}
 import org.apache.spark.sql.types.StructType
 
 /**
@@ -230,7 +230,7 @@ class UnsafeHybridAggregationIterator(
     }
 
     // Step 5: Get the sorted iterator from the externalSorter.
-    val sortedKVIterator: KVIterator[UnsafeRow, UnsafeRow] = externalSorter.sortedIterator()
+    val sortedKVIterator: UnsafeKVExternalSorter#KVSorterIterator = externalSorter.sortedIterator()
 
     // Step 6: We now create a SortBasedAggregationIterator based on sortedKVIterator.
     // For a aggregate function with mode Partial, its mode in the SortBasedAggregationIterator
@@ -368,31 +368,5 @@ object UnsafeHybridAggregationIterator {
       newMutableProjection,
       outputsUnsafeRows)
   }
-
-  def createFromKVIterator(
-      groupingKeyAttributes: Seq[Attribute],
-      valueAttributes: Seq[Attribute],
-      inputKVIterator: KVIterator[UnsafeRow, InternalRow],
-      nonCompleteAggregateExpressions: Seq[AggregateExpression2],
-      nonCompleteAggregateAttributes: Seq[Attribute],
-      completeAggregateExpressions: Seq[AggregateExpression2],
-      completeAggregateAttributes: Seq[Attribute],
-      initialInputBufferOffset: Int,
-      resultExpressions: Seq[NamedExpression],
-      newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-      outputsUnsafeRows: Boolean): UnsafeHybridAggregationIterator = {
-    new UnsafeHybridAggregationIterator(
-      groupingKeyAttributes,
-      valueAttributes,
-      inputKVIterator,
-      nonCompleteAggregateExpressions,
-      nonCompleteAggregateAttributes,
-      completeAggregateExpressions,
-      completeAggregateAttributes,
-      initialInputBufferOffset,
-      resultExpressions,
-      newMutableProjection,
-      outputsUnsafeRows)
-  }
   // scalastyle:on
 }

From 0afa6fbf525723e97c6dacfdba3ad1762637ffa9 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 4 Aug 2015 13:48:22 +0900
Subject: [PATCH 017/633] [SPARK-9521] [DOCS] Addendum. Require Maven 3.3.3+ in
 the build

Follow on for #7852: Building Spark doc needs to refer to new Maven requirement too

Author: Sean Owen <sowen@cloudera.com>

Closes #7905 from srowen/SPARK-9521.2 and squashes the following commits:

73285df [Sean Owen] Follow on for #7852: Building Spark doc needs to refer to new Maven requirement too
---
 docs/building-spark.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/building-spark.md b/docs/building-spark.md
index a5da3b39502e2..f133eb96d9a21 100644
--- a/docs/building-spark.md
+++ b/docs/building-spark.md
@@ -7,7 +7,8 @@ redirect_from: "building-with-maven.html"
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-Building Spark using Maven requires Maven 3.0.4 or newer and Java 7+.
+Building Spark using Maven requires Maven 3.3.3 or newer and Java 7+.
+The Spark build can supply a suitable Maven binary; see below.
 
 # Building with `build/mvn`
 

From 9e952ecbce670e9b532a1c664a4d03b66e404112 Mon Sep 17 00:00:00 2001
From: Ankur Dave <ankurdave@gmail.com>
Date: Mon, 3 Aug 2015 23:07:32 -0700
Subject: [PATCH 018/633] [SPARK-3190] [GRAPHX] Fix VertexRDD.count() overflow
 regression

SPARK-3190 was originally fixed by 96df92906978c5f58e0cc8ff5eebe5b35a08be3b, but a5ef58113667ff73562ce6db381cff96a0b354b0 introduced a regression during refactoring. This commit fixes the regression.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #7923 from ankurdave/SPARK-3190-reopening and squashes the following commits:

a3e1b23 [Ankur Dave] Fix VertexRDD.count() overflow regression
---
 .../main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala
index 33ac7b0ed6095..7f4e7e9d79d6b 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/VertexRDDImpl.scala
@@ -87,7 +87,7 @@ class VertexRDDImpl[VD] private[graphx] (
 
   /** The number of vertices in the RDD. */
   override def count(): Long = {
-    partitionsRDD.map(_.size).reduce(_ + _)
+    partitionsRDD.map(_.size.toLong).reduce(_ + _)
   }
 
   override private[graphx] def mapVertexPartitions[VD2: ClassTag](

From 76d74090d60f74412bd45487e8db6aff2e8343a2 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 4 Aug 2015 12:02:26 +0100
Subject: [PATCH 019/633] [SPARK-9534] [BUILD] Enable javac lint for scalac
 parity; fix a lot of build warnings, 1.5.0 edition

Enable most javac lint warnings; fix a lot of build warnings. In a few cases, touch up surrounding code in the process.

I'll explain several of the changes inline in comments.

Author: Sean Owen <sowen@cloudera.com>

Closes #7862 from srowen/SPARK-9534 and squashes the following commits:

ea51618 [Sean Owen] Enable most javac lint warnings; fix a lot of build warnings. In a few cases, touch up surrounding code in the process.
---
 .../JavaSparkContextVarargsWorkaround.java    | 19 +++---
 .../spark/storage/TachyonBlockManager.scala   |  9 ++-
 .../master/PersistenceEngineSuite.scala       | 13 ++--
 .../mesos/MesosSchedulerUtilsSuite.scala      |  3 +
 .../examples/ml/JavaOneVsRestExample.java     |  1 +
 .../JavaStatefulNetworkWordCount.java         |  4 +-
 .../kafka/JavaDirectKafkaStreamSuite.java     |  2 +-
 .../evaluation/JavaRankingMetricsSuite.java   | 14 ++---
 .../ml/classification/NaiveBayesSuite.scala   |  4 +-
 .../network/protocol/ChunkFetchFailure.java   |  5 ++
 .../network/protocol/ChunkFetchRequest.java   |  5 ++
 .../network/protocol/ChunkFetchSuccess.java   |  5 ++
 .../spark/network/protocol/RpcFailure.java    |  5 ++
 .../spark/network/protocol/RpcRequest.java    |  5 ++
 .../spark/network/protocol/RpcResponse.java   |  5 ++
 .../spark/network/TestManagedBuffer.java      |  5 ++
 .../spark/network/sasl/SparkSaslSuite.java    | 16 ++---
 .../ExternalShuffleBlockHandlerSuite.java     |  6 +-
 .../shuffle/RetryingBlockFetcherSuite.java    | 47 +++++++-------
 pom.xml                                       |  4 ++
 .../apache/spark/sql/JavaDataFrameSuite.java  |  1 +
 .../spark/sql/sources/TableScanSuite.scala    | 16 +++--
 .../hive/client/IsolatedClientLoader.scala    |  2 +-
 .../spark/sql/hive/execution/commands.scala   |  2 +-
 .../spark/sql/hive/JavaDataFrameSuite.java    |  2 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |  4 +-
 .../streaming/scheduler/JobScheduler.scala    |  4 +-
 .../streaming/JavaWriteAheadLogSuite.java     | 62 ++++++++++---------
 .../spark/streaming/UISeleniumSuite.scala     |  4 +-
 29 files changed, 167 insertions(+), 107 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java b/core/src/main/java/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java
index 2090efd3b9990..d4c42b38ac224 100644
--- a/core/src/main/java/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java
+++ b/core/src/main/java/org/apache/spark/api/java/JavaSparkContextVarargsWorkaround.java
@@ -23,11 +23,13 @@
 // See
 // http://scala-programming-language.1934581.n4.nabble.com/Workaround-for-implementing-java-varargs-in-2-7-2-final-tp1944767p1944772.html
 abstract class JavaSparkContextVarargsWorkaround {
-  public <T> JavaRDD<T> union(JavaRDD<T>... rdds) {
+
+  @SafeVarargs
+  public final <T> JavaRDD<T> union(JavaRDD<T>... rdds) {
     if (rdds.length == 0) {
       throw new IllegalArgumentException("Union called on empty list");
     }
-    ArrayList<JavaRDD<T>> rest = new ArrayList<JavaRDD<T>>(rdds.length - 1);
+    List<JavaRDD<T>> rest = new ArrayList<>(rdds.length - 1);
     for (int i = 1; i < rdds.length; i++) {
       rest.add(rdds[i]);
     }
@@ -38,18 +40,19 @@ public JavaDoubleRDD union(JavaDoubleRDD... rdds) {
     if (rdds.length == 0) {
       throw new IllegalArgumentException("Union called on empty list");
     }
-    ArrayList<JavaDoubleRDD> rest = new ArrayList<JavaDoubleRDD>(rdds.length - 1);
+    List<JavaDoubleRDD> rest = new ArrayList<>(rdds.length - 1);
     for (int i = 1; i < rdds.length; i++) {
       rest.add(rdds[i]);
     }
     return union(rdds[0], rest);
   }
 
-  public <K, V> JavaPairRDD<K, V> union(JavaPairRDD<K, V>... rdds) {
+  @SafeVarargs
+  public final <K, V> JavaPairRDD<K, V> union(JavaPairRDD<K, V>... rdds) {
     if (rdds.length == 0) {
       throw new IllegalArgumentException("Union called on empty list");
     }
-    ArrayList<JavaPairRDD<K, V>> rest = new ArrayList<JavaPairRDD<K, V>>(rdds.length - 1);
+    List<JavaPairRDD<K, V>> rest = new ArrayList<>(rdds.length - 1);
     for (int i = 1; i < rdds.length; i++) {
       rest.add(rdds[i]);
     }
@@ -57,7 +60,7 @@ public <K, V> JavaPairRDD<K, V> union(JavaPairRDD<K, V>... rdds) {
   }
 
   // These methods take separate "first" and "rest" elements to avoid having the same type erasure
-  abstract public <T> JavaRDD<T> union(JavaRDD<T> first, List<JavaRDD<T>> rest);
-  abstract public JavaDoubleRDD union(JavaDoubleRDD first, List<JavaDoubleRDD> rest);
-  abstract public <K, V> JavaPairRDD<K, V> union(JavaPairRDD<K, V> first, List<JavaPairRDD<K, V>> rest);
+  public abstract <T> JavaRDD<T> union(JavaRDD<T> first, List<JavaRDD<T>> rest);
+  public abstract JavaDoubleRDD union(JavaDoubleRDD first, List<JavaDoubleRDD> rest);
+  public abstract <K, V> JavaPairRDD<K, V> union(JavaPairRDD<K, V> first, List<JavaPairRDD<K, V>> rest);
 }
diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
index b53c86e89a273..ebad5bc5ab28d 100644
--- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
@@ -27,9 +27,10 @@ import scala.util.control.NonFatal
 import com.google.common.io.ByteStreams
 
 import tachyon.client.{ReadType, WriteType, TachyonFS, TachyonFile}
+import tachyon.conf.TachyonConf
 import tachyon.TachyonURI
 
-import org.apache.spark.{SparkException, SparkConf, Logging}
+import org.apache.spark.Logging
 import org.apache.spark.executor.ExecutorExitCode
 import org.apache.spark.util.Utils
 
@@ -60,7 +61,11 @@ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Log
 
     rootDirs = s"$storeDir/$appFolderName/$executorId"
     master = blockManager.conf.get(ExternalBlockStore.MASTER_URL, "tachyon://localhost:19998")
-    client = if (master != null && master != "") TachyonFS.get(new TachyonURI(master)) else null
+    client = if (master != null && master != "") {
+      TachyonFS.get(new TachyonURI(master), new TachyonConf())
+    } else {
+      null
+    }
     // original implementation call System.exit, we change it to run without extblkstore support
     if (client == null) {
       logError("Failed to connect to the Tachyon as the master address is not configured")
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala
index 11e87bd1dd8eb..34775577de8a3 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/PersistenceEngineSuite.scala
@@ -73,11 +73,11 @@ class PersistenceEngineSuite extends SparkFunSuite {
     assert(persistenceEngine.read[String]("test_").isEmpty)
 
     // Test deserializing objects that contain RpcEndpointRef
-    val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
+    val testRpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
     try {
       // Create a real endpoint so that we can test RpcEndpointRef deserialization
-      val workerEndpoint = rpcEnv.setupEndpoint("worker", new RpcEndpoint {
-        override val rpcEnv: RpcEnv = rpcEnv
+      val workerEndpoint = testRpcEnv.setupEndpoint("worker", new RpcEndpoint {
+        override val rpcEnv: RpcEnv = testRpcEnv
       })
 
       val workerToPersist = new WorkerInfo(
@@ -93,7 +93,8 @@ class PersistenceEngineSuite extends SparkFunSuite {
 
       persistenceEngine.addWorker(workerToPersist)
 
-      val (storedApps, storedDrivers, storedWorkers) = persistenceEngine.readPersistedData(rpcEnv)
+      val (storedApps, storedDrivers, storedWorkers) =
+        persistenceEngine.readPersistedData(testRpcEnv)
 
       assert(storedApps.isEmpty)
       assert(storedDrivers.isEmpty)
@@ -110,8 +111,8 @@ class PersistenceEngineSuite extends SparkFunSuite {
       assert(workerToPersist.webUiPort === recoveryWorkerInfo.webUiPort)
       assert(workerToPersist.publicAddress === recoveryWorkerInfo.publicAddress)
     } finally {
-      rpcEnv.shutdown()
-      rpcEnv.awaitTermination()
+      testRpcEnv.shutdown()
+      testRpcEnv.awaitTermination()
     }
   }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala
index b354914b6ffd0..2eb43b7313381 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtilsSuite.scala
@@ -17,10 +17,13 @@
 
 package org.apache.spark.scheduler.cluster.mesos
 
+import scala.language.reflectiveCalls
+
 import org.apache.mesos.Protos.Value
 import org.mockito.Mockito._
 import org.scalatest._
 import org.scalatest.mock.MockitoSugar
+
 import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
 
 class MesosSchedulerUtilsSuite extends SparkFunSuite with Matchers with MockitoSugar {
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
index 75063dbf800d8..e7f2f6f615070 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaOneVsRestExample.java
@@ -178,6 +178,7 @@ private static Params parse(String[] args) {
     return params;
   }
 
+  @SuppressWarnings("static")
   private static Options generateCommandlineOptions() {
     Option input = OptionBuilder.withArgName("input")
       .hasArg()
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
index dbf2ef02d7b76..02f58f48b07ab 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
@@ -85,7 +85,7 @@ public Optional<Integer> call(List<Integer> values, Optional<Integer> state) {
     @SuppressWarnings("unchecked")
     List<Tuple2<String, Integer>> tuples = Arrays.asList(new Tuple2<String, Integer>("hello", 1),
             new Tuple2<String, Integer>("world", 1));
-    JavaPairRDD<String, Integer> initialRDD = ssc.sc().parallelizePairs(tuples);
+    JavaPairRDD<String, Integer> initialRDD = ssc.sparkContext().parallelizePairs(tuples);
 
     JavaReceiverInputDStream<String> lines = ssc.socketTextStream(
             args[0], Integer.parseInt(args[1]), StorageLevels.MEMORY_AND_DISK_SER_2);
@@ -107,7 +107,7 @@ public Tuple2<String, Integer> call(String s) {
 
     // This will give a Dstream made of state (which is the cumulative count of the words)
     JavaPairDStream<String, Integer> stateDstream = wordsDstream.updateStateByKey(updateFunction,
-            new HashPartitioner(ssc.sc().defaultParallelism()), initialRDD);
+            new HashPartitioner(ssc.sparkContext().defaultParallelism()), initialRDD);
 
     stateDstream.print();
     ssc.start();
diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
index 02cd24a35906f..9db07d0507fea 100644
--- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
+++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
@@ -70,7 +70,7 @@ public void testKafkaStream() throws InterruptedException {
     final String topic1 = "topic1";
     final String topic2 = "topic2";
     // hold a reference to the current offset ranges, so it can be used downstream
-    final AtomicReference<OffsetRange[]> offsetRanges = new AtomicReference();
+    final AtomicReference<OffsetRange[]> offsetRanges = new AtomicReference<>();
 
     String[] topic1data = createTopicAndSendData(topic1);
     String[] topic2data = createTopicAndSendData(topic2);
diff --git a/mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java
index effc8a1a6dabc..fa4d334801ce4 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/evaluation/JavaRankingMetricsSuite.java
@@ -18,12 +18,12 @@
 package org.apache.spark.mllib.evaluation;
 
 import java.io.Serializable;
-import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
 
 import scala.Tuple2;
 import scala.Tuple2$;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -34,18 +34,18 @@
 
 public class JavaRankingMetricsSuite implements Serializable {
   private transient JavaSparkContext sc;
-  private transient JavaRDD<Tuple2<ArrayList<Integer>, ArrayList<Integer>>> predictionAndLabels;
+  private transient JavaRDD<Tuple2<List<Integer>, List<Integer>>> predictionAndLabels;
 
   @Before
   public void setUp() {
     sc = new JavaSparkContext("local", "JavaRankingMetricsSuite");
-    predictionAndLabels = sc.parallelize(Lists.newArrayList(
+    predictionAndLabels = sc.parallelize(Arrays.asList(
       Tuple2$.MODULE$.apply(
-        Lists.newArrayList(1, 6, 2, 7, 8, 3, 9, 10, 4, 5), Lists.newArrayList(1, 2, 3, 4, 5)),
+        Arrays.asList(1, 6, 2, 7, 8, 3, 9, 10, 4, 5), Arrays.asList(1, 2, 3, 4, 5)),
       Tuple2$.MODULE$.apply(
-        Lists.newArrayList(4, 1, 5, 6, 2, 7, 3, 8, 9, 10), Lists.newArrayList(1, 2, 3)),
+          Arrays.asList(4, 1, 5, 6, 2, 7, 3, 8, 9, 10), Arrays.asList(1, 2, 3)),
       Tuple2$.MODULE$.apply(
-        Lists.newArrayList(1, 2, 3, 4, 5), Lists.<Integer>newArrayList())), 2);
+          Arrays.asList(1, 2, 3, 4, 5), Arrays.<Integer>asList())), 2);
   }
 
   @After
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
index aea3d9b694490..98bc9511163e7 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/NaiveBayesSuite.scala
@@ -21,7 +21,7 @@ import breeze.linalg.{Vector => BV}
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
-import org.apache.spark.mllib.classification.NaiveBayes
+import org.apache.spark.mllib.classification.NaiveBayes.{Multinomial, Bernoulli}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
@@ -31,8 +31,6 @@ import org.apache.spark.sql.Row
 
 class NaiveBayesSuite extends SparkFunSuite with MLlibTestSparkContext {
 
-  import NaiveBayes.{Multinomial, Bernoulli}
-
   def validatePrediction(predictionAndLabels: DataFrame): Unit = {
     val numOfErrorPredictions = predictionAndLabels.collect().count {
       case Row(prediction: Double, label: Double) =>
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java
index f76bb49e874fc..f0363830b61ac 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchFailure.java
@@ -52,6 +52,11 @@ public static ChunkFetchFailure decode(ByteBuf buf) {
     return new ChunkFetchFailure(streamChunkId, errorString);
   }
 
+  @Override
+  public int hashCode() {
+    return Objects.hashCode(streamChunkId, errorString);
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof ChunkFetchFailure) {
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchRequest.java b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchRequest.java
index 980947cf13f6b..5a173af54f618 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchRequest.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchRequest.java
@@ -48,6 +48,11 @@ public static ChunkFetchRequest decode(ByteBuf buf) {
     return new ChunkFetchRequest(StreamChunkId.decode(buf));
   }
 
+  @Override
+  public int hashCode() {
+    return streamChunkId.hashCode();
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof ChunkFetchRequest) {
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchSuccess.java b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchSuccess.java
index ff4936470c697..c962fb7ecf76d 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchSuccess.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/ChunkFetchSuccess.java
@@ -61,6 +61,11 @@ public static ChunkFetchSuccess decode(ByteBuf buf) {
     return new ChunkFetchSuccess(streamChunkId, managedBuf);
   }
 
+  @Override
+  public int hashCode() {
+    return Objects.hashCode(streamChunkId, buffer);
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof ChunkFetchSuccess) {
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java b/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java
index 6b991375fc486..2dfc7876ba328 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/RpcFailure.java
@@ -50,6 +50,11 @@ public static RpcFailure decode(ByteBuf buf) {
     return new RpcFailure(requestId, errorString);
   }
 
+  @Override
+  public int hashCode() {
+    return Objects.hashCode(requestId, errorString);
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof RpcFailure) {
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/RpcRequest.java b/network/common/src/main/java/org/apache/spark/network/protocol/RpcRequest.java
index cdee0b0e0316b..745039db742fa 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/RpcRequest.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/RpcRequest.java
@@ -59,6 +59,11 @@ public static RpcRequest decode(ByteBuf buf) {
     return new RpcRequest(requestId, message);
   }
 
+  @Override
+  public int hashCode() {
+    return Objects.hashCode(requestId, Arrays.hashCode(message));
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof RpcRequest) {
diff --git a/network/common/src/main/java/org/apache/spark/network/protocol/RpcResponse.java b/network/common/src/main/java/org/apache/spark/network/protocol/RpcResponse.java
index 0a62e09a8115c..1671cd444f039 100644
--- a/network/common/src/main/java/org/apache/spark/network/protocol/RpcResponse.java
+++ b/network/common/src/main/java/org/apache/spark/network/protocol/RpcResponse.java
@@ -50,6 +50,11 @@ public static RpcResponse decode(ByteBuf buf) {
     return new RpcResponse(requestId, response);
   }
 
+  @Override
+  public int hashCode() {
+    return Objects.hashCode(requestId, Arrays.hashCode(response));
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof RpcResponse) {
diff --git a/network/common/src/test/java/org/apache/spark/network/TestManagedBuffer.java b/network/common/src/test/java/org/apache/spark/network/TestManagedBuffer.java
index 38113a918f795..83c90f9eff2b1 100644
--- a/network/common/src/test/java/org/apache/spark/network/TestManagedBuffer.java
+++ b/network/common/src/test/java/org/apache/spark/network/TestManagedBuffer.java
@@ -80,6 +80,11 @@ public Object convertToNetty() throws IOException {
     return underlying.convertToNetty();
   }
 
+  @Override
+  public int hashCode() {
+    return underlying.hashCode();
+  }
+
   @Override
   public boolean equals(Object other) {
     if (other instanceof ManagedBuffer) {
diff --git a/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java b/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java
index be6632bb8cf49..8104004847a24 100644
--- a/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java
+++ b/network/common/src/test/java/org/apache/spark/network/sasl/SparkSaslSuite.java
@@ -17,11 +17,11 @@
 
 package org.apache.spark.network.sasl;
 
-import static com.google.common.base.Charsets.UTF_8;
 import static org.junit.Assert.*;
 import static org.mockito.Mockito.*;
 
 import java.io.File;
+import java.nio.charset.StandardCharsets;
 import java.util.Arrays;
 import java.util.List;
 import java.util.Random;
@@ -138,8 +138,8 @@ private void testBasicSasl(boolean encrypt) throws Exception {
         public Void answer(InvocationOnMock invocation) {
           byte[] message = (byte[]) invocation.getArguments()[1];
           RpcResponseCallback cb = (RpcResponseCallback) invocation.getArguments()[2];
-          assertEquals("Ping", new String(message, UTF_8));
-          cb.onSuccess("Pong".getBytes(UTF_8));
+          assertEquals("Ping", new String(message, StandardCharsets.UTF_8));
+          cb.onSuccess("Pong".getBytes(StandardCharsets.UTF_8));
           return null;
         }
       })
@@ -148,8 +148,9 @@ public Void answer(InvocationOnMock invocation) {
 
     SaslTestCtx ctx = new SaslTestCtx(rpcHandler, encrypt, false);
     try {
-      byte[] response = ctx.client.sendRpcSync("Ping".getBytes(UTF_8), TimeUnit.SECONDS.toMillis(10));
-      assertEquals("Pong", new String(response, UTF_8));
+      byte[] response = ctx.client.sendRpcSync("Ping".getBytes(StandardCharsets.UTF_8),
+                                               TimeUnit.SECONDS.toMillis(10));
+      assertEquals("Pong", new String(response, StandardCharsets.UTF_8));
     } finally {
       ctx.close();
     }
@@ -235,7 +236,7 @@ public void testFileRegionEncryption() throws Exception {
     final String blockSizeConf = "spark.network.sasl.maxEncryptedBlockSize";
     System.setProperty(blockSizeConf, "1k");
 
-    final AtomicReference<ManagedBuffer> response = new AtomicReference();
+    final AtomicReference<ManagedBuffer> response = new AtomicReference<>();
     final File file = File.createTempFile("sasltest", ".txt");
     SaslTestCtx ctx = null;
     try {
@@ -321,7 +322,8 @@ public void testDataEncryptionIsActuallyEnabled() throws Exception {
     SaslTestCtx ctx = null;
     try {
       ctx = new SaslTestCtx(mock(RpcHandler.class), true, true);
-      ctx.client.sendRpcSync("Ping".getBytes(UTF_8), TimeUnit.SECONDS.toMillis(10));
+      ctx.client.sendRpcSync("Ping".getBytes(StandardCharsets.UTF_8),
+                             TimeUnit.SECONDS.toMillis(10));
       fail("Should have failed to send RPC to server.");
     } catch (Exception e) {
       assertFalse(e.getCause() instanceof TimeoutException);
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
index 73374cdc77a23..1d197497b7c8f 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
@@ -90,9 +90,11 @@ public void testOpenShuffleBlocks() {
       (StreamHandle) BlockTransferMessage.Decoder.fromByteArray(response.getValue());
     assertEquals(2, handle.numChunks);
 
-    ArgumentCaptor<Iterator> stream = ArgumentCaptor.forClass(Iterator.class);
+    @SuppressWarnings("unchecked")
+    ArgumentCaptor<Iterator<ManagedBuffer>> stream = (ArgumentCaptor<Iterator<ManagedBuffer>>)
+        (ArgumentCaptor<?>) ArgumentCaptor.forClass(Iterator.class);
     verify(streamManager, times(1)).registerStream(stream.capture());
-    Iterator<ManagedBuffer> buffers = (Iterator<ManagedBuffer>) stream.getValue();
+    Iterator<ManagedBuffer> buffers = stream.getValue();
     assertEquals(block0Marker, buffers.next());
     assertEquals(block1Marker, buffers.next());
     assertFalse(buffers.hasNext());
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/RetryingBlockFetcherSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/RetryingBlockFetcherSuite.java
index 1ad0d72ae5ec5..06e46f9241094 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/RetryingBlockFetcherSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/RetryingBlockFetcherSuite.java
@@ -20,7 +20,9 @@
 
 import java.io.IOException;
 import java.nio.ByteBuffer;
+import java.util.Arrays;
 import java.util.LinkedHashSet;
+import java.util.List;
 import java.util.Map;
 
 import com.google.common.collect.ImmutableMap;
@@ -67,13 +69,13 @@ public void afterEach() {
   public void testNoFailures() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // Immediately return both blocks successfully.
       ImmutableMap.<String, Object>builder()
         .put("b0", block0)
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+      );
 
     performInteractions(interactions, listener);
 
@@ -86,13 +88,13 @@ public void testNoFailures() throws IOException {
   public void testUnrecoverableFailure() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // b0 throws a non-IOException error, so it will be failed without retry.
       ImmutableMap.<String, Object>builder()
         .put("b0", new RuntimeException("Ouch!"))
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -105,7 +107,7 @@ public void testUnrecoverableFailure() throws IOException {
   public void testSingleIOExceptionOnFirst() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // IOException will cause a retry. Since b0 fails, we will retry both.
       ImmutableMap.<String, Object>builder()
         .put("b0", new IOException("Connection failed or something"))
@@ -114,8 +116,8 @@ public void testSingleIOExceptionOnFirst() throws IOException {
       ImmutableMap.<String, Object>builder()
         .put("b0", block0)
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -128,7 +130,7 @@ public void testSingleIOExceptionOnFirst() throws IOException {
   public void testSingleIOExceptionOnSecond() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // IOException will cause a retry. Since b1 fails, we will not retry b0.
       ImmutableMap.<String, Object>builder()
         .put("b0", block0)
@@ -136,8 +138,8 @@ public void testSingleIOExceptionOnSecond() throws IOException {
         .build(),
       ImmutableMap.<String, Object>builder()
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -150,7 +152,7 @@ public void testSingleIOExceptionOnSecond() throws IOException {
   public void testTwoIOExceptions() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // b0's IOException will trigger retry, b1's will be ignored.
       ImmutableMap.<String, Object>builder()
         .put("b0", new IOException())
@@ -164,8 +166,8 @@ public void testTwoIOExceptions() throws IOException {
       // b1 returns successfully within 2 retries.
       ImmutableMap.<String, Object>builder()
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -178,7 +180,7 @@ public void testTwoIOExceptions() throws IOException {
   public void testThreeIOExceptions() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // b0's IOException will trigger retry, b1's will be ignored.
       ImmutableMap.<String, Object>builder()
         .put("b0", new IOException())
@@ -196,8 +198,8 @@ public void testThreeIOExceptions() throws IOException {
       // This is not reached -- b1 has failed.
       ImmutableMap.<String, Object>builder()
         .put("b1", block1)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -210,7 +212,7 @@ public void testThreeIOExceptions() throws IOException {
   public void testRetryAndUnrecoverable() throws IOException {
     BlockFetchingListener listener = mock(BlockFetchingListener.class);
 
-    Map[] interactions = new Map[] {
+    List<? extends Map<String, Object>> interactions = Arrays.asList(
       // b0's IOException will trigger retry, subsequent messages will be ignored.
       ImmutableMap.<String, Object>builder()
         .put("b0", new IOException())
@@ -226,8 +228,8 @@ public void testRetryAndUnrecoverable() throws IOException {
       // b2 succeeds in its last retry.
       ImmutableMap.<String, Object>builder()
         .put("b2", block2)
-        .build(),
-    };
+        .build()
+    );
 
     performInteractions(interactions, listener);
 
@@ -248,7 +250,8 @@ public void testRetryAndUnrecoverable() throws IOException {
    * subset of the original blocks in a second interaction.
    */
   @SuppressWarnings("unchecked")
-  private void performInteractions(final Map[] interactions, BlockFetchingListener listener)
+  private static void performInteractions(List<? extends Map<String, Object>> interactions,
+                                          BlockFetchingListener listener)
     throws IOException {
 
     TransportConf conf = new TransportConf(new SystemPropertyConfigProvider());
diff --git a/pom.xml b/pom.xml
index a958cec867eae..b4ee3ccb0bff5 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1849,6 +1849,7 @@
               <javacArg>${java.version}</javacArg>
               <javacArg>-target</javacArg>
               <javacArg>${java.version}</javacArg>
+              <javacArg>-Xlint:all,-serial,-path</javacArg>
             </javacArgs>
           </configuration>
         </plugin>
@@ -1862,6 +1863,9 @@
             <encoding>UTF-8</encoding>
             <maxmem>1024m</maxmem>
             <fork>true</fork>
+            <compilerArgs>
+              <arg>-Xlint:all,-serial,-path</arg>
+            </compilerArgs>
           </configuration>
         </plugin>
         <!-- Surefire runs all Java tests -->
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 2c669bb59a0b5..7302361ab9fdb 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -167,6 +167,7 @@ public void testCreateDataFrameFromJavaBeans() {
     for (int i = 0; i < result.length(); i++) {
       Assert.assertEquals(bean.getB()[i], result.apply(i));
     }
+    @SuppressWarnings("unchecked")
     Seq<Integer> outputBuffer = (Seq<Integer>) first.getJavaMap(2).get("hello");
     Assert.assertArrayEquals(
       bean.getC().get("hello"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
index cfb03ff485b7c..e34e0956d1fdd 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.sql.sources
 
+import java.nio.charset.StandardCharsets
 import java.sql.{Date, Timestamp}
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
 
 class DefaultSource extends SimpleScanSource
 
@@ -73,7 +71,7 @@ case class AllDataTypesScan(
     sqlContext.sparkContext.parallelize(from to to).map { i =>
       Row(
         s"str_$i",
-        s"str_$i".getBytes(),
+        s"str_$i".getBytes(StandardCharsets.UTF_8),
         i % 2 == 0,
         i.toByte,
         i.toShort,
@@ -83,7 +81,7 @@ case class AllDataTypesScan(
         i.toDouble,
         new java.math.BigDecimal(i),
         new java.math.BigDecimal(i),
-        new Date(1970, 1, 1),
+        Date.valueOf("1970-01-01"),
         new Timestamp(20000 + i),
         s"varchar_$i",
         Seq(i, i + 1),
@@ -92,7 +90,7 @@ case class AllDataTypesScan(
         Map(Map(s"str_$i" -> i.toFloat) -> Row(i.toLong)),
         Row(i, i.toString),
           Row(Seq(s"str_$i", s"str_${i + 1}"),
-            Row(Seq(new Date(1970, 1, i + 1)))))
+            Row(Seq(Date.valueOf(s"1970-01-${i + 1}")))))
     }
   }
 }
@@ -113,7 +111,7 @@ class TableScanSuite extends DataSourceTest {
       i.toDouble,
       new java.math.BigDecimal(i),
       new java.math.BigDecimal(i),
-      new Date(1970, 1, 1),
+      Date.valueOf("1970-01-01"),
       new Timestamp(20000 + i),
       s"varchar_$i",
       Seq(i, i + 1),
@@ -121,7 +119,7 @@ class TableScanSuite extends DataSourceTest {
       Map(i -> i.toString),
       Map(Map(s"str_$i" -> i.toFloat) -> Row(i.toLong)),
       Row(i, i.toString),
-      Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(new Date(1970, 1, i + 1)))))
+      Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(Date.valueOf(s"1970-01-${i + 1}")))))
   }.toSeq
 
   before {
@@ -280,7 +278,7 @@ class TableScanSuite extends DataSourceTest {
 
   sqlTest(
     "SELECT structFieldComplex.Value.`value_(2)` FROM tableWithSchema",
-    (1 to 10).map(i => Row(Seq(new Date(1970, 1, i + 1)))).toSeq)
+    (1 to 10).map(i => Row(Seq(Date.valueOf(s"1970-01-${i + 1}")))).toSeq)
 
   test("Caching")  {
     // Cached Query Execution
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
index f58bc7d7a0af4..a7d5a991948d9 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -77,7 +77,7 @@ private[hive] object IsolatedClientLoader {
     // TODO: Remove copy logic.
     val tempDir = Utils.createTempDir(namePrefix = s"hive-${version}")
     allFiles.foreach(f => FileUtils.copyFileToDirectory(f, tempDir))
-    tempDir.listFiles().map(_.toURL)
+    tempDir.listFiles().map(_.toURI.toURL)
   }
 
   private def resolvedVersions = new scala.collection.mutable.HashMap[HiveVersion, Seq[URL]]
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
index a47f9a4feb21b..05a78930afe3d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
@@ -88,7 +88,7 @@ case class AddJar(path: String) extends RunnableCommand {
     val currentClassLoader = Utils.getContextOrSparkClassLoader
 
     // Add jar to current context
-    val jarURL = new java.io.File(path).toURL
+    val jarURL = new java.io.File(path).toURI.toURL
     val newClassLoader = new java.net.URLClassLoader(Array(jarURL), currentClassLoader)
     Thread.currentThread.setContextClassLoader(newClassLoader)
     // We need to explicitly set the class loader associated with the conf in executionHive's
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
index 741a3cd31c603..613b2bcc80e37 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
@@ -54,7 +54,7 @@ public void setUp() throws IOException {
     for (int i = 0; i < 10; i++) {
       jsonObjects.add("{\"key\":" + i + ", \"value\":\"str" + i + "\"}");
     }
-    df = hc.jsonRDD(sc.parallelize(jsonObjects));
+    df = hc.read().json(sc.parallelize(jsonObjects));
     df.registerTempTable("window_table");
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 95c1da6e9796c..fb41451803634 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -660,7 +660,7 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
 
   test("resolve udtf in projection #2") {
     val rdd = sparkContext.makeRDD((1 to 2).map(i => s"""{"a":[$i, ${i + 1}]}"""))
-    jsonRDD(rdd).registerTempTable("data")
+    read.json(rdd).registerTempTable("data")
     checkAnswer(sql("SELECT explode(map(1, 1)) FROM data LIMIT 1"), Row(1, 1) :: Nil)
     checkAnswer(sql("SELECT explode(map(1, 1)) as (k1, k2) FROM data LIMIT 1"), Row(1, 1) :: Nil)
     intercept[AnalysisException] {
@@ -675,7 +675,7 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
   // TGF with non-TGF in project is allowed in Spark SQL, but not in Hive
   test("TGF with non-TGF in projection") {
     val rdd = sparkContext.makeRDD( """{"a": "1", "b":"1"}""" :: Nil)
-    jsonRDD(rdd).registerTempTable("data")
+    read.json(rdd).registerTempTable("data")
     checkAnswer(
       sql("SELECT explode(map(a, b)) as (k1, k2), a, b FROM data"),
       Row("1", "1", "1", "1") :: Nil)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
index 58bdda7794bf2..7e735562dca33 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
@@ -40,7 +40,9 @@ private[scheduler] case class ErrorReported(msg: String, e: Throwable) extends J
 private[streaming]
 class JobScheduler(val ssc: StreamingContext) extends Logging {
 
-  private val jobSets = new ConcurrentHashMap[Time, JobSet]
+  // Use of ConcurrentHashMap.keySet later causes an odd runtime problem due to Java 7/8 diff
+  // https://gist.github.com/AlainODea/1375759b8720a3f9f094
+  private val jobSets: java.util.Map[Time, JobSet] = new ConcurrentHashMap[Time, JobSet]
   private val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1)
   private val jobExecutor = Executors.newFixedThreadPool(numConcurrentJobs)
   private val jobGenerator = new JobGenerator(this)
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java
index 50e8f9fc159c8..175b8a496b4e5 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaWriteAheadLogSuite.java
@@ -17,13 +17,15 @@
 
 package org.apache.spark.streaming;
 
+import java.nio.charset.StandardCharsets;
 import java.util.ArrayList;
 import java.nio.ByteBuffer;
 import java.util.Arrays;
-import java.util.Collection;
+import java.util.Iterator;
+import java.util.List;
 
-import org.apache.commons.collections.CollectionUtils;
-import org.apache.commons.collections.Transformer;
+import com.google.common.base.Function;
+import com.google.common.collect.Iterators;
 import org.apache.spark.SparkConf;
 import org.apache.spark.streaming.util.WriteAheadLog;
 import org.apache.spark.streaming.util.WriteAheadLogRecordHandle;
@@ -32,40 +34,40 @@
 import org.junit.Test;
 import org.junit.Assert;
 
-class JavaWriteAheadLogSuiteHandle extends WriteAheadLogRecordHandle {
-  int index = -1;
-  public JavaWriteAheadLogSuiteHandle(int idx) {
-    index = idx;
-  }
-}
-
 public class JavaWriteAheadLogSuite extends WriteAheadLog {
 
-  class Record {
+  static class JavaWriteAheadLogSuiteHandle extends WriteAheadLogRecordHandle {
+    int index = -1;
+    JavaWriteAheadLogSuiteHandle(int idx) {
+      index = idx;
+    }
+  }
+
+  static class Record {
     long time;
     int index;
     ByteBuffer buffer;
 
-    public Record(long tym, int idx, ByteBuffer buf) {
+    Record(long tym, int idx, ByteBuffer buf) {
       index = idx;
       time = tym;
       buffer = buf;
     }
   }
   private int index = -1;
-  private ArrayList<Record> records = new ArrayList<Record>();
+  private final List<Record> records = new ArrayList<>();
 
 
   // Methods for WriteAheadLog
   @Override
-  public WriteAheadLogRecordHandle write(java.nio.ByteBuffer record, long time) {
+  public WriteAheadLogRecordHandle write(ByteBuffer record, long time) {
     index += 1;
-    records.add(new org.apache.spark.streaming.JavaWriteAheadLogSuite.Record(time, index, record));
+    records.add(new Record(time, index, record));
     return new JavaWriteAheadLogSuiteHandle(index);
   }
 
   @Override
-  public java.nio.ByteBuffer read(WriteAheadLogRecordHandle handle) {
+  public ByteBuffer read(WriteAheadLogRecordHandle handle) {
     if (handle instanceof JavaWriteAheadLogSuiteHandle) {
       int reqdIndex = ((JavaWriteAheadLogSuiteHandle) handle).index;
       for (Record record: records) {
@@ -78,14 +80,13 @@ public java.nio.ByteBuffer read(WriteAheadLogRecordHandle handle) {
   }
 
   @Override
-  public java.util.Iterator<java.nio.ByteBuffer> readAll() {
-    Collection<ByteBuffer> buffers = CollectionUtils.collect(records, new Transformer() {
+  public Iterator<ByteBuffer> readAll() {
+    return Iterators.transform(records.iterator(), new Function<Record,ByteBuffer>() {
       @Override
-      public Object transform(Object input) {
-        return ((Record) input).buffer;
+      public ByteBuffer apply(Record input) {
+        return input.buffer;
       }
     });
-    return buffers.iterator();
   }
 
   @Override
@@ -110,20 +111,21 @@ public void testCustomWAL() {
     WriteAheadLog wal = WriteAheadLogUtils.createLogForDriver(conf, null, null);
 
     String data1 = "data1";
-    WriteAheadLogRecordHandle handle = wal.write(ByteBuffer.wrap(data1.getBytes()), 1234);
+    WriteAheadLogRecordHandle handle =
+        wal.write(ByteBuffer.wrap(data1.getBytes(StandardCharsets.UTF_8)), 1234);
     Assert.assertTrue(handle instanceof JavaWriteAheadLogSuiteHandle);
-    Assert.assertTrue(new String(wal.read(handle).array()).equals(data1));
+    Assert.assertEquals(new String(wal.read(handle).array(), StandardCharsets.UTF_8), data1);
 
-    wal.write(ByteBuffer.wrap("data2".getBytes()), 1235);
-    wal.write(ByteBuffer.wrap("data3".getBytes()), 1236);
-    wal.write(ByteBuffer.wrap("data4".getBytes()), 1237);
+    wal.write(ByteBuffer.wrap("data2".getBytes(StandardCharsets.UTF_8)), 1235);
+    wal.write(ByteBuffer.wrap("data3".getBytes(StandardCharsets.UTF_8)), 1236);
+    wal.write(ByteBuffer.wrap("data4".getBytes(StandardCharsets.UTF_8)), 1237);
     wal.clean(1236, false);
 
-    java.util.Iterator<java.nio.ByteBuffer> dataIterator = wal.readAll();
-    ArrayList<String> readData = new ArrayList<String>();
+    Iterator<ByteBuffer> dataIterator = wal.readAll();
+    List<String> readData = new ArrayList<>();
     while (dataIterator.hasNext()) {
-      readData.add(new String(dataIterator.next().array()));
+      readData.add(new String(dataIterator.next().array(), StandardCharsets.UTF_8));
     }
-    Assert.assertTrue(readData.equals(Arrays.asList("data3", "data4")));
+    Assert.assertEquals(readData, Arrays.asList("data3", "data4"));
   }
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala
index a08578680cff9..068a6cb0e8fa4 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/UISeleniumSuite.scala
@@ -100,8 +100,8 @@ class UISeleniumSuite
         // Check stat table
         val statTableHeaders = findAll(cssSelector("#stat-table th")).map(_.text).toSeq
         statTableHeaders.exists(
-          _.matches("Timelines \\(Last \\d+ batches, \\d+ active, \\d+ completed\\)")) should be
-          (true)
+          _.matches("Timelines \\(Last \\d+ batches, \\d+ active, \\d+ completed\\)")
+        ) should be (true)
         statTableHeaders should contain ("Histograms")
 
         val statTableCells = findAll(cssSelector("#stat-table td")).map(_.text).toSeq

From b211cbc7369af5eb2cb65d93c4c57c4db7143f47 Mon Sep 17 00:00:00 2001
From: tedyu <yuzhihong@gmail.com>
Date: Tue, 4 Aug 2015 12:22:53 +0100
Subject: [PATCH 020/633] [SPARK-8064] [BUILD] Follow-up. Undo change from
 SPARK-9507 that was accidentally reverted

This PR removes the dependency reduced POM hack brought back by #7191

Author: tedyu <yuzhihong@gmail.com>

Closes #7919 from tedyu/master and squashes the following commits:

1bfbd7b [tedyu] [BUILD] Remove dependency reduced POM hack
---
 pom.xml | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/pom.xml b/pom.xml
index b4ee3ccb0bff5..2bcc55b040a26 100644
--- a/pom.xml
+++ b/pom.xml
@@ -179,9 +179,6 @@
     <jsr305.version>1.3.9</jsr305.version>
     <libthrift.version>0.9.2</libthrift.version>
 
-    <!-- For maven shade plugin (see SPARK-8819) -->
-    <create.dependency.reduced.pom>false</create.dependency.reduced.pom>
-
     <test.java.home>${java.home}</test.java.home>
 
     <!--

From cb7fa0aa93dae5a25a8e7e387dbd6b55a5a23fb0 Mon Sep 17 00:00:00 2001
From: Carson Wang <carson.wang@intel.com>
Date: Tue, 4 Aug 2015 22:12:30 +0900
Subject: [PATCH 021/633] [SPARK-2016] [WEBUI] RDD partition table pagination
 for the RDD Page

Add pagination for the RDD page to avoid unresponsive UI when the number of the RDD partitions is large.
Before:
![rddpagebefore](https://cloud.githubusercontent.com/assets/9278199/8951533/3d9add54-3601-11e5-99d0-5653b473c49b.png)
After:
![rddpageafter](https://cloud.githubusercontent.com/assets/9278199/8951536/439d66e0-3601-11e5-9cee-1b380fe6620d.png)

Author: Carson Wang <carson.wang@intel.com>

Closes #7692 from carsonwang/SPARK-2016 and squashes the following commits:

03c7168 [Carson Wang] Fix style issues
612c18c [Carson Wang] RDD partition table pagination for the RDD Page
---
 .../org/apache/spark/ui/PagedTable.scala      |  16 +-
 .../org/apache/spark/ui/jobs/StagePage.scala  |  10 +-
 .../org/apache/spark/ui/storage/RDDPage.scala | 228 +++++++++++++++---
 3 files changed, 209 insertions(+), 45 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/PagedTable.scala b/core/src/main/scala/org/apache/spark/ui/PagedTable.scala
index 17d7b39c2d951..6e2375477a688 100644
--- a/core/src/main/scala/org/apache/spark/ui/PagedTable.scala
+++ b/core/src/main/scala/org/apache/spark/ui/PagedTable.scala
@@ -159,9 +159,9 @@ private[ui] trait PagedTable[T] {
       // "goButtonJsFuncName"
       val formJs =
         s"""$$(function(){
-          |  $$( "#form-task-page" ).submit(function(event) {
-          |    var page = $$("#form-task-page-no").val()
-          |    var pageSize = $$("#form-task-page-size").val()
+          |  $$( "#form-$tableId-page" ).submit(function(event) {
+          |    var page = $$("#form-$tableId-page-no").val()
+          |    var pageSize = $$("#form-$tableId-page-size").val()
           |    pageSize = pageSize ? pageSize: 100;
           |    if (page != "") {
           |      ${goButtonJsFuncName}(page, pageSize);
@@ -173,12 +173,14 @@ private[ui] trait PagedTable[T] {
 
       <div>
         <div>
-          <form id="form-task-page" class="form-inline pull-right" style="margin-bottom: 0px;">
+          <form id={s"form-$tableId-page"}
+                class="form-inline pull-right" style="margin-bottom: 0px;">
             <label>{totalPages} Pages. Jump to</label>
-            <input type="text" id="form-task-page-no" value={page.toString} class="span1" />
+            <input type="text" id={s"form-$tableId-page-no"} value={page.toString} class="span1" />
             <label>. Show </label>
-            <input type="text" id="form-task-page-size" value={pageSize.toString} class="span1" />
-            <label>tasks in a page.</label>
+            <input type="text"
+                   id={s"form-$tableId-page-size"} value={pageSize.toString} class="span1" />
+            <label>items in a page.</label>
             <button type="submit" class="btn">Go</button>
           </form>
         </div>
diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 3954c3d1ef894..0c94204df6530 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -988,8 +988,7 @@ private[ui] class TaskDataSource(
       shuffleRead,
       shuffleWrite,
       bytesSpilled,
-      errorMessage.getOrElse("")
-    )
+      errorMessage.getOrElse(""))
   }
 
   /**
@@ -1197,7 +1196,7 @@ private[ui] class TaskPagedTable(
   private val displayPeakExecutionMemory =
     conf.getOption("spark.sql.unsafe.enabled").exists(_.toBoolean)
 
-  override def tableId: String = ""
+  override def tableId: String = "task-table"
 
   override def tableCssClass: String = "table table-bordered table-condensed table-striped"
 
@@ -1212,8 +1211,7 @@ private[ui] class TaskPagedTable(
     currentTime,
     pageSize,
     sortColumn,
-    desc
-  )
+    desc)
 
   override def pageLink(page: Int): String = {
     val encodedSortColumn = URLEncoder.encode(sortColumn, "UTF-8")
@@ -1277,7 +1275,7 @@ private[ui] class TaskPagedTable(
         Seq(("Errors", ""))
 
     if (!taskHeadersAndCssClasses.map(_._1).contains(sortColumn)) {
-      new IllegalArgumentException(s"Unknown column: $sortColumn")
+      throw new IllegalArgumentException(s"Unknown column: $sortColumn")
     }
 
     val headerRow: Seq[Node] = {
diff --git a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
index 36943978ff594..fd6cc3ed759b3 100644
--- a/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/storage/RDDPage.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.ui.storage
 
+import java.net.URLEncoder
 import javax.servlet.http.HttpServletRequest
 
-import scala.xml.Node
+import scala.xml.{Node, Unparsed}
 
 import org.apache.spark.status.api.v1.{AllRDDResource, RDDDataDistribution, RDDPartitionInfo}
-import org.apache.spark.ui.{UIUtils, WebUIPage}
+import org.apache.spark.ui.{PagedDataSource, PagedTable, UIUtils, WebUIPage}
 import org.apache.spark.util.Utils
 
 /** Page showing storage details for a given RDD */
@@ -32,6 +33,17 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
   def render(request: HttpServletRequest): Seq[Node] = {
     val parameterId = request.getParameter("id")
     require(parameterId != null && parameterId.nonEmpty, "Missing id parameter")
+
+    val parameterBlockPage = request.getParameter("block.page")
+    val parameterBlockSortColumn = request.getParameter("block.sort")
+    val parameterBlockSortDesc = request.getParameter("block.desc")
+    val parameterBlockPageSize = request.getParameter("block.pageSize")
+
+    val blockPage = Option(parameterBlockPage).map(_.toInt).getOrElse(1)
+    val blockSortColumn = Option(parameterBlockSortColumn).getOrElse("Block Name")
+    val blockSortDesc = Option(parameterBlockSortDesc).map(_.toBoolean).getOrElse(false)
+    val blockPageSize = Option(parameterBlockPageSize).map(_.toInt).getOrElse(100)
+
     val rddId = parameterId.toInt
     val rddStorageInfo = AllRDDResource.getRDDStorageInfo(rddId, listener, includeDetails = true)
       .getOrElse {
@@ -44,8 +56,34 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
       rddStorageInfo.dataDistribution.get, id = Some("rdd-storage-by-worker-table"))
 
     // Block table
-    val blockTable = UIUtils.listingTable(blockHeader, blockRow, rddStorageInfo.partitions.get,
-      id = Some("rdd-storage-by-block-table"))
+    val (blockTable, blockTableHTML) = try {
+      val _blockTable = new BlockPagedTable(
+        UIUtils.prependBaseUri(parent.basePath) + s"/storage/rdd/?id=${rddId}",
+        rddStorageInfo.partitions.get,
+        blockPageSize,
+        blockSortColumn,
+        blockSortDesc)
+      (_blockTable, _blockTable.table(blockPage))
+    } catch {
+      case e @ (_ : IllegalArgumentException | _ : IndexOutOfBoundsException) =>
+        (null, <div class="alert alert-error">{e.getMessage}</div>)
+    }
+
+    val jsForScrollingDownToBlockTable =
+      <script>
+        {
+          Unparsed {
+            """
+              |$(function() {
+              |  if (/.*&block.sort=.*$/.test(location.search)) {
+              |    var topOffset = $("#blocks-section").offset().top;
+              |    $("html,body").animate({scrollTop: topOffset}, 200);
+              |  }
+              |});
+            """.stripMargin
+          }
+        }
+      </script>
 
     val content =
       <div class="row-fluid">
@@ -85,11 +123,11 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
         </div>
       </div>
 
-      <div class="row-fluid">
-        <div class="span12">
-          <h4> {rddStorageInfo.partitions.map(_.size).getOrElse(0)} Partitions </h4>
-          {blockTable}
-        </div>
+      <div>
+        <h4 id="blocks-section">
+          {rddStorageInfo.partitions.map(_.size).getOrElse(0)} Partitions
+        </h4>
+        {blockTableHTML ++ jsForScrollingDownToBlockTable}
       </div>;
 
     UIUtils.headerSparkPage("RDD Storage Info for " + rddStorageInfo.name, content, parent)
@@ -101,14 +139,6 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
     "Memory Usage",
     "Disk Usage")
 
-  /** Header fields for the block table */
-  private def blockHeader = Seq(
-    "Block Name",
-    "Storage Level",
-    "Size in Memory",
-    "Size on Disk",
-    "Executors")
-
   /** Render an HTML row representing a worker */
   private def workerRow(worker: RDDDataDistribution): Seq[Node] = {
     <tr>
@@ -120,23 +150,157 @@ private[ui] class RDDPage(parent: StorageTab) extends WebUIPage("rdd") {
       <td>{Utils.bytesToString(worker.diskUsed)}</td>
     </tr>
   }
+}
+
+private[ui] case class BlockTableRowData(
+    blockName: String,
+    storageLevel: String,
+    memoryUsed: Long,
+    diskUsed: Long,
+    executors: String)
+
+private[ui] class BlockDataSource(
+    rddPartitions: Seq[RDDPartitionInfo],
+    pageSize: Int,
+    sortColumn: String,
+    desc: Boolean) extends PagedDataSource[BlockTableRowData](pageSize) {
+
+  private val data = rddPartitions.map(blockRow).sorted(ordering(sortColumn, desc))
+
+  override def dataSize: Int = data.size
+
+  override def sliceData(from: Int, to: Int): Seq[BlockTableRowData] = {
+    data.slice(from, to)
+  }
+
+  private def blockRow(rddPartition: RDDPartitionInfo): BlockTableRowData = {
+    BlockTableRowData(
+      rddPartition.blockName,
+      rddPartition.storageLevel,
+      rddPartition.memoryUsed,
+      rddPartition.diskUsed,
+      rddPartition.executors.mkString(" "))
+  }
+
+  /**
+   * Return Ordering according to sortColumn and desc
+   */
+  private def ordering(sortColumn: String, desc: Boolean): Ordering[BlockTableRowData] = {
+    val ordering = sortColumn match {
+      case "Block Name" => new Ordering[BlockTableRowData] {
+        override def compare(x: BlockTableRowData, y: BlockTableRowData): Int =
+          Ordering.String.compare(x.blockName, y.blockName)
+      }
+      case "Storage Level" => new Ordering[BlockTableRowData] {
+        override def compare(x: BlockTableRowData, y: BlockTableRowData): Int =
+          Ordering.String.compare(x.storageLevel, y.storageLevel)
+      }
+      case "Size in Memory" => new Ordering[BlockTableRowData] {
+        override def compare(x: BlockTableRowData, y: BlockTableRowData): Int =
+          Ordering.Long.compare(x.memoryUsed, y.memoryUsed)
+      }
+      case "Size on Disk" => new Ordering[BlockTableRowData] {
+        override def compare(x: BlockTableRowData, y: BlockTableRowData): Int =
+          Ordering.Long.compare(x.diskUsed, y.diskUsed)
+      }
+      case "Executors" => new Ordering[BlockTableRowData] {
+        override def compare(x: BlockTableRowData, y: BlockTableRowData): Int =
+          Ordering.String.compare(x.executors, y.executors)
+      }
+      case unknownColumn => throw new IllegalArgumentException(s"Unknown column: $unknownColumn")
+    }
+    if (desc) {
+      ordering.reverse
+    } else {
+      ordering
+    }
+  }
+}
+
+private[ui] class BlockPagedTable(
+    basePath: String,
+    rddPartitions: Seq[RDDPartitionInfo],
+    pageSize: Int,
+    sortColumn: String,
+    desc: Boolean) extends PagedTable[BlockTableRowData] {
+
+  override def tableId: String = "rdd-storage-by-block-table"
+
+  override def tableCssClass: String = "table table-bordered table-condensed table-striped"
+
+  override val dataSource: BlockDataSource = new BlockDataSource(
+    rddPartitions,
+    pageSize,
+    sortColumn,
+    desc)
+
+  override def pageLink(page: Int): String = {
+    val encodedSortColumn = URLEncoder.encode(sortColumn, "UTF-8")
+    s"${basePath}&block.page=$page&block.sort=${encodedSortColumn}&block.desc=${desc}" +
+      s"&block.pageSize=${pageSize}"
+  }
+
+  override def goButtonJavascriptFunction: (String, String) = {
+    val jsFuncName = "goToBlockPage"
+    val encodedSortColumn = URLEncoder.encode(sortColumn, "UTF-8")
+    val jsFunc = s"""
+      |currentBlockPageSize = ${pageSize}
+      |function goToBlockPage(page, pageSize) {
+      |  // Set page to 1 if the page size changes
+      |  page = pageSize == currentBlockPageSize ? page : 1;
+      |  var url = "${basePath}&block.sort=${encodedSortColumn}&block.desc=${desc}" +
+      |    "&block.page=" + page + "&block.pageSize=" + pageSize;
+      |  window.location.href = url;
+      |}
+     """.stripMargin
+    (jsFuncName, jsFunc)
+  }
 
-  /** Render an HTML row representing a block */
-  private def blockRow(row: RDDPartitionInfo): Seq[Node] = {
+  override def headers: Seq[Node] = {
+    val blockHeaders = Seq(
+      "Block Name",
+      "Storage Level",
+      "Size in Memory",
+      "Size on Disk",
+      "Executors")
+
+    if (!blockHeaders.contains(sortColumn)) {
+      throw new IllegalArgumentException(s"Unknown column: $sortColumn")
+    }
+
+    val headerRow: Seq[Node] = {
+      blockHeaders.map { header =>
+        if (header == sortColumn) {
+          val headerLink =
+            s"$basePath&block.sort=${URLEncoder.encode(header, "UTF-8")}&block.desc=${!desc}" +
+              s"&block.pageSize=${pageSize}"
+          val js = Unparsed(s"window.location.href='${headerLink}'")
+          val arrow = if (desc) "&#x25BE;" else "&#x25B4;" // UP or DOWN
+          <th onclick={js} style="cursor: pointer;">
+            {header}
+            <span>&nbsp;{Unparsed(arrow)}</span>
+          </th>
+        } else {
+          val headerLink =
+            s"$basePath&block.sort=${URLEncoder.encode(header, "UTF-8")}" +
+              s"&block.pageSize=${pageSize}"
+          val js = Unparsed(s"window.location.href='${headerLink}'")
+          <th onclick={js} style="cursor: pointer;">
+            {header}
+          </th>
+        }
+      }
+    }
+    <thead>{headerRow}</thead>
+  }
+
+  override def row(block: BlockTableRowData): Seq[Node] = {
     <tr>
-      <td>{row.blockName}</td>
-      <td>
-        {row.storageLevel}
-      </td>
-      <td sorttable_customkey={row.memoryUsed.toString}>
-        {Utils.bytesToString(row.memoryUsed)}
-      </td>
-      <td sorttable_customkey={row.diskUsed.toString}>
-        {Utils.bytesToString(row.diskUsed)}
-      </td>
-      <td>
-        {row.executors.map(l => <span>{l}<br/></span>)}
-      </td>
+      <td>{block.blockName}</td>
+      <td>{block.storageLevel}</td>
+      <td>{Utils.bytesToString(block.memoryUsed)}</td>
+      <td>{Utils.bytesToString(block.diskUsed)}</td>
+      <td>{block.executors}</td>
     </tr>
   }
 }

From d702d53732b44e8242448ce5302738bd130717d8 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 4 Aug 2015 22:19:11 +0900
Subject: [PATCH 022/633] [SPARK-9583] [BUILD] Do not print mvn debug messages
 to stdout.

This allows build/mvn to be used by make-distribution.sh.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7915 from vanzin/SPARK-9583 and squashes the following commits:

6469e60 [Marcelo Vanzin] [SPARK-9583] [build] Do not print mvn debug messages to stdout.
---
 build/mvn | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/build/mvn b/build/mvn
index f62f61ee1c416..4a1664b04053d 100755
--- a/build/mvn
+++ b/build/mvn
@@ -51,11 +51,11 @@ install_app() {
     # check if we have curl installed
     # download application
     [ ! -f "${local_tarball}" ] && [ $(command -v curl) ] && \
-      echo "exec: curl ${curl_opts} ${remote_tarball}" && \
+      echo "exec: curl ${curl_opts} ${remote_tarball}" 1>&2 && \
       curl ${curl_opts} "${remote_tarball}" > "${local_tarball}"
     # if the file still doesn't exist, lets try `wget` and cross our fingers
     [ ! -f "${local_tarball}" ] && [ $(command -v wget) ] && \
-      echo "exec: wget ${wget_opts} ${remote_tarball}" && \
+      echo "exec: wget ${wget_opts} ${remote_tarball}" 1>&2 && \
       wget ${wget_opts} -O "${local_tarball}" "${remote_tarball}"
     # if both were unsuccessful, exit
     [ ! -f "${local_tarball}" ] && \
@@ -146,7 +146,7 @@ fi
 # Set any `mvn` options if not already present
 export MAVEN_OPTS=${MAVEN_OPTS:-"$_COMPILE_JVM_OPTS"}
 
-echo "Using \`mvn\` from path: $MVN_BIN"
+echo "Using \`mvn\` from path: $MVN_BIN" 1>&2
 
 # Last, call the `mvn` command as usual
 ${MVN_BIN} "$@"

From b1f88a38d53aebe7cabb762cdd2f1cc64726b0b4 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Tue, 4 Aug 2015 08:59:42 -0700
Subject: [PATCH 023/633] [SPARK-8244] [SQL] string function: find in set

This PR is based on #7186 (just fix the conflict), thanks to tarekauel .

find_in_set(string str, string strList): int

Returns the first occurance of str in strList where strList is a comma-delimited string. Returns null if either argument is null. Returns 0 if the first argument contains any commas. For example, find_in_set('ab', 'abc,b,ab,c,def') returns 3.

Only add this to SQL, not DataFrame.

Closes #7186

Author: Tarek Auel <tarek.auel@googlemail.com>
Author: Davies Liu <davies@databricks.com>

Closes #7900 from davies/find_in_set and squashes the following commits:

4334209 [Davies Liu] Merge branch 'master' of github.com:apache/spark into find_in_set
8f00572 [Davies Liu] Merge branch 'master' of github.com:apache/spark into find_in_set
243ede4 [Tarek Auel] [SPARK-8244][SQL] hive compatibility
1aaf64e [Tarek Auel] [SPARK-8244][SQL] unit test fix
e4093a4 [Tarek Auel] [SPARK-8244][SQL] final modifier for COMMA_UTF8
0d05df5 [Tarek Auel] Merge branch 'master' into SPARK-8244
208d710 [Tarek Auel] [SPARK-8244] address comments & bug fix
71b2e69 [Tarek Auel] [SPARK-8244] find_in_set
66c7fda [Tarek Auel] Merge branch 'master' into SPARK-8244
61b8ca2 [Tarek Auel] [SPARK-8224] removed loop and split; use unsafe String comparison
4f75a65 [Tarek Auel] Merge branch 'master' into SPARK-8244
e3b20c8 [Tarek Auel] [SPARK-8244] added type check
1c2bbb7 [Tarek Auel] [SPARK-8244] findInSet
---
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../expressions/stringOperations.scala        | 25 +++++++++++--
 .../expressions/StringExpressionsSuite.scala  | 10 ++++++
 .../spark/sql/DataFrameFunctionsSuite.scala   |  8 +++++
 .../apache/spark/unsafe/types/UTF8String.java | 35 +++++++++++++++++--
 .../spark/unsafe/types/UTF8StringSuite.java   | 12 +++++++
 6 files changed, 87 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index bc0846646174a..6140d1b129c64 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -177,6 +177,7 @@ object FunctionRegistry {
     expression[ConcatWs]("concat_ws"),
     expression[Encode]("encode"),
     expression[Decode]("decode"),
+    expression[FindInSet]("find_in_set"),
     expression[FormatNumber]("format_number"),
     expression[InitCap]("initcap"),
     expression[Lower]("lcase"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 56225290cd6b1..0cc785d9f3a49 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import java.text.DecimalFormat
-import java.util.Arrays
-import java.util.Locale
+import java.util.{Arrays, Locale}
 import java.util.regex.{MatchResult, Pattern}
 
 import org.apache.commons.lang3.StringEscapeUtils
@@ -350,6 +349,28 @@ case class EndsWith(left: Expression, right: Expression)
   }
 }
 
+/**
+ * A function that returns the index (1-based) of the given string (left) in the comma-
+ * delimited list (right). Returns 0, if the string wasn't found or if the given
+ * string (left) contains a comma.
+ */
+case class FindInSet(left: Expression, right: Expression) extends BinaryExpression
+    with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType)
+
+  override protected def nullSafeEval(word: Any, set: Any): Any =
+    set.asInstanceOf[UTF8String].findInSet(word.asInstanceOf[UTF8String])
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, (word, set) =>
+      s"${ev.primitive} = $set.findInSet($word);"
+    )
+  }
+
+  override def dataType: DataType = IntegerType
+}
+
 /**
  * A function that trim the spaces from both ends for the specified string.
  */
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 906be701beed7..23f36ca43d663 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -675,4 +675,14 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(FormatNumber(Literal.create(null, IntegerType), Literal(3)), null)
     checkEvaluation(FormatNumber(Literal.create(null, NullType), Literal(3)), null)
   }
+
+  test("find in set") {
+    checkEvaluation(
+      FindInSet(Literal.create(null, StringType), Literal.create(null, StringType)), null)
+    checkEvaluation(FindInSet(Literal("ab"), Literal.create(null, StringType)), null)
+    checkEvaluation(FindInSet(Literal.create(null, StringType), Literal("abc,b,ab,c,def")), null)
+    checkEvaluation(FindInSet(Literal("ab"), Literal("abc,b,ab,c,def")), 3)
+    checkEvaluation(FindInSet(Literal("abf"), Literal("abc,b,ab,c,def")), 0)
+    checkEvaluation(FindInSet(Literal("ab,"), Literal("abc,b,ab,c,def")), 0)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 431dcf7382f16..6137527757f85 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -208,6 +208,14 @@ class DataFrameFunctionsSuite extends QueryTest {
       Row(2743272264L, 2180413220L))
   }
 
+  test("string function find_in_set") {
+    val df = Seq(("abc,b,ab,c,def", "abc,b,ab,c,def")).toDF("a", "b")
+
+    checkAnswer(
+      df.selectExpr("find_in_set('ab', a)", "find_in_set('x', b)"),
+      Row(3, 0))
+  }
+
   test("conditional function: least") {
     checkAnswer(
       testData2.select(least(lit(-1), lit(0), col("a"), col("b"))).limit(1),
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index d80bd57bd2048..febbe3d4e54d1 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -54,8 +54,9 @@ public final class UTF8String implements Comparable<UTF8String>, Serializable {
     5, 5, 5, 5,
     6, 6};
 
-  private static ByteOrder byteOrder = ByteOrder.nativeOrder();
+  private static boolean isLittleEndian = ByteOrder.nativeOrder() == ByteOrder.LITTLE_ENDIAN;
 
+  private static final UTF8String COMMA_UTF8 = UTF8String.fromString(",");
   public static final UTF8String EMPTY_UTF8 = UTF8String.fromString("");
 
   /**
@@ -179,7 +180,7 @@ public long getPrefix() {
     // After getting the data, we use a mask to mask out data that is not part of the string.
     long p;
     long mask = 0;
-    if (byteOrder == ByteOrder.LITTLE_ENDIAN) {
+    if (isLittleEndian) {
       if (numBytes >= 8) {
         p = PlatformDependent.UNSAFE.getLong(base, offset);
       } else if (numBytes > 4) {
@@ -411,6 +412,36 @@ private UTF8String toTitleCaseSlow() {
     return fromString(sb.toString());
   }
 
+  /*
+   * Returns the index of the string `match` in this String. This string has to be a comma separated
+   * list. If `match` contains a comma 0 will be returned. If the `match` isn't part of this String,
+   * 0 will be returned, else the index of match (1-based index)
+   */
+  public int findInSet(UTF8String match) {
+    if (match.contains(COMMA_UTF8)) {
+      return 0;
+    }
+
+    int n = 1, lastComma = -1;
+    for (int i = 0; i < numBytes; i++) {
+      if (getByte(i) == (byte) ',') {
+        if (i - (lastComma + 1) == match.numBytes &&
+          ByteArrayMethods.arrayEquals(base, offset + (lastComma + 1), match.base, match.offset,
+            match.numBytes)) {
+          return n;
+        }
+        lastComma = i;
+        n++;
+      }
+    }
+    if (numBytes - (lastComma + 1) == match.numBytes &&
+      ByteArrayMethods.arrayEquals(base, offset + (lastComma + 1), match.base, match.offset,
+        match.numBytes)) {
+      return n;
+    }
+    return 0;
+  }
+
   /**
    * Copy the bytes from the current UTF8String, and make a new UTF8String.
    * @param start the start position of the current UTF8String in bytes.
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index 9b3190f8f0c3f..b30c94c1c1f80 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -399,6 +399,18 @@ public void createBlankString() {
     assertEquals(fromString(""), blankString(0));
   }
 
+  @Test
+  public void findInSet() {
+    assertEquals(fromString("ab").findInSet(fromString("ab")), 1);
+    assertEquals(fromString("a,b").findInSet(fromString("b")), 2);
+    assertEquals(fromString("abc,b,ab,c,def").findInSet(fromString("ab")), 3);
+    assertEquals(fromString("ab,abc,b,ab,c,def").findInSet(fromString("ab")), 1);
+    assertEquals(fromString(",,,ab,abc,b,ab,c,def").findInSet(fromString("ab")), 4);
+    assertEquals(fromString(",ab,abc,b,ab,c,def").findInSet(fromString("")), 1);
+    assertEquals(fromString("数据砖头,abc,b,ab,c,def").findInSet(fromString("ab")), 4);
+    assertEquals(fromString("数据砖头,abc,b,ab,c,def").findInSet(fromString("def")), 6);
+  }
+
   @Test
   public void soundex() {
     assertEquals(fromString("Robert").soundex(), fromString("R163"));

From 73dedb589d06f7c7a525cc4f07721a77f480c434 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 4 Aug 2015 09:07:09 -0700
Subject: [PATCH 024/633] [SPARK-8246] [SQL] Implement get_json_object
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This is based on #7485 , thanks to NathanHowell

Tests were copied from Hive, but do not seem to be super comprehensive. I've generally replicated Hive's unusual behavior rather than following a JSONPath reference, except for one case (as noted in the comments). I don't know if there is a way of fully replicating Hive's behavior without a slower TreeNode implementation, so I've erred on the side of performance instead.

Author: Davies Liu <davies@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Author: Nathan Howell <nhowell@godaddy.com>

Closes #7901 from davies/get_json_object and squashes the following commits:

3ace9b9 [Davies Liu] Merge branch 'get_json_object' of github.com:davies/spark into get_json_object
98766fc [Davies Liu] Merge branch 'master' of github.com:apache/spark into get_json_object
a7dc6d0 [Davies Liu] Update JsonExpressionsSuite.scala
c818519 [Yin Huai] new results.
18ce26b [Davies Liu] fix tests
6ac29fb [Yin Huai] Golden files.
25eebef [Davies Liu] use HiveQuerySuite
e0ac6ec [Yin Huai] Golden answer files.
940c060 [Davies Liu] tweat code style
44084c5 [Davies Liu] Merge branch 'master' of github.com:apache/spark into get_json_object
9192d09 [Nathan Howell] Match Hive’s behavior for unwrapping arrays of one element
8dab647 [Nathan Howell] [SPARK-8246] [SQL] Implement get_json_object
---
 .../catalyst/analysis/FunctionRegistry.scala  |   1 +
 .../catalyst/expressions/jsonFunctions.scala  | 309 ++++++++++++++++++
 .../expressions/JsonExpressionsSuite.scala    | 202 ++++++++++++
 .../apache/spark/sql/JsonFunctionsSuite.scala |  32 ++
 .../execution/HiveCompatibilitySuite.scala    |   3 +
 .../apache/spark/sql/hive/test/TestHive.scala |   6 +-
 ...ject #1-0-f01b340b5662c45bb5f1e3b7c6900e1f |   1 +
 ...ect #10-0-f3f47d06d7c51d493d68112b0bd6c1fc |   1 +
 ...bject #2-0-e84c2f8136919830fd665a278e4158a |   1 +
 ...ject #3-0-bf140c65c31f8d892ec23e41e16e58bb |   1 +
 ...ject #4-0-f0bd902edc1990c9a6c65a6bb672c4d5 |   1 +
 ...bject #5-0-3c09f4316a1533049aee8af749cdcab |   1 +
 ...ject #6-0-8334d1ddbe0f41fc7b80d4e6b45409da |   1 +
 ...ject #7-0-40d7dff94b26a2e3f4ab71baee3d3ce0 |   1 +
 ...ject #8-0-180b4b6fdb26011fec05a7ca99fd9844 |   1 +
 ...ject #9-0-47c451a969d856f008f4d6b3d378d94b |   1 +
 .../sql/hive/execution/HiveQuerySuite.scala   |  51 +++
 17 files changed, 613 insertions(+), 1 deletion(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #1-0-f01b340b5662c45bb5f1e3b7c6900e1f
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #10-0-f3f47d06d7c51d493d68112b0bd6c1fc
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #2-0-e84c2f8136919830fd665a278e4158a
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #3-0-bf140c65c31f8d892ec23e41e16e58bb
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #4-0-f0bd902edc1990c9a6c65a6bb672c4d5
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #5-0-3c09f4316a1533049aee8af749cdcab
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #6-0-8334d1ddbe0f41fc7b80d4e6b45409da
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #7-0-40d7dff94b26a2e3f4ab71baee3d3ce0
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #8-0-180b4b6fdb26011fec05a7ca99fd9844
 create mode 100644 sql/hive/src/test/resources/golden/get_json_object #9-0-47c451a969d856f008f4d6b3d378d94b

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 6140d1b129c64..43e3e9b910e7d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -179,6 +179,7 @@ object FunctionRegistry {
     expression[Decode]("decode"),
     expression[FindInSet]("find_in_set"),
     expression[FormatNumber]("format_number"),
+    expression[GetJsonObject]("get_json_object"),
     expression[InitCap]("initcap"),
     expression[Lower]("lcase"),
     expression[Lower]("lower"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala
new file mode 100644
index 0000000000000..23bfa18c94286
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala
@@ -0,0 +1,309 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import java.io.{StringWriter, ByteArrayOutputStream}
+
+import com.fasterxml.jackson.core._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
+import org.apache.spark.sql.types.{StringType, DataType}
+import org.apache.spark.unsafe.types.UTF8String
+
+import scala.util.parsing.combinator.RegexParsers
+
+private[this] sealed trait PathInstruction
+private[this] object PathInstruction {
+  private[expressions] case object Subscript extends PathInstruction
+  private[expressions] case object Wildcard extends PathInstruction
+  private[expressions] case object Key extends PathInstruction
+  private[expressions] case class Index(index: Long) extends PathInstruction
+  private[expressions] case class Named(name: String) extends PathInstruction
+}
+
+private[this] sealed trait WriteStyle
+private[this] object WriteStyle {
+  private[expressions] case object RawStyle extends WriteStyle
+  private[expressions] case object QuotedStyle extends WriteStyle
+  private[expressions] case object FlattenStyle extends WriteStyle
+}
+
+private[this] object JsonPathParser extends RegexParsers {
+  import PathInstruction._
+
+  def root: Parser[Char] = '$'
+
+  def long: Parser[Long] = "\\d+".r ^? {
+    case x => x.toLong
+  }
+
+  // parse `[*]` and `[123]` subscripts
+  def subscript: Parser[List[PathInstruction]] =
+    for {
+      operand <- '[' ~> ('*' ^^^ Wildcard | long ^^ Index) <~ ']'
+    } yield {
+      Subscript :: operand :: Nil
+    }
+
+  // parse `.name` or `['name']` child expressions
+  def named: Parser[List[PathInstruction]] =
+    for {
+      name <- '.' ~> "[^\\.\\[]+".r | "[\\'" ~> "[^\\'\\?]+" <~ "\\']"
+    } yield {
+      Key :: Named(name) :: Nil
+    }
+
+  // child wildcards: `..`, `.*` or `['*']`
+  def wildcard: Parser[List[PathInstruction]] =
+    (".*" | "['*']") ^^^ List(Wildcard)
+
+  def node: Parser[List[PathInstruction]] =
+    wildcard |
+      named |
+      subscript
+
+  val expression: Parser[List[PathInstruction]] = {
+    phrase(root ~> rep(node) ^^ (x => x.flatten))
+  }
+
+  def parse(str: String): Option[List[PathInstruction]] = {
+    this.parseAll(expression, str) match {
+      case Success(result, _) =>
+        Some(result)
+
+      case NoSuccess(msg, next) =>
+        None
+    }
+  }
+}
+
+private[this] object GetJsonObject {
+  private val jsonFactory = new JsonFactory()
+
+  // Enabled for Hive compatibility
+  jsonFactory.enable(JsonParser.Feature.ALLOW_UNQUOTED_CONTROL_CHARS)
+}
+
+/**
+ * Extracts json object from a json string based on json path specified, and returns json string
+ * of the extracted json object. It will return null if the input json string is invalid.
+ */
+case class GetJsonObject(json: Expression, path: Expression)
+  extends BinaryExpression with ExpectsInputTypes with CodegenFallback {
+
+  import GetJsonObject._
+  import PathInstruction._
+  import WriteStyle._
+  import com.fasterxml.jackson.core.JsonToken._
+
+  override def left: Expression = json
+  override def right: Expression = path
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
+  override def dataType: DataType = StringType
+  override def prettyName: String = "get_json_object"
+
+  @transient private lazy val parsedPath = parsePath(path.eval().asInstanceOf[UTF8String])
+
+  override def eval(input: InternalRow): Any = {
+    val jsonStr = json.eval(input).asInstanceOf[UTF8String]
+    if (jsonStr == null) {
+      return null
+    }
+
+    val parsed = if (path.foldable) {
+      parsedPath
+    } else {
+      parsePath(path.eval(input).asInstanceOf[UTF8String])
+    }
+
+    if (parsed.isDefined) {
+      try {
+        val parser = jsonFactory.createParser(jsonStr.getBytes)
+        val output = new ByteArrayOutputStream()
+        val generator = jsonFactory.createGenerator(output, JsonEncoding.UTF8)
+        parser.nextToken()
+        val matched = evaluatePath(parser, generator, RawStyle, parsed.get)
+        generator.close()
+        if (matched) {
+          UTF8String.fromBytes(output.toByteArray)
+        } else {
+          null
+        }
+      } catch {
+        case _: JsonProcessingException => null
+      }
+    } else {
+      null
+    }
+  }
+
+  private def parsePath(path: UTF8String): Option[List[PathInstruction]] = {
+    if (path != null) {
+      JsonPathParser.parse(path.toString)
+    } else {
+      None
+    }
+  }
+
+  // advance to the desired array index, assumes to start at the START_ARRAY token
+  private def arrayIndex(p: JsonParser, f: () => Boolean): Long => Boolean = {
+    case _ if p.getCurrentToken == END_ARRAY =>
+      // terminate, nothing has been written
+      false
+
+    case 0 =>
+      // we've reached the desired index
+      val dirty = f()
+
+      while (p.nextToken() != END_ARRAY) {
+        // advance the token stream to the end of the array
+        p.skipChildren()
+      }
+
+      dirty
+
+    case i if i > 0 =>
+      // skip this token and evaluate the next
+      p.skipChildren()
+      p.nextToken()
+      arrayIndex(p, f)(i - 1)
+  }
+
+  /**
+   * Evaluate a list of JsonPath instructions, returning a bool that indicates if any leaf nodes
+   * have been written to the generator
+   */
+  private def evaluatePath(
+      p: JsonParser,
+      g: JsonGenerator,
+      style: WriteStyle,
+      path: List[PathInstruction]): Boolean = {
+    (p.getCurrentToken, path) match {
+      case (VALUE_STRING, Nil) if style == RawStyle =>
+        // there is no array wildcard or slice parent, emit this string without quotes
+        if (p.hasTextCharacters) {
+          g.writeRaw(p.getTextCharacters, p.getTextOffset, p.getTextLength)
+        } else {
+          g.writeRaw(p.getText)
+        }
+        true
+
+      case (START_ARRAY, Nil) if style == FlattenStyle =>
+        // flatten this array into the parent
+        var dirty = false
+        while (p.nextToken() != END_ARRAY) {
+          dirty |= evaluatePath(p, g, style, Nil)
+        }
+        dirty
+
+      case (_, Nil) =>
+        // general case: just copy the child tree verbatim
+        g.copyCurrentStructure(p)
+        true
+
+      case (START_OBJECT, Key :: xs) =>
+        var dirty = false
+        while (p.nextToken() != END_OBJECT) {
+          if (dirty) {
+            // once a match has been found we can skip other fields
+            p.skipChildren()
+          } else {
+            dirty = evaluatePath(p, g, style, xs)
+          }
+        }
+        dirty
+
+      case (START_ARRAY, Subscript :: Wildcard :: Subscript :: Wildcard :: xs) =>
+        // special handling for the non-structure preserving double wildcard behavior in Hive
+        var dirty = false
+        g.writeStartArray()
+        while (p.nextToken() != END_ARRAY) {
+          dirty |= evaluatePath(p, g, FlattenStyle, xs)
+        }
+        g.writeEndArray()
+        dirty
+
+      case (START_ARRAY, Subscript :: Wildcard :: xs) if style != QuotedStyle =>
+        // retain Flatten, otherwise use Quoted... cannot use Raw within an array
+        val nextStyle = style match {
+          case RawStyle => QuotedStyle
+          case FlattenStyle => FlattenStyle
+          case QuotedStyle => throw new IllegalStateException()
+        }
+
+        // temporarily buffer child matches, the emitted json will need to be
+        // modified slightly if there is only a single element written
+        val buffer = new StringWriter()
+        val flattenGenerator = jsonFactory.createGenerator(buffer)
+        flattenGenerator.writeStartArray()
+
+        var dirty = 0
+        while (p.nextToken() != END_ARRAY) {
+          // track the number of array elements and only emit an outer array if
+          // we've written more than one element, this matches Hive's behavior
+          dirty += (if (evaluatePath(p, flattenGenerator, nextStyle, xs)) 1 else 0)
+        }
+        flattenGenerator.writeEndArray()
+        flattenGenerator.close()
+
+        val buf = buffer.getBuffer
+        if (dirty > 1) {
+          g.writeRawValue(buf.toString)
+        } else if (dirty == 1) {
+          // remove outer array tokens
+          g.writeRawValue(buf.substring(1, buf.length()-1))
+        } // else do not write anything
+
+        dirty > 0
+
+      case (START_ARRAY, Subscript :: Wildcard :: xs) =>
+        var dirty = false
+        g.writeStartArray()
+        while (p.nextToken() != END_ARRAY) {
+          // wildcards can have multiple matches, continually update the dirty count
+          dirty |= evaluatePath(p, g, QuotedStyle, xs)
+        }
+        g.writeEndArray()
+
+        dirty
+
+      case (START_ARRAY, Subscript :: Index(idx) :: (xs@Subscript :: Wildcard :: _)) =>
+        p.nextToken()
+        // we're going to have 1 or more results, switch to QuotedStyle
+        arrayIndex(p, () => evaluatePath(p, g, QuotedStyle, xs))(idx)
+
+      case (START_ARRAY, Subscript :: Index(idx) :: xs) =>
+        p.nextToken()
+        arrayIndex(p, () => evaluatePath(p, g, style, xs))(idx)
+
+      case (FIELD_NAME, Named(name) :: xs) if p.getCurrentName == name =>
+        // exact field match
+        p.nextToken()
+        evaluatePath(p, g, style, xs)
+
+      case (FIELD_NAME, Wildcard :: xs) =>
+        // wildcard field match
+        p.nextToken()
+        evaluatePath(p, g, style, xs)
+
+      case _ =>
+        p.skipChildren()
+        false
+    }
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
new file mode 100644
index 0000000000000..4addbaf0cbce7
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/JsonExpressionsSuite.scala
@@ -0,0 +1,202 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.SparkFunSuite
+
+class JsonExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
+  val json =
+    """
+      |{"store":{"fruit":[{"weight":8,"type":"apple"},{"weight":9,"type":"pear"}],
+      |"basket":[[1,2,{"b":"y","a":"x"}],[3,4],[5,6]],"book":[{"author":"Nigel Rees",
+      |"title":"Sayings of the Century","category":"reference","price":8.95},
+      |{"author":"Herman Melville","title":"Moby Dick","category":"fiction","price":8.99,
+      |"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":"The Lord of the Rings",
+      |"category":"fiction","reader":[{"age":25,"name":"bob"},{"age":26,"name":"jack"}],
+      |"price":22.99,"isbn":"0-395-19395-8"}],"bicycle":{"price":19.95,"color":"red"}},
+      |"email":"amy@only_for_json_udf_test.net","owner":"amy","zip code":"94025",
+      |"fb:testid":"1234"}
+      |""".stripMargin
+
+  test("$.store.bicycle") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.bicycle")),
+      """{"price":19.95,"color":"red"}""")
+  }
+
+  test("$.store.book") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book")),
+      """[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference",
+        |"price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction",
+        |"price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":
+        |"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},
+        |{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}]
+        |""".stripMargin.replace("\n", ""))
+  }
+
+  test("$.store.book[0]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[0]")),
+      """{"author":"Nigel Rees","title":"Sayings of the Century",
+        |"category":"reference","price":8.95}""".stripMargin.replace("\n", ""))
+  }
+
+  test("$.store.book[*]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[*]")),
+      """[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference",
+        |"price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction",
+        |"price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":
+        |"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},
+        |{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}]
+        |""".stripMargin.replace("\n", ""))
+  }
+
+  test("$") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$")),
+      json.replace("\n", ""))
+  }
+
+  test("$.store.book[0].category") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[0].category")),
+      "reference")
+  }
+
+  test("$.store.book[*].category") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[*].category")),
+      """["reference","fiction","fiction"]""")
+  }
+
+  test("$.store.book[*].isbn") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[*].isbn")),
+      """["0-553-21311-3","0-395-19395-8"]""")
+  }
+
+  test("$.store.book[*].reader") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[*].reader")),
+      """[{"age":25,"name":"bob"},{"age":26,"name":"jack"}]""")
+  }
+
+  test("$.store.basket[0][1]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[0][1]")),
+      "2")
+  }
+
+  test("$.store.basket[*]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[*]")),
+      """[[1,2,{"b":"y","a":"x"}],[3,4],[5,6]]""")
+  }
+
+  test("$.store.basket[*][0]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[*][0]")),
+      "[1,3,5]")
+  }
+
+  test("$.store.basket[0][*]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[0][*]")),
+      """[1,2,{"b":"y","a":"x"}]""")
+  }
+
+  test("$.store.basket[*][*]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[*][*]")),
+      """[1,2,{"b":"y","a":"x"},3,4,5,6]""")
+  }
+
+  test("$.store.basket[0][2].b") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[0][2].b")),
+      "y")
+  }
+
+  test("$.store.basket[0][*].b") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[0][*].b")),
+      """["y"]""")
+  }
+
+  test("$.zip code") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.zip code")),
+      "94025")
+  }
+
+  test("$.fb:testid") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.fb:testid")),
+      "1234")
+  }
+
+  test("preserve newlines") {
+    checkEvaluation(
+      GetJsonObject(Literal("""{"a":"b\nc"}"""), Literal("$.a")),
+      "b\nc")
+  }
+
+  test("escape") {
+    checkEvaluation(
+      GetJsonObject(Literal("""{"a":"b\"c"}"""), Literal("$.a")),
+      "b\"c")
+  }
+
+  test("$.non_exist_key") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.non_exist_key")),
+      null)
+  }
+
+  test("$..no_recursive") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$..no_recursive")),
+      null)
+  }
+
+  test("$.store.book[10]") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[10]")),
+      null)
+  }
+
+  test("$.store.book[0].non_exist_key") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.book[0].non_exist_key")),
+      null)
+  }
+
+  test("$.store.basket[*].non_exist_key") {
+    checkEvaluation(
+      GetJsonObject(Literal(json), Literal("$.store.basket[*].non_exist_key")),
+      null)
+  }
+
+  test("non foldable literal") {
+    checkEvaluation(
+      GetJsonObject(NonFoldableLiteral(json), NonFoldableLiteral("$.fb:testid")),
+      "1234")
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
new file mode 100644
index 0000000000000..71c26a6f8d367
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+class JsonFunctionsSuite extends QueryTest {
+
+  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+  import ctx.implicits._
+
+  test("function get_json_object") {
+    val df: DataFrame = Seq(("""{"name": "alice", "age": 5}""", "")).toDF("a", "b")
+    checkAnswer(
+      df.selectExpr("get_json_object(a, '$.name')", "get_json_object(a, '$.age')"),
+      Row("alice", "5"))
+  }
+
+}
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index c46a4a4b0be54..d4fc6c2b6ebc0 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -266,6 +266,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     // Hive returns string from UTC formatted timestamp, spark returns timestamp type
     "date_udf",
 
+    // Can't compare the result that have newline in it
+    "udf_get_json_object",
+
     // Unlike Hive, we do support log base in (0, 1.0], therefore disable this
     "udf7",
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 8d0bf46e8fad7..167086db5bfe2 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -374,7 +374,11 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
         INSERT OVERWRITE TABLE episodes_part PARTITION (doctor_pt=1)
         SELECT title, air_date, doctor FROM episodes
       """.cmd
-      )
+      ),
+    TestTable("src_json",
+      s"""CREATE TABLE src_json (json STRING) STORED AS TEXTFILE
+       """.stripMargin.cmd,
+      s"LOAD DATA LOCAL INPATH '${getHiveFile("data/files/json.txt")}' INTO TABLE src_json".cmd)
   )
 
   hiveQTestUtilTables.foreach(registerTestTable)
diff --git a/sql/hive/src/test/resources/golden/get_json_object #1-0-f01b340b5662c45bb5f1e3b7c6900e1f b/sql/hive/src/test/resources/golden/get_json_object #1-0-f01b340b5662c45bb5f1e3b7c6900e1f
new file mode 100644
index 0000000000000..1dcda4315a14d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #1-0-f01b340b5662c45bb5f1e3b7c6900e1f	
@@ -0,0 +1 @@
+{"store":{"fruit":[{"weight":8,"type":"apple"},{"weight":9,"type":"pear"}],"basket":[[1,2,{"b":"y","a":"x"}],[3,4],[5,6]],"book":[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference","price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction","price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}],"bicycle":{"price":19.95,"color":"red"}},"email":"amy@only_for_json_udf_test.net","owner":"amy","zip code":"94025","fb:testid":"1234"}
diff --git a/sql/hive/src/test/resources/golden/get_json_object #10-0-f3f47d06d7c51d493d68112b0bd6c1fc b/sql/hive/src/test/resources/golden/get_json_object #10-0-f3f47d06d7c51d493d68112b0bd6c1fc
new file mode 100644
index 0000000000000..81c545efebe5f
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #10-0-f3f47d06d7c51d493d68112b0bd6c1fc	
@@ -0,0 +1 @@
+1234
diff --git a/sql/hive/src/test/resources/golden/get_json_object #2-0-e84c2f8136919830fd665a278e4158a b/sql/hive/src/test/resources/golden/get_json_object #2-0-e84c2f8136919830fd665a278e4158a
new file mode 100644
index 0000000000000..99127db9e311c
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #2-0-e84c2f8136919830fd665a278e4158a	
@@ -0,0 +1 @@
+amy	{"fruit":[{"weight":8,"type":"apple"},{"weight":9,"type":"pear"}],"basket":[[1,2,{"b":"y","a":"x"}],[3,4],[5,6]],"book":[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference","price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction","price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}],"bicycle":{"price":19.95,"color":"red"}}
diff --git a/sql/hive/src/test/resources/golden/get_json_object #3-0-bf140c65c31f8d892ec23e41e16e58bb b/sql/hive/src/test/resources/golden/get_json_object #3-0-bf140c65c31f8d892ec23e41e16e58bb
new file mode 100644
index 0000000000000..0bc03998296af
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #3-0-bf140c65c31f8d892ec23e41e16e58bb	
@@ -0,0 +1 @@
+{"price":19.95,"color":"red"}	[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference","price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction","price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}]
diff --git a/sql/hive/src/test/resources/golden/get_json_object #4-0-f0bd902edc1990c9a6c65a6bb672c4d5 b/sql/hive/src/test/resources/golden/get_json_object #4-0-f0bd902edc1990c9a6c65a6bb672c4d5
new file mode 100644
index 0000000000000..4f7e09bd3fa73
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #4-0-f0bd902edc1990c9a6c65a6bb672c4d5	
@@ -0,0 +1 @@
+{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference","price":8.95}	[{"author":"Nigel Rees","title":"Sayings of the Century","category":"reference","price":8.95},{"author":"Herman Melville","title":"Moby Dick","category":"fiction","price":8.99,"isbn":"0-553-21311-3"},{"author":"J. R. R. Tolkien","title":"The Lord of the Rings","category":"fiction","reader":[{"age":25,"name":"bob"},{"age":26,"name":"jack"}],"price":22.99,"isbn":"0-395-19395-8"}]
diff --git a/sql/hive/src/test/resources/golden/get_json_object #5-0-3c09f4316a1533049aee8af749cdcab b/sql/hive/src/test/resources/golden/get_json_object #5-0-3c09f4316a1533049aee8af749cdcab
new file mode 100644
index 0000000000000..b2d212a597d99
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #5-0-3c09f4316a1533049aee8af749cdcab	
@@ -0,0 +1 @@
+reference	["reference","fiction","fiction"]	["0-553-21311-3","0-395-19395-8"]	[{"age":25,"name":"bob"},{"age":26,"name":"jack"}]
diff --git a/sql/hive/src/test/resources/golden/get_json_object #6-0-8334d1ddbe0f41fc7b80d4e6b45409da b/sql/hive/src/test/resources/golden/get_json_object #6-0-8334d1ddbe0f41fc7b80d4e6b45409da
new file mode 100644
index 0000000000000..21d88629fcdb6
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #6-0-8334d1ddbe0f41fc7b80d4e6b45409da	
@@ -0,0 +1 @@
+25	[25,26]
diff --git a/sql/hive/src/test/resources/golden/get_json_object #7-0-40d7dff94b26a2e3f4ab71baee3d3ce0 b/sql/hive/src/test/resources/golden/get_json_object #7-0-40d7dff94b26a2e3f4ab71baee3d3ce0
new file mode 100644
index 0000000000000..e60721e1dd240
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #7-0-40d7dff94b26a2e3f4ab71baee3d3ce0	
@@ -0,0 +1 @@
+2	[[1,2,{"b":"y","a":"x"}],[3,4],[5,6]]	1	[1,2,{"b":"y","a":"x"}]	[1,2,{"b":"y","a":"x"},3,4,5,6]	y	["y"]
diff --git a/sql/hive/src/test/resources/golden/get_json_object #8-0-180b4b6fdb26011fec05a7ca99fd9844 b/sql/hive/src/test/resources/golden/get_json_object #8-0-180b4b6fdb26011fec05a7ca99fd9844
new file mode 100644
index 0000000000000..356fcdf7139bd
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #8-0-180b4b6fdb26011fec05a7ca99fd9844	
@@ -0,0 +1 @@
+NULL	NULL	NULL	NULL	NULL	NULL
diff --git a/sql/hive/src/test/resources/golden/get_json_object #9-0-47c451a969d856f008f4d6b3d378d94b b/sql/hive/src/test/resources/golden/get_json_object #9-0-47c451a969d856f008f4d6b3d378d94b
new file mode 100644
index 0000000000000..ef4a39675ed6d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/get_json_object #9-0-47c451a969d856f008f4d6b3d378d94b	
@@ -0,0 +1 @@
+94025
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index a7cfac51cc097..edb27553671d1 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -587,6 +587,57 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
       |select * where key = 4
     """.stripMargin)
 
+  // test get_json_object again Hive, because the HiveCompatabilitySuite cannot handle result
+  // with newline in it.
+  createQueryTest("get_json_object #1",
+    "SELECT get_json_object(src_json.json, '$') FROM src_json")
+
+  createQueryTest("get_json_object #2",
+    "SELECT get_json_object(src_json.json, '$.owner'), get_json_object(src_json.json, '$.store')" +
+      " FROM src_json")
+
+  createQueryTest("get_json_object #3",
+    "SELECT get_json_object(src_json.json, '$.store.bicycle'), " +
+      "get_json_object(src_json.json, '$.store.book') FROM src_json")
+
+  createQueryTest("get_json_object #4",
+    "SELECT get_json_object(src_json.json, '$.store.book[0]'), " +
+      "get_json_object(src_json.json, '$.store.book[*]') FROM src_json")
+
+  createQueryTest("get_json_object #5",
+    "SELECT get_json_object(src_json.json, '$.store.book[0].category'), " +
+      "get_json_object(src_json.json, '$.store.book[*].category'), " +
+      "get_json_object(src_json.json, '$.store.book[*].isbn'), " +
+      "get_json_object(src_json.json, '$.store.book[*].reader') FROM src_json")
+
+  createQueryTest("get_json_object #6",
+    "SELECT get_json_object(src_json.json, '$.store.book[*].reader[0].age'), " +
+      "get_json_object(src_json.json, '$.store.book[*].reader[*].age') FROM src_json")
+
+  createQueryTest("get_json_object #7",
+    "SELECT get_json_object(src_json.json, '$.store.basket[0][1]'), " +
+      "get_json_object(src_json.json, '$.store.basket[*]'), " +
+      // Hive returns wrong result with [*][0], so this expression is change to make test pass
+      "get_json_object(src_json.json, '$.store.basket[0][0]'), " +
+      "get_json_object(src_json.json, '$.store.basket[0][*]'), " +
+      "get_json_object(src_json.json, '$.store.basket[*][*]'), " +
+      "get_json_object(src_json.json, '$.store.basket[0][2].b'), " +
+      "get_json_object(src_json.json, '$.store.basket[0][*].b') FROM src_json")
+
+  createQueryTest("get_json_object #8",
+    "SELECT get_json_object(src_json.json, '$.non_exist_key'), " +
+      "get_json_object(src_json.json, '$..no_recursive'), " +
+      "get_json_object(src_json.json, '$.store.book[10]'), " +
+      "get_json_object(src_json.json, '$.store.book[0].non_exist_key'), " +
+      "get_json_object(src_json.json, '$.store.basket[*].non_exist_key'), " +
+      "get_json_object(src_json.json, '$.store.basket[0][*].non_exist_key') FROM src_json")
+
+  createQueryTest("get_json_object #9",
+    "SELECT get_json_object(src_json.json, '$.zip code') FROM src_json")
+
+  createQueryTest("get_json_object #10",
+    "SELECT get_json_object(src_json.json, '$.fb:testid') FROM src_json")
+
   test("predicates contains an empty AttributeSet() references") {
     sql(
       """

From b5034c9c59947f20423faa46bc6606aad56836b0 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Tue, 4 Aug 2015 09:09:52 -0700
Subject: [PATCH 025/633] [SPARK-9541] [SQL] DataTimeUtils cleanup

JIRA: https://issues.apache.org/jira/browse/SPARK-9541

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7870 from yjshen/datetime_cleanup and squashes the following commits:

9203e33 [Yijie Shen] revert getMonth & getDayOfMonth
5cad119 [Yijie Shen] rebase code
7d62a74 [Yijie Shen] remove tmp tuple inside split date
e98aaac [Yijie Shen] DataTimeUtils cleanup
---
 .../sql/catalyst/util/DateTimeUtils.scala     | 142 +++++++-----------
 1 file changed, 57 insertions(+), 85 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index f645eb5f7bb01..063940cb9e2c3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -493,6 +493,50 @@ object DateTimeUtils {
     }
   }
 
+  /**
+   * Split date (expressed in days since 1.1.1970) into four fields:
+   * year, month (Jan is Month 1), dayInMonth, daysToMonthEnd (0 if it's last day of month).
+   */
+  def splitDate(date: Int): (Int, Int, Int, Int) = {
+    var (year, dayInYear) = getYearAndDayInYear(date)
+    val isLeap = isLeapYear(year)
+    if (isLeap && dayInYear == 60) {
+      (year, 2, 29, 0)
+    } else {
+      if (isLeap && dayInYear > 60) dayInYear -= 1
+
+      if (dayInYear <= 181) {
+        if (dayInYear <= 31) {
+          (year, 1, dayInYear, 31 - dayInYear)
+        } else if (dayInYear <= 59) {
+          (year, 2, dayInYear - 31, if (isLeap) 60 - dayInYear else 59 - dayInYear)
+        } else if (dayInYear <= 90) {
+          (year, 3, dayInYear - 59, 90 - dayInYear)
+        } else if (dayInYear <= 120) {
+          (year, 4, dayInYear - 90, 120 - dayInYear)
+        } else if (dayInYear <= 151) {
+          (year, 5, dayInYear - 120, 151 - dayInYear)
+        } else {
+          (year, 6, dayInYear - 151, 181 - dayInYear)
+        }
+      } else {
+        if (dayInYear <= 212) {
+          (year, 7, dayInYear - 181, 212 - dayInYear)
+        } else if (dayInYear <= 243) {
+          (year, 8, dayInYear - 212, 243 - dayInYear)
+        } else if (dayInYear <= 273) {
+          (year, 9, dayInYear - 243, 273 - dayInYear)
+        } else if (dayInYear <= 304) {
+          (year, 10, dayInYear - 273, 304 - dayInYear)
+        } else if (dayInYear <= 334) {
+          (year, 11, dayInYear - 304, 334 - dayInYear)
+        } else {
+          (year, 12, dayInYear - 334, 365 - dayInYear)
+        }
+      }
+    }
+  }
+
   /**
    * Returns the month value for the given date. The date is expressed in days
    * since 1.1.1970. January is month 1.
@@ -613,15 +657,16 @@ object DateTimeUtils {
    * Returns a date value, expressed in days since 1.1.1970.
    */
   def dateAddMonths(days: Int, months: Int): Int = {
-    val absoluteMonth = (getYear(days) - YearZero) * 12 + getMonth(days) - 1 + months
+    val (year, monthInYear, dayOfMonth, daysToMonthEnd) = splitDate(days)
+    val absoluteMonth = (year - YearZero) * 12 + monthInYear - 1 + months
     val nonNegativeMonth = if (absoluteMonth >= 0) absoluteMonth else 0
     val currentMonthInYear = nonNegativeMonth % 12
     val currentYear = nonNegativeMonth / 12
+
     val leapDay = if (currentMonthInYear == 1 && isLeapYear(currentYear + YearZero)) 1 else 0
     val lastDayOfMonth = monthDays(currentMonthInYear) + leapDay
 
-    val dayOfMonth = getDayOfMonth(days)
-    val currentDayInMonth = if (getDayOfMonth(days + 1) == 1 || dayOfMonth >= lastDayOfMonth) {
+    val currentDayInMonth = if (daysToMonthEnd == 0 || dayOfMonth >= lastDayOfMonth) {
       // last day of the month
       lastDayOfMonth
     } else {
@@ -640,46 +685,6 @@ object DateTimeUtils {
     daysToMillis(newDays) * 1000L + start - daysToMillis(days) * 1000L + microseconds
   }
 
-  /**
-   * Returns the last dayInMonth in the month it belongs to. The date is expressed
-   * in days since 1.1.1970. the return value starts from 1.
-   */
-  private def getLastDayInMonthOfMonth(date: Int): Int = {
-    var (year, dayInYear) = getYearAndDayInYear(date)
-    if (isLeapYear(year)) {
-      if (dayInYear > 31 && dayInYear <= 60) {
-        return 29
-      } else if (dayInYear > 60) {
-        dayInYear = dayInYear - 1
-      }
-    }
-    if (dayInYear <= 31) {
-      31
-    } else if (dayInYear <= 59) {
-      28
-    } else if (dayInYear <= 90) {
-      31
-    } else if (dayInYear <= 120) {
-      30
-    } else if (dayInYear <= 151) {
-      31
-    } else if (dayInYear <= 181) {
-      30
-    } else if (dayInYear <= 212) {
-      31
-    } else if (dayInYear <= 243) {
-      31
-    } else if (dayInYear <= 273) {
-      30
-    } else if (dayInYear <= 304) {
-      31
-    } else if (dayInYear <= 334) {
-      30
-    } else {
-      31
-    }
-  }
-
   /**
    * Returns number of months between time1 and time2. time1 and time2 are expressed in
    * microseconds since 1.1.1970.
@@ -695,14 +700,13 @@ object DateTimeUtils {
     val millis2 = time2 / 1000L
     val date1 = millisToDays(millis1)
     val date2 = millisToDays(millis2)
-    // TODO(davies): get year, month, dayOfMonth from single function
-    val dayInMonth1 = getDayOfMonth(date1)
-    val dayInMonth2 = getDayOfMonth(date2)
-    val months1 = getYear(date1) * 12 + getMonth(date1)
-    val months2 = getYear(date2) * 12 + getMonth(date2)
-
-    if (dayInMonth1 == dayInMonth2 || (dayInMonth1 == getLastDayInMonthOfMonth(date1)
-      && dayInMonth2 == getLastDayInMonthOfMonth(date2))) {
+    val (year1, monthInYear1, dayInMonth1, daysToMonthEnd1) = splitDate(date1)
+    val (year2, monthInYear2, dayInMonth2, daysToMonthEnd2) = splitDate(date2)
+
+    val months1 = year1 * 12 + monthInYear1
+    val months2 = year2 * 12 + monthInYear2
+
+    if (dayInMonth1 == dayInMonth2 || ((daysToMonthEnd1 == 0) && (daysToMonthEnd2 == 0))) {
       return (months1 - months2).toDouble
     }
     // milliseconds is enough for 8 digits precision on the right side
@@ -745,40 +749,8 @@ object DateTimeUtils {
    * since 1.1.1970.
    */
   def getLastDayOfMonth(date: Int): Int = {
-    var (year, dayInYear) = getYearAndDayInYear(date)
-    if (isLeapYear(year)) {
-      if (dayInYear > 31 && dayInYear <= 60) {
-        return date + (60 - dayInYear)
-      } else if (dayInYear > 60) {
-        dayInYear = dayInYear - 1
-      }
-    }
-    val lastDayOfMonthInYear = if (dayInYear <= 31) {
-      31
-    } else if (dayInYear <= 59) {
-      59
-    } else if (dayInYear <= 90) {
-      90
-    } else if (dayInYear <= 120) {
-      120
-    } else if (dayInYear <= 151) {
-      151
-    } else if (dayInYear <= 181) {
-      181
-    } else if (dayInYear <= 212) {
-      212
-    } else if (dayInYear <= 243) {
-      243
-    } else if (dayInYear <= 273) {
-      273
-    } else if (dayInYear <= 304) {
-      304
-    } else if (dayInYear <= 334) {
-      334
-    } else {
-      365
-    }
-    date + (lastDayOfMonthInYear - dayInYear)
+    val (_, _, _, daysToMonthEnd) = splitDate(date)
+    date + daysToMonthEnd
   }
 
   private val TRUNC_TO_YEAR = 1

From 6a0f8b994de36b7a7bdfb9958d39dbd011776107 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Tue, 4 Aug 2015 09:40:07 -0700
Subject: [PATCH 026/633] [SPARK-9562] Change reference to amplab/spark-ec2
 from mesos/

cc srowen pwendell nchammas

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #7899 from shivaram/spark-ec2-move and squashes the following commits:

7cc22c9 [Shivaram Venkataraman] Change reference to amplab/spark-ec2 from mesos/
---
 ec2/spark_ec2.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index ccf922d9371fb..11fd7ee0ec8df 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -90,7 +90,7 @@
 DEFAULT_SPARK_GITHUB_REPO = "https://github.com/apache/spark"
 
 # Default location to get the spark-ec2 scripts (and ami-list) from
-DEFAULT_SPARK_EC2_GITHUB_REPO = "https://github.com/mesos/spark-ec2"
+DEFAULT_SPARK_EC2_GITHUB_REPO = "https://github.com/amplab/spark-ec2"
 DEFAULT_SPARK_EC2_BRANCH = "branch-1.4"
 
 

From 34a0eb2e89d59b0823efc035ddf2dc93f19540c1 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 4 Aug 2015 10:07:53 -0700
Subject: [PATCH 027/633] [SPARK-9512][SQL] Revert SPARK-9251, Allow evaluation
 while sorting

The analysis rule has a bug and we ended up making the sorter still capable of doing evaluation, so lets revert this for now.

Author: Michael Armbrust <michael@databricks.com>

Closes #7906 from marmbrus/revertSortProjection and squashes the following commits:

2da6972 [Michael Armbrust] unrevert unrelated changes
4f2b00c [Michael Armbrust] Revert "[SPARK-9251][SQL] do not order by expressions which still need evaluation"
---
 .../sql/catalyst/analysis/Analyzer.scala      | 58 -------------------
 .../plans/logical/basicOperators.scala        | 13 ++---
 .../sql/catalyst/analysis/AnalysisSuite.scala | 36 ++----------
 3 files changed, 8 insertions(+), 99 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index f5daba1543da9..ca17f3e3d06ff 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -79,7 +79,6 @@ class Analyzer(
       ExtractWindowExpressions ::
       GlobalAggregates ::
       UnresolvedHavingClauseAttributes ::
-      RemoveEvaluationFromSort ::
       HiveTypeCoercion.typeCoercionRules ++
       extendedResolutionRules : _*),
     Batch("Nondeterministic", Once,
@@ -955,63 +954,6 @@ class Analyzer(
         Project(p.output, newPlan.withNewChildren(newChild :: Nil))
     }
   }
-
-  /**
-   * Removes all still-need-evaluate ordering expressions from sort and use an inner project to
-   * materialize them, finally use a outer project to project them away to keep the result same.
-   * Then we can make sure we only sort by [[AttributeReference]]s.
-   *
-   * As an example,
-   * {{{
-   *   Sort('a, 'b + 1,
-   *     Relation('a, 'b))
-   * }}}
-   * will be turned into:
-   * {{{
-   *   Project('a, 'b,
-   *     Sort('a, '_sortCondition,
-   *       Project('a, 'b, ('b + 1).as("_sortCondition"),
-   *         Relation('a, 'b))))
-   * }}}
-   */
-  object RemoveEvaluationFromSort extends Rule[LogicalPlan] {
-    private def hasAlias(expr: Expression) = {
-      expr.find {
-        case a: Alias => true
-        case _ => false
-      }.isDefined
-    }
-
-    override def apply(plan: LogicalPlan): LogicalPlan = plan transform {
-      // The ordering expressions have no effect to the output schema of `Sort`,
-      // so `Alias`s in ordering expressions are unnecessary and we should remove them.
-      case s @ Sort(ordering, _, _) if ordering.exists(hasAlias) =>
-        val newOrdering = ordering.map(_.transformUp {
-          case Alias(child, _) => child
-        }.asInstanceOf[SortOrder])
-        s.copy(order = newOrdering)
-
-      case s @ Sort(ordering, global, child)
-        if s.expressions.forall(_.resolved) && s.childrenResolved && !s.hasNoEvaluation =>
-
-        val (ref, needEval) = ordering.partition(_.child.isInstanceOf[AttributeReference])
-
-        val namedExpr = needEval.map(_.child match {
-          case n: NamedExpression => n
-          case e => Alias(e, "_sortCondition")()
-        })
-
-        val newOrdering = ref ++ needEval.zip(namedExpr).map { case (order, ne) =>
-          order.copy(child = ne.toAttribute)
-        }
-
-        // Add still-need-evaluate ordering expressions into inner project and then project
-        // them away after the sort.
-        Project(child.output,
-          Sort(newOrdering, global,
-            Project(child.output ++ namedExpr, child)))
-    }
-  }
 }
 
 /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index aacfc86ab0e49..7c404722d811c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -34,7 +34,7 @@ case class Project(projectList: Seq[NamedExpression], child: LogicalPlan) extend
       }.nonEmpty
     )
 
-    expressions.forall(_.resolved) && childrenResolved && !hasSpecialExpressions
+    !expressions.exists(!_.resolved) && childrenResolved && !hasSpecialExpressions
   }
 }
 
@@ -68,7 +68,7 @@ case class Generate(
     generator.resolved &&
       childrenResolved &&
       generator.elementTypes.length == generatorOutput.length &&
-      generatorOutput.forall(_.resolved)
+      !generatorOutput.exists(!_.resolved)
   }
 
   // we don't want the gOutput to be taken as part of the expressions
@@ -188,7 +188,7 @@ case class WithWindowDefinition(
 }
 
 /**
- * @param order  The ordering expressions, should all be [[AttributeReference]]
+ * @param order  The ordering expressions
  * @param global True means global sorting apply for entire data set,
  *               False means sorting only apply within the partition.
  * @param child  Child logical plan
@@ -198,11 +198,6 @@ case class Sort(
     global: Boolean,
     child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
-
-  def hasNoEvaluation: Boolean = order.forall(_.child.isInstanceOf[AttributeReference])
-
-  override lazy val resolved: Boolean =
-    expressions.forall(_.resolved) && childrenResolved && hasNoEvaluation
 }
 
 case class Aggregate(
@@ -217,7 +212,7 @@ case class Aggregate(
       }.nonEmpty
     )
 
-    expressions.forall(_.resolved) && childrenResolved && !hasWindowExpressions
+    !expressions.exists(!_.resolved) && childrenResolved && !hasWindowExpressions
   }
 
   lazy val newAggregation: Option[Aggregate] = Utils.tryConvert(this)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index a86cefe941e8e..221b4e92f086c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -165,39 +165,11 @@ class AnalysisSuite extends AnalysisTest {
 
   test("pull out nondeterministic expressions from Sort") {
     val plan = Sort(Seq(SortOrder(Rand(33), Ascending)), false, testRelation)
-    val analyzed = caseSensitiveAnalyzer.execute(plan)
-    analyzed.transform {
-      case s: Sort if s.expressions.exists(!_.deterministic) =>
-        fail("nondeterministic expressions are not allowed in Sort")
-    }
-  }
-
-  test("remove still-need-evaluate ordering expressions from sort") {
-    val a = testRelation2.output(0)
-    val b = testRelation2.output(1)
-
-    def makeOrder(e: Expression): SortOrder = SortOrder(e, Ascending)
-
-    val noEvalOrdering = makeOrder(a)
-    val noEvalOrderingWithAlias = makeOrder(Alias(Alias(b, "name1")(), "name2")())
-
-    val needEvalExpr = Coalesce(Seq(a, Literal("1")))
-    val needEvalExpr2 = Coalesce(Seq(a, b))
-    val needEvalOrdering = makeOrder(needEvalExpr)
-    val needEvalOrdering2 = makeOrder(needEvalExpr2)
-
-    val plan = Sort(
-      Seq(noEvalOrdering, noEvalOrderingWithAlias, needEvalOrdering, needEvalOrdering2),
-      false, testRelation2)
-
-    val evaluatedOrdering = makeOrder(AttributeReference("_sortCondition", StringType)())
-    val materializedExprs = Seq(needEvalExpr, needEvalExpr2).map(e => Alias(e, "_sortCondition")())
-
+    val projected = Alias(Rand(33), "_nondeterministic")()
     val expected =
-      Project(testRelation2.output,
-        Sort(Seq(makeOrder(a), makeOrder(b), evaluatedOrdering, evaluatedOrdering), false,
-          Project(testRelation2.output ++ materializedExprs, testRelation2)))
-
+      Project(testRelation.output,
+        Sort(Seq(SortOrder(projected.toAttribute, Ascending)), false,
+          Project(testRelation.output :+ projected, testRelation)))
     checkAnalysis(plan, expected)
   }
 }

From 5a23213c148bfe362514f9c71f5273ebda0a848a Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 4 Aug 2015 10:12:22 -0700
Subject: [PATCH 028/633] [SPARK-8069] [ML] Add multiclass thresholds for
 ProbabilisticClassifier

This PR replaces the old "threshold" with a generalized "thresholds" Param.  We keep getThreshold,setThreshold for backwards compatibility for binary classification.

Note that the primary author of this PR is holdenk

Author: Holden Karau <holden@pigscanfly.ca>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7909 from jkbradley/holdenk-SPARK-8069-add-cutoff-aka-threshold-to-random-forest and squashes the following commits:

3952977 [Joseph K. Bradley] fixed pyspark doc test
85febc8 [Joseph K. Bradley] made python unit tests a little more robust
7eb1d86 [Joseph K. Bradley] small cleanups
6cc2ed8 [Joseph K. Bradley] Fixed remaining merge issues.
0255e44 [Joseph K. Bradley] Many cleanups for thresholds, some more tests
7565a60 [Holden Karau] fix pep8 style checks, add a getThreshold method similar to our LogisticRegression.scala one for API compat
be87f26 [Holden Karau] Convert threshold to thresholds in the python code, add specialized support for Array[Double] to shared parems codegen, etc.
6747dad [Holden Karau] Override raw2prediction for ProbabilisticClassifier, fix some tests
25df168 [Holden Karau] Fix handling of thresholds in LogisticRegression
c02d6c0 [Holden Karau] No default for thresholds
5e43628 [Holden Karau] CR feedback and fixed the renamed test
f3fbbd1 [Holden Karau] revert the changes to random forest :(
51f581c [Holden Karau] Add explicit types to public methods, fix long line
f7032eb [Holden Karau] Fix a java test bug, remove some unecessary changes
adf15b4 [Holden Karau] rename the classifier suite test to ProbabilisticClassifierSuite now that we only have it in Probabilistic
398078a [Holden Karau] move the thresholding around a bunch based on the design doc
4893bdc [Holden Karau] Use numtrees of 3 since previous result was tied (one tree for each) and the switch from different max methods picked a different element (since they were equal I think this is ok)
638854c [Holden Karau] Add a scala RandomForestClassifierSuite test based on corresponding python test
e09919c [Holden Karau] Fix return type, I need more coffee....
8d92cac [Holden Karau] Use ClassifierParams as the head
3456ed3 [Holden Karau] Add explicit return types even though just test
a0f3b0c [Holden Karau] scala style fixes
6f14314 [Holden Karau] Since hasthreshold/hasthresholds is in root classifier now
ffc8dab [Holden Karau] Update the sharedParams
0420290 [Holden Karau] Allow us to override the get methods selectively
978e77a [Holden Karau] Move HasThreshold into classifier params and start defining the overloaded getThreshold/getThresholds functions
1433e52 [Holden Karau] Revert "try and hide threshold but chainges the API so no dice there"
1f09a2e [Holden Karau] try and hide threshold but chainges the API so no dice there
efb9084 [Holden Karau] move setThresholds only to where its used
6b34809 [Holden Karau] Add a test with thresholding for the RFCS
74f54c3 [Holden Karau] Fix creation of vote array
1986fa8 [Holden Karau] Setting the thresholds only makes sense if the underlying class hasn't overridden predict, so lets push it down.
2f44b18 [Holden Karau] Add a global default of null for thresholds param
f338cfc [Holden Karau] Wait that wasn't a good idea, Revert "Some progress towards unifying threshold and thresholds"
634b06f [Holden Karau] Some progress towards unifying threshold and thresholds
85c9e01 [Holden Karau] Test passes again... little fnur
099c0f3 [Holden Karau] Move thresholds around some more (set on model not trainer)
0f46836 [Holden Karau] Start adding a classifiersuite
f70eb5e [Holden Karau] Fix test compile issues
a7d59c8 [Holden Karau] Move thresholding into Classifier trait
5d999d2 [Holden Karau] Some more progress, start adding a test (maybe try and see if we can find a better thing to use for the base of the test)
1fed644 [Holden Karau] Use thresholds to scale scores in random forest classifcation
31d6bf2 [Holden Karau] Start threading the threshold info through
0ef228c [Holden Karau] Add hasthresholds
---
 .../examples/ml/JavaSimpleParamsExample.java  |  3 +-
 .../main/python/ml/simple_params_example.py   |  2 +-
 .../examples/ml/SimpleParamsExample.scala     |  2 +-
 .../spark/ml/classification/Classifier.scala  |  3 +-
 .../classification/LogisticRegression.scala   | 47 ++++++++++--
 .../ProbabilisticClassifier.scala             | 41 +++++++++--
 .../ml/param/shared/SharedParamsCodeGen.scala | 19 ++++-
 .../spark/ml/param/shared/sharedParams.scala  | 17 ++++-
 .../org/apache/spark/ml/tree/treeParams.scala |  3 +-
 .../JavaLogisticRegressionSuite.java          |  9 ++-
 .../LogisticRegressionSuite.scala             | 28 +++++++-
 .../ml/classification/OneVsRestSuite.scala    |  2 +-
 .../ProbabilisticClassifierSuite.scala        | 57 +++++++++++++++
 python/pyspark/ml/classification.py           | 72 +++++++++++++++----
 14 files changed, 265 insertions(+), 40 deletions(-)
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
index dac649d1d5ae6..94beeced3d479 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaSimpleParamsExample.java
@@ -77,7 +77,8 @@ public static void main(String[] args) {
     ParamMap paramMap = new ParamMap();
     paramMap.put(lr.maxIter().w(20)); // Specify 1 Param.
     paramMap.put(lr.maxIter(), 30); // This overwrites the original maxIter.
-    paramMap.put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params.
+    double thresholds[] = {0.45, 0.55};
+    paramMap.put(lr.regParam().w(0.1), lr.thresholds().w(thresholds)); // Specify multiple Params.
 
     // One can also combine ParamMaps.
     ParamMap paramMap2 = new ParamMap();
diff --git a/examples/src/main/python/ml/simple_params_example.py b/examples/src/main/python/ml/simple_params_example.py
index a9f29dab2d602..2d6d115d54d02 100644
--- a/examples/src/main/python/ml/simple_params_example.py
+++ b/examples/src/main/python/ml/simple_params_example.py
@@ -70,7 +70,7 @@
 
     # We may alternatively specify parameters using a parameter map.
     # paramMap overrides all lr parameters set earlier.
-    paramMap = {lr.maxIter: 20, lr.threshold: 0.55, lr.probabilityCol: "myProbability"}
+    paramMap = {lr.maxIter: 20, lr.thresholds: [0.45, 0.55], lr.probabilityCol: "myProbability"}
 
     # Now learn a new model using the new parameters.
     model2 = lr.fit(training, paramMap)
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
index 58d7b67674ff7..f4d1fe57856a1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/SimpleParamsExample.scala
@@ -70,7 +70,7 @@ object SimpleParamsExample {
     // which supports several methods for specifying parameters.
     val paramMap = ParamMap(lr.maxIter -> 20)
     paramMap.put(lr.maxIter, 30) // Specify 1 Param.  This overwrites the original maxIter.
-    paramMap.put(lr.regParam -> 0.1, lr.threshold -> 0.55) // Specify multiple Params.
+    paramMap.put(lr.regParam -> 0.1, lr.thresholds -> Array(0.45, 0.55)) // Specify multiple Params.
 
     // One can also combine ParamMaps.
     val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability") // Change output column name
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
index 581d8fa7749be..45df557a89908 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
@@ -18,14 +18,13 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.{PredictionModel, PredictorParams, Predictor}
 import org.apache.spark.ml.param.shared.HasRawPredictionCol
 import org.apache.spark.ml.util.SchemaUtils
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
+import org.apache.spark.sql.types.{DataType, StructType}
 
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 8fc9199fb4602..c937b9602b8c1 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -41,7 +41,39 @@ import org.apache.spark.storage.StorageLevel
  */
 private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams
   with HasRegParam with HasElasticNetParam with HasMaxIter with HasFitIntercept with HasTol
-  with HasThreshold with HasStandardization
+  with HasStandardization {
+
+  /**
+   * Version of setThresholds() for binary classification, available for backwards
+   * compatibility.
+   *
+   * Calling this with threshold p will effectively call `setThresholds(Array(1-p, p))`.
+   *
+   * Default is effectively 0.5.
+   * @group setParam
+   */
+  def setThreshold(value: Double): this.type = set(thresholds, Array(1.0 - value, value))
+
+  /**
+   * Version of [[getThresholds()]] for binary classification, available for backwards
+   * compatibility.
+   *
+   * Param thresholds must have length 2 (or not be specified).
+   * This returns {{{1 / (1 + thresholds(0) / thresholds(1))}}}.
+   * @group getParam
+   */
+  def getThreshold: Double = {
+    if (isDefined(thresholds)) {
+      val thresholdValues = $(thresholds)
+      assert(thresholdValues.length == 2, "Logistic Regression getThreshold only applies to" +
+        " binary classification, but thresholds has length != 2." +
+        s"  thresholds: ${thresholdValues.mkString(",")}")
+      1.0 / (1.0 + thresholdValues(0) / thresholdValues(1))
+    } else {
+      0.5
+    }
+  }
+}
 
 /**
  * :: Experimental ::
@@ -110,9 +142,9 @@ class LogisticRegression(override val uid: String)
   def setStandardization(value: Boolean): this.type = set(standardization, value)
   setDefault(standardization -> true)
 
-  /** @group setParam */
-  def setThreshold(value: Double): this.type = set(threshold, value)
-  setDefault(threshold -> 0.5)
+  override def setThreshold(value: Double): this.type = super.setThreshold(value)
+
+  override def getThreshold: Double = super.getThreshold
 
   override protected def train(dataset: DataFrame): LogisticRegressionModel = {
     // Extract columns from data.  If dataset is persisted, do not persist oldDataset.
@@ -270,8 +302,9 @@ class LogisticRegressionModel private[ml] (
   extends ProbabilisticClassificationModel[Vector, LogisticRegressionModel]
   with LogisticRegressionParams {
 
-  /** @group setParam */
-  def setThreshold(value: Double): this.type = set(threshold, value)
+  override def setThreshold(value: Double): this.type = super.setThreshold(value)
+
+  override def getThreshold: Double = super.getThreshold
 
   /** Margin (rawPrediction) for class label 1.  For binary classification only. */
   private val margin: Vector => Double = (features) => {
@@ -288,7 +321,7 @@ class LogisticRegressionModel private[ml] (
 
   /**
    * Predict label for the given feature vector.
-   * The behavior of this can be adjusted using [[threshold]].
+   * The behavior of this can be adjusted using [[thresholds]].
    */
   override protected def predict(features: Vector): Double = {
     if (score(features) > getThreshold) 1 else 0
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
index f9c9c2371f5cd..1e50a895a9a05 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
@@ -20,17 +20,16 @@ package org.apache.spark.ml.classification
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.SchemaUtils
-import org.apache.spark.mllib.linalg.{SparseVector, DenseVector, Vector, VectorUDT}
+import org.apache.spark.mllib.linalg.{DenseVector, Vector, VectorUDT, Vectors}
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.{DoubleType, DataType, StructType}
+import org.apache.spark.sql.types.{DataType, StructType}
 
 /**
  * (private[classification])  Params for probabilistic classification.
  */
 private[classification] trait ProbabilisticClassifierParams
-  extends ClassifierParams with HasProbabilityCol {
-
+  extends ClassifierParams with HasProbabilityCol with HasThresholds {
   override protected def validateAndTransformSchema(
       schema: StructType,
       fitting: Boolean,
@@ -59,6 +58,9 @@ private[spark] abstract class ProbabilisticClassifier[
 
   /** @group setParam */
   def setProbabilityCol(value: String): E = set(probabilityCol, value).asInstanceOf[E]
+
+  /** @group setParam */
+  def setThresholds(value: Array[Double]): E = set(thresholds, value).asInstanceOf[E]
 }
 
 
@@ -80,6 +82,9 @@ private[spark] abstract class ProbabilisticClassificationModel[
   /** @group setParam */
   def setProbabilityCol(value: String): M = set(probabilityCol, value).asInstanceOf[M]
 
+  /** @group setParam */
+  def setThresholds(value: Array[Double]): M = set(thresholds, value).asInstanceOf[M]
+
   /**
    * Transforms dataset by reading from [[featuresCol]], and appending new columns as specified by
    * parameters:
@@ -92,6 +97,11 @@ private[spark] abstract class ProbabilisticClassificationModel[
    */
   override def transform(dataset: DataFrame): DataFrame = {
     transformSchema(dataset.schema, logging = true)
+    if (isDefined(thresholds)) {
+      require($(thresholds).length == numClasses, this.getClass.getSimpleName +
+        ".transform() called with non-matching numClasses and thresholds.length." +
+        s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
+    }
 
     // Output selected columns only.
     // This is a bit complicated since it tries to avoid repeated computation.
@@ -155,6 +165,14 @@ private[spark] abstract class ProbabilisticClassificationModel[
     raw2probabilityInPlace(probs)
   }
 
+  override protected def raw2prediction(rawPrediction: Vector): Double = {
+    if (!isDefined(thresholds)) {
+      rawPrediction.argmax
+    } else {
+      probability2prediction(raw2probability(rawPrediction))
+    }
+  }
+
   /**
    * Predict the probability of each class given the features.
    * These predictions are also called class conditional probabilities.
@@ -170,10 +188,21 @@ private[spark] abstract class ProbabilisticClassificationModel[
 
   /**
    * Given a vector of class conditional probabilities, select the predicted label.
-   * This may be overridden to support thresholds which favor particular labels.
+   * This supports thresholds which favor particular labels.
    * @return  predicted label
    */
-  protected def probability2prediction(probability: Vector): Double = probability.argmax
+  protected def probability2prediction(probability: Vector): Double = {
+    if (!isDefined(thresholds)) {
+      probability.argmax
+    } else {
+      val thresholds: Array[Double] = getThresholds
+      val scaledProbability: Array[Double] =
+        probability.toArray.zip(thresholds).map { case (p, t) =>
+          if (t == 0.0) Double.PositiveInfinity else p / t
+        }
+      Vectors.dense(scaledProbability).argmax
+    }
+  }
 }
 
 private[ml] object ProbabilisticClassificationModel {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index f7ae1de522e01..a97c8059b8d45 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -46,7 +46,13 @@ private[shared] object SharedParamsCodeGen {
         Some("\"probability\"")),
       ParamDesc[Double]("threshold",
         "threshold in binary classification prediction, in range [0, 1]",
-        isValid = "ParamValidators.inRange(0, 1)"),
+        isValid = "ParamValidators.inRange(0, 1)", finalMethods = false),
+      ParamDesc[Array[Double]]("thresholds", "Thresholds in multi-class classification" +
+        " to adjust the probability of predicting each class." +
+        " Array must have length equal to the number of classes, with values >= 0." +
+        " The class with largest value p/t is predicted, where p is the original probability" +
+        " of that class and t is the class' threshold.",
+        isValid = "(t: Array[Double]) => t.forall(_ >= 0)"),
       ParamDesc[String]("inputCol", "input column name"),
       ParamDesc[Array[String]]("inputCols", "input column names"),
       ParamDesc[String]("outputCol", "output column name", Some("uid + \"__output\"")),
@@ -74,7 +80,8 @@ private[shared] object SharedParamsCodeGen {
       name: String,
       doc: String,
       defaultValueStr: Option[String] = None,
-      isValid: String = "") {
+      isValid: String = "",
+      finalMethods: Boolean = true) {
 
     require(name.matches("[a-z][a-zA-Z0-9]*"), s"Param name $name is invalid.")
     require(doc.nonEmpty) // TODO: more rigorous on doc
@@ -88,6 +95,7 @@ private[shared] object SharedParamsCodeGen {
         case _ if c == classOf[Double] => "DoubleParam"
         case _ if c == classOf[Boolean] => "BooleanParam"
         case _ if c.isArray && c.getComponentType == classOf[String] => s"StringArrayParam"
+        case _ if c.isArray && c.getComponentType == classOf[Double] => s"DoubleArrayParam"
         case _ => s"Param[${getTypeString(c)}]"
       }
     }
@@ -131,6 +139,11 @@ private[shared] object SharedParamsCodeGen {
     } else {
       ""
     }
+    val methodStr = if (param.finalMethods) {
+      "final def"
+    } else {
+      "def"
+    }
 
     s"""
       |/**
@@ -145,7 +158,7 @@ private[shared] object SharedParamsCodeGen {
       |  final val $name: $Param = new $Param(this, "$name", "$doc"$isValid)
       |$setDefault
       |  /** @group getParam */
-      |  final def get$Name: $T = $$($name)
+      |  $methodStr get$Name: $T = $$($name)
       |}
       |""".stripMargin
   }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index 65e48e4ee5083..f332630c32f1b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -150,7 +150,22 @@ private[ml] trait HasThreshold extends Params {
   final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction, in range [0, 1]", ParamValidators.inRange(0, 1))
 
   /** @group getParam */
-  final def getThreshold: Double = $(threshold)
+  def getThreshold: Double = $(threshold)
+}
+
+/**
+ * Trait for shared param thresholds.
+ */
+private[ml] trait HasThresholds extends Params {
+
+  /**
+   * Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold..
+   * @group param
+   */
+  final val thresholds: DoubleArrayParam = new DoubleArrayParam(this, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.", (t: Array[Double]) => t.forall(_ >= 0))
+
+  /** @group getParam */
+  final def getThresholds: Array[Double] = $(thresholds)
 }
 
 /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
index a0c5238d966bf..e817090f8a16b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
@@ -17,9 +17,10 @@
 
 package org.apache.spark.ml.tree
 
+import org.apache.spark.ml.classification.ClassifierParams
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed}
+import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed, HasThresholds}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldGini, Impurity => OldImpurity, Variance => OldVariance}
 import org.apache.spark.mllib.tree.loss.{Loss => OldLoss}
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index f75e024a713ee..fb1de51163f2e 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -87,6 +87,8 @@ public void logisticRegressionWithSetters() {
     LogisticRegression parent = (LogisticRegression) model.parent();
     assert(parent.getMaxIter() == 10);
     assert(parent.getRegParam() == 1.0);
+    assert(parent.getThresholds()[0] == 0.4);
+    assert(parent.getThresholds()[1] == 0.6);
     assert(parent.getThreshold() == 0.6);
     assert(model.getThreshold() == 0.6);
 
@@ -98,7 +100,9 @@ public void logisticRegressionWithSetters() {
       assert(r.getDouble(0) == 0.0);
     }
     // Call transform with params, and check that the params worked.
-    model.transform(dataset, model.threshold().w(0.0), model.probabilityCol().w("myProb"))
+    double[] thresholds = {1.0, 0.0};
+    model.transform(
+      dataset, model.thresholds().w(thresholds), model.probabilityCol().w("myProb"))
       .registerTempTable("predNotAllZero");
     DataFrame predNotAllZero = jsql.sql("SELECT prediction, myProb FROM predNotAllZero");
     boolean foundNonZero = false;
@@ -108,8 +112,9 @@ public void logisticRegressionWithSetters() {
     assert(foundNonZero);
 
     // Call fit() with new params, and check as many params as we can.
+    double[] thresholds2 = {0.6, 0.4};
     LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1),
-        lr.threshold().w(0.4), lr.probabilityCol().w("theProb"));
+        lr.thresholds().w(thresholds2), lr.probabilityCol().w("theProb"));
     LogisticRegression parent2 = (LogisticRegression) model2.parent();
     assert(parent2.getMaxIter() == 5);
     assert(parent2.getRegParam() == 0.1);
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index b7dd44753896a..da13dcb42d1ca 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -91,6 +91,28 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(model.hasParent)
   }
 
+  test("setThreshold, getThreshold") {
+    val lr = new LogisticRegression
+    // default
+    withClue("LogisticRegression should not have thresholds set by default") {
+      intercept[java.util.NoSuchElementException] {
+        lr.getThresholds
+      }
+    }
+    // Set via thresholds.
+    // Intuition: Large threshold or large thresholds(1) makes class 0 more likely.
+    lr.setThreshold(1.0)
+    assert(lr.getThresholds === Array(0.0, 1.0))
+    lr.setThreshold(0.0)
+    assert(lr.getThresholds === Array(1.0, 0.0))
+    lr.setThreshold(0.5)
+    assert(lr.getThresholds === Array(0.5, 0.5))
+    // Test getThreshold
+    lr.setThresholds(Array(0.3, 0.7))
+    val expectedThreshold = 1.0 / (1.0 + 0.3 / 0.7)
+    assert(lr.getThreshold ~== expectedThreshold relTol 1E-7)
+  }
+
   test("logistic regression doesn't fit intercept when fitIntercept is off") {
     val lr = new LogisticRegression
     lr.setFitIntercept(false)
@@ -123,14 +145,16 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       s" ${predAllZero.count(_ === 0)} of ${dataset.count()} were 0.")
     // Call transform with params, and check that the params worked.
     val predNotAllZero =
-      model.transform(dataset, model.threshold -> 0.0, model.probabilityCol -> "myProb")
+      model.transform(dataset, model.thresholds -> Array(1.0, 0.0),
+        model.probabilityCol -> "myProb")
         .select("prediction", "myProb")
         .collect()
         .map { case Row(pred: Double, prob: Vector) => pred }
     assert(predNotAllZero.exists(_ !== 0.0))
 
     // Call fit() with new params, and check as many params as we can.
-    val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.threshold -> 0.4,
+    val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1,
+      lr.thresholds -> Array(0.6, 0.4),
       lr.probabilityCol -> "theProb")
     val parent2 = model2.parent.asInstanceOf[LogisticRegression]
     assert(parent2.getMaxIter === 5)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
index 3775292f6dca7..bd8e819f6926c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
@@ -151,7 +151,7 @@ class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext {
     require(ovr1.getClassifier.getOrDefault(lr.maxIter) === 10,
       "copy should handle extra classifier params")
 
-    val ovrModel = ovr1.fit(dataset).copy(ParamMap(lr.threshold -> 0.1))
+    val ovrModel = ovr1.fit(dataset).copy(ParamMap(lr.thresholds -> Array(0.9, 0.1)))
     ovrModel.models.foreach { case m: LogisticRegressionModel =>
       require(m.getThreshold === 0.1, "copy should handle extra model params")
     }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala
new file mode 100644
index 0000000000000..8f50cb924e64d
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/ProbabilisticClassifierSuite.scala
@@ -0,0 +1,57 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.classification
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+
+final class TestProbabilisticClassificationModel(
+    override val uid: String,
+    override val numClasses: Int)
+  extends ProbabilisticClassificationModel[Vector, TestProbabilisticClassificationModel] {
+
+  override def copy(extra: org.apache.spark.ml.param.ParamMap): this.type = defaultCopy(extra)
+
+  override protected def predictRaw(input: Vector): Vector = {
+    input
+  }
+
+  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
+    rawPrediction
+  }
+
+  def friendlyPredict(input: Vector): Double = {
+    predict(input)
+  }
+}
+
+
+class ProbabilisticClassifierSuite extends SparkFunSuite {
+
+  test("test thresholding") {
+    val thresholds = Array(0.5, 0.2)
+    val testModel = new TestProbabilisticClassificationModel("myuid", 2).setThresholds(thresholds)
+    assert(testModel.friendlyPredict(Vectors.dense(Array(1.0, 1.0))) === 1.0)
+    assert(testModel.friendlyPredict(Vectors.dense(Array(1.0, 0.2))) === 0.0)
+  }
+
+  test("test thresholding not required") {
+    val testModel = new TestProbabilisticClassificationModel("myuid", 2)
+    assert(testModel.friendlyPredict(Vectors.dense(Array(1.0, 2.0))) === 1.0)
+  }
+}
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index b5814f76de000..291320f881e7c 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -69,17 +69,25 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
               "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
               "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
     fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.")
-    threshold = Param(Params._dummy(), "threshold",
-                      "threshold in binary classification prediction, in range [0, 1].")
+    thresholds = Param(Params._dummy(), "thresholds",
+                       "Thresholds in multi-class classification" +
+                       " to adjust the probability of predicting each class." +
+                       " Array must have length equal to the number of classes, with values >= 0." +
+                       " The class with largest value p/t is predicted, where p is the original" +
+                       " probability of that class and t is the class' threshold.")
 
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                 threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction"):
+                 threshold=None, thresholds=None,
+                 probabilityCol="probability", rawPredictionCol="rawPrediction"):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction")
+                 threshold=None, thresholds=None, \
+                 probabilityCol="probability", rawPredictionCol="rawPrediction")
+        Param thresholds overrides Param threshold; threshold is provided
+        for backwards compatibility and only applies to binary classification.
         """
         super(LogisticRegression, self).__init__()
         self._java_obj = self._new_java_obj(
@@ -93,23 +101,35 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
         #: param for whether to fit an intercept term.
         self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
         #: param for threshold in binary classification prediction, in range [0, 1].
-        self.threshold = Param(self, "threshold",
-                               "threshold in binary classification prediction, in range [0, 1].")
+        self.thresholds = \
+            Param(self, "thresholds",
+                  "Thresholds in multi-class classification" +
+                  " to adjust the probability of predicting each class." +
+                  " Array must have length equal to the number of classes, with values >= 0." +
+                  " The class with largest value p/t is predicted, where p is the original" +
+                  " probability of that class and t is the class' threshold.")
         self._setDefault(maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1E-6,
-                         fitIntercept=True, threshold=0.5)
+                         fitIntercept=True)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                  threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction"):
+                  threshold=None, thresholds=None,
+                  probabilityCol="probability", rawPredictionCol="rawPrediction"):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 threshold=0.5, probabilityCol="probability", rawPredictionCol="rawPrediction")
+                  threshold=None, thresholds=None, \
+                  probabilityCol="probability", rawPredictionCol="rawPrediction")
         Sets params for logistic regression.
+        Param thresholds overrides Param threshold; threshold is provided
+        for backwards compatibility and only applies to binary classification.
         """
+        # Under the hood we use thresholds so translate threshold to thresholds if applicable
+        if thresholds is None and threshold is not None:
+            kwargs[thresholds] = [1-threshold, threshold]
         kwargs = self.setParams._input_kwargs
         return self._set(**kwargs)
 
@@ -144,16 +164,44 @@ def getFitIntercept(self):
 
     def setThreshold(self, value):
         """
-        Sets the value of :py:attr:`threshold`.
+        Sets the value of :py:attr:`thresholds` using [1-value, value].
+
+        >>> lr = LogisticRegression()
+        >>> lr.getThreshold()
+        0.5
+        >>> lr.setThreshold(0.6)
+        LogisticRegression_...
+        >>> abs(lr.getThreshold() - 0.6) < 1e-5
+        True
+        """
+        return self.setThresholds([1-value, value])
+
+    def setThresholds(self, value):
+        """
+        Sets the value of :py:attr:`thresholds`.
         """
-        self._paramMap[self.threshold] = value
+        self._paramMap[self.thresholds] = value
         return self
 
+    def getThresholds(self):
+        """
+        Gets the value of thresholds or its default value.
+        """
+        return self.getOrDefault(self.thresholds)
+
     def getThreshold(self):
         """
         Gets the value of threshold or its default value.
         """
-        return self.getOrDefault(self.threshold)
+        if self.isDefined(self.thresholds):
+            thresholds = self.getOrDefault(self.thresholds)
+            if len(thresholds) != 2:
+                raise ValueError("Logistic Regression getThreshold only applies to" +
+                                 " binary classification, but thresholds has length != 2." +
+                                 "  thresholds: " + ",".join(thresholds))
+            return 1.0/(1.0+thresholds[0]/thresholds[1])
+        else:
+            return 0.5
 
 
 class LogisticRegressionModel(JavaModel):

From a0cc01759b0c2cecf340c885d391976eb4e3fad6 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 4 Aug 2015 12:19:52 -0700
Subject: [PATCH 029/633] [SPARK-9606] [SQL] Ignore flaky thrift server tests

Author: Michael Armbrust <michael@databricks.com>

Closes #7939 from marmbrus/turnOffThriftTests and squashes the following commits:

80d618e [Michael Armbrust] [SPARK-9606][SQL] Ignore flaky thrift server tests
---
 .../spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
index 8374629b5d45a..17e7044c46ecb 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
@@ -38,7 +38,7 @@ import org.apache.hive.service.cli.thrift.TCLIService.Client
 import org.apache.hive.service.cli.thrift.ThriftCLIServiceClient
 import org.apache.thrift.protocol.TBinaryProtocol
 import org.apache.thrift.transport.TSocket
-import org.scalatest.BeforeAndAfterAll
+import org.scalatest.{Ignore, BeforeAndAfterAll}
 
 import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.sql.hive.HiveContext
@@ -53,6 +53,7 @@ object TestData {
   val smallKvWithNull = getTestDataFilePath("small_kv_with_null.txt")
 }
 
+@Ignore // SPARK-9606
 class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest {
   override def mode: ServerMode.Value = ServerMode.binary
 
@@ -379,6 +380,7 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest {
   }
 }
 
+@Ignore // SPARK-9606
 class HiveThriftHttpServerSuite extends HiveThriftJdbcTest {
   override def mode: ServerMode.Value = ServerMode.http
 

From f4b1ac08a1327e6d0ddc317cdf3997a0f68dec72 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 4 Aug 2015 14:40:46 -0700
Subject: [PATCH 030/633] [SPARK-9553][SQL] remove the no-longer-necessary
 createCode and createStructCode, and replace the usage

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7890 from cloud-fan/minor and squashes the following commits:

c3b1be3 [Wenchen Fan] fix style
b0cbe2e [Wenchen Fan] remove the createCode and createStructCode, and replace the usage of them by createStructCode
---
 .../codegen/GenerateUnsafeProjection.scala    | 161 +-----------------
 .../expressions/complexTypeCreator.scala      |  10 +-
 2 files changed, 17 insertions(+), 154 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index fc3ecf5451426..71f8ea09f0770 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -116,154 +116,6 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       throw new UnsupportedOperationException(s"Not supported DataType: $fieldType")
   }
 
-  /**
-   * Generates the code to create an [[UnsafeRow]] object based on the input expressions.
-   * @param ctx context for code generation
-   * @param ev specifies the name of the variable for the output [[UnsafeRow]] object
-   * @param expressions input expressions
-   * @return generated code to put the expression output into an [[UnsafeRow]]
-   */
-  def createCode(ctx: CodeGenContext, ev: GeneratedExpressionCode, expressions: Seq[Expression])
-    : String = {
-
-    val ret = ev.primitive
-    ctx.addMutableState("UnsafeRow", ret, s"$ret = new UnsafeRow();")
-    val buffer = ctx.freshName("buffer")
-    ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
-    val cursor = ctx.freshName("cursor")
-    val numBytes = ctx.freshName("numBytes")
-
-    val exprs = expressions.map { e => e.dataType match {
-      case st: StructType => createCodeForStruct(ctx, e.gen(ctx), st)
-      case _ => e.gen(ctx)
-    }}
-    val allExprs = exprs.map(_.code).mkString("\n")
-
-    val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
-    val additionalSize = expressions.zipWithIndex.map {
-      case (e, i) => genAdditionalSize(e.dataType, exprs(i))
-    }.mkString("")
-
-    val writers = expressions.zipWithIndex.map { case (e, i) =>
-      val update = genFieldWriter(ctx, e.dataType, exprs(i), ret, i, cursor)
-      s"""if (${exprs(i).isNull}) {
-            $ret.setNullAt($i);
-          } else {
-            $update;
-          }"""
-    }.mkString("\n          ")
-
-    s"""
-      $allExprs
-      int $numBytes = $fixedSize $additionalSize;
-      if ($numBytes > $buffer.length) {
-        $buffer = new byte[$numBytes];
-      }
-
-      $ret.pointTo(
-        $buffer,
-        $PlatformDependent.BYTE_ARRAY_OFFSET,
-        ${expressions.size},
-        $numBytes);
-      int $cursor = $fixedSize;
-
-      $writers
-      boolean ${ev.isNull} = false;
-     """
-  }
-
-  /**
-   * Generates the Java code to convert a struct (backed by InternalRow) to UnsafeRow.
-   *
-   * This function also handles nested structs by recursively generating the code to do conversion.
-   *
-   * @param ctx code generation context
-   * @param input the input struct, identified by a [[GeneratedExpressionCode]]
-   * @param schema schema of the struct field
-   */
-  // TODO: refactor createCode and this function to reduce code duplication.
-  private def createCodeForStruct(
-      ctx: CodeGenContext,
-      input: GeneratedExpressionCode,
-      schema: StructType): GeneratedExpressionCode = {
-
-    val isNull = input.isNull
-    val primitive = ctx.freshName("structConvert")
-    ctx.addMutableState("UnsafeRow", primitive, s"$primitive = new UnsafeRow();")
-    val buffer = ctx.freshName("buffer")
-    ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
-    val cursor = ctx.freshName("cursor")
-
-    val exprs: Seq[GeneratedExpressionCode] = schema.map(_.dataType).zipWithIndex.map {
-      case (dt, i) => dt match {
-        case st: StructType =>
-          val nestedStructEv = GeneratedExpressionCode(
-            code = "",
-            isNull = s"${input.primitive}.isNullAt($i)",
-            primitive = s"${ctx.getValue(input.primitive, dt, i.toString)}"
-          )
-          createCodeForStruct(ctx, nestedStructEv, st)
-        case _ =>
-          GeneratedExpressionCode(
-            code = "",
-            isNull = s"${input.primitive}.isNullAt($i)",
-            primitive = s"${ctx.getValue(input.primitive, dt, i.toString)}"
-          )
-        }
-    }
-    val allExprs = exprs.map(_.code).mkString("\n")
-
-    val fixedSize = 8 * exprs.length + UnsafeRow.calculateBitSetWidthInBytes(exprs.length)
-    val additionalSize = schema.toSeq.map(_.dataType).zip(exprs).map { case (dt, ev) =>
-      genAdditionalSize(dt, ev)
-    }.mkString("")
-
-    val writers = schema.toSeq.map(_.dataType).zip(exprs).zipWithIndex.map { case ((dt, ev), i) =>
-      val update = genFieldWriter(ctx, dt, ev, primitive, i, cursor)
-      s"""
-          if (${exprs(i).isNull}) {
-            $primitive.setNullAt($i);
-          } else {
-            $update;
-          }
-        """
-    }.mkString("\n          ")
-
-    // Note that we add a shortcut here for performance: if the input is already an UnsafeRow,
-    // just copy the bytes directly into our buffer space without running any conversion.
-    // We also had to use a hack to introduce a "tmp" variable, to avoid the Java compiler from
-    // complaining that a GenericMutableRow (generated by expressions) cannot be cast to UnsafeRow.
-    val tmp = ctx.freshName("tmp")
-    val numBytes = ctx.freshName("numBytes")
-    val code = s"""
-       |${input.code}
-       |if (!${input.isNull}) {
-       |  Object $tmp = (Object) ${input.primitive};
-       |  if ($tmp instanceof UnsafeRow) {
-       |    $primitive = (UnsafeRow) $tmp;
-       |  } else {
-       |    $allExprs
-       |
-       |    int $numBytes = $fixedSize $additionalSize;
-       |    if ($numBytes > $buffer.length) {
-       |      $buffer = new byte[$numBytes];
-       |    }
-       |
-       |    $primitive.pointTo(
-       |      $buffer,
-       |      $PlatformDependent.BYTE_ARRAY_OFFSET,
-       |      ${exprs.size},
-       |      $numBytes);
-       |    int $cursor = $fixedSize;
-       |
-       |    $writers
-       |  }
-       |}
-     """.stripMargin
-
-    GeneratedExpressionCode(code, isNull, primitive)
-  }
-
   /**
    * Generates the Java code to convert a struct (backed by InternalRow) to UnsafeRow.
    *
@@ -271,7 +123,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
    * @param inputs could be the codes for expressions or input struct fields.
    * @param inputTypes types of the inputs
    */
-  private def createCodeForStruct2(
+  private def createCodeForStruct(
       ctx: CodeGenContext,
       inputs: Seq[GeneratedExpressionCode],
       inputTypes: Seq[DataType]): GeneratedExpressionCode = {
@@ -537,7 +389,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         val fieldIsNull = s"$tmp.isNullAt($i)"
         GeneratedExpressionCode("", fieldIsNull, getFieldCode)
       }
-      val converter = createCodeForStruct2(ctx, fieldEvals, fieldTypes)
+      val converter = createCodeForStruct(ctx, fieldEvals, fieldTypes)
       val code = s"""
         ${input.code}
          UnsafeRow $output = null;
@@ -561,6 +413,12 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     case _ => input
   }
 
+  def createCode(ctx: CodeGenContext, expressions: Seq[Expression]): GeneratedExpressionCode = {
+    val exprEvals = expressions.map(e => e.gen(ctx))
+    val exprTypes = expressions.map(_.dataType)
+    createCodeForStruct(ctx, exprEvals, exprTypes)
+  }
+
   protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
     in.map(ExpressionCanonicalizer.execute)
 
@@ -570,8 +428,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
   protected def create(expressions: Seq[Expression]): UnsafeProjection = {
     val ctx = newCodeGenContext()
 
-    val exprEvals = expressions.map(e => e.gen(ctx))
-    val eval = createCodeForStruct2(ctx, exprEvals, expressions.map(_.dataType))
+    val eval = createCode(ctx, expressions)
 
     val code = s"""
       public Object generate($exprType[] exprs) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index a145dfb4bbf08..4a071e663e0d1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -211,7 +211,10 @@ case class CreateStructUnsafe(children: Seq[Expression]) extends Expression {
   override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    GenerateUnsafeProjection.createCode(ctx, ev, children)
+    val eval = GenerateUnsafeProjection.createCode(ctx, children)
+    ev.isNull = eval.isNull
+    ev.primitive = eval.primitive
+    eval.code
   }
 
   override def prettyName: String = "struct_unsafe"
@@ -246,7 +249,10 @@ case class CreateNamedStructUnsafe(children: Seq[Expression]) extends Expression
   override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    GenerateUnsafeProjection.createCode(ctx, ev, valExprs)
+    val eval = GenerateUnsafeProjection.createCode(ctx, valExprs)
+    ev.isNull = eval.isNull
+    ev.primitive = eval.primitive
+    eval.code
   }
 
   override def prettyName: String = "named_struct_unsafe"

From ab8ee1a3b93286a62949569615086ef5030e9fae Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 4 Aug 2015 14:42:11 -0700
Subject: [PATCH 031/633] [SPARK-9452] [SQL] Support records larger than page
 size in UnsafeExternalSorter

This patch extends UnsafeExternalSorter to support records larger than the page size. The basic strategy is the same as in #7762: store large records in their own overflow pages.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7891 from JoshRosen/large-records-in-sql-sorter and squashes the following commits:

967580b [Josh Rosen] Merge remote-tracking branch 'origin/master' into large-records-in-sql-sorter
948c344 [Josh Rosen] Add large records tests for KV sorter.
3c17288 [Josh Rosen] Combine memory and disk cleanup into general cleanupResources() method
380f217 [Josh Rosen] Merge remote-tracking branch 'origin/master' into large-records-in-sql-sorter
27eafa0 [Josh Rosen] Fix page size in PackedRecordPointerSuite
a49baef [Josh Rosen] Address initial round of review comments
3edb931 [Josh Rosen] Remove accidentally-committed debug statements.
2b164e2 [Josh Rosen] Support large records in UnsafeExternalSorter.
---
 .../unsafe/sort/UnsafeExternalSorter.java     | 173 ++++++++++-----
 .../unsafe/PackedRecordPointerSuite.java      |   8 +-
 .../sort/UnsafeExternalSorterSuite.java       | 129 ++++++++---
 .../execution/UnsafeExternalRowSorter.java    |   2 +-
 .../sql/execution/UnsafeKVExternalSorter.java |  11 +-
 .../UnsafeKVExternalSorterSuite.scala         | 210 +++++++++++-------
 .../unsafe/memory/HeapMemoryAllocator.java    |   3 +
 .../unsafe/memory/UnsafeMemoryAllocator.java  |   3 +
 8 files changed, 372 insertions(+), 167 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index dec7fcfa0ddc1..e6ddd08e5fa99 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -34,6 +34,7 @@
 import org.apache.spark.executor.ShuffleWriteMetrics;
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.storage.BlockManager;
+import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
@@ -143,8 +144,7 @@ private UnsafeExternalSorter(
     taskContext.addOnCompleteCallback(new AbstractFunction0<BoxedUnit>() {
       @Override
       public BoxedUnit apply() {
-        deleteSpillFiles();
-        freeMemory();
+        cleanupResources();
         return null;
       }
     });
@@ -249,7 +249,7 @@ public int getNumberOfAllocatedPages() {
    *
    * @return the number of bytes freed.
    */
-  public long freeMemory() {
+  private long freeMemory() {
     updatePeakMemoryUsed();
     long memoryFreed = 0;
     for (MemoryBlock block : allocatedPages) {
@@ -275,44 +275,32 @@ public long freeMemory() {
   /**
    * Deletes any spill files created by this sorter.
    */
-  public void deleteSpillFiles() {
+  private void deleteSpillFiles() {
     for (UnsafeSorterSpillWriter spill : spillWriters) {
       File file = spill.getFile();
       if (file != null && file.exists()) {
         if (!file.delete()) {
           logger.error("Was unable to delete spill file {}", file.getAbsolutePath());
-        };
+        }
       }
     }
   }
 
   /**
-   * Checks whether there is enough space to insert a new record into the sorter.
-   *
-   * @param requiredSpace the required space in the data page, in bytes, including space for storing
-   *                      the record size.
-
-   * @return true if the record can be inserted without requiring more allocations, false otherwise.
+   * Frees this sorter's in-memory data structures and cleans up its spill files.
    */
-  private boolean haveSpaceForRecord(int requiredSpace) {
-    assert(requiredSpace > 0);
-    assert(inMemSorter != null);
-    return (inMemSorter.hasSpaceForAnotherRecord() && (requiredSpace <= freeSpaceInCurrentPage));
+  public void cleanupResources() {
+    deleteSpillFiles();
+    freeMemory();
   }
 
   /**
-   * Allocates more memory in order to insert an additional record. This will request additional
-   * memory from the {@link ShuffleMemoryManager} and spill if the requested memory can not be
-   * obtained.
-   *
-   * @param requiredSpace the required space in the data page, in bytes, including space for storing
-   *                      the record size.
+   * Checks whether there is enough space to insert an additional record in to the sort pointer
+   * array and grows the array if additional space is required. If the required space cannot be
+   * obtained, then the in-memory data will be spilled to disk.
    */
-  private void allocateSpaceForRecord(int requiredSpace) throws IOException {
+  private void growPointerArrayIfNecessary() throws IOException {
     assert(inMemSorter != null);
-    // TODO: merge these steps to first calculate total memory requirements for this insert,
-    // then try to acquire; no point in acquiring sort buffer only to spill due to no space in the
-    // data page.
     if (!inMemSorter.hasSpaceForAnotherRecord()) {
       logger.debug("Attempting to expand sort pointer array");
       final long oldPointerArrayMemoryUsage = inMemSorter.getMemoryUsage();
@@ -326,7 +314,20 @@ private void allocateSpaceForRecord(int requiredSpace) throws IOException {
         shuffleMemoryManager.release(oldPointerArrayMemoryUsage);
       }
     }
+  }
 
+  /**
+   * Allocates more memory in order to insert an additional record. This will request additional
+   * memory from the {@link ShuffleMemoryManager} and spill if the requested memory can not be
+   * obtained.
+   *
+   * @param requiredSpace the required space in the data page, in bytes, including space for storing
+   *                      the record size. This must be less than or equal to the page size (records
+   *                      that exceed the page size are handled via a different code path which uses
+   *                      special overflow pages).
+   */
+  private void acquireNewPageIfNecessary(int requiredSpace) throws IOException {
+    assert (requiredSpace <= pageSizeBytes);
     if (requiredSpace > freeSpaceInCurrentPage) {
       logger.trace("Required space {} is less than free space in current page ({})", requiredSpace,
         freeSpaceInCurrentPage);
@@ -339,9 +340,7 @@ private void allocateSpaceForRecord(int requiredSpace) throws IOException {
       } else {
         final long memoryAcquired = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
         if (memoryAcquired < pageSizeBytes) {
-          if (memoryAcquired > 0) {
-            shuffleMemoryManager.release(memoryAcquired);
-          }
+          shuffleMemoryManager.release(memoryAcquired);
           spill();
           final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
           if (memoryAcquiredAfterSpilling != pageSizeBytes) {
@@ -365,26 +364,59 @@ public void insertRecord(
       long recordBaseOffset,
       int lengthInBytes,
       long prefix) throws IOException {
+
+    growPointerArrayIfNecessary();
     // Need 4 bytes to store the record length.
     final int totalSpaceRequired = lengthInBytes + 4;
-    if (!haveSpaceForRecord(totalSpaceRequired)) {
-      allocateSpaceForRecord(totalSpaceRequired);
+
+    // --- Figure out where to insert the new record ----------------------------------------------
+
+    final MemoryBlock dataPage;
+    long dataPagePosition;
+    boolean useOverflowPage = totalSpaceRequired > pageSizeBytes;
+    if (useOverflowPage) {
+      long overflowPageSize = ByteArrayMethods.roundNumberOfBytesToNearestWord(totalSpaceRequired);
+      // The record is larger than the page size, so allocate a special overflow page just to hold
+      // that record.
+      final long memoryGranted = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+      if (memoryGranted != overflowPageSize) {
+        shuffleMemoryManager.release(memoryGranted);
+        spill();
+        final long memoryGrantedAfterSpill = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+        if (memoryGrantedAfterSpill != overflowPageSize) {
+          shuffleMemoryManager.release(memoryGrantedAfterSpill);
+          throw new IOException("Unable to acquire " + overflowPageSize + " bytes of memory");
+        }
+      }
+      MemoryBlock overflowPage = taskMemoryManager.allocatePage(overflowPageSize);
+      allocatedPages.add(overflowPage);
+      dataPage = overflowPage;
+      dataPagePosition = overflowPage.getBaseOffset();
+    } else {
+      // The record is small enough to fit in a regular data page, but the current page might not
+      // have enough space to hold it (or no pages have been allocated yet).
+      acquireNewPageIfNecessary(totalSpaceRequired);
+      dataPage = currentPage;
+      dataPagePosition = currentPagePosition;
+      // Update bookkeeping information
+      freeSpaceInCurrentPage -= totalSpaceRequired;
+      currentPagePosition += totalSpaceRequired;
     }
-    assert(inMemSorter != null);
+    final Object dataPageBaseObject = dataPage.getBaseObject();
+
+    // --- Insert the record ----------------------------------------------------------------------
 
     final long recordAddress =
-      taskMemoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
-    final Object dataPageBaseObject = currentPage.getBaseObject();
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, lengthInBytes);
-    currentPagePosition += 4;
+      taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
+    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
+    dataPagePosition += 4;
     PlatformDependent.copyMemory(
       recordBaseObject,
       recordBaseOffset,
       dataPageBaseObject,
-      currentPagePosition,
+      dataPagePosition,
       lengthInBytes);
-    currentPagePosition += lengthInBytes;
-    freeSpaceInCurrentPage -= totalSpaceRequired;
+    assert(inMemSorter != null);
     inMemSorter.insertRecord(recordAddress, prefix);
   }
 
@@ -399,33 +431,70 @@ public void insertRecord(
   public void insertKVRecord(
       Object keyBaseObj, long keyOffset, int keyLen,
       Object valueBaseObj, long valueOffset, int valueLen, long prefix) throws IOException {
+
+    growPointerArrayIfNecessary();
     final int totalSpaceRequired = keyLen + valueLen + 4 + 4;
-    if (!haveSpaceForRecord(totalSpaceRequired)) {
-      allocateSpaceForRecord(totalSpaceRequired);
+
+    // --- Figure out where to insert the new record ----------------------------------------------
+
+    final MemoryBlock dataPage;
+    long dataPagePosition;
+    boolean useOverflowPage = totalSpaceRequired > pageSizeBytes;
+    if (useOverflowPage) {
+      long overflowPageSize = ByteArrayMethods.roundNumberOfBytesToNearestWord(totalSpaceRequired);
+      // The record is larger than the page size, so allocate a special overflow page just to hold
+      // that record.
+      final long memoryGranted = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+      if (memoryGranted != overflowPageSize) {
+        shuffleMemoryManager.release(memoryGranted);
+        spill();
+        final long memoryGrantedAfterSpill = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+        if (memoryGrantedAfterSpill != overflowPageSize) {
+          shuffleMemoryManager.release(memoryGrantedAfterSpill);
+          throw new IOException("Unable to acquire " + overflowPageSize + " bytes of memory");
+        }
+      }
+      MemoryBlock overflowPage = taskMemoryManager.allocatePage(overflowPageSize);
+      allocatedPages.add(overflowPage);
+      dataPage = overflowPage;
+      dataPagePosition = overflowPage.getBaseOffset();
+    } else {
+      // The record is small enough to fit in a regular data page, but the current page might not
+      // have enough space to hold it (or no pages have been allocated yet).
+      acquireNewPageIfNecessary(totalSpaceRequired);
+      dataPage = currentPage;
+      dataPagePosition = currentPagePosition;
+      // Update bookkeeping information
+      freeSpaceInCurrentPage -= totalSpaceRequired;
+      currentPagePosition += totalSpaceRequired;
     }
-    assert(inMemSorter != null);
+    final Object dataPageBaseObject = dataPage.getBaseObject();
+
+    // --- Insert the record ----------------------------------------------------------------------
 
     final long recordAddress =
-      taskMemoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
-    final Object dataPageBaseObject = currentPage.getBaseObject();
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, keyLen + valueLen + 4);
-    currentPagePosition += 4;
+      taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
+    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, keyLen + valueLen + 4);
+    dataPagePosition += 4;
 
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, keyLen);
-    currentPagePosition += 4;
+    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, keyLen);
+    dataPagePosition += 4;
 
     PlatformDependent.copyMemory(
-      keyBaseObj, keyOffset, dataPageBaseObject, currentPagePosition, keyLen);
-    currentPagePosition += keyLen;
+      keyBaseObj, keyOffset, dataPageBaseObject, dataPagePosition, keyLen);
+    dataPagePosition += keyLen;
 
     PlatformDependent.copyMemory(
-      valueBaseObj, valueOffset, dataPageBaseObject, currentPagePosition, valueLen);
-    currentPagePosition += valueLen;
+      valueBaseObj, valueOffset, dataPageBaseObject, dataPagePosition, valueLen);
 
-    freeSpaceInCurrentPage -= totalSpaceRequired;
+    assert(inMemSorter != null);
     inMemSorter.insertRecord(recordAddress, prefix);
   }
 
+  /**
+   * Returns a sorted iterator. It is the caller's responsibility to call `cleanupResources()`
+   * after consuming this iterator.
+   */
   public UnsafeSorterIterator getSortedIterator() throws IOException {
     assert(inMemSorter != null);
     final UnsafeInMemorySorter.SortedIterator inMemoryIterator = inMemSorter.getSortedIterator();
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/PackedRecordPointerSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/PackedRecordPointerSuite.java
index db9e82759090a..934b7e03050b6 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/PackedRecordPointerSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/PackedRecordPointerSuite.java
@@ -32,8 +32,8 @@ public class PackedRecordPointerSuite {
   public void heap() {
     final TaskMemoryManager memoryManager =
       new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP));
-    final MemoryBlock page0 = memoryManager.allocatePage(100);
-    final MemoryBlock page1 = memoryManager.allocatePage(100);
+    final MemoryBlock page0 = memoryManager.allocatePage(128);
+    final MemoryBlock page1 = memoryManager.allocatePage(128);
     final long addressInPage1 = memoryManager.encodePageNumberAndOffset(page1,
       page1.getBaseOffset() + 42);
     PackedRecordPointer packedPointer = new PackedRecordPointer();
@@ -50,8 +50,8 @@ public void heap() {
   public void offHeap() {
     final TaskMemoryManager memoryManager =
       new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.UNSAFE));
-    final MemoryBlock page0 = memoryManager.allocatePage(100);
-    final MemoryBlock page1 = memoryManager.allocatePage(100);
+    final MemoryBlock page0 = memoryManager.allocatePage(128);
+    final MemoryBlock page1 = memoryManager.allocatePage(128);
     final long addressInPage1 = memoryManager.encodePageNumberAndOffset(page1,
       page1.getBaseOffset() + 42);
     PackedRecordPointer packedPointer = new PackedRecordPointer();
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index c11949d57a0ea..968185bde78ab 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -18,8 +18,10 @@
 package org.apache.spark.util.collection.unsafe.sort;
 
 import java.io.File;
+import java.io.IOException;
 import java.io.InputStream;
 import java.io.OutputStream;
+import java.util.Arrays;
 import java.util.LinkedList;
 import java.util.UUID;
 
@@ -34,6 +36,7 @@
 import org.mockito.MockitoAnnotations;
 import org.mockito.invocation.InvocationOnMock;
 import org.mockito.stubbing.Answer;
+import static org.hamcrest.Matchers.greaterThanOrEqualTo;
 import static org.junit.Assert.*;
 import static org.mockito.AdditionalAnswers.returnsSecondArg;
 import static org.mockito.Answers.RETURNS_SMART_NULLS;
@@ -77,12 +80,13 @@ public int compare(
     }
   };
 
+  SparkConf sparkConf;
+  File tempDir;
   ShuffleMemoryManager shuffleMemoryManager;
   @Mock(answer = RETURNS_SMART_NULLS) BlockManager blockManager;
   @Mock(answer = RETURNS_SMART_NULLS) DiskBlockManager diskBlockManager;
   @Mock(answer = RETURNS_SMART_NULLS) TaskContext taskContext;
 
-  File tempDir;
 
   private final long pageSizeBytes = new SparkConf().getSizeAsBytes("spark.buffer.pageSize", "64m");
 
@@ -96,6 +100,7 @@ public OutputStream apply(OutputStream stream) {
   @Before
   public void setUp() {
     MockitoAnnotations.initMocks(this);
+    sparkConf = new SparkConf();
     tempDir = new File(Utils.createTempDir$default$1());
     shuffleMemoryManager = new ShuffleMemoryManager(Long.MAX_VALUE);
     spillFilesCreated.clear();
@@ -155,14 +160,19 @@ private void assertSpillFilesWereCleanedUp() {
   }
 
   private static void insertNumber(UnsafeExternalSorter sorter, int value) throws Exception {
-    final int[] arr = new int[] { value };
+    final int[] arr = new int[]{ value };
     sorter.insertRecord(arr, PlatformDependent.INT_ARRAY_OFFSET, 4, value);
   }
 
-  @Test
-  public void testSortingOnlyByPrefix() throws Exception {
+  private static void insertRecord(
+      UnsafeExternalSorter sorter,
+      int[] record,
+      long prefix) throws IOException {
+    sorter.insertRecord(record, PlatformDependent.INT_ARRAY_OFFSET, record.length * 4, prefix);
+  }
 
-    final UnsafeExternalSorter sorter = UnsafeExternalSorter.create(
+  private UnsafeExternalSorter newSorter() throws IOException {
+    return UnsafeExternalSorter.create(
       taskMemoryManager,
       shuffleMemoryManager,
       blockManager,
@@ -171,7 +181,11 @@ public void testSortingOnlyByPrefix() throws Exception {
       prefixComparator,
       /* initialSize */ 1024,
       pageSizeBytes);
+  }
 
+  @Test
+  public void testSortingOnlyByPrefix() throws Exception {
+    final UnsafeExternalSorter sorter = newSorter();
     insertNumber(sorter, 5);
     insertNumber(sorter, 1);
     insertNumber(sorter, 3);
@@ -186,26 +200,16 @@ public void testSortingOnlyByPrefix() throws Exception {
       iter.loadNext();
       assertEquals(i, iter.getKeyPrefix());
       assertEquals(4, iter.getRecordLength());
-      // TODO: read rest of value.
+      assertEquals(i, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
     }
 
-    sorter.freeMemory();
+    sorter.cleanupResources();
     assertSpillFilesWereCleanedUp();
   }
 
   @Test
   public void testSortingEmptyArrays() throws Exception {
-
-    final UnsafeExternalSorter sorter = UnsafeExternalSorter.create(
-      taskMemoryManager,
-      shuffleMemoryManager,
-      blockManager,
-      taskContext,
-      recordComparator,
-      prefixComparator,
-      /* initialSize */ 1024,
-      pageSizeBytes);
-
+    final UnsafeExternalSorter sorter = newSorter();
     sorter.insertRecord(null, 0, 0, 0);
     sorter.insertRecord(null, 0, 0, 0);
     sorter.spill();
@@ -222,28 +226,89 @@ public void testSortingEmptyArrays() throws Exception {
       assertEquals(0, iter.getRecordLength());
     }
 
-    sorter.freeMemory();
+    sorter.cleanupResources();
     assertSpillFilesWereCleanedUp();
   }
 
   @Test
-  public void testFillingPage() throws Exception {
+  public void spillingOccursInResponseToMemoryPressure() throws Exception {
+    shuffleMemoryManager = new ShuffleMemoryManager(pageSizeBytes * 2);
+    final UnsafeExternalSorter sorter = newSorter();
+    final int numRecords = 100000;
+    for (int i = 0; i <= numRecords; i++) {
+      insertNumber(sorter, numRecords - i);
+    }
+    // Ensure that spill files were created
+    assertThat(tempDir.listFiles().length, greaterThanOrEqualTo(1));
+    // Read back the sorted data:
+    UnsafeSorterIterator iter = sorter.getSortedIterator();
 
-    final UnsafeExternalSorter sorter = UnsafeExternalSorter.create(
-      taskMemoryManager,
-      shuffleMemoryManager,
-      blockManager,
-      taskContext,
-      recordComparator,
-      prefixComparator,
-      /* initialSize */ 1024,
-      pageSizeBytes);
+    int i = 0;
+    while (iter.hasNext()) {
+      iter.loadNext();
+      assertEquals(i, iter.getKeyPrefix());
+      assertEquals(4, iter.getRecordLength());
+      assertEquals(i, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+      i++;
+    }
+    sorter.cleanupResources();
+    assertSpillFilesWereCleanedUp();
+  }
 
+  @Test
+  public void testFillingPage() throws Exception {
+    final UnsafeExternalSorter sorter = newSorter();
     byte[] record = new byte[16];
     while (sorter.getNumberOfAllocatedPages() < 2) {
       sorter.insertRecord(record, PlatformDependent.BYTE_ARRAY_OFFSET, record.length, 0);
     }
-    sorter.freeMemory();
+    sorter.cleanupResources();
+    assertSpillFilesWereCleanedUp();
+  }
+
+  @Test
+  public void sortingRecordsThatExceedPageSize() throws Exception {
+    final UnsafeExternalSorter sorter = newSorter();
+    final int[] largeRecord = new int[(int) pageSizeBytes + 16];
+    Arrays.fill(largeRecord, 456);
+    final int[] smallRecord = new int[100];
+    Arrays.fill(smallRecord, 123);
+
+    insertRecord(sorter, largeRecord, 456);
+    sorter.spill();
+    insertRecord(sorter, smallRecord, 123);
+    sorter.spill();
+    insertRecord(sorter, smallRecord, 123);
+    insertRecord(sorter, largeRecord, 456);
+
+    UnsafeSorterIterator iter = sorter.getSortedIterator();
+    // Small record
+    assertTrue(iter.hasNext());
+    iter.loadNext();
+    assertEquals(123, iter.getKeyPrefix());
+    assertEquals(smallRecord.length * 4, iter.getRecordLength());
+    assertEquals(123, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    // Small record
+    assertTrue(iter.hasNext());
+    iter.loadNext();
+    assertEquals(123, iter.getKeyPrefix());
+    assertEquals(smallRecord.length * 4, iter.getRecordLength());
+    assertEquals(123, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    // Large record
+    assertTrue(iter.hasNext());
+    iter.loadNext();
+    assertEquals(456, iter.getKeyPrefix());
+    assertEquals(largeRecord.length * 4, iter.getRecordLength());
+    assertEquals(456, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    // Large record
+    assertTrue(iter.hasNext());
+    iter.loadNext();
+    assertEquals(456, iter.getKeyPrefix());
+    assertEquals(largeRecord.length * 4, iter.getRecordLength());
+    assertEquals(456, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+
+    assertFalse(iter.hasNext());
+    sorter.cleanupResources();
     assertSpillFilesWereCleanedUp();
   }
 
@@ -289,8 +354,10 @@ public void testPeakMemoryUsed() throws Exception {
       newPeakMemory = sorter.getPeakMemoryUsedBytes();
       assertEquals(previousPeakMemory, newPeakMemory);
     } finally {
-      sorter.freeMemory();
+      sorter.cleanupResources();
+      assertSpillFilesWereCleanedUp();
     }
   }
 
 }
+
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index 193906d24790e..a5ae2b9736527 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -114,7 +114,7 @@ public long getPeakMemoryUsage() {
   }
 
   private void cleanupResources() {
-    sorter.freeMemory();
+    sorter.cleanupResources();
   }
 
   @VisibleForTesting
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index 312ec8ea0dd9d..86a563df992d0 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -134,6 +134,10 @@ public void insertKV(UnsafeRow key, UnsafeRow value) throws IOException {
       value.getBaseObject(), value.getBaseOffset(), value.getSizeInBytes(), prefix);
   }
 
+  /**
+   * Returns a sorted iterator. It is the caller's responsibility to call `cleanupResources()`
+   * after consuming this iterator.
+   */
   public KVSorterIterator sortedIterator() throws IOException {
     try {
       final UnsafeSorterIterator underlying = sorter.getSortedIterator();
@@ -158,8 +162,11 @@ void closeCurrentPage() {
     sorter.closeCurrentPage();
   }
 
-  private void cleanupResources() {
-    sorter.freeMemory();
+  /**
+   * Frees this sorter's in-memory data structures and cleans up its spill files.
+   */
+  public void cleanupResources() {
+    sorter.cleanupResources();
   }
 
   private static final class KVComparator extends RecordComparator {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
index 601a5a07ad002..08156f0e39ce8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.execution
 import scala.util.Random
 
 import org.apache.spark._
-import org.apache.spark.sql.RandomDataGenerator
+import org.apache.spark.sql.{RandomDataGenerator, Row}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, RowOrdering, UnsafeProjection}
 import org.apache.spark.sql.test.TestSQLContext
@@ -46,6 +46,7 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite {
     testKVSorter(keySchema, valueSchema, spill = i > 3)
   }
 
+
   /**
    * Create a test case using randomly generated data for the given key and value schema.
    *
@@ -60,96 +61,151 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite {
    * If spill is set to true, the sorter will spill probabilistically roughly every 100 records.
    */
   private def testKVSorter(keySchema: StructType, valueSchema: StructType, spill: Boolean): Unit = {
+    // Create the data converters
+    val kExternalConverter = CatalystTypeConverters.createToCatalystConverter(keySchema)
+    val vExternalConverter = CatalystTypeConverters.createToCatalystConverter(valueSchema)
+    val kConverter = UnsafeProjection.create(keySchema)
+    val vConverter = UnsafeProjection.create(valueSchema)
+
+    val keyDataGen = RandomDataGenerator.forType(keySchema, nullable = false).get
+    val valueDataGen = RandomDataGenerator.forType(valueSchema, nullable = false).get
+
+    val inputData = Seq.fill(1024) {
+      val k = kConverter(kExternalConverter.apply(keyDataGen.apply()).asInstanceOf[InternalRow])
+      val v = vConverter(vExternalConverter.apply(valueDataGen.apply()).asInstanceOf[InternalRow])
+      (k.asInstanceOf[InternalRow].copy(), v.asInstanceOf[InternalRow].copy())
+    }
 
     val keySchemaStr = keySchema.map(_.dataType.simpleString).mkString("[", ",", "]")
     val valueSchemaStr = valueSchema.map(_.dataType.simpleString).mkString("[", ",", "]")
 
     test(s"kv sorting key schema $keySchemaStr and value schema $valueSchemaStr") {
-      // Calling this make sure we have block manager and everything else setup.
-      TestSQLContext
-
-      val taskMemMgr = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
-      val shuffleMemMgr = new TestShuffleMemoryManager
-      TaskContext.setTaskContext(new TaskContextImpl(
-        stageId = 0,
-        partitionId = 0,
-        taskAttemptId = 98456,
-        attemptNumber = 0,
-        taskMemoryManager = taskMemMgr,
-        metricsSystem = null,
-        internalAccumulators = Seq.empty))
-
-      // Create the data converters
-      val kExternalConverter = CatalystTypeConverters.createToCatalystConverter(keySchema)
-      val vExternalConverter = CatalystTypeConverters.createToCatalystConverter(valueSchema)
-      val kConverter = UnsafeProjection.create(keySchema)
-      val vConverter = UnsafeProjection.create(valueSchema)
-
-      val keyDataGen = RandomDataGenerator.forType(keySchema, nullable = false).get
-      val valueDataGen = RandomDataGenerator.forType(valueSchema, nullable = false).get
-
-      val input = Seq.fill(1024) {
-        val k = kConverter(kExternalConverter.apply(keyDataGen.apply()).asInstanceOf[InternalRow])
-        val v = vConverter(vExternalConverter.apply(valueDataGen.apply()).asInstanceOf[InternalRow])
-        (k.asInstanceOf[InternalRow].copy(), v.asInstanceOf[InternalRow].copy())
-      }
-
-      val sorter = new UnsafeKVExternalSorter(
-        keySchema, valueSchema, SparkEnv.get.blockManager, shuffleMemMgr, 16 * 1024 * 1024)
-
-      // Insert generated keys and values into the sorter
-      input.foreach { case (k, v) =>
-        sorter.insertKV(k.asInstanceOf[UnsafeRow], v.asInstanceOf[UnsafeRow])
-        // 1% chance we will spill
-        if (rand.nextDouble() < 0.01 && spill) {
-          shuffleMemMgr.markAsOutOfMemory()
-          sorter.closeCurrentPage()
-        }
-      }
+      testKVSorter(
+        keySchema,
+        valueSchema,
+        inputData,
+        pageSize = 16 * 1024 * 1024,
+        spill
+      )
+    }
+  }
 
-      // Collect the sorted output
-      val out = new scala.collection.mutable.ArrayBuffer[(InternalRow, InternalRow)]
-      val iter = sorter.sortedIterator()
-      while (iter.next()) {
-        out += Tuple2(iter.getKey.copy(), iter.getValue.copy())
+  /**
+   * Create a test case using the given input data for the given key and value schema.
+   *
+   * The approach works as follows:
+   *
+   * - Create input by randomly generating data based on the given schema
+   * - Run [[UnsafeKVExternalSorter]] on the input data
+   * - Collect the output from the sorter, and make sure the keys are sorted in ascending order
+   * - Sort the input by both key and value, and sort the sorter output also by both key and value.
+   *   Compare the sorted input and sorted output together to make sure all the key/values match.
+   *
+   * If spill is set to true, the sorter will spill probabilistically roughly every 100 records.
+   */
+  private def testKVSorter(
+      keySchema: StructType,
+      valueSchema: StructType,
+      inputData: Seq[(InternalRow, InternalRow)],
+      pageSize: Long,
+      spill: Boolean): Unit = {
+    // Calling this make sure we have block manager and everything else setup.
+    TestSQLContext
+
+    val taskMemMgr = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
+    val shuffleMemMgr = new TestShuffleMemoryManager
+    TaskContext.setTaskContext(new TaskContextImpl(
+      stageId = 0,
+      partitionId = 0,
+      taskAttemptId = 98456,
+      attemptNumber = 0,
+      taskMemoryManager = taskMemMgr,
+      metricsSystem = null,
+      internalAccumulators = Seq.empty))
+
+    val sorter = new UnsafeKVExternalSorter(
+      keySchema, valueSchema, SparkEnv.get.blockManager, shuffleMemMgr, pageSize)
+
+    // Insert the keys and values into the sorter
+    inputData.foreach { case (k, v) =>
+      sorter.insertKV(k.asInstanceOf[UnsafeRow], v.asInstanceOf[UnsafeRow])
+      // 1% chance we will spill
+      if (rand.nextDouble() < 0.01 && spill) {
+        shuffleMemMgr.markAsOutOfMemory()
+        sorter.closeCurrentPage()
       }
+    }
 
-      val keyOrdering = RowOrdering.forSchema(keySchema.map(_.dataType))
-      val valueOrdering = RowOrdering.forSchema(valueSchema.map(_.dataType))
-      val kvOrdering = new Ordering[(InternalRow, InternalRow)] {
-        override def compare(x: (InternalRow, InternalRow), y: (InternalRow, InternalRow)): Int = {
-          keyOrdering.compare(x._1, y._1) match {
-            case 0 => valueOrdering.compare(x._2, y._2)
-            case cmp => cmp
-          }
+    // Collect the sorted output
+    val out = new scala.collection.mutable.ArrayBuffer[(InternalRow, InternalRow)]
+    val iter = sorter.sortedIterator()
+    while (iter.next()) {
+      out += Tuple2(iter.getKey.copy(), iter.getValue.copy())
+    }
+    sorter.cleanupResources()
+
+    val keyOrdering = RowOrdering.forSchema(keySchema.map(_.dataType))
+    val valueOrdering = RowOrdering.forSchema(valueSchema.map(_.dataType))
+    val kvOrdering = new Ordering[(InternalRow, InternalRow)] {
+      override def compare(x: (InternalRow, InternalRow), y: (InternalRow, InternalRow)): Int = {
+        keyOrdering.compare(x._1, y._1) match {
+          case 0 => valueOrdering.compare(x._2, y._2)
+          case cmp => cmp
         }
       }
+    }
 
-      // Testing to make sure output from the sorter is sorted by key
-      var prevK: InternalRow = null
-      out.zipWithIndex.foreach { case ((k, v), i) =>
-        if (prevK != null) {
-          assert(keyOrdering.compare(prevK, k) <= 0,
-            s"""
-               |key is not in sorted order:
-               |previous key: $prevK
-               |current key : $k
-               """.stripMargin)
-        }
-        prevK = k
+    // Testing to make sure output from the sorter is sorted by key
+    var prevK: InternalRow = null
+    out.zipWithIndex.foreach { case ((k, v), i) =>
+      if (prevK != null) {
+        assert(keyOrdering.compare(prevK, k) <= 0,
+          s"""
+             |key is not in sorted order:
+             |previous key: $prevK
+             |current key : $k
+             """.stripMargin)
       }
+      prevK = k
+    }
 
-      // Testing to make sure the key/value in output matches input
-      assert(out.sorted(kvOrdering) === input.sorted(kvOrdering))
+    // Testing to make sure the key/value in output matches input
+    assert(out.sorted(kvOrdering) === inputData.sorted(kvOrdering))
 
-      // Make sure there is no memory leak
-      val leakedUnsafeMemory: Long = taskMemMgr.cleanUpAllAllocatedMemory
-      if (shuffleMemMgr != null) {
-        val leakedShuffleMemory: Long = shuffleMemMgr.getMemoryConsumptionForThisTask()
-        assert(0L === leakedShuffleMemory)
-      }
-      assert(0 === leakedUnsafeMemory)
-      TaskContext.unset()
+    // Make sure there is no memory leak
+    val leakedUnsafeMemory: Long = taskMemMgr.cleanUpAllAllocatedMemory
+    if (shuffleMemMgr != null) {
+      val leakedShuffleMemory: Long = shuffleMemMgr.getMemoryConsumptionForThisTask()
+      assert(0L === leakedShuffleMemory)
     }
+    assert(0 === leakedUnsafeMemory)
+    TaskContext.unset()
+  }
+
+  test("kv sorting with records that exceed page size") {
+    val pageSize = 128
+
+    val schema = StructType(StructField("b", BinaryType) :: Nil)
+    val externalConverter = CatalystTypeConverters.createToCatalystConverter(schema)
+    val converter = UnsafeProjection.create(schema)
+
+    val rand = new Random()
+    val inputData = Seq.fill(1024) {
+      val kBytes = new Array[Byte](rand.nextInt(pageSize))
+      val vBytes = new Array[Byte](rand.nextInt(pageSize))
+      rand.nextBytes(kBytes)
+      rand.nextBytes(vBytes)
+      val k = converter(externalConverter.apply(Row(kBytes)).asInstanceOf[InternalRow])
+      val v = converter(externalConverter.apply(Row(vBytes)).asInstanceOf[InternalRow])
+      (k.asInstanceOf[InternalRow].copy(), v.asInstanceOf[InternalRow].copy())
+    }
+
+    testKVSorter(
+      schema,
+      schema,
+      inputData,
+      pageSize,
+      spill = true
+    )
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java
index bbe83d36cf36b..6722301df19d1 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/HeapMemoryAllocator.java
@@ -24,6 +24,9 @@ public class HeapMemoryAllocator implements MemoryAllocator {
 
   @Override
   public MemoryBlock allocate(long size) throws OutOfMemoryError {
+    if (size % 8 != 0) {
+      throw new IllegalArgumentException("Size " + size + " was not a multiple of 8");
+    }
     long[] array = new long[(int) (size / 8)];
     return MemoryBlock.fromLongArray(array);
   }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
index 15898771fef25..62f4459696c28 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
@@ -26,6 +26,9 @@ public class UnsafeMemoryAllocator implements MemoryAllocator {
 
   @Override
   public MemoryBlock allocate(long size) throws OutOfMemoryError {
+    if (size % 8 != 0) {
+      throw new IllegalArgumentException("Size " + size + " was not a multiple of 8");
+    }
     long address = PlatformDependent.UNSAFE.allocateMemory(size);
     return new MemoryBlock(null, address, size);
   }

From 9d668b73687e697cad2ef7fd3c3ba405e9795593 Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Tue, 4 Aug 2015 14:54:11 -0700
Subject: [PATCH 032/633] [SPARK-9602] remove "Akka/Actor" words from comments

https://issues.apache.org/jira/browse/SPARK-9602

Although we have hidden Akka behind RPC interface, I found that the Akka/Actor-related comments are still spreading everywhere. To make it consistent, we shall remove "actor"/"akka" words from the comments...

Author: CodingCat <zhunansjtu@gmail.com>

Closes #7936 from CodingCat/SPARK-9602 and squashes the following commits:

e8296a3 [CodingCat] remove actor words from comments
---
 .../main/scala/org/apache/spark/api/python/PythonRDD.scala | 2 +-
 .../scala/org/apache/spark/deploy/LocalSparkCluster.scala  | 4 ----
 .../scala/org/apache/spark/deploy/client/AppClient.scala   | 2 +-
 .../apache/spark/deploy/master/LeaderElectionAgent.scala   | 6 +++---
 .../org/apache/spark/deploy/master/MasterMessages.scala    | 2 +-
 .../spark/deploy/master/ZooKeeperLeaderElectionAgent.scala | 6 +++---
 .../main/scala/org/apache/spark/deploy/worker/Worker.scala | 7 ++++---
 .../org/apache/spark/deploy/worker/WorkerWatcher.scala     | 4 ++--
 .../main/scala/org/apache/spark/rpc/RpcEndpointRef.scala   | 2 +-
 .../apache/spark/scheduler/OutputCommitCoordinator.scala   | 2 +-
 .../org/apache/spark/scheduler/cluster/ExecutorData.scala  | 2 +-
 .../src/main/scala/org/apache/spark/util/IdGenerator.scala | 6 +++---
 .../spark/deploy/master/CustomRecoveryModeFactory.scala    | 4 ++--
 .../apache/spark/deploy/worker/WorkerWatcherSuite.scala    | 5 ++---
 project/MimaExcludes.scala                                 | 2 +-
 .../src/main/scala/org/apache/spark/repl/SparkILoop.scala  | 2 +-
 16 files changed, 27 insertions(+), 31 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 55e563ee968be..2a56bf28d7027 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -794,7 +794,7 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort:
 
   /**
    * We try to reuse a single Socket to transfer accumulator updates, as they are all added
-   * by the DAGScheduler's single-threaded actor anyway.
+   * by the DAGScheduler's single-threaded RpcEndpoint anyway.
    */
   @transient var socket: Socket = _
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala
index 53356addf6edb..83ccaadfe7447 100644
--- a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala
@@ -73,12 +73,8 @@ class LocalSparkCluster(
   def stop() {
     logInfo("Shutting down local Spark cluster.")
     // Stop the workers before the master so they don't get upset that it disconnected
-    // TODO: In Akka 2.1.x, ActorSystem.awaitTermination hangs when you have remote actors!
-    //       This is unfortunate, but for now we just comment it out.
     workerRpcEnvs.foreach(_.shutdown())
-    // workerActorSystems.foreach(_.awaitTermination())
     masterRpcEnvs.foreach(_.shutdown())
-    // masterActorSystems.foreach(_.awaitTermination())
     masterRpcEnvs.clear()
     workerRpcEnvs.clear()
   }
diff --git a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
index 7576a2985ee7b..25ea6925434ab 100644
--- a/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala
@@ -257,7 +257,7 @@ private[spark] class AppClient(
   }
 
   def start() {
-    // Just launch an actor; it will call back into the listener.
+    // Just launch an rpcEndpoint; it will call back into the listener.
     endpoint = rpcEnv.setupEndpoint("AppClient", new ClientEndpoint(rpcEnv))
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/LeaderElectionAgent.scala b/core/src/main/scala/org/apache/spark/deploy/master/LeaderElectionAgent.scala
index cf77c86d760cf..70f21fbe0de85 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/LeaderElectionAgent.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/LeaderElectionAgent.scala
@@ -26,7 +26,7 @@ import org.apache.spark.annotation.DeveloperApi
  */
 @DeveloperApi
 trait LeaderElectionAgent {
-  val masterActor: LeaderElectable
+  val masterInstance: LeaderElectable
   def stop() {} // to avoid noops in implementations.
 }
 
@@ -37,7 +37,7 @@ trait LeaderElectable {
 }
 
 /** Single-node implementation of LeaderElectionAgent -- we're initially and always the leader. */
-private[spark] class MonarchyLeaderAgent(val masterActor: LeaderElectable)
+private[spark] class MonarchyLeaderAgent(val masterInstance: LeaderElectable)
   extends LeaderElectionAgent {
-  masterActor.electedLeader()
+  masterInstance.electedLeader()
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala b/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala
index 68c937188b333..a952cee36eb44 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/MasterMessages.scala
@@ -38,5 +38,5 @@ private[master] object MasterMessages {
 
   case object BoundPortsRequest
 
-  case class BoundPortsResponse(actorPort: Int, webUIPort: Int, restPort: Option[Int])
+  case class BoundPortsResponse(rpcEndpointPort: Int, webUIPort: Int, restPort: Option[Int])
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala
index 6fdff86f66e01..d317206a614fb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala
@@ -22,7 +22,7 @@ import org.apache.curator.framework.CuratorFramework
 import org.apache.curator.framework.recipes.leader.{LeaderLatchListener, LeaderLatch}
 import org.apache.spark.deploy.SparkCuratorUtil
 
-private[master] class ZooKeeperLeaderElectionAgent(val masterActor: LeaderElectable,
+private[master] class ZooKeeperLeaderElectionAgent(val masterInstance: LeaderElectable,
     conf: SparkConf) extends LeaderLatchListener with LeaderElectionAgent with Logging  {
 
   val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/leader_election"
@@ -73,10 +73,10 @@ private[master] class ZooKeeperLeaderElectionAgent(val masterActor: LeaderElecta
   private def updateLeadershipStatus(isLeader: Boolean) {
     if (isLeader && status == LeadershipStatus.NOT_LEADER) {
       status = LeadershipStatus.LEADER
-      masterActor.electedLeader()
+      masterInstance.electedLeader()
     } else if (!isLeader && status == LeadershipStatus.LEADER) {
       status = LeadershipStatus.NOT_LEADER
-      masterActor.revokedLeadership()
+      masterInstance.revokedLeadership()
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index c82a7ccab54dc..6792d3310b06c 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -228,7 +228,7 @@ private[deploy] class Worker(
   /**
    * Re-register with the master because a network failure or a master failure has occurred.
    * If the re-registration attempt threshold is exceeded, the worker exits with error.
-   * Note that for thread-safety this should only be called from the actor.
+   * Note that for thread-safety this should only be called from the rpcEndpoint.
    */
   private def reregisterWithMaster(): Unit = {
     Utils.tryOrExit {
@@ -365,7 +365,8 @@ private[deploy] class Worker(
       if (connected) { sendToMaster(Heartbeat(workerId, self)) }
 
     case WorkDirCleanup =>
-      // Spin up a separate thread (in a future) to do the dir cleanup; don't tie up worker actor
+      // Spin up a separate thread (in a future) to do the dir cleanup; don't tie up worker
+      // rpcEndpoint.
       // Copy ids so that it can be used in the cleanup thread.
       val appIds = executors.values.map(_.appId).toSet
       val cleanupFuture = concurrent.future {
@@ -684,7 +685,7 @@ private[deploy] object Worker extends Logging {
       workerNumber: Option[Int] = None,
       conf: SparkConf = new SparkConf): RpcEnv = {
 
-    // The LocalSparkCluster runs multiple local sparkWorkerX actor systems
+    // The LocalSparkCluster runs multiple local sparkWorkerX RPC Environments
     val systemName = SYSTEM_NAME + workerNumber.map(_.toString).getOrElse("")
     val securityMgr = new SecurityManager(conf)
     val rpcEnv = RpcEnv.create(systemName, host, port, conf, securityMgr)
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala
index fae5640b9a213..735c4f0927150 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala
@@ -43,7 +43,7 @@ private[spark] class WorkerWatcher(override val rpcEnv: RpcEnv, workerUrl: Strin
   private[deploy] def setTesting(testing: Boolean) = isTesting = testing
   private var isTesting = false
 
-  // Lets us filter events only from the worker's actor system
+  // Lets filter events only from the worker's rpc system
   private val expectedAddress = RpcAddress.fromURIString(workerUrl)
   private def isWorker(address: RpcAddress) = expectedAddress == address
 
@@ -62,7 +62,7 @@ private[spark] class WorkerWatcher(override val rpcEnv: RpcEnv, workerUrl: Strin
   override def onDisconnected(remoteAddress: RpcAddress): Unit = {
     if (isWorker(remoteAddress)) {
       // This log message will never be seen
-      logError(s"Lost connection to worker actor $workerUrl. Exiting.")
+      logError(s"Lost connection to worker rpc endpoint $workerUrl. Exiting.")
       exitNonZero()
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
index 6ae47894598be..7409ac8859991 100644
--- a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
@@ -100,7 +100,7 @@ private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf)
         val future = ask[T](message, timeout)
         val result = timeout.awaitResult(future)
         if (result == null) {
-          throw new SparkException("Actor returned null")
+          throw new SparkException("RpcEndpoint returned null")
         }
         return result
       } catch {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala
index 8321037cdc026..5d926377ce86b 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/OutputCommitCoordinator.scala
@@ -162,7 +162,7 @@ private[spark] class OutputCommitCoordinator(conf: SparkConf, isDriver: Boolean)
 
 private[spark] object OutputCommitCoordinator {
 
-  // This actor is used only for RPC
+  // This endpoint is used only for RPC
   private[spark] class OutputCommitCoordinatorEndpoint(
       override val rpcEnv: RpcEnv, outputCommitCoordinator: OutputCommitCoordinator)
     extends RpcEndpoint with Logging {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala
index 26e72c0bff38d..626a2b7d69abe 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/ExecutorData.scala
@@ -22,7 +22,7 @@ import org.apache.spark.rpc.{RpcEndpointRef, RpcAddress}
 /**
  * Grouping of data for an executor used by CoarseGrainedSchedulerBackend.
  *
- * @param executorEndpoint The ActorRef representing this executor
+ * @param executorEndpoint The RpcEndpointRef representing this executor
  * @param executorAddress The network address of this executor
  * @param executorHost The hostname that this executor is running on
  * @param freeCores  The current number of cores available for work on the executor
diff --git a/core/src/main/scala/org/apache/spark/util/IdGenerator.scala b/core/src/main/scala/org/apache/spark/util/IdGenerator.scala
index 17e55f7996bf7..53934ad4ce477 100644
--- a/core/src/main/scala/org/apache/spark/util/IdGenerator.scala
+++ b/core/src/main/scala/org/apache/spark/util/IdGenerator.scala
@@ -22,10 +22,10 @@ import java.util.concurrent.atomic.AtomicInteger
 /**
  * A util used to get a unique generation ID. This is a wrapper around Java's
  * AtomicInteger. An example usage is in BlockManager, where each BlockManager
- * instance would start an Akka actor and we use this utility to assign the Akka
- * actors unique names.
+ * instance would start an RpcEndpoint and we use this utility to assign the RpcEndpoints'
+ * unique names.
  */
 private[spark] class IdGenerator {
-  private var id = new AtomicInteger
+  private val id = new AtomicInteger
   def next: Int = id.incrementAndGet
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala b/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala
index 8c96b0e71dfdd..4b86da536768c 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/CustomRecoveryModeFactory.scala
@@ -99,7 +99,7 @@ object CustomPersistenceEngine {
   @volatile var lastInstance: Option[CustomPersistenceEngine] = None
 }
 
-class CustomLeaderElectionAgent(val masterActor: LeaderElectable) extends LeaderElectionAgent {
-  masterActor.electedLeader()
+class CustomLeaderElectionAgent(val masterInstance: LeaderElectable) extends LeaderElectionAgent {
+  masterInstance.electedLeader()
 }
 
diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala
index cd24d79423316..e9034e39a715c 100644
--- a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala
@@ -38,12 +38,11 @@ class WorkerWatcherSuite extends SparkFunSuite {
     val conf = new SparkConf()
     val rpcEnv = RpcEnv.create("test", "localhost", 12345, conf, new SecurityManager(conf))
     val targetWorkerUrl = rpcEnv.uriOf("test", RpcAddress("1.2.3.4", 1234), "Worker")
-    val otherAddress = "akka://test@4.3.2.1:1234/user/OtherActor"
-    val otherAkkaAddress = RpcAddress("4.3.2.1", 1234)
+    val otherRpcAddress = RpcAddress("4.3.2.1", 1234)
     val workerWatcher = new WorkerWatcher(rpcEnv, targetWorkerUrl)
     workerWatcher.setTesting(testing = true)
     rpcEnv.setupEndpoint("worker-watcher", workerWatcher)
-    workerWatcher.onDisconnected(otherAkkaAddress)
+    workerWatcher.onDisconnected(otherRpcAddress)
     assert(!workerWatcher.isShutDown)
     rpcEnv.shutdown()
   }
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 280aac931915d..b60ae784c3798 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -182,7 +182,7 @@ object MimaExcludes {
             ProblemFilters.exclude[IncompatibleResultTypeProblem](
               "org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast"),
             ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorActor")
+              "org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorEndpoint")
           ) ++ Seq(
             // SPARK-4655 - Making Stage an Abstract class broke binary compatility even though
             // the stage class is defined as private[spark]
diff --git a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 8130868fe1487..304b1e8cdbed5 100644
--- a/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/scala-2.10/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -981,7 +981,7 @@ class SparkILoop(
     // which spins off a separate thread, then print the prompt and try
     // our best to look ready.  The interlocking lazy vals tend to
     // inter-deadlock, so we break the cycle with a single asynchronous
-    // message to an actor.
+    // message to an rpcEndpoint.
     if (isAsync) {
       intp initialize initializedCallback()
       createAsyncListener() // listens for signal to run postInitialization

From e375456063617cd7000d796024f41e5927f21edd Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Tue, 4 Aug 2015 14:54:26 -0700
Subject: [PATCH 033/633] [SPARK-9447] [ML] [PYTHON] Added HasRawPredictionCol,
 HasProbabilityCol to RandomForestClassifier

Added HasRawPredictionCol, HasProbabilityCol to RandomForestClassifier, plus doc tests for those columns.

CC: holdenk yanboliang

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7903 from jkbradley/rf-prob-python and squashes the following commits:

c62a83f [Joseph K. Bradley] made unit test more robust
14eeba2 [Joseph K. Bradley] added HasRawPredictionCol, HasProbabilityCol to RandomForestClassifier in PySpark
---
 python/pyspark/ml/classification.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 291320f881e7c..5978d8f4d3a01 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -347,6 +347,7 @@ class DecisionTreeClassificationModel(DecisionTreeModel):
 
 @inherit_doc
 class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
+                             HasRawPredictionCol, HasProbabilityCol,
                              DecisionTreeParams, HasCheckpointInterval):
     """
     `http://en.wikipedia.org/wiki/Random_forest  Random Forest`
@@ -354,6 +355,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     It supports both binary and multiclass labels, as well as both continuous and categorical
     features.
 
+    >>> import numpy
     >>> from numpy import allclose
     >>> from pyspark.mllib.linalg import Vectors
     >>> from pyspark.ml.feature import StringIndexer
@@ -368,8 +370,13 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     >>> allclose(model.treeWeights, [1.0, 1.0, 1.0])
     True
     >>> test0 = sqlContext.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
-    >>> model.transform(test0).head().prediction
+    >>> result = model.transform(test0).head()
+    >>> result.prediction
     0.0
+    >>> numpy.argmax(result.probability)
+    0
+    >>> numpy.argmax(result.rawPrediction)
+    0
     >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
     >>> model.transform(test1).head().prediction
     1.0
@@ -390,11 +397,13 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
 
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                 probabilityCol="probability", rawPredictionCol="rawPrediction",
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini",
                  numTrees=20, featureSubsetStrategy="auto", seed=None):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 probabilityCol="probability", rawPredictionCol="rawPrediction", \
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="gini", \
                  numTrees=20, featureSubsetStrategy="auto", seed=None)
@@ -427,11 +436,13 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
 
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                  probabilityCol="probability", rawPredictionCol="rawPrediction",
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
                   impurity="gini", numTrees=20, featureSubsetStrategy="auto"):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 probabilityCol="probability", rawPredictionCol="rawPrediction", \
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                   maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, \
                   impurity="gini", numTrees=20, featureSubsetStrategy="auto")

From 1833d9c08f021d991334424d0a6d5ec21d1fccb2 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Tue, 4 Aug 2015 15:43:13 -0700
Subject: [PATCH 034/633] [SPARK-9582] [ML] LDA cleanups

Small cleanups to recent LDA additions and docs.

CC: feynmanliang

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7916 from jkbradley/lda-cleanups and squashes the following commits:

f7021d9 [Joseph K. Bradley] broadcasting large matrices for LDA in local model and online learning
97947aa [Joseph K. Bradley] a few more cleanups
5b03f88 [Joseph K. Bradley] reverted split of lda log likelihood
c566915 [Joseph K. Bradley] small edit to make review easier
63f6c7d [Joseph K. Bradley] clarified log likelihood for lda models
---
 .../spark/mllib/clustering/LDAModel.scala     | 82 +++++++++++--------
 .../spark/mllib/clustering/LDAOptimizer.scala | 19 +++--
 2 files changed, 58 insertions(+), 43 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 6af90d7287ff8..33babda69bbb9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -27,6 +27,7 @@ import org.json4s.jackson.JsonMethods._
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaPairRDD
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.graphx.{Edge, EdgeContext, Graph, VertexId}
 import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -217,26 +218,28 @@ class LocalLDAModel private[clustering] (
   // TODO: declare in LDAModel and override once implemented in DistributedLDAModel
   /**
    * Calculates a lower bound on the log likelihood of the entire corpus.
+   *
+   * See Equation (16) in original Online LDA paper.
+   *
    * @param documents test corpus to use for calculating log likelihood
    * @return variational lower bound on the log likelihood of the entire corpus
    */
-  def logLikelihood(documents: RDD[(Long, Vector)]): Double = bound(documents,
+  def logLikelihood(documents: RDD[(Long, Vector)]): Double = logLikelihoodBound(documents,
     docConcentration, topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k,
     vocabSize)
 
   /**
-   * Calculate an upper bound bound on perplexity. See Equation (16) in original Online
-   * LDA paper.
+   * Calculate an upper bound bound on perplexity.  (Lower is better.)
+   * See Equation (16) in original Online LDA paper.
+   *
    * @param documents test corpus to use for calculating perplexity
-   * @return variational upper bound on log perplexity per word
+   * @return Variational upper bound on log perplexity per token.
    */
   def logPerplexity(documents: RDD[(Long, Vector)]): Double = {
-    val corpusWords = documents
+    val corpusTokenCount = documents
       .map { case (_, termCounts) => termCounts.toArray.sum }
       .sum()
-    val perWordBound = -logLikelihood(documents) / corpusWords
-
-    perWordBound
+    -logLikelihood(documents) / corpusTokenCount
   }
 
   /**
@@ -244,17 +247,20 @@ class LocalLDAModel private[clustering] (
    *    log p(documents) >= E_q[log p(documents)] - E_q[log q(documents)]
    * This bound is derived by decomposing the LDA model to:
    *    log p(documents) = E_q[log p(documents)] - E_q[log q(documents)] + D(q|p)
-   * and noting that the KL-divergence D(q|p) >= 0. See Equation (16) in original Online LDA paper.
+   * and noting that the KL-divergence D(q|p) >= 0.
+   *
+   * See Equation (16) in original Online LDA paper, as well as Appendix A.3 in the JMLR version of
+   * the original LDA paper.
    * @param documents a subset of the test corpus
    * @param alpha document-topic Dirichlet prior parameters
-   * @param eta topic-word Dirichlet prior parameters
+   * @param eta topic-word Dirichlet prior parameter
    * @param lambda parameters for variational q(beta | lambda) topic-word distributions
    * @param gammaShape shape parameter for random initialization of variational q(theta | gamma)
    *                   topic mixture distributions
    * @param k number of topics
    * @param vocabSize number of unique terms in the entire test corpus
    */
-  private def bound(
+  private def logLikelihoodBound(
       documents: RDD[(Long, Vector)],
       alpha: Vector,
       eta: Double,
@@ -266,33 +272,38 @@ class LocalLDAModel private[clustering] (
     // transpose because dirichletExpectation normalizes by row and we need to normalize
     // by topic (columns of lambda)
     val Elogbeta = LDAUtils.dirichletExpectation(lambda.t).t
+    val ElogbetaBc = documents.sparkContext.broadcast(Elogbeta)
+
+    // Sum bound components for each document:
+    //  component for prob(tokens) + component for prob(document-topic distribution)
+    val corpusPart =
+      documents.filter(_._2.numNonzeros > 0).map { case (id: Long, termCounts: Vector) =>
+        val localElogbeta = ElogbetaBc.value
+        var docBound = 0.0D
+        val (gammad: BDV[Double], _) = OnlineLDAOptimizer.variationalTopicInference(
+          termCounts, exp(localElogbeta), brzAlpha, gammaShape, k)
+        val Elogthetad: BDV[Double] = LDAUtils.dirichletExpectation(gammad)
+
+        // E[log p(doc | theta, beta)]
+        termCounts.foreachActive { case (idx, count) =>
+          docBound += count * LDAUtils.logSumExp(Elogthetad + localElogbeta(idx, ::).t)
+        }
+        // E[log p(theta | alpha) - log q(theta | gamma)]
+        docBound += sum((brzAlpha - gammad) :* Elogthetad)
+        docBound += sum(lgamma(gammad) - lgamma(brzAlpha))
+        docBound += lgamma(sum(brzAlpha)) - lgamma(sum(gammad))
 
-    var score = documents.filter(_._2.numNonzeros > 0).map { case (id: Long, termCounts: Vector) =>
-      var docScore = 0.0D
-      val (gammad: BDV[Double], _) = OnlineLDAOptimizer.variationalTopicInference(
-        termCounts, exp(Elogbeta), brzAlpha, gammaShape, k)
-      val Elogthetad: BDV[Double] = LDAUtils.dirichletExpectation(gammad)
-
-      // E[log p(doc | theta, beta)]
-      termCounts.foreachActive { case (idx, count) =>
-        docScore += count * LDAUtils.logSumExp(Elogthetad + Elogbeta(idx, ::).t)
-      }
-      // E[log p(theta | alpha) - log q(theta | gamma)]; assumes alpha is a vector
-      docScore += sum((brzAlpha - gammad) :* Elogthetad)
-      docScore += sum(lgamma(gammad) - lgamma(brzAlpha))
-      docScore += lgamma(sum(brzAlpha)) - lgamma(sum(gammad))
-
-      docScore
-    }.sum()
-
-    // E[log p(beta | eta) - log q (beta | lambda)]; assumes eta is a scalar
-    score += sum((eta - lambda) :* Elogbeta)
-    score += sum(lgamma(lambda) - lgamma(eta))
+        docBound
+      }.sum()
 
+    // Bound component for prob(topic-term distributions):
+    //   E[log p(beta | eta) - log q(beta | lambda)]
     val sumEta = eta * vocabSize
-    score += sum(lgamma(sumEta) - lgamma(sum(lambda(::, breeze.linalg.*))))
+    val topicsPart = sum((eta - lambda) :* Elogbeta) +
+      sum(lgamma(lambda) - lgamma(eta)) +
+      sum(lgamma(sumEta) - lgamma(sum(lambda(::, breeze.linalg.*))))
 
-    score
+    corpusPart + topicsPart
   }
 
   /**
@@ -310,6 +321,7 @@ class LocalLDAModel private[clustering] (
     // Double transpose because dirichletExpectation normalizes by row and we need to normalize
     // by topic (columns of lambda)
     val expElogbeta = exp(LDAUtils.dirichletExpectation(topicsMatrix.toBreeze.toDenseMatrix.t).t)
+    val expElogbetaBc = documents.sparkContext.broadcast(expElogbeta)
     val docConcentrationBrz = this.docConcentration.toBreeze
     val gammaShape = this.gammaShape
     val k = this.k
@@ -320,7 +332,7 @@ class LocalLDAModel private[clustering] (
       } else {
         val (gamma, _) = OnlineLDAOptimizer.variationalTopicInference(
           termCounts,
-          expElogbeta,
+          expElogbetaBc.value,
           docConcentrationBrz,
           gammaShape,
           k)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index b0e14cb8296a6..afba2866c7040 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -419,6 +419,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
     val k = this.k
     val vocabSize = this.vocabSize
     val expElogbeta = exp(LDAUtils.dirichletExpectation(lambda)).t
+    val expElogbetaBc = batch.sparkContext.broadcast(expElogbeta)
     val alpha = this.alpha.toBreeze
     val gammaShape = this.gammaShape
 
@@ -433,13 +434,14 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
           case v: SparseVector => v.indices.toList
         }
         val (gammad, sstats) = OnlineLDAOptimizer.variationalTopicInference(
-          termCounts, expElogbeta, alpha, gammaShape, k)
+          termCounts, expElogbetaBc.value, alpha, gammaShape, k)
         stat(::, ids) := stat(::, ids).toDenseMatrix + sstats
         gammaPart = gammad :: gammaPart
       }
       Iterator((stat, gammaPart))
     }
     val statsSum: BDM[Double] = stats.map(_._1).reduce(_ += _)
+    expElogbetaBc.unpersist()
     val gammat: BDM[Double] = breeze.linalg.DenseMatrix.vertcat(
       stats.map(_._2).reduce(_ ++ _).map(_.toDenseMatrix): _*)
     val batchResult = statsSum :* expElogbeta.t
@@ -540,21 +542,22 @@ private[clustering] object OnlineLDAOptimizer {
     val expElogthetad: BDV[Double] = exp(LDAUtils.dirichletExpectation(gammad))  // K
     val expElogbetad = expElogbeta(ids, ::).toDenseMatrix                        // ids * K
 
-    val phinorm: BDV[Double] = expElogbetad * expElogthetad :+ 1e-100            // ids
-    var meanchange = 1D
+    val phiNorm: BDV[Double] = expElogbetad * expElogthetad :+ 1e-100            // ids
+    var meanGammaChange = 1D
     val ctsVector = new BDV[Double](cts)                                         // ids
 
     // Iterate between gamma and phi until convergence
-    while (meanchange > 1e-3) {
+    while (meanGammaChange > 1e-3) {
       val lastgamma = gammad.copy
       //        K                  K * ids               ids
-      gammad := (expElogthetad :* (expElogbetad.t * (ctsVector :/ phinorm))) :+ alpha
+      gammad := (expElogthetad :* (expElogbetad.t * (ctsVector :/ phiNorm))) :+ alpha
       expElogthetad := exp(LDAUtils.dirichletExpectation(gammad))
-      phinorm := expElogbetad * expElogthetad :+ 1e-100
-      meanchange = sum(abs(gammad - lastgamma)) / k
+      // TODO: Keep more values in log space, and only exponentiate when needed.
+      phiNorm := expElogbetad * expElogthetad :+ 1e-100
+      meanGammaChange = sum(abs(gammad - lastgamma)) / k
     }
 
-    val sstatsd = expElogthetad.asDenseMatrix.t * (ctsVector :/ phinorm).asDenseMatrix
+    val sstatsd = expElogthetad.asDenseMatrix.t * (ctsVector :/ phiNorm).asDenseMatrix
     (gammad, sstatsd)
   }
 }

From 571d5b5363ff4dbbce1f7019ab8e86cbc3cba4d5 Mon Sep 17 00:00:00 2001
From: Mike Dusenberry <mwdusenb@us.ibm.com>
Date: Tue, 4 Aug 2015 16:30:03 -0700
Subject: [PATCH 035/633] [SPARK-6485] [MLLIB] [PYTHON] Add
 CoordinateMatrix/RowMatrix/IndexedRowMatrix to PySpark.

This PR adds the RowMatrix, IndexedRowMatrix, and CoordinateMatrix distributed matrices to PySpark.  Each distributed matrix class acts as a wrapper around the Scala/Java counterpart by maintaining a reference to the Java object.  New distributed matrices can be created using factory methods added to DistributedMatrices, which creates the Java distributed matrix and then wraps it with the corresponding PySpark class.  This design allows for simple conversion between the various distributed matrices, and lets us re-use the Scala code.  Serialization between Python and Java is implemented using DataFrames as needed for IndexedRowMatrix and CoordinateMatrix for simplicity.  Associated documentation and unit-tests have also been added.  To facilitate code review, this PR implements access to the rows/entries as RDDs, the number of rows & columns, and conversions between the various distributed matrices (not including BlockMatrix), and does not implement the other linear algebra functions of the matrices, although this will be very simple to add now.

Author: Mike Dusenberry <mwdusenb@us.ibm.com>

Closes #7554 from dusenberrymw/SPARK-6485_Add_CoordinateMatrix_RowMatrix_IndexedMatrix_to_PySpark and squashes the following commits:

bb039cb [Mike Dusenberry] Minor documentation update.
b887c18 [Mike Dusenberry] Updating the matrix conversion logic again to make it even cleaner.  Now, we allow the 'rows' parameter in the constructors to be either an RDD or the Java matrix object. If 'rows' is an RDD, we create a Java matrix object, wrap it, and then store that.  If 'rows' is a Java matrix object of the correct type, we just wrap and store that directly.  This is only for internal usage, and publicly, we still require 'rows' to be an RDD.  We no longer store the 'rows' RDD, and instead just compute it from the Java object when needed.  The point of this is that when we do matrix conversions, we do the conversion on the Scala/Java side, which returns a Java object, so we should use that directly, but exposing 'java_matrix' parameter in the public API is not ideal. This non-public feature of allowing 'rows' to be a Java matrix object is documented in the '__init__' constructor docstrings, which are not part of the generated public API, and doctests are also included.
7f0dcb6 [Mike Dusenberry] Updating module docstring.
cfc1be5 [Mike Dusenberry] Use 'new SQLContext(matrix.rows.sparkContext)' rather than 'SQLContext.getOrCreate', as the later doesn't guarantee that the SparkContext will be the same as for the matrix.rows data.
687e345 [Mike Dusenberry] Improving conversion performance.  This adds an optional 'java_matrix' parameter to the constructors, and pulls the conversion logic out into a '_create_from_java' function. Now, if the constructors are given a valid Java distributed matrix object as 'java_matrix', they will store those internally, rather than create a new one on the Scala/Java side.
3e50b6e [Mike Dusenberry] Moving the distributed matrices to pyspark.mllib.linalg.distributed.
308f197 [Mike Dusenberry] Using properties for better documentation.
1633f86 [Mike Dusenberry] Minor documentation cleanup.
f0c13a7 [Mike Dusenberry] CoordinateMatrix should inherit from DistributedMatrix.
ffdd724 [Mike Dusenberry] Updating doctests to make documentation cleaner.
3fd4016 [Mike Dusenberry] Updating docstrings.
27cd5f6 [Mike Dusenberry] Simplifying input conversions in the constructors for each distributed matrix.
a409cf5 [Mike Dusenberry] Updating doctests to be less verbose by using lists instead of DenseVectors explicitly.
d19b0ba [Mike Dusenberry] Updating code and documentation to note that a vector-like object (numpy array, list, etc.) can be used in place of explicit Vector object, and adding conversions when necessary to RowMatrix construction.
4bd756d [Mike Dusenberry] Adding param documentation to IndexedRow and MatrixEntry.
c6bded5 [Mike Dusenberry] Move conversion logic from tuples to IndexedRow or MatrixEntry types from within the IndexedRowMatrix and CoordinateMatrix constructors to separate _convert_to_indexed_row and _convert_to_matrix_entry functions.
329638b [Mike Dusenberry] Moving the Experimental tag to the top of each docstring.
0be6826 [Mike Dusenberry] Simplifying doctests by removing duplicated rows/entries RDDs within the various tests.
c0900df [Mike Dusenberry] Adding the colons that were accidentally not inserted.
4ad6819 [Mike Dusenberry] Documenting the  and  parameters.
3b854b9 [Mike Dusenberry] Minor updates to documentation.
10046e8 [Mike Dusenberry] Updating documentation to use class constructors instead of the removed DistributedMatrices factory methods.
119018d [Mike Dusenberry] Adding static  methods to each of the distributed matrix classes to consolidate conversion logic.
4d7af86 [Mike Dusenberry] Adding type checks to the constructors.  Although it is slightly verbose, it is better for the user to have a good error message than a cryptic stacktrace.
93b6a3d [Mike Dusenberry] Pulling the DistributedMatrices Python class out of this pull request.
f6f3c68 [Mike Dusenberry] Pulling the DistributedMatrices Scala class out of this pull request.
6a3ecb7 [Mike Dusenberry] Updating pattern matching.
08f287b [Mike Dusenberry] Slight reformatting of the documentation.
a245dc0 [Mike Dusenberry] Updating Python doctests for compatability between Python 2 & 3. Since Python 3 removed the idea of a separate 'long' type, all values that would have been outputted as a 'long' (ex: '4L') will now be treated as an 'int' and outputed as one (ex: '4').  The doctests now explicitly convert to ints so that both Python 2 and 3 will have the same output.  This is fine since the values are all small, and thus can be easily represented as ints.
4d3a37e [Mike Dusenberry] Reformatting a few long Python doctest lines.
7e3ca16 [Mike Dusenberry] Fixing long lines.
f721ead [Mike Dusenberry] Updating documentation for each of the distributed matrices.
ab0e8b6 [Mike Dusenberry] Updating unit test to be more useful.
dda2f89 [Mike Dusenberry] Added wrappers for the conversions between the various distributed matrices.  Added logic to be able to access the rows/entries of the distributed matrices, which requires serialization through DataFrames for IndexedRowMatrix and CoordinateMatrix types. Added unit tests.
0cd7166 [Mike Dusenberry] Implemented the CoordinateMatrix API in PySpark, following the idea of the IndexedRowMatrix API, including using DataFrames for serialization.
3c369cb [Mike Dusenberry] Updating the architecture a bit to make conversions between the various distributed matrix types easier.  The different distributed matrix classes are now only wrappers around the Java objects, and take the Java object as an argument during construction.  This way, we can call  for example on an , which returns a reference to a Java RowMatrix object, and then construct a PySpark RowMatrix object wrapped around the Java object.  This is analogous to the behavior of PySpark RDDs and DataFrames.  We now delegate creation of the various distributed matrices from scratch in PySpark to the factory methods on .
4bdd09b [Mike Dusenberry] Implemented the IndexedRowMatrix API in PySpark, following the idea of the RowMatrix API.  Note that for the IndexedRowMatrix, we use DataFrames to serialize the data between Python and Scala/Java, so we accept PySpark RDDs, then convert to a DataFrame, then convert back to RDDs on the Scala/Java side before constructing the IndexedRowMatrix.
23bf1ec [Mike Dusenberry] Updating documentation to add PySpark RowMatrix. Inserting newline above doctest so that it renders properly in API docs.
b194623 [Mike Dusenberry] Updating design to have a PySpark RowMatrix simply create and keep a reference to a wrapper over a Java RowMatrix.  Updating DistributedMatrices factory methods to accept numRows and numCols with default values.  Updating PySpark DistributedMatrices factory method to simply create a PySpark RowMatrix. Adding additional doctests for numRows and numCols parameters.
bc2d220 [Mike Dusenberry] Adding unit tests for RowMatrix methods.
d7e316f [Mike Dusenberry] Implemented the RowMatrix API in PySpark by doing the following: Added a DistributedMatrices class to contain factory methods for creating the various distributed matrices.  Added a factory method for creating a RowMatrix from an RDD of Vectors.  Added a createRowMatrix function to the PythonMLlibAPI to interface with the factory method.  Added DistributedMatrix, DistributedMatrices, and RowMatrix classes to the pyspark.mllib.linalg api.
---
 dev/sparktestsupport/modules.py               |   1 +
 docs/mllib-data-types.md                      | 106 +++-
 .../mllib/api/python/PythonMLLibAPI.scala     |  53 +-
 python/docs/pyspark.mllib.rst                 |   8 +
 python/pyspark/mllib/common.py                |   2 +
 python/pyspark/mllib/linalg/distributed.py    | 537 ++++++++++++++++++
 6 files changed, 704 insertions(+), 3 deletions(-)
 create mode 100644 python/pyspark/mllib/linalg/distributed.py

diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index 956dc81b62e93..a9717ff9569c7 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -331,6 +331,7 @@ def contains_file(self, filename):
         "pyspark.mllib.feature",
         "pyspark.mllib.fpm",
         "pyspark.mllib.linalg.__init__",
+        "pyspark.mllib.linalg.distributed",
         "pyspark.mllib.random",
         "pyspark.mllib.recommendation",
         "pyspark.mllib.regression",
diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md
index 3aa040046fca5..11033bf4f9c37 100644
--- a/docs/mllib-data-types.md
+++ b/docs/mllib-data-types.md
@@ -372,12 +372,37 @@ long m = mat.numRows();
 long n = mat.numCols();
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+
+A [`RowMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.RowMatrix) can be 
+created from an `RDD` of vectors.
+
+{% highlight python %}
+from pyspark.mllib.linalg.distributed import RowMatrix
+
+# Create an RDD of vectors.
+rows = sc.parallelize([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
+
+# Create a RowMatrix from an RDD of vectors.
+mat = RowMatrix(rows)
+
+# Get its size.
+m = mat.numRows()  # 4
+n = mat.numCols()  # 3
+
+# Get the rows as an RDD of vectors again.
+rowsRDD = mat.rows
+{% endhighlight %}
+</div>
+
 </div>
 
 ### IndexedRowMatrix
 
 An `IndexedRowMatrix` is similar to a `RowMatrix` but with meaningful row indices.  It is backed by
-an RDD of indexed rows, so that each row is represented by its index (long-typed) and a local vector.
+an RDD of indexed rows, so that each row is represented by its index (long-typed) and a local 
+vector.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
@@ -431,7 +456,48 @@ long n = mat.numCols();
 // Drop its row indices.
 RowMatrix rowMat = mat.toRowMatrix();
 {% endhighlight %}
-</div></div>
+</div>
+
+<div data-lang="python" markdown="1">
+
+An [`IndexedRowMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.IndexedRowMatrix)
+can be created from an `RDD` of `IndexedRow`s, where 
+[`IndexedRow`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.IndexedRow) is a 
+wrapper over `(long, vector)`.  An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping
+its row indices.
+
+{% highlight python %}
+from pyspark.mllib.linalg.distributed import IndexedRow, IndexedRowMatrix
+
+# Create an RDD of indexed rows.
+#   - This can be done explicitly with the IndexedRow class:
+indexedRows = sc.parallelize([IndexedRow(0, [1, 2, 3]), 
+                              IndexedRow(1, [4, 5, 6]), 
+                              IndexedRow(2, [7, 8, 9]), 
+                              IndexedRow(3, [10, 11, 12])])
+#   - or by using (long, vector) tuples:
+indexedRows = sc.parallelize([(0, [1, 2, 3]), (1, [4, 5, 6]), 
+                              (2, [7, 8, 9]), (3, [10, 11, 12])])
+
+# Create an IndexedRowMatrix from an RDD of IndexedRows.
+mat = IndexedRowMatrix(indexedRows)
+
+# Get its size.
+m = mat.numRows()  # 4
+n = mat.numCols()  # 3
+
+# Get the rows as an RDD of IndexedRows.
+rowsRDD = mat.rows
+
+# Convert to a RowMatrix by dropping the row indices.
+rowMat = mat.toRowMatrix()
+
+# Convert to a CoordinateMatrix.
+coordinateMat = mat.toCoordinateMatrix()
+{% endhighlight %}
+</div>
+
+</div>
 
 ### CoordinateMatrix
 
@@ -495,6 +561,42 @@ long n = mat.numCols();
 IndexedRowMatrix indexedRowMatrix = mat.toIndexedRowMatrix();
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+
+A [`CoordinateMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.CoordinateMatrix)
+can be created from an `RDD` of `MatrixEntry` entries, where 
+[`MatrixEntry`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.MatrixEntry) is a 
+wrapper over `(long, long, float)`.  A `CoordinateMatrix` can be converted to a `RowMatrix` by 
+calling `toRowMatrix`, or to an `IndexedRowMatrix` with sparse rows by calling `toIndexedRowMatrix`.
+
+{% highlight python %}
+from pyspark.mllib.linalg.distributed import CoordinateMatrix, MatrixEntry
+
+# Create an RDD of coordinate entries.
+#   - This can be done explicitly with the MatrixEntry class:
+entries = sc.parallelize([MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7)])
+#   - or using (long, long, float) tuples:
+entries = sc.parallelize([(0, 0, 1.2), (1, 0, 2.1), (2, 1, 3.7)])
+
+# Create an CoordinateMatrix from an RDD of MatrixEntries.
+mat = CoordinateMatrix(entries)
+
+# Get its size.
+m = mat.numRows()  # 3
+n = mat.numCols()  # 2
+
+# Get the entries as an RDD of MatrixEntries.
+entriesRDD = mat.entries
+
+# Convert to a RowMatrix.
+rowMat = mat.toRowMatrix()
+
+# Convert to an IndexedRowMatrix.
+indexedRowMat = mat.toIndexedRowMatrix()
+{% endhighlight %}
+</div>
+
 </div>
 
 ### BlockMatrix
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index 6f080d32bbf4d..d2b3fae381acb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -37,6 +37,7 @@ import org.apache.spark.mllib.evaluation.RankingMetrics
 import org.apache.spark.mllib.feature._
 import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel}
 import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.linalg.distributed._
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.random.{RandomRDDs => RG}
 import org.apache.spark.mllib.recommendation._
@@ -54,7 +55,7 @@ import org.apache.spark.mllib.tree.{DecisionTree, GradientBoostedTrees, RandomFo
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.mllib.util.LinearDataGenerator
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.Utils
 
@@ -1096,6 +1097,56 @@ private[python] class PythonMLLibAPI extends Serializable {
     Statistics.kolmogorovSmirnovTest(data, distName, paramsSeq: _*)
   }
 
+  /**
+   * Wrapper around RowMatrix constructor.
+   */
+  def createRowMatrix(rows: JavaRDD[Vector], numRows: Long, numCols: Int): RowMatrix = {
+    new RowMatrix(rows.rdd, numRows, numCols)
+  }
+
+  /**
+   * Wrapper around IndexedRowMatrix constructor.
+   */
+  def createIndexedRowMatrix(rows: DataFrame, numRows: Long, numCols: Int): IndexedRowMatrix = {
+    // We use DataFrames for serialization of IndexedRows from Python,
+    // so map each Row in the DataFrame back to an IndexedRow.
+    val indexedRows = rows.map {
+      case Row(index: Long, vector: Vector) => IndexedRow(index, vector)
+    }
+    new IndexedRowMatrix(indexedRows, numRows, numCols)
+  }
+
+  /**
+   * Wrapper around CoordinateMatrix constructor.
+   */
+  def createCoordinateMatrix(rows: DataFrame, numRows: Long, numCols: Long): CoordinateMatrix = {
+    // We use DataFrames for serialization of MatrixEntry entries from
+    // Python, so map each Row in the DataFrame back to a MatrixEntry.
+    val entries = rows.map {
+      case Row(i: Long, j: Long, value: Double) => MatrixEntry(i, j, value)
+    }
+    new CoordinateMatrix(entries, numRows, numCols)
+  }
+
+  /**
+   * Return the rows of an IndexedRowMatrix.
+   */
+  def getIndexedRows(indexedRowMatrix: IndexedRowMatrix): DataFrame = {
+    // We use DataFrames for serialization of IndexedRows to Python,
+    // so return a DataFrame.
+    val sqlContext = new SQLContext(indexedRowMatrix.rows.sparkContext)
+    sqlContext.createDataFrame(indexedRowMatrix.rows)
+  }
+
+  /**
+   * Return the entries of a CoordinateMatrix.
+   */
+  def getMatrixEntries(coordinateMatrix: CoordinateMatrix): DataFrame = {
+    // We use DataFrames for serialization of MatrixEntry entries to
+    // Python, so return a DataFrame.
+    val sqlContext = new SQLContext(coordinateMatrix.entries.sparkContext)
+    sqlContext.createDataFrame(coordinateMatrix.entries)
+  }
 }
 
 /**
diff --git a/python/docs/pyspark.mllib.rst b/python/docs/pyspark.mllib.rst
index 26ece4c2c389a..2d54ab118b94b 100644
--- a/python/docs/pyspark.mllib.rst
+++ b/python/docs/pyspark.mllib.rst
@@ -46,6 +46,14 @@ pyspark.mllib.linalg module
     :undoc-members:
     :show-inheritance:
 
+pyspark.mllib.linalg.distributed module
+---------------------------------------
+
+.. automodule:: pyspark.mllib.linalg.distributed
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 pyspark.mllib.random module
 ---------------------------
 
diff --git a/python/pyspark/mllib/common.py b/python/pyspark/mllib/common.py
index 855e85f57155e..a439a488de5cc 100644
--- a/python/pyspark/mllib/common.py
+++ b/python/pyspark/mllib/common.py
@@ -73,6 +73,8 @@ def _py2java(sc, obj):
     """ Convert Python object into Java """
     if isinstance(obj, RDD):
         obj = _to_java_object_rdd(obj)
+    elif isinstance(obj, DataFrame):
+        obj = obj._jdf
     elif isinstance(obj, SparkContext):
         obj = obj._jsc
     elif isinstance(obj, list):
diff --git a/python/pyspark/mllib/linalg/distributed.py b/python/pyspark/mllib/linalg/distributed.py
new file mode 100644
index 0000000000000..666d833019562
--- /dev/null
+++ b/python/pyspark/mllib/linalg/distributed.py
@@ -0,0 +1,537 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+Package for distributed linear algebra.
+"""
+
+import sys
+
+if sys.version >= '3':
+    long = int
+
+from py4j.java_gateway import JavaObject
+
+from pyspark import RDD
+from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
+from pyspark.mllib.linalg import _convert_to_vector
+
+
+__all__ = ['DistributedMatrix', 'RowMatrix', 'IndexedRow',
+           'IndexedRowMatrix', 'MatrixEntry', 'CoordinateMatrix']
+
+
+class DistributedMatrix(object):
+    """
+    .. note:: Experimental
+
+    Represents a distributively stored matrix backed by one or
+    more RDDs.
+
+    """
+    def numRows(self):
+        """Get or compute the number of rows."""
+        raise NotImplementedError
+
+    def numCols(self):
+        """Get or compute the number of cols."""
+        raise NotImplementedError
+
+
+class RowMatrix(DistributedMatrix):
+    """
+    .. note:: Experimental
+
+    Represents a row-oriented distributed Matrix with no meaningful
+    row indices.
+
+    :param rows: An RDD of vectors.
+    :param numRows: Number of rows in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of rows will be determined by the number of
+                    records in the `rows` RDD.
+    :param numCols: Number of columns in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of columns will be determined by the size of
+                    the first row.
+    """
+    def __init__(self, rows, numRows=0, numCols=0):
+        """
+        Note: This docstring is not shown publicly.
+
+        Create a wrapper over a Java RowMatrix.
+
+        Publicly, we require that `rows` be an RDD.  However, for
+        internal usage, `rows` can also be a Java RowMatrix
+        object, in which case we can wrap it directly.  This
+        assists in clean matrix conversions.
+
+        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6]])
+        >>> mat = RowMatrix(rows)
+
+        >>> mat_diff = RowMatrix(rows)
+        >>> (mat_diff._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        False
+
+        >>> mat_same = RowMatrix(mat._java_matrix_wrapper._java_model)
+        >>> (mat_same._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        True
+        """
+        if isinstance(rows, RDD):
+            rows = rows.map(_convert_to_vector)
+            java_matrix = callMLlibFunc("createRowMatrix", rows, long(numRows), int(numCols))
+        elif (isinstance(rows, JavaObject)
+              and rows.getClass().getSimpleName() == "RowMatrix"):
+            java_matrix = rows
+        else:
+            raise TypeError("rows should be an RDD of vectors, got %s" % type(rows))
+
+        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)
+
+    @property
+    def rows(self):
+        """
+        Rows of the RowMatrix stored as an RDD of vectors.
+
+        >>> mat = RowMatrix(sc.parallelize([[1, 2, 3], [4, 5, 6]]))
+        >>> rows = mat.rows
+        >>> rows.first()
+        DenseVector([1.0, 2.0, 3.0])
+        """
+        return self._java_matrix_wrapper.call("rows")
+
+    def numRows(self):
+        """
+        Get or compute the number of rows.
+
+        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6],
+        ...                        [7, 8, 9], [10, 11, 12]])
+
+        >>> mat = RowMatrix(rows)
+        >>> print(mat.numRows())
+        4
+
+        >>> mat = RowMatrix(rows, 7, 6)
+        >>> print(mat.numRows())
+        7
+        """
+        return self._java_matrix_wrapper.call("numRows")
+
+    def numCols(self):
+        """
+        Get or compute the number of cols.
+
+        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6],
+        ...                        [7, 8, 9], [10, 11, 12]])
+
+        >>> mat = RowMatrix(rows)
+        >>> print(mat.numCols())
+        3
+
+        >>> mat = RowMatrix(rows, 7, 6)
+        >>> print(mat.numCols())
+        6
+        """
+        return self._java_matrix_wrapper.call("numCols")
+
+
+class IndexedRow(object):
+    """
+    .. note:: Experimental
+
+    Represents a row of an IndexedRowMatrix.
+
+    Just a wrapper over a (long, vector) tuple.
+
+    :param index: The index for the given row.
+    :param vector: The row in the matrix at the given index.
+    """
+    def __init__(self, index, vector):
+        self.index = long(index)
+        self.vector = _convert_to_vector(vector)
+
+    def __repr__(self):
+        return "IndexedRow(%s, %s)" % (self.index, self.vector)
+
+
+def _convert_to_indexed_row(row):
+    if isinstance(row, IndexedRow):
+        return row
+    elif isinstance(row, tuple) and len(row) == 2:
+        return IndexedRow(*row)
+    else:
+        raise TypeError("Cannot convert type %s into IndexedRow" % type(row))
+
+
+class IndexedRowMatrix(DistributedMatrix):
+    """
+    .. note:: Experimental
+
+    Represents a row-oriented distributed Matrix with indexed rows.
+
+    :param rows: An RDD of IndexedRows or (long, vector) tuples.
+    :param numRows: Number of rows in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of rows will be determined by the max row
+                    index plus one.
+    :param numCols: Number of columns in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of columns will be determined by the size of
+                    the first row.
+    """
+    def __init__(self, rows, numRows=0, numCols=0):
+        """
+        Note: This docstring is not shown publicly.
+
+        Create a wrapper over a Java IndexedRowMatrix.
+
+        Publicly, we require that `rows` be an RDD.  However, for
+        internal usage, `rows` can also be a Java IndexedRowMatrix
+        object, in which case we can wrap it directly.  This
+        assists in clean matrix conversions.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                        IndexedRow(1, [4, 5, 6])])
+        >>> mat = IndexedRowMatrix(rows)
+
+        >>> mat_diff = IndexedRowMatrix(rows)
+        >>> (mat_diff._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        False
+
+        >>> mat_same = IndexedRowMatrix(mat._java_matrix_wrapper._java_model)
+        >>> (mat_same._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        True
+        """
+        if isinstance(rows, RDD):
+            rows = rows.map(_convert_to_indexed_row)
+            # We use DataFrames for serialization of IndexedRows from
+            # Python, so first convert the RDD to a DataFrame on this
+            # side. This will convert each IndexedRow to a Row
+            # containing the 'index' and 'vector' values, which can
+            # both be easily serialized.  We will convert back to
+            # IndexedRows on the Scala side.
+            java_matrix = callMLlibFunc("createIndexedRowMatrix", rows.toDF(),
+                                        long(numRows), int(numCols))
+        elif (isinstance(rows, JavaObject)
+              and rows.getClass().getSimpleName() == "IndexedRowMatrix"):
+            java_matrix = rows
+        else:
+            raise TypeError("rows should be an RDD of IndexedRows or (long, vector) tuples, "
+                            "got %s" % type(rows))
+
+        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)
+
+    @property
+    def rows(self):
+        """
+        Rows of the IndexedRowMatrix stored as an RDD of IndexedRows.
+
+        >>> mat = IndexedRowMatrix(sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                                        IndexedRow(1, [4, 5, 6])]))
+        >>> rows = mat.rows
+        >>> rows.first()
+        IndexedRow(0, [1.0,2.0,3.0])
+        """
+        # We use DataFrames for serialization of IndexedRows from
+        # Java, so we first convert the RDD of rows to a DataFrame
+        # on the Scala/Java side. Then we map each Row in the
+        # DataFrame back to an IndexedRow on this side.
+        rows_df = callMLlibFunc("getIndexedRows", self._java_matrix_wrapper._java_model)
+        rows = rows_df.map(lambda row: IndexedRow(row[0], row[1]))
+        return rows
+
+    def numRows(self):
+        """
+        Get or compute the number of rows.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                        IndexedRow(1, [4, 5, 6]),
+        ...                        IndexedRow(2, [7, 8, 9]),
+        ...                        IndexedRow(3, [10, 11, 12])])
+
+        >>> mat = IndexedRowMatrix(rows)
+        >>> print(mat.numRows())
+        4
+
+        >>> mat = IndexedRowMatrix(rows, 7, 6)
+        >>> print(mat.numRows())
+        7
+        """
+        return self._java_matrix_wrapper.call("numRows")
+
+    def numCols(self):
+        """
+        Get or compute the number of cols.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                        IndexedRow(1, [4, 5, 6]),
+        ...                        IndexedRow(2, [7, 8, 9]),
+        ...                        IndexedRow(3, [10, 11, 12])])
+
+        >>> mat = IndexedRowMatrix(rows)
+        >>> print(mat.numCols())
+        3
+
+        >>> mat = IndexedRowMatrix(rows, 7, 6)
+        >>> print(mat.numCols())
+        6
+        """
+        return self._java_matrix_wrapper.call("numCols")
+
+    def toRowMatrix(self):
+        """
+        Convert this matrix to a RowMatrix.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                        IndexedRow(6, [4, 5, 6])])
+        >>> mat = IndexedRowMatrix(rows).toRowMatrix()
+        >>> mat.rows.collect()
+        [DenseVector([1.0, 2.0, 3.0]), DenseVector([4.0, 5.0, 6.0])]
+        """
+        java_row_matrix = self._java_matrix_wrapper.call("toRowMatrix")
+        return RowMatrix(java_row_matrix)
+
+    def toCoordinateMatrix(self):
+        """
+        Convert this matrix to a CoordinateMatrix.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 0]),
+        ...                        IndexedRow(6, [0, 5])])
+        >>> mat = IndexedRowMatrix(rows).toCoordinateMatrix()
+        >>> mat.entries.take(3)
+        [MatrixEntry(0, 0, 1.0), MatrixEntry(0, 1, 0.0), MatrixEntry(6, 0, 0.0)]
+        """
+        java_coordinate_matrix = self._java_matrix_wrapper.call("toCoordinateMatrix")
+        return CoordinateMatrix(java_coordinate_matrix)
+
+
+class MatrixEntry(object):
+    """
+    .. note:: Experimental
+
+    Represents an entry of a CoordinateMatrix.
+
+    Just a wrapper over a (long, long, float) tuple.
+
+    :param i: The row index of the matrix.
+    :param j: The column index of the matrix.
+    :param value: The (i, j)th entry of the matrix, as a float.
+    """
+    def __init__(self, i, j, value):
+        self.i = long(i)
+        self.j = long(j)
+        self.value = float(value)
+
+    def __repr__(self):
+        return "MatrixEntry(%s, %s, %s)" % (self.i, self.j, self.value)
+
+
+def _convert_to_matrix_entry(entry):
+    if isinstance(entry, MatrixEntry):
+        return entry
+    elif isinstance(entry, tuple) and len(entry) == 3:
+        return MatrixEntry(*entry)
+    else:
+        raise TypeError("Cannot convert type %s into MatrixEntry" % type(entry))
+
+
+class CoordinateMatrix(DistributedMatrix):
+    """
+    .. note:: Experimental
+
+    Represents a matrix in coordinate format.
+
+    :param entries: An RDD of MatrixEntry inputs or
+                    (long, long, float) tuples.
+    :param numRows: Number of rows in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of rows will be determined by the max row
+                    index plus one.
+    :param numCols: Number of columns in the matrix. A non-positive
+                    value means unknown, at which point the number
+                    of columns will be determined by the max row
+                    index plus one.
+    """
+    def __init__(self, entries, numRows=0, numCols=0):
+        """
+        Note: This docstring is not shown publicly.
+
+        Create a wrapper over a Java CoordinateMatrix.
+
+        Publicly, we require that `rows` be an RDD.  However, for
+        internal usage, `rows` can also be a Java CoordinateMatrix
+        object, in which case we can wrap it directly.  This
+        assists in clean matrix conversions.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(6, 4, 2.1)])
+        >>> mat = CoordinateMatrix(entries)
+
+        >>> mat_diff = CoordinateMatrix(entries)
+        >>> (mat_diff._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        False
+
+        >>> mat_same = CoordinateMatrix(mat._java_matrix_wrapper._java_model)
+        >>> (mat_same._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        True
+        """
+        if isinstance(entries, RDD):
+            entries = entries.map(_convert_to_matrix_entry)
+            # We use DataFrames for serialization of MatrixEntry entries
+            # from Python, so first convert the RDD to a DataFrame on
+            # this side. This will convert each MatrixEntry to a Row
+            # containing the 'i', 'j', and 'value' values, which can
+            # each be easily serialized. We will convert back to
+            # MatrixEntry inputs on the Scala side.
+            java_matrix = callMLlibFunc("createCoordinateMatrix", entries.toDF(),
+                                        long(numRows), long(numCols))
+        elif (isinstance(entries, JavaObject)
+              and entries.getClass().getSimpleName() == "CoordinateMatrix"):
+            java_matrix = entries
+        else:
+            raise TypeError("entries should be an RDD of MatrixEntry entries or "
+                            "(long, long, float) tuples, got %s" % type(entries))
+
+        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)
+
+    @property
+    def entries(self):
+        """
+        Entries of the CoordinateMatrix stored as an RDD of
+        MatrixEntries.
+
+        >>> mat = CoordinateMatrix(sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                                        MatrixEntry(6, 4, 2.1)]))
+        >>> entries = mat.entries
+        >>> entries.first()
+        MatrixEntry(0, 0, 1.2)
+        """
+        # We use DataFrames for serialization of MatrixEntry entries
+        # from Java, so we first convert the RDD of entries to a
+        # DataFrame on the Scala/Java side. Then we map each Row in
+        # the DataFrame back to a MatrixEntry on this side.
+        entries_df = callMLlibFunc("getMatrixEntries", self._java_matrix_wrapper._java_model)
+        entries = entries_df.map(lambda row: MatrixEntry(row[0], row[1], row[2]))
+        return entries
+
+    def numRows(self):
+        """
+        Get or compute the number of rows.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(1, 0, 2),
+        ...                           MatrixEntry(2, 1, 3.7)])
+
+        >>> mat = CoordinateMatrix(entries)
+        >>> print(mat.numRows())
+        3
+
+        >>> mat = CoordinateMatrix(entries, 7, 6)
+        >>> print(mat.numRows())
+        7
+        """
+        return self._java_matrix_wrapper.call("numRows")
+
+    def numCols(self):
+        """
+        Get or compute the number of cols.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(1, 0, 2),
+        ...                           MatrixEntry(2, 1, 3.7)])
+
+        >>> mat = CoordinateMatrix(entries)
+        >>> print(mat.numCols())
+        2
+
+        >>> mat = CoordinateMatrix(entries, 7, 6)
+        >>> print(mat.numCols())
+        6
+        """
+        return self._java_matrix_wrapper.call("numCols")
+
+    def toRowMatrix(self):
+        """
+        Convert this matrix to a RowMatrix.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(6, 4, 2.1)])
+
+        >>> # This CoordinateMatrix will have 7 effective rows, due to
+        >>> # the highest row index being 6, but the ensuing RowMatrix
+        >>> # will only have 2 rows since there are only entries on 2
+        >>> # unique rows.
+        >>> mat = CoordinateMatrix(entries).toRowMatrix()
+        >>> print(mat.numRows())
+        2
+
+        >>> # This CoordinateMatrix will have 5 columns, due to the
+        >>> # highest column index being 4, and the ensuing RowMatrix
+        >>> # will have 5 columns as well.
+        >>> mat = CoordinateMatrix(entries).toRowMatrix()
+        >>> print(mat.numCols())
+        5
+        """
+        java_row_matrix = self._java_matrix_wrapper.call("toRowMatrix")
+        return RowMatrix(java_row_matrix)
+
+    def toIndexedRowMatrix(self):
+        """
+        Convert this matrix to an IndexedRowMatrix.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(6, 4, 2.1)])
+
+        >>> # This CoordinateMatrix will have 7 effective rows, due to
+        >>> # the highest row index being 6, and the ensuing
+        >>> # IndexedRowMatrix will have 7 rows as well.
+        >>> mat = CoordinateMatrix(entries).toIndexedRowMatrix()
+        >>> print(mat.numRows())
+        7
+
+        >>> # This CoordinateMatrix will have 5 columns, due to the
+        >>> # highest column index being 4, and the ensuing
+        >>> # IndexedRowMatrix will have 5 columns as well.
+        >>> mat = CoordinateMatrix(entries).toIndexedRowMatrix()
+        >>> print(mat.numCols())
+        5
+        """
+        java_indexed_row_matrix = self._java_matrix_wrapper.call("toIndexedRowMatrix")
+        return IndexedRowMatrix(java_indexed_row_matrix)
+
+
+def _test():
+    import doctest
+    from pyspark import SparkContext
+    from pyspark.sql import SQLContext
+    import pyspark.mllib.linalg.distributed
+    globs = pyspark.mllib.linalg.distributed.__dict__.copy()
+    globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
+    globs['sqlContext'] = SQLContext(globs['sc'])
+    (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
+    globs['sc'].stop()
+    if failure_count:
+        exit(-1)
+
+if __name__ == "__main__":
+    _test()

From b77d3b9688d56d33737909375d1d0db07da5827b Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Tue, 4 Aug 2015 16:52:43 -0700
Subject: [PATCH 036/633] [SPARK-9586] [ML] Update
 BinaryClassificationEvaluator to use setRawPredictionCol

Update BinaryClassificationEvaluator to use setRawPredictionCol, rather than setScoreCol. Deprecated setScoreCol.

I don't think setScoreCol was actually used anywhere (based on search).

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7921 from jkbradley/binary-eval-rawpred and squashes the following commits:

e5d7dfa [Joseph K. Bradley] Update BinaryClassificationEvaluator to use setRawPredictionCol
---
 .../ml/evaluation/BinaryClassificationEvaluator.scala    | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
index 4a82b77f0edcb..5d5cb7e94f45b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.types.DoubleType
 
 /**
  * :: Experimental ::
- * Evaluator for binary classification, which expects two input columns: score and label.
+ * Evaluator for binary classification, which expects two input columns: rawPrediction and label.
  */
 @Experimental
 class BinaryClassificationEvaluator(override val uid: String)
@@ -50,6 +50,13 @@ class BinaryClassificationEvaluator(override val uid: String)
   def setMetricName(value: String): this.type = set(metricName, value)
 
   /** @group setParam */
+  def setRawPredictionCol(value: String): this.type = set(rawPredictionCol, value)
+
+  /**
+   * @group setParam
+   * @deprecated use [[setRawPredictionCol()]] instead
+   */
+  @deprecated("use setRawPredictionCol instead", "1.5.0")
   def setScoreCol(value: String): this.type = set(rawPredictionCol, value)
 
   /** @group setParam */

From 7c8fc1f7cb837ff5c32811fdeb3ee2b84de2dea4 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 4 Aug 2015 17:05:19 -0700
Subject: [PATCH 037/633] [SPARK-9598][SQL] do not expose generic getter in
 internal row

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7932 from cloud-fan/generic-getter and squashes the following commits:

c60de4c [Wenchen Fan] do not expose generic getter in internal row
---
 .../sql/catalyst/expressions/UnsafeRow.java   |  5 --
 .../spark/sql/catalyst/InternalRow.scala      | 37 +++++++++--
 .../GenericSpecializedGetters.scala           | 61 -------------------
 .../sql/catalyst/expressions/Projection.scala |  4 +-
 .../expressions/SpecificMutableRow.scala      |  2 +-
 .../sql/catalyst/expressions/aggregates.scala |  2 +-
 .../codegen/GenerateProjection.scala          |  2 +-
 .../spark/sql/catalyst/expressions/rows.scala | 12 ++--
 .../spark/sql/types/GenericArrayData.scala    | 37 +++++++----
 .../datasources/DataSourceStrategy.scala      |  6 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala | 20 +++---
 11 files changed, 80 insertions(+), 108 deletions(-)
 delete mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index e6750fce4fa80..e3e1622de08ba 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -253,11 +253,6 @@ public void setDecimal(int ordinal, Decimal value, int precision) {
     }
   }
 
-  @Override
-  public Object genericGet(int ordinal) {
-    throw new UnsupportedOperationException();
-  }
-
   @Override
   public Object get(int ordinal, DataType dataType) {
     if (isNullAt(ordinal) || dataType instanceof NullType) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 7656d054dc361..7d17cca808791 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -17,15 +17,15 @@
 
 package org.apache.spark.sql.catalyst
 
-import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.{DataType, MapData, ArrayData, Decimal}
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
  * internal types.
  */
-// todo: make InternalRow just extends SpecializedGetters, remove generic getter
-abstract class InternalRow extends GenericSpecializedGetters with Serializable {
+abstract class InternalRow extends SpecializedGetters with Serializable {
 
   def numFields: Int
 
@@ -50,6 +50,31 @@ abstract class InternalRow extends GenericSpecializedGetters with Serializable {
     false
   }
 
+  // Subclasses of InternalRow should implement all special getters and equals/hashCode,
+  // or implement this genericGet.
+  protected def genericGet(ordinal: Int): Any = throw new IllegalStateException(
+    "Concrete internal rows should implement genericGet, " +
+      "or implement all special getters and equals/hashCode")
+
+  // default implementation (slow)
+  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
+  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
+  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+  override def getMap(ordinal: Int): MapData = getAs(ordinal)
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+
   override def equals(o: Any): Boolean = {
     if (!o.isInstanceOf[InternalRow]) {
       return false
@@ -159,15 +184,15 @@ abstract class InternalRow extends GenericSpecializedGetters with Serializable {
 
 object InternalRow {
   /**
-   * This method can be used to construct a [[Row]] with the given values.
+   * This method can be used to construct a [[InternalRow]] with the given values.
    */
   def apply(values: Any*): InternalRow = new GenericInternalRow(values.toArray)
 
   /**
-   * This method can be used to construct a [[Row]] from a [[Seq]] of values.
+   * This method can be used to construct a [[InternalRow]] from a [[Seq]] of values.
    */
   def fromSeq(values: Seq[Any]): InternalRow = new GenericInternalRow(values.toArray)
 
-  /** Returns an empty row. */
+  /** Returns an empty [[InternalRow]]. */
   val empty = apply()
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala
deleted file mode 100644
index 6e957928e02a8..0000000000000
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/GenericSpecializedGetters.scala
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.expressions
-
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.types.{DataType, MapData, ArrayData, Decimal}
-import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
-
-trait GenericSpecializedGetters extends SpecializedGetters {
-
-  def genericGet(ordinal: Int): Any
-
-  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
-
-  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
-
-  override def get(ordinal: Int, elementType: DataType): AnyRef = getAs(ordinal)
-
-  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
-
-  override def getByte(ordinal: Int): Byte = getAs(ordinal)
-
-  override def getShort(ordinal: Int): Short = getAs(ordinal)
-
-  override def getInt(ordinal: Int): Int = getAs(ordinal)
-
-  override def getLong(ordinal: Int): Long = getAs(ordinal)
-
-  override def getFloat(ordinal: Int): Float = getAs(ordinal)
-
-  override def getDouble(ordinal: Int): Double = getAs(ordinal)
-
-  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
-
-  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
-
-  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
-
-  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
-
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
-
-  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
-
-  override def getMap(ordinal: Int): MapData = getAs(ordinal)
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 79649741025a3..4296b4b123fc0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -207,8 +207,8 @@ class JoinedRow extends InternalRow {
 
   override def numFields: Int = row1.numFields + row2.numFields
 
-  override def genericGet(i: Int): Any =
-    if (i < row1.numFields) row1.genericGet(i) else row2.genericGet(i - row1.numFields)
+  override def get(i: Int, dt: DataType): AnyRef =
+    if (i < row1.numFields) row1.get(i, dt) else row2.get(i - row1.numFields, dt)
 
   override def isNullAt(i: Int): Boolean =
     if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index d149a5b179b63..b94df6bd66e04 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -232,7 +232,7 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
     new GenericInternalRow(newValues)
   }
 
-  override def genericGet(i: Int): Any = values(i).boxed
+  override protected def genericGet(i: Int): Any = values(i).boxed
 
   override def update(ordinal: Int, value: Any) {
     if (value == null) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 5d4b349b1597a..2cf8312ea59aa 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -623,7 +623,7 @@ case class CombineSetsAndSumFunction(
       null
     } else {
       Cast(Literal(
-        casted.iterator.map(f => f.genericGet(0)).reduceLeft(
+        casted.iterator.map(f => f.get(0, null)).reduceLeft(
           base.dataType.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]].plus)),
         base.dataType).eval(null)
     }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index 1572b2b99ab66..c04fe734d554e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -184,7 +184,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         public void setNullAt(int i) { nullBits[i] = true; }
         public boolean isNullAt(int i) { return nullBits[i]; }
 
-        public Object genericGet(int i) {
+        protected Object genericGet(int i) {
           if (isNullAt(i)) return null;
           switch (i) {
           $getCases
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index d04434b953e41..5e5de1d1dc6a7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -19,8 +19,8 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.types.{Decimal, DataType, StructType, AtomicType}
-import org.apache.spark.unsafe.types.UTF8String
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.
@@ -76,13 +76,13 @@ class GenericRowWithSchema(values: Array[Any], override val schema: StructType)
  * Note that, while the array is not copied, and thus could technically be mutated after creation,
  * this is not allowed.
  */
-class GenericInternalRow(protected[sql] val values: Array[Any]) extends InternalRow {
+class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
   def this(size: Int) = this(new Array[Any](size))
 
-  override def genericGet(ordinal: Int): Any = values(ordinal)
+  override protected def genericGet(ordinal: Int) = values(ordinal)
 
   override def toSeq: Seq[Any] = values
 
@@ -103,13 +103,13 @@ class GenericInternalRowWithSchema(values: Array[Any], val schema: StructType)
   def fieldIndex(name: String): Int = schema.fieldIndex(name)
 }
 
-class GenericMutableRow(val values: Array[Any]) extends MutableRow {
+class GenericMutableRow(values: Array[Any]) extends MutableRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
   def this(size: Int) = this(new Array[Any](size))
 
-  override def genericGet(ordinal: Int): Any = values(ordinal)
+  override protected def genericGet(ordinal: Int) = values(ordinal)
 
   override def toSeq: Seq[Any] = values
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
index b314acdfe3644..459fcb6fc0acc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/GenericArrayData.scala
@@ -17,22 +17,33 @@
 
 package org.apache.spark.sql.types
 
-import scala.reflect.ClassTag
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
-import org.apache.spark.sql.catalyst.expressions.GenericSpecializedGetters
-
-class GenericArrayData(private[sql] val array: Array[Any])
-  extends ArrayData with GenericSpecializedGetters {
-
-  override def genericGet(ordinal: Int): Any = array(ordinal)
+class GenericArrayData(private[sql] val array: Array[Any]) extends ArrayData {
 
   override def copy(): ArrayData = new GenericArrayData(array.clone())
 
-  // todo: Array is invariant in scala, maybe use toSeq instead?
-  override def toArray[T: ClassTag](elementType: DataType): Array[T] = array.map(_.asInstanceOf[T])
-
   override def numElements(): Int = array.length
 
+  private def getAs[T](ordinal: Int) = array(ordinal).asInstanceOf[T]
+  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
+  override def get(ordinal: Int, elementType: DataType): AnyRef = getAs(ordinal)
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+  override def getMap(ordinal: Int): MapData = getAs(ordinal)
+
   override def toString(): String = array.mkString("[", ",", "]")
 
   override def equals(o: Any): Boolean = {
@@ -56,8 +67,8 @@ class GenericArrayData(private[sql] val array: Array[Any])
         return false
       }
       if (!isNullAt(i)) {
-        val o1 = genericGet(i)
-        val o2 = other.genericGet(i)
+        val o1 = array(i)
+        val o2 = other.array(i)
         o1 match {
           case b1: Array[Byte] =>
             if (!o2.isInstanceOf[Array[Byte]] ||
@@ -91,7 +102,7 @@ class GenericArrayData(private[sql] val array: Array[Any])
         if (isNullAt(i)) {
           0
         } else {
-          genericGet(i) match {
+          array(i) match {
             case b: Boolean => if (b) 0 else 1
             case b: Byte => b.toInt
             case s: Short => s.toInt
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 6b91e51ca52fb..d9d7bc19bd419 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -187,15 +187,17 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         // To see whether the `index`-th column is a partition column...
         val i = partitionColumns.indexOf(name)
         if (i != -1) {
+          val dt = schema(partitionColumns(i)).dataType
           // If yes, gets column value from partition values.
           (mutableRow: MutableRow, dataRow: InternalRow, ordinal: Int) => {
-            mutableRow(ordinal) = partitionValues.genericGet(i)
+            mutableRow(ordinal) = partitionValues.get(i, dt)
           }
         } else {
           // Otherwise, inherits the value from scanned data.
           val i = nonPartitionColumns.indexOf(name)
+          val dt = schema(nonPartitionColumns(i)).dataType
           (mutableRow: MutableRow, dataRow: InternalRow, ordinal: Int) => {
-            mutableRow(ordinal) = dataRow.genericGet(i)
+            mutableRow(ordinal) = dataRow.get(i, dt)
           }
         }
       }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 66014ddca0596..16e0187ed20a0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -61,11 +61,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.genericGet(0))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.genericGet(1))
-      assertResult(10, "Wrong null count")(stats.genericGet(2))
-      assertResult(20, "Wrong row count")(stats.genericGet(3))
-      assertResult(stats.genericGet(4), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
+      assertResult(10, "Wrong null count")(stats.get(2, null))
+      assertResult(20, "Wrong row count")(stats.get(3, null))
+      assertResult(stats.get(4, null), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
@@ -96,11 +96,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.genericGet(0))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.genericGet(1))
-      assertResult(10, "Wrong null count")(stats.genericGet(2))
-      assertResult(20, "Wrong row count")(stats.genericGet(3))
-      assertResult(stats.genericGet(4), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
+      assertResult(10, "Wrong null count")(stats.get(2, null))
+      assertResult(20, "Wrong row count")(stats.get(3, null))
+      assertResult(stats.get(4, null), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum

From 629e26f7ee916e70f59b017cb6083aa441b26b2c Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 4 Aug 2015 18:13:18 -0700
Subject: [PATCH 038/633] [SPARK-9609] [MLLIB] Fix spelling of
 Strategy.defaultStrategy

jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #7941 from feynmanliang/SPARK-9609-stategy-spelling and squashes the following commits:

d2aafb1 [Feynman Liang] Add deprecated backwards compatibility
aa090a8 [Feynman Liang] Fix spelling
---
 .../main/scala/org/apache/spark/ml/tree/treeParams.scala  | 2 +-
 .../spark/mllib/tree/configuration/BoostingStrategy.scala | 2 +-
 .../apache/spark/mllib/tree/configuration/Strategy.scala  | 8 ++++++--
 3 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
index e817090f8a16b..dbd8d31571d2e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
@@ -163,7 +163,7 @@ private[ml] trait DecisionTreeParams extends PredictorParams {
       oldAlgo: OldAlgo.Algo,
       oldImpurity: OldImpurity,
       subsamplingRate: Double): OldStrategy = {
-    val strategy = OldStrategy.defaultStategy(oldAlgo)
+    val strategy = OldStrategy.defaultStrategy(oldAlgo)
     strategy.impurity = oldImpurity
     strategy.checkpointInterval = getCheckpointInterval
     strategy.maxBins = getMaxBins
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index 9fd30c9b56319..50fe2ac53da9d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -90,7 +90,7 @@ object BoostingStrategy {
    * @return Configuration for boosting algorithm
    */
   def defaultParams(algo: Algo): BoostingStrategy = {
-    val treeStrategy = Strategy.defaultStategy(algo)
+    val treeStrategy = Strategy.defaultStrategy(algo)
     treeStrategy.maxDepth = 3
     algo match {
       case Algo.Classification =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index ada227c200a79..de2c784809443 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -178,14 +178,14 @@ object Strategy {
    * @param algo  "Classification" or "Regression"
    */
   def defaultStrategy(algo: String): Strategy = {
-    defaultStategy(Algo.fromString(algo))
+    defaultStrategy(Algo.fromString(algo))
   }
 
   /**
    * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
    * @param algo Algo.Classification or Algo.Regression
    */
-  def defaultStategy(algo: Algo): Strategy = algo match {
+  def defaultStrategy(algo: Algo): Strategy = algo match {
     case Algo.Classification =>
       new Strategy(algo = Classification, impurity = Gini, maxDepth = 10,
         numClasses = 2)
@@ -193,4 +193,8 @@ object Strategy {
       new Strategy(algo = Regression, impurity = Variance, maxDepth = 10,
         numClasses = 0)
   }
+
+  @deprecated("Use Strategy.defaultStrategy instead.", "1.5.0")
+  def defaultStategy(algo: Algo): Strategy = defaultStrategy(algo)
+
 }

From d92fa14179287c996407d9c7d249103109f9cdef Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 4 Aug 2015 18:15:26 -0700
Subject: [PATCH 039/633] [SPARK-8601] [ML] Add an option to disable
 standardization for linear regression

All compressed sensing applications, and some of the regression use-cases will have better result by turning the feature scaling off. However, if we implement this naively by training the dataset without doing any standardization, the rate of convergency will not be good. This can be implemented by still standardizing the training dataset but we penalize each component differently to get effectively the same objective function but a better numerical problem. As a result, for those columns with high variances, they will be penalized less, and vice versa. Without this, since all the features are standardized, so they will be penalized the same.

In R, there is an option for this.
standardize

Logical flag for x variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE. If variables are in the same units already, you might not wish to standardize. See details below for y standardization with family="gaussian".

Note that the primary author for this PR is holdenk

Author: Holden Karau <holden@pigscanfly.ca>
Author: DB Tsai <dbt@netflix.com>

Closes #7875 from dbtsai/SPARK-8522 and squashes the following commits:

e856036 [DB Tsai] scala doc
596e96c [DB Tsai] minor
bbff347 [DB Tsai] naming
baa0805 [DB Tsai] touch up
d6234ba [DB Tsai] Merge branch 'master' into SPARK-8522-Disable-Linear_featureScaling-Spark-8601-in-Linear_regression
6b1dc09 [Holden Karau] Merge branch 'master' into SPARK-8522-Disable-Linear_featureScaling-Spark-8601-in-Linear_regression
332f140 [Holden Karau] Merge in master
eebe10a [Holden Karau] Use same comparision operator throughout the test
3f92935 [Holden Karau] merge
b83a41e [Holden Karau] Expand the tests and make them similar to the other PR also providing an option to disable standardization (but for LoR).
0c334a2 [Holden Karau] Remove extra line
99ce053 [Holden Karau] merge in master
e54a8a9 [Holden Karau] Fix long line
e47c574 [Holden Karau] Add support for L2 without standardization.
55d3a66 [Holden Karau] Add standardization param for linear regression
00a1dc5 [Holden Karau] Add the param to the linearregression impl
---
 .../classification/LogisticRegression.scala   |   6 +-
 .../ml/regression/LinearRegression.scala      |  70 ++++-
 .../ml/regression/LinearRegressionSuite.scala | 278 +++++++++++++-----
 3 files changed, 268 insertions(+), 86 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index c937b9602b8c1..0d073839259c6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -133,9 +133,9 @@ class LogisticRegression(override val uid: String)
   /**
    * Whether to standardize the training features before fitting the model.
    * The coefficients of models will be always returned on the original scale,
-   * so it will be transparent for users. Note that when no regularization,
-   * with or without standardization, the models should be always converged to
-   * the same solution.
+   * so it will be transparent for users. Note that with/without standardization,
+   * the models should be always converged to the same solution when no regularization
+   * is applied. In R's GLMNET package, the default behavior is true as well.
    * Default is true.
    * @group setParam
    * */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 3b85ba001b128..92d819bad8654 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -45,7 +45,7 @@ import org.apache.spark.util.StatCounter
  */
 private[regression] trait LinearRegressionParams extends PredictorParams
     with HasRegParam with HasElasticNetParam with HasMaxIter with HasTol
-    with HasFitIntercept
+    with HasFitIntercept with HasStandardization
 
 /**
  * :: Experimental ::
@@ -84,6 +84,18 @@ class LinearRegression(override val uid: String)
   def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
   setDefault(fitIntercept -> true)
 
+  /**
+   * Whether to standardize the training features before fitting the model.
+   * The coefficients of models will be always returned on the original scale,
+   * so it will be transparent for users. Note that with/without standardization,
+   * the models should be always converged to the same solution when no regularization
+   * is applied. In R's GLMNET package, the default behavior is true as well.
+   * Default is true.
+   * @group setParam
+   */
+  def setStandardization(value: Boolean): this.type = set(standardization, value)
+  setDefault(standardization -> true)
+
   /**
    * Set the ElasticNet mixing parameter.
    * For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
@@ -165,12 +177,24 @@ class LinearRegression(override val uid: String)
     val effectiveL2RegParam = (1.0 - $(elasticNetParam)) * effectiveRegParam
 
     val costFun = new LeastSquaresCostFun(instances, yStd, yMean, $(fitIntercept),
-      featuresStd, featuresMean, effectiveL2RegParam)
+      $(standardization), featuresStd, featuresMean, effectiveL2RegParam)
 
     val optimizer = if ($(elasticNetParam) == 0.0 || effectiveRegParam == 0.0) {
       new BreezeLBFGS[BDV[Double]]($(maxIter), 10, $(tol))
     } else {
-      new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, effectiveL1RegParam, $(tol))
+      def effectiveL1RegFun = (index: Int) => {
+        if ($(standardization)) {
+          effectiveL1RegParam
+        } else {
+          // If `standardization` is false, we still standardize the data
+          // to improve the rate of convergence; as a result, we have to
+          // perform this reverse standardization by penalizing each component
+          // differently to get effectively the same objective function when
+          // the training dataset is not standardized.
+          if (featuresStd(index) != 0.0) effectiveL1RegParam / featuresStd(index) else 0.0
+        }
+      }
+      new BreezeOWLQN[Int, BDV[Double]]($(maxIter), 10, effectiveL1RegFun, $(tol))
     }
 
     val initialWeights = Vectors.zeros(numFeatures)
@@ -456,6 +480,7 @@ class LinearRegressionSummary private[regression] (
  * @param weights The weights/coefficients corresponding to the features.
  * @param labelStd The standard deviation value of the label.
  * @param labelMean The mean value of the label.
+ * @param fitIntercept Whether to fit an intercept term.
  * @param featuresStd The standard deviation values of the features.
  * @param featuresMean The mean values of the features.
  */
@@ -568,6 +593,7 @@ private class LeastSquaresCostFun(
     labelStd: Double,
     labelMean: Double,
     fitIntercept: Boolean,
+    standardization: Boolean,
     featuresStd: Array[Double],
     featuresMean: Array[Double],
     effectiveL2regParam: Double) extends DiffFunction[BDV[Double]] {
@@ -584,14 +610,38 @@ private class LeastSquaresCostFun(
           case (aggregator1, aggregator2) => aggregator1.merge(aggregator2)
         })
 
-    // regVal is the sum of weight squares for L2 regularization
-    val norm = brzNorm(weights, 2.0)
-    val regVal = 0.5 * effectiveL2regParam * norm * norm
+    val totalGradientArray = leastSquaresAggregator.gradient.toArray
 
-    val loss = leastSquaresAggregator.loss + regVal
-    val gradient = leastSquaresAggregator.gradient
-    axpy(effectiveL2regParam, w, gradient)
+    val regVal = if (effectiveL2regParam == 0.0) {
+      0.0
+    } else {
+      var sum = 0.0
+      w.foreachActive { (index, value) =>
+        // The following code will compute the loss of the regularization; also
+        // the gradient of the regularization, and add back to totalGradientArray.
+        sum += {
+          if (standardization) {
+            totalGradientArray(index) += effectiveL2regParam * value
+            value * value
+          } else {
+            if (featuresStd(index) != 0.0) {
+              // If `standardization` is false, we still standardize the data
+              // to improve the rate of convergence; as a result, we have to
+              // perform this reverse standardization by penalizing each component
+              // differently to get effectively the same objective function when
+              // the training dataset is not standardized.
+              val temp = value / (featuresStd(index) * featuresStd(index))
+              totalGradientArray(index) += effectiveL2regParam * temp
+              value * temp
+            } else {
+              0.0
+            }
+          }
+        }
+      }
+      0.5 * effectiveL2regParam * sum
+    }
 
-    (loss, gradient.toBreeze.asInstanceOf[BDV[Double]])
+    (leastSquaresAggregator.loss + regVal, new BDV(totalGradientArray))
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index 7cdda3db88ad1..21ad8225bd9f7 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -70,6 +70,7 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(lir.getRegParam === 0.0)
     assert(lir.getElasticNetParam === 0.0)
     assert(lir.getFitIntercept)
+    assert(lir.getStandardization)
     val model = lir.fit(dataset)
     model.transform(dataset)
       .select("label", "prediction")
@@ -81,8 +82,11 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("linear regression with intercept without regularization") {
-    val trainer = new LinearRegression
-    val model = trainer.fit(dataset)
+    val trainer1 = new LinearRegression
+    // The result should be the same regardless of standardization without regularization
+    val trainer2 = (new LinearRegression).setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        Using the following R code to load the data and train the model using glmnet package.
@@ -95,28 +99,36 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        > weights
         3 x 1 sparse Matrix of class "dgCMatrix"
                                  s0
-       (Intercept)         6.300528
-       as.numeric.data.V2. 4.701024
-       as.numeric.data.V3. 7.198257
+       (Intercept)         6.298698
+       as.numeric.data.V2. 4.700706
+       as.numeric.data.V3. 7.199082
      */
     val interceptR = 6.298698
     val weightsR = Vectors.dense(4.700706, 7.199082)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR relTol 1E-3)
+    assert(model1.weights ~= weightsR relTol 1E-3)
+    assert(model2.intercept ~== interceptR relTol 1E-3)
+    assert(model2.weights ~= weightsR relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression without intercept without regularization") {
-    val trainer = (new LinearRegression).setFitIntercept(false)
-    val model = trainer.fit(dataset)
-    val modelWithoutIntercept = trainer.fit(datasetWithoutIntercept)
+    val trainer1 = (new LinearRegression).setFitIntercept(false)
+    // Without regularization the results should be the same
+    val trainer2 = (new LinearRegression).setFitIntercept(false).setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val modelWithoutIntercept1 = trainer1.fit(datasetWithoutIntercept)
+    val model2 = trainer2.fit(dataset)
+    val modelWithoutIntercept2 = trainer2.fit(datasetWithoutIntercept)
+
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 0, lambda = 0,
@@ -130,26 +142,34 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
      */
     val weightsR = Vectors.dense(6.995908, 5.275131)
 
-    assert(model.intercept ~== 0 absTol 1E-3)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== 0 absTol 1E-3)
+    assert(model1.weights ~= weightsR relTol 1E-3)
+    assert(model2.intercept ~== 0 absTol 1E-3)
+    assert(model2.weights ~= weightsR relTol 1E-3)
+
     /*
        Then again with the data with no intercept:
        > weightsWithoutIntercept
        3 x 1 sparse Matrix of class "dgCMatrix"
-                                   s0
+                                 s0
        (Intercept)           .
        as.numeric.data3.V2. 4.70011
        as.numeric.data3.V3. 7.19943
      */
     val weightsWithoutInterceptR = Vectors.dense(4.70011, 7.19943)
 
-    assert(modelWithoutIntercept.intercept ~== 0 absTol 1E-3)
-    assert(modelWithoutIntercept.weights ~= weightsWithoutInterceptR relTol 1E-3)
+    assert(modelWithoutIntercept1.intercept ~== 0 absTol 1E-3)
+    assert(modelWithoutIntercept1.weights ~= weightsWithoutInterceptR relTol 1E-3)
+    assert(modelWithoutIntercept2.intercept ~== 0 absTol 1E-3)
+    assert(modelWithoutIntercept2.weights ~= weightsWithoutInterceptR relTol 1E-3)
   }
 
   test("linear regression with intercept with L1 regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
-    val model = trainer.fit(dataset)
+    val trainer1 = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
+    val trainer2 = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
+      .setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57))
@@ -160,24 +180,44 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V2. 4.024821
        as.numeric.data.V3. 6.679841
      */
-    val interceptR = 6.24300
-    val weightsR = Vectors.dense(4.024821, 6.679841)
+    val interceptR1 = 6.24300
+    val weightsR1 = Vectors.dense(4.024821, 6.679841)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 relTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+      weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57,
+        standardize=FALSE))
+      > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+                                s0
+      (Intercept)         6.416948
+      as.numeric.data.V2. 3.893869
+      as.numeric.data.V3. 6.724286
+     */
+    val interceptR2 = 6.416948
+    val weightsR2 = Vectors.dense(3.893869, 6.724286)
+
+    assert(model2.intercept ~== interceptR2 relTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression without intercept with L1 regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
+    val trainer1 = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
       .setFitIntercept(false)
-    val model = trainer.fit(dataset)
+    val trainer2 = (new LinearRegression).setElasticNetParam(1.0).setRegParam(0.57)
+      .setFitIntercept(false).setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57,
@@ -189,51 +229,90 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V2. 6.299752
        as.numeric.data.V3. 4.772913
      */
-    val interceptR = 0.0
-    val weightsR = Vectors.dense(6.299752, 4.772913)
+    val interceptR1 = 0.0
+    val weightsR1 = Vectors.dense(6.299752, 4.772913)
 
-    assert(model.intercept ~== interceptR absTol 1E-5)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 absTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 1.0, lambda = 0.57,
+         intercept=FALSE, standardize=FALSE))
+       > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         .
+       as.numeric.data.V2. 6.232193
+       as.numeric.data.V3. 4.764229
+     */
+    val interceptR2 = 0.0
+    val weightsR2 = Vectors.dense(6.232193, 4.764229)
+
+    assert(model2.intercept ~== interceptR2 absTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression with intercept with L2 regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
-    val model = trainer.fit(dataset)
+    val trainer1 = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
+    val trainer2 = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
+      .setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
-       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3))
-       > weights
-        3 x 1 sparse Matrix of class "dgCMatrix"
-                                 s0
-       (Intercept)         6.328062
-       as.numeric.data.V2. 3.222034
-       as.numeric.data.V3. 4.926260
+      weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3))
+      > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+                                s0
+      (Intercept)         5.269376
+      as.numeric.data.V2. 3.736216
+      as.numeric.data.V3. 5.712356)
      */
-    val interceptR = 5.269376
-    val weightsR = Vectors.dense(3.736216, 5.712356)
+    val interceptR1 = 5.269376
+    val weightsR1 = Vectors.dense(3.736216, 5.712356)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 relTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+      weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3,
+        standardize=FALSE))
+      > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+                                s0
+      (Intercept)         5.791109
+      as.numeric.data.V2. 3.435466
+      as.numeric.data.V3. 5.910406
+     */
+    val interceptR2 = 5.791109
+    val weightsR2 = Vectors.dense(3.435466, 5.910406)
+
+    assert(model2.intercept ~== interceptR2 relTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression without intercept with L2 regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
+    val trainer1 = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
       .setFitIntercept(false)
-    val model = trainer.fit(dataset)
+    val trainer2 = (new LinearRegression).setElasticNetParam(0.0).setRegParam(2.3)
+      .setFitIntercept(false).setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3,
@@ -245,23 +324,42 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V2. 5.522875
        as.numeric.data.V3. 4.214502
      */
-    val interceptR = 0.0
-    val weightsR = Vectors.dense(5.522875, 4.214502)
+    val interceptR1 = 0.0
+    val weightsR1 = Vectors.dense(5.522875, 4.214502)
 
-    assert(model.intercept ~== interceptR absTol 1E-3)
-    assert(model.weights ~== weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 absTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.0, lambda = 2.3,
+         intercept = FALSE, standardize=FALSE))
+       > weights
+        3 x 1 sparse Matrix of class "dgCMatrix"
+                                 s0
+       (Intercept)         .
+       as.numeric.data.V2. 5.263704
+       as.numeric.data.V3. 4.187419
+     */
+    val interceptR2 = 0.0
+    val weightsR2 = Vectors.dense(5.263704, 4.187419)
+
+    assert(model2.intercept ~== interceptR2 absTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression with intercept with ElasticNet regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
-    val model = trainer.fit(dataset)
+    val trainer1 = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
+    val trainer2 = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
+      .setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6))
@@ -272,24 +370,43 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V2. 3.168435
        as.numeric.data.V3. 5.200403
      */
-    val interceptR = 5.696056
-    val weightsR = Vectors.dense(3.670489, 6.001122)
+    val interceptR1 = 5.696056
+    val weightsR1 = Vectors.dense(3.670489, 6.001122)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights ~== weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 relTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+      weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6
+       standardize=FALSE))
+      > weights
+      3 x 1 sparse Matrix of class "dgCMatrix"
+      s0
+      (Intercept)         6.114723
+      as.numeric.data.V2. 3.409937
+      as.numeric.data.V3. 6.146531
+     */
+    val interceptR2 = 6.114723
+    val weightsR2 = Vectors.dense(3.409937, 6.146531)
+
+    assert(model2.intercept ~== interceptR2 relTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
 
   test("linear regression without intercept with ElasticNet regularization") {
-    val trainer = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
+    val trainer1 = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
       .setFitIntercept(false)
-    val model = trainer.fit(dataset)
+    val trainer2 = (new LinearRegression).setElasticNetParam(0.3).setRegParam(1.6)
+      .setFitIntercept(false).setStandardization(false)
+    val model1 = trainer1.fit(dataset)
+    val model2 = trainer2.fit(dataset)
 
     /*
        weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6,
@@ -301,16 +418,32 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.dataM.V2. 5.673348
        as.numeric.dataM.V3. 4.322251
      */
-    val interceptR = 0.0
-    val weightsR = Vectors.dense(5.673348, 4.322251)
+    val interceptR1 = 0.0
+    val weightsR1 = Vectors.dense(5.673348, 4.322251)
 
-    assert(model.intercept ~== interceptR absTol 1E-3)
-    assert(model.weights ~= weightsR relTol 1E-3)
+    assert(model1.intercept ~== interceptR1 absTol 1E-3)
+    assert(model1.weights ~= weightsR1 relTol 1E-3)
 
-    model.transform(dataset).select("features", "prediction").collect().foreach {
+    /*
+       weights <- coef(glmnet(features, label, family="gaussian", alpha = 0.3, lambda = 1.6,
+         intercept=FALSE, standardize=FALSE))
+       > weights
+       3 x 1 sparse Matrix of class "dgCMatrix"
+       s0
+       (Intercept)         .
+       as.numeric.data.V2. 5.477988
+       as.numeric.data.V3. 4.297622
+     */
+    val interceptR2 = 0.0
+    val weightsR2 = Vectors.dense(5.477988, 4.297622)
+
+    assert(model2.intercept ~== interceptR2 absTol 1E-3)
+    assert(model2.weights ~= weightsR2 relTol 1E-3)
+
+    model1.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
         val prediction2 =
-          features(0) * model.weights(0) + features(1) * model.weights(1) + model.intercept
+          features(0) * model1.weights(0) + features(1) * model1.weights(1) + model1.intercept
         assert(prediction1 ~== prediction2 relTol 1E-5)
     }
   }
@@ -372,5 +505,4 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       .zip(testSummary.residuals.select("residuals").collect())
       .forall { case (Row(r1: Double), Row(r2: Double)) => r1 ~== r2 relTol 1E-5 }
   }
-
 }

From a7fe48f68727d5c0247698cff329fb12faff1d50 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Tue, 4 Aug 2015 18:19:26 -0700
Subject: [PATCH 040/633] [SPARK-9432][SQL] Audit expression unit tests to make
 sure we pass the proper numeric ranges

JIRA: https://issues.apache.org/jira/browse/SPARK-9432

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7933 from yjshen/numeric_ranges and squashes the following commits:

e719f78 [Yijie Shen] proper integral range check
---
 .../ArithmeticExpressionSuite.scala           | 51 +++++++++++++++++--
 .../expressions/BitwiseFunctionsSuite.scala   | 21 ++++++++
 .../expressions/DateExpressionsSuite.scala    | 14 +++++
 .../IntegralLiteralTestUtils.scala            | 42 +++++++++++++++
 .../expressions/MathFunctionsSuite.scala      | 40 +++++++++++++++
 5 files changed, 164 insertions(+), 4 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/IntegralLiteralTestUtils.scala

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
index 0bae8fe2fd8aa..a1f15e4f0f25a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
@@ -23,6 +23,8 @@ import org.apache.spark.sql.types._
 
 class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
 
+  import IntegralLiteralTestUtils._
+
   /**
    * Runs through the testFunc for all numeric data types.
    *
@@ -47,6 +49,9 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Add(Literal.create(null, left.dataType), right), null)
       checkEvaluation(Add(left, Literal.create(null, right.dataType)), null)
     }
+    checkEvaluation(Add(positiveShortLit, negativeShortLit), -1.toShort)
+    checkEvaluation(Add(positiveIntLit, negativeIntLit), -1)
+    checkEvaluation(Add(positiveLongLit, negativeLongLit), -1L)
   }
 
   test("- (UnaryMinus)") {
@@ -60,6 +65,12 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(UnaryMinus(Literal(Int.MinValue)), Int.MinValue)
     checkEvaluation(UnaryMinus(Literal(Short.MinValue)), Short.MinValue)
     checkEvaluation(UnaryMinus(Literal(Byte.MinValue)), Byte.MinValue)
+    checkEvaluation(UnaryMinus(positiveShortLit), (- positiveShort).toShort)
+    checkEvaluation(UnaryMinus(negativeShortLit), (- negativeShort).toShort)
+    checkEvaluation(UnaryMinus(positiveIntLit), - positiveInt)
+    checkEvaluation(UnaryMinus(negativeIntLit), - negativeInt)
+    checkEvaluation(UnaryMinus(positiveLongLit), - positiveLong)
+    checkEvaluation(UnaryMinus(negativeLongLit), - negativeLong)
   }
 
   test("- (Minus)") {
@@ -70,6 +81,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Subtract(Literal.create(null, left.dataType), right), null)
       checkEvaluation(Subtract(left, Literal.create(null, right.dataType)), null)
     }
+    checkEvaluation(Subtract(positiveShortLit, negativeShortLit),
+      (positiveShort - negativeShort).toShort)
+    checkEvaluation(Subtract(positiveIntLit, negativeIntLit), positiveInt - negativeInt)
+    checkEvaluation(Subtract(positiveLongLit, negativeLongLit), positiveLong - negativeLong)
   }
 
   test("* (Multiply)") {
@@ -80,6 +95,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Multiply(Literal.create(null, left.dataType), right), null)
       checkEvaluation(Multiply(left, Literal.create(null, right.dataType)), null)
     }
+    checkEvaluation(Multiply(positiveShortLit, negativeShortLit),
+      (positiveShort * negativeShort).toShort)
+    checkEvaluation(Multiply(positiveIntLit, negativeIntLit), positiveInt * negativeInt)
+    checkEvaluation(Multiply(positiveLongLit, negativeLongLit), positiveLong * negativeLong)
   }
 
   test("/ (Divide) basic") {
@@ -99,6 +118,9 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Divide(Literal(1.toShort), Literal(2.toShort)), 0.toShort)
     checkEvaluation(Divide(Literal(1), Literal(2)), 0)
     checkEvaluation(Divide(Literal(1.toLong), Literal(2.toLong)), 0.toLong)
+    checkEvaluation(Divide(positiveShortLit, negativeShortLit), 0.toShort)
+    checkEvaluation(Divide(positiveIntLit, negativeIntLit), 0)
+    checkEvaluation(Divide(positiveLongLit, negativeLongLit), 0L)
   }
 
   test("/ (Divide) for floating point") {
@@ -116,6 +138,12 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Remainder(left, Literal.create(null, right.dataType)), null)
       checkEvaluation(Remainder(left, Literal(convert(0))), null)  // mod by 0
     }
+    checkEvaluation(Remainder(positiveShortLit, positiveShortLit), 0.toShort)
+    checkEvaluation(Remainder(negativeShortLit, negativeShortLit), 0.toShort)
+    checkEvaluation(Remainder(positiveIntLit, positiveIntLit), 0)
+    checkEvaluation(Remainder(negativeIntLit, negativeIntLit), 0)
+    checkEvaluation(Remainder(positiveLongLit, positiveLongLit), 0L)
+    checkEvaluation(Remainder(negativeLongLit, negativeLongLit), 0L)
   }
 
   test("Abs") {
@@ -127,6 +155,12 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Abs(Literal(convert(-1))), convert(1))
       checkEvaluation(Abs(Literal.create(null, dataType)), null)
     }
+    checkEvaluation(Abs(positiveShortLit), positiveShort)
+    checkEvaluation(Abs(negativeShortLit), (- negativeShort).toShort)
+    checkEvaluation(Abs(positiveIntLit), positiveInt)
+    checkEvaluation(Abs(negativeIntLit), - negativeInt)
+    checkEvaluation(Abs(positiveLongLit), positiveLong)
+    checkEvaluation(Abs(negativeLongLit), - negativeLong)
   }
 
   test("MaxOf basic") {
@@ -138,6 +172,9 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(MaxOf(Literal.create(null, small.dataType), large), convert(2))
       checkEvaluation(MaxOf(large, Literal.create(null, small.dataType)), convert(2))
     }
+    checkEvaluation(MaxOf(positiveShortLit, negativeShortLit), (positiveShort).toShort)
+    checkEvaluation(MaxOf(positiveIntLit, negativeIntLit), positiveInt)
+    checkEvaluation(MaxOf(positiveLongLit, negativeLongLit), positiveLong)
   }
 
   test("MaxOf for atomic type") {
@@ -156,6 +193,9 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(MinOf(Literal.create(null, small.dataType), large), convert(2))
       checkEvaluation(MinOf(small, Literal.create(null, small.dataType)), convert(1))
     }
+    checkEvaluation(MinOf(positiveShortLit, negativeShortLit), (negativeShort).toShort)
+    checkEvaluation(MinOf(positiveIntLit, negativeIntLit), negativeInt)
+    checkEvaluation(MinOf(positiveLongLit, negativeLongLit), negativeLong)
   }
 
   test("MinOf for atomic type") {
@@ -174,9 +214,12 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Pmod(left, Literal.create(null, right.dataType)), null)
       checkEvaluation(Remainder(left, Literal(convert(0))), null)  // mod by 0
     }
-    checkEvaluation(Pmod(-7, 3), 2)
-    checkEvaluation(Pmod(7.2D, 4.1D), 3.1000000000000005)
-    checkEvaluation(Pmod(Decimal(0.7), Decimal(0.2)), Decimal(0.1))
-    checkEvaluation(Pmod(2L, Long.MaxValue), 2L)
+    checkEvaluation(Pmod(Literal(-7), Literal(3)), 2)
+    checkEvaluation(Pmod(Literal(7.2D), Literal(4.1D)), 3.1000000000000005)
+    checkEvaluation(Pmod(Literal(Decimal(0.7)), Literal(Decimal(0.2))), Decimal(0.1))
+    checkEvaluation(Pmod(Literal(2L), Literal(Long.MaxValue)), 2L)
+    checkEvaluation(Pmod(positiveShort, negativeShort), positiveShort.toShort)
+    checkEvaluation(Pmod(positiveInt, negativeInt), positiveInt)
+    checkEvaluation(Pmod(positiveLong, negativeLong), positiveLong)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
index fa30fbe528479..4fc1c06153595 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
@@ -23,6 +23,8 @@ import org.apache.spark.sql.types._
 
 class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
+  import IntegralLiteralTestUtils._
+
   test("BitwiseNOT") {
     def check(input: Any, expected: Any): Unit = {
       val expr = BitwiseNot(Literal(input))
@@ -37,6 +39,12 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     check(123456789123L, ~123456789123L)
 
     checkEvaluation(BitwiseNot(Literal.create(null, IntegerType)), null)
+    checkEvaluation(BitwiseNot(positiveShortLit), (~positiveShort).toShort)
+    checkEvaluation(BitwiseNot(negativeShortLit), (~negativeShort).toShort)
+    checkEvaluation(BitwiseNot(positiveIntLit), ~positiveInt)
+    checkEvaluation(BitwiseNot(negativeIntLit), ~negativeInt)
+    checkEvaluation(BitwiseNot(positiveLongLit), ~positiveLong)
+    checkEvaluation(BitwiseNot(negativeLongLit), ~negativeLong)
   }
 
   test("BitwiseAnd") {
@@ -56,6 +64,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(BitwiseAnd(nullLit, Literal(1)), null)
     checkEvaluation(BitwiseAnd(Literal(1), nullLit), null)
     checkEvaluation(BitwiseAnd(nullLit, nullLit), null)
+    checkEvaluation(BitwiseAnd(positiveShortLit, negativeShortLit),
+      (positiveShort & negativeShort).toShort)
+    checkEvaluation(BitwiseAnd(positiveIntLit, negativeIntLit), positiveInt & negativeInt)
+    checkEvaluation(BitwiseAnd(positiveLongLit, negativeLongLit), positiveLong & negativeLong)
   }
 
   test("BitwiseOr") {
@@ -75,6 +87,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(BitwiseOr(nullLit, Literal(1)), null)
     checkEvaluation(BitwiseOr(Literal(1), nullLit), null)
     checkEvaluation(BitwiseOr(nullLit, nullLit), null)
+    checkEvaluation(BitwiseOr(positiveShortLit, negativeShortLit),
+      (positiveShort | negativeShort).toShort)
+    checkEvaluation(BitwiseOr(positiveIntLit, negativeIntLit), positiveInt | negativeInt)
+    checkEvaluation(BitwiseOr(positiveLongLit, negativeLongLit), positiveLong | negativeLong)
   }
 
   test("BitwiseXor") {
@@ -94,5 +110,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(BitwiseXor(nullLit, Literal(1)), null)
     checkEvaluation(BitwiseXor(Literal(1), nullLit), null)
     checkEvaluation(BitwiseXor(nullLit, nullLit), null)
+
+    checkEvaluation(BitwiseXor(positiveShortLit, negativeShortLit),
+      (positiveShort ^ negativeShort).toShort)
+    checkEvaluation(BitwiseXor(positiveIntLit, negativeIntLit), positiveInt ^ negativeInt)
+    checkEvaluation(BitwiseXor(positiveLongLit, negativeLongLit), positiveLong ^ negativeLong)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index e6e8790e90926..f9b73f1a75e73 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -28,6 +28,8 @@ import org.apache.spark.unsafe.types.CalendarInterval
 
 class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
+  import IntegralLiteralTestUtils._
+
   val sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
   val sdfDate = new SimpleDateFormat("yyyy-MM-dd")
   val d = new Date(sdf.parse("2015-04-08 13:10:15").getTime)
@@ -212,6 +214,10 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       null)
     checkEvaluation(DateAdd(Literal.create(null, DateType), Literal.create(null, IntegerType)),
       null)
+    checkEvaluation(
+      DateAdd(Literal(Date.valueOf("2016-02-28")), positiveIntLit), 49627)
+    checkEvaluation(
+      DateAdd(Literal(Date.valueOf("2016-02-28")), negativeIntLit), -15910)
   }
 
   test("date_sub") {
@@ -226,6 +232,10 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       null)
     checkEvaluation(DateSub(Literal.create(null, DateType), Literal.create(null, IntegerType)),
       null)
+    checkEvaluation(
+      DateSub(Literal(Date.valueOf("2016-02-28")), positiveIntLit), -15909)
+    checkEvaluation(
+      DateSub(Literal(Date.valueOf("2016-02-28")), negativeIntLit), 49628)
   }
 
   test("time_add") {
@@ -282,6 +292,10 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       null)
     checkEvaluation(
       AddMonths(Literal(Date.valueOf("2015-01-30")), Literal(Int.MinValue)), -7293498)
+    checkEvaluation(
+      AddMonths(Literal(Date.valueOf("2016-02-28")), positiveIntLit), 1014213)
+    checkEvaluation(
+      AddMonths(Literal(Date.valueOf("2016-02-28")), negativeIntLit), -980528)
   }
 
   test("months_between") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/IntegralLiteralTestUtils.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/IntegralLiteralTestUtils.scala
new file mode 100644
index 0000000000000..2e5a121f4ec56
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/IntegralLiteralTestUtils.scala
@@ -0,0 +1,42 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+/**
+ * Utilities to make sure we pass the proper numeric ranges
+ */
+object IntegralLiteralTestUtils {
+
+  val positiveShort: Short = (Byte.MaxValue + 1).toShort
+  val negativeShort: Short = (Byte.MinValue - 1).toShort
+
+  val positiveShortLit: Literal = Literal(positiveShort)
+  val negativeShortLit: Literal = Literal(negativeShort)
+
+  val positiveInt: Int = Short.MaxValue + 1
+  val negativeInt: Int = Short.MinValue - 1
+
+  val positiveIntLit: Literal = Literal(positiveInt)
+  val negativeIntLit: Literal = Literal(negativeInt)
+
+  val positiveLong: Long = Int.MaxValue + 1L
+  val negativeLong: Long = Int.MinValue - 1L
+
+  val positiveLongLit: Literal = Literal(positiveLong)
+  val negativeLongLit: Literal = Literal(negativeLong)
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 9fcb548af6bbb..033792eee6c0f 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -30,6 +30,8 @@ import org.apache.spark.sql.types._
 
 class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
+  import IntegralLiteralTestUtils._
+
   /**
    * Used for testing leaf math expressions.
    *
@@ -293,6 +295,9 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Bin(l3), java.lang.Long.toBinaryString(123), row)
     checkEvaluation(Bin(l4), java.lang.Long.toBinaryString(1234), row)
     checkEvaluation(Bin(l5), java.lang.Long.toBinaryString(-123), row)
+
+    checkEvaluation(Bin(positiveLongLit), java.lang.Long.toBinaryString(positiveLong))
+    checkEvaluation(Bin(negativeLongLit), java.lang.Long.toBinaryString(negativeLong))
   }
 
   test("log2") {
@@ -324,6 +329,15 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     checkEvaluation(ShiftLeft(Literal(21.toLong), Literal(1)), 42.toLong)
     checkEvaluation(ShiftLeft(Literal(-21.toLong), Literal(1)), -42.toLong)
+
+    checkEvaluation(ShiftLeft(positiveIntLit, positiveIntLit), positiveInt << positiveInt)
+    checkEvaluation(ShiftLeft(positiveIntLit, negativeIntLit), positiveInt << negativeInt)
+    checkEvaluation(ShiftLeft(negativeIntLit, positiveIntLit), negativeInt << positiveInt)
+    checkEvaluation(ShiftLeft(negativeIntLit, negativeIntLit), negativeInt << negativeInt)
+    checkEvaluation(ShiftLeft(positiveLongLit, positiveIntLit), positiveLong << positiveInt)
+    checkEvaluation(ShiftLeft(positiveLongLit, negativeIntLit), positiveLong << negativeInt)
+    checkEvaluation(ShiftLeft(negativeLongLit, positiveIntLit), negativeLong << positiveInt)
+    checkEvaluation(ShiftLeft(negativeLongLit, negativeIntLit), negativeLong << negativeInt)
   }
 
   test("shift right") {
@@ -335,6 +349,15 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     checkEvaluation(ShiftRight(Literal(42.toLong), Literal(1)), 21.toLong)
     checkEvaluation(ShiftRight(Literal(-42.toLong), Literal(1)), -21.toLong)
+
+    checkEvaluation(ShiftRight(positiveIntLit, positiveIntLit), positiveInt >> positiveInt)
+    checkEvaluation(ShiftRight(positiveIntLit, negativeIntLit), positiveInt >> negativeInt)
+    checkEvaluation(ShiftRight(negativeIntLit, positiveIntLit), negativeInt >> positiveInt)
+    checkEvaluation(ShiftRight(negativeIntLit, negativeIntLit), negativeInt >> negativeInt)
+    checkEvaluation(ShiftRight(positiveLongLit, positiveIntLit), positiveLong >> positiveInt)
+    checkEvaluation(ShiftRight(positiveLongLit, negativeIntLit), positiveLong >> negativeInt)
+    checkEvaluation(ShiftRight(negativeLongLit, positiveIntLit), negativeLong >> positiveInt)
+    checkEvaluation(ShiftRight(negativeLongLit, negativeIntLit), negativeLong >> negativeInt)
   }
 
   test("shift right unsigned") {
@@ -346,6 +369,23 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     checkEvaluation(ShiftRightUnsigned(Literal(42.toLong), Literal(1)), 21.toLong)
     checkEvaluation(ShiftRightUnsigned(Literal(-42.toLong), Literal(1)), 9223372036854775787L)
+
+    checkEvaluation(ShiftRightUnsigned(positiveIntLit, positiveIntLit),
+      positiveInt >>> positiveInt)
+    checkEvaluation(ShiftRightUnsigned(positiveIntLit, negativeIntLit),
+      positiveInt >>> negativeInt)
+    checkEvaluation(ShiftRightUnsigned(negativeIntLit, positiveIntLit),
+      negativeInt >>> positiveInt)
+    checkEvaluation(ShiftRightUnsigned(negativeIntLit, negativeIntLit),
+      negativeInt >>> negativeInt)
+    checkEvaluation(ShiftRightUnsigned(positiveLongLit, positiveIntLit),
+      positiveLong >>> positiveInt)
+    checkEvaluation(ShiftRightUnsigned(positiveLongLit, negativeIntLit),
+      positiveLong >>> negativeInt)
+    checkEvaluation(ShiftRightUnsigned(negativeLongLit, positiveIntLit),
+      negativeLong >>> positiveInt)
+    checkEvaluation(ShiftRightUnsigned(negativeLongLit, negativeIntLit),
+      negativeLong >>> negativeInt)
   }
 
   test("hex") {

From c9a4c36d052456c2dd1f7e0a871c6b764b5064d2 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Tue, 4 Aug 2015 18:20:12 -0700
Subject: [PATCH 041/633] [SPARK-8313] R Spark packages support

shivaram cafreeman Could you please help me in testing this out? Exposing and running `rPackageBuilder` from inside the shell works, but for some reason, I can't get it to work during Spark Submit. It just starts relaunching Spark Submit.

For testing, you may use the R branch with [sbt-spark-package](https://github.com/databricks/sbt-spark-package). You can call spPackage, and then pass the jar using `--jars`.

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #7139 from brkyvz/r-submit and squashes the following commits:

0de384f [Burak Yavuz] remove unused imports 2
d253708 [Burak Yavuz] removed unused imports
6603d0d [Burak Yavuz] addressed comments
4258ffe [Burak Yavuz] merged master
ddfcc06 [Burak Yavuz] added zipping test
3a1be7d [Burak Yavuz] don't zip
77995df [Burak Yavuz] fix URI
ac45527 [Burak Yavuz] added zipping of all libs
e6bf7b0 [Burak Yavuz] add println ignores
1bc5554 [Burak Yavuz] add assumes for tests
9778e03 [Burak Yavuz] addressed comments
b42b300 [Burak Yavuz] merged master
ffd134e [Burak Yavuz] Merge branch 'master' of github.com:apache/spark into r-submit
d867756 [Burak Yavuz] add apache header
eff5ba1 [Burak Yavuz] ready for review
8838edb [Burak Yavuz] Merge branch 'master' of github.com:apache/spark into r-submit
e5b5a06 [Burak Yavuz] added doc
bb751ce [Burak Yavuz] fix null bug
0226768 [Burak Yavuz] fixed issues
8810beb [Burak Yavuz] R packages support
---
 R/install-dev.sh                              |   4 -
 R/pkg/inst/tests/packageInAJarTest.R          |  30 +++
 .../scala/org/apache/spark/api/r/RUtils.scala |  14 +-
 .../apache/spark/deploy/RPackageUtils.scala   | 232 ++++++++++++++++++
 .../org/apache/spark/deploy/SparkSubmit.scala |  11 +-
 .../spark/deploy/SparkSubmitArguments.scala   |   1 -
 .../apache/spark/deploy/IvyTestUtils.scala    | 101 ++++++--
 .../spark/deploy/RPackageUtilsSuite.scala     | 156 ++++++++++++
 .../spark/deploy/SparkSubmitSuite.scala       |  24 ++
 9 files changed, 538 insertions(+), 35 deletions(-)
 create mode 100644 R/pkg/inst/tests/packageInAJarTest.R
 create mode 100644 core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
 create mode 100644 core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala

diff --git a/R/install-dev.sh b/R/install-dev.sh
index 4972bb9217072..59d98c9c7a646 100755
--- a/R/install-dev.sh
+++ b/R/install-dev.sh
@@ -42,8 +42,4 @@ Rscript -e ' if("devtools" %in% rownames(installed.packages())) { library(devtoo
 # Install SparkR to $LIB_DIR
 R CMD INSTALL --library=$LIB_DIR $FWDIR/pkg/
 
-# Zip the SparkR package so that it can be distributed to worker nodes on YARN
-cd $LIB_DIR
-jar cfM "$LIB_DIR/sparkr.zip" SparkR
-
 popd > /dev/null
diff --git a/R/pkg/inst/tests/packageInAJarTest.R b/R/pkg/inst/tests/packageInAJarTest.R
new file mode 100644
index 0000000000000..207a37a0cb47f
--- /dev/null
+++ b/R/pkg/inst/tests/packageInAJarTest.R
@@ -0,0 +1,30 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+library(SparkR)
+library(sparkPackageTest)
+
+sc <- sparkR.init()
+
+run1 <- myfunc(5L)
+
+run2 <- myfunc(-4L)
+
+sparkR.stop()
+
+if(run1 != 6) quit(save = "no", status = 1)
+
+if(run2 != -3) quit(save = "no", status = 1)
diff --git a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
index d53abd3408c55..93b3bea578676 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
@@ -19,6 +19,8 @@ package org.apache.spark.api.r
 
 import java.io.File
 
+import scala.collection.JavaConversions._
+
 import org.apache.spark.{SparkEnv, SparkException}
 
 private[spark] object RUtils {
@@ -26,7 +28,7 @@ private[spark] object RUtils {
    * Get the SparkR package path in the local spark distribution.
    */
   def localSparkRPackagePath: Option[String] = {
-    val sparkHome = sys.env.get("SPARK_HOME")
+    val sparkHome = sys.env.get("SPARK_HOME").orElse(sys.props.get("spark.test.home"))
     sparkHome.map(
       Seq(_, "R", "lib").mkString(File.separator)
     )
@@ -46,8 +48,8 @@ private[spark] object RUtils {
         (sparkConf.get("spark.master"), sparkConf.get("spark.submit.deployMode"))
       }
 
-    val isYarnCluster = master.contains("yarn") && deployMode == "cluster"
-    val isYarnClient = master.contains("yarn") && deployMode == "client"
+    val isYarnCluster = master != null && master.contains("yarn") && deployMode == "cluster"
+    val isYarnClient = master != null && master.contains("yarn") && deployMode == "client"
 
     // In YARN mode, the SparkR package is distributed as an archive symbolically
     // linked to the "sparkr" file in the current directory. Note that this does not apply
@@ -62,4 +64,10 @@ private[spark] object RUtils {
       }
     }
   }
+
+  /** Check if R is installed before running tests that use R commands. */
+  def isRInstalled: Boolean = {
+    val builder = new ProcessBuilder(Seq("R", "--version"))
+    builder.start().waitFor() == 0
+  }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala b/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
new file mode 100644
index 0000000000000..ed1e972955679
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
@@ -0,0 +1,232 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy
+
+import java.io._
+import java.util.jar.JarFile
+import java.util.logging.Level
+import java.util.zip.{ZipEntry, ZipOutputStream}
+
+import scala.collection.JavaConversions._
+
+import com.google.common.io.{ByteStreams, Files}
+
+import org.apache.spark.{SparkException, Logging}
+import org.apache.spark.api.r.RUtils
+import org.apache.spark.util.{RedirectThread, Utils}
+
+private[deploy] object RPackageUtils extends Logging {
+
+  /** The key in the MANIFEST.mf that we look for, in case a jar contains R code. */
+  private final val hasRPackage = "Spark-HasRPackage"
+
+  /** Base of the shell command used in order to install R packages. */
+  private final val baseInstallCmd = Seq("R", "CMD", "INSTALL", "-l")
+
+  /** R source code should exist under R/pkg in a jar. */
+  private final val RJarEntries = "R/pkg"
+
+  /** Documentation on how the R source file layout should be in the jar. */
+  private[deploy] final val RJarDoc =
+    s"""In order for Spark to build R packages that are parts of Spark Packages, there are a few
+      |requirements. The R source code must be shipped in a jar, with additional Java/Scala
+      |classes. The jar must be in the following format:
+      |  1- The Manifest (META-INF/MANIFEST.mf) must contain the key-value: $hasRPackage: true
+      |  2- The standard R package layout must be preserved under R/pkg/ inside the jar. More
+      |  information on the standard R package layout can be found in:
+      |  http://cran.r-project.org/doc/contrib/Leisch-CreatingPackages.pdf
+      |  An example layout is given below. After running `jar tf $$JAR_FILE | sort`:
+      |
+      |META-INF/MANIFEST.MF
+      |R/
+      |R/pkg/
+      |R/pkg/DESCRIPTION
+      |R/pkg/NAMESPACE
+      |R/pkg/R/
+      |R/pkg/R/myRcode.R
+      |org/
+      |org/apache/
+      |...
+    """.stripMargin.trim
+
+  /** Internal method for logging. We log to a printStream in tests, for debugging purposes. */
+  private def print(
+      msg: String,
+      printStream: PrintStream,
+      level: Level = Level.FINE,
+      e: Throwable = null): Unit = {
+    if (printStream != null) {
+      // scalastyle:off println
+      printStream.println(msg)
+      // scalastyle:on println
+      if (e != null) {
+        e.printStackTrace(printStream)
+      }
+    } else {
+      level match {
+        case Level.INFO => logInfo(msg)
+        case Level.WARNING => logWarning(msg)
+        case Level.SEVERE => logError(msg, e)
+        case _ => logDebug(msg)
+      }
+    }
+  }
+
+  /**
+   * Checks the manifest of the Jar whether there is any R source code bundled with it.
+   * Exposed for testing.
+   */
+  private[deploy] def checkManifestForR(jar: JarFile): Boolean = {
+    val manifest = jar.getManifest.getMainAttributes
+    manifest.getValue(hasRPackage) != null && manifest.getValue(hasRPackage).trim == "true"
+  }
+
+  /**
+   * Runs the standard R package installation code to build the R package from source.
+   * Multiple runs don't cause problems.
+   */
+  private def rPackageBuilder(dir: File, printStream: PrintStream, verbose: Boolean): Boolean = {
+    // this code should be always running on the driver.
+    val pathToSparkR = RUtils.localSparkRPackagePath.getOrElse(
+      throw new SparkException("SPARK_HOME not set. Can't locate SparkR package."))
+    val pathToPkg = Seq(dir, "R", "pkg").mkString(File.separator)
+    val installCmd = baseInstallCmd ++ Seq(pathToSparkR, pathToPkg)
+    if (verbose) {
+      print(s"Building R package with the command: $installCmd", printStream)
+    }
+    try {
+      val builder = new ProcessBuilder(installCmd)
+      builder.redirectErrorStream(true)
+      val env = builder.environment()
+      env.clear()
+      val process = builder.start()
+      new RedirectThread(process.getInputStream, printStream, "redirect R packaging").start()
+      process.waitFor() == 0
+    } catch {
+      case e: Throwable =>
+        print("Failed to build R package.", printStream, Level.SEVERE, e)
+        false
+    }
+  }
+
+  /**
+   * Extracts the files under /R in the jar to a temporary directory for building.
+   */
+  private def extractRFolder(jar: JarFile, printStream: PrintStream, verbose: Boolean): File = {
+    val tempDir = Utils.createTempDir(null)
+    val jarEntries = jar.entries()
+    while (jarEntries.hasMoreElements) {
+      val entry = jarEntries.nextElement()
+      val entryRIndex = entry.getName.indexOf(RJarEntries)
+      if (entryRIndex > -1) {
+        val entryPath = entry.getName.substring(entryRIndex)
+        if (entry.isDirectory) {
+          val dir = new File(tempDir, entryPath)
+          if (verbose) {
+            print(s"Creating directory: $dir", printStream)
+          }
+          dir.mkdirs
+        } else {
+          val inStream = jar.getInputStream(entry)
+          val outPath = new File(tempDir, entryPath)
+          Files.createParentDirs(outPath)
+          val outStream = new FileOutputStream(outPath)
+          if (verbose) {
+            print(s"Extracting $entry to $outPath", printStream)
+          }
+          Utils.copyStream(inStream, outStream, closeStreams = true)
+        }
+      }
+    }
+    tempDir
+  }
+
+  /**
+   * Extracts the files under /R in the jar to a temporary directory for building.
+   */
+  private[deploy] def checkAndBuildRPackage(
+      jars: String,
+      printStream: PrintStream = null,
+      verbose: Boolean = false): Unit = {
+    jars.split(",").foreach { jarPath =>
+      val file = new File(Utils.resolveURI(jarPath))
+      if (file.exists()) {
+        val jar = new JarFile(file)
+        if (checkManifestForR(jar)) {
+          print(s"$file contains R source code. Now installing package.", printStream, Level.INFO)
+          val rSource = extractRFolder(jar, printStream, verbose)
+          try {
+            if (!rPackageBuilder(rSource, printStream, verbose)) {
+              print(s"ERROR: Failed to build R package in $file.", printStream)
+              print(RJarDoc, printStream)
+            }
+          } finally {
+            rSource.delete() // clean up
+          }
+        } else {
+          if (verbose) {
+            print(s"$file doesn't contain R source code, skipping...", printStream)
+          }
+        }
+      } else {
+        print(s"WARN: $file resolved as dependency, but not found.", printStream, Level.WARNING)
+      }
+    }
+  }
+
+  private def listFilesRecursively(dir: File, excludePatterns: Seq[String]): Set[File] = {
+    if (!dir.exists()) {
+      Set.empty[File]
+    } else {
+      if (dir.isDirectory) {
+        val subDir = dir.listFiles(new FilenameFilter {
+          override def accept(dir: File, name: String): Boolean = {
+            !excludePatterns.map(name.contains).reduce(_ || _) // exclude files with given pattern
+          }
+        })
+        subDir.flatMap(listFilesRecursively(_, excludePatterns)).toSet
+      } else {
+        Set(dir)
+      }
+    }
+  }
+
+  /** Zips all the libraries found with SparkR in the R/lib directory for distribution with Yarn. */
+  private[deploy] def zipRLibraries(dir: File, name: String): File = {
+    val filesToBundle = listFilesRecursively(dir, Seq(".zip"))
+    // create a zip file from scratch, do not append to existing file.
+    val zipFile = new File(dir, name)
+    zipFile.delete()
+    val zipOutputStream = new ZipOutputStream(new FileOutputStream(zipFile, false))
+    try {
+      filesToBundle.foreach { file =>
+        // get the relative paths for proper naming in the zip file
+        val relPath = file.getAbsolutePath.replaceFirst(dir.getAbsolutePath, "")
+        val fis = new FileInputStream(file)
+        val zipEntry = new ZipEntry(relPath)
+        zipOutputStream.putNextEntry(zipEntry)
+        ByteStreams.copy(fis, zipOutputStream)
+        zipOutputStream.closeEntry()
+        fis.close()
+      }
+    } finally {
+      zipOutputStream.close()
+    }
+    zipFile
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 31185c8e77def..1186bed485250 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -292,6 +292,12 @@ object SparkSubmit {
       }
     }
 
+    // install any R packages that may have been passed through --jars or --packages.
+    // Spark Packages may contain R source code inside the jar.
+    if (args.isR && !StringUtils.isBlank(args.jars)) {
+      RPackageUtils.checkAndBuildRPackage(args.jars, printStream, args.verbose)
+    }
+
     // Require all python files to be local, so we can add them to the PYTHONPATH
     // In YARN cluster mode, python files are distributed as regular files, which can be non-local
     if (args.isPython && !isYarnCluster) {
@@ -361,7 +367,8 @@ object SparkSubmit {
       if (rPackagePath.isEmpty) {
         printErrorAndExit("SPARK_HOME does not exist for R application in YARN mode.")
       }
-      val rPackageFile = new File(rPackagePath.get, SPARKR_PACKAGE_ARCHIVE)
+      val rPackageFile =
+        RPackageUtils.zipRLibraries(new File(rPackagePath.get), SPARKR_PACKAGE_ARCHIVE)
       if (!rPackageFile.exists()) {
         printErrorAndExit(s"$SPARKR_PACKAGE_ARCHIVE does not exist for R application in YARN mode.")
       }
@@ -987,11 +994,9 @@ private[spark] object SparkSubmitUtils {
         addExclusionRules(ivySettings, ivyConfName, md)
         // add all supplied maven artifacts as dependencies
         addDependenciesToIvy(md, artifacts, ivyConfName)
-
         exclusions.foreach { e =>
           md.addExcludeRule(createExclusion(e + ":*", ivySettings, ivyConfName))
         }
-
         // resolve dependencies
         val rr: ResolveReport = ivy.resolve(md, resolveOptions)
         if (rr.hasError) {
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 44852ce4e84ac..3f3c6627c21fb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -611,5 +611,4 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
       System.setErr(currentErr)
     }
   }
-
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala b/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala
index 823050b0aabbe..d93febcfd23fd 100644
--- a/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/IvyTestUtils.scala
@@ -19,6 +19,10 @@ package org.apache.spark.deploy
 
 import java.io.{File, FileInputStream, FileOutputStream}
 import java.util.jar.{JarEntry, JarOutputStream}
+import java.util.jar.Attributes.Name
+import java.util.jar.Manifest
+
+import scala.collection.mutable.ArrayBuffer
 
 import com.google.common.io.{Files, ByteStreams}
 
@@ -35,7 +39,7 @@ private[deploy] object IvyTestUtils {
    * Create the path for the jar and pom from the maven coordinate. Extension should be `jar`
    * or `pom`.
    */
-  private def pathFromCoordinate(
+  private[deploy] def pathFromCoordinate(
       artifact: MavenCoordinate,
       prefix: File,
       ext: String,
@@ -52,7 +56,7 @@ private[deploy] object IvyTestUtils {
   }
 
   /** Returns the artifact naming based on standard ivy or maven format. */
-  private def artifactName(
+  private[deploy] def artifactName(
       artifact: MavenCoordinate,
       useIvyLayout: Boolean,
       ext: String = ".jar"): String = {
@@ -73,7 +77,7 @@ private[deploy] object IvyTestUtils {
   }
 
   /** Write the contents to a file to the supplied directory. */
-  private def writeFile(dir: File, fileName: String, contents: String): File = {
+  private[deploy] def writeFile(dir: File, fileName: String, contents: String): File = {
     val outputFile = new File(dir, fileName)
     val outputStream = new FileOutputStream(outputFile)
     outputStream.write(contents.toCharArray.map(_.toByte))
@@ -90,6 +94,42 @@ private[deploy] object IvyTestUtils {
     writeFile(dir, "mylib.py", contents)
   }
 
+  /** Create an example R package that calls the given Java class. */
+  private def createRFiles(
+      dir: File,
+      className: String,
+      packageName: String): Seq[(String, File)] = {
+    val rFilesDir = new File(dir, "R" + File.separator + "pkg")
+    Files.createParentDirs(new File(rFilesDir, "R" + File.separator + "mylib.R"))
+    val contents =
+      s"""myfunc <- function(x) {
+        |  SparkR:::callJStatic("$packageName.$className", "myFunc", x)
+        |}
+      """.stripMargin
+    val source = writeFile(new File(rFilesDir, "R"), "mylib.R", contents)
+    val description =
+      """Package: sparkPackageTest
+        |Type: Package
+        |Title: Test for building an R package
+        |Version: 0.1
+        |Date: 2015-07-08
+        |Author: Burak Yavuz
+        |Imports: methods, SparkR
+        |Depends: R (>= 3.1), methods, SparkR
+        |Suggests: testthat
+        |Description: Test for building an R package within a jar
+        |License: Apache License (== 2.0)
+        |Collate: 'mylib.R'
+      """.stripMargin
+    val descFile = writeFile(rFilesDir, "DESCRIPTION", description)
+    val namespace =
+      """import(SparkR)
+        |export("myfunc")
+      """.stripMargin
+    val nameFile = writeFile(rFilesDir, "NAMESPACE", namespace)
+    Seq(("R/pkg/R/mylib.R", source), ("R/pkg/DESCRIPTION", descFile), ("R/pkg/NAMESPACE", nameFile))
+  }
+
   /** Create a simple testable Class. */
   private def createJavaClass(dir: File, className: String, packageName: String): File = {
     val contents =
@@ -97,17 +137,14 @@ private[deploy] object IvyTestUtils {
         |
         |import java.lang.Integer;
         |
-        |class $className implements java.io.Serializable {
-        |
-        | public $className() {}
-        |
-        | public Integer myFunc(Integer x) {
+        |public class $className implements java.io.Serializable {
+        | public static Integer myFunc(Integer x) {
         |   return x + 1;
         | }
         |}
       """.stripMargin
     val sourceFile =
-      new JavaSourceFromString(new File(dir, className + ".java").getAbsolutePath, contents)
+      new JavaSourceFromString(new File(dir, className).getAbsolutePath, contents)
     createCompiledClass(className, dir, sourceFile, Seq.empty)
   }
 
@@ -199,14 +236,25 @@ private[deploy] object IvyTestUtils {
   }
 
   /** Create the jar for the given maven coordinate, using the supplied files. */
-  private def packJar(
+  private[deploy] def packJar(
       dir: File,
       artifact: MavenCoordinate,
       files: Seq[(String, File)],
-      useIvyLayout: Boolean): File = {
+      useIvyLayout: Boolean,
+      withR: Boolean,
+      withManifest: Option[Manifest] = None): File = {
     val jarFile = new File(dir, artifactName(artifact, useIvyLayout))
     val jarFileStream = new FileOutputStream(jarFile)
-    val jarStream = new JarOutputStream(jarFileStream, new java.util.jar.Manifest())
+    val manifest = withManifest.getOrElse {
+      val mani = new Manifest()
+      if (withR) {
+        val attr = mani.getMainAttributes
+        attr.put(Name.MANIFEST_VERSION, "1.0")
+        attr.put(new Name("Spark-HasRPackage"), "true")
+      }
+      mani
+    }
+    val jarStream = new JarOutputStream(jarFileStream, manifest)
 
     for (file <- files) {
       val jarEntry = new JarEntry(file._1)
@@ -239,7 +287,8 @@ private[deploy] object IvyTestUtils {
       dependencies: Option[Seq[MavenCoordinate]] = None,
       tempDir: Option[File] = None,
       useIvyLayout: Boolean = false,
-      withPython: Boolean = false): File = {
+      withPython: Boolean = false,
+      withR: Boolean = false): File = {
     // Where the root of the repository exists, and what Ivy will search in
     val tempPath = tempDir.getOrElse(Files.createTempDir())
     // Create directory if it doesn't exist
@@ -255,14 +304,16 @@ private[deploy] object IvyTestUtils {
       val javaClass = createJavaClass(root, className, artifact.groupId)
       // A tuple of files representation in the jar, and the file
       val javaFile = (artifact.groupId.replace(".", "/") + "/" + javaClass.getName, javaClass)
-      val allFiles =
-        if (withPython) {
-          val pythonFile = createPythonFile(root)
-          Seq(javaFile, (pythonFile.getName, pythonFile))
-        } else {
-          Seq(javaFile)
-        }
-      val jarFile = packJar(jarPath, artifact, allFiles, useIvyLayout)
+      val allFiles = ArrayBuffer[(String, File)](javaFile)
+      if (withPython) {
+        val pythonFile = createPythonFile(root)
+        allFiles.append((pythonFile.getName, pythonFile))
+      }
+      if (withR) {
+        val rFiles = createRFiles(root, className, artifact.groupId)
+        allFiles.append(rFiles: _*)
+      }
+      val jarFile = packJar(jarPath, artifact, allFiles, useIvyLayout, withR)
       assert(jarFile.exists(), "Problem creating Jar file")
       val descriptor = createDescriptor(tempPath, artifact, dependencies, useIvyLayout)
       assert(descriptor.exists(), "Problem creating Pom file")
@@ -286,9 +337,10 @@ private[deploy] object IvyTestUtils {
       dependencies: Option[String],
       rootDir: Option[File],
       useIvyLayout: Boolean = false,
-      withPython: Boolean = false): File = {
+      withPython: Boolean = false,
+      withR: Boolean = false): File = {
     val deps = dependencies.map(SparkSubmitUtils.extractMavenCoordinates)
-    val mainRepo = createLocalRepository(artifact, deps, rootDir, useIvyLayout, withPython)
+    val mainRepo = createLocalRepository(artifact, deps, rootDir, useIvyLayout, withPython, withR)
     deps.foreach { seq => seq.foreach { dep =>
       createLocalRepository(dep, None, Some(mainRepo), useIvyLayout, withPython = false)
     }}
@@ -311,11 +363,12 @@ private[deploy] object IvyTestUtils {
       rootDir: Option[File],
       useIvyLayout: Boolean = false,
       withPython: Boolean = false,
+      withR: Boolean = false,
       ivySettings: IvySettings = new IvySettings)(f: String => Unit): Unit = {
     val deps = dependencies.map(SparkSubmitUtils.extractMavenCoordinates)
     purgeLocalIvyCache(artifact, deps, ivySettings)
     val repo = createLocalRepositoryForTests(artifact, dependencies, rootDir, useIvyLayout,
-      withPython)
+      withPython, withR)
     try {
       f(repo.toURI.toString)
     } finally {
diff --git a/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala
new file mode 100644
index 0000000000000..47a64081e297e
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala
@@ -0,0 +1,156 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy
+
+import java.io.{PrintStream, OutputStream, File}
+import java.net.URI
+import java.util.jar.Attributes.Name
+import java.util.jar.{JarFile, Manifest}
+import java.util.zip.{ZipEntry, ZipFile}
+
+import org.scalatest.BeforeAndAfterEach
+import scala.collection.JavaConversions._
+import scala.collection.mutable.ArrayBuffer
+
+import com.google.common.io.Files
+import org.apache.commons.io.FileUtils
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.api.r.RUtils
+import org.apache.spark.deploy.SparkSubmitUtils.MavenCoordinate
+
+class RPackageUtilsSuite extends SparkFunSuite with BeforeAndAfterEach {
+
+  private val main = MavenCoordinate("a", "b", "c")
+  private val dep1 = MavenCoordinate("a", "dep1", "c")
+  private val dep2 = MavenCoordinate("a", "dep2", "d")
+
+  private def getJarPath(coord: MavenCoordinate, repo: File): File = {
+    new File(IvyTestUtils.pathFromCoordinate(coord, repo, "jar", useIvyLayout = false),
+      IvyTestUtils.artifactName(coord, useIvyLayout = false, ".jar"))
+  }
+
+  private val lineBuffer = ArrayBuffer[String]()
+
+  private val noOpOutputStream = new OutputStream {
+    def write(b: Int) = {}
+  }
+
+  /** Simple PrintStream that reads data into a buffer */
+  private class BufferPrintStream extends PrintStream(noOpOutputStream) {
+    // scalastyle:off println
+    override def println(line: String) {
+    // scalastyle:on println
+      lineBuffer += line
+    }
+  }
+
+  def beforeAll() {
+    System.setProperty("spark.testing", "true")
+  }
+
+  override def beforeEach(): Unit = {
+    lineBuffer.clear()
+  }
+
+  test("pick which jars to unpack using the manifest") {
+    val deps = Seq(dep1, dep2).mkString(",")
+    IvyTestUtils.withRepository(main, Some(deps), None, withR = true) { repo =>
+      val jars = Seq(main, dep1, dep2).map(c => new JarFile(getJarPath(c, new File(new URI(repo)))))
+      assert(RPackageUtils.checkManifestForR(jars(0)), "should have R code")
+      assert(!RPackageUtils.checkManifestForR(jars(1)), "should not have R code")
+      assert(!RPackageUtils.checkManifestForR(jars(2)), "should not have R code")
+    }
+  }
+
+  test("build an R package from a jar end to end") {
+    assume(RUtils.isRInstalled, "R isn't installed on this machine.")
+    val deps = Seq(dep1, dep2).mkString(",")
+    IvyTestUtils.withRepository(main, Some(deps), None, withR = true) { repo =>
+      val jars = Seq(main, dep1, dep2).map { c =>
+        getJarPath(c, new File(new URI(repo)))
+      }.mkString(",")
+      RPackageUtils.checkAndBuildRPackage(jars, new BufferPrintStream, verbose = true)
+      val firstJar = jars.substring(0, jars.indexOf(","))
+      val output = lineBuffer.mkString("\n")
+      assert(output.contains("Building R package"))
+      assert(output.contains("Extracting"))
+      assert(output.contains(s"$firstJar contains R source code. Now installing package."))
+      assert(output.contains("doesn't contain R source code, skipping..."))
+    }
+  }
+
+  test("jars that don't exist are skipped and print warning") {
+    assume(RUtils.isRInstalled, "R isn't installed on this machine.")
+    val deps = Seq(dep1, dep2).mkString(",")
+    IvyTestUtils.withRepository(main, Some(deps), None, withR = true) { repo =>
+      val jars = Seq(main, dep1, dep2).map { c =>
+        getJarPath(c, new File(new URI(repo))) + "dummy"
+      }.mkString(",")
+      RPackageUtils.checkAndBuildRPackage(jars, new BufferPrintStream, verbose = true)
+      val individualJars = jars.split(",")
+      val output = lineBuffer.mkString("\n")
+      individualJars.foreach { jarFile =>
+        assert(output.contains(s"$jarFile"))
+      }
+    }
+  }
+
+  test("faulty R package shows documentation") {
+    assume(RUtils.isRInstalled, "R isn't installed on this machine.")
+    IvyTestUtils.withRepository(main, None, None) { repo =>
+      val manifest = new Manifest
+      val attr = manifest.getMainAttributes
+      attr.put(Name.MANIFEST_VERSION, "1.0")
+      attr.put(new Name("Spark-HasRPackage"), "true")
+      val jar = IvyTestUtils.packJar(new File(new URI(repo)), dep1, Nil,
+        useIvyLayout = false, withR = false, Some(manifest))
+      RPackageUtils.checkAndBuildRPackage(jar.getAbsolutePath, new BufferPrintStream,
+        verbose = true)
+      val output = lineBuffer.mkString("\n")
+      assert(output.contains(RPackageUtils.RJarDoc))
+    }
+  }
+
+  test("SparkR zipping works properly") {
+    val tempDir = Files.createTempDir()
+    try {
+      IvyTestUtils.writeFile(tempDir, "test.R", "abc")
+      val fakeSparkRDir = new File(tempDir, "SparkR")
+      assert(fakeSparkRDir.mkdirs())
+      IvyTestUtils.writeFile(fakeSparkRDir, "abc.R", "abc")
+      IvyTestUtils.writeFile(fakeSparkRDir, "DESCRIPTION", "abc")
+      IvyTestUtils.writeFile(tempDir, "package.zip", "abc") // fake zip file :)
+      val fakePackageDir = new File(tempDir, "packageTest")
+      assert(fakePackageDir.mkdirs())
+      IvyTestUtils.writeFile(fakePackageDir, "def.R", "abc")
+      IvyTestUtils.writeFile(fakePackageDir, "DESCRIPTION", "abc")
+      val finalZip = RPackageUtils.zipRLibraries(tempDir, "sparkr.zip")
+      assert(finalZip.exists())
+      val entries = new ZipFile(finalZip).entries().toSeq.map(_.getName)
+      assert(entries.contains("/test.R"))
+      assert(entries.contains("/SparkR/abc.R"))
+      assert(entries.contains("/SparkR/DESCRIPTION"))
+      assert(!entries.contains("/package.zip"))
+      assert(entries.contains("/packageTest/def.R"))
+      assert(entries.contains("/packageTest/DESCRIPTION"))
+    } finally {
+      FileUtils.deleteDirectory(tempDir)
+    }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index aa78bfe30974c..757e0ce3d278b 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -362,6 +362,30 @@ class SparkSubmitSuite
     }
   }
 
+  test("correctly builds R packages included in a jar with --packages") {
+    // TODO(SPARK-9603): Building a package to $SPARK_HOME/R/lib is unavailable on Jenkins.
+    // It's hard to write the test in SparkR (because we can't create the repository dynamically)
+    /*
+    assume(RUtils.isRInstalled, "R isn't installed on this machine.")
+    val main = MavenCoordinate("my.great.lib", "mylib", "0.1")
+    val sparkHome = sys.props.getOrElse("spark.test.home", fail("spark.test.home is not set!"))
+    val rScriptDir =
+      Seq(sparkHome, "R", "pkg", "inst", "tests", "packageInAJarTest.R").mkString(File.separator)
+    assert(new File(rScriptDir).exists)
+    IvyTestUtils.withRepository(main, None, None, withR = true) { repo =>
+      val args = Seq(
+        "--name", "testApp",
+        "--master", "local-cluster[2,1,1024]",
+        "--packages", main.toString,
+        "--repositories", repo,
+        "--verbose",
+        "--conf", "spark.ui.enabled=false",
+        rScriptDir)
+      runSparkSubmit(args)
+    }
+    */
+  }
+
   test("resolves command line argument paths correctly") {
     val jars = "/jar1,/jar2"                 // --jars
     val files = "hdfs:/file1,file2"          // --files

From 6f8f0e265a29e89bd5192a8d5217cba19f0875da Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Tue, 4 Aug 2015 18:26:05 -0700
Subject: [PATCH 042/633] [SPARK-7119] [SQL] Give script a default serde with
 the user specific types

This is to address this issue that there would be not compatible type exception when running this:
`from (from src select transform(key, value) using 'cat' as (thing1 int, thing2 string)) t select thing1 + 2;`

15/04/24 00:58:55 ERROR CliDriver: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 1 times, most recent failure: Lost task 0.0 in stage 0.0 (TID 0, localhost): java.lang.ClassCastException: org.apache.spark.sql.types.UTF8String cannot be cast to java.lang.Integer
	at scala.runtime.BoxesRunTime.unboxToInt(BoxesRunTime.java:106)
	at scala.math.Numeric$IntIsIntegral$.plus(Numeric.scala:57)
	at org.apache.spark.sql.catalyst.expressions.Add.eval(arithmetic.scala:127)
	at org.apache.spark.sql.catalyst.expressions.Alias.eval(namedExpressions.scala:118)
	at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:68)
	at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.apply(Projection.scala:52)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
	at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
	at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
	at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
	at scala.collection.AbstractIterator.to(Iterator.scala:1157)
	at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
	at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
	at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
	at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
	at org.apache.spark.rdd.RDD$$anonfun$17.apply(RDD.scala:819)
	at org.apache.spark.rdd.RDD$$anonfun$17.apply(RDD.scala:819)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1618)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1618)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
	at org.apache.spark.scheduler.Task.run(Task.scala:64)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:209)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1110)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:603)
	at java.lang.Thread.run(Thread.java:722)

chenghao-intel marmbrus

Author: zhichao.li <zhichao.li@intel.com>

Closes #6638 from zhichao-li/transDataType2 and squashes the following commits:

a36cc7c [zhichao.li] style
b9252a8 [zhichao.li] delete cacheRow
f6968a4 [zhichao.li] give script a default serde
---
 .../org/apache/spark/sql/hive/HiveQl.scala    |  3 +-
 .../hive/execution/ScriptTransformation.scala | 96 +++++++------------
 .../sql/hive/execution/SQLQuerySuite.scala    | 10 ++
 3 files changed, 49 insertions(+), 60 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index e2fdfc6163a00..f43e403ce9a9d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -21,6 +21,7 @@ import java.sql.Date
 import java.util.Locale
 
 import org.apache.hadoop.hive.conf.HiveConf
+import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.serde.serdeConstants
 import org.apache.hadoop.hive.ql.{ErrorMsg, Context}
 import org.apache.hadoop.hive.ql.exec.{FunctionRegistry, FunctionInfo}
@@ -907,7 +908,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
                 }
                 (Nil, Some(BaseSemanticAnalyzer.unescapeSQLString(serdeClass)), serdeProps)
 
-              case Nil => (Nil, None, Nil)
+              case Nil => (Nil, Option(hiveConf().getVar(ConfVars.HIVESCRIPTSERDE)), Nil)
             }
 
             val (inRowFormat, inSerdeClass, inSerdeProps) = matchSerDe(inputSerdeClause)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index fbb86406f40cb..97e4ea2081d56 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -27,11 +27,11 @@ import scala.util.control.NonFatal
 import org.apache.hadoop.hive.serde.serdeConstants
 import org.apache.hadoop.hive.serde2.AbstractSerDe
 import org.apache.hadoop.hive.serde2.objectinspector._
+import org.apache.hadoop.io.Writable
 
 import org.apache.spark.{TaskContext, Logging}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.ScriptInputOutputSchema
 import org.apache.spark.sql.execution._
@@ -106,9 +106,15 @@ case class ScriptTransformation(
 
       val reader = new BufferedReader(new InputStreamReader(inputStream))
       val outputIterator: Iterator[InternalRow] = new Iterator[InternalRow] with HiveInspectors {
-        var cacheRow: InternalRow = null
         var curLine: String = null
-        var eof: Boolean = false
+        val scriptOutputStream = new DataInputStream(inputStream)
+        var scriptOutputWritable: Writable = null
+        val reusedWritableObject: Writable = if (null != outputSerde) {
+          outputSerde.getSerializedClass().newInstance
+        } else {
+          null
+        }
+        val mutableRow = new SpecificMutableRow(output.map(_.dataType))
 
         override def hasNext: Boolean = {
           if (outputSerde == null) {
@@ -125,45 +131,20 @@ case class ScriptTransformation(
             } else {
               true
             }
-          } else {
-            if (eof) {
-              if (writerThread.exception.isDefined) {
-                throw writerThread.exception.get
-              }
-              false
-            } else {
+          } else if (scriptOutputWritable == null) {
+            scriptOutputWritable = reusedWritableObject
+            try {
+              scriptOutputWritable.readFields(scriptOutputStream)
               true
+            } catch {
+              case _: EOFException =>
+                if (writerThread.exception.isDefined) {
+                  throw writerThread.exception.get
+                }
+                false
             }
-          }
-        }
-
-        def deserialize(): InternalRow = {
-          if (cacheRow != null) return cacheRow
-
-          val mutableRow = new SpecificMutableRow(output.map(_.dataType))
-          try {
-            val dataInputStream = new DataInputStream(inputStream)
-            val writable = outputSerde.getSerializedClass().newInstance
-            writable.readFields(dataInputStream)
-
-            val raw = outputSerde.deserialize(writable)
-            val dataList = outputSoi.getStructFieldsDataAsList(raw)
-            val fieldList = outputSoi.getAllStructFieldRefs()
-
-            var i = 0
-            dataList.foreach( element => {
-              if (element == null) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow(i) = unwrap(element, fieldList(i).getFieldObjectInspector)
-              }
-              i += 1
-            })
-            mutableRow
-          } catch {
-            case e: EOFException =>
-              eof = true
-              null
+          } else {
+            true
           }
         }
 
@@ -171,7 +152,6 @@ case class ScriptTransformation(
           if (!hasNext) {
             throw new NoSuchElementException
           }
-
           if (outputSerde == null) {
             val prevLine = curLine
             curLine = reader.readLine()
@@ -185,12 +165,20 @@ case class ScriptTransformation(
                   .map(CatalystTypeConverters.convertToCatalyst))
             }
           } else {
-            val ret = deserialize()
-            if (!eof) {
-              cacheRow = null
-              cacheRow = deserialize()
+            val raw = outputSerde.deserialize(scriptOutputWritable)
+            scriptOutputWritable = null
+            val dataList = outputSoi.getStructFieldsDataAsList(raw)
+            val fieldList = outputSoi.getAllStructFieldRefs()
+            var i = 0
+            while (i < dataList.size()) {
+              if (dataList(i) == null) {
+                mutableRow.setNullAt(i)
+              } else {
+                mutableRow(i) = unwrap(dataList(i), fieldList(i).getFieldObjectInspector)
+              }
+              i += 1
             }
-            ret
+            mutableRow
           }
         }
       }
@@ -320,18 +308,8 @@ case class HiveScriptIOSchema (
   }
 
   private def parseAttrs(attrs: Seq[Expression]): (Seq[String], Seq[DataType]) = {
-    val columns = attrs.map {
-      case aref: AttributeReference => aref.name
-      case e: NamedExpression => e.name
-      case _ => null
-    }
-
-    val columnTypes = attrs.map {
-      case aref: AttributeReference => aref.dataType
-      case e: NamedExpression => e.dataType
-      case _ => null
-    }
-
+    val columns = attrs.zipWithIndex.map(e => s"${e._1.prettyName}_${e._2}")
+    val columnTypes = attrs.map(_.dataType)
     (columns, columnTypes)
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index fb41451803634..ff9a3694d612e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -751,6 +751,16 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
         .queryExecution.toRdd.count())
   }
 
+  test("test script transform data type") {
+    val data = (1 to 5).map { i => (i, i) }
+    data.toDF("key", "value").registerTempTable("test")
+    checkAnswer(
+      sql("""FROM
+          |(FROM test SELECT TRANSFORM(key, value) USING 'cat' AS (thing1 int, thing2 string)) t
+          |SELECT thing1 + 1
+        """.stripMargin), (2 to 6).map(i => Row(i)))
+  }
+
   test("window function: udaf with aggregate expressin") {
     val data = Seq(
       WindowData(1, "a", 5),

From 2b67fdb60be95778e016efae4f0a9cdf2fbfe779 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 4 Aug 2015 19:25:24 -0700
Subject: [PATCH 043/633] [SPARK-9513] [SQL] [PySpark] Add python API for
 DataFrame functions

This adds Python API for those DataFrame functions that is introduced in 1.5.

There is issue with serialize byte_array in Python 3, so some of functions (for BinaryType) does not have tests.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #7922 from davies/python_functions and squashes the following commits:

8ad942f [Davies Liu] fix test
5fb6ec3 [Davies Liu] fix bugs
3495ed3 [Davies Liu] fix issues
ea5f7bb [Davies Liu] Add python API for DataFrame functions
---
 python/pyspark/sql/functions.py               | 885 ++++++++++++------
 .../org/apache/spark/sql/functions.scala      |  80 +-
 2 files changed, 628 insertions(+), 337 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index a73ecc7d93361..e65b14dc0e2b3 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -32,41 +32,6 @@
 from pyspark.sql.column import Column, _to_java_column, _to_seq
 
 
-__all__ = [
-    'array',
-    'approxCountDistinct',
-    'bin',
-    'coalesce',
-    'countDistinct',
-    'explode',
-    'format_number',
-    'length',
-    'log2',
-    'md5',
-    'monotonicallyIncreasingId',
-    'rand',
-    'randn',
-    'regexp_extract',
-    'regexp_replace',
-    'sha1',
-    'sha2',
-    'size',
-    'sort_array',
-    'sparkPartitionId',
-    'struct',
-    'udf',
-    'when']
-
-__all__ += ['lag', 'lead', 'ntile']
-
-__all__ += [
-    'date_format', 'date_add', 'date_sub', 'add_months', 'months_between',
-    'year', 'quarter', 'month', 'hour', 'minute', 'second',
-    'dayofmonth', 'dayofyear', 'weekofyear']
-
-__all__ += ['soundex', 'substring', 'substring_index']
-
-
 def _create_function(name, doc=""):
     """ Create a function for aggregator by name"""
     def _(col):
@@ -208,30 +173,6 @@ def _():
 for _name, _doc in _window_functions.items():
     globals()[_name] = since(1.4)(_create_window_function(_name, _doc))
 del _name, _doc
-__all__ += _functions.keys()
-__all__ += _functions_1_4.keys()
-__all__ += _binary_mathfunctions.keys()
-__all__ += _window_functions.keys()
-__all__.sort()
-
-
-@since(1.4)
-def array(*cols):
-    """Creates a new array column.
-
-    :param cols: list of column names (string) or list of :class:`Column` expressions that have
-        the same data type.
-
-    >>> df.select(array('age', 'age').alias("arr")).collect()
-    [Row(arr=[2, 2]), Row(arr=[5, 5])]
-    >>> df.select(array([df.age, df.age]).alias("arr")).collect()
-    [Row(arr=[2, 2]), Row(arr=[5, 5])]
-    """
-    sc = SparkContext._active_spark_context
-    if len(cols) == 1 and isinstance(cols[0], (list, set)):
-        cols = cols[0]
-    jc = sc._jvm.functions.array(_to_seq(sc, cols, _to_java_column))
-    return Column(jc)
 
 
 @since(1.3)
@@ -249,19 +190,6 @@ def approxCountDistinct(col, rsd=None):
     return Column(jc)
 
 
-@ignore_unicode_prefix
-@since(1.5)
-def bin(col):
-    """Returns the string representation of the binary value of the given column.
-
-    >>> df.select(bin(df.age).alias('c')).collect()
-    [Row(c=u'10'), Row(c=u'101')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.bin(_to_java_column(col))
-    return Column(jc)
-
-
 @since(1.4)
 def coalesce(*cols):
     """Returns the first column that is not null.
@@ -314,82 +242,6 @@ def countDistinct(col, *cols):
     return Column(jc)
 
 
-@since(1.4)
-def explode(col):
-    """Returns a new row for each element in the given array or map.
-
-    >>> from pyspark.sql import Row
-    >>> eDF = sqlContext.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})])
-    >>> eDF.select(explode(eDF.intlist).alias("anInt")).collect()
-    [Row(anInt=1), Row(anInt=2), Row(anInt=3)]
-
-    >>> eDF.select(explode(eDF.mapfield).alias("key", "value")).show()
-    +---+-----+
-    |key|value|
-    +---+-----+
-    |  a|    b|
-    +---+-----+
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.explode(_to_java_column(col))
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def levenshtein(left, right):
-    """Computes the Levenshtein distance of the two given strings.
-
-    >>> df0 = sqlContext.createDataFrame([('kitten', 'sitting',)], ['l', 'r'])
-    >>> df0.select(levenshtein('l', 'r').alias('d')).collect()
-    [Row(d=3)]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.levenshtein(_to_java_column(left), _to_java_column(right))
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def regexp_extract(str, pattern, idx):
-    """Extract a specific(idx) group identified by a java regex, from the specified string column.
-
-    >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])
-    >>> df.select(regexp_extract('str', '(\d+)-(\d+)', 1).alias('d')).collect()
-    [Row(d=u'100')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.regexp_extract(_to_java_column(str), pattern, idx)
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def regexp_replace(str, pattern, replacement):
-    """Replace all substrings of the specified string value that match regexp with rep.
-
-    >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])
-    >>> df.select(regexp_replace('str', '(\\d+)', '##').alias('d')).collect()
-    [Row(d=u'##-##')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.regexp_replace(_to_java_column(str), pattern, replacement)
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def md5(col):
-    """Calculates the MD5 digest and returns the value as a 32 character hex string.
-
-    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(md5('a').alias('hash')).collect()
-    [Row(hash=u'902fbdd2b1df0c4f70b4a5d23525e932')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.md5(_to_java_column(col))
-    return Column(jc)
-
-
 @since(1.4)
 def monotonicallyIncreasingId():
     """A column that generates monotonically increasing 64-bit integers.
@@ -435,63 +287,17 @@ def randn(seed=None):
     return Column(jc)
 
 
-@ignore_unicode_prefix
-@since(1.5)
-def hex(col):
-    """Computes hex value of the given column, which could be StringType,
-    BinaryType, IntegerType or LongType.
-
-    >>> sqlContext.createDataFrame([('ABC', 3)], ['a', 'b']).select(hex('a'), hex('b')).collect()
-    [Row(hex(a)=u'414243', hex(b)=u'3')]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.hex(_to_java_column(col))
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def unhex(col):
-    """Inverse of hex. Interprets each pair of characters as a hexadecimal number
-    and converts to the byte representation of number.
-
-    >>> sqlContext.createDataFrame([('414243',)], ['a']).select(unhex('a')).collect()
-    [Row(unhex(a)=bytearray(b'ABC'))]
-    """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.unhex(_to_java_column(col))
-    return Column(jc)
-
-
-@ignore_unicode_prefix
 @since(1.5)
-def sha1(col):
-    """Returns the hex string result of SHA-1.
-
-    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(sha1('a').alias('hash')).collect()
-    [Row(hash=u'3c01bdbb26f358bab27f267924aa2c9a03fcfdb8')]
+def round(col, scale=0):
     """
-    sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.sha1(_to_java_column(col))
-    return Column(jc)
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def sha2(col, numBits):
-    """Returns the hex string result of SHA-2 family of hash functions (SHA-224, SHA-256, SHA-384,
-    and SHA-512). The numBits indicates the desired bit length of the result, which must have a
-    value of 224, 256, 384, 512, or 0 (which is equivalent to 256).
+    Round the value of `e` to `scale` decimal places if `scale` >= 0
+    or at integral part when `scale` < 0.
 
-    >>> digests = df.select(sha2(df.name, 256).alias('s')).collect()
-    >>> digests[0]
-    Row(s=u'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043')
-    >>> digests[1]
-    Row(s=u'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961')
+    >>> sqlContext.createDataFrame([(2.546,)], ['a']).select(round('a', 1).alias('r')).collect()
+    [Row(r=2.5)]
     """
     sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.sha2(_to_java_column(col), numBits)
-    return Column(jc)
+    return Column(sc._jvm.functions.round(_to_java_column(col), scale))
 
 
 @since(1.5)
@@ -502,8 +308,7 @@ def shiftLeft(col, numBits):
     [Row(r=42)]
     """
     sc = SparkContext._active_spark_context
-    jc = sc._jvm.functions.shiftLeft(_to_java_column(col), numBits)
-    return Column(jc)
+    return Column(sc._jvm.functions.shiftLeft(_to_java_column(col), numBits))
 
 
 @since(1.5)
@@ -522,8 +327,8 @@ def shiftRight(col, numBits):
 def shiftRightUnsigned(col, numBits):
     """Unsigned shift the the given value numBits right.
 
-    >>> sqlContext.createDataFrame([(-42,)], ['a']).select(shiftRightUnsigned('a', 1).alias('r'))\
-    .collect()
+    >>> df = sqlContext.createDataFrame([(-42,)], ['a'])
+    >>> df.select(shiftRightUnsigned('a', 1).alias('r')).collect()
     [Row(r=9223372036854775787)]
     """
     sc = SparkContext._active_spark_context
@@ -544,6 +349,7 @@ def sparkPartitionId():
     return Column(sc._jvm.functions.sparkPartitionId())
 
 
+@since(1.5)
 def expr(str):
     """Parses the expression string into the column that it represents
 
@@ -554,34 +360,6 @@ def expr(str):
     return Column(sc._jvm.functions.expr(str))
 
 
-@ignore_unicode_prefix
-@since(1.5)
-def length(col):
-    """Calculates the length of a string or binary expression.
-
-    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(length('a').alias('length')).collect()
-    [Row(length=3)]
-    """
-    sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.length(_to_java_column(col)))
-
-
-@ignore_unicode_prefix
-@since(1.5)
-def format_number(col, d):
-    """Formats the number X to a format like '#,###,###.##', rounded to d decimal places,
-       and returns the result as a string.
-
-    :param col: the column name of the numeric value to be formatted
-    :param d: the N decimal places
-
-    >>> sqlContext.createDataFrame([(5,)], ['a']).select(format_number('a', 4).alias('v')).collect()
-    [Row(v=u'5.0000')]
-    """
-    sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.format_number(_to_java_column(col), d))
-
-
 @ignore_unicode_prefix
 @since(1.4)
 def struct(*cols):
@@ -601,6 +379,38 @@ def struct(*cols):
     return Column(jc)
 
 
+@since(1.5)
+def greatest(*cols):
+    """
+    Returns the greatest value of the list of column names, skipping null values.
+    This function takes at least 2 parameters. It will return null iff all parameters are null.
+
+    >>> df = sqlContext.createDataFrame([(1, 4, 3)], ['a', 'b', 'c'])
+    >>> df.select(greatest(df.a, df.b, df.c).alias("greatest")).collect()
+    [Row(greatest=4)]
+    """
+    if len(cols) < 2:
+        raise ValueError("greatest should take at least two columns")
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.greatest(_to_seq(sc, cols, _to_java_column)))
+
+
+@since(1.5)
+def least(*cols):
+    """
+    Returns the least value of the list of column names, skipping null values.
+    This function takes at least 2 parameters. It will return null iff all parameters are null.
+
+    >>> df = sqlContext.createDataFrame([(1, 4, 3)], ['a', 'b', 'c'])
+    >>> df.select(least(df.a, df.b, df.c).alias("least")).collect()
+    [Row(least=1)]
+    """
+    if len(cols) < 2:
+        raise ValueError("least should take at least two columns")
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.least(_to_seq(sc, cols, _to_java_column)))
+
+
 @since(1.4)
 def when(condition, value):
     """Evaluates a list of conditions and returns one of multiple possible result expressions.
@@ -654,6 +464,35 @@ def log2(col):
     return Column(sc._jvm.functions.log2(_to_java_column(col)))
 
 
+@since(1.5)
+@ignore_unicode_prefix
+def conv(col, fromBase, toBase):
+    """
+    Convert a number in a string column from one base to another.
+
+    >>> df = sqlContext.createDataFrame([("010101",)], ['n'])
+    >>> df.select(conv(df.n, 2, 16).alias('hex')).collect()
+    [Row(hex=u'15')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.conv(_to_java_column(col), fromBase, toBase))
+
+
+@since(1.5)
+def factorial(col):
+    """
+    Computes the factorial of the given value.
+
+    >>> df = sqlContext.createDataFrame([(5,)], ['n'])
+    >>> df.select(factorial(df.n).alias('f')).collect()
+    [Row(f=120)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.factorial(_to_java_column(col)))
+
+
+# ---------------  Window functions ------------------------
+
 @since(1.4)
 def lag(col, count=1, default=None):
     """
@@ -703,9 +542,28 @@ def ntile(n):
     return Column(sc._jvm.functions.ntile(int(n)))
 
 
+# ---------------------- Date/Timestamp functions ------------------------------
+
+@since(1.5)
+def current_date():
+    """
+    Returns the current date as a date column.
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.current_date())
+
+
+def current_timestamp():
+    """
+    Returns the current timestamp as a timestamp column.
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.current_timestamp())
+
+
 @ignore_unicode_prefix
 @since(1.5)
-def date_format(dateCol, format):
+def date_format(date, format):
     """
     Converts a date/timestamp/string to a value of string in the format specified by the date
     format given by the second argument.
@@ -721,7 +579,7 @@ def date_format(dateCol, format):
     [Row(date=u'04/08/2015')]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.date_format(_to_java_column(dateCol), format))
+    return Column(sc._jvm.functions.date_format(_to_java_column(date), format))
 
 
 @since(1.5)
@@ -867,6 +725,19 @@ def date_sub(start, days):
     return Column(sc._jvm.functions.date_sub(_to_java_column(start), days))
 
 
+@since(1.5)
+def datediff(end, start):
+    """
+    Returns the number of days from `start` to `end`.
+
+    >>> df = sqlContext.createDataFrame([('2015-04-08','2015-05-10')], ['d1', 'd2'])
+    >>> df.select(datediff(df.d2, df.d1).alias('diff')).collect()
+    [Row(diff=32)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.datediff(_to_java_column(end), _to_java_column(start)))
+
+
 @since(1.5)
 def add_months(start, months):
     """
@@ -924,33 +795,296 @@ def trunc(date, format):
 
 
 @since(1.5)
-@ignore_unicode_prefix
-def substring(str, pos, len):
+def next_day(date, dayOfWeek):
     """
-    Substring starts at `pos` and is of length `len` when str is String type or
-    returns the slice of byte array that starts at `pos` in byte and is of length `len`
-    when str is Binary type
+    Returns the first date which is later than the value of the date column.
 
-    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
-    >>> df.select(substring(df.s, 1, 2).alias('s')).collect()
-    [Row(s=u'ab')]
+    Day of the week parameter is case insensitive, and accepts:
+        "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun".
+
+    >>> df = sqlContext.createDataFrame([('2015-07-27',)], ['d'])
+    >>> df.select(next_day(df.d, 'Sun').alias('date')).collect()
+    [Row(date=datetime.date(2015, 8, 2))]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.substring(_to_java_column(str), pos, len))
+    return Column(sc._jvm.functions.next_day(_to_java_column(date), dayOfWeek))
 
 
 @since(1.5)
-@ignore_unicode_prefix
-def substring_index(str, delim, count):
+def last_day(date):
     """
-    Returns the substring from string str before count occurrences of the delimiter delim.
-    If count is positive, everything the left of the final delimiter (counting from left) is
-    returned. If count is negative, every to the right of the final delimiter (counting from the
-    right) is returned. substring_index performs a case-sensitive match when searching for delim.
+    Returns the last day of the month which the given date belongs to.
 
-    >>> df = sqlContext.createDataFrame([('a.b.c.d',)], ['s'])
-    >>> df.select(substring_index(df.s, '.', 2).alias('s')).collect()
-    [Row(s=u'a.b')]
+    >>> df = sqlContext.createDataFrame([('1997-02-10',)], ['d'])
+    >>> df.select(last_day(df.d).alias('date')).collect()
+    [Row(date=datetime.date(1997, 2, 28))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.last_day(_to_java_column(date)))
+
+
+@since(1.5)
+def from_unixtime(timestamp, format="yyyy-MM-dd HH:mm:ss"):
+    """
+    Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
+    representing the timestamp of that moment in the current system time zone in the given
+    format.
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.from_unixtime(_to_java_column(timestamp), format))
+
+
+@since(1.5)
+def unix_timestamp(timestamp=None, format='yyyy-MM-dd HH:mm:ss'):
+    """
+    Convert time string with given pattern ('yyyy-MM-dd HH:mm:ss', by default)
+    to Unix time stamp (in seconds), using the default timezone and the default
+    locale, return null if fail.
+
+    if `timestamp` is None, then it returns current timestamp.
+    """
+    sc = SparkContext._active_spark_context
+    if timestamp is None:
+        return Column(sc._jvm.functions.unix_timestamp())
+    return Column(sc._jvm.functions.unix_timestamp(_to_java_column(timestamp), format))
+
+
+@since(1.5)
+def from_utc_timestamp(timestamp, tz):
+    """
+    Assumes given timestamp is UTC and converts to given timezone.
+
+    >>> df = sqlContext.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
+    >>> df.select(from_utc_timestamp(df.t, "PST").alias('t')).collect()
+    [Row(t=datetime.datetime(1997, 2, 28, 2, 30))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.from_utc_timestamp(_to_java_column(timestamp), tz))
+
+
+@since(1.5)
+def to_utc_timestamp(timestamp, tz):
+    """
+    Assumes given timestamp is in given timezone and converts to UTC.
+
+    >>> df = sqlContext.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
+    >>> df.select(to_utc_timestamp(df.t, "PST").alias('t')).collect()
+    [Row(t=datetime.datetime(1997, 2, 28, 18, 30))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.to_utc_timestamp(_to_java_column(timestamp), tz))
+
+
+# ---------------------------- misc functions ----------------------------------
+
+@since(1.5)
+@ignore_unicode_prefix
+def crc32(col):
+    """
+    Calculates the cyclic redundancy check value  (CRC32) of a binary column and
+    returns the value as a bigint.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(crc32('a').alias('crc32')).collect()
+    [Row(crc32=u'902fbdd2b1df0c4f70b4a5d23525e932')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.md5(_to_java_column(col)))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def md5(col):
+    """Calculates the MD5 digest and returns the value as a 32 character hex string.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(md5('a').alias('hash')).collect()
+    [Row(hash=u'902fbdd2b1df0c4f70b4a5d23525e932')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.md5(_to_java_column(col))
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def sha1(col):
+    """Returns the hex string result of SHA-1.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(sha1('a').alias('hash')).collect()
+    [Row(hash=u'3c01bdbb26f358bab27f267924aa2c9a03fcfdb8')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.sha1(_to_java_column(col))
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def sha2(col, numBits):
+    """Returns the hex string result of SHA-2 family of hash functions (SHA-224, SHA-256, SHA-384,
+    and SHA-512). The numBits indicates the desired bit length of the result, which must have a
+    value of 224, 256, 384, 512, or 0 (which is equivalent to 256).
+
+    >>> digests = df.select(sha2(df.name, 256).alias('s')).collect()
+    >>> digests[0]
+    Row(s=u'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043')
+    >>> digests[1]
+    Row(s=u'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961')
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.sha2(_to_java_column(col), numBits)
+    return Column(jc)
+
+
+# ---------------------- String/Binary functions ------------------------------
+
+_string_functions = {
+    'ascii': 'Computes the numeric value of the first character of the string column.',
+    'base64': 'Computes the BASE64 encoding of a binary column and returns it as a string column.',
+    'unbase64': 'Decodes a BASE64 encoded string column and returns it as a binary column.',
+    'initcap': 'Returns a new string column by converting the first letter of each word to ' +
+               'uppercase. Words are delimited by whitespace.',
+    'lower': 'Converts a string column to lower case.',
+    'upper': 'Converts a string column to upper case.',
+    'reverse': 'Reverses the string column and returns it as a new string column.',
+    'ltrim': 'Trim the spaces from right end for the specified string value.',
+    'rtrim': 'Trim the spaces from right end for the specified string value.',
+    'trim': 'Trim the spaces from both ends for the specified string column.',
+}
+
+
+for _name, _doc in _string_functions.items():
+    globals()[_name] = since(1.5)(_create_function(_name, _doc))
+del _name, _doc
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def concat(*cols):
+    """
+    Concatenates multiple input string columns together into a single string column.
+
+    >>> df = sqlContext.createDataFrame([('abcd','123')], ['s', 'd'])
+    >>> df.select(concat(df.s, df.d).alias('s')).collect()
+    [Row(s=u'abcd123')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.concat(_to_seq(sc, cols, _to_java_column)))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def concat_ws(sep, *cols):
+    """
+    Concatenates multiple input string columns together into a single string column,
+    using the given separator.
+
+    >>> df = sqlContext.createDataFrame([('abcd','123')], ['s', 'd'])
+    >>> df.select(concat_ws('-', df.s, df.d).alias('s')).collect()
+    [Row(s=u'abcd-123')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.concat_ws(sep, _to_seq(sc, cols, _to_java_column)))
+
+
+@since(1.5)
+def decode(col, charset):
+    """
+    Computes the first argument into a string from a binary using the provided character set
+    (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.decode(_to_java_column(col), charset))
+
+
+@since(1.5)
+def encode(col, charset):
+    """
+    Computes the first argument into a binary from a string using the provided character set
+    (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.encode(_to_java_column(col), charset))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def format_number(col, d):
+    """
+    Formats the number X to a format like '#,--#,--#.--', rounded to d decimal places,
+    and returns the result as a string.
+
+    :param col: the column name of the numeric value to be formatted
+    :param d: the N decimal places
+
+    >>> sqlContext.createDataFrame([(5,)], ['a']).select(format_number('a', 4).alias('v')).collect()
+    [Row(v=u'5.0000')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.format_number(_to_java_column(col), d))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def format_string(format, *cols):
+    """
+    Formats the arguments in printf-style and returns the result as a string column.
+
+    :param col: the column name of the numeric value to be formatted
+    :param d: the N decimal places
+
+    >>> df = sqlContext.createDataFrame([(5, "hello")], ['a', 'b'])
+    >>> df.select(format_string('%d %s', df.a, df.b).alias('v')).collect()
+    [Row(v=u'5 hello')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.format_string(format, _to_seq(sc, cols, _to_java_column)))
+
+
+@since(1.5)
+def instr(str, substr):
+    """
+    Locate the position of the first occurrence of substr column in the given string.
+    Returns null if either of the arguments are null.
+
+    NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+    could not be found in str.
+
+    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
+    >>> df.select(instr(df.s, 'b').alias('s')).collect()
+    [Row(s=2)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.instr(_to_java_column(str), substr))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def substring(str, pos, len):
+    """
+    Substring starts at `pos` and is of length `len` when str is String type or
+    returns the slice of byte array that starts at `pos` in byte and is of length `len`
+    when str is Binary type
+
+    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
+    >>> df.select(substring(df.s, 1, 2).alias('s')).collect()
+    [Row(s=u'ab')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.substring(_to_java_column(str), pos, len))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def substring_index(str, delim, count):
+    """
+    Returns the substring from string str before count occurrences of the delimiter delim.
+    If count is positive, everything the left of the final delimiter (counting from left) is
+    returned. If count is negative, every to the right of the final delimiter (counting from the
+    right) is returned. substring_index performs a case-sensitive match when searching for delim.
+
+    >>> df = sqlContext.createDataFrame([('a.b.c.d',)], ['s'])
+    >>> df.select(substring_index(df.s, '.', 2).alias('s')).collect()
+    [Row(s=u'a.b')]
     >>> df.select(substring_index(df.s, '.', -3).alias('s')).collect()
     [Row(s=u'b.c.d')]
     """
@@ -958,6 +1092,126 @@ def substring_index(str, delim, count):
     return Column(sc._jvm.functions.substring_index(_to_java_column(str), delim, count))
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def levenshtein(left, right):
+    """Computes the Levenshtein distance of the two given strings.
+
+    >>> df0 = sqlContext.createDataFrame([('kitten', 'sitting',)], ['l', 'r'])
+    >>> df0.select(levenshtein('l', 'r').alias('d')).collect()
+    [Row(d=3)]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.levenshtein(_to_java_column(left), _to_java_column(right))
+    return Column(jc)
+
+
+@since(1.5)
+def locate(substr, str, pos=0):
+    """
+    Locate the position of the first occurrence of substr in a string column, after position pos.
+
+    NOTE: The position is not zero based, but 1 based index. returns 0 if substr
+    could not be found in str.
+
+    :param substr: a string
+    :param str: a Column of StringType
+    :param pos: start position (zero based)
+
+    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
+    >>> df.select(locate('b', df.s, 1).alias('s')).collect()
+    [Row(s=2)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.locate(substr, _to_java_column(str), pos))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def lpad(col, len, pad):
+    """
+    Left-pad the string column to width `len` with `pad`.
+
+    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
+    >>> df.select(lpad(df.s, 6, '#').alias('s')).collect()
+    [Row(s=u'##abcd')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.lpad(_to_java_column(col), len, pad))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def rpad(col, len, pad):
+    """
+    Right-pad the string column to width `len` with `pad`.
+
+    >>> df = sqlContext.createDataFrame([('abcd',)], ['s',])
+    >>> df.select(rpad(df.s, 6, '#').alias('s')).collect()
+    [Row(s=u'abcd##')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.rpad(_to_java_column(col), len, pad))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def repeat(col, n):
+    """
+    Repeats a string column n times, and returns it as a new string column.
+
+    >>> df = sqlContext.createDataFrame([('ab',)], ['s',])
+    >>> df.select(repeat(df.s, 3).alias('s')).collect()
+    [Row(s=u'ababab')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.repeat(_to_java_column(col), n))
+
+
+@since(1.5)
+@ignore_unicode_prefix
+def split(str, pattern):
+    """
+    Splits str around pattern (pattern is a regular expression).
+
+    NOTE: pattern is a string represent the regular expression.
+
+    >>> df = sqlContext.createDataFrame([('ab12cd',)], ['s',])
+    >>> df.select(split(df.s, '[0-9]+').alias('s')).collect()
+    [Row(s=[u'ab', u'cd'])]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.split(_to_java_column(str), pattern))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def regexp_extract(str, pattern, idx):
+    """Extract a specific(idx) group identified by a java regex, from the specified string column.
+
+    >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])
+    >>> df.select(regexp_extract('str', '(\d+)-(\d+)', 1).alias('d')).collect()
+    [Row(d=u'100')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.regexp_extract(_to_java_column(str), pattern, idx)
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def regexp_replace(str, pattern, replacement):
+    """Replace all substrings of the specified string value that match regexp with rep.
+
+    >>> df = sqlContext.createDataFrame([('100-200',)], ['str'])
+    >>> df.select(regexp_replace('str', '(\\d+)', '--').alias('d')).collect()
+    [Row(d=u'-----')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.regexp_replace(_to_java_column(str), pattern, replacement)
+    return Column(jc)
+
+
 @ignore_unicode_prefix
 @since(1.5)
 def initcap(col):
@@ -970,6 +1224,114 @@ def initcap(col):
     return Column(sc._jvm.functions.initcap(_to_java_column(col)))
 
 
+@since(1.5)
+@ignore_unicode_prefix
+def soundex(col):
+    """
+    Returns the SoundEx encoding for a string
+
+    >>> df = sqlContext.createDataFrame([("Peters",),("Uhrbach",)], ['name'])
+    >>> df.select(soundex(df.name).alias("soundex")).collect()
+    [Row(soundex=u'P362'), Row(soundex=u'U612')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.soundex(_to_java_column(col)))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def bin(col):
+    """Returns the string representation of the binary value of the given column.
+
+    >>> df.select(bin(df.age).alias('c')).collect()
+    [Row(c=u'10'), Row(c=u'101')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.bin(_to_java_column(col))
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def hex(col):
+    """Computes hex value of the given column, which could be StringType,
+    BinaryType, IntegerType or LongType.
+
+    >>> sqlContext.createDataFrame([('ABC', 3)], ['a', 'b']).select(hex('a'), hex('b')).collect()
+    [Row(hex(a)=u'414243', hex(b)=u'3')]
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.hex(_to_java_column(col))
+    return Column(jc)
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def unhex(col):
+    """Inverse of hex. Interprets each pair of characters as a hexadecimal number
+    and converts to the byte representation of number.
+
+    >>> sqlContext.createDataFrame([('414243',)], ['a']).select(unhex('a')).collect()
+    [Row(unhex(a)=bytearray(b'ABC'))]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.unhex(_to_java_column(col)))
+
+
+@ignore_unicode_prefix
+@since(1.5)
+def length(col):
+    """Calculates the length of a string or binary expression.
+
+    >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(length('a').alias('length')).collect()
+    [Row(length=3)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.length(_to_java_column(col)))
+
+
+# ---------------------- Collection functions ------------------------------
+
+@since(1.4)
+def array(*cols):
+    """Creates a new array column.
+
+    :param cols: list of column names (string) or list of :class:`Column` expressions that have
+        the same data type.
+
+    >>> df.select(array('age', 'age').alias("arr")).collect()
+    [Row(arr=[2, 2]), Row(arr=[5, 5])]
+    >>> df.select(array([df.age, df.age]).alias("arr")).collect()
+    [Row(arr=[2, 2]), Row(arr=[5, 5])]
+    """
+    sc = SparkContext._active_spark_context
+    if len(cols) == 1 and isinstance(cols[0], (list, set)):
+        cols = cols[0]
+    jc = sc._jvm.functions.array(_to_seq(sc, cols, _to_java_column))
+    return Column(jc)
+
+
+@since(1.4)
+def explode(col):
+    """Returns a new row for each element in the given array or map.
+
+    >>> from pyspark.sql import Row
+    >>> eDF = sqlContext.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})])
+    >>> eDF.select(explode(eDF.intlist).alias("anInt")).collect()
+    [Row(anInt=1), Row(anInt=2), Row(anInt=3)]
+
+    >>> eDF.select(explode(eDF.mapfield).alias("key", "value")).show()
+    +---+-----+
+    |key|value|
+    +---+-----+
+    |  a|    b|
+    +---+-----+
+    """
+    sc = SparkContext._active_spark_context
+    jc = sc._jvm.functions.explode(_to_java_column(col))
+    return Column(jc)
+
+
 @since(1.5)
 def size(col):
     """
@@ -1002,19 +1364,7 @@ def sort_array(col, asc=True):
     return Column(sc._jvm.functions.sort_array(_to_java_column(col), asc))
 
 
-@since
-@ignore_unicode_prefix
-def soundex(col):
-    """
-    Returns the SoundEx encoding for a string
-
-    >>> df = sqlContext.createDataFrame([("Peters",),("Uhrbach",)], ['name'])
-    >>> df.select(soundex(df.name).alias("soundex")).collect()
-    [Row(soundex=u'P362'), Row(soundex=u'U612')]
-    """
-    sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.size(_to_java_column(col)))
-
+# ---------------------------- User Defined Function ----------------------------------
 
 class UserDefinedFunction(object):
     """
@@ -1066,6 +1416,11 @@ def udf(f, returnType=StringType()):
     """
     return UserDefinedFunction(f, returnType)
 
+blacklist = ['map', 'since', 'ignore_unicode_prefix']
+__all__ = [k for k, v in globals().items()
+           if not k.startswith('_') and k[0].islower() and callable(v) and k not in blacklist]
+__all__.sort()
+
 
 def _test():
     import doctest
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 35958299076ce..bff70172546ed 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1058,14 +1058,6 @@ object functions {
    */
   def factorial(e: Column): Column = Factorial(e.expr)
 
-  /**
-   * Computes the factorial of the given column.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def factorial(columnName: String): Column = factorial(Column(columnName))
-
   /**
    * Computes the floor of the given value.
    *
@@ -1366,15 +1358,6 @@ object functions {
    */
   def pmod(dividend: Column, divisor: Column): Column = Pmod(dividend.expr, divisor.expr)
 
-  /**
-   * Returns the positive value of dividend mod divisor.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def pmod(dividendColName: String, divisorColName: String): Column =
-    pmod(Column(dividendColName), Column(divisorColName))
-
   /**
    * Returns the double value that is closest in value to the argument and
    * is equal to a mathematical integer.
@@ -1401,14 +1384,6 @@ object functions {
    */
   def round(e: Column): Column = round(e.expr, 0)
 
-  /**
-   * Returns the value of the given column rounded to 0 decimal places.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def round(columnName: String): Column = round(Column(columnName), 0)
-
   /**
    * Round the value of `e` to `scale` decimal places if `scale` >= 0
    * or at integral part when `scale` < 0.
@@ -1418,15 +1393,6 @@ object functions {
    */
   def round(e: Column, scale: Int): Column = Round(e.expr, Literal(scale))
 
-  /**
-   * Round the value of the given column to `scale` decimal places if `scale` >= 0
-   * or at integral part when `scale` < 0.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def round(columnName: String, scale: Int): Column = round(Column(columnName), scale)
-
   /**
    * Shift the the given value numBits left. If the given value is a long value, this function
    * will return a long value else it will return an integer value.
@@ -1436,16 +1402,6 @@ object functions {
    */
   def shiftLeft(e: Column, numBits: Int): Column = ShiftLeft(e.expr, lit(numBits).expr)
 
-  /**
-   * Shift the the given value numBits left. If the given value is a long value, this function
-   * will return a long value else it will return an integer value.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def shiftLeft(columnName: String, numBits: Int): Column =
-    shiftLeft(Column(columnName), numBits)
-
   /**
    * Shift the the given value numBits right. If the given value is a long value, it will return
    * a long value else it will return an integer value.
@@ -1455,16 +1411,6 @@ object functions {
    */
   def shiftRight(e: Column, numBits: Int): Column = ShiftRight(e.expr, lit(numBits).expr)
 
-  /**
-   * Unsigned shift the the given value numBits right. If the given value is a long value,
-   * it will return a long value else it will return an integer value.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def shiftRightUnsigned(columnName: String, numBits: Int): Column =
-    shiftRightUnsigned(Column(columnName), numBits)
-
   /**
    * Unsigned shift the the given value numBits right. If the given value is a long value,
    * it will return a long value else it will return an integer value.
@@ -1475,16 +1421,6 @@ object functions {
   def shiftRightUnsigned(e: Column, numBits: Int): Column =
     ShiftRightUnsigned(e.expr, lit(numBits).expr)
 
-  /**
-   * Shift the the given value numBits right. If the given value is a long value, it will return
-   * a long value else it will return an integer value.
-   *
-   * @group math_funcs
-   * @since 1.5.0
-   */
-  def shiftRight(columnName: String, numBits: Int): Column =
-    shiftRight(Column(columnName), numBits)
-
   /**
    * Computes the signum of the given value.
    *
@@ -1788,14 +1724,6 @@ object functions {
     new StringLocate(lit(substr).expr, str.expr)
   }
 
-  /**
-   * Trim the spaces from left end for the specified string value.
-   *
-   * @group string_funcs
-   * @since 1.5.0
-   */
-  def ltrim(e: Column): Column = StringTrimLeft(e.expr)
-
   /**
    * Locate the position of the first occurrence of substr in a string column, after position pos.
    *
@@ -1819,6 +1747,14 @@ object functions {
     StringLPad(str.expr, lit(len).expr, lit(pad).expr)
   }
 
+  /**
+   * Trim the spaces from left end for the specified string value.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
+  def ltrim(e: Column): Column = StringTrimLeft(e.expr)
+
   /**
    * Extract a specific(idx) group identified by a java regex, from the specified string column.
    *

From d34bac0e156432ca6a260db73dbe1318060e309c Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 4 Aug 2015 20:09:15 -0700
Subject: [PATCH 044/633] [SPARK-9504] [STREAMING] [TESTS] Fix
 o.a.s.streaming.StreamingContextSuite.stop gracefully again

The test failure is here: https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-SBT/3150/AMPLAB_JENKINS_BUILD_PROFILE=hadoop1.0,label=centos/testReport/junit/org.apache.spark.streaming/StreamingContextSuite/stop_gracefully/

There is a race condition in TestReceiver that it may add 1 record and increase `TestReceiver.counter` after stopping `BlockGenerator`. This PR just adds `join` to wait the pushing thread.

Author: zsxwing <zsxwing@gmail.com>

Closes #7934 from zsxwing/SPARK-9504-2 and squashes the following commits:

cfd7973 [zsxwing] Wait for the thread to make sure we won't change TestReceiver.counter after stopping BlockGenerator
---
 .../org/apache/spark/streaming/StreamingContextSuite.scala     | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index b7db280f63588..7423ef6bcb6ea 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -789,7 +789,8 @@ class TestReceiver extends Receiver[Int](StorageLevel.MEMORY_ONLY) with Logging
   }
 
   def onStop() {
-    // no clean to be done, the receiving thread should stop on it own
+    // no clean to be done, the receiving thread should stop on it own, so just wait for it.
+    receivingThreadOption.foreach(_.join())
   }
 }
 

From f7abd6bec9d51ed4ab6359e50eac853e64ecae86 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 4 Aug 2015 22:17:14 -0700
Subject: [PATCH 045/633] Update docs/README.md to put all prereqs together.

This pull request groups all the prereq requirements into a single section.

cc srowen shivaram

Author: Reynold Xin <rxin@databricks.com>

Closes #7951 from rxin/readme-docs and squashes the following commits:

ab7ded0 [Reynold Xin] Updated docs/README.md to put all prereqs together.
---
 docs/README.md | 43 ++++++++-----------------------------------
 1 file changed, 8 insertions(+), 35 deletions(-)

diff --git a/docs/README.md b/docs/README.md
index 50209896f986c..1f4fd3e56ed5f 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -9,12 +9,13 @@ documentation yourself. Why build it yourself? So that you have the docs that co
 whichever version of Spark you currently have checked out of revision control.
 
 ## Prerequisites
-The Spark documenation build uses a number of tools to build HTML docs and API docs in Scala, Python
-and R. To get started you can run the following commands
+The Spark documentation build uses a number of tools to build HTML docs and API docs in Scala,
+Python and R. To get started you can run the following commands
 
     $ sudo gem install jekyll
     $ sudo gem install jekyll-redirect-from
     $ sudo pip install Pygments
+    $ sudo pip install sphinx
     $ Rscript -e 'install.packages(c("knitr", "devtools"), repos="http://cran.stat.ucla.edu/")'
 
 
@@ -29,17 +30,12 @@ you have checked out or downloaded.
 In this directory you will find textfiles formatted using Markdown, with an ".md" suffix. You can
 read those text files directly if you want. Start with index.md.
 
-The markdown code can be compiled to HTML using the [Jekyll tool](http://jekyllrb.com).
-`Jekyll` and a few dependencies must be installed for this to work. We recommend
-installing via the Ruby Gem dependency manager. Since the exact HTML output
-varies between versions of Jekyll and its dependencies, we list specific versions here
-in some cases:
+Execute `jekyll build` from the `docs/` directory to compile the site. Compiling the site with
+Jekyll will create a directory called `_site` containing index.html as well as the rest of the
+compiled files.
 
-    $ sudo gem install jekyll
-    $ sudo gem install jekyll-redirect-from
-
-Execute `jekyll build` from the `docs/` directory to compile the site. Compiling the site with Jekyll will create a directory
-called `_site` containing index.html as well as the rest of the compiled files.
+    $ cd docs
+    $ jekyll build
 
 You can modify the default Jekyll build as follows:
 
@@ -50,29 +46,6 @@ You can modify the default Jekyll build as follows:
     # Build the site with extra features used on the live page
     $ PRODUCTION=1 jekyll build
 
-## Pygments
-
-We also use pygments (http://pygments.org) for syntax highlighting in documentation markdown pages,
-so you will also need to install that (it requires Python) by running `sudo pip install Pygments`.
-
-To mark a block of code in your markdown to be syntax highlighted by jekyll during the compile
-phase, use the following sytax:
-
-    {% highlight scala %}
-    // Your scala code goes here, you can replace scala with many other
-    // supported languages too.
-    {% endhighlight %}
-
-## Sphinx
-
-We use Sphinx to generate Python API docs, so you will need to install it by running
-`sudo pip install sphinx`.
-
-## knitr, devtools
-
-SparkR documentation is written using `roxygen2` and we use `knitr`, `devtools` to generate
-documentation. To install these packages you can run `install.packages(c("knitr", "devtools"))` from a
-R console.
 
 ## API Docs (Scaladoc, Sphinx, roxygen2)
 

From a02bcf20c4fc9e2e182630d197221729e996afc2 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 4 Aug 2015 22:28:49 -0700
Subject: [PATCH 046/633] [SPARK-9540] [MLLIB] optimize PrefixSpan
 implementation

This is a major refactoring of the PrefixSpan implementation. It contains the following changes:

1. Expand prefix with one item at a time. The existing implementation generates all subsets for each itemset, which might have scalability issue when the itemset is large.
2. Use a new internal format. `<(12)(31)>` is represented by `[0, 1, 2, 0, 1, 3, 0]` internally. We use `0` because negative numbers are used to indicates partial prefix items, e.g., `_2` is represented by `-2`.
3. Remember the start indices of all partial projections in the projected postfix to help next projection.
4. Reuse the original sequence array for projected postfixes.
5. Use `Prefix` IDs in aggregation rather than its content.
6. Use `ArrayBuilder` for building primitive arrays.
7. Expose `maxLocalProjDBSize`.
8. Tests are not changed except using `0` instead of `-1` as the delimiter.

`Postfix`'s API doc should be a good place to start.

Closes #7594

feynmanliang zhangjiajin

Author: Xiangrui Meng <meng@databricks.com>

Closes #7937 from mengxr/SPARK-9540 and squashes the following commits:

2d0ec31 [Xiangrui Meng] address more comments
48f450c [Xiangrui Meng] address comments from Feynman; fixed a bug in project and added a test
65f90e8 [Xiangrui Meng] naming and documentation
8afc86a [Xiangrui Meng] refactor impl
---
 .../spark/mllib/fpm/LocalPrefixSpan.scala     | 132 ++--
 .../apache/spark/mllib/fpm/PrefixSpan.scala   | 587 ++++++++++++------
 .../spark/mllib/fpm/PrefixSpanSuite.scala     | 271 ++++----
 3 files changed, 599 insertions(+), 391 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
index ccebf951c850d..3ea10779a1837 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/LocalPrefixSpan.scala
@@ -22,85 +22,89 @@ import scala.collection.mutable
 import org.apache.spark.Logging
 
 /**
- * Calculate all patterns of a projected database in local.
+ * Calculate all patterns of a projected database in local mode.
+ *
+ * @param minCount minimal count for a frequent pattern
+ * @param maxPatternLength max pattern length for a frequent pattern
  */
-private[fpm] object LocalPrefixSpan extends Logging with Serializable {
-  import PrefixSpan._
+private[fpm] class LocalPrefixSpan(
+    val minCount: Long,
+    val maxPatternLength: Int) extends Logging with Serializable {
+  import PrefixSpan.Postfix
+  import LocalPrefixSpan.ReversedPrefix
+
   /**
-   * Calculate all patterns of a projected database.
-   * @param minCount minimum count
-   * @param maxPatternLength maximum pattern length
-   * @param prefixes prefixes in reversed order
-   * @param database the projected database
-   * @return a set of sequential pattern pairs,
-   *         the key of pair is sequential pattern (a list of items in reversed order),
-   *         the value of pair is the pattern's count.
+   * Generates frequent patterns on the input array of postfixes.
+   * @param postfixes an array of postfixes
+   * @return an iterator of (frequent pattern, count)
    */
-  def run(
-      minCount: Long,
-      maxPatternLength: Int,
-      prefixes: List[Set[Int]],
-      database: Iterable[List[Set[Int]]]): Iterator[(List[Set[Int]], Long)] = {
-    if (prefixes.length == maxPatternLength || database.isEmpty) {
-      return Iterator.empty
-    }
-    val freqItemSetsAndCounts = getFreqItemAndCounts(minCount, database)
-    val freqItems = freqItemSetsAndCounts.keys.flatten.toSet
-    val filteredDatabase = database.map { suffix =>
-      suffix
-        .map(item => freqItems.intersect(item))
-        .filter(_.nonEmpty)
-    }
-    freqItemSetsAndCounts.iterator.flatMap { case (item, count) =>
-      val newPrefixes = item :: prefixes
-      val newProjected = project(filteredDatabase, item)
-      Iterator.single((newPrefixes, count)) ++
-        run(minCount, maxPatternLength, newPrefixes, newProjected)
+  def run(postfixes: Array[Postfix]): Iterator[(Array[Int], Long)] = {
+    genFreqPatterns(ReversedPrefix.empty, postfixes).map { case (prefix, count) =>
+      (prefix.toSequence, count)
     }
   }
 
   /**
-   * Calculate suffix sequence immediately after the first occurrence of an item.
-   * @param item itemset to get suffix after
-   * @param sequence sequence to extract suffix from
-   * @return suffix sequence
+   * Recursively generates frequent patterns.
+   * @param prefix current prefix
+   * @param postfixes projected postfixes w.r.t. the prefix
+   * @return an iterator of (prefix, count)
    */
-  def getSuffix(item: Set[Int], sequence: List[Set[Int]]): List[Set[Int]] = {
-    val itemsetSeq = sequence
-    val index = itemsetSeq.indexWhere(item.subsetOf(_))
-    if (index == -1) {
-      List()
-    } else {
-      itemsetSeq.drop(index + 1)
+  private def genFreqPatterns(
+      prefix: ReversedPrefix,
+      postfixes: Array[Postfix]): Iterator[(ReversedPrefix, Long)] = {
+    if (maxPatternLength == prefix.length || postfixes.length < minCount) {
+      return Iterator.empty
+    }
+    // find frequent items
+    val counts = mutable.Map.empty[Int, Long].withDefaultValue(0)
+    postfixes.foreach { postfix =>
+      postfix.genPrefixItems.foreach { case (x, _) =>
+        counts(x) += 1L
+      }
+    }
+    val freqItems = counts.toSeq.filter { case (_, count) =>
+      count >= minCount
+    }.sorted
+    // project and recursively call genFreqPatterns
+    freqItems.toIterator.flatMap { case (item, count) =>
+      val newPrefix = prefix :+ item
+      Iterator.single((newPrefix, count)) ++ {
+        val projected = postfixes.map(_.project(item)).filter(_.nonEmpty)
+        genFreqPatterns(newPrefix, projected)
+      }
     }
   }
+}
 
-  def project(
-      database: Iterable[List[Set[Int]]],
-      prefix: Set[Int]): Iterable[List[Set[Int]]] = {
-    database
-      .map(getSuffix(prefix, _))
-      .filter(_.nonEmpty)
-  }
+private object LocalPrefixSpan {
 
   /**
-   * Generates frequent items by filtering the input data using minimal count level.
-   * @param minCount the minimum count for an item to be frequent
-   * @param database database of sequences
-   * @return freq item to count map
+   * Represents a prefix stored as a list in reversed order.
+   * @param items items in the prefix in reversed order
+   * @param length length of the prefix, not counting delimiters
    */
-  private def getFreqItemAndCounts(
-      minCount: Long,
-      database: Iterable[List[Set[Int]]]): Map[Set[Int], Long] = {
-    // TODO: use PrimitiveKeyOpenHashMap
-    val counts = mutable.Map[Set[Int], Long]().withDefaultValue(0L)
-    database.foreach { sequence =>
-      sequence.flatMap(nonemptySubsets(_)).distinct.foreach { item =>
-        counts(item) += 1L
+  class ReversedPrefix private (val items: List[Int], val length: Int) extends Serializable {
+    /**
+     * Expands the prefix by one item.
+     */
+    def :+(item: Int): ReversedPrefix = {
+      require(item != 0)
+      if (item < 0) {
+        new ReversedPrefix(-item :: items, length + 1)
+      } else {
+        new ReversedPrefix(item :: 0 :: items, length + 1)
       }
     }
-    counts
-      .filter { case (_, count) => count >= minCount }
-      .toMap
+
+    /**
+     * Converts this prefix to a sequence.
+     */
+    def toSequence: Array[Int] = (0 :: items).toArray.reverse
+  }
+
+  object ReversedPrefix {
+    /** An empty prefix. */
+    val empty: ReversedPrefix = new ReversedPrefix(List.empty, 0)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index 9eaf733fada22..d5f0c926c69bb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -18,9 +18,10 @@
 package org.apache.spark.mllib.fpm
 
 import java.{lang => jl, util => ju}
+import java.util.concurrent.atomic.AtomicInteger
 
+import scala.collection.mutable
 import scala.collection.JavaConverters._
-import scala.collection.mutable.ArrayBuilder
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
@@ -31,17 +32,20 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
 /**
- *
  * :: Experimental ::
  *
- * A parallel PrefixSpan algorithm to mine sequential pattern.
- * The PrefixSpan algorithm is described in
- * [[http://doi.org/10.1109/ICDE.2001.914830]].
+ * A parallel PrefixSpan algorithm to mine frequent sequential patterns.
+ * The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan: Mining Sequential Patterns
+ * Efficiently by Prefix-Projected Pattern Growth ([[http://doi.org/10.1109/ICDE.2001.914830]]).
  *
  * @param minSupport the minimal support level of the sequential pattern, any pattern appears
  *                   more than  (minSupport * size-of-the-dataset) times will be output
  * @param maxPatternLength the maximal length of the sequential pattern, any pattern appears
- *                   less than maxPatternLength will be output
+ *                         less than maxPatternLength will be output
+ * @param maxLocalProjDBSize The maximum number of items (including delimiters used in the internal
+ *                           storage format) allowed in a projected database before local
+ *                           processing. If a projected database exceeds this size, another
+ *                           iteration of distributed prefix growth is run.
  *
  * @see [[https://en.wikipedia.org/wiki/Sequential_Pattern_Mining Sequential Pattern Mining
  *       (Wikipedia)]]
@@ -49,33 +53,28 @@ import org.apache.spark.storage.StorageLevel
 @Experimental
 class PrefixSpan private (
     private var minSupport: Double,
-    private var maxPatternLength: Int) extends Logging with Serializable {
+    private var maxPatternLength: Int,
+    private var maxLocalProjDBSize: Long) extends Logging with Serializable {
   import PrefixSpan._
 
-  /**
-   * The maximum number of items allowed in a projected database before local processing. If a
-   * projected database exceeds this size, another iteration of distributed PrefixSpan is run.
-   */
-  // TODO: make configurable with a better default value
-  private val maxLocalProjDBSize: Long = 32000000L
-
   /**
    * Constructs a default instance with default parameters
-   * {minSupport: `0.1`, maxPatternLength: `10`}.
+   * {minSupport: `0.1`, maxPatternLength: `10`, maxLocalProjDBSize: `32000000L`}.
    */
-  def this() = this(0.1, 10)
+  def this() = this(0.1, 10, 32000000L)
 
   /**
    * Get the minimal support (i.e. the frequency of occurrence before a pattern is considered
    * frequent).
    */
-  def getMinSupport: Double = this.minSupport
+  def getMinSupport: Double = minSupport
 
   /**
    * Sets the minimal support level (default: `0.1`).
    */
   def setMinSupport(minSupport: Double): this.type = {
-    require(minSupport >= 0 && minSupport <= 1, "The minimum support value must be in [0, 1].")
+    require(minSupport >= 0 && minSupport <= 1,
+      s"The minimum support value must be in [0, 1], but got $minSupport.")
     this.minSupport = minSupport
     this
   }
@@ -83,45 +82,115 @@ class PrefixSpan private (
   /**
    * Gets the maximal pattern length (i.e. the length of the longest sequential pattern to consider.
    */
-  def getMaxPatternLength: Double = this.maxPatternLength
+  def getMaxPatternLength: Double = maxPatternLength
 
   /**
    * Sets maximal pattern length (default: `10`).
    */
   def setMaxPatternLength(maxPatternLength: Int): this.type = {
     // TODO: support unbounded pattern length when maxPatternLength = 0
-    require(maxPatternLength >= 1, "The maximum pattern length value must be greater than 0.")
+    require(maxPatternLength >= 1,
+      s"The maximum pattern length value must be greater than 0, but got $maxPatternLength.")
     this.maxPatternLength = maxPatternLength
     this
   }
 
   /**
-   * Find the complete set of sequential patterns in the input sequences of itemsets.
-   * @param data ordered sequences of itemsets.
-   * @return a [[PrefixSpanModel]] that contains the frequent sequences
+   * Gets the maximum number of items allowed in a projected database before local processing.
+   */
+  def getMaxLocalProjDBSize: Long = maxLocalProjDBSize
+
+  /**
+   * Sets the maximum number of items (including delimiters used in the internal storage format)
+   * allowed in a projected database before local processing (default: `32000000L`).
+   */
+  def setMaxLocalProjDBSize(maxLocalProjDBSize: Long): this.type = {
+    require(maxLocalProjDBSize >= 0L,
+      s"The maximum local projected database size must be nonnegative, but got $maxLocalProjDBSize")
+    this.maxLocalProjDBSize = maxLocalProjDBSize
+    this
+  }
+
+  /**
+   * Finds the complete set of frequent sequential patterns in the input sequences of itemsets.
+   * @param data sequences of itemsets.
+   * @return a [[PrefixSpanModel]] that contains the frequent patterns
    */
   def run[Item: ClassTag](data: RDD[Array[Array[Item]]]): PrefixSpanModel[Item] = {
-    val itemToInt = data.aggregate(Set[Item]())(
-      seqOp = { (uniqItems, item) => uniqItems ++ item.flatten.toSet },
-      combOp = { _ ++ _ }
-    ).zipWithIndex.toMap
-    val intToItem = Map() ++ (itemToInt.map { case (k, v) => (v, k) })
-
-    val dataInternalRepr = data.map { seq =>
-      seq.map(itemset => itemset.map(itemToInt)).reduce((a, b) => a ++ (DELIMITER +: b))
+    if (data.getStorageLevel == StorageLevel.NONE) {
+      logWarning("Input data is not cached.")
     }
-    val results = run(dataInternalRepr)
 
-    def toPublicRepr(pattern: Iterable[Int]): List[Array[Item]] = {
-      pattern.span(_ != DELIMITER) match {
-        case (x, xs) if xs.size > 1 => x.map(intToItem).toArray :: toPublicRepr(xs.tail)
-        case (x, xs) => List(x.map(intToItem).toArray)
+    val totalCount = data.count()
+    logInfo(s"number of sequences: $totalCount")
+    val minCount = math.ceil(minSupport * totalCount).toLong
+    logInfo(s"minimum count for a frequent pattern: $minCount")
+
+    // Find frequent items.
+    val freqItemAndCounts = data.flatMap { itemsets =>
+        val uniqItems = mutable.Set.empty[Item]
+        itemsets.foreach { _.foreach { item =>
+          uniqItems += item
+        }}
+        uniqItems.toIterator.map((_, 1L))
+      }.reduceByKey(_ + _)
+      .filter { case (_, count) =>
+        count >= minCount
+      }.collect()
+    val freqItems = freqItemAndCounts.sortBy(-_._2).map(_._1)
+    logInfo(s"number of frequent items: ${freqItems.length}")
+
+    // Keep only frequent items from input sequences and convert them to internal storage.
+    val itemToInt = freqItems.zipWithIndex.toMap
+    val dataInternalRepr = data.flatMap { itemsets =>
+      val allItems = mutable.ArrayBuilder.make[Int]
+      var containsFreqItems = false
+      allItems += 0
+      itemsets.foreach { itemsets =>
+        val items = mutable.ArrayBuilder.make[Int]
+        itemsets.foreach { item =>
+          if (itemToInt.contains(item)) {
+            items += itemToInt(item) + 1 // using 1-indexing in internal format
+          }
+        }
+        val result = items.result()
+        if (result.nonEmpty) {
+          containsFreqItems = true
+          allItems ++= result.sorted
+        }
+        allItems += 0
+      }
+      if (containsFreqItems) {
+        Iterator.single(allItems.result())
+      } else {
+        Iterator.empty
+      }
+    }.persist(StorageLevel.MEMORY_AND_DISK)
+
+    val results = genFreqPatterns(dataInternalRepr, minCount, maxPatternLength, maxLocalProjDBSize)
+
+    def toPublicRepr(pattern: Array[Int]): Array[Array[Item]] = {
+      val sequenceBuilder = mutable.ArrayBuilder.make[Array[Item]]
+      val itemsetBuilder = mutable.ArrayBuilder.make[Item]
+      val n = pattern.length
+      var i = 1
+      while (i < n) {
+        val x = pattern(i)
+        if (x == 0) {
+          sequenceBuilder += itemsetBuilder.result()
+          itemsetBuilder.clear()
+        } else {
+          itemsetBuilder += freqItems(x - 1) // using 1-indexing in internal format
+        }
+        i += 1
       }
+      sequenceBuilder.result()
     }
+
     val freqSequences = results.map { case (seq: Array[Int], count: Long) =>
-      new FreqSequence[Item](toPublicRepr(seq).toArray, count)
+      new FreqSequence(toPublicRepr(seq), count)
     }
-    new PrefixSpanModel[Item](freqSequences)
+    new PrefixSpanModel(freqSequences)
   }
 
   /**
@@ -131,7 +200,7 @@ class PrefixSpan private (
    * @tparam Item item type
    * @tparam Itemset itemset type, which is an Iterable of Items
    * @tparam Sequence sequence type, which is an Iterable of Itemsets
-   * @return a [[PrefixSpanModel]] that contains the frequent sequences
+   * @return a [[PrefixSpanModel]] that contains the frequent sequential patterns
    */
   def run[Item, Itemset <: jl.Iterable[Item], Sequence <: jl.Iterable[Itemset]](
       data: JavaRDD[Sequence]): PrefixSpanModel[Item] = {
@@ -139,200 +208,320 @@ class PrefixSpan private (
     run(data.rdd.map(_.asScala.map(_.asScala.toArray).toArray))
   }
 
+}
+
+@Experimental
+object PrefixSpan extends Logging {
+
   /**
-   * Find the complete set of sequential patterns in the input sequences. This method utilizes
-   * the internal representation of itemsets as Array[Int] where each itemset is represented by
-   * a contiguous sequence of non-negative integers and delimiters represented by [[DELIMITER]].
-   * @param data ordered sequences of itemsets. Items are represented by non-negative integers.
-   *             Each itemset has one or more items and is delimited by [[DELIMITER]].
-   * @return a set of sequential pattern pairs,
-   *         the key of pair is pattern (a list of elements),
-   *         the value of pair is the pattern's count.
+   * Find the complete set of frequent sequential patterns in the input sequences.
+   * @param data ordered sequences of itemsets. We represent a sequence internally as Array[Int],
+   *             where each itemset is represented by a contiguous sequence of distinct and ordered
+   *             positive integers. We use 0 as the delimiter at itemset boundaries, including the
+   *             first and the last position.
+   * @return an RDD of (frequent sequential pattern, count) pairs,
+   * @see [[Postfix]]
    */
-  private[fpm] def run(data: RDD[Array[Int]]): RDD[(Array[Int], Long)] = {
+  private[fpm] def genFreqPatterns(
+      data: RDD[Array[Int]],
+      minCount: Long,
+      maxPatternLength: Int,
+      maxLocalProjDBSize: Long): RDD[(Array[Int], Long)] = {
     val sc = data.sparkContext
 
     if (data.getStorageLevel == StorageLevel.NONE) {
       logWarning("Input data is not cached.")
     }
 
-    // Use List[Set[Item]] for internal computation
-    val sequences = data.map { seq => splitSequence(seq.toList) }
-
-    // Convert min support to a min number of transactions for this dataset
-    val minCount = if (minSupport == 0) 0L else math.ceil(sequences.count() * minSupport).toLong
-
-    // (Frequent items -> number of occurrences, all items here satisfy the `minSupport` threshold
-    val freqItemCounts = sequences
-      .flatMap(seq => seq.flatMap(nonemptySubsets(_)).distinct.map(item => (item, 1L)))
-      .reduceByKey(_ + _)
-      .filter { case (item, count) => (count >= minCount) }
-      .collect()
-      .toMap
-
-    // Pairs of (length 1 prefix, suffix consisting of frequent items)
-    val itemSuffixPairs = {
-      val freqItemSets = freqItemCounts.keys.toSet
-      val freqItems = freqItemSets.flatten
-      sequences.flatMap { seq =>
-        val filteredSeq = seq.map(item => freqItems.intersect(item)).filter(_.nonEmpty)
-        freqItemSets.flatMap { item =>
-          val candidateSuffix = LocalPrefixSpan.getSuffix(item, filteredSeq)
-          candidateSuffix match {
-            case suffix if !suffix.isEmpty => Some((List(item), suffix))
-            case _ => None
+    val postfixes = data.map(items => new Postfix(items))
+
+    // Local frequent patterns (prefixes) and their counts.
+    val localFreqPatterns = mutable.ArrayBuffer.empty[(Array[Int], Long)]
+    // Prefixes whose projected databases are small.
+    val smallPrefixes = mutable.Map.empty[Int, Prefix]
+    val emptyPrefix = Prefix.empty
+    // Prefixes whose projected databases are large.
+    var largePrefixes = mutable.Map(emptyPrefix.id -> emptyPrefix)
+    while (largePrefixes.nonEmpty) {
+      val numLocalFreqPatterns = localFreqPatterns.length
+      logInfo(s"number of local frequent patterns: $numLocalFreqPatterns")
+      if (numLocalFreqPatterns > 1000000) {
+        logWarning(
+          s"""
+             | Collected $numLocalFreqPatterns local frequent patterns. You may want to consider:
+             |   1. increase minSupport,
+             |   2. decrease maxPatternLength,
+             |   3. increase maxLocalProjDBSize.
+           """.stripMargin)
+      }
+      logInfo(s"number of small prefixes: ${smallPrefixes.size}")
+      logInfo(s"number of large prefixes: ${largePrefixes.size}")
+      val largePrefixArray = largePrefixes.values.toArray
+      val freqPrefixes = postfixes.flatMap { postfix =>
+          largePrefixArray.flatMap { prefix =>
+            postfix.project(prefix).genPrefixItems.map { case (item, postfixSize) =>
+              ((prefix.id, item), (1L, postfixSize))
+            }
+          }
+        }.reduceByKey { case ((c0, s0), (c1, s1)) =>
+          (c0 + c1, s0 + s1)
+        }.filter { case (_, (c, _)) => c >= minCount }
+        .collect()
+      val newLargePrefixes = mutable.Map.empty[Int, Prefix]
+      freqPrefixes.foreach { case ((id, item), (count, projDBSize)) =>
+        val newPrefix = largePrefixes(id) :+ item
+        localFreqPatterns += ((newPrefix.items :+ 0, count))
+        if (newPrefix.length < maxPatternLength) {
+          if (projDBSize > maxLocalProjDBSize) {
+            newLargePrefixes += newPrefix.id -> newPrefix
+          } else {
+            smallPrefixes += newPrefix.id -> newPrefix
           }
         }
       }
+      largePrefixes = newLargePrefixes
     }
 
-    // Accumulator for the computed results to be returned, initialized to the frequent items (i.e.
-    // frequent length-one prefixes)
-    var resultsAccumulator = freqItemCounts.map { case (item, count) => (List(item), count) }.toList
-
-    // Remaining work to be locally and distributively processed respectfully
-    var (pairsForLocal, pairsForDistributed) = partitionByProjDBSize(itemSuffixPairs)
-
-    // Continue processing until no pairs for distributed processing remain (i.e. all prefixes have
-    // projected database sizes <= `maxLocalProjDBSize`) or `maxPatternLength` is reached
-    var patternLength = 1
-    while (pairsForDistributed.count() != 0 && patternLength < maxPatternLength) {
-      val (nextPatternAndCounts, nextPrefixSuffixPairs) =
-        extendPrefixes(minCount, pairsForDistributed)
-      pairsForDistributed.unpersist()
-      val (smallerPairsPart, largerPairsPart) = partitionByProjDBSize(nextPrefixSuffixPairs)
-      pairsForDistributed = largerPairsPart
-      pairsForDistributed.persist(StorageLevel.MEMORY_AND_DISK)
-      pairsForLocal ++= smallerPairsPart
-      resultsAccumulator ++= nextPatternAndCounts.collect()
-      patternLength += 1 // pattern length grows one per iteration
+    // Switch to local processing.
+    val bcSmallPrefixes = sc.broadcast(smallPrefixes)
+    val distributedFreqPattern = postfixes.flatMap { postfix =>
+      bcSmallPrefixes.value.values.map { prefix =>
+        (prefix.id, postfix.project(prefix).compressed)
+      }.filter(_._2.nonEmpty)
+    }.groupByKey().flatMap { case (id, projPostfixes) =>
+      val prefix = bcSmallPrefixes.value(id)
+      val localPrefixSpan = new LocalPrefixSpan(minCount, maxPatternLength - prefix.length)
+      // TODO: We collect projected postfixes into memory. We should also compare the performance
+      // TODO: of keeping them on shuffle files.
+      localPrefixSpan.run(projPostfixes.toArray).map { case (pattern, count) =>
+        (prefix.items ++ pattern, count)
+      }
     }
 
-    // Process the small projected databases locally
-    val remainingResults = getPatternsInLocal(
-      minCount, sc.parallelize(pairsForLocal, 1).groupByKey())
-
-    (sc.parallelize(resultsAccumulator, 1) ++ remainingResults)
-      .map { case (pattern, count) => (flattenSequence(pattern.reverse).toArray, count) }
+    // Union local frequent patterns and distributed ones.
+    val freqPatterns = (sc.parallelize(localFreqPatterns, 1) ++ distributedFreqPattern)
+      .persist(StorageLevel.MEMORY_AND_DISK)
+    freqPatterns
   }
 
-
   /**
-   * Partitions the prefix-suffix pairs by projected database size.
-   * @param prefixSuffixPairs prefix (length n) and suffix pairs,
-   * @return prefix-suffix pairs partitioned by whether their projected database size is <= or
-   *         greater than [[maxLocalProjDBSize]]
+   * Represents a prefix.
+   * @param items items in this prefix, using the internal format
+   * @param length length of this prefix, not counting 0
    */
-  private def partitionByProjDBSize(prefixSuffixPairs: RDD[(List[Set[Int]], List[Set[Int]])])
-    : (List[(List[Set[Int]], List[Set[Int]])], RDD[(List[Set[Int]], List[Set[Int]])]) = {
-    val prefixToSuffixSize = prefixSuffixPairs
-      .aggregateByKey(0)(
-        seqOp = { case (count, suffix) => count + suffix.length },
-        combOp = { _ + _ })
-    val smallPrefixes = prefixToSuffixSize
-      .filter(_._2 <= maxLocalProjDBSize)
-      .keys
-      .collect()
-      .toSet
-    val small = prefixSuffixPairs.filter { case (prefix, _) => smallPrefixes.contains(prefix) }
-    val large = prefixSuffixPairs.filter { case (prefix, _) => !smallPrefixes.contains(prefix) }
-    (small.collect().toList, large)
+  private[fpm] class Prefix private (val items: Array[Int], val length: Int) extends Serializable {
+
+    /** A unique id for this prefix. */
+    val id: Int = Prefix.nextId
+
+    /** Expands this prefix by the input item. */
+    def :+(item: Int): Prefix = {
+      require(item != 0)
+      if (item < 0) {
+        new Prefix(items :+ -item, length + 1)
+      } else {
+        new Prefix(items ++ Array(0, item), length + 1)
+      }
+    }
   }
 
-  /**
-   * Extends all prefixes by one itemset from their suffix and computes the resulting frequent
-   * prefixes and remaining work.
-   * @param minCount minimum count
-   * @param prefixSuffixPairs prefix (length N) and suffix pairs,
-   * @return (frequent length N+1 extended prefix, count) pairs and (frequent length N+1 extended
-   *         prefix, corresponding suffix) pairs.
-   */
-  private def extendPrefixes(
-      minCount: Long,
-      prefixSuffixPairs: RDD[(List[Set[Int]], List[Set[Int]])])
-    : (RDD[(List[Set[Int]], Long)], RDD[(List[Set[Int]], List[Set[Int]])]) = {
-
-    // (length N prefix, itemset from suffix) pairs and their corresponding number of occurrences
-    // Every (prefix :+ suffix) is guaranteed to have support exceeding `minSupport`
-    val prefixItemPairAndCounts = prefixSuffixPairs
-      .flatMap { case (prefix, suffix) =>
-      suffix.flatMap(nonemptySubsets(_)).distinct.map(y => ((prefix, y), 1L)) }
-      .reduceByKey(_ + _)
-      .filter { case (item, count) => (count >= minCount) }
-
-    // Map from prefix to set of possible next items from suffix
-    val prefixToNextItems = prefixItemPairAndCounts
-      .keys
-      .groupByKey()
-      .mapValues(_.toSet)
-      .collect()
-      .toMap
-
-    // Frequent patterns with length N+1 and their corresponding counts
-    val extendedPrefixAndCounts = prefixItemPairAndCounts
-      .map { case ((prefix, item), count) => (item :: prefix, count) }
-
-    // Remaining work, all prefixes will have length N+1
-    val extendedPrefixAndSuffix = prefixSuffixPairs
-      .filter(x => prefixToNextItems.contains(x._1))
-      .flatMap { case (prefix, suffix) =>
-        val frequentNextItemSets = prefixToNextItems(prefix)
-        val frequentNextItems = frequentNextItemSets.flatten
-        val filteredSuffix = suffix
-          .map(item => frequentNextItems.intersect(item))
-          .filter(_.nonEmpty)
-        frequentNextItemSets.flatMap { item =>
-          LocalPrefixSpan.getSuffix(item, filteredSuffix) match {
-            case suffix if !suffix.isEmpty => Some(item :: prefix, suffix)
-            case _ => None
-          }
-        }
-      }
+  private[fpm] object Prefix {
+    /** Internal counter to generate unique IDs. */
+    private val counter: AtomicInteger = new AtomicInteger(-1)
 
-    (extendedPrefixAndCounts, extendedPrefixAndSuffix)
+    /** Gets the next unique ID. */
+    private def nextId: Int = counter.incrementAndGet()
+
+    /** An empty [[Prefix]] instance. */
+    val empty: Prefix = new Prefix(Array.empty, 0)
   }
 
   /**
-   * Calculate the patterns in local.
-   * @param minCount the absolute minimum count
-   * @param data prefixes and projected sequences data data
-   * @return patterns
+   * An internal representation of a postfix from some projection.
+   * We use one int array to store the items, which might also contains other items from the
+   * original sequence.
+   * Items are represented by positive integers, and items in each itemset must be distinct and
+   * ordered.
+   * we use 0 as the delimiter between itemsets.
+   * For example, a sequence `<(12)(31)1>` is represented by `[0, 1, 2, 0, 1, 3, 0, 1, 0]`.
+   * The postfix of this sequence w.r.t. to prefix `<1>` is `<(_2)(13)1>`.
+   * We may reuse the original items array `[0, 1, 2, 0, 1, 3, 0, 1, 0]` to represent the postfix,
+   * and mark the start index of the postfix, which is `2` in this example.
+   * So the active items in this postfix are `[2, 0, 1, 3, 0, 1, 0]`.
+   * We also remember the start indices of partial projections, the ones that split an itemset.
+   * For example, another possible partial projection w.r.t. `<1>` is `<(_3)1>`.
+   * We remember the start indices of partial projections, which is `[2, 5]` in this example.
+   * This data structure makes it easier to do projections.
+   *
+   * @param items a sequence stored as `Array[Int]` containing this postfix
+   * @param start the start index of this postfix in items
+   * @param partialStarts start indices of possible partial projections, strictly increasing
    */
-  private def getPatternsInLocal(
-      minCount: Long,
-      data: RDD[(List[Set[Int]], Iterable[List[Set[Int]]])]): RDD[(List[Set[Int]], Long)] = {
-    data.flatMap {
-      case (prefix, projDB) => LocalPrefixSpan.run(minCount, maxPatternLength, prefix, projDB)
+  private[fpm] class Postfix(
+      val items: Array[Int],
+      val start: Int = 0,
+      val partialStarts: Array[Int] = Array.empty) extends Serializable {
+
+    require(items.last == 0, s"The last item in a postfix must be zero, but got ${items.last}.")
+    if (partialStarts.nonEmpty) {
+      require(partialStarts.head >= start,
+        "The first partial start cannot be smaller than the start index," +
+          s"but got partialStarts.head = ${partialStarts.head} < start = $start.")
     }
-  }
 
-}
+    /**
+     * Start index of the first full itemset contained in this postfix.
+     */
+    private[this] def fullStart: Int = {
+      var i = start
+      while (items(i) != 0) {
+        i += 1
+      }
+      i
+    }
+
+    /**
+     * Generates length-1 prefix items of this postfix with the corresponding postfix sizes.
+     * There are two types of prefix items:
+     *   a) The item can be assembled to the last itemset of the prefix. For example,
+     *      the postfix of `<(12)(123)>1` w.r.t. `<1>` is `<(_2)(123)1>`. The prefix items of this
+     *      postfix can be assembled to `<1>` is `_2` and `_3`, resulting new prefixes `<(12)>` and
+     *      `<(13)>`. We flip the sign in the output to indicate that this is a partial prefix item.
+     *   b) The item can be appended to the prefix. Taking the same example above, the prefix items
+     *      can be appended to `<1>` is `1`, `2`, and `3`, resulting new prefixes `<11>`, `<12>`,
+     *      and `<13>`.
+     * @return an iterator of (prefix item, corresponding postfix size). If the item is negative, it
+     *         indicates a partial prefix item, which should be assembled to the last itemset of the
+     *         current prefix. Otherwise, the item should be appended to the current prefix.
+     */
+    def genPrefixItems: Iterator[(Int, Long)] = {
+      val n1 = items.length - 1
+      // For each unique item (subject to sign) in this sequence, we output exact one split.
+      // TODO: use PrimitiveKeyOpenHashMap
+      val prefixes = mutable.Map.empty[Int, Long]
+      // a) items that can be assembled to the last itemset of the prefix
+      partialStarts.foreach { start =>
+        var i = start
+        var x = -items(i)
+        while (x != 0) {
+          if (!prefixes.contains(x)) {
+            prefixes(x) = n1 - i
+          }
+          i += 1
+          x = -items(i)
+        }
+      }
+      // b) items that can be appended to the prefix
+      var i = fullStart
+      while (i < n1) {
+        val x = items(i)
+        if (x != 0 && !prefixes.contains(x)) {
+          prefixes(x) = n1 - i
+        }
+        i += 1
+      }
+      prefixes.toIterator
+    }
 
-object PrefixSpan {
-  private[fpm] val DELIMITER = -1
+    /** Tests whether this postfix is non-empty. */
+    def nonEmpty: Boolean = items.length > start + 1
 
-  /** Splits an array of itemsets delimited by [[DELIMITER]]. */
-  private[fpm] def splitSequence(sequence: List[Int]): List[Set[Int]] = {
-    sequence.span(_ != DELIMITER) match {
-      case (x, xs) if xs.length > 1 => x.toSet :: splitSequence(xs.tail)
-      case (x, xs) => List(x.toSet)
+    /**
+     * Projects this postfix with respect to the input prefix item.
+     * @param prefix prefix item. If prefix is positive, we match items in any full itemset; if it
+     *               is negative, we do partial projections.
+     * @return the projected postfix
+     */
+    def project(prefix: Int): Postfix = {
+      require(prefix != 0)
+      val n1 = items.length - 1
+      var matched = false
+      var newStart = n1
+      val newPartialStarts = mutable.ArrayBuilder.make[Int]
+      if (prefix < 0) {
+        // Search for partial projections.
+        val target = -prefix
+        partialStarts.foreach { start =>
+          var i = start
+          var x = items(i)
+          while (x != target && x != 0) {
+            i += 1
+            x = items(i)
+          }
+          if (x == target) {
+            i += 1
+            if (!matched) {
+              newStart = i
+              matched = true
+            }
+            if (items(i) != 0) {
+              newPartialStarts += i
+            }
+          }
+        }
+      } else {
+        // Search for items in full itemsets.
+        // Though the items are ordered in each itemsets, they should be small in practice.
+        // So a sequential scan is sufficient here, compared to bisection search.
+        val target = prefix
+        var i = fullStart
+        while (i < n1) {
+          val x = items(i)
+          if (x == target) {
+            if (!matched) {
+              newStart = i
+              matched = true
+            }
+            if (items(i + 1) != 0) {
+              newPartialStarts += i + 1
+            }
+          }
+          i += 1
+        }
+      }
+      new Postfix(items, newStart, newPartialStarts.result())
     }
-  }
 
-  /** Flattens a sequence of itemsets into an Array, inserting[[DELIMITER]] between itemsets. */
-  private[fpm] def flattenSequence(sequence: List[Set[Int]]): List[Int] = {
-    val builder = ArrayBuilder.make[Int]()
-    for (itemSet <- sequence) {
-      builder += DELIMITER
-      builder ++= itemSet.toSeq.sorted
+    /**
+     * Projects this postfix with respect to the input prefix.
+     */
+    private def project(prefix: Array[Int]): Postfix = {
+      var partial = true
+      var cur = this
+      var i = 0
+      val np = prefix.length
+      while (i < np && cur.nonEmpty) {
+        val x = prefix(i)
+        if (x == 0) {
+          partial = false
+        } else {
+          if (partial) {
+            cur = cur.project(-x)
+          } else {
+            cur = cur.project(x)
+            partial = true
+          }
+        }
+        i += 1
+      }
+      cur
     }
-    builder.result().toList.drop(1) // drop trailing delimiter
-  }
 
-  /** Returns an iterator over all non-empty subsets of `itemSet` */
-  private[fpm] def nonemptySubsets(itemSet: Set[Int]): Iterator[Set[Int]] = {
-    // TODO: improve complexity by using partial prefixes, considering one item at a time
-    itemSet.subsets.filter(_ != Set.empty[Int])
+    /**
+     * Projects this postfix with respect to the input prefix.
+     */
+    def project(prefix: Prefix): Postfix = project(prefix.items)
+
+    /**
+     * Returns the same sequence with compressed storage if possible.
+     */
+    def compressed: Postfix = {
+      if (start > 0) {
+        new Postfix(items.slice(start, items.length), 0, partialStarts.map(_ - start))
+      } else {
+        this
+      }
+    }
   }
 
   /**
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
index 0ae48d62cc6b5..a83e543859b8a 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/fpm/PrefixSpanSuite.scala
@@ -21,7 +21,7 @@ import org.apache.spark.mllib.util.MLlibTestSparkContext
 
 class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
 
-  test("PrefixSpan internal (integer seq, -1 delim) run, singleton itemsets") {
+  test("PrefixSpan internal (integer seq, 0 delim) run, singleton itemsets") {
 
     /*
       library("arulesSequences")
@@ -35,83 +35,81 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
     */
 
     val sequences = Array(
-      Array(1, -1, 3, -1, 4, -1, 5),
-      Array(2, -1, 3, -1, 1),
-      Array(2, -1, 4, -1, 1),
-      Array(3, -1, 1, -1, 3, -1, 4, -1, 5),
-      Array(3, -1, 4, -1, 4, -1, 3),
-      Array(6, -1, 5, -1, 3))
+      Array(0, 1, 0, 3, 0, 4, 0, 5, 0),
+      Array(0, 2, 0, 3, 0, 1, 0),
+      Array(0, 2, 0, 4, 0, 1, 0),
+      Array(0, 3, 0, 1, 0, 3, 0, 4, 0, 5, 0),
+      Array(0, 3, 0, 4, 0, 4, 0, 3, 0),
+      Array(0, 6, 0, 5, 0, 3, 0))
 
     val rdd = sc.parallelize(sequences, 2).cache()
 
-    val prefixspan = new PrefixSpan()
-      .setMinSupport(0.33)
-      .setMaxPatternLength(50)
-    val result1 = prefixspan.run(rdd)
+    val result1 = PrefixSpan.genFreqPatterns(
+      rdd, minCount = 2L, maxPatternLength = 50, maxLocalProjDBSize = 16L)
     val expectedValue1 = Array(
-      (Array(1), 4L),
-      (Array(1, -1, 3), 2L),
-      (Array(1, -1, 3, -1, 4), 2L),
-      (Array(1, -1, 3, -1, 4, -1, 5), 2L),
-      (Array(1, -1, 3, -1, 5), 2L),
-      (Array(1, -1, 4), 2L),
-      (Array(1, -1, 4, -1, 5), 2L),
-      (Array(1, -1, 5), 2L),
-      (Array(2), 2L),
-      (Array(2, -1, 1), 2L),
-      (Array(3), 5L),
-      (Array(3, -1, 1), 2L),
-      (Array(3, -1, 3), 2L),
-      (Array(3, -1, 4), 3L),
-      (Array(3, -1, 4, -1, 5), 2L),
-      (Array(3, -1, 5), 2L),
-      (Array(4), 4L),
-      (Array(4, -1, 5), 2L),
-      (Array(5), 3L)
+      (Array(0, 1, 0), 4L),
+      (Array(0, 1, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 4, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 4, 0, 5, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 5, 0), 2L),
+      (Array(0, 1, 0, 4, 0), 2L),
+      (Array(0, 1, 0, 4, 0, 5, 0), 2L),
+      (Array(0, 1, 0, 5, 0), 2L),
+      (Array(0, 2, 0), 2L),
+      (Array(0, 2, 0, 1, 0), 2L),
+      (Array(0, 3, 0), 5L),
+      (Array(0, 3, 0, 1, 0), 2L),
+      (Array(0, 3, 0, 3, 0), 2L),
+      (Array(0, 3, 0, 4, 0), 3L),
+      (Array(0, 3, 0, 4, 0, 5, 0), 2L),
+      (Array(0, 3, 0, 5, 0), 2L),
+      (Array(0, 4, 0), 4L),
+      (Array(0, 4, 0, 5, 0), 2L),
+      (Array(0, 5, 0), 3L)
     )
     compareInternalResults(expectedValue1, result1.collect())
 
-    prefixspan.setMinSupport(0.5).setMaxPatternLength(50)
-    val result2 = prefixspan.run(rdd)
+    val result2 = PrefixSpan.genFreqPatterns(
+      rdd, minCount = 3, maxPatternLength = 50, maxLocalProjDBSize = 32L)
     val expectedValue2 = Array(
-      (Array(1), 4L),
-      (Array(3), 5L),
-      (Array(3, -1, 4), 3L),
-      (Array(4), 4L),
-      (Array(5), 3L)
+      (Array(0, 1, 0), 4L),
+      (Array(0, 3, 0), 5L),
+      (Array(0, 3, 0, 4, 0), 3L),
+      (Array(0, 4, 0), 4L),
+      (Array(0, 5, 0), 3L)
     )
     compareInternalResults(expectedValue2, result2.collect())
 
-    prefixspan.setMinSupport(0.33).setMaxPatternLength(2)
-    val result3 = prefixspan.run(rdd)
+    val result3 = PrefixSpan.genFreqPatterns(
+      rdd, minCount = 2, maxPatternLength = 2, maxLocalProjDBSize = 32L)
     val expectedValue3 = Array(
-      (Array(1), 4L),
-      (Array(1, -1, 3), 2L),
-      (Array(1, -1, 4), 2L),
-      (Array(1, -1, 5), 2L),
-      (Array(2, -1, 1), 2L),
-      (Array(2), 2L),
-      (Array(3), 5L),
-      (Array(3, -1, 1), 2L),
-      (Array(3, -1, 3), 2L),
-      (Array(3, -1, 4), 3L),
-      (Array(3, -1, 5), 2L),
-      (Array(4), 4L),
-      (Array(4, -1, 5), 2L),
-      (Array(5), 3L)
+      (Array(0, 1, 0), 4L),
+      (Array(0, 1, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 4, 0), 2L),
+      (Array(0, 1, 0, 5, 0), 2L),
+      (Array(0, 2, 0, 1, 0), 2L),
+      (Array(0, 2, 0), 2L),
+      (Array(0, 3, 0), 5L),
+      (Array(0, 3, 0, 1, 0), 2L),
+      (Array(0, 3, 0, 3, 0), 2L),
+      (Array(0, 3, 0, 4, 0), 3L),
+      (Array(0, 3, 0, 5, 0), 2L),
+      (Array(0, 4, 0), 4L),
+      (Array(0, 4, 0, 5, 0), 2L),
+      (Array(0, 5, 0), 3L)
     )
     compareInternalResults(expectedValue3, result3.collect())
   }
 
   test("PrefixSpan internal (integer seq, -1 delim) run, variable-size itemsets") {
     val sequences = Array(
-      Array(1, -1, 1, 2, 3, -1, 1, 3, -1, 4, -1, 3, 6),
-      Array(1, 4, -1, 3, -1, 2, 3, -1, 1, 5),
-      Array(5, 6, -1, 1, 2, -1, 4, 6, -1, 3, -1, 2),
-      Array(5, -1, 7, -1, 1, 6, -1, 3, -1, 2, -1, 3))
+      Array(0, 1, 0, 1, 2, 3, 0, 1, 3, 0, 4, 0, 3, 6, 0),
+      Array(0, 1, 4, 0, 3, 0, 2, 3, 0, 1, 5, 0),
+      Array(0, 5, 6, 0, 1, 2, 0, 4, 6, 0, 3, 0, 2, 0),
+      Array(0, 5, 0, 7, 0, 1, 6, 0, 3, 0, 2, 0, 3, 0))
     val rdd = sc.parallelize(sequences, 2).cache()
-    val prefixspan = new PrefixSpan().setMinSupport(0.5).setMaxPatternLength(5)
-    val result = prefixspan.run(rdd)
+    val result = PrefixSpan.genFreqPatterns(
+      rdd, minCount = 2, maxPatternLength = 5, maxLocalProjDBSize = 128L)
 
     /*
       To verify results, create file "prefixSpanSeqs" with content
@@ -200,63 +198,87 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
         53     <{1},{2},{1}>    0.50
      */
     val expectedValue = Array(
-      (Array(1), 4L),
-      (Array(2), 4L),
-      (Array(3), 4L),
-      (Array(4), 3L),
-      (Array(5), 3L),
-      (Array(6), 3L),
-      (Array(1, -1, 6), 2L),
-      (Array(2, -1, 6), 2L),
-      (Array(5, -1, 6), 2L),
-      (Array(1, 2, -1, 6), 2L),
-      (Array(1, -1, 4), 2L),
-      (Array(2, -1, 4), 2L),
-      (Array(1, 2, -1, 4), 2L),
-      (Array(1, -1, 3), 4L),
-      (Array(2, -1, 3), 3L),
-      (Array(2, 3), 2L),
-      (Array(3, -1, 3), 3L),
-      (Array(4, -1, 3), 3L),
-      (Array(5, -1, 3), 2L),
-      (Array(6, -1, 3), 2L),
-      (Array(5, -1, 6, -1, 3), 2L),
-      (Array(6, -1, 2, -1, 3), 2L),
-      (Array(5, -1, 2, -1, 3), 2L),
-      (Array(5, -1, 1, -1, 3), 2L),
-      (Array(2, -1, 4, -1, 3), 2L),
-      (Array(1, -1, 4, -1, 3), 2L),
-      (Array(1, 2, -1, 4, -1, 3), 2L),
-      (Array(1, -1, 3, -1, 3), 3L),
-      (Array(1, 2, -1, 3), 2L),
-      (Array(1, -1, 2, -1, 3), 2L),
-      (Array(1, -1, 2, 3), 2L),
-      (Array(1, -1, 2), 4L),
-      (Array(1, 2), 2L),
-      (Array(3, -1, 2), 3L),
-      (Array(4, -1, 2), 2L),
-      (Array(5, -1, 2), 2L),
-      (Array(6, -1, 2), 2L),
-      (Array(5, -1, 6, -1, 2), 2L),
-      (Array(6, -1, 3, -1, 2), 2L),
-      (Array(5, -1, 3, -1, 2), 2L),
-      (Array(5, -1, 1, -1, 2), 2L),
-      (Array(4, -1, 3, -1, 2), 2L),
-      (Array(1, -1, 3, -1, 2), 3L),
-      (Array(5, -1, 6, -1, 3, -1, 2), 2L),
-      (Array(5, -1, 1, -1, 3, -1, 2), 2L),
-      (Array(1, -1, 1), 2L),
-      (Array(2, -1, 1), 2L),
-      (Array(3, -1, 1), 2L),
-      (Array(5, -1, 1), 2L),
-      (Array(2, 3, -1, 1), 2L),
-      (Array(1, -1, 3, -1, 1), 2L),
-      (Array(1, -1, 2, 3, -1, 1), 2L),
-      (Array(1, -1, 2, -1, 1), 2L))
+      (Array(0, 1, 0), 4L),
+      (Array(0, 2, 0), 4L),
+      (Array(0, 3, 0), 4L),
+      (Array(0, 4, 0), 3L),
+      (Array(0, 5, 0), 3L),
+      (Array(0, 6, 0), 3L),
+      (Array(0, 1, 0, 6, 0), 2L),
+      (Array(0, 2, 0, 6, 0), 2L),
+      (Array(0, 5, 0, 6, 0), 2L),
+      (Array(0, 1, 2, 0, 6, 0), 2L),
+      (Array(0, 1, 0, 4, 0), 2L),
+      (Array(0, 2, 0, 4, 0), 2L),
+      (Array(0, 1, 2, 0, 4, 0), 2L),
+      (Array(0, 1, 0, 3, 0), 4L),
+      (Array(0, 2, 0, 3, 0), 3L),
+      (Array(0, 2, 3, 0), 2L),
+      (Array(0, 3, 0, 3, 0), 3L),
+      (Array(0, 4, 0, 3, 0), 3L),
+      (Array(0, 5, 0, 3, 0), 2L),
+      (Array(0, 6, 0, 3, 0), 2L),
+      (Array(0, 5, 0, 6, 0, 3, 0), 2L),
+      (Array(0, 6, 0, 2, 0, 3, 0), 2L),
+      (Array(0, 5, 0, 2, 0, 3, 0), 2L),
+      (Array(0, 5, 0, 1, 0, 3, 0), 2L),
+      (Array(0, 2, 0, 4, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 4, 0, 3, 0), 2L),
+      (Array(0, 1, 2, 0, 4, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 3, 0), 3L),
+      (Array(0, 1, 2, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 2, 0, 3, 0), 2L),
+      (Array(0, 1, 0, 2, 3, 0), 2L),
+      (Array(0, 1, 0, 2, 0), 4L),
+      (Array(0, 1, 2, 0), 2L),
+      (Array(0, 3, 0, 2, 0), 3L),
+      (Array(0, 4, 0, 2, 0), 2L),
+      (Array(0, 5, 0, 2, 0), 2L),
+      (Array(0, 6, 0, 2, 0), 2L),
+      (Array(0, 5, 0, 6, 0, 2, 0), 2L),
+      (Array(0, 6, 0, 3, 0, 2, 0), 2L),
+      (Array(0, 5, 0, 3, 0, 2, 0), 2L),
+      (Array(0, 5, 0, 1, 0, 2, 0), 2L),
+      (Array(0, 4, 0, 3, 0, 2, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 2, 0), 3L),
+      (Array(0, 5, 0, 6, 0, 3, 0, 2, 0), 2L),
+      (Array(0, 5, 0, 1, 0, 3, 0, 2, 0), 2L),
+      (Array(0, 1, 0, 1, 0), 2L),
+      (Array(0, 2, 0, 1, 0), 2L),
+      (Array(0, 3, 0, 1, 0), 2L),
+      (Array(0, 5, 0, 1, 0), 2L),
+      (Array(0, 2, 3, 0, 1, 0), 2L),
+      (Array(0, 1, 0, 3, 0, 1, 0), 2L),
+      (Array(0, 1, 0, 2, 3, 0, 1, 0), 2L),
+      (Array(0, 1, 0, 2, 0, 1, 0), 2L))
 
     compareInternalResults(expectedValue, result.collect())
   }
 
+  test("PrefixSpan projections with multiple partial starts") {
+    val sequences = Seq(
+      Array(Array(1, 2), Array(1, 2, 3)))
+    val rdd = sc.parallelize(sequences, 2)
+    val prefixSpan = new PrefixSpan()
+      .setMinSupport(1.0)
+      .setMaxPatternLength(2)
+    val model = prefixSpan.run(rdd)
+    val expected = Array(
+      (Array(Array(1)), 1L),
+      (Array(Array(1, 2)), 1L),
+      (Array(Array(1), Array(1)), 1L),
+      (Array(Array(1), Array(2)), 1L),
+      (Array(Array(1), Array(3)), 1L),
+      (Array(Array(1, 3)), 1L),
+      (Array(Array(2)), 1L),
+      (Array(Array(2, 3)), 1L),
+      (Array(Array(2), Array(1)), 1L),
+      (Array(Array(2), Array(2)), 1L),
+      (Array(Array(2), Array(3)), 1L),
+      (Array(Array(3)), 1L))
+    compareResults(expected, model.freqSequences.collect())
+  }
+
   test("PrefixSpan Integer type, variable-size itemsets") {
     val sequences = Seq(
       Array(Array(1, 2), Array(3)),
@@ -265,7 +287,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       Array(Array(6)))
     val rdd = sc.parallelize(sequences, 2).cache()
 
-    val prefixspan = new PrefixSpan()
+    val prefixSpan = new PrefixSpan()
       .setMinSupport(0.5)
       .setMaxPatternLength(5)
 
@@ -296,7 +318,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
         5   <{1,2}>    0.75
      */
 
-    val model = prefixspan.run(rdd)
+    val model = prefixSpan.run(rdd)
     val expected = Array(
       (Array(Array(1)), 3L),
       (Array(Array(2)), 3L),
@@ -304,7 +326,7 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       (Array(Array(1), Array(3)), 2L),
       (Array(Array(1, 2)), 3L)
     )
-    compareResults(expected, model.freqSequences.collect().map(x => (x.sequence, x.freq)))
+    compareResults(expected, model.freqSequences.collect())
   }
 
   test("PrefixSpan String type, variable-size itemsets") {
@@ -318,11 +340,11 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
       Array(Array(6))).map(seq => seq.map(itemSet => itemSet.map(intToString)))
     val rdd = sc.parallelize(sequences, 2).cache()
 
-    val prefixspan = new PrefixSpan()
+    val prefixSpan = new PrefixSpan()
       .setMinSupport(0.5)
       .setMaxPatternLength(5)
 
-    val model = prefixspan.run(rdd)
+    val model = prefixSpan.run(rdd)
     val expected = Array(
       (Array(Array(1)), 3L),
       (Array(Array(2)), 3L),
@@ -332,17 +354,17 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
     ).map { case (pattern, count) =>
       (pattern.map(itemSet => itemSet.map(intToString)), count)
     }
-    compareResults(expected, model.freqSequences.collect().map(x => (x.sequence, x.freq)))
+    compareResults(expected, model.freqSequences.collect())
   }
 
   private def compareResults[Item](
       expectedValue: Array[(Array[Array[Item]], Long)],
-      actualValue: Array[(Array[Array[Item]], Long)]): Unit = {
+      actualValue: Array[PrefixSpan.FreqSequence[Item]]): Unit = {
     val expectedSet = expectedValue.map { case (pattern: Array[Array[Item]], count: Long) =>
       (pattern.map(itemSet => itemSet.toSet).toSeq, count)
     }.toSet
-    val actualSet = actualValue.map { case (pattern: Array[Array[Item]], count: Long) =>
-      (pattern.map(itemSet => itemSet.toSet).toSeq, count)
+    val actualSet = actualValue.map { x =>
+      (x.sequence.map(_.toSet).toSeq, x.freq)
     }.toSet
     assert(expectedSet === actualSet)
   }
@@ -354,11 +376,4 @@ class PrefixSpanSuite extends SparkFunSuite with MLlibTestSparkContext {
     val actualSet = actualValue.map(x => (x._1.toSeq, x._2)).toSet
     assert(expectedSet === actualSet)
   }
-
-  private def insertDelimiter(sequence: Array[Int]): Array[Int] = {
-    sequence.zip(Seq.fill(sequence.length)(PrefixSpan.DELIMITER)).map { case (a, b) =>
-      List(a, b)
-    }.flatten
-  }
-
 }

From d34548587ab55bc2136c8f823b9e6ae96e1355a4 Mon Sep 17 00:00:00 2001
From: Pedro Rodriguez <prodriguez@trulia.com>
Date: Tue, 4 Aug 2015 22:32:21 -0700
Subject: [PATCH 047/633] [SPARK-8231] [SQL] Add array_contains

This PR is based on #7580 , thanks to EntilZha

PR for work on https://issues.apache.org/jira/browse/SPARK-8231

Currently, I have an initial implementation for contains. Based on discussion on JIRA, it should behave same as Hive: https://github.com/apache/hive/blob/master/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDFArrayContains.java#L102-L128

Main points are:
1. If the array is empty, null, or the value is null, return false
2. If there is a type mismatch, throw error
3. If comparison is not supported, throw error

Closes #7580

Author: Pedro Rodriguez <prodriguez@trulia.com>
Author: Pedro Rodriguez <ski.rodriguez@gmail.com>
Author: Davies Liu <davies@databricks.com>

Closes #7949 from davies/array_contains and squashes the following commits:

d3c08bc [Davies Liu] use foreach() to avoid copy
bc3d1fe [Davies Liu] fix array_contains
719e37d [Davies Liu] Merge branch 'master' of github.com:apache/spark into array_contains
e352cf9 [Pedro Rodriguez] fixed diff from master
4d5b0ff [Pedro Rodriguez] added docs and another type check
ffc0591 [Pedro Rodriguez] fixed unit test
7a22deb [Pedro Rodriguez] Changed test to use strings instead of long/ints which are different between python 2 an 3
b5ffae8 [Pedro Rodriguez] fixed pyspark test
4e7dce3 [Pedro Rodriguez] added more docs
3082399 [Pedro Rodriguez] fixed unit test
46f9789 [Pedro Rodriguez] reverted change
d3ca013 [Pedro Rodriguez] Fixed type checking to match hive behavior, then added tests to insure this
8528027 [Pedro Rodriguez] added more tests
686e029 [Pedro Rodriguez] fix scala style
d262e9d [Pedro Rodriguez] reworked type checking code and added more tests
2517a58 [Pedro Rodriguez] removed unused import
28b4f71 [Pedro Rodriguez] fixed bug with type conversions and re-added tests
12f8795 [Pedro Rodriguez] fix scala style checks
e8a20a9 [Pedro Rodriguez] added python df (broken atm)
65b562c [Pedro Rodriguez] made array_contains nullable false
33b45aa [Pedro Rodriguez] reordered test
9623c64 [Pedro Rodriguez] fixed test
4b4425b [Pedro Rodriguez] changed Arrays in tests to Seqs
72cb4b1 [Pedro Rodriguez] added checkInputTypes and docs
69c46fb [Pedro Rodriguez] added tests and codegen
9e0bfc4 [Pedro Rodriguez] initial attempt at implementation
---
 python/pyspark/sql/functions.py               | 17 ++++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../expressions/collectionOperations.scala    | 78 ++++++++++++++++++-
 .../CollectionFunctionsSuite.scala            | 15 ++++
 .../org/apache/spark/sql/functions.scala      |  8 ++
 .../spark/sql/DataFrameFunctionsSuite.scala   | 49 +++++++++++-
 6 files changed, 163 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index e65b14dc0e2b3..9f0d71d7960cf 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -1311,6 +1311,23 @@ def array(*cols):
     return Column(jc)
 
 
+@since(1.5)
+def array_contains(col, value):
+    """
+    Collection function: returns True if the array contains the given value. The collection
+    elements and value must be of the same type.
+
+    :param col: name of column containing array
+    :param value: value to check for in array
+
+    >>> df = sqlContext.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
+    >>> df.select(array_contains(df.data, "a")).collect()
+    [Row(array_contains(data,a)=True), Row(array_contains(data,a)=False)]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.array_contains(_to_java_column(col), value))
+
+
 @since(1.4)
 def explode(col):
     """Returns a new row for each element in the given array or map.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 43e3e9b910e7d..94c355f838fa0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -240,6 +240,7 @@ object FunctionRegistry {
     // collection functions
     expression[Size]("size"),
     expression[SortArray]("sort_array"),
+    expression[ArrayContains]("array_contains"),
 
     // misc functions
     expression[Crc32]("crc32"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
index 6ccb56578f790..646afa4047d84 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -19,8 +19,9 @@ package org.apache.spark.sql.catalyst.expressions
 import java.util.Comparator
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenFallback, CodeGenContext, GeneratedExpressionCode}
-import org.apache.spark.sql.catalyst.util.TypeUtils
+import org.apache.spark.sql.catalyst.expressions.codegen.{
+  CodegenFallback, CodeGenContext, GeneratedExpressionCode}
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 
 /**
@@ -115,3 +116,76 @@ case class SortArray(base: Expression, ascendingOrder: Expression)
 
   override def prettyName: String = "sort_array"
 }
+
+/**
+ * Checks if the array (left) has the element (right)
+ */
+case class ArrayContains(left: Expression, right: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def dataType: DataType = BooleanType
+
+  override def inputTypes: Seq[AbstractDataType] = right.dataType match {
+    case NullType => Seq()
+    case _ => left.dataType match {
+      case n @ ArrayType(element, _) => Seq(n, element)
+      case _ => Seq()
+    }
+  }
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (right.dataType == NullType) {
+      TypeCheckResult.TypeCheckFailure("Null typed values cannot be used as arguments")
+    } else if (!left.dataType.isInstanceOf[ArrayType]
+      || left.dataType.asInstanceOf[ArrayType].elementType != right.dataType) {
+      TypeCheckResult.TypeCheckFailure(
+        "Arguments must be an array followed by a value of same type as the array members")
+    } else {
+      TypeCheckResult.TypeCheckSuccess
+    }
+  }
+
+  override def nullable: Boolean = false
+
+  override def eval(input: InternalRow): Boolean = {
+    val arr = left.eval(input)
+    if (arr == null) {
+      false
+    } else {
+      val value = right.eval(input)
+      if (value == null) {
+        false
+      } else {
+        arr.asInstanceOf[ArrayData].foreach(right.dataType, (i, v) =>
+          if (v == value) return true
+        )
+        false
+      }
+    }
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val arrGen = left.gen(ctx)
+    val elementGen = right.gen(ctx)
+    val i = ctx.freshName("i")
+    val getValue = ctx.getValue(arrGen.primitive, right.dataType, i)
+    s"""
+      ${arrGen.code}
+      boolean ${ev.isNull} = false;
+      boolean ${ev.primitive} = false;
+      if (!${arrGen.isNull}) {
+        ${elementGen.code}
+        if (!${elementGen.isNull}) {
+          for (int $i = 0; $i < ${arrGen.primitive}.numElements(); $i ++) {
+            if (${ctx.genEqual(right.dataType, elementGen.primitive, getValue)}) {
+              ${ev.primitive} = true;
+              break;
+            }
+          }
+        }
+      }
+     """
+  }
+
+  override def prettyName: String = "array_contains"
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
index 2c7e85c446ec6..95f0e38212a1a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
@@ -65,4 +65,19 @@ class CollectionFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     checkEvaluation(Literal.create(null, ArrayType(StringType)), null)
   }
+
+  test("Array contains") {
+    val a0 = Literal.create(Seq(1, 2, 3), ArrayType(IntegerType))
+    val a1 = Literal.create(Seq[String](null, ""), ArrayType(StringType))
+    val a2 = Literal.create(Seq(null), ArrayType(LongType))
+
+    checkEvaluation(ArrayContains(a0, Literal(1)), true)
+    checkEvaluation(ArrayContains(a0, Literal(0)), false)
+    checkEvaluation(ArrayContains(a0, Literal(null)), false)
+
+    checkEvaluation(ArrayContains(a1, Literal("")), true)
+    checkEvaluation(ArrayContains(a1, Literal(null)), false)
+
+    checkEvaluation(ArrayContains(a2, Literal(null)), false)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index bff70172546ed..5a10c3891ad6c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2120,6 +2120,14 @@ object functions {
   // Collection functions
   //////////////////////////////////////////////////////////////////////////////////////////////
 
+  /**
+   * Returns true if the array contain the value
+   * @group collection_funcs
+   * @since 1.5.0
+   */
+  def array_contains(column: Column, value: Any): Column =
+    ArrayContains(column.expr, Literal(value))
+
   /**
    * Creates a new row for each element in the given array or map column.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 6137527757f85..03116a374f3be 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -323,9 +323,9 @@ class DataFrameFunctionsSuite extends QueryTest {
 
   test("array size function") {
     val df = Seq(
-      (Array[Int](1, 2), "x"),
-      (Array[Int](), "y"),
-      (Array[Int](1, 2, 3), "z")
+      (Seq[Int](1, 2), "x"),
+      (Seq[Int](), "y"),
+      (Seq[Int](1, 2, 3), "z")
     ).toDF("a", "b")
     checkAnswer(
       df.select(size($"a")),
@@ -352,4 +352,47 @@ class DataFrameFunctionsSuite extends QueryTest {
       Seq(Row(2), Row(0), Row(3))
     )
   }
+
+  test("array contains function") {
+    val df = Seq(
+      (Seq[Int](1, 2), "x"),
+      (Seq[Int](), "x")
+    ).toDF("a", "b")
+
+    // Simple test cases
+    checkAnswer(
+      df.select(array_contains(df("a"), 1)),
+      Seq(Row(true), Row(false))
+    )
+    checkAnswer(
+      df.selectExpr("array_contains(a, 1)"),
+      Seq(Row(true), Row(false))
+    )
+    checkAnswer(
+      df.select(array_contains(array(lit(2), lit(null)), 1)),
+      Seq(Row(false), Row(false))
+    )
+
+    // In hive, this errors because null has no type information
+    intercept[AnalysisException] {
+      df.select(array_contains(df("a"), null))
+    }
+    intercept[AnalysisException] {
+      df.selectExpr("array_contains(a, null)")
+    }
+    intercept[AnalysisException] {
+      df.selectExpr("array_contains(null, 1)")
+    }
+
+    // In hive, if either argument has a matching type has a null value, return false, even if
+    // the first argument array contains a null and the second argument is null
+    checkAnswer(
+      df.selectExpr("array_contains(array(array(1), null)[1], 1)"),
+      Seq(Row(false), Row(false))
+    )
+    checkAnswer(
+      df.selectExpr("array_contains(array(0, null), array(1, null)[1])"),
+      Seq(Row(false), Row(false))
+    )
+  }
 }

From 781c8d71a0a6a86c84048a4f22cb3a7d035a5be2 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 4 Aug 2015 23:12:49 -0700
Subject: [PATCH 048/633] [SPARK-9119] [SPARK-8359] [SQL] match
 Decimal.precision/scale with DecimalType

Let Decimal carry the correct precision and scale with DecimalType.

cc rxin yhuai

Author: Davies Liu <davies@databricks.com>

Closes #7925 from davies/decimal_scale and squashes the following commits:

e19701a [Davies Liu] some tweaks
57d78d2 [Davies Liu] fix tests
5d5bc69 [Davies Liu] match precision and scale with DecimalType
---
 .../main/scala/org/apache/spark/sql/Row.scala |  4 ++
 .../sql/catalyst/CatalystTypeConverters.scala | 21 +++++++----
 .../catalyst/analysis/HiveTypeCoercion.scala  |  4 +-
 .../spark/sql/catalyst/expressions/Cast.scala |  6 +--
 .../sql/catalyst/expressions/arithmetic.scala |  2 +-
 .../org/apache/spark/sql/types/Decimal.scala  | 37 +++++++++++++++----
 .../sql/types/decimal/DecimalSuite.scala      | 21 +++++++++++
 .../sql/execution/SparkSqlSerializer2.scala   |  3 +-
 .../spark/sql/execution/pythonUDFs.scala      |  3 +-
 .../apache/spark/sql/json/InferSchema.scala   | 21 +++++++++--
 .../apache/spark/sql/json/JacksonParser.scala |  5 +--
 .../spark/sql/JavaApplySchemaSuite.java       |  2 +-
 .../columnar/InMemoryColumnarQuerySuite.scala |  2 +-
 .../org/apache/spark/sql/json/JsonSuite.scala | 26 ++++++-------
 .../spark/sql/parquet/ParquetQuerySuite.scala | 13 +++++++
 .../hive/execution/ScriptTransformation.scala |  2 +-
 16 files changed, 122 insertions(+), 50 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index 91449479fa539..40159aaf14d34 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -417,6 +417,10 @@ trait Row extends Serializable {
             if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
               return false
             }
+          case d1: java.math.BigDecimal if o2.isInstanceOf[java.math.BigDecimal] =>
+            if (d1.compareTo(o2.asInstanceOf[java.math.BigDecimal]) != 0) {
+              return false
+            }
           case _ => if (o1 != o2) {
             return false
           }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index c666864e43abd..8d0c64eae4774 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -317,18 +317,23 @@ object CatalystTypeConverters {
 
   private class DecimalConverter(dataType: DecimalType)
     extends CatalystTypeConverter[Any, JavaBigDecimal, Decimal] {
-    override def toCatalystImpl(scalaValue: Any): Decimal = scalaValue match {
-      case d: BigDecimal => Decimal(d)
-      case d: JavaBigDecimal => Decimal(d)
-      case d: Decimal => d
+    override def toCatalystImpl(scalaValue: Any): Decimal = {
+      val decimal = scalaValue match {
+        case d: BigDecimal => Decimal(d)
+        case d: JavaBigDecimal => Decimal(d)
+        case d: Decimal => d
+      }
+      if (decimal.changePrecision(dataType.precision, dataType.scale)) {
+        decimal
+      } else {
+        null
+      }
     }
     override def toScala(catalystValue: Decimal): JavaBigDecimal = catalystValue.toJavaBigDecimal
     override def toScalaImpl(row: InternalRow, column: Int): JavaBigDecimal =
       row.getDecimal(column, dataType.precision, dataType.scale).toJavaBigDecimal
   }
 
-  private object BigDecimalConverter extends DecimalConverter(DecimalType.SYSTEM_DEFAULT)
-
   private abstract class PrimitiveConverter[T] extends CatalystTypeConverter[T, Any, Any] {
     final override def toScala(catalystValue: Any): Any = catalystValue
     final override def toCatalystImpl(scalaValue: T): Any = scalaValue
@@ -413,8 +418,8 @@ object CatalystTypeConverters {
     case s: String => StringConverter.toCatalyst(s)
     case d: Date => DateConverter.toCatalyst(d)
     case t: Timestamp => TimestampConverter.toCatalyst(t)
-    case d: BigDecimal => BigDecimalConverter.toCatalyst(d)
-    case d: JavaBigDecimal => BigDecimalConverter.toCatalyst(d)
+    case d: BigDecimal => new DecimalConverter(DecimalType(d.precision, d.scale)).toCatalyst(d)
+    case d: JavaBigDecimal => new DecimalConverter(DecimalType(d.precision, d.scale)).toCatalyst(d)
     case seq: Seq[Any] => new GenericArrayData(seq.map(convertToCatalyst).toArray)
     case r: Row => InternalRow(r.toSeq.map(convertToCatalyst): _*)
     case arr: Array[Any] => new GenericArrayData(arr.map(convertToCatalyst))
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 422d423747026..490f3dc07b6ed 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -442,8 +442,8 @@ object HiveTypeCoercion {
    * Changes numeric values to booleans so that expressions like true = 1 can be evaluated.
    */
   object BooleanEquality extends Rule[LogicalPlan] {
-    private val trueValues = Seq(1.toByte, 1.toShort, 1, 1L, Decimal(1))
-    private val falseValues = Seq(0.toByte, 0.toShort, 0, 0L, Decimal(0))
+    private val trueValues = Seq(1.toByte, 1.toShort, 1, 1L, Decimal.ONE)
+    private val falseValues = Seq(0.toByte, 0.toShort, 0, 0L, Decimal.ZERO)
 
     private def buildCaseKeyWhen(booleanExpr: Expression, numericExpr: Expression) = {
       CaseKeyWhen(numericExpr, Seq(
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 88429bb84b1e3..39f99700c8a26 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -26,8 +26,6 @@ import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
-import scala.collection.mutable
-
 
 object Cast {
 
@@ -157,7 +155,7 @@ case class Cast(child: Expression, dataType: DataType)
     case ByteType =>
       buildCast[Byte](_, _ != 0)
     case DecimalType() =>
-      buildCast[Decimal](_, _ != Decimal(0))
+      buildCast[Decimal](_, _ != Decimal.ZERO)
     case DoubleType =>
       buildCast[Double](_, _ != 0)
     case FloatType =>
@@ -311,7 +309,7 @@ case class Cast(child: Expression, dataType: DataType)
         case _: NumberFormatException => null
       })
     case BooleanType =>
-      buildCast[Boolean](_, b => changePrecision(if (b) Decimal(1) else Decimal(0), target))
+      buildCast[Boolean](_, b => changePrecision(if (b) Decimal.ONE else Decimal.ZERO, target))
     case DateType =>
       buildCast[Int](_, d => null) // date can't cast to decimal in Hive
     case TimestampType =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 0891b55494710..5808e3f66de3c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -511,6 +511,6 @@ case class Pmod(left: Expression, right: Expression) extends BinaryArithmetic {
 
   private def pmod(a: Decimal, n: Decimal): Decimal = {
     val r = a % n
-    if (r.compare(Decimal(0)) < 0) {(r + n) % n} else r
+    if (r.compare(Decimal.ZERO) < 0) {(r + n) % n} else r
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index c0155eeb450a6..624c3f3d7fa97 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.types
 
+import java.math.{RoundingMode, MathContext}
+
 import org.apache.spark.annotation.DeveloperApi
 
 /**
@@ -28,7 +30,7 @@ import org.apache.spark.annotation.DeveloperApi
  * - Otherwise, the decimal value is longVal / (10 ** _scale)
  */
 final class Decimal extends Ordered[Decimal] with Serializable {
-  import org.apache.spark.sql.types.Decimal.{BIG_DEC_ZERO, MAX_LONG_DIGITS, POW_10, ROUNDING_MODE}
+  import org.apache.spark.sql.types.Decimal._
 
   private var decimalVal: BigDecimal = null
   private var longVal: Long = 0L
@@ -137,9 +139,9 @@ final class Decimal extends Ordered[Decimal] with Serializable {
 
   def toBigDecimal: BigDecimal = {
     if (decimalVal.ne(null)) {
-      decimalVal
+      decimalVal(MATH_CONTEXT)
     } else {
-      BigDecimal(longVal, _scale)
+      BigDecimal(longVal, _scale)(MATH_CONTEXT)
     }
   }
 
@@ -261,10 +263,23 @@ final class Decimal extends Ordered[Decimal] with Serializable {
 
   def isZero: Boolean = if (decimalVal.ne(null)) decimalVal == BIG_DEC_ZERO else longVal == 0
 
-  def + (that: Decimal): Decimal = Decimal(toBigDecimal + that.toBigDecimal)
+  def + (that: Decimal): Decimal = {
+    if (decimalVal.eq(null) && that.decimalVal.eq(null) && scale == that.scale) {
+      Decimal(longVal + that.longVal, Math.max(precision, that.precision), scale)
+    } else {
+      Decimal(toBigDecimal + that.toBigDecimal, precision, scale)
+    }
+  }
 
-  def - (that: Decimal): Decimal = Decimal(toBigDecimal - that.toBigDecimal)
+  def - (that: Decimal): Decimal = {
+    if (decimalVal.eq(null) && that.decimalVal.eq(null) && scale == that.scale) {
+      Decimal(longVal - that.longVal, Math.max(precision, that.precision), scale)
+    } else {
+      Decimal(toBigDecimal - that.toBigDecimal, precision, scale)
+    }
+  }
 
+  // HiveTypeCoercion will take care of the precision, scale of result
   def * (that: Decimal): Decimal = Decimal(toBigDecimal * that.toBigDecimal)
 
   def / (that: Decimal): Decimal =
@@ -277,13 +292,13 @@ final class Decimal extends Ordered[Decimal] with Serializable {
 
   def unary_- : Decimal = {
     if (decimalVal.ne(null)) {
-      Decimal(-decimalVal)
+      Decimal(-decimalVal, precision, scale)
     } else {
       Decimal(-longVal, precision, scale)
     }
   }
 
-  def abs: Decimal = if (this.compare(Decimal(0)) < 0) this.unary_- else this
+  def abs: Decimal = if (this.compare(Decimal.ZERO) < 0) this.unary_- else this
 }
 
 object Decimal {
@@ -296,6 +311,11 @@ object Decimal {
 
   private val BIG_DEC_ZERO = BigDecimal(0)
 
+  private val MATH_CONTEXT = new MathContext(DecimalType.MAX_PRECISION, RoundingMode.HALF_UP)
+
+  private[sql] val ZERO = Decimal(0)
+  private[sql] val ONE = Decimal(1)
+
   def apply(value: Double): Decimal = new Decimal().set(value)
 
   def apply(value: Long): Decimal = new Decimal().set(value)
@@ -309,6 +329,9 @@ object Decimal {
   def apply(value: BigDecimal, precision: Int, scale: Int): Decimal =
     new Decimal().set(value, precision, scale)
 
+  def apply(value: java.math.BigDecimal, precision: Int, scale: Int): Decimal =
+    new Decimal().set(value, precision, scale)
+
   def apply(unscaled: Long, precision: Int, scale: Int): Decimal =
     new Decimal().set(unscaled, precision, scale)
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
index 1d297beb3868d..6921d15958a55 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/decimal/DecimalSuite.scala
@@ -166,6 +166,27 @@ class DecimalSuite extends SparkFunSuite with PrivateMethodTester {
     assert(Decimal(100) % Decimal(0) === null)
   }
 
+  // regression test for SPARK-8359
+  test("accurate precision after multiplication") {
+    val decimal = (Decimal(Long.MaxValue, 38, 0) * Decimal(Long.MaxValue, 38, 0)).toJavaBigDecimal
+    assert(decimal.unscaledValue.toString === "85070591730234615847396907784232501249")
+  }
+
+  // regression test for SPARK-8677
+  test("fix non-terminating decimal expansion problem") {
+    val decimal = Decimal(1.0, 10, 3) / Decimal(3.0, 10, 3)
+    // The difference between decimal should not be more than 0.001.
+    assert(decimal.toDouble - 0.333 < 0.001)
+  }
+
+  // regression test for SPARK-8800
+  test("fix loss of precision/scale when doing division operation") {
+    val a = Decimal(2) / Decimal(3)
+    assert(a.toDouble < 1.0 && a.toDouble > 0.6)
+    val b = Decimal(1) / Decimal(8)
+    assert(b.toDouble === 0.125)
+  }
+
   test("set/setOrNull") {
     assert(new Decimal().set(10L, 10, 0).toUnscaledLong === 10L)
     assert(new Decimal().set(100L, 10, 0).toUnscaledLong === 100L)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
index e5bbd0aaed0a5..e811f1de3e6dd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
@@ -412,7 +412,8 @@ private[sql] object SparkSqlSerializer2 {
                 // Then, read the scale.
                 val scale = in.readInt()
                 // Finally, create the Decimal object and set it in the row.
-                mutableRow.update(i, Decimal(new BigDecimal(unscaledVal, scale)))
+                mutableRow.update(i,
+                  Decimal(new BigDecimal(unscaledVal, scale), decimal.precision, decimal.scale))
               }
           }
           i += 1
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index aade2e769ccd6..dedc7c4dfb4d1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -21,7 +21,6 @@ import java.io.OutputStream
 import java.util.{List => JList, Map => JMap}
 
 import scala.collection.JavaConversions._
-import scala.collection.JavaConverters._
 
 import net.razorvine.pickle._
 
@@ -182,7 +181,7 @@ object EvaluatePython {
 
     case (c: Double, DoubleType) => c
 
-    case (c: java.math.BigDecimal, dt: DecimalType) => Decimal(c)
+    case (c: java.math.BigDecimal, dt: DecimalType) => Decimal(c, dt.precision, dt.scale)
 
     case (c: Int, DateType) => c
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
index 04ab5e2217882..ec5668c6b95a1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
@@ -113,8 +113,12 @@ private[sql] object InferSchema {
           case INT | LONG => LongType
           // Since we do not have a data type backed by BigInteger,
           // when we see a Java BigInteger, we use DecimalType.
-          case BIG_INTEGER | BIG_DECIMAL => DecimalType.SYSTEM_DEFAULT
-          case FLOAT | DOUBLE => DoubleType
+          case BIG_INTEGER | BIG_DECIMAL =>
+            val v = parser.getDecimalValue
+            DecimalType(v.precision(), v.scale())
+          case FLOAT | DOUBLE =>
+            // TODO(davies): Should we use decimal if possible?
+            DoubleType
         }
 
       case VALUE_TRUE | VALUE_FALSE => BooleanType
@@ -171,9 +175,18 @@ private[sql] object InferSchema {
         // Double support larger range than fixed decimal, DecimalType.Maximum should be enough
         // in most case, also have better precision.
         case (DoubleType, t: DecimalType) =>
-          if (t == DecimalType.SYSTEM_DEFAULT) t else DoubleType
+          DoubleType
         case (t: DecimalType, DoubleType) =>
-          if (t == DecimalType.SYSTEM_DEFAULT) t else DoubleType
+          DoubleType
+        case (t1: DecimalType, t2: DecimalType) =>
+          val scale = math.max(t1.scale, t2.scale)
+          val range = math.max(t1.precision - t1.scale, t2.precision - t2.scale)
+          if (range + scale > 38) {
+            // DecimalType can't support precision > 38
+            DoubleType
+          } else {
+            DecimalType(range + scale, scale)
+          }
 
         case (StructType(fields1), StructType(fields2)) =>
           val newFields = (fields1 ++ fields2).groupBy(field => field.name).map {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
index bf0448ee9645d..f1a66c84fc96a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
@@ -84,9 +84,8 @@ private[sql] object JacksonParser {
       case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DoubleType) =>
         parser.getDoubleValue
 
-      case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DecimalType()) =>
-        // TODO: add fixed precision and scale handling
-        Decimal(parser.getDecimalValue)
+      case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, dt: DecimalType) =>
+        Decimal(parser.getDecimalValue, dt.precision, dt.scale)
 
       case (VALUE_NUMBER_INT, ByteType) =>
         parser.getByteValue
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
index cb84e78d628ca..e912eb835d169 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
@@ -164,7 +164,7 @@ public void applySchemaToJSON() {
         "\"bigInteger\":92233720368547758069, \"double\":1.7976931348623157E305, " +
         "\"boolean\":false, \"null\":null}"));
     List<StructField> fields = new ArrayList<StructField>(7);
-    fields.add(DataTypes.createStructField("bigInteger", DataTypes.createDecimalType(38, 18),
+    fields.add(DataTypes.createStructField("bigInteger", DataTypes.createDecimalType(20, 0),
       true));
     fields.add(DataTypes.createStructField("boolean", DataTypes.BooleanType, true));
     fields.add(DataTypes.createStructField("double", DataTypes.DoubleType, true));
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 037e2048a8631..9bca4e7e660d6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -148,7 +148,7 @@ class InMemoryColumnarQuerySuite extends QueryTest {
     val dataTypes =
       Seq(StringType, BinaryType, NullType, BooleanType,
         ByteType, ShortType, IntegerType, LongType,
-        FloatType, DoubleType, DecimalType.SYSTEM_DEFAULT, DecimalType(6, 5),
+        FloatType, DoubleType, DecimalType(25, 5), DecimalType(6, 5),
         DateType, TimestampType,
         ArrayType(IntegerType), MapType(StringType, LongType), struct)
     val fields = dataTypes.zipWithIndex.map { case (dataType, index) =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index f19f22fca7d54..16a5c5706009a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -73,8 +73,6 @@ class JsonSuite extends QueryTest with TestJsonData {
 
     val doubleNumber: Double = 1.7976931348623157E308d
     checkTypePromotion(doubleNumber.toDouble, enforceCorrectType(doubleNumber, DoubleType))
-    checkTypePromotion(
-      Decimal(doubleNumber), enforceCorrectType(doubleNumber, DecimalType.SYSTEM_DEFAULT))
 
     checkTypePromotion(DateTimeUtils.fromJavaTimestamp(new Timestamp(intNumber)),
         enforceCorrectType(intNumber, TimestampType))
@@ -150,7 +148,7 @@ class JsonSuite extends QueryTest with TestJsonData {
 
     // DoubleType
     checkDataType(DoubleType, DoubleType, DoubleType)
-    checkDataType(DoubleType, DecimalType.SYSTEM_DEFAULT, DecimalType.SYSTEM_DEFAULT)
+    checkDataType(DoubleType, DecimalType.SYSTEM_DEFAULT, DoubleType)
     checkDataType(DoubleType, StringType, StringType)
     checkDataType(DoubleType, ArrayType(IntegerType), StringType)
     checkDataType(DoubleType, StructType(Nil), StringType)
@@ -241,7 +239,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val jsonDF = ctx.read.json(primitiveFieldAndType)
 
     val expectedSchema = StructType(
-      StructField("bigInteger", DecimalType.SYSTEM_DEFAULT, true) ::
+      StructField("bigInteger", DecimalType(20, 0), true) ::
       StructField("boolean", BooleanType, true) ::
       StructField("double", DoubleType, true) ::
       StructField("integer", LongType, true) ::
@@ -271,7 +269,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val expectedSchema = StructType(
       StructField("arrayOfArray1", ArrayType(ArrayType(StringType, true), true), true) ::
       StructField("arrayOfArray2", ArrayType(ArrayType(DoubleType, true), true), true) ::
-      StructField("arrayOfBigInteger", ArrayType(DecimalType.SYSTEM_DEFAULT, true), true) ::
+      StructField("arrayOfBigInteger", ArrayType(DecimalType(21, 0), true), true) ::
       StructField("arrayOfBoolean", ArrayType(BooleanType, true), true) ::
       StructField("arrayOfDouble", ArrayType(DoubleType, true), true) ::
       StructField("arrayOfInteger", ArrayType(LongType, true), true) ::
@@ -285,7 +283,7 @@ class JsonSuite extends QueryTest with TestJsonData {
           StructField("field3", StringType, true) :: Nil), true), true) ::
       StructField("struct", StructType(
         StructField("field1", BooleanType, true) ::
-        StructField("field2", DecimalType.SYSTEM_DEFAULT, true) :: Nil), true) ::
+        StructField("field2", DecimalType(20, 0), true) :: Nil), true) ::
       StructField("structWithArrayFields", StructType(
         StructField("field1", ArrayType(LongType, true), true) ::
         StructField("field2", ArrayType(StringType, true), true) :: Nil), true) :: Nil)
@@ -386,7 +384,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val expectedSchema = StructType(
       StructField("num_bool", StringType, true) ::
       StructField("num_num_1", LongType, true) ::
-      StructField("num_num_2", DecimalType.SYSTEM_DEFAULT, true) ::
+      StructField("num_num_2", DoubleType, true) ::
       StructField("num_num_3", DoubleType, true) ::
       StructField("num_str", StringType, true) ::
       StructField("str_bool", StringType, true) :: Nil)
@@ -398,11 +396,9 @@ class JsonSuite extends QueryTest with TestJsonData {
     checkAnswer(
       sql("select * from jsonTable"),
       Row("true", 11L, null, 1.1, "13.1", "str1") ::
-        Row("12", null, new java.math.BigDecimal("21474836470.9"), null, null, "true") ::
-        Row("false", 21474836470L,
-          new java.math.BigDecimal("92233720368547758070"), 100, "str1", "false") ::
-        Row(null, 21474836570L,
-          new java.math.BigDecimal("1.1"), 21474836470L, "92233720368547758070", null) :: Nil
+        Row("12", null, 21474836470.9, null, null, "true") ::
+        Row("false", 21474836470L, 92233720368547758070d, 100, "str1", "false") ::
+        Row(null, 21474836570L, 1.1, 21474836470L, "92233720368547758070", null) :: Nil
     )
 
     // Number and Boolean conflict: resolve the type as number in this query.
@@ -425,8 +421,8 @@ class JsonSuite extends QueryTest with TestJsonData {
     // Widening to DecimalType
     checkAnswer(
       sql("select num_num_2 + 1.3 from jsonTable where num_num_2 > 1.1"),
-      Row(BigDecimal("21474836472.2")) ::
-        Row(BigDecimal("92233720368547758071.3")) :: Nil
+      Row(21474836472.2) ::
+        Row(92233720368547758071.3) :: Nil
     )
 
     // Widening to Double
@@ -611,7 +607,7 @@ class JsonSuite extends QueryTest with TestJsonData {
     val jsonDF = ctx.read.json(path)
 
     val expectedSchema = StructType(
-      StructField("bigInteger", DecimalType.SYSTEM_DEFAULT, true) ::
+      StructField("bigInteger", DecimalType(20, 0), true) ::
       StructField("boolean", BooleanType, true) ::
       StructField("double", DoubleType, true) ::
       StructField("integer", LongType, true) ::
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
index a95f70f2bba69..5c65a8ec57f00 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
@@ -189,4 +189,17 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
       }
     }
   }
+
+  test("SPARK-9119 Decimal should be correctly written into parquet") {
+    withTempPath { dir =>
+      val basePath = dir.getCanonicalPath
+      val schema = StructType(Array(StructField("name", DecimalType(10, 5), false)))
+      val rowRDD = sqlContext.sparkContext.parallelize(Array(Row(Decimal("67123.45"))))
+      val df = sqlContext.createDataFrame(rowRDD, schema)
+      df.write.parquet(basePath)
+
+      val decimal = sqlContext.read.parquet(basePath).first().getDecimal(0)
+      assert(Decimal("67123.45") === Decimal(decimal))
+    }
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index 97e4ea2081d56..a6a343d395995 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -29,7 +29,6 @@ import org.apache.hadoop.hive.serde2.AbstractSerDe
 import org.apache.hadoop.hive.serde2.objectinspector._
 import org.apache.hadoop.io.Writable
 
-import org.apache.spark.{TaskContext, Logging}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions._
@@ -39,6 +38,7 @@ import org.apache.spark.sql.hive.HiveShim._
 import org.apache.spark.sql.hive.{HiveContext, HiveInspectors}
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.util.{CircularBuffer, RedirectThread, Utils}
+import org.apache.spark.{Logging, TaskContext}
 
 /**
  * Transforms the input by forking and running the specified script.

From c2a71f0714b7a6ab30c1c4998f606f782428971c Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed, 5 Aug 2015 00:20:26 -0700
Subject: [PATCH 049/633] [SPARK-9217] [STREAMING] Make the kinesis receiver
 reliable by recording sequence numbers

This PR is the second one in the larger issue of making the Kinesis integration reliable and provide WAL-free at-least once guarantee. It is based on the design doc - https://docs.google.com/document/d/1k0dl270EnK7uExrsCE7jYw7PYx0YC935uBcxn3p0f58/edit

In this PR, I have updated the Kinesis Receiver to do the following.
- Control the block generation, by creating its own BlockGenerator with own callback methods and using it to keep track of the ranges of sequence numbers that go into each block.
- More specifically, as the KinesisRecordProcessor provides small batches of records, the records are atomically inserted into the block (that is, either the whole batch is in the block, or not). Accordingly the sequence number range of the batch is recorded. Since there may be many batches added to a block, the receiver tracks all the range of sequence numbers that is added to a block.
- When the block is ready to be pushed, the block is pushed and the ranges are reported as metadata of the block. In addition, the ranges are used to find out the latest sequence number for each shard that can be checkpointed through the DynamoDB.
- Periodically, each KinesisRecordProcessor checkpoints the latest successfully stored sequence number for it own shard.
- The array of ranges in the block metadata is used to create KinesisBackedBlockRDDs. The ReceiverInputDStream has been slightly refactored to allow the creation of KinesisBackedBlockRDDs instead of the WALBackedBlockRDDs.

Things to be done
- [x] Add new test to verify that the sequence numbers are recovered.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7825 from tdas/kinesis-receiver and squashes the following commits:

2159be9 [Tathagata Das] Fixed bug
569be83 [Tathagata Das] Fix scala style issue
bf31e22 [Tathagata Das] Added more documentation to make the kinesis test endpoint more configurable
3ad8361 [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into kinesis-receiver
c693a63 [Tathagata Das] Removed unnecessary constructor params from KinesisTestUtils
e1f1d0a [Tathagata Das] Addressed PR comments
b9fa6bf [Tathagata Das] Fix serialization issues
f8b7680 [Tathagata Das] Updated doc
33fe43a [Tathagata Das] Added more tests
7997138 [Tathagata Das] Fix style errors
a806710 [Tathagata Das] Fixed unit test and use KinesisInputDStream
40a1709 [Tathagata Das] Fixed KinesisReceiverSuite tests
7e44df6 [Tathagata Das] Added documentation and fixed checkpointing
096383f [Tathagata Das] Added test, and addressed some of the comments.
84a7892 [Tathagata Das] fixed scala style issue
e19e37d [Tathagata Das] Added license
1cd7b66 [Tathagata Das] Updated kinesis receiver
---
 .../kinesis/KinesisBackedBlockRDD.scala       |  20 +-
 .../kinesis/KinesisInputDStream.scala         |  71 ++++++
 .../streaming/kinesis/KinesisReceiver.scala   | 195 +++++++++++++--
 .../kinesis/KinesisRecordProcessor.scala      |  76 +++---
 .../streaming/kinesis/KinesisTestUtils.scala  |  63 +++--
 .../streaming/kinesis/KinesisUtils.scala      |  21 +-
 .../kinesis/KinesisBackedBlockRDDSuite.scala  |  18 +-
 .../streaming/kinesis/KinesisFunSuite.scala   |   4 +-
 .../kinesis/KinesisReceiverSuite.scala        |  41 +++-
 .../kinesis/KinesisStreamSuite.scala          | 222 +++++++++++++++---
 .../dstream/ReceiverInputDStream.scala        |  71 +++---
 11 files changed, 605 insertions(+), 197 deletions(-)
 create mode 100644 extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisInputDStream.scala

diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
index 8f144a4d974a8..a003ddf325e6e 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
@@ -37,16 +37,18 @@ case class SequenceNumberRange(
 
 /** Class representing an array of Kinesis sequence number ranges */
 private[kinesis]
-case class SequenceNumberRanges(ranges: Array[SequenceNumberRange]) {
+case class SequenceNumberRanges(ranges: Seq[SequenceNumberRange]) {
   def isEmpty(): Boolean = ranges.isEmpty
+
   def nonEmpty(): Boolean = ranges.nonEmpty
+
   override def toString(): String = ranges.mkString("SequenceNumberRanges(", ", ", ")")
 }
 
 private[kinesis]
 object SequenceNumberRanges {
   def apply(range: SequenceNumberRange): SequenceNumberRanges = {
-    new SequenceNumberRanges(Array(range))
+    new SequenceNumberRanges(Seq(range))
   }
 }
 
@@ -66,14 +68,14 @@ class KinesisBackedBlockRDDPartition(
  */
 private[kinesis]
 class KinesisBackedBlockRDD(
-    sc: SparkContext,
-    regionId: String,
-    endpointUrl: String,
+    @transient sc: SparkContext,
+    val regionName: String,
+    val endpointUrl: String,
     @transient blockIds: Array[BlockId],
-    @transient arrayOfseqNumberRanges: Array[SequenceNumberRanges],
+    @transient val arrayOfseqNumberRanges: Array[SequenceNumberRanges],
     @transient isBlockIdValid: Array[Boolean] = Array.empty,
-    retryTimeoutMs: Int = 10000,
-    awsCredentialsOption: Option[SerializableAWSCredentials] = None
+    val retryTimeoutMs: Int = 10000,
+    val awsCredentialsOption: Option[SerializableAWSCredentials] = None
   ) extends BlockRDD[Array[Byte]](sc, blockIds) {
 
   require(blockIds.length == arrayOfseqNumberRanges.length,
@@ -104,7 +106,7 @@ class KinesisBackedBlockRDD(
       }
       partition.seqNumberRanges.ranges.iterator.flatMap { range =>
         new KinesisSequenceRangeIterator(
-          credenentials, endpointUrl, regionId, range, retryTimeoutMs)
+          credenentials, endpointUrl, regionName, range, retryTimeoutMs)
       }
     }
     if (partition.isBlockIdValid) {
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisInputDStream.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisInputDStream.scala
new file mode 100644
index 0000000000000..2e4204dcb6f1a
--- /dev/null
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisInputDStream.scala
@@ -0,0 +1,71 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.kinesis
+
+import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.{BlockId, StorageLevel}
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
+import org.apache.spark.streaming.receiver.Receiver
+import org.apache.spark.streaming.scheduler.ReceivedBlockInfo
+import org.apache.spark.streaming.{Duration, StreamingContext, Time}
+
+private[kinesis] class KinesisInputDStream(
+    @transient _ssc: StreamingContext,
+    streamName: String,
+    endpointUrl: String,
+    regionName: String,
+    initialPositionInStream: InitialPositionInStream,
+    checkpointAppName: String,
+    checkpointInterval: Duration,
+    storageLevel: StorageLevel,
+    awsCredentialsOption: Option[SerializableAWSCredentials]
+  ) extends ReceiverInputDStream[Array[Byte]](_ssc) {
+
+  private[streaming]
+  override def createBlockRDD(time: Time, blockInfos: Seq[ReceivedBlockInfo]): RDD[Array[Byte]] = {
+
+    // This returns true even for when blockInfos is empty
+    val allBlocksHaveRanges = blockInfos.map { _.metadataOption }.forall(_.nonEmpty)
+
+    if (allBlocksHaveRanges) {
+      // Create a KinesisBackedBlockRDD, even when there are no blocks
+      val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray
+      val seqNumRanges = blockInfos.map {
+        _.metadataOption.get.asInstanceOf[SequenceNumberRanges] }.toArray
+      val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray
+      logDebug(s"Creating KinesisBackedBlockRDD for $time with ${seqNumRanges.length} " +
+          s"seq number ranges: ${seqNumRanges.mkString(", ")} ")
+      new KinesisBackedBlockRDD(
+        context.sc, regionName, endpointUrl, blockIds, seqNumRanges,
+        isBlockIdValid = isBlockIdValid,
+        retryTimeoutMs = ssc.graph.batchDuration.milliseconds.toInt,
+        awsCredentialsOption = awsCredentialsOption)
+    } else {
+      logWarning("Kinesis sequence number information was not present with some block metadata," +
+        " it may not be possible to recover from failures")
+      super.createBlockRDD(time, blockInfos)
+    }
+  }
+
+  override def getReceiver(): Receiver[Array[Byte]] = {
+    new KinesisReceiver(streamName, endpointUrl, regionName, initialPositionInStream,
+      checkpointAppName, checkpointInterval, storageLevel, awsCredentialsOption)
+  }
+}
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
index 1a8a4cecc1141..a4baeec0846b4 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
@@ -18,17 +18,20 @@ package org.apache.spark.streaming.kinesis
 
 import java.util.UUID
 
+import scala.collection.JavaConversions.asScalaIterator
+import scala.collection.mutable
 import scala.util.control.NonFatal
 
-import com.amazonaws.auth.{AWSCredentials, AWSCredentialsProvider, BasicAWSCredentials, DefaultAWSCredentialsProviderChain}
+import com.amazonaws.auth.{AWSCredentials, AWSCredentialsProvider, DefaultAWSCredentialsProviderChain}
 import com.amazonaws.services.kinesis.clientlibrary.interfaces.{IRecordProcessor, IRecordProcessorFactory}
 import com.amazonaws.services.kinesis.clientlibrary.lib.worker.{InitialPositionInStream, KinesisClientLibConfiguration, Worker}
+import com.amazonaws.services.kinesis.model.Record
 
-import org.apache.spark.Logging
-import org.apache.spark.storage.StorageLevel
+import org.apache.spark.storage.{StorageLevel, StreamBlockId}
 import org.apache.spark.streaming.Duration
-import org.apache.spark.streaming.receiver.Receiver
+import org.apache.spark.streaming.receiver.{BlockGenerator, BlockGeneratorListener, Receiver}
 import org.apache.spark.util.Utils
+import org.apache.spark.{Logging, SparkEnv}
 
 
 private[kinesis]
@@ -42,38 +45,47 @@ case class SerializableAWSCredentials(accessKeyId: String, secretKey: String)
  * Custom AWS Kinesis-specific implementation of Spark Streaming's Receiver.
  * This implementation relies on the Kinesis Client Library (KCL) Worker as described here:
  * https://github.com/awslabs/amazon-kinesis-client
- * This is a custom receiver used with StreamingContext.receiverStream(Receiver) as described here:
- *   http://spark.apache.org/docs/latest/streaming-custom-receivers.html
- * Instances of this class will get shipped to the Spark Streaming Workers to run within a
- *   Spark Executor.
  *
- * @param appName  Kinesis application name. Kinesis Apps are mapped to Kinesis Streams
- *                 by the Kinesis Client Library.  If you change the App name or Stream name,
- *                 the KCL will throw errors.  This usually requires deleting the backing
- *                 DynamoDB table with the same name this Kinesis application.
+ * The way this Receiver works is as follows:
+ * - The receiver starts a KCL Worker, which is essentially runs a threadpool of multiple
+ *   KinesisRecordProcessor
+ * - Each KinesisRecordProcessor receives data from a Kinesis shard in batches. Each batch is
+ *   inserted into a Block Generator, and the corresponding range of sequence numbers is recorded.
+ * - When the block generator defines a block, then the recorded sequence number ranges that were
+ *   inserted into the block are recorded separately for being used later.
+ * - When the block is ready to be pushed, the block is pushed and the ranges are reported as
+ *   metadata of the block. In addition, the ranges are used to find out the latest sequence
+ *   number for each shard that can be checkpointed through the DynamoDB.
+ * - Periodically, each KinesisRecordProcessor checkpoints the latest successfully stored sequence
+ *   number for it own shard.
+ *
  * @param streamName   Kinesis stream name
  * @param endpointUrl  Url of Kinesis service (e.g., https://kinesis.us-east-1.amazonaws.com)
  * @param regionName  Region name used by the Kinesis Client Library for
  *                    DynamoDB (lease coordination and checkpointing) and CloudWatch (metrics)
- * @param checkpointInterval  Checkpoint interval for Kinesis checkpointing.
- *                            See the Kinesis Spark Streaming documentation for more
- *                            details on the different types of checkpoints.
  * @param initialPositionInStream  In the absence of Kinesis checkpoint info, this is the
  *                                 worker's initial starting position in the stream.
  *                                 The values are either the beginning of the stream
  *                                 per Kinesis' limit of 24 hours
  *                                 (InitialPositionInStream.TRIM_HORIZON) or
  *                                 the tip of the stream (InitialPositionInStream.LATEST).
+ * @param checkpointAppName  Kinesis application name. Kinesis Apps are mapped to Kinesis Streams
+ *                 by the Kinesis Client Library.  If you change the App name or Stream name,
+ *                 the KCL will throw errors.  This usually requires deleting the backing
+ *                 DynamoDB table with the same name this Kinesis application.
+ * @param checkpointInterval  Checkpoint interval for Kinesis checkpointing.
+ *                            See the Kinesis Spark Streaming documentation for more
+ *                            details on the different types of checkpoints.
  * @param storageLevel Storage level to use for storing the received objects
  * @param awsCredentialsOption Optional AWS credentials, used when user directly specifies
  *                             the credentials
  */
 private[kinesis] class KinesisReceiver(
-    appName: String,
-    streamName: String,
+    val streamName: String,
     endpointUrl: String,
     regionName: String,
     initialPositionInStream: InitialPositionInStream,
+    checkpointAppName: String,
     checkpointInterval: Duration,
     storageLevel: StorageLevel,
     awsCredentialsOption: Option[SerializableAWSCredentials]
@@ -90,7 +102,7 @@ private[kinesis] class KinesisReceiver(
    * workerId is used by the KCL should be based on the ip address of the actual Spark Worker
    * where this code runs (not the driver's IP address.)
    */
-  private var workerId: String = null
+  @volatile private var workerId: String = null
 
   /**
    * Worker is the core client abstraction from the Kinesis Client Library (KCL).
@@ -98,22 +110,40 @@ private[kinesis] class KinesisReceiver(
    * Each shard is assigned its own IRecordProcessor and the worker run multiple such
    * processors.
    */
-  private var worker: Worker = null
+  @volatile private var worker: Worker = null
+  @volatile private var workerThread: Thread = null
 
-  /** Thread running the worker */
-  private var workerThread: Thread = null
+  /** BlockGenerator used to generates blocks out of Kinesis data */
+  @volatile private var blockGenerator: BlockGenerator = null
 
+  /**
+   * Sequence number ranges added to the current block being generated.
+   * Accessing and updating of this map is synchronized by locks in BlockGenerator.
+   */
+  private val seqNumRangesInCurrentBlock = new mutable.ArrayBuffer[SequenceNumberRange]
+
+  /** Sequence number ranges of data added to each generated block */
+  private val blockIdToSeqNumRanges = new mutable.HashMap[StreamBlockId, SequenceNumberRanges]
+    with mutable.SynchronizedMap[StreamBlockId, SequenceNumberRanges]
+
+  /**
+   * Latest sequence number ranges that have been stored successfully.
+   * This is used for checkpointing through KCL */
+  private val shardIdToLatestStoredSeqNum = new mutable.HashMap[String, String]
+    with mutable.SynchronizedMap[String, String]
   /**
    * This is called when the KinesisReceiver starts and must be non-blocking.
    * The KCL creates and manages the receiving/processing thread pool through Worker.run().
    */
   override def onStart() {
+    blockGenerator = new BlockGenerator(new GeneratedBlockHandler, streamId, SparkEnv.get.conf)
+
     workerId = Utils.localHostName() + ":" + UUID.randomUUID()
 
     // KCL config instance
     val awsCredProvider = resolveAWSCredentialsProvider()
     val kinesisClientLibConfiguration =
-      new KinesisClientLibConfiguration(appName, streamName, awsCredProvider, workerId)
+      new KinesisClientLibConfiguration(checkpointAppName, streamName, awsCredProvider, workerId)
       .withKinesisEndpoint(endpointUrl)
       .withInitialPositionInStream(initialPositionInStream)
       .withTaskBackoffTimeMillis(500)
@@ -141,6 +171,10 @@ private[kinesis] class KinesisReceiver(
         }
       }
     }
+
+    blockIdToSeqNumRanges.clear()
+    blockGenerator.start()
+
     workerThread.setName(s"Kinesis Receiver ${streamId}")
     workerThread.setDaemon(true)
     workerThread.start()
@@ -165,6 +199,81 @@ private[kinesis] class KinesisReceiver(
     workerId = null
   }
 
+  /** Add records of the given shard to the current block being generated */
+  private[kinesis] def addRecords(shardId: String, records: java.util.List[Record]): Unit = {
+    if (records.size > 0) {
+      val dataIterator = records.iterator().map { record =>
+        val byteBuffer = record.getData()
+        val byteArray = new Array[Byte](byteBuffer.remaining())
+        byteBuffer.get(byteArray)
+        byteArray
+      }
+      val metadata = SequenceNumberRange(streamName, shardId,
+        records.get(0).getSequenceNumber(), records.get(records.size() - 1).getSequenceNumber())
+      blockGenerator.addMultipleDataWithCallback(dataIterator, metadata)
+
+    }
+  }
+
+  /** Get the latest sequence number for the given shard that can be checkpointed through KCL */
+  private[kinesis] def getLatestSeqNumToCheckpoint(shardId: String): Option[String] = {
+    shardIdToLatestStoredSeqNum.get(shardId)
+  }
+
+  /**
+   * Remember the range of sequence numbers that was added to the currently active block.
+   * Internally, this is synchronized with `finalizeRangesForCurrentBlock()`.
+   */
+  private def rememberAddedRange(range: SequenceNumberRange): Unit = {
+    seqNumRangesInCurrentBlock += range
+  }
+
+  /**
+   * Finalize the ranges added to the block that was active and prepare the ranges buffer
+   * for next block. Internally, this is synchronized with `rememberAddedRange()`.
+   */
+  private def finalizeRangesForCurrentBlock(blockId: StreamBlockId): Unit = {
+    blockIdToSeqNumRanges(blockId) = SequenceNumberRanges(seqNumRangesInCurrentBlock.toArray)
+    seqNumRangesInCurrentBlock.clear()
+    logDebug(s"Generated block $blockId has $blockIdToSeqNumRanges")
+  }
+
+  /** Store the block along with its associated ranges */
+  private def storeBlockWithRanges(
+      blockId: StreamBlockId, arrayBuffer: mutable.ArrayBuffer[Array[Byte]]): Unit = {
+    val rangesToReportOption = blockIdToSeqNumRanges.remove(blockId)
+    if (rangesToReportOption.isEmpty) {
+      stop("Error while storing block into Spark, could not find sequence number ranges " +
+        s"for block $blockId")
+      return
+    }
+
+    val rangesToReport = rangesToReportOption.get
+    var attempt = 0
+    var stored = false
+    var throwable: Throwable = null
+    while (!stored && attempt <= 3) {
+      try {
+        store(arrayBuffer, rangesToReport)
+        stored = true
+      } catch {
+        case NonFatal(th) =>
+          attempt += 1
+          throwable = th
+      }
+    }
+    if (!stored) {
+      stop("Error while storing block into Spark", throwable)
+    }
+
+    // Update the latest sequence number that have been successfully stored for each shard
+    // Note that we are doing this sequentially because the array of sequence number ranges
+    // is assumed to be
+    rangesToReport.ranges.foreach { range =>
+      shardIdToLatestStoredSeqNum(range.shardId) = range.toSeqNumber
+    }
+  }
+
   /**
    * If AWS credential is provided, return a AWSCredentialProvider returning that credential.
    * Otherwise, return the DefaultAWSCredentialsProviderChain.
@@ -182,4 +291,46 @@ private[kinesis] class KinesisReceiver(
         new DefaultAWSCredentialsProviderChain()
     }
   }
+
+
+  /**
+   * Class to handle blocks generated by this receiver's block generator. Specifically, in
+   * the context of the Kinesis Receiver, this handler does the following.
+   *
+   * - When an array of records is added to the current active block in the block generator,
+   *   this handler keeps track of the corresponding sequence number range.
+   * - When the currently active block is ready to sealed (not more records), this handler
+   *   keep track of the list of ranges added into this block in another H
+   */
+  private class GeneratedBlockHandler extends BlockGeneratorListener {
+
+    /**
+     * Callback method called after a data item is added into the BlockGenerator.
+     * The data addition, block generation, and calls to onAddData and onGenerateBlock
+     * are all synchronized through the same lock.
+     */
+    def onAddData(data: Any, metadata: Any): Unit = {
+      rememberAddedRange(metadata.asInstanceOf[SequenceNumberRange])
+    }
+
+    /**
+     * Callback method called after a block has been generated.
+     * The data addition, block generation, and calls to onAddData and onGenerateBlock
+     * are all synchronized through the same lock.
+     */
+    def onGenerateBlock(blockId: StreamBlockId): Unit = {
+      finalizeRangesForCurrentBlock(blockId)
+    }
+
+    /** Callback method called when a block is ready to be pushed / stored. */
+    def onPushBlock(blockId: StreamBlockId, arrayBuffer: mutable.ArrayBuffer[_]): Unit = {
+      storeBlockWithRanges(blockId,
+        arrayBuffer.asInstanceOf[mutable.ArrayBuffer[Array[Byte]]])
+    }
+
+    /** Callback called in case of any error in internal of the BlockGenerator */
+    def onError(message: String, throwable: Throwable): Unit = {
+      reportError(message, throwable)
+    }
+  }
 }
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala
index fe9e3a0c793e2..b2405123321e3 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisRecordProcessor.scala
@@ -18,20 +18,16 @@ package org.apache.spark.streaming.kinesis
 
 import java.util.List
 
-import scala.collection.JavaConversions.asScalaBuffer
 import scala.util.Random
+import scala.util.control.NonFatal
 
-import org.apache.spark.Logging
-
-import com.amazonaws.services.kinesis.clientlibrary.exceptions.InvalidStateException
-import com.amazonaws.services.kinesis.clientlibrary.exceptions.KinesisClientLibDependencyException
-import com.amazonaws.services.kinesis.clientlibrary.exceptions.ShutdownException
-import com.amazonaws.services.kinesis.clientlibrary.exceptions.ThrottlingException
-import com.amazonaws.services.kinesis.clientlibrary.interfaces.IRecordProcessor
-import com.amazonaws.services.kinesis.clientlibrary.interfaces.IRecordProcessorCheckpointer
+import com.amazonaws.services.kinesis.clientlibrary.exceptions.{InvalidStateException, KinesisClientLibDependencyException, ShutdownException, ThrottlingException}
+import com.amazonaws.services.kinesis.clientlibrary.interfaces.{IRecordProcessor, IRecordProcessorCheckpointer}
 import com.amazonaws.services.kinesis.clientlibrary.types.ShutdownReason
 import com.amazonaws.services.kinesis.model.Record
 
+import org.apache.spark.Logging
+
 /**
  * Kinesis-specific implementation of the Kinesis Client Library (KCL) IRecordProcessor.
  * This implementation operates on the Array[Byte] from the KinesisReceiver.
@@ -51,6 +47,7 @@ private[kinesis] class KinesisRecordProcessor(
     checkpointState: KinesisCheckpointState) extends IRecordProcessor with Logging {
 
   // shardId to be populated during initialize()
+  @volatile
   private var shardId: String = _
 
   /**
@@ -75,47 +72,38 @@ private[kinesis] class KinesisRecordProcessor(
   override def processRecords(batch: List[Record], checkpointer: IRecordProcessorCheckpointer) {
     if (!receiver.isStopped()) {
       try {
-        /*
-         * Notes:
-         * 1) If we try to store the raw ByteBuffer from record.getData(), the Spark Streaming
-         *    Receiver.store(ByteBuffer) attempts to deserialize the ByteBuffer using the
-         *    internally-configured Spark serializer (kryo, etc).
-         * 2) This is not desirable, so we instead store a raw Array[Byte] and decouple
-         *    ourselves from Spark's internal serialization strategy.
-         * 3) For performance, the BlockGenerator is asynchronously queuing elements within its
-         *    memory before creating blocks.  This prevents the small block scenario, but requires
-         *    that you register callbacks to know when a block has been generated and stored
-         *    (WAL is sufficient for storage) before can checkpoint back to the source.
-        */
-        batch.foreach(record => receiver.store(record.getData().array()))
-
-        logDebug(s"Stored:  Worker $workerId stored ${batch.size} records for shardId $shardId")
+        receiver.addRecords(shardId, batch)
+        logDebug(s"Stored: Worker $workerId stored ${batch.size} records for shardId $shardId")
 
         /*
-         * Checkpoint the sequence number of the last record successfully processed/stored
-         *   in the batch.
-         * In this implementation, we're checkpointing after the given checkpointIntervalMillis.
-         * Note that this logic requires that processRecords() be called AND that it's time to
-         *   checkpoint.  I point this out because there is no background thread running the
-         *   checkpointer.  Checkpointing is tested and trigger only when a new batch comes in.
-         * If the worker is shutdown cleanly, checkpoint will happen (see shutdown() below).
-         * However, if the worker dies unexpectedly, a checkpoint may not happen.
-         * This could lead to records being processed more than once.
+         *
+         * Checkpoint the sequence number of the last record successfully stored.
+         * Note that in this current implementation, the checkpointing occurs only when after
+         * checkpointIntervalMillis from the last checkpoint, AND when there is new record
+         * to process. This leads to the checkpointing lagging behind what records have been
+         * stored by the receiver. Ofcourse, this can lead records processed more than once,
+         * under failures and restarts.
+         *
+         * TODO: Instead of checkpointing here, run a separate timer task to perform
+         * checkpointing so that it checkpoints in a timely manner independent of whether
+         * new records are available or not.
          */
         if (checkpointState.shouldCheckpoint()) {
-          /* Perform the checkpoint */
-          KinesisRecordProcessor.retryRandom(checkpointer.checkpoint(), 4, 100)
+          receiver.getLatestSeqNumToCheckpoint(shardId).foreach { latestSeqNum =>
+            /* Perform the checkpoint */
+            KinesisRecordProcessor.retryRandom(checkpointer.checkpoint(latestSeqNum), 4, 100)
 
-          /* Update the next checkpoint time */
-          checkpointState.advanceCheckpoint()
+            /* Update the next checkpoint time */
+            checkpointState.advanceCheckpoint()
 
-          logDebug(s"Checkpoint:  WorkerId $workerId completed checkpoint of ${batch.size}" +
+            logDebug(s"Checkpoint:  WorkerId $workerId completed checkpoint of ${batch.size}" +
               s" records for shardId $shardId")
-          logDebug(s"Checkpoint:  Next checkpoint is at " +
+            logDebug(s"Checkpoint:  Next checkpoint is at " +
               s" ${checkpointState.checkpointClock.getTimeMillis()} for shardId $shardId")
+          }
         }
       } catch {
-        case e: Throwable => {
+        case NonFatal(e) => {
           /*
            *  If there is a failure within the batch, the batch will not be checkpointed.
            *  This will potentially cause records since the last checkpoint to be processed
@@ -130,7 +118,7 @@ private[kinesis] class KinesisRecordProcessor(
       }
     } else {
       /* RecordProcessor has been stopped. */
-      logInfo(s"Stopped:  The Spark KinesisReceiver has stopped for workerId $workerId" +
+      logInfo(s"Stopped:  KinesisReceiver has stopped for workerId $workerId" +
           s" and shardId $shardId.  No more records will be processed.")
     }
   }
@@ -154,7 +142,11 @@ private[kinesis] class KinesisRecordProcessor(
        * It's now OK to read from the new shards that resulted from a resharding event.
        */
       case ShutdownReason.TERMINATE =>
-        KinesisRecordProcessor.retryRandom(checkpointer.checkpoint(), 4, 100)
+        val latestSeqNumToCheckpointOption = receiver.getLatestSeqNumToCheckpoint(shardId)
+        if (latestSeqNumToCheckpointOption.nonEmpty) {
+          KinesisRecordProcessor.retryRandom(
+            checkpointer.checkpoint(latestSeqNumToCheckpointOption.get), 4, 100)
+        }
 
       /*
        * ZOMBIE Use Case.  NoOp.
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
index 255ac27f793ba..711aade182945 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
@@ -36,22 +36,10 @@ import org.apache.spark.Logging
 /**
  * Shared utility methods for performing Kinesis tests that actually transfer data
  */
-private class KinesisTestUtils(val endpointUrl: String, _regionName: String) extends Logging {
-
-  def this() {
-    this("https://kinesis.us-west-2.amazonaws.com", "")
-  }
-
-  def this(endpointUrl: String) {
-    this(endpointUrl, "")
-  }
-
-  val regionName = if (_regionName.length == 0) {
-    RegionUtils.getRegionByEndpoint(endpointUrl).getName()
-  } else {
-    RegionUtils.getRegion(_regionName).getName()
-  }
+private class KinesisTestUtils extends Logging {
 
+  val endpointUrl = KinesisTestUtils.endpointUrl
+  val regionName = RegionUtils.getRegionByEndpoint(endpointUrl).getName()
   val streamShardCount = 2
 
   private val createStreamTimeoutSeconds = 300
@@ -81,11 +69,11 @@ private class KinesisTestUtils(val endpointUrl: String, _regionName: String) ext
   }
 
   def createStream(): Unit = {
-    logInfo("Creating stream")
     require(!streamCreated, "Stream already created")
     _streamName = findNonExistentStreamName()
 
     // Create a stream. The number of shards determines the provisioned throughput.
+    logInfo(s"Creating stream ${_streamName}")
     val createStreamRequest = new CreateStreamRequest()
     createStreamRequest.setStreamName(_streamName)
     createStreamRequest.setShardCount(2)
@@ -94,7 +82,7 @@ private class KinesisTestUtils(val endpointUrl: String, _regionName: String) ext
     // The stream is now being created. Wait for it to become active.
     waitForStreamToBeActive(_streamName)
     streamCreated = true
-    logInfo("Created stream")
+    logInfo(s"Created stream ${_streamName}")
   }
 
   /**
@@ -191,9 +179,38 @@ private class KinesisTestUtils(val endpointUrl: String, _regionName: String) ext
 
 private[kinesis] object KinesisTestUtils {
 
-  val envVarName = "ENABLE_KINESIS_TESTS"
+  val envVarNameForEnablingTests = "ENABLE_KINESIS_TESTS"
+  val endVarNameForEndpoint = "KINESIS_TEST_ENDPOINT_URL"
+  val defaultEndpointUrl = "https://kinesis.us-west-2.amazonaws.com"
+
+  lazy val shouldRunTests = {
+    val isEnvSet = sys.env.get(envVarNameForEnablingTests) == Some("1")
+    if (isEnvSet) {
+      // scalastyle:off println
+      // Print this so that they are easily visible on the console and not hidden in the log4j logs.
+      println(
+        s"""
+          |Kinesis tests that actually send data has been enabled by setting the environment
+          |variable $envVarNameForEnablingTests to 1. This will create Kinesis Streams and
+          |DynamoDB tables in AWS. Please be aware that this may incur some AWS costs.
+          |By default, the tests use the endpoint URL $defaultEndpointUrl to create Kinesis streams.
+          |To change this endpoint URL to a different region, you can set the environment variable
+          |$endVarNameForEndpoint to the desired endpoint URL
+          |(e.g. $endVarNameForEndpoint="https://kinesis.us-west-2.amazonaws.com").
+        """.stripMargin)
+      // scalastyle:on println
+    }
+    isEnvSet
+  }
 
-  val shouldRunTests = sys.env.get(envVarName) == Some("1")
+  lazy val endpointUrl = {
+    val url = sys.env.getOrElse(endVarNameForEndpoint, defaultEndpointUrl)
+    // scalastyle:off println
+    // Print this so that they are easily visible on the console and not hidden in the log4j logs.
+    println(s"Using endpoint URL $url for creating Kinesis streams for tests.")
+    // scalastyle:on println
+    url
+  }
 
   def isAWSCredentialsPresent: Boolean = {
     Try { new DefaultAWSCredentialsProviderChain().getCredentials() }.isSuccess
@@ -205,7 +222,13 @@ private[kinesis] object KinesisTestUtils {
     Try { new DefaultAWSCredentialsProviderChain().getCredentials() } match {
       case Success(cred) => cred
       case Failure(e) =>
-        throw new Exception("Kinesis tests enabled, but could get not AWS credentials")
+        throw new Exception(
+          s"""
+             |Kinesis tests enabled using environment variable $envVarNameForEnablingTests
+             |but could not find AWS credentials. Please follow instructions in AWS documentation
+             |to set the credentials in your system such that the DefaultAWSCredentialsProviderChain
+             |can find the credentials.
+           """.stripMargin)
     }
   }
 }
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
index 7dab17eba8483..c799fadf2d5ce 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisUtils.scala
@@ -65,9 +65,8 @@ object KinesisUtils {
     ): ReceiverInputDStream[Array[Byte]] = {
     // Setting scope to override receiver stream's scope of "receiver stream"
     ssc.withNamedScope("kinesis stream") {
-      ssc.receiverStream(
-        new KinesisReceiver(kinesisAppName, streamName, endpointUrl, validateRegion(regionName),
-          initialPositionInStream, checkpointInterval, storageLevel, None))
+      new KinesisInputDStream(ssc, streamName, endpointUrl, validateRegion(regionName),
+        initialPositionInStream, kinesisAppName, checkpointInterval, storageLevel, None)
     }
   }
 
@@ -112,10 +111,11 @@ object KinesisUtils {
       awsAccessKeyId: String,
       awsSecretKey: String
     ): ReceiverInputDStream[Array[Byte]] = {
-    ssc.receiverStream(
-      new KinesisReceiver(kinesisAppName, streamName, endpointUrl, validateRegion(regionName),
-        initialPositionInStream, checkpointInterval, storageLevel,
-        Some(SerializableAWSCredentials(awsAccessKeyId, awsSecretKey))))
+    ssc.withNamedScope("kinesis stream") {
+      new KinesisInputDStream(ssc, streamName, endpointUrl, validateRegion(regionName),
+        initialPositionInStream, kinesisAppName, checkpointInterval, storageLevel,
+        Some(SerializableAWSCredentials(awsAccessKeyId, awsSecretKey)))
+    }
   }
 
   /**
@@ -155,9 +155,10 @@ object KinesisUtils {
       initialPositionInStream: InitialPositionInStream,
       storageLevel: StorageLevel
     ): ReceiverInputDStream[Array[Byte]] = {
-    ssc.receiverStream(
-      new KinesisReceiver(ssc.sc.appName, streamName, endpointUrl, getRegionByEndpoint(endpointUrl),
-        initialPositionInStream, checkpointInterval, storageLevel, None))
+    ssc.withNamedScope("kinesis stream") {
+      new KinesisInputDStream(ssc, streamName, endpointUrl, getRegionByEndpoint(endpointUrl),
+        initialPositionInStream, ssc.sc.appName, checkpointInterval, storageLevel, None)
+    }
   }
 
   /**
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala
index e81fb11e5959f..a89e5627e014c 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDDSuite.scala
@@ -24,8 +24,6 @@ import org.apache.spark.{SparkConf, SparkContext, SparkException}
 
 class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll {
 
-  private val regionId = "us-east-1"
-  private val endpointUrl = "https://kinesis.us-east-1.amazonaws.com"
   private val testData = 1 to 8
 
   private var testUtils: KinesisTestUtils = null
@@ -42,7 +40,7 @@ class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll
 
   override def beforeAll(): Unit = {
     runIfTestsEnabled("Prepare KinesisTestUtils") {
-      testUtils = new KinesisTestUtils(endpointUrl)
+      testUtils = new KinesisTestUtils()
       testUtils.createStream()
 
       shardIdToDataAndSeqNumbers = testUtils.pushData(testData)
@@ -75,21 +73,21 @@ class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll
 
   testIfEnabled("Basic reading from Kinesis") {
     // Verify all data using multiple ranges in a single RDD partition
-    val receivedData1 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl,
+    val receivedData1 = new KinesisBackedBlockRDD(sc, testUtils.regionName, testUtils.endpointUrl,
       fakeBlockIds(1),
       Array(SequenceNumberRanges(allRanges.toArray))
     ).map { bytes => new String(bytes).toInt }.collect()
     assert(receivedData1.toSet === testData.toSet)
 
     // Verify all data using one range in each of the multiple RDD partitions
-    val receivedData2 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl,
+    val receivedData2 = new KinesisBackedBlockRDD(sc, testUtils.regionName, testUtils.endpointUrl,
       fakeBlockIds(allRanges.size),
       allRanges.map { range => SequenceNumberRanges(Array(range)) }.toArray
     ).map { bytes => new String(bytes).toInt }.collect()
     assert(receivedData2.toSet === testData.toSet)
 
     // Verify ordering within each partition
-    val receivedData3 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl,
+    val receivedData3 = new KinesisBackedBlockRDD(sc, testUtils.regionName, testUtils.endpointUrl,
       fakeBlockIds(allRanges.size),
       allRanges.map { range => SequenceNumberRanges(Array(range)) }.toArray
     ).map { bytes => new String(bytes).toInt }.collectPartitions()
@@ -211,7 +209,8 @@ class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll
       }, "Incorrect configuration of RDD, unexpected ranges set"
     )
 
-    val rdd = new KinesisBackedBlockRDD(sc, regionId, endpointUrl, blockIds, ranges)
+    val rdd = new KinesisBackedBlockRDD(
+      sc, testUtils.regionName, testUtils.endpointUrl, blockIds, ranges)
     val collectedData = rdd.map { bytes =>
       new String(bytes).toInt
     }.collect()
@@ -224,8 +223,9 @@ class KinesisBackedBlockRDDSuite extends KinesisFunSuite with BeforeAndAfterAll
     if (testIsBlockValid) {
       require(numPartitionsInBM === numPartitions, "All partitions must be in BlockManager")
       require(numPartitionsInKinesis === 0, "No partitions must be in Kinesis")
-      val rdd2 = new KinesisBackedBlockRDD(sc, regionId, endpointUrl, blockIds.toArray,
-        ranges, isBlockIdValid = Array.fill(blockIds.length)(false))
+      val rdd2 = new KinesisBackedBlockRDD(
+        sc, testUtils.regionName, testUtils.endpointUrl, blockIds.toArray, ranges,
+        isBlockIdValid = Array.fill(blockIds.length)(false))
       intercept[SparkException] {
         rdd2.collect()
       }
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
index 8373138785a89..ee428f31d6ce3 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisFunSuite.scala
@@ -31,7 +31,7 @@ trait KinesisFunSuite extends SparkFunSuite  {
     if (shouldRunTests) {
       test(testName)(testBody)
     } else {
-      ignore(s"$testName [enable by setting env var $envVarName=1]")(testBody)
+      ignore(s"$testName [enable by setting env var $envVarNameForEnablingTests=1]")(testBody)
     }
   }
 
@@ -40,7 +40,7 @@ trait KinesisFunSuite extends SparkFunSuite  {
     if (shouldRunTests) {
       body
     } else {
-      ignore(s"$message [enable by setting env var $envVarName=1]")()
+      ignore(s"$message [enable by setting env var $envVarNameForEnablingTests=1]")()
     }
   }
 }
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
index 98f2c7c4f1bfb..ceb135e0651aa 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
@@ -22,15 +22,14 @@ import scala.collection.JavaConversions.seqAsJavaList
 
 import com.amazonaws.services.kinesis.clientlibrary.exceptions.{InvalidStateException, KinesisClientLibDependencyException, ShutdownException, ThrottlingException}
 import com.amazonaws.services.kinesis.clientlibrary.interfaces.IRecordProcessorCheckpointer
-import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
 import com.amazonaws.services.kinesis.clientlibrary.types.ShutdownReason
 import com.amazonaws.services.kinesis.model.Record
+import org.mockito.Matchers._
 import org.mockito.Mockito._
-import org.scalatest.{BeforeAndAfter, Matchers}
 import org.scalatest.mock.MockitoSugar
+import org.scalatest.{BeforeAndAfter, Matchers}
 
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.streaming.{Milliseconds, Seconds, StreamingContext, TestSuiteBase}
+import org.apache.spark.streaming.{Milliseconds, TestSuiteBase}
 import org.apache.spark.util.{Clock, ManualClock, Utils}
 
 /**
@@ -44,6 +43,8 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
   val endpoint = "endpoint-url"
   val workerId = "dummyWorkerId"
   val shardId = "dummyShardId"
+  val seqNum = "dummySeqNum"
+  val someSeqNum = Some(seqNum)
 
   val record1 = new Record()
   record1.setData(ByteBuffer.wrap("Spark In Action".getBytes()))
@@ -80,16 +81,18 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
 
   test("process records including store and checkpoint") {
     when(receiverMock.isStopped()).thenReturn(false)
+    when(receiverMock.getLatestSeqNumToCheckpoint(shardId)).thenReturn(someSeqNum)
     when(checkpointStateMock.shouldCheckpoint()).thenReturn(true)
 
     val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock)
+    recordProcessor.initialize(shardId)
     recordProcessor.processRecords(batch, checkpointerMock)
 
     verify(receiverMock, times(1)).isStopped()
-    verify(receiverMock, times(1)).store(record1.getData().array())
-    verify(receiverMock, times(1)).store(record2.getData().array())
+    verify(receiverMock, times(1)).addRecords(shardId, batch)
+    verify(receiverMock, times(1)).getLatestSeqNumToCheckpoint(shardId)
     verify(checkpointStateMock, times(1)).shouldCheckpoint()
-    verify(checkpointerMock, times(1)).checkpoint()
+    verify(checkpointerMock, times(1)).checkpoint(anyString)
     verify(checkpointStateMock, times(1)).advanceCheckpoint()
   }
 
@@ -100,19 +103,25 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
     recordProcessor.processRecords(batch, checkpointerMock)
 
     verify(receiverMock, times(1)).isStopped()
+    verify(receiverMock, never).addRecords(anyString, anyListOf(classOf[Record]))
+    verify(checkpointerMock, never).checkpoint(anyString)
   }
 
   test("shouldn't checkpoint when exception occurs during store") {
     when(receiverMock.isStopped()).thenReturn(false)
-    when(receiverMock.store(record1.getData().array())).thenThrow(new RuntimeException())
+    when(
+      receiverMock.addRecords(anyString, anyListOf(classOf[Record]))
+    ).thenThrow(new RuntimeException())
 
     intercept[RuntimeException] {
       val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock)
+      recordProcessor.initialize(shardId)
       recordProcessor.processRecords(batch, checkpointerMock)
     }
 
     verify(receiverMock, times(1)).isStopped()
-    verify(receiverMock, times(1)).store(record1.getData().array())
+    verify(receiverMock, times(1)).addRecords(shardId, batch)
+    verify(checkpointerMock, never).checkpoint(anyString)
   }
 
   test("should set checkpoint time to currentTime + checkpoint interval upon instantiation") {
@@ -158,19 +167,25 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
   }
 
   test("shutdown should checkpoint if the reason is TERMINATE") {
+    when(receiverMock.getLatestSeqNumToCheckpoint(shardId)).thenReturn(someSeqNum)
+
     val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock)
-    val reason = ShutdownReason.TERMINATE
-    recordProcessor.shutdown(checkpointerMock, reason)
+    recordProcessor.initialize(shardId)
+    recordProcessor.shutdown(checkpointerMock, ShutdownReason.TERMINATE)
 
-    verify(checkpointerMock, times(1)).checkpoint()
+    verify(receiverMock, times(1)).getLatestSeqNumToCheckpoint(shardId)
+    verify(checkpointerMock, times(1)).checkpoint(anyString)
   }
 
   test("shutdown should not checkpoint if the reason is something other than TERMINATE") {
+    when(receiverMock.getLatestSeqNumToCheckpoint(shardId)).thenReturn(someSeqNum)
+
     val recordProcessor = new KinesisRecordProcessor(receiverMock, workerId, checkpointStateMock)
+    recordProcessor.initialize(shardId)
     recordProcessor.shutdown(checkpointerMock, ShutdownReason.ZOMBIE)
     recordProcessor.shutdown(checkpointerMock, null)
 
-    verify(checkpointerMock, never()).checkpoint()
+    verify(checkpointerMock, never).checkpoint(anyString)
   }
 
   test("retry success on first attempt") {
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
index b88c9c6478d56..1177dc758100d 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisStreamSuite.scala
@@ -22,34 +22,67 @@ import scala.concurrent.duration._
 import scala.language.postfixOps
 import scala.util.Random
 
+import com.amazonaws.regions.RegionUtils
 import com.amazonaws.services.kinesis.clientlibrary.lib.worker.InitialPositionInStream
+import org.scalatest.Matchers._
 import org.scalatest.concurrent.Eventually
 import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
 
-import org.apache.spark.storage.StorageLevel
+import org.apache.spark.rdd.RDD
+import org.apache.spark.storage.{StorageLevel, StreamBlockId}
 import org.apache.spark.streaming._
-import org.apache.spark.{SparkConf, SparkContext, SparkFunSuite}
+import org.apache.spark.streaming.kinesis.KinesisTestUtils._
+import org.apache.spark.streaming.receiver.BlockManagerBasedStoreResult
+import org.apache.spark.streaming.scheduler.ReceivedBlockInfo
+import org.apache.spark.util.Utils
+import org.apache.spark.{SparkConf, SparkContext}
 
 class KinesisStreamSuite extends KinesisFunSuite
   with Eventually with BeforeAndAfter with BeforeAndAfterAll {
 
-  // This is the name that KCL uses to save metadata to DynamoDB
-  private val kinesisAppName = s"KinesisStreamSuite-${math.abs(Random.nextLong())}"
+  // This is the name that KCL will use to save metadata to DynamoDB
+  private val appName = s"KinesisStreamSuite-${math.abs(Random.nextLong())}"
+  private val batchDuration = Seconds(1)
 
-  private var ssc: StreamingContext = _
-  private var sc: SparkContext = _
+  // Dummy parameters for API testing
+  private val dummyEndpointUrl = defaultEndpointUrl
+  private val dummyRegionName = RegionUtils.getRegionByEndpoint(dummyEndpointUrl).getName()
+  private val dummyAWSAccessKey = "dummyAccessKey"
+  private val dummyAWSSecretKey = "dummySecretKey"
+
+  private var testUtils: KinesisTestUtils = null
+  private var ssc: StreamingContext = null
+  private var sc: SparkContext = null
 
   override def beforeAll(): Unit = {
     val conf = new SparkConf()
       .setMaster("local[4]")
       .setAppName("KinesisStreamSuite") // Setting Spark app name to Kinesis app name
     sc = new SparkContext(conf)
+
+    runIfTestsEnabled("Prepare KinesisTestUtils") {
+      testUtils = new KinesisTestUtils()
+      testUtils.createStream()
+    }
   }
 
   override def afterAll(): Unit = {
-    sc.stop()
-    // Delete the Kinesis stream as well as the DynamoDB table generated by
-    // Kinesis Client Library when consuming the stream
+    if (ssc != null) {
+      ssc.stop()
+    }
+    if (sc != null) {
+      sc.stop()
+    }
+    if (testUtils != null) {
+      // Delete the Kinesis stream as well as the DynamoDB table generated by
+      // Kinesis Client Library when consuming the stream
+      testUtils.deleteStream()
+      testUtils.deleteDynamoDBTable(appName)
+    }
+  }
+
+  before {
+    ssc = new StreamingContext(sc, batchDuration)
   }
 
   after {
@@ -57,21 +90,75 @@ class KinesisStreamSuite extends KinesisFunSuite
       ssc.stop(stopSparkContext = false)
       ssc = null
     }
+    if (testUtils != null) {
+      testUtils.deleteDynamoDBTable(appName)
+    }
   }
 
   test("KinesisUtils API") {
-    ssc = new StreamingContext(sc, Seconds(1))
     // Tests the API, does not actually test data receiving
     val kinesisStream1 = KinesisUtils.createStream(ssc, "mySparkStream",
-      "https://kinesis.us-west-2.amazonaws.com", Seconds(2),
+      dummyEndpointUrl, Seconds(2),
       InitialPositionInStream.LATEST, StorageLevel.MEMORY_AND_DISK_2)
     val kinesisStream2 = KinesisUtils.createStream(ssc, "myAppNam", "mySparkStream",
-      "https://kinesis.us-west-2.amazonaws.com", "us-west-2",
+      dummyEndpointUrl, dummyRegionName,
       InitialPositionInStream.LATEST, Seconds(2), StorageLevel.MEMORY_AND_DISK_2)
     val kinesisStream3 = KinesisUtils.createStream(ssc, "myAppNam", "mySparkStream",
-      "https://kinesis.us-west-2.amazonaws.com", "us-west-2",
+      dummyEndpointUrl, dummyRegionName,
       InitialPositionInStream.LATEST, Seconds(2), StorageLevel.MEMORY_AND_DISK_2,
-      "awsAccessKey", "awsSecretKey")
+      dummyAWSAccessKey, dummyAWSSecretKey)
+  }
+
+  test("RDD generation") {
+    val inputStream = KinesisUtils.createStream(ssc, appName, "dummyStream",
+      dummyEndpointUrl, dummyRegionName, InitialPositionInStream.LATEST, Seconds(2),
+      StorageLevel.MEMORY_AND_DISK_2, dummyAWSAccessKey, dummyAWSSecretKey)
+    assert(inputStream.isInstanceOf[KinesisInputDStream])
+
+    val kinesisStream = inputStream.asInstanceOf[KinesisInputDStream]
+    val time = Time(1000)
+
+    // Generate block info data for testing
+    val seqNumRanges1 = SequenceNumberRanges(
+      SequenceNumberRange("fakeStream", "fakeShardId", "xxx", "yyy"))
+    val blockId1 = StreamBlockId(kinesisStream.id, 123)
+    val blockInfo1 = ReceivedBlockInfo(
+      0, None, Some(seqNumRanges1), new BlockManagerBasedStoreResult(blockId1, None))
+
+    val seqNumRanges2 = SequenceNumberRanges(
+      SequenceNumberRange("fakeStream", "fakeShardId", "aaa", "bbb"))
+    val blockId2 = StreamBlockId(kinesisStream.id, 345)
+    val blockInfo2 = ReceivedBlockInfo(
+      0, None, Some(seqNumRanges2), new BlockManagerBasedStoreResult(blockId2, None))
+
+    // Verify that the generated KinesisBackedBlockRDD has the all the right information
+    val blockInfos = Seq(blockInfo1, blockInfo2)
+    val nonEmptyRDD = kinesisStream.createBlockRDD(time, blockInfos)
+    nonEmptyRDD shouldBe a [KinesisBackedBlockRDD]
+    val kinesisRDD = nonEmptyRDD.asInstanceOf[KinesisBackedBlockRDD]
+    assert(kinesisRDD.regionName === dummyRegionName)
+    assert(kinesisRDD.endpointUrl === dummyEndpointUrl)
+    assert(kinesisRDD.retryTimeoutMs === batchDuration.milliseconds)
+    assert(kinesisRDD.awsCredentialsOption ===
+      Some(SerializableAWSCredentials(dummyAWSAccessKey, dummyAWSSecretKey)))
+    assert(nonEmptyRDD.partitions.size === blockInfos.size)
+    nonEmptyRDD.partitions.foreach { _ shouldBe a [KinesisBackedBlockRDDPartition] }
+    val partitions = nonEmptyRDD.partitions.map {
+      _.asInstanceOf[KinesisBackedBlockRDDPartition] }.toSeq
+    assert(partitions.map { _.seqNumberRanges } === Seq(seqNumRanges1, seqNumRanges2))
+    assert(partitions.map { _.blockId } === Seq(blockId1, blockId2))
+    assert(partitions.forall { _.isBlockIdValid === true })
+
+    // Verify that KinesisBackedBlockRDD is generated even when there are no blocks
+    val emptyRDD = kinesisStream.createBlockRDD(time, Seq.empty)
+    emptyRDD shouldBe a [KinesisBackedBlockRDD]
+    emptyRDD.partitions shouldBe empty
+
+    // Verify that the KinesisBackedBlockRDD has isBlockValid = false when blocks are invalid
+    blockInfos.foreach { _.setBlockIdInvalid() }
+    kinesisStream.createBlockRDD(time, blockInfos).partitions.foreach { partition =>
+      assert(partition.asInstanceOf[KinesisBackedBlockRDDPartition].isBlockIdValid === false)
+    }
   }
 
 
@@ -84,32 +171,91 @@ class KinesisStreamSuite extends KinesisFunSuite
    * and you have to set the system environment variable RUN_KINESIS_TESTS=1 .
    */
   testIfEnabled("basic operation") {
-    val kinesisTestUtils = new KinesisTestUtils()
-    try {
-      kinesisTestUtils.createStream()
-      ssc = new StreamingContext(sc, Seconds(1))
-      val awsCredentials = KinesisTestUtils.getAWSCredentials()
-      val stream = KinesisUtils.createStream(ssc, kinesisAppName, kinesisTestUtils.streamName,
-        kinesisTestUtils.endpointUrl, kinesisTestUtils.regionName, InitialPositionInStream.LATEST,
-        Seconds(10), StorageLevel.MEMORY_ONLY,
-        awsCredentials.getAWSAccessKeyId, awsCredentials.getAWSSecretKey)
-
-      val collected = new mutable.HashSet[Int] with mutable.SynchronizedSet[Int]
-      stream.map { bytes => new String(bytes).toInt }.foreachRDD { rdd =>
-        collected ++= rdd.collect()
-        logInfo("Collected = " + rdd.collect().toSeq.mkString(", "))
-      }
-      ssc.start()
+    val awsCredentials = KinesisTestUtils.getAWSCredentials()
+    val stream = KinesisUtils.createStream(ssc, appName, testUtils.streamName,
+      testUtils.endpointUrl, testUtils.regionName, InitialPositionInStream.LATEST,
+      Seconds(10), StorageLevel.MEMORY_ONLY,
+      awsCredentials.getAWSAccessKeyId, awsCredentials.getAWSSecretKey)
 
-      val testData = 1 to 10
-      eventually(timeout(120 seconds), interval(10 second)) {
-        kinesisTestUtils.pushData(testData)
-        assert(collected === testData.toSet, "\nData received does not match data sent")
+    val collected = new mutable.HashSet[Int] with mutable.SynchronizedSet[Int]
+    stream.map { bytes => new String(bytes).toInt }.foreachRDD { rdd =>
+      collected ++= rdd.collect()
+      logInfo("Collected = " + rdd.collect().toSeq.mkString(", "))
+    }
+    ssc.start()
+
+    val testData = 1 to 10
+    eventually(timeout(120 seconds), interval(10 second)) {
+      testUtils.pushData(testData)
+      assert(collected === testData.toSet, "\nData received does not match data sent")
+    }
+    ssc.stop(stopSparkContext = false)
+  }
+
+  testIfEnabled("failure recovery") {
+    val sparkConf = new SparkConf().setMaster("local[4]").setAppName(this.getClass.getSimpleName)
+    val checkpointDir = Utils.createTempDir().getAbsolutePath
+
+    ssc = new StreamingContext(sc, Milliseconds(1000))
+    ssc.checkpoint(checkpointDir)
+
+    val awsCredentials = KinesisTestUtils.getAWSCredentials()
+    val collectedData = new mutable.HashMap[Time, (Array[SequenceNumberRanges], Seq[Int])]
+      with mutable.SynchronizedMap[Time, (Array[SequenceNumberRanges], Seq[Int])]
+
+    val kinesisStream = KinesisUtils.createStream(ssc, appName, testUtils.streamName,
+      testUtils.endpointUrl, testUtils.regionName, InitialPositionInStream.LATEST,
+      Seconds(10), StorageLevel.MEMORY_ONLY,
+      awsCredentials.getAWSAccessKeyId, awsCredentials.getAWSSecretKey)
+
+    // Verify that the generated RDDs are KinesisBackedBlockRDDs, and collect the data in each batch
+    kinesisStream.foreachRDD((rdd: RDD[Array[Byte]], time: Time) => {
+      val kRdd = rdd.asInstanceOf[KinesisBackedBlockRDD]
+      val data = rdd.map { bytes => new String(bytes).toInt }.collect().toSeq
+      collectedData(time) = (kRdd.arrayOfseqNumberRanges, data)
+    })
+
+    ssc.remember(Minutes(60)) // remember all the batches so that they are all saved in checkpoint
+    ssc.start()
+
+    def numBatchesWithData: Int = collectedData.count(_._2._2.nonEmpty)
+
+    def isCheckpointPresent: Boolean = Checkpoint.getCheckpointFiles(checkpointDir).nonEmpty
+
+    // Run until there are at least 10 batches with some data in them
+    // If this times out because numBatchesWithData is empty, then its likely that foreachRDD
+    // function failed with exceptions, and nothing got added to `collectedData`
+    eventually(timeout(2 minutes), interval(1 seconds)) {
+      testUtils.pushData(1 to 5)
+      assert(isCheckpointPresent && numBatchesWithData > 10)
+    }
+    ssc.stop(stopSparkContext = true)  // stop the SparkContext so that the blocks are not reused
+
+    // Restart the context from checkpoint and verify whether the
+    logInfo("Restarting from checkpoint")
+    ssc = new StreamingContext(checkpointDir)
+    ssc.start()
+    val recoveredKinesisStream = ssc.graph.getInputStreams().head
+
+    // Verify that the recomputed RDDs are KinesisBackedBlockRDDs with the same sequence ranges
+    // and return the same data
+    val times = collectedData.keySet
+    times.foreach { time =>
+      val (arrayOfSeqNumRanges, data) = collectedData(time)
+      val rdd = recoveredKinesisStream.getOrCompute(time).get.asInstanceOf[RDD[Array[Byte]]]
+      rdd shouldBe a [KinesisBackedBlockRDD]
+
+      // Verify the recovered sequence ranges
+      val kRdd = rdd.asInstanceOf[KinesisBackedBlockRDD]
+      assert(kRdd.arrayOfseqNumberRanges.size === arrayOfSeqNumRanges.size)
+      arrayOfSeqNumRanges.zip(kRdd.arrayOfseqNumberRanges).foreach { case (expected, found) =>
+        assert(expected.ranges.toSeq === found.ranges.toSeq)
       }
-      ssc.stop()
-    } finally {
-      kinesisTestUtils.deleteStream()
-      kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
+
+      // Verify the recovered data
+      assert(rdd.map { bytes => new String(bytes).toInt }.collect().toSeq === data)
     }
+    ssc.stop()
   }
+
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index 670ef8d296a0b..a15800917c6f4 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -21,12 +21,12 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.rdd.{BlockRDD, RDD}
 import org.apache.spark.storage.BlockId
-import org.apache.spark.streaming.{StreamingContext, Time}
 import org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD
 import org.apache.spark.streaming.receiver.Receiver
-import org.apache.spark.streaming.scheduler.{RateController, StreamInputInfo}
 import org.apache.spark.streaming.scheduler.rate.RateEstimator
+import org.apache.spark.streaming.scheduler.{ReceivedBlockInfo, RateController, StreamInputInfo}
 import org.apache.spark.streaming.util.WriteAheadLogUtils
+import org.apache.spark.streaming.{StreamingContext, Time}
 
 /**
  * Abstract class for defining any [[org.apache.spark.streaming.dstream.InputDStream]]
@@ -79,48 +79,55 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont
         // for this batch
         val receiverTracker = ssc.scheduler.receiverTracker
         val blockInfos = receiverTracker.getBlocksOfBatch(validTime).getOrElse(id, Seq.empty)
-        val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray
 
         // Register the input blocks information into InputInfoTracker
         val inputInfo = StreamInputInfo(id, blockInfos.flatMap(_.numRecords).sum)
         ssc.scheduler.inputInfoTracker.reportInfo(validTime, inputInfo)
 
-        if (blockInfos.nonEmpty) {
-          // Are WAL record handles present with all the blocks
-          val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty }
+        // Create the BlockRDD
+        createBlockRDD(validTime, blockInfos)
+      }
+    }
+    Some(blockRDD)
+  }
 
-          if (areWALRecordHandlesPresent) {
-            // If all the blocks have WAL record handle, then create a WALBackedBlockRDD
-            val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray
-            val walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArray
-            new WriteAheadLogBackedBlockRDD[T](
-              ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid)
-          } else {
-            // Else, create a BlockRDD. However, if there are some blocks with WAL info but not
-            // others then that is unexpected and log a warning accordingly.
-            if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) {
-              if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {
-                logError("Some blocks do not have Write Ahead Log information; " +
-                  "this is unexpected and data may not be recoverable after driver failures")
-              } else {
-                logWarning("Some blocks have Write Ahead Log information; this is unexpected")
-              }
-            }
-            new BlockRDD[T](ssc.sc, blockIds)
-          }
-        } else {
-          // If no block is ready now, creating WriteAheadLogBackedBlockRDD or BlockRDD
-          // according to the configuration
+  private[streaming] def createBlockRDD(time: Time, blockInfos: Seq[ReceivedBlockInfo]): RDD[T] = {
+
+    if (blockInfos.nonEmpty) {
+      val blockIds = blockInfos.map { _.blockId.asInstanceOf[BlockId] }.toArray
+
+      // Are WAL record handles present with all the blocks
+      val areWALRecordHandlesPresent = blockInfos.forall { _.walRecordHandleOption.nonEmpty }
+
+      if (areWALRecordHandlesPresent) {
+        // If all the blocks have WAL record handle, then create a WALBackedBlockRDD
+        val isBlockIdValid = blockInfos.map { _.isBlockIdValid() }.toArray
+        val walRecordHandles = blockInfos.map { _.walRecordHandleOption.get }.toArray
+        new WriteAheadLogBackedBlockRDD[T](
+          ssc.sparkContext, blockIds, walRecordHandles, isBlockIdValid)
+      } else {
+        // Else, create a BlockRDD. However, if there are some blocks with WAL info but not
+        // others then that is unexpected and log a warning accordingly.
+        if (blockInfos.find(_.walRecordHandleOption.nonEmpty).nonEmpty) {
           if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {
-            new WriteAheadLogBackedBlockRDD[T](
-              ssc.sparkContext, Array.empty, Array.empty, Array.empty)
+            logError("Some blocks do not have Write Ahead Log information; " +
+              "this is unexpected and data may not be recoverable after driver failures")
           } else {
-            new BlockRDD[T](ssc.sc, Array.empty)
+            logWarning("Some blocks have Write Ahead Log information; this is unexpected")
           }
         }
+        new BlockRDD[T](ssc.sc, blockIds)
+      }
+    } else {
+      // If no block is ready now, creating WriteAheadLogBackedBlockRDD or BlockRDD
+      // according to the configuration
+      if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) {
+        new WriteAheadLogBackedBlockRDD[T](
+          ssc.sparkContext, Array.empty, Array.empty, Array.empty)
+      } else {
+        new BlockRDD[T](ssc.sc, Array.empty)
       }
     }
-    Some(blockRDD)
   }
 
   /**

From 1d1a76c8c56148beec70dd14e4fdeb86de65018a Mon Sep 17 00:00:00 2001
From: Emiliano Leporati <emiliano.leporati@gmail.com>
Date: Wed, 5 Aug 2015 00:42:08 -0700
Subject: [PATCH 050/633] [SPARK-9581][SQL] Add unit test for JSON UDT

This brings #7416 up-to-date by drubbo.

Author: Emiliano Leporati <emiliano.leporati@gmail.com>
Author: Reynold Xin <rxin@databricks.com>

Closes #7917 from rxin/udt-json-test and squashes the following commits:

93e3954 [Reynold Xin] Fix test.
7035308 [Reynold Xin] Merge pull request #7416 from drubbo/master
b5bcd94 [Emiliano Leporati] removed unneded case in MyDenseVector::equals
508a399 [Emiliano Leporati] Merge remote branch 'upstream/master'
7569e42 [Emiliano Leporati] using checkAnswer
62daccd [Emiliano Leporati] added coverage for UDTs in JSON RDDs
---
 .../apache/spark/sql/json/JacksonParser.scala |  2 +-
 .../spark/sql/UserDefinedTypeSuite.scala      | 20 +++++++++++++++++++
 2 files changed, 21 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
index f1a66c84fc96a..b8fd3b9cc150e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
@@ -125,7 +125,7 @@ private[sql] object JacksonParser {
         convertMap(factory, parser, kt)
 
       case (_, udt: UserDefinedType[_]) =>
-        udt.deserialize(convertField(factory, parser, udt.sqlType))
+        convertField(factory, parser, udt.sqlType)
     }
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
index f29935224e5b6..9181222f6922b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
@@ -138,4 +138,24 @@ class UserDefinedTypeSuite extends QueryTest {
     val actual = openHashSetUDT.deserialize(openHashSetUDT.serialize(set))
     assert(actual.iterator.toSet === set.iterator.toSet)
   }
+
+  test("UDTs with JSON") {
+    val data = Seq(
+      "{\"id\":1,\"vec\":[1.1,2.2,3.3,4.4]}",
+      "{\"id\":2,\"vec\":[2.25,4.5,8.75]}"
+    )
+    val schema = StructType(Seq(
+      StructField("id", IntegerType, false),
+      StructField("vec", new MyDenseVectorUDT, false)
+    ))
+
+    val stringRDD = ctx.sparkContext.parallelize(data)
+    val jsonRDD = ctx.read.schema(schema).json(stringRDD)
+    checkAnswer(
+      jsonRDD,
+      Row(1, new MyDenseVector(Array(1.1, 2.2, 3.3, 4.4))) ::
+        Row(2, new MyDenseVector(Array(2.25, 4.5, 8.75))) ::
+        Nil
+    )
+  }
 }

From d8ef538e56645ebda29a64f1456822aad92c2bde Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 5 Aug 2015 00:44:31 -0700
Subject: [PATCH 051/633] Closes #7917


From 6d8a6e4161176e391514153d7535da14b52194be Mon Sep 17 00:00:00 2001
From: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Date: Wed, 5 Aug 2015 00:54:31 -0700
Subject: [PATCH 052/633] [SPARK-9360] [SQL] Support BinaryType in
 PrefixComparators for UnsafeExternalSort

The current implementation of UnsafeExternalSort uses NoOpPrefixComparator for binary-typed data.
So, we need to add BinaryPrefixComparator in PrefixComparators.

Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>

Closes #7676 from maropu/BinaryTypePrefixComparator and squashes the following commits:

fe6f31b [Takeshi YAMAMURO] Apply comments
d943c04 [Takeshi YAMAMURO] Add a codegen'd entry for BinaryType in SortPrefix
ecf3ac5 [Takeshi YAMAMURO] Support BinaryType in PrefixComparator
---
 .../unsafe/sort/PrefixComparators.java        | 35 +++++++++++++++++
 .../unsafe/sort/PrefixComparatorsSuite.scala  | 38 +++++++++++++++++++
 .../sql/catalyst/expressions/SortOrder.scala  |  3 ++
 .../spark/sql/execution/SortPrefixUtils.scala |  2 +
 4 files changed, 78 insertions(+)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
index 4d7e5b3dfba6e..b5f661c0d5481 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -20,6 +20,7 @@
 import com.google.common.primitives.UnsignedLongs;
 
 import org.apache.spark.annotation.Private;
+import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.types.UTF8String;
 import org.apache.spark.util.Utils;
 
@@ -29,6 +30,8 @@ private PrefixComparators() {}
 
   public static final StringPrefixComparator STRING = new StringPrefixComparator();
   public static final StringPrefixComparatorDesc STRING_DESC = new StringPrefixComparatorDesc();
+  public static final BinaryPrefixComparator BINARY = new BinaryPrefixComparator();
+  public static final BinaryPrefixComparatorDesc BINARY_DESC = new BinaryPrefixComparatorDesc();
   public static final LongPrefixComparator LONG = new LongPrefixComparator();
   public static final LongPrefixComparatorDesc LONG_DESC = new LongPrefixComparatorDesc();
   public static final DoublePrefixComparator DOUBLE = new DoublePrefixComparator();
@@ -52,6 +55,38 @@ public int compare(long bPrefix, long aPrefix) {
     }
   }
 
+  public static final class BinaryPrefixComparator extends PrefixComparator {
+    @Override
+    public int compare(long aPrefix, long bPrefix) {
+      return UnsignedLongs.compare(aPrefix, bPrefix);
+    }
+
+    public static long computePrefix(byte[] bytes) {
+      if (bytes == null) {
+        return 0L;
+      } else {
+        /**
+         * TODO: If a wrapper for BinaryType is created (SPARK-8786),
+         * these codes below will be in the wrapper class.
+         */
+        final int minLen = Math.min(bytes.length, 8);
+        long p = 0;
+        for (int i = 0; i < minLen; ++i) {
+          p |= (128L + PlatformDependent.UNSAFE.getByte(bytes, BYTE_ARRAY_OFFSET + i))
+              << (56 - 8 * i);
+        }
+        return p;
+      }
+    }
+  }
+
+  public static final class BinaryPrefixComparatorDesc extends PrefixComparator {
+    @Override
+    public int compare(long bPrefix, long aPrefix) {
+      return UnsignedLongs.compare(aPrefix, bPrefix);
+    }
+  }
+
   public static final class LongPrefixComparator extends PrefixComparator {
     @Override
     public int compare(long a, long b) {
diff --git a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
index 26a2e96edaaa2..0326ed70b5edb 100644
--- a/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/collection/unsafe/sort/PrefixComparatorsSuite.scala
@@ -55,6 +55,44 @@ class PrefixComparatorsSuite extends SparkFunSuite with PropertyChecks {
     forAll { (s1: String, s2: String) => testPrefixComparison(s1, s2) }
   }
 
+  test("Binary prefix comparator") {
+
+     def compareBinary(x: Array[Byte], y: Array[Byte]): Int = {
+      for (i <- 0 until x.length; if i < y.length) {
+        val res = x(i).compare(y(i))
+        if (res != 0) return res
+      }
+      x.length - y.length
+    }
+
+    def testPrefixComparison(x: Array[Byte], y: Array[Byte]): Unit = {
+      val s1Prefix = PrefixComparators.BinaryPrefixComparator.computePrefix(x)
+      val s2Prefix = PrefixComparators.BinaryPrefixComparator.computePrefix(y)
+      val prefixComparisonResult =
+        PrefixComparators.BINARY.compare(s1Prefix, s2Prefix)
+      assert(
+        (prefixComparisonResult == 0) ||
+        (prefixComparisonResult < 0 && compareBinary(x, y) < 0) ||
+        (prefixComparisonResult > 0 && compareBinary(x, y) > 0))
+    }
+
+    // scalastyle:off
+    val regressionTests = Table(
+      ("s1", "s2"),
+      ("abc", "世界"),
+      ("你好", "世界"),
+      ("你好123", "你好122")
+    )
+    // scalastyle:on
+
+    forAll (regressionTests) { (s1: String, s2: String) =>
+      testPrefixComparison(s1.getBytes("UTF-8"), s2.getBytes("UTF-8"))
+    }
+    forAll { (s1: String, s2: String) =>
+      testPrefixComparison(s1.getBytes("UTF-8"), s2.getBytes("UTF-8"))
+    }
+  }
+
   test("double prefix comparator handles NaNs properly") {
     val nan1: Double = java.lang.Double.longBitsToDouble(0x7ff0000000000001L)
     val nan2: Double = java.lang.Double.longBitsToDouble(0x7fffffffffffffffL)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
index f6a872ba446eb..98e029035ab6f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala
@@ -21,6 +21,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.types._
+import org.apache.spark.util.collection.unsafe.sort.PrefixComparators.BinaryPrefixComparator
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparators.DoublePrefixComparator
 
 abstract sealed class SortDirection
@@ -63,6 +64,7 @@ case class SortPrefix(child: SortOrder) extends UnaryExpression {
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val childCode = child.child.gen(ctx)
     val input = childCode.primitive
+    val BinaryPrefixCmp = classOf[BinaryPrefixComparator].getName
     val DoublePrefixCmp = classOf[DoublePrefixComparator].getName
 
     val (nullValue: Long, prefixCode: String) = child.child.dataType match {
@@ -76,6 +78,7 @@ case class SortPrefix(child: SortOrder) extends UnaryExpression {
         (DoublePrefixComparator.computePrefix(Double.NegativeInfinity),
           s"$DoublePrefixCmp.computePrefix((double)$input)")
       case StringType => (0L, s"$input.getPrefix()")
+      case BinaryType => (0L, s"$BinaryPrefixCmp.computePrefix($input)")
       case dt: DecimalType if dt.precision - dt.scale <= Decimal.MAX_LONG_DIGITS =>
         val prefix = if (dt.precision <= Decimal.MAX_LONG_DIGITS) {
           s"$input.toUnscaledLong()"
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
index 49adf215379cf..e17b50edc62dd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SortPrefixUtils.scala
@@ -38,6 +38,8 @@ object SortPrefixUtils {
     sortOrder.dataType match {
       case StringType =>
         if (sortOrder.isAscending) PrefixComparators.STRING else PrefixComparators.STRING_DESC
+      case BinaryType =>
+        if (sortOrder.isAscending) PrefixComparators.BINARY else PrefixComparators.BINARY_DESC
       case BooleanType | ByteType | ShortType | IntegerType | LongType | DateType | TimestampType =>
         if (sortOrder.isAscending) PrefixComparators.LONG else PrefixComparators.LONG_DESC
       case dt: DecimalType if dt.precision - dt.scale <= Decimal.MAX_LONG_DIGITS =>

From 1bf608b5ef1e6e2ae4325e13c2bd5e34db62450f Mon Sep 17 00:00:00 2001
From: Namit Katariya <katariya.namit@gmail.com>
Date: Wed, 5 Aug 2015 01:07:33 -0700
Subject: [PATCH 053/633] [SPARK-9601] [DOCS] Fix JavaPairDStream signature for
 stream-stream and windowed join in streaming guide doc

Author: Namit Katariya <katariya.namit@gmail.com>

Closes #7935 from namitk/SPARK-9601 and squashes the following commits:

03b5784 [Namit Katariya] [SPARK-9601] Fix signature of JavaPairDStream for stream-stream and windowed join in streaming guide doc
---
 docs/streaming-programming-guide.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 4663b3f14c527..dbfdb619f89e2 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -1141,7 +1141,7 @@ val joinedStream = stream1.join(stream2)
 {% highlight java %}
 JavaPairDStream<String, String> stream1 = ...
 JavaPairDStream<String, String> stream2 = ...
-JavaPairDStream<String, String> joinedStream = stream1.join(stream2);
+JavaPairDStream<String, Tuple2<String, String>> joinedStream = stream1.join(stream2);
 {% endhighlight %}
 </div>
 <div data-lang="python" markdown="1">
@@ -1166,7 +1166,7 @@ val joinedStream = windowedStream1.join(windowedStream2)
 {% highlight java %}
 JavaPairDStream<String, String> windowedStream1 = stream1.window(Durations.seconds(20));
 JavaPairDStream<String, String> windowedStream2 = stream2.window(Durations.minutes(1));
-JavaPairDStream<String, String> joinedStream = windowedStream1.join(windowedStream2);
+JavaPairDStream<String, Tuple2<String, String>> joinedStream = windowedStream1.join(windowedStream2);
 {% endhighlight %}
 </div>
 <div data-lang="python" markdown="1">

From 1b0317f64cfe99ff70580eeb99753cd0d31f849a Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 5 Aug 2015 01:51:22 -0700
Subject: [PATCH 054/633] [SPARK-8861][SPARK-8862][SQL] Add basic
 instrumentation to each SparkPlan operator and add a new SQL tab

This PR includes the following changes:

### SPARK-8862: Add basic instrumentation to each SparkPlan operator

A SparkPlan can override `def accumulators: Map[String, Accumulator[_]]` to expose its metrics that can be displayed in UI. The UI will use them to track the updates and show them in the web page in real-time.

### SparkSQLExecution and SQLSparkListener

`SparkSQLExecution.withNewExecutionId` will set `spark.sql.execution.id` to the local properties so that we can use it to track all jobs that belong to the same query.

SQLSparkListener is a listener to track all accumulator updates of all tasks for a query. It receives them from heartbeats can the UI can query them in real-time.

When running a query, `SQLSparkListener.onExecutionStart` will be called. When a query is finished,  `SQLSparkListener.onExecutionEnd` will be called. And the Spark jobs with the same execution id will be tracked and stored with this query.

`SQLSparkListener` has to store all accumulator updates for tasks separately. When a task fails and starts to retry, we need to drop the old accumulator updates. Because we can not revert our changes to an accumulator, we have to maintain these accumulator updates by ourselves so as to drop accumulator updates for a failed task.

### SPARK-8862: A new SQL tab
Includes two pages:
#### A page for all DataFrame/SQL queries
It will show the running, completed and failed queries in 3 tables. It also displays the jobs and their links for a query in each row.
#### A detail page for a DataFrame/SQL query
In this page, it also shows the SparkPlan metrics in real-time. Run a long-running query, such as
```
val testData = sc.parallelize((1 to 1000000).map(i => (i, i.toString))).toDF()
testData.select($"_1").filter($"_1" < 1000).foreach(_ => Thread.sleep(60))
```
and you will see the metrics keep updating in real-time.

<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/7774)
<!-- Reviewable:end -->

Author: zsxwing <zsxwing@gmail.com>

Closes #7774 from zsxwing/sql-ui and squashes the following commits:

5a2bc99 [zsxwing] Remove UISeleniumSuite and its dependency
57d4cd2 [zsxwing] Use VisibleForTesting annotation
cc1c736 [zsxwing] Add SparkPlan.trackNumOfRowsEnabled to make subclasses easy to track the number of rows; fix the issue that the "save" action cannot collect metrics
3771ab0 [zsxwing] Register SQL metrics accmulators
3a101c0 [zsxwing] Change prepareCalled's type to AtomicBoolean for thread-safety
b8d5605 [zsxwing] Make prepare idempotent; call children's prepare in SparkPlan.prepare; change doPrepare to def
4ed11a1 [zsxwing] var -> val
332639c [zsxwing] Ignore UISeleniumSuite and SQLListenerSuite."no memory leak" because of SPARK-9580
bb52359 [zsxwing] Address other commens in SQLListener
c4d0f5d [zsxwing] Move newPredicate out of the iterator loop
957473c [zsxwing] Move STATIC_RESOURCE_DIR to object SQLTab
7ab4816 [zsxwing] Make SparkPlan accumulator API private[sql]
dae195e [zsxwing] Fix the code style and comments
3a66207 [zsxwing] Ignore irrelevant accumulators
b8484a1 [zsxwing] Merge branch 'master' into sql-ui
9406592 [zsxwing] Implement the SparkPlan viz
4ebce68 [zsxwing] Add SparkPlan.prepare to support BroadcastHashJoin to run background work in parallel
ca1811f [zsxwing] Merge branch 'master' into sql-ui
fef6fc6 [zsxwing] Fix a corner case
25f335c [zsxwing] Fix the code style
6eae828 [zsxwing] SQLSparkListener -> SQLListener; SparkSQLExecutionUIData -> SQLExecutionUIData; SparkSQLExecution -> SQLExecution
822af75 [zsxwing] Add SQLSparkListenerSuite and fix the issue about onExecutionEnd and onJobEnd
6be626f [zsxwing] Add UISeleniumSuite to test UI
d02a24d [zsxwing] Make ExecutionPage private
23abf73 [zsxwing] [SPARK-8862][SPARK-8862][SQL] Add basic instrumentation to each SparkPlan operator and add a new SQL tab
---
 .../scala/org/apache/spark/Accumulators.scala |  15 +-
 .../scala/org/apache/spark/SparkContext.scala |  15 +
 .../org/apache/spark/executor/Executor.scala  |   2 +
 .../spark/sql/ui/static/spark-sql-viz.css     |  37 ++
 .../spark/sql/ui/static/spark-sql-viz.js      | 160 ++++++++
 .../org/apache/spark/sql/DataFrame.scala      |  29 +-
 .../org/apache/spark/sql/SQLContext.scala     |   6 +
 .../spark/sql/execution/ExistingRDD.scala     |   3 +
 .../spark/sql/execution/LocalTableScan.scala  |   2 +
 .../spark/sql/execution/SQLExecution.scala    | 105 ++++++
 .../spark/sql/execution/SparkPlan.scala       |  62 ++-
 .../spark/sql/execution/basicOperators.scala  |  18 +-
 .../sql/execution/datasources/commands.scala  |  39 +-
 .../execution/joins/BroadcastHashJoin.scala   |  31 +-
 .../joins/BroadcastHashOuterJoin.scala        |  29 +-
 .../execution/joins/ShuffledHashJoin.scala    |   2 +
 .../sql/execution/joins/SortMergeJoin.scala   |   2 +
 .../spark/sql/ui/AllExecutionsPage.scala      | 238 ++++++++++++
 .../apache/spark/sql/ui/ExecutionPage.scala   | 127 +++++++
 .../org/apache/spark/sql/ui/SQLListener.scala | 354 ++++++++++++++++++
 .../org/apache/spark/sql/ui/SQLTab.scala      |  41 ++
 .../apache/spark/sql/ui/SparkPlanGraph.scala  | 118 ++++++
 .../apache/spark/sql/CachedTableSuite.scala   |   8 +-
 .../spark/sql/ui/SQLListenerSuite.scala       | 347 +++++++++++++++++
 24 files changed, 1735 insertions(+), 55 deletions(-)
 create mode 100644 sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css
 create mode 100644 sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index b6a0119c696fd..462d5c96d480b 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -153,11 +153,12 @@ class Accumulable[R, T] private[spark] (
     value_ = zero
     deserialized = true
     // Automatically register the accumulator when it is deserialized with the task closure.
-    // Note that internal accumulators are deserialized before the TaskContext is created and
-    // are registered in the TaskContext constructor.
-    if (!isInternal) {
-      val taskContext = TaskContext.get()
-      assume(taskContext != null, "Task context was null when deserializing user accumulators")
+    //
+    // Note internal accumulators sent with task are deserialized before the TaskContext is created
+    // and are registered in the TaskContext constructor. Other internal accumulators, such SQL
+    // metrics, still need to register here.
+    val taskContext = TaskContext.get()
+    if (taskContext != null) {
       taskContext.registerAccumulator(this)
     }
   }
@@ -255,8 +256,8 @@ GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Serializa
  * @tparam T result type
  */
 class Accumulator[T] private[spark] (
-    @transient initialValue: T,
-    param: AccumulatorParam[T],
+    @transient private[spark] val initialValue: T,
+    private[spark] val param: AccumulatorParam[T],
     name: Option[String],
     internal: Boolean)
   extends Accumulable[T, T](initialValue, param, name, internal) {
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 4380cf45cc1b0..0c0705325b169 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1238,6 +1238,21 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     acc
   }
 
+  /**
+   * Create an [[org.apache.spark.Accumulator]] variable of a given type, with a name for display
+   * in the Spark UI. Tasks can "add" values to the accumulator using the `+=` method. Only the
+   * driver can access the accumulator's `value`. The latest local value of such accumulator will be
+   * sent back to the driver via heartbeats.
+   *
+   * @tparam T type that can be added to the accumulator, must be thread safe
+   */
+  private[spark] def internalAccumulator[T](initialValue: T, name: String)(
+    implicit param: AccumulatorParam[T]): Accumulator[T] = {
+    val acc = new Accumulator(initialValue, param, Some(name), internal = true)
+    cleaner.foreach(_.registerAccumulatorForCleanup(acc))
+    acc
+  }
+
   /**
    * Create an [[org.apache.spark.Accumulable]] shared variable, to which tasks can add values
    * with `+=`. Only the driver can access the accumuable's `value`.
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 7bc7fce7ae8dd..5d78a9dc8885e 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -249,6 +249,7 @@ private[spark] class Executor(
           m.setExecutorRunTime((taskFinish - taskStart) - task.executorDeserializeTime)
           m.setJvmGCTime(computeTotalGcTime() - startGCTime)
           m.setResultSerializationTime(afterSerialization - beforeSerialization)
+          m.updateAccumulators()
         }
 
         val directResult = new DirectTaskResult(valueBytes, accumUpdates, task.metrics.orNull)
@@ -300,6 +301,7 @@ private[spark] class Executor(
             task.metrics.map { m =>
               m.setExecutorRunTime(System.currentTimeMillis() - taskStart)
               m.setJvmGCTime(computeTotalGcTime() - startGCTime)
+              m.updateAccumulators()
               m
             }
           }
diff --git a/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css b/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css
new file mode 100644
index 0000000000000..ddd3a91dd8ef8
--- /dev/null
+++ b/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#plan-viz-graph .label {
+  font-weight: normal;
+  text-shadow: none;
+}
+
+#plan-viz-graph svg g.node rect {
+  fill: #C3EBFF;
+  stroke: #3EC0FF;
+  stroke-width: 1px;
+}
+
+/* Hightlight the SparkPlan node name */
+#plan-viz-graph svg text :first-child {
+  font-weight: bold;
+}
+
+#plan-viz-graph svg path {
+  stroke: #444;
+  stroke-width: 1.5px;
+}
diff --git a/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js b/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js
new file mode 100644
index 0000000000000..5161fcde669e7
--- /dev/null
+++ b/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js
@@ -0,0 +1,160 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+var PlanVizConstants = {
+  svgMarginX: 16,
+  svgMarginY: 16
+};
+
+function renderPlanViz() {
+  var svg = planVizContainer().append("svg");
+  var metadata = d3.select("#plan-viz-metadata");
+  var dot = metadata.select(".dot-file").text().trim();
+  var graph = svg.append("g");
+
+  var g = graphlibDot.read(dot);
+  preprocessGraphLayout(g);
+  var renderer = new dagreD3.render();
+  renderer(graph, g);
+
+  // Round corners on rectangles
+  svg
+    .selectAll("rect")
+    .attr("rx", "5")
+    .attr("ry", "5");
+
+  var nodeSize = parseInt($("#plan-viz-metadata-size").text());
+  for (var i = 0; i < nodeSize; i++) {
+    setupTooltipForSparkPlanNode(i);
+  }
+
+  resizeSvg(svg)
+}
+
+/* -------------------- *
+ * | Helper functions | *
+ * -------------------- */
+
+function planVizContainer() { return d3.select("#plan-viz-graph"); }
+
+/*
+ * Set up the tooltip for a SparkPlan node using metadata. When the user moves the mouse on the
+ * node, it will display the details of this SparkPlan node in the right.
+ */
+function setupTooltipForSparkPlanNode(nodeId) {
+  var nodeTooltip = d3.select("#plan-meta-data-" + nodeId).text()
+  d3.select("svg g .node_" + nodeId)
+    .on('mouseover', function(d) {
+      var domNode = d3.select(this).node();
+      $(domNode).tooltip({
+        title: nodeTooltip, trigger: "manual", container: "body", placement: "right"
+      });
+      $(domNode).tooltip("show");
+    })
+    .on('mouseout', function(d) {
+      var domNode = d3.select(this).node();
+      $(domNode).tooltip("destroy");
+    })
+}
+
+/*
+ * Helper function to pre-process the graph layout.
+ * This step is necessary for certain styles that affect the positioning
+ * and sizes of graph elements, e.g. padding, font style, shape.
+ */
+function preprocessGraphLayout(g) {
+  var nodes = g.nodes();
+  for (var i = 0; i < nodes.length; i++) {
+      var node = g.node(nodes[i]);
+      node.padding = "5";
+  }
+  // Curve the edges
+  var edges = g.edges();
+  for (var j = 0; j < edges.length; j++) {
+    var edge = g.edge(edges[j]);
+    edge.lineInterpolate = "basis";
+  }
+}
+
+/*
+ * Helper function to size the SVG appropriately such that all elements are displayed.
+ * This assumes that all outermost elements are clusters (rectangles).
+ */
+function resizeSvg(svg) {
+  var allClusters = svg.selectAll("g rect")[0];
+  console.log(allClusters);
+  var startX = -PlanVizConstants.svgMarginX +
+    toFloat(d3.min(allClusters, function(e) {
+      console.log(e);
+      return getAbsolutePosition(d3.select(e)).x;
+    }));
+  var startY = -PlanVizConstants.svgMarginY +
+    toFloat(d3.min(allClusters, function(e) {
+      return getAbsolutePosition(d3.select(e)).y;
+    }));
+  var endX = PlanVizConstants.svgMarginX +
+    toFloat(d3.max(allClusters, function(e) {
+      var t = d3.select(e);
+      return getAbsolutePosition(t).x + toFloat(t.attr("width"));
+    }));
+  var endY = PlanVizConstants.svgMarginY +
+    toFloat(d3.max(allClusters, function(e) {
+      var t = d3.select(e);
+      return getAbsolutePosition(t).y + toFloat(t.attr("height"));
+    }));
+  var width = endX - startX;
+  var height = endY - startY;
+  svg.attr("viewBox", startX + " " + startY + " " + width + " " + height)
+     .attr("width", width)
+     .attr("height", height);
+}
+
+/* Helper function to convert attributes to numeric values. */
+function toFloat(f) {
+  if (f) {
+    return parseFloat(f.toString().replace(/px$/, ""));
+  } else {
+    return f;
+  }
+}
+
+/*
+ * Helper function to compute the absolute position of the specified element in our graph.
+ */
+function getAbsolutePosition(d3selection) {
+  if (d3selection.empty()) {
+    throw "Attempted to get absolute position of an empty selection.";
+  }
+  var obj = d3selection;
+  var _x = toFloat(obj.attr("x")) || 0;
+  var _y = toFloat(obj.attr("y")) || 0;
+  while (!obj.empty()) {
+    var transformText = obj.attr("transform");
+    if (transformText) {
+      var translate = d3.transform(transformText).translate;
+      _x += toFloat(translate[0]);
+      _y += toFloat(translate[1]);
+    }
+    // Climb upwards to find how our parents are translated
+    obj = d3.select(obj.node().parentNode);
+    // Stop when we've reached the graph container itself
+    if (obj.node() == planVizContainer().node()) {
+      break;
+    }
+  }
+  return { x: _x, y: _y };
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 3ea0f9ed3bddd..1f83567df88cc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -39,7 +39,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical.{Filter, _}
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
-import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD}
+import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD, SQLExecution}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation}
 import org.apache.spark.sql.json.{JacksonGenerator, JSONRelation}
 import org.apache.spark.sql.sources.HadoopFsRelation
@@ -119,6 +119,9 @@ class DataFrame private[sql](
     @transient val sqlContext: SQLContext,
     @DeveloperApi @transient val queryExecution: SQLContext#QueryExecution) extends Serializable {
 
+  // Note for Spark contributors: if adding or updating any action in `DataFrame`, please make sure
+  // you wrap it with `withNewExecutionId` if this actions doesn't call other action.
+
   /**
    * A constructor that automatically analyzes the logical plan.
    *
@@ -1356,14 +1359,18 @@ class DataFrame private[sql](
    * @group rdd
    * @since 1.3.0
    */
-  def foreach(f: Row => Unit): Unit = rdd.foreach(f)
+  def foreach(f: Row => Unit): Unit = withNewExecutionId {
+    rdd.foreach(f)
+  }
 
   /**
    * Applies a function f to each partition of this [[DataFrame]].
    * @group rdd
    * @since 1.3.0
    */
-  def foreachPartition(f: Iterator[Row] => Unit): Unit = rdd.foreachPartition(f)
+  def foreachPartition(f: Iterator[Row] => Unit): Unit = withNewExecutionId {
+    rdd.foreachPartition(f)
+  }
 
   /**
    * Returns the first `n` rows in the [[DataFrame]].
@@ -1377,14 +1384,18 @@ class DataFrame private[sql](
    * @group action
    * @since 1.3.0
    */
-  def collect(): Array[Row] = queryExecution.executedPlan.executeCollect()
+  def collect(): Array[Row] = withNewExecutionId {
+    queryExecution.executedPlan.executeCollect()
+  }
 
   /**
    * Returns a Java list that contains all of [[Row]]s in this [[DataFrame]].
    * @group action
    * @since 1.3.0
    */
-  def collectAsList(): java.util.List[Row] = java.util.Arrays.asList(rdd.collect() : _*)
+  def collectAsList(): java.util.List[Row] = withNewExecutionId {
+    java.util.Arrays.asList(rdd.collect() : _*)
+  }
 
   /**
    * Returns the number of rows in the [[DataFrame]].
@@ -1863,6 +1874,14 @@ class DataFrame private[sql](
     write.mode(SaveMode.Append).insertInto(tableName)
   }
 
+  /**
+   * Wrap a DataFrame action to track all Spark jobs in the body so that we can connect them with
+   * an execution.
+   */
+  private[sql] def withNewExecutionId[T](body: => T): T = {
+    SQLExecution.withNewExecutionId(sqlContext, queryExecution)(body)
+  }
+
   ////////////////////////////////////////////////////////////////////////////
   ////////////////////////////////////////////////////////////////////////////
   // End of deprecated methods
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index dbb2a09846548..ffc2baf7a8826 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -43,6 +43,7 @@ import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.ui.{SQLListener, SQLTab}
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.Utils
 
@@ -74,6 +75,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
    */
   protected[sql] def conf = currentSession().conf
 
+  // `listener` should be only used in the driver
+  @transient private[sql] val listener = new SQLListener(this)
+  sparkContext.addSparkListener(listener)
+  sparkContext.ui.foreach(new SQLTab(this, _))
+
   /**
    * Set Spark SQL configuration properties.
    *
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
index da27a753a710f..fbaa8e276ddb7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
@@ -96,6 +96,9 @@ private[sql] case class LogicalRDD(
 private[sql] case class PhysicalRDD(
     output: Seq[Attribute],
     rdd: RDD[InternalRow]) extends LeafNode {
+
+  override protected[sql] val trackNumOfRowsEnabled = true
+
   protected override def doExecute(): RDD[InternalRow] = rdd
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
index 34e926e4582be..858dd85fd1fa6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
@@ -30,6 +30,8 @@ private[sql] case class LocalTableScan(
     output: Seq[Attribute],
     rows: Seq[InternalRow]) extends LeafNode {
 
+  override protected[sql] val trackNumOfRowsEnabled = true
+
   private lazy val rdd = sqlContext.sparkContext.parallelize(rows)
 
   protected override def doExecute(): RDD[InternalRow] = rdd
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
new file mode 100644
index 0000000000000..97f1323e97835
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
@@ -0,0 +1,105 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import java.util.concurrent.atomic.AtomicLong
+
+import org.apache.spark.SparkContext
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.ui.SparkPlanGraph
+import org.apache.spark.util.Utils
+
+private[sql] object SQLExecution {
+
+  val EXECUTION_ID_KEY = "spark.sql.execution.id"
+
+  private val _nextExecutionId = new AtomicLong(0)
+
+  private def nextExecutionId: Long = _nextExecutionId.getAndIncrement
+
+  /**
+   * Wrap an action that will execute "queryExecution" to track all Spark jobs in the body so that
+   * we can connect them with an execution.
+   */
+  def withNewExecutionId[T](
+      sqlContext: SQLContext, queryExecution: SQLContext#QueryExecution)(body: => T): T = {
+    val sc = sqlContext.sparkContext
+    val oldExecutionId = sc.getLocalProperty(EXECUTION_ID_KEY)
+    if (oldExecutionId == null) {
+      val executionId = SQLExecution.nextExecutionId
+      sc.setLocalProperty(EXECUTION_ID_KEY, executionId.toString)
+      val r = try {
+        val callSite = Utils.getCallSite()
+        sqlContext.listener.onExecutionStart(
+          executionId,
+          callSite.shortForm,
+          callSite.longForm,
+          queryExecution.toString,
+          SparkPlanGraph(queryExecution.executedPlan),
+          System.currentTimeMillis())
+        try {
+          body
+        } finally {
+          // Ideally, we need to make sure onExecutionEnd happens after onJobStart and onJobEnd.
+          // However, onJobStart and onJobEnd run in the listener thread. Because we cannot add new
+          // SQL event types to SparkListener since it's a public API, we cannot guarantee that.
+          //
+          // SQLListener should handle the case that onExecutionEnd happens before onJobEnd.
+          //
+          // The worst case is onExecutionEnd may happen before onJobStart when the listener thread
+          // is very busy. If so, we cannot track the jobs for the execution. It seems acceptable.
+          sqlContext.listener.onExecutionEnd(executionId, System.currentTimeMillis())
+        }
+      } finally {
+        sc.setLocalProperty(EXECUTION_ID_KEY, null)
+      }
+      r
+    } else {
+      // Don't support nested `withNewExecutionId`. This is an example of the nested
+      // `withNewExecutionId`:
+      //
+      // class DataFrame {
+      //   def foo: T = withNewExecutionId { something.createNewDataFrame().collect() }
+      // }
+      //
+      // Note: `collect` will call withNewExecutionId
+      // In this case, only the "executedPlan" for "collect" will be executed. The "executedPlan"
+      // for the outer DataFrame won't be executed. So it's meaningless to create a new Execution
+      // for the outer DataFrame. Even if we track it, since its "executedPlan" doesn't run,
+      // all accumulator metrics will be 0. It will confuse people if we show them in Web UI.
+      //
+      // A real case is the `DataFrame.count` method.
+      throw new IllegalArgumentException(s"$EXECUTION_ID_KEY is already set")
+    }
+  }
+
+  /**
+   * Wrap an action with a known executionId. When running a different action in a different
+   * thread from the original one, this method can be used to connect the Spark jobs in this action
+   * with the known executionId, e.g., `BroadcastHashJoin.broadcastFuture`.
+   */
+  def withExecutionId[T](sc: SparkContext, executionId: String)(body: => T): T = {
+    val oldExecutionId = sc.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
+    try {
+      sc.setLocalProperty(SQLExecution.EXECUTION_ID_KEY, executionId)
+      body
+    } finally {
+      sc.setLocalProperty(SQLExecution.EXECUTION_ID_KEY, oldExecutionId)
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 50c27def8ea54..73b237fffece8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -17,9 +17,11 @@
 
 package org.apache.spark.sql.execution
 
+import java.util.concurrent.atomic.AtomicBoolean
+
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.Logging
+import org.apache.spark.{Accumulator, Logging}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.{RDD, RDDOperationScope}
 import org.apache.spark.sql.SQLContext
@@ -59,12 +61,39 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
     false
   }
 
+  /**
+   * Whether the "prepare" method is called.
+   */
+  private val prepareCalled = new AtomicBoolean(false)
+
   /** Overridden make copy also propogates sqlContext to copied plan. */
   override def makeCopy(newArgs: Array[AnyRef]): this.type = {
     SparkPlan.currentContext.set(sqlContext)
     super.makeCopy(newArgs)
   }
 
+  /**
+   * Whether track the number of rows output by this SparkPlan
+   */
+  protected[sql] def trackNumOfRowsEnabled: Boolean = false
+
+  private lazy val numOfRowsAccumulator = sparkContext.internalAccumulator(0L, "number of rows")
+
+  /**
+   * Return all accumulators containing metrics of this SparkPlan.
+   */
+  private[sql] def accumulators: Map[String, Accumulator[_]] = if (trackNumOfRowsEnabled) {
+      Map("numRows" -> numOfRowsAccumulator)
+    } else {
+      Map.empty
+    }
+
+  /**
+   * Return the accumulator according to the name.
+   */
+  private[sql] def accumulator[T](name: String): Accumulator[T] =
+    accumulators(name).asInstanceOf[Accumulator[T]]
+
   // TODO: Move to `DistributedPlan`
   /** Specifies how data is partitioned across different nodes in the cluster. */
   def outputPartitioning: Partitioning = UnknownPartitioning(0) // TODO: WRONG WIDTH!
@@ -110,10 +139,39 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
         "Operator will receive unsafe rows as input but cannot process unsafe rows")
     }
     RDDOperationScope.withScope(sparkContext, nodeName, false, true) {
-      doExecute()
+      prepare()
+      if (trackNumOfRowsEnabled) {
+        val numRows = accumulator[Long]("numRows")
+        doExecute().map { row =>
+          numRows += 1
+          row
+        }
+      } else {
+        doExecute()
+      }
     }
   }
 
+  /**
+   * Prepare a SparkPlan for execution. It's idempotent.
+   */
+  final def prepare(): Unit = {
+    if (prepareCalled.compareAndSet(false, true)) {
+      doPrepare
+      children.foreach(_.prepare())
+    }
+  }
+
+  /**
+   * Overridden by concrete implementations of SparkPlan. It is guaranteed to run before any
+   * `execute` of SparkPlan. This is helpful if we want to set up some state before executing the
+   * query, e.g., `BroadcastHashJoin` uses it to broadcast asynchronously.
+   *
+   * Note: the prepare method has already walked down the tree, so the implementation doesn't need
+   * to call children's prepare methods.
+   */
+  protected def doPrepare(): Unit = {}
+
   /**
    * Overridden by concrete implementations of SparkPlan.
    * Produces the result of the query as an RDD[InternalRow]
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 5a1b000e89875..477170297c2ac 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -81,8 +81,22 @@ case class TungstenProject(projectList: Seq[NamedExpression], child: SparkPlan)
 case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 
-  protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter =>
-    iter.filter(newPredicate(condition, child.output))
+  private[sql] override lazy val accumulators = Map(
+    "numInputRows" -> sparkContext.internalAccumulator(0L, "number of input rows"),
+    "numOutputRows" -> sparkContext.internalAccumulator(0L, "number of output rows"))
+
+  protected override def doExecute(): RDD[InternalRow] = {
+    val numInputRows = accumulator[Long]("numInputRows")
+    val numOutputRows = accumulator[Long]("numOutputRows")
+    child.execute().mapPartitions { iter =>
+      val predicate = newPredicate(condition, child.output)
+      iter.filter { row =>
+        numInputRows += 1
+        val r = predicate(row)
+        if (r) numOutputRows += 1
+        r
+      }
+    }
   }
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
index d551f386eee6e..cf199118287dc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
@@ -33,7 +33,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
-import org.apache.spark.sql.execution.RunnableCommand
+import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StringType
 import org.apache.spark.util.SerializableConfiguration
@@ -122,25 +122,26 @@ private[sql] case class InsertIntoHadoopFsRelation(
 
       // We create a DataFrame by applying the schema of relation to the data to make sure.
       // We are writing data based on the expected schema,
-      val df = {
-        // For partitioned relation r, r.schema's column ordering can be different from the column
-        // ordering of data.logicalPlan (partition columns are all moved after data column). We
-        // need a Project to adjust the ordering, so that inside InsertIntoHadoopFsRelation, we can
-        // safely apply the schema of r.schema to the data.
-        val project = Project(
-          relation.schema.map(field => new UnresolvedAttribute(Seq(field.name))), query)
-
-        sqlContext.internalCreateDataFrame(
-          DataFrame(sqlContext, project).queryExecution.toRdd, relation.schema)
-      }
 
-      val partitionColumns = relation.partitionColumns.fieldNames
-      if (partitionColumns.isEmpty) {
-        insert(new DefaultWriterContainer(relation, job, isAppend), df)
-      } else {
-        val writerContainer = new DynamicPartitionWriterContainer(
-          relation, job, partitionColumns, PartitioningUtils.DEFAULT_PARTITION_NAME, isAppend)
-        insertWithDynamicPartitions(sqlContext, writerContainer, df, partitionColumns)
+      // For partitioned relation r, r.schema's column ordering can be different from the column
+      // ordering of data.logicalPlan (partition columns are all moved after data column). We
+      // need a Project to adjust the ordering, so that inside InsertIntoHadoopFsRelation, we can
+      // safely apply the schema of r.schema to the data.
+      val project = Project(
+        relation.schema.map(field => new UnresolvedAttribute(Seq(field.name))), query)
+
+      val queryExecution = DataFrame(sqlContext, project).queryExecution
+      SQLExecution.withNewExecutionId(sqlContext, queryExecution) {
+        val df = sqlContext.internalCreateDataFrame(queryExecution.toRdd, relation.schema)
+
+        val partitionColumns = relation.partitionColumns.fieldNames
+        if (partitionColumns.isEmpty) {
+          insert(new DefaultWriterContainer(relation, job, isAppend), df)
+        } else {
+          val writerContainer = new DynamicPartitionWriterContainer(
+            relation, job, partitionColumns, PartitioningUtils.DEFAULT_PARTITION_NAME, isAppend)
+          insertWithDynamicPartitions(sqlContext, writerContainer, df, partitionColumns)
+        }
       }
     }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
index e73e2523a777f..ec1a148342fc6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
@@ -26,7 +26,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution}
-import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.{BinaryNode, SparkPlan, SQLExecution}
 import org.apache.spark.util.ThreadUtils
 
 /**
@@ -45,6 +45,8 @@ case class BroadcastHashJoin(
     right: SparkPlan)
   extends BinaryNode with HashJoin {
 
+  override protected[sql] val trackNumOfRowsEnabled = true
+
   val timeout: Duration = {
     val timeoutValue = sqlContext.conf.broadcastTimeout
     if (timeoutValue < 0) {
@@ -59,13 +61,28 @@ case class BroadcastHashJoin(
   override def requiredChildDistribution: Seq[Distribution] =
     UnspecifiedDistribution :: UnspecifiedDistribution :: Nil
 
+  // Use lazy so that we won't do broadcast when calling explain but still cache the broadcast value
+  // for the same query.
   @transient
-  private val broadcastFuture = future {
-    // Note that we use .execute().collect() because we don't want to convert data to Scala types
-    val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-    val hashed = HashedRelation(input.iterator, buildSideKeyGenerator, input.size)
-    sparkContext.broadcast(hashed)
-  }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
+  private lazy val broadcastFuture = {
+    // broadcastFuture is used in "doExecute". Therefore we can get the execution id correctly here.
+    val executionId = sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
+    future {
+      // This will run in another thread. Set the execution id so that we can connect these jobs
+      // with the correct execution.
+      SQLExecution.withExecutionId(sparkContext, executionId) {
+        // Note that we use .execute().collect() because we don't want to convert data to Scala
+        // types
+        val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
+        val hashed = HashedRelation(input.iterator, buildSideKeyGenerator, input.size)
+        sparkContext.broadcast(hashed)
+      }
+    }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
+  }
+
+  protected override def doPrepare(): Unit = {
+    broadcastFuture
+  }
 
   protected override def doExecute(): RDD[InternalRow] = {
     val broadcastRelation = Await.result(broadcastFuture, timeout)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index c35e439cc9deb..e342fd914d321 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -27,7 +27,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, Distribution, UnspecifiedDistribution}
 import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
-import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.{BinaryNode, SparkPlan, SQLExecution}
 import org.apache.spark.util.ThreadUtils
 
 /**
@@ -60,13 +60,28 @@ case class BroadcastHashOuterJoin(
 
   override def outputPartitioning: Partitioning = streamedPlan.outputPartitioning
 
+  // Use lazy so that we won't do broadcast when calling explain but still cache the broadcast value
+  // for the same query.
   @transient
-  private val broadcastFuture = future {
-    // Note that we use .execute().collect() because we don't want to convert data to Scala types
-    val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-    val hashed = HashedRelation(input.iterator, buildKeyGenerator, input.size)
-    sparkContext.broadcast(hashed)
-  }(BroadcastHashOuterJoin.broadcastHashOuterJoinExecutionContext)
+  private lazy val broadcastFuture = {
+    // broadcastFuture is used in "doExecute". Therefore we can get the execution id correctly here.
+    val executionId = sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
+    future {
+      // This will run in another thread. Set the execution id so that we can connect these jobs
+      // with the correct execution.
+      SQLExecution.withExecutionId(sparkContext, executionId) {
+        // Note that we use .execute().collect() because we don't want to convert data to Scala
+        // types
+        val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
+        val hashed = HashedRelation(input.iterator, buildKeyGenerator, input.size)
+        sparkContext.broadcast(hashed)
+      }
+    }(BroadcastHashOuterJoin.broadcastHashOuterJoinExecutionContext)
+  }
+
+  protected override def doPrepare(): Unit = {
+    broadcastFuture
+  }
 
   override def doExecute(): RDD[InternalRow] = {
     val broadcastRelation = Await.result(broadcastFuture, timeout)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
index fc6efe87bceb5..c923dc837c449 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
@@ -38,6 +38,8 @@ case class ShuffledHashJoin(
     right: SparkPlan)
   extends BinaryNode with HashJoin {
 
+  override protected[sql] val trackNumOfRowsEnabled = true
+
   override def outputPartitioning: Partitioning =
     PartitioningCollection(Seq(left.outputPartitioning, right.outputPartitioning))
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index 41be78afd37e6..eb595490fbf28 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -38,6 +38,8 @@ case class SortMergeJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode {
 
+  override protected[sql] val trackNumOfRowsEnabled = true
+
   override def output: Seq[Attribute] = left.output ++ right.output
 
   override def outputPartitioning: Partitioning =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
new file mode 100644
index 0000000000000..727fc4b37fa48
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
@@ -0,0 +1,238 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.collection.mutable
+import scala.xml.Node
+
+import org.apache.commons.lang3.StringEscapeUtils
+
+import org.apache.spark.Logging
+import org.apache.spark.ui.{UIUtils, WebUIPage}
+
+private[ui] class AllExecutionsPage(parent: SQLTab) extends WebUIPage("") with Logging {
+
+  private val listener = parent.listener
+
+  override def render(request: HttpServletRequest): Seq[Node] = {
+    val currentTime = System.currentTimeMillis()
+    val content = listener.synchronized {
+      val _content = mutable.ListBuffer[Node]()
+      if (listener.getRunningExecutions.nonEmpty) {
+        _content ++=
+          new RunningExecutionTable(
+            parent, "Running Queries", currentTime,
+            listener.getRunningExecutions.sortBy(_.submissionTime).reverse).toNodeSeq
+      }
+      if (listener.getCompletedExecutions.nonEmpty) {
+        _content ++=
+          new CompletedExecutionTable(
+            parent, "Completed Queries", currentTime,
+            listener.getCompletedExecutions.sortBy(_.submissionTime).reverse).toNodeSeq
+      }
+      if (listener.getFailedExecutions.nonEmpty) {
+        _content ++=
+          new FailedExecutionTable(
+            parent, "Failed Queries", currentTime,
+            listener.getFailedExecutions.sortBy(_.submissionTime).reverse).toNodeSeq
+      }
+      _content
+    }
+    UIUtils.headerSparkPage("SQL", content, parent, Some(5000))
+  }
+}
+
+private[ui] abstract class ExecutionTable(
+    parent: SQLTab,
+    tableId: String,
+    tableName: String,
+    currentTime: Long,
+    executionUIDatas: Seq[SQLExecutionUIData],
+    showRunningJobs: Boolean,
+    showSucceededJobs: Boolean,
+    showFailedJobs: Boolean) {
+
+  protected def baseHeader: Seq[String] = Seq(
+    "ID",
+    "Description",
+    "Submitted",
+    "Duration")
+
+  protected def header: Seq[String]
+
+  protected def row(currentTime: Long, executionUIData: SQLExecutionUIData): Seq[Node] = {
+    val submissionTime = executionUIData.submissionTime
+    val duration = executionUIData.completionTime.getOrElse(currentTime) - submissionTime
+
+    val runningJobs = executionUIData.runningJobs.map { jobId =>
+      <a href={jobURL(jobId)}>{jobId.toString}</a><br/>
+    }
+    val succeededJobs = executionUIData.succeededJobs.sorted.map { jobId =>
+      <a href={jobURL(jobId)}>{jobId.toString}</a><br/>
+    }
+    val failedJobs = executionUIData.failedJobs.sorted.map { jobId =>
+      <a href={jobURL(jobId)}>{jobId.toString}</a><br/>
+    }
+    <tr>
+      <td>
+        {executionUIData.executionId.toString}
+      </td>
+      <td>
+        {descriptionCell(executionUIData)}
+      </td>
+      <td sorttable_customkey={submissionTime.toString}>
+        {UIUtils.formatDate(submissionTime)}
+      </td>
+      <td sorttable_customkey={duration.toString}>
+        {UIUtils.formatDuration(duration)}
+      </td>
+      {if (showRunningJobs) {
+        <td>
+          {runningJobs}
+        </td>
+      }}
+      {if (showSucceededJobs) {
+        <td>
+          {succeededJobs}
+        </td>
+      }}
+      {if (showFailedJobs) {
+        <td>
+          {failedJobs}
+        </td>
+      }}
+      {detailCell(executionUIData.physicalPlanDescription)}
+    </tr>
+  }
+
+  private def descriptionCell(execution: SQLExecutionUIData): Seq[Node] = {
+    val details = if (execution.details.nonEmpty) {
+      <span onclick="this.parentNode.querySelector('.stage-details').classList.toggle('collapsed')"
+            class="expand-details">
+        +details
+      </span> ++
+      <div class="stage-details collapsed">
+        <pre>{execution.details}</pre>
+      </div>
+    } else {
+      Nil
+    }
+
+    val desc = {
+      <a href={executionURL(execution.executionId)}>{execution.description}</a>
+    }
+
+    <div>{desc} {details}</div>
+  }
+
+  private def detailCell(physicalPlan: String): Seq[Node] = {
+    val isMultiline = physicalPlan.indexOf('\n') >= 0
+    val summary = StringEscapeUtils.escapeHtml4(
+      if (isMultiline) {
+        physicalPlan.substring(0, physicalPlan.indexOf('\n'))
+      } else {
+        physicalPlan
+      })
+    val details = if (isMultiline) {
+      // scalastyle:off
+      <span onclick="this.parentNode.querySelector('.stacktrace-details').classList.toggle('collapsed')"
+            class="expand-details">
+        +details
+      </span> ++
+        <div class="stacktrace-details collapsed">
+          <pre>{physicalPlan}</pre>
+        </div>
+      // scalastyle:on
+    } else {
+      ""
+    }
+    <td>{summary}{details}</td>
+  }
+
+  def toNodeSeq: Seq[Node] = {
+    <div>
+      <h4>{tableName}</h4>
+      {UIUtils.listingTable[SQLExecutionUIData](
+        header, row(currentTime, _), executionUIDatas, id = Some(tableId))}
+    </div>
+  }
+
+  private def jobURL(jobId: Long): String =
+    "%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), jobId)
+
+  private def executionURL(executionID: Long): String =
+    "%s/sql/execution?id=%s".format(UIUtils.prependBaseUri(parent.basePath), executionID)
+}
+
+private[ui] class RunningExecutionTable(
+    parent: SQLTab,
+    tableName: String,
+    currentTime: Long,
+    executionUIDatas: Seq[SQLExecutionUIData])
+  extends ExecutionTable(
+    parent,
+    "running-execution-table",
+    tableName,
+    currentTime,
+    executionUIDatas,
+    showRunningJobs = true,
+    showSucceededJobs = true,
+    showFailedJobs = true) {
+
+  override protected def header: Seq[String] =
+    baseHeader ++ Seq("Running Jobs", "Succeeded Jobs", "Failed Jobs", "Detail")
+}
+
+private[ui] class CompletedExecutionTable(
+    parent: SQLTab,
+    tableName: String,
+    currentTime: Long,
+    executionUIDatas: Seq[SQLExecutionUIData])
+  extends ExecutionTable(
+    parent,
+    "completed-execution-table",
+    tableName,
+    currentTime,
+    executionUIDatas,
+    showRunningJobs = false,
+    showSucceededJobs = true,
+    showFailedJobs = false) {
+
+  override protected def header: Seq[String] = baseHeader ++ Seq("Jobs", "Detail")
+}
+
+private[ui] class FailedExecutionTable(
+    parent: SQLTab,
+    tableName: String,
+    currentTime: Long,
+    executionUIDatas: Seq[SQLExecutionUIData])
+  extends ExecutionTable(
+    parent,
+    "failed-execution-table",
+    tableName,
+    currentTime,
+    executionUIDatas,
+    showRunningJobs = false,
+    showSucceededJobs = true,
+    showFailedJobs = true) {
+
+  override protected def header: Seq[String] =
+    baseHeader ++ Seq("Succeeded Jobs", "Failed Jobs", "Detail")
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala
new file mode 100644
index 0000000000000..52ddf99e9266a
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala
@@ -0,0 +1,127 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import javax.servlet.http.HttpServletRequest
+
+import scala.xml.{Node, Unparsed}
+
+import org.apache.commons.lang3.StringEscapeUtils
+
+import org.apache.spark.Logging
+import org.apache.spark.ui.{UIUtils, WebUIPage}
+
+private[sql] class ExecutionPage(parent: SQLTab) extends WebUIPage("execution") with Logging {
+
+  private val listener = parent.listener
+
+  override def render(request: HttpServletRequest): Seq[Node] = listener.synchronized {
+    val parameterExecutionId = request.getParameter("id")
+    require(parameterExecutionId != null && parameterExecutionId.nonEmpty,
+      "Missing execution id parameter")
+
+    val executionId = parameterExecutionId.toLong
+    val content = listener.getExecution(executionId).map { executionUIData =>
+      val currentTime = System.currentTimeMillis()
+      val duration =
+        executionUIData.completionTime.getOrElse(currentTime) - executionUIData.submissionTime
+
+      val summary =
+        <div>
+          <ul class="unstyled">
+            <li>
+              <strong>Submitted Time: </strong>{UIUtils.formatDate(executionUIData.submissionTime)}
+            </li>
+            <li>
+              <strong>Duration: </strong>{UIUtils.formatDuration(duration)}
+            </li>
+            {if (executionUIData.runningJobs.nonEmpty) {
+              <li>
+                <strong>Running Jobs: </strong>
+                {executionUIData.runningJobs.sorted.map { jobId =>
+                <a href={jobURL(jobId)}>{jobId.toString}</a><span>&nbsp;</span>
+              }}
+              </li>
+            }}
+            {if (executionUIData.succeededJobs.nonEmpty) {
+              <li>
+                <strong>Succeeded Jobs: </strong>
+                {executionUIData.succeededJobs.sorted.map { jobId =>
+                  <a href={jobURL(jobId)}>{jobId.toString}</a><span>&nbsp;</span>
+                }}
+              </li>
+            }}
+            {if (executionUIData.failedJobs.nonEmpty) {
+              <li>
+                <strong>Failed Jobs: </strong>
+                {executionUIData.failedJobs.sorted.map { jobId =>
+                  <a href={jobURL(jobId)}>{jobId.toString}</a><span>&nbsp;</span>
+                }}
+              </li>
+            }}
+            <li>
+              <strong>Detail: </strong><br/>
+              <pre>{executionUIData.physicalPlanDescription}</pre>
+            </li>
+          </ul>
+        </div>
+
+      val metrics = listener.getExecutionMetrics(executionId)
+
+      summary ++ planVisualization(metrics, executionUIData.physicalPlanGraph)
+    }.getOrElse {
+      <div>No information to display for Plan {executionId}</div>
+    }
+
+    UIUtils.headerSparkPage(s"Details for Query $executionId", content, parent, Some(5000))
+  }
+
+
+  private def planVisualizationResources: Seq[Node] = {
+    // scalastyle:off
+    <link rel="stylesheet" href={UIUtils.prependBaseUri("/static/sql/spark-sql-viz.css")} type="text/css"/>
+    <script src={UIUtils.prependBaseUri("/static/d3.min.js")}></script>
+    <script src={UIUtils.prependBaseUri("/static/dagre-d3.min.js")}></script>
+    <script src={UIUtils.prependBaseUri("/static/graphlib-dot.min.js")}></script>
+    <script src={UIUtils.prependBaseUri("/static/sql/spark-sql-viz.js")}></script>
+    // scalastyle:on
+  }
+
+  private def planVisualization(metrics: Map[Long, Any], graph: SparkPlanGraph): Seq[Node] = {
+    val metadata = graph.nodes.flatMap { node =>
+      val nodeId = s"plan-meta-data-${node.id}"
+      <div id={nodeId}>{node.desc}</div>
+    }
+
+    <div>
+      <div id="plan-viz-graph"></div>
+      <div id="plan-viz-metadata" style="display:none">
+        <div class="dot-file">
+          {graph.makeDotFile(metrics)}
+        </div>
+        <div id="plan-viz-metadata-size">{graph.nodes.size.toString}</div>
+        {metadata}
+      </div>
+      {planVisualizationResources}
+      <script>$(function(){{ renderPlanViz(); }})</script>
+    </div>
+  }
+
+  private def jobURL(jobId: Long): String =
+    "%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), jobId)
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
new file mode 100644
index 0000000000000..e7b1dd1ffac68
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
@@ -0,0 +1,354 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import scala.collection.mutable
+
+import com.google.common.annotations.VisibleForTesting
+
+import org.apache.spark.{AccumulatorParam, JobExecutionStatus, Logging}
+import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.scheduler._
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.execution.SQLExecution
+
+private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener with Logging {
+
+  private val retainedExecutions =
+    sqlContext.sparkContext.conf.getInt("spark.sql.ui.retainedExecutions", 1000)
+
+  private val activeExecutions = mutable.HashMap[Long, SQLExecutionUIData]()
+
+  // Old data in the following fields must be removed in "trimExecutionsIfNecessary".
+  // If adding new fields, make sure "trimExecutionsIfNecessary" can clean up old data
+
+  // VisibleForTesting
+  private val _executionIdToData = mutable.HashMap[Long, SQLExecutionUIData]()
+
+  /**
+   * Maintain the relation between job id and execution id so that we can get the execution id in
+   * the "onJobEnd" method.
+   */
+  private val _jobIdToExecutionId = mutable.HashMap[Long, Long]()
+
+  private val _stageIdToStageMetrics = mutable.HashMap[Long, SQLStageMetrics]()
+
+  private val failedExecutions = mutable.ListBuffer[SQLExecutionUIData]()
+
+  private val completedExecutions = mutable.ListBuffer[SQLExecutionUIData]()
+
+  @VisibleForTesting
+  def executionIdToData: Map[Long, SQLExecutionUIData] = synchronized {
+    _executionIdToData.toMap
+  }
+
+  @VisibleForTesting
+  def jobIdToExecutionId: Map[Long, Long] = synchronized {
+    _jobIdToExecutionId.toMap
+  }
+
+  @VisibleForTesting
+  def stageIdToStageMetrics: Map[Long, SQLStageMetrics] = synchronized {
+    _stageIdToStageMetrics.toMap
+  }
+
+  private def trimExecutionsIfNecessary(
+      executions: mutable.ListBuffer[SQLExecutionUIData]): Unit = {
+    if (executions.size > retainedExecutions) {
+      val toRemove = math.max(retainedExecutions / 10, 1)
+      executions.take(toRemove).foreach { execution =>
+        for (executionUIData <- _executionIdToData.remove(execution.executionId)) {
+          for (jobId <- executionUIData.jobs.keys) {
+            _jobIdToExecutionId.remove(jobId)
+          }
+          for (stageId <- executionUIData.stages) {
+            _stageIdToStageMetrics.remove(stageId)
+          }
+        }
+      }
+      executions.trimStart(toRemove)
+    }
+  }
+
+  override def onJobStart(jobStart: SparkListenerJobStart): Unit = {
+    val executionIdString = jobStart.properties.getProperty(SQLExecution.EXECUTION_ID_KEY)
+    if (executionIdString == null) {
+      // This is not a job created by SQL
+      return
+    }
+    val executionId = executionIdString.toLong
+    val jobId = jobStart.jobId
+    val stageIds = jobStart.stageIds
+
+    synchronized {
+      activeExecutions.get(executionId).foreach { executionUIData =>
+        executionUIData.jobs(jobId) = JobExecutionStatus.RUNNING
+        executionUIData.stages ++= stageIds
+        stageIds.foreach(stageId =>
+          _stageIdToStageMetrics(stageId) = new SQLStageMetrics(stageAttemptId = 0))
+        _jobIdToExecutionId(jobId) = executionId
+      }
+    }
+  }
+
+  override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = synchronized {
+    val jobId = jobEnd.jobId
+    for (executionId <- _jobIdToExecutionId.get(jobId);
+         executionUIData <- _executionIdToData.get(executionId)) {
+      jobEnd.jobResult match {
+        case JobSucceeded => executionUIData.jobs(jobId) = JobExecutionStatus.SUCCEEDED
+        case JobFailed(_) => executionUIData.jobs(jobId) = JobExecutionStatus.FAILED
+      }
+      if (executionUIData.completionTime.nonEmpty && !executionUIData.hasRunningJobs) {
+        // We are the last job of this execution, so mark the execution as finished. Note that
+        // `onExecutionEnd` also does this, but currently that can be called before `onJobEnd`
+        // since these are called on different threads.
+        markExecutionFinished(executionId)
+      }
+    }
+  }
+
+  override def onExecutorMetricsUpdate(
+      executorMetricsUpdate: SparkListenerExecutorMetricsUpdate): Unit = synchronized {
+    for ((taskId, stageId, stageAttemptID, metrics) <- executorMetricsUpdate.taskMetrics) {
+      updateTaskAccumulatorValues(taskId, stageId, stageAttemptID, metrics, finishTask = false)
+    }
+  }
+
+  override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted): Unit = synchronized {
+    val stageId = stageSubmitted.stageInfo.stageId
+    val stageAttemptId = stageSubmitted.stageInfo.attemptId
+    // Always override metrics for old stage attempt
+    _stageIdToStageMetrics(stageId) = new SQLStageMetrics(stageAttemptId)
+  }
+
+  override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = synchronized {
+    updateTaskAccumulatorValues(
+      taskEnd.taskInfo.taskId,
+      taskEnd.stageId,
+      taskEnd.stageAttemptId,
+      taskEnd.taskMetrics,
+      finishTask = true)
+  }
+
+  /**
+   * Update the accumulator values of a task with the latest metrics for this task. This is called
+   * every time we receive an executor heartbeat or when a task finishes.
+   */
+  private def updateTaskAccumulatorValues(
+      taskId: Long,
+      stageId: Int,
+      stageAttemptID: Int,
+      metrics: TaskMetrics,
+      finishTask: Boolean): Unit = {
+    if (metrics == null) {
+      return
+    }
+
+    _stageIdToStageMetrics.get(stageId) match {
+      case Some(stageMetrics) =>
+        if (stageAttemptID < stageMetrics.stageAttemptId) {
+          // A task of an old stage attempt. Because a new stage is submitted, we can ignore it.
+        } else if (stageAttemptID > stageMetrics.stageAttemptId) {
+          logWarning(s"A task should not have a higher stageAttemptID ($stageAttemptID) then " +
+            s"what we have seen (${stageMetrics.stageAttemptId}})")
+        } else {
+          // TODO We don't know the attemptId. Currently, what we can do is overriding the
+          // accumulator updates. However, if there are two same task are running, such as
+          // speculation, the accumulator updates will be overriding by different task attempts,
+          // the results will be weird.
+          stageMetrics.taskIdToMetricUpdates.get(taskId) match {
+            case Some(taskMetrics) =>
+              if (finishTask) {
+                taskMetrics.finished = true
+                taskMetrics.accumulatorUpdates = metrics.accumulatorUpdates()
+              } else if (!taskMetrics.finished) {
+                taskMetrics.accumulatorUpdates = metrics.accumulatorUpdates()
+              } else {
+                // If a task is finished, we should not override with accumulator updates from
+                // heartbeat reports
+              }
+            case None =>
+              // TODO Now just set attemptId to 0. Should fix here when we can get the attempt
+              // id from SparkListenerExecutorMetricsUpdate
+              stageMetrics.taskIdToMetricUpdates(taskId) = new SQLTaskMetrics(
+                  attemptId = 0, finished = finishTask, metrics.accumulatorUpdates())
+          }
+        }
+      case None =>
+      // This execution and its stage have been dropped
+    }
+  }
+
+  def onExecutionStart(
+      executionId: Long,
+      description: String,
+      details: String,
+      physicalPlanDescription: String,
+      physicalPlanGraph: SparkPlanGraph,
+      time: Long): Unit = {
+    val sqlPlanMetrics = physicalPlanGraph.nodes.flatMap { node =>
+      node.metrics.map(metric => metric.accumulatorId -> metric)
+    }
+
+    val executionUIData = new SQLExecutionUIData(executionId, description, details,
+      physicalPlanDescription, physicalPlanGraph, sqlPlanMetrics.toMap, time)
+    synchronized {
+      activeExecutions(executionId) = executionUIData
+      _executionIdToData(executionId) = executionUIData
+    }
+  }
+
+  def onExecutionEnd(executionId: Long, time: Long): Unit = synchronized {
+    _executionIdToData.get(executionId).foreach { executionUIData =>
+      executionUIData.completionTime = Some(time)
+      if (!executionUIData.hasRunningJobs) {
+        // onExecutionEnd happens after all "onJobEnd"s
+        // So we should update the execution lists.
+        markExecutionFinished(executionId)
+      } else {
+        // There are some running jobs, onExecutionEnd happens before some "onJobEnd"s.
+        // Then we don't if the execution is successful, so let the last onJobEnd updates the
+        // execution lists.
+      }
+    }
+  }
+
+  private def markExecutionFinished(executionId: Long): Unit = {
+    activeExecutions.remove(executionId).foreach { executionUIData =>
+      if (executionUIData.isFailed) {
+        failedExecutions += executionUIData
+        trimExecutionsIfNecessary(failedExecutions)
+      } else {
+        completedExecutions += executionUIData
+        trimExecutionsIfNecessary(completedExecutions)
+      }
+    }
+  }
+
+  def getRunningExecutions: Seq[SQLExecutionUIData] = synchronized {
+    activeExecutions.values.toSeq
+  }
+
+  def getFailedExecutions: Seq[SQLExecutionUIData] = synchronized {
+    failedExecutions
+  }
+
+  def getCompletedExecutions: Seq[SQLExecutionUIData] = synchronized {
+    completedExecutions
+  }
+
+  def getExecution(executionId: Long): Option[SQLExecutionUIData] = synchronized {
+    _executionIdToData.get(executionId)
+  }
+
+  /**
+   * Get all accumulator updates from all tasks which belong to this execution and merge them.
+   */
+  def getExecutionMetrics(executionId: Long): Map[Long, Any] = synchronized {
+    _executionIdToData.get(executionId) match {
+      case Some(executionUIData) =>
+        val accumulatorUpdates = {
+          for (stageId <- executionUIData.stages;
+               stageMetrics <- _stageIdToStageMetrics.get(stageId).toIterable;
+               taskMetrics <- stageMetrics.taskIdToMetricUpdates.values;
+               accumulatorUpdate <- taskMetrics.accumulatorUpdates.toSeq) yield {
+            accumulatorUpdate
+          }
+        }.filter { case (id, _) => executionUIData.accumulatorMetrics.keySet(id) }
+        mergeAccumulatorUpdates(accumulatorUpdates, accumulatorId =>
+          executionUIData.accumulatorMetrics(accumulatorId).accumulatorParam)
+      case None =>
+        // This execution has been dropped
+        Map.empty
+    }
+  }
+
+  private def mergeAccumulatorUpdates(
+      accumulatorUpdates: Seq[(Long, Any)],
+      paramFunc: Long => AccumulatorParam[Any]): Map[Long, Any] = {
+    accumulatorUpdates.groupBy(_._1).map { case (accumulatorId, values) =>
+      val param = paramFunc(accumulatorId)
+      (accumulatorId, values.map(_._2).reduceLeft(param.addInPlace))
+    }
+  }
+
+}
+
+/**
+ * Represent all necessary data for an execution that will be used in Web UI.
+ */
+private[ui] class SQLExecutionUIData(
+    val executionId: Long,
+    val description: String,
+    val details: String,
+    val physicalPlanDescription: String,
+    val physicalPlanGraph: SparkPlanGraph,
+    val accumulatorMetrics: Map[Long, SQLPlanMetric],
+    val submissionTime: Long,
+    var completionTime: Option[Long] = None,
+    val jobs: mutable.HashMap[Long, JobExecutionStatus] = mutable.HashMap.empty,
+    val stages: mutable.ArrayBuffer[Int] = mutable.ArrayBuffer()) {
+
+  /**
+   * Return whether there are running jobs in this execution.
+   */
+  def hasRunningJobs: Boolean = jobs.values.exists(_ == JobExecutionStatus.RUNNING)
+
+  /**
+   * Return whether there are any failed jobs in this execution.
+   */
+  def isFailed: Boolean = jobs.values.exists(_ == JobExecutionStatus.FAILED)
+
+  def runningJobs: Seq[Long] =
+    jobs.filter { case (_, status) => status == JobExecutionStatus.RUNNING }.keys.toSeq
+
+  def succeededJobs: Seq[Long] =
+    jobs.filter { case (_, status) => status == JobExecutionStatus.SUCCEEDED }.keys.toSeq
+
+  def failedJobs: Seq[Long] =
+    jobs.filter { case (_, status) => status == JobExecutionStatus.FAILED }.keys.toSeq
+}
+
+/**
+ * Represent a metric in a SQLPlan.
+ *
+ * Because we cannot revert our changes for an "Accumulator", we need to maintain accumulator
+ * updates for each task. So that if a task is retried, we can simply override the old updates with
+ * the new updates of the new attempt task. Since we cannot add them to accumulator, we need to use
+ * "AccumulatorParam" to get the aggregation value.
+ */
+private[ui] case class SQLPlanMetric(
+    name: String,
+    accumulatorId: Long,
+    accumulatorParam: AccumulatorParam[Any])
+
+/**
+ * Store all accumulatorUpdates for all tasks in a Spark stage.
+ */
+private[ui] class SQLStageMetrics(
+    val stageAttemptId: Long,
+    val taskIdToMetricUpdates: mutable.HashMap[Long, SQLTaskMetrics] = mutable.HashMap.empty)
+
+/**
+ * Store all accumulatorUpdates for a Spark task.
+ */
+private[ui] class SQLTaskMetrics(
+    val attemptId: Long, // TODO not used yet
+    var finished: Boolean,
+    var accumulatorUpdates: Map[Long, Any])
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
new file mode 100644
index 0000000000000..a9e5226303978
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
@@ -0,0 +1,41 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.ui.{SparkUI, SparkUITab}
+
+private[sql] class SQLTab(sqlContext: SQLContext, sparkUI: SparkUI)
+  extends SparkUITab(sparkUI, "sql") with Logging {
+
+
+  val parent = sparkUI
+  val listener = sqlContext.listener
+
+  attachPage(new AllExecutionsPage(this))
+  attachPage(new ExecutionPage(this))
+  parent.attachTab(this)
+
+  parent.addStaticHandler(SQLTab.STATIC_RESOURCE_DIR, "/static/sql")
+}
+
+private[sql] object SQLTab {
+
+  private val STATIC_RESOURCE_DIR = "org/apache/spark/sql/ui/static"
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
new file mode 100644
index 0000000000000..7910c163ba453
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
@@ -0,0 +1,118 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import java.util.concurrent.atomic.AtomicLong
+
+import scala.collection.mutable
+
+import org.apache.spark.AccumulatorParam
+import org.apache.spark.sql.execution.SparkPlan
+
+/**
+ * A graph used for storing information of an executionPlan of DataFrame.
+ *
+ * Each graph is defined with a set of nodes and a set of edges. Each node represents a node in the
+ * SparkPlan tree, and each edge represents a parent-child relationship between two nodes.
+ */
+private[ui] case class SparkPlanGraph(
+    nodes: Seq[SparkPlanGraphNode], edges: Seq[SparkPlanGraphEdge]) {
+
+  def makeDotFile(metrics: Map[Long, Any]): String = {
+    val dotFile = new StringBuilder
+    dotFile.append("digraph G {\n")
+    nodes.foreach(node => dotFile.append(node.makeDotNode(metrics) + "\n"))
+    edges.foreach(edge => dotFile.append(edge.makeDotEdge + "\n"))
+    dotFile.append("}")
+    dotFile.toString()
+  }
+}
+
+private[sql] object SparkPlanGraph {
+
+  /**
+   * Build a SparkPlanGraph from the root of a SparkPlan tree.
+   */
+  def apply(plan: SparkPlan): SparkPlanGraph = {
+    val nodeIdGenerator = new AtomicLong(0)
+    val nodes = mutable.ArrayBuffer[SparkPlanGraphNode]()
+    val edges = mutable.ArrayBuffer[SparkPlanGraphEdge]()
+    buildSparkPlanGraphNode(plan, nodeIdGenerator, nodes, edges)
+    new SparkPlanGraph(nodes, edges)
+  }
+
+  private def buildSparkPlanGraphNode(
+      plan: SparkPlan,
+      nodeIdGenerator: AtomicLong,
+      nodes: mutable.ArrayBuffer[SparkPlanGraphNode],
+      edges: mutable.ArrayBuffer[SparkPlanGraphEdge]): SparkPlanGraphNode = {
+    val metrics = plan.accumulators.toSeq.map { case (key, accumulator) =>
+      SQLPlanMetric(accumulator.name.getOrElse(key), accumulator.id,
+        accumulator.param.asInstanceOf[AccumulatorParam[Any]])
+    }
+    val node = SparkPlanGraphNode(
+      nodeIdGenerator.getAndIncrement(), plan.nodeName, plan.simpleString, metrics)
+    nodes += node
+    val childrenNodes = plan.children.map(
+      child => buildSparkPlanGraphNode(child, nodeIdGenerator, nodes, edges))
+    for (child <- childrenNodes) {
+      edges += SparkPlanGraphEdge(child.id, node.id)
+    }
+    node
+  }
+}
+
+/**
+ * Represent a node in the SparkPlan tree, along with its metrics.
+ *
+ * @param id generated by "SparkPlanGraph". There is no duplicate id in a graph
+ * @param name the name of this SparkPlan node
+ * @param metrics metrics that this SparkPlan node will track
+ */
+private[ui] case class SparkPlanGraphNode(
+    id: Long, name: String, desc: String, metrics: Seq[SQLPlanMetric]) {
+
+  def makeDotNode(metricsValue: Map[Long, Any]): String = {
+    val values = {
+      for (metric <- metrics;
+           value <- metricsValue.get(metric.accumulatorId)) yield {
+        metric.name + ": " + value
+      }
+    }
+    val label = if (values.isEmpty) {
+        name
+      } else {
+        // If there are metrics, display all metrics in a separate line. We should use an escaped
+        // "\n" here to follow the dot syntax.
+        //
+        // Note: whitespace between two "\n"s is to create an empty line between the name of
+        // SparkPlan and metrics. If removing it, it won't display the empty line in UI.
+        name + "\\n \\n" + values.mkString("\\n")
+      }
+    s"""  $id [label="$label"];"""
+  }
+}
+
+/**
+ * Represent an edge in the SparkPlan tree. `fromId` is the parent node id, and `toId` is the child
+ * node id.
+ */
+private[ui] case class SparkPlanGraphEdge(fromId: Long, toId: Long) {
+
+  def makeDotEdge: String = s"""  $fromId->$toId;\n"""
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index eb3e913322062..e9dd7ef226e42 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -305,12 +305,8 @@ class CachedTableSuite extends QueryTest {
     sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1")
     sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2")
 
-    Accumulators.synchronized {
-      val accsSize = Accumulators.originals.size
-      ctx.cacheTable("t1")
-      ctx.cacheTable("t2")
-      assert((accsSize + 2) == Accumulators.originals.size)
-    }
+    ctx.cacheTable("t1")
+    ctx.cacheTable("t2")
 
     sql("SELECT * FROM t1").count()
     sql("SELECT * FROM t2").count()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
new file mode 100644
index 0000000000000..f1fcaf59532b8
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
@@ -0,0 +1,347 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.ui
+
+import java.util.Properties
+
+import org.apache.spark.{SparkException, SparkContext, SparkConf, SparkFunSuite}
+import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.scheduler._
+import org.apache.spark.sql.{DataFrame, SQLContext}
+import org.apache.spark.sql.execution.SQLExecution
+import org.apache.spark.sql.test.TestSQLContext
+
+class SQLListenerSuite extends SparkFunSuite {
+
+  private def createTestDataFrame: DataFrame = {
+    import TestSQLContext.implicits._
+    Seq(
+      (1, 1),
+      (2, 2)
+    ).toDF().filter("_1 > 1")
+  }
+
+  private def createProperties(executionId: Long): Properties = {
+    val properties = new Properties()
+    properties.setProperty(SQLExecution.EXECUTION_ID_KEY, executionId.toString)
+    properties
+  }
+
+  private def createStageInfo(stageId: Int, attemptId: Int): StageInfo = new StageInfo(
+    stageId = stageId,
+    attemptId = attemptId,
+    // The following fields are not used in tests
+    name = "",
+    numTasks = 0,
+    rddInfos = Nil,
+    parentIds = Nil,
+    details = ""
+  )
+
+  private def createTaskInfo(taskId: Int, attempt: Int): TaskInfo = new TaskInfo(
+    taskId = taskId,
+    attempt = attempt,
+    // The following fields are not used in tests
+    index = 0,
+    launchTime = 0,
+    executorId = "",
+    host = "",
+    taskLocality = null,
+    speculative = false
+  )
+
+  private def createTaskMetrics(accumulatorUpdates: Map[Long, Any]): TaskMetrics = {
+    val metrics = new TaskMetrics
+    metrics.setAccumulatorsUpdater(() => accumulatorUpdates)
+    metrics.updateAccumulators()
+    metrics
+  }
+
+  test("basic") {
+    val listener = new SQLListener(TestSQLContext)
+    val executionId = 0
+    val df = createTestDataFrame
+    val accumulatorIds =
+      SparkPlanGraph(df.queryExecution.executedPlan).nodes.flatMap(_.metrics.map(_.accumulatorId))
+    // Assume all accumulators are long
+    var accumulatorValue = 0L
+    val accumulatorUpdates = accumulatorIds.map { id =>
+      accumulatorValue += 1L
+      (id, accumulatorValue)
+    }.toMap
+
+    listener.onExecutionStart(
+      executionId,
+      "test",
+      "test",
+      df.queryExecution.toString,
+      SparkPlanGraph(df.queryExecution.executedPlan),
+      System.currentTimeMillis())
+
+    val executionUIData = listener.executionIdToData(0)
+
+    listener.onJobStart(SparkListenerJobStart(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      stageInfos = Seq(
+        createStageInfo(0, 0),
+        createStageInfo(1, 0)
+      ),
+      createProperties(executionId)))
+    listener.onStageSubmitted(SparkListenerStageSubmitted(createStageInfo(0, 0)))
+
+    assert(listener.getExecutionMetrics(0).isEmpty)
+
+    listener.onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate("", Seq(
+      // (task id, stage id, stage attempt, metrics)
+      (0L, 0, 0, createTaskMetrics(accumulatorUpdates)),
+      (1L, 0, 0, createTaskMetrics(accumulatorUpdates))
+    )))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 2))
+
+    listener.onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate("", Seq(
+      // (task id, stage id, stage attempt, metrics)
+      (0L, 0, 0, createTaskMetrics(accumulatorUpdates)),
+      (1L, 0, 0, createTaskMetrics(accumulatorUpdates.mapValues(_ * 2)))
+    )))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 3))
+
+    // Retrying a stage should reset the metrics
+    listener.onStageSubmitted(SparkListenerStageSubmitted(createStageInfo(0, 1)))
+
+    listener.onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate("", Seq(
+      // (task id, stage id, stage attempt, metrics)
+      (0L, 0, 1, createTaskMetrics(accumulatorUpdates)),
+      (1L, 0, 1, createTaskMetrics(accumulatorUpdates))
+    )))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 2))
+
+    // Ignore the task end for the first attempt
+    listener.onTaskEnd(SparkListenerTaskEnd(
+      stageId = 0,
+      stageAttemptId = 0,
+      taskType = "",
+      reason = null,
+      createTaskInfo(0, 0),
+      createTaskMetrics(accumulatorUpdates.mapValues(_ * 100))))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 2))
+
+    // Finish two tasks
+    listener.onTaskEnd(SparkListenerTaskEnd(
+      stageId = 0,
+      stageAttemptId = 1,
+      taskType = "",
+      reason = null,
+      createTaskInfo(0, 0),
+      createTaskMetrics(accumulatorUpdates.mapValues(_ * 2))))
+    listener.onTaskEnd(SparkListenerTaskEnd(
+      stageId = 0,
+      stageAttemptId = 1,
+      taskType = "",
+      reason = null,
+      createTaskInfo(1, 0),
+      createTaskMetrics(accumulatorUpdates.mapValues(_ * 3))))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 5))
+
+    // Summit a new stage
+    listener.onStageSubmitted(SparkListenerStageSubmitted(createStageInfo(1, 0)))
+
+    listener.onExecutorMetricsUpdate(SparkListenerExecutorMetricsUpdate("", Seq(
+      // (task id, stage id, stage attempt, metrics)
+      (0L, 1, 0, createTaskMetrics(accumulatorUpdates)),
+      (1L, 1, 0, createTaskMetrics(accumulatorUpdates))
+    )))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 7))
+
+    // Finish two tasks
+    listener.onTaskEnd(SparkListenerTaskEnd(
+      stageId = 1,
+      stageAttemptId = 0,
+      taskType = "",
+      reason = null,
+      createTaskInfo(0, 0),
+      createTaskMetrics(accumulatorUpdates.mapValues(_ * 3))))
+    listener.onTaskEnd(SparkListenerTaskEnd(
+      stageId = 1,
+      stageAttemptId = 0,
+      taskType = "",
+      reason = null,
+      createTaskInfo(1, 0),
+      createTaskMetrics(accumulatorUpdates.mapValues(_ * 3))))
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 11))
+
+    assert(executionUIData.runningJobs === Seq(0))
+    assert(executionUIData.succeededJobs.isEmpty)
+    assert(executionUIData.failedJobs.isEmpty)
+
+    listener.onJobEnd(SparkListenerJobEnd(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      JobSucceeded
+    ))
+    listener.onExecutionEnd(executionId, System.currentTimeMillis())
+
+    assert(executionUIData.runningJobs.isEmpty)
+    assert(executionUIData.succeededJobs === Seq(0))
+    assert(executionUIData.failedJobs.isEmpty)
+
+    assert(listener.getExecutionMetrics(0) === accumulatorUpdates.mapValues(_ * 11))
+  }
+
+  test("onExecutionEnd happens before onJobEnd(JobSucceeded)") {
+    val listener = new SQLListener(TestSQLContext)
+    val executionId = 0
+    val df = createTestDataFrame
+    listener.onExecutionStart(
+      executionId,
+      "test",
+      "test",
+      df.queryExecution.toString,
+      SparkPlanGraph(df.queryExecution.executedPlan),
+      System.currentTimeMillis())
+    listener.onJobStart(SparkListenerJobStart(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      stageInfos = Nil,
+      createProperties(executionId)))
+    listener.onExecutionEnd(executionId, System.currentTimeMillis())
+    listener.onJobEnd(SparkListenerJobEnd(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      JobSucceeded
+    ))
+
+    val executionUIData = listener.executionIdToData(0)
+    assert(executionUIData.runningJobs.isEmpty)
+    assert(executionUIData.succeededJobs === Seq(0))
+    assert(executionUIData.failedJobs.isEmpty)
+  }
+
+  test("onExecutionEnd happens before multiple onJobEnd(JobSucceeded)s") {
+    val listener = new SQLListener(TestSQLContext)
+    val executionId = 0
+    val df = createTestDataFrame
+    listener.onExecutionStart(
+      executionId,
+      "test",
+      "test",
+      df.queryExecution.toString,
+      SparkPlanGraph(df.queryExecution.executedPlan),
+      System.currentTimeMillis())
+    listener.onJobStart(SparkListenerJobStart(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      stageInfos = Nil,
+      createProperties(executionId)))
+    listener.onJobEnd(SparkListenerJobEnd(
+        jobId = 0,
+        time = System.currentTimeMillis(),
+        JobSucceeded
+    ))
+
+    listener.onJobStart(SparkListenerJobStart(
+      jobId = 1,
+      time = System.currentTimeMillis(),
+      stageInfos = Nil,
+      createProperties(executionId)))
+    listener.onExecutionEnd(executionId, System.currentTimeMillis())
+    listener.onJobEnd(SparkListenerJobEnd(
+      jobId = 1,
+      time = System.currentTimeMillis(),
+      JobSucceeded
+    ))
+
+    val executionUIData = listener.executionIdToData(0)
+    assert(executionUIData.runningJobs.isEmpty)
+    assert(executionUIData.succeededJobs.sorted === Seq(0, 1))
+    assert(executionUIData.failedJobs.isEmpty)
+  }
+
+  test("onExecutionEnd happens before onJobEnd(JobFailed)") {
+    val listener = new SQLListener(TestSQLContext)
+    val executionId = 0
+    val df = createTestDataFrame
+    listener.onExecutionStart(
+      executionId,
+      "test",
+      "test",
+      df.queryExecution.toString,
+      SparkPlanGraph(df.queryExecution.executedPlan),
+      System.currentTimeMillis())
+    listener.onJobStart(SparkListenerJobStart(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      stageInfos = Seq.empty,
+      createProperties(executionId)))
+    listener.onExecutionEnd(executionId, System.currentTimeMillis())
+    listener.onJobEnd(SparkListenerJobEnd(
+      jobId = 0,
+      time = System.currentTimeMillis(),
+      JobFailed(new RuntimeException("Oops"))
+    ))
+
+    val executionUIData = listener.executionIdToData(0)
+    assert(executionUIData.runningJobs.isEmpty)
+    assert(executionUIData.succeededJobs.isEmpty)
+    assert(executionUIData.failedJobs === Seq(0))
+  }
+
+  ignore("no memory leak") {
+    val conf = new SparkConf()
+      .setMaster("local")
+      .setAppName("test")
+      .set("spark.task.maxFailures", "1") // Don't retry the tasks to run this test quickly
+      .set("spark.sql.ui.retainedExecutions", "50") // Set it to 50 to run this test quickly
+    val sc = new SparkContext(conf)
+    try {
+      val sqlContext = new SQLContext(sc)
+      import sqlContext.implicits._
+      // Run 100 successful executions and 100 failed executions.
+      // Each execution only has one job and one stage.
+      for (i <- 0 until 100) {
+        val df = Seq(
+          (1, 1),
+          (2, 2)
+        ).toDF()
+        df.collect()
+        try {
+          df.foreach(_ => throw new RuntimeException("Oops"))
+        } catch {
+          case e: SparkException => // This is expected for a failed job
+        }
+      }
+      sc.listenerBus.waitUntilEmpty(10000)
+      assert(sqlContext.listener.getCompletedExecutions.size <= 50)
+      assert(sqlContext.listener.getFailedExecutions.size <= 50)
+      // 50 for successful executions and 50 for failed executions
+      assert(sqlContext.listener.executionIdToData.size <= 100)
+      assert(sqlContext.listener.jobIdToExecutionId.size <= 100)
+      assert(sqlContext.listener.stageIdToStageMetrics.size <= 100)
+    } finally {
+      sc.stop()
+    }
+  }
+
+}

From 84ca3183b6fdd07ce56c5eb6686ac38dc5e96f9e Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Wed, 5 Aug 2015 02:04:28 -0700
Subject: [PATCH 055/633] [SPARK-9628][SQL]Rename int to SQLDate, long to
 SQLTimestamp for better readability

JIRA: https://issues.apache.org/jira/browse/SPARK-9628

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7953 from yjshen/datetime_alias and squashes the following commits:

3cac3cc [Yijie Shen] rename int to SQLDate, long to SQLTimestamp for better readability
---
 .../sql/catalyst/util/DateTimeUtils.scala     | 69 ++++++++++---------
 1 file changed, 37 insertions(+), 32 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 063940cb9e2c3..672620460c3c5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -31,6 +31,11 @@ import org.apache.spark.unsafe.types.UTF8String
  * precision.
  */
 object DateTimeUtils {
+
+  // we use Int and Long internally to represent [[DateType]] and [[TimestampType]]
+  type SQLDate = Int
+  type SQLTimestamp = Long
+
   // see http://stackoverflow.com/questions/466321/convert-unix-timestamp-to-julian
   final val JULIAN_DAY_OF_EPOCH = 2440587  // and .5
   final val SECONDS_PER_DAY = 60 * 60 * 24L
@@ -72,7 +77,7 @@ object DateTimeUtils {
   }
 
   // we should use the exact day as Int, for example, (year, month, day) -> day
-  def millisToDays(millisUtc: Long): Int = {
+  def millisToDays(millisUtc: Long): SQLDate = {
     // SPARK-6785: use Math.floor so negative number of days (dates before 1970)
     // will correctly work as input for function toJavaDate(Int)
     val millisLocal = millisUtc + threadLocalLocalTimeZone.get().getOffset(millisUtc)
@@ -80,16 +85,16 @@ object DateTimeUtils {
   }
 
   // reverse of millisToDays
-  def daysToMillis(days: Int): Long = {
+  def daysToMillis(days: SQLDate): Long = {
     val millisUtc = days.toLong * MILLIS_PER_DAY
     millisUtc - threadLocalLocalTimeZone.get().getOffset(millisUtc)
   }
 
-  def dateToString(days: Int): String =
+  def dateToString(days: SQLDate): String =
     threadLocalDateFormat.get.format(toJavaDate(days))
 
   // Converts Timestamp to string according to Hive TimestampWritable convention.
-  def timestampToString(us: Long): String = {
+  def timestampToString(us: SQLTimestamp): String = {
     val ts = toJavaTimestamp(us)
     val timestampString = ts.toString
     val formatted = threadLocalTimestampFormat.get.format(ts)
@@ -132,21 +137,21 @@ object DateTimeUtils {
   /**
    * Returns the number of days since epoch from from java.sql.Date.
    */
-  def fromJavaDate(date: Date): Int = {
+  def fromJavaDate(date: Date): SQLDate = {
     millisToDays(date.getTime)
   }
 
   /**
    * Returns a java.sql.Date from number of days since epoch.
    */
-  def toJavaDate(daysSinceEpoch: Int): Date = {
+  def toJavaDate(daysSinceEpoch: SQLDate): Date = {
     new Date(daysToMillis(daysSinceEpoch))
   }
 
   /**
    * Returns a java.sql.Timestamp from number of micros since epoch.
    */
-  def toJavaTimestamp(us: Long): Timestamp = {
+  def toJavaTimestamp(us: SQLTimestamp): Timestamp = {
     // setNanos() will overwrite the millisecond part, so the milliseconds should be
     // cut off at seconds
     var seconds = us / MICROS_PER_SECOND
@@ -164,7 +169,7 @@ object DateTimeUtils {
   /**
    * Returns the number of micros since epoch from java.sql.Timestamp.
    */
-  def fromJavaTimestamp(t: Timestamp): Long = {
+  def fromJavaTimestamp(t: Timestamp): SQLTimestamp = {
     if (t != null) {
       t.getTime() * 1000L + (t.getNanos().toLong / 1000) % 1000L
     } else {
@@ -176,7 +181,7 @@ object DateTimeUtils {
    * Returns the number of microseconds since epoch from Julian day
    * and nanoseconds in a day
    */
-  def fromJulianDay(day: Int, nanoseconds: Long): Long = {
+  def fromJulianDay(day: Int, nanoseconds: Long): SQLTimestamp = {
     // use Long to avoid rounding errors
     val seconds = (day - JULIAN_DAY_OF_EPOCH).toLong * SECONDS_PER_DAY - SECONDS_PER_DAY / 2
     seconds * MICROS_PER_SECOND + nanoseconds / 1000L
@@ -185,7 +190,7 @@ object DateTimeUtils {
   /**
    * Returns Julian day and nanoseconds in a day from the number of microseconds
    */
-  def toJulianDay(us: Long): (Int, Long) = {
+  def toJulianDay(us: SQLTimestamp): (Int, Long) = {
     val seconds = us / MICROS_PER_SECOND + SECONDS_PER_DAY / 2
     val day = seconds / SECONDS_PER_DAY + JULIAN_DAY_OF_EPOCH
     val secondsInDay = seconds % SECONDS_PER_DAY
@@ -219,7 +224,7 @@ object DateTimeUtils {
    * `T[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]-[h]h:[m]m`
    * `T[h]h:[m]m:[s]s.[ms][ms][ms][us][us][us]+[h]h:[m]m`
    */
-  def stringToTimestamp(s: UTF8String): Option[Long] = {
+  def stringToTimestamp(s: UTF8String): Option[SQLTimestamp] = {
     if (s == null) {
       return None
     }
@@ -355,7 +360,7 @@ object DateTimeUtils {
    * `yyyy-[m]m-[d]d *`
    * `yyyy-[m]m-[d]dT*`
    */
-  def stringToDate(s: UTF8String): Option[Int] = {
+  def stringToDate(s: UTF8String): Option[SQLDate] = {
     if (s == null) {
       return None
     }
@@ -394,7 +399,7 @@ object DateTimeUtils {
   /**
    * Returns the hour value of a given timestamp value. The timestamp is expressed in microseconds.
    */
-  def getHours(timestamp: Long): Int = {
+  def getHours(timestamp: SQLTimestamp): Int = {
     val localTs = (timestamp / 1000) + defaultTimeZone.getOffset(timestamp / 1000)
     ((localTs / 1000 / 3600) % 24).toInt
   }
@@ -403,7 +408,7 @@ object DateTimeUtils {
    * Returns the minute value of a given timestamp value. The timestamp is expressed in
    * microseconds.
    */
-  def getMinutes(timestamp: Long): Int = {
+  def getMinutes(timestamp: SQLTimestamp): Int = {
     val localTs = (timestamp / 1000) + defaultTimeZone.getOffset(timestamp / 1000)
     ((localTs / 1000 / 60) % 60).toInt
   }
@@ -412,7 +417,7 @@ object DateTimeUtils {
    * Returns the second value of a given timestamp value. The timestamp is expressed in
    * microseconds.
    */
-  def getSeconds(timestamp: Long): Int = {
+  def getSeconds(timestamp: SQLTimestamp): Int = {
     ((timestamp / 1000 / 1000) % 60).toInt
   }
 
@@ -447,7 +452,7 @@ object DateTimeUtils {
    * The calculation uses the fact that the period 1.1.2001 until 31.12.2400 is
    * equals to the period 1.1.1601 until 31.12.2000.
    */
-  private[this] def getYearAndDayInYear(daysSince1970: Int): (Int, Int) = {
+  private[this] def getYearAndDayInYear(daysSince1970: SQLDate): (Int, Int) = {
     // add the difference (in days) between 1.1.1970 and the artificial year 0 (-17999)
     val daysNormalized = daysSince1970 + toYearZero
     val numOfQuarterCenturies = daysNormalized / daysIn400Years
@@ -461,7 +466,7 @@ object DateTimeUtils {
    * Returns the 'day in year' value for the given date. The date is expressed in days
    * since 1.1.1970.
    */
-  def getDayInYear(date: Int): Int = {
+  def getDayInYear(date: SQLDate): Int = {
     getYearAndDayInYear(date)._2
   }
 
@@ -469,7 +474,7 @@ object DateTimeUtils {
    * Returns the year value for the given date. The date is expressed in days
    * since 1.1.1970.
    */
-  def getYear(date: Int): Int = {
+  def getYear(date: SQLDate): Int = {
     getYearAndDayInYear(date)._1
   }
 
@@ -477,7 +482,7 @@ object DateTimeUtils {
    * Returns the quarter for the given date. The date is expressed in days
    * since 1.1.1970.
    */
-  def getQuarter(date: Int): Int = {
+  def getQuarter(date: SQLDate): Int = {
     var (year, dayInYear) = getYearAndDayInYear(date)
     if (isLeapYear(year)) {
       dayInYear = dayInYear - 1
@@ -497,7 +502,7 @@ object DateTimeUtils {
    * Split date (expressed in days since 1.1.1970) into four fields:
    * year, month (Jan is Month 1), dayInMonth, daysToMonthEnd (0 if it's last day of month).
    */
-  def splitDate(date: Int): (Int, Int, Int, Int) = {
+  def splitDate(date: SQLDate): (Int, Int, Int, Int) = {
     var (year, dayInYear) = getYearAndDayInYear(date)
     val isLeap = isLeapYear(year)
     if (isLeap && dayInYear == 60) {
@@ -541,7 +546,7 @@ object DateTimeUtils {
    * Returns the month value for the given date. The date is expressed in days
    * since 1.1.1970. January is month 1.
    */
-  def getMonth(date: Int): Int = {
+  def getMonth(date: SQLDate): Int = {
     var (year, dayInYear) = getYearAndDayInYear(date)
     if (isLeapYear(year)) {
       if (dayInYear == 60) {
@@ -582,7 +587,7 @@ object DateTimeUtils {
    * Returns the 'day of month' value for the given date. The date is expressed in days
    * since 1.1.1970.
    */
-  def getDayOfMonth(date: Int): Int = {
+  def getDayOfMonth(date: SQLDate): Int = {
     var (year, dayInYear) = getYearAndDayInYear(date)
     if (isLeapYear(year)) {
       if (dayInYear == 60) {
@@ -628,7 +633,7 @@ object DateTimeUtils {
    * Returns the date value for the first day of the given month.
    * The month is expressed in months since year zero (17999 BC), starting from 0.
    */
-  private def firstDayOfMonth(absoluteMonth: Int): Int = {
+  private def firstDayOfMonth(absoluteMonth: Int): SQLDate = {
     val absoluteYear = absoluteMonth / 12
     var monthInYear = absoluteMonth - absoluteYear * 12
     var date = getDateFromYear(absoluteYear)
@@ -646,7 +651,7 @@ object DateTimeUtils {
    * Returns the date value for January 1 of the given year.
    * The year is expressed in years since year zero (17999 BC), starting from 0.
    */
-  private def getDateFromYear(absoluteYear: Int): Int = {
+  private def getDateFromYear(absoluteYear: Int): SQLDate = {
     val absoluteDays = (absoluteYear * 365 + absoluteYear / 400 - absoluteYear / 100
       + absoluteYear / 4)
     absoluteDays - toYearZero
@@ -656,7 +661,7 @@ object DateTimeUtils {
    * Add date and year-month interval.
    * Returns a date value, expressed in days since 1.1.1970.
    */
-  def dateAddMonths(days: Int, months: Int): Int = {
+  def dateAddMonths(days: SQLDate, months: Int): SQLDate = {
     val (year, monthInYear, dayOfMonth, daysToMonthEnd) = splitDate(days)
     val absoluteMonth = (year - YearZero) * 12 + monthInYear - 1 + months
     val nonNegativeMonth = if (absoluteMonth >= 0) absoluteMonth else 0
@@ -679,7 +684,7 @@ object DateTimeUtils {
    * Add timestamp and full interval.
    * Returns a timestamp value, expressed in microseconds since 1.1.1970 00:00:00.
    */
-  def timestampAddInterval(start: Long, months: Int, microseconds: Long): Long = {
+  def timestampAddInterval(start: SQLTimestamp, months: Int, microseconds: Long): SQLTimestamp = {
     val days = millisToDays(start / 1000L)
     val newDays = dateAddMonths(days, months)
     daysToMillis(newDays) * 1000L + start - daysToMillis(days) * 1000L + microseconds
@@ -695,7 +700,7 @@ object DateTimeUtils {
    * Otherwise, the difference is calculated based on 31 days per month, and rounding to
    * 8 digits.
    */
-  def monthsBetween(time1: Long, time2: Long): Double = {
+  def monthsBetween(time1: SQLTimestamp, time2: SQLTimestamp): Double = {
     val millis1 = time1 / 1000L
     val millis2 = time2 / 1000L
     val date1 = millisToDays(millis1)
@@ -740,7 +745,7 @@ object DateTimeUtils {
    * Returns the first date which is later than startDate and is of the given dayOfWeek.
    * dayOfWeek is an integer ranges in [0, 6], and 0 is Thu, 1 is Fri, etc,.
    */
-  def getNextDateForDayOfWeek(startDate: Int, dayOfWeek: Int): Int = {
+  def getNextDateForDayOfWeek(startDate: SQLDate, dayOfWeek: Int): SQLDate = {
     startDate + 1 + ((dayOfWeek - 1 - startDate) % 7 + 7) % 7
   }
 
@@ -748,7 +753,7 @@ object DateTimeUtils {
    * Returns last day of the month for the given date. The date is expressed in days
    * since 1.1.1970.
    */
-  def getLastDayOfMonth(date: Int): Int = {
+  def getLastDayOfMonth(date: SQLDate): SQLDate = {
     val (_, _, _, daysToMonthEnd) = splitDate(date)
     date + daysToMonthEnd
   }
@@ -761,7 +766,7 @@ object DateTimeUtils {
    * Returns the trunc date from original date and trunc level.
    * Trunc level should be generated using `parseTruncLevel()`, should only be 1 or 2.
    */
-  def truncDate(d: Int, level: Int): Int = {
+  def truncDate(d: SQLDate, level: Int): SQLDate = {
     if (level == TRUNC_TO_YEAR) {
       d - DateTimeUtils.getDayInYear(d) + 1
     } else if (level == TRUNC_TO_MONTH) {
@@ -792,7 +797,7 @@ object DateTimeUtils {
    * Returns a timestamp of given timezone from utc timestamp, with the same string
    * representation in their timezone.
    */
-  def fromUTCTime(time: Long, timeZone: String): Long = {
+  def fromUTCTime(time: SQLTimestamp, timeZone: String): SQLTimestamp = {
     val tz = TimeZone.getTimeZone(timeZone)
     val offset = tz.getOffset(time / 1000L)
     time + offset * 1000L
@@ -802,7 +807,7 @@ object DateTimeUtils {
    * Returns a utc timestamp from a given timestamp from a given timezone, with the same
    * string representation in their timezone.
    */
-  def toUTCTime(time: Long, timeZone: String): Long = {
+  def toUTCTime(time: SQLTimestamp, timeZone: String): SQLTimestamp = {
     val tz = TimeZone.getTimeZone(timeZone)
     val offset = tz.getOffset(time / 1000L)
     time - offset * 1000L

From 26b06f1c4630ccc98b0eb1bf7294347899c4314f Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 5 Aug 2015 02:39:41 -0700
Subject: [PATCH 056/633] [HOTFIX] Add static import to fix build break from
 #7676.

---
 .../spark/util/collection/unsafe/sort/PrefixComparators.java     | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
index b5f661c0d5481..5e002ae1b7568 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -23,6 +23,7 @@
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.types.UTF8String;
 import org.apache.spark.util.Utils;
+import static org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET;
 
 @Private
 public class PrefixComparators {

From e27a8c4cb3564f1b2d1ee5445dff341c8e0087b0 Mon Sep 17 00:00:00 2001
From: Ryan Williams <ryan.blake.williams@gmail.com>
Date: Wed, 5 Aug 2015 11:10:47 +0100
Subject: [PATCH 057/633] [SPARK-9607] [SPARK-9608] fix zinc-port handling in
 build/mvn

- pass `$ZINC_PORT` to zinc status/shutdown commands
- fix path check that sets `$ZINC_INSTALL_FLAG`, which was incorrectly
  causing zinc to be shutdown and restarted every time (with mismatched
  ports on those commands to boot)
- pass `-DzincPort=${ZINC_PORT}` to maven, to use the correct zinc port
  when building

Author: Ryan Williams <ryan.blake.williams@gmail.com>

Closes #7944 from ryan-williams/zinc-status and squashes the following commits:

619c520 [Ryan Williams] fix zinc status/shutdown commands
---
 build/mvn | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/build/mvn b/build/mvn
index 4a1664b04053d..ec0380afad319 100755
--- a/build/mvn
+++ b/build/mvn
@@ -82,7 +82,7 @@ install_mvn() {
 # Install zinc under the build/ folder
 install_zinc() {
   local zinc_path="zinc-0.3.5.3/bin/zinc"
-  [ ! -f "${zinc_path}" ] && ZINC_INSTALL_FLAG=1
+  [ ! -f "${_DIR}/${zinc_path}" ] && ZINC_INSTALL_FLAG=1
   install_app \
     "http://downloads.typesafe.com/zinc/0.3.5.3" \
     "zinc-0.3.5.3.tgz" \
@@ -135,9 +135,9 @@ cd "${_CALLING_DIR}"
 
 # Now that zinc is ensured to be installed, check its status and, if its
 # not running or just installed, start it
-if [ -n "${ZINC_INSTALL_FLAG}" -o -z "`${ZINC_BIN} -status`" ]; then
+if [ -n "${ZINC_INSTALL_FLAG}" -o -z "`${ZINC_BIN} -status -port ${ZINC_PORT}`" ]; then
   export ZINC_OPTS=${ZINC_OPTS:-"$_COMPILE_JVM_OPTS"}
-  ${ZINC_BIN} -shutdown
+  ${ZINC_BIN} -shutdown -port ${ZINC_PORT}
   ${ZINC_BIN} -start -port ${ZINC_PORT} \
     -scala-compiler "${SCALA_COMPILER}" \
     -scala-library "${SCALA_LIBRARY}" &>/dev/null
@@ -149,4 +149,4 @@ export MAVEN_OPTS=${MAVEN_OPTS:-"$_COMPILE_JVM_OPTS"}
 echo "Using \`mvn\` from path: $MVN_BIN" 1>&2
 
 # Last, call the `mvn` command as usual
-${MVN_BIN} "$@"
+${MVN_BIN} -DzincPort=${ZINC_PORT} "$@"

From 70112ff22bd1aee7689c5d3af9b66c9b8ceb3ec3 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 5 Aug 2015 20:03:54 +0800
Subject: [PATCH 058/633] [SPARK-9593] [SQL] Fixes Hadoop shims loading

This PR is used to workaround CDH Hadoop versions like 2.0.0-mr1-cdh4.1.1.

Internally, Hive `ShimLoader` tries to load different versions of Hadoop shims by checking version information gathered from Hadoop jar files.  If the major version number is 1, `Hadoop20SShims` will be loaded.  Otherwise, if the major version number is 2, `Hadoop23Shims` will be chosen.  However, CDH Hadoop versions like 2.0.0-mr1-cdh4.1.1 have 2 as major version number, but contain Hadoop 1 code.  This confuses Hive `ShimLoader` and loads wrong version of shims.

In this PR we check for existence of the `Path.getPathWithoutSchemeAndAuthority` method, which doesn't exist in Hadoop 1 (it's also the method that reveals this shims loading issue), and load `Hadoop20SShims` when it doesn't exist.

Author: Cheng Lian <lian@databricks.com>

Closes #7929 from liancheng/spark-9593/fix-hadoop-shims-loading and squashes the following commits:

c99b497 [Cheng Lian] Narrows down the fix to handle "2.0.0-*cdh4*" Hadoop versions only
b17e955 [Cheng Lian] Updates comments
490d8f2 [Cheng Lian] Fixes Scala style issue
9c6c12d [Cheng Lian] Fixes Hadoop shims loading
---
 .../spark/sql/hive/client/ClientWrapper.scala | 48 +++++++++++++++++++
 1 file changed, 48 insertions(+)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index dc372be0e5a37..211a3b879c1b3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -32,6 +32,8 @@ import org.apache.hadoop.hive.ql.metadata.Hive
 import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.ql.session.SessionState
 import org.apache.hadoop.hive.ql.{Driver, metadata}
+import org.apache.hadoop.hive.shims.{HadoopShims, ShimLoader}
+import org.apache.hadoop.util.VersionInfo
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.expressions.Expression
@@ -62,6 +64,52 @@ private[hive] class ClientWrapper(
   extends ClientInterface
   with Logging {
 
+  overrideHadoopShims()
+
+  // !! HACK ALERT !!
+  //
+  // This method is a surgical fix for Hadoop version 2.0.0-mr1-cdh4.1.1, which is used by Spark EC2
+  // scripts.  We should remove this after upgrading Spark EC2 scripts to some more recent Hadoop
+  // version in the future.
+  //
+  // Internally, Hive `ShimLoader` tries to load different versions of Hadoop shims by checking
+  // version information gathered from Hadoop jar files.  If the major version number is 1,
+  // `Hadoop20SShims` will be loaded.  Otherwise, if the major version number is 2, `Hadoop23Shims`
+  // will be chosen.
+  //
+  // However, part of APIs in Hadoop 2.0.x and 2.1.x versions were in flux due to historical
+  // reasons. So 2.0.0-mr1-cdh4.1.1 is actually more Hadoop-1-like and should be used together with
+  // `Hadoop20SShims`, but `Hadoop20SShims` is chose because the major version number here is 2.
+  //
+  // Here we check for this specific version and loads `Hadoop20SShims` via reflection.  Note that
+  // we can't check for string literal "2.0.0-mr1-cdh4.1.1" because the obtained version string
+  // comes from Maven artifact org.apache.hadoop:hadoop-common:2.0.0-cdh4.1.1, which doesn't have
+  // the "mr1" tag in its version string.
+  private def overrideHadoopShims(): Unit = {
+    val VersionPattern = """2\.0\.0.*cdh4.*""".r
+
+    VersionInfo.getVersion match {
+      case VersionPattern() =>
+        val shimClassName = "org.apache.hadoop.hive.shims.Hadoop20SShims"
+        logInfo(s"Loading Hadoop shims $shimClassName")
+
+        try {
+          val shimsField = classOf[ShimLoader].getDeclaredField("hadoopShims")
+          // scalastyle:off classforname
+          val shimsClass = Class.forName(shimClassName)
+          // scalastyle:on classforname
+          val shims = classOf[HadoopShims].cast(shimsClass.newInstance())
+          shimsField.setAccessible(true)
+          shimsField.set(null, shims)
+        } catch { case cause: Throwable =>
+          logError(s"Failed to load $shimClassName")
+          // Falls back to normal Hive `ShimLoader` logic
+        }
+
+      case _ =>
+    }
+  }
+
   // Circular buffer to hold what hive prints to STDOUT and ERR.  Only printed when failures occur.
   private val outputBuffer = new CircularBuffer()
 

From eb8bfa3eaa0846d685e4d12f9ee2e4273b85edcf Mon Sep 17 00:00:00 2001
From: Nathan Howell <nhowell@godaddy.com>
Date: Wed, 5 Aug 2015 22:16:56 +0800
Subject: [PATCH 059/633] [SPARK-9618] [SQL] Use the specified schema when
 reading Parquet files

The user specified schema is currently ignored when loading Parquet files.

One workaround is to use the `format` and `load` methods instead of `parquet`, e.g.:

```
val schema = ???

// schema is ignored
sqlContext.read.schema(schema).parquet("hdfs:///test")

// schema is retained
sqlContext.read.schema(schema).format("parquet").load("hdfs:///test")
```

The fix is simple, but I wonder if the `parquet` method should instead be written in a similar fashion to `orc`:

```
def parquet(path: String): DataFrame = format("parquet").load(path)
```

Author: Nathan Howell <nhowell@godaddy.com>

Closes #7947 from NathanHowell/SPARK-9618 and squashes the following commits:

d1ea62c [Nathan Howell] [SPARK-9618] [SQL] Use the specified schema when reading Parquet files
---
 .../src/main/scala/org/apache/spark/sql/DataFrameReader.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index eb09807f9d9c2..b90de8ef09048 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -260,7 +260,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
 
       sqlContext.baseRelationToDataFrame(
         new ParquetRelation(
-          globbedPaths.map(_.toString), None, None, extraOptions.toMap)(sqlContext))
+          globbedPaths.map(_.toString), userSpecifiedSchema, None, extraOptions.toMap)(sqlContext))
     }
   }
 

From 519cf6d3f764a977770266784d6902fe205a070f Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Wed, 5 Aug 2015 22:35:55 +0800
Subject: [PATCH 060/633] [SPARK-9381] [SQL] Migrate JSON data source to the
 new partitioning data source

Support partitioning for the JSON data source.

Still 2 open issues for the `HadoopFsRelation`
- `refresh()` will invoke the `discoveryPartition()`, which will auto infer the data type for the partition columns, and maybe conflict with the given partition columns. (TODO enable `HadoopFsRelationSuite.Partition column type casting"
- When insert data into a cached HadoopFsRelation based table, we need to invalidate the cache after the insertion (TODO enable `InsertSuite.Caching`)

Author: Cheng Hao <hao.cheng@intel.com>

Closes #7696 from chenghao-intel/json and squashes the following commits:

d90b104 [Cheng Hao] revert the change for JacksonGenerator.apply
307111d [Cheng Hao] fix bug in the unit test
8738c8a [Cheng Hao] fix bug in unit testing
35f2cde [Cheng Hao] support partition for json format
---
 .../org/apache/spark/sql/DataFrame.scala      |   2 -
 .../apache/spark/sql/DataFrameReader.scala    |   2 +-
 .../datasources/DataSourceStrategy.scala      |   2 +
 .../sql/execution/datasources/commands.scala  |  10 +-
 .../sql/execution/datasources/rules.scala     |  14 +-
 .../apache/spark/sql/json/JSONRelation.scala  | 285 ++++++++----------
 .../spark/sql/json/JacksonGenerator.scala     |  58 ++++
 .../org/apache/spark/sql/DataFrameSuite.scala |   9 +-
 .../org/apache/spark/sql/json/JsonSuite.scala |  96 +++++-
 .../apache/spark/sql/json/TestJsonData.scala  |   4 +
 .../spark/sql/sources/InsertSuite.scala       |  47 ++-
 .../spark/sql/sources/SaveLoadSuite.scala     |  27 +-
 .../sql/hive/MetastoreDataSourcesSuite.scala  |  15 +-
 .../SimpleTextHadoopFsRelationSuite.scala     |  30 ++
 .../sql/sources/hadoopFsRelationSuites.scala  |   4 +-
 15 files changed, 382 insertions(+), 223 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 1f83567df88cc..db15711202b77 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1567,8 +1567,6 @@ class DataFrame private[sql](
     val files: Seq[String] = logicalPlan.collect {
       case LogicalRelation(fsBasedRelation: HadoopFsRelation) =>
         fsBasedRelation.paths.toSeq
-      case LogicalRelation(jsonRelation: JSONRelation) =>
-        jsonRelation.path.toSeq
     }.flatten
     files.toSet.toArray
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index b90de8ef09048..85f33c5e99523 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -237,7 +237,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
   def json(jsonRDD: RDD[String]): DataFrame = {
     val samplingRatio = extraOptions.getOrElse("samplingRatio", "1.0").toDouble
     sqlContext.baseRelationToDataFrame(
-      new JSONRelation(() => jsonRDD, None, samplingRatio, userSpecifiedSchema)(sqlContext))
+      new JSONRelation(Some(jsonRDD), samplingRatio, userSpecifiedSchema, None, None)(sqlContext))
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index d9d7bc19bd419..a43bccbe6927c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -60,6 +60,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
     // Scanning partitioned HadoopFsRelation
     case PhysicalOperation(projects, filters, l @ LogicalRelation(t: HadoopFsRelation))
         if t.partitionSpec.partitionColumns.nonEmpty =>
+      t.refresh()
       val selectedPartitions = prunePartitions(filters, t.partitionSpec).toArray
 
       logInfo {
@@ -87,6 +88,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
 
     // Scanning non-partitioned HadoopFsRelation
     case PhysicalOperation(projects, filters, l @ LogicalRelation(t: HadoopFsRelation)) =>
+      t.refresh()
       // See buildPartitionedTableScan for the reason that we need to create a shard
       // broadcast HadoopConf.
       val sharedHadoopConf = SparkHadoopUtil.get.conf
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
index cf199118287dc..42668979c9a32 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution.datasources
 
+import java.io.IOException
 import java.util.{Date, UUID}
 
 import scala.collection.JavaConversions.asScalaIterator
@@ -36,7 +37,7 @@ import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StringType
-import org.apache.spark.util.SerializableConfiguration
+import org.apache.spark.util.{Utils, SerializableConfiguration}
 
 
 private[sql] case class InsertIntoDataSource(
@@ -102,7 +103,12 @@ private[sql] case class InsertIntoHadoopFsRelation(
       case (SaveMode.ErrorIfExists, true) =>
         throw new AnalysisException(s"path $qualifiedOutputPath already exists.")
       case (SaveMode.Overwrite, true) =>
-        fs.delete(qualifiedOutputPath, true)
+        Utils.tryOrIOException {
+          if (!fs.delete(qualifiedOutputPath, true /* recursively */)) {
+            throw new IOException(s"Unable to clear output " +
+              s"directory $qualifiedOutputPath prior to writing to it")
+          }
+        }
         true
       case (SaveMode.Append, _) | (SaveMode.Overwrite, _) | (SaveMode.ErrorIfExists, false) =>
         true
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
index 11bb49b8d83de..40ca8bf4095d8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
@@ -101,7 +101,8 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           }
         }
 
-      case logical.InsertIntoTable(LogicalRelation(r: HadoopFsRelation), part, _, _, _) =>
+      case logical.InsertIntoTable(
+        LogicalRelation(r: HadoopFsRelation), part, query, overwrite, _) =>
         // We need to make sure the partition columns specified by users do match partition
         // columns of the relation.
         val existingPartitionColumns = r.partitionColumns.fieldNames.toSet
@@ -115,6 +116,17 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           // OK
         }
 
+        // Get all input data source relations of the query.
+        val srcRelations = query.collect {
+          case LogicalRelation(src: BaseRelation) => src
+        }
+        if (srcRelations.contains(r)) {
+          failAnalysis(
+            "Cannot insert overwrite into table that is also being read from.")
+        } else {
+          // OK
+        }
+
       case logical.InsertIntoTable(l: LogicalRelation, _, _, _, _) =>
         // The relation in l is not an InsertableRelation.
         failAnalysis(s"$l does not allow insertion.")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index 562b058414d07..5d371402877c6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -17,31 +17,52 @@
 
 package org.apache.spark.sql.json
 
-import java.io.IOException
-
-import org.apache.hadoop.fs.{FileSystem, Path}
+import java.io.CharArrayWriter
+
+import com.fasterxml.jackson.core.JsonFactory
+import com.google.common.base.Objects
+import org.apache.hadoop.fs.{FileStatus, Path}
+import org.apache.hadoop.io.{Text, LongWritable, NullWritable}
+import org.apache.hadoop.mapred.{JobConf, TextInputFormat}
+import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat
+import org.apache.hadoop.mapreduce.{RecordWriter, TaskAttemptContext, Job}
+import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
+import org.apache.spark.Logging
+import org.apache.spark.mapred.SparkHadoopMapRedUtil
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.AnalysisException
-import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.datasources.PartitionSpec
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StructType
-import org.apache.spark.sql.{DataFrame, Row, SQLContext, SaveMode}
-
+import org.apache.spark.sql.{AnalysisException, Row, SQLContext}
 
-private[sql] class DefaultSource
-  extends RelationProvider
-  with SchemaRelationProvider
-  with CreatableRelationProvider {
+private[sql] class DefaultSource extends HadoopFsRelationProvider {
+  override def createRelation(
+      sqlContext: SQLContext,
+      paths: Array[String],
+      dataSchema: Option[StructType],
+      partitionColumns: Option[StructType],
+      parameters: Map[String, String]): HadoopFsRelation = {
+    val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0)
 
-  private def checkPath(parameters: Map[String, String]): String = {
-    parameters.getOrElse("path", sys.error("'path' must be specified for json data."))
+    new JSONRelation(None, samplingRatio, dataSchema, None, partitionColumns, paths)(sqlContext)
   }
+}
 
-  /** Constraints to be imposed on dataframe to be stored. */
-  private def checkConstraints(data: DataFrame): Unit = {
-    if (data.schema.fieldNames.length != data.schema.fieldNames.distinct.length) {
-      val duplicateColumns = data.schema.fieldNames.groupBy(identity).collect {
+private[sql] class JSONRelation(
+    val inputRDD: Option[RDD[String]],
+    val samplingRatio: Double,
+    val maybeDataSchema: Option[StructType],
+    val maybePartitionSpec: Option[PartitionSpec],
+    override val userDefinedPartitionColumns: Option[StructType],
+    override val paths: Array[String] = Array.empty[String])(@transient val sqlContext: SQLContext)
+  extends HadoopFsRelation(maybePartitionSpec) {
+
+  /** Constraints to be imposed on schema to be stored. */
+  private def checkConstraints(schema: StructType): Unit = {
+    if (schema.fieldNames.length != schema.fieldNames.distinct.length) {
+      val duplicateColumns = schema.fieldNames.groupBy(identity).collect {
         case (x, ys) if ys.length > 1 => "\"" + x + "\""
       }.mkString(", ")
       throw new AnalysisException(s"Duplicate column(s) : $duplicateColumns found, " +
@@ -49,176 +70,118 @@ private[sql] class DefaultSource
     }
   }
 
-  /** Returns a new base relation with the parameters. */
-  override def createRelation(
-      sqlContext: SQLContext,
-      parameters: Map[String, String]): BaseRelation = {
-    val path = checkPath(parameters)
-    val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0)
+  override val needConversion: Boolean = false
 
-    new JSONRelation(path, samplingRatio, None, sqlContext)
-  }
+  private def createBaseRdd(inputPaths: Array[FileStatus]): RDD[String] = {
+    val job = new Job(sqlContext.sparkContext.hadoopConfiguration)
+    val conf = job.getConfiguration
 
-  /** Returns a new base relation with the given schema and parameters. */
-  override def createRelation(
-      sqlContext: SQLContext,
-      parameters: Map[String, String],
-      schema: StructType): BaseRelation = {
-    val path = checkPath(parameters)
-    val samplingRatio = parameters.get("samplingRatio").map(_.toDouble).getOrElse(1.0)
-
-    new JSONRelation(path, samplingRatio, Some(schema), sqlContext)
-  }
+    val paths = inputPaths.map(_.getPath)
 
-  override def createRelation(
-      sqlContext: SQLContext,
-      mode: SaveMode,
-      parameters: Map[String, String],
-      data: DataFrame): BaseRelation = {
-    // check if dataframe satisfies the constraints
-    // before moving forward
-    checkConstraints(data)
-
-    val path = checkPath(parameters)
-    val filesystemPath = new Path(path)
-    val fs = filesystemPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
-    val doSave = if (fs.exists(filesystemPath)) {
-      mode match {
-        case SaveMode.Append =>
-          sys.error(s"Append mode is not supported by ${this.getClass.getCanonicalName}")
-        case SaveMode.Overwrite => {
-          JSONRelation.delete(filesystemPath, fs)
-          true
-        }
-        case SaveMode.ErrorIfExists =>
-          sys.error(s"path $path already exists.")
-        case SaveMode.Ignore => false
-      }
-    } else {
-      true
-    }
-    if (doSave) {
-      // Only save data when the save mode is not ignore.
-      data.toJSON.saveAsTextFile(path)
+    if (paths.nonEmpty) {
+      FileInputFormat.setInputPaths(job, paths: _*)
     }
 
-    createRelation(sqlContext, parameters, data.schema)
+    sqlContext.sparkContext.hadoopRDD(
+      conf.asInstanceOf[JobConf],
+      classOf[TextInputFormat],
+      classOf[LongWritable],
+      classOf[Text]).map(_._2.toString) // get the text line
   }
-}
 
-private[sql] class JSONRelation(
-    // baseRDD is not immutable with respect to INSERT OVERWRITE
-    // and so it must be recreated at least as often as the
-    // underlying inputs are modified. To be safe, a function is
-    // used instead of a regular RDD value to ensure a fresh RDD is
-    // recreated for each and every operation.
-    baseRDD: () => RDD[String],
-    val path: Option[String],
-    val samplingRatio: Double,
-    userSpecifiedSchema: Option[StructType])(
-    @transient val sqlContext: SQLContext)
-  extends BaseRelation
-  with TableScan
-  with InsertableRelation
-  with CatalystScan {
-
-  def this(
-      path: String,
-      samplingRatio: Double,
-      userSpecifiedSchema: Option[StructType],
-      sqlContext: SQLContext) =
-    this(
-      () => sqlContext.sparkContext.textFile(path),
-      Some(path),
-      samplingRatio,
-      userSpecifiedSchema)(sqlContext)
-
-  /** Constraints to be imposed on dataframe to be stored. */
-  private def checkConstraints(data: DataFrame): Unit = {
-    if (data.schema.fieldNames.length != data.schema.fieldNames.distinct.length) {
-      val duplicateColumns = data.schema.fieldNames.groupBy(identity).collect {
-        case (x, ys) if ys.length > 1 => "\"" + x + "\""
-      }.mkString(", ")
-      throw new AnalysisException(s"Duplicate column(s) : $duplicateColumns found, " +
-        s"cannot save to JSON format")
+  override lazy val dataSchema = {
+    val jsonSchema = maybeDataSchema.getOrElse {
+      val files = cachedLeafStatuses().filterNot { status =>
+        val name = status.getPath.getName
+        name.startsWith("_") || name.startsWith(".")
+      }.toArray
+      InferSchema(
+        inputRDD.getOrElse(createBaseRdd(files)),
+        samplingRatio,
+        sqlContext.conf.columnNameOfCorruptRecord)
     }
-  }
+    checkConstraints(jsonSchema)
 
-  override val needConversion: Boolean = false
-
-  override lazy val schema = userSpecifiedSchema.getOrElse {
-    InferSchema(
-      baseRDD(),
-      samplingRatio,
-      sqlContext.conf.columnNameOfCorruptRecord)
+    jsonSchema
   }
 
-  override def buildScan(): RDD[Row] = {
-    // Rely on type erasure hack to pass RDD[InternalRow] back as RDD[Row]
+  override def buildScan(
+      requiredColumns: Array[String],
+      filters: Array[Filter],
+      inputPaths: Array[FileStatus]): RDD[Row] = {
     JacksonParser(
-      baseRDD(),
-      schema,
+      inputRDD.getOrElse(createBaseRdd(inputPaths)),
+      StructType(requiredColumns.map(dataSchema(_))),
       sqlContext.conf.columnNameOfCorruptRecord).asInstanceOf[RDD[Row]]
   }
 
-  override def buildScan(requiredColumns: Seq[Attribute], filters: Seq[Expression]): RDD[Row] = {
-    // Rely on a type erasure hack to pass RDD[InternalRow] back as RDD[Row]
-    JacksonParser(
-      baseRDD(),
-      StructType.fromAttributes(requiredColumns),
-      sqlContext.conf.columnNameOfCorruptRecord).asInstanceOf[RDD[Row]]
+  override def equals(other: Any): Boolean = other match {
+    case that: JSONRelation =>
+      ((inputRDD, that.inputRDD) match {
+        case (Some(thizRdd), Some(thatRdd)) => thizRdd eq thatRdd
+        case (None, None) => true
+        case _ => false
+      }) && paths.toSet == that.paths.toSet &&
+        dataSchema == that.dataSchema &&
+        schema == that.schema
+    case _ => false
   }
 
-  override def insert(data: DataFrame, overwrite: Boolean): Unit = {
-    // check if dataframe satisfies constraints
-    // before moving forward
-    checkConstraints(data)
+  override def hashCode(): Int = {
+    Objects.hashCode(
+      inputRDD,
+      paths.toSet,
+      dataSchema,
+      schema,
+      partitionColumns)
+  }
 
-    val filesystemPath = path match {
-      case Some(p) => new Path(p)
-      case None =>
-        throw new IOException(s"Cannot INSERT into table with no path defined")
+  override def prepareJobForWrite(job: Job): OutputWriterFactory = {
+    new OutputWriterFactory {
+      override def newInstance(
+          path: String,
+          dataSchema: StructType,
+          context: TaskAttemptContext): OutputWriter = {
+        new JsonOutputWriter(path, dataSchema, context)
+      }
     }
+  }
+}
+
+private[json] class JsonOutputWriter(
+    path: String,
+    dataSchema: StructType,
+    context: TaskAttemptContext)
+  extends OutputWriterInternal with SparkHadoopMapRedUtil with Logging {
 
-    val fs = filesystemPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
+  val writer = new CharArrayWriter()
+  // create the Generator without separator inserted between 2 records
+  val gen = new JsonFactory().createGenerator(writer).setRootValueSeparator(null)
 
-    if (overwrite) {
-      if (fs.exists(filesystemPath)) {
-        JSONRelation.delete(filesystemPath, fs)
+  val result = new Text()
+
+  private val recordWriter: RecordWriter[NullWritable, Text] = {
+    new TextOutputFormat[NullWritable, Text]() {
+      override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
+        val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
+        val split = context.getTaskAttemptID.getTaskID.getId
+        new Path(path, f"part-r-$split%05d-$uniqueWriteJobId$extension")
       }
-      // Write the data.
-      data.toJSON.saveAsTextFile(filesystemPath.toString)
-      // Right now, we assume that the schema is not changed. We will not update the schema.
-      // schema = data.schema
-    } else {
-      // TODO: Support INSERT INTO
-      sys.error("JSON table only support INSERT OVERWRITE for now.")
-    }
+    }.getRecordWriter(context)
   }
 
-  override def hashCode(): Int = 41 * (41 + path.hashCode) + schema.hashCode()
+  override def writeInternal(row: InternalRow): Unit = {
+    JacksonGenerator(dataSchema, gen, row)
+    gen.flush()
 
-  override def equals(other: Any): Boolean = other match {
-    case that: JSONRelation =>
-      (this.path == that.path) && this.schema.sameType(that.schema)
-    case _ => false
+    result.set(writer.toString)
+    writer.reset()
+
+    recordWriter.write(NullWritable.get(), result)
   }
-}
 
-private object JSONRelation {
-
-  /** Delete the specified directory to overwrite it with new JSON data. */
-  def delete(dir: Path, fs: FileSystem): Unit = {
-    var success: Boolean = false
-    val failMessage = s"Unable to clear output directory $dir prior to writing to JSON table"
-    try {
-      success = fs.delete(dir, true /* recursive */)
-    } catch {
-      case e: IOException =>
-        throw new IOException(s"$failMessage\n${e.toString}")
-    }
-    if (!success) {
-      throw new IOException(failMessage)
-    }
+  override def close(): Unit = {
+    gen.close()
+    recordWriter.close(context)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
index 1e6b1198d245b..d734e7e8904bd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.json
 
+import org.apache.spark.sql.catalyst.InternalRow
+
 import scala.collection.Map
 
 import com.fasterxml.jackson.core._
@@ -74,4 +76,60 @@ private[sql] object JacksonGenerator {
 
     valWriter(rowSchema, row)
   }
+
+  /** Transforms a single InternalRow to JSON using Jackson
+   *
+   * TODO: make the code shared with the other apply method.
+   *
+   * @param rowSchema the schema object used for conversion
+   * @param gen a JsonGenerator object
+   * @param row The row to convert
+   */
+  def apply(rowSchema: StructType, gen: JsonGenerator, row: InternalRow): Unit = {
+    def valWriter: (DataType, Any) => Unit = {
+      case (_, null) | (NullType, _) => gen.writeNull()
+      case (StringType, v) => gen.writeString(v.toString)
+      case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString)
+      case (IntegerType, v: Int) => gen.writeNumber(v)
+      case (ShortType, v: Short) => gen.writeNumber(v)
+      case (FloatType, v: Float) => gen.writeNumber(v)
+      case (DoubleType, v: Double) => gen.writeNumber(v)
+      case (LongType, v: Long) => gen.writeNumber(v)
+      case (DecimalType(), v: java.math.BigDecimal) => gen.writeNumber(v)
+      case (ByteType, v: Byte) => gen.writeNumber(v.toInt)
+      case (BinaryType, v: Array[Byte]) => gen.writeBinary(v)
+      case (BooleanType, v: Boolean) => gen.writeBoolean(v)
+      case (DateType, v) => gen.writeString(v.toString)
+      case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, udt.serialize(v))
+
+      case (ArrayType(ty, _), v: ArrayData) =>
+        gen.writeStartArray()
+        v.foreach(ty, (_, value) => valWriter(ty, value))
+        gen.writeEndArray()
+
+      case (MapType(kv, vv, _), v: Map[_, _]) =>
+        gen.writeStartObject()
+        v.foreach { p =>
+          gen.writeFieldName(p._1.toString)
+          valWriter(vv, p._2)
+        }
+        gen.writeEndObject()
+
+      case (StructType(ty), v: InternalRow) =>
+        gen.writeStartObject()
+        var i = 0
+        while (i < ty.length) {
+          val field = ty(i)
+          val value = v.get(i, field.dataType)
+          if (value != null) {
+            gen.writeFieldName(field.name)
+            valWriter(field.dataType, value)
+          }
+          i += 1
+        }
+        gen.writeEndObject()
+    }
+
+    valWriter(rowSchema, row)
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index aef940a526675..b8f10b00f5690 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -492,15 +492,16 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
     val df1 = DataFrame(sqlContext, LogicalRelation(fakeRelation1))
     assert(df1.inputFiles.toSet == fakeRelation1.paths.toSet)
 
-    val fakeRelation2 = new JSONRelation("/json/path", 1, Some(testData.schema), sqlContext)
+    val fakeRelation2 = new JSONRelation(
+      None, 1, Some(testData.schema), None, None, Array("/json/path"))(sqlContext)
     val df2 = DataFrame(sqlContext, LogicalRelation(fakeRelation2))
-    assert(df2.inputFiles.toSet == fakeRelation2.path.toSet)
+    assert(df2.inputFiles.toSet == fakeRelation2.paths.toSet)
 
     val unionDF = df1.unionAll(df2)
-    assert(unionDF.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.path)
+    assert(unionDF.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.paths)
 
     val filtered = df1.filter("false").unionAll(df2.intersect(df2))
-    assert(filtered.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.path)
+    assert(filtered.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.paths)
   }
 
   ignore("show") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
index 16a5c5706009a..92022ff23d2c3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
@@ -17,23 +17,27 @@
 
 package org.apache.spark.sql.json
 
-import java.io.StringWriter
+import java.io.{File, StringWriter}
 import java.sql.{Date, Timestamp}
 
 import com.fasterxml.jackson.core.JsonFactory
+import org.apache.spark.rdd.RDD
 import org.scalactic.Tolerance._
 
-import org.apache.spark.sql.{QueryTest, Row, SQLConf}
+import org.apache.spark.sql.{SQLContext, QueryTest, Row, SQLConf}
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
-import org.apache.spark.sql.execution.datasources.LogicalRelation
+import org.apache.spark.sql.execution.datasources.{ResolvedDataSource, LogicalRelation}
 import org.apache.spark.sql.json.InferSchema.compatibleType
 import org.apache.spark.sql.types._
+import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.util.Utils
 
-class JsonSuite extends QueryTest with TestJsonData {
+class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
 
   protected lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+  override def sqlContext: SQLContext = ctx // used by SQLTestUtils
+
   import ctx.sql
   import ctx.implicits._
 
@@ -574,7 +578,7 @@ class JsonSuite extends QueryTest with TestJsonData {
   test("jsonFile should be based on JSONRelation") {
     val dir = Utils.createTempDir()
     dir.delete()
-    val path = dir.getCanonicalPath
+    val path = dir.getCanonicalFile.toURI.toString
     ctx.sparkContext.parallelize(1 to 100)
       .map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path)
     val jsonDF = ctx.read.option("samplingRatio", "0.49").json(path)
@@ -587,14 +591,14 @@ class JsonSuite extends QueryTest with TestJsonData {
     assert(
       relation.isInstanceOf[JSONRelation],
       "The DataFrame returned by jsonFile should be based on JSONRelation.")
-    assert(relation.asInstanceOf[JSONRelation].path === Some(path))
+    assert(relation.asInstanceOf[JSONRelation].paths === Array(path))
     assert(relation.asInstanceOf[JSONRelation].samplingRatio === (0.49 +- 0.001))
 
     val schema = StructType(StructField("a", LongType, true) :: Nil)
     val logicalRelation =
       ctx.read.schema(schema).json(path).queryExecution.analyzed.asInstanceOf[LogicalRelation]
     val relationWithSchema = logicalRelation.relation.asInstanceOf[JSONRelation]
-    assert(relationWithSchema.path === Some(path))
+    assert(relationWithSchema.paths === Array(path))
     assert(relationWithSchema.schema === schema)
     assert(relationWithSchema.samplingRatio > 0.99)
   }
@@ -1037,25 +1041,36 @@ class JsonSuite extends QueryTest with TestJsonData {
 
   test("JSONRelation equality test") {
     val context = org.apache.spark.sql.test.TestSQLContext
+
+    val relation0 = new JSONRelation(
+      Some(empty),
+      1.0,
+      Some(StructType(StructField("a", IntegerType, true) :: Nil)),
+      None, None)(context)
+    val logicalRelation0 = LogicalRelation(relation0)
     val relation1 = new JSONRelation(
-      "path",
+      Some(singleRow),
       1.0,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      context)
+      None, None)(context)
     val logicalRelation1 = LogicalRelation(relation1)
     val relation2 = new JSONRelation(
-      "path",
+      Some(singleRow),
       0.5,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      context)
+      None, None)(context)
     val logicalRelation2 = LogicalRelation(relation2)
     val relation3 = new JSONRelation(
-      "path",
+      Some(singleRow),
       1.0,
-      Some(StructType(StructField("b", StringType, true) :: Nil)),
-      context)
+      Some(StructType(StructField("b", IntegerType, true) :: Nil)),
+      None, None)(context)
     val logicalRelation3 = LogicalRelation(relation3)
 
+    assert(relation0 !== relation1)
+    assert(!logicalRelation0.sameResult(logicalRelation1),
+      s"$logicalRelation0 and $logicalRelation1 should be considered not having the same result.")
+
     assert(relation1 === relation2)
     assert(logicalRelation1.sameResult(logicalRelation2),
       s"$logicalRelation1 and $logicalRelation2 should be considered having the same result.")
@@ -1067,6 +1082,27 @@ class JsonSuite extends QueryTest with TestJsonData {
     assert(relation2 !== relation3)
     assert(!logicalRelation2.sameResult(logicalRelation3),
       s"$logicalRelation2 and $logicalRelation3 should be considered not having the same result.")
+
+    withTempPath(dir => {
+      val path = dir.getCanonicalFile.toURI.toString
+      ctx.sparkContext.parallelize(1 to 100)
+        .map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path)
+
+      val d1 = ResolvedDataSource(
+        context,
+        userSpecifiedSchema = None,
+        partitionColumns = Array.empty[String],
+        provider = classOf[DefaultSource].getCanonicalName,
+        options = Map("path" -> path))
+
+      val d2 = ResolvedDataSource(
+        context,
+        userSpecifiedSchema = None,
+        partitionColumns = Array.empty[String],
+        provider = classOf[DefaultSource].getCanonicalName,
+        options = Map("path" -> path))
+      assert(d1 === d2)
+    })
   }
 
   test("SPARK-6245 JsonRDD.inferSchema on empty RDD") {
@@ -1101,4 +1137,36 @@ class JsonSuite extends QueryTest with TestJsonData {
     val emptySchema = InferSchema(emptyRecords, 1.0, "")
     assert(StructType(Seq()) === emptySchema)
   }
+
+  test("JSON with Partition") {
+    def makePartition(rdd: RDD[String], parent: File, partName: String, partValue: Any): File = {
+      val p = new File(parent, s"$partName=${partValue.toString}")
+      rdd.saveAsTextFile(p.getCanonicalPath)
+      p
+    }
+
+    withTempPath(root => {
+      val d1 = new File(root, "d1=1")
+      // root/dt=1/col1=abc
+      val p1_col1 = makePartition(
+        ctx.sparkContext.parallelize(2 to 5).map(i => s"""{"a": 1, "b": "str$i"}"""),
+        d1,
+        "col1",
+        "abc")
+
+      // root/dt=1/col1=abd
+      val p2 = makePartition(
+        ctx.sparkContext.parallelize(6 to 10).map(i => s"""{"a": 1, "b": "str$i"}"""),
+        d1,
+        "col1",
+        "abd")
+
+        ctx.read.json(root.getAbsolutePath).registerTempTable("test_myjson_with_part")
+        checkAnswer(
+          sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abc'"), Row(4))
+        checkAnswer(
+          sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abd'"), Row(5))
+        checkAnswer(sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1"), Row(9))
+    })
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
index eb62066ac6430..369df5653060b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
@@ -198,5 +198,9 @@ trait TestJsonData {
         """{"b": [{"c": {}}]}""" ::
         """]""" :: Nil)
 
+  lazy val singleRow: RDD[String] =
+    ctx.sparkContext.parallelize(
+      """{"a":123}""" :: Nil)
+
   def empty: RDD[String] = ctx.sparkContext.parallelize(Seq[String]())
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
index 0b7c46c482c88..39d18d712ef8c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
@@ -146,13 +146,24 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
     caseInsensitiveContext.dropTempTable("jt2")
   }
 
-  test("INSERT INTO not supported for JSONRelation for now") {
-    intercept[RuntimeException]{
-      sql(
-        s"""
-        |INSERT INTO TABLE jsonTable SELECT a, b FROM jt
-      """.stripMargin)
-    }
+  test("INSERT INTO JSONRelation for now") {
+    sql(
+      s"""
+      |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt
+    """.stripMargin)
+    checkAnswer(
+      sql("SELECT a, b FROM jsonTable"),
+      sql("SELECT a, b FROM jt").collect()
+    )
+
+    sql(
+      s"""
+         |INSERT INTO TABLE jsonTable SELECT a, b FROM jt
+    """.stripMargin)
+    checkAnswer(
+      sql("SELECT a, b FROM jsonTable"),
+      sql("SELECT a, b FROM jt UNION ALL SELECT a, b FROM jt").collect()
+    )
   }
 
   test("save directly to the path of a JSON table") {
@@ -183,6 +194,11 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
   }
 
   test("Caching")  {
+    // write something to the jsonTable
+    sql(
+      s"""
+         |INSERT OVERWRITE TABLE jsonTable SELECT a, b FROM jt
+      """.stripMargin)
     // Cached Query Execution
     caseInsensitiveContext.cacheTable("jsonTable")
     assertCached(sql("SELECT * FROM jsonTable"))
@@ -217,14 +233,15 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
       """.stripMargin)
     // jsonTable should be recached.
     assertCached(sql("SELECT * FROM jsonTable"))
-    // The cached data is the new data.
-    checkAnswer(
-      sql("SELECT a, b FROM jsonTable"),
-      sql("SELECT a * 2, b FROM jt").collect())
-
-    // Verify uncaching
-    caseInsensitiveContext.uncacheTable("jsonTable")
-    assertCached(sql("SELECT * FROM jsonTable"), 0)
+    // TODO we need to invalidate the cached data in InsertIntoHadoopFsRelation
+//    // The cached data is the new data.
+//    checkAnswer(
+//      sql("SELECT a, b FROM jsonTable"),
+//      sql("SELECT a * 2, b FROM jt").collect())
+//
+//    // Verify uncaching
+//    caseInsensitiveContext.uncacheTable("jsonTable")
+//    assertCached(sql("SELECT * FROM jsonTable"), 0)
   }
 
   test("it's not allowed to insert into a relation that is not an InsertableRelation") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
index b032515a9d28c..31730a3d3f8d3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
@@ -21,7 +21,7 @@ import java.io.File
 
 import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.sql.{SaveMode, SQLConf, DataFrame}
+import org.apache.spark.sql.{AnalysisException, SaveMode, SQLConf, DataFrame}
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -57,19 +57,21 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll {
     Utils.deleteRecursively(path)
   }
 
-  def checkLoad(): Unit = {
+  def checkLoad(expectedDF: DataFrame = df, tbl: String = "jsonTable"): Unit = {
     caseInsensitiveContext.conf.setConf(
       SQLConf.DEFAULT_DATA_SOURCE_NAME, "org.apache.spark.sql.json")
-    checkAnswer(caseInsensitiveContext.read.load(path.toString), df.collect())
+    checkAnswer(caseInsensitiveContext.read.load(path.toString), expectedDF.collect())
 
     // Test if we can pick up the data source name passed in load.
     caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, "not a source name")
-    checkAnswer(caseInsensitiveContext.read.format("json").load(path.toString), df.collect())
-    checkAnswer(caseInsensitiveContext.read.format("json").load(path.toString), df.collect())
+    checkAnswer(caseInsensitiveContext.read.format("json").load(path.toString),
+      expectedDF.collect())
+    checkAnswer(caseInsensitiveContext.read.format("json").load(path.toString),
+      expectedDF.collect())
     val schema = StructType(StructField("b", StringType, true) :: Nil)
     checkAnswer(
       caseInsensitiveContext.read.format("json").schema(schema).load(path.toString),
-      sql("SELECT b FROM jsonTable").collect())
+      sql(s"SELECT b FROM $tbl").collect())
   }
 
   test("save with path and load") {
@@ -102,7 +104,7 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll {
   test("save and save again") {
     df.write.json(path.toString)
 
-    var message = intercept[RuntimeException] {
+    val message = intercept[AnalysisException] {
       df.write.json(path.toString)
     }.getMessage
 
@@ -118,12 +120,11 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll {
     df.write.mode(SaveMode.Overwrite).json(path.toString)
     checkLoad()
 
-    message = intercept[RuntimeException] {
-      df.write.mode(SaveMode.Append).json(path.toString)
-    }.getMessage
+    // verify the append mode
+    df.write.mode(SaveMode.Append).json(path.toString)
+    val df2 = df.unionAll(df)
+    df2.registerTempTable("jsonTable2")
 
-    assert(
-      message.contains("Append mode is not supported"),
-      "We should complain that 'Append mode is not supported' for JSON source.")
+    checkLoad(df2, "jsonTable2")
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index 4fdf774ead75e..b73d6665755d0 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive
 
-import java.io.File
+import java.io.{IOException, File}
 
 import scala.collection.mutable.ArrayBuffer
 
@@ -463,23 +463,20 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeA
 
           checkAnswer(sql("SELECT * FROM savedJsonTable"), df)
 
-          // Right now, we cannot append to an existing JSON table.
-          intercept[RuntimeException] {
-            df.write.mode(SaveMode.Append).saveAsTable("savedJsonTable")
-          }
-
           // We can overwrite it.
           df.write.mode(SaveMode.Overwrite).saveAsTable("savedJsonTable")
           checkAnswer(sql("SELECT * FROM savedJsonTable"), df)
 
           // When the save mode is Ignore, we will do nothing when the table already exists.
           df.select("b").write.mode(SaveMode.Ignore).saveAsTable("savedJsonTable")
-          assert(df.schema === table("savedJsonTable").schema)
+          // TODO in ResolvedDataSource, will convert the schema into nullable = true
+          // hence the df.schema is not exactly the same as table("savedJsonTable").schema
+          // assert(df.schema === table("savedJsonTable").schema)
           checkAnswer(sql("SELECT * FROM savedJsonTable"), df)
 
           // Drop table will also delete the data.
           sql("DROP TABLE savedJsonTable")
-          intercept[InvalidInputException] {
+          intercept[IOException] {
             read.json(catalog.hiveDefaultTableFilePath("savedJsonTable"))
           }
         }
@@ -555,7 +552,7 @@ class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeA
                 "org.apache.spark.sql.json",
                 schema,
                 Map.empty[String, String])
-            }.getMessage.contains("'path' must be specified for json data."),
+            }.getMessage.contains("key not found: path"),
             "We should complain that path is not specified.")
         }
       }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
index e8975e5f5cd08..1813cc33226d1 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -50,3 +50,33 @@ class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
     }
   }
 }
+
+class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
+  override val dataSourceName: String =
+    classOf[org.apache.spark.sql.json.DefaultSource].getCanonicalName
+
+  import sqlContext._
+
+  test("save()/load() - partitioned table - simple queries - partition columns in data") {
+    withTempDir { file =>
+      val basePath = new Path(file.getCanonicalPath)
+      val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf)
+      val qualifiedBasePath = fs.makeQualified(basePath)
+
+      for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) {
+        val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2")
+        sparkContext
+          .parallelize(for (i <- 1 to 3) yield s"""{"a":$i,"b":"val_$i"}""")
+          .saveAsTextFile(partitionDir.toString)
+      }
+
+      val dataSchemaWithPartition =
+        StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
+
+      checkQueries(
+        read.format(dataSourceName)
+          .option("dataSchema", dataSchemaWithPartition.json)
+          .load(file.getCanonicalPath))
+    }
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index dd274023a1cf5..2a69d331b6e52 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -444,7 +444,9 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
     }
   }
 
-  test("Partition column type casting") {
+  // HadoopFsRelation.discoverPartitions() called by refresh(), which will ignore
+  // the given partition data type.
+  ignore("Partition column type casting") {
     withTempPath { file =>
       val input = partitionedTestDF.select('a, 'b, 'p1.cast(StringType).as('ps), 'p2)
 

From 34dcf10104460816382908b2b8eeb6c925e862bf Mon Sep 17 00:00:00 2001
From: Mike Dusenberry <mwdusenb@us.ibm.com>
Date: Wed, 5 Aug 2015 07:40:50 -0700
Subject: [PATCH 061/633] [SPARK-6486] [MLLIB] [PYTHON] Add BlockMatrix to
 PySpark.

mengxr This adds the `BlockMatrix` to PySpark.  I have the conversions to `IndexedRowMatrix` and `CoordinateMatrix` ready as well, so once PR #7554 is completed (which relies on PR #7746), this PR can be finished.

Author: Mike Dusenberry <mwdusenb@us.ibm.com>

Closes #7761 from dusenberrymw/SPARK-6486_Add_BlockMatrix_to_PySpark and squashes the following commits:

27195c2 [Mike Dusenberry] Adding one more check to _convert_to_matrix_block_tuple, and a few minor documentation changes.
ae50883 [Mike Dusenberry] Minor update: BlockMatrix should inherit from DistributedMatrix.
b8acc1c [Mike Dusenberry] Moving BlockMatrix to pyspark.mllib.linalg.distributed, updating the logic to match that of the other distributed matrices, adding conversions, and adding documentation.
c014002 [Mike Dusenberry] Using properties for better documentation.
3bda6ab [Mike Dusenberry] Adding documentation.
8fb3095 [Mike Dusenberry] Small cleanup.
e17af2e [Mike Dusenberry] Adding BlockMatrix to PySpark.
---
 docs/mllib-data-types.md                      |  41 +++
 .../mllib/api/python/PythonMLLibAPI.scala     |  25 ++
 python/pyspark/mllib/linalg/distributed.py    | 328 +++++++++++++++++-
 3 files changed, 388 insertions(+), 6 deletions(-)

diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md
index 11033bf4f9c37..f0e8d5495675d 100644
--- a/docs/mllib-data-types.md
+++ b/docs/mllib-data-types.md
@@ -494,6 +494,9 @@ rowMat = mat.toRowMatrix()
 
 # Convert to a CoordinateMatrix.
 coordinateMat = mat.toCoordinateMatrix()
+
+# Convert to a BlockMatrix.
+blockMat = mat.toBlockMatrix()
 {% endhighlight %}
 </div>
 
@@ -594,6 +597,9 @@ rowMat = mat.toRowMatrix()
 
 # Convert to an IndexedRowMatrix.
 indexedRowMat = mat.toIndexedRowMatrix()
+
+# Convert to a BlockMatrix.
+blockMat = mat.toBlockMatrix()
 {% endhighlight %}
 </div>
 
@@ -661,4 +667,39 @@ matA.validate();
 BlockMatrix ata = matA.transpose().multiply(matA);
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+
+A [`BlockMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.BlockMatrix) 
+can be created from an `RDD` of sub-matrix blocks, where a sub-matrix block is a 
+`((blockRowIndex, blockColIndex), sub-matrix)` tuple.
+
+{% highlight python %}
+from pyspark.mllib.linalg import Matrices
+from pyspark.mllib.linalg.distributed import BlockMatrix
+
+# Create an RDD of sub-matrix blocks.
+blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])), 
+                         ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+
+# Create a BlockMatrix from an RDD of sub-matrix blocks.
+mat = BlockMatrix(blocks, 3, 2)
+
+# Get its size.
+m = mat.numRows() # 6
+n = mat.numCols() # 2
+
+# Get the blocks as an RDD of sub-matrix blocks.
+blocksRDD = mat.blocks
+
+# Convert to a LocalMatrix.
+localMat = mat.toLocalMatrix()
+
+# Convert to an IndexedRowMatrix.
+indexedRowMat = mat.toIndexedRowMatrix()
+
+# Convert to a CoordinateMatrix.
+coordinateMat = mat.toCoordinateMatrix()
+{% endhighlight %}
+</div>
 </div>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index d2b3fae381acb..f585aacd452e0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -1128,6 +1128,21 @@ private[python] class PythonMLLibAPI extends Serializable {
     new CoordinateMatrix(entries, numRows, numCols)
   }
 
+  /**
+   * Wrapper around BlockMatrix constructor.
+   */
+  def createBlockMatrix(blocks: DataFrame, rowsPerBlock: Int, colsPerBlock: Int,
+                        numRows: Long, numCols: Long): BlockMatrix = {
+    // We use DataFrames for serialization of sub-matrix blocks from
+    // Python, so map each Row in the DataFrame back to a
+    // ((blockRowIndex, blockColIndex), sub-matrix) tuple.
+    val blockTuples = blocks.map {
+      case Row(Row(blockRowIndex: Long, blockColIndex: Long), subMatrix: Matrix) =>
+        ((blockRowIndex.toInt, blockColIndex.toInt), subMatrix)
+    }
+    new BlockMatrix(blockTuples, rowsPerBlock, colsPerBlock, numRows, numCols)
+  }
+
   /**
    * Return the rows of an IndexedRowMatrix.
    */
@@ -1147,6 +1162,16 @@ private[python] class PythonMLLibAPI extends Serializable {
     val sqlContext = new SQLContext(coordinateMatrix.entries.sparkContext)
     sqlContext.createDataFrame(coordinateMatrix.entries)
   }
+
+  /**
+   * Return the sub-matrix blocks of a BlockMatrix.
+   */
+  def getMatrixBlocks(blockMatrix: BlockMatrix): DataFrame = {
+    // We use DataFrames for serialization of sub-matrix blocks to
+    // Python, so return a DataFrame.
+    val sqlContext = new SQLContext(blockMatrix.blocks.sparkContext)
+    sqlContext.createDataFrame(blockMatrix.blocks)
+  }
 }
 
 /**
diff --git a/python/pyspark/mllib/linalg/distributed.py b/python/pyspark/mllib/linalg/distributed.py
index 666d833019562..aec407de90aa3 100644
--- a/python/pyspark/mllib/linalg/distributed.py
+++ b/python/pyspark/mllib/linalg/distributed.py
@@ -28,11 +28,12 @@
 
 from pyspark import RDD
 from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
-from pyspark.mllib.linalg import _convert_to_vector
+from pyspark.mllib.linalg import _convert_to_vector, Matrix
 
 
 __all__ = ['DistributedMatrix', 'RowMatrix', 'IndexedRow',
-           'IndexedRowMatrix', 'MatrixEntry', 'CoordinateMatrix']
+           'IndexedRowMatrix', 'MatrixEntry', 'CoordinateMatrix',
+           'BlockMatrix']
 
 
 class DistributedMatrix(object):
@@ -322,6 +323,35 @@ def toCoordinateMatrix(self):
         java_coordinate_matrix = self._java_matrix_wrapper.call("toCoordinateMatrix")
         return CoordinateMatrix(java_coordinate_matrix)
 
+    def toBlockMatrix(self, rowsPerBlock=1024, colsPerBlock=1024):
+        """
+        Convert this matrix to a BlockMatrix.
+
+        :param rowsPerBlock: Number of rows that make up each block.
+                             The blocks forming the final rows are not
+                             required to have the given number of rows.
+        :param colsPerBlock: Number of columns that make up each block.
+                             The blocks forming the final columns are not
+                             required to have the given number of columns.
+
+        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
+        ...                        IndexedRow(6, [4, 5, 6])])
+        >>> mat = IndexedRowMatrix(rows).toBlockMatrix()
+
+        >>> # This IndexedRowMatrix will have 7 effective rows, due to
+        >>> # the highest row index being 6, and the ensuing
+        >>> # BlockMatrix will have 7 rows as well.
+        >>> print(mat.numRows())
+        7
+
+        >>> print(mat.numCols())
+        3
+        """
+        java_block_matrix = self._java_matrix_wrapper.call("toBlockMatrix",
+                                                           rowsPerBlock,
+                                                           colsPerBlock)
+        return BlockMatrix(java_block_matrix, rowsPerBlock, colsPerBlock)
+
 
 class MatrixEntry(object):
     """
@@ -476,19 +506,18 @@ def toRowMatrix(self):
 
         >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
         ...                           MatrixEntry(6, 4, 2.1)])
+        >>> mat = CoordinateMatrix(entries).toRowMatrix()
 
         >>> # This CoordinateMatrix will have 7 effective rows, due to
         >>> # the highest row index being 6, but the ensuing RowMatrix
         >>> # will only have 2 rows since there are only entries on 2
         >>> # unique rows.
-        >>> mat = CoordinateMatrix(entries).toRowMatrix()
         >>> print(mat.numRows())
         2
 
         >>> # This CoordinateMatrix will have 5 columns, due to the
         >>> # highest column index being 4, and the ensuing RowMatrix
         >>> # will have 5 columns as well.
-        >>> mat = CoordinateMatrix(entries).toRowMatrix()
         >>> print(mat.numCols())
         5
         """
@@ -501,33 +530,320 @@ def toIndexedRowMatrix(self):
 
         >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
         ...                           MatrixEntry(6, 4, 2.1)])
+        >>> mat = CoordinateMatrix(entries).toIndexedRowMatrix()
 
         >>> # This CoordinateMatrix will have 7 effective rows, due to
         >>> # the highest row index being 6, and the ensuing
         >>> # IndexedRowMatrix will have 7 rows as well.
-        >>> mat = CoordinateMatrix(entries).toIndexedRowMatrix()
         >>> print(mat.numRows())
         7
 
         >>> # This CoordinateMatrix will have 5 columns, due to the
         >>> # highest column index being 4, and the ensuing
         >>> # IndexedRowMatrix will have 5 columns as well.
-        >>> mat = CoordinateMatrix(entries).toIndexedRowMatrix()
         >>> print(mat.numCols())
         5
         """
         java_indexed_row_matrix = self._java_matrix_wrapper.call("toIndexedRowMatrix")
         return IndexedRowMatrix(java_indexed_row_matrix)
 
+    def toBlockMatrix(self, rowsPerBlock=1024, colsPerBlock=1024):
+        """
+        Convert this matrix to a BlockMatrix.
+
+        :param rowsPerBlock: Number of rows that make up each block.
+                             The blocks forming the final rows are not
+                             required to have the given number of rows.
+        :param colsPerBlock: Number of columns that make up each block.
+                             The blocks forming the final columns are not
+                             required to have the given number of columns.
+
+        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
+        ...                           MatrixEntry(6, 4, 2.1)])
+        >>> mat = CoordinateMatrix(entries).toBlockMatrix()
+
+        >>> # This CoordinateMatrix will have 7 effective rows, due to
+        >>> # the highest row index being 6, and the ensuing
+        >>> # BlockMatrix will have 7 rows as well.
+        >>> print(mat.numRows())
+        7
+
+        >>> # This CoordinateMatrix will have 5 columns, due to the
+        >>> # highest column index being 4, and the ensuing
+        >>> # BlockMatrix will have 5 columns as well.
+        >>> print(mat.numCols())
+        5
+        """
+        java_block_matrix = self._java_matrix_wrapper.call("toBlockMatrix",
+                                                           rowsPerBlock,
+                                                           colsPerBlock)
+        return BlockMatrix(java_block_matrix, rowsPerBlock, colsPerBlock)
+
+
+def _convert_to_matrix_block_tuple(block):
+    if (isinstance(block, tuple) and len(block) == 2
+            and isinstance(block[0], tuple) and len(block[0]) == 2
+            and isinstance(block[1], Matrix)):
+        blockRowIndex = int(block[0][0])
+        blockColIndex = int(block[0][1])
+        subMatrix = block[1]
+        return ((blockRowIndex, blockColIndex), subMatrix)
+    else:
+        raise TypeError("Cannot convert type %s into a sub-matrix block tuple" % type(block))
+
+
+class BlockMatrix(DistributedMatrix):
+    """
+    .. note:: Experimental
+
+    Represents a distributed matrix in blocks of local matrices.
+
+    :param blocks: An RDD of sub-matrix blocks
+                   ((blockRowIndex, blockColIndex), sub-matrix) that
+                   form this distributed matrix. If multiple blocks
+                   with the same index exist, the results for
+                   operations like add and multiply will be
+                   unpredictable.
+    :param rowsPerBlock: Number of rows that make up each block.
+                         The blocks forming the final rows are not
+                         required to have the given number of rows.
+    :param colsPerBlock: Number of columns that make up each block.
+                         The blocks forming the final columns are not
+                         required to have the given number of columns.
+    :param numRows: Number of rows of this matrix. If the supplied
+                    value is less than or equal to zero, the number
+                    of rows will be calculated when `numRows` is
+                    invoked.
+    :param numCols: Number of columns of this matrix. If the supplied
+                    value is less than or equal to zero, the number
+                    of columns will be calculated when `numCols` is
+                    invoked.
+    """
+    def __init__(self, blocks, rowsPerBlock, colsPerBlock, numRows=0, numCols=0):
+        """
+        Note: This docstring is not shown publicly.
+
+        Create a wrapper over a Java BlockMatrix.
+
+        Publicly, we require that `blocks` be an RDD.  However, for
+        internal usage, `blocks` can also be a Java BlockMatrix
+        object, in which case we can wrap it directly.  This
+        assists in clean matrix conversions.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2)
+
+        >>> mat_diff = BlockMatrix(blocks, 3, 2)
+        >>> (mat_diff._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        False
+
+        >>> mat_same = BlockMatrix(mat._java_matrix_wrapper._java_model, 3, 2)
+        >>> (mat_same._java_matrix_wrapper._java_model ==
+        ...  mat._java_matrix_wrapper._java_model)
+        True
+        """
+        if isinstance(blocks, RDD):
+            blocks = blocks.map(_convert_to_matrix_block_tuple)
+            # We use DataFrames for serialization of sub-matrix blocks
+            # from Python, so first convert the RDD to a DataFrame on
+            # this side. This will convert each sub-matrix block
+            # tuple to a Row containing the 'blockRowIndex',
+            # 'blockColIndex', and 'subMatrix' values, which can
+            # each be easily serialized.  We will convert back to
+            # ((blockRowIndex, blockColIndex), sub-matrix) tuples on
+            # the Scala side.
+            java_matrix = callMLlibFunc("createBlockMatrix", blocks.toDF(),
+                                        int(rowsPerBlock), int(colsPerBlock),
+                                        long(numRows), long(numCols))
+        elif (isinstance(blocks, JavaObject)
+              and blocks.getClass().getSimpleName() == "BlockMatrix"):
+            java_matrix = blocks
+        else:
+            raise TypeError("blocks should be an RDD of sub-matrix blocks as "
+                            "((int, int), matrix) tuples, got %s" % type(blocks))
+
+        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)
+
+    @property
+    def blocks(self):
+        """
+        The RDD of sub-matrix blocks
+        ((blockRowIndex, blockColIndex), sub-matrix) that form this
+        distributed matrix.
+
+        >>> mat = BlockMatrix(
+        ...     sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                     ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]), 3, 2)
+        >>> blocks = mat.blocks
+        >>> blocks.first()
+        ((0, 0), DenseMatrix(3, 2, [1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 0))
+
+        """
+        # We use DataFrames for serialization of sub-matrix blocks
+        # from Java, so we first convert the RDD of blocks to a
+        # DataFrame on the Scala/Java side. Then we map each Row in
+        # the DataFrame back to a sub-matrix block on this side.
+        blocks_df = callMLlibFunc("getMatrixBlocks", self._java_matrix_wrapper._java_model)
+        blocks = blocks_df.map(lambda row: ((row[0][0], row[0][1]), row[1]))
+        return blocks
+
+    @property
+    def rowsPerBlock(self):
+        """
+        Number of rows that make up each block.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> mat.rowsPerBlock
+        3
+        """
+        return self._java_matrix_wrapper.call("rowsPerBlock")
+
+    @property
+    def colsPerBlock(self):
+        """
+        Number of columns that make up each block.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> mat.colsPerBlock
+        2
+        """
+        return self._java_matrix_wrapper.call("colsPerBlock")
+
+    @property
+    def numRowBlocks(self):
+        """
+        Number of rows of blocks in the BlockMatrix.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> mat.numRowBlocks
+        2
+        """
+        return self._java_matrix_wrapper.call("numRowBlocks")
+
+    @property
+    def numColBlocks(self):
+        """
+        Number of columns of blocks in the BlockMatrix.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> mat.numColBlocks
+        1
+        """
+        return self._java_matrix_wrapper.call("numColBlocks")
+
+    def numRows(self):
+        """
+        Get or compute the number of rows.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> print(mat.numRows())
+        6
+
+        >>> mat = BlockMatrix(blocks, 3, 2, 7, 6)
+        >>> print(mat.numRows())
+        7
+        """
+        return self._java_matrix_wrapper.call("numRows")
+
+    def numCols(self):
+        """
+        Get or compute the number of cols.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+
+        >>> mat = BlockMatrix(blocks, 3, 2)
+        >>> print(mat.numCols())
+        2
+
+        >>> mat = BlockMatrix(blocks, 3, 2, 7, 6)
+        >>> print(mat.numCols())
+        6
+        """
+        return self._java_matrix_wrapper.call("numCols")
+
+    def toLocalMatrix(self):
+        """
+        Collect the distributed matrix on the driver as a DenseMatrix.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2).toLocalMatrix()
+
+        >>> # This BlockMatrix will have 6 effective rows, due to
+        >>> # having two sub-matrix blocks stacked, each with 3 rows.
+        >>> # The ensuing DenseMatrix will also have 6 rows.
+        >>> print(mat.numRows)
+        6
+
+        >>> # This BlockMatrix will have 2 effective columns, due to
+        >>> # having two sub-matrix blocks stacked, each with 2
+        >>> # columns. The ensuing DenseMatrix will also have 2 columns.
+        >>> print(mat.numCols)
+        2
+        """
+        return self._java_matrix_wrapper.call("toLocalMatrix")
+
+    def toIndexedRowMatrix(self):
+        """
+        Convert this matrix to an IndexedRowMatrix.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
+        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
+        >>> mat = BlockMatrix(blocks, 3, 2).toIndexedRowMatrix()
+
+        >>> # This BlockMatrix will have 6 effective rows, due to
+        >>> # having two sub-matrix blocks stacked, each with 3 rows.
+        >>> # The ensuing IndexedRowMatrix will also have 6 rows.
+        >>> print(mat.numRows())
+        6
+
+        >>> # This BlockMatrix will have 2 effective columns, due to
+        >>> # having two sub-matrix blocks stacked, each with 2 columns.
+        >>> # The ensuing IndexedRowMatrix will also have 2 columns.
+        >>> print(mat.numCols())
+        2
+        """
+        java_indexed_row_matrix = self._java_matrix_wrapper.call("toIndexedRowMatrix")
+        return IndexedRowMatrix(java_indexed_row_matrix)
+
+    def toCoordinateMatrix(self):
+        """
+        Convert this matrix to a CoordinateMatrix.
+
+        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(1, 2, [1, 2])),
+        ...                          ((1, 0), Matrices.dense(1, 2, [7, 8]))])
+        >>> mat = BlockMatrix(blocks, 1, 2).toCoordinateMatrix()
+        >>> mat.entries.take(3)
+        [MatrixEntry(0, 0, 1.0), MatrixEntry(0, 1, 2.0), MatrixEntry(1, 0, 7.0)]
+        """
+        java_coordinate_matrix = self._java_matrix_wrapper.call("toCoordinateMatrix")
+        return CoordinateMatrix(java_coordinate_matrix)
+
 
 def _test():
     import doctest
     from pyspark import SparkContext
     from pyspark.sql import SQLContext
+    from pyspark.mllib.linalg import Matrices
     import pyspark.mllib.linalg.distributed
     globs = pyspark.mllib.linalg.distributed.__dict__.copy()
     globs['sc'] = SparkContext('local[2]', 'PythonTest', batchSize=2)
     globs['sqlContext'] = SQLContext(globs['sc'])
+    globs['Matrices'] = Matrices
     (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
     globs['sc'].stop()
     if failure_count:

From 23d982204bb9ef74d3b788a32ce6608116968719 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 5 Aug 2015 09:01:45 -0700
Subject: [PATCH 062/633] [SPARK-9141] [SQL] Remove project collapsing from
 DataFrame API

Currently we collapse successive projections that are added by `withColumn`.  However, this optimization violates the constraint that adding nodes to a plan will never change its analyzed form and thus breaks caching.  Instead of doing early optimization, in this PR I just fix some low-hanging slowness in the analyzer.  In particular, I add a mechanism for skipping already analyzed subplans, `resolveOperators` and `resolveExpression`.  Since trees are generally immutable after construction, it's safe to annotate a plan as already analyzed as any transformation will create a new tree with this bit no longer set.

Together these result in a faster analyzer than before, even with added timing instrumentation.

```
Original Code
[info] 3430ms
[info] 2205ms
[info] 1973ms
[info] 1982ms
[info] 1916ms

Without Project Collapsing in DataFrame
[info] 44610ms
[info] 45977ms
[info] 46423ms
[info] 46306ms
[info] 54723ms

With analyzer optimizations
[info] 6394ms
[info] 4630ms
[info] 4388ms
[info] 4093ms
[info] 4113ms

With resolveOperators
[info] 2495ms
[info] 1380ms
[info] 1685ms
[info] 1414ms
[info] 1240ms
```

Author: Michael Armbrust <michael@databricks.com>

Closes #7920 from marmbrus/withColumnCache and squashes the following commits:

2145031 [Michael Armbrust] fix hive udfs tests
5a5a525 [Michael Armbrust] remove wrong comment
7a507d5 [Michael Armbrust] style
b59d710 [Michael Armbrust] revert small change
1fa5949 [Michael Armbrust] move logic into LogicalPlan, add tests
0e2cb43 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into withColumnCache
c926e24 [Michael Armbrust] naming
e593a2d [Michael Armbrust] style
f5a929e [Michael Armbrust] [SPARK-9141][SQL] Remove project collapsing from DataFrame API
38b1c83 [Michael Armbrust] WIP
---
 .../sql/catalyst/analysis/Analyzer.scala      | 28 ++++----
 .../sql/catalyst/analysis/CheckAnalysis.scala |  3 +
 .../catalyst/analysis/HiveTypeCoercion.scala  | 30 ++++----
 .../spark/sql/catalyst/plans/QueryPlan.scala  |  5 +-
 .../catalyst/plans/logical/LogicalPlan.scala  | 51 ++++++++++++-
 .../sql/catalyst/rules/RuleExecutor.scala     | 22 ++++++
 .../spark/sql/catalyst/trees/TreeNode.scala   | 64 ++++-------------
 .../sql/catalyst/plans/LogicalPlanSuite.scala | 72 +++++++++++++++++++
 .../org/apache/spark/sql/DataFrame.scala      |  6 +-
 .../spark/sql/execution/SparkPlan.scala       |  2 +-
 .../spark/sql/execution/pythonUDFs.scala      |  2 +-
 .../apache/spark/sql/CachedTableSuite.scala   | 20 ++++++
 .../org/apache/spark/sql/DataFrameSuite.scala | 12 ----
 .../execution/HiveCompatibilitySuite.scala    |  5 ++
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  5 +-
 .../org/apache/spark/sql/hive/hiveUDFs.scala  |  3 +-
 .../sql/hive/execution/HiveUDFSuite.scala     |  8 +--
 .../sql/hive/execution/SQLQuerySuite.scala    |  2 +
 18 files changed, 234 insertions(+), 106 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index ca17f3e3d06ff..6de31f42dd30c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -90,7 +90,7 @@ class Analyzer(
    */
   object CTESubstitution extends Rule[LogicalPlan] {
     // TODO allow subquery to define CTE
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform  {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators  {
       case With(child, relations) => substituteCTE(child, relations)
       case other => other
     }
@@ -116,7 +116,7 @@ class Analyzer(
    * Substitute child plan with WindowSpecDefinitions.
    */
   object WindowsSubstitution extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       // Lookup WindowSpecDefinitions. This rule works with unresolved children.
       case WithWindowDefinition(windowDefinitions, child) =>
         child.transform {
@@ -140,7 +140,7 @@ class Analyzer(
   object ResolveAliases extends Rule[LogicalPlan] {
     private def assignAliases(exprs: Seq[NamedExpression]) = {
       // The `UnresolvedAlias`s will appear only at root of a expression tree, we don't need
-      // to transform down the whole tree.
+      // to resolveOperator down the whole tree.
       exprs.zipWithIndex.map {
         case (u @ UnresolvedAlias(child), i) =>
           child match {
@@ -156,7 +156,7 @@ class Analyzer(
       }
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case Aggregate(groups, aggs, child)
         if child.resolved && aggs.exists(_.isInstanceOf[UnresolvedAlias]) =>
         Aggregate(groups, assignAliases(aggs), child)
@@ -198,7 +198,7 @@ class Analyzer(
       Seq.tabulate(1 << c.groupByExprs.length)(i => i)
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case a if !a.childrenResolved => a // be sure all of the children are resolved.
       case a: Cube =>
         GroupingSets(bitmasks(a), a.groupByExprs, a.child, a.aggregations)
@@ -261,7 +261,7 @@ class Analyzer(
       }
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case i @ InsertIntoTable(u: UnresolvedRelation, _, _, _, _) =>
         i.copy(table = EliminateSubQueries(getTable(u)))
       case u: UnresolvedRelation =>
@@ -274,7 +274,7 @@ class Analyzer(
    * a logical plan node's children.
    */
   object ResolveReferences extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case p: LogicalPlan if !p.childrenResolved => p
 
       // If the projection list contains Stars, expand it.
@@ -444,7 +444,7 @@ class Analyzer(
    * remove these attributes after sorting.
    */
   object ResolveSortReferences extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case s @ Sort(ordering, global, p @ Project(projectList, child))
           if !s.resolved && p.resolved =>
         val (newOrdering, missing) = resolveAndFindMissing(ordering, p, child)
@@ -519,7 +519,7 @@ class Analyzer(
    * Replaces [[UnresolvedFunction]]s with concrete [[Expression]]s.
    */
   object ResolveFunctions extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case q: LogicalPlan =>
         q transformExpressions {
           case u @ UnresolvedFunction(name, children, isDistinct) =>
@@ -551,7 +551,7 @@ class Analyzer(
    * Turns projections that contain aggregate expressions into aggregations.
    */
   object GlobalAggregates extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case Project(projectList, child) if containsAggregates(projectList) =>
         Aggregate(Nil, projectList, child)
     }
@@ -571,7 +571,7 @@ class Analyzer(
    * aggregates and then projects them away above the filter.
    */
   object UnresolvedHavingClauseAttributes extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformUp {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case filter @ Filter(havingCondition, aggregate @ Aggregate(_, originalAggExprs, _))
           if aggregate.resolved && containsAggregate(havingCondition) =>
 
@@ -601,7 +601,7 @@ class Analyzer(
    * [[AnalysisException]] is throw.
    */
   object ResolveGenerate extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case p: Generate if !p.child.resolved || !p.generator.resolved => p
       case g: Generate if !g.resolved =>
         g.copy(generatorOutput = makeGeneratorOutput(g.generator, g.generatorOutput.map(_.name)))
@@ -872,6 +872,8 @@ class Analyzer(
     // We have to use transformDown at here to make sure the rule of
     // "Aggregate with Having clause" will be triggered.
     def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
+
+
       // Aggregate with Having clause. This rule works with an unresolved Aggregate because
       // a resolved Aggregate will not have Window Functions.
       case f @ Filter(condition, a @ Aggregate(groupingExprs, aggregateExprs, child))
@@ -927,7 +929,7 @@ class Analyzer(
    * put them into an inner Project and finally project them away at the outer Project.
    */
   object PullOutNondeterministic extends Rule[LogicalPlan] {
-    override def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    override def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case p: Project => p
       case f: Filter => f
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 187b238045f85..39f554c137c98 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -47,6 +47,7 @@ trait CheckAnalysis {
     // We transform up and order the rules so as to catch the first possible failure instead
     // of the result of cascading resolution failures.
     plan.foreachUp {
+      case p if p.analyzed => // Skip already analyzed sub-plans
 
       case operator: LogicalPlan =>
         operator transformExpressionsUp {
@@ -179,5 +180,7 @@ trait CheckAnalysis {
         }
     }
     extendedCheckRules.foreach(_(plan))
+
+    plan.foreach(_.setAnalyzed())
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 490f3dc07b6ed..970f3c8282c81 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -144,7 +144,8 @@ object HiveTypeCoercion {
    * instances higher in the query tree.
    */
   object PropagateTypes extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+
       // No propagation required for leaf nodes.
       case q: LogicalPlan if q.children.isEmpty => q
 
@@ -225,7 +226,9 @@ object HiveTypeCoercion {
       }
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+      case p if p.analyzed => p
+
       case u @ Union(left, right) if u.childrenResolved && !u.resolved =>
         val (newLeft, newRight) = widenOutputTypes(u.nodeName, left, right)
         Union(newLeft, newRight)
@@ -242,7 +245,7 @@ object HiveTypeCoercion {
    * Promotes strings that appear in arithmetic expressions.
    */
   object PromoteStrings extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -305,7 +308,7 @@ object HiveTypeCoercion {
    * Convert all expressions in in() list to the left operator type
    */
   object InConversion extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -372,7 +375,8 @@ object HiveTypeCoercion {
       ChangeDecimalPrecision(Cast(e, dataType))
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+
       // fix decimal precision for expressions
       case q => q.transformExpressions {
         // Skip nodes whose children have not been resolved yet
@@ -466,7 +470,7 @@ object HiveTypeCoercion {
       ))
     }
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -508,7 +512,7 @@ object HiveTypeCoercion {
    * truncated version of this number.
    */
   object StringToIntegralCasts extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -521,7 +525,7 @@ object HiveTypeCoercion {
    * This ensure that the types for various functions are as expected.
    */
   object FunctionArgumentConversion extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -575,7 +579,7 @@ object HiveTypeCoercion {
    * converted to fractional types.
    */
   object Division extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who has not been resolved yet,
       // as this is an extra rule which should be applied at last.
       case e if !e.resolved => e
@@ -592,7 +596,7 @@ object HiveTypeCoercion {
    * Coerces the type of different branches of a CASE WHEN statement to a common type.
    */
   object CaseWhenCoercion extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       case c: CaseWhenLike if c.childrenResolved && !c.valueTypesEqual =>
         logDebug(s"Input values for null casting ${c.valueTypes.mkString(",")}")
         val maybeCommonType = findTightestCommonTypeAndPromoteToString(c.valueTypes)
@@ -628,7 +632,7 @@ object HiveTypeCoercion {
    * Coerces the type of different branches of If statement to a common type.
    */
   object IfCoercion extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Find tightest common type for If, if the true value and false value have different types.
       case i @ If(pred, left, right) if left.dataType != right.dataType =>
         findTightestCommonTypeToString(left.dataType, right.dataType).map { widestType =>
@@ -652,7 +656,7 @@ object HiveTypeCoercion {
 
     private val acceptedTypes = Seq(DateType, TimestampType, StringType)
 
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
@@ -669,7 +673,7 @@ object HiveTypeCoercion {
    * Casts types according to the expected input types for [[Expression]]s.
    */
   object ImplicitTypeCasts extends Rule[LogicalPlan] {
-    def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
+    def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       // Skip nodes who's children have not been resolved yet.
       case e if !e.childrenResolved => e
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
index c610f70d38437..55286f9f2fc5c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.plans
 
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeSet, Expression, VirtualColumn}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.types.{DataType, StructType}
 
@@ -92,7 +93,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy
 
     val newArgs = productIterator.map(recursiveTransform).toArray
 
-    if (changed) makeCopy(newArgs) else this
+    if (changed) makeCopy(newArgs).asInstanceOf[this.type] else this
   }
 
   /**
@@ -124,7 +125,7 @@ abstract class QueryPlan[PlanType <: TreeNode[PlanType]] extends TreeNode[PlanTy
 
     val newArgs = productIterator.map(recursiveTransform).toArray
 
-    if (changed) makeCopy(newArgs) else this
+    if (changed) makeCopy(newArgs).asInstanceOf[this.type] else this
   }
 
   /** Returns the result of running [[transformExpressions]] on this node
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index bedeaf06adf12..9b52f020093f0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -22,11 +22,60 @@ import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
-import org.apache.spark.sql.catalyst.trees.TreeNode
+import org.apache.spark.sql.catalyst.trees.{CurrentOrigin, TreeNode}
 
 
 abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
 
+  private var _analyzed: Boolean = false
+
+  /**
+   * Marks this plan as already analyzed.  This should only be called by CheckAnalysis.
+   */
+  private[catalyst] def setAnalyzed(): Unit = { _analyzed = true }
+
+  /**
+   * Returns true if this node and its children have already been gone through analysis and
+   * verification.  Note that this is only an optimization used to avoid analyzing trees that
+   * have already been analyzed, and can be reset by transformations.
+   */
+  def analyzed: Boolean = _analyzed
+
+  /**
+   * Returns a copy of this node where `rule` has been recursively applied first to all of its
+   * children and then itself (post-order). When `rule` does not apply to a given node, it is left
+   * unchanged.  This function is similar to `transformUp`, but skips sub-trees that have already
+   * been marked as analyzed.
+   *
+   * @param rule the function use to transform this nodes children
+   */
+  def resolveOperators(rule: PartialFunction[LogicalPlan, LogicalPlan]): LogicalPlan = {
+    if (!analyzed) {
+      val afterRuleOnChildren = transformChildren(rule, (t, r) => t.resolveOperators(r))
+      if (this fastEquals afterRuleOnChildren) {
+        CurrentOrigin.withOrigin(origin) {
+          rule.applyOrElse(this, identity[LogicalPlan])
+        }
+      } else {
+        CurrentOrigin.withOrigin(origin) {
+          rule.applyOrElse(afterRuleOnChildren, identity[LogicalPlan])
+        }
+      }
+    } else {
+      this
+    }
+  }
+
+  /**
+   * Recursively transforms the expressions of a tree, skipping nodes that have already
+   * been analyzed.
+   */
+  def resolveExpressions(r: PartialFunction[Expression, Expression]): LogicalPlan = {
+    this resolveOperators  {
+      case p => p.transformExpressions(r)
+    }
+  }
+
   /**
    * Computes [[Statistics]] for this plan. The default implementation assumes the output
    * cardinality is the product of of all child plan's cardinality, i.e. applies in the case
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
index 3f9858b0c4a43..8b824511a79da 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
@@ -21,6 +21,23 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.catalyst.util.sideBySide
 
+import scala.collection.mutable
+
+object RuleExecutor {
+  protected val timeMap = new mutable.HashMap[String, Long].withDefault(_ => 0)
+
+  /** Resets statistics about time spent running specific rules */
+  def resetTime(): Unit = timeMap.clear()
+
+  /** Dump statistics about time spent running specific rules. */
+  def dumpTimeSpent(): String = {
+    val maxSize = timeMap.keys.map(_.toString.length).max
+    timeMap.toSeq.sortBy(_._2).reverseMap { case (k, v) =>
+      s"${k.padTo(maxSize, " ").mkString} $v"
+    }.mkString("\n")
+  }
+}
+
 abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging {
 
   /**
@@ -41,6 +58,7 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging {
   /** Defines a sequence of rule batches, to be overridden by the implementation. */
   protected val batches: Seq[Batch]
 
+
   /**
    * Executes the batches of rules defined by the subclass. The batches are executed serially
    * using the defined execution strategy. Within each batch, rules are also executed serially.
@@ -58,7 +76,11 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging {
       while (continue) {
         curPlan = batch.rules.foldLeft(curPlan) {
           case (plan, rule) =>
+            val startTime = System.nanoTime()
             val result = rule(plan)
+            val runTime = System.nanoTime() - startTime
+            RuleExecutor.timeMap(rule.ruleName) = RuleExecutor.timeMap(rule.ruleName) + runTime
+
             if (!result.fastEquals(plan)) {
               logTrace(
                 s"""
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
index 122e9fc5ed77f..7971e25188e8d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/trees/TreeNode.scala
@@ -149,7 +149,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
   /**
    * Returns a copy of this node where `f` has been applied to all the nodes children.
    */
-  def mapChildren(f: BaseType => BaseType): this.type = {
+  def mapChildren(f: BaseType => BaseType): BaseType = {
     var changed = false
     val newArgs = productIterator.map {
       case arg: TreeNode[_] if containsChild(arg) =>
@@ -170,7 +170,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
    * Returns a copy of this node with the children replaced.
    * TODO: Validate somewhere (in debug mode?) that children are ordered correctly.
    */
-  def withNewChildren(newChildren: Seq[BaseType]): this.type = {
+  def withNewChildren(newChildren: Seq[BaseType]): BaseType = {
     assert(newChildren.size == children.size, "Incorrect number of children")
     var changed = false
     val remainingNewChildren = newChildren.toBuffer
@@ -229,9 +229,9 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
 
     // Check if unchanged and then possibly return old copy to avoid gc churn.
     if (this fastEquals afterRule) {
-      transformChildrenDown(rule)
+      transformChildren(rule, (t, r) => t.transformDown(r))
     } else {
-      afterRule.transformChildrenDown(rule)
+      afterRule.transformChildren(rule, (t, r) => t.transformDown(r))
     }
   }
 
@@ -240,11 +240,13 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
    * this node.  When `rule` does not apply to a given node it is left unchanged.
    * @param rule the function used to transform this nodes children
    */
-  def transformChildrenDown(rule: PartialFunction[BaseType, BaseType]): this.type = {
+  protected def transformChildren(
+      rule: PartialFunction[BaseType, BaseType],
+      nextOperation: (BaseType, PartialFunction[BaseType, BaseType]) => BaseType): BaseType = {
     var changed = false
     val newArgs = productIterator.map {
       case arg: TreeNode[_] if containsChild(arg) =>
-        val newChild = arg.asInstanceOf[BaseType].transformDown(rule)
+        val newChild = nextOperation(arg.asInstanceOf[BaseType], rule)
         if (!(newChild fastEquals arg)) {
           changed = true
           newChild
@@ -252,7 +254,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
           arg
         }
       case Some(arg: TreeNode[_]) if containsChild(arg) =>
-        val newChild = arg.asInstanceOf[BaseType].transformDown(rule)
+        val newChild = nextOperation(arg.asInstanceOf[BaseType], rule)
         if (!(newChild fastEquals arg)) {
           changed = true
           Some(newChild)
@@ -263,7 +265,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
       case d: DataType => d // Avoid unpacking Structs
       case args: Traversable[_] => args.map {
         case arg: TreeNode[_] if containsChild(arg) =>
-          val newChild = arg.asInstanceOf[BaseType].transformDown(rule)
+          val newChild = nextOperation(arg.asInstanceOf[BaseType], rule)
           if (!(newChild fastEquals arg)) {
             changed = true
             newChild
@@ -285,7 +287,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
    * @param rule the function use to transform this nodes children
    */
   def transformUp(rule: PartialFunction[BaseType, BaseType]): BaseType = {
-    val afterRuleOnChildren = transformChildrenUp(rule)
+    val afterRuleOnChildren = transformChildren(rule, (t, r) => t.transformUp(r))
     if (this fastEquals afterRuleOnChildren) {
       CurrentOrigin.withOrigin(origin) {
         rule.applyOrElse(this, identity[BaseType])
@@ -297,44 +299,6 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
     }
   }
 
-  def transformChildrenUp(rule: PartialFunction[BaseType, BaseType]): this.type = {
-    var changed = false
-    val newArgs = productIterator.map {
-      case arg: TreeNode[_] if containsChild(arg) =>
-        val newChild = arg.asInstanceOf[BaseType].transformUp(rule)
-        if (!(newChild fastEquals arg)) {
-          changed = true
-          newChild
-        } else {
-          arg
-        }
-      case Some(arg: TreeNode[_]) if containsChild(arg) =>
-        val newChild = arg.asInstanceOf[BaseType].transformUp(rule)
-        if (!(newChild fastEquals arg)) {
-          changed = true
-          Some(newChild)
-        } else {
-          Some(arg)
-        }
-      case m: Map[_, _] => m
-      case d: DataType => d // Avoid unpacking Structs
-      case args: Traversable[_] => args.map {
-        case arg: TreeNode[_] if containsChild(arg) =>
-          val newChild = arg.asInstanceOf[BaseType].transformUp(rule)
-          if (!(newChild fastEquals arg)) {
-            changed = true
-            newChild
-          } else {
-            arg
-          }
-        case other => other
-      }
-      case nonChild: AnyRef => nonChild
-      case null => null
-    }.toArray
-    if (changed) makeCopy(newArgs) else this
-  }
-
   /**
    * Args to the constructor that should be copied, but not transformed.
    * These are appended to the transformed args automatically by makeCopy
@@ -348,7 +312,7 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
    * that are not present in the productIterator.
    * @param newArgs the new product arguments.
    */
-  def makeCopy(newArgs: Array[AnyRef]): this.type = attachTree(this, "makeCopy") {
+  def makeCopy(newArgs: Array[AnyRef]): BaseType = attachTree(this, "makeCopy") {
     val ctors = getClass.getConstructors.filter(_.getParameterTypes.size != 0)
     if (ctors.isEmpty) {
       sys.error(s"No valid constructor for $nodeName")
@@ -359,9 +323,9 @@ abstract class TreeNode[BaseType <: TreeNode[BaseType]] extends Product {
       CurrentOrigin.withOrigin(origin) {
         // Skip no-arg constructors that are just there for kryo.
         if (otherCopyArgs.isEmpty) {
-          defaultCtor.newInstance(newArgs: _*).asInstanceOf[this.type]
+          defaultCtor.newInstance(newArgs: _*).asInstanceOf[BaseType]
         } else {
-          defaultCtor.newInstance((newArgs ++ otherCopyArgs).toArray: _*).asInstanceOf[this.type]
+          defaultCtor.newInstance((newArgs ++ otherCopyArgs).toArray: _*).asInstanceOf[BaseType]
         }
       }
     } catch {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala
new file mode 100644
index 0000000000000..797b29f23cbb9
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala
@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.plans
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.logical._
+import org.apache.spark.sql.catalyst.util._
+
+/**
+ * Provides helper methods for comparing plans.
+ */
+class LogicalPlanSuite extends SparkFunSuite {
+  private var invocationCount = 0
+  private val function: PartialFunction[LogicalPlan, LogicalPlan] = {
+    case p: Project =>
+      invocationCount += 1
+      p
+  }
+
+  private val testRelation = LocalRelation()
+
+  test("resolveOperator runs on operators") {
+    invocationCount = 0
+    val plan = Project(Nil, testRelation)
+    plan resolveOperators function
+
+    assert(invocationCount === 1)
+  }
+
+  test("resolveOperator runs on operators recursively") {
+    invocationCount = 0
+    val plan = Project(Nil, Project(Nil, testRelation))
+    plan resolveOperators function
+
+    assert(invocationCount === 2)
+  }
+
+  test("resolveOperator skips all ready resolved plans") {
+    invocationCount = 0
+    val plan = Project(Nil, Project(Nil, testRelation))
+    plan.foreach(_.setAnalyzed())
+    plan resolveOperators function
+
+    assert(invocationCount === 0)
+  }
+
+  test("resolveOperator skips partially resolved plans") {
+    invocationCount = 0
+    val plan1 = Project(Nil, testRelation)
+    val plan2 = Project(Nil, plan1)
+    plan1.foreach(_.setAnalyzed())
+    plan2 resolveOperators function
+
+    assert(invocationCount === 1)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index db15711202b77..e57acec59d327 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql
 import java.io.CharArrayWriter
 import java.util.Properties
 
+import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.unsafe.types.UTF8String
 
 import scala.language.implicitConversions
@@ -54,7 +55,6 @@ private[sql] object DataFrame {
   }
 }
 
-
 /**
  * :: Experimental ::
  * A distributed collection of data organized into named columns.
@@ -690,9 +690,7 @@ class DataFrame private[sql](
       case Column(explode: Explode) => MultiAlias(explode, Nil)
       case Column(expr: Expression) => Alias(expr, expr.prettyString)()
     }
-    // When user continuously call `select`, speed up analysis by collapsing `Project`
-    import org.apache.spark.sql.catalyst.optimizer.ProjectCollapsing
-    Project(namedExpressions.toSeq, ProjectCollapsing(logicalPlan))
+    Project(namedExpressions.toSeq, logicalPlan)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 73b237fffece8..dbc0cefbe2e10 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -67,7 +67,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
   private val prepareCalled = new AtomicBoolean(false)
 
   /** Overridden make copy also propogates sqlContext to copied plan. */
-  override def makeCopy(newArgs: Array[AnyRef]): this.type = {
+  override def makeCopy(newArgs: Array[AnyRef]): SparkPlan = {
     SparkPlan.currentContext.set(sqlContext)
     super.makeCopy(newArgs)
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index dedc7c4dfb4d1..59f8b079ab333 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -65,7 +65,7 @@ private[spark] case class PythonUDF(
  * multiple child operators.
  */
 private[spark] object ExtractPythonUDFs extends Rule[LogicalPlan] {
-  def apply(plan: LogicalPlan): LogicalPlan = plan transform {
+  def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
     // Skip EvaluatePython nodes.
     case plan: EvaluatePython => plan
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index e9dd7ef226e42..a88df91b1001c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -25,6 +25,7 @@ import org.scalatest.concurrent.Eventually._
 import org.apache.spark.Accumulators
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.columnar._
+import org.apache.spark.sql.functions._
 import org.apache.spark.storage.{StorageLevel, RDDBlockId}
 
 case class BigData(s: String)
@@ -50,6 +51,25 @@ class CachedTableSuite extends QueryTest {
     ctx.sparkContext.env.blockManager.get(RDDBlockId(rddId, 0)).nonEmpty
   }
 
+  test("withColumn doesn't invalidate cached dataframe") {
+    var evalCount = 0
+    val myUDF = udf((x: String) => { evalCount += 1; "result" })
+    val df = Seq(("test", 1)).toDF("s", "i").select(myUDF($"s"))
+    df.cache()
+
+    df.collect()
+    assert(evalCount === 1)
+
+    df.collect()
+    assert(evalCount === 1)
+
+    val df2 = df.withColumn("newColumn", lit(1))
+    df2.collect()
+
+    // We should not reevaluate the cached dataframe
+    assert(evalCount === 1)
+  }
+
   test("cache temp table") {
     testData.select('key).registerTempTable("tempTable")
     assertCached(sql("SELECT COUNT(*) FROM tempTable"), 0)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index b8f10b00f5690..f9cc6d1f3c250 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -686,18 +686,6 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
       Seq(Row(2, 1, 2), Row(1, 1, 1)))
   }
 
-  test("SPARK-7276: Project collapse for continuous select") {
-    var df = testData
-    for (i <- 1 to 5) {
-      df = df.select($"*")
-    }
-
-    import org.apache.spark.sql.catalyst.plans.logical.Project
-    // make sure df have at most two Projects
-    val p = df.logicalPlan.asInstanceOf[Project].child.asInstanceOf[Project]
-    assert(!p.child.isInstanceOf[Project])
-  }
-
   test("SPARK-7150 range api") {
     // numSlice is greater than length
     val res1 = sqlContext.range(0, 10, 1, 15).select("id")
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index d4fc6c2b6ebc0..ab309e0a1d36b 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.hive.execution
 import java.io.File
 import java.util.{Locale, TimeZone}
 
+import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.SQLConf
@@ -50,6 +51,7 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     TestHive.setConf(SQLConf.COLUMN_BATCH_SIZE, 5)
     // Enable in-memory partition pruning for testing purposes
     TestHive.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, true)
+    RuleExecutor.resetTime()
   }
 
   override def afterAll() {
@@ -58,6 +60,9 @@ class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
     Locale.setDefault(originalLocale)
     TestHive.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize)
     TestHive.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning)
+
+    // For debugging dump some statistics about how much time was spent in various optimizer rules.
+    logWarning(RuleExecutor.dumpTimeSpent())
   }
 
   /** A list of tests deemed out of scope currently and thus completely disregarded. */
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 16c186627f6cc..6b37af99f4677 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -391,7 +391,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
    */
   object ParquetConversions extends Rule[LogicalPlan] {
     override def apply(plan: LogicalPlan): LogicalPlan = {
-      if (!plan.resolved) {
+      if (!plan.resolved || plan.analyzed) {
         return plan
       }
 
@@ -418,8 +418,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
           (relation, parquetRelation, attributedRewrites)
 
         // Read path
-        case p @ PhysicalOperation(_, _, relation: MetastoreRelation)
-            if hive.convertMetastoreParquet &&
+        case relation: MetastoreRelation if hive.convertMetastoreParquet &&
               relation.tableDesc.getSerdeClassName.toLowerCase.contains("parquet") =>
           val parquetRelation = convertToParquetRelation(relation)
           val attributedRewrites = relation.output.zip(parquetRelation.output)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 8a86a87368f29..7182246e466a4 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -133,8 +133,7 @@ private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, childre
   @transient
   private lazy val conversionHelper = new ConversionHelper(method, arguments)
 
-  @transient
-  lazy val dataType = javaClassToDataType(method.getReturnType)
+  val dataType = javaClassToDataType(method.getReturnType)
 
   @transient
   lazy val returnInspector = ObjectInspectorFactory.getReflectionObjectInspector(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index 7069afc9f7da2..10f2902e5eef0 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -182,7 +182,7 @@ class HiveUDFSuite extends QueryTest {
     val errMsg = intercept[AnalysisException] {
       sql("SELECT testUDFToListString(s) FROM inputTable")
     }
-    assert(errMsg.getMessage === "List type in java is unsupported because " +
+    assert(errMsg.getMessage contains "List type in java is unsupported because " +
       "JVM type erasure makes spark fail to catch a component type in List<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToListString")
@@ -197,7 +197,7 @@ class HiveUDFSuite extends QueryTest {
     val errMsg = intercept[AnalysisException] {
       sql("SELECT testUDFToListInt(s) FROM inputTable")
     }
-    assert(errMsg.getMessage === "List type in java is unsupported because " +
+    assert(errMsg.getMessage contains "List type in java is unsupported because " +
       "JVM type erasure makes spark fail to catch a component type in List<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToListInt")
@@ -213,7 +213,7 @@ class HiveUDFSuite extends QueryTest {
     val errMsg = intercept[AnalysisException] {
       sql("SELECT testUDFToStringIntMap(s) FROM inputTable")
     }
-    assert(errMsg.getMessage === "Map type in java is unsupported because " +
+    assert(errMsg.getMessage contains "Map type in java is unsupported because " +
       "JVM type erasure makes spark fail to catch key and value types in Map<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToStringIntMap")
@@ -229,7 +229,7 @@ class HiveUDFSuite extends QueryTest {
     val errMsg = intercept[AnalysisException] {
       sql("SELECT testUDFToIntIntMap(s) FROM inputTable")
     }
-    assert(errMsg.getMessage === "Map type in java is unsupported because " +
+    assert(errMsg.getMessage contains "Map type in java is unsupported because " +
       "JVM type erasure makes spark fail to catch key and value types in Map<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToIntIntMap")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index ff9a3694d612e..1dff07a6de8ad 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -948,6 +948,8 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
   }
 
   test("SPARK-7595: Window will cause resolve failed with self join") {
+    sql("SELECT * FROM src") // Force loading of src table.
+
     checkAnswer(sql(
       """
         |with

From 7a969a6967c4ecc0f004b73bff27a75257a94e86 Mon Sep 17 00:00:00 2001
From: linweizhong <linweizhong@huawei.com>
Date: Wed, 5 Aug 2015 10:16:12 -0700
Subject: [PATCH 063/633] [SPARK-9519] [YARN] Confirm stop sc successfully when
 application was killed

Currently, when we kill application on Yarn, then will call sc.stop() at Yarn application state monitor thread, then in YarnClientSchedulerBackend.stop() will call interrupt this will cause SparkContext not stop fully as we will wait executor to exit.

Author: linweizhong <linweizhong@huawei.com>

Closes #7846 from Sephiroth-Lin/SPARK-9519 and squashes the following commits:

1ae736d [linweizhong] Update comments
2e8e365 [linweizhong] Add comment explaining the code
ad0e23b [linweizhong] Update
243d2c7 [linweizhong] Confirm stop sc successfully when application was killed
---
 .../cluster/YarnClientSchedulerBackend.scala  | 47 +++++++++++++------
 1 file changed, 32 insertions(+), 15 deletions(-)

diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index d97fa2e2151bc..d225061fcd1b4 100644
--- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -33,7 +33,7 @@ private[spark] class YarnClientSchedulerBackend(
 
   private var client: Client = null
   private var appId: ApplicationId = null
-  private var monitorThread: Thread = null
+  private var monitorThread: MonitorThread = null
 
   /**
    * Create a Yarn client to submit an application to the ResourceManager.
@@ -131,24 +131,42 @@ private[spark] class YarnClientSchedulerBackend(
     }
   }
 
+  /**
+   * We create this class for SPARK-9519. Basically when we interrupt the monitor thread it's
+   * because the SparkContext is being shut down(sc.stop() called by user code), but if
+   * monitorApplication return, it means the Yarn application finished before sc.stop() was called,
+   * which means we should call sc.stop() here, and we don't allow the monitor to be interrupted
+   * before SparkContext stops successfully.
+   */
+  private class MonitorThread extends Thread {
+    private var allowInterrupt = true
+
+    override def run() {
+      try {
+        val (state, _) = client.monitorApplication(appId, logApplicationReport = false)
+        logError(s"Yarn application has already exited with state $state!")
+        allowInterrupt = false
+        sc.stop()
+      } catch {
+        case e: InterruptedException => logInfo("Interrupting monitor thread")
+      }
+    }
+
+    def stopMonitor(): Unit = {
+      if (allowInterrupt) {
+        this.interrupt()
+      }
+    }
+  }
+
   /**
    * Monitor the application state in a separate thread.
    * If the application has exited for any reason, stop the SparkContext.
    * This assumes both `client` and `appId` have already been set.
    */
-  private def asyncMonitorApplication(): Thread = {
+  private def asyncMonitorApplication(): MonitorThread = {
     assert(client != null && appId != null, "Application has not been submitted yet!")
-    val t = new Thread {
-      override def run() {
-        try {
-          val (state, _) = client.monitorApplication(appId, logApplicationReport = false)
-          logError(s"Yarn application has already exited with state $state!")
-          sc.stop()
-        } catch {
-          case e: InterruptedException => logInfo("Interrupting monitor thread")
-        }
-      }
-    }
+    val t = new MonitorThread
     t.setName("Yarn application state monitor")
     t.setDaemon(true)
     t
@@ -160,7 +178,7 @@ private[spark] class YarnClientSchedulerBackend(
   override def stop() {
     assert(client != null, "Attempted to stop this scheduler before starting it!")
     if (monitorThread != null) {
-      monitorThread.interrupt()
+      monitorThread.stopMonitor()
     }
     super.stop()
     client.stop()
@@ -174,5 +192,4 @@ private[spark] class YarnClientSchedulerBackend(
       super.applicationId
     }
   }
-
 }

From 1f8c364b9c6636f06986f5f80d5a49b7a7772ac3 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 5 Aug 2015 11:03:02 -0700
Subject: [PATCH 064/633] [SPARK-9141] [SQL] [MINOR] Fix comments of PR #7920

This is a follow-up of https://github.com/apache/spark/pull/7920 to fix comments.

Author: Yin Huai <yhuai@databricks.com>

Closes #7964 from yhuai/SPARK-9141-follow-up and squashes the following commits:

4d0ee80 [Yin Huai] Fix comments.
---
 .../org/apache/spark/sql/catalyst/analysis/Analyzer.scala      | 3 +--
 .../org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala | 3 ++-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 6de31f42dd30c..82158e61e3fb5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -140,7 +140,7 @@ class Analyzer(
   object ResolveAliases extends Rule[LogicalPlan] {
     private def assignAliases(exprs: Seq[NamedExpression]) = {
       // The `UnresolvedAlias`s will appear only at root of a expression tree, we don't need
-      // to resolveOperator down the whole tree.
+      // to traverse the whole tree.
       exprs.zipWithIndex.map {
         case (u @ UnresolvedAlias(child), i) =>
           child match {
@@ -873,7 +873,6 @@ class Analyzer(
     // "Aggregate with Having clause" will be triggered.
     def apply(plan: LogicalPlan): LogicalPlan = plan transformDown {
 
-
       // Aggregate with Having clause. This rule works with an unresolved Aggregate because
       // a resolved Aggregate will not have Window Functions.
       case f @ Filter(condition, a @ Aggregate(groupingExprs, aggregateExprs, child))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala
index 797b29f23cbb9..455a3810c719e 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/LogicalPlanSuite.scala
@@ -23,7 +23,8 @@ import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.util._
 
 /**
- * Provides helper methods for comparing plans.
+ * This suite is used to test [[LogicalPlan]]'s `resolveOperators` and make sure it can correctly
+ * skips sub-trees that have already been marked as analyzed.
  */
 class LogicalPlanSuite extends SparkFunSuite {
   private var invocationCount = 0

From e1e05873fc75781b6dd3f7fadbfb57824f83054e Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Wed, 5 Aug 2015 11:38:56 -0700
Subject: [PATCH 065/633] [SPARK-9403] [SQL] Add codegen support in In and
 InSet

This continues tarekauel's work in #7778.

Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Tarek Auel <tarek.auel@googlemail.com>

Closes #7893 from viirya/codegen_in and squashes the following commits:

81ff97b [Liang-Chi Hsieh] For comments.
47761c6 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into codegen_in
cf4bf41 [Liang-Chi Hsieh] For comments.
f532b3c [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into codegen_in
446bbcd [Liang-Chi Hsieh] Fix bug.
b3d0ab4 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into codegen_in
4610eff [Liang-Chi Hsieh] Relax the types of references and update optimizer test.
224f18e [Liang-Chi Hsieh] Beef up the test cases for In and InSet to include all primitive data types.
86dc8aa [Liang-Chi Hsieh] Only convert In to InSet when the number of items in set is more than the threshold.
b7ded7e [Tarek Auel] [SPARK-9403][SQL] codeGen in / inSet
---
 .../sql/catalyst/expressions/predicates.scala | 63 +++++++++++++++++--
 .../sql/catalyst/optimizer/Optimizer.scala    |  2 +-
 .../catalyst/expressions/PredicateSuite.scala | 37 ++++++++++-
 .../catalyst/optimizer/OptimizeInSuite.scala  | 14 ++++-
 .../datasources/DataSourceStrategy.scala      |  7 +++
 .../spark/sql/ColumnExpressionSuite.scala     |  6 ++
 6 files changed, 119 insertions(+), 10 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index b69bbabee7e81..68c832d7194d4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -17,6 +17,9 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import scala.collection.mutable
+
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenFallback, GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
@@ -97,32 +100,80 @@ case class Not(child: Expression)
 /**
  * Evaluates to `true` if `list` contains `value`.
  */
-case class In(value: Expression, list: Seq[Expression]) extends Predicate with CodegenFallback {
+case class In(value: Expression, list: Seq[Expression]) extends Predicate
+    with ImplicitCastInputTypes {
+
+  override def inputTypes: Seq[AbstractDataType] = value.dataType +: list.map(_.dataType)
+
+  override def checkInputDataTypes(): TypeCheckResult = {
+    if (list.exists(l => l.dataType != value.dataType)) {
+      TypeCheckResult.TypeCheckFailure(
+        "Arguments must be same type")
+    } else {
+      TypeCheckResult.TypeCheckSuccess
+    }
+  }
+
   override def children: Seq[Expression] = value +: list
 
-  override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN.
+  override def nullable: Boolean = false // TODO: Figure out correct nullability semantics of IN.
   override def toString: String = s"$value IN ${list.mkString("(", ",", ")")}"
 
   override def eval(input: InternalRow): Any = {
     val evaluatedValue = value.eval(input)
     list.exists(e => e.eval(input) == evaluatedValue)
   }
-}
 
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val valueGen = value.gen(ctx)
+    val listGen = list.map(_.gen(ctx))
+    val listCode = listGen.map(x =>
+      s"""
+        if (!${ev.primitive}) {
+          ${x.code}
+          if (${ctx.genEqual(value.dataType, valueGen.primitive, x.primitive)}) {
+            ${ev.primitive} = true;
+          }
+        }
+       """).mkString("\n")
+    s"""
+      ${valueGen.code}
+      boolean ${ev.primitive} = false;
+      boolean ${ev.isNull} = false;
+      $listCode
+    """
+  }
+}
 
 /**
  * Optimized version of In clause, when all filter values of In clause are
  * static.
  */
-case class InSet(child: Expression, hset: Set[Any])
-  extends UnaryExpression with Predicate with CodegenFallback {
+case class InSet(child: Expression, hset: Set[Any]) extends UnaryExpression with Predicate {
 
-  override def nullable: Boolean = true // TODO: Figure out correct nullability semantics of IN.
+  override def nullable: Boolean = false // TODO: Figure out correct nullability semantics of IN.
   override def toString: String = s"$child INSET ${hset.mkString("(", ",", ")")}"
 
   override def eval(input: InternalRow): Any = {
     hset.contains(child.eval(input))
   }
+
+  def getHSet(): Set[Any] = hset
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val setName = classOf[Set[Any]].getName
+    val InSetName = classOf[InSet].getName
+    val childGen = child.gen(ctx)
+    ctx.references += this
+    val hsetTerm = ctx.freshName("hset")
+    ctx.addMutableState(setName, hsetTerm,
+      s"$hsetTerm = (($InSetName)expressions[${ctx.references.size - 1}]).getHSet();")
+    s"""
+      ${childGen.code}
+      boolean ${ev.isNull} = false;
+      boolean ${ev.primitive} = $hsetTerm.contains(${childGen.primitive});
+     """
+  }
 }
 
 case class And(left: Expression, right: Expression) extends BinaryOperator with Predicate {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 29d706dcb39a7..4ab5ac2c61e3c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -393,7 +393,7 @@ object ConstantFolding extends Rule[LogicalPlan] {
 object OptimizeIn extends Rule[LogicalPlan] {
   def apply(plan: LogicalPlan): LogicalPlan = plan transform {
     case q: LogicalPlan => q transformExpressionsDown {
-      case In(v, list) if !list.exists(!_.isInstanceOf[Literal]) =>
+      case In(v, list) if !list.exists(!_.isInstanceOf[Literal]) && list.size > 10 =>
         val hSet = list.map(e => e.eval(EmptyRow))
         InSet(v, HashSet() ++ hSet)
     }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index d7eb13c50b134..7beef71845e43 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -21,7 +21,8 @@ import scala.collection.immutable.HashSet
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.types.{Decimal, DoubleType, IntegerType, BooleanType}
+import org.apache.spark.sql.RandomDataGenerator
+import org.apache.spark.sql.types._
 
 
 class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
@@ -118,6 +119,23 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("^Ba*n"))), true)
     checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("aa"), Literal("^Ba*n"))), true)
     checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("aa"), Literal("^n"))), false)
+
+    val primitiveTypes = Seq(IntegerType, FloatType, DoubleType, StringType, ByteType, ShortType,
+      LongType, BinaryType, BooleanType, DecimalType.USER_DEFAULT, TimestampType)
+    primitiveTypes.map { t =>
+      val dataGen = RandomDataGenerator.forType(t, nullable = false).get
+      val inputData = Seq.fill(10) {
+        val value = dataGen.apply()
+        value match {
+          case d: Double if d.isNaN => 0.0d
+          case f: Float if f.isNaN => 0.0f
+          case _ => value
+        }
+      }
+      val input = inputData.map(Literal(_))
+      checkEvaluation(In(input(0), input.slice(1, 10)),
+        inputData.slice(1, 10).contains(inputData(0)))
+    }
   }
 
   test("INSET") {
@@ -134,6 +152,23 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(InSet(three, hS), false)
     checkEvaluation(InSet(three, nS), false)
     checkEvaluation(And(InSet(one, hS), InSet(two, hS)), true)
+
+    val primitiveTypes = Seq(IntegerType, FloatType, DoubleType, StringType, ByteType, ShortType,
+      LongType, BinaryType, BooleanType, DecimalType.USER_DEFAULT, TimestampType)
+    primitiveTypes.map { t =>
+      val dataGen = RandomDataGenerator.forType(t, nullable = false).get
+      val inputData = Seq.fill(10) {
+        val value = dataGen.apply()
+        value match {
+          case d: Double if d.isNaN => 0.0d
+          case f: Float if f.isNaN => 0.0f
+          case _ => value
+        }
+      }
+      val input = inputData.map(Literal(_))
+      checkEvaluation(InSet(input(0), inputData.slice(1, 10).toSet),
+        inputData.slice(1, 10).contains(inputData(0)))
+    }
   }
 
   private val smallValues = Seq(1, Decimal(1), Array(1.toByte), "a", 0f, 0d, false).map(Literal(_))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala
index 1d433275fed2e..6f7b5b9572e22 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala
@@ -43,16 +43,26 @@ class OptimizeInSuite extends PlanTest {
 
   val testRelation = LocalRelation('a.int, 'b.int, 'c.int)
 
-  test("OptimizedIn test: In clause optimized to InSet") {
+  test("OptimizedIn test: In clause not optimized to InSet when less than 10 items") {
     val originalQuery =
       testRelation
         .where(In(UnresolvedAttribute("a"), Seq(Literal(1), Literal(2))))
         .analyze
 
+    val optimized = Optimize.execute(originalQuery.analyze)
+    comparePlans(optimized, originalQuery)
+  }
+
+  test("OptimizedIn test: In clause optimized to InSet when more than 10 items") {
+    val originalQuery =
+      testRelation
+        .where(In(UnresolvedAttribute("a"), (1 to 11).map(Literal(_))))
+        .analyze
+
     val optimized = Optimize.execute(originalQuery.analyze)
     val correctAnswer =
       testRelation
-        .where(InSet(UnresolvedAttribute("a"), HashSet[Any]() + 1 + 2))
+        .where(InSet(UnresolvedAttribute("a"), (1 to 11).toSet))
         .analyze
 
     comparePlans(optimized, correctAnswer)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index a43bccbe6927c..e5dc676b87841 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -366,6 +366,13 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
       case expressions.InSet(a: Attribute, set) =>
         Some(sources.In(a.name, set.toArray))
 
+      // Because we only convert In to InSet in Optimizer when there are more than certain
+      // items. So it is possible we still get an In expression here that needs to be pushed
+      // down.
+      case expressions.In(a: Attribute, list) if !list.exists(!_.isInstanceOf[Literal]) =>
+        val hSet = list.map(e => e.eval(EmptyRow))
+        Some(sources.In(a.name, hSet.toArray))
+
       case expressions.IsNull(a: Attribute) =>
         Some(sources.IsNull(a.name))
       case expressions.IsNotNull(a: Attribute) =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 35ca0b4c7cc21..b351380373259 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -357,6 +357,12 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
       df.collect().toSeq.filter(r => r.getString(1) == "z" || r.getString(1) == "x"))
     checkAnswer(df.filter($"b".in("z", "y")),
       df.collect().toSeq.filter(r => r.getString(1) == "z" || r.getString(1) == "y"))
+
+    val df2 = Seq((1, Seq(1)), (2, Seq(2)), (3, Seq(3))).toDF("a", "b")
+
+    intercept[AnalysisException] {
+      df2.filter($"a".in($"b"))
+    }
   }
 
   val booleanData = ctx.createDataFrame(ctx.sparkContext.parallelize(

From eb5b8f4a603e0f289bdaa0a2164cde2cfe4feecb Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 5 Aug 2015 12:51:12 -0700
Subject: [PATCH 066/633] Closes #7778 since it is done as #7893.


From 5f0fb6466f5e3607f7fca9b2371a73b3deef3fdf Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Wed, 5 Aug 2015 14:12:22 -0700
Subject: [PATCH 067/633] [SPARK-9649] Fix flaky test MasterSuite - randomize
 ports

```
Error Message

Failed to bind to: /127.0.0.1:7093: Service 'sparkMaster' failed after 16 retries!
Stacktrace

      java.net.BindException: Failed to bind to: /127.0.0.1:7093: Service 'sparkMaster' failed after 16 retries!
      at org.jboss.netty.bootstrap.ServerBootstrap.bind(ServerBootstrap.java:272)
      at akka.remote.transport.netty.NettyTransport$$anonfun$listen$1.apply(NettyTransport.scala:393)
      at akka.remote.transport.netty.NettyTransport$$anonfun$listen$1.apply(NettyTransport.scala:389)
      at scala.util.Success$$anonfun$map$1.apply(Try.scala:206)
      at scala.util.Try$.apply(Try.scala:161)
```

Author: Andrew Or <andrew@databricks.com>

Closes #7968 from andrewor14/fix-master-flaky-test and squashes the following commits:

fcc42ef [Andrew Or] Randomize port
---
 .../org/apache/spark/deploy/master/MasterSuite.scala      | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
index 30780a0da7f8d..ae0e037d822ea 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
@@ -93,8 +93,8 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
       publicAddress = ""
     )
 
-    val (rpcEnv, uiPort, restPort) =
-      Master.startRpcEnvAndEndpoint("127.0.0.1", 7077, 8080, conf)
+    val (rpcEnv, _, _) =
+      Master.startRpcEnvAndEndpoint("127.0.0.1", 0, 0, conf)
 
     try {
       rpcEnv.setupEndpointRef(Master.SYSTEM_NAME, rpcEnv.address, Master.ENDPOINT_NAME)
@@ -343,8 +343,8 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
 
   private def makeMaster(conf: SparkConf = new SparkConf): Master = {
     val securityMgr = new SecurityManager(conf)
-    val rpcEnv = RpcEnv.create(Master.SYSTEM_NAME, "localhost", 7077, conf, securityMgr)
-    val master = new Master(rpcEnv, rpcEnv.address, 8080, securityMgr, conf)
+    val rpcEnv = RpcEnv.create(Master.SYSTEM_NAME, "localhost", 0, conf, securityMgr)
+    val master = new Master(rpcEnv, rpcEnv.address, 0, securityMgr, conf)
     master
   }
 

From f9c2a2af1e883b36c5e51b87ef660a1b9ad0f586 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 5 Aug 2015 14:15:57 -0700
Subject: [PATCH 068/633] Closes #7474 since it's marked as won't fix.


From dac090d1e9be7dec6c5ebdb2a81105b87e853193 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 5 Aug 2015 15:42:18 -0700
Subject: [PATCH 069/633] [SPARK-9657] Fix return type of getMaxPatternLength

mengxr

Author: Feynman Liang <fliang@databricks.com>

Closes #7974 from feynmanliang/SPARK-9657 and squashes the following commits:

7ca533f [Feynman Liang] Fix return type of getMaxPatternLength
---
 .../src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index d5f0c926c69bb..ad6715b52f337 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -82,7 +82,7 @@ class PrefixSpan private (
   /**
    * Gets the maximal pattern length (i.e. the length of the longest sequential pattern to consider.
    */
-  def getMaxPatternLength: Double = maxPatternLength
+  def getMaxPatternLength: Int = maxPatternLength
 
   /**
    * Sets maximal pattern length (default: `10`).

From 9c878923db6634effed98c99bf24dd263bb7c6ad Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 5 Aug 2015 16:33:42 -0700
Subject: [PATCH 070/633] [SPARK-9054] [SQL] Rename RowOrdering to
 InterpretedOrdering; use newOrdering in SMJ

This patches renames `RowOrdering` to `InterpretedOrdering` and updates SortMergeJoin to use the `SparkPlan` methods for constructing its ordering so that it may benefit from codegen.

This is an updated version of #7408.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7973 from JoshRosen/SPARK-9054 and squashes the following commits:

e610655 [Josh Rosen] Add comment RE: Ascending ordering
34b8e0c [Josh Rosen] Import ordering
be19a0f [Josh Rosen] [SPARK-9054] [SQL] Rename RowOrdering to InterpretedOrdering; use newOrdering in more places.
---
 .../sql/catalyst/expressions/arithmetic.scala |  4 +--
 .../catalyst/expressions/conditionals.scala   |  4 +--
 .../{RowOrdering.scala => ordering.scala}     | 27 ++++++++++---------
 .../sql/catalyst/expressions/predicates.scala |  8 +++---
 .../spark/sql/catalyst/util/TypeUtils.scala   |  4 +--
 .../apache/spark/sql/types/StructType.scala   |  4 +--
 .../expressions/CodeGenerationSuite.scala     |  2 +-
 .../apache/spark/sql/execution/Exchange.scala |  5 +++-
 .../spark/sql/execution/SparkPlan.scala       | 14 ++++++++--
 .../spark/sql/execution/basicOperators.scala  |  4 ++-
 .../sql/execution/joins/SortMergeJoin.scala   |  9 ++++---
 .../UnsafeKVExternalSorterSuite.scala         |  6 ++---
 12 files changed, 55 insertions(+), 36 deletions(-)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{RowOrdering.scala => ordering.scala} (85%)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
index 5808e3f66de3c..98464edf4d390 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala
@@ -320,7 +320,7 @@ case class MaxOf(left: Expression, right: Expression) extends BinaryArithmetic {
 
   override def nullable: Boolean = left.nullable && right.nullable
 
-  private lazy val ordering = TypeUtils.getOrdering(dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(dataType)
 
   override def eval(input: InternalRow): Any = {
     val input1 = left.eval(input)
@@ -374,7 +374,7 @@ case class MinOf(left: Expression, right: Expression) extends BinaryArithmetic {
 
   override def nullable: Boolean = left.nullable && right.nullable
 
-  private lazy val ordering = TypeUtils.getOrdering(dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(dataType)
 
   override def eval(input: InternalRow): Any = {
     val input1 = left.eval(input)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
index 961b1d8616801..d51f3d3cef588 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
@@ -319,7 +319,7 @@ case class Least(children: Seq[Expression]) extends Expression {
   override def nullable: Boolean = children.forall(_.nullable)
   override def foldable: Boolean = children.forall(_.foldable)
 
-  private lazy val ordering = TypeUtils.getOrdering(dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(dataType)
 
   override def checkInputDataTypes(): TypeCheckResult = {
     if (children.length <= 1) {
@@ -374,7 +374,7 @@ case class Greatest(children: Seq[Expression]) extends Expression {
   override def nullable: Boolean = children.forall(_.nullable)
   override def foldable: Boolean = children.forall(_.foldable)
 
-  private lazy val ordering = TypeUtils.getOrdering(dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(dataType)
 
   override def checkInputDataTypes(): TypeCheckResult = {
     if (children.length <= 1) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/RowOrdering.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ordering.scala
similarity index 85%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/RowOrdering.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ordering.scala
index 873f5324c573e..6407c73bc97d9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/RowOrdering.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/ordering.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.types._
 /**
  * An interpreted row ordering comparator.
  */
-class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
+class InterpretedOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
 
   def this(ordering: Seq[SortOrder], inputSchema: Seq[Attribute]) =
     this(ordering.map(BindReferences.bindReference(_, inputSchema)))
@@ -49,9 +49,9 @@ class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
           case dt: AtomicType if order.direction == Descending =>
             dt.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
           case s: StructType if order.direction == Ascending =>
-            s.ordering.asInstanceOf[Ordering[Any]].compare(left, right)
+            s.interpretedOrdering.asInstanceOf[Ordering[Any]].compare(left, right)
           case s: StructType if order.direction == Descending =>
-            s.ordering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
+            s.interpretedOrdering.asInstanceOf[Ordering[Any]].reverse.compare(left, right)
           case other =>
             throw new IllegalArgumentException(s"Type $other does not support ordered operations")
         }
@@ -65,6 +65,18 @@ class RowOrdering(ordering: Seq[SortOrder]) extends Ordering[InternalRow] {
   }
 }
 
+object InterpretedOrdering {
+
+  /**
+   * Creates a [[InterpretedOrdering]] for the given schema, in natural ascending order.
+   */
+  def forSchema(dataTypes: Seq[DataType]): InterpretedOrdering = {
+    new InterpretedOrdering(dataTypes.zipWithIndex.map {
+      case (dt, index) => new SortOrder(BoundReference(index, dt, nullable = true), Ascending)
+    })
+  }
+}
+
 object RowOrdering {
 
   /**
@@ -81,13 +93,4 @@ object RowOrdering {
    * Returns true iff outputs from the expressions can be ordered.
    */
   def isOrderable(exprs: Seq[Expression]): Boolean = exprs.forall(e => isOrderable(e.dataType))
-
-  /**
-   * Creates a [[RowOrdering]] for the given schema, in natural ascending order.
-   */
-  def forSchema(dataTypes: Seq[DataType]): RowOrdering = {
-    new RowOrdering(dataTypes.zipWithIndex.map {
-      case (dt, index) => new SortOrder(BoundReference(index, dt, nullable = true), Ascending)
-    })
-  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 68c832d7194d4..fe7dffb815987 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -376,7 +376,7 @@ case class LessThan(left: Expression, right: Expression) extends BinaryCompariso
 
   override def symbol: String = "<"
 
-  private lazy val ordering = TypeUtils.getOrdering(left.dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(left.dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.lt(input1, input2)
 }
@@ -388,7 +388,7 @@ case class LessThanOrEqual(left: Expression, right: Expression) extends BinaryCo
 
   override def symbol: String = "<="
 
-  private lazy val ordering = TypeUtils.getOrdering(left.dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(left.dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.lteq(input1, input2)
 }
@@ -400,7 +400,7 @@ case class GreaterThan(left: Expression, right: Expression) extends BinaryCompar
 
   override def symbol: String = ">"
 
-  private lazy val ordering = TypeUtils.getOrdering(left.dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(left.dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.gt(input1, input2)
 }
@@ -412,7 +412,7 @@ case class GreaterThanOrEqual(left: Expression, right: Expression) extends Binar
 
   override def symbol: String = ">="
 
-  private lazy val ordering = TypeUtils.getOrdering(left.dataType)
+  private lazy val ordering = TypeUtils.getInterpretedOrdering(left.dataType)
 
   protected override def nullSafeEval(input1: Any, input2: Any): Any = ordering.gteq(input1, input2)
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
index 0b41f92c6193c..bcf4d78fb9371 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/TypeUtils.scala
@@ -54,10 +54,10 @@ object TypeUtils {
   def getNumeric(t: DataType): Numeric[Any] =
     t.asInstanceOf[NumericType].numeric.asInstanceOf[Numeric[Any]]
 
-  def getOrdering(t: DataType): Ordering[Any] = {
+  def getInterpretedOrdering(t: DataType): Ordering[Any] = {
     t match {
       case i: AtomicType => i.ordering.asInstanceOf[Ordering[Any]]
-      case s: StructType => s.ordering.asInstanceOf[Ordering[Any]]
+      case s: StructType => s.interpretedOrdering.asInstanceOf[Ordering[Any]]
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index 6928707f7bf6e..9cbc207538d4f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -24,7 +24,7 @@ import org.json4s.JsonDSL._
 
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute, RowOrdering}
+import org.apache.spark.sql.catalyst.expressions.{InterpretedOrdering, AttributeReference, Attribute, InterpretedOrdering$}
 
 
 /**
@@ -301,7 +301,7 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
     StructType(newFields)
   }
 
-  private[sql] val ordering = RowOrdering.forSchema(this.fields.map(_.dataType))
+  private[sql] val interpretedOrdering = InterpretedOrdering.forSchema(this.fields.map(_.dataType))
 }
 
 object StructType extends AbstractDataType {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index cc82f7c3f5a73..e310aee221666 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -54,7 +54,7 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
   // GenerateOrdering agrees with RowOrdering.
   (DataTypeTestUtils.atomicTypes ++ Set(NullType)).foreach { dataType =>
     test(s"GenerateOrdering with $dataType") {
-      val rowOrdering = RowOrdering.forSchema(Seq(dataType, dataType))
+      val rowOrdering = InterpretedOrdering.forSchema(Seq(dataType, dataType))
       val genOrdering = GenerateOrdering.generate(
         BoundReference(0, dataType, nullable = true).asc ::
           BoundReference(1, dataType, nullable = true).asc :: Nil)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 05b009d1935bb..6ea5eeedf1bbe 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -156,7 +156,10 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
           val mutablePair = new MutablePair[InternalRow, Null]()
           iter.map(row => mutablePair.update(row.copy(), null))
         }
-        implicit val ordering = new RowOrdering(sortingExpressions, child.output)
+        // We need to use an interpreted ordering here because generated orderings cannot be
+        // serialized and this ordering needs to be created on the driver in order to be passed into
+        // Spark core code.
+        implicit val ordering = new InterpretedOrdering(sortingExpressions, child.output)
         new RangePartitioner(numPartitions, rddForSampling, ascending = true)
       case SinglePartition =>
         new Partitioner {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index dbc0cefbe2e10..2f29067f5646a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -32,6 +32,7 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.types.DataType
 
 object SparkPlan {
   protected[sql] val currentContext = new ThreadLocal[SQLContext]()
@@ -309,13 +310,22 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
             throw e
           } else {
             log.error("Failed to generate ordering, fallback to interpreted", e)
-            new RowOrdering(order, inputSchema)
+            new InterpretedOrdering(order, inputSchema)
           }
       }
     } else {
-      new RowOrdering(order, inputSchema)
+      new InterpretedOrdering(order, inputSchema)
     }
   }
+  /**
+   * Creates a row ordering for the given schema, in natural ascending order.
+   */
+  protected def newNaturalAscendingOrdering(dataTypes: Seq[DataType]): Ordering[InternalRow] = {
+    val order: Seq[SortOrder] = dataTypes.zipWithIndex.map {
+      case (dt, index) => new SortOrder(BoundReference(index, dt, nullable = true), Ascending)
+    }
+    newOrdering(order, Seq.empty)
+  }
 }
 
 private[sql] trait LeafNode extends SparkPlan {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 477170297c2ac..f4677b4ee86bb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -212,7 +212,9 @@ case class TakeOrderedAndProject(
 
   override def outputPartitioning: Partitioning = SinglePartition
 
-  private val ord: RowOrdering = new RowOrdering(sortOrder, child.output)
+  // We need to use an interpreted ordering here because generated orderings cannot be serialized
+  // and this ordering needs to be created on the driver in order to be passed into Spark core code.
+  private val ord: InterpretedOrdering = new InterpretedOrdering(sortOrder, child.output)
 
   // TODO: remove @transient after figure out how to clean closure at InsertIntoHiveTable.
   @transient private val projection = projectList.map(new InterpretedProjection(_, child.output))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index eb595490fbf28..4ae23c186cf7b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -48,9 +48,6 @@ case class SortMergeJoin(
   override def requiredChildDistribution: Seq[Distribution] =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
-  // this is to manually construct an ordering that can be used to compare keys from both sides
-  private val keyOrdering: RowOrdering = RowOrdering.forSchema(leftKeys.map(_.dataType))
-
   override def outputOrdering: Seq[SortOrder] = requiredOrders(leftKeys)
 
   override def requiredChildOrdering: Seq[Seq[SortOrder]] =
@@ -59,8 +56,10 @@ case class SortMergeJoin(
   @transient protected lazy val leftKeyGenerator = newProjection(leftKeys, left.output)
   @transient protected lazy val rightKeyGenerator = newProjection(rightKeys, right.output)
 
-  private def requiredOrders(keys: Seq[Expression]): Seq[SortOrder] =
+  private def requiredOrders(keys: Seq[Expression]): Seq[SortOrder] = {
+    // This must be ascending in order to agree with the `keyOrdering` defined in `doExecute()`.
     keys.map(SortOrder(_, Ascending))
+  }
 
   protected override def doExecute(): RDD[InternalRow] = {
     val leftResults = left.execute().map(_.copy())
@@ -68,6 +67,8 @@ case class SortMergeJoin(
 
     leftResults.zipPartitions(rightResults) { (leftIter, rightIter) =>
       new Iterator[InternalRow] {
+        // An ordering that can be used to compare keys from both sides.
+        private[this] val keyOrdering = newNaturalAscendingOrdering(leftKeys.map(_.dataType))
         // Mutable per row objects.
         private[this] val joinRow = new JoinedRow
         private[this] var leftElement: InternalRow = _
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
index 08156f0e39ce8..a9515a03acf2c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
@@ -22,7 +22,7 @@ import scala.util.Random
 import org.apache.spark._
 import org.apache.spark.sql.{RandomDataGenerator, Row}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
-import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, RowOrdering, UnsafeProjection}
+import org.apache.spark.sql.catalyst.expressions.{InterpretedOrdering, UnsafeRow, UnsafeProjection}
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
@@ -144,8 +144,8 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite {
     }
     sorter.cleanupResources()
 
-    val keyOrdering = RowOrdering.forSchema(keySchema.map(_.dataType))
-    val valueOrdering = RowOrdering.forSchema(valueSchema.map(_.dataType))
+    val keyOrdering = InterpretedOrdering.forSchema(keySchema.map(_.dataType))
+    val valueOrdering = InterpretedOrdering.forSchema(valueSchema.map(_.dataType))
     val kvOrdering = new Ordering[(InternalRow, InternalRow)] {
       override def compare(x: (InternalRow, InternalRow), y: (InternalRow, InternalRow)): Int = {
         keyOrdering.compare(x._1, y._1) match {

From a018b85716fd510ae95a3c66d676bbdb90f8d4e7 Mon Sep 17 00:00:00 2001
From: Xusen Yin <yinxusen@gmail.com>
Date: Wed, 5 Aug 2015 17:07:55 -0700
Subject: [PATCH 071/633] [SPARK-5895] [ML] Add VectorSlicer - updated

Add VectorSlicer transformer to spark.ml, with features specified as either indices or names.  Transfers feature attributes for selected features.

Updated version of [https://github.com/apache/spark/pull/5731]

CC: yinxusen This updates your PR.  You'll still be the primary author of this PR.

CC: mengxr

Author: Xusen Yin <yinxusen@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7972 from jkbradley/yinxusen-SPARK-5895 and squashes the following commits:

b16e86e [Joseph K. Bradley] fixed scala style
71c65d2 [Joseph K. Bradley] fix import order
86e9739 [Joseph K. Bradley] cleanups per code review
9d8d6f1 [Joseph K. Bradley] style fix
83bc2e9 [Joseph K. Bradley] Updated VectorSlicer
98c6939 [Xusen Yin] fix style error
ecbf2d3 [Xusen Yin] change interfaces and params
f6be302 [Xusen Yin] Merge branch 'master' into SPARK-5895
e4781f2 [Xusen Yin] fix commit error
fd154d7 [Xusen Yin] add test suite of vector slicer
17171f8 [Xusen Yin] fix slicer
9ab9747 [Xusen Yin] add vector slicer
aa5a0bf [Xusen Yin] add vector slicer
---
 .../spark/ml/feature/VectorSlicer.scala       | 170 ++++++++++++++++++
 .../apache/spark/ml/util/MetadataUtils.scala  |  17 ++
 .../apache/spark/mllib/linalg/Vectors.scala   |  24 +++
 .../spark/ml/feature/VectorSlicerSuite.scala  | 109 +++++++++++
 .../spark/mllib/linalg/VectorsSuite.scala     |   7 +
 5 files changed, 327 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
new file mode 100644
index 0000000000000..772bebeff214b
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
@@ -0,0 +1,170 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.attribute.{Attribute, AttributeGroup}
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.param.{IntArrayParam, ParamMap, StringArrayParam}
+import org.apache.spark.ml.util.{Identifiable, MetadataUtils, SchemaUtils}
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.StructType
+
+/**
+ * :: Experimental ::
+ * This class takes a feature vector and outputs a new feature vector with a subarray of the
+ * original features.
+ *
+ * The subset of features can be specified with either indices ([[setIndices()]])
+ * or names ([[setNames()]]).  At least one feature must be selected. Duplicate features
+ * are not allowed, so there can be no overlap between selected indices and names.
+ *
+ * The output vector will order features with the selected indices first (in the order given),
+ * followed by the selected names (in the order given).
+ */
+@Experimental
+final class VectorSlicer(override val uid: String)
+  extends Transformer with HasInputCol with HasOutputCol {
+
+  def this() = this(Identifiable.randomUID("vectorSlicer"))
+
+  /**
+   * An array of indices to select features from a vector column.
+   * There can be no overlap with [[names]].
+   * @group param
+   */
+  val indices = new IntArrayParam(this, "indices",
+    "An array of indices to select features from a vector column." +
+      " There can be no overlap with names.", VectorSlicer.validIndices)
+
+  setDefault(indices -> Array.empty[Int])
+
+  /** @group getParam */
+  def getIndices: Array[Int] = $(indices)
+
+  /** @group setParam */
+  def setIndices(value: Array[Int]): this.type = set(indices, value)
+
+  /**
+   * An array of feature names to select features from a vector column.
+   * These names must be specified by ML [[org.apache.spark.ml.attribute.Attribute]]s.
+   * There can be no overlap with [[indices]].
+   * @group param
+   */
+  val names = new StringArrayParam(this, "names",
+    "An array of feature names to select features from a vector column." +
+      " There can be no overlap with indices.", VectorSlicer.validNames)
+
+  setDefault(names -> Array.empty[String])
+
+  /** @group getParam */
+  def getNames: Array[String] = $(names)
+
+  /** @group setParam */
+  def setNames(value: Array[String]): this.type = set(names, value)
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  override def validateParams(): Unit = {
+    require($(indices).length > 0 || $(names).length > 0,
+      s"VectorSlicer requires that at least one feature be selected.")
+  }
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    // Validity checks
+    transformSchema(dataset.schema)
+    val inputAttr = AttributeGroup.fromStructField(dataset.schema($(inputCol)))
+    inputAttr.numAttributes.foreach { numFeatures =>
+      val maxIndex = $(indices).max
+      require(maxIndex < numFeatures,
+        s"Selected feature index $maxIndex invalid for only $numFeatures input features.")
+    }
+
+    // Prepare output attributes
+    val inds = getSelectedFeatureIndices(dataset.schema)
+    val selectedAttrs: Option[Array[Attribute]] = inputAttr.attributes.map { attrs =>
+      inds.map(index => attrs(index))
+    }
+    val outputAttr = selectedAttrs match {
+      case Some(attrs) => new AttributeGroup($(outputCol), attrs)
+      case None => new AttributeGroup($(outputCol), inds.length)
+    }
+
+    // Select features
+    val slicer = udf { vec: Vector =>
+      vec match {
+        case features: DenseVector => Vectors.dense(inds.map(features.apply))
+        case features: SparseVector => features.slice(inds)
+      }
+    }
+    dataset.withColumn($(outputCol),
+      slicer(dataset($(inputCol))).as($(outputCol), outputAttr.toMetadata()))
+  }
+
+  /** Get the feature indices in order: indices, names */
+  private def getSelectedFeatureIndices(schema: StructType): Array[Int] = {
+    val nameFeatures = MetadataUtils.getFeatureIndicesFromNames(schema($(inputCol)), $(names))
+    val indFeatures = $(indices)
+    val numDistinctFeatures = (nameFeatures ++ indFeatures).distinct.length
+    lazy val errMsg = "VectorSlicer requires indices and names to be disjoint" +
+      s" sets of features, but they overlap." +
+      s" indices: ${indFeatures.mkString("[", ",", "]")}." +
+      s" names: " +
+      nameFeatures.zip($(names)).map { case (i, n) => s"$i:$n" }.mkString("[", ",", "]")
+    require(nameFeatures.length + indFeatures.length == numDistinctFeatures, errMsg)
+    indFeatures ++ nameFeatures
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT)
+
+    if (schema.fieldNames.contains($(outputCol))) {
+      throw new IllegalArgumentException(s"Output column ${$(outputCol)} already exists.")
+    }
+    val numFeaturesSelected = $(indices).length + $(names).length
+    val outputAttr = new AttributeGroup($(outputCol), numFeaturesSelected)
+    val outputFields = schema.fields :+ outputAttr.toStructField()
+    StructType(outputFields)
+  }
+
+  override def copy(extra: ParamMap): VectorSlicer = defaultCopy(extra)
+}
+
+private[feature] object VectorSlicer {
+
+  /** Return true if given feature indices are valid */
+  def validIndices(indices: Array[Int]): Boolean = {
+    if (indices.isEmpty) {
+      true
+    } else {
+      indices.length == indices.distinct.length && indices.forall(_ >= 0)
+    }
+  }
+
+  /** Return true if given feature names are valid */
+  def validNames(names: Array[String]): Boolean = {
+    names.forall(_.nonEmpty) && names.length == names.distinct.length
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
index 2a1db90f2ca2b..fcb517b5f735e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
@@ -20,6 +20,7 @@ package org.apache.spark.ml.util
 import scala.collection.immutable.HashMap
 
 import org.apache.spark.ml.attribute._
+import org.apache.spark.mllib.linalg.VectorUDT
 import org.apache.spark.sql.types.StructField
 
 
@@ -74,4 +75,20 @@ private[spark] object MetadataUtils {
     }
   }
 
+  /**
+   * Takes a Vector column and a list of feature names, and returns the corresponding list of
+   * feature indices in the column, in order.
+   * @param col  Vector column which must have feature names specified via attributes
+   * @param names  List of feature names
+   */
+  def getFeatureIndicesFromNames(col: StructField, names: Array[String]): Array[Int] = {
+    require(col.dataType.isInstanceOf[VectorUDT], s"getFeatureIndicesFromNames expected column $col"
+      + s" to be Vector type, but it was type ${col.dataType} instead.")
+    val inputAttr = AttributeGroup.fromStructField(col)
+    names.map { name =>
+      require(inputAttr.hasAttr(name),
+        s"getFeatureIndicesFromNames found no feature with name $name in column $col.")
+      inputAttr.getAttr(name).index.get
+    }
+  }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 96d1f48ba2ba3..86c461fa91633 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -766,6 +766,30 @@ class SparseVector(
       maxIdx
     }
   }
+
+  /**
+   * Create a slice of this vector based on the given indices.
+   * @param selectedIndices Unsorted list of indices into the vector.
+   *                        This does NOT do bound checking.
+   * @return  New SparseVector with values in the order specified by the given indices.
+   *
+   * NOTE: The API needs to be discussed before making this public.
+   *       Also, if we have a version assuming indices are sorted, we should optimize it.
+   */
+  private[spark] def slice(selectedIndices: Array[Int]): SparseVector = {
+    var currentIdx = 0
+    val (sliceInds, sliceVals) = selectedIndices.flatMap { origIdx =>
+      val iIdx = java.util.Arrays.binarySearch(this.indices, origIdx)
+      val i_v = if (iIdx >= 0) {
+        Iterator((currentIdx, this.values(iIdx)))
+      } else {
+        Iterator()
+      }
+      currentIdx += 1
+      i_v
+    }.unzip
+    new SparseVector(selectedIndices.length, sliceInds.toArray, sliceVals.toArray)
+  }
 }
 
 object SparseVector {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala
new file mode 100644
index 0000000000000..a6c2fba8360dd
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorSlicerSuite.scala
@@ -0,0 +1,109 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute}
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+
+class VectorSlicerSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("params") {
+    val slicer = new VectorSlicer
+    ParamsSuite.checkParams(slicer)
+    assert(slicer.getIndices.length === 0)
+    assert(slicer.getNames.length === 0)
+    withClue("VectorSlicer should not have any features selected by default") {
+      intercept[IllegalArgumentException] {
+        slicer.validateParams()
+      }
+    }
+  }
+
+  test("feature validity checks") {
+    import VectorSlicer._
+    assert(validIndices(Array(0, 1, 8, 2)))
+    assert(validIndices(Array.empty[Int]))
+    assert(!validIndices(Array(-1)))
+    assert(!validIndices(Array(1, 2, 1)))
+
+    assert(validNames(Array("a", "b")))
+    assert(validNames(Array.empty[String]))
+    assert(!validNames(Array("", "b")))
+    assert(!validNames(Array("a", "b", "a")))
+  }
+
+  test("Test vector slicer") {
+    val sqlContext = new SQLContext(sc)
+
+    val data = Array(
+      Vectors.sparse(5, Seq((0, -2.0), (1, 2.3))),
+      Vectors.dense(-2.0, 2.3, 0.0, 0.0, 1.0),
+      Vectors.dense(0.0, 0.0, 0.0, 0.0, 0.0),
+      Vectors.dense(0.6, -1.1, -3.0, 4.5, 3.3),
+      Vectors.sparse(5, Seq())
+    )
+
+    // Expected after selecting indices 1, 4
+    val expected = Array(
+      Vectors.sparse(2, Seq((0, 2.3))),
+      Vectors.dense(2.3, 1.0),
+      Vectors.dense(0.0, 0.0),
+      Vectors.dense(-1.1, 3.3),
+      Vectors.sparse(2, Seq())
+    )
+
+    val defaultAttr = NumericAttribute.defaultAttr
+    val attrs = Array("f0", "f1", "f2", "f3", "f4").map(defaultAttr.withName)
+    val attrGroup = new AttributeGroup("features", attrs.asInstanceOf[Array[Attribute]])
+
+    val resultAttrs = Array("f1", "f4").map(defaultAttr.withName)
+    val resultAttrGroup = new AttributeGroup("expected", resultAttrs.asInstanceOf[Array[Attribute]])
+
+    val rdd = sc.parallelize(data.zip(expected)).map { case (a, b) => Row(a, b) }
+    val df = sqlContext.createDataFrame(rdd,
+      StructType(Array(attrGroup.toStructField(), resultAttrGroup.toStructField())))
+
+    val vectorSlicer = new VectorSlicer().setInputCol("features").setOutputCol("result")
+
+    def validateResults(df: DataFrame): Unit = {
+      df.select("result", "expected").collect().foreach { case Row(vec1: Vector, vec2: Vector) =>
+        assert(vec1 === vec2)
+      }
+      val resultMetadata = AttributeGroup.fromStructField(df.schema("result"))
+      val expectedMetadata = AttributeGroup.fromStructField(df.schema("expected"))
+      assert(resultMetadata.numAttributes === expectedMetadata.numAttributes)
+      resultMetadata.attributes.get.zip(expectedMetadata.attributes.get).foreach { case (a, b) =>
+        assert(a === b)
+      }
+    }
+
+    vectorSlicer.setIndices(Array(1, 4)).setNames(Array.empty)
+    validateResults(vectorSlicer.transform(df))
+
+    vectorSlicer.setIndices(Array(1)).setNames(Array("f4"))
+    validateResults(vectorSlicer.transform(df))
+
+    vectorSlicer.setIndices(Array.empty).setNames(Array("f1", "f4"))
+    validateResults(vectorSlicer.transform(df))
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
index 1c37ea5123e82..6508ddeba4206 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala
@@ -367,4 +367,11 @@ class VectorsSuite extends SparkFunSuite with Logging {
     val sv1c = sv1.compressed.asInstanceOf[DenseVector]
     assert(sv1 === sv1c)
   }
+
+  test("SparseVector.slice") {
+    val v = new SparseVector(5, Array(1, 2, 4), Array(1.1, 2.2, 4.4))
+    assert(v.slice(Array(0, 2)) === new SparseVector(2, Array(1), Array(2.2)))
+    assert(v.slice(Array(2, 0)) === new SparseVector(2, Array(0), Array(2.2)))
+    assert(v.slice(Array(2, 0, 3, 4)) === new SparseVector(4, Array(0, 3), Array(2.2, 4.4)))
+  }
 }

From 8c320e45b5c9ffd7f0e35c1c7e6b5fc355377ea6 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Wed, 5 Aug 2015 17:28:23 -0700
Subject: [PATCH 072/633] [SPARK-6591] [SQL] Python data source load options
 should auto convert common types into strings

JIRA: https://issues.apache.org/jira/browse/SPARK-6591

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7926 from yjshen/py_dsload_opt and squashes the following commits:

b207832 [Yijie Shen] fix style
efdf834 [Yijie Shen] resolve comment
7a8f6a2 [Yijie Shen] lowercase
822e769 [Yijie Shen] convert load opts to string
---
 python/pyspark/sql/readwriter.py | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index dea8bad79e187..bf6ac084bbbf8 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -24,6 +24,16 @@
 __all__ = ["DataFrameReader", "DataFrameWriter"]
 
 
+def to_str(value):
+    """
+    A wrapper over str(), but convert bool values to lower case string
+    """
+    if isinstance(value, bool):
+        return str(value).lower()
+    else:
+        return str(value)
+
+
 class DataFrameReader(object):
     """
     Interface used to load a :class:`DataFrame` from external storage systems
@@ -77,7 +87,7 @@ def schema(self, schema):
     def option(self, key, value):
         """Adds an input option for the underlying data source.
         """
-        self._jreader = self._jreader.option(key, value)
+        self._jreader = self._jreader.option(key, to_str(value))
         return self
 
     @since(1.4)
@@ -85,7 +95,7 @@ def options(self, **options):
         """Adds input options for the underlying data source.
         """
         for k in options:
-            self._jreader = self._jreader.option(k, options[k])
+            self._jreader = self._jreader.option(k, to_str(options[k]))
         return self
 
     @since(1.4)
@@ -97,7 +107,8 @@ def load(self, path=None, format=None, schema=None, **options):
         :param schema: optional :class:`StructType` for the input schema.
         :param options: all other string options
 
-        >>> df = sqlContext.read.load('python/test_support/sql/parquet_partitioned')
+        >>> df = sqlContext.read.load('python/test_support/sql/parquet_partitioned', opt1=True,
+        ...     opt2=1, opt3='str')
         >>> df.dtypes
         [('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
         """

From 4399b7b0903d830313ab7e69731c11d587ae567c Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 5 Aug 2015 17:58:36 -0700
Subject: [PATCH 073/633] [SPARK-9651] Fix UnsafeExternalSorterSuite.

First, it's probably a bad idea to call generated Scala methods
from Java. In this case, the method being called wasn't actually
"Utils.createTempDir()", but actually the method that returns the
first default argument to the actual createTempDir method, which
is just the location of java.io.tmpdir; meaning that all tests in
the class were using the same temp dir, and thus affecting each
other.

Second, spillingOccursInResponseToMemoryPressure was not writing
enough records to actually cause a spill.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7970 from vanzin/SPARK-9651 and squashes the following commits:

74d357f [Marcelo Vanzin] Clean up temp dir on test tear down.
a64f36a [Marcelo Vanzin] [SPARK-9651] Fix UnsafeExternalSorterSuite.
---
 .../sort/UnsafeExternalSorterSuite.java       | 21 ++++++++++++-------
 1 file changed, 13 insertions(+), 8 deletions(-)

diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index 968185bde78ab..117745f9a9c00 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -101,7 +101,7 @@ public OutputStream apply(OutputStream stream) {
   public void setUp() {
     MockitoAnnotations.initMocks(this);
     sparkConf = new SparkConf();
-    tempDir = new File(Utils.createTempDir$default$1());
+    tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "unsafe-test");
     shuffleMemoryManager = new ShuffleMemoryManager(Long.MAX_VALUE);
     spillFilesCreated.clear();
     taskContext = mock(TaskContext.class);
@@ -143,13 +143,18 @@ public DiskBlockObjectWriter answer(InvocationOnMock invocationOnMock) throws Th
 
   @After
   public void tearDown() {
-    long leakedUnsafeMemory = taskMemoryManager.cleanUpAllAllocatedMemory();
-    if (shuffleMemoryManager != null) {
-      long leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask();
-      shuffleMemoryManager = null;
-      assertEquals(0L, leakedShuffleMemory);
+    try {
+      long leakedUnsafeMemory = taskMemoryManager.cleanUpAllAllocatedMemory();
+      if (shuffleMemoryManager != null) {
+        long leakedShuffleMemory = shuffleMemoryManager.getMemoryConsumptionForThisTask();
+        shuffleMemoryManager = null;
+        assertEquals(0L, leakedShuffleMemory);
+      }
+      assertEquals(0, leakedUnsafeMemory);
+    } finally {
+      Utils.deleteRecursively(tempDir);
+      tempDir = null;
     }
-    assertEquals(0, leakedUnsafeMemory);
   }
 
   private void assertSpillFilesWereCleanedUp() {
@@ -234,7 +239,7 @@ public void testSortingEmptyArrays() throws Exception {
   public void spillingOccursInResponseToMemoryPressure() throws Exception {
     shuffleMemoryManager = new ShuffleMemoryManager(pageSizeBytes * 2);
     final UnsafeExternalSorter sorter = newSorter();
-    final int numRecords = 100000;
+    final int numRecords = (int) pageSizeBytes / 4;
     for (int i = 0; i <= numRecords; i++) {
       insertNumber(sorter, numRecords - i);
     }

From 4581badbc8aa7e5a37ba7f7f83cc3860240f5dd3 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 5 Aug 2015 19:19:09 -0700
Subject: [PATCH 074/633] [SPARK-9611] [SQL] Fixes a few corner cases when we
 spill a UnsafeFixedWidthAggregationMap

This PR has the following three small fixes.

1. UnsafeKVExternalSorter does not use 0 as the initialSize to create an UnsafeInMemorySorter if its BytesToBytesMap is empty.
2. We will not not spill a InMemorySorter if it is empty.
3. We will not add a SpillReader to a SpillMerger if this SpillReader is empty.

JIRA: https://issues.apache.org/jira/browse/SPARK-9611

Author: Yin Huai <yhuai@databricks.com>

Closes #7948 from yhuai/unsafeEmptyMap and squashes the following commits:

9727abe [Yin Huai] Address Josh's comments.
34b6f76 [Yin Huai] 1. UnsafeKVExternalSorter does not use 0 as the initialSize to create an UnsafeInMemorySorter if its BytesToBytesMap is empty. 2. Do not spill a InMemorySorter if it is empty. 3. Do not add spill to SpillMerger if this spill is empty.
---
 .../unsafe/sort/UnsafeExternalSorter.java     |  36 +++---
 .../unsafe/sort/UnsafeSorterSpillMerger.java  |  12 +-
 .../sql/execution/UnsafeKVExternalSorter.java |   6 +-
 .../UnsafeFixedWidthAggregationMapSuite.scala | 108 +++++++++++++++++-
 4 files changed, 141 insertions(+), 21 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index e6ddd08e5fa99..8f78fc5a41629 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -191,24 +191,29 @@ public void spill() throws IOException {
       spillWriters.size(),
       spillWriters.size() > 1 ? " times" : " time");
 
-    final UnsafeSorterSpillWriter spillWriter =
-      new UnsafeSorterSpillWriter(blockManager, fileBufferSizeBytes, writeMetrics,
-        inMemSorter.numRecords());
-    spillWriters.add(spillWriter);
-    final UnsafeSorterIterator sortedRecords = inMemSorter.getSortedIterator();
-    while (sortedRecords.hasNext()) {
-      sortedRecords.loadNext();
-      final Object baseObject = sortedRecords.getBaseObject();
-      final long baseOffset = sortedRecords.getBaseOffset();
-      final int recordLength = sortedRecords.getRecordLength();
-      spillWriter.write(baseObject, baseOffset, recordLength, sortedRecords.getKeyPrefix());
+    // We only write out contents of the inMemSorter if it is not empty.
+    if (inMemSorter.numRecords() > 0) {
+      final UnsafeSorterSpillWriter spillWriter =
+        new UnsafeSorterSpillWriter(blockManager, fileBufferSizeBytes, writeMetrics,
+          inMemSorter.numRecords());
+      spillWriters.add(spillWriter);
+      final UnsafeSorterIterator sortedRecords = inMemSorter.getSortedIterator();
+      while (sortedRecords.hasNext()) {
+        sortedRecords.loadNext();
+        final Object baseObject = sortedRecords.getBaseObject();
+        final long baseOffset = sortedRecords.getBaseOffset();
+        final int recordLength = sortedRecords.getRecordLength();
+        spillWriter.write(baseObject, baseOffset, recordLength, sortedRecords.getKeyPrefix());
+      }
+      spillWriter.close();
     }
-    spillWriter.close();
+
     final long spillSize = freeMemory();
     // Note that this is more-or-less going to be a multiple of the page size, so wasted space in
     // pages will currently be counted as memory spilled even though that space isn't actually
     // written to disk. This also counts the space needed to store the sorter's pointer array.
     taskContext.taskMetrics().incMemoryBytesSpilled(spillSize);
+
     initializeForWriting();
   }
 
@@ -505,12 +510,11 @@ public UnsafeSorterIterator getSortedIterator() throws IOException {
       final UnsafeSorterSpillMerger spillMerger =
         new UnsafeSorterSpillMerger(recordComparator, prefixComparator, numIteratorsToMerge);
       for (UnsafeSorterSpillWriter spillWriter : spillWriters) {
-        spillMerger.addSpill(spillWriter.getReader(blockManager));
+        spillMerger.addSpillIfNotEmpty(spillWriter.getReader(blockManager));
       }
       spillWriters.clear();
-      if (inMemoryIterator.hasNext()) {
-        spillMerger.addSpill(inMemoryIterator);
-      }
+      spillMerger.addSpillIfNotEmpty(inMemoryIterator);
+
       return spillMerger.getSortedIterator();
     }
   }
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java
index 8272c2a5be0d1..3874a9f9cbdb6 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillMerger.java
@@ -47,11 +47,19 @@ public int compare(UnsafeSorterIterator left, UnsafeSorterIterator right) {
     priorityQueue = new PriorityQueue<UnsafeSorterIterator>(numSpills, comparator);
   }
 
-  public void addSpill(UnsafeSorterIterator spillReader) throws IOException {
+  /**
+   * Add an UnsafeSorterIterator to this merger
+   */
+  public void addSpillIfNotEmpty(UnsafeSorterIterator spillReader) throws IOException {
     if (spillReader.hasNext()) {
+      // We only add the spillReader to the priorityQueue if it is not empty. We do this to
+      // make sure the hasNext method of UnsafeSorterIterator returned by getSortedIterator
+      // does not return wrong result because hasNext will returns true
+      // at least priorityQueue.size() times. If we allow n spillReaders in the
+      // priorityQueue, we will have n extra empty records in the result of the UnsafeSorterIterator.
       spillReader.loadNext();
+      priorityQueue.add(spillReader);
     }
-    priorityQueue.add(spillReader);
   }
 
   public UnsafeSorterIterator getSortedIterator() throws IOException {
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index 86a563df992d0..6c1cf136d9b81 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -82,8 +82,11 @@ public UnsafeKVExternalSorter(StructType keySchema, StructType valueSchema,
         pageSizeBytes);
     } else {
       // Insert the records into the in-memory sorter.
+      // We will use the number of elements in the map as the initialSize of the
+      // UnsafeInMemorySorter. Because UnsafeInMemorySorter does not accept 0 as the initialSize,
+      // we will use 1 as its initial size if the map is empty.
       final UnsafeInMemorySorter inMemSorter = new UnsafeInMemorySorter(
-        taskMemoryManager, recordComparator, prefixComparator, map.numElements());
+        taskMemoryManager, recordComparator, prefixComparator, Math.max(1, map.numElements()));
 
       final int numKeyFields = keySchema.size();
       BytesToBytesMap.BytesToBytesMapIterator iter = map.iterator();
@@ -214,7 +217,6 @@ public boolean next() throws IOException {
           // Note that recordLen = keyLen + valueLen + 4 bytes (for the keyLen itself)
           int keyLen = PlatformDependent.UNSAFE.getInt(baseObj, recordOffset);
           int valueLen = recordLen - keyLen - 4;
-
           key.pointTo(baseObj, recordOffset + 4, numKeyFields, keyLen);
           value.pointTo(baseObj, recordOffset + 4 + keyLen, numValueFields, valueLen);
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index ef827b0fe9b5b..b513c970ccfe2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -23,7 +23,7 @@ import scala.util.{Try, Random}
 
 import org.scalatest.Matchers
 
-import org.apache.spark.sql.catalyst.expressions.UnsafeProjection
+import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeProjection}
 import org.apache.spark.{TaskContextImpl, TaskContext, SparkFunSuite}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.test.TestSQLContext
@@ -231,4 +231,110 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
 
     map.free()
   }
+
+  testWithMemoryLeakDetection("test external sorting with an empty map") {
+    // Calling this make sure we have block manager and everything else setup.
+    TestSQLContext
+
+    val map = new UnsafeFixedWidthAggregationMap(
+      emptyAggregationBuffer,
+      aggBufferSchema,
+      groupKeySchema,
+      taskMemoryManager,
+      shuffleMemoryManager,
+      128, // initial capacity
+      PAGE_SIZE_BYTES,
+      false // disable perf metrics
+    )
+
+    // Convert the map into a sorter
+    val sorter = map.destructAndCreateExternalSorter()
+
+    // Add more keys to the sorter and make sure the results come out sorted.
+    val additionalKeys = randomStrings(1024)
+    val keyConverter = UnsafeProjection.create(groupKeySchema)
+    val valueConverter = UnsafeProjection.create(aggBufferSchema)
+
+    additionalKeys.zipWithIndex.foreach { case (str, i) =>
+      val k = InternalRow(UTF8String.fromString(str))
+      val v = InternalRow(str.length)
+      sorter.insertKV(keyConverter.apply(k), valueConverter.apply(v))
+
+      if ((i % 100) == 0) {
+        shuffleMemoryManager.markAsOutOfMemory()
+        sorter.closeCurrentPage()
+      }
+    }
+
+    val out = new scala.collection.mutable.ArrayBuffer[String]
+    val iter = sorter.sortedIterator()
+    while (iter.next()) {
+      // At here, we also test if copy is correct.
+      val key = iter.getKey.copy()
+      val value = iter.getValue.copy()
+      assert(key.getString(0).length === value.getInt(0))
+      out += key.getString(0)
+    }
+
+    assert(out === (additionalKeys).sorted)
+
+    map.free()
+  }
+
+  testWithMemoryLeakDetection("test external sorting with empty records") {
+    // Calling this make sure we have block manager and everything else setup.
+    TestSQLContext
+
+    // Memory consumption in the beginning of the task.
+    val initialMemoryConsumption = shuffleMemoryManager.getMemoryConsumptionForThisTask()
+
+    val map = new UnsafeFixedWidthAggregationMap(
+      emptyAggregationBuffer,
+      StructType(Nil),
+      StructType(Nil),
+      taskMemoryManager,
+      shuffleMemoryManager,
+      128, // initial capacity
+      PAGE_SIZE_BYTES,
+      false // disable perf metrics
+    )
+
+    (1 to 10).foreach { i =>
+      val buf = map.getAggregationBuffer(UnsafeRow.createFromByteArray(0, 0))
+      assert(buf != null)
+    }
+
+    // Convert the map into a sorter. Right now, it contains one record.
+    val sorter = map.destructAndCreateExternalSorter()
+
+    withClue(s"destructAndCreateExternalSorter should release memory used by the map") {
+      // 4096 * 16 is the initial size allocated for the pointer/prefix array in the in-mem sorter.
+      assert(shuffleMemoryManager.getMemoryConsumptionForThisTask() ===
+        initialMemoryConsumption + 4096 * 16)
+    }
+
+    // Add more keys to the sorter and make sure the results come out sorted.
+    (1 to 4096).foreach { i =>
+      sorter.insertKV(UnsafeRow.createFromByteArray(0, 0), UnsafeRow.createFromByteArray(0, 0))
+
+      if ((i % 100) == 0) {
+        shuffleMemoryManager.markAsOutOfMemory()
+        sorter.closeCurrentPage()
+      }
+    }
+
+    var count = 0
+    val iter = sorter.sortedIterator()
+    while (iter.next()) {
+      // At here, we also test if copy is correct.
+      iter.getKey.copy()
+      iter.getValue.copy()
+      count += 1;
+    }
+
+    // 1 record was from the map and 4096 records were explicitly inserted.
+    assert(count === 4097)
+
+    map.free()
+  }
 }

From 119b59053870df7be899bf5c1c0d321406af96f9 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 6 Aug 2015 11:13:44 +0800
Subject: [PATCH 075/633] [SPARK-6923] [SPARK-7550] [SQL] Persists data source
 relations in Hive compatible format when possible

This PR is a fork of PR #5733 authored by chenghao-intel.  For committers who's going to merge this PR, please set the author to "Cheng Hao <hao.chengintel.com>".

----

When a data source relation meets the following requirements, we persist it in Hive compatible format, so that other systems like Hive can access it:

1. It's a `HadoopFsRelation`
2. It has only one input path
3. It's non-partitioned
4. It's data source provider can be naturally mapped to a Hive builtin SerDe (e.g. ORC and Parquet)

Author: Cheng Lian <lian@databricks.com>
Author: Cheng Hao <hao.cheng@intel.com>

Closes #7967 from liancheng/spark-6923/refactoring-pr-5733 and squashes the following commits:

5175ee6 [Cheng Lian] Fixes an oudated comment
3870166 [Cheng Lian] Fixes build error and comments
864acee [Cheng Lian] Refactors PR #5733
3490cdc [Cheng Hao] update the scaladoc
6f57669 [Cheng Hao] write schema info to hivemetastore for data source
---
 .../org/apache/spark/sql/DataFrame.scala      |  53 +++++--
 .../apache/spark/sql/DataFrameWriter.scala    |   7 +
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 146 +++++++++++++++++-
 .../org/apache/spark/sql/hive/HiveQl.scala    |  49 ++----
 .../spark/sql/hive/orc/OrcRelation.scala      |   6 +-
 .../sql/hive/HiveMetastoreCatalogSuite.scala  | 133 ++++++++++++++--
 6 files changed, 324 insertions(+), 70 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index e57acec59d327..405b5a4a9a7f9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -20,9 +20,6 @@ package org.apache.spark.sql
 import java.io.CharArrayWriter
 import java.util.Properties
 
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.unsafe.types.UTF8String
-
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
@@ -42,7 +39,7 @@ import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
 import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD, SQLExecution}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation}
-import org.apache.spark.sql.json.{JacksonGenerator, JSONRelation}
+import org.apache.spark.sql.json.JacksonGenerator
 import org.apache.spark.sql.sources.HadoopFsRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.storage.StorageLevel
@@ -1650,8 +1647,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by `write().saveAsTable(tableName)`.
    */
@@ -1669,8 +1670,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by `write().mode(mode).saveAsTable(tableName)`.
    */
@@ -1689,8 +1694,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by `write().format(source).saveAsTable(tableName)`.
    */
@@ -1709,8 +1718,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by `write().mode(mode).saveAsTable(tableName)`.
    */
@@ -1728,8 +1741,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by
    *            `write().format(source).mode(mode).options(options).saveAsTable(tableName)`.
@@ -1754,8 +1771,12 @@ class DataFrame private[sql](
    * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
    * be the target of an `insertInto`.
    *
-   * Also note that while this function can persist the table metadata into Hive's metastore,
-   * the table will NOT be accessible from Hive, until SPARK-7550 is resolved.
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @group output
    * @deprecated As of 1.4.0, replaced by
    *            `write().format(source).mode(mode).options(options).saveAsTable(tableName)`.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index 7e3318cefe62c..2a4992db09bc2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -25,6 +25,7 @@ import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.catalyst.plans.logical.InsertIntoTable
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, ResolvedDataSource}
 import org.apache.spark.sql.jdbc.{JDBCWriteDetails, JdbcUtils}
+import org.apache.spark.sql.sources.HadoopFsRelation
 
 
 /**
@@ -185,6 +186,12 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    * When `mode` is `Append`, the schema of the [[DataFrame]] need to be
    * the same as that of the existing table, and format or options will be ignored.
    *
+   * When the DataFrame is created from a non-partitioned [[HadoopFsRelation]] with a single input
+   * path, and the data source provider can be mapped to an existing Hive builtin SerDe (i.e. ORC
+   * and Parquet), the table is persisted in a Hive compatible format, which means other systems
+   * like Hive will be able to read this table. Otherwise, the table is persisted in a Spark SQL
+   * specific format.
+   *
    * @since 1.4.0
    */
   def saveAsTable(tableName: String): Unit = {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 6b37af99f4677..1523ebe9d5493 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -18,11 +18,13 @@
 package org.apache.spark.sql.hive
 
 import scala.collection.JavaConversions._
+import scala.collection.mutable
 
 import com.google.common.base.Objects
 import com.google.common.cache.{CacheBuilder, CacheLoader, LoadingCache}
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.hive.common.StatsSetupConst
+import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.metastore.Warehouse
 import org.apache.hadoop.hive.metastore.api.FieldSchema
 import org.apache.hadoop.hive.ql.metadata._
@@ -40,9 +42,59 @@ import org.apache.spark.sql.execution.datasources
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource}
 import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode}
 
+private[hive] case class HiveSerDe(
+    inputFormat: Option[String] = None,
+    outputFormat: Option[String] = None,
+    serde: Option[String] = None)
+
+private[hive] object HiveSerDe {
+  /**
+   * Get the Hive SerDe information from the data source abbreviation string or classname.
+   *
+   * @param source Currently the source abbreviation can be one of the following:
+   *               SequenceFile, RCFile, ORC, PARQUET, and case insensitive.
+   * @param hiveConf Hive Conf
+   * @return HiveSerDe associated with the specified source
+   */
+  def sourceToSerDe(source: String, hiveConf: HiveConf): Option[HiveSerDe] = {
+    val serdeMap = Map(
+      "sequencefile" ->
+        HiveSerDe(
+          inputFormat = Option("org.apache.hadoop.mapred.SequenceFileInputFormat"),
+          outputFormat = Option("org.apache.hadoop.mapred.SequenceFileOutputFormat")),
+
+      "rcfile" ->
+        HiveSerDe(
+          inputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat"),
+          outputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat"),
+          serde = Option(hiveConf.getVar(HiveConf.ConfVars.HIVEDEFAULTRCFILESERDE))),
+
+      "orc" ->
+        HiveSerDe(
+          inputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat"),
+          outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat"),
+          serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde")),
+
+      "parquet" ->
+        HiveSerDe(
+          inputFormat = Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"),
+          outputFormat = Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"),
+          serde = Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe")))
+
+    val key = source.toLowerCase match {
+      case _ if source.startsWith("org.apache.spark.sql.parquet") => "parquet"
+      case _ if source.startsWith("org.apache.spark.sql.orc") => "orc"
+      case _ => source.toLowerCase
+    }
+
+    serdeMap.get(key)
+  }
+}
+
 private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: HiveContext)
   extends Catalog with Logging {
 
@@ -164,15 +216,15 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       processDatabaseAndTableName(database, tableIdent.table)
     }
 
-    val tableProperties = new scala.collection.mutable.HashMap[String, String]
+    val tableProperties = new mutable.HashMap[String, String]
     tableProperties.put("spark.sql.sources.provider", provider)
 
     // Saves optional user specified schema.  Serialized JSON schema string may be too long to be
     // stored into a single metastore SerDe property.  In this case, we split the JSON string and
     // store each part as a separate SerDe property.
-    if (userSpecifiedSchema.isDefined) {
+    userSpecifiedSchema.foreach { schema =>
       val threshold = conf.schemaStringLengthThreshold
-      val schemaJsonString = userSpecifiedSchema.get.json
+      val schemaJsonString = schema.json
       // Split the JSON string.
       val parts = schemaJsonString.grouped(threshold).toSeq
       tableProperties.put("spark.sql.sources.schema.numParts", parts.size.toString)
@@ -194,7 +246,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
         // The table does not have a specified schema, which means that the schema will be inferred
         // when we load the table. So, we are not expecting partition columns and we will discover
         // partitions when we load the table. However, if there are specified partition columns,
-        // we simplily ignore them and provide a warning message..
+        // we simply ignore them and provide a warning message.
         logWarning(
           s"The schema and partitions of table $tableIdent will be inferred when it is loaded. " +
             s"Specified partition columns (${partitionColumns.mkString(",")}) will be ignored.")
@@ -210,7 +262,11 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       ManagedTable
     }
 
-    client.createTable(
+    val maybeSerDe = HiveSerDe.sourceToSerDe(provider, hive.hiveconf)
+    val dataSource = ResolvedDataSource(
+      hive, userSpecifiedSchema, partitionColumns, provider, options)
+
+    def newSparkSQLSpecificMetastoreTable(): HiveTable = {
       HiveTable(
         specifiedDatabase = Option(dbName),
         name = tblName,
@@ -218,7 +274,83 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
         partitionColumns = metastorePartitionColumns,
         tableType = tableType,
         properties = tableProperties.toMap,
-        serdeProperties = options))
+        serdeProperties = options)
+    }
+
+    def newHiveCompatibleMetastoreTable(relation: HadoopFsRelation, serde: HiveSerDe): HiveTable = {
+      def schemaToHiveColumn(schema: StructType): Seq[HiveColumn] = {
+        schema.map { field =>
+          HiveColumn(
+            name = field.name,
+            hiveType = HiveMetastoreTypes.toMetastoreType(field.dataType),
+            comment = "")
+        }
+      }
+
+      val partitionColumns = schemaToHiveColumn(relation.partitionColumns)
+      val dataColumns = schemaToHiveColumn(relation.schema).filterNot(partitionColumns.contains)
+
+      HiveTable(
+        specifiedDatabase = Option(dbName),
+        name = tblName,
+        schema = dataColumns,
+        partitionColumns = partitionColumns,
+        tableType = tableType,
+        properties = tableProperties.toMap,
+        serdeProperties = options,
+        location = Some(relation.paths.head),
+        viewText = None, // TODO We need to place the SQL string here.
+        inputFormat = serde.inputFormat,
+        outputFormat = serde.outputFormat,
+        serde = serde.serde)
+    }
+
+    // TODO: Support persisting partitioned data source relations in Hive compatible format
+    val hiveTable = (maybeSerDe, dataSource.relation) match {
+      case (Some(serde), relation: HadoopFsRelation)
+          if relation.paths.length == 1 && relation.partitionColumns.isEmpty =>
+        logInfo {
+          "Persisting data source relation with a single input path into Hive metastore in Hive " +
+            s"compatible format.  Input path: ${relation.paths.head}"
+        }
+        newHiveCompatibleMetastoreTable(relation, serde)
+
+      case (Some(serde), relation: HadoopFsRelation) if relation.partitionColumns.nonEmpty =>
+        logWarning {
+          val paths = relation.paths.mkString(", ")
+          "Persisting partitioned data source relation into Hive metastore in " +
+            s"Spark SQL specific format, which is NOT compatible with Hive.  Input path(s): " +
+            paths.mkString("\n", "\n", "")
+        }
+        newSparkSQLSpecificMetastoreTable()
+
+      case (Some(serde), relation: HadoopFsRelation) =>
+        logWarning {
+          val paths = relation.paths.mkString(", ")
+          "Persisting data source relation with multiple input paths into Hive metastore in " +
+            s"Spark SQL specific format, which is NOT compatible with Hive.  Input paths: " +
+            paths.mkString("\n", "\n", "")
+        }
+        newSparkSQLSpecificMetastoreTable()
+
+      case (Some(serde), _) =>
+        logWarning {
+          s"Data source relation is not a ${classOf[HadoopFsRelation].getSimpleName}. " +
+            "Persisting it into Hive metastore in Spark SQL specific format, " +
+            "which is NOT compatible with Hive."
+        }
+        newSparkSQLSpecificMetastoreTable()
+
+      case _ =>
+        logWarning {
+          s"Couldn't find corresponding Hive SerDe for data source provider $provider. " +
+            "Persisting data source relation into Hive metastore in Spark SQL specific format, " +
+            "which is NOT compatible with Hive."
+        }
+        newSparkSQLSpecificMetastoreTable()
+    }
+
+    client.createTable(hiveTable)
   }
 
   def hiveDefaultTableFilePath(tableName: String): String = {
@@ -463,7 +595,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       case p: LogicalPlan if !p.childrenResolved => p
       case p: LogicalPlan if p.resolved => p
       case p @ CreateTableAsSelect(table, child, allowExisting) =>
-        val schema = if (table.schema.size > 0) {
+        val schema = if (table.schema.nonEmpty) {
           table.schema
         } else {
           child.output.map {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index f43e403ce9a9d..7d7b4b9167306 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -32,6 +32,7 @@ import org.apache.hadoop.hive.ql.session.SessionState
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.AnalysisException
+import org.apache.spark.sql.catalyst
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
@@ -261,8 +262,8 @@ private[hive] object HiveQl extends Logging {
   /**
    * Returns the HiveConf
    */
-  private[this] def hiveConf(): HiveConf = {
-    val ss = SessionState.get() // SessionState is lazy initializaion, it can be null here
+  private[this] def hiveConf: HiveConf = {
+    val ss = SessionState.get() // SessionState is lazy initialization, it can be null here
     if (ss == null) {
       new HiveConf()
     } else {
@@ -604,38 +605,18 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         serde = None,
         viewText = None)
 
-      // default storage type abbriviation (e.g. RCFile, ORC, PARQUET etc.)
+      // default storage type abbreviation (e.g. RCFile, ORC, PARQUET etc.)
       val defaultStorageType = hiveConf.getVar(HiveConf.ConfVars.HIVEDEFAULTFILEFORMAT)
-      // handle the default format for the storage type abbriviation
-      tableDesc = if ("SequenceFile".equalsIgnoreCase(defaultStorageType)) {
-          tableDesc.copy(
-            inputFormat = Option("org.apache.hadoop.mapred.SequenceFileInputFormat"),
-            outputFormat = Option("org.apache.hadoop.mapred.SequenceFileOutputFormat"))
-        } else if ("RCFile".equalsIgnoreCase(defaultStorageType)) {
-          tableDesc.copy(
-            inputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileInputFormat"),
-            outputFormat = Option("org.apache.hadoop.hive.ql.io.RCFileOutputFormat"),
-            serde = Option(hiveConf.getVar(HiveConf.ConfVars.HIVEDEFAULTRCFILESERDE)))
-        } else if ("ORC".equalsIgnoreCase(defaultStorageType)) {
-          tableDesc.copy(
-            inputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcInputFormat"),
-            outputFormat = Option("org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat"),
-            serde = Option("org.apache.hadoop.hive.ql.io.orc.OrcSerde"))
-        } else if ("PARQUET".equalsIgnoreCase(defaultStorageType)) {
-          tableDesc.copy(
-            inputFormat =
-              Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat"),
-            outputFormat =
-              Option("org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat"),
-            serde =
-              Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe"))
-        } else {
-          tableDesc.copy(
-            inputFormat =
-              Option("org.apache.hadoop.mapred.TextInputFormat"),
-            outputFormat =
-              Option("org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat"))
-        }
+      // handle the default format for the storage type abbreviation
+      val hiveSerDe = HiveSerDe.sourceToSerDe(defaultStorageType, hiveConf).getOrElse {
+        HiveSerDe(
+          inputFormat = Option("org.apache.hadoop.mapred.TextInputFormat"),
+          outputFormat = Option("org.apache.hadoop.hive.ql.io.IgnoreKeyTextOutputFormat"))
+      }
+
+      hiveSerDe.inputFormat.foreach(f => tableDesc = tableDesc.copy(inputFormat = Some(f)))
+      hiveSerDe.outputFormat.foreach(f => tableDesc = tableDesc.copy(outputFormat = Some(f)))
+      hiveSerDe.serde.foreach(f => tableDesc = tableDesc.copy(serde = Some(f)))
 
       children.collect {
         case list @ Token("TOK_TABCOLLIST", _) =>
@@ -908,7 +889,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
                 }
                 (Nil, Some(BaseSemanticAnalyzer.unescapeSQLString(serdeClass)), serdeProps)
 
-              case Nil => (Nil, Option(hiveConf().getVar(ConfVars.HIVESCRIPTSERDE)), Nil)
+              case Nil => (Nil, Option(hiveConf.getVar(ConfVars.HIVESCRIPTSERDE)), Nil)
             }
 
             val (inRowFormat, inSerdeClass, inSerdeProps) = matchSerDe(inputSerdeClause)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 6fa599734892b..4a310ff4e9016 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -291,9 +291,11 @@ private[orc] case class OrcTableScan(
     // Sets requested columns
     addColumnIds(attributes, relation, conf)
 
-    if (inputPaths.nonEmpty) {
-      FileInputFormat.setInputPaths(job, inputPaths.map(_.getPath): _*)
+    if (inputPaths.isEmpty) {
+      // the input path probably be pruned, return an empty RDD.
+      return sqlContext.sparkContext.emptyRDD[InternalRow]
     }
+    FileInputFormat.setInputPaths(job, inputPaths.map(_.getPath): _*)
 
     val inputFormatClass =
       classOf[OrcInputFormat]
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
index 983c013bcf86a..332c3ec0c28b8 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
@@ -17,31 +17,142 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.{Logging, SparkFunSuite}
-import org.apache.spark.sql.hive.test.TestHive
+import java.io.File
 
-import org.apache.spark.sql.test.ExamplePointUDT
+import org.apache.spark.sql.hive.client.{ExternalTable, HiveColumn, ManagedTable}
+import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.sources.DataSourceTest
+import org.apache.spark.sql.test.{ExamplePointUDT, SQLTestUtils}
 import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.{Row, SaveMode}
+import org.apache.spark.{Logging, SparkFunSuite}
+
 
 class HiveMetastoreCatalogSuite extends SparkFunSuite with Logging {
 
   test("struct field should accept underscore in sub-column name") {
-    val metastr = "struct<a: int, b_1: string, c: string>"
-
-    val datatype = HiveMetastoreTypes.toDataType(metastr)
-    assert(datatype.isInstanceOf[StructType])
+    val hiveTypeStr = "struct<a: int, b_1: string, c: string>"
+    val dateType = HiveMetastoreTypes.toDataType(hiveTypeStr)
+    assert(dateType.isInstanceOf[StructType])
   }
 
   test("udt to metastore type conversion") {
     val udt = new ExamplePointUDT
-    assert(HiveMetastoreTypes.toMetastoreType(udt) ===
-      HiveMetastoreTypes.toMetastoreType(udt.sqlType))
+    assertResult(HiveMetastoreTypes.toMetastoreType(udt.sqlType)) {
+      HiveMetastoreTypes.toMetastoreType(udt)
+    }
   }
 
   test("duplicated metastore relations") {
-    import TestHive.implicits._
-    val df = TestHive.sql("SELECT * FROM src")
+    val df = sql("SELECT * FROM src")
     logInfo(df.queryExecution.toString)
     df.as('a).join(df.as('b), $"a.key" === $"b.key")
   }
 }
+
+class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTestUtils {
+  override val sqlContext = TestHive
+
+  private val testDF = (1 to 2).map(i => (i, s"val_$i")).toDF("d1", "d2").coalesce(1)
+
+  Seq(
+    "parquet" -> (
+      "org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat",
+      "org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat",
+      "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe"
+    ),
+
+    "orc" -> (
+      "org.apache.hadoop.hive.ql.io.orc.OrcInputFormat",
+      "org.apache.hadoop.hive.ql.io.orc.OrcOutputFormat",
+      "org.apache.hadoop.hive.ql.io.orc.OrcSerde"
+    )
+  ).foreach { case (provider, (inputFormat, outputFormat, serde)) =>
+    test(s"Persist non-partitioned $provider relation into metastore as managed table") {
+      withTable("t") {
+        testDF
+          .write
+          .mode(SaveMode.Overwrite)
+          .format(provider)
+          .saveAsTable("t")
+
+        val hiveTable = catalog.client.getTable("default", "t")
+        assert(hiveTable.inputFormat === Some(inputFormat))
+        assert(hiveTable.outputFormat === Some(outputFormat))
+        assert(hiveTable.serde === Some(serde))
+
+        assert(!hiveTable.isPartitioned)
+        assert(hiveTable.tableType === ManagedTable)
+
+        val columns = hiveTable.schema
+        assert(columns.map(_.name) === Seq("d1", "d2"))
+        assert(columns.map(_.hiveType) === Seq("int", "string"))
+
+        checkAnswer(table("t"), testDF)
+        assert(runSqlHive("SELECT * FROM t") === Seq("1\tval_1", "2\tval_2"))
+      }
+    }
+
+    test(s"Persist non-partitioned $provider relation into metastore as external table") {
+      withTempPath { dir =>
+        withTable("t") {
+          val path = dir.getCanonicalFile
+
+          testDF
+            .write
+            .mode(SaveMode.Overwrite)
+            .format(provider)
+            .option("path", path.toString)
+            .saveAsTable("t")
+
+          val hiveTable = catalog.client.getTable("default", "t")
+          assert(hiveTable.inputFormat === Some(inputFormat))
+          assert(hiveTable.outputFormat === Some(outputFormat))
+          assert(hiveTable.serde === Some(serde))
+
+          assert(hiveTable.tableType === ExternalTable)
+          assert(hiveTable.location.get === path.toURI.toString.stripSuffix(File.separator))
+
+          val columns = hiveTable.schema
+          assert(columns.map(_.name) === Seq("d1", "d2"))
+          assert(columns.map(_.hiveType) === Seq("int", "string"))
+
+          checkAnswer(table("t"), testDF)
+          assert(runSqlHive("SELECT * FROM t") === Seq("1\tval_1", "2\tval_2"))
+        }
+      }
+    }
+
+    test(s"Persist non-partitioned $provider relation into metastore as managed table using CTAS") {
+      withTempPath { dir =>
+        withTable("t") {
+          val path = dir.getCanonicalPath
+
+          sql(
+            s"""CREATE TABLE t USING $provider
+               |OPTIONS (path '$path')
+               |AS SELECT 1 AS d1, "val_1" AS d2
+             """.stripMargin)
+
+          val hiveTable = catalog.client.getTable("default", "t")
+          assert(hiveTable.inputFormat === Some(inputFormat))
+          assert(hiveTable.outputFormat === Some(outputFormat))
+          assert(hiveTable.serde === Some(serde))
+
+          assert(hiveTable.isPartitioned === false)
+          assert(hiveTable.tableType === ExternalTable)
+          assert(hiveTable.partitionColumns.length === 0)
+
+          val columns = hiveTable.schema
+          assert(columns.map(_.name) === Seq("d1", "d2"))
+          assert(columns.map(_.hiveType) === Seq("int", "string"))
+
+          checkAnswer(table("t"), Row(1, "val_1"))
+          assert(runSqlHive("SELECT * FROM t") === Seq("1\tval_1"))
+        }
+      }
+    }
+  }
+}

From 9270bd06fd0b16892e3f37213b5bc7813ea11fdd Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 5 Aug 2015 21:50:14 -0700
Subject: [PATCH 076/633] [SPARK-9674][SQL] Remove GeneratedAggregate.

The new aggregate replaces the old GeneratedAggregate.

Author: Reynold Xin <rxin@databricks.com>

Closes #7983 from rxin/remove-generated-agg and squashes the following commits:

8334aae [Reynold Xin] [SPARK-9674][SQL] Remove GeneratedAggregate.
---
 .../sql/execution/GeneratedAggregate.scala    | 352 ------------------
 .../spark/sql/execution/SparkStrategies.scala |  34 --
 .../org/apache/spark/sql/SQLQuerySuite.scala  |   5 +-
 .../spark/sql/execution/AggregateSuite.scala  |  48 ---
 4 files changed, 2 insertions(+), 437 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
 delete mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
deleted file mode 100644
index bf4905dc1eef9..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/GeneratedAggregate.scala
+++ /dev/null
@@ -1,352 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution
-
-import java.io.IOException
-
-import org.apache.spark.{InternalAccumulator, SparkEnv, TaskContext}
-import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.catalyst.trees._
-import org.apache.spark.sql.types._
-
-case class AggregateEvaluation(
-    schema: Seq[Attribute],
-    initialValues: Seq[Expression],
-    update: Seq[Expression],
-    result: Expression)
-
-/**
- * :: DeveloperApi ::
- * Alternate version of aggregation that leverages projection and thus code generation.
- * Aggregations are converted into a set of projections from a aggregation buffer tuple back onto
- * itself. Currently only used for simple aggregations like SUM, COUNT, or AVERAGE are supported.
- *
- * @param partial if true then aggregation is done partially on local data without shuffling to
- *                ensure all values where `groupingExpressions` are equal are present.
- * @param groupingExpressions expressions that are evaluated to determine grouping.
- * @param aggregateExpressions expressions that are computed for each group.
- * @param unsafeEnabled whether to allow Unsafe-based aggregation buffers to be used.
- * @param child the input data source.
- */
-@DeveloperApi
-case class GeneratedAggregate(
-    partial: Boolean,
-    groupingExpressions: Seq[Expression],
-    aggregateExpressions: Seq[NamedExpression],
-    unsafeEnabled: Boolean,
-    child: SparkPlan)
-  extends UnaryNode {
-
-  override def requiredChildDistribution: Seq[Distribution] =
-    if (partial) {
-      UnspecifiedDistribution :: Nil
-    } else {
-      if (groupingExpressions == Nil) {
-        AllTuples :: Nil
-      } else {
-        ClusteredDistribution(groupingExpressions) :: Nil
-      }
-    }
-
-  override def output: Seq[Attribute] = aggregateExpressions.map(_.toAttribute)
-
-  protected override def doExecute(): RDD[InternalRow] = {
-    val aggregatesToCompute = aggregateExpressions.flatMap { a =>
-      a.collect { case agg: AggregateExpression1 => agg}
-    }
-
-    // If you add any new function support, please add tests in org.apache.spark.sql.SQLQuerySuite
-    // (in test "aggregation with codegen").
-    val computeFunctions = aggregatesToCompute.map {
-      case c @ Count(expr) =>
-        // If we're evaluating UnscaledValue(x), we can do Count on x directly, since its
-        // UnscaledValue will be null if and only if x is null; helps with Average on decimals
-        val toCount = expr match {
-          case UnscaledValue(e) => e
-          case _ => expr
-        }
-        val currentCount = AttributeReference("currentCount", LongType, nullable = false)()
-        val initialValue = Literal(0L)
-        val updateFunction = If(IsNotNull(toCount), Add(currentCount, Literal(1L)), currentCount)
-        val result = currentCount
-
-        AggregateEvaluation(currentCount :: Nil, initialValue :: Nil, updateFunction :: Nil, result)
-
-      case s @ Sum(expr) =>
-        val calcType =
-          expr.dataType match {
-            case DecimalType.Fixed(p, s) =>
-              DecimalType.bounded(p + 10, s)
-            case _ =>
-              expr.dataType
-          }
-
-        val currentSum = AttributeReference("currentSum", calcType, nullable = true)()
-        val initialValue = Literal.create(null, calcType)
-
-        // Coalesce avoids double calculation...
-        // but really, common sub expression elimination would be better....
-        val zero = Cast(Literal(0), calcType)
-        val updateFunction = Coalesce(
-          Add(
-            Coalesce(currentSum :: zero :: Nil),
-            Cast(expr, calcType)
-          ) :: currentSum :: Nil)
-        val result =
-          expr.dataType match {
-            case DecimalType.Fixed(_, _) =>
-              Cast(currentSum, s.dataType)
-            case _ => currentSum
-          }
-
-        AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result)
-
-      case m @ Max(expr) =>
-        val currentMax = AttributeReference("currentMax", expr.dataType, nullable = true)()
-        val initialValue = Literal.create(null, expr.dataType)
-        val updateMax = MaxOf(currentMax, expr)
-
-        AggregateEvaluation(
-          currentMax :: Nil,
-          initialValue :: Nil,
-          updateMax :: Nil,
-          currentMax)
-
-      case m @ Min(expr) =>
-        val currentMin = AttributeReference("currentMin", expr.dataType, nullable = true)()
-        val initialValue = Literal.create(null, expr.dataType)
-        val updateMin = MinOf(currentMin, expr)
-
-        AggregateEvaluation(
-          currentMin :: Nil,
-          initialValue :: Nil,
-          updateMin :: Nil,
-          currentMin)
-
-      case CollectHashSet(Seq(expr)) =>
-        val set =
-          AttributeReference("hashSet", new OpenHashSetUDT(expr.dataType), nullable = false)()
-        val initialValue = NewSet(expr.dataType)
-        val addToSet = AddItemToSet(expr, set)
-
-        AggregateEvaluation(
-          set :: Nil,
-          initialValue :: Nil,
-          addToSet :: Nil,
-          set)
-
-      case CombineSetsAndCount(inputSet) =>
-        val elementType = inputSet.dataType.asInstanceOf[OpenHashSetUDT].elementType
-        val set =
-          AttributeReference("hashSet", new OpenHashSetUDT(elementType), nullable = false)()
-        val initialValue = NewSet(elementType)
-        val collectSets = CombineSets(set, inputSet)
-
-        AggregateEvaluation(
-          set :: Nil,
-          initialValue :: Nil,
-          collectSets :: Nil,
-          CountSet(set))
-
-      case o => sys.error(s"$o can't be codegened.")
-    }
-
-    val computationSchema = computeFunctions.flatMap(_.schema)
-
-    val resultMap: Map[TreeNodeRef, Expression] =
-      aggregatesToCompute.zip(computeFunctions).map {
-        case (agg, func) => new TreeNodeRef(agg) -> func.result
-      }.toMap
-
-    val namedGroups = groupingExpressions.zipWithIndex.map {
-      case (ne: NamedExpression, _) => (ne, ne.toAttribute)
-      case (e, i) => (e, Alias(e, s"GroupingExpr$i")().toAttribute)
-    }
-
-    // The set of expressions that produce the final output given the aggregation buffer and the
-    // grouping expressions.
-    val resultExpressions = aggregateExpressions.map(_.transform {
-      case e: Expression if resultMap.contains(new TreeNodeRef(e)) => resultMap(new TreeNodeRef(e))
-      case e: Expression =>
-        namedGroups.collectFirst {
-          case (expr, attr) if expr semanticEquals e => attr
-        }.getOrElse(e)
-    })
-
-    val aggregationBufferSchema: StructType = StructType.fromAttributes(computationSchema)
-
-    val groupKeySchema: StructType = {
-      val fields = groupingExpressions.zipWithIndex.map { case (expr, idx) =>
-        // This is a dummy field name
-        StructField(idx.toString, expr.dataType, expr.nullable)
-      }
-      StructType(fields)
-    }
-
-    val schemaSupportsUnsafe: Boolean = {
-      UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) &&
-        UnsafeProjection.canSupport(groupKeySchema)
-    }
-
-    child.execute().mapPartitions { iter =>
-      // Builds a new custom class for holding the results of aggregation for a group.
-      val initialValues = computeFunctions.flatMap(_.initialValues)
-      val newAggregationBuffer = newProjection(initialValues, child.output)
-      log.info(s"Initial values: ${initialValues.mkString(",")}")
-
-      // A projection that computes the group given an input tuple.
-      val groupProjection = newProjection(groupingExpressions, child.output)
-      log.info(s"Grouping Projection: ${groupingExpressions.mkString(",")}")
-
-      // A projection that is used to update the aggregate values for a group given a new tuple.
-      // This projection should be targeted at the current values for the group and then applied
-      // to a joined row of the current values with the new input row.
-      val updateExpressions = computeFunctions.flatMap(_.update)
-      val updateSchema = computeFunctions.flatMap(_.schema) ++ child.output
-      val updateProjection = newMutableProjection(updateExpressions, updateSchema)()
-      log.info(s"Update Expressions: ${updateExpressions.mkString(",")}")
-
-      // A projection that produces the final result, given a computation.
-      val resultProjectionBuilder =
-        newMutableProjection(
-          resultExpressions,
-          namedGroups.map(_._2) ++ computationSchema)
-      log.info(s"Result Projection: ${resultExpressions.mkString(",")}")
-
-      val joinedRow = new JoinedRow
-
-      if (!iter.hasNext) {
-        // This is an empty input, so return early so that we do not allocate data structures
-        // that won't be cleaned up (see SPARK-8357).
-        if (groupingExpressions.isEmpty) {
-          // This is a global aggregate, so return an empty aggregation buffer.
-          val resultProjection = resultProjectionBuilder()
-          Iterator(resultProjection(newAggregationBuffer(EmptyRow)))
-        } else {
-          // This is a grouped aggregate, so return an empty iterator.
-          Iterator[InternalRow]()
-        }
-      } else if (groupingExpressions.isEmpty) {
-        // TODO: Codegening anything other than the updateProjection is probably over kill.
-        val buffer = newAggregationBuffer(EmptyRow).asInstanceOf[MutableRow]
-        var currentRow: InternalRow = null
-        updateProjection.target(buffer)
-
-        while (iter.hasNext) {
-          currentRow = iter.next()
-          updateProjection(joinedRow(buffer, currentRow))
-        }
-
-        val resultProjection = resultProjectionBuilder()
-        Iterator(resultProjection(buffer))
-
-      } else if (unsafeEnabled && schemaSupportsUnsafe) {
-        assert(iter.hasNext, "There should be at least one row for this path")
-        log.info("Using Unsafe-based aggregator")
-        val pageSizeBytes = SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
-        val taskContext = TaskContext.get()
-        val aggregationMap = new UnsafeFixedWidthAggregationMap(
-          newAggregationBuffer(EmptyRow),
-          aggregationBufferSchema,
-          groupKeySchema,
-          taskContext.taskMemoryManager(),
-          SparkEnv.get.shuffleMemoryManager,
-          1024 * 16, // initial capacity
-          pageSizeBytes,
-          false // disable tracking of performance metrics
-        )
-
-        while (iter.hasNext) {
-          val currentRow: InternalRow = iter.next()
-          val groupKey: InternalRow = groupProjection(currentRow)
-          val aggregationBuffer = aggregationMap.getAggregationBuffer(groupKey)
-          if (aggregationBuffer == null) {
-            throw new IOException("Could not allocate memory to grow aggregation buffer")
-          }
-          updateProjection.target(aggregationBuffer)(joinedRow(aggregationBuffer, currentRow))
-        }
-
-        // Record memory used in the process
-        taskContext.internalMetricsToAccumulators(
-          InternalAccumulator.PEAK_EXECUTION_MEMORY).add(aggregationMap.getMemoryUsage)
-
-        new Iterator[InternalRow] {
-          private[this] val mapIterator = aggregationMap.iterator()
-          private[this] val resultProjection = resultProjectionBuilder()
-          private[this] var _hasNext = mapIterator.next()
-
-          def hasNext: Boolean = _hasNext
-
-          def next(): InternalRow = {
-            if (_hasNext) {
-              val result = resultProjection(joinedRow(mapIterator.getKey, mapIterator.getValue))
-              _hasNext = mapIterator.next()
-              if (_hasNext) {
-                result
-              } else {
-                // This is the last element in the iterator, so let's free the buffer. Before we do,
-                // though, we need to make a defensive copy of the result so that we don't return an
-                // object that might contain dangling pointers to the freed memory.
-                val resultCopy = result.copy()
-                aggregationMap.free()
-                resultCopy
-              }
-            } else {
-              throw new java.util.NoSuchElementException
-            }
-          }
-        }
-      } else {
-        if (unsafeEnabled) {
-          log.info("Not using Unsafe-based aggregator because it is not supported for this schema")
-        }
-        val buffers = new java.util.HashMap[InternalRow, MutableRow]()
-
-        var currentRow: InternalRow = null
-        while (iter.hasNext) {
-          currentRow = iter.next()
-          val currentGroup = groupProjection(currentRow)
-          var currentBuffer = buffers.get(currentGroup)
-          if (currentBuffer == null) {
-            currentBuffer = newAggregationBuffer(EmptyRow).asInstanceOf[MutableRow]
-            buffers.put(currentGroup, currentBuffer)
-          }
-          // Target the projection at the current aggregation buffer and then project the updated
-          // values.
-          updateProjection.target(currentBuffer)(joinedRow(currentBuffer, currentRow))
-        }
-
-        new Iterator[InternalRow] {
-          private[this] val resultIterator = buffers.entrySet.iterator()
-          private[this] val resultProjection = resultProjectionBuilder()
-
-          def hasNext: Boolean = resultIterator.hasNext
-
-          def next(): InternalRow = {
-            val currentGroup = resultIterator.next()
-            resultProjection(joinedRow(currentGroup.getKey, currentGroup.getValue))
-          }
-        }
-      }
-    }
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 952ba7d45c13e..a730ffbb217c0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -136,32 +136,6 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   object HashAggregation extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       // Aggregations that can be performed in two phases, before and after the shuffle.
-
-      // Cases where all aggregates can be codegened.
-      case PartialAggregation(
-             namedGroupingAttributes,
-             rewrittenAggregateExpressions,
-             groupingExpressions,
-             partialComputation,
-             child)
-             if canBeCodeGened(
-                  allAggregates(partialComputation) ++
-                  allAggregates(rewrittenAggregateExpressions)) &&
-               codegenEnabled &&
-               !canBeConvertedToNewAggregation(plan) =>
-          execution.GeneratedAggregate(
-            partial = false,
-            namedGroupingAttributes,
-            rewrittenAggregateExpressions,
-            unsafeEnabled,
-            execution.GeneratedAggregate(
-              partial = true,
-              groupingExpressions,
-              partialComputation,
-              unsafeEnabled,
-              planLater(child))) :: Nil
-
-      // Cases where some aggregate can not be codegened
       case PartialAggregation(
              namedGroupingAttributes,
              rewrittenAggregateExpressions,
@@ -192,14 +166,6 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case _ => false
     }
 
-    def canBeCodeGened(aggs: Seq[AggregateExpression1]): Boolean = aggs.forall {
-      case _: Sum | _: Count | _: Max | _: Min |  _: CombineSetsAndCount => true
-      // The generated set implementation is pretty limited ATM.
-      case CollectHashSet(exprs) if exprs.size == 1  &&
-           Seq(IntegerType, LongType).contains(exprs.head.dataType) => true
-      case _ => false
-    }
-
     def allAggregates(exprs: Seq[Expression]): Seq[AggregateExpression1] =
       exprs.flatMap(_.collect { case a: AggregateExpression1 => a })
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 29dfcf2575227..cef40dd324d9e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -26,7 +26,6 @@ import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.aggregate
-import org.apache.spark.sql.execution.GeneratedAggregate
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.test.SQLTestUtils
@@ -263,7 +262,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     val df = sql(sqlText)
     // First, check if we have GeneratedAggregate.
     val hasGeneratedAgg = df.queryExecution.executedPlan
-      .collect { case _: GeneratedAggregate | _: aggregate.Aggregate => true }
+      .collect { case _: aggregate.Aggregate => true }
       .nonEmpty
     if (!hasGeneratedAgg) {
       fail(
@@ -1603,7 +1602,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       Row(new CalendarInterval(-(12 * 3 - 3), -(7L * MICROS_PER_WEEK + 123))))
   }
 
-  test("aggregation with codegen updates peak execution memory") {
+  ignore("aggregation with codegen updates peak execution memory") {
     withSQLConf(
         (SQLConf.CODEGEN_ENABLED.key, "true"),
         (SQLConf.USE_SQL_AGGREGATE2.key, "false")) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala
deleted file mode 100644
index 20def6bef0c17..0000000000000
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/AggregateSuite.scala
+++ /dev/null
@@ -1,48 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution
-
-import org.apache.spark.sql.SQLConf
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.test.TestSQLContext
-
-class AggregateSuite extends SparkPlanTest {
-
-  test("SPARK-8357 unsafe aggregation path should not leak memory with empty input") {
-    val codegenDefault = TestSQLContext.getConf(SQLConf.CODEGEN_ENABLED)
-    val unsafeDefault = TestSQLContext.getConf(SQLConf.UNSAFE_ENABLED)
-    try {
-      TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, true)
-      TestSQLContext.setConf(SQLConf.UNSAFE_ENABLED, true)
-      val df = Seq.empty[(Int, Int)].toDF("a", "b")
-      checkAnswer(
-        df,
-        GeneratedAggregate(
-          partial = true,
-          Seq(df.col("b").expr),
-          Seq(Alias(Count(df.col("a").expr), "cnt")()),
-          unsafeEnabled = true,
-          _: SparkPlan),
-        Seq.empty
-      )
-    } finally {
-      TestSQLContext.setConf(SQLConf.CODEGEN_ENABLED, codegenDefault)
-      TestSQLContext.setConf(SQLConf.UNSAFE_ENABLED, unsafeDefault)
-    }
-  }
-}

From d5a9af3230925c347d0904fe7f2402e468e80bc8 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 5 Aug 2015 21:50:35 -0700
Subject: [PATCH 077/633] [SPARK-9664] [SQL] Remove UDAFRegistration and add
 apply to UserDefinedAggregateFunction.

https://issues.apache.org/jira/browse/SPARK-9664

Author: Yin Huai <yhuai@databricks.com>

Closes #7982 from yhuai/udafRegister and squashes the following commits:

0cc2287 [Yin Huai] Remove UDAFRegistration and add apply to UserDefinedAggregateFunction.
---
 .../org/apache/spark/sql/SQLContext.scala     |  3 --
 .../apache/spark/sql/UDAFRegistration.scala   | 36 -------------------
 .../apache/spark/sql/UDFRegistration.scala    | 16 +++++++++
 .../spark/sql/execution/aggregate/udaf.scala  |  8 ++---
 .../apache/spark/sql/expressions/udaf.scala   | 32 ++++++++++++++++-
 .../org/apache/spark/sql/functions.scala      |  1 +
 .../spark/sql/hive/JavaDataFrameSuite.java    | 26 ++++++++++++++
 .../execution/AggregationQuerySuite.scala     |  4 +--
 8 files changed, 80 insertions(+), 46 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index ffc2baf7a8826..6f8ffb54402a7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -291,9 +291,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
   @transient
   val udf: UDFRegistration = new UDFRegistration(this)
 
-  @transient
-  val udaf: UDAFRegistration = new UDAFRegistration(this)
-
   /**
    * Returns true if the table is currently cached in-memory.
    * @group cachemgmt
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala
deleted file mode 100644
index 0d4e30f29255e..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/UDAFRegistration.scala
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql
-
-import org.apache.spark.Logging
-import org.apache.spark.sql.catalyst.expressions.{Expression}
-import org.apache.spark.sql.execution.aggregate.ScalaUDAF
-import org.apache.spark.sql.expressions.UserDefinedAggregateFunction
-
-class UDAFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
-
-  private val functionRegistry = sqlContext.functionRegistry
-
-  def register(
-      name: String,
-      func: UserDefinedAggregateFunction): UserDefinedAggregateFunction = {
-    def builder(children: Seq[Expression]) = ScalaUDAF(children, func)
-    functionRegistry.registerFunction(name, builder)
-    func
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
index 7cd7421a518c9..1f270560d7bc1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
@@ -26,6 +26,8 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.api.java._
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.expressions.{Expression, ScalaUDF}
+import org.apache.spark.sql.execution.aggregate.ScalaUDAF
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction
 import org.apache.spark.sql.types.DataType
 
 /**
@@ -52,6 +54,20 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
     functionRegistry.registerFunction(name, udf.builder)
   }
 
+  /**
+   * Register a user-defined aggregate function (UDAF).
+   * @param name the name of the UDAF.
+   * @param udaf the UDAF needs to be registered.
+   * @return the registered UDAF.
+   */
+  def register(
+      name: String,
+      udaf: UserDefinedAggregateFunction): UserDefinedAggregateFunction = {
+    def builder(children: Seq[Expression]) = ScalaUDAF(children, udaf)
+    functionRegistry.registerFunction(name, builder)
+    udaf
+  }
+
   // scalastyle:off
 
   /* register 0-22 were generated by this script
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
index 5fafc916bfa0b..7619f3ec9f0a7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
@@ -316,7 +316,7 @@ private[sql] case class ScalaUDAF(
 
   override lazy val cloneBufferAttributes = bufferAttributes.map(_.newInstance())
 
-  private[this] val childrenSchema: StructType = {
+  private[this] lazy val childrenSchema: StructType = {
     val inputFields = children.zipWithIndex.map {
       case (child, index) =>
         StructField(s"input$index", child.dataType, child.nullable, Metadata.empty)
@@ -337,16 +337,16 @@ private[sql] case class ScalaUDAF(
     }
   }
 
-  private[this] val inputToScalaConverters: Any => Any =
+  private[this] lazy val inputToScalaConverters: Any => Any =
     CatalystTypeConverters.createToScalaConverter(childrenSchema)
 
-  private[this] val bufferValuesToCatalystConverters: Array[Any => Any] = {
+  private[this] lazy val bufferValuesToCatalystConverters: Array[Any => Any] = {
     bufferSchema.fields.map { field =>
       CatalystTypeConverters.createToCatalystConverter(field.dataType)
     }
   }
 
-  private[this] val bufferValuesToScalaConverters: Array[Any => Any] = {
+  private[this] lazy val bufferValuesToScalaConverters: Array[Any => Any] = {
     bufferSchema.fields.map { field =>
       CatalystTypeConverters.createToScalaConverter(field.dataType)
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
index 278dd438fab4a..5180871585f25 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
@@ -17,7 +17,10 @@
 
 package org.apache.spark.sql.expressions
 
-import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.expressions.ScalaUDF
+import org.apache.spark.sql.catalyst.expressions.aggregate.{Complete, AggregateExpression2}
+import org.apache.spark.sql.execution.aggregate.ScalaUDAF
+import org.apache.spark.sql.{Column, Row}
 import org.apache.spark.sql.types._
 import org.apache.spark.annotation.Experimental
 
@@ -87,6 +90,33 @@ abstract class UserDefinedAggregateFunction extends Serializable {
    * aggregation buffer.
    */
   def evaluate(buffer: Row): Any
+
+  /**
+   * Creates a [[Column]] for this UDAF with given [[Column]]s as arguments.
+   */
+  @scala.annotation.varargs
+  def apply(exprs: Column*): Column = {
+    val aggregateExpression =
+      AggregateExpression2(
+        ScalaUDAF(exprs.map(_.expr), this),
+        Complete,
+        isDistinct = false)
+    Column(aggregateExpression)
+  }
+
+  /**
+   * Creates a [[Column]] for this UDAF with given [[Column]]s as arguments.
+   * If `isDistinct` is true, this UDAF is working on distinct input values.
+   */
+  @scala.annotation.varargs
+  def apply(isDistinct: Boolean, exprs: Column*): Column = {
+    val aggregateExpression =
+      AggregateExpression2(
+        ScalaUDAF(exprs.map(_.expr), this),
+        Complete,
+        isDistinct = isDistinct)
+    Column(aggregateExpression)
+  }
 }
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 5a10c3891ad6c..39aa905c8532a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -2500,6 +2500,7 @@ object functions {
    * @group udf_funcs
    * @since 1.5.0
    */
+  @scala.annotation.varargs
   def callUDF(udfName: String, cols: Column*): Column = {
     UnresolvedFunction(udfName, cols.map(_.expr), isDistinct = false)
   }
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
index 613b2bcc80e37..21b053f07a3ba 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
@@ -29,8 +29,12 @@
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.sql.*;
 import org.apache.spark.sql.expressions.Window;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import static org.apache.spark.sql.functions.*;
 import org.apache.spark.sql.hive.HiveContext;
 import org.apache.spark.sql.hive.test.TestHive$;
+import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
+import test.org.apache.spark.sql.hive.aggregate.MyDoubleSum;
 
 public class JavaDataFrameSuite {
   private transient JavaSparkContext sc;
@@ -77,4 +81,26 @@ public void saveTableAndQueryIt() {
         "      ROWS BETWEEN 1 preceding and 1 following) " +
         "FROM window_table").collectAsList());
   }
+
+  @Test
+  public void testUDAF() {
+    DataFrame df = hc.range(0, 100).unionAll(hc.range(0, 100)).select(col("id").as("value"));
+    UserDefinedAggregateFunction udaf = new MyDoubleSum();
+    UserDefinedAggregateFunction registeredUDAF = hc.udf().register("mydoublesum", udaf);
+    // Create Columns for the UDAF. For now, callUDF does not take an argument to specific if
+    // we want to use distinct aggregation.
+    DataFrame aggregatedDF =
+      df.groupBy()
+        .agg(
+          udaf.apply(true, col("value")),
+          udaf.apply(col("value")),
+          registeredUDAF.apply(col("value")),
+          callUDF("mydoublesum", col("value")));
+
+    List<Row> expectedResult = new ArrayList<Row>();
+    expectedResult.add(RowFactory.create(4950.0, 9900.0, 9900.0, 9900.0));
+    checkAnswer(
+      aggregatedDF,
+      expectedResult);
+  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index 6f0db27775e4d..4b35c8fd83533 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -73,8 +73,8 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
     emptyDF.registerTempTable("emptyTable")
 
     // Register UDAFs
-    sqlContext.udaf.register("mydoublesum", new MyDoubleSum)
-    sqlContext.udaf.register("mydoubleavg", new MyDoubleAvg)
+    sqlContext.udf.register("mydoublesum", new MyDoubleSum)
+    sqlContext.udf.register("mydoubleavg", new MyDoubleAvg)
   }
 
   override def afterAll(): Unit = {

From aead18ffca36830e854fba32a1cac11a0b2e31d5 Mon Sep 17 00:00:00 2001
From: "zhichao.li" <zhichao.li@intel.com>
Date: Thu, 6 Aug 2015 09:02:30 -0700
Subject: [PATCH 078/633] [SPARK-8266] [SQL] add function translate

![translate](http://www.w3resource.com/PostgreSQL/postgresql-translate-function.png)

Author: zhichao.li <zhichao.li@intel.com>

Closes #7709 from zhichao-li/translate and squashes the following commits:

9418088 [zhichao.li] refine checking condition
f2ab77a [zhichao.li] clone string
9d88f2d [zhichao.li] fix indent
6aa2962 [zhichao.li] style
e575ead [zhichao.li] add python api
9d4bab0 [zhichao.li] add special case for fodable and refactor unittest
eda7ad6 [zhichao.li] update to use TernaryExpression
cdfd4be [zhichao.li] add function translate
---
 python/pyspark/sql/functions.py               | 16 ++++
 .../catalyst/analysis/FunctionRegistry.scala  |  1 +
 .../sql/catalyst/expressions/Expression.scala |  4 +-
 .../expressions/stringOperations.scala        | 79 ++++++++++++++++++-
 .../expressions/StringExpressionsSuite.scala  | 14 ++++
 .../org/apache/spark/sql/functions.scala      | 21 +++--
 .../spark/sql/StringFunctionsSuite.scala      |  6 ++
 .../apache/spark/unsafe/types/UTF8String.java | 16 ++++
 .../spark/unsafe/types/UTF8StringSuite.java   | 31 ++++++++
 9 files changed, 180 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 9f0d71d7960cf..b5c6a01f18858 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -1290,6 +1290,22 @@ def length(col):
     return Column(sc._jvm.functions.length(_to_java_column(col)))
 
 
+@ignore_unicode_prefix
+@since(1.5)
+def translate(srcCol, matching, replace):
+    """A function translate any character in the `srcCol` by a character in `matching`.
+    The characters in `replace` is corresponding to the characters in `matching`.
+    The translate will happen when any character in the string matching with the character
+    in the `matching`.
+
+    >>> sqlContext.createDataFrame([('translate',)], ['a']).select(translate('a', "rnlt", "123")\
+    .alias('r')).collect()
+    [Row(r=u'1a2s3ae')]
+    """
+    sc = SparkContext._active_spark_context
+    return Column(sc._jvm.functions.translate(_to_java_column(srcCol), matching, replace))
+
+
 # ---------------------- Collection functions ------------------------------
 
 @since(1.4)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index 94c355f838fa0..cd5a90d788151 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -203,6 +203,7 @@ object FunctionRegistry {
     expression[Substring]("substr"),
     expression[Substring]("substring"),
     expression[SubstringIndex]("substring_index"),
+    expression[StringTranslate]("translate"),
     expression[StringTrim]("trim"),
     expression[UnBase64]("unbase64"),
     expression[Upper]("ucase"),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
index ef2fc2e8c29d4..0b98f555a1d60 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala
@@ -444,7 +444,7 @@ abstract class TernaryExpression extends Expression {
   override def nullable: Boolean = children.exists(_.nullable)
 
   /**
-   * Default behavior of evaluation according to the default nullability of BinaryExpression.
+   * Default behavior of evaluation according to the default nullability of TernaryExpression.
    * If subclass of BinaryExpression override nullable, probably should also override this.
    */
   override def eval(input: InternalRow): Any = {
@@ -463,7 +463,7 @@ abstract class TernaryExpression extends Expression {
   }
 
   /**
-   * Called by default [[eval]] implementation.  If subclass of BinaryExpression keep the default
+   * Called by default [[eval]] implementation.  If subclass of TernaryExpression keep the default
    * nullability, they can override this method to save null-check code.  If we need full control
    * of evaluation process, we should override [[eval]].
    */
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 0cc785d9f3a49..76666bd6b3d27 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -18,7 +18,9 @@
 package org.apache.spark.sql.catalyst.expressions
 
 import java.text.DecimalFormat
-import java.util.{Arrays, Locale}
+import java.util.Arrays
+import java.util.{Map => JMap, HashMap}
+import java.util.Locale
 import java.util.regex.{MatchResult, Pattern}
 
 import org.apache.commons.lang3.StringEscapeUtils
@@ -349,6 +351,81 @@ case class EndsWith(left: Expression, right: Expression)
   }
 }
 
+object StringTranslate {
+
+  def buildDict(matchingString: UTF8String, replaceString: UTF8String)
+    : JMap[Character, Character] = {
+    val matching = matchingString.toString()
+    val replace = replaceString.toString()
+    val dict = new HashMap[Character, Character]()
+    var i = 0
+    while (i < matching.length()) {
+      val rep = if (i < replace.length()) replace.charAt(i) else '\0'
+      if (null == dict.get(matching.charAt(i))) {
+        dict.put(matching.charAt(i), rep)
+      }
+      i += 1
+    }
+    dict
+  }
+}
+
+/**
+ * A function translate any character in the `srcExpr` by a character in `replaceExpr`.
+ * The characters in `replaceExpr` is corresponding to the characters in `matchingExpr`.
+ * The translate will happen when any character in the string matching with the character
+ * in the `matchingExpr`.
+ */
+case class StringTranslate(srcExpr: Expression, matchingExpr: Expression, replaceExpr: Expression)
+  extends TernaryExpression with ImplicitCastInputTypes {
+
+  @transient private var lastMatching: UTF8String = _
+  @transient private var lastReplace: UTF8String = _
+  @transient private var dict: JMap[Character, Character] = _
+
+  override def nullSafeEval(srcEval: Any, matchingEval: Any, replaceEval: Any): Any = {
+    if (matchingEval != lastMatching || replaceEval != lastReplace) {
+      lastMatching = matchingEval.asInstanceOf[UTF8String].clone()
+      lastReplace = replaceEval.asInstanceOf[UTF8String].clone()
+      dict = StringTranslate.buildDict(lastMatching, lastReplace)
+    }
+    srcEval.asInstanceOf[UTF8String].translate(dict)
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val termLastMatching = ctx.freshName("lastMatching")
+    val termLastReplace = ctx.freshName("lastReplace")
+    val termDict = ctx.freshName("dict")
+    val classNameDict = classOf[JMap[Character, Character]].getCanonicalName
+
+    ctx.addMutableState("UTF8String", termLastMatching, s"${termLastMatching} = null;")
+    ctx.addMutableState("UTF8String", termLastReplace, s"${termLastReplace} = null;")
+    ctx.addMutableState(classNameDict, termDict, s"${termDict} = null;")
+
+    nullSafeCodeGen(ctx, ev, (src, matching, replace) => {
+      val check = if (matchingExpr.foldable && replaceExpr.foldable) {
+        s"${termDict} == null"
+      } else {
+        s"!${matching}.equals(${termLastMatching}) || !${replace}.equals(${termLastReplace})"
+      }
+      s"""if ($check) {
+        // Not all of them is literal or matching or replace value changed
+        ${termLastMatching} = ${matching}.clone();
+        ${termLastReplace} = ${replace}.clone();
+        ${termDict} = org.apache.spark.sql.catalyst.expressions.StringTranslate
+          .buildDict(${termLastMatching}, ${termLastReplace});
+      }
+      ${ev.primitive} = ${src}.translate(${termDict});
+      """
+    })
+  }
+
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType, StringType)
+  override def children: Seq[Expression] = srcExpr :: matchingExpr :: replaceExpr :: Nil
+  override def prettyName: String = "translate"
+}
+
 /**
  * A function that returns the index (1-based) of the given string (left) in the comma-
  * delimited list (right). Returns 0, if the string wasn't found or if the given
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 23f36ca43d663..426dc272471ae 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -431,6 +431,20 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(SoundEx(Literal("!!")), "!!")
   }
 
+  test("translate") {
+    checkEvaluation(
+      StringTranslate(Literal("translate"), Literal("rnlt"), Literal("123")), "1a2s3ae")
+    checkEvaluation(StringTranslate(Literal("translate"), Literal(""), Literal("123")), "translate")
+    checkEvaluation(StringTranslate(Literal("translate"), Literal("rnlt"), Literal("")), "asae")
+    // test for multiple mapping
+    checkEvaluation(StringTranslate(Literal("abcd"), Literal("aba"), Literal("123")), "12cd")
+    checkEvaluation(StringTranslate(Literal("abcd"), Literal("aba"), Literal("12")), "12cd")
+    // scalastyle:off
+    // non ascii characters are not allowed in the source code, so we disable the scalastyle.
+    checkEvaluation(StringTranslate(Literal("花花世界"), Literal("花界"), Literal("ab")), "aa世b")
+    // scalastyle:on
+  }
+
   test("TRIM/LTRIM/RTRIM") {
     val s = 'a.string.at(0)
     checkEvaluation(StringTrim(Literal(" aa  ")), "aa", create_row(" abdef "))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 39aa905c8532a..79c5f596661d4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1100,11 +1100,11 @@ object functions {
   }
 
   /**
-    * Computes hex value of the given column.
-    *
-    * @group math_funcs
-    * @since 1.5.0
-    */
+   * Computes hex value of the given column.
+   *
+   * @group math_funcs
+   * @since 1.5.0
+   */
   def hex(column: Column): Column = Hex(column.expr)
 
   /**
@@ -1863,6 +1863,17 @@ object functions {
   def substring_index(str: Column, delim: String, count: Int): Column =
     SubstringIndex(str.expr, lit(delim).expr, lit(count).expr)
 
+  /* Translate any character in the src by a character in replaceString.
+  * The characters in replaceString is corresponding to the characters in matchingString.
+  * The translate will happen when any character in the string matching with the character
+  * in the matchingString.
+  *
+  * @group string_funcs
+  * @since 1.5.0
+  */
+  def translate(src: Column, matchingString: String, replaceString: String): Column =
+    StringTranslate(src.expr, lit(matchingString).expr, lit(replaceString).expr)
+
   /**
    * Trim the spaces from both ends for the specified string column.
    *
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index ab5da6ee79f1b..ca298b2434410 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -128,6 +128,12 @@ class StringFunctionsSuite extends QueryTest {
     // scalastyle:on
   }
 
+  test("string translate") {
+    val df = Seq(("translate", "")).toDF("a", "b")
+    checkAnswer(df.select(translate($"a", "rnlt", "123")), Row("1a2s3ae"))
+    checkAnswer(df.selectExpr("""translate(a, "rnlt", "")"""), Row("asae"))
+  }
+
   test("string trim functions") {
     val df = Seq(("  example  ", "")).toDF("a", "b")
 
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index febbe3d4e54d1..d1014426c0f49 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -22,6 +22,7 @@
 import java.io.UnsupportedEncodingException;
 import java.nio.ByteOrder;
 import java.util.Arrays;
+import java.util.Map;
 
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
@@ -795,6 +796,21 @@ public UTF8String[] split(UTF8String pattern, int limit) {
     return res;
   }
 
+  // TODO: Need to use `Code Point` here instead of Char in case the character longer than 2 bytes
+  public UTF8String translate(Map<Character, Character> dict) {
+    String srcStr = this.toString();
+
+    StringBuilder sb = new StringBuilder();
+    for(int k = 0; k< srcStr.length(); k++) {
+      if (null == dict.get(srcStr.charAt(k))) {
+        sb.append(srcStr.charAt(k));
+      } else if ('\0' != dict.get(srcStr.charAt(k))){
+        sb.append(dict.get(srcStr.charAt(k)));
+      }
+    }
+    return fromString(sb.toString());
+  }
+
   @Override
   public String toString() {
     try {
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
index b30c94c1c1f80..98aa8a2469a75 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/UTF8StringSuite.java
@@ -19,7 +19,9 @@
 
 import java.io.UnsupportedEncodingException;
 import java.util.Arrays;
+import java.util.HashMap;
 
+import com.google.common.collect.ImmutableMap;
 import org.junit.Test;
 
 import static junit.framework.Assert.*;
@@ -391,6 +393,35 @@ public void levenshteinDistance() {
     assertEquals(fromString("世界千世").levenshteinDistance(fromString("千a世b")),4);
   }
 
+  @Test
+  public void translate() {
+    assertEquals(
+      fromString("1a2s3ae"),
+      fromString("translate").translate(ImmutableMap.of(
+        'r', '1',
+        'n', '2',
+        'l', '3',
+        't', '\0'
+      )));
+    assertEquals(
+      fromString("translate"),
+      fromString("translate").translate(new HashMap<Character, Character>()));
+    assertEquals(
+      fromString("asae"),
+      fromString("translate").translate(ImmutableMap.of(
+        'r', '\0',
+        'n', '\0',
+        'l', '\0',
+        't', '\0'
+      )));
+    assertEquals(
+      fromString("aa世b"),
+      fromString("花花世界").translate(ImmutableMap.of(
+        '花', 'a',
+        '界', 'b'
+      )));
+  }
+
   @Test
   public void createBlankString() {
     assertEquals(fromString(" "), blankString(1));

From 5b965d64ee1687145ba793da749659c8f67384e8 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 6 Aug 2015 09:10:57 -0700
Subject: [PATCH 079/633] [SPARK-9644] [SQL] Support update DecimalType with
 precision > 18 in UnsafeRow

In order to support update a varlength (actually fixed length) object, the space should be preserved even  it's null. And, we can't call setNullAt(i) for it anymore, we because setNullAt(i) will remove the offset of the preserved space, should call setDecimal(i, null, precision) instead.

After this, we can do hash based aggregation on DecimalType with precision > 18. In a tests, this could decrease the end-to-end run time of aggregation query from 37 seconds (sort based) to 24 seconds (hash based).

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #7978 from davies/update_decimal and squashes the following commits:

bed8100 [Davies Liu] isSettable -> isMutable
923c9eb [Davies Liu] address comments and fix bug
385891d [Davies Liu] Merge branch 'master' of github.com:apache/spark into update_decimal
36a1872 [Davies Liu] fix tests
cd6c524 [Davies Liu] support set decimal with precision > 18
---
 .../sql/catalyst/expressions/UnsafeRow.java   | 74 +++++++++++++++----
 .../expressions/UnsafeRowWriters.java         | 41 ++++++----
 .../codegen/GenerateMutableProjection.scala   | 15 +++-
 .../codegen/GenerateUnsafeProjection.scala    | 53 +++++++------
 .../spark/sql/catalyst/expressions/rows.scala |  8 +-
 .../expressions/UnsafeRowConverterSuite.scala | 17 ++++-
 .../UnsafeFixedWidthAggregationMap.java       |  4 +-
 .../SortBasedAggregationIterator.scala        |  4 +-
 .../UnsafeFixedWidthAggregationMapSuite.scala |  2 +-
 .../spark/unsafe/PlatformDependent.java       | 26 +++++++
 10 files changed, 183 insertions(+), 61 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index e3e1622de08ba..e829acb6285f1 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -65,11 +65,11 @@ public static int calculateBitSetWidthInBytes(int numFields) {
   /**
    * Field types that can be updated in place in UnsafeRows (e.g. we support set() for these types)
    */
-  public static final Set<DataType> settableFieldTypes;
+  public static final Set<DataType> mutableFieldTypes;
 
-  // DecimalType(precision <= 18) is settable
+  // DecimalType is also mutable
   static {
-    settableFieldTypes = Collections.unmodifiableSet(
+    mutableFieldTypes = Collections.unmodifiableSet(
       new HashSet<>(
         Arrays.asList(new DataType[] {
           NullType,
@@ -87,12 +87,16 @@ public static int calculateBitSetWidthInBytes(int numFields) {
 
   public static boolean isFixedLength(DataType dt) {
     if (dt instanceof DecimalType) {
-      return ((DecimalType) dt).precision() < Decimal.MAX_LONG_DIGITS();
+      return ((DecimalType) dt).precision() <= Decimal.MAX_LONG_DIGITS();
     } else {
-      return settableFieldTypes.contains(dt);
+      return mutableFieldTypes.contains(dt);
     }
   }
 
+  public static boolean isMutable(DataType dt) {
+    return mutableFieldTypes.contains(dt) || dt instanceof DecimalType;
+  }
+
   //////////////////////////////////////////////////////////////////////////////
   // Private fields and methods
   //////////////////////////////////////////////////////////////////////////////
@@ -238,17 +242,45 @@ public void setFloat(int ordinal, float value) {
     PlatformDependent.UNSAFE.putFloat(baseObject, getFieldOffset(ordinal), value);
   }
 
+  /**
+   * Updates the decimal column.
+   *
+   * Note: In order to support update a decimal with precision > 18, CAN NOT call
+   * setNullAt() for this column.
+   */
   @Override
   public void setDecimal(int ordinal, Decimal value, int precision) {
     assertIndexIsValid(ordinal);
-    if (value == null) {
-      setNullAt(ordinal);
-    } else {
-      if (precision <= Decimal.MAX_LONG_DIGITS()) {
+    if (precision <= Decimal.MAX_LONG_DIGITS()) {
+      // compact format
+      if (value == null) {
+        setNullAt(ordinal);
+      } else {
         setLong(ordinal, value.toUnscaledLong());
+      }
+    } else {
+      // fixed length
+      long cursor = getLong(ordinal) >>> 32;
+      assert cursor > 0 : "invalid cursor " + cursor;
+      // zero-out the bytes
+      PlatformDependent.UNSAFE.putLong(baseObject, baseOffset + cursor, 0L);
+      PlatformDependent.UNSAFE.putLong(baseObject, baseOffset + cursor + 8, 0L);
+
+      if (value == null) {
+        setNullAt(ordinal);
+        // keep the offset for future update
+        PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(ordinal), cursor << 32);
       } else {
-        // TODO(davies): support update decimal (hold a bounded space even it's null)
-        throw new UnsupportedOperationException();
+
+        final BigInteger integer = value.toJavaBigDecimal().unscaledValue();
+        final int[] mag = (int[]) PlatformDependent.UNSAFE.getObjectVolatile(integer,
+          PlatformDependent.BIG_INTEGER_MAG_OFFSET);
+        assert(mag.length <= 4);
+
+        // Write the bytes to the variable length portion.
+        PlatformDependent.copyMemory(mag, PlatformDependent.INT_ARRAY_OFFSET,
+          baseObject, baseOffset + cursor, mag.length * 4);
+        setLong(ordinal, (cursor << 32) | ((long) (((integer.signum() + 1) << 8) + mag.length)));
       }
     }
   }
@@ -343,6 +375,8 @@ public double getDouble(int ordinal) {
     return PlatformDependent.UNSAFE.getDouble(baseObject, getFieldOffset(ordinal));
   }
 
+  private static byte[] EMPTY = new byte[0];
+
   @Override
   public Decimal getDecimal(int ordinal, int precision, int scale) {
     if (isNullAt(ordinal)) {
@@ -351,10 +385,20 @@ public Decimal getDecimal(int ordinal, int precision, int scale) {
     if (precision <= Decimal.MAX_LONG_DIGITS()) {
       return Decimal.apply(getLong(ordinal), precision, scale);
     } else {
-      byte[] bytes = getBinary(ordinal);
-      BigInteger bigInteger = new BigInteger(bytes);
-      BigDecimal javaDecimal = new BigDecimal(bigInteger, scale);
-      return Decimal.apply(new scala.math.BigDecimal(javaDecimal), precision, scale);
+      long offsetAndSize = getLong(ordinal);
+      long offset = offsetAndSize >>> 32;
+      int signum = ((int) (offsetAndSize & 0xfff) >> 8);
+      assert signum >=0 && signum <= 2 : "invalid signum " + signum;
+      int size = (int) (offsetAndSize & 0xff);
+      int[] mag = new int[size];
+      PlatformDependent.copyMemory(baseObject, baseOffset + offset,
+        mag, PlatformDependent.INT_ARRAY_OFFSET, size * 4);
+
+      // create a BigInteger using signum and mag
+      BigInteger v = new BigInteger(0, EMPTY);  // create the initial object
+      PlatformDependent.UNSAFE.putInt(v, PlatformDependent.BIG_INTEGER_SIGNUM_OFFSET, signum - 1);
+      PlatformDependent.UNSAFE.putObjectVolatile(v, PlatformDependent.BIG_INTEGER_MAG_OFFSET, mag);
+      return Decimal.apply(new BigDecimal(v, scale), precision, scale);
     }
   }
 
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index 31928731545da..28e7ec0a0f120 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -17,9 +17,10 @@
 
 package org.apache.spark.sql.catalyst.expressions;
 
+import java.math.BigInteger;
+
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.types.Decimal;
-import org.apache.spark.sql.types.MapData;
 import org.apache.spark.unsafe.PlatformDependent;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.types.ByteArray;
@@ -47,29 +48,41 @@ public static int write(UnsafeRow target, int ordinal, int cursor, Decimal input
 
   /** Writer for Decimal with precision larger than 18. */
   public static class DecimalWriter {
-
+    private static final int SIZE = 16;
     public static int getSize(Decimal input) {
       // bounded size
-      return 16;
+      return SIZE;
     }
 
     public static int write(UnsafeRow target, int ordinal, int cursor, Decimal input) {
+      final Object base = target.getBaseObject();
       final long offset = target.getBaseOffset() + cursor;
-      final byte[] bytes = input.toJavaBigDecimal().unscaledValue().toByteArray();
-      final int numBytes = bytes.length;
-      assert(numBytes <= 16);
-
       // zero-out the bytes
-      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset, 0L);
-      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset + 8, 0L);
+      PlatformDependent.UNSAFE.putLong(base, offset, 0L);
+      PlatformDependent.UNSAFE.putLong(base, offset + 8, 0L);
+
+      if (input == null) {
+        target.setNullAt(ordinal);
+        // keep the offset and length for update
+        int fieldOffset = UnsafeRow.calculateBitSetWidthInBytes(target.numFields()) + ordinal * 8;
+        PlatformDependent.UNSAFE.putLong(base, target.getBaseOffset() + fieldOffset,
+          ((long) cursor) << 32);
+        return SIZE;
+      }
 
-      // Write the bytes to the variable length portion.
-      PlatformDependent.copyMemory(bytes, PlatformDependent.BYTE_ARRAY_OFFSET,
-        target.getBaseObject(), offset, numBytes);
+      final BigInteger integer = input.toJavaBigDecimal().unscaledValue();
+      int signum = integer.signum() + 1;
+      final int[] mag = (int[]) PlatformDependent.UNSAFE.getObjectVolatile(integer,
+        PlatformDependent.BIG_INTEGER_MAG_OFFSET);
+      assert(mag.length <= 4);
 
+      // Write the bytes to the variable length portion.
+      PlatformDependent.copyMemory(mag, PlatformDependent.INT_ARRAY_OFFSET,
+        base, target.getBaseOffset() + cursor, mag.length * 4);
       // Set the fixed length portion.
-      target.setLong(ordinal, (((long) cursor) << 32) | ((long) numBytes));
-      return 16;
+      target.setLong(ordinal, (((long) cursor) << 32) | ((long) ((signum << 8) + mag.length)));
+
+      return SIZE;
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index e4a8fc24dac2f..ac58423cd884d 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -21,6 +21,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
+import org.apache.spark.sql.types.DecimalType
 
 // MutableProjection is not accessible in Java
 abstract class BaseMutableProjection extends MutableProjection
@@ -43,14 +44,26 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
       case (NoOp, _) => ""
       case (e, i) =>
         val evaluationCode = e.gen(ctx)
-        evaluationCode.code +
+        if (e.dataType.isInstanceOf[DecimalType]) {
+          // Can't call setNullAt on DecimalType, because we need to keep the offset
           s"""
+            ${evaluationCode.code}
+            if (${evaluationCode.isNull}) {
+              ${ctx.setColumn("mutableRow", e.dataType, i, null)};
+            } else {
+              ${ctx.setColumn("mutableRow", e.dataType, i, evaluationCode.primitive)};
+            }
+          """
+        } else {
+          s"""
+            ${evaluationCode.code}
             if (${evaluationCode.isNull}) {
               mutableRow.setNullAt($i);
             } else {
               ${ctx.setColumn("mutableRow", e.dataType, i, evaluationCode.primitive)};
             }
           """
+        }
     }
     // collect projections into blocks as function has 64kb codesize limit in JVM
     val projectionBlocks = new ArrayBuffer[String]()
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 71f8ea09f0770..d8912df694a10 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -45,10 +45,10 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
   /** Returns true iff we support this data type. */
   def canSupport(dataType: DataType): Boolean = dataType match {
+    case NullType => true
     case t: AtomicType => true
     case _: CalendarIntervalType => true
     case t: StructType => t.toSeq.forall(field => canSupport(field.dataType))
-    case NullType => true
     case t: ArrayType if canSupport(t.elementType) => true
     case MapType(kt, vt, _) if canSupport(kt) && canSupport(vt) => true
     case _ => false
@@ -56,7 +56,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
   def genAdditionalSize(dt: DataType, ev: GeneratedExpressionCode): String = dt match {
     case t: DecimalType if t.precision > Decimal.MAX_LONG_DIGITS =>
-      s" + (${ev.isNull} ? 0 : $DecimalWriter.getSize(${ev.primitive}))"
+      s" + $DecimalWriter.getSize(${ev.primitive})"
     case StringType =>
       s" + (${ev.isNull} ? 0 : $StringWriter.getSize(${ev.primitive}))"
     case BinaryType =>
@@ -76,41 +76,41 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       ctx: CodeGenContext,
       fieldType: DataType,
       ev: GeneratedExpressionCode,
-      primitive: String,
+      target: String,
       index: Int,
       cursor: String): String = fieldType match {
     case _ if ctx.isPrimitiveType(fieldType) =>
-      s"${ctx.setColumn(primitive, fieldType, index, ev.primitive)}"
+      s"${ctx.setColumn(target, fieldType, index, ev.primitive)}"
     case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
       s"""
        // make sure Decimal object has the same scale as DecimalType
        if (${ev.primitive}.changePrecision(${t.precision}, ${t.scale})) {
-         $CompactDecimalWriter.write($primitive, $index, $cursor, ${ev.primitive});
+         $CompactDecimalWriter.write($target, $index, $cursor, ${ev.primitive});
        } else {
-         $primitive.setNullAt($index);
+         $target.setNullAt($index);
        }
        """
     case t: DecimalType if t.precision > Decimal.MAX_LONG_DIGITS =>
       s"""
        // make sure Decimal object has the same scale as DecimalType
        if (${ev.primitive}.changePrecision(${t.precision}, ${t.scale})) {
-         $cursor += $DecimalWriter.write($primitive, $index, $cursor, ${ev.primitive});
+         $cursor += $DecimalWriter.write($target, $index, $cursor, ${ev.primitive});
        } else {
-         $primitive.setNullAt($index);
+         $cursor += $DecimalWriter.write($target, $index, $cursor, null);
        }
        """
     case StringType =>
-      s"$cursor += $StringWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $StringWriter.write($target, $index, $cursor, ${ev.primitive})"
     case BinaryType =>
-      s"$cursor += $BinaryWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $BinaryWriter.write($target, $index, $cursor, ${ev.primitive})"
     case CalendarIntervalType =>
-      s"$cursor += $IntervalWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $IntervalWriter.write($target, $index, $cursor, ${ev.primitive})"
     case _: StructType =>
-      s"$cursor += $StructWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $StructWriter.write($target, $index, $cursor, ${ev.primitive})"
     case _: ArrayType =>
-      s"$cursor += $ArrayWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $ArrayWriter.write($target, $index, $cursor, ${ev.primitive})"
     case _: MapType =>
-      s"$cursor += $MapWriter.write($primitive, $index, $cursor, ${ev.primitive})"
+      s"$cursor += $MapWriter.write($target, $index, $cursor, ${ev.primitive})"
     case NullType => ""
     case _ =>
       throw new UnsupportedOperationException(s"Not supported DataType: $fieldType")
@@ -146,13 +146,24 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     val fieldWriters = inputTypes.zip(convertedFields).zipWithIndex.map { case ((dt, ev), i) =>
       val update = genFieldWriter(ctx, dt, ev, output, i, cursor)
-      s"""
-        if (${ev.isNull}) {
-          $output.setNullAt($i);
-        } else {
-          $update;
-        }
-      """
+      if (dt.isInstanceOf[DecimalType]) {
+        // Can't call setNullAt() for DecimalType
+        s"""
+          if (${ev.isNull}) {
+           $cursor += $DecimalWriter.write($output, $i, $cursor, null);
+          } else {
+           $update;
+          }
+        """
+      } else {
+        s"""
+          if (${ev.isNull}) {
+            $output.setNullAt($i);
+          } else {
+            $update;
+          }
+        """
+      }
     }.mkString("\n")
 
     val code = s"""
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 5e5de1d1dc6a7..7657fb535dcf4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -20,7 +20,6 @@ package org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.
@@ -39,6 +38,13 @@ abstract class MutableRow extends InternalRow {
   def setLong(i: Int, value: Long): Unit = { update(i, value) }
   def setFloat(i: Int, value: Float): Unit = { update(i, value) }
   def setDouble(i: Int, value: Double): Unit = { update(i, value) }
+
+  /**
+   * Update the decimal column at `i`.
+   *
+   * Note: In order to support update decimal with precision > 18 in UnsafeRow,
+   * CAN NOT call setNullAt() for decimal column on UnsafeRow, call setDecimal(i, null, precision).
+   */
   def setDecimal(i: Int, value: Decimal, precision: Int) { update(i, value) }
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
index 59491c5ba160e..8c72203193630 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/UnsafeRowConverterSuite.scala
@@ -123,7 +123,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       DoubleType,
       StringType,
       BinaryType,
-      DecimalType.USER_DEFAULT
+      DecimalType.USER_DEFAULT,
+      DecimalType.SYSTEM_DEFAULT
       // ArrayType(IntegerType)
     )
     val converter = UnsafeProjection.create(fieldTypes)
@@ -151,6 +152,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(createdFromNull.getUTF8String(8) === null)
     assert(createdFromNull.getBinary(9) === null)
     assert(createdFromNull.getDecimal(10, 10, 0) === null)
+    assert(createdFromNull.getDecimal(11, 38, 18) === null)
     // assert(createdFromNull.get(11) === null)
 
     // If we have an UnsafeRow with columns that are initially non-null and we null out those
@@ -169,6 +171,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
       r.update(8, UTF8String.fromString("hello"))
       r.update(9, "world".getBytes)
       r.setDecimal(10, Decimal(10), 10)
+      r.setDecimal(11, Decimal(10.00, 38, 18), 38)
       // r.update(11, Array(11))
       r
     }
@@ -187,10 +190,17 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     assert(setToNullAfterCreation.getBinary(9) === rowWithNoNullColumns.getBinary(9))
     assert(setToNullAfterCreation.getDecimal(10, 10, 0) ===
       rowWithNoNullColumns.getDecimal(10, 10, 0))
+    assert(setToNullAfterCreation.getDecimal(11, 38, 18) ===
+      rowWithNoNullColumns.getDecimal(11, 38, 18))
     // assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
 
     for (i <- fieldTypes.indices) {
-      setToNullAfterCreation.setNullAt(i)
+      // Cann't call setNullAt() on DecimalType
+      if (i == 11) {
+        setToNullAfterCreation.setDecimal(11, null, 38)
+      } else {
+        setToNullAfterCreation.setNullAt(i)
+      }
     }
     // There are some garbage left in the var-length area
     assert(Arrays.equals(createdFromNull.getBytes, setToNullAfterCreation.getBytes()))
@@ -206,6 +216,7 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     // setToNullAfterCreation.update(8, UTF8String.fromString("hello"))
     // setToNullAfterCreation.update(9, "world".getBytes)
     setToNullAfterCreation.setDecimal(10, Decimal(10), 10)
+    setToNullAfterCreation.setDecimal(11, Decimal(10.00, 38, 18), 38)
     // setToNullAfterCreation.update(11, Array(11))
 
     assert(setToNullAfterCreation.isNullAt(0) === rowWithNoNullColumns.isNullAt(0))
@@ -220,6 +231,8 @@ class UnsafeRowConverterSuite extends SparkFunSuite with Matchers {
     // assert(setToNullAfterCreation.get(9) === rowWithNoNullColumns.get(9))
     assert(setToNullAfterCreation.getDecimal(10, 10, 0) ===
       rowWithNoNullColumns.getDecimal(10, 10, 0))
+    assert(setToNullAfterCreation.getDecimal(11, 38, 18) ===
+      rowWithNoNullColumns.getDecimal(11, 38, 18))
     // assert(setToNullAfterCreation.get(11) === rowWithNoNullColumns.get(11))
   }
 
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 43d06ce9bdfa3..02458030b00e9 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -72,7 +72,7 @@ public final class UnsafeFixedWidthAggregationMap {
    */
   public static boolean supportsAggregationBufferSchema(StructType schema) {
     for (StructField field: schema.fields()) {
-      if (!UnsafeRow.isFixedLength(field.dataType())) {
+      if (!UnsafeRow.isMutable(field.dataType())) {
         return false;
       }
     }
@@ -111,8 +111,6 @@ public UnsafeFixedWidthAggregationMap(
     // Initialize the buffer for aggregation value
     final UnsafeProjection valueProjection = UnsafeProjection.create(aggregationBufferSchema);
     this.emptyAggregationBuffer = valueProjection.apply(emptyAggregationBuffer).getBytes();
-    assert(this.emptyAggregationBuffer.length == aggregationBufferSchema.length() * 8 +
-      UnsafeRow.calculateBitSetWidthInBytes(aggregationBufferSchema.length()));
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
index 78bcee16c9d00..40f6bff53d2b7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
@@ -20,8 +20,6 @@ package org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression2, AggregateFunction2}
-import org.apache.spark.sql.execution.UnsafeFixedWidthAggregationMap
-import org.apache.spark.sql.types.StructType
 import org.apache.spark.unsafe.KVIterator
 
 /**
@@ -57,7 +55,7 @@ class SortBasedAggregationIterator(
     val bufferRowSize: Int = bufferSchema.length
 
     val genericMutableBuffer = new GenericMutableRow(bufferRowSize)
-    val useUnsafeBuffer = bufferSchema.map(_.dataType).forall(UnsafeRow.isFixedLength)
+    val useUnsafeBuffer = bufferSchema.map(_.dataType).forall(UnsafeRow.isMutable)
 
     val buffer = if (useUnsafeBuffer) {
       val unsafeProjection =
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index b513c970ccfe2..e03473041c3e9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -93,7 +93,7 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
   testWithMemoryLeakDetection("supported schemas") {
     assert(supportsAggregationBufferSchema(
       StructType(StructField("x", DecimalType.USER_DEFAULT) :: Nil)))
-    assert(!supportsAggregationBufferSchema(
+    assert(supportsAggregationBufferSchema(
       StructType(StructField("x", DecimalType.SYSTEM_DEFAULT) :: Nil)))
     assert(!supportsAggregationBufferSchema(StructType(StructField("x", StringType) :: Nil)))
     assert(
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java
index 192c6714b2406..b2de2a2590f05 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java
@@ -18,6 +18,7 @@
 package org.apache.spark.unsafe;
 
 import java.lang.reflect.Field;
+import java.math.BigInteger;
 
 import sun.misc.Unsafe;
 
@@ -87,6 +88,14 @@ public static void putDouble(Object object, long offset, double value) {
       _UNSAFE.putDouble(object, offset, value);
     }
 
+    public static Object getObjectVolatile(Object object, long offset) {
+      return _UNSAFE.getObjectVolatile(object, offset);
+    }
+
+    public static void putObjectVolatile(Object object, long offset, Object value) {
+      _UNSAFE.putObjectVolatile(object, offset, value);
+    }
+
     public static long allocateMemory(long size) {
       return _UNSAFE.allocateMemory(size);
     }
@@ -107,6 +116,10 @@ public static void freeMemory(long address) {
 
   public static final int DOUBLE_ARRAY_OFFSET;
 
+  // Support for resetting final fields while deserializing
+  public static final long BIG_INTEGER_SIGNUM_OFFSET;
+  public static final long BIG_INTEGER_MAG_OFFSET;
+
   /**
    * Limits the number of bytes to copy per {@link Unsafe#copyMemory(long, long, long)} to
    * allow safepoint polling during a large copy.
@@ -129,11 +142,24 @@ public static void freeMemory(long address) {
       INT_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(int[].class);
       LONG_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(long[].class);
       DOUBLE_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(double[].class);
+
+      long signumOffset = 0;
+      long magOffset = 0;
+      try {
+        signumOffset = _UNSAFE.objectFieldOffset(BigInteger.class.getDeclaredField("signum"));
+        magOffset = _UNSAFE.objectFieldOffset(BigInteger.class.getDeclaredField("mag"));
+      } catch (Exception ex) {
+        // should not happen
+      }
+      BIG_INTEGER_SIGNUM_OFFSET = signumOffset;
+      BIG_INTEGER_MAG_OFFSET = magOffset;
     } else {
       BYTE_ARRAY_OFFSET = 0;
       INT_ARRAY_OFFSET = 0;
       LONG_ARRAY_OFFSET = 0;
       DOUBLE_ARRAY_OFFSET = 0;
+      BIG_INTEGER_SIGNUM_OFFSET = 0;
+      BIG_INTEGER_MAG_OFFSET = 0;
     }
   }
 

From 93085c992e40dbc06714cb1a64c838e25e683a6f Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 6 Aug 2015 09:12:41 -0700
Subject: [PATCH 080/633] [SPARK-9482] [SQL] Fix thread-safey issue of using
 UnsafeProjection in join

This PR also change to use `def` instead of `lazy val` for UnsafeProjection, because it's not thread safe.

TODO: cleanup the debug code once the flaky test passed 100 times.

Author: Davies Liu <davies@databricks.com>

Closes #7940 from davies/semijoin and squashes the following commits:

93baac7 [Davies Liu] fix outerjoin
5c40ded [Davies Liu] address comments
aa3de46 [Davies Liu] Merge branch 'master' of github.com:apache/spark into semijoin
7590a25 [Davies Liu] Merge branch 'master' of github.com:apache/spark into semijoin
2d4085b [Davies Liu] use def for resultProjection
0833407 [Davies Liu] Merge branch 'semijoin' of github.com:davies/spark into semijoin
e0d8c71 [Davies Liu] use lazy val
6a59e8f [Davies Liu] Update HashedRelation.scala
0fdacaf [Davies Liu] fix broadcast and thread-safety of UnsafeProjection
2fc3ef6 [Davies Liu] reproduce failure in semijoin
---
 .../execution/joins/BroadcastHashJoin.scala   |  6 ++---
 .../joins/BroadcastHashOuterJoin.scala        | 20 ++++++----------
 .../joins/BroadcastNestedLoopJoin.scala       | 17 ++++++++------
 .../spark/sql/execution/joins/HashJoin.scala  |  4 ++--
 .../sql/execution/joins/HashOuterJoin.scala   | 23 ++++++++++---------
 .../sql/execution/joins/HashSemiJoin.scala    |  8 +++----
 .../sql/execution/joins/HashedRelation.scala  |  4 ++--
 .../joins/ShuffledHashOuterJoin.scala         |  6 +++--
 8 files changed, 44 insertions(+), 44 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
index ec1a148342fc6..f7a68e4f5d445 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
@@ -20,14 +20,14 @@ package org.apache.spark.sql.execution.joins
 import scala.concurrent._
 import scala.concurrent.duration._
 
-import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution}
-import org.apache.spark.sql.execution.{BinaryNode, SparkPlan, SQLExecution}
+import org.apache.spark.sql.execution.{BinaryNode, SQLExecution, SparkPlan}
 import org.apache.spark.util.ThreadUtils
+import org.apache.spark.{InternalAccumulator, TaskContext}
 
 /**
  * :: DeveloperApi ::
@@ -102,6 +102,6 @@ case class BroadcastHashJoin(
 
 object BroadcastHashJoin {
 
-  private val broadcastHashJoinExecutionContext = ExecutionContext.fromExecutorService(
+  private[joins] val broadcastHashJoinExecutionContext = ExecutionContext.fromExecutorService(
     ThreadUtils.newDaemonCachedThreadPool("broadcast-hash-join", 128))
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index e342fd914d321..a3626de49aeab 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -20,15 +20,14 @@ package org.apache.spark.sql.execution.joins
 import scala.concurrent._
 import scala.concurrent.duration._
 
-import org.apache.spark.{InternalAccumulator, TaskContext}
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, Distribution, UnspecifiedDistribution}
+import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution}
 import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
-import org.apache.spark.sql.execution.{BinaryNode, SparkPlan, SQLExecution}
-import org.apache.spark.util.ThreadUtils
+import org.apache.spark.sql.execution.{BinaryNode, SQLExecution, SparkPlan}
+import org.apache.spark.{InternalAccumulator, TaskContext}
 
 /**
  * :: DeveloperApi ::
@@ -76,7 +75,7 @@ case class BroadcastHashOuterJoin(
         val hashed = HashedRelation(input.iterator, buildKeyGenerator, input.size)
         sparkContext.broadcast(hashed)
       }
-    }(BroadcastHashOuterJoin.broadcastHashOuterJoinExecutionContext)
+    }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
   }
 
   protected override def doPrepare(): Unit = {
@@ -98,19 +97,20 @@ case class BroadcastHashOuterJoin(
         case _ =>
       }
 
+      val resultProj = resultProjection
       joinType match {
         case LeftOuter =>
           streamedIter.flatMap(currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withLeft(currentRow)
-            leftOuterIterator(rowKey, joinedRow, hashTable.get(rowKey))
+            leftOuterIterator(rowKey, joinedRow, hashTable.get(rowKey), resultProj)
           })
 
         case RightOuter =>
           streamedIter.flatMap(currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withRight(currentRow)
-            rightOuterIterator(rowKey, hashTable.get(rowKey), joinedRow)
+            rightOuterIterator(rowKey, hashTable.get(rowKey), joinedRow, resultProj)
           })
 
         case x =>
@@ -120,9 +120,3 @@ case class BroadcastHashOuterJoin(
     }
   }
 }
-
-object BroadcastHashOuterJoin {
-
-  private val broadcastHashOuterJoinExecutionContext = ExecutionContext.fromExecutorService(
-    ThreadUtils.newDaemonCachedThreadPool("broadcast-hash-outer-join", 128))
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
index 83b726a8e2897..23aebf4b068b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
@@ -47,7 +47,7 @@ case class BroadcastNestedLoopJoin(
   override def outputsUnsafeRows: Boolean = left.outputsUnsafeRows || right.outputsUnsafeRows
   override def canProcessUnsafeRows: Boolean = true
 
-  @transient private[this] lazy val resultProjection: InternalRow => InternalRow = {
+  private[this] def genResultProjection: InternalRow => InternalRow = {
     if (outputsUnsafeRows) {
       UnsafeProjection.create(schema)
     } else {
@@ -88,6 +88,7 @@ case class BroadcastNestedLoopJoin(
 
       val leftNulls = new GenericMutableRow(left.output.size)
       val rightNulls = new GenericMutableRow(right.output.size)
+      val resultProj = genResultProjection
 
       streamedIter.foreach { streamedRow =>
         var i = 0
@@ -97,11 +98,11 @@ case class BroadcastNestedLoopJoin(
           val broadcastedRow = broadcastedRelation.value(i)
           buildSide match {
             case BuildRight if boundCondition(joinedRow(streamedRow, broadcastedRow)) =>
-              matchedRows += resultProjection(joinedRow(streamedRow, broadcastedRow)).copy()
+              matchedRows += resultProj(joinedRow(streamedRow, broadcastedRow)).copy()
               streamRowMatched = true
               includedBroadcastTuples += i
             case BuildLeft if boundCondition(joinedRow(broadcastedRow, streamedRow)) =>
-              matchedRows += resultProjection(joinedRow(broadcastedRow, streamedRow)).copy()
+              matchedRows += resultProj(joinedRow(broadcastedRow, streamedRow)).copy()
               streamRowMatched = true
               includedBroadcastTuples += i
             case _ =>
@@ -111,9 +112,9 @@ case class BroadcastNestedLoopJoin(
 
         (streamRowMatched, joinType, buildSide) match {
           case (false, LeftOuter | FullOuter, BuildRight) =>
-            matchedRows += resultProjection(joinedRow(streamedRow, rightNulls)).copy()
+            matchedRows += resultProj(joinedRow(streamedRow, rightNulls)).copy()
           case (false, RightOuter | FullOuter, BuildLeft) =>
-            matchedRows += resultProjection(joinedRow(leftNulls, streamedRow)).copy()
+            matchedRows += resultProj(joinedRow(leftNulls, streamedRow)).copy()
           case _ =>
         }
       }
@@ -127,6 +128,8 @@ case class BroadcastNestedLoopJoin(
 
     val leftNulls = new GenericMutableRow(left.output.size)
     val rightNulls = new GenericMutableRow(right.output.size)
+    val resultProj = genResultProjection
+
     /** Rows from broadcasted joined with nulls. */
     val broadcastRowsWithNulls: Seq[InternalRow] = {
       val buf: CompactBuffer[InternalRow] = new CompactBuffer()
@@ -138,7 +141,7 @@ case class BroadcastNestedLoopJoin(
           joinedRow.withLeft(leftNulls)
           while (i < rel.length) {
             if (!allIncludedBroadcastTuples.contains(i)) {
-              buf += resultProjection(joinedRow.withRight(rel(i))).copy()
+              buf += resultProj(joinedRow.withRight(rel(i))).copy()
             }
             i += 1
           }
@@ -147,7 +150,7 @@ case class BroadcastNestedLoopJoin(
           joinedRow.withRight(rightNulls)
           while (i < rel.length) {
             if (!allIncludedBroadcastTuples.contains(i)) {
-              buf += resultProjection(joinedRow.withLeft(rel(i))).copy()
+              buf += resultProj(joinedRow.withLeft(rel(i))).copy()
             }
             i += 1
           }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 6b3d1652923fd..5e9cd9fd2345a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -52,14 +52,14 @@ trait HashJoin {
   override def canProcessUnsafeRows: Boolean = isUnsafeMode
   override def canProcessSafeRows: Boolean = !isUnsafeMode
 
-  @transient protected lazy val buildSideKeyGenerator: Projection =
+  protected def buildSideKeyGenerator: Projection =
     if (isUnsafeMode) {
       UnsafeProjection.create(buildKeys, buildPlan.output)
     } else {
       newMutableProjection(buildKeys, buildPlan.output)()
     }
 
-  @transient protected lazy val streamSideKeyGenerator: Projection =
+  protected def streamSideKeyGenerator: Projection =
     if (isUnsafeMode) {
       UnsafeProjection.create(streamedKeys, streamedPlan.output)
     } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index a323aea4ea2c4..346337e64245c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -76,14 +76,14 @@ trait HashOuterJoin {
   override def canProcessUnsafeRows: Boolean = isUnsafeMode
   override def canProcessSafeRows: Boolean = !isUnsafeMode
 
-  @transient protected lazy val buildKeyGenerator: Projection =
+  protected def buildKeyGenerator: Projection =
     if (isUnsafeMode) {
       UnsafeProjection.create(buildKeys, buildPlan.output)
     } else {
       newMutableProjection(buildKeys, buildPlan.output)()
     }
 
-  @transient protected[this] lazy val streamedKeyGenerator: Projection = {
+  protected[this] def streamedKeyGenerator: Projection = {
     if (isUnsafeMode) {
       UnsafeProjection.create(streamedKeys, streamedPlan.output)
     } else {
@@ -91,7 +91,7 @@ trait HashOuterJoin {
     }
   }
 
-  @transient private[this] lazy val resultProjection: InternalRow => InternalRow = {
+  protected[this] def resultProjection: InternalRow => InternalRow = {
     if (isUnsafeMode) {
       UnsafeProjection.create(self.schema)
     } else {
@@ -113,7 +113,8 @@ trait HashOuterJoin {
   protected[this] def leftOuterIterator(
       key: InternalRow,
       joinedRow: JoinedRow,
-      rightIter: Iterable[InternalRow]): Iterator[InternalRow] = {
+      rightIter: Iterable[InternalRow],
+      resultProjection: InternalRow => InternalRow): Iterator[InternalRow] = {
     val ret: Iterable[InternalRow] = {
       if (!key.anyNull) {
         val temp = if (rightIter != null) {
@@ -124,12 +125,12 @@ trait HashOuterJoin {
           List.empty
         }
         if (temp.isEmpty) {
-          resultProjection(joinedRow.withRight(rightNullRow)).copy :: Nil
+          resultProjection(joinedRow.withRight(rightNullRow)) :: Nil
         } else {
           temp
         }
       } else {
-        resultProjection(joinedRow.withRight(rightNullRow)).copy :: Nil
+        resultProjection(joinedRow.withRight(rightNullRow)) :: Nil
       }
     }
     ret.iterator
@@ -138,24 +139,24 @@ trait HashOuterJoin {
   protected[this] def rightOuterIterator(
       key: InternalRow,
       leftIter: Iterable[InternalRow],
-      joinedRow: JoinedRow): Iterator[InternalRow] = {
+      joinedRow: JoinedRow,
+      resultProjection: InternalRow => InternalRow): Iterator[InternalRow] = {
     val ret: Iterable[InternalRow] = {
       if (!key.anyNull) {
         val temp = if (leftIter != null) {
           leftIter.collect {
-            case l if boundCondition(joinedRow.withLeft(l)) =>
-              resultProjection(joinedRow).copy()
+            case l if boundCondition(joinedRow.withLeft(l)) => resultProjection(joinedRow).copy()
           }
         } else {
           List.empty
         }
         if (temp.isEmpty) {
-          resultProjection(joinedRow.withLeft(leftNullRow)).copy :: Nil
+          resultProjection(joinedRow.withLeft(leftNullRow)) :: Nil
         } else {
           temp
         }
       } else {
-        resultProjection(joinedRow.withLeft(leftNullRow)).copy :: Nil
+        resultProjection(joinedRow.withLeft(leftNullRow)) :: Nil
       }
     }
     ret.iterator
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 97fde8f975bfd..47a7d370f5415 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -43,14 +43,14 @@ trait HashSemiJoin {
   override def canProcessUnsafeRows: Boolean = supportUnsafe
   override def canProcessSafeRows: Boolean = !supportUnsafe
 
-  @transient protected lazy val leftKeyGenerator: Projection =
+  protected def leftKeyGenerator: Projection =
     if (supportUnsafe) {
       UnsafeProjection.create(leftKeys, left.output)
     } else {
       newMutableProjection(leftKeys, left.output)()
     }
 
-  @transient protected lazy val rightKeyGenerator: Projection =
+  protected def rightKeyGenerator: Projection =
     if (supportUnsafe) {
       UnsafeProjection.create(rightKeys, right.output)
     } else {
@@ -62,12 +62,11 @@ trait HashSemiJoin {
 
   protected def buildKeyHashSet(buildIter: Iterator[InternalRow]): java.util.Set[InternalRow] = {
     val hashSet = new java.util.HashSet[InternalRow]()
-    var currentRow: InternalRow = null
 
     // Create a Hash set of buildKeys
     val rightKey = rightKeyGenerator
     while (buildIter.hasNext) {
-      currentRow = buildIter.next()
+      val currentRow = buildIter.next()
       val rowKey = rightKey(currentRow)
       if (!rowKey.anyNull) {
         val keyExists = hashSet.contains(rowKey)
@@ -76,6 +75,7 @@ trait HashSemiJoin {
         }
       }
     }
+
     hashSet
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 58b4236f7b5b5..3f257ecdd156c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -17,12 +17,11 @@
 
 package org.apache.spark.sql.execution.joins
 
-import java.io.{IOException, Externalizable, ObjectInput, ObjectOutput}
+import java.io.{Externalizable, IOException, ObjectInput, ObjectOutput}
 import java.nio.ByteOrder
 import java.util.{HashMap => JavaHashMap}
 
 import org.apache.spark.shuffle.ShuffleMemoryManager
-import org.apache.spark.{SparkConf, SparkEnv, TaskContext}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkSqlSerializer
@@ -31,6 +30,7 @@ import org.apache.spark.unsafe.map.BytesToBytesMap
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.util.Utils
 import org.apache.spark.util.collection.CompactBuffer
+import org.apache.spark.{SparkConf, SparkEnv}
 
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
index eee8ad800f98e..6a8c35efca8f4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
@@ -60,19 +60,21 @@ case class ShuffledHashOuterJoin(
         case LeftOuter =>
           val hashed = HashedRelation(rightIter, buildKeyGenerator)
           val keyGenerator = streamedKeyGenerator
+          val resultProj = resultProjection
           leftIter.flatMap( currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withLeft(currentRow)
-            leftOuterIterator(rowKey, joinedRow, hashed.get(rowKey))
+            leftOuterIterator(rowKey, joinedRow, hashed.get(rowKey), resultProj)
           })
 
         case RightOuter =>
           val hashed = HashedRelation(leftIter, buildKeyGenerator)
           val keyGenerator = streamedKeyGenerator
+          val resultProj = resultProjection
           rightIter.flatMap ( currentRow => {
             val rowKey = keyGenerator(currentRow)
             joinedRow.withRight(currentRow)
-            rightOuterIterator(rowKey, hashed.get(rowKey), joinedRow)
+            rightOuterIterator(rowKey, hashed.get(rowKey), joinedRow, resultProj)
           })
 
         case FullOuter =>

From 9f94c85ff35df6289371f80edde51c2aa6c4bcdc Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 6 Aug 2015 09:53:53 -0700
Subject: [PATCH 081/633] [SPARK-9593] [SQL] [HOTFIX] Makes the Hadoop shims
 loading fix more robust

This is a follow-up of #7929.

We found that Jenkins SBT master build still fails because of the Hadoop shims loading issue. But the failure doesn't appear to be deterministic. My suspect is that Hadoop `VersionInfo` class may fail to inspect Hadoop version, and the shims loading branch is skipped.

This PR tries to make the fix more robust:

1. When Hadoop version is available, we load `Hadoop20SShims` for versions <= 2.0.x as srowen suggested in PR #7929.
2. Otherwise, we use `Path.getPathWithoutSchemeAndAuthority` as a probe method, which doesn't exist in Hadoop 1.x or 2.0.x. If this method is not found, `Hadoop20SShims` is also loaded.

Author: Cheng Lian <lian@databricks.com>

Closes #7994 from liancheng/spark-9593/fix-hadoop-shims and squashes the following commits:

e1d3d70 [Cheng Lian] Fixes typo in comments
8d971da [Cheng Lian] Makes the Hadoop shims loading fix more robust
---
 .../spark/sql/hive/client/ClientWrapper.scala | 88 ++++++++++++-------
 1 file changed, 55 insertions(+), 33 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 211a3b879c1b3..3d05b583cf9e0 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -68,45 +68,67 @@ private[hive] class ClientWrapper(
 
   // !! HACK ALERT !!
   //
-  // This method is a surgical fix for Hadoop version 2.0.0-mr1-cdh4.1.1, which is used by Spark EC2
-  // scripts.  We should remove this after upgrading Spark EC2 scripts to some more recent Hadoop
-  // version in the future.
-  //
   // Internally, Hive `ShimLoader` tries to load different versions of Hadoop shims by checking
-  // version information gathered from Hadoop jar files.  If the major version number is 1,
-  // `Hadoop20SShims` will be loaded.  Otherwise, if the major version number is 2, `Hadoop23Shims`
-  // will be chosen.
+  // major version number gathered from Hadoop jar files:
+  //
+  // - For major version number 1, load `Hadoop20SShims`, where "20S" stands for Hadoop 0.20 with
+  //   security.
+  // - For major version number 2, load `Hadoop23Shims`, where "23" stands for Hadoop 0.23.
   //
-  // However, part of APIs in Hadoop 2.0.x and 2.1.x versions were in flux due to historical
-  // reasons. So 2.0.0-mr1-cdh4.1.1 is actually more Hadoop-1-like and should be used together with
-  // `Hadoop20SShims`, but `Hadoop20SShims` is chose because the major version number here is 2.
+  // However, APIs in Hadoop 2.0.x and 2.1.x versions were in flux due to historical reasons. It
+  // turns out that Hadoop 2.0.x versions should also be used together with `Hadoop20SShims`, but
+  // `Hadoop23Shims` is chosen because the major version number here is 2.
   //
-  // Here we check for this specific version and loads `Hadoop20SShims` via reflection.  Note that
-  // we can't check for string literal "2.0.0-mr1-cdh4.1.1" because the obtained version string
-  // comes from Maven artifact org.apache.hadoop:hadoop-common:2.0.0-cdh4.1.1, which doesn't have
-  // the "mr1" tag in its version string.
+  // To fix this issue, we try to inspect Hadoop version via `org.apache.hadoop.utils.VersionInfo`
+  // and load `Hadoop20SShims` for Hadoop 1.x and 2.0.x versions.  If Hadoop version information is
+  // not available, we decide whether to override the shims or not by checking for existence of a
+  // probe method which doesn't exist in Hadoop 1.x or 2.0.x versions.
   private def overrideHadoopShims(): Unit = {
-    val VersionPattern = """2\.0\.0.*cdh4.*""".r
-
-    VersionInfo.getVersion match {
-      case VersionPattern() =>
-        val shimClassName = "org.apache.hadoop.hive.shims.Hadoop20SShims"
-        logInfo(s"Loading Hadoop shims $shimClassName")
-
-        try {
-          val shimsField = classOf[ShimLoader].getDeclaredField("hadoopShims")
-          // scalastyle:off classforname
-          val shimsClass = Class.forName(shimClassName)
-          // scalastyle:on classforname
-          val shims = classOf[HadoopShims].cast(shimsClass.newInstance())
-          shimsField.setAccessible(true)
-          shimsField.set(null, shims)
-        } catch { case cause: Throwable =>
-          logError(s"Failed to load $shimClassName")
-          // Falls back to normal Hive `ShimLoader` logic
+    val hadoopVersion = VersionInfo.getVersion
+    val VersionPattern = """(\d+)\.(\d+).*""".r
+
+    hadoopVersion match {
+      case null =>
+        logError("Failed to inspect Hadoop version")
+
+        // Using "Path.getPathWithoutSchemeAndAuthority" as the probe method.
+        val probeMethod = "getPathWithoutSchemeAndAuthority"
+        if (!classOf[Path].getDeclaredMethods.exists(_.getName == probeMethod)) {
+          logInfo(
+            s"Method ${classOf[Path].getCanonicalName}.$probeMethod not found, " +
+              s"we are probably using Hadoop 1.x or 2.0.x")
+          loadHadoop20SShims()
+        }
+
+      case VersionPattern(majorVersion, minorVersion) =>
+        logInfo(s"Inspected Hadoop version: $hadoopVersion")
+
+        // Loads Hadoop20SShims for 1.x and 2.0.x versions
+        val (major, minor) = (majorVersion.toInt, minorVersion.toInt)
+        if (major < 2 || (major == 2 && minor == 0)) {
+          loadHadoop20SShims()
         }
+    }
+
+    // Logs the actual loaded Hadoop shims class
+    val loadedShimsClassName = ShimLoader.getHadoopShims.getClass.getCanonicalName
+    logInfo(s"Loaded $loadedShimsClassName for Hadoop version $hadoopVersion")
+  }
 
-      case _ =>
+  private def loadHadoop20SShims(): Unit = {
+    val hadoop20SShimsClassName = "org.apache.hadoop.hive.shims.Hadoop20SShims"
+    logInfo(s"Loading Hadoop shims $hadoop20SShimsClassName")
+
+    try {
+      val shimsField = classOf[ShimLoader].getDeclaredField("hadoopShims")
+      // scalastyle:off classforname
+      val shimsClass = Class.forName(hadoop20SShimsClassName)
+      // scalastyle:on classforname
+      val shims = classOf[HadoopShims].cast(shimsClass.newInstance())
+      shimsField.setAccessible(true)
+      shimsField.set(null, shims)
+    } catch { case cause: Throwable =>
+      throw new RuntimeException(s"Failed to load $hadoop20SShimsClassName", cause)
     }
   }
 

From c5c6aded641048a3e66ac79d9e84d34e4b1abae7 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 6 Aug 2015 10:08:33 -0700
Subject: [PATCH 082/633] [SPARK-9112] [ML] Implement Stats for
 LogisticRegression

I have added support for stats in LogisticRegression. The API is similar to that of LinearRegression with LogisticRegressionTrainingSummary and LogisticRegressionSummary

I have some queries and asked them inline.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7538 from MechCoder/log_reg_stats and squashes the following commits:

2e9f7c7 [MechCoder] Change defs into lazy vals
d775371 [MechCoder] Clean up class inheritance
9586125 [MechCoder] Add abstraction to handle Multiclass Metrics
40ad8ef [MechCoder] minor
640376a [MechCoder] remove unnecessary dataframe stuff and add docs
80d9954 [MechCoder] Added tests
fbed861 [MechCoder] DataFrame support for metrics
70a0fc4 [MechCoder] [SPARK-9112] [ML] Implement Stats for LogisticRegression
---
 .../classification/LogisticRegression.scala   | 166 +++++++++++++++++-
 .../JavaLogisticRegressionSuite.java          |   9 +
 .../LogisticRegressionSuite.scala             |  37 +++-
 3 files changed, 209 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 0d073839259c6..f55134d258857 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -30,10 +30,12 @@ import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.linalg.BLAS._
 import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+import org.apache.spark.sql.functions.{col, udf}
 import org.apache.spark.storage.StorageLevel
 
 /**
@@ -284,7 +286,13 @@ class LogisticRegression(override val uid: String)
 
     if (handlePersistence) instances.unpersist()
 
-    copyValues(new LogisticRegressionModel(uid, weights, intercept))
+    val model = copyValues(new LogisticRegressionModel(uid, weights, intercept))
+    val logRegSummary = new BinaryLogisticRegressionTrainingSummary(
+      model.transform(dataset),
+      $(probabilityCol),
+      $(labelCol),
+      objectiveHistory)
+    model.setSummary(logRegSummary)
   }
 
   override def copy(extra: ParamMap): LogisticRegression = defaultCopy(extra)
@@ -319,6 +327,38 @@ class LogisticRegressionModel private[ml] (
 
   override val numClasses: Int = 2
 
+  private var trainingSummary: Option[LogisticRegressionTrainingSummary] = None
+
+  /**
+   * Gets summary of model on training set. An exception is
+   * thrown if `trainingSummary == None`.
+   */
+  def summary: LogisticRegressionTrainingSummary = trainingSummary match {
+    case Some(summ) => summ
+    case None =>
+      throw new SparkException(
+        "No training summary available for this LogisticRegressionModel",
+        new NullPointerException())
+  }
+
+  private[classification] def setSummary(
+      summary: LogisticRegressionTrainingSummary): this.type = {
+    this.trainingSummary = Some(summary)
+    this
+  }
+
+  /** Indicates whether a training summary exists for this model instance. */
+  def hasSummary: Boolean = trainingSummary.isDefined
+
+  /**
+   * Evaluates the model on a testset.
+   * @param dataset Test dataset to evaluate model on.
+   */
+  // TODO: decide on a good name before exposing to public API
+  private[classification] def evaluate(dataset: DataFrame): LogisticRegressionSummary = {
+    new BinaryLogisticRegressionSummary(this.transform(dataset), $(probabilityCol), $(labelCol))
+  }
+
   /**
    * Predict label for the given feature vector.
    * The behavior of this can be adjusted using [[thresholds]].
@@ -440,6 +480,128 @@ private[classification] class MultiClassSummarizer extends Serializable {
   }
 }
 
+/**
+ * Abstraction for multinomial Logistic Regression Training results.
+ */
+sealed trait LogisticRegressionTrainingSummary extends LogisticRegressionSummary {
+
+  /** objective function (scaled loss + regularization) at each iteration. */
+  def objectiveHistory: Array[Double]
+
+  /** Number of training iterations until termination */
+  def totalIterations: Int = objectiveHistory.length
+
+}
+
+/**
+ * Abstraction for Logistic Regression Results for a given model.
+ */
+sealed trait LogisticRegressionSummary extends Serializable {
+
+  /** Dataframe outputted by the model's `transform` method. */
+  def predictions: DataFrame
+
+  /** Field in "predictions" which gives the calibrated probability of each sample as a vector. */
+  def probabilityCol: String
+
+  /** Field in "predictions" which gives the the true label of each sample. */
+  def labelCol: String
+
+}
+
+/**
+ * :: Experimental ::
+ * Logistic regression training results.
+ * @param predictions dataframe outputted by the model's `transform` method.
+ * @param probabilityCol field in "predictions" which gives the calibrated probability of
+ *                       each sample as a vector.
+ * @param labelCol field in "predictions" which gives the true label of each sample.
+ * @param objectiveHistory objective function (scaled loss + regularization) at each iteration.
+ */
+@Experimental
+class BinaryLogisticRegressionTrainingSummary private[classification] (
+    predictions: DataFrame,
+    probabilityCol: String,
+    labelCol: String,
+    val objectiveHistory: Array[Double])
+  extends BinaryLogisticRegressionSummary(predictions, probabilityCol, labelCol)
+  with LogisticRegressionTrainingSummary {
+
+}
+
+/**
+ * :: Experimental ::
+ * Binary Logistic regression results for a given model.
+ * @param predictions dataframe outputted by the model's `transform` method.
+ * @param probabilityCol field in "predictions" which gives the calibrated probability of
+ *                       each sample.
+ * @param labelCol field in "predictions" which gives the true label of each sample.
+ */
+@Experimental
+class BinaryLogisticRegressionSummary private[classification] (
+    @transient override val predictions: DataFrame,
+    override val probabilityCol: String,
+    override val labelCol: String) extends LogisticRegressionSummary {
+
+  private val sqlContext = predictions.sqlContext
+  import sqlContext.implicits._
+
+  /**
+   * Returns a BinaryClassificationMetrics object.
+   */
+  // TODO: Allow the user to vary the number of bins using a setBins method in
+  // BinaryClassificationMetrics. For now the default is set to 100.
+  @transient private val binaryMetrics = new BinaryClassificationMetrics(
+    predictions.select(probabilityCol, labelCol).map {
+      case Row(score: Vector, label: Double) => (score(1), label)
+    }, 100
+  )
+
+  /**
+   * Returns the receiver operating characteristic (ROC) curve,
+   * which is an Dataframe having two fields (FPR, TPR)
+   * with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
+   * @see http://en.wikipedia.org/wiki/Receiver_operating_characteristic
+   */
+  @transient lazy val roc: DataFrame = binaryMetrics.roc().toDF("FPR", "TPR")
+
+  /**
+   * Computes the area under the receiver operating characteristic (ROC) curve.
+   */
+  lazy val areaUnderROC: Double = binaryMetrics.areaUnderROC()
+
+  /**
+   * Returns the precision-recall curve, which is an Dataframe containing
+   * two fields recall, precision with (0.0, 1.0) prepended to it.
+   */
+  @transient lazy val pr: DataFrame = binaryMetrics.pr().toDF("recall", "precision")
+
+  /**
+   * Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.
+   */
+  @transient lazy val fMeasureByThreshold: DataFrame = {
+    binaryMetrics.fMeasureByThreshold().toDF("threshold", "F-Measure")
+  }
+
+  /**
+   * Returns a dataframe with two fields (threshold, precision) curve.
+   * Every possible probability obtained in transforming the dataset are used
+   * as thresholds used in calculating the precision.
+   */
+  @transient lazy val precisionByThreshold: DataFrame = {
+    binaryMetrics.precisionByThreshold().toDF("threshold", "precision")
+  }
+
+  /**
+   * Returns a dataframe with two fields (threshold, recall) curve.
+   * Every possible probability obtained in transforming the dataset are used
+   * as thresholds used in calculating the recall.
+   */
+  @transient lazy val recallByThreshold: DataFrame = {
+    binaryMetrics.recallByThreshold().toDF("threshold", "recall")
+  }
+}
+
 /**
  * LogisticAggregator computes the gradient and loss for binary logistic loss function, as used
  * in binary classification for samples in sparse or dense vector in a online fashion.
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index fb1de51163f2e..7e9aa383728f0 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -152,4 +152,13 @@ public void logisticRegressionPredictorClassifierMethods() {
       }
     }
   }
+
+  @Test
+  public void logisticRegressionTrainingSummary() {
+    LogisticRegression lr = new LogisticRegression();
+    LogisticRegressionModel model = lr.fit(dataset);
+
+    LogisticRegressionTrainingSummary summary = model.summary();
+    assert(summary.totalIterations() == summary.objectiveHistory().length);
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index da13dcb42d1ca..8c3d4590f5ae9 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -723,6 +723,41 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val weightsR = Vectors.dense(0.0, 0.0, 0.0, 0.0)
 
     assert(model1.intercept ~== interceptR relTol 1E-5)
-    assert(model1.weights ~= weightsR absTol 1E-6)
+    assert(model1.weights ~== weightsR absTol 1E-6)
+  }
+
+  test("evaluate on test set") {
+    // Evaluate on test set should be same as that of the transformed training data.
+    val lr = new LogisticRegression()
+      .setMaxIter(10)
+      .setRegParam(1.0)
+      .setThreshold(0.6)
+    val model = lr.fit(dataset)
+    val summary = model.summary.asInstanceOf[BinaryLogisticRegressionSummary]
+
+    val sameSummary = model.evaluate(dataset).asInstanceOf[BinaryLogisticRegressionSummary]
+    assert(summary.areaUnderROC === sameSummary.areaUnderROC)
+    assert(summary.roc.collect() === sameSummary.roc.collect())
+    assert(summary.pr.collect === sameSummary.pr.collect())
+    assert(
+      summary.fMeasureByThreshold.collect() === sameSummary.fMeasureByThreshold.collect())
+    assert(summary.recallByThreshold.collect() === sameSummary.recallByThreshold.collect())
+    assert(
+      summary.precisionByThreshold.collect() === sameSummary.precisionByThreshold.collect())
+  }
+
+  test("statistics on training data") {
+    // Test that loss is monotonically decreasing.
+    val lr = new LogisticRegression()
+      .setMaxIter(10)
+      .setRegParam(1.0)
+      .setThreshold(0.6)
+    val model = lr.fit(dataset)
+    assert(
+      model.summary
+        .objectiveHistory
+        .sliding(2)
+        .forall(x => x(0) >= x(1)))
+
   }
 }

From 076ec056818a65216eaf51aa5b3bd8f697c34748 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 6 Aug 2015 10:09:58 -0700
Subject: [PATCH 083/633] [SPARK-9533] [PYSPARK] [ML] Add missing methods in
 Word2Vec ML

After https://github.com/apache/spark/pull/7263 it is pretty straightforward to Python wrappers.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #7930 from MechCoder/spark-9533 and squashes the following commits:

1bea394 [MechCoder] make getVectors a lazy val
5522756 [MechCoder] [SPARK-9533] [PySpark] [ML] Add missing methods in Word2Vec ML
---
 .../apache/spark/ml/feature/Word2Vec.scala    |  2 +-
 python/pyspark/ml/feature.py                  | 40 +++++++++++++++++++
 2 files changed, 41 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index b4f46cef798dd..29acc3eb5865f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -153,7 +153,7 @@ class Word2VecModel private[ml] (
    * Returns a dataframe with two fields, "word" and "vector", with "word" being a String and
    * and the vector the DenseVector that it is mapped to.
    */
-  val getVectors: DataFrame = {
+  @transient lazy val getVectors: DataFrame = {
     val sc = SparkContext.getOrCreate()
     val sqlContext = SQLContext.getOrCreate(sc)
     import sqlContext.implicits._
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 3f04c41ac5ab6..cb4dfa21298ce 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -15,11 +15,16 @@
 # limitations under the License.
 #
 
+import sys
+if sys.version > '3':
+    basestring = str
+
 from pyspark.rdd import ignore_unicode_prefix
 from pyspark.ml.param.shared import *
 from pyspark.ml.util import keyword_only
 from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer
 from pyspark.mllib.common import inherit_doc
+from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer', 'OneHotEncoder',
            'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', 'StandardScalerModel',
@@ -954,6 +959,23 @@ class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, Has
     >>> sent = ("a b " * 100 + "a c " * 10).split(" ")
     >>> doc = sqlContext.createDataFrame([(sent,), (sent,)], ["sentence"])
     >>> model = Word2Vec(vectorSize=5, seed=42, inputCol="sentence", outputCol="model").fit(doc)
+    >>> model.getVectors().show()
+    +----+--------------------+
+    |word|              vector|
+    +----+--------------------+
+    |   a|[-0.3511952459812...|
+    |   b|[0.29077222943305...|
+    |   c|[0.02315592765808...|
+    +----+--------------------+
+    ...
+    >>> model.findSynonyms("a", 2).show()
+    +----+-------------------+
+    |word|         similarity|
+    +----+-------------------+
+    |   b|0.29255685145799626|
+    |   c|-0.5414068302988307|
+    +----+-------------------+
+    ...
     >>> model.transform(doc).head().model
     DenseVector([-0.0422, -0.5138, -0.2546, 0.6885, 0.276])
     """
@@ -1047,6 +1069,24 @@ class Word2VecModel(JavaModel):
     Model fitted by Word2Vec.
     """
 
+    def getVectors(self):
+        """
+        Returns the vector representation of the words as a dataframe
+        with two fields, word and vector.
+        """
+        return self._call_java("getVectors")
+
+    def findSynonyms(self, word, num):
+        """
+        Find "num" number of words closest in similarity to "word".
+        word can be a string or vector representation.
+        Returns a dataframe with two fields word and similarity (which
+        gives the cosine similarity).
+        """
+        if not isinstance(word, basestring):
+            word = _convert_to_vector(word)
+        return self._call_java("findSynonyms", word, num)
+
 
 @inherit_doc
 class PCA(JavaEstimator, HasInputCol, HasOutputCol):

From 98e69467d4fda2c26a951409b5b7c6f1e9345ce4 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Thu, 6 Aug 2015 10:29:40 -0700
Subject: [PATCH 084/633] [SPARK-9615] [SPARK-9616] [SQL] [MLLIB] Bugs related
 to FrequentItems when merging and with Tungsten

In short:
1- FrequentItems should not use the InternalRow representation, because the keys in the map get messed up. For example, every key in the Map correspond to the very last element observed in the partition, when the elements are strings.

2- Merging two partitions had a bug:

**Existing behavior with size 3**
Partition A -> Map(1 -> 3, 2 -> 3, 3 -> 4)
Partition B -> Map(4 -> 25)
Result -> Map()

**Correct Behavior:**
Partition A -> Map(1 -> 3, 2 -> 3, 3 -> 4)
Partition B -> Map(4 -> 25)
Result -> Map(3 -> 1, 4 -> 22)

cc mengxr rxin JoshRosen

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #7945 from brkyvz/freq-fix and squashes the following commits:

07fa001 [Burak Yavuz] address 2
1dc61a8 [Burak Yavuz] address 1
506753e [Burak Yavuz] fixed and added reg test
47bfd50 [Burak Yavuz] pushing
---
 .../sql/execution/stat/FrequentItems.scala    | 26 +++++++++++--------
 .../apache/spark/sql/DataFrameStatSuite.scala | 24 ++++++++++++++---
 2 files changed, 36 insertions(+), 14 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
index 9329148aa233c..db463029aedf7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala
@@ -20,17 +20,15 @@ package org.apache.spark.sql.execution.stat
 import scala.collection.mutable.{Map => MutableMap}
 
 import org.apache.spark.Logging
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{Column, DataFrame}
+import org.apache.spark.sql.{Row, Column, DataFrame}
 
 private[sql] object FrequentItems extends Logging {
 
   /** A helper class wrapping `MutableMap[Any, Long]` for simplicity. */
   private class FreqItemCounter(size: Int) extends Serializable {
     val baseMap: MutableMap[Any, Long] = MutableMap.empty[Any, Long]
-
     /**
      * Add a new example to the counts if it exists, otherwise deduct the count
      * from existing items.
@@ -42,9 +40,15 @@ private[sql] object FrequentItems extends Logging {
         if (baseMap.size < size) {
           baseMap += key -> count
         } else {
-          // TODO: Make this more efficient... A flatMap?
-          baseMap.retain((k, v) => v > count)
-          baseMap.transform((k, v) => v - count)
+          val minCount = baseMap.values.min
+          val remainder = count - minCount
+          if (remainder >= 0) {
+            baseMap += key -> count // something will get kicked out, so we can add this
+            baseMap.retain((k, v) => v > minCount)
+            baseMap.transform((k, v) => v - minCount)
+          } else {
+            baseMap.transform((k, v) => v - count)
+          }
         }
       }
       this
@@ -90,12 +94,12 @@ private[sql] object FrequentItems extends Logging {
       (name, originalSchema.fields(index).dataType)
     }.toArray
 
-    val freqItems = df.select(cols.map(Column(_)) : _*).queryExecution.toRdd.aggregate(countMaps)(
+    val freqItems = df.select(cols.map(Column(_)) : _*).rdd.aggregate(countMaps)(
       seqOp = (counts, row) => {
         var i = 0
         while (i < numCols) {
           val thisMap = counts(i)
-          val key = row.get(i, colInfo(i)._2)
+          val key = row.get(i)
           thisMap.add(key, 1L)
           i += 1
         }
@@ -110,13 +114,13 @@ private[sql] object FrequentItems extends Logging {
         baseCounts
       }
     )
-    val justItems = freqItems.map(m => m.baseMap.keys.toArray).map(new GenericArrayData(_))
-    val resultRow = InternalRow(justItems : _*)
+    val justItems = freqItems.map(m => m.baseMap.keys.toArray)
+    val resultRow = Row(justItems : _*)
     // append frequent Items to the column name for easy debugging
     val outputCols = colInfo.map { v =>
       StructField(v._1 + "_freqItems", ArrayType(v._2, false))
     }
     val schema = StructType(outputCols).toAttributes
-    new DataFrame(df.sqlContext, LocalRelation(schema, Seq(resultRow)))
+    new DataFrame(df.sqlContext, LocalRelation.fromExternalRows(schema, Seq(resultRow)))
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 07a675e64f527..0e7659f443ecd 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -123,12 +123,30 @@ class DataFrameStatSuite extends QueryTest {
 
     val results = df.stat.freqItems(Array("numbers", "letters"), 0.1)
     val items = results.collect().head
-    items.getSeq[Int](0) should contain (1)
-    items.getSeq[String](1) should contain (toLetter(1))
+    assert(items.getSeq[Int](0).contains(1))
+    assert(items.getSeq[String](1).contains(toLetter(1)))
 
     val singleColResults = df.stat.freqItems(Array("negDoubles"), 0.1)
     val items2 = singleColResults.collect().head
-    items2.getSeq[Double](0) should contain (-1.0)
+    assert(items2.getSeq[Double](0).contains(-1.0))
+  }
+
+  test("Frequent Items 2") {
+    val rows = sqlCtx.sparkContext.parallelize(Seq.empty[Int], 4)
+    // this is a regression test, where when merging partitions, we omitted values with higher
+    // counts than those that existed in the map when the map was full. This test should also fail
+    // if anything like SPARK-9614 is observed once again
+    val df = rows.mapPartitionsWithIndex { (idx, iter) =>
+      if (idx == 3) { // must come from one of the later merges, therefore higher partition index
+        Iterator("3", "3", "3", "3", "3")
+      } else {
+        Iterator("0", "1", "2", "3", "4")
+      }
+    }.toDF("a")
+    val results = df.stat.freqItems(Array("a"), 0.25)
+    val items = results.collect().head.getSeq[String](0)
+    assert(items.contains("3"))
+    assert(items.length === 1)
   }
 
   test("sampleBy") {

From 5e1b0ef07942a041195b3decd05d86c289bc8d2b Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 6 Aug 2015 10:39:16 -0700
Subject: [PATCH 085/633] [SPARK-9659][SQL] Rename inSet to isin to match
 Pandas function.

Inspiration drawn from this blog post: https://lab.getbase.com/pandarize-spark-dataframes/

Author: Reynold Xin <rxin@databricks.com>

Closes #7977 from rxin/isin and squashes the following commits:

9b1d3d6 [Reynold Xin] Added return.
2197d37 [Reynold Xin] Fixed test case.
7c1b6cf [Reynold Xin] Import warnings.
4f4a35d [Reynold Xin] [SPARK-9659][SQL] Rename inSet to isin to match Pandas function.
---
 python/pyspark/sql/column.py                  | 20 ++++++++++++++++++-
 .../scala/org/apache/spark/sql/Column.scala   | 13 +++++++++++-
 .../spark/sql/ColumnExpressionSuite.scala     | 14 ++++++-------
 3 files changed, 38 insertions(+), 9 deletions(-)

diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 0a85da7443d3d..8af8637cf948d 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -16,6 +16,7 @@
 #
 
 import sys
+import warnings
 
 if sys.version >= '3':
     basestring = str
@@ -254,12 +255,29 @@ def inSet(self, *cols):
         [Row(age=5, name=u'Bob')]
         >>> df[df.age.inSet([1, 2, 3])].collect()
         [Row(age=2, name=u'Alice')]
+
+        .. note:: Deprecated in 1.5, use :func:`Column.isin` instead.
+        """
+        warnings.warn("inSet is deprecated. Use isin() instead.")
+        return self.isin(*cols)
+
+    @ignore_unicode_prefix
+    @since(1.5)
+    def isin(self, *cols):
+        """
+        A boolean expression that is evaluated to true if the value of this
+        expression is contained by the evaluated values of the arguments.
+
+        >>> df[df.name.isin("Bob", "Mike")].collect()
+        [Row(age=5, name=u'Bob')]
+        >>> df[df.age.isin([1, 2, 3])].collect()
+        [Row(age=2, name=u'Alice')]
         """
         if len(cols) == 1 and isinstance(cols[0], (list, set)):
             cols = cols[0]
         cols = [c._jc if isinstance(c, Column) else _create_column_from_literal(c) for c in cols]
         sc = SparkContext._active_spark_context
-        jc = getattr(self._jc, "in")(_to_seq(sc, cols))
+        jc = getattr(self._jc, "isin")(_to_seq(sc, cols))
         return Column(jc)
 
     # order
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index b25dcbca82b9f..75365fbcec757 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -627,8 +627,19 @@ class Column(protected[sql] val expr: Expression) extends Logging {
    * @group expr_ops
    * @since 1.3.0
    */
+  @deprecated("use isin", "1.5.0")
   @scala.annotation.varargs
-  def in(list: Any*): Column = In(expr, list.map(lit(_).expr))
+  def in(list: Any*): Column = isin(list : _*)
+
+  /**
+   * A boolean expression that is evaluated to true if the value of this expression is contained
+   * by the evaluated values of the arguments.
+   *
+   * @group expr_ops
+   * @since 1.5.0
+   */
+  @scala.annotation.varargs
+  def isin(list: Any*): Column = In(expr, list.map(lit(_).expr))
 
   /**
    * SQL like expression.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index b351380373259..e1b3443d74993 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -345,23 +345,23 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
 
   test("in") {
     val df = Seq((1, "x"), (2, "y"), (3, "z")).toDF("a", "b")
-    checkAnswer(df.filter($"a".in(1, 2)),
+    checkAnswer(df.filter($"a".isin(1, 2)),
       df.collect().toSeq.filter(r => r.getInt(0) == 1 || r.getInt(0) == 2))
-    checkAnswer(df.filter($"a".in(3, 2)),
+    checkAnswer(df.filter($"a".isin(3, 2)),
       df.collect().toSeq.filter(r => r.getInt(0) == 3 || r.getInt(0) == 2))
-    checkAnswer(df.filter($"a".in(3, 1)),
+    checkAnswer(df.filter($"a".isin(3, 1)),
       df.collect().toSeq.filter(r => r.getInt(0) == 3 || r.getInt(0) == 1))
-    checkAnswer(df.filter($"b".in("y", "x")),
+    checkAnswer(df.filter($"b".isin("y", "x")),
       df.collect().toSeq.filter(r => r.getString(1) == "y" || r.getString(1) == "x"))
-    checkAnswer(df.filter($"b".in("z", "x")),
+    checkAnswer(df.filter($"b".isin("z", "x")),
       df.collect().toSeq.filter(r => r.getString(1) == "z" || r.getString(1) == "x"))
-    checkAnswer(df.filter($"b".in("z", "y")),
+    checkAnswer(df.filter($"b".isin("z", "y")),
       df.collect().toSeq.filter(r => r.getString(1) == "z" || r.getString(1) == "y"))
 
     val df2 = Seq((1, Seq(1)), (2, Seq(2)), (3, Seq(3))).toDF("a", "b")
 
     intercept[AnalysisException] {
-      df2.filter($"a".in($"b"))
+      df2.filter($"a".isin($"b"))
     }
   }
 

From 6e009cb9c4d7a395991e10dab427f37019283758 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 6 Aug 2015 10:40:54 -0700
Subject: [PATCH 086/633] [SPARK-9632][SQL] update InternalRow.toSeq to make it
 accept data type info

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7955 from cloud-fan/toSeq and squashes the following commits:

21665e2 [Wenchen Fan] fix hive again...
4addf29 [Wenchen Fan] fix hive
bc16c59 [Wenchen Fan] minor fix
33d802c [Wenchen Fan] pass data type info to InternalRow.toSeq
3dd033e [Wenchen Fan] move the default special getters implementation from InternalRow to BaseGenericInternalRow
---
 .../spark/sql/catalyst/InternalRow.scala      | 132 ++----------------
 .../sql/catalyst/expressions/Projection.scala |  12 +-
 .../expressions/SpecificMutableRow.scala      |   5 +-
 .../codegen/GenerateProjection.scala          |   8 +-
 .../spark/sql/catalyst/expressions/rows.scala | 132 +++++++++++++++++-
 .../expressions/CodeGenerationSuite.scala     |   2 +-
 .../spark/sql/columnar/ColumnStats.scala      |  51 +++----
 .../columnar/InMemoryColumnarTableScan.scala  |  11 +-
 .../spark/sql/execution/debug/package.scala   |   4 +-
 .../apache/spark/sql/sources/interfaces.scala |   4 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala |  54 +++----
 .../spark/sql/hive/HiveInspectors.scala       |   6 +-
 .../hive/execution/ScriptTransformation.scala |  21 ++-
 .../spark/sql/hive/hiveWriterContainers.scala |  24 ++--
 .../spark/sql/hive/HiveInspectorSuite.scala   |  10 +-
 15 files changed, 259 insertions(+), 217 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 7d17cca808791..85b4bf3b6aef5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{DataType, MapData, ArrayData, Decimal}
-import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
+import org.apache.spark.sql.types.{DataType, StructType}
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
@@ -32,8 +31,6 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
   // This is only use for test and will throw a null pointer exception if the position is null.
   def getString(ordinal: Int): String = getUTF8String(ordinal).toString
 
-  override def toString: String = mkString("[", ",", "]")
-
   /**
    * Make a copy of the current [[InternalRow]] object.
    */
@@ -50,136 +47,25 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
     false
   }
 
-  // Subclasses of InternalRow should implement all special getters and equals/hashCode,
-  // or implement this genericGet.
-  protected def genericGet(ordinal: Int): Any = throw new IllegalStateException(
-    "Concrete internal rows should implement genericGet, " +
-      "or implement all special getters and equals/hashCode")
-
-  // default implementation (slow)
-  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
-  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
-  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
-  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
-  override def getByte(ordinal: Int): Byte = getAs(ordinal)
-  override def getShort(ordinal: Int): Short = getAs(ordinal)
-  override def getInt(ordinal: Int): Int = getAs(ordinal)
-  override def getLong(ordinal: Int): Long = getAs(ordinal)
-  override def getFloat(ordinal: Int): Float = getAs(ordinal)
-  override def getDouble(ordinal: Int): Double = getAs(ordinal)
-  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
-  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
-  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
-  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
-  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
-  override def getMap(ordinal: Int): MapData = getAs(ordinal)
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
-
-  override def equals(o: Any): Boolean = {
-    if (!o.isInstanceOf[InternalRow]) {
-      return false
-    }
-
-    val other = o.asInstanceOf[InternalRow]
-    if (other eq null) {
-      return false
-    }
-
-    val len = numFields
-    if (len != other.numFields) {
-      return false
-    }
-
-    var i = 0
-    while (i < len) {
-      if (isNullAt(i) != other.isNullAt(i)) {
-        return false
-      }
-      if (!isNullAt(i)) {
-        val o1 = genericGet(i)
-        val o2 = other.genericGet(i)
-        o1 match {
-          case b1: Array[Byte] =>
-            if (!o2.isInstanceOf[Array[Byte]] ||
-              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
-              return false
-            }
-          case f1: Float if java.lang.Float.isNaN(f1) =>
-            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
-              return false
-            }
-          case d1: Double if java.lang.Double.isNaN(d1) =>
-            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
-              return false
-            }
-          case _ => if (o1 != o2) {
-            return false
-          }
-        }
-      }
-      i += 1
-    }
-    true
-  }
-
-  // Custom hashCode function that matches the efficient code generated version.
-  override def hashCode: Int = {
-    var result: Int = 37
-    var i = 0
-    val len = numFields
-    while (i < len) {
-      val update: Int =
-        if (isNullAt(i)) {
-          0
-        } else {
-          genericGet(i) match {
-            case b: Boolean => if (b) 0 else 1
-            case b: Byte => b.toInt
-            case s: Short => s.toInt
-            case i: Int => i
-            case l: Long => (l ^ (l >>> 32)).toInt
-            case f: Float => java.lang.Float.floatToIntBits(f)
-            case d: Double =>
-              val b = java.lang.Double.doubleToLongBits(d)
-              (b ^ (b >>> 32)).toInt
-            case a: Array[Byte] => java.util.Arrays.hashCode(a)
-            case other => other.hashCode()
-          }
-        }
-      result = 37 * result + update
-      i += 1
-    }
-    result
-  }
-
   /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
    * Return a Scala Seq representing the row. Elements are placed in the same order in the Seq.
    */
-  // todo: remove this as it needs the generic getter
-  def toSeq: Seq[Any] = {
-    val n = numFields
-    val values = new Array[Any](n)
+  def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
+    val len = numFields
+    assert(len == fieldTypes.length)
+
+    val values = new Array[Any](len)
     var i = 0
-    while (i < n) {
-      values.update(i, genericGet(i))
+    while (i < len) {
+      values(i) = get(i, fieldTypes(i))
       i += 1
     }
     values
   }
 
-  /** Displays all elements of this sequence in a string (without a separator). */
-  def mkString: String = toSeq.mkString
-
-  /** Displays all elements of this sequence in a string using a separator string. */
-  def mkString(sep: String): String = toSeq.mkString(sep)
-
-  /**
-   * Displays all elements of this traversable or iterator in a string using
-   * start, end, and separator strings.
-   */
-  def mkString(start: String, sep: String, end: String): String = toSeq.mkString(start, sep, end)
+  def toSeq(schema: StructType): Seq[Any] = toSeq(schema.map(_.dataType))
 }
 
 object InternalRow {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 4296b4b123fc0..59ce7fc4f2c63 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -203,7 +203,11 @@ class JoinedRow extends InternalRow {
     this
   }
 
-  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
+    assert(fieldTypes.length == row1.numFields + row2.numFields)
+    val (left, right) = fieldTypes.splitAt(row1.numFields)
+    row1.toSeq(left) ++ row2.toSeq(right)
+  }
 
   override def numFields: Int = row1.numFields + row2.numFields
 
@@ -276,11 +280,11 @@ class JoinedRow extends InternalRow {
     if ((row1 eq null) && (row2 eq null)) {
       "[ empty row ]"
     } else if (row1 eq null) {
-      row2.mkString("[", ",", "]")
+      row2.toString
     } else if (row2 eq null) {
-      row1.mkString("[", ",", "]")
+      row1.toString
     } else {
-      mkString("[", ",", "]")
+      s"{${row1.toString} + ${row2.toString}}"
     }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index b94df6bd66e04..4f56f94bd4ca4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -192,7 +192,8 @@ final class MutableAny extends MutableValue {
  * based on the dataTypes of each column.  The intent is to decrease garbage when modifying the
  * values of primitive columns.
  */
-final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableRow {
+final class SpecificMutableRow(val values: Array[MutableValue])
+  extends MutableRow with BaseGenericInternalRow {
 
   def this(dataTypes: Seq[DataType]) =
     this(
@@ -213,8 +214,6 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
 
   override def numFields: Int = values.length
 
-  override def toSeq: Seq[Any] = values.map(_.boxed)
-
   override def setNullAt(i: Int): Unit = {
     values(i).isNull = true
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index c04fe734d554e..c744e84d822e8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
 
@@ -25,6 +26,8 @@ import org.apache.spark.sql.types._
  */
 abstract class BaseProjection extends Projection {}
 
+abstract class CodeGenMutableRow extends MutableRow with BaseGenericInternalRow
+
 /**
  * Generates bytecode that produces a new [[InternalRow]] object based on a fixed set of input
  * [[Expression Expressions]] and a given input [[InternalRow]].  The returned [[InternalRow]]
@@ -171,7 +174,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         return new SpecificRow((InternalRow) r);
       }
 
-      final class SpecificRow extends ${classOf[MutableRow].getName} {
+      final class SpecificRow extends ${classOf[CodeGenMutableRow].getName} {
 
         $columns
 
@@ -184,7 +187,8 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         public void setNullAt(int i) { nullBits[i] = true; }
         public boolean isNullAt(int i) { return nullBits[i]; }
 
-        protected Object genericGet(int i) {
+        @Override
+        public Object genericGet(int i) {
           if (isNullAt(i)) return null;
           switch (i) {
           $getCases
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 7657fb535dcf4..207e667792660 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -21,6 +21,130 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 
+/**
+ * An extended version of [[InternalRow]] that implements all special getters, toString
+ * and equals/hashCode by `genericGet`.
+ */
+trait BaseGenericInternalRow extends InternalRow {
+
+  protected def genericGet(ordinal: Int): Any
+
+  // default implementation (slow)
+  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
+  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
+  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+  override def getMap(ordinal: Int): MapData = getAs(ordinal)
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+
+  override def toString(): String = {
+    if (numFields == 0) {
+      "[empty row]"
+    } else {
+      val sb = new StringBuilder
+      sb.append("[")
+      sb.append(genericGet(0))
+      val len = numFields
+      var i = 1
+      while (i < len) {
+        sb.append(",")
+        sb.append(genericGet(i))
+        i += 1
+      }
+      sb.append("]")
+      sb.toString()
+    }
+  }
+
+  override def equals(o: Any): Boolean = {
+    if (!o.isInstanceOf[BaseGenericInternalRow]) {
+      return false
+    }
+
+    val other = o.asInstanceOf[BaseGenericInternalRow]
+    if (other eq null) {
+      return false
+    }
+
+    val len = numFields
+    if (len != other.numFields) {
+      return false
+    }
+
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i) != other.isNullAt(i)) {
+        return false
+      }
+      if (!isNullAt(i)) {
+        val o1 = genericGet(i)
+        val o2 = other.genericGet(i)
+        o1 match {
+          case b1: Array[Byte] =>
+            if (!o2.isInstanceOf[Array[Byte]] ||
+              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+              return false
+            }
+          case f1: Float if java.lang.Float.isNaN(f1) =>
+            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
+              return false
+            }
+          case d1: Double if java.lang.Double.isNaN(d1) =>
+            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
+              return false
+            }
+          case _ => if (o1 != o2) {
+            return false
+          }
+        }
+      }
+      i += 1
+    }
+    true
+  }
+
+  // Custom hashCode function that matches the efficient code generated version.
+  override def hashCode: Int = {
+    var result: Int = 37
+    var i = 0
+    val len = numFields
+    while (i < len) {
+      val update: Int =
+        if (isNullAt(i)) {
+          0
+        } else {
+          genericGet(i) match {
+            case b: Boolean => if (b) 0 else 1
+            case b: Byte => b.toInt
+            case s: Short => s.toInt
+            case i: Int => i
+            case l: Long => (l ^ (l >>> 32)).toInt
+            case f: Float => java.lang.Float.floatToIntBits(f)
+            case d: Double =>
+              val b = java.lang.Double.doubleToLongBits(d)
+              (b ^ (b >>> 32)).toInt
+            case a: Array[Byte] => java.util.Arrays.hashCode(a)
+            case other => other.hashCode()
+          }
+        }
+      result = 37 * result + update
+      i += 1
+    }
+    result
+  }
+}
+
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.
  * Setting a value through a primitive function implicitly marks that column as not null.
@@ -82,7 +206,7 @@ class GenericRowWithSchema(values: Array[Any], override val schema: StructType)
  * Note that, while the array is not copied, and thus could technically be mutated after creation,
  * this is not allowed.
  */
-class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRow {
+class GenericInternalRow(private[sql] val values: Array[Any]) extends BaseGenericInternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -90,7 +214,7 @@ class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRo
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq: Seq[Any] = values
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
 
   override def numFields: Int = values.length
 
@@ -109,7 +233,7 @@ class GenericInternalRowWithSchema(values: Array[Any], val schema: StructType)
   def fieldIndex(name: String): Int = schema.fieldIndex(name)
 }
 
-class GenericMutableRow(values: Array[Any]) extends MutableRow {
+class GenericMutableRow(values: Array[Any]) extends MutableRow with BaseGenericInternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -117,7 +241,7 @@ class GenericMutableRow(values: Array[Any]) extends MutableRow {
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq: Seq[Any] = values
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
 
   override def numFields: Int = values.length
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index e310aee221666..e323467af5f4a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -87,7 +87,7 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
     val length = 5000
     val expressions = List.fill(length)(EqualTo(Literal(1), Literal(1)))
     val plan = GenerateMutableProjection.generate(expressions)()
-    val actual = plan(new GenericMutableRow(length)).toSeq
+    val actual = plan(new GenericMutableRow(length)).toSeq(expressions.map(_.dataType))
     val expected = Seq.fill(length)(true)
 
     if (!checkResult(actual, expected)) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index af1a8ecca9b57..5cbd52bc0590e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.columnar
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, AttributeReference}
+import org.apache.spark.sql.catalyst.expressions.{GenericInternalRow, Attribute, AttributeMap, AttributeReference}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -66,7 +66,7 @@ private[sql] sealed trait ColumnStats extends Serializable {
    * Column statistics represented as a single row, currently including closed lower bound, closed
    * upper bound and null count.
    */
-  def collectedStatistics: InternalRow
+  def collectedStatistics: GenericInternalRow
 }
 
 /**
@@ -75,7 +75,8 @@ private[sql] sealed trait ColumnStats extends Serializable {
 private[sql] class NoopColumnStats extends ColumnStats {
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = super.gatherStats(row, ordinal)
 
-  override def collectedStatistics: InternalRow = InternalRow(null, null, nullCount, count, 0L)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, 0L))
 }
 
 private[sql] class BooleanColumnStats extends ColumnStats {
@@ -92,8 +93,8 @@ private[sql] class BooleanColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class ByteColumnStats extends ColumnStats {
@@ -110,8 +111,8 @@ private[sql] class ByteColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class ShortColumnStats extends ColumnStats {
@@ -128,8 +129,8 @@ private[sql] class ShortColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class IntColumnStats extends ColumnStats {
@@ -146,8 +147,8 @@ private[sql] class IntColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class LongColumnStats extends ColumnStats {
@@ -164,8 +165,8 @@ private[sql] class LongColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class FloatColumnStats extends ColumnStats {
@@ -182,8 +183,8 @@ private[sql] class FloatColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class DoubleColumnStats extends ColumnStats {
@@ -200,8 +201,8 @@ private[sql] class DoubleColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class StringColumnStats extends ColumnStats {
@@ -218,8 +219,8 @@ private[sql] class StringColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class BinaryColumnStats extends ColumnStats {
@@ -230,8 +231,8 @@ private[sql] class BinaryColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(null, null, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
 }
 
 private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends ColumnStats {
@@ -248,8 +249,8 @@ private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends C
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
@@ -262,8 +263,8 @@ private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(null, null, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
 }
 
 private[sql] class DateColumnStats extends IntColumnStats
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index 5d5b0697d7016..d553bb6169ecc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -148,7 +148,7 @@ private[sql] case class InMemoryRelation(
           }
 
           val stats = InternalRow.fromSeq(columnBuilders.map(_.columnStats.collectedStatistics)
-                        .flatMap(_.toSeq))
+                        .flatMap(_.values))
 
           batchStats += stats
           CachedBatch(columnBuilders.map(_.build().array()), stats)
@@ -330,10 +330,11 @@ private[sql] case class InMemoryColumnarTableScan(
         if (inMemoryPartitionPruningEnabled) {
           cachedBatchIterator.filter { cachedBatch =>
             if (!partitionFilter(cachedBatch.stats)) {
-              def statsString: String = relation.partitionStatistics.schema
-                .zip(cachedBatch.stats.toSeq)
-                .map { case (a, s) => s"${a.name}: $s" }
-                .mkString(", ")
+              def statsString: String = relation.partitionStatistics.schema.zipWithIndex.map {
+                case (a, i) =>
+                  val value = cachedBatch.stats.get(i, a.dataType)
+                  s"${a.name}: $value"
+              }.mkString(", ")
               logInfo(s"Skipping partition based on stats $statsString")
               false
             } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index c37007f1eece7..dd3858ea2b520 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -156,8 +156,8 @@ package object debug {
     def typeCheck(data: Any, schema: DataType): Unit = (data, schema) match {
       case (null, _) =>
 
-      case (row: InternalRow, StructType(fields)) =>
-        row.toSeq.zip(fields.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
+      case (row: InternalRow, s: StructType) =>
+        row.toSeq(s).zip(s.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
       case (a: ArrayData, ArrayType(elemType, _)) =>
         a.foreach(elemType, (_, e) => {
           typeCheck(e, elemType)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 7126145ddc010..c04557e5a0818 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -461,8 +461,8 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
             val spec = discoverPartitions()
             val partitionColumnTypes = spec.partitionColumns.map(_.dataType)
             val castedPartitions = spec.partitions.map { case p @ Partition(values, path) =>
-              val literals = values.toSeq.zip(partitionColumnTypes).map {
-                case (value, dataType) => Literal.create(value, dataType)
+              val literals = partitionColumnTypes.zipWithIndex.map { case (dt, i) =>
+                Literal.create(values.get(i, dt), dt)
               }
               val castedValues = partitionSchema.zip(literals).map { case (field, literal) =>
                 Cast(literal, field.dataType).eval()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 16e0187ed20a0..d0430d2a60e75 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -19,33 +19,36 @@ package org.apache.spark.sql.columnar
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.GenericInternalRow
 import org.apache.spark.sql.types._
 
 class ColumnStatsSuite extends SparkFunSuite {
-  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, InternalRow(true, false, 0))
-  testColumnStats(classOf[ByteColumnStats], BYTE, InternalRow(Byte.MaxValue, Byte.MinValue, 0))
-  testColumnStats(classOf[ShortColumnStats], SHORT, InternalRow(Short.MaxValue, Short.MinValue, 0))
-  testColumnStats(classOf[IntColumnStats], INT, InternalRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[DateColumnStats], DATE, InternalRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[LongColumnStats], LONG, InternalRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, createRow(true, false, 0))
+  testColumnStats(classOf[ByteColumnStats], BYTE, createRow(Byte.MaxValue, Byte.MinValue, 0))
+  testColumnStats(classOf[ShortColumnStats], SHORT, createRow(Short.MaxValue, Short.MinValue, 0))
+  testColumnStats(classOf[IntColumnStats], INT, createRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[DateColumnStats], DATE, createRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[LongColumnStats], LONG, createRow(Long.MaxValue, Long.MinValue, 0))
   testColumnStats(classOf[TimestampColumnStats], TIMESTAMP,
-    InternalRow(Long.MaxValue, Long.MinValue, 0))
-  testColumnStats(classOf[FloatColumnStats], FLOAT, InternalRow(Float.MaxValue, Float.MinValue, 0))
+    createRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[FloatColumnStats], FLOAT, createRow(Float.MaxValue, Float.MinValue, 0))
   testColumnStats(classOf[DoubleColumnStats], DOUBLE,
-    InternalRow(Double.MaxValue, Double.MinValue, 0))
-  testColumnStats(classOf[StringColumnStats], STRING, InternalRow(null, null, 0))
-  testDecimalColumnStats(InternalRow(null, null, 0))
+    createRow(Double.MaxValue, Double.MinValue, 0))
+  testColumnStats(classOf[StringColumnStats], STRING, createRow(null, null, 0))
+  testDecimalColumnStats(createRow(null, null, 0))
+
+  def createRow(values: Any*): GenericInternalRow = new GenericInternalRow(values.toArray)
 
   def testColumnStats[T <: AtomicType, U <: ColumnStats](
       columnStatsClass: Class[U],
       columnType: NativeColumnType[T],
-      initialStatistics: InternalRow): Unit = {
+      initialStatistics: GenericInternalRow): Unit = {
 
     val columnStatsName = columnStatsClass.getSimpleName
 
     test(s"$columnStatsName: empty") {
       val columnStats = columnStatsClass.newInstance()
-      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
+      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -61,11 +64,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
-      assertResult(10, "Wrong null count")(stats.get(2, null))
-      assertResult(20, "Wrong row count")(stats.get(3, null))
-      assertResult(stats.get(4, null), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
+      assertResult(10, "Wrong null count")(stats.values(2))
+      assertResult(20, "Wrong row count")(stats.values(3))
+      assertResult(stats.values(4), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
@@ -73,14 +76,15 @@ class ColumnStatsSuite extends SparkFunSuite {
     }
   }
 
-  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](initialStatistics: InternalRow) {
+  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](
+      initialStatistics: GenericInternalRow): Unit = {
 
     val columnStatsName = classOf[FixedDecimalColumnStats].getSimpleName
     val columnType = FIXED_DECIMAL(15, 10)
 
     test(s"$columnStatsName: empty") {
       val columnStats = new FixedDecimalColumnStats(15, 10)
-      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
+      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -96,11 +100,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
-      assertResult(10, "Wrong null count")(stats.get(2, null))
-      assertResult(20, "Wrong row count")(stats.get(3, null))
-      assertResult(stats.get(4, null), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
+      assertResult(10, "Wrong null count")(stats.values(2))
+      assertResult(20, "Wrong row count")(stats.values(3))
+      assertResult(stats.values(4), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 39d798d072aeb..9824dad239596 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -390,8 +390,10 @@ private[hive] trait HiveInspectors {
       (o: Any) => {
         if (o != null) {
           val struct = soi.create()
-          (soi.getAllStructFieldRefs, wrappers, o.asInstanceOf[InternalRow].toSeq).zipped.foreach {
-            (field, wrapper, data) => soi.setStructFieldData(struct, field, wrapper(data))
+          val row = o.asInstanceOf[InternalRow]
+          soi.getAllStructFieldRefs.zip(wrappers).zipWithIndex.foreach {
+            case ((field, wrapper), i) =>
+              soi.setStructFieldData(struct, field, wrapper(row.get(i, schema(i).dataType)))
           }
           struct
         } else {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index a6a343d395995..ade27454b9d29 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -88,6 +88,7 @@ case class ScriptTransformation(
       // external process. That process's output will be read by this current thread.
       val writerThread = new ScriptTransformationWriterThread(
         inputIterator,
+        input.map(_.dataType),
         outputProjection,
         inputSerde,
         inputSoi,
@@ -201,6 +202,7 @@ case class ScriptTransformation(
 
 private class ScriptTransformationWriterThread(
     iter: Iterator[InternalRow],
+    inputSchema: Seq[DataType],
     outputProjection: Projection,
     @Nullable inputSerde: AbstractSerDe,
     @Nullable inputSoi: ObjectInspector,
@@ -226,12 +228,25 @@ private class ScriptTransformationWriterThread(
     // We can't use Utils.tryWithSafeFinally here because we also need a `catch` block, so
     // let's use a variable to record whether the `finally` block was hit due to an exception
     var threwException: Boolean = true
+    val len = inputSchema.length
     try {
       iter.map(outputProjection).foreach { row =>
         if (inputSerde == null) {
-          val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"),
-            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8")
-          outputStream.write(data)
+          val data = if (len == 0) {
+            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")
+          } else {
+            val sb = new StringBuilder
+            sb.append(row.get(0, inputSchema(0)))
+            var i = 1
+            while (i < len) {
+              sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
+              sb.append(row.get(i, inputSchema(i)))
+              i += 1
+            }
+            sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES"))
+            sb.toString()
+          }
+          outputStream.write(data.getBytes("utf-8"))
         } else {
           val writable = inputSerde.serialize(
             row.asInstanceOf[GenericInternalRow].values, inputSoi)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 684ea1d137b49..8dc796b056a72 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -211,18 +211,18 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
       }
     }
 
-    val dynamicPartPath = dynamicPartColNames
-      .zip(row.toSeq.takeRight(dynamicPartColNames.length))
-      .map { case (col, rawVal) =>
-        val string = if (rawVal == null) null else convertToHiveRawString(col, rawVal)
-        val colString =
-          if (string == null || string.isEmpty) {
-            defaultPartName
-          } else {
-            FileUtils.escapePathName(string, defaultPartName)
-          }
-        s"/$col=$colString"
-      }.mkString
+    val nonDynamicPartLen = row.numFields - dynamicPartColNames.length
+    val dynamicPartPath = dynamicPartColNames.zipWithIndex.map { case (colName, i) =>
+      val rawVal = row.get(nonDynamicPartLen + i, schema(colName).dataType)
+      val string = if (rawVal == null) null else convertToHiveRawString(colName, rawVal)
+      val colString =
+        if (string == null || string.isEmpty) {
+          defaultPartName
+        } else {
+          FileUtils.escapePathName(string, defaultPartName)
+        }
+      s"/$colName=$colString"
+    }.mkString
 
     def newWriter(): FileSinkOperator.RecordWriter = {
       val newFileSinkDesc = new FileSinkDesc(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
index 99e95fb921301..81a70b8d42267 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
@@ -133,8 +133,8 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
     }
   }
 
-  def checkValues(row1: Seq[Any], row2: InternalRow): Unit = {
-    row1.zip(row2.toSeq).foreach { case (r1, r2) =>
+  def checkValues(row1: Seq[Any], row2: InternalRow, row2Schema: StructType): Unit = {
+    row1.zip(row2.toSeq(row2Schema)).foreach { case (r1, r2) =>
       checkValue(r1, r2)
     }
   }
@@ -211,8 +211,10 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
       case (t, idx) => StructField(s"c_$idx", t)
     })
     val inspector = toInspector(dt)
-    checkValues(row,
-      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow])
+    checkValues(
+      row,
+      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow],
+      dt)
     checkValue(null, unwrap(wrap(null, toInspector(dt), dt), toInspector(dt)))
   }
 

From 2eca46a17a3d46a605804ff89c010017da91e1bc Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Thu, 6 Aug 2015 11:15:37 -0700
Subject: [PATCH 087/633] Revert "[SPARK-9632][SQL] update InternalRow.toSeq to
 make it accept data type info"

This reverts commit 6e009cb9c4d7a395991e10dab427f37019283758.
---
 .../spark/sql/catalyst/InternalRow.scala      | 132 ++++++++++++++++--
 .../sql/catalyst/expressions/Projection.scala |  12 +-
 .../expressions/SpecificMutableRow.scala      |   5 +-
 .../codegen/GenerateProjection.scala          |   8 +-
 .../spark/sql/catalyst/expressions/rows.scala | 132 +-----------------
 .../expressions/CodeGenerationSuite.scala     |   2 +-
 .../spark/sql/columnar/ColumnStats.scala      |  51 ++++---
 .../columnar/InMemoryColumnarTableScan.scala  |  11 +-
 .../spark/sql/execution/debug/package.scala   |   4 +-
 .../apache/spark/sql/sources/interfaces.scala |   4 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala |  54 ++++---
 .../spark/sql/hive/HiveInspectors.scala       |   6 +-
 .../hive/execution/ScriptTransformation.scala |  21 +--
 .../spark/sql/hive/hiveWriterContainers.scala |  24 ++--
 .../spark/sql/hive/HiveInspectorSuite.scala   |  10 +-
 15 files changed, 217 insertions(+), 259 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 85b4bf3b6aef5..7d17cca808791 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -18,7 +18,8 @@
 package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{DataType, StructType}
+import org.apache.spark.sql.types.{DataType, MapData, ArrayData, Decimal}
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
@@ -31,6 +32,8 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
   // This is only use for test and will throw a null pointer exception if the position is null.
   def getString(ordinal: Int): String = getUTF8String(ordinal).toString
 
+  override def toString: String = mkString("[", ",", "]")
+
   /**
    * Make a copy of the current [[InternalRow]] object.
    */
@@ -47,25 +50,136 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
     false
   }
 
+  // Subclasses of InternalRow should implement all special getters and equals/hashCode,
+  // or implement this genericGet.
+  protected def genericGet(ordinal: Int): Any = throw new IllegalStateException(
+    "Concrete internal rows should implement genericGet, " +
+      "or implement all special getters and equals/hashCode")
+
+  // default implementation (slow)
+  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
+  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
+  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+  override def getMap(ordinal: Int): MapData = getAs(ordinal)
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+
+  override def equals(o: Any): Boolean = {
+    if (!o.isInstanceOf[InternalRow]) {
+      return false
+    }
+
+    val other = o.asInstanceOf[InternalRow]
+    if (other eq null) {
+      return false
+    }
+
+    val len = numFields
+    if (len != other.numFields) {
+      return false
+    }
+
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i) != other.isNullAt(i)) {
+        return false
+      }
+      if (!isNullAt(i)) {
+        val o1 = genericGet(i)
+        val o2 = other.genericGet(i)
+        o1 match {
+          case b1: Array[Byte] =>
+            if (!o2.isInstanceOf[Array[Byte]] ||
+              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+              return false
+            }
+          case f1: Float if java.lang.Float.isNaN(f1) =>
+            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
+              return false
+            }
+          case d1: Double if java.lang.Double.isNaN(d1) =>
+            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
+              return false
+            }
+          case _ => if (o1 != o2) {
+            return false
+          }
+        }
+      }
+      i += 1
+    }
+    true
+  }
+
+  // Custom hashCode function that matches the efficient code generated version.
+  override def hashCode: Int = {
+    var result: Int = 37
+    var i = 0
+    val len = numFields
+    while (i < len) {
+      val update: Int =
+        if (isNullAt(i)) {
+          0
+        } else {
+          genericGet(i) match {
+            case b: Boolean => if (b) 0 else 1
+            case b: Byte => b.toInt
+            case s: Short => s.toInt
+            case i: Int => i
+            case l: Long => (l ^ (l >>> 32)).toInt
+            case f: Float => java.lang.Float.floatToIntBits(f)
+            case d: Double =>
+              val b = java.lang.Double.doubleToLongBits(d)
+              (b ^ (b >>> 32)).toInt
+            case a: Array[Byte] => java.util.Arrays.hashCode(a)
+            case other => other.hashCode()
+          }
+        }
+      result = 37 * result + update
+      i += 1
+    }
+    result
+  }
+
   /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
    * Return a Scala Seq representing the row. Elements are placed in the same order in the Seq.
    */
-  def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
-    val len = numFields
-    assert(len == fieldTypes.length)
-
-    val values = new Array[Any](len)
+  // todo: remove this as it needs the generic getter
+  def toSeq: Seq[Any] = {
+    val n = numFields
+    val values = new Array[Any](n)
     var i = 0
-    while (i < len) {
-      values(i) = get(i, fieldTypes(i))
+    while (i < n) {
+      values.update(i, genericGet(i))
       i += 1
     }
     values
   }
 
-  def toSeq(schema: StructType): Seq[Any] = toSeq(schema.map(_.dataType))
+  /** Displays all elements of this sequence in a string (without a separator). */
+  def mkString: String = toSeq.mkString
+
+  /** Displays all elements of this sequence in a string using a separator string. */
+  def mkString(sep: String): String = toSeq.mkString(sep)
+
+  /**
+   * Displays all elements of this traversable or iterator in a string using
+   * start, end, and separator strings.
+   */
+  def mkString(start: String, sep: String, end: String): String = toSeq.mkString(start, sep, end)
 }
 
 object InternalRow {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 59ce7fc4f2c63..4296b4b123fc0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -203,11 +203,7 @@ class JoinedRow extends InternalRow {
     this
   }
 
-  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
-    assert(fieldTypes.length == row1.numFields + row2.numFields)
-    val (left, right) = fieldTypes.splitAt(row1.numFields)
-    row1.toSeq(left) ++ row2.toSeq(right)
-  }
+  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
 
   override def numFields: Int = row1.numFields + row2.numFields
 
@@ -280,11 +276,11 @@ class JoinedRow extends InternalRow {
     if ((row1 eq null) && (row2 eq null)) {
       "[ empty row ]"
     } else if (row1 eq null) {
-      row2.toString
+      row2.mkString("[", ",", "]")
     } else if (row2 eq null) {
-      row1.toString
+      row1.mkString("[", ",", "]")
     } else {
-      s"{${row1.toString} + ${row2.toString}}"
+      mkString("[", ",", "]")
     }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index 4f56f94bd4ca4..b94df6bd66e04 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -192,8 +192,7 @@ final class MutableAny extends MutableValue {
  * based on the dataTypes of each column.  The intent is to decrease garbage when modifying the
  * values of primitive columns.
  */
-final class SpecificMutableRow(val values: Array[MutableValue])
-  extends MutableRow with BaseGenericInternalRow {
+final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableRow {
 
   def this(dataTypes: Seq[DataType]) =
     this(
@@ -214,6 +213,8 @@ final class SpecificMutableRow(val values: Array[MutableValue])
 
   override def numFields: Int = values.length
 
+  override def toSeq: Seq[Any] = values.map(_.boxed)
+
   override def setNullAt(i: Int): Unit = {
     values(i).isNull = true
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index c744e84d822e8..c04fe734d554e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
 
@@ -26,8 +25,6 @@ import org.apache.spark.sql.types._
  */
 abstract class BaseProjection extends Projection {}
 
-abstract class CodeGenMutableRow extends MutableRow with BaseGenericInternalRow
-
 /**
  * Generates bytecode that produces a new [[InternalRow]] object based on a fixed set of input
  * [[Expression Expressions]] and a given input [[InternalRow]].  The returned [[InternalRow]]
@@ -174,7 +171,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         return new SpecificRow((InternalRow) r);
       }
 
-      final class SpecificRow extends ${classOf[CodeGenMutableRow].getName} {
+      final class SpecificRow extends ${classOf[MutableRow].getName} {
 
         $columns
 
@@ -187,8 +184,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         public void setNullAt(int i) { nullBits[i] = true; }
         public boolean isNullAt(int i) { return nullBits[i]; }
 
-        @Override
-        public Object genericGet(int i) {
+        protected Object genericGet(int i) {
           if (isNullAt(i)) return null;
           switch (i) {
           $getCases
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 207e667792660..7657fb535dcf4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -21,130 +21,6 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 
-/**
- * An extended version of [[InternalRow]] that implements all special getters, toString
- * and equals/hashCode by `genericGet`.
- */
-trait BaseGenericInternalRow extends InternalRow {
-
-  protected def genericGet(ordinal: Int): Any
-
-  // default implementation (slow)
-  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
-  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
-  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
-  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
-  override def getByte(ordinal: Int): Byte = getAs(ordinal)
-  override def getShort(ordinal: Int): Short = getAs(ordinal)
-  override def getInt(ordinal: Int): Int = getAs(ordinal)
-  override def getLong(ordinal: Int): Long = getAs(ordinal)
-  override def getFloat(ordinal: Int): Float = getAs(ordinal)
-  override def getDouble(ordinal: Int): Double = getAs(ordinal)
-  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
-  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
-  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
-  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
-  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
-  override def getMap(ordinal: Int): MapData = getAs(ordinal)
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
-
-  override def toString(): String = {
-    if (numFields == 0) {
-      "[empty row]"
-    } else {
-      val sb = new StringBuilder
-      sb.append("[")
-      sb.append(genericGet(0))
-      val len = numFields
-      var i = 1
-      while (i < len) {
-        sb.append(",")
-        sb.append(genericGet(i))
-        i += 1
-      }
-      sb.append("]")
-      sb.toString()
-    }
-  }
-
-  override def equals(o: Any): Boolean = {
-    if (!o.isInstanceOf[BaseGenericInternalRow]) {
-      return false
-    }
-
-    val other = o.asInstanceOf[BaseGenericInternalRow]
-    if (other eq null) {
-      return false
-    }
-
-    val len = numFields
-    if (len != other.numFields) {
-      return false
-    }
-
-    var i = 0
-    while (i < len) {
-      if (isNullAt(i) != other.isNullAt(i)) {
-        return false
-      }
-      if (!isNullAt(i)) {
-        val o1 = genericGet(i)
-        val o2 = other.genericGet(i)
-        o1 match {
-          case b1: Array[Byte] =>
-            if (!o2.isInstanceOf[Array[Byte]] ||
-              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
-              return false
-            }
-          case f1: Float if java.lang.Float.isNaN(f1) =>
-            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
-              return false
-            }
-          case d1: Double if java.lang.Double.isNaN(d1) =>
-            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
-              return false
-            }
-          case _ => if (o1 != o2) {
-            return false
-          }
-        }
-      }
-      i += 1
-    }
-    true
-  }
-
-  // Custom hashCode function that matches the efficient code generated version.
-  override def hashCode: Int = {
-    var result: Int = 37
-    var i = 0
-    val len = numFields
-    while (i < len) {
-      val update: Int =
-        if (isNullAt(i)) {
-          0
-        } else {
-          genericGet(i) match {
-            case b: Boolean => if (b) 0 else 1
-            case b: Byte => b.toInt
-            case s: Short => s.toInt
-            case i: Int => i
-            case l: Long => (l ^ (l >>> 32)).toInt
-            case f: Float => java.lang.Float.floatToIntBits(f)
-            case d: Double =>
-              val b = java.lang.Double.doubleToLongBits(d)
-              (b ^ (b >>> 32)).toInt
-            case a: Array[Byte] => java.util.Arrays.hashCode(a)
-            case other => other.hashCode()
-          }
-        }
-      result = 37 * result + update
-      i += 1
-    }
-    result
-  }
-}
-
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.
  * Setting a value through a primitive function implicitly marks that column as not null.
@@ -206,7 +82,7 @@ class GenericRowWithSchema(values: Array[Any], override val schema: StructType)
  * Note that, while the array is not copied, and thus could technically be mutated after creation,
  * this is not allowed.
  */
-class GenericInternalRow(private[sql] val values: Array[Any]) extends BaseGenericInternalRow {
+class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -214,7 +90,7 @@ class GenericInternalRow(private[sql] val values: Array[Any]) extends BaseGeneri
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
+  override def toSeq: Seq[Any] = values
 
   override def numFields: Int = values.length
 
@@ -233,7 +109,7 @@ class GenericInternalRowWithSchema(values: Array[Any], val schema: StructType)
   def fieldIndex(name: String): Int = schema.fieldIndex(name)
 }
 
-class GenericMutableRow(values: Array[Any]) extends MutableRow with BaseGenericInternalRow {
+class GenericMutableRow(values: Array[Any]) extends MutableRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -241,7 +117,7 @@ class GenericMutableRow(values: Array[Any]) extends MutableRow with BaseGenericI
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
+  override def toSeq: Seq[Any] = values
 
   override def numFields: Int = values.length
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index e323467af5f4a..e310aee221666 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -87,7 +87,7 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
     val length = 5000
     val expressions = List.fill(length)(EqualTo(Literal(1), Literal(1)))
     val plan = GenerateMutableProjection.generate(expressions)()
-    val actual = plan(new GenericMutableRow(length)).toSeq(expressions.map(_.dataType))
+    val actual = plan(new GenericMutableRow(length)).toSeq
     val expected = Seq.fill(length)(true)
 
     if (!checkResult(actual, expected)) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index 5cbd52bc0590e..af1a8ecca9b57 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.columnar
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{GenericInternalRow, Attribute, AttributeMap, AttributeReference}
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, AttributeReference}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -66,7 +66,7 @@ private[sql] sealed trait ColumnStats extends Serializable {
    * Column statistics represented as a single row, currently including closed lower bound, closed
    * upper bound and null count.
    */
-  def collectedStatistics: GenericInternalRow
+  def collectedStatistics: InternalRow
 }
 
 /**
@@ -75,8 +75,7 @@ private[sql] sealed trait ColumnStats extends Serializable {
 private[sql] class NoopColumnStats extends ColumnStats {
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = super.gatherStats(row, ordinal)
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](null, null, nullCount, count, 0L))
+  override def collectedStatistics: InternalRow = InternalRow(null, null, nullCount, count, 0L)
 }
 
 private[sql] class BooleanColumnStats extends ColumnStats {
@@ -93,8 +92,8 @@ private[sql] class BooleanColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class ByteColumnStats extends ColumnStats {
@@ -111,8 +110,8 @@ private[sql] class ByteColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class ShortColumnStats extends ColumnStats {
@@ -129,8 +128,8 @@ private[sql] class ShortColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class IntColumnStats extends ColumnStats {
@@ -147,8 +146,8 @@ private[sql] class IntColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class LongColumnStats extends ColumnStats {
@@ -165,8 +164,8 @@ private[sql] class LongColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class FloatColumnStats extends ColumnStats {
@@ -183,8 +182,8 @@ private[sql] class FloatColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class DoubleColumnStats extends ColumnStats {
@@ -201,8 +200,8 @@ private[sql] class DoubleColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class StringColumnStats extends ColumnStats {
@@ -219,8 +218,8 @@ private[sql] class StringColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class BinaryColumnStats extends ColumnStats {
@@ -231,8 +230,8 @@ private[sql] class BinaryColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(null, null, nullCount, count, sizeInBytes)
 }
 
 private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends ColumnStats {
@@ -249,8 +248,8 @@ private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends C
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(lower, upper, nullCount, count, sizeInBytes)
 }
 
 private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
@@ -263,8 +262,8 @@ private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: GenericInternalRow =
-    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
+  override def collectedStatistics: InternalRow =
+    InternalRow(null, null, nullCount, count, sizeInBytes)
 }
 
 private[sql] class DateColumnStats extends IntColumnStats
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index d553bb6169ecc..5d5b0697d7016 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -148,7 +148,7 @@ private[sql] case class InMemoryRelation(
           }
 
           val stats = InternalRow.fromSeq(columnBuilders.map(_.columnStats.collectedStatistics)
-                        .flatMap(_.values))
+                        .flatMap(_.toSeq))
 
           batchStats += stats
           CachedBatch(columnBuilders.map(_.build().array()), stats)
@@ -330,11 +330,10 @@ private[sql] case class InMemoryColumnarTableScan(
         if (inMemoryPartitionPruningEnabled) {
           cachedBatchIterator.filter { cachedBatch =>
             if (!partitionFilter(cachedBatch.stats)) {
-              def statsString: String = relation.partitionStatistics.schema.zipWithIndex.map {
-                case (a, i) =>
-                  val value = cachedBatch.stats.get(i, a.dataType)
-                  s"${a.name}: $value"
-              }.mkString(", ")
+              def statsString: String = relation.partitionStatistics.schema
+                .zip(cachedBatch.stats.toSeq)
+                .map { case (a, s) => s"${a.name}: $s" }
+                .mkString(", ")
               logInfo(s"Skipping partition based on stats $statsString")
               false
             } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index dd3858ea2b520..c37007f1eece7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -156,8 +156,8 @@ package object debug {
     def typeCheck(data: Any, schema: DataType): Unit = (data, schema) match {
       case (null, _) =>
 
-      case (row: InternalRow, s: StructType) =>
-        row.toSeq(s).zip(s.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
+      case (row: InternalRow, StructType(fields)) =>
+        row.toSeq.zip(fields.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
       case (a: ArrayData, ArrayType(elemType, _)) =>
         a.foreach(elemType, (_, e) => {
           typeCheck(e, elemType)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index c04557e5a0818..7126145ddc010 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -461,8 +461,8 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
             val spec = discoverPartitions()
             val partitionColumnTypes = spec.partitionColumns.map(_.dataType)
             val castedPartitions = spec.partitions.map { case p @ Partition(values, path) =>
-              val literals = partitionColumnTypes.zipWithIndex.map { case (dt, i) =>
-                Literal.create(values.get(i, dt), dt)
+              val literals = values.toSeq.zip(partitionColumnTypes).map {
+                case (value, dataType) => Literal.create(value, dataType)
               }
               val castedValues = partitionSchema.zip(literals).map { case (field, literal) =>
                 Cast(literal, field.dataType).eval()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index d0430d2a60e75..16e0187ed20a0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -19,36 +19,33 @@ package org.apache.spark.sql.columnar
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.GenericInternalRow
 import org.apache.spark.sql.types._
 
 class ColumnStatsSuite extends SparkFunSuite {
-  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, createRow(true, false, 0))
-  testColumnStats(classOf[ByteColumnStats], BYTE, createRow(Byte.MaxValue, Byte.MinValue, 0))
-  testColumnStats(classOf[ShortColumnStats], SHORT, createRow(Short.MaxValue, Short.MinValue, 0))
-  testColumnStats(classOf[IntColumnStats], INT, createRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[DateColumnStats], DATE, createRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[LongColumnStats], LONG, createRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, InternalRow(true, false, 0))
+  testColumnStats(classOf[ByteColumnStats], BYTE, InternalRow(Byte.MaxValue, Byte.MinValue, 0))
+  testColumnStats(classOf[ShortColumnStats], SHORT, InternalRow(Short.MaxValue, Short.MinValue, 0))
+  testColumnStats(classOf[IntColumnStats], INT, InternalRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[DateColumnStats], DATE, InternalRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[LongColumnStats], LONG, InternalRow(Long.MaxValue, Long.MinValue, 0))
   testColumnStats(classOf[TimestampColumnStats], TIMESTAMP,
-    createRow(Long.MaxValue, Long.MinValue, 0))
-  testColumnStats(classOf[FloatColumnStats], FLOAT, createRow(Float.MaxValue, Float.MinValue, 0))
+    InternalRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[FloatColumnStats], FLOAT, InternalRow(Float.MaxValue, Float.MinValue, 0))
   testColumnStats(classOf[DoubleColumnStats], DOUBLE,
-    createRow(Double.MaxValue, Double.MinValue, 0))
-  testColumnStats(classOf[StringColumnStats], STRING, createRow(null, null, 0))
-  testDecimalColumnStats(createRow(null, null, 0))
-
-  def createRow(values: Any*): GenericInternalRow = new GenericInternalRow(values.toArray)
+    InternalRow(Double.MaxValue, Double.MinValue, 0))
+  testColumnStats(classOf[StringColumnStats], STRING, InternalRow(null, null, 0))
+  testDecimalColumnStats(InternalRow(null, null, 0))
 
   def testColumnStats[T <: AtomicType, U <: ColumnStats](
       columnStatsClass: Class[U],
       columnType: NativeColumnType[T],
-      initialStatistics: GenericInternalRow): Unit = {
+      initialStatistics: InternalRow): Unit = {
 
     val columnStatsName = columnStatsClass.getSimpleName
 
     test(s"$columnStatsName: empty") {
       val columnStats = columnStatsClass.newInstance()
-      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
+      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -64,11 +61,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
-      assertResult(10, "Wrong null count")(stats.values(2))
-      assertResult(20, "Wrong row count")(stats.values(3))
-      assertResult(stats.values(4), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
+      assertResult(10, "Wrong null count")(stats.get(2, null))
+      assertResult(20, "Wrong row count")(stats.get(3, null))
+      assertResult(stats.get(4, null), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
@@ -76,15 +73,14 @@ class ColumnStatsSuite extends SparkFunSuite {
     }
   }
 
-  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](
-      initialStatistics: GenericInternalRow): Unit = {
+  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](initialStatistics: InternalRow) {
 
     val columnStatsName = classOf[FixedDecimalColumnStats].getSimpleName
     val columnType = FIXED_DECIMAL(15, 10)
 
     test(s"$columnStatsName: empty") {
       val columnStats = new FixedDecimalColumnStats(15, 10)
-      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
+      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -100,11 +96,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
-      assertResult(10, "Wrong null count")(stats.values(2))
-      assertResult(20, "Wrong row count")(stats.values(3))
-      assertResult(stats.values(4), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
+      assertResult(10, "Wrong null count")(stats.get(2, null))
+      assertResult(20, "Wrong row count")(stats.get(3, null))
+      assertResult(stats.get(4, null), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 9824dad239596..39d798d072aeb 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -390,10 +390,8 @@ private[hive] trait HiveInspectors {
       (o: Any) => {
         if (o != null) {
           val struct = soi.create()
-          val row = o.asInstanceOf[InternalRow]
-          soi.getAllStructFieldRefs.zip(wrappers).zipWithIndex.foreach {
-            case ((field, wrapper), i) =>
-              soi.setStructFieldData(struct, field, wrapper(row.get(i, schema(i).dataType)))
+          (soi.getAllStructFieldRefs, wrappers, o.asInstanceOf[InternalRow].toSeq).zipped.foreach {
+            (field, wrapper, data) => soi.setStructFieldData(struct, field, wrapper(data))
           }
           struct
         } else {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index ade27454b9d29..a6a343d395995 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -88,7 +88,6 @@ case class ScriptTransformation(
       // external process. That process's output will be read by this current thread.
       val writerThread = new ScriptTransformationWriterThread(
         inputIterator,
-        input.map(_.dataType),
         outputProjection,
         inputSerde,
         inputSoi,
@@ -202,7 +201,6 @@ case class ScriptTransformation(
 
 private class ScriptTransformationWriterThread(
     iter: Iterator[InternalRow],
-    inputSchema: Seq[DataType],
     outputProjection: Projection,
     @Nullable inputSerde: AbstractSerDe,
     @Nullable inputSoi: ObjectInspector,
@@ -228,25 +226,12 @@ private class ScriptTransformationWriterThread(
     // We can't use Utils.tryWithSafeFinally here because we also need a `catch` block, so
     // let's use a variable to record whether the `finally` block was hit due to an exception
     var threwException: Boolean = true
-    val len = inputSchema.length
     try {
       iter.map(outputProjection).foreach { row =>
         if (inputSerde == null) {
-          val data = if (len == 0) {
-            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")
-          } else {
-            val sb = new StringBuilder
-            sb.append(row.get(0, inputSchema(0)))
-            var i = 1
-            while (i < len) {
-              sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
-              sb.append(row.get(i, inputSchema(i)))
-              i += 1
-            }
-            sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES"))
-            sb.toString()
-          }
-          outputStream.write(data.getBytes("utf-8"))
+          val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"),
+            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8")
+          outputStream.write(data)
         } else {
           val writable = inputSerde.serialize(
             row.asInstanceOf[GenericInternalRow].values, inputSoi)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 8dc796b056a72..684ea1d137b49 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -211,18 +211,18 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
       }
     }
 
-    val nonDynamicPartLen = row.numFields - dynamicPartColNames.length
-    val dynamicPartPath = dynamicPartColNames.zipWithIndex.map { case (colName, i) =>
-      val rawVal = row.get(nonDynamicPartLen + i, schema(colName).dataType)
-      val string = if (rawVal == null) null else convertToHiveRawString(colName, rawVal)
-      val colString =
-        if (string == null || string.isEmpty) {
-          defaultPartName
-        } else {
-          FileUtils.escapePathName(string, defaultPartName)
-        }
-      s"/$colName=$colString"
-    }.mkString
+    val dynamicPartPath = dynamicPartColNames
+      .zip(row.toSeq.takeRight(dynamicPartColNames.length))
+      .map { case (col, rawVal) =>
+        val string = if (rawVal == null) null else convertToHiveRawString(col, rawVal)
+        val colString =
+          if (string == null || string.isEmpty) {
+            defaultPartName
+          } else {
+            FileUtils.escapePathName(string, defaultPartName)
+          }
+        s"/$col=$colString"
+      }.mkString
 
     def newWriter(): FileSinkOperator.RecordWriter = {
       val newFileSinkDesc = new FileSinkDesc(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
index 81a70b8d42267..99e95fb921301 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
@@ -133,8 +133,8 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
     }
   }
 
-  def checkValues(row1: Seq[Any], row2: InternalRow, row2Schema: StructType): Unit = {
-    row1.zip(row2.toSeq(row2Schema)).foreach { case (r1, r2) =>
+  def checkValues(row1: Seq[Any], row2: InternalRow): Unit = {
+    row1.zip(row2.toSeq).foreach { case (r1, r2) =>
       checkValue(r1, r2)
     }
   }
@@ -211,10 +211,8 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
       case (t, idx) => StructField(s"c_$idx", t)
     })
     val inspector = toInspector(dt)
-    checkValues(
-      row,
-      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow],
-      dt)
+    checkValues(row,
+      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow])
     checkValue(null, unwrap(wrap(null, toInspector(dt), dt), toInspector(dt)))
   }
 

From cdd53b762bf358616b313e3334b5f6945caf9ab1 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 6 Aug 2015 11:15:54 -0700
Subject: [PATCH 088/633] [SPARK-9632] [SQL] [HOT-FIX] Fix build.

seems https://github.com/apache/spark/pull/7955 breaks the build.

Author: Yin Huai <yhuai@databricks.com>

Closes #8001 from yhuai/SPARK-9632-fixBuild and squashes the following commits:

6c257dd [Yin Huai] Fix build.
---
 .../scala/org/apache/spark/sql/catalyst/expressions/rows.scala   | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 7657fb535dcf4..fd42fac3d2cd4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.catalyst.expressions
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.

From 0d7aac99da660cc42eb5a9be8e262bd9bd8a770f Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Thu, 6 Aug 2015 19:29:42 +0100
Subject: [PATCH 089/633] [SPARK-9641] [DOCS] spark.shuffle.service.port is not
 documented

Document spark.shuffle.service.{enabled,port}

CC sryza tgravescs
This is pretty minimal; is there more to say here about the service?

Author: Sean Owen <sowen@cloudera.com>

Closes #7991 from srowen/SPARK-9641 and squashes the following commits:

3bb946e [Sean Owen] Add link to docs for setup and config of external shuffle service
2302e01 [Sean Owen] Document spark.shuffle.service.{enabled,port}
---
 docs/configuration.md | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/docs/configuration.md b/docs/configuration.md
index 24b606356a149..c60dd16839c02 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -473,6 +473,25 @@ Apart from these, the following properties are also available, and may be useful
     <code>spark.storage.memoryFraction</code>.
   </td>
 </tr>
+<tr>
+  <td><code>spark.shuffle.service.enabled</code></td>
+  <td>false</td>
+  <td>
+    Enables the external shuffle service. This service preserves the shuffle files written by 
+    executors so the executors can be safely removed. This must be enabled if 
+    <code>spark.dynamicAllocation.enabled</code> is "true". The external shuffle service
+    must be set up in order to enable it. See
+    <a href="job-scheduling.html#configuration-and-setup">dynamic allocation 
+    configuration and setup documentation</a> for more information.
+  </td>
+</tr>
+<tr>
+  <td><code>spark.shuffle.service.port</code></td>
+  <td>7337</td>
+  <td>
+    Port on which the external shuffle service will run.
+  </td>
+</tr>
 <tr>
   <td><code>spark.shuffle.sort.bypassMergeThreshold</code></td>
   <td>200</td>

From a1bbf1bc5c51cd796015ac159799cf024de6fa07 Mon Sep 17 00:00:00 2001
From: Nilanjan Raychaudhuri <nraychaudhuri@gmail.com>
Date: Thu, 6 Aug 2015 12:50:08 -0700
Subject: [PATCH 090/633] [SPARK-8978] [STREAMING] Implements the
 DirectKafkaRateController
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Author: Dean Wampler <dean@concurrentthought.com>
Author: Nilanjan Raychaudhuri <nraychaudhuri@gmail.com>
Author: François Garillot <francois@garillot.net>

Closes #7796 from dragos/topic/streaming-bp/kafka-direct and squashes the following commits:

50d1f21 [Nilanjan Raychaudhuri] Taking care of the remaining nits
648c8b1 [Dean Wampler] Refactored rate controller test to be more predictable and run faster.
e43f678 [Nilanjan Raychaudhuri] fixing doc and nits
ce19d2a [Dean Wampler] Removing an unreliable assertion.
9615320 [Dean Wampler] Give me a break...
6372478 [Dean Wampler] Found a few ways to make this test more robust...
9e69e37 [Dean Wampler] Attempt to fix flakey test that fails in CI, but not locally :(
d3db1ea [Dean Wampler] Fixing stylecheck errors.
d04a288 [Nilanjan Raychaudhuri] adding test to make sure rate controller is used to calculate maxMessagesPerPartition
b6ecb67 [Nilanjan Raychaudhuri] Fixed styling issue
3110267 [Nilanjan Raychaudhuri] [SPARK-8978][Streaming] Implements the DirectKafkaRateController
393c580 [François Garillot] [SPARK-8978][Streaming] Implements the DirectKafkaRateController
51e78c6 [Nilanjan Raychaudhuri] Rename and fix build failure
2795509 [Nilanjan Raychaudhuri] Added missing RateController
19200f5 [Dean Wampler] Removed usage of infix notation. Changed a private variable name to be more consistent with usage.
aa4a70b [François Garillot] [SPARK-8978][Streaming] Implements the DirectKafkaController
---
 .../kafka/DirectKafkaInputDStream.scala       | 47 ++++++++--
 .../kafka/DirectKafkaStreamSuite.scala        | 89 +++++++++++++++++++
 2 files changed, 127 insertions(+), 9 deletions(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
index 48a1933d92f85..8a177077775c6 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
@@ -29,7 +29,8 @@ import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.streaming.{StreamingContext, Time}
 import org.apache.spark.streaming.dstream._
 import org.apache.spark.streaming.kafka.KafkaCluster.LeaderOffset
-import org.apache.spark.streaming.scheduler.StreamInputInfo
+import org.apache.spark.streaming.scheduler.{RateController, StreamInputInfo}
+import org.apache.spark.streaming.scheduler.rate.RateEstimator
 
 /**
  *  A stream of {@link org.apache.spark.streaming.kafka.KafkaRDD} where
@@ -61,7 +62,7 @@ class DirectKafkaInputDStream[
     val kafkaParams: Map[String, String],
     val fromOffsets: Map[TopicAndPartition, Long],
     messageHandler: MessageAndMetadata[K, V] => R
-) extends InputDStream[R](ssc_) with Logging {
+  ) extends InputDStream[R](ssc_) with Logging {
   val maxRetries = context.sparkContext.getConf.getInt(
     "spark.streaming.kafka.maxRetries", 1)
 
@@ -71,14 +72,35 @@ class DirectKafkaInputDStream[
   protected[streaming] override val checkpointData =
     new DirectKafkaInputDStreamCheckpointData
 
+
+  /**
+   * Asynchronously maintains & sends new rate limits to the receiver through the receiver tracker.
+   */
+  override protected[streaming] val rateController: Option[RateController] = {
+    if (RateController.isBackPressureEnabled(ssc.conf)) {
+      Some(new DirectKafkaRateController(id,
+        RateEstimator.create(ssc.conf, ssc_.graph.batchDuration)))
+    } else {
+      None
+    }
+  }
+
   protected val kc = new KafkaCluster(kafkaParams)
 
-  protected val maxMessagesPerPartition: Option[Long] = {
-    val ratePerSec = context.sparkContext.getConf.getInt(
+  private val maxRateLimitPerPartition: Int = context.sparkContext.getConf.getInt(
       "spark.streaming.kafka.maxRatePerPartition", 0)
-    if (ratePerSec > 0) {
+  protected def maxMessagesPerPartition: Option[Long] = {
+    val estimatedRateLimit = rateController.map(_.getLatestRate().toInt)
+    val numPartitions = currentOffsets.keys.size
+
+    val effectiveRateLimitPerPartition = estimatedRateLimit
+      .filter(_ > 0)
+      .map(limit => Math.min(maxRateLimitPerPartition, (limit / numPartitions)))
+      .getOrElse(maxRateLimitPerPartition)
+
+    if (effectiveRateLimitPerPartition > 0) {
       val secsPerBatch = context.graph.batchDuration.milliseconds.toDouble / 1000
-      Some((secsPerBatch * ratePerSec).toLong)
+      Some((secsPerBatch * effectiveRateLimitPerPartition).toLong)
     } else {
       None
     }
@@ -170,11 +192,18 @@ class DirectKafkaInputDStream[
       val leaders = KafkaCluster.checkErrors(kc.findLeaders(topics))
 
       batchForTime.toSeq.sortBy(_._1)(Time.ordering).foreach { case (t, b) =>
-          logInfo(s"Restoring KafkaRDD for time $t ${b.mkString("[", ", ", "]")}")
-          generatedRDDs += t -> new KafkaRDD[K, V, U, T, R](
-            context.sparkContext, kafkaParams, b.map(OffsetRange(_)), leaders, messageHandler)
+         logInfo(s"Restoring KafkaRDD for time $t ${b.mkString("[", ", ", "]")}")
+         generatedRDDs += t -> new KafkaRDD[K, V, U, T, R](
+           context.sparkContext, kafkaParams, b.map(OffsetRange(_)), leaders, messageHandler)
       }
     }
   }
 
+  /**
+   * A RateController to retrieve the rate from RateEstimator.
+   */
+  private[streaming] class DirectKafkaRateController(id: Int, estimator: RateEstimator)
+    extends RateController(id, estimator) {
+    override def publish(rate: Long): Unit = ()
+  }
 }
diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
index 5b3c79444aa68..02225d5aa7cc5 100644
--- a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
+++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/DirectKafkaStreamSuite.scala
@@ -20,6 +20,9 @@ package org.apache.spark.streaming.kafka
 import java.io.File
 import java.util.concurrent.atomic.AtomicLong
 
+import org.apache.spark.streaming.kafka.KafkaCluster.LeaderOffset
+import org.apache.spark.streaming.scheduler.rate.RateEstimator
+
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.duration._
@@ -350,6 +353,77 @@ class DirectKafkaStreamSuite
     ssc.stop()
   }
 
+  test("using rate controller") {
+    val topic = "backpressure"
+    val topicPartition = TopicAndPartition(topic, 0)
+    kafkaTestUtils.createTopic(topic)
+    val kafkaParams = Map(
+      "metadata.broker.list" -> kafkaTestUtils.brokerAddress,
+      "auto.offset.reset" -> "smallest"
+    )
+
+    val batchIntervalMilliseconds = 100
+    val estimator = new ConstantEstimator(100)
+    val messageKeys = (1 to 200).map(_.toString)
+    val messages = messageKeys.map((_, 1)).toMap
+
+    val sparkConf = new SparkConf()
+      // Safe, even with streaming, because we're using the direct API.
+      // Using 1 core is useful to make the test more predictable.
+      .setMaster("local[1]")
+      .setAppName(this.getClass.getSimpleName)
+      .set("spark.streaming.kafka.maxRatePerPartition", "100")
+
+    // Setup the streaming context
+    ssc = new StreamingContext(sparkConf, Milliseconds(batchIntervalMilliseconds))
+
+    val kafkaStream = withClue("Error creating direct stream") {
+      val kc = new KafkaCluster(kafkaParams)
+      val messageHandler = (mmd: MessageAndMetadata[String, String]) => (mmd.key, mmd.message)
+      val m = kc.getEarliestLeaderOffsets(Set(topicPartition))
+        .fold(e => Map.empty[TopicAndPartition, Long], m => m.mapValues(lo => lo.offset))
+
+      new DirectKafkaInputDStream[String, String, StringDecoder, StringDecoder, (String, String)](
+        ssc, kafkaParams, m, messageHandler) {
+        override protected[streaming] val rateController =
+          Some(new DirectKafkaRateController(id, estimator))
+      }
+    }
+
+    val collectedData =
+      new mutable.ArrayBuffer[Array[String]]() with mutable.SynchronizedBuffer[Array[String]]
+
+    // Used for assertion failure messages.
+    def dataToString: String =
+      collectedData.map(_.mkString("[", ",", "]")).mkString("{", ", ", "}")
+
+    // This is to collect the raw data received from Kafka
+    kafkaStream.foreachRDD { (rdd: RDD[(String, String)], time: Time) =>
+      val data = rdd.map { _._2 }.collect()
+      collectedData += data
+    }
+
+    ssc.start()
+
+    // Try different rate limits.
+    // Send data to Kafka and wait for arrays of data to appear matching the rate.
+    Seq(100, 50, 20).foreach { rate =>
+      collectedData.clear()       // Empty this buffer on each pass.
+      estimator.updateRate(rate)  // Set a new rate.
+      // Expect blocks of data equal to "rate", scaled by the interval length in secs.
+      val expectedSize = Math.round(rate * batchIntervalMilliseconds * 0.001)
+      kafkaTestUtils.sendMessages(topic, messages)
+      eventually(timeout(5.seconds), interval(batchIntervalMilliseconds.milliseconds)) {
+        // Assert that rate estimator values are used to determine maxMessagesPerPartition.
+        // Funky "-" in message makes the complete assertion message read better.
+        assert(collectedData.exists(_.size == expectedSize),
+          s" - No arrays of size $expectedSize for rate $rate found in $dataToString")
+      }
+    }
+
+    ssc.stop()
+  }
+
   /** Get the generated offset ranges from the DirectKafkaStream */
   private def getOffsetRanges[K, V](
       kafkaStream: DStream[(K, V)]): Seq[(Time, Array[OffsetRange])] = {
@@ -381,3 +455,18 @@ object DirectKafkaStreamSuite {
     }
   }
 }
+
+private[streaming] class ConstantEstimator(@volatile private var rate: Long)
+  extends RateEstimator {
+
+  def updateRate(newRate: Long): Unit = {
+    rate = newRate
+  }
+
+  def compute(
+      time: Long,
+      elements: Long,
+      processingDelay: Long,
+      schedulingDelay: Long): Option[Double] = Some(rate)
+}
+

From 1f62f104c7a2aeac625b17d9e5ac62f1f10a2b21 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 6 Aug 2015 13:11:59 -0700
Subject: [PATCH 091/633] [SPARK-9632][SQL] update InternalRow.toSeq to make it
 accept data type info

This re-applies #7955, which was reverted due to a race condition to fix build breaking.

Author: Wenchen Fan <cloud0fan@outlook.com>
Author: Reynold Xin <rxin@databricks.com>

Closes #8002 from rxin/InternalRow-toSeq and squashes the following commits:

332416a [Reynold Xin] Merge pull request #7955 from cloud-fan/toSeq
21665e2 [Wenchen Fan] fix hive again...
4addf29 [Wenchen Fan] fix hive
bc16c59 [Wenchen Fan] minor fix
33d802c [Wenchen Fan] pass data type info to InternalRow.toSeq
3dd033e [Wenchen Fan] move the default special getters implementation from InternalRow to BaseGenericInternalRow
---
 .../spark/sql/catalyst/InternalRow.scala      | 132 ++----------------
 .../sql/catalyst/expressions/Projection.scala |  12 +-
 .../expressions/SpecificMutableRow.scala      |   5 +-
 .../codegen/GenerateProjection.scala          |   8 +-
 .../spark/sql/catalyst/expressions/rows.scala | 132 +++++++++++++++++-
 .../expressions/CodeGenerationSuite.scala     |   2 +-
 .../spark/sql/columnar/ColumnStats.scala      |  51 +++----
 .../columnar/InMemoryColumnarTableScan.scala  |  11 +-
 .../spark/sql/execution/debug/package.scala   |   4 +-
 .../apache/spark/sql/sources/interfaces.scala |   4 +-
 .../spark/sql/columnar/ColumnStatsSuite.scala |  54 +++----
 .../spark/sql/hive/HiveInspectors.scala       |   6 +-
 .../hive/execution/ScriptTransformation.scala |  21 ++-
 .../spark/sql/hive/hiveWriterContainers.scala |  24 ++--
 .../spark/sql/hive/HiveInspectorSuite.scala   |  10 +-
 15 files changed, 259 insertions(+), 217 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 7d17cca808791..85b4bf3b6aef5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.catalyst
 
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{DataType, MapData, ArrayData, Decimal}
-import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
+import org.apache.spark.sql.types.{DataType, StructType}
 
 /**
  * An abstract class for row used internal in Spark SQL, which only contain the columns as
@@ -32,8 +31,6 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
   // This is only use for test and will throw a null pointer exception if the position is null.
   def getString(ordinal: Int): String = getUTF8String(ordinal).toString
 
-  override def toString: String = mkString("[", ",", "]")
-
   /**
    * Make a copy of the current [[InternalRow]] object.
    */
@@ -50,136 +47,25 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
     false
   }
 
-  // Subclasses of InternalRow should implement all special getters and equals/hashCode,
-  // or implement this genericGet.
-  protected def genericGet(ordinal: Int): Any = throw new IllegalStateException(
-    "Concrete internal rows should implement genericGet, " +
-      "or implement all special getters and equals/hashCode")
-
-  // default implementation (slow)
-  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
-  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
-  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
-  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
-  override def getByte(ordinal: Int): Byte = getAs(ordinal)
-  override def getShort(ordinal: Int): Short = getAs(ordinal)
-  override def getInt(ordinal: Int): Int = getAs(ordinal)
-  override def getLong(ordinal: Int): Long = getAs(ordinal)
-  override def getFloat(ordinal: Int): Float = getAs(ordinal)
-  override def getDouble(ordinal: Int): Double = getAs(ordinal)
-  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
-  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
-  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
-  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
-  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
-  override def getMap(ordinal: Int): MapData = getAs(ordinal)
-  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
-
-  override def equals(o: Any): Boolean = {
-    if (!o.isInstanceOf[InternalRow]) {
-      return false
-    }
-
-    val other = o.asInstanceOf[InternalRow]
-    if (other eq null) {
-      return false
-    }
-
-    val len = numFields
-    if (len != other.numFields) {
-      return false
-    }
-
-    var i = 0
-    while (i < len) {
-      if (isNullAt(i) != other.isNullAt(i)) {
-        return false
-      }
-      if (!isNullAt(i)) {
-        val o1 = genericGet(i)
-        val o2 = other.genericGet(i)
-        o1 match {
-          case b1: Array[Byte] =>
-            if (!o2.isInstanceOf[Array[Byte]] ||
-              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
-              return false
-            }
-          case f1: Float if java.lang.Float.isNaN(f1) =>
-            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
-              return false
-            }
-          case d1: Double if java.lang.Double.isNaN(d1) =>
-            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
-              return false
-            }
-          case _ => if (o1 != o2) {
-            return false
-          }
-        }
-      }
-      i += 1
-    }
-    true
-  }
-
-  // Custom hashCode function that matches the efficient code generated version.
-  override def hashCode: Int = {
-    var result: Int = 37
-    var i = 0
-    val len = numFields
-    while (i < len) {
-      val update: Int =
-        if (isNullAt(i)) {
-          0
-        } else {
-          genericGet(i) match {
-            case b: Boolean => if (b) 0 else 1
-            case b: Byte => b.toInt
-            case s: Short => s.toInt
-            case i: Int => i
-            case l: Long => (l ^ (l >>> 32)).toInt
-            case f: Float => java.lang.Float.floatToIntBits(f)
-            case d: Double =>
-              val b = java.lang.Double.doubleToLongBits(d)
-              (b ^ (b >>> 32)).toInt
-            case a: Array[Byte] => java.util.Arrays.hashCode(a)
-            case other => other.hashCode()
-          }
-        }
-      result = 37 * result + update
-      i += 1
-    }
-    result
-  }
-
   /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
    * Return a Scala Seq representing the row. Elements are placed in the same order in the Seq.
    */
-  // todo: remove this as it needs the generic getter
-  def toSeq: Seq[Any] = {
-    val n = numFields
-    val values = new Array[Any](n)
+  def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
+    val len = numFields
+    assert(len == fieldTypes.length)
+
+    val values = new Array[Any](len)
     var i = 0
-    while (i < n) {
-      values.update(i, genericGet(i))
+    while (i < len) {
+      values(i) = get(i, fieldTypes(i))
       i += 1
     }
     values
   }
 
-  /** Displays all elements of this sequence in a string (without a separator). */
-  def mkString: String = toSeq.mkString
-
-  /** Displays all elements of this sequence in a string using a separator string. */
-  def mkString(sep: String): String = toSeq.mkString(sep)
-
-  /**
-   * Displays all elements of this traversable or iterator in a string using
-   * start, end, and separator strings.
-   */
-  def mkString(start: String, sep: String, end: String): String = toSeq.mkString(start, sep, end)
+  def toSeq(schema: StructType): Seq[Any] = toSeq(schema.map(_.dataType))
 }
 
 object InternalRow {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 4296b4b123fc0..59ce7fc4f2c63 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -203,7 +203,11 @@ class JoinedRow extends InternalRow {
     this
   }
 
-  override def toSeq: Seq[Any] = row1.toSeq ++ row2.toSeq
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
+    assert(fieldTypes.length == row1.numFields + row2.numFields)
+    val (left, right) = fieldTypes.splitAt(row1.numFields)
+    row1.toSeq(left) ++ row2.toSeq(right)
+  }
 
   override def numFields: Int = row1.numFields + row2.numFields
 
@@ -276,11 +280,11 @@ class JoinedRow extends InternalRow {
     if ((row1 eq null) && (row2 eq null)) {
       "[ empty row ]"
     } else if (row1 eq null) {
-      row2.mkString("[", ",", "]")
+      row2.toString
     } else if (row2 eq null) {
-      row1.mkString("[", ",", "]")
+      row1.toString
     } else {
-      mkString("[", ",", "]")
+      s"{${row1.toString} + ${row2.toString}}"
     }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
index b94df6bd66e04..4f56f94bd4ca4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SpecificMutableRow.scala
@@ -192,7 +192,8 @@ final class MutableAny extends MutableValue {
  * based on the dataTypes of each column.  The intent is to decrease garbage when modifying the
  * values of primitive columns.
  */
-final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableRow {
+final class SpecificMutableRow(val values: Array[MutableValue])
+  extends MutableRow with BaseGenericInternalRow {
 
   def this(dataTypes: Seq[DataType]) =
     this(
@@ -213,8 +214,6 @@ final class SpecificMutableRow(val values: Array[MutableValue]) extends MutableR
 
   override def numFields: Int = values.length
 
-  override def toSeq: Seq[Any] = values.map(_.boxed)
-
   override def setNullAt(i: Int): Unit = {
     values(i).isNull = true
   }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index c04fe734d554e..c744e84d822e8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
 
@@ -25,6 +26,8 @@ import org.apache.spark.sql.types._
  */
 abstract class BaseProjection extends Projection {}
 
+abstract class CodeGenMutableRow extends MutableRow with BaseGenericInternalRow
+
 /**
  * Generates bytecode that produces a new [[InternalRow]] object based on a fixed set of input
  * [[Expression Expressions]] and a given input [[InternalRow]].  The returned [[InternalRow]]
@@ -171,7 +174,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         return new SpecificRow((InternalRow) r);
       }
 
-      final class SpecificRow extends ${classOf[MutableRow].getName} {
+      final class SpecificRow extends ${classOf[CodeGenMutableRow].getName} {
 
         $columns
 
@@ -184,7 +187,8 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         public void setNullAt(int i) { nullBits[i] = true; }
         public boolean isNullAt(int i) { return nullBits[i]; }
 
-        protected Object genericGet(int i) {
+        @Override
+        public Object genericGet(int i) {
           if (isNullAt(i)) return null;
           switch (i) {
           $getCases
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index fd42fac3d2cd4..11d10b2d8a48b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -22,6 +22,130 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
+/**
+ * An extended version of [[InternalRow]] that implements all special getters, toString
+ * and equals/hashCode by `genericGet`.
+ */
+trait BaseGenericInternalRow extends InternalRow {
+
+  protected def genericGet(ordinal: Int): Any
+
+  // default implementation (slow)
+  private def getAs[T](ordinal: Int) = genericGet(ordinal).asInstanceOf[T]
+  override def isNullAt(ordinal: Int): Boolean = getAs[AnyRef](ordinal) eq null
+  override def get(ordinal: Int, dataType: DataType): AnyRef = getAs(ordinal)
+  override def getBoolean(ordinal: Int): Boolean = getAs(ordinal)
+  override def getByte(ordinal: Int): Byte = getAs(ordinal)
+  override def getShort(ordinal: Int): Short = getAs(ordinal)
+  override def getInt(ordinal: Int): Int = getAs(ordinal)
+  override def getLong(ordinal: Int): Long = getAs(ordinal)
+  override def getFloat(ordinal: Int): Float = getAs(ordinal)
+  override def getDouble(ordinal: Int): Double = getAs(ordinal)
+  override def getDecimal(ordinal: Int, precision: Int, scale: Int): Decimal = getAs(ordinal)
+  override def getUTF8String(ordinal: Int): UTF8String = getAs(ordinal)
+  override def getBinary(ordinal: Int): Array[Byte] = getAs(ordinal)
+  override def getArray(ordinal: Int): ArrayData = getAs(ordinal)
+  override def getInterval(ordinal: Int): CalendarInterval = getAs(ordinal)
+  override def getMap(ordinal: Int): MapData = getAs(ordinal)
+  override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
+
+  override def toString(): String = {
+    if (numFields == 0) {
+      "[empty row]"
+    } else {
+      val sb = new StringBuilder
+      sb.append("[")
+      sb.append(genericGet(0))
+      val len = numFields
+      var i = 1
+      while (i < len) {
+        sb.append(",")
+        sb.append(genericGet(i))
+        i += 1
+      }
+      sb.append("]")
+      sb.toString()
+    }
+  }
+
+  override def equals(o: Any): Boolean = {
+    if (!o.isInstanceOf[BaseGenericInternalRow]) {
+      return false
+    }
+
+    val other = o.asInstanceOf[BaseGenericInternalRow]
+    if (other eq null) {
+      return false
+    }
+
+    val len = numFields
+    if (len != other.numFields) {
+      return false
+    }
+
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i) != other.isNullAt(i)) {
+        return false
+      }
+      if (!isNullAt(i)) {
+        val o1 = genericGet(i)
+        val o2 = other.genericGet(i)
+        o1 match {
+          case b1: Array[Byte] =>
+            if (!o2.isInstanceOf[Array[Byte]] ||
+              !java.util.Arrays.equals(b1, o2.asInstanceOf[Array[Byte]])) {
+              return false
+            }
+          case f1: Float if java.lang.Float.isNaN(f1) =>
+            if (!o2.isInstanceOf[Float] || ! java.lang.Float.isNaN(o2.asInstanceOf[Float])) {
+              return false
+            }
+          case d1: Double if java.lang.Double.isNaN(d1) =>
+            if (!o2.isInstanceOf[Double] || ! java.lang.Double.isNaN(o2.asInstanceOf[Double])) {
+              return false
+            }
+          case _ => if (o1 != o2) {
+            return false
+          }
+        }
+      }
+      i += 1
+    }
+    true
+  }
+
+  // Custom hashCode function that matches the efficient code generated version.
+  override def hashCode: Int = {
+    var result: Int = 37
+    var i = 0
+    val len = numFields
+    while (i < len) {
+      val update: Int =
+        if (isNullAt(i)) {
+          0
+        } else {
+          genericGet(i) match {
+            case b: Boolean => if (b) 0 else 1
+            case b: Byte => b.toInt
+            case s: Short => s.toInt
+            case i: Int => i
+            case l: Long => (l ^ (l >>> 32)).toInt
+            case f: Float => java.lang.Float.floatToIntBits(f)
+            case d: Double =>
+              val b = java.lang.Double.doubleToLongBits(d)
+              (b ^ (b >>> 32)).toInt
+            case a: Array[Byte] => java.util.Arrays.hashCode(a)
+            case other => other.hashCode()
+          }
+        }
+      result = 37 * result + update
+      i += 1
+    }
+    result
+  }
+}
+
 /**
  * An extended interface to [[InternalRow]] that allows the values for each column to be updated.
  * Setting a value through a primitive function implicitly marks that column as not null.
@@ -83,7 +207,7 @@ class GenericRowWithSchema(values: Array[Any], override val schema: StructType)
  * Note that, while the array is not copied, and thus could technically be mutated after creation,
  * this is not allowed.
  */
-class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRow {
+class GenericInternalRow(private[sql] val values: Array[Any]) extends BaseGenericInternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -91,7 +215,7 @@ class GenericInternalRow(private[sql] val values: Array[Any]) extends InternalRo
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq: Seq[Any] = values
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
 
   override def numFields: Int = values.length
 
@@ -110,7 +234,7 @@ class GenericInternalRowWithSchema(values: Array[Any], val schema: StructType)
   def fieldIndex(name: String): Int = schema.fieldIndex(name)
 }
 
-class GenericMutableRow(values: Array[Any]) extends MutableRow {
+class GenericMutableRow(values: Array[Any]) extends MutableRow with BaseGenericInternalRow {
   /** No-arg constructor for serialization. */
   protected def this() = this(null)
 
@@ -118,7 +242,7 @@ class GenericMutableRow(values: Array[Any]) extends MutableRow {
 
   override protected def genericGet(ordinal: Int) = values(ordinal)
 
-  override def toSeq: Seq[Any] = values
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = values
 
   override def numFields: Int = values.length
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
index e310aee221666..e323467af5f4a 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CodeGenerationSuite.scala
@@ -87,7 +87,7 @@ class CodeGenerationSuite extends SparkFunSuite with ExpressionEvalHelper {
     val length = 5000
     val expressions = List.fill(length)(EqualTo(Literal(1), Literal(1)))
     val plan = GenerateMutableProjection.generate(expressions)()
-    val actual = plan(new GenericMutableRow(length)).toSeq
+    val actual = plan(new GenericMutableRow(length)).toSeq(expressions.map(_.dataType))
     val expected = Seq.fill(length)(true)
 
     if (!checkResult(actual, expected)) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
index af1a8ecca9b57..5cbd52bc0590e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnStats.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.columnar
 
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeMap, AttributeReference}
+import org.apache.spark.sql.catalyst.expressions.{GenericInternalRow, Attribute, AttributeMap, AttributeReference}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -66,7 +66,7 @@ private[sql] sealed trait ColumnStats extends Serializable {
    * Column statistics represented as a single row, currently including closed lower bound, closed
    * upper bound and null count.
    */
-  def collectedStatistics: InternalRow
+  def collectedStatistics: GenericInternalRow
 }
 
 /**
@@ -75,7 +75,8 @@ private[sql] sealed trait ColumnStats extends Serializable {
 private[sql] class NoopColumnStats extends ColumnStats {
   override def gatherStats(row: InternalRow, ordinal: Int): Unit = super.gatherStats(row, ordinal)
 
-  override def collectedStatistics: InternalRow = InternalRow(null, null, nullCount, count, 0L)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, 0L))
 }
 
 private[sql] class BooleanColumnStats extends ColumnStats {
@@ -92,8 +93,8 @@ private[sql] class BooleanColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class ByteColumnStats extends ColumnStats {
@@ -110,8 +111,8 @@ private[sql] class ByteColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class ShortColumnStats extends ColumnStats {
@@ -128,8 +129,8 @@ private[sql] class ShortColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class IntColumnStats extends ColumnStats {
@@ -146,8 +147,8 @@ private[sql] class IntColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class LongColumnStats extends ColumnStats {
@@ -164,8 +165,8 @@ private[sql] class LongColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class FloatColumnStats extends ColumnStats {
@@ -182,8 +183,8 @@ private[sql] class FloatColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class DoubleColumnStats extends ColumnStats {
@@ -200,8 +201,8 @@ private[sql] class DoubleColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class StringColumnStats extends ColumnStats {
@@ -218,8 +219,8 @@ private[sql] class StringColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class BinaryColumnStats extends ColumnStats {
@@ -230,8 +231,8 @@ private[sql] class BinaryColumnStats extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(null, null, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
 }
 
 private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends ColumnStats {
@@ -248,8 +249,8 @@ private[sql] class FixedDecimalColumnStats(precision: Int, scale: Int) extends C
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(lower, upper, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](lower, upper, nullCount, count, sizeInBytes))
 }
 
 private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
@@ -262,8 +263,8 @@ private[sql] class GenericColumnStats(dataType: DataType) extends ColumnStats {
     }
   }
 
-  override def collectedStatistics: InternalRow =
-    InternalRow(null, null, nullCount, count, sizeInBytes)
+  override def collectedStatistics: GenericInternalRow =
+    new GenericInternalRow(Array[Any](null, null, nullCount, count, sizeInBytes))
 }
 
 private[sql] class DateColumnStats extends IntColumnStats
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index 5d5b0697d7016..d553bb6169ecc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -148,7 +148,7 @@ private[sql] case class InMemoryRelation(
           }
 
           val stats = InternalRow.fromSeq(columnBuilders.map(_.columnStats.collectedStatistics)
-                        .flatMap(_.toSeq))
+                        .flatMap(_.values))
 
           batchStats += stats
           CachedBatch(columnBuilders.map(_.build().array()), stats)
@@ -330,10 +330,11 @@ private[sql] case class InMemoryColumnarTableScan(
         if (inMemoryPartitionPruningEnabled) {
           cachedBatchIterator.filter { cachedBatch =>
             if (!partitionFilter(cachedBatch.stats)) {
-              def statsString: String = relation.partitionStatistics.schema
-                .zip(cachedBatch.stats.toSeq)
-                .map { case (a, s) => s"${a.name}: $s" }
-                .mkString(", ")
+              def statsString: String = relation.partitionStatistics.schema.zipWithIndex.map {
+                case (a, i) =>
+                  val value = cachedBatch.stats.get(i, a.dataType)
+                  s"${a.name}: $value"
+              }.mkString(", ")
               logInfo(s"Skipping partition based on stats $statsString")
               false
             } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index c37007f1eece7..dd3858ea2b520 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -156,8 +156,8 @@ package object debug {
     def typeCheck(data: Any, schema: DataType): Unit = (data, schema) match {
       case (null, _) =>
 
-      case (row: InternalRow, StructType(fields)) =>
-        row.toSeq.zip(fields.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
+      case (row: InternalRow, s: StructType) =>
+        row.toSeq(s).zip(s.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
       case (a: ArrayData, ArrayType(elemType, _)) =>
         a.foreach(elemType, (_, e) => {
           typeCheck(e, elemType)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 7126145ddc010..c04557e5a0818 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -461,8 +461,8 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
             val spec = discoverPartitions()
             val partitionColumnTypes = spec.partitionColumns.map(_.dataType)
             val castedPartitions = spec.partitions.map { case p @ Partition(values, path) =>
-              val literals = values.toSeq.zip(partitionColumnTypes).map {
-                case (value, dataType) => Literal.create(value, dataType)
+              val literals = partitionColumnTypes.zipWithIndex.map { case (dt, i) =>
+                Literal.create(values.get(i, dt), dt)
               }
               val castedValues = partitionSchema.zip(literals).map { case (field, literal) =>
                 Cast(literal, field.dataType).eval()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
index 16e0187ed20a0..d0430d2a60e75 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/ColumnStatsSuite.scala
@@ -19,33 +19,36 @@ package org.apache.spark.sql.columnar
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.GenericInternalRow
 import org.apache.spark.sql.types._
 
 class ColumnStatsSuite extends SparkFunSuite {
-  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, InternalRow(true, false, 0))
-  testColumnStats(classOf[ByteColumnStats], BYTE, InternalRow(Byte.MaxValue, Byte.MinValue, 0))
-  testColumnStats(classOf[ShortColumnStats], SHORT, InternalRow(Short.MaxValue, Short.MinValue, 0))
-  testColumnStats(classOf[IntColumnStats], INT, InternalRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[DateColumnStats], DATE, InternalRow(Int.MaxValue, Int.MinValue, 0))
-  testColumnStats(classOf[LongColumnStats], LONG, InternalRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[BooleanColumnStats], BOOLEAN, createRow(true, false, 0))
+  testColumnStats(classOf[ByteColumnStats], BYTE, createRow(Byte.MaxValue, Byte.MinValue, 0))
+  testColumnStats(classOf[ShortColumnStats], SHORT, createRow(Short.MaxValue, Short.MinValue, 0))
+  testColumnStats(classOf[IntColumnStats], INT, createRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[DateColumnStats], DATE, createRow(Int.MaxValue, Int.MinValue, 0))
+  testColumnStats(classOf[LongColumnStats], LONG, createRow(Long.MaxValue, Long.MinValue, 0))
   testColumnStats(classOf[TimestampColumnStats], TIMESTAMP,
-    InternalRow(Long.MaxValue, Long.MinValue, 0))
-  testColumnStats(classOf[FloatColumnStats], FLOAT, InternalRow(Float.MaxValue, Float.MinValue, 0))
+    createRow(Long.MaxValue, Long.MinValue, 0))
+  testColumnStats(classOf[FloatColumnStats], FLOAT, createRow(Float.MaxValue, Float.MinValue, 0))
   testColumnStats(classOf[DoubleColumnStats], DOUBLE,
-    InternalRow(Double.MaxValue, Double.MinValue, 0))
-  testColumnStats(classOf[StringColumnStats], STRING, InternalRow(null, null, 0))
-  testDecimalColumnStats(InternalRow(null, null, 0))
+    createRow(Double.MaxValue, Double.MinValue, 0))
+  testColumnStats(classOf[StringColumnStats], STRING, createRow(null, null, 0))
+  testDecimalColumnStats(createRow(null, null, 0))
+
+  def createRow(values: Any*): GenericInternalRow = new GenericInternalRow(values.toArray)
 
   def testColumnStats[T <: AtomicType, U <: ColumnStats](
       columnStatsClass: Class[U],
       columnType: NativeColumnType[T],
-      initialStatistics: InternalRow): Unit = {
+      initialStatistics: GenericInternalRow): Unit = {
 
     val columnStatsName = columnStatsClass.getSimpleName
 
     test(s"$columnStatsName: empty") {
       val columnStats = columnStatsClass.newInstance()
-      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
+      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -61,11 +64,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
-      assertResult(10, "Wrong null count")(stats.get(2, null))
-      assertResult(20, "Wrong row count")(stats.get(3, null))
-      assertResult(stats.get(4, null), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
+      assertResult(10, "Wrong null count")(stats.values(2))
+      assertResult(20, "Wrong row count")(stats.values(3))
+      assertResult(stats.values(4), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
@@ -73,14 +76,15 @@ class ColumnStatsSuite extends SparkFunSuite {
     }
   }
 
-  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](initialStatistics: InternalRow) {
+  def testDecimalColumnStats[T <: AtomicType, U <: ColumnStats](
+      initialStatistics: GenericInternalRow): Unit = {
 
     val columnStatsName = classOf[FixedDecimalColumnStats].getSimpleName
     val columnType = FIXED_DECIMAL(15, 10)
 
     test(s"$columnStatsName: empty") {
       val columnStats = new FixedDecimalColumnStats(15, 10)
-      columnStats.collectedStatistics.toSeq.zip(initialStatistics.toSeq).foreach {
+      columnStats.collectedStatistics.values.zip(initialStatistics.values).foreach {
         case (actual, expected) => assert(actual === expected)
       }
     }
@@ -96,11 +100,11 @@ class ColumnStatsSuite extends SparkFunSuite {
       val ordering = columnType.dataType.ordering.asInstanceOf[Ordering[T#InternalType]]
       val stats = columnStats.collectedStatistics
 
-      assertResult(values.min(ordering), "Wrong lower bound")(stats.get(0, null))
-      assertResult(values.max(ordering), "Wrong upper bound")(stats.get(1, null))
-      assertResult(10, "Wrong null count")(stats.get(2, null))
-      assertResult(20, "Wrong row count")(stats.get(3, null))
-      assertResult(stats.get(4, null), "Wrong size in bytes") {
+      assertResult(values.min(ordering), "Wrong lower bound")(stats.values(0))
+      assertResult(values.max(ordering), "Wrong upper bound")(stats.values(1))
+      assertResult(10, "Wrong null count")(stats.values(2))
+      assertResult(20, "Wrong row count")(stats.values(3))
+      assertResult(stats.values(4), "Wrong size in bytes") {
         rows.map { row =>
           if (row.isNullAt(0)) 4 else columnType.actualSize(row, 0)
         }.sum
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 39d798d072aeb..9824dad239596 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -390,8 +390,10 @@ private[hive] trait HiveInspectors {
       (o: Any) => {
         if (o != null) {
           val struct = soi.create()
-          (soi.getAllStructFieldRefs, wrappers, o.asInstanceOf[InternalRow].toSeq).zipped.foreach {
-            (field, wrapper, data) => soi.setStructFieldData(struct, field, wrapper(data))
+          val row = o.asInstanceOf[InternalRow]
+          soi.getAllStructFieldRefs.zip(wrappers).zipWithIndex.foreach {
+            case ((field, wrapper), i) =>
+              soi.setStructFieldData(struct, field, wrapper(row.get(i, schema(i).dataType)))
           }
           struct
         } else {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index a6a343d395995..ade27454b9d29 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -88,6 +88,7 @@ case class ScriptTransformation(
       // external process. That process's output will be read by this current thread.
       val writerThread = new ScriptTransformationWriterThread(
         inputIterator,
+        input.map(_.dataType),
         outputProjection,
         inputSerde,
         inputSoi,
@@ -201,6 +202,7 @@ case class ScriptTransformation(
 
 private class ScriptTransformationWriterThread(
     iter: Iterator[InternalRow],
+    inputSchema: Seq[DataType],
     outputProjection: Projection,
     @Nullable inputSerde: AbstractSerDe,
     @Nullable inputSoi: ObjectInspector,
@@ -226,12 +228,25 @@ private class ScriptTransformationWriterThread(
     // We can't use Utils.tryWithSafeFinally here because we also need a `catch` block, so
     // let's use a variable to record whether the `finally` block was hit due to an exception
     var threwException: Boolean = true
+    val len = inputSchema.length
     try {
       iter.map(outputProjection).foreach { row =>
         if (inputSerde == null) {
-          val data = row.mkString("", ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"),
-            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")).getBytes("utf-8")
-          outputStream.write(data)
+          val data = if (len == 0) {
+            ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES")
+          } else {
+            val sb = new StringBuilder
+            sb.append(row.get(0, inputSchema(0)))
+            var i = 1
+            while (i < len) {
+              sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATFIELD"))
+              sb.append(row.get(i, inputSchema(i)))
+              i += 1
+            }
+            sb.append(ioschema.inputRowFormatMap("TOK_TABLEROWFORMATLINES"))
+            sb.toString()
+          }
+          outputStream.write(data.getBytes("utf-8"))
         } else {
           val writable = inputSerde.serialize(
             row.asInstanceOf[GenericInternalRow].values, inputSoi)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 684ea1d137b49..8dc796b056a72 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -211,18 +211,18 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
       }
     }
 
-    val dynamicPartPath = dynamicPartColNames
-      .zip(row.toSeq.takeRight(dynamicPartColNames.length))
-      .map { case (col, rawVal) =>
-        val string = if (rawVal == null) null else convertToHiveRawString(col, rawVal)
-        val colString =
-          if (string == null || string.isEmpty) {
-            defaultPartName
-          } else {
-            FileUtils.escapePathName(string, defaultPartName)
-          }
-        s"/$col=$colString"
-      }.mkString
+    val nonDynamicPartLen = row.numFields - dynamicPartColNames.length
+    val dynamicPartPath = dynamicPartColNames.zipWithIndex.map { case (colName, i) =>
+      val rawVal = row.get(nonDynamicPartLen + i, schema(colName).dataType)
+      val string = if (rawVal == null) null else convertToHiveRawString(colName, rawVal)
+      val colString =
+        if (string == null || string.isEmpty) {
+          defaultPartName
+        } else {
+          FileUtils.escapePathName(string, defaultPartName)
+        }
+      s"/$colName=$colString"
+    }.mkString
 
     def newWriter(): FileSinkOperator.RecordWriter = {
       val newFileSinkDesc = new FileSinkDesc(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
index 99e95fb921301..81a70b8d42267 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveInspectorSuite.scala
@@ -133,8 +133,8 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
     }
   }
 
-  def checkValues(row1: Seq[Any], row2: InternalRow): Unit = {
-    row1.zip(row2.toSeq).foreach { case (r1, r2) =>
+  def checkValues(row1: Seq[Any], row2: InternalRow, row2Schema: StructType): Unit = {
+    row1.zip(row2.toSeq(row2Schema)).foreach { case (r1, r2) =>
       checkValue(r1, r2)
     }
   }
@@ -211,8 +211,10 @@ class HiveInspectorSuite extends SparkFunSuite with HiveInspectors {
       case (t, idx) => StructField(s"c_$idx", t)
     })
     val inspector = toInspector(dt)
-    checkValues(row,
-      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow])
+    checkValues(
+      row,
+      unwrap(wrap(InternalRow.fromSeq(row), inspector, dt), inspector).asInstanceOf[InternalRow],
+      dt)
     checkValue(null, unwrap(wrap(null, toInspector(dt), dt), toInspector(dt)))
   }
 

From 54c0789a05a783ce90e0e9848079be442a82966b Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 6 Aug 2015 13:29:31 -0700
Subject: [PATCH 092/633] [SPARK-9493] [ML] add featureIndex to handle vector
 features in IsotonicRegression

This PR contains the following changes:
* add `featureIndex` to handle vector features (in order to chain isotonic regression easily with output from logistic regression
* make getter/setter names consistent with params
* remove inheritance from Regressor because it is tricky to handle both `DoubleType` and `VectorType`
* simplify test data generation

jkbradley zapletal-martin

Author: Xiangrui Meng <meng@databricks.com>

Closes #7952 from mengxr/SPARK-9493 and squashes the following commits:

8818ac3 [Xiangrui Meng] address comments
05e2216 [Xiangrui Meng] address comments
8d08090 [Xiangrui Meng] add featureIndex to handle vector features make getter/setter names consistent with params remove inheritance from Regressor
---
 .../ml/regression/IsotonicRegression.scala    | 202 +++++++++++++-----
 .../regression/IsotonicRegressionSuite.scala  |  82 ++++---
 2 files changed, 194 insertions(+), 90 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
index 4ece8cf8cf0b6..f570590960a62 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
@@ -17,44 +17,113 @@
 
 package org.apache.spark.ml.regression
 
+import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.ml.PredictorParams
-import org.apache.spark.ml.param.{Param, ParamMap, BooleanParam}
-import org.apache.spark.ml.util.{SchemaUtils, Identifiable}
-import org.apache.spark.mllib.regression.{IsotonicRegression => MLlibIsotonicRegression}
-import org.apache.spark.mllib.regression.{IsotonicRegressionModel => MLlibIsotonicRegressionModel}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasPredictionCol}
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
+import org.apache.spark.mllib.regression.{IsotonicRegression => MLlibIsotonicRegression, IsotonicRegressionModel => MLlibIsotonicRegressionModel}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{DoubleType, DataType}
-import org.apache.spark.sql.{Row, DataFrame}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.functions.{col, lit, udf}
+import org.apache.spark.sql.types.{DoubleType, StructType}
 import org.apache.spark.storage.StorageLevel
 
 /**
  * Params for isotonic regression.
  */
-private[regression] trait IsotonicRegressionParams extends PredictorParams {
+private[regression] trait IsotonicRegressionBase extends Params with HasFeaturesCol
+  with HasLabelCol with HasPredictionCol with Logging {
 
   /**
-   * Param for weight column name.
-   * TODO: Move weightCol to sharedParams.
-   *
+   * Param for weight column name (default: none).
    * @group param
    */
+  // TODO: Move weightCol to sharedParams.
   final val weightCol: Param[String] =
-    new Param[String](this, "weightCol", "weight column name")
+    new Param[String](this, "weightCol",
+      "weight column name. If this is not set or empty, we treat all instance weights as 1.0.")
 
   /** @group getParam */
   final def getWeightCol: String = $(weightCol)
 
   /**
-   * Param for isotonic parameter.
-   * Isotonic (increasing) or antitonic (decreasing) sequence.
+   * Param for whether the output sequence should be isotonic/increasing (true) or
+   * antitonic/decreasing (false).
    * @group param
    */
   final val isotonic: BooleanParam =
-    new BooleanParam(this, "isotonic", "isotonic (increasing) or antitonic (decreasing) sequence")
+    new BooleanParam(this, "isotonic",
+      "whether the output sequence should be isotonic/increasing (true) or" +
+        "antitonic/decreasing (false)")
 
   /** @group getParam */
-  final def getIsotonicParam: Boolean = $(isotonic)
+  final def getIsotonic: Boolean = $(isotonic)
+
+  /**
+   * Param for the index of the feature if [[featuresCol]] is a vector column (default: `0`), no
+   * effect otherwise.
+   * @group param
+   */
+  final val featureIndex: IntParam = new IntParam(this, "featureIndex",
+    "The index of the feature if featuresCol is a vector column, no effect otherwise.")
+
+  /** @group getParam */
+  final def getFeatureIndex: Int = $(featureIndex)
+
+  setDefault(isotonic -> true, featureIndex -> 0)
+
+  /** Checks whether the input has weight column. */
+  protected[ml] def hasWeightCol: Boolean = {
+    isDefined(weightCol) && $(weightCol) != ""
+  }
+
+  /**
+   * Extracts (label, feature, weight) from input dataset.
+   */
+  protected[ml] def extractWeightedLabeledPoints(
+      dataset: DataFrame): RDD[(Double, Double, Double)] = {
+    val f = if (dataset.schema($(featuresCol)).dataType.isInstanceOf[VectorUDT]) {
+      val idx = $(featureIndex)
+      val extract = udf { v: Vector => v(idx) }
+      extract(col($(featuresCol)))
+    } else {
+      col($(featuresCol))
+    }
+    val w = if (hasWeightCol) {
+      col($(weightCol))
+    } else {
+      lit(1.0)
+    }
+    dataset.select(col($(labelCol)), f, w)
+      .map { case Row(label: Double, feature: Double, weights: Double) =>
+      (label, feature, weights)
+    }
+  }
+
+  /**
+   * Validates and transforms input schema.
+   * @param schema input schema
+   * @param fitting whether this is in fitting or prediction
+   * @return output schema
+   */
+  protected[ml] def validateAndTransformSchema(
+      schema: StructType,
+      fitting: Boolean): StructType = {
+    if (fitting) {
+      SchemaUtils.checkColumnType(schema, $(labelCol), DoubleType)
+      if (hasWeightCol) {
+        SchemaUtils.checkColumnType(schema, $(weightCol), DoubleType)
+      } else {
+        logInfo("The weight column is not defined. Treat all instance weights as 1.0.")
+      }
+    }
+    val featuresType = schema($(featuresCol)).dataType
+    require(featuresType == DoubleType || featuresType.isInstanceOf[VectorUDT])
+    SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType)
+  }
 }
 
 /**
@@ -67,52 +136,46 @@ private[regression] trait IsotonicRegressionParams extends PredictorParams {
  * Uses [[org.apache.spark.mllib.regression.IsotonicRegression]].
  */
 @Experimental
-class IsotonicRegression(override val uid: String)
-  extends Regressor[Double, IsotonicRegression, IsotonicRegressionModel]
-  with IsotonicRegressionParams {
+class IsotonicRegression(override val uid: String) extends Estimator[IsotonicRegressionModel]
+  with IsotonicRegressionBase {
 
   def this() = this(Identifiable.randomUID("isoReg"))
 
-  /**
-   * Set the isotonic parameter.
-   * Default is true.
-   * @group setParam
-   */
-  def setIsotonicParam(value: Boolean): this.type = set(isotonic, value)
-  setDefault(isotonic -> true)
+  /** @group setParam */
+  def setLabelCol(value: String): this.type = set(labelCol, value)
 
-  /**
-   * Set weight column param.
-   * Default is weight.
-   * @group setParam
-   */
-  def setWeightParam(value: String): this.type = set(weightCol, value)
-  setDefault(weightCol -> "weight")
+  /** @group setParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
 
-  override private[ml] def featuresDataType: DataType = DoubleType
+  /** @group setParam */
+  def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
-  override def copy(extra: ParamMap): IsotonicRegression = defaultCopy(extra)
+  /** @group setParam */
+  def setIsotonic(value: Boolean): this.type = set(isotonic, value)
 
-  private[this] def extractWeightedLabeledPoints(
-      dataset: DataFrame): RDD[(Double, Double, Double)] = {
+  /** @group setParam */
+  def setWeightCol(value: String): this.type = set(weightCol, value)
 
-    dataset.select($(labelCol), $(featuresCol), $(weightCol))
-      .map { case Row(label: Double, features: Double, weights: Double) =>
-        (label, features, weights)
-      }
-  }
+  /** @group setParam */
+  def setFeatureIndex(value: Int): this.type = set(featureIndex, value)
 
-  override protected def train(dataset: DataFrame): IsotonicRegressionModel = {
-    SchemaUtils.checkColumnType(dataset.schema, $(weightCol), DoubleType)
+  override def copy(extra: ParamMap): IsotonicRegression = defaultCopy(extra)
+
+  override def fit(dataset: DataFrame): IsotonicRegressionModel = {
+    validateAndTransformSchema(dataset.schema, fitting = true)
     // Extract columns from data.  If dataset is persisted, do not persist oldDataset.
     val instances = extractWeightedLabeledPoints(dataset)
     val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
     if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
 
     val isotonicRegression = new MLlibIsotonicRegression().setIsotonic($(isotonic))
-    val parentModel = isotonicRegression.run(instances)
+    val oldModel = isotonicRegression.run(instances)
 
-    new IsotonicRegressionModel(uid, parentModel)
+    copyValues(new IsotonicRegressionModel(uid, oldModel).setParent(this))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema, fitting = true)
   }
 }
 
@@ -123,22 +186,49 @@ class IsotonicRegression(override val uid: String)
  *
  * For detailed rules see [[org.apache.spark.mllib.regression.IsotonicRegressionModel.predict()]].
  *
- * @param parentModel A [[org.apache.spark.mllib.regression.IsotonicRegressionModel]]
- *                    model trained by [[org.apache.spark.mllib.regression.IsotonicRegression]].
+ * @param oldModel A [[org.apache.spark.mllib.regression.IsotonicRegressionModel]]
+ *                 model trained by [[org.apache.spark.mllib.regression.IsotonicRegression]].
  */
+@Experimental
 class IsotonicRegressionModel private[ml] (
     override val uid: String,
-    private[ml] val parentModel: MLlibIsotonicRegressionModel)
-  extends RegressionModel[Double, IsotonicRegressionModel]
-  with IsotonicRegressionParams {
+    private val oldModel: MLlibIsotonicRegressionModel)
+  extends Model[IsotonicRegressionModel] with IsotonicRegressionBase {
 
-  override def featuresDataType: DataType = DoubleType
+  /** @group setParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
 
-  override protected def predict(features: Double): Double = {
-    parentModel.predict(features)
-  }
+  /** @group setParam */
+  def setPredictionCol(value: String): this.type = set(predictionCol, value)
+
+  /** @group setParam */
+  def setFeatureIndex(value: Int): this.type = set(featureIndex, value)
+
+  /** Boundaries in increasing order for which predictions are known. */
+  def boundaries: Vector = Vectors.dense(oldModel.boundaries)
+
+  /**
+   * Predictions associated with the boundaries at the same index, monotone because of isotonic
+   * regression.
+   */
+  def predictions: Vector = Vectors.dense(oldModel.predictions)
 
   override def copy(extra: ParamMap): IsotonicRegressionModel = {
-    copyValues(new IsotonicRegressionModel(uid, parentModel), extra)
+    copyValues(new IsotonicRegressionModel(uid, oldModel), extra)
+  }
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    val predict = dataset.schema($(featuresCol)).dataType match {
+      case DoubleType =>
+        udf { feature: Double => oldModel.predict(feature) }
+      case _: VectorUDT =>
+        val idx = $(featureIndex)
+        udf { features: Vector => oldModel.predict(features(idx)) }
+    }
+    dataset.withColumn($(predictionCol), predict(col($(featuresCol))))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema, fitting = false)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
index 66e4b170bae80..c0ab00b68a2f3 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
@@ -19,57 +19,46 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
 import org.apache.spark.sql.{DataFrame, Row}
 
 class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
-  private val schema = StructType(
-    Array(
-      StructField("label", DoubleType),
-      StructField("features", DoubleType),
-      StructField("weight", DoubleType)))
-
-  private val predictionSchema = StructType(Array(StructField("features", DoubleType)))
-
   private def generateIsotonicInput(labels: Seq[Double]): DataFrame = {
-    val data = Seq.tabulate(labels.size)(i => Row(labels(i), i.toDouble, 1d))
-    val parallelData = sc.parallelize(data)
-
-    sqlContext.createDataFrame(parallelData, schema)
+    sqlContext.createDataFrame(
+      labels.zipWithIndex.map { case (label, i) => (label, i.toDouble, 1.0) }
+    ).toDF("label", "features", "weight")
   }
 
   private def generatePredictionInput(features: Seq[Double]): DataFrame = {
-    val data = Seq.tabulate(features.size)(i => Row(features(i)))
-
-    val parallelData = sc.parallelize(data)
-    sqlContext.createDataFrame(parallelData, predictionSchema)
+    sqlContext.createDataFrame(features.map(Tuple1.apply))
+      .toDF("features")
   }
 
   test("isotonic regression predictions") {
     val dataset = generateIsotonicInput(Seq(1, 2, 3, 1, 6, 17, 16, 17, 18))
-    val trainer = new IsotonicRegression().setIsotonicParam(true)
+    val ir = new IsotonicRegression().setIsotonic(true)
 
-    val model = trainer.fit(dataset)
+    val model = ir.fit(dataset)
 
     val predictions = model
       .transform(dataset)
-      .select("prediction").map {
-        case Row(pred) => pred
+      .select("prediction").map { case Row(pred) =>
+        pred
       }.collect()
 
     assert(predictions === Array(1, 2, 2, 2, 6, 16.5, 16.5, 17, 18))
 
-    assert(model.parentModel.boundaries === Array(0, 1, 3, 4, 5, 6, 7, 8))
-    assert(model.parentModel.predictions === Array(1, 2, 2, 6, 16.5, 16.5, 17.0, 18.0))
-    assert(model.parentModel.isotonic)
+    assert(model.boundaries === Vectors.dense(0, 1, 3, 4, 5, 6, 7, 8))
+    assert(model.predictions === Vectors.dense(1, 2, 2, 6, 16.5, 16.5, 17.0, 18.0))
+    assert(model.getIsotonic)
   }
 
   test("antitonic regression predictions") {
     val dataset = generateIsotonicInput(Seq(7, 5, 3, 5, 1))
-    val trainer = new IsotonicRegression().setIsotonicParam(false)
+    val ir = new IsotonicRegression().setIsotonic(false)
 
-    val model = trainer.fit(dataset)
+    val model = ir.fit(dataset)
     val features = generatePredictionInput(Seq(-2.0, -1.0, 0.5, 0.75, 1.0, 2.0, 9.0))
 
     val predictions = model
@@ -94,9 +83,10 @@ class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val ir = new IsotonicRegression()
     assert(ir.getLabelCol === "label")
     assert(ir.getFeaturesCol === "features")
-    assert(ir.getWeightCol === "weight")
     assert(ir.getPredictionCol === "prediction")
-    assert(ir.getIsotonicParam === true)
+    assert(!ir.isDefined(ir.weightCol))
+    assert(ir.getIsotonic)
+    assert(ir.getFeatureIndex === 0)
 
     val model = ir.fit(dataset)
     model.transform(dataset)
@@ -105,21 +95,22 @@ class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     assert(model.getLabelCol === "label")
     assert(model.getFeaturesCol === "features")
-    assert(model.getWeightCol === "weight")
     assert(model.getPredictionCol === "prediction")
-    assert(model.getIsotonicParam === true)
+    assert(!model.isDefined(model.weightCol))
+    assert(model.getIsotonic)
+    assert(model.getFeatureIndex === 0)
     assert(model.hasParent)
   }
 
   test("set parameters") {
     val isotonicRegression = new IsotonicRegression()
-      .setIsotonicParam(false)
-      .setWeightParam("w")
+      .setIsotonic(false)
+      .setWeightCol("w")
       .setFeaturesCol("f")
       .setLabelCol("l")
       .setPredictionCol("p")
 
-    assert(isotonicRegression.getIsotonicParam === false)
+    assert(!isotonicRegression.getIsotonic)
     assert(isotonicRegression.getWeightCol === "w")
     assert(isotonicRegression.getFeaturesCol === "f")
     assert(isotonicRegression.getLabelCol === "l")
@@ -130,7 +121,7 @@ class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     val dataset = generateIsotonicInput(Seq(1, 2, 3))
 
     intercept[IllegalArgumentException] {
-      new IsotonicRegression().setWeightParam("w").fit(dataset)
+      new IsotonicRegression().setWeightCol("w").fit(dataset)
     }
 
     intercept[IllegalArgumentException] {
@@ -145,4 +136,27 @@ class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       new IsotonicRegression().fit(dataset).setFeaturesCol("f").transform(dataset)
     }
   }
+
+  test("vector features column with feature index") {
+    val dataset = sqlContext.createDataFrame(Seq(
+      (4.0, Vectors.dense(0.0, 1.0)),
+      (3.0, Vectors.dense(0.0, 2.0)),
+      (5.0, Vectors.sparse(2, Array(1), Array(3.0))))
+    ).toDF("label", "features")
+
+    val ir = new IsotonicRegression()
+      .setFeatureIndex(1)
+
+    val model = ir.fit(dataset)
+
+    val features = generatePredictionInput(Seq(2.0, 3.0, 4.0, 5.0))
+
+    val predictions = model
+      .transform(features)
+      .select("prediction").map {
+      case Row(pred) => pred
+    }.collect()
+
+    assert(predictions === Array(3.5, 5.0, 5.0, 5.0))
+  }
 }

From abfedb9cd70af60c8290bd2f5a5cec1047845ba0 Mon Sep 17 00:00:00 2001
From: Christian Kadner <ckadner@us.ibm.com>
Date: Thu, 6 Aug 2015 14:15:42 -0700
Subject: [PATCH 093/633] [SPARK-9211] [SQL] [TEST] normalize line separators
 before generating MD5 hash

The golden answer file names for the existing Hive comparison tests were generated using a MD5 hash of the query text which uses Unix-style line separator characters `\n` (LF).
This PR ensures that all occurrences of the Windows-style line separator `\r\n` (CR) are replaced with `\n` (LF) before generating the MD5 hash to produce an identical MD5 hash for golden answer file names generated on Windows.

Author: Christian Kadner <ckadner@us.ibm.com>

Closes #7563 from ckadner/SPARK-9211_working and squashes the following commits:

d541db0 [Christian Kadner] [SPARK-9211][SQL] normalize line separators before MD5 hash
---
 .../spark/sql/hive/execution/HiveComparisonTest.scala     | 2 +-
 .../apache/spark/sql/hive/execution/HiveQuerySuite.scala  | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 638b9c810372a..2bdb0e11878e5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -124,7 +124,7 @@ abstract class HiveComparisonTest
   protected val cacheDigest = java.security.MessageDigest.getInstance("MD5")
   protected def getMd5(str: String): String = {
     val digest = java.security.MessageDigest.getInstance("MD5")
-    digest.update(str.getBytes("utf-8"))
+    digest.update(str.replaceAll(System.lineSeparator(), "\n").getBytes("utf-8"))
     new java.math.BigInteger(1, digest.digest).toString(16)
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index edb27553671d1..83f9f3eaa3a5e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -427,7 +427,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
       |'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
       |USING 'cat' AS (tKey, tValue) ROW FORMAT SERDE
       |'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' FROM src;
-    """.stripMargin.replaceAll("\n", " "))
+    """.stripMargin.replaceAll(System.lineSeparator(), " "))
 
   test("transform with SerDe2") {
 
@@ -446,7 +446,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
         |('avro.schema.literal'='{"namespace": "testing.hive.avro.serde","name":
         |"src","type": "record","fields": [{"name":"key","type":"int"}]}')
         |FROM small_src
-      """.stripMargin.replaceAll("\n", " ")).collect().head
+      """.stripMargin.replaceAll(System.lineSeparator(), " ")).collect().head
 
     assert(expected(0) === res(0))
   }
@@ -458,7 +458,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
       |('serialization.last.column.takes.rest'='true') USING 'cat' AS (tKey, tValue)
       |ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe'
       |WITH SERDEPROPERTIES ('serialization.last.column.takes.rest'='true') FROM src;
-    """.stripMargin.replaceAll("\n", " "))
+    """.stripMargin.replaceAll(System.lineSeparator(), " "))
 
   createQueryTest("transform with SerDe4",
     """
@@ -467,7 +467,7 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
       |('serialization.last.column.takes.rest'='true') USING 'cat' ROW FORMAT SERDE
       |'org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe' WITH SERDEPROPERTIES
       |('serialization.last.column.takes.rest'='true') FROM src;
-    """.stripMargin.replaceAll("\n", " "))
+    """.stripMargin.replaceAll(System.lineSeparator(), " "))
 
   createQueryTest("LIKE",
     "SELECT * FROM src WHERE value LIKE '%1%'")

From 21fdfd7d6f89adbd37066c169e6ba9ccd337683e Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Thu, 6 Aug 2015 14:33:29 -0700
Subject: [PATCH 094/633] [SPARK-9548][SQL] Add a destructive iterator for
 BytesToBytesMap

This pull request adds a destructive iterator to BytesToBytesMap. When used, the iterator frees pages as it traverses them. This is part of the effort to avoid starving when we have more than one operators that can exhaust memory.

This is based on #7924, but fixes a bug there (Don't use destructive iterator in UnsafeKVExternalSorter).

Closes #7924.

Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Reynold Xin <rxin@databricks.com>

Closes #8003 from rxin/map-destructive-iterator and squashes the following commits:

6b618c3 [Reynold Xin] Don't use destructive iterator in UnsafeKVExternalSorter.
a7bd8ec [Reynold Xin] Merge remote-tracking branch 'viirya/destructive_iter' into map-destructive-iterator
7652083 [Liang-Chi Hsieh] For comments: add destructiveIterator(), modify unit test, remove code block.
4a3e9de [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into destructive_iter
581e9e3 [Liang-Chi Hsieh] Merge remote-tracking branch 'upstream/master' into destructive_iter
f0ff783 [Liang-Chi Hsieh] No need to free last page.
9e9d2a3 [Liang-Chi Hsieh] Add a destructive iterator for BytesToBytesMap.
---
 .../spark/unsafe/map/BytesToBytesMap.java     | 33 +++++++++++++++--
 .../map/AbstractBytesToBytesMapSuite.java     | 37 ++++++++++++++++---
 .../UnsafeFixedWidthAggregationMap.java       |  7 +++-
 .../sql/execution/UnsafeKVExternalSorter.java |  5 ++-
 4 files changed, 71 insertions(+), 11 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 20347433e16b2..5ac3736ac62aa 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -227,22 +227,35 @@ public static final class BytesToBytesMapIterator implements Iterator<Location>
     private final Iterator<MemoryBlock> dataPagesIterator;
     private final Location loc;
 
-    private MemoryBlock currentPage;
+    private MemoryBlock currentPage = null;
     private int currentRecordNumber = 0;
     private Object pageBaseObject;
     private long offsetInPage;
 
+    // If this iterator destructive or not. When it is true, it frees each page as it moves onto
+    // next one.
+    private boolean destructive = false;
+    private BytesToBytesMap bmap;
+
     private BytesToBytesMapIterator(
-        int numRecords, Iterator<MemoryBlock> dataPagesIterator, Location loc) {
+        int numRecords, Iterator<MemoryBlock> dataPagesIterator, Location loc,
+        boolean destructive, BytesToBytesMap bmap) {
       this.numRecords = numRecords;
       this.dataPagesIterator = dataPagesIterator;
       this.loc = loc;
+      this.destructive = destructive;
+      this.bmap = bmap;
       if (dataPagesIterator.hasNext()) {
         advanceToNextPage();
       }
     }
 
     private void advanceToNextPage() {
+      if (destructive && currentPage != null) {
+        dataPagesIterator.remove();
+        this.bmap.taskMemoryManager.freePage(currentPage);
+        this.bmap.shuffleMemoryManager.release(currentPage.size());
+      }
       currentPage = dataPagesIterator.next();
       pageBaseObject = currentPage.getBaseObject();
       offsetInPage = currentPage.getBaseOffset();
@@ -281,7 +294,21 @@ public void remove() {
    * `lookup()`, the behavior of the returned iterator is undefined.
    */
   public BytesToBytesMapIterator iterator() {
-    return new BytesToBytesMapIterator(numElements, dataPages.iterator(), loc);
+    return new BytesToBytesMapIterator(numElements, dataPages.iterator(), loc, false, this);
+  }
+
+  /**
+   * Returns a destructive iterator for iterating over the entries of this map. It frees each page
+   * as it moves onto next one. Notice: it is illegal to call any method on the map after
+   * `destructiveIterator()` has been called.
+   *
+   * For efficiency, all calls to `next()` will return the same {@link Location} object.
+   *
+   * If any other lookups or operations are performed on this map while iterating over it, including
+   * `lookup()`, the behavior of the returned iterator is undefined.
+   */
+  public BytesToBytesMapIterator destructiveIterator() {
+    return new BytesToBytesMapIterator(numElements, dataPages.iterator(), loc, true, this);
   }
 
   /**
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 0e23a64fb74bb..3c5003380162f 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -183,8 +183,7 @@ public void setAndRetrieveAKey() {
     }
   }
 
-  @Test
-  public void iteratorTest() throws Exception {
+  private void iteratorTestBase(boolean destructive) throws Exception {
     final int size = 4096;
     BytesToBytesMap map = new BytesToBytesMap(
       taskMemoryManager, shuffleMemoryManager, size / 2, PAGE_SIZE_BYTES);
@@ -216,7 +215,14 @@ public void iteratorTest() throws Exception {
         }
       }
       final java.util.BitSet valuesSeen = new java.util.BitSet(size);
-      final Iterator<BytesToBytesMap.Location> iter = map.iterator();
+      final Iterator<BytesToBytesMap.Location> iter;
+      if (destructive) {
+        iter = map.destructiveIterator();
+      } else {
+        iter = map.iterator();
+      }
+      int numPages = map.getNumDataPages();
+      int countFreedPages = 0;
       while (iter.hasNext()) {
         final BytesToBytesMap.Location loc = iter.next();
         Assert.assertTrue(loc.isDefined());
@@ -228,11 +234,22 @@ public void iteratorTest() throws Exception {
         if (keyLength == 0) {
           Assert.assertTrue("value " + value + " was not divisible by 5", value % 5 == 0);
         } else {
-        final long key = PlatformDependent.UNSAFE.getLong(
-          keyAddress.getBaseObject(), keyAddress.getBaseOffset());
+          final long key = PlatformDependent.UNSAFE.getLong(
+            keyAddress.getBaseObject(), keyAddress.getBaseOffset());
           Assert.assertEquals(value, key);
         }
         valuesSeen.set((int) value);
+        if (destructive) {
+          // The iterator moves onto next page and frees previous page
+          if (map.getNumDataPages() < numPages) {
+            numPages = map.getNumDataPages();
+            countFreedPages++;
+          }
+        }
+      }
+      if (destructive) {
+        // Latest page is not freed by iterator but by map itself
+        Assert.assertEquals(countFreedPages, numPages - 1);
       }
       Assert.assertEquals(size, valuesSeen.cardinality());
     } finally {
@@ -240,6 +257,16 @@ public void iteratorTest() throws Exception {
     }
   }
 
+  @Test
+  public void iteratorTest() throws Exception {
+    iteratorTestBase(false);
+  }
+
+  @Test
+  public void destructiveIteratorTest() throws Exception {
+    iteratorTestBase(true);
+  }
+
   @Test
   public void iteratingOverDataPagesWithWastedSpace() throws Exception {
     final int NUM_ENTRIES = 1000 * 1000;
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 02458030b00e9..efb33530dac86 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -154,14 +154,17 @@ public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
   }
 
   /**
-   * Returns an iterator over the keys and values in this map.
+   * Returns an iterator over the keys and values in this map. This uses destructive iterator of
+   * BytesToBytesMap. So it is illegal to call any other method on this map after `iterator()` has
+   * been called.
    *
    * For efficiency, each call returns the same object.
    */
   public KVIterator<UnsafeRow, UnsafeRow> iterator() {
     return new KVIterator<UnsafeRow, UnsafeRow>() {
 
-      private final BytesToBytesMap.BytesToBytesMapIterator mapLocationIterator = map.iterator();
+      private final BytesToBytesMap.BytesToBytesMapIterator mapLocationIterator =
+        map.destructiveIterator();
       private final UnsafeRow key = new UnsafeRow();
       private final UnsafeRow value = new UnsafeRow();
 
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index 6c1cf136d9b81..9a65c9d3a404a 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -88,8 +88,11 @@ public UnsafeKVExternalSorter(StructType keySchema, StructType valueSchema,
       final UnsafeInMemorySorter inMemSorter = new UnsafeInMemorySorter(
         taskMemoryManager, recordComparator, prefixComparator, Math.max(1, map.numElements()));
 
-      final int numKeyFields = keySchema.size();
+      // We cannot use the destructive iterator here because we are reusing the existing memory
+      // pages in BytesToBytesMap to hold records during sorting.
+      // The only new memory we are allocating is the pointer/prefix array.
       BytesToBytesMap.BytesToBytesMapIterator iter = map.iterator();
+      final int numKeyFields = keySchema.size();
       UnsafeRow row = new UnsafeRow();
       while (iter.hasNext()) {
         final BytesToBytesMap.Location loc = iter.next();

From 0a078303d08ad2bb92b9a8a6969563d75b512290 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu, 6 Aug 2015 14:35:30 -0700
Subject: [PATCH 095/633] [SPARK-9556] [SPARK-9619] [SPARK-9624] [STREAMING]
 Make BlockGenerator more robust and make all BlockGenerators subscribe to
 rate limit updates

In some receivers, instead of using the default `BlockGenerator` in `ReceiverSupervisorImpl`, custom generator with their custom listeners are used for reliability (see [`ReliableKafkaReceiver`](https://github.com/apache/spark/blob/master/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala#L99) and [updated `KinesisReceiver`](https://github.com/apache/spark/pull/7825/files)). These custom generators do not receive rate updates. This PR modifies the code to allow custom `BlockGenerator`s to be created through the `ReceiverSupervisorImpl` so that they can be kept track and rate updates can be applied.

In the process, I did some simplification, and de-flaki-fication of some rate controller related tests. In particular.
- Renamed `Receiver.executor` to `Receiver.supervisor` (to match `ReceiverSupervisor`)
- Made `RateControllerSuite` faster (by increasing batch interval) and less flaky
- Changed a few internal API to return the current rate of block generators as Long instead of Option\[Long\] (was inconsistent at places).
- Updated existing `ReceiverTrackerSuite` to test that custom block generators get rate updates as well.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7913 from tdas/SPARK-9556 and squashes the following commits:

41d4461 [Tathagata Das] fix scala style
eb9fd59 [Tathagata Das] Updated kinesis receiver
d24994d [Tathagata Das] Updated BlockGeneratorSuite to use manual clock in BlockGenerator
d70608b [Tathagata Das] Updated BlockGenerator with states and proper synchronization
f6bd47e [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into SPARK-9556
31da173 [Tathagata Das] Fix bug
12116df [Tathagata Das] Add BlockGeneratorSuite
74bd069 [Tathagata Das] Fix style
989bb5c [Tathagata Das] Made BlockGenerator fail is used after stop, and added better unit tests for it
3ff618c [Tathagata Das] Fix test
b40eff8 [Tathagata Das] slight refactoring
f0df0f1 [Tathagata Das] Scala style fixes
51759cb [Tathagata Das] Refactored rate controller tests and added the ability to update rate of any custom block generator
---
 .../org/apache/spark/util/ManualClock.scala   |   2 +-
 .../kafka/ReliableKafkaReceiver.scala         |   2 +-
 .../streaming/kinesis/KinesisReceiver.scala   |   2 +-
 .../streaming/receiver/ActorReceiver.scala    |   8 +-
 .../streaming/receiver/BlockGenerator.scala   | 131 ++++++---
 .../streaming/receiver/RateLimiter.scala      |   3 +-
 .../spark/streaming/receiver/Receiver.scala   |  52 ++--
 .../receiver/ReceiverSupervisor.scala         |  27 +-
 .../receiver/ReceiverSupervisorImpl.scala     |  33 ++-
 .../spark/streaming/CheckpointSuite.scala     |  16 +-
 .../spark/streaming/ReceiverSuite.scala       |  31 +--
 .../receiver/BlockGeneratorSuite.scala        | 253 ++++++++++++++++++
 .../scheduler/RateControllerSuite.scala       |  64 ++---
 .../scheduler/ReceiverTrackerSuite.scala      | 129 +++++----
 14 files changed, 534 insertions(+), 219 deletions(-)
 create mode 100644 streaming/src/test/scala/org/apache/spark/streaming/receiver/BlockGeneratorSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/util/ManualClock.scala b/core/src/main/scala/org/apache/spark/util/ManualClock.scala
index 1718554061985..e7a65d74a440e 100644
--- a/core/src/main/scala/org/apache/spark/util/ManualClock.scala
+++ b/core/src/main/scala/org/apache/spark/util/ManualClock.scala
@@ -58,7 +58,7 @@ private[spark] class ManualClock(private var time: Long) extends Clock {
    */
   def waitTillTime(targetTime: Long): Long = synchronized {
     while (time < targetTime) {
-      wait(100)
+      wait(10)
     }
     getTimeMillis()
   }
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala
index 75f0dfc22b9dc..764d170934aa6 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/ReliableKafkaReceiver.scala
@@ -96,7 +96,7 @@ class ReliableKafkaReceiver[
     blockOffsetMap = new ConcurrentHashMap[StreamBlockId, Map[TopicAndPartition, Long]]()
 
     // Initialize the block generator for storing Kafka message.
-    blockGenerator = new BlockGenerator(new GeneratedBlockHandler, streamId, conf)
+    blockGenerator = supervisor.createBlockGenerator(new GeneratedBlockHandler)
 
     if (kafkaParams.contains(AUTO_OFFSET_COMMIT) && kafkaParams(AUTO_OFFSET_COMMIT) == "true") {
       logWarning(s"$AUTO_OFFSET_COMMIT should be set to false in ReliableKafkaReceiver, " +
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
index a4baeec0846b4..22324e821ce94 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
@@ -136,7 +136,7 @@ private[kinesis] class KinesisReceiver(
    * The KCL creates and manages the receiving/processing thread pool through Worker.run().
    */
   override def onStart() {
-    blockGenerator = new BlockGenerator(new GeneratedBlockHandler, streamId, SparkEnv.get.conf)
+    blockGenerator = supervisor.createBlockGenerator(new GeneratedBlockHandler)
 
     workerId = Utils.localHostName() + ":" + UUID.randomUUID()
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
index cd309788a7717..7ec74016a1c2c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ActorReceiver.scala
@@ -144,7 +144,7 @@ private[streaming] class ActorReceiver[T: ClassTag](
     receiverSupervisorStrategy: SupervisorStrategy
   ) extends Receiver[T](storageLevel) with Logging {
 
-  protected lazy val supervisor = SparkEnv.get.actorSystem.actorOf(Props(new Supervisor),
+  protected lazy val actorSupervisor = SparkEnv.get.actorSystem.actorOf(Props(new Supervisor),
     "Supervisor" + streamId)
 
   class Supervisor extends Actor {
@@ -191,11 +191,11 @@ private[streaming] class ActorReceiver[T: ClassTag](
   }
 
   def onStart(): Unit = {
-    supervisor
-    logInfo("Supervision tree for receivers initialized at:" + supervisor.path)
+    actorSupervisor
+    logInfo("Supervision tree for receivers initialized at:" + actorSupervisor.path)
   }
 
   def onStop(): Unit = {
-    supervisor ! PoisonPill
+    actorSupervisor ! PoisonPill
   }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
index 92b51ce39234c..794dece370b2c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
@@ -21,10 +21,10 @@ import java.util.concurrent.{ArrayBlockingQueue, TimeUnit}
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.{Logging, SparkConf}
+import org.apache.spark.{SparkException, Logging, SparkConf}
 import org.apache.spark.storage.StreamBlockId
 import org.apache.spark.streaming.util.RecurringTimer
-import org.apache.spark.util.SystemClock
+import org.apache.spark.util.{Clock, SystemClock}
 
 /** Listener object for BlockGenerator events */
 private[streaming] trait BlockGeneratorListener {
@@ -69,16 +69,35 @@ private[streaming] trait BlockGeneratorListener {
  * named blocks at regular intervals. This class starts two threads,
  * one to periodically start a new batch and prepare the previous batch of as a block,
  * the other to push the blocks into the block manager.
+ *
+ * Note: Do not create BlockGenerator instances directly inside receivers. Use
+ * `ReceiverSupervisor.createBlockGenerator` to create a BlockGenerator and use it.
  */
 private[streaming] class BlockGenerator(
     listener: BlockGeneratorListener,
     receiverId: Int,
-    conf: SparkConf
+    conf: SparkConf,
+    clock: Clock = new SystemClock()
   ) extends RateLimiter(conf) with Logging {
 
   private case class Block(id: StreamBlockId, buffer: ArrayBuffer[Any])
 
-  private val clock = new SystemClock()
+  /**
+   * The BlockGenerator can be in 5 possible states, in the order as follows.
+   * - Initialized: Nothing has been started
+   * - Active: start() has been called, and it is generating blocks on added data.
+   * - StoppedAddingData: stop() has been called, the adding of data has been stopped,
+   *                      but blocks are still being generated and pushed.
+   * - StoppedGeneratingBlocks: Generating of blocks has been stopped, but
+   *                            they are still being pushed.
+   * - StoppedAll: Everything has stopped, and the BlockGenerator object can be GCed.
+   */
+  private object GeneratorState extends Enumeration {
+    type GeneratorState = Value
+    val Initialized, Active, StoppedAddingData, StoppedGeneratingBlocks, StoppedAll = Value
+  }
+  import GeneratorState._
+
   private val blockIntervalMs = conf.getTimeAsMs("spark.streaming.blockInterval", "200ms")
   require(blockIntervalMs > 0, s"'spark.streaming.blockInterval' should be a positive value")
 
@@ -89,59 +108,100 @@ private[streaming] class BlockGenerator(
   private val blockPushingThread = new Thread() { override def run() { keepPushingBlocks() } }
 
   @volatile private var currentBuffer = new ArrayBuffer[Any]
-  @volatile private var stopped = false
+  @volatile private var state = Initialized
 
   /** Start block generating and pushing threads. */
-  def start() {
-    blockIntervalTimer.start()
-    blockPushingThread.start()
-    logInfo("Started BlockGenerator")
+  def start(): Unit = synchronized {
+    if (state == Initialized) {
+      state = Active
+      blockIntervalTimer.start()
+      blockPushingThread.start()
+      logInfo("Started BlockGenerator")
+    } else {
+      throw new SparkException(
+        s"Cannot start BlockGenerator as its not in the Initialized state [state = $state]")
+    }
   }
 
-  /** Stop all threads. */
-  def stop() {
+  /**
+   * Stop everything in the right order such that all the data added is pushed out correctly.
+   * - First, stop adding data to the current buffer.
+   * - Second, stop generating blocks.
+   * - Finally, wait for queue of to-be-pushed blocks to be drained.
+   */
+  def stop(): Unit = {
+    // Set the state to stop adding data
+    synchronized {
+      if (state == Active) {
+        state = StoppedAddingData
+      } else {
+        logWarning(s"Cannot stop BlockGenerator as its not in the Active state [state = $state]")
+        return
+      }
+    }
+
+    // Stop generating blocks and set the state for block pushing thread to start draining the queue
     logInfo("Stopping BlockGenerator")
     blockIntervalTimer.stop(interruptTimer = false)
-    stopped = true
-    logInfo("Waiting for block pushing thread")
+    synchronized { state = StoppedGeneratingBlocks }
+
+    // Wait for the queue to drain and mark generated as stopped
+    logInfo("Waiting for block pushing thread to terminate")
     blockPushingThread.join()
+    synchronized { state = StoppedAll }
     logInfo("Stopped BlockGenerator")
   }
 
   /**
-   * Push a single data item into the buffer. All received data items
-   * will be periodically pushed into BlockManager.
+   * Push a single data item into the buffer.
    */
-  def addData (data: Any): Unit = synchronized {
-    waitToPush()
-    currentBuffer += data
+  def addData(data: Any): Unit = synchronized {
+    if (state == Active) {
+      waitToPush()
+      currentBuffer += data
+    } else {
+      throw new SparkException(
+        "Cannot add data as BlockGenerator has not been started or has been stopped")
+    }
   }
 
   /**
    * Push a single data item into the buffer. After buffering the data, the
-   * `BlockGeneratorListener.onAddData` callback will be called. All received data items
-   * will be periodically pushed into BlockManager.
+   * `BlockGeneratorListener.onAddData` callback will be called.
    */
   def addDataWithCallback(data: Any, metadata: Any): Unit = synchronized {
-    waitToPush()
-    currentBuffer += data
-    listener.onAddData(data, metadata)
+    if (state == Active) {
+      waitToPush()
+      currentBuffer += data
+      listener.onAddData(data, metadata)
+    } else {
+      throw new SparkException(
+        "Cannot add data as BlockGenerator has not been started or has been stopped")
+    }
   }
 
   /**
    * Push multiple data items into the buffer. After buffering the data, the
-   * `BlockGeneratorListener.onAddData` callback will be called. All received data items
-   * will be periodically pushed into BlockManager. Note that all the data items is guaranteed
-   * to be present in a single block.
+   * `BlockGeneratorListener.onAddData` callback will be called. Note that all the data items
+   * are atomically added to the buffer, and are hence guaranteed to be present in a single block.
    */
   def addMultipleDataWithCallback(dataIterator: Iterator[Any], metadata: Any): Unit = synchronized {
-    dataIterator.foreach { data =>
-      waitToPush()
-      currentBuffer += data
+    if (state == Active) {
+      dataIterator.foreach { data =>
+        waitToPush()
+        currentBuffer += data
+      }
+      listener.onAddData(dataIterator, metadata)
+    } else {
+      throw new SparkException(
+        "Cannot add data as BlockGenerator has not been started or has been stopped")
     }
-    listener.onAddData(dataIterator, metadata)
   }
 
+  def isActive(): Boolean = state == Active
+
+  def isStopped(): Boolean = state == StoppedAll
+
   /** Change the buffer to which single records are added to. */
   private def updateCurrentBuffer(time: Long): Unit = synchronized {
     try {
@@ -165,18 +225,21 @@ private[streaming] class BlockGenerator(
   /** Keep pushing blocks to the BlockManager. */
   private def keepPushingBlocks() {
     logInfo("Started block pushing thread")
+
+    def isGeneratingBlocks = synchronized { state == Active || state == StoppedAddingData }
     try {
-      while (!stopped) {
-        Option(blocksForPushing.poll(100, TimeUnit.MILLISECONDS)) match {
+      while (isGeneratingBlocks) {
+        Option(blocksForPushing.poll(10, TimeUnit.MILLISECONDS)) match {
           case Some(block) => pushBlock(block)
           case None =>
         }
       }
-      // Push out the blocks that are still left
+
+      // At this point, state is StoppedGeneratingBlock. So drain the queue of to-be-pushed blocks.
       logInfo("Pushing out the last " + blocksForPushing.size() + " blocks")
       while (!blocksForPushing.isEmpty) {
-        logDebug("Getting block ")
         val block = blocksForPushing.take()
+        logDebug(s"Pushing block $block")
         pushBlock(block)
         logInfo("Blocks left to push " + blocksForPushing.size())
       }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala
index f663def4c0511..bca1fbc8fda2f 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/RateLimiter.scala
@@ -45,8 +45,7 @@ private[receiver] abstract class RateLimiter(conf: SparkConf) extends Logging {
   /**
    * Return the current rate limit. If no limit has been set so far, it returns {{{Long.MaxValue}}}.
    */
-  def getCurrentLimit: Long =
-    rateLimiter.getRate.toLong
+  def getCurrentLimit: Long = rateLimiter.getRate.toLong
 
   /**
    * Set the rate limit to `newRate`. The new rate will not exceed the maximum rate configured by
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
index 7504fa44d9fae..554aae0117b24 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
@@ -116,12 +116,12 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * being pushed into Spark's memory.
    */
   def store(dataItem: T) {
-    executor.pushSingle(dataItem)
+    supervisor.pushSingle(dataItem)
   }
 
   /** Store an ArrayBuffer of received data as a data block into Spark's memory. */
   def store(dataBuffer: ArrayBuffer[T]) {
-    executor.pushArrayBuffer(dataBuffer, None, None)
+    supervisor.pushArrayBuffer(dataBuffer, None, None)
   }
 
   /**
@@ -130,12 +130,12 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * for being used in the corresponding InputDStream.
    */
   def store(dataBuffer: ArrayBuffer[T], metadata: Any) {
-    executor.pushArrayBuffer(dataBuffer, Some(metadata), None)
+    supervisor.pushArrayBuffer(dataBuffer, Some(metadata), None)
   }
 
   /** Store an iterator of received data as a data block into Spark's memory. */
   def store(dataIterator: Iterator[T]) {
-    executor.pushIterator(dataIterator, None, None)
+    supervisor.pushIterator(dataIterator, None, None)
   }
 
   /**
@@ -144,12 +144,12 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * for being used in the corresponding InputDStream.
    */
   def store(dataIterator: java.util.Iterator[T], metadata: Any) {
-    executor.pushIterator(dataIterator, Some(metadata), None)
+    supervisor.pushIterator(dataIterator, Some(metadata), None)
   }
 
   /** Store an iterator of received data as a data block into Spark's memory. */
   def store(dataIterator: java.util.Iterator[T]) {
-    executor.pushIterator(dataIterator, None, None)
+    supervisor.pushIterator(dataIterator, None, None)
   }
 
   /**
@@ -158,7 +158,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * for being used in the corresponding InputDStream.
    */
   def store(dataIterator: Iterator[T], metadata: Any) {
-    executor.pushIterator(dataIterator, Some(metadata), None)
+    supervisor.pushIterator(dataIterator, Some(metadata), None)
   }
 
   /**
@@ -167,7 +167,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * that Spark is configured to use.
    */
   def store(bytes: ByteBuffer) {
-    executor.pushBytes(bytes, None, None)
+    supervisor.pushBytes(bytes, None, None)
   }
 
   /**
@@ -176,12 +176,12 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * for being used in the corresponding InputDStream.
    */
   def store(bytes: ByteBuffer, metadata: Any) {
-    executor.pushBytes(bytes, Some(metadata), None)
+    supervisor.pushBytes(bytes, Some(metadata), None)
   }
 
   /** Report exceptions in receiving data. */
   def reportError(message: String, throwable: Throwable) {
-    executor.reportError(message, throwable)
+    supervisor.reportError(message, throwable)
   }
 
   /**
@@ -193,7 +193,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * The `message` will be reported to the driver.
    */
   def restart(message: String) {
-    executor.restartReceiver(message)
+    supervisor.restartReceiver(message)
   }
 
   /**
@@ -205,7 +205,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * The `message` and `exception` will be reported to the driver.
    */
   def restart(message: String, error: Throwable) {
-    executor.restartReceiver(message, Some(error))
+    supervisor.restartReceiver(message, Some(error))
   }
 
   /**
@@ -215,22 +215,22 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * in a background thread.
    */
   def restart(message: String, error: Throwable, millisecond: Int) {
-    executor.restartReceiver(message, Some(error), millisecond)
+    supervisor.restartReceiver(message, Some(error), millisecond)
   }
 
   /** Stop the receiver completely. */
   def stop(message: String) {
-    executor.stop(message, None)
+    supervisor.stop(message, None)
   }
 
   /** Stop the receiver completely due to an exception */
   def stop(message: String, error: Throwable) {
-    executor.stop(message, Some(error))
+    supervisor.stop(message, Some(error))
   }
 
   /** Check if the receiver has started or not. */
   def isStarted(): Boolean = {
-    executor.isReceiverStarted()
+    supervisor.isReceiverStarted()
   }
 
   /**
@@ -238,7 +238,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * the receiving of data should be stopped.
    */
   def isStopped(): Boolean = {
-    executor.isReceiverStopped()
+    supervisor.isReceiverStopped()
   }
 
   /**
@@ -257,7 +257,7 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
   private var id: Int = -1
 
   /** Handler object that runs the receiver. This is instantiated lazily in the worker. */
-  private[streaming] var executor_ : ReceiverSupervisor = null
+  @transient private var _supervisor : ReceiverSupervisor = null
 
   /** Set the ID of the DStream that this receiver is associated with. */
   private[streaming] def setReceiverId(id_ : Int) {
@@ -265,15 +265,17 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
   }
 
   /** Attach Network Receiver executor to this receiver. */
-  private[streaming] def attachExecutor(exec: ReceiverSupervisor) {
-    assert(executor_ == null)
-    executor_ = exec
+  private[streaming] def attachSupervisor(exec: ReceiverSupervisor) {
+    assert(_supervisor == null)
+    _supervisor = exec
   }
 
-  /** Get the attached executor. */
-  private def executor: ReceiverSupervisor = {
-    assert(executor_ != null, "Executor has not been attached to this receiver")
-    executor_
+  /** Get the attached supervisor. */
+  private[streaming] def supervisor: ReceiverSupervisor = {
+    assert(_supervisor != null,
+      "A ReceiverSupervisor have not been attached to the receiver yet. Maybe you are starting " +
+        "some computation in the receiver before the Receiver.onStart() has been called.")
+    _supervisor
   }
 }
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
index e98017a63756e..158d1ba2f183a 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisor.scala
@@ -44,8 +44,8 @@ private[streaming] abstract class ReceiverSupervisor(
   }
   import ReceiverState._
 
-  // Attach the executor to the receiver
-  receiver.attachExecutor(this)
+  // Attach the supervisor to the receiver
+  receiver.attachSupervisor(this)
 
   private val futureExecutionContext = ExecutionContext.fromExecutorService(
     ThreadUtils.newDaemonCachedThreadPool("receiver-supervisor-future", 128))
@@ -60,7 +60,7 @@ private[streaming] abstract class ReceiverSupervisor(
   private val defaultRestartDelay = conf.getInt("spark.streaming.receiverRestartDelay", 2000)
 
   /** The current maximum rate limit for this receiver. */
-  private[streaming] def getCurrentRateLimit: Option[Long] = None
+  private[streaming] def getCurrentRateLimit: Long = Long.MaxValue
 
   /** Exception associated with the stopping of the receiver */
   @volatile protected var stoppingError: Throwable = null
@@ -92,13 +92,30 @@ private[streaming] abstract class ReceiverSupervisor(
       optionalBlockId: Option[StreamBlockId]
     )
 
+  /**
+   * Create a custom [[BlockGenerator]] that the receiver implementation can directly control
+   * using their provided [[BlockGeneratorListener]].
+   *
+   * Note: Do not explicitly start or stop the `BlockGenerator`, the `ReceiverSupervisorImpl`
+   * will take care of it.
+   */
+  def createBlockGenerator(blockGeneratorListener: BlockGeneratorListener): BlockGenerator
+
   /** Report errors. */
   def reportError(message: String, throwable: Throwable)
 
-  /** Called when supervisor is started */
+  /**
+   * Called when supervisor is started.
+   * Note that this must be called before the receiver.onStart() is called to ensure
+   * things like [[BlockGenerator]]s are started before the receiver starts sending data.
+   */
   protected def onStart() { }
 
-  /** Called when supervisor is stopped */
+  /**
+   * Called when supervisor is stopped.
+   * Note that this must be called after the receiver.onStop() is called to ensure
+   * things like [[BlockGenerator]]s are cleaned up after the receiver stops sending data.
+   */
   protected def onStop(message: String, error: Option[Throwable]) { }
 
   /** Called when receiver is started. Return true if the driver accepts us */
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
index 0d802f83549af..59ef58d232ee7 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceiverSupervisorImpl.scala
@@ -20,6 +20,7 @@ package org.apache.spark.streaming.receiver
 import java.nio.ByteBuffer
 import java.util.concurrent.atomic.AtomicLong
 
+import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
 import com.google.common.base.Throwables
@@ -81,15 +82,20 @@ private[streaming] class ReceiverSupervisorImpl(
           cleanupOldBlocks(threshTime)
         case UpdateRateLimit(eps) =>
           logInfo(s"Received a new rate limit: $eps.")
-          blockGenerator.updateRate(eps)
+          registeredBlockGenerators.foreach { bg =>
+            bg.updateRate(eps)
+          }
       }
     })
 
   /** Unique block ids if one wants to add blocks directly */
   private val newBlockId = new AtomicLong(System.currentTimeMillis())
 
+  private val registeredBlockGenerators = new mutable.ArrayBuffer[BlockGenerator]
+    with mutable.SynchronizedBuffer[BlockGenerator]
+
   /** Divides received data records into data blocks for pushing in BlockManager. */
-  private val blockGenerator = new BlockGenerator(new BlockGeneratorListener {
+  private val defaultBlockGeneratorListener = new BlockGeneratorListener {
     def onAddData(data: Any, metadata: Any): Unit = { }
 
     def onGenerateBlock(blockId: StreamBlockId): Unit = { }
@@ -101,14 +107,15 @@ private[streaming] class ReceiverSupervisorImpl(
     def onPushBlock(blockId: StreamBlockId, arrayBuffer: ArrayBuffer[_]) {
       pushArrayBuffer(arrayBuffer, None, Some(blockId))
     }
-  }, streamId, env.conf)
+  }
+  private val defaultBlockGenerator = createBlockGenerator(defaultBlockGeneratorListener)
 
-  override private[streaming] def getCurrentRateLimit: Option[Long] =
-    Some(blockGenerator.getCurrentLimit)
+  /** Get the current rate limit of the default block generator */
+  override private[streaming] def getCurrentRateLimit: Long = defaultBlockGenerator.getCurrentLimit
 
   /** Push a single record of received data into block generator. */
   def pushSingle(data: Any) {
-    blockGenerator.addData(data)
+    defaultBlockGenerator.addData(data)
   }
 
   /** Store an ArrayBuffer of received data as a data block into Spark's memory. */
@@ -162,11 +169,11 @@ private[streaming] class ReceiverSupervisorImpl(
   }
 
   override protected def onStart() {
-    blockGenerator.start()
+    registeredBlockGenerators.foreach { _.start() }
   }
 
   override protected def onStop(message: String, error: Option[Throwable]) {
-    blockGenerator.stop()
+    registeredBlockGenerators.foreach { _.stop() }
     env.rpcEnv.stop(endpoint)
   }
 
@@ -183,6 +190,16 @@ private[streaming] class ReceiverSupervisorImpl(
     logInfo("Stopped receiver " + streamId)
   }
 
+  override def createBlockGenerator(
+      blockGeneratorListener: BlockGeneratorListener): BlockGenerator = {
+    // Cleanup BlockGenerators that have already been stopped
+    registeredBlockGenerators --= registeredBlockGenerators.filter{ _.isStopped() }
+
+    val newBlockGenerator = new BlockGenerator(blockGeneratorListener, streamId, env.conf)
+    registeredBlockGenerators += newBlockGenerator
+    newBlockGenerator
+  }
+
   /** Generate new block ID */
   private def nextBlockId = StreamBlockId(streamId, newBlockId.getAndIncrement)
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
index 67c2d900940ab..1bba7a143edf2 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming
 
 import java.io.File
 
-import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
+import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer}
 import scala.reflect.ClassTag
 
 import com.google.common.base.Charsets
@@ -33,7 +33,7 @@ import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
 
 import org.apache.spark.streaming.dstream.{DStream, FileInputDStream}
-import org.apache.spark.streaming.scheduler.{RateLimitInputDStream, ConstantEstimator, SingletonTestRateReceiver}
+import org.apache.spark.streaming.scheduler.{ConstantEstimator, RateTestInputDStream, RateTestReceiver}
 import org.apache.spark.util.{Clock, ManualClock, Utils}
 
 /**
@@ -397,26 +397,24 @@ class CheckpointSuite extends TestSuiteBase {
     ssc = new StreamingContext(conf, batchDuration)
     ssc.checkpoint(checkpointDir)
 
-    val dstream = new RateLimitInputDStream(ssc) {
+    val dstream = new RateTestInputDStream(ssc) {
       override val rateController =
-        Some(new ReceiverRateController(id, new ConstantEstimator(200.0)))
+        Some(new ReceiverRateController(id, new ConstantEstimator(200)))
     }
-    SingletonTestRateReceiver.reset()
 
     val output = new TestOutputStreamWithPartitions(dstream.checkpoint(batchDuration * 2))
     output.register()
     runStreams(ssc, 5, 5)
 
-    SingletonTestRateReceiver.reset()
     ssc = new StreamingContext(checkpointDir)
     ssc.start()
     val outputNew = advanceTimeWithRealDelay(ssc, 2)
 
-    eventually(timeout(5.seconds)) {
-      assert(dstream.getCurrentRateLimit === Some(200))
+    eventually(timeout(10.seconds)) {
+      assert(RateTestReceiver.getActive().nonEmpty)
+      assert(RateTestReceiver.getActive().get.getDefaultBlockGeneratorRateLimit() === 200)
     }
     ssc.stop()
-    ssc = null
   }
 
   // This tests whether file input stream remembers what files were seen before
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
index 13b4d17c86183..01279b34f73dc 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverSuite.scala
@@ -129,32 +129,6 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable {
     }
   }
 
-  test("block generator") {
-    val blockGeneratorListener = new FakeBlockGeneratorListener
-    val blockIntervalMs = 200
-    val conf = new SparkConf().set("spark.streaming.blockInterval", s"${blockIntervalMs}ms")
-    val blockGenerator = new BlockGenerator(blockGeneratorListener, 1, conf)
-    val expectedBlocks = 5
-    val waitTime = expectedBlocks * blockIntervalMs + (blockIntervalMs / 2)
-    val generatedData = new ArrayBuffer[Int]
-
-    // Generate blocks
-    val startTime = System.currentTimeMillis()
-    blockGenerator.start()
-    var count = 0
-    while(System.currentTimeMillis - startTime < waitTime) {
-      blockGenerator.addData(count)
-      generatedData += count
-      count += 1
-      Thread.sleep(10)
-    }
-    blockGenerator.stop()
-
-    val recordedData = blockGeneratorListener.arrayBuffers.flatten
-    assert(blockGeneratorListener.arrayBuffers.size > 0)
-    assert(recordedData.toSet === generatedData.toSet)
-  }
-
   ignore("block generator throttling") {
     val blockGeneratorListener = new FakeBlockGeneratorListener
     val blockIntervalMs = 100
@@ -348,6 +322,11 @@ class ReceiverSuite extends TestSuiteBase with Timeouts with Serializable {
     }
 
     override protected def onReceiverStart(): Boolean = true
+
+    override def createBlockGenerator(
+        blockGeneratorListener: BlockGeneratorListener): BlockGenerator = {
+      null
+    }
   }
 
   /**
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/receiver/BlockGeneratorSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/receiver/BlockGeneratorSuite.scala
new file mode 100644
index 0000000000000..a38cc603f2190
--- /dev/null
+++ b/streaming/src/test/scala/org/apache/spark/streaming/receiver/BlockGeneratorSuite.scala
@@ -0,0 +1,253 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.receiver
+
+import scala.collection.mutable
+
+import org.scalatest.BeforeAndAfter
+import org.scalatest.Matchers._
+import org.scalatest.concurrent.Timeouts._
+import org.scalatest.concurrent.Eventually._
+import org.scalatest.time.SpanSugar._
+
+import org.apache.spark.storage.StreamBlockId
+import org.apache.spark.util.ManualClock
+import org.apache.spark.{SparkException, SparkConf, SparkFunSuite}
+
+class BlockGeneratorSuite extends SparkFunSuite with BeforeAndAfter {
+
+  private val blockIntervalMs = 10
+  private val conf = new SparkConf().set("spark.streaming.blockInterval", s"${blockIntervalMs}ms")
+  @volatile private var blockGenerator: BlockGenerator = null
+
+  after {
+    if (blockGenerator != null) {
+      blockGenerator.stop()
+    }
+  }
+
+  test("block generation and data callbacks") {
+    val listener = new TestBlockGeneratorListener
+    val clock = new ManualClock()
+
+    require(blockIntervalMs > 5)
+    require(listener.onAddDataCalled === false)
+    require(listener.onGenerateBlockCalled === false)
+    require(listener.onPushBlockCalled === false)
+
+    // Verify that creating the generator does not start it
+    blockGenerator = new BlockGenerator(listener, 0, conf, clock)
+    assert(blockGenerator.isActive() === false, "block generator active before start()")
+    assert(blockGenerator.isStopped() === false, "block generator stopped before start()")
+    assert(listener.onAddDataCalled === false)
+    assert(listener.onGenerateBlockCalled === false)
+    assert(listener.onPushBlockCalled === false)
+
+    // Verify start marks the generator active, but does not call the callbacks
+    blockGenerator.start()
+    assert(blockGenerator.isActive() === true, "block generator active after start()")
+    assert(blockGenerator.isStopped() === false, "block generator stopped after start()")
+    withClue("callbacks called before adding data") {
+      assert(listener.onAddDataCalled === false)
+      assert(listener.onGenerateBlockCalled === false)
+      assert(listener.onPushBlockCalled === false)
+    }
+
+    // Verify whether addData() adds data that is present in generated blocks
+    val data1 = 1 to 10
+    data1.foreach { blockGenerator.addData _ }
+    withClue("callbacks called on adding data without metadata and without block generation") {
+      assert(listener.onAddDataCalled === false) // should be called only with addDataWithCallback()
+      assert(listener.onGenerateBlockCalled === false)
+      assert(listener.onPushBlockCalled === false)
+    }
+    clock.advance(blockIntervalMs)  // advance clock to generate blocks
+    withClue("blocks not generated or pushed") {
+      eventually(timeout(1 second)) {
+        assert(listener.onGenerateBlockCalled === true)
+        assert(listener.onPushBlockCalled === true)
+      }
+    }
+    listener.pushedData should contain theSameElementsInOrderAs (data1)
+    assert(listener.onAddDataCalled === false) // should be called only with addDataWithCallback()
+
+    // Verify addDataWithCallback() add data+metadata and and callbacks are called correctly
+    val data2 = 11 to 20
+    val metadata2 = data2.map { _.toString }
+    data2.zip(metadata2).foreach { case (d, m) => blockGenerator.addDataWithCallback(d, m) }
+    assert(listener.onAddDataCalled === true)
+    listener.addedData should contain theSameElementsInOrderAs (data2)
+    listener.addedMetadata should contain theSameElementsInOrderAs (metadata2)
+    clock.advance(blockIntervalMs)  // advance clock to generate blocks
+    eventually(timeout(1 second)) {
+      listener.pushedData should contain theSameElementsInOrderAs (data1 ++ data2)
+    }
+
+    // Verify addMultipleDataWithCallback() add data+metadata and and callbacks are called correctly
+    val data3 = 21 to 30
+    val metadata3 = "metadata"
+    blockGenerator.addMultipleDataWithCallback(data3.iterator, metadata3)
+    listener.addedMetadata should contain theSameElementsInOrderAs (metadata2 :+ metadata3)
+    clock.advance(blockIntervalMs)  // advance clock to generate blocks
+    eventually(timeout(1 second)) {
+      listener.pushedData should contain theSameElementsInOrderAs (data1 ++ data2 ++ data3)
+    }
+
+    // Stop the block generator by starting the stop on a different thread and
+    // then advancing the manual clock for the stopping to proceed.
+    val thread = stopBlockGenerator(blockGenerator)
+    eventually(timeout(1 second), interval(10 milliseconds)) {
+      clock.advance(blockIntervalMs)
+      assert(blockGenerator.isStopped() === true)
+    }
+    thread.join()
+
+    // Verify that the generator cannot be used any more
+    intercept[SparkException] {
+      blockGenerator.addData(1)
+    }
+    intercept[SparkException] {
+      blockGenerator.addDataWithCallback(1, 1)
+    }
+    intercept[SparkException] {
+      blockGenerator.addMultipleDataWithCallback(Iterator(1), 1)
+    }
+    intercept[SparkException] {
+      blockGenerator.start()
+    }
+    blockGenerator.stop()   // Calling stop again should be fine
+  }
+
+  test("stop ensures correct shutdown") {
+    val listener = new TestBlockGeneratorListener
+    val clock = new ManualClock()
+    blockGenerator = new BlockGenerator(listener, 0, conf, clock)
+    require(listener.onGenerateBlockCalled === false)
+    blockGenerator.start()
+    assert(blockGenerator.isActive() === true, "block generator")
+    assert(blockGenerator.isStopped() === false)
+
+    val data = 1 to 1000
+    data.foreach { blockGenerator.addData _ }
+
+    // Verify that stop() shutdowns everything in the right order
+    // - First, stop receiving new data
+    // - Second, wait for final block with all buffered data to be generated
+    // - Finally, wait for all blocks to be pushed
+    clock.advance(1) // to make sure that the timer for another interval to complete
+    val thread = stopBlockGenerator(blockGenerator)
+    eventually(timeout(1 second), interval(10 milliseconds)) {
+      assert(blockGenerator.isActive() === false)
+    }
+    assert(blockGenerator.isStopped() === false)
+
+    // Verify that data cannot be added
+    intercept[SparkException] {
+      blockGenerator.addData(1)
+    }
+    intercept[SparkException] {
+      blockGenerator.addDataWithCallback(1, null)
+    }
+    intercept[SparkException] {
+      blockGenerator.addMultipleDataWithCallback(Iterator(1), null)
+    }
+
+    // Verify that stop() stays blocked until another block containing all the data is generated
+    // This intercept always succeeds, as the body either will either throw a timeout exception
+    // (expected as stop() should never complete) or a SparkException (unexpected as stop()
+    // completed and thread terminated).
+    val exception = intercept[Exception] {
+      failAfter(200 milliseconds) {
+        thread.join()
+        throw new SparkException(
+          "BlockGenerator.stop() completed before generating timer was stopped")
+      }
+    }
+    exception should not be a [SparkException]
+
+
+    // Verify that the final data is present in the final generated block and
+    // pushed before complete stop
+    assert(blockGenerator.isStopped() === false) // generator has not stopped yet
+    clock.advance(blockIntervalMs)   // force block generation
+    failAfter(1 second) {
+      thread.join()
+    }
+    assert(blockGenerator.isStopped() === true) // generator has finally been completely stopped
+    assert(listener.pushedData === data, "All data not pushed by stop()")
+  }
+
+  test("block push errors are reported") {
+    val listener = new TestBlockGeneratorListener {
+      @volatile var errorReported = false
+      override def onPushBlock(
+          blockId: StreamBlockId, arrayBuffer: mutable.ArrayBuffer[_]): Unit = {
+        throw new SparkException("test")
+      }
+      override def onError(message: String, throwable: Throwable): Unit = {
+        errorReported = true
+      }
+    }
+    blockGenerator = new BlockGenerator(listener, 0, conf)
+    blockGenerator.start()
+    assert(listener.errorReported === false)
+    blockGenerator.addData(1)
+    eventually(timeout(1 second), interval(10 milliseconds)) {
+      assert(listener.errorReported === true)
+    }
+    blockGenerator.stop()
+  }
+
+  /**
+   * Helper method to stop the block generator with manual clock in a different thread,
+   * so that the main thread can advance the clock that allows the stopping to proceed.
+   */
+  private def stopBlockGenerator(blockGenerator: BlockGenerator): Thread = {
+    val thread = new Thread() {
+      override def run(): Unit = {
+        blockGenerator.stop()
+      }
+    }
+    thread.start()
+    thread
+  }
+
+  /** A listener for BlockGenerator that records the data in the callbacks */
+  private class TestBlockGeneratorListener extends BlockGeneratorListener {
+    val pushedData = new mutable.ArrayBuffer[Any] with mutable.SynchronizedBuffer[Any]
+    val addedData = new mutable.ArrayBuffer[Any] with mutable.SynchronizedBuffer[Any]
+    val addedMetadata = new mutable.ArrayBuffer[Any] with mutable.SynchronizedBuffer[Any]
+    @volatile var onGenerateBlockCalled = false
+    @volatile var onAddDataCalled = false
+    @volatile var onPushBlockCalled = false
+
+    override def onPushBlock(blockId: StreamBlockId, arrayBuffer: mutable.ArrayBuffer[_]): Unit = {
+      pushedData ++= arrayBuffer
+      onPushBlockCalled = true
+    }
+    override def onError(message: String, throwable: Throwable): Unit = {}
+    override def onGenerateBlock(blockId: StreamBlockId): Unit = {
+      onGenerateBlockCalled = true
+    }
+    override def onAddData(data: Any, metadata: Any): Unit = {
+      addedData += data
+      addedMetadata += metadata
+      onAddDataCalled = true
+    }
+  }
+}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala
index 921da773f6c11..1eb52b7029a21 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala
@@ -18,10 +18,7 @@
 package org.apache.spark.streaming.scheduler
 
 import scala.collection.mutable
-import scala.reflect.ClassTag
-import scala.util.control.NonFatal
 
-import org.scalatest.Matchers._
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
 
@@ -32,72 +29,63 @@ class RateControllerSuite extends TestSuiteBase {
 
   override def useManualClock: Boolean = false
 
-  test("rate controller publishes updates") {
+  override def batchDuration: Duration = Milliseconds(50)
+
+  test("RateController - rate controller publishes updates after batches complete") {
     val ssc = new StreamingContext(conf, batchDuration)
     withStreamingContext(ssc) { ssc =>
-      val dstream = new RateLimitInputDStream(ssc)
+      val dstream = new RateTestInputDStream(ssc)
       dstream.register()
       ssc.start()
 
       eventually(timeout(10.seconds)) {
-        assert(dstream.publishCalls > 0)
+        assert(dstream.publishedRates > 0)
       }
     }
   }
 
-  test("publish rates reach receivers") {
+  test("ReceiverRateController - published rates reach receivers") {
     val ssc = new StreamingContext(conf, batchDuration)
     withStreamingContext(ssc) { ssc =>
-      val dstream = new RateLimitInputDStream(ssc) {
+      val estimator = new ConstantEstimator(100)
+      val dstream = new RateTestInputDStream(ssc) {
         override val rateController =
-          Some(new ReceiverRateController(id, new ConstantEstimator(200.0)))
+          Some(new ReceiverRateController(id, estimator))
       }
       dstream.register()
-      SingletonTestRateReceiver.reset()
       ssc.start()
 
-      eventually(timeout(10.seconds)) {
-        assert(dstream.getCurrentRateLimit === Some(200))
+      // Wait for receiver to start
+      eventually(timeout(5.seconds)) {
+        RateTestReceiver.getActive().nonEmpty
       }
-    }
-  }
 
-  test("multiple publish rates reach receivers") {
-    val ssc = new StreamingContext(conf, batchDuration)
-    withStreamingContext(ssc) { ssc =>
-      val rates = Seq(100L, 200L, 300L)
-
-      val dstream = new RateLimitInputDStream(ssc) {
-        override val rateController =
-          Some(new ReceiverRateController(id, new ConstantEstimator(rates.map(_.toDouble): _*)))
+      // Update rate in the estimator and verify whether the rate was published to the receiver
+      def updateRateAndVerify(rate: Long): Unit = {
+        estimator.updateRate(rate)
+        eventually(timeout(5.seconds)) {
+          assert(RateTestReceiver.getActive().get.getDefaultBlockGeneratorRateLimit() === rate)
+        }
       }
-      SingletonTestRateReceiver.reset()
-      dstream.register()
-
-      val observedRates = mutable.HashSet.empty[Long]
-      ssc.start()
 
-      eventually(timeout(20.seconds)) {
-        dstream.getCurrentRateLimit.foreach(observedRates += _)
-        // Long.MaxValue (essentially, no rate limit) is the initial rate limit for any Receiver
-        observedRates should contain theSameElementsAs (rates :+ Long.MaxValue)
+      // Verify multiple rate update
+      Seq(100, 200, 300).foreach { rate =>
+        updateRateAndVerify(rate)
       }
     }
   }
 }
 
-private[streaming] class ConstantEstimator(rates: Double*) extends RateEstimator {
-  private var idx: Int = 0
+private[streaming] class ConstantEstimator(@volatile private var rate: Long)
+  extends RateEstimator {
 
-  private def nextRate(): Double = {
-    val rate = rates(idx)
-    idx = (idx + 1) % rates.size
-    rate
+  def updateRate(newRate: Long): Unit = {
+    rate = newRate
   }
 
   def compute(
       time: Long,
       elements: Long,
       processingDelay: Long,
-      schedulingDelay: Long): Option[Double] = Some(nextRate())
+      schedulingDelay: Long): Option[Double] = Some(rate)
 }
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
index afad5f16dbc71..dd292ba4dd949 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
@@ -17,48 +17,43 @@
 
 package org.apache.spark.streaming.scheduler
 
+import scala.collection.mutable.ArrayBuffer
+
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.time.SpanSugar._
 
-import org.apache.spark.SparkConf
+import org.apache.spark.storage.{StorageLevel, StreamBlockId}
 import org.apache.spark.streaming._
-import org.apache.spark.streaming.receiver._
 import org.apache.spark.streaming.dstream.ReceiverInputDStream
-import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.receiver._
 
 /** Testsuite for receiver scheduling */
 class ReceiverTrackerSuite extends TestSuiteBase {
-  val sparkConf = new SparkConf().setMaster("local[8]").setAppName("test")
-
-  test("Receiver tracker - propagates rate limit") {
-    withStreamingContext(new StreamingContext(sparkConf, Milliseconds(100))) { ssc =>
-      object ReceiverStartedWaiter extends StreamingListener {
-        @volatile
-        var started = false
-
-        override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted): Unit = {
-          started = true
-        }
-      }
 
-      ssc.addStreamingListener(ReceiverStartedWaiter)
+  test("send rate update to receivers") {
+    withStreamingContext(new StreamingContext(conf, Milliseconds(100))) { ssc =>
       ssc.scheduler.listenerBus.start(ssc.sc)
-      SingletonTestRateReceiver.reset()
 
       val newRateLimit = 100L
-      val inputDStream = new RateLimitInputDStream(ssc)
+      val inputDStream = new RateTestInputDStream(ssc)
       val tracker = new ReceiverTracker(ssc)
       tracker.start()
       try {
         // we wait until the Receiver has registered with the tracker,
         // otherwise our rate update is lost
         eventually(timeout(5 seconds)) {
-          assert(ReceiverStartedWaiter.started)
+          assert(RateTestReceiver.getActive().nonEmpty)
         }
+
+
+        // Verify that the rate of the block generator in the receiver get updated
+        val activeReceiver = RateTestReceiver.getActive().get
         tracker.sendRateUpdate(inputDStream.id, newRateLimit)
-        // this is an async message, we need to wait a bit for it to be processed
-        eventually(timeout(3 seconds)) {
-          assert(inputDStream.getCurrentRateLimit.get === newRateLimit)
+        eventually(timeout(5 seconds)) {
+          assert(activeReceiver.getDefaultBlockGeneratorRateLimit() === newRateLimit,
+            "default block generator did not receive rate update")
+          assert(activeReceiver.getCustomBlockGeneratorRateLimit() === newRateLimit,
+            "other block generator did not receive rate update")
         }
       } finally {
         tracker.stop(false)
@@ -67,69 +62,73 @@ class ReceiverTrackerSuite extends TestSuiteBase {
   }
 }
 
-/**
- * An input DStream with a hard-coded receiver that gives access to internals for testing.
- *
- * @note Make sure to call {{{SingletonDummyReceiver.reset()}}} before using this in a test,
- *       or otherwise you may get {{{NotSerializableException}}} when trying to serialize
- *       the receiver.
- * @see [[[SingletonDummyReceiver]]].
- */
-private[streaming] class RateLimitInputDStream(@transient ssc_ : StreamingContext)
+/** An input DStream with for testing rate controlling */
+private[streaming] class RateTestInputDStream(@transient ssc_ : StreamingContext)
   extends ReceiverInputDStream[Int](ssc_) {
 
-  override def getReceiver(): RateTestReceiver = SingletonTestRateReceiver
-
-  def getCurrentRateLimit: Option[Long] = {
-    invokeExecutorMethod.getCurrentRateLimit
-  }
+  override def getReceiver(): Receiver[Int] = new RateTestReceiver(id)
 
   @volatile
-  var publishCalls = 0
+  var publishedRates = 0
 
   override val rateController: Option[RateController] = {
-    Some(new RateController(id, new ConstantEstimator(100.0)) {
+    Some(new RateController(id, new ConstantEstimator(100)) {
       override def publish(rate: Long): Unit = {
-        publishCalls += 1
+        publishedRates += 1
       }
     })
   }
+}
 
-  private def invokeExecutorMethod: ReceiverSupervisor = {
-    val c = classOf[Receiver[_]]
-    val ex = c.getDeclaredMethod("executor")
-    ex.setAccessible(true)
-    ex.invoke(SingletonTestRateReceiver).asInstanceOf[ReceiverSupervisor]
+/** A receiver implementation for testing rate controlling */
+private[streaming] class RateTestReceiver(receiverId: Int, host: Option[String] = None)
+  extends Receiver[Int](StorageLevel.MEMORY_ONLY) {
+
+  private lazy val customBlockGenerator = supervisor.createBlockGenerator(
+    new BlockGeneratorListener {
+      override def onPushBlock(blockId: StreamBlockId, arrayBuffer: ArrayBuffer[_]): Unit = {}
+      override def onError(message: String, throwable: Throwable): Unit = {}
+      override def onGenerateBlock(blockId: StreamBlockId): Unit = {}
+      override def onAddData(data: Any, metadata: Any): Unit = {}
+    }
+  )
+
+  setReceiverId(receiverId)
+
+  override def onStart(): Unit = {
+    customBlockGenerator
+    RateTestReceiver.registerReceiver(this)
   }
-}
 
-/**
- * A Receiver as an object so we can read its rate limit. Make sure to call `reset()` when
- * reusing this receiver, otherwise a non-null `executor_` field will prevent it from being
- * serialized when receivers are installed on executors.
- *
- * @note It's necessary to be a top-level object, or else serialization would create another
- *       one on the executor side and we won't be able to read its rate limit.
- */
-private[streaming] object SingletonTestRateReceiver extends RateTestReceiver(0) {
+  override def onStop(): Unit = {
+    RateTestReceiver.deregisterReceiver()
+  }
+
+  override def preferredLocation: Option[String] = host
 
-  /** Reset the object to be usable in another test. */
-  def reset(): Unit = {
-    executor_ = null
+  def getDefaultBlockGeneratorRateLimit(): Long = {
+    supervisor.getCurrentRateLimit
+  }
+
+  def getCustomBlockGeneratorRateLimit(): Long = {
+    customBlockGenerator.getCurrentLimit
   }
 }
 
 /**
- * Dummy receiver implementation
+ * A helper object to RateTestReceiver that give access to the currently active RateTestReceiver
+ * instance.
  */
-private[streaming] class RateTestReceiver(receiverId: Int, host: Option[String] = None)
-  extends Receiver[Int](StorageLevel.MEMORY_ONLY) {
+private[streaming] object RateTestReceiver {
+  @volatile private var activeReceiver: RateTestReceiver = null
 
-  setReceiverId(receiverId)
-
-  override def onStart(): Unit = {}
+  def registerReceiver(receiver: RateTestReceiver): Unit = {
+    activeReceiver = receiver
+  }
 
-  override def onStop(): Unit = {}
+  def deregisterReceiver(): Unit = {
+    activeReceiver = null
+  }
 
-  override def preferredLocation: Option[String] = host
+  def getActive(): Option[RateTestReceiver] = Option(activeReceiver)
 }

From 1723e34893f9b087727ea0e5c8b335645f42c295 Mon Sep 17 00:00:00 2001
From: cody koeninger <cody@koeninger.org>
Date: Thu, 6 Aug 2015 14:37:25 -0700
Subject: [PATCH 096/633] =?UTF-8?q?[DOCS]=20[STREAMING]=20make=20the=20exi?=
 =?UTF-8?q?sting=20parameter=20docs=20for=20OffsetRange=20ac=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…tually visible

Author: cody koeninger <cody@koeninger.org>

Closes #7995 from koeninger/doc-fixes and squashes the following commits:

87af9ea [cody koeninger] [Docs][Streaming] make the existing parameter docs for OffsetRange actually visible
---
 .../org/apache/spark/streaming/kafka/OffsetRange.scala    | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
index f326e7f1f6f8d..2f8981d4898bd 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
@@ -42,16 +42,16 @@ trait HasOffsetRanges {
  * :: Experimental ::
  * Represents a range of offsets from a single Kafka TopicAndPartition. Instances of this class
  * can be created with `OffsetRange.create()`.
+ * @param topic Kafka topic name
+ * @param partition Kafka partition id
+ * @param fromOffset Inclusive starting offset
+ * @param untilOffset Exclusive ending offset
  */
 @Experimental
 final class OffsetRange private(
-    /** Kafka topic name */
     val topic: String,
-    /** Kafka partition id */
     val partition: Int,
-    /** inclusive starting offset */
     val fromOffset: Long,
-    /** exclusive ending offset */
     val untilOffset: Long) extends Serializable {
   import OffsetRange.OffsetRangeTuple
 

From 346209097e88fe79015359e40b49c32cc0bdc439 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 6 Aug 2015 14:39:36 -0700
Subject: [PATCH 097/633] [SPARK-9639] [STREAMING] Fix a potential NPE in
 Streaming JobScheduler

Because `JobScheduler.stop(false)` may set `eventLoop` to null when `JobHandler` is running, then it's possible that when `post` is called, `eventLoop` happens to null.

This PR fixed this bug and also set threads in `jobExecutor` to `daemon`.

Author: zsxwing <zsxwing@gmail.com>

Closes #7960 from zsxwing/fix-npe and squashes the following commits:

b0864c4 [zsxwing] Fix a potential NPE in Streaming JobScheduler
---
 .../streaming/scheduler/JobScheduler.scala    | 32 +++++++++++++------
 1 file changed, 22 insertions(+), 10 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
index 7e735562dca33..6d4cdc4aa6b10 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.streaming.scheduler
 
-import java.util.concurrent.{TimeUnit, ConcurrentHashMap, Executors}
+import java.util.concurrent.{ConcurrentHashMap, TimeUnit}
 
 import scala.collection.JavaConversions._
 import scala.util.{Failure, Success}
@@ -25,7 +25,7 @@ import scala.util.{Failure, Success}
 import org.apache.spark.Logging
 import org.apache.spark.rdd.PairRDDFunctions
 import org.apache.spark.streaming._
-import org.apache.spark.util.EventLoop
+import org.apache.spark.util.{EventLoop, ThreadUtils}
 
 
 private[scheduler] sealed trait JobSchedulerEvent
@@ -44,7 +44,8 @@ class JobScheduler(val ssc: StreamingContext) extends Logging {
   // https://gist.github.com/AlainODea/1375759b8720a3f9f094
   private val jobSets: java.util.Map[Time, JobSet] = new ConcurrentHashMap[Time, JobSet]
   private val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1)
-  private val jobExecutor = Executors.newFixedThreadPool(numConcurrentJobs)
+  private val jobExecutor =
+    ThreadUtils.newDaemonFixedThreadPool(numConcurrentJobs, "streaming-job-executor")
   private val jobGenerator = new JobGenerator(this)
   val clock = jobGenerator.clock
   val listenerBus = new StreamingListenerBus()
@@ -193,14 +194,25 @@ class JobScheduler(val ssc: StreamingContext) extends Logging {
       ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, job.time.milliseconds.toString)
       ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, job.outputOpId.toString)
       try {
-        eventLoop.post(JobStarted(job))
-        // Disable checks for existing output directories in jobs launched by the streaming
-        // scheduler, since we may need to write output to an existing directory during checkpoint
-        // recovery; see SPARK-4835 for more details.
-        PairRDDFunctions.disableOutputSpecValidation.withValue(true) {
-          job.run()
+        // We need to assign `eventLoop` to a temp variable. Otherwise, because
+        // `JobScheduler.stop(false)` may set `eventLoop` to null when this method is running, then
+        // it's possible that when `post` is called, `eventLoop` happens to null.
+        var _eventLoop = eventLoop
+        if (_eventLoop != null) {
+          _eventLoop.post(JobStarted(job))
+          // Disable checks for existing output directories in jobs launched by the streaming
+          // scheduler, since we may need to write output to an existing directory during checkpoint
+          // recovery; see SPARK-4835 for more details.
+          PairRDDFunctions.disableOutputSpecValidation.withValue(true) {
+            job.run()
+          }
+          _eventLoop = eventLoop
+          if (_eventLoop != null) {
+            _eventLoop.post(JobCompleted(job))
+          }
+        } else {
+          // JobScheduler has been stopped.
         }
-        eventLoop.post(JobCompleted(job))
       } finally {
         ssc.sc.setLocalProperty(JobScheduler.BATCH_TIME_PROPERTY_KEY, null)
         ssc.sc.setLocalProperty(JobScheduler.OUTPUT_OP_ID_PROPERTY_KEY, null)

From 3504bf3aa9f7b75c0985f04ce2944833d8c5b5bd Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 6 Aug 2015 15:04:44 -0700
Subject: [PATCH 098/633] [SPARK-9630] [SQL] Clean up new aggregate operators
 (SPARK-9240 follow up)

This is the followup of https://github.com/apache/spark/pull/7813. It renames `HybridUnsafeAggregationIterator` to `TungstenAggregationIterator` and makes it only work with `UnsafeRow`. Also, I add a `TungstenAggregate` that uses `TungstenAggregationIterator` and make `SortBasedAggregate` (renamed from `SortBasedAggregate`) only works with `SafeRow`.

Author: Yin Huai <yhuai@databricks.com>

Closes #7954 from yhuai/agg-followUp and squashes the following commits:

4d2f4fc [Yin Huai] Add comments and free map.
0d7ddb9 [Yin Huai] Add TungstenAggregationQueryWithControlledFallbackSuite to test fall back process.
91d69c2 [Yin Huai] Rename UnsafeHybridAggregationIterator to  TungstenAggregateIteraotr and make it only work with UnsafeRow.
---
 .../expressions/aggregate/functions.scala     |  14 +-
 .../spark/sql/execution/SparkStrategies.scala |   3 +-
 .../sql/execution/UnsafeRowSerializer.scala   |  20 +-
 .../sql/execution/aggregate/Aggregate.scala   | 182 -----
 .../aggregate/SortBasedAggregate.scala        | 103 +++
 .../SortBasedAggregationIterator.scala        |  26 -
 .../aggregate/TungstenAggregate.scala         | 102 +++
 .../TungstenAggregationIterator.scala         | 667 ++++++++++++++++++
 .../UnsafeHybridAggregationIterator.scala     | 372 ----------
 .../spark/sql/execution/aggregate/utils.scala | 260 +++++--
 .../org/apache/spark/sql/SQLQuerySuite.scala  |   2 +-
 .../execution/AggregationQuerySuite.scala     | 104 ++-
 12 files changed, 1192 insertions(+), 663 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
index 88fb516e64aaf..a73024d6adba1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
@@ -31,8 +31,11 @@ case class Average(child: Expression) extends AlgebraicAggregate {
   override def dataType: DataType = resultType
 
   // Expected input data type.
-  // TODO: Once we remove the old code path, we can use our analyzer to cast NullType
-  // to the default data type of the NumericType.
+  // TODO: Right now, we replace old aggregate functions (based on AggregateExpression1) to the
+  // new version at planning time (after analysis phase). For now, NullType is added at here
+  // to make it resolved when we have cases like `select avg(null)`.
+  // We can use our analyzer to cast NullType to the default data type of the NumericType once
+  // we remove the old aggregate functions. Then, we will not need NullType at here.
   override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(NumericType, NullType))
 
   private val resultType = child.dataType match {
@@ -256,12 +259,19 @@ case class Sum(child: Expression) extends AlgebraicAggregate {
   override def dataType: DataType = resultType
 
   // Expected input data type.
+  // TODO: Right now, we replace old aggregate functions (based on AggregateExpression1) to the
+  // new version at planning time (after analysis phase). For now, NullType is added at here
+  // to make it resolved when we have cases like `select sum(null)`.
+  // We can use our analyzer to cast NullType to the default data type of the NumericType once
+  // we remove the old aggregate functions. Then, we will not need NullType at here.
   override def inputTypes: Seq[AbstractDataType] =
     Seq(TypeCollection(LongType, DoubleType, DecimalType, NullType))
 
   private val resultType = child.dataType match {
     case DecimalType.Fixed(precision, scale) =>
       DecimalType.bounded(precision + 10, scale)
+    // TODO: Remove this line once we remove the NullType from inputTypes.
+    case NullType => IntegerType
     case _ => child.dataType
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index a730ffbb217c0..c5aaebe673225 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -191,8 +191,9 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
             // aggregate function to the corresponding attribute of the function.
             val aggregateFunctionMap = aggregateExpressions.map { agg =>
               val aggregateFunction = agg.aggregateFunction
+              val attribtue = Alias(aggregateFunction, aggregateFunction.toString)().toAttribute
               (aggregateFunction, agg.isDistinct) ->
-                Alias(aggregateFunction, aggregateFunction.toString)().toAttribute
+                (aggregateFunction -> attribtue)
             }.toMap
 
             val (functionsWithDistinct, functionsWithoutDistinct) =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 16498da080c88..39f8f992a9f00 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution
 
-import java.io.{DataInputStream, DataOutputStream, OutputStream, InputStream}
+import java.io._
 import java.nio.ByteBuffer
 
 import scala.reflect.ClassTag
@@ -58,11 +58,26 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
    */
   override def serializeStream(out: OutputStream): SerializationStream = new SerializationStream {
     private[this] var writeBuffer: Array[Byte] = new Array[Byte](4096)
+    // When `out` is backed by ChainedBufferOutputStream, we will get an
+    // UnsupportedOperationException when we call dOut.writeInt because it internally calls
+    // ChainedBufferOutputStream's write(b: Int), which is not supported.
+    // To workaround this issue, we create an array for sorting the int value.
+    // To reproduce the problem, use dOut.writeInt(row.getSizeInBytes) and
+    // run SparkSqlSerializer2SortMergeShuffleSuite.
+    private[this] var intBuffer: Array[Byte] = new Array[Byte](4)
     private[this] val dOut: DataOutputStream = new DataOutputStream(out)
 
     override def writeValue[T: ClassTag](value: T): SerializationStream = {
       val row = value.asInstanceOf[UnsafeRow]
-      dOut.writeInt(row.getSizeInBytes)
+      val size = row.getSizeInBytes
+      // This part is based on DataOutputStream's writeInt.
+      // It is for dOut.writeInt(row.getSizeInBytes).
+      intBuffer(0) = ((size >>> 24) & 0xFF).toByte
+      intBuffer(1) = ((size >>> 16) & 0xFF).toByte
+      intBuffer(2) = ((size >>> 8) & 0xFF).toByte
+      intBuffer(3) = ((size >>> 0) & 0xFF).toByte
+      dOut.write(intBuffer, 0, 4)
+
       row.writeToStream(out, writeBuffer)
       this
     }
@@ -90,6 +105,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
 
     override def close(): Unit = {
       writeBuffer = null
+      intBuffer = null
       dOut.writeInt(EOF)
       dOut.close()
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala
deleted file mode 100644
index cf568dc048674..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/Aggregate.scala
+++ /dev/null
@@ -1,182 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution.aggregate
-
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.errors._
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
-import org.apache.spark.sql.execution.{UnsafeFixedWidthAggregationMap, SparkPlan, UnaryNode}
-import org.apache.spark.sql.types.StructType
-
-/**
- * An Aggregate Operator used to evaluate [[AggregateFunction2]]. Based on the data types
- * of the grouping expressions and aggregate functions, it determines if it uses
- * sort-based aggregation and hybrid (hash-based with sort-based as the fallback) to
- * process input rows.
- */
-case class Aggregate(
-    requiredChildDistributionExpressions: Option[Seq[Expression]],
-    groupingExpressions: Seq[NamedExpression],
-    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
-    nonCompleteAggregateAttributes: Seq[Attribute],
-    completeAggregateExpressions: Seq[AggregateExpression2],
-    completeAggregateAttributes: Seq[Attribute],
-    initialInputBufferOffset: Int,
-    resultExpressions: Seq[NamedExpression],
-    child: SparkPlan)
-  extends UnaryNode {
-
-  private[this] val allAggregateExpressions =
-    nonCompleteAggregateExpressions ++ completeAggregateExpressions
-
-  private[this] val hasNonAlgebricAggregateFunctions =
-    !allAggregateExpressions.forall(_.aggregateFunction.isInstanceOf[AlgebraicAggregate])
-
-  // Use the hybrid iterator if (1) unsafe is enabled, (2) the schemata of
-  // grouping key and aggregation buffer is supported; and (3) all
-  // aggregate functions are algebraic.
-  private[this] val supportsHybridIterator: Boolean = {
-    val aggregationBufferSchema: StructType =
-      StructType.fromAttributes(
-        allAggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes))
-    val groupKeySchema: StructType =
-      StructType.fromAttributes(groupingExpressions.map(_.toAttribute))
-
-    val schemaSupportsUnsafe: Boolean =
-      UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) &&
-        UnsafeProjection.canSupport(groupKeySchema)
-
-    // TODO: Use the hybrid iterator for non-algebric aggregate functions.
-    sqlContext.conf.unsafeEnabled && schemaSupportsUnsafe && !hasNonAlgebricAggregateFunctions
-  }
-
-  // We need to use sorted input if we have grouping expressions, and
-  // we cannot use the hybrid iterator or the hybrid is disabled.
-  private[this] val requiresSortedInput: Boolean = {
-    groupingExpressions.nonEmpty && !supportsHybridIterator
-  }
-
-  override def canProcessUnsafeRows: Boolean = !hasNonAlgebricAggregateFunctions
-
-  // If result expressions' data types are all fixed length, we generate unsafe rows
-  // (We have this requirement instead of check the result of UnsafeProjection.canSupport
-  // is because we use a mutable projection to generate the result).
-  override def outputsUnsafeRows: Boolean = {
-    // resultExpressions.map(_.dataType).forall(UnsafeRow.isFixedLength)
-    // TODO: Supports generating UnsafeRows. We can just re-enable the line above and fix
-    // any issue we get.
-    false
-  }
-
-  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
-
-  override def requiredChildDistribution: List[Distribution] = {
-    requiredChildDistributionExpressions match {
-      case Some(exprs) if exprs.length == 0 => AllTuples :: Nil
-      case Some(exprs) if exprs.length > 0 => ClusteredDistribution(exprs) :: Nil
-      case None => UnspecifiedDistribution :: Nil
-    }
-  }
-
-  override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
-    if (requiresSortedInput) {
-      // TODO: We should not sort the input rows if they are just in reversed order.
-      groupingExpressions.map(SortOrder(_, Ascending)) :: Nil
-    } else {
-      Seq.fill(children.size)(Nil)
-    }
-  }
-
-  override def outputOrdering: Seq[SortOrder] = {
-    if (requiresSortedInput) {
-      // It is possible that the child.outputOrdering starts with the required
-      // ordering expressions (e.g. we require [a] as the sort expression and the
-      // child's outputOrdering is [a, b]). We can only guarantee the output rows
-      // are sorted by values of groupingExpressions.
-      groupingExpressions.map(SortOrder(_, Ascending))
-    } else {
-      Nil
-    }
-  }
-
-  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
-    child.execute().mapPartitions { iter =>
-      // Because the constructor of an aggregation iterator will read at least the first row,
-      // we need to get the value of iter.hasNext first.
-      val hasInput = iter.hasNext
-      val useHybridIterator =
-        hasInput &&
-          supportsHybridIterator &&
-          groupingExpressions.nonEmpty
-      if (useHybridIterator) {
-        UnsafeHybridAggregationIterator.createFromInputIterator(
-          groupingExpressions,
-          nonCompleteAggregateExpressions,
-          nonCompleteAggregateAttributes,
-          completeAggregateExpressions,
-          completeAggregateAttributes,
-          initialInputBufferOffset,
-          resultExpressions,
-          newMutableProjection _,
-          child.output,
-          iter,
-          outputsUnsafeRows)
-      } else {
-        if (!hasInput && groupingExpressions.nonEmpty) {
-          // This is a grouped aggregate and the input iterator is empty,
-          // so return an empty iterator.
-          Iterator[InternalRow]()
-        } else {
-          val outputIter = SortBasedAggregationIterator.createFromInputIterator(
-            groupingExpressions,
-            nonCompleteAggregateExpressions,
-            nonCompleteAggregateAttributes,
-            completeAggregateExpressions,
-            completeAggregateAttributes,
-            initialInputBufferOffset,
-            resultExpressions,
-            newMutableProjection _ ,
-            newProjection _,
-            child.output,
-            iter,
-            outputsUnsafeRows)
-          if (!hasInput && groupingExpressions.isEmpty) {
-            // There is no input and there is no grouping expressions.
-            // We need to output a single row as the output.
-            Iterator[InternalRow](outputIter.outputForEmptyGroupingKeyWithoutInput())
-          } else {
-            outputIter
-          }
-        }
-      }
-    }
-  }
-
-  override def simpleString: String = {
-    val iterator = if (supportsHybridIterator && groupingExpressions.nonEmpty) {
-      classOf[UnsafeHybridAggregationIterator].getSimpleName
-    } else {
-      classOf[SortBasedAggregationIterator].getSimpleName
-    }
-
-    s"""NewAggregate with $iterator ${groupingExpressions} ${allAggregateExpressions}"""
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
new file mode 100644
index 0000000000000..ad428ad663f30
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
@@ -0,0 +1,103 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.errors._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
+import org.apache.spark.sql.execution.{UnsafeFixedWidthAggregationMap, SparkPlan, UnaryNode}
+import org.apache.spark.sql.types.StructType
+
+case class SortBasedAggregate(
+    requiredChildDistributionExpressions: Option[Seq[Expression]],
+    groupingExpressions: Seq[NamedExpression],
+    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+    nonCompleteAggregateAttributes: Seq[Attribute],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateAttributes: Seq[Attribute],
+    initialInputBufferOffset: Int,
+    resultExpressions: Seq[NamedExpression],
+    child: SparkPlan)
+  extends UnaryNode {
+
+  override def outputsUnsafeRows: Boolean = false
+
+  override def canProcessUnsafeRows: Boolean = false
+
+  override def canProcessSafeRows: Boolean = true
+
+  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
+
+  override def requiredChildDistribution: List[Distribution] = {
+    requiredChildDistributionExpressions match {
+      case Some(exprs) if exprs.length == 0 => AllTuples :: Nil
+      case Some(exprs) if exprs.length > 0 => ClusteredDistribution(exprs) :: Nil
+      case None => UnspecifiedDistribution :: Nil
+    }
+  }
+
+  override def requiredChildOrdering: Seq[Seq[SortOrder]] = {
+    groupingExpressions.map(SortOrder(_, Ascending)) :: Nil
+  }
+
+  override def outputOrdering: Seq[SortOrder] = {
+    groupingExpressions.map(SortOrder(_, Ascending))
+  }
+
+  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    child.execute().mapPartitions { iter =>
+      // Because the constructor of an aggregation iterator will read at least the first row,
+      // we need to get the value of iter.hasNext first.
+      val hasInput = iter.hasNext
+      if (!hasInput && groupingExpressions.nonEmpty) {
+        // This is a grouped aggregate and the input iterator is empty,
+        // so return an empty iterator.
+        Iterator[InternalRow]()
+      } else {
+        val outputIter = SortBasedAggregationIterator.createFromInputIterator(
+          groupingExpressions,
+          nonCompleteAggregateExpressions,
+          nonCompleteAggregateAttributes,
+          completeAggregateExpressions,
+          completeAggregateAttributes,
+          initialInputBufferOffset,
+          resultExpressions,
+          newMutableProjection _,
+          newProjection _,
+          child.output,
+          iter,
+          outputsUnsafeRows)
+        if (!hasInput && groupingExpressions.isEmpty) {
+          // There is no input and there is no grouping expressions.
+          // We need to output a single row as the output.
+          Iterator[InternalRow](outputIter.outputForEmptyGroupingKeyWithoutInput())
+        } else {
+          outputIter
+        }
+      }
+    }
+  }
+
+  override def simpleString: String = {
+    val allAggregateExpressions = nonCompleteAggregateExpressions ++ completeAggregateExpressions
+    s"""SortBasedAggregate ${groupingExpressions} ${allAggregateExpressions}"""
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
index 40f6bff53d2b7..67ebafde25ad3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
@@ -204,31 +204,5 @@ object SortBasedAggregationIterator {
       newMutableProjection,
       outputsUnsafeRows)
   }
-
-  def createFromKVIterator(
-      groupingKeyAttributes: Seq[Attribute],
-      valueAttributes: Seq[Attribute],
-      inputKVIterator: KVIterator[InternalRow, InternalRow],
-      nonCompleteAggregateExpressions: Seq[AggregateExpression2],
-      nonCompleteAggregateAttributes: Seq[Attribute],
-      completeAggregateExpressions: Seq[AggregateExpression2],
-      completeAggregateAttributes: Seq[Attribute],
-      initialInputBufferOffset: Int,
-      resultExpressions: Seq[NamedExpression],
-      newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-      outputsUnsafeRows: Boolean): SortBasedAggregationIterator = {
-    new SortBasedAggregationIterator(
-      groupingKeyAttributes,
-      valueAttributes,
-      inputKVIterator,
-      nonCompleteAggregateExpressions,
-      nonCompleteAggregateAttributes,
-      completeAggregateExpressions,
-      completeAggregateAttributes,
-      initialInputBufferOffset,
-      resultExpressions,
-      newMutableProjection,
-      outputsUnsafeRows)
-  }
   // scalastyle:on
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
new file mode 100644
index 0000000000000..5a0b4d47d62f8
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -0,0 +1,102 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.errors._
+import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
+import org.apache.spark.sql.execution.{UnaryNode, SparkPlan}
+
+case class TungstenAggregate(
+    requiredChildDistributionExpressions: Option[Seq[Expression]],
+    groupingExpressions: Seq[NamedExpression],
+    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    initialInputBufferOffset: Int,
+    resultExpressions: Seq[NamedExpression],
+    child: SparkPlan)
+  extends UnaryNode {
+
+  override def outputsUnsafeRows: Boolean = true
+
+  override def canProcessUnsafeRows: Boolean = true
+
+  override def canProcessSafeRows: Boolean = false
+
+  override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
+
+  override def requiredChildDistribution: List[Distribution] = {
+    requiredChildDistributionExpressions match {
+      case Some(exprs) if exprs.length == 0 => AllTuples :: Nil
+      case Some(exprs) if exprs.length > 0 => ClusteredDistribution(exprs) :: Nil
+      case None => UnspecifiedDistribution :: Nil
+    }
+  }
+
+  // This is for testing. We force TungstenAggregationIterator to fall back to sort-based
+  // aggregation once it has processed a given number of input rows.
+  private val testFallbackStartsAt: Option[Int] = {
+    sqlContext.getConf("spark.sql.TungstenAggregate.testFallbackStartsAt", null) match {
+      case null | "" => None
+      case fallbackStartsAt => Some(fallbackStartsAt.toInt)
+    }
+  }
+
+  protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    child.execute().mapPartitions { iter =>
+      val hasInput = iter.hasNext
+      if (!hasInput && groupingExpressions.nonEmpty) {
+        // This is a grouped aggregate and the input iterator is empty,
+        // so return an empty iterator.
+        Iterator.empty.asInstanceOf[Iterator[UnsafeRow]]
+      } else {
+        val aggregationIterator =
+          new TungstenAggregationIterator(
+            groupingExpressions,
+            nonCompleteAggregateExpressions,
+            completeAggregateExpressions,
+            initialInputBufferOffset,
+            resultExpressions,
+            newMutableProjection,
+            child.output,
+            iter.asInstanceOf[Iterator[UnsafeRow]],
+            testFallbackStartsAt)
+
+        if (!hasInput && groupingExpressions.isEmpty) {
+          Iterator.single[UnsafeRow](aggregationIterator.outputForEmptyGroupingKeyWithoutInput())
+        } else {
+          aggregationIterator
+        }
+      }
+    }
+  }
+
+  override def simpleString: String = {
+    val allAggregateExpressions = nonCompleteAggregateExpressions ++ completeAggregateExpressions
+
+    testFallbackStartsAt match {
+      case None => s"TungstenAggregate ${groupingExpressions} ${allAggregateExpressions}"
+      case Some(fallbackStartsAt) =>
+        s"TungstenAggregateWithControlledFallback ${groupingExpressions} " +
+          s"${allAggregateExpressions} fallbackStartsAt=$fallbackStartsAt"
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
new file mode 100644
index 0000000000000..b9d44aace1009
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -0,0 +1,667 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark.unsafe.KVIterator
+import org.apache.spark.{Logging, SparkEnv, TaskContext}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.aggregate._
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeRowJoiner
+import org.apache.spark.sql.execution.{UnsafeKVExternalSorter, UnsafeFixedWidthAggregationMap}
+import org.apache.spark.sql.types.StructType
+
+/**
+ * An iterator used to evaluate aggregate functions. It operates on [[UnsafeRow]]s.
+ *
+ * This iterator first uses hash-based aggregation to process input rows. It uses
+ * a hash map to store groups and their corresponding aggregation buffers. If we
+ * this map cannot allocate memory from [[org.apache.spark.shuffle.ShuffleMemoryManager]],
+ * it switches to sort-based aggregation. The process of the switch has the following step:
+ *  - Step 1: Sort all entries of the hash map based on values of grouping expressions and
+ *            spill them to disk.
+ *  - Step 2: Create a external sorter based on the spilled sorted map entries.
+ *  - Step 3: Redirect all input rows to the external sorter.
+ *  - Step 4: Get a sorted [[KVIterator]] from the external sorter.
+ *  - Step 5: Initialize sort-based aggregation.
+ * Then, this iterator works in the way of sort-based aggregation.
+ *
+ * The code of this class is organized as follows:
+ *  - Part 1: Initializing aggregate functions.
+ *  - Part 2: Methods and fields used by setting aggregation buffer values,
+ *            processing input rows from inputIter, and generating output
+ *            rows.
+ *  - Part 3: Methods and fields used by hash-based aggregation.
+ *  - Part 4: The function used to switch this iterator from hash-based
+ *            aggregation to sort-based aggregation.
+ *  - Part 5: Methods and fields used by sort-based aggregation.
+ *  - Part 6: Loads input and process input rows.
+ *  - Part 7: Public methods of this iterator.
+ *  - Part 8: A utility function used to generate a result when there is no
+ *            input and there is no grouping expression.
+ *
+ * @param groupingExpressions
+ *   expressions for grouping keys
+ * @param nonCompleteAggregateExpressions
+ *   [[AggregateExpression2]] containing [[AggregateFunction2]]s with mode [[Partial]],
+ *   [[PartialMerge]], or [[Final]].
+ * @param completeAggregateExpressions
+ *   [[AggregateExpression2]] containing [[AggregateFunction2]]s with mode [[Complete]].
+ * @param initialInputBufferOffset
+ *   If this iterator is used to handle functions with mode [[PartialMerge]] or [[Final]].
+ *   The input rows have the format of `grouping keys + aggregation buffer`.
+ *   This offset indicates the starting position of aggregation buffer in a input row.
+ * @param resultExpressions
+ *   expressions for generating output rows.
+ * @param newMutableProjection
+ *   the function used to create mutable projections.
+ * @param originalInputAttributes
+ *   attributes of representing input rows from `inputIter`.
+ * @param inputIter
+ *   the iterator containing input [[UnsafeRow]]s.
+ */
+class TungstenAggregationIterator(
+    groupingExpressions: Seq[NamedExpression],
+    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
+    completeAggregateExpressions: Seq[AggregateExpression2],
+    initialInputBufferOffset: Int,
+    resultExpressions: Seq[NamedExpression],
+    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
+    originalInputAttributes: Seq[Attribute],
+    inputIter: Iterator[UnsafeRow],
+    testFallbackStartsAt: Option[Int])
+  extends Iterator[UnsafeRow] with Logging {
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 1: Initializing aggregate functions.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // A Seq containing all AggregateExpressions.
+  // It is important that all AggregateExpressions with the mode Partial, PartialMerge or Final
+  // are at the beginning of the allAggregateExpressions.
+  private[this] val allAggregateExpressions: Seq[AggregateExpression2] =
+    nonCompleteAggregateExpressions ++ completeAggregateExpressions
+
+  // Check to make sure we do not have more than three modes in our AggregateExpressions.
+  // If we have, users are hitting a bug and we throw an IllegalStateException.
+  if (allAggregateExpressions.map(_.mode).distinct.length > 2) {
+    throw new IllegalStateException(
+      s"$allAggregateExpressions should have no more than 2 kinds of modes.")
+  }
+
+  //
+  // The modes of AggregateExpressions. Right now, we can handle the following mode:
+  //  - Partial-only:
+  //      All AggregateExpressions have the mode of Partial.
+  //      For this case, aggregationMode is (Some(Partial), None).
+  //  - PartialMerge-only:
+  //      All AggregateExpressions have the mode of PartialMerge).
+  //      For this case, aggregationMode is (Some(PartialMerge), None).
+  //  - Final-only:
+  //      All AggregateExpressions have the mode of Final.
+  //      For this case, aggregationMode is (Some(Final), None).
+  //  - Final-Complete:
+  //      Some AggregateExpressions have the mode of Final and
+  //      others have the mode of Complete. For this case,
+  //      aggregationMode is (Some(Final), Some(Complete)).
+  //  - Complete-only:
+  //      nonCompleteAggregateExpressions is empty and we have AggregateExpressions
+  //      with mode Complete in completeAggregateExpressions. For this case,
+  //      aggregationMode is (None, Some(Complete)).
+  //  - Grouping-only:
+  //      There is no AggregateExpression. For this case, AggregationMode is (None,None).
+  //
+  private[this] var aggregationMode: (Option[AggregateMode], Option[AggregateMode]) = {
+    nonCompleteAggregateExpressions.map(_.mode).distinct.headOption ->
+      completeAggregateExpressions.map(_.mode).distinct.headOption
+  }
+
+  // All aggregate functions. TungstenAggregationIterator only handles AlgebraicAggregates.
+  // If there is any functions that is not an AlgebraicAggregate, we throw an
+  // IllegalStateException.
+  private[this] val allAggregateFunctions: Array[AlgebraicAggregate] = {
+    if (!allAggregateExpressions.forall(_.aggregateFunction.isInstanceOf[AlgebraicAggregate])) {
+      throw new IllegalStateException(
+        "Only AlgebraicAggregates should be passed in TungstenAggregationIterator.")
+    }
+
+    allAggregateExpressions
+      .map(_.aggregateFunction.asInstanceOf[AlgebraicAggregate])
+      .toArray
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 2: Methods and fields used by setting aggregation buffer values,
+  //         processing input rows from inputIter, and generating output
+  //         rows.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // The projection used to initialize buffer values.
+  private[this] val algebraicInitialProjection: MutableProjection = {
+    val initExpressions = allAggregateFunctions.flatMap(_.initialValues)
+    newMutableProjection(initExpressions, Nil)()
+  }
+
+  // Creates a new aggregation buffer and initializes buffer values.
+  // This functions should be only called at most three times (when we create the hash map,
+  // when we switch to sort-based aggregation, and when we create the re-used buffer for
+  // sort-based aggregation).
+  private def createNewAggregationBuffer(): UnsafeRow = {
+    val bufferSchema = allAggregateFunctions.flatMap(_.bufferAttributes)
+    val bufferRowSize: Int = bufferSchema.length
+
+    val genericMutableBuffer = new GenericMutableRow(bufferRowSize)
+    val unsafeProjection =
+      UnsafeProjection.create(bufferSchema.map(_.dataType))
+    val buffer = unsafeProjection.apply(genericMutableBuffer)
+    algebraicInitialProjection.target(buffer)(EmptyRow)
+    buffer
+  }
+
+  // Creates a function used to process a row based on the given inputAttributes.
+  private def generateProcessRow(
+      inputAttributes: Seq[Attribute]): (UnsafeRow, UnsafeRow) => Unit = {
+
+    val aggregationBufferAttributes = allAggregateFunctions.flatMap(_.bufferAttributes)
+    val aggregationBufferSchema = StructType.fromAttributes(aggregationBufferAttributes)
+    val inputSchema = StructType.fromAttributes(inputAttributes)
+    val unsafeRowJoiner =
+      GenerateUnsafeRowJoiner.create(aggregationBufferSchema, inputSchema)
+
+    aggregationMode match {
+      // Partial-only
+      case (Some(Partial), None) =>
+        val updateExpressions = allAggregateFunctions.flatMap(_.updateExpressions)
+        val algebraicUpdateProjection =
+          newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
+
+        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+          algebraicUpdateProjection.target(currentBuffer)
+          algebraicUpdateProjection(unsafeRowJoiner.join(currentBuffer, row))
+        }
+
+      // PartialMerge-only or Final-only
+      case (Some(PartialMerge), None) | (Some(Final), None) =>
+        val mergeExpressions = allAggregateFunctions.flatMap(_.mergeExpressions)
+        // This projection is used to merge buffer values for all AlgebraicAggregates.
+        val algebraicMergeProjection =
+          newMutableProjection(
+            mergeExpressions,
+            aggregationBufferAttributes ++ inputAttributes)()
+
+        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+          // Process all algebraic aggregate functions.
+          algebraicMergeProjection.target(currentBuffer)
+          algebraicMergeProjection(unsafeRowJoiner.join(currentBuffer, row))
+        }
+
+      // Final-Complete
+      case (Some(Final), Some(Complete)) =>
+        val nonCompleteAggregateFunctions: Array[AlgebraicAggregate] =
+          allAggregateFunctions.take(nonCompleteAggregateExpressions.length)
+        val completeAggregateFunctions: Array[AlgebraicAggregate] =
+          allAggregateFunctions.takeRight(completeAggregateExpressions.length)
+
+        val completeOffsetExpressions =
+          Seq.fill(completeAggregateFunctions.map(_.bufferAttributes.length).sum)(NoOp)
+        val mergeExpressions =
+          nonCompleteAggregateFunctions.flatMap(_.mergeExpressions) ++ completeOffsetExpressions
+        val finalAlgebraicMergeProjection =
+          newMutableProjection(
+            mergeExpressions,
+            aggregationBufferAttributes ++ inputAttributes)()
+
+        // We do not touch buffer values of aggregate functions with the Final mode.
+        val finalOffsetExpressions =
+          Seq.fill(nonCompleteAggregateFunctions.map(_.bufferAttributes.length).sum)(NoOp)
+        val updateExpressions =
+          finalOffsetExpressions ++ completeAggregateFunctions.flatMap(_.updateExpressions)
+        val completeAlgebraicUpdateProjection =
+          newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
+
+        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+          val input = unsafeRowJoiner.join(currentBuffer, row)
+          // For all aggregate functions with mode Complete, update the given currentBuffer.
+          completeAlgebraicUpdateProjection.target(currentBuffer)(input)
+
+          // For all aggregate functions with mode Final, merge buffer values in row to
+          // currentBuffer.
+          finalAlgebraicMergeProjection.target(currentBuffer)(input)
+        }
+
+      // Complete-only
+      case (None, Some(Complete)) =>
+        val completeAggregateFunctions: Array[AlgebraicAggregate] =
+          allAggregateFunctions.takeRight(completeAggregateExpressions.length)
+
+        val updateExpressions =
+          completeAggregateFunctions.flatMap(_.updateExpressions)
+        val completeAlgebraicUpdateProjection =
+          newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
+
+        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+          completeAlgebraicUpdateProjection.target(currentBuffer)
+          // For all aggregate functions with mode Complete, update the given currentBuffer.
+          completeAlgebraicUpdateProjection(unsafeRowJoiner.join(currentBuffer, row))
+        }
+
+      // Grouping only.
+      case (None, None) => (currentBuffer: UnsafeRow, row: UnsafeRow) => {}
+
+      case other =>
+        throw new IllegalStateException(
+          s"${aggregationMode} should not be passed into TungstenAggregationIterator.")
+    }
+  }
+
+  // Creates a function used to generate output rows.
+  private def generateResultProjection(): (UnsafeRow, UnsafeRow) => UnsafeRow = {
+
+    val groupingAttributes = groupingExpressions.map(_.toAttribute)
+    val groupingKeySchema = StructType.fromAttributes(groupingAttributes)
+    val bufferAttributes = allAggregateFunctions.flatMap(_.bufferAttributes)
+    val bufferSchema = StructType.fromAttributes(bufferAttributes)
+    val unsafeRowJoiner = GenerateUnsafeRowJoiner.create(groupingKeySchema, bufferSchema)
+
+    aggregationMode match {
+      // Partial-only or PartialMerge-only: every output row is basically the values of
+      // the grouping expressions and the corresponding aggregation buffer.
+      case (Some(Partial), None) | (Some(PartialMerge), None) =>
+        (currentGroupingKey: UnsafeRow, currentBuffer: UnsafeRow) => {
+          unsafeRowJoiner.join(currentGroupingKey, currentBuffer)
+        }
+
+      // Final-only, Complete-only and Final-Complete: a output row is generated based on
+      // resultExpressions.
+      case (Some(Final), None) | (Some(Final) | None, Some(Complete)) =>
+        val resultProjection =
+          UnsafeProjection.create(resultExpressions, groupingAttributes ++ bufferAttributes)
+
+        (currentGroupingKey: UnsafeRow, currentBuffer: UnsafeRow) => {
+          resultProjection(unsafeRowJoiner.join(currentGroupingKey, currentBuffer))
+        }
+
+      // Grouping-only: a output row is generated from values of grouping expressions.
+      case (None, None) =>
+        val resultProjection =
+          UnsafeProjection.create(resultExpressions, groupingAttributes)
+
+        (currentGroupingKey: UnsafeRow, currentBuffer: UnsafeRow) => {
+          resultProjection(currentGroupingKey)
+        }
+
+      case other =>
+        throw new IllegalStateException(
+          s"${aggregationMode} should not be passed into TungstenAggregationIterator.")
+    }
+  }
+
+  // An UnsafeProjection used to extract grouping keys from the input rows.
+  private[this] val groupProjection =
+    UnsafeProjection.create(groupingExpressions, originalInputAttributes)
+
+  // A function used to process a input row. Its first argument is the aggregation buffer
+  // and the second argument is the input row.
+  private[this] var processRow: (UnsafeRow, UnsafeRow) => Unit =
+    generateProcessRow(originalInputAttributes)
+
+  // A function used to generate output rows based on the grouping keys (first argument)
+  // and the corresponding aggregation buffer (second argument).
+  private[this] var generateOutput: (UnsafeRow, UnsafeRow) => UnsafeRow =
+    generateResultProjection()
+
+  // An aggregation buffer containing initial buffer values. It is used to
+  // initialize other aggregation buffers.
+  private[this] val initialAggregationBuffer: UnsafeRow = createNewAggregationBuffer()
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 3: Methods and fields used by hash-based aggregation.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // This is the hash map used for hash-based aggregation. It is backed by an
+  // UnsafeFixedWidthAggregationMap and it is used to store
+  // all groups and their corresponding aggregation buffers for hash-based aggregation.
+  private[this] val hashMap = new UnsafeFixedWidthAggregationMap(
+    initialAggregationBuffer,
+    StructType.fromAttributes(allAggregateFunctions.flatMap(_.bufferAttributes)),
+    StructType.fromAttributes(groupingExpressions.map(_.toAttribute)),
+    TaskContext.get.taskMemoryManager(),
+    SparkEnv.get.shuffleMemoryManager,
+    1024 * 16, // initial capacity
+    SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m"),
+    false // disable tracking of performance metrics
+  )
+
+  // The function used to read and process input rows. When processing input rows,
+  // it first uses hash-based aggregation by putting groups and their buffers in
+  // hashMap. If we could not allocate more memory for the map, we switch to
+  // sort-based aggregation (by calling switchToSortBasedAggregation).
+  private def processInputs(): Unit = {
+    while (!sortBased && inputIter.hasNext) {
+      val newInput = inputIter.next()
+      val groupingKey = groupProjection.apply(newInput)
+      val buffer: UnsafeRow = hashMap.getAggregationBuffer(groupingKey)
+      if (buffer == null) {
+        // buffer == null means that we could not allocate more memory.
+        // Now, we need to spill the map and switch to sort-based aggregation.
+        switchToSortBasedAggregation(groupingKey, newInput)
+      } else {
+        processRow(buffer, newInput)
+      }
+    }
+  }
+
+  // This function is only used for testing. It basically the same as processInputs except
+  // that it switch to sort-based aggregation after `fallbackStartsAt` input rows have
+  // been processed.
+  private def processInputsWithControlledFallback(fallbackStartsAt: Int): Unit = {
+    var i = 0
+    while (!sortBased && inputIter.hasNext) {
+      val newInput = inputIter.next()
+      val groupingKey = groupProjection.apply(newInput)
+      val buffer: UnsafeRow = if (i < fallbackStartsAt) {
+        hashMap.getAggregationBuffer(groupingKey)
+      } else {
+        null
+      }
+      if (buffer == null) {
+        // buffer == null means that we could not allocate more memory.
+        // Now, we need to spill the map and switch to sort-based aggregation.
+        switchToSortBasedAggregation(groupingKey, newInput)
+      } else {
+        processRow(buffer, newInput)
+      }
+      i += 1
+    }
+  }
+
+  // The iterator created from hashMap. It is used to generate output rows when we
+  // are using hash-based aggregation.
+  private[this] var aggregationBufferMapIterator: KVIterator[UnsafeRow, UnsafeRow] = null
+
+  // Indicates if aggregationBufferMapIterator still has key-value pairs.
+  private[this] var mapIteratorHasNext: Boolean = false
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 4: The function used to switch this iterator from hash-based
+  // aggregation to sort-based aggregation.
+  ///////////////////////////////////////////////////////////////////////////
+
+  private def switchToSortBasedAggregation(firstKey: UnsafeRow, firstInput: UnsafeRow): Unit = {
+    logInfo("falling back to sort based aggregation.")
+    // Step 1: Get the ExternalSorter containing sorted entries of the map.
+    val externalSorter: UnsafeKVExternalSorter = hashMap.destructAndCreateExternalSorter()
+
+    // Step 2: Free the memory used by the map.
+    hashMap.free()
+
+    // Step 3: If we have aggregate function with mode Partial or Complete,
+    // we need to process input rows to get aggregation buffer.
+    // So, later in the sort-based aggregation iterator, we can do merge.
+    // If aggregate functions are with mode Final and PartialMerge,
+    // we just need to project the aggregation buffer from an input row.
+    val needsProcess = aggregationMode match {
+      case (Some(Partial), None) => true
+      case (None, Some(Complete)) => true
+      case (Some(Final), Some(Complete)) => true
+      case _ => false
+    }
+
+    if (needsProcess) {
+      // First, we create a buffer.
+      val buffer = createNewAggregationBuffer()
+
+      // Process firstKey and firstInput.
+      // Initialize buffer.
+      buffer.copyFrom(initialAggregationBuffer)
+      processRow(buffer, firstInput)
+      externalSorter.insertKV(firstKey, buffer)
+
+      // Process the rest of input rows.
+      while (inputIter.hasNext) {
+        val newInput = inputIter.next()
+        val groupingKey = groupProjection.apply(newInput)
+        buffer.copyFrom(initialAggregationBuffer)
+        processRow(buffer, newInput)
+        externalSorter.insertKV(groupingKey, buffer)
+      }
+    } else {
+      // When needsProcess is false, the format of input rows is groupingKey + aggregation buffer.
+      // We need to project the aggregation buffer part from an input row.
+      val buffer = createNewAggregationBuffer()
+      // The originalInputAttributes are using cloneBufferAttributes. So, we need to use
+      // allAggregateFunctions.flatMap(_.cloneBufferAttributes).
+      val bufferExtractor = newMutableProjection(
+        allAggregateFunctions.flatMap(_.cloneBufferAttributes),
+        originalInputAttributes)()
+      bufferExtractor.target(buffer)
+
+      // Insert firstKey and its buffer.
+      bufferExtractor(firstInput)
+      externalSorter.insertKV(firstKey, buffer)
+
+      // Insert the rest of input rows.
+      while (inputIter.hasNext) {
+        val newInput = inputIter.next()
+        val groupingKey = groupProjection.apply(newInput)
+        bufferExtractor(newInput)
+        externalSorter.insertKV(groupingKey, buffer)
+      }
+    }
+
+    // Set aggregationMode, processRow, and generateOutput for sort-based aggregation.
+    val newAggregationMode = aggregationMode match {
+      case (Some(Partial), None) => (Some(PartialMerge), None)
+      case (None, Some(Complete)) => (Some(Final), None)
+      case (Some(Final), Some(Complete)) => (Some(Final), None)
+      case other => other
+    }
+    aggregationMode = newAggregationMode
+
+    // Basically the value of the KVIterator returned by externalSorter
+    // will just aggregation buffer. At here, we use cloneBufferAttributes.
+    val newInputAttributes: Seq[Attribute] =
+      allAggregateFunctions.flatMap(_.cloneBufferAttributes)
+
+    // Set up new processRow and generateOutput.
+    processRow = generateProcessRow(newInputAttributes)
+    generateOutput = generateResultProjection()
+
+    // Step 5: Get the sorted iterator from the externalSorter.
+    sortedKVIterator = externalSorter.sortedIterator()
+
+    // Step 6: Pre-load the first key-value pair from the sorted iterator to make
+    // hasNext idempotent.
+    sortedInputHasNewGroup = sortedKVIterator.next()
+
+    // Copy the first key and value (aggregation buffer).
+    if (sortedInputHasNewGroup) {
+      val key = sortedKVIterator.getKey
+      val value = sortedKVIterator.getValue
+      nextGroupingKey = key.copy()
+      currentGroupingKey = key.copy()
+      firstRowInNextGroup = value.copy()
+    }
+
+    // Step 7: set sortBased to true.
+    sortBased = true
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 5: Methods and fields used by sort-based aggregation.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // Indicates if we are using sort-based aggregation. Because we first try to use
+  // hash-based aggregation, its initial value is false.
+  private[this] var sortBased: Boolean = false
+
+  // The KVIterator containing input rows for the sort-based aggregation. It will be
+  // set in switchToSortBasedAggregation when we switch to sort-based aggregation.
+  private[this] var sortedKVIterator: UnsafeKVExternalSorter#KVSorterIterator = null
+
+  // The grouping key of the current group.
+  private[this] var currentGroupingKey: UnsafeRow = null
+
+  // The grouping key of next group.
+  private[this] var nextGroupingKey: UnsafeRow = null
+
+  // The first row of next group.
+  private[this] var firstRowInNextGroup: UnsafeRow = null
+
+  // Indicates if we has new group of rows from the sorted input iterator.
+  private[this] var sortedInputHasNewGroup: Boolean = false
+
+  // The aggregation buffer used by the sort-based aggregation.
+  private[this] val sortBasedAggregationBuffer: UnsafeRow = createNewAggregationBuffer()
+
+  // Processes rows in the current group. It will stop when it find a new group.
+  private def processCurrentSortedGroup(): Unit = {
+    // First, we need to copy nextGroupingKey to currentGroupingKey.
+    currentGroupingKey.copyFrom(nextGroupingKey)
+    // Now, we will start to find all rows belonging to this group.
+    // We create a variable to track if we see the next group.
+    var findNextPartition = false
+    // firstRowInNextGroup is the first row of this group. We first process it.
+    processRow(sortBasedAggregationBuffer, firstRowInNextGroup)
+
+    // The search will stop when we see the next group or there is no
+    // input row left in the iter.
+    // Pre-load the first key-value pair to make the condition of the while loop
+    // has no action (we do not trigger loading a new key-value pair
+    // when we evaluate the condition).
+    var hasNext = sortedKVIterator.next()
+    while (!findNextPartition && hasNext) {
+      // Get the grouping key and value (aggregation buffer).
+      val groupingKey = sortedKVIterator.getKey
+      val inputAggregationBuffer = sortedKVIterator.getValue
+
+      // Check if the current row belongs the current input row.
+      if (currentGroupingKey.equals(groupingKey)) {
+        processRow(sortBasedAggregationBuffer, inputAggregationBuffer)
+
+        hasNext = sortedKVIterator.next()
+      } else {
+        // We find a new group.
+        findNextPartition = true
+        // copyFrom will fail when
+        nextGroupingKey.copyFrom(groupingKey) // = groupingKey.copy()
+        firstRowInNextGroup.copyFrom(inputAggregationBuffer) // = inputAggregationBuffer.copy()
+
+      }
+    }
+    // We have not seen a new group. It means that there is no new row in the input
+    // iter. The current group is the last group of the sortedKVIterator.
+    if (!findNextPartition) {
+      sortedInputHasNewGroup = false
+      sortedKVIterator.close()
+    }
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 6: Loads input rows and setup aggregationBufferMapIterator if we
+  //         have not switched to sort-based aggregation.
+  ///////////////////////////////////////////////////////////////////////////
+
+  // Starts to process input rows.
+  testFallbackStartsAt match {
+    case None =>
+      processInputs()
+    case Some(fallbackStartsAt) =>
+      // This is the testing path. processInputsWithControlledFallback is same as processInputs
+      // except that it switches to sort-based aggregation after `fallbackStartsAt` input rows
+      // have been processed.
+      processInputsWithControlledFallback(fallbackStartsAt)
+  }
+
+  // If we did not switch to sort-based aggregation in processInputs,
+  // we pre-load the first key-value pair from the map (to make hasNext idempotent).
+  if (!sortBased) {
+    // First, set aggregationBufferMapIterator.
+    aggregationBufferMapIterator = hashMap.iterator()
+    // Pre-load the first key-value pair from the aggregationBufferMapIterator.
+    mapIteratorHasNext = aggregationBufferMapIterator.next()
+    // If the map is empty, we just free it.
+    if (!mapIteratorHasNext) {
+      hashMap.free()
+    }
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Par 7: Iterator's public methods.
+  ///////////////////////////////////////////////////////////////////////////
+
+  override final def hasNext: Boolean = {
+    (sortBased && sortedInputHasNewGroup) || (!sortBased && mapIteratorHasNext)
+  }
+
+  override final def next(): UnsafeRow = {
+    if (hasNext) {
+      if (sortBased) {
+        // Process the current group.
+        processCurrentSortedGroup()
+        // Generate output row for the current group.
+        val outputRow = generateOutput(currentGroupingKey, sortBasedAggregationBuffer)
+        // Initialize buffer values for the next group.
+        sortBasedAggregationBuffer.copyFrom(initialAggregationBuffer)
+
+        outputRow
+      } else {
+        // We did not fall back to sort-based aggregation.
+        val result =
+          generateOutput(
+            aggregationBufferMapIterator.getKey,
+            aggregationBufferMapIterator.getValue)
+
+        // Pre-load next key-value pair form aggregationBufferMapIterator to make hasNext
+        // idempotent.
+        mapIteratorHasNext = aggregationBufferMapIterator.next()
+
+        if (!mapIteratorHasNext) {
+          // If there is no input from aggregationBufferMapIterator, we copy current result.
+          val resultCopy = result.copy()
+          // Then, we free the map.
+          hashMap.free()
+
+          resultCopy
+        } else {
+          result
+        }
+      }
+    } else {
+      // no more result
+      throw new NoSuchElementException
+    }
+  }
+
+  ///////////////////////////////////////////////////////////////////////////
+  // Part 8: A utility function used to generate a output row when there is no
+  // input and there is no grouping expression.
+  ///////////////////////////////////////////////////////////////////////////
+  def outputForEmptyGroupingKeyWithoutInput(): UnsafeRow = {
+    if (groupingExpressions.isEmpty) {
+      sortBasedAggregationBuffer.copyFrom(initialAggregationBuffer)
+      // We create a output row and copy it. So, we can free the map.
+      val resultCopy =
+        generateOutput(UnsafeRow.createFromByteArray(0, 0), sortBasedAggregationBuffer).copy()
+      hashMap.free()
+      resultCopy
+    } else {
+      throw new IllegalStateException(
+        "This method should not be called when groupingExpressions is not empty.")
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
deleted file mode 100644
index b465787fe8cbd..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/UnsafeHybridAggregationIterator.scala
+++ /dev/null
@@ -1,372 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution.aggregate
-
-import org.apache.spark.unsafe.KVIterator
-import org.apache.spark.{SparkEnv, TaskContext}
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.execution.{UnsafeKVExternalSorter, UnsafeFixedWidthAggregationMap}
-import org.apache.spark.sql.types.StructType
-
-/**
- * An iterator used to evaluate [[AggregateFunction2]].
- * It first tries to use in-memory hash-based aggregation. If we cannot allocate more
- * space for the hash map, we spill the sorted map entries, free the map, and then
- * switch to sort-based aggregation.
- */
-class UnsafeHybridAggregationIterator(
-    groupingKeyAttributes: Seq[Attribute],
-    valueAttributes: Seq[Attribute],
-    inputKVIterator: KVIterator[UnsafeRow, InternalRow],
-    nonCompleteAggregateExpressions: Seq[AggregateExpression2],
-    nonCompleteAggregateAttributes: Seq[Attribute],
-    completeAggregateExpressions: Seq[AggregateExpression2],
-    completeAggregateAttributes: Seq[Attribute],
-    initialInputBufferOffset: Int,
-    resultExpressions: Seq[NamedExpression],
-    newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-    outputsUnsafeRows: Boolean)
-  extends AggregationIterator(
-    groupingKeyAttributes,
-    valueAttributes,
-    nonCompleteAggregateExpressions,
-    nonCompleteAggregateAttributes,
-    completeAggregateExpressions,
-    completeAggregateAttributes,
-    initialInputBufferOffset,
-    resultExpressions,
-    newMutableProjection,
-    outputsUnsafeRows) {
-
-  require(groupingKeyAttributes.nonEmpty)
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Unsafe Aggregation buffers
-  ///////////////////////////////////////////////////////////////////////////
-
-  // This is the Unsafe Aggregation Map used to store all buffers.
-  private[this] val buffers = new UnsafeFixedWidthAggregationMap(
-    newBuffer,
-    StructType.fromAttributes(allAggregateFunctions.flatMap(_.bufferAttributes)),
-    StructType.fromAttributes(groupingKeyAttributes),
-    TaskContext.get.taskMemoryManager(),
-    SparkEnv.get.shuffleMemoryManager,
-    1024 * 16, // initial capacity
-    SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m"),
-    false // disable tracking of performance metrics
-  )
-
-  override protected def newBuffer: UnsafeRow = {
-    val bufferSchema = allAggregateFunctions.flatMap(_.bufferAttributes)
-    val bufferRowSize: Int = bufferSchema.length
-
-    val genericMutableBuffer = new GenericMutableRow(bufferRowSize)
-    val unsafeProjection =
-      UnsafeProjection.create(bufferSchema.map(_.dataType))
-    val buffer = unsafeProjection.apply(genericMutableBuffer)
-    initializeBuffer(buffer)
-    buffer
-  }
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Methods and variables related to switching to sort-based aggregation
-  ///////////////////////////////////////////////////////////////////////////
-  private[this] var sortBased = false
-
-  private[this] var sortBasedAggregationIterator: SortBasedAggregationIterator = _
-
-  // The value part of the input KV iterator is used to store original input values of
-  // aggregate functions, we need to convert them to aggregation buffers.
-  private def processOriginalInput(
-      firstKey: UnsafeRow,
-      firstValue: InternalRow): KVIterator[UnsafeRow, UnsafeRow] = {
-    new KVIterator[UnsafeRow, UnsafeRow] {
-      private[this] var isFirstRow = true
-
-      private[this] var groupingKey: UnsafeRow = _
-
-      private[this] val buffer: UnsafeRow = newBuffer
-
-      override def next(): Boolean = {
-        initializeBuffer(buffer)
-        if (isFirstRow) {
-          isFirstRow = false
-          groupingKey = firstKey
-          processRow(buffer, firstValue)
-
-          true
-        } else if (inputKVIterator.next()) {
-          groupingKey = inputKVIterator.getKey()
-          val value = inputKVIterator.getValue()
-          processRow(buffer, value)
-
-          true
-        } else {
-          false
-        }
-      }
-
-      override def getKey(): UnsafeRow = {
-        groupingKey
-      }
-
-      override def getValue(): UnsafeRow = {
-        buffer
-      }
-
-      override def close(): Unit = {
-        // Do nothing.
-      }
-    }
-  }
-
-  // The value of the input KV Iterator has the format of groupingExprs + aggregation buffer.
-  // We need to project the aggregation buffer out.
-  private def projectInputBufferToUnsafe(
-      firstKey: UnsafeRow,
-      firstValue: InternalRow): KVIterator[UnsafeRow, UnsafeRow] = {
-    new KVIterator[UnsafeRow, UnsafeRow] {
-      private[this] var isFirstRow = true
-
-      private[this] var groupingKey: UnsafeRow = _
-
-      private[this] val bufferSchema = allAggregateFunctions.flatMap(_.bufferAttributes)
-
-      private[this] val value: UnsafeRow = {
-        val genericMutableRow = new GenericMutableRow(bufferSchema.length)
-        UnsafeProjection.create(bufferSchema.map(_.dataType)).apply(genericMutableRow)
-      }
-
-      private[this] val projectInputBuffer = {
-        newMutableProjection(bufferSchema, valueAttributes)().target(value)
-      }
-
-      override def next(): Boolean = {
-        if (isFirstRow) {
-          isFirstRow = false
-          groupingKey = firstKey
-          projectInputBuffer(firstValue)
-
-          true
-        } else if (inputKVIterator.next()) {
-          groupingKey = inputKVIterator.getKey()
-          projectInputBuffer(inputKVIterator.getValue())
-
-          true
-        } else {
-          false
-        }
-      }
-
-      override def getKey(): UnsafeRow = {
-        groupingKey
-      }
-
-      override def getValue(): UnsafeRow = {
-        value
-      }
-
-      override def close(): Unit = {
-        // Do nothing.
-      }
-    }
-  }
-
-  /**
-   * We need to fall back to sort based aggregation because we do not have enough memory
-   * for our in-memory hash map (i.e. `buffers`).
-   */
-  private def switchToSortBasedAggregation(
-      currentGroupingKey: UnsafeRow,
-      currentRow: InternalRow): Unit = {
-    logInfo("falling back to sort based aggregation.")
-
-    // Step 1: Get the ExternalSorter containing entries of the map.
-    val externalSorter = buffers.destructAndCreateExternalSorter()
-
-    // Step 2: Free the memory used by the map.
-    buffers.free()
-
-    // Step 3: If we have aggregate function with mode Partial or Complete,
-    // we need to process them to get aggregation buffer.
-    // So, later in the sort-based aggregation iterator, we can do merge.
-    // If aggregate functions are with mode Final and PartialMerge,
-    // we just need to project the aggregation buffer from the input.
-    val needsProcess = aggregationMode match {
-      case (Some(Partial), None) => true
-      case (None, Some(Complete)) => true
-      case (Some(Final), Some(Complete)) => true
-      case _ => false
-    }
-
-    val processedIterator = if (needsProcess) {
-      processOriginalInput(currentGroupingKey, currentRow)
-    } else {
-      // The input value's format is groupingExprs + buffer.
-      // We need to project the buffer part out.
-      projectInputBufferToUnsafe(currentGroupingKey, currentRow)
-    }
-
-    // Step 4: Redirect processedIterator to externalSorter.
-    while (processedIterator.next()) {
-      externalSorter.insertKV(processedIterator.getKey(), processedIterator.getValue())
-    }
-
-    // Step 5: Get the sorted iterator from the externalSorter.
-    val sortedKVIterator: UnsafeKVExternalSorter#KVSorterIterator = externalSorter.sortedIterator()
-
-    // Step 6: We now create a SortBasedAggregationIterator based on sortedKVIterator.
-    // For a aggregate function with mode Partial, its mode in the SortBasedAggregationIterator
-    // will be PartialMerge. For a aggregate function with mode Complete,
-    // its mode in the SortBasedAggregationIterator will be Final.
-    val newNonCompleteAggregateExpressions = allAggregateExpressions.map {
-        case AggregateExpression2(func, Partial, isDistinct) =>
-          AggregateExpression2(func, PartialMerge, isDistinct)
-        case AggregateExpression2(func, Complete, isDistinct) =>
-          AggregateExpression2(func, Final, isDistinct)
-        case other => other
-      }
-    val newNonCompleteAggregateAttributes =
-      nonCompleteAggregateAttributes ++ completeAggregateAttributes
-
-    val newValueAttributes =
-      allAggregateExpressions.flatMap(_.aggregateFunction.cloneBufferAttributes)
-
-    sortBasedAggregationIterator = SortBasedAggregationIterator.createFromKVIterator(
-      groupingKeyAttributes = groupingKeyAttributes,
-      valueAttributes = newValueAttributes,
-      inputKVIterator = sortedKVIterator.asInstanceOf[KVIterator[InternalRow, InternalRow]],
-      nonCompleteAggregateExpressions = newNonCompleteAggregateExpressions,
-      nonCompleteAggregateAttributes = newNonCompleteAggregateAttributes,
-      completeAggregateExpressions = Nil,
-      completeAggregateAttributes = Nil,
-      initialInputBufferOffset = 0,
-      resultExpressions = resultExpressions,
-      newMutableProjection = newMutableProjection,
-      outputsUnsafeRows = outputsUnsafeRows)
-  }
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Methods used to initialize this iterator.
-  ///////////////////////////////////////////////////////////////////////////
-
-  /** Starts to read input rows and falls back to sort-based aggregation if necessary. */
-  protected def initialize(): Unit = {
-    var hasNext = inputKVIterator.next()
-    while (!sortBased && hasNext) {
-      val groupingKey = inputKVIterator.getKey()
-      val currentRow = inputKVIterator.getValue()
-      val buffer = buffers.getAggregationBuffer(groupingKey)
-      if (buffer == null) {
-        // buffer == null means that we could not allocate more memory.
-        // Now, we need to spill the map and switch to sort-based aggregation.
-        switchToSortBasedAggregation(groupingKey, currentRow)
-        sortBased = true
-      } else {
-        processRow(buffer, currentRow)
-        hasNext = inputKVIterator.next()
-      }
-    }
-  }
-
-  // This is the starting point of this iterator.
-  initialize()
-
-  // Creates the iterator for the Hash Aggregation Map after we have populated
-  // contents of that map.
-  private[this] val aggregationBufferMapIterator = buffers.iterator()
-
-  private[this] var _mapIteratorHasNext = false
-
-  // Pre-load the first key-value pair from the map to make hasNext idempotent.
-  if (!sortBased) {
-    _mapIteratorHasNext = aggregationBufferMapIterator.next()
-    // If the map is empty, we just free it.
-    if (!_mapIteratorHasNext) {
-      buffers.free()
-    }
-  }
-
-  ///////////////////////////////////////////////////////////////////////////
-  // Iterator's public methods
-  ///////////////////////////////////////////////////////////////////////////
-
-  override final def hasNext: Boolean = {
-    (sortBased && sortBasedAggregationIterator.hasNext) || (!sortBased && _mapIteratorHasNext)
-  }
-
-
-  override final def next(): InternalRow = {
-    if (hasNext) {
-      if (sortBased) {
-        sortBasedAggregationIterator.next()
-      } else {
-        // We did not fall back to the sort-based aggregation.
-        val result =
-          generateOutput(
-            aggregationBufferMapIterator.getKey,
-            aggregationBufferMapIterator.getValue)
-        // Pre-load next key-value pair form aggregationBufferMapIterator.
-        _mapIteratorHasNext = aggregationBufferMapIterator.next()
-
-        if (!_mapIteratorHasNext) {
-          val resultCopy = result.copy()
-          buffers.free()
-          resultCopy
-        } else {
-          result
-        }
-      }
-    } else {
-      // no more result
-      throw new NoSuchElementException
-    }
-  }
-}
-
-object UnsafeHybridAggregationIterator {
-  // scalastyle:off
-  def createFromInputIterator(
-      groupingExprs: Seq[NamedExpression],
-      nonCompleteAggregateExpressions: Seq[AggregateExpression2],
-      nonCompleteAggregateAttributes: Seq[Attribute],
-      completeAggregateExpressions: Seq[AggregateExpression2],
-      completeAggregateAttributes: Seq[Attribute],
-      initialInputBufferOffset: Int,
-      resultExpressions: Seq[NamedExpression],
-      newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-      inputAttributes: Seq[Attribute],
-      inputIter: Iterator[InternalRow],
-      outputsUnsafeRows: Boolean): UnsafeHybridAggregationIterator = {
-    new UnsafeHybridAggregationIterator(
-      groupingExprs.map(_.toAttribute),
-      inputAttributes,
-      AggregationIterator.unsafeKVIterator(groupingExprs, inputAttributes, inputIter),
-      nonCompleteAggregateExpressions,
-      nonCompleteAggregateAttributes,
-      completeAggregateExpressions,
-      completeAggregateAttributes,
-      initialInputBufferOffset,
-      resultExpressions,
-      newMutableProjection,
-      outputsUnsafeRows)
-  }
-  // scalastyle:on
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
index 960be08f84d94..80816a095ea8c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/utils.scala
@@ -17,20 +17,41 @@
 
 package org.apache.spark.sql.execution.aggregate
 
+import scala.collection.mutable
+
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
-import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.{UnsafeFixedWidthAggregationMap, SparkPlan}
+import org.apache.spark.sql.types.StructType
 
 /**
  * Utility functions used by the query planner to convert our plan to new aggregation code path.
  */
 object Utils {
+  def supportsTungstenAggregate(
+      groupingExpressions: Seq[Expression],
+      aggregateBufferAttributes: Seq[Attribute]): Boolean = {
+    val aggregationBufferSchema = StructType.fromAttributes(aggregateBufferAttributes)
+
+    UnsafeFixedWidthAggregationMap.supportsAggregationBufferSchema(aggregationBufferSchema) &&
+      UnsafeProjection.canSupport(groupingExpressions)
+  }
+
   def planAggregateWithoutDistinct(
       groupingExpressions: Seq[Expression],
       aggregateExpressions: Seq[AggregateExpression2],
-      aggregateFunctionMap: Map[(AggregateFunction2, Boolean), Attribute],
+      aggregateFunctionMap: Map[(AggregateFunction2, Boolean), (AggregateFunction2, Attribute)],
       resultExpressions: Seq[NamedExpression],
       child: SparkPlan): Seq[SparkPlan] = {
+    // Check if we can use TungstenAggregate.
+    val usesTungstenAggregate =
+      child.sqlContext.conf.unsafeEnabled &&
+      aggregateExpressions.forall(_.aggregateFunction.isInstanceOf[AlgebraicAggregate]) &&
+      supportsTungstenAggregate(
+        groupingExpressions,
+        aggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes))
+
+
     // 1. Create an Aggregate Operator for partial aggregations.
     val namedGroupingExpressions = groupingExpressions.map {
       case ne: NamedExpression => ne -> ne
@@ -44,11 +65,23 @@ object Utils {
     val groupExpressionMap = namedGroupingExpressions.toMap
     val namedGroupingAttributes = namedGroupingExpressions.map(_._2.toAttribute)
     val partialAggregateExpressions = aggregateExpressions.map(_.copy(mode = Partial))
-    val partialAggregateAttributes = partialAggregateExpressions.flatMap { agg =>
-      agg.aggregateFunction.bufferAttributes
-    }
-    val partialAggregate =
-      Aggregate(
+    val partialAggregateAttributes =
+      partialAggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes)
+    val partialResultExpressions =
+      namedGroupingAttributes ++
+        partialAggregateExpressions.flatMap(_.aggregateFunction.cloneBufferAttributes)
+
+    val partialAggregate = if (usesTungstenAggregate) {
+      TungstenAggregate(
+        requiredChildDistributionExpressions = None: Option[Seq[Expression]],
+        groupingExpressions = namedGroupingExpressions.map(_._2),
+        nonCompleteAggregateExpressions = partialAggregateExpressions,
+        completeAggregateExpressions = Nil,
+        initialInputBufferOffset = 0,
+        resultExpressions = partialResultExpressions,
+        child = child)
+    } else {
+      SortBasedAggregate(
         requiredChildDistributionExpressions = None: Option[Seq[Expression]],
         groupingExpressions = namedGroupingExpressions.map(_._2),
         nonCompleteAggregateExpressions = partialAggregateExpressions,
@@ -56,29 +89,57 @@ object Utils {
         completeAggregateExpressions = Nil,
         completeAggregateAttributes = Nil,
         initialInputBufferOffset = 0,
-        resultExpressions = namedGroupingAttributes ++ partialAggregateAttributes,
+        resultExpressions = partialResultExpressions,
         child = child)
+    }
 
     // 2. Create an Aggregate Operator for final aggregations.
     val finalAggregateExpressions = aggregateExpressions.map(_.copy(mode = Final))
     val finalAggregateAttributes =
       finalAggregateExpressions.map {
-        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)
+        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)._2
       }
-    val rewrittenResultExpressions = resultExpressions.map { expr =>
-      expr.transformDown {
-        case agg: AggregateExpression2 =>
-          aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct).toAttribute
-        case expression =>
-          // We do not rely on the equality check at here since attributes may
-          // different cosmetically. Instead, we use semanticEquals.
-          groupExpressionMap.collectFirst {
-            case (expr, ne) if expr semanticEquals expression => ne.toAttribute
-          }.getOrElse(expression)
-      }.asInstanceOf[NamedExpression]
-    }
-    val finalAggregate =
-      Aggregate(
+
+    val finalAggregate = if (usesTungstenAggregate) {
+      val rewrittenResultExpressions = resultExpressions.map { expr =>
+        expr.transformDown {
+          case agg: AggregateExpression2 =>
+            // aggregateFunctionMap contains unique aggregate functions.
+            val aggregateFunction =
+              aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct)._1
+            aggregateFunction.asInstanceOf[AlgebraicAggregate].evaluateExpression
+          case expression =>
+            // We do not rely on the equality check at here since attributes may
+            // different cosmetically. Instead, we use semanticEquals.
+            groupExpressionMap.collectFirst {
+              case (expr, ne) if expr semanticEquals expression => ne.toAttribute
+            }.getOrElse(expression)
+        }.asInstanceOf[NamedExpression]
+      }
+
+      TungstenAggregate(
+        requiredChildDistributionExpressions = Some(namedGroupingAttributes),
+        groupingExpressions = namedGroupingAttributes,
+        nonCompleteAggregateExpressions = finalAggregateExpressions,
+        completeAggregateExpressions = Nil,
+        initialInputBufferOffset = namedGroupingAttributes.length,
+        resultExpressions = rewrittenResultExpressions,
+        child = partialAggregate)
+    } else {
+      val rewrittenResultExpressions = resultExpressions.map { expr =>
+        expr.transformDown {
+          case agg: AggregateExpression2 =>
+            aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct)._2
+          case expression =>
+            // We do not rely on the equality check at here since attributes may
+            // different cosmetically. Instead, we use semanticEquals.
+            groupExpressionMap.collectFirst {
+              case (expr, ne) if expr semanticEquals expression => ne.toAttribute
+            }.getOrElse(expression)
+        }.asInstanceOf[NamedExpression]
+      }
+
+      SortBasedAggregate(
         requiredChildDistributionExpressions = Some(namedGroupingAttributes),
         groupingExpressions = namedGroupingAttributes,
         nonCompleteAggregateExpressions = finalAggregateExpressions,
@@ -88,6 +149,7 @@ object Utils {
         initialInputBufferOffset = namedGroupingAttributes.length,
         resultExpressions = rewrittenResultExpressions,
         child = partialAggregate)
+    }
 
     finalAggregate :: Nil
   }
@@ -96,10 +158,18 @@ object Utils {
       groupingExpressions: Seq[Expression],
       functionsWithDistinct: Seq[AggregateExpression2],
       functionsWithoutDistinct: Seq[AggregateExpression2],
-      aggregateFunctionMap: Map[(AggregateFunction2, Boolean), Attribute],
+      aggregateFunctionMap: Map[(AggregateFunction2, Boolean), (AggregateFunction2, Attribute)],
       resultExpressions: Seq[NamedExpression],
       child: SparkPlan): Seq[SparkPlan] = {
 
+    val aggregateExpressions = functionsWithDistinct ++ functionsWithoutDistinct
+    val usesTungstenAggregate =
+      child.sqlContext.conf.unsafeEnabled &&
+        aggregateExpressions.forall(_.aggregateFunction.isInstanceOf[AlgebraicAggregate]) &&
+        supportsTungstenAggregate(
+          groupingExpressions,
+          aggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes))
+
     // 1. Create an Aggregate Operator for partial aggregations.
     // The grouping expressions are original groupingExpressions and
     // distinct columns. For example, for avg(distinct value) ... group by key
@@ -129,19 +199,26 @@ object Utils {
     val distinctColumnExpressionMap = namedDistinctColumnExpressions.toMap
     val distinctColumnAttributes = namedDistinctColumnExpressions.map(_._2.toAttribute)
 
-    val partialAggregateExpressions = functionsWithoutDistinct.map {
-      case AggregateExpression2(aggregateFunction, mode, _) =>
-        AggregateExpression2(aggregateFunction, Partial, false)
-    }
-    val partialAggregateAttributes = partialAggregateExpressions.flatMap { agg =>
-      agg.aggregateFunction.bufferAttributes
-    }
+    val partialAggregateExpressions = functionsWithoutDistinct.map(_.copy(mode = Partial))
+    val partialAggregateAttributes =
+      partialAggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes)
     val partialAggregateGroupingExpressions =
       (namedGroupingExpressions ++ namedDistinctColumnExpressions).map(_._2)
     val partialAggregateResult =
-      namedGroupingAttributes ++ distinctColumnAttributes ++ partialAggregateAttributes
-    val partialAggregate =
-      Aggregate(
+      namedGroupingAttributes ++
+        distinctColumnAttributes ++
+        partialAggregateExpressions.flatMap(_.aggregateFunction.cloneBufferAttributes)
+    val partialAggregate = if (usesTungstenAggregate) {
+      TungstenAggregate(
+        requiredChildDistributionExpressions = None: Option[Seq[Expression]],
+        groupingExpressions = partialAggregateGroupingExpressions,
+        nonCompleteAggregateExpressions = partialAggregateExpressions,
+        completeAggregateExpressions = Nil,
+        initialInputBufferOffset = 0,
+        resultExpressions = partialAggregateResult,
+        child = child)
+    } else {
+      SortBasedAggregate(
         requiredChildDistributionExpressions = None: Option[Seq[Expression]],
         groupingExpressions = partialAggregateGroupingExpressions,
         nonCompleteAggregateExpressions = partialAggregateExpressions,
@@ -151,20 +228,27 @@ object Utils {
         initialInputBufferOffset = 0,
         resultExpressions = partialAggregateResult,
         child = child)
+    }
 
     // 2. Create an Aggregate Operator for partial merge aggregations.
-    val partialMergeAggregateExpressions = functionsWithoutDistinct.map {
-      case AggregateExpression2(aggregateFunction, mode, _) =>
-        AggregateExpression2(aggregateFunction, PartialMerge, false)
-    }
+    val partialMergeAggregateExpressions = functionsWithoutDistinct.map(_.copy(mode = PartialMerge))
     val partialMergeAggregateAttributes =
-      partialMergeAggregateExpressions.flatMap { agg =>
-        agg.aggregateFunction.bufferAttributes
-      }
+      partialMergeAggregateExpressions.flatMap(_.aggregateFunction.bufferAttributes)
     val partialMergeAggregateResult =
-      namedGroupingAttributes ++ distinctColumnAttributes ++ partialMergeAggregateAttributes
-    val partialMergeAggregate =
-      Aggregate(
+      namedGroupingAttributes ++
+        distinctColumnAttributes ++
+        partialMergeAggregateExpressions.flatMap(_.aggregateFunction.cloneBufferAttributes)
+    val partialMergeAggregate = if (usesTungstenAggregate) {
+      TungstenAggregate(
+        requiredChildDistributionExpressions = Some(namedGroupingAttributes),
+        groupingExpressions = namedGroupingAttributes ++ distinctColumnAttributes,
+        nonCompleteAggregateExpressions = partialMergeAggregateExpressions,
+        completeAggregateExpressions = Nil,
+        initialInputBufferOffset = (namedGroupingAttributes ++ distinctColumnAttributes).length,
+        resultExpressions = partialMergeAggregateResult,
+        child = partialAggregate)
+    } else {
+      SortBasedAggregate(
         requiredChildDistributionExpressions = Some(namedGroupingAttributes),
         groupingExpressions = namedGroupingAttributes ++ distinctColumnAttributes,
         nonCompleteAggregateExpressions = partialMergeAggregateExpressions,
@@ -174,48 +258,91 @@ object Utils {
         initialInputBufferOffset = (namedGroupingAttributes ++ distinctColumnAttributes).length,
         resultExpressions = partialMergeAggregateResult,
         child = partialAggregate)
+    }
 
     // 3. Create an Aggregate Operator for partial merge aggregations.
-    val finalAggregateExpressions = functionsWithoutDistinct.map {
-      case AggregateExpression2(aggregateFunction, mode, _) =>
-        AggregateExpression2(aggregateFunction, Final, false)
-    }
+    val finalAggregateExpressions = functionsWithoutDistinct.map(_.copy(mode = Final))
     val finalAggregateAttributes =
       finalAggregateExpressions.map {
-        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)
+        expr => aggregateFunctionMap(expr.aggregateFunction, expr.isDistinct)._2
       }
+    // Create a map to store those rewritten aggregate functions. We always need to use
+    // both function and its corresponding isDistinct flag as the key because function itself
+    // does not knows if it is has distinct keyword or now.
+    val rewrittenAggregateFunctions =
+      mutable.Map.empty[(AggregateFunction2, Boolean), AggregateFunction2]
     val (completeAggregateExpressions, completeAggregateAttributes) = functionsWithDistinct.map {
       // Children of an AggregateFunction with DISTINCT keyword has already
       // been evaluated. At here, we need to replace original children
       // to AttributeReferences.
-      case agg @ AggregateExpression2(aggregateFunction, mode, isDistinct) =>
+      case agg @ AggregateExpression2(aggregateFunction, mode, true) =>
         val rewrittenAggregateFunction = aggregateFunction.transformDown {
           case expr if distinctColumnExpressionMap.contains(expr) =>
             distinctColumnExpressionMap(expr).toAttribute
         }.asInstanceOf[AggregateFunction2]
+        // Because we have rewritten the aggregate function, we use rewrittenAggregateFunctions
+        // to track the old version and the new version of this function.
+        rewrittenAggregateFunctions += (aggregateFunction, true) -> rewrittenAggregateFunction
         // We rewrite the aggregate function to a non-distinct aggregation because
         // its input will have distinct arguments.
+        // We just keep the isDistinct setting to true, so when users look at the query plan,
+        // they still can see distinct aggregations.
         val rewrittenAggregateExpression =
-          AggregateExpression2(rewrittenAggregateFunction, Complete, false)
+          AggregateExpression2(rewrittenAggregateFunction, Complete, true)
 
-        val aggregateFunctionAttribute = aggregateFunctionMap(agg.aggregateFunction, isDistinct)
+        val aggregateFunctionAttribute =
+          aggregateFunctionMap(agg.aggregateFunction, true)._2
         (rewrittenAggregateExpression -> aggregateFunctionAttribute)
     }.unzip
 
-    val rewrittenResultExpressions = resultExpressions.map { expr =>
-      expr.transform {
-        case agg: AggregateExpression2 =>
-          aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct).toAttribute
-        case expression =>
-          // We do not rely on the equality check at here since attributes may
-          // different cosmetically. Instead, we use semanticEquals.
-          groupExpressionMap.collectFirst {
-            case (expr, ne) if expr semanticEquals expression => ne.toAttribute
-          }.getOrElse(expression)
-      }.asInstanceOf[NamedExpression]
-    }
-    val finalAndCompleteAggregate =
-      Aggregate(
+    val finalAndCompleteAggregate = if (usesTungstenAggregate) {
+      val rewrittenResultExpressions = resultExpressions.map { expr =>
+        expr.transform {
+          case agg: AggregateExpression2 =>
+            val function = agg.aggregateFunction
+            val isDistinct = agg.isDistinct
+            val aggregateFunction =
+              if (rewrittenAggregateFunctions.contains(function, isDistinct)) {
+                // If this function has been rewritten, we get the rewritten version from
+                // rewrittenAggregateFunctions.
+                rewrittenAggregateFunctions(function, isDistinct)
+              } else {
+                // Oterwise, we get it from aggregateFunctionMap, which contains unique
+                // aggregate functions that have not been rewritten.
+                aggregateFunctionMap(function, isDistinct)._1
+              }
+            aggregateFunction.asInstanceOf[AlgebraicAggregate].evaluateExpression
+          case expression =>
+            // We do not rely on the equality check at here since attributes may
+            // different cosmetically. Instead, we use semanticEquals.
+            groupExpressionMap.collectFirst {
+              case (expr, ne) if expr semanticEquals expression => ne.toAttribute
+            }.getOrElse(expression)
+        }.asInstanceOf[NamedExpression]
+      }
+
+      TungstenAggregate(
+        requiredChildDistributionExpressions = Some(namedGroupingAttributes),
+        groupingExpressions = namedGroupingAttributes,
+        nonCompleteAggregateExpressions = finalAggregateExpressions,
+        completeAggregateExpressions = completeAggregateExpressions,
+        initialInputBufferOffset = (namedGroupingAttributes ++ distinctColumnAttributes).length,
+        resultExpressions = rewrittenResultExpressions,
+        child = partialMergeAggregate)
+    } else {
+      val rewrittenResultExpressions = resultExpressions.map { expr =>
+        expr.transform {
+          case agg: AggregateExpression2 =>
+            aggregateFunctionMap(agg.aggregateFunction, agg.isDistinct)._2
+          case expression =>
+            // We do not rely on the equality check at here since attributes may
+            // different cosmetically. Instead, we use semanticEquals.
+            groupExpressionMap.collectFirst {
+              case (expr, ne) if expr semanticEquals expression => ne.toAttribute
+            }.getOrElse(expression)
+        }.asInstanceOf[NamedExpression]
+      }
+      SortBasedAggregate(
         requiredChildDistributionExpressions = Some(namedGroupingAttributes),
         groupingExpressions = namedGroupingAttributes,
         nonCompleteAggregateExpressions = finalAggregateExpressions,
@@ -225,6 +352,7 @@ object Utils {
         initialInputBufferOffset = (namedGroupingAttributes ++ distinctColumnAttributes).length,
         resultExpressions = rewrittenResultExpressions,
         child = partialMergeAggregate)
+    }
 
     finalAndCompleteAggregate :: Nil
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index cef40dd324d9e..c64aa7a07dc2b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -262,7 +262,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     val df = sql(sqlText)
     // First, check if we have GeneratedAggregate.
     val hasGeneratedAgg = df.queryExecution.executedPlan
-      .collect { case _: aggregate.Aggregate => true }
+      .collect { case _: aggregate.TungstenAggregate => true }
       .nonEmpty
     if (!hasGeneratedAgg) {
       fail(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index 4b35c8fd83533..7b5aa4763fd9e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -21,9 +21,9 @@ import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
-import org.apache.spark.sql.{SQLConf, AnalysisException, QueryTest, Row}
+import org.apache.spark.sql._
 import org.scalatest.BeforeAndAfterAll
-import test.org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
+import _root_.test.org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
 
 abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
 
@@ -141,6 +141,22 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
       Nil)
   }
 
+  test("null literal") {
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  AVG(null),
+          |  COUNT(null),
+          |  FIRST(null),
+          |  LAST(null),
+          |  MAX(null),
+          |  MIN(null),
+          |  SUM(null)
+        """.stripMargin),
+      Row(null, 0, null, null, null, null, null) :: Nil)
+  }
+
   test("only do grouping") {
     checkAnswer(
       sqlContext.sql(
@@ -266,13 +282,6 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
           |SELECT avg(value) FROM agg1
         """.stripMargin),
       Row(11.125) :: Nil)
-
-    checkAnswer(
-      sqlContext.sql(
-        """
-          |SELECT avg(null)
-        """.stripMargin),
-      Row(null) :: Nil)
   }
 
   test("udaf") {
@@ -364,7 +373,7 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
           |  max(distinct value1)
           |FROM agg2
         """.stripMargin),
-      Row(-60, 70.0, 101.0/9.0, 5.6, 100.0))
+      Row(-60, 70.0, 101.0/9.0, 5.6, 100))
 
     checkAnswer(
       sqlContext.sql(
@@ -402,6 +411,23 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
         Row(2, 100.0, 3.0, 0.0, 100.0, 1.0/3.0 + 100.0) ::
         Row(3, null, 3.0, null, null, null) ::
         Row(null, 110.0, 60.0, 30.0, 110.0, 110.0) :: Nil)
+
+    checkAnswer(
+      sqlContext.sql(
+        """
+          |SELECT
+          |  count(value1),
+          |  count(*),
+          |  count(1),
+          |  count(DISTINCT value1),
+          |  key
+          |FROM agg2
+          |GROUP BY key
+        """.stripMargin),
+      Row(3, 3, 3, 2, 1) ::
+        Row(3, 4, 4, 2, 2) ::
+        Row(0, 2, 2, 0, 3) ::
+        Row(3, 4, 4, 3, null) :: Nil)
   }
 
   test("test count") {
@@ -496,7 +522,8 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
           |FROM agg1
           |GROUP BY key
         """.stripMargin).queryExecution.executedPlan.collect {
-        case agg: aggregate.Aggregate => agg
+        case agg: aggregate.SortBasedAggregate => agg
+        case agg: aggregate.TungstenAggregate => agg
       }
       val message =
         "We should fallback to the old aggregation code path if " +
@@ -537,3 +564,58 @@ class TungstenAggregationQuerySuite extends AggregationQuerySuite {
     sqlContext.setConf(SQLConf.UNSAFE_ENABLED.key, originalUnsafeEnabled.toString)
   }
 }
+
+class TungstenAggregationQueryWithControlledFallbackSuite extends AggregationQuerySuite {
+
+  var originalUnsafeEnabled: Boolean = _
+
+  override def beforeAll(): Unit = {
+    originalUnsafeEnabled = sqlContext.conf.unsafeEnabled
+    sqlContext.setConf(SQLConf.UNSAFE_ENABLED.key, "true")
+    super.beforeAll()
+  }
+
+  override def afterAll(): Unit = {
+    super.afterAll()
+    sqlContext.setConf(SQLConf.UNSAFE_ENABLED.key, originalUnsafeEnabled.toString)
+    sqlContext.conf.unsetConf("spark.sql.TungstenAggregate.testFallbackStartsAt")
+  }
+
+  override protected def checkAnswer(actual: DataFrame, expectedAnswer: Seq[Row]): Unit = {
+    (0 to 2).foreach { fallbackStartsAt =>
+      sqlContext.setConf(
+        "spark.sql.TungstenAggregate.testFallbackStartsAt",
+        fallbackStartsAt.toString)
+
+      // Create a new df to make sure its physical operator picks up
+      // spark.sql.TungstenAggregate.testFallbackStartsAt.
+      val newActual = DataFrame(sqlContext, actual.logicalPlan)
+
+      QueryTest.checkAnswer(newActual, expectedAnswer) match {
+        case Some(errorMessage) =>
+          val newErrorMessage =
+            s"""
+              |The following aggregation query failed when using TungstenAggregate with
+              |controlled fallback (it falls back to sort-based aggregation once it has processed
+              |$fallbackStartsAt input rows). The query is
+              |${actual.queryExecution}
+              |
+              |$errorMessage
+            """.stripMargin
+
+          fail(newErrorMessage)
+        case None =>
+      }
+    }
+  }
+
+  // Override it to make sure we call the actually overridden checkAnswer.
+  override protected def checkAnswer(df: DataFrame, expectedAnswer: Row): Unit = {
+    checkAnswer(df, Seq(expectedAnswer))
+  }
+
+  // Override it to make sure we call the actually overridden checkAnswer.
+  override protected def checkAnswer(df: DataFrame, expectedAnswer: DataFrame): Unit = {
+    checkAnswer(df, expectedAnswer.collect())
+  }
+}

From e234ea1b49d30bb6c8b8c001bd98c43de290dcff Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Thu, 6 Aug 2015 15:30:27 -0700
Subject: [PATCH 099/633] [SPARK-9645] [YARN] [CORE] Allow shuffle service to
 read shuffle files.

Spark should not mess with the permissions of directories created
by the cluster manager. Here, by setting the block manager dir
permissions to 700, the shuffle service (running as the YARN user)
wouldn't be able to serve shuffle files created by applications.

Also, the code to protect the local app dir was missing in standalone's
Worker; that has been now added. Since all processes run as the same
user in standalone, `chmod 700` should not cause problems.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7966 from vanzin/SPARK-9645 and squashes the following commits:

6e07b31 [Marcelo Vanzin] Protect the app dir in standalone mode.
384ba6a [Marcelo Vanzin] [SPARK-9645] [yarn] [core] Allow shuffle service to read shuffle files.
---
 .../main/scala/org/apache/spark/deploy/worker/Worker.scala    | 4 +++-
 .../scala/org/apache/spark/storage/DiskBlockManager.scala     | 1 -
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 6792d3310b06c..79b1536d94016 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -428,7 +428,9 @@ private[deploy] class Worker(
           // application finishes.
           val appLocalDirs = appDirectories.get(appId).getOrElse {
             Utils.getOrCreateLocalRootDirs(conf).map { dir =>
-              Utils.createDirectory(dir, namePrefix = "executor").getAbsolutePath()
+              val appDir = Utils.createDirectory(dir, namePrefix = "executor")
+              Utils.chmod700(appDir)
+              appDir.getAbsolutePath()
             }.toSeq
           }
           appDirectories(appId) = appLocalDirs
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 5f537692a16c5..56a33d5ca7d60 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -133,7 +133,6 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
     Utils.getConfiguredLocalDirs(conf).flatMap { rootDir =>
       try {
         val localDir = Utils.createDirectory(rootDir, "blockmgr")
-        Utils.chmod700(localDir)
         logInfo(s"Created local directory at $localDir")
         Some(localDir)
       } catch {

From 681e3024b6c2fcb54b42180d94d3ba3eed52a2d4 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Thu, 6 Aug 2015 23:43:52 +0100
Subject: [PATCH 100/633] [SPARK-9633] [BUILD] SBT download locations outdated;
 need an update

Remove 2 defunct SBT download URLs and replace with the 1 known download URL. Also, use https.
Follow up on https://github.com/apache/spark/pull/7792

Author: Sean Owen <sowen@cloudera.com>

Closes #7956 from srowen/SPARK-9633 and squashes the following commits:

caa40bd [Sean Owen] Remove 2 defunct SBT download URLs and replace with the 1 known download URL. Also, use https.
---
 build/sbt-launch-lib.bash | 9 +++------
 1 file changed, 3 insertions(+), 6 deletions(-)

diff --git a/build/sbt-launch-lib.bash b/build/sbt-launch-lib.bash
index 7930a38b9674a..615f848394650 100755
--- a/build/sbt-launch-lib.bash
+++ b/build/sbt-launch-lib.bash
@@ -38,8 +38,7 @@ dlog () {
 
 acquire_sbt_jar () {
   SBT_VERSION=`awk -F "=" '/sbt\.version/ {print $2}' ./project/build.properties`
-  URL1=http://typesafe.artifactoryonline.com/typesafe/ivy-releases/org.scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch.jar
-  URL2=http://repo.typesafe.com/typesafe/ivy-releases/org.scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch.jar
+  URL1=https://dl.bintray.com/typesafe/ivy-releases/org.scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch.jar
   JAR=build/sbt-launch-${SBT_VERSION}.jar
 
   sbt_jar=$JAR
@@ -51,12 +50,10 @@ acquire_sbt_jar () {
     printf "Attempting to fetch sbt\n"
     JAR_DL="${JAR}.part"
     if [ $(command -v curl) ]; then
-      (curl --fail --location --silent ${URL1} > "${JAR_DL}" ||\
-        (rm -f "${JAR_DL}" && curl --fail --location --silent ${URL2} > "${JAR_DL}")) &&\
+      curl --fail --location --silent ${URL1} > "${JAR_DL}" &&\
         mv "${JAR_DL}" "${JAR}"
     elif [ $(command -v wget) ]; then
-      (wget --quiet ${URL1} -O "${JAR_DL}" ||\
-        (rm -f "${JAR_DL}" && wget --quiet ${URL2} -O "${JAR_DL}")) &&\
+      wget --quiet ${URL1} -O "${JAR_DL}" &&\
         mv "${JAR_DL}" "${JAR}"
     else
       printf "You do not have curl or wget installed, please install sbt manually from http://www.scala-sbt.org/\n"

From baf4587a569b49e39020c04c2785041bdd00789b Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 6 Aug 2015 17:03:14 -0700
Subject: [PATCH 101/633] [SPARK-9691] [SQL] PySpark SQL rand function treats
 seed 0 as no seed

https://issues.apache.org/jira/browse/SPARK-9691

jkbradley rxin

Author: Yin Huai <yhuai@databricks.com>

Closes #7999 from yhuai/pythonRand and squashes the following commits:

4187e0c [Yin Huai] Regression test.
a985ef9 [Yin Huai] Use "if seed is not None" instead "if seed" because "if seed" returns false when seed is 0.
---
 python/pyspark/sql/functions.py |  4 ++--
 python/pyspark/sql/tests.py     | 10 ++++++++++
 2 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index b5c6a01f18858..95f46044d324a 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -268,7 +268,7 @@ def rand(seed=None):
     """Generates a random column with i.i.d. samples from U[0.0, 1.0].
     """
     sc = SparkContext._active_spark_context
-    if seed:
+    if seed is not None:
         jc = sc._jvm.functions.rand(seed)
     else:
         jc = sc._jvm.functions.rand()
@@ -280,7 +280,7 @@ def randn(seed=None):
     """Generates a column with i.i.d. samples from the standard normal distribution.
     """
     sc = SparkContext._active_spark_context
-    if seed:
+    if seed is not None:
         jc = sc._jvm.functions.randn(seed)
     else:
         jc = sc._jvm.functions.randn()
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index ebd3ea8db6a43..1e3444dd9e3b4 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -629,6 +629,16 @@ def test_rand_functions(self):
         for row in rndn:
             assert row[1] >= -4.0 and row[1] <= 4.0, "got: %s" % row[1]
 
+        # If the specified seed is 0, we should use it.
+        # https://issues.apache.org/jira/browse/SPARK-9691
+        rnd1 = df.select('key', functions.rand(0)).collect()
+        rnd2 = df.select('key', functions.rand(0)).collect()
+        self.assertEqual(sorted(rnd1), sorted(rnd2))
+
+        rndn1 = df.select('key', functions.randn(0)).collect()
+        rndn2 = df.select('key', functions.randn(0)).collect()
+        self.assertEqual(sorted(rndn1), sorted(rndn2))
+
     def test_between_function(self):
         df = self.sc.parallelize([
             Row(a=1, b=2, c=3),

From 4e70e8256ce2f45b438642372329eac7b1e9e8cf Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 6 Aug 2015 17:30:31 -0700
Subject: [PATCH 102/633] [SPARK-9228] [SQL] use tungsten.enabled in public for
 both of codegen/unsafe

spark.sql.tungsten.enabled will be the default value for both codegen and unsafe, they are kept internally for debug/testing.

cc marmbrus rxin

Author: Davies Liu <davies@databricks.com>

Closes #7998 from davies/tungsten and squashes the following commits:

c1c16da [Davies Liu] update doc
1a47be1 [Davies Liu] use tungsten.enabled for both of codegen/unsafe
---
 docs/sql-programming-guide.md                 |  6 +++---
 .../scala/org/apache/spark/sql/SQLConf.scala  | 20 ++++++++++++-------
 .../spark/sql/execution/SparkPlan.scala       |  8 +++++++-
 .../spark/sql/execution/joins/HashJoin.scala  |  3 ++-
 .../sql/execution/joins/HashOuterJoin.scala   |  2 +-
 .../sql/execution/joins/HashSemiJoin.scala    |  3 ++-
 6 files changed, 28 insertions(+), 14 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 3ea77e82422fb..6c317175d3278 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1884,11 +1884,11 @@ that these options will be deprecated in future release as more optimizations ar
     </td>
   </tr>
   <tr>
-    <td><code>spark.sql.codegen</code></td>
+    <td><code>spark.sql.tungsten.enabled</code></td>
     <td>true</td>
     <td>
-      When true, code will be dynamically generated at runtime for expression evaluation in a specific
-      query. For some queries with complicated expression this option can lead to significant speed-ups.
+      When true, use the optimized Tungsten physical execution backend which explicitly manages memory
+      and dynamically generates bytecode for expression evaluation.
     </td>
   </tr>
   <tr>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index f836122b3e0e4..ef35c133d9cc3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -223,14 +223,21 @@ private[spark] object SQLConf {
     defaultValue = Some(200),
     doc = "The default number of partitions to use when shuffling data for joins or aggregations.")
 
-  val CODEGEN_ENABLED = booleanConf("spark.sql.codegen",
+  val TUNGSTEN_ENABLED = booleanConf("spark.sql.tungsten.enabled",
     defaultValue = Some(true),
+    doc = "When true, use the optimized Tungsten physical execution backend which explicitly " +
+          "manages memory and dynamically generates bytecode for expression evaluation.")
+
+  val CODEGEN_ENABLED = booleanConf("spark.sql.codegen",
+    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
     doc = "When true, code will be dynamically generated at runtime for expression evaluation in" +
-      " a specific query.")
+      " a specific query.",
+    isPublic = false)
 
   val UNSAFE_ENABLED = booleanConf("spark.sql.unsafe.enabled",
-    defaultValue = Some(true),
-    doc = "When true, use the new optimized Tungsten physical execution backend.")
+    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
+    doc = "When true, use the new optimized Tungsten physical execution backend.",
+    isPublic = false)
 
   val DIALECT = stringConf(
     "spark.sql.dialect",
@@ -427,7 +434,6 @@ private[spark] object SQLConf {
  *
  * SQLConf is thread-safe (internally synchronized, so safe to be used in multiple threads).
  */
-
 private[sql] class SQLConf extends Serializable with CatalystConf {
   import SQLConf._
 
@@ -474,11 +480,11 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN)
 
-  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED)
+  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED, getConf(TUNGSTEN_ENABLED))
 
   def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE)
 
-  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED)
+  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED, getConf(TUNGSTEN_ENABLED))
 
   private[spark] def useSqlAggregate2: Boolean = getConf(USE_SQL_AGGREGATE2)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 2f29067f5646a..3fff79cd1b281 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -55,12 +55,18 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
   protected def sparkContext = sqlContext.sparkContext
 
   // sqlContext will be null when we are being deserialized on the slaves.  In this instance
-  // the value of codegenEnabled will be set by the desserializer after the constructor has run.
+  // the value of codegenEnabled/unsafeEnabled will be set by the desserializer after the
+  // constructor has run.
   val codegenEnabled: Boolean = if (sqlContext != null) {
     sqlContext.conf.codegenEnabled
   } else {
     false
   }
+  val unsafeEnabled: Boolean = if (sqlContext != null) {
+    sqlContext.conf.unsafeEnabled
+  } else {
+    false
+  }
 
   /**
    * Whether the "prepare" method is called.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 5e9cd9fd2345a..22d46d1c3e3b7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -44,7 +44,8 @@ trait HashJoin {
   override def output: Seq[Attribute] = left.output ++ right.output
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && UnsafeProjection.canSupport(buildKeys)
+    (self.codegenEnabled && self.unsafeEnabled
+      && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 346337e64245c..701bd3cd86372 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -67,7 +67,7 @@ trait HashOuterJoin {
   }
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && joinType != FullOuter
+    (self.codegenEnabled && self.unsafeEnabled && joinType != FullOuter
       && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 47a7d370f5415..82dd6eb7e7ed0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -33,7 +33,8 @@ trait HashSemiJoin {
   override def output: Seq[Attribute] = left.output
 
   protected[this] def supportUnsafe: Boolean = {
-    (self.codegenEnabled && UnsafeProjection.canSupport(leftKeys)
+    (self.codegenEnabled && self.unsafeEnabled
+      && UnsafeProjection.canSupport(leftKeys)
       && UnsafeProjection.canSupport(rightKeys)
       && UnsafeProjection.canSupport(left.schema)
       && UnsafeProjection.canSupport(right.schema))

From 0867b23c74a3e6347d718b67ddabff17b468eded Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 6 Aug 2015 17:31:16 -0700
Subject: [PATCH 103/633] [SPARK-9650][SQL] Fix quoting behavior on
 interpolated column names

Make sure that `$"column"` is consistent with other methods with respect to backticks.  Adds a bunch of tests for various ways of constructing columns.

Author: Michael Armbrust <michael@databricks.com>

Closes #7969 from marmbrus/namesWithDots and squashes the following commits:

53ef3d7 [Michael Armbrust] [SPARK-9650][SQL] Fix quoting behavior on interpolated column names
2bf7a92 [Michael Armbrust] WIP
---
 .../sql/catalyst/analysis/unresolved.scala    | 57 ++++++++++++++++
 .../catalyst/plans/logical/LogicalPlan.scala  | 42 +-----------
 .../scala/org/apache/spark/sql/Column.scala   |  2 +-
 .../org/apache/spark/sql/SQLContext.scala     |  2 +-
 .../spark/sql/ColumnExpressionSuite.scala     | 68 +++++++++++++++++++
 5 files changed, 128 insertions(+), 43 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
index 03da45b09f928..43ee3191935eb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/unresolved.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
+import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.catalyst.errors
 import org.apache.spark.sql.catalyst.expressions._
@@ -69,8 +70,64 @@ case class UnresolvedAttribute(nameParts: Seq[String]) extends Attribute with Un
 }
 
 object UnresolvedAttribute {
+  /**
+   * Creates an [[UnresolvedAttribute]], parsing segments separated by dots ('.').
+   */
   def apply(name: String): UnresolvedAttribute = new UnresolvedAttribute(name.split("\\."))
+
+  /**
+   * Creates an [[UnresolvedAttribute]], from a single quoted string (for example using backticks in
+   * HiveQL.  Since the string is consider quoted, no processing is done on the name.
+   */
   def quoted(name: String): UnresolvedAttribute = new UnresolvedAttribute(Seq(name))
+
+  /**
+   * Creates an [[UnresolvedAttribute]] from a string in an embedded language.  In this case
+   * we treat it as a quoted identifier, except for '.', which must be further quoted using
+   * backticks if it is part of a column name.
+   */
+  def quotedString(name: String): UnresolvedAttribute =
+    new UnresolvedAttribute(parseAttributeName(name))
+
+  /**
+   * Used to split attribute name by dot with backticks rule.
+   * Backticks must appear in pairs, and the quoted string must be a complete name part,
+   * which means `ab..c`e.f is not allowed.
+   * Escape character is not supported now, so we can't use backtick inside name part.
+   */
+  def parseAttributeName(name: String): Seq[String] = {
+    def e = new AnalysisException(s"syntax error in attribute name: $name")
+    val nameParts = scala.collection.mutable.ArrayBuffer.empty[String]
+    val tmp = scala.collection.mutable.ArrayBuffer.empty[Char]
+    var inBacktick = false
+    var i = 0
+    while (i < name.length) {
+      val char = name(i)
+      if (inBacktick) {
+        if (char == '`') {
+          inBacktick = false
+          if (i + 1 < name.length && name(i + 1) != '.') throw e
+        } else {
+          tmp += char
+        }
+      } else {
+        if (char == '`') {
+          if (tmp.nonEmpty) throw e
+          inBacktick = true
+        } else if (char == '.') {
+          if (name(i - 1) == '.' || i == name.length - 1) throw e
+          nameParts += tmp.mkString
+          tmp.clear()
+        } else {
+          tmp += char
+        }
+      }
+      i += 1
+    }
+    if (inBacktick) throw e
+    nameParts += tmp.mkString
+    nameParts.toSeq
+  }
 }
 
 case class UnresolvedFunction(
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index 9b52f020093f0..c290e6acb361c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -179,47 +179,7 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
   def resolveQuoted(
       name: String,
       resolver: Resolver): Option[NamedExpression] = {
-    resolve(parseAttributeName(name), output, resolver)
-  }
-
-  /**
-   * Internal method, used to split attribute name by dot with backticks rule.
-   * Backticks must appear in pairs, and the quoted string must be a complete name part,
-   * which means `ab..c`e.f is not allowed.
-   * Escape character is not supported now, so we can't use backtick inside name part.
-   */
-  private def parseAttributeName(name: String): Seq[String] = {
-    val e = new AnalysisException(s"syntax error in attribute name: $name")
-    val nameParts = scala.collection.mutable.ArrayBuffer.empty[String]
-    val tmp = scala.collection.mutable.ArrayBuffer.empty[Char]
-    var inBacktick = false
-    var i = 0
-    while (i < name.length) {
-      val char = name(i)
-      if (inBacktick) {
-        if (char == '`') {
-          inBacktick = false
-          if (i + 1 < name.length && name(i + 1) != '.') throw e
-        } else {
-          tmp += char
-        }
-      } else {
-        if (char == '`') {
-          if (tmp.nonEmpty) throw e
-          inBacktick = true
-        } else if (char == '.') {
-          if (name(i - 1) == '.' || i == name.length - 1) throw e
-          nameParts += tmp.mkString
-          tmp.clear()
-        } else {
-          tmp += char
-        }
-      }
-      i += 1
-    }
-    if (inBacktick) throw e
-    nameParts += tmp.mkString
-    nameParts.toSeq
+    resolve(UnresolvedAttribute.parseAttributeName(name), output, resolver)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index 75365fbcec757..27bd084847346 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -54,7 +54,7 @@ class Column(protected[sql] val expr: Expression) extends Logging {
   def this(name: String) = this(name match {
     case "*" => UnresolvedStar(None)
     case _ if name.endsWith(".*") => UnresolvedStar(Some(name.substring(0, name.length - 2)))
-    case _ => UnresolvedAttribute(name)
+    case _ => UnresolvedAttribute.quotedString(name)
   })
 
   /** Creates a column based on the given expression. */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 6f8ffb54402a7..075c0ea2544b2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -343,7 +343,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
      */
     implicit class StringToColumn(val sc: StringContext) {
       def $(args: Any*): ColumnName = {
-        new ColumnName(sc.s(args : _*))
+        new ColumnName(sc.s(args: _*))
       }
     }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index e1b3443d74993..6a09a3b72c081 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -32,6 +32,74 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
 
   override def sqlContext(): SQLContext = ctx
 
+  test("column names with space") {
+    val df = Seq((1, "a")).toDF("name with space", "name.with.dot")
+
+    checkAnswer(
+      df.select(df("name with space")),
+      Row(1) :: Nil)
+
+    checkAnswer(
+      df.select($"name with space"),
+      Row(1) :: Nil)
+
+    checkAnswer(
+      df.select(col("name with space")),
+      Row(1) :: Nil)
+
+    checkAnswer(
+      df.select("name with space"),
+      Row(1) :: Nil)
+
+    checkAnswer(
+      df.select(expr("`name with space`")),
+      Row(1) :: Nil)
+  }
+
+  test("column names with dot") {
+    val df = Seq((1, "a")).toDF("name with space", "name.with.dot").as("a")
+
+    checkAnswer(
+      df.select(df("`name.with.dot`")),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select($"`name.with.dot`"),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select(col("`name.with.dot`")),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select("`name.with.dot`"),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select(expr("`name.with.dot`")),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select(df("a.`name.with.dot`")),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select($"a.`name.with.dot`"),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select(col("a.`name.with.dot`")),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select("a.`name.with.dot`"),
+      Row("a") :: Nil)
+
+    checkAnswer(
+      df.select(expr("a.`name.with.dot`")),
+      Row("a") :: Nil)
+  }
+
   test("alias") {
     val df = Seq((1, Seq(1, 2, 3))).toDF("a", "intList")
     assert(df.select(df("a").as("b")).columns.head === "b")

From 49b1504fe3733eb36a7fc6317ec19aeba5d46f97 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies.liu@gmail.com>
Date: Thu, 6 Aug 2015 17:36:12 -0700
Subject: [PATCH 104/633] Revert "[SPARK-9228] [SQL] use tungsten.enabled in
 public for both of codegen/unsafe"

This reverts commit 4e70e8256ce2f45b438642372329eac7b1e9e8cf.
---
 docs/sql-programming-guide.md                 |  6 +++---
 .../scala/org/apache/spark/sql/SQLConf.scala  | 20 +++++++------------
 .../spark/sql/execution/SparkPlan.scala       |  8 +-------
 .../spark/sql/execution/joins/HashJoin.scala  |  3 +--
 .../sql/execution/joins/HashOuterJoin.scala   |  2 +-
 .../sql/execution/joins/HashSemiJoin.scala    |  3 +--
 6 files changed, 14 insertions(+), 28 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 6c317175d3278..3ea77e82422fb 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1884,11 +1884,11 @@ that these options will be deprecated in future release as more optimizations ar
     </td>
   </tr>
   <tr>
-    <td><code>spark.sql.tungsten.enabled</code></td>
+    <td><code>spark.sql.codegen</code></td>
     <td>true</td>
     <td>
-      When true, use the optimized Tungsten physical execution backend which explicitly manages memory
-      and dynamically generates bytecode for expression evaluation.
+      When true, code will be dynamically generated at runtime for expression evaluation in a specific
+      query. For some queries with complicated expression this option can lead to significant speed-ups.
     </td>
   </tr>
   <tr>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index ef35c133d9cc3..f836122b3e0e4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -223,21 +223,14 @@ private[spark] object SQLConf {
     defaultValue = Some(200),
     doc = "The default number of partitions to use when shuffling data for joins or aggregations.")
 
-  val TUNGSTEN_ENABLED = booleanConf("spark.sql.tungsten.enabled",
-    defaultValue = Some(true),
-    doc = "When true, use the optimized Tungsten physical execution backend which explicitly " +
-          "manages memory and dynamically generates bytecode for expression evaluation.")
-
   val CODEGEN_ENABLED = booleanConf("spark.sql.codegen",
-    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
+    defaultValue = Some(true),
     doc = "When true, code will be dynamically generated at runtime for expression evaluation in" +
-      " a specific query.",
-    isPublic = false)
+      " a specific query.")
 
   val UNSAFE_ENABLED = booleanConf("spark.sql.unsafe.enabled",
-    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
-    doc = "When true, use the new optimized Tungsten physical execution backend.",
-    isPublic = false)
+    defaultValue = Some(true),
+    doc = "When true, use the new optimized Tungsten physical execution backend.")
 
   val DIALECT = stringConf(
     "spark.sql.dialect",
@@ -434,6 +427,7 @@ private[spark] object SQLConf {
  *
  * SQLConf is thread-safe (internally synchronized, so safe to be used in multiple threads).
  */
+
 private[sql] class SQLConf extends Serializable with CatalystConf {
   import SQLConf._
 
@@ -480,11 +474,11 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN)
 
-  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED, getConf(TUNGSTEN_ENABLED))
+  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED)
 
   def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE)
 
-  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED, getConf(TUNGSTEN_ENABLED))
+  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED)
 
   private[spark] def useSqlAggregate2: Boolean = getConf(USE_SQL_AGGREGATE2)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 3fff79cd1b281..2f29067f5646a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -55,18 +55,12 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
   protected def sparkContext = sqlContext.sparkContext
 
   // sqlContext will be null when we are being deserialized on the slaves.  In this instance
-  // the value of codegenEnabled/unsafeEnabled will be set by the desserializer after the
-  // constructor has run.
+  // the value of codegenEnabled will be set by the desserializer after the constructor has run.
   val codegenEnabled: Boolean = if (sqlContext != null) {
     sqlContext.conf.codegenEnabled
   } else {
     false
   }
-  val unsafeEnabled: Boolean = if (sqlContext != null) {
-    sqlContext.conf.unsafeEnabled
-  } else {
-    false
-  }
 
   /**
    * Whether the "prepare" method is called.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 22d46d1c3e3b7..5e9cd9fd2345a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -44,8 +44,7 @@ trait HashJoin {
   override def output: Seq[Attribute] = left.output ++ right.output
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && self.unsafeEnabled
-      && UnsafeProjection.canSupport(buildKeys)
+    (self.codegenEnabled && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 701bd3cd86372..346337e64245c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -67,7 +67,7 @@ trait HashOuterJoin {
   }
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && self.unsafeEnabled && joinType != FullOuter
+    (self.codegenEnabled && joinType != FullOuter
       && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 82dd6eb7e7ed0..47a7d370f5415 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -33,8 +33,7 @@ trait HashSemiJoin {
   override def output: Seq[Attribute] = left.output
 
   protected[this] def supportUnsafe: Boolean = {
-    (self.codegenEnabled && self.unsafeEnabled
-      && UnsafeProjection.canSupport(leftKeys)
+    (self.codegenEnabled && UnsafeProjection.canSupport(leftKeys)
       && UnsafeProjection.canSupport(rightKeys)
       && UnsafeProjection.canSupport(left.schema)
       && UnsafeProjection.canSupport(right.schema))

From b87825310ac87485672868bf6a9ed01d154a3626 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 6 Aug 2015 18:25:38 -0700
Subject: [PATCH 105/633] [SPARK-9692] Remove SqlNewHadoopRDD's generated
 Tuple2 and InterruptibleIterator.
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

A small performance optimization – we don't need to generate a Tuple2 and then immediately discard the key. We also don't need an extra wrapper from InterruptibleIterator.

Author: Reynold Xin <rxin@databricks.com>

Closes #8000 from rxin/SPARK-9692 and squashes the following commits:

1d4d0b3 [Reynold Xin] [SPARK-9692] Remove SqlNewHadoopRDD's generated Tuple2 and InterruptibleIterator.
---
 .../apache/spark/rdd/SqlNewHadoopRDD.scala    | 44 +++++++------------
 .../spark/sql/parquet/ParquetRelation.scala   |  3 +-
 2 files changed, 18 insertions(+), 29 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
index 35e44cb59c1be..6a95e44c57fec 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
@@ -26,14 +26,12 @@ import org.apache.hadoop.conf.{Configurable, Configuration}
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.{CombineFileSplit, FileSplit}
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.executor.DataReadMethod
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.{Partition => SparkPartition, _}
-import org.apache.spark.rdd.NewHadoopRDD.NewHadoopMapPartitionsWithSplitRDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.{SerializableConfiguration, Utils}
 
@@ -60,18 +58,16 @@ private[spark] class SqlNewHadoopPartition(
  *    and the executor side to the shared Hadoop Configuration.
  *
  * Note: This is RDD is basically a cloned version of [[org.apache.spark.rdd.NewHadoopRDD]] with
- * changes based on [[org.apache.spark.rdd.HadoopRDD]]. In future, this functionality will be
- * folded into core.
+ * changes based on [[org.apache.spark.rdd.HadoopRDD]].
  */
-private[spark] class SqlNewHadoopRDD[K, V](
+private[spark] class SqlNewHadoopRDD[V: ClassTag](
     @transient sc : SparkContext,
     broadcastedConf: Broadcast[SerializableConfiguration],
     @transient initDriverSideJobFuncOpt: Option[Job => Unit],
     initLocalJobFuncOpt: Option[Job => Unit],
-    inputFormatClass: Class[_ <: InputFormat[K, V]],
-    keyClass: Class[K],
+    inputFormatClass: Class[_ <: InputFormat[Void, V]],
     valueClass: Class[V])
-  extends RDD[(K, V)](sc, Nil)
+  extends RDD[V](sc, Nil)
   with SparkHadoopMapReduceUtil
   with Logging {
 
@@ -120,8 +116,8 @@ private[spark] class SqlNewHadoopRDD[K, V](
 
   override def compute(
       theSplit: SparkPartition,
-      context: TaskContext): InterruptibleIterator[(K, V)] = {
-    val iter = new Iterator[(K, V)] {
+      context: TaskContext): Iterator[V] = {
+    val iter = new Iterator[V] {
       val split = theSplit.asInstanceOf[SqlNewHadoopPartition]
       logInfo("Input split: " + split.serializableHadoopSplit)
       val conf = getConf(isDriverSide = false)
@@ -154,17 +150,20 @@ private[spark] class SqlNewHadoopRDD[K, V](
           configurable.setConf(conf)
         case _ =>
       }
-      private var reader = format.createRecordReader(
+      private[this] var reader = format.createRecordReader(
         split.serializableHadoopSplit.value, hadoopAttemptContext)
       reader.initialize(split.serializableHadoopSplit.value, hadoopAttemptContext)
 
       // Register an on-task-completion callback to close the input stream.
       context.addTaskCompletionListener(context => close())
-      var havePair = false
-      var finished = false
-      var recordsSinceMetricsUpdate = 0
+
+      private[this] var havePair = false
+      private[this] var finished = false
 
       override def hasNext: Boolean = {
+        if (context.isInterrupted) {
+          throw new TaskKilledException
+        }
         if (!finished && !havePair) {
           finished = !reader.nextKeyValue
           if (finished) {
@@ -178,7 +177,7 @@ private[spark] class SqlNewHadoopRDD[K, V](
         !finished
       }
 
-      override def next(): (K, V) = {
+      override def next(): V = {
         if (!hasNext) {
           throw new java.util.NoSuchElementException("End of stream")
         }
@@ -186,7 +185,7 @@ private[spark] class SqlNewHadoopRDD[K, V](
         if (!finished) {
           inputMetrics.incRecordsRead(1)
         }
-        (reader.getCurrentKey, reader.getCurrentValue)
+        reader.getCurrentValue
       }
 
       private def close() {
@@ -212,23 +211,14 @@ private[spark] class SqlNewHadoopRDD[K, V](
             }
           }
         } catch {
-          case e: Exception => {
+          case e: Exception =>
             if (!Utils.inShutdown()) {
               logWarning("Exception in RecordReader.close()", e)
             }
-          }
         }
       }
     }
-    new InterruptibleIterator(context, iter)
-  }
-
-  /** Maps over a partition, providing the InputSplit that was used as the base of the partition. */
-  @DeveloperApi
-  def mapPartitionsWithInputSplit[U: ClassTag](
-      f: (InputSplit, Iterator[(K, V)]) => Iterator[U],
-      preservesPartitioning: Boolean = false): RDD[U] = {
-    new NewHadoopMapPartitionsWithSplitRDD(this, f, preservesPartitioning)
+    iter
   }
 
   override def getPreferredLocations(hsplit: SparkPartition): Seq[String] = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index b4337a48dbd80..29c388c22ef93 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -291,7 +291,6 @@ private[sql] class ParquetRelation(
         initDriverSideJobFuncOpt = Some(setInputPaths),
         initLocalJobFuncOpt = Some(initLocalJobFuncOpt),
         inputFormatClass = classOf[ParquetInputFormat[InternalRow]],
-        keyClass = classOf[Void],
         valueClass = classOf[InternalRow]) {
 
         val cacheMetadata = useMetadataCache
@@ -328,7 +327,7 @@ private[sql] class ParquetRelation(
             new SqlNewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
           }
         }
-      }.values.asInstanceOf[RDD[Row]]  // type erasure hack to pass RDD[InternalRow] as RDD[Row]
+      }.asInstanceOf[RDD[Row]]  // type erasure hack to pass RDD[InternalRow] as RDD[Row]
     }
   }
 

From 014a9f9d8c9521180f7a448cc7cc96cc00537d5c Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 6 Aug 2015 19:04:57 -0700
Subject: [PATCH 106/633] [SPARK-9709] [SQL] Avoid starving unsafe operators
 that use sort

The issue is that a task may run multiple sorts, and the sorts run by the child operator (i.e. parent RDD) may acquire all available memory such that other sorts in the same task do not have enough to proceed. This manifests itself in an `IOException("Unable to acquire X bytes of memory")` thrown by `UnsafeExternalSorter`.

The solution is to reserve a page in each sorter in the chain before computing the child operator's (parent RDD's) partitions. This requires us to use a new special RDD that does some preparation before computing the parent's partitions.

Author: Andrew Or <andrew@databricks.com>

Closes #8011 from andrewor14/unsafe-starve-memory and squashes the following commits:

35b69a4 [Andrew Or] Simplify test
0b07782 [Andrew Or] Minor: update comments
5d5afdf [Andrew Or] Merge branch 'master' of github.com:apache/spark into unsafe-starve-memory
254032e [Andrew Or] Add tests
234acbd [Andrew Or] Reserve a page in sorter when preparing each partition
b889e08 [Andrew Or] MapPartitionsWithPreparationRDD
---
 .../unsafe/sort/UnsafeExternalSorter.java     | 43 ++++++++-----
 .../apache/spark/rdd/MapPartitionsRDD.scala   |  3 +
 .../rdd/MapPartitionsWithPreparationRDD.scala | 49 +++++++++++++++
 .../spark/shuffle/ShuffleMemoryManager.scala  |  2 +-
 .../sort/UnsafeExternalSorterSuite.java       | 19 +++++-
 ...MapPartitionsWithPreparationRDDSuite.scala | 60 +++++++++++++++++++
 .../spark/sql/execution/SparkPlan.scala       |  2 +-
 .../org/apache/spark/sql/execution/sort.scala | 28 +++++++--
 8 files changed, 184 insertions(+), 22 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
 create mode 100644 core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala

diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index 8f78fc5a41629..4c54ba4bce408 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -138,6 +138,11 @@ private UnsafeExternalSorter(
       this.inMemSorter = existingInMemorySorter;
     }
 
+    // Acquire a new page as soon as we construct the sorter to ensure that we have at
+    // least one page to work with. Otherwise, other operators in the same task may starve
+    // this sorter (SPARK-9709).
+    acquireNewPage();
+
     // Register a cleanup task with TaskContext to ensure that memory is guaranteed to be freed at
     // the end of the task. This is necessary to avoid memory leaks in when the downstream operator
     // does not fully consume the sorter's output (e.g. sort followed by limit).
@@ -343,22 +348,32 @@ private void acquireNewPageIfNecessary(int requiredSpace) throws IOException {
         throw new IOException("Required space " + requiredSpace + " is greater than page size (" +
           pageSizeBytes + ")");
       } else {
-        final long memoryAcquired = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
-        if (memoryAcquired < pageSizeBytes) {
-          shuffleMemoryManager.release(memoryAcquired);
-          spill();
-          final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
-          if (memoryAcquiredAfterSpilling != pageSizeBytes) {
-            shuffleMemoryManager.release(memoryAcquiredAfterSpilling);
-            throw new IOException("Unable to acquire " + pageSizeBytes + " bytes of memory");
-          }
-        }
-        currentPage = taskMemoryManager.allocatePage(pageSizeBytes);
-        currentPagePosition = currentPage.getBaseOffset();
-        freeSpaceInCurrentPage = pageSizeBytes;
-        allocatedPages.add(currentPage);
+        acquireNewPage();
+      }
+    }
+  }
+
+  /**
+   * Acquire a new page from the {@link ShuffleMemoryManager}.
+   *
+   * If there is not enough space to allocate the new page, spill all existing ones
+   * and try again. If there is still not enough space, report error to the caller.
+   */
+  private void acquireNewPage() throws IOException {
+    final long memoryAcquired = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
+    if (memoryAcquired < pageSizeBytes) {
+      shuffleMemoryManager.release(memoryAcquired);
+      spill();
+      final long memoryAcquiredAfterSpilling = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
+      if (memoryAcquiredAfterSpilling != pageSizeBytes) {
+        shuffleMemoryManager.release(memoryAcquiredAfterSpilling);
+        throw new IOException("Unable to acquire " + pageSizeBytes + " bytes of memory");
       }
     }
+    currentPage = taskMemoryManager.allocatePage(pageSizeBytes);
+    currentPagePosition = currentPage.getBaseOffset();
+    freeSpaceInCurrentPage = pageSizeBytes;
+    allocatedPages.add(currentPage);
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala
index a838aac6e8d1a..4312d3a417759 100644
--- a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsRDD.scala
@@ -21,6 +21,9 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.{Partition, TaskContext}
 
+/**
+ * An RDD that applies the provided function to every partition of the parent RDD.
+ */
 private[spark] class MapPartitionsRDD[U: ClassTag, T: ClassTag](
     prev: RDD[T],
     f: (TaskContext, Int, Iterator[T]) => Iterator[U],  // (TaskContext, partition index, iterator)
diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
new file mode 100644
index 0000000000000..b475bd8d79f85
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
@@ -0,0 +1,49 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.{Partition, Partitioner, TaskContext}
+
+/**
+ * An RDD that applies a user provided function to every partition of the parent RDD, and
+ * additionally allows the user to prepare each partition before computing the parent partition.
+ */
+private[spark] class MapPartitionsWithPreparationRDD[U: ClassTag, T: ClassTag, M: ClassTag](
+    prev: RDD[T],
+    preparePartition: () => M,
+    executePartition: (TaskContext, Int, M, Iterator[T]) => Iterator[U],
+    preservesPartitioning: Boolean = false)
+  extends RDD[U](prev) {
+
+  override val partitioner: Option[Partitioner] = {
+    if (preservesPartitioning) firstParent[T].partitioner else None
+  }
+
+  override def getPartitions: Array[Partition] = firstParent[T].partitions
+
+  /**
+   * Prepare a partition before computing it from its parent.
+   */
+  override def compute(partition: Partition, context: TaskContext): Iterator[U] = {
+    val preparedArgument = preparePartition()
+    val parentIterator = firstParent[T].iterator(partition, context)
+    executePartition(context, partition.index, preparedArgument, parentIterator)
+  }
+}
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
index 00c1e078a441c..e3d229cc99821 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
@@ -124,7 +124,7 @@ private[spark] class ShuffleMemoryManager(maxMemory: Long) extends Logging {
   }
 }
 
-private object ShuffleMemoryManager {
+private[spark] object ShuffleMemoryManager {
   /**
    * Figure out the shuffle memory limit from a SparkConf. We currently have both a fraction
    * of the memory pool and a safety factor since collections can sometimes grow bigger than
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index 117745f9a9c00..f5300373d87ea 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -340,7 +340,8 @@ public void testPeakMemoryUsed() throws Exception {
       for (int i = 0; i < numRecordsPerPage * 10; i++) {
         insertNumber(sorter, i);
         newPeakMemory = sorter.getPeakMemoryUsedBytes();
-        if (i % numRecordsPerPage == 0) {
+        // The first page is pre-allocated on instantiation
+        if (i % numRecordsPerPage == 0 && i > 0) {
           // We allocated a new page for this record, so peak memory should change
           assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
         } else {
@@ -364,5 +365,21 @@ public void testPeakMemoryUsed() throws Exception {
     }
   }
 
+  @Test
+  public void testReservePageOnInstantiation() throws Exception {
+    final UnsafeExternalSorter sorter = newSorter();
+    try {
+      assertEquals(1, sorter.getNumberOfAllocatedPages());
+      // Inserting a new record doesn't allocate more memory since we already have a page
+      long peakMemory = sorter.getPeakMemoryUsedBytes();
+      insertNumber(sorter, 100);
+      assertEquals(peakMemory, sorter.getPeakMemoryUsedBytes());
+      assertEquals(1, sorter.getNumberOfAllocatedPages());
+    } finally {
+      sorter.cleanupResources();
+      assertSpillFilesWereCleanedUp();
+    }
+  }
+
 }
 
diff --git a/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala
new file mode 100644
index 0000000000000..c16930e7d6491
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import scala.collection.mutable
+
+import org.apache.spark.{LocalSparkContext, SparkContext, SparkFunSuite, TaskContext}
+
+class MapPartitionsWithPreparationRDDSuite extends SparkFunSuite with LocalSparkContext {
+
+  test("prepare called before parent partition is computed") {
+    sc = new SparkContext("local", "test")
+
+    // Have the parent partition push a number to the list
+    val parent = sc.parallelize(1 to 100, 1).mapPartitions { iter =>
+      TestObject.things.append(20)
+      iter
+    }
+
+    // Push a different number during the prepare phase
+    val preparePartition = () => { TestObject.things.append(10) }
+
+    // Push yet another number during the execution phase
+    val executePartition = (
+        taskContext: TaskContext,
+        partitionIndex: Int,
+        notUsed: Unit,
+        parentIterator: Iterator[Int]) => {
+      TestObject.things.append(30)
+      TestObject.things.iterator
+    }
+
+    // Verify that the numbers are pushed in the order expected
+    val result = {
+      new MapPartitionsWithPreparationRDD[Int, Int, Unit](
+        parent, preparePartition, executePartition).collect()
+    }
+    assert(result === Array(10, 20, 30))
+  }
+
+}
+
+private object TestObject {
+  val things = new mutable.ListBuffer[Int]
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 2f29067f5646a..490428965a61d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -158,7 +158,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
    */
   final def prepare(): Unit = {
     if (prepareCalled.compareAndSet(false, true)) {
-      doPrepare
+      doPrepare()
       children.foreach(_.prepare())
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index 3192b6ebe9075..7f69cdb08aa78 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.{InternalAccumulator, TaskContext}
-import org.apache.spark.rdd.RDD
+import org.apache.spark.rdd.{MapPartitionsWithPreparationRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
@@ -123,7 +123,12 @@ case class TungstenSort(
     val schema = child.schema
     val childOutput = child.output
     val pageSize = sparkContext.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
-    child.execute().mapPartitions({ iter =>
+
+    /**
+     * Set up the sorter in each partition before computing the parent partition.
+     * This makes sure our sorter is not starved by other sorters used in the same task.
+     */
+    def preparePartition(): UnsafeExternalRowSorter = {
       val ordering = newOrdering(sortOrder, childOutput)
 
       // The comparator for comparing prefix
@@ -143,12 +148,25 @@ case class TungstenSort(
       if (testSpillFrequency > 0) {
         sorter.setTestSpillFrequency(testSpillFrequency)
       }
-      val sortedIterator = sorter.sort(iter.asInstanceOf[Iterator[UnsafeRow]])
-      val taskContext = TaskContext.get()
+      sorter
+    }
+
+    /** Compute a partition using the sorter already set up previously. */
+    def executePartition(
+        taskContext: TaskContext,
+        partitionIndex: Int,
+        sorter: UnsafeExternalRowSorter,
+        parentIterator: Iterator[InternalRow]): Iterator[InternalRow] = {
+      val sortedIterator = sorter.sort(parentIterator.asInstanceOf[Iterator[UnsafeRow]])
       taskContext.internalMetricsToAccumulators(
         InternalAccumulator.PEAK_EXECUTION_MEMORY).add(sorter.getPeakMemoryUsage)
       sortedIterator
-    }, preservesPartitioning = true)
+    }
+
+    // Note: we need to set up the external sorter in each partition before computing
+    // the parent partition, so we cannot simply use `mapPartitions` here (SPARK-9709).
+    new MapPartitionsWithPreparationRDD[InternalRow, InternalRow, UnsafeExternalRowSorter](
+      child.execute(), preparePartition, executePartition, preservesPartitioning = true)
   }
 
 }

From 17284db314f52bdb2065482b8a49656f7683d30a Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 6 Aug 2015 17:30:31 -0700
Subject: [PATCH 107/633] [SPARK-9228] [SQL] use tungsten.enabled in public for
 both of codegen/unsafe

spark.sql.tungsten.enabled will be the default value for both codegen and unsafe, they are kept internally for debug/testing.

cc marmbrus rxin

Author: Davies Liu <davies@databricks.com>

Closes #7998 from davies/tungsten and squashes the following commits:

c1c16da [Davies Liu] update doc
1a47be1 [Davies Liu] use tungsten.enabled for both of codegen/unsafe

(cherry picked from commit 4e70e8256ce2f45b438642372329eac7b1e9e8cf)
Signed-off-by: Reynold Xin <rxin@databricks.com>
---
 docs/sql-programming-guide.md                 |  6 +++---
 .../scala/org/apache/spark/sql/SQLConf.scala  | 20 ++++++++++++-------
 .../spark/sql/execution/SparkPlan.scala       |  8 +++++++-
 .../spark/sql/execution/joins/HashJoin.scala  |  3 ++-
 .../sql/execution/joins/HashOuterJoin.scala   |  2 +-
 .../sql/execution/joins/HashSemiJoin.scala    |  3 ++-
 6 files changed, 28 insertions(+), 14 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 3ea77e82422fb..6c317175d3278 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1884,11 +1884,11 @@ that these options will be deprecated in future release as more optimizations ar
     </td>
   </tr>
   <tr>
-    <td><code>spark.sql.codegen</code></td>
+    <td><code>spark.sql.tungsten.enabled</code></td>
     <td>true</td>
     <td>
-      When true, code will be dynamically generated at runtime for expression evaluation in a specific
-      query. For some queries with complicated expression this option can lead to significant speed-ups.
+      When true, use the optimized Tungsten physical execution backend which explicitly manages memory
+      and dynamically generates bytecode for expression evaluation.
     </td>
   </tr>
   <tr>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index f836122b3e0e4..ef35c133d9cc3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -223,14 +223,21 @@ private[spark] object SQLConf {
     defaultValue = Some(200),
     doc = "The default number of partitions to use when shuffling data for joins or aggregations.")
 
-  val CODEGEN_ENABLED = booleanConf("spark.sql.codegen",
+  val TUNGSTEN_ENABLED = booleanConf("spark.sql.tungsten.enabled",
     defaultValue = Some(true),
+    doc = "When true, use the optimized Tungsten physical execution backend which explicitly " +
+          "manages memory and dynamically generates bytecode for expression evaluation.")
+
+  val CODEGEN_ENABLED = booleanConf("spark.sql.codegen",
+    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
     doc = "When true, code will be dynamically generated at runtime for expression evaluation in" +
-      " a specific query.")
+      " a specific query.",
+    isPublic = false)
 
   val UNSAFE_ENABLED = booleanConf("spark.sql.unsafe.enabled",
-    defaultValue = Some(true),
-    doc = "When true, use the new optimized Tungsten physical execution backend.")
+    defaultValue = Some(true),  // use TUNGSTEN_ENABLED as default
+    doc = "When true, use the new optimized Tungsten physical execution backend.",
+    isPublic = false)
 
   val DIALECT = stringConf(
     "spark.sql.dialect",
@@ -427,7 +434,6 @@ private[spark] object SQLConf {
  *
  * SQLConf is thread-safe (internally synchronized, so safe to be used in multiple threads).
  */
-
 private[sql] class SQLConf extends Serializable with CatalystConf {
   import SQLConf._
 
@@ -474,11 +480,11 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def sortMergeJoinEnabled: Boolean = getConf(SORTMERGE_JOIN)
 
-  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED)
+  private[spark] def codegenEnabled: Boolean = getConf(CODEGEN_ENABLED, getConf(TUNGSTEN_ENABLED))
 
   def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE)
 
-  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED)
+  private[spark] def unsafeEnabled: Boolean = getConf(UNSAFE_ENABLED, getConf(TUNGSTEN_ENABLED))
 
   private[spark] def useSqlAggregate2: Boolean = getConf(USE_SQL_AGGREGATE2)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 490428965a61d..719ad432e2fe0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -55,12 +55,18 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
   protected def sparkContext = sqlContext.sparkContext
 
   // sqlContext will be null when we are being deserialized on the slaves.  In this instance
-  // the value of codegenEnabled will be set by the desserializer after the constructor has run.
+  // the value of codegenEnabled/unsafeEnabled will be set by the desserializer after the
+  // constructor has run.
   val codegenEnabled: Boolean = if (sqlContext != null) {
     sqlContext.conf.codegenEnabled
   } else {
     false
   }
+  val unsafeEnabled: Boolean = if (sqlContext != null) {
+    sqlContext.conf.unsafeEnabled
+  } else {
+    false
+  }
 
   /**
    * Whether the "prepare" method is called.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 5e9cd9fd2345a..22d46d1c3e3b7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -44,7 +44,8 @@ trait HashJoin {
   override def output: Seq[Attribute] = left.output ++ right.output
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && UnsafeProjection.canSupport(buildKeys)
+    (self.codegenEnabled && self.unsafeEnabled
+      && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 346337e64245c..701bd3cd86372 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -67,7 +67,7 @@ trait HashOuterJoin {
   }
 
   protected[this] def isUnsafeMode: Boolean = {
-    (self.codegenEnabled && joinType != FullOuter
+    (self.codegenEnabled && self.unsafeEnabled && joinType != FullOuter
       && UnsafeProjection.canSupport(buildKeys)
       && UnsafeProjection.canSupport(self.schema))
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 47a7d370f5415..82dd6eb7e7ed0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -33,7 +33,8 @@ trait HashSemiJoin {
   override def output: Seq[Attribute] = left.output
 
   protected[this] def supportUnsafe: Boolean = {
-    (self.codegenEnabled && UnsafeProjection.canSupport(leftKeys)
+    (self.codegenEnabled && self.unsafeEnabled
+      && UnsafeProjection.canSupport(leftKeys)
       && UnsafeProjection.canSupport(rightKeys)
       && UnsafeProjection.canSupport(left.schema)
       && UnsafeProjection.canSupport(right.schema))

From fe12277b40082585e40e1bdf6aa2ebcfe80ed83f Mon Sep 17 00:00:00 2001
From: Jeff Zhang <zjffdu@apache.org>
Date: Thu, 6 Aug 2015 21:03:47 -0700
Subject: [PATCH 108/633] Fix doc typo

Straightforward fix on doc typo

Author: Jeff Zhang <zjffdu@apache.org>

Closes #8019 from zjffdu/master and squashes the following commits:

aed6e64 [Jeff Zhang] Fix doc typo
---
 docs/tuning.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/tuning.md b/docs/tuning.md
index 572c7270e4999..6936912a6be54 100644
--- a/docs/tuning.md
+++ b/docs/tuning.md
@@ -240,7 +240,7 @@ worth optimizing.
 ## Data Locality
 
 Data locality can have a major impact on the performance of Spark jobs.  If data and the code that
-operates on it are together than computation tends to be fast.  But if code and data are separated,
+operates on it are together then computation tends to be fast.  But if code and data are separated,
 one must move to the other.  Typically it is faster to ship serialized code from place to place than
 a chunk of data because code size is much smaller than data.  Spark builds its scheduling around
 this general principle of data locality.

From 672f467668da1cf20895ee57652489c306120288 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 6 Aug 2015 21:42:42 -0700
Subject: [PATCH 109/633] [SPARK-8057][Core]Call
 TaskAttemptContext.getTaskAttemptID using Reflection

Someone may use the Spark core jar in the maven repo with hadoop 1. SPARK-2075 has already resolved the compatibility issue to support it. But `SparkHadoopMapRedUtil.commitTask` broke it recently.

This PR uses Reflection to call `TaskAttemptContext.getTaskAttemptID` to fix the compatibility issue.

Author: zsxwing <zsxwing@gmail.com>

Closes #6599 from zsxwing/SPARK-8057 and squashes the following commits:

f7a343c [zsxwing] Remove the redundant import
6b7f1af [zsxwing] Call TaskAttemptContext.getTaskAttemptID using Reflection
---
 .../org/apache/spark/deploy/SparkHadoopUtil.scala  | 14 ++++++++++++++
 .../spark/mapred/SparkHadoopMapRedUtil.scala       |  3 ++-
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index e06b06e06fb4a..7e9dba42bebd8 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -34,6 +34,8 @@ import org.apache.hadoop.fs.{FileStatus, FileSystem, Path, PathFilter}
 import org.apache.hadoop.hdfs.security.token.delegation.DelegationTokenIdentifier
 import org.apache.hadoop.mapred.JobConf
 import org.apache.hadoop.mapreduce.JobContext
+import org.apache.hadoop.mapreduce.{TaskAttemptContext => MapReduceTaskAttemptContext}
+import org.apache.hadoop.mapreduce.{TaskAttemptID => MapReduceTaskAttemptID}
 import org.apache.hadoop.security.{Credentials, UserGroupInformation}
 
 import org.apache.spark.annotation.DeveloperApi
@@ -194,6 +196,18 @@ class SparkHadoopUtil extends Logging {
     method.invoke(context).asInstanceOf[Configuration]
   }
 
+  /**
+   * Using reflection to call `getTaskAttemptID` from TaskAttemptContext. If we directly
+   * call `TaskAttemptContext.getTaskAttemptID`, it will generate different byte codes
+   * for Hadoop 1.+ and Hadoop 2.+ because TaskAttemptContext is class in Hadoop 1.+
+   * while it's interface in Hadoop 2.+.
+   */
+  def getTaskAttemptIDFromTaskAttemptContext(
+      context: MapReduceTaskAttemptContext): MapReduceTaskAttemptID = {
+    val method = context.getClass.getMethod("getTaskAttemptID")
+    method.invoke(context).asInstanceOf[MapReduceTaskAttemptID]
+  }
+
   /**
    * Get [[FileStatus]] objects for all leaf children (files) under the given base path. If the
    * given path points to a file, return a single-element collection containing [[FileStatus]] of
diff --git a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
index 87df42748be44..f405b732e4725 100644
--- a/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
+++ b/core/src/main/scala/org/apache/spark/mapred/SparkHadoopMapRedUtil.scala
@@ -24,6 +24,7 @@ import org.apache.hadoop.mapred._
 import org.apache.hadoop.mapreduce.{TaskAttemptContext => MapReduceTaskAttemptContext}
 import org.apache.hadoop.mapreduce.{OutputCommitter => MapReduceOutputCommitter}
 
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.executor.CommitDeniedException
 import org.apache.spark.{Logging, SparkEnv, TaskContext}
 import org.apache.spark.util.{Utils => SparkUtils}
@@ -93,7 +94,7 @@ object SparkHadoopMapRedUtil extends Logging {
       splitId: Int,
       attemptId: Int): Unit = {
 
-    val mrTaskAttemptID = mrTaskContext.getTaskAttemptID
+    val mrTaskAttemptID = SparkHadoopUtil.get.getTaskAttemptIDFromTaskAttemptContext(mrTaskContext)
 
     // Called after we have decided to commit
     def performCommit(): Unit = {

From f0cda587fb80bf2f1ba53d35dc9dc87bf72ee338 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 6 Aug 2015 22:49:01 -0700
Subject: [PATCH 110/633] [SPARK-7550] [SQL] [MINOR] Fixes logs when persisting
 DataFrames

Author: Cheng Lian <lian@databricks.com>

Closes #8021 from liancheng/spark-7550/fix-logs and squashes the following commits:

b7bd0ed [Cheng Lian] Fixes logs
---
 .../org/apache/spark/sql/hive/HiveMetastoreCatalog.scala    | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 1523ebe9d5493..7198a32df4a02 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -317,19 +317,17 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
 
       case (Some(serde), relation: HadoopFsRelation) if relation.partitionColumns.nonEmpty =>
         logWarning {
-          val paths = relation.paths.mkString(", ")
           "Persisting partitioned data source relation into Hive metastore in " +
             s"Spark SQL specific format, which is NOT compatible with Hive.  Input path(s): " +
-            paths.mkString("\n", "\n", "")
+            relation.paths.mkString("\n", "\n", "")
         }
         newSparkSQLSpecificMetastoreTable()
 
       case (Some(serde), relation: HadoopFsRelation) =>
         logWarning {
-          val paths = relation.paths.mkString(", ")
           "Persisting data source relation with multiple input paths into Hive metastore in " +
             s"Spark SQL specific format, which is NOT compatible with Hive.  Input paths: " +
-            paths.mkString("\n", "\n", "")
+            relation.paths.mkString("\n", "\n", "")
         }
         newSparkSQLSpecificMetastoreTable()
 

From 7aaed1b114751a24835204b8c588533d5c5ffaf0 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 6 Aug 2015 22:52:23 -0700
Subject: [PATCH 111/633] [SPARK-8862][SQL]Support multiple SQLContexts in Web
 UI

This is a follow-up PR to solve the UI issue when there are multiple SQLContexts. Each SQLContext has a separate tab and contains queries which are executed by this SQLContext.

<img width="1366" alt="multiple sqlcontexts" src="https://cloud.githubusercontent.com/assets/1000778/9088391/54584434-3bc2-11e5-9caf-94c2b0da528e.png">

Author: zsxwing <zsxwing@gmail.com>

Closes #7962 from zsxwing/multi-sqlcontext-ui and squashes the following commits:

cf661e1 [zsxwing] sql -> SQL
39b0c97 [zsxwing] Support multiple SQLContexts in Web UI
---
 .../org/apache/spark/sql/ui/AllExecutionsPage.scala  |  2 +-
 .../main/scala/org/apache/spark/sql/ui/SQLTab.scala  | 12 ++++++++++--
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
index 727fc4b37fa48..cb7ca60b2fe48 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
@@ -178,7 +178,7 @@ private[ui] abstract class ExecutionTable(
     "%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), jobId)
 
   private def executionURL(executionID: Long): String =
-    "%s/sql/execution?id=%s".format(UIUtils.prependBaseUri(parent.basePath), executionID)
+    s"${UIUtils.prependBaseUri(parent.basePath)}/${parent.prefix}/execution?id=$executionID"
 }
 
 private[ui] class RunningExecutionTable(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
index a9e5226303978..3bba0afaf14eb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
@@ -17,13 +17,14 @@
 
 package org.apache.spark.sql.ui
 
+import java.util.concurrent.atomic.AtomicInteger
+
 import org.apache.spark.Logging
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.ui.{SparkUI, SparkUITab}
 
 private[sql] class SQLTab(sqlContext: SQLContext, sparkUI: SparkUI)
-  extends SparkUITab(sparkUI, "sql") with Logging {
-
+  extends SparkUITab(sparkUI, SQLTab.nextTabName) with Logging {
 
   val parent = sparkUI
   val listener = sqlContext.listener
@@ -38,4 +39,11 @@ private[sql] class SQLTab(sqlContext: SQLContext, sparkUI: SparkUI)
 private[sql] object SQLTab {
 
   private val STATIC_RESOURCE_DIR = "org/apache/spark/sql/ui/static"
+
+  private val nextTabId = new AtomicInteger(0)
+
+  private def nextTabName: String = {
+    val nextId = nextTabId.getAndIncrement()
+    if (nextId == 0) "SQL" else s"SQL${nextId}"
+  }
 }

From 4309262ec9146d7158ee9957a128bb152289d557 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 6 Aug 2015 23:18:29 -0700
Subject: [PATCH 112/633] [SPARK-9700] Pick default page size more
 intelligently.

Previously, we use 64MB as the default page size, which was way too big for a lot of Spark applications (especially for single node).

This patch changes it so that the default page size, if unset by the user, is determined by the number of cores available and the total execution memory available.

Author: Reynold Xin <rxin@databricks.com>

Closes #8012 from rxin/pagesize and squashes the following commits:

16f4756 [Reynold Xin] Fixed failing test.
5afd570 [Reynold Xin] private...
0d5fb98 [Reynold Xin] Update default value.
674a6cd [Reynold Xin] Address review feedback.
dc00e05 [Reynold Xin] Merge with master.
73ebdb6 [Reynold Xin] [SPARK-9700] Pick default page size more intelligently.
---
 R/run-tests.sh                                |  2 +-
 .../unsafe/UnsafeShuffleExternalSorter.java   |  3 +-
 .../spark/unsafe/map/BytesToBytesMap.java     |  8 +--
 .../unsafe/sort/UnsafeExternalSorter.java     |  1 -
 .../scala/org/apache/spark/SparkConf.scala    |  7 +++
 .../scala/org/apache/spark/SparkContext.scala |  2 +-
 .../scala/org/apache/spark/SparkEnv.scala     |  2 +-
 .../spark/shuffle/ShuffleMemoryManager.scala  | 53 +++++++++++++++++--
 .../unsafe/UnsafeShuffleWriterSuite.java      |  5 +-
 .../map/AbstractBytesToBytesMapSuite.java     |  6 +--
 .../sort/UnsafeExternalSorterSuite.java       |  4 +-
 .../shuffle/ShuffleMemoryManagerSuite.scala   | 14 ++---
 python/pyspark/java_gateway.py                |  1 -
 .../TungstenAggregationIterator.scala         |  2 +-
 .../sql/execution/joins/HashedRelation.scala  | 16 +++---
 .../org/apache/spark/sql/execution/sort.scala |  4 +-
 ...ypes.scala => ParquetTypesConverter.scala} |  0
 .../execution/TestShuffleMemoryManager.scala  |  2 +-
 .../apache/spark/sql/hive/test/TestHive.scala |  1 -
 .../spark/unsafe/array/ByteArrayMethods.java  |  6 +++
 20 files changed, 93 insertions(+), 46 deletions(-)
 rename sql/core/src/main/scala/org/apache/spark/sql/parquet/{ParquetTypes.scala => ParquetTypesConverter.scala} (100%)

diff --git a/R/run-tests.sh b/R/run-tests.sh
index 18a1e13bdc655..e82ad0ba2cd06 100755
--- a/R/run-tests.sh
+++ b/R/run-tests.sh
@@ -23,7 +23,7 @@ FAILED=0
 LOGFILE=$FWDIR/unit-tests.out
 rm -f $LOGFILE
 
-SPARK_TESTING=1 $FWDIR/../bin/sparkR --conf spark.buffer.pageSize=4m --driver-java-options "-Dlog4j.configuration=file:$FWDIR/log4j.properties" $FWDIR/pkg/tests/run-all.R 2>&1 | tee -a $LOGFILE
+SPARK_TESTING=1 $FWDIR/../bin/sparkR --driver-java-options "-Dlog4j.configuration=file:$FWDIR/log4j.properties" $FWDIR/pkg/tests/run-all.R 2>&1 | tee -a $LOGFILE
 FAILED=$((PIPESTATUS[0]||$FAILED))
 
 if [[ $FAILED != 0 ]]; then
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index bf4eaa59ff589..f6e0913a7a0b3 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -115,8 +115,7 @@ public UnsafeShuffleExternalSorter(
     // Use getSizeAsKb (not bytes) to maintain backwards compatibility if no units are provided
     this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
     this.pageSizeBytes = (int) Math.min(
-      PackedRecordPointer.MAXIMUM_PAGE_SIZE_BYTES,
-      conf.getSizeAsBytes("spark.buffer.pageSize", "64m"));
+      PackedRecordPointer.MAXIMUM_PAGE_SIZE_BYTES, shuffleMemoryManager.pageSizeBytes());
     this.maxRecordSizeBytes = pageSizeBytes - 4;
     this.writeMetrics = writeMetrics;
     initializeForWriting();
diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 5ac3736ac62aa..0636ae7c8df1a 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -642,7 +642,7 @@ public boolean putNewKey(
   private void allocate(int capacity) {
     assert (capacity >= 0);
     // The capacity needs to be divisible by 64 so that our bit set can be sized properly
-    capacity = Math.max((int) Math.min(MAX_CAPACITY, nextPowerOf2(capacity)), 64);
+    capacity = Math.max((int) Math.min(MAX_CAPACITY, ByteArrayMethods.nextPowerOf2(capacity)), 64);
     assert (capacity <= MAX_CAPACITY);
     longArray = new LongArray(MemoryBlock.fromLongArray(new long[capacity * 2]));
     bitset = new BitSet(MemoryBlock.fromLongArray(new long[capacity / 64]));
@@ -770,10 +770,4 @@ void growAndRehash() {
       timeSpentResizingNs += System.nanoTime() - resizeStartTime;
     }
   }
-
-  /** Returns the next number greater or equal num that is power of 2. */
-  private static long nextPowerOf2(long num) {
-    final long highBit = Long.highestOneBit(num);
-    return (highBit == num) ? num : highBit << 1;
-  }
 }
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index 4c54ba4bce408..5ebbf9b068fd6 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -127,7 +127,6 @@ private UnsafeExternalSorter(
     // Use getSizeAsKb (not bytes) to maintain backwards compatibility for units
     // this.fileBufferSizeBytes = (int) conf.getSizeAsKb("spark.shuffle.file.buffer", "32k") * 1024;
     this.fileBufferSizeBytes = 32 * 1024;
-    // this.pageSizeBytes = conf.getSizeAsBytes("spark.buffer.pageSize", "64m");
     this.pageSizeBytes = pageSizeBytes;
     this.writeMetrics = new ShuffleWriteMetrics();
 
diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index 08bab4bf2739f..8ff154fb5e334 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -249,6 +249,13 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
     Utils.byteStringAsBytes(get(key, defaultValue))
   }
 
+  /**
+   * Get a size parameter as bytes, falling back to a default if not set.
+   */
+  def getSizeAsBytes(key: String, defaultValue: Long): Long = {
+    Utils.byteStringAsBytes(get(key, defaultValue + "B"))
+  }
+
   /**
    * Get a size parameter as Kibibytes; throws a NoSuchElementException if it's not set. If no
    * suffix is provided then Kibibytes are assumed.
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 0c0705325b169..5662686436900 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -629,7 +629,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * [[org.apache.spark.SparkContext.setLocalProperty]].
    */
   def getLocalProperty(key: String): String =
-    Option(localProperties.get).map(_.getProperty(key)).getOrElse(null)
+    Option(localProperties.get).map(_.getProperty(key)).orNull
 
   /** Set a human readable description of the current job. */
   def setJobDescription(value: String) {
diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index adfece4d6e7c0..a796e72850191 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -324,7 +324,7 @@ object SparkEnv extends Logging {
     val shuffleMgrClass = shortShuffleMgrNames.getOrElse(shuffleMgrName.toLowerCase, shuffleMgrName)
     val shuffleManager = instantiateClass[ShuffleManager](shuffleMgrClass)
 
-    val shuffleMemoryManager = new ShuffleMemoryManager(conf)
+    val shuffleMemoryManager = ShuffleMemoryManager.create(conf, numUsableCores)
 
     val blockTransferService =
       conf.get("spark.shuffle.blockTransferService", "netty").toLowerCase match {
diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
index e3d229cc99821..8c3a72644c38a 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
@@ -19,6 +19,9 @@ package org.apache.spark.shuffle
 
 import scala.collection.mutable
 
+import com.google.common.annotations.VisibleForTesting
+
+import org.apache.spark.unsafe.array.ByteArrayMethods
 import org.apache.spark.{Logging, SparkException, SparkConf, TaskContext}
 
 /**
@@ -34,11 +37,19 @@ import org.apache.spark.{Logging, SparkException, SparkConf, TaskContext}
  * set of active tasks and redo the calculations of 1 / 2N and 1 / N in waiting tasks whenever
  * this set changes. This is all done by synchronizing access on "this" to mutate state and using
  * wait() and notifyAll() to signal changes.
+ *
+ * Use `ShuffleMemoryManager.create()` factory method to create a new instance.
+ *
+ * @param maxMemory total amount of memory available for execution, in bytes.
+ * @param pageSizeBytes number of bytes for each page, by default.
  */
-private[spark] class ShuffleMemoryManager(maxMemory: Long) extends Logging {
-  private val taskMemory = new mutable.HashMap[Long, Long]()  // taskAttemptId -> memory bytes
+private[spark]
+class ShuffleMemoryManager protected (
+    val maxMemory: Long,
+    val pageSizeBytes: Long)
+  extends Logging {
 
-  def this(conf: SparkConf) = this(ShuffleMemoryManager.getMaxMemory(conf))
+  private val taskMemory = new mutable.HashMap[Long, Long]()  // taskAttemptId -> memory bytes
 
   private def currentTaskAttemptId(): Long = {
     // In case this is called on the driver, return an invalid task attempt id.
@@ -124,15 +135,49 @@ private[spark] class ShuffleMemoryManager(maxMemory: Long) extends Logging {
   }
 }
 
+
 private[spark] object ShuffleMemoryManager {
+
+  def create(conf: SparkConf, numCores: Int): ShuffleMemoryManager = {
+    val maxMemory = ShuffleMemoryManager.getMaxMemory(conf)
+    val pageSize = ShuffleMemoryManager.getPageSize(conf, maxMemory, numCores)
+    new ShuffleMemoryManager(maxMemory, pageSize)
+  }
+
+  def create(maxMemory: Long, pageSizeBytes: Long): ShuffleMemoryManager = {
+    new ShuffleMemoryManager(maxMemory, pageSizeBytes)
+  }
+
+  @VisibleForTesting
+  def createForTesting(maxMemory: Long): ShuffleMemoryManager = {
+    new ShuffleMemoryManager(maxMemory, 4 * 1024 * 1024)
+  }
+
   /**
    * Figure out the shuffle memory limit from a SparkConf. We currently have both a fraction
    * of the memory pool and a safety factor since collections can sometimes grow bigger than
    * the size we target before we estimate their sizes again.
    */
-  def getMaxMemory(conf: SparkConf): Long = {
+  private def getMaxMemory(conf: SparkConf): Long = {
     val memoryFraction = conf.getDouble("spark.shuffle.memoryFraction", 0.2)
     val safetyFraction = conf.getDouble("spark.shuffle.safetyFraction", 0.8)
     (Runtime.getRuntime.maxMemory * memoryFraction * safetyFraction).toLong
   }
+
+  /**
+   * Sets the page size, in bytes.
+   *
+   * If user didn't explicitly set "spark.buffer.pageSize", we figure out the default value
+   * by looking at the number of cores available to the process, and the total amount of memory,
+   * and then divide it by a factor of safety.
+   */
+  private def getPageSize(conf: SparkConf, maxMemory: Long, numCores: Int): Long = {
+    val minPageSize = 1L * 1024 * 1024   // 1MB
+    val maxPageSize = 64L * minPageSize  // 64MB
+    val cores = if (numCores > 0) numCores else Runtime.getRuntime.availableProcessors()
+    val safetyFactor = 8
+    val size = ByteArrayMethods.nextPowerOf2(maxMemory / cores / safetyFactor)
+    val default = math.min(maxPageSize, math.max(minPageSize, size))
+    conf.getSizeAsBytes("spark.buffer.pageSize", default)
+  }
 }
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
index 98c32bbc298d7..c68354ba49a46 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
@@ -115,6 +115,7 @@ public void setUp() throws IOException {
     taskMetrics = new TaskMetrics();
 
     when(shuffleMemoryManager.tryToAcquire(anyLong())).then(returnsFirstArg());
+    when(shuffleMemoryManager.pageSizeBytes()).thenReturn(128L * 1024 * 1024);
 
     when(blockManager.diskBlockManager()).thenReturn(diskBlockManager);
     when(blockManager.getDiskWriter(
@@ -549,14 +550,14 @@ public void testPeakMemoryUsed() throws Exception {
     final long recordLengthBytes = 8;
     final long pageSizeBytes = 256;
     final long numRecordsPerPage = pageSizeBytes / recordLengthBytes;
-    final SparkConf conf = new SparkConf().set("spark.buffer.pageSize", pageSizeBytes + "b");
+    when(shuffleMemoryManager.pageSizeBytes()).thenReturn(pageSizeBytes);
     final UnsafeShuffleWriter<Object, Object> writer =
       new UnsafeShuffleWriter<Object, Object>(
         blockManager,
         shuffleBlockResolver,
         taskMemoryManager,
         shuffleMemoryManager,
-        new UnsafeShuffleHandle<Object, Object>(0, 1, shuffleDep),
+        new UnsafeShuffleHandle<>(0, 1, shuffleDep),
         0, // map id
         taskContext,
         conf);
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 3c5003380162f..0b11562980b8e 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -48,7 +48,7 @@ public abstract class AbstractBytesToBytesMapSuite {
 
   @Before
   public void setup() {
-    shuffleMemoryManager = new ShuffleMemoryManager(Long.MAX_VALUE);
+    shuffleMemoryManager = ShuffleMemoryManager.create(Long.MAX_VALUE, PAGE_SIZE_BYTES);
     taskMemoryManager = new TaskMemoryManager(new ExecutorMemoryManager(getMemoryAllocator()));
     // Mocked memory manager for tests that check the maximum array size, since actually allocating
     // such large arrays will cause us to run out of memory in our tests.
@@ -441,7 +441,7 @@ public void randomizedTestWithRecordsLargerThanPageSize() {
 
   @Test
   public void failureToAllocateFirstPage() {
-    shuffleMemoryManager = new ShuffleMemoryManager(1024);
+    shuffleMemoryManager = ShuffleMemoryManager.createForTesting(1024);
     BytesToBytesMap map =
       new BytesToBytesMap(taskMemoryManager, shuffleMemoryManager, 1, PAGE_SIZE_BYTES);
     try {
@@ -461,7 +461,7 @@ public void failureToAllocateFirstPage() {
 
   @Test
   public void failureToGrow() {
-    shuffleMemoryManager = new ShuffleMemoryManager(1024 * 10);
+    shuffleMemoryManager = ShuffleMemoryManager.createForTesting(1024 * 10);
     BytesToBytesMap map = new BytesToBytesMap(taskMemoryManager, shuffleMemoryManager, 1, 1024);
     try {
       boolean success = true;
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index f5300373d87ea..83049b8a21fcf 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -102,7 +102,7 @@ public void setUp() {
     MockitoAnnotations.initMocks(this);
     sparkConf = new SparkConf();
     tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "unsafe-test");
-    shuffleMemoryManager = new ShuffleMemoryManager(Long.MAX_VALUE);
+    shuffleMemoryManager = ShuffleMemoryManager.create(Long.MAX_VALUE, pageSizeBytes);
     spillFilesCreated.clear();
     taskContext = mock(TaskContext.class);
     when(taskContext.taskMetrics()).thenReturn(new TaskMetrics());
@@ -237,7 +237,7 @@ public void testSortingEmptyArrays() throws Exception {
 
   @Test
   public void spillingOccursInResponseToMemoryPressure() throws Exception {
-    shuffleMemoryManager = new ShuffleMemoryManager(pageSizeBytes * 2);
+    shuffleMemoryManager = ShuffleMemoryManager.create(pageSizeBytes * 2, pageSizeBytes);
     final UnsafeExternalSorter sorter = newSorter();
     final int numRecords = (int) pageSizeBytes / 4;
     for (int i = 0; i <= numRecords; i++) {
diff --git a/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala b/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala
index f495b6a037958..6d45b1a101be6 100644
--- a/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/shuffle/ShuffleMemoryManagerSuite.scala
@@ -24,7 +24,7 @@ import org.mockito.Mockito._
 import org.scalatest.concurrent.Timeouts
 import org.scalatest.time.SpanSugar._
 
-import org.apache.spark.{SparkFunSuite, TaskContext}
+import org.apache.spark.{SparkConf, SparkFunSuite, TaskContext}
 
 class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
 
@@ -50,7 +50,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
   }
 
   test("single task requesting memory") {
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
 
     assert(manager.tryToAcquire(100L) === 100L)
     assert(manager.tryToAcquire(400L) === 400L)
@@ -72,7 +72,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     // Two threads request 500 bytes first, wait for each other to get it, and then request
     // 500 more; we should immediately return 0 as both are now at 1 / N
 
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
 
     class State {
       var t1Result1 = -1L
@@ -124,7 +124,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     // Two tasks request 250 bytes first, wait for each other to get it, and then request
     // 500 more; we should only grant 250 bytes to each of them on this second request
 
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
 
     class State {
       var t1Result1 = -1L
@@ -176,7 +176,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     // for a bit and releases 250 bytes, which should then be granted to t2. Further requests
     // by t2 will return false right away because it now has 1 / 2N of the memory.
 
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
 
     class State {
       var t1Requested = false
@@ -241,7 +241,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
     // t1 grabs 1000 bytes and then waits until t2 is ready to make a request. It sleeps
     // for a bit and releases all its memory. t2 should now be able to grab all the memory.
 
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
 
     class State {
       var t1Requested = false
@@ -307,7 +307,7 @@ class ShuffleMemoryManagerSuite extends SparkFunSuite with Timeouts {
   }
 
   test("tasks should not be granted a negative size") {
-    val manager = new ShuffleMemoryManager(1000L)
+    val manager = ShuffleMemoryManager.createForTesting(maxMemory = 1000L)
     manager.tryToAcquire(700L)
 
     val latch = new CountDownLatch(1)
diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py
index 60be85e53e2aa..cd4c55f79f18c 100644
--- a/python/pyspark/java_gateway.py
+++ b/python/pyspark/java_gateway.py
@@ -54,7 +54,6 @@ def launch_gateway():
         if os.environ.get("SPARK_TESTING"):
             submit_args = ' '.join([
                 "--conf spark.ui.enabled=false",
-                "--conf spark.buffer.pageSize=4mb",
                 submit_args
             ])
         command = [os.path.join(SPARK_HOME, script)] + shlex.split(submit_args)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index b9d44aace1009..4d5e98a3e90c8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -342,7 +342,7 @@ class TungstenAggregationIterator(
     TaskContext.get.taskMemoryManager(),
     SparkEnv.get.shuffleMemoryManager,
     1024 * 16, // initial capacity
-    SparkEnv.get.conf.getSizeAsBytes("spark.buffer.pageSize", "64m"),
+    SparkEnv.get.shuffleMemoryManager.pageSizeBytes,
     false // disable tracking of performance metrics
   )
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 3f257ecdd156c..953abf409f220 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -282,17 +282,15 @@ private[joins] final class UnsafeHashedRelation(
     // This is used in Broadcast, shared by multiple tasks, so we use on-heap memory
     val taskMemoryManager = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
 
+    val pageSizeBytes = Option(SparkEnv.get).map(_.shuffleMemoryManager.pageSizeBytes)
+      .getOrElse(new SparkConf().getSizeAsBytes("spark.buffer.pageSize", "16m"))
+
     // Dummy shuffle memory manager which always grants all memory allocation requests.
     // We use this because it doesn't make sense count shared broadcast variables' memory usage
     // towards individual tasks' quotas. In the future, we should devise a better way of handling
     // this.
-    val shuffleMemoryManager = new ShuffleMemoryManager(new SparkConf()) {
-      override def tryToAcquire(numBytes: Long): Long = numBytes
-      override def release(numBytes: Long): Unit = {}
-    }
-
-    val pageSizeBytes = Option(SparkEnv.get).map(_.conf).getOrElse(new SparkConf())
-      .getSizeAsBytes("spark.buffer.pageSize", "64m")
+    val shuffleMemoryManager =
+      ShuffleMemoryManager.create(maxMemory = Long.MaxValue, pageSizeBytes = pageSizeBytes)
 
     binaryMap = new BytesToBytesMap(
       taskMemoryManager,
@@ -306,11 +304,11 @@ private[joins] final class UnsafeHashedRelation(
     while (i < nKeys) {
       val keySize = in.readInt()
       val valuesSize = in.readInt()
-      if (keySize > keyBuffer.size) {
+      if (keySize > keyBuffer.length) {
         keyBuffer = new Array[Byte](keySize)
       }
       in.readFully(keyBuffer, 0, keySize)
-      if (valuesSize > valuesBuffer.size) {
+      if (valuesSize > valuesBuffer.length) {
         valuesBuffer = new Array[Byte](valuesSize)
       }
       in.readFully(valuesBuffer, 0, valuesSize)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index 7f69cdb08aa78..e316930470127 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.{InternalAccumulator, TaskContext}
+import org.apache.spark.{SparkEnv, InternalAccumulator, TaskContext}
 import org.apache.spark.rdd.{MapPartitionsWithPreparationRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
@@ -122,7 +122,7 @@ case class TungstenSort(
   protected override def doExecute(): RDD[InternalRow] = {
     val schema = child.schema
     val childOutput = child.output
-    val pageSize = sparkContext.conf.getSizeAsBytes("spark.buffer.pageSize", "64m")
+    val pageSize = SparkEnv.get.shuffleMemoryManager.pageSizeBytes
 
     /**
      * Set up the sorter in each partition before computing the parent partition.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypesConverter.scala
similarity index 100%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypes.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypesConverter.scala
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala
index 53de2d0f0771f..48c3938ff87ba 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TestShuffleMemoryManager.scala
@@ -22,7 +22,7 @@ import org.apache.spark.shuffle.ShuffleMemoryManager
 /**
  * A [[ShuffleMemoryManager]] that can be controlled to run out of memory.
  */
-class TestShuffleMemoryManager extends ShuffleMemoryManager(Long.MaxValue) {
+class TestShuffleMemoryManager extends ShuffleMemoryManager(Long.MaxValue, 4 * 1024 * 1024) {
   private var oom = false
 
   override def tryToAcquire(numBytes: Long): Long = {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 167086db5bfe2..296cc5c5e0b04 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -52,7 +52,6 @@ object TestHive
         .set("spark.sql.test", "")
         .set("spark.sql.hive.metastore.barrierPrefixes",
           "org.apache.spark.sql.hive.execution.PairSerDe")
-        .set("spark.buffer.pageSize", "4m")
         // SPARK-8910
         .set("spark.ui.enabled", "false")))
 
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
index cf693d01a4f5b..70b81ce015ddc 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
@@ -25,6 +25,12 @@ private ByteArrayMethods() {
     // Private constructor, since this class only contains static methods.
   }
 
+  /** Returns the next number greater or equal num that is power of 2. */
+  public static long nextPowerOf2(long num) {
+    final long highBit = Long.highestOneBit(num);
+    return (highBit == num) ? num : highBit << 1;
+  }
+
   public static int roundNumberOfBytesToNearestWord(int numBytes) {
     int remainder = numBytes & 0x07;  // This is equivalent to `numBytes % 8`
     if (remainder == 0) {

From 15bd6f338dff4bcab4a1a3a2c568655022e49c32 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 6 Aug 2015 23:40:38 -0700
Subject: [PATCH 113/633] [SPARK-9453] [SQL] support records larger than page
 size in UnsafeShuffleExternalSorter

This patch follows exactly #7891 (except testing)

Author: Davies Liu <davies@databricks.com>

Closes #8005 from davies/larger_record and squashes the following commits:

f9c4aff [Davies Liu] address comments
9de5c72 [Davies Liu] support records larger than page size in UnsafeShuffleExternalSorter
---
 .../unsafe/UnsafeShuffleExternalSorter.java   | 143 +++++++++++-------
 .../shuffle/unsafe/UnsafeShuffleWriter.java   |  10 +-
 .../unsafe/UnsafeShuffleWriterSuite.java      |  60 ++------
 3 files changed, 103 insertions(+), 110 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index f6e0913a7a0b3..925b60a145886 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -17,10 +17,10 @@
 
 package org.apache.spark.shuffle.unsafe;
 
+import javax.annotation.Nullable;
 import java.io.File;
 import java.io.IOException;
 import java.util.LinkedList;
-import javax.annotation.Nullable;
 
 import scala.Tuple2;
 
@@ -34,8 +34,11 @@
 import org.apache.spark.serializer.DummySerializerInstance;
 import org.apache.spark.serializer.SerializerInstance;
 import org.apache.spark.shuffle.ShuffleMemoryManager;
-import org.apache.spark.storage.*;
+import org.apache.spark.storage.BlockManager;
+import org.apache.spark.storage.DiskBlockObjectWriter;
+import org.apache.spark.storage.TempShuffleBlockId;
 import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
 import org.apache.spark.util.Utils;
@@ -68,7 +71,7 @@ final class UnsafeShuffleExternalSorter {
   private final int pageSizeBytes;
   @VisibleForTesting
   final int maxRecordSizeBytes;
-  private final TaskMemoryManager memoryManager;
+  private final TaskMemoryManager taskMemoryManager;
   private final ShuffleMemoryManager shuffleMemoryManager;
   private final BlockManager blockManager;
   private final TaskContext taskContext;
@@ -91,7 +94,7 @@ final class UnsafeShuffleExternalSorter {
   private long peakMemoryUsedBytes;
 
   // These variables are reset after spilling:
-  @Nullable private UnsafeShuffleInMemorySorter sorter;
+  @Nullable private UnsafeShuffleInMemorySorter inMemSorter;
   @Nullable private MemoryBlock currentPage = null;
   private long currentPagePosition = -1;
   private long freeSpaceInCurrentPage = 0;
@@ -105,7 +108,7 @@ public UnsafeShuffleExternalSorter(
       int numPartitions,
       SparkConf conf,
       ShuffleWriteMetrics writeMetrics) throws IOException {
-    this.memoryManager = memoryManager;
+    this.taskMemoryManager = memoryManager;
     this.shuffleMemoryManager = shuffleMemoryManager;
     this.blockManager = blockManager;
     this.taskContext = taskContext;
@@ -133,7 +136,7 @@ private void initializeForWriting() throws IOException {
       throw new IOException("Could not acquire " + memoryRequested + " bytes of memory");
     }
 
-    this.sorter = new UnsafeShuffleInMemorySorter(initialSize);
+    this.inMemSorter = new UnsafeShuffleInMemorySorter(initialSize);
   }
 
   /**
@@ -160,7 +163,7 @@ private void writeSortedFile(boolean isLastFile) throws IOException {
 
     // This call performs the actual sort.
     final UnsafeShuffleInMemorySorter.UnsafeShuffleSorterIterator sortedRecords =
-      sorter.getSortedIterator();
+      inMemSorter.getSortedIterator();
 
     // Currently, we need to open a new DiskBlockObjectWriter for each partition; we can avoid this
     // after SPARK-5581 is fixed.
@@ -206,8 +209,8 @@ private void writeSortedFile(boolean isLastFile) throws IOException {
       }
 
       final long recordPointer = sortedRecords.packedRecordPointer.getRecordPointer();
-      final Object recordPage = memoryManager.getPage(recordPointer);
-      final long recordOffsetInPage = memoryManager.getOffsetInPage(recordPointer);
+      final Object recordPage = taskMemoryManager.getPage(recordPointer);
+      final long recordOffsetInPage = taskMemoryManager.getOffsetInPage(recordPointer);
       int dataRemaining = PlatformDependent.UNSAFE.getInt(recordPage, recordOffsetInPage);
       long recordReadPosition = recordOffsetInPage + 4; // skip over record length
       while (dataRemaining > 0) {
@@ -269,9 +272,9 @@ void spill() throws IOException {
       spills.size() > 1 ? " times" : " time");
 
     writeSortedFile(false);
-    final long sorterMemoryUsage = sorter.getMemoryUsage();
-    sorter = null;
-    shuffleMemoryManager.release(sorterMemoryUsage);
+    final long inMemSorterMemoryUsage = inMemSorter.getMemoryUsage();
+    inMemSorter = null;
+    shuffleMemoryManager.release(inMemSorterMemoryUsage);
     final long spillSize = freeMemory();
     taskContext.taskMetrics().incMemoryBytesSpilled(spillSize);
 
@@ -283,7 +286,7 @@ private long getMemoryUsage() {
     for (MemoryBlock page : allocatedPages) {
       totalPageSize += page.size();
     }
-    return ((sorter == null) ? 0 : sorter.getMemoryUsage()) + totalPageSize;
+    return ((inMemSorter == null) ? 0 : inMemSorter.getMemoryUsage()) + totalPageSize;
   }
 
   private void updatePeakMemoryUsed() {
@@ -305,7 +308,7 @@ private long freeMemory() {
     updatePeakMemoryUsed();
     long memoryFreed = 0;
     for (MemoryBlock block : allocatedPages) {
-      memoryManager.freePage(block);
+      taskMemoryManager.freePage(block);
       shuffleMemoryManager.release(block.size());
       memoryFreed += block.size();
     }
@@ -319,54 +322,53 @@ private long freeMemory() {
   /**
    * Force all memory and spill files to be deleted; called by shuffle error-handling code.
    */
-  public void cleanupAfterError() {
+  public void cleanupResources() {
     freeMemory();
     for (SpillInfo spill : spills) {
       if (spill.file.exists() && !spill.file.delete()) {
         logger.error("Unable to delete spill file {}", spill.file.getPath());
       }
     }
-    if (sorter != null) {
-      shuffleMemoryManager.release(sorter.getMemoryUsage());
-      sorter = null;
+    if (inMemSorter != null) {
+      shuffleMemoryManager.release(inMemSorter.getMemoryUsage());
+      inMemSorter = null;
     }
   }
 
   /**
-   * Checks whether there is enough space to insert a new record into the sorter.
-   *
-   * @param requiredSpace the required space in the data page, in bytes, including space for storing
-   *                      the record size.
-
-   * @return true if the record can be inserted without requiring more allocations, false otherwise.
-   */
-  private boolean haveSpaceForRecord(int requiredSpace) {
-    assert (requiredSpace > 0);
-    return (sorter.hasSpaceForAnotherRecord() && (requiredSpace <= freeSpaceInCurrentPage));
-  }
-
-  /**
-   * Allocates more memory in order to insert an additional record. This will request additional
-   * memory from the {@link ShuffleMemoryManager} and spill if the requested memory can not be
-   * obtained.
-   *
-   * @param requiredSpace the required space in the data page, in bytes, including space for storing
-   *                      the record size.
+   * Checks whether there is enough space to insert an additional record in to the sort pointer
+   * array and grows the array if additional space is required. If the required space cannot be
+   * obtained, then the in-memory data will be spilled to disk.
    */
-  private void allocateSpaceForRecord(int requiredSpace) throws IOException {
-    if (!sorter.hasSpaceForAnotherRecord()) {
+  private void growPointerArrayIfNecessary() throws IOException {
+    assert(inMemSorter != null);
+    if (!inMemSorter.hasSpaceForAnotherRecord()) {
       logger.debug("Attempting to expand sort pointer array");
-      final long oldPointerArrayMemoryUsage = sorter.getMemoryUsage();
+      final long oldPointerArrayMemoryUsage = inMemSorter.getMemoryUsage();
       final long memoryToGrowPointerArray = oldPointerArrayMemoryUsage * 2;
       final long memoryAcquired = shuffleMemoryManager.tryToAcquire(memoryToGrowPointerArray);
       if (memoryAcquired < memoryToGrowPointerArray) {
         shuffleMemoryManager.release(memoryAcquired);
         spill();
       } else {
-        sorter.expandPointerArray();
+        inMemSorter.expandPointerArray();
         shuffleMemoryManager.release(oldPointerArrayMemoryUsage);
       }
     }
+  }
+  
+  /**
+   * Allocates more memory in order to insert an additional record. This will request additional
+   * memory from the {@link ShuffleMemoryManager} and spill if the requested memory can not be
+   * obtained.
+   *
+   * @param requiredSpace the required space in the data page, in bytes, including space for storing
+   *                      the record size. This must be less than or equal to the page size (records
+   *                      that exceed the page size are handled via a different code path which uses
+   *                      special overflow pages).
+   */
+  private void acquireNewPageIfNecessary(int requiredSpace) throws IOException {
+    growPointerArrayIfNecessary();
     if (requiredSpace > freeSpaceInCurrentPage) {
       logger.trace("Required space {} is less than free space in current page ({})", requiredSpace,
         freeSpaceInCurrentPage);
@@ -387,7 +389,7 @@ private void allocateSpaceForRecord(int requiredSpace) throws IOException {
             throw new IOException("Unable to acquire " + pageSizeBytes + " bytes of memory");
           }
         }
-        currentPage = memoryManager.allocatePage(pageSizeBytes);
+        currentPage = taskMemoryManager.allocatePage(pageSizeBytes);
         currentPagePosition = currentPage.getBaseOffset();
         freeSpaceInCurrentPage = pageSizeBytes;
         allocatedPages.add(currentPage);
@@ -403,27 +405,58 @@ public void insertRecord(
       long recordBaseOffset,
       int lengthInBytes,
       int partitionId) throws IOException {
+
+    growPointerArrayIfNecessary();
     // Need 4 bytes to store the record length.
     final int totalSpaceRequired = lengthInBytes + 4;
-    if (!haveSpaceForRecord(totalSpaceRequired)) {
-      allocateSpaceForRecord(totalSpaceRequired);
+
+    // --- Figure out where to insert the new record ----------------------------------------------
+
+    final MemoryBlock dataPage;
+    long dataPagePosition;
+    boolean useOverflowPage = totalSpaceRequired > pageSizeBytes;
+    if (useOverflowPage) {
+      long overflowPageSize = ByteArrayMethods.roundNumberOfBytesToNearestWord(totalSpaceRequired);
+      // The record is larger than the page size, so allocate a special overflow page just to hold
+      // that record.
+      final long memoryGranted = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+      if (memoryGranted != overflowPageSize) {
+        shuffleMemoryManager.release(memoryGranted);
+        spill();
+        final long memoryGrantedAfterSpill = shuffleMemoryManager.tryToAcquire(overflowPageSize);
+        if (memoryGrantedAfterSpill != overflowPageSize) {
+          shuffleMemoryManager.release(memoryGrantedAfterSpill);
+          throw new IOException("Unable to acquire " + overflowPageSize + " bytes of memory");
+        }
+      }
+      MemoryBlock overflowPage = taskMemoryManager.allocatePage(overflowPageSize);
+      allocatedPages.add(overflowPage);
+      dataPage = overflowPage;
+      dataPagePosition = overflowPage.getBaseOffset();
+    } else {
+      // The record is small enough to fit in a regular data page, but the current page might not
+      // have enough space to hold it (or no pages have been allocated yet).
+      acquireNewPageIfNecessary(totalSpaceRequired);
+      dataPage = currentPage;
+      dataPagePosition = currentPagePosition;
+      // Update bookkeeping information
+      freeSpaceInCurrentPage -= totalSpaceRequired;
+      currentPagePosition += totalSpaceRequired;
     }
+    final Object dataPageBaseObject = dataPage.getBaseObject();
 
     final long recordAddress =
-      memoryManager.encodePageNumberAndOffset(currentPage, currentPagePosition);
-    final Object dataPageBaseObject = currentPage.getBaseObject();
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, currentPagePosition, lengthInBytes);
-    currentPagePosition += 4;
-    freeSpaceInCurrentPage -= 4;
+      taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
+    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
+    dataPagePosition += 4;
     PlatformDependent.copyMemory(
       recordBaseObject,
       recordBaseOffset,
       dataPageBaseObject,
-      currentPagePosition,
+      dataPagePosition,
       lengthInBytes);
-    currentPagePosition += lengthInBytes;
-    freeSpaceInCurrentPage -= lengthInBytes;
-    sorter.insertRecord(recordAddress, partitionId);
+    assert(inMemSorter != null);
+    inMemSorter.insertRecord(recordAddress, partitionId);
   }
 
   /**
@@ -435,14 +468,14 @@ public void insertRecord(
    */
   public SpillInfo[] closeAndGetSpills() throws IOException {
     try {
-      if (sorter != null) {
+      if (inMemSorter != null) {
         // Do not count the final file towards the spill count.
         writeSortedFile(true);
         freeMemory();
       }
       return spills.toArray(new SpillInfo[spills.size()]);
     } catch (IOException e) {
-      cleanupAfterError();
+      cleanupResources();
       throw e;
     }
   }
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
index 6e2eeb37c86f1..02084f9122e00 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
@@ -17,17 +17,17 @@
 
 package org.apache.spark.shuffle.unsafe;
 
+import javax.annotation.Nullable;
 import java.io.*;
 import java.nio.channels.FileChannel;
 import java.util.Iterator;
-import javax.annotation.Nullable;
 
 import scala.Option;
 import scala.Product2;
 import scala.collection.JavaConversions;
+import scala.collection.immutable.Map;
 import scala.reflect.ClassTag;
 import scala.reflect.ClassTag$;
-import scala.collection.immutable.Map;
 
 import com.google.common.annotations.VisibleForTesting;
 import com.google.common.io.ByteStreams;
@@ -38,10 +38,10 @@
 
 import org.apache.spark.*;
 import org.apache.spark.annotation.Private;
+import org.apache.spark.executor.ShuffleWriteMetrics;
 import org.apache.spark.io.CompressionCodec;
 import org.apache.spark.io.CompressionCodec$;
 import org.apache.spark.io.LZFCompressionCodec;
-import org.apache.spark.executor.ShuffleWriteMetrics;
 import org.apache.spark.network.util.LimitedInputStream;
 import org.apache.spark.scheduler.MapStatus;
 import org.apache.spark.scheduler.MapStatus$;
@@ -178,7 +178,7 @@ public void write(scala.collection.Iterator<Product2<K, V>> records) throws IOEx
     } finally {
       if (sorter != null) {
         try {
-          sorter.cleanupAfterError();
+          sorter.cleanupResources();
         } catch (Exception e) {
           // Only throw this error if we won't be masking another
           // error.
@@ -482,7 +482,7 @@ public Option<MapStatus> stop(boolean success) {
       if (sorter != null) {
         // If sorter is non-null, then this implies that we called stop() in response to an error,
         // so we need to clean up memory and spill files created by the sorter
-        sorter.cleanupAfterError();
+        sorter.cleanupResources();
       }
     }
   }
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
index c68354ba49a46..94650be536b5f 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
@@ -475,62 +475,22 @@ public void writeRecordsThatAreBiggerThanDiskWriteBufferSize() throws Exception
 
   @Test
   public void writeRecordsThatAreBiggerThanMaxRecordSize() throws Exception {
-    // Use a custom serializer so that we have exact control over the size of serialized data.
-    final Serializer byteArraySerializer = new Serializer() {
-      @Override
-      public SerializerInstance newInstance() {
-        return new SerializerInstance() {
-          @Override
-          public SerializationStream serializeStream(final OutputStream s) {
-            return new SerializationStream() {
-              @Override
-              public void flush() { }
-
-              @Override
-              public <T> SerializationStream writeObject(T t, ClassTag<T> ev1) {
-                byte[] bytes = (byte[]) t;
-                try {
-                  s.write(bytes);
-                } catch (IOException e) {
-                  throw new RuntimeException(e);
-                }
-                return this;
-              }
-
-              @Override
-              public void close() { }
-            };
-          }
-          public <T> ByteBuffer serialize(T t, ClassTag<T> ev1) { return null; }
-          public DeserializationStream deserializeStream(InputStream s) { return null; }
-          public <T> T deserialize(ByteBuffer b, ClassLoader l, ClassTag<T> ev1) { return null; }
-          public <T> T deserialize(ByteBuffer bytes, ClassTag<T> ev1) { return null; }
-        };
-      }
-    };
-    when(shuffleDep.serializer()).thenReturn(Option.<Serializer>apply(byteArraySerializer));
     final UnsafeShuffleWriter<Object, Object> writer = createWriter(false);
-    // Insert a record and force a spill so that there's something to clean up:
-    writer.insertRecordIntoSorter(new Tuple2<Object, Object>(new byte[1], new byte[1]));
-    writer.forceSorterToSpill();
+    final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<Product2<Object, Object>>();
+    dataToWrite.add(new Tuple2<Object, Object>(1, ByteBuffer.wrap(new byte[1])));
     // We should be able to write a record that's right _at_ the max record size
     final byte[] atMaxRecordSize = new byte[writer.maxRecordSizeBytes()];
     new Random(42).nextBytes(atMaxRecordSize);
-    writer.insertRecordIntoSorter(new Tuple2<Object, Object>(new byte[0], atMaxRecordSize));
-    writer.forceSorterToSpill();
-    // Inserting a record that's larger than the max record size should fail:
+    dataToWrite.add(new Tuple2<Object, Object>(2, ByteBuffer.wrap(atMaxRecordSize)));
+    // Inserting a record that's larger than the max record size
     final byte[] exceedsMaxRecordSize = new byte[writer.maxRecordSizeBytes() + 1];
     new Random(42).nextBytes(exceedsMaxRecordSize);
-    Product2<Object, Object> hugeRecord =
-      new Tuple2<Object, Object>(new byte[0], exceedsMaxRecordSize);
-    try {
-      // Here, we write through the public `write()` interface instead of the test-only
-      // `insertRecordIntoSorter` interface:
-      writer.write(Collections.singletonList(hugeRecord).iterator());
-      fail("Expected exception to be thrown");
-    } catch (IOException e) {
-      // Pass
-    }
+    dataToWrite.add(new Tuple2<Object, Object>(3, ByteBuffer.wrap(exceedsMaxRecordSize)));
+    writer.write(dataToWrite.iterator());
+    writer.stop(true);
+    assertEquals(
+      HashMultiset.create(dataToWrite),
+      HashMultiset.create(readRecordsFromFile()));
     assertSpillFilesWereCleanedUp();
   }
 

From e57d6b56137bf3557efe5acea3ad390c1987b257 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 7 Aug 2015 00:00:43 -0700
Subject: [PATCH 114/633] [SPARK-9683] [SQL] copy UTF8String when convert
 unsafe array/map to safe

When we convert unsafe row to safe row, we will do copy if the column is struct or string type. However, the string inside unsafe array/map are not copied, which may cause problems.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #7990 from cloud-fan/copy and squashes the following commits:

c13d1e3 [Wenchen Fan] change test name
fe36294 [Wenchen Fan] we should deep copy UTF8String when convert unsafe row to safe row
---
 .../sql/catalyst/expressions/FromUnsafe.scala |  3 ++
 .../execution/RowFormatConvertersSuite.scala  | 38 ++++++++++++++++++-
 2 files changed, 40 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
index 3caf0fb3410c4..9b960b136f984 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
@@ -19,6 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 
 import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
 
 case class FromUnsafe(child: Expression) extends UnaryExpression
   with ExpectsInputTypes with CodegenFallback {
@@ -52,6 +53,8 @@ case class FromUnsafe(child: Expression) extends UnaryExpression
       }
       new GenericArrayData(result)
 
+    case StringType => value.asInstanceOf[UTF8String].clone()
+
     case MapType(kt, vt, _) =>
       val map = value.asInstanceOf[UnsafeMapData]
       val safeKeyArray = convert(map.keys, ArrayType(kt)).asInstanceOf[GenericArrayData]
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index 707cd9c6d939b..8208b25b5708c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -17,9 +17,13 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.expressions.{Literal, IsNull}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute, Literal, IsNull}
 import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.types.{GenericArrayData, ArrayType, StructType, StringType}
+import org.apache.spark.unsafe.types.UTF8String
 
 class RowFormatConvertersSuite extends SparkPlanTest {
 
@@ -87,4 +91,36 @@ class RowFormatConvertersSuite extends SparkPlanTest {
       input.map(Row.fromTuple)
     )
   }
+
+  test("SPARK-9683: copy UTF8String when convert unsafe array/map to safe") {
+    SparkPlan.currentContext.set(TestSQLContext)
+    val schema = ArrayType(StringType)
+    val rows = (1 to 100).map { i =>
+      InternalRow(new GenericArrayData(Array[Any](UTF8String.fromString(i.toString))))
+    }
+    val relation = LocalTableScan(Seq(AttributeReference("t", schema)()), rows)
+
+    val plan =
+      DummyPlan(
+        ConvertToSafe(
+          ConvertToUnsafe(relation)))
+    assert(plan.execute().collect().map(_.getUTF8String(0).toString) === (1 to 100).map(_.toString))
+  }
+}
+
+case class DummyPlan(child: SparkPlan) extends UnaryNode {
+
+  override protected def doExecute(): RDD[InternalRow] = {
+    child.execute().mapPartitions { iter =>
+      // cache all strings to make sure we have deep copied UTF8String inside incoming
+      // safe InternalRow.
+      val strings = new scala.collection.mutable.ArrayBuffer[UTF8String]
+      iter.foreach { row =>
+        strings += row.getArray(0).getUTF8String(0)
+      }
+      strings.map(InternalRow(_)).iterator
+    }
+  }
+
+  override def output: Seq[Attribute] = Seq(AttributeReference("a", StringType)())
 }

From ebfd91c542aaead343cb154277fcf9114382fee7 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 7 Aug 2015 00:09:58 -0700
Subject: [PATCH 115/633] [SPARK-9467][SQL]Add SQLMetric to specialize
 accumulators to avoid boxing

This PR adds SQLMetric/SQLMetricParam/SQLMetricValue to specialize accumulators to avoid boxing. All SQL metrics should use these classes rather than `Accumulator`.

Author: zsxwing <zsxwing@gmail.com>

Closes #7996 from zsxwing/sql-accu and squashes the following commits:

14a5f0a [zsxwing] Address comments
367ca23 [zsxwing] Use localValue directly to avoid changing Accumulable
42f50c3 [zsxwing] Add SQLMetric to specialize accumulators to avoid boxing
---
 .../scala/org/apache/spark/Accumulators.scala |   2 +-
 .../scala/org/apache/spark/SparkContext.scala |  15 --
 .../spark/sql/execution/SparkPlan.scala       |  33 ++--
 .../spark/sql/execution/basicOperators.scala  |  11 +-
 .../apache/spark/sql/metric/SQLMetrics.scala  | 149 ++++++++++++++++++
 .../org/apache/spark/sql/ui/SQLListener.scala |  17 +-
 .../apache/spark/sql/ui/SparkPlanGraph.scala  |   8 +-
 .../spark/sql/metric/SQLMetricsSuite.scala    | 145 +++++++++++++++++
 .../spark/sql/ui/SQLListenerSuite.scala       |   5 +-
 9 files changed, 338 insertions(+), 47 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index 462d5c96d480b..064246dfa7fc3 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -257,7 +257,7 @@ GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Serializa
  */
 class Accumulator[T] private[spark] (
     @transient private[spark] val initialValue: T,
-    private[spark] val param: AccumulatorParam[T],
+    param: AccumulatorParam[T],
     name: Option[String],
     internal: Boolean)
   extends Accumulable[T, T](initialValue, param, name, internal) {
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 5662686436900..9ced44131b0d9 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1238,21 +1238,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     acc
   }
 
-  /**
-   * Create an [[org.apache.spark.Accumulator]] variable of a given type, with a name for display
-   * in the Spark UI. Tasks can "add" values to the accumulator using the `+=` method. Only the
-   * driver can access the accumulator's `value`. The latest local value of such accumulator will be
-   * sent back to the driver via heartbeats.
-   *
-   * @tparam T type that can be added to the accumulator, must be thread safe
-   */
-  private[spark] def internalAccumulator[T](initialValue: T, name: String)(
-    implicit param: AccumulatorParam[T]): Accumulator[T] = {
-    val acc = new Accumulator(initialValue, param, Some(name), internal = true)
-    cleaner.foreach(_.registerAccumulatorForCleanup(acc))
-    acc
-  }
-
   /**
    * Create an [[org.apache.spark.Accumulable]] shared variable, to which tasks can add values
    * with `+=`. Only the driver can access the accumuable's `value`.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 719ad432e2fe0..1915496d16205 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -21,7 +21,7 @@ import java.util.concurrent.atomic.AtomicBoolean
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.{Accumulator, Logging}
+import org.apache.spark.Logging
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.{RDD, RDDOperationScope}
 import org.apache.spark.sql.SQLContext
@@ -32,6 +32,7 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.metric.{IntSQLMetric, LongSQLMetric, SQLMetric, SQLMetrics}
 import org.apache.spark.sql.types.DataType
 
 object SparkPlan {
@@ -84,22 +85,30 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
    */
   protected[sql] def trackNumOfRowsEnabled: Boolean = false
 
-  private lazy val numOfRowsAccumulator = sparkContext.internalAccumulator(0L, "number of rows")
+  private lazy val defaultMetrics: Map[String, SQLMetric[_, _]] =
+    if (trackNumOfRowsEnabled) {
+      Map("numRows" -> SQLMetrics.createLongMetric(sparkContext, "number of rows"))
+    }
+    else {
+      Map.empty
+    }
 
   /**
-   * Return all accumulators containing metrics of this SparkPlan.
+   * Return all metrics containing metrics of this SparkPlan.
    */
-  private[sql] def accumulators: Map[String, Accumulator[_]] = if (trackNumOfRowsEnabled) {
-      Map("numRows" -> numOfRowsAccumulator)
-    } else {
-      Map.empty
-    }
+  private[sql] def metrics: Map[String, SQLMetric[_, _]] = defaultMetrics
+
+  /**
+   * Return a IntSQLMetric according to the name.
+   */
+  private[sql] def intMetric(name: String): IntSQLMetric =
+    metrics(name).asInstanceOf[IntSQLMetric]
 
   /**
-   * Return the accumulator according to the name.
+   * Return a LongSQLMetric according to the name.
    */
-  private[sql] def accumulator[T](name: String): Accumulator[T] =
-    accumulators(name).asInstanceOf[Accumulator[T]]
+  private[sql] def longMetric(name: String): LongSQLMetric =
+    metrics(name).asInstanceOf[LongSQLMetric]
 
   // TODO: Move to `DistributedPlan`
   /** Specifies how data is partitioned across different nodes in the cluster. */
@@ -148,7 +157,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
     RDDOperationScope.withScope(sparkContext, nodeName, false, true) {
       prepare()
       if (trackNumOfRowsEnabled) {
-        val numRows = accumulator[Long]("numRows")
+        val numRows = longMetric("numRows")
         doExecute().map { row =>
           numRows += 1
           row
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index f4677b4ee86bb..0680f31d40f6d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -26,6 +26,7 @@ import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.metric.SQLMetrics
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.collection.ExternalSorter
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator
@@ -81,13 +82,13 @@ case class TungstenProject(projectList: Seq[NamedExpression], child: SparkPlan)
 case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 
-  private[sql] override lazy val accumulators = Map(
-    "numInputRows" -> sparkContext.internalAccumulator(0L, "number of input rows"),
-    "numOutputRows" -> sparkContext.internalAccumulator(0L, "number of output rows"))
+  private[sql] override lazy val metrics = Map(
+    "numInputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of input rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
 
   protected override def doExecute(): RDD[InternalRow] = {
-    val numInputRows = accumulator[Long]("numInputRows")
-    val numOutputRows = accumulator[Long]("numOutputRows")
+    val numInputRows = longMetric("numInputRows")
+    val numOutputRows = longMetric("numOutputRows")
     child.execute().mapPartitions { iter =>
       val predicate = newPredicate(condition, child.output)
       iter.filter { row =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala b/sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala
new file mode 100644
index 0000000000000..3b907e5da7897
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala
@@ -0,0 +1,149 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.metric
+
+import org.apache.spark.{Accumulable, AccumulableParam, SparkContext}
+
+/**
+ * Create a layer for specialized metric. We cannot add `@specialized` to
+ * `Accumulable/AccumulableParam` because it will break Java source compatibility.
+ *
+ * An implementation of SQLMetric should override `+=` and `add` to avoid boxing.
+ */
+private[sql] abstract class SQLMetric[R <: SQLMetricValue[T], T](
+    name: String, val param: SQLMetricParam[R, T])
+  extends Accumulable[R, T](param.zero, param, Some(name), true)
+
+/**
+ * Create a layer for specialized metric. We cannot add `@specialized` to
+ * `Accumulable/AccumulableParam` because it will break Java source compatibility.
+ */
+private[sql] trait SQLMetricParam[R <: SQLMetricValue[T], T] extends AccumulableParam[R, T] {
+
+  def zero: R
+}
+
+/**
+ * Create a layer for specialized metric. We cannot add `@specialized` to
+ * `Accumulable/AccumulableParam` because it will break Java source compatibility.
+ */
+private[sql] trait SQLMetricValue[T] extends Serializable {
+
+  def value: T
+
+  override def toString: String = value.toString
+}
+
+/**
+ * A wrapper of Long to avoid boxing and unboxing when using Accumulator
+ */
+private[sql] class LongSQLMetricValue(private var _value : Long) extends SQLMetricValue[Long] {
+
+  def add(incr: Long): LongSQLMetricValue = {
+    _value += incr
+    this
+  }
+
+  // Although there is a boxing here, it's fine because it's only called in SQLListener
+  override def value: Long = _value
+}
+
+/**
+ * A wrapper of Int to avoid boxing and unboxing when using Accumulator
+ */
+private[sql] class IntSQLMetricValue(private var _value: Int) extends SQLMetricValue[Int] {
+
+  def add(term: Int): IntSQLMetricValue = {
+    _value += term
+    this
+  }
+
+  // Although there is a boxing here, it's fine because it's only called in SQLListener
+  override def value: Int = _value
+}
+
+/**
+ * A specialized long Accumulable to avoid boxing and unboxing when using Accumulator's
+ * `+=` and `add`.
+ */
+private[sql] class LongSQLMetric private[metric](name: String)
+  extends SQLMetric[LongSQLMetricValue, Long](name, LongSQLMetricParam) {
+
+  override def +=(term: Long): Unit = {
+    localValue.add(term)
+  }
+
+  override def add(term: Long): Unit = {
+    localValue.add(term)
+  }
+}
+
+/**
+ * A specialized int Accumulable to avoid boxing and unboxing when using Accumulator's
+ * `+=` and `add`.
+ */
+private[sql] class IntSQLMetric private[metric](name: String)
+  extends SQLMetric[IntSQLMetricValue, Int](name, IntSQLMetricParam) {
+
+  override def +=(term: Int): Unit = {
+    localValue.add(term)
+  }
+
+  override def add(term: Int): Unit = {
+    localValue.add(term)
+  }
+}
+
+private object LongSQLMetricParam extends SQLMetricParam[LongSQLMetricValue, Long] {
+
+  override def addAccumulator(r: LongSQLMetricValue, t: Long): LongSQLMetricValue = r.add(t)
+
+  override def addInPlace(r1: LongSQLMetricValue, r2: LongSQLMetricValue): LongSQLMetricValue =
+    r1.add(r2.value)
+
+  override def zero(initialValue: LongSQLMetricValue): LongSQLMetricValue = zero
+
+  override def zero: LongSQLMetricValue = new LongSQLMetricValue(0L)
+}
+
+private object IntSQLMetricParam extends SQLMetricParam[IntSQLMetricValue, Int] {
+
+  override def addAccumulator(r: IntSQLMetricValue, t: Int): IntSQLMetricValue = r.add(t)
+
+  override def addInPlace(r1: IntSQLMetricValue, r2: IntSQLMetricValue): IntSQLMetricValue =
+    r1.add(r2.value)
+
+  override def zero(initialValue: IntSQLMetricValue): IntSQLMetricValue = zero
+
+  override def zero: IntSQLMetricValue = new IntSQLMetricValue(0)
+}
+
+private[sql] object SQLMetrics {
+
+  def createIntMetric(sc: SparkContext, name: String): IntSQLMetric = {
+    val acc = new IntSQLMetric(name)
+    sc.cleaner.foreach(_.registerAccumulatorForCleanup(acc))
+    acc
+  }
+
+  def createLongMetric(sc: SparkContext, name: String): LongSQLMetric = {
+    val acc = new LongSQLMetric(name)
+    sc.cleaner.foreach(_.registerAccumulatorForCleanup(acc))
+    acc
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
index e7b1dd1ffac68..2fd4fc658d068 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
@@ -21,11 +21,12 @@ import scala.collection.mutable
 
 import com.google.common.annotations.VisibleForTesting
 
-import org.apache.spark.{AccumulatorParam, JobExecutionStatus, Logging}
+import org.apache.spark.{JobExecutionStatus, Logging}
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler._
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.execution.SQLExecution
+import org.apache.spark.sql.metric.{SQLMetricParam, SQLMetricValue}
 
 private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener with Logging {
 
@@ -36,8 +37,6 @@ private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener wit
 
   // Old data in the following fields must be removed in "trimExecutionsIfNecessary".
   // If adding new fields, make sure "trimExecutionsIfNecessary" can clean up old data
-
-  // VisibleForTesting
   private val _executionIdToData = mutable.HashMap[Long, SQLExecutionUIData]()
 
   /**
@@ -270,9 +269,10 @@ private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener wit
                accumulatorUpdate <- taskMetrics.accumulatorUpdates.toSeq) yield {
             accumulatorUpdate
           }
-        }.filter { case (id, _) => executionUIData.accumulatorMetrics.keySet(id) }
+        }.filter { case (id, _) => executionUIData.accumulatorMetrics.contains(id) }
         mergeAccumulatorUpdates(accumulatorUpdates, accumulatorId =>
-          executionUIData.accumulatorMetrics(accumulatorId).accumulatorParam)
+          executionUIData.accumulatorMetrics(accumulatorId).metricParam).
+          mapValues(_.asInstanceOf[SQLMetricValue[_]].value)
       case None =>
         // This execution has been dropped
         Map.empty
@@ -281,10 +281,11 @@ private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener wit
 
   private def mergeAccumulatorUpdates(
       accumulatorUpdates: Seq[(Long, Any)],
-      paramFunc: Long => AccumulatorParam[Any]): Map[Long, Any] = {
+      paramFunc: Long => SQLMetricParam[SQLMetricValue[Any], Any]): Map[Long, Any] = {
     accumulatorUpdates.groupBy(_._1).map { case (accumulatorId, values) =>
       val param = paramFunc(accumulatorId)
-      (accumulatorId, values.map(_._2).reduceLeft(param.addInPlace))
+      (accumulatorId,
+        values.map(_._2.asInstanceOf[SQLMetricValue[Any]]).foldLeft(param.zero)(param.addInPlace))
     }
   }
 
@@ -336,7 +337,7 @@ private[ui] class SQLExecutionUIData(
 private[ui] case class SQLPlanMetric(
     name: String,
     accumulatorId: Long,
-    accumulatorParam: AccumulatorParam[Any])
+    metricParam: SQLMetricParam[SQLMetricValue[Any], Any])
 
 /**
  * Store all accumulatorUpdates for all tasks in a Spark stage.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala b/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
index 7910c163ba453..1ba50b95becc1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
@@ -21,8 +21,8 @@ import java.util.concurrent.atomic.AtomicLong
 
 import scala.collection.mutable
 
-import org.apache.spark.AccumulatorParam
 import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.metric.{SQLMetricParam, SQLMetricValue}
 
 /**
  * A graph used for storing information of an executionPlan of DataFrame.
@@ -61,9 +61,9 @@ private[sql] object SparkPlanGraph {
       nodeIdGenerator: AtomicLong,
       nodes: mutable.ArrayBuffer[SparkPlanGraphNode],
       edges: mutable.ArrayBuffer[SparkPlanGraphEdge]): SparkPlanGraphNode = {
-    val metrics = plan.accumulators.toSeq.map { case (key, accumulator) =>
-      SQLPlanMetric(accumulator.name.getOrElse(key), accumulator.id,
-        accumulator.param.asInstanceOf[AccumulatorParam[Any]])
+    val metrics = plan.metrics.toSeq.map { case (key, metric) =>
+      SQLPlanMetric(metric.name.getOrElse(key), metric.id,
+        metric.param.asInstanceOf[SQLMetricParam[SQLMetricValue[Any], Any]])
     }
     val node = SparkPlanGraphNode(
       nodeIdGenerator.getAndIncrement(), plan.nodeName, plan.simpleString, metrics)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala
new file mode 100644
index 0000000000000..d22160f5384f4
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala
@@ -0,0 +1,145 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.metric
+
+import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
+
+import scala.collection.mutable
+
+import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm._
+import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.Opcodes._
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.util.Utils
+
+
+class SQLMetricsSuite extends SparkFunSuite {
+
+  test("LongSQLMetric should not box Long") {
+    val l = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "long")
+    val f = () => { l += 1L }
+    BoxingFinder.getClassReader(f.getClass).foreach { cl =>
+      val boxingFinder = new BoxingFinder()
+      cl.accept(boxingFinder, 0)
+      assert(boxingFinder.boxingInvokes.isEmpty, s"Found boxing: ${boxingFinder.boxingInvokes}")
+    }
+  }
+
+  test("IntSQLMetric should not box Int") {
+    val l = SQLMetrics.createIntMetric(TestSQLContext.sparkContext, "Int")
+    val f = () => { l += 1 }
+    BoxingFinder.getClassReader(f.getClass).foreach { cl =>
+      val boxingFinder = new BoxingFinder()
+      cl.accept(boxingFinder, 0)
+      assert(boxingFinder.boxingInvokes.isEmpty, s"Found boxing: ${boxingFinder.boxingInvokes}")
+    }
+  }
+
+  test("Normal accumulator should do boxing") {
+    // We need this test to make sure BoxingFinder works.
+    val l = TestSQLContext.sparkContext.accumulator(0L)
+    val f = () => { l += 1L }
+    BoxingFinder.getClassReader(f.getClass).foreach { cl =>
+      val boxingFinder = new BoxingFinder()
+      cl.accept(boxingFinder, 0)
+      assert(boxingFinder.boxingInvokes.nonEmpty, "Found find boxing in this test")
+    }
+  }
+}
+
+private case class MethodIdentifier[T](cls: Class[T], name: String, desc: String)
+
+/**
+ * If `method` is null, search all methods of this class recursively to find if they do some boxing.
+ * If `method` is specified, only search this method of the class to speed up the searching.
+ *
+ * This method will skip the methods in `visitedMethods` to avoid potential infinite cycles.
+ */
+private class BoxingFinder(
+    method: MethodIdentifier[_] = null,
+    val boxingInvokes: mutable.Set[String] = mutable.Set.empty,
+    visitedMethods: mutable.Set[MethodIdentifier[_]] = mutable.Set.empty)
+  extends ClassVisitor(ASM4) {
+
+  private val primitiveBoxingClassName =
+    Set("java/lang/Long",
+      "java/lang/Double",
+      "java/lang/Integer",
+      "java/lang/Float",
+      "java/lang/Short",
+      "java/lang/Character",
+      "java/lang/Byte",
+      "java/lang/Boolean")
+
+  override def visitMethod(
+      access: Int, name: String, desc: String, sig: String, exceptions: Array[String]):
+    MethodVisitor = {
+    if (method != null && (method.name != name || method.desc != desc)) {
+      // If method is specified, skip other methods.
+      return new MethodVisitor(ASM4) {}
+    }
+
+    new MethodVisitor(ASM4) {
+      override def visitMethodInsn(op: Int, owner: String, name: String, desc: String) {
+        if (op == INVOKESPECIAL && name == "<init>" || op == INVOKESTATIC && name == "valueOf") {
+          if (primitiveBoxingClassName.contains(owner)) {
+            // Find boxing methods, e.g, new java.lang.Long(l) or java.lang.Long.valueOf(l)
+            boxingInvokes.add(s"$owner.$name")
+          }
+        } else {
+          // scalastyle:off classforname
+          val classOfMethodOwner = Class.forName(owner.replace('/', '.'), false,
+            Thread.currentThread.getContextClassLoader)
+          // scalastyle:on classforname
+          val m = MethodIdentifier(classOfMethodOwner, name, desc)
+          if (!visitedMethods.contains(m)) {
+            // Keep track of visited methods to avoid potential infinite cycles
+            visitedMethods += m
+            BoxingFinder.getClassReader(classOfMethodOwner).foreach { cl =>
+              visitedMethods += m
+              cl.accept(new BoxingFinder(m, boxingInvokes, visitedMethods), 0)
+            }
+          }
+        }
+      }
+    }
+  }
+}
+
+private object BoxingFinder {
+
+  def getClassReader(cls: Class[_]): Option[ClassReader] = {
+    val className = cls.getName.replaceFirst("^.*\\.", "") + ".class"
+    val resourceStream = cls.getResourceAsStream(className)
+    val baos = new ByteArrayOutputStream(128)
+    // Copy data over, before delegating to ClassReader -
+    // else we can run out of open file handles.
+    Utils.copyStream(resourceStream, baos, true)
+    // ASM4 doesn't support Java 8 classes, which requires ASM5.
+    // So if the class is ASM5 (E.g., java.lang.Long when using JDK8 runtime to run these codes),
+    // then ClassReader will throw IllegalArgumentException,
+    // However, since this is only for testing, it's safe to skip these classes.
+    try {
+      Some(new ClassReader(new ByteArrayInputStream(baos.toByteArray)))
+    } catch {
+      case _: IllegalArgumentException => None
+    }
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
index f1fcaf59532b8..69a561e16aa17 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
@@ -21,6 +21,7 @@ import java.util.Properties
 
 import org.apache.spark.{SparkException, SparkContext, SparkConf, SparkFunSuite}
 import org.apache.spark.executor.TaskMetrics
+import org.apache.spark.sql.metric.LongSQLMetricValue
 import org.apache.spark.scheduler._
 import org.apache.spark.sql.{DataFrame, SQLContext}
 import org.apache.spark.sql.execution.SQLExecution
@@ -65,9 +66,9 @@ class SQLListenerSuite extends SparkFunSuite {
     speculative = false
   )
 
-  private def createTaskMetrics(accumulatorUpdates: Map[Long, Any]): TaskMetrics = {
+  private def createTaskMetrics(accumulatorUpdates: Map[Long, Long]): TaskMetrics = {
     val metrics = new TaskMetrics
-    metrics.setAccumulatorsUpdater(() => accumulatorUpdates)
+    metrics.setAccumulatorsUpdater(() => accumulatorUpdates.mapValues(new LongSQLMetricValue(_)))
     metrics.updateAccumulators()
     metrics
   }

From 76eaa701833a2ff23b50147d70ced41e85719572 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 7 Aug 2015 11:02:53 -0700
Subject: [PATCH 116/633] [SPARK-9674][SPARK-9667] Remove SparkSqlSerializer2

It is now subsumed by various Tungsten operators.

Author: Reynold Xin <rxin@databricks.com>

Closes #7981 from rxin/SPARK-9674 and squashes the following commits:

144f96e [Reynold Xin] Re-enable test
58b7332 [Reynold Xin] Disable failing list.
fb797e3 [Reynold Xin] Match all UDTs.
be9f243 [Reynold Xin] Updated if.
71fc99c [Reynold Xin] [SPARK-9674][SPARK-9667] Remove GeneratedAggregate & SparkSqlSerializer2.
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |   6 -
 .../apache/spark/sql/execution/Exchange.scala |  48 +-
 .../sql/execution/SparkSqlSerializer2.scala   | 426 ------------------
 .../execution/SparkSqlSerializer2Suite.scala  | 221 ---------
 4 files changed, 24 insertions(+), 677 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
 delete mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index ef35c133d9cc3..45d3d8c863512 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -416,10 +416,6 @@ private[spark] object SQLConf {
   val USE_SQL_AGGREGATE2 = booleanConf("spark.sql.useAggregate2",
     defaultValue = Some(true), doc = "<TODO>")
 
-  val USE_SQL_SERIALIZER2 = booleanConf(
-    "spark.sql.useSerializer2",
-    defaultValue = Some(true), isPublic = false)
-
   object Deprecated {
     val MAPRED_REDUCE_TASKS = "mapred.reduce.tasks"
   }
@@ -488,8 +484,6 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   private[spark] def useSqlAggregate2: Boolean = getConf(USE_SQL_AGGREGATE2)
 
-  private[spark] def useSqlSerializer2: Boolean = getConf(USE_SQL_SERIALIZER2)
-
   private[spark] def autoBroadcastJoinThreshold: Int = getConf(AUTO_BROADCASTJOIN_THRESHOLD)
 
   private[spark] def defaultSizeInBytes: Long =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 6ea5eeedf1bbe..60087f2ca4a3e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -29,6 +29,7 @@ import org.apache.spark.sql.catalyst.errors.attachTree
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.types.UserDefinedType
 import org.apache.spark.util.MutablePair
 import org.apache.spark.{HashPartitioner, Partitioner, RangePartitioner, SparkEnv}
 
@@ -39,21 +40,34 @@ import org.apache.spark.{HashPartitioner, Partitioner, RangePartitioner, SparkEn
 @DeveloperApi
 case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends UnaryNode {
 
-  override def outputPartitioning: Partitioning = newPartitioning
-
-  override def output: Seq[Attribute] = child.output
-
-  override def outputsUnsafeRows: Boolean = child.outputsUnsafeRows
+  override def nodeName: String = if (tungstenMode) "TungstenExchange" else "Exchange"
 
-  override def canProcessSafeRows: Boolean = true
-
-  override def canProcessUnsafeRows: Boolean = {
+  /**
+   * Returns true iff the children outputs aggregate UDTs that are not part of the SQL type.
+   * This only happens with the old aggregate implementation and should be removed in 1.6.
+   */
+  private lazy val tungstenMode: Boolean = {
+    val unserializableUDT = child.schema.exists(_.dataType match {
+      case _: UserDefinedType[_] => true
+      case _ => false
+    })
     // Do not use the Unsafe path if we are using a RangePartitioning, since this may lead to
     // an interpreted RowOrdering being applied to an UnsafeRow, which will lead to
     // ClassCastExceptions at runtime. This check can be removed after SPARK-9054 is fixed.
-    !newPartitioning.isInstanceOf[RangePartitioning]
+    !unserializableUDT && !newPartitioning.isInstanceOf[RangePartitioning]
   }
 
+  override def outputPartitioning: Partitioning = newPartitioning
+
+  override def output: Seq[Attribute] = child.output
+
+  // This setting is somewhat counterintuitive:
+  // If the schema works with UnsafeRow, then we tell the planner that we don't support safe row,
+  // so the planner inserts a converter to convert data into UnsafeRow if needed.
+  override def outputsUnsafeRows: Boolean = tungstenMode
+  override def canProcessSafeRows: Boolean = !tungstenMode
+  override def canProcessUnsafeRows: Boolean = tungstenMode
+
   /**
    * Determines whether records must be defensively copied before being sent to the shuffle.
    * Several of Spark's shuffle components will buffer deserialized Java objects in memory. The
@@ -124,23 +138,9 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
 
   private val serializer: Serializer = {
     val rowDataTypes = child.output.map(_.dataType).toArray
-    // It is true when there is no field that needs to be write out.
-    // For now, we will not use SparkSqlSerializer2 when noField is true.
-    val noField = rowDataTypes == null || rowDataTypes.length == 0
-
-    val useSqlSerializer2 =
-        child.sqlContext.conf.useSqlSerializer2 &&   // SparkSqlSerializer2 is enabled.
-        SparkSqlSerializer2.support(rowDataTypes) &&  // The schema of row is supported.
-        !noField
-
-    if (child.outputsUnsafeRows) {
-      logInfo("Using UnsafeRowSerializer.")
+    if (tungstenMode) {
       new UnsafeRowSerializer(child.output.size)
-    } else if (useSqlSerializer2) {
-      logInfo("Using SparkSqlSerializer2.")
-      new SparkSqlSerializer2(rowDataTypes)
     } else {
-      logInfo("Using SparkSqlSerializer.")
       new SparkSqlSerializer(sparkConf)
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
deleted file mode 100644
index e811f1de3e6dd..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkSqlSerializer2.scala
+++ /dev/null
@@ -1,426 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution
-
-import java.io._
-import java.math.{BigDecimal, BigInteger}
-import java.nio.ByteBuffer
-
-import scala.reflect.ClassTag
-
-import org.apache.spark.Logging
-import org.apache.spark.serializer._
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.{MutableRow, SpecificMutableRow}
-import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
-
-/**
- * The serialization stream for [[SparkSqlSerializer2]]. It assumes that the object passed in
- * its `writeObject` are [[Product2]]. The serialization functions for the key and value of the
- * [[Product2]] are constructed based on their schemata.
- * The benefit of this serialization stream is that compared with general-purpose serializers like
- * Kryo and Java serializer, it can significantly reduce the size of serialized and has a lower
- * allocation cost, which can benefit the shuffle operation. Right now, its main limitations are:
- *  1. It does not support complex types, i.e. Map, Array, and Struct.
- *  2. It assumes that the objects passed in are [[Product2]]. So, it cannot be used when
- *     [[org.apache.spark.util.collection.ExternalSorter]]'s merge sort operation is used because
- *     the objects passed in the serializer are not in the type of [[Product2]]. Also also see
- *     the comment of the `serializer` method in [[Exchange]] for more information on it.
- */
-private[sql] class Serializer2SerializationStream(
-    rowSchema: Array[DataType],
-    out: OutputStream)
-  extends SerializationStream with Logging {
-
-  private val rowOut = new DataOutputStream(new BufferedOutputStream(out))
-  private val writeRowFunc = SparkSqlSerializer2.createSerializationFunction(rowSchema, rowOut)
-
-  override def writeObject[T: ClassTag](t: T): SerializationStream = {
-    val kv = t.asInstanceOf[Product2[InternalRow, InternalRow]]
-    writeKey(kv._1)
-    writeValue(kv._2)
-
-    this
-  }
-
-  override def writeKey[T: ClassTag](t: T): SerializationStream = {
-    // No-op.
-    this
-  }
-
-  override def writeValue[T: ClassTag](t: T): SerializationStream = {
-    writeRowFunc(t.asInstanceOf[InternalRow])
-    this
-  }
-
-  def flush(): Unit = {
-    rowOut.flush()
-  }
-
-  def close(): Unit = {
-    rowOut.close()
-  }
-}
-
-/**
- * The corresponding deserialization stream for [[Serializer2SerializationStream]].
- */
-private[sql] class Serializer2DeserializationStream(
-    rowSchema: Array[DataType],
-    in: InputStream)
-  extends DeserializationStream with Logging  {
-
-  private val rowIn = new DataInputStream(new BufferedInputStream(in))
-
-  private def rowGenerator(schema: Array[DataType]): () => (MutableRow) = {
-    if (schema == null) {
-      () => null
-    } else {
-      // It is safe to reuse the mutable row.
-      val mutableRow = new SpecificMutableRow(schema)
-      () => mutableRow
-    }
-  }
-
-  // Functions used to return rows for key and value.
-  private val getRow = rowGenerator(rowSchema)
-  // Functions used to read a serialized row from the InputStream and deserialize it.
-  private val readRowFunc = SparkSqlSerializer2.createDeserializationFunction(rowSchema, rowIn)
-
-  override def readObject[T: ClassTag](): T = {
-    readValue()
-  }
-
-  override def readKey[T: ClassTag](): T = {
-    null.asInstanceOf[T] // intentionally left blank.
-  }
-
-  override def readValue[T: ClassTag](): T = {
-    readRowFunc(getRow()).asInstanceOf[T]
-  }
-
-  override def close(): Unit = {
-    rowIn.close()
-  }
-}
-
-private[sql] class SparkSqlSerializer2Instance(
-    rowSchema: Array[DataType])
-  extends SerializerInstance {
-
-  def serialize[T: ClassTag](t: T): ByteBuffer =
-    throw new UnsupportedOperationException("Not supported.")
-
-  def deserialize[T: ClassTag](bytes: ByteBuffer): T =
-    throw new UnsupportedOperationException("Not supported.")
-
-  def deserialize[T: ClassTag](bytes: ByteBuffer, loader: ClassLoader): T =
-    throw new UnsupportedOperationException("Not supported.")
-
-  def serializeStream(s: OutputStream): SerializationStream = {
-    new Serializer2SerializationStream(rowSchema, s)
-  }
-
-  def deserializeStream(s: InputStream): DeserializationStream = {
-    new Serializer2DeserializationStream(rowSchema, s)
-  }
-}
-
-/**
- * SparkSqlSerializer2 is a special serializer that creates serialization function and
- * deserialization function based on the schema of data. It assumes that values passed in
- * are Rows.
- */
-private[sql] class SparkSqlSerializer2(rowSchema: Array[DataType])
-  extends Serializer
-  with Logging
-  with Serializable{
-
-  def newInstance(): SerializerInstance = new SparkSqlSerializer2Instance(rowSchema)
-
-  override def supportsRelocationOfSerializedObjects: Boolean = {
-    // SparkSqlSerializer2 is stateless and writes no stream headers
-    true
-  }
-}
-
-private[sql] object SparkSqlSerializer2 {
-
-  final val NULL = 0
-  final val NOT_NULL = 1
-
-  /**
-   * Check if rows with the given schema can be serialized with ShuffleSerializer.
-   * Right now, we do not support a schema having complex types or UDTs, or all data types
-   * of fields are NullTypes.
-   */
-  def support(schema: Array[DataType]): Boolean = {
-    if (schema == null) return true
-
-    var allNullTypes = true
-    var i = 0
-    while (i < schema.length) {
-      schema(i) match {
-        case NullType => // Do nothing
-        case udt: UserDefinedType[_] =>
-          allNullTypes = false
-          return false
-        case array: ArrayType =>
-          allNullTypes = false
-          return false
-        case map: MapType =>
-          allNullTypes = false
-          return false
-        case struct: StructType =>
-          allNullTypes = false
-          return false
-        case _ =>
-          allNullTypes = false
-      }
-      i += 1
-    }
-
-    // If types of fields are all NullTypes, we return false.
-    // Otherwise, we return true.
-    return !allNullTypes
-  }
-
-  /**
-   * The util function to create the serialization function based on the given schema.
-   */
-  def createSerializationFunction(schema: Array[DataType], out: DataOutputStream)
-    : InternalRow => Unit = {
-    (row: InternalRow) =>
-      // If the schema is null, the returned function does nothing when it get called.
-      if (schema != null) {
-        var i = 0
-        while (i < schema.length) {
-          schema(i) match {
-            // When we write values to the underlying stream, we also first write the null byte
-            // first. Then, if the value is not null, we write the contents out.
-
-            case NullType => // Write nothing.
-
-            case BooleanType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeBoolean(row.getBoolean(i))
-              }
-
-            case ByteType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeByte(row.getByte(i))
-              }
-
-            case ShortType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeShort(row.getShort(i))
-              }
-
-            case IntegerType | DateType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeInt(row.getInt(i))
-              }
-
-            case LongType | TimestampType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeLong(row.getLong(i))
-              }
-
-            case FloatType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeFloat(row.getFloat(i))
-              }
-
-            case DoubleType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                out.writeDouble(row.getDouble(i))
-              }
-
-            case StringType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                val bytes = row.getUTF8String(i).getBytes
-                out.writeInt(bytes.length)
-                out.write(bytes)
-              }
-
-            case BinaryType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                val bytes = row.getBinary(i)
-                out.writeInt(bytes.length)
-                out.write(bytes)
-              }
-
-            case decimal: DecimalType =>
-              if (row.isNullAt(i)) {
-                out.writeByte(NULL)
-              } else {
-                out.writeByte(NOT_NULL)
-                val value = row.getDecimal(i, decimal.precision, decimal.scale)
-                val javaBigDecimal = value.toJavaBigDecimal
-                // First, write out the unscaled value.
-                val bytes: Array[Byte] = javaBigDecimal.unscaledValue().toByteArray
-                out.writeInt(bytes.length)
-                out.write(bytes)
-                // Then, write out the scale.
-                out.writeInt(javaBigDecimal.scale())
-              }
-          }
-          i += 1
-        }
-      }
-  }
-
-  /**
-   * The util function to create the deserialization function based on the given schema.
-   */
-  def createDeserializationFunction(
-      schema: Array[DataType],
-      in: DataInputStream): (MutableRow) => InternalRow = {
-    if (schema == null) {
-      (mutableRow: MutableRow) => null
-    } else {
-      (mutableRow: MutableRow) => {
-        var i = 0
-        while (i < schema.length) {
-          schema(i) match {
-            // When we read values from the underlying stream, we also first read the null byte
-            // first. Then, if the value is not null, we update the field of the mutable row.
-
-            case NullType => mutableRow.setNullAt(i) // Read nothing.
-
-            case BooleanType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setBoolean(i, in.readBoolean())
-              }
-
-            case ByteType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setByte(i, in.readByte())
-              }
-
-            case ShortType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setShort(i, in.readShort())
-              }
-
-            case IntegerType | DateType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setInt(i, in.readInt())
-              }
-
-            case LongType | TimestampType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setLong(i, in.readLong())
-              }
-
-            case FloatType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setFloat(i, in.readFloat())
-              }
-
-            case DoubleType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                mutableRow.setDouble(i, in.readDouble())
-              }
-
-            case StringType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                val length = in.readInt()
-                val bytes = new Array[Byte](length)
-                in.readFully(bytes)
-                mutableRow.update(i, UTF8String.fromBytes(bytes))
-              }
-
-            case BinaryType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                val length = in.readInt()
-                val bytes = new Array[Byte](length)
-                in.readFully(bytes)
-                mutableRow.update(i, bytes)
-              }
-
-            case decimal: DecimalType =>
-              if (in.readByte() == NULL) {
-                mutableRow.setNullAt(i)
-              } else {
-                // First, read in the unscaled value.
-                val length = in.readInt()
-                val bytes = new Array[Byte](length)
-                in.readFully(bytes)
-                val unscaledVal = new BigInteger(bytes)
-                // Then, read the scale.
-                val scale = in.readInt()
-                // Finally, create the Decimal object and set it in the row.
-                mutableRow.update(i,
-                  Decimal(new BigDecimal(unscaledVal, scale), decimal.precision, decimal.scale))
-              }
-          }
-          i += 1
-        }
-
-        mutableRow
-      }
-    }
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
deleted file mode 100644
index 7978ed57a937e..0000000000000
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkSqlSerializer2Suite.scala
+++ /dev/null
@@ -1,221 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution
-
-import java.sql.{Timestamp, Date}
-
-import org.apache.spark.sql.test.TestSQLContext
-import org.scalatest.BeforeAndAfterAll
-
-import org.apache.spark.rdd.ShuffledRDD
-import org.apache.spark.serializer.Serializer
-import org.apache.spark.{ShuffleDependency, SparkFunSuite}
-import org.apache.spark.sql.types._
-import org.apache.spark.sql.Row
-import org.apache.spark.sql.{MyDenseVectorUDT, QueryTest}
-
-class SparkSqlSerializer2DataTypeSuite extends SparkFunSuite {
-  // Make sure that we will not use serializer2 for unsupported data types.
-  def checkSupported(dataType: DataType, isSupported: Boolean): Unit = {
-    val testName =
-      s"${if (dataType == null) null else dataType.toString} is " +
-        s"${if (isSupported) "supported" else "unsupported"}"
-
-    test(testName) {
-      assert(SparkSqlSerializer2.support(Array(dataType)) === isSupported)
-    }
-  }
-
-  checkSupported(null, isSupported = true)
-  checkSupported(BooleanType, isSupported = true)
-  checkSupported(ByteType, isSupported = true)
-  checkSupported(ShortType, isSupported = true)
-  checkSupported(IntegerType, isSupported = true)
-  checkSupported(LongType, isSupported = true)
-  checkSupported(FloatType, isSupported = true)
-  checkSupported(DoubleType, isSupported = true)
-  checkSupported(DateType, isSupported = true)
-  checkSupported(TimestampType, isSupported = true)
-  checkSupported(StringType, isSupported = true)
-  checkSupported(BinaryType, isSupported = true)
-  checkSupported(DecimalType(10, 5), isSupported = true)
-  checkSupported(DecimalType.SYSTEM_DEFAULT, isSupported = true)
-
-  // If NullType is the only data type in the schema, we do not support it.
-  checkSupported(NullType, isSupported = false)
-  // For now, ArrayType, MapType, and StructType are not supported.
-  checkSupported(ArrayType(DoubleType, true), isSupported = false)
-  checkSupported(ArrayType(StringType, false), isSupported = false)
-  checkSupported(MapType(IntegerType, StringType, true), isSupported = false)
-  checkSupported(MapType(IntegerType, ArrayType(DoubleType), false), isSupported = false)
-  checkSupported(StructType(StructField("a", IntegerType, true) :: Nil), isSupported = false)
-  // UDTs are not supported right now.
-  checkSupported(new MyDenseVectorUDT, isSupported = false)
-}
-
-abstract class SparkSqlSerializer2Suite extends QueryTest with BeforeAndAfterAll {
-  var allColumns: String = _
-  val serializerClass: Class[Serializer] =
-    classOf[SparkSqlSerializer2].asInstanceOf[Class[Serializer]]
-  var numShufflePartitions: Int = _
-  var useSerializer2: Boolean = _
-
-  protected lazy val ctx = TestSQLContext
-
-  override def beforeAll(): Unit = {
-    numShufflePartitions = ctx.conf.numShufflePartitions
-    useSerializer2 = ctx.conf.useSqlSerializer2
-
-    ctx.sql("set spark.sql.useSerializer2=true")
-
-    val supportedTypes =
-      Seq(StringType, BinaryType, NullType, BooleanType,
-        ByteType, ShortType, IntegerType, LongType,
-        FloatType, DoubleType, DecimalType.SYSTEM_DEFAULT, DecimalType(6, 5),
-        DateType, TimestampType)
-
-    val fields = supportedTypes.zipWithIndex.map { case (dataType, index) =>
-      StructField(s"col$index", dataType, true)
-    }
-    allColumns = fields.map(_.name).mkString(",")
-    val schema = StructType(fields)
-
-    // Create a RDD with all data types supported by SparkSqlSerializer2.
-    val rdd =
-      ctx.sparkContext.parallelize((1 to 1000), 10).map { i =>
-        Row(
-          s"str${i}: test serializer2.",
-          s"binary${i}: test serializer2.".getBytes("UTF-8"),
-          null,
-          i % 2 == 0,
-          i.toByte,
-          i.toShort,
-          i,
-          Long.MaxValue - i.toLong,
-          (i + 0.25).toFloat,
-          (i + 0.75),
-          BigDecimal(Long.MaxValue.toString + ".12345"),
-          new java.math.BigDecimal(s"${i % 9 + 1}" + ".23456"),
-          new Date(i),
-          new Timestamp(i))
-      }
-
-    ctx.createDataFrame(rdd, schema).registerTempTable("shuffle")
-
-    super.beforeAll()
-  }
-
-  override def afterAll(): Unit = {
-    ctx.dropTempTable("shuffle")
-    ctx.sql(s"set spark.sql.shuffle.partitions=$numShufflePartitions")
-    ctx.sql(s"set spark.sql.useSerializer2=$useSerializer2")
-    super.afterAll()
-  }
-
-  def checkSerializer[T <: Serializer](
-      executedPlan: SparkPlan,
-      expectedSerializerClass: Class[T]): Unit = {
-    executedPlan.foreach {
-      case exchange: Exchange =>
-        val shuffledRDD = exchange.execute()
-        val dependency = shuffledRDD.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]]
-        val serializerNotSetMessage =
-          s"Expected $expectedSerializerClass as the serializer of Exchange. " +
-          s"However, the serializer was not set."
-        val serializer = dependency.serializer.getOrElse(fail(serializerNotSetMessage))
-        val isExpectedSerializer =
-          serializer.getClass == expectedSerializerClass ||
-            serializer.getClass == classOf[UnsafeRowSerializer]
-        val wrongSerializerErrorMessage =
-          s"Expected ${expectedSerializerClass.getCanonicalName} or " +
-            s"${classOf[UnsafeRowSerializer].getCanonicalName}. But " +
-            s"${serializer.getClass.getCanonicalName} is used."
-        assert(isExpectedSerializer, wrongSerializerErrorMessage)
-      case _ => // Ignore other nodes.
-    }
-  }
-
-  test("key schema and value schema are not nulls") {
-    val df = ctx.sql(s"SELECT DISTINCT ${allColumns} FROM shuffle")
-    checkSerializer(df.queryExecution.executedPlan, serializerClass)
-    checkAnswer(
-      df,
-      ctx.table("shuffle").collect())
-  }
-
-  test("key schema is null") {
-    val aggregations = allColumns.split(",").map(c => s"COUNT($c)").mkString(",")
-    val df = ctx.sql(s"SELECT $aggregations FROM shuffle")
-    checkSerializer(df.queryExecution.executedPlan, serializerClass)
-    checkAnswer(
-      df,
-      Row(1000, 1000, 0, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000))
-  }
-
-  test("value schema is null") {
-    val df = ctx.sql(s"SELECT col0 FROM shuffle ORDER BY col0")
-    checkSerializer(df.queryExecution.executedPlan, serializerClass)
-    assert(df.map(r => r.getString(0)).collect().toSeq ===
-      ctx.table("shuffle").select("col0").map(r => r.getString(0)).collect().sorted.toSeq)
-  }
-
-  test("no map output field") {
-    val df = ctx.sql(s"SELECT 1 + 1 FROM shuffle")
-    checkSerializer(df.queryExecution.executedPlan, classOf[SparkSqlSerializer])
-  }
-
-  test("types of fields are all NullTypes") {
-    // Test range partitioning code path.
-    val nulls = ctx.sql(s"SELECT null as a, null as b, null as c")
-    val df = nulls.unionAll(nulls).sort("a")
-    checkSerializer(df.queryExecution.executedPlan, classOf[SparkSqlSerializer])
-    checkAnswer(
-      df,
-      Row(null, null, null) :: Row(null, null, null) :: Nil)
-
-    // Test hash partitioning code path.
-    val oneRow = ctx.sql(s"SELECT DISTINCT null, null, null FROM shuffle")
-    checkSerializer(oneRow.queryExecution.executedPlan, classOf[SparkSqlSerializer])
-    checkAnswer(
-      oneRow,
-      Row(null, null, null))
-  }
-}
-
-/** Tests SparkSqlSerializer2 with sort based shuffle without sort merge. */
-class SparkSqlSerializer2SortShuffleSuite extends SparkSqlSerializer2Suite {
-  override def beforeAll(): Unit = {
-    super.beforeAll()
-    // Sort merge will not be triggered.
-    val bypassMergeThreshold =
-      ctx.sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200)
-    ctx.sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold-1}")
-  }
-}
-
-/** For now, we will use SparkSqlSerializer for sort based shuffle with sort merge. */
-class SparkSqlSerializer2SortMergeShuffleSuite extends SparkSqlSerializer2Suite {
-
-  override def beforeAll(): Unit = {
-    super.beforeAll()
-    // To trigger the sort merge.
-    val bypassMergeThreshold =
-      ctx.sparkContext.conf.getInt("spark.shuffle.sort.bypassMergeThreshold", 200)
-    ctx.sql(s"set spark.sql.shuffle.partitions=${bypassMergeThreshold + 1}")
-  }
-}

From 2432c2e239f66049a7a7d7e0591204abcc993f1a Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 7 Aug 2015 11:28:43 -0700
Subject: [PATCH 117/633] [SPARK-8382] [SQL] Improve Analysis Unit test
 framework

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8025 from cloud-fan/analysis and squashes the following commits:

51461b1 [Wenchen Fan] move test file to test folder
ec88ace [Wenchen Fan] Improve Analysis Unit test framework
---
 .../analysis/AnalysisErrorSuite.scala         | 48 +++++-----------
 .../sql/catalyst/analysis/AnalysisSuite.scala | 55 +------------------
 .../sql/catalyst/analysis/AnalysisTest.scala  | 33 +----------
 .../sql/catalyst/analysis/TestRelations.scala | 51 +++++++++++++++++
 .../BooleanSimplificationSuite.scala          | 19 ++++---
 5 files changed, 79 insertions(+), 127 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TestRelations.scala

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index 26935c6e3b24f..63b475b6366c2 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -42,8 +42,8 @@ case class UnresolvedTestPlan() extends LeafNode {
   override def output: Seq[Attribute] = Nil
 }
 
-class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
-  import AnalysisSuite._
+class AnalysisErrorSuite extends AnalysisTest with BeforeAndAfter {
+  import TestRelations._
 
   def errorTest(
       name: String,
@@ -51,15 +51,7 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
       errorMessages: Seq[String],
       caseSensitive: Boolean = true): Unit = {
     test(name) {
-      val error = intercept[AnalysisException] {
-        if (caseSensitive) {
-          caseSensitiveAnalyze(plan)
-        } else {
-          caseInsensitiveAnalyze(plan)
-        }
-      }
-
-      errorMessages.foreach(m => assert(error.getMessage.toLowerCase.contains(m.toLowerCase)))
+      assertAnalysisError(plan, errorMessages, caseSensitive)
     }
   }
 
@@ -69,21 +61,21 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
     "single invalid type, single arg",
     testRelation.select(TestFunction(dateLit :: Nil, IntegerType :: Nil).as('a)),
     "cannot resolve" :: "testfunction" :: "argument 1" :: "requires int type" ::
-    "'null' is of date type" ::Nil)
+    "'null' is of date type" :: Nil)
 
   errorTest(
     "single invalid type, second arg",
     testRelation.select(
       TestFunction(dateLit :: dateLit :: Nil, DateType :: IntegerType :: Nil).as('a)),
     "cannot resolve" :: "testfunction" :: "argument 2" :: "requires int type" ::
-    "'null' is of date type" ::Nil)
+    "'null' is of date type" :: Nil)
 
   errorTest(
     "multiple invalid type",
     testRelation.select(
       TestFunction(dateLit :: dateLit :: Nil, IntegerType :: IntegerType :: Nil).as('a)),
     "cannot resolve" :: "testfunction" :: "argument 1" :: "argument 2" ::
-    "requires int type" :: "'null' is of date type" ::Nil)
+    "requires int type" :: "'null' is of date type" :: Nil)
 
   errorTest(
     "unresolved window function",
@@ -169,11 +161,7 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
 
     assert(plan.resolved)
 
-    val message = intercept[AnalysisException] {
-      caseSensitiveAnalyze(plan)
-    }.getMessage
-
-    assert(message.contains("resolved attribute(s) a#1 missing from a#2"))
+    assertAnalysisError(plan, "resolved attribute(s) a#1 missing from a#2" :: Nil)
   }
 
   test("error test for self-join") {
@@ -194,10 +182,8 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
           AttributeReference("a", BinaryType)(exprId = ExprId(2)),
           AttributeReference("b", IntegerType)(exprId = ExprId(1))))
 
-    val error = intercept[AnalysisException] {
-      caseSensitiveAnalyze(plan)
-    }
-    assert(error.message.contains("binary type expression a cannot be used in grouping expression"))
+    assertAnalysisError(plan,
+      "binary type expression a cannot be used in grouping expression" :: Nil)
 
     val plan2 =
       Aggregate(
@@ -207,10 +193,8 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
           AttributeReference("a", MapType(IntegerType, StringType))(exprId = ExprId(2)),
           AttributeReference("b", IntegerType)(exprId = ExprId(1))))
 
-    val error2 = intercept[AnalysisException] {
-      caseSensitiveAnalyze(plan2)
-    }
-    assert(error2.message.contains("map type expression a cannot be used in grouping expression"))
+    assertAnalysisError(plan2,
+      "map type expression a cannot be used in grouping expression" :: Nil)
   }
 
   test("Join can't work on binary and map types") {
@@ -226,10 +210,7 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
         Some(EqualTo(AttributeReference("a", BinaryType)(exprId = ExprId(2)),
           AttributeReference("c", BinaryType)(exprId = ExprId(4)))))
 
-    val error = intercept[AnalysisException] {
-      caseSensitiveAnalyze(plan)
-    }
-    assert(error.message.contains("binary type expression a cannot be used in join conditions"))
+    assertAnalysisError(plan, "binary type expression a cannot be used in join conditions" :: Nil)
 
     val plan2 =
       Join(
@@ -243,9 +224,6 @@ class AnalysisErrorSuite extends SparkFunSuite with BeforeAndAfter {
         Some(EqualTo(AttributeReference("a", MapType(IntegerType, StringType))(exprId = ExprId(2)),
           AttributeReference("c", MapType(IntegerType, StringType))(exprId = ExprId(4)))))
 
-    val error2 = intercept[AnalysisException] {
-      caseSensitiveAnalyze(plan2)
-    }
-    assert(error2.message.contains("map type expression a cannot be used in join conditions"))
+    assertAnalysisError(plan2, "map type expression a cannot be used in join conditions" :: Nil)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index 221b4e92f086c..c944bc69e25b0 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -24,61 +24,8 @@ import org.apache.spark.sql.catalyst.SimpleCatalystConf
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.dsl.plans._
 
-// todo: remove this and use AnalysisTest instead.
-object AnalysisSuite {
-  val caseSensitiveConf = new SimpleCatalystConf(true)
-  val caseInsensitiveConf = new SimpleCatalystConf(false)
-
-  val caseSensitiveCatalog = new SimpleCatalog(caseSensitiveConf)
-  val caseInsensitiveCatalog = new SimpleCatalog(caseInsensitiveConf)
-
-  val caseSensitiveAnalyzer =
-    new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitiveConf) {
-      override val extendedResolutionRules = EliminateSubQueries :: Nil
-    }
-  val caseInsensitiveAnalyzer =
-    new Analyzer(caseInsensitiveCatalog, EmptyFunctionRegistry, caseInsensitiveConf) {
-      override val extendedResolutionRules = EliminateSubQueries :: Nil
-    }
-
-  def caseSensitiveAnalyze(plan: LogicalPlan): Unit =
-    caseSensitiveAnalyzer.checkAnalysis(caseSensitiveAnalyzer.execute(plan))
-
-  def caseInsensitiveAnalyze(plan: LogicalPlan): Unit =
-    caseInsensitiveAnalyzer.checkAnalysis(caseInsensitiveAnalyzer.execute(plan))
-
-  val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)())
-  val testRelation2 = LocalRelation(
-    AttributeReference("a", StringType)(),
-    AttributeReference("b", StringType)(),
-    AttributeReference("c", DoubleType)(),
-    AttributeReference("d", DecimalType(10, 2))(),
-    AttributeReference("e", ShortType)())
-
-  val nestedRelation = LocalRelation(
-    AttributeReference("top", StructType(
-      StructField("duplicateField", StringType) ::
-        StructField("duplicateField", StringType) ::
-        StructField("differentCase", StringType) ::
-        StructField("differentcase", StringType) :: Nil
-    ))())
-
-  val nestedRelation2 = LocalRelation(
-    AttributeReference("top", StructType(
-      StructField("aField", StringType) ::
-        StructField("bField", StringType) ::
-        StructField("cField", StringType) :: Nil
-    ))())
-
-  val listRelation = LocalRelation(
-    AttributeReference("list", ArrayType(IntegerType))())
-
-  caseSensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
-  caseInsensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
-}
-
-
 class AnalysisSuite extends AnalysisTest {
+  import TestRelations._
 
   test("union project *") {
     val plan = (1 to 100)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
index fdb4f28950daf..ee1f8f54251e0 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
@@ -17,40 +17,11 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
-import org.apache.spark.sql.AnalysisException
-import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.PlanTest
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.SimpleCatalystConf
-import org.apache.spark.sql.types._
 
 trait AnalysisTest extends PlanTest {
-  val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)())
-
-  val testRelation2 = LocalRelation(
-    AttributeReference("a", StringType)(),
-    AttributeReference("b", StringType)(),
-    AttributeReference("c", DoubleType)(),
-    AttributeReference("d", DecimalType(10, 2))(),
-    AttributeReference("e", ShortType)())
-
-  val nestedRelation = LocalRelation(
-    AttributeReference("top", StructType(
-      StructField("duplicateField", StringType) ::
-        StructField("duplicateField", StringType) ::
-        StructField("differentCase", StringType) ::
-        StructField("differentcase", StringType) :: Nil
-    ))())
-
-  val nestedRelation2 = LocalRelation(
-    AttributeReference("top", StructType(
-      StructField("aField", StringType) ::
-        StructField("bField", StringType) ::
-        StructField("cField", StringType) :: Nil
-    ))())
-
-  val listRelation = LocalRelation(
-    AttributeReference("list", ArrayType(IntegerType))())
 
   val (caseSensitiveAnalyzer, caseInsensitiveAnalyzer) = {
     val caseSensitiveConf = new SimpleCatalystConf(true)
@@ -59,8 +30,8 @@ trait AnalysisTest extends PlanTest {
     val caseSensitiveCatalog = new SimpleCatalog(caseSensitiveConf)
     val caseInsensitiveCatalog = new SimpleCatalog(caseInsensitiveConf)
 
-    caseSensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
-    caseInsensitiveCatalog.registerTable(Seq("TaBlE"), testRelation)
+    caseSensitiveCatalog.registerTable(Seq("TaBlE"), TestRelations.testRelation)
+    caseInsensitiveCatalog.registerTable(Seq("TaBlE"), TestRelations.testRelation)
 
     new Analyzer(caseSensitiveCatalog, EmptyFunctionRegistry, caseSensitiveConf) {
       override val extendedResolutionRules = EliminateSubQueries :: Nil
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TestRelations.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TestRelations.scala
new file mode 100644
index 0000000000000..05b870705e7ea
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/TestRelations.scala
@@ -0,0 +1,51 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.analysis
+
+import org.apache.spark.sql.catalyst.expressions.AttributeReference
+import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
+import org.apache.spark.sql.types._
+
+object TestRelations {
+  val testRelation = LocalRelation(AttributeReference("a", IntegerType, nullable = true)())
+
+  val testRelation2 = LocalRelation(
+    AttributeReference("a", StringType)(),
+    AttributeReference("b", StringType)(),
+    AttributeReference("c", DoubleType)(),
+    AttributeReference("d", DecimalType(10, 2))(),
+    AttributeReference("e", ShortType)())
+
+  val nestedRelation = LocalRelation(
+    AttributeReference("top", StructType(
+      StructField("duplicateField", StringType) ::
+        StructField("duplicateField", StringType) ::
+        StructField("differentCase", StringType) ::
+        StructField("differentcase", StringType) :: Nil
+    ))())
+
+  val nestedRelation2 = LocalRelation(
+    AttributeReference("top", StructType(
+      StructField("aField", StringType) ::
+        StructField("bField", StringType) ::
+        StructField("cField", StringType) :: Nil
+    ))())
+
+  val listRelation = LocalRelation(
+    AttributeReference("list", ArrayType(IntegerType))())
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
index d4916ea8d273a..1877cff1334bd 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
@@ -17,7 +17,8 @@
 
 package org.apache.spark.sql.catalyst.optimizer
 
-import org.apache.spark.sql.catalyst.analysis.{AnalysisSuite, EliminateSubQueries}
+import org.apache.spark.sql.catalyst.SimpleCatalystConf
+import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.PlanTest
@@ -88,20 +89,24 @@ class BooleanSimplificationSuite extends PlanTest with PredicateHelper {
       ('a === 'b || 'b > 3 && 'a > 3 && 'a < 5))
   }
 
-  private def caseInsensitiveAnalyse(plan: LogicalPlan) =
-    AnalysisSuite.caseInsensitiveAnalyzer.execute(plan)
+  private val caseInsensitiveAnalyzer =
+    new Analyzer(EmptyCatalog, EmptyFunctionRegistry, new SimpleCatalystConf(false))
 
   test("(a && b) || (a && c) => a && (b || c) when case insensitive") {
-    val plan = caseInsensitiveAnalyse(testRelation.where(('a > 2 && 'b > 3) || ('A > 2 && 'b < 5)))
+    val plan = caseInsensitiveAnalyzer.execute(
+      testRelation.where(('a > 2 && 'b > 3) || ('A > 2 && 'b < 5)))
     val actual = Optimize.execute(plan)
-    val expected = caseInsensitiveAnalyse(testRelation.where('a > 2 && ('b > 3 || 'b < 5)))
+    val expected = caseInsensitiveAnalyzer.execute(
+      testRelation.where('a > 2 && ('b > 3 || 'b < 5)))
     comparePlans(actual, expected)
   }
 
   test("(a || b) && (a || c) => a || (b && c) when case insensitive") {
-    val plan = caseInsensitiveAnalyse(testRelation.where(('a > 2 || 'b > 3) && ('A > 2 || 'b < 5)))
+    val plan = caseInsensitiveAnalyzer.execute(
+      testRelation.where(('a > 2 || 'b > 3) && ('A > 2 || 'b < 5)))
     val actual = Optimize.execute(plan)
-    val expected = caseInsensitiveAnalyse(testRelation.where('a > 2 || ('b > 3 && 'b < 5)))
+    val expected = caseInsensitiveAnalyzer.execute(
+      testRelation.where('a > 2 || ('b > 3 && 'b < 5)))
     comparePlans(actual, expected)
   }
 }

From 9897cc5e3d6c70f7e45e887e2c6fc24dfa1adada Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 7 Aug 2015 11:29:13 -0700
Subject: [PATCH 118/633] [SPARK-9736] [SQL] JoinedRow.anyNull should delegate
 to the underlying rows.

JoinedRow.anyNull currently loops through every field to check for null, which is inefficient if the underlying rows are UnsafeRows. It should just delegate to the underlying implementation.

Author: Reynold Xin <rxin@databricks.com>

Closes #8027 from rxin/SPARK-9736 and squashes the following commits:

03a2e92 [Reynold Xin] Include all files.
90f1add [Reynold Xin] [SPARK-9736][SQL] JoinedRow.anyNull should delegate to the underlying rows.
---
 .../spark/sql/catalyst/InternalRow.scala      |  10 +-
 .../sql/catalyst/expressions/JoinedRow.scala  | 144 ++++++++++++++++++
 .../sql/catalyst/expressions/Projection.scala | 119 ---------------
 .../spark/sql/catalyst/expressions/rows.scala |  12 +-
 4 files changed, 156 insertions(+), 129 deletions(-)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
index 85b4bf3b6aef5..eba95c5c8b908 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/InternalRow.scala
@@ -37,15 +37,7 @@ abstract class InternalRow extends SpecializedGetters with Serializable {
   def copy(): InternalRow
 
   /** Returns true if there are any NULL values in this row. */
-  def anyNull: Boolean = {
-    val len = numFields
-    var i = 0
-    while (i < len) {
-      if (isNullAt(i)) { return true }
-      i += 1
-    }
-    false
-  }
+  def anyNull: Boolean
 
   /* ---------------------- utility methods for Scala ---------------------- */
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala
new file mode 100644
index 0000000000000..b76757c93523d
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala
@@ -0,0 +1,144 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
+
+
+/**
+ * A mutable wrapper that makes two rows appear as a single concatenated row.  Designed to
+ * be instantiated once per thread and reused.
+ */
+class JoinedRow extends InternalRow {
+  private[this] var row1: InternalRow = _
+  private[this] var row2: InternalRow = _
+
+  def this(left: InternalRow, right: InternalRow) = {
+    this()
+    row1 = left
+    row2 = right
+  }
+
+  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
+  def apply(r1: InternalRow, r2: InternalRow): InternalRow = {
+    row1 = r1
+    row2 = r2
+    this
+  }
+
+  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
+  def withLeft(newLeft: InternalRow): InternalRow = {
+    row1 = newLeft
+    this
+  }
+
+  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
+  def withRight(newRight: InternalRow): InternalRow = {
+    row2 = newRight
+    this
+  }
+
+  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
+    assert(fieldTypes.length == row1.numFields + row2.numFields)
+    val (left, right) = fieldTypes.splitAt(row1.numFields)
+    row1.toSeq(left) ++ row2.toSeq(right)
+  }
+
+  override def numFields: Int = row1.numFields + row2.numFields
+
+  override def get(i: Int, dt: DataType): AnyRef =
+    if (i < row1.numFields) row1.get(i, dt) else row2.get(i - row1.numFields, dt)
+
+  override def isNullAt(i: Int): Boolean =
+    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
+
+  override def getBoolean(i: Int): Boolean =
+    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
+
+  override def getByte(i: Int): Byte =
+    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
+
+  override def getShort(i: Int): Short =
+    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
+
+  override def getInt(i: Int): Int =
+    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
+
+  override def getLong(i: Int): Long =
+    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
+
+  override def getFloat(i: Int): Float =
+    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
+
+  override def getDouble(i: Int): Double =
+    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
+
+  override def getDecimal(i: Int, precision: Int, scale: Int): Decimal = {
+    if (i < row1.numFields) {
+      row1.getDecimal(i, precision, scale)
+    } else {
+      row2.getDecimal(i - row1.numFields, precision, scale)
+    }
+  }
+
+  override def getUTF8String(i: Int): UTF8String =
+    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
+
+  override def getBinary(i: Int): Array[Byte] =
+    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
+
+  override def getArray(i: Int): ArrayData =
+    if (i < row1.numFields) row1.getArray(i) else row2.getArray(i - row1.numFields)
+
+  override def getInterval(i: Int): CalendarInterval =
+    if (i < row1.numFields) row1.getInterval(i) else row2.getInterval(i - row1.numFields)
+
+  override def getMap(i: Int): MapData =
+    if (i < row1.numFields) row1.getMap(i) else row2.getMap(i - row1.numFields)
+
+  override def getStruct(i: Int, numFields: Int): InternalRow = {
+    if (i < row1.numFields) {
+      row1.getStruct(i, numFields)
+    } else {
+      row2.getStruct(i - row1.numFields, numFields)
+    }
+  }
+
+  override def anyNull: Boolean = row1.anyNull || row2.anyNull
+
+  override def copy(): InternalRow = {
+    val copy1 = row1.copy()
+    val copy2 = row2.copy()
+    new JoinedRow(copy1, copy2)
+  }
+
+  override def toString: String = {
+    // Make sure toString never throws NullPointerException.
+    if ((row1 eq null) && (row2 eq null)) {
+      "[ empty row ]"
+    } else if (row1 eq null) {
+      row2.toString
+    } else if (row2 eq null) {
+      row1.toString
+    } else {
+      s"{${row1.toString} + ${row2.toString}}"
+    }
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 59ce7fc4f2c63..796bc327a3db1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -169,122 +169,3 @@ object FromUnsafeProjection {
     GenerateSafeProjection.generate(exprs)
   }
 }
-
-/**
- * A mutable wrapper that makes two rows appear as a single concatenated row.  Designed to
- * be instantiated once per thread and reused.
- */
-class JoinedRow extends InternalRow {
-  private[this] var row1: InternalRow = _
-  private[this] var row2: InternalRow = _
-
-  def this(left: InternalRow, right: InternalRow) = {
-    this()
-    row1 = left
-    row2 = right
-  }
-
-  /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
-  def apply(r1: InternalRow, r2: InternalRow): InternalRow = {
-    row1 = r1
-    row2 = r2
-    this
-  }
-
-  /** Updates this JoinedRow by updating its left base row.  Returns itself. */
-  def withLeft(newLeft: InternalRow): InternalRow = {
-    row1 = newLeft
-    this
-  }
-
-  /** Updates this JoinedRow by updating its right base row.  Returns itself. */
-  def withRight(newRight: InternalRow): InternalRow = {
-    row2 = newRight
-    this
-  }
-
-  override def toSeq(fieldTypes: Seq[DataType]): Seq[Any] = {
-    assert(fieldTypes.length == row1.numFields + row2.numFields)
-    val (left, right) = fieldTypes.splitAt(row1.numFields)
-    row1.toSeq(left) ++ row2.toSeq(right)
-  }
-
-  override def numFields: Int = row1.numFields + row2.numFields
-
-  override def get(i: Int, dt: DataType): AnyRef =
-    if (i < row1.numFields) row1.get(i, dt) else row2.get(i - row1.numFields, dt)
-
-  override def isNullAt(i: Int): Boolean =
-    if (i < row1.numFields) row1.isNullAt(i) else row2.isNullAt(i - row1.numFields)
-
-  override def getBoolean(i: Int): Boolean =
-    if (i < row1.numFields) row1.getBoolean(i) else row2.getBoolean(i - row1.numFields)
-
-  override def getByte(i: Int): Byte =
-    if (i < row1.numFields) row1.getByte(i) else row2.getByte(i - row1.numFields)
-
-  override def getShort(i: Int): Short =
-    if (i < row1.numFields) row1.getShort(i) else row2.getShort(i - row1.numFields)
-
-  override def getInt(i: Int): Int =
-    if (i < row1.numFields) row1.getInt(i) else row2.getInt(i - row1.numFields)
-
-  override def getLong(i: Int): Long =
-    if (i < row1.numFields) row1.getLong(i) else row2.getLong(i - row1.numFields)
-
-  override def getFloat(i: Int): Float =
-    if (i < row1.numFields) row1.getFloat(i) else row2.getFloat(i - row1.numFields)
-
-  override def getDouble(i: Int): Double =
-    if (i < row1.numFields) row1.getDouble(i) else row2.getDouble(i - row1.numFields)
-
-  override def getDecimal(i: Int, precision: Int, scale: Int): Decimal = {
-    if (i < row1.numFields) {
-      row1.getDecimal(i, precision, scale)
-    } else {
-      row2.getDecimal(i - row1.numFields, precision, scale)
-    }
-  }
-
-  override def getUTF8String(i: Int): UTF8String =
-    if (i < row1.numFields) row1.getUTF8String(i) else row2.getUTF8String(i - row1.numFields)
-
-  override def getBinary(i: Int): Array[Byte] =
-    if (i < row1.numFields) row1.getBinary(i) else row2.getBinary(i - row1.numFields)
-
-  override def getArray(i: Int): ArrayData =
-    if (i < row1.numFields) row1.getArray(i) else row2.getArray(i - row1.numFields)
-
-  override def getInterval(i: Int): CalendarInterval =
-    if (i < row1.numFields) row1.getInterval(i) else row2.getInterval(i - row1.numFields)
-
-  override def getMap(i: Int): MapData =
-    if (i < row1.numFields) row1.getMap(i) else row2.getMap(i - row1.numFields)
-
-  override def getStruct(i: Int, numFields: Int): InternalRow = {
-    if (i < row1.numFields) {
-      row1.getStruct(i, numFields)
-    } else {
-      row2.getStruct(i - row1.numFields, numFields)
-    }
-  }
-
-  override def copy(): InternalRow = {
-    val copy1 = row1.copy()
-    val copy2 = row2.copy()
-    new JoinedRow(copy1, copy2)
-  }
-
-  override def toString: String = {
-    // Make sure toString never throws NullPointerException.
-    if ((row1 eq null) && (row2 eq null)) {
-      "[ empty row ]"
-    } else if (row1 eq null) {
-      row2.toString
-    } else if (row2 eq null) {
-      row1.toString
-    } else {
-      s"{${row1.toString} + ${row2.toString}}"
-    }
-  }
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
index 11d10b2d8a48b..017efd2a166a7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/rows.scala
@@ -49,7 +49,17 @@ trait BaseGenericInternalRow extends InternalRow {
   override def getMap(ordinal: Int): MapData = getAs(ordinal)
   override def getStruct(ordinal: Int, numFields: Int): InternalRow = getAs(ordinal)
 
-  override def toString(): String = {
+  override def anyNull: Boolean = {
+    val len = numFields
+    var i = 0
+    while (i < len) {
+      if (isNullAt(i)) { return true }
+      i += 1
+    }
+    false
+  }
+
+  override def toString: String = {
     if (numFields == 0) {
       "[empty row]"
     } else {

From aeddeafc03d77a5149d2c8f9489b0ca83e6b3e03 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 7 Aug 2015 13:26:03 -0700
Subject: [PATCH 119/633] [SPARK-9667][SQL] followup: Use
 GenerateUnsafeProjection.canSupport to test Exchange supported data types.

This way we recursively test the data types.

cc chenghao-intel

Author: Reynold Xin <rxin@databricks.com>

Closes #8036 from rxin/cansupport and squashes the following commits:

f7302ff [Reynold Xin] Can GenerateUnsafeProjection.canSupport to test Exchange supported data types.
---
 .../org/apache/spark/sql/execution/Exchange.scala | 15 ++++-----------
 1 file changed, 4 insertions(+), 11 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 60087f2ca4a3e..49bb729800863 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -27,9 +27,9 @@ import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors.attachTree
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.rules.Rule
-import org.apache.spark.sql.types.UserDefinedType
 import org.apache.spark.util.MutablePair
 import org.apache.spark.{HashPartitioner, Partitioner, RangePartitioner, SparkEnv}
 
@@ -43,18 +43,11 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
   override def nodeName: String = if (tungstenMode) "TungstenExchange" else "Exchange"
 
   /**
-   * Returns true iff the children outputs aggregate UDTs that are not part of the SQL type.
-   * This only happens with the old aggregate implementation and should be removed in 1.6.
+   * Returns true iff we can support the data type, and we are not doing range partitioning.
    */
   private lazy val tungstenMode: Boolean = {
-    val unserializableUDT = child.schema.exists(_.dataType match {
-      case _: UserDefinedType[_] => true
-      case _ => false
-    })
-    // Do not use the Unsafe path if we are using a RangePartitioning, since this may lead to
-    // an interpreted RowOrdering being applied to an UnsafeRow, which will lead to
-    // ClassCastExceptions at runtime. This check can be removed after SPARK-9054 is fixed.
-    !unserializableUDT && !newPartitioning.isInstanceOf[RangePartitioning]
+    GenerateUnsafeProjection.canSupport(child.schema) &&
+      !newPartitioning.isInstanceOf[RangePartitioning]
   }
 
   override def outputPartitioning: Partitioning = newPartitioning

From 05d04e10a8ea030bea840c3c5ba93ecac479a039 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 7 Aug 2015 13:41:45 -0700
Subject: [PATCH 120/633] [SPARK-9733][SQL] Improve physical plan explain for
 data sources

All data sources show up as "PhysicalRDD" in physical plan explain. It'd be better if we can show the name of the data source.

Without this patch:
```
== Physical Plan ==
NewAggregate with UnsafeHybridAggregationIterator ArrayBuffer(date#0, cat#1) ArrayBuffer((sum(CAST((CAST(count#2, IntegerType) + 1), LongType))2,mode=Final,isDistinct=false))
 Exchange hashpartitioning(date#0,cat#1)
  NewAggregate with UnsafeHybridAggregationIterator ArrayBuffer(date#0, cat#1) ArrayBuffer((sum(CAST((CAST(count#2, IntegerType) + 1), LongType))2,mode=Partial,isDistinct=false))
   PhysicalRDD [date#0,cat#1,count#2], MapPartitionsRDD[3] at
```

With this patch:
```
== Physical Plan ==
TungstenAggregate(key=[date#0,cat#1], value=[(sum(CAST((CAST(count#2, IntegerType) + 1), LongType)),mode=Final,isDistinct=false)]
 Exchange hashpartitioning(date#0,cat#1)
  TungstenAggregate(key=[date#0,cat#1], value=[(sum(CAST((CAST(count#2, IntegerType) + 1), LongType)),mode=Partial,isDistinct=false)]
   ConvertToUnsafe
    Scan ParquetRelation[file:/scratch/rxin/spark/sales4][date#0,cat#1,count#2]
```

Author: Reynold Xin <rxin@databricks.com>

Closes #8024 from rxin/SPARK-9733 and squashes the following commits:

811b90e [Reynold Xin] Fixed Python test case.
52cab77 [Reynold Xin] Cast.
eea9ccc [Reynold Xin] Fix test case.
fcecb22 [Reynold Xin] [SPARK-9733][SQL] Improve explain message for data source scan node.
---
 python/pyspark/sql/dataframe.py               |  4 +---
 .../spark/sql/catalyst/expressions/Cast.scala |  4 ++--
 .../expressions/aggregate/interfaces.scala    |  2 +-
 .../org/apache/spark/sql/SQLContext.scala     |  4 ----
 .../spark/sql/execution/ExistingRDD.scala     | 15 ++++++++++++-
 .../spark/sql/execution/SparkStrategies.scala |  4 ++--
 .../aggregate/TungstenAggregate.scala         |  9 +++++---
 .../datasources/DataSourceStrategy.scala      | 22 +++++++++++++------
 .../apache/spark/sql/sources/interfaces.scala |  2 +-
 .../execution/RowFormatConvertersSuite.scala  |  4 ++--
 .../sql/hive/execution/HiveExplainSuite.scala |  2 +-
 11 files changed, 45 insertions(+), 27 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 0f3480c239187..47d5a6a43a84d 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -212,8 +212,7 @@ def explain(self, extended=False):
         :param extended: boolean, default ``False``. If ``False``, prints only the physical plan.
 
         >>> df.explain()
-        PhysicalRDD [age#0,name#1], MapPartitionsRDD[...] at applySchemaToPythonRDD at\
-          NativeMethodAccessorImpl.java:...
+        Scan PhysicalRDD[age#0,name#1]
 
         >>> df.explain(True)
         == Parsed Logical Plan ==
@@ -224,7 +223,6 @@ def explain(self, extended=False):
         ...
         == Physical Plan ==
         ...
-        == RDD ==
         """
         if extended:
             print(self._jdf.queryExecution().toString())
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 39f99700c8a26..946c5a9c04f14 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -107,6 +107,8 @@ object Cast {
 case class Cast(child: Expression, dataType: DataType)
   extends UnaryExpression with CodegenFallback {
 
+  override def toString: String = s"cast($child as ${dataType.simpleString})"
+
   override def checkInputDataTypes(): TypeCheckResult = {
     if (Cast.canCast(child.dataType, dataType)) {
       TypeCheckResult.TypeCheckSuccess
@@ -118,8 +120,6 @@ case class Cast(child: Expression, dataType: DataType)
 
   override def nullable: Boolean = Cast.forceNullable(child.dataType, dataType) || child.nullable
 
-  override def toString: String = s"CAST($child, $dataType)"
-
   // [[func]] assumes the input is no longer null because eval already does the null check.
   @inline private[this] def buildCast[T](a: Any, func: T => Any): Any = func(a.asInstanceOf[T])
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
index 4abfdfe87d5e9..576d8c7a3a68a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/interfaces.scala
@@ -93,7 +93,7 @@ private[sql] case class AggregateExpression2(
     AttributeSet(childReferences)
   }
 
-  override def toString: String = s"(${aggregateFunction}2,mode=$mode,isDistinct=$isDistinct)"
+  override def toString: String = s"(${aggregateFunction},mode=$mode,isDistinct=$isDistinct)"
 }
 
 abstract class AggregateFunction2
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 075c0ea2544b2..832572571cabd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -1011,9 +1011,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
       def output =
         analyzed.output.map(o => s"${o.name}: ${o.dataType.simpleString}").mkString(", ")
 
-      // TODO previously will output RDD details by run (${stringOrError(toRdd.toDebugString)})
-      // however, the `toRdd` will cause the real execution, which is not what we want.
-      // We need to think about how to avoid the side effect.
       s"""== Parsed Logical Plan ==
          |${stringOrError(logical)}
          |== Analyzed Logical Plan ==
@@ -1024,7 +1021,6 @@ class SQLContext(@transient val sparkContext: SparkContext)
          |== Physical Plan ==
          |${stringOrError(executedPlan)}
          |Code Generation: ${stringOrError(executedPlan.codegenEnabled)}
-         |== RDD ==
       """.stripMargin.trim
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
index fbaa8e276ddb7..cae7ca5cbdc88 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.{InternalRow, CatalystTypeConverters}
 import org.apache.spark.sql.catalyst.analysis.MultiInstanceRelation
 import org.apache.spark.sql.catalyst.expressions.{Attribute, GenericMutableRow}
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Statistics}
+import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.sql.{Row, SQLContext}
 
@@ -95,11 +96,23 @@ private[sql] case class LogicalRDD(
 /** Physical plan node for scanning data from an RDD. */
 private[sql] case class PhysicalRDD(
     output: Seq[Attribute],
-    rdd: RDD[InternalRow]) extends LeafNode {
+    rdd: RDD[InternalRow],
+    extraInformation: String) extends LeafNode {
 
   override protected[sql] val trackNumOfRowsEnabled = true
 
   protected override def doExecute(): RDD[InternalRow] = rdd
+
+  override def simpleString: String = "Scan " + extraInformation + output.mkString("[", ",", "]")
+}
+
+private[sql] object PhysicalRDD {
+  def createFromDataSource(
+      output: Seq[Attribute],
+      rdd: RDD[InternalRow],
+      relation: BaseRelation): PhysicalRDD = {
+    PhysicalRDD(output, rdd, relation.toString)
+  }
 }
 
 /** Logical plan node for scanning data from a local collection. */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index c5aaebe673225..c4b9b5acea4de 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -363,12 +363,12 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         execution.Generate(
           generator, join = join, outer = outer, g.output, planLater(child)) :: Nil
       case logical.OneRowRelation =>
-        execution.PhysicalRDD(Nil, singleRowRdd) :: Nil
+        execution.PhysicalRDD(Nil, singleRowRdd, "OneRowRelation") :: Nil
       case logical.RepartitionByExpression(expressions, child) =>
         execution.Exchange(HashPartitioning(expressions, numPartitions), planLater(child)) :: Nil
       case e @ EvaluatePython(udf, child, _) =>
         BatchPythonEvaluation(udf, e.output, planLater(child)) :: Nil
-      case LogicalRDD(output, rdd) => PhysicalRDD(output, rdd) :: Nil
+      case LogicalRDD(output, rdd) => PhysicalRDD(output, rdd, "PhysicalRDD") :: Nil
       case BroadcastHint(child) => apply(child)
       case _ => Nil
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index 5a0b4d47d62f8..c3dcbd2b71ee8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -93,10 +93,13 @@ case class TungstenAggregate(
     val allAggregateExpressions = nonCompleteAggregateExpressions ++ completeAggregateExpressions
 
     testFallbackStartsAt match {
-      case None => s"TungstenAggregate ${groupingExpressions} ${allAggregateExpressions}"
+      case None =>
+        val keyString = groupingExpressions.mkString("[", ",", "]")
+        val valueString = allAggregateExpressions.mkString("[", ",", "]")
+        s"TungstenAggregate(key=$keyString, value=$valueString"
       case Some(fallbackStartsAt) =>
-        s"TungstenAggregateWithControlledFallback ${groupingExpressions} " +
-          s"${allAggregateExpressions} fallbackStartsAt=$fallbackStartsAt"
+        s"TungstenAggregateWithControlledFallback $groupingExpressions " +
+          s"$allAggregateExpressions fallbackStartsAt=$fallbackStartsAt"
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index e5dc676b87841..5b5fa8c93ec52 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -101,8 +101,9 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         (a, f) =>
           toCatalystRDD(l, a, t.buildScan(a.map(_.name).toArray, f, t.paths, confBroadcast))) :: Nil
 
-    case l @ LogicalRelation(t: TableScan) =>
-      execution.PhysicalRDD(l.output, toCatalystRDD(l, t.buildScan())) :: Nil
+    case l @ LogicalRelation(baseRelation: TableScan) =>
+      execution.PhysicalRDD.createFromDataSource(
+        l.output, toCatalystRDD(l, baseRelation.buildScan()), baseRelation) :: Nil
 
     case i @ logical.InsertIntoTable(
       l @ LogicalRelation(t: InsertableRelation), part, query, overwrite, false) if part.isEmpty =>
@@ -169,7 +170,10 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         new UnionRDD(relation.sqlContext.sparkContext, perPartitionRows)
       }
 
-    execution.PhysicalRDD(projections.map(_.toAttribute), unionedRows)
+    execution.PhysicalRDD.createFromDataSource(
+      projections.map(_.toAttribute),
+      unionedRows,
+      logicalRelation.relation)
   }
 
   // TODO: refactor this thing. It is very complicated because it does projection internally.
@@ -299,14 +303,18 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         projects.asInstanceOf[Seq[Attribute]] // Safe due to if above.
           .map(relation.attributeMap)            // Match original case of attributes.
 
-      val scan = execution.PhysicalRDD(projects.map(_.toAttribute),
-        scanBuilder(requestedColumns, pushedFilters))
+      val scan = execution.PhysicalRDD.createFromDataSource(
+        projects.map(_.toAttribute),
+        scanBuilder(requestedColumns, pushedFilters),
+        relation.relation)
       filterCondition.map(execution.Filter(_, scan)).getOrElse(scan)
     } else {
       val requestedColumns = (projectSet ++ filterSet).map(relation.attributeMap).toSeq
 
-      val scan = execution.PhysicalRDD(requestedColumns,
-        scanBuilder(requestedColumns, pushedFilters))
+      val scan = execution.PhysicalRDD.createFromDataSource(
+        requestedColumns,
+        scanBuilder(requestedColumns, pushedFilters),
+        relation.relation)
       execution.Project(projects, filterCondition.map(execution.Filter(_, scan)).getOrElse(scan))
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index c04557e5a0818..0b2929661b657 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -383,7 +383,7 @@ private[sql] abstract class OutputWriterInternal extends OutputWriter {
 abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[PartitionSpec])
   extends BaseRelation with Logging {
 
-  logInfo("Constructing HadoopFsRelation")
+  override def toString: String = getClass.getSimpleName + paths.mkString("[", ",", "]")
 
   def this() = this(None)
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index 8208b25b5708c..322966f423784 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -32,9 +32,9 @@ class RowFormatConvertersSuite extends SparkPlanTest {
     case c: ConvertToSafe => c
   }
 
-  private val outputsSafe = ExternalSort(Nil, false, PhysicalRDD(Seq.empty, null))
+  private val outputsSafe = ExternalSort(Nil, false, PhysicalRDD(Seq.empty, null, "name"))
   assert(!outputsSafe.outputsUnsafeRows)
-  private val outputsUnsafe = TungstenSort(Nil, false, PhysicalRDD(Seq.empty, null))
+  private val outputsUnsafe = TungstenSort(Nil, false, PhysicalRDD(Seq.empty, null, "name"))
   assert(outputsUnsafe.outputsUnsafeRows)
 
   test("planner should insert unsafe->safe conversions when required") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
index 697211222b90c..8215dd6c2e711 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
@@ -36,7 +36,7 @@ class HiveExplainSuite extends QueryTest {
                    "== Analyzed Logical Plan ==",
                    "== Optimized Logical Plan ==",
                    "== Physical Plan ==",
-                   "Code Generation", "== RDD ==")
+                   "Code Generation")
   }
 
   test("explain create table command") {

From 881548ab20fa4c4b635c51d956b14bd13981e2f4 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 7 Aug 2015 14:20:13 -0700
Subject: [PATCH 121/633] [SPARK-9674] Re-enable ignored test in SQLQuerySuite

The original code that this test tests is removed in https://github.com/apache/spark/commit/9270bd06fd0b16892e3f37213b5bc7813ea11fdd. It was ignored shortly before that so we never caught it. This patch re-enables the test and adds the code necessary to make it pass.

JoshRosen yhuai

Author: Andrew Or <andrew@databricks.com>

Closes #8015 from andrewor14/SPARK-9674 and squashes the following commits:

225eac2 [Andrew Or] Merge branch 'master' of github.com:apache/spark into SPARK-9674
8c24209 [Andrew Or] Fix NPE
e541d64 [Andrew Or] Track aggregation memory for both sort and hash
0be3a42 [Andrew Or] Fix test
---
 .../spark/unsafe/map/BytesToBytesMap.java     | 37 +++++++++++++++++--
 .../map/AbstractBytesToBytesMapSuite.java     | 20 ++++++----
 .../UnsafeFixedWidthAggregationMap.java       |  7 ++--
 .../sql/execution/UnsafeKVExternalSorter.java |  7 ++++
 .../TungstenAggregationIterator.scala         | 32 +++++++++++++---
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  8 ++--
 6 files changed, 85 insertions(+), 26 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 0636ae7c8df1a..7f79cd13aab43 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -109,7 +109,7 @@ public final class BytesToBytesMap {
    * Position {@code 2 * i} in the array is used to track a pointer to the key at index {@code i},
    * while position {@code 2 * i + 1} in the array holds key's full 32-bit hashcode.
    */
-  private LongArray longArray;
+  @Nullable private LongArray longArray;
   // TODO: we're wasting 32 bits of space here; we can probably store fewer bits of the hashcode
   // and exploit word-alignment to use fewer bits to hold the address.  This might let us store
   // only one long per map entry, increasing the chance that this array will fit in cache at the
@@ -124,7 +124,7 @@ public final class BytesToBytesMap {
    * A {@link BitSet} used to track location of the map where the key is set.
    * Size of the bitset should be half of the size of the long array.
    */
-  private BitSet bitset;
+  @Nullable private BitSet bitset;
 
   private final double loadFactor;
 
@@ -166,6 +166,8 @@ public final class BytesToBytesMap {
 
   private long numHashCollisions = 0;
 
+  private long peakMemoryUsedBytes = 0L;
+
   public BytesToBytesMap(
       TaskMemoryManager taskMemoryManager,
       ShuffleMemoryManager shuffleMemoryManager,
@@ -321,6 +323,9 @@ public Location lookup(
       Object keyBaseObject,
       long keyBaseOffset,
       int keyRowLengthBytes) {
+    assert(bitset != null);
+    assert(longArray != null);
+
     if (enablePerfMetrics) {
       numKeyLookups++;
     }
@@ -410,6 +415,7 @@ private void updateAddressesAndSizes(final Object page, final long offsetInPage)
     }
 
     private Location with(int pos, int keyHashcode, boolean isDefined) {
+      assert(longArray != null);
       this.pos = pos;
       this.isDefined = isDefined;
       this.keyHashcode = keyHashcode;
@@ -525,6 +531,9 @@ public boolean putNewKey(
       assert (!isDefined) : "Can only set value once for a key";
       assert (keyLengthBytes % 8 == 0);
       assert (valueLengthBytes % 8 == 0);
+      assert(bitset != null);
+      assert(longArray != null);
+
       if (numElements == MAX_CAPACITY) {
         throw new IllegalStateException("BytesToBytesMap has reached maximum capacity");
       }
@@ -658,6 +667,7 @@ private void allocate(int capacity) {
    * This method is idempotent and can be called multiple times.
    */
   public void free() {
+    updatePeakMemoryUsed();
     longArray = null;
     bitset = null;
     Iterator<MemoryBlock> dataPagesIterator = dataPages.iterator();
@@ -684,14 +694,30 @@ public long getPageSizeBytes() {
 
   /**
    * Returns the total amount of memory, in bytes, consumed by this map's managed structures.
-   * Note that this is also the peak memory used by this map, since the map is append-only.
    */
   public long getTotalMemoryConsumption() {
     long totalDataPagesSize = 0L;
     for (MemoryBlock dataPage : dataPages) {
       totalDataPagesSize += dataPage.size();
     }
-    return totalDataPagesSize + bitset.memoryBlock().size() + longArray.memoryBlock().size();
+    return totalDataPagesSize +
+      ((bitset != null) ? bitset.memoryBlock().size() : 0L) +
+      ((longArray != null) ? longArray.memoryBlock().size() : 0L);
+  }
+
+  private void updatePeakMemoryUsed() {
+    long mem = getTotalMemoryConsumption();
+    if (mem > peakMemoryUsedBytes) {
+      peakMemoryUsedBytes = mem;
+    }
+  }
+
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  public long getPeakMemoryUsedBytes() {
+    updatePeakMemoryUsed();
+    return peakMemoryUsedBytes;
   }
 
   /**
@@ -731,6 +757,9 @@ int getNumDataPages() {
    */
   @VisibleForTesting
   void growAndRehash() {
+    assert(bitset != null);
+    assert(longArray != null);
+
     long resizeStartTime = -1;
     if (enablePerfMetrics) {
       resizeStartTime = System.nanoTime();
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 0b11562980b8e..e56a3f0b6d12c 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -525,7 +525,7 @@ public void resizingLargeMap() {
   }
 
   @Test
-  public void testTotalMemoryConsumption() {
+  public void testPeakMemoryUsed() {
     final long recordLengthBytes = 24;
     final long pageSizeBytes = 256 + 8; // 8 bytes for end-of-page marker
     final long numRecordsPerPage = (pageSizeBytes - 8) / recordLengthBytes;
@@ -536,8 +536,8 @@ public void testTotalMemoryConsumption() {
     // monotonically increasing. More specifically, every time we allocate a new page it
     // should increase by exactly the size of the page. In this regard, the memory usage
     // at any given time is also the peak memory used.
-    long previousMemory = map.getTotalMemoryConsumption();
-    long newMemory;
+    long previousPeakMemory = map.getPeakMemoryUsedBytes();
+    long newPeakMemory;
     try {
       for (long i = 0; i < numRecordsPerPage * 10; i++) {
         final long[] value = new long[]{i};
@@ -548,15 +548,21 @@ public void testTotalMemoryConsumption() {
           value,
           PlatformDependent.LONG_ARRAY_OFFSET,
           8);
-        newMemory = map.getTotalMemoryConsumption();
+        newPeakMemory = map.getPeakMemoryUsedBytes();
         if (i % numRecordsPerPage == 0) {
           // We allocated a new page for this record, so peak memory should change
-          assertEquals(previousMemory + pageSizeBytes, newMemory);
+          assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
         } else {
-          assertEquals(previousMemory, newMemory);
+          assertEquals(previousPeakMemory, newPeakMemory);
         }
-        previousMemory = newMemory;
+        previousPeakMemory = newPeakMemory;
       }
+
+      // Freeing the map should not change the peak memory
+      map.free();
+      newPeakMemory = map.getPeakMemoryUsedBytes();
+      assertEquals(previousPeakMemory, newPeakMemory);
+
     } finally {
       map.free();
     }
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index efb33530dac86..b08a4a13a28be 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -210,11 +210,10 @@ public void close() {
   }
 
   /**
-   * The memory used by this map's managed structures, in bytes.
-   * Note that this is also the peak memory used by this map, since the map is append-only.
+   * Return the peak memory used so far, in bytes.
    */
-  public long getMemoryUsage() {
-    return map.getTotalMemoryConsumption();
+  public long getPeakMemoryUsedBytes() {
+    return map.getPeakMemoryUsedBytes();
   }
 
   /**
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index 9a65c9d3a404a..69d6784713a24 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -159,6 +159,13 @@ public KVSorterIterator sortedIterator() throws IOException {
     }
   }
 
+  /**
+   * Return the peak memory used so far, in bytes.
+   */
+  public long getPeakMemoryUsedBytes() {
+    return sorter.getPeakMemoryUsedBytes();
+  }
+
   /**
    * Marks the current page as no-more-space-available, and as a result, either allocate a
    * new page or spill when we see the next record.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index 4d5e98a3e90c8..440bef32f4e9b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution.aggregate
 
 import org.apache.spark.unsafe.KVIterator
-import org.apache.spark.{Logging, SparkEnv, TaskContext}
+import org.apache.spark.{InternalAccumulator, Logging, SparkEnv, TaskContext}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeRowJoiner
@@ -397,14 +397,20 @@ class TungstenAggregationIterator(
   private[this] var mapIteratorHasNext: Boolean = false
 
   ///////////////////////////////////////////////////////////////////////////
-  // Part 4: The function used to switch this iterator from hash-based
-  // aggregation to sort-based aggregation.
+  // Part 3: Methods and fields used by sort-based aggregation.
   ///////////////////////////////////////////////////////////////////////////
 
+  // This sorter is used for sort-based aggregation. It is initialized as soon as
+  // we switch from hash-based to sort-based aggregation. Otherwise, it is not used.
+  private[this] var externalSorter: UnsafeKVExternalSorter = null
+
+  /**
+   * Switch to sort-based aggregation when the hash-based approach is unable to acquire memory.
+   */
   private def switchToSortBasedAggregation(firstKey: UnsafeRow, firstInput: UnsafeRow): Unit = {
     logInfo("falling back to sort based aggregation.")
     // Step 1: Get the ExternalSorter containing sorted entries of the map.
-    val externalSorter: UnsafeKVExternalSorter = hashMap.destructAndCreateExternalSorter()
+    externalSorter = hashMap.destructAndCreateExternalSorter()
 
     // Step 2: Free the memory used by the map.
     hashMap.free()
@@ -601,7 +607,7 @@ class TungstenAggregationIterator(
   }
 
   ///////////////////////////////////////////////////////////////////////////
-  // Par 7: Iterator's public methods.
+  // Part 7: Iterator's public methods.
   ///////////////////////////////////////////////////////////////////////////
 
   override final def hasNext: Boolean = {
@@ -610,7 +616,7 @@ class TungstenAggregationIterator(
 
   override final def next(): UnsafeRow = {
     if (hasNext) {
-      if (sortBased) {
+      val res = if (sortBased) {
         // Process the current group.
         processCurrentSortedGroup()
         // Generate output row for the current group.
@@ -641,6 +647,19 @@ class TungstenAggregationIterator(
           result
         }
       }
+
+      // If this is the last record, update the task's peak memory usage. Since we destroy
+      // the map to create the sorter, their memory usages should not overlap, so it is safe
+      // to just use the max of the two.
+      if (!hasNext) {
+        val mapMemory = hashMap.getPeakMemoryUsedBytes
+        val sorterMemory = Option(externalSorter).map(_.getPeakMemoryUsedBytes).getOrElse(0L)
+        val peakMemory = Math.max(mapMemory, sorterMemory)
+        TaskContext.get().internalMetricsToAccumulators(
+          InternalAccumulator.PEAK_EXECUTION_MEMORY).add(peakMemory)
+      }
+
+      res
     } else {
       // no more result
       throw new NoSuchElementException
@@ -651,6 +670,7 @@ class TungstenAggregationIterator(
   // Part 8: A utility function used to generate a output row when there is no
   // input and there is no grouping expression.
   ///////////////////////////////////////////////////////////////////////////
+
   def outputForEmptyGroupingKeyWithoutInput(): UnsafeRow = {
     if (groupingExpressions.isEmpty) {
       sortBasedAggregationBuffer.copyFrom(initialAggregationBuffer)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index c64aa7a07dc2b..b14ef9bab90cb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -267,7 +267,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     if (!hasGeneratedAgg) {
       fail(
         s"""
-           |Codegen is enabled, but query $sqlText does not have GeneratedAggregate in the plan.
+           |Codegen is enabled, but query $sqlText does not have TungstenAggregate in the plan.
            |${df.queryExecution.simpleString}
          """.stripMargin)
     }
@@ -1602,10 +1602,8 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       Row(new CalendarInterval(-(12 * 3 - 3), -(7L * MICROS_PER_WEEK + 123))))
   }
 
-  ignore("aggregation with codegen updates peak execution memory") {
-    withSQLConf(
-        (SQLConf.CODEGEN_ENABLED.key, "true"),
-        (SQLConf.USE_SQL_AGGREGATE2.key, "false")) {
+  test("aggregation with codegen updates peak execution memory") {
+    withSQLConf((SQLConf.CODEGEN_ENABLED.key, "true")) {
       val sc = sqlContext.sparkContext
       AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "aggregation with codegen") {
         testCodeGen(

From e2fbbe73111d4624390f596a19a1799c86a05f6c Mon Sep 17 00:00:00 2001
From: Dariusz Kobylarz <darek.kobylarz@gmail.com>
Date: Fri, 7 Aug 2015 14:51:03 -0700
Subject: [PATCH 122/633] [SPARK-8481] [MLLIB] GaussianMixtureModel predict
 accepting single vector

Resubmit of [https://github.com/apache/spark/pull/6906] for adding single-vec predict to GMMs

CC: dkobylarz  mengxr

To be merged with master and branch-1.5
Primary author: dkobylarz

Author: Dariusz Kobylarz <darek.kobylarz@gmail.com>

Closes #8039 from jkbradley/gmm-predict-vec and squashes the following commits:

bfbedc4 [Dariusz Kobylarz] [SPARK-8481] [MLlib] GaussianMixtureModel predict accepting single vector
---
 .../mllib/clustering/GaussianMixtureModel.scala     | 13 +++++++++++++
 .../mllib/clustering/GaussianMixtureSuite.scala     | 10 ++++++++++
 2 files changed, 23 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index cb807c8038101..76aeebd703d4e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -66,6 +66,12 @@ class GaussianMixtureModel(
     responsibilityMatrix.map(r => r.indexOf(r.max))
   }
 
+  /** Maps given point to its cluster index. */
+  def predict(point: Vector): Int = {
+    val r = computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
+    r.indexOf(r.max)
+  }
+
   /** Java-friendly version of [[predict()]] */
   def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
     predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
@@ -83,6 +89,13 @@ class GaussianMixtureModel(
     }
   }
 
+  /**
+   * Given the input vector, return the membership values to all mixture components.
+   */
+  def predictSoft(point: Vector): Array[Double] = {
+    computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
+  }
+
   /**
    * Compute the partial assignments for each vector
    */
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
index b218d72f1268a..b636d02f786e6 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
@@ -148,6 +148,16 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext {
     }
   }
 
+  test("model prediction, parallel and local") {
+    val data = sc.parallelize(GaussianTestData.data)
+    val gmm = new GaussianMixture().setK(2).setSeed(0).run(data)
+
+    val batchPredictions = gmm.predict(data)
+    batchPredictions.zip(data).collect().foreach { case (batchPred, datum) =>
+      assert(batchPred === gmm.predict(datum))
+    }
+  }
+
   object GaussianTestData {
 
     val data = Array(

From 902334fd55bbe40a57c1de2a9bdb25eddf1c8cf6 Mon Sep 17 00:00:00 2001
From: Bertrand Dechoux <BertrandDechoux@users.noreply.github.com>
Date: Fri, 7 Aug 2015 16:07:24 -0700
Subject: [PATCH 123/633] [SPARK-9748] [MLLIB] Centriod typo in KMeansModel

A minor typo (centriod -> centroid). Readable variable names help every users.

Author: Bertrand Dechoux <BertrandDechoux@users.noreply.github.com>

Closes #8037 from BertrandDechoux/kmeans-typo and squashes the following commits:

47632fe [Bertrand Dechoux] centriod typo
---
 .../apache/spark/mllib/clustering/KMeansModel.scala    | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index 8ecb3df11d95e..96359024fa228 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -120,11 +120,11 @@ object KMeansModel extends Loader[KMeansModel] {
       assert(className == thisClassName)
       assert(formatVersion == thisFormatVersion)
       val k = (metadata \ "k").extract[Int]
-      val centriods = sqlContext.read.parquet(Loader.dataPath(path))
-      Loader.checkSchema[Cluster](centriods.schema)
-      val localCentriods = centriods.map(Cluster.apply).collect()
-      assert(k == localCentriods.size)
-      new KMeansModel(localCentriods.sortBy(_.id).map(_.point))
+      val centroids = sqlContext.read.parquet(Loader.dataPath(path))
+      Loader.checkSchema[Cluster](centroids.schema)
+      val localCentroids = centroids.map(Cluster.apply).collect()
+      assert(k == localCentroids.size)
+      new KMeansModel(localCentroids.sortBy(_.id).map(_.point))
     }
   }
 }

From 49702bd738de681255a7177339510e0e1b25a8db Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Fri, 7 Aug 2015 16:24:50 -0700
Subject: [PATCH 124/633] [SPARK-8890] [SQL] Fallback on sorting when writing
 many dynamic partitions

Previously, we would open a new file for each new dynamic written out using `HadoopFsRelation`.  For formats like parquet this is very costly due to the buffers required to get good compression.  In this PR I refactor the code allowing us to fall back on an external sort when many partitions are seen.  As such each task will open no more than `spark.sql.sources.maxFiles` files.  I also did the following cleanup:

 - Instead of keying the file HashMap on an expensive to compute string representation of the partition, we now use a fairly cheap UnsafeProjection that avoids heap allocations.
 - The control flow for instantiating and invoking a writer container has been simplified.  Now instead of switching in two places based on the use of partitioning, the specific writer container must implement a single method `writeRows` that is invoked using `runJob`.
 - `InternalOutputWriter` has been removed.  Instead we have a `private[sql]` method `writeInternal` that converts and calls the public method.  This method can be overridden by internal datasources to avoid the conversion.  This change remove a lot of code duplication and per-row `asInstanceOf` checks.
 - `commands.scala` has been split up.

Author: Michael Armbrust <michael@databricks.com>

Closes #8010 from marmbrus/fsWriting and squashes the following commits:

00804fe [Michael Armbrust] use shuffleMemoryManager.pageSizeBytes
775cc49 [Michael Armbrust] Merge remote-tracking branch 'origin/master' into fsWriting
17b690e [Michael Armbrust] remove comment
40f0372 [Michael Armbrust] address comments
f5675bd [Michael Armbrust] char -> string
7e2d0a4 [Michael Armbrust] make sure we close current writer
8100100 [Michael Armbrust] delete empty commands.scala
71cc717 [Michael Armbrust] update comment
8ec75ac [Michael Armbrust] [SPARK-8890][SQL] Fallback on sorting when writing many dynamic partitions
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |   8 +-
 .../datasources/InsertIntoDataSource.scala    |  64 ++
 .../InsertIntoHadoopFsRelation.scala          | 165 +++++
 .../datasources/WriterContainer.scala         | 404 ++++++++++++
 .../sql/execution/datasources/commands.scala  | 606 ------------------
 .../apache/spark/sql/json/JSONRelation.scala  |   6 +-
 .../spark/sql/parquet/ParquetRelation.scala   |   6 +-
 .../apache/spark/sql/sources/interfaces.scala |  17 +-
 .../sql/sources/PartitionedWriteSuite.scala   |  56 ++
 .../spark/sql/hive/orc/OrcRelation.scala      |   6 +-
 10 files changed, 715 insertions(+), 623 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelation.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index 45d3d8c863512..e9de14f025502 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -366,17 +366,21 @@ private[spark] object SQLConf {
       "storing additional schema information in Hive's metastore.",
     isPublic = false)
 
-  // Whether to perform partition discovery when loading external data sources.  Default to true.
   val PARTITION_DISCOVERY_ENABLED = booleanConf("spark.sql.sources.partitionDiscovery.enabled",
     defaultValue = Some(true),
     doc = "When true, automtically discover data partitions.")
 
-  // Whether to perform partition column type inference. Default to true.
   val PARTITION_COLUMN_TYPE_INFERENCE =
     booleanConf("spark.sql.sources.partitionColumnTypeInference.enabled",
       defaultValue = Some(true),
       doc = "When true, automatically infer the data types for partitioned columns.")
 
+  val PARTITION_MAX_FILES =
+    intConf("spark.sql.sources.maxConcurrentWrites",
+      defaultValue = Some(5),
+      doc = "The maximum number of concurent files to open before falling back on sorting when " +
+            "writing out files using dynamic partitioning.")
+
   // The output committer class used by HadoopFsRelation. The specified class needs to be a
   // subclass of org.apache.hadoop.mapreduce.OutputCommitter.
   //
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
new file mode 100644
index 0000000000000..6ccde7693bd34
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
@@ -0,0 +1,64 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources
+
+import java.io.IOException
+import java.util.{Date, UUID}
+
+import scala.collection.JavaConversions.asScalaIterator
+
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.mapreduce._
+import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter, FileOutputFormat}
+import org.apache.spark._
+import org.apache.spark.mapred.SparkHadoopMapRedUtil
+import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
+import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
+import org.apache.spark.sql.sources._
+import org.apache.spark.sql.types.StringType
+import org.apache.spark.util.{Utils, SerializableConfiguration}
+
+
+/**
+ * Inserts the results of `query` in to a relation that extends [[InsertableRelation]].
+ */
+private[sql] case class InsertIntoDataSource(
+    logicalRelation: LogicalRelation,
+    query: LogicalPlan,
+    overwrite: Boolean)
+  extends RunnableCommand {
+
+  override def run(sqlContext: SQLContext): Seq[Row] = {
+    val relation = logicalRelation.relation.asInstanceOf[InsertableRelation]
+    val data = DataFrame(sqlContext, query)
+    // Apply the schema of the existing table to the new data.
+    val df = sqlContext.internalCreateDataFrame(data.queryExecution.toRdd, logicalRelation.schema)
+    relation.insert(df, overwrite)
+
+    // Invalidate the cache.
+    sqlContext.cacheManager.invalidateCache(logicalRelation)
+
+    Seq.empty[Row]
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelation.scala
new file mode 100644
index 0000000000000..735d52f808868
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoHadoopFsRelation.scala
@@ -0,0 +1,165 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources
+
+import java.io.IOException
+
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.mapreduce._
+import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat
+import org.apache.spark._
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
+import org.apache.spark.sql.sources._
+import org.apache.spark.util.Utils
+
+
+/**
+ * A command for writing data to a [[HadoopFsRelation]].  Supports both overwriting and appending.
+ * Writing to dynamic partitions is also supported.  Each [[InsertIntoHadoopFsRelation]] issues a
+ * single write job, and owns a UUID that identifies this job.  Each concrete implementation of
+ * [[HadoopFsRelation]] should use this UUID together with task id to generate unique file path for
+ * each task output file.  This UUID is passed to executor side via a property named
+ * `spark.sql.sources.writeJobUUID`.
+ *
+ * Different writer containers, [[DefaultWriterContainer]] and [[DynamicPartitionWriterContainer]]
+ * are used to write to normal tables and tables with dynamic partitions.
+ *
+ * Basic work flow of this command is:
+ *
+ *   1. Driver side setup, including output committer initialization and data source specific
+ *      preparation work for the write job to be issued.
+ *   2. Issues a write job consists of one or more executor side tasks, each of which writes all
+ *      rows within an RDD partition.
+ *   3. If no exception is thrown in a task, commits that task, otherwise aborts that task;  If any
+ *      exception is thrown during task commitment, also aborts that task.
+ *   4. If all tasks are committed, commit the job, otherwise aborts the job;  If any exception is
+ *      thrown during job commitment, also aborts the job.
+ */
+private[sql] case class InsertIntoHadoopFsRelation(
+    @transient relation: HadoopFsRelation,
+    @transient query: LogicalPlan,
+    mode: SaveMode)
+  extends RunnableCommand {
+
+  override def run(sqlContext: SQLContext): Seq[Row] = {
+    require(
+      relation.paths.length == 1,
+      s"Cannot write to multiple destinations: ${relation.paths.mkString(",")}")
+
+    val hadoopConf = sqlContext.sparkContext.hadoopConfiguration
+    val outputPath = new Path(relation.paths.head)
+    val fs = outputPath.getFileSystem(hadoopConf)
+    val qualifiedOutputPath = outputPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
+
+    val pathExists = fs.exists(qualifiedOutputPath)
+    val doInsertion = (mode, pathExists) match {
+      case (SaveMode.ErrorIfExists, true) =>
+        throw new AnalysisException(s"path $qualifiedOutputPath already exists.")
+      case (SaveMode.Overwrite, true) =>
+        Utils.tryOrIOException {
+          if (!fs.delete(qualifiedOutputPath, true /* recursively */)) {
+            throw new IOException(s"Unable to clear output " +
+              s"directory $qualifiedOutputPath prior to writing to it")
+          }
+        }
+        true
+      case (SaveMode.Append, _) | (SaveMode.Overwrite, _) | (SaveMode.ErrorIfExists, false) =>
+        true
+      case (SaveMode.Ignore, exists) =>
+        !exists
+      case (s, exists) =>
+        throw new IllegalStateException(s"unsupported save mode $s ($exists)")
+    }
+    // If we are appending data to an existing dir.
+    val isAppend = pathExists && (mode == SaveMode.Append)
+
+    if (doInsertion) {
+      val job = new Job(hadoopConf)
+      job.setOutputKeyClass(classOf[Void])
+      job.setOutputValueClass(classOf[InternalRow])
+      FileOutputFormat.setOutputPath(job, qualifiedOutputPath)
+
+      // A partitioned relation schema's can be different from the input logicalPlan, since
+      // partition columns are all moved after data column. We Project to adjust the ordering.
+      // TODO: this belongs in the analyzer.
+      val project = Project(
+        relation.schema.map(field => UnresolvedAttribute.quoted(field.name)), query)
+      val queryExecution = DataFrame(sqlContext, project).queryExecution
+
+      SQLExecution.withNewExecutionId(sqlContext, queryExecution) {
+        val df = sqlContext.internalCreateDataFrame(queryExecution.toRdd, relation.schema)
+        val partitionColumns = relation.partitionColumns.fieldNames
+
+        // Some pre-flight checks.
+        require(
+          df.schema == relation.schema,
+          s"""DataFrame must have the same schema as the relation to which is inserted.
+             |DataFrame schema: ${df.schema}
+             |Relation schema: ${relation.schema}
+          """.stripMargin)
+        val partitionColumnsInSpec = relation.partitionColumns.fieldNames
+        require(
+          partitionColumnsInSpec.sameElements(partitionColumns),
+          s"""Partition columns mismatch.
+             |Expected: ${partitionColumnsInSpec.mkString(", ")}
+             |Actual: ${partitionColumns.mkString(", ")}
+          """.stripMargin)
+
+        val writerContainer = if (partitionColumns.isEmpty) {
+          new DefaultWriterContainer(relation, job, isAppend)
+        } else {
+          val output = df.queryExecution.executedPlan.output
+          val (partitionOutput, dataOutput) =
+            output.partition(a => partitionColumns.contains(a.name))
+
+          new DynamicPartitionWriterContainer(
+            relation,
+            job,
+            partitionOutput,
+            dataOutput,
+            output,
+            PartitioningUtils.DEFAULT_PARTITION_NAME,
+            sqlContext.conf.getConf(SQLConf.PARTITION_MAX_FILES),
+            isAppend)
+        }
+
+        // This call shouldn't be put into the `try` block below because it only initializes and
+        // prepares the job, any exception thrown from here shouldn't cause abortJob() to be called.
+        writerContainer.driverSideSetup()
+
+        try {
+          sqlContext.sparkContext.runJob(df.queryExecution.toRdd, writerContainer.writeRows _)
+          writerContainer.commitJob()
+          relation.refresh()
+        } catch { case cause: Throwable =>
+          logError("Aborting job.", cause)
+          writerContainer.abortJob()
+          throw new SparkException("Job aborted.", cause)
+        }
+      }
+    } else {
+      logInfo("Skipping insertion into a relation that already exists.")
+    }
+
+    Seq.empty[Row]
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
new file mode 100644
index 0000000000000..2f11f40422402
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -0,0 +1,404 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources
+
+import java.util.{Date, UUID}
+
+import scala.collection.JavaConverters._
+
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.mapreduce._
+import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter}
+import org.apache.spark._
+import org.apache.spark.mapred.SparkHadoopMapRedUtil
+import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.execution.UnsafeKVExternalSorter
+import org.apache.spark.sql.sources.{HadoopFsRelation, OutputWriter, OutputWriterFactory}
+import org.apache.spark.sql.types.{StructType, StringType}
+import org.apache.spark.util.SerializableConfiguration
+
+
+private[sql] abstract class BaseWriterContainer(
+    @transient val relation: HadoopFsRelation,
+    @transient job: Job,
+    isAppend: Boolean)
+  extends SparkHadoopMapReduceUtil
+  with Logging
+  with Serializable {
+
+  protected val dataSchema = relation.dataSchema
+
+  protected val serializableConf = new SerializableConfiguration(job.getConfiguration)
+
+  // This UUID is used to avoid output file name collision between different appending write jobs.
+  // These jobs may belong to different SparkContext instances. Concrete data source implementations
+  // may use this UUID to generate unique file names (e.g., `part-r-<task-id>-<job-uuid>.parquet`).
+  //  The reason why this ID is used to identify a job rather than a single task output file is
+  // that, speculative tasks must generate the same output file name as the original task.
+  private val uniqueWriteJobId = UUID.randomUUID()
+
+  // This is only used on driver side.
+  @transient private val jobContext: JobContext = job
+
+  // The following fields are initialized and used on both driver and executor side.
+  @transient protected var outputCommitter: OutputCommitter = _
+  @transient private var jobId: JobID = _
+  @transient private var taskId: TaskID = _
+  @transient private var taskAttemptId: TaskAttemptID = _
+  @transient protected var taskAttemptContext: TaskAttemptContext = _
+
+  protected val outputPath: String = {
+    assert(
+      relation.paths.length == 1,
+      s"Cannot write to multiple destinations: ${relation.paths.mkString(",")}")
+    relation.paths.head
+  }
+
+  protected var outputWriterFactory: OutputWriterFactory = _
+
+  private var outputFormatClass: Class[_ <: OutputFormat[_, _]] = _
+
+  def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit
+
+  def driverSideSetup(): Unit = {
+    setupIDs(0, 0, 0)
+    setupConf()
+
+    // This UUID is sent to executor side together with the serialized `Configuration` object within
+    // the `Job` instance.  `OutputWriters` on the executor side should use this UUID to generate
+    // unique task output files.
+    job.getConfiguration.set("spark.sql.sources.writeJobUUID", uniqueWriteJobId.toString)
+
+    // Order of the following two lines is important.  For Hadoop 1, TaskAttemptContext constructor
+    // clones the Configuration object passed in.  If we initialize the TaskAttemptContext first,
+    // configurations made in prepareJobForWrite(job) are not populated into the TaskAttemptContext.
+    //
+    // Also, the `prepareJobForWrite` call must happen before initializing output format and output
+    // committer, since their initialization involve the job configuration, which can be potentially
+    // decorated in `prepareJobForWrite`.
+    outputWriterFactory = relation.prepareJobForWrite(job)
+    taskAttemptContext = newTaskAttemptContext(serializableConf.value, taskAttemptId)
+
+    outputFormatClass = job.getOutputFormatClass
+    outputCommitter = newOutputCommitter(taskAttemptContext)
+    outputCommitter.setupJob(jobContext)
+  }
+
+  def executorSideSetup(taskContext: TaskContext): Unit = {
+    setupIDs(taskContext.stageId(), taskContext.partitionId(), taskContext.attemptNumber())
+    setupConf()
+    taskAttemptContext = newTaskAttemptContext(serializableConf.value, taskAttemptId)
+    outputCommitter = newOutputCommitter(taskAttemptContext)
+    outputCommitter.setupTask(taskAttemptContext)
+  }
+
+  protected def getWorkPath: String = {
+    outputCommitter match {
+      // FileOutputCommitter writes to a temporary location returned by `getWorkPath`.
+      case f: MapReduceFileOutputCommitter => f.getWorkPath.toString
+      case _ => outputPath
+    }
+  }
+
+  private def newOutputCommitter(context: TaskAttemptContext): OutputCommitter = {
+    val defaultOutputCommitter = outputFormatClass.newInstance().getOutputCommitter(context)
+
+    if (isAppend) {
+      // If we are appending data to an existing dir, we will only use the output committer
+      // associated with the file output format since it is not safe to use a custom
+      // committer for appending. For example, in S3, direct parquet output committer may
+      // leave partial data in the destination dir when the the appending job fails.
+      logInfo(
+        s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName} " +
+          "for appending.")
+      defaultOutputCommitter
+    } else {
+      val committerClass = context.getConfiguration.getClass(
+        SQLConf.OUTPUT_COMMITTER_CLASS.key, null, classOf[OutputCommitter])
+
+      Option(committerClass).map { clazz =>
+        logInfo(s"Using user defined output committer class ${clazz.getCanonicalName}")
+
+        // Every output format based on org.apache.hadoop.mapreduce.lib.output.OutputFormat
+        // has an associated output committer. To override this output committer,
+        // we will first try to use the output committer set in SQLConf.OUTPUT_COMMITTER_CLASS.
+        // If a data source needs to override the output committer, it needs to set the
+        // output committer in prepareForWrite method.
+        if (classOf[MapReduceFileOutputCommitter].isAssignableFrom(clazz)) {
+          // The specified output committer is a FileOutputCommitter.
+          // So, we will use the FileOutputCommitter-specified constructor.
+          val ctor = clazz.getDeclaredConstructor(classOf[Path], classOf[TaskAttemptContext])
+          ctor.newInstance(new Path(outputPath), context)
+        } else {
+          // The specified output committer is just a OutputCommitter.
+          // So, we will use the no-argument constructor.
+          val ctor = clazz.getDeclaredConstructor()
+          ctor.newInstance()
+        }
+      }.getOrElse {
+        // If output committer class is not set, we will use the one associated with the
+        // file output format.
+        logInfo(
+          s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName}")
+        defaultOutputCommitter
+      }
+    }
+  }
+
+  private def setupIDs(jobId: Int, splitId: Int, attemptId: Int): Unit = {
+    this.jobId = SparkHadoopWriter.createJobID(new Date, jobId)
+    this.taskId = new TaskID(this.jobId, true, splitId)
+    this.taskAttemptId = new TaskAttemptID(taskId, attemptId)
+  }
+
+  private def setupConf(): Unit = {
+    serializableConf.value.set("mapred.job.id", jobId.toString)
+    serializableConf.value.set("mapred.tip.id", taskAttemptId.getTaskID.toString)
+    serializableConf.value.set("mapred.task.id", taskAttemptId.toString)
+    serializableConf.value.setBoolean("mapred.task.is.map", true)
+    serializableConf.value.setInt("mapred.task.partition", 0)
+  }
+
+  def commitTask(): Unit = {
+    SparkHadoopMapRedUtil.commitTask(
+      outputCommitter, taskAttemptContext, jobId.getId, taskId.getId, taskAttemptId.getId)
+  }
+
+  def abortTask(): Unit = {
+    if (outputCommitter != null) {
+      outputCommitter.abortTask(taskAttemptContext)
+    }
+    logError(s"Task attempt $taskAttemptId aborted.")
+  }
+
+  def commitJob(): Unit = {
+    outputCommitter.commitJob(jobContext)
+    logInfo(s"Job $jobId committed.")
+  }
+
+  def abortJob(): Unit = {
+    if (outputCommitter != null) {
+      outputCommitter.abortJob(jobContext, JobStatus.State.FAILED)
+    }
+    logError(s"Job $jobId aborted.")
+  }
+}
+
+/**
+ * A writer that writes all of the rows in a partition to a single file.
+ */
+private[sql] class DefaultWriterContainer(
+    @transient relation: HadoopFsRelation,
+    @transient job: Job,
+    isAppend: Boolean)
+  extends BaseWriterContainer(relation, job, isAppend) {
+
+  def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = {
+    executorSideSetup(taskContext)
+    taskAttemptContext.getConfiguration.set("spark.sql.sources.output.path", outputPath)
+    val writer = outputWriterFactory.newInstance(getWorkPath, dataSchema, taskAttemptContext)
+    writer.initConverter(dataSchema)
+
+    // If anything below fails, we should abort the task.
+    try {
+      while (iterator.hasNext) {
+        val internalRow = iterator.next()
+        writer.writeInternal(internalRow)
+      }
+
+      commitTask()
+    } catch {
+      case cause: Throwable =>
+        logError("Aborting task.", cause)
+        abortTask()
+        throw new SparkException("Task failed while writing rows.", cause)
+    }
+
+    def commitTask(): Unit = {
+      try {
+        assert(writer != null, "OutputWriter instance should have been initialized")
+        writer.close()
+        super.commitTask()
+      } catch {
+        case cause: Throwable =>
+          // This exception will be handled in `InsertIntoHadoopFsRelation.insert$writeRows`, and
+          // will cause `abortTask()` to be invoked.
+          throw new RuntimeException("Failed to commit task", cause)
+      }
+    }
+
+    def abortTask(): Unit = {
+      try {
+        writer.close()
+      } finally {
+        super.abortTask()
+      }
+    }
+  }
+}
+
+/**
+ * A writer that dynamically opens files based on the given partition columns.  Internally this is
+ * done by maintaining a HashMap of open files until `maxFiles` is reached.  If this occurs, the
+ * writer externally sorts the remaining rows and then writes out them out one file at a time.
+ */
+private[sql] class DynamicPartitionWriterContainer(
+    @transient relation: HadoopFsRelation,
+    @transient job: Job,
+    partitionColumns: Seq[Attribute],
+    dataColumns: Seq[Attribute],
+    inputSchema: Seq[Attribute],
+    defaultPartitionName: String,
+    maxOpenFiles: Int,
+    isAppend: Boolean)
+  extends BaseWriterContainer(relation, job, isAppend) {
+
+  def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = {
+    val outputWriters = new java.util.HashMap[InternalRow, OutputWriter]
+    executorSideSetup(taskContext)
+
+    // Returns the partition key given an input row
+    val getPartitionKey = UnsafeProjection.create(partitionColumns, inputSchema)
+    // Returns the data columns to be written given an input row
+    val getOutputRow = UnsafeProjection.create(dataColumns, inputSchema)
+
+    // Expressions that given a partition key build a string like: col1=val/col2=val/...
+    val partitionStringExpression = partitionColumns.zipWithIndex.flatMap { case (c, i) =>
+      val escaped =
+        ScalaUDF(
+          PartitioningUtils.escapePathName _, StringType, Seq(Cast(c, StringType)), Seq(StringType))
+      val str = If(IsNull(c), Literal(defaultPartitionName), escaped)
+      val partitionName = Literal(c.name + "=") :: str :: Nil
+      if (i == 0) partitionName else Literal(Path.SEPARATOR_CHAR.toString) :: partitionName
+    }
+
+    // Returns the partition path given a partition key.
+    val getPartitionString =
+      UnsafeProjection.create(Concat(partitionStringExpression) :: Nil, partitionColumns)
+
+    // If anything below fails, we should abort the task.
+    try {
+      // This will be filled in if we have to fall back on sorting.
+      var sorter: UnsafeKVExternalSorter = null
+      while (iterator.hasNext && sorter == null) {
+        val inputRow = iterator.next()
+        val currentKey = getPartitionKey(inputRow)
+        var currentWriter = outputWriters.get(currentKey)
+
+        if (currentWriter == null) {
+          if (outputWriters.size < maxOpenFiles) {
+            currentWriter = newOutputWriter(currentKey)
+            outputWriters.put(currentKey.copy(), currentWriter)
+            currentWriter.writeInternal(getOutputRow(inputRow))
+          } else {
+            logInfo(s"Maximum partitions reached, falling back on sorting.")
+            sorter = new UnsafeKVExternalSorter(
+              StructType.fromAttributes(partitionColumns),
+              StructType.fromAttributes(dataColumns),
+              SparkEnv.get.blockManager,
+              SparkEnv.get.shuffleMemoryManager,
+              SparkEnv.get.shuffleMemoryManager.pageSizeBytes)
+            sorter.insertKV(currentKey, getOutputRow(inputRow))
+          }
+        } else {
+          currentWriter.writeInternal(getOutputRow(inputRow))
+        }
+      }
+
+      // If the sorter is not null that means that we reached the maxFiles above and need to finish
+      // using external sort.
+      if (sorter != null) {
+        while (iterator.hasNext) {
+          val currentRow = iterator.next()
+          sorter.insertKV(getPartitionKey(currentRow), getOutputRow(currentRow))
+        }
+
+        logInfo(s"Sorting complete. Writing out partition files one at a time.")
+
+        val sortedIterator = sorter.sortedIterator()
+        var currentKey: InternalRow = null
+        var currentWriter: OutputWriter = null
+        try {
+          while (sortedIterator.next()) {
+            if (currentKey != sortedIterator.getKey) {
+              if (currentWriter != null) {
+                currentWriter.close()
+              }
+              currentKey = sortedIterator.getKey.copy()
+              logDebug(s"Writing partition: $currentKey")
+
+              // Either use an existing file from before, or open a new one.
+              currentWriter = outputWriters.remove(currentKey)
+              if (currentWriter == null) {
+                currentWriter = newOutputWriter(currentKey)
+              }
+            }
+
+            currentWriter.writeInternal(sortedIterator.getValue)
+          }
+        } finally {
+          if (currentWriter != null) { currentWriter.close() }
+        }
+      }
+
+      commitTask()
+    } catch {
+      case cause: Throwable =>
+        logError("Aborting task.", cause)
+        abortTask()
+        throw new SparkException("Task failed while writing rows.", cause)
+    }
+
+    /** Open and returns a new OutputWriter given a partition key. */
+    def newOutputWriter(key: InternalRow): OutputWriter = {
+      val partitionPath = getPartitionString(key).getString(0)
+      val path = new Path(getWorkPath, partitionPath)
+      taskAttemptContext.getConfiguration.set(
+        "spark.sql.sources.output.path", new Path(outputPath, partitionPath).toString)
+      val newWriter = outputWriterFactory.newInstance(path.toString, dataSchema, taskAttemptContext)
+      newWriter.initConverter(dataSchema)
+      newWriter
+    }
+
+    def clearOutputWriters(): Unit = {
+      outputWriters.asScala.values.foreach(_.close())
+      outputWriters.clear()
+    }
+
+    def commitTask(): Unit = {
+      try {
+        clearOutputWriters()
+        super.commitTask()
+      } catch {
+        case cause: Throwable =>
+          throw new RuntimeException("Failed to commit task", cause)
+      }
+    }
+
+    def abortTask(): Unit = {
+      try {
+        clearOutputWriters()
+      } finally {
+        super.abortTask()
+      }
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
deleted file mode 100644
index 42668979c9a32..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/commands.scala
+++ /dev/null
@@ -1,606 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution.datasources
-
-import java.io.IOException
-import java.util.{Date, UUID}
-
-import scala.collection.JavaConversions.asScalaIterator
-
-import org.apache.hadoop.fs.Path
-import org.apache.hadoop.mapreduce._
-import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter, FileOutputFormat}
-import org.apache.spark._
-import org.apache.spark.mapred.SparkHadoopMapRedUtil
-import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
-import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
-import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
-import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
-import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
-import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.StringType
-import org.apache.spark.util.{Utils, SerializableConfiguration}
-
-
-private[sql] case class InsertIntoDataSource(
-    logicalRelation: LogicalRelation,
-    query: LogicalPlan,
-    overwrite: Boolean)
-  extends RunnableCommand {
-
-  override def run(sqlContext: SQLContext): Seq[Row] = {
-    val relation = logicalRelation.relation.asInstanceOf[InsertableRelation]
-    val data = DataFrame(sqlContext, query)
-    // Apply the schema of the existing table to the new data.
-    val df = sqlContext.internalCreateDataFrame(data.queryExecution.toRdd, logicalRelation.schema)
-    relation.insert(df, overwrite)
-
-    // Invalidate the cache.
-    sqlContext.cacheManager.invalidateCache(logicalRelation)
-
-    Seq.empty[Row]
-  }
-}
-
-/**
- * A command for writing data to a [[HadoopFsRelation]].  Supports both overwriting and appending.
- * Writing to dynamic partitions is also supported.  Each [[InsertIntoHadoopFsRelation]] issues a
- * single write job, and owns a UUID that identifies this job.  Each concrete implementation of
- * [[HadoopFsRelation]] should use this UUID together with task id to generate unique file path for
- * each task output file.  This UUID is passed to executor side via a property named
- * `spark.sql.sources.writeJobUUID`.
- *
- * Different writer containers, [[DefaultWriterContainer]] and [[DynamicPartitionWriterContainer]]
- * are used to write to normal tables and tables with dynamic partitions.
- *
- * Basic work flow of this command is:
- *
- *   1. Driver side setup, including output committer initialization and data source specific
- *      preparation work for the write job to be issued.
- *   2. Issues a write job consists of one or more executor side tasks, each of which writes all
- *      rows within an RDD partition.
- *   3. If no exception is thrown in a task, commits that task, otherwise aborts that task;  If any
- *      exception is thrown during task commitment, also aborts that task.
- *   4. If all tasks are committed, commit the job, otherwise aborts the job;  If any exception is
- *      thrown during job commitment, also aborts the job.
- */
-private[sql] case class InsertIntoHadoopFsRelation(
-    @transient relation: HadoopFsRelation,
-    @transient query: LogicalPlan,
-    mode: SaveMode)
-  extends RunnableCommand {
-
-  override def run(sqlContext: SQLContext): Seq[Row] = {
-    require(
-      relation.paths.length == 1,
-      s"Cannot write to multiple destinations: ${relation.paths.mkString(",")}")
-
-    val hadoopConf = sqlContext.sparkContext.hadoopConfiguration
-    val outputPath = new Path(relation.paths.head)
-    val fs = outputPath.getFileSystem(hadoopConf)
-    val qualifiedOutputPath = outputPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
-
-    val pathExists = fs.exists(qualifiedOutputPath)
-    val doInsertion = (mode, pathExists) match {
-      case (SaveMode.ErrorIfExists, true) =>
-        throw new AnalysisException(s"path $qualifiedOutputPath already exists.")
-      case (SaveMode.Overwrite, true) =>
-        Utils.tryOrIOException {
-          if (!fs.delete(qualifiedOutputPath, true /* recursively */)) {
-            throw new IOException(s"Unable to clear output " +
-              s"directory $qualifiedOutputPath prior to writing to it")
-          }
-        }
-        true
-      case (SaveMode.Append, _) | (SaveMode.Overwrite, _) | (SaveMode.ErrorIfExists, false) =>
-        true
-      case (SaveMode.Ignore, exists) =>
-        !exists
-      case (s, exists) =>
-        throw new IllegalStateException(s"unsupported save mode $s ($exists)")
-    }
-    // If we are appending data to an existing dir.
-    val isAppend = pathExists && (mode == SaveMode.Append)
-
-    if (doInsertion) {
-      val job = new Job(hadoopConf)
-      job.setOutputKeyClass(classOf[Void])
-      job.setOutputValueClass(classOf[InternalRow])
-      FileOutputFormat.setOutputPath(job, qualifiedOutputPath)
-
-      // We create a DataFrame by applying the schema of relation to the data to make sure.
-      // We are writing data based on the expected schema,
-
-      // For partitioned relation r, r.schema's column ordering can be different from the column
-      // ordering of data.logicalPlan (partition columns are all moved after data column). We
-      // need a Project to adjust the ordering, so that inside InsertIntoHadoopFsRelation, we can
-      // safely apply the schema of r.schema to the data.
-      val project = Project(
-        relation.schema.map(field => new UnresolvedAttribute(Seq(field.name))), query)
-
-      val queryExecution = DataFrame(sqlContext, project).queryExecution
-      SQLExecution.withNewExecutionId(sqlContext, queryExecution) {
-        val df = sqlContext.internalCreateDataFrame(queryExecution.toRdd, relation.schema)
-
-        val partitionColumns = relation.partitionColumns.fieldNames
-        if (partitionColumns.isEmpty) {
-          insert(new DefaultWriterContainer(relation, job, isAppend), df)
-        } else {
-          val writerContainer = new DynamicPartitionWriterContainer(
-            relation, job, partitionColumns, PartitioningUtils.DEFAULT_PARTITION_NAME, isAppend)
-          insertWithDynamicPartitions(sqlContext, writerContainer, df, partitionColumns)
-        }
-      }
-    }
-
-    Seq.empty[Row]
-  }
-
-  /**
-   * Inserts the content of the [[DataFrame]] into a table without any partitioning columns.
-   */
-  private def insert(writerContainer: BaseWriterContainer, df: DataFrame): Unit = {
-    // Uses local vals for serialization
-    val needsConversion = relation.needConversion
-    val dataSchema = relation.dataSchema
-
-    // This call shouldn't be put into the `try` block below because it only initializes and
-    // prepares the job, any exception thrown from here shouldn't cause abortJob() to be called.
-    writerContainer.driverSideSetup()
-
-    try {
-      df.sqlContext.sparkContext.runJob(df.queryExecution.toRdd, writeRows _)
-      writerContainer.commitJob()
-      relation.refresh()
-    } catch { case cause: Throwable =>
-      logError("Aborting job.", cause)
-      writerContainer.abortJob()
-      throw new SparkException("Job aborted.", cause)
-    }
-
-    def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = {
-      // If anything below fails, we should abort the task.
-      try {
-        writerContainer.executorSideSetup(taskContext)
-
-        if (needsConversion) {
-          val converter = CatalystTypeConverters.createToScalaConverter(dataSchema)
-            .asInstanceOf[InternalRow => Row]
-          while (iterator.hasNext) {
-            val internalRow = iterator.next()
-            writerContainer.outputWriterForRow(internalRow).write(converter(internalRow))
-          }
-        } else {
-          while (iterator.hasNext) {
-            val internalRow = iterator.next()
-            writerContainer.outputWriterForRow(internalRow)
-              .asInstanceOf[OutputWriterInternal].writeInternal(internalRow)
-          }
-        }
-
-        writerContainer.commitTask()
-      } catch { case cause: Throwable =>
-        logError("Aborting task.", cause)
-        writerContainer.abortTask()
-        throw new SparkException("Task failed while writing rows.", cause)
-      }
-    }
-  }
-
-  /**
-   * Inserts the content of the [[DataFrame]] into a table with partitioning columns.
-   */
-  private def insertWithDynamicPartitions(
-      sqlContext: SQLContext,
-      writerContainer: BaseWriterContainer,
-      df: DataFrame,
-      partitionColumns: Array[String]): Unit = {
-    // Uses a local val for serialization
-    val needsConversion = relation.needConversion
-    val dataSchema = relation.dataSchema
-
-    require(
-      df.schema == relation.schema,
-      s"""DataFrame must have the same schema as the relation to which is inserted.
-         |DataFrame schema: ${df.schema}
-         |Relation schema: ${relation.schema}
-       """.stripMargin)
-
-    val partitionColumnsInSpec = relation.partitionColumns.fieldNames
-    require(
-      partitionColumnsInSpec.sameElements(partitionColumns),
-      s"""Partition columns mismatch.
-         |Expected: ${partitionColumnsInSpec.mkString(", ")}
-         |Actual: ${partitionColumns.mkString(", ")}
-       """.stripMargin)
-
-    val output = df.queryExecution.executedPlan.output
-    val (partitionOutput, dataOutput) = output.partition(a => partitionColumns.contains(a.name))
-    val codegenEnabled = df.sqlContext.conf.codegenEnabled
-
-    // This call shouldn't be put into the `try` block below because it only initializes and
-    // prepares the job, any exception thrown from here shouldn't cause abortJob() to be called.
-    writerContainer.driverSideSetup()
-
-    try {
-      df.sqlContext.sparkContext.runJob(df.queryExecution.toRdd, writeRows _)
-      writerContainer.commitJob()
-      relation.refresh()
-    } catch { case cause: Throwable =>
-      logError("Aborting job.", cause)
-      writerContainer.abortJob()
-      throw new SparkException("Job aborted.", cause)
-    }
-
-    def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = {
-      // If anything below fails, we should abort the task.
-      try {
-        writerContainer.executorSideSetup(taskContext)
-
-        // Projects all partition columns and casts them to strings to build partition directories.
-        val partitionCasts = partitionOutput.map(Cast(_, StringType))
-        val partitionProj = newProjection(codegenEnabled, partitionCasts, output)
-        val dataProj = newProjection(codegenEnabled, dataOutput, output)
-
-        if (needsConversion) {
-          val converter = CatalystTypeConverters.createToScalaConverter(dataSchema)
-            .asInstanceOf[InternalRow => Row]
-          while (iterator.hasNext) {
-            val internalRow = iterator.next()
-            val partitionPart = partitionProj(internalRow)
-            val dataPart = converter(dataProj(internalRow))
-            writerContainer.outputWriterForRow(partitionPart).write(dataPart)
-          }
-        } else {
-          while (iterator.hasNext) {
-            val internalRow = iterator.next()
-            val partitionPart = partitionProj(internalRow)
-            val dataPart = dataProj(internalRow)
-            writerContainer.outputWriterForRow(partitionPart)
-              .asInstanceOf[OutputWriterInternal].writeInternal(dataPart)
-          }
-        }
-
-        writerContainer.commitTask()
-      } catch { case cause: Throwable =>
-        logError("Aborting task.", cause)
-        writerContainer.abortTask()
-        throw new SparkException("Task failed while writing rows.", cause)
-      }
-    }
-  }
-
-  // This is copied from SparkPlan, probably should move this to a more general place.
-  private def newProjection(
-      codegenEnabled: Boolean,
-      expressions: Seq[Expression],
-      inputSchema: Seq[Attribute]): Projection = {
-    log.debug(
-      s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
-    if (codegenEnabled) {
-
-      try {
-        GenerateProjection.generate(expressions, inputSchema)
-      } catch {
-        case e: Exception =>
-          if (sys.props.contains("spark.testing")) {
-            throw e
-          } else {
-            log.error("failed to generate projection, fallback to interpreted", e)
-            new InterpretedProjection(expressions, inputSchema)
-          }
-      }
-    } else {
-      new InterpretedProjection(expressions, inputSchema)
-    }
-  }
-}
-
-private[sql] abstract class BaseWriterContainer(
-    @transient val relation: HadoopFsRelation,
-    @transient job: Job,
-    isAppend: Boolean)
-  extends SparkHadoopMapReduceUtil
-  with Logging
-  with Serializable {
-
-  protected val serializableConf = new SerializableConfiguration(job.getConfiguration)
-
-  // This UUID is used to avoid output file name collision between different appending write jobs.
-  // These jobs may belong to different SparkContext instances. Concrete data source implementations
-  // may use this UUID to generate unique file names (e.g., `part-r-<task-id>-<job-uuid>.parquet`).
-  //  The reason why this ID is used to identify a job rather than a single task output file is
-  // that, speculative tasks must generate the same output file name as the original task.
-  private val uniqueWriteJobId = UUID.randomUUID()
-
-  // This is only used on driver side.
-  @transient private val jobContext: JobContext = job
-
-  // The following fields are initialized and used on both driver and executor side.
-  @transient protected var outputCommitter: OutputCommitter = _
-  @transient private var jobId: JobID = _
-  @transient private var taskId: TaskID = _
-  @transient private var taskAttemptId: TaskAttemptID = _
-  @transient protected var taskAttemptContext: TaskAttemptContext = _
-
-  protected val outputPath: String = {
-    assert(
-      relation.paths.length == 1,
-      s"Cannot write to multiple destinations: ${relation.paths.mkString(",")}")
-    relation.paths.head
-  }
-
-  protected val dataSchema = relation.dataSchema
-
-  protected var outputWriterFactory: OutputWriterFactory = _
-
-  private var outputFormatClass: Class[_ <: OutputFormat[_, _]] = _
-
-  def driverSideSetup(): Unit = {
-    setupIDs(0, 0, 0)
-    setupConf()
-
-    // This UUID is sent to executor side together with the serialized `Configuration` object within
-    // the `Job` instance.  `OutputWriters` on the executor side should use this UUID to generate
-    // unique task output files.
-    job.getConfiguration.set("spark.sql.sources.writeJobUUID", uniqueWriteJobId.toString)
-
-    // Order of the following two lines is important.  For Hadoop 1, TaskAttemptContext constructor
-    // clones the Configuration object passed in.  If we initialize the TaskAttemptContext first,
-    // configurations made in prepareJobForWrite(job) are not populated into the TaskAttemptContext.
-    //
-    // Also, the `prepareJobForWrite` call must happen before initializing output format and output
-    // committer, since their initialization involve the job configuration, which can be potentially
-    // decorated in `prepareJobForWrite`.
-    outputWriterFactory = relation.prepareJobForWrite(job)
-    taskAttemptContext = newTaskAttemptContext(serializableConf.value, taskAttemptId)
-
-    outputFormatClass = job.getOutputFormatClass
-    outputCommitter = newOutputCommitter(taskAttemptContext)
-    outputCommitter.setupJob(jobContext)
-  }
-
-  def executorSideSetup(taskContext: TaskContext): Unit = {
-    setupIDs(taskContext.stageId(), taskContext.partitionId(), taskContext.attemptNumber())
-    setupConf()
-    taskAttemptContext = newTaskAttemptContext(serializableConf.value, taskAttemptId)
-    outputCommitter = newOutputCommitter(taskAttemptContext)
-    outputCommitter.setupTask(taskAttemptContext)
-    initWriters()
-  }
-
-  protected def getWorkPath: String = {
-    outputCommitter match {
-      // FileOutputCommitter writes to a temporary location returned by `getWorkPath`.
-      case f: MapReduceFileOutputCommitter => f.getWorkPath.toString
-      case _ => outputPath
-    }
-  }
-
-  private def newOutputCommitter(context: TaskAttemptContext): OutputCommitter = {
-    val defaultOutputCommitter = outputFormatClass.newInstance().getOutputCommitter(context)
-
-    if (isAppend) {
-      // If we are appending data to an existing dir, we will only use the output committer
-      // associated with the file output format since it is not safe to use a custom
-      // committer for appending. For example, in S3, direct parquet output committer may
-      // leave partial data in the destination dir when the the appending job fails.
-      logInfo(
-        s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName} " +
-        "for appending.")
-      defaultOutputCommitter
-    } else {
-      val committerClass = context.getConfiguration.getClass(
-        SQLConf.OUTPUT_COMMITTER_CLASS.key, null, classOf[OutputCommitter])
-
-      Option(committerClass).map { clazz =>
-        logInfo(s"Using user defined output committer class ${clazz.getCanonicalName}")
-
-        // Every output format based on org.apache.hadoop.mapreduce.lib.output.OutputFormat
-        // has an associated output committer. To override this output committer,
-        // we will first try to use the output committer set in SQLConf.OUTPUT_COMMITTER_CLASS.
-        // If a data source needs to override the output committer, it needs to set the
-        // output committer in prepareForWrite method.
-        if (classOf[MapReduceFileOutputCommitter].isAssignableFrom(clazz)) {
-          // The specified output committer is a FileOutputCommitter.
-          // So, we will use the FileOutputCommitter-specified constructor.
-          val ctor = clazz.getDeclaredConstructor(classOf[Path], classOf[TaskAttemptContext])
-          ctor.newInstance(new Path(outputPath), context)
-        } else {
-          // The specified output committer is just a OutputCommitter.
-          // So, we will use the no-argument constructor.
-          val ctor = clazz.getDeclaredConstructor()
-          ctor.newInstance()
-        }
-      }.getOrElse {
-        // If output committer class is not set, we will use the one associated with the
-        // file output format.
-        logInfo(
-          s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName}")
-        defaultOutputCommitter
-      }
-    }
-  }
-
-  private def setupIDs(jobId: Int, splitId: Int, attemptId: Int): Unit = {
-    this.jobId = SparkHadoopWriter.createJobID(new Date, jobId)
-    this.taskId = new TaskID(this.jobId, true, splitId)
-    this.taskAttemptId = new TaskAttemptID(taskId, attemptId)
-  }
-
-  private def setupConf(): Unit = {
-    serializableConf.value.set("mapred.job.id", jobId.toString)
-    serializableConf.value.set("mapred.tip.id", taskAttemptId.getTaskID.toString)
-    serializableConf.value.set("mapred.task.id", taskAttemptId.toString)
-    serializableConf.value.setBoolean("mapred.task.is.map", true)
-    serializableConf.value.setInt("mapred.task.partition", 0)
-  }
-
-  // Called on executor side when writing rows
-  def outputWriterForRow(row: InternalRow): OutputWriter
-
-  protected def initWriters(): Unit
-
-  def commitTask(): Unit = {
-    SparkHadoopMapRedUtil.commitTask(
-      outputCommitter, taskAttemptContext, jobId.getId, taskId.getId, taskAttemptId.getId)
-  }
-
-  def abortTask(): Unit = {
-    if (outputCommitter != null) {
-      outputCommitter.abortTask(taskAttemptContext)
-    }
-    logError(s"Task attempt $taskAttemptId aborted.")
-  }
-
-  def commitJob(): Unit = {
-    outputCommitter.commitJob(jobContext)
-    logInfo(s"Job $jobId committed.")
-  }
-
-  def abortJob(): Unit = {
-    if (outputCommitter != null) {
-      outputCommitter.abortJob(jobContext, JobStatus.State.FAILED)
-    }
-    logError(s"Job $jobId aborted.")
-  }
-}
-
-private[sql] class DefaultWriterContainer(
-    @transient relation: HadoopFsRelation,
-    @transient job: Job,
-    isAppend: Boolean)
-  extends BaseWriterContainer(relation, job, isAppend) {
-
-  @transient private var writer: OutputWriter = _
-
-  override protected def initWriters(): Unit = {
-    taskAttemptContext.getConfiguration.set("spark.sql.sources.output.path", outputPath)
-    writer = outputWriterFactory.newInstance(getWorkPath, dataSchema, taskAttemptContext)
-  }
-
-  override def outputWriterForRow(row: InternalRow): OutputWriter = writer
-
-  override def commitTask(): Unit = {
-    try {
-      assert(writer != null, "OutputWriter instance should have been initialized")
-      writer.close()
-      super.commitTask()
-    } catch { case cause: Throwable =>
-      // This exception will be handled in `InsertIntoHadoopFsRelation.insert$writeRows`, and will
-      // cause `abortTask()` to be invoked.
-      throw new RuntimeException("Failed to commit task", cause)
-    }
-  }
-
-  override def abortTask(): Unit = {
-    try {
-      // It's possible that the task fails before `writer` gets initialized
-      if (writer != null) {
-        writer.close()
-      }
-    } finally {
-      super.abortTask()
-    }
-  }
-}
-
-private[sql] class DynamicPartitionWriterContainer(
-    @transient relation: HadoopFsRelation,
-    @transient job: Job,
-    partitionColumns: Array[String],
-    defaultPartitionName: String,
-    isAppend: Boolean)
-  extends BaseWriterContainer(relation, job, isAppend) {
-
-  // All output writers are created on executor side.
-  @transient protected var outputWriters: java.util.HashMap[String, OutputWriter] = _
-
-  override protected def initWriters(): Unit = {
-    outputWriters = new java.util.HashMap[String, OutputWriter]
-  }
-
-  // The `row` argument is supposed to only contain partition column values which have been casted
-  // to strings.
-  override def outputWriterForRow(row: InternalRow): OutputWriter = {
-    val partitionPath = {
-      val partitionPathBuilder = new StringBuilder
-      var i = 0
-
-      while (i < partitionColumns.length) {
-        val col = partitionColumns(i)
-        val partitionValueString = {
-          val string = row.getUTF8String(i)
-          if (string.eq(null)) {
-            defaultPartitionName
-          } else {
-            PartitioningUtils.escapePathName(string.toString)
-          }
-        }
-
-        if (i > 0) {
-          partitionPathBuilder.append(Path.SEPARATOR_CHAR)
-        }
-
-        partitionPathBuilder.append(s"$col=$partitionValueString")
-        i += 1
-      }
-
-      partitionPathBuilder.toString()
-    }
-
-    val writer = outputWriters.get(partitionPath)
-    if (writer.eq(null)) {
-      val path = new Path(getWorkPath, partitionPath)
-      taskAttemptContext.getConfiguration.set(
-        "spark.sql.sources.output.path", new Path(outputPath, partitionPath).toString)
-      val newWriter = outputWriterFactory.newInstance(path.toString, dataSchema, taskAttemptContext)
-      outputWriters.put(partitionPath, newWriter)
-      newWriter
-    } else {
-      writer
-    }
-  }
-
-  private def clearOutputWriters(): Unit = {
-    if (!outputWriters.isEmpty) {
-      asScalaIterator(outputWriters.values().iterator()).foreach(_.close())
-      outputWriters.clear()
-    }
-  }
-
-  override def commitTask(): Unit = {
-    try {
-      clearOutputWriters()
-      super.commitTask()
-    } catch { case cause: Throwable =>
-      throw new RuntimeException("Failed to commit task", cause)
-    }
-  }
-
-  override def abortTask(): Unit = {
-    try {
-      clearOutputWriters()
-    } finally {
-      super.abortTask()
-    }
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index 5d371402877c6..10f1367e6984c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -152,7 +152,7 @@ private[json] class JsonOutputWriter(
     path: String,
     dataSchema: StructType,
     context: TaskAttemptContext)
-  extends OutputWriterInternal with SparkHadoopMapRedUtil with Logging {
+  extends OutputWriter with SparkHadoopMapRedUtil with Logging {
 
   val writer = new CharArrayWriter()
   // create the Generator without separator inserted between 2 records
@@ -170,7 +170,9 @@ private[json] class JsonOutputWriter(
     }.getRecordWriter(context)
   }
 
-  override def writeInternal(row: InternalRow): Unit = {
+  override def write(row: Row): Unit = throw new UnsupportedOperationException("call writeInternal")
+
+  override protected[sql] def writeInternal(row: InternalRow): Unit = {
     JacksonGenerator(dataSchema, gen, row)
     gen.flush()
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 29c388c22ef93..48009b2fd007d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -62,7 +62,7 @@ private[sql] class DefaultSource extends HadoopFsRelationProvider {
 
 // NOTE: This class is instantiated and used on executor side only, no need to be serializable.
 private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext)
-  extends OutputWriterInternal {
+  extends OutputWriter {
 
   private val recordWriter: RecordWriter[Void, InternalRow] = {
     val outputFormat = {
@@ -87,7 +87,9 @@ private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext
     outputFormat.getRecordWriter(context)
   }
 
-  override def writeInternal(row: InternalRow): Unit = recordWriter.write(null, row)
+  override def write(row: Row): Unit = throw new UnsupportedOperationException("call writeInternal")
+
+  override protected[sql] def writeInternal(row: InternalRow): Unit = recordWriter.write(null, row)
 
   override def close(): Unit = recordWriter.close(context)
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 0b2929661b657..c5b7ee73eb784 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -342,18 +342,17 @@ abstract class OutputWriter {
    * @since 1.4.0
    */
   def close(): Unit
-}
 
-/**
- * This is an internal, private version of [[OutputWriter]] with an writeInternal method that
- * accepts an [[InternalRow]] rather than an [[Row]]. Data sources that return this must have
- * the conversion flag set to false.
- */
-private[sql] abstract class OutputWriterInternal extends OutputWriter {
+  private var converter: InternalRow => Row = _
 
-  override def write(row: Row): Unit = throw new UnsupportedOperationException
+  protected[sql] def initConverter(dataSchema: StructType) = {
+    converter =
+      CatalystTypeConverters.createToScalaConverter(dataSchema).asInstanceOf[InternalRow => Row]
+  }
 
-  def writeInternal(row: InternalRow): Unit
+  protected[sql] def writeInternal(row: InternalRow): Unit = {
+    write(converter(row))
+  }
 }
 
 /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
new file mode 100644
index 0000000000000..c86ddd7c83e53
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.sources
+
+import org.apache.spark.sql.{Row, QueryTest}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.util.Utils
+
+class PartitionedWriteSuite extends QueryTest {
+  import TestSQLContext.implicits._
+
+  test("write many partitions") {
+    val path = Utils.createTempDir()
+    path.delete()
+
+    val df = TestSQLContext.range(100).select($"id", lit(1).as("data"))
+    df.write.partitionBy("id").save(path.getCanonicalPath)
+
+    checkAnswer(
+      TestSQLContext.read.load(path.getCanonicalPath),
+      (0 to 99).map(Row(1, _)).toSeq)
+
+    Utils.deleteRecursively(path)
+  }
+
+  test("write many partitions with repeats") {
+    val path = Utils.createTempDir()
+    path.delete()
+
+    val base = TestSQLContext.range(100)
+    val df = base.unionAll(base).select($"id", lit(1).as("data"))
+    df.write.partitionBy("id").save(path.getCanonicalPath)
+
+    checkAnswer(
+      TestSQLContext.read.load(path.getCanonicalPath),
+      (0 to 99).map(Row(1, _)).toSeq ++ (0 to 99).map(Row(1, _)).toSeq)
+
+    Utils.deleteRecursively(path)
+  }
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 4a310ff4e9016..7c8704b47f286 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -66,7 +66,7 @@ private[orc] class OrcOutputWriter(
     path: String,
     dataSchema: StructType,
     context: TaskAttemptContext)
-  extends OutputWriterInternal with SparkHadoopMapRedUtil with HiveInspectors {
+  extends OutputWriter with SparkHadoopMapRedUtil with HiveInspectors {
 
   private val serializer = {
     val table = new Properties()
@@ -120,7 +120,9 @@ private[orc] class OrcOutputWriter(
     ).asInstanceOf[RecordWriter[NullWritable, Writable]]
   }
 
-  override def writeInternal(row: InternalRow): Unit = {
+  override def write(row: Row): Unit = throw new UnsupportedOperationException("call writeInternal")
+
+  override protected[sql] def writeInternal(row: InternalRow): Unit = {
     var i = 0
     while (i < row.numFields) {
       reusableOutputBuffer(i) = wrappers(i)(row.get(i, dataSchema(i).dataType))

From cd540c1e59561ad1fdac59af6170944c60e685d8 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Fri, 7 Aug 2015 17:19:48 -0700
Subject: [PATCH 125/633] [SPARK-9756] [ML] Make constructors in ML decision
 trees private

These should be made private until there is a public constructor for providing `rootNode: Node` to use these constructors.

jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8046 from feynmanliang/SPARK-9756 and squashes the following commits:

2cbdf08 [Feynman Liang] Make RFRegressionModel aux constructor private
a06f596 [Feynman Liang] Make constructors in ML decision trees private
---
 .../spark/ml/classification/DecisionTreeClassifier.scala     | 2 +-
 .../spark/ml/classification/RandomForestClassifier.scala     | 5 ++++-
 .../apache/spark/ml/regression/DecisionTreeRegressor.scala   | 2 +-
 .../apache/spark/ml/regression/RandomForestRegressor.scala   | 2 +-
 4 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index f2b992f8ba249..29598f3f05c2d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -117,7 +117,7 @@ final class DecisionTreeClassificationModel private[ml] (
    * Construct a decision tree classification model.
    * @param rootNode  Root node of tree, with other nodes attached.
    */
-  def this(rootNode: Node, numClasses: Int) =
+  private[ml] def this(rootNode: Node, numClasses: Int) =
     this(Identifiable.randomUID("dtc"), rootNode, numClasses)
 
   override protected def predict(features: Vector): Double = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index b59826a59499a..156050aaf7a45 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -136,7 +136,10 @@ final class RandomForestClassificationModel private[ml] (
    * Construct a random forest classification model, with all trees weighted equally.
    * @param trees  Component trees
    */
-  def this(trees: Array[DecisionTreeClassificationModel], numFeatures: Int, numClasses: Int) =
+  private[ml] def this(
+      trees: Array[DecisionTreeClassificationModel],
+      numFeatures: Int,
+      numClasses: Int) =
     this(Identifiable.randomUID("rfc"), trees, numFeatures, numClasses)
 
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index 4d30e4b5548aa..dc94a14014542 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -107,7 +107,7 @@ final class DecisionTreeRegressionModel private[ml] (
    * Construct a decision tree regression model.
    * @param rootNode  Root node of tree, with other nodes attached.
    */
-  def this(rootNode: Node) = this(Identifiable.randomUID("dtr"), rootNode)
+  private[ml] def this(rootNode: Node) = this(Identifiable.randomUID("dtr"), rootNode)
 
   override protected def predict(features: Vector): Double = {
     rootNode.predictImpl(features).prediction
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 1ee43c8725732..db75c0d26392f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -125,7 +125,7 @@ final class RandomForestRegressionModel private[ml] (
    * Construct a random forest regression model, with all trees weighted equally.
    * @param trees  Component trees
    */
-  def this(trees: Array[DecisionTreeRegressionModel], numFeatures: Int) =
+  private[ml] def this(trees: Array[DecisionTreeRegressionModel], numFeatures: Int) =
     this(Identifiable.randomUID("rfr"), trees, numFeatures)
 
   override def trees: Array[DecisionTreeModel] = _trees.asInstanceOf[Array[DecisionTreeModel]]

From 85be65b39ce669f937a898195a844844d757666b Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Fri, 7 Aug 2015 17:21:12 -0700
Subject: [PATCH 126/633] [SPARK-9719] [ML] Clean up Naive Bayes doc

Small documentation cleanups, including:
 * Adds documentation for `pi` and `theta`
 * setParam to `setModelType`

Author: Feynman Liang <fliang@databricks.com>

Closes #8047 from feynmanliang/SPARK-9719 and squashes the following commits:

b372438 [Feynman Liang] Clean up naive bayes doc
---
 .../scala/org/apache/spark/ml/classification/NaiveBayes.scala | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index b46b676204e0e..97cbaf1fa8761 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -86,6 +86,7 @@ class NaiveBayes(override val uid: String)
    * Set the model type using a string (case-sensitive).
    * Supported options: "multinomial" and "bernoulli".
    * Default is "multinomial"
+   * @group setParam
    */
   def setModelType(value: String): this.type = set(modelType, value)
   setDefault(modelType -> OldNaiveBayes.Multinomial)
@@ -101,6 +102,9 @@ class NaiveBayes(override val uid: String)
 
 /**
  * Model produced by [[NaiveBayes]]
+ * @param pi log of class priors, whose dimension is C (number of classes)
+ * @param theta log of class conditional probabilities, whose dimension is C (number of classes)
+ *              by D (number of features)
  */
 class NaiveBayesModel private[ml] (
     override val uid: String,

From 998f4ff94df1d9db1c9e32c04091017c25cd4e81 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 7 Aug 2015 19:09:28 -0700
Subject: [PATCH 127/633] [SPARK-9754][SQL] Remove TypeCheck in debug package.

TypeCheck no longer applies in the new "Tungsten" world.

Author: Reynold Xin <rxin@databricks.com>

Closes #8043 from rxin/SPARK-9754 and squashes the following commits:

4ec471e [Reynold Xin] [SPARK-9754][SQL] Remove TypeCheck in debug package.
---
 .../spark/sql/execution/debug/package.scala   | 104 +-----------------
 .../sql/execution/debug/DebuggingSuite.scala  |   4 -
 2 files changed, 4 insertions(+), 104 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
index dd3858ea2b520..74892e4e13fa4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
@@ -17,21 +17,16 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.catalyst.expressions.Attribute
-import org.apache.spark.unsafe.types.UTF8String
-
 import scala.collection.mutable.HashSet
 
-import org.apache.spark.{AccumulatorParam, Accumulator, Logging}
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.trees.TreeNodeRef
-import org.apache.spark.sql.types._
+import org.apache.spark.{Accumulator, AccumulatorParam, Logging}
 
 /**
- * :: DeveloperApi ::
  * Contains methods for debugging query execution.
  *
  * Usage:
@@ -53,10 +48,8 @@ package object debug {
   }
 
   /**
-   * :: DeveloperApi ::
    * Augments [[DataFrame]]s with debug methods.
    */
-  @DeveloperApi
   implicit class DebugQuery(query: DataFrame) extends Logging {
     def debug(): Unit = {
       val plan = query.queryExecution.executedPlan
@@ -72,23 +65,6 @@ package object debug {
         case _ =>
       }
     }
-
-    def typeCheck(): Unit = {
-      val plan = query.queryExecution.executedPlan
-      val visited = new collection.mutable.HashSet[TreeNodeRef]()
-      val debugPlan = plan transform {
-        case s: SparkPlan if !visited.contains(new TreeNodeRef(s)) =>
-          visited += new TreeNodeRef(s)
-          TypeCheck(s)
-      }
-      try {
-        logDebug(s"Results returned: ${debugPlan.execute().count()}")
-      } catch {
-        case e: Exception =>
-          def unwrap(e: Throwable): Throwable = if (e.getCause == null) e else unwrap(e.getCause)
-          logDebug(s"Deepest Error: ${unwrap(e)}")
-      }
-    }
   }
 
   private[sql] case class DebugNode(child: SparkPlan) extends UnaryNode {
@@ -148,76 +124,4 @@ package object debug {
       }
     }
   }
-
-  /**
-   * Helper functions for checking that runtime types match a given schema.
-   */
-  private[sql] object TypeCheck {
-    def typeCheck(data: Any, schema: DataType): Unit = (data, schema) match {
-      case (null, _) =>
-
-      case (row: InternalRow, s: StructType) =>
-        row.toSeq(s).zip(s.map(_.dataType)).foreach { case(d, t) => typeCheck(d, t) }
-      case (a: ArrayData, ArrayType(elemType, _)) =>
-        a.foreach(elemType, (_, e) => {
-          typeCheck(e, elemType)
-        })
-      case (m: MapData, MapType(keyType, valueType, _)) =>
-        m.keyArray().foreach(keyType, (_, e) => {
-          typeCheck(e, keyType)
-        })
-        m.valueArray().foreach(valueType, (_, e) => {
-          typeCheck(e, valueType)
-        })
-
-      case (_: Long, LongType) =>
-      case (_: Int, IntegerType) =>
-      case (_: UTF8String, StringType) =>
-      case (_: Float, FloatType) =>
-      case (_: Byte, ByteType) =>
-      case (_: Short, ShortType) =>
-      case (_: Boolean, BooleanType) =>
-      case (_: Double, DoubleType) =>
-      case (_: Int, DateType) =>
-      case (_: Long, TimestampType) =>
-      case (v, udt: UserDefinedType[_]) => typeCheck(v, udt.sqlType)
-
-      case (d, t) => sys.error(s"Invalid data found: got $d (${d.getClass}) expected $t")
-    }
-  }
-
-  /**
-   * Augments [[DataFrame]]s with debug methods.
-   */
-  private[sql] case class TypeCheck(child: SparkPlan) extends SparkPlan {
-    import TypeCheck._
-
-    override def nodeName: String = ""
-
-    /* Only required when defining this class in a REPL.
-    override def makeCopy(args: Array[Object]): this.type =
-      TypeCheck(args(0).asInstanceOf[SparkPlan]).asInstanceOf[this.type]
-    */
-
-    def output: Seq[Attribute] = child.output
-
-    def children: List[SparkPlan] = child :: Nil
-
-    protected override def doExecute(): RDD[InternalRow] = {
-      child.execute().map { row =>
-        try typeCheck(row, child.schema) catch {
-          case e: Exception =>
-            sys.error(
-              s"""
-                  |ERROR WHEN TYPE CHECKING QUERY
-                  |==============================
-                  |$e
-                  |======== BAD TREE ============
-                  |$child
-             """.stripMargin)
-        }
-        row
-      }
-    }
-  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
index 8ec3985e00360..239deb7973845 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
@@ -25,8 +25,4 @@ class DebuggingSuite extends SparkFunSuite {
   test("DataFrame.debug()") {
     testData.debug()
   }
-
-  test("DataFrame.typeCheck()") {
-    testData.typeCheck()
-  }
 }

From c564b27447ed99e55b359b3df1d586d5766b85ea Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 7 Aug 2015 20:04:17 -0700
Subject: [PATCH 128/633] [SPARK-9753] [SQL] TungstenAggregate should also
 accept InternalRow instead of just UnsafeRow

https://issues.apache.org/jira/browse/SPARK-9753

This PR makes TungstenAggregate to accept `InternalRow` instead of just `UnsafeRow`. Also, it adds an `getAggregationBufferFromUnsafeRow` method to `UnsafeFixedWidthAggregationMap`. It is useful when we already have grouping keys stored in `UnsafeRow`s. Finally, it wraps `InputStream` and `OutputStream` in `UnsafeRowSerializer` with `BufferedInputStream` and `BufferedOutputStream`, respectively.

Author: Yin Huai <yhuai@databricks.com>

Closes #8041 from yhuai/joinedRowForProjection and squashes the following commits:

7753e34 [Yin Huai] Use BufferedInputStream and BufferedOutputStream.
d68b74e [Yin Huai] Use joinedRow instead of UnsafeRowJoiner.
e93c009 [Yin Huai] Add getAggregationBufferFromUnsafeRow for cases that the given groupingKeyRow is already an UnsafeRow.
---
 .../UnsafeFixedWidthAggregationMap.java       |  4 ++
 .../sql/execution/UnsafeRowSerializer.scala   | 30 +++--------
 .../aggregate/TungstenAggregate.scala         |  4 +-
 .../TungstenAggregationIterator.scala         | 51 +++++++++----------
 4 files changed, 39 insertions(+), 50 deletions(-)

diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index b08a4a13a28be..00218f213054b 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -121,6 +121,10 @@ public UnsafeFixedWidthAggregationMap(
   public UnsafeRow getAggregationBuffer(InternalRow groupingKey) {
     final UnsafeRow unsafeGroupingKeyRow = this.groupingKeyProjection.apply(groupingKey);
 
+    return getAggregationBufferFromUnsafeRow(unsafeGroupingKeyRow);
+  }
+
+  public UnsafeRow getAggregationBufferFromUnsafeRow(UnsafeRow unsafeGroupingKeyRow) {
     // Probe our map using the serialized key
     final BytesToBytesMap.Location loc = map.lookup(
       unsafeGroupingKeyRow.getBaseObject(),
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 39f8f992a9f00..6c7e5cacc99e7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -58,27 +58,14 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
    */
   override def serializeStream(out: OutputStream): SerializationStream = new SerializationStream {
     private[this] var writeBuffer: Array[Byte] = new Array[Byte](4096)
-    // When `out` is backed by ChainedBufferOutputStream, we will get an
-    // UnsupportedOperationException when we call dOut.writeInt because it internally calls
-    // ChainedBufferOutputStream's write(b: Int), which is not supported.
-    // To workaround this issue, we create an array for sorting the int value.
-    // To reproduce the problem, use dOut.writeInt(row.getSizeInBytes) and
-    // run SparkSqlSerializer2SortMergeShuffleSuite.
-    private[this] var intBuffer: Array[Byte] = new Array[Byte](4)
-    private[this] val dOut: DataOutputStream = new DataOutputStream(out)
+    private[this] val dOut: DataOutputStream =
+      new DataOutputStream(new BufferedOutputStream(out))
 
     override def writeValue[T: ClassTag](value: T): SerializationStream = {
       val row = value.asInstanceOf[UnsafeRow]
-      val size = row.getSizeInBytes
-      // This part is based on DataOutputStream's writeInt.
-      // It is for dOut.writeInt(row.getSizeInBytes).
-      intBuffer(0) = ((size >>> 24) & 0xFF).toByte
-      intBuffer(1) = ((size >>> 16) & 0xFF).toByte
-      intBuffer(2) = ((size >>> 8) & 0xFF).toByte
-      intBuffer(3) = ((size >>> 0) & 0xFF).toByte
-      dOut.write(intBuffer, 0, 4)
-
-      row.writeToStream(out, writeBuffer)
+
+      dOut.writeInt(row.getSizeInBytes)
+      row.writeToStream(dOut, writeBuffer)
       this
     }
 
@@ -105,7 +92,6 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
 
     override def close(): Unit = {
       writeBuffer = null
-      intBuffer = null
       dOut.writeInt(EOF)
       dOut.close()
     }
@@ -113,7 +99,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
 
   override def deserializeStream(in: InputStream): DeserializationStream = {
     new DeserializationStream {
-      private[this] val dIn: DataInputStream = new DataInputStream(in)
+      private[this] val dIn: DataInputStream = new DataInputStream(new BufferedInputStream(in))
       // 1024 is a default buffer size; this buffer will grow to accommodate larger rows
       private[this] var rowBuffer: Array[Byte] = new Array[Byte](1024)
       private[this] var row: UnsafeRow = new UnsafeRow()
@@ -129,7 +115,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
             if (rowBuffer.length < rowSize) {
               rowBuffer = new Array[Byte](rowSize)
             }
-            ByteStreams.readFully(in, rowBuffer, 0, rowSize)
+            ByteStreams.readFully(dIn, rowBuffer, 0, rowSize)
             row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize)
             rowSize = dIn.readInt() // read the next row's size
             if (rowSize == EOF) { // We are returning the last row in this stream
@@ -163,7 +149,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
         if (rowBuffer.length < rowSize) {
           rowBuffer = new Array[Byte](rowSize)
         }
-        ByteStreams.readFully(in, rowBuffer, 0, rowSize)
+        ByteStreams.readFully(dIn, rowBuffer, 0, rowSize)
         row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize)
         row.asInstanceOf[T]
       }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index c3dcbd2b71ee8..1694794a53d9f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -39,7 +39,7 @@ case class TungstenAggregate(
 
   override def canProcessUnsafeRows: Boolean = true
 
-  override def canProcessSafeRows: Boolean = false
+  override def canProcessSafeRows: Boolean = true
 
   override def output: Seq[Attribute] = resultExpressions.map(_.toAttribute)
 
@@ -77,7 +77,7 @@ case class TungstenAggregate(
             resultExpressions,
             newMutableProjection,
             child.output,
-            iter.asInstanceOf[Iterator[UnsafeRow]],
+            iter,
             testFallbackStartsAt)
 
         if (!hasInput && groupingExpressions.isEmpty) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index 440bef32f4e9b..32160906c3bc8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -22,6 +22,7 @@ import org.apache.spark.{InternalAccumulator, Logging, SparkEnv, TaskContext}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeRowJoiner
+import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.execution.{UnsafeKVExternalSorter, UnsafeFixedWidthAggregationMap}
 import org.apache.spark.sql.types.StructType
 
@@ -46,8 +47,7 @@ import org.apache.spark.sql.types.StructType
  *            processing input rows from inputIter, and generating output
  *            rows.
  *  - Part 3: Methods and fields used by hash-based aggregation.
- *  - Part 4: The function used to switch this iterator from hash-based
- *            aggregation to sort-based aggregation.
+ *  - Part 4: Methods and fields used when we switch to sort-based aggregation.
  *  - Part 5: Methods and fields used by sort-based aggregation.
  *  - Part 6: Loads input and process input rows.
  *  - Part 7: Public methods of this iterator.
@@ -82,7 +82,7 @@ class TungstenAggregationIterator(
     resultExpressions: Seq[NamedExpression],
     newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
     originalInputAttributes: Seq[Attribute],
-    inputIter: Iterator[UnsafeRow],
+    inputIter: Iterator[InternalRow],
     testFallbackStartsAt: Option[Int])
   extends Iterator[UnsafeRow] with Logging {
 
@@ -174,13 +174,10 @@ class TungstenAggregationIterator(
 
   // Creates a function used to process a row based on the given inputAttributes.
   private def generateProcessRow(
-      inputAttributes: Seq[Attribute]): (UnsafeRow, UnsafeRow) => Unit = {
+      inputAttributes: Seq[Attribute]): (UnsafeRow, InternalRow) => Unit = {
 
     val aggregationBufferAttributes = allAggregateFunctions.flatMap(_.bufferAttributes)
-    val aggregationBufferSchema = StructType.fromAttributes(aggregationBufferAttributes)
-    val inputSchema = StructType.fromAttributes(inputAttributes)
-    val unsafeRowJoiner =
-      GenerateUnsafeRowJoiner.create(aggregationBufferSchema, inputSchema)
+    val joinedRow = new JoinedRow()
 
     aggregationMode match {
       // Partial-only
@@ -189,9 +186,9 @@ class TungstenAggregationIterator(
         val algebraicUpdateProjection =
           newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
 
-        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+        (currentBuffer: UnsafeRow, row: InternalRow) => {
           algebraicUpdateProjection.target(currentBuffer)
-          algebraicUpdateProjection(unsafeRowJoiner.join(currentBuffer, row))
+          algebraicUpdateProjection(joinedRow(currentBuffer, row))
         }
 
       // PartialMerge-only or Final-only
@@ -203,10 +200,10 @@ class TungstenAggregationIterator(
             mergeExpressions,
             aggregationBufferAttributes ++ inputAttributes)()
 
-        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+        (currentBuffer: UnsafeRow, row: InternalRow) => {
           // Process all algebraic aggregate functions.
           algebraicMergeProjection.target(currentBuffer)
-          algebraicMergeProjection(unsafeRowJoiner.join(currentBuffer, row))
+          algebraicMergeProjection(joinedRow(currentBuffer, row))
         }
 
       // Final-Complete
@@ -233,8 +230,8 @@ class TungstenAggregationIterator(
         val completeAlgebraicUpdateProjection =
           newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
 
-        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
-          val input = unsafeRowJoiner.join(currentBuffer, row)
+        (currentBuffer: UnsafeRow, row: InternalRow) => {
+          val input = joinedRow(currentBuffer, row)
           // For all aggregate functions with mode Complete, update the given currentBuffer.
           completeAlgebraicUpdateProjection.target(currentBuffer)(input)
 
@@ -253,14 +250,14 @@ class TungstenAggregationIterator(
         val completeAlgebraicUpdateProjection =
           newMutableProjection(updateExpressions, aggregationBufferAttributes ++ inputAttributes)()
 
-        (currentBuffer: UnsafeRow, row: UnsafeRow) => {
+        (currentBuffer: UnsafeRow, row: InternalRow) => {
           completeAlgebraicUpdateProjection.target(currentBuffer)
           // For all aggregate functions with mode Complete, update the given currentBuffer.
-          completeAlgebraicUpdateProjection(unsafeRowJoiner.join(currentBuffer, row))
+          completeAlgebraicUpdateProjection(joinedRow(currentBuffer, row))
         }
 
       // Grouping only.
-      case (None, None) => (currentBuffer: UnsafeRow, row: UnsafeRow) => {}
+      case (None, None) => (currentBuffer: UnsafeRow, row: InternalRow) => {}
 
       case other =>
         throw new IllegalStateException(
@@ -272,15 +269,16 @@ class TungstenAggregationIterator(
   private def generateResultProjection(): (UnsafeRow, UnsafeRow) => UnsafeRow = {
 
     val groupingAttributes = groupingExpressions.map(_.toAttribute)
-    val groupingKeySchema = StructType.fromAttributes(groupingAttributes)
     val bufferAttributes = allAggregateFunctions.flatMap(_.bufferAttributes)
-    val bufferSchema = StructType.fromAttributes(bufferAttributes)
-    val unsafeRowJoiner = GenerateUnsafeRowJoiner.create(groupingKeySchema, bufferSchema)
 
     aggregationMode match {
       // Partial-only or PartialMerge-only: every output row is basically the values of
       // the grouping expressions and the corresponding aggregation buffer.
       case (Some(Partial), None) | (Some(PartialMerge), None) =>
+        val groupingKeySchema = StructType.fromAttributes(groupingAttributes)
+        val bufferSchema = StructType.fromAttributes(bufferAttributes)
+        val unsafeRowJoiner = GenerateUnsafeRowJoiner.create(groupingKeySchema, bufferSchema)
+
         (currentGroupingKey: UnsafeRow, currentBuffer: UnsafeRow) => {
           unsafeRowJoiner.join(currentGroupingKey, currentBuffer)
         }
@@ -288,11 +286,12 @@ class TungstenAggregationIterator(
       // Final-only, Complete-only and Final-Complete: a output row is generated based on
       // resultExpressions.
       case (Some(Final), None) | (Some(Final) | None, Some(Complete)) =>
+        val joinedRow = new JoinedRow()
         val resultProjection =
           UnsafeProjection.create(resultExpressions, groupingAttributes ++ bufferAttributes)
 
         (currentGroupingKey: UnsafeRow, currentBuffer: UnsafeRow) => {
-          resultProjection(unsafeRowJoiner.join(currentGroupingKey, currentBuffer))
+          resultProjection(joinedRow(currentGroupingKey, currentBuffer))
         }
 
       // Grouping-only: a output row is generated from values of grouping expressions.
@@ -316,7 +315,7 @@ class TungstenAggregationIterator(
 
   // A function used to process a input row. Its first argument is the aggregation buffer
   // and the second argument is the input row.
-  private[this] var processRow: (UnsafeRow, UnsafeRow) => Unit =
+  private[this] var processRow: (UnsafeRow, InternalRow) => Unit =
     generateProcessRow(originalInputAttributes)
 
   // A function used to generate output rows based on the grouping keys (first argument)
@@ -354,7 +353,7 @@ class TungstenAggregationIterator(
     while (!sortBased && inputIter.hasNext) {
       val newInput = inputIter.next()
       val groupingKey = groupProjection.apply(newInput)
-      val buffer: UnsafeRow = hashMap.getAggregationBuffer(groupingKey)
+      val buffer: UnsafeRow = hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
       if (buffer == null) {
         // buffer == null means that we could not allocate more memory.
         // Now, we need to spill the map and switch to sort-based aggregation.
@@ -374,7 +373,7 @@ class TungstenAggregationIterator(
       val newInput = inputIter.next()
       val groupingKey = groupProjection.apply(newInput)
       val buffer: UnsafeRow = if (i < fallbackStartsAt) {
-        hashMap.getAggregationBuffer(groupingKey)
+        hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
       } else {
         null
       }
@@ -397,7 +396,7 @@ class TungstenAggregationIterator(
   private[this] var mapIteratorHasNext: Boolean = false
 
   ///////////////////////////////////////////////////////////////////////////
-  // Part 3: Methods and fields used by sort-based aggregation.
+  // Part 4: Methods and fields used when we switch to sort-based aggregation.
   ///////////////////////////////////////////////////////////////////////////
 
   // This sorter is used for sort-based aggregation. It is initialized as soon as
@@ -407,7 +406,7 @@ class TungstenAggregationIterator(
   /**
    * Switch to sort-based aggregation when the hash-based approach is unable to acquire memory.
    */
-  private def switchToSortBasedAggregation(firstKey: UnsafeRow, firstInput: UnsafeRow): Unit = {
+  private def switchToSortBasedAggregation(firstKey: UnsafeRow, firstInput: InternalRow): Unit = {
     logInfo("falling back to sort based aggregation.")
     // Step 1: Get the ExternalSorter containing sorted entries of the map.
     externalSorter = hashMap.destructAndCreateExternalSorter()

From ef062c15992b0d08554495b8ea837bef3fabf6e9 Mon Sep 17 00:00:00 2001
From: Carson Wang <carson.wang@intel.com>
Date: Fri, 7 Aug 2015 23:36:26 -0700
Subject: [PATCH 129/633] [SPARK-9731] Standalone scheduling incorrect cores if
 spark.executor.cores is not set

The issue only happens if `spark.executor.cores` is not set and executor memory is set to a high value.
For example, if we have a worker with 4G and 10 cores and we set `spark.executor.memory` to 3G, then only 1 core is assigned to the executor. The correct number should be 10 cores.
I've added a unit test to illustrate the issue.

Author: Carson Wang <carson.wang@intel.com>

Closes #8017 from carsonwang/SPARK-9731 and squashes the following commits:

d09ec48 [Carson Wang] Fix code style
86b651f [Carson Wang] Simplify the code
943cc4c [Carson Wang] fix scheduling correct cores to executors
---
 .../apache/spark/deploy/master/Master.scala   | 26 ++++++++++---------
 .../spark/deploy/master/MasterSuite.scala     | 15 +++++++++++
 2 files changed, 29 insertions(+), 12 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index e38e437fe1c5a..9217202b69a66 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -581,20 +581,22 @@ private[deploy] class Master(
 
     /** Return whether the specified worker can launch an executor for this app. */
     def canLaunchExecutor(pos: Int): Boolean = {
+      val keepScheduling = coresToAssign >= minCoresPerExecutor
+      val enoughCores = usableWorkers(pos).coresFree - assignedCores(pos) >= minCoresPerExecutor
+
       // If we allow multiple executors per worker, then we can always launch new executors.
-      // Otherwise, we may have already started assigning cores to the executor on this worker.
+      // Otherwise, if there is already an executor on this worker, just give it more cores.
       val launchingNewExecutor = !oneExecutorPerWorker || assignedExecutors(pos) == 0
-      val underLimit =
-        if (launchingNewExecutor) {
-          assignedExecutors.sum + app.executors.size < app.executorLimit
-        } else {
-          true
-        }
-      val assignedMemory = assignedExecutors(pos) * memoryPerExecutor
-      usableWorkers(pos).memoryFree - assignedMemory >= memoryPerExecutor &&
-      usableWorkers(pos).coresFree - assignedCores(pos) >= minCoresPerExecutor &&
-      coresToAssign >= minCoresPerExecutor &&
-      underLimit
+      if (launchingNewExecutor) {
+        val assignedMemory = assignedExecutors(pos) * memoryPerExecutor
+        val enoughMemory = usableWorkers(pos).memoryFree - assignedMemory >= memoryPerExecutor
+        val underLimit = assignedExecutors.sum + app.executors.size < app.executorLimit
+        keepScheduling && enoughCores && enoughMemory && underLimit
+      } else {
+        // We're adding cores to an existing executor, so no need
+        // to check memory and executor limits
+        keepScheduling && enoughCores
+      }
     }
 
     // Keep launching executors until no more workers can accommodate any
diff --git a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
index ae0e037d822ea..20d0201a364ab 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
@@ -151,6 +151,14 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
     basicScheduling(spreadOut = false)
   }
 
+  test("basic scheduling with more memory - spread out") {
+    basicSchedulingWithMoreMemory(spreadOut = true)
+  }
+
+  test("basic scheduling with more memory - no spread out") {
+    basicSchedulingWithMoreMemory(spreadOut = false)
+  }
+
   test("scheduling with max cores - spread out") {
     schedulingWithMaxCores(spreadOut = true)
   }
@@ -214,6 +222,13 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
     assert(scheduledCores === Array(10, 10, 10))
   }
 
+  private def basicSchedulingWithMoreMemory(spreadOut: Boolean): Unit = {
+    val master = makeMaster()
+    val appInfo = makeAppInfo(3072)
+    val scheduledCores = scheduleExecutorsOnWorkers(master, appInfo, workerInfos, spreadOut)
+    assert(scheduledCores === Array(10, 10, 10))
+  }
+
   private def schedulingWithMaxCores(spreadOut: Boolean): Unit = {
     val master = makeMaster()
     val appInfo1 = makeAppInfo(1024, maxCores = Some(8))

From 11caf1ce290b6931647c2f71268f847d1d48930e Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sat, 8 Aug 2015 18:09:48 +0800
Subject: [PATCH 130/633] [SPARK-4176] [SQL] [MINOR] Should use unscaled Long
 to write decimals for precision <= 18 rather than 8

This PR fixes a minor bug introduced in #7455: when writing decimals, we should use the unscaled Long for better performance when the precision <= 18 rather than 8 (should be a typo). This bug doesn't affect correctness, but hurts Parquet decimal writing performance.

This PR also replaced similar magic numbers with newly defined constants.

Author: Cheng Lian <lian@databricks.com>

Closes #8031 from liancheng/spark-4176/minor-fix-for-writing-decimals and squashes the following commits:

10d4ea3 [Cheng Lian] Should use unscaled Long to write decimals for precision <= 18 rather than 8
---
 .../sql/parquet/CatalystRowConverter.scala    |  2 +-
 .../sql/parquet/CatalystSchemaConverter.scala | 29 +++++++++++--------
 2 files changed, 18 insertions(+), 13 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
index 6938b071065cd..4fe8a39f20abd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
@@ -264,7 +264,7 @@ private[parquet] class CatalystRowConverter(
       val scale = decimalType.scale
       val bytes = value.getBytes
 
-      if (precision <= 8) {
+      if (precision <= CatalystSchemaConverter.MAX_PRECISION_FOR_INT64) {
         // Constructs a `Decimal` with an unscaled `Long` value if possible.
         var unscaled = 0L
         var i = 0
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
index d43ca95b4eea0..b12149dcf1c92 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
@@ -25,6 +25,7 @@ import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName._
 import org.apache.parquet.schema.Type.Repetition._
 import org.apache.parquet.schema._
 
+import org.apache.spark.sql.parquet.CatalystSchemaConverter.{MAX_PRECISION_FOR_INT32, MAX_PRECISION_FOR_INT64, maxPrecisionForBytes}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, SQLConf}
 
@@ -155,7 +156,7 @@ private[parquet] class CatalystSchemaConverter(
           case INT_16 => ShortType
           case INT_32 | null => IntegerType
           case DATE => DateType
-          case DECIMAL => makeDecimalType(maxPrecisionForBytes(4))
+          case DECIMAL => makeDecimalType(MAX_PRECISION_FOR_INT32)
           case TIME_MILLIS => typeNotImplemented()
           case _ => illegalType()
         }
@@ -163,7 +164,7 @@ private[parquet] class CatalystSchemaConverter(
       case INT64 =>
         originalType match {
           case INT_64 | null => LongType
-          case DECIMAL => makeDecimalType(maxPrecisionForBytes(8))
+          case DECIMAL => makeDecimalType(MAX_PRECISION_FOR_INT64)
           case TIMESTAMP_MILLIS => typeNotImplemented()
           case _ => illegalType()
         }
@@ -405,7 +406,7 @@ private[parquet] class CatalystSchemaConverter(
 
       // Uses INT32 for 1 <= precision <= 9
       case DecimalType.Fixed(precision, scale)
-        if precision <= maxPrecisionForBytes(4) && followParquetFormatSpec =>
+          if precision <= MAX_PRECISION_FOR_INT32 && followParquetFormatSpec =>
         Types
           .primitive(INT32, repetition)
           .as(DECIMAL)
@@ -415,7 +416,7 @@ private[parquet] class CatalystSchemaConverter(
 
       // Uses INT64 for 1 <= precision <= 18
       case DecimalType.Fixed(precision, scale)
-        if precision <= maxPrecisionForBytes(8) && followParquetFormatSpec =>
+          if precision <= MAX_PRECISION_FOR_INT64 && followParquetFormatSpec =>
         Types
           .primitive(INT64, repetition)
           .as(DECIMAL)
@@ -534,14 +535,6 @@ private[parquet] class CatalystSchemaConverter(
         throw new AnalysisException(s"Unsupported data type $field.dataType")
     }
   }
-
-  // Max precision of a decimal value stored in `numBytes` bytes
-  private def maxPrecisionForBytes(numBytes: Int): Int = {
-    Math.round(                               // convert double to long
-      Math.floor(Math.log10(                  // number of base-10 digits
-        Math.pow(2, 8 * numBytes - 1) - 1)))  // max value stored in numBytes
-      .asInstanceOf[Int]
-  }
 }
 
 
@@ -584,4 +577,16 @@ private[parquet] object CatalystSchemaConverter {
       computeMinBytesForPrecision(precision)
     }
   }
+
+  val MAX_PRECISION_FOR_INT32 = maxPrecisionForBytes(4)
+
+  val MAX_PRECISION_FOR_INT64 = maxPrecisionForBytes(8)
+
+  // Max precision of a decimal value stored in `numBytes` bytes
+  def maxPrecisionForBytes(numBytes: Int): Int = {
+    Math.round(                               // convert double to long
+      Math.floor(Math.log10(                  // number of base-10 digits
+        Math.pow(2, 8 * numBytes - 1) - 1)))  // max value stored in numBytes
+      .asInstanceOf[Int]
+  }
 }

From 106c0789d8c83c7081bc9a335df78ba728e95872 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sat, 8 Aug 2015 08:33:14 -0700
Subject: [PATCH 131/633] [SPARK-9738] [SQL] remove FromUnsafe and add its
 codegen version to GenerateSafe

In https://github.com/apache/spark/pull/7752 we added `FromUnsafe` to convert nexted unsafe data like array/map/struct to safe versions. It's a quick solution and we already have `GenerateSafe` to do the conversion which is codegened. So we should remove `FromUnsafe` and implement its codegen version in `GenerateSafe`.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8029 from cloud-fan/from-unsafe and squashes the following commits:

ed40d8f [Wenchen Fan] add the copy back
a93fd4b [Wenchen Fan] cogengen FromUnsafe
---
 .../sql/catalyst/expressions/FromUnsafe.scala |  70 ----------
 .../sql/catalyst/expressions/Projection.scala |   8 +-
 .../codegen/GenerateSafeProjection.scala      | 120 +++++++++++++-----
 .../execution/RowFormatConvertersSuite.scala  |   4 +-
 4 files changed, 95 insertions(+), 107 deletions(-)
 delete mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
deleted file mode 100644
index 9b960b136f984..0000000000000
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/FromUnsafe.scala
+++ /dev/null
@@ -1,70 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.catalyst.expressions
-
-import org.apache.spark.sql.catalyst.expressions.codegen.CodegenFallback
-import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
-
-case class FromUnsafe(child: Expression) extends UnaryExpression
-  with ExpectsInputTypes with CodegenFallback {
-
-  override def inputTypes: Seq[AbstractDataType] =
-    Seq(TypeCollection(ArrayType, StructType, MapType))
-
-  override def dataType: DataType = child.dataType
-
-  private def convert(value: Any, dt: DataType): Any = dt match {
-    case StructType(fields) =>
-      val row = value.asInstanceOf[UnsafeRow]
-      val result = new Array[Any](fields.length)
-      fields.map(_.dataType).zipWithIndex.foreach { case (dt, i) =>
-        if (!row.isNullAt(i)) {
-          result(i) = convert(row.get(i, dt), dt)
-        }
-      }
-      new GenericInternalRow(result)
-
-    case ArrayType(elementType, _) =>
-      val array = value.asInstanceOf[UnsafeArrayData]
-      val length = array.numElements()
-      val result = new Array[Any](length)
-      var i = 0
-      while (i < length) {
-        if (!array.isNullAt(i)) {
-          result(i) = convert(array.get(i, elementType), elementType)
-        }
-        i += 1
-      }
-      new GenericArrayData(result)
-
-    case StringType => value.asInstanceOf[UTF8String].clone()
-
-    case MapType(kt, vt, _) =>
-      val map = value.asInstanceOf[UnsafeMapData]
-      val safeKeyArray = convert(map.keys, ArrayType(kt)).asInstanceOf[GenericArrayData]
-      val safeValueArray = convert(map.values, ArrayType(vt)).asInstanceOf[GenericArrayData]
-      new ArrayBasedMapData(safeKeyArray, safeValueArray)
-
-    case _ => value
-  }
-
-  override def nullSafeEval(input: Any): Any = {
-    convert(input, dataType)
-  }
-}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
index 796bc327a3db1..afe52e6a667eb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Projection.scala
@@ -152,13 +152,7 @@ object FromUnsafeProjection {
    */
   def apply(fields: Seq[DataType]): Projection = {
     create(fields.zipWithIndex.map(x => {
-      val b = new BoundReference(x._2, x._1, true)
-      // todo: this is quite slow, maybe remove this whole projection after remove generic getter of
-      // InternalRow?
-      b.dataType match {
-        case _: StructType | _: ArrayType | _: MapType => FromUnsafe(b)
-        case _ => b
-      }
+      new BoundReference(x._2, x._1, true)
     }))
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
index f06ffc5449e76..ef08ddf041afc 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
@@ -21,7 +21,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
-import org.apache.spark.sql.types.{StringType, StructType, DataType}
+import org.apache.spark.sql.types._
 
 
 /**
@@ -36,34 +36,94 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
   protected def bind(in: Seq[Expression], inputSchema: Seq[Attribute]): Seq[Expression] =
     in.map(BindReferences.bindReference(_, inputSchema))
 
-  private def genUpdater(
+  private def createCodeForStruct(
       ctx: CodeGenContext,
-      setter: String,
-      dataType: DataType,
-      ordinal: Int,
-      value: String): String = {
-    dataType match {
-      case struct: StructType =>
-        val rowTerm = ctx.freshName("row")
-        val updates = struct.map(_.dataType).zipWithIndex.map { case (dt, i) =>
-          val colTerm = ctx.freshName("col")
-          s"""
-            if ($value.isNullAt($i)) {
-              $rowTerm.setNullAt($i);
-            } else {
-              ${ctx.javaType(dt)} $colTerm = ${ctx.getValue(value, dt, s"$i")};
-              ${genUpdater(ctx, rowTerm, dt, i, colTerm)};
-            }
-           """
-        }.mkString("\n")
-        s"""
-          $genericMutableRowType $rowTerm = new $genericMutableRowType(${struct.fields.length});
-          $updates
-          $setter.update($ordinal, $rowTerm.copy());
-        """
-      case _ =>
-        ctx.setColumn(setter, dataType, ordinal, value)
-    }
+      input: String,
+      schema: StructType): GeneratedExpressionCode = {
+    val tmp = ctx.freshName("tmp")
+    val output = ctx.freshName("safeRow")
+    val values = ctx.freshName("values")
+    val rowClass = classOf[GenericInternalRow].getName
+
+    val fieldWriters = schema.map(_.dataType).zipWithIndex.map { case (dt, i) =>
+      val converter = convertToSafe(ctx, ctx.getValue(tmp, dt, i.toString), dt)
+      s"""
+        if (!$tmp.isNullAt($i)) {
+          ${converter.code}
+          $values[$i] = ${converter.primitive};
+        }
+      """
+    }.mkString("\n")
+
+    val code = s"""
+      final InternalRow $tmp = $input;
+      final Object[] $values = new Object[${schema.length}];
+      $fieldWriters
+      final InternalRow $output = new $rowClass($values);
+    """
+
+    GeneratedExpressionCode(code, "false", output)
+  }
+
+  private def createCodeForArray(
+      ctx: CodeGenContext,
+      input: String,
+      elementType: DataType): GeneratedExpressionCode = {
+    val tmp = ctx.freshName("tmp")
+    val output = ctx.freshName("safeArray")
+    val values = ctx.freshName("values")
+    val numElements = ctx.freshName("numElements")
+    val index = ctx.freshName("index")
+    val arrayClass = classOf[GenericArrayData].getName
+
+    val elementConverter = convertToSafe(ctx, ctx.getValue(tmp, elementType, index), elementType)
+    val code = s"""
+      final ArrayData $tmp = $input;
+      final int $numElements = $tmp.numElements();
+      final Object[] $values = new Object[$numElements];
+      for (int $index = 0; $index < $numElements; $index++) {
+        if (!$tmp.isNullAt($index)) {
+          ${elementConverter.code}
+          $values[$index] = ${elementConverter.primitive};
+        }
+      }
+      final ArrayData $output = new $arrayClass($values);
+    """
+
+    GeneratedExpressionCode(code, "false", output)
+  }
+
+  private def createCodeForMap(
+      ctx: CodeGenContext,
+      input: String,
+      keyType: DataType,
+      valueType: DataType): GeneratedExpressionCode = {
+    val tmp = ctx.freshName("tmp")
+    val output = ctx.freshName("safeMap")
+    val mapClass = classOf[ArrayBasedMapData].getName
+
+    val keyConverter = createCodeForArray(ctx, s"$tmp.keyArray()", keyType)
+    val valueConverter = createCodeForArray(ctx, s"$tmp.valueArray()", valueType)
+    val code = s"""
+      final MapData $tmp = $input;
+      ${keyConverter.code}
+      ${valueConverter.code}
+      final MapData $output = new $mapClass(${keyConverter.primitive}, ${valueConverter.primitive});
+    """
+
+    GeneratedExpressionCode(code, "false", output)
+  }
+
+  private def convertToSafe(
+      ctx: CodeGenContext,
+      input: String,
+      dataType: DataType): GeneratedExpressionCode = dataType match {
+    case s: StructType => createCodeForStruct(ctx, input, s)
+    case ArrayType(elementType, _) => createCodeForArray(ctx, input, elementType)
+    case MapType(keyType, valueType, _) => createCodeForMap(ctx, input, keyType, valueType)
+    // UTF8String act as a pointer if it's inside UnsafeRow, so copy it to make it safe.
+    case StringType => GeneratedExpressionCode("", "false", s"$input.clone()")
+    case _ => GeneratedExpressionCode("", "false", input)
   }
 
   protected def create(expressions: Seq[Expression]): Projection = {
@@ -72,12 +132,14 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
       case (NoOp, _) => ""
       case (e, i) =>
         val evaluationCode = e.gen(ctx)
+        val converter = convertToSafe(ctx, evaluationCode.primitive, e.dataType)
         evaluationCode.code +
           s"""
             if (${evaluationCode.isNull}) {
               mutableRow.setNullAt($i);
             } else {
-              ${genUpdater(ctx, "mutableRow", e.dataType, i, evaluationCode.primitive)};
+              ${converter.code}
+              ${ctx.setColumn("mutableRow", e.dataType, i, converter.primitive)};
             }
           """
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index 322966f423784..dd08e9025a927 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -112,7 +112,9 @@ case class DummyPlan(child: SparkPlan) extends UnaryNode {
 
   override protected def doExecute(): RDD[InternalRow] = {
     child.execute().mapPartitions { iter =>
-      // cache all strings to make sure we have deep copied UTF8String inside incoming
+      // This `DummyPlan` is in safe mode, so we don't need to do copy even we hold some
+      // values gotten from the incoming rows.
+      // we cache all strings here to make sure we have deep copied UTF8String inside incoming
       // safe InternalRow.
       val strings = new scala.collection.mutable.ArrayBuffer[UTF8String]
       iter.foreach { row =>

From 74a6541aa82bcd7a052b2e57b5ca55b7c316495b Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Sat, 8 Aug 2015 08:36:14 -0700
Subject: [PATCH 132/633] [SPARK-4561] [PYSPARK] [SQL] turn Row into dict
 recursively

Add an option `recursive` to `Row.asDict()`, when True (default is False), it will convert the nested Row into dict.

Author: Davies Liu <davies@databricks.com>

Closes #8006 from davies/as_dict and squashes the following commits:

922cc5a [Davies Liu] turn Row into dict recursively
---
 python/pyspark/sql/types.py | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 6f74b7162f7cc..e2e6f03ae9fd7 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -1197,13 +1197,36 @@ def __new__(self, *args, **kwargs):
         else:
             raise ValueError("No args or kwargs")
 
-    def asDict(self):
+    def asDict(self, recursive=False):
         """
         Return as an dict
+
+        :param recursive: turns the nested Row as dict (default: False).
+
+        >>> Row(name="Alice", age=11).asDict() == {'name': 'Alice', 'age': 11}
+        True
+        >>> row = Row(key=1, value=Row(name='a', age=2))
+        >>> row.asDict() == {'key': 1, 'value': Row(age=2, name='a')}
+        True
+        >>> row.asDict(True) == {'key': 1, 'value': {'name': 'a', 'age': 2}}
+        True
         """
         if not hasattr(self, "__fields__"):
             raise TypeError("Cannot convert a Row class into dict")
-        return dict(zip(self.__fields__, self))
+
+        if recursive:
+            def conv(obj):
+                if isinstance(obj, Row):
+                    return obj.asDict(True)
+                elif isinstance(obj, list):
+                    return [conv(o) for o in obj]
+                elif isinstance(obj, dict):
+                    return dict((k, conv(v)) for k, v in obj.items())
+                else:
+                    return obj
+            return dict(zip(self.__fields__, (conv(o) for o in self)))
+        else:
+            return dict(zip(self.__fields__, self))
 
     # let object acts like class
     def __call__(self, *args):

From ac507a03c3371cd5404ca195ee0ba0306badfc23 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Sat, 8 Aug 2015 08:38:18 -0700
Subject: [PATCH 133/633] [SPARK-6902] [SQL] [PYSPARK] Row should be read-only

Raise an read-only exception when user try to mutable a Row.

Author: Davies Liu <davies@databricks.com>

Closes #8009 from davies/readonly_row and squashes the following commits:

8722f3f [Davies Liu] add tests
05a3d36 [Davies Liu] Row should be read-only
---
 python/pyspark/sql/tests.py | 15 +++++++++++++++
 python/pyspark/sql/types.py |  5 +++++
 2 files changed, 20 insertions(+)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 1e3444dd9e3b4..38c83c427a747 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -179,6 +179,21 @@ def tearDownClass(cls):
         ReusedPySparkTestCase.tearDownClass()
         shutil.rmtree(cls.tempdir.name, ignore_errors=True)
 
+    def test_row_should_be_read_only(self):
+        row = Row(a=1, b=2)
+        self.assertEqual(1, row.a)
+
+        def foo():
+            row.a = 3
+        self.assertRaises(Exception, foo)
+
+        row2 = self.sqlCtx.range(10).first()
+        self.assertEqual(0, row2.id)
+
+        def foo2():
+            row2.id = 2
+        self.assertRaises(Exception, foo2)
+
     def test_range(self):
         self.assertEqual(self.sqlCtx.range(1, 1).count(), 0)
         self.assertEqual(self.sqlCtx.range(1, 0, -1).count(), 1)
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index e2e6f03ae9fd7..c083bf89905bf 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -1246,6 +1246,11 @@ def __getattr__(self, item):
         except ValueError:
             raise AttributeError(item)
 
+    def __setattr__(self, key, value):
+        if key != '__fields__':
+            raise Exception("Row is read-only")
+        self.__dict__[key] = value
+
     def __reduce__(self):
         """Returns a tuple so Python knows how to pickle Row."""
         if hasattr(self, "__fields__"):

From 23695f1d2d7ef9f3ea92cebcd96b1cf0e8904eb4 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Sat, 8 Aug 2015 11:01:25 -0700
Subject: [PATCH 134/633] [SPARK-9728][SQL]Support CalendarIntervalType in
 HiveQL

This PR enables converting interval term in HiveQL to CalendarInterval Literal.

JIRA: https://issues.apache.org/jira/browse/SPARK-9728

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8034 from yjshen/interval_hiveql and squashes the following commits:

7fe9a5e [Yijie Shen] declare throw exception and add unit test
fce7795 [Yijie Shen] convert hiveql interval term into CalendarInterval literal
---
 .../org/apache/spark/sql/hive/HiveQl.scala    |  25 +++
 .../apache/spark/sql/hive/HiveQlSuite.scala   |  15 ++
 .../sql/hive/execution/SQLQuerySuite.scala    |  22 +++
 .../spark/unsafe/types/CalendarInterval.java  | 156 ++++++++++++++++++
 .../unsafe/types/CalendarIntervalSuite.java   |  91 ++++++++++
 5 files changed, 309 insertions(+)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index 7d7b4b9167306..c3f29350101d3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -45,6 +45,7 @@ import org.apache.spark.sql.hive.HiveShim._
 import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.hive.execution.{HiveNativeCommand, DropTable, AnalyzeTable, HiveScriptIOSchema}
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.CalendarInterval
 import org.apache.spark.util.random.RandomSampler
 
 /* Implicit conversions */
@@ -1519,6 +1520,30 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
     case ast: ASTNode if ast.getType == HiveParser.TOK_CHARSETLITERAL =>
       Literal(BaseSemanticAnalyzer.charSetString(ast.getChild(0).getText, ast.getChild(1).getText))
 
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_YEAR_MONTH_LITERAL =>
+      Literal(CalendarInterval.fromYearMonthString(ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_DAY_TIME_LITERAL =>
+      Literal(CalendarInterval.fromDayTimeString(ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_YEAR_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("year", ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_MONTH_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("month", ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_DAY_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("day", ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_HOUR_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("hour", ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_MINUTE_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("minute", ast.getText))
+
+    case ast: ASTNode if ast.getType == HiveParser.TOK_INTERVAL_SECOND_LITERAL =>
+      Literal(CalendarInterval.fromSingleUnitString("second", ast.getText))
+
     case a: ASTNode =>
       throw new NotImplementedError(
         s"""No parse rules for ASTNode type: ${a.getType}, text: ${a.getText} :
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveQlSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveQlSuite.scala
index f765395e148af..79cf40aba4bf2 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveQlSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveQlSuite.scala
@@ -175,4 +175,19 @@ class HiveQlSuite extends SparkFunSuite with BeforeAndAfterAll {
     assert(desc.serde == Option("org.apache.hadoop.hive.serde2.columnar.ColumnarSerDe"))
     assert(desc.properties == Map(("tbl_p1" -> "p11"), ("tbl_p2" -> "p22")))
   }
+
+  test("Invalid interval term should throw AnalysisException") {
+    def assertError(sql: String, errorMessage: String): Unit = {
+      val e = intercept[AnalysisException] {
+        HiveQl.parseSql(sql)
+      }
+      assert(e.getMessage.contains(errorMessage))
+    }
+    assertError("select interval '42-32' year to month",
+      "month 32 outside range [0, 11]")
+    assertError("select interval '5 49:12:15' day to second",
+      "hour 49 outside range [0, 23]")
+    assertError("select interval '.1111111111' second",
+      "nanosecond 1111111111 outside range")
+  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 1dff07a6de8ad..2fa7ae3fa2e12 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -33,6 +33,7 @@ import org.apache.spark.sql.hive.{HiveContext, HiveQLDialect, MetastoreRelation}
 import org.apache.spark.sql.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.CalendarInterval
 
 case class Nested1(f1: Nested2)
 case class Nested2(f2: Nested3)
@@ -1115,4 +1116,25 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
 
     checkAnswer(sql("SELECT a.`c.b`, `b.$q`[0].`a@!.q`, `q.w`.`w.i&`[0] FROM t"), Row(1, 1, 1))
   }
+
+  test("Convert hive interval term into Literal of CalendarIntervalType") {
+    checkAnswer(sql("select interval '10-9' year to month"),
+      Row(CalendarInterval.fromString("interval 10 years 9 months")))
+    checkAnswer(sql("select interval '20 15:40:32.99899999' day to second"),
+      Row(CalendarInterval.fromString("interval 2 weeks 6 days 15 hours 40 minutes " +
+        "32 seconds 99 milliseconds 899 microseconds")))
+    checkAnswer(sql("select interval '30' year"),
+      Row(CalendarInterval.fromString("interval 30 years")))
+    checkAnswer(sql("select interval '25' month"),
+      Row(CalendarInterval.fromString("interval 25 months")))
+    checkAnswer(sql("select interval '-100' day"),
+      Row(CalendarInterval.fromString("interval -14 weeks -2 days")))
+    checkAnswer(sql("select interval '40' hour"),
+      Row(CalendarInterval.fromString("interval 1 days 16 hours")))
+    checkAnswer(sql("select interval '80' minute"),
+      Row(CalendarInterval.fromString("interval 1 hour 20 minutes")))
+    checkAnswer(sql("select interval '299.889987299' second"),
+      Row(CalendarInterval.fromString(
+        "interval 4 minutes 59 seconds 889 milliseconds 987 microseconds")))
+  }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java
index 92a5e4f86f234..30e1758076361 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/CalendarInterval.java
@@ -50,6 +50,14 @@ private static String unitRegex(String unit) {
     unitRegex("week") + unitRegex("day") + unitRegex("hour") + unitRegex("minute") +
     unitRegex("second") + unitRegex("millisecond") + unitRegex("microsecond"));
 
+  private static Pattern yearMonthPattern =
+    Pattern.compile("^(?:['|\"])?([+|-])?(\\d+)-(\\d+)(?:['|\"])?$");
+
+  private static Pattern dayTimePattern =
+    Pattern.compile("^(?:['|\"])?([+|-])?(\\d+) (\\d+):(\\d+):(\\d+)(\\.(\\d+))?(?:['|\"])?$");
+
+  private static Pattern quoteTrimPattern = Pattern.compile("^(?:['|\"])?(.*?)(?:['|\"])?$");
+
   private static long toLong(String s) {
     if (s == null) {
       return 0;
@@ -79,6 +87,154 @@ public static CalendarInterval fromString(String s) {
     }
   }
 
+  public static long toLongWithRange(String fieldName,
+      String s, long minValue, long maxValue) throws IllegalArgumentException {
+    long result = 0;
+    if (s != null) {
+      result = Long.valueOf(s);
+      if (result < minValue || result > maxValue) {
+        throw new IllegalArgumentException(String.format("%s %d outside range [%d, %d]",
+          fieldName, result, minValue, maxValue));
+      }
+    }
+    return result;
+  }
+
+  /**
+   * Parse YearMonth string in form: [-]YYYY-MM
+   *
+   * adapted from HiveIntervalYearMonth.valueOf
+   */
+  public static CalendarInterval fromYearMonthString(String s) throws IllegalArgumentException {
+    CalendarInterval result = null;
+    if (s == null) {
+      throw new IllegalArgumentException("Interval year-month string was null");
+    }
+    s = s.trim();
+    Matcher m = yearMonthPattern.matcher(s);
+    if (!m.matches()) {
+      throw new IllegalArgumentException(
+        "Interval string does not match year-month format of 'y-m': " + s);
+    } else {
+      try {
+        int sign = m.group(1) != null && m.group(1).equals("-") ? -1 : 1;
+        int years = (int) toLongWithRange("year", m.group(2), 0, Integer.MAX_VALUE);
+        int months = (int) toLongWithRange("month", m.group(3), 0, 11);
+        result = new CalendarInterval(sign * (years * 12 + months), 0);
+      } catch (Exception e) {
+        throw new IllegalArgumentException(
+          "Error parsing interval year-month string: " + e.getMessage(), e);
+      }
+    }
+    return result;
+  }
+
+  /**
+   * Parse dayTime string in form: [-]d HH:mm:ss.nnnnnnnnn
+   *
+   * adapted from HiveIntervalDayTime.valueOf
+   */
+  public static CalendarInterval fromDayTimeString(String s) throws IllegalArgumentException {
+    CalendarInterval result = null;
+    if (s == null) {
+      throw new IllegalArgumentException("Interval day-time string was null");
+    }
+    s = s.trim();
+    Matcher m = dayTimePattern.matcher(s);
+    if (!m.matches()) {
+      throw new IllegalArgumentException(
+        "Interval string does not match day-time format of 'd h:m:s.n': " + s);
+    } else {
+      try {
+        int sign = m.group(1) != null && m.group(1).equals("-") ? -1 : 1;
+        long days = toLongWithRange("day", m.group(2), 0, Integer.MAX_VALUE);
+        long hours = toLongWithRange("hour", m.group(3), 0, 23);
+        long minutes = toLongWithRange("minute", m.group(4), 0, 59);
+        long seconds = toLongWithRange("second", m.group(5), 0, 59);
+        // Hive allow nanosecond precision interval
+        long nanos = toLongWithRange("nanosecond", m.group(7), 0L, 999999999L);
+        result = new CalendarInterval(0, sign * (
+          days * MICROS_PER_DAY + hours * MICROS_PER_HOUR + minutes * MICROS_PER_MINUTE +
+          seconds * MICROS_PER_SECOND + nanos / 1000L));
+      } catch (Exception e) {
+        throw new IllegalArgumentException(
+          "Error parsing interval day-time string: " + e.getMessage(), e);
+      }
+    }
+    return result;
+  }
+
+  public static CalendarInterval fromSingleUnitString(String unit, String s)
+      throws IllegalArgumentException {
+
+    CalendarInterval result = null;
+    if (s == null) {
+      throw new IllegalArgumentException(String.format("Interval %s string was null", unit));
+    }
+    s = s.trim();
+    Matcher m = quoteTrimPattern.matcher(s);
+    if (!m.matches()) {
+      throw new IllegalArgumentException(
+        "Interval string does not match day-time format of 'd h:m:s.n': " + s);
+    } else {
+      try {
+        if (unit.equals("year")) {
+          int year = (int) toLongWithRange("year", m.group(1),
+            Integer.MIN_VALUE / 12, Integer.MAX_VALUE / 12);
+          result = new CalendarInterval(year * 12, 0L);
+
+        } else if (unit.equals("month")) {
+          int month = (int) toLongWithRange("month", m.group(1),
+            Integer.MIN_VALUE, Integer.MAX_VALUE);
+          result = new CalendarInterval(month, 0L);
+
+        } else if (unit.equals("day")) {
+          long day = toLongWithRange("day", m.group(1),
+            Long.MIN_VALUE / MICROS_PER_DAY, Long.MAX_VALUE / MICROS_PER_DAY);
+          result = new CalendarInterval(0, day * MICROS_PER_DAY);
+
+        } else if (unit.equals("hour")) {
+          long hour = toLongWithRange("hour", m.group(1),
+            Long.MIN_VALUE / MICROS_PER_HOUR, Long.MAX_VALUE / MICROS_PER_HOUR);
+          result = new CalendarInterval(0, hour * MICROS_PER_HOUR);
+
+        } else if (unit.equals("minute")) {
+          long minute = toLongWithRange("minute", m.group(1),
+            Long.MIN_VALUE / MICROS_PER_MINUTE, Long.MAX_VALUE / MICROS_PER_MINUTE);
+          result = new CalendarInterval(0, minute * MICROS_PER_MINUTE);
+
+        } else if (unit.equals("second")) {
+          long micros = parseSecondNano(m.group(1));
+          result = new CalendarInterval(0, micros);
+        }
+      } catch (Exception e) {
+        throw new IllegalArgumentException("Error parsing interval string: " + e.getMessage(), e);
+      }
+    }
+    return result;
+  }
+
+  /**
+   * Parse second_nano string in ss.nnnnnnnnn format to microseconds
+   */
+  public static long parseSecondNano(String secondNano) throws IllegalArgumentException {
+    String[] parts = secondNano.split("\\.");
+    if (parts.length == 1) {
+      return toLongWithRange("second", parts[0], Long.MIN_VALUE / MICROS_PER_SECOND,
+        Long.MAX_VALUE / MICROS_PER_SECOND) * MICROS_PER_SECOND;
+
+    } else if (parts.length == 2) {
+      long seconds = parts[0].equals("") ? 0L : toLongWithRange("second", parts[0],
+        Long.MIN_VALUE / MICROS_PER_SECOND, Long.MAX_VALUE / MICROS_PER_SECOND);
+      long nanos = toLongWithRange("nanosecond", parts[1], 0L, 999999999L);
+      return seconds * MICROS_PER_SECOND + nanos / 1000L;
+
+    } else {
+      throw new IllegalArgumentException(
+        "Interval string does not match second-nano format of ss.nnnnnnnnn");
+    }
+  }
+
   public final int months;
   public final long microseconds;
 
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/types/CalendarIntervalSuite.java b/unsafe/src/test/java/org/apache/spark/unsafe/types/CalendarIntervalSuite.java
index 6274b92b47dd4..80d4982c4b576 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/types/CalendarIntervalSuite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/types/CalendarIntervalSuite.java
@@ -101,6 +101,97 @@ public void fromStringTest() {
     assertEquals(CalendarInterval.fromString(input), null);
   }
 
+  @Test
+  public void fromYearMonthStringTest() {
+    String input;
+    CalendarInterval i;
+
+    input = "99-10";
+    i = new CalendarInterval(99 * 12 + 10, 0L);
+    assertEquals(CalendarInterval.fromYearMonthString(input), i);
+
+    input = "-8-10";
+    i = new CalendarInterval(-8 * 12 - 10, 0L);
+    assertEquals(CalendarInterval.fromYearMonthString(input), i);
+
+    try {
+      input = "99-15";
+      CalendarInterval.fromYearMonthString(input);
+      fail("Expected to throw an exception for the invalid input");
+    } catch (IllegalArgumentException e) {
+      assertTrue(e.getMessage().contains("month 15 outside range"));
+    }
+  }
+
+  @Test
+  public void fromDayTimeStringTest() {
+    String input;
+    CalendarInterval i;
+
+    input = "5 12:40:30.999999999";
+    i = new CalendarInterval(0, 5 * MICROS_PER_DAY + 12 * MICROS_PER_HOUR +
+      40 * MICROS_PER_MINUTE + 30 * MICROS_PER_SECOND + 999999L);
+    assertEquals(CalendarInterval.fromDayTimeString(input), i);
+
+    input = "10 0:12:0.888";
+    i = new CalendarInterval(0, 10 * MICROS_PER_DAY + 12 * MICROS_PER_MINUTE);
+    assertEquals(CalendarInterval.fromDayTimeString(input), i);
+
+    input = "-3 0:0:0";
+    i = new CalendarInterval(0, -3 * MICROS_PER_DAY);
+    assertEquals(CalendarInterval.fromDayTimeString(input), i);
+
+    try {
+      input = "5 30:12:20";
+      CalendarInterval.fromDayTimeString(input);
+      fail("Expected to throw an exception for the invalid input");
+    } catch (IllegalArgumentException e) {
+      assertTrue(e.getMessage().contains("hour 30 outside range"));
+    }
+
+    try {
+      input = "5 30-12";
+      CalendarInterval.fromDayTimeString(input);
+      fail("Expected to throw an exception for the invalid input");
+    } catch (IllegalArgumentException e) {
+      assertTrue(e.getMessage().contains("not match day-time format"));
+    }
+  }
+
+  @Test
+  public void fromSingleUnitStringTest() {
+    String input;
+    CalendarInterval i;
+
+    input = "12";
+    i = new CalendarInterval(12 * 12, 0L);
+    assertEquals(CalendarInterval.fromSingleUnitString("year", input), i);
+
+    input = "100";
+    i = new CalendarInterval(0, 100 * MICROS_PER_DAY);
+    assertEquals(CalendarInterval.fromSingleUnitString("day", input), i);
+
+    input = "1999.38888";
+    i = new CalendarInterval(0, 1999 * MICROS_PER_SECOND + 38);
+    assertEquals(CalendarInterval.fromSingleUnitString("second", input), i);
+
+    try {
+      input = String.valueOf(Integer.MAX_VALUE);
+      CalendarInterval.fromSingleUnitString("year", input);
+      fail("Expected to throw an exception for the invalid input");
+    } catch (IllegalArgumentException e) {
+      assertTrue(e.getMessage().contains("outside range"));
+    }
+
+    try {
+      input = String.valueOf(Long.MAX_VALUE / MICROS_PER_HOUR + 1);
+      CalendarInterval.fromSingleUnitString("hour", input);
+      fail("Expected to throw an exception for the invalid input");
+    } catch (IllegalArgumentException e) {
+      assertTrue(e.getMessage().contains("outside range"));
+    }
+  }
+
   @Test
   public void addTest() {
     String input = "interval 3 month 1 hour";

From a3aec918bed22f8e33cf91dc0d6e712e6653c7d2 Mon Sep 17 00:00:00 2001
From: Joseph Batchik <joseph.batchik@cloudera.com>
Date: Sat, 8 Aug 2015 11:03:01 -0700
Subject: [PATCH 135/633] [SPARK-9486][SQL] Add data source aliasing for
 external packages

Users currently have to provide the full class name for external data sources, like:

`sqlContext.read.format("com.databricks.spark.avro").load(path)`

This allows external data source packages to register themselves using a Service Loader so that they can add custom alias like:

`sqlContext.read.format("avro").load(path)`

This makes it so that using external data source packages uses the same format as the internal data sources like parquet, json, etc.

Author: Joseph Batchik <joseph.batchik@cloudera.com>
Author: Joseph Batchik <josephbatchik@gmail.com>

Closes #7802 from JDrit/service_loader and squashes the following commits:

49a01ec [Joseph Batchik] fixed a couple of format / error bugs
e5e93b2 [Joseph Batchik] modified rat file to only excluded added services
72b349a [Joseph Batchik] fixed error with orc data source actually
9f93ea7 [Joseph Batchik] fixed error with orc data source
87b7f1c [Joseph Batchik] fixed typo
101cd22 [Joseph Batchik] removing unneeded changes
8f3cf43 [Joseph Batchik] merged in changes
b63d337 [Joseph Batchik] merged in master
95ae030 [Joseph Batchik] changed the new trait to be used as a mixin for data source to register themselves
74db85e [Joseph Batchik] reformatted class loader
ac2270d [Joseph Batchik] removing some added test
a6926db [Joseph Batchik] added test cases for data source loader
208a2a8 [Joseph Batchik] changes to do error catching if there are multiple data sources
946186e [Joseph Batchik] started working on service loader
---
 .rat-excludes                                 |  1 +
 ...pache.spark.sql.sources.DataSourceRegister |  3 +
 .../spark/sql/execution/datasources/ddl.scala | 52 ++++++------
 .../apache/spark/sql/jdbc/JDBCRelation.scala  |  5 +-
 .../apache/spark/sql/json/JSONRelation.scala  |  5 +-
 .../spark/sql/parquet/ParquetRelation.scala   |  5 +-
 .../apache/spark/sql/sources/interfaces.scala | 21 +++++
 ...pache.spark.sql.sources.DataSourceRegister |  3 +
 .../sql/sources/DDLSourceLoadSuite.scala      | 85 +++++++++++++++++++
 ...pache.spark.sql.sources.DataSourceRegister |  1 +
 .../spark/sql/hive/orc/OrcRelation.scala      |  5 +-
 11 files changed, 156 insertions(+), 30 deletions(-)
 create mode 100644 sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
 create mode 100644 sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
 create mode 100644 sql/hive/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister

diff --git a/.rat-excludes b/.rat-excludes
index 236c2db05367c..72771465846b8 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -93,3 +93,4 @@ INDEX
 .lintr
 gen-java.*
 .*avpr
+org.apache.spark.sql.sources.DataSourceRegister
diff --git a/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
new file mode 100644
index 0000000000000..cc32d4b72748e
--- /dev/null
+++ b/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -0,0 +1,3 @@
+org.apache.spark.sql.jdbc.DefaultSource
+org.apache.spark.sql.json.DefaultSource
+org.apache.spark.sql.parquet.DefaultSource
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
index 0cdb407ad57b9..8c2f297e42458 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
@@ -17,7 +17,12 @@
 
 package org.apache.spark.sql.execution.datasources
 
+import java.util.ServiceLoader
+
+import scala.collection.Iterator
+import scala.collection.JavaConversions._
 import scala.language.{existentials, implicitConversions}
+import scala.util.{Failure, Success, Try}
 import scala.util.matching.Regex
 
 import org.apache.hadoop.fs.Path
@@ -190,37 +195,32 @@ private[sql] class DDLParser(
     }
 }
 
-private[sql] object ResolvedDataSource {
-
-  private val builtinSources = Map(
-    "jdbc" -> "org.apache.spark.sql.jdbc.DefaultSource",
-    "json" -> "org.apache.spark.sql.json.DefaultSource",
-    "parquet" -> "org.apache.spark.sql.parquet.DefaultSource",
-    "orc" -> "org.apache.spark.sql.hive.orc.DefaultSource"
-  )
+private[sql] object ResolvedDataSource extends Logging {
 
   /** Given a provider name, look up the data source class definition. */
   def lookupDataSource(provider: String): Class[_] = {
+    val provider2 = s"$provider.DefaultSource"
     val loader = Utils.getContextOrSparkClassLoader
-
-    if (builtinSources.contains(provider)) {
-      return loader.loadClass(builtinSources(provider))
-    }
-
-    try {
-      loader.loadClass(provider)
-    } catch {
-      case cnf: java.lang.ClassNotFoundException =>
-        try {
-          loader.loadClass(provider + ".DefaultSource")
-        } catch {
-          case cnf: java.lang.ClassNotFoundException =>
-            if (provider.startsWith("org.apache.spark.sql.hive.orc")) {
-              sys.error("The ORC data source must be used with Hive support enabled.")
-            } else {
-              sys.error(s"Failed to load class for data source: $provider")
-            }
+    val serviceLoader = ServiceLoader.load(classOf[DataSourceRegister], loader)
+
+    serviceLoader.iterator().filter(_.format().equalsIgnoreCase(provider)).toList match {
+      /** the provider format did not match any given registered aliases */
+      case Nil => Try(loader.loadClass(provider)).orElse(Try(loader.loadClass(provider2))) match {
+        case Success(dataSource) => dataSource
+        case Failure(error) => if (provider.startsWith("org.apache.spark.sql.hive.orc")) {
+          throw new ClassNotFoundException(
+            "The ORC data source must be used with Hive support enabled.", error)
+        } else {
+          throw new ClassNotFoundException(
+            s"Failed to load class for data source: $provider", error)
         }
+      }
+      /** there is exactly one registered alias */
+      case head :: Nil => head.getClass
+      /** There are multiple registered aliases for the input */
+      case sources => sys.error(s"Multiple sources found for $provider, " +
+        s"(${sources.map(_.getClass.getName).mkString(", ")}), " +
+        "please specify the fully qualified class name")
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
index 41d0ecb4bbfbf..48d97ced9ca0a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
@@ -77,7 +77,10 @@ private[sql] object JDBCRelation {
   }
 }
 
-private[sql] class DefaultSource extends RelationProvider {
+private[sql] class DefaultSource extends RelationProvider with DataSourceRegister {
+
+  def format(): String = "jdbc"
+
   /** Returns a new base relation with the given parameters. */
   override def createRelation(
       sqlContext: SQLContext,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index 10f1367e6984c..b34a272ec547f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -37,7 +37,10 @@ import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.{AnalysisException, Row, SQLContext}
 
-private[sql] class DefaultSource extends HadoopFsRelationProvider {
+private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
+
+  def format(): String = "json"
+
   override def createRelation(
       sqlContext: SQLContext,
       paths: Array[String],
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
index 48009b2fd007d..b6db71b5b8a62 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
@@ -49,7 +49,10 @@ import org.apache.spark.sql.types.{DataType, StructType}
 import org.apache.spark.util.{SerializableConfiguration, Utils}
 
 
-private[sql] class DefaultSource extends HadoopFsRelationProvider {
+private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
+
+  def format(): String = "parquet"
+
   override def createRelation(
       sqlContext: SQLContext,
       paths: Array[String],
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index c5b7ee73eb784..4aafec0e2df27 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -37,6 +37,27 @@ import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql._
 import org.apache.spark.util.SerializableConfiguration
 
+/**
+ * ::DeveloperApi::
+ * Data sources should implement this trait so that they can register an alias to their data source.
+ * This allows users to give the data source alias as the format type over the fully qualified
+ * class name.
+ *
+ * ex: parquet.DefaultSource.format = "parquet".
+ *
+ * A new instance of this class with be instantiated each time a DDL call is made.
+ */
+@DeveloperApi
+trait DataSourceRegister {
+
+  /**
+   * The string that represents the format that this data source provider uses. This is
+   * overridden by children to provide a nice alias for the data source,
+   * ex: override def format(): String = "parquet"
+   */
+  def format(): String
+}
+
 /**
  * ::DeveloperApi::
  * Implemented by objects that produce relations for a specific kind of data source.  When
diff --git a/sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
new file mode 100644
index 0000000000000..cfd7889b4ac2c
--- /dev/null
+++ b/sql/core/src/test/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -0,0 +1,3 @@
+org.apache.spark.sql.sources.FakeSourceOne
+org.apache.spark.sql.sources.FakeSourceTwo
+org.apache.spark.sql.sources.FakeSourceThree
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
new file mode 100644
index 0000000000000..1a4d41b02ca68
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
@@ -0,0 +1,85 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.sources
+
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.types.{StringType, StructField, StructType}
+
+class FakeSourceOne extends RelationProvider with DataSourceRegister {
+
+  def format(): String = "Fluet da Bomb"
+
+  override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
+    new BaseRelation {
+      override def sqlContext: SQLContext = cont
+
+      override def schema: StructType =
+        StructType(Seq(StructField("stringType", StringType, nullable = false)))
+    }
+}
+
+class FakeSourceTwo extends RelationProvider  with DataSourceRegister {
+
+  def format(): String = "Fluet da Bomb"
+
+  override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
+    new BaseRelation {
+      override def sqlContext: SQLContext = cont
+
+      override def schema: StructType =
+        StructType(Seq(StructField("stringType", StringType, nullable = false)))
+    }
+}
+
+class FakeSourceThree extends RelationProvider with DataSourceRegister {
+
+  def format(): String = "gathering quorum"
+
+  override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
+    new BaseRelation {
+      override def sqlContext: SQLContext = cont
+
+      override def schema: StructType =
+        StructType(Seq(StructField("stringType", StringType, nullable = false)))
+    }
+}
+// please note that the META-INF/services had to be modified for the test directory for this to work
+class DDLSourceLoadSuite extends DataSourceTest {
+
+  test("data sources with the same name") {
+    intercept[RuntimeException] {
+      caseInsensitiveContext.read.format("Fluet da Bomb").load()
+    }
+  }
+
+  test("load data source from format alias") {
+    caseInsensitiveContext.read.format("gathering quorum").load().schema ==
+      StructType(Seq(StructField("stringType", StringType, nullable = false)))
+  }
+
+  test("specify full classname with duplicate formats") {
+    caseInsensitiveContext.read.format("org.apache.spark.sql.sources.FakeSourceOne")
+      .load().schema == StructType(Seq(StructField("stringType", StringType, nullable = false)))
+  }
+
+  test("Loading Orc") {
+    intercept[ClassNotFoundException] {
+      caseInsensitiveContext.read.format("orc").load()
+    }
+  }
+}
diff --git a/sql/hive/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/sql/hive/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
new file mode 100644
index 0000000000000..4a774fbf1fdf8
--- /dev/null
+++ b/sql/hive/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -0,0 +1 @@
+org.apache.spark.sql.hive.orc.DefaultSource
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 7c8704b47f286..0c344c63fde3f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -47,7 +47,10 @@ import org.apache.spark.util.SerializableConfiguration
 /* Implicit conversions */
 import scala.collection.JavaConversions._
 
-private[sql] class DefaultSource extends HadoopFsRelationProvider {
+private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
+
+  def format(): String = "orc"
+
   def createRelation(
       sqlContext: SQLContext,
       paths: Array[String],

From 25c363e93bc79119c5ba5c228fcad620061cff62 Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Sat, 8 Aug 2015 18:22:46 -0700
Subject: [PATCH 136/633] [MINOR] inaccurate comments for showString()

Author: CodingCat <zhunansjtu@gmail.com>

Closes #8050 from CodingCat/minor and squashes the following commits:

5bc4b89 [CodingCat] inaccurate comments
---
 sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 405b5a4a9a7f9..570b8b2d5928d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -168,7 +168,7 @@ class DataFrame private[sql](
   }
 
   /**
-   * Internal API for Python
+   * Compose the string representing rows for output
    * @param _numRows Number of rows to show
    * @param truncate Whether truncate long strings and align cells right
    */

From 3ca995b78f373251081f6877623649bfba3040b2 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Sat, 8 Aug 2015 21:05:50 -0700
Subject: [PATCH 137/633] [SPARK-6212] [SQL] The EXPLAIN output of CTAS only
 shows the analyzed plan

JIRA: https://issues.apache.org/jira/browse/SPARK-6212

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7986 from yjshen/ctas_explain and squashes the following commits:

bb6fee5 [Yijie Shen] refine test
f731041 [Yijie Shen] address comment
b2cf8ab [Yijie Shen] bug fix
bd7eb20 [Yijie Shen] ctas explain
---
 .../apache/spark/sql/execution/commands.scala |  2 ++
 .../hive/execution/CreateTableAsSelect.scala  |  4 ++-
 .../sql/hive/execution/HiveExplainSuite.scala | 35 +++++++++++++++++--
 3 files changed, 38 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
index 6b83025d5a153..95209e6634519 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala
@@ -69,6 +69,8 @@ private[sql] case class ExecutedCommand(cmd: RunnableCommand) extends SparkPlan
     val converted = sideEffectResult.map(convert(_).asInstanceOf[InternalRow])
     sqlContext.sparkContext.parallelize(converted, 1)
   }
+
+  override def argString: String = cmd.toString
 }
 
 /**
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala
index 84358cb73c9e3..8422287e177e5 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/CreateTableAsSelect.scala
@@ -40,6 +40,8 @@ case class CreateTableAsSelect(
   def database: String = tableDesc.database
   def tableName: String = tableDesc.name
 
+  override def children: Seq[LogicalPlan] = Seq(query)
+
   override def run(sqlContext: SQLContext): Seq[Row] = {
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
     lazy val metastoreRelation: MetastoreRelation = {
@@ -91,6 +93,6 @@ case class CreateTableAsSelect(
   }
 
   override def argString: String = {
-    s"[Database:$database, TableName: $tableName, InsertIntoHiveTable]\n" + query.toString
+    s"[Database:$database, TableName: $tableName, InsertIntoHiveTable]"
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
index 8215dd6c2e711..44c5b80392fa5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
@@ -17,13 +17,18 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.{SQLContext, QueryTest}
+import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.test.SQLTestUtils
 
 /**
  * A set of tests that validates support for Hive Explain command.
  */
-class HiveExplainSuite extends QueryTest {
+class HiveExplainSuite extends QueryTest with SQLTestUtils {
+
+  def sqlContext: SQLContext = TestHive
+
   test("explain extended command") {
     checkExistence(sql(" explain   select * from src where key=123 "), true,
                    "== Physical Plan ==")
@@ -74,4 +79,30 @@ class HiveExplainSuite extends QueryTest {
       "Limit",
       "src")
   }
+
+  test("SPARK-6212: The EXPLAIN output of CTAS only shows the analyzed plan") {
+    withTempTable("jt") {
+      val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""))
+      read.json(rdd).registerTempTable("jt")
+      val outputs = sql(
+        s"""
+           |EXPLAIN EXTENDED
+           |CREATE TABLE t1
+           |AS
+           |SELECT * FROM jt
+      """.stripMargin).collect().map(_.mkString).mkString
+
+      val shouldContain =
+        "== Parsed Logical Plan ==" :: "== Analyzed Logical Plan ==" :: "Subquery" ::
+        "== Optimized Logical Plan ==" :: "== Physical Plan ==" ::
+        "CreateTableAsSelect" :: "InsertIntoHiveTable" :: "jt" :: Nil
+      for (key <- shouldContain) {
+        assert(outputs.contains(key), s"$key doesn't exist in result")
+      }
+
+      val physicalIndex = outputs.indexOf("== Physical Plan ==")
+      assert(!outputs.substring(physicalIndex).contains("Subquery"),
+        "Physical Plan should not contain Subquery since it's eliminated by optimizer")
+    }
+  }
 }

From e9c36938ba972b6fe3c9f6228508e3c9f1c876b2 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 9 Aug 2015 10:58:36 -0700
Subject: [PATCH 138/633] [SPARK-9752][SQL] Support UnsafeRow in Sample
 operator.

In order for this to work, I had to disable gap sampling.

Author: Reynold Xin <rxin@databricks.com>

Closes #8040 from rxin/SPARK-9752 and squashes the following commits:

f9e248c [Reynold Xin] Fix the test case for real this time.
adbccb3 [Reynold Xin] Fixed test case.
589fb23 [Reynold Xin] Merge branch 'SPARK-9752' of github.com:rxin/spark into SPARK-9752
55ccddc [Reynold Xin] Fixed core test.
78fa895 [Reynold Xin] [SPARK-9752][SQL] Support UnsafeRow in Sample operator.
c9e7112 [Reynold Xin] [SPARK-9752][SQL] Support UnsafeRow in Sample operator.
---
 .../spark/util/random/RandomSampler.scala     | 18 ++++++----
 .../spark/sql/execution/basicOperators.scala  | 18 +++++++---
 .../apache/spark/sql/DataFrameStatSuite.scala | 35 +++++++++++++++++++
 .../org/apache/spark/sql/DataFrameSuite.scala | 17 ---------
 4 files changed, 61 insertions(+), 27 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
index 786b97ad7b9ec..c156b03cdb7c4 100644
--- a/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
+++ b/core/src/main/scala/org/apache/spark/util/random/RandomSampler.scala
@@ -176,10 +176,15 @@ class BernoulliSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T
  * A sampler for sampling with replacement, based on values drawn from Poisson distribution.
  *
  * @param fraction the sampling fraction (with replacement)
+ * @param useGapSamplingIfPossible if true, use gap sampling when sampling ratio is low.
  * @tparam T item type
  */
 @DeveloperApi
-class PoissonSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T] {
+class PoissonSampler[T: ClassTag](
+    fraction: Double,
+    useGapSamplingIfPossible: Boolean) extends RandomSampler[T, T] {
+
+  def this(fraction: Double) = this(fraction, useGapSamplingIfPossible = true)
 
   /** Epsilon slop to avoid failure from floating point jitter. */
   require(
@@ -199,17 +204,18 @@ class PoissonSampler[T: ClassTag](fraction: Double) extends RandomSampler[T, T]
   override def sample(items: Iterator[T]): Iterator[T] = {
     if (fraction <= 0.0) {
       Iterator.empty
-    } else if (fraction <= RandomSampler.defaultMaxGapSamplingFraction) {
-        new GapSamplingReplacementIterator(items, fraction, rngGap, RandomSampler.rngEpsilon)
+    } else if (useGapSamplingIfPossible &&
+               fraction <= RandomSampler.defaultMaxGapSamplingFraction) {
+      new GapSamplingReplacementIterator(items, fraction, rngGap, RandomSampler.rngEpsilon)
     } else {
-      items.flatMap { item => {
+      items.flatMap { item =>
         val count = rng.sample()
         if (count == 0) Iterator.empty else Iterator.fill(count)(item)
-      }}
+      }
     }
   }
 
-  override def clone: PoissonSampler[T] = new PoissonSampler[T](fraction)
+  override def clone: PoissonSampler[T] = new PoissonSampler[T](fraction, useGapSamplingIfPossible)
 }
 
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 0680f31d40f6d..c5d1ed0937b19 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.rdd.{RDD, ShuffledRDD}
+import org.apache.spark.rdd.{PartitionwiseSampledRDD, RDD, ShuffledRDD}
 import org.apache.spark.shuffle.sort.SortShuffleManager
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
@@ -30,6 +30,7 @@ import org.apache.spark.sql.metric.SQLMetrics
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.collection.ExternalSorter
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator
+import org.apache.spark.util.random.PoissonSampler
 import org.apache.spark.util.{CompletionIterator, MutablePair}
 import org.apache.spark.{HashPartitioner, SparkEnv}
 
@@ -130,12 +131,21 @@ case class Sample(
 {
   override def output: Seq[Attribute] = child.output
 
-  // TODO: How to pick seed?
+  override def outputsUnsafeRows: Boolean = child.outputsUnsafeRows
+  override def canProcessUnsafeRows: Boolean = true
+  override def canProcessSafeRows: Boolean = true
+
   protected override def doExecute(): RDD[InternalRow] = {
     if (withReplacement) {
-      child.execute().map(_.copy()).sample(withReplacement, upperBound - lowerBound, seed)
+      // Disable gap sampling since the gap sampling method buffers two rows internally,
+      // requiring us to copy the row, which is more expensive than the random number generator.
+      new PartitionwiseSampledRDD[InternalRow, InternalRow](
+        child.execute(),
+        new PoissonSampler[InternalRow](upperBound - lowerBound, useGapSamplingIfPossible = false),
+        preservesPartitioning = true,
+        seed)
     } else {
-      child.execute().map(_.copy()).randomSampleWithRange(lowerBound, upperBound, seed)
+      child.execute().randomSampleWithRange(lowerBound, upperBound, seed)
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 0e7659f443ecd..8f5984e4a8ce2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -30,6 +30,41 @@ class DataFrameStatSuite extends QueryTest {
 
   private def toLetter(i: Int): String = (i + 97).toChar.toString
 
+  test("sample with replacement") {
+    val n = 100
+    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    checkAnswer(
+      data.sample(withReplacement = true, 0.05, seed = 13),
+      Seq(5, 10, 52, 73).map(Row(_))
+    )
+  }
+
+  test("sample without replacement") {
+    val n = 100
+    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    checkAnswer(
+      data.sample(withReplacement = false, 0.05, seed = 13),
+      Seq(16, 23, 88, 100).map(Row(_))
+    )
+  }
+
+  test("randomSplit") {
+    val n = 600
+    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    for (seed <- 1 to 5) {
+      val splits = data.randomSplit(Array[Double](1, 2, 3), seed)
+      assert(splits.length == 3, "wrong number of splits")
+
+      assert(splits.reduce((a, b) => a.unionAll(b)).sort("id").collect().toList ==
+        data.collect().toList, "incomplete or wrong split")
+
+      val s = splits.map(_.count())
+      assert(math.abs(s(0) - 100) < 50) // std =  9.13
+      assert(math.abs(s(1) - 200) < 50) // std = 11.55
+      assert(math.abs(s(2) - 300) < 50) // std = 12.25
+    }
+  }
+
   test("pearson correlation") {
     val df = Seq.tabulate(10)(i => (i, 2 * i, i * -1.0)).toDF("a", "b", "c")
     val corr1 = df.stat.corr("a", "b", "pearson")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index f9cc6d1f3c250..0212637a829e5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -415,23 +415,6 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
     assert(df.schema.map(_.name) === Seq("key", "valueRenamed", "newCol"))
   }
 
-  test("randomSplit") {
-    val n = 600
-    val data = sqlContext.sparkContext.parallelize(1 to n, 2).toDF("id")
-    for (seed <- 1 to 5) {
-      val splits = data.randomSplit(Array[Double](1, 2, 3), seed)
-      assert(splits.length == 3, "wrong number of splits")
-
-      assert(splits.reduce((a, b) => a.unionAll(b)).sort("id").collect().toList ==
-        data.collect().toList, "incomplete or wrong split")
-
-      val s = splits.map(_.count())
-      assert(math.abs(s(0) - 100) < 50) // std =  9.13
-      assert(math.abs(s(1) - 200) < 50) // std = 11.55
-      assert(math.abs(s(2) - 300) < 50) // std = 12.25
-    }
-  }
-
   test("describe") {
     val describeTestData = Seq(
       ("Bob", 16, 176),

From 68ccc6e184598822b19a880fdd4597b66a1c2d92 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Sun, 9 Aug 2015 11:44:51 -0700
Subject: [PATCH 139/633] [SPARK-8930] [SQL] Throw a AnalysisException with
 meaningful messages if DataFrame#explode takes a star in expressions

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8057 from yjshen/explode_star and squashes the following commits:

eae181d [Yijie Shen] change explaination message
54c9d11 [Yijie Shen] meaning message for * in explode
---
 .../spark/sql/catalyst/analysis/Analyzer.scala    |  4 +++-
 .../sql/catalyst/analysis/AnalysisTest.scala      |  4 +++-
 .../org/apache/spark/sql/DataFrameSuite.scala     | 15 +++++++++++++++
 3 files changed, 21 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 82158e61e3fb5..a684dbc3afa42 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -408,7 +408,7 @@ class Analyzer(
     /**
      * Returns true if `exprs` contains a [[Star]].
      */
-    protected def containsStar(exprs: Seq[Expression]): Boolean =
+    def containsStar(exprs: Seq[Expression]): Boolean =
       exprs.exists(_.collect { case _: Star => true }.nonEmpty)
   }
 
@@ -602,6 +602,8 @@ class Analyzer(
    */
   object ResolveGenerate extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+      case g: Generate if ResolveReferences.containsStar(g.generator.children) =>
+        failAnalysis("Cannot explode *, explode can only be applied on a specific column.")
       case p: Generate if !p.child.resolved || !p.generator.resolved => p
       case g: Generate if !g.resolved =>
         g.copy(generatorOutput = makeGeneratorOutput(g.generator, g.generatorOutput.map(_.name)))
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
index ee1f8f54251e0..53b3695a86be5 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisTest.scala
@@ -71,6 +71,8 @@ trait AnalysisTest extends PlanTest {
     val e = intercept[Exception] {
       analyzer.checkAnalysis(analyzer.execute(inputPlan))
     }
-    expectedErrors.forall(e.getMessage.contains)
+    assert(expectedErrors.map(_.toLowerCase).forall(e.getMessage.toLowerCase.contains),
+      s"Expected to throw Exception contains: ${expectedErrors.mkString(", ")}, " +
+        s"actually we get ${e.getMessage}")
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 0212637a829e5..c49f256be5501 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -134,6 +134,21 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
     )
   }
 
+  test("SPARK-8930: explode should fail with a meaningful message if it takes a star") {
+    val df = Seq(("1", "1,2"), ("2", "4"), ("3", "7,8,9")).toDF("prefix", "csv")
+    val e = intercept[AnalysisException] {
+      df.explode($"*") { case Row(prefix: String, csv: String) =>
+        csv.split(",").map(v => Tuple1(prefix + ":" + v)).toSeq
+      }.queryExecution.assertAnalyzed()
+    }
+    assert(e.getMessage.contains(
+      "Cannot explode *, explode can only be applied on a specific column."))
+
+    df.explode('prefix, 'csv) { case Row(prefix: String, csv: String) =>
+      csv.split(",").map(v => Tuple1(prefix + ":" + v)).toSeq
+    }.queryExecution.assertAnalyzed()
+  }
+
   test("explode alias and star") {
     val df = Seq((Array("a"), 1)).toDF("a", "b")
 

From 86fa4ba6d13f909cb508b7cb3b153d586fe59bc3 Mon Sep 17 00:00:00 2001
From: Yadong Qi <qiyadong2010@gmail.com>
Date: Sun, 9 Aug 2015 19:54:05 +0100
Subject: [PATCH 140/633] [SPARK-9737] [YARN] Add the suggested configuration
 when required executor memory is above the max threshold of this cluster on
 YARN mode

Author: Yadong Qi <qiyadong2010@gmail.com>

Closes #8028 from watermen/SPARK-9737 and squashes the following commits:

48bdf3d [Yadong Qi] Add suggested configuration.
---
 .../main/scala/org/apache/spark/deploy/yarn/Client.scala    | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index fc11bbf97e2ec..b4ba3f0221600 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -203,12 +203,14 @@ private[spark] class Client(
     val executorMem = args.executorMemory + executorMemoryOverhead
     if (executorMem > maxMem) {
       throw new IllegalArgumentException(s"Required executor memory (${args.executorMemory}" +
-        s"+$executorMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster!")
+        s"+$executorMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster! " +
+        "Please increase the value of 'yarn.scheduler.maximum-allocation-mb'.")
     }
     val amMem = args.amMemory + amMemoryOverhead
     if (amMem > maxMem) {
       throw new IllegalArgumentException(s"Required AM memory (${args.amMemory}" +
-        s"+$amMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster!")
+        s"+$amMemoryOverhead MB) is above the max threshold ($maxMem MB) of this cluster! " +
+        "Please increase the value of 'yarn.scheduler.maximum-allocation-mb'.")
     }
     logInfo("Will allocate AM container, with %d MB memory including %d MB overhead".format(
       amMem,

From a863348fd85848e0d4325c4de359da12e5f548d2 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Sun, 9 Aug 2015 13:43:31 -0700
Subject: [PATCH 141/633] Disable JobGeneratorSuite "Do not clear received
 block data too soon".

---
 .../apache/spark/streaming/scheduler/JobGeneratorSuite.scala   | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala
index a2dbae149f311..9b6cd4bc4e315 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/JobGeneratorSuite.scala
@@ -56,7 +56,8 @@ class JobGeneratorSuite extends TestSuiteBase {
   // 4. allow subsequent batches to be generated (to allow premature deletion of 3rd batch metadata)
   // 5. verify whether 3rd batch's block metadata still exists
   //
-  test("SPARK-6222: Do not clear received block data too soon") {
+  // TODO: SPARK-7420 enable this test
+  ignore("SPARK-6222: Do not clear received block data too soon") {
     import JobGeneratorSuite._
     val checkpointDir = Utils.createTempDir()
     val testConf = conf

From 23cf5af08d98da771c41571c00a2f5cafedfebdd Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sun, 9 Aug 2015 14:26:01 -0700
Subject: [PATCH 142/633] [SPARK-9703] [SQL] Refactor EnsureRequirements to
 avoid certain unnecessary shuffles

This pull request refactors the `EnsureRequirements` planning rule in order to avoid the addition of certain unnecessary shuffles.

As an example of how unnecessary shuffles can occur, consider SortMergeJoin, which requires clustered distribution and sorted ordering of its children's input rows. Say that both of SMJ's children produce unsorted output but are both SinglePartition. In this case, we will need to inject sort operators but should not need to inject Exchanges. Unfortunately, it looks like the EnsureRequirements unnecessarily repartitions using a hash partitioning.

This patch solves this problem by refactoring `EnsureRequirements` to properly implement the `compatibleWith` checks that were broken in earlier implementations. See the significant inline comments for a better description of how this works. The majority of this PR is new comments and test cases, with few actual changes to the code.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7988 from JoshRosen/exchange-fixes and squashes the following commits:

38006e7 [Josh Rosen] Rewrite EnsureRequirements _yet again_ to make things even simpler
0983f75 [Josh Rosen] More guarantees vs. compatibleWith cleanup; delete BroadcastPartitioning.
8784bd9 [Josh Rosen] Giant comment explaining compatibleWith vs. guarantees
1307c50 [Josh Rosen] Update conditions for requiring child compatibility.
18cddeb [Josh Rosen] Rename DummyPlan to DummySparkPlan.
2c7e126 [Josh Rosen] Merge remote-tracking branch 'origin/master' into exchange-fixes
fee65c4 [Josh Rosen] Further refinement to comments / reasoning
642b0bb [Josh Rosen] Further expand comment / reasoning
06aba0c [Josh Rosen] Add more comments
8dbc845 [Josh Rosen] Add even more tests.
4f08278 [Josh Rosen] Fix the test by adding the compatibility check to EnsureRequirements
a1c12b9 [Josh Rosen] Add failing test to demonstrate allCompatible bug
0725a34 [Josh Rosen] Small assertion cleanup.
5172ac5 [Josh Rosen] Add test for requiresChildrenToProduceSameNumberOfPartitions.
2e0f33a [Josh Rosen] Write a more generic test for EnsureRequirements.
752b8de [Josh Rosen] style fix
c628daf [Josh Rosen] Revert accidental ExchangeSuite change.
c9fb231 [Josh Rosen] Rewrite exchange to fix better handle this case.
adcc742 [Josh Rosen] Move test to PlannerSuite.
0675956 [Josh Rosen] Preserving ordering and partitioning in row format converters also does not help.
cc5669c [Josh Rosen] Adding outputPartitioning to Repartition does not fix the test.
2dfc648 [Josh Rosen] Add failing test illustrating bad exchange planning.
---
 .../plans/physical/partitioning.scala         | 128 +++++++++++++--
 .../apache/spark/sql/execution/Exchange.scala | 104 ++++++------
 .../spark/sql/execution/basicOperators.scala  |   5 +
 .../sql/execution/rowFormatConverters.scala   |   5 +
 .../spark/sql/execution/PlannerSuite.scala    | 151 ++++++++++++++++++
 5 files changed, 328 insertions(+), 65 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
index ec659ce789c27..5a89a90b735a6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
@@ -75,6 +75,37 @@ case class OrderedDistribution(ordering: Seq[SortOrder]) extends Distribution {
   def clustering: Set[Expression] = ordering.map(_.child).toSet
 }
 
+/**
+ * Describes how an operator's output is split across partitions. The `compatibleWith`,
+ * `guarantees`, and `satisfies` methods describe relationships between child partitionings,
+ * target partitionings, and [[Distribution]]s. These relations are described more precisely in
+ * their individual method docs, but at a high level:
+ *
+ *  - `satisfies` is a relationship between partitionings and distributions.
+ *  - `compatibleWith` is relationships between an operator's child output partitionings.
+ *  - `guarantees` is a relationship between a child's existing output partitioning and a target
+ *     output partitioning.
+ *
+ *  Diagrammatically:
+ *
+ *            +--------------+
+ *            | Distribution |
+ *            +--------------+
+ *                    ^
+ *                    |
+ *               satisfies
+ *                    |
+ *            +--------------+                  +--------------+
+ *            |    Child     |                  |    Target    |
+ *       +----| Partitioning |----guarantees--->| Partitioning |
+ *       |    +--------------+                  +--------------+
+ *       |            ^
+ *       |            |
+ *       |     compatibleWith
+ *       |            |
+ *       +------------+
+ *
+ */
 sealed trait Partitioning {
   /** Returns the number of partitions that the data is split across */
   val numPartitions: Int
@@ -90,9 +121,66 @@ sealed trait Partitioning {
   /**
    * Returns true iff we can say that the partitioning scheme of this [[Partitioning]]
    * guarantees the same partitioning scheme described by `other`.
+   *
+   * Compatibility of partitionings is only checked for operators that have multiple children
+   * and that require a specific child output [[Distribution]], such as joins.
+   *
+   * Intuitively, partitionings are compatible if they route the same partitioning key to the same
+   * partition. For instance, two hash partitionings are only compatible if they produce the same
+   * number of output partitionings and hash records according to the same hash function and
+   * same partitioning key schema.
+   *
+   * Put another way, two partitionings are compatible with each other if they satisfy all of the
+   * same distribution guarantees.
    */
-  // TODO: Add an example once we have the `nullSafe` concept.
-  def guarantees(other: Partitioning): Boolean
+  def compatibleWith(other: Partitioning): Boolean
+
+  /**
+   * Returns true iff we can say that the partitioning scheme of this [[Partitioning]] guarantees
+   * the same partitioning scheme described by `other`. If a `A.guarantees(B)`, then repartitioning
+   * the child's output according to `B` will be unnecessary. `guarantees` is used as a performance
+   * optimization to allow the exchange planner to avoid redundant repartitionings. By default,
+   * a partitioning only guarantees partitionings that are equal to itself (i.e. the same number
+   * of partitions, same strategy (range or hash), etc).
+   *
+   * In order to enable more aggressive optimization, this strict equality check can be relaxed.
+   * For example, say that the planner needs to repartition all of an operator's children so that
+   * they satisfy the [[AllTuples]] distribution. One way to do this is to repartition all children
+   * to have the [[SinglePartition]] partitioning. If one of the operator's children already happens
+   * to be hash-partitioned with a single partition then we do not need to re-shuffle this child;
+   * this repartitioning can be avoided if a single-partition [[HashPartitioning]] `guarantees`
+   * [[SinglePartition]].
+   *
+   * The SinglePartition example given above is not particularly interesting; guarantees' real
+   * value occurs for more advanced partitioning strategies. SPARK-7871 will introduce a notion
+   * of null-safe partitionings, under which partitionings can specify whether rows whose
+   * partitioning keys contain null values will be grouped into the same partition or whether they
+   * will have an unknown / random distribution. If a partitioning does not require nulls to be
+   * clustered then a partitioning which _does_ cluster nulls will guarantee the null clustered
+   * partitioning. The converse is not true, however: a partitioning which clusters nulls cannot
+   * be guaranteed by one which does not cluster them. Thus, in general `guarantees` is not a
+   * symmetric relation.
+   *
+   * Another way to think about `guarantees`: if `A.guarantees(B)`, then any partitioning of rows
+   * produced by `A` could have also been produced by `B`.
+   */
+  def guarantees(other: Partitioning): Boolean = this == other
+}
+
+object Partitioning {
+  def allCompatible(partitionings: Seq[Partitioning]): Boolean = {
+    // Note: this assumes transitivity
+    partitionings.sliding(2).map {
+      case Seq(a) => true
+      case Seq(a, b) =>
+        if (a.numPartitions != b.numPartitions) {
+          assert(!a.compatibleWith(b) && !b.compatibleWith(a))
+          false
+        } else {
+          a.compatibleWith(b) && b.compatibleWith(a)
+        }
+    }.forall(_ == true)
+  }
 }
 
 case class UnknownPartitioning(numPartitions: Int) extends Partitioning {
@@ -101,6 +189,8 @@ case class UnknownPartitioning(numPartitions: Int) extends Partitioning {
     case _ => false
   }
 
+  override def compatibleWith(other: Partitioning): Boolean = false
+
   override def guarantees(other: Partitioning): Boolean = false
 }
 
@@ -109,21 +199,9 @@ case object SinglePartition extends Partitioning {
 
   override def satisfies(required: Distribution): Boolean = true
 
-  override def guarantees(other: Partitioning): Boolean = other match {
-    case SinglePartition => true
-    case _ => false
-  }
-}
-
-case object BroadcastPartitioning extends Partitioning {
-  val numPartitions = 1
+  override def compatibleWith(other: Partitioning): Boolean = other.numPartitions == 1
 
-  override def satisfies(required: Distribution): Boolean = true
-
-  override def guarantees(other: Partitioning): Boolean = other match {
-    case BroadcastPartitioning => true
-    case _ => false
-  }
+  override def guarantees(other: Partitioning): Boolean = other.numPartitions == 1
 }
 
 /**
@@ -147,6 +225,12 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
     case _ => false
   }
 
+  override def compatibleWith(other: Partitioning): Boolean = other match {
+    case o: HashPartitioning =>
+      this.clusteringSet == o.clusteringSet && this.numPartitions == o.numPartitions
+    case _ => false
+  }
+
   override def guarantees(other: Partitioning): Boolean = other match {
     case o: HashPartitioning =>
       this.clusteringSet == o.clusteringSet && this.numPartitions == o.numPartitions
@@ -185,6 +269,11 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int)
     case _ => false
   }
 
+  override def compatibleWith(other: Partitioning): Boolean = other match {
+    case o: RangePartitioning => this == o
+    case _ => false
+  }
+
   override def guarantees(other: Partitioning): Boolean = other match {
     case o: RangePartitioning => this == o
     case _ => false
@@ -228,6 +317,13 @@ case class PartitioningCollection(partitionings: Seq[Partitioning])
   override def satisfies(required: Distribution): Boolean =
     partitionings.exists(_.satisfies(required))
 
+  /**
+   * Returns true if any `partitioning` of this collection is compatible with
+   * the given [[Partitioning]].
+   */
+  override def compatibleWith(other: Partitioning): Boolean =
+    partitionings.exists(_.compatibleWith(other))
+
   /**
    * Returns true if any `partitioning` of this collection guarantees
    * the given [[Partitioning]].
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index 49bb729800863..b89e634761eb1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -190,66 +190,72 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
  * of input data meets the
  * [[org.apache.spark.sql.catalyst.plans.physical.Distribution Distribution]] requirements for
  * each operator by inserting [[Exchange]] Operators where required.  Also ensure that the
- * required input partition ordering requirements are met.
+ * input partition ordering requirements are met.
  */
 private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[SparkPlan] {
   // TODO: Determine the number of partitions.
-  def numPartitions: Int = sqlContext.conf.numShufflePartitions
+  private def numPartitions: Int = sqlContext.conf.numShufflePartitions
 
-  def apply(plan: SparkPlan): SparkPlan = plan.transformUp {
-    case operator: SparkPlan =>
-      // Adds Exchange or Sort operators as required
-      def addOperatorsIfNecessary(
-          partitioning: Partitioning,
-          rowOrdering: Seq[SortOrder],
-          child: SparkPlan): SparkPlan = {
-
-        def addShuffleIfNecessary(child: SparkPlan): SparkPlan = {
-          if (!child.outputPartitioning.guarantees(partitioning)) {
-            Exchange(partitioning, child)
-          } else {
-            child
-          }
-        }
+  /**
+   * Given a required distribution, returns a partitioning that satisfies that distribution.
+   */
+  private def canonicalPartitioning(requiredDistribution: Distribution): Partitioning = {
+    requiredDistribution match {
+      case AllTuples => SinglePartition
+      case ClusteredDistribution(clustering) => HashPartitioning(clustering, numPartitions)
+      case OrderedDistribution(ordering) => RangePartitioning(ordering, numPartitions)
+      case dist => sys.error(s"Do not know how to satisfy distribution $dist")
+    }
+  }
 
-        def addSortIfNecessary(child: SparkPlan): SparkPlan = {
-
-          if (rowOrdering.nonEmpty) {
-            // If child.outputOrdering is [a, b] and rowOrdering is [a], we do not need to sort.
-            val minSize = Seq(rowOrdering.size, child.outputOrdering.size).min
-            if (minSize == 0 || rowOrdering.take(minSize) != child.outputOrdering.take(minSize)) {
-              sqlContext.planner.BasicOperators.getSortOperator(rowOrdering, global = false, child)
-            } else {
-              child
-            }
-          } else {
-            child
-          }
-        }
+  private def ensureDistributionAndOrdering(operator: SparkPlan): SparkPlan = {
+    val requiredChildDistributions: Seq[Distribution] = operator.requiredChildDistribution
+    val requiredChildOrderings: Seq[Seq[SortOrder]] = operator.requiredChildOrdering
+    var children: Seq[SparkPlan] = operator.children
 
-        addSortIfNecessary(addShuffleIfNecessary(child))
+    // Ensure that the operator's children satisfy their output distribution requirements:
+    children = children.zip(requiredChildDistributions).map { case (child, distribution) =>
+      if (child.outputPartitioning.satisfies(distribution)) {
+        child
+      } else {
+        Exchange(canonicalPartitioning(distribution), child)
       }
+    }
 
-      val requirements =
-        (operator.requiredChildDistribution, operator.requiredChildOrdering, operator.children)
-
-      val fixedChildren = requirements.zipped.map {
-        case (AllTuples, rowOrdering, child) =>
-          addOperatorsIfNecessary(SinglePartition, rowOrdering, child)
-        case (ClusteredDistribution(clustering), rowOrdering, child) =>
-          addOperatorsIfNecessary(HashPartitioning(clustering, numPartitions), rowOrdering, child)
-        case (OrderedDistribution(ordering), rowOrdering, child) =>
-          addOperatorsIfNecessary(RangePartitioning(ordering, numPartitions), rowOrdering, child)
-
-        case (UnspecifiedDistribution, Seq(), child) =>
+    // If the operator has multiple children and specifies child output distributions (e.g. join),
+    // then the children's output partitionings must be compatible:
+    if (children.length > 1
+        && requiredChildDistributions.toSet != Set(UnspecifiedDistribution)
+        && !Partitioning.allCompatible(children.map(_.outputPartitioning))) {
+      children = children.zip(requiredChildDistributions).map { case (child, distribution) =>
+        val targetPartitioning = canonicalPartitioning(distribution)
+        if (child.outputPartitioning.guarantees(targetPartitioning)) {
           child
-        case (UnspecifiedDistribution, rowOrdering, child) =>
-          sqlContext.planner.BasicOperators.getSortOperator(rowOrdering, global = false, child)
+        } else {
+          Exchange(targetPartitioning, child)
+        }
+      }
+    }
 
-        case (dist, ordering, _) =>
-          sys.error(s"Don't know how to ensure $dist with ordering $ordering")
+    // Now that we've performed any necessary shuffles, add sorts to guarantee output orderings:
+    children = children.zip(requiredChildOrderings).map { case (child, requiredOrdering) =>
+      if (requiredOrdering.nonEmpty) {
+        // If child.outputOrdering is [a, b] and requiredOrdering is [a], we do not need to sort.
+        val minSize = Seq(requiredOrdering.size, child.outputOrdering.size).min
+        if (minSize == 0 || requiredOrdering.take(minSize) != child.outputOrdering.take(minSize)) {
+          sqlContext.planner.BasicOperators.getSortOperator(requiredOrdering, global = false, child)
+        } else {
+          child
+        }
+      } else {
+        child
       }
+    }
 
-      operator.withNewChildren(fixedChildren)
+    operator.withNewChildren(children)
+  }
+
+  def apply(plan: SparkPlan): SparkPlan = plan.transformUp {
+    case operator: SparkPlan => ensureDistributionAndOrdering(operator)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index c5d1ed0937b19..24950f26061f7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -256,6 +256,11 @@ case class Repartition(numPartitions: Int, shuffle: Boolean, child: SparkPlan)
   extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 
+  override def outputPartitioning: Partitioning = {
+    if (numPartitions == 1) SinglePartition
+    else UnknownPartitioning(numPartitions)
+  }
+
   protected override def doExecute(): RDD[InternalRow] = {
     child.execute().map(_.copy()).coalesce(numPartitions, shuffle)
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
index 29f3beb3cb3c8..855555dd1d4c4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/rowFormatConverters.scala
@@ -21,6 +21,7 @@ import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.physical.Partitioning
 import org.apache.spark.sql.catalyst.rules.Rule
 
 /**
@@ -33,6 +34,8 @@ case class ConvertToUnsafe(child: SparkPlan) extends UnaryNode {
   require(UnsafeProjection.canSupport(child.schema), s"Cannot convert ${child.schema} to Unsafe")
 
   override def output: Seq[Attribute] = child.output
+  override def outputPartitioning: Partitioning = child.outputPartitioning
+  override def outputOrdering: Seq[SortOrder] = child.outputOrdering
   override def outputsUnsafeRows: Boolean = true
   override def canProcessUnsafeRows: Boolean = false
   override def canProcessSafeRows: Boolean = true
@@ -51,6 +54,8 @@ case class ConvertToUnsafe(child: SparkPlan) extends UnaryNode {
 @DeveloperApi
 case class ConvertToSafe(child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
+  override def outputPartitioning: Partitioning = child.outputPartitioning
+  override def outputOrdering: Seq[SortOrder] = child.outputOrdering
   override def outputsUnsafeRows: Boolean = false
   override def canProcessUnsafeRows: Boolean = true
   override def canProcessSafeRows: Boolean = false
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 18b0e54dc7c53..5582caa0d366e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -18,9 +18,13 @@
 package org.apache.spark.sql.execution
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.TestData._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute, Literal, SortOrder}
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.joins.{BroadcastHashJoin, ShuffledHashJoin}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.test.{SQLTestUtils, TestSQLContext}
@@ -202,4 +206,151 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
       }
     }
   }
+
+  // --- Unit tests of EnsureRequirements ---------------------------------------------------------
+
+  // When it comes to testing whether EnsureRequirements properly ensures distribution requirements,
+  // there two dimensions that need to be considered: are the child partitionings compatible and
+  // do they satisfy the distribution requirements? As a result, we need at least four test cases.
+
+  private def assertDistributionRequirementsAreSatisfied(outputPlan: SparkPlan): Unit = {
+    if (outputPlan.children.length > 1
+        && outputPlan.requiredChildDistribution.toSet != Set(UnspecifiedDistribution)) {
+      val childPartitionings = outputPlan.children.map(_.outputPartitioning)
+      if (!Partitioning.allCompatible(childPartitionings)) {
+        fail(s"Partitionings are not compatible: $childPartitionings")
+      }
+    }
+    outputPlan.children.zip(outputPlan.requiredChildDistribution).foreach {
+      case (child, requiredDist) =>
+        assert(child.outputPartitioning.satisfies(requiredDist),
+          s"$child output partitioning does not satisfy $requiredDist:\n$outputPlan")
+    }
+  }
+
+  test("EnsureRequirements with incompatible child partitionings which satisfy distribution") {
+    // Consider an operator that requires inputs that are clustered by two expressions (e.g.
+    // sort merge join where there are multiple columns in the equi-join condition)
+    val clusteringA = Literal(1) :: Nil
+    val clusteringB = Literal(2) :: Nil
+    val distribution = ClusteredDistribution(clusteringA ++ clusteringB)
+    // Say that the left and right inputs are each partitioned by _one_ of the two join columns:
+    val leftPartitioning = HashPartitioning(clusteringA, 1)
+    val rightPartitioning = HashPartitioning(clusteringB, 1)
+    // Individually, each input's partitioning satisfies the clustering distribution:
+    assert(leftPartitioning.satisfies(distribution))
+    assert(rightPartitioning.satisfies(distribution))
+    // However, these partitionings are not compatible with each other, so we still need to
+    // repartition both inputs prior to performing the join:
+    assert(!leftPartitioning.compatibleWith(rightPartitioning))
+    assert(!rightPartitioning.compatibleWith(leftPartitioning))
+    val inputPlan = DummySparkPlan(
+      children = Seq(
+        DummySparkPlan(outputPartitioning = leftPartitioning),
+        DummySparkPlan(outputPartitioning = rightPartitioning)
+      ),
+      requiredChildDistribution = Seq(distribution, distribution),
+      requiredChildOrdering = Seq(Seq.empty, Seq.empty)
+    )
+    val outputPlan = EnsureRequirements(sqlContext).apply(inputPlan)
+    assertDistributionRequirementsAreSatisfied(outputPlan)
+    if (outputPlan.collect { case Exchange(_, _) => true }.isEmpty) {
+      fail(s"Exchange should have been added:\n$outputPlan")
+    }
+  }
+
+  test("EnsureRequirements with child partitionings with different numbers of output partitions") {
+    // This is similar to the previous test, except it checks that partitionings are not compatible
+    // unless they produce the same number of partitions.
+    val clustering = Literal(1) :: Nil
+    val distribution = ClusteredDistribution(clustering)
+    val inputPlan = DummySparkPlan(
+      children = Seq(
+        DummySparkPlan(outputPartitioning = HashPartitioning(clustering, 1)),
+        DummySparkPlan(outputPartitioning = HashPartitioning(clustering, 2))
+      ),
+      requiredChildDistribution = Seq(distribution, distribution),
+      requiredChildOrdering = Seq(Seq.empty, Seq.empty)
+    )
+    val outputPlan = EnsureRequirements(sqlContext).apply(inputPlan)
+    assertDistributionRequirementsAreSatisfied(outputPlan)
+  }
+
+  test("EnsureRequirements with compatible child partitionings that do not satisfy distribution") {
+    val distribution = ClusteredDistribution(Literal(1) :: Nil)
+    // The left and right inputs have compatible partitionings but they do not satisfy the
+    // distribution because they are clustered on different columns. Thus, we need to shuffle.
+    val childPartitioning = HashPartitioning(Literal(2) :: Nil, 1)
+    assert(!childPartitioning.satisfies(distribution))
+    val inputPlan = DummySparkPlan(
+      children = Seq(
+        DummySparkPlan(outputPartitioning = childPartitioning),
+        DummySparkPlan(outputPartitioning = childPartitioning)
+      ),
+      requiredChildDistribution = Seq(distribution, distribution),
+      requiredChildOrdering = Seq(Seq.empty, Seq.empty)
+    )
+    val outputPlan = EnsureRequirements(sqlContext).apply(inputPlan)
+    assertDistributionRequirementsAreSatisfied(outputPlan)
+    if (outputPlan.collect { case Exchange(_, _) => true }.isEmpty) {
+      fail(s"Exchange should have been added:\n$outputPlan")
+    }
+  }
+
+  test("EnsureRequirements with compatible child partitionings that satisfy distribution") {
+    // In this case, all requirements are satisfied and no exchange should be added.
+    val distribution = ClusteredDistribution(Literal(1) :: Nil)
+    val childPartitioning = HashPartitioning(Literal(1) :: Nil, 5)
+    assert(childPartitioning.satisfies(distribution))
+    val inputPlan = DummySparkPlan(
+      children = Seq(
+        DummySparkPlan(outputPartitioning = childPartitioning),
+        DummySparkPlan(outputPartitioning = childPartitioning)
+      ),
+      requiredChildDistribution = Seq(distribution, distribution),
+      requiredChildOrdering = Seq(Seq.empty, Seq.empty)
+    )
+    val outputPlan = EnsureRequirements(sqlContext).apply(inputPlan)
+    assertDistributionRequirementsAreSatisfied(outputPlan)
+    if (outputPlan.collect { case Exchange(_, _) => true }.nonEmpty) {
+      fail(s"Exchange should not have been added:\n$outputPlan")
+    }
+  }
+
+  // This is a regression test for SPARK-9703
+  test("EnsureRequirements should not repartition if only ordering requirement is unsatisfied") {
+    // Consider an operator that imposes both output distribution and  ordering requirements on its
+    // children, such as sort sort merge join. If the distribution requirements are satisfied but
+    // the output ordering requirements are unsatisfied, then the planner should only add sorts and
+    // should not need to add additional shuffles / exchanges.
+    val outputOrdering = Seq(SortOrder(Literal(1), Ascending))
+    val distribution = ClusteredDistribution(Literal(1) :: Nil)
+    val inputPlan = DummySparkPlan(
+      children = Seq(
+        DummySparkPlan(outputPartitioning = SinglePartition),
+        DummySparkPlan(outputPartitioning = SinglePartition)
+      ),
+      requiredChildDistribution = Seq(distribution, distribution),
+      requiredChildOrdering = Seq(outputOrdering, outputOrdering)
+    )
+    val outputPlan = EnsureRequirements(sqlContext).apply(inputPlan)
+    assertDistributionRequirementsAreSatisfied(outputPlan)
+    if (outputPlan.collect { case Exchange(_, _) => true }.nonEmpty) {
+      fail(s"No Exchanges should have been added:\n$outputPlan")
+    }
+  }
+
+  // ---------------------------------------------------------------------------------------------
+}
+
+// Used for unit-testing EnsureRequirements
+private case class DummySparkPlan(
+    override val children: Seq[SparkPlan] = Nil,
+    override val outputOrdering: Seq[SortOrder] = Nil,
+    override val outputPartitioning: Partitioning = UnknownPartitioning(0),
+    override val requiredChildDistribution: Seq[Distribution] = Nil,
+    override val requiredChildOrdering: Seq[Seq[SortOrder]] = Nil
+  ) extends SparkPlan {
+  override protected def doExecute(): RDD[InternalRow] = throw new NotImplementedError
+  override def output: Seq[Attribute] = Seq.empty
 }

From 46025616b414eaf1da01fcc1255d8041ea1554bc Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Sun, 9 Aug 2015 14:30:30 -0700
Subject: [PATCH 143/633] [CORE] [SPARK-9760] Use Option instead of Some for
 Ivy repos

This was introduced in #7599

cc rxin brkyvz

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #8055 from shivaram/spark-packages-repo-fix and squashes the following commits:

890f306 [Shivaram Venkataraman] Remove test case
51d69ee [Shivaram Venkataraman] Add test case for --packages without --repository
c02e0b4 [Shivaram Venkataraman] Use Option instead of Some for Ivy repos
---
 core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 1186bed485250..7ac6cbce4cd1d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -284,7 +284,7 @@ object SparkSubmit {
         Nil
       }
     val resolvedMavenCoordinates = SparkSubmitUtils.resolveMavenCoordinates(args.packages,
-      Some(args.repositories), Some(args.ivyRepoPath), exclusions = exclusions)
+      Option(args.repositories), Option(args.ivyRepoPath), exclusions = exclusions)
     if (!StringUtils.isBlank(resolvedMavenCoordinates)) {
       args.jars = mergeFileLists(args.jars, resolvedMavenCoordinates)
       if (args.isPython) {

From be80def0d07ed0f45d60453f4f82500d8c4c9106 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Sun, 9 Aug 2015 22:33:53 -0700
Subject: [PATCH 144/633] [SPARK-9777] [SQL] Window operator can accept
 UnsafeRows

https://issues.apache.org/jira/browse/SPARK-9777

Author: Yin Huai <yhuai@databricks.com>

Closes #8064 from yhuai/windowUnsafe and squashes the following commits:

8fb3537 [Yin Huai] Set canProcessUnsafeRows to true.
---
 .../src/main/scala/org/apache/spark/sql/execution/Window.scala  | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
index fe9f2c7028171..0269d6d4b7a1c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Window.scala
@@ -101,6 +101,8 @@ case class Window(
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
 
+  override def canProcessUnsafeRows: Boolean = true
+
   /**
    * Create a bound ordering object for a given frame type and offset. A bound ordering object is
    * used to determine which input row lies within the frame boundaries of an output row.

From e3fef0f9e17b1766a3869cb80ce7e4cd521cb7b6 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 10 Aug 2015 09:07:08 -0700
Subject: [PATCH 145/633] [SPARK-9743] [SQL] Fixes JSONRelation refreshing

PR #7696 added two `HadoopFsRelation.refresh()` calls ([this] [1], and [this] [2]) in `DataSourceStrategy` to make test case `InsertSuite.save directly to the path of a JSON table` pass. However, this forces every `HadoopFsRelation` table scan to do a refresh, which can be super expensive for tables with large number of partitions.

The reason why the original test case fails without the `refresh()` calls is that, the old JSON relation builds the base RDD with the input paths, while `HadoopFsRelation` provides `FileStatus`es of leaf files. With the old JSON relation, we can create a temporary table based on a path, writing data to that, and then read newly written data without refreshing the table. This is no long true for `HadoopFsRelation`.

This PR removes those two expensive refresh calls, and moves the refresh into `JSONRelation` to fix this issue. We might want to update `HadoopFsRelation` interface to provide better support for this use case.

[1]: https://github.com/apache/spark/blob/ebfd91c542aaead343cb154277fcf9114382fee7/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala#L63
[2]: https://github.com/apache/spark/blob/ebfd91c542aaead343cb154277fcf9114382fee7/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala#L91

Author: Cheng Lian <lian@databricks.com>

Closes #8035 from liancheng/spark-9743/fix-json-relation-refreshing and squashes the following commits:

ec1957d [Cheng Lian] Fixes JSONRelation refreshing
---
 .../datasources/DataSourceStrategy.scala      |  2 --
 .../apache/spark/sql/json/JSONRelation.scala  | 19 +++++++++++++++----
 .../apache/spark/sql/sources/interfaces.scala |  2 +-
 .../spark/sql/sources/InsertSuite.scala       | 10 +++++-----
 4 files changed, 21 insertions(+), 12 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 5b5fa8c93ec52..78a4acdf4b1bf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -60,7 +60,6 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
     // Scanning partitioned HadoopFsRelation
     case PhysicalOperation(projects, filters, l @ LogicalRelation(t: HadoopFsRelation))
         if t.partitionSpec.partitionColumns.nonEmpty =>
-      t.refresh()
       val selectedPartitions = prunePartitions(filters, t.partitionSpec).toArray
 
       logInfo {
@@ -88,7 +87,6 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
 
     // Scanning non-partitioned HadoopFsRelation
     case PhysicalOperation(projects, filters, l @ LogicalRelation(t: HadoopFsRelation)) =>
-      t.refresh()
       // See buildPartitionedTableScan for the reason that we need to create a shard
       // broadcast HadoopConf.
       val sharedHadoopConf = SparkHadoopUtil.get.conf
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
index b34a272ec547f..5bb9e62310a50 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
@@ -22,20 +22,22 @@ import java.io.CharArrayWriter
 import com.fasterxml.jackson.core.JsonFactory
 import com.google.common.base.Objects
 import org.apache.hadoop.fs.{FileStatus, Path}
-import org.apache.hadoop.io.{Text, LongWritable, NullWritable}
+import org.apache.hadoop.io.{LongWritable, NullWritable, Text}
 import org.apache.hadoop.mapred.{JobConf, TextInputFormat}
-import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat
-import org.apache.hadoop.mapreduce.{RecordWriter, TaskAttemptContext, Job}
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
+import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat
+import org.apache.hadoop.mapreduce.{Job, RecordWriter, TaskAttemptContext}
+
 import org.apache.spark.Logging
+import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
-
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.execution.datasources.PartitionSpec
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.{AnalysisException, Row, SQLContext}
+import org.apache.spark.util.SerializableConfiguration
 
 private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
 
@@ -108,6 +110,15 @@ private[sql] class JSONRelation(
     jsonSchema
   }
 
+  override private[sql] def buildScan(
+      requiredColumns: Array[String],
+      filters: Array[Filter],
+      inputPaths: Array[String],
+      broadcastedConf: Broadcast[SerializableConfiguration]): RDD[Row] = {
+    refresh()
+    super.buildScan(requiredColumns, filters, inputPaths, broadcastedConf)
+  }
+
   override def buildScan(
       requiredColumns: Array[String],
       filters: Array[Filter],
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 4aafec0e2df27..6bcabbab4f77b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -555,7 +555,7 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
     })
   }
 
-  private[sql] final def buildScan(
+  private[sql] def buildScan(
       requiredColumns: Array[String],
       filters: Array[Filter],
       inputPaths: Array[String],
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
index 39d18d712ef8c..cdbfaf6455fe4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
@@ -32,9 +32,9 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
 
   var path: File = null
 
-  override def beforeAll: Unit = {
+  override def beforeAll(): Unit = {
     path = Utils.createTempDir()
-    val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""))
+    val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""))
     caseInsensitiveContext.read.json(rdd).registerTempTable("jt")
     sql(
       s"""
@@ -46,7 +46,7 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
       """.stripMargin)
   }
 
-  override def afterAll: Unit = {
+  override def afterAll(): Unit = {
     caseInsensitiveContext.dropTempTable("jsonTable")
     caseInsensitiveContext.dropTempTable("jt")
     Utils.deleteRecursively(path)
@@ -110,7 +110,7 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
     )
 
     // Writing the table to less part files.
-    val rdd1 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""), 5)
+    val rdd1 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""), 5)
     caseInsensitiveContext.read.json(rdd1).registerTempTable("jt1")
     sql(
       s"""
@@ -122,7 +122,7 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
     )
 
     // Writing the table to more part files.
-    val rdd2 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""), 10)
+    val rdd2 = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""), 10)
     caseInsensitiveContext.read.json(rdd2).registerTempTable("jt2")
     sql(
       s"""

From 0f3366a4c740147a7a7519922642912e2dd238f8 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Mon, 10 Aug 2015 10:10:40 -0700
Subject: [PATCH 146/633] [SPARK-9710] [TEST] Fix RPackageUtilsSuite when R is
 not available.

RUtils.isRInstalled throws an exception if R is not installed,
instead of returning false. Fix that.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8008 from vanzin/SPARK-9710 and squashes the following commits:

df72d8c [Marcelo Vanzin] [SPARK-9710] [test] Fix RPackageUtilsSuite when R is not available.
---
 core/src/main/scala/org/apache/spark/api/r/RUtils.scala | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
index 93b3bea578676..427b2bc7cbcbb 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
@@ -67,7 +67,11 @@ private[spark] object RUtils {
 
   /** Check if R is installed before running tests that use R commands. */
   def isRInstalled: Boolean = {
-    val builder = new ProcessBuilder(Seq("R", "--version"))
-    builder.start().waitFor() == 0
+    try {
+      val builder = new ProcessBuilder(Seq("R", "--version"))
+      builder.start().waitFor() == 0
+    } catch {
+      case e: Exception => false
+    }
   }
 }

From 00b655cced637e1c3b750c19266086b9dcd7c158 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 10 Aug 2015 11:01:45 -0700
Subject: [PATCH 147/633] [SPARK-9755] [MLLIB] Add docs to
 MultivariateOnlineSummarizer methods

Adds method documentations back to `MultivariateOnlineSummarizer`, which were present in 1.4 but disappeared somewhere along the way to 1.5.

jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8045 from feynmanliang/SPARK-9755 and squashes the following commits:

af67fde [Feynman Liang] Add MultivariateOnlineSummarizer docs
---
 .../stat/MultivariateOnlineSummarizer.scala      | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
index 62da9f2ef22a3..64e4be0ebb97e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
@@ -153,6 +153,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * Sample mean of each dimension.
+   *
    * @since 1.1.0
    */
   override def mean: Vector = {
@@ -168,6 +170,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * Sample variance of each dimension.
+   *
    * @since 1.1.0
    */
   override def variance: Vector = {
@@ -193,11 +197,15 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * Sample size.
+   *
    * @since 1.1.0
    */
   override def count: Long = totalCnt
 
   /**
+   * Number of nonzero elements in each dimension.
+   *
    * @since 1.1.0
    */
   override def numNonzeros: Vector = {
@@ -207,6 +215,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * Maximum value of each dimension.
+   *
    * @since 1.1.0
    */
   override def max: Vector = {
@@ -221,6 +231,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * Minimum value of each dimension.
+   *
    * @since 1.1.0
    */
   override def min: Vector = {
@@ -235,6 +247,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * L2 (Euclidian) norm of each dimension.
+   *
    * @since 1.2.0
    */
   override def normL2: Vector = {
@@ -252,6 +266,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   }
 
   /**
+   * L1 norm of each dimension.
+   *
    * @since 1.2.0
    */
   override def normL1: Vector = {

From d285212756168200383bf4df2c951bd80a492a7c Mon Sep 17 00:00:00 2001
From: Mahmoud Lababidi <lababidi@gmail.com>
Date: Mon, 10 Aug 2015 13:02:01 -0700
Subject: [PATCH 148/633] Fixed AtmoicReference<> Example

Author: Mahmoud Lababidi <lababidi@gmail.com>

Closes #8076 from lababidi/master and squashes the following commits:

af4553b [Mahmoud Lababidi] Fixed AtmoicReference<> Example
---
 docs/streaming-kafka-integration.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md
index 775d508d4879b..7571e22575efd 100644
--- a/docs/streaming-kafka-integration.md
+++ b/docs/streaming-kafka-integration.md
@@ -152,7 +152,7 @@ Next, we discuss how to use this approach in your streaming application.
 	</div>
 	<div data-lang="java" markdown="1">
 		// Hold a reference to the current offset ranges, so it can be used downstream
-		final AtomicReference<OffsetRange[]> offsetRanges = new AtomicReference();
+		final AtomicReference<OffsetRange[]> offsetRanges = new AtomicReference<>();
 		
 		directKafkaStream.transformToPair(
 		  new Function<JavaPairRDD<String, String>, JavaPairRDD<String, String>>() {

From 0fe66744f16854fc8cd8a72174de93a788e3cf6c Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 10 Aug 2015 13:05:03 -0700
Subject: [PATCH 149/633] [SPARK-9784] [SQL] Exchange.isUnsafe should check
 whether codegen and unsafe are enabled

Exchange.isUnsafe should check whether codegen and unsafe are enabled.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8073 from JoshRosen/SPARK-9784 and squashes the following commits:

7a1019f [Josh Rosen] [SPARK-9784] Exchange.isUnsafe should check whether codegen and unsafe are enabled
---
 .../main/scala/org/apache/spark/sql/execution/Exchange.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
index b89e634761eb1..029f2264a6a27 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala
@@ -46,7 +46,7 @@ case class Exchange(newPartitioning: Partitioning, child: SparkPlan) extends Una
    * Returns true iff we can support the data type, and we are not doing range partitioning.
    */
   private lazy val tungstenMode: Boolean = {
-    GenerateUnsafeProjection.canSupport(child.schema) &&
+    unsafeEnabled && codegenEnabled && GenerateUnsafeProjection.canSupport(child.schema) &&
       !newPartitioning.isInstanceOf[RangePartitioning]
   }
 

From 40ed2af587cedadc6e5249031857a922b3b234ca Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 10 Aug 2015 13:49:23 -0700
Subject: [PATCH 150/633] [SPARK-9763][SQL] Minimize exposure of internal SQL
 classes.

There are a few changes in this pull request:

1. Moved all data sources to execution.datasources, except the public JDBC APIs.
2. In order to maintain backward compatibility from 1, added a backward compatibility translation map in data source resolution.
3. Moved ui and metric package into execution.
4. Added more documentation on some internal classes.
5. Renamed DataSourceRegister.format -> shortName.
6. Added "override" modifier on shortName.
7. Removed IntSQLMetric.

Author: Reynold Xin <rxin@databricks.com>

Closes #8056 from rxin/SPARK-9763 and squashes the following commits:

9df4801 [Reynold Xin] Removed hardcoded name in test cases.
d9babc6 [Reynold Xin] Shorten.
e484419 [Reynold Xin] Removed VisibleForTesting.
171b812 [Reynold Xin] MimaExcludes.
2041389 [Reynold Xin] Compile ...
79dda42 [Reynold Xin] Compile.
0818ba3 [Reynold Xin] Removed IntSQLMetric.
c46884f [Reynold Xin] Two more fixes.
f9aa88d [Reynold Xin] [SPARK-9763][SQL] Minimize exposure of internal SQL classes.
---
 project/MimaExcludes.scala                    |  24 +-
 ...pache.spark.sql.sources.DataSourceRegister |   6 +-
 .../ui/static/spark-sql-viz.css               |   0
 .../ui/static/spark-sql-viz.js                |   0
 .../org/apache/spark/sql/DataFrame.scala      |   2 +-
 .../apache/spark/sql/DataFrameReader.scala    |   6 +-
 .../apache/spark/sql/DataFrameWriter.scala    |   6 +-
 .../org/apache/spark/sql/SQLContext.scala     |   2 +-
 .../spark/sql/execution/SQLExecution.scala    |   2 +-
 .../spark/sql/execution/SparkPlan.scala       |   8 +-
 .../spark/sql/execution/basicOperators.scala  |   2 +-
 .../sql/execution/datasources/DDLParser.scala | 185 +++++++++
 .../execution/datasources/DefaultSource.scala |  64 ++++
 .../datasources/InsertIntoDataSource.scala    |  23 +-
 .../datasources/ResolvedDataSource.scala      | 204 ++++++++++
 .../spark/sql/execution/datasources/ddl.scala | 352 +-----------------
 .../datasources/jdbc/DefaultSource.scala      |  62 +++
 .../datasources/jdbc/DriverRegistry.scala     |  60 +++
 .../datasources/jdbc/DriverWrapper.scala      |  48 +++
 .../datasources}/jdbc/JDBCRDD.scala           |   9 +-
 .../datasources}/jdbc/JDBCRelation.scala      |  41 +-
 .../datasources/jdbc/JdbcUtils.scala          | 219 +++++++++++
 .../datasources}/json/InferSchema.scala       |   4 +-
 .../datasources}/json/JSONRelation.scala      |   7 +-
 .../datasources}/json/JacksonGenerator.scala  |   2 +-
 .../datasources}/json/JacksonParser.scala     |   4 +-
 .../datasources}/json/JacksonUtils.scala      |   2 +-
 .../parquet/CatalystReadSupport.scala         |   2 +-
 .../parquet/CatalystRecordMaterializer.scala  |   2 +-
 .../parquet/CatalystRowConverter.scala        |   2 +-
 .../parquet/CatalystSchemaConverter.scala     |   4 +-
 .../DirectParquetOutputCommitter.scala        |   2 +-
 .../parquet/ParquetConverter.scala            |   2 +-
 .../datasources}/parquet/ParquetFilters.scala |   2 +-
 .../parquet/ParquetRelation.scala             |   4 +-
 .../parquet/ParquetTableSupport.scala         |   2 +-
 .../parquet/ParquetTypesConverter.scala       |   2 +-
 .../{ => execution}/metric/SQLMetrics.scala   |  36 +-
 .../apache/spark/sql/execution/package.scala  |   8 +-
 .../ui/AllExecutionsPage.scala                |   2 +-
 .../{ => execution}/ui/ExecutionPage.scala    |   2 +-
 .../sql/{ => execution}/ui/SQLListener.scala  |   7 +-
 .../spark/sql/{ => execution}/ui/SQLTab.scala |   6 +-
 .../{ => execution}/ui/SparkPlanGraph.scala   |   4 +-
 .../org/apache/spark/sql/jdbc/JdbcUtils.scala |  52 ---
 .../org/apache/spark/sql/jdbc/jdbc.scala      | 250 -------------
 .../apache/spark/sql/sources/interfaces.scala |  15 +-
 .../parquet/test/avro/CompatibilityTest.java  |   4 +-
 .../parquet/test/avro/Nested.java             |  30 +-
 .../parquet/test/avro/ParquetAvroCompat.java  | 106 +++---
 .../org/apache/spark/sql/DataFrameSuite.scala |   4 +-
 .../datasources}/json/JsonSuite.scala         |   4 +-
 .../datasources}/json/TestJsonData.scala      |   2 +-
 .../ParquetAvroCompatibilitySuite.scala       |   4 +-
 .../parquet/ParquetCompatibilityTest.scala    |   2 +-
 .../parquet/ParquetFilterSuite.scala          |   2 +-
 .../datasources}/parquet/ParquetIOSuite.scala |   4 +-
 .../ParquetPartitionDiscoverySuite.scala      |   2 +-
 .../parquet/ParquetQuerySuite.scala           |   2 +-
 .../parquet/ParquetSchemaSuite.scala          |   2 +-
 .../datasources}/parquet/ParquetTest.scala    |   2 +-
 .../ParquetThriftCompatibilitySuite.scala     |   2 +-
 .../metric/SQLMetricsSuite.scala              |  12 +-
 .../{ => execution}/ui/SQLListenerSuite.scala |   4 +-
 .../sources/CreateTableAsSelectSuite.scala    |  20 +-
 .../sql/sources/DDLSourceLoadSuite.scala      |  59 +--
 .../sql/sources/ResolvedDataSourceSuite.scala |  39 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |   2 +-
 .../spark/sql/hive/orc/OrcRelation.scala      |   6 +-
 .../spark/sql/hive/HiveParquetSuite.scala     |   2 +-
 .../sql/hive/MetastoreDataSourcesSuite.scala  |   2 +-
 .../hive/ParquetHiveCompatibilitySuite.scala  |   2 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |   2 +-
 .../apache/spark/sql/hive/parquetSuites.scala |   2 +-
 .../ParquetHadoopFsRelationSuite.scala        |   4 +-
 .../SimpleTextHadoopFsRelationSuite.scala     |   2 +-
 76 files changed, 1114 insertions(+), 966 deletions(-)
 rename sql/core/src/main/resources/org/apache/spark/sql/{ => execution}/ui/static/spark-sql-viz.css (100%)
 rename sql/core/src/main/resources/org/apache/spark/sql/{ => execution}/ui/static/spark-sql-viz.js (100%)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DefaultSource.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverWrapper.scala
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/jdbc/JDBCRDD.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/jdbc/JDBCRelation.scala (71%)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/json/InferSchema.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/json/JSONRelation.scala (97%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/json/JacksonGenerator.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/json/JacksonParser.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/json/JacksonUtils.scala (95%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/CatalystReadSupport.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/CatalystRecordMaterializer.scala (96%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/CatalystRowConverter.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/CatalystSchemaConverter.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/DirectParquetOutputCommitter.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetConverter.scala (96%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetFilters.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetRelation.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetTableSupport.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetTypesConverter.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/metric/SQLMetrics.scala (77%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/ui/AllExecutionsPage.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/ui/ExecutionPage.scala (99%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/ui/SQLListener.scala (98%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/ui/SQLTab.scala (90%)
 rename sql/core/src/main/scala/org/apache/spark/sql/{ => execution}/ui/SparkPlanGraph.scala (97%)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcUtils.scala
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
 rename sql/core/src/test/gen-java/org/apache/spark/sql/{ => execution/datasources}/parquet/test/avro/CompatibilityTest.java (93%)
 rename sql/core/src/test/gen-java/org/apache/spark/sql/{ => execution/datasources}/parquet/test/avro/Nested.java (78%)
 rename sql/core/src/test/gen-java/org/apache/spark/sql/{ => execution/datasources}/parquet/test/avro/ParquetAvroCompat.java (83%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/json/JsonSuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/json/TestJsonData.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetAvroCompatibilitySuite.scala (96%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetCompatibilityTest.scala (97%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetFilterSuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetIOSuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetPartitionDiscoverySuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetQuerySuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetSchemaSuite.scala (99%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetTest.scala (98%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution/datasources}/parquet/ParquetThriftCompatibilitySuite.scala (98%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution}/metric/SQLMetricsSuite.scala (92%)
 rename sql/core/src/test/scala/org/apache/spark/sql/{ => execution}/ui/SQLListenerSuite.scala (99%)

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index b60ae784c3798..90261ca3d61aa 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -62,8 +62,6 @@ object MimaExcludes {
               "org.apache.spark.ml.classification.LogisticCostFun.this"),
             // SQL execution is considered private.
             excludePackage("org.apache.spark.sql.execution"),
-            // Parquet support is considered private.
-            excludePackage("org.apache.spark.sql.parquet"),
             // The old JSON RDD is removed in favor of streaming Jackson
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD$"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD"),
@@ -155,7 +153,27 @@ object MimaExcludes {
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD$"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec$"),
             ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLException")
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLException"),
+            // SPARK-9763 Minimize exposure of internal SQL classes
+            excludePackage("org.apache.spark.sql.parquet"),
+            excludePackage("org.apache.spark.sql.json"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$JDBCConversion"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$DriverWrapper"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DefaultSource"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$"),
+            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation")
           ) ++ Seq(
             // SPARK-4751 Dynamic allocation for standalone mode
             ProblemFilters.exclude[MissingMethodProblem](
diff --git a/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
index cc32d4b72748e..ca50000b4756e 100644
--- a/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
+++ b/sql/core/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -1,3 +1,3 @@
-org.apache.spark.sql.jdbc.DefaultSource
-org.apache.spark.sql.json.DefaultSource
-org.apache.spark.sql.parquet.DefaultSource
+org.apache.spark.sql.execution.datasources.jdbc.DefaultSource
+org.apache.spark.sql.execution.datasources.json.DefaultSource
+org.apache.spark.sql.execution.datasources.parquet.DefaultSource
diff --git a/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css b/sql/core/src/main/resources/org/apache/spark/sql/execution/ui/static/spark-sql-viz.css
similarity index 100%
rename from sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.css
rename to sql/core/src/main/resources/org/apache/spark/sql/execution/ui/static/spark-sql-viz.css
diff --git a/sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js b/sql/core/src/main/resources/org/apache/spark/sql/execution/ui/static/spark-sql-viz.js
similarity index 100%
rename from sql/core/src/main/resources/org/apache/spark/sql/ui/static/spark-sql-viz.js
rename to sql/core/src/main/resources/org/apache/spark/sql/execution/ui/static/spark-sql-viz.js
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 570b8b2d5928d..27b994f1f0caf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -39,7 +39,7 @@ import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
 import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD, SQLExecution}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation}
-import org.apache.spark.sql.json.JacksonGenerator
+import org.apache.spark.sql.execution.datasources.json.JacksonGenerator
 import org.apache.spark.sql.sources.HadoopFsRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.storage.StorageLevel
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 85f33c5e99523..9ea955b010017 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -25,10 +25,10 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.execution.datasources.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation}
+import org.apache.spark.sql.execution.datasources.json.JSONRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.execution.datasources.{LogicalRelation, ResolvedDataSource}
-import org.apache.spark.sql.jdbc.{JDBCPartition, JDBCPartitioningInfo, JDBCRelation}
-import org.apache.spark.sql.json.JSONRelation
-import org.apache.spark.sql.parquet.ParquetRelation
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.{Logging, Partition}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index 2a4992db09bc2..5fa11da4c38cd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -23,8 +23,8 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.{SqlParser, TableIdentifier}
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.catalyst.plans.logical.InsertIntoTable
+import org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, ResolvedDataSource}
-import org.apache.spark.sql.jdbc.{JDBCWriteDetails, JdbcUtils}
 import org.apache.spark.sql.sources.HadoopFsRelation
 
 
@@ -264,7 +264,7 @@ final class DataFrameWriter private[sql](df: DataFrame) {
 
       // Create the table if the table didn't exist.
       if (!tableExists) {
-        val schema = JDBCWriteDetails.schemaString(df, url)
+        val schema = JdbcUtils.schemaString(df, url)
         val sql = s"CREATE TABLE $table ($schema)"
         conn.prepareStatement(sql).executeUpdate()
       }
@@ -272,7 +272,7 @@ final class DataFrameWriter private[sql](df: DataFrame) {
       conn.close()
     }
 
-    JDBCWriteDetails.saveTable(df, url, table, connectionProperties)
+    JdbcUtils.saveTable(df, url, table, connectionProperties)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 832572571cabd..f73bb0488c984 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -43,7 +43,7 @@ import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.ui.{SQLListener, SQLTab}
+import org.apache.spark.sql.execution.ui.{SQLListener, SQLTab}
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.Utils
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
index 97f1323e97835..cee58218a885b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
@@ -21,7 +21,7 @@ import java.util.concurrent.atomic.AtomicLong
 
 import org.apache.spark.SparkContext
 import org.apache.spark.sql.SQLContext
-import org.apache.spark.sql.ui.SparkPlanGraph
+import org.apache.spark.sql.execution.ui.SparkPlanGraph
 import org.apache.spark.util.Utils
 
 private[sql] object SQLExecution {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 1915496d16205..9ba5cf2d2b39e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -32,7 +32,7 @@ import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.plans.QueryPlan
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.metric.{IntSQLMetric, LongSQLMetric, SQLMetric, SQLMetrics}
+import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetric, SQLMetrics}
 import org.apache.spark.sql.types.DataType
 
 object SparkPlan {
@@ -98,12 +98,6 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
    */
   private[sql] def metrics: Map[String, SQLMetric[_, _]] = defaultMetrics
 
-  /**
-   * Return a IntSQLMetric according to the name.
-   */
-  private[sql] def intMetric(name: String): IntSQLMetric =
-    metrics(name).asInstanceOf[IntSQLMetric]
-
   /**
    * Return a LongSQLMetric according to the name.
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 24950f26061f7..bf2de244c8e4a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -26,7 +26,7 @@ import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.metric.SQLMetrics
+import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.collection.ExternalSorter
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
new file mode 100644
index 0000000000000..6c462fa30461b
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
@@ -0,0 +1,185 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.datasources
+
+import scala.language.implicitConversions
+import scala.util.matching.Regex
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.SaveMode
+import org.apache.spark.sql.catalyst.{TableIdentifier, AbstractSparkSQLParser}
+import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.types._
+
+
+/**
+ * A parser for foreign DDL commands.
+ */
+class DDLParser(parseQuery: String => LogicalPlan)
+  extends AbstractSparkSQLParser with DataTypeParser with Logging {
+
+  def parse(input: String, exceptionOnError: Boolean): LogicalPlan = {
+    try {
+      parse(input)
+    } catch {
+      case ddlException: DDLException => throw ddlException
+      case _ if !exceptionOnError => parseQuery(input)
+      case x: Throwable => throw x
+    }
+  }
+
+  // Keyword is a convention with AbstractSparkSQLParser, which will scan all of the `Keyword`
+  // properties via reflection the class in runtime for constructing the SqlLexical object
+  protected val CREATE = Keyword("CREATE")
+  protected val TEMPORARY = Keyword("TEMPORARY")
+  protected val TABLE = Keyword("TABLE")
+  protected val IF = Keyword("IF")
+  protected val NOT = Keyword("NOT")
+  protected val EXISTS = Keyword("EXISTS")
+  protected val USING = Keyword("USING")
+  protected val OPTIONS = Keyword("OPTIONS")
+  protected val DESCRIBE = Keyword("DESCRIBE")
+  protected val EXTENDED = Keyword("EXTENDED")
+  protected val AS = Keyword("AS")
+  protected val COMMENT = Keyword("COMMENT")
+  protected val REFRESH = Keyword("REFRESH")
+
+  protected lazy val ddl: Parser[LogicalPlan] = createTable | describeTable | refreshTable
+
+  protected def start: Parser[LogicalPlan] = ddl
+
+  /**
+   * `CREATE [TEMPORARY] TABLE avroTable [IF NOT EXISTS]
+   * USING org.apache.spark.sql.avro
+   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
+   * or
+   * `CREATE [TEMPORARY] TABLE avroTable(intField int, stringField string...) [IF NOT EXISTS]
+   * USING org.apache.spark.sql.avro
+   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
+   * or
+   * `CREATE [TEMPORARY] TABLE avroTable [IF NOT EXISTS]
+   * USING org.apache.spark.sql.avro
+   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
+   * AS SELECT ...
+   */
+  protected lazy val createTable: Parser[LogicalPlan] = {
+    // TODO: Support database.table.
+    (CREATE ~> TEMPORARY.? <~ TABLE) ~ (IF ~> NOT <~ EXISTS).? ~ ident ~
+      tableCols.? ~ (USING ~> className) ~ (OPTIONS ~> options).? ~ (AS ~> restInput).? ^^ {
+      case temp ~ allowExisting ~ tableName ~ columns ~ provider ~ opts ~ query =>
+        if (temp.isDefined && allowExisting.isDefined) {
+          throw new DDLException(
+            "a CREATE TEMPORARY TABLE statement does not allow IF NOT EXISTS clause.")
+        }
+
+        val options = opts.getOrElse(Map.empty[String, String])
+        if (query.isDefined) {
+          if (columns.isDefined) {
+            throw new DDLException(
+              "a CREATE TABLE AS SELECT statement does not allow column definitions.")
+          }
+          // When IF NOT EXISTS clause appears in the query, the save mode will be ignore.
+          val mode = if (allowExisting.isDefined) {
+            SaveMode.Ignore
+          } else if (temp.isDefined) {
+            SaveMode.Overwrite
+          } else {
+            SaveMode.ErrorIfExists
+          }
+
+          val queryPlan = parseQuery(query.get)
+          CreateTableUsingAsSelect(tableName,
+            provider,
+            temp.isDefined,
+            Array.empty[String],
+            mode,
+            options,
+            queryPlan)
+        } else {
+          val userSpecifiedSchema = columns.flatMap(fields => Some(StructType(fields)))
+          CreateTableUsing(
+            tableName,
+            userSpecifiedSchema,
+            provider,
+            temp.isDefined,
+            options,
+            allowExisting.isDefined,
+            managedIfNoPath = false)
+        }
+    }
+  }
+
+  protected lazy val tableCols: Parser[Seq[StructField]] = "(" ~> repsep(column, ",") <~ ")"
+
+  /*
+   * describe [extended] table avroTable
+   * This will display all columns of table `avroTable` includes column_name,column_type,comment
+   */
+  protected lazy val describeTable: Parser[LogicalPlan] =
+    (DESCRIBE ~> opt(EXTENDED)) ~ (ident <~ ".").? ~ ident  ^^ {
+      case e ~ db ~ tbl =>
+        val tblIdentifier = db match {
+          case Some(dbName) =>
+            Seq(dbName, tbl)
+          case None =>
+            Seq(tbl)
+        }
+        DescribeCommand(UnresolvedRelation(tblIdentifier, None), e.isDefined)
+    }
+
+  protected lazy val refreshTable: Parser[LogicalPlan] =
+    REFRESH ~> TABLE ~> (ident <~ ".").? ~ ident ^^ {
+      case maybeDatabaseName ~ tableName =>
+        RefreshTable(TableIdentifier(tableName, maybeDatabaseName))
+    }
+
+  protected lazy val options: Parser[Map[String, String]] =
+    "(" ~> repsep(pair, ",") <~ ")" ^^ { case s: Seq[(String, String)] => s.toMap }
+
+  protected lazy val className: Parser[String] = repsep(ident, ".") ^^ { case s => s.mkString(".")}
+
+  override implicit def regexToParser(regex: Regex): Parser[String] = acceptMatch(
+    s"identifier matching regex $regex", {
+      case lexical.Identifier(str) if regex.unapplySeq(str).isDefined => str
+      case lexical.Keyword(str) if regex.unapplySeq(str).isDefined => str
+    }
+  )
+
+  protected lazy val optionPart: Parser[String] = "[_a-zA-Z][_a-zA-Z0-9]*".r ^^ {
+    case name => name
+  }
+
+  protected lazy val optionName: Parser[String] = repsep(optionPart, ".") ^^ {
+    case parts => parts.mkString(".")
+  }
+
+  protected lazy val pair: Parser[(String, String)] =
+    optionName ~ stringLit ^^ { case k ~ v => (k, v) }
+
+  protected lazy val column: Parser[StructField] =
+    ident ~ dataType ~ (COMMENT ~> stringLit).?  ^^ { case columnName ~ typ ~ cm =>
+      val meta = cm match {
+        case Some(comment) =>
+          new MetadataBuilder().putString(COMMENT.str.toLowerCase, comment).build()
+        case None => Metadata.empty
+      }
+
+      StructField(columnName, typ, nullable = true, meta)
+    }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala
new file mode 100644
index 0000000000000..6e4cc4de7f651
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala
@@ -0,0 +1,64 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.datasources
+
+import java.util.Properties
+
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.execution.datasources.jdbc.{JDBCRelation, JDBCPartitioningInfo, DriverRegistry}
+import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
+
+
+class DefaultSource extends RelationProvider with DataSourceRegister {
+
+  override def shortName(): String = "jdbc"
+
+  /** Returns a new base relation with the given parameters. */
+  override def createRelation(
+      sqlContext: SQLContext,
+      parameters: Map[String, String]): BaseRelation = {
+    val url = parameters.getOrElse("url", sys.error("Option 'url' not specified"))
+    val driver = parameters.getOrElse("driver", null)
+    val table = parameters.getOrElse("dbtable", sys.error("Option 'dbtable' not specified"))
+    val partitionColumn = parameters.getOrElse("partitionColumn", null)
+    val lowerBound = parameters.getOrElse("lowerBound", null)
+    val upperBound = parameters.getOrElse("upperBound", null)
+    val numPartitions = parameters.getOrElse("numPartitions", null)
+
+    if (driver != null) DriverRegistry.register(driver)
+
+    if (partitionColumn != null
+      && (lowerBound == null || upperBound == null || numPartitions == null)) {
+      sys.error("Partitioning incompletely specified")
+    }
+
+    val partitionInfo = if (partitionColumn == null) {
+      null
+    } else {
+      JDBCPartitioningInfo(
+        partitionColumn,
+        lowerBound.toLong,
+        upperBound.toLong,
+        numPartitions.toInt)
+    }
+    val parts = JDBCRelation.columnPartition(partitionInfo)
+    val properties = new Properties() // Additional properties that we will pass to getConnection
+    parameters.foreach(kv => properties.setProperty(kv._1, kv._2))
+    JDBCRelation(url, table, parts, properties)(sqlContext)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
index 6ccde7693bd34..3b7dc2e8d0210 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/InsertIntoDataSource.scala
@@ -17,27 +17,10 @@
 
 package org.apache.spark.sql.execution.datasources
 
-import java.io.IOException
-import java.util.{Date, UUID}
-
-import scala.collection.JavaConversions.asScalaIterator
-
-import org.apache.hadoop.fs.Path
-import org.apache.hadoop.mapreduce._
-import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter, FileOutputFormat}
-import org.apache.spark._
-import org.apache.spark.mapred.SparkHadoopMapRedUtil
-import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
 import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.codegen.GenerateProjection
-import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Project}
-import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
-import org.apache.spark.sql.execution.{RunnableCommand, SQLExecution}
-import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.StringType
-import org.apache.spark.util.{Utils, SerializableConfiguration}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.RunnableCommand
+import org.apache.spark.sql.sources.InsertableRelation
 
 
 /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
new file mode 100644
index 0000000000000..7770bbd712f04
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
@@ -0,0 +1,204 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.datasources
+
+import java.util.ServiceLoader
+
+import scala.collection.JavaConversions._
+import scala.language.{existentials, implicitConversions}
+import scala.util.{Success, Failure, Try}
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.Logging
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.sql.{DataFrame, SaveMode, AnalysisException, SQLContext}
+import org.apache.spark.sql.sources._
+import org.apache.spark.sql.types.{CalendarIntervalType, StructType}
+import org.apache.spark.util.Utils
+
+
+case class ResolvedDataSource(provider: Class[_], relation: BaseRelation)
+
+
+object ResolvedDataSource extends Logging {
+
+  /** A map to maintain backward compatibility in case we move data sources around. */
+  private val backwardCompatibilityMap = Map(
+    "org.apache.spark.sql.jdbc" -> classOf[jdbc.DefaultSource].getCanonicalName,
+    "org.apache.spark.sql.jdbc.DefaultSource" -> classOf[jdbc.DefaultSource].getCanonicalName,
+    "org.apache.spark.sql.json" -> classOf[json.DefaultSource].getCanonicalName,
+    "org.apache.spark.sql.json.DefaultSource" -> classOf[json.DefaultSource].getCanonicalName,
+    "org.apache.spark.sql.parquet" -> classOf[parquet.DefaultSource].getCanonicalName,
+    "org.apache.spark.sql.parquet.DefaultSource" -> classOf[parquet.DefaultSource].getCanonicalName
+  )
+
+  /** Given a provider name, look up the data source class definition. */
+  def lookupDataSource(provider0: String): Class[_] = {
+    val provider = backwardCompatibilityMap.getOrElse(provider0, provider0)
+    val provider2 = s"$provider.DefaultSource"
+    val loader = Utils.getContextOrSparkClassLoader
+    val serviceLoader = ServiceLoader.load(classOf[DataSourceRegister], loader)
+
+    serviceLoader.iterator().filter(_.shortName().equalsIgnoreCase(provider)).toList match {
+      /** the provider format did not match any given registered aliases */
+      case Nil => Try(loader.loadClass(provider)).orElse(Try(loader.loadClass(provider2))) match {
+        case Success(dataSource) => dataSource
+        case Failure(error) =>
+          if (provider.startsWith("org.apache.spark.sql.hive.orc")) {
+            throw new ClassNotFoundException(
+              "The ORC data source must be used with Hive support enabled.", error)
+          } else {
+            throw new ClassNotFoundException(
+              s"Failed to load class for data source: $provider.", error)
+          }
+      }
+      /** there is exactly one registered alias */
+      case head :: Nil => head.getClass
+      /** There are multiple registered aliases for the input */
+      case sources => sys.error(s"Multiple sources found for $provider, " +
+        s"(${sources.map(_.getClass.getName).mkString(", ")}), " +
+        "please specify the fully qualified class name.")
+    }
+  }
+
+  /** Create a [[ResolvedDataSource]] for reading data in. */
+  def apply(
+      sqlContext: SQLContext,
+      userSpecifiedSchema: Option[StructType],
+      partitionColumns: Array[String],
+      provider: String,
+      options: Map[String, String]): ResolvedDataSource = {
+    val clazz: Class[_] = lookupDataSource(provider)
+    def className: String = clazz.getCanonicalName
+    val relation = userSpecifiedSchema match {
+      case Some(schema: StructType) => clazz.newInstance() match {
+        case dataSource: SchemaRelationProvider =>
+          dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options), schema)
+        case dataSource: HadoopFsRelationProvider =>
+          val maybePartitionsSchema = if (partitionColumns.isEmpty) {
+            None
+          } else {
+            Some(partitionColumnsSchema(schema, partitionColumns))
+          }
+
+          val caseInsensitiveOptions = new CaseInsensitiveMap(options)
+          val paths = {
+            val patternPath = new Path(caseInsensitiveOptions("path"))
+            val fs = patternPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
+            val qualifiedPattern = patternPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
+            SparkHadoopUtil.get.globPathIfNecessary(qualifiedPattern).map(_.toString).toArray
+          }
+
+          val dataSchema =
+            StructType(schema.filterNot(f => partitionColumns.contains(f.name))).asNullable
+
+          dataSource.createRelation(
+            sqlContext,
+            paths,
+            Some(dataSchema),
+            maybePartitionsSchema,
+            caseInsensitiveOptions)
+        case dataSource: org.apache.spark.sql.sources.RelationProvider =>
+          throw new AnalysisException(s"$className does not allow user-specified schemas.")
+        case _ =>
+          throw new AnalysisException(s"$className is not a RelationProvider.")
+      }
+
+      case None => clazz.newInstance() match {
+        case dataSource: RelationProvider =>
+          dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options))
+        case dataSource: HadoopFsRelationProvider =>
+          val caseInsensitiveOptions = new CaseInsensitiveMap(options)
+          val paths = {
+            val patternPath = new Path(caseInsensitiveOptions("path"))
+            val fs = patternPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
+            val qualifiedPattern = patternPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
+            SparkHadoopUtil.get.globPathIfNecessary(qualifiedPattern).map(_.toString).toArray
+          }
+          dataSource.createRelation(sqlContext, paths, None, None, caseInsensitiveOptions)
+        case dataSource: org.apache.spark.sql.sources.SchemaRelationProvider =>
+          throw new AnalysisException(
+            s"A schema needs to be specified when using $className.")
+        case _ =>
+          throw new AnalysisException(
+            s"$className is neither a RelationProvider nor a FSBasedRelationProvider.")
+      }
+    }
+    new ResolvedDataSource(clazz, relation)
+  }
+
+  private def partitionColumnsSchema(
+      schema: StructType,
+      partitionColumns: Array[String]): StructType = {
+    StructType(partitionColumns.map { col =>
+      schema.find(_.name == col).getOrElse {
+        throw new RuntimeException(s"Partition column $col not found in schema $schema")
+      }
+    }).asNullable
+  }
+
+  /** Create a [[ResolvedDataSource]] for saving the content of the given DataFrame. */
+  def apply(
+      sqlContext: SQLContext,
+      provider: String,
+      partitionColumns: Array[String],
+      mode: SaveMode,
+      options: Map[String, String],
+      data: DataFrame): ResolvedDataSource = {
+    if (data.schema.map(_.dataType).exists(_.isInstanceOf[CalendarIntervalType])) {
+      throw new AnalysisException("Cannot save interval data type into external storage.")
+    }
+    val clazz: Class[_] = lookupDataSource(provider)
+    val relation = clazz.newInstance() match {
+      case dataSource: CreatableRelationProvider =>
+        dataSource.createRelation(sqlContext, mode, options, data)
+      case dataSource: HadoopFsRelationProvider =>
+        // Don't glob path for the write path.  The contracts here are:
+        //  1. Only one output path can be specified on the write path;
+        //  2. Output path must be a legal HDFS style file system path;
+        //  3. It's OK that the output path doesn't exist yet;
+        val caseInsensitiveOptions = new CaseInsensitiveMap(options)
+        val outputPath = {
+          val path = new Path(caseInsensitiveOptions("path"))
+          val fs = path.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
+          path.makeQualified(fs.getUri, fs.getWorkingDirectory)
+        }
+        val dataSchema = StructType(data.schema.filterNot(f => partitionColumns.contains(f.name)))
+        val r = dataSource.createRelation(
+          sqlContext,
+          Array(outputPath.toString),
+          Some(dataSchema.asNullable),
+          Some(partitionColumnsSchema(data.schema, partitionColumns)),
+          caseInsensitiveOptions)
+
+        // For partitioned relation r, r.schema's column ordering can be different from the column
+        // ordering of data.logicalPlan (partition columns are all moved after data column).  This
+        // will be adjusted within InsertIntoHadoopFsRelation.
+        sqlContext.executePlan(
+          InsertIntoHadoopFsRelation(
+            r,
+            data.logicalPlan,
+            mode)).toRdd
+        r
+      case _ =>
+        sys.error(s"${clazz.getCanonicalName} does not allow create table as select.")
+    }
+    ResolvedDataSource(clazz, relation)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
index 8c2f297e42458..ecd304c30cdee 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
@@ -17,340 +17,12 @@
 
 package org.apache.spark.sql.execution.datasources
 
-import java.util.ServiceLoader
-
-import scala.collection.Iterator
-import scala.collection.JavaConversions._
-import scala.language.{existentials, implicitConversions}
-import scala.util.{Failure, Success, Try}
-import scala.util.matching.Regex
-
-import org.apache.hadoop.fs.Path
-
-import org.apache.spark.Logging
-import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
+import org.apache.spark.sql.catalyst.TableIdentifier
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
 import org.apache.spark.sql.catalyst.plans.logical._
-import org.apache.spark.sql.catalyst.{AbstractSparkSQLParser, TableIdentifier}
 import org.apache.spark.sql.execution.RunnableCommand
-import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{AnalysisException, DataFrame, Row, SQLContext, SaveMode}
-import org.apache.spark.util.Utils
-
-/**
- * A parser for foreign DDL commands.
- */
-private[sql] class DDLParser(
-    parseQuery: String => LogicalPlan)
-  extends AbstractSparkSQLParser with DataTypeParser with Logging {
-
-  def parse(input: String, exceptionOnError: Boolean): LogicalPlan = {
-    try {
-      parse(input)
-    } catch {
-      case ddlException: DDLException => throw ddlException
-      case _ if !exceptionOnError => parseQuery(input)
-      case x: Throwable => throw x
-    }
-  }
-
-  // Keyword is a convention with AbstractSparkSQLParser, which will scan all of the `Keyword`
-  // properties via reflection the class in runtime for constructing the SqlLexical object
-  protected val CREATE = Keyword("CREATE")
-  protected val TEMPORARY = Keyword("TEMPORARY")
-  protected val TABLE = Keyword("TABLE")
-  protected val IF = Keyword("IF")
-  protected val NOT = Keyword("NOT")
-  protected val EXISTS = Keyword("EXISTS")
-  protected val USING = Keyword("USING")
-  protected val OPTIONS = Keyword("OPTIONS")
-  protected val DESCRIBE = Keyword("DESCRIBE")
-  protected val EXTENDED = Keyword("EXTENDED")
-  protected val AS = Keyword("AS")
-  protected val COMMENT = Keyword("COMMENT")
-  protected val REFRESH = Keyword("REFRESH")
-
-  protected lazy val ddl: Parser[LogicalPlan] = createTable | describeTable | refreshTable
-
-  protected def start: Parser[LogicalPlan] = ddl
-
-  /**
-   * `CREATE [TEMPORARY] TABLE avroTable [IF NOT EXISTS]
-   * USING org.apache.spark.sql.avro
-   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
-   * or
-   * `CREATE [TEMPORARY] TABLE avroTable(intField int, stringField string...) [IF NOT EXISTS]
-   * USING org.apache.spark.sql.avro
-   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
-   * or
-   * `CREATE [TEMPORARY] TABLE avroTable [IF NOT EXISTS]
-   * USING org.apache.spark.sql.avro
-   * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
-   * AS SELECT ...
-   */
-  protected lazy val createTable: Parser[LogicalPlan] =
-    // TODO: Support database.table.
-    (CREATE ~> TEMPORARY.? <~ TABLE) ~ (IF ~> NOT <~ EXISTS).? ~ ident ~
-      tableCols.? ~ (USING ~> className) ~ (OPTIONS ~> options).? ~ (AS ~> restInput).? ^^ {
-      case temp ~ allowExisting ~ tableName ~ columns ~ provider ~ opts ~ query =>
-        if (temp.isDefined && allowExisting.isDefined) {
-          throw new DDLException(
-            "a CREATE TEMPORARY TABLE statement does not allow IF NOT EXISTS clause.")
-        }
-
-        val options = opts.getOrElse(Map.empty[String, String])
-        if (query.isDefined) {
-          if (columns.isDefined) {
-            throw new DDLException(
-              "a CREATE TABLE AS SELECT statement does not allow column definitions.")
-          }
-          // When IF NOT EXISTS clause appears in the query, the save mode will be ignore.
-          val mode = if (allowExisting.isDefined) {
-            SaveMode.Ignore
-          } else if (temp.isDefined) {
-            SaveMode.Overwrite
-          } else {
-            SaveMode.ErrorIfExists
-          }
-
-          val queryPlan = parseQuery(query.get)
-          CreateTableUsingAsSelect(tableName,
-            provider,
-            temp.isDefined,
-            Array.empty[String],
-            mode,
-            options,
-            queryPlan)
-        } else {
-          val userSpecifiedSchema = columns.flatMap(fields => Some(StructType(fields)))
-          CreateTableUsing(
-            tableName,
-            userSpecifiedSchema,
-            provider,
-            temp.isDefined,
-            options,
-            allowExisting.isDefined,
-            managedIfNoPath = false)
-        }
-    }
-
-  protected lazy val tableCols: Parser[Seq[StructField]] = "(" ~> repsep(column, ",") <~ ")"
-
-  /*
-   * describe [extended] table avroTable
-   * This will display all columns of table `avroTable` includes column_name,column_type,comment
-   */
-  protected lazy val describeTable: Parser[LogicalPlan] =
-    (DESCRIBE ~> opt(EXTENDED)) ~ (ident <~ ".").? ~ ident  ^^ {
-      case e ~ db ~ tbl =>
-        val tblIdentifier = db match {
-          case Some(dbName) =>
-            Seq(dbName, tbl)
-          case None =>
-            Seq(tbl)
-        }
-        DescribeCommand(UnresolvedRelation(tblIdentifier, None), e.isDefined)
-   }
-
-  protected lazy val refreshTable: Parser[LogicalPlan] =
-    REFRESH ~> TABLE ~> (ident <~ ".").? ~ ident ^^ {
-      case maybeDatabaseName ~ tableName =>
-        RefreshTable(TableIdentifier(tableName, maybeDatabaseName))
-    }
-
-  protected lazy val options: Parser[Map[String, String]] =
-    "(" ~> repsep(pair, ",") <~ ")" ^^ { case s: Seq[(String, String)] => s.toMap }
-
-  protected lazy val className: Parser[String] = repsep(ident, ".") ^^ { case s => s.mkString(".")}
-
-  override implicit def regexToParser(regex: Regex): Parser[String] = acceptMatch(
-    s"identifier matching regex $regex", {
-      case lexical.Identifier(str) if regex.unapplySeq(str).isDefined => str
-      case lexical.Keyword(str) if regex.unapplySeq(str).isDefined => str
-    }
-  )
-
-  protected lazy val optionPart: Parser[String] = "[_a-zA-Z][_a-zA-Z0-9]*".r ^^ {
-    case name => name
-  }
-
-  protected lazy val optionName: Parser[String] = repsep(optionPart, ".") ^^ {
-    case parts => parts.mkString(".")
-  }
-
-  protected lazy val pair: Parser[(String, String)] =
-    optionName ~ stringLit ^^ { case k ~ v => (k, v) }
-
-  protected lazy val column: Parser[StructField] =
-    ident ~ dataType ~ (COMMENT ~> stringLit).?  ^^ { case columnName ~ typ ~ cm =>
-      val meta = cm match {
-        case Some(comment) =>
-          new MetadataBuilder().putString(COMMENT.str.toLowerCase, comment).build()
-        case None => Metadata.empty
-      }
-
-      StructField(columnName, typ, nullable = true, meta)
-    }
-}
-
-private[sql] object ResolvedDataSource extends Logging {
-
-  /** Given a provider name, look up the data source class definition. */
-  def lookupDataSource(provider: String): Class[_] = {
-    val provider2 = s"$provider.DefaultSource"
-    val loader = Utils.getContextOrSparkClassLoader
-    val serviceLoader = ServiceLoader.load(classOf[DataSourceRegister], loader)
-
-    serviceLoader.iterator().filter(_.format().equalsIgnoreCase(provider)).toList match {
-      /** the provider format did not match any given registered aliases */
-      case Nil => Try(loader.loadClass(provider)).orElse(Try(loader.loadClass(provider2))) match {
-        case Success(dataSource) => dataSource
-        case Failure(error) => if (provider.startsWith("org.apache.spark.sql.hive.orc")) {
-          throw new ClassNotFoundException(
-            "The ORC data source must be used with Hive support enabled.", error)
-        } else {
-          throw new ClassNotFoundException(
-            s"Failed to load class for data source: $provider", error)
-        }
-      }
-      /** there is exactly one registered alias */
-      case head :: Nil => head.getClass
-      /** There are multiple registered aliases for the input */
-      case sources => sys.error(s"Multiple sources found for $provider, " +
-        s"(${sources.map(_.getClass.getName).mkString(", ")}), " +
-        "please specify the fully qualified class name")
-    }
-  }
-
-  /** Create a [[ResolvedDataSource]] for reading data in. */
-  def apply(
-      sqlContext: SQLContext,
-      userSpecifiedSchema: Option[StructType],
-      partitionColumns: Array[String],
-      provider: String,
-      options: Map[String, String]): ResolvedDataSource = {
-    val clazz: Class[_] = lookupDataSource(provider)
-    def className: String = clazz.getCanonicalName
-    val relation = userSpecifiedSchema match {
-      case Some(schema: StructType) => clazz.newInstance() match {
-        case dataSource: SchemaRelationProvider =>
-          dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options), schema)
-        case dataSource: HadoopFsRelationProvider =>
-          val maybePartitionsSchema = if (partitionColumns.isEmpty) {
-            None
-          } else {
-            Some(partitionColumnsSchema(schema, partitionColumns))
-          }
-
-          val caseInsensitiveOptions = new CaseInsensitiveMap(options)
-          val paths = {
-            val patternPath = new Path(caseInsensitiveOptions("path"))
-            val fs = patternPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
-            val qualifiedPattern = patternPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
-            SparkHadoopUtil.get.globPathIfNecessary(qualifiedPattern).map(_.toString).toArray
-          }
-
-          val dataSchema =
-            StructType(schema.filterNot(f => partitionColumns.contains(f.name))).asNullable
-
-          dataSource.createRelation(
-            sqlContext,
-            paths,
-            Some(dataSchema),
-            maybePartitionsSchema,
-            caseInsensitiveOptions)
-        case dataSource: org.apache.spark.sql.sources.RelationProvider =>
-          throw new AnalysisException(s"$className does not allow user-specified schemas.")
-        case _ =>
-          throw new AnalysisException(s"$className is not a RelationProvider.")
-      }
-
-      case None => clazz.newInstance() match {
-        case dataSource: RelationProvider =>
-          dataSource.createRelation(sqlContext, new CaseInsensitiveMap(options))
-        case dataSource: HadoopFsRelationProvider =>
-          val caseInsensitiveOptions = new CaseInsensitiveMap(options)
-          val paths = {
-            val patternPath = new Path(caseInsensitiveOptions("path"))
-            val fs = patternPath.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
-            val qualifiedPattern = patternPath.makeQualified(fs.getUri, fs.getWorkingDirectory)
-            SparkHadoopUtil.get.globPathIfNecessary(qualifiedPattern).map(_.toString).toArray
-          }
-          dataSource.createRelation(sqlContext, paths, None, None, caseInsensitiveOptions)
-        case dataSource: org.apache.spark.sql.sources.SchemaRelationProvider =>
-          throw new AnalysisException(
-            s"A schema needs to be specified when using $className.")
-        case _ =>
-          throw new AnalysisException(
-            s"$className is neither a RelationProvider nor a FSBasedRelationProvider.")
-      }
-    }
-    new ResolvedDataSource(clazz, relation)
-  }
-
-  private def partitionColumnsSchema(
-      schema: StructType,
-      partitionColumns: Array[String]): StructType = {
-    StructType(partitionColumns.map { col =>
-      schema.find(_.name == col).getOrElse {
-        throw new RuntimeException(s"Partition column $col not found in schema $schema")
-      }
-    }).asNullable
-  }
-
-  /** Create a [[ResolvedDataSource]] for saving the content of the given [[DataFrame]]. */
-  def apply(
-      sqlContext: SQLContext,
-      provider: String,
-      partitionColumns: Array[String],
-      mode: SaveMode,
-      options: Map[String, String],
-      data: DataFrame): ResolvedDataSource = {
-    if (data.schema.map(_.dataType).exists(_.isInstanceOf[CalendarIntervalType])) {
-      throw new AnalysisException("Cannot save interval data type into external storage.")
-    }
-    val clazz: Class[_] = lookupDataSource(provider)
-    val relation = clazz.newInstance() match {
-      case dataSource: CreatableRelationProvider =>
-        dataSource.createRelation(sqlContext, mode, options, data)
-      case dataSource: HadoopFsRelationProvider =>
-        // Don't glob path for the write path.  The contracts here are:
-        //  1. Only one output path can be specified on the write path;
-        //  2. Output path must be a legal HDFS style file system path;
-        //  3. It's OK that the output path doesn't exist yet;
-        val caseInsensitiveOptions = new CaseInsensitiveMap(options)
-        val outputPath = {
-          val path = new Path(caseInsensitiveOptions("path"))
-          val fs = path.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
-          path.makeQualified(fs.getUri, fs.getWorkingDirectory)
-        }
-        val dataSchema = StructType(data.schema.filterNot(f => partitionColumns.contains(f.name)))
-        val r = dataSource.createRelation(
-          sqlContext,
-          Array(outputPath.toString),
-          Some(dataSchema.asNullable),
-          Some(partitionColumnsSchema(data.schema, partitionColumns)),
-          caseInsensitiveOptions)
-
-        // For partitioned relation r, r.schema's column ordering can be different from the column
-        // ordering of data.logicalPlan (partition columns are all moved after data column).  This
-        // will be adjusted within InsertIntoHadoopFsRelation.
-        sqlContext.executePlan(
-          InsertIntoHadoopFsRelation(
-            r,
-            data.logicalPlan,
-            mode)).toRdd
-        r
-      case _ =>
-        sys.error(s"${clazz.getCanonicalName} does not allow create table as select.")
-    }
-    new ResolvedDataSource(clazz, relation)
-  }
-}
-
-private[sql] case class ResolvedDataSource(provider: Class[_], relation: BaseRelation)
+import org.apache.spark.sql.{DataFrame, Row, SQLContext, SaveMode}
 
 /**
  * Returned for the "DESCRIBE [EXTENDED] [dbName.]tableName" command.
@@ -358,11 +30,12 @@ private[sql] case class ResolvedDataSource(provider: Class[_], relation: BaseRel
  * @param isExtended True if "DESCRIBE EXTENDED" is used. Otherwise, false.
  *                   It is effective only when the table is a Hive table.
  */
-private[sql] case class DescribeCommand(
+case class DescribeCommand(
     table: LogicalPlan,
     isExtended: Boolean) extends LogicalPlan with Command {
 
   override def children: Seq[LogicalPlan] = Seq.empty
+
   override val output: Seq[Attribute] = Seq(
     // Column names are based on Hive.
     AttributeReference("col_name", StringType, nullable = false,
@@ -370,7 +43,8 @@ private[sql] case class DescribeCommand(
     AttributeReference("data_type", StringType, nullable = false,
       new MetadataBuilder().putString("comment", "data type of the column").build())(),
     AttributeReference("comment", StringType, nullable = false,
-      new MetadataBuilder().putString("comment", "comment of the column").build())())
+      new MetadataBuilder().putString("comment", "comment of the column").build())()
+  )
 }
 
 /**
@@ -378,7 +52,7 @@ private[sql] case class DescribeCommand(
   * @param allowExisting If it is true, we will do nothing when the table already exists.
   *                      If it is false, an exception will be thrown
   */
-private[sql] case class CreateTableUsing(
+case class CreateTableUsing(
     tableName: String,
     userSpecifiedSchema: Option[StructType],
     provider: String,
@@ -397,7 +71,7 @@ private[sql] case class CreateTableUsing(
  * can analyze the logical plan that will be used to populate the table.
  * So, [[PreWriteCheck]] can detect cases that are not allowed.
  */
-private[sql] case class CreateTableUsingAsSelect(
+case class CreateTableUsingAsSelect(
     tableName: String,
     provider: String,
     temporary: Boolean,
@@ -410,7 +84,7 @@ private[sql] case class CreateTableUsingAsSelect(
   // override lazy val resolved = databaseName != None && childrenResolved
 }
 
-private[sql] case class CreateTempTableUsing(
+case class CreateTempTableUsing(
     tableName: String,
     userSpecifiedSchema: Option[StructType],
     provider: String,
@@ -425,7 +99,7 @@ private[sql] case class CreateTempTableUsing(
   }
 }
 
-private[sql] case class CreateTempTableUsingAsSelect(
+case class CreateTempTableUsingAsSelect(
     tableName: String,
     provider: String,
     partitionColumns: Array[String],
@@ -443,7 +117,7 @@ private[sql] case class CreateTempTableUsingAsSelect(
   }
 }
 
-private[sql] case class RefreshTable(tableIdent: TableIdentifier)
+case class RefreshTable(tableIdent: TableIdentifier)
   extends RunnableCommand {
 
   override def run(sqlContext: SQLContext): Seq[Row] = {
@@ -472,7 +146,7 @@ private[sql] case class RefreshTable(tableIdent: TableIdentifier)
 /**
  * Builds a map in which keys are case insensitive
  */
-protected[sql] class CaseInsensitiveMap(map: Map[String, String]) extends Map[String, String]
+class CaseInsensitiveMap(map: Map[String, String]) extends Map[String, String]
   with Serializable {
 
   val baseMap = map.map(kv => kv.copy(_1 = kv._1.toLowerCase))
@@ -490,4 +164,4 @@ protected[sql] class CaseInsensitiveMap(map: Map[String, String]) extends Map[St
 /**
  * The exception thrown from the DDL parser.
  */
-protected[sql] class DDLException(message: String) extends Exception(message)
+class DDLException(message: String) extends RuntimeException(message)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DefaultSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DefaultSource.scala
new file mode 100644
index 0000000000000..6773afc794f9c
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DefaultSource.scala
@@ -0,0 +1,62 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.datasources.jdbc
+
+import java.util.Properties
+
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.sources.{BaseRelation, RelationProvider, DataSourceRegister}
+
+class DefaultSource extends RelationProvider with DataSourceRegister {
+
+  override def shortName(): String = "jdbc"
+
+  /** Returns a new base relation with the given parameters. */
+  override def createRelation(
+      sqlContext: SQLContext,
+      parameters: Map[String, String]): BaseRelation = {
+    val url = parameters.getOrElse("url", sys.error("Option 'url' not specified"))
+    val driver = parameters.getOrElse("driver", null)
+    val table = parameters.getOrElse("dbtable", sys.error("Option 'dbtable' not specified"))
+    val partitionColumn = parameters.getOrElse("partitionColumn", null)
+    val lowerBound = parameters.getOrElse("lowerBound", null)
+    val upperBound = parameters.getOrElse("upperBound", null)
+    val numPartitions = parameters.getOrElse("numPartitions", null)
+
+    if (driver != null) DriverRegistry.register(driver)
+
+    if (partitionColumn != null
+      && (lowerBound == null || upperBound == null || numPartitions == null)) {
+      sys.error("Partitioning incompletely specified")
+    }
+
+    val partitionInfo = if (partitionColumn == null) {
+      null
+    } else {
+      JDBCPartitioningInfo(
+        partitionColumn,
+        lowerBound.toLong,
+        upperBound.toLong,
+        numPartitions.toInt)
+    }
+    val parts = JDBCRelation.columnPartition(partitionInfo)
+    val properties = new Properties() // Additional properties that we will pass to getConnection
+    parameters.foreach(kv => properties.setProperty(kv._1, kv._2))
+    JDBCRelation(url, table, parts, properties)(sqlContext)
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala
new file mode 100644
index 0000000000000..7ccd61ed469e9
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverRegistry.scala
@@ -0,0 +1,60 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.jdbc
+
+import java.sql.{Driver, DriverManager}
+
+import scala.collection.mutable
+
+import org.apache.spark.Logging
+import org.apache.spark.util.Utils
+
+/**
+ * java.sql.DriverManager is always loaded by bootstrap classloader,
+ * so it can't load JDBC drivers accessible by Spark ClassLoader.
+ *
+ * To solve the problem, drivers from user-supplied jars are wrapped into thin wrapper.
+ */
+object DriverRegistry extends Logging {
+
+  private val wrapperMap: mutable.Map[String, DriverWrapper] = mutable.Map.empty
+
+  def register(className: String): Unit = {
+    val cls = Utils.getContextOrSparkClassLoader.loadClass(className)
+    if (cls.getClassLoader == null) {
+      logTrace(s"$className has been loaded with bootstrap ClassLoader, wrapper is not required")
+    } else if (wrapperMap.get(className).isDefined) {
+      logTrace(s"Wrapper for $className already exists")
+    } else {
+      synchronized {
+        if (wrapperMap.get(className).isEmpty) {
+          val wrapper = new DriverWrapper(cls.newInstance().asInstanceOf[Driver])
+          DriverManager.registerDriver(wrapper)
+          wrapperMap(className) = wrapper
+          logTrace(s"Wrapper for $className registered")
+        }
+      }
+    }
+  }
+
+  def getDriverClassName(url: String): String = DriverManager.getDriver(url) match {
+    case wrapper: DriverWrapper => wrapper.wrapped.getClass.getCanonicalName
+    case driver => driver.getClass.getCanonicalName
+  }
+}
+
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverWrapper.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverWrapper.scala
new file mode 100644
index 0000000000000..18263fe227d04
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/DriverWrapper.scala
@@ -0,0 +1,48 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.jdbc
+
+import java.sql.{Connection, Driver, DriverPropertyInfo, SQLFeatureNotSupportedException}
+import java.util.Properties
+
+/**
+ * A wrapper for a JDBC Driver to work around SPARK-6913.
+ *
+ * The problem is in `java.sql.DriverManager` class that can't access drivers loaded by
+ * Spark ClassLoader.
+ */
+class DriverWrapper(val wrapped: Driver) extends Driver {
+  override def acceptsURL(url: String): Boolean = wrapped.acceptsURL(url)
+
+  override def jdbcCompliant(): Boolean = wrapped.jdbcCompliant()
+
+  override def getPropertyInfo(url: String, info: Properties): Array[DriverPropertyInfo] = {
+    wrapped.getPropertyInfo(url, info)
+  }
+
+  override def getMinorVersion: Int = wrapped.getMinorVersion
+
+  def getParentLogger: java.util.logging.Logger = {
+    throw new SQLFeatureNotSupportedException(
+      s"${this.getClass.getName}.getParentLogger is not yet implemented.")
+  }
+
+  override def connect(url: String, info: Properties): Connection = wrapped.connect(url, info)
+
+  override def getMajorVersion: Int = wrapped.getMajorVersion
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
index 3cf70db6b7b09..8eab6a0adccc4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.jdbc
+package org.apache.spark.sql.execution.datasources.jdbc
 
 import java.sql.{Connection, DriverManager, ResultSet, ResultSetMetaData, SQLException}
 import java.util.Properties
@@ -26,6 +26,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.jdbc.JdbcDialects
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -180,9 +181,8 @@ private[sql] object JDBCRDD extends Logging {
       try {
         if (driver != null) DriverRegistry.register(driver)
       } catch {
-        case e: ClassNotFoundException => {
-          logWarning(s"Couldn't find class $driver", e);
-        }
+        case e: ClassNotFoundException =>
+          logWarning(s"Couldn't find class $driver", e)
       }
       DriverManager.getConnection(url, properties)
     }
@@ -344,7 +344,6 @@ private[sql] class JDBCRDD(
     }).toArray
   }
 
-
   /**
    * Runs the SQL query against the JDBC driver.
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRelation.scala
similarity index 71%
rename from sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRelation.scala
index 48d97ced9ca0a..f9300dc2cb529 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRelation.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.jdbc
+package org.apache.spark.sql.execution.datasources.jdbc
 
 import java.util.Properties
 
@@ -77,45 +77,6 @@ private[sql] object JDBCRelation {
   }
 }
 
-private[sql] class DefaultSource extends RelationProvider with DataSourceRegister {
-
-  def format(): String = "jdbc"
-
-  /** Returns a new base relation with the given parameters. */
-  override def createRelation(
-      sqlContext: SQLContext,
-      parameters: Map[String, String]): BaseRelation = {
-    val url = parameters.getOrElse("url", sys.error("Option 'url' not specified"))
-    val driver = parameters.getOrElse("driver", null)
-    val table = parameters.getOrElse("dbtable", sys.error("Option 'dbtable' not specified"))
-    val partitionColumn = parameters.getOrElse("partitionColumn", null)
-    val lowerBound = parameters.getOrElse("lowerBound", null)
-    val upperBound = parameters.getOrElse("upperBound", null)
-    val numPartitions = parameters.getOrElse("numPartitions", null)
-
-    if (driver != null) DriverRegistry.register(driver)
-
-    if (partitionColumn != null
-        && (lowerBound == null || upperBound == null || numPartitions == null)) {
-      sys.error("Partitioning incompletely specified")
-    }
-
-    val partitionInfo = if (partitionColumn == null) {
-      null
-    } else {
-      JDBCPartitioningInfo(
-        partitionColumn,
-        lowerBound.toLong,
-        upperBound.toLong,
-        numPartitions.toInt)
-    }
-    val parts = JDBCRelation.columnPartition(partitionInfo)
-    val properties = new Properties() // Additional properties that we will pass to getConnection
-    parameters.foreach(kv => properties.setProperty(kv._1, kv._2))
-    JDBCRelation(url, table, parts, properties)(sqlContext)
-  }
-}
-
 private[sql] case class JDBCRelation(
     url: String,
     table: String,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
new file mode 100644
index 0000000000000..039c13bf163ca
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
@@ -0,0 +1,219 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.jdbc
+
+import java.sql.{Connection, DriverManager, PreparedStatement}
+import java.util.Properties
+
+import scala.util.Try
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.jdbc.JdbcDialects
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.{DataFrame, Row}
+
+/**
+ * Util functions for JDBC tables.
+ */
+object JdbcUtils extends Logging {
+
+  /**
+   * Establishes a JDBC connection.
+   */
+  def createConnection(url: String, connectionProperties: Properties): Connection = {
+    DriverManager.getConnection(url, connectionProperties)
+  }
+
+  /**
+   * Returns true if the table already exists in the JDBC database.
+   */
+  def tableExists(conn: Connection, table: String): Boolean = {
+    // Somewhat hacky, but there isn't a good way to identify whether a table exists for all
+    // SQL database systems, considering "table" could also include the database name.
+    Try(conn.prepareStatement(s"SELECT 1 FROM $table LIMIT 1").executeQuery().next()).isSuccess
+  }
+
+  /**
+   * Drops a table from the JDBC database.
+   */
+  def dropTable(conn: Connection, table: String): Unit = {
+    conn.prepareStatement(s"DROP TABLE $table").executeUpdate()
+  }
+
+  /**
+   * Returns a PreparedStatement that inserts a row into table via conn.
+   */
+  def insertStatement(conn: Connection, table: String, rddSchema: StructType): PreparedStatement = {
+    val sql = new StringBuilder(s"INSERT INTO $table VALUES (")
+    var fieldsLeft = rddSchema.fields.length
+    while (fieldsLeft > 0) {
+      sql.append("?")
+      if (fieldsLeft > 1) sql.append(", ") else sql.append(")")
+      fieldsLeft = fieldsLeft - 1
+    }
+    conn.prepareStatement(sql.toString())
+  }
+
+  /**
+   * Saves a partition of a DataFrame to the JDBC database.  This is done in
+   * a single database transaction in order to avoid repeatedly inserting
+   * data as much as possible.
+   *
+   * It is still theoretically possible for rows in a DataFrame to be
+   * inserted into the database more than once if a stage somehow fails after
+   * the commit occurs but before the stage can return successfully.
+   *
+   * This is not a closure inside saveTable() because apparently cosmetic
+   * implementation changes elsewhere might easily render such a closure
+   * non-Serializable.  Instead, we explicitly close over all variables that
+   * are used.
+   */
+  def savePartition(
+      getConnection: () => Connection,
+      table: String,
+      iterator: Iterator[Row],
+      rddSchema: StructType,
+      nullTypes: Array[Int]): Iterator[Byte] = {
+    val conn = getConnection()
+    var committed = false
+    try {
+      conn.setAutoCommit(false) // Everything in the same db transaction.
+      val stmt = insertStatement(conn, table, rddSchema)
+      try {
+        while (iterator.hasNext) {
+          val row = iterator.next()
+          val numFields = rddSchema.fields.length
+          var i = 0
+          while (i < numFields) {
+            if (row.isNullAt(i)) {
+              stmt.setNull(i + 1, nullTypes(i))
+            } else {
+              rddSchema.fields(i).dataType match {
+                case IntegerType => stmt.setInt(i + 1, row.getInt(i))
+                case LongType => stmt.setLong(i + 1, row.getLong(i))
+                case DoubleType => stmt.setDouble(i + 1, row.getDouble(i))
+                case FloatType => stmt.setFloat(i + 1, row.getFloat(i))
+                case ShortType => stmt.setInt(i + 1, row.getShort(i))
+                case ByteType => stmt.setInt(i + 1, row.getByte(i))
+                case BooleanType => stmt.setBoolean(i + 1, row.getBoolean(i))
+                case StringType => stmt.setString(i + 1, row.getString(i))
+                case BinaryType => stmt.setBytes(i + 1, row.getAs[Array[Byte]](i))
+                case TimestampType => stmt.setTimestamp(i + 1, row.getAs[java.sql.Timestamp](i))
+                case DateType => stmt.setDate(i + 1, row.getAs[java.sql.Date](i))
+                case t: DecimalType => stmt.setBigDecimal(i + 1, row.getDecimal(i))
+                case _ => throw new IllegalArgumentException(
+                  s"Can't translate non-null value for field $i")
+              }
+            }
+            i = i + 1
+          }
+          stmt.executeUpdate()
+        }
+      } finally {
+        stmt.close()
+      }
+      conn.commit()
+      committed = true
+    } finally {
+      if (!committed) {
+        // The stage must fail.  We got here through an exception path, so
+        // let the exception through unless rollback() or close() want to
+        // tell the user about another problem.
+        conn.rollback()
+        conn.close()
+      } else {
+        // The stage must succeed.  We cannot propagate any exception close() might throw.
+        try {
+          conn.close()
+        } catch {
+          case e: Exception => logWarning("Transaction succeeded, but closing failed", e)
+        }
+      }
+    }
+    Array[Byte]().iterator
+  }
+
+  /**
+   * Compute the schema string for this RDD.
+   */
+  def schemaString(df: DataFrame, url: String): String = {
+    val sb = new StringBuilder()
+    val dialect = JdbcDialects.get(url)
+    df.schema.fields foreach { field => {
+      val name = field.name
+      val typ: String =
+        dialect.getJDBCType(field.dataType).map(_.databaseTypeDefinition).getOrElse(
+          field.dataType match {
+            case IntegerType => "INTEGER"
+            case LongType => "BIGINT"
+            case DoubleType => "DOUBLE PRECISION"
+            case FloatType => "REAL"
+            case ShortType => "INTEGER"
+            case ByteType => "BYTE"
+            case BooleanType => "BIT(1)"
+            case StringType => "TEXT"
+            case BinaryType => "BLOB"
+            case TimestampType => "TIMESTAMP"
+            case DateType => "DATE"
+            case t: DecimalType => s"DECIMAL(${t.precision}},${t.scale}})"
+            case _ => throw new IllegalArgumentException(s"Don't know how to save $field to JDBC")
+          })
+      val nullable = if (field.nullable) "" else "NOT NULL"
+      sb.append(s", $name $typ $nullable")
+    }}
+    if (sb.length < 2) "" else sb.substring(2)
+  }
+
+  /**
+   * Saves the RDD to the database in a single transaction.
+   */
+  def saveTable(
+      df: DataFrame,
+      url: String,
+      table: String,
+      properties: Properties = new Properties()) {
+    val dialect = JdbcDialects.get(url)
+    val nullTypes: Array[Int] = df.schema.fields.map { field =>
+      dialect.getJDBCType(field.dataType).map(_.jdbcNullType).getOrElse(
+        field.dataType match {
+          case IntegerType => java.sql.Types.INTEGER
+          case LongType => java.sql.Types.BIGINT
+          case DoubleType => java.sql.Types.DOUBLE
+          case FloatType => java.sql.Types.REAL
+          case ShortType => java.sql.Types.INTEGER
+          case ByteType => java.sql.Types.INTEGER
+          case BooleanType => java.sql.Types.BIT
+          case StringType => java.sql.Types.CLOB
+          case BinaryType => java.sql.Types.BLOB
+          case TimestampType => java.sql.Types.TIMESTAMP
+          case DateType => java.sql.Types.DATE
+          case t: DecimalType => java.sql.Types.DECIMAL
+          case _ => throw new IllegalArgumentException(
+            s"Can't translate null value for field $field")
+        })
+    }
+
+    val rddSchema = df.schema
+    val driver: String = DriverRegistry.getDriverClassName(url)
+    val getConnection: () => Connection = JDBCRDD.getConnector(driver, url, properties)
+    df.foreachPartition { iterator =>
+      savePartition(getConnection, table, iterator, rddSchema, nullTypes)
+    }
+  }
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/InferSchema.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/InferSchema.scala
index ec5668c6b95a1..b6f3410bad690 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/InferSchema.scala
@@ -15,13 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import com.fasterxml.jackson.core._
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.analysis.HiveTypeCoercion
-import org.apache.spark.sql.json.JacksonUtils.nextUntil
+import org.apache.spark.sql.execution.datasources.json.JacksonUtils.nextUntil
 import org.apache.spark.sql.types._
 
 private[sql] object InferSchema {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
similarity index 97%
rename from sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
index 5bb9e62310a50..114c8b211891e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import java.io.CharArrayWriter
 
@@ -39,9 +39,10 @@ import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.{AnalysisException, Row, SQLContext}
 import org.apache.spark.util.SerializableConfiguration
 
-private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
 
-  def format(): String = "json"
+class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
+
+  override def shortName(): String = "json"
 
   override def createRelation(
       sqlContext: SQLContext,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
index d734e7e8904bd..37c2b5a296c15 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import org.apache.spark.sql.catalyst.InternalRow
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
index b8fd3b9cc150e..cd68bd667c5c4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import java.io.ByteArrayOutputStream
 
@@ -27,7 +27,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
-import org.apache.spark.sql.json.JacksonUtils.nextUntil
+import org.apache.spark.sql.execution.datasources.json.JacksonUtils.nextUntil
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonUtils.scala
similarity index 95%
rename from sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonUtils.scala
index fde96852ce68e..005546f37dda0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/json/JacksonUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonUtils.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import com.fasterxml.jackson.core.{JsonParser, JsonToken}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystReadSupport.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index 975fec101d9c2..4049795ed3bad 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.util.{Map => JMap}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRecordMaterializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRecordMaterializer.scala
similarity index 96%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRecordMaterializer.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRecordMaterializer.scala
index 84f1dccfeb788..ed9e0aa65977b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRecordMaterializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRecordMaterializer.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import org.apache.parquet.io.api.{GroupConverter, RecordMaterializer}
 import org.apache.parquet.schema.MessageType
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index 4fe8a39f20abd..3542dfbae1292 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.math.{BigDecimal, BigInteger}
 import java.nio.ByteOrder
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index b12149dcf1c92..a3fc74cf7929b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import scala.collection.JavaConversions._
 
@@ -25,7 +25,7 @@ import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName._
 import org.apache.parquet.schema.Type.Repetition._
 import org.apache.parquet.schema._
 
-import org.apache.spark.sql.parquet.CatalystSchemaConverter.{MAX_PRECISION_FOR_INT32, MAX_PRECISION_FOR_INT64, maxPrecisionForBytes}
+import org.apache.spark.sql.execution.datasources.parquet.CatalystSchemaConverter.{MAX_PRECISION_FOR_INT32, MAX_PRECISION_FOR_INT64, maxPrecisionForBytes}
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, SQLConf}
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
index 1551afd7b7bf2..2c6b914328b60 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/DirectParquetOutputCommitter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetConverter.scala
similarity index 96%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetConverter.scala
index 6ed3580af0729..ccd7ebf319af9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetConverter.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types.{MapData, ArrayData}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
index d57b789f5c1c7..9e2e232f50167 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetFilters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.Serializable
 import java.nio.ByteBuffer
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index b6db71b5b8a62..4086a139bed72 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.net.URI
 import java.util.logging.{Level, Logger => JLogger}
@@ -51,7 +51,7 @@ import org.apache.spark.util.{SerializableConfiguration, Utils}
 
 private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
 
-  def format(): String = "parquet"
+  override def shortName(): String = "parquet"
 
   override def createRelation(
       sqlContext: SQLContext,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
index 9cd0250f9c510..3191cf3d121bb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.math.BigInteger
 import java.nio.{ByteBuffer, ByteOrder}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypesConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypesConverter.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
index 3854f5bd39fb1..019db34fc666d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/parquet/ParquetTypesConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.IOException
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
similarity index 77%
rename from sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
index 3b907e5da7897..1b51a5e5c8a8e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/metric/SQLMetrics.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
@@ -15,7 +15,7 @@
 * limitations under the License.
 */
 
-package org.apache.spark.sql.metric
+package org.apache.spark.sql.execution.metric
 
 import org.apache.spark.{Accumulable, AccumulableParam, SparkContext}
 
@@ -93,22 +93,6 @@ private[sql] class LongSQLMetric private[metric](name: String)
   }
 }
 
-/**
- * A specialized int Accumulable to avoid boxing and unboxing when using Accumulator's
- * `+=` and `add`.
- */
-private[sql] class IntSQLMetric private[metric](name: String)
-  extends SQLMetric[IntSQLMetricValue, Int](name, IntSQLMetricParam) {
-
-  override def +=(term: Int): Unit = {
-    localValue.add(term)
-  }
-
-  override def add(term: Int): Unit = {
-    localValue.add(term)
-  }
-}
-
 private object LongSQLMetricParam extends SQLMetricParam[LongSQLMetricValue, Long] {
 
   override def addAccumulator(r: LongSQLMetricValue, t: Long): LongSQLMetricValue = r.add(t)
@@ -121,26 +105,8 @@ private object LongSQLMetricParam extends SQLMetricParam[LongSQLMetricValue, Lon
   override def zero: LongSQLMetricValue = new LongSQLMetricValue(0L)
 }
 
-private object IntSQLMetricParam extends SQLMetricParam[IntSQLMetricValue, Int] {
-
-  override def addAccumulator(r: IntSQLMetricValue, t: Int): IntSQLMetricValue = r.add(t)
-
-  override def addInPlace(r1: IntSQLMetricValue, r2: IntSQLMetricValue): IntSQLMetricValue =
-    r1.add(r2.value)
-
-  override def zero(initialValue: IntSQLMetricValue): IntSQLMetricValue = zero
-
-  override def zero: IntSQLMetricValue = new IntSQLMetricValue(0)
-}
-
 private[sql] object SQLMetrics {
 
-  def createIntMetric(sc: SparkContext, name: String): IntSQLMetric = {
-    val acc = new IntSQLMetric(name)
-    sc.cleaner.foreach(_.registerAccumulatorForCleanup(acc))
-    acc
-  }
-
   def createLongMetric(sc: SparkContext, name: String): LongSQLMetric = {
     val acc = new LongSQLMetric(name)
     sc.cleaner.foreach(_.registerAccumulatorForCleanup(acc))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/package.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/package.scala
index 66237f8f1314b..28fa231e722d0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/package.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/package.scala
@@ -18,12 +18,6 @@
 package org.apache.spark.sql
 
 /**
- * :: DeveloperApi ::
- * An execution engine for relational query plans that runs on top Spark and returns RDDs.
- *
- * Note that the operators in this package are created automatically by a query planner using a
- * [[SQLContext]] and are not intended to be used directly by end users of Spark SQL.  They are
- * documented here in order to make it easier for others to understand the performance
- * characteristics of query plans that are generated by Spark SQL.
+ * The physical execution component of Spark SQL. Note that this is a private package.
  */
 package object execution
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/AllExecutionsPage.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/ui/AllExecutionsPage.scala
index cb7ca60b2fe48..49646a99d68c8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/AllExecutionsPage.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/AllExecutionsPage.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import javax.servlet.http.HttpServletRequest
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
similarity index 99%
rename from sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
index 52ddf99e9266a..f0b56c2eb7a53 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/ExecutionPage.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import javax.servlet.http.HttpServletRequest
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
similarity index 98%
rename from sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
index 2fd4fc658d068..0b9bad987c488 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLListener.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import scala.collection.mutable
 
@@ -26,7 +26,7 @@ import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler._
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.execution.SQLExecution
-import org.apache.spark.sql.metric.{SQLMetricParam, SQLMetricValue}
+import org.apache.spark.sql.execution.metric.{SQLMetricParam, SQLMetricValue}
 
 private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener with Logging {
 
@@ -51,17 +51,14 @@ private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener wit
 
   private val completedExecutions = mutable.ListBuffer[SQLExecutionUIData]()
 
-  @VisibleForTesting
   def executionIdToData: Map[Long, SQLExecutionUIData] = synchronized {
     _executionIdToData.toMap
   }
 
-  @VisibleForTesting
   def jobIdToExecutionId: Map[Long, Long] = synchronized {
     _jobIdToExecutionId.toMap
   }
 
-  @VisibleForTesting
   def stageIdToStageMetrics: Map[Long, SQLStageMetrics] = synchronized {
     _stageIdToStageMetrics.toMap
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLTab.scala
similarity index 90%
rename from sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLTab.scala
index 3bba0afaf14eb..0b0867f67eb6e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SQLTab.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLTab.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import java.util.concurrent.atomic.AtomicInteger
 
@@ -38,12 +38,12 @@ private[sql] class SQLTab(sqlContext: SQLContext, sparkUI: SparkUI)
 
 private[sql] object SQLTab {
 
-  private val STATIC_RESOURCE_DIR = "org/apache/spark/sql/ui/static"
+  private val STATIC_RESOURCE_DIR = "org/apache/spark/sql/execution/ui/static"
 
   private val nextTabId = new AtomicInteger(0)
 
   private def nextTabName: String = {
     val nextId = nextTabId.getAndIncrement()
-    if (nextId == 0) "SQL" else s"SQL${nextId}"
+    if (nextId == 0) "SQL" else s"SQL$nextId"
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SparkPlanGraph.scala
similarity index 97%
rename from sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
rename to sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SparkPlanGraph.scala
index 1ba50b95becc1..ae3d752dde348 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/ui/SparkPlanGraph.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SparkPlanGraph.scala
@@ -15,14 +15,14 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import java.util.concurrent.atomic.AtomicLong
 
 import scala.collection.mutable
 
 import org.apache.spark.sql.execution.SparkPlan
-import org.apache.spark.sql.metric.{SQLMetricParam, SQLMetricValue}
+import org.apache.spark.sql.execution.metric.{SQLMetricParam, SQLMetricValue}
 
 /**
  * A graph used for storing information of an executionPlan of DataFrame.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcUtils.scala
deleted file mode 100644
index cc918c237192b..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcUtils.scala
+++ /dev/null
@@ -1,52 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.jdbc
-
-import java.sql.{Connection, DriverManager}
-import java.util.Properties
-
-import scala.util.Try
-
-/**
- * Util functions for JDBC tables.
- */
-private[sql] object JdbcUtils {
-
-  /**
-   * Establishes a JDBC connection.
-   */
-  def createConnection(url: String, connectionProperties: Properties): Connection = {
-    DriverManager.getConnection(url, connectionProperties)
-  }
-
-  /**
-   * Returns true if the table already exists in the JDBC database.
-   */
-  def tableExists(conn: Connection, table: String): Boolean = {
-    // Somewhat hacky, but there isn't a good way to identify whether a table exists for all
-    // SQL database systems, considering "table" could also include the database name.
-    Try(conn.prepareStatement(s"SELECT 1 FROM $table LIMIT 1").executeQuery().next()).isSuccess
-  }
-
-  /**
-   * Drops a table from the JDBC database.
-   */
-  def dropTable(conn: Connection, table: String): Unit = {
-    conn.prepareStatement(s"DROP TABLE $table").executeUpdate()
-  }
-}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
deleted file mode 100644
index 035e0510080ff..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/jdbc.scala
+++ /dev/null
@@ -1,250 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql
-
-import java.sql.{Connection, Driver, DriverManager, DriverPropertyInfo, PreparedStatement, SQLFeatureNotSupportedException}
-import java.util.Properties
-
-import scala.collection.mutable
-
-import org.apache.spark.Logging
-import org.apache.spark.sql.types._
-import org.apache.spark.util.Utils
-
-package object jdbc {
-  private[sql] object JDBCWriteDetails extends Logging {
-    /**
-     * Returns a PreparedStatement that inserts a row into table via conn.
-     */
-    def insertStatement(conn: Connection, table: String, rddSchema: StructType):
-        PreparedStatement = {
-      val sql = new StringBuilder(s"INSERT INTO $table VALUES (")
-      var fieldsLeft = rddSchema.fields.length
-      while (fieldsLeft > 0) {
-        sql.append("?")
-        if (fieldsLeft > 1) sql.append(", ") else sql.append(")")
-        fieldsLeft = fieldsLeft - 1
-      }
-      conn.prepareStatement(sql.toString)
-    }
-
-    /**
-     * Saves a partition of a DataFrame to the JDBC database.  This is done in
-     * a single database transaction in order to avoid repeatedly inserting
-     * data as much as possible.
-     *
-     * It is still theoretically possible for rows in a DataFrame to be
-     * inserted into the database more than once if a stage somehow fails after
-     * the commit occurs but before the stage can return successfully.
-     *
-     * This is not a closure inside saveTable() because apparently cosmetic
-     * implementation changes elsewhere might easily render such a closure
-     * non-Serializable.  Instead, we explicitly close over all variables that
-     * are used.
-     */
-    def savePartition(
-        getConnection: () => Connection,
-        table: String,
-        iterator: Iterator[Row],
-        rddSchema: StructType,
-        nullTypes: Array[Int]): Iterator[Byte] = {
-      val conn = getConnection()
-      var committed = false
-      try {
-        conn.setAutoCommit(false) // Everything in the same db transaction.
-        val stmt = insertStatement(conn, table, rddSchema)
-        try {
-          while (iterator.hasNext) {
-            val row = iterator.next()
-            val numFields = rddSchema.fields.length
-            var i = 0
-            while (i < numFields) {
-              if (row.isNullAt(i)) {
-                stmt.setNull(i + 1, nullTypes(i))
-              } else {
-                rddSchema.fields(i).dataType match {
-                  case IntegerType => stmt.setInt(i + 1, row.getInt(i))
-                  case LongType => stmt.setLong(i + 1, row.getLong(i))
-                  case DoubleType => stmt.setDouble(i + 1, row.getDouble(i))
-                  case FloatType => stmt.setFloat(i + 1, row.getFloat(i))
-                  case ShortType => stmt.setInt(i + 1, row.getShort(i))
-                  case ByteType => stmt.setInt(i + 1, row.getByte(i))
-                  case BooleanType => stmt.setBoolean(i + 1, row.getBoolean(i))
-                  case StringType => stmt.setString(i + 1, row.getString(i))
-                  case BinaryType => stmt.setBytes(i + 1, row.getAs[Array[Byte]](i))
-                  case TimestampType => stmt.setTimestamp(i + 1, row.getAs[java.sql.Timestamp](i))
-                  case DateType => stmt.setDate(i + 1, row.getAs[java.sql.Date](i))
-                  case t: DecimalType => stmt.setBigDecimal(i + 1, row.getDecimal(i))
-                  case _ => throw new IllegalArgumentException(
-                      s"Can't translate non-null value for field $i")
-                }
-              }
-              i = i + 1
-            }
-            stmt.executeUpdate()
-          }
-        } finally {
-          stmt.close()
-        }
-        conn.commit()
-        committed = true
-      } finally {
-        if (!committed) {
-          // The stage must fail.  We got here through an exception path, so
-          // let the exception through unless rollback() or close() want to
-          // tell the user about another problem.
-          conn.rollback()
-          conn.close()
-        } else {
-          // The stage must succeed.  We cannot propagate any exception close() might throw.
-          try {
-            conn.close()
-          } catch {
-            case e: Exception => logWarning("Transaction succeeded, but closing failed", e)
-          }
-        }
-      }
-      Array[Byte]().iterator
-    }
-
-    /**
-     * Compute the schema string for this RDD.
-     */
-    def schemaString(df: DataFrame, url: String): String = {
-      val sb = new StringBuilder()
-      val dialect = JdbcDialects.get(url)
-      df.schema.fields foreach { field => {
-        val name = field.name
-        val typ: String =
-          dialect.getJDBCType(field.dataType).map(_.databaseTypeDefinition).getOrElse(
-          field.dataType match {
-            case IntegerType => "INTEGER"
-            case LongType => "BIGINT"
-            case DoubleType => "DOUBLE PRECISION"
-            case FloatType => "REAL"
-            case ShortType => "INTEGER"
-            case ByteType => "BYTE"
-            case BooleanType => "BIT(1)"
-            case StringType => "TEXT"
-            case BinaryType => "BLOB"
-            case TimestampType => "TIMESTAMP"
-            case DateType => "DATE"
-            case t: DecimalType => s"DECIMAL(${t.precision}},${t.scale}})"
-            case _ => throw new IllegalArgumentException(s"Don't know how to save $field to JDBC")
-          })
-        val nullable = if (field.nullable) "" else "NOT NULL"
-        sb.append(s", $name $typ $nullable")
-      }}
-      if (sb.length < 2) "" else sb.substring(2)
-    }
-
-    /**
-     * Saves the RDD to the database in a single transaction.
-     */
-    def saveTable(
-        df: DataFrame,
-        url: String,
-        table: String,
-        properties: Properties = new Properties()) {
-      val dialect = JdbcDialects.get(url)
-      val nullTypes: Array[Int] = df.schema.fields.map { field =>
-        dialect.getJDBCType(field.dataType).map(_.jdbcNullType).getOrElse(
-          field.dataType match {
-            case IntegerType => java.sql.Types.INTEGER
-            case LongType => java.sql.Types.BIGINT
-            case DoubleType => java.sql.Types.DOUBLE
-            case FloatType => java.sql.Types.REAL
-            case ShortType => java.sql.Types.INTEGER
-            case ByteType => java.sql.Types.INTEGER
-            case BooleanType => java.sql.Types.BIT
-            case StringType => java.sql.Types.CLOB
-            case BinaryType => java.sql.Types.BLOB
-            case TimestampType => java.sql.Types.TIMESTAMP
-            case DateType => java.sql.Types.DATE
-            case t: DecimalType => java.sql.Types.DECIMAL
-            case _ => throw new IllegalArgumentException(
-              s"Can't translate null value for field $field")
-          })
-      }
-
-      val rddSchema = df.schema
-      val driver: String = DriverRegistry.getDriverClassName(url)
-      val getConnection: () => Connection = JDBCRDD.getConnector(driver, url, properties)
-      df.foreachPartition { iterator =>
-        JDBCWriteDetails.savePartition(getConnection, table, iterator, rddSchema, nullTypes)
-      }
-    }
-
-  }
-
-  private [sql] class DriverWrapper(val wrapped: Driver) extends Driver {
-    override def acceptsURL(url: String): Boolean = wrapped.acceptsURL(url)
-
-    override def jdbcCompliant(): Boolean = wrapped.jdbcCompliant()
-
-    override def getPropertyInfo(url: String, info: Properties): Array[DriverPropertyInfo] = {
-      wrapped.getPropertyInfo(url, info)
-    }
-
-    override def getMinorVersion: Int = wrapped.getMinorVersion
-
-    def getParentLogger: java.util.logging.Logger =
-      throw new SQLFeatureNotSupportedException(
-        s"${this.getClass().getName}.getParentLogger is not yet implemented.")
-
-    override def connect(url: String, info: Properties): Connection = wrapped.connect(url, info)
-
-    override def getMajorVersion: Int = wrapped.getMajorVersion
-  }
-
-  /**
-   * java.sql.DriverManager is always loaded by bootstrap classloader,
-   * so it can't load JDBC drivers accessible by Spark ClassLoader.
-   *
-   * To solve the problem, drivers from user-supplied jars are wrapped
-   * into thin wrapper.
-   */
-  private [sql] object DriverRegistry extends Logging {
-
-    private val wrapperMap: mutable.Map[String, DriverWrapper] = mutable.Map.empty
-
-    def register(className: String): Unit = {
-      val cls = Utils.getContextOrSparkClassLoader.loadClass(className)
-      if (cls.getClassLoader == null) {
-        logTrace(s"$className has been loaded with bootstrap ClassLoader, wrapper is not required")
-      } else if (wrapperMap.get(className).isDefined) {
-        logTrace(s"Wrapper for $className already exists")
-      } else {
-        synchronized {
-          if (wrapperMap.get(className).isEmpty) {
-            val wrapper = new DriverWrapper(cls.newInstance().asInstanceOf[Driver])
-            DriverManager.registerDriver(wrapper)
-            wrapperMap(className) = wrapper
-            logTrace(s"Wrapper for $className registered")
-          }
-        }
-      }
-    }
-
-    def getDriverClassName(url: String): String = DriverManager.getDriver(url) match {
-      case wrapper: DriverWrapper => wrapper.wrapped.getClass.getCanonicalName
-      case driver => driver.getClass.getCanonicalName
-    }
-  }
-
-} // package object jdbc
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 6bcabbab4f77b..2f8417a48d32e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -43,19 +43,24 @@ import org.apache.spark.util.SerializableConfiguration
  * This allows users to give the data source alias as the format type over the fully qualified
  * class name.
  *
- * ex: parquet.DefaultSource.format = "parquet".
- *
  * A new instance of this class with be instantiated each time a DDL call is made.
+ *
+ * @since 1.5.0
  */
 @DeveloperApi
 trait DataSourceRegister {
 
   /**
    * The string that represents the format that this data source provider uses. This is
-   * overridden by children to provide a nice alias for the data source,
-   * ex: override def format(): String = "parquet"
+   * overridden by children to provide a nice alias for the data source. For example:
+   *
+   * {{{
+   *   override def format(): String = "parquet"
+   * }}}
+   *
+   * @since 1.5.0
    */
-  def format(): String
+  def shortName(): String
 }
 
 /**
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/CompatibilityTest.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
similarity index 93%
rename from sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/CompatibilityTest.java
rename to sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
index daec65a5bbe57..70dec1a9d3c92 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/CompatibilityTest.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
@@ -3,7 +3,7 @@
  * 
  * DO NOT EDIT DIRECTLY
  */
-package org.apache.spark.sql.parquet.test.avro;
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;
 
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
@@ -12,6 +12,6 @@ public interface CompatibilityTest {
 
   @SuppressWarnings("all")
   public interface Callback extends CompatibilityTest {
-    public static final org.apache.avro.Protocol PROTOCOL = org.apache.spark.sql.parquet.test.avro.CompatibilityTest.PROTOCOL;
+    public static final org.apache.avro.Protocol PROTOCOL = org.apache.spark.sql.execution.datasources.parquet.test.avro.CompatibilityTest.PROTOCOL;
   }
 }
\ No newline at end of file
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/Nested.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
similarity index 78%
rename from sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/Nested.java
rename to sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
index 051f1ee903863..a0a406bcd10c1 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/Nested.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
@@ -3,7 +3,7 @@
  * 
  * DO NOT EDIT DIRECTLY
  */
-package org.apache.spark.sql.parquet.test.avro;  
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public class Nested extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
@@ -77,18 +77,18 @@ public void setNestedStringColumn(java.lang.String value) {
   }
 
   /** Creates a new Nested RecordBuilder */
-  public static org.apache.spark.sql.parquet.test.avro.Nested.Builder newBuilder() {
-    return new org.apache.spark.sql.parquet.test.avro.Nested.Builder();
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder();
   }
   
   /** Creates a new Nested RecordBuilder by copying an existing Builder */
-  public static org.apache.spark.sql.parquet.test.avro.Nested.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.Nested.Builder other) {
-    return new org.apache.spark.sql.parquet.test.avro.Nested.Builder(other);
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder(other);
   }
   
   /** Creates a new Nested RecordBuilder by copying an existing Nested instance */
-  public static org.apache.spark.sql.parquet.test.avro.Nested.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.Nested other) {
-    return new org.apache.spark.sql.parquet.test.avro.Nested.Builder(other);
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder(other);
   }
   
   /**
@@ -102,11 +102,11 @@ public static class Builder extends org.apache.avro.specific.SpecificRecordBuild
 
     /** Creates a new Builder */
     private Builder() {
-      super(org.apache.spark.sql.parquet.test.avro.Nested.SCHEMA$);
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.SCHEMA$);
     }
     
     /** Creates a Builder by copying an existing Builder */
-    private Builder(org.apache.spark.sql.parquet.test.avro.Nested.Builder other) {
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder other) {
       super(other);
       if (isValidValue(fields()[0], other.nested_ints_column)) {
         this.nested_ints_column = data().deepCopy(fields()[0].schema(), other.nested_ints_column);
@@ -119,8 +119,8 @@ private Builder(org.apache.spark.sql.parquet.test.avro.Nested.Builder other) {
     }
     
     /** Creates a Builder by copying an existing Nested instance */
-    private Builder(org.apache.spark.sql.parquet.test.avro.Nested other) {
-            super(org.apache.spark.sql.parquet.test.avro.Nested.SCHEMA$);
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.SCHEMA$);
       if (isValidValue(fields()[0], other.nested_ints_column)) {
         this.nested_ints_column = data().deepCopy(fields()[0].schema(), other.nested_ints_column);
         fieldSetFlags()[0] = true;
@@ -137,7 +137,7 @@ public java.util.List<java.lang.Integer> getNestedIntsColumn() {
     }
     
     /** Sets the value of the 'nested_ints_column' field */
-    public org.apache.spark.sql.parquet.test.avro.Nested.Builder setNestedIntsColumn(java.util.List<java.lang.Integer> value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder setNestedIntsColumn(java.util.List<java.lang.Integer> value) {
       validate(fields()[0], value);
       this.nested_ints_column = value;
       fieldSetFlags()[0] = true;
@@ -150,7 +150,7 @@ public boolean hasNestedIntsColumn() {
     }
     
     /** Clears the value of the 'nested_ints_column' field */
-    public org.apache.spark.sql.parquet.test.avro.Nested.Builder clearNestedIntsColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder clearNestedIntsColumn() {
       nested_ints_column = null;
       fieldSetFlags()[0] = false;
       return this;
@@ -162,7 +162,7 @@ public java.lang.String getNestedStringColumn() {
     }
     
     /** Sets the value of the 'nested_string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.Nested.Builder setNestedStringColumn(java.lang.String value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder setNestedStringColumn(java.lang.String value) {
       validate(fields()[1], value);
       this.nested_string_column = value;
       fieldSetFlags()[1] = true;
@@ -175,7 +175,7 @@ public boolean hasNestedStringColumn() {
     }
     
     /** Clears the value of the 'nested_string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.Nested.Builder clearNestedStringColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested.Builder clearNestedStringColumn() {
       nested_string_column = null;
       fieldSetFlags()[1] = false;
       return this;
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/ParquetAvroCompat.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
similarity index 83%
rename from sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/ParquetAvroCompat.java
rename to sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
index 354c9d73cca31..6198b00b1e3ca 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/parquet/test/avro/ParquetAvroCompat.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
@@ -3,7 +3,7 @@
  * 
  * DO NOT EDIT DIRECTLY
  */
-package org.apache.spark.sql.parquet.test.avro;  
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public class ParquetAvroCompat extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
@@ -25,7 +25,7 @@ public class ParquetAvroCompat extends org.apache.avro.specific.SpecificRecordBa
   @Deprecated public java.lang.String maybe_string_column;
   @Deprecated public java.util.List<java.lang.String> strings_column;
   @Deprecated public java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column;
-  @Deprecated public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> complex_column;
+  @Deprecated public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column;
 
   /**
    * Default constructor.  Note that this does not initialize fields
@@ -37,7 +37,7 @@ public ParquetAvroCompat() {}
   /**
    * All-args constructor.
    */
-  public ParquetAvroCompat(java.lang.Boolean bool_column, java.lang.Integer int_column, java.lang.Long long_column, java.lang.Float float_column, java.lang.Double double_column, java.nio.ByteBuffer binary_column, java.lang.String string_column, java.lang.Boolean maybe_bool_column, java.lang.Integer maybe_int_column, java.lang.Long maybe_long_column, java.lang.Float maybe_float_column, java.lang.Double maybe_double_column, java.nio.ByteBuffer maybe_binary_column, java.lang.String maybe_string_column, java.util.List<java.lang.String> strings_column, java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column, java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> complex_column) {
+  public ParquetAvroCompat(java.lang.Boolean bool_column, java.lang.Integer int_column, java.lang.Long long_column, java.lang.Float float_column, java.lang.Double double_column, java.nio.ByteBuffer binary_column, java.lang.String string_column, java.lang.Boolean maybe_bool_column, java.lang.Integer maybe_int_column, java.lang.Long maybe_long_column, java.lang.Float maybe_float_column, java.lang.Double maybe_double_column, java.nio.ByteBuffer maybe_binary_column, java.lang.String maybe_string_column, java.util.List<java.lang.String> strings_column, java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column, java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column) {
     this.bool_column = bool_column;
     this.int_column = int_column;
     this.long_column = long_column;
@@ -101,7 +101,7 @@ public void put(int field$, java.lang.Object value$) {
     case 13: maybe_string_column = (java.lang.String)value$; break;
     case 14: strings_column = (java.util.List<java.lang.String>)value$; break;
     case 15: string_to_int_column = (java.util.Map<java.lang.String,java.lang.Integer>)value$; break;
-    case 16: complex_column = (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>>)value$; break;
+    case 16: complex_column = (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>)value$; break;
     default: throw new org.apache.avro.AvroRuntimeException("Bad index");
     }
   }
@@ -349,7 +349,7 @@ public void setStringToIntColumn(java.util.Map<java.lang.String,java.lang.Intege
   /**
    * Gets the value of the 'complex_column' field.
    */
-  public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> getComplexColumn() {
+  public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> getComplexColumn() {
     return complex_column;
   }
 
@@ -357,23 +357,23 @@ public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parque
    * Sets the value of the 'complex_column' field.
    * @param value the value to set.
    */
-  public void setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> value) {
+  public void setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> value) {
     this.complex_column = value;
   }
 
   /** Creates a new ParquetAvroCompat RecordBuilder */
-  public static org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder newBuilder() {
-    return new org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder();
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder();
   }
   
   /** Creates a new ParquetAvroCompat RecordBuilder by copying an existing Builder */
-  public static org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder other) {
-    return new org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder(other);
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder(other);
   }
   
   /** Creates a new ParquetAvroCompat RecordBuilder by copying an existing ParquetAvroCompat instance */
-  public static org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder newBuilder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat other) {
-    return new org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder(other);
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder(other);
   }
   
   /**
@@ -398,15 +398,15 @@ public static class Builder extends org.apache.avro.specific.SpecificRecordBuild
     private java.lang.String maybe_string_column;
     private java.util.List<java.lang.String> strings_column;
     private java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column;
-    private java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> complex_column;
+    private java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column;
 
     /** Creates a new Builder */
     private Builder() {
-      super(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
     }
     
     /** Creates a Builder by copying an existing Builder */
-    private Builder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder other) {
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder other) {
       super(other);
       if (isValidValue(fields()[0], other.bool_column)) {
         this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
@@ -479,8 +479,8 @@ private Builder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder
     }
     
     /** Creates a Builder by copying an existing ParquetAvroCompat instance */
-    private Builder(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat other) {
-            super(org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
       if (isValidValue(fields()[0], other.bool_column)) {
         this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
         fieldSetFlags()[0] = true;
@@ -557,7 +557,7 @@ public java.lang.Boolean getBoolColumn() {
     }
     
     /** Sets the value of the 'bool_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setBoolColumn(boolean value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setBoolColumn(boolean value) {
       validate(fields()[0], value);
       this.bool_column = value;
       fieldSetFlags()[0] = true;
@@ -570,7 +570,7 @@ public boolean hasBoolColumn() {
     }
     
     /** Clears the value of the 'bool_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearBoolColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearBoolColumn() {
       fieldSetFlags()[0] = false;
       return this;
     }
@@ -581,7 +581,7 @@ public java.lang.Integer getIntColumn() {
     }
     
     /** Sets the value of the 'int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setIntColumn(int value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setIntColumn(int value) {
       validate(fields()[1], value);
       this.int_column = value;
       fieldSetFlags()[1] = true;
@@ -594,7 +594,7 @@ public boolean hasIntColumn() {
     }
     
     /** Clears the value of the 'int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearIntColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearIntColumn() {
       fieldSetFlags()[1] = false;
       return this;
     }
@@ -605,7 +605,7 @@ public java.lang.Long getLongColumn() {
     }
     
     /** Sets the value of the 'long_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setLongColumn(long value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setLongColumn(long value) {
       validate(fields()[2], value);
       this.long_column = value;
       fieldSetFlags()[2] = true;
@@ -618,7 +618,7 @@ public boolean hasLongColumn() {
     }
     
     /** Clears the value of the 'long_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearLongColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearLongColumn() {
       fieldSetFlags()[2] = false;
       return this;
     }
@@ -629,7 +629,7 @@ public java.lang.Float getFloatColumn() {
     }
     
     /** Sets the value of the 'float_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setFloatColumn(float value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setFloatColumn(float value) {
       validate(fields()[3], value);
       this.float_column = value;
       fieldSetFlags()[3] = true;
@@ -642,7 +642,7 @@ public boolean hasFloatColumn() {
     }
     
     /** Clears the value of the 'float_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearFloatColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearFloatColumn() {
       fieldSetFlags()[3] = false;
       return this;
     }
@@ -653,7 +653,7 @@ public java.lang.Double getDoubleColumn() {
     }
     
     /** Sets the value of the 'double_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setDoubleColumn(double value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setDoubleColumn(double value) {
       validate(fields()[4], value);
       this.double_column = value;
       fieldSetFlags()[4] = true;
@@ -666,7 +666,7 @@ public boolean hasDoubleColumn() {
     }
     
     /** Clears the value of the 'double_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearDoubleColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearDoubleColumn() {
       fieldSetFlags()[4] = false;
       return this;
     }
@@ -677,7 +677,7 @@ public java.nio.ByteBuffer getBinaryColumn() {
     }
     
     /** Sets the value of the 'binary_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setBinaryColumn(java.nio.ByteBuffer value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setBinaryColumn(java.nio.ByteBuffer value) {
       validate(fields()[5], value);
       this.binary_column = value;
       fieldSetFlags()[5] = true;
@@ -690,7 +690,7 @@ public boolean hasBinaryColumn() {
     }
     
     /** Clears the value of the 'binary_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearBinaryColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearBinaryColumn() {
       binary_column = null;
       fieldSetFlags()[5] = false;
       return this;
@@ -702,7 +702,7 @@ public java.lang.String getStringColumn() {
     }
     
     /** Sets the value of the 'string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setStringColumn(java.lang.String value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringColumn(java.lang.String value) {
       validate(fields()[6], value);
       this.string_column = value;
       fieldSetFlags()[6] = true;
@@ -715,7 +715,7 @@ public boolean hasStringColumn() {
     }
     
     /** Clears the value of the 'string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearStringColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringColumn() {
       string_column = null;
       fieldSetFlags()[6] = false;
       return this;
@@ -727,7 +727,7 @@ public java.lang.Boolean getMaybeBoolColumn() {
     }
     
     /** Sets the value of the 'maybe_bool_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBoolColumn(java.lang.Boolean value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBoolColumn(java.lang.Boolean value) {
       validate(fields()[7], value);
       this.maybe_bool_column = value;
       fieldSetFlags()[7] = true;
@@ -740,7 +740,7 @@ public boolean hasMaybeBoolColumn() {
     }
     
     /** Clears the value of the 'maybe_bool_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBoolColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBoolColumn() {
       maybe_bool_column = null;
       fieldSetFlags()[7] = false;
       return this;
@@ -752,7 +752,7 @@ public java.lang.Integer getMaybeIntColumn() {
     }
     
     /** Sets the value of the 'maybe_int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeIntColumn(java.lang.Integer value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeIntColumn(java.lang.Integer value) {
       validate(fields()[8], value);
       this.maybe_int_column = value;
       fieldSetFlags()[8] = true;
@@ -765,7 +765,7 @@ public boolean hasMaybeIntColumn() {
     }
     
     /** Clears the value of the 'maybe_int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeIntColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeIntColumn() {
       maybe_int_column = null;
       fieldSetFlags()[8] = false;
       return this;
@@ -777,7 +777,7 @@ public java.lang.Long getMaybeLongColumn() {
     }
     
     /** Sets the value of the 'maybe_long_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeLongColumn(java.lang.Long value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeLongColumn(java.lang.Long value) {
       validate(fields()[9], value);
       this.maybe_long_column = value;
       fieldSetFlags()[9] = true;
@@ -790,7 +790,7 @@ public boolean hasMaybeLongColumn() {
     }
     
     /** Clears the value of the 'maybe_long_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeLongColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeLongColumn() {
       maybe_long_column = null;
       fieldSetFlags()[9] = false;
       return this;
@@ -802,7 +802,7 @@ public java.lang.Float getMaybeFloatColumn() {
     }
     
     /** Sets the value of the 'maybe_float_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeFloatColumn(java.lang.Float value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeFloatColumn(java.lang.Float value) {
       validate(fields()[10], value);
       this.maybe_float_column = value;
       fieldSetFlags()[10] = true;
@@ -815,7 +815,7 @@ public boolean hasMaybeFloatColumn() {
     }
     
     /** Clears the value of the 'maybe_float_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeFloatColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeFloatColumn() {
       maybe_float_column = null;
       fieldSetFlags()[10] = false;
       return this;
@@ -827,7 +827,7 @@ public java.lang.Double getMaybeDoubleColumn() {
     }
     
     /** Sets the value of the 'maybe_double_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeDoubleColumn(java.lang.Double value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeDoubleColumn(java.lang.Double value) {
       validate(fields()[11], value);
       this.maybe_double_column = value;
       fieldSetFlags()[11] = true;
@@ -840,7 +840,7 @@ public boolean hasMaybeDoubleColumn() {
     }
     
     /** Clears the value of the 'maybe_double_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeDoubleColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeDoubleColumn() {
       maybe_double_column = null;
       fieldSetFlags()[11] = false;
       return this;
@@ -852,7 +852,7 @@ public java.nio.ByteBuffer getMaybeBinaryColumn() {
     }
     
     /** Sets the value of the 'maybe_binary_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBinaryColumn(java.nio.ByteBuffer value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBinaryColumn(java.nio.ByteBuffer value) {
       validate(fields()[12], value);
       this.maybe_binary_column = value;
       fieldSetFlags()[12] = true;
@@ -865,7 +865,7 @@ public boolean hasMaybeBinaryColumn() {
     }
     
     /** Clears the value of the 'maybe_binary_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBinaryColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBinaryColumn() {
       maybe_binary_column = null;
       fieldSetFlags()[12] = false;
       return this;
@@ -877,7 +877,7 @@ public java.lang.String getMaybeStringColumn() {
     }
     
     /** Sets the value of the 'maybe_string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setMaybeStringColumn(java.lang.String value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeStringColumn(java.lang.String value) {
       validate(fields()[13], value);
       this.maybe_string_column = value;
       fieldSetFlags()[13] = true;
@@ -890,7 +890,7 @@ public boolean hasMaybeStringColumn() {
     }
     
     /** Clears the value of the 'maybe_string_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeStringColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeStringColumn() {
       maybe_string_column = null;
       fieldSetFlags()[13] = false;
       return this;
@@ -902,7 +902,7 @@ public java.util.List<java.lang.String> getStringsColumn() {
     }
     
     /** Sets the value of the 'strings_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setStringsColumn(java.util.List<java.lang.String> value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringsColumn(java.util.List<java.lang.String> value) {
       validate(fields()[14], value);
       this.strings_column = value;
       fieldSetFlags()[14] = true;
@@ -915,7 +915,7 @@ public boolean hasStringsColumn() {
     }
     
     /** Clears the value of the 'strings_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearStringsColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringsColumn() {
       strings_column = null;
       fieldSetFlags()[14] = false;
       return this;
@@ -927,7 +927,7 @@ public java.util.Map<java.lang.String,java.lang.Integer> getStringToIntColumn()
     }
     
     /** Sets the value of the 'string_to_int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setStringToIntColumn(java.util.Map<java.lang.String,java.lang.Integer> value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringToIntColumn(java.util.Map<java.lang.String,java.lang.Integer> value) {
       validate(fields()[15], value);
       this.string_to_int_column = value;
       fieldSetFlags()[15] = true;
@@ -940,19 +940,19 @@ public boolean hasStringToIntColumn() {
     }
     
     /** Clears the value of the 'string_to_int_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearStringToIntColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringToIntColumn() {
       string_to_int_column = null;
       fieldSetFlags()[15] = false;
       return this;
     }
 
     /** Gets the value of the 'complex_column' field */
-    public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> getComplexColumn() {
+    public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> getComplexColumn() {
       return complex_column;
     }
     
     /** Sets the value of the 'complex_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>> value) {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> value) {
       validate(fields()[16], value);
       this.complex_column = value;
       fieldSetFlags()[16] = true;
@@ -965,7 +965,7 @@ public boolean hasComplexColumn() {
     }
     
     /** Clears the value of the 'complex_column' field */
-    public org.apache.spark.sql.parquet.test.avro.ParquetAvroCompat.Builder clearComplexColumn() {
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearComplexColumn() {
       complex_column = null;
       fieldSetFlags()[16] = false;
       return this;
@@ -991,7 +991,7 @@ public ParquetAvroCompat build() {
         record.maybe_string_column = fieldSetFlags()[13] ? this.maybe_string_column : (java.lang.String) defaultValue(fields()[13]);
         record.strings_column = fieldSetFlags()[14] ? this.strings_column : (java.util.List<java.lang.String>) defaultValue(fields()[14]);
         record.string_to_int_column = fieldSetFlags()[15] ? this.string_to_int_column : (java.util.Map<java.lang.String,java.lang.Integer>) defaultValue(fields()[15]);
-        record.complex_column = fieldSetFlags()[16] ? this.complex_column : (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.parquet.test.avro.Nested>>) defaultValue(fields()[16]);
+        record.complex_column = fieldSetFlags()[16] ? this.complex_column : (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>) defaultValue(fields()[16]);
         return record;
       } catch (Exception e) {
         throw new org.apache.avro.AvroRuntimeException(e);
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index c49f256be5501..adbd95197d7ca 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -25,8 +25,8 @@ import scala.util.Random
 import org.apache.spark.sql.catalyst.plans.logical.OneRowRelation
 import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.json.JSONRelation
-import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.execution.datasources.json.JSONRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, SQLTestUtils}
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
index 92022ff23d2c3..73d5621897819 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import java.io.{File, StringWriter}
 import java.sql.{Date, Timestamp}
@@ -28,7 +28,7 @@ import org.apache.spark.sql.{SQLContext, QueryTest, Row, SQLConf}
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.execution.datasources.{ResolvedDataSource, LogicalRelation}
-import org.apache.spark.sql.json.InferSchema.compatibleType
+import org.apache.spark.sql.execution.datasources.json.InferSchema.compatibleType
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.util.Utils
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
index 369df5653060b..6b62c9a003df6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.json
+package org.apache.spark.sql.execution.datasources.json
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SQLContext
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
similarity index 96%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetAvroCompatibilitySuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index bfa427349ff6a..4d9c07bb7a570 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.nio.ByteBuffer
 import java.util.{List => JList, Map => JMap}
@@ -25,7 +25,7 @@ import scala.collection.JavaConversions._
 import org.apache.hadoop.fs.Path
 import org.apache.parquet.avro.AvroParquetWriter
 
-import org.apache.spark.sql.parquet.test.avro.{Nested, ParquetAvroCompat}
+import org.apache.spark.sql.execution.datasources.parquet.test.avro.{Nested, ParquetAvroCompat}
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.{Row, SQLContext}
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
similarity index 97%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index 57478931cd509..68f35b1f3aa83 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 import java.io.File
 
 import scala.collection.JavaConversions._
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
index b6a7c4fbddbdc..7dd9680d8cd65 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetFilterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import org.apache.parquet.filter2.predicate.Operators._
 import org.apache.parquet.filter2.predicate.{FilterPredicate, Operators}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index b415da5b8c136..ee925afe08508 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
@@ -373,7 +373,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest {
     // _temporary should be missing if direct output committer works.
     try {
       configuration.set("spark.sql.parquet.output.committer.class",
-        "org.apache.spark.sql.parquet.DirectParquetOutputCommitter")
+        classOf[DirectParquetOutputCommitter].getCanonicalName)
       sqlContext.udf.register("div0", (x: Int) => x / 0)
       withTempPath { dir =>
         intercept[org.apache.spark.SparkException] {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
index 2eef10189f11c..73152de244759 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetPartitionDiscoverySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.File
 import java.math.BigInteger
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index 5c65a8ec57f00..5e6d9c1cd44a8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.File
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 4a0b3b60f419d..8f06de7ce7c4f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
similarity index 98%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
index 64e94056f209a..3c6e54db4bca7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.File
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
similarity index 98%
rename from sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
index 1c532d78790d2..92b1d822172d5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/parquet/ParquetThriftCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.parquet
+package org.apache.spark.sql.execution.datasources.parquet
 
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.{Row, SQLContext}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
similarity index 92%
rename from sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
index d22160f5384f4..953284c98b208 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/metric/SQLMetricsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
@@ -15,7 +15,7 @@
 * limitations under the License.
 */
 
-package org.apache.spark.sql.metric
+package org.apache.spark.sql.execution.metric
 
 import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
 
@@ -41,16 +41,6 @@ class SQLMetricsSuite extends SparkFunSuite {
     }
   }
 
-  test("IntSQLMetric should not box Int") {
-    val l = SQLMetrics.createIntMetric(TestSQLContext.sparkContext, "Int")
-    val f = () => { l += 1 }
-    BoxingFinder.getClassReader(f.getClass).foreach { cl =>
-      val boxingFinder = new BoxingFinder()
-      cl.accept(boxingFinder, 0)
-      assert(boxingFinder.boxingInvokes.isEmpty, s"Found boxing: ${boxingFinder.boxingInvokes}")
-    }
-  }
-
   test("Normal accumulator should do boxing") {
     // We need this test to make sure BoxingFinder works.
     val l = TestSQLContext.sparkContext.accumulator(0L)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
similarity index 99%
rename from sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
index 69a561e16aa17..41dd1896c15df 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ui/SQLListenerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
@@ -15,13 +15,13 @@
  * limitations under the License.
  */
 
-package org.apache.spark.sql.ui
+package org.apache.spark.sql.execution.ui
 
 import java.util.Properties
 
 import org.apache.spark.{SparkException, SparkContext, SparkConf, SparkFunSuite}
 import org.apache.spark.executor.TaskMetrics
-import org.apache.spark.sql.metric.LongSQLMetricValue
+import org.apache.spark.sql.execution.metric.LongSQLMetricValue
 import org.apache.spark.scheduler._
 import org.apache.spark.sql.{DataFrame, SQLContext}
 import org.apache.spark.sql.execution.SQLExecution
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
index 1907e643c85dd..562c279067048 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
@@ -51,7 +51,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
     sql(
       s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -75,7 +75,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
       sql(
         s"""
            |CREATE TEMPORARY TABLE jsonTable
-           |USING org.apache.spark.sql.json.DefaultSource
+           |USING json
            |OPTIONS (
            |  path '${path.toString}'
            |) AS
@@ -92,7 +92,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
     sql(
       s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -107,7 +107,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
       sql(
         s"""
         |CREATE TEMPORARY TABLE IF NOT EXISTS jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -122,7 +122,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
     sql(
       s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -139,7 +139,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
     sql(
       s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -158,7 +158,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
       sql(
         s"""
         |CREATE TEMPORARY TABLE IF NOT EXISTS jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -175,7 +175,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
       sql(
         s"""
         |CREATE TEMPORARY TABLE jsonTable (a int, b string)
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -188,7 +188,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
     sql(
       s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
@@ -199,7 +199,7 @@ class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
       sql(
         s"""
         |CREATE TEMPORARY TABLE jsonTable
-        |USING org.apache.spark.sql.json.DefaultSource
+        |USING json
         |OPTIONS (
         |  path '${path.toString}'
         |) AS
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
index 1a4d41b02ca68..392da0b0826b5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
@@ -20,9 +20,37 @@ package org.apache.spark.sql.sources
 import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 
+
+// please note that the META-INF/services had to be modified for the test directory for this to work
+class DDLSourceLoadSuite extends DataSourceTest {
+
+  test("data sources with the same name") {
+    intercept[RuntimeException] {
+      caseInsensitiveContext.read.format("Fluet da Bomb").load()
+    }
+  }
+
+  test("load data source from format alias") {
+    caseInsensitiveContext.read.format("gathering quorum").load().schema ==
+      StructType(Seq(StructField("stringType", StringType, nullable = false)))
+  }
+
+  test("specify full classname with duplicate formats") {
+    caseInsensitiveContext.read.format("org.apache.spark.sql.sources.FakeSourceOne")
+      .load().schema == StructType(Seq(StructField("stringType", StringType, nullable = false)))
+  }
+
+  test("should fail to load ORC without HiveContext") {
+    intercept[ClassNotFoundException] {
+      caseInsensitiveContext.read.format("orc").load()
+    }
+  }
+}
+
+
 class FakeSourceOne extends RelationProvider with DataSourceRegister {
 
-  def format(): String = "Fluet da Bomb"
+  def shortName(): String = "Fluet da Bomb"
 
   override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
     new BaseRelation {
@@ -35,7 +63,7 @@ class FakeSourceOne extends RelationProvider with DataSourceRegister {
 
 class FakeSourceTwo extends RelationProvider  with DataSourceRegister {
 
-  def format(): String = "Fluet da Bomb"
+  def shortName(): String = "Fluet da Bomb"
 
   override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
     new BaseRelation {
@@ -48,7 +76,7 @@ class FakeSourceTwo extends RelationProvider  with DataSourceRegister {
 
 class FakeSourceThree extends RelationProvider with DataSourceRegister {
 
-  def format(): String = "gathering quorum"
+  def shortName(): String = "gathering quorum"
 
   override def createRelation(cont: SQLContext, param: Map[String, String]): BaseRelation =
     new BaseRelation {
@@ -58,28 +86,3 @@ class FakeSourceThree extends RelationProvider with DataSourceRegister {
         StructType(Seq(StructField("stringType", StringType, nullable = false)))
     }
 }
-// please note that the META-INF/services had to be modified for the test directory for this to work
-class DDLSourceLoadSuite extends DataSourceTest {
-
-  test("data sources with the same name") {
-    intercept[RuntimeException] {
-      caseInsensitiveContext.read.format("Fluet da Bomb").load()
-    }
-  }
-
-  test("load data source from format alias") {
-    caseInsensitiveContext.read.format("gathering quorum").load().schema ==
-      StructType(Seq(StructField("stringType", StringType, nullable = false)))
-  }
-
-  test("specify full classname with duplicate formats") {
-    caseInsensitiveContext.read.format("org.apache.spark.sql.sources.FakeSourceOne")
-      .load().schema == StructType(Seq(StructField("stringType", StringType, nullable = false)))
-  }
-
-  test("Loading Orc") {
-    intercept[ClassNotFoundException] {
-      caseInsensitiveContext.read.format("orc").load()
-    }
-  }
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala
index 3cbf5467b253a..27d1cd92fca1a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/ResolvedDataSourceSuite.scala
@@ -22,14 +22,39 @@ import org.apache.spark.sql.execution.datasources.ResolvedDataSource
 
 class ResolvedDataSourceSuite extends SparkFunSuite {
 
-  test("builtin sources") {
-    assert(ResolvedDataSource.lookupDataSource("jdbc") ===
-      classOf[org.apache.spark.sql.jdbc.DefaultSource])
+  test("jdbc") {
+    assert(
+      ResolvedDataSource.lookupDataSource("jdbc") ===
+      classOf[org.apache.spark.sql.execution.datasources.jdbc.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.execution.datasources.jdbc") ===
+      classOf[org.apache.spark.sql.execution.datasources.jdbc.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.jdbc") ===
+        classOf[org.apache.spark.sql.execution.datasources.jdbc.DefaultSource])
+  }
 
-    assert(ResolvedDataSource.lookupDataSource("json") ===
-      classOf[org.apache.spark.sql.json.DefaultSource])
+  test("json") {
+    assert(
+      ResolvedDataSource.lookupDataSource("json") ===
+      classOf[org.apache.spark.sql.execution.datasources.json.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.execution.datasources.json") ===
+        classOf[org.apache.spark.sql.execution.datasources.json.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.json") ===
+        classOf[org.apache.spark.sql.execution.datasources.json.DefaultSource])
+  }
 
-    assert(ResolvedDataSource.lookupDataSource("parquet") ===
-      classOf[org.apache.spark.sql.parquet.DefaultSource])
+  test("parquet") {
+    assert(
+      ResolvedDataSource.lookupDataSource("parquet") ===
+      classOf[org.apache.spark.sql.execution.datasources.parquet.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.execution.datasources.parquet") ===
+        classOf[org.apache.spark.sql.execution.datasources.parquet.DefaultSource])
+    assert(
+      ResolvedDataSource.lookupDataSource("org.apache.spark.sql.parquet") ===
+        classOf[org.apache.spark.sql.execution.datasources.parquet.DefaultSource])
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 7198a32df4a02..ac9aaed19d566 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -41,7 +41,7 @@ import org.apache.spark.sql.catalyst.{InternalRow, SqlParser, TableIdentifier}
 import org.apache.spark.sql.execution.datasources
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource}
 import org.apache.spark.sql.hive.client._
-import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 0c344c63fde3f..9f4f8b5789afe 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -32,7 +32,6 @@ import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.rdd.{HadoopRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
@@ -49,9 +48,9 @@ import scala.collection.JavaConversions._
 
 private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
 
-  def format(): String = "orc"
+  override def shortName(): String = "orc"
 
-  def createRelation(
+  override def createRelation(
       sqlContext: SQLContext,
       paths: Array[String],
       dataSchema: Option[StructType],
@@ -144,7 +143,6 @@ private[orc] class OrcOutputWriter(
   }
 }
 
-@DeveloperApi
 private[sql] class OrcRelation(
     override val paths: Array[String],
     maybeDataSchema: Option[StructType],
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
index a45c2d957278f..1fa005d5f9a15 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.parquet.ParquetTest
+import org.apache.spark.sql.execution.datasources.parquet.ParquetTest
 import org.apache.spark.sql.{QueryTest, Row}
 
 case class Cases(lower: String, UPPER: String)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index b73d6665755d0..7f36a483a3965 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -32,7 +32,7 @@ import org.apache.spark.sql.hive.client.{HiveTable, ManagedTable}
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
-import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index f00d3754c364a..80eb9f122ad90 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.hive
 import org.apache.hadoop.hive.conf.HiveConf
 
 import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.parquet.ParquetCompatibilityTest
+import org.apache.spark.sql.execution.datasources.parquet.ParquetCompatibilityTest
 import org.apache.spark.sql.{Row, SQLConf, SQLContext}
 
 class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 2fa7ae3fa2e12..79a136ae6f619 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -30,7 +30,7 @@ import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.hive.{HiveContext, HiveQLDialect, MetastoreRelation}
-import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.CalendarInterval
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
index c4bc60086f6e1..50f02432dacce 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.hive.execution.HiveTableScan
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
-import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
index d280543a071d9..cb4cedddbfddd 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
@@ -23,12 +23,12 @@ import com.google.common.io.Files
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.{AnalysisException, SaveMode, parquet}
+import org.apache.spark.sql.{AnalysisException, SaveMode}
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 
 
 class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
-  override val dataSourceName: String = classOf[parquet.DefaultSource].getCanonicalName
+  override val dataSourceName: String = "parquet"
 
   import sqlContext._
   import sqlContext.implicits._
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
index 1813cc33226d1..48c37a1fa1022 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -53,7 +53,7 @@ class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
 
 class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String =
-    classOf[org.apache.spark.sql.json.DefaultSource].getCanonicalName
+    classOf[org.apache.spark.sql.execution.datasources.json.DefaultSource].getCanonicalName
 
   import sqlContext._
 

From fe2fb7fb7189d183a4273ad27514af4b6b461f26 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 10 Aug 2015 13:52:18 -0700
Subject: [PATCH 151/633] [SPARK-9620] [SQL] generated UnsafeProjection should
 support many columns or large exressions

Currently, generated UnsafeProjection can reach 64k byte code limit of Java. This patch will split the generated expressions into multiple functions, to avoid the limitation.

After this patch, we can work well with table that have up to 64k columns (hit max number of constants limit in Java), it should be enough in practice.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #8044 from davies/wider_table and squashes the following commits:

9192e6c [Davies Liu] fix generated safe projection
d1ef81a [Davies Liu] fix failed tests
737b3d3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into wider_table
ffcd132 [Davies Liu] address comments
1b95be4 [Davies Liu] put the generated class into sql package
77ed72d [Davies Liu] address comments
4518e17 [Davies Liu] Merge branch 'master' of github.com:apache/spark into wider_table
75ccd01 [Davies Liu] Merge branch 'master' of github.com:apache/spark into wider_table
495e932 [Davies Liu] support wider table with more than 1k columns for generated projections
---
 .../expressions/codegen/CodeGenerator.scala   |  48 ++++++-
 .../codegen/GenerateMutableProjection.scala   |  43 +-----
 .../codegen/GenerateSafeProjection.scala      |  52 ++------
 .../codegen/GenerateUnsafeProjection.scala    | 122 +++++++++---------
 .../codegen/GenerateUnsafeRowJoiner.scala     |   2 +-
 .../codegen/GeneratedProjectionSuite.scala    |  82 ++++++++++++
 6 files changed, 207 insertions(+), 142 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index 7b41c9a3f3b8e..c21f4d626a74e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions.codegen
 
 import scala.collection.mutable
+import scala.collection.mutable.ArrayBuffer
 import scala.language.existentials
 
 import com.google.common.cache.{CacheBuilder, CacheLoader}
@@ -265,6 +266,45 @@ class CodeGenContext {
   def isPrimitiveType(jt: String): Boolean = primitiveTypes.contains(jt)
 
   def isPrimitiveType(dt: DataType): Boolean = isPrimitiveType(javaType(dt))
+
+  /**
+   * Splits the generated code of expressions into multiple functions, because function has
+   * 64kb code size limit in JVM
+   *
+   * @param row the variable name of row that is used by expressions
+   */
+  def splitExpressions(row: String, expressions: Seq[String]): String = {
+    val blocks = new ArrayBuffer[String]()
+    val blockBuilder = new StringBuilder()
+    for (code <- expressions) {
+      // We can't know how many byte code will be generated, so use the number of bytes as limit
+      if (blockBuilder.length > 64 * 1000) {
+        blocks.append(blockBuilder.toString())
+        blockBuilder.clear()
+      }
+      blockBuilder.append(code)
+    }
+    blocks.append(blockBuilder.toString())
+
+    if (blocks.length == 1) {
+      // inline execution if only one block
+      blocks.head
+    } else {
+      val apply = freshName("apply")
+      val functions = blocks.zipWithIndex.map { case (body, i) =>
+        val name = s"${apply}_$i"
+        val code = s"""
+           |private void $name(InternalRow $row) {
+           |  $body
+           |}
+         """.stripMargin
+         addNewFunction(name, code)
+         name
+      }
+
+      functions.map(name => s"$name($row);").mkString("\n")
+    }
+  }
 }
 
 /**
@@ -289,15 +329,15 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   protected def declareMutableStates(ctx: CodeGenContext): String = {
     ctx.mutableStates.map { case (javaType, variableName, _) =>
       s"private $javaType $variableName;"
-    }.mkString
+    }.mkString("\n")
   }
 
   protected def initMutableStates(ctx: CodeGenContext): String = {
-    ctx.mutableStates.map(_._3).mkString
+    ctx.mutableStates.map(_._3).mkString("\n")
   }
 
   protected def declareAddedFunctions(ctx: CodeGenContext): String = {
-    ctx.addedFuntions.map { case (funcName, funcCode) => funcCode }.mkString
+    ctx.addedFuntions.map { case (funcName, funcCode) => funcCode }.mkString("\n")
   }
 
   /**
@@ -328,6 +368,8 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   private[this] def doCompile(code: String): GeneratedClass = {
     val evaluator = new ClassBodyEvaluator()
     evaluator.setParentClassLoader(getClass.getClassLoader)
+    // Cannot be under package codegen, or fail with java.lang.InstantiationException
+    evaluator.setClassName("org.apache.spark.sql.catalyst.expressions.GeneratedClass")
     evaluator.setDefaultImports(Array(
       classOf[PlatformDependent].getName,
       classOf[InternalRow].getName,
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index ac58423cd884d..b4d4df8934bd4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -40,7 +40,7 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
 
   protected def create(expressions: Seq[Expression]): (() => MutableProjection) = {
     val ctx = newCodeGenContext()
-    val projectionCode = expressions.zipWithIndex.map {
+    val projectionCodes = expressions.zipWithIndex.map {
       case (NoOp, _) => ""
       case (e, i) =>
         val evaluationCode = e.gen(ctx)
@@ -65,49 +65,21 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
           """
         }
     }
-    // collect projections into blocks as function has 64kb codesize limit in JVM
-    val projectionBlocks = new ArrayBuffer[String]()
-    val blockBuilder = new StringBuilder()
-    for (projection <- projectionCode) {
-      if (blockBuilder.length > 16 * 1000) {
-        projectionBlocks.append(blockBuilder.toString())
-        blockBuilder.clear()
-      }
-      blockBuilder.append(projection)
-    }
-    projectionBlocks.append(blockBuilder.toString())
-
-    val (projectionFuns, projectionCalls) = {
-      // inline execution if codesize limit was not broken
-      if (projectionBlocks.length == 1) {
-        ("", projectionBlocks.head)
-      } else {
-        (
-          projectionBlocks.zipWithIndex.map { case (body, i) =>
-            s"""
-               |private void apply$i(InternalRow i) {
-               |  $body
-               |}
-             """.stripMargin
-          }.mkString,
-          projectionBlocks.indices.map(i => s"apply$i(i);").mkString("\n")
-        )
-      }
-    }
+    val allProjections = ctx.splitExpressions("i", projectionCodes)
 
     val code = s"""
       public Object generate($exprType[] expr) {
-        return new SpecificProjection(expr);
+        return new SpecificMutableProjection(expr);
       }
 
-      class SpecificProjection extends ${classOf[BaseMutableProjection].getName} {
+      class SpecificMutableProjection extends ${classOf[BaseMutableProjection].getName} {
 
         private $exprType[] expressions;
         private $mutableRowType mutableRow;
         ${declareMutableStates(ctx)}
         ${declareAddedFunctions(ctx)}
 
-        public SpecificProjection($exprType[] expr) {
+        public SpecificMutableProjection($exprType[] expr) {
           expressions = expr;
           mutableRow = new $genericMutableRowType(${expressions.size});
           ${initMutableStates(ctx)}
@@ -123,12 +95,9 @@ object GenerateMutableProjection extends CodeGenerator[Seq[Expression], () => Mu
           return (InternalRow) mutableRow;
         }
 
-        $projectionFuns
-
         public Object apply(Object _i) {
           InternalRow i = (InternalRow) _i;
-          $projectionCalls
-
+          $allProjections
           return mutableRow;
         }
       }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
index ef08ddf041afc..7ad352d7ce3e9 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateSafeProjection.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
-import scala.collection.mutable.ArrayBuffer
-
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
 import org.apache.spark.sql.types._
@@ -43,6 +41,9 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
     val tmp = ctx.freshName("tmp")
     val output = ctx.freshName("safeRow")
     val values = ctx.freshName("values")
+    // These expressions could be splitted into multiple functions
+    ctx.addMutableState("Object[]", values, s"this.$values = null;")
+
     val rowClass = classOf[GenericInternalRow].getName
 
     val fieldWriters = schema.map(_.dataType).zipWithIndex.map { case (dt, i) =>
@@ -53,12 +54,12 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
           $values[$i] = ${converter.primitive};
         }
       """
-    }.mkString("\n")
-
+    }
+    val allFields = ctx.splitExpressions(tmp, fieldWriters)
     val code = s"""
       final InternalRow $tmp = $input;
-      final Object[] $values = new Object[${schema.length}];
-      $fieldWriters
+      this.$values = new Object[${schema.length}];
+      $allFields
       final InternalRow $output = new $rowClass($values);
     """
 
@@ -128,7 +129,7 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
 
   protected def create(expressions: Seq[Expression]): Projection = {
     val ctx = newCodeGenContext()
-    val projectionCode = expressions.zipWithIndex.map {
+    val expressionCodes = expressions.zipWithIndex.map {
       case (NoOp, _) => ""
       case (e, i) =>
         val evaluationCode = e.gen(ctx)
@@ -143,36 +144,7 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
             }
           """
     }
-    // collect projections into blocks as function has 64kb codesize limit in JVM
-    val projectionBlocks = new ArrayBuffer[String]()
-    val blockBuilder = new StringBuilder()
-    for (projection <- projectionCode) {
-      if (blockBuilder.length > 16 * 1000) {
-        projectionBlocks.append(blockBuilder.toString())
-        blockBuilder.clear()
-      }
-      blockBuilder.append(projection)
-    }
-    projectionBlocks.append(blockBuilder.toString())
-
-    val (projectionFuns, projectionCalls) = {
-      // inline it if we have only one block
-      if (projectionBlocks.length == 1) {
-        ("", projectionBlocks.head)
-      } else {
-        (
-          projectionBlocks.zipWithIndex.map { case (body, i) =>
-            s"""
-               |private void apply$i(InternalRow i) {
-               |  $body
-               |}
-             """.stripMargin
-          }.mkString,
-          projectionBlocks.indices.map(i => s"apply$i(i);").mkString("\n")
-          )
-      }
-    }
-
+    val allExpressions = ctx.splitExpressions("i", expressionCodes)
     val code = s"""
       public Object generate($exprType[] expr) {
         return new SpecificSafeProjection(expr);
@@ -183,6 +155,7 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
         private $exprType[] expressions;
         private $mutableRowType mutableRow;
         ${declareMutableStates(ctx)}
+        ${declareAddedFunctions(ctx)}
 
         public SpecificSafeProjection($exprType[] expr) {
           expressions = expr;
@@ -190,12 +163,9 @@ object GenerateSafeProjection extends CodeGenerator[Seq[Expression], Projection]
           ${initMutableStates(ctx)}
         }
 
-        $projectionFuns
-
         public Object apply(Object _i) {
           InternalRow i = (InternalRow) _i;
-          $projectionCalls
-
+          $allExpressions
           return mutableRow;
         }
       }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index d8912df694a10..29f6a7b981752 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -19,7 +19,6 @@ package org.apache.spark.sql.catalyst.expressions.codegen
 
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.PlatformDependent
 
 /**
  * Generates a [[Projection]] that returns an [[UnsafeRow]].
@@ -41,8 +40,6 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
   private val ArrayWriter = classOf[UnsafeRowWriters.ArrayWriter].getName
   private val MapWriter = classOf[UnsafeRowWriters.MapWriter].getName
 
-  private val PlatformDependent = classOf[PlatformDependent].getName
-
   /** Returns true iff we support this data type. */
   def canSupport(dataType: DataType): Boolean = dataType match {
     case NullType => true
@@ -56,19 +53,19 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
   def genAdditionalSize(dt: DataType, ev: GeneratedExpressionCode): String = dt match {
     case t: DecimalType if t.precision > Decimal.MAX_LONG_DIGITS =>
-      s" + $DecimalWriter.getSize(${ev.primitive})"
+      s"$DecimalWriter.getSize(${ev.primitive})"
     case StringType =>
-      s" + (${ev.isNull} ? 0 : $StringWriter.getSize(${ev.primitive}))"
+      s"${ev.isNull} ? 0 : $StringWriter.getSize(${ev.primitive})"
     case BinaryType =>
-      s" + (${ev.isNull} ? 0 : $BinaryWriter.getSize(${ev.primitive}))"
+      s"${ev.isNull} ? 0 : $BinaryWriter.getSize(${ev.primitive})"
     case CalendarIntervalType =>
-      s" + (${ev.isNull} ? 0 : 16)"
+      s"${ev.isNull} ? 0 : 16"
     case _: StructType =>
-      s" + (${ev.isNull} ? 0 : $StructWriter.getSize(${ev.primitive}))"
+      s"${ev.isNull} ? 0 : $StructWriter.getSize(${ev.primitive})"
     case _: ArrayType =>
-      s" + (${ev.isNull} ? 0 : $ArrayWriter.getSize(${ev.primitive}))"
+      s"${ev.isNull} ? 0 : $ArrayWriter.getSize(${ev.primitive})"
     case _: MapType =>
-      s" + (${ev.isNull} ? 0 : $MapWriter.getSize(${ev.primitive}))"
+      s"${ev.isNull} ? 0 : $MapWriter.getSize(${ev.primitive})"
     case _ => ""
   }
 
@@ -125,64 +122,69 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
    */
   private def createCodeForStruct(
       ctx: CodeGenContext,
+      row: String,
       inputs: Seq[GeneratedExpressionCode],
       inputTypes: Seq[DataType]): GeneratedExpressionCode = {
 
+    val fixedSize = 8 * inputTypes.length + UnsafeRow.calculateBitSetWidthInBytes(inputTypes.length)
+
     val output = ctx.freshName("convertedStruct")
-    ctx.addMutableState("UnsafeRow", output, s"$output = new UnsafeRow();")
+    ctx.addMutableState("UnsafeRow", output, s"this.$output = new UnsafeRow();")
     val buffer = ctx.freshName("buffer")
-    ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
-    val numBytes = ctx.freshName("numBytes")
+    ctx.addMutableState("byte[]", buffer, s"this.$buffer = new byte[$fixedSize];")
     val cursor = ctx.freshName("cursor")
+    ctx.addMutableState("int", cursor, s"this.$cursor = 0;")
+    val tmp = ctx.freshName("tmpBuffer")
 
-    val convertedFields = inputTypes.zip(inputs).map { case (dt, input) =>
-      createConvertCode(ctx, input, dt)
-    }
-
-    val fixedSize = 8 * inputTypes.length + UnsafeRow.calculateBitSetWidthInBytes(inputTypes.length)
-    val additionalSize = inputTypes.zip(convertedFields).map { case (dt, ev) =>
-      genAdditionalSize(dt, ev)
-    }.mkString("")
-
-    val fieldWriters = inputTypes.zip(convertedFields).zipWithIndex.map { case ((dt, ev), i) =>
-      val update = genFieldWriter(ctx, dt, ev, output, i, cursor)
-      if (dt.isInstanceOf[DecimalType]) {
-        // Can't call setNullAt() for DecimalType
+    val convertedFields = inputTypes.zip(inputs).zipWithIndex.map { case ((dt, input), i) =>
+      val ev = createConvertCode(ctx, input, dt)
+      val growBuffer = if (!UnsafeRow.isFixedLength(dt)) {
+        val numBytes = ctx.freshName("numBytes")
         s"""
+          int $numBytes = $cursor + (${genAdditionalSize(dt, ev)});
+          if ($buffer.length < $numBytes) {
+            // This will not happen frequently, because the buffer is re-used.
+            byte[] $tmp = new byte[$numBytes * 2];
+            PlatformDependent.copyMemory($buffer, PlatformDependent.BYTE_ARRAY_OFFSET,
+              $tmp, PlatformDependent.BYTE_ARRAY_OFFSET, $buffer.length);
+            $buffer = $tmp;
+          }
+          $output.pointTo($buffer, PlatformDependent.BYTE_ARRAY_OFFSET,
+            ${inputTypes.length}, $numBytes);
+         """
+      } else {
+        ""
+      }
+      val update = dt match {
+        case dt: DecimalType if dt.precision > Decimal.MAX_LONG_DIGITS =>
+          // Can't call setNullAt() for DecimalType
+          s"""
           if (${ev.isNull}) {
-           $cursor += $DecimalWriter.write($output, $i, $cursor, null);
+            $cursor += $DecimalWriter.write($output, $i, $cursor, null);
           } else {
-           $update;
+            ${genFieldWriter(ctx, dt, ev, output, i, cursor)};
           }
         """
-      } else {
-        s"""
+        case _ =>
+          s"""
           if (${ev.isNull}) {
             $output.setNullAt($i);
           } else {
-            $update;
+            ${genFieldWriter(ctx, dt, ev, output, i, cursor)};
           }
         """
       }
-    }.mkString("\n")
+      s"""
+        ${ev.code}
+        $growBuffer
+        $update
+      """
+    }
 
     val code = s"""
-      ${convertedFields.map(_.code).mkString("\n")}
-
-      final int $numBytes = $fixedSize $additionalSize;
-      if ($numBytes > $buffer.length) {
-        $buffer = new byte[$numBytes];
-      }
-
-      $output.pointTo(
-        $buffer,
-        $PlatformDependent.BYTE_ARRAY_OFFSET,
-        ${inputTypes.length},
-        $numBytes);
-
-      int $cursor = $fixedSize;
-
-      $fieldWriters
+      $cursor = $fixedSize;
+      $output.pointTo($buffer, PlatformDependent.BYTE_ARRAY_OFFSET, ${inputTypes.length}, $cursor);
+      ${ctx.splitExpressions(row, convertedFields)}
       """
     GeneratedExpressionCode(code, "false", output)
   }
@@ -265,17 +267,17 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
-          $PlatformDependent.UNSAFE.put${ctx.primitiveTypeName(elementType)}(
+          PlatformDependent.UNSAFE.put${ctx.primitiveTypeName(elementType)}(
             $buffer,
-            $PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
             ${convertedElement.primitive});
           $cursor += $elementSize;
         """
       case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
         s"""
-          $PlatformDependent.UNSAFE.putLong(
+          PlatformDependent.UNSAFE.putLong(
             $buffer,
-            $PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
             ${convertedElement.primitive}.toUnscaledLong());
           $cursor += 8;
         """
@@ -284,7 +286,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         s"""
           $cursor += $writer.write(
             $buffer,
-            $PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
             $elements[$index]);
         """
     }
@@ -318,14 +320,14 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           for (int $index = 0; $index < $numElements; $index++) {
             if ($checkNull) {
               // If element is null, write the negative value address into offset region.
-              $PlatformDependent.UNSAFE.putInt(
+              PlatformDependent.UNSAFE.putInt(
                 $buffer,
-                $PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
+                PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
                 -$cursor);
             } else {
-              $PlatformDependent.UNSAFE.putInt(
+              PlatformDependent.UNSAFE.putInt(
                 $buffer,
-                $PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
+                PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
                 $cursor);
 
               $writeElement
@@ -334,7 +336,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
           $output.pointTo(
             $buffer,
-            $PlatformDependent.BYTE_ARRAY_OFFSET,
+            PlatformDependent.BYTE_ARRAY_OFFSET,
             $numElements,
             $numBytes);
         }
@@ -400,7 +402,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         val fieldIsNull = s"$tmp.isNullAt($i)"
         GeneratedExpressionCode("", fieldIsNull, getFieldCode)
       }
-      val converter = createCodeForStruct(ctx, fieldEvals, fieldTypes)
+      val converter = createCodeForStruct(ctx, tmp, fieldEvals, fieldTypes)
       val code = s"""
         ${input.code}
          UnsafeRow $output = null;
@@ -427,7 +429,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
   def createCode(ctx: CodeGenContext, expressions: Seq[Expression]): GeneratedExpressionCode = {
     val exprEvals = expressions.map(e => e.gen(ctx))
     val exprTypes = expressions.map(_.dataType)
-    createCodeForStruct(ctx, exprEvals, exprTypes)
+    createCodeForStruct(ctx, "i", exprEvals, exprTypes)
   }
 
   protected def canonicalize(in: Seq[Expression]): Seq[Expression] =
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
index 30b51dd83fa9a..8aaa5b4300044 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
@@ -155,7 +155,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
            |$putLong(buf, $cursor, $getLong(buf, $cursor) + ($shift << 32));
          """.stripMargin
       }
-    }.mkString
+    }.mkString("\n")
 
     // ------------------------ Finally, put everything together  --------------------------- //
     val code = s"""
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala
new file mode 100644
index 0000000000000..8c7ee8720f7bb
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala
@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions.codegen
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.{StringType, IntegerType, StructField, StructType}
+import org.apache.spark.unsafe.types.UTF8String
+
+/**
+ * A test suite for generated projections
+ */
+class GeneratedProjectionSuite extends SparkFunSuite {
+
+  test("generated projections on wider table") {
+    val N = 1000
+    val wideRow1 = new GenericInternalRow((1 to N).toArray[Any])
+    val schema1 = StructType((1 to N).map(i => StructField("", IntegerType)))
+    val wideRow2 = new GenericInternalRow(
+      (1 to N).map(i => UTF8String.fromString(i.toString)).toArray[Any])
+    val schema2 = StructType((1 to N).map(i => StructField("", StringType)))
+    val joined = new JoinedRow(wideRow1, wideRow2)
+    val joinedSchema = StructType(schema1 ++ schema2)
+    val nested = new JoinedRow(InternalRow(joined, joined), joined)
+    val nestedSchema = StructType(
+      Seq(StructField("", joinedSchema), StructField("", joinedSchema)) ++ joinedSchema)
+
+    // test generated UnsafeProjection
+    val unsafeProj = UnsafeProjection.create(nestedSchema)
+    val unsafe: UnsafeRow = unsafeProj(nested)
+    (0 until N).foreach { i =>
+      val s = UTF8String.fromString((i + 1).toString)
+      assert(i + 1 === unsafe.getInt(i + 2))
+      assert(s === unsafe.getUTF8String(i + 2 + N))
+      assert(i + 1 === unsafe.getStruct(0, N * 2).getInt(i))
+      assert(s === unsafe.getStruct(0, N * 2).getUTF8String(i + N))
+      assert(i + 1 === unsafe.getStruct(1, N * 2).getInt(i))
+      assert(s === unsafe.getStruct(1, N * 2).getUTF8String(i + N))
+    }
+
+    // test generated SafeProjection
+    val safeProj = FromUnsafeProjection(nestedSchema)
+    val result = safeProj(unsafe)
+    // Can't compare GenericInternalRow with JoinedRow directly
+    (0 until N).foreach { i =>
+      val r = i + 1
+      val s = UTF8String.fromString((i + 1).toString)
+      assert(r === result.getInt(i + 2))
+      assert(s === result.getUTF8String(i + 2 + N))
+      assert(r === result.getStruct(0, N * 2).getInt(i))
+      assert(s === result.getStruct(0, N * 2).getUTF8String(i + N))
+      assert(r === result.getStruct(1, N * 2).getInt(i))
+      assert(s === result.getStruct(1, N * 2).getUTF8String(i + N))
+    }
+
+    // test generated MutableProjection
+    val exprs = nestedSchema.fields.zipWithIndex.map { case (f, i) =>
+      BoundReference(i, f.dataType, true)
+    }
+    val mutableProj = GenerateMutableProjection.generate(exprs)()
+    val row1 = mutableProj(result)
+    assert(result === row1)
+    val row2 = mutableProj(result)
+    assert(result === row2)
+  }
+}

From c4fd2a242228ee101904770446e3f37d49e39b76 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 10 Aug 2015 13:55:11 -0700
Subject: [PATCH 152/633] [SPARK-9759] [SQL] improve decimal.times() and
 cast(int, decimalType)

This patch optimize two things:

1. passing MathContext to JavaBigDecimal.multiply/divide/reminder to do right rounding, because java.math.BigDecimal.apply(MathContext) is expensive

2. Cast integer/short/byte to decimal directly (without double)

This two optimizations could speed up the end-to-end time of a aggregation (SUM(short * decimal(5, 2)) 75% (from 19s -> 10.8s)

Author: Davies Liu <davies@databricks.com>

Closes #8052 from davies/optimize_decimal and squashes the following commits:

225efad [Davies Liu] improve decimal.times() and cast(int, decimalType)
---
 .../spark/sql/catalyst/expressions/Cast.scala | 42 +++++++------------
 .../org/apache/spark/sql/types/Decimal.scala  | 12 +++---
 2 files changed, 22 insertions(+), 32 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 946c5a9c04f14..616b9e0e65b78 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -155,7 +155,7 @@ case class Cast(child: Expression, dataType: DataType)
     case ByteType =>
       buildCast[Byte](_, _ != 0)
     case DecimalType() =>
-      buildCast[Decimal](_, _ != Decimal.ZERO)
+      buildCast[Decimal](_, !_.isZero)
     case DoubleType =>
       buildCast[Double](_, _ != 0)
     case FloatType =>
@@ -315,13 +315,13 @@ case class Cast(child: Expression, dataType: DataType)
     case TimestampType =>
       // Note that we lose precision here.
       buildCast[Long](_, t => changePrecision(Decimal(timestampToDouble(t)), target))
-    case DecimalType() =>
+    case dt: DecimalType =>
       b => changePrecision(b.asInstanceOf[Decimal].clone(), target)
-    case LongType =>
-      b => changePrecision(Decimal(b.asInstanceOf[Long]), target)
-    case x: NumericType => // All other numeric types can be represented precisely as Doubles
+    case t: IntegralType =>
+      b => changePrecision(Decimal(t.integral.asInstanceOf[Integral[Any]].toLong(b)), target)
+    case x: FractionalType =>
       b => try {
-        changePrecision(Decimal(x.numeric.asInstanceOf[Numeric[Any]].toDouble(b)), target)
+        changePrecision(Decimal(x.fractional.asInstanceOf[Fractional[Any]].toDouble(b)), target)
       } catch {
         case _: NumberFormatException => null
       }
@@ -534,10 +534,7 @@ case class Cast(child: Expression, dataType: DataType)
         (c, evPrim, evNull) =>
           s"""
             try {
-              org.apache.spark.sql.types.Decimal tmpDecimal =
-                new org.apache.spark.sql.types.Decimal().set(
-                  new scala.math.BigDecimal(
-                    new java.math.BigDecimal($c.toString())));
+              Decimal tmpDecimal = Decimal.apply(new java.math.BigDecimal($c.toString()));
               ${changePrecision("tmpDecimal", target, evPrim, evNull)}
             } catch (java.lang.NumberFormatException e) {
               $evNull = true;
@@ -546,12 +543,7 @@ case class Cast(child: Expression, dataType: DataType)
       case BooleanType =>
         (c, evPrim, evNull) =>
           s"""
-            org.apache.spark.sql.types.Decimal tmpDecimal = null;
-            if ($c) {
-              tmpDecimal = new org.apache.spark.sql.types.Decimal().set(1);
-            } else {
-              tmpDecimal = new org.apache.spark.sql.types.Decimal().set(0);
-            }
+            Decimal tmpDecimal = $c ? Decimal.apply(1) : Decimal.apply(0);
             ${changePrecision("tmpDecimal", target, evPrim, evNull)}
           """
       case DateType =>
@@ -561,32 +553,28 @@ case class Cast(child: Expression, dataType: DataType)
         // Note that we lose precision here.
         (c, evPrim, evNull) =>
           s"""
-            org.apache.spark.sql.types.Decimal tmpDecimal =
-              new org.apache.spark.sql.types.Decimal().set(
-                scala.math.BigDecimal.valueOf(${timestampToDoubleCode(c)}));
+            Decimal tmpDecimal = Decimal.apply(
+              scala.math.BigDecimal.valueOf(${timestampToDoubleCode(c)}));
             ${changePrecision("tmpDecimal", target, evPrim, evNull)}
           """
       case DecimalType() =>
         (c, evPrim, evNull) =>
           s"""
-            org.apache.spark.sql.types.Decimal tmpDecimal = $c.clone();
+            Decimal tmpDecimal = $c.clone();
             ${changePrecision("tmpDecimal", target, evPrim, evNull)}
           """
-      case LongType =>
+      case x: IntegralType =>
         (c, evPrim, evNull) =>
           s"""
-            org.apache.spark.sql.types.Decimal tmpDecimal =
-              new org.apache.spark.sql.types.Decimal().set($c);
+            Decimal tmpDecimal = Decimal.apply((long) $c);
             ${changePrecision("tmpDecimal", target, evPrim, evNull)}
           """
-      case x: NumericType =>
+      case x: FractionalType =>
         // All other numeric types can be represented precisely as Doubles
         (c, evPrim, evNull) =>
           s"""
             try {
-              org.apache.spark.sql.types.Decimal tmpDecimal =
-                new org.apache.spark.sql.types.Decimal().set(
-                  scala.math.BigDecimal.valueOf((double) $c));
+              Decimal tmpDecimal = Decimal.apply(scala.math.BigDecimal.valueOf((double) $c));
               ${changePrecision("tmpDecimal", target, evPrim, evNull)}
             } catch (java.lang.NumberFormatException e) {
               $evNull = true;
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index 624c3f3d7fa97..d95805c24521c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -139,9 +139,9 @@ final class Decimal extends Ordered[Decimal] with Serializable {
 
   def toBigDecimal: BigDecimal = {
     if (decimalVal.ne(null)) {
-      decimalVal(MATH_CONTEXT)
+      decimalVal
     } else {
-      BigDecimal(longVal, _scale)(MATH_CONTEXT)
+      BigDecimal(longVal, _scale)
     }
   }
 
@@ -280,13 +280,15 @@ final class Decimal extends Ordered[Decimal] with Serializable {
   }
 
   // HiveTypeCoercion will take care of the precision, scale of result
-  def * (that: Decimal): Decimal = Decimal(toBigDecimal * that.toBigDecimal)
+  def * (that: Decimal): Decimal =
+    Decimal(toJavaBigDecimal.multiply(that.toJavaBigDecimal, MATH_CONTEXT))
 
   def / (that: Decimal): Decimal =
-    if (that.isZero) null else Decimal(toBigDecimal / that.toBigDecimal)
+    if (that.isZero) null else Decimal(toJavaBigDecimal.divide(that.toJavaBigDecimal, MATH_CONTEXT))
 
   def % (that: Decimal): Decimal =
-    if (that.isZero) null else Decimal(toBigDecimal % that.toBigDecimal)
+    if (that.isZero) null
+    else Decimal(toJavaBigDecimal.remainder(that.toJavaBigDecimal, MATH_CONTEXT))
 
   def remainder(that: Decimal): Decimal = this % that
 

From 853809e948e7c5092643587a30738115b6591a59 Mon Sep 17 00:00:00 2001
From: Prabeesh K <prabsmails@gmail.com>
Date: Mon, 10 Aug 2015 16:33:23 -0700
Subject: [PATCH 153/633] [SPARK-5155] [PYSPARK] [STREAMING] Mqtt streaming
 support in Python

This PR is based on #4229, thanks prabeesh.

Closes #4229

Author: Prabeesh K <prabsmails@gmail.com>
Author: zsxwing <zsxwing@gmail.com>
Author: prabs <prabsmails@gmail.com>
Author: Prabeesh K <prabeesh.k@namshi.com>

Closes #7833 from zsxwing/pr4229 and squashes the following commits:

9570bec [zsxwing] Fix the variable name and check null in finally
4a9c79e [zsxwing] Fix pom.xml indentation
abf5f18 [zsxwing] Merge branch 'master' into pr4229
935615c [zsxwing] Fix the flaky MQTT tests
47278c5 [zsxwing] Include the project class files
478f844 [zsxwing] Add unpack
5f8a1d4 [zsxwing] Make the maven build generate the test jar for Python MQTT tests
734db99 [zsxwing] Merge branch 'master' into pr4229
126608a [Prabeesh K] address the comments
b90b709 [Prabeesh K] Merge pull request #1 from zsxwing/pr4229
d07f454 [zsxwing] Register StreamingListerner before starting StreamingContext; Revert unncessary changes; fix the python unit test
a6747cb [Prabeesh K] wait for starting the receiver before publishing data
87fc677 [Prabeesh K] address the comments:
97244ec [zsxwing] Make sbt build the assembly test jar for streaming mqtt
80474d1 [Prabeesh K] fix
1f0cfe9 [Prabeesh K] python style fix
e1ee016 [Prabeesh K] scala style fix
a5a8f9f [Prabeesh K] added Python test
9767d82 [Prabeesh K] implemented Python-friendly class
a11968b [Prabeesh K] fixed python style
795ec27 [Prabeesh K] address comments
ee387ae [Prabeesh K] Fix assembly jar location of mqtt-assembly
3f4df12 [Prabeesh K] updated version
b34c3c1 [prabs] adress comments
3aa7fff [prabs] Added Python streaming mqtt word count example
b7d42ff [prabs] Mqtt streaming support in Python
---
 dev/run-tests.py                              |   2 +
 dev/sparktestsupport/modules.py               |   2 +
 docs/streaming-programming-guide.md           |   2 +-
 .../main/python/streaming/mqtt_wordcount.py   |  58 +++++++++
 external/mqtt-assembly/pom.xml                | 102 +++++++++++++++
 external/mqtt/pom.xml                         |  28 +++++
 external/mqtt/src/main/assembly/assembly.xml  |  44 +++++++
 .../spark/streaming/mqtt/MQTTUtils.scala      |  16 +++
 .../streaming/mqtt/MQTTStreamSuite.scala      | 118 +++---------------
 .../spark/streaming/mqtt/MQTTTestUtils.scala  | 111 ++++++++++++++++
 pom.xml                                       |   1 +
 project/SparkBuild.scala                      |  12 +-
 python/pyspark/streaming/mqtt.py              |  72 +++++++++++
 python/pyspark/streaming/tests.py             | 106 +++++++++++++++-
 14 files changed, 565 insertions(+), 109 deletions(-)
 create mode 100644 examples/src/main/python/streaming/mqtt_wordcount.py
 create mode 100644 external/mqtt-assembly/pom.xml
 create mode 100644 external/mqtt/src/main/assembly/assembly.xml
 create mode 100644 external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
 create mode 100644 python/pyspark/streaming/mqtt.py

diff --git a/dev/run-tests.py b/dev/run-tests.py
index d1852b95bb292..f689425ee40b6 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -303,6 +303,8 @@ def build_spark_sbt(hadoop_version):
                  "assembly/assembly",
                  "streaming-kafka-assembly/assembly",
                  "streaming-flume-assembly/assembly",
+                 "streaming-mqtt-assembly/assembly",
+                 "streaming-mqtt/test:assembly",
                  "streaming-kinesis-asl-assembly/assembly"]
     profiles_and_goals = build_profiles + sbt_goals
 
diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index a9717ff9569c7..d82c0cca37bc6 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -181,6 +181,7 @@ def contains_file(self, filename):
     dependencies=[streaming],
     source_file_regexes=[
         "external/mqtt",
+        "external/mqtt-assembly",
     ],
     sbt_test_goals=[
         "streaming-mqtt/test",
@@ -306,6 +307,7 @@ def contains_file(self, filename):
         streaming,
         streaming_kafka,
         streaming_flume_assembly,
+        streaming_mqtt,
         streaming_kinesis_asl
     ],
     source_file_regexes=[
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index dbfdb619f89e2..c59d936b43c88 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -683,7 +683,7 @@ for Java, and [StreamingContext](api/python/pyspark.streaming.html#pyspark.strea
 {:.no_toc}
 
 <span class="badge" style="background-color: grey">Python API</span> As of Spark {{site.SPARK_VERSION_SHORT}},
-out of these sources, *only* Kafka and Flume are available in the Python API. We will add more advanced sources in the Python API in future.
+out of these sources, *only* Kafka, Flume and MQTT are available in the Python API. We will add more advanced sources in the Python API in future.
 
 This category of sources require interfacing with external non-Spark libraries, some of them with
 complex dependencies (e.g., Kafka and Flume). Hence, to minimize issues related to version conflicts
diff --git a/examples/src/main/python/streaming/mqtt_wordcount.py b/examples/src/main/python/streaming/mqtt_wordcount.py
new file mode 100644
index 0000000000000..617ce5ea6775e
--- /dev/null
+++ b/examples/src/main/python/streaming/mqtt_wordcount.py
@@ -0,0 +1,58 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""
+ A sample wordcount with MqttStream stream
+ Usage: mqtt_wordcount.py <broker url> <topic>
+
+ To run this in your local machine, you need to setup a MQTT broker and publisher first,
+ Mosquitto is one of the open source MQTT Brokers, see
+ http://mosquitto.org/
+ Eclipse paho project provides number of clients and utilities for working with MQTT, see
+ http://www.eclipse.org/paho/#getting-started
+
+ and then run the example
+    `$ bin/spark-submit --jars external/mqtt-assembly/target/scala-*/\
+      spark-streaming-mqtt-assembly-*.jar examples/src/main/python/streaming/mqtt_wordcount.py \
+      tcp://localhost:1883 foo`
+"""
+
+import sys
+
+from pyspark import SparkContext
+from pyspark.streaming import StreamingContext
+from pyspark.streaming.mqtt import MQTTUtils
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: mqtt_wordcount.py <broker url> <topic>"
+        exit(-1)
+
+    sc = SparkContext(appName="PythonStreamingMQTTWordCount")
+    ssc = StreamingContext(sc, 1)
+
+    brokerUrl = sys.argv[1]
+    topic = sys.argv[2]
+
+    lines = MQTTUtils.createStream(ssc, brokerUrl, topic)
+    counts = lines.flatMap(lambda line: line.split(" ")) \
+        .map(lambda word: (word, 1)) \
+        .reduceByKey(lambda a, b: a+b)
+    counts.pprint()
+
+    ssc.start()
+    ssc.awaitTermination()
diff --git a/external/mqtt-assembly/pom.xml b/external/mqtt-assembly/pom.xml
new file mode 100644
index 0000000000000..9c94473053d96
--- /dev/null
+++ b/external/mqtt-assembly/pom.xml
@@ -0,0 +1,102 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+  <modelVersion>4.0.0</modelVersion>
+  <parent>
+    <groupId>org.apache.spark</groupId>
+    <artifactId>spark-parent_2.10</artifactId>
+    <version>1.5.0-SNAPSHOT</version>
+    <relativePath>../../pom.xml</relativePath>
+  </parent>
+
+  <groupId>org.apache.spark</groupId>
+  <artifactId>spark-streaming-mqtt-assembly_2.10</artifactId>
+  <packaging>jar</packaging>
+  <name>Spark Project External MQTT Assembly</name>
+  <url>http://spark.apache.org/</url>
+
+  <properties>
+    <sbt.project.name>streaming-mqtt-assembly</sbt.project.name>
+  </properties>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming-mqtt_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <scope>provided</scope>
+    </dependency>
+  </dependencies>
+
+  <build>
+    <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
+    <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+    <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-shade-plugin</artifactId>
+        <configuration>
+          <shadedArtifactAttached>false</shadedArtifactAttached>
+          <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-mqtt-assembly-${project.version}.jar</outputFile>
+          <artifactSet>
+            <includes>
+              <include>*:*</include>
+            </includes>
+          </artifactSet>
+          <filters>
+            <filter>
+              <artifact>*:*</artifact>
+              <excludes>
+                <exclude>META-INF/*.SF</exclude>
+                <exclude>META-INF/*.DSA</exclude>
+                <exclude>META-INF/*.RSA</exclude>
+              </excludes>
+            </filter>
+          </filters>
+        </configuration>
+        <executions>
+          <execution>
+            <phase>package</phase>
+            <goals>
+              <goal>shade</goal>
+            </goals>
+            <configuration>
+              <transformers>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
+                  <resource>reference.conf</resource>
+                </transformer>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.DontIncludeResourceTransformer">
+                  <resource>log4j.properties</resource>
+                </transformer>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheLicenseResourceTransformer"/>
+                <transformer implementation="org.apache.maven.plugins.shade.resource.ApacheNoticeResourceTransformer"/>
+              </transformers>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
+    </plugins>
+  </build>
+</project>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 0e41e5781784b..69b309876a0db 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -78,5 +78,33 @@
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>
+
+    <plugins>
+      <!-- Assemble a jar with test dependencies for Python tests -->
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-assembly-plugin</artifactId>
+        <executions>
+          <execution>
+            <id>test-jar-with-dependencies</id>
+            <phase>package</phase>
+            <goals>
+              <goal>single</goal>
+            </goals>
+            <configuration>
+              <!-- Make sure the file path is same as the sbt build -->
+              <finalName>spark-streaming-mqtt-test-${project.version}</finalName>
+              <outputDirectory>${project.build.directory}/scala-${scala.binary.version}/</outputDirectory>
+              <appendAssemblyId>false</appendAssemblyId>
+              <!-- Don't publish it since it's only for Python tests -->
+              <attach>false</attach>
+              <descriptors>
+                <descriptor>src/main/assembly/assembly.xml</descriptor>
+              </descriptors>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
+    </plugins>
   </build>
 </project>
diff --git a/external/mqtt/src/main/assembly/assembly.xml b/external/mqtt/src/main/assembly/assembly.xml
new file mode 100644
index 0000000000000..ecab5b360eb3e
--- /dev/null
+++ b/external/mqtt/src/main/assembly/assembly.xml
@@ -0,0 +1,44 @@
+<!--
+  ~ Licensed to the Apache Software Foundation (ASF) under one or more
+  ~ contributor license agreements.  See the NOTICE file distributed with
+  ~ this work for additional information regarding copyright ownership.
+  ~ The ASF licenses this file to You under the Apache License, Version 2.0
+  ~ (the "License"); you may not use this file except in compliance with
+  ~ the License.  You may obtain a copy of the License at
+  ~
+  ~    http://www.apache.org/licenses/LICENSE-2.0
+  ~
+  ~ Unless required by applicable law or agreed to in writing, software
+  ~ distributed under the License is distributed on an "AS IS" BASIS,
+  ~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+  ~ See the License for the specific language governing permissions and
+  ~ limitations under the License.
+  -->
+<assembly>
+  <id>test-jar-with-dependencies</id>
+  <formats>
+    <format>jar</format>
+  </formats>
+  <includeBaseDirectory>false</includeBaseDirectory>
+
+  <fileSets>
+    <fileSet>
+      <directory>${project.build.directory}/scala-${scala.binary.version}/test-classes</directory>
+      <outputDirectory>/</outputDirectory>
+    </fileSet>
+  </fileSets>
+
+  <dependencySets>
+    <dependencySet>
+      <useTransitiveDependencies>true</useTransitiveDependencies>
+      <scope>test</scope>
+      <unpack>true</unpack>
+      <excludes>
+        <exclude>org.apache.hadoop:*:jar</exclude>
+        <exclude>org.apache.zookeeper:*:jar</exclude>
+        <exclude>org.apache.avro:*:jar</exclude>
+      </excludes>
+    </dependencySet>
+  </dependencySets>
+
+</assembly>
diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
index 1142d0f56ba34..38a1114863d15 100644
--- a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
+++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
@@ -74,3 +74,19 @@ object MQTTUtils {
     createStream(jssc.ssc, brokerUrl, topic, storageLevel)
   }
 }
+
+/**
+ * This is a helper class that wraps the methods in MQTTUtils into more Python-friendly class and
+ * function so that it can be easily instantiated and called from Python's MQTTUtils.
+ */
+private class MQTTUtilsPythonHelper {
+
+  def createStream(
+      jssc: JavaStreamingContext,
+      brokerUrl: String,
+      topic: String,
+      storageLevel: StorageLevel
+    ): JavaDStream[String] = {
+    MQTTUtils.createStream(jssc, brokerUrl, topic, storageLevel)
+  }
+}
diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala
index c4bf5aa7869bb..a6a9249db8ed7 100644
--- a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala
+++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala
@@ -17,46 +17,30 @@
 
 package org.apache.spark.streaming.mqtt
 
-import java.net.{URI, ServerSocket}
-import java.util.concurrent.CountDownLatch
-import java.util.concurrent.TimeUnit
-
 import scala.concurrent.duration._
 import scala.language.postfixOps
 
-import org.apache.activemq.broker.{TransportConnector, BrokerService}
-import org.apache.commons.lang3.RandomUtils
-import org.eclipse.paho.client.mqttv3._
-import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence
-
 import org.scalatest.BeforeAndAfter
 import org.scalatest.concurrent.Eventually
 
-import org.apache.spark.streaming.{Milliseconds, StreamingContext}
-import org.apache.spark.storage.StorageLevel
-import org.apache.spark.streaming.dstream.ReceiverInputDStream
-import org.apache.spark.streaming.scheduler.StreamingListener
-import org.apache.spark.streaming.scheduler.StreamingListenerReceiverStarted
 import org.apache.spark.{SparkConf, SparkFunSuite}
-import org.apache.spark.util.Utils
+import org.apache.spark.storage.StorageLevel
+import org.apache.spark.streaming.{Milliseconds, StreamingContext}
 
 class MQTTStreamSuite extends SparkFunSuite with Eventually with BeforeAndAfter {
 
   private val batchDuration = Milliseconds(500)
   private val master = "local[2]"
   private val framework = this.getClass.getSimpleName
-  private val freePort = findFreePort()
-  private val brokerUri = "//localhost:" + freePort
   private val topic = "def"
-  private val persistenceDir = Utils.createTempDir()
 
   private var ssc: StreamingContext = _
-  private var broker: BrokerService = _
-  private var connector: TransportConnector = _
+  private var mqttTestUtils: MQTTTestUtils = _
 
   before {
     ssc = new StreamingContext(master, framework, batchDuration)
-    setupMQTT()
+    mqttTestUtils = new MQTTTestUtils
+    mqttTestUtils.setup()
   }
 
   after {
@@ -64,14 +48,17 @@ class MQTTStreamSuite extends SparkFunSuite with Eventually with BeforeAndAfter
       ssc.stop()
       ssc = null
     }
-    Utils.deleteRecursively(persistenceDir)
-    tearDownMQTT()
+    if (mqttTestUtils != null) {
+      mqttTestUtils.teardown()
+      mqttTestUtils = null
+    }
   }
 
   test("mqtt input stream") {
     val sendMessage = "MQTT demo for spark streaming"
-    val receiveStream =
-      MQTTUtils.createStream(ssc, "tcp:" + brokerUri, topic, StorageLevel.MEMORY_ONLY)
+    val receiveStream = MQTTUtils.createStream(ssc, "tcp://" + mqttTestUtils.brokerUri, topic,
+      StorageLevel.MEMORY_ONLY)
+
     @volatile var receiveMessage: List[String] = List()
     receiveStream.foreachRDD { rdd =>
       if (rdd.collect.length > 0) {
@@ -79,89 +66,14 @@ class MQTTStreamSuite extends SparkFunSuite with Eventually with BeforeAndAfter
         receiveMessage
       }
     }
-    ssc.start()
 
-    // wait for the receiver to start before publishing data, or we risk failing
-    // the test nondeterministically. See SPARK-4631
-    waitForReceiverToStart()
+    ssc.start()
 
-    publishData(sendMessage)
+    // Retry it because we don't know when the receiver will start.
     eventually(timeout(10000 milliseconds), interval(100 milliseconds)) {
+      mqttTestUtils.publishData(topic, sendMessage)
       assert(sendMessage.equals(receiveMessage(0)))
     }
     ssc.stop()
   }
-
-  private def setupMQTT() {
-    broker = new BrokerService()
-    broker.setDataDirectoryFile(Utils.createTempDir())
-    connector = new TransportConnector()
-    connector.setName("mqtt")
-    connector.setUri(new URI("mqtt:" + brokerUri))
-    broker.addConnector(connector)
-    broker.start()
-  }
-
-  private def tearDownMQTT() {
-    if (broker != null) {
-      broker.stop()
-      broker = null
-    }
-    if (connector != null) {
-      connector.stop()
-      connector = null
-    }
-  }
-
-  private def findFreePort(): Int = {
-    val candidatePort = RandomUtils.nextInt(1024, 65536)
-    Utils.startServiceOnPort(candidatePort, (trialPort: Int) => {
-      val socket = new ServerSocket(trialPort)
-      socket.close()
-      (null, trialPort)
-    }, new SparkConf())._2
-  }
-
-  def publishData(data: String): Unit = {
-    var client: MqttClient = null
-    try {
-      val persistence = new MqttDefaultFilePersistence(persistenceDir.getAbsolutePath)
-      client = new MqttClient("tcp:" + brokerUri, MqttClient.generateClientId(), persistence)
-      client.connect()
-      if (client.isConnected) {
-        val msgTopic = client.getTopic(topic)
-        val message = new MqttMessage(data.getBytes("utf-8"))
-        message.setQos(1)
-        message.setRetained(true)
-
-        for (i <- 0 to 10) {
-          try {
-            msgTopic.publish(message)
-          } catch {
-            case e: MqttException if e.getReasonCode == MqttException.REASON_CODE_MAX_INFLIGHT =>
-              // wait for Spark streaming to consume something from the message queue
-              Thread.sleep(50)
-          }
-        }
-      }
-    } finally {
-      client.disconnect()
-      client.close()
-      client = null
-    }
-  }
-
-  /**
-   * Block until at least one receiver has started or timeout occurs.
-   */
-  private def waitForReceiverToStart() = {
-    val latch = new CountDownLatch(1)
-    ssc.addStreamingListener(new StreamingListener {
-      override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted) {
-        latch.countDown()
-      }
-    })
-
-    assert(latch.await(10, TimeUnit.SECONDS), "Timeout waiting for receiver to start.")
-  }
 }
diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
new file mode 100644
index 0000000000000..1a371b7008824
--- /dev/null
+++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
@@ -0,0 +1,111 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming.mqtt
+
+import java.net.{ServerSocket, URI}
+
+import scala.language.postfixOps
+
+import com.google.common.base.Charsets.UTF_8
+import org.apache.activemq.broker.{BrokerService, TransportConnector}
+import org.apache.commons.lang3.RandomUtils
+import org.eclipse.paho.client.mqttv3._
+import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence
+
+import org.apache.spark.util.Utils
+import org.apache.spark.{Logging, SparkConf}
+
+/**
+ * Share codes for Scala and Python unit tests
+ */
+private class MQTTTestUtils extends Logging {
+
+  private val persistenceDir = Utils.createTempDir()
+  private val brokerHost = "localhost"
+  private val brokerPort = findFreePort()
+
+  private var broker: BrokerService = _
+  private var connector: TransportConnector = _
+
+  def brokerUri: String = {
+    s"$brokerHost:$brokerPort"
+  }
+
+  def setup(): Unit = {
+    broker = new BrokerService()
+    broker.setDataDirectoryFile(Utils.createTempDir())
+    connector = new TransportConnector()
+    connector.setName("mqtt")
+    connector.setUri(new URI("mqtt://" + brokerUri))
+    broker.addConnector(connector)
+    broker.start()
+  }
+
+  def teardown(): Unit = {
+    if (broker != null) {
+      broker.stop()
+      broker = null
+    }
+    if (connector != null) {
+      connector.stop()
+      connector = null
+    }
+    Utils.deleteRecursively(persistenceDir)
+  }
+
+  private def findFreePort(): Int = {
+    val candidatePort = RandomUtils.nextInt(1024, 65536)
+    Utils.startServiceOnPort(candidatePort, (trialPort: Int) => {
+      val socket = new ServerSocket(trialPort)
+      socket.close()
+      (null, trialPort)
+    }, new SparkConf())._2
+  }
+
+  def publishData(topic: String, data: String): Unit = {
+    var client: MqttClient = null
+    try {
+      val persistence = new MqttDefaultFilePersistence(persistenceDir.getAbsolutePath)
+      client = new MqttClient("tcp://" + brokerUri, MqttClient.generateClientId(), persistence)
+      client.connect()
+      if (client.isConnected) {
+        val msgTopic = client.getTopic(topic)
+        val message = new MqttMessage(data.getBytes(UTF_8))
+        message.setQos(1)
+        message.setRetained(true)
+
+        for (i <- 0 to 10) {
+          try {
+            msgTopic.publish(message)
+          } catch {
+            case e: MqttException if e.getReasonCode == MqttException.REASON_CODE_MAX_INFLIGHT =>
+              // wait for Spark streaming to consume something from the message queue
+              Thread.sleep(50)
+          }
+        }
+      }
+    } finally {
+      if (client != null) {
+        client.disconnect()
+        client.close()
+        client = null
+      }
+    }
+  }
+
+}
diff --git a/pom.xml b/pom.xml
index 2bcc55b040a26..8942836a7da16 100644
--- a/pom.xml
+++ b/pom.xml
@@ -104,6 +104,7 @@
     <module>external/flume-sink</module>
     <module>external/flume-assembly</module>
     <module>external/mqtt</module>
+    <module>external/mqtt-assembly</module>
     <module>external/zeromq</module>
     <module>examples</module>
     <module>repl</module>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 9a33baa7c6ce1..41a85fa9de778 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -45,8 +45,8 @@ object BuildCommons {
     sparkKinesisAsl) = Seq("yarn", "yarn-stable", "java8-tests", "ganglia-lgpl",
     "kinesis-asl").map(ProjectRef(buildLocation, _))
 
-  val assemblyProjects@Seq(assembly, examples, networkYarn, streamingFlumeAssembly, streamingKafkaAssembly, streamingKinesisAslAssembly) =
-    Seq("assembly", "examples", "network-yarn", "streaming-flume-assembly", "streaming-kafka-assembly", "streaming-kinesis-asl-assembly")
+  val assemblyProjects@Seq(assembly, examples, networkYarn, streamingFlumeAssembly, streamingKafkaAssembly, streamingMqttAssembly, streamingKinesisAslAssembly) =
+    Seq("assembly", "examples", "network-yarn", "streaming-flume-assembly", "streaming-kafka-assembly", "streaming-mqtt-assembly", "streaming-kinesis-asl-assembly")
       .map(ProjectRef(buildLocation, _))
 
   val tools = ProjectRef(buildLocation, "tools")
@@ -212,6 +212,9 @@ object SparkBuild extends PomBuild {
   /* Enable Assembly for all assembly projects */
   assemblyProjects.foreach(enable(Assembly.settings))
 
+  /* Enable Assembly for streamingMqtt test */
+  enable(inConfig(Test)(Assembly.settings))(streamingMqtt)
+
   /* Package pyspark artifacts in a separate zip file for YARN. */
   enable(PySparkAssembly.settings)(assembly)
 
@@ -382,13 +385,16 @@ object Assembly {
         .getOrElse(SbtPomKeys.effectivePom.value.getProperties.get("hadoop.version").asInstanceOf[String])
     },
     jarName in assembly <<= (version, moduleName, hadoopVersion) map { (v, mName, hv) =>
-      if (mName.contains("streaming-flume-assembly") || mName.contains("streaming-kafka-assembly") || mName.contains("streaming-kinesis-asl-assembly")) {
+      if (mName.contains("streaming-flume-assembly") || mName.contains("streaming-kafka-assembly") || mName.contains("streaming-mqtt-assembly") || mName.contains("streaming-kinesis-asl-assembly")) {
         // This must match the same name used in maven (see external/kafka-assembly/pom.xml)
         s"${mName}-${v}.jar"
       } else {
         s"${mName}-${v}-hadoop${hv}.jar"
       }
     },
+    jarName in (Test, assembly) <<= (version, moduleName, hadoopVersion) map { (v, mName, hv) =>
+      s"${mName}-test-${v}.jar"
+    },
     mergeStrategy in assembly := {
       case PathList("org", "datanucleus", xs @ _*)             => MergeStrategy.discard
       case m if m.toLowerCase.endsWith("manifest.mf")          => MergeStrategy.discard
diff --git a/python/pyspark/streaming/mqtt.py b/python/pyspark/streaming/mqtt.py
new file mode 100644
index 0000000000000..f06598971c548
--- /dev/null
+++ b/python/pyspark/streaming/mqtt.py
@@ -0,0 +1,72 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from py4j.java_gateway import Py4JJavaError
+
+from pyspark.storagelevel import StorageLevel
+from pyspark.serializers import UTF8Deserializer
+from pyspark.streaming import DStream
+
+__all__ = ['MQTTUtils']
+
+
+class MQTTUtils(object):
+
+    @staticmethod
+    def createStream(ssc, brokerUrl, topic,
+                     storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2):
+        """
+        Create an input stream that pulls messages from a Mqtt Broker.
+        :param ssc:  StreamingContext object
+        :param brokerUrl:  Url of remote mqtt publisher
+        :param topic:  topic name to subscribe to
+        :param storageLevel:  RDD storage level.
+        :return: A DStream object
+        """
+        jlevel = ssc._sc._getJavaStorageLevel(storageLevel)
+
+        try:
+            helperClass = ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
+                .loadClass("org.apache.spark.streaming.mqtt.MQTTUtilsPythonHelper")
+            helper = helperClass.newInstance()
+            jstream = helper.createStream(ssc._jssc, brokerUrl, topic, jlevel)
+        except Py4JJavaError as e:
+            if 'ClassNotFoundException' in str(e.java_exception):
+                MQTTUtils._printErrorMsg(ssc.sparkContext)
+            raise e
+
+        return DStream(jstream, ssc, UTF8Deserializer())
+
+    @staticmethod
+    def _printErrorMsg(sc):
+        print("""
+________________________________________________________________________________________________
+
+  Spark Streaming's MQTT libraries not found in class path. Try one of the following.
+
+  1. Include the MQTT library and its dependencies with in the
+     spark-submit command as
+
+     $ bin/spark-submit --packages org.apache.spark:spark-streaming-mqtt:%s ...
+
+  2. Download the JAR of the artifact from Maven Central http://search.maven.org/,
+     Group Id = org.apache.spark, Artifact Id = spark-streaming-mqtt-assembly, Version = %s.
+     Then, include the jar in the spark-submit command as
+
+     $ bin/spark-submit --jars <spark-streaming-mqtt-assembly.jar> ...
+________________________________________________________________________________________________
+""" % (sc.version, sc.version))
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 5cd544b2144ef..66ae3345f468f 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -40,6 +40,7 @@
 from pyspark.streaming.context import StreamingContext
 from pyspark.streaming.kafka import Broker, KafkaUtils, OffsetRange, TopicAndPartition
 from pyspark.streaming.flume import FlumeUtils
+from pyspark.streaming.mqtt import MQTTUtils
 from pyspark.streaming.kinesis import KinesisUtils, InitialPositionInStream
 
 
@@ -893,6 +894,68 @@ def test_flume_polling_multiple_hosts(self):
         self._testMultipleTimes(self._testFlumePollingMultipleHosts)
 
 
+class MQTTStreamTests(PySparkStreamingTestCase):
+    timeout = 20  # seconds
+    duration = 1
+
+    def setUp(self):
+        super(MQTTStreamTests, self).setUp()
+
+        MQTTTestUtilsClz = self.ssc._jvm.java.lang.Thread.currentThread().getContextClassLoader() \
+            .loadClass("org.apache.spark.streaming.mqtt.MQTTTestUtils")
+        self._MQTTTestUtils = MQTTTestUtilsClz.newInstance()
+        self._MQTTTestUtils.setup()
+
+    def tearDown(self):
+        if self._MQTTTestUtils is not None:
+            self._MQTTTestUtils.teardown()
+            self._MQTTTestUtils = None
+
+        super(MQTTStreamTests, self).tearDown()
+
+    def _randomTopic(self):
+        return "topic-%d" % random.randint(0, 10000)
+
+    def _startContext(self, topic):
+        # Start the StreamingContext and also collect the result
+        stream = MQTTUtils.createStream(self.ssc, "tcp://" + self._MQTTTestUtils.brokerUri(), topic)
+        result = []
+
+        def getOutput(_, rdd):
+            for data in rdd.collect():
+                result.append(data)
+
+        stream.foreachRDD(getOutput)
+        self.ssc.start()
+        return result
+
+    def test_mqtt_stream(self):
+        """Test the Python MQTT stream API."""
+        sendData = "MQTT demo for spark streaming"
+        topic = self._randomTopic()
+        result = self._startContext(topic)
+
+        def retry():
+            self._MQTTTestUtils.publishData(topic, sendData)
+            # Because "publishData" sends duplicate messages, here we should use > 0
+            self.assertTrue(len(result) > 0)
+            self.assertEqual(sendData, result[0])
+
+        # Retry it because we don't know when the receiver will start.
+        self._retry_or_timeout(retry)
+
+    def _retry_or_timeout(self, test_func):
+        start_time = time.time()
+        while True:
+            try:
+                test_func()
+                break
+            except:
+                if time.time() - start_time > self.timeout:
+                    raise
+                time.sleep(0.01)
+
+
 class KinesisStreamTests(PySparkStreamingTestCase):
 
     def test_kinesis_stream_api(self):
@@ -985,7 +1048,42 @@ def search_flume_assembly_jar():
             "'build/mvn package' before running this test")
     elif len(jars) > 1:
         raise Exception(("Found multiple Spark Streaming Flume assembly JARs in %s; please "
-                         "remove all but one") % flume_assembly_dir)
+                        "remove all but one") % flume_assembly_dir)
+    else:
+        return jars[0]
+
+
+def search_mqtt_assembly_jar():
+    SPARK_HOME = os.environ["SPARK_HOME"]
+    mqtt_assembly_dir = os.path.join(SPARK_HOME, "external/mqtt-assembly")
+    jars = glob.glob(
+        os.path.join(mqtt_assembly_dir, "target/scala-*/spark-streaming-mqtt-assembly-*.jar"))
+    if not jars:
+        raise Exception(
+            ("Failed to find Spark Streaming MQTT assembly jar in %s. " % mqtt_assembly_dir) +
+            "You need to build Spark with "
+            "'build/sbt assembly/assembly streaming-mqtt-assembly/assembly' or "
+            "'build/mvn package' before running this test")
+    elif len(jars) > 1:
+        raise Exception(("Found multiple Spark Streaming MQTT assembly JARs in %s; please "
+                         "remove all but one") % mqtt_assembly_dir)
+    else:
+        return jars[0]
+
+
+def search_mqtt_test_jar():
+    SPARK_HOME = os.environ["SPARK_HOME"]
+    mqtt_test_dir = os.path.join(SPARK_HOME, "external/mqtt")
+    jars = glob.glob(
+        os.path.join(mqtt_test_dir, "target/scala-*/spark-streaming-mqtt-test-*.jar"))
+    if not jars:
+        raise Exception(
+            ("Failed to find Spark Streaming MQTT test jar in %s. " % mqtt_test_dir) +
+            "You need to build Spark with "
+            "'build/sbt assembly/assembly streaming-mqtt/test:assembly'")
+    elif len(jars) > 1:
+        raise Exception(("Found multiple Spark Streaming MQTT test JARs in %s; please "
+                         "remove all but one") % mqtt_test_dir)
     else:
         return jars[0]
 
@@ -1012,8 +1110,12 @@ def search_kinesis_asl_assembly_jar():
 if __name__ == "__main__":
     kafka_assembly_jar = search_kafka_assembly_jar()
     flume_assembly_jar = search_flume_assembly_jar()
+    mqtt_assembly_jar = search_mqtt_assembly_jar()
+    mqtt_test_jar = search_mqtt_test_jar()
     kinesis_asl_assembly_jar = search_kinesis_asl_assembly_jar()
-    jars = "%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, kinesis_asl_assembly_jar)
+
+    jars = "%s,%s,%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, kinesis_asl_assembly_jar,
+                               mqtt_assembly_jar, mqtt_test_jar)
 
     os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars
     unittest.main()

From 3c9802d9400bea802984456683b2736a450ee17e Mon Sep 17 00:00:00 2001
From: Hao Zhu <viadeazhu@gmail.com>
Date: Mon, 10 Aug 2015 17:17:22 -0700
Subject: [PATCH 154/633] [SPARK-9801] [STREAMING] Check if file exists before
 deleting temporary files.

Spark streaming deletes the temp file and backup files without checking if they exist or not

Author: Hao Zhu <viadeazhu@gmail.com>

Closes #8082 from viadea/master and squashes the following commits:

242d05f [Hao Zhu] [SPARK-9801][Streaming]No need to check the existence of those files
fd143f2 [Hao Zhu] [SPARK-9801][Streaming]Check if backupFile exists before deleting backupFile files.
087daf0 [Hao Zhu] SPARK-9801
---
 .../scala/org/apache/spark/streaming/Checkpoint.scala     | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index 2780d5b6adbcf..6f6b449accc3c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -192,7 +192,9 @@ class CheckpointWriter(
             + "'")
 
           // Write checkpoint to temp file
-          fs.delete(tempFile, true)   // just in case it exists
+          if (fs.exists(tempFile)) {
+            fs.delete(tempFile, true)   // just in case it exists
+          }
           val fos = fs.create(tempFile)
           Utils.tryWithSafeFinally {
             fos.write(bytes)
@@ -203,7 +205,9 @@ class CheckpointWriter(
           // If the checkpoint file exists, back it up
           // If the backup exists as well, just delete it, otherwise rename will fail
           if (fs.exists(checkpointFile)) {
-            fs.delete(backupFile, true) // just in case it exists
+            if (fs.exists(backupFile)){
+              fs.delete(backupFile, true) // just in case it exists
+            }
             if (!fs.rename(checkpointFile, backupFile)) {
               logWarning("Could not rename " + checkpointFile + " to " + backupFile)
             }

From 071bbad5db1096a548c886762b611a8484a52753 Mon Sep 17 00:00:00 2001
From: Damian Guy <damian.guy@gmail.com>
Date: Tue, 11 Aug 2015 12:46:33 +0800
Subject: [PATCH 155/633] [SPARK-9340] [SQL] Fixes converting unannotated
 Parquet lists

This PR is inspired by #8063 authored by dguy. Especially, testing Parquet files added here are all taken from that PR.

**Committer who merges this PR should attribute it to "Damian Guy <damian.guygmail.com>".**

----

SPARK-6776 and SPARK-6777 followed `parquet-avro` to implement backwards-compatibility rules defined in `parquet-format` spec. However, both Spark SQL and `parquet-avro` neglected the following statement in `parquet-format`:

> This does not affect repeated fields that are not annotated: A repeated field that is neither contained by a `LIST`- or `MAP`-annotated group nor annotated by `LIST` or `MAP` should be interpreted as a required list of required elements where the element type is the type of the field.

One of the consequences is that, Parquet files generated by `parquet-protobuf` containing unannotated repeated fields are not correctly converted to Catalyst arrays.

This PR fixes this issue by

1. Handling unannotated repeated fields in `CatalystSchemaConverter`.
2. Converting this kind of special repeated fields to Catalyst arrays in `CatalystRowConverter`.

   Two special converters, `RepeatedPrimitiveConverter` and `RepeatedGroupConverter`, are added. They delegate actual conversion work to a child `elementConverter` and accumulates elements in an `ArrayBuffer`.

   Two extra methods, `start()` and `end()`, are added to `ParentContainerUpdater`. So that they can be used to initialize new `ArrayBuffer`s for unannotated repeated fields, and propagate converted array values to upstream.

Author: Cheng Lian <lian@databricks.com>

Closes #8070 from liancheng/spark-9340/unannotated-parquet-list and squashes the following commits:

ace6df7 [Cheng Lian] Moves ParquetProtobufCompatibilitySuite
f1c7bfd [Cheng Lian] Updates .rat-excludes
420ad2b [Cheng Lian] Fixes converting unannotated Parquet lists
---
 .rat-excludes                                 |   1 +
 .../parquet/CatalystRowConverter.scala        | 151 ++++++++++++++----
 .../parquet/CatalystSchemaConverter.scala     |   7 +-
 .../resources/nested-array-struct.parquet     | Bin 0 -> 775 bytes
 .../test/resources/old-repeated-int.parquet   | Bin 0 -> 389 bytes
 .../resources/old-repeated-message.parquet    | Bin 0 -> 600 bytes
 .../src/test/resources/old-repeated.parquet   | Bin 0 -> 432 bytes
 .../parquet-thrift-compat.snappy.parquet      | Bin
 .../resources/proto-repeated-string.parquet   | Bin 0 -> 411 bytes
 .../resources/proto-repeated-struct.parquet   | Bin 0 -> 608 bytes
 .../proto-struct-with-array-many.parquet      | Bin 0 -> 802 bytes
 .../resources/proto-struct-with-array.parquet | Bin 0 -> 1576 bytes
 .../ParquetProtobufCompatibilitySuite.scala   |  91 +++++++++++
 .../parquet/ParquetSchemaSuite.scala          |  30 ++++
 14 files changed, 247 insertions(+), 33 deletions(-)
 create mode 100644 sql/core/src/test/resources/nested-array-struct.parquet
 create mode 100644 sql/core/src/test/resources/old-repeated-int.parquet
 create mode 100644 sql/core/src/test/resources/old-repeated-message.parquet
 create mode 100644 sql/core/src/test/resources/old-repeated.parquet
 mode change 100755 => 100644 sql/core/src/test/resources/parquet-thrift-compat.snappy.parquet
 create mode 100644 sql/core/src/test/resources/proto-repeated-string.parquet
 create mode 100644 sql/core/src/test/resources/proto-repeated-struct.parquet
 create mode 100644 sql/core/src/test/resources/proto-struct-with-array-many.parquet
 create mode 100644 sql/core/src/test/resources/proto-struct-with-array.parquet
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala

diff --git a/.rat-excludes b/.rat-excludes
index 72771465846b8..9165872b9fb27 100644
--- a/.rat-excludes
+++ b/.rat-excludes
@@ -94,3 +94,4 @@ INDEX
 gen-java.*
 .*avpr
 org.apache.spark.sql.sources.DataSourceRegister
+.*parquet
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index 3542dfbae1292..ab5a6ddd41cfc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -21,11 +21,11 @@ import java.math.{BigDecimal, BigInteger}
 import java.nio.ByteOrder
 
 import scala.collection.JavaConversions._
-import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.parquet.column.Dictionary
 import org.apache.parquet.io.api.{Binary, Converter, GroupConverter, PrimitiveConverter}
+import org.apache.parquet.schema.OriginalType.LIST
 import org.apache.parquet.schema.Type.Repetition
 import org.apache.parquet.schema.{GroupType, PrimitiveType, Type}
 
@@ -42,6 +42,12 @@ import org.apache.spark.unsafe.types.UTF8String
  * values to an [[ArrayBuffer]].
  */
 private[parquet] trait ParentContainerUpdater {
+  /** Called before a record field is being converted */
+  def start(): Unit = ()
+
+  /** Called after a record field is being converted */
+  def end(): Unit = ()
+
   def set(value: Any): Unit = ()
   def setBoolean(value: Boolean): Unit = set(value)
   def setByte(value: Byte): Unit = set(value)
@@ -55,6 +61,32 @@ private[parquet] trait ParentContainerUpdater {
 /** A no-op updater used for root converter (who doesn't have a parent). */
 private[parquet] object NoopUpdater extends ParentContainerUpdater
 
+private[parquet] trait HasParentContainerUpdater {
+  def updater: ParentContainerUpdater
+}
+
+/**
+ * A convenient converter class for Parquet group types with an [[HasParentContainerUpdater]].
+ */
+private[parquet] abstract class CatalystGroupConverter(val updater: ParentContainerUpdater)
+  extends GroupConverter with HasParentContainerUpdater
+
+/**
+ * Parquet converter for Parquet primitive types.  Note that not all Spark SQL atomic types
+ * are handled by this converter.  Parquet primitive types are only a subset of those of Spark
+ * SQL.  For example, BYTE, SHORT, and INT in Spark SQL are all covered by INT32 in Parquet.
+ */
+private[parquet] class CatalystPrimitiveConverter(val updater: ParentContainerUpdater)
+  extends PrimitiveConverter with HasParentContainerUpdater {
+
+  override def addBoolean(value: Boolean): Unit = updater.setBoolean(value)
+  override def addInt(value: Int): Unit = updater.setInt(value)
+  override def addLong(value: Long): Unit = updater.setLong(value)
+  override def addFloat(value: Float): Unit = updater.setFloat(value)
+  override def addDouble(value: Double): Unit = updater.setDouble(value)
+  override def addBinary(value: Binary): Unit = updater.set(value.getBytes)
+}
+
 /**
  * A [[CatalystRowConverter]] is used to convert Parquet "structs" into Spark SQL [[InternalRow]]s.
  * Since any Parquet record is also a struct, this converter can also be used as root converter.
@@ -70,7 +102,7 @@ private[parquet] class CatalystRowConverter(
     parquetType: GroupType,
     catalystType: StructType,
     updater: ParentContainerUpdater)
-  extends GroupConverter {
+  extends CatalystGroupConverter(updater) {
 
   /**
    * Updater used together with field converters within a [[CatalystRowConverter]].  It propagates
@@ -89,13 +121,11 @@ private[parquet] class CatalystRowConverter(
 
   /**
    * Represents the converted row object once an entire Parquet record is converted.
-   *
-   * @todo Uses [[UnsafeRow]] for better performance.
    */
   val currentRow = new SpecificMutableRow(catalystType.map(_.dataType))
 
   // Converters for each field.
-  private val fieldConverters: Array[Converter] = {
+  private val fieldConverters: Array[Converter with HasParentContainerUpdater] = {
     parquetType.getFields.zip(catalystType).zipWithIndex.map {
       case ((parquetFieldType, catalystField), ordinal) =>
         // Converted field value should be set to the `ordinal`-th cell of `currentRow`
@@ -105,11 +135,19 @@ private[parquet] class CatalystRowConverter(
 
   override def getConverter(fieldIndex: Int): Converter = fieldConverters(fieldIndex)
 
-  override def end(): Unit = updater.set(currentRow)
+  override def end(): Unit = {
+    var i = 0
+    while (i < currentRow.numFields) {
+      fieldConverters(i).updater.end()
+      i += 1
+    }
+    updater.set(currentRow)
+  }
 
   override def start(): Unit = {
     var i = 0
     while (i < currentRow.numFields) {
+      fieldConverters(i).updater.start()
       currentRow.setNullAt(i)
       i += 1
     }
@@ -122,20 +160,20 @@ private[parquet] class CatalystRowConverter(
   private def newConverter(
       parquetType: Type,
       catalystType: DataType,
-      updater: ParentContainerUpdater): Converter = {
+      updater: ParentContainerUpdater): Converter with HasParentContainerUpdater = {
 
     catalystType match {
       case BooleanType | IntegerType | LongType | FloatType | DoubleType | BinaryType =>
         new CatalystPrimitiveConverter(updater)
 
       case ByteType =>
-        new PrimitiveConverter {
+        new CatalystPrimitiveConverter(updater) {
           override def addInt(value: Int): Unit =
             updater.setByte(value.asInstanceOf[ByteType#InternalType])
         }
 
       case ShortType =>
-        new PrimitiveConverter {
+        new CatalystPrimitiveConverter(updater) {
           override def addInt(value: Int): Unit =
             updater.setShort(value.asInstanceOf[ShortType#InternalType])
         }
@@ -148,7 +186,7 @@ private[parquet] class CatalystRowConverter(
 
       case TimestampType =>
         // TODO Implements `TIMESTAMP_MICROS` once parquet-mr has that.
-        new PrimitiveConverter {
+        new CatalystPrimitiveConverter(updater) {
           // Converts nanosecond timestamps stored as INT96
           override def addBinary(value: Binary): Unit = {
             assert(
@@ -164,13 +202,23 @@ private[parquet] class CatalystRowConverter(
         }
 
       case DateType =>
-        new PrimitiveConverter {
+        new CatalystPrimitiveConverter(updater) {
           override def addInt(value: Int): Unit = {
             // DateType is not specialized in `SpecificMutableRow`, have to box it here.
             updater.set(value.asInstanceOf[DateType#InternalType])
           }
         }
 
+      // A repeated field that is neither contained by a `LIST`- or `MAP`-annotated group nor
+      // annotated by `LIST` or `MAP` should be interpreted as a required list of required
+      // elements where the element type is the type of the field.
+      case t: ArrayType if parquetType.getOriginalType != LIST =>
+        if (parquetType.isPrimitive) {
+          new RepeatedPrimitiveConverter(parquetType, t.elementType, updater)
+        } else {
+          new RepeatedGroupConverter(parquetType, t.elementType, updater)
+        }
+
       case t: ArrayType =>
         new CatalystArrayConverter(parquetType.asGroupType(), t, updater)
 
@@ -195,27 +243,11 @@ private[parquet] class CatalystRowConverter(
     }
   }
 
-  /**
-   * Parquet converter for Parquet primitive types.  Note that not all Spark SQL atomic types
-   * are handled by this converter.  Parquet primitive types are only a subset of those of Spark
-   * SQL.  For example, BYTE, SHORT, and INT in Spark SQL are all covered by INT32 in Parquet.
-   */
-  private final class CatalystPrimitiveConverter(updater: ParentContainerUpdater)
-    extends PrimitiveConverter {
-
-    override def addBoolean(value: Boolean): Unit = updater.setBoolean(value)
-    override def addInt(value: Int): Unit = updater.setInt(value)
-    override def addLong(value: Long): Unit = updater.setLong(value)
-    override def addFloat(value: Float): Unit = updater.setFloat(value)
-    override def addDouble(value: Double): Unit = updater.setDouble(value)
-    override def addBinary(value: Binary): Unit = updater.set(value.getBytes)
-  }
-
   /**
    * Parquet converter for strings. A dictionary is used to minimize string decoding cost.
    */
   private final class CatalystStringConverter(updater: ParentContainerUpdater)
-    extends PrimitiveConverter {
+    extends CatalystPrimitiveConverter(updater) {
 
     private var expandedDictionary: Array[UTF8String] = null
 
@@ -242,7 +274,7 @@ private[parquet] class CatalystRowConverter(
   private final class CatalystDecimalConverter(
       decimalType: DecimalType,
       updater: ParentContainerUpdater)
-    extends PrimitiveConverter {
+    extends CatalystPrimitiveConverter(updater) {
 
     // Converts decimals stored as INT32
     override def addInt(value: Int): Unit = {
@@ -306,7 +338,7 @@ private[parquet] class CatalystRowConverter(
       parquetSchema: GroupType,
       catalystSchema: ArrayType,
       updater: ParentContainerUpdater)
-    extends GroupConverter {
+    extends CatalystGroupConverter(updater) {
 
     private var currentArray: ArrayBuffer[Any] = _
 
@@ -383,7 +415,7 @@ private[parquet] class CatalystRowConverter(
       parquetType: GroupType,
       catalystType: MapType,
       updater: ParentContainerUpdater)
-    extends GroupConverter {
+    extends CatalystGroupConverter(updater) {
 
     private var currentKeys: ArrayBuffer[Any] = _
     private var currentValues: ArrayBuffer[Any] = _
@@ -446,4 +478,61 @@ private[parquet] class CatalystRowConverter(
       }
     }
   }
+
+  private trait RepeatedConverter {
+    private var currentArray: ArrayBuffer[Any] = _
+
+    protected def newArrayUpdater(updater: ParentContainerUpdater) = new ParentContainerUpdater {
+      override def start(): Unit = currentArray = ArrayBuffer.empty[Any]
+      override def end(): Unit = updater.set(new GenericArrayData(currentArray.toArray))
+      override def set(value: Any): Unit = currentArray += value
+    }
+  }
+
+  /**
+   * A primitive converter for converting unannotated repeated primitive values to required arrays
+   * of required primitives values.
+   */
+  private final class RepeatedPrimitiveConverter(
+      parquetType: Type,
+      catalystType: DataType,
+      parentUpdater: ParentContainerUpdater)
+    extends PrimitiveConverter with RepeatedConverter with HasParentContainerUpdater {
+
+    val updater: ParentContainerUpdater = newArrayUpdater(parentUpdater)
+
+    private val elementConverter: PrimitiveConverter =
+      newConverter(parquetType, catalystType, updater).asPrimitiveConverter()
+
+    override def addBoolean(value: Boolean): Unit = elementConverter.addBoolean(value)
+    override def addInt(value: Int): Unit = elementConverter.addInt(value)
+    override def addLong(value: Long): Unit = elementConverter.addLong(value)
+    override def addFloat(value: Float): Unit = elementConverter.addFloat(value)
+    override def addDouble(value: Double): Unit = elementConverter.addDouble(value)
+    override def addBinary(value: Binary): Unit = elementConverter.addBinary(value)
+
+    override def setDictionary(dict: Dictionary): Unit = elementConverter.setDictionary(dict)
+    override def hasDictionarySupport: Boolean = elementConverter.hasDictionarySupport
+    override def addValueFromDictionary(id: Int): Unit = elementConverter.addValueFromDictionary(id)
+  }
+
+  /**
+   * A group converter for converting unannotated repeated group values to required arrays of
+   * required struct values.
+   */
+  private final class RepeatedGroupConverter(
+      parquetType: Type,
+      catalystType: DataType,
+      parentUpdater: ParentContainerUpdater)
+    extends GroupConverter with HasParentContainerUpdater with RepeatedConverter {
+
+    val updater: ParentContainerUpdater = newArrayUpdater(parentUpdater)
+
+    private val elementConverter: GroupConverter =
+      newConverter(parquetType, catalystType, updater).asGroupConverter()
+
+    override def getConverter(field: Int): Converter = elementConverter.getConverter(field)
+    override def end(): Unit = elementConverter.end()
+    override def start(): Unit = elementConverter.start()
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index a3fc74cf7929b..275646e8181ad 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -100,8 +100,11 @@ private[parquet] class CatalystSchemaConverter(
           StructField(field.getName, convertField(field), nullable = false)
 
         case REPEATED =>
-          throw new AnalysisException(
-            s"REPEATED not supported outside LIST or MAP. Type: $field")
+          // A repeated field that is neither contained by a `LIST`- or `MAP`-annotated group nor
+          // annotated by `LIST` or `MAP` should be interpreted as a required list of required
+          // elements where the element type is the type of the field.
+          val arrayType = ArrayType(convertField(field), containsNull = false)
+          StructField(field.getName, arrayType, nullable = false)
       }
     }
 
diff --git a/sql/core/src/test/resources/nested-array-struct.parquet b/sql/core/src/test/resources/nested-array-struct.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..41a43fa35d39685e56ba4849a16cba4bb1aa86ae
GIT binary patch
literal 775
zcmaKr-%G+!6vvO#=8u;i;*JSE3{j~lAyWtuV%0Q3*U(Y)B(q&>u(@?NBZ;2+Px=dc
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S1!5siKXe$p;U(Ab7x)6M)yB5~

literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/old-repeated-int.parquet b/sql/core/src/test/resources/old-repeated-int.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..520922f73ebb75950c4e65dec6689af817cb7a33
GIT binary patch
literal 389
zcmZWmO-sW-5FMkeB_3tN1_Ca@_7p=~Z@EPbSg5juTs)Ocvz0>9#NEw7ivQhdDcI1<
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literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/old-repeated-message.parquet b/sql/core/src/test/resources/old-repeated-message.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..548db991627774512b856b33b045327678b5a7ab
GIT binary patch
literal 600
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literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/old-repeated.parquet b/sql/core/src/test/resources/old-repeated.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..213f1a90291b30a8a3161b51c38f008f3ae9f6e5
GIT binary patch
literal 432
zcmZWm!D@p*5ZxNF!5+)X3PMGioUA16Ei?y9g$B|h;-#ms>Ld--Xm{5`DgF13A<&42
znSH!BJNtMWhsm50I-@h68VC$(I7}ZALYRJm-NGUo*2p;a%Z@xEJgH{;FE=Sj6^mNc
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literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/parquet-thrift-compat.snappy.parquet b/sql/core/src/test/resources/parquet-thrift-compat.snappy.parquet
old mode 100755
new mode 100644
diff --git a/sql/core/src/test/resources/proto-repeated-string.parquet b/sql/core/src/test/resources/proto-repeated-string.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..8a7eea601d0164b1177ab90efb7116c6e4c900da
GIT binary patch
literal 411
zcmY*W!D_-l5S^$E62wc{umKMtR4+{f-b!vM4Q)Y6&|G?w#H^aKansF;gi?C#*Yq1Z
zv6e;{_C4Mk<_)t^FrN}2UmBK6hDddy19SkO`+9soFOY8;=b|A8A$itAvJoQdBBnKK
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h7Au}q6}>(e6`sLs-{PvZ8BpWYeN#xd)c_*=mLHLcZu|fM

literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/proto-repeated-struct.parquet b/sql/core/src/test/resources/proto-repeated-struct.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..c29eee35c350e84efa2818febb2528309a8ac3ea
GIT binary patch
literal 608
zcma))-D<)x6vvP811a8(bSZdI%9Js*tjca=2puciK%r=F1_P}cr%>B2jf^q&4ts<>
z%r1PaC9^8^%A4e$li&GFTzg<)z+K#J;DQh(TmnD<kVkpBDo_;LwcgY%DJ&^Y<6Aqv
zbsFE^@okK>m&bFDCfsEak0$H6=|yp$CW-$_F@l={DK5j1GEpn8)DX!>A+15G`Fpg}
zMZREE-l#~cYPa=r6<4%c3AD?tzx2bP7Sypiu9pGoi(^0p+XD*$Y;s4$HpQOV<V3O`
zq$1E7gv363TT^bvvJG=alPO!qJYwWK7y2vO(Tzi2hB9B$V5Wa6x}IMC_h`uTdNrY}
zqgz4;{)7e{y=UJ!z0tsN9ouv6g`I!IBB1x|I-|Zb9NG^~Y8<7}%*;Ta<owc+8p@Xf
k<Sbk5H1LyzTxE@1yh$d`#i0QpN(whu41UERT=BR50I{KoDgXcg

literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/proto-struct-with-array-many.parquet b/sql/core/src/test/resources/proto-struct-with-array-many.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..ff9809675fc04edbbfc43f4033593a5aa6141dfa
GIT binary patch
literal 802
zcmb7DK}y3w6rChC)R3J_oIn>L*dnD3MsZWzL<}ml5ZY`6p``6p3uzOR6cO<bE<A+?
zaNPrV1eZOCGt(qZx+_eQ|NeXPU;ZD)vHB(i@P{L-1!WS7Py~Pn00>QizQ3hI@;TGm
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zc(o7;z363$6C<SU7!w?iqgj{)5&Gm7-W+!{ecPWdO=sAjXz!Ayr6lWBr+7IZ;i#jT
zB=)@#_B&eFy0*K+zU|nSYu}K3@)nYU?t^0wd-h;p-P&n#m{v1NgT1G7Oh>Cr9}+-E
j<-Z;KUL2!lIhl~NDb+dIHUN;6ire!D{E~S&<aYc6_=2b7

literal 0
HcmV?d00001

diff --git a/sql/core/src/test/resources/proto-struct-with-array.parquet b/sql/core/src/test/resources/proto-struct-with-array.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..325a8370ad20ec31010fde0c816895da9f5dbd27
GIT binary patch
literal 1576
zcmcIlO>5gg5S?rsql%Icnq5|qgD{N<#x?oCY2!n{ZAEKv64iDOJsH{F!~)4uB-v0(
z@BJM;_owufA5^-l4@Z{ekVAVh>zOxi-n`LDMyq>_0q^0x8b<t3#u5MmfXxk=|MG~0
zr8vb80MFte#Wcm26MS4HDz@=<n&cImf-GVxYLf6(akYj^QU8<=MTK(IF_MCmUmvwT
z3qr$pLw7j4srN<`=9ur?>74l?^SjJrp%q&06h8-y4iMdSJ@MDH4c~HjX3j($=&sN1
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z5A#DO!$$Zca>TK`;67WBib&?m76{4rqTmNgwH)UCc)*uPhjcg;fc!=Tfl{N?GyS_6
z3+tZPeSOS=k#BjS>(f75Q`2EhwX*hMsK_@Kv&ZT;SydBky3mDR6_J}cL*_TtV}7>H
zA+wumr}b9v46coS`}(TY;qmaR$9wg^82X@n)jvIvzps*~J`|Fl<jK4Gr_b%K{{G42
zxdmpGpRSS{daM6r^3;}OgIVg=dFo<QF7;lo^+8XD86<YsAG@_Z%kgZsX!(et3(Ui=
z-sEK!+xeelE#2p=Tj#5?iWc!NWC4A#d^bk683govIT-TS=F%2kuDvcm<)rqN<HLLl
Iz~JAAzx9@|oB#j-

literal 0
HcmV?d00001

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala
new file mode 100644
index 0000000000000..981334cf771cf
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala
@@ -0,0 +1,91 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.parquet
+
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+
+class ParquetProtobufCompatibilitySuite extends ParquetCompatibilityTest {
+  override def sqlContext: SQLContext = TestSQLContext
+
+  private def readParquetProtobufFile(name: String): DataFrame = {
+    val url = Thread.currentThread().getContextClassLoader.getResource(name)
+    sqlContext.read.parquet(url.toString)
+  }
+
+  test("unannotated array of primitive type") {
+    checkAnswer(readParquetProtobufFile("old-repeated-int.parquet"), Row(Seq(1, 2, 3)))
+  }
+
+  test("unannotated array of struct") {
+    checkAnswer(
+      readParquetProtobufFile("old-repeated-message.parquet"),
+      Row(
+        Seq(
+          Row("First inner", null, null),
+          Row(null, "Second inner", null),
+          Row(null, null, "Third inner"))))
+
+    checkAnswer(
+      readParquetProtobufFile("proto-repeated-struct.parquet"),
+      Row(
+        Seq(
+          Row("0 - 1", "0 - 2", "0 - 3"),
+          Row("1 - 1", "1 - 2", "1 - 3"))))
+
+    checkAnswer(
+      readParquetProtobufFile("proto-struct-with-array-many.parquet"),
+      Seq(
+        Row(
+          Seq(
+            Row("0 - 0 - 1", "0 - 0 - 2", "0 - 0 - 3"),
+            Row("0 - 1 - 1", "0 - 1 - 2", "0 - 1 - 3"))),
+        Row(
+          Seq(
+            Row("1 - 0 - 1", "1 - 0 - 2", "1 - 0 - 3"),
+            Row("1 - 1 - 1", "1 - 1 - 2", "1 - 1 - 3"))),
+        Row(
+          Seq(
+            Row("2 - 0 - 1", "2 - 0 - 2", "2 - 0 - 3"),
+            Row("2 - 1 - 1", "2 - 1 - 2", "2 - 1 - 3")))))
+  }
+
+  test("struct with unannotated array") {
+    checkAnswer(
+      readParquetProtobufFile("proto-struct-with-array.parquet"),
+      Row(10, 9, Seq.empty, null, Row(9), Seq(Row(9), Row(10))))
+  }
+
+  test("unannotated array of struct with unannotated array") {
+    checkAnswer(
+      readParquetProtobufFile("nested-array-struct.parquet"),
+      Seq(
+        Row(2, Seq(Row(1, Seq(Row(3))))),
+        Row(5, Seq(Row(4, Seq(Row(6))))),
+        Row(8, Seq(Row(7, Seq(Row(9)))))))
+  }
+
+  test("unannotated array of string") {
+    checkAnswer(
+      readParquetProtobufFile("proto-repeated-string.parquet"),
+      Seq(
+        Row(Seq("hello", "world")),
+        Row(Seq("good", "bye")),
+        Row(Seq("one", "two", "three"))))
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 8f06de7ce7c4f..971f71e27bfc6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -585,6 +585,36 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
       |}
     """.stripMargin)
 
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with non-nullable element type 7 - " +
+      "parquet-protobuf primitive lists",
+    new StructType()
+      .add("f1", ArrayType(IntegerType, containsNull = false), nullable = false),
+    """message root {
+      |  repeated int32 f1;
+      |}
+    """.stripMargin)
+
+  testParquetToCatalyst(
+    "Backwards-compatibility: LIST with non-nullable element type 8 - " +
+      "parquet-protobuf non-primitive lists",
+    {
+      val elementType =
+        new StructType()
+          .add("c1", StringType, nullable = true)
+          .add("c2", IntegerType, nullable = false)
+
+      new StructType()
+        .add("f1", ArrayType(elementType, containsNull = false), nullable = false)
+    },
+    """message root {
+      |  repeated group f1 {
+      |    optional binary c1 (UTF8);
+      |    required int32 c2;
+      |  }
+      |}
+    """.stripMargin)
+
   // =======================================================
   // Tests for converting Catalyst ArrayType to Parquet LIST
   // =======================================================

From 91e9389f39509e63654bd4bcb7bd919eaedda910 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 10 Aug 2015 22:04:41 -0700
Subject: [PATCH 156/633] [SPARK-9729] [SPARK-9363] [SQL] Use sort merge join
 for left and right outer join

This patch adds a new `SortMergeOuterJoin` operator that performs left and right outer joins using sort merge join.  It also refactors `SortMergeJoin` in order to improve performance and code clarity.

Along the way, I also performed a couple pieces of minor cleanup and optimization:

- Rename the `HashJoin` physical planner rule to `EquiJoinSelection`, since it's also used for non-hash joins.
- Rewrite the comment at the top of `HashJoin` to better explain the precedence for choosing join operators.
- Update `JoinSuite` to use `SqlTestUtils.withConf` for changing SQLConf settings.

This patch incorporates several ideas from adrian-wang's patch, #5717.

Closes #5717.

<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/7904)
<!-- Reviewable:end -->

Author: Josh Rosen <joshrosen@databricks.com>
Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #7904 from JoshRosen/outer-join-smj and squashes 1 commits.
---
 .../sql/catalyst/expressions/JoinedRow.scala  |   6 +-
 .../org/apache/spark/sql/SQLContext.scala     |   2 +-
 .../spark/sql/execution/RowIterator.scala     |  93 +++++
 .../spark/sql/execution/SparkStrategies.scala |  45 ++-
 .../joins/BroadcastNestedLoopJoin.scala       |   5 +-
 .../sql/execution/joins/SortMergeJoin.scala   | 331 +++++++++++++-----
 .../execution/joins/SortMergeOuterJoin.scala  | 251 +++++++++++++
 .../org/apache/spark/sql/JoinSuite.scala      | 132 ++++---
 .../sql/execution/joins/InnerJoinSuite.scala  | 180 ++++++++++
 .../sql/execution/joins/OuterJoinSuite.scala  | 310 +++++++++++-----
 .../sql/execution/joins/SemiJoinSuite.scala   | 125 ++++---
 .../apache/spark/sql/test/SQLTestUtils.scala  |   2 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   2 +-
 13 files changed, 1165 insertions(+), 319 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/RowIterator.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala
index b76757c93523d..d3560df0792eb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/JoinedRow.scala
@@ -37,20 +37,20 @@ class JoinedRow extends InternalRow {
   }
 
   /** Updates this JoinedRow to used point at two new base rows.  Returns itself. */
-  def apply(r1: InternalRow, r2: InternalRow): InternalRow = {
+  def apply(r1: InternalRow, r2: InternalRow): JoinedRow = {
     row1 = r1
     row2 = r2
     this
   }
 
   /** Updates this JoinedRow by updating its left base row.  Returns itself. */
-  def withLeft(newLeft: InternalRow): InternalRow = {
+  def withLeft(newLeft: InternalRow): JoinedRow = {
     row1 = newLeft
     this
   }
 
   /** Updates this JoinedRow by updating its right base row.  Returns itself. */
-  def withRight(newRight: InternalRow): InternalRow = {
+  def withRight(newRight: InternalRow): JoinedRow = {
     row2 = newRight
     this
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index f73bb0488c984..4bf00b3399e7a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -873,7 +873,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       HashAggregation ::
       Aggregation ::
       LeftSemiJoin ::
-      HashJoin ::
+      EquiJoinSelection ::
       InMemoryScans ::
       BasicOperators ::
       CartesianProduct ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/RowIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/RowIterator.scala
new file mode 100644
index 0000000000000..7462dbc4eba3a
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/RowIterator.scala
@@ -0,0 +1,93 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import java.util.NoSuchElementException
+
+import org.apache.spark.sql.catalyst.InternalRow
+
+/**
+ * An internal iterator interface which presents a more restrictive API than
+ * [[scala.collection.Iterator]].
+ *
+ * One major departure from the Scala iterator API is the fusing of the `hasNext()` and `next()`
+ * calls: Scala's iterator allows users to call `hasNext()` without immediately advancing the
+ * iterator to consume the next row, whereas RowIterator combines these calls into a single
+ * [[advanceNext()]] method.
+ */
+private[sql] abstract class RowIterator {
+  /**
+   * Advance this iterator by a single row. Returns `false` if this iterator has no more rows
+   * and `true` otherwise. If this returns `true`, then the new row can be retrieved by calling
+   * [[getRow]].
+   */
+  def advanceNext(): Boolean
+
+  /**
+   * Retrieve the row from this iterator. This method is idempotent. It is illegal to call this
+   * method after [[advanceNext()]] has returned `false`.
+   */
+  def getRow: InternalRow
+
+  /**
+   * Convert this RowIterator into a [[scala.collection.Iterator]].
+   */
+  def toScala: Iterator[InternalRow] = new RowIteratorToScala(this)
+}
+
+object RowIterator {
+  def fromScala(scalaIter: Iterator[InternalRow]): RowIterator = {
+    scalaIter match {
+      case wrappedRowIter: RowIteratorToScala => wrappedRowIter.rowIter
+      case _ => new RowIteratorFromScala(scalaIter)
+    }
+  }
+}
+
+private final class RowIteratorToScala(val rowIter: RowIterator) extends Iterator[InternalRow] {
+  private [this] var hasNextWasCalled: Boolean = false
+  private [this] var _hasNext: Boolean = false
+  override def hasNext: Boolean = {
+    // Idempotency:
+    if (!hasNextWasCalled) {
+      _hasNext = rowIter.advanceNext()
+      hasNextWasCalled = true
+    }
+    _hasNext
+  }
+  override def next(): InternalRow = {
+    if (!hasNext) throw new NoSuchElementException
+    hasNextWasCalled = false
+    rowIter.getRow
+  }
+}
+
+private final class RowIteratorFromScala(scalaIter: Iterator[InternalRow]) extends RowIterator {
+  private[this] var _next: InternalRow = null
+  override def advanceNext(): Boolean = {
+    if (scalaIter.hasNext) {
+      _next = scalaIter.next()
+      true
+    } else {
+      _next = null
+      false
+    }
+  }
+  override def getRow: InternalRow = _next
+  override def toScala: Iterator[InternalRow] = scalaIter
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index c4b9b5acea4de..1fc870d44b578 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -63,19 +63,23 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
   }
 
   /**
-   * Uses the ExtractEquiJoinKeys pattern to find joins where at least some of the predicates can be
-   * evaluated by matching hash keys.
+   * Uses the [[ExtractEquiJoinKeys]] pattern to find joins where at least some of the predicates
+   * can be evaluated by matching join keys.
    *
-   * This strategy applies a simple optimization based on the estimates of the physical sizes of
-   * the two join sides.  When planning a [[joins.BroadcastHashJoin]], if one side has an
-   * estimated physical size smaller than the user-settable threshold
-   * [[org.apache.spark.sql.SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]], the planner would mark it as the
-   * ''build'' relation and mark the other relation as the ''stream'' side.  The build table will be
-   * ''broadcasted'' to all of the executors involved in the join, as a
-   * [[org.apache.spark.broadcast.Broadcast]] object.  If both estimates exceed the threshold, they
-   * will instead be used to decide the build side in a [[joins.ShuffledHashJoin]].
+   * Join implementations are chosen with the following precedence:
+   *
+   * - Broadcast: if one side of the join has an estimated physical size that is smaller than the
+   *     user-configurable [[org.apache.spark.sql.SQLConf.AUTO_BROADCASTJOIN_THRESHOLD]] threshold
+   *     or if that side has an explicit broadcast hint (e.g. the user applied the
+   *     [[org.apache.spark.sql.functions.broadcast()]] function to a DataFrame), then that side
+   *     of the join will be broadcasted and the other side will be streamed, with no shuffling
+   *     performed. If both sides of the join are eligible to be broadcasted then the
+   * - Sort merge: if the matching join keys are sortable and
+   *     [[org.apache.spark.sql.SQLConf.SORTMERGE_JOIN]] is enabled (default), then sort merge join
+   *     will be used.
+   * - Hash: will be chosen if neither of the above optimizations apply to this join.
    */
-  object HashJoin extends Strategy with PredicateHelper {
+  object EquiJoinSelection extends Strategy with PredicateHelper {
 
     private[this] def makeBroadcastHashJoin(
         leftKeys: Seq[Expression],
@@ -90,14 +94,15 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     }
 
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+
+      // --- Inner joins --------------------------------------------------------------------------
+
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
         makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, joins.BuildRight)
 
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, CanBroadcast(left), right) =>
         makeBroadcastHashJoin(leftKeys, rightKeys, left, right, condition, joins.BuildLeft)
 
-      // If the sort merge join option is set, we want to use sort merge join prior to hashjoin
-      // for now let's support inner join first, then add outer join
       case ExtractEquiJoinKeys(Inner, leftKeys, rightKeys, condition, left, right)
         if sqlContext.conf.sortMergeJoinEnabled && RowOrdering.isOrderable(leftKeys) =>
         val mergeJoin =
@@ -115,6 +120,8 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
           leftKeys, rightKeys, buildSide, planLater(left), planLater(right))
         condition.map(Filter(_, hashJoin)).getOrElse(hashJoin) :: Nil
 
+      // --- Outer joins --------------------------------------------------------------------------
+
       case ExtractEquiJoinKeys(
              LeftOuter, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
         joins.BroadcastHashOuterJoin(
@@ -125,10 +132,22 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         joins.BroadcastHashOuterJoin(
           leftKeys, rightKeys, RightOuter, condition, planLater(left), planLater(right)) :: Nil
 
+      case ExtractEquiJoinKeys(LeftOuter, leftKeys, rightKeys, condition, left, right)
+        if sqlContext.conf.sortMergeJoinEnabled && RowOrdering.isOrderable(leftKeys) =>
+        joins.SortMergeOuterJoin(
+          leftKeys, rightKeys, LeftOuter, condition, planLater(left), planLater(right)) :: Nil
+
+      case ExtractEquiJoinKeys(RightOuter, leftKeys, rightKeys, condition, left, right)
+        if sqlContext.conf.sortMergeJoinEnabled && RowOrdering.isOrderable(leftKeys) =>
+        joins.SortMergeOuterJoin(
+          leftKeys, rightKeys, RightOuter, condition, planLater(left), planLater(right)) :: Nil
+
       case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right) =>
         joins.ShuffledHashOuterJoin(
           leftKeys, rightKeys, joinType, condition, planLater(left), planLater(right)) :: Nil
 
+      // --- Cases where this strategy does not apply ---------------------------------------------
+
       case _ => Nil
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
index 23aebf4b068b4..017a44b9ca863 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
@@ -65,8 +65,9 @@ case class BroadcastNestedLoopJoin(
         left.output.map(_.withNullability(true)) ++ right.output
       case FullOuter =>
         left.output.map(_.withNullability(true)) ++ right.output.map(_.withNullability(true))
-      case _ =>
-        left.output ++ right.output
+      case x =>
+        throw new IllegalArgumentException(
+          s"BroadcastNestedLoopJoin should not take $x as the JoinType")
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index 4ae23c186cf7b..6d656ea2849a9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -17,15 +17,14 @@
 
 package org.apache.spark.sql.execution.joins
 
-import java.util.NoSuchElementException
+import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
-import org.apache.spark.util.collection.CompactBuffer
+import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
 
 /**
  * :: DeveloperApi ::
@@ -38,8 +37,6 @@ case class SortMergeJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode {
 
-  override protected[sql] val trackNumOfRowsEnabled = true
-
   override def output: Seq[Attribute] = left.output ++ right.output
 
   override def outputPartitioning: Partitioning =
@@ -56,117 +53,265 @@ case class SortMergeJoin(
   @transient protected lazy val leftKeyGenerator = newProjection(leftKeys, left.output)
   @transient protected lazy val rightKeyGenerator = newProjection(rightKeys, right.output)
 
+  protected[this] def isUnsafeMode: Boolean = {
+    (codegenEnabled && unsafeEnabled
+      && UnsafeProjection.canSupport(leftKeys)
+      && UnsafeProjection.canSupport(rightKeys)
+      && UnsafeProjection.canSupport(schema))
+  }
+
+  override def outputsUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessSafeRows: Boolean = !isUnsafeMode
+
   private def requiredOrders(keys: Seq[Expression]): Seq[SortOrder] = {
     // This must be ascending in order to agree with the `keyOrdering` defined in `doExecute()`.
     keys.map(SortOrder(_, Ascending))
   }
 
   protected override def doExecute(): RDD[InternalRow] = {
-    val leftResults = left.execute().map(_.copy())
-    val rightResults = right.execute().map(_.copy())
-
-    leftResults.zipPartitions(rightResults) { (leftIter, rightIter) =>
-      new Iterator[InternalRow] {
+    left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
+      new RowIterator {
         // An ordering that can be used to compare keys from both sides.
         private[this] val keyOrdering = newNaturalAscendingOrdering(leftKeys.map(_.dataType))
-        // Mutable per row objects.
+        private[this] var currentLeftRow: InternalRow = _
+        private[this] var currentRightMatches: ArrayBuffer[InternalRow] = _
+        private[this] var currentMatchIdx: Int = -1
+        private[this] val smjScanner = new SortMergeJoinScanner(
+          leftKeyGenerator,
+          rightKeyGenerator,
+          keyOrdering,
+          RowIterator.fromScala(leftIter),
+          RowIterator.fromScala(rightIter)
+        )
         private[this] val joinRow = new JoinedRow
-        private[this] var leftElement: InternalRow = _
-        private[this] var rightElement: InternalRow = _
-        private[this] var leftKey: InternalRow = _
-        private[this] var rightKey: InternalRow = _
-        private[this] var rightMatches: CompactBuffer[InternalRow] = _
-        private[this] var rightPosition: Int = -1
-        private[this] var stop: Boolean = false
-        private[this] var matchKey: InternalRow = _
-
-        // initialize iterator
-        initialize()
-
-        override final def hasNext: Boolean = nextMatchingPair()
-
-        override final def next(): InternalRow = {
-          if (hasNext) {
-            // we are using the buffered right rows and run down left iterator
-            val joinedRow = joinRow(leftElement, rightMatches(rightPosition))
-            rightPosition += 1
-            if (rightPosition >= rightMatches.size) {
-              rightPosition = 0
-              fetchLeft()
-              if (leftElement == null || keyOrdering.compare(leftKey, matchKey) != 0) {
-                stop = false
-                rightMatches = null
-              }
-            }
-            joinedRow
+        private[this] val resultProjection: (InternalRow) => InternalRow = {
+          if (isUnsafeMode) {
+            UnsafeProjection.create(schema)
           } else {
-            // no more result
-            throw new NoSuchElementException
+            identity[InternalRow]
           }
         }
 
-        private def fetchLeft() = {
-          if (leftIter.hasNext) {
-            leftElement = leftIter.next()
-            leftKey = leftKeyGenerator(leftElement)
-          } else {
-            leftElement = null
+        override def advanceNext(): Boolean = {
+          if (currentMatchIdx == -1 || currentMatchIdx == currentRightMatches.length) {
+            if (smjScanner.findNextInnerJoinRows()) {
+              currentRightMatches = smjScanner.getBufferedMatches
+              currentLeftRow = smjScanner.getStreamedRow
+              currentMatchIdx = 0
+            } else {
+              currentRightMatches = null
+              currentLeftRow = null
+              currentMatchIdx = -1
+            }
           }
-        }
-
-        private def fetchRight() = {
-          if (rightIter.hasNext) {
-            rightElement = rightIter.next()
-            rightKey = rightKeyGenerator(rightElement)
+          if (currentLeftRow != null) {
+            joinRow(currentLeftRow, currentRightMatches(currentMatchIdx))
+            currentMatchIdx += 1
+            true
           } else {
-            rightElement = null
+            false
           }
         }
 
-        private def initialize() = {
-          fetchLeft()
-          fetchRight()
+        override def getRow: InternalRow = resultProjection(joinRow)
+      }.toScala
+    }
+  }
+}
+
+/**
+ * Helper class that is used to implement [[SortMergeJoin]] and [[SortMergeOuterJoin]].
+ *
+ * To perform an inner (outer) join, users of this class call [[findNextInnerJoinRows()]]
+ * ([[findNextOuterJoinRows()]]), which returns `true` if a result has been produced and `false`
+ * otherwise. If a result has been produced, then the caller may call [[getStreamedRow]] to return
+ * the matching row from the streamed input and may call [[getBufferedMatches]] to return the
+ * sequence of matching rows from the buffered input (in the case of an outer join, this will return
+ * an empty sequence if there are no matches from the buffered input). For efficiency, both of these
+ * methods return mutable objects which are re-used across calls to the `findNext*JoinRows()`
+ * methods.
+ *
+ * @param streamedKeyGenerator a projection that produces join keys from the streamed input.
+ * @param bufferedKeyGenerator a projection that produces join keys from the buffered input.
+ * @param keyOrdering an ordering which can be used to compare join keys.
+ * @param streamedIter an input whose rows will be streamed.
+ * @param bufferedIter an input whose rows will be buffered to construct sequences of rows that
+ *                     have the same join key.
+ */
+private[joins] class SortMergeJoinScanner(
+    streamedKeyGenerator: Projection,
+    bufferedKeyGenerator: Projection,
+    keyOrdering: Ordering[InternalRow],
+    streamedIter: RowIterator,
+    bufferedIter: RowIterator) {
+  private[this] var streamedRow: InternalRow = _
+  private[this] var streamedRowKey: InternalRow = _
+  private[this] var bufferedRow: InternalRow = _
+  // Note: this is guaranteed to never have any null columns:
+  private[this] var bufferedRowKey: InternalRow = _
+  /**
+   * The join key for the rows buffered in `bufferedMatches`, or null if `bufferedMatches` is empty
+   */
+  private[this] var matchJoinKey: InternalRow = _
+  /** Buffered rows from the buffered side of the join. This is empty if there are no matches. */
+  private[this] val bufferedMatches: ArrayBuffer[InternalRow] = new ArrayBuffer[InternalRow]
+
+  // Initialization (note: do _not_ want to advance streamed here).
+  advancedBufferedToRowWithNullFreeJoinKey()
+
+  // --- Public methods ---------------------------------------------------------------------------
+
+  def getStreamedRow: InternalRow = streamedRow
+
+  def getBufferedMatches: ArrayBuffer[InternalRow] = bufferedMatches
+
+  /**
+   * Advances both input iterators, stopping when we have found rows with matching join keys.
+   * @return true if matching rows have been found and false otherwise. If this returns true, then
+   *         [[getStreamedRow]] and [[getBufferedMatches]] can be called to construct the join
+   *         results.
+   */
+  final def findNextInnerJoinRows(): Boolean = {
+    while (advancedStreamed() && streamedRowKey.anyNull) {
+      // Advance the streamed side of the join until we find the next row whose join key contains
+      // no nulls or we hit the end of the streamed iterator.
+    }
+    if (streamedRow == null) {
+      // We have consumed the entire streamed iterator, so there can be no more matches.
+      matchJoinKey = null
+      bufferedMatches.clear()
+      false
+    } else if (matchJoinKey != null && keyOrdering.compare(streamedRowKey, matchJoinKey) == 0) {
+      // The new streamed row has the same join key as the previous row, so return the same matches.
+      true
+    } else if (bufferedRow == null) {
+      // The streamed row's join key does not match the current batch of buffered rows and there are
+      // no more rows to read from the buffered iterator, so there can be no more matches.
+      matchJoinKey = null
+      bufferedMatches.clear()
+      false
+    } else {
+      // Advance both the streamed and buffered iterators to find the next pair of matching rows.
+      var comp = keyOrdering.compare(streamedRowKey, bufferedRowKey)
+      do {
+        if (streamedRowKey.anyNull) {
+          advancedStreamed()
+        } else {
+          assert(!bufferedRowKey.anyNull)
+          comp = keyOrdering.compare(streamedRowKey, bufferedRowKey)
+          if (comp > 0) advancedBufferedToRowWithNullFreeJoinKey()
+          else if (comp < 0) advancedStreamed()
         }
+      } while (streamedRow != null && bufferedRow != null && comp != 0)
+      if (streamedRow == null || bufferedRow == null) {
+        // We have either hit the end of one of the iterators, so there can be no more matches.
+        matchJoinKey = null
+        bufferedMatches.clear()
+        false
+      } else {
+        // The streamed row's join key matches the current buffered row's join, so walk through the
+        // buffered iterator to buffer the rest of the matching rows.
+        assert(comp == 0)
+        bufferMatchingRows()
+        true
+      }
+    }
+  }
 
-        /**
-         * Searches the right iterator for the next rows that have matches in left side, and store
-         * them in a buffer.
-         *
-         * @return true if the search is successful, and false if the right iterator runs out of
-         *         tuples.
-         */
-        private def nextMatchingPair(): Boolean = {
-          if (!stop && rightElement != null) {
-            // run both side to get the first match pair
-            while (!stop && leftElement != null && rightElement != null) {
-              val comparing = keyOrdering.compare(leftKey, rightKey)
-              // for inner join, we need to filter those null keys
-              stop = comparing == 0 && !leftKey.anyNull
-              if (comparing > 0 || rightKey.anyNull) {
-                fetchRight()
-              } else if (comparing < 0 || leftKey.anyNull) {
-                fetchLeft()
-              }
-            }
-            rightMatches = new CompactBuffer[InternalRow]()
-            if (stop) {
-              stop = false
-              // iterate the right side to buffer all rows that matches
-              // as the records should be ordered, exit when we meet the first that not match
-              while (!stop && rightElement != null) {
-                rightMatches += rightElement
-                fetchRight()
-                stop = keyOrdering.compare(leftKey, rightKey) != 0
-              }
-              if (rightMatches.size > 0) {
-                rightPosition = 0
-                matchKey = leftKey
-              }
-            }
+  /**
+   * Advances the streamed input iterator and buffers all rows from the buffered input that
+   * have matching keys.
+   * @return true if the streamed iterator returned a row, false otherwise. If this returns true,
+   *         then [getStreamedRow and [[getBufferedMatches]] can be called to produce the outer
+   *         join results.
+   */
+  final def findNextOuterJoinRows(): Boolean = {
+    if (!advancedStreamed()) {
+      // We have consumed the entire streamed iterator, so there can be no more matches.
+      matchJoinKey = null
+      bufferedMatches.clear()
+      false
+    } else {
+      if (matchJoinKey != null && keyOrdering.compare(streamedRowKey, matchJoinKey) == 0) {
+        // Matches the current group, so do nothing.
+      } else {
+        // The streamed row does not match the current group.
+        matchJoinKey = null
+        bufferedMatches.clear()
+        if (bufferedRow != null && !streamedRowKey.anyNull) {
+          // The buffered iterator could still contain matching rows, so we'll need to walk through
+          // it until we either find matches or pass where they would be found.
+          var comp = 1
+          do {
+            comp = keyOrdering.compare(streamedRowKey, bufferedRowKey)
+          } while (comp > 0 && advancedBufferedToRowWithNullFreeJoinKey())
+          if (comp == 0) {
+            // We have found matches, so buffer them (this updates matchJoinKey)
+            bufferMatchingRows()
+          } else {
+            // We have overshot the position where the row would be found, hence no matches.
           }
-          rightMatches != null && rightMatches.size > 0
         }
       }
+      // If there is a streamed input then we always return true
+      true
     }
   }
+
+  // --- Private methods --------------------------------------------------------------------------
+
+  /**
+   * Advance the streamed iterator and compute the new row's join key.
+   * @return true if the streamed iterator returned a row and false otherwise.
+   */
+  private def advancedStreamed(): Boolean = {
+    if (streamedIter.advanceNext()) {
+      streamedRow = streamedIter.getRow
+      streamedRowKey = streamedKeyGenerator(streamedRow)
+      true
+    } else {
+      streamedRow = null
+      streamedRowKey = null
+      false
+    }
+  }
+
+  /**
+   * Advance the buffered iterator until we find a row with join key that does not contain nulls.
+   * @return true if the buffered iterator returned a row and false otherwise.
+   */
+  private def advancedBufferedToRowWithNullFreeJoinKey(): Boolean = {
+    var foundRow: Boolean = false
+    while (!foundRow && bufferedIter.advanceNext()) {
+      bufferedRow = bufferedIter.getRow
+      bufferedRowKey = bufferedKeyGenerator(bufferedRow)
+      foundRow = !bufferedRowKey.anyNull
+    }
+    if (!foundRow) {
+      bufferedRow = null
+      bufferedRowKey = null
+      false
+    } else {
+      true
+    }
+  }
+
+  /**
+   * Called when the streamed and buffered join keys match in order to buffer the matching rows.
+   */
+  private def bufferMatchingRows(): Unit = {
+    assert(streamedRowKey != null)
+    assert(!streamedRowKey.anyNull)
+    assert(bufferedRowKey != null)
+    assert(!bufferedRowKey.anyNull)
+    assert(keyOrdering.compare(streamedRowKey, bufferedRowKey) == 0)
+    // This join key may have been produced by a mutable projection, so we need to make a copy:
+    matchJoinKey = streamedRowKey.copy()
+    bufferedMatches.clear()
+    do {
+      bufferedMatches += bufferedRow.copy() // need to copy mutable rows before buffering them
+      advancedBufferedToRowWithNullFreeJoinKey()
+    } while (bufferedRow != null && keyOrdering.compare(streamedRowKey, bufferedRowKey) == 0)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
new file mode 100644
index 0000000000000..5326966b07a66
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
@@ -0,0 +1,251 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
+import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
+
+/**
+ * :: DeveloperApi ::
+ * Performs an sort merge outer join of two child relations.
+ *
+ * Note: this does not support full outer join yet; see SPARK-9730 for progress on this.
+ */
+@DeveloperApi
+case class SortMergeOuterJoin(
+    leftKeys: Seq[Expression],
+    rightKeys: Seq[Expression],
+    joinType: JoinType,
+    condition: Option[Expression],
+    left: SparkPlan,
+    right: SparkPlan) extends BinaryNode {
+
+  override def output: Seq[Attribute] = {
+    joinType match {
+      case LeftOuter =>
+        left.output ++ right.output.map(_.withNullability(true))
+      case RightOuter =>
+        left.output.map(_.withNullability(true)) ++ right.output
+      case x =>
+        throw new IllegalArgumentException(
+          s"${getClass.getSimpleName} should not take $x as the JoinType")
+    }
+  }
+
+  override def outputPartitioning: Partitioning = joinType match {
+    // For left and right outer joins, the output is partitioned by the streamed input's join keys.
+    case LeftOuter => left.outputPartitioning
+    case RightOuter => right.outputPartitioning
+    case x =>
+      throw new IllegalArgumentException(
+        s"${getClass.getSimpleName} should not take $x as the JoinType")
+  }
+
+  override def outputOrdering: Seq[SortOrder] = joinType match {
+    // For left and right outer joins, the output is ordered by the streamed input's join keys.
+    case LeftOuter => requiredOrders(leftKeys)
+    case RightOuter => requiredOrders(rightKeys)
+    case x => throw new IllegalArgumentException(
+      s"SortMergeOuterJoin should not take $x as the JoinType")
+  }
+
+  override def requiredChildDistribution: Seq[Distribution] =
+    ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
+
+  override def requiredChildOrdering: Seq[Seq[SortOrder]] =
+    requiredOrders(leftKeys) :: requiredOrders(rightKeys) :: Nil
+
+  private def requiredOrders(keys: Seq[Expression]): Seq[SortOrder] = {
+    // This must be ascending in order to agree with the `keyOrdering` defined in `doExecute()`.
+    keys.map(SortOrder(_, Ascending))
+  }
+
+  private def isUnsafeMode: Boolean = {
+    (codegenEnabled && unsafeEnabled
+      && UnsafeProjection.canSupport(leftKeys)
+      && UnsafeProjection.canSupport(rightKeys)
+      && UnsafeProjection.canSupport(schema))
+  }
+
+  override def outputsUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessUnsafeRows: Boolean = isUnsafeMode
+  override def canProcessSafeRows: Boolean = !isUnsafeMode
+
+  private def createLeftKeyGenerator(): Projection = {
+    if (isUnsafeMode) {
+      UnsafeProjection.create(leftKeys, left.output)
+    } else {
+      newProjection(leftKeys, left.output)
+    }
+  }
+
+  private def createRightKeyGenerator(): Projection = {
+    if (isUnsafeMode) {
+      UnsafeProjection.create(rightKeys, right.output)
+    } else {
+      newProjection(rightKeys, right.output)
+    }
+  }
+
+  override def doExecute(): RDD[InternalRow] = {
+    left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
+      // An ordering that can be used to compare keys from both sides.
+      val keyOrdering = newNaturalAscendingOrdering(leftKeys.map(_.dataType))
+      val boundCondition: (InternalRow) => Boolean = {
+        condition.map { cond =>
+          newPredicate(cond, left.output ++ right.output)
+        }.getOrElse {
+          (r: InternalRow) => true
+        }
+      }
+      val resultProj: InternalRow => InternalRow = {
+        if (isUnsafeMode) {
+          UnsafeProjection.create(schema)
+        } else {
+          identity[InternalRow]
+        }
+      }
+
+      joinType match {
+        case LeftOuter =>
+          val smjScanner = new SortMergeJoinScanner(
+            streamedKeyGenerator = createLeftKeyGenerator(),
+            bufferedKeyGenerator = createRightKeyGenerator(),
+            keyOrdering,
+            streamedIter = RowIterator.fromScala(leftIter),
+            bufferedIter = RowIterator.fromScala(rightIter)
+          )
+          val rightNullRow = new GenericInternalRow(right.output.length)
+          new LeftOuterIterator(smjScanner, rightNullRow, boundCondition, resultProj).toScala
+
+        case RightOuter =>
+          val smjScanner = new SortMergeJoinScanner(
+            streamedKeyGenerator = createRightKeyGenerator(),
+            bufferedKeyGenerator = createLeftKeyGenerator(),
+            keyOrdering,
+            streamedIter = RowIterator.fromScala(rightIter),
+            bufferedIter = RowIterator.fromScala(leftIter)
+          )
+          val leftNullRow = new GenericInternalRow(left.output.length)
+          new RightOuterIterator(smjScanner, leftNullRow, boundCondition, resultProj).toScala
+
+        case x =>
+          throw new IllegalArgumentException(
+            s"SortMergeOuterJoin should not take $x as the JoinType")
+      }
+    }
+  }
+}
+
+
+private class LeftOuterIterator(
+    smjScanner: SortMergeJoinScanner,
+    rightNullRow: InternalRow,
+    boundCondition: InternalRow => Boolean,
+    resultProj: InternalRow => InternalRow
+  ) extends RowIterator {
+  private[this] val joinedRow: JoinedRow = new JoinedRow()
+  private[this] var rightIdx: Int = 0
+  assert(smjScanner.getBufferedMatches.length == 0)
+
+  private def advanceLeft(): Boolean = {
+    rightIdx = 0
+    if (smjScanner.findNextOuterJoinRows()) {
+      joinedRow.withLeft(smjScanner.getStreamedRow)
+      if (smjScanner.getBufferedMatches.isEmpty) {
+        // There are no matching right rows, so return nulls for the right row
+        joinedRow.withRight(rightNullRow)
+      } else {
+        // Find the next row from the right input that satisfied the bound condition
+        if (!advanceRightUntilBoundConditionSatisfied()) {
+          joinedRow.withRight(rightNullRow)
+        }
+      }
+      true
+    } else {
+      // Left input has been exhausted
+      false
+    }
+  }
+
+  private def advanceRightUntilBoundConditionSatisfied(): Boolean = {
+    var foundMatch: Boolean = false
+    while (!foundMatch && rightIdx < smjScanner.getBufferedMatches.length) {
+      foundMatch = boundCondition(joinedRow.withRight(smjScanner.getBufferedMatches(rightIdx)))
+      rightIdx += 1
+    }
+    foundMatch
+  }
+
+  override def advanceNext(): Boolean = {
+    advanceRightUntilBoundConditionSatisfied() || advanceLeft()
+  }
+
+  override def getRow: InternalRow = resultProj(joinedRow)
+}
+
+private class RightOuterIterator(
+    smjScanner: SortMergeJoinScanner,
+    leftNullRow: InternalRow,
+    boundCondition: InternalRow => Boolean,
+    resultProj: InternalRow => InternalRow
+  ) extends RowIterator {
+  private[this] val joinedRow: JoinedRow = new JoinedRow()
+  private[this] var leftIdx: Int = 0
+  assert(smjScanner.getBufferedMatches.length == 0)
+
+  private def advanceRight(): Boolean = {
+    leftIdx = 0
+    if (smjScanner.findNextOuterJoinRows()) {
+      joinedRow.withRight(smjScanner.getStreamedRow)
+      if (smjScanner.getBufferedMatches.isEmpty) {
+        // There are no matching left rows, so return nulls for the left row
+        joinedRow.withLeft(leftNullRow)
+      } else {
+        // Find the next row from the left input that satisfied the bound condition
+        if (!advanceLeftUntilBoundConditionSatisfied()) {
+          joinedRow.withLeft(leftNullRow)
+        }
+      }
+      true
+    } else {
+      // Right input has been exhausted
+      false
+    }
+  }
+
+  private def advanceLeftUntilBoundConditionSatisfied(): Boolean = {
+    var foundMatch: Boolean = false
+    while (!foundMatch && leftIdx < smjScanner.getBufferedMatches.length) {
+      foundMatch = boundCondition(joinedRow.withLeft(smjScanner.getBufferedMatches(leftIdx)))
+      leftIdx += 1
+    }
+    foundMatch
+  }
+
+  override def advanceNext(): Boolean = {
+    advanceLeftUntilBoundConditionSatisfied() || advanceRight()
+  }
+
+  override def getRow: InternalRow = resultProj(joinedRow)
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index 5bef1d8966031..ae07eaf91c872 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -22,13 +22,14 @@ import org.scalatest.BeforeAndAfterEach
 import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.execution.joins._
-import org.apache.spark.sql.types.BinaryType
+import org.apache.spark.sql.test.SQLTestUtils
 
 
-class JoinSuite extends QueryTest with BeforeAndAfterEach {
+class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
   // Ensures tables are loaded.
   TestData
 
+  override def sqlContext: SQLContext = org.apache.spark.sql.test.TestSQLContext
   lazy val ctx = org.apache.spark.sql.test.TestSQLContext
   import ctx.implicits._
   import ctx.logicalPlanToSparkQuery
@@ -37,7 +38,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
     val x = testData2.as("x")
     val y = testData2.as("y")
     val join = x.join(y, $"x.a" === $"y.a", "inner").queryExecution.optimizedPlan
-    val planned = ctx.planner.HashJoin(join)
+    val planned = ctx.planner.EquiJoinSelection(join)
     assert(planned.size === 1)
   }
 
@@ -55,6 +56,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
       case j: BroadcastNestedLoopJoin => j
       case j: BroadcastLeftSemiJoinHash => j
       case j: SortMergeJoin => j
+      case j: SortMergeOuterJoin => j
     }
 
     assert(operators.size === 1)
@@ -66,7 +68,6 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
   test("join operator selection") {
     ctx.cacheManager.clearCache()
 
-    val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled
     Seq(
       ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash]),
       ("SELECT * FROM testData LEFT SEMI JOIN testData2", classOf[LeftSemiJoinBNL]),
@@ -83,11 +84,11 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
       ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[SortMergeJoin]),
       ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2", classOf[SortMergeJoin]),
       ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2", classOf[SortMergeJoin]),
-      ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
+      ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[SortMergeOuterJoin]),
       ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
-        classOf[ShuffledHashOuterJoin]),
+        classOf[SortMergeOuterJoin]),
       ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
-        classOf[ShuffledHashOuterJoin]),
+        classOf[SortMergeOuterJoin]),
       ("SELECT * FROM testData full outer join testData2 ON key = a",
         classOf[ShuffledHashOuterJoin]),
       ("SELECT * FROM testData left JOIN testData2 ON (key * a != key + a)",
@@ -97,82 +98,75 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
       ("SELECT * FROM testData full JOIN testData2 ON (key * a != key + a)",
         classOf[BroadcastNestedLoopJoin])
     ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    try {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true)
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
       Seq(
-        ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[SortMergeJoin]),
-        ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2", classOf[SortMergeJoin]),
-        ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2", classOf[SortMergeJoin])
+        ("SELECT * FROM testData JOIN testData2 ON key = a", classOf[ShuffledHashJoin]),
+        ("SELECT * FROM testData JOIN testData2 ON key = a and key = 2",
+          classOf[ShuffledHashJoin]),
+        ("SELECT * FROM testData JOIN testData2 ON key = a where key = 2",
+          classOf[ShuffledHashJoin]),
+        ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
+        ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
+          classOf[ShuffledHashOuterJoin]),
+        ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
+          classOf[ShuffledHashOuterJoin]),
+        ("SELECT * FROM testData full outer join testData2 ON key = a",
+          classOf[ShuffledHashOuterJoin])
       ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    } finally {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED)
     }
   }
 
   test("SortMergeJoin shouldn't work on unsortable columns") {
-    val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled
-    try {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true)
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
       Seq(
         ("SELECT * FROM arrayData JOIN complexData ON data = a", classOf[ShuffledHashJoin])
       ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    } finally {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED)
     }
   }
 
   test("broadcasted hash join operator selection") {
     ctx.cacheManager.clearCache()
     ctx.sql("CACHE TABLE testData")
-
-    val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled
-    Seq(
-      ("SELECT * FROM testData join testData2 ON key = a", classOf[BroadcastHashJoin]),
-      ("SELECT * FROM testData join testData2 ON key = a and key = 2", classOf[BroadcastHashJoin]),
-      ("SELECT * FROM testData join testData2 ON key = a where key = 2",
-        classOf[BroadcastHashJoin])
-    ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    try {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true)
-      Seq(
-        ("SELECT * FROM testData join testData2 ON key = a", classOf[BroadcastHashJoin]),
-        ("SELECT * FROM testData join testData2 ON key = a and key = 2",
-          classOf[BroadcastHashJoin]),
-        ("SELECT * FROM testData join testData2 ON key = a where key = 2",
-          classOf[BroadcastHashJoin])
-      ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    } finally {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED)
+    for (sortMergeJoinEnabled <- Seq(true, false)) {
+      withClue(s"sortMergeJoinEnabled=$sortMergeJoinEnabled") {
+        withSQLConf(SQLConf.SORTMERGE_JOIN.key -> s"$sortMergeJoinEnabled") {
+          Seq(
+            ("SELECT * FROM testData join testData2 ON key = a",
+              classOf[BroadcastHashJoin]),
+            ("SELECT * FROM testData join testData2 ON key = a and key = 2",
+              classOf[BroadcastHashJoin]),
+            ("SELECT * FROM testData join testData2 ON key = a where key = 2",
+              classOf[BroadcastHashJoin])
+          ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
+        }
+      }
     }
-
     ctx.sql("UNCACHE TABLE testData")
   }
 
   test("broadcasted hash outer join operator selection") {
     ctx.cacheManager.clearCache()
     ctx.sql("CACHE TABLE testData")
-
-    val SORTMERGEJOIN_ENABLED: Boolean = ctx.conf.sortMergeJoinEnabled
-    Seq(
-      ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
-      ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
-        classOf[BroadcastHashOuterJoin]),
-      ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
-        classOf[BroadcastHashOuterJoin])
-    ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    try {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, true)
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
       Seq(
-        ("SELECT * FROM testData LEFT JOIN testData2 ON key = a", classOf[ShuffledHashOuterJoin]),
+        ("SELECT * FROM testData LEFT JOIN testData2 ON key = a",
+          classOf[SortMergeOuterJoin]),
+        ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
+          classOf[BroadcastHashOuterJoin]),
+        ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
+          classOf[BroadcastHashOuterJoin])
+      ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
+    }
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      Seq(
+        ("SELECT * FROM testData LEFT JOIN testData2 ON key = a",
+          classOf[ShuffledHashOuterJoin]),
         ("SELECT * FROM testData RIGHT JOIN testData2 ON key = a where key = 2",
           classOf[BroadcastHashOuterJoin]),
         ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
           classOf[BroadcastHashOuterJoin])
       ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
-    } finally {
-      ctx.conf.setConf(SQLConf.SORTMERGE_JOIN, SORTMERGEJOIN_ENABLED)
     }
-
     ctx.sql("UNCACHE TABLE testData")
   }
 
@@ -180,7 +174,7 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
     val x = testData2.as("x")
     val y = testData2.as("y")
     val join = x.join(y, ($"x.a" === $"y.a") && ($"x.b" === $"y.b")).queryExecution.optimizedPlan
-    val planned = ctx.planner.HashJoin(join)
+    val planned = ctx.planner.EquiJoinSelection(join)
     assert(planned.size === 1)
   }
 
@@ -457,25 +451,24 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
   test("broadcasted left semi join operator selection") {
     ctx.cacheManager.clearCache()
     ctx.sql("CACHE TABLE testData")
-    val tmp = ctx.conf.autoBroadcastJoinThreshold
 
-    ctx.sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=1000000000")
-    Seq(
-      ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a",
-        classOf[BroadcastLeftSemiJoinHash])
-    ).foreach {
-      case (query, joinClass) => assertJoin(query, joinClass)
+    withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "1000000000") {
+      Seq(
+        ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a",
+          classOf[BroadcastLeftSemiJoinHash])
+      ).foreach {
+        case (query, joinClass) => assertJoin(query, joinClass)
+      }
     }
 
-    ctx.sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=-1")
-
-    Seq(
-      ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash])
-    ).foreach {
-      case (query, joinClass) => assertJoin(query, joinClass)
+    withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1") {
+      Seq(
+        ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash])
+      ).foreach {
+        case (query, joinClass) => assertJoin(query, joinClass)
+      }
     }
 
-    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, tmp)
     ctx.sql("UNCACHE TABLE testData")
   }
 
@@ -488,6 +481,5 @@ class JoinSuite extends QueryTest with BeforeAndAfterEach {
         Row(2, 2) ::
         Row(3, 1) ::
         Row(3, 2) :: Nil)
-
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
new file mode 100644
index 0000000000000..ddff7cebcc17d
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
@@ -0,0 +1,180 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.Inner
+import org.apache.spark.sql.catalyst.plans.logical.Join
+import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.types.{IntegerType, StringType, StructType}
+import org.apache.spark.sql.{SQLConf, execution, Row, DataFrame}
+import org.apache.spark.sql.catalyst.expressions.Expression
+import org.apache.spark.sql.execution._
+
+class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
+
+  private def testInnerJoin(
+      testName: String,
+      leftRows: DataFrame,
+      rightRows: DataFrame,
+      condition: Expression,
+      expectedAnswer: Seq[Product]): Unit = {
+    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
+    ExtractEquiJoinKeys.unapply(join).foreach {
+      case (joinType, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
+
+        def makeBroadcastHashJoin(left: SparkPlan, right: SparkPlan, side: BuildSide) = {
+          val broadcastHashJoin =
+            execution.joins.BroadcastHashJoin(leftKeys, rightKeys, side, left, right)
+          boundCondition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin)
+        }
+
+        def makeShuffledHashJoin(left: SparkPlan, right: SparkPlan, side: BuildSide) = {
+          val shuffledHashJoin =
+            execution.joins.ShuffledHashJoin(leftKeys, rightKeys, side, left, right)
+          val filteredJoin =
+            boundCondition.map(Filter(_, shuffledHashJoin)).getOrElse(shuffledHashJoin)
+          EnsureRequirements(sqlContext).apply(filteredJoin)
+        }
+
+        def makeSortMergeJoin(left: SparkPlan, right: SparkPlan) = {
+          val sortMergeJoin =
+            execution.joins.SortMergeJoin(leftKeys, rightKeys, left, right)
+          val filteredJoin = boundCondition.map(Filter(_, sortMergeJoin)).getOrElse(sortMergeJoin)
+          EnsureRequirements(sqlContext).apply(filteredJoin)
+        }
+
+        test(s"$testName using BroadcastHashJoin (build=left)") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              makeBroadcastHashJoin(left, right, joins.BuildLeft),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+
+        test(s"$testName using BroadcastHashJoin (build=right)") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              makeBroadcastHashJoin(left, right, joins.BuildRight),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+
+        test(s"$testName using ShuffledHashJoin (build=left)") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              makeShuffledHashJoin(left, right, joins.BuildLeft),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+
+        test(s"$testName using ShuffledHashJoin (build=right)") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              makeShuffledHashJoin(left, right, joins.BuildRight),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+
+        test(s"$testName using SortMergeJoin") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              makeSortMergeJoin(left, right),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+    }
+  }
+
+  {
+    val upperCaseData = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+      Row(1, "A"),
+      Row(2, "B"),
+      Row(3, "C"),
+      Row(4, "D"),
+      Row(5, "E"),
+      Row(6, "F"),
+      Row(null, "G")
+    )), new StructType().add("N", IntegerType).add("L", StringType))
+
+    val lowerCaseData = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+      Row(1, "a"),
+      Row(2, "b"),
+      Row(3, "c"),
+      Row(4, "d"),
+      Row(null, "e")
+    )), new StructType().add("n", IntegerType).add("l", StringType))
+
+    testInnerJoin(
+      "inner join, one match per row",
+      upperCaseData,
+      lowerCaseData,
+      (upperCaseData.col("N") === lowerCaseData.col("n")).expr,
+      Seq(
+        (1, "A", 1, "a"),
+        (2, "B", 2, "b"),
+        (3, "C", 3, "c"),
+        (4, "D", 4, "d")
+      )
+    )
+  }
+
+  private val testData2 = Seq(
+    (1, 1),
+    (1, 2),
+    (2, 1),
+    (2, 2),
+    (3, 1),
+    (3, 2)
+  ).toDF("a", "b")
+
+  {
+    val left = testData2.where("a = 1")
+    val right = testData2.where("a = 1")
+    testInnerJoin(
+      "inner join, multiple matches",
+      left,
+      right,
+      (left.col("a") === right.col("a")).expr,
+      Seq(
+        (1, 1, 1, 1),
+        (1, 1, 1, 2),
+        (1, 2, 1, 1),
+        (1, 2, 1, 2)
+      )
+    )
+  }
+
+  {
+    val left = testData2.where("a = 1")
+    val right = testData2.where("a = 2")
+    testInnerJoin(
+      "inner join, no matches",
+      left,
+      right,
+      (left.col("a") === right.col("a")).expr,
+      Seq.empty
+    )
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
index 2c27da596bc4f..e16f5e39aa2f4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
@@ -1,89 +1,221 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.execution.joins
-
-import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.catalyst.expressions.{Expression, LessThan}
-import org.apache.spark.sql.catalyst.plans.{FullOuter, LeftOuter, RightOuter}
-import org.apache.spark.sql.execution.{SparkPlan, SparkPlanTest}
-
-class OuterJoinSuite extends SparkPlanTest {
-
-  val left = Seq(
-    (1, 2.0),
-    (2, 1.0),
-    (3, 3.0)
-  ).toDF("a", "b")
-
-  val right = Seq(
-    (2, 3.0),
-    (3, 2.0),
-    (4, 1.0)
-  ).toDF("c", "d")
-
-  val leftKeys: List[Expression] = 'a :: Nil
-  val rightKeys: List[Expression] = 'c :: Nil
-  val condition = Some(LessThan('b, 'd))
-
-  test("shuffled hash outer join") {
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      ShuffledHashOuterJoin(leftKeys, rightKeys, LeftOuter, condition, left, right),
-      Seq(
-        (1, 2.0, null, null),
-        (2, 1.0, 2, 3.0),
-        (3, 3.0, null, null)
-      ).map(Row.fromTuple))
-
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      ShuffledHashOuterJoin(leftKeys, rightKeys, RightOuter, condition, left, right),
-      Seq(
-        (2, 1.0, 2, 3.0),
-        (null, null, 3, 2.0),
-        (null, null, 4, 1.0)
-      ).map(Row.fromTuple))
-
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      ShuffledHashOuterJoin(leftKeys, rightKeys, FullOuter, condition, left, right),
-      Seq(
-        (1, 2.0, null, null),
-        (2, 1.0, 2, 3.0),
-        (3, 3.0, null, null),
-        (null, null, 3, 2.0),
-        (null, null, 4, 1.0)
-      ).map(Row.fromTuple))
-  }
-
-  test("broadcast hash outer join") {
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      BroadcastHashOuterJoin(leftKeys, rightKeys, LeftOuter, condition, left, right),
-      Seq(
-        (1, 2.0, null, null),
-        (2, 1.0, 2, 3.0),
-        (3, 3.0, null, null)
-      ).map(Row.fromTuple))
-
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      BroadcastHashOuterJoin(leftKeys, rightKeys, RightOuter, condition, left, right),
-      Seq(
-        (2, 1.0, 2, 3.0),
-        (null, null, 3, 2.0),
-        (null, null, 4, 1.0)
-      ).map(Row.fromTuple))
-  }
-}
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.joins
+
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.logical.Join
+import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.types.{IntegerType, DoubleType, StructType}
+import org.apache.spark.sql.{SQLConf, DataFrame, Row}
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans._
+import org.apache.spark.sql.execution.{EnsureRequirements, joins, SparkPlan, SparkPlanTest}
+
+class OuterJoinSuite extends SparkPlanTest with SQLTestUtils {
+
+  private def testOuterJoin(
+      testName: String,
+      leftRows: DataFrame,
+      rightRows: DataFrame,
+      joinType: JoinType,
+      condition: Expression,
+      expectedAnswer: Seq[Product]): Unit = {
+    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
+    ExtractEquiJoinKeys.unapply(join).foreach {
+      case (_, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
+        test(s"$testName using ShuffledHashOuterJoin") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              EnsureRequirements(sqlContext).apply(
+                ShuffledHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+
+        if (joinType != FullOuter) {
+          test(s"$testName using BroadcastHashOuterJoin") {
+            withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+              checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+                BroadcastHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right),
+                expectedAnswer.map(Row.fromTuple),
+                sortAnswers = true)
+            }
+          }
+
+          test(s"$testName using SortMergeOuterJoin") {
+            withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+              checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+                EnsureRequirements(sqlContext).apply(
+                  SortMergeOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
+                expectedAnswer.map(Row.fromTuple),
+                sortAnswers = false)
+            }
+          }
+        }
+    }
+
+    test(s"$testName using BroadcastNestedLoopJoin (build=left)") {
+      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+          joins.BroadcastNestedLoopJoin(left, right, joins.BuildLeft, joinType, Some(condition)),
+          expectedAnswer.map(Row.fromTuple),
+          sortAnswers = true)
+      }
+    }
+
+    test(s"$testName using BroadcastNestedLoopJoin (build=right)") {
+      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+          joins.BroadcastNestedLoopJoin(left, right, joins.BuildRight, joinType, Some(condition)),
+          expectedAnswer.map(Row.fromTuple),
+          sortAnswers = true)
+      }
+    }
+  }
+
+  val left = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+    Row(1, 2.0),
+    Row(2, 100.0),
+    Row(2, 1.0), // This row is duplicated to ensure that we will have multiple buffered matches
+    Row(2, 1.0),
+    Row(3, 3.0),
+    Row(5, 1.0),
+    Row(6, 6.0),
+    Row(null, null)
+  )), new StructType().add("a", IntegerType).add("b", DoubleType))
+
+  val right = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+    Row(0, 0.0),
+    Row(2, 3.0), // This row is duplicated to ensure that we will have multiple buffered matches
+    Row(2, -1.0),
+    Row(2, -1.0),
+    Row(2, 3.0),
+    Row(3, 2.0),
+    Row(4, 1.0),
+    Row(5, 3.0),
+    Row(7, 7.0),
+    Row(null, null)
+  )), new StructType().add("c", IntegerType).add("d", DoubleType))
+
+  val condition = {
+    And(
+      (left.col("a") === right.col("c")).expr,
+      LessThan(left.col("b").expr, right.col("d").expr))
+  }
+
+  // --- Basic outer joins ------------------------------------------------------------------------
+
+  testOuterJoin(
+    "basic left outer join",
+    left,
+    right,
+    LeftOuter,
+    condition,
+    Seq(
+      (null, null, null, null),
+      (1, 2.0, null, null),
+      (2, 100.0, null, null),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (3, 3.0, null, null),
+      (5, 1.0, 5, 3.0),
+      (6, 6.0, null, null)
+    )
+  )
+
+  testOuterJoin(
+    "basic right outer join",
+    left,
+    right,
+    RightOuter,
+    condition,
+    Seq(
+      (null, null, null, null),
+      (null, null, 0, 0.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (null, null, 2, -1.0),
+      (null, null, 2, -1.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (null, null, 3, 2.0),
+      (null, null, 4, 1.0),
+      (5, 1.0, 5, 3.0),
+      (null, null, 7, 7.0)
+    )
+  )
+
+  testOuterJoin(
+    "basic full outer join",
+    left,
+    right,
+    FullOuter,
+    condition,
+    Seq(
+      (1, 2.0, null, null),
+      (null, null, 2, -1.0),
+      (null, null, 2, -1.0),
+      (2, 100.0, null, null),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (2, 1.0, 2, 3.0),
+      (3, 3.0, null, null),
+      (5, 1.0, 5, 3.0),
+      (6, 6.0, null, null),
+      (null, null, 0, 0.0),
+      (null, null, 3, 2.0),
+      (null, null, 4, 1.0),
+      (null, null, 7, 7.0),
+      (null, null, null, null),
+      (null, null, null, null)
+    )
+  )
+
+  // --- Both inputs empty ------------------------------------------------------------------------
+
+  testOuterJoin(
+    "left outer join with both inputs empty",
+    left.filter("false"),
+    right.filter("false"),
+    LeftOuter,
+    condition,
+    Seq.empty
+  )
+
+  testOuterJoin(
+    "right outer join with both inputs empty",
+    left.filter("false"),
+    right.filter("false"),
+    RightOuter,
+    condition,
+    Seq.empty
+  )
+
+  testOuterJoin(
+    "full outer join with both inputs empty",
+    left.filter("false"),
+    right.filter("false"),
+    FullOuter,
+    condition,
+    Seq.empty
+  )
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
index 927e85a7db3dc..4503ed251fcb1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
@@ -17,58 +17,91 @@
 
 package org.apache.spark.sql.execution.joins
 
-import org.apache.spark.sql.Row
-import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.catalyst.expressions.{LessThan, Expression}
-import org.apache.spark.sql.execution.{SparkPlan, SparkPlanTest}
+import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans.Inner
+import org.apache.spark.sql.catalyst.plans.logical.Join
+import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.types.{DoubleType, IntegerType, StructType}
+import org.apache.spark.sql.{SQLConf, DataFrame, Row}
+import org.apache.spark.sql.catalyst.expressions.{And, LessThan, Expression}
+import org.apache.spark.sql.execution.{EnsureRequirements, SparkPlan, SparkPlanTest}
 
+class SemiJoinSuite extends SparkPlanTest with SQLTestUtils {
 
-class SemiJoinSuite extends SparkPlanTest{
-  val left = Seq(
-    (1, 2.0),
-    (1, 2.0),
-    (2, 1.0),
-    (2, 1.0),
-    (3, 3.0)
-  ).toDF("a", "b")
+  private def testLeftSemiJoin(
+      testName: String,
+      leftRows: DataFrame,
+      rightRows: DataFrame,
+      condition: Expression,
+      expectedAnswer: Seq[Product]): Unit = {
+    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
+    ExtractEquiJoinKeys.unapply(join).foreach {
+      case (joinType, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
+        test(s"$testName using LeftSemiJoinHash") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              EnsureRequirements(left.sqlContext).apply(
+                LeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition)),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
 
-  val right = Seq(
-    (2, 3.0),
-    (2, 3.0),
-    (3, 2.0),
-    (4, 1.0)
-  ).toDF("c", "d")
+        test(s"$testName using BroadcastLeftSemiJoinHash") {
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              BroadcastLeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
+        }
+    }
 
-  val leftKeys: List[Expression] = 'a :: Nil
-  val rightKeys: List[Expression] = 'c :: Nil
-  val condition = Some(LessThan('b, 'd))
-
-  test("left semi join hash") {
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      LeftSemiJoinHash(leftKeys, rightKeys, left, right, condition),
-      Seq(
-        (2, 1.0),
-        (2, 1.0)
-      ).map(Row.fromTuple))
+    test(s"$testName using LeftSemiJoinBNL") {
+      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+          LeftSemiJoinBNL(left, right, Some(condition)),
+          expectedAnswer.map(Row.fromTuple),
+          sortAnswers = true)
+      }
+    }
   }
 
-  test("left semi join BNL") {
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      LeftSemiJoinBNL(left, right, condition),
-      Seq(
-        (1, 2.0),
-        (1, 2.0),
-        (2, 1.0),
-        (2, 1.0)
-      ).map(Row.fromTuple))
-  }
+  val left = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+    Row(1, 2.0),
+    Row(1, 2.0),
+    Row(2, 1.0),
+    Row(2, 1.0),
+    Row(3, 3.0),
+    Row(null, null),
+    Row(null, 5.0),
+    Row(6, null)
+  )), new StructType().add("a", IntegerType).add("b", DoubleType))
 
-  test("broadcast left semi join hash") {
-    checkAnswer2(left, right, (left: SparkPlan, right: SparkPlan) =>
-      BroadcastLeftSemiJoinHash(leftKeys, rightKeys, left, right, condition),
-      Seq(
-        (2, 1.0),
-        (2, 1.0)
-      ).map(Row.fromTuple))
+  val right = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+    Row(2, 3.0),
+    Row(2, 3.0),
+    Row(3, 2.0),
+    Row(4, 1.0),
+    Row(null, null),
+    Row(null, 5.0),
+    Row(6, null)
+  )), new StructType().add("c", IntegerType).add("d", DoubleType))
+
+  val condition = {
+    And(
+      (left.col("a") === right.col("c")).expr,
+      LessThan(left.col("b").expr, right.col("d").expr))
   }
+
+  testLeftSemiJoin(
+    "basic test",
+    left,
+    right,
+    condition,
+    Seq(
+      (2, 1.0),
+      (2, 1.0)
+    )
+  )
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
index 4c11acdab9ec0..1066695589778 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
@@ -27,7 +27,7 @@ import org.apache.spark.sql.SQLContext
 import org.apache.spark.util.Utils
 
 trait SQLTestUtils { this: SparkFunSuite =>
-  def sqlContext: SQLContext
+  protected def sqlContext: SQLContext
 
   protected def configuration = sqlContext.sparkContext.hadoopConfiguration
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 567d7fa12ff14..f17177a771c3b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -531,7 +531,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
       HashAggregation,
       Aggregation,
       LeftSemiJoin,
-      HashJoin,
+      EquiJoinSelection,
       BasicOperators,
       CartesianProduct,
       BroadcastNestedLoopJoin

From 0f90d6055e5bea9ceb1d454db84f4aa1d59b284d Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Mon, 10 Aug 2015 23:41:53 -0700
Subject: [PATCH 157/633] [SPARK-9640] [STREAMING] [TEST] Do not run Python
 Kinesis tests when the Kinesis assembly JAR has not been generated

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #7961 from tdas/SPARK-9640 and squashes the following commits:

974ce19 [Tathagata Das] Undo changes related to SPARK-9727
004ae26 [Tathagata Das] style fixes
9bbb97d [Tathagata Das] Minor style fies
e6a677e [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into SPARK-9640
ca90719 [Tathagata Das] Removed extra line
ba9cfc7 [Tathagata Das] Improved kinesis test selection logic
88d59bd [Tathagata Das] updated test modules
871fcc8 [Tathagata Das] Fixed SparkBuild
94be631 [Tathagata Das] Fixed style
b858196 [Tathagata Das] Fixed conditions and few other things based on PR comments.
e292e64 [Tathagata Das] Added filters for Kinesis python tests
---
 python/pyspark/streaming/tests.py | 56 ++++++++++++++++++++++++-------
 1 file changed, 44 insertions(+), 12 deletions(-)

diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 66ae3345f468f..f0ed415f97120 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -971,8 +971,10 @@ def test_kinesis_stream_api(self):
             "awsAccessKey", "awsSecretKey")
 
     def test_kinesis_stream(self):
-        if os.environ.get('ENABLE_KINESIS_TESTS') != '1':
-            print("Skip test_kinesis_stream")
+        if not are_kinesis_tests_enabled:
+            sys.stderr.write(
+                "Skipped test_kinesis_stream (enable by setting environment variable %s=1"
+                % kinesis_test_environ_var)
             return
 
         import random
@@ -1013,6 +1015,7 @@ def get_output(_, rdd):
             traceback.print_exc()
             raise
         finally:
+            self.ssc.stop(False)
             kinesisTestUtils.deleteStream()
             kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
 
@@ -1027,7 +1030,7 @@ def search_kafka_assembly_jar():
             ("Failed to find Spark Streaming kafka assembly jar in %s. " % kafka_assembly_dir) +
             "You need to build Spark with "
             "'build/sbt assembly/assembly streaming-kafka-assembly/assembly' or "
-            "'build/mvn package' before running this test")
+            "'build/mvn package' before running this test.")
     elif len(jars) > 1:
         raise Exception(("Found multiple Spark Streaming Kafka assembly JARs in %s; please "
                          "remove all but one") % kafka_assembly_dir)
@@ -1045,7 +1048,7 @@ def search_flume_assembly_jar():
             ("Failed to find Spark Streaming Flume assembly jar in %s. " % flume_assembly_dir) +
             "You need to build Spark with "
             "'build/sbt assembly/assembly streaming-flume-assembly/assembly' or "
-            "'build/mvn package' before running this test")
+            "'build/mvn package' before running this test.")
     elif len(jars) > 1:
         raise Exception(("Found multiple Spark Streaming Flume assembly JARs in %s; please "
                         "remove all but one") % flume_assembly_dir)
@@ -1095,11 +1098,7 @@ def search_kinesis_asl_assembly_jar():
         os.path.join(kinesis_asl_assembly_dir,
                      "target/scala-*/spark-streaming-kinesis-asl-assembly-*.jar"))
     if not jars:
-        raise Exception(
-            ("Failed to find Spark Streaming Kinesis ASL assembly jar in %s. " %
-             kinesis_asl_assembly_dir) + "You need to build Spark with "
-            "'build/sbt -Pkinesis-asl assembly/assembly streaming-kinesis-asl-assembly/assembly' "
-            "or 'build/mvn -Pkinesis-asl package' before running this test")
+        return None
     elif len(jars) > 1:
         raise Exception(("Found multiple Spark Streaming Kinesis ASL assembly JARs in %s; please "
                          "remove all but one") % kinesis_asl_assembly_dir)
@@ -1107,6 +1106,10 @@ def search_kinesis_asl_assembly_jar():
         return jars[0]
 
 
+# Must be same as the variable and condition defined in KinesisTestUtils.scala
+kinesis_test_environ_var = "ENABLE_KINESIS_TESTS"
+are_kinesis_tests_enabled = os.environ.get(kinesis_test_environ_var) == '1'
+
 if __name__ == "__main__":
     kafka_assembly_jar = search_kafka_assembly_jar()
     flume_assembly_jar = search_flume_assembly_jar()
@@ -1114,8 +1117,37 @@ def search_kinesis_asl_assembly_jar():
     mqtt_test_jar = search_mqtt_test_jar()
     kinesis_asl_assembly_jar = search_kinesis_asl_assembly_jar()
 
-    jars = "%s,%s,%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, kinesis_asl_assembly_jar,
-                               mqtt_assembly_jar, mqtt_test_jar)
+    if kinesis_asl_assembly_jar is None:
+        kinesis_jar_present = False
+        jars = "%s,%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, mqtt_assembly_jar,
+                                mqtt_test_jar)
+    else:
+        kinesis_jar_present = True
+        jars = "%s,%s,%s,%s,%s" % (kafka_assembly_jar, flume_assembly_jar, mqtt_assembly_jar,
+                                   mqtt_test_jar, kinesis_asl_assembly_jar)
 
     os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars
-    unittest.main()
+    testcases = [BasicOperationTests, WindowFunctionTests, StreamingContextTests,
+                 CheckpointTests, KafkaStreamTests, FlumeStreamTests, FlumePollingStreamTests]
+
+    if kinesis_jar_present is True:
+        testcases.append(KinesisStreamTests)
+    elif are_kinesis_tests_enabled is False:
+        sys.stderr.write("Skipping all Kinesis Python tests as the optional Kinesis project was "
+                         "not compiled with -Pkinesis-asl profile. To run these tests, "
+                         "you need to build Spark with 'build/sbt -Pkinesis-asl assembly/assembly "
+                         "streaming-kinesis-asl-assembly/assembly' or "
+                         "'build/mvn -Pkinesis-asl package' before running this test.")
+    else:
+        raise Exception(
+            ("Failed to find Spark Streaming Kinesis assembly jar in %s. "
+             % kinesis_asl_assembly_dir) +
+            "You need to build Spark with 'build/sbt -Pkinesis-asl "
+            "assembly/assembly streaming-kinesis-asl-assembly/assembly'"
+            "or 'build/mvn -Pkinesis-asl package' before running this test.")
+
+    sys.stderr.write("Running tests: %s \n" % (str(testcases)))
+    for testcase in testcases:
+        sys.stderr.write("[Running %s]\n" % (testcase))
+        tests = unittest.TestLoader().loadTestsFromTestCase(testcase)
+        unittest.TextTestRunner(verbosity=2).run(tests)

From 55752d88321925da815823f968128832de6fdbbb Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 11 Aug 2015 01:08:30 -0700
Subject: [PATCH 158/633] [SPARK-9810] [BUILD] Remove individual commit
 messages from the squash commit message

For more information, please see the JIRA ticket and the associated dev list discussion.

https://issues.apache.org/jira/browse/SPARK-9810

http://apache-spark-developers-list.1001551.n3.nabble.com/discuss-Removing-individual-commit-messages-from-the-squash-commit-message-td13295.html

Author: Reynold Xin <rxin@databricks.com>

Closes #8091 from rxin/SPARK-9810.
---
 dev/merge_spark_pr.py | 6 +-----
 1 file changed, 1 insertion(+), 5 deletions(-)

diff --git a/dev/merge_spark_pr.py b/dev/merge_spark_pr.py
index ad4b76695c9ff..b9bdec3d70864 100755
--- a/dev/merge_spark_pr.py
+++ b/dev/merge_spark_pr.py
@@ -159,11 +159,7 @@ def merge_pr(pr_num, target_ref, title, body, pr_repo_desc):
         merge_message_flags += ["-m", message]
 
     # The string "Closes #%s" string is required for GitHub to correctly close the PR
-    merge_message_flags += [
-        "-m",
-        "Closes #%s from %s and squashes the following commits:" % (pr_num, pr_repo_desc)]
-    for c in commits:
-        merge_message_flags += ["-m", c]
+    merge_message_flags += ["-m", "Closes #%s from %s." % (pr_num, pr_repo_desc)]
 
     run_cmd(['git', 'commit', '--author="%s"' % primary_author] + merge_message_flags)
 

From 600031ebe27473d8fffe6ea436c2149223b82896 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 11 Aug 2015 02:41:03 -0700
Subject: [PATCH 159/633] [SPARK-9727] [STREAMING] [BUILD] Updated streaming
 kinesis SBT project name to be more consistent

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8092 from tdas/SPARK-9727 and squashes the following commits:

b1b01fd [Tathagata Das] Updated streaming kinesis project name
---
 dev/sparktestsupport/modules.py | 4 ++--
 extras/kinesis-asl/pom.xml      | 2 +-
 project/SparkBuild.scala        | 4 ++--
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index d82c0cca37bc6..346452f3174e4 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -134,7 +134,7 @@ def contains_file(self, filename):
 # files in streaming_kinesis_asl are changed, so that if Kinesis experiences an outage, we don't
 # fail other PRs.
 streaming_kinesis_asl = Module(
-    name="kinesis-asl",
+    name="streaming-kinesis-asl",
     dependencies=[],
     source_file_regexes=[
         "extras/kinesis-asl/",
@@ -147,7 +147,7 @@ def contains_file(self, filename):
         "ENABLE_KINESIS_TESTS": "1"
     },
     sbt_test_goals=[
-        "kinesis-asl/test",
+        "streaming-kinesis-asl/test",
     ]
 )
 
diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml
index c242e7a57b9ab..521b53e230c4a 100644
--- a/extras/kinesis-asl/pom.xml
+++ b/extras/kinesis-asl/pom.xml
@@ -31,7 +31,7 @@
   <name>Spark Kinesis Integration</name>
 
   <properties>
-    <sbt.project.name>kinesis-asl</sbt.project.name>
+    <sbt.project.name>streaming-kinesis-asl</sbt.project.name>
   </properties>
 
   <dependencies>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 41a85fa9de778..cad7067ade8c1 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -42,8 +42,8 @@ object BuildCommons {
       "streaming-zeromq", "launcher", "unsafe").map(ProjectRef(buildLocation, _))
 
   val optionallyEnabledProjects@Seq(yarn, yarnStable, java8Tests, sparkGangliaLgpl,
-    sparkKinesisAsl) = Seq("yarn", "yarn-stable", "java8-tests", "ganglia-lgpl",
-    "kinesis-asl").map(ProjectRef(buildLocation, _))
+    streamingKinesisAsl) = Seq("yarn", "yarn-stable", "java8-tests", "ganglia-lgpl",
+    "streaming-kinesis-asl").map(ProjectRef(buildLocation, _))
 
   val assemblyProjects@Seq(assembly, examples, networkYarn, streamingFlumeAssembly, streamingKafkaAssembly, streamingMqttAssembly, streamingKinesisAslAssembly) =
     Seq("assembly", "examples", "network-yarn", "streaming-flume-assembly", "streaming-kafka-assembly", "streaming-mqtt-assembly", "streaming-kinesis-asl-assembly")

From d378396f86f625f006738d87fe5dbc2ff8fd913d Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 11 Aug 2015 08:41:06 -0700
Subject: [PATCH 160/633] [SPARK-9815] Rename PlatformDependent.UNSAFE ->
 Platform.

PlatformDependent.UNSAFE is way too verbose.

Author: Reynold Xin <rxin@databricks.com>

Closes #8094 from rxin/SPARK-9815 and squashes the following commits:

229b603 [Reynold Xin] [SPARK-9815] Rename PlatformDependent.UNSAFE -> Platform.
---
 .../serializer/DummySerializerInstance.java   |   6 +-
 .../unsafe/UnsafeShuffleExternalSorter.java   |  22 +--
 .../shuffle/unsafe/UnsafeShuffleWriter.java   |   4 +-
 .../spark/unsafe/map/BytesToBytesMap.java     |  20 +--
 .../unsafe/sort/PrefixComparators.java        |   5 +-
 .../unsafe/sort/UnsafeExternalSorter.java     |  22 +--
 .../unsafe/sort/UnsafeInMemorySorter.java     |   4 +-
 .../unsafe/sort/UnsafeSorterSpillReader.java  |   4 +-
 .../unsafe/sort/UnsafeSorterSpillWriter.java  |   6 +-
 .../UnsafeShuffleInMemorySorterSuite.java     |  20 +--
 .../map/AbstractBytesToBytesMapSuite.java     |  94 +++++-----
 .../sort/UnsafeExternalSorterSuite.java       |  20 +--
 .../sort/UnsafeInMemorySorterSuite.java       |  20 +--
 .../catalyst/expressions/UnsafeArrayData.java |  51 ++----
 .../catalyst/expressions/UnsafeReaders.java   |   8 +-
 .../sql/catalyst/expressions/UnsafeRow.java   | 108 +++++------
 .../expressions/UnsafeRowWriters.java         |  41 ++---
 .../catalyst/expressions/UnsafeWriters.java   |  43 ++---
 .../execution/UnsafeExternalRowSorter.java    |   4 +-
 .../expressions/codegen/CodeGenerator.scala   |   4 +-
 .../codegen/GenerateUnsafeProjection.scala    |  32 ++--
 .../codegen/GenerateUnsafeRowJoiner.scala     |  16 +-
 .../expressions/stringOperations.scala        |   4 +-
 .../GenerateUnsafeRowJoinerBitsetSuite.scala  |   4 +-
 .../UnsafeFixedWidthAggregationMap.java       |   4 +-
 .../sql/execution/UnsafeKVExternalSorter.java |   4 +-
 .../sql/execution/UnsafeRowSerializer.scala   |   6 +-
 .../sql/execution/joins/HashedRelation.scala  |  13 +-
 .../org/apache/spark/sql/UnsafeRowSuite.scala |   4 +-
 .../{PlatformDependent.java => Platform.java} | 170 ++++++++----------
 .../spark/unsafe/array/ByteArrayMethods.java  |  14 +-
 .../apache/spark/unsafe/array/LongArray.java  |   6 +-
 .../spark/unsafe/bitset/BitSetMethods.java    |  19 +-
 .../spark/unsafe/hash/Murmur3_x86_32.java     |   4 +-
 .../spark/unsafe/memory/MemoryBlock.java      |   4 +-
 .../unsafe/memory/UnsafeMemoryAllocator.java  |   6 +-
 .../apache/spark/unsafe/types/ByteArray.java  |  10 +-
 .../apache/spark/unsafe/types/UTF8String.java |  30 ++--
 .../unsafe/hash/Murmur3_x86_32Suite.java      |  14 +-
 39 files changed, 371 insertions(+), 499 deletions(-)
 rename unsafe/src/main/java/org/apache/spark/unsafe/{PlatformDependent.java => Platform.java} (55%)

diff --git a/core/src/main/java/org/apache/spark/serializer/DummySerializerInstance.java b/core/src/main/java/org/apache/spark/serializer/DummySerializerInstance.java
index 0399abc63c235..0e58bb4f7101c 100644
--- a/core/src/main/java/org/apache/spark/serializer/DummySerializerInstance.java
+++ b/core/src/main/java/org/apache/spark/serializer/DummySerializerInstance.java
@@ -25,7 +25,7 @@
 import scala.reflect.ClassTag;
 
 import org.apache.spark.annotation.Private;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * Unfortunately, we need a serializer instance in order to construct a DiskBlockObjectWriter.
@@ -49,7 +49,7 @@ public void flush() {
         try {
           s.flush();
         } catch (IOException e) {
-          PlatformDependent.throwException(e);
+          Platform.throwException(e);
         }
       }
 
@@ -64,7 +64,7 @@ public void close() {
         try {
           s.close();
         } catch (IOException e) {
-          PlatformDependent.throwException(e);
+          Platform.throwException(e);
         }
       }
     };
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index 925b60a145886..3d1ef0c48adc5 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -37,7 +37,7 @@
 import org.apache.spark.storage.BlockManager;
 import org.apache.spark.storage.DiskBlockObjectWriter;
 import org.apache.spark.storage.TempShuffleBlockId;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
@@ -211,16 +211,12 @@ private void writeSortedFile(boolean isLastFile) throws IOException {
       final long recordPointer = sortedRecords.packedRecordPointer.getRecordPointer();
       final Object recordPage = taskMemoryManager.getPage(recordPointer);
       final long recordOffsetInPage = taskMemoryManager.getOffsetInPage(recordPointer);
-      int dataRemaining = PlatformDependent.UNSAFE.getInt(recordPage, recordOffsetInPage);
+      int dataRemaining = Platform.getInt(recordPage, recordOffsetInPage);
       long recordReadPosition = recordOffsetInPage + 4; // skip over record length
       while (dataRemaining > 0) {
         final int toTransfer = Math.min(DISK_WRITE_BUFFER_SIZE, dataRemaining);
-        PlatformDependent.copyMemory(
-          recordPage,
-          recordReadPosition,
-          writeBuffer,
-          PlatformDependent.BYTE_ARRAY_OFFSET,
-          toTransfer);
+        Platform.copyMemory(
+          recordPage, recordReadPosition, writeBuffer, Platform.BYTE_ARRAY_OFFSET, toTransfer);
         writer.write(writeBuffer, 0, toTransfer);
         recordReadPosition += toTransfer;
         dataRemaining -= toTransfer;
@@ -447,14 +443,10 @@ public void insertRecord(
 
     final long recordAddress =
       taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
+    Platform.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
     dataPagePosition += 4;
-    PlatformDependent.copyMemory(
-      recordBaseObject,
-      recordBaseOffset,
-      dataPageBaseObject,
-      dataPagePosition,
-      lengthInBytes);
+    Platform.copyMemory(
+      recordBaseObject, recordBaseOffset, dataPageBaseObject, dataPagePosition, lengthInBytes);
     assert(inMemSorter != null);
     inMemSorter.insertRecord(recordAddress, partitionId);
   }
diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
index 02084f9122e00..2389c28b28395 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
@@ -53,7 +53,7 @@
 import org.apache.spark.shuffle.ShuffleWriter;
 import org.apache.spark.storage.BlockManager;
 import org.apache.spark.storage.TimeTrackingOutputStream;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
 
 @Private
@@ -244,7 +244,7 @@ void insertRecordIntoSorter(Product2<K, V> record) throws IOException {
     assert (serializedRecordSize > 0);
 
     sorter.insertRecord(
-      serBuffer.getBuf(), PlatformDependent.BYTE_ARRAY_OFFSET, serializedRecordSize, partitionId);
+      serBuffer.getBuf(), Platform.BYTE_ARRAY_OFFSET, serializedRecordSize, partitionId);
   }
 
   @VisibleForTesting
diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 7f79cd13aab43..85b46ec8bfae3 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -270,10 +270,10 @@ public boolean hasNext() {
 
     @Override
     public Location next() {
-      int totalLength = PlatformDependent.UNSAFE.getInt(pageBaseObject, offsetInPage);
+      int totalLength = Platform.getInt(pageBaseObject, offsetInPage);
       if (totalLength == END_OF_PAGE_MARKER) {
         advanceToNextPage();
-        totalLength = PlatformDependent.UNSAFE.getInt(pageBaseObject, offsetInPage);
+        totalLength = Platform.getInt(pageBaseObject, offsetInPage);
       }
       loc.with(currentPage, offsetInPage);
       offsetInPage += 4 + totalLength;
@@ -402,9 +402,9 @@ private void updateAddressesAndSizes(long fullKeyAddress) {
 
     private void updateAddressesAndSizes(final Object page, final long offsetInPage) {
       long position = offsetInPage;
-      final int totalLength = PlatformDependent.UNSAFE.getInt(page, position);
+      final int totalLength = Platform.getInt(page, position);
       position += 4;
-      keyLength = PlatformDependent.UNSAFE.getInt(page, position);
+      keyLength = Platform.getInt(page, position);
       position += 4;
       valueLength = totalLength - keyLength - 4;
 
@@ -572,7 +572,7 @@ public boolean putNewKey(
           // There wasn't enough space in the current page, so write an end-of-page marker:
           final Object pageBaseObject = currentDataPage.getBaseObject();
           final long lengthOffsetInPage = currentDataPage.getBaseOffset() + pageCursor;
-          PlatformDependent.UNSAFE.putInt(pageBaseObject, lengthOffsetInPage, END_OF_PAGE_MARKER);
+          Platform.putInt(pageBaseObject, lengthOffsetInPage, END_OF_PAGE_MARKER);
         }
         final long memoryGranted = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
         if (memoryGranted != pageSizeBytes) {
@@ -608,21 +608,21 @@ public boolean putNewKey(
       final long valueDataOffsetInPage = insertCursor;
       insertCursor += valueLengthBytes; // word used to store the value size
 
-      PlatformDependent.UNSAFE.putInt(dataPageBaseObject, recordOffset,
+      Platform.putInt(dataPageBaseObject, recordOffset,
         keyLengthBytes + valueLengthBytes + 4);
-      PlatformDependent.UNSAFE.putInt(dataPageBaseObject, keyLengthOffset, keyLengthBytes);
+      Platform.putInt(dataPageBaseObject, keyLengthOffset, keyLengthBytes);
       // Copy the key
-      PlatformDependent.copyMemory(
+      Platform.copyMemory(
         keyBaseObject, keyBaseOffset, dataPageBaseObject, keyDataOffsetInPage, keyLengthBytes);
       // Copy the value
-      PlatformDependent.copyMemory(valueBaseObject, valueBaseOffset, dataPageBaseObject,
+      Platform.copyMemory(valueBaseObject, valueBaseOffset, dataPageBaseObject,
         valueDataOffsetInPage, valueLengthBytes);
 
       // --- Update bookeeping data structures -----------------------------------------------------
 
       if (useOverflowPage) {
         // Store the end-of-page marker at the end of the data page
-        PlatformDependent.UNSAFE.putInt(dataPageBaseObject, insertCursor, END_OF_PAGE_MARKER);
+        Platform.putInt(dataPageBaseObject, insertCursor, END_OF_PAGE_MARKER);
       } else {
         pageCursor += requiredSize;
       }
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
index 5e002ae1b7568..71b76d5ddfaa7 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/PrefixComparators.java
@@ -20,10 +20,9 @@
 import com.google.common.primitives.UnsignedLongs;
 
 import org.apache.spark.annotation.Private;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.types.UTF8String;
 import org.apache.spark.util.Utils;
-import static org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET;
 
 @Private
 public class PrefixComparators {
@@ -73,7 +72,7 @@ public static long computePrefix(byte[] bytes) {
         final int minLen = Math.min(bytes.length, 8);
         long p = 0;
         for (int i = 0; i < minLen; ++i) {
-          p |= (128L + PlatformDependent.UNSAFE.getByte(bytes, BYTE_ARRAY_OFFSET + i))
+          p |= (128L + Platform.getByte(bytes, Platform.BYTE_ARRAY_OFFSET + i))
               << (56 - 8 * i);
         }
         return p;
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index 5ebbf9b068fd6..9601aafe55464 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -35,7 +35,7 @@
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.storage.BlockManager;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
 import org.apache.spark.util.Utils;
@@ -427,14 +427,10 @@ public void insertRecord(
 
     final long recordAddress =
       taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
+    Platform.putInt(dataPageBaseObject, dataPagePosition, lengthInBytes);
     dataPagePosition += 4;
-    PlatformDependent.copyMemory(
-      recordBaseObject,
-      recordBaseOffset,
-      dataPageBaseObject,
-      dataPagePosition,
-      lengthInBytes);
+    Platform.copyMemory(
+      recordBaseObject, recordBaseOffset, dataPageBaseObject, dataPagePosition, lengthInBytes);
     assert(inMemSorter != null);
     inMemSorter.insertRecord(recordAddress, prefix);
   }
@@ -493,18 +489,16 @@ public void insertKVRecord(
 
     final long recordAddress =
       taskMemoryManager.encodePageNumberAndOffset(dataPage, dataPagePosition);
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, keyLen + valueLen + 4);
+    Platform.putInt(dataPageBaseObject, dataPagePosition, keyLen + valueLen + 4);
     dataPagePosition += 4;
 
-    PlatformDependent.UNSAFE.putInt(dataPageBaseObject, dataPagePosition, keyLen);
+    Platform.putInt(dataPageBaseObject, dataPagePosition, keyLen);
     dataPagePosition += 4;
 
-    PlatformDependent.copyMemory(
-      keyBaseObj, keyOffset, dataPageBaseObject, dataPagePosition, keyLen);
+    Platform.copyMemory(keyBaseObj, keyOffset, dataPageBaseObject, dataPagePosition, keyLen);
     dataPagePosition += keyLen;
 
-    PlatformDependent.copyMemory(
-      valueBaseObj, valueOffset, dataPageBaseObject, dataPagePosition, valueLen);
+    Platform.copyMemory(valueBaseObj, valueOffset, dataPageBaseObject, dataPagePosition, valueLen);
 
     assert(inMemSorter != null);
     inMemSorter.insertRecord(recordAddress, prefix);
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
index 1e4b8a116e11a..f7787e1019c2b 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorter.java
@@ -19,7 +19,7 @@
 
 import java.util.Comparator;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.util.collection.Sorter;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
 
@@ -164,7 +164,7 @@ public void loadNext() {
       final long recordPointer = sortBuffer[position];
       baseObject = memoryManager.getPage(recordPointer);
       baseOffset = memoryManager.getOffsetInPage(recordPointer) + 4;  // Skip over record length
-      recordLength = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset - 4);
+      recordLength = Platform.getInt(baseObject, baseOffset - 4);
       keyPrefix = sortBuffer[position + 1];
       position += 2;
     }
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
index ca1ccedc93c8e..4989b05d63e23 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillReader.java
@@ -23,7 +23,7 @@
 
 import org.apache.spark.storage.BlockId;
 import org.apache.spark.storage.BlockManager;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * Reads spill files written by {@link UnsafeSorterSpillWriter} (see that class for a description
@@ -42,7 +42,7 @@ final class UnsafeSorterSpillReader extends UnsafeSorterIterator {
 
   private byte[] arr = new byte[1024 * 1024];
   private Object baseObject = arr;
-  private final long baseOffset = PlatformDependent.BYTE_ARRAY_OFFSET;
+  private final long baseOffset = Platform.BYTE_ARRAY_OFFSET;
 
   public UnsafeSorterSpillReader(
       BlockManager blockManager,
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
index 44cf6c756d7c3..e59a84ff8d118 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeSorterSpillWriter.java
@@ -28,7 +28,7 @@
 import org.apache.spark.storage.BlockManager;
 import org.apache.spark.storage.DiskBlockObjectWriter;
 import org.apache.spark.storage.TempLocalBlockId;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * Spills a list of sorted records to disk. Spill files have the following format:
@@ -117,11 +117,11 @@ public void write(
     long recordReadPosition = baseOffset;
     while (dataRemaining > 0) {
       final int toTransfer = Math.min(freeSpaceInWriteBuffer, dataRemaining);
-      PlatformDependent.copyMemory(
+      Platform.copyMemory(
         baseObject,
         recordReadPosition,
         writeBuffer,
-        PlatformDependent.BYTE_ARRAY_OFFSET + (DISK_WRITE_BUFFER_SIZE - freeSpaceInWriteBuffer),
+        Platform.BYTE_ARRAY_OFFSET + (DISK_WRITE_BUFFER_SIZE - freeSpaceInWriteBuffer),
         toTransfer);
       writer.write(writeBuffer, 0, (DISK_WRITE_BUFFER_SIZE - freeSpaceInWriteBuffer) + toTransfer);
       recordReadPosition += toTransfer;
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleInMemorySorterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleInMemorySorterSuite.java
index 8fa72597db24d..40fefe2c9d140 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleInMemorySorterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleInMemorySorterSuite.java
@@ -24,7 +24,7 @@
 import org.junit.Test;
 
 import org.apache.spark.HashPartitioner;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.ExecutorMemoryManager;
 import org.apache.spark.unsafe.memory.MemoryAllocator;
 import org.apache.spark.unsafe.memory.MemoryBlock;
@@ -34,11 +34,7 @@ public class UnsafeShuffleInMemorySorterSuite {
 
   private static String getStringFromDataPage(Object baseObject, long baseOffset, int strLength) {
     final byte[] strBytes = new byte[strLength];
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset,
-      strBytes,
-      PlatformDependent.BYTE_ARRAY_OFFSET, strLength);
+    Platform.copyMemory(baseObject, baseOffset, strBytes, Platform.BYTE_ARRAY_OFFSET, strLength);
     return new String(strBytes);
   }
 
@@ -74,14 +70,10 @@ public void testBasicSorting() throws Exception {
     for (String str : dataToSort) {
       final long recordAddress = memoryManager.encodePageNumberAndOffset(dataPage, position);
       final byte[] strBytes = str.getBytes("utf-8");
-      PlatformDependent.UNSAFE.putInt(baseObject, position, strBytes.length);
+      Platform.putInt(baseObject, position, strBytes.length);
       position += 4;
-      PlatformDependent.copyMemory(
-        strBytes,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
-        baseObject,
-        position,
-        strBytes.length);
+      Platform.copyMemory(
+        strBytes, Platform.BYTE_ARRAY_OFFSET, baseObject, position, strBytes.length);
       position += strBytes.length;
       sorter.insertRecord(recordAddress, hashPartitioner.getPartition(str));
     }
@@ -98,7 +90,7 @@ public void testBasicSorting() throws Exception {
       Assert.assertTrue("Partition id " + partitionId + " should be >= prev id " + prevPartitionId,
         partitionId >= prevPartitionId);
       final long recordAddress = iter.packedRecordPointer.getRecordPointer();
-      final int recordLength = PlatformDependent.UNSAFE.getInt(
+      final int recordLength = Platform.getInt(
         memoryManager.getPage(recordAddress), memoryManager.getOffsetInPage(recordAddress));
       final String str = getStringFromDataPage(
         memoryManager.getPage(recordAddress),
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index e56a3f0b6d12c..1a79c20c35246 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -32,9 +32,7 @@
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.memory.*;
-import org.apache.spark.unsafe.PlatformDependent;
-import static org.apache.spark.unsafe.PlatformDependent.BYTE_ARRAY_OFFSET;
-import static org.apache.spark.unsafe.PlatformDependent.LONG_ARRAY_OFFSET;
+import org.apache.spark.unsafe.Platform;
 
 
 public abstract class AbstractBytesToBytesMapSuite {
@@ -80,13 +78,8 @@ public void tearDown() {
 
   private static byte[] getByteArray(MemoryLocation loc, int size) {
     final byte[] arr = new byte[size];
-    PlatformDependent.copyMemory(
-      loc.getBaseObject(),
-      loc.getBaseOffset(),
-      arr,
-      BYTE_ARRAY_OFFSET,
-      size
-    );
+    Platform.copyMemory(
+      loc.getBaseObject(), loc.getBaseOffset(), arr, Platform.BYTE_ARRAY_OFFSET, size);
     return arr;
   }
 
@@ -108,7 +101,7 @@ private static boolean arrayEquals(
       long actualLengthBytes) {
     return (actualLengthBytes == expected.length) && ByteArrayMethods.arrayEquals(
       expected,
-      BYTE_ARRAY_OFFSET,
+      Platform.BYTE_ARRAY_OFFSET,
       actualAddr.getBaseObject(),
       actualAddr.getBaseOffset(),
       expected.length
@@ -124,7 +117,7 @@ public void emptyMap() {
       final int keyLengthInWords = 10;
       final int keyLengthInBytes = keyLengthInWords * 8;
       final byte[] key = getRandomByteArray(keyLengthInWords);
-      Assert.assertFalse(map.lookup(key, BYTE_ARRAY_OFFSET, keyLengthInBytes).isDefined());
+      Assert.assertFalse(map.lookup(key, Platform.BYTE_ARRAY_OFFSET, keyLengthInBytes).isDefined());
       Assert.assertFalse(map.iterator().hasNext());
     } finally {
       map.free();
@@ -141,14 +134,14 @@ public void setAndRetrieveAKey() {
     final byte[] valueData = getRandomByteArray(recordLengthWords);
     try {
       final BytesToBytesMap.Location loc =
-        map.lookup(keyData, BYTE_ARRAY_OFFSET, recordLengthBytes);
+        map.lookup(keyData, Platform.BYTE_ARRAY_OFFSET, recordLengthBytes);
       Assert.assertFalse(loc.isDefined());
       Assert.assertTrue(loc.putNewKey(
         keyData,
-        BYTE_ARRAY_OFFSET,
+        Platform.BYTE_ARRAY_OFFSET,
         recordLengthBytes,
         valueData,
-        BYTE_ARRAY_OFFSET,
+        Platform.BYTE_ARRAY_OFFSET,
         recordLengthBytes
       ));
       // After storing the key and value, the other location methods should return results that
@@ -159,7 +152,8 @@ public void setAndRetrieveAKey() {
       Assert.assertArrayEquals(valueData, getByteArray(loc.getValueAddress(), recordLengthBytes));
 
       // After calling lookup() the location should still point to the correct data.
-      Assert.assertTrue(map.lookup(keyData, BYTE_ARRAY_OFFSET, recordLengthBytes).isDefined());
+      Assert.assertTrue(
+        map.lookup(keyData, Platform.BYTE_ARRAY_OFFSET, recordLengthBytes).isDefined());
       Assert.assertEquals(recordLengthBytes, loc.getKeyLength());
       Assert.assertEquals(recordLengthBytes, loc.getValueLength());
       Assert.assertArrayEquals(keyData, getByteArray(loc.getKeyAddress(), recordLengthBytes));
@@ -168,10 +162,10 @@ public void setAndRetrieveAKey() {
       try {
         Assert.assertTrue(loc.putNewKey(
           keyData,
-          BYTE_ARRAY_OFFSET,
+          Platform.BYTE_ARRAY_OFFSET,
           recordLengthBytes,
           valueData,
-          BYTE_ARRAY_OFFSET,
+          Platform.BYTE_ARRAY_OFFSET,
           recordLengthBytes
         ));
         Assert.fail("Should not be able to set a new value for a key");
@@ -191,25 +185,25 @@ private void iteratorTestBase(boolean destructive) throws Exception {
       for (long i = 0; i < size; i++) {
         final long[] value = new long[] { i };
         final BytesToBytesMap.Location loc =
-          map.lookup(value, PlatformDependent.LONG_ARRAY_OFFSET, 8);
+          map.lookup(value, Platform.LONG_ARRAY_OFFSET, 8);
         Assert.assertFalse(loc.isDefined());
         // Ensure that we store some zero-length keys
         if (i % 5 == 0) {
           Assert.assertTrue(loc.putNewKey(
             null,
-            PlatformDependent.LONG_ARRAY_OFFSET,
+            Platform.LONG_ARRAY_OFFSET,
             0,
             value,
-            PlatformDependent.LONG_ARRAY_OFFSET,
+            Platform.LONG_ARRAY_OFFSET,
             8
           ));
         } else {
           Assert.assertTrue(loc.putNewKey(
             value,
-            PlatformDependent.LONG_ARRAY_OFFSET,
+            Platform.LONG_ARRAY_OFFSET,
             8,
             value,
-            PlatformDependent.LONG_ARRAY_OFFSET,
+            Platform.LONG_ARRAY_OFFSET,
             8
           ));
         }
@@ -228,14 +222,13 @@ private void iteratorTestBase(boolean destructive) throws Exception {
         Assert.assertTrue(loc.isDefined());
         final MemoryLocation keyAddress = loc.getKeyAddress();
         final MemoryLocation valueAddress = loc.getValueAddress();
-        final long value = PlatformDependent.UNSAFE.getLong(
+        final long value = Platform.getLong(
           valueAddress.getBaseObject(), valueAddress.getBaseOffset());
         final long keyLength = loc.getKeyLength();
         if (keyLength == 0) {
           Assert.assertTrue("value " + value + " was not divisible by 5", value % 5 == 0);
         } else {
-          final long key = PlatformDependent.UNSAFE.getLong(
-            keyAddress.getBaseObject(), keyAddress.getBaseOffset());
+          final long key = Platform.getLong(keyAddress.getBaseObject(), keyAddress.getBaseOffset());
           Assert.assertEquals(value, key);
         }
         valuesSeen.set((int) value);
@@ -284,16 +277,16 @@ public void iteratingOverDataPagesWithWastedSpace() throws Exception {
         final long[] value = new long[] { i, i, i, i, i }; // 5 * 8 = 40 bytes
         final BytesToBytesMap.Location loc = map.lookup(
           key,
-          LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           KEY_LENGTH
         );
         Assert.assertFalse(loc.isDefined());
         Assert.assertTrue(loc.putNewKey(
           key,
-          LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           KEY_LENGTH,
           value,
-          LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           VALUE_LENGTH
         ));
       }
@@ -308,18 +301,18 @@ public void iteratingOverDataPagesWithWastedSpace() throws Exception {
         Assert.assertTrue(loc.isDefined());
         Assert.assertEquals(KEY_LENGTH, loc.getKeyLength());
         Assert.assertEquals(VALUE_LENGTH, loc.getValueLength());
-        PlatformDependent.copyMemory(
+        Platform.copyMemory(
           loc.getKeyAddress().getBaseObject(),
           loc.getKeyAddress().getBaseOffset(),
           key,
-          LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           KEY_LENGTH
         );
-        PlatformDependent.copyMemory(
+        Platform.copyMemory(
           loc.getValueAddress().getBaseObject(),
           loc.getValueAddress().getBaseOffset(),
           value,
-          LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           VALUE_LENGTH
         );
         for (long j : key) {
@@ -354,16 +347,16 @@ public void randomizedStressTest() {
           expected.put(ByteBuffer.wrap(key), value);
           final BytesToBytesMap.Location loc = map.lookup(
             key,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             key.length
           );
           Assert.assertFalse(loc.isDefined());
           Assert.assertTrue(loc.putNewKey(
             key,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             key.length,
             value,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             value.length
           ));
           // After calling putNewKey, the following should be true, even before calling
@@ -379,7 +372,8 @@ public void randomizedStressTest() {
       for (Map.Entry<ByteBuffer, byte[]> entry : expected.entrySet()) {
         final byte[] key = entry.getKey().array();
         final byte[] value = entry.getValue();
-        final BytesToBytesMap.Location loc = map.lookup(key, BYTE_ARRAY_OFFSET, key.length);
+        final BytesToBytesMap.Location loc =
+          map.lookup(key, Platform.BYTE_ARRAY_OFFSET, key.length);
         Assert.assertTrue(loc.isDefined());
         Assert.assertTrue(arrayEquals(key, loc.getKeyAddress(), loc.getKeyLength()));
         Assert.assertTrue(arrayEquals(value, loc.getValueAddress(), loc.getValueLength()));
@@ -405,16 +399,16 @@ public void randomizedTestWithRecordsLargerThanPageSize() {
           expected.put(ByteBuffer.wrap(key), value);
           final BytesToBytesMap.Location loc = map.lookup(
             key,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             key.length
           );
           Assert.assertFalse(loc.isDefined());
           Assert.assertTrue(loc.putNewKey(
             key,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             key.length,
             value,
-            BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             value.length
           ));
           // After calling putNewKey, the following should be true, even before calling
@@ -429,7 +423,8 @@ public void randomizedTestWithRecordsLargerThanPageSize() {
       for (Map.Entry<ByteBuffer, byte[]> entry : expected.entrySet()) {
         final byte[] key = entry.getKey().array();
         final byte[] value = entry.getValue();
-        final BytesToBytesMap.Location loc = map.lookup(key, BYTE_ARRAY_OFFSET, key.length);
+        final BytesToBytesMap.Location loc =
+          map.lookup(key, Platform.BYTE_ARRAY_OFFSET, key.length);
         Assert.assertTrue(loc.isDefined());
         Assert.assertTrue(arrayEquals(key, loc.getKeyAddress(), loc.getKeyLength()));
         Assert.assertTrue(arrayEquals(value, loc.getValueAddress(), loc.getValueLength()));
@@ -447,12 +442,10 @@ public void failureToAllocateFirstPage() {
     try {
       final long[] emptyArray = new long[0];
       final BytesToBytesMap.Location loc =
-        map.lookup(emptyArray, PlatformDependent.LONG_ARRAY_OFFSET, 0);
+        map.lookup(emptyArray, Platform.LONG_ARRAY_OFFSET, 0);
       Assert.assertFalse(loc.isDefined());
       Assert.assertFalse(loc.putNewKey(
-        emptyArray, LONG_ARRAY_OFFSET, 0,
-        emptyArray, LONG_ARRAY_OFFSET, 0
-      ));
+        emptyArray, Platform.LONG_ARRAY_OFFSET, 0, emptyArray, Platform.LONG_ARRAY_OFFSET, 0));
     } finally {
       map.free();
     }
@@ -468,8 +461,9 @@ public void failureToGrow() {
       int i;
       for (i = 0; i < 1024; i++) {
         final long[] arr = new long[]{i};
-        final BytesToBytesMap.Location loc = map.lookup(arr, PlatformDependent.LONG_ARRAY_OFFSET, 8);
-        success = loc.putNewKey(arr, LONG_ARRAY_OFFSET, 8, arr, LONG_ARRAY_OFFSET, 8);
+        final BytesToBytesMap.Location loc = map.lookup(arr, Platform.LONG_ARRAY_OFFSET, 8);
+        success =
+          loc.putNewKey(arr, Platform.LONG_ARRAY_OFFSET, 8, arr, Platform.LONG_ARRAY_OFFSET, 8);
         if (!success) {
           break;
         }
@@ -541,12 +535,12 @@ public void testPeakMemoryUsed() {
     try {
       for (long i = 0; i < numRecordsPerPage * 10; i++) {
         final long[] value = new long[]{i};
-        map.lookup(value, PlatformDependent.LONG_ARRAY_OFFSET, 8).putNewKey(
+        map.lookup(value, Platform.LONG_ARRAY_OFFSET, 8).putNewKey(
           value,
-          PlatformDependent.LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           8,
           value,
-          PlatformDependent.LONG_ARRAY_OFFSET,
+          Platform.LONG_ARRAY_OFFSET,
           8);
         newPeakMemory = map.getPeakMemoryUsedBytes();
         if (i % numRecordsPerPage == 0) {
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
index 83049b8a21fcf..445a37b83e98a 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorterSuite.java
@@ -49,7 +49,7 @@
 import org.apache.spark.serializer.SerializerInstance;
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.storage.*;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.ExecutorMemoryManager;
 import org.apache.spark.unsafe.memory.MemoryAllocator;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
@@ -166,14 +166,14 @@ private void assertSpillFilesWereCleanedUp() {
 
   private static void insertNumber(UnsafeExternalSorter sorter, int value) throws Exception {
     final int[] arr = new int[]{ value };
-    sorter.insertRecord(arr, PlatformDependent.INT_ARRAY_OFFSET, 4, value);
+    sorter.insertRecord(arr, Platform.INT_ARRAY_OFFSET, 4, value);
   }
 
   private static void insertRecord(
       UnsafeExternalSorter sorter,
       int[] record,
       long prefix) throws IOException {
-    sorter.insertRecord(record, PlatformDependent.INT_ARRAY_OFFSET, record.length * 4, prefix);
+    sorter.insertRecord(record, Platform.INT_ARRAY_OFFSET, record.length * 4, prefix);
   }
 
   private UnsafeExternalSorter newSorter() throws IOException {
@@ -205,7 +205,7 @@ public void testSortingOnlyByPrefix() throws Exception {
       iter.loadNext();
       assertEquals(i, iter.getKeyPrefix());
       assertEquals(4, iter.getRecordLength());
-      assertEquals(i, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+      assertEquals(i, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
     }
 
     sorter.cleanupResources();
@@ -253,7 +253,7 @@ public void spillingOccursInResponseToMemoryPressure() throws Exception {
       iter.loadNext();
       assertEquals(i, iter.getKeyPrefix());
       assertEquals(4, iter.getRecordLength());
-      assertEquals(i, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+      assertEquals(i, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
       i++;
     }
     sorter.cleanupResources();
@@ -265,7 +265,7 @@ public void testFillingPage() throws Exception {
     final UnsafeExternalSorter sorter = newSorter();
     byte[] record = new byte[16];
     while (sorter.getNumberOfAllocatedPages() < 2) {
-      sorter.insertRecord(record, PlatformDependent.BYTE_ARRAY_OFFSET, record.length, 0);
+      sorter.insertRecord(record, Platform.BYTE_ARRAY_OFFSET, record.length, 0);
     }
     sorter.cleanupResources();
     assertSpillFilesWereCleanedUp();
@@ -292,25 +292,25 @@ public void sortingRecordsThatExceedPageSize() throws Exception {
     iter.loadNext();
     assertEquals(123, iter.getKeyPrefix());
     assertEquals(smallRecord.length * 4, iter.getRecordLength());
-    assertEquals(123, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    assertEquals(123, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
     // Small record
     assertTrue(iter.hasNext());
     iter.loadNext();
     assertEquals(123, iter.getKeyPrefix());
     assertEquals(smallRecord.length * 4, iter.getRecordLength());
-    assertEquals(123, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    assertEquals(123, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
     // Large record
     assertTrue(iter.hasNext());
     iter.loadNext();
     assertEquals(456, iter.getKeyPrefix());
     assertEquals(largeRecord.length * 4, iter.getRecordLength());
-    assertEquals(456, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    assertEquals(456, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
     // Large record
     assertTrue(iter.hasNext());
     iter.loadNext();
     assertEquals(456, iter.getKeyPrefix());
     assertEquals(largeRecord.length * 4, iter.getRecordLength());
-    assertEquals(456, PlatformDependent.UNSAFE.getInt(iter.getBaseObject(), iter.getBaseOffset()));
+    assertEquals(456, Platform.getInt(iter.getBaseObject(), iter.getBaseOffset()));
 
     assertFalse(iter.hasNext());
     sorter.cleanupResources();
diff --git a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java
index 909500930539c..778e813df6b54 100644
--- a/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java
+++ b/core/src/test/java/org/apache/spark/util/collection/unsafe/sort/UnsafeInMemorySorterSuite.java
@@ -26,7 +26,7 @@
 import static org.mockito.Mockito.mock;
 
 import org.apache.spark.HashPartitioner;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.ExecutorMemoryManager;
 import org.apache.spark.unsafe.memory.MemoryAllocator;
 import org.apache.spark.unsafe.memory.MemoryBlock;
@@ -36,11 +36,7 @@ public class UnsafeInMemorySorterSuite {
 
   private static String getStringFromDataPage(Object baseObject, long baseOffset, int length) {
     final byte[] strBytes = new byte[length];
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset,
-      strBytes,
-      PlatformDependent.BYTE_ARRAY_OFFSET, length);
+    Platform.copyMemory(baseObject, baseOffset, strBytes, Platform.BYTE_ARRAY_OFFSET, length);
     return new String(strBytes);
   }
 
@@ -76,14 +72,10 @@ public void testSortingOnlyByIntegerPrefix() throws Exception {
     long position = dataPage.getBaseOffset();
     for (String str : dataToSort) {
       final byte[] strBytes = str.getBytes("utf-8");
-      PlatformDependent.UNSAFE.putInt(baseObject, position, strBytes.length);
+      Platform.putInt(baseObject, position, strBytes.length);
       position += 4;
-      PlatformDependent.copyMemory(
-        strBytes,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
-        baseObject,
-        position,
-        strBytes.length);
+      Platform.copyMemory(
+        strBytes, Platform.BYTE_ARRAY_OFFSET, baseObject, position, strBytes.length);
       position += strBytes.length;
     }
     // Since the key fits within the 8-byte prefix, we don't need to do any record comparison, so
@@ -113,7 +105,7 @@ public int compare(long prefix1, long prefix2) {
     position = dataPage.getBaseOffset();
     for (int i = 0; i < dataToSort.length; i++) {
       // position now points to the start of a record (which holds its length).
-      final int recordLength = PlatformDependent.UNSAFE.getInt(baseObject, position);
+      final int recordLength = Platform.getInt(baseObject, position);
       final long address = memoryManager.encodePageNumberAndOffset(dataPage, position);
       final String str = getStringFromDataPage(baseObject, position + 4, recordLength);
       final int partitionId = hashPartitioner.getPartition(str);
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java
index 0374846d71674..501dff090313c 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeArrayData.java
@@ -22,7 +22,7 @@
 
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.types.*;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.hash.Murmur3_x86_32;
 import org.apache.spark.unsafe.types.CalendarInterval;
@@ -59,7 +59,7 @@ public class UnsafeArrayData extends ArrayData {
   private int sizeInBytes;
 
   private int getElementOffset(int ordinal) {
-    return PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + ordinal * 4L);
+    return Platform.getInt(baseObject, baseOffset + ordinal * 4L);
   }
 
   private int getElementSize(int offset, int ordinal) {
@@ -157,7 +157,7 @@ public boolean getBoolean(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return false;
-    return PlatformDependent.UNSAFE.getBoolean(baseObject, baseOffset + offset);
+    return Platform.getBoolean(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -165,7 +165,7 @@ public byte getByte(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getByte(baseObject, baseOffset + offset);
+    return Platform.getByte(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -173,7 +173,7 @@ public short getShort(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getShort(baseObject, baseOffset + offset);
+    return Platform.getShort(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -181,7 +181,7 @@ public int getInt(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + offset);
+    return Platform.getInt(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -189,7 +189,7 @@ public long getLong(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
+    return Platform.getLong(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -197,7 +197,7 @@ public float getFloat(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getFloat(baseObject, baseOffset + offset);
+    return Platform.getFloat(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -205,7 +205,7 @@ public double getDouble(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return 0;
-    return PlatformDependent.UNSAFE.getDouble(baseObject, baseOffset + offset);
+    return Platform.getDouble(baseObject, baseOffset + offset);
   }
 
   @Override
@@ -215,7 +215,7 @@ public Decimal getDecimal(int ordinal, int precision, int scale) {
     if (offset < 0) return null;
 
     if (precision <= Decimal.MAX_LONG_DIGITS()) {
-      final long value = PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
+      final long value = Platform.getLong(baseObject, baseOffset + offset);
       return Decimal.apply(value, precision, scale);
     } else {
       final byte[] bytes = getBinary(ordinal);
@@ -241,12 +241,7 @@ public byte[] getBinary(int ordinal) {
     if (offset < 0) return null;
     final int size = getElementSize(offset, ordinal);
     final byte[] bytes = new byte[size];
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset + offset,
-      bytes,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
-      size);
+    Platform.copyMemory(baseObject, baseOffset + offset, bytes, Platform.BYTE_ARRAY_OFFSET, size);
     return bytes;
   }
 
@@ -255,9 +250,8 @@ public CalendarInterval getInterval(int ordinal) {
     assertIndexIsValid(ordinal);
     final int offset = getElementOffset(ordinal);
     if (offset < 0) return null;
-    final int months = (int) PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
-    final long microseconds =
-      PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset + 8);
+    final int months = (int) Platform.getLong(baseObject, baseOffset + offset);
+    final long microseconds = Platform.getLong(baseObject, baseOffset + offset + 8);
     return new CalendarInterval(months, microseconds);
   }
 
@@ -307,27 +301,16 @@ public boolean equals(Object other) {
   }
 
   public void writeToMemory(Object target, long targetOffset) {
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset,
-      target,
-      targetOffset,
-      sizeInBytes
-    );
+    Platform.copyMemory(baseObject, baseOffset, target, targetOffset, sizeInBytes);
   }
 
   @Override
   public UnsafeArrayData copy() {
     UnsafeArrayData arrayCopy = new UnsafeArrayData();
     final byte[] arrayDataCopy = new byte[sizeInBytes];
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset,
-      arrayDataCopy,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
-      sizeInBytes
-    );
-    arrayCopy.pointTo(arrayDataCopy, PlatformDependent.BYTE_ARRAY_OFFSET, numElements, sizeInBytes);
+    Platform.copyMemory(
+      baseObject, baseOffset, arrayDataCopy, Platform.BYTE_ARRAY_OFFSET, sizeInBytes);
+    arrayCopy.pointTo(arrayDataCopy, Platform.BYTE_ARRAY_OFFSET, numElements, sizeInBytes);
     return arrayCopy;
   }
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java
index b521b703389d3..7b03185a30e3c 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeReaders.java
@@ -17,13 +17,13 @@
 
 package org.apache.spark.sql.catalyst.expressions;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 public class UnsafeReaders {
 
   public static UnsafeArrayData readArray(Object baseObject, long baseOffset, int numBytes) {
     // Read the number of elements from first 4 bytes.
-    final int numElements = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset);
+    final int numElements = Platform.getInt(baseObject, baseOffset);
     final UnsafeArrayData array = new UnsafeArrayData();
     // Skip the first 4 bytes.
     array.pointTo(baseObject, baseOffset + 4, numElements, numBytes - 4);
@@ -32,9 +32,9 @@ public static UnsafeArrayData readArray(Object baseObject, long baseOffset, int
 
   public static UnsafeMapData readMap(Object baseObject, long baseOffset, int numBytes) {
     // Read the number of elements from first 4 bytes.
-    final int numElements = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset);
+    final int numElements = Platform.getInt(baseObject, baseOffset);
     // Read the numBytes of key array in second 4 bytes.
-    final int keyArraySize = PlatformDependent.UNSAFE.getInt(baseObject, baseOffset + 4);
+    final int keyArraySize = Platform.getInt(baseObject, baseOffset + 4);
     final int valueArraySize = numBytes - 8 - keyArraySize;
 
     final UnsafeArrayData keyArray = new UnsafeArrayData();
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index e829acb6285f1..7fd94772090df 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -27,7 +27,7 @@
 import java.util.Set;
 
 import org.apache.spark.sql.types.*;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.bitset.BitSetMethods;
 import org.apache.spark.unsafe.hash.Murmur3_x86_32;
@@ -169,7 +169,7 @@ public void pointTo(Object baseObject, long baseOffset, int numFields, int sizeI
    * @param sizeInBytes the number of bytes valid in the byte array
    */
   public void pointTo(byte[] buf, int numFields, int sizeInBytes) {
-    pointTo(buf, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, sizeInBytes);
+    pointTo(buf, Platform.BYTE_ARRAY_OFFSET, numFields, sizeInBytes);
   }
 
   @Override
@@ -179,7 +179,7 @@ public void setNullAt(int i) {
     // To preserve row equality, zero out the value when setting the column to null.
     // Since this row does does not currently support updates to variable-length values, we don't
     // have to worry about zeroing out that data.
-    PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(i), 0);
+    Platform.putLong(baseObject, getFieldOffset(i), 0);
   }
 
   @Override
@@ -191,14 +191,14 @@ public void update(int ordinal, Object value) {
   public void setInt(int ordinal, int value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
-    PlatformDependent.UNSAFE.putInt(baseObject, getFieldOffset(ordinal), value);
+    Platform.putInt(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
   public void setLong(int ordinal, long value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
-    PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(ordinal), value);
+    Platform.putLong(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
@@ -208,28 +208,28 @@ public void setDouble(int ordinal, double value) {
     if (Double.isNaN(value)) {
       value = Double.NaN;
     }
-    PlatformDependent.UNSAFE.putDouble(baseObject, getFieldOffset(ordinal), value);
+    Platform.putDouble(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
   public void setBoolean(int ordinal, boolean value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
-    PlatformDependent.UNSAFE.putBoolean(baseObject, getFieldOffset(ordinal), value);
+    Platform.putBoolean(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
   public void setShort(int ordinal, short value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
-    PlatformDependent.UNSAFE.putShort(baseObject, getFieldOffset(ordinal), value);
+    Platform.putShort(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
   public void setByte(int ordinal, byte value) {
     assertIndexIsValid(ordinal);
     setNotNullAt(ordinal);
-    PlatformDependent.UNSAFE.putByte(baseObject, getFieldOffset(ordinal), value);
+    Platform.putByte(baseObject, getFieldOffset(ordinal), value);
   }
 
   @Override
@@ -239,7 +239,7 @@ public void setFloat(int ordinal, float value) {
     if (Float.isNaN(value)) {
       value = Float.NaN;
     }
-    PlatformDependent.UNSAFE.putFloat(baseObject, getFieldOffset(ordinal), value);
+    Platform.putFloat(baseObject, getFieldOffset(ordinal), value);
   }
 
   /**
@@ -263,23 +263,23 @@ public void setDecimal(int ordinal, Decimal value, int precision) {
       long cursor = getLong(ordinal) >>> 32;
       assert cursor > 0 : "invalid cursor " + cursor;
       // zero-out the bytes
-      PlatformDependent.UNSAFE.putLong(baseObject, baseOffset + cursor, 0L);
-      PlatformDependent.UNSAFE.putLong(baseObject, baseOffset + cursor + 8, 0L);
+      Platform.putLong(baseObject, baseOffset + cursor, 0L);
+      Platform.putLong(baseObject, baseOffset + cursor + 8, 0L);
 
       if (value == null) {
         setNullAt(ordinal);
         // keep the offset for future update
-        PlatformDependent.UNSAFE.putLong(baseObject, getFieldOffset(ordinal), cursor << 32);
+        Platform.putLong(baseObject, getFieldOffset(ordinal), cursor << 32);
       } else {
 
         final BigInteger integer = value.toJavaBigDecimal().unscaledValue();
-        final int[] mag = (int[]) PlatformDependent.UNSAFE.getObjectVolatile(integer,
-          PlatformDependent.BIG_INTEGER_MAG_OFFSET);
+        final int[] mag = (int[]) Platform.getObjectVolatile(integer,
+          Platform.BIG_INTEGER_MAG_OFFSET);
         assert(mag.length <= 4);
 
         // Write the bytes to the variable length portion.
-        PlatformDependent.copyMemory(mag, PlatformDependent.INT_ARRAY_OFFSET,
-          baseObject, baseOffset + cursor, mag.length * 4);
+        Platform.copyMemory(
+          mag, Platform.INT_ARRAY_OFFSET, baseObject, baseOffset + cursor, mag.length * 4);
         setLong(ordinal, (cursor << 32) | ((long) (((integer.signum() + 1) << 8) + mag.length)));
       }
     }
@@ -336,43 +336,43 @@ public boolean isNullAt(int ordinal) {
   @Override
   public boolean getBoolean(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getBoolean(baseObject, getFieldOffset(ordinal));
+    return Platform.getBoolean(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public byte getByte(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getByte(baseObject, getFieldOffset(ordinal));
+    return Platform.getByte(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public short getShort(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getShort(baseObject, getFieldOffset(ordinal));
+    return Platform.getShort(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public int getInt(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getInt(baseObject, getFieldOffset(ordinal));
+    return Platform.getInt(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public long getLong(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getLong(baseObject, getFieldOffset(ordinal));
+    return Platform.getLong(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public float getFloat(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getFloat(baseObject, getFieldOffset(ordinal));
+    return Platform.getFloat(baseObject, getFieldOffset(ordinal));
   }
 
   @Override
   public double getDouble(int ordinal) {
     assertIndexIsValid(ordinal);
-    return PlatformDependent.UNSAFE.getDouble(baseObject, getFieldOffset(ordinal));
+    return Platform.getDouble(baseObject, getFieldOffset(ordinal));
   }
 
   private static byte[] EMPTY = new byte[0];
@@ -391,13 +391,13 @@ public Decimal getDecimal(int ordinal, int precision, int scale) {
       assert signum >=0 && signum <= 2 : "invalid signum " + signum;
       int size = (int) (offsetAndSize & 0xff);
       int[] mag = new int[size];
-      PlatformDependent.copyMemory(baseObject, baseOffset + offset,
-        mag, PlatformDependent.INT_ARRAY_OFFSET, size * 4);
+      Platform.copyMemory(
+        baseObject, baseOffset + offset, mag, Platform.INT_ARRAY_OFFSET, size * 4);
 
       // create a BigInteger using signum and mag
       BigInteger v = new BigInteger(0, EMPTY);  // create the initial object
-      PlatformDependent.UNSAFE.putInt(v, PlatformDependent.BIG_INTEGER_SIGNUM_OFFSET, signum - 1);
-      PlatformDependent.UNSAFE.putObjectVolatile(v, PlatformDependent.BIG_INTEGER_MAG_OFFSET, mag);
+      Platform.putInt(v, Platform.BIG_INTEGER_SIGNUM_OFFSET, signum - 1);
+      Platform.putObjectVolatile(v, Platform.BIG_INTEGER_MAG_OFFSET, mag);
       return Decimal.apply(new BigDecimal(v, scale), precision, scale);
     }
   }
@@ -420,11 +420,11 @@ public byte[] getBinary(int ordinal) {
       final int offset = (int) (offsetAndSize >> 32);
       final int size = (int) (offsetAndSize & ((1L << 32) - 1));
       final byte[] bytes = new byte[size];
-      PlatformDependent.copyMemory(
+      Platform.copyMemory(
         baseObject,
         baseOffset + offset,
         bytes,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
+        Platform.BYTE_ARRAY_OFFSET,
         size
       );
       return bytes;
@@ -438,9 +438,8 @@ public CalendarInterval getInterval(int ordinal) {
     } else {
       final long offsetAndSize = getLong(ordinal);
       final int offset = (int) (offsetAndSize >> 32);
-      final int months = (int) PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset);
-      final long microseconds =
-        PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + offset + 8);
+      final int months = (int) Platform.getLong(baseObject, baseOffset + offset);
+      final long microseconds = Platform.getLong(baseObject, baseOffset + offset + 8);
       return new CalendarInterval(months, microseconds);
     }
   }
@@ -491,14 +490,14 @@ public MapData getMap(int ordinal) {
   public UnsafeRow copy() {
     UnsafeRow rowCopy = new UnsafeRow();
     final byte[] rowDataCopy = new byte[sizeInBytes];
-    PlatformDependent.copyMemory(
+    Platform.copyMemory(
       baseObject,
       baseOffset,
       rowDataCopy,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
+      Platform.BYTE_ARRAY_OFFSET,
       sizeInBytes
     );
-    rowCopy.pointTo(rowDataCopy, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, sizeInBytes);
+    rowCopy.pointTo(rowDataCopy, Platform.BYTE_ARRAY_OFFSET, numFields, sizeInBytes);
     return rowCopy;
   }
 
@@ -518,18 +517,13 @@ public static UnsafeRow createFromByteArray(int numBytes, int numFields) {
    */
   public void copyFrom(UnsafeRow row) {
     // copyFrom is only available for UnsafeRow created from byte array.
-    assert (baseObject instanceof byte[]) && baseOffset == PlatformDependent.BYTE_ARRAY_OFFSET;
+    assert (baseObject instanceof byte[]) && baseOffset == Platform.BYTE_ARRAY_OFFSET;
     if (row.sizeInBytes > this.sizeInBytes) {
       // resize the underlying byte[] if it's not large enough.
       this.baseObject = new byte[row.sizeInBytes];
     }
-    PlatformDependent.copyMemory(
-      row.baseObject,
-      row.baseOffset,
-      this.baseObject,
-      this.baseOffset,
-      row.sizeInBytes
-    );
+    Platform.copyMemory(
+      row.baseObject, row.baseOffset, this.baseObject, this.baseOffset, row.sizeInBytes);
     // update the sizeInBytes.
     this.sizeInBytes = row.sizeInBytes;
   }
@@ -544,19 +538,15 @@ public void copyFrom(UnsafeRow row) {
    */
   public void writeToStream(OutputStream out, byte[] writeBuffer) throws IOException {
     if (baseObject instanceof byte[]) {
-      int offsetInByteArray = (int) (PlatformDependent.BYTE_ARRAY_OFFSET - baseOffset);
+      int offsetInByteArray = (int) (Platform.BYTE_ARRAY_OFFSET - baseOffset);
       out.write((byte[]) baseObject, offsetInByteArray, sizeInBytes);
     } else {
       int dataRemaining = sizeInBytes;
       long rowReadPosition = baseOffset;
       while (dataRemaining > 0) {
         int toTransfer = Math.min(writeBuffer.length, dataRemaining);
-        PlatformDependent.copyMemory(
-          baseObject,
-          rowReadPosition,
-          writeBuffer,
-          PlatformDependent.BYTE_ARRAY_OFFSET,
-          toTransfer);
+        Platform.copyMemory(
+          baseObject, rowReadPosition, writeBuffer, Platform.BYTE_ARRAY_OFFSET, toTransfer);
         out.write(writeBuffer, 0, toTransfer);
         rowReadPosition += toTransfer;
         dataRemaining -= toTransfer;
@@ -584,13 +574,12 @@ public boolean equals(Object other) {
    * Returns the underlying bytes for this UnsafeRow.
    */
   public byte[] getBytes() {
-    if (baseObject instanceof byte[] && baseOffset == PlatformDependent.BYTE_ARRAY_OFFSET
+    if (baseObject instanceof byte[] && baseOffset == Platform.BYTE_ARRAY_OFFSET
       && (((byte[]) baseObject).length == sizeInBytes)) {
       return (byte[]) baseObject;
     } else {
       byte[] bytes = new byte[sizeInBytes];
-      PlatformDependent.copyMemory(baseObject, baseOffset, bytes,
-        PlatformDependent.BYTE_ARRAY_OFFSET, sizeInBytes);
+      Platform.copyMemory(baseObject, baseOffset, bytes, Platform.BYTE_ARRAY_OFFSET, sizeInBytes);
       return bytes;
     }
   }
@@ -600,8 +589,7 @@ public byte[] getBytes() {
   public String toString() {
     StringBuilder build = new StringBuilder("[");
     for (int i = 0; i < sizeInBytes; i += 8) {
-      build.append(java.lang.Long.toHexString(
-        PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + i)));
+      build.append(java.lang.Long.toHexString(Platform.getLong(baseObject, baseOffset + i)));
       build.append(',');
     }
     build.append(']');
@@ -619,12 +607,6 @@ public boolean anyNull() {
    * bytes in this string.
    */
   public void writeToMemory(Object target, long targetOffset) {
-    PlatformDependent.copyMemory(
-      baseObject,
-      baseOffset,
-      target,
-      targetOffset,
-      sizeInBytes
-    );
+    Platform.copyMemory(baseObject, baseOffset, target, targetOffset, sizeInBytes);
   }
 }
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index 28e7ec0a0f120..005351f0883e5 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -21,7 +21,7 @@
 
 import org.apache.spark.sql.catalyst.InternalRow;
 import org.apache.spark.sql.types.Decimal;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.types.ByteArray;
 import org.apache.spark.unsafe.types.CalendarInterval;
@@ -58,27 +58,27 @@ public static int write(UnsafeRow target, int ordinal, int cursor, Decimal input
       final Object base = target.getBaseObject();
       final long offset = target.getBaseOffset() + cursor;
       // zero-out the bytes
-      PlatformDependent.UNSAFE.putLong(base, offset, 0L);
-      PlatformDependent.UNSAFE.putLong(base, offset + 8, 0L);
+      Platform.putLong(base, offset, 0L);
+      Platform.putLong(base, offset + 8, 0L);
 
       if (input == null) {
         target.setNullAt(ordinal);
         // keep the offset and length for update
         int fieldOffset = UnsafeRow.calculateBitSetWidthInBytes(target.numFields()) + ordinal * 8;
-        PlatformDependent.UNSAFE.putLong(base, target.getBaseOffset() + fieldOffset,
+        Platform.putLong(base, target.getBaseOffset() + fieldOffset,
           ((long) cursor) << 32);
         return SIZE;
       }
 
       final BigInteger integer = input.toJavaBigDecimal().unscaledValue();
       int signum = integer.signum() + 1;
-      final int[] mag = (int[]) PlatformDependent.UNSAFE.getObjectVolatile(integer,
-        PlatformDependent.BIG_INTEGER_MAG_OFFSET);
+      final int[] mag = (int[]) Platform.getObjectVolatile(
+        integer, Platform.BIG_INTEGER_MAG_OFFSET);
       assert(mag.length <= 4);
 
       // Write the bytes to the variable length portion.
-      PlatformDependent.copyMemory(mag, PlatformDependent.INT_ARRAY_OFFSET,
-        base, target.getBaseOffset() + cursor, mag.length * 4);
+      Platform.copyMemory(
+        mag, Platform.INT_ARRAY_OFFSET, base, target.getBaseOffset() + cursor, mag.length * 4);
       // Set the fixed length portion.
       target.setLong(ordinal, (((long) cursor) << 32) | ((long) ((signum << 8) + mag.length)));
 
@@ -99,8 +99,7 @@ public static int write(UnsafeRow target, int ordinal, int cursor, UTF8String in
 
       // zero-out the padding bytes
       if ((numBytes & 0x07) > 0) {
-        PlatformDependent.UNSAFE.putLong(
-          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+        Platform.putLong(target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
       }
 
       // Write the bytes to the variable length portion.
@@ -125,8 +124,7 @@ public static int write(UnsafeRow target, int ordinal, int cursor, byte[] input)
 
       // zero-out the padding bytes
       if ((numBytes & 0x07) > 0) {
-        PlatformDependent.UNSAFE.putLong(
-          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+        Platform.putLong(target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
       }
 
       // Write the bytes to the variable length portion.
@@ -167,8 +165,7 @@ public static int write(UnsafeRow target, int ordinal, int cursor, InternalRow i
 
         // zero-out the padding bytes
         if ((numBytes & 0x07) > 0) {
-          PlatformDependent.UNSAFE.putLong(
-            target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+          Platform.putLong(target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
         }
 
         // Write the bytes to the variable length portion.
@@ -191,8 +188,8 @@ public static int write(UnsafeRow target, int ordinal, int cursor, CalendarInter
       final long offset = target.getBaseOffset() + cursor;
 
       // Write the months and microseconds fields of Interval to the variable length portion.
-      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset, input.months);
-      PlatformDependent.UNSAFE.putLong(target.getBaseObject(), offset + 8, input.microseconds);
+      Platform.putLong(target.getBaseObject(), offset, input.months);
+      Platform.putLong(target.getBaseObject(), offset + 8, input.microseconds);
 
       // Set the fixed length portion.
       target.setLong(ordinal, ((long) cursor) << 32);
@@ -212,12 +209,11 @@ public static int write(UnsafeRow target, int ordinal, int cursor, UnsafeArrayDa
       final long offset = target.getBaseOffset() + cursor;
 
       // write the number of elements into first 4 bytes.
-      PlatformDependent.UNSAFE.putInt(target.getBaseObject(), offset, input.numElements());
+      Platform.putInt(target.getBaseObject(), offset, input.numElements());
 
       // zero-out the padding bytes
       if ((numBytes & 0x07) > 0) {
-        PlatformDependent.UNSAFE.putLong(
-          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+        Platform.putLong(target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
       }
 
       // Write the bytes to the variable length portion.
@@ -247,14 +243,13 @@ public static int write(UnsafeRow target, int ordinal, int cursor, UnsafeMapData
       final int numBytes = 4 + 4 + keysNumBytes + valuesNumBytes;
 
       // write the number of elements into first 4 bytes.
-      PlatformDependent.UNSAFE.putInt(target.getBaseObject(), offset, input.numElements());
+      Platform.putInt(target.getBaseObject(), offset, input.numElements());
       // write the numBytes of key array into second 4 bytes.
-      PlatformDependent.UNSAFE.putInt(target.getBaseObject(), offset + 4, keysNumBytes);
+      Platform.putInt(target.getBaseObject(), offset + 4, keysNumBytes);
 
       // zero-out the padding bytes
       if ((numBytes & 0x07) > 0) {
-        PlatformDependent.UNSAFE.putLong(
-          target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
+        Platform.putLong(target.getBaseObject(), offset + ((numBytes >> 3) << 3), 0L);
       }
 
       // Write the bytes of key array to the variable length portion.
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java
index 0e8e405d055de..cd83695fca033 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeWriters.java
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.catalyst.expressions;
 
 import org.apache.spark.sql.types.Decimal;
-import org.apache.spark.unsafe.PlatformDependent;
-import org.apache.spark.unsafe.array.ByteArrayMethods;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.types.CalendarInterval;
 import org.apache.spark.unsafe.types.UTF8String;
 
@@ -36,17 +35,11 @@ public static void writeToMemory(
 
     // zero-out the padding bytes
 //    if ((numBytes & 0x07) > 0) {
-//      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset + ((numBytes >> 3) << 3), 0L);
+//      Platform.putLong(targetObject, targetOffset + ((numBytes >> 3) << 3), 0L);
 //    }
 
     // Write the UnsafeData to the target memory.
-    PlatformDependent.copyMemory(
-      inputObject,
-      inputOffset,
-      targetObject,
-      targetOffset,
-      numBytes
-    );
+    Platform.copyMemory(inputObject, inputOffset, targetObject, targetOffset, numBytes);
   }
 
   public static int getRoundedSize(int size) {
@@ -68,16 +61,11 @@ public static int write(Object targetObject, long targetOffset, Decimal input) {
       assert(numBytes <= 16);
 
       // zero-out the bytes
-      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset, 0L);
-      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset + 8, 0L);
+      Platform.putLong(targetObject, targetOffset, 0L);
+      Platform.putLong(targetObject, targetOffset + 8, 0L);
 
       // Write the bytes to the variable length portion.
-      PlatformDependent.copyMemory(bytes,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
-        targetObject,
-        targetOffset,
-        numBytes);
-
+      Platform.copyMemory(bytes, Platform.BYTE_ARRAY_OFFSET, targetObject, targetOffset, numBytes);
       return 16;
     }
   }
@@ -111,8 +99,7 @@ public static int write(Object targetObject, long targetOffset, byte[] input) {
       final int numBytes = input.length;
 
       // Write the bytes to the variable length portion.
-      writeToMemory(input, PlatformDependent.BYTE_ARRAY_OFFSET,
-        targetObject, targetOffset, numBytes);
+      writeToMemory(input, Platform.BYTE_ARRAY_OFFSET, targetObject, targetOffset, numBytes);
 
       return getRoundedSize(numBytes);
     }
@@ -144,11 +131,9 @@ public static int getSize(UnsafeRow input) {
     }
 
     public static int write(Object targetObject, long targetOffset, CalendarInterval input) {
-
       // Write the months and microseconds fields of Interval to the variable length portion.
-      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset, input.months);
-      PlatformDependent.UNSAFE.putLong(targetObject, targetOffset + 8, input.microseconds);
-
+      Platform.putLong(targetObject, targetOffset, input.months);
+      Platform.putLong(targetObject, targetOffset + 8, input.microseconds);
       return 16;
     }
   }
@@ -165,11 +150,11 @@ public static int write(Object targetObject, long targetOffset, UnsafeArrayData
       final int numBytes = input.getSizeInBytes();
 
       // write the number of elements into first 4 bytes.
-      PlatformDependent.UNSAFE.putInt(targetObject, targetOffset, input.numElements());
+      Platform.putInt(targetObject, targetOffset, input.numElements());
 
       // Write the bytes to the variable length portion.
-      writeToMemory(input.getBaseObject(), input.getBaseOffset(),
-        targetObject, targetOffset + 4, numBytes);
+      writeToMemory(
+        input.getBaseObject(), input.getBaseOffset(), targetObject, targetOffset + 4, numBytes);
 
       return getRoundedSize(numBytes + 4);
     }
@@ -190,9 +175,9 @@ public static int write(Object targetObject, long targetOffset, UnsafeMapData in
       final int numBytes = 4 + 4 + keysNumBytes + valuesNumBytes;
 
       // write the number of elements into first 4 bytes.
-      PlatformDependent.UNSAFE.putInt(targetObject, targetOffset, input.numElements());
+      Platform.putInt(targetObject, targetOffset, input.numElements());
       // write the numBytes of key array into second 4 bytes.
-      PlatformDependent.UNSAFE.putInt(targetObject, targetOffset + 4, keysNumBytes);
+      Platform.putInt(targetObject, targetOffset + 4, keysNumBytes);
 
       // Write the bytes of key array to the variable length portion.
       writeToMemory(keyArray.getBaseObject(), keyArray.getBaseOffset(),
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
index a5ae2b9736527..1d27182912c8a 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java
@@ -31,7 +31,7 @@
 import org.apache.spark.sql.catalyst.expressions.UnsafeProjection;
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow;
 import org.apache.spark.sql.types.StructType;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.util.collection.unsafe.sort.PrefixComparator;
 import org.apache.spark.util.collection.unsafe.sort.RecordComparator;
 import org.apache.spark.util.collection.unsafe.sort.UnsafeExternalSorter;
@@ -157,7 +157,7 @@ public UnsafeRow next() {
             cleanupResources();
             // Scala iterators don't declare any checked exceptions, so we need to use this hack
             // to re-throw the exception:
-            PlatformDependent.throwException(e);
+            Platform.throwException(e);
           }
           throw new RuntimeException("Exception should have been re-thrown in next()");
         };
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index c21f4d626a74e..bf96248feaef7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -28,7 +28,7 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.types._
 
 
@@ -371,7 +371,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
     // Cannot be under package codegen, or fail with java.lang.InstantiationException
     evaluator.setClassName("org.apache.spark.sql.catalyst.expressions.GeneratedClass")
     evaluator.setDefaultImports(Array(
-      classOf[PlatformDependent].getName,
+      classOf[Platform].getName,
       classOf[InternalRow].getName,
       classOf[UnsafeRow].getName,
       classOf[UTF8String].getName,
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 29f6a7b981752..b2fb913850794 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -145,12 +145,11 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           if ($buffer.length < $numBytes) {
             // This will not happen frequently, because the buffer is re-used.
             byte[] $tmp = new byte[$numBytes * 2];
-            PlatformDependent.copyMemory($buffer, PlatformDependent.BYTE_ARRAY_OFFSET,
-              $tmp, PlatformDependent.BYTE_ARRAY_OFFSET, $buffer.length);
+            Platform.copyMemory($buffer, Platform.BYTE_ARRAY_OFFSET,
+              $tmp, Platform.BYTE_ARRAY_OFFSET, $buffer.length);
             $buffer = $tmp;
           }
-          $output.pointTo($buffer, PlatformDependent.BYTE_ARRAY_OFFSET,
-            ${inputTypes.length}, $numBytes);
+          $output.pointTo($buffer, Platform.BYTE_ARRAY_OFFSET, ${inputTypes.length}, $numBytes);
          """
       } else {
         ""
@@ -183,7 +182,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     val code = s"""
       $cursor = $fixedSize;
-      $output.pointTo($buffer, PlatformDependent.BYTE_ARRAY_OFFSET, ${inputTypes.length}, $cursor);
+      $output.pointTo($buffer, Platform.BYTE_ARRAY_OFFSET, ${inputTypes.length}, $cursor);
       ${ctx.splitExpressions(row, convertedFields)}
       """
     GeneratedExpressionCode(code, "false", output)
@@ -267,17 +266,17 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
-          PlatformDependent.UNSAFE.put${ctx.primitiveTypeName(elementType)}(
+          Platform.put${ctx.primitiveTypeName(elementType)}(
             $buffer,
-            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            Platform.BYTE_ARRAY_OFFSET + $cursor,
             ${convertedElement.primitive});
           $cursor += $elementSize;
         """
       case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
         s"""
-          PlatformDependent.UNSAFE.putLong(
+          Platform.putLong(
             $buffer,
-            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            Platform.BYTE_ARRAY_OFFSET + $cursor,
             ${convertedElement.primitive}.toUnscaledLong());
           $cursor += 8;
         """
@@ -286,7 +285,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         s"""
           $cursor += $writer.write(
             $buffer,
-            PlatformDependent.BYTE_ARRAY_OFFSET + $cursor,
+            Platform.BYTE_ARRAY_OFFSET + $cursor,
             $elements[$index]);
         """
     }
@@ -320,23 +319,16 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           for (int $index = 0; $index < $numElements; $index++) {
             if ($checkNull) {
               // If element is null, write the negative value address into offset region.
-              PlatformDependent.UNSAFE.putInt(
-                $buffer,
-                PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
-                -$cursor);
+              Platform.putInt($buffer, Platform.BYTE_ARRAY_OFFSET + 4 * $index, -$cursor);
             } else {
-              PlatformDependent.UNSAFE.putInt(
-                $buffer,
-                PlatformDependent.BYTE_ARRAY_OFFSET + 4 * $index,
-                $cursor);
-
+              Platform.putInt($buffer, Platform.BYTE_ARRAY_OFFSET + 4 * $index, $cursor);
               $writeElement
             }
           }
 
           $output.pointTo(
             $buffer,
-            PlatformDependent.BYTE_ARRAY_OFFSET,
+            Platform.BYTE_ARRAY_OFFSET,
             $numElements,
             $numBytes);
         }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
index 8aaa5b4300044..da91ff29537b3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoiner.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.expressions.codegen
 
 import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, Attribute}
 import org.apache.spark.sql.types.StructType
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 
 
 abstract class UnsafeRowJoiner {
@@ -52,9 +52,9 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
   }
 
   def create(schema1: StructType, schema2: StructType): UnsafeRowJoiner = {
-    val offset = PlatformDependent.BYTE_ARRAY_OFFSET
-    val getLong = "PlatformDependent.UNSAFE.getLong"
-    val putLong = "PlatformDependent.UNSAFE.putLong"
+    val offset = Platform.BYTE_ARRAY_OFFSET
+    val getLong = "Platform.getLong"
+    val putLong = "Platform.putLong"
 
     val bitset1Words = (schema1.size + 63) / 64
     val bitset2Words = (schema2.size + 63) / 64
@@ -96,7 +96,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
     var cursor = offset + outputBitsetWords * 8
     val copyFixedLengthRow1 = s"""
        |// Copy fixed length data for row1
-       |PlatformDependent.copyMemory(
+       |Platform.copyMemory(
        |  obj1, offset1 + ${bitset1Words * 8},
        |  buf, $cursor,
        |  ${schema1.size * 8});
@@ -106,7 +106,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
     // --------------------- copy fixed length portion from row 2 ----------------------- //
     val copyFixedLengthRow2 = s"""
        |// Copy fixed length data for row2
-       |PlatformDependent.copyMemory(
+       |Platform.copyMemory(
        |  obj2, offset2 + ${bitset2Words * 8},
        |  buf, $cursor,
        |  ${schema2.size * 8});
@@ -118,7 +118,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
     val copyVariableLengthRow1 = s"""
        |// Copy variable length data for row1
        |long numBytesVariableRow1 = row1.getSizeInBytes() - $numBytesBitsetAndFixedRow1;
-       |PlatformDependent.copyMemory(
+       |Platform.copyMemory(
        |  obj1, offset1 + ${(bitset1Words + schema1.size) * 8},
        |  buf, $cursor,
        |  numBytesVariableRow1);
@@ -129,7 +129,7 @@ object GenerateUnsafeRowJoiner extends CodeGenerator[(StructType, StructType), U
     val copyVariableLengthRow2 = s"""
        |// Copy variable length data for row2
        |long numBytesVariableRow2 = row2.getSizeInBytes() - $numBytesBitsetAndFixedRow2;
-       |PlatformDependent.copyMemory(
+       |Platform.copyMemory(
        |  obj2, offset2 + ${(bitset2Words + schema2.size) * 8},
        |  buf, $cursor + numBytesVariableRow1,
        |  numBytesVariableRow2);
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 76666bd6b3d27..134f1aa2af9a8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -1013,7 +1013,7 @@ case class Decode(bin: Expression, charset: Expression)
         try {
           ${ev.primitive} = UTF8String.fromString(new String($bytes, $charset.toString()));
         } catch (java.io.UnsupportedEncodingException e) {
-          org.apache.spark.unsafe.PlatformDependent.throwException(e);
+          org.apache.spark.unsafe.Platform.throwException(e);
         }
       """)
   }
@@ -1043,7 +1043,7 @@ case class Encode(value: Expression, charset: Expression)
         try {
           ${ev.primitive} = $string.toString().getBytes($charset.toString());
         } catch (java.io.UnsupportedEncodingException e) {
-          org.apache.spark.unsafe.PlatformDependent.throwException(e);
+          org.apache.spark.unsafe.Platform.throwException(e);
         }""")
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
index aff1bee99faad..796d60032e1a6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeRowJoinerBitsetSuite.scala
@@ -22,7 +22,7 @@ import scala.util.Random
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 
 /**
  * A test suite for the bitset portion of the row concatenation.
@@ -96,7 +96,7 @@ class GenerateUnsafeRowJoinerBitsetSuite extends SparkFunSuite {
     // This way we can test the joiner when the input UnsafeRows are not the entire arrays.
     val offset = numFields * 8
     val buf = new Array[Byte](sizeInBytes + offset)
-    row.pointTo(buf, PlatformDependent.BYTE_ARRAY_OFFSET + offset, numFields, sizeInBytes)
+    row.pointTo(buf, Platform.BYTE_ARRAY_OFFSET + offset, numFields, sizeInBytes)
     row
   }
 
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 00218f213054b..5cce41d5a7569 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -27,7 +27,7 @@
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 import org.apache.spark.unsafe.KVIterator;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.map.BytesToBytesMap;
 import org.apache.spark.unsafe.memory.MemoryLocation;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
@@ -138,7 +138,7 @@ public UnsafeRow getAggregationBufferFromUnsafeRow(UnsafeRow unsafeGroupingKeyRo
         unsafeGroupingKeyRow.getBaseOffset(),
         unsafeGroupingKeyRow.getSizeInBytes(),
         emptyAggregationBuffer,
-        PlatformDependent.BYTE_ARRAY_OFFSET,
+        Platform.BYTE_ARRAY_OFFSET,
         emptyAggregationBuffer.length
       );
       if (!putSucceeded) {
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
index 69d6784713a24..7db6b7ff50f22 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeKVExternalSorter.java
@@ -31,7 +31,7 @@
 import org.apache.spark.sql.types.StructType;
 import org.apache.spark.storage.BlockManager;
 import org.apache.spark.unsafe.KVIterator;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.map.BytesToBytesMap;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 import org.apache.spark.unsafe.memory.TaskMemoryManager;
@@ -225,7 +225,7 @@ public boolean next() throws IOException {
           int recordLen = underlying.getRecordLength();
 
           // Note that recordLen = keyLen + valueLen + 4 bytes (for the keyLen itself)
-          int keyLen = PlatformDependent.UNSAFE.getInt(baseObj, recordOffset);
+          int keyLen = Platform.getInt(baseObj, recordOffset);
           int valueLen = recordLen - keyLen - 4;
           key.pointTo(baseObj, recordOffset + 4, numKeyFields, keyLen);
           value.pointTo(baseObj, recordOffset + 4 + keyLen, numValueFields, valueLen);
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 6c7e5cacc99e7..3860c4bba9a99 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -26,7 +26,7 @@ import com.google.common.io.ByteStreams
 
 import org.apache.spark.serializer.{SerializationStream, DeserializationStream, SerializerInstance, Serializer}
 import org.apache.spark.sql.catalyst.expressions.UnsafeRow
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 
 /**
  * Serializer for serializing [[UnsafeRow]]s during shuffle. Since UnsafeRows are already stored as
@@ -116,7 +116,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
               rowBuffer = new Array[Byte](rowSize)
             }
             ByteStreams.readFully(dIn, rowBuffer, 0, rowSize)
-            row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize)
+            row.pointTo(rowBuffer, Platform.BYTE_ARRAY_OFFSET, numFields, rowSize)
             rowSize = dIn.readInt() // read the next row's size
             if (rowSize == EOF) { // We are returning the last row in this stream
               val _rowTuple = rowTuple
@@ -150,7 +150,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
           rowBuffer = new Array[Byte](rowSize)
         }
         ByteStreams.readFully(dIn, rowBuffer, 0, rowSize)
-        row.pointTo(rowBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, numFields, rowSize)
+        row.pointTo(rowBuffer, Platform.BYTE_ARRAY_OFFSET, numFields, rowSize)
         row.asInstanceOf[T]
       }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 953abf409f220..63d35d0f02622 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -25,7 +25,7 @@ import org.apache.spark.shuffle.ShuffleMemoryManager
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkSqlSerializer
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.map.BytesToBytesMap
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.util.Utils
@@ -218,8 +218,8 @@ private[joins] final class UnsafeHashedRelation(
         var offset = loc.getValueAddress.getBaseOffset
         val last = loc.getValueAddress.getBaseOffset + loc.getValueLength
         while (offset < last) {
-          val numFields = PlatformDependent.UNSAFE.getInt(base, offset)
-          val sizeInBytes = PlatformDependent.UNSAFE.getInt(base, offset + 4)
+          val numFields = Platform.getInt(base, offset)
+          val sizeInBytes = Platform.getInt(base, offset + 4)
           offset += 8
 
           val row = new UnsafeRow
@@ -314,10 +314,11 @@ private[joins] final class UnsafeHashedRelation(
       in.readFully(valuesBuffer, 0, valuesSize)
 
       // put it into binary map
-      val loc = binaryMap.lookup(keyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, keySize)
+      val loc = binaryMap.lookup(keyBuffer, Platform.BYTE_ARRAY_OFFSET, keySize)
       assert(!loc.isDefined, "Duplicated key found!")
-      val putSuceeded = loc.putNewKey(keyBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, keySize,
-        valuesBuffer, PlatformDependent.BYTE_ARRAY_OFFSET, valuesSize)
+      val putSuceeded = loc.putNewKey(
+        keyBuffer, Platform.BYTE_ARRAY_OFFSET, keySize,
+        valuesBuffer, Platform.BYTE_ARRAY_OFFSET, valuesSize)
       if (!putSuceeded) {
         throw new IOException("Could not allocate memory to grow BytesToBytesMap")
       }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
index 89bad1bfdab0a..219435dff5bc8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
@@ -23,7 +23,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeProjection}
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.PlatformDependent
+import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.memory.MemoryAllocator
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -51,7 +51,7 @@ class UnsafeRowSuite extends SparkFunSuite {
     val bytesFromOffheapRow: Array[Byte] = {
       val offheapRowPage = MemoryAllocator.UNSAFE.allocate(arrayBackedUnsafeRow.getSizeInBytes)
       try {
-        PlatformDependent.copyMemory(
+        Platform.copyMemory(
           arrayBackedUnsafeRow.getBaseObject,
           arrayBackedUnsafeRow.getBaseOffset,
           offheapRowPage.getBaseObject,
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java b/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
similarity index 55%
rename from unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java
rename to unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
index b2de2a2590f05..18343efdc3437 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/PlatformDependent.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
@@ -22,103 +22,111 @@
 
 import sun.misc.Unsafe;
 
-public final class PlatformDependent {
+public final class Platform {
 
-  /**
-   * Facade in front of {@link sun.misc.Unsafe}, used to avoid directly exposing Unsafe outside of
-   * this package. This also lets us avoid accidental use of deprecated methods.
-   */
-  public static final class UNSAFE {
-
-    private UNSAFE() { }
+  private static final Unsafe _UNSAFE;
 
-    public static int getInt(Object object, long offset) {
-      return _UNSAFE.getInt(object, offset);
-    }
+  public static final int BYTE_ARRAY_OFFSET;
 
-    public static void putInt(Object object, long offset, int value) {
-      _UNSAFE.putInt(object, offset, value);
-    }
+  public static final int INT_ARRAY_OFFSET;
 
-    public static boolean getBoolean(Object object, long offset) {
-      return _UNSAFE.getBoolean(object, offset);
-    }
+  public static final int LONG_ARRAY_OFFSET;
 
-    public static void putBoolean(Object object, long offset, boolean value) {
-      _UNSAFE.putBoolean(object, offset, value);
-    }
+  public static final int DOUBLE_ARRAY_OFFSET;
 
-    public static byte getByte(Object object, long offset) {
-      return _UNSAFE.getByte(object, offset);
-    }
+  // Support for resetting final fields while deserializing
+  public static final long BIG_INTEGER_SIGNUM_OFFSET;
+  public static final long BIG_INTEGER_MAG_OFFSET;
 
-    public static void putByte(Object object, long offset, byte value) {
-      _UNSAFE.putByte(object, offset, value);
-    }
+  public static int getInt(Object object, long offset) {
+    return _UNSAFE.getInt(object, offset);
+  }
 
-    public static short getShort(Object object, long offset) {
-      return _UNSAFE.getShort(object, offset);
-    }
+  public static void putInt(Object object, long offset, int value) {
+    _UNSAFE.putInt(object, offset, value);
+  }
 
-    public static void putShort(Object object, long offset, short value) {
-      _UNSAFE.putShort(object, offset, value);
-    }
+  public static boolean getBoolean(Object object, long offset) {
+    return _UNSAFE.getBoolean(object, offset);
+  }
 
-    public static long getLong(Object object, long offset) {
-      return _UNSAFE.getLong(object, offset);
-    }
+  public static void putBoolean(Object object, long offset, boolean value) {
+    _UNSAFE.putBoolean(object, offset, value);
+  }
 
-    public static void putLong(Object object, long offset, long value) {
-      _UNSAFE.putLong(object, offset, value);
-    }
+  public static byte getByte(Object object, long offset) {
+    return _UNSAFE.getByte(object, offset);
+  }
 
-    public static float getFloat(Object object, long offset) {
-      return _UNSAFE.getFloat(object, offset);
-    }
+  public static void putByte(Object object, long offset, byte value) {
+    _UNSAFE.putByte(object, offset, value);
+  }
 
-    public static void putFloat(Object object, long offset, float value) {
-      _UNSAFE.putFloat(object, offset, value);
-    }
+  public static short getShort(Object object, long offset) {
+    return _UNSAFE.getShort(object, offset);
+  }
 
-    public static double getDouble(Object object, long offset) {
-      return _UNSAFE.getDouble(object, offset);
-    }
+  public static void putShort(Object object, long offset, short value) {
+    _UNSAFE.putShort(object, offset, value);
+  }
 
-    public static void putDouble(Object object, long offset, double value) {
-      _UNSAFE.putDouble(object, offset, value);
-    }
+  public static long getLong(Object object, long offset) {
+    return _UNSAFE.getLong(object, offset);
+  }
 
-    public static Object getObjectVolatile(Object object, long offset) {
-      return _UNSAFE.getObjectVolatile(object, offset);
-    }
+  public static void putLong(Object object, long offset, long value) {
+    _UNSAFE.putLong(object, offset, value);
+  }
 
-    public static void putObjectVolatile(Object object, long offset, Object value) {
-      _UNSAFE.putObjectVolatile(object, offset, value);
-    }
+  public static float getFloat(Object object, long offset) {
+    return _UNSAFE.getFloat(object, offset);
+  }
 
-    public static long allocateMemory(long size) {
-      return _UNSAFE.allocateMemory(size);
-    }
+  public static void putFloat(Object object, long offset, float value) {
+    _UNSAFE.putFloat(object, offset, value);
+  }
 
-    public static void freeMemory(long address) {
-      _UNSAFE.freeMemory(address);
-    }
+  public static double getDouble(Object object, long offset) {
+    return _UNSAFE.getDouble(object, offset);
+  }
 
+  public static void putDouble(Object object, long offset, double value) {
+    _UNSAFE.putDouble(object, offset, value);
   }
 
-  private static final Unsafe _UNSAFE;
+  public static Object getObjectVolatile(Object object, long offset) {
+    return _UNSAFE.getObjectVolatile(object, offset);
+  }
 
-  public static final int BYTE_ARRAY_OFFSET;
+  public static void putObjectVolatile(Object object, long offset, Object value) {
+    _UNSAFE.putObjectVolatile(object, offset, value);
+  }
 
-  public static final int INT_ARRAY_OFFSET;
+  public static long allocateMemory(long size) {
+    return _UNSAFE.allocateMemory(size);
+  }
 
-  public static final int LONG_ARRAY_OFFSET;
+  public static void freeMemory(long address) {
+    _UNSAFE.freeMemory(address);
+  }
 
-  public static final int DOUBLE_ARRAY_OFFSET;
+  public static void copyMemory(
+    Object src, long srcOffset, Object dst, long dstOffset, long length) {
+    while (length > 0) {
+      long size = Math.min(length, UNSAFE_COPY_THRESHOLD);
+      _UNSAFE.copyMemory(src, srcOffset, dst, dstOffset, size);
+      length -= size;
+      srcOffset += size;
+      dstOffset += size;
+    }
+  }
 
-  // Support for resetting final fields while deserializing
-  public static final long BIG_INTEGER_SIGNUM_OFFSET;
-  public static final long BIG_INTEGER_MAG_OFFSET;
+  /**
+   * Raises an exception bypassing compiler checks for checked exceptions.
+   */
+  public static void throwException(Throwable t) {
+    _UNSAFE.throwException(t);
+  }
 
   /**
    * Limits the number of bytes to copy per {@link Unsafe#copyMemory(long, long, long)} to
@@ -162,26 +170,4 @@ public static void freeMemory(long address) {
       BIG_INTEGER_MAG_OFFSET = 0;
     }
   }
-
-  static public void copyMemory(
-      Object src,
-      long srcOffset,
-      Object dst,
-      long dstOffset,
-      long length) {
-    while (length > 0) {
-      long size = Math.min(length, UNSAFE_COPY_THRESHOLD);
-      _UNSAFE.copyMemory(src, srcOffset, dst, dstOffset, size);
-      length -= size;
-      srcOffset += size;
-      dstOffset += size;
-    }
-  }
-
-  /**
-   * Raises an exception bypassing compiler checks for checked exceptions.
-   */
-  public static void throwException(Throwable t) {
-    _UNSAFE.throwException(t);
-  }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
index 70b81ce015ddc..cf42877bf9fd4 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/array/ByteArrayMethods.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.array;
 
-import static org.apache.spark.unsafe.PlatformDependent.*;
+import org.apache.spark.unsafe.Platform;
 
 public class ByteArrayMethods {
 
@@ -45,20 +45,18 @@ public static int roundNumberOfBytesToNearestWord(int numBytes) {
    * @return true if the arrays are equal, false otherwise
    */
   public static boolean arrayEquals(
-      Object leftBase,
-      long leftOffset,
-      Object rightBase,
-      long rightOffset,
-      final long length) {
+      Object leftBase, long leftOffset, Object rightBase, long rightOffset, final long length) {
     int i = 0;
     while (i <= length - 8) {
-      if (UNSAFE.getLong(leftBase, leftOffset + i) != UNSAFE.getLong(rightBase, rightOffset + i)) {
+      if (Platform.getLong(leftBase, leftOffset + i) !=
+        Platform.getLong(rightBase, rightOffset + i)) {
         return false;
       }
       i += 8;
     }
     while (i < length) {
-      if (UNSAFE.getByte(leftBase, leftOffset + i) != UNSAFE.getByte(rightBase, rightOffset + i)) {
+      if (Platform.getByte(leftBase, leftOffset + i) !=
+        Platform.getByte(rightBase, rightOffset + i)) {
         return false;
       }
       i += 1;
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java b/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java
index 18d1f0d2d7eb2..74105050e4191 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/array/LongArray.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.array;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.memory.MemoryBlock;
 
 /**
@@ -64,7 +64,7 @@ public long size() {
   public void set(int index, long value) {
     assert index >= 0 : "index (" + index + ") should >= 0";
     assert index < length : "index (" + index + ") should < length (" + length + ")";
-    PlatformDependent.UNSAFE.putLong(baseObj, baseOffset + index * WIDTH, value);
+    Platform.putLong(baseObj, baseOffset + index * WIDTH, value);
   }
 
   /**
@@ -73,6 +73,6 @@ public void set(int index, long value) {
   public long get(int index) {
     assert index >= 0 : "index (" + index + ") should >= 0";
     assert index < length : "index (" + index + ") should < length (" + length + ")";
-    return PlatformDependent.UNSAFE.getLong(baseObj, baseOffset + index * WIDTH);
+    return Platform.getLong(baseObj, baseOffset + index * WIDTH);
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
index 27462c7fa5e62..7857bf66a72ad 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/bitset/BitSetMethods.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.bitset;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * Methods for working with fixed-size uncompressed bitsets.
@@ -41,8 +41,8 @@ public static void set(Object baseObject, long baseOffset, int index) {
     assert index >= 0 : "index (" + index + ") should >= 0";
     final long mask = 1L << (index & 0x3f);  // mod 64 and shift
     final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE;
-    final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset);
-    PlatformDependent.UNSAFE.putLong(baseObject, wordOffset, word | mask);
+    final long word = Platform.getLong(baseObject, wordOffset);
+    Platform.putLong(baseObject, wordOffset, word | mask);
   }
 
   /**
@@ -52,8 +52,8 @@ public static void unset(Object baseObject, long baseOffset, int index) {
     assert index >= 0 : "index (" + index + ") should >= 0";
     final long mask = 1L << (index & 0x3f);  // mod 64 and shift
     final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE;
-    final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset);
-    PlatformDependent.UNSAFE.putLong(baseObject, wordOffset, word & ~mask);
+    final long word = Platform.getLong(baseObject, wordOffset);
+    Platform.putLong(baseObject, wordOffset, word & ~mask);
   }
 
   /**
@@ -63,7 +63,7 @@ public static boolean isSet(Object baseObject, long baseOffset, int index) {
     assert index >= 0 : "index (" + index + ") should >= 0";
     final long mask = 1L << (index & 0x3f);  // mod 64 and shift
     final long wordOffset = baseOffset + (index >> 6) * WORD_SIZE;
-    final long word = PlatformDependent.UNSAFE.getLong(baseObject, wordOffset);
+    final long word = Platform.getLong(baseObject, wordOffset);
     return (word & mask) != 0;
   }
 
@@ -73,7 +73,7 @@ public static boolean isSet(Object baseObject, long baseOffset, int index) {
   public static boolean anySet(Object baseObject, long baseOffset, long bitSetWidthInWords) {
     long addr = baseOffset;
     for (int i = 0; i < bitSetWidthInWords; i++, addr += WORD_SIZE) {
-      if (PlatformDependent.UNSAFE.getLong(baseObject, addr) != 0) {
+      if (Platform.getLong(baseObject, addr) != 0) {
         return true;
       }
     }
@@ -109,8 +109,7 @@ public static int nextSetBit(
 
     // Try to find the next set bit in the current word
     final int subIndex = fromIndex & 0x3f;
-    long word =
-      PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + wi * WORD_SIZE) >> subIndex;
+    long word = Platform.getLong(baseObject, baseOffset + wi * WORD_SIZE) >> subIndex;
     if (word != 0) {
       return (wi << 6) + subIndex + java.lang.Long.numberOfTrailingZeros(word);
     }
@@ -118,7 +117,7 @@ public static int nextSetBit(
     // Find the next set bit in the rest of the words
     wi += 1;
     while (wi < bitsetSizeInWords) {
-      word = PlatformDependent.UNSAFE.getLong(baseObject, baseOffset + wi * WORD_SIZE);
+      word = Platform.getLong(baseObject, baseOffset + wi * WORD_SIZE);
       if (word != 0) {
         return (wi << 6) + java.lang.Long.numberOfTrailingZeros(word);
       }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java b/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java
index 61f483ced3217..4276f25c2165b 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/hash/Murmur3_x86_32.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.hash;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * 32-bit Murmur3 hasher.  This is based on Guava's Murmur3_32HashFunction.
@@ -53,7 +53,7 @@ public static int hashUnsafeWords(Object base, long offset, int lengthInBytes, i
     assert (lengthInBytes % 8 == 0): "lengthInBytes must be a multiple of 8 (word-aligned)";
     int h1 = seed;
     for (int i = 0; i < lengthInBytes; i += 4) {
-      int halfWord = PlatformDependent.UNSAFE.getInt(base, offset + i);
+      int halfWord = Platform.getInt(base, offset + i);
       int k1 = mixK1(halfWord);
       h1 = mixH1(h1, k1);
     }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java
index 91be46ba21ff8..dd75820834370 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/MemoryBlock.java
@@ -19,7 +19,7 @@
 
 import javax.annotation.Nullable;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * A consecutive block of memory, starting at a {@link MemoryLocation} with a fixed size.
@@ -50,6 +50,6 @@ public long size() {
    * Creates a memory block pointing to the memory used by the long array.
    */
   public static MemoryBlock fromLongArray(final long[] array) {
-    return new MemoryBlock(array, PlatformDependent.LONG_ARRAY_OFFSET, array.length * 8);
+    return new MemoryBlock(array, Platform.LONG_ARRAY_OFFSET, array.length * 8);
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
index 62f4459696c28..cda7826c8c99b 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/UnsafeMemoryAllocator.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.memory;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 /**
  * A simple {@link MemoryAllocator} that uses {@code Unsafe} to allocate off-heap memory.
@@ -29,7 +29,7 @@ public MemoryBlock allocate(long size) throws OutOfMemoryError {
     if (size % 8 != 0) {
       throw new IllegalArgumentException("Size " + size + " was not a multiple of 8");
     }
-    long address = PlatformDependent.UNSAFE.allocateMemory(size);
+    long address = Platform.allocateMemory(size);
     return new MemoryBlock(null, address, size);
   }
 
@@ -37,6 +37,6 @@ public MemoryBlock allocate(long size) throws OutOfMemoryError {
   public void free(MemoryBlock memory) {
     assert (memory.obj == null) :
       "baseObject not null; are you trying to use the off-heap allocator to free on-heap memory?";
-    PlatformDependent.UNSAFE.freeMemory(memory.offset);
+    Platform.freeMemory(memory.offset);
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java
index 69b0e206cef18..c08c9c73d2396 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/ByteArray.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.unsafe.types;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 
 public class ByteArray {
 
@@ -27,12 +27,6 @@ public class ByteArray {
    * hold all the bytes in this string.
    */
   public static void writeToMemory(byte[] src, Object target, long targetOffset) {
-    PlatformDependent.copyMemory(
-      src,
-      PlatformDependent.BYTE_ARRAY_OFFSET,
-      target,
-      targetOffset,
-      src.length
-    );
+    Platform.copyMemory(src, Platform.BYTE_ARRAY_OFFSET, target, targetOffset, src.length);
   }
 }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index d1014426c0f49..667c00900f2c5 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -24,10 +24,10 @@
 import java.util.Arrays;
 import java.util.Map;
 
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 
-import static org.apache.spark.unsafe.PlatformDependent.*;
+import static org.apache.spark.unsafe.Platform.*;
 
 
 /**
@@ -133,13 +133,7 @@ protected UTF8String(Object base, long offset, int numBytes) {
    * bytes in this string.
    */
   public void writeToMemory(Object target, long targetOffset) {
-    PlatformDependent.copyMemory(
-      base,
-      offset,
-      target,
-      targetOffset,
-      numBytes
-    );
+    Platform.copyMemory(base, offset, target, targetOffset, numBytes);
   }
 
   /**
@@ -183,12 +177,12 @@ public long getPrefix() {
     long mask = 0;
     if (isLittleEndian) {
       if (numBytes >= 8) {
-        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        p = Platform.getLong(base, offset);
       } else if (numBytes > 4) {
-        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        p = Platform.getLong(base, offset);
         mask = (1L << (8 - numBytes) * 8) - 1;
       } else if (numBytes > 0) {
-        p = (long) PlatformDependent.UNSAFE.getInt(base, offset);
+        p = (long) Platform.getInt(base, offset);
         mask = (1L << (8 - numBytes) * 8) - 1;
       } else {
         p = 0;
@@ -197,12 +191,12 @@ public long getPrefix() {
     } else {
       // byteOrder == ByteOrder.BIG_ENDIAN
       if (numBytes >= 8) {
-        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        p = Platform.getLong(base, offset);
       } else if (numBytes > 4) {
-        p = PlatformDependent.UNSAFE.getLong(base, offset);
+        p = Platform.getLong(base, offset);
         mask = (1L << (8 - numBytes) * 8) - 1;
       } else if (numBytes > 0) {
-        p = ((long) PlatformDependent.UNSAFE.getInt(base, offset)) << 32;
+        p = ((long) Platform.getInt(base, offset)) << 32;
         mask = (1L << (8 - numBytes) * 8) - 1;
       } else {
         p = 0;
@@ -293,7 +287,7 @@ public boolean contains(final UTF8String substring) {
    * Returns the byte at position `i`.
    */
   private byte getByte(int i) {
-    return UNSAFE.getByte(base, offset + i);
+    return Platform.getByte(base, offset + i);
   }
 
   private boolean matchAt(final UTF8String s, int pos) {
@@ -769,7 +763,7 @@ public static UTF8String concatWs(UTF8String separator, UTF8String... inputs) {
         int len = inputs[i].numBytes;
         copyMemory(
           inputs[i].base, inputs[i].offset,
-          result, PlatformDependent.BYTE_ARRAY_OFFSET + offset,
+          result, BYTE_ARRAY_OFFSET + offset,
           len);
         offset += len;
 
@@ -778,7 +772,7 @@ public static UTF8String concatWs(UTF8String separator, UTF8String... inputs) {
         if (j < numInputs) {
           copyMemory(
             separator.base, separator.offset,
-            result, PlatformDependent.BYTE_ARRAY_OFFSET + offset,
+            result, BYTE_ARRAY_OFFSET + offset,
             separator.numBytes);
           offset += separator.numBytes;
         }
diff --git a/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java b/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java
index 3b9175835229c..2f8cb132ac8b4 100644
--- a/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java
+++ b/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java
@@ -22,7 +22,7 @@
 import java.util.Set;
 
 import junit.framework.Assert;
-import org.apache.spark.unsafe.PlatformDependent;
+import org.apache.spark.unsafe.Platform;
 import org.junit.Test;
 
 /**
@@ -83,11 +83,11 @@ public void randomizedStressTestBytes() {
       rand.nextBytes(bytes);
 
       Assert.assertEquals(
-        hasher.hashUnsafeWords(bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize),
-        hasher.hashUnsafeWords(bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize));
+        hasher.hashUnsafeWords(bytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize),
+        hasher.hashUnsafeWords(bytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize));
 
       hashcodes.add(hasher.hashUnsafeWords(
-        bytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize));
+        bytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize));
     }
 
     // A very loose bound.
@@ -106,11 +106,11 @@ public void randomizedStressTestPaddedStrings() {
       System.arraycopy(strBytes, 0, paddedBytes, 0, strBytes.length);
 
       Assert.assertEquals(
-        hasher.hashUnsafeWords(paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize),
-        hasher.hashUnsafeWords(paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize));
+        hasher.hashUnsafeWords(paddedBytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize),
+        hasher.hashUnsafeWords(paddedBytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize));
 
       hashcodes.add(hasher.hashUnsafeWords(
-        paddedBytes, PlatformDependent.BYTE_ARRAY_OFFSET, byteArrSize));
+        paddedBytes, Platform.BYTE_ARRAY_OFFSET, byteArrSize));
     }
 
     // A very loose bound.

From dfe347d2cae3eb05d7539aaf72db3d309e711213 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 11 Aug 2015 08:52:15 -0700
Subject: [PATCH 161/633] [SPARK-9785] [SQL] HashPartitioning compatibility
 should consider expression ordering

HashPartitioning compatibility is currently defined w.r.t the _set_ of expressions, but the ordering of those expressions matters when computing hash codes; this could lead to incorrect answers if we mistakenly avoided a shuffle based on the assumption that HashPartitionings with the same expressions in different orders will produce equivalent row hashcodes. The first commit adds a regression test which illustrates this problem.

The fix for this is simple: make `HashPartitioning.compatibleWith` and `HashPartitioning.guarantees` sensitive to the expression ordering (i.e. do not perform set comparison).

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8074 from JoshRosen/hashpartitioning-compatiblewith-fixes and squashes the following commits:

b61412f [Josh Rosen] Demonstrate that I haven't cheated in my fix
0b4d7d9 [Josh Rosen] Update so that clusteringSet is only used in satisfies().
dc9c9d7 [Josh Rosen] Add failing regression test for SPARK-9785
---
 .../plans/physical/partitioning.scala         | 15 ++---
 .../sql/catalyst/PartitioningSuite.scala      | 55 +++++++++++++++++++
 2 files changed, 60 insertions(+), 10 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/PartitioningSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
index 5a89a90b735a6..5ac3f1f5b0cac 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/physical/partitioning.scala
@@ -216,26 +216,23 @@ case class HashPartitioning(expressions: Seq[Expression], numPartitions: Int)
   override def nullable: Boolean = false
   override def dataType: DataType = IntegerType
 
-  lazy val clusteringSet = expressions.toSet
-
   override def satisfies(required: Distribution): Boolean = required match {
     case UnspecifiedDistribution => true
     case ClusteredDistribution(requiredClustering) =>
-      clusteringSet.subsetOf(requiredClustering.toSet)
+      expressions.toSet.subsetOf(requiredClustering.toSet)
     case _ => false
   }
 
   override def compatibleWith(other: Partitioning): Boolean = other match {
-    case o: HashPartitioning =>
-      this.clusteringSet == o.clusteringSet && this.numPartitions == o.numPartitions
+    case o: HashPartitioning => this == o
     case _ => false
   }
 
   override def guarantees(other: Partitioning): Boolean = other match {
-    case o: HashPartitioning =>
-      this.clusteringSet == o.clusteringSet && this.numPartitions == o.numPartitions
+    case o: HashPartitioning => this == o
     case _ => false
   }
+
 }
 
 /**
@@ -257,15 +254,13 @@ case class RangePartitioning(ordering: Seq[SortOrder], numPartitions: Int)
   override def nullable: Boolean = false
   override def dataType: DataType = IntegerType
 
-  private[this] lazy val clusteringSet = ordering.map(_.child).toSet
-
   override def satisfies(required: Distribution): Boolean = required match {
     case UnspecifiedDistribution => true
     case OrderedDistribution(requiredOrdering) =>
       val minSize = Seq(requiredOrdering.size, ordering.size).min
       requiredOrdering.take(minSize) == ordering.take(minSize)
     case ClusteredDistribution(requiredClustering) =>
-      clusteringSet.subsetOf(requiredClustering.toSet)
+      ordering.map(_.child).toSet.subsetOf(requiredClustering.toSet)
     case _ => false
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/PartitioningSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/PartitioningSuite.scala
new file mode 100644
index 0000000000000..5b802ccc637dd
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/PartitioningSuite.scala
@@ -0,0 +1,55 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.expressions.{InterpretedMutableProjection, Literal}
+import org.apache.spark.sql.catalyst.plans.physical.{ClusteredDistribution, HashPartitioning}
+
+class PartitioningSuite extends SparkFunSuite {
+  test("HashPartitioning compatibility should be sensitive to expression ordering (SPARK-9785)") {
+    val expressions = Seq(Literal(2), Literal(3))
+    // Consider two HashPartitionings that have the same _set_ of hash expressions but which are
+    // created with different orderings of those expressions:
+    val partitioningA = HashPartitioning(expressions, 100)
+    val partitioningB = HashPartitioning(expressions.reverse, 100)
+    // These partitionings are not considered equal:
+    assert(partitioningA != partitioningB)
+    // However, they both satisfy the same clustered distribution:
+    val distribution = ClusteredDistribution(expressions)
+    assert(partitioningA.satisfies(distribution))
+    assert(partitioningB.satisfies(distribution))
+    // These partitionings compute different hashcodes for the same input row:
+    def computeHashCode(partitioning: HashPartitioning): Int = {
+      val hashExprProj = new InterpretedMutableProjection(partitioning.expressions, Seq.empty)
+      hashExprProj.apply(InternalRow.empty).hashCode()
+    }
+    assert(computeHashCode(partitioningA) != computeHashCode(partitioningB))
+    // Thus, these partitionings are incompatible:
+    assert(!partitioningA.compatibleWith(partitioningB))
+    assert(!partitioningB.compatibleWith(partitioningA))
+    assert(!partitioningA.guarantees(partitioningB))
+    assert(!partitioningB.guarantees(partitioningA))
+
+    // Just to be sure that we haven't cheated by having these methods always return false,
+    // check that identical partitionings are still compatible with and guarantee each other:
+    assert(partitioningA === partitioningA)
+    assert(partitioningA.guarantees(partitioningA))
+    assert(partitioningA.compatibleWith(partitioningA))
+  }
+}

From bce72797f3499f14455722600b0d0898d4fd87c9 Mon Sep 17 00:00:00 2001
From: Jeff Zhang <zjffdu@apache.org>
Date: Tue, 11 Aug 2015 10:42:17 -0700
Subject: [PATCH 162/633] Fix comment error

API is updated but its doc comment is not updated.

Author: Jeff Zhang <zjffdu@apache.org>

Closes #8097 from zjffdu/dev.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 9ced44131b0d9..6aafb4c5644d7 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -866,7 +866,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * }}}
    *
    * Do
-   * `val rdd = sparkContext.dataStreamFiles("hdfs://a-hdfs-path")`,
+   * `val rdd = sparkContext.binaryFiles("hdfs://a-hdfs-path")`,
    *
    * then `rdd` contains
    * {{{

From 8cad854ef6a2066de5adffcca6b79a205ccfd5f3 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 11 Aug 2015 11:01:59 -0700
Subject: [PATCH 163/633] [SPARK-8345] [ML] Add an SQL node as a feature
 transformer

Implements the transforms which are defined by SQL statement.
Currently we only support SQL syntax like 'SELECT ... FROM __THIS__'
where '__THIS__' represents the underlying table of the input dataset.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #7465 from yanboliang/spark-8345 and squashes the following commits:

b403fcb [Yanbo Liang] address comments
0d4bb15 [Yanbo Liang] a better transformSchema() implementation
51eb9e7 [Yanbo Liang] Add an SQL node as a feature transformer
---
 .../spark/ml/feature/SQLTransformer.scala     | 72 +++++++++++++++++++
 .../ml/feature/SQLTransformerSuite.scala      | 44 ++++++++++++
 2 files changed, 116 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala
new file mode 100644
index 0000000000000..95e4305638730
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala
@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.param.{ParamMap, Param}
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.sql.{SQLContext, DataFrame, Row}
+import org.apache.spark.sql.types.StructType
+
+/**
+ * :: Experimental ::
+ * Implements the transforms which are defined by SQL statement.
+ * Currently we only support SQL syntax like 'SELECT ... FROM __THIS__'
+ * where '__THIS__' represents the underlying table of the input dataset.
+ */
+@Experimental
+class SQLTransformer (override val uid: String) extends Transformer {
+
+  def this() = this(Identifiable.randomUID("sql"))
+
+  /**
+   * SQL statement parameter. The statement is provided in string form.
+   * @group param
+   */
+  final val statement: Param[String] = new Param[String](this, "statement", "SQL statement")
+
+  /** @group setParam */
+  def setStatement(value: String): this.type = set(statement, value)
+
+  /** @group getParam */
+  def getStatement: String = $(statement)
+
+  private val tableIdentifier: String = "__THIS__"
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    val tableName = Identifiable.randomUID(uid)
+    dataset.registerTempTable(tableName)
+    val realStatement = $(statement).replace(tableIdentifier, tableName)
+    val outputDF = dataset.sqlContext.sql(realStatement)
+    outputDF
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    val sc = SparkContext.getOrCreate()
+    val sqlContext = SQLContext.getOrCreate(sc)
+    val dummyRDD = sc.parallelize(Seq(Row.empty))
+    val dummyDF = sqlContext.createDataFrame(dummyRDD, schema)
+    dummyDF.registerTempTable(tableIdentifier)
+    val outputSchema = sqlContext.sql($(statement)).schema
+    outputSchema
+  }
+
+  override def copy(extra: ParamMap): SQLTransformer = defaultCopy(extra)
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala
new file mode 100644
index 0000000000000..d19052881ae45
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/SQLTransformerSuite.scala
@@ -0,0 +1,44 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+
+class SQLTransformerSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("params") {
+    ParamsSuite.checkParams(new SQLTransformer())
+  }
+
+  test("transform numeric data") {
+    val original = sqlContext.createDataFrame(
+      Seq((0, 1.0, 3.0), (2, 2.0, 5.0))).toDF("id", "v1", "v2")
+    val sqlTrans = new SQLTransformer().setStatement(
+      "SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__")
+    val result = sqlTrans.transform(original)
+    val resultSchema = sqlTrans.transformSchema(original.schema)
+    val expected = sqlContext.createDataFrame(
+      Seq((0, 1.0, 3.0, 4.0, 3.0), (2, 2.0, 5.0, 7.0, 10.0)))
+      .toDF("id", "v1", "v2", "v3", "v4")
+    assert(result.schema.toString == resultSchema.toString)
+    assert(resultSchema == expected.schema)
+    assert(result.collect().toSeq == expected.collect().toSeq)
+  }
+}

From dbd778d84d094ca142bc08c351478595b280bc2a Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 11 Aug 2015 11:33:36 -0700
Subject: [PATCH 164/633] [SPARK-8764] [ML] string indexer should take option
 to handle unseen values

As a precursor to adding a public constructor add an option to handle unseen values by skipping rather than throwing an exception (default remains throwing an exception),

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7266 from holdenk/SPARK-8764-string-indexer-should-take-option-to-handle-unseen-values and squashes the following commits:

38a4de9 [Holden Karau] fix long line
045bf22 [Holden Karau] Add a second b entry so b gets 0 for sure
81dd312 [Holden Karau] Update the docs for handleInvalid param to be more descriptive
7f37f6e [Holden Karau] remove extra space (scala style)
414e249 [Holden Karau] And switch to using handleInvalid instead of skipInvalid
1e53f9b [Holden Karau] update the param (codegen side)
7a22215 [Holden Karau] fix typo
100a39b [Holden Karau] Merge in master
aa5b093 [Holden Karau] Since we filter we should never go down this code path if getSkipInvalid is true
75ffa69 [Holden Karau] Remove extra newline
d69ef5e [Holden Karau] Add a test
b5734be [Holden Karau] Add support for unseen labels
afecd4e [Holden Karau] Add a param to skip invalid entries.
---
 .../spark/ml/feature/StringIndexer.scala      | 26 ++++++++++++---
 .../ml/param/shared/SharedParamsCodeGen.scala |  4 +++
 .../spark/ml/param/shared/sharedParams.scala  | 15 +++++++++
 .../spark/ml/feature/StringIndexerSuite.scala | 32 +++++++++++++++++++
 4 files changed, 73 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index ebfa972532358..e4485eb038409 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -33,7 +33,8 @@ import org.apache.spark.util.collection.OpenHashMap
 /**
  * Base trait for [[StringIndexer]] and [[StringIndexerModel]].
  */
-private[feature] trait StringIndexerBase extends Params with HasInputCol with HasOutputCol {
+private[feature] trait StringIndexerBase extends Params with HasInputCol with HasOutputCol
+    with HasHandleInvalid {
 
   /** Validates and transforms the input schema. */
   protected def validateAndTransformSchema(schema: StructType): StructType = {
@@ -65,13 +66,16 @@ class StringIndexer(override val uid: String) extends Estimator[StringIndexerMod
 
   def this() = this(Identifiable.randomUID("strIdx"))
 
+  /** @group setParam */
+  def setHandleInvalid(value: String): this.type = set(handleInvalid, value)
+  setDefault(handleInvalid, "error")
+
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)
 
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  // TODO: handle unseen labels
 
   override def fit(dataset: DataFrame): StringIndexerModel = {
     val counts = dataset.select(col($(inputCol)).cast(StringType))
@@ -111,6 +115,10 @@ class StringIndexerModel private[ml] (
     map
   }
 
+  /** @group setParam */
+  def setHandleInvalid(value: String): this.type = set(handleInvalid, value)
+  setDefault(handleInvalid, "error")
+
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)
 
@@ -128,14 +136,24 @@ class StringIndexerModel private[ml] (
       if (labelToIndex.contains(label)) {
         labelToIndex(label)
       } else {
-        // TODO: handle unseen labels
         throw new SparkException(s"Unseen label: $label.")
       }
     }
+
     val outputColName = $(outputCol)
     val metadata = NominalAttribute.defaultAttr
       .withName(outputColName).withValues(labels).toMetadata()
-    dataset.select(col("*"),
+    // If we are skipping invalid records, filter them out.
+    val filteredDataset = (getHandleInvalid) match {
+      case "skip" => {
+        val filterer = udf { label: String =>
+          labelToIndex.contains(label)
+        }
+        dataset.where(filterer(dataset($(inputCol))))
+      }
+      case _ => dataset
+    }
+    filteredDataset.select(col("*"),
       indexer(dataset($(inputCol)).cast(StringType)).as(outputColName, metadata))
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index a97c8059b8d45..da4c076830391 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -59,6 +59,10 @@ private[shared] object SharedParamsCodeGen {
       ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1)",
         isValid = "ParamValidators.gtEq(1)"),
       ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")),
+      ParamDesc[String]("handleInvalid", "how to handle invalid entries. Options are skip (which " +
+        "will filter out rows with bad values), or error (which will throw an errror). More " +
+        "options may be added later.",
+        isValid = "ParamValidators.inArray(Array(\"skip\", \"error\"))"),
       ParamDesc[Boolean]("standardization", "whether to standardize the training features" +
         " before fitting the model.", Some("true")),
       ParamDesc[Long]("seed", "random seed", Some("this.getClass.getName.hashCode.toLong")),
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index f332630c32f1b..23e2b6cc43996 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -247,6 +247,21 @@ private[ml] trait HasFitIntercept extends Params {
   final def getFitIntercept: Boolean = $(fitIntercept)
 }
 
+/**
+ * Trait for shared param handleInvalid.
+ */
+private[ml] trait HasHandleInvalid extends Params {
+
+  /**
+   * Param for how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later..
+   * @group param
+   */
+  final val handleInvalid: Param[String] = new Param[String](this, "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.", ParamValidators.inArray(Array("skip", "error")))
+
+  /** @group getParam */
+  final def getHandleInvalid: String = $(handleInvalid)
+}
+
 /**
  * Trait for shared param standardization (default: true).
  */
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index d0295a0fe2fc1..b111036087e6a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.ml.feature
 
+import org.apache.spark.SparkException
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
 import org.apache.spark.ml.param.ParamsSuite
@@ -62,6 +63,37 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
       reversed2.collect().map(r => (r.getInt(0), r.getString(1))).toSet)
   }
 
+  test("StringIndexerUnseen") {
+    val data = sc.parallelize(Seq((0, "a"), (1, "b"), (4, "b")), 2)
+    val data2 = sc.parallelize(Seq((0, "a"), (1, "b"), (2, "c")), 2)
+    val df = sqlContext.createDataFrame(data).toDF("id", "label")
+    val df2 = sqlContext.createDataFrame(data2).toDF("id", "label")
+    val indexer = new StringIndexer()
+      .setInputCol("label")
+      .setOutputCol("labelIndex")
+      .fit(df)
+    // Verify we throw by default with unseen values
+    intercept[SparkException] {
+      indexer.transform(df2).collect()
+    }
+    val indexerSkipInvalid = new StringIndexer()
+      .setInputCol("label")
+      .setOutputCol("labelIndex")
+      .setHandleInvalid("skip")
+      .fit(df)
+    // Verify that we skip the c record
+    val transformed = indexerSkipInvalid.transform(df2)
+    val attr = Attribute.fromStructField(transformed.schema("labelIndex"))
+      .asInstanceOf[NominalAttribute]
+    assert(attr.values.get === Array("b", "a"))
+    val output = transformed.select("id", "labelIndex").map { r =>
+      (r.getInt(0), r.getDouble(1))
+    }.collect().toSet
+    // a -> 1, b -> 0
+    val expected = Set((0, 1.0), (1, 0.0))
+    assert(output === expected)
+  }
+
   test("StringIndexer with a numeric input column") {
     val data = sc.parallelize(Seq((0, 100), (1, 200), (2, 300), (3, 100), (4, 100), (5, 300)), 2)
     val df = sqlContext.createDataFrame(data).toDF("id", "label")

From 5b8bb1b213b8738f563fcd00747604410fbb3087 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 11 Aug 2015 12:02:28 -0700
Subject: [PATCH 165/633] [SPARK-9572] [STREAMING] [PYSPARK] Added
 StreamingContext.getActiveOrCreate() in Python

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8080 from tdas/SPARK-9572 and squashes the following commits:

64a231d [Tathagata Das] Fix based on comments
741a0d0 [Tathagata Das] Fixed style
f4f094c [Tathagata Das] Tweaked test
9afcdbe [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into SPARK-9572
e21488d [Tathagata Das] Minor update
1a371d9 [Tathagata Das] Addressed comments.
60479da [Tathagata Das] Fixed indent
9c2da9c [Tathagata Das] Fixed bugs
b5bd32c [Tathagata Das] Merge remote-tracking branch 'apache-github/master' into SPARK-9572
b55b348 [Tathagata Das] Removed prints
5781728 [Tathagata Das] Fix style issues
b711214 [Tathagata Das] Reverted run-tests.py
643b59d [Tathagata Das] Revert unnecessary change
150e58c [Tathagata Das] Added StreamingContext.getActiveOrCreate() in Python
---
 python/pyspark/streaming/context.py |  57 +++++++++++-
 python/pyspark/streaming/tests.py   | 133 +++++++++++++++++++++++++---
 python/run-tests.py                 |   2 +-
 3 files changed, 177 insertions(+), 15 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index ac5ba69e8dbbb..e3ba70e4e5e88 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -86,6 +86,9 @@ class StreamingContext(object):
     """
     _transformerSerializer = None
 
+    # Reference to a currently active StreamingContext
+    _activeContext = None
+
     def __init__(self, sparkContext, batchDuration=None, jssc=None):
         """
         Create a new StreamingContext.
@@ -142,10 +145,10 @@ def getOrCreate(cls, checkpointPath, setupFunc):
         Either recreate a StreamingContext from checkpoint data or create a new StreamingContext.
         If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be
         recreated from the checkpoint data. If the data does not exist, then the provided setupFunc
-        will be used to create a JavaStreamingContext.
+        will be used to create a new context.
 
-        @param checkpointPath: Checkpoint directory used in an earlier JavaStreamingContext program
-        @param setupFunc:      Function to create a new JavaStreamingContext and setup DStreams
+        @param checkpointPath: Checkpoint directory used in an earlier streaming program
+        @param setupFunc:      Function to create a new context and setup DStreams
         """
         # TODO: support checkpoint in HDFS
         if not os.path.exists(checkpointPath) or not os.listdir(checkpointPath):
@@ -170,6 +173,52 @@ def getOrCreate(cls, checkpointPath, setupFunc):
         cls._transformerSerializer.ctx = sc
         return StreamingContext(sc, None, jssc)
 
+    @classmethod
+    def getActive(cls):
+        """
+        Return either the currently active StreamingContext (i.e., if there is a context started
+        but not stopped) or None.
+        """
+        activePythonContext = cls._activeContext
+        if activePythonContext is not None:
+            # Verify that the current running Java StreamingContext is active and is the same one
+            # backing the supposedly active Python context
+            activePythonContextJavaId = activePythonContext._jssc.ssc().hashCode()
+            activeJvmContextOption = activePythonContext._jvm.StreamingContext.getActive()
+
+            if activeJvmContextOption.isEmpty():
+                cls._activeContext = None
+            elif activeJvmContextOption.get().hashCode() != activePythonContextJavaId:
+                cls._activeContext = None
+                raise Exception("JVM's active JavaStreamingContext is not the JavaStreamingContext "
+                                "backing the action Python StreamingContext. This is unexpected.")
+        return cls._activeContext
+
+    @classmethod
+    def getActiveOrCreate(cls, checkpointPath, setupFunc):
+        """
+        Either return the active StreamingContext (i.e. currently started but not stopped),
+        or recreate a StreamingContext from checkpoint data or create a new StreamingContext
+        using the provided setupFunc function. If the checkpointPath is None or does not contain
+        valid checkpoint data, then setupFunc will be called to create a new context and setup
+        DStreams.
+
+        @param checkpointPath: Checkpoint directory used in an earlier streaming program. Can be
+                               None if the intention is to always create a new context when there
+                               is no active context.
+        @param setupFunc:      Function to create a new JavaStreamingContext and setup DStreams
+        """
+
+        if setupFunc is None:
+            raise Exception("setupFunc cannot be None")
+        activeContext = cls.getActive()
+        if activeContext is not None:
+            return activeContext
+        elif checkpointPath is not None:
+            return cls.getOrCreate(checkpointPath, setupFunc)
+        else:
+            return setupFunc()
+
     @property
     def sparkContext(self):
         """
@@ -182,6 +231,7 @@ def start(self):
         Start the execution of the streams.
         """
         self._jssc.start()
+        StreamingContext._activeContext = self
 
     def awaitTermination(self, timeout=None):
         """
@@ -212,6 +262,7 @@ def stop(self, stopSparkContext=True, stopGraceFully=False):
                               of all received data to be completed
         """
         self._jssc.stop(stopSparkContext, stopGraceFully)
+        StreamingContext._activeContext = None
         if stopSparkContext:
             self._sc.stop()
 
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index f0ed415f97120..6108c845c1efe 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -24,6 +24,7 @@
 import tempfile
 import random
 import struct
+import shutil
 from functools import reduce
 
 if sys.version_info[:2] <= (2, 6):
@@ -59,12 +60,21 @@ def setUpClass(cls):
     @classmethod
     def tearDownClass(cls):
         cls.sc.stop()
+        # Clean up in the JVM just in case there has been some issues in Python API
+        jSparkContextOption = SparkContext._jvm.SparkContext.get()
+        if jSparkContextOption.nonEmpty():
+            jSparkContextOption.get().stop()
 
     def setUp(self):
         self.ssc = StreamingContext(self.sc, self.duration)
 
     def tearDown(self):
-        self.ssc.stop(False)
+        if self.ssc is not None:
+            self.ssc.stop(False)
+        # Clean up in the JVM just in case there has been some issues in Python API
+        jStreamingContextOption = StreamingContext._jvm.SparkContext.getActive()
+        if jStreamingContextOption.nonEmpty():
+            jStreamingContextOption.get().stop(False)
 
     def wait_for(self, result, n):
         start_time = time.time()
@@ -442,6 +452,7 @@ def test_reduce_by_invalid_window(self):
 class StreamingContextTests(PySparkStreamingTestCase):
 
     duration = 0.1
+    setupCalled = False
 
     def _add_input_stream(self):
         inputs = [range(1, x) for x in range(101)]
@@ -515,10 +526,85 @@ def func(rdds):
 
         self.assertEqual([2, 3, 1], self._take(dstream, 3))
 
+    def test_get_active(self):
+        self.assertEqual(StreamingContext.getActive(), None)
+
+        # Verify that getActive() returns the active context
+        self.ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count())
+        self.ssc.start()
+        self.assertEqual(StreamingContext.getActive(), self.ssc)
+
+        # Verify that getActive() returns None
+        self.ssc.stop(False)
+        self.assertEqual(StreamingContext.getActive(), None)
+
+        # Verify that if the Java context is stopped, then getActive() returns None
+        self.ssc = StreamingContext(self.sc, self.duration)
+        self.ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count())
+        self.ssc.start()
+        self.assertEqual(StreamingContext.getActive(), self.ssc)
+        self.ssc._jssc.stop(False)
+        self.assertEqual(StreamingContext.getActive(), None)
+
+    def test_get_active_or_create(self):
+        # Test StreamingContext.getActiveOrCreate() without checkpoint data
+        # See CheckpointTests for tests with checkpoint data
+        self.ssc = None
+        self.assertEqual(StreamingContext.getActive(), None)
+
+        def setupFunc():
+            ssc = StreamingContext(self.sc, self.duration)
+            ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count())
+            self.setupCalled = True
+            return ssc
+
+        # Verify that getActiveOrCreate() (w/o checkpoint) calls setupFunc when no context is active
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(None, setupFunc)
+        self.assertTrue(self.setupCalled)
+
+        # Verify that getActiveOrCreate() retuns active context and does not call the setupFunc
+        self.ssc.start()
+        self.setupCalled = False
+        self.assertEqual(StreamingContext.getActiveOrCreate(None, setupFunc), self.ssc)
+        self.assertFalse(self.setupCalled)
+
+        # Verify that getActiveOrCreate() calls setupFunc after active context is stopped
+        self.ssc.stop(False)
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(None, setupFunc)
+        self.assertTrue(self.setupCalled)
+
+        # Verify that if the Java context is stopped, then getActive() returns None
+        self.ssc = StreamingContext(self.sc, self.duration)
+        self.ssc.queueStream([[1]]).foreachRDD(lambda rdd: rdd.count())
+        self.ssc.start()
+        self.assertEqual(StreamingContext.getActive(), self.ssc)
+        self.ssc._jssc.stop(False)
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(None, setupFunc)
+        self.assertTrue(self.setupCalled)
+
 
 class CheckpointTests(unittest.TestCase):
 
-    def test_get_or_create(self):
+    setupCalled = False
+
+    @staticmethod
+    def tearDownClass():
+        # Clean up in the JVM just in case there has been some issues in Python API
+        jStreamingContextOption = StreamingContext._jvm.SparkContext.getActive()
+        if jStreamingContextOption.nonEmpty():
+            jStreamingContextOption.get().stop()
+        jSparkContextOption = SparkContext._jvm.SparkContext.get()
+        if jSparkContextOption.nonEmpty():
+            jSparkContextOption.get().stop()
+
+    def tearDown(self):
+        if self.ssc is not None:
+            self.ssc.stop(True)
+
+    def test_get_or_create_and_get_active_or_create(self):
         inputd = tempfile.mkdtemp()
         outputd = tempfile.mkdtemp() + "/"
 
@@ -533,11 +619,12 @@ def setup():
             wc = dstream.updateStateByKey(updater)
             wc.map(lambda x: "%s,%d" % x).saveAsTextFiles(outputd + "test")
             wc.checkpoint(.5)
+            self.setupCalled = True
             return ssc
 
         cpd = tempfile.mkdtemp("test_streaming_cps")
-        ssc = StreamingContext.getOrCreate(cpd, setup)
-        ssc.start()
+        self.ssc = StreamingContext.getOrCreate(cpd, setup)
+        self.ssc.start()
 
         def check_output(n):
             while not os.listdir(outputd):
@@ -552,7 +639,7 @@ def check_output(n):
                     # not finished
                     time.sleep(0.01)
                     continue
-                ordd = ssc.sparkContext.textFile(p).map(lambda line: line.split(","))
+                ordd = self.ssc.sparkContext.textFile(p).map(lambda line: line.split(","))
                 d = ordd.values().map(int).collect()
                 if not d:
                     time.sleep(0.01)
@@ -568,13 +655,37 @@ def check_output(n):
 
         check_output(1)
         check_output(2)
-        ssc.stop(True, True)
 
+        # Verify the getOrCreate() recovers from checkpoint files
+        self.ssc.stop(True, True)
         time.sleep(1)
-        ssc = StreamingContext.getOrCreate(cpd, setup)
-        ssc.start()
+        self.setupCalled = False
+        self.ssc = StreamingContext.getOrCreate(cpd, setup)
+        self.assertFalse(self.setupCalled)
+        self.ssc.start()
         check_output(3)
-        ssc.stop(True, True)
+
+        # Verify the getActiveOrCreate() recovers from checkpoint files
+        self.ssc.stop(True, True)
+        time.sleep(1)
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(cpd, setup)
+        self.assertFalse(self.setupCalled)
+        self.ssc.start()
+        check_output(4)
+
+        # Verify that getActiveOrCreate() returns active context
+        self.setupCalled = False
+        self.assertEquals(StreamingContext.getActiveOrCreate(cpd, setup), self.ssc)
+        self.assertFalse(self.setupCalled)
+
+        # Verify that getActiveOrCreate() calls setup() in absence of checkpoint files
+        self.ssc.stop(True, True)
+        shutil.rmtree(cpd)  # delete checkpoint directory
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(cpd, setup)
+        self.assertTrue(self.setupCalled)
+        self.ssc.stop(True, True)
 
 
 class KafkaStreamTests(PySparkStreamingTestCase):
@@ -1134,7 +1245,7 @@ def search_kinesis_asl_assembly_jar():
         testcases.append(KinesisStreamTests)
     elif are_kinesis_tests_enabled is False:
         sys.stderr.write("Skipping all Kinesis Python tests as the optional Kinesis project was "
-                         "not compiled with -Pkinesis-asl profile. To run these tests, "
+                         "not compiled into a JAR. To run these tests, "
                          "you need to build Spark with 'build/sbt -Pkinesis-asl assembly/assembly "
                          "streaming-kinesis-asl-assembly/assembly' or "
                          "'build/mvn -Pkinesis-asl package' before running this test.")
@@ -1150,4 +1261,4 @@ def search_kinesis_asl_assembly_jar():
     for testcase in testcases:
         sys.stderr.write("[Running %s]\n" % (testcase))
         tests = unittest.TestLoader().loadTestsFromTestCase(testcase)
-        unittest.TextTestRunner(verbosity=2).run(tests)
+        unittest.TextTestRunner(verbosity=3).run(tests)
diff --git a/python/run-tests.py b/python/run-tests.py
index cc560779373b3..fd56c7ab6e0e2 100755
--- a/python/run-tests.py
+++ b/python/run-tests.py
@@ -158,7 +158,7 @@ def main():
     else:
         log_level = logging.INFO
     logging.basicConfig(stream=sys.stdout, level=log_level, format="%(message)s")
-    LOGGER.info("Running PySpark tests. Output is in python/%s", LOG_FILE)
+    LOGGER.info("Running PySpark tests. Output is in %s", LOG_FILE)
     if os.path.exists(LOG_FILE):
         os.remove(LOG_FILE)
     python_execs = opts.python_executables.split(',')

From 5831294a7a8fa2524133c5d718cbc8187d2b0620 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 11 Aug 2015 12:39:13 -0700
Subject: [PATCH 166/633] [SPARK-9646] [SQL] Add metrics for all join and
 aggregate operators

This PR added metrics for all join and aggregate operators. However, I found the metrics may be confusing in the following two case:
1. The iterator is not totally consumed and the metric values will be less.
2. Recreating the iterators will make metric values look bigger than the size of the input source, such as `CartesianProduct`.

Author: zsxwing <zsxwing@gmail.com>

Closes #8060 from zsxwing/sql-metrics and squashes the following commits:

40f3fc1 [zsxwing] Mark LongSQLMetric private[metric] to avoid using incorrectly and leak memory
b1b9071 [zsxwing] Merge branch 'master' into sql-metrics
4bef25a [zsxwing] Add metrics for SortMergeOuterJoin
95ccfc6 [zsxwing] Merge branch 'master' into sql-metrics
67cb4dd [zsxwing] Add metrics for Project and TungstenProject; remove metrics from PhysicalRDD and LocalTableScan
0eb47d4 [zsxwing] Merge branch 'master' into sql-metrics
dd9d932 [zsxwing] Avoid creating new Iterators
589ea26 [zsxwing] Add metrics for all join and aggregate operators
---
 .../spark/sql/execution/Aggregate.scala       |  11 +
 .../spark/sql/execution/ExistingRDD.scala     |   2 -
 .../spark/sql/execution/LocalTableScan.scala  |   2 -
 .../spark/sql/execution/SparkPlan.scala       |  25 +-
 .../aggregate/SortBasedAggregate.scala        |  12 +-
 .../SortBasedAggregationIterator.scala        |  18 +-
 .../aggregate/TungstenAggregate.scala         |  12 +-
 .../TungstenAggregationIterator.scala         |  11 +-
 .../spark/sql/execution/basicOperators.scala  |  36 +-
 .../execution/joins/BroadcastHashJoin.scala   |  30 +-
 .../joins/BroadcastHashOuterJoin.scala        |  40 +-
 .../joins/BroadcastLeftSemiJoinHash.scala     |  24 +-
 .../joins/BroadcastNestedLoopJoin.scala       |  27 +-
 .../execution/joins/CartesianProduct.scala    |  25 +-
 .../spark/sql/execution/joins/HashJoin.scala  |   7 +-
 .../sql/execution/joins/HashOuterJoin.scala   |  30 +-
 .../sql/execution/joins/HashSemiJoin.scala    |  23 +-
 .../sql/execution/joins/HashedRelation.scala  |   8 +-
 .../sql/execution/joins/LeftSemiJoinBNL.scala |  19 +-
 .../execution/joins/LeftSemiJoinHash.scala    |  18 +-
 .../execution/joins/ShuffledHashJoin.scala    |  16 +-
 .../joins/ShuffledHashOuterJoin.scala         |  29 +-
 .../sql/execution/joins/SortMergeJoin.scala   |  21 +-
 .../execution/joins/SortMergeOuterJoin.scala  |  38 +-
 .../sql/execution/metric/SQLMetrics.scala     |   6 +
 .../execution/joins/HashedRelationSuite.scala |  14 +-
 .../execution/metric/SQLMetricsSuite.scala    | 450 +++++++++++++++++-
 27 files changed, 847 insertions(+), 107 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
index e8c6a0f8f801d..f3b6a3a5f4a33 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/Aggregate.scala
@@ -25,6 +25,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -45,6 +46,10 @@ case class Aggregate(
     child: SparkPlan)
   extends UnaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numInputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of input rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def requiredChildDistribution: List[Distribution] = {
     if (partial) {
       UnspecifiedDistribution :: Nil
@@ -121,12 +126,15 @@ case class Aggregate(
   }
 
   protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    val numInputRows = longMetric("numInputRows")
+    val numOutputRows = longMetric("numOutputRows")
     if (groupingExpressions.isEmpty) {
       child.execute().mapPartitions { iter =>
         val buffer = newAggregateBuffer()
         var currentRow: InternalRow = null
         while (iter.hasNext) {
           currentRow = iter.next()
+          numInputRows += 1
           var i = 0
           while (i < buffer.length) {
             buffer(i).update(currentRow)
@@ -142,6 +150,7 @@ case class Aggregate(
           i += 1
         }
 
+        numOutputRows += 1
         Iterator(resultProjection(aggregateResults))
       }
     } else {
@@ -152,6 +161,7 @@ case class Aggregate(
         var currentRow: InternalRow = null
         while (iter.hasNext) {
           currentRow = iter.next()
+          numInputRows += 1
           val currentGroup = groupingProjection(currentRow)
           var currentBuffer = hashTable.get(currentGroup)
           if (currentBuffer == null) {
@@ -180,6 +190,7 @@ case class Aggregate(
             val currentEntry = hashTableIter.next()
             val currentGroup = currentEntry.getKey
             val currentBuffer = currentEntry.getValue
+            numOutputRows += 1
 
             var i = 0
             while (i < currentBuffer.length) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
index cae7ca5cbdc88..abb60cf12e3a5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ExistingRDD.scala
@@ -99,8 +99,6 @@ private[sql] case class PhysicalRDD(
     rdd: RDD[InternalRow],
     extraInformation: String) extends LeafNode {
 
-  override protected[sql] val trackNumOfRowsEnabled = true
-
   protected override def doExecute(): RDD[InternalRow] = rdd
 
   override def simpleString: String = "Scan " + extraInformation + output.mkString("[", ",", "]")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
index 858dd85fd1fa6..34e926e4582be 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/LocalTableScan.scala
@@ -30,8 +30,6 @@ private[sql] case class LocalTableScan(
     output: Seq[Attribute],
     rows: Seq[InternalRow]) extends LeafNode {
 
-  override protected[sql] val trackNumOfRowsEnabled = true
-
   private lazy val rdd = sqlContext.sparkContext.parallelize(rows)
 
   protected override def doExecute(): RDD[InternalRow] = rdd
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
index 9ba5cf2d2b39e..72f5450510a10 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlan.scala
@@ -80,23 +80,10 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
     super.makeCopy(newArgs)
   }
 
-  /**
-   * Whether track the number of rows output by this SparkPlan
-   */
-  protected[sql] def trackNumOfRowsEnabled: Boolean = false
-
-  private lazy val defaultMetrics: Map[String, SQLMetric[_, _]] =
-    if (trackNumOfRowsEnabled) {
-      Map("numRows" -> SQLMetrics.createLongMetric(sparkContext, "number of rows"))
-    }
-    else {
-      Map.empty
-    }
-
   /**
    * Return all metrics containing metrics of this SparkPlan.
    */
-  private[sql] def metrics: Map[String, SQLMetric[_, _]] = defaultMetrics
+  private[sql] def metrics: Map[String, SQLMetric[_, _]] = Map.empty
 
   /**
    * Return a LongSQLMetric according to the name.
@@ -150,15 +137,7 @@ abstract class SparkPlan extends QueryPlan[SparkPlan] with Logging with Serializ
     }
     RDDOperationScope.withScope(sparkContext, nodeName, false, true) {
       prepare()
-      if (trackNumOfRowsEnabled) {
-        val numRows = longMetric("numRows")
-        doExecute().map { row =>
-          numRows += 1
-          row
-        }
-      } else {
-        doExecute()
-      }
+      doExecute()
     }
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
index ad428ad663f30..ab26f9c58aa2e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate._
 import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
 import org.apache.spark.sql.execution.{UnsafeFixedWidthAggregationMap, SparkPlan, UnaryNode}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.sql.types.StructType
 
 case class SortBasedAggregate(
@@ -38,6 +39,10 @@ case class SortBasedAggregate(
     child: SparkPlan)
   extends UnaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numInputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of input rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def outputsUnsafeRows: Boolean = false
 
   override def canProcessUnsafeRows: Boolean = false
@@ -63,6 +68,8 @@ case class SortBasedAggregate(
   }
 
   protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    val numInputRows = longMetric("numInputRows")
+    val numOutputRows = longMetric("numOutputRows")
     child.execute().mapPartitions { iter =>
       // Because the constructor of an aggregation iterator will read at least the first row,
       // we need to get the value of iter.hasNext first.
@@ -84,10 +91,13 @@ case class SortBasedAggregate(
           newProjection _,
           child.output,
           iter,
-          outputsUnsafeRows)
+          outputsUnsafeRows,
+          numInputRows,
+          numOutputRows)
         if (!hasInput && groupingExpressions.isEmpty) {
           // There is no input and there is no grouping expressions.
           // We need to output a single row as the output.
+          numOutputRows += 1
           Iterator[InternalRow](outputIter.outputForEmptyGroupingKeyWithoutInput())
         } else {
           outputIter
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
index 67ebafde25ad3..73d50e07cf0b5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregationIterator.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.{AggregateExpression2, AggregateFunction2}
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 import org.apache.spark.unsafe.KVIterator
 
 /**
@@ -37,7 +38,9 @@ class SortBasedAggregationIterator(
     initialInputBufferOffset: Int,
     resultExpressions: Seq[NamedExpression],
     newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
-    outputsUnsafeRows: Boolean)
+    outputsUnsafeRows: Boolean,
+    numInputRows: LongSQLMetric,
+    numOutputRows: LongSQLMetric)
   extends AggregationIterator(
     groupingKeyAttributes,
     valueAttributes,
@@ -103,6 +106,7 @@ class SortBasedAggregationIterator(
       // Get the grouping key.
       val groupingKey = inputKVIterator.getKey
       val currentRow = inputKVIterator.getValue
+      numInputRows += 1
 
       // Check if the current row belongs the current input row.
       if (currentGroupingKey == groupingKey) {
@@ -137,7 +141,7 @@ class SortBasedAggregationIterator(
       val outputRow = generateOutput(currentGroupingKey, sortBasedAggregationBuffer)
       // Initialize buffer values for the next group.
       initializeBuffer(sortBasedAggregationBuffer)
-
+      numOutputRows += 1
       outputRow
     } else {
       // no more result
@@ -151,7 +155,7 @@ class SortBasedAggregationIterator(
 
       nextGroupingKey = inputKVIterator.getKey().copy()
       firstRowInNextGroup = inputKVIterator.getValue().copy()
-
+      numInputRows += 1
       sortedInputHasNewGroup = true
     } else {
       // This inputIter is empty.
@@ -181,7 +185,9 @@ object SortBasedAggregationIterator {
       newProjection: (Seq[Expression], Seq[Attribute]) => Projection,
       inputAttributes: Seq[Attribute],
       inputIter: Iterator[InternalRow],
-      outputsUnsafeRows: Boolean): SortBasedAggregationIterator = {
+      outputsUnsafeRows: Boolean,
+      numInputRows: LongSQLMetric,
+      numOutputRows: LongSQLMetric): SortBasedAggregationIterator = {
     val kvIterator = if (UnsafeProjection.canSupport(groupingExprs)) {
       AggregationIterator.unsafeKVIterator(
         groupingExprs,
@@ -202,7 +208,9 @@ object SortBasedAggregationIterator {
       initialInputBufferOffset,
       resultExpressions,
       newMutableProjection,
-      outputsUnsafeRows)
+      outputsUnsafeRows,
+      numInputRows,
+      numOutputRows)
   }
   // scalastyle:on
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index 1694794a53d9f..6b5935a7ce296 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
 import org.apache.spark.sql.execution.{UnaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 case class TungstenAggregate(
     requiredChildDistributionExpressions: Option[Seq[Expression]],
@@ -35,6 +36,10 @@ case class TungstenAggregate(
     child: SparkPlan)
   extends UnaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numInputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of input rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def outputsUnsafeRows: Boolean = true
 
   override def canProcessUnsafeRows: Boolean = true
@@ -61,6 +66,8 @@ case class TungstenAggregate(
   }
 
   protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
+    val numInputRows = longMetric("numInputRows")
+    val numOutputRows = longMetric("numOutputRows")
     child.execute().mapPartitions { iter =>
       val hasInput = iter.hasNext
       if (!hasInput && groupingExpressions.nonEmpty) {
@@ -78,9 +85,12 @@ case class TungstenAggregate(
             newMutableProjection,
             child.output,
             iter,
-            testFallbackStartsAt)
+            testFallbackStartsAt,
+            numInputRows,
+            numOutputRows)
 
         if (!hasInput && groupingExpressions.isEmpty) {
+          numOutputRows += 1
           Iterator.single[UnsafeRow](aggregationIterator.outputForEmptyGroupingKeyWithoutInput())
         } else {
           aggregationIterator
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index 32160906c3bc8..1f383dd04482f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions.aggregate._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeRowJoiner
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.execution.{UnsafeKVExternalSorter, UnsafeFixedWidthAggregationMap}
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 import org.apache.spark.sql.types.StructType
 
 /**
@@ -83,7 +84,9 @@ class TungstenAggregationIterator(
     newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
     originalInputAttributes: Seq[Attribute],
     inputIter: Iterator[InternalRow],
-    testFallbackStartsAt: Option[Int])
+    testFallbackStartsAt: Option[Int],
+    numInputRows: LongSQLMetric,
+    numOutputRows: LongSQLMetric)
   extends Iterator[UnsafeRow] with Logging {
 
   ///////////////////////////////////////////////////////////////////////////
@@ -352,6 +355,7 @@ class TungstenAggregationIterator(
   private def processInputs(): Unit = {
     while (!sortBased && inputIter.hasNext) {
       val newInput = inputIter.next()
+      numInputRows += 1
       val groupingKey = groupProjection.apply(newInput)
       val buffer: UnsafeRow = hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
       if (buffer == null) {
@@ -371,6 +375,7 @@ class TungstenAggregationIterator(
     var i = 0
     while (!sortBased && inputIter.hasNext) {
       val newInput = inputIter.next()
+      numInputRows += 1
       val groupingKey = groupProjection.apply(newInput)
       val buffer: UnsafeRow = if (i < fallbackStartsAt) {
         hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
@@ -439,6 +444,7 @@ class TungstenAggregationIterator(
       // Process the rest of input rows.
       while (inputIter.hasNext) {
         val newInput = inputIter.next()
+        numInputRows += 1
         val groupingKey = groupProjection.apply(newInput)
         buffer.copyFrom(initialAggregationBuffer)
         processRow(buffer, newInput)
@@ -462,6 +468,7 @@ class TungstenAggregationIterator(
       // Insert the rest of input rows.
       while (inputIter.hasNext) {
         val newInput = inputIter.next()
+        numInputRows += 1
         val groupingKey = groupProjection.apply(newInput)
         bufferExtractor(newInput)
         externalSorter.insertKV(groupingKey, buffer)
@@ -657,7 +664,7 @@ class TungstenAggregationIterator(
         TaskContext.get().internalMetricsToAccumulators(
           InternalAccumulator.PEAK_EXECUTION_MEMORY).add(peakMemory)
       }
-
+      numOutputRows += 1
       res
     } else {
       // no more result
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index bf2de244c8e4a..247c900baae9e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -41,11 +41,20 @@ import org.apache.spark.{HashPartitioner, SparkEnv}
 case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends UnaryNode {
   override def output: Seq[Attribute] = projectList.map(_.toAttribute)
 
+  override private[sql] lazy val metrics = Map(
+    "numRows" -> SQLMetrics.createLongMetric(sparkContext, "number of rows"))
+
   @transient lazy val buildProjection = newMutableProjection(projectList, child.output)
 
-  protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter =>
-    val reusableProjection = buildProjection()
-    iter.map(reusableProjection)
+  protected override def doExecute(): RDD[InternalRow] = {
+    val numRows = longMetric("numRows")
+    child.execute().mapPartitions { iter =>
+      val reusableProjection = buildProjection()
+      iter.map { row =>
+        numRows += 1
+        reusableProjection(row)
+      }
+    }
   }
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
@@ -57,19 +66,28 @@ case class Project(projectList: Seq[NamedExpression], child: SparkPlan) extends
  */
 case class TungstenProject(projectList: Seq[NamedExpression], child: SparkPlan) extends UnaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numRows" -> SQLMetrics.createLongMetric(sparkContext, "number of rows"))
+
   override def outputsUnsafeRows: Boolean = true
   override def canProcessUnsafeRows: Boolean = true
   override def canProcessSafeRows: Boolean = true
 
   override def output: Seq[Attribute] = projectList.map(_.toAttribute)
 
-  protected override def doExecute(): RDD[InternalRow] = child.execute().mapPartitions { iter =>
-    this.transformAllExpressions {
-      case CreateStruct(children) => CreateStructUnsafe(children)
-      case CreateNamedStruct(children) => CreateNamedStructUnsafe(children)
+  protected override def doExecute(): RDD[InternalRow] = {
+    val numRows = longMetric("numRows")
+    child.execute().mapPartitions { iter =>
+      this.transformAllExpressions {
+        case CreateStruct(children) => CreateStructUnsafe(children)
+        case CreateNamedStruct(children) => CreateNamedStructUnsafe(children)
+      }
+      val project = UnsafeProjection.create(projectList, child.output)
+      iter.map { row =>
+        numRows += 1
+        project(row)
+      }
     }
-    val project = UnsafeProjection.create(projectList, child.output)
-    iter.map(project)
   }
 
   override def outputOrdering: Seq[SortOrder] = child.outputOrdering
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
index f7a68e4f5d445..2e108cb814516 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashJoin.scala
@@ -26,6 +26,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution}
 import org.apache.spark.sql.execution.{BinaryNode, SQLExecution, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.util.ThreadUtils
 import org.apache.spark.{InternalAccumulator, TaskContext}
 
@@ -45,7 +46,10 @@ case class BroadcastHashJoin(
     right: SparkPlan)
   extends BinaryNode with HashJoin {
 
-  override protected[sql] val trackNumOfRowsEnabled = true
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
 
   val timeout: Duration = {
     val timeoutValue = sqlContext.conf.broadcastTimeout
@@ -65,6 +69,11 @@ case class BroadcastHashJoin(
   // for the same query.
   @transient
   private lazy val broadcastFuture = {
+    val numBuildRows = buildSide match {
+      case BuildLeft => longMetric("numLeftRows")
+      case BuildRight => longMetric("numRightRows")
+    }
+
     // broadcastFuture is used in "doExecute". Therefore we can get the execution id correctly here.
     val executionId = sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
     future {
@@ -73,8 +82,15 @@ case class BroadcastHashJoin(
       SQLExecution.withExecutionId(sparkContext, executionId) {
         // Note that we use .execute().collect() because we don't want to convert data to Scala
         // types
-        val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-        val hashed = HashedRelation(input.iterator, buildSideKeyGenerator, input.size)
+        val input: Array[InternalRow] = buildPlan.execute().map { row =>
+          numBuildRows += 1
+          row.copy()
+        }.collect()
+        // The following line doesn't run in a job so we cannot track the metric value. However, we
+        // have already tracked it in the above lines. So here we can use
+        // `SQLMetrics.nullLongMetric` to ignore it.
+        val hashed = HashedRelation(
+          input.iterator, SQLMetrics.nullLongMetric, buildSideKeyGenerator, input.size)
         sparkContext.broadcast(hashed)
       }
     }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
@@ -85,6 +101,12 @@ case class BroadcastHashJoin(
   }
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val numStreamedRows = buildSide match {
+      case BuildLeft => longMetric("numRightRows")
+      case BuildRight => longMetric("numLeftRows")
+    }
+    val numOutputRows = longMetric("numOutputRows")
+
     val broadcastRelation = Await.result(broadcastFuture, timeout)
 
     streamedPlan.execute().mapPartitions { streamedIter =>
@@ -95,7 +117,7 @@ case class BroadcastHashJoin(
             InternalAccumulator.PEAK_EXECUTION_MEMORY).add(unsafe.getUnsafeSize)
         case _ =>
       }
-      hashJoin(streamedIter, hashedRelation)
+      hashJoin(streamedIter, numStreamedRows, hashedRelation, numOutputRows)
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
index a3626de49aeab..69a8b95eaa7ec 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastHashOuterJoin.scala
@@ -27,6 +27,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{Distribution, Partitioning, UnspecifiedDistribution}
 import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.{BinaryNode, SQLExecution, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.{InternalAccumulator, TaskContext}
 
 /**
@@ -45,6 +46,11 @@ case class BroadcastHashOuterJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode with HashOuterJoin {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   val timeout = {
     val timeoutValue = sqlContext.conf.broadcastTimeout
     if (timeoutValue < 0) {
@@ -63,6 +69,14 @@ case class BroadcastHashOuterJoin(
   // for the same query.
   @transient
   private lazy val broadcastFuture = {
+    val numBuildRows = joinType match {
+      case RightOuter => longMetric("numLeftRows")
+      case LeftOuter => longMetric("numRightRows")
+      case x =>
+        throw new IllegalArgumentException(
+          s"HashOuterJoin should not take $x as the JoinType")
+    }
+
     // broadcastFuture is used in "doExecute". Therefore we can get the execution id correctly here.
     val executionId = sparkContext.getLocalProperty(SQLExecution.EXECUTION_ID_KEY)
     future {
@@ -71,8 +85,15 @@ case class BroadcastHashOuterJoin(
       SQLExecution.withExecutionId(sparkContext, executionId) {
         // Note that we use .execute().collect() because we don't want to convert data to Scala
         // types
-        val input: Array[InternalRow] = buildPlan.execute().map(_.copy()).collect()
-        val hashed = HashedRelation(input.iterator, buildKeyGenerator, input.size)
+        val input: Array[InternalRow] = buildPlan.execute().map { row =>
+          numBuildRows += 1
+          row.copy()
+        }.collect()
+        // The following line doesn't run in a job so we cannot track the metric value. However, we
+        // have already tracked it in the above lines. So here we can use
+        // `SQLMetrics.nullLongMetric` to ignore it.
+        val hashed = HashedRelation(
+          input.iterator, SQLMetrics.nullLongMetric, buildKeyGenerator, input.size)
         sparkContext.broadcast(hashed)
       }
     }(BroadcastHashJoin.broadcastHashJoinExecutionContext)
@@ -83,6 +104,15 @@ case class BroadcastHashOuterJoin(
   }
 
   override def doExecute(): RDD[InternalRow] = {
+    val numStreamedRows = joinType match {
+      case RightOuter => longMetric("numRightRows")
+      case LeftOuter => longMetric("numLeftRows")
+      case x =>
+        throw new IllegalArgumentException(
+          s"HashOuterJoin should not take $x as the JoinType")
+    }
+    val numOutputRows = longMetric("numOutputRows")
+
     val broadcastRelation = Await.result(broadcastFuture, timeout)
 
     streamedPlan.execute().mapPartitions { streamedIter =>
@@ -101,16 +131,18 @@ case class BroadcastHashOuterJoin(
       joinType match {
         case LeftOuter =>
           streamedIter.flatMap(currentRow => {
+            numStreamedRows += 1
             val rowKey = keyGenerator(currentRow)
             joinedRow.withLeft(currentRow)
-            leftOuterIterator(rowKey, joinedRow, hashTable.get(rowKey), resultProj)
+            leftOuterIterator(rowKey, joinedRow, hashTable.get(rowKey), resultProj, numOutputRows)
           })
 
         case RightOuter =>
           streamedIter.flatMap(currentRow => {
+            numStreamedRows += 1
             val rowKey = keyGenerator(currentRow)
             joinedRow.withRight(currentRow)
-            rightOuterIterator(rowKey, hashTable.get(rowKey), joinedRow, resultProj)
+            rightOuterIterator(rowKey, hashTable.get(rowKey), joinedRow, resultProj, numOutputRows)
           })
 
         case x =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
index 5bd06fbdca605..78a8c16c62bca 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala
@@ -23,6 +23,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -37,18 +38,31 @@ case class BroadcastLeftSemiJoinHash(
     right: SparkPlan,
     condition: Option[Expression]) extends BinaryNode with HashSemiJoin {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   protected override def doExecute(): RDD[InternalRow] = {
-    val input = right.execute().map(_.copy()).collect()
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
+    val input = right.execute().map { row =>
+      numRightRows += 1
+      row.copy()
+    }.collect()
 
     if (condition.isEmpty) {
-      val hashSet = buildKeyHashSet(input.toIterator)
+      val hashSet = buildKeyHashSet(input.toIterator, SQLMetrics.nullLongMetric)
       val broadcastedRelation = sparkContext.broadcast(hashSet)
 
       left.execute().mapPartitions { streamIter =>
-        hashSemiJoin(streamIter, broadcastedRelation.value)
+        hashSemiJoin(streamIter, numLeftRows, broadcastedRelation.value, numOutputRows)
       }
     } else {
-      val hashRelation = HashedRelation(input.toIterator, rightKeyGenerator, input.size)
+      val hashRelation =
+        HashedRelation(input.toIterator, SQLMetrics.nullLongMetric, rightKeyGenerator, input.size)
       val broadcastedRelation = sparkContext.broadcast(hashRelation)
 
       left.execute().mapPartitions { streamIter =>
@@ -59,7 +73,7 @@ case class BroadcastLeftSemiJoinHash(
               InternalAccumulator.PEAK_EXECUTION_MEMORY).add(unsafe.getUnsafeSize)
           case _ =>
         }
-        hashSemiJoin(streamIter, hashedRelation)
+        hashSemiJoin(streamIter, numLeftRows, hashedRelation, numOutputRows)
       }
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
index 017a44b9ca863..28c88b1b03d02 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastNestedLoopJoin.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.Partitioning
 import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 import org.apache.spark.util.collection.CompactBuffer
 
 /**
@@ -38,6 +39,11 @@ case class BroadcastNestedLoopJoin(
     condition: Option[Expression]) extends BinaryNode {
   // TODO: Override requiredChildDistribution.
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   /** BuildRight means the right relation <=> the broadcast relation. */
   private val (streamed, broadcast) = buildSide match {
     case BuildRight => (left, right)
@@ -75,9 +81,17 @@ case class BroadcastNestedLoopJoin(
     newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output)
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val (numStreamedRows, numBuildRows) = buildSide match {
+      case BuildRight => (longMetric("numLeftRows"), longMetric("numRightRows"))
+      case BuildLeft => (longMetric("numRightRows"), longMetric("numLeftRows"))
+    }
+    val numOutputRows = longMetric("numOutputRows")
+
     val broadcastedRelation =
-      sparkContext.broadcast(broadcast.execute().map(_.copy())
-        .collect().toIndexedSeq)
+      sparkContext.broadcast(broadcast.execute().map { row =>
+        numBuildRows += 1
+        row.copy()
+      }.collect().toIndexedSeq)
 
     /** All rows that either match both-way, or rows from streamed joined with nulls. */
     val matchesOrStreamedRowsWithNulls = streamed.execute().mapPartitions { streamedIter =>
@@ -94,6 +108,7 @@ case class BroadcastNestedLoopJoin(
       streamedIter.foreach { streamedRow =>
         var i = 0
         var streamRowMatched = false
+        numStreamedRows += 1
 
         while (i < broadcastedRelation.value.size) {
           val broadcastedRow = broadcastedRelation.value(i)
@@ -162,6 +177,12 @@ case class BroadcastNestedLoopJoin(
 
     // TODO: Breaks lineage.
     sparkContext.union(
-      matchesOrStreamedRowsWithNulls.flatMap(_._1), sparkContext.makeRDD(broadcastRowsWithNulls))
+      matchesOrStreamedRowsWithNulls.flatMap(_._1),
+      sparkContext.makeRDD(broadcastRowsWithNulls)
+    ).map { row =>
+      // `broadcastRowsWithNulls` doesn't run in a job so that we have to track numOutputRows here.
+      numOutputRows += 1
+      row
+    }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala
index 261b4724159fb..2115f40702286 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/CartesianProduct.scala
@@ -22,6 +22,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Attribute, JoinedRow}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -30,13 +31,31 @@ import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
 case class CartesianProduct(left: SparkPlan, right: SparkPlan) extends BinaryNode {
   override def output: Seq[Attribute] = left.output ++ right.output
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   protected override def doExecute(): RDD[InternalRow] = {
-    val leftResults = left.execute().map(_.copy())
-    val rightResults = right.execute().map(_.copy())
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
+    val leftResults = left.execute().map { row =>
+      numLeftRows += 1
+      row.copy()
+    }
+    val rightResults = right.execute().map { row =>
+      numRightRows += 1
+      row.copy()
+    }
 
     leftResults.cartesian(rightResults).mapPartitions { iter =>
       val joinedRow = new JoinedRow
-      iter.map(r => joinedRow(r._1, r._2))
+      iter.map { r =>
+        numOutputRows += 1
+        joinedRow(r._1, r._2)
+      }
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
index 22d46d1c3e3b7..7ce4a517838cb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashJoin.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.joins
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 
 
 trait HashJoin {
@@ -69,7 +70,9 @@ trait HashJoin {
 
   protected def hashJoin(
       streamIter: Iterator[InternalRow],
-      hashedRelation: HashedRelation): Iterator[InternalRow] =
+      numStreamRows: LongSQLMetric,
+      hashedRelation: HashedRelation,
+      numOutputRows: LongSQLMetric): Iterator[InternalRow] =
   {
     new Iterator[InternalRow] {
       private[this] var currentStreamedRow: InternalRow = _
@@ -98,6 +101,7 @@ trait HashJoin {
           case BuildLeft => joinRow(currentHashMatches(currentMatchPosition), currentStreamedRow)
         }
         currentMatchPosition += 1
+        numOutputRows += 1
         resultProjection(ret)
       }
 
@@ -113,6 +117,7 @@ trait HashJoin {
 
         while (currentHashMatches == null && streamIter.hasNext) {
           currentStreamedRow = streamIter.next()
+          numStreamRows += 1
           val key = joinKeys(currentStreamedRow)
           if (!key.anyNull) {
             currentHashMatches = hashedRelation.get(key)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
index 701bd3cd86372..66903347c88c1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashOuterJoin.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 import org.apache.spark.util.collection.CompactBuffer
 
 @DeveloperApi
@@ -114,22 +115,28 @@ trait HashOuterJoin {
       key: InternalRow,
       joinedRow: JoinedRow,
       rightIter: Iterable[InternalRow],
-      resultProjection: InternalRow => InternalRow): Iterator[InternalRow] = {
+      resultProjection: InternalRow => InternalRow,
+      numOutputRows: LongSQLMetric): Iterator[InternalRow] = {
     val ret: Iterable[InternalRow] = {
       if (!key.anyNull) {
         val temp = if (rightIter != null) {
           rightIter.collect {
-            case r if boundCondition(joinedRow.withRight(r)) => resultProjection(joinedRow).copy()
+            case r if boundCondition(joinedRow.withRight(r)) => {
+              numOutputRows += 1
+              resultProjection(joinedRow).copy()
+            }
           }
         } else {
           List.empty
         }
         if (temp.isEmpty) {
+          numOutputRows += 1
           resultProjection(joinedRow.withRight(rightNullRow)) :: Nil
         } else {
           temp
         }
       } else {
+        numOutputRows += 1
         resultProjection(joinedRow.withRight(rightNullRow)) :: Nil
       }
     }
@@ -140,22 +147,28 @@ trait HashOuterJoin {
       key: InternalRow,
       leftIter: Iterable[InternalRow],
       joinedRow: JoinedRow,
-      resultProjection: InternalRow => InternalRow): Iterator[InternalRow] = {
+      resultProjection: InternalRow => InternalRow,
+      numOutputRows: LongSQLMetric): Iterator[InternalRow] = {
     val ret: Iterable[InternalRow] = {
       if (!key.anyNull) {
         val temp = if (leftIter != null) {
           leftIter.collect {
-            case l if boundCondition(joinedRow.withLeft(l)) => resultProjection(joinedRow).copy()
+            case l if boundCondition(joinedRow.withLeft(l)) => {
+              numOutputRows += 1
+              resultProjection(joinedRow).copy()
+            }
           }
         } else {
           List.empty
         }
         if (temp.isEmpty) {
+          numOutputRows += 1
           resultProjection(joinedRow.withLeft(leftNullRow)) :: Nil
         } else {
           temp
         }
       } else {
+        numOutputRows += 1
         resultProjection(joinedRow.withLeft(leftNullRow)) :: Nil
       }
     }
@@ -164,7 +177,7 @@ trait HashOuterJoin {
 
   protected[this] def fullOuterIterator(
       key: InternalRow, leftIter: Iterable[InternalRow], rightIter: Iterable[InternalRow],
-      joinedRow: JoinedRow): Iterator[InternalRow] = {
+      joinedRow: JoinedRow, numOutputRows: LongSQLMetric): Iterator[InternalRow] = {
     if (!key.anyNull) {
       // Store the positions of records in right, if one of its associated row satisfy
       // the join condition.
@@ -177,6 +190,7 @@ trait HashOuterJoin {
           //    append them directly
 
           case (r, idx) if boundCondition(joinedRow.withRight(r)) =>
+            numOutputRows += 1
             matched = true
             // if the row satisfy the join condition, add its index into the matched set
             rightMatchedSet.add(idx)
@@ -189,6 +203,7 @@ trait HashOuterJoin {
           // as we don't know whether we need to append it until finish iterating all
           // of the records in right side.
           // If we didn't get any proper row, then append a single row with empty right.
+          numOutputRows += 1
           joinedRow.withRight(rightNullRow).copy()
         })
       } ++ rightIter.zipWithIndex.collect {
@@ -197,12 +212,15 @@ trait HashOuterJoin {
         // Re-visiting the records in right, and append additional row with empty left, if its not
         // in the matched set.
         case (r, idx) if !rightMatchedSet.contains(idx) =>
+          numOutputRows += 1
           joinedRow(leftNullRow, r).copy()
       }
     } else {
       leftIter.iterator.map[InternalRow] { l =>
+        numOutputRows += 1
         joinedRow(l, rightNullRow).copy()
       } ++ rightIter.iterator.map[InternalRow] { r =>
+        numOutputRows += 1
         joinedRow(leftNullRow, r).copy()
       }
     }
@@ -211,10 +229,12 @@ trait HashOuterJoin {
   // This is only used by FullOuter
   protected[this] def buildHashTable(
       iter: Iterator[InternalRow],
+      numIterRows: LongSQLMetric,
       keyGenerator: Projection): JavaHashMap[InternalRow, CompactBuffer[InternalRow]] = {
     val hashTable = new JavaHashMap[InternalRow, CompactBuffer[InternalRow]]()
     while (iter.hasNext) {
       val currentRow = iter.next()
+      numIterRows += 1
       val rowKey = keyGenerator(currentRow)
 
       var existingMatchList = hashTable.get(rowKey)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
index 82dd6eb7e7ed0..beb141ade616d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashSemiJoin.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.execution.joins
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 
 
 trait HashSemiJoin {
@@ -61,13 +62,15 @@ trait HashSemiJoin {
   @transient private lazy val boundCondition =
     newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output)
 
-  protected def buildKeyHashSet(buildIter: Iterator[InternalRow]): java.util.Set[InternalRow] = {
+  protected def buildKeyHashSet(
+      buildIter: Iterator[InternalRow], numBuildRows: LongSQLMetric): java.util.Set[InternalRow] = {
     val hashSet = new java.util.HashSet[InternalRow]()
 
     // Create a Hash set of buildKeys
     val rightKey = rightKeyGenerator
     while (buildIter.hasNext) {
       val currentRow = buildIter.next()
+      numBuildRows += 1
       val rowKey = rightKey(currentRow)
       if (!rowKey.anyNull) {
         val keyExists = hashSet.contains(rowKey)
@@ -82,25 +85,35 @@ trait HashSemiJoin {
 
   protected def hashSemiJoin(
     streamIter: Iterator[InternalRow],
-    hashSet: java.util.Set[InternalRow]): Iterator[InternalRow] = {
+    numStreamRows: LongSQLMetric,
+    hashSet: java.util.Set[InternalRow],
+    numOutputRows: LongSQLMetric): Iterator[InternalRow] = {
     val joinKeys = leftKeyGenerator
     streamIter.filter(current => {
+      numStreamRows += 1
       val key = joinKeys(current)
-      !key.anyNull && hashSet.contains(key)
+      val r = !key.anyNull && hashSet.contains(key)
+      if (r) numOutputRows += 1
+      r
     })
   }
 
   protected def hashSemiJoin(
       streamIter: Iterator[InternalRow],
-      hashedRelation: HashedRelation): Iterator[InternalRow] = {
+      numStreamRows: LongSQLMetric,
+      hashedRelation: HashedRelation,
+      numOutputRows: LongSQLMetric): Iterator[InternalRow] = {
     val joinKeys = leftKeyGenerator
     val joinedRow = new JoinedRow
     streamIter.filter { current =>
+      numStreamRows += 1
       val key = joinKeys(current)
       lazy val rowBuffer = hashedRelation.get(key)
-      !key.anyNull && rowBuffer != null && rowBuffer.exists {
+      val r = !key.anyNull && rowBuffer != null && rowBuffer.exists {
         (row: InternalRow) => boundCondition(joinedRow(current, row))
       }
+      if (r) numOutputRows += 1
+      r
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 63d35d0f02622..c1bc7947aa39c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -25,6 +25,7 @@ import org.apache.spark.shuffle.ShuffleMemoryManager
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkSqlSerializer
+import org.apache.spark.sql.execution.metric.LongSQLMetric
 import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.map.BytesToBytesMap
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
@@ -112,11 +113,13 @@ private[joins] object HashedRelation {
 
   def apply(
       input: Iterator[InternalRow],
+      numInputRows: LongSQLMetric,
       keyGenerator: Projection,
       sizeEstimate: Int = 64): HashedRelation = {
 
     if (keyGenerator.isInstanceOf[UnsafeProjection]) {
-      return UnsafeHashedRelation(input, keyGenerator.asInstanceOf[UnsafeProjection], sizeEstimate)
+      return UnsafeHashedRelation(
+        input, numInputRows, keyGenerator.asInstanceOf[UnsafeProjection], sizeEstimate)
     }
 
     // TODO: Use Spark's HashMap implementation.
@@ -130,6 +133,7 @@ private[joins] object HashedRelation {
     // Create a mapping of buildKeys -> rows
     while (input.hasNext) {
       currentRow = input.next()
+      numInputRows += 1
       val rowKey = keyGenerator(currentRow)
       if (!rowKey.anyNull) {
         val existingMatchList = hashTable.get(rowKey)
@@ -331,6 +335,7 @@ private[joins] object UnsafeHashedRelation {
 
   def apply(
       input: Iterator[InternalRow],
+      numInputRows: LongSQLMetric,
       keyGenerator: UnsafeProjection,
       sizeEstimate: Int): HashedRelation = {
 
@@ -340,6 +345,7 @@ private[joins] object UnsafeHashedRelation {
     // Create a mapping of buildKeys -> rows
     while (input.hasNext) {
       val unsafeRow = input.next().asInstanceOf[UnsafeRow]
+      numInputRows += 1
       val rowKey = keyGenerator(unsafeRow)
       if (!rowKey.anyNull) {
         val existingMatchList = hashTable.get(rowKey)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
index 4443455ef11fe..ad6362542f2ff 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinBNL.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.Partitioning
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -35,6 +36,11 @@ case class LeftSemiJoinBNL(
   extends BinaryNode {
   // TODO: Override requiredChildDistribution.
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def outputPartitioning: Partitioning = streamed.outputPartitioning
 
   override def output: Seq[Attribute] = left.output
@@ -52,13 +58,21 @@ case class LeftSemiJoinBNL(
     newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output)
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
     val broadcastedRelation =
-      sparkContext.broadcast(broadcast.execute().map(_.copy()).collect().toIndexedSeq)
+      sparkContext.broadcast(broadcast.execute().map { row =>
+        numRightRows += 1
+        row.copy()
+      }.collect().toIndexedSeq)
 
     streamed.execute().mapPartitions { streamedIter =>
       val joinedRow = new JoinedRow
 
       streamedIter.filter(streamedRow => {
+        numLeftRows += 1
         var i = 0
         var matched = false
 
@@ -69,6 +83,9 @@ case class LeftSemiJoinBNL(
           }
           i += 1
         }
+        if (matched) {
+          numOutputRows += 1
+        }
         matched
       })
     }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
index 68ccd34d8ed9b..18808adaac63f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/LeftSemiJoinHash.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical.{Partitioning, Distribution, ClusteredDistribution}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -37,19 +38,28 @@ case class LeftSemiJoinHash(
     right: SparkPlan,
     condition: Option[Expression]) extends BinaryNode with HashSemiJoin {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def outputPartitioning: Partitioning = left.outputPartitioning
 
   override def requiredChildDistribution: Seq[Distribution] =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
     right.execute().zipPartitions(left.execute()) { (buildIter, streamIter) =>
       if (condition.isEmpty) {
-        val hashSet = buildKeyHashSet(buildIter)
-        hashSemiJoin(streamIter, hashSet)
+        val hashSet = buildKeyHashSet(buildIter, numRightRows)
+        hashSemiJoin(streamIter, numLeftRows, hashSet, numOutputRows)
       } else {
-        val hashRelation = HashedRelation(buildIter, rightKeyGenerator)
-        hashSemiJoin(streamIter, hashRelation)
+        val hashRelation = HashedRelation(buildIter, numRightRows, rightKeyGenerator)
+        hashSemiJoin(streamIter, numLeftRows, hashRelation, numOutputRows)
       }
     }
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
index c923dc837c449..fc8c9439a6f07 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashJoin.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -38,7 +39,10 @@ case class ShuffledHashJoin(
     right: SparkPlan)
   extends BinaryNode with HashJoin {
 
-  override protected[sql] val trackNumOfRowsEnabled = true
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
 
   override def outputPartitioning: Partitioning =
     PartitioningCollection(Seq(left.outputPartitioning, right.outputPartitioning))
@@ -47,9 +51,15 @@ case class ShuffledHashJoin(
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val (numBuildRows, numStreamedRows) = buildSide match {
+      case BuildLeft => (longMetric("numLeftRows"), longMetric("numRightRows"))
+      case BuildRight => (longMetric("numRightRows"), longMetric("numLeftRows"))
+    }
+    val numOutputRows = longMetric("numOutputRows")
+
     buildPlan.execute().zipPartitions(streamedPlan.execute()) { (buildIter, streamIter) =>
-      val hashed = HashedRelation(buildIter, buildSideKeyGenerator)
-      hashJoin(streamIter, hashed)
+      val hashed = HashedRelation(buildIter, numBuildRows, buildSideKeyGenerator)
+      hashJoin(streamIter, numStreamedRows, hashed, numOutputRows)
     }
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
index 6a8c35efca8f4..ed282f98b7d71 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
@@ -26,6 +26,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.{BinaryNode, SparkPlan}
+import org.apache.spark.sql.execution.metric.SQLMetrics
 
 /**
  * :: DeveloperApi ::
@@ -41,6 +42,11 @@ case class ShuffledHashOuterJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode with HashOuterJoin {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def requiredChildDistribution: Seq[Distribution] =
     ClusteredDistribution(leftKeys) :: ClusteredDistribution(rightKeys) :: Nil
 
@@ -53,39 +59,48 @@ case class ShuffledHashOuterJoin(
   }
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
     val joinedRow = new JoinedRow()
     left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
       // TODO this probably can be replaced by external sort (sort merged join?)
       joinType match {
         case LeftOuter =>
-          val hashed = HashedRelation(rightIter, buildKeyGenerator)
+          val hashed = HashedRelation(rightIter, numRightRows, buildKeyGenerator)
           val keyGenerator = streamedKeyGenerator
           val resultProj = resultProjection
           leftIter.flatMap( currentRow => {
+            numLeftRows += 1
             val rowKey = keyGenerator(currentRow)
             joinedRow.withLeft(currentRow)
-            leftOuterIterator(rowKey, joinedRow, hashed.get(rowKey), resultProj)
+            leftOuterIterator(rowKey, joinedRow, hashed.get(rowKey), resultProj, numOutputRows)
           })
 
         case RightOuter =>
-          val hashed = HashedRelation(leftIter, buildKeyGenerator)
+          val hashed = HashedRelation(leftIter, numLeftRows, buildKeyGenerator)
           val keyGenerator = streamedKeyGenerator
           val resultProj = resultProjection
           rightIter.flatMap ( currentRow => {
+            numRightRows += 1
             val rowKey = keyGenerator(currentRow)
             joinedRow.withRight(currentRow)
-            rightOuterIterator(rowKey, hashed.get(rowKey), joinedRow, resultProj)
+            rightOuterIterator(rowKey, hashed.get(rowKey), joinedRow, resultProj, numOutputRows)
           })
 
         case FullOuter =>
           // TODO(davies): use UnsafeRow
-          val leftHashTable = buildHashTable(leftIter, newProjection(leftKeys, left.output))
-          val rightHashTable = buildHashTable(rightIter, newProjection(rightKeys, right.output))
+          val leftHashTable =
+            buildHashTable(leftIter, numLeftRows, newProjection(leftKeys, left.output))
+          val rightHashTable =
+            buildHashTable(rightIter, numRightRows, newProjection(rightKeys, right.output))
           (leftHashTable.keySet ++ rightHashTable.keySet).iterator.flatMap { key =>
             fullOuterIterator(key,
               leftHashTable.getOrElse(key, EMPTY_LIST),
               rightHashTable.getOrElse(key, EMPTY_LIST),
-              joinedRow)
+              joinedRow,
+              numOutputRows)
           }
 
         case x =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index 6d656ea2849a9..6b7322671d6b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -25,6 +25,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
+import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetrics}
 
 /**
  * :: DeveloperApi ::
@@ -37,6 +38,11 @@ case class SortMergeJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def output: Seq[Attribute] = left.output ++ right.output
 
   override def outputPartitioning: Partitioning =
@@ -70,6 +76,10 @@ case class SortMergeJoin(
   }
 
   protected override def doExecute(): RDD[InternalRow] = {
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
     left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
       new RowIterator {
         // An ordering that can be used to compare keys from both sides.
@@ -82,7 +92,9 @@ case class SortMergeJoin(
           rightKeyGenerator,
           keyOrdering,
           RowIterator.fromScala(leftIter),
-          RowIterator.fromScala(rightIter)
+          numLeftRows,
+          RowIterator.fromScala(rightIter),
+          numRightRows
         )
         private[this] val joinRow = new JoinedRow
         private[this] val resultProjection: (InternalRow) => InternalRow = {
@@ -108,6 +120,7 @@ case class SortMergeJoin(
           if (currentLeftRow != null) {
             joinRow(currentLeftRow, currentRightMatches(currentMatchIdx))
             currentMatchIdx += 1
+            numOutputRows += 1
             true
           } else {
             false
@@ -144,7 +157,9 @@ private[joins] class SortMergeJoinScanner(
     bufferedKeyGenerator: Projection,
     keyOrdering: Ordering[InternalRow],
     streamedIter: RowIterator,
-    bufferedIter: RowIterator) {
+    numStreamedRows: LongSQLMetric,
+    bufferedIter: RowIterator,
+    numBufferedRows: LongSQLMetric) {
   private[this] var streamedRow: InternalRow = _
   private[this] var streamedRowKey: InternalRow = _
   private[this] var bufferedRow: InternalRow = _
@@ -269,6 +284,7 @@ private[joins] class SortMergeJoinScanner(
     if (streamedIter.advanceNext()) {
       streamedRow = streamedIter.getRow
       streamedRowKey = streamedKeyGenerator(streamedRow)
+      numStreamedRows += 1
       true
     } else {
       streamedRow = null
@@ -286,6 +302,7 @@ private[joins] class SortMergeJoinScanner(
     while (!foundRow && bufferedIter.advanceNext()) {
       bufferedRow = bufferedIter.getRow
       bufferedRowKey = bufferedKeyGenerator(bufferedRow)
+      numBufferedRows += 1
       foundRow = !bufferedRowKey.anyNull
     }
     if (!foundRow) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
index 5326966b07a66..dea9e5e580a1e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
@@ -24,6 +24,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
+import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetrics}
 
 /**
  * :: DeveloperApi ::
@@ -40,6 +41,11 @@ case class SortMergeOuterJoin(
     left: SparkPlan,
     right: SparkPlan) extends BinaryNode {
 
+  override private[sql] lazy val metrics = Map(
+    "numLeftRows" -> SQLMetrics.createLongMetric(sparkContext, "number of left rows"),
+    "numRightRows" -> SQLMetrics.createLongMetric(sparkContext, "number of right rows"),
+    "numOutputRows" -> SQLMetrics.createLongMetric(sparkContext, "number of output rows"))
+
   override def output: Seq[Attribute] = {
     joinType match {
       case LeftOuter =>
@@ -108,6 +114,10 @@ case class SortMergeOuterJoin(
   }
 
   override def doExecute(): RDD[InternalRow] = {
+    val numLeftRows = longMetric("numLeftRows")
+    val numRightRows = longMetric("numRightRows")
+    val numOutputRows = longMetric("numOutputRows")
+
     left.execute().zipPartitions(right.execute()) { (leftIter, rightIter) =>
       // An ordering that can be used to compare keys from both sides.
       val keyOrdering = newNaturalAscendingOrdering(leftKeys.map(_.dataType))
@@ -133,10 +143,13 @@ case class SortMergeOuterJoin(
             bufferedKeyGenerator = createRightKeyGenerator(),
             keyOrdering,
             streamedIter = RowIterator.fromScala(leftIter),
-            bufferedIter = RowIterator.fromScala(rightIter)
+            numLeftRows,
+            bufferedIter = RowIterator.fromScala(rightIter),
+            numRightRows
           )
           val rightNullRow = new GenericInternalRow(right.output.length)
-          new LeftOuterIterator(smjScanner, rightNullRow, boundCondition, resultProj).toScala
+          new LeftOuterIterator(
+            smjScanner, rightNullRow, boundCondition, resultProj, numOutputRows).toScala
 
         case RightOuter =>
           val smjScanner = new SortMergeJoinScanner(
@@ -144,10 +157,13 @@ case class SortMergeOuterJoin(
             bufferedKeyGenerator = createLeftKeyGenerator(),
             keyOrdering,
             streamedIter = RowIterator.fromScala(rightIter),
-            bufferedIter = RowIterator.fromScala(leftIter)
+            numRightRows,
+            bufferedIter = RowIterator.fromScala(leftIter),
+            numLeftRows
           )
           val leftNullRow = new GenericInternalRow(left.output.length)
-          new RightOuterIterator(smjScanner, leftNullRow, boundCondition, resultProj).toScala
+          new RightOuterIterator(
+            smjScanner, leftNullRow, boundCondition, resultProj, numOutputRows).toScala
 
         case x =>
           throw new IllegalArgumentException(
@@ -162,7 +178,8 @@ private class LeftOuterIterator(
     smjScanner: SortMergeJoinScanner,
     rightNullRow: InternalRow,
     boundCondition: InternalRow => Boolean,
-    resultProj: InternalRow => InternalRow
+    resultProj: InternalRow => InternalRow,
+    numRows: LongSQLMetric
   ) extends RowIterator {
   private[this] val joinedRow: JoinedRow = new JoinedRow()
   private[this] var rightIdx: Int = 0
@@ -198,7 +215,9 @@ private class LeftOuterIterator(
   }
 
   override def advanceNext(): Boolean = {
-    advanceRightUntilBoundConditionSatisfied() || advanceLeft()
+    val r = advanceRightUntilBoundConditionSatisfied() || advanceLeft()
+    if (r) numRows += 1
+    r
   }
 
   override def getRow: InternalRow = resultProj(joinedRow)
@@ -208,7 +227,8 @@ private class RightOuterIterator(
     smjScanner: SortMergeJoinScanner,
     leftNullRow: InternalRow,
     boundCondition: InternalRow => Boolean,
-    resultProj: InternalRow => InternalRow
+    resultProj: InternalRow => InternalRow,
+    numRows: LongSQLMetric
   ) extends RowIterator {
   private[this] val joinedRow: JoinedRow = new JoinedRow()
   private[this] var leftIdx: Int = 0
@@ -244,7 +264,9 @@ private class RightOuterIterator(
   }
 
   override def advanceNext(): Boolean = {
-    advanceLeftUntilBoundConditionSatisfied() || advanceRight()
+    val r = advanceLeftUntilBoundConditionSatisfied() || advanceRight()
+    if (r) numRows += 1
+    r
   }
 
   override def getRow: InternalRow = resultProj(joinedRow)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
index 1b51a5e5c8a8e..7a2a98ec18cb8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/metric/SQLMetrics.scala
@@ -112,4 +112,10 @@ private[sql] object SQLMetrics {
     sc.cleaner.foreach(_.registerAccumulatorForCleanup(acc))
     acc
   }
+
+  /**
+   * A metric that its value will be ignored. Use this one when we need a metric parameter but don't
+   * care about the value.
+   */
+  val nullLongMetric = new LongSQLMetric("null")
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index 8b1a9b21a96b9..a1fa2c3864bdb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -22,6 +22,8 @@ import java.io.{ByteArrayInputStream, ByteArrayOutputStream, ObjectInputStream,
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.metric.SQLMetrics
+import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 import org.apache.spark.util.collection.CompactBuffer
 
@@ -35,7 +37,8 @@ class HashedRelationSuite extends SparkFunSuite {
 
   test("GeneralHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
-    val hashed = HashedRelation(data.iterator, keyProjection)
+    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
+    val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[GeneralHashedRelation])
 
     assert(hashed.get(data(0)) === CompactBuffer[InternalRow](data(0)))
@@ -45,11 +48,13 @@ class HashedRelationSuite extends SparkFunSuite {
     val data2 = CompactBuffer[InternalRow](data(2))
     data2 += data(2)
     assert(hashed.get(data(2)) === data2)
+    assert(numDataRows.value.value === data.length)
   }
 
   test("UniqueKeyHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2))
-    val hashed = HashedRelation(data.iterator, keyProjection)
+    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
+    val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[UniqueKeyHashedRelation])
 
     assert(hashed.get(data(0)) === CompactBuffer[InternalRow](data(0)))
@@ -62,17 +67,19 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(uniqHashed.getValue(data(1)) === data(1))
     assert(uniqHashed.getValue(data(2)) === data(2))
     assert(uniqHashed.getValue(InternalRow(10)) === null)
+    assert(numDataRows.value.value === data.length)
   }
 
   test("UnsafeHashedRelation") {
     val schema = StructType(StructField("a", IntegerType, true) :: Nil)
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
+    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
     val toUnsafe = UnsafeProjection.create(schema)
     val unsafeData = data.map(toUnsafe(_).copy()).toArray
 
     val buildKey = Seq(BoundReference(0, IntegerType, false))
     val keyGenerator = UnsafeProjection.create(buildKey)
-    val hashed = UnsafeHashedRelation(unsafeData.iterator, keyGenerator, 1)
+    val hashed = UnsafeHashedRelation(unsafeData.iterator, numDataRows, keyGenerator, 1)
     assert(hashed.isInstanceOf[UnsafeHashedRelation])
 
     assert(hashed.get(unsafeData(0)) === CompactBuffer[InternalRow](unsafeData(0)))
@@ -94,5 +101,6 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(hashed2.get(unsafeData(1)) === CompactBuffer[InternalRow](unsafeData(1)))
     assert(hashed2.get(toUnsafe(InternalRow(10))) === null)
     assert(hashed2.get(unsafeData(2)) === data2)
+    assert(numDataRows.value.value === data.length)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
index 953284c98b208..7383d3f8fe024 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
@@ -25,15 +25,24 @@ import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm._
 import com.esotericsoftware.reflectasm.shaded.org.objectweb.asm.Opcodes._
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql._
+import org.apache.spark.sql.execution.ui.SparkPlanGraph
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.{SQLTestUtils, TestSQLContext}
 import org.apache.spark.util.Utils
 
+class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
-class SQLMetricsSuite extends SparkFunSuite {
+  override val sqlContext = TestSQLContext
+
+  import sqlContext.implicits._
 
   test("LongSQLMetric should not box Long") {
     val l = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "long")
-    val f = () => { l += 1L }
+    val f = () => {
+      l += 1L
+      l.add(1L)
+    }
     BoxingFinder.getClassReader(f.getClass).foreach { cl =>
       val boxingFinder = new BoxingFinder()
       cl.accept(boxingFinder, 0)
@@ -51,6 +60,441 @@ class SQLMetricsSuite extends SparkFunSuite {
       assert(boxingFinder.boxingInvokes.nonEmpty, "Found find boxing in this test")
     }
   }
+
+  /**
+   * Call `df.collect()` and verify if the collected metrics are same as "expectedMetrics".
+   *
+   * @param df `DataFrame` to run
+   * @param expectedNumOfJobs number of jobs that will run
+   * @param expectedMetrics the expected metrics. The format is
+   *                        `nodeId -> (operatorName, metric name -> metric value)`.
+   */
+  private def testSparkPlanMetrics(
+      df: DataFrame,
+      expectedNumOfJobs: Int,
+      expectedMetrics: Map[Long, (String, Map[String, Any])]): Unit = {
+    val previousExecutionIds = TestSQLContext.listener.executionIdToData.keySet
+    df.collect()
+    TestSQLContext.sparkContext.listenerBus.waitUntilEmpty(10000)
+    val executionIds = TestSQLContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
+    assert(executionIds.size === 1)
+    val executionId = executionIds.head
+    val jobs = TestSQLContext.listener.getExecution(executionId).get.jobs
+    // Use "<=" because there is a race condition that we may miss some jobs
+    // TODO Change it to "=" once we fix the race condition that missing the JobStarted event.
+    assert(jobs.size <= expectedNumOfJobs)
+    if (jobs.size == expectedNumOfJobs) {
+      // If we can track all jobs, check the metric values
+      val metricValues = TestSQLContext.listener.getExecutionMetrics(executionId)
+      val actualMetrics = SparkPlanGraph(df.queryExecution.executedPlan).nodes.filter { node =>
+        expectedMetrics.contains(node.id)
+      }.map { node =>
+        val nodeMetrics = node.metrics.map { metric =>
+          val metricValue = metricValues(metric.accumulatorId)
+          (metric.name, metricValue)
+        }.toMap
+        (node.id, node.name -> nodeMetrics)
+      }.toMap
+      assert(expectedMetrics === actualMetrics)
+    } else {
+      // TODO Remove this "else" once we fix the race condition that missing the JobStarted event.
+      // Since we cannot track all jobs, the metric values could be wrong and we should not check
+      // them.
+      logWarning("Due to a race condition, we miss some jobs and cannot verify the metric values")
+    }
+  }
+
+  test("Project metrics") {
+    withSQLConf(
+      SQLConf.UNSAFE_ENABLED.key -> "false",
+      SQLConf.CODEGEN_ENABLED.key -> "false",
+      SQLConf.TUNGSTEN_ENABLED.key -> "false") {
+      // Assume the execution plan is
+      // PhysicalRDD(nodeId = 1) -> Project(nodeId = 0)
+      val df = TestData.person.select('name)
+      testSparkPlanMetrics(df, 1, Map(
+        0L ->("Project", Map(
+          "number of rows" -> 2L)))
+      )
+    }
+  }
+
+  test("TungstenProject metrics") {
+    withSQLConf(
+      SQLConf.UNSAFE_ENABLED.key -> "true",
+      SQLConf.CODEGEN_ENABLED.key -> "true",
+      SQLConf.TUNGSTEN_ENABLED.key -> "true") {
+      // Assume the execution plan is
+      // PhysicalRDD(nodeId = 1) -> TungstenProject(nodeId = 0)
+      val df = TestData.person.select('name)
+      testSparkPlanMetrics(df, 1, Map(
+        0L ->("TungstenProject", Map(
+          "number of rows" -> 2L)))
+      )
+    }
+  }
+
+  test("Filter metrics") {
+    // Assume the execution plan is
+    // PhysicalRDD(nodeId = 1) -> Filter(nodeId = 0)
+    val df = TestData.person.filter('age < 25)
+    testSparkPlanMetrics(df, 1, Map(
+      0L -> ("Filter", Map(
+        "number of input rows" -> 2L,
+        "number of output rows" -> 1L)))
+    )
+  }
+
+  test("Aggregate metrics") {
+    withSQLConf(
+      SQLConf.UNSAFE_ENABLED.key -> "false",
+      SQLConf.CODEGEN_ENABLED.key -> "false",
+      SQLConf.TUNGSTEN_ENABLED.key -> "false") {
+      // Assume the execution plan is
+      // ... -> Aggregate(nodeId = 2) -> TungstenExchange(nodeId = 1) -> Aggregate(nodeId = 0)
+      val df = TestData.testData2.groupBy().count() // 2 partitions
+      testSparkPlanMetrics(df, 1, Map(
+        2L -> ("Aggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 2L)),
+        0L -> ("Aggregate", Map(
+          "number of input rows" -> 2L,
+          "number of output rows" -> 1L)))
+      )
+
+      // 2 partitions and each partition contains 2 keys
+      val df2 = TestData.testData2.groupBy('a).count()
+      testSparkPlanMetrics(df2, 1, Map(
+        2L -> ("Aggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 4L)),
+        0L -> ("Aggregate", Map(
+          "number of input rows" -> 4L,
+          "number of output rows" -> 3L)))
+      )
+    }
+  }
+
+  test("SortBasedAggregate metrics") {
+    // Because SortBasedAggregate may skip different rows if the number of partitions is different,
+    // this test should use the deterministic number of partitions.
+    withSQLConf(
+      SQLConf.UNSAFE_ENABLED.key -> "false",
+      SQLConf.CODEGEN_ENABLED.key -> "true",
+      SQLConf.TUNGSTEN_ENABLED.key -> "true") {
+      // Assume the execution plan is
+      // ... -> SortBasedAggregate(nodeId = 2) -> TungstenExchange(nodeId = 1) ->
+      // SortBasedAggregate(nodeId = 0)
+      val df = TestData.testData2.groupBy().count() // 2 partitions
+      testSparkPlanMetrics(df, 1, Map(
+        2L -> ("SortBasedAggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 2L)),
+        0L -> ("SortBasedAggregate", Map(
+          "number of input rows" -> 2L,
+          "number of output rows" -> 1L)))
+      )
+
+      // Assume the execution plan is
+      // ... -> SortBasedAggregate(nodeId = 3) -> TungstenExchange(nodeId = 2)
+      // -> ExternalSort(nodeId = 1)-> SortBasedAggregate(nodeId = 0)
+      // 2 partitions and each partition contains 2 keys
+      val df2 = TestData.testData2.groupBy('a).count()
+      testSparkPlanMetrics(df2, 1, Map(
+        3L -> ("SortBasedAggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 4L)),
+        0L -> ("SortBasedAggregate", Map(
+          "number of input rows" -> 4L,
+          "number of output rows" -> 3L)))
+      )
+    }
+  }
+
+  test("TungstenAggregate metrics") {
+    withSQLConf(
+      SQLConf.UNSAFE_ENABLED.key -> "true",
+      SQLConf.CODEGEN_ENABLED.key -> "true",
+      SQLConf.TUNGSTEN_ENABLED.key -> "true") {
+      // Assume the execution plan is
+      // ... -> TungstenAggregate(nodeId = 2) -> Exchange(nodeId = 1)
+      // -> TungstenAggregate(nodeId = 0)
+      val df = TestData.testData2.groupBy().count() // 2 partitions
+      testSparkPlanMetrics(df, 1, Map(
+        2L -> ("TungstenAggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 2L)),
+        0L -> ("TungstenAggregate", Map(
+          "number of input rows" -> 2L,
+          "number of output rows" -> 1L)))
+      )
+
+      // 2 partitions and each partition contains 2 keys
+      val df2 = TestData.testData2.groupBy('a).count()
+      testSparkPlanMetrics(df2, 1, Map(
+        2L -> ("TungstenAggregate", Map(
+          "number of input rows" -> 6L,
+          "number of output rows" -> 4L)),
+        0L -> ("TungstenAggregate", Map(
+          "number of input rows" -> 4L,
+          "number of output rows" -> 3L)))
+      )
+    }
+  }
+
+  test("SortMergeJoin metrics") {
+    // Because SortMergeJoin may skip different rows if the number of partitions is different, this
+    // test should use the deterministic number of partitions.
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
+      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      testDataForJoin.registerTempTable("testDataForJoin")
+      withTempTable("testDataForJoin") {
+        // Assume the execution plan is
+        // ... -> SortMergeJoin(nodeId = 1) -> TungstenProject(nodeId = 0)
+        val df = sqlContext.sql(
+          "SELECT * FROM testData2 JOIN testDataForJoin ON testData2.a = testDataForJoin.a")
+        testSparkPlanMetrics(df, 1, Map(
+          1L -> ("SortMergeJoin", Map(
+            // It's 4 because we only read 3 rows in the first partition and 1 row in the second one
+            "number of left rows" -> 4L,
+            "number of right rows" -> 2L,
+            "number of output rows" -> 4L)))
+        )
+      }
+    }
+  }
+
+  test("SortMergeOuterJoin metrics") {
+    // Because SortMergeOuterJoin may skip different rows if the number of partitions is different,
+    // this test should use the deterministic number of partitions.
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
+      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      testDataForJoin.registerTempTable("testDataForJoin")
+      withTempTable("testDataForJoin") {
+        // Assume the execution plan is
+        // ... -> SortMergeOuterJoin(nodeId = 1) -> TungstenProject(nodeId = 0)
+        val df = sqlContext.sql(
+          "SELECT * FROM testData2 left JOIN testDataForJoin ON testData2.a = testDataForJoin.a")
+        testSparkPlanMetrics(df, 1, Map(
+          1L -> ("SortMergeOuterJoin", Map(
+            // It's 4 because we only read 3 rows in the first partition and 1 row in the second one
+            "number of left rows" -> 6L,
+            "number of right rows" -> 2L,
+            "number of output rows" -> 8L)))
+        )
+
+        val df2 = sqlContext.sql(
+          "SELECT * FROM testDataForJoin right JOIN testData2 ON testData2.a = testDataForJoin.a")
+        testSparkPlanMetrics(df2, 1, Map(
+          1L -> ("SortMergeOuterJoin", Map(
+            // It's 4 because we only read 3 rows in the first partition and 1 row in the second one
+            "number of left rows" -> 2L,
+            "number of right rows" -> 6L,
+            "number of output rows" -> 8L)))
+        )
+      }
+    }
+  }
+
+  test("BroadcastHashJoin metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+      val df2 = Seq((1, "1"), (2, "2"), (3, "3"), (4, "4")).toDF("key", "value")
+      // Assume the execution plan is
+      // ... -> BroadcastHashJoin(nodeId = 1) -> TungstenProject(nodeId = 0)
+      val df = df1.join(broadcast(df2), "key")
+      testSparkPlanMetrics(df, 2, Map(
+        1L -> ("BroadcastHashJoin", Map(
+          "number of left rows" -> 2L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 2L)))
+      )
+    }
+  }
+
+  test("ShuffledHashJoin metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      testDataForJoin.registerTempTable("testDataForJoin")
+      withTempTable("testDataForJoin") {
+        // Assume the execution plan is
+        // ... -> ShuffledHashJoin(nodeId = 1) -> TungstenProject(nodeId = 0)
+        val df = sqlContext.sql(
+          "SELECT * FROM testData2 JOIN testDataForJoin ON testData2.a = testDataForJoin.a")
+        testSparkPlanMetrics(df, 1, Map(
+          1L -> ("ShuffledHashJoin", Map(
+            "number of left rows" -> 6L,
+            "number of right rows" -> 2L,
+            "number of output rows" -> 4L)))
+        )
+      }
+    }
+  }
+
+  test("ShuffledHashOuterJoin metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false",
+      SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "0") {
+      val df1 = Seq((1, "a"), (1, "b"), (4, "c")).toDF("key", "value")
+      val df2 = Seq((1, "a"), (1, "b"), (2, "c"), (3, "d")).toDF("key2", "value")
+      // Assume the execution plan is
+      // ... -> ShuffledHashOuterJoin(nodeId = 0)
+      val df = df1.join(df2, $"key" === $"key2", "left_outer")
+      testSparkPlanMetrics(df, 1, Map(
+        0L -> ("ShuffledHashOuterJoin", Map(
+          "number of left rows" -> 3L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 5L)))
+      )
+
+      val df3 = df1.join(df2, $"key" === $"key2", "right_outer")
+      testSparkPlanMetrics(df3, 1, Map(
+        0L -> ("ShuffledHashOuterJoin", Map(
+          "number of left rows" -> 3L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 6L)))
+      )
+
+      val df4 = df1.join(df2, $"key" === $"key2", "outer")
+      testSparkPlanMetrics(df4, 1, Map(
+        0L -> ("ShuffledHashOuterJoin", Map(
+          "number of left rows" -> 3L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 7L)))
+      )
+    }
+  }
+
+  test("BroadcastHashOuterJoin metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      val df1 = Seq((1, "a"), (1, "b"), (4, "c")).toDF("key", "value")
+      val df2 = Seq((1, "a"), (1, "b"), (2, "c"), (3, "d")).toDF("key2", "value")
+      // Assume the execution plan is
+      // ... -> BroadcastHashOuterJoin(nodeId = 0)
+      val df = df1.join(broadcast(df2), $"key" === $"key2", "left_outer")
+      testSparkPlanMetrics(df, 2, Map(
+        0L -> ("BroadcastHashOuterJoin", Map(
+          "number of left rows" -> 3L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 5L)))
+      )
+
+      val df3 = df1.join(broadcast(df2), $"key" === $"key2", "right_outer")
+      testSparkPlanMetrics(df3, 2, Map(
+        0L -> ("BroadcastHashOuterJoin", Map(
+          "number of left rows" -> 3L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 6L)))
+      )
+    }
+  }
+
+  test("BroadcastNestedLoopJoin metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
+      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      testDataForJoin.registerTempTable("testDataForJoin")
+      withTempTable("testDataForJoin") {
+        // Assume the execution plan is
+        // ... -> BroadcastNestedLoopJoin(nodeId = 1) -> TungstenProject(nodeId = 0)
+        val df = sqlContext.sql(
+          "SELECT * FROM testData2 left JOIN testDataForJoin ON " +
+            "testData2.a * testDataForJoin.a != testData2.a + testDataForJoin.a")
+        testSparkPlanMetrics(df, 3, Map(
+          1L -> ("BroadcastNestedLoopJoin", Map(
+            "number of left rows" -> 12L, // left needs to be scanned twice
+            "number of right rows" -> 2L,
+            "number of output rows" -> 12L)))
+        )
+      }
+    }
+  }
+
+  test("BroadcastLeftSemiJoinHash metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+      val df2 = Seq((1, "1"), (2, "2"), (3, "3"), (4, "4")).toDF("key2", "value")
+      // Assume the execution plan is
+      // ... -> BroadcastLeftSemiJoinHash(nodeId = 0)
+      val df = df1.join(broadcast(df2), $"key" === $"key2", "leftsemi")
+      testSparkPlanMetrics(df, 2, Map(
+        0L -> ("BroadcastLeftSemiJoinHash", Map(
+          "number of left rows" -> 2L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 2L)))
+      )
+    }
+  }
+
+  test("LeftSemiJoinHash metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true",
+      SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "0") {
+      val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+      val df2 = Seq((1, "1"), (2, "2"), (3, "3"), (4, "4")).toDF("key2", "value")
+      // Assume the execution plan is
+      // ... -> LeftSemiJoinHash(nodeId = 0)
+      val df = df1.join(df2, $"key" === $"key2", "leftsemi")
+      testSparkPlanMetrics(df, 1, Map(
+        0L -> ("LeftSemiJoinHash", Map(
+          "number of left rows" -> 2L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 2L)))
+      )
+    }
+  }
+
+  test("LeftSemiJoinBNL metrics") {
+    withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
+      val df1 = Seq((1, "1"), (2, "2")).toDF("key", "value")
+      val df2 = Seq((1, "1"), (2, "2"), (3, "3"), (4, "4")).toDF("key2", "value")
+      // Assume the execution plan is
+      // ... -> LeftSemiJoinBNL(nodeId = 0)
+      val df = df1.join(df2, $"key" < $"key2", "leftsemi")
+      testSparkPlanMetrics(df, 2, Map(
+        0L -> ("LeftSemiJoinBNL", Map(
+          "number of left rows" -> 2L,
+          "number of right rows" -> 4L,
+          "number of output rows" -> 2L)))
+      )
+    }
+  }
+
+  test("CartesianProduct metrics") {
+    val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+    testDataForJoin.registerTempTable("testDataForJoin")
+    withTempTable("testDataForJoin") {
+      // Assume the execution plan is
+      // ... -> CartesianProduct(nodeId = 1) -> TungstenProject(nodeId = 0)
+      val df = sqlContext.sql(
+        "SELECT * FROM testData2 JOIN testDataForJoin")
+      testSparkPlanMetrics(df, 1, Map(
+        1L -> ("CartesianProduct", Map(
+          "number of left rows" -> 12L, // left needs to be scanned twice
+          "number of right rows" -> 12L, // right is read 6 times
+          "number of output rows" -> 12L)))
+      )
+    }
+  }
+
+  test("save metrics") {
+    withTempPath { file =>
+      val previousExecutionIds = TestSQLContext.listener.executionIdToData.keySet
+      // Assume the execution plan is
+      // PhysicalRDD(nodeId = 0)
+      TestData.person.select('name).write.format("json").save(file.getAbsolutePath)
+      TestSQLContext.sparkContext.listenerBus.waitUntilEmpty(10000)
+      val executionIds = TestSQLContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
+      assert(executionIds.size === 1)
+      val executionId = executionIds.head
+      val jobs = TestSQLContext.listener.getExecution(executionId).get.jobs
+      // Use "<=" because there is a race condition that we may miss some jobs
+      // TODO Change "<=" to "=" once we fix the race condition that missing the JobStarted event.
+      assert(jobs.size <= 1)
+      val metricValues = TestSQLContext.listener.getExecutionMetrics(executionId)
+      // Because "save" will create a new DataFrame internally, we cannot get the real metric id.
+      // However, we still can check the value.
+      assert(metricValues.values.toSeq === Seq(2L))
+    }
+  }
+
 }
 
 private case class MethodIdentifier[T](cls: Class[T], name: String, desc: String)

From 520ad44b17f72e6465bf990f64b4e289f8a83447 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 11 Aug 2015 12:49:47 -0700
Subject: [PATCH 167/633] [SPARK-9750] [MLLIB] Improve equals on SparseMatrix
 and DenseMatrix

Adds unit test for `equals` on `mllib.linalg.Matrix` class and `equals` to both `SparseMatrix` and `DenseMatrix`. Supports equality testing between `SparseMatrix` and `DenseMatrix`.

mengxr

Author: Feynman Liang <fliang@databricks.com>

Closes #8042 from feynmanliang/SPARK-9750 and squashes the following commits:

bb70d5e [Feynman Liang] Breeze compare for dense matrices as well, in case other is sparse
ab6f3c8 [Feynman Liang] Sparse matrix compare for equals
22782df [Feynman Liang] Add equality based on matrix semantics, not representation
78f9426 [Feynman Liang] Add casts
43d28fa [Feynman Liang] Fix failing test
6416fa0 [Feynman Liang] Add failing sparse matrix equals tests
---
 .../apache/spark/mllib/linalg/Matrices.scala   |  8 ++++++--
 .../spark/mllib/linalg/MatricesSuite.scala     | 18 ++++++++++++++++++
 2 files changed, 24 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 1c858348bf20e..1139ce36d50b8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -257,8 +257,7 @@ class DenseMatrix(
     this(numRows, numCols, values, false)
 
   override def equals(o: Any): Boolean = o match {
-    case m: DenseMatrix =>
-      m.numRows == numRows && m.numCols == numCols && Arrays.equals(toArray, m.toArray)
+    case m: Matrix => toBreeze == m.toBreeze
     case _ => false
   }
 
@@ -519,6 +518,11 @@ class SparseMatrix(
       rowIndices: Array[Int],
       values: Array[Double]) = this(numRows, numCols, colPtrs, rowIndices, values, false)
 
+  override def equals(o: Any): Boolean = o match {
+    case m: Matrix => toBreeze == m.toBreeze
+    case _ => false
+  }
+
   private[mllib] def toBreeze: BM[Double] = {
      if (!isTransposed) {
        new BSM[Double](values, numRows, numCols, colPtrs, rowIndices)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
index a270ba2562db9..bfd6d5495f5e0 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/MatricesSuite.scala
@@ -74,6 +74,24 @@ class MatricesSuite extends SparkFunSuite {
     }
   }
 
+  test("equals") {
+    val dm1 = Matrices.dense(2, 2, Array(0.0, 1.0, 2.0, 3.0))
+    assert(dm1 === dm1)
+    assert(dm1 !== dm1.transpose)
+
+    val dm2 = Matrices.dense(2, 2, Array(0.0, 2.0, 1.0, 3.0))
+    assert(dm1 === dm2.transpose)
+
+    val sm1 = dm1.asInstanceOf[DenseMatrix].toSparse
+    assert(sm1 === sm1)
+    assert(sm1 === dm1)
+    assert(sm1 !== sm1.transpose)
+
+    val sm2 = dm2.asInstanceOf[DenseMatrix].toSparse
+    assert(sm1 === sm2.transpose)
+    assert(sm1 === dm2.transpose)
+  }
+
   test("matrix copies are deep copies") {
     val m = 3
     val n = 2

From 2a3be4ddf9d9527353f07ea0ab204ce17dbcba9a Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Tue, 11 Aug 2015 14:02:23 -0700
Subject: [PATCH 168/633] [SPARK-7726] Add import so Scaladoc doesn't fail.

This is another import needed so Scala 2.11 doc generation doesn't fail.
See SPARK-7726 for more detail. I tested this locally and the 2.11
install goes from failing to succeeding with this patch.

Author: Patrick Wendell <patrick@databricks.com>

Closes #8095 from pwendell/scaladoc.
---
 .../spark/network/shuffle/protocol/mesos/RegisterDriver.java   | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java
index 1c28fc1dff246..94a61d6caadc4 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/mesos/RegisterDriver.java
@@ -23,6 +23,9 @@
 import org.apache.spark.network.protocol.Encoders;
 import org.apache.spark.network.shuffle.protocol.BlockTransferMessage;
 
+// Needed by ScalaDoc. See SPARK-7726
+import static org.apache.spark.network.shuffle.protocol.BlockTransferMessage.Type;
+
 /**
  * A message sent from the driver to register with the MesosExternalShuffleService.
  */

From 00c02728a6c6c4282c389ca90641dd78dd5e3d32 Mon Sep 17 00:00:00 2001
From: hyukjinkwon <gurwls223@gmail.com>
Date: Tue, 11 Aug 2015 14:04:09 -0700
Subject: [PATCH 169/633] [SPARK-9814] [SQL] EqualNotNull not passing to data
 sources
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Author: hyukjinkwon <gurwls223@gmail.com>
Author: 권혁진 <gurwls223@gmail.com>

Closes #8096 from HyukjinKwon/master.
---
 .../sql/execution/datasources/DataSourceStrategy.scala   | 5 +++++
 .../scala/org/apache/spark/sql/sources/filters.scala     | 9 +++++++++
 .../org/apache/spark/sql/sources/FilteredScanSuite.scala | 1 +
 3 files changed, 15 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 78a4acdf4b1bf..2a4c40db8bb66 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -349,6 +349,11 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
       case expressions.EqualTo(Literal(v, _), a: Attribute) =>
         Some(sources.EqualTo(a.name, v))
 
+      case expressions.EqualNullSafe(a: Attribute, Literal(v, _)) =>
+        Some(sources.EqualNullSafe(a.name, v))
+      case expressions.EqualNullSafe(Literal(v, _), a: Attribute) =>
+        Some(sources.EqualNullSafe(a.name, v))
+
       case expressions.GreaterThan(a: Attribute, Literal(v, _)) =>
         Some(sources.GreaterThan(a.name, v))
       case expressions.GreaterThan(Literal(v, _), a: Attribute) =>
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala
index 4d942e4f9287a..3780cbbcc9631 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/filters.scala
@@ -36,6 +36,15 @@ abstract class Filter
  */
 case class EqualTo(attribute: String, value: Any) extends Filter
 
+/**
+ * Performs equality comparison, similar to [[EqualTo]]. However, this differs from [[EqualTo]]
+ * in that it returns `true` (rather than NULL) if both inputs are NULL, and `false`
+ * (rather than NULL) if one of the input is NULL and the other is not NULL.
+ *
+ * @since 1.5.0
+ */
+case class EqualNullSafe(attribute: String, value: Any) extends Filter
+
 /**
  * A filter that evaluates to `true` iff the attribute evaluates to a value
  * greater than `value`.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
index 81b3a0f0c5b3a..5ef365797eace 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
@@ -56,6 +56,7 @@ case class SimpleFilteredScan(from: Int, to: Int)(@transient val sqlContext: SQL
     // Predicate test on integer column
     def translateFilterOnA(filter: Filter): Int => Boolean = filter match {
       case EqualTo("a", v) => (a: Int) => a == v
+      case EqualNullSafe("a", v) => (a: Int) => a == v
       case LessThan("a", v: Int) => (a: Int) => a < v
       case LessThanOrEqual("a", v: Int) => (a: Int) => a <= v
       case GreaterThan("a", v: Int) => (a: Int) => a > v

From f16bc68dfb25c7b746ae031a57840ace9bafa87f Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 11 Aug 2015 14:06:23 -0700
Subject: [PATCH 170/633] [SPARK-9824] [CORE] Fix the issue that
 InternalAccumulator leaks WeakReference

`InternalAccumulator.create` doesn't call `registerAccumulatorForCleanup` to register itself with ContextCleaner, so `WeakReference`s for these accumulators in `Accumulators.originals` won't be removed.

This PR added `registerAccumulatorForCleanup` for internal accumulators to avoid the memory leak.

Author: zsxwing <zsxwing@gmail.com>

Closes #8108 from zsxwing/internal-accumulators-leak.
---
 .../scala/org/apache/spark/Accumulators.scala | 22 +++++++++++--------
 .../org/apache/spark/scheduler/Stage.scala    |  2 +-
 .../org/apache/spark/AccumulatorSuite.scala   |  3 ++-
 3 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index 064246dfa7fc3..c39c8667d013e 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -382,14 +382,18 @@ private[spark] object InternalAccumulator {
    * add to the same set of accumulators. We do this to report the distribution of accumulator
    * values across all tasks within each stage.
    */
-  def create(): Seq[Accumulator[Long]] = {
-    Seq(
-      // Execution memory refers to the memory used by internal data structures created
-      // during shuffles, aggregations and joins. The value of this accumulator should be
-      // approximately the sum of the peak sizes across all such data structures created
-      // in this task. For SQL jobs, this only tracks all unsafe operators and ExternalSort.
-      new Accumulator(
-        0L, AccumulatorParam.LongAccumulatorParam, Some(PEAK_EXECUTION_MEMORY), internal = true)
-    ) ++ maybeTestAccumulator.toSeq
+  def create(sc: SparkContext): Seq[Accumulator[Long]] = {
+    val internalAccumulators = Seq(
+        // Execution memory refers to the memory used by internal data structures created
+        // during shuffles, aggregations and joins. The value of this accumulator should be
+        // approximately the sum of the peak sizes across all such data structures created
+        // in this task. For SQL jobs, this only tracks all unsafe operators and ExternalSort.
+        new Accumulator(
+          0L, AccumulatorParam.LongAccumulatorParam, Some(PEAK_EXECUTION_MEMORY), internal = true)
+      ) ++ maybeTestAccumulator.toSeq
+    internalAccumulators.foreach { accumulator =>
+      sc.cleaner.foreach(_.registerAccumulatorForCleanup(accumulator))
+    }
+    internalAccumulators
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index de05ee256dbfc..1cf06856ffbc2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -81,7 +81,7 @@ private[spark] abstract class Stage(
    * accumulators here again will override partial values from the finished tasks.
    */
   def resetInternalAccumulators(): Unit = {
-    _internalAccumulators = InternalAccumulator.create()
+    _internalAccumulators = InternalAccumulator.create(rdd.sparkContext)
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
index 48f549575f4d1..0eb2293a9d063 100644
--- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
@@ -160,7 +160,8 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
   }
 
   test("internal accumulators in TaskContext") {
-    val accums = InternalAccumulator.create()
+    sc = new SparkContext("local", "test")
+    val accums = InternalAccumulator.create(sc)
     val taskContext = new TaskContextImpl(0, 0, 0, 0, null, null, accums)
     val internalMetricsToAccums = taskContext.internalMetricsToAccumulators
     val collectedInternalAccums = taskContext.collectInternalAccumulators()

From 423cdfd83d7fd02a4f8cf3e714db913fd3f9ca09 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 11 Aug 2015 14:08:09 -0700
Subject: [PATCH 171/633] Closes #1290 Closes #4934


From be3e27164133025db860781bd5cdd3ca233edd21 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 11 Aug 2015 14:21:53 -0700
Subject: [PATCH 172/633] [SPARK-9788] [MLLIB] Fix LDA Binary Compatibility
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

1. Add “asymmetricDocConcentration” and revert docConcentration changes. If the (internal) doc concentration vector is a single value, “getDocConcentration" returns it. If it is a constant vector, getDocConcentration returns the first item, and fails otherwise.
2. Give `LDAModel.gammaShape` a default value in `LDAModel` concrete class constructors.

jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8077 from feynmanliang/SPARK-9788 and squashes the following commits:

6b07bc8 [Feynman Liang] Code review changes
9d6a71e [Feynman Liang] Add asymmetricAlpha alias
bf4e685 [Feynman Liang] Asymmetric docConcentration
4cab972 [Feynman Liang] Default gammaShape
---
 .../apache/spark/mllib/clustering/LDA.scala   | 27 ++++++++++++++++--
 .../spark/mllib/clustering/LDAModel.scala     | 11 ++++----
 .../spark/mllib/clustering/LDAOptimizer.scala | 28 +++++++++----------
 .../spark/mllib/clustering/LDASuite.scala     |  4 +--
 4 files changed, 46 insertions(+), 24 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index ab124e6d77c5e..0fc9b1ac4d716 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -79,7 +79,24 @@ class LDA private (
    *
    * This is the parameter to a Dirichlet distribution.
    */
-  def getDocConcentration: Vector = this.docConcentration
+  def getAsymmetricDocConcentration: Vector = this.docConcentration
+
+  /**
+   * Concentration parameter (commonly named "alpha") for the prior placed on documents'
+   * distributions over topics ("theta").
+   *
+   * This method assumes the Dirichlet distribution is symmetric and can be described by a single
+   * [[Double]] parameter. It should fail if docConcentration is asymmetric.
+   */
+  def getDocConcentration: Double = {
+    val parameter = docConcentration(0)
+    if (docConcentration.size == 1) {
+      parameter
+    } else {
+      require(docConcentration.toArray.forall(_ == parameter))
+      parameter
+    }
+  }
 
   /**
    * Concentration parameter (commonly named "alpha") for the prior placed on documents'
@@ -106,18 +123,22 @@ class LDA private (
    *       [[https://github.com/Blei-Lab/onlineldavb]].
    */
   def setDocConcentration(docConcentration: Vector): this.type = {
+    require(docConcentration.size > 0, "docConcentration must have > 0 elements")
     this.docConcentration = docConcentration
     this
   }
 
-  /** Replicates Double to create a symmetric prior */
+  /** Replicates a [[Double]] docConcentration to create a symmetric prior. */
   def setDocConcentration(docConcentration: Double): this.type = {
     this.docConcentration = Vectors.dense(docConcentration)
     this
   }
 
+  /** Alias for [[getAsymmetricDocConcentration]] */
+  def getAsymmetricAlpha: Vector = getAsymmetricDocConcentration
+
   /** Alias for [[getDocConcentration]] */
-  def getAlpha: Vector = getDocConcentration
+  def getAlpha: Double = getDocConcentration
 
   /** Alias for [[setDocConcentration()]] */
   def setAlpha(alpha: Vector): this.type = setDocConcentration(alpha)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 33babda69bbb9..5dc637ebdc133 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -27,7 +27,6 @@ import org.json4s.jackson.JsonMethods._
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.api.java.JavaPairRDD
-import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.graphx.{Edge, EdgeContext, Graph, VertexId}
 import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -190,7 +189,8 @@ class LocalLDAModel private[clustering] (
     val topics: Matrix,
     override val docConcentration: Vector,
     override val topicConcentration: Double,
-    override protected[clustering] val gammaShape: Double) extends LDAModel with Serializable {
+    override protected[clustering] val gammaShape: Double = 100)
+  extends LDAModel with Serializable {
 
   override def k: Int = topics.numCols
 
@@ -455,8 +455,9 @@ class DistributedLDAModel private[clustering] (
     val vocabSize: Int,
     override val docConcentration: Vector,
     override val topicConcentration: Double,
-    override protected[clustering] val gammaShape: Double,
-    private[spark] val iterationTimes: Array[Double]) extends LDAModel {
+    private[spark] val iterationTimes: Array[Double],
+    override protected[clustering] val gammaShape: Double = 100)
+  extends LDAModel {
 
   import LDA._
 
@@ -756,7 +757,7 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
       val graph: Graph[LDA.TopicCounts, LDA.TokenCount] = Graph(vertices, edges)
 
       new DistributedLDAModel(graph, globalTopicTotals, globalTopicTotals.length, vocabSize,
-        docConcentration, topicConcentration, gammaShape, iterationTimes)
+        docConcentration, topicConcentration, iterationTimes, gammaShape)
     }
 
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index afba2866c7040..a0008f9c99ad7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -95,10 +95,8 @@ final class EMLDAOptimizer extends LDAOptimizer {
    * Compute bipartite term/doc graph.
    */
   override private[clustering] def initialize(docs: RDD[(Long, Vector)], lda: LDA): LDAOptimizer = {
-    val docConcentration = lda.getDocConcentration(0)
-    require({
-      lda.getDocConcentration.toArray.forall(_ == docConcentration)
-    }, "EMLDAOptimizer currently only supports symmetric document-topic priors")
+    // EMLDAOptimizer currently only supports symmetric document-topic priors
+    val docConcentration = lda.getDocConcentration
 
     val topicConcentration = lda.getTopicConcentration
     val k = lda.getK
@@ -209,11 +207,11 @@ final class EMLDAOptimizer extends LDAOptimizer {
   override private[clustering] def getLDAModel(iterationTimes: Array[Double]): LDAModel = {
     require(graph != null, "graph is null, EMLDAOptimizer not initialized.")
     this.graphCheckpointer.deleteAllCheckpoints()
-    // This assumes gammaShape = 100 in OnlineLDAOptimizer to ensure equivalence in LDAModel.toLocal
-    // conversion
+    // The constructor's default arguments assume gammaShape = 100 to ensure equivalence in
+    // LDAModel.toLocal conversion
     new DistributedLDAModel(this.graph, this.globalTopicTotals, this.k, this.vocabSize,
       Vectors.dense(Array.fill(this.k)(this.docConcentration)), this.topicConcentration,
-      100, iterationTimes)
+      iterationTimes)
   }
 }
 
@@ -378,18 +376,20 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
     this.k = lda.getK
     this.corpusSize = docs.count()
     this.vocabSize = docs.first()._2.size
-    this.alpha = if (lda.getDocConcentration.size == 1) {
-      if (lda.getDocConcentration(0) == -1) Vectors.dense(Array.fill(k)(1.0 / k))
+    this.alpha = if (lda.getAsymmetricDocConcentration.size == 1) {
+      if (lda.getAsymmetricDocConcentration(0) == -1) Vectors.dense(Array.fill(k)(1.0 / k))
       else {
-        require(lda.getDocConcentration(0) >= 0, s"all entries in alpha must be >=0, got: $alpha")
-        Vectors.dense(Array.fill(k)(lda.getDocConcentration(0)))
+        require(lda.getAsymmetricDocConcentration(0) >= 0,
+          s"all entries in alpha must be >=0, got: $alpha")
+        Vectors.dense(Array.fill(k)(lda.getAsymmetricDocConcentration(0)))
       }
     } else {
-      require(lda.getDocConcentration.size == k, s"alpha must have length k, got: $alpha")
-      lda.getDocConcentration.foreachActive { case (_, x) =>
+      require(lda.getAsymmetricDocConcentration.size == k,
+        s"alpha must have length k, got: $alpha")
+      lda.getAsymmetricDocConcentration.foreachActive { case (_, x) =>
         require(x >= 0, s"all entries in alpha must be >= 0, got: $alpha")
       }
-      lda.getDocConcentration
+      lda.getAsymmetricDocConcentration
     }
     this.eta = if (lda.getTopicConcentration == -1) 1.0 / k else lda.getTopicConcentration
     this.randomGenerator = new Random(lda.getSeed)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index fdc2554ab853e..ce6a8eb8e8c46 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -160,8 +160,8 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
 
   test("setter alias") {
     val lda = new LDA().setAlpha(2.0).setBeta(3.0)
-    assert(lda.getAlpha.toArray.forall(_ === 2.0))
-    assert(lda.getDocConcentration.toArray.forall(_ === 2.0))
+    assert(lda.getAsymmetricAlpha.toArray.forall(_ === 2.0))
+    assert(lda.getAsymmetricDocConcentration.toArray.forall(_ === 2.0))
     assert(lda.getBeta === 3.0)
     assert(lda.getTopicConcentration === 3.0)
   }

From 017b5de07ef6cff249e984a2ab781c520249ac76 Mon Sep 17 00:00:00 2001
From: Sudhakar Thota <sudhakarthota@yahoo.com>
Date: Tue, 11 Aug 2015 14:31:51 -0700
Subject: [PATCH 173/633] [SPARK-8925] [MLLIB] Add @since tags to mllib.util

Went thru the history of changes the file MLUtils.scala and picked up the version that the change went in.

Author: Sudhakar Thota <sudhakarthota@yahoo.com>
Author: Sudhakar Thota <sudhakarthota@sudhakars-mbp-2.usca.ibm.com>

Closes #7436 from sthota2014/SPARK-8925_thotas.
---
 .../org/apache/spark/mllib/util/MLUtils.scala | 22 ++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index 7c5cfa7bd84ce..26eb84a8dc0b0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -64,6 +64,7 @@ object MLUtils {
    *                    feature dimensions.
    * @param minPartitions min number of partitions
    * @return labeled data stored as an RDD[LabeledPoint]
+   * @since 1.0.0
    */
   def loadLibSVMFile(
       sc: SparkContext,
@@ -113,7 +114,10 @@ object MLUtils {
   }
 
   // Convenient methods for `loadLibSVMFile`.
-
+  
+  /**
+   * @since 1.0.0
+   */
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -126,6 +130,7 @@ object MLUtils {
   /**
    * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of
    * partitions.
+   * @since 1.0.0
    */
   def loadLibSVMFile(
       sc: SparkContext,
@@ -133,6 +138,9 @@ object MLUtils {
       numFeatures: Int): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, numFeatures, sc.defaultMinPartitions)
 
+  /**
+   * @since 1.0.0
+   */
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -141,6 +149,9 @@ object MLUtils {
       numFeatures: Int): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, numFeatures)
 
+  /**
+   * @since 1.0.0
+   */
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -151,6 +162,7 @@ object MLUtils {
   /**
    * Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of
    * features determined automatically and the default number of partitions.
+   * @since 1.0.0
    */
   def loadLibSVMFile(sc: SparkContext, path: String): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, -1)
@@ -181,12 +193,14 @@ object MLUtils {
    * @param path file or directory path in any Hadoop-supported file system URI
    * @param minPartitions min number of partitions
    * @return vectors stored as an RDD[Vector]
+   * @since 1.1.0
    */
   def loadVectors(sc: SparkContext, path: String, minPartitions: Int): RDD[Vector] =
     sc.textFile(path, minPartitions).map(Vectors.parse)
 
   /**
    * Loads vectors saved using `RDD[Vector].saveAsTextFile` with the default number of partitions.
+   * @since 1.1.0
    */
   def loadVectors(sc: SparkContext, path: String): RDD[Vector] =
     sc.textFile(path, sc.defaultMinPartitions).map(Vectors.parse)
@@ -197,6 +211,7 @@ object MLUtils {
    * @param path file or directory path in any Hadoop-supported file system URI
    * @param minPartitions min number of partitions
    * @return labeled points stored as an RDD[LabeledPoint]
+   * @since 1.1.0
    */
   def loadLabeledPoints(sc: SparkContext, path: String, minPartitions: Int): RDD[LabeledPoint] =
     sc.textFile(path, minPartitions).map(LabeledPoint.parse)
@@ -204,6 +219,7 @@ object MLUtils {
   /**
    * Loads labeled points saved using `RDD[LabeledPoint].saveAsTextFile` with the default number of
    * partitions.
+   * @since 1.1.0
    */
   def loadLabeledPoints(sc: SparkContext, dir: String): RDD[LabeledPoint] =
     loadLabeledPoints(sc, dir, sc.defaultMinPartitions)
@@ -220,6 +236,7 @@ object MLUtils {
    *
    * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
    *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
+   * @since 1.0.0
    */
   @deprecated("Should use MLUtils.loadLabeledPoints instead.", "1.0.1")
   def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
@@ -241,6 +258,7 @@ object MLUtils {
    *
    * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
    *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
+   * @since 1.0.0
    */
   @deprecated("Should use RDD[LabeledPoint].saveAsTextFile instead.", "1.0.1")
   def saveLabeledData(data: RDD[LabeledPoint], dir: String) {
@@ -253,6 +271,7 @@ object MLUtils {
    * Return a k element array of pairs of RDDs with the first element of each pair
    * containing the training data, a complement of the validation data and the second
    * element, the validation data, containing a unique 1/kth of the data. Where k=numFolds.
+   * @since 1.0.0
    */
   @Experimental
   def kFold[T: ClassTag](rdd: RDD[T], numFolds: Int, seed: Int): Array[(RDD[T], RDD[T])] = {
@@ -268,6 +287,7 @@ object MLUtils {
 
   /**
    * Returns a new vector with `1.0` (bias) appended to the input vector.
+   * @since 1.0.0
    */
   def appendBias(vector: Vector): Vector = {
     vector match {

From 736af95bd0c41723d455246b634a0fb68b38a7c7 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Tue, 11 Aug 2015 14:52:52 -0700
Subject: [PATCH 174/633] [HOTFIX] Fix style error caused by 017b5de

---
 mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index 26eb84a8dc0b0..11ed23176fc12 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -114,7 +114,7 @@ object MLUtils {
   }
 
   // Convenient methods for `loadLibSVMFile`.
-  
+
   /**
    * @since 1.0.0
    */

From 5a5bbc29961630d649d4bd4acd5d19eb537b5fd0 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 11 Aug 2015 16:33:08 -0700
Subject: [PATCH 175/633] [SPARK-9074] [LAUNCHER] Allow arbitrary Spark args to
 be set.

This change allows any Spark argument to be added to the app to
be started using SparkLauncher. Known arguments are properly
validated, while unknown arguments are allowed so that the
library can launch newer Spark versions (in case SPARK_HOME points
at one).

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7975 from vanzin/SPARK-9074 and squashes the following commits:

b5e451a [Marcelo Vanzin] [SPARK-9074] [launcher] Allow arbitrary Spark args to be set.
---
 .../apache/spark/launcher/SparkLauncher.java  | 101 +++++++++++++++++-
 .../launcher/SparkSubmitCommandBuilder.java   |   2 +-
 .../spark/launcher/SparkLauncherSuite.java    |  50 +++++++++
 3 files changed, 150 insertions(+), 3 deletions(-)

diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
index c0f89c9230692..03c9358bc865d 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
@@ -20,12 +20,13 @@
 import java.io.File;
 import java.io.IOException;
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 import java.util.Map;
 
 import static org.apache.spark.launcher.CommandBuilderUtils.*;
 
-/** 
+/**
  * Launcher for Spark applications.
  * <p>
  * Use this class to start Spark applications programmatically. The class uses a builder pattern
@@ -57,7 +58,8 @@ public class SparkLauncher {
   /** Configuration key for the number of executor CPU cores. */
   public static final String EXECUTOR_CORES = "spark.executor.cores";
 
-  private final SparkSubmitCommandBuilder builder;
+  // Visible for testing.
+  final SparkSubmitCommandBuilder builder;
 
   public SparkLauncher() {
     this(null);
@@ -187,6 +189,73 @@ public SparkLauncher setMainClass(String mainClass) {
     return this;
   }
 
+  /**
+   * Adds a no-value argument to the Spark invocation. If the argument is known, this method
+   * validates whether the argument is indeed a no-value argument, and throws an exception
+   * otherwise.
+   * <p/>
+   * Use this method with caution. It is possible to create an invalid Spark command by passing
+   * unknown arguments to this method, since those are allowed for forward compatibility.
+   *
+   * @param arg Argument to add.
+   * @return This launcher.
+   */
+  public SparkLauncher addSparkArg(String arg) {
+    SparkSubmitOptionParser validator = new ArgumentValidator(false);
+    validator.parse(Arrays.asList(arg));
+    builder.sparkArgs.add(arg);
+    return this;
+  }
+
+  /**
+   * Adds an argument with a value to the Spark invocation. If the argument name corresponds to
+   * a known argument, the code validates that the argument actually expects a value, and throws
+   * an exception otherwise.
+   * <p/>
+   * It is safe to add arguments modified by other methods in this class (such as
+   * {@link #setMaster(String)} - the last invocation will be the one to take effect.
+   * <p/>
+   * Use this method with caution. It is possible to create an invalid Spark command by passing
+   * unknown arguments to this method, since those are allowed for forward compatibility.
+   *
+   * @param name Name of argument to add.
+   * @param value Value of the argument.
+   * @return This launcher.
+   */
+  public SparkLauncher addSparkArg(String name, String value) {
+    SparkSubmitOptionParser validator = new ArgumentValidator(true);
+    if (validator.MASTER.equals(name)) {
+      setMaster(value);
+    } else if (validator.PROPERTIES_FILE.equals(name)) {
+      setPropertiesFile(value);
+    } else if (validator.CONF.equals(name)) {
+      String[] vals = value.split("=", 2);
+      setConf(vals[0], vals[1]);
+    } else if (validator.CLASS.equals(name)) {
+      setMainClass(value);
+    } else if (validator.JARS.equals(name)) {
+      builder.jars.clear();
+      for (String jar : value.split(",")) {
+        addJar(jar);
+      }
+    } else if (validator.FILES.equals(name)) {
+      builder.files.clear();
+      for (String file : value.split(",")) {
+        addFile(file);
+      }
+    } else if (validator.PY_FILES.equals(name)) {
+      builder.pyFiles.clear();
+      for (String file : value.split(",")) {
+        addPyFile(file);
+      }
+    } else {
+      validator.parse(Arrays.asList(name, value));
+      builder.sparkArgs.add(name);
+      builder.sparkArgs.add(value);
+    }
+    return this;
+  }
+
   /**
    * Adds command line arguments for the application.
    *
@@ -277,4 +346,32 @@ public Process launch() throws IOException {
     return pb.start();
   }
 
+  private static class ArgumentValidator extends SparkSubmitOptionParser {
+
+    private final boolean hasValue;
+
+    ArgumentValidator(boolean hasValue) {
+      this.hasValue = hasValue;
+    }
+
+    @Override
+    protected boolean handle(String opt, String value) {
+      if (value == null && hasValue) {
+        throw new IllegalArgumentException(String.format("'%s' does not expect a value.", opt));
+      }
+      return true;
+    }
+
+    @Override
+    protected boolean handleUnknown(String opt) {
+      // Do not fail on unknown arguments, to support future arguments added to SparkSubmit.
+      return true;
+    }
+
+    protected void handleExtraArgs(List<String> extra) {
+      // No op.
+    }
+
+  };
+
 }
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
index 87c43aa9980e1..4f354cedee66f 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
@@ -76,7 +76,7 @@ class SparkSubmitCommandBuilder extends AbstractCommandBuilder {
       "spark-internal");
   }
 
-  private final List<String> sparkArgs;
+  final List<String> sparkArgs;
   private final boolean printHelp;
 
   /**
diff --git a/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java b/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
index 252d5abae1ca3..d0c26dd05679b 100644
--- a/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
+++ b/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
@@ -20,6 +20,7 @@
 import java.io.BufferedReader;
 import java.io.InputStream;
 import java.io.InputStreamReader;
+import java.util.Arrays;
 import java.util.HashMap;
 import java.util.Map;
 
@@ -35,8 +36,54 @@ public class SparkLauncherSuite {
 
   private static final Logger LOG = LoggerFactory.getLogger(SparkLauncherSuite.class);
 
+  @Test
+  public void testSparkArgumentHandling() throws Exception {
+    SparkLauncher launcher = new SparkLauncher()
+      .setSparkHome(System.getProperty("spark.test.home"));
+    SparkSubmitOptionParser opts = new SparkSubmitOptionParser();
+
+    launcher.addSparkArg(opts.HELP);
+    try {
+      launcher.addSparkArg(opts.PROXY_USER);
+      fail("Expected IllegalArgumentException.");
+    } catch (IllegalArgumentException e) {
+      // Expected.
+    }
+
+    launcher.addSparkArg(opts.PROXY_USER, "someUser");
+    try {
+      launcher.addSparkArg(opts.HELP, "someValue");
+      fail("Expected IllegalArgumentException.");
+    } catch (IllegalArgumentException e) {
+      // Expected.
+    }
+
+    launcher.addSparkArg("--future-argument");
+    launcher.addSparkArg("--future-argument", "someValue");
+
+    launcher.addSparkArg(opts.MASTER, "myMaster");
+    assertEquals("myMaster", launcher.builder.master);
+
+    launcher.addJar("foo");
+    launcher.addSparkArg(opts.JARS, "bar");
+    assertEquals(Arrays.asList("bar"), launcher.builder.jars);
+
+    launcher.addFile("foo");
+    launcher.addSparkArg(opts.FILES, "bar");
+    assertEquals(Arrays.asList("bar"), launcher.builder.files);
+
+    launcher.addPyFile("foo");
+    launcher.addSparkArg(opts.PY_FILES, "bar");
+    assertEquals(Arrays.asList("bar"), launcher.builder.pyFiles);
+
+    launcher.setConf("spark.foo", "foo");
+    launcher.addSparkArg(opts.CONF, "spark.foo=bar");
+    assertEquals("bar", launcher.builder.conf.get("spark.foo"));
+  }
+
   @Test
   public void testChildProcLauncher() throws Exception {
+    SparkSubmitOptionParser opts = new SparkSubmitOptionParser();
     Map<String, String> env = new HashMap<String, String>();
     env.put("SPARK_PRINT_LAUNCH_COMMAND", "1");
 
@@ -44,9 +91,12 @@ public void testChildProcLauncher() throws Exception {
       .setSparkHome(System.getProperty("spark.test.home"))
       .setMaster("local")
       .setAppResource("spark-internal")
+      .addSparkArg(opts.CONF,
+        String.format("%s=-Dfoo=ShouldBeOverriddenBelow", SparkLauncher.DRIVER_EXTRA_JAVA_OPTIONS))
       .setConf(SparkLauncher.DRIVER_EXTRA_JAVA_OPTIONS,
         "-Dfoo=bar -Dtest.name=-testChildProcLauncher")
       .setConf(SparkLauncher.DRIVER_EXTRA_CLASSPATH, System.getProperty("java.class.path"))
+      .addSparkArg(opts.CLASS, "ShouldBeOverriddenBelow")
       .setMainClass(SparkLauncherTestApp.class.getName())
       .addAppArgs("proc");
     final Process app = launcher.launch();

From afa757c98c537965007cad4c61c436887f3ac6a6 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 11 Aug 2015 18:08:49 -0700
Subject: [PATCH 176/633] [SPARK-9849] [SQL] DirectParquetOutputCommitter
 qualified name should be backward compatible

DirectParquetOutputCommitter was moved in SPARK-9763. However, users can explicitly set the class as a config option, so we must be able to resolve the old committer qualified name.

Author: Reynold Xin <rxin@databricks.com>

Closes #8114 from rxin/SPARK-9849.
---
 .../datasources/parquet/ParquetRelation.scala |  7 +++++
 .../datasources/parquet/ParquetIOSuite.scala  | 27 ++++++++++++++++++-
 2 files changed, 33 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index 4086a139bed72..c71c69b6e80b1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -209,6 +209,13 @@ private[sql] class ParquetRelation(
   override def prepareJobForWrite(job: Job): OutputWriterFactory = {
     val conf = ContextUtil.getConfiguration(job)
 
+    // SPARK-9849 DirectParquetOutputCommitter qualified name should be backward compatible
+    val committerClassname = conf.get(SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key)
+    if (committerClassname == "org.apache.spark.sql.parquet.DirectParquetOutputCommitter") {
+      conf.set(SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key,
+        classOf[DirectParquetOutputCommitter].getCanonicalName)
+    }
+
     val committerClass =
       conf.getClass(
         SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index ee925afe08508..cb166349fdb26 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -390,7 +390,32 @@ class ParquetIOSuite extends QueryTest with ParquetTest {
     }
   }
 
-  test("SPARK-8121: spark.sql.parquet.output.committer.class shouldn't be overriden") {
+  test("SPARK-9849 DirectParquetOutputCommitter qualified name should be backward compatible") {
+    val clonedConf = new Configuration(configuration)
+
+    // Write to a parquet file and let it fail.
+    // _temporary should be missing if direct output committer works.
+    try {
+      configuration.set("spark.sql.parquet.output.committer.class",
+        "org.apache.spark.sql.parquet.DirectParquetOutputCommitter")
+      sqlContext.udf.register("div0", (x: Int) => x / 0)
+      withTempPath { dir =>
+        intercept[org.apache.spark.SparkException] {
+          sqlContext.sql("select div0(1)").write.parquet(dir.getCanonicalPath)
+        }
+        val path = new Path(dir.getCanonicalPath, "_temporary")
+        val fs = path.getFileSystem(configuration)
+        assert(!fs.exists(path))
+      }
+    } finally {
+      // Hadoop 1 doesn't have `Configuration.unset`
+      configuration.clear()
+      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+    }
+  }
+
+
+  test("SPARK-8121: spark.sql.parquet.output.committer.class shouldn't be overridden") {
     withTempPath { dir =>
       val clonedConf = new Configuration(configuration)
 

From ca8f70e9d473d2c81866f3c330cc6545c33bdac7 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Tue, 11 Aug 2015 20:46:58 -0700
Subject: [PATCH 177/633] [SPARK-9649] Fix flaky test MasterSuite again -
 disable REST

The REST server is not actually used in most tests and so we can disable it. It is a source of flakiness because it tries to bind to a specific port in vain. There was also some code that avoided the shuffle service in tests. This is actually not necessary because the shuffle service is already off by default.

Author: Andrew Or <andrew@databricks.com>

Closes #8084 from andrewor14/fix-master-suite-again.
---
 pom.xml                  | 1 +
 project/SparkBuild.scala | 1 +
 2 files changed, 2 insertions(+)

diff --git a/pom.xml b/pom.xml
index 8942836a7da16..cfd7d32563f2a 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1895,6 +1895,7 @@
               <java.io.tmpdir>${project.build.directory}/tmp</java.io.tmpdir>
               <spark.test.home>${spark.test.home}</spark.test.home>
               <spark.testing>1</spark.testing>
+              <spark.master.rest.enabled>false</spark.master.rest.enabled>
               <spark.ui.enabled>false</spark.ui.enabled>
               <spark.ui.showConsoleProgress>false</spark.ui.showConsoleProgress>
               <spark.driver.allowMultipleContexts>true</spark.driver.allowMultipleContexts>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index cad7067ade8c1..74f815f941d5b 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -546,6 +546,7 @@ object TestSettings {
     javaOptions in Test += "-Dspark.test.home=" + sparkHome,
     javaOptions in Test += "-Dspark.testing=1",
     javaOptions in Test += "-Dspark.port.maxRetries=100",
+    javaOptions in Test += "-Dspark.master.rest.enabled=false",
     javaOptions in Test += "-Dspark.ui.enabled=false",
     javaOptions in Test += "-Dspark.ui.showConsoleProgress=false",
     javaOptions in Test += "-Dspark.driver.allowMultipleContexts=true",

From 3ef0f32928fc383ad3edd5ad167212aeb9eba6e1 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Tue, 11 Aug 2015 21:16:48 -0700
Subject: [PATCH 178/633] [SPARK-1517] Refactor release scripts to facilitate
 nightly publishing

This update contains some code changes to the release scripts that allow easier nightly publishing. I've been using these new scripts on Jenkins for cutting and publishing nightly snapshots for the last month or so, and it has been going well. I'd like to get them merged back upstream so this can be maintained by the community.

The main changes are:
1. Separates the release tagging from various build possibilities for an already tagged release (`release-tag.sh` and `release-build.sh`).
2. Allow for injecting credentials through the environment, including GPG keys. This is then paired with secure key injection in Jenkins.
3. Support for copying build results to a remote directory, and also "rotating" results, e.g. the ability to keep the last N copies of binary or doc builds.

I'm happy if anyone wants to take a look at this - it's not user facing but an internal utility used for generating releases.

Author: Patrick Wendell <patrick@databricks.com>

Closes #7411 from pwendell/release-script-updates and squashes the following commits:

74f9beb [Patrick Wendell] Moving maven build command to a variable
233ce85 [Patrick Wendell] [SPARK-1517] Refactor release scripts to facilitate nightly publishing
---
 dev/create-release/create-release.sh | 267 ----------------------
 dev/create-release/release-build.sh  | 321 +++++++++++++++++++++++++++
 dev/create-release/release-tag.sh    |  79 +++++++
 3 files changed, 400 insertions(+), 267 deletions(-)
 delete mode 100755 dev/create-release/create-release.sh
 create mode 100755 dev/create-release/release-build.sh
 create mode 100755 dev/create-release/release-tag.sh

diff --git a/dev/create-release/create-release.sh b/dev/create-release/create-release.sh
deleted file mode 100755
index 4311c8c9e4ca6..0000000000000
--- a/dev/create-release/create-release.sh
+++ /dev/null
@@ -1,267 +0,0 @@
-#!/usr/bin/env bash
-
-#
-# Licensed to the Apache Software Foundation (ASF) under one or more
-# contributor license agreements.  See the NOTICE file distributed with
-# this work for additional information regarding copyright ownership.
-# The ASF licenses this file to You under the Apache License, Version 2.0
-# (the "License"); you may not use this file except in compliance with
-# the License.  You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-
-# Quick-and-dirty automation of making maven and binary releases. Not robust at all.
-# Publishes releases to Maven and packages/copies binary release artifacts.
-# Expects to be run in a totally empty directory.
-#
-# Options:
-#  --skip-create-release	Assume the desired release tag already exists
-#  --skip-publish 		Do not publish to Maven central
-#  --skip-package		Do not package and upload binary artifacts
-# Would be nice to add:
-#  - Send output to stderr and have useful logging in stdout
-
-# Note: The following variables must be set before use!
-ASF_USERNAME=${ASF_USERNAME:-pwendell}
-ASF_PASSWORD=${ASF_PASSWORD:-XXX}
-GPG_PASSPHRASE=${GPG_PASSPHRASE:-XXX}
-GIT_BRANCH=${GIT_BRANCH:-branch-1.0}
-RELEASE_VERSION=${RELEASE_VERSION:-1.2.0}
-# Allows publishing under a different version identifier than
-# was present in the actual release sources (e.g. rc-X)
-PUBLISH_VERSION=${PUBLISH_VERSION:-$RELEASE_VERSION} 
-NEXT_VERSION=${NEXT_VERSION:-1.2.1}
-RC_NAME=${RC_NAME:-rc2}
-
-M2_REPO=~/.m2/repository
-SPARK_REPO=$M2_REPO/org/apache/spark
-NEXUS_ROOT=https://repository.apache.org/service/local/staging
-NEXUS_PROFILE=d63f592e7eac0 # Profile for Spark staging uploads
-
-if [ -z "$JAVA_HOME" ]; then
-  echo "Error: JAVA_HOME is not set, cannot proceed."
-  exit -1
-fi
-JAVA_7_HOME=${JAVA_7_HOME:-$JAVA_HOME}
-
-set -e
-
-GIT_TAG=v$RELEASE_VERSION-$RC_NAME
-
-if [[ ! "$@" =~ --skip-create-release ]]; then
-  echo "Creating release commit and publishing to Apache repository"
-  # Artifact publishing
-  git clone https://$ASF_USERNAME:$ASF_PASSWORD@git-wip-us.apache.org/repos/asf/spark.git \
-    -b $GIT_BRANCH
-  pushd spark
-  export MAVEN_OPTS="-Xmx3g -XX:MaxPermSize=1g -XX:ReservedCodeCacheSize=1g"
-
-  # Create release commits and push them to github
-  # NOTE: This is done "eagerly" i.e. we don't check if we can succesfully build
-  # or before we coin the release commit. This helps avoid races where
-  # other people add commits to this branch while we are in the middle of building.
-  cur_ver="${RELEASE_VERSION}-SNAPSHOT"
-  rel_ver="${RELEASE_VERSION}"
-  next_ver="${NEXT_VERSION}-SNAPSHOT"
-
-  old="^\( \{2,4\}\)<version>${cur_ver}<\/version>$"
-  new="\1<version>${rel_ver}<\/version>"
-  find . -name pom.xml | grep -v dev | xargs -I {} sed -i \
-    -e "s/${old}/${new}/" {}
-  find . -name package.scala | grep -v dev | xargs -I {} sed -i \
-    -e "s/${old}/${new}/" {}
-
-  git commit -a -m "Preparing Spark release $GIT_TAG"
-  echo "Creating tag $GIT_TAG at the head of $GIT_BRANCH"
-  git tag $GIT_TAG
-
-  old="^\( \{2,4\}\)<version>${rel_ver}<\/version>$"
-  new="\1<version>${next_ver}<\/version>"
-  find . -name pom.xml | grep -v dev | xargs -I {} sed -i \
-    -e "s/$old/$new/" {}
-  find . -name package.scala | grep -v dev | xargs -I {} sed -i \
-    -e "s/${old}/${new}/" {}
-  git commit -a -m "Preparing development version $next_ver"
-  git push origin $GIT_TAG
-  git push origin HEAD:$GIT_BRANCH
-  popd
-  rm -rf spark
-fi
-
-if [[ ! "$@" =~ --skip-publish ]]; then
-  git clone https://$ASF_USERNAME:$ASF_PASSWORD@git-wip-us.apache.org/repos/asf/spark.git
-  pushd spark
-  git checkout --force $GIT_TAG 
-  
-  # Substitute in case published version is different than released
-  old="^\( \{2,4\}\)<version>${RELEASE_VERSION}<\/version>$"
-  new="\1<version>${PUBLISH_VERSION}<\/version>"
-  find . -name pom.xml | grep -v dev | xargs -I {} sed -i \
-    -e "s/${old}/${new}/" {}
-
-  # Using Nexus API documented here:
-  # https://support.sonatype.com/entries/39720203-Uploading-to-a-Staging-Repository-via-REST-API
-  echo "Creating Nexus staging repository"
-  repo_request="<promoteRequest><data><description>Apache Spark $GIT_TAG (published as $PUBLISH_VERSION)</description></data></promoteRequest>"
-  out=$(curl -X POST -d "$repo_request" -u $ASF_USERNAME:$ASF_PASSWORD \
-    -H "Content-Type:application/xml" -v \
-    $NEXUS_ROOT/profiles/$NEXUS_PROFILE/start)
-  staged_repo_id=$(echo $out | sed -e "s/.*\(orgapachespark-[0-9]\{4\}\).*/\1/")
-  echo "Created Nexus staging repository: $staged_repo_id"
-
-  rm -rf $SPARK_REPO
-
-  build/mvn -DskipTests -Pyarn -Phive \
-    -Phive-thriftserver -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
-    clean install
-
-  ./dev/change-scala-version.sh 2.11
-  
-  build/mvn -DskipTests -Pyarn -Phive \
-    -Dscala-2.11 -Phadoop-2.2 -Pspark-ganglia-lgpl -Pkinesis-asl \
-    clean install
-
-  ./dev/change-scala-version.sh 2.10
-
-  pushd $SPARK_REPO
-
-  # Remove any extra files generated during install
-  find . -type f |grep -v \.jar |grep -v \.pom | xargs rm
-
-  echo "Creating hash and signature files"
-  for file in $(find . -type f)
-  do
-    echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --output $file.asc --detach-sig --armour $file;
-    if [ $(command -v md5) ]; then
-      # Available on OS X; -q to keep only hash
-      md5 -q $file > $file.md5
-    else
-      # Available on Linux; cut to keep only hash
-      md5sum $file | cut -f1 -d' ' > $file.md5
-    fi
-    shasum -a 1 $file | cut -f1 -d' ' > $file.sha1
-  done
-
-  nexus_upload=$NEXUS_ROOT/deployByRepositoryId/$staged_repo_id
-  echo "Uplading files to $nexus_upload"
-  for file in $(find . -type f)
-  do
-    # strip leading ./
-    file_short=$(echo $file | sed -e "s/\.\///")
-    dest_url="$nexus_upload/org/apache/spark/$file_short"
-    echo "  Uploading $file_short"
-    curl -u $ASF_USERNAME:$ASF_PASSWORD --upload-file $file_short $dest_url
-  done
-
-  echo "Closing nexus staging repository"
-  repo_request="<promoteRequest><data><stagedRepositoryId>$staged_repo_id</stagedRepositoryId><description>Apache Spark $GIT_TAG (published as $PUBLISH_VERSION)</description></data></promoteRequest>"
-  out=$(curl -X POST -d "$repo_request" -u $ASF_USERNAME:$ASF_PASSWORD \
-    -H "Content-Type:application/xml" -v \
-    $NEXUS_ROOT/profiles/$NEXUS_PROFILE/finish)
-  echo "Closed Nexus staging repository: $staged_repo_id"
-
-  popd
-  popd
-  rm -rf spark
-fi
-
-if [[ ! "$@" =~ --skip-package ]]; then
-  # Source and binary tarballs
-  echo "Packaging release tarballs"
-  git clone https://git-wip-us.apache.org/repos/asf/spark.git
-  cd spark
-  git checkout --force $GIT_TAG
-  release_hash=`git rev-parse HEAD`
-
-  rm .gitignore
-  rm -rf .git
-  cd ..
-
-  cp -r spark spark-$RELEASE_VERSION
-  tar cvzf spark-$RELEASE_VERSION.tgz spark-$RELEASE_VERSION
-  echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --armour --output spark-$RELEASE_VERSION.tgz.asc \
-    --detach-sig spark-$RELEASE_VERSION.tgz
-  echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --print-md MD5 spark-$RELEASE_VERSION.tgz > \
-    spark-$RELEASE_VERSION.tgz.md5
-  echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --print-md SHA512 spark-$RELEASE_VERSION.tgz > \
-    spark-$RELEASE_VERSION.tgz.sha
-  rm -rf spark-$RELEASE_VERSION
-  
-  # Updated for each binary build
-  make_binary_release() {
-    NAME=$1
-    FLAGS=$2
-    ZINC_PORT=$3
-    cp -r spark spark-$RELEASE_VERSION-bin-$NAME
-    
-    cd spark-$RELEASE_VERSION-bin-$NAME
-
-    # TODO There should probably be a flag to make-distribution to allow 2.11 support
-    if [[ $FLAGS == *scala-2.11* ]]; then
-      ./dev/change-scala-version.sh 2.11
-    fi
-
-    export ZINC_PORT=$ZINC_PORT
-    echo "Creating distribution: $NAME ($FLAGS)"
-    ./make-distribution.sh --name $NAME --tgz $FLAGS -DzincPort=$ZINC_PORT 2>&1 > \
-      ../binary-release-$NAME.log
-    cd ..
-    cp spark-$RELEASE_VERSION-bin-$NAME/spark-$RELEASE_VERSION-bin-$NAME.tgz .
-
-    echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --armour \
-      --output spark-$RELEASE_VERSION-bin-$NAME.tgz.asc \
-      --detach-sig spark-$RELEASE_VERSION-bin-$NAME.tgz
-    echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --print-md \
-      MD5 spark-$RELEASE_VERSION-bin-$NAME.tgz > \
-      spark-$RELEASE_VERSION-bin-$NAME.tgz.md5
-    echo $GPG_PASSPHRASE | gpg --passphrase-fd 0 --print-md \
-      SHA512 spark-$RELEASE_VERSION-bin-$NAME.tgz > \
-      spark-$RELEASE_VERSION-bin-$NAME.tgz.sha
-  }
-
-  # We increment the Zinc port each time to avoid OOM's and other craziness if multiple builds
-  # share the same Zinc server.
-  make_binary_release "hadoop1" "-Psparkr -Phadoop-1 -Phive -Phive-thriftserver" "3030" &
-  make_binary_release "hadoop1-scala2.11" "-Psparkr -Phadoop-1 -Phive -Dscala-2.11" "3031" &
-  make_binary_release "cdh4" "-Psparkr -Phadoop-1 -Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0" "3032" &
-  make_binary_release "hadoop2.3" "-Psparkr -Phadoop-2.3 -Phive -Phive-thriftserver -Pyarn" "3033" &
-  make_binary_release "hadoop2.4" "-Psparkr -Phadoop-2.4 -Phive -Phive-thriftserver -Pyarn" "3034" &
-  make_binary_release "mapr3" "-Pmapr3 -Psparkr -Phive -Phive-thriftserver" "3035" &
-  make_binary_release "mapr4" "-Pmapr4 -Psparkr -Pyarn -Phive -Phive-thriftserver" "3036" &
-  make_binary_release "hadoop2.4-without-hive" "-Psparkr -Phadoop-2.4 -Pyarn" "3037" &
-  wait
-  rm -rf spark-$RELEASE_VERSION-bin-*/
-
-  # Copy data
-  echo "Copying release tarballs"
-  rc_folder=spark-$RELEASE_VERSION-$RC_NAME
-  ssh $ASF_USERNAME@people.apache.org \
-    mkdir /home/$ASF_USERNAME/public_html/$rc_folder
-  scp spark-* \
-    $ASF_USERNAME@people.apache.org:/home/$ASF_USERNAME/public_html/$rc_folder/
-
-  # Docs
-  cd spark
-  sbt/sbt clean
-  cd docs
-  # Compile docs with Java 7 to use nicer format
-  JAVA_HOME="$JAVA_7_HOME" PRODUCTION=1 RELEASE_VERSION="$RELEASE_VERSION" jekyll build
-  echo "Copying release documentation"
-  rc_docs_folder=${rc_folder}-docs
-  ssh $ASF_USERNAME@people.apache.org \
-    mkdir /home/$ASF_USERNAME/public_html/$rc_docs_folder
-  rsync -r _site/* $ASF_USERNAME@people.apache.org:/home/$ASF_USERNAME/public_html/$rc_docs_folder
-
-  echo "Release $RELEASE_VERSION completed:"
-  echo "Git tag:\t $GIT_TAG"
-  echo "Release commit:\t $release_hash"
-  echo "Binary location:\t http://people.apache.org/~$ASF_USERNAME/$rc_folder"
-  echo "Doc location:\t http://people.apache.org/~$ASF_USERNAME/$rc_docs_folder"
-fi
diff --git a/dev/create-release/release-build.sh b/dev/create-release/release-build.sh
new file mode 100755
index 0000000000000..399c73e7bf6bc
--- /dev/null
+++ b/dev/create-release/release-build.sh
@@ -0,0 +1,321 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+function exit_with_usage {
+  cat << EOF
+usage: release-build.sh <package|docs|publish-snapshot|publish-release>
+Creates build deliverables from a Spark commit.
+
+Top level targets are
+  package: Create binary packages and copy them to people.apache
+  docs: Build docs and copy them to people.apache
+  publish-snapshot: Publish snapshot release to Apache snapshots
+  publish-release: Publish a release to Apache release repo
+
+All other inputs are environment variables
+
+GIT_REF - Release tag or commit to build from
+SPARK_VERSION - Release identifier used when publishing
+SPARK_PACKAGE_VERSION - Release identifier in top level package directory
+REMOTE_PARENT_DIR - Parent in which to create doc or release builds.
+REMOTE_PARENT_MAX_LENGTH - If set, parent directory will be cleaned to only
+ have this number of subdirectories (by deleting old ones). WARNING: This deletes data.
+
+ASF_USERNAME - Username of ASF committer account
+ASF_PASSWORD - Password of ASF committer account
+ASF_RSA_KEY - RSA private key file for ASF committer account
+
+GPG_KEY - GPG key used to sign release artifacts
+GPG_PASSPHRASE - Passphrase for GPG key
+EOF
+  exit 1
+}
+
+set -e
+
+if [ $# -eq 0 ]; then
+  exit_with_usage
+fi
+
+if [[ $@ == *"help"* ]]; then
+  exit_with_usage
+fi
+
+for env in ASF_USERNAME ASF_RSA_KEY GPG_PASSPHRASE GPG_KEY; do
+  if [ -z "${!env}" ]; then
+    echo "ERROR: $env must be set to run this script"
+    exit_with_usage
+  fi
+done
+
+# Commit ref to checkout when building
+GIT_REF=${GIT_REF:-master}
+
+# Destination directory parent on remote server
+REMOTE_PARENT_DIR=${REMOTE_PARENT_DIR:-/home/$ASF_USERNAME/public_html}
+
+SSH="ssh -o StrictHostKeyChecking=no -i $ASF_RSA_KEY"
+GPG="gpg --no-tty --batch"
+NEXUS_ROOT=https://repository.apache.org/service/local/staging
+NEXUS_PROFILE=d63f592e7eac0 # Profile for Spark staging uploads
+BASE_DIR=$(pwd)
+
+MVN="build/mvn --force"
+PUBLISH_PROFILES="-Pyarn -Phive -Phadoop-2.2"
+PUBLISH_PROFILES="$PUBLISH_PROFILES -Pspark-ganglia-lgpl -Pkinesis-asl"
+
+rm -rf spark
+git clone https://git-wip-us.apache.org/repos/asf/spark.git
+cd spark
+git checkout $GIT_REF
+git_hash=`git rev-parse --short HEAD`
+echo "Checked out Spark git hash $git_hash"
+
+if [ -z "$SPARK_VERSION" ]; then
+  SPARK_VERSION=$($MVN help:evaluate -Dexpression=project.version \
+    | grep -v INFO | grep -v WARNING | grep -v Download)
+fi
+
+if [ -z "$SPARK_PACKAGE_VERSION" ]; then
+  SPARK_PACKAGE_VERSION="${SPARK_VERSION}-$(date +%Y_%m_%d_%H_%M)-${git_hash}"
+fi
+
+DEST_DIR_NAME="spark-$SPARK_PACKAGE_VERSION"
+USER_HOST="$ASF_USERNAME@people.apache.org"
+
+rm .gitignore
+rm -rf .git
+cd ..
+
+if [ -n "$REMOTE_PARENT_MAX_LENGTH" ]; then
+  old_dirs=$($SSH $USER_HOST ls -t $REMOTE_PARENT_DIR | tail -n +$REMOTE_PARENT_MAX_LENGTH)
+  for old_dir in $old_dirs; do
+    echo "Removing directory: $old_dir"
+    $SSH $USER_HOST rm -r $REMOTE_PARENT_DIR/$old_dir
+  done
+fi
+
+if [[ "$1" == "package" ]]; then
+  # Source and binary tarballs
+  echo "Packaging release tarballs"
+  cp -r spark spark-$SPARK_VERSION
+  tar cvzf spark-$SPARK_VERSION.tgz spark-$SPARK_VERSION
+  echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --armour --output spark-$SPARK_VERSION.tgz.asc \
+    --detach-sig spark-$SPARK_VERSION.tgz
+  echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md MD5 spark-$SPARK_VERSION.tgz > \
+    spark-$SPARK_VERSION.tgz.md5
+  echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \
+    SHA512 spark-$SPARK_VERSION.tgz > spark-$SPARK_VERSION.tgz.sha
+  rm -rf spark-$SPARK_VERSION
+
+  # Updated for each binary build
+  make_binary_release() {
+    NAME=$1
+    FLAGS=$2
+    ZINC_PORT=$3
+    cp -r spark spark-$SPARK_VERSION-bin-$NAME
+
+    cd spark-$SPARK_VERSION-bin-$NAME
+
+    # TODO There should probably be a flag to make-distribution to allow 2.11 support
+    if [[ $FLAGS == *scala-2.11* ]]; then
+      ./dev/change-scala-version.sh 2.11
+    fi
+
+    export ZINC_PORT=$ZINC_PORT
+    echo "Creating distribution: $NAME ($FLAGS)"
+    ./make-distribution.sh --name $NAME --tgz $FLAGS -DzincPort=$ZINC_PORT 2>&1 > \
+      ../binary-release-$NAME.log
+    cd ..
+    cp spark-$SPARK_VERSION-bin-$NAME/spark-$SPARK_VERSION-bin-$NAME.tgz .
+
+    echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --armour \
+      --output spark-$SPARK_VERSION-bin-$NAME.tgz.asc \
+      --detach-sig spark-$SPARK_VERSION-bin-$NAME.tgz
+    echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \
+      MD5 spark-$SPARK_VERSION-bin-$NAME.tgz > \
+      spark-$SPARK_VERSION-bin-$NAME.tgz.md5
+    echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --print-md \
+      SHA512 spark-$SPARK_VERSION-bin-$NAME.tgz > \
+      spark-$SPARK_VERSION-bin-$NAME.tgz.sha
+  }
+
+  # TODO: Check exit codes of children here:
+  # http://stackoverflow.com/questions/1570262/shell-get-exit-code-of-background-process
+
+  # We increment the Zinc port each time to avoid OOM's and other craziness if multiple builds
+  # share the same Zinc server.
+  make_binary_release "hadoop1" "-Psparkr -Phadoop-1 -Phive -Phive-thriftserver" "3030" &
+  make_binary_release "hadoop1-scala2.11" "-Psparkr -Phadoop-1 -Phive -Dscala-2.11" "3031" &
+  make_binary_release "cdh4" "-Psparkr -Phadoop-1 -Phive -Phive-thriftserver -Dhadoop.version=2.0.0-mr1-cdh4.2.0" "3032" &
+  make_binary_release "hadoop2.3" "-Psparkr -Phadoop-2.3 -Phive -Phive-thriftserver -Pyarn" "3033" &
+  make_binary_release "hadoop2.4" "-Psparkr -Phadoop-2.4 -Phive -Phive-thriftserver -Pyarn" "3034" &
+  make_binary_release "hadoop2.6" "-Psparkr -Phadoop-2.6 -Phive -Phive-thriftserver -Pyarn" "3034" &
+  make_binary_release "hadoop2.4-without-hive" "-Psparkr -Phadoop-2.4 -Pyarn" "3037" &
+  make_binary_release "without-hadoop" "-Psparkr -Phadoop-provided -Pyarn" "3038" &
+  wait
+  rm -rf spark-$SPARK_VERSION-bin-*/
+
+  # Copy data
+  dest_dir="$REMOTE_PARENT_DIR/${DEST_DIR_NAME}-bin"
+  echo "Copying release tarballs to $dest_dir"
+  $SSH $USER_HOST mkdir $dest_dir
+  rsync -e "$SSH" spark-* $USER_HOST:$dest_dir
+  echo "Linking /latest to $dest_dir"
+  $SSH $USER_HOST rm -f "$REMOTE_PARENT_DIR/latest"
+  $SSH $USER_HOST ln -s $dest_dir "$REMOTE_PARENT_DIR/latest"
+  exit 0
+fi
+
+if [[ "$1" == "docs" ]]; then
+  # Documentation
+  cd spark
+  echo "Building Spark docs"
+  dest_dir="$REMOTE_PARENT_DIR/${DEST_DIR_NAME}-docs"
+  cd docs
+  # Compile docs with Java 7 to use nicer format
+  # TODO: Make configurable to add this: PRODUCTION=1
+  PRODUCTION=1 RELEASE_VERSION="$SPARK_VERSION" jekyll build
+  echo "Copying release documentation to $dest_dir"
+  $SSH $USER_HOST mkdir $dest_dir
+  echo "Linking /latest to $dest_dir"
+  $SSH $USER_HOST rm -f "$REMOTE_PARENT_DIR/latest"
+  $SSH $USER_HOST ln -s $dest_dir "$REMOTE_PARENT_DIR/latest"
+  rsync -e "$SSH" -r _site/* $USER_HOST:$dest_dir
+  cd ..
+  exit 0
+fi
+
+if [[ "$1" == "publish-snapshot" ]]; then
+  cd spark
+  # Publish Spark to Maven release repo
+  echo "Deploying Spark SNAPSHOT at '$GIT_REF' ($git_hash)"
+  echo "Publish version is $SPARK_VERSION"
+  if [[ ! $SPARK_VERSION == *"SNAPSHOT"* ]]; then
+    echo "ERROR: Snapshots must have a version containing SNAPSHOT"
+    echo "ERROR: You gave version '$SPARK_VERSION'"
+    exit 1
+  fi
+  # Coerce the requested version
+  $MVN versions:set -DnewVersion=$SPARK_VERSION
+  tmp_settings="tmp-settings.xml"
+  echo "<settings><servers><server>" > $tmp_settings
+  echo "<id>apache.snapshots.https</id><username>$ASF_USERNAME</username>" >> $tmp_settings
+  echo "<password>$ASF_PASSWORD</password>" >> $tmp_settings
+  echo "</server></servers></settings>" >> $tmp_settings
+
+  # Generate random point for Zinc
+  export ZINC_PORT=$(python -S -c "import random; print random.randrange(3030,4030)")
+
+  $MVN -DzincPort=$ZINC_PORT --settings $tmp_settings -DskipTests $PUBLISH_PROFILES \
+    -Phive-thriftserver deploy
+  ./dev/change-scala-version.sh 2.10
+  $MVN -DzincPort=$ZINC_PORT -Dscala-2.11 --settings $tmp_settings \
+    -DskipTests $PUBLISH_PROFILES deploy
+
+  # Clean-up Zinc nailgun process
+  /usr/sbin/lsof -P |grep $ZINC_PORT | grep LISTEN | awk '{ print $2; }' | xargs kill
+
+  rm $tmp_settings
+  cd ..
+  exit 0
+fi
+
+if [[ "$1" == "publish-release" ]]; then
+  cd spark
+  # Publish Spark to Maven release repo
+  echo "Publishing Spark checkout at '$GIT_REF' ($git_hash)"
+  echo "Publish version is $SPARK_VERSION"
+  # Coerce the requested version
+  $MVN versions:set -DnewVersion=$SPARK_VERSION
+
+  # Using Nexus API documented here:
+  # https://support.sonatype.com/entries/39720203-Uploading-to-a-Staging-Repository-via-REST-API
+  echo "Creating Nexus staging repository"
+  repo_request="<promoteRequest><data><description>Apache Spark $SPARK_VERSION (commit $git_hash)</description></data></promoteRequest>"
+  out=$(curl -X POST -d "$repo_request" -u $ASF_USERNAME:$ASF_PASSWORD \
+    -H "Content-Type:application/xml" -v \
+    $NEXUS_ROOT/profiles/$NEXUS_PROFILE/start)
+  staged_repo_id=$(echo $out | sed -e "s/.*\(orgapachespark-[0-9]\{4\}\).*/\1/")
+  echo "Created Nexus staging repository: $staged_repo_id"
+
+  tmp_repo=$(mktemp -d spark-repo-XXXXX)
+
+  # Generate random point for Zinc
+  export ZINC_PORT=$(python -S -c "import random; print random.randrange(3030,4030)")
+
+  $MVN -DzincPort=$ZINC_PORT -Dmaven.repo.local=$tmp_repo -DskipTests $PUBLISH_PROFILES \
+    -Phive-thriftserver clean install
+
+  ./dev/change-scala-version.sh 2.11
+
+  $MVN -DzincPort=$ZINC_PORT -Dmaven.repo.local=$tmp_repo -Dscala-2.11 \
+    -DskipTests $PUBLISH_PROFILES clean install
+
+  # Clean-up Zinc nailgun process
+  /usr/sbin/lsof -P |grep $ZINC_PORT | grep LISTEN | awk '{ print $2; }' | xargs kill
+
+  ./dev/change-version-to-2.10.sh
+
+  pushd $tmp_repo/org/apache/spark
+
+  # Remove any extra files generated during install
+  find . -type f |grep -v \.jar |grep -v \.pom | xargs rm
+
+  echo "Creating hash and signature files"
+  for file in $(find . -type f)
+  do
+    echo $GPG_PASSPHRASE | $GPG --passphrase-fd 0 --output $file.asc \
+      --detach-sig --armour $file;
+    if [ $(command -v md5) ]; then
+      # Available on OS X; -q to keep only hash
+      md5 -q $file > $file.md5
+    else
+      # Available on Linux; cut to keep only hash
+      md5sum $file | cut -f1 -d' ' > $file.md5
+    fi
+    sha1sum $file | cut -f1 -d' ' > $file.sha1
+  done
+
+  nexus_upload=$NEXUS_ROOT/deployByRepositoryId/$staged_repo_id
+  echo "Uplading files to $nexus_upload"
+  for file in $(find . -type f)
+  do
+    # strip leading ./
+    file_short=$(echo $file | sed -e "s/\.\///")
+    dest_url="$nexus_upload/org/apache/spark/$file_short"
+    echo "  Uploading $file_short"
+    curl -u $ASF_USERNAME:$ASF_PASSWORD --upload-file $file_short $dest_url
+  done
+
+  echo "Closing nexus staging repository"
+  repo_request="<promoteRequest><data><stagedRepositoryId>$staged_repo_id</stagedRepositoryId><description>Apache Spark $SPARK_VERSION (commit $git_hash)</description></data></promoteRequest>"
+  out=$(curl -X POST -d "$repo_request" -u $ASF_USERNAME:$ASF_PASSWORD \
+    -H "Content-Type:application/xml" -v \
+    $NEXUS_ROOT/profiles/$NEXUS_PROFILE/finish)
+  echo "Closed Nexus staging repository: $staged_repo_id"
+  popd
+  rm -rf $tmp_repo
+  cd ..
+  exit 0
+fi
+
+cd ..
+rm -rf spark
+echo "ERROR: expects to be called with 'package', 'docs', 'publish-release' or 'publish-snapshot'"
diff --git a/dev/create-release/release-tag.sh b/dev/create-release/release-tag.sh
new file mode 100755
index 0000000000000..b0a3374becc6a
--- /dev/null
+++ b/dev/create-release/release-tag.sh
@@ -0,0 +1,79 @@
+#!/usr/bin/env bash
+
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+function exit_with_usage {
+  cat << EOF
+usage: tag-release.sh
+Tags a Spark release on a particular branch.
+
+Inputs are specified with the following environment variables:
+ASF_USERNAME - Apache Username
+ASF_PASSWORD - Apache Password
+GIT_NAME - Name to use with git
+GIT_EMAIL - E-mail address to use with git
+GIT_BRANCH - Git branch on which to make release
+RELEASE_VERSION - Version used in pom files for release
+RELEASE_TAG - Name of release tag
+NEXT_VERSION - Development version after release
+EOF
+  exit 1
+}
+
+set -e
+
+if [[ $@ == *"help"* ]]; then
+  exit_with_usage
+fi
+
+for env in ASF_USERNAME ASF_PASSWORD RELEASE_VERSION RELEASE_TAG NEXT_VERSION GIT_EMAIL GIT_NAME GIT_BRANCH; do
+  if [ -z "${!env}" ]; then
+    echo "$env must be set to run this script"
+    exit 1
+  fi
+done
+
+ASF_SPARK_REPO="git-wip-us.apache.org/repos/asf/spark.git"
+MVN="build/mvn --force"
+
+rm -rf spark
+git clone https://$ASF_USERNAME:$ASF_PASSWORD@$ASF_SPARK_REPO -b $GIT_BRANCH
+cd spark
+
+git config user.name "$GIT_NAME"
+git config user.email $GIT_EMAIL
+
+# Create release version
+$MVN versions:set -DnewVersion=$RELEASE_VERSION | grep -v "no value" # silence logs
+git commit -a -m "Preparing Spark release $RELEASE_TAG"
+echo "Creating tag $RELEASE_TAG at the head of $GIT_BRANCH"
+git tag $RELEASE_TAG
+
+# TODO: It would be nice to do some verifications here
+#       i.e. check whether ec2 scripts have the new version
+
+# Create next version
+$MVN versions:set -DnewVersion=$NEXT_VERSION | grep -v "no value" # silence logs
+git commit -a -m "Preparing development version $NEXT_VERSION"
+
+# Push changes
+git push origin $RELEASE_TAG
+git push origin HEAD:$GIT_BRANCH
+
+cd ..
+rm -rf spark

From 74a293f4537c6982345166f8883538f81d850872 Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Tue, 11 Aug 2015 21:26:03 -0700
Subject: [PATCH 179/633] [SPARK-9713] [ML] Document SparkR MLlib glm()
 integration in Spark 1.5

This documents the use of R model formulae in the SparkR guide. Also fixes some bugs in the R api doc.

mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #8085 from ericl/docs.
---
 R/pkg/R/generics.R |  4 ++--
 R/pkg/R/mllib.R    |  8 ++++----
 docs/sparkr.md     | 37 ++++++++++++++++++++++++++++++++++++-
 3 files changed, 42 insertions(+), 7 deletions(-)

diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index c43b947129e87..379a78b1d833e 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -535,8 +535,8 @@ setGeneric("showDF", function(x,...) { standardGeneric("showDF") })
 #' @export
 setGeneric("summarize", function(x,...) { standardGeneric("summarize") })
 
-##' rdname summary
-##' @export
+#' @rdname summary
+#' @export
 setGeneric("summary", function(x, ...) { standardGeneric("summary") })
 
 # @rdname tojson
diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R
index b524d1fd87496..cea3d760d05fe 100644
--- a/R/pkg/R/mllib.R
+++ b/R/pkg/R/mllib.R
@@ -56,10 +56,10 @@ setMethod("glm", signature(formula = "formula", family = "ANY", data = "DataFram
 #'
 #' Makes predictions from a model produced by glm(), similarly to R's predict().
 #'
-#' @param model A fitted MLlib model
+#' @param object A fitted MLlib model
 #' @param newData DataFrame for testing
 #' @return DataFrame containing predicted values
-#' @rdname glm
+#' @rdname predict
 #' @export
 #' @examples
 #'\dontrun{
@@ -76,10 +76,10 @@ setMethod("predict", signature(object = "PipelineModel"),
 #'
 #' Returns the summary of a model produced by glm(), similarly to R's summary().
 #'
-#' @param model A fitted MLlib model
+#' @param x A fitted MLlib model
 #' @return a list with a 'coefficient' component, which is the matrix of coefficients. See
 #'         summary.glm for more information.
-#' @rdname glm
+#' @rdname summary
 #' @export
 #' @examples
 #'\dontrun{
diff --git a/docs/sparkr.md b/docs/sparkr.md
index 4385a4eeacd5c..7139d16b4a068 100644
--- a/docs/sparkr.md
+++ b/docs/sparkr.md
@@ -11,7 +11,8 @@ title: SparkR (R on Spark)
 SparkR is an R package that provides a light-weight frontend to use Apache Spark from R.
 In Spark {{site.SPARK_VERSION}}, SparkR provides a distributed data frame implementation that
 supports operations like selection, filtering, aggregation etc. (similar to R data frames,
-[dplyr](https://github.com/hadley/dplyr)) but on large datasets.
+[dplyr](https://github.com/hadley/dplyr)) but on large datasets. SparkR also supports distributed
+machine learning using MLlib.
 
 # SparkR DataFrames
 
@@ -230,3 +231,37 @@ head(teenagers)
 
 {% endhighlight %}
 </div>
+
+# Machine Learning
+
+SparkR allows the fitting of generalized linear models over DataFrames using the [glm()](api/R/glm.html) function. Under the hood, SparkR uses MLlib to train a model of the specified family. Currently the gaussian and binomial families are supported. We support a subset of the available R formula operators for model fitting, including '~', '.', '+', and '-'. The example below shows the use of building a gaussian GLM model using SparkR.
+
+<div data-lang="r"  markdown="1">
+{% highlight r %}
+# Create the DataFrame
+df <- createDataFrame(sqlContext, iris)
+
+# Fit a linear model over the dataset.
+model <- glm(Sepal_Length ~ Sepal_Width + Species, data = df, family = "gaussian")
+
+# Model coefficients are returned in a similar format to R's native glm().
+summary(model)
+##$coefficients
+##                    Estimate
+##(Intercept)        2.2513930
+##Sepal_Width        0.8035609
+##Species_versicolor 1.4587432
+##Species_virginica  1.9468169
+
+# Make predictions based on the model.
+predictions <- predict(model, newData = df)
+head(select(predictions, "Sepal_Length", "prediction"))
+##  Sepal_Length prediction
+##1          5.1   5.063856
+##2          4.9   4.662076
+##3          4.7   4.822788
+##4          4.6   4.742432
+##5          5.0   5.144212
+##6          5.4   5.385281
+{% endhighlight %}
+</div>

From c3e9a120e33159fb45cd99f3a55fc5cf16cd7c6c Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 11 Aug 2015 22:45:18 -0700
Subject: [PATCH 180/633] [SPARK-9831] [SQL] fix serialization with empty
 broadcast

Author: Davies Liu <davies@databricks.com>

Closes #8117 from davies/fix_serialization and squashes the following commits:

d21ac71 [Davies Liu] fix serialization with empty broadcast
---
 .../sql/execution/joins/HashedRelation.scala    |  2 +-
 .../execution/joins/HashedRelationSuite.scala   | 17 +++++++++++++++++
 2 files changed, 18 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index c1bc7947aa39c..076afe6e4e960 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -299,7 +299,7 @@ private[joins] final class UnsafeHashedRelation(
     binaryMap = new BytesToBytesMap(
       taskMemoryManager,
       shuffleMemoryManager,
-      nKeys * 2, // reduce hash collision
+      (nKeys * 1.5 + 1).toInt, // reduce hash collision
       pageSizeBytes)
 
     var i = 0
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index a1fa2c3864bdb..c635b2d51f464 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -103,4 +103,21 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(hashed2.get(unsafeData(2)) === data2)
     assert(numDataRows.value.value === data.length)
   }
+
+  test("test serialization empty hash map") {
+    val os = new ByteArrayOutputStream()
+    val out = new ObjectOutputStream(os)
+    val hashed = new UnsafeHashedRelation(
+      new java.util.HashMap[UnsafeRow, CompactBuffer[UnsafeRow]])
+    hashed.writeExternal(out)
+    out.flush()
+    val in = new ObjectInputStream(new ByteArrayInputStream(os.toByteArray))
+    val hashed2 = new UnsafeHashedRelation()
+    hashed2.readExternal(in)
+
+    val schema = StructType(StructField("a", IntegerType, true) :: Nil)
+    val toUnsafe = UnsafeProjection.create(schema)
+    val row = toUnsafe(InternalRow(0))
+    assert(hashed2.get(row) === null)
+  }
 }

From b1581ac28840a4d2209ef8bb5c9f8700b4c1b286 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 11 Aug 2015 22:46:59 -0700
Subject: [PATCH 181/633] [SPARK-9854] [SQL] RuleExecutor.timeMap should be
 thread-safe

`RuleExecutor.timeMap` is currently a non-thread-safe mutable HashMap; this can lead to infinite loops if multiple threads are concurrently modifying the map.  I believe that this is responsible for some hangs that I've observed in HiveQuerySuite.

This patch addresses this by using a Guava `AtomicLongMap`.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8120 from JoshRosen/rule-executor-time-map-fix.
---
 .../spark/sql/catalyst/rules/RuleExecutor.scala   | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
index 8b824511a79da..f80d2a93241d1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/rules/RuleExecutor.scala
@@ -17,22 +17,25 @@
 
 package org.apache.spark.sql.catalyst.rules
 
+import scala.collection.JavaConverters._
+
+import com.google.common.util.concurrent.AtomicLongMap
+
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.catalyst.util.sideBySide
 
-import scala.collection.mutable
-
 object RuleExecutor {
-  protected val timeMap = new mutable.HashMap[String, Long].withDefault(_ => 0)
+  protected val timeMap = AtomicLongMap.create[String]()
 
   /** Resets statistics about time spent running specific rules */
   def resetTime(): Unit = timeMap.clear()
 
   /** Dump statistics about time spent running specific rules. */
   def dumpTimeSpent(): String = {
-    val maxSize = timeMap.keys.map(_.toString.length).max
-    timeMap.toSeq.sortBy(_._2).reverseMap { case (k, v) =>
+    val map = timeMap.asMap().asScala
+    val maxSize = map.keys.map(_.toString.length).max
+    map.toSeq.sortBy(_._2).reverseMap { case (k, v) =>
       s"${k.padTo(maxSize, " ").mkString} $v"
     }.mkString("\n")
   }
@@ -79,7 +82,7 @@ abstract class RuleExecutor[TreeType <: TreeNode[_]] extends Logging {
             val startTime = System.nanoTime()
             val result = rule(plan)
             val runTime = System.nanoTime() - startTime
-            RuleExecutor.timeMap(rule.ruleName) = RuleExecutor.timeMap(rule.ruleName) + runTime
+            RuleExecutor.timeMap.addAndGet(rule.ruleName, runTime)
 
             if (!result.fastEquals(plan)) {
               logTrace(

From b85f9a242a12e8096e331fa77d5ebd16e93c844d Mon Sep 17 00:00:00 2001
From: xutingjun <xutingjun@huawei.com>
Date: Tue, 11 Aug 2015 23:19:35 -0700
Subject: [PATCH 182/633] [SPARK-8366] maxNumExecutorsNeeded should properly
 handle failed tasks

Author: xutingjun <xutingjun@huawei.com>
Author: meiyoula <1039320815@qq.com>

Closes #6817 from XuTingjun/SPARK-8366.
---
 .../spark/ExecutorAllocationManager.scala     | 22 ++++++++++++-------
 .../ExecutorAllocationManagerSuite.scala      | 22 +++++++++++++++++--
 2 files changed, 34 insertions(+), 10 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
index 1877aaf2cac55..b93536e6536e2 100644
--- a/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
+++ b/core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
@@ -599,14 +599,8 @@ private[spark] class ExecutorAllocationManager(
 
         // If this is the last pending task, mark the scheduler queue as empty
         stageIdToTaskIndices.getOrElseUpdate(stageId, new mutable.HashSet[Int]) += taskIndex
-        val numTasksScheduled = stageIdToTaskIndices(stageId).size
-        val numTasksTotal = stageIdToNumTasks.getOrElse(stageId, -1)
-        if (numTasksScheduled == numTasksTotal) {
-          // No more pending tasks for this stage
-          stageIdToNumTasks -= stageId
-          if (stageIdToNumTasks.isEmpty) {
-            allocationManager.onSchedulerQueueEmpty()
-          }
+        if (totalPendingTasks() == 0) {
+          allocationManager.onSchedulerQueueEmpty()
         }
 
         // Mark the executor on which this task is scheduled as busy
@@ -618,6 +612,8 @@ private[spark] class ExecutorAllocationManager(
     override def onTaskEnd(taskEnd: SparkListenerTaskEnd): Unit = {
       val executorId = taskEnd.taskInfo.executorId
       val taskId = taskEnd.taskInfo.taskId
+      val taskIndex = taskEnd.taskInfo.index
+      val stageId = taskEnd.stageId
       allocationManager.synchronized {
         numRunningTasks -= 1
         // If the executor is no longer running any scheduled tasks, mark it as idle
@@ -628,6 +624,16 @@ private[spark] class ExecutorAllocationManager(
             allocationManager.onExecutorIdle(executorId)
           }
         }
+
+        // If the task failed, we expect it to be resubmitted later. To ensure we have
+        // enough resources to run the resubmitted task, we need to mark the scheduler
+        // as backlogged again if it's not already marked as such (SPARK-8366)
+        if (taskEnd.reason != Success) {
+          if (totalPendingTasks() == 0) {
+            allocationManager.onSchedulerBacklogged()
+          }
+          stageIdToTaskIndices.get(stageId).foreach { _.remove(taskIndex) }
+        }
       }
     }
 
diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
index 34caca892891c..f374f97f87448 100644
--- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
@@ -206,8 +206,8 @@ class ExecutorAllocationManagerSuite
 
     val task2Info = createTaskInfo(1, 0, "executor-1")
     sc.listenerBus.postToAll(SparkListenerTaskStart(2, 0, task2Info))
-    sc.listenerBus.postToAll(SparkListenerTaskEnd(2, 0, null, null, task1Info, null))
-    sc.listenerBus.postToAll(SparkListenerTaskEnd(2, 0, null, null, task2Info, null))
+    sc.listenerBus.postToAll(SparkListenerTaskEnd(2, 0, null, Success, task1Info, null))
+    sc.listenerBus.postToAll(SparkListenerTaskEnd(2, 0, null, Success, task2Info, null))
 
     assert(adjustRequestedExecutors(manager) === -1)
   }
@@ -787,6 +787,24 @@ class ExecutorAllocationManagerSuite
       Map("host2" -> 1, "host3" -> 2, "host4" -> 1, "host5" -> 2))
   }
 
+  test("SPARK-8366: maxNumExecutorsNeeded should properly handle failed tasks") {
+    sc = createSparkContext()
+    val manager = sc.executorAllocationManager.get
+    assert(maxNumExecutorsNeeded(manager) === 0)
+
+    sc.listenerBus.postToAll(SparkListenerStageSubmitted(createStageInfo(0, 1)))
+    assert(maxNumExecutorsNeeded(manager) === 1)
+
+    val taskInfo = createTaskInfo(1, 1, "executor-1")
+    sc.listenerBus.postToAll(SparkListenerTaskStart(0, 0, taskInfo))
+    assert(maxNumExecutorsNeeded(manager) === 1)
+
+    // If the task is failed, we expect it to be resubmitted later.
+    val taskEndReason = ExceptionFailure(null, null, null, null, null)
+    sc.listenerBus.postToAll(SparkListenerTaskEnd(0, 0, null, taskEndReason, taskInfo, null))
+    assert(maxNumExecutorsNeeded(manager) === 1)
+  }
+
   private def createSparkContext(
       minExecutors: Int = 1,
       maxExecutors: Int = 5,

From a807fcbe50b2ce18751d80d39e9d21842f7da32a Mon Sep 17 00:00:00 2001
From: Rohit Agarwal <rohita@qubole.com>
Date: Tue, 11 Aug 2015 23:20:39 -0700
Subject: [PATCH 183/633] [SPARK-9806] [WEB UI] Don't share ReplayListenerBus
 between multiple applications

Author: Rohit Agarwal <rohita@qubole.com>

Closes #8088 from mindprince/SPARK-9806.
---
 .../org/apache/spark/deploy/history/FsHistoryProvider.scala     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index e3060ac3fa1a9..53c18ca3ff50c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -272,9 +272,9 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
    * Replay the log files in the list and merge the list of old applications with new ones
    */
   private def mergeApplicationListing(logs: Seq[FileStatus]): Unit = {
-    val bus = new ReplayListenerBus()
     val newAttempts = logs.flatMap { fileStatus =>
       try {
+        val bus = new ReplayListenerBus()
         val res = replay(fileStatus, bus)
         res match {
           case Some(r) => logDebug(s"Application log ${r.logPath} loaded successfully.")

From 4e3f4b934f74e8c7c06f4940d6381343f9fd4918 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 11 Aug 2015 23:23:17 -0700
Subject: [PATCH 184/633] [SPARK-9829] [WEBUI] Display the update value for
 peak execution memory

The peak execution memory is not correct because it shows the sum of finished tasks' values when a task finishes.

This PR fixes it by using the update value rather than the accumulator value.

Author: zsxwing <zsxwing@gmail.com>

Closes #8121 from zsxwing/SPARK-9829.
---
 core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 0c94204df6530..fb4556b836859 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -860,7 +860,7 @@ private[ui] class TaskDataSource(
     }
     val peakExecutionMemoryUsed = taskInternalAccumulables
       .find { acc => acc.name == InternalAccumulator.PEAK_EXECUTION_MEMORY }
-      .map { acc => acc.value.toLong }
+      .map { acc => acc.update.getOrElse("0").toLong }
       .getOrElse(0L)
 
     val maybeInput = metrics.flatMap(_.inputMetrics)

From bab89232854de7554e88f29cab76f1a1c349edc1 Mon Sep 17 00:00:00 2001
From: Carson Wang <carson.wang@intel.com>
Date: Tue, 11 Aug 2015 23:25:02 -0700
Subject: [PATCH 185/633] [SPARK-9426] [WEBUI] Job page DAG visualization is
 not shown

To reproduce the issue, go to the stage page and click DAG Visualization once, then go to the job page to show the job DAG visualization. You will only see the first stage of the job.
Root cause: the java script use local storage to remember your selection. Once you click the stage DAG visualization, the local storage set `expand-dag-viz-arrow-stage` to true. When you go to the job page, the js checks `expand-dag-viz-arrow-stage` in the local storage first and will try to show stage DAG visualization on the job page.
To fix this, I set an id to the DAG span to differ job page and stage page. In the js code, we check the id and local storage together to make sure we show the correct DAG visualization.

Author: Carson Wang <carson.wang@intel.com>

Closes #8104 from carsonwang/SPARK-9426.
---
 .../resources/org/apache/spark/ui/static/spark-dag-viz.js | 8 ++++----
 core/src/main/scala/org/apache/spark/ui/UIUtils.scala     | 3 ++-
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
index 4a893bc0189aa..83dbea40b63f3 100644
--- a/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
+++ b/core/src/main/resources/org/apache/spark/ui/static/spark-dag-viz.js
@@ -109,13 +109,13 @@ function toggleDagViz(forJob) {
 }
 
 $(function (){
-  if (window.localStorage.getItem(expandDagVizArrowKey(false)) == "true") {
+  if ($("#stage-dag-viz").length &&
+      window.localStorage.getItem(expandDagVizArrowKey(false)) == "true") {
     // Set it to false so that the click function can revert it
     window.localStorage.setItem(expandDagVizArrowKey(false), "false");
     toggleDagViz(false);
-  }
-
-  if (window.localStorage.getItem(expandDagVizArrowKey(true)) == "true") {
+  } else if ($("#job-dag-viz").length &&
+      window.localStorage.getItem(expandDagVizArrowKey(true)) == "true") {
     // Set it to false so that the click function can revert it
     window.localStorage.setItem(expandDagVizArrowKey(true), "false");
     toggleDagViz(true);
diff --git a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
index 718aea7e1dc22..f2da417724104 100644
--- a/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/UIUtils.scala
@@ -352,7 +352,8 @@ private[spark] object UIUtils extends Logging {
    */
   private def showDagViz(graphs: Seq[RDDOperationGraph], forJob: Boolean): Seq[Node] = {
     <div>
-      <span class="expand-dag-viz" onclick={s"toggleDagViz($forJob);"}>
+      <span id={if (forJob) "job-dag-viz" else "stage-dag-viz"}
+            class="expand-dag-viz" onclick={s"toggleDagViz($forJob);"}>
         <span class="expand-dag-viz-arrow arrow-closed"></span>
         <a data-toggle="tooltip" title={if (forJob) ToolTips.JOB_DAG else ToolTips.STAGE_DAG}
            data-placement="right">

From 5c99d8bf98cbf7f568345d02a814fc318cbfca75 Mon Sep 17 00:00:00 2001
From: Timothy Chen <tnachen@gmail.com>
Date: Tue, 11 Aug 2015 23:26:33 -0700
Subject: [PATCH 186/633] [SPARK-8798] [MESOS] Allow additional uris to be
 fetched with mesos

Some users like to download additional files in their sandbox that they can refer to from their spark program, or even later mount these files to another directory.

Author: Timothy Chen <tnachen@gmail.com>

Closes #7195 from tnachen/mesos_files.
---
 .../cluster/mesos/CoarseMesosSchedulerBackend.scala       | 5 +++++
 .../scheduler/cluster/mesos/MesosClusterScheduler.scala   | 3 +++
 .../scheduler/cluster/mesos/MesosSchedulerBackend.scala   | 5 +++++
 .../scheduler/cluster/mesos/MesosSchedulerUtils.scala     | 6 ++++++
 docs/running-on-mesos.md                                  | 8 ++++++++
 5 files changed, 27 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index 15a0915708c7c..d6e1e9e5bebc2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -194,6 +194,11 @@ private[spark] class CoarseMesosSchedulerBackend(
         s" --app-id $appId")
       command.addUris(CommandInfo.URI.newBuilder().setValue(uri.get))
     }
+
+    conf.getOption("spark.mesos.uris").map { uris =>
+      setupUris(uris, command)
+    }
+
     command.build()
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
index f078547e71352..64ec2b8e3db15 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
@@ -403,6 +403,9 @@ private[spark] class MesosClusterScheduler(
     }
     builder.setValue(s"$executable $cmdOptions $jar $appArguments")
     builder.setEnvironment(envBuilder.build())
+    conf.getOption("spark.mesos.uris").map { uris =>
+      setupUris(uris, builder)
+    }
     builder.build()
   }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index 3f63ec1c5832f..5c20606d58715 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -133,6 +133,11 @@ private[spark] class MesosSchedulerBackend(
 
     builder.addAllResources(usedCpuResources)
     builder.addAllResources(usedMemResources)
+
+    sc.conf.getOption("spark.mesos.uris").map { uris =>
+      setupUris(uris, command)
+    }
+
     val executorInfo = builder
       .setExecutorId(ExecutorID.newBuilder().setValue(execId).build())
       .setCommand(command)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
index c04920e4f5873..5b854aa5c2754 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
@@ -331,4 +331,10 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
       sc.executorMemory
   }
 
+  def setupUris(uris: String, builder: CommandInfo.Builder): Unit = {
+    uris.split(",").foreach { uri =>
+      builder.addUris(CommandInfo.URI.newBuilder().setValue(uri.trim()))
+    }
+  }
+
 }
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index debdd2adf22d6..55e6d4e83a725 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -306,6 +306,14 @@ See the [configuration page](configuration.html) for information on Spark config
     the final overhead will be this value.
   </td>
 </tr>
+<tr>
+  <td><code>spark.mesos.uris</code></td>
+  <td>(none)</td>
+  <td>
+    A list of URIs to be downloaded to the sandbox when driver or executor is launched by Mesos.
+    This applies to both coarse-grain and fine-grain mode.
+  </td>
+</tr>
 <tr>
   <td><code>spark.mesos.principal</code></td>
   <td>Framework principal to authenticate to Mesos</td>

From 741a29f98945538a475579ccc974cd42c1613be4 Mon Sep 17 00:00:00 2001
From: Timothy Chen <tnachen@gmail.com>
Date: Tue, 11 Aug 2015 23:33:22 -0700
Subject: [PATCH 187/633] [SPARK-9575] [MESOS] Add docuemntation around Mesos
 shuffle service.

andrewor14

Author: Timothy Chen <tnachen@gmail.com>

Closes #7907 from tnachen/mesos_shuffle.
---
 docs/running-on-mesos.md | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index 55e6d4e83a725..cfd219ab02e26 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -216,6 +216,20 @@ node. Please refer to [Hadoop on Mesos](https://github.com/mesos/hadoop).
 
 In either case, HDFS runs separately from Hadoop MapReduce, without being scheduled through Mesos.
 
+# Dynamic Resource Allocation with Mesos
+
+Mesos supports dynamic allocation only with coarse grain mode, which can resize the number of executors based on statistics
+of the application. While dynamic allocation supports both scaling up and scaling down the number of executors, the coarse grain scheduler only supports scaling down
+since it is already designed to run one executor per slave with the configured amount of resources. However, after scaling down the number of executors the coarse grain scheduler
+can scale back up to the same amount of executors when Spark signals more executors are needed.
+
+Users that like to utilize this feature should launch the Mesos Shuffle Service that
+provides shuffle data cleanup functionality on top of the Shuffle Service since Mesos doesn't yet support notifying another framework's
+termination. To launch/stop the Mesos Shuffle Service please use the provided sbin/start-mesos-shuffle-service.sh and sbin/stop-mesos-shuffle-service.sh
+scripts accordingly.
+
+The Shuffle Service is expected to be running on each slave node that will run Spark executors. One way to easily achieve this with Mesos
+is to launch the Shuffle Service with Marathon with a unique host constraint.
 
 # Configuration
 

From 9d0822455ddc8d765440d58c463367a4d67ef456 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Wed, 12 Aug 2015 19:54:00 +0800
Subject: [PATCH 188/633] [SPARK-9182] [SQL] Filters are not passed through to
 jdbc source

This PR fixes unable to push filter down to JDBC source caused by `Cast` during pattern matching.

While we are comparing columns of different type, there's a big chance we need a cast on the column, therefore not match the pattern directly on Attribute and would fail to push down.

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8049 from yjshen/jdbc_pushdown.
---
 .../datasources/DataSourceStrategy.scala      | 30 ++++++++++++++--
 .../execution/datasources/jdbc/JDBCRDD.scala  |  2 +-
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala | 34 +++++++++++++++++++
 3 files changed, 63 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 2a4c40db8bb66..9eea2b0382535 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -20,13 +20,13 @@ package org.apache.spark.sql.execution.datasources
 import org.apache.spark.{Logging, TaskContext}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.{MapPartitionsRDD, RDD, UnionRDD}
-import org.apache.spark.sql.catalyst.{InternalRow, expressions}
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow, expressions}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.{StringType, StructType}
+import org.apache.spark.sql.types.{TimestampType, DateType, StringType, StructType}
 import org.apache.spark.sql.{SaveMode, Strategy, execution, sources, _}
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.{SerializableConfiguration, Utils}
@@ -343,11 +343,17 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
    * and convert them.
    */
   protected[sql] def selectFilters(filters: Seq[Expression]) = {
+    import CatalystTypeConverters._
+
     def translate(predicate: Expression): Option[Filter] = predicate match {
       case expressions.EqualTo(a: Attribute, Literal(v, _)) =>
         Some(sources.EqualTo(a.name, v))
       case expressions.EqualTo(Literal(v, _), a: Attribute) =>
         Some(sources.EqualTo(a.name, v))
+      case expressions.EqualTo(Cast(a: Attribute, _), l: Literal) =>
+        Some(sources.EqualTo(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
+      case expressions.EqualTo(l: Literal, Cast(a: Attribute, _)) =>
+        Some(sources.EqualTo(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.EqualNullSafe(a: Attribute, Literal(v, _)) =>
         Some(sources.EqualNullSafe(a.name, v))
@@ -358,21 +364,41 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         Some(sources.GreaterThan(a.name, v))
       case expressions.GreaterThan(Literal(v, _), a: Attribute) =>
         Some(sources.LessThan(a.name, v))
+      case expressions.GreaterThan(Cast(a: Attribute, _), l: Literal) =>
+        Some(sources.GreaterThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
+      case expressions.GreaterThan(l: Literal, Cast(a: Attribute, _)) =>
+        Some(sources.LessThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.LessThan(a: Attribute, Literal(v, _)) =>
         Some(sources.LessThan(a.name, v))
       case expressions.LessThan(Literal(v, _), a: Attribute) =>
         Some(sources.GreaterThan(a.name, v))
+      case expressions.LessThan(Cast(a: Attribute, _), l: Literal) =>
+        Some(sources.LessThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
+      case expressions.LessThan(l: Literal, Cast(a: Attribute, _)) =>
+        Some(sources.GreaterThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.GreaterThanOrEqual(a: Attribute, Literal(v, _)) =>
         Some(sources.GreaterThanOrEqual(a.name, v))
       case expressions.GreaterThanOrEqual(Literal(v, _), a: Attribute) =>
         Some(sources.LessThanOrEqual(a.name, v))
+      case expressions.GreaterThanOrEqual(Cast(a: Attribute, _), l: Literal) =>
+        Some(sources.GreaterThanOrEqual(a.name,
+          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
+      case expressions.GreaterThanOrEqual(l: Literal, Cast(a: Attribute, _)) =>
+        Some(sources.LessThanOrEqual(a.name,
+          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.LessThanOrEqual(a: Attribute, Literal(v, _)) =>
         Some(sources.LessThanOrEqual(a.name, v))
       case expressions.LessThanOrEqual(Literal(v, _), a: Attribute) =>
         Some(sources.GreaterThanOrEqual(a.name, v))
+      case expressions.LessThanOrEqual(Cast(a: Attribute, _), l: Literal) =>
+        Some(sources.LessThanOrEqual(a.name,
+          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
+      case expressions.LessThanOrEqual(l: Literal, Cast(a: Attribute, _)) =>
+        Some(sources.GreaterThanOrEqual(a.name,
+          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.InSet(a: Attribute, set) =>
         Some(sources.In(a.name, set.toArray))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
index 8eab6a0adccc4..281943e23fcff 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
@@ -284,7 +284,7 @@ private[sql] class JDBCRDD(
   /**
    * `filters`, but as a WHERE clause suitable for injection into a SQL query.
    */
-  private val filterWhereClause: String = {
+  val filterWhereClause: String = {
     val filterStrings = filters map compileFilter filter (_ != null)
     if (filterStrings.size > 0) {
       val sb = new StringBuilder("WHERE ")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index 42f2449afb0f9..b9cfae51e809c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -25,6 +25,8 @@ import org.h2.jdbc.JdbcSQLException
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD
+import org.apache.spark.sql.execution.PhysicalRDD
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -148,6 +150,18 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
         |OPTIONS (url '$url', dbtable 'TEST.FLTTYPES', user 'testUser', password 'testPass')
       """.stripMargin.replaceAll("\n", " "))
 
+    conn.prepareStatement("create table test.decimals (a DECIMAL(7, 2), b DECIMAL(4, 0))").
+      executeUpdate()
+    conn.prepareStatement("insert into test.decimals values (12345.67, 1234)").executeUpdate()
+    conn.prepareStatement("insert into test.decimals values (34567.89, 1428)").executeUpdate()
+    conn.commit()
+    sql(
+      s"""
+         |CREATE TEMPORARY TABLE decimals
+         |USING org.apache.spark.sql.jdbc
+         |OPTIONS (url '$url', dbtable 'TEST.DECIMALS', user 'testUser', password 'testPass')
+      """.stripMargin.replaceAll("\n", " "))
+
     conn.prepareStatement(
       s"""
         |create table test.nulltypes (a INT, b BOOLEAN, c TINYINT, d BINARY(20), e VARCHAR(20),
@@ -445,4 +459,24 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
     assert(agg.getCatalystType(1, "", 1, null) === Some(StringType))
   }
 
+  test("SPARK-9182: filters are not passed through to jdbc source") {
+    def checkPushedFilter(query: String, filterStr: String): Unit = {
+      val rddOpt = sql(query).queryExecution.executedPlan.collectFirst {
+        case PhysicalRDD(_, rdd: JDBCRDD, _) => rdd
+      }
+      assert(rddOpt.isDefined)
+      val pushedFilterStr = rddOpt.get.filterWhereClause
+      assert(pushedFilterStr.contains(filterStr),
+        s"Expected to push [$filterStr], actually we pushed [$pushedFilterStr]")
+    }
+
+    checkPushedFilter("select * from foobar where NAME = 'fred'", "NAME = 'fred'")
+    checkPushedFilter("select * from inttypes where A > '15'", "A > 15")
+    checkPushedFilter("select * from inttypes where C <= 20", "C <= 20")
+    checkPushedFilter("select * from decimals where A > 1000", "A > 1000.00")
+    checkPushedFilter("select * from decimals where A > 1000 AND A < 2000",
+      "A > 1000.00 AND A < 2000.00")
+    checkPushedFilter("select * from decimals where A = 2000 AND B > 20", "A = 2000.00 AND B > 20")
+    checkPushedFilter("select * from timetypes where B > '1998-09-10'", "B > 1998-09-10")
+  }
 }

From 3ecb3794302dc12d0989f8d725483b2cc37762cf Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 12 Aug 2015 20:01:34 +0800
Subject: [PATCH 189/633] [SPARK-9407] [SQL] Relaxes Parquet ValidTypeMap to
 allow ENUM predicates to be pushed down

This PR adds a hacky workaround for PARQUET-201, and should be removed once we upgrade to parquet-mr 1.8.1 or higher versions.

In Parquet, not all types of columns can be used for filter push-down optimization.  The set of valid column types is controlled by `ValidTypeMap`.  Unfortunately, in parquet-mr 1.7.0 and prior versions, this limitation is too strict, and doesn't allow `BINARY (ENUM)` columns to be pushed down.  On the other hand, `BINARY (ENUM)` is commonly seen in Parquet files written by libraries like `parquet-avro`.

This restriction is problematic for Spark SQL, because Spark SQL doesn't have a type that maps to Parquet `BINARY (ENUM)` directly, and always converts `BINARY (ENUM)` to Catalyst `StringType`.  Thus, a predicate involving a `BINARY (ENUM)` is recognized as one involving a string field instead and can be pushed down by the query optimizer.  Such predicates are actually perfectly legal except that it fails the `ValidTypeMap` check.

The workaround added here is relaxing `ValidTypeMap` to include `BINARY (ENUM)`.  I also took the chance to simplify `ParquetCompatibilityTest` a little bit when adding regression test.

Author: Cheng Lian <lian@databricks.com>

Closes #8107 from liancheng/spark-9407/parquet-enum-filter-push-down.
---
 .../datasources/parquet/ParquetFilters.scala  |  38 ++++-
 .../datasources/parquet/ParquetRelation.scala |   2 +-
 sql/core/src/test/README.md                   |  16 +-
 sql/core/src/test/avro/parquet-compat.avdl    |  13 +-
 sql/core/src/test/avro/parquet-compat.avpr    |  13 +-
 .../parquet/test/avro/CompatibilityTest.java  |   2 +-
 .../datasources/parquet/test/avro/Nested.java |   4 +-
 .../parquet/test/avro/ParquetAvroCompat.java  |   4 +-
 .../parquet/test/avro/ParquetEnum.java        | 142 ++++++++++++++++++
 .../datasources/parquet/test/avro/Suit.java   |  13 ++
 .../ParquetAvroCompatibilitySuite.scala       | 105 +++++++------
 .../parquet/ParquetCompatibilityTest.scala    |  33 +---
 .../test/scripts/{gen-code.sh => gen-avro.sh} |  13 +-
 sql/core/src/test/scripts/gen-thrift.sh       |  27 ++++
 .../src/test/thrift/parquet-compat.thrift     |   2 +-
 .../hive/ParquetHiveCompatibilitySuite.scala  |  83 +++++-----
 16 files changed, 374 insertions(+), 136 deletions(-)
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetEnum.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Suit.java
 rename sql/core/src/test/scripts/{gen-code.sh => gen-avro.sh} (76%)
 create mode 100755 sql/core/src/test/scripts/gen-thrift.sh

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
index 9e2e232f50167..63915e0a28655 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
@@ -25,9 +25,10 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.parquet.filter2.compat.FilterCompat
 import org.apache.parquet.filter2.compat.FilterCompat._
 import org.apache.parquet.filter2.predicate.FilterApi._
-import org.apache.parquet.filter2.predicate.{FilterApi, FilterPredicate, Statistics}
-import org.apache.parquet.filter2.predicate.UserDefinedPredicate
+import org.apache.parquet.filter2.predicate._
 import org.apache.parquet.io.api.Binary
+import org.apache.parquet.schema.OriginalType
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName
 
 import org.apache.spark.SparkEnv
 import org.apache.spark.sql.catalyst.expressions._
@@ -197,6 +198,8 @@ private[sql] object ParquetFilters {
   def createFilter(schema: StructType, predicate: sources.Filter): Option[FilterPredicate] = {
     val dataTypeOf = schema.map(f => f.name -> f.dataType).toMap
 
+    relaxParquetValidTypeMap
+
     // NOTE:
     //
     // For any comparison operator `cmp`, both `a cmp NULL` and `NULL cmp a` evaluate to `NULL`,
@@ -239,6 +242,37 @@ private[sql] object ParquetFilters {
     }
   }
 
+  // !! HACK ALERT !!
+  //
+  // This lazy val is a workaround for PARQUET-201, and should be removed once we upgrade to
+  // parquet-mr 1.8.1 or higher versions.
+  //
+  // In Parquet, not all types of columns can be used for filter push-down optimization.  The set
+  // of valid column types is controlled by `ValidTypeMap`.  Unfortunately, in parquet-mr 1.7.0 and
+  // prior versions, the limitation is too strict, and doesn't allow `BINARY (ENUM)` columns to be
+  // pushed down.
+  //
+  // This restriction is problematic for Spark SQL, because Spark SQL doesn't have a type that maps
+  // to Parquet original type `ENUM` directly, and always converts `ENUM` to `StringType`.  Thus,
+  // a predicate involving a `ENUM` field can be pushed-down as a string column, which is perfectly
+  // legal except that it fails the `ValidTypeMap` check.
+  //
+  // Here we add `BINARY (ENUM)` into `ValidTypeMap` lazily via reflection to workaround this issue.
+  private lazy val relaxParquetValidTypeMap: Unit = {
+    val constructor = Class
+      .forName(classOf[ValidTypeMap].getCanonicalName + "$FullTypeDescriptor")
+      .getDeclaredConstructor(classOf[PrimitiveTypeName], classOf[OriginalType])
+
+    constructor.setAccessible(true)
+    val enumTypeDescriptor = constructor
+      .newInstance(PrimitiveTypeName.BINARY, OriginalType.ENUM)
+      .asInstanceOf[AnyRef]
+
+    val addMethod = classOf[ValidTypeMap].getDeclaredMethods.find(_.getName == "add").get
+    addMethod.setAccessible(true)
+    addMethod.invoke(null, classOf[Binary], enumTypeDescriptor)
+  }
+
   /**
    * Converts Catalyst predicate expressions to Parquet filter predicates.
    *
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index c71c69b6e80b1..52fac18ba187a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -678,7 +678,7 @@ private[sql] object ParquetRelation extends Logging {
     val followParquetFormatSpec = sqlContext.conf.followParquetFormatSpec
     val serializedConf = new SerializableConfiguration(sqlContext.sparkContext.hadoopConfiguration)
 
-    // HACK ALERT:
+    // !! HACK ALERT !!
     //
     // Parquet requires `FileStatus`es to read footers.  Here we try to send cached `FileStatus`es
     // to executor side to avoid fetching them again.  However, `FileStatus` is not `Serializable`
diff --git a/sql/core/src/test/README.md b/sql/core/src/test/README.md
index 3dd9861b4896d..421c2ea4f7aed 100644
--- a/sql/core/src/test/README.md
+++ b/sql/core/src/test/README.md
@@ -6,23 +6,19 @@ The following directories and files are used for Parquet compatibility tests:
 .
 ├── README.md                   # This file
 ├── avro
-│   ├── parquet-compat.avdl     # Testing Avro IDL
-│   └── parquet-compat.avpr     # !! NO TOUCH !! Protocol file generated from parquet-compat.avdl
+│   ├── *.avdl                  # Testing Avro IDL(s)
+│   └── *.avpr                  # !! NO TOUCH !! Protocol files generated from Avro IDL(s)
 ├── gen-java                    # !! NO TOUCH !! Generated Java code
 ├── scripts
-│   └── gen-code.sh             # Script used to generate Java code for Thrift and Avro
+│   ├── gen-avro.sh             # Script used to generate Java code for Avro
+│   └── gen-thrift.sh           # Script used to generate Java code for Thrift
 └── thrift
-    └── parquet-compat.thrift   # Testing Thrift schema
+    └── *.thrift                # Testing Thrift schema(s)
 ```
 
-Generated Java code are used in the following test suites:
-
-- `org.apache.spark.sql.parquet.ParquetAvroCompatibilitySuite`
-- `org.apache.spark.sql.parquet.ParquetThriftCompatibilitySuite`
-
 To avoid code generation during build time, Java code generated from testing Thrift schema and Avro IDL are also checked in.
 
-When updating the testing Thrift schema and Avro IDL, please run `gen-code.sh` to update all the generated Java code.
+When updating the testing Thrift schema and Avro IDL, please run `gen-avro.sh` and `gen-thrift.sh` accordingly to update generated Java code.
 
 ## Prerequisites
 
diff --git a/sql/core/src/test/avro/parquet-compat.avdl b/sql/core/src/test/avro/parquet-compat.avdl
index 24729f6143e6c..8070d0a9170a3 100644
--- a/sql/core/src/test/avro/parquet-compat.avdl
+++ b/sql/core/src/test/avro/parquet-compat.avdl
@@ -16,8 +16,19 @@
  */
 
 // This is a test protocol for testing parquet-avro compatibility.
-@namespace("org.apache.spark.sql.parquet.test.avro")
+@namespace("org.apache.spark.sql.execution.datasources.parquet.test.avro")
 protocol CompatibilityTest {
+    enum Suit {
+        SPADES,
+        HEARTS,
+        DIAMONDS,
+        CLUBS
+    }
+
+    record ParquetEnum {
+        Suit suit;
+    }
+
     record Nested {
         array<int> nested_ints_column;
         string nested_string_column;
diff --git a/sql/core/src/test/avro/parquet-compat.avpr b/sql/core/src/test/avro/parquet-compat.avpr
index a83b7c990dd2e..060391765034b 100644
--- a/sql/core/src/test/avro/parquet-compat.avpr
+++ b/sql/core/src/test/avro/parquet-compat.avpr
@@ -1,7 +1,18 @@
 {
   "protocol" : "CompatibilityTest",
-  "namespace" : "org.apache.spark.sql.parquet.test.avro",
+  "namespace" : "org.apache.spark.sql.execution.datasources.parquet.test.avro",
   "types" : [ {
+    "type" : "enum",
+    "name" : "Suit",
+    "symbols" : [ "SPADES", "HEARTS", "DIAMONDS", "CLUBS" ]
+  }, {
+    "type" : "record",
+    "name" : "ParquetEnum",
+    "fields" : [ {
+      "name" : "suit",
+      "type" : "Suit"
+    } ]
+  }, {
     "type" : "record",
     "name" : "Nested",
     "fields" : [ {
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
index 70dec1a9d3c92..2368323cb36b9 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
@@ -8,7 +8,7 @@
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public interface CompatibilityTest {
-  public static final org.apache.avro.Protocol PROTOCOL = org.apache.avro.Protocol.parse("{\"protocol\":\"CompatibilityTest\",\"namespace\":\"org.apache.spark.sql.parquet.test.avro\",\"types\":[{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"Nested\"},\"avro.java.string\":\"String\"}}]}],\"messages\":{}}");
+  public static final org.apache.avro.Protocol PROTOCOL = org.apache.avro.Protocol.parse("{\"protocol\":\"CompatibilityTest\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"types\":[{\"type\":\"enum\",\"name\":\"Suit\",\"symbols\":[\"SPADES\",\"HEARTS\",\"DIAMONDS\",\"CLUBS\"]},{\"type\":\"record\",\"name\":\"ParquetEnum\",\"fields\":[{\"name\":\"suit\",\"type\":\"Suit\"}]},{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"Nested\"},\"avro.java.string\":\"String\"}}]}],\"messages\":{}}");
 
   @SuppressWarnings("all")
   public interface Callback extends CompatibilityTest {
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
index a0a406bcd10c1..a7bf4841919c5 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Nested.java
@@ -3,11 +3,11 @@
  * 
  * DO NOT EDIT DIRECTLY
  */
-package org.apache.spark.sql.execution.datasources.parquet.test.avro;
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public class Nested extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
-  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"Nested\",\"namespace\":\"org.apache.spark.sql.parquet.test.avro\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}");
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"Nested\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}");
   public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
   @Deprecated public java.util.List<java.lang.Integer> nested_ints_column;
   @Deprecated public java.lang.String nested_string_column;
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
index 6198b00b1e3ca..681cacbd12c7c 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
@@ -3,11 +3,11 @@
  * 
  * DO NOT EDIT DIRECTLY
  */
-package org.apache.spark.sql.execution.datasources.parquet.test.avro;
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public class ParquetAvroCompat extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
-  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"namespace\":\"org.apache.spark.sql.parquet.test.avro\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}},\"avro.java.string\":\"String\"}}]}");
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}},\"avro.java.string\":\"String\"}}]}");
   public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
   @Deprecated public boolean bool_column;
   @Deprecated public int int_column;
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetEnum.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetEnum.java
new file mode 100644
index 0000000000000..05fefe4cee754
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetEnum.java
@@ -0,0 +1,142 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class ParquetEnum extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"ParquetEnum\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"suit\",\"type\":{\"type\":\"enum\",\"name\":\"Suit\",\"symbols\":[\"SPADES\",\"HEARTS\",\"DIAMONDS\",\"CLUBS\"]}}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit suit;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public ParquetEnum() {}
+
+  /**
+   * All-args constructor.
+   */
+  public ParquetEnum(org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit suit) {
+    this.suit = suit;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return suit;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: suit = (org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'suit' field.
+   */
+  public org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit getSuit() {
+    return suit;
+  }
+
+  /**
+   * Sets the value of the 'suit' field.
+   * @param value the value to set.
+   */
+  public void setSuit(org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit value) {
+    this.suit = value;
+  }
+
+  /** Creates a new ParquetEnum RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder();
+  }
+  
+  /** Creates a new ParquetEnum RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder(other);
+  }
+  
+  /** Creates a new ParquetEnum RecordBuilder by copying an existing ParquetEnum instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for ParquetEnum instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<ParquetEnum>
+    implements org.apache.avro.data.RecordBuilder<ParquetEnum> {
+
+    private org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit suit;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.suit)) {
+        this.suit = data().deepCopy(fields()[0].schema(), other.suit);
+        fieldSetFlags()[0] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing ParquetEnum instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.SCHEMA$);
+      if (isValidValue(fields()[0], other.suit)) {
+        this.suit = data().deepCopy(fields()[0].schema(), other.suit);
+        fieldSetFlags()[0] = true;
+      }
+    }
+
+    /** Gets the value of the 'suit' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit getSuit() {
+      return suit;
+    }
+    
+    /** Sets the value of the 'suit' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder setSuit(org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit value) {
+      validate(fields()[0], value);
+      this.suit = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'suit' field has been set */
+    public boolean hasSuit() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'suit' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetEnum.Builder clearSuit() {
+      suit = null;
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    @Override
+    public ParquetEnum build() {
+      try {
+        ParquetEnum record = new ParquetEnum();
+        record.suit = fieldSetFlags()[0] ? this.suit : (org.apache.spark.sql.execution.datasources.parquet.test.avro.Suit) defaultValue(fields()[0]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Suit.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Suit.java
new file mode 100644
index 0000000000000..00711a0c2a267
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/Suit.java
@@ -0,0 +1,13 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public enum Suit { 
+  SPADES, HEARTS, DIAMONDS, CLUBS  ;
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"enum\",\"name\":\"Suit\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"symbols\":[\"SPADES\",\"HEARTS\",\"DIAMONDS\",\"CLUBS\"]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index 4d9c07bb7a570..866a975ad5404 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -22,10 +22,12 @@ import java.util.{List => JList, Map => JMap}
 
 import scala.collection.JavaConversions._
 
+import org.apache.avro.Schema
+import org.apache.avro.generic.IndexedRecord
 import org.apache.hadoop.fs.Path
 import org.apache.parquet.avro.AvroParquetWriter
 
-import org.apache.spark.sql.execution.datasources.parquet.test.avro.{Nested, ParquetAvroCompat}
+import org.apache.spark.sql.execution.datasources.parquet.test.avro.{Nested, ParquetAvroCompat, ParquetEnum, Suit}
 import org.apache.spark.sql.test.TestSQLContext
 import org.apache.spark.sql.{Row, SQLContext}
 
@@ -34,52 +36,55 @@ class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest {
 
   override val sqlContext: SQLContext = TestSQLContext
 
-  override protected def beforeAll(): Unit = {
-    super.beforeAll()
-
-    val writer =
-      new AvroParquetWriter[ParquetAvroCompat](
-        new Path(parquetStore.getCanonicalPath),
-        ParquetAvroCompat.getClassSchema)
-
-    (0 until 10).foreach(i => writer.write(makeParquetAvroCompat(i)))
-    writer.close()
+  private def withWriter[T <: IndexedRecord]
+      (path: String, schema: Schema)
+      (f: AvroParquetWriter[T] => Unit) = {
+    val writer = new AvroParquetWriter[T](new Path(path), schema)
+    try f(writer) finally writer.close()
   }
 
   test("Read Parquet file generated by parquet-avro") {
-    logInfo(
-      s"""Schema of the Parquet file written by parquet-avro:
-         |${readParquetSchema(parquetStore.getCanonicalPath)}
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[ParquetAvroCompat](path, ParquetAvroCompat.getClassSchema) { writer =>
+        (0 until 10).foreach(i => writer.write(makeParquetAvroCompat(i)))
+      }
+
+      logInfo(
+        s"""Schema of the Parquet file written by parquet-avro:
+           |${readParquetSchema(path)}
        """.stripMargin)
 
-    checkAnswer(sqlContext.read.parquet(parquetStore.getCanonicalPath), (0 until 10).map { i =>
-      def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
-
-      Row(
-        i % 2 == 0,
-        i,
-        i.toLong * 10,
-        i.toFloat + 0.1f,
-        i.toDouble + 0.2d,
-        s"val_$i".getBytes,
-        s"val_$i",
-
-        nullable(i % 2 == 0: java.lang.Boolean),
-        nullable(i: Integer),
-        nullable(i.toLong: java.lang.Long),
-        nullable(i.toFloat + 0.1f: java.lang.Float),
-        nullable(i.toDouble + 0.2d: java.lang.Double),
-        nullable(s"val_$i".getBytes),
-        nullable(s"val_$i"),
-
-        Seq.tabulate(3)(n => s"arr_${i + n}"),
-        Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap,
-        Seq.tabulate(3) { n =>
-          (i + n).toString -> Seq.tabulate(3) { m =>
-            Row(Seq.tabulate(3)(j => i + j + m), s"val_${i + m}")
-          }
-        }.toMap)
-    })
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
+
+        Row(
+          i % 2 == 0,
+          i,
+          i.toLong * 10,
+          i.toFloat + 0.1f,
+          i.toDouble + 0.2d,
+          s"val_$i".getBytes,
+          s"val_$i",
+
+          nullable(i % 2 == 0: java.lang.Boolean),
+          nullable(i: Integer),
+          nullable(i.toLong: java.lang.Long),
+          nullable(i.toFloat + 0.1f: java.lang.Float),
+          nullable(i.toDouble + 0.2d: java.lang.Double),
+          nullable(s"val_$i".getBytes),
+          nullable(s"val_$i"),
+
+          Seq.tabulate(3)(n => s"arr_${i + n}"),
+          Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap,
+          Seq.tabulate(3) { n =>
+            (i + n).toString -> Seq.tabulate(3) { m =>
+              Row(Seq.tabulate(3)(j => i + j + m), s"val_${i + m}")
+            }
+          }.toMap)
+      })
+    }
   }
 
   def makeParquetAvroCompat(i: Int): ParquetAvroCompat = {
@@ -122,4 +127,20 @@ class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest {
 
       .build()
   }
+
+  test("SPARK-9407 Don't push down predicates involving Parquet ENUM columns") {
+    import sqlContext.implicits._
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[ParquetEnum](path, ParquetEnum.getClassSchema) { writer =>
+        (0 until 4).foreach { i =>
+          writer.write(ParquetEnum.newBuilder().setSuit(Suit.values.apply(i)).build())
+        }
+      }
+
+      checkAnswer(sqlContext.read.parquet(path).filter('suit === "SPADES"), Row("SPADES"))
+    }
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index 68f35b1f3aa83..0ea64aa2a509b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -16,45 +16,28 @@
  */
 
 package org.apache.spark.sql.execution.datasources.parquet
-import java.io.File
 
 import scala.collection.JavaConversions._
 
-import org.apache.hadoop.fs.Path
+import org.apache.hadoop.fs.{Path, PathFilter}
 import org.apache.parquet.hadoop.ParquetFileReader
 import org.apache.parquet.schema.MessageType
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql.QueryTest
-import org.apache.spark.util.Utils
 
 abstract class ParquetCompatibilityTest extends QueryTest with ParquetTest with BeforeAndAfterAll {
-  protected var parquetStore: File = _
-
-  /**
-   * Optional path to a staging subdirectory which may be created during query processing
-   * (Hive does this).
-   * Parquet files under this directory will be ignored in [[readParquetSchema()]]
-   * @return an optional staging directory to ignore when scanning for parquet files.
-   */
-  protected def stagingDir: Option[String] = None
-
-  override protected def beforeAll(): Unit = {
-    parquetStore = Utils.createTempDir(namePrefix = "parquet-compat_")
-    parquetStore.delete()
-  }
-
-  override protected def afterAll(): Unit = {
-    Utils.deleteRecursively(parquetStore)
+  def readParquetSchema(path: String): MessageType = {
+    readParquetSchema(path, { path => !path.getName.startsWith("_") })
   }
 
-  def readParquetSchema(path: String): MessageType = {
+  def readParquetSchema(path: String, pathFilter: Path => Boolean): MessageType = {
     val fsPath = new Path(path)
     val fs = fsPath.getFileSystem(configuration)
-    val parquetFiles = fs.listStatus(fsPath).toSeq.filterNot { status =>
-      status.getPath.getName.startsWith("_") ||
-        stagingDir.map(status.getPath.getName.startsWith).getOrElse(false)
-    }
+    val parquetFiles = fs.listStatus(fsPath, new PathFilter {
+      override def accept(path: Path): Boolean = pathFilter(path)
+    }).toSeq
+
     val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
     footers.head.getParquetMetadata.getFileMetaData.getSchema
   }
diff --git a/sql/core/src/test/scripts/gen-code.sh b/sql/core/src/test/scripts/gen-avro.sh
similarity index 76%
rename from sql/core/src/test/scripts/gen-code.sh
rename to sql/core/src/test/scripts/gen-avro.sh
index 5d8d8ad08555c..48174b287fd7c 100755
--- a/sql/core/src/test/scripts/gen-code.sh
+++ b/sql/core/src/test/scripts/gen-avro.sh
@@ -22,10 +22,9 @@ cd -
 rm -rf $BASEDIR/gen-java
 mkdir -p $BASEDIR/gen-java
 
-thrift\
-    --gen java\
-    -out $BASEDIR/gen-java\
-    $BASEDIR/thrift/parquet-compat.thrift
-
-avro-tools idl $BASEDIR/avro/parquet-compat.avdl > $BASEDIR/avro/parquet-compat.avpr
-avro-tools compile -string protocol $BASEDIR/avro/parquet-compat.avpr $BASEDIR/gen-java
+for input in `ls $BASEDIR/avro/*.avdl`; do
+    filename=$(basename "$input")
+    filename="${filename%.*}"
+    avro-tools idl $input> $BASEDIR/avro/${filename}.avpr
+    avro-tools compile -string protocol $BASEDIR/avro/${filename}.avpr $BASEDIR/gen-java
+done
diff --git a/sql/core/src/test/scripts/gen-thrift.sh b/sql/core/src/test/scripts/gen-thrift.sh
new file mode 100755
index 0000000000000..ada432c68ab95
--- /dev/null
+++ b/sql/core/src/test/scripts/gen-thrift.sh
@@ -0,0 +1,27 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+cd $(dirname $0)/..
+BASEDIR=`pwd`
+cd -
+
+rm -rf $BASEDIR/gen-java
+mkdir -p $BASEDIR/gen-java
+
+for input in `ls $BASEDIR/thrift/*.thrift`; do
+    thrift --gen java -out $BASEDIR/gen-java $input
+done
diff --git a/sql/core/src/test/thrift/parquet-compat.thrift b/sql/core/src/test/thrift/parquet-compat.thrift
index fa5ed8c62306a..98bf778aec5d6 100644
--- a/sql/core/src/test/thrift/parquet-compat.thrift
+++ b/sql/core/src/test/thrift/parquet-compat.thrift
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-namespace java org.apache.spark.sql.parquet.test.thrift
+namespace java org.apache.spark.sql.execution.datasources.parquet.test.thrift
 
 enum Suit {
     SPADES,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index 80eb9f122ad90..251e0324bfa5f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -32,53 +32,54 @@ class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
    * Set the staging directory (and hence path to ignore Parquet files under)
    * to that set by [[HiveConf.ConfVars.STAGINGDIR]].
    */
-  override val stagingDir: Option[String] =
-    Some(new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR))
+  private val stagingDir = new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR)
 
-  override protected def beforeAll(): Unit = {
-    super.beforeAll()
+  test("Read Parquet file generated by parquet-hive") {
+    withTable("parquet_compat") {
+      withTempPath { dir =>
+        val path = dir.getCanonicalPath
 
-    withSQLConf(HiveContext.CONVERT_METASTORE_PARQUET.key -> "false") {
-      withTempTable("data") {
-        sqlContext.sql(
-          s"""CREATE TABLE parquet_compat(
-             |  bool_column BOOLEAN,
-             |  byte_column TINYINT,
-             |  short_column SMALLINT,
-             |  int_column INT,
-             |  long_column BIGINT,
-             |  float_column FLOAT,
-             |  double_column DOUBLE,
-             |
-             |  strings_column ARRAY<STRING>,
-             |  int_to_string_column MAP<INT, STRING>
-             |)
-             |STORED AS PARQUET
-             |LOCATION '${parquetStore.getCanonicalPath}'
-           """.stripMargin)
+        withSQLConf(HiveContext.CONVERT_METASTORE_PARQUET.key -> "false") {
+          withTempTable("data") {
+            sqlContext.sql(
+              s"""CREATE TABLE parquet_compat(
+                 |  bool_column BOOLEAN,
+                 |  byte_column TINYINT,
+                 |  short_column SMALLINT,
+                 |  int_column INT,
+                 |  long_column BIGINT,
+                 |  float_column FLOAT,
+                 |  double_column DOUBLE,
+                 |
+                 |  strings_column ARRAY<STRING>,
+                 |  int_to_string_column MAP<INT, STRING>
+                 |)
+                 |STORED AS PARQUET
+                 |LOCATION '$path'
+               """.stripMargin)
 
-        val schema = sqlContext.table("parquet_compat").schema
-        val rowRDD = sqlContext.sparkContext.parallelize(makeRows).coalesce(1)
-        sqlContext.createDataFrame(rowRDD, schema).registerTempTable("data")
-        sqlContext.sql("INSERT INTO TABLE parquet_compat SELECT * FROM data")
-      }
-    }
-  }
+            val schema = sqlContext.table("parquet_compat").schema
+            val rowRDD = sqlContext.sparkContext.parallelize(makeRows).coalesce(1)
+            sqlContext.createDataFrame(rowRDD, schema).registerTempTable("data")
+            sqlContext.sql("INSERT INTO TABLE parquet_compat SELECT * FROM data")
+          }
+        }
 
-  override protected def afterAll(): Unit = {
-    sqlContext.sql("DROP TABLE parquet_compat")
-  }
+        val schema = readParquetSchema(path, { path =>
+          !path.getName.startsWith("_") && !path.getName.startsWith(stagingDir)
+        })
 
-  test("Read Parquet file generated by parquet-hive") {
-    logInfo(
-      s"""Schema of the Parquet file written by parquet-hive:
-         |${readParquetSchema(parquetStore.getCanonicalPath)}
-       """.stripMargin)
+        logInfo(
+          s"""Schema of the Parquet file written by parquet-hive:
+             |$schema
+           """.stripMargin)
 
-    // Unfortunately parquet-hive doesn't add `UTF8` annotation to BINARY when writing strings.
-    // Have to assume all BINARY values are strings here.
-    withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING.key -> "true") {
-      checkAnswer(sqlContext.read.parquet(parquetStore.getCanonicalPath), makeRows)
+        // Unfortunately parquet-hive doesn't add `UTF8` annotation to BINARY when writing strings.
+        // Have to assume all BINARY values are strings here.
+        withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING.key -> "true") {
+          checkAnswer(sqlContext.read.parquet(path), makeRows)
+        }
+      }
     }
   }
 

From 2e680668f7b6fc158aa068aedd19c1878ecf759e Mon Sep 17 00:00:00 2001
From: Tom White <tom@cloudera.com>
Date: Wed, 12 Aug 2015 10:06:27 -0500
Subject: [PATCH 190/633] [SPARK-8625] [CORE] Propagate user exceptions in
 tasks back to driver

This allows clients to retrieve the original exception from the
cause field of the SparkException that is thrown by the driver.
If the original exception is not in fact Serializable then it will
not be returned, but the message and stacktrace will be. (All Java
Throwables implement the Serializable interface, but this is no
guarantee that a particular implementation can actually be
serialized.)

Author: Tom White <tom@cloudera.com>

Closes #7014 from tomwhite/propagate-user-exceptions.
---
 .../org/apache/spark/TaskEndReason.scala      | 44 ++++++++++++-
 .../org/apache/spark/executor/Executor.scala  | 14 +++-
 .../apache/spark/scheduler/DAGScheduler.scala | 44 ++++++++-----
 .../spark/scheduler/DAGSchedulerEvent.scala   |  3 +-
 .../spark/scheduler/TaskSetManager.scala      | 12 ++--
 .../org/apache/spark/util/JsonProtocol.scala  |  2 +-
 .../ExecutorAllocationManagerSuite.scala      |  2 +-
 .../scala/org/apache/spark/FailureSuite.scala | 66 ++++++++++++++++++-
 .../spark/scheduler/DAGSchedulerSuite.scala   |  2 +-
 .../spark/scheduler/TaskSetManagerSuite.scala |  5 +-
 .../ui/jobs/JobProgressListenerSuite.scala    |  2 +-
 .../apache/spark/util/JsonProtocolSuite.scala |  3 +-
 12 files changed, 165 insertions(+), 34 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/TaskEndReason.scala b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
index 48fd3e7e23d52..934d00dc708b9 100644
--- a/core/src/main/scala/org/apache/spark/TaskEndReason.scala
+++ b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark
 
+import java.io.{IOException, ObjectInputStream, ObjectOutputStream}
+
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.storage.BlockManagerId
@@ -90,6 +92,10 @@ case class FetchFailed(
  *
  * `fullStackTrace` is a better representation of the stack trace because it contains the whole
  * stack trace including the exception and its causes
+ *
+ * `exception` is the actual exception that caused the task to fail. It may be `None` in
+ * the case that the exception is not in fact serializable. If a task fails more than
+ * once (due to retries), `exception` is that one that caused the last failure.
  */
 @DeveloperApi
 case class ExceptionFailure(
@@ -97,11 +103,26 @@ case class ExceptionFailure(
     description: String,
     stackTrace: Array[StackTraceElement],
     fullStackTrace: String,
-    metrics: Option[TaskMetrics])
+    metrics: Option[TaskMetrics],
+    private val exceptionWrapper: Option[ThrowableSerializationWrapper])
   extends TaskFailedReason {
 
+  /**
+   * `preserveCause` is used to keep the exception itself so it is available to the
+   * driver. This may be set to `false` in the event that the exception is not in fact
+   * serializable.
+   */
+  private[spark] def this(e: Throwable, metrics: Option[TaskMetrics], preserveCause: Boolean) {
+    this(e.getClass.getName, e.getMessage, e.getStackTrace, Utils.exceptionString(e), metrics,
+      if (preserveCause) Some(new ThrowableSerializationWrapper(e)) else None)
+  }
+
   private[spark] def this(e: Throwable, metrics: Option[TaskMetrics]) {
-    this(e.getClass.getName, e.getMessage, e.getStackTrace, Utils.exceptionString(e), metrics)
+    this(e, metrics, preserveCause = true)
+  }
+
+  def exception: Option[Throwable] = exceptionWrapper.flatMap {
+    (w: ThrowableSerializationWrapper) => Option(w.exception)
   }
 
   override def toErrorString: String =
@@ -127,6 +148,25 @@ case class ExceptionFailure(
   }
 }
 
+/**
+ * A class for recovering from exceptions when deserializing a Throwable that was
+ * thrown in user task code. If the Throwable cannot be deserialized it will be null,
+ * but the stacktrace and message will be preserved correctly in SparkException.
+ */
+private[spark] class ThrowableSerializationWrapper(var exception: Throwable) extends
+    Serializable with Logging {
+  private def writeObject(out: ObjectOutputStream): Unit = {
+    out.writeObject(exception)
+  }
+  private def readObject(in: ObjectInputStream): Unit = {
+    try {
+      exception = in.readObject().asInstanceOf[Throwable]
+    } catch {
+      case e : Exception => log.warn("Task exception could not be deserialized", e)
+    }
+  }
+}
+
 /**
  * :: DeveloperApi ::
  * The task finished successfully, but the result was lost from the executor's block manager before
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 5d78a9dc8885e..42a85e42ea2b6 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.executor
 
-import java.io.File
+import java.io.{File, NotSerializableException}
 import java.lang.management.ManagementFactory
 import java.net.URL
 import java.nio.ByteBuffer
@@ -305,8 +305,16 @@ private[spark] class Executor(
               m
             }
           }
-          val taskEndReason = new ExceptionFailure(t, metrics)
-          execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(taskEndReason))
+          val serializedTaskEndReason = {
+            try {
+              ser.serialize(new ExceptionFailure(t, metrics))
+            } catch {
+              case _: NotSerializableException =>
+                // t is not serializable so just send the stacktrace
+                ser.serialize(new ExceptionFailure(t, metrics, false))
+            }
+          }
+          execBackend.statusUpdate(taskId, TaskState.FAILED, serializedTaskEndReason)
 
           // Don't forcibly exit unless the exception was inherently fatal, to avoid
           // stopping other tasks unnecessarily.
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index bb489c6b6e98f..7ab5ccf50adb7 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -200,8 +200,8 @@ class DAGScheduler(
 
   // Called by TaskScheduler to cancel an entire TaskSet due to either repeated failures or
   // cancellation of the job itself.
-  def taskSetFailed(taskSet: TaskSet, reason: String): Unit = {
-    eventProcessLoop.post(TaskSetFailed(taskSet, reason))
+  def taskSetFailed(taskSet: TaskSet, reason: String, exception: Option[Throwable]): Unit = {
+    eventProcessLoop.post(TaskSetFailed(taskSet, reason, exception))
   }
 
   private[scheduler]
@@ -677,8 +677,11 @@ class DAGScheduler(
     submitWaitingStages()
   }
 
-  private[scheduler] def handleTaskSetFailed(taskSet: TaskSet, reason: String) {
-    stageIdToStage.get(taskSet.stageId).foreach {abortStage(_, reason) }
+  private[scheduler] def handleTaskSetFailed(
+      taskSet: TaskSet,
+      reason: String,
+      exception: Option[Throwable]): Unit = {
+    stageIdToStage.get(taskSet.stageId).foreach { abortStage(_, reason, exception) }
     submitWaitingStages()
   }
 
@@ -762,7 +765,7 @@ class DAGScheduler(
         }
       }
     } else {
-      abortStage(stage, "No active job for stage " + stage.id)
+      abortStage(stage, "No active job for stage " + stage.id, None)
     }
   }
 
@@ -816,7 +819,7 @@ class DAGScheduler(
       case NonFatal(e) =>
         stage.makeNewStageAttempt(partitionsToCompute.size)
         listenerBus.post(SparkListenerStageSubmitted(stage.latestInfo, properties))
-        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}")
+        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}", Some(e))
         runningStages -= stage
         return
     }
@@ -845,13 +848,13 @@ class DAGScheduler(
     } catch {
       // In the case of a failure during serialization, abort the stage.
       case e: NotSerializableException =>
-        abortStage(stage, "Task not serializable: " + e.toString)
+        abortStage(stage, "Task not serializable: " + e.toString, Some(e))
         runningStages -= stage
 
         // Abort execution
         return
       case NonFatal(e) =>
-        abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}")
+        abortStage(stage, s"Task serialization failed: $e\n${e.getStackTraceString}", Some(e))
         runningStages -= stage
         return
     }
@@ -878,7 +881,7 @@ class DAGScheduler(
       }
     } catch {
       case NonFatal(e) =>
-        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}")
+        abortStage(stage, s"Task creation failed: $e\n${e.getStackTraceString}", Some(e))
         runningStages -= stage
         return
     }
@@ -1098,7 +1101,8 @@ class DAGScheduler(
           }
 
           if (disallowStageRetryForTest) {
-            abortStage(failedStage, "Fetch failure will not retry stage due to testing config")
+            abortStage(failedStage, "Fetch failure will not retry stage due to testing config",
+              None)
           } else if (failedStages.isEmpty) {
             // Don't schedule an event to resubmit failed stages if failed isn't empty, because
             // in that case the event will already have been scheduled.
@@ -1126,7 +1130,7 @@ class DAGScheduler(
       case commitDenied: TaskCommitDenied =>
         // Do nothing here, left up to the TaskScheduler to decide how to handle denied commits
 
-      case ExceptionFailure(className, description, stackTrace, fullStackTrace, metrics) =>
+      case exceptionFailure: ExceptionFailure =>
         // Do nothing here, left up to the TaskScheduler to decide how to handle user failures
 
       case TaskResultLost =>
@@ -1235,7 +1239,10 @@ class DAGScheduler(
    * Aborts all jobs depending on a particular Stage. This is called in response to a task set
    * being canceled by the TaskScheduler. Use taskSetFailed() to inject this event from outside.
    */
-  private[scheduler] def abortStage(failedStage: Stage, reason: String) {
+  private[scheduler] def abortStage(
+      failedStage: Stage,
+      reason: String,
+      exception: Option[Throwable]): Unit = {
     if (!stageIdToStage.contains(failedStage.id)) {
       // Skip all the actions if the stage has been removed.
       return
@@ -1244,7 +1251,7 @@ class DAGScheduler(
       activeJobs.filter(job => stageDependsOn(job.finalStage, failedStage)).toSeq
     failedStage.latestInfo.completionTime = Some(clock.getTimeMillis())
     for (job <- dependentJobs) {
-      failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason")
+      failJobAndIndependentStages(job, s"Job aborted due to stage failure: $reason", exception)
     }
     if (dependentJobs.isEmpty) {
       logInfo("Ignoring failure of " + failedStage + " because all jobs depending on it are done")
@@ -1252,8 +1259,11 @@ class DAGScheduler(
   }
 
   /** Fails a job and all stages that are only used by that job, and cleans up relevant state. */
-  private def failJobAndIndependentStages(job: ActiveJob, failureReason: String) {
-    val error = new SparkException(failureReason)
+  private def failJobAndIndependentStages(
+      job: ActiveJob,
+      failureReason: String,
+      exception: Option[Throwable] = None): Unit = {
+    val error = new SparkException(failureReason, exception.getOrElse(null))
     var ableToCancelStages = true
 
     val shouldInterruptThread =
@@ -1462,8 +1472,8 @@ private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler
     case completion @ CompletionEvent(task, reason, _, _, taskInfo, taskMetrics) =>
       dagScheduler.handleTaskCompletion(completion)
 
-    case TaskSetFailed(taskSet, reason) =>
-      dagScheduler.handleTaskSetFailed(taskSet, reason)
+    case TaskSetFailed(taskSet, reason, exception) =>
+      dagScheduler.handleTaskSetFailed(taskSet, reason, exception)
 
     case ResubmitFailedStages =>
       dagScheduler.resubmitFailedStages()
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
index a213d419cf033..f72a52e85dc15 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
@@ -73,6 +73,7 @@ private[scheduler] case class ExecutorAdded(execId: String, host: String) extend
 private[scheduler] case class ExecutorLost(execId: String) extends DAGSchedulerEvent
 
 private[scheduler]
-case class TaskSetFailed(taskSet: TaskSet, reason: String) extends DAGSchedulerEvent
+case class TaskSetFailed(taskSet: TaskSet, reason: String, exception: Option[Throwable])
+  extends DAGSchedulerEvent
 
 private[scheduler] case object ResubmitFailedStages extends DAGSchedulerEvent
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
index 82455b0426a5d..818b95d67f6be 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
@@ -662,7 +662,7 @@ private[spark] class TaskSetManager(
 
     val failureReason = s"Lost task ${info.id} in stage ${taskSet.id} (TID $tid, ${info.host}): " +
       reason.asInstanceOf[TaskFailedReason].toErrorString
-    reason match {
+    val failureException: Option[Throwable] = reason match {
       case fetchFailed: FetchFailed =>
         logWarning(failureReason)
         if (!successful(index)) {
@@ -671,6 +671,7 @@ private[spark] class TaskSetManager(
         }
         // Not adding to failed executors for FetchFailed.
         isZombie = true
+        None
 
       case ef: ExceptionFailure =>
         taskMetrics = ef.metrics.orNull
@@ -706,12 +707,15 @@ private[spark] class TaskSetManager(
             s"Lost task ${info.id} in stage ${taskSet.id} (TID $tid) on executor ${info.host}: " +
             s"${ef.className} (${ef.description}) [duplicate $dupCount]")
         }
+        ef.exception
 
       case e: TaskFailedReason =>  // TaskResultLost, TaskKilled, and others
         logWarning(failureReason)
+        None
 
       case e: TaskEndReason =>
         logError("Unknown TaskEndReason: " + e)
+        None
     }
     // always add to failed executors
     failedExecutors.getOrElseUpdate(index, new HashMap[String, Long]()).
@@ -728,16 +732,16 @@ private[spark] class TaskSetManager(
         logError("Task %d in stage %s failed %d times; aborting job".format(
           index, taskSet.id, maxTaskFailures))
         abort("Task %d in stage %s failed %d times, most recent failure: %s\nDriver stacktrace:"
-          .format(index, taskSet.id, maxTaskFailures, failureReason))
+          .format(index, taskSet.id, maxTaskFailures, failureReason), failureException)
         return
       }
     }
     maybeFinishTaskSet()
   }
 
-  def abort(message: String): Unit = sched.synchronized {
+  def abort(message: String, exception: Option[Throwable] = None): Unit = sched.synchronized {
     // TODO: Kill running tasks if we were not terminated due to a Mesos error
-    sched.dagScheduler.taskSetFailed(taskSet, message)
+    sched.dagScheduler.taskSetFailed(taskSet, message, exception)
     isZombie = true
     maybeFinishTaskSet()
   }
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index c600319d9ddb4..cbc94fd6d54d9 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -790,7 +790,7 @@ private[spark] object JsonProtocol {
         val fullStackTrace = Utils.jsonOption(json \ "Full Stack Trace").
           map(_.extract[String]).orNull
         val metrics = Utils.jsonOption(json \ "Metrics").map(taskMetricsFromJson)
-        ExceptionFailure(className, description, stackTrace, fullStackTrace, metrics)
+        ExceptionFailure(className, description, stackTrace, fullStackTrace, metrics, None)
       case `taskResultLost` => TaskResultLost
       case `taskKilled` => TaskKilled
       case `executorLostFailure` =>
diff --git a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
index f374f97f87448..116f027a0f987 100644
--- a/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ExecutorAllocationManagerSuite.scala
@@ -800,7 +800,7 @@ class ExecutorAllocationManagerSuite
     assert(maxNumExecutorsNeeded(manager) === 1)
 
     // If the task is failed, we expect it to be resubmitted later.
-    val taskEndReason = ExceptionFailure(null, null, null, null, null)
+    val taskEndReason = ExceptionFailure(null, null, null, null, null, None)
     sc.listenerBus.postToAll(SparkListenerTaskEnd(0, 0, null, taskEndReason, taskInfo, null))
     assert(maxNumExecutorsNeeded(manager) === 1)
   }
diff --git a/core/src/test/scala/org/apache/spark/FailureSuite.scala b/core/src/test/scala/org/apache/spark/FailureSuite.scala
index 69cb4b44cf7ef..aa50a49c50232 100644
--- a/core/src/test/scala/org/apache/spark/FailureSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FailureSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark
 
 import org.apache.spark.util.NonSerializable
 
-import java.io.NotSerializableException
+import java.io.{IOException, NotSerializableException, ObjectInputStream}
 
 // Common state shared by FailureSuite-launched tasks. We use a global object
 // for this because any local variables used in the task closures will rightfully
@@ -166,5 +166,69 @@ class FailureSuite extends SparkFunSuite with LocalSparkContext {
     assert(thrownDueToMemoryLeak.getMessage.contains("memory leak"))
   }
 
+  // Run a 3-task map job in which task 1 always fails with a exception message that
+  // depends on the failure number, and check that we get the last failure.
+  test("last failure cause is sent back to driver") {
+    sc = new SparkContext("local[1,2]", "test")
+    val data = sc.makeRDD(1 to 3, 3).map { x =>
+      FailureSuiteState.synchronized {
+        FailureSuiteState.tasksRun += 1
+        if (x == 3) {
+          FailureSuiteState.tasksFailed += 1
+          throw new UserException("oops",
+            new IllegalArgumentException("failed=" + FailureSuiteState.tasksFailed))
+        }
+      }
+      x * x
+    }
+    val thrown = intercept[SparkException] {
+      data.collect()
+    }
+    FailureSuiteState.synchronized {
+      assert(FailureSuiteState.tasksRun === 4)
+    }
+    assert(thrown.getClass === classOf[SparkException])
+    assert(thrown.getCause.getClass === classOf[UserException])
+    assert(thrown.getCause.getMessage === "oops")
+    assert(thrown.getCause.getCause.getClass === classOf[IllegalArgumentException])
+    assert(thrown.getCause.getCause.getMessage === "failed=2")
+    FailureSuiteState.clear()
+  }
+
+  test("failure cause stacktrace is sent back to driver if exception is not serializable") {
+    sc = new SparkContext("local", "test")
+    val thrown = intercept[SparkException] {
+      sc.makeRDD(1 to 3).foreach { _ => throw new NonSerializableUserException }
+    }
+    assert(thrown.getClass === classOf[SparkException])
+    assert(thrown.getCause === null)
+    assert(thrown.getMessage.contains("NonSerializableUserException"))
+    FailureSuiteState.clear()
+  }
+
+  test("failure cause stacktrace is sent back to driver if exception is not deserializable") {
+    sc = new SparkContext("local", "test")
+    val thrown = intercept[SparkException] {
+      sc.makeRDD(1 to 3).foreach { _ => throw new NonDeserializableUserException }
+    }
+    assert(thrown.getClass === classOf[SparkException])
+    assert(thrown.getCause === null)
+    assert(thrown.getMessage.contains("NonDeserializableUserException"))
+    FailureSuiteState.clear()
+  }
+
   // TODO: Need to add tests with shuffle fetch failures.
 }
+
+class UserException(message: String, cause: Throwable)
+  extends RuntimeException(message, cause)
+
+class NonSerializableUserException extends RuntimeException {
+  val nonSerializableInstanceVariable = new NonSerializable
+}
+
+class NonDeserializableUserException extends RuntimeException {
+  private def readObject(in: ObjectInputStream): Unit = {
+    throw new IOException("Intentional exception during deserialization.")
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 86dff8fb577d5..b0ca49cbea4f7 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -242,7 +242,7 @@ class DAGSchedulerSuite
 
   /** Sends TaskSetFailed to the scheduler. */
   private def failed(taskSet: TaskSet, message: String) {
-    runEvent(TaskSetFailed(taskSet, message))
+    runEvent(TaskSetFailed(taskSet, message, None))
   }
 
   /** Sends JobCancelled to the DAG scheduler. */
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index f7cc4bb61d574..edbdb485c5ea4 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -48,7 +48,10 @@ class FakeDAGScheduler(sc: SparkContext, taskScheduler: FakeTaskScheduler)
 
   override def executorLost(execId: String) {}
 
-  override def taskSetFailed(taskSet: TaskSet, reason: String) {
+  override def taskSetFailed(
+      taskSet: TaskSet,
+      reason: String,
+      exception: Option[Throwable]): Unit = {
     taskScheduler.taskSetsFailed += taskSet.id
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
index 56f7b9cf1f358..b140387d309f3 100644
--- a/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/jobs/JobProgressListenerSuite.scala
@@ -240,7 +240,7 @@ class JobProgressListenerSuite extends SparkFunSuite with LocalSparkContext with
     val taskFailedReasons = Seq(
       Resubmitted,
       new FetchFailed(null, 0, 0, 0, "ignored"),
-      ExceptionFailure("Exception", "description", null, null, None),
+      ExceptionFailure("Exception", "description", null, null, None, None),
       TaskResultLost,
       TaskKilled,
       ExecutorLostFailure("0"),
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index dde95f3778434..343a4139b0ca8 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -163,7 +163,8 @@ class JsonProtocolSuite extends SparkFunSuite {
   }
 
   test("ExceptionFailure backward compatibility") {
-    val exceptionFailure = ExceptionFailure("To be", "or not to be", stackTrace, null, None)
+    val exceptionFailure = ExceptionFailure("To be", "or not to be", stackTrace, null,
+      None, None)
     val oldEvent = JsonProtocol.taskEndReasonToJson(exceptionFailure)
       .removeField({ _._1 == "Full Stack Trace" })
     assertEquals(exceptionFailure, JsonProtocol.taskEndReasonFromJson(oldEvent))

From be5d1912076c2ffd21ec88611e53d3b3c59b7ecc Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Wed, 12 Aug 2015 09:24:50 -0700
Subject: [PATCH 191/633] [SPARK-9795] Dynamic allocation: avoid double
 counting when killing same executor twice

This is based on KaiXinXiaoLei's changes in #7716.

The issue is that when someone calls `sc.killExecutor("1")` on the same executor twice quickly, then the executor target will be adjusted downwards by 2 instead of 1 even though we're only actually killing one executor. In certain cases where we don't adjust the target back upwards quickly, we'll end up with jobs hanging.

This is a common danger because there are many places where this is called:
- `HeartbeatReceiver` kills an executor that has not been sending heartbeats
- `ExecutorAllocationManager` kills an executor that has been idle
- The user code might call this, which may interfere with the previous callers

While it's not clear whether this fixes SPARK-9745, fixing this potential race condition seems like a strict improvement. I've added a regression test to illustrate the issue.

Author: Andrew Or <andrew@databricks.com>

Closes #8078 from andrewor14/da-double-kill.
---
 .../CoarseGrainedSchedulerBackend.scala       | 11 ++++++----
 .../StandaloneDynamicAllocationSuite.scala    | 20 +++++++++++++++++++
 2 files changed, 27 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index 6acf8a9a5e9b4..5730a87f960a0 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -422,16 +422,19 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
       logWarning(s"Executor to kill $id does not exist!")
     }
 
+    // If an executor is already pending to be removed, do not kill it again (SPARK-9795)
+    val executorsToKill = knownExecutors.filter { id => !executorsPendingToRemove.contains(id) }
+    executorsPendingToRemove ++= executorsToKill
+
     // If we do not wish to replace the executors we kill, sync the target number of executors
     // with the cluster manager to avoid allocating new ones. When computing the new target,
     // take into account executors that are pending to be added or removed.
     if (!replace) {
-      doRequestTotalExecutors(numExistingExecutors + numPendingExecutors
-        - executorsPendingToRemove.size - knownExecutors.size)
+      doRequestTotalExecutors(
+        numExistingExecutors + numPendingExecutors - executorsPendingToRemove.size)
     }
 
-    executorsPendingToRemove ++= knownExecutors
-    doKillExecutors(knownExecutors)
+    doKillExecutors(executorsToKill)
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala b/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala
index 08c41a897a861..1f2a0f0d309ce 100644
--- a/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/StandaloneDynamicAllocationSuite.scala
@@ -283,6 +283,26 @@ class StandaloneDynamicAllocationSuite
     assert(master.apps.head.getExecutorLimit === 1000)
   }
 
+  test("kill the same executor twice (SPARK-9795)") {
+    sc = new SparkContext(appConf)
+    val appId = sc.applicationId
+    assert(master.apps.size === 1)
+    assert(master.apps.head.id === appId)
+    assert(master.apps.head.executors.size === 2)
+    assert(master.apps.head.getExecutorLimit === Int.MaxValue)
+    // sync executors between the Master and the driver, needed because
+    // the driver refuses to kill executors it does not know about
+    syncExecutors(sc)
+    // kill the same executor twice
+    val executors = getExecutorIds(sc)
+    assert(executors.size === 2)
+    assert(sc.killExecutor(executors.head))
+    assert(sc.killExecutor(executors.head))
+    assert(master.apps.head.executors.size === 1)
+    // The limit should not be lowered twice
+    assert(master.apps.head.getExecutorLimit === 1)
+  }
+
   // ===============================
   // | Utility methods for testing |
   // ===============================

From 66d87c1d76bea2b81993156ac1fa7dad6c312ebf Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Wed, 12 Aug 2015 09:35:32 -0700
Subject: [PATCH 192/633] [SPARK-7583] [MLLIB] User guide update for
 RegexTokenizer

jira: https://issues.apache.org/jira/browse/SPARK-7583

User guide update for RegexTokenizer

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #7828 from hhbyyh/regexTokenizerDoc.
---
 docs/ml-features.md | 41 ++++++++++++++++++++++++++++++-----------
 1 file changed, 30 insertions(+), 11 deletions(-)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index fa0ad1f00ab12..cec2cbe673407 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -217,21 +217,32 @@ for feature in result.select("result").take(3):
 
 [Tokenization](http://en.wikipedia.org/wiki/Lexical_analysis#Tokenization) is the process of taking text (such as a sentence) and breaking it into individual terms (usually words).  A simple [Tokenizer](api/scala/index.html#org.apache.spark.ml.feature.Tokenizer) class provides this functionality.  The example below shows how to split sentences into sequences of words.
 
-Note: A more advanced tokenizer is provided via [RegexTokenizer](api/scala/index.html#org.apache.spark.ml.feature.RegexTokenizer).
+[RegexTokenizer](api/scala/index.html#org.apache.spark.ml.feature.RegexTokenizer) allows more
+ advanced tokenization based on regular expression (regex) matching.
+ By default, the parameter "pattern" (regex, default: \\s+) is used as delimiters to split the input text.
+ Alternatively, users can set parameter "gaps" to false indicating the regex "pattern" denotes
+ "tokens" rather than splitting gaps, and find all matching occurrences as the tokenization result.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
-import org.apache.spark.ml.feature.Tokenizer
+import org.apache.spark.ml.feature.{Tokenizer, RegexTokenizer}
 
 val sentenceDataFrame = sqlContext.createDataFrame(Seq(
   (0, "Hi I heard about Spark"),
-  (0, "I wish Java could use case classes"),
-  (1, "Logistic regression models are neat")
+  (1, "I wish Java could use case classes"),
+  (2, "Logistic,regression,models,are,neat")
 )).toDF("label", "sentence")
 val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words")
-val wordsDataFrame = tokenizer.transform(sentenceDataFrame)
-wordsDataFrame.select("words", "label").take(3).foreach(println)
+val regexTokenizer = new RegexTokenizer()
+  .setInputCol("sentence")
+  .setOutputCol("words")
+  .setPattern("\\W")  // alternatively .setPattern("\\w+").setGaps(false)
+
+val tokenized = tokenizer.transform(sentenceDataFrame)
+tokenized.select("words", "label").take(3).foreach(println)
+val regexTokenized = regexTokenizer.transform(sentenceDataFrame)
+regexTokenized.select("words", "label").take(3).foreach(println)
 {% endhighlight %}
 </div>
 
@@ -240,6 +251,7 @@ wordsDataFrame.select("words", "label").take(3).foreach(println)
 import com.google.common.collect.Lists;
 
 import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.RegexTokenizer;
 import org.apache.spark.ml.feature.Tokenizer;
 import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.sql.DataFrame;
@@ -252,8 +264,8 @@ import org.apache.spark.sql.types.StructType;
 
 JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
   RowFactory.create(0, "Hi I heard about Spark"),
-  RowFactory.create(0, "I wish Java could use case classes"),
-  RowFactory.create(1, "Logistic regression models are neat")
+  RowFactory.create(1, "I wish Java could use case classes"),
+  RowFactory.create(2, "Logistic,regression,models,are,neat")
 ));
 StructType schema = new StructType(new StructField[]{
   new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
@@ -267,22 +279,29 @@ for (Row r : wordsDataFrame.select("words", "label").take(3)) {
   for (String word : words) System.out.print(word + " ");
   System.out.println();
 }
+
+RegexTokenizer regexTokenizer = new RegexTokenizer()
+  .setInputCol("sentence")
+  .setOutputCol("words")
+  .setPattern("\\W");  // alternatively .setPattern("\\w+").setGaps(false);
 {% endhighlight %}
 </div>
 
 <div data-lang="python" markdown="1">
 {% highlight python %}
-from pyspark.ml.feature import Tokenizer
+from pyspark.ml.feature import Tokenizer, RegexTokenizer
 
 sentenceDataFrame = sqlContext.createDataFrame([
   (0, "Hi I heard about Spark"),
-  (0, "I wish Java could use case classes"),
-  (1, "Logistic regression models are neat")
+  (1, "I wish Java could use case classes"),
+  (2, "Logistic,regression,models,are,neat")
 ], ["label", "sentence"])
 tokenizer = Tokenizer(inputCol="sentence", outputCol="words")
 wordsDataFrame = tokenizer.transform(sentenceDataFrame)
 for words_label in wordsDataFrame.select("words", "label").take(3):
   print(words_label)
+regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern="\\W")
+# alternatively, pattern="\\w+", gaps(False)
 {% endhighlight %}
 </div>
 </div>

From e0110792ef71ebfd3727b970346a2e13695990a4 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Wed, 12 Aug 2015 10:08:35 -0700
Subject: [PATCH 193/633] [SPARK-9747] [SQL] Avoid starving an unsafe operator
 in aggregation

This is the sister patch to #8011, but for aggregation.

In a nutshell: create the `TungstenAggregationIterator` before computing the parent partition. Internally this creates a `BytesToBytesMap` which acquires a page in the constructor as of this patch. This ensures that the aggregation operator is not starved since we reserve at least 1 page in advance.

rxin yhuai

Author: Andrew Or <andrew@databricks.com>

Closes #8038 from andrewor14/unsafe-starve-memory-agg.
---
 .../spark/unsafe/map/BytesToBytesMap.java     | 34 +++++--
 .../unsafe/sort/UnsafeExternalSorter.java     |  9 +-
 .../map/AbstractBytesToBytesMapSuite.java     | 11 ++-
 .../UnsafeFixedWidthAggregationMap.java       |  7 ++
 .../aggregate/TungstenAggregate.scala         | 72 +++++++++------
 .../TungstenAggregationIterator.scala         | 88 +++++++++++--------
 .../TungstenAggregationIteratorSuite.scala    | 56 ++++++++++++
 7 files changed, 201 insertions(+), 76 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 85b46ec8bfae3..87ed47e88c4ef 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -193,6 +193,11 @@ public BytesToBytesMap(
         TaskMemoryManager.MAXIMUM_PAGE_SIZE_BYTES);
     }
     allocate(initialCapacity);
+
+    // Acquire a new page as soon as we construct the map to ensure that we have at least
+    // one page to work with. Otherwise, other operators in the same task may starve this
+    // map (SPARK-9747).
+    acquireNewPage();
   }
 
   public BytesToBytesMap(
@@ -574,16 +579,9 @@ public boolean putNewKey(
           final long lengthOffsetInPage = currentDataPage.getBaseOffset() + pageCursor;
           Platform.putInt(pageBaseObject, lengthOffsetInPage, END_OF_PAGE_MARKER);
         }
-        final long memoryGranted = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
-        if (memoryGranted != pageSizeBytes) {
-          shuffleMemoryManager.release(memoryGranted);
-          logger.debug("Failed to acquire {} bytes of memory", pageSizeBytes);
+        if (!acquireNewPage()) {
           return false;
         }
-        MemoryBlock newPage = taskMemoryManager.allocatePage(pageSizeBytes);
-        dataPages.add(newPage);
-        pageCursor = 0;
-        currentDataPage = newPage;
         dataPage = currentDataPage;
         dataPageBaseObject = currentDataPage.getBaseObject();
         dataPageInsertOffset = currentDataPage.getBaseOffset();
@@ -642,6 +640,24 @@ public boolean putNewKey(
     }
   }
 
+  /**
+   * Acquire a new page from the {@link ShuffleMemoryManager}.
+   * @return whether there is enough space to allocate the new page.
+   */
+  private boolean acquireNewPage() {
+    final long memoryGranted = shuffleMemoryManager.tryToAcquire(pageSizeBytes);
+    if (memoryGranted != pageSizeBytes) {
+      shuffleMemoryManager.release(memoryGranted);
+      logger.debug("Failed to acquire {} bytes of memory", pageSizeBytes);
+      return false;
+    }
+    MemoryBlock newPage = taskMemoryManager.allocatePage(pageSizeBytes);
+    dataPages.add(newPage);
+    pageCursor = 0;
+    currentDataPage = newPage;
+    return true;
+  }
+
   /**
    * Allocate new data structures for this map. When calling this outside of the constructor,
    * make sure to keep references to the old data structures so that you can free them.
@@ -748,7 +764,7 @@ public long getNumHashCollisions() {
   }
 
   @VisibleForTesting
-  int getNumDataPages() {
+  public int getNumDataPages() {
     return dataPages.size();
   }
 
diff --git a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
index 9601aafe55464..fc364e0a895b1 100644
--- a/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java
@@ -132,16 +132,15 @@ private UnsafeExternalSorter(
 
     if (existingInMemorySorter == null) {
       initializeForWriting();
+      // Acquire a new page as soon as we construct the sorter to ensure that we have at
+      // least one page to work with. Otherwise, other operators in the same task may starve
+      // this sorter (SPARK-9709). We don't need to do this if we already have an existing sorter.
+      acquireNewPage();
     } else {
       this.isInMemSorterExternal = true;
       this.inMemSorter = existingInMemorySorter;
     }
 
-    // Acquire a new page as soon as we construct the sorter to ensure that we have at
-    // least one page to work with. Otherwise, other operators in the same task may starve
-    // this sorter (SPARK-9709).
-    acquireNewPage();
-
     // Register a cleanup task with TaskContext to ensure that memory is guaranteed to be freed at
     // the end of the task. This is necessary to avoid memory leaks in when the downstream operator
     // does not fully consume the sorter's output (e.g. sort followed by limit).
diff --git a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
index 1a79c20c35246..ab480b60adaed 100644
--- a/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
+++ b/core/src/test/java/org/apache/spark/unsafe/map/AbstractBytesToBytesMapSuite.java
@@ -543,7 +543,7 @@ public void testPeakMemoryUsed() {
           Platform.LONG_ARRAY_OFFSET,
           8);
         newPeakMemory = map.getPeakMemoryUsedBytes();
-        if (i % numRecordsPerPage == 0) {
+        if (i % numRecordsPerPage == 0 && i > 0) {
           // We allocated a new page for this record, so peak memory should change
           assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
         } else {
@@ -561,4 +561,13 @@ public void testPeakMemoryUsed() {
       map.free();
     }
   }
+
+  @Test
+  public void testAcquirePageInConstructor() {
+    final BytesToBytesMap map = new BytesToBytesMap(
+      taskMemoryManager, shuffleMemoryManager, 1, PAGE_SIZE_BYTES);
+    assertEquals(1, map.getNumDataPages());
+    map.free();
+  }
+
 }
diff --git a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
index 5cce41d5a7569..09511ff35f785 100644
--- a/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
+++ b/sql/core/src/main/java/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMap.java
@@ -19,6 +19,8 @@
 
 import java.io.IOException;
 
+import com.google.common.annotations.VisibleForTesting;
+
 import org.apache.spark.SparkEnv;
 import org.apache.spark.shuffle.ShuffleMemoryManager;
 import org.apache.spark.sql.catalyst.InternalRow;
@@ -220,6 +222,11 @@ public long getPeakMemoryUsedBytes() {
     return map.getPeakMemoryUsedBytes();
   }
 
+  @VisibleForTesting
+  public int getNumDataPages() {
+    return map.getNumDataPages();
+  }
+
   /**
    * Free the memory associated with this map. This is idempotent and can be called multiple times.
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index 6b5935a7ce296..c40ca973796a6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.sql.execution.aggregate
 
-import org.apache.spark.rdd.RDD
+import org.apache.spark.TaskContext
+import org.apache.spark.rdd.{MapPartitionsWithPreparationRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateExpression2
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, ClusteredDistribution, AllTuples, Distribution}
+import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.{UnaryNode, SparkPlan}
 import org.apache.spark.sql.execution.metric.SQLMetrics
 
@@ -68,35 +69,56 @@ case class TungstenAggregate(
   protected override def doExecute(): RDD[InternalRow] = attachTree(this, "execute") {
     val numInputRows = longMetric("numInputRows")
     val numOutputRows = longMetric("numOutputRows")
-    child.execute().mapPartitions { iter =>
-      val hasInput = iter.hasNext
-      if (!hasInput && groupingExpressions.nonEmpty) {
-        // This is a grouped aggregate and the input iterator is empty,
-        // so return an empty iterator.
-        Iterator.empty.asInstanceOf[Iterator[UnsafeRow]]
-      } else {
-        val aggregationIterator =
-          new TungstenAggregationIterator(
-            groupingExpressions,
-            nonCompleteAggregateExpressions,
-            completeAggregateExpressions,
-            initialInputBufferOffset,
-            resultExpressions,
-            newMutableProjection,
-            child.output,
-            iter,
-            testFallbackStartsAt,
-            numInputRows,
-            numOutputRows)
-
-        if (!hasInput && groupingExpressions.isEmpty) {
+
+    /**
+     * Set up the underlying unsafe data structures used before computing the parent partition.
+     * This makes sure our iterator is not starved by other operators in the same task.
+     */
+    def preparePartition(): TungstenAggregationIterator = {
+      new TungstenAggregationIterator(
+        groupingExpressions,
+        nonCompleteAggregateExpressions,
+        completeAggregateExpressions,
+        initialInputBufferOffset,
+        resultExpressions,
+        newMutableProjection,
+        child.output,
+        testFallbackStartsAt,
+        numInputRows,
+        numOutputRows)
+    }
+
+    /** Compute a partition using the iterator already set up previously. */
+    def executePartition(
+        context: TaskContext,
+        partitionIndex: Int,
+        aggregationIterator: TungstenAggregationIterator,
+        parentIterator: Iterator[InternalRow]): Iterator[UnsafeRow] = {
+      val hasInput = parentIterator.hasNext
+      if (!hasInput) {
+        // We're not using the underlying map, so we just can free it here
+        aggregationIterator.free()
+        if (groupingExpressions.isEmpty) {
           numOutputRows += 1
           Iterator.single[UnsafeRow](aggregationIterator.outputForEmptyGroupingKeyWithoutInput())
         } else {
-          aggregationIterator
+          // This is a grouped aggregate and the input iterator is empty,
+          // so return an empty iterator.
+          Iterator[UnsafeRow]()
         }
+      } else {
+        aggregationIterator.start(parentIterator)
+        aggregationIterator
       }
     }
+
+    // Note: we need to set up the iterator in each partition before computing the
+    // parent partition, so we cannot simply use `mapPartitions` here (SPARK-9747).
+    val resultRdd = {
+      new MapPartitionsWithPreparationRDD[UnsafeRow, InternalRow, TungstenAggregationIterator](
+        child.execute(), preparePartition, executePartition, preservesPartitioning = true)
+    }
+    resultRdd.asInstanceOf[RDD[InternalRow]]
   }
 
   override def simpleString: String = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index 1f383dd04482f..af7e0fcedbe4e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -72,8 +72,6 @@ import org.apache.spark.sql.types.StructType
  *   the function used to create mutable projections.
  * @param originalInputAttributes
  *   attributes of representing input rows from `inputIter`.
- * @param inputIter
- *   the iterator containing input [[UnsafeRow]]s.
  */
 class TungstenAggregationIterator(
     groupingExpressions: Seq[NamedExpression],
@@ -83,12 +81,14 @@ class TungstenAggregationIterator(
     resultExpressions: Seq[NamedExpression],
     newMutableProjection: (Seq[Expression], Seq[Attribute]) => (() => MutableProjection),
     originalInputAttributes: Seq[Attribute],
-    inputIter: Iterator[InternalRow],
     testFallbackStartsAt: Option[Int],
     numInputRows: LongSQLMetric,
     numOutputRows: LongSQLMetric)
   extends Iterator[UnsafeRow] with Logging {
 
+  // The parent partition iterator, to be initialized later in `start`
+  private[this] var inputIter: Iterator[InternalRow] = null
+
   ///////////////////////////////////////////////////////////////////////////
   // Part 1: Initializing aggregate functions.
   ///////////////////////////////////////////////////////////////////////////
@@ -348,11 +348,15 @@ class TungstenAggregationIterator(
     false // disable tracking of performance metrics
   )
 
+  // Exposed for testing
+  private[aggregate] def getHashMap: UnsafeFixedWidthAggregationMap = hashMap
+
   // The function used to read and process input rows. When processing input rows,
   // it first uses hash-based aggregation by putting groups and their buffers in
   // hashMap. If we could not allocate more memory for the map, we switch to
   // sort-based aggregation (by calling switchToSortBasedAggregation).
   private def processInputs(): Unit = {
+    assert(inputIter != null, "attempted to process input when iterator was null")
     while (!sortBased && inputIter.hasNext) {
       val newInput = inputIter.next()
       numInputRows += 1
@@ -372,6 +376,7 @@ class TungstenAggregationIterator(
   // that it switch to sort-based aggregation after `fallbackStartsAt` input rows have
   // been processed.
   private def processInputsWithControlledFallback(fallbackStartsAt: Int): Unit = {
+    assert(inputIter != null, "attempted to process input when iterator was null")
     var i = 0
     while (!sortBased && inputIter.hasNext) {
       val newInput = inputIter.next()
@@ -412,6 +417,7 @@ class TungstenAggregationIterator(
    * Switch to sort-based aggregation when the hash-based approach is unable to acquire memory.
    */
   private def switchToSortBasedAggregation(firstKey: UnsafeRow, firstInput: InternalRow): Unit = {
+    assert(inputIter != null, "attempted to process input when iterator was null")
     logInfo("falling back to sort based aggregation.")
     // Step 1: Get the ExternalSorter containing sorted entries of the map.
     externalSorter = hashMap.destructAndCreateExternalSorter()
@@ -431,6 +437,11 @@ class TungstenAggregationIterator(
       case _ => false
     }
 
+    // Note: Since we spill the sorter's contents immediately after creating it, we must insert
+    // something into the sorter here to ensure that we acquire at least a page of memory.
+    // This is done through `externalSorter.insertKV`, which will trigger the page allocation.
+    // Otherwise, children operators may steal the window of opportunity and starve our sorter.
+
     if (needsProcess) {
       // First, we create a buffer.
       val buffer = createNewAggregationBuffer()
@@ -588,27 +599,33 @@ class TungstenAggregationIterator(
   //         have not switched to sort-based aggregation.
   ///////////////////////////////////////////////////////////////////////////
 
-  // Starts to process input rows.
-  testFallbackStartsAt match {
-    case None =>
-      processInputs()
-    case Some(fallbackStartsAt) =>
-      // This is the testing path. processInputsWithControlledFallback is same as processInputs
-      // except that it switches to sort-based aggregation after `fallbackStartsAt` input rows
-      // have been processed.
-      processInputsWithControlledFallback(fallbackStartsAt)
-  }
+  /**
+   * Start processing input rows.
+   * Only after this method is called will this iterator be non-empty.
+   */
+  def start(parentIter: Iterator[InternalRow]): Unit = {
+    inputIter = parentIter
+    testFallbackStartsAt match {
+      case None =>
+        processInputs()
+      case Some(fallbackStartsAt) =>
+        // This is the testing path. processInputsWithControlledFallback is same as processInputs
+        // except that it switches to sort-based aggregation after `fallbackStartsAt` input rows
+        // have been processed.
+        processInputsWithControlledFallback(fallbackStartsAt)
+    }
 
-  // If we did not switch to sort-based aggregation in processInputs,
-  // we pre-load the first key-value pair from the map (to make hasNext idempotent).
-  if (!sortBased) {
-    // First, set aggregationBufferMapIterator.
-    aggregationBufferMapIterator = hashMap.iterator()
-    // Pre-load the first key-value pair from the aggregationBufferMapIterator.
-    mapIteratorHasNext = aggregationBufferMapIterator.next()
-    // If the map is empty, we just free it.
-    if (!mapIteratorHasNext) {
-      hashMap.free()
+    // If we did not switch to sort-based aggregation in processInputs,
+    // we pre-load the first key-value pair from the map (to make hasNext idempotent).
+    if (!sortBased) {
+      // First, set aggregationBufferMapIterator.
+      aggregationBufferMapIterator = hashMap.iterator()
+      // Pre-load the first key-value pair from the aggregationBufferMapIterator.
+      mapIteratorHasNext = aggregationBufferMapIterator.next()
+      // If the map is empty, we just free it.
+      if (!mapIteratorHasNext) {
+        hashMap.free()
+      }
     }
   }
 
@@ -673,21 +690,20 @@ class TungstenAggregationIterator(
   }
 
   ///////////////////////////////////////////////////////////////////////////
-  // Part 8: A utility function used to generate a output row when there is no
-  // input and there is no grouping expression.
+  // Part 8: Utility functions
   ///////////////////////////////////////////////////////////////////////////
 
+  /**
+   * Generate a output row when there is no input and there is no grouping expression.
+   */
   def outputForEmptyGroupingKeyWithoutInput(): UnsafeRow = {
-    if (groupingExpressions.isEmpty) {
-      sortBasedAggregationBuffer.copyFrom(initialAggregationBuffer)
-      // We create a output row and copy it. So, we can free the map.
-      val resultCopy =
-        generateOutput(UnsafeRow.createFromByteArray(0, 0), sortBasedAggregationBuffer).copy()
-      hashMap.free()
-      resultCopy
-    } else {
-      throw new IllegalStateException(
-        "This method should not be called when groupingExpressions is not empty.")
-    }
+    assert(groupingExpressions.isEmpty)
+    assert(inputIter == null)
+    generateOutput(UnsafeRow.createFromByteArray(0, 0), initialAggregationBuffer)
+  }
+
+  /** Free memory used in the underlying map. */
+  def free(): Unit = {
+    hashMap.free()
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
new file mode 100644
index 0000000000000..ac22c2f3c0a58
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
@@ -0,0 +1,56 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.aggregate
+
+import org.apache.spark._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection
+import org.apache.spark.sql.execution.metric.SQLMetrics
+import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.unsafe.memory.TaskMemoryManager
+
+class TungstenAggregationIteratorSuite extends SparkFunSuite {
+
+  test("memory acquired on construction") {
+    // set up environment
+    val ctx = TestSQLContext
+
+    val taskMemoryManager = new TaskMemoryManager(SparkEnv.get.executorMemoryManager)
+    val taskContext = new TaskContextImpl(0, 0, 0, 0, taskMemoryManager, null, Seq.empty)
+    TaskContext.setTaskContext(taskContext)
+
+    // Assert that a page is allocated before processing starts
+    var iter: TungstenAggregationIterator = null
+    try {
+      val newMutableProjection = (expr: Seq[Expression], schema: Seq[Attribute]) => {
+        () => new InterpretedMutableProjection(expr, schema)
+      }
+      val dummyAccum = SQLMetrics.createLongMetric(ctx.sparkContext, "dummy")
+      iter = new TungstenAggregationIterator(Seq.empty, Seq.empty, Seq.empty, 0,
+        Seq.empty, newMutableProjection, Seq.empty, None, dummyAccum, dummyAccum)
+      val numPages = iter.getHashMap.getNumDataPages
+      assert(numPages === 1)
+    } finally {
+      // Clean up
+      if (iter != null) {
+        iter.free()
+      }
+      TaskContext.unset()
+    }
+  }
+}

From 57ec27dd7784ce15a2ece8a6c8ac7bd5fd25aea2 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 12 Aug 2015 10:38:30 -0700
Subject: [PATCH 194/633] [SPARK-9804] [HIVE] Use correct value for isSrcLocal
 parameter.

If the correct parameter is not provided, Hive will run into an error
because it calls methods that are specific to the local filesystem to
copy the data.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8086 from vanzin/SPARK-9804.
---
 .../org/apache/spark/sql/hive/client/HiveShim.scala | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index 6e826ce552204..8fc8935b1dc3c 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -25,7 +25,7 @@ import java.util.concurrent.TimeUnit
 
 import scala.collection.JavaConversions._
 
-import org.apache.hadoop.fs.Path
+import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.ql.Driver
 import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table}
@@ -429,7 +429,7 @@ private[client] class Shim_v0_14 extends Shim_v0_13 {
       isSkewedStoreAsSubdir: Boolean): Unit = {
     loadPartitionMethod.invoke(hive, loadPath, tableName, partSpec, replace: JBoolean,
       holdDDLTime: JBoolean, inheritTableSpecs: JBoolean, isSkewedStoreAsSubdir: JBoolean,
-      JBoolean.TRUE, JBoolean.FALSE)
+      isSrcLocal(loadPath, hive.getConf()): JBoolean, JBoolean.FALSE)
   }
 
   override def loadTable(
@@ -439,7 +439,7 @@ private[client] class Shim_v0_14 extends Shim_v0_13 {
       replace: Boolean,
       holdDDLTime: Boolean): Unit = {
     loadTableMethod.invoke(hive, loadPath, tableName, replace: JBoolean, holdDDLTime: JBoolean,
-      JBoolean.TRUE, JBoolean.FALSE, JBoolean.FALSE)
+      isSrcLocal(loadPath, hive.getConf()): JBoolean, JBoolean.FALSE, JBoolean.FALSE)
   }
 
   override def loadDynamicPartitions(
@@ -461,6 +461,13 @@ private[client] class Shim_v0_14 extends Shim_v0_13 {
       HiveConf.ConfVars.METASTORE_CLIENT_CONNECT_RETRY_DELAY,
       TimeUnit.MILLISECONDS).asInstanceOf[Long]
   }
+
+  protected def isSrcLocal(path: Path, conf: HiveConf): Boolean = {
+    val localFs = FileSystem.getLocal(conf)
+    val pathFs = FileSystem.get(path.toUri(), conf)
+    localFs.getUri() == pathFs.getUri()
+  }
+
 }
 
 private[client] class Shim_v1_0 extends Shim_v0_14 {

From 70fe558867ccb4bcff6ec673438b03608bb02252 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Wed, 12 Aug 2015 10:48:52 -0700
Subject: [PATCH 195/633] [SPARK-9847] [ML] Modified copyValues to distinguish
 between default, explicit param values

From JIRA: Currently, Params.copyValues copies default parameter values to the paramMap of the target instance, rather than the defaultParamMap. It should copy to the defaultParamMap because explicitly setting a parameter can change the semantics.
This issue arose in SPARK-9789, where 2 params "threshold" and "thresholds" for LogisticRegression can have mutually exclusive values. If thresholds is set, then fit() will copy the default value of threshold as well, easily resulting in inconsistent settings for the 2 params.

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8115 from jkbradley/copyvalues-fix.
---
 .../org/apache/spark/ml/param/params.scala    | 19 ++++++++++++++++---
 .../apache/spark/ml/param/ParamsSuite.scala   |  8 ++++++++
 2 files changed, 24 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index d68f5ff0053c9..91c0a5631319d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -559,13 +559,26 @@ trait Params extends Identifiable with Serializable {
 
   /**
    * Copies param values from this instance to another instance for params shared by them.
-   * @param to the target instance
-   * @param extra extra params to be copied
+   *
+   * This handles default Params and explicitly set Params separately.
+   * Default Params are copied from and to [[defaultParamMap]], and explicitly set Params are
+   * copied from and to [[paramMap]].
+   * Warning: This implicitly assumes that this [[Params]] instance and the target instance
+   *          share the same set of default Params.
+   *
+   * @param to the target instance, which should work with the same set of default Params as this
+   *           source instance
+   * @param extra extra params to be copied to the target's [[paramMap]]
    * @return the target instance with param values copied
    */
   protected def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T = {
-    val map = extractParamMap(extra)
+    val map = paramMap ++ extra
     params.foreach { param =>
+      // copy default Params
+      if (defaultParamMap.contains(param) && to.hasParam(param.name)) {
+        to.defaultParamMap.put(to.getParam(param.name), defaultParamMap(param))
+      }
+      // copy explicitly set Params
       if (map.contains(param) && to.hasParam(param.name)) {
         to.set(param.name, map(param))
       }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
index 050d4170ea017..be95638d81686 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
@@ -200,6 +200,14 @@ class ParamsSuite extends SparkFunSuite {
     val inArray = ParamValidators.inArray[Int](Array(1, 2))
     assert(inArray(1) && inArray(2) && !inArray(0))
   }
+
+  test("Params.copyValues") {
+    val t = new TestParams()
+    val t2 = t.copy(ParamMap.empty)
+    assert(!t2.isSet(t2.maxIter))
+    val t3 = t.copy(ParamMap(t.maxIter -> 20))
+    assert(t3.isSet(t3.maxIter))
+  }
 }
 
 object ParamsSuite extends SparkFunSuite {

From 60103ecd3d9c92709a5878be7ebd57012813ab48 Mon Sep 17 00:00:00 2001
From: Brennan Ashton <bashton@brennanashton.com>
Date: Wed, 12 Aug 2015 11:57:30 -0700
Subject: [PATCH 196/633] [SPARK-9726] [PYTHON] PySpark DF join no longer
 accepts on=None

rxin

First pull request for Spark so let me know if I am missing anything
The contribution is my original work and I license the work to the project under the project's open source license.

Author: Brennan Ashton <bashton@brennanashton.com>

Closes #8016 from btashton/patch-1.
---
 python/pyspark/sql/dataframe.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 47d5a6a43a84d..09647ff6d0749 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -566,8 +566,7 @@ def join(self, other, on=None, how=None):
 
         if on is None or len(on) == 0:
             jdf = self._jdf.join(other._jdf)
-
-        if isinstance(on[0], basestring):
+        elif isinstance(on[0], basestring):
             jdf = self._jdf.join(other._jdf, self._jseq(on))
         else:
             assert isinstance(on[0], Column), "on should be Column or list of Column"

From 762bacc16ac5e74c8b05a7c1e3e367d1d1633cef Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Wed, 12 Aug 2015 13:24:18 -0700
Subject: [PATCH 197/633] [SPARK-9766] [ML] [PySpark] check and add miss docs
 for PySpark ML

Check and add miss docs for PySpark ML (this issue only check miss docs for o.a.s.ml not o.a.s.mllib).

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8059 from yanboliang/SPARK-9766.
---
 python/pyspark/ml/classification.py | 12 ++++++++++--
 python/pyspark/ml/clustering.py     |  4 +++-
 python/pyspark/ml/evaluation.py     |  3 ++-
 python/pyspark/ml/feature.py        |  9 +++++----
 4 files changed, 20 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 5978d8f4d3a01..6702dce5545a9 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -34,6 +34,7 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
                          HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol):
     """
     Logistic regression.
+    Currently, this class only supports binary classification.
 
     >>> from pyspark.sql import Row
     >>> from pyspark.mllib.linalg import Vectors
@@ -96,8 +97,8 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
         #  is an L2 penalty. For alpha = 1, it is an L1 penalty.
         self.elasticNetParam = \
             Param(self, "elasticNetParam",
-                  "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty " +
-                  "is an L2 penalty. For alpha = 1, it is an L1 penalty.")
+                  "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
+                  "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
         #: param for whether to fit an intercept term.
         self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
         #: param for threshold in binary classification prediction, in range [0, 1].
@@ -656,6 +657,13 @@ class NaiveBayes(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, H
                  HasRawPredictionCol):
     """
     Naive Bayes Classifiers.
+    It supports both Multinomial and Bernoulli NB. Multinomial NB
+    (`http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html`)
+    can handle finitely supported discrete data. For example, by converting documents into
+    TF-IDF vectors, it can be used for document classification. By making every vector a
+    binary (0/1) data, it can also be used as Bernoulli NB
+    (`http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html`).
+    The input feature values must be nonnegative.
 
     >>> from pyspark.sql import Row
     >>> from pyspark.mllib.linalg import Vectors
diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py
index b5e9b6549d9f1..48338713a29ea 100644
--- a/python/pyspark/ml/clustering.py
+++ b/python/pyspark/ml/clustering.py
@@ -37,7 +37,9 @@ def clusterCenters(self):
 @inherit_doc
 class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
     """
-    K-means Clustering
+    K-means clustering with support for multiple parallel runs and a k-means++ like initialization
+    mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested,
+    they are executed together with joint passes over the data for efficiency.
 
     >>> from pyspark.mllib.linalg import Vectors
     >>> data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index 06e809352225b..2734092575ad9 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -23,7 +23,8 @@
 from pyspark.ml.util import keyword_only
 from pyspark.mllib.common import inherit_doc
 
-__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator']
+__all__ = ['Evaluator', 'BinaryClassificationEvaluator', 'RegressionEvaluator',
+           'MulticlassClassificationEvaluator']
 
 
 @inherit_doc
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index cb4dfa21298ce..535d55326646c 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -26,10 +26,11 @@
 from pyspark.mllib.common import inherit_doc
 from pyspark.mllib.linalg import _convert_to_vector
 
-__all__ = ['Binarizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer', 'OneHotEncoder',
-           'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler', 'StandardScalerModel',
-           'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer',
-           'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
+__all__ = ['Binarizer', 'Bucketizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer',
+           'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler',
+           'StandardScalerModel', 'StringIndexer', 'StringIndexerModel', 'Tokenizer',
+           'VectorAssembler', 'VectorIndexer', 'Word2Vec', 'Word2VecModel', 'PCA',
+           'PCAModel', 'RFormula', 'RFormulaModel']
 
 
 @inherit_doc

From 551def5d6972440365bd7436d484a67138d9a8f3 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Wed, 12 Aug 2015 14:27:13 -0700
Subject: [PATCH 198/633] [SPARK-9789] [ML] Added logreg threshold param back

Reinstated LogisticRegression.threshold Param for binary compatibility.  Param thresholds overrides threshold, if set.

CC: mengxr dbtsai feynmanliang

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8079 from jkbradley/logreg-reinstate-threshold.
---
 .../classification/LogisticRegression.scala   | 127 ++++++++++++++----
 .../ml/param/shared/SharedParamsCodeGen.scala |   4 +-
 .../spark/ml/param/shared/sharedParams.scala  |   6 +-
 .../JavaLogisticRegressionSuite.java          |   7 +-
 .../LogisticRegressionSuite.scala             |  33 +++--
 python/pyspark/ml/classification.py           |  98 +++++++++-----
 6 files changed, 199 insertions(+), 76 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index f55134d258857..5bcd7117b668c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -34,8 +34,7 @@ import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Row, SQLContext}
-import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.storage.StorageLevel
 
 /**
@@ -43,44 +42,115 @@ import org.apache.spark.storage.StorageLevel
  */
 private[classification] trait LogisticRegressionParams extends ProbabilisticClassifierParams
   with HasRegParam with HasElasticNetParam with HasMaxIter with HasFitIntercept with HasTol
-  with HasStandardization {
+  with HasStandardization with HasThreshold {
 
   /**
-   * Version of setThresholds() for binary classification, available for backwards
-   * compatibility.
+   * Set threshold in binary classification, in range [0, 1].
    *
-   * Calling this with threshold p will effectively call `setThresholds(Array(1-p, p))`.
+   * If the estimated probability of class label 1 is > threshold, then predict 1, else 0.
+   * A high threshold encourages the model to predict 0 more often;
+   * a low threshold encourages the model to predict 1 more often.
+   *
+   * Note: Calling this with threshold p is equivalent to calling `setThresholds(Array(1-p, p))`.
+   *       When [[setThreshold()]] is called, any user-set value for [[thresholds]] will be cleared.
+   *       If both [[threshold]] and [[thresholds]] are set in a ParamMap, then they must be
+   *       equivalent.
+   *
+   * Default is 0.5.
+   * @group setParam
+   */
+  def setThreshold(value: Double): this.type = {
+    if (isSet(thresholds)) clear(thresholds)
+    set(threshold, value)
+  }
+
+  /**
+   * Get threshold for binary classification.
+   *
+   * If [[threshold]] is set, returns that value.
+   * Otherwise, if [[thresholds]] is set with length 2 (i.e., binary classification),
+   * this returns the equivalent threshold: {{{1 / (1 + thresholds(0) / thresholds(1))}}}.
+   * Otherwise, returns [[threshold]] default value.
+   *
+   * @group getParam
+   * @throws IllegalArgumentException if [[thresholds]] is set to an array of length other than 2.
+   */
+  override def getThreshold: Double = {
+    checkThresholdConsistency()
+    if (isSet(thresholds)) {
+      val ts = $(thresholds)
+      require(ts.length == 2, "Logistic Regression getThreshold only applies to" +
+        " binary classification, but thresholds has length != 2.  thresholds: " + ts.mkString(","))
+      1.0 / (1.0 + ts(0) / ts(1))
+    } else {
+      $(threshold)
+    }
+  }
+
+  /**
+   * Set thresholds in multiclass (or binary) classification to adjust the probability of
+   * predicting each class. Array must have length equal to the number of classes, with values >= 0.
+   * The class with largest value p/t is predicted, where p is the original probability of that
+   * class and t is the class' threshold.
+   *
+   * Note: When [[setThresholds()]] is called, any user-set value for [[threshold]] will be cleared.
+   *       If both [[threshold]] and [[thresholds]] are set in a ParamMap, then they must be
+   *       equivalent.
    *
-   * Default is effectively 0.5.
    * @group setParam
    */
-  def setThreshold(value: Double): this.type = set(thresholds, Array(1.0 - value, value))
+  def setThresholds(value: Array[Double]): this.type = {
+    if (isSet(threshold)) clear(threshold)
+    set(thresholds, value)
+  }
 
   /**
-   * Version of [[getThresholds()]] for binary classification, available for backwards
-   * compatibility.
+   * Get thresholds for binary or multiclass classification.
+   *
+   * If [[thresholds]] is set, return its value.
+   * Otherwise, if [[threshold]] is set, return the equivalent thresholds for binary
+   * classification: (1-threshold, threshold).
+   * If neither are set, throw an exception.
    *
-   * Param thresholds must have length 2 (or not be specified).
-   * This returns {{{1 / (1 + thresholds(0) / thresholds(1))}}}.
    * @group getParam
    */
-  def getThreshold: Double = {
-    if (isDefined(thresholds)) {
-      val thresholdValues = $(thresholds)
-      assert(thresholdValues.length == 2, "Logistic Regression getThreshold only applies to" +
-        " binary classification, but thresholds has length != 2." +
-        s"  thresholds: ${thresholdValues.mkString(",")}")
-      1.0 / (1.0 + thresholdValues(0) / thresholdValues(1))
+  override def getThresholds: Array[Double] = {
+    checkThresholdConsistency()
+    if (!isSet(thresholds) && isSet(threshold)) {
+      val t = $(threshold)
+      Array(1-t, t)
     } else {
-      0.5
+      $(thresholds)
+    }
+  }
+
+  /**
+   * If [[threshold]] and [[thresholds]] are both set, ensures they are consistent.
+   * @throws IllegalArgumentException if [[threshold]] and [[thresholds]] are not equivalent
+   */
+  protected def checkThresholdConsistency(): Unit = {
+    if (isSet(threshold) && isSet(thresholds)) {
+      val ts = $(thresholds)
+      require(ts.length == 2, "Logistic Regression found inconsistent values for threshold and" +
+        s" thresholds.  Param threshold is set (${$(threshold)}), indicating binary" +
+        s" classification, but Param thresholds is set with length ${ts.length}." +
+        " Clear one Param value to fix this problem.")
+      val t = 1.0 / (1.0 + ts(0) / ts(1))
+      require(math.abs($(threshold) - t) < 1E-5, "Logistic Regression getThreshold found" +
+        s" inconsistent values for threshold (${$(threshold)}) and thresholds (equivalent to $t)")
     }
   }
+
+  override def validateParams(): Unit = {
+    checkThresholdConsistency()
+  }
 }
 
 /**
  * :: Experimental ::
  * Logistic regression.
- * Currently, this class only supports binary classification.
+ * Currently, this class only supports binary classification.  It will support multiclass
+ * in the future.
  */
 @Experimental
 class LogisticRegression(override val uid: String)
@@ -128,7 +198,7 @@ class LogisticRegression(override val uid: String)
    * Whether to fit an intercept term.
    * Default is true.
    * @group setParam
-   * */
+   */
   def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)
   setDefault(fitIntercept -> true)
 
@@ -140,7 +210,7 @@ class LogisticRegression(override val uid: String)
    * is applied. In R's GLMNET package, the default behavior is true as well.
    * Default is true.
    * @group setParam
-   * */
+   */
   def setStandardization(value: Boolean): this.type = set(standardization, value)
   setDefault(standardization -> true)
 
@@ -148,6 +218,10 @@ class LogisticRegression(override val uid: String)
 
   override def getThreshold: Double = super.getThreshold
 
+  override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value)
+
+  override def getThresholds: Array[Double] = super.getThresholds
+
   override protected def train(dataset: DataFrame): LogisticRegressionModel = {
     // Extract columns from data.  If dataset is persisted, do not persist oldDataset.
     val instances = extractLabeledPoints(dataset).map {
@@ -314,6 +388,10 @@ class LogisticRegressionModel private[ml] (
 
   override def getThreshold: Double = super.getThreshold
 
+  override def setThresholds(value: Array[Double]): this.type = super.setThresholds(value)
+
+  override def getThresholds: Array[Double] = super.getThresholds
+
   /** Margin (rawPrediction) for class label 1.  For binary classification only. */
   private val margin: Vector => Double = (features) => {
     BLAS.dot(features, weights) + intercept
@@ -364,6 +442,7 @@ class LogisticRegressionModel private[ml] (
    * The behavior of this can be adjusted using [[thresholds]].
    */
   override protected def predict(features: Vector): Double = {
+    // Note: We should use getThreshold instead of $(threshold) since getThreshold is overridden.
     if (score(features) > getThreshold) 1 else 0
   }
 
@@ -393,6 +472,7 @@ class LogisticRegressionModel private[ml] (
   }
 
   override protected def raw2prediction(rawPrediction: Vector): Double = {
+    // Note: We should use getThreshold instead of $(threshold) since getThreshold is overridden.
     val t = getThreshold
     val rawThreshold = if (t == 0.0) {
       Double.NegativeInfinity
@@ -405,6 +485,7 @@ class LogisticRegressionModel private[ml] (
   }
 
   override protected def probability2prediction(probability: Vector): Double = {
+    // Note: We should use getThreshold instead of $(threshold) since getThreshold is overridden.
     if (probability(1) > getThreshold) 1 else 0
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index da4c076830391..9e12f1856a940 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -45,14 +45,14 @@ private[shared] object SharedParamsCodeGen {
         " These probabilities should be treated as confidences, not precise probabilities.",
         Some("\"probability\"")),
       ParamDesc[Double]("threshold",
-        "threshold in binary classification prediction, in range [0, 1]",
+        "threshold in binary classification prediction, in range [0, 1]", Some("0.5"),
         isValid = "ParamValidators.inRange(0, 1)", finalMethods = false),
       ParamDesc[Array[Double]]("thresholds", "Thresholds in multi-class classification" +
         " to adjust the probability of predicting each class." +
         " Array must have length equal to the number of classes, with values >= 0." +
         " The class with largest value p/t is predicted, where p is the original probability" +
         " of that class and t is the class' threshold.",
-        isValid = "(t: Array[Double]) => t.forall(_ >= 0)"),
+        isValid = "(t: Array[Double]) => t.forall(_ >= 0)", finalMethods = false),
       ParamDesc[String]("inputCol", "input column name"),
       ParamDesc[Array[String]]("inputCols", "input column names"),
       ParamDesc[String]("outputCol", "output column name", Some("uid + \"__output\"")),
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index 23e2b6cc43996..a17d4ea960a90 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -139,7 +139,7 @@ private[ml] trait HasProbabilityCol extends Params {
 }
 
 /**
- * Trait for shared param threshold.
+ * Trait for shared param threshold (default: 0.5).
  */
 private[ml] trait HasThreshold extends Params {
 
@@ -149,6 +149,8 @@ private[ml] trait HasThreshold extends Params {
    */
   final val threshold: DoubleParam = new DoubleParam(this, "threshold", "threshold in binary classification prediction, in range [0, 1]", ParamValidators.inRange(0, 1))
 
+  setDefault(threshold, 0.5)
+
   /** @group getParam */
   def getThreshold: Double = $(threshold)
 }
@@ -165,7 +167,7 @@ private[ml] trait HasThresholds extends Params {
   final val thresholds: DoubleArrayParam = new DoubleArrayParam(this, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.", (t: Array[Double]) => t.forall(_ >= 0))
 
   /** @group getParam */
-  final def getThresholds: Array[Double] = $(thresholds)
+  def getThresholds: Array[Double] = $(thresholds)
 }
 
 /**
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index 7e9aa383728f0..618b95b9bd126 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -100,9 +100,7 @@ public void logisticRegressionWithSetters() {
       assert(r.getDouble(0) == 0.0);
     }
     // Call transform with params, and check that the params worked.
-    double[] thresholds = {1.0, 0.0};
-    model.transform(
-      dataset, model.thresholds().w(thresholds), model.probabilityCol().w("myProb"))
+    model.transform(dataset, model.threshold().w(0.0), model.probabilityCol().w("myProb"))
       .registerTempTable("predNotAllZero");
     DataFrame predNotAllZero = jsql.sql("SELECT prediction, myProb FROM predNotAllZero");
     boolean foundNonZero = false;
@@ -112,9 +110,8 @@ public void logisticRegressionWithSetters() {
     assert(foundNonZero);
 
     // Call fit() with new params, and check as many params as we can.
-    double[] thresholds2 = {0.6, 0.4};
     LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1),
-        lr.thresholds().w(thresholds2), lr.probabilityCol().w("theProb"));
+        lr.threshold().w(0.4), lr.probabilityCol().w("theProb"));
     LogisticRegression parent2 = (LogisticRegression) model2.parent();
     assert(parent2.getMaxIter() == 5);
     assert(parent2.getRegParam() == 0.1);
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index 8c3d4590f5ae9..e354e161c6dee 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -94,12 +94,13 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
   test("setThreshold, getThreshold") {
     val lr = new LogisticRegression
     // default
-    withClue("LogisticRegression should not have thresholds set by default") {
-      intercept[java.util.NoSuchElementException] {
+    assert(lr.getThreshold === 0.5, "LogisticRegression.threshold should default to 0.5")
+    withClue("LogisticRegression should not have thresholds set by default.") {
+      intercept[java.util.NoSuchElementException] { // Note: The exception type may change in future
         lr.getThresholds
       }
     }
-    // Set via thresholds.
+    // Set via threshold.
     // Intuition: Large threshold or large thresholds(1) makes class 0 more likely.
     lr.setThreshold(1.0)
     assert(lr.getThresholds === Array(0.0, 1.0))
@@ -107,10 +108,26 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(lr.getThresholds === Array(1.0, 0.0))
     lr.setThreshold(0.5)
     assert(lr.getThresholds === Array(0.5, 0.5))
-    // Test getThreshold
-    lr.setThresholds(Array(0.3, 0.7))
+    // Set via thresholds
+    val lr2 = new LogisticRegression
+    lr2.setThresholds(Array(0.3, 0.7))
     val expectedThreshold = 1.0 / (1.0 + 0.3 / 0.7)
-    assert(lr.getThreshold ~== expectedThreshold relTol 1E-7)
+    assert(lr2.getThreshold ~== expectedThreshold relTol 1E-7)
+    // thresholds and threshold must be consistent
+    lr2.setThresholds(Array(0.1, 0.2, 0.3))
+    withClue("getThreshold should throw error if thresholds has length != 2.") {
+      intercept[IllegalArgumentException] {
+        lr2.getThreshold
+      }
+    }
+    // thresholds and threshold must be consistent: values
+    withClue("fit with ParamMap should throw error if threshold, thresholds do not match.") {
+      intercept[IllegalArgumentException] {
+        val lr2model = lr2.fit(dataset,
+          lr2.thresholds -> Array(0.3, 0.7), lr2.threshold -> (expectedThreshold / 2.0))
+        lr2model.getThreshold
+      }
+    }
   }
 
   test("logistic regression doesn't fit intercept when fitIntercept is off") {
@@ -145,7 +162,7 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
       s" ${predAllZero.count(_ === 0)} of ${dataset.count()} were 0.")
     // Call transform with params, and check that the params worked.
     val predNotAllZero =
-      model.transform(dataset, model.thresholds -> Array(1.0, 0.0),
+      model.transform(dataset, model.threshold -> 0.0,
         model.probabilityCol -> "myProb")
         .select("prediction", "myProb")
         .collect()
@@ -153,8 +170,8 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(predNotAllZero.exists(_ !== 0.0))
 
     // Call fit() with new params, and check as many params as we can.
+    lr.setThresholds(Array(0.6, 0.4))
     val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1,
-      lr.thresholds -> Array(0.6, 0.4),
       lr.probabilityCol -> "theProb")
     val parent2 = model2.parent.asInstanceOf[LogisticRegression]
     assert(parent2.getMaxIter === 5)
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 6702dce5545a9..83f808efc3bf0 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -76,19 +76,21 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
                        " Array must have length equal to the number of classes, with values >= 0." +
                        " The class with largest value p/t is predicted, where p is the original" +
                        " probability of that class and t is the class' threshold.")
+    threshold = Param(Params._dummy(), "threshold",
+                      "Threshold in binary classification prediction, in range [0, 1]." +
+                      " If threshold and thresholds are both set, they must match.")
 
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                 threshold=None, thresholds=None,
+                 threshold=0.5, thresholds=None,
                  probabilityCol="probability", rawPredictionCol="rawPrediction"):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 threshold=None, thresholds=None, \
+                 threshold=0.5, thresholds=None, \
                  probabilityCol="probability", rawPredictionCol="rawPrediction")
-        Param thresholds overrides Param threshold; threshold is provided
-        for backwards compatibility and only applies to binary classification.
+        If the threshold and thresholds Params are both set, they must be equivalent.
         """
         super(LogisticRegression, self).__init__()
         self._java_obj = self._new_java_obj(
@@ -101,7 +103,11 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                   "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
         #: param for whether to fit an intercept term.
         self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
-        #: param for threshold in binary classification prediction, in range [0, 1].
+        #: param for threshold in binary classification, in range [0, 1].
+        self.threshold = Param(self, "threshold",
+                               "Threshold in binary classification prediction, in range [0, 1]." +
+                               " If threshold and thresholds are both set, they must match.")
+        #: param for thresholds or cutoffs in binary or multiclass classification
         self.thresholds = \
             Param(self, "thresholds",
                   "Thresholds in multi-class classification" +
@@ -110,29 +116,28 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
                   " The class with largest value p/t is predicted, where p is the original" +
                   " probability of that class and t is the class' threshold.")
         self._setDefault(maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1E-6,
-                         fitIntercept=True)
+                         fitIntercept=True, threshold=0.5)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
+        self._checkThresholdConsistency()
 
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                  threshold=None, thresholds=None,
+                  threshold=0.5, thresholds=None,
                   probabilityCol="probability", rawPredictionCol="rawPrediction"):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                  threshold=None, thresholds=None, \
+                  threshold=0.5, thresholds=None, \
                   probabilityCol="probability", rawPredictionCol="rawPrediction")
         Sets params for logistic regression.
-        Param thresholds overrides Param threshold; threshold is provided
-        for backwards compatibility and only applies to binary classification.
+        If the threshold and thresholds Params are both set, they must be equivalent.
         """
-        # Under the hood we use thresholds so translate threshold to thresholds if applicable
-        if thresholds is None and threshold is not None:
-            kwargs[thresholds] = [1-threshold, threshold]
         kwargs = self.setParams._input_kwargs
-        return self._set(**kwargs)
+        self._set(**kwargs)
+        self._checkThresholdConsistency()
+        return self
 
     def _create_model(self, java_model):
         return LogisticRegressionModel(java_model)
@@ -165,44 +170,65 @@ def getFitIntercept(self):
 
     def setThreshold(self, value):
         """
-        Sets the value of :py:attr:`thresholds` using [1-value, value].
+        Sets the value of :py:attr:`threshold`.
+        Clears value of :py:attr:`thresholds` if it has been set.
+        """
+        self._paramMap[self.threshold] = value
+        if self.isSet(self.thresholds):
+            del self._paramMap[self.thresholds]
+        return self
 
-        >>> lr = LogisticRegression()
-        >>> lr.getThreshold()
-        0.5
-        >>> lr.setThreshold(0.6)
-        LogisticRegression_...
-        >>> abs(lr.getThreshold() - 0.6) < 1e-5
-        True
+    def getThreshold(self):
+        """
+        Gets the value of threshold or its default value.
         """
-        return self.setThresholds([1-value, value])
+        self._checkThresholdConsistency()
+        if self.isSet(self.thresholds):
+            ts = self.getOrDefault(self.thresholds)
+            if len(ts) != 2:
+                raise ValueError("Logistic Regression getThreshold only applies to" +
+                                 " binary classification, but thresholds has length != 2." +
+                                 "  thresholds: " + ",".join(ts))
+            return 1.0/(1.0 + ts[0]/ts[1])
+        else:
+            return self.getOrDefault(self.threshold)
 
     def setThresholds(self, value):
         """
         Sets the value of :py:attr:`thresholds`.
+        Clears value of :py:attr:`threshold` if it has been set.
         """
         self._paramMap[self.thresholds] = value
+        if self.isSet(self.threshold):
+            del self._paramMap[self.threshold]
         return self
 
     def getThresholds(self):
         """
-        Gets the value of thresholds or its default value.
+        If :py:attr:`thresholds` is set, return its value.
+        Otherwise, if :py:attr:`threshold` is set, return the equivalent thresholds for binary
+        classification: (1-threshold, threshold).
+        If neither are set, throw an error.
         """
-        return self.getOrDefault(self.thresholds)
+        self._checkThresholdConsistency()
+        if not self.isSet(self.thresholds) and self.isSet(self.threshold):
+            t = self.getOrDefault(self.threshold)
+            return [1.0-t, t]
+        else:
+            return self.getOrDefault(self.thresholds)
 
-    def getThreshold(self):
-        """
-        Gets the value of threshold or its default value.
-        """
-        if self.isDefined(self.thresholds):
-            thresholds = self.getOrDefault(self.thresholds)
-            if len(thresholds) != 2:
+    def _checkThresholdConsistency(self):
+        if self.isSet(self.threshold) and self.isSet(self.thresholds):
+            ts = self.getParam(self.thresholds)
+            if len(ts) != 2:
                 raise ValueError("Logistic Regression getThreshold only applies to" +
                                  " binary classification, but thresholds has length != 2." +
-                                 "  thresholds: " + ",".join(thresholds))
-            return 1.0/(1.0+thresholds[0]/thresholds[1])
-        else:
-            return 0.5
+                                 " thresholds: " + ",".join(ts))
+            t = 1.0/(1.0 + ts[0]/ts[1])
+            t2 = self.getParam(self.threshold)
+            if abs(t2 - t) >= 1E-5:
+                raise ValueError("Logistic Regression getThreshold found inconsistent values for" +
+                                 " threshold (%g) and thresholds (equivalent to %g)" % (t2, t))
 
 
 class LogisticRegressionModel(JavaModel):

From 6f60298b1d7aa97268a42eca1e3b4851a7e88cb5 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 14:28:23 -0700
Subject: [PATCH 199/633] [SPARK-8967] [DOC] add Since annotation

Add `Since` as a Scala annotation. The benefit is that we can use it without having explicit JavaDoc. This is useful for inherited methods. The limitation is that is doesn't show up in the generated Java API documentation. This might be fixed by modifying genjavadoc. I think we could leave it as a TODO.

This is how the generated Scala doc looks:

`since` JavaDoc tag:

![screen shot 2015-08-11 at 10 00 37 pm](https://cloud.githubusercontent.com/assets/829644/9230761/fa72865c-40d8-11e5-807e-0f3c815c5acd.png)

`Since` annotation:

![screen shot 2015-08-11 at 10 00 28 pm](https://cloud.githubusercontent.com/assets/829644/9230764/0041d7f4-40d9-11e5-8124-c3f3e5d5b31f.png)

rxin

Author: Xiangrui Meng <meng@databricks.com>

Closes #8131 from mengxr/SPARK-8967.
---
 .../org/apache/spark/annotation/Since.scala   | 28 +++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 core/src/main/scala/org/apache/spark/annotation/Since.scala

diff --git a/core/src/main/scala/org/apache/spark/annotation/Since.scala b/core/src/main/scala/org/apache/spark/annotation/Since.scala
new file mode 100644
index 0000000000000..fa59393c22476
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/annotation/Since.scala
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.annotation
+
+import scala.annotation.StaticAnnotation
+
+/**
+ * A Scala annotation that specifies the Spark version when a definition was added.
+ * Different from the `@since` tag in JavaDoc, this annotation does not require explicit JavaDoc and
+ * hence works for overridden methods that inherit API documentation directly from parents.
+ * The limitation is that it does not show up in the generated Java API documentation.
+ */
+private[spark] class Since(version: String) extends StaticAnnotation

From a17384fa343628cec44437da5b80b9403ecd5838 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 12 Aug 2015 15:27:52 -0700
Subject: [PATCH 200/633] [SPARK-9907] [SQL] Python crc32 is mistakenly calling
 md5

Author: Reynold Xin <rxin@databricks.com>

Closes #8138 from rxin/SPARK-9907.
---
 python/pyspark/sql/functions.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 95f46044d324a..e98979533f901 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -885,10 +885,10 @@ def crc32(col):
     returns the value as a bigint.
 
     >>> sqlContext.createDataFrame([('ABC',)], ['a']).select(crc32('a').alias('crc32')).collect()
-    [Row(crc32=u'902fbdd2b1df0c4f70b4a5d23525e932')]
+    [Row(crc32=2743272264)]
     """
     sc = SparkContext._active_spark_context
-    return Column(sc._jvm.functions.md5(_to_java_column(col)))
+    return Column(sc._jvm.functions.crc32(_to_java_column(col)))
 
 
 @ignore_unicode_prefix

From 738f353988dbf02704bd63f5e35d94402c59ed79 Mon Sep 17 00:00:00 2001
From: Niranjan Padmanabhan <niranjan.padmanabhan@cloudera.com>
Date: Wed, 12 Aug 2015 16:10:21 -0700
Subject: [PATCH 201/633] [SPARK-9092] Fixed incompatibility when both
 num-executors and dynamic...
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

… allocation are set. Now, dynamic allocation is set to false when num-executors is explicitly specified as an argument. Consequently, executorAllocationManager in not initialized in the SparkContext.

Author: Niranjan Padmanabhan <niranjan.padmanabhan@cloudera.com>

Closes #7657 from neurons/SPARK-9092.
---
 .../scala/org/apache/spark/SparkConf.scala    | 19 +++++++++++++++++++
 .../scala/org/apache/spark/SparkContext.scala |  6 +++++-
 .../org/apache/spark/deploy/SparkSubmit.scala |  4 ++--
 .../scala/org/apache/spark/util/Utils.scala   | 11 +++++++++++
 .../org/apache/spark/SparkContextSuite.scala  |  8 ++++++++
 .../spark/deploy/SparkSubmitSuite.scala       |  1 -
 docs/running-on-yarn.md                       |  2 +-
 .../spark/deploy/yarn/ApplicationMaster.scala |  4 ++--
 .../yarn/ApplicationMasterArguments.scala     |  5 -----
 .../org/apache/spark/deploy/yarn/Client.scala |  5 ++++-
 .../spark/deploy/yarn/ClientArguments.scala   |  8 +-------
 .../spark/deploy/yarn/YarnAllocator.scala     |  9 ++++++++-
 .../cluster/YarnClientSchedulerBackend.scala  |  3 ---
 .../deploy/yarn/YarnAllocatorSuite.scala      |  5 +++--
 14 files changed, 64 insertions(+), 26 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala
index 8ff154fb5e334..b344b5e173d67 100644
--- a/core/src/main/scala/org/apache/spark/SparkConf.scala
+++ b/core/src/main/scala/org/apache/spark/SparkConf.scala
@@ -389,6 +389,7 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
     val driverOptsKey = "spark.driver.extraJavaOptions"
     val driverClassPathKey = "spark.driver.extraClassPath"
     val driverLibraryPathKey = "spark.driver.extraLibraryPath"
+    val sparkExecutorInstances = "spark.executor.instances"
 
     // Used by Yarn in 1.1 and before
     sys.props.get("spark.driver.libraryPath").foreach { value =>
@@ -476,6 +477,24 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
         }
       }
     }
+
+    if (!contains(sparkExecutorInstances)) {
+      sys.env.get("SPARK_WORKER_INSTANCES").foreach { value =>
+        val warning =
+          s"""
+             |SPARK_WORKER_INSTANCES was detected (set to '$value').
+             |This is deprecated in Spark 1.0+.
+             |
+             |Please instead use:
+             | - ./spark-submit with --num-executors to specify the number of executors
+             | - Or set SPARK_EXECUTOR_INSTANCES
+             | - spark.executor.instances to configure the number of instances in the spark config.
+        """.stripMargin
+        logWarning(warning)
+
+        set("spark.executor.instances", value)
+      }
+    }
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 6aafb4c5644d7..207a0c1bffeb3 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -528,7 +528,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       }
 
     // Optionally scale number of executors dynamically based on workload. Exposed for testing.
-    val dynamicAllocationEnabled = _conf.getBoolean("spark.dynamicAllocation.enabled", false)
+    val dynamicAllocationEnabled = Utils.isDynamicAllocationEnabled(_conf)
+    if (!dynamicAllocationEnabled && _conf.getBoolean("spark.dynamicAllocation.enabled", false)) {
+      logInfo("Dynamic Allocation and num executors both set, thus dynamic allocation disabled.")
+    }
+
     _executorAllocationManager =
       if (dynamicAllocationEnabled) {
         Some(new ExecutorAllocationManager(this, listenerBus, _conf))
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 7ac6cbce4cd1d..02fa3088eded0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -422,7 +422,8 @@ object SparkSubmit {
 
       // Yarn client only
       OptionAssigner(args.queue, YARN, CLIENT, sysProp = "spark.yarn.queue"),
-      OptionAssigner(args.numExecutors, YARN, CLIENT, sysProp = "spark.executor.instances"),
+      OptionAssigner(args.numExecutors, YARN, ALL_DEPLOY_MODES,
+        sysProp = "spark.executor.instances"),
       OptionAssigner(args.files, YARN, CLIENT, sysProp = "spark.yarn.dist.files"),
       OptionAssigner(args.archives, YARN, CLIENT, sysProp = "spark.yarn.dist.archives"),
       OptionAssigner(args.principal, YARN, CLIENT, sysProp = "spark.yarn.principal"),
@@ -433,7 +434,6 @@ object SparkSubmit {
       OptionAssigner(args.driverMemory, YARN, CLUSTER, clOption = "--driver-memory"),
       OptionAssigner(args.driverCores, YARN, CLUSTER, clOption = "--driver-cores"),
       OptionAssigner(args.queue, YARN, CLUSTER, clOption = "--queue"),
-      OptionAssigner(args.numExecutors, YARN, CLUSTER, clOption = "--num-executors"),
       OptionAssigner(args.executorMemory, YARN, CLUSTER, clOption = "--executor-memory"),
       OptionAssigner(args.executorCores, YARN, CLUSTER, clOption = "--executor-cores"),
       OptionAssigner(args.files, YARN, CLUSTER, clOption = "--files"),
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index c4012d0e83f7d..a90d8541366f4 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -2286,6 +2286,17 @@ private[spark] object Utils extends Logging {
     isInDirectory(parent, child.getParentFile)
   }
 
+  /**
+   * Return whether dynamic allocation is enabled in the given conf
+   * Dynamic allocation and explicitly setting the number of executors are inherently
+   * incompatible. In environments where dynamic allocation is turned on by default,
+   * the latter should override the former (SPARK-9092).
+   */
+  def isDynamicAllocationEnabled(conf: SparkConf): Boolean = {
+    conf.contains("spark.dynamicAllocation.enabled") &&
+      conf.getInt("spark.executor.instances", 0) == 0
+  }
+
 }
 
 private [util] class SparkShutdownHookManager {
diff --git a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala
index 5c57940fa5f77..d4f2ea87650a9 100644
--- a/core/src/test/scala/org/apache/spark/SparkContextSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkContextSuite.scala
@@ -285,4 +285,12 @@ class SparkContextSuite extends SparkFunSuite with LocalSparkContext {
     }
   }
 
+  test("No exception when both num-executors and dynamic allocation set.") {
+    noException should be thrownBy {
+      sc = new SparkContext(new SparkConf().setAppName("test").setMaster("local")
+        .set("spark.dynamicAllocation.enabled", "true").set("spark.executor.instances", "6"))
+      assert(sc.executorAllocationManager.isEmpty)
+      assert(sc.getConf.getInt("spark.executor.instances", 0) === 6)
+    }
+  }
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 757e0ce3d278b..2456c5d0d49b0 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -159,7 +159,6 @@ class SparkSubmitSuite
     childArgsStr should include ("--executor-cores 5")
     childArgsStr should include ("--arg arg1 --arg arg2")
     childArgsStr should include ("--queue thequeue")
-    childArgsStr should include ("--num-executors 6")
     childArgsStr should include regex ("--jar .*thejar.jar")
     childArgsStr should include regex ("--addJars .*one.jar,.*two.jar,.*three.jar")
     childArgsStr should include regex ("--files .*file1.txt,.*file2.txt")
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index cac08a91b97d9..ec32c419b7c51 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -199,7 +199,7 @@ If you need a reference to the proper location to put log files in the YARN so t
  <td><code>spark.executor.instances</code></td>
   <td>2</td>
   <td>
-    The number of executors. Note that this property is incompatible with <code>spark.dynamicAllocation.enabled</code>.
+    The number of executors. Note that this property is incompatible with <code>spark.dynamicAllocation.enabled</code>. If both <code>spark.dynamicAllocation.enabled</code> and <code>spark.executor.instances</code> are specified, dynamic allocation is turned off and the specified number of <code>spark.executor.instances</code> is used. 
   </td>
 </tr>
 <tr>
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 1d67b3ebb51b7..e19940d8d6642 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -64,7 +64,8 @@ private[spark] class ApplicationMaster(
 
   // Default to numExecutors * 2, with minimum of 3
   private val maxNumExecutorFailures = sparkConf.getInt("spark.yarn.max.executor.failures",
-    sparkConf.getInt("spark.yarn.max.worker.failures", math.max(args.numExecutors * 2, 3)))
+    sparkConf.getInt("spark.yarn.max.worker.failures",
+      math.max(sparkConf.getInt("spark.executor.instances", 0) *  2, 3)))
 
   @volatile private var exitCode = 0
   @volatile private var unregistered = false
@@ -493,7 +494,6 @@ private[spark] class ApplicationMaster(
    */
   private def startUserApplication(): Thread = {
     logInfo("Starting the user application in a separate Thread")
-    System.setProperty("spark.executor.instances", args.numExecutors.toString)
 
     val classpath = Client.getUserClasspath(sparkConf)
     val urls = classpath.map { entry =>
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
index 37f793763367e..b08412414aa1c 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
@@ -29,7 +29,6 @@ class ApplicationMasterArguments(val args: Array[String]) {
   var userArgs: Seq[String] = Nil
   var executorMemory = 1024
   var executorCores = 1
-  var numExecutors = DEFAULT_NUMBER_EXECUTORS
   var propertiesFile: String = null
 
   parseArgs(args.toList)
@@ -63,10 +62,6 @@ class ApplicationMasterArguments(val args: Array[String]) {
           userArgsBuffer += value
           args = tail
 
-        case ("--num-workers" | "--num-executors") :: IntParam(value) :: tail =>
-          numExecutors = value
-          args = tail
-
         case ("--worker-memory" | "--executor-memory") :: MemoryParam(value) :: tail =>
           executorMemory = value
           args = tail
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index b4ba3f0221600..6d63ddaf15852 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -751,7 +751,6 @@ private[spark] class Client(
         userArgs ++ Seq(
           "--executor-memory", args.executorMemory.toString + "m",
           "--executor-cores", args.executorCores.toString,
-          "--num-executors ", args.numExecutors.toString,
           "--properties-file", buildPath(YarnSparkHadoopUtil.expandEnvironment(Environment.PWD),
             LOCALIZED_CONF_DIR, SPARK_CONF_FILE))
 
@@ -960,6 +959,10 @@ object Client extends Logging {
     val sparkConf = new SparkConf
 
     val args = new ClientArguments(argStrings, sparkConf)
+    // to maintain backwards-compatibility
+    if (!Utils.isDynamicAllocationEnabled(sparkConf)) {
+      sparkConf.setIfMissing("spark.executor.instances", args.numExecutors.toString)
+    }
     new Client(args, sparkConf).run()
   }
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
index 20d63d40cf605..4f42ffefa77f9 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
@@ -53,8 +53,7 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
   private val amMemOverheadKey = "spark.yarn.am.memoryOverhead"
   private val driverCoresKey = "spark.driver.cores"
   private val amCoresKey = "spark.yarn.am.cores"
-  private val isDynamicAllocationEnabled =
-    sparkConf.getBoolean("spark.dynamicAllocation.enabled", false)
+  private val isDynamicAllocationEnabled = Utils.isDynamicAllocationEnabled(sparkConf)
 
   parseArgs(args.toList)
   loadEnvironmentArgs()
@@ -196,11 +195,6 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
           if (args(0) == "--num-workers") {
             println("--num-workers is deprecated. Use --num-executors instead.")
           }
-          // Dynamic allocation is not compatible with this option
-          if (isDynamicAllocationEnabled) {
-            throw new IllegalArgumentException("Explicitly setting the number " +
-              "of executors is not compatible with spark.dynamicAllocation.enabled!")
-          }
           numExecutors = value
           args = tail
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index 59caa787b6e20..ccf753e69f4b6 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -21,6 +21,8 @@ import java.util.Collections
 import java.util.concurrent._
 import java.util.regex.Pattern
 
+import org.apache.spark.util.Utils
+
 import scala.collection.JavaConversions._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
@@ -86,7 +88,12 @@ private[yarn] class YarnAllocator(
   private var executorIdCounter = 0
   @volatile private var numExecutorsFailed = 0
 
-  @volatile private var targetNumExecutors = args.numExecutors
+  @volatile private var targetNumExecutors =
+    if (Utils.isDynamicAllocationEnabled(sparkConf)) {
+      sparkConf.getInt("spark.dynamicAllocation.initialExecutors", 0)
+    } else {
+      sparkConf.getInt("spark.executor.instances", YarnSparkHadoopUtil.DEFAULT_NUMBER_EXECUTORS)
+    }
 
   // Keep track of which container is running which executor to remove the executors later
   // Visible for testing.
diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
index d225061fcd1b4..d06d95140438c 100644
--- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
+++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala
@@ -81,8 +81,6 @@ private[spark] class YarnClientSchedulerBackend(
     // List of (target Client argument, environment variable, Spark property)
     val optionTuples =
       List(
-        ("--num-executors", "SPARK_WORKER_INSTANCES", "spark.executor.instances"),
-        ("--num-executors", "SPARK_EXECUTOR_INSTANCES", "spark.executor.instances"),
         ("--executor-memory", "SPARK_WORKER_MEMORY", "spark.executor.memory"),
         ("--executor-memory", "SPARK_EXECUTOR_MEMORY", "spark.executor.memory"),
         ("--executor-cores", "SPARK_WORKER_CORES", "spark.executor.cores"),
@@ -92,7 +90,6 @@ private[spark] class YarnClientSchedulerBackend(
       )
     // Warn against the following deprecated environment variables: env var -> suggestion
     val deprecatedEnvVars = Map(
-      "SPARK_WORKER_INSTANCES" -> "SPARK_WORKER_INSTANCES or --num-executors through spark-submit",
       "SPARK_WORKER_MEMORY" -> "SPARK_EXECUTOR_MEMORY or --executor-memory through spark-submit",
       "SPARK_WORKER_CORES" -> "SPARK_EXECUTOR_CORES or --executor-cores through spark-submit")
     optionTuples.foreach { case (optionName, envVar, sparkProp) =>
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
index 58318bf9bcc08..5d05f514adde3 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnAllocatorSuite.scala
@@ -87,16 +87,17 @@ class YarnAllocatorSuite extends SparkFunSuite with Matchers with BeforeAndAfter
 
   def createAllocator(maxExecutors: Int = 5): YarnAllocator = {
     val args = Array(
-      "--num-executors", s"$maxExecutors",
       "--executor-cores", "5",
       "--executor-memory", "2048",
       "--jar", "somejar.jar",
       "--class", "SomeClass")
+    val sparkConfClone = sparkConf.clone()
+    sparkConfClone.set("spark.executor.instances", maxExecutors.toString)
     new YarnAllocator(
       "not used",
       mock(classOf[RpcEndpointRef]),
       conf,
-      sparkConf,
+      sparkConfClone,
       rmClient,
       appAttemptId,
       new ApplicationMasterArguments(args),

From ab7e721cfec63155641e81e72b4ad43cf6a7d4c7 Mon Sep 17 00:00:00 2001
From: Michel Lemay <mlemay@gmail.com>
Date: Wed, 12 Aug 2015 16:17:58 -0700
Subject: [PATCH 202/633] [SPARK-9826] [CORE] Fix cannot use custom classes in
 log4j.properties

Refactor Utils class and create ShutdownHookManager.

NOTE: Wasn't able to run /dev/run-tests on windows machine.
Manual tests were conducted locally using custom log4j.properties file with Redis appender and logstash formatter (bundled in the fat-jar submitted to spark)

ex:
log4j.rootCategory=WARN,console,redis
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

log4j.logger.org.eclipse.jetty=WARN
log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
log4j.logger.org.apache.spark.graphx.Pregel=INFO

log4j.appender.redis=com.ryantenney.log4j.FailoverRedisAppender
log4j.appender.redis.endpoints=hostname:port
log4j.appender.redis.key=mykey
log4j.appender.redis.alwaysBatch=false
log4j.appender.redis.layout=net.logstash.log4j.JSONEventLayoutV1

Author: michellemay <mlemay@gmail.com>

Closes #8109 from michellemay/SPARK-9826.
---
 .../scala/org/apache/spark/SparkContext.scala |   5 +-
 .../spark/deploy/history/HistoryServer.scala  |   4 +-
 .../spark/deploy/worker/ExecutorRunner.scala  |   7 +-
 .../org/apache/spark/rdd/HadoopRDD.scala      |   4 +-
 .../org/apache/spark/rdd/NewHadoopRDD.scala   |   4 +-
 .../apache/spark/rdd/SqlNewHadoopRDD.scala    |   4 +-
 .../spark/storage/DiskBlockManager.scala      |  10 +-
 .../spark/storage/TachyonBlockManager.scala   |   6 +-
 .../spark/util/ShutdownHookManager.scala      | 266 ++++++++++++++++++
 .../util/SparkUncaughtExceptionHandler.scala  |   2 +-
 .../scala/org/apache/spark/util/Utils.scala   | 222 +--------------
 .../hive/thriftserver/HiveThriftServer2.scala |   4 +-
 .../hive/thriftserver/SparkSQLCLIDriver.scala |   4 +-
 .../apache/spark/sql/hive/test/TestHive.scala |   4 +-
 .../spark/streaming/StreamingContext.scala    |   8 +-
 .../spark/deploy/yarn/ApplicationMaster.scala |   5 +-
 16 files changed, 307 insertions(+), 252 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 207a0c1bffeb3..2e01a9a18c784 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -563,7 +563,8 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     // Make sure the context is stopped if the user forgets about it. This avoids leaving
     // unfinished event logs around after the JVM exits cleanly. It doesn't help if the JVM
     // is killed, though.
-    _shutdownHookRef = Utils.addShutdownHook(Utils.SPARK_CONTEXT_SHUTDOWN_PRIORITY) { () =>
+    _shutdownHookRef = ShutdownHookManager.addShutdownHook(
+      ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY) { () =>
       logInfo("Invoking stop() from shutdown hook")
       stop()
     }
@@ -1671,7 +1672,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       return
     }
     if (_shutdownHookRef != null) {
-      Utils.removeShutdownHook(_shutdownHookRef)
+      ShutdownHookManager.removeShutdownHook(_shutdownHookRef)
     }
 
     Utils.tryLogNonFatalError {
diff --git a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
index a076a9c3f984d..d4f327cc588fe 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/HistoryServer.scala
@@ -30,7 +30,7 @@ import org.apache.spark.status.api.v1.{ApiRootResource, ApplicationInfo, Applica
   UIRoot}
 import org.apache.spark.ui.{SparkUI, UIUtils, WebUI}
 import org.apache.spark.ui.JettyUtils._
-import org.apache.spark.util.{SignalLogger, Utils}
+import org.apache.spark.util.{ShutdownHookManager, SignalLogger, Utils}
 
 /**
  * A web server that renders SparkUIs of completed applications.
@@ -238,7 +238,7 @@ object HistoryServer extends Logging {
     val server = new HistoryServer(conf, provider, securityManager, port)
     server.bind()
 
-    Utils.addShutdownHook { () => server.stop() }
+    ShutdownHookManager.addShutdownHook { () => server.stop() }
 
     // Wait until the end of the world... or if the HistoryServer process is manually stopped
     while(true) { Thread.sleep(Int.MaxValue) }
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index 29a5042285578..ab3fea475c2a5 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -28,7 +28,7 @@ import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.{SecurityManager, SparkConf, Logging}
 import org.apache.spark.deploy.{ApplicationDescription, ExecutorState}
 import org.apache.spark.deploy.DeployMessages.ExecutorStateChanged
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 import org.apache.spark.util.logging.FileAppender
 
 /**
@@ -70,7 +70,8 @@ private[deploy] class ExecutorRunner(
     }
     workerThread.start()
     // Shutdown hook that kills actors on shutdown.
-    shutdownHook = Utils.addShutdownHook { () => killProcess(Some("Worker shutting down")) }
+    shutdownHook = ShutdownHookManager.addShutdownHook { () =>
+      killProcess(Some("Worker shutting down")) }
   }
 
   /**
@@ -102,7 +103,7 @@ private[deploy] class ExecutorRunner(
       workerThread = null
       state = ExecutorState.KILLED
       try {
-        Utils.removeShutdownHook(shutdownHook)
+        ShutdownHookManager.removeShutdownHook(shutdownHook)
       } catch {
         case e: IllegalStateException => None
       }
diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index f1c17369cb48c..e1f8719eead02 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -44,7 +44,7 @@ import org.apache.spark.broadcast.Broadcast
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.executor.DataReadMethod
 import org.apache.spark.rdd.HadoopRDD.HadoopMapPartitionsWithSplitRDD
-import org.apache.spark.util.{SerializableConfiguration, NextIterator, Utils}
+import org.apache.spark.util.{SerializableConfiguration, ShutdownHookManager, NextIterator, Utils}
 import org.apache.spark.scheduler.{HostTaskLocation, HDFSCacheTaskLocation}
 import org.apache.spark.storage.StorageLevel
 
@@ -274,7 +274,7 @@ class HadoopRDD[K, V](
           }
         } catch {
           case e: Exception => {
-            if (!Utils.inShutdown()) {
+            if (!ShutdownHookManager.inShutdown()) {
               logWarning("Exception in RecordReader.close()", e)
             }
           }
diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
index f83a051f5da11..6a9c004d65cff 100644
--- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
@@ -33,7 +33,7 @@ import org.apache.spark._
 import org.apache.spark.executor.DataReadMethod
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
 import org.apache.spark.rdd.NewHadoopRDD.NewHadoopMapPartitionsWithSplitRDD
-import org.apache.spark.util.{SerializableConfiguration, Utils}
+import org.apache.spark.util.{SerializableConfiguration, ShutdownHookManager, Utils}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.storage.StorageLevel
 
@@ -186,7 +186,7 @@ class NewHadoopRDD[K, V](
           }
         } catch {
           case e: Exception => {
-            if (!Utils.inShutdown()) {
+            if (!ShutdownHookManager.inShutdown()) {
               logWarning("Exception in RecordReader.close()", e)
             }
           }
diff --git a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
index 6a95e44c57fec..fa3fecc80cb63 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
@@ -33,7 +33,7 @@ import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.{Partition => SparkPartition, _}
 import org.apache.spark.storage.StorageLevel
-import org.apache.spark.util.{SerializableConfiguration, Utils}
+import org.apache.spark.util.{SerializableConfiguration, ShutdownHookManager, Utils}
 
 
 private[spark] class SqlNewHadoopPartition(
@@ -212,7 +212,7 @@ private[spark] class SqlNewHadoopRDD[V: ClassTag](
           }
         } catch {
           case e: Exception =>
-            if (!Utils.inShutdown()) {
+            if (!ShutdownHookManager.inShutdown()) {
               logWarning("Exception in RecordReader.close()", e)
             }
         }
diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 56a33d5ca7d60..3f8d26e1d4cab 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -22,7 +22,7 @@ import java.io.{IOException, File}
 
 import org.apache.spark.{SparkConf, Logging}
 import org.apache.spark.executor.ExecutorExitCode
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 
 /**
  * Creates and maintains the logical mapping between logical blocks and physical on-disk
@@ -144,7 +144,7 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
   }
 
   private def addShutdownHook(): AnyRef = {
-    Utils.addShutdownHook(Utils.TEMP_DIR_SHUTDOWN_PRIORITY + 1) { () =>
+    ShutdownHookManager.addShutdownHook(ShutdownHookManager.TEMP_DIR_SHUTDOWN_PRIORITY + 1) { () =>
       logInfo("Shutdown hook called")
       DiskBlockManager.this.doStop()
     }
@@ -154,7 +154,7 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
   private[spark] def stop() {
     // Remove the shutdown hook.  It causes memory leaks if we leave it around.
     try {
-      Utils.removeShutdownHook(shutdownHook)
+      ShutdownHookManager.removeShutdownHook(shutdownHook)
     } catch {
       case e: Exception =>
         logError(s"Exception while removing shutdown hook.", e)
@@ -168,7 +168,9 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
       localDirs.foreach { localDir =>
         if (localDir.isDirectory() && localDir.exists()) {
           try {
-            if (!Utils.hasRootAsShutdownDeleteDir(localDir)) Utils.deleteRecursively(localDir)
+            if (!ShutdownHookManager.hasRootAsShutdownDeleteDir(localDir)) {
+              Utils.deleteRecursively(localDir)
+            }
           } catch {
             case e: Exception =>
               logError(s"Exception while deleting local spark dir: $localDir", e)
diff --git a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
index ebad5bc5ab28d..22878783fca67 100644
--- a/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/TachyonBlockManager.scala
@@ -32,7 +32,7 @@ import tachyon.TachyonURI
 
 import org.apache.spark.Logging
 import org.apache.spark.executor.ExecutorExitCode
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 
 
 /**
@@ -80,7 +80,7 @@ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Log
     // in order to avoid having really large inodes at the top level in Tachyon.
     tachyonDirs = createTachyonDirs()
     subDirs = Array.fill(tachyonDirs.length)(new Array[TachyonFile](subDirsPerTachyonDir))
-    tachyonDirs.foreach(tachyonDir => Utils.registerShutdownDeleteDir(tachyonDir))
+    tachyonDirs.foreach(tachyonDir => ShutdownHookManager.registerShutdownDeleteDir(tachyonDir))
   }
 
   override def toString: String = {"ExternalBlockStore-Tachyon"}
@@ -240,7 +240,7 @@ private[spark] class TachyonBlockManager() extends ExternalBlockManager with Log
     logDebug("Shutdown hook called")
     tachyonDirs.foreach { tachyonDir =>
       try {
-        if (!Utils.hasRootAsShutdownDeleteDir(tachyonDir)) {
+        if (!ShutdownHookManager.hasRootAsShutdownDeleteDir(tachyonDir)) {
           Utils.deleteRecursively(tachyonDir, client)
         }
       } catch {
diff --git a/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala b/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala
new file mode 100644
index 0000000000000..61ff9b89ec1c1
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala
@@ -0,0 +1,266 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.util
+
+import java.io.File
+import java.util.PriorityQueue
+
+import scala.util.{Failure, Success, Try}
+import tachyon.client.TachyonFile
+
+import org.apache.hadoop.fs.FileSystem
+import org.apache.spark.Logging
+
+/**
+ * Various utility methods used by Spark.
+ */
+private[spark] object ShutdownHookManager extends Logging {
+  val DEFAULT_SHUTDOWN_PRIORITY = 100
+
+  /**
+   * The shutdown priority of the SparkContext instance. This is lower than the default
+   * priority, so that by default hooks are run before the context is shut down.
+   */
+  val SPARK_CONTEXT_SHUTDOWN_PRIORITY = 50
+
+  /**
+   * The shutdown priority of temp directory must be lower than the SparkContext shutdown
+   * priority. Otherwise cleaning the temp directories while Spark jobs are running can
+   * throw undesirable errors at the time of shutdown.
+   */
+  val TEMP_DIR_SHUTDOWN_PRIORITY = 25
+
+  private lazy val shutdownHooks = {
+    val manager = new SparkShutdownHookManager()
+    manager.install()
+    manager
+  }
+
+  private val shutdownDeletePaths = new scala.collection.mutable.HashSet[String]()
+  private val shutdownDeleteTachyonPaths = new scala.collection.mutable.HashSet[String]()
+
+  // Add a shutdown hook to delete the temp dirs when the JVM exits
+  addShutdownHook(TEMP_DIR_SHUTDOWN_PRIORITY) { () =>
+    logInfo("Shutdown hook called")
+    shutdownDeletePaths.foreach { dirPath =>
+      try {
+        logInfo("Deleting directory " + dirPath)
+        Utils.deleteRecursively(new File(dirPath))
+      } catch {
+        case e: Exception => logError(s"Exception while deleting Spark temp dir: $dirPath", e)
+      }
+    }
+  }
+
+  // Register the path to be deleted via shutdown hook
+  def registerShutdownDeleteDir(file: File) {
+    val absolutePath = file.getAbsolutePath()
+    shutdownDeletePaths.synchronized {
+      shutdownDeletePaths += absolutePath
+    }
+  }
+
+  // Register the tachyon path to be deleted via shutdown hook
+  def registerShutdownDeleteDir(tachyonfile: TachyonFile) {
+    val absolutePath = tachyonfile.getPath()
+    shutdownDeleteTachyonPaths.synchronized {
+      shutdownDeleteTachyonPaths += absolutePath
+    }
+  }
+
+  // Remove the path to be deleted via shutdown hook
+  def removeShutdownDeleteDir(file: File) {
+    val absolutePath = file.getAbsolutePath()
+    shutdownDeletePaths.synchronized {
+      shutdownDeletePaths.remove(absolutePath)
+    }
+  }
+
+  // Remove the tachyon path to be deleted via shutdown hook
+  def removeShutdownDeleteDir(tachyonfile: TachyonFile) {
+    val absolutePath = tachyonfile.getPath()
+    shutdownDeleteTachyonPaths.synchronized {
+      shutdownDeleteTachyonPaths.remove(absolutePath)
+    }
+  }
+
+  // Is the path already registered to be deleted via a shutdown hook ?
+  def hasShutdownDeleteDir(file: File): Boolean = {
+    val absolutePath = file.getAbsolutePath()
+    shutdownDeletePaths.synchronized {
+      shutdownDeletePaths.contains(absolutePath)
+    }
+  }
+
+  // Is the path already registered to be deleted via a shutdown hook ?
+  def hasShutdownDeleteTachyonDir(file: TachyonFile): Boolean = {
+    val absolutePath = file.getPath()
+    shutdownDeleteTachyonPaths.synchronized {
+      shutdownDeleteTachyonPaths.contains(absolutePath)
+    }
+  }
+
+  // Note: if file is child of some registered path, while not equal to it, then return true;
+  // else false. This is to ensure that two shutdown hooks do not try to delete each others
+  // paths - resulting in IOException and incomplete cleanup.
+  def hasRootAsShutdownDeleteDir(file: File): Boolean = {
+    val absolutePath = file.getAbsolutePath()
+    val retval = shutdownDeletePaths.synchronized {
+      shutdownDeletePaths.exists { path =>
+        !absolutePath.equals(path) && absolutePath.startsWith(path)
+      }
+    }
+    if (retval) {
+      logInfo("path = " + file + ", already present as root for deletion.")
+    }
+    retval
+  }
+
+  // Note: if file is child of some registered path, while not equal to it, then return true;
+  // else false. This is to ensure that two shutdown hooks do not try to delete each others
+  // paths - resulting in Exception and incomplete cleanup.
+  def hasRootAsShutdownDeleteDir(file: TachyonFile): Boolean = {
+    val absolutePath = file.getPath()
+    val retval = shutdownDeleteTachyonPaths.synchronized {
+      shutdownDeleteTachyonPaths.exists { path =>
+        !absolutePath.equals(path) && absolutePath.startsWith(path)
+      }
+    }
+    if (retval) {
+      logInfo("path = " + file + ", already present as root for deletion.")
+    }
+    retval
+  }
+
+  /**
+   * Detect whether this thread might be executing a shutdown hook. Will always return true if
+   * the current thread is a running a shutdown hook but may spuriously return true otherwise (e.g.
+   * if System.exit was just called by a concurrent thread).
+   *
+   * Currently, this detects whether the JVM is shutting down by Runtime#addShutdownHook throwing
+   * an IllegalStateException.
+   */
+  def inShutdown(): Boolean = {
+    try {
+      val hook = new Thread {
+        override def run() {}
+      }
+      Runtime.getRuntime.addShutdownHook(hook)
+      Runtime.getRuntime.removeShutdownHook(hook)
+    } catch {
+      case ise: IllegalStateException => return true
+    }
+    false
+  }
+
+  /**
+   * Adds a shutdown hook with default priority.
+   *
+   * @param hook The code to run during shutdown.
+   * @return A handle that can be used to unregister the shutdown hook.
+   */
+  def addShutdownHook(hook: () => Unit): AnyRef = {
+    addShutdownHook(DEFAULT_SHUTDOWN_PRIORITY)(hook)
+  }
+
+  /**
+   * Adds a shutdown hook with the given priority. Hooks with lower priority values run
+   * first.
+   *
+   * @param hook The code to run during shutdown.
+   * @return A handle that can be used to unregister the shutdown hook.
+   */
+  def addShutdownHook(priority: Int)(hook: () => Unit): AnyRef = {
+    shutdownHooks.add(priority, hook)
+  }
+
+  /**
+   * Remove a previously installed shutdown hook.
+   *
+   * @param ref A handle returned by `addShutdownHook`.
+   * @return Whether the hook was removed.
+   */
+  def removeShutdownHook(ref: AnyRef): Boolean = {
+    shutdownHooks.remove(ref)
+  }
+
+}
+
+private [util] class SparkShutdownHookManager {
+
+  private val hooks = new PriorityQueue[SparkShutdownHook]()
+  private var shuttingDown = false
+
+  /**
+   * Install a hook to run at shutdown and run all registered hooks in order. Hadoop 1.x does not
+   * have `ShutdownHookManager`, so in that case we just use the JVM's `Runtime` object and hope for
+   * the best.
+   */
+  def install(): Unit = {
+    val hookTask = new Runnable() {
+      override def run(): Unit = runAll()
+    }
+    Try(Utils.classForName("org.apache.hadoop.util.ShutdownHookManager")) match {
+      case Success(shmClass) =>
+        val fsPriority = classOf[FileSystem].getField("SHUTDOWN_HOOK_PRIORITY").get()
+          .asInstanceOf[Int]
+        val shm = shmClass.getMethod("get").invoke(null)
+        shm.getClass().getMethod("addShutdownHook", classOf[Runnable], classOf[Int])
+          .invoke(shm, hookTask, Integer.valueOf(fsPriority + 30))
+
+      case Failure(_) =>
+        Runtime.getRuntime.addShutdownHook(new Thread(hookTask, "Spark Shutdown Hook"));
+    }
+  }
+
+  def runAll(): Unit = synchronized {
+    shuttingDown = true
+    while (!hooks.isEmpty()) {
+      Try(Utils.logUncaughtExceptions(hooks.poll().run()))
+    }
+  }
+
+  def add(priority: Int, hook: () => Unit): AnyRef = synchronized {
+    checkState()
+    val hookRef = new SparkShutdownHook(priority, hook)
+    hooks.add(hookRef)
+    hookRef
+  }
+
+  def remove(ref: AnyRef): Boolean = synchronized {
+    hooks.remove(ref)
+  }
+
+  private def checkState(): Unit = {
+    if (shuttingDown) {
+      throw new IllegalStateException("Shutdown hooks cannot be modified during shutdown.")
+    }
+  }
+
+}
+
+private class SparkShutdownHook(private val priority: Int, hook: () => Unit)
+  extends Comparable[SparkShutdownHook] {
+
+  override def compareTo(other: SparkShutdownHook): Int = {
+    other.priority - priority
+  }
+
+  def run(): Unit = hook()
+
+}
diff --git a/core/src/main/scala/org/apache/spark/util/SparkUncaughtExceptionHandler.scala b/core/src/main/scala/org/apache/spark/util/SparkUncaughtExceptionHandler.scala
index ad3db1fbb57ed..7248187247330 100644
--- a/core/src/main/scala/org/apache/spark/util/SparkUncaughtExceptionHandler.scala
+++ b/core/src/main/scala/org/apache/spark/util/SparkUncaughtExceptionHandler.scala
@@ -33,7 +33,7 @@ private[spark] object SparkUncaughtExceptionHandler
 
       // We may have been called from a shutdown hook. If so, we must not call System.exit().
       // (If we do, we will deadlock.)
-      if (!Utils.inShutdown()) {
+      if (!ShutdownHookManager.inShutdown()) {
         if (exception.isInstanceOf[OutOfMemoryError]) {
           System.exit(SparkExitCode.OOM)
         } else {
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index a90d8541366f4..f2abf227dc129 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -21,7 +21,7 @@ import java.io._
 import java.lang.management.ManagementFactory
 import java.net._
 import java.nio.ByteBuffer
-import java.util.{PriorityQueue, Properties, Locale, Random, UUID}
+import java.util.{Properties, Locale, Random, UUID}
 import java.util.concurrent._
 import javax.net.ssl.HttpsURLConnection
 
@@ -65,21 +65,6 @@ private[spark] object CallSite {
 private[spark] object Utils extends Logging {
   val random = new Random()
 
-  val DEFAULT_SHUTDOWN_PRIORITY = 100
-
-  /**
-   * The shutdown priority of the SparkContext instance. This is lower than the default
-   * priority, so that by default hooks are run before the context is shut down.
-   */
-  val SPARK_CONTEXT_SHUTDOWN_PRIORITY = 50
-
-  /**
-   * The shutdown priority of temp directory must be lower than the SparkContext shutdown
-   * priority. Otherwise cleaning the temp directories while Spark jobs are running can
-   * throw undesirable errors at the time of shutdown.
-   */
-  val TEMP_DIR_SHUTDOWN_PRIORITY = 25
-
   /**
    * Define a default value for driver memory here since this value is referenced across the code
    * base and nearly all files already use Utils.scala
@@ -90,9 +75,6 @@ private[spark] object Utils extends Logging {
   @volatile private var localRootDirs: Array[String] = null
 
 
-  private val shutdownHooks = new SparkShutdownHookManager()
-  shutdownHooks.install()
-
   /** Serialize an object using Java serialization */
   def serialize[T](o: T): Array[Byte] = {
     val bos = new ByteArrayOutputStream()
@@ -205,86 +187,6 @@ private[spark] object Utils extends Logging {
     }
   }
 
-  private val shutdownDeletePaths = new scala.collection.mutable.HashSet[String]()
-  private val shutdownDeleteTachyonPaths = new scala.collection.mutable.HashSet[String]()
-
-  // Add a shutdown hook to delete the temp dirs when the JVM exits
-  addShutdownHook(TEMP_DIR_SHUTDOWN_PRIORITY) { () =>
-    logInfo("Shutdown hook called")
-    shutdownDeletePaths.foreach { dirPath =>
-      try {
-        logInfo("Deleting directory " + dirPath)
-        Utils.deleteRecursively(new File(dirPath))
-      } catch {
-        case e: Exception => logError(s"Exception while deleting Spark temp dir: $dirPath", e)
-      }
-    }
-  }
-
-  // Register the path to be deleted via shutdown hook
-  def registerShutdownDeleteDir(file: File) {
-    val absolutePath = file.getAbsolutePath()
-    shutdownDeletePaths.synchronized {
-      shutdownDeletePaths += absolutePath
-    }
-  }
-
-  // Register the tachyon path to be deleted via shutdown hook
-  def registerShutdownDeleteDir(tachyonfile: TachyonFile) {
-    val absolutePath = tachyonfile.getPath()
-    shutdownDeleteTachyonPaths.synchronized {
-      shutdownDeleteTachyonPaths += absolutePath
-    }
-  }
-
-  // Is the path already registered to be deleted via a shutdown hook ?
-  def hasShutdownDeleteDir(file: File): Boolean = {
-    val absolutePath = file.getAbsolutePath()
-    shutdownDeletePaths.synchronized {
-      shutdownDeletePaths.contains(absolutePath)
-    }
-  }
-
-  // Is the path already registered to be deleted via a shutdown hook ?
-  def hasShutdownDeleteTachyonDir(file: TachyonFile): Boolean = {
-    val absolutePath = file.getPath()
-    shutdownDeleteTachyonPaths.synchronized {
-      shutdownDeleteTachyonPaths.contains(absolutePath)
-    }
-  }
-
-  // Note: if file is child of some registered path, while not equal to it, then return true;
-  // else false. This is to ensure that two shutdown hooks do not try to delete each others
-  // paths - resulting in IOException and incomplete cleanup.
-  def hasRootAsShutdownDeleteDir(file: File): Boolean = {
-    val absolutePath = file.getAbsolutePath()
-    val retval = shutdownDeletePaths.synchronized {
-      shutdownDeletePaths.exists { path =>
-        !absolutePath.equals(path) && absolutePath.startsWith(path)
-      }
-    }
-    if (retval) {
-      logInfo("path = " + file + ", already present as root for deletion.")
-    }
-    retval
-  }
-
-  // Note: if file is child of some registered path, while not equal to it, then return true;
-  // else false. This is to ensure that two shutdown hooks do not try to delete each others
-  // paths - resulting in Exception and incomplete cleanup.
-  def hasRootAsShutdownDeleteDir(file: TachyonFile): Boolean = {
-    val absolutePath = file.getPath()
-    val retval = shutdownDeleteTachyonPaths.synchronized {
-      shutdownDeleteTachyonPaths.exists { path =>
-        !absolutePath.equals(path) && absolutePath.startsWith(path)
-      }
-    }
-    if (retval) {
-      logInfo("path = " + file + ", already present as root for deletion.")
-    }
-    retval
-  }
-
   /**
    * JDK equivalent of `chmod 700 file`.
    *
@@ -333,7 +235,7 @@ private[spark] object Utils extends Logging {
       root: String = System.getProperty("java.io.tmpdir"),
       namePrefix: String = "spark"): File = {
     val dir = createDirectory(root, namePrefix)
-    registerShutdownDeleteDir(dir)
+    ShutdownHookManager.registerShutdownDeleteDir(dir)
     dir
   }
 
@@ -973,9 +875,7 @@ private[spark] object Utils extends Logging {
           if (savedIOException != null) {
             throw savedIOException
           }
-          shutdownDeletePaths.synchronized {
-            shutdownDeletePaths.remove(file.getAbsolutePath)
-          }
+          ShutdownHookManager.removeShutdownDeleteDir(file)
         }
       } finally {
         if (!file.delete()) {
@@ -1478,27 +1378,6 @@ private[spark] object Utils extends Logging {
     serializer.deserialize[T](serializer.serialize(value))
   }
 
-  /**
-   * Detect whether this thread might be executing a shutdown hook. Will always return true if
-   * the current thread is a running a shutdown hook but may spuriously return true otherwise (e.g.
-   * if System.exit was just called by a concurrent thread).
-   *
-   * Currently, this detects whether the JVM is shutting down by Runtime#addShutdownHook throwing
-   * an IllegalStateException.
-   */
-  def inShutdown(): Boolean = {
-    try {
-      val hook = new Thread {
-        override def run() {}
-      }
-      Runtime.getRuntime.addShutdownHook(hook)
-      Runtime.getRuntime.removeShutdownHook(hook)
-    } catch {
-      case ise: IllegalStateException => return true
-    }
-    false
-  }
-
   private def isSpace(c: Char): Boolean = {
     " \t\r\n".indexOf(c) != -1
   }
@@ -2221,37 +2100,6 @@ private[spark] object Utils extends Logging {
     msg.startsWith(BACKUP_STANDALONE_MASTER_PREFIX)
   }
 
-  /**
-   * Adds a shutdown hook with default priority.
-   *
-   * @param hook The code to run during shutdown.
-   * @return A handle that can be used to unregister the shutdown hook.
-   */
-  def addShutdownHook(hook: () => Unit): AnyRef = {
-    addShutdownHook(DEFAULT_SHUTDOWN_PRIORITY)(hook)
-  }
-
-  /**
-   * Adds a shutdown hook with the given priority. Hooks with lower priority values run
-   * first.
-   *
-   * @param hook The code to run during shutdown.
-   * @return A handle that can be used to unregister the shutdown hook.
-   */
-  def addShutdownHook(priority: Int)(hook: () => Unit): AnyRef = {
-    shutdownHooks.add(priority, hook)
-  }
-
-  /**
-   * Remove a previously installed shutdown hook.
-   *
-   * @param ref A handle returned by `addShutdownHook`.
-   * @return Whether the hook was removed.
-   */
-  def removeShutdownHook(ref: AnyRef): Boolean = {
-    shutdownHooks.remove(ref)
-  }
-
   /**
    * To avoid calling `Utils.getCallSite` for every single RDD we create in the body,
    * set a dummy call site that RDDs use instead. This is for performance optimization.
@@ -2299,70 +2147,6 @@ private[spark] object Utils extends Logging {
 
 }
 
-private [util] class SparkShutdownHookManager {
-
-  private val hooks = new PriorityQueue[SparkShutdownHook]()
-  private var shuttingDown = false
-
-  /**
-   * Install a hook to run at shutdown and run all registered hooks in order. Hadoop 1.x does not
-   * have `ShutdownHookManager`, so in that case we just use the JVM's `Runtime` object and hope for
-   * the best.
-   */
-  def install(): Unit = {
-    val hookTask = new Runnable() {
-      override def run(): Unit = runAll()
-    }
-    Try(Utils.classForName("org.apache.hadoop.util.ShutdownHookManager")) match {
-      case Success(shmClass) =>
-        val fsPriority = classOf[FileSystem].getField("SHUTDOWN_HOOK_PRIORITY").get()
-          .asInstanceOf[Int]
-        val shm = shmClass.getMethod("get").invoke(null)
-        shm.getClass().getMethod("addShutdownHook", classOf[Runnable], classOf[Int])
-          .invoke(shm, hookTask, Integer.valueOf(fsPriority + 30))
-
-      case Failure(_) =>
-        Runtime.getRuntime.addShutdownHook(new Thread(hookTask, "Spark Shutdown Hook"));
-    }
-  }
-
-  def runAll(): Unit = synchronized {
-    shuttingDown = true
-    while (!hooks.isEmpty()) {
-      Try(Utils.logUncaughtExceptions(hooks.poll().run()))
-    }
-  }
-
-  def add(priority: Int, hook: () => Unit): AnyRef = synchronized {
-    checkState()
-    val hookRef = new SparkShutdownHook(priority, hook)
-    hooks.add(hookRef)
-    hookRef
-  }
-
-  def remove(ref: AnyRef): Boolean = synchronized {
-    hooks.remove(ref)
-  }
-
-  private def checkState(): Unit = {
-    if (shuttingDown) {
-      throw new IllegalStateException("Shutdown hooks cannot be modified during shutdown.")
-    }
-  }
-
-}
-
-private class SparkShutdownHook(private val priority: Int, hook: () => Unit)
-  extends Comparable[SparkShutdownHook] {
-
-  override def compareTo(other: SparkShutdownHook): Int = {
-    other.priority - priority
-  }
-
-  def run(): Unit = hook()
-
-}
-
 /**
  * A utility class to redirect the child process's stdout or stderr.
  */
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
index 9c047347cb58d..2c9fa595b2dad 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
@@ -35,7 +35,7 @@ import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
 import org.apache.spark.sql.hive.thriftserver.ui.ThriftServerTab
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 import org.apache.spark.{Logging, SparkContext}
 
 
@@ -76,7 +76,7 @@ object HiveThriftServer2 extends Logging {
     logInfo("Starting SparkContext")
     SparkSQLEnv.init()
 
-    Utils.addShutdownHook { () =>
+    ShutdownHookManager.addShutdownHook { () =>
       SparkSQLEnv.stop()
       uiTab.foreach(_.detach())
     }
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
index d3886142b388d..7799704c819d9 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -39,7 +39,7 @@ import org.apache.thrift.transport.TSocket
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.hive.HiveContext
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 
 /**
  * This code doesn't support remote connections in Hive 1.2+, as the underlying CliDriver
@@ -114,7 +114,7 @@ private[hive] object SparkSQLCLIDriver extends Logging {
     SessionState.start(sessionState)
 
     // Clean up after we exit
-    Utils.addShutdownHook { () => SparkSQLEnv.stop() }
+    ShutdownHookManager.addShutdownHook { () => SparkSQLEnv.stop() }
 
     val remoteMode = isRemoteMode(sessionState)
     // "-h" option has been passed, so connect to Hive thrift server.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 296cc5c5e0b04..4eae699ac3b51 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -36,7 +36,7 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.CacheTableCommand
 import org.apache.spark.sql.hive._
 import org.apache.spark.sql.hive.execution.HiveNativeCommand
-import org.apache.spark.util.Utils
+import org.apache.spark.util.{ShutdownHookManager, Utils}
 import org.apache.spark.{SparkConf, SparkContext}
 
 /* Implicit conversions */
@@ -154,7 +154,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
   val hiveFilesTemp = File.createTempFile("catalystHiveFiles", "")
   hiveFilesTemp.delete()
   hiveFilesTemp.mkdir()
-  Utils.registerShutdownDeleteDir(hiveFilesTemp)
+  ShutdownHookManager.registerShutdownDeleteDir(hiveFilesTemp)
 
   val inRepoTests = if (System.getProperty("user.dir").endsWith("sql" + File.separator + "hive")) {
     new File("src" + File.separator + "test" + File.separator + "resources" + File.separator)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index 177e710ace54b..b496d1f341a0b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
@@ -44,7 +44,7 @@ import org.apache.spark.streaming.dstream._
 import org.apache.spark.streaming.receiver.{ActorReceiver, ActorSupervisorStrategy, Receiver}
 import org.apache.spark.streaming.scheduler.{JobScheduler, StreamingListener}
 import org.apache.spark.streaming.ui.{StreamingJobProgressListener, StreamingTab}
-import org.apache.spark.util.{CallSite, Utils}
+import org.apache.spark.util.{CallSite, ShutdownHookManager, Utils}
 
 /**
  * Main entry point for Spark Streaming functionality. It provides methods used to create
@@ -604,7 +604,7 @@ class StreamingContext private[streaming] (
           }
           StreamingContext.setActiveContext(this)
         }
-        shutdownHookRef = Utils.addShutdownHook(
+        shutdownHookRef = ShutdownHookManager.addShutdownHook(
           StreamingContext.SHUTDOWN_HOOK_PRIORITY)(stopOnShutdown)
         // Registering Streaming Metrics at the start of the StreamingContext
         assert(env.metricsSystem != null)
@@ -691,7 +691,7 @@ class StreamingContext private[streaming] (
           StreamingContext.setActiveContext(null)
           waiter.notifyStop()
           if (shutdownHookRef != null) {
-            Utils.removeShutdownHook(shutdownHookRef)
+            ShutdownHookManager.removeShutdownHook(shutdownHookRef)
           }
           logInfo("StreamingContext stopped successfully")
       }
@@ -725,7 +725,7 @@ object StreamingContext extends Logging {
    */
   private val ACTIVATION_LOCK = new Object()
 
-  private val SHUTDOWN_HOOK_PRIORITY = Utils.SPARK_CONTEXT_SHUTDOWN_PRIORITY + 1
+  private val SHUTDOWN_HOOK_PRIORITY = ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY + 1
 
   private val activeContext = new AtomicReference[StreamingContext](null)
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index e19940d8d6642..6a8ddb37b29e8 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -112,7 +112,8 @@ private[spark] class ApplicationMaster(
       val fs = FileSystem.get(yarnConf)
 
       // This shutdown hook should run *after* the SparkContext is shut down.
-      Utils.addShutdownHook(Utils.SPARK_CONTEXT_SHUTDOWN_PRIORITY - 1) { () =>
+      val priority = ShutdownHookManager.SPARK_CONTEXT_SHUTDOWN_PRIORITY - 1
+      ShutdownHookManager.addShutdownHook(priority) { () =>
         val maxAppAttempts = client.getMaxRegAttempts(sparkConf, yarnConf)
         val isLastAttempt = client.getAttemptId().getAttemptId() >= maxAppAttempts
 
@@ -199,7 +200,7 @@ private[spark] class ApplicationMaster(
   final def finish(status: FinalApplicationStatus, code: Int, msg: String = null): Unit = {
     synchronized {
       if (!finished) {
-        val inShutdown = Utils.inShutdown()
+        val inShutdown = ShutdownHookManager.inShutdown()
         logInfo(s"Final app status: $status, exitCode: $code" +
           Option(msg).map(msg => s", (reason: $msg)").getOrElse(""))
         exitCode = code

From 7035d880a0cf06910c19b4afd49645124c620f14 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 12 Aug 2015 16:45:15 -0700
Subject: [PATCH 203/633] [SPARK-9894] [SQL] Json writer should handle MapData.

https://issues.apache.org/jira/browse/SPARK-9894

Author: Yin Huai <yhuai@databricks.com>

Closes #8137 from yhuai/jsonMapData.
---
 .../datasources/json/JacksonGenerator.scala   | 10 +--
 .../sources/JsonHadoopFsRelationSuite.scala   | 78 +++++++++++++++++++
 .../SimpleTextHadoopFsRelationSuite.scala     | 30 -------
 3 files changed, 83 insertions(+), 35 deletions(-)
 create mode 100644 sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
index 37c2b5a296c15..99ac7730bd1c9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
@@ -107,12 +107,12 @@ private[sql] object JacksonGenerator {
         v.foreach(ty, (_, value) => valWriter(ty, value))
         gen.writeEndArray()
 
-      case (MapType(kv, vv, _), v: Map[_, _]) =>
+      case (MapType(kt, vt, _), v: MapData) =>
         gen.writeStartObject()
-        v.foreach { p =>
-          gen.writeFieldName(p._1.toString)
-          valWriter(vv, p._2)
-        }
+        v.foreach(kt, vt, { (k, v) =>
+          gen.writeFieldName(k.toString)
+          valWriter(vt, v)
+        })
         gen.writeEndObject()
 
       case (StructType(ty), v: InternalRow) =>
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
new file mode 100644
index 0000000000000..ed6d512ab36fe
--- /dev/null
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
@@ -0,0 +1,78 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.sources
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.types._
+
+class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
+  override val dataSourceName: String = "json"
+
+  import sqlContext._
+
+  test("save()/load() - partitioned table - simple queries - partition columns in data") {
+    withTempDir { file =>
+      val basePath = new Path(file.getCanonicalPath)
+      val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf)
+      val qualifiedBasePath = fs.makeQualified(basePath)
+
+      for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) {
+        val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2")
+        sparkContext
+          .parallelize(for (i <- 1 to 3) yield s"""{"a":$i,"b":"val_$i"}""")
+          .saveAsTextFile(partitionDir.toString)
+      }
+
+      val dataSchemaWithPartition =
+        StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
+
+      checkQueries(
+        read.format(dataSourceName)
+          .option("dataSchema", dataSchemaWithPartition.json)
+          .load(file.getCanonicalPath))
+    }
+  }
+
+  test("SPARK-9894: save complex types to JSON") {
+    withTempDir { file =>
+      file.delete()
+
+      val schema =
+        new StructType()
+          .add("array", ArrayType(LongType))
+          .add("map", MapType(StringType, new StructType().add("innerField", LongType)))
+
+      val data =
+        Row(Seq(1L, 2L, 3L), Map("m1" -> Row(4L))) ::
+          Row(Seq(5L, 6L, 7L), Map("m2" -> Row(10L))) :: Nil
+      val df = createDataFrame(sparkContext.parallelize(data), schema)
+
+      // Write the data out.
+      df.write.format(dataSourceName).save(file.getCanonicalPath)
+
+      // Read it back and check the result.
+      checkAnswer(
+        read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
+        df
+      )
+    }
+  }
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
index 48c37a1fa1022..e8975e5f5cd08 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -50,33 +50,3 @@ class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
     }
   }
 }
-
-class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
-  override val dataSourceName: String =
-    classOf[org.apache.spark.sql.execution.datasources.json.DefaultSource].getCanonicalName
-
-  import sqlContext._
-
-  test("save()/load() - partitioned table - simple queries - partition columns in data") {
-    withTempDir { file =>
-      val basePath = new Path(file.getCanonicalPath)
-      val fs = basePath.getFileSystem(SparkHadoopUtil.get.conf)
-      val qualifiedBasePath = fs.makeQualified(basePath)
-
-      for (p1 <- 1 to 2; p2 <- Seq("foo", "bar")) {
-        val partitionDir = new Path(qualifiedBasePath, s"p1=$p1/p2=$p2")
-        sparkContext
-          .parallelize(for (i <- 1 to 3) yield s"""{"a":$i,"b":"val_$i"}""")
-          .saveAsTextFile(partitionDir.toString)
-      }
-
-      val dataSchemaWithPartition =
-        StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
-
-      checkQueries(
-        read.format(dataSourceName)
-          .option("dataSchema", dataSchemaWithPartition.json)
-          .load(file.getCanonicalPath))
-    }
-  }
-}

From caa14d9dc9e2eb1102052b22445b63b0e004e3c7 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 16:53:47 -0700
Subject: [PATCH 204/633] [SPARK-9913] [MLLIB] LDAUtils should be private

feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8142 from mengxr/SPARK-9913.
---
 .../main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala
index f7e5ce1665fe6..a9ba7b60bad08 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAUtils.scala
@@ -22,7 +22,7 @@ import breeze.numerics._
 /**
  * Utility methods for LDA.
  */
-object LDAUtils {
+private[clustering] object LDAUtils {
   /**
    * Log Sum Exp with overflow protection using the identity:
    * For any a: \log \sum_{n=1}^N \exp\{x_n\} = a + \log \sum_{n=1}^N \exp\{x_n - a\}

From 6e409bc1357f49de2efdfc4226d074b943fb1153 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Wed, 12 Aug 2015 16:54:45 -0700
Subject: [PATCH 205/633] [SPARK-9909] [ML] [TRIVIAL] move weightCol to shared
 params

As per the TODO move weightCol to Shared Params.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8144 from holdenk/SPARK-9909-move-weightCol-toSharedParams.
---
 .../ml/param/shared/SharedParamsCodeGen.scala    |  4 +++-
 .../spark/ml/param/shared/sharedParams.scala     | 15 +++++++++++++++
 .../spark/ml/regression/IsotonicRegression.scala | 16 ++--------------
 3 files changed, 20 insertions(+), 15 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index 9e12f1856a940..8c16c6149b40d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -70,7 +70,9 @@ private[shared] object SharedParamsCodeGen {
         " For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.",
         isValid = "ParamValidators.inRange(0, 1)"),
       ParamDesc[Double]("tol", "the convergence tolerance for iterative algorithms"),
-      ParamDesc[Double]("stepSize", "Step size to be used for each iteration of optimization."))
+      ParamDesc[Double]("stepSize", "Step size to be used for each iteration of optimization."),
+      ParamDesc[String]("weightCol", "weight column name. If this is not set or empty, we treat " +
+        "all instance weights as 1.0."))
 
     val code = genSharedParams(params)
     val file = "src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala"
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index a17d4ea960a90..c26768953e3db 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -342,4 +342,19 @@ private[ml] trait HasStepSize extends Params {
   /** @group getParam */
   final def getStepSize: Double = $(stepSize)
 }
+
+/**
+ * Trait for shared param weightCol.
+ */
+private[ml] trait HasWeightCol extends Params {
+
+  /**
+   * Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0..
+   * @group param
+   */
+  final val weightCol: Param[String] = new Param[String](this, "weightCol", "weight column name. If this is not set or empty, we treat all instance weights as 1.0.")
+
+  /** @group getParam */
+  final def getWeightCol: String = $(weightCol)
+}
 // scalastyle:on
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
index f570590960a62..0f33bae30e622 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
@@ -21,7 +21,7 @@ import org.apache.spark.Logging
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasPredictionCol}
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol, HasPredictionCol, HasWeightCol}
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
 import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
 import org.apache.spark.mllib.regression.{IsotonicRegression => MLlibIsotonicRegression, IsotonicRegressionModel => MLlibIsotonicRegressionModel}
@@ -35,19 +35,7 @@ import org.apache.spark.storage.StorageLevel
  * Params for isotonic regression.
  */
 private[regression] trait IsotonicRegressionBase extends Params with HasFeaturesCol
-  with HasLabelCol with HasPredictionCol with Logging {
-
-  /**
-   * Param for weight column name (default: none).
-   * @group param
-   */
-  // TODO: Move weightCol to sharedParams.
-  final val weightCol: Param[String] =
-    new Param[String](this, "weightCol",
-      "weight column name. If this is not set or empty, we treat all instance weights as 1.0.")
-
-  /** @group getParam */
-  final def getWeightCol: String = $(weightCol)
+  with HasLabelCol with HasPredictionCol with HasWeightCol with Logging {
 
   /**
    * Param for whether the output sequence should be isotonic/increasing (true) or

From e6aef55766d0e2a48e0f9cb6eda0e31a71b962f3 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 17:04:31 -0700
Subject: [PATCH 206/633] [SPARK-9912] [MLLIB] QRDecomposition should use QType
 and RType for type names instead of UType and VType

hhbyyh

Author: Xiangrui Meng <meng@databricks.com>

Closes #8140 from mengxr/SPARK-9912.
---
 .../apache/spark/mllib/linalg/SingularValueDecomposition.scala  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index b416d50a5631e..cff5dbeee3e57 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -31,5 +31,5 @@ case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VTyp
  * Represents QR factors.
  */
 @Experimental
-case class QRDecomposition[UType, VType](Q: UType, R: VType)
+case class QRDecomposition[QType, RType](Q: QType, R: RType)
 

From fc1c7fd66e64ccea53b31cd2fbb98bc6d307329c Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 17:06:12 -0700
Subject: [PATCH 207/633] [SPARK-9915] [ML] stopWords should use
 StringArrayParam

hhbyyh

Author: Xiangrui Meng <meng@databricks.com>

Closes #8141 from mengxr/SPARK-9915.
---
 .../org/apache/spark/ml/feature/StopWordsRemover.scala      | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
index 3cc41424460f2..5d77ea08db657 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
@@ -19,12 +19,12 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.param.{BooleanParam, ParamMap, StringArrayParam}
 import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
-import org.apache.spark.ml.param.{ParamMap, BooleanParam, Param}
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.sql.DataFrame
-import org.apache.spark.sql.types.{StringType, StructField, ArrayType, StructType}
 import org.apache.spark.sql.functions.{col, udf}
+import org.apache.spark.sql.types.{ArrayType, StringType, StructField, StructType}
 
 /**
  * stop words list
@@ -100,7 +100,7 @@ class StopWordsRemover(override val uid: String)
    * the stop words set to be filtered out
    * @group param
    */
-  val stopWords: Param[Array[String]] = new Param(this, "stopWords", "stop words")
+  val stopWords: StringArrayParam = new StringArrayParam(this, "stopWords", "stop words")
 
   /** @group setParam */
   def setStopWords(value: Array[String]): this.type = set(stopWords, value)

From 660e6dcff8125b83cc73dbe00c90cbe58744bc66 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Wed, 12 Aug 2015 17:07:29 -0700
Subject: [PATCH 208/633] [SPARK-9449] [SQL] Include MetastoreRelation's
 inputFiles

Author: Michael Armbrust <michael@databricks.com>

Closes #8119 from marmbrus/metastoreInputFiles.
---
 .../org/apache/spark/sql/DataFrame.scala      | 10 ++++---
 .../spark/sql/execution/FileRelation.scala    | 28 +++++++++++++++++++
 .../apache/spark/sql/sources/interfaces.scala |  6 ++--
 .../org/apache/spark/sql/DataFrameSuite.scala | 26 +++++++++--------
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 16 +++++++++--
 5 files changed, 66 insertions(+), 20 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/FileRelation.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 27b994f1f0caf..c466d9e6cb349 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -34,10 +34,10 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.logical.{Filter, _}
+import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
-import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, LogicalRDD, SQLExecution}
+import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, FileRelation, LogicalRDD, SQLExecution}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation}
 import org.apache.spark.sql.execution.datasources.json.JacksonGenerator
 import org.apache.spark.sql.sources.HadoopFsRelation
@@ -1560,8 +1560,10 @@ class DataFrame private[sql](
    */
   def inputFiles: Array[String] = {
     val files: Seq[String] = logicalPlan.collect {
-      case LogicalRelation(fsBasedRelation: HadoopFsRelation) =>
-        fsBasedRelation.paths.toSeq
+      case LogicalRelation(fsBasedRelation: FileRelation) =>
+        fsBasedRelation.inputFiles
+      case fr: FileRelation =>
+        fr.inputFiles
     }.flatten
     files.toSet.toArray
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/FileRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/FileRelation.scala
new file mode 100644
index 0000000000000..7a2a9eed5807d
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/FileRelation.scala
@@ -0,0 +1,28 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+/**
+ * An interface for relations that are backed by files.  When a class implements this interface,
+ * the list of paths that it returns will be returned to a user who calls `inputPaths` on any
+ * DataFrame that queries this relation.
+ */
+private[sql] trait FileRelation {
+  /** Returns the list of files that will be read when scanning this relation. */
+  def inputFiles: Array[String]
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index 2f8417a48d32e..b3b326fe612c7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -31,7 +31,7 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
-import org.apache.spark.sql.execution.RDDConversions
+import org.apache.spark.sql.execution.{FileRelation, RDDConversions}
 import org.apache.spark.sql.execution.datasources.{PartitioningUtils, PartitionSpec, Partition}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql._
@@ -406,7 +406,7 @@ abstract class OutputWriter {
  */
 @Experimental
 abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[PartitionSpec])
-  extends BaseRelation with Logging {
+  extends BaseRelation with FileRelation with Logging {
 
   override def toString: String = getClass.getSimpleName + paths.mkString("[", ",", "]")
 
@@ -516,6 +516,8 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
    */
   def paths: Array[String]
 
+  override def inputFiles: Array[String] = cachedLeafStatuses().map(_.getPath.toString).toArray
+
   /**
    * Partition columns.  Can be either defined by [[userDefinedPartitionColumns]] or automatically
    * discovered.  Note that they should always be nullable.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index adbd95197d7ca..2feec29955bc8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -485,21 +485,23 @@ class DataFrameSuite extends QueryTest with SQLTestUtils {
   }
 
   test("inputFiles") {
-    val fakeRelation1 = new ParquetRelation(Array("/my/path", "/my/other/path"),
-      Some(testData.schema), None, Map.empty)(sqlContext)
-    val df1 = DataFrame(sqlContext, LogicalRelation(fakeRelation1))
-    assert(df1.inputFiles.toSet == fakeRelation1.paths.toSet)
+    withTempDir { dir =>
+      val df = Seq((1, 22)).toDF("a", "b")
 
-    val fakeRelation2 = new JSONRelation(
-      None, 1, Some(testData.schema), None, None, Array("/json/path"))(sqlContext)
-    val df2 = DataFrame(sqlContext, LogicalRelation(fakeRelation2))
-    assert(df2.inputFiles.toSet == fakeRelation2.paths.toSet)
+      val parquetDir = new File(dir, "parquet").getCanonicalPath
+      df.write.parquet(parquetDir)
+      val parquetDF = sqlContext.read.parquet(parquetDir)
+      assert(parquetDF.inputFiles.nonEmpty)
 
-    val unionDF = df1.unionAll(df2)
-    assert(unionDF.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.paths)
+      val jsonDir = new File(dir, "json").getCanonicalPath
+      df.write.json(jsonDir)
+      val jsonDF = sqlContext.read.json(jsonDir)
+      assert(parquetDF.inputFiles.nonEmpty)
 
-    val filtered = df1.filter("false").unionAll(df2.intersect(df2))
-    assert(filtered.inputFiles.toSet == fakeRelation1.paths.toSet ++ fakeRelation2.paths)
+      val unioned = jsonDF.unionAll(parquetDF).inputFiles.sorted
+      val allFiles = (jsonDF.inputFiles ++ parquetDF.inputFiles).toSet.toArray.sorted
+      assert(unioned === allFiles)
+    }
   }
 
   ignore("show") {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index ac9aaed19d566..5e5497837a393 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -38,7 +38,7 @@ import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.{InternalRow, SqlParser, TableIdentifier}
-import org.apache.spark.sql.execution.datasources
+import org.apache.spark.sql.execution.{FileRelation, datasources}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource}
 import org.apache.spark.sql.hive.client._
 import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
@@ -739,7 +739,7 @@ private[hive] case class MetastoreRelation
     (databaseName: String, tableName: String, alias: Option[String])
     (val table: HiveTable)
     (@transient sqlContext: SQLContext)
-  extends LeafNode with MultiInstanceRelation {
+  extends LeafNode with MultiInstanceRelation with FileRelation {
 
   override def equals(other: Any): Boolean = other match {
     case relation: MetastoreRelation =>
@@ -888,6 +888,18 @@ private[hive] case class MetastoreRelation
   /** An attribute map for determining the ordinal for non-partition columns. */
   val columnOrdinals = AttributeMap(attributes.zipWithIndex)
 
+  override def inputFiles: Array[String] = {
+    val partLocations = table.getPartitions(Nil).map(_.storage.location).toArray
+    if (partLocations.nonEmpty) {
+      partLocations
+    } else {
+      Array(
+        table.location.getOrElse(
+          sys.error(s"Could not get the location of ${table.qualifiedName}.")))
+    }
+  }
+
+
   override def newInstance(): MetastoreRelation = {
     MetastoreRelation(databaseName, tableName, alias)(table)(sqlContext)
   }

From 8ce60963cb0928058ef7b6e29ff94eb69d1143af Mon Sep 17 00:00:00 2001
From: cody koeninger <cody@koeninger.org>
Date: Wed, 12 Aug 2015 17:44:16 -0700
Subject: [PATCH 209/633] =?UTF-8?q?[SPARK-9780]=20[STREAMING]=20[KAFKA]=20?=
 =?UTF-8?q?prevent=20NPE=20if=20KafkaRDD=20instantiation=20=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…fails

Author: cody koeninger <cody@koeninger.org>

Closes #8133 from koeninger/SPARK-9780 and squashes the following commits:

406259d [cody koeninger] [SPARK-9780][Streaming][Kafka] prevent NPE if KafkaRDD instantiation fails
---
 .../scala/org/apache/spark/streaming/kafka/KafkaRDD.scala   | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala
index 1a9d78c0d4f59..ea5f842c6cafe 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaRDD.scala
@@ -197,7 +197,11 @@ class KafkaRDD[
         .dropWhile(_.offset < requestOffset)
     }
 
-    override def close(): Unit = consumer.close()
+    override def close(): Unit = {
+      if (consumer != null) {
+        consumer.close()
+      }
+    }
 
     override def getNext(): R = {
       if (iter == null || !iter.hasNext) {

From 0d1d146c220f0d47d0e62b368d5b94d3bd9dd197 Mon Sep 17 00:00:00 2001
From: Rohit Agarwal <rohita@qubole.com>
Date: Wed, 12 Aug 2015 17:48:43 -0700
Subject: [PATCH 210/633] [SPARK-9724] [WEB UI] Avoid unnecessary redirects in
 the Spark Web UI.

Author: Rohit Agarwal <rohita@qubole.com>

Closes #8014 from mindprince/SPARK-9724 and squashes the following commits:

a7af5ff [Rohit Agarwal] [SPARK-9724] [WEB UI] Inline attachPrefix and attachPrefixForRedirect. Fix logic of attachPrefix
8a977cd [Rohit Agarwal] [SPARK-9724] [WEB UI] Address review comments: Remove unneeded code, update scaladoc.
b257844 [Rohit Agarwal] [SPARK-9724] [WEB UI] Avoid unnecessary redirects in the Spark Web UI.
---
 .../main/scala/org/apache/spark/ui/JettyUtils.scala | 13 ++++++-------
 .../main/scala/org/apache/spark/ui/SparkUI.scala    |  4 ++--
 2 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
index c8356467fab87..779c0ba083596 100644
--- a/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
+++ b/core/src/main/scala/org/apache/spark/ui/JettyUtils.scala
@@ -106,7 +106,11 @@ private[spark] object JettyUtils extends Logging {
       path: String,
       servlet: HttpServlet,
       basePath: String): ServletContextHandler = {
-    val prefixedPath = attachPrefix(basePath, path)
+    val prefixedPath = if (basePath == "" && path == "/") {
+      path
+    } else {
+      (basePath + path).stripSuffix("/")
+    }
     val contextHandler = new ServletContextHandler
     val holder = new ServletHolder(servlet)
     contextHandler.setContextPath(prefixedPath)
@@ -121,7 +125,7 @@ private[spark] object JettyUtils extends Logging {
       beforeRedirect: HttpServletRequest => Unit = x => (),
       basePath: String = "",
       httpMethods: Set[String] = Set("GET")): ServletContextHandler = {
-    val prefixedDestPath = attachPrefix(basePath, destPath)
+    val prefixedDestPath = basePath + destPath
     val servlet = new HttpServlet {
       override def doGet(request: HttpServletRequest, response: HttpServletResponse): Unit = {
         if (httpMethods.contains("GET")) {
@@ -246,11 +250,6 @@ private[spark] object JettyUtils extends Logging {
     val (server, boundPort) = Utils.startServiceOnPort[Server](port, connect, conf, serverName)
     ServerInfo(server, boundPort, collection)
   }
-
-  /** Attach a prefix to the given path, but avoid returning an empty path */
-  private def attachPrefix(basePath: String, relativePath: String): String = {
-    if (basePath == "") relativePath else (basePath + relativePath).stripSuffix("/")
-  }
 }
 
 private[spark] case class ServerInfo(
diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
index 3788916cf39bb..d8b90568b7b9a 100644
--- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
+++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala
@@ -64,11 +64,11 @@ private[spark] class SparkUI private (
     attachTab(new EnvironmentTab(this))
     attachTab(new ExecutorsTab(this))
     attachHandler(createStaticHandler(SparkUI.STATIC_RESOURCE_DIR, "/static"))
-    attachHandler(createRedirectHandler("/", "/jobs", basePath = basePath))
+    attachHandler(createRedirectHandler("/", "/jobs/", basePath = basePath))
     attachHandler(ApiRootResource.getServletHandler(this))
     // This should be POST only, but, the YARN AM proxy won't proxy POSTs
     attachHandler(createRedirectHandler(
-      "/stages/stage/kill", "/stages", stagesTab.handleKillRequest,
+      "/stages/stage/kill", "/stages/", stagesTab.handleKillRequest,
       httpMethods = Set("GET", "POST")))
   }
   initialize()

From f4bc01f1f33a93e6affe5c8a3e33ffbd92d03f38 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Wed, 12 Aug 2015 18:33:27 -0700
Subject: [PATCH 211/633] [SPARK-9855] [SPARKR] Add expression functions into
 SparkR whose params are simple

I added lots of expression functions for SparkR. This PR includes only functions whose params  are only `(Column)` or `(Column, Column)`.  And I think we need to improve how to test those functions. However, it would be better to work on another issue.

## Diff Summary

- Add lots of functions in `functions.R` and their generic in `generic.R`
- Add aliases for `ceiling` and `sign`
- Move expression functions from `column.R` to `functions.R`
- Modify `rdname` from `column` to `functions`

I haven't supported `not` function, because the name has a collesion with `testthat` package. I didn't think of the way  to define it.

## New Supported Functions

```
approxCountDistinct
ascii
base64
bin
bitwiseNOT
ceil (alias: ceiling)
crc32
dayofmonth
dayofyear
explode
factorial
hex
hour
initcap
isNaN
last_day
length
log2
ltrim
md5
minute
month
negate
quarter
reverse
round
rtrim
second
sha1
signum (alias: sign)
size
soundex
to_date
trim
unbase64
unhex
weekofyear
year

datediff
levenshtein
months_between
nanvl
pmod
```

## JIRA
[[SPARK-9855] Add expression functions into SparkR whose params are simple - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9855)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8123 from yu-iskw/SPARK-9855.
---
 R/pkg/DESCRIPTION                |   1 +
 R/pkg/R/column.R                 |  81 --------------
 R/pkg/R/functions.R              | 123 ++++++++++++++++++++
 R/pkg/R/generics.R               | 185 +++++++++++++++++++++++++++++--
 R/pkg/inst/tests/test_sparkSQL.R |  21 ++--
 5 files changed, 309 insertions(+), 102 deletions(-)
 create mode 100644 R/pkg/R/functions.R

diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index 4949d86d20c91..83e64897216b1 100644
--- a/R/pkg/DESCRIPTION
+++ b/R/pkg/DESCRIPTION
@@ -29,6 +29,7 @@ Collate:
     'client.R'
     'context.R'
     'deserialize.R'
+    'functions.R'
     'mllib.R'
     'serialize.R'
     'sparkR.R'
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index eeaf9f193b728..328f595d0805f 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -60,12 +60,6 @@ operators <- list(
 )
 column_functions1 <- c("asc", "desc", "isNull", "isNotNull")
 column_functions2 <- c("like", "rlike", "startsWith", "endsWith", "getField", "getItem", "contains")
-functions <- c("min", "max", "sum", "avg", "mean", "count", "abs", "sqrt",
-               "first", "last", "lower", "upper", "sumDistinct",
-               "acos", "asin", "atan", "cbrt", "ceiling", "cos", "cosh", "exp",
-               "expm1", "floor", "log", "log10", "log1p", "rint", "sign",
-               "sin", "sinh", "tan", "tanh", "toDegrees", "toRadians")
-binary_mathfunctions <- c("atan2", "hypot")
 
 createOperator <- function(op) {
   setMethod(op,
@@ -111,33 +105,6 @@ createColumnFunction2 <- function(name) {
             })
 }
 
-createStaticFunction <- function(name) {
-  setMethod(name,
-            signature(x = "Column"),
-            function(x) {
-              if (name == "ceiling") {
-                  name <- "ceil"
-              }
-              if (name == "sign") {
-                  name <- "signum"
-              }
-              jc <- callJStatic("org.apache.spark.sql.functions", name, x@jc)
-              column(jc)
-            })
-}
-
-createBinaryMathfunctions <- function(name) {
-  setMethod(name,
-            signature(y = "Column"),
-            function(y, x) {
-              if (class(x) == "Column") {
-                x <- x@jc
-              }
-              jc <- callJStatic("org.apache.spark.sql.functions", name, y@jc, x)
-              column(jc)
-            })
-}
-
 createMethods <- function() {
   for (op in names(operators)) {
     createOperator(op)
@@ -148,12 +115,6 @@ createMethods <- function() {
   for (name in column_functions2) {
     createColumnFunction2(name)
   }
-  for (x in functions) {
-    createStaticFunction(x)
-  }
-  for (name in binary_mathfunctions) {
-    createBinaryMathfunctions(name)
-  }
 }
 
 createMethods()
@@ -242,45 +203,3 @@ setMethod("%in%",
             jc <- callJMethod(x@jc, "in", table)
             return(column(jc))
           })
-
-#' Approx Count Distinct
-#'
-#' @rdname column
-#' @return the approximate number of distinct items in a group.
-setMethod("approxCountDistinct",
-          signature(x = "Column"),
-          function(x, rsd = 0.95) {
-            jc <- callJStatic("org.apache.spark.sql.functions", "approxCountDistinct", x@jc, rsd)
-            column(jc)
-          })
-
-#' Count Distinct
-#'
-#' @rdname column
-#' @return the number of distinct items in a group.
-setMethod("countDistinct",
-          signature(x = "Column"),
-          function(x, ...) {
-            jcol <- lapply(list(...), function (x) {
-              x@jc
-            })
-            jc <- callJStatic("org.apache.spark.sql.functions", "countDistinct", x@jc,
-                              listToSeq(jcol))
-            column(jc)
-          })
-
-#' @rdname column
-#' @aliases countDistinct
-setMethod("n_distinct",
-          signature(x = "Column"),
-          function(x, ...) {
-            countDistinct(x, ...)
-          })
-
-#' @rdname column
-#' @aliases count
-setMethod("n",
-          signature(x = "Column"),
-          function(x) {
-            count(x)
-          })
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
new file mode 100644
index 0000000000000..a15d2d5da534e
--- /dev/null
+++ b/R/pkg/R/functions.R
@@ -0,0 +1,123 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+#' @include generics.R column.R
+NULL
+
+#' @title S4 expression functions for DataFrame column(s)
+#' @description These are expression functions on DataFrame columns
+
+functions1 <- c(
+  "abs", "acos", "approxCountDistinct", "ascii", "asin", "atan",
+  "avg", "base64", "bin", "bitwiseNOT", "cbrt", "ceil", "cos", "cosh", "count",
+  "crc32", "dayofmonth", "dayofyear", "exp", "explode", "expm1", "factorial",
+  "first", "floor", "hex", "hour", "initcap", "isNaN", "last", "last_day",
+  "length", "log", "log10", "log1p", "log2", "lower", "ltrim", "max", "md5",
+  "mean", "min", "minute", "month", "negate", "quarter", "reverse",
+  "rint", "round", "rtrim", "second", "sha1", "signum", "sin", "sinh", "size",
+  "soundex", "sqrt", "sum", "sumDistinct", "tan", "tanh", "toDegrees",
+  "toRadians", "to_date", "trim", "unbase64", "unhex", "upper", "weekofyear",
+  "year")
+functions2 <- c(
+  "atan2", "datediff", "hypot", "levenshtein", "months_between", "nanvl", "pmod")
+
+createFunction1 <- function(name) {
+  setMethod(name,
+            signature(x = "Column"),
+            function(x) {
+              jc <- callJStatic("org.apache.spark.sql.functions", name, x@jc)
+              column(jc)
+            })
+}
+
+createFunction2 <- function(name) {
+  setMethod(name,
+            signature(y = "Column"),
+            function(y, x) {
+              if (class(x) == "Column") {
+                x <- x@jc
+              }
+              jc <- callJStatic("org.apache.spark.sql.functions", name, y@jc, x)
+              column(jc)
+            })
+}
+
+createFunctions <- function() {
+  for (name in functions1) {
+    createFunction1(name)
+  }
+  for (name in functions2) {
+    createFunction2(name)
+  }
+}
+
+createFunctions()
+
+#' Approx Count Distinct
+#'
+#' @rdname functions
+#' @return the approximate number of distinct items in a group.
+setMethod("approxCountDistinct",
+          signature(x = "Column"),
+          function(x, rsd = 0.95) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "approxCountDistinct", x@jc, rsd)
+            column(jc)
+          })
+
+#' Count Distinct
+#'
+#' @rdname functions
+#' @return the number of distinct items in a group.
+setMethod("countDistinct",
+          signature(x = "Column"),
+          function(x, ...) {
+            jcol <- lapply(list(...), function (x) {
+              x@jc
+            })
+            jc <- callJStatic("org.apache.spark.sql.functions", "countDistinct", x@jc,
+                              listToSeq(jcol))
+            column(jc)
+          })
+
+#' @rdname functions
+#' @aliases ceil
+setMethod("ceiling",
+          signature(x = "Column"),
+          function(x) {
+            ceil(x)
+          })
+
+#' @rdname functions
+#' @aliases signum
+setMethod("sign", signature(x = "Column"),
+          function(x) {
+            signum(x)
+          })
+
+#' @rdname functions
+#' @aliases countDistinct
+setMethod("n_distinct", signature(x = "Column"),
+          function(x, ...) {
+            countDistinct(x, ...)
+          })
+
+#' @rdname functions
+#' @aliases count
+setMethod("n", signature(x = "Column"),
+          function(x) {
+            count(x)
+          })
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 379a78b1d833e..f11e7fcb6a07c 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -575,10 +575,6 @@ setGeneric("approxCountDistinct", function(x, ...) { standardGeneric("approxCoun
 #' @export
 setGeneric("asc", function(x) { standardGeneric("asc") })
 
-#' @rdname column
-#' @export
-setGeneric("avg", function(x, ...) { standardGeneric("avg") })
-
 #' @rdname column
 #' @export
 setGeneric("between", function(x, bounds) { standardGeneric("between") })
@@ -587,13 +583,10 @@ setGeneric("between", function(x, bounds) { standardGeneric("between") })
 #' @export
 setGeneric("cast", function(x, dataType) { standardGeneric("cast") })
 
-#' @rdname column
-#' @export
-setGeneric("cbrt", function(x) { standardGeneric("cbrt") })
-
 #' @rdname column
 #' @export
 setGeneric("contains", function(x, ...) { standardGeneric("contains") })
+
 #' @rdname column
 #' @export
 setGeneric("countDistinct", function(x, ...) { standardGeneric("countDistinct") })
@@ -658,22 +651,190 @@ setGeneric("rlike", function(x, ...) { standardGeneric("rlike") })
 #' @export
 setGeneric("startsWith", function(x, ...) { standardGeneric("startsWith") })
 
-#' @rdname column
+
+###################### Expression Function Methods ##########################
+
+#' @rdname functions
+#' @export
+setGeneric("ascii", function(x) { standardGeneric("ascii") })
+
+#' @rdname functions
+#' @export
+setGeneric("avg", function(x, ...) { standardGeneric("avg") })
+
+#' @rdname functions
+#' @export
+setGeneric("base64", function(x) { standardGeneric("base64") })
+
+#' @rdname functions
+#' @export
+setGeneric("bin", function(x) { standardGeneric("bin") })
+
+#' @rdname functions
+#' @export
+setGeneric("bitwiseNOT", function(x) { standardGeneric("bitwiseNOT") })
+
+#' @rdname functions
+#' @export
+setGeneric("cbrt", function(x) { standardGeneric("cbrt") })
+
+#' @rdname functions
+#' @export
+setGeneric("ceil", function(x) { standardGeneric("ceil") })
+
+#' @rdname functions
+#' @export
+setGeneric("crc32", function(x) { standardGeneric("crc32") })
+
+#' @rdname functions
+#' @export
+setGeneric("datediff", function(y, x) { standardGeneric("datediff") })
+
+#' @rdname functions
+#' @export
+setGeneric("dayofmonth", function(x) { standardGeneric("dayofmonth") })
+
+#' @rdname functions
+#' @export
+setGeneric("dayofyear", function(x) { standardGeneric("dayofyear") })
+
+#' @rdname functions
+#' @export
+setGeneric("explode", function(x) { standardGeneric("explode") })
+
+#' @rdname functions
+#' @export
+setGeneric("hex", function(x) { standardGeneric("hex") })
+
+#' @rdname functions
+#' @export
+setGeneric("hour", function(x) { standardGeneric("hour") })
+
+#' @rdname functions
+#' @export
+setGeneric("initcap", function(x) { standardGeneric("initcap") })
+
+#' @rdname functions
+#' @export
+setGeneric("isNaN", function(x) { standardGeneric("isNaN") })
+
+#' @rdname functions
+#' @export
+setGeneric("last_day", function(x) { standardGeneric("last_day") })
+
+#' @rdname functions
+#' @export
+setGeneric("levenshtein", function(y, x) { standardGeneric("levenshtein") })
+
+#' @rdname functions
+#' @export
+setGeneric("lower", function(x) { standardGeneric("lower") })
+
+#' @rdname functions
+#' @export
+setGeneric("ltrim", function(x) { standardGeneric("ltrim") })
+
+#' @rdname functions
+#' @export
+setGeneric("md5", function(x) { standardGeneric("md5") })
+
+#' @rdname functions
+#' @export
+setGeneric("minute", function(x) { standardGeneric("minute") })
+
+#' @rdname functions
+#' @export
+setGeneric("month", function(x) { standardGeneric("month") })
+
+#' @rdname functions
+#' @export
+setGeneric("months_between", function(y, x) { standardGeneric("months_between") })
+
+#' @rdname functions
+#' @export
+setGeneric("nanvl", function(y, x) { standardGeneric("nanvl") })
+
+#' @rdname functions
+#' @export
+setGeneric("negate", function(x) { standardGeneric("negate") })
+
+#' @rdname functions
+#' @export
+setGeneric("pmod", function(y, x) { standardGeneric("pmod") })
+
+#' @rdname functions
+#' @export
+setGeneric("quarter", function(x) { standardGeneric("quarter") })
+
+#' @rdname functions
+#' @export
+setGeneric("reverse", function(x) { standardGeneric("reverse") })
+
+#' @rdname functions
+#' @export
+setGeneric("rtrim", function(x) { standardGeneric("rtrim") })
+
+#' @rdname functions
+#' @export
+setGeneric("second", function(x) { standardGeneric("second") })
+
+#' @rdname functions
+#' @export
+setGeneric("sha1", function(x) { standardGeneric("sha1") })
+
+#' @rdname functions
+#' @export
+setGeneric("signum", function(x) { standardGeneric("signum") })
+
+#' @rdname functions
+#' @export
+setGeneric("size", function(x) { standardGeneric("size") })
+
+#' @rdname functions
+#' @export
+setGeneric("soundex", function(x) { standardGeneric("soundex") })
+
+#' @rdname functions
 #' @export
 setGeneric("sumDistinct", function(x) { standardGeneric("sumDistinct") })
 
-#' @rdname column
+#' @rdname functions
 #' @export
 setGeneric("toDegrees", function(x) { standardGeneric("toDegrees") })
 
-#' @rdname column
+#' @rdname functions
 #' @export
 setGeneric("toRadians", function(x) { standardGeneric("toRadians") })
 
-#' @rdname column
+#' @rdname functions
+#' @export
+setGeneric("to_date", function(x) { standardGeneric("to_date") })
+
+#' @rdname functions
+#' @export
+setGeneric("trim", function(x) { standardGeneric("trim") })
+
+#' @rdname functions
+#' @export
+setGeneric("unbase64", function(x) { standardGeneric("unbase64") })
+
+#' @rdname functions
+#' @export
+setGeneric("unhex", function(x) { standardGeneric("unhex") })
+
+#' @rdname functions
 #' @export
 setGeneric("upper", function(x) { standardGeneric("upper") })
 
+#' @rdname functions
+#' @export
+setGeneric("weekofyear", function(x) { standardGeneric("weekofyear") })
+
+#' @rdname functions
+#' @export
+setGeneric("year", function(x) { standardGeneric("year") })
+
+
 #' @rdname glm
 #' @export
 setGeneric("glm")
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 7377fc8f1ca9c..e6d3b21ff825b 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -640,15 +640,18 @@ test_that("column operators", {
 
 test_that("column functions", {
   c <- SparkR:::col("a")
-  c2 <- min(c) + max(c) + sum(c) + avg(c) + count(c) + abs(c) + sqrt(c)
-  c3 <- lower(c) + upper(c) + first(c) + last(c)
-  c4 <- approxCountDistinct(c) + countDistinct(c) + cast(c, "string")
-  c5 <- n(c) + n_distinct(c)
-  c5 <- acos(c) + asin(c) + atan(c) + cbrt(c)
-  c6 <- ceiling(c) + cos(c) + cosh(c) + exp(c) + expm1(c)
-  c7 <- floor(c) + log(c) + log10(c) + log1p(c) + rint(c)
-  c8 <- sign(c) + sin(c) + sinh(c) + tan(c) + tanh(c)
-  c9 <- toDegrees(c) + toRadians(c)
+  c1 <- abs(c) + acos(c) + approxCountDistinct(c) + ascii(c) + asin(c) + atan(c)
+  c2 <- avg(c) + base64(c) + bin(c) + bitwiseNOT(c) + cbrt(c) + ceil(c) + cos(c)
+  c3 <- cosh(c) + count(c) + crc32(c) + dayofmonth(c) + dayofyear(c) + exp(c)
+  c4 <- explode(c) + expm1(c) + factorial(c) + first(c) + floor(c) + hex(c)
+  c5 <- hour(c) + initcap(c) + isNaN(c) + last(c) + last_day(c) + length(c)
+  c6 <- log(c) + (c) + log1p(c) + log2(c) + lower(c) + ltrim(c) + max(c) + md5(c)
+  c7 <- mean(c) + min(c) + minute(c) + month(c) + negate(c) + quarter(c)
+  c8 <- reverse(c) + rint(c) + round(c) + rtrim(c) + second(c) + sha1(c)
+  c9 <- signum(c) + sin(c) + sinh(c) + size(c) + soundex(c) + sqrt(c) + sum(c)
+  c10 <- sumDistinct(c) + tan(c) + tanh(c) + toDegrees(c) + toRadians(c)
+  c11 <- to_date(c) + trim(c) + unbase64(c) + unhex(c) + upper(c) + weekofyear(c)
+  c12 <- year(c)
 
   df <- jsonFile(sqlContext, jsonPath)
   df2 <- select(df, between(df$age, c(20, 30)), between(df$age, c(10, 20)))

From 7b13ed27c1296cf76d0946e400f3449c335c8471 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Wed, 12 Aug 2015 18:52:11 -0700
Subject: [PATCH 212/633] [SPARK-9870] Disable driver UI and Master REST server
 in SparkSubmitSuite

I think that we should pass additional configuration flags to disable the driver UI and Master REST server in SparkSubmitSuite and HiveSparkSubmitSuite. This might cut down on port-contention-related flakiness in Jenkins.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8124 from JoshRosen/disable-ui-in-sparksubmitsuite.
---
 .../org/apache/spark/deploy/SparkSubmitSuite.scala     |  7 +++++++
 .../apache/spark/sql/hive/HiveSparkSubmitSuite.scala   | 10 +++++++++-
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
index 2456c5d0d49b0..1110ca6051a40 100644
--- a/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/SparkSubmitSuite.scala
@@ -324,6 +324,8 @@ class SparkSubmitSuite
       "--class", SimpleApplicationTest.getClass.getName.stripSuffix("$"),
       "--name", "testApp",
       "--master", "local",
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
       unusedJar.toString)
     runSparkSubmit(args)
   }
@@ -337,6 +339,8 @@ class SparkSubmitSuite
       "--class", JarCreationTest.getClass.getName.stripSuffix("$"),
       "--name", "testApp",
       "--master", "local-cluster[2,1,1024]",
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
       "--jars", jarsString,
       unusedJar.toString, "SparkSubmitClassA", "SparkSubmitClassB")
     runSparkSubmit(args)
@@ -355,6 +359,7 @@ class SparkSubmitSuite
         "--packages", Seq(main, dep).mkString(","),
         "--repositories", repo,
         "--conf", "spark.ui.enabled=false",
+        "--conf", "spark.master.rest.enabled=false",
         unusedJar.toString,
         "my.great.lib.MyLib", "my.great.dep.MyLib")
       runSparkSubmit(args)
@@ -500,6 +505,8 @@ class SparkSubmitSuite
       "--master", "local",
       "--conf", "spark.driver.extraClassPath=" + systemJar,
       "--conf", "spark.driver.userClassPathFirst=true",
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
       userJar.toString)
     runSparkSubmit(args)
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index b8d41065d3f02..1e1972d1ac353 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -57,6 +57,8 @@ class HiveSparkSubmitSuite
       "--class", SparkSubmitClassLoaderTest.getClass.getName.stripSuffix("$"),
       "--name", "SparkSubmitClassLoaderTest",
       "--master", "local-cluster[2,1,1024]",
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
       "--jars", jarsString,
       unusedJar.toString, "SparkSubmitClassA", "SparkSubmitClassB")
     runSparkSubmit(args)
@@ -68,6 +70,8 @@ class HiveSparkSubmitSuite
       "--class", SparkSQLConfTest.getClass.getName.stripSuffix("$"),
       "--name", "SparkSQLConfTest",
       "--master", "local-cluster[2,1,1024]",
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
       unusedJar.toString)
     runSparkSubmit(args)
   }
@@ -79,7 +83,11 @@ class HiveSparkSubmitSuite
     // the HiveContext code mistakenly overrides the class loader that contains user classes.
     // For more detail, see sql/hive/src/test/resources/regression-test-SPARK-8489/*scala.
     val testJar = "sql/hive/src/test/resources/regression-test-SPARK-8489/test.jar"
-    val args = Seq("--class", "Main", testJar)
+    val args = Seq(
+      "--conf", "spark.ui.enabled=false",
+      "--conf", "spark.master.rest.enabled=false",
+      "--class", "Main",
+      testJar)
     runSparkSubmit(args)
   }
 

From 7c35746c916cf0019367850e75a080d7e739dba0 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 12 Aug 2015 20:02:55 -0700
Subject: [PATCH 213/633] [SPARK-9827] [SQL] fix fd leak in UnsafeRowSerializer

Currently, UnsafeRowSerializer does not close the InputStream, will cause fd leak if the InputStream has an open fd in it.

TODO: the fd could still be leaked, if any items in the stream is not consumed. Currently it replies on GC to close the fd in this case.

cc JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #8116 from davies/fd_leak.
---
 .../sql/execution/UnsafeRowSerializer.scala   |  2 ++
 .../execution/UnsafeRowSerializerSuite.scala  | 31 +++++++++++++++++--
 2 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 3860c4bba9a99..5c18558f9bde7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -108,6 +108,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
       override def asKeyValueIterator: Iterator[(Int, UnsafeRow)] = {
         new Iterator[(Int, UnsafeRow)] {
           private[this] var rowSize: Int = dIn.readInt()
+          if (rowSize == EOF) dIn.close()
 
           override def hasNext: Boolean = rowSize != EOF
 
@@ -119,6 +120,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
             row.pointTo(rowBuffer, Platform.BYTE_ARRAY_OFFSET, numFields, rowSize)
             rowSize = dIn.readInt() // read the next row's size
             if (rowSize == EOF) { // We are returning the last row in this stream
+              dIn.close()
               val _rowTuple = rowTuple
               // Null these out so that the byte array can be garbage collected once the entire
               // iterator has been consumed
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
index 40b47ae18d648..bd02c73a26ace 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution
 
-import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
+import java.io.{DataOutputStream, ByteArrayInputStream, ByteArrayOutputStream}
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.Row
@@ -25,6 +25,18 @@ import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, UnsafeRow}
 import org.apache.spark.sql.types._
 
+
+/**
+ * used to test close InputStream in UnsafeRowSerializer
+ */
+class ClosableByteArrayInputStream(buf: Array[Byte]) extends ByteArrayInputStream(buf) {
+  var closed: Boolean = false
+  override def close(): Unit = {
+    closed = true
+    super.close()
+  }
+}
+
 class UnsafeRowSerializerSuite extends SparkFunSuite {
 
   private def toUnsafeRow(row: Row, schema: Array[DataType]): UnsafeRow = {
@@ -52,8 +64,8 @@ class UnsafeRowSerializerSuite extends SparkFunSuite {
       serializerStream.writeValue(unsafeRow)
     }
     serializerStream.close()
-    val deserializerIter = serializer.deserializeStream(
-      new ByteArrayInputStream(baos.toByteArray)).asKeyValueIterator
+    val input = new ClosableByteArrayInputStream(baos.toByteArray)
+    val deserializerIter = serializer.deserializeStream(input).asKeyValueIterator
     for (expectedRow <- unsafeRows) {
       val actualRow = deserializerIter.next().asInstanceOf[(Integer, UnsafeRow)]._2
       assert(expectedRow.getSizeInBytes === actualRow.getSizeInBytes)
@@ -61,5 +73,18 @@ class UnsafeRowSerializerSuite extends SparkFunSuite {
       assert(expectedRow.getInt(1) === actualRow.getInt(1))
     }
     assert(!deserializerIter.hasNext)
+    assert(input.closed)
+  }
+
+  test("close empty input stream") {
+    val baos = new ByteArrayOutputStream()
+    val dout = new DataOutputStream(baos)
+    dout.writeInt(-1)  // EOF
+    dout.flush()
+    val input = new ClosableByteArrayInputStream(baos.toByteArray)
+    val serializer = new UnsafeRowSerializer(numFields = 2).newInstance()
+    val deserializerIter = serializer.deserializeStream(input).asKeyValueIterator
+    assert(!deserializerIter.hasNext)
+    assert(input.closed)
   }
 }

From 4413d0855aaba5cb00f737dc6934a0b92d9bc05d Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 12 Aug 2015 20:03:55 -0700
Subject: [PATCH 214/633] [SPARK-9908] [SQL] When spark.sql.tungsten.enabled is
 false, broadcast join does not work

https://issues.apache.org/jira/browse/SPARK-9908

Author: Yin Huai <yhuai@databricks.com>

Closes #8149 from yhuai/SPARK-9908.
---
 .../apache/spark/sql/execution/joins/HashedRelation.scala   | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 076afe6e4e960..bb333b4d5ed18 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -66,7 +66,8 @@ private[joins] final class GeneralHashedRelation(
     private var hashTable: JavaHashMap[InternalRow, CompactBuffer[InternalRow]])
   extends HashedRelation with Externalizable {
 
-  private def this() = this(null) // Needed for serialization
+  // Needed for serialization (it is public to make Java serialization work)
+  def this() = this(null)
 
   override def get(key: InternalRow): Seq[InternalRow] = hashTable.get(key)
 
@@ -88,7 +89,8 @@ private[joins]
 final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[InternalRow, InternalRow])
   extends HashedRelation with Externalizable {
 
-  private def this() = this(null) // Needed for serialization
+  // Needed for serialization (it is public to make Java serialization work)
+  def this() = this(null)
 
   override def get(key: InternalRow): Seq[InternalRow] = {
     val v = hashTable.get(key)

From d2d5e7fe2df582e1c866334b3014d7cb351f5b70 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Wed, 12 Aug 2015 20:43:36 -0700
Subject: [PATCH 215/633] [SPARK-9704] [ML] Made ProbabilisticClassifier,
 Identifiable, VectorUDT public APIs

Made ProbabilisticClassifier, Identifiable, VectorUDT public.  All are annotated as DeveloperApi.

CC: mengxr EronWright

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8004 from jkbradley/ml-api-public-items and squashes the following commits:

7ebefda [Joseph K. Bradley] update per code review
7ff0768 [Joseph K. Bradley] attepting to add mima fix
756d84c [Joseph K. Bradley] VectorUDT annotated as AlphaComponent
ae7767d [Joseph K. Bradley] added another warning
94fd553 [Joseph K. Bradley] Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
---
 .../classification/ProbabilisticClassifier.scala |  4 ++--
 .../org/apache/spark/ml/util/Identifiable.scala  | 16 ++++++++++++++--
 .../org/apache/spark/mllib/linalg/Vectors.scala  | 10 ++++------
 project/MimaExcludes.scala                       |  4 ++++
 4 files changed, 24 insertions(+), 10 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
index 1e50a895a9a05..fdd1851ae5508 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/ProbabilisticClassifier.scala
@@ -50,7 +50,7 @@ private[classification] trait ProbabilisticClassifierParams
  * @tparam M  Concrete Model type
  */
 @DeveloperApi
-private[spark] abstract class ProbabilisticClassifier[
+abstract class ProbabilisticClassifier[
     FeaturesType,
     E <: ProbabilisticClassifier[FeaturesType, E, M],
     M <: ProbabilisticClassificationModel[FeaturesType, M]]
@@ -74,7 +74,7 @@ private[spark] abstract class ProbabilisticClassifier[
  * @tparam M  Concrete Model type
  */
 @DeveloperApi
-private[spark] abstract class ProbabilisticClassificationModel[
+abstract class ProbabilisticClassificationModel[
     FeaturesType,
     M <: ProbabilisticClassificationModel[FeaturesType, M]]
   extends ClassificationModel[FeaturesType, M] with ProbabilisticClassifierParams {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala b/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala
index ddd34a54503a6..bd213e7362e94 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/Identifiable.scala
@@ -19,11 +19,19 @@ package org.apache.spark.ml.util
 
 import java.util.UUID
 
+import org.apache.spark.annotation.DeveloperApi
+
 
 /**
+ * :: DeveloperApi ::
+ *
  * Trait for an object with an immutable unique ID that identifies itself and its derivatives.
+ *
+ * WARNING: There have not yet been final discussions on this API, so it may be broken in future
+ *          releases.
  */
-private[spark] trait Identifiable {
+@DeveloperApi
+trait Identifiable {
 
   /**
    * An immutable unique ID for the object and its derivatives.
@@ -33,7 +41,11 @@ private[spark] trait Identifiable {
   override def toString: String = uid
 }
 
-private[spark] object Identifiable {
+/**
+ * :: DeveloperApi ::
+ */
+@DeveloperApi
+object Identifiable {
 
   /**
    * Returns a random UID that concatenates the given prefix, "_", and 12 random hex chars.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 86c461fa91633..df15d985c814c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -26,7 +26,7 @@ import scala.collection.JavaConverters._
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
 
 import org.apache.spark.SparkException
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.mllib.util.NumericParser
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
@@ -159,15 +159,13 @@ sealed trait Vector extends Serializable {
 }
 
 /**
- * :: DeveloperApi ::
+ * :: AlphaComponent ::
  *
  * User-defined type for [[Vector]] which allows easy interaction with SQL
  * via [[org.apache.spark.sql.DataFrame]].
- *
- * NOTE: This is currently private[spark] but will be made public later once it is stabilized.
  */
-@DeveloperApi
-private[spark] class VectorUDT extends UserDefinedType[Vector] {
+@AlphaComponent
+class VectorUDT extends UserDefinedType[Vector] {
 
   override def sqlType: StructType = {
     // type: 0 = sparse, 1 = dense
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 90261ca3d61aa..784f83c10e023 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -178,6 +178,10 @@ object MimaExcludes {
             // SPARK-4751 Dynamic allocation for standalone mode
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.SparkContext.supportDynamicAllocation")
+          ) ++ Seq(
+            // SPARK-9704 Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
+            ProblemFilters.exclude[IncompatibleResultTypeProblem](
+              "org.apache.spark.mllib.linalg.VectorUDT.serialize")
           )
 
         case v if v.startsWith("1.4") =>

From d7053bea985679c514b3add029631ea23e1730ce Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 20:44:40 -0700
Subject: [PATCH 216/633] [SPARK-9903] [MLLIB] skip local processing in
 PrefixSpan if there are no small prefixes

There exists a chance that the prefixes keep growing to the maximum pattern length. Then the final local processing step becomes unnecessary. feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8136 from mengxr/SPARK-9903.
---
 .../apache/spark/mllib/fpm/PrefixSpan.scala   | 37 +++++++++++--------
 1 file changed, 21 insertions(+), 16 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index ad6715b52f337..dc4ae1d0b69ed 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -282,25 +282,30 @@ object PrefixSpan extends Logging {
       largePrefixes = newLargePrefixes
     }
 
-    // Switch to local processing.
-    val bcSmallPrefixes = sc.broadcast(smallPrefixes)
-    val distributedFreqPattern = postfixes.flatMap { postfix =>
-      bcSmallPrefixes.value.values.map { prefix =>
-        (prefix.id, postfix.project(prefix).compressed)
-      }.filter(_._2.nonEmpty)
-    }.groupByKey().flatMap { case (id, projPostfixes) =>
-      val prefix = bcSmallPrefixes.value(id)
-      val localPrefixSpan = new LocalPrefixSpan(minCount, maxPatternLength - prefix.length)
-      // TODO: We collect projected postfixes into memory. We should also compare the performance
-      // TODO: of keeping them on shuffle files.
-      localPrefixSpan.run(projPostfixes.toArray).map { case (pattern, count) =>
-        (prefix.items ++ pattern, count)
+    var freqPatterns = sc.parallelize(localFreqPatterns, 1)
+
+    val numSmallPrefixes = smallPrefixes.size
+    logInfo(s"number of small prefixes for local processing: $numSmallPrefixes")
+    if (numSmallPrefixes > 0) {
+      // Switch to local processing.
+      val bcSmallPrefixes = sc.broadcast(smallPrefixes)
+      val distributedFreqPattern = postfixes.flatMap { postfix =>
+        bcSmallPrefixes.value.values.map { prefix =>
+          (prefix.id, postfix.project(prefix).compressed)
+        }.filter(_._2.nonEmpty)
+      }.groupByKey().flatMap { case (id, projPostfixes) =>
+        val prefix = bcSmallPrefixes.value(id)
+        val localPrefixSpan = new LocalPrefixSpan(minCount, maxPatternLength - prefix.length)
+        // TODO: We collect projected postfixes into memory. We should also compare the performance
+        // TODO: of keeping them on shuffle files.
+        localPrefixSpan.run(projPostfixes.toArray).map { case (pattern, count) =>
+          (prefix.items ++ pattern, count)
+        }
       }
+      // Union local frequent patterns and distributed ones.
+      freqPatterns = freqPatterns ++ distributedFreqPattern
     }
 
-    // Union local frequent patterns and distributed ones.
-    val freqPatterns = (sc.parallelize(localFreqPatterns, 1) ++ distributedFreqPattern)
-      .persist(StorageLevel.MEMORY_AND_DISK)
     freqPatterns
   }
 

From 2fb4901b71cee65d40a43e61e3f4411c30cdefc3 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Wed, 12 Aug 2015 20:59:38 -0700
Subject: [PATCH 217/633] [SPARK-9916] [BUILD] [SPARKR] removed left-over
 sparkr.zip copy/create commands from codebase

sparkr.zip is now built by SparkSubmit on a need-to-build basis.

cc shivaram

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #8147 from brkyvz/make-dist-fix.
---
 R/install-dev.bat    | 5 -----
 make-distribution.sh | 1 -
 2 files changed, 6 deletions(-)

diff --git a/R/install-dev.bat b/R/install-dev.bat
index f32670b67de96..008a5c668bc45 100644
--- a/R/install-dev.bat
+++ b/R/install-dev.bat
@@ -25,8 +25,3 @@ set SPARK_HOME=%~dp0..
 MKDIR %SPARK_HOME%\R\lib
 
 R.exe CMD INSTALL --library="%SPARK_HOME%\R\lib"  %SPARK_HOME%\R\pkg\
-
-rem Zip the SparkR package so that it can be distributed to worker nodes on YARN
-pushd %SPARK_HOME%\R\lib
-%JAVA_HOME%\bin\jar.exe cfM "%SPARK_HOME%\R\lib\sparkr.zip" SparkR
-popd
diff --git a/make-distribution.sh b/make-distribution.sh
index 4789b0e09cc8a..247a81341e4a4 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -219,7 +219,6 @@ cp -r "$SPARK_HOME/ec2" "$DISTDIR"
 if [ -d "$SPARK_HOME"/R/lib/SparkR ]; then
   mkdir -p "$DISTDIR"/R/lib
   cp -r "$SPARK_HOME/R/lib/SparkR" "$DISTDIR"/R/lib
-  cp "$SPARK_HOME/R/lib/sparkr.zip" "$DISTDIR"/R/lib
 fi
 
 # Download and copy in tachyon, if requested

From 2278219054314f1d31ffc358a59aa5067f9f5de9 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 12 Aug 2015 21:24:15 -0700
Subject: [PATCH 218/633] [SPARK-9920] [SQL] The simpleString of
 TungstenAggregate does not show its output

https://issues.apache.org/jira/browse/SPARK-9920

Taking `sqlContext.sql("select i, sum(j1) as sum from testAgg group by i").explain()` as an example, the output of our current master is
```
== Physical Plan ==
TungstenAggregate(key=[i#0], value=[(sum(cast(j1#1 as bigint)),mode=Final,isDistinct=false)]
 TungstenExchange hashpartitioning(i#0)
  TungstenAggregate(key=[i#0], value=[(sum(cast(j1#1 as bigint)),mode=Partial,isDistinct=false)]
   Scan ParquetRelation[file:/user/hive/warehouse/testagg][i#0,j1#1]
```
With this PR, the output will be
```
== Physical Plan ==
TungstenAggregate(key=[i#0], functions=[(sum(cast(j1#1 as bigint)),mode=Final,isDistinct=false)], output=[i#0,sum#18L])
 TungstenExchange hashpartitioning(i#0)
  TungstenAggregate(key=[i#0], functions=[(sum(cast(j1#1 as bigint)),mode=Partial,isDistinct=false)], output=[i#0,currentSum#22L])
   Scan ParquetRelation[file:/user/hive/warehouse/testagg][i#0,j1#1]
```

Author: Yin Huai <yhuai@databricks.com>

Closes #8150 from yhuai/SPARK-9920.
---
 .../spark/sql/execution/aggregate/SortBasedAggregate.scala | 6 +++++-
 .../spark/sql/execution/aggregate/TungstenAggregate.scala  | 7 ++++---
 2 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
index ab26f9c58aa2e..f4c14a9b3556f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/SortBasedAggregate.scala
@@ -108,6 +108,10 @@ case class SortBasedAggregate(
 
   override def simpleString: String = {
     val allAggregateExpressions = nonCompleteAggregateExpressions ++ completeAggregateExpressions
-    s"""SortBasedAggregate ${groupingExpressions} ${allAggregateExpressions}"""
+
+    val keyString = groupingExpressions.mkString("[", ",", "]")
+    val functionString = allAggregateExpressions.mkString("[", ",", "]")
+    val outputString = output.mkString("[", ",", "]")
+    s"SortBasedAggregate(key=$keyString, functions=$functionString, output=$outputString)"
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index c40ca973796a6..99f51ba5b6935 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -127,11 +127,12 @@ case class TungstenAggregate(
     testFallbackStartsAt match {
       case None =>
         val keyString = groupingExpressions.mkString("[", ",", "]")
-        val valueString = allAggregateExpressions.mkString("[", ",", "]")
-        s"TungstenAggregate(key=$keyString, value=$valueString"
+        val functionString = allAggregateExpressions.mkString("[", ",", "]")
+        val outputString = output.mkString("[", ",", "]")
+        s"TungstenAggregate(key=$keyString, functions=$functionString, output=$outputString)"
       case Some(fallbackStartsAt) =>
         s"TungstenAggregateWithControlledFallback $groupingExpressions " +
-          s"$allAggregateExpressions fallbackStartsAt=$fallbackStartsAt"
+          s"$allAggregateExpressions $resultExpressions fallbackStartsAt=$fallbackStartsAt"
     }
   }
 }

From a8ab2634c1eee143a4deaf309204df8add727f9e Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 12 Aug 2015 21:26:00 -0700
Subject: [PATCH 219/633] [SPARK-9832] [SQL] add a thread-safe lookup for
 BytesToBytseMap

This patch add a thread-safe lookup for BytesToBytseMap, and use that in broadcasted HashedRelation.

Author: Davies Liu <davies@databricks.com>

Closes #8151 from davies/safeLookup.
---
 .../spark/unsafe/map/BytesToBytesMap.java     | 30 ++++++++++++++-----
 .../sql/execution/joins/HashedRelation.scala  |  6 ++--
 2 files changed, 26 insertions(+), 10 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 87ed47e88c4ef..5f3a4fcf4d585 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -17,25 +17,24 @@
 
 package org.apache.spark.unsafe.map;
 
-import java.lang.Override;
-import java.lang.UnsupportedOperationException;
+import javax.annotation.Nullable;
 import java.util.Iterator;
 import java.util.LinkedList;
 import java.util.List;
 
-import javax.annotation.Nullable;
-
 import com.google.common.annotations.VisibleForTesting;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 import org.apache.spark.shuffle.ShuffleMemoryManager;
-import org.apache.spark.unsafe.*;
+import org.apache.spark.unsafe.Platform;
 import org.apache.spark.unsafe.array.ByteArrayMethods;
 import org.apache.spark.unsafe.array.LongArray;
 import org.apache.spark.unsafe.bitset.BitSet;
 import org.apache.spark.unsafe.hash.Murmur3_x86_32;
-import org.apache.spark.unsafe.memory.*;
+import org.apache.spark.unsafe.memory.MemoryBlock;
+import org.apache.spark.unsafe.memory.MemoryLocation;
+import org.apache.spark.unsafe.memory.TaskMemoryManager;
 
 /**
  * An append-only hash map where keys and values are contiguous regions of bytes.
@@ -328,6 +327,20 @@ public Location lookup(
       Object keyBaseObject,
       long keyBaseOffset,
       int keyRowLengthBytes) {
+    safeLookup(keyBaseObject, keyBaseOffset, keyRowLengthBytes, loc);
+    return loc;
+  }
+
+  /**
+   * Looks up a key, and saves the result in provided `loc`.
+   *
+   * This is a thread-safe version of `lookup`, could be used by multiple threads.
+   */
+  public void safeLookup(
+      Object keyBaseObject,
+      long keyBaseOffset,
+      int keyRowLengthBytes,
+      Location loc) {
     assert(bitset != null);
     assert(longArray != null);
 
@@ -343,7 +356,8 @@ public Location lookup(
       }
       if (!bitset.isSet(pos)) {
         // This is a new key.
-        return loc.with(pos, hashcode, false);
+        loc.with(pos, hashcode, false);
+        return;
       } else {
         long stored = longArray.get(pos * 2 + 1);
         if ((int) (stored) == hashcode) {
@@ -361,7 +375,7 @@ public Location lookup(
               keyRowLengthBytes
             );
             if (areEqual) {
-              return loc;
+              return;
             } else {
               if (enablePerfMetrics) {
                 numHashCollisions++;
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index bb333b4d5ed18..ea02076b41a6f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -215,8 +215,10 @@ private[joins] final class UnsafeHashedRelation(
 
     if (binaryMap != null) {
       // Used in Broadcast join
-      val loc = binaryMap.lookup(unsafeKey.getBaseObject, unsafeKey.getBaseOffset,
-        unsafeKey.getSizeInBytes)
+      val map = binaryMap  // avoid the compiler error
+      val loc = new map.Location  // this could be allocated in stack
+      binaryMap.safeLookup(unsafeKey.getBaseObject, unsafeKey.getBaseOffset,
+        unsafeKey.getSizeInBytes, loc)
       if (loc.isDefined) {
         val buffer = CompactBuffer[UnsafeRow]()
 

From 5fc058a1fc5d83ad53feec936475484aef3800b3 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 21:33:38 -0700
Subject: [PATCH 220/633] [SPARK-9917] [ML] add getMin/getMax and doc for
 originalMin/origianlMax in MinMaxScaler

hhbyyh

Author: Xiangrui Meng <meng@databricks.com>

Closes #8145 from mengxr/SPARK-9917.
---
 .../org/apache/spark/ml/feature/MinMaxScaler.scala     | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
index b30adf3df48d2..9a473dd23772d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
@@ -41,6 +41,9 @@ private[feature] trait MinMaxScalerParams extends Params with HasInputCol with H
   val min: DoubleParam = new DoubleParam(this, "min",
     "lower bound of the output feature range")
 
+  /** @group getParam */
+  def getMin: Double = $(min)
+
   /**
    * upper bound after transformation, shared by all features
    * Default: 1.0
@@ -49,6 +52,9 @@ private[feature] trait MinMaxScalerParams extends Params with HasInputCol with H
   val max: DoubleParam = new DoubleParam(this, "max",
     "upper bound of the output feature range")
 
+  /** @group getParam */
+  def getMax: Double = $(max)
+
   /** Validates and transforms the input schema. */
   protected def validateAndTransformSchema(schema: StructType): StructType = {
     val inputType = schema($(inputCol)).dataType
@@ -115,6 +121,9 @@ class MinMaxScaler(override val uid: String)
  * :: Experimental ::
  * Model fitted by [[MinMaxScaler]].
  *
+ * @param originalMin min value for each original column during fitting
+ * @param originalMax max value for each original column during fitting
+ *
  * TODO: The transformer does not yet set the metadata in the output column (SPARK-8529).
  */
 @Experimental
@@ -136,7 +145,6 @@ class MinMaxScalerModel private[ml] (
   /** @group setParam */
   def setMax(value: Double): this.type = set(max, value)
 
-
   override def transform(dataset: DataFrame): DataFrame = {
     val originalRange = (originalMax.toBreeze - originalMin.toBreeze).toArray
     val minArray = originalMin.toArray

From df543892122342b97e5137b266959ba97589b3ef Mon Sep 17 00:00:00 2001
From: "shikai.tang" <tar.sky06@gmail.com>
Date: Wed, 12 Aug 2015 21:53:15 -0700
Subject: [PATCH 221/633] [SPARK-8922] [DOCUMENTATION, MLLIB] Add @since tags
 to mllib.evaluation

Author: shikai.tang <tar.sky06@gmail.com>

Closes #7429 from mosessky/master.
---
 .../BinaryClassificationMetrics.scala         | 32 ++++++++++++++++---
 .../mllib/evaluation/MulticlassMetrics.scala  |  9 ++++++
 .../mllib/evaluation/MultilabelMetrics.scala  |  4 +++
 .../mllib/evaluation/RankingMetrics.scala     |  4 +++
 .../mllib/evaluation/RegressionMetrics.scala  |  6 ++++
 5 files changed, 50 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
index c1d1a224817e8..486741edd6f5a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
@@ -41,6 +41,7 @@ import org.apache.spark.sql.DataFrame
  *                of bins may not exactly equal numBins. The last bin in each partition may
  *                be smaller as a result, meaning there may be an extra sample at
  *                partition boundaries.
+ * @since 1.3.0
  */
 @Experimental
 class BinaryClassificationMetrics(
@@ -51,6 +52,7 @@ class BinaryClassificationMetrics(
 
   /**
    * Defaults `numBins` to 0.
+   * @since 1.0.0
    */
   def this(scoreAndLabels: RDD[(Double, Double)]) = this(scoreAndLabels, 0)
 
@@ -61,12 +63,18 @@ class BinaryClassificationMetrics(
   private[mllib] def this(scoreAndLabels: DataFrame) =
     this(scoreAndLabels.map(r => (r.getDouble(0), r.getDouble(1))))
 
-  /** Unpersist intermediate RDDs used in the computation. */
+  /**
+   * Unpersist intermediate RDDs used in the computation.
+   * @since 1.0.0
+   */
   def unpersist() {
     cumulativeCounts.unpersist()
   }
 
-  /** Returns thresholds in descending order. */
+  /**
+   * Returns thresholds in descending order.
+   * @since 1.0.0
+   */
   def thresholds(): RDD[Double] = cumulativeCounts.map(_._1)
 
   /**
@@ -74,6 +82,7 @@ class BinaryClassificationMetrics(
    * which is an RDD of (false positive rate, true positive rate)
    * with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
    * @see http://en.wikipedia.org/wiki/Receiver_operating_characteristic
+   * @since 1.0.0
    */
   def roc(): RDD[(Double, Double)] = {
     val rocCurve = createCurve(FalsePositiveRate, Recall)
@@ -85,6 +94,7 @@ class BinaryClassificationMetrics(
 
   /**
    * Computes the area under the receiver operating characteristic (ROC) curve.
+   * @since 1.0.0
    */
   def areaUnderROC(): Double = AreaUnderCurve.of(roc())
 
@@ -92,6 +102,7 @@ class BinaryClassificationMetrics(
    * Returns the precision-recall curve, which is an RDD of (recall, precision),
    * NOT (precision, recall), with (0.0, 1.0) prepended to it.
    * @see http://en.wikipedia.org/wiki/Precision_and_recall
+   * @since 1.0.0
    */
   def pr(): RDD[(Double, Double)] = {
     val prCurve = createCurve(Recall, Precision)
@@ -102,6 +113,7 @@ class BinaryClassificationMetrics(
 
   /**
    * Computes the area under the precision-recall curve.
+   * @since 1.0.0
    */
   def areaUnderPR(): Double = AreaUnderCurve.of(pr())
 
@@ -110,16 +122,26 @@ class BinaryClassificationMetrics(
    * @param beta the beta factor in F-Measure computation.
    * @return an RDD of (threshold, F-Measure) pairs.
    * @see http://en.wikipedia.org/wiki/F1_score
+   * @since 1.0.0
    */
   def fMeasureByThreshold(beta: Double): RDD[(Double, Double)] = createCurve(FMeasure(beta))
 
-  /** Returns the (threshold, F-Measure) curve with beta = 1.0. */
+  /**
+   * Returns the (threshold, F-Measure) curve with beta = 1.0.
+   * @since 1.0.0
+   */
   def fMeasureByThreshold(): RDD[(Double, Double)] = fMeasureByThreshold(1.0)
 
-  /** Returns the (threshold, precision) curve. */
+  /**
+   * Returns the (threshold, precision) curve.
+   * @since 1.0.0
+   */
   def precisionByThreshold(): RDD[(Double, Double)] = createCurve(Precision)
 
-  /** Returns the (threshold, recall) curve. */
+  /**
+   * Returns the (threshold, recall) curve.
+   * @since 1.0.0
+   */
   def recallByThreshold(): RDD[(Double, Double)] = createCurve(Recall)
 
   private lazy val (
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
index 4628dc5690913..dddfa3ea5b800 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
@@ -30,6 +30,7 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for multiclass classification.
  *
  * @param predictionAndLabels an RDD of (prediction, label) pairs.
+ * @since 1.1.0
  */
 @Experimental
 class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
@@ -64,6 +65,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
    * predicted classes are in columns,
    * they are ordered by class label ascending,
    * as in "labels"
+   * @since 1.1.0
    */
   def confusionMatrix: Matrix = {
     val n = labels.size
@@ -83,12 +85,14 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns true positive rate for a given label (category)
    * @param label the label.
+   * @since 1.1.0
    */
   def truePositiveRate(label: Double): Double = recall(label)
 
   /**
    * Returns false positive rate for a given label (category)
    * @param label the label.
+   * @since 1.1.0
    */
   def falsePositiveRate(label: Double): Double = {
     val fp = fpByClass.getOrElse(label, 0)
@@ -98,6 +102,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns precision for a given label (category)
    * @param label the label.
+   * @since 1.1.0
    */
   def precision(label: Double): Double = {
     val tp = tpByClass(label)
@@ -108,6 +113,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns recall for a given label (category)
    * @param label the label.
+   * @since 1.1.0
    */
   def recall(label: Double): Double = tpByClass(label).toDouble / labelCountByClass(label)
 
@@ -115,6 +121,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
    * Returns f-measure for a given label (category)
    * @param label the label.
    * @param beta the beta parameter.
+   * @since 1.1.0
    */
   def fMeasure(label: Double, beta: Double): Double = {
     val p = precision(label)
@@ -126,6 +133,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns f1-measure for a given label (category)
    * @param label the label.
+   * @since 1.1.0
    */
   def fMeasure(label: Double): Double = fMeasure(label, 1.0)
 
@@ -179,6 +187,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns weighted averaged f-measure
    * @param beta the beta parameter.
+   * @since 1.1.0
    */
   def weightedFMeasure(beta: Double): Double = labelCountByClass.map { case (category, count) =>
     fMeasure(category, beta) * count.toDouble / labelCount
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
index bf6eb1d5bd2ab..77cb1e09bdbb5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
@@ -25,6 +25,7 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for multilabel classification.
  * @param predictionAndLabels an RDD of (predictions, labels) pairs,
  * both are non-null Arrays, each with unique elements.
+ * @since 1.2.0
  */
 class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) {
 
@@ -103,6 +104,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns precision for a given label (category)
    * @param label the label.
+   * @since 1.2.0
    */
   def precision(label: Double): Double = {
     val tp = tpPerClass(label)
@@ -113,6 +115,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns recall for a given label (category)
    * @param label the label.
+   * @since 1.2.0
    */
   def recall(label: Double): Double = {
     val tp = tpPerClass(label)
@@ -123,6 +126,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns f1-measure for a given label (category)
    * @param label the label.
+   * @since 1.2.0
    */
   def f1Measure(label: Double): Double = {
     val p = precision(label)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
index 5b5a2a1450f7f..063fbed8cdeea 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
@@ -34,6 +34,7 @@ import org.apache.spark.rdd.RDD
  * Java users should use [[RankingMetrics$.of]] to create a [[RankingMetrics]] instance.
  *
  * @param predictionAndLabels an RDD of (predicted ranking, ground truth set) pairs.
+ * @since 1.2.0
  */
 @Experimental
 class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])])
@@ -55,6 +56,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
    *
    * @param k the position to compute the truncated precision, must be positive
    * @return the average precision at the first k ranking positions
+   * @since 1.2.0
    */
   def precisionAt(k: Int): Double = {
     require(k > 0, "ranking position k should be positive")
@@ -124,6 +126,7 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
    *
    * @param k the position to compute the truncated ndcg, must be positive
    * @return the average ndcg at the first k ranking positions
+   * @since 1.2.0
    */
   def ndcgAt(k: Int): Double = {
     require(k > 0, "ranking position k should be positive")
@@ -162,6 +165,7 @@ object RankingMetrics {
   /**
    * Creates a [[RankingMetrics]] instance (for Java users).
    * @param predictionAndLabels a JavaRDD of (predicted ranking, ground truth set) pairs
+   * @since 1.4.0
    */
   def of[E, T <: jl.Iterable[E]](predictionAndLabels: JavaRDD[(T, T)]): RankingMetrics[E] = {
     implicit val tag = JavaSparkContext.fakeClassTag[E]
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
index 408847afa800d..54dfd8c099494 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
@@ -29,6 +29,7 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for regression.
  *
  * @param predictionAndObservations an RDD of (prediction, observation) pairs.
+ * @since 1.2.0
  */
 @Experimental
 class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extends Logging {
@@ -66,6 +67,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
    * Returns the variance explained by regression.
    * explainedVariance = \sum_i (\hat{y_i} - \bar{y})^2 / n
    * @see [[https://en.wikipedia.org/wiki/Fraction_of_variance_unexplained]]
+   * @since 1.2.0
    */
   def explainedVariance: Double = {
     SSreg / summary.count
@@ -74,6 +76,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the mean absolute error, which is a risk function corresponding to the
    * expected value of the absolute error loss or l1-norm loss.
+   * @since 1.2.0
    */
   def meanAbsoluteError: Double = {
     summary.normL1(1) / summary.count
@@ -82,6 +85,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the mean squared error, which is a risk function corresponding to the
    * expected value of the squared error loss or quadratic loss.
+   * @since 1.2.0
    */
   def meanSquaredError: Double = {
     SSerr / summary.count
@@ -90,6 +94,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the root mean squared error, which is defined as the square root of
    * the mean squared error.
+   * @since 1.2.0
    */
   def rootMeanSquaredError: Double = {
     math.sqrt(this.meanSquaredError)
@@ -98,6 +103,7 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns R^2^, the unadjusted coefficient of determination.
    * @see [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
+   * @since 1.2.0
    */
   def r2: Double = {
     1 - SSerr / SStot

From d7eb371eb6369a34e58a09179efe058c4101de9e Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 22:30:33 -0700
Subject: [PATCH 222/633] [SPARK-9914] [ML] define setters explicitly for Java
 and use setParam group in RFormula

The problem with defining setters in the base class is that it doesn't return the correct type in Java.

ericl

Author: Xiangrui Meng <meng@databricks.com>

Closes #8143 from mengxr/SPARK-9914 and squashes the following commits:

d36c887 [Xiangrui Meng] remove setters from model
a49021b [Xiangrui Meng] define setters explicitly for Java and use setParam group
---
 .../scala/org/apache/spark/ml/feature/RFormula.scala  | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index d5360c9217ea9..a752dacd72d95 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -33,11 +33,6 @@ import org.apache.spark.sql.types._
  * Base trait for [[RFormula]] and [[RFormulaModel]].
  */
 private[feature] trait RFormulaBase extends HasFeaturesCol with HasLabelCol {
-  /** @group getParam */
-  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
-
-  /** @group getParam */
-  def setLabelCol(value: String): this.type = set(labelCol, value)
 
   protected def hasLabelCol(schema: StructType): Boolean = {
     schema.map(_.name).contains($(labelCol))
@@ -71,6 +66,12 @@ class RFormula(override val uid: String) extends Estimator[RFormulaModel] with R
   /** @group getParam */
   def getFormula: String = $(formula)
 
+  /** @group setParam */
+  def setFeaturesCol(value: String): this.type = set(featuresCol, value)
+
+  /** @group setParam */
+  def setLabelCol(value: String): this.type = set(labelCol, value)
+
   /** Whether the formula specifies fitting an intercept. */
   private[ml] def hasIntercept: Boolean = {
     require(isDefined(formula), "Formula must be defined first.")

From d0b18919d16e6a2f19159516bd2767b60b595279 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Thu, 13 Aug 2015 13:33:39 +0800
Subject: [PATCH 223/633] [SPARK-9927] [SQL] Revert 8049 since it's pushing
 wrong filter down

I made a mistake in #8049 by casting literal value to attribute's data type, which would cause simply truncate the literal value and push a wrong filter down.

JIRA: https://issues.apache.org/jira/browse/SPARK-9927

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8157 from yjshen/rever8049.
---
 .../datasources/DataSourceStrategy.scala      | 30 ++--------------
 .../execution/datasources/jdbc/JDBCRDD.scala  |  2 +-
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala | 35 -------------------
 3 files changed, 3 insertions(+), 64 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 9eea2b0382535..2a4c40db8bb66 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -20,13 +20,13 @@ package org.apache.spark.sql.execution.datasources
 import org.apache.spark.{Logging, TaskContext}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.{MapPartitionsRDD, RDD, UnionRDD}
-import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow, expressions}
+import org.apache.spark.sql.catalyst.{InternalRow, expressions}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.sources._
-import org.apache.spark.sql.types.{TimestampType, DateType, StringType, StructType}
+import org.apache.spark.sql.types.{StringType, StructType}
 import org.apache.spark.sql.{SaveMode, Strategy, execution, sources, _}
 import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.{SerializableConfiguration, Utils}
@@ -343,17 +343,11 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
    * and convert them.
    */
   protected[sql] def selectFilters(filters: Seq[Expression]) = {
-    import CatalystTypeConverters._
-
     def translate(predicate: Expression): Option[Filter] = predicate match {
       case expressions.EqualTo(a: Attribute, Literal(v, _)) =>
         Some(sources.EqualTo(a.name, v))
       case expressions.EqualTo(Literal(v, _), a: Attribute) =>
         Some(sources.EqualTo(a.name, v))
-      case expressions.EqualTo(Cast(a: Attribute, _), l: Literal) =>
-        Some(sources.EqualTo(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
-      case expressions.EqualTo(l: Literal, Cast(a: Attribute, _)) =>
-        Some(sources.EqualTo(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.EqualNullSafe(a: Attribute, Literal(v, _)) =>
         Some(sources.EqualNullSafe(a.name, v))
@@ -364,41 +358,21 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
         Some(sources.GreaterThan(a.name, v))
       case expressions.GreaterThan(Literal(v, _), a: Attribute) =>
         Some(sources.LessThan(a.name, v))
-      case expressions.GreaterThan(Cast(a: Attribute, _), l: Literal) =>
-        Some(sources.GreaterThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
-      case expressions.GreaterThan(l: Literal, Cast(a: Attribute, _)) =>
-        Some(sources.LessThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.LessThan(a: Attribute, Literal(v, _)) =>
         Some(sources.LessThan(a.name, v))
       case expressions.LessThan(Literal(v, _), a: Attribute) =>
         Some(sources.GreaterThan(a.name, v))
-      case expressions.LessThan(Cast(a: Attribute, _), l: Literal) =>
-        Some(sources.LessThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
-      case expressions.LessThan(l: Literal, Cast(a: Attribute, _)) =>
-        Some(sources.GreaterThan(a.name, convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.GreaterThanOrEqual(a: Attribute, Literal(v, _)) =>
         Some(sources.GreaterThanOrEqual(a.name, v))
       case expressions.GreaterThanOrEqual(Literal(v, _), a: Attribute) =>
         Some(sources.LessThanOrEqual(a.name, v))
-      case expressions.GreaterThanOrEqual(Cast(a: Attribute, _), l: Literal) =>
-        Some(sources.GreaterThanOrEqual(a.name,
-          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
-      case expressions.GreaterThanOrEqual(l: Literal, Cast(a: Attribute, _)) =>
-        Some(sources.LessThanOrEqual(a.name,
-          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.LessThanOrEqual(a: Attribute, Literal(v, _)) =>
         Some(sources.LessThanOrEqual(a.name, v))
       case expressions.LessThanOrEqual(Literal(v, _), a: Attribute) =>
         Some(sources.GreaterThanOrEqual(a.name, v))
-      case expressions.LessThanOrEqual(Cast(a: Attribute, _), l: Literal) =>
-        Some(sources.LessThanOrEqual(a.name,
-          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
-      case expressions.LessThanOrEqual(l: Literal, Cast(a: Attribute, _)) =>
-        Some(sources.GreaterThanOrEqual(a.name,
-          convertToScala(Cast(l, a.dataType).eval(), a.dataType)))
 
       case expressions.InSet(a: Attribute, set) =>
         Some(sources.In(a.name, set.toArray))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
index 281943e23fcff..8eab6a0adccc4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
@@ -284,7 +284,7 @@ private[sql] class JDBCRDD(
   /**
    * `filters`, but as a WHERE clause suitable for injection into a SQL query.
    */
-  val filterWhereClause: String = {
+  private val filterWhereClause: String = {
     val filterStrings = filters map compileFilter filter (_ != null)
     if (filterStrings.size > 0) {
       val sb = new StringBuilder("WHERE ")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index b9cfae51e809c..e4dcf4c75d208 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -25,8 +25,6 @@ import org.h2.jdbc.JdbcSQLException
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD
-import org.apache.spark.sql.execution.PhysicalRDD
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
@@ -150,18 +148,6 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
         |OPTIONS (url '$url', dbtable 'TEST.FLTTYPES', user 'testUser', password 'testPass')
       """.stripMargin.replaceAll("\n", " "))
 
-    conn.prepareStatement("create table test.decimals (a DECIMAL(7, 2), b DECIMAL(4, 0))").
-      executeUpdate()
-    conn.prepareStatement("insert into test.decimals values (12345.67, 1234)").executeUpdate()
-    conn.prepareStatement("insert into test.decimals values (34567.89, 1428)").executeUpdate()
-    conn.commit()
-    sql(
-      s"""
-         |CREATE TEMPORARY TABLE decimals
-         |USING org.apache.spark.sql.jdbc
-         |OPTIONS (url '$url', dbtable 'TEST.DECIMALS', user 'testUser', password 'testPass')
-      """.stripMargin.replaceAll("\n", " "))
-
     conn.prepareStatement(
       s"""
         |create table test.nulltypes (a INT, b BOOLEAN, c TINYINT, d BINARY(20), e VARCHAR(20),
@@ -458,25 +444,4 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
     assert(agg.getCatalystType(0, "", 1, null) === Some(LongType))
     assert(agg.getCatalystType(1, "", 1, null) === Some(StringType))
   }
-
-  test("SPARK-9182: filters are not passed through to jdbc source") {
-    def checkPushedFilter(query: String, filterStr: String): Unit = {
-      val rddOpt = sql(query).queryExecution.executedPlan.collectFirst {
-        case PhysicalRDD(_, rdd: JDBCRDD, _) => rdd
-      }
-      assert(rddOpt.isDefined)
-      val pushedFilterStr = rddOpt.get.filterWhereClause
-      assert(pushedFilterStr.contains(filterStr),
-        s"Expected to push [$filterStr], actually we pushed [$pushedFilterStr]")
-    }
-
-    checkPushedFilter("select * from foobar where NAME = 'fred'", "NAME = 'fred'")
-    checkPushedFilter("select * from inttypes where A > '15'", "A > 15")
-    checkPushedFilter("select * from inttypes where C <= 20", "C <= 20")
-    checkPushedFilter("select * from decimals where A > 1000", "A > 1000.00")
-    checkPushedFilter("select * from decimals where A > 1000 AND A < 2000",
-      "A > 1000.00 AND A < 2000.00")
-    checkPushedFilter("select * from decimals where A = 2000 AND B > 20", "A = 2000.00 AND B > 20")
-    checkPushedFilter("select * from timetypes where B > '1998-09-10'", "B > 1998-09-10")
-  }
 }

From 68f99571492f67596b3656e9f076deeb96616f4a Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 12 Aug 2015 23:04:59 -0700
Subject: [PATCH 224/633] [SPARK-9918] [MLLIB] remove runs from k-means and
 rename epsilon to tol

This requires some discussion. I'm not sure whether `runs` is a useful parameter. It certainly complicates the implementation. We might want to optimize the k-means implementation with block matrix operations. In this case, having `runs` may not be worth the trade-off. Also it increases the communication cost in a single job, which might cause other issues.

This PR also renames `epsilon` to `tol` to have consistent naming among algorithms. The Python constructor is updated to include all parameters.

jkbradley yu-iskw

Author: Xiangrui Meng <meng@databricks.com>

Closes #8148 from mengxr/SPARK-9918 and squashes the following commits:

149b9e5 [Xiangrui Meng] fix constructor in Python and rename epsilon to tol
3cc15b3 [Xiangrui Meng] fix test and change initStep to initSteps in python
a0a0274 [Xiangrui Meng] remove runs from k-means in the pipeline API
---
 .../apache/spark/ml/clustering/KMeans.scala   | 51 +++------------
 .../spark/ml/clustering/KMeansSuite.scala     | 12 +---
 python/pyspark/ml/clustering.py               | 63 ++++---------------
 3 files changed, 26 insertions(+), 100 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
index dc192add6ca13..47a18cdb31b53 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
@@ -18,8 +18,8 @@
 package org.apache.spark.ml.clustering
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.ml.param.{Param, Params, IntParam, DoubleParam, ParamMap}
-import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasMaxIter, HasPredictionCol, HasSeed}
+import org.apache.spark.ml.param.{Param, Params, IntParam, ParamMap}
+import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.mllib.clustering.{KMeans => MLlibKMeans, KMeansModel => MLlibKMeansModel}
@@ -27,14 +27,13 @@ import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.sql.functions.{col, udf}
 import org.apache.spark.sql.types.{IntegerType, StructType}
 import org.apache.spark.sql.{DataFrame, Row}
-import org.apache.spark.util.Utils
 
 
 /**
  * Common params for KMeans and KMeansModel
  */
-private[clustering] trait KMeansParams
-    extends Params with HasMaxIter with HasFeaturesCol with HasSeed with HasPredictionCol {
+private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFeaturesCol
+  with HasSeed with HasPredictionCol with HasTol {
 
   /**
    * Set the number of clusters to create (k). Must be > 1. Default: 2.
@@ -45,31 +44,6 @@ private[clustering] trait KMeansParams
   /** @group getParam */
   def getK: Int = $(k)
 
-  /**
-   * Param the number of runs of the algorithm to execute in parallel. We initialize the algorithm
-   * this many times with random starting conditions (configured by the initialization mode), then
-   * return the best clustering found over any run. Must be >= 1. Default: 1.
-   * @group param
-   */
-  final val runs = new IntParam(this, "runs",
-    "number of runs of the algorithm to execute in parallel", (value: Int) => value >= 1)
-
-  /** @group getParam */
-  def getRuns: Int = $(runs)
-
-  /**
-   * Param the distance threshold within which we've consider centers to have converged.
-   * If all centers move less than this Euclidean distance, we stop iterating one run.
-   * Must be >= 0.0. Default: 1e-4
-   * @group param
-   */
-  final val epsilon = new DoubleParam(this, "epsilon",
-    "distance threshold within which we've consider centers to have converge",
-    (value: Double) => value >= 0.0)
-
-  /** @group getParam */
-  def getEpsilon: Double = $(epsilon)
-
   /**
    * Param for the initialization algorithm. This can be either "random" to choose random points as
    * initial cluster centers, or "k-means||" to use a parallel variant of k-means++
@@ -136,9 +110,9 @@ class KMeansModel private[ml] (
 
 /**
  * :: Experimental ::
- * K-means clustering with support for multiple parallel runs and a k-means++ like initialization
- * mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested,
- * they are executed together with joint passes over the data for efficiency.
+ * K-means clustering with support for k-means|| initialization proposed by Bahmani et al.
+ *
+ * @see [[http://dx.doi.org/10.14778/2180912.2180915 Bahmani et al., Scalable k-means++.]]
  */
 @Experimental
 class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMeansParams {
@@ -146,10 +120,9 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
   setDefault(
     k -> 2,
     maxIter -> 20,
-    runs -> 1,
     initMode -> MLlibKMeans.K_MEANS_PARALLEL,
     initSteps -> 5,
-    epsilon -> 1e-4)
+    tol -> 1e-4)
 
   override def copy(extra: ParamMap): KMeans = defaultCopy(extra)
 
@@ -174,10 +147,7 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
   def setMaxIter(value: Int): this.type = set(maxIter, value)
 
   /** @group setParam */
-  def setRuns(value: Int): this.type = set(runs, value)
-
-  /** @group setParam */
-  def setEpsilon(value: Double): this.type = set(epsilon, value)
+  def setTol(value: Double): this.type = set(tol, value)
 
   /** @group setParam */
   def setSeed(value: Long): this.type = set(seed, value)
@@ -191,8 +161,7 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
       .setInitializationSteps($(initSteps))
       .setMaxIterations($(maxIter))
       .setSeed($(seed))
-      .setEpsilon($(epsilon))
-      .setRuns($(runs))
+      .setEpsilon($(tol))
     val parentModel = algo.run(rdd)
     val model = new KMeansModel(uid, parentModel)
     copyValues(model)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
index 1f15ac02f4008..688b0e31f91dc 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala
@@ -52,10 +52,9 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(kmeans.getFeaturesCol === "features")
     assert(kmeans.getPredictionCol === "prediction")
     assert(kmeans.getMaxIter === 20)
-    assert(kmeans.getRuns === 1)
     assert(kmeans.getInitMode === MLlibKMeans.K_MEANS_PARALLEL)
     assert(kmeans.getInitSteps === 5)
-    assert(kmeans.getEpsilon === 1e-4)
+    assert(kmeans.getTol === 1e-4)
   }
 
   test("set parameters") {
@@ -64,21 +63,19 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setFeaturesCol("test_feature")
       .setPredictionCol("test_prediction")
       .setMaxIter(33)
-      .setRuns(7)
       .setInitMode(MLlibKMeans.RANDOM)
       .setInitSteps(3)
       .setSeed(123)
-      .setEpsilon(1e-3)
+      .setTol(1e-3)
 
     assert(kmeans.getK === 9)
     assert(kmeans.getFeaturesCol === "test_feature")
     assert(kmeans.getPredictionCol === "test_prediction")
     assert(kmeans.getMaxIter === 33)
-    assert(kmeans.getRuns === 7)
     assert(kmeans.getInitMode === MLlibKMeans.RANDOM)
     assert(kmeans.getInitSteps === 3)
     assert(kmeans.getSeed === 123)
-    assert(kmeans.getEpsilon === 1e-3)
+    assert(kmeans.getTol === 1e-3)
   }
 
   test("parameters validation") {
@@ -91,9 +88,6 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
     intercept[IllegalArgumentException] {
       new KMeans().setInitSteps(0)
     }
-    intercept[IllegalArgumentException] {
-      new KMeans().setRuns(0)
-    }
   }
 
   test("fit & transform") {
diff --git a/python/pyspark/ml/clustering.py b/python/pyspark/ml/clustering.py
index 48338713a29ea..cb4c16e25a7a3 100644
--- a/python/pyspark/ml/clustering.py
+++ b/python/pyspark/ml/clustering.py
@@ -19,7 +19,6 @@
 from pyspark.ml.wrapper import JavaEstimator, JavaModel
 from pyspark.ml.param.shared import *
 from pyspark.mllib.common import inherit_doc
-from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['KMeans', 'KMeansModel']
 
@@ -35,7 +34,7 @@ def clusterCenters(self):
 
 
 @inherit_doc
-class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
+class KMeans(JavaEstimator, HasFeaturesCol, HasPredictionCol, HasMaxIter, HasTol, HasSeed):
     """
     K-means clustering with support for multiple parallel runs and a k-means++ like initialization
     mode (the k-means|| algorithm by Bahmani et al). When multiple concurrent runs are requested,
@@ -45,7 +44,7 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
     >>> data = [(Vectors.dense([0.0, 0.0]),), (Vectors.dense([1.0, 1.0]),),
     ...         (Vectors.dense([9.0, 8.0]),), (Vectors.dense([8.0, 9.0]),)]
     >>> df = sqlContext.createDataFrame(data, ["features"])
-    >>> kmeans = KMeans().setK(2).setSeed(1).setFeaturesCol("features")
+    >>> kmeans = KMeans(k=2, seed=1)
     >>> model = kmeans.fit(df)
     >>> centers = model.clusterCenters()
     >>> len(centers)
@@ -60,10 +59,6 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
 
     # a placeholder to make it appear in the generated doc
     k = Param(Params._dummy(), "k", "number of clusters to create")
-    epsilon = Param(Params._dummy(), "epsilon",
-                    "distance threshold within which " +
-                    "we've consider centers to have converged")
-    runs = Param(Params._dummy(), "runs", "number of runs of the algorithm to execute in parallel")
     initMode = Param(Params._dummy(), "initMode",
                      "the initialization algorithm. This can be either \"random\" to " +
                      "choose random points as initial cluster centers, or \"k-means||\" " +
@@ -71,21 +66,21 @@ class KMeans(JavaEstimator, HasFeaturesCol, HasMaxIter, HasSeed):
     initSteps = Param(Params._dummy(), "initSteps", "steps for k-means initialization mode")
 
     @keyword_only
-    def __init__(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initStep=5):
+    def __init__(self, featuresCol="features", predictionCol="prediction", k=2,
+                 initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None):
+        """
+        __init__(self, featuresCol="features", predictionCol="prediction", k=2, \
+                 initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None)
+        """
         super(KMeans, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.clustering.KMeans", self.uid)
         self.k = Param(self, "k", "number of clusters to create")
-        self.epsilon = Param(self, "epsilon",
-                             "distance threshold within which " +
-                             "we've consider centers to have converged")
-        self.runs = Param(self, "runs", "number of runs of the algorithm to execute in parallel")
-        self.seed = Param(self, "seed", "random seed")
         self.initMode = Param(self, "initMode",
                               "the initialization algorithm. This can be either \"random\" to " +
                               "choose random points as initial cluster centers, or \"k-means||\" " +
                               "to use a parallel variant of k-means++")
         self.initSteps = Param(self, "initSteps", "steps for k-means initialization mode")
-        self._setDefault(k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5)
+        self._setDefault(k=2, initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
@@ -93,9 +88,11 @@ def _create_model(self, java_model):
         return KMeansModel(java_model)
 
     @keyword_only
-    def setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5):
+    def setParams(self, featuresCol="features", predictionCol="prediction", k=2,
+                  initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None):
         """
-        setParams(self, k=2, maxIter=20, runs=1, epsilon=1e-4, initMode="k-means||", initSteps=5):
+        setParams(self, featuresCol="features", predictionCol="prediction", k=2, \
+                  initMode="k-means||", initSteps=5, tol=1e-4, maxIter=20, seed=None)
 
         Sets params for KMeans.
         """
@@ -119,40 +116,6 @@ def getK(self):
         """
         return self.getOrDefault(self.k)
 
-    def setEpsilon(self, value):
-        """
-        Sets the value of :py:attr:`epsilon`.
-
-        >>> algo = KMeans().setEpsilon(1e-5)
-        >>> abs(algo.getEpsilon() - 1e-5) < 1e-5
-        True
-        """
-        self._paramMap[self.epsilon] = value
-        return self
-
-    def getEpsilon(self):
-        """
-        Gets the value of `epsilon`
-        """
-        return self.getOrDefault(self.epsilon)
-
-    def setRuns(self, value):
-        """
-        Sets the value of :py:attr:`runs`.
-
-        >>> algo = KMeans().setRuns(10)
-        >>> algo.getRuns()
-        10
-        """
-        self._paramMap[self.runs] = value
-        return self
-
-    def getRuns(self):
-        """
-        Gets the value of `runs`
-        """
-        return self.getOrDefault(self.runs)
-
     def setInitMode(self, value):
         """
         Sets the value of :py:attr:`initMode`.

From 84a27916a62980c8fcb0977c3a7fdb73c0bd5812 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 13 Aug 2015 15:08:57 +0800
Subject: [PATCH 225/633] [SPARK-9885] [SQL] Also pass barrierPrefixes and
 sharedPrefixes to IsolatedClientLoader when hiveMetastoreJars is set to
 maven.

https://issues.apache.org/jira/browse/SPARK-9885

cc marmbrus liancheng

Author: Yin Huai <yhuai@databricks.com>

Closes #8158 from yhuai/classloaderMaven.
---
 .../scala/org/apache/spark/sql/hive/HiveContext.scala |  6 +++++-
 .../spark/sql/hive/client/IsolatedClientLoader.scala  | 11 +++++++++--
 2 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index f17177a771c3b..17762649fd70d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -231,7 +231,11 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
       // TODO: Support for loading the jars from an already downloaded location.
       logInfo(
         s"Initializing HiveMetastoreConnection version $hiveMetastoreVersion using maven.")
-      IsolatedClientLoader.forVersion(hiveMetastoreVersion, allConfig)
+      IsolatedClientLoader.forVersion(
+        version = hiveMetastoreVersion,
+        config = allConfig,
+        barrierPrefixes = hiveMetastoreBarrierPrefixes,
+        sharedPrefixes = hiveMetastoreSharedPrefixes)
     } else {
       // Convert to files and expand any directories.
       val jars =
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
index a7d5a991948d9..7856037508412 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -42,11 +42,18 @@ private[hive] object IsolatedClientLoader {
   def forVersion(
       version: String,
       config: Map[String, String] = Map.empty,
-      ivyPath: Option[String] = None): IsolatedClientLoader = synchronized {
+      ivyPath: Option[String] = None,
+      sharedPrefixes: Seq[String] = Seq.empty,
+      barrierPrefixes: Seq[String] = Seq.empty): IsolatedClientLoader = synchronized {
     val resolvedVersion = hiveVersion(version)
     val files = resolvedVersions.getOrElseUpdate(resolvedVersion,
       downloadVersion(resolvedVersion, ivyPath))
-    new IsolatedClientLoader(hiveVersion(version), files, config)
+    new IsolatedClientLoader(
+      version = hiveVersion(version),
+      execJars = files,
+      config = config,
+      sharedPrefixes = sharedPrefixes,
+      barrierPrefixes = barrierPrefixes)
   }
 
   def hiveVersion(version: String): HiveVersion = version match {

From 69930310115501f0de094fe6f5c6c60dade342bd Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 13 Aug 2015 16:16:50 +0800
Subject: [PATCH 226/633] [SPARK-9757] [SQL] Fixes persistence of Parquet
 relation with decimal column

PR #7967 enables us to save data source relations to metastore in Hive compatible format when possible. But it fails to persist Parquet relations with decimal column(s) to Hive metastore of versions lower than 1.2.0. This is because `ParquetHiveSerDe` in Hive versions prior to 1.2.0 doesn't support decimal. This PR checks for this case and falls back to Spark SQL specific metastore table format.

Author: Yin Huai <yhuai@databricks.com>
Author: Cheng Lian <lian@databricks.com>

Closes #8130 from liancheng/spark-9757/old-hive-parquet-decimal.
---
 .../apache/spark/sql/types/ArrayType.scala    |  6 +-
 .../org/apache/spark/sql/types/DataType.scala |  5 ++
 .../org/apache/spark/sql/types/MapType.scala  |  6 +-
 .../apache/spark/sql/types/StructType.scala   |  8 ++-
 .../spark/sql/types/DataTypeSuite.scala       | 24 +++++++
 .../spark/sql/hive/HiveMetastoreCatalog.scala | 39 ++++++++---
 .../sql/hive/client/ClientInterface.scala     |  3 +
 .../spark/sql/hive/client/ClientWrapper.scala |  2 +-
 .../spark/sql/hive/client/package.scala       |  2 +-
 .../sql/hive/HiveMetastoreCatalogSuite.scala  | 17 +++--
 .../spark/sql/hive/HiveSparkSubmitSuite.scala | 68 +++++++++++++++++--
 11 files changed, 150 insertions(+), 30 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
index 5094058164b2f..5770f59b53077 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/ArrayType.scala
@@ -75,6 +75,10 @@ case class ArrayType(elementType: DataType, containsNull: Boolean) extends DataT
 
   override def simpleString: String = s"array<${elementType.simpleString}>"
 
-  private[spark] override def asNullable: ArrayType =
+  override private[spark] def asNullable: ArrayType =
     ArrayType(elementType.asNullable, containsNull = true)
+
+  override private[spark] def existsRecursively(f: (DataType) => Boolean): Boolean = {
+    f(this) || elementType.existsRecursively(f)
+  }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index f4428c2e8b202..7bcd623b3f33e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -77,6 +77,11 @@ abstract class DataType extends AbstractDataType {
    */
   private[spark] def asNullable: DataType
 
+  /**
+   * Returns true if any `DataType` of this DataType tree satisfies the given function `f`.
+   */
+  private[spark] def existsRecursively(f: (DataType) => Boolean): Boolean = f(this)
+
   override private[sql] def defaultConcreteType: DataType = this
 
   override private[sql] def acceptsType(other: DataType): Boolean = sameType(other)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
index ac34b642827ca..00461e529ca0a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/MapType.scala
@@ -62,8 +62,12 @@ case class MapType(
 
   override def simpleString: String = s"map<${keyType.simpleString},${valueType.simpleString}>"
 
-  private[spark] override def asNullable: MapType =
+  override private[spark] def asNullable: MapType =
     MapType(keyType.asNullable, valueType.asNullable, valueContainsNull = true)
+
+  override private[spark] def existsRecursively(f: (DataType) => Boolean): Boolean = {
+    f(this) || keyType.existsRecursively(f) || valueType.existsRecursively(f)
+  }
 }
 
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
index 9cbc207538d4f..d8968ef806390 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala
@@ -24,7 +24,7 @@ import org.json4s.JsonDSL._
 
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.DeveloperApi
-import org.apache.spark.sql.catalyst.expressions.{InterpretedOrdering, AttributeReference, Attribute, InterpretedOrdering$}
+import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference, InterpretedOrdering}
 
 
 /**
@@ -292,7 +292,7 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
   private[sql] def merge(that: StructType): StructType =
     StructType.merge(this, that).asInstanceOf[StructType]
 
-  private[spark] override def asNullable: StructType = {
+  override private[spark] def asNullable: StructType = {
     val newFields = fields.map {
       case StructField(name, dataType, nullable, metadata) =>
         StructField(name, dataType.asNullable, nullable = true, metadata)
@@ -301,6 +301,10 @@ case class StructType(fields: Array[StructField]) extends DataType with Seq[Stru
     StructType(newFields)
   }
 
+  override private[spark] def existsRecursively(f: (DataType) => Boolean): Boolean = {
+    f(this) || fields.exists(field => field.dataType.existsRecursively(f))
+  }
+
   private[sql] val interpretedOrdering = InterpretedOrdering.forSchema(this.fields.map(_.dataType))
 }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
index 88b221cd81d74..706ecd29d1355 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala
@@ -170,6 +170,30 @@ class DataTypeSuite extends SparkFunSuite {
     }
   }
 
+  test("existsRecursively") {
+    val struct = StructType(
+      StructField("a", LongType) ::
+      StructField("b", FloatType) :: Nil)
+    assert(struct.existsRecursively(_.isInstanceOf[LongType]))
+    assert(struct.existsRecursively(_.isInstanceOf[StructType]))
+    assert(!struct.existsRecursively(_.isInstanceOf[IntegerType]))
+
+    val mapType = MapType(struct, StringType)
+    assert(mapType.existsRecursively(_.isInstanceOf[LongType]))
+    assert(mapType.existsRecursively(_.isInstanceOf[StructType]))
+    assert(mapType.existsRecursively(_.isInstanceOf[StringType]))
+    assert(mapType.existsRecursively(_.isInstanceOf[MapType]))
+    assert(!mapType.existsRecursively(_.isInstanceOf[IntegerType]))
+
+    val arrayType = ArrayType(mapType)
+    assert(arrayType.existsRecursively(_.isInstanceOf[LongType]))
+    assert(arrayType.existsRecursively(_.isInstanceOf[StructType]))
+    assert(arrayType.existsRecursively(_.isInstanceOf[StringType]))
+    assert(arrayType.existsRecursively(_.isInstanceOf[MapType]))
+    assert(arrayType.existsRecursively(_.isInstanceOf[ArrayType]))
+    assert(!arrayType.existsRecursively(_.isInstanceOf[IntegerType]))
+  }
+
   def checkDataTypeJsonRepr(dataType: DataType): Unit = {
     test(s"JSON - $dataType") {
       assert(DataType.fromJson(dataType.json) === dataType)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 5e5497837a393..6770462bb0ad3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -33,15 +33,14 @@ import org.apache.hadoop.hive.ql.plan.TableDesc
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.analysis.{Catalog, MultiInstanceRelation, OverrideCatalog}
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.{InternalRow, SqlParser, TableIdentifier}
-import org.apache.spark.sql.execution.{FileRelation, datasources}
+import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation, Partition => ParquetPartition, PartitionSpec, ResolvedDataSource}
+import org.apache.spark.sql.execution.{FileRelation, datasources}
 import org.apache.spark.sql.hive.client._
-import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, SQLContext, SaveMode}
@@ -86,9 +85,9 @@ private[hive] object HiveSerDe {
           serde = Option("org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe")))
 
     val key = source.toLowerCase match {
-      case _ if source.startsWith("org.apache.spark.sql.parquet") => "parquet"
-      case _ if source.startsWith("org.apache.spark.sql.orc") => "orc"
-      case _ => source.toLowerCase
+      case s if s.startsWith("org.apache.spark.sql.parquet") => "parquet"
+      case s if s.startsWith("org.apache.spark.sql.orc") => "orc"
+      case s => s
     }
 
     serdeMap.get(key)
@@ -309,11 +308,31 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
     val hiveTable = (maybeSerDe, dataSource.relation) match {
       case (Some(serde), relation: HadoopFsRelation)
           if relation.paths.length == 1 && relation.partitionColumns.isEmpty =>
-        logInfo {
-          "Persisting data source relation with a single input path into Hive metastore in Hive " +
-            s"compatible format.  Input path: ${relation.paths.head}"
+        // Hive ParquetSerDe doesn't support decimal type until 1.2.0.
+        val isParquetSerDe = serde.inputFormat.exists(_.toLowerCase.contains("parquet"))
+        val hasDecimalFields = relation.schema.existsRecursively(_.isInstanceOf[DecimalType])
+
+        val hiveParquetSupportsDecimal = client.version match {
+          case org.apache.spark.sql.hive.client.hive.v1_2 => true
+          case _ => false
+        }
+
+        if (isParquetSerDe && !hiveParquetSupportsDecimal && hasDecimalFields) {
+          // If Hive version is below 1.2.0, we cannot save Hive compatible schema to
+          // metastore when the file format is Parquet and the schema has DecimalType.
+          logWarning {
+            "Persisting Parquet relation with decimal field(s) into Hive metastore in Spark SQL " +
+              "specific format, which is NOT compatible with Hive. Because ParquetHiveSerDe in " +
+              s"Hive ${client.version.fullVersion} doesn't support decimal type. See HIVE-6384."
+          }
+          newSparkSQLSpecificMetastoreTable()
+        } else {
+          logInfo {
+            "Persisting data source relation with a single input path into Hive metastore in " +
+              s"Hive compatible format. Input path: ${relation.paths.head}"
+          }
+          newHiveCompatibleMetastoreTable(relation, serde)
         }
-        newHiveCompatibleMetastoreTable(relation, serde)
 
       case (Some(serde), relation: HadoopFsRelation) if relation.partitionColumns.nonEmpty =>
         logWarning {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
index a82e152dcda2c..3811c152a7ae6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientInterface.scala
@@ -88,6 +88,9 @@ private[hive] case class HiveTable(
  */
 private[hive] trait ClientInterface {
 
+  /** Returns the Hive Version of this client. */
+  def version: HiveVersion
+
   /** Returns the configuration for the given key in the current session. */
   def getConf(key: String, defaultValue: String): String
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index 3d05b583cf9e0..f49c97de8ff4e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -58,7 +58,7 @@ import org.apache.spark.util.{CircularBuffer, Utils}
  *                        this ClientWrapper.
  */
 private[hive] class ClientWrapper(
-    version: HiveVersion,
+    override val version: HiveVersion,
     config: Map[String, String],
     initClassLoader: ClassLoader)
   extends ClientInterface
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
index 0503691a44249..b1b8439efa011 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/package.scala
@@ -25,7 +25,7 @@ package object client {
       val exclusions: Seq[String] = Nil)
 
   // scalastyle:off
-  private[client] object hive {
+  private[hive] object hive {
     case object v12 extends HiveVersion("0.12.0")
     case object v13 extends HiveVersion("0.13.1")
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
index 332c3ec0c28b8..59e65ff97b8e0 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
@@ -19,13 +19,13 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
-import org.apache.spark.sql.hive.client.{ExternalTable, HiveColumn, ManagedTable}
+import org.apache.spark.sql.hive.client.{ExternalTable, ManagedTable}
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.sources.DataSourceTest
 import org.apache.spark.sql.test.{ExamplePointUDT, SQLTestUtils}
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.types.{DecimalType, StringType, StructType}
 import org.apache.spark.sql.{Row, SaveMode}
 import org.apache.spark.{Logging, SparkFunSuite}
 
@@ -55,7 +55,10 @@ class HiveMetastoreCatalogSuite extends SparkFunSuite with Logging {
 class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTestUtils {
   override val sqlContext = TestHive
 
-  private val testDF = (1 to 2).map(i => (i, s"val_$i")).toDF("d1", "d2").coalesce(1)
+  private val testDF = range(1, 3).select(
+    ('id + 0.1) cast DecimalType(10, 3) as 'd1,
+    'id cast StringType as 'd2
+  ).coalesce(1)
 
   Seq(
     "parquet" -> (
@@ -88,10 +91,10 @@ class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTes
 
         val columns = hiveTable.schema
         assert(columns.map(_.name) === Seq("d1", "d2"))
-        assert(columns.map(_.hiveType) === Seq("int", "string"))
+        assert(columns.map(_.hiveType) === Seq("decimal(10,3)", "string"))
 
         checkAnswer(table("t"), testDF)
-        assert(runSqlHive("SELECT * FROM t") === Seq("1\tval_1", "2\tval_2"))
+        assert(runSqlHive("SELECT * FROM t") === Seq("1.1\t1", "2.1\t2"))
       }
     }
 
@@ -117,10 +120,10 @@ class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTes
 
           val columns = hiveTable.schema
           assert(columns.map(_.name) === Seq("d1", "d2"))
-          assert(columns.map(_.hiveType) === Seq("int", "string"))
+          assert(columns.map(_.hiveType) === Seq("decimal(10,3)", "string"))
 
           checkAnswer(table("t"), testDF)
-          assert(runSqlHive("SELECT * FROM t") === Seq("1\tval_1", "2\tval_2"))
+          assert(runSqlHive("SELECT * FROM t") === Seq("1.1\t1", "2.1\t2"))
         }
       }
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index 1e1972d1ac353..0c29646114465 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -20,16 +20,18 @@ package org.apache.spark.sql.hive
 import java.io.File
 
 import scala.collection.mutable.ArrayBuffer
-import scala.sys.process.{ProcessLogger, Process}
+import scala.sys.process.{Process, ProcessLogger}
 
+import org.scalatest.Matchers
+import org.scalatest.concurrent.Timeouts
 import org.scalatest.exceptions.TestFailedDueToTimeoutException
+import org.scalatest.time.SpanSugar._
 
 import org.apache.spark._
+import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.hive.test.{TestHive, TestHiveContext}
+import org.apache.spark.sql.types.DecimalType
 import org.apache.spark.util.{ResetSystemProperties, Utils}
-import org.scalatest.Matchers
-import org.scalatest.concurrent.Timeouts
-import org.scalatest.time.SpanSugar._
 
 /**
  * This suite tests spark-submit with applications using HiveContext.
@@ -50,8 +52,8 @@ class HiveSparkSubmitSuite
     val unusedJar = TestUtils.createJarWithClasses(Seq.empty)
     val jar1 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassA"))
     val jar2 = TestUtils.createJarWithClasses(Seq("SparkSubmitClassB"))
-    val jar3 = TestHive.getHiveFile("hive-contrib-0.13.1.jar").getCanonicalPath()
-    val jar4 = TestHive.getHiveFile("hive-hcatalog-core-0.13.1.jar").getCanonicalPath()
+    val jar3 = TestHive.getHiveFile("hive-contrib-0.13.1.jar").getCanonicalPath
+    val jar4 = TestHive.getHiveFile("hive-hcatalog-core-0.13.1.jar").getCanonicalPath
     val jarsString = Seq(jar1, jar2, jar3, jar4).map(j => j.toString).mkString(",")
     val args = Seq(
       "--class", SparkSubmitClassLoaderTest.getClass.getName.stripSuffix("$"),
@@ -91,6 +93,16 @@ class HiveSparkSubmitSuite
     runSparkSubmit(args)
   }
 
+  test("SPARK-9757 Persist Parquet relation with decimal column") {
+    val unusedJar = TestUtils.createJarWithClasses(Seq.empty)
+    val args = Seq(
+      "--class", SPARK_9757.getClass.getName.stripSuffix("$"),
+      "--name", "SparkSQLConfTest",
+      "--master", "local-cluster[2,1,1024]",
+      unusedJar.toString)
+    runSparkSubmit(args)
+  }
+
   // NOTE: This is an expensive operation in terms of time (10 seconds+). Use sparingly.
   // This is copied from org.apache.spark.deploy.SparkSubmitSuite
   private def runSparkSubmit(args: Seq[String]): Unit = {
@@ -213,7 +225,7 @@ object SparkSQLConfTest extends Logging {
     // before spark.sql.hive.metastore.jars get set, we will see the following exception:
     // Exception in thread "main" java.lang.IllegalArgumentException: Builtin jars can only
     // be used when hive execution version == hive metastore version.
-    // Execution: 0.13.1 != Metastore: 0.12. Specify a vaild path to the correct hive jars
+    // Execution: 0.13.1 != Metastore: 0.12. Specify a valid path to the correct hive jars
     // using $HIVE_METASTORE_JARS or change spark.sql.hive.metastore.version to 0.13.1.
     val conf = new SparkConf() {
       override def getAll: Array[(String, String)] = {
@@ -239,3 +251,45 @@ object SparkSQLConfTest extends Logging {
     sc.stop()
   }
 }
+
+object SPARK_9757 extends QueryTest with Logging {
+  def main(args: Array[String]): Unit = {
+    Utils.configTestLog4j("INFO")
+
+    val sparkContext = new SparkContext(
+      new SparkConf()
+        .set("spark.sql.hive.metastore.version", "0.13.1")
+        .set("spark.sql.hive.metastore.jars", "maven"))
+
+    val hiveContext = new TestHiveContext(sparkContext)
+    import hiveContext.implicits._
+    import org.apache.spark.sql.functions._
+
+    val dir = Utils.createTempDir()
+    dir.delete()
+
+    try {
+      {
+        val df =
+          hiveContext
+            .range(10)
+            .select(('id + 0.1) cast DecimalType(10, 3) as 'dec)
+        df.write.option("path", dir.getCanonicalPath).mode("overwrite").saveAsTable("t")
+        checkAnswer(hiveContext.table("t"), df)
+      }
+
+      {
+        val df =
+          hiveContext
+            .range(10)
+            .select(callUDF("struct", ('id + 0.2) cast DecimalType(10, 3)) as 'dec_struct)
+        df.write.option("path", dir.getCanonicalPath).mode("overwrite").saveAsTable("t")
+        checkAnswer(hiveContext.table("t"), df)
+      }
+    } finally {
+      dir.delete()
+      hiveContext.sql("DROP TABLE t")
+      sparkContext.stop()
+    }
+  }
+}

From 2932e25da4532de9e86b01d08bce0cb680874e70 Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Thu, 13 Aug 2015 09:17:19 -0700
Subject: [PATCH 227/633] [SPARK-9073] [ML] spark.ml Models copy() should call
 setParent when there is a parent

Copied ML models must have the same parent of original ones

Author: lewuathe <lewuathe@me.com>
Author: Lewuathe <lewuathe@me.com>

Closes #7447 from Lewuathe/SPARK-9073.
---
 .../examples/ml/JavaDeveloperApiExample.java  |  3 +-
 .../examples/ml/DeveloperApiExample.scala     |  2 +-
 .../scala/org/apache/spark/ml/Pipeline.scala  |  2 +-
 .../DecisionTreeClassifier.scala              |  1 +
 .../ml/classification/GBTClassifier.scala     |  2 +-
 .../classification/LogisticRegression.scala   |  2 +-
 .../spark/ml/classification/OneVsRest.scala   |  2 +-
 .../RandomForestClassifier.scala              |  1 +
 .../apache/spark/ml/feature/Bucketizer.scala  |  4 ++-
 .../org/apache/spark/ml/feature/IDF.scala     |  2 +-
 .../spark/ml/feature/MinMaxScaler.scala       |  2 +-
 .../org/apache/spark/ml/feature/PCA.scala     |  2 +-
 .../spark/ml/feature/StandardScaler.scala     |  2 +-
 .../spark/ml/feature/StringIndexer.scala      |  2 +-
 .../spark/ml/feature/VectorIndexer.scala      |  2 +-
 .../apache/spark/ml/feature/Word2Vec.scala    |  2 +-
 .../apache/spark/ml/recommendation/ALS.scala  |  2 +-
 .../ml/regression/DecisionTreeRegressor.scala |  2 +-
 .../spark/ml/regression/GBTRegressor.scala    |  2 +-
 .../ml/regression/LinearRegression.scala      |  2 +-
 .../ml/regression/RandomForestRegressor.scala |  2 +-
 .../spark/ml/tuning/CrossValidator.scala      |  2 +-
 .../org/apache/spark/ml/PipelineSuite.scala   |  3 ++
 .../DecisionTreeClassifierSuite.scala         |  4 +++
 .../classification/GBTClassifierSuite.scala   |  4 +++
 .../LogisticRegressionSuite.scala             |  4 +++
 .../ml/classification/OneVsRestSuite.scala    |  6 +++-
 .../RandomForestClassifierSuite.scala         |  4 +++
 .../spark/ml/feature/BucketizerSuite.scala    |  1 +
 .../spark/ml/feature/MinMaxScalerSuite.scala  |  4 +++
 .../apache/spark/ml/feature/PCASuite.scala    |  4 +++
 .../spark/ml/feature/StringIndexerSuite.scala |  5 ++++
 .../spark/ml/feature/VectorIndexerSuite.scala |  5 ++++
 .../spark/ml/feature/Word2VecSuite.scala      |  4 +++
 .../spark/ml/recommendation/ALSSuite.scala    |  4 +++
 .../DecisionTreeRegressorSuite.scala          | 11 +++++++
 .../ml/regression/GBTRegressorSuite.scala     |  5 ++++
 .../ml/regression/LinearRegressionSuite.scala |  5 ++++
 .../RandomForestRegressorSuite.scala          |  7 ++++-
 .../spark/ml/tuning/CrossValidatorSuite.scala |  5 ++++
 .../apache/spark/ml/util/MLTestingUtils.scala | 30 +++++++++++++++++++
 41 files changed, 138 insertions(+), 22 deletions(-)
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala

diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
index 9df26ffca5775..3f1fe900b0008 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
@@ -230,6 +230,7 @@ public Vector predictRaw(Vector features) {
    */
   @Override
   public MyJavaLogisticRegressionModel copy(ParamMap extra) {
-    return copyValues(new MyJavaLogisticRegressionModel(uid(), weights_), extra);
+    return copyValues(new MyJavaLogisticRegressionModel(uid(), weights_), extra)
+      .setParent(parent());
   }
 }
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
index 78f31b4ffe56a..340c3559b15ef 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/DeveloperApiExample.scala
@@ -179,7 +179,7 @@ private class MyLogisticRegressionModel(
    * This is used for the default implementation of [[transform()]].
    */
   override def copy(extra: ParamMap): MyLogisticRegressionModel = {
-    copyValues(new MyLogisticRegressionModel(uid, weights), extra)
+    copyValues(new MyLogisticRegressionModel(uid, weights), extra).setParent(parent)
   }
 }
 // scalastyle:on println
diff --git a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
index aef2c019d2871..a3e59401c5cfb 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/Pipeline.scala
@@ -198,6 +198,6 @@ class PipelineModel private[ml] (
   }
 
   override def copy(extra: ParamMap): PipelineModel = {
-    new PipelineModel(uid, stages.map(_.copy(extra)))
+    new PipelineModel(uid, stages.map(_.copy(extra))).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 29598f3f05c2d..6f70b96b17ec6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -141,6 +141,7 @@ final class DecisionTreeClassificationModel private[ml] (
 
   override def copy(extra: ParamMap): DecisionTreeClassificationModel = {
     copyValues(new DecisionTreeClassificationModel(uid, rootNode, numClasses), extra)
+      .setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index c3891a9599262..3073a2a61ce83 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -196,7 +196,7 @@ final class GBTClassificationModel(
   }
 
   override def copy(extra: ParamMap): GBTClassificationModel = {
-    copyValues(new GBTClassificationModel(uid, _trees, _treeWeights), extra)
+    copyValues(new GBTClassificationModel(uid, _trees, _treeWeights), extra).setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 5bcd7117b668c..21fbe38ca8233 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -468,7 +468,7 @@ class LogisticRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): LogisticRegressionModel = {
-    copyValues(new LogisticRegressionModel(uid, weights, intercept), extra)
+    copyValues(new LogisticRegressionModel(uid, weights, intercept), extra).setParent(parent)
   }
 
   override protected def raw2prediction(rawPrediction: Vector): Double = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index 1741f19dc911c..1132d8046df67 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -138,7 +138,7 @@ final class OneVsRestModel private[ml] (
   override def copy(extra: ParamMap): OneVsRestModel = {
     val copied = new OneVsRestModel(
       uid, labelMetadata, models.map(_.copy(extra).asInstanceOf[ClassificationModel[_, _]]))
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 156050aaf7a45..11a6d72468333 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -189,6 +189,7 @@ final class RandomForestClassificationModel private[ml] (
 
   override def copy(extra: ParamMap): RandomForestClassificationModel = {
     copyValues(new RandomForestClassificationModel(uid, _trees, numFeatures, numClasses), extra)
+      .setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
index 67e4785bc3553..cfca494dcf468 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
@@ -90,7 +90,9 @@ final class Bucketizer(override val uid: String)
     SchemaUtils.appendColumn(schema, prepOutputField(schema))
   }
 
-  override def copy(extra: ParamMap): Bucketizer = defaultCopy(extra)
+  override def copy(extra: ParamMap): Bucketizer = {
+    defaultCopy[Bucketizer](extra).setParent(parent)
+  }
 }
 
 private[feature] object Bucketizer {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
index ecde80810580c..938447447a0a2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
@@ -114,6 +114,6 @@ class IDFModel private[ml] (
 
   override def copy(extra: ParamMap): IDFModel = {
     val copied = new IDFModel(uid, idfModel)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
index 9a473dd23772d..1b494ec8b1727 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala
@@ -173,6 +173,6 @@ class MinMaxScalerModel private[ml] (
 
   override def copy(extra: ParamMap): MinMaxScalerModel = {
     val copied = new MinMaxScalerModel(uid, originalMin, originalMax)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
index 2d3bb680cf309..539084704b653 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PCA.scala
@@ -125,6 +125,6 @@ class PCAModel private[ml] (
 
   override def copy(extra: ParamMap): PCAModel = {
     val copied = new PCAModel(uid, pcaModel)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
index 72b545e5db3e4..f6d0b0c0e9e75 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StandardScaler.scala
@@ -136,6 +136,6 @@ class StandardScalerModel private[ml] (
 
   override def copy(extra: ParamMap): StandardScalerModel = {
     val copied = new StandardScalerModel(uid, scaler)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index e4485eb038409..9e4b0f0add612 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -168,7 +168,7 @@ class StringIndexerModel private[ml] (
 
   override def copy(extra: ParamMap): StringIndexerModel = {
     val copied = new StringIndexerModel(uid, labels)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
index c73bdccdef5fa..6875aefe065bb 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
@@ -405,6 +405,6 @@ class VectorIndexerModel private[ml] (
 
   override def copy(extra: ParamMap): VectorIndexerModel = {
     val copied = new VectorIndexerModel(uid, numFeatures, categoryMaps)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index 29acc3eb5865f..5af775a4159ad 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -221,6 +221,6 @@ class Word2VecModel private[ml] (
 
   override def copy(extra: ParamMap): Word2VecModel = {
     val copied = new Word2VecModel(uid, wordVectors)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
index 2e44cd4cc6a22..7db8ad8d27918 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -219,7 +219,7 @@ class ALSModel private[ml] (
 
   override def copy(extra: ParamMap): ALSModel = {
     val copied = new ALSModel(uid, rank, userFactors, itemFactors)
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index dc94a14014542..a2bcd67401d08 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -114,7 +114,7 @@ final class DecisionTreeRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): DecisionTreeRegressionModel = {
-    copyValues(new DecisionTreeRegressionModel(uid, rootNode), extra)
+    copyValues(new DecisionTreeRegressionModel(uid, rootNode), extra).setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 5633bc320273a..b66e61f37dd5e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -185,7 +185,7 @@ final class GBTRegressionModel(
   }
 
   override def copy(extra: ParamMap): GBTRegressionModel = {
-    copyValues(new GBTRegressionModel(uid, _trees, _treeWeights), extra)
+    copyValues(new GBTRegressionModel(uid, _trees, _treeWeights), extra).setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 92d819bad8654..884003eb38524 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -312,7 +312,7 @@ class LinearRegressionModel private[ml] (
   override def copy(extra: ParamMap): LinearRegressionModel = {
     val newModel = copyValues(new LinearRegressionModel(uid, weights, intercept))
     if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
-    newModel
+    newModel.setParent(parent)
   }
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index db75c0d26392f..2f36da371f577 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -151,7 +151,7 @@ final class RandomForestRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): RandomForestRegressionModel = {
-    copyValues(new RandomForestRegressionModel(uid, _trees, numFeatures), extra)
+    copyValues(new RandomForestRegressionModel(uid, _trees, numFeatures), extra).setParent(parent)
   }
 
   override def toString: String = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index f979319cc4b58..4792eb0f0a288 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -160,6 +160,6 @@ class CrossValidatorModel private[ml] (
       uid,
       bestModel.copy(extra).asInstanceOf[Model[_]],
       avgMetrics.clone())
-    copyValues(copied, extra)
+    copyValues(copied, extra).setParent(parent)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
index 63d2fa31c7499..1f2c9b75b617b 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/PipelineSuite.scala
@@ -26,6 +26,7 @@ import org.scalatest.mock.MockitoSugar.mock
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.feature.HashingTF
 import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.sql.DataFrame
 
 class PipelineSuite extends SparkFunSuite {
@@ -65,6 +66,8 @@ class PipelineSuite extends SparkFunSuite {
       .setStages(Array(estimator0, transformer1, estimator2, transformer3))
     val pipelineModel = pipeline.fit(dataset0)
 
+    MLTestingUtils.checkCopy(pipelineModel)
+
     assert(pipelineModel.stages.length === 4)
     assert(pipelineModel.stages(0).eq(model0))
     assert(pipelineModel.stages(1).eq(transformer1))
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
index c7bbf1ce07a23..4b7c5d3f23d2c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
@@ -21,6 +21,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.tree.LeafNode
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree, DecisionTreeSuite => OldDecisionTreeSuite}
@@ -244,6 +245,9 @@ class DecisionTreeClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
     val newData: DataFrame = TreeTests.setMetadata(rdd, categoricalFeatures, numClasses)
     val newTree = dt.fit(newData)
 
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(newTree)
+
     val predictions = newTree.transform(newData)
       .select(newTree.getPredictionCol, newTree.getRawPredictionCol, newTree.getProbabilityCol)
       .collect()
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
index d4b5896c12c06..e3909bccaa5ca 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala
@@ -22,6 +22,7 @@ import org.apache.spark.ml.impl.TreeTests
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel
 import org.apache.spark.ml.tree.LeafNode
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
@@ -92,6 +93,9 @@ class GBTClassifierSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setCheckpointInterval(2)
     val model = gbt.fit(df)
 
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     sc.checkpointDir = None
     Utils.deleteRecursively(tempDir)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
index e354e161c6dee..cce39f382f738 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.classification.LogisticRegressionSuite._
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
@@ -135,6 +136,9 @@ class LogisticRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     lr.setFitIntercept(false)
     val model = lr.fit(dataset)
     assert(model.intercept === 0.0)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
   }
 
   test("logistic regression with setters") {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
index bd8e819f6926c..977f0e0b70c1a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
@@ -21,7 +21,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute.NominalAttribute
 import org.apache.spark.ml.feature.StringIndexer
 import org.apache.spark.ml.param.{ParamMap, ParamsSuite}
-import org.apache.spark.ml.util.MetadataUtils
+import org.apache.spark.ml.util.{MLTestingUtils, MetadataUtils}
 import org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS
 import org.apache.spark.mllib.classification.LogisticRegressionSuite._
 import org.apache.spark.mllib.evaluation.MulticlassMetrics
@@ -70,6 +70,10 @@ class OneVsRestSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(ova.getLabelCol === "label")
     assert(ova.getPredictionCol === "prediction")
     val ovaModel = ova.fit(dataset)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(ovaModel)
+
     assert(ovaModel.models.size === numClasses)
     val transformedDataset = ovaModel.transform(dataset)
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
index 6ca4b5aa5fde8..b4403ec30049a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala
@@ -21,6 +21,7 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.tree.LeafNode
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
@@ -135,6 +136,9 @@ class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
     val df: DataFrame = TreeTests.setMetadata(rdd, categoricalFeatures, numClasses)
     val model = rf.fit(df)
 
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     val predictions = model.transform(df)
       .select(rf.getPredictionCol, rf.getRawPredictionCol, rf.getProbabilityCol)
       .collect()
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala
index ec85e0d151e07..0eba34fda6228 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala
@@ -21,6 +21,7 @@ import scala.util.Random
 
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala
index c452054bec92f..c04dda41eea34 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{Row, SQLContext}
@@ -51,6 +52,9 @@ class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext {
       .foreach { case Row(vector1: Vector, vector2: Vector) =>
         assert(vector1.equals(vector2), "Transformed vector is different with expected.")
     }
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
   }
 
   test("MinMaxScaler arguments max must be larger than min") {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala
index d0ae36b28c7a9..30c500f87a769 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/PCASuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
 import org.apache.spark.mllib.linalg.{Vector, Vectors, DenseMatrix, Matrices}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
@@ -56,6 +57,9 @@ class PCASuite extends SparkFunSuite with MLlibTestSparkContext {
       .setK(3)
       .fit(df)
 
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(pca)
+
     pca.transform(df).select("pca_features", "expected").collect().foreach {
       case Row(x: Vector, y: Vector) =>
         assert(x ~== y absTol 1e-5, "Transformed vector is different with expected vector.")
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index b111036087e6a..2d24914cb91f6 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -21,6 +21,7 @@ import org.apache.spark.SparkException
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 
 class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
@@ -38,6 +39,10 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setInputCol("label")
       .setOutputCol("labelIndex")
       .fit(df)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(indexer)
+
     val transformed = indexer.transform(df)
     val attr = Attribute.fromStructField(transformed.schema("labelIndex"))
       .asInstanceOf[NominalAttribute]
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
index 03120c828ca96..8cb0a2cf14d37 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorIndexerSuite.scala
@@ -22,6 +22,7 @@ import scala.beans.{BeanInfo, BeanProperty}
 import org.apache.spark.{Logging, SparkException, SparkFunSuite}
 import org.apache.spark.ml.attribute._
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.rdd.RDD
@@ -109,6 +110,10 @@ class VectorIndexerSuite extends SparkFunSuite with MLlibTestSparkContext with L
   test("Throws error when given RDDs with different size vectors") {
     val vectorIndexer = getIndexer
     val model = vectorIndexer.fit(densePoints1) // vectors of length 3
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     model.transform(densePoints1) // should work
     model.transform(sparsePoints1) // should work
     intercept[SparkException] {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
index adcda0e623b25..a2e46f2029956 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/Word2VecSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.feature
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
@@ -62,6 +63,9 @@ class Word2VecSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setSeed(42L)
       .fit(docDF)
 
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     model.transform(docDF).select("result", "expected").collect().foreach {
       case Row(vector1: Vector, vector2: Vector) =>
         assert(vector1 ~== vector2 absTol 1E-5, "Transformed vector is different with expected.")
diff --git a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
index 2e5cfe7027eb6..eadc80e0e62b1 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala
@@ -28,6 +28,7 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.{Logging, SparkException, SparkFunSuite}
 import org.apache.spark.ml.recommendation.ALS._
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
@@ -374,6 +375,9 @@ class ALSSuite extends SparkFunSuite with MLlibTestSparkContext with Logging {
       }
     logInfo(s"Test RMSE is $rmse.")
     assert(rmse < targetRMSE)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
   }
 
   test("exact rank-1 matrix") {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
index 33aa9d0d62343..b092bcd6a7e86 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{DecisionTree => OldDecisionTree,
   DecisionTreeSuite => OldDecisionTreeSuite}
@@ -61,6 +62,16 @@ class DecisionTreeRegressorSuite extends SparkFunSuite with MLlibTestSparkContex
     compareAPIs(categoricalDataPointsRDD, dt, categoricalFeatures)
   }
 
+  test("copied model must have the same parent") {
+    val categoricalFeatures = Map(0 -> 2, 1-> 2)
+    val df = TreeTests.setMetadata(categoricalDataPointsRDD, categoricalFeatures, numClasses = 0)
+    val model = new DecisionTreeRegressor()
+      .setImpurity("variance")
+      .setMaxDepth(2)
+      .setMaxBins(8).fit(df)
+    MLTestingUtils.checkCopy(model)
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
index dbdce0c9dea54..a68197b59193d 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GBTRegressorSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, GradientBoostedTrees => OldGBT}
@@ -82,6 +83,9 @@ class GBTRegressorSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setMaxDepth(2)
       .setMaxIter(2)
     val model = gbt.fit(df)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
     val preds = model.transform(df)
     val predictions = preds.select("prediction").map(_.getDouble(0))
     // Checks based on SPARK-8736 (to ensure it is not doing classification)
@@ -104,6 +108,7 @@ class GBTRegressorSuite extends SparkFunSuite with MLlibTestSparkContext {
 
     sc.checkpointDir = None
     Utils.deleteRecursively(tempDir)
+
   }
 
   // TODO: Reinstate test once runWithValidation is implemented  SPARK-7132
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index 21ad8225bd9f7..2aaee71ecc734 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.{DenseVector, Vectors}
 import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
 import org.apache.spark.mllib.util.TestingUtils._
@@ -72,6 +73,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(lir.getFitIntercept)
     assert(lir.getStandardization)
     val model = lir.fit(dataset)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     model.transform(dataset)
       .select("label", "prediction")
       .collect()
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
index 992ce9562434e..7b1b3f11481de 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/RandomForestRegressorSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.impl.TreeTests
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
@@ -91,7 +92,11 @@ class RandomForestRegressorSuite extends SparkFunSuite with MLlibTestSparkContex
     val categoricalFeatures = Map.empty[Int, Int]
     val df: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, 0)
 
-    val importances = rf.fit(df).featureImportances
+    val model = rf.fit(df)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+    val importances = model.featureImportances
     val mostImportantFeature = importances.argmax
     assert(mostImportantFeature === 1)
   }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
index db64511a76055..aaca08bb61a45 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.ml.tuning
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.evaluation.{BinaryClassificationEvaluator, Evaluator, RegressionEvaluator}
@@ -53,6 +54,10 @@ class CrossValidatorSuite extends SparkFunSuite with MLlibTestSparkContext {
       .setEvaluator(eval)
       .setNumFolds(3)
     val cvModel = cv.fit(dataset)
+
+    // copied model must have the same paren.
+    MLTestingUtils.checkCopy(cvModel)
+
     val parent = cvModel.bestModel.parent.asInstanceOf[LogisticRegression]
     assert(parent.getRegParam === 0.001)
     assert(parent.getMaxIter === 10)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala b/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
new file mode 100644
index 0000000000000..d290cc9b06e73
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
@@ -0,0 +1,30 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.util
+
+import org.apache.spark.ml.Model
+import org.apache.spark.ml.param.ParamMap
+
+object MLTestingUtils {
+  def checkCopy(model: Model[_]): Unit = {
+    val copied = model.copy(ParamMap.empty)
+      .asInstanceOf[Model[_]]
+    assert(copied.parent.uid == model.parent.uid)
+    assert(copied.parent == model.parent)
+  }
+}

From 7a539ef3b1792764f866fa88c84c78ad59903f21 Mon Sep 17 00:00:00 2001
From: Rosstin <asterazul@gmail.com>
Date: Thu, 13 Aug 2015 09:18:39 -0700
Subject: [PATCH 228/633] [SPARK-8965] [DOCS] Add ml-guide Python Example:
 Estimator, Transformer, and Param

Added ml-guide Python Example: Estimator, Transformer, and Param
/docs/_site/ml-guide.html

Author: Rosstin <asterazul@gmail.com>

Closes #8081 from Rosstin/SPARK-8965.
---
 docs/ml-guide.md | 68 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index b6ca50e98db02..a03ab4356a413 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -355,6 +355,74 @@ jsc.stop();
 {% endhighlight %}
 </div>
 
+<div data-lang="python">
+{% highlight python %}
+from pyspark import SparkContext
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.ml.classification import LogisticRegression
+from pyspark.ml.param import Param, Params
+from pyspark.sql import Row, SQLContext
+
+sc = SparkContext(appName="SimpleParamsExample")
+sqlContext = SQLContext(sc)
+
+# Prepare training data.
+# We use LabeledPoint.
+# Spark SQL can convert RDDs of LabeledPoints into DataFrames.
+training = sc.parallelize([LabeledPoint(1.0, [0.0, 1.1,  0.1]),
+                           LabeledPoint(0.0, [2.0, 1.0, -1.0]),
+                           LabeledPoint(0.0, [2.0, 1.3,  1.0]),
+                           LabeledPoint(1.0, [0.0, 1.2, -0.5])])
+
+# Create a LogisticRegression instance. This instance is an Estimator.
+lr = LogisticRegression(maxIter=10, regParam=0.01)
+# Print out the parameters, documentation, and any default values.
+print "LogisticRegression parameters:\n" + lr.explainParams() + "\n"
+
+# Learn a LogisticRegression model. This uses the parameters stored in lr.
+model1 = lr.fit(training.toDF())
+
+# Since model1 is a Model (i.e., a transformer produced by an Estimator),
+# we can view the parameters it used during fit().
+# This prints the parameter (name: value) pairs, where names are unique IDs for this
+# LogisticRegression instance.
+print "Model 1 was fit using parameters: "
+print model1.extractParamMap()
+
+# We may alternatively specify parameters using a Python dictionary as a paramMap
+paramMap = {lr.maxIter: 20}
+paramMap[lr.maxIter] = 30 # Specify 1 Param, overwriting the original maxIter.
+paramMap.update({lr.regParam: 0.1, lr.threshold: 0.55}) # Specify multiple Params.
+
+# You can combine paramMaps, which are python dictionaries.
+paramMap2 = {lr.probabilityCol: "myProbability"} # Change output column name
+paramMapCombined = paramMap.copy()
+paramMapCombined.update(paramMap2)
+
+# Now learn a new model using the paramMapCombined parameters.
+# paramMapCombined overrides all parameters set earlier via lr.set* methods.
+model2 = lr.fit(training.toDF(), paramMapCombined)
+print "Model 2 was fit using parameters: "
+print model2.extractParamMap()
+
+# Prepare test data
+test = sc.parallelize([LabeledPoint(1.0, [-1.0, 1.5,  1.3]),
+                       LabeledPoint(0.0, [ 3.0, 2.0, -0.1]),
+                       LabeledPoint(1.0, [ 0.0, 2.2, -1.5])])
+
+# Make predictions on test data using the Transformer.transform() method.
+# LogisticRegression.transform will only use the 'features' column.
+# Note that model2.transform() outputs a "myProbability" column instead of the usual
+# 'probability' column since we renamed the lr.probabilityCol parameter previously.
+prediction = model2.transform(test.toDF())
+selected = prediction.select("features", "label", "myProbability", "prediction")
+for row in selected.collect():
+    print row
+
+sc.stop()
+{% endhighlight %}
+</div>
+
 </div>
 
 ## Example: Pipeline

From 4b70798c96b0a784b85fda461426ec60f609be12 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 13 Aug 2015 09:31:14 -0700
Subject: [PATCH 229/633] [MINOR] [ML] change
 MultilayerPerceptronClassifierModel to
 MultilayerPerceptronClassificationModel

To follow the naming rule of ML, change `MultilayerPerceptronClassifierModel` to `MultilayerPerceptronClassificationModel` like `DecisionTreeClassificationModel`, `GBTClassificationModel` and so on.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8164 from yanboliang/mlp-name.
---
 .../MultilayerPerceptronClassifier.scala         | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index 8cd2103d7d5e6..c154561886585 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -131,7 +131,7 @@ private object LabelConverter {
  */
 @Experimental
 class MultilayerPerceptronClassifier(override val uid: String)
-  extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassifierModel]
+  extends Predictor[Vector, MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel]
   with MultilayerPerceptronParams {
 
   def this() = this(Identifiable.randomUID("mlpc"))
@@ -146,7 +146,7 @@ class MultilayerPerceptronClassifier(override val uid: String)
    * @param dataset Training dataset
    * @return Fitted model
    */
-  override protected def train(dataset: DataFrame): MultilayerPerceptronClassifierModel = {
+  override protected def train(dataset: DataFrame): MultilayerPerceptronClassificationModel = {
     val myLayers = $(layers)
     val labels = myLayers.last
     val lpData = extractLabeledPoints(dataset)
@@ -156,13 +156,13 @@ class MultilayerPerceptronClassifier(override val uid: String)
     FeedForwardTrainer.LBFGSOptimizer.setConvergenceTol($(tol)).setNumIterations($(maxIter))
     FeedForwardTrainer.setStackSize($(blockSize))
     val mlpModel = FeedForwardTrainer.train(data)
-    new MultilayerPerceptronClassifierModel(uid, myLayers, mlpModel.weights())
+    new MultilayerPerceptronClassificationModel(uid, myLayers, mlpModel.weights())
   }
 }
 
 /**
  * :: Experimental ::
- * Classifier model based on the Multilayer Perceptron.
+ * Classification model based on the Multilayer Perceptron.
  * Each layer has sigmoid activation function, output layer has softmax.
  * @param uid uid
  * @param layers array of layer sizes including input and output layers
@@ -170,11 +170,11 @@ class MultilayerPerceptronClassifier(override val uid: String)
  * @return prediction model
  */
 @Experimental
-class MultilayerPerceptronClassifierModel private[ml] (
+class MultilayerPerceptronClassificationModel private[ml] (
     override val uid: String,
     layers: Array[Int],
     weights: Vector)
-  extends PredictionModel[Vector, MultilayerPerceptronClassifierModel]
+  extends PredictionModel[Vector, MultilayerPerceptronClassificationModel]
   with Serializable {
 
   private val mlpModel = FeedForwardTopology.multiLayerPerceptron(layers, true).getInstance(weights)
@@ -187,7 +187,7 @@ class MultilayerPerceptronClassifierModel private[ml] (
     LabelConverter.decodeLabel(mlpModel.predict(features))
   }
 
-  override def copy(extra: ParamMap): MultilayerPerceptronClassifierModel = {
-    copyValues(new MultilayerPerceptronClassifierModel(uid, layers, weights), extra)
+  override def copy(extra: ParamMap): MultilayerPerceptronClassificationModel = {
+    copyValues(new MultilayerPerceptronClassificationModel(uid, layers, weights), extra)
   }
 }

From 65fec798ce52ca6b8b0fe14b78a16712778ad04c Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 13 Aug 2015 10:16:40 -0700
Subject: [PATCH 230/633] [MINOR] [DOC] fix mllib pydoc warnings

Switch to correct Sphinx syntax. MechCoder

Author: Xiangrui Meng <meng@databricks.com>

Closes #8169 from mengxr/mllib-pydoc-fix.
---
 python/pyspark/mllib/regression.py | 14 ++++++++++----
 python/pyspark/mllib/util.py       |  1 +
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 5b7afc15ddfba..41946e3674fbe 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -207,8 +207,10 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
         Train a linear regression model using Stochastic Gradient
         Descent (SGD).
         This solves the least squares regression formulation
-                f(weights) = 1/n ||A weights-y||^2^
-        (which is the mean squared error).
+
+            f(weights) = 1/(2n) ||A weights - y||^2,
+
+        which is the mean squared error.
         Here the data matrix has n rows, and the input RDD holds the
         set of rows of A, each with its corresponding right hand side
         label y. See also the documentation for the precise formulation.
@@ -334,7 +336,9 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01,
         Stochastic Gradient Descent.
         This solves the l1-regularized least squares regression
         formulation
-            f(weights) = 1/2n ||A weights-y||^2^  + regParam ||weights||_1
+
+            f(weights) = 1/(2n) ||A weights - y||^2  + regParam ||weights||_1.
+
         Here the data matrix has n rows, and the input RDD holds the
         set of rows of A, each with its corresponding right hand side
         label y. See also the documentation for the precise formulation.
@@ -451,7 +455,9 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01,
         Stochastic Gradient Descent.
         This solves the l2-regularized least squares regression
         formulation
-            f(weights) = 1/2n ||A weights-y||^2^  + regParam/2 ||weights||^2^
+
+            f(weights) = 1/(2n) ||A weights - y||^2 + regParam/2 ||weights||^2.
+
         Here the data matrix has n rows, and the input RDD holds the
         set of rows of A, each with its corresponding right hand side
         label y. See also the documentation for the precise formulation.
diff --git a/python/pyspark/mllib/util.py b/python/pyspark/mllib/util.py
index 916de2d6fcdbd..10a1e4b3eb0fc 100644
--- a/python/pyspark/mllib/util.py
+++ b/python/pyspark/mllib/util.py
@@ -300,6 +300,7 @@ def generateLinearInput(intercept, weights, xMean, xVariance,
         :param: seed      Random Seed
         :param: eps       Used to scale the noise. If eps is set high,
                           the amount of gaussian noise added is more.
+
         Returns a list of LabeledPoints of length nPoints
         """
         weights = [float(weight) for weight in weights]

From 8815ba2f674dbb18eb499216df9942b411e10daa Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 13 Aug 2015 11:31:10 -0700
Subject: [PATCH 231/633] [SPARK-9649] Fix MasterSuite, third time's a charm

This particular test did not load the default configurations so
it continued to start the REST server, which causes port bind
exceptions.
---
 .../test/scala/org/apache/spark/deploy/master/MasterSuite.scala  | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
index 20d0201a364ab..242bf4b5566eb 100644
--- a/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/master/MasterSuite.scala
@@ -40,6 +40,7 @@ class MasterSuite extends SparkFunSuite with Matchers with Eventually with Priva
     conf.set("spark.deploy.recoveryMode", "CUSTOM")
     conf.set("spark.deploy.recoveryMode.factory",
       classOf[CustomRecoveryModeFactory].getCanonicalName)
+    conf.set("spark.master.rest.enabled", "false")
 
     val instantiationAttempts = CustomRecoveryModeFactory.instantiationAttempts
 

From 864de8eaf4b6ad5c9099f6f29e251c56b029f631 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 13 Aug 2015 13:42:35 -0700
Subject: [PATCH 232/633] [SPARK-9661] [MLLIB] [ML] Java compatibility

I skimmed through the docs for various instance of Object and replaced them with Java compaible versions of the same.

1. Some methods in LDAModel.
2. runMiniBatchSGD
3. kolmogorovSmirnovTest

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8126 from MechCoder/java_incop.
---
 .../spark/mllib/clustering/LDAModel.scala     | 27 +++++++++++++++++--
 .../apache/spark/mllib/stat/Statistics.scala  | 16 ++++++++++-
 .../spark/mllib/clustering/JavaLDASuite.java  | 24 +++++++++++++++++
 .../spark/mllib/stat/JavaStatisticsSuite.java | 22 +++++++++++++++
 .../spark/mllib/clustering/LDASuite.scala     | 13 +++++++++
 5 files changed, 99 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 5dc637ebdc133..f31949f13a4cf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -26,7 +26,7 @@ import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.api.java.JavaPairRDD
+import org.apache.spark.api.java.{JavaPairRDD, JavaRDD}
 import org.apache.spark.graphx.{Edge, EdgeContext, Graph, VertexId}
 import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -228,6 +228,11 @@ class LocalLDAModel private[clustering] (
     docConcentration, topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k,
     vocabSize)
 
+  /** Java-friendly version of [[logLikelihood]] */
+  def logLikelihood(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
+    logLikelihood(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
+  }
+
   /**
    * Calculate an upper bound bound on perplexity.  (Lower is better.)
    * See Equation (16) in original Online LDA paper.
@@ -242,6 +247,11 @@ class LocalLDAModel private[clustering] (
     -logLikelihood(documents) / corpusTokenCount
   }
 
+  /** Java-friendly version of [[logPerplexity]] */
+  def logPerplexity(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
+    logPerplexity(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
+  }
+
   /**
    * Estimate the variational likelihood bound of from `documents`:
    *    log p(documents) >= E_q[log p(documents)] - E_q[log q(documents)]
@@ -341,8 +351,14 @@ class LocalLDAModel private[clustering] (
     }
   }
 
-}
+  /** Java-friendly version of [[topicDistributions]] */
+  def topicDistributions(
+      documents: JavaPairRDD[java.lang.Long, Vector]): JavaPairRDD[java.lang.Long, Vector] = {
+    val distributions = topicDistributions(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
+    JavaPairRDD.fromRDD(distributions.asInstanceOf[RDD[(java.lang.Long, Vector)]])
+  }
 
+}
 
 @Experimental
 object LocalLDAModel extends Loader[LocalLDAModel] {
@@ -657,6 +673,13 @@ class DistributedLDAModel private[clustering] (
     }
   }
 
+  /** Java-friendly version of [[topTopicsPerDocument]] */
+  def javaTopTopicsPerDocument(
+      k: Int): JavaRDD[(java.lang.Long, Array[Int], Array[java.lang.Double])] = {
+    val topics = topTopicsPerDocument(k)
+    topics.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[java.lang.Double])]].toJavaRDD()
+  }
+
   // TODO:
   // override def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = ???
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
index f84502919e381..24fe48cb8f71f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.stat
 import scala.annotation.varargs
 
 import org.apache.spark.annotation.Experimental
-import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.api.java.{JavaRDD, JavaDoubleRDD}
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
 import org.apache.spark.mllib.linalg.{Matrix, Vector}
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -178,6 +178,9 @@ object Statistics {
     ChiSqTest.chiSquaredFeatures(data)
   }
 
+  /** Java-friendly version of [[chiSqTest()]] */
+  def chiSqTest(data: JavaRDD[LabeledPoint]): Array[ChiSqTestResult] = chiSqTest(data.rdd)
+
   /**
    * Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a
    * continuous distribution. By comparing the largest difference between the empirical cumulative
@@ -212,4 +215,15 @@ object Statistics {
     : KolmogorovSmirnovTestResult = {
     KolmogorovSmirnovTest.testOneSample(data, distName, params: _*)
   }
+
+  /** Java-friendly version of [[kolmogorovSmirnovTest()]] */
+  @varargs
+  def kolmogorovSmirnovTest(
+      data: JavaDoubleRDD,
+      distName: String,
+      params: java.lang.Double*): KolmogorovSmirnovTestResult = {
+    val javaParams = params.asInstanceOf[Seq[Double]]
+    KolmogorovSmirnovTest.testOneSample(data.rdd.asInstanceOf[RDD[Double]],
+      distName, javaParams: _*)
+  }
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index d272a42c8576f..427be9430d820 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -124,6 +124,10 @@ public Boolean call(Tuple2<Long, Vector> tuple2) {
         }
     });
     assertEquals(topicDistributions.count(), nonEmptyCorpus.count());
+
+    // Check: javaTopTopicsPerDocuments
+    JavaRDD<scala.Tuple3<java.lang.Long, int[], java.lang.Double[]>> topTopics =
+      model.javaTopTopicsPerDocument(3);
   }
 
   @Test
@@ -160,11 +164,31 @@ public void OnlineOptimizerCompatibility() {
     assertEquals(roundedLocalTopicSummary.length, k);
   }
 
+  @Test
+  public void localLdaMethods() {
+    JavaRDD<Tuple2<Long, Vector>> docs = sc.parallelize(toyData, 2);
+    JavaPairRDD<Long, Vector> pairedDocs = JavaPairRDD.fromJavaRDD(docs);
+
+    // check: topicDistributions
+    assertEquals(toyModel.topicDistributions(pairedDocs).count(), pairedDocs.count());
+
+    // check: logPerplexity
+    double logPerplexity = toyModel.logPerplexity(pairedDocs);
+
+    // check: logLikelihood.
+    ArrayList<Tuple2<Long, Vector>> docsSingleWord = new ArrayList<Tuple2<Long, Vector>>();
+    docsSingleWord.add(new Tuple2<Long, Vector>(Long.valueOf(0), Vectors.dense(1.0, 0.0, 0.0)));
+    JavaPairRDD<Long, Vector> single = JavaPairRDD.fromJavaRDD(sc.parallelize(docsSingleWord));
+    double logLikelihood = toyModel.logLikelihood(single);
+  }
+
   private static int tinyK = LDASuite$.MODULE$.tinyK();
   private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize();
   private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics();
   private static Tuple2<int[], double[]>[] tinyTopicDescription =
       LDASuite$.MODULE$.tinyTopicDescription();
   private JavaPairRDD<Long, Vector> corpus;
+  private LocalLDAModel toyModel = LDASuite$.MODULE$.toyModel();
+  private ArrayList<Tuple2<Long, Vector>> toyData = LDASuite$.MODULE$.javaToyData();
 
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
index 62f7f26b7c98f..eb4e3698624bc 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
@@ -27,7 +27,12 @@
 import static org.junit.Assert.assertEquals;
 
 import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaDoubleRDD;
 import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.stat.test.ChiSqTestResult;
+import org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult;
 
 public class JavaStatisticsSuite implements Serializable {
   private transient JavaSparkContext sc;
@@ -53,4 +58,21 @@ public void testCorr() {
     // Check default method
     assertEquals(corr1, corr2);
   }
+
+  @Test
+  public void kolmogorovSmirnovTest() {
+    JavaDoubleRDD data = sc.parallelizeDoubles(Lists.newArrayList(0.2, 1.0, -1.0, 2.0));
+    KolmogorovSmirnovTestResult testResult1 = Statistics.kolmogorovSmirnovTest(data, "norm");
+    KolmogorovSmirnovTestResult testResult2 = Statistics.kolmogorovSmirnovTest(
+      data, "norm", 0.0, 1.0);
+  }
+
+  @Test
+  public void chiSqTest() {
+    JavaRDD<LabeledPoint> data = sc.parallelize(Lists.newArrayList(
+      new LabeledPoint(0.0, Vectors.dense(0.1, 2.3)),
+      new LabeledPoint(1.0, Vectors.dense(1.5, 5.1)),
+      new LabeledPoint(0.0, Vectors.dense(2.4, 8.1))));
+    ChiSqTestResult[] testResults = Statistics.chiSqTest(data);
+  }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index ce6a8eb8e8c46..926185e90bcf9 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.mllib.clustering
 
+import java.util.{ArrayList => JArrayList}
+
 import breeze.linalg.{DenseMatrix => BDM, argtopk, max, argmax}
 
 import org.apache.spark.SparkFunSuite
@@ -575,6 +577,17 @@ private[clustering] object LDASuite {
     Vectors.sparse(6, Array(4, 5), Array(1, 1))
   ).zipWithIndex.map { case (wordCounts, docId) => (docId.toLong, wordCounts) }
 
+  /** Used in the Java Test Suite */
+  def javaToyData: JArrayList[(java.lang.Long, Vector)] = {
+    val javaData = new JArrayList[(java.lang.Long, Vector)]
+    var i = 0
+    while (i < toyData.size) {
+      javaData.add((toyData(i)._1, toyData(i)._2))
+      i += 1
+    }
+    javaData
+  }
+
   def toyModel: LocalLDAModel = {
     val k = 2
     val vocabSize = 6

From a8d2f4c5f92210a09c846711bd7cc89a43e2edd2 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 13 Aug 2015 14:03:55 -0700
Subject: [PATCH 233/633] [SPARK-9942] [PYSPARK] [SQL] ignore exceptions while
 try to import pandas

If pandas is broken (can't be imported, raise other exceptions other than ImportError), pyspark can't be imported, we should ignore all the exceptions.

Author: Davies Liu <davies@databricks.com>

Closes #8173 from davies/fix_pandas.
---
 python/pyspark/sql/context.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index 917de24f3536b..0ef46c44644ab 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -39,7 +39,7 @@
 try:
     import pandas
     has_pandas = True
-except ImportError:
+except Exception:
     has_pandas = False
 
 __all__ = ["SQLContext", "HiveContext", "UDFRegistration"]

From c2520f501a200cf794bbe5dc9c385100f518d020 Mon Sep 17 00:00:00 2001
From: hyukjinkwon <gurwls223@gmail.com>
Date: Thu, 13 Aug 2015 16:07:03 -0700
Subject: [PATCH 234/633] [SPARK-9935] [SQL] EqualNotNull not processed in ORC

https://issues.apache.org/jira/browse/SPARK-9935

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #8163 from HyukjinKwon/master.
---
 .../scala/org/apache/spark/sql/hive/orc/OrcFilters.scala     | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
index 86142e5d66f37..b3d9f7f71a27d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcFilters.scala
@@ -107,6 +107,11 @@ private[orc] object OrcFilters extends Logging {
           .filter(isSearchableLiteral)
           .map(builder.equals(attribute, _))
 
+      case EqualNullSafe(attribute, value) =>
+        Option(value)
+          .filter(isSearchableLiteral)
+          .map(builder.nullSafeEquals(attribute, _))
+
       case LessThan(attribute, value) =>
         Option(value)
           .filter(isSearchableLiteral)

From 6c5858bc65c8a8602422b46bfa9cf0a1fb296b88 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 13 Aug 2015 16:52:17 -0700
Subject: [PATCH 235/633] [SPARK-9922] [ML] rename StringIndexerReverse to
 IndexToString

What `StringIndexerInverse` does is not strictly associated with `StringIndexer`, and the name is not clearly describing the transformation. Renaming to `IndexToString` might be better.

~~I also changed `invert` to `inverse` without arguments. `inputCol` and `outputCol` could be set after.~~
I also removed `invert`.

jkbradley holdenk

Author: Xiangrui Meng <meng@databricks.com>

Closes #8152 from mengxr/SPARK-9922.
---
 .../spark/ml/feature/StringIndexer.scala      | 34 +++++--------
 .../spark/ml/feature/StringIndexerSuite.scala | 50 +++++++++++++------
 2 files changed, 48 insertions(+), 36 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 9e4b0f0add612..9f6e7b6b6b274 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -24,7 +24,7 @@ import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.Transformer
-import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
+import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DoubleType, NumericType, StringType, StructType}
@@ -59,6 +59,8 @@ private[feature] trait StringIndexerBase extends Params with HasInputCol with Ha
  * If the input column is numeric, we cast it to string and index the string values.
  * The indices are in [0, numLabels), ordered by label frequencies.
  * So the most frequent label gets index 0.
+ *
+ * @see [[IndexToString]] for the inverse transformation
  */
 @Experimental
 class StringIndexer(override val uid: String) extends Estimator[StringIndexerModel]
@@ -170,34 +172,24 @@ class StringIndexerModel private[ml] (
     val copied = new StringIndexerModel(uid, labels)
     copyValues(copied, extra).setParent(parent)
   }
-
-  /**
-   * Return a model to perform the inverse transformation.
-   * Note: By default we keep the original columns during this transformation, so the inverse
-   * should only be used on new columns such as predicted labels.
-   */
-  def invert(inputCol: String, outputCol: String): StringIndexerInverse = {
-    new StringIndexerInverse()
-      .setInputCol(inputCol)
-      .setOutputCol(outputCol)
-      .setLabels(labels)
-  }
 }
 
 /**
  * :: Experimental ::
- * Transform a provided column back to the original input types using either the metadata
- * on the input column, or if provided using the labels supplied by the user.
- * Note: By default we keep the original columns during this transformation,
- * so the inverse should only be used on new columns such as predicted labels.
+ * A [[Transformer]] that maps a column of string indices back to a new column of corresponding
+ * string values using either the ML attributes of the input column, or if provided using the labels
+ * supplied by the user.
+ * All original columns are kept during transformation.
+ *
+ * @see [[StringIndexer]] for converting strings into indices
  */
 @Experimental
-class StringIndexerInverse private[ml] (
+class IndexToString private[ml] (
   override val uid: String) extends Transformer
     with HasInputCol with HasOutputCol {
 
   def this() =
-    this(Identifiable.randomUID("strIdxInv"))
+    this(Identifiable.randomUID("idxToStr"))
 
   /** @group setParam */
   def setInputCol(value: String): this.type = set(inputCol, value)
@@ -257,7 +249,7 @@ class StringIndexerInverse private[ml] (
     }
     val indexer = udf { index: Double =>
       val idx = index.toInt
-      if (0 <= idx && idx < values.size) {
+      if (0 <= idx && idx < values.length) {
         values(idx)
       } else {
         throw new SparkException(s"Unseen index: $index ??")
@@ -268,7 +260,7 @@ class StringIndexerInverse private[ml] (
       indexer(dataset($(inputCol)).cast(DoubleType)).as(outputColName))
   }
 
-  override def copy(extra: ParamMap): StringIndexerInverse = {
+  override def copy(extra: ParamMap): IndexToString = {
     defaultCopy(extra)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index 2d24914cb91f6..fa918ce64877c 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.ml.feature
 
-import org.apache.spark.SparkException
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
 import org.apache.spark.ml.param.ParamsSuite
 import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.functions.col
 
 class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
 
@@ -53,19 +54,6 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
     // a -> 0, b -> 2, c -> 1
     val expected = Set((0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0))
     assert(output === expected)
-    // convert reverse our transform
-    val reversed = indexer.invert("labelIndex", "label2")
-      .transform(transformed)
-      .select("id", "label2")
-    assert(df.collect().map(r => (r.getInt(0), r.getString(1))).toSet ===
-      reversed.collect().map(r => (r.getInt(0), r.getString(1))).toSet)
-    // Check invert using only metadata
-    val inverse2 = new StringIndexerInverse()
-      .setInputCol("labelIndex")
-      .setOutputCol("label2")
-    val reversed2 = inverse2.transform(transformed).select("id", "label2")
-    assert(df.collect().map(r => (r.getInt(0), r.getString(1))).toSet ===
-      reversed2.collect().map(r => (r.getInt(0), r.getString(1))).toSet)
   }
 
   test("StringIndexerUnseen") {
@@ -125,4 +113,36 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
     val df = sqlContext.range(0L, 10L)
     assert(indexerModel.transform(df).eq(df))
   }
+
+  test("IndexToString params") {
+    val idxToStr = new IndexToString()
+    ParamsSuite.checkParams(idxToStr)
+  }
+
+  test("IndexToString.transform") {
+    val labels = Array("a", "b", "c")
+    val df0 = sqlContext.createDataFrame(Seq(
+      (0, "a"), (1, "b"), (2, "c"), (0, "a")
+    )).toDF("index", "expected")
+
+    val idxToStr0 = new IndexToString()
+      .setInputCol("index")
+      .setOutputCol("actual")
+      .setLabels(labels)
+    idxToStr0.transform(df0).select("actual", "expected").collect().foreach {
+      case Row(actual, expected) =>
+        assert(actual === expected)
+    }
+
+    val attr = NominalAttribute.defaultAttr.withValues(labels)
+    val df1 = df0.select(col("index").as("indexWithAttr", attr.toMetadata()), col("expected"))
+
+    val idxToStr1 = new IndexToString()
+      .setInputCol("indexWithAttr")
+      .setOutputCol("actual")
+    idxToStr1.transform(df1).select("actual", "expected").collect().foreach {
+      case Row(actual, expected) =>
+        assert(actual === expected)
+    }
+  }
 }

From 693949ba4096c01a0b41da2542ff316823464a16 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 13 Aug 2015 17:33:37 -0700
Subject: [PATCH 236/633] [SPARK-8976] [PYSPARK] fix open mode in python3

This bug only happen on Python 3 and Windows.

I tested this manually with python 3 and disable python daemon, no unit test yet.

Author: Davies Liu <davies@databricks.com>

Closes #8181 from davies/open_mode.
---
 python/pyspark/worker.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/worker.py b/python/pyspark/worker.py
index 93df9002be377..42c2f8b75933e 100644
--- a/python/pyspark/worker.py
+++ b/python/pyspark/worker.py
@@ -146,5 +146,5 @@ def process():
     java_port = int(sys.stdin.readline())
     sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
     sock.connect(("127.0.0.1", java_port))
-    sock_file = sock.makefile("a+", 65536)
+    sock_file = sock.makefile("rwb", 65536)
     main(sock_file, sock_file)

From c50f97dafd2d5bf5a8351efcc1c8d3e2b87efc72 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 13 Aug 2015 17:35:11 -0700
Subject: [PATCH 237/633] [SPARK-9943] [SQL] deserialized UnsafeHashedRelation
 should be serializable

When the free memory in executor goes low, the cached broadcast objects need to serialized into disk, but currently the deserialized UnsafeHashedRelation can't be serialized , fail with NPE. This PR fixes that.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #8174 from davies/serialize_hashed.
---
 .../sql/execution/joins/HashedRelation.scala  | 93 ++++++++++++-------
 .../execution/joins/HashedRelationSuite.scala | 14 +++
 2 files changed, 74 insertions(+), 33 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index ea02076b41a6f..6c0196c21a0d1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.execution.SparkSqlSerializer
 import org.apache.spark.sql.execution.metric.LongSQLMetric
 import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.map.BytesToBytesMap
-import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
+import org.apache.spark.unsafe.memory.{MemoryLocation, ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.util.Utils
 import org.apache.spark.util.collection.CompactBuffer
 import org.apache.spark.{SparkConf, SparkEnv}
@@ -247,40 +247,67 @@ private[joins] final class UnsafeHashedRelation(
   }
 
   override def writeExternal(out: ObjectOutput): Unit = Utils.tryOrIOException {
-    out.writeInt(hashTable.size())
-
-    val iter = hashTable.entrySet().iterator()
-    while (iter.hasNext) {
-      val entry = iter.next()
-      val key = entry.getKey
-      val values = entry.getValue
-
-      // write all the values as single byte array
-      var totalSize = 0L
-      var i = 0
-      while (i < values.length) {
-        totalSize += values(i).getSizeInBytes + 4 + 4
-        i += 1
+    if (binaryMap != null) {
+      // This could happen when a cached broadcast object need to be dumped into disk to free memory
+      out.writeInt(binaryMap.numElements())
+
+      var buffer = new Array[Byte](64)
+      def write(addr: MemoryLocation, length: Int): Unit = {
+        if (buffer.length < length) {
+          buffer = new Array[Byte](length)
+        }
+        Platform.copyMemory(addr.getBaseObject, addr.getBaseOffset,
+          buffer, Platform.BYTE_ARRAY_OFFSET, length)
+        out.write(buffer, 0, length)
       }
-      assert(totalSize < Integer.MAX_VALUE, "values are too big")
-
-      // [key size] [values size] [key bytes] [values bytes]
-      out.writeInt(key.getSizeInBytes)
-      out.writeInt(totalSize.toInt)
-      out.write(key.getBytes)
-      i = 0
-      while (i < values.length) {
-        // [num of fields] [num of bytes] [row bytes]
-        // write the integer in native order, so they can be read by UNSAFE.getInt()
-        if (ByteOrder.nativeOrder() == ByteOrder.BIG_ENDIAN) {
-          out.writeInt(values(i).numFields())
-          out.writeInt(values(i).getSizeInBytes)
-        } else {
-          out.writeInt(Integer.reverseBytes(values(i).numFields()))
-          out.writeInt(Integer.reverseBytes(values(i).getSizeInBytes))
+
+      val iter = binaryMap.iterator()
+      while (iter.hasNext) {
+        val loc = iter.next()
+        // [key size] [values size] [key bytes] [values bytes]
+        out.writeInt(loc.getKeyLength)
+        out.writeInt(loc.getValueLength)
+        write(loc.getKeyAddress, loc.getKeyLength)
+        write(loc.getValueAddress, loc.getValueLength)
+      }
+
+    } else {
+      assert(hashTable != null)
+      out.writeInt(hashTable.size())
+
+      val iter = hashTable.entrySet().iterator()
+      while (iter.hasNext) {
+        val entry = iter.next()
+        val key = entry.getKey
+        val values = entry.getValue
+
+        // write all the values as single byte array
+        var totalSize = 0L
+        var i = 0
+        while (i < values.length) {
+          totalSize += values(i).getSizeInBytes + 4 + 4
+          i += 1
+        }
+        assert(totalSize < Integer.MAX_VALUE, "values are too big")
+
+        // [key size] [values size] [key bytes] [values bytes]
+        out.writeInt(key.getSizeInBytes)
+        out.writeInt(totalSize.toInt)
+        out.write(key.getBytes)
+        i = 0
+        while (i < values.length) {
+          // [num of fields] [num of bytes] [row bytes]
+          // write the integer in native order, so they can be read by UNSAFE.getInt()
+          if (ByteOrder.nativeOrder() == ByteOrder.BIG_ENDIAN) {
+            out.writeInt(values(i).numFields())
+            out.writeInt(values(i).getSizeInBytes)
+          } else {
+            out.writeInt(Integer.reverseBytes(values(i).numFields()))
+            out.writeInt(Integer.reverseBytes(values(i).getSizeInBytes))
+          }
+          out.write(values(i).getBytes)
+          i += 1
         }
-        out.write(values(i).getBytes)
-        i += 1
       }
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index c635b2d51f464..d33a967093ca5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -102,6 +102,14 @@ class HashedRelationSuite extends SparkFunSuite {
     assert(hashed2.get(toUnsafe(InternalRow(10))) === null)
     assert(hashed2.get(unsafeData(2)) === data2)
     assert(numDataRows.value.value === data.length)
+
+    val os2 = new ByteArrayOutputStream()
+    val out2 = new ObjectOutputStream(os2)
+    hashed2.asInstanceOf[UnsafeHashedRelation].writeExternal(out2)
+    out2.flush()
+    // This depends on that the order of items in BytesToBytesMap.iterator() is exactly the same
+    // as they are inserted
+    assert(java.util.Arrays.equals(os2.toByteArray, os.toByteArray))
   }
 
   test("test serialization empty hash map") {
@@ -119,5 +127,11 @@ class HashedRelationSuite extends SparkFunSuite {
     val toUnsafe = UnsafeProjection.create(schema)
     val row = toUnsafe(InternalRow(0))
     assert(hashed2.get(row) === null)
+
+    val os2 = new ByteArrayOutputStream()
+    val out2 = new ObjectOutputStream(os2)
+    hashed2.writeExternal(out2)
+    out2.flush()
+    assert(java.util.Arrays.equals(os2.toByteArray, os.toByteArray))
   }
 }

From 8187b3ae477e2b2987ae9acc5368d57b1d5653b2 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 13 Aug 2015 17:42:01 -0700
Subject: [PATCH 238/633] [SPARK-9580] [SQL] Replace singletons in SQL tests

A fundamental limitation of the existing SQL tests is that *there is simply no way to create your own `SparkContext`*. This is a serious limitation because the user may wish to use a different master or config. As a case in point, `BroadcastJoinSuite` is entirely commented out because there is no way to make it pass with the existing infrastructure.

This patch removes the singletons `TestSQLContext` and `TestData`, and instead introduces a `SharedSQLContext` that starts a context per suite. Unfortunately the singletons were so ingrained in the SQL tests that this patch necessarily needed to touch *all* the SQL test files.

<!-- Reviewable:start -->
[<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/8111)
<!-- Reviewable:end -->

Author: Andrew Or <andrew@databricks.com>

Closes #8111 from andrewor14/sql-tests-refactor.
---
 project/MimaExcludes.scala                    |  10 +
 project/SparkBuild.scala                      |  16 +-
 .../analysis/AnalysisErrorSuite.scala         |   6 +-
 .../org/apache/spark/sql/SQLContext.scala     |  97 +-----
 .../org/apache/spark/sql/SQLImplicits.scala   | 123 ++++++++
 .../spark/sql/JavaApplySchemaSuite.java       |  10 +-
 .../apache/spark/sql/JavaDataFrameSuite.java  |  39 +--
 .../org/apache/spark/sql/JavaUDFSuite.java    |  10 +-
 .../spark/sql/sources/JavaSaveLoadSuite.java  |  15 +-
 .../apache/spark/sql/CachedTableSuite.scala   |  14 +-
 .../spark/sql/ColumnExpressionSuite.scala     |  37 +--
 .../spark/sql/DataFrameAggregateSuite.scala   |  10 +-
 .../spark/sql/DataFrameFunctionsSuite.scala   |  18 +-
 .../spark/sql/DataFrameImplicitsSuite.scala   |   6 +-
 .../apache/spark/sql/DataFrameJoinSuite.scala |  10 +-
 .../spark/sql/DataFrameNaFunctionsSuite.scala |   6 +-
 .../apache/spark/sql/DataFrameStatSuite.scala |  19 +-
 .../org/apache/spark/sql/DataFrameSuite.scala |  12 +-
 .../spark/sql/DataFrameTungstenSuite.scala    |   8 +-
 .../apache/spark/sql/DateFunctionsSuite.scala |  13 +-
 .../org/apache/spark/sql/JoinSuite.scala      |  47 ++-
 .../apache/spark/sql/JsonFunctionsSuite.scala |   6 +-
 .../apache/spark/sql/ListTablesSuite.scala    |  15 +-
 .../spark/sql/MathExpressionsSuite.scala      |  28 +-
 .../org/apache/spark/sql/QueryTest.scala      |   6 -
 .../scala/org/apache/spark/sql/RowSuite.scala |   7 +-
 .../org/apache/spark/sql/SQLConfSuite.scala   |  19 +-
 .../apache/spark/sql/SQLContextSuite.scala    |  13 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  29 +-
 .../sql/ScalaReflectionRelationSuite.scala    |  17 +-
 .../apache/spark/sql/SerializationSuite.scala |   9 +-
 .../spark/sql/StringFunctionsSuite.scala      |   7 +-
 .../scala/org/apache/spark/sql/TestData.scala | 197 ------------
 .../scala/org/apache/spark/sql/UDFSuite.scala |  39 ++-
 .../spark/sql/UserDefinedTypeSuite.scala      |   9 +-
 .../columnar/InMemoryColumnarQuerySuite.scala |  14 +-
 .../columnar/PartitionBatchPruningSuite.scala |  31 +-
 .../spark/sql/execution/ExchangeSuite.scala   |   3 +-
 .../spark/sql/execution/PlannerSuite.scala    |  39 +--
 .../execution/RowFormatConvertersSuite.scala  |  22 +-
 .../spark/sql/execution/SortSuite.scala       |   3 +-
 .../spark/sql/execution/SparkPlanTest.scala   |  36 +--
 .../sql/execution/TungstenSortSuite.scala     |  21 +-
 .../UnsafeFixedWidthAggregationMapSuite.scala |  14 +-
 .../UnsafeKVExternalSorterSuite.scala         |   7 +-
 .../TungstenAggregationIteratorSuite.scala    |   7 +-
 .../datasources/json/JsonSuite.scala          |  42 ++-
 .../datasources/json/TestJsonData.scala       |  40 ++-
 .../ParquetAvroCompatibilitySuite.scala       |  14 +-
 .../parquet/ParquetCompatibilityTest.scala    |  10 +-
 .../parquet/ParquetFilterSuite.scala          |   8 +-
 .../datasources/parquet/ParquetIOSuite.scala  |   6 +-
 .../ParquetPartitionDiscoverySuite.scala      |  12 +-
 .../ParquetProtobufCompatibilitySuite.scala   |   7 +-
 .../parquet/ParquetQuerySuite.scala           |  43 ++-
 .../parquet/ParquetSchemaSuite.scala          |   6 +-
 .../datasources/parquet/ParquetTest.scala     |  15 +-
 .../ParquetThriftCompatibilitySuite.scala     |   8 +-
 .../sql/execution/debug/DebuggingSuite.scala  |   6 +-
 .../execution/joins/HashedRelationSuite.scala |  10 +-
 .../sql/execution/joins/InnerJoinSuite.scala  | 248 ++++++++-------
 .../sql/execution/joins/OuterJoinSuite.scala  | 125 ++++----
 .../sql/execution/joins/SemiJoinSuite.scala   | 113 +++----
 .../execution/metric/SQLMetricsSuite.scala    |  62 ++--
 .../sql/execution/ui/SQLListenerSuite.scala   |  14 +-
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala |   9 +-
 .../spark/sql/jdbc/JDBCWriteSuite.scala       |  20 +-
 .../sources/CreateTableAsSelectSuite.scala    |  18 +-
 .../sql/sources/DDLSourceLoadSuite.scala      |   3 +-
 .../spark/sql/sources/DDLTestSuite.scala      |  11 +-
 .../spark/sql/sources/DataSourceTest.scala    |  17 +-
 .../spark/sql/sources/FilteredScanSuite.scala |   9 +-
 .../spark/sql/sources/InsertSuite.scala       |  30 +-
 .../sql/sources/PartitionedWriteSuite.scala   |  14 +-
 .../spark/sql/sources/PrunedScanSuite.scala   |  11 +-
 .../spark/sql/sources/SaveLoadSuite.scala     |  26 +-
 .../spark/sql/sources/TableScanSuite.scala    |  15 +-
 .../apache/spark/sql/test/SQLTestData.scala   | 290 ++++++++++++++++++
 .../apache/spark/sql/test/SQLTestUtils.scala  |  92 +++++-
 .../spark/sql/test/SharedSQLContext.scala     |  68 ++++
 .../spark/sql/test/TestSQLContext.scala       |  46 ++-
 .../hive/thriftserver/UISeleniumSuite.scala   |   2 -
 .../sql/hive/HiveMetastoreCatalogSuite.scala  |   5 +-
 .../spark/sql/hive/HiveParquetSuite.scala     |  11 +-
 .../sql/hive/MetastoreDataSourcesSuite.scala  |   4 +-
 .../spark/sql/hive/MultiDatabaseSuite.scala   |   5 +-
 .../hive/ParquetHiveCompatibilitySuite.scala  |   3 +-
 .../org/apache/spark/sql/hive/UDFSuite.scala  |   4 +-
 .../execution/AggregationQuerySuite.scala     |   9 +-
 .../sql/hive/execution/HiveExplainSuite.scala |   4 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |   3 +-
 .../execution/ScriptTransformationSuite.scala |   3 +-
 .../apache/spark/sql/hive/orc/OrcTest.scala   |   4 +-
 .../apache/spark/sql/hive/parquetSuites.scala |   3 +-
 .../CommitFailureTestRelationSuite.scala      |   6 +-
 .../sql/sources/hadoopFsRelationSuites.scala  |   5 +-
 96 files changed, 1460 insertions(+), 1203 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
 delete mode 100644 sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
 rename sql/core/src/{main => test}/scala/org/apache/spark/sql/test/TestSQLContext.scala (54%)

diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 784f83c10e023..88745dc086a04 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -178,6 +178,16 @@ object MimaExcludes {
             // SPARK-4751 Dynamic allocation for standalone mode
             ProblemFilters.exclude[MissingMethodProblem](
               "org.apache.spark.SparkContext.supportDynamicAllocation")
+          ) ++ Seq(
+            // SPARK-9580: Remove SQL test singletons
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.test.LocalSQLContext$SQLSession"),
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.test.LocalSQLContext"),
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.test.TestSQLContext"),
+            ProblemFilters.exclude[MissingClassProblem](
+              "org.apache.spark.sql.test.TestSQLContext$")
           ) ++ Seq(
             // SPARK-9704 Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
             ProblemFilters.exclude[IncompatibleResultTypeProblem](
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 74f815f941d5b..04e0d49b178cf 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -319,6 +319,8 @@ object SQL {
   lazy val settings = Seq(
     initialCommands in console :=
       """
+        |import org.apache.spark.SparkContext
+        |import org.apache.spark.sql.SQLContext
         |import org.apache.spark.sql.catalyst.analysis._
         |import org.apache.spark.sql.catalyst.dsl._
         |import org.apache.spark.sql.catalyst.errors._
@@ -328,9 +330,14 @@ object SQL {
         |import org.apache.spark.sql.catalyst.util._
         |import org.apache.spark.sql.execution
         |import org.apache.spark.sql.functions._
-        |import org.apache.spark.sql.test.TestSQLContext._
-        |import org.apache.spark.sql.types._""".stripMargin,
-    cleanupCommands in console := "sparkContext.stop()"
+        |import org.apache.spark.sql.types._
+        |
+        |val sc = new SparkContext("local[*]", "dev-shell")
+        |val sqlContext = new SQLContext(sc)
+        |import sqlContext.implicits._
+        |import sqlContext._
+      """.stripMargin,
+    cleanupCommands in console := "sc.stop()"
   )
 }
 
@@ -340,8 +347,6 @@ object Hive {
     javaOptions += "-XX:MaxPermSize=256m",
     // Specially disable assertions since some Hive tests fail them
     javaOptions in Test := (javaOptions in Test).value.filterNot(_ == "-ea"),
-    // Multiple queries rely on the TestHive singleton. See comments there for more details.
-    parallelExecution in Test := false,
     // Supporting all SerDes requires us to depend on deprecated APIs, so we turn off the warnings
     // only for this subproject.
     scalacOptions <<= scalacOptions map { currentOpts: Seq[String] =>
@@ -349,6 +354,7 @@ object Hive {
     },
     initialCommands in console :=
       """
+        |import org.apache.spark.SparkContext
         |import org.apache.spark.sql.catalyst.analysis._
         |import org.apache.spark.sql.catalyst.dsl._
         |import org.apache.spark.sql.catalyst.errors._
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index 63b475b6366c2..f60d11c988ef8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -17,14 +17,10 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
-import org.scalatest.BeforeAndAfter
-
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.Inner
-import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.types._
@@ -42,7 +38,7 @@ case class UnresolvedTestPlan() extends LeafNode {
   override def output: Seq[Attribute] = Nil
 }
 
-class AnalysisErrorSuite extends AnalysisTest with BeforeAndAfter {
+class AnalysisErrorSuite extends AnalysisTest {
   import TestRelations._
 
   def errorTest(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 4bf00b3399e7a..53de10d5fa9da 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -23,7 +23,6 @@ import java.util.concurrent.atomic.AtomicReference
 
 import scala.collection.JavaConversions._
 import scala.collection.immutable
-import scala.language.implicitConversions
 import scala.reflect.runtime.universe.TypeTag
 import scala.util.control.NonFatal
 
@@ -41,10 +40,9 @@ import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.apache.spark.sql.catalyst.{InternalRow, ParserDialect, _}
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.datasources._
+import org.apache.spark.sql.execution.ui.{SQLListener, SQLTab}
 import org.apache.spark.sql.sources.BaseRelation
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.execution.ui.{SQLListener, SQLTab}
-import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.util.Utils
 
 /**
@@ -334,97 +332,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * @since 1.3.0
    */
   @Experimental
-  object implicits extends Serializable {
-    // scalastyle:on
-
-    /**
-     * Converts $"col name" into an [[Column]].
-     * @since 1.3.0
-     */
-    implicit class StringToColumn(val sc: StringContext) {
-      def $(args: Any*): ColumnName = {
-        new ColumnName(sc.s(args: _*))
-      }
-    }
-
-    /**
-     * An implicit conversion that turns a Scala `Symbol` into a [[Column]].
-     * @since 1.3.0
-     */
-    implicit def symbolToColumn(s: Symbol): ColumnName = new ColumnName(s.name)
-
-    /**
-     * Creates a DataFrame from an RDD of case classes or tuples.
-     * @since 1.3.0
-     */
-    implicit def rddToDataFrameHolder[A <: Product : TypeTag](rdd: RDD[A]): DataFrameHolder = {
-      DataFrameHolder(self.createDataFrame(rdd))
-    }
-
-    /**
-     * Creates a DataFrame from a local Seq of Product.
-     * @since 1.3.0
-     */
-    implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder =
-    {
-      DataFrameHolder(self.createDataFrame(data))
-    }
-
-    // Do NOT add more implicit conversions. They are likely to break source compatibility by
-    // making existing implicit conversions ambiguous. In particular, RDD[Double] is dangerous
-    // because of [[DoubleRDDFunctions]].
-
-    /**
-     * Creates a single column DataFrame from an RDD[Int].
-     * @since 1.3.0
-     */
-    implicit def intRddToDataFrameHolder(data: RDD[Int]): DataFrameHolder = {
-      val dataType = IntegerType
-      val rows = data.mapPartitions { iter =>
-        val row = new SpecificMutableRow(dataType :: Nil)
-        iter.map { v =>
-          row.setInt(0, v)
-          row: InternalRow
-        }
-      }
-      DataFrameHolder(
-        self.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
-    }
-
-    /**
-     * Creates a single column DataFrame from an RDD[Long].
-     * @since 1.3.0
-     */
-    implicit def longRddToDataFrameHolder(data: RDD[Long]): DataFrameHolder = {
-      val dataType = LongType
-      val rows = data.mapPartitions { iter =>
-        val row = new SpecificMutableRow(dataType :: Nil)
-        iter.map { v =>
-          row.setLong(0, v)
-          row: InternalRow
-        }
-      }
-      DataFrameHolder(
-        self.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
-    }
-
-    /**
-     * Creates a single column DataFrame from an RDD[String].
-     * @since 1.3.0
-     */
-    implicit def stringRddToDataFrameHolder(data: RDD[String]): DataFrameHolder = {
-      val dataType = StringType
-      val rows = data.mapPartitions { iter =>
-        val row = new SpecificMutableRow(dataType :: Nil)
-        iter.map { v =>
-          row.update(0, UTF8String.fromString(v))
-          row: InternalRow
-        }
-      }
-      DataFrameHolder(
-        self.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
-    }
+  object implicits extends SQLImplicits with Serializable {
+    protected override def _sqlContext: SQLContext = self
   }
+  // scalastyle:on
 
   /**
    * :: Experimental ::
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
new file mode 100644
index 0000000000000..5f82372700f2c
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
@@ -0,0 +1,123 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import scala.language.implicitConversions
+import scala.reflect.runtime.universe.TypeTag
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
+import org.apache.spark.sql.types.StructField
+import org.apache.spark.unsafe.types.UTF8String
+
+/**
+ * A collection of implicit methods for converting common Scala objects into [[DataFrame]]s.
+ */
+private[sql] abstract class SQLImplicits {
+  protected def _sqlContext: SQLContext
+
+  /**
+   * Converts $"col name" into an [[Column]].
+   * @since 1.3.0
+   */
+  implicit class StringToColumn(val sc: StringContext) {
+    def $(args: Any*): ColumnName = {
+      new ColumnName(sc.s(args: _*))
+    }
+  }
+
+  /**
+   * An implicit conversion that turns a Scala `Symbol` into a [[Column]].
+   * @since 1.3.0
+   */
+  implicit def symbolToColumn(s: Symbol): ColumnName = new ColumnName(s.name)
+
+  /**
+   * Creates a DataFrame from an RDD of case classes or tuples.
+   * @since 1.3.0
+   */
+  implicit def rddToDataFrameHolder[A <: Product : TypeTag](rdd: RDD[A]): DataFrameHolder = {
+    DataFrameHolder(_sqlContext.createDataFrame(rdd))
+  }
+
+  /**
+   * Creates a DataFrame from a local Seq of Product.
+   * @since 1.3.0
+   */
+  implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder =
+  {
+    DataFrameHolder(_sqlContext.createDataFrame(data))
+  }
+
+  // Do NOT add more implicit conversions. They are likely to break source compatibility by
+  // making existing implicit conversions ambiguous. In particular, RDD[Double] is dangerous
+  // because of [[DoubleRDDFunctions]].
+
+  /**
+   * Creates a single column DataFrame from an RDD[Int].
+   * @since 1.3.0
+   */
+  implicit def intRddToDataFrameHolder(data: RDD[Int]): DataFrameHolder = {
+    val dataType = IntegerType
+    val rows = data.mapPartitions { iter =>
+      val row = new SpecificMutableRow(dataType :: Nil)
+      iter.map { v =>
+        row.setInt(0, v)
+        row: InternalRow
+      }
+    }
+    DataFrameHolder(
+      _sqlContext.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
+  }
+
+  /**
+   * Creates a single column DataFrame from an RDD[Long].
+   * @since 1.3.0
+   */
+  implicit def longRddToDataFrameHolder(data: RDD[Long]): DataFrameHolder = {
+    val dataType = LongType
+    val rows = data.mapPartitions { iter =>
+      val row = new SpecificMutableRow(dataType :: Nil)
+      iter.map { v =>
+        row.setLong(0, v)
+        row: InternalRow
+      }
+    }
+    DataFrameHolder(
+      _sqlContext.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
+  }
+
+  /**
+   * Creates a single column DataFrame from an RDD[String].
+   * @since 1.3.0
+   */
+  implicit def stringRddToDataFrameHolder(data: RDD[String]): DataFrameHolder = {
+    val dataType = StringType
+    val rows = data.mapPartitions { iter =>
+      val row = new SpecificMutableRow(dataType :: Nil)
+      iter.map { v =>
+        row.update(0, UTF8String.fromString(v))
+        row: InternalRow
+      }
+    }
+    DataFrameHolder(
+      _sqlContext.internalCreateDataFrame(rows, StructType(StructField("_1", dataType) :: Nil)))
+  }
+}
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
index e912eb835d169..bf693c7c393f6 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
@@ -27,6 +27,7 @@
 import org.junit.Before;
 import org.junit.Test;
 
+import org.apache.spark.SparkContext;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -34,7 +35,6 @@
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.RowFactory;
 import org.apache.spark.sql.SQLContext;
-import org.apache.spark.sql.test.TestSQLContext$;
 import org.apache.spark.sql.types.DataTypes;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
@@ -48,14 +48,16 @@ public class JavaApplySchemaSuite implements Serializable {
 
   @Before
   public void setUp() {
-    sqlContext = TestSQLContext$.MODULE$;
-    javaCtx = new JavaSparkContext(sqlContext.sparkContext());
+    SparkContext context = new SparkContext("local[*]", "testing");
+    javaCtx = new JavaSparkContext(context);
+    sqlContext = new SQLContext(context);
   }
 
   @After
   public void tearDown() {
-    javaCtx = null;
+    sqlContext.sparkContext().stop();
     sqlContext = null;
+    javaCtx = null;
   }
 
   public static class Person implements Serializable {
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 7302361ab9fdb..7abdd3db80341 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -17,44 +17,45 @@
 
 package test.org.apache.spark.sql;
 
+import java.io.Serializable;
+import java.util.Arrays;
+import java.util.Comparator;
+import java.util.List;
+import java.util.Map;
+
+import scala.collection.JavaConversions;
+import scala.collection.Seq;
+
 import com.google.common.collect.ImmutableMap;
 import com.google.common.primitives.Ints;
+import org.junit.*;
 
+import org.apache.spark.SparkContext;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.sql.*;
+import static org.apache.spark.sql.functions.*;
 import org.apache.spark.sql.test.TestSQLContext;
-import org.apache.spark.sql.test.TestSQLContext$;
 import org.apache.spark.sql.types.*;
-import org.junit.*;
-
-import scala.collection.JavaConversions;
-import scala.collection.Seq;
-
-import java.io.Serializable;
-import java.util.Arrays;
-import java.util.Comparator;
-import java.util.List;
-import java.util.Map;
-
-import static org.apache.spark.sql.functions.*;
 
 public class JavaDataFrameSuite {
   private transient JavaSparkContext jsc;
-  private transient SQLContext context;
+  private transient TestSQLContext context;
 
   @Before
   public void setUp() {
     // Trigger static initializer of TestData
-    TestData$.MODULE$.testData();
-    jsc = new JavaSparkContext(TestSQLContext.sparkContext());
-    context = TestSQLContext$.MODULE$;
+    SparkContext sc = new SparkContext("local[*]", "testing");
+    jsc = new JavaSparkContext(sc);
+    context = new TestSQLContext(sc);
+    context.loadTestData();
   }
 
   @After
   public void tearDown() {
-    jsc = null;
+    context.sparkContext().stop();
     context = null;
+    jsc = null;
   }
 
   @Test
@@ -230,7 +231,7 @@ public void testCovariance() {
 
   @Test
   public void testSampleBy() {
-    DataFrame df = context.range(0, 100).select(col("id").mod(3).as("key"));
+    DataFrame df = context.range(0, 100, 1, 2).select(col("id").mod(3).as("key"));
     DataFrame sampled = df.stat().<Integer>sampleBy("key", ImmutableMap.of(0, 0.1, 1, 0.2), 0L);
     Row[] actual = sampled.groupBy("key").count().orderBy("key").collect();
     Row[] expected = new Row[] {RowFactory.create(0, 5), RowFactory.create(1, 8)};
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
index 79d92734ff375..bb02b58cca9be 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
@@ -23,12 +23,12 @@
 import org.junit.Before;
 import org.junit.Test;
 
+import org.apache.spark.SparkContext;
 import org.apache.spark.sql.Row;
 import org.apache.spark.sql.SQLContext;
 import org.apache.spark.sql.api.java.UDF1;
 import org.apache.spark.sql.api.java.UDF2;
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.sql.test.TestSQLContext$;
 import org.apache.spark.sql.types.DataTypes;
 
 // The test suite itself is Serializable so that anonymous Function implementations can be
@@ -40,12 +40,16 @@ public class JavaUDFSuite implements Serializable {
 
   @Before
   public void setUp() {
-    sqlContext = TestSQLContext$.MODULE$;
-    sc = new JavaSparkContext(sqlContext.sparkContext());
+    SparkContext _sc = new SparkContext("local[*]", "testing");
+    sqlContext = new SQLContext(_sc);
+    sc = new JavaSparkContext(_sc);
   }
 
   @After
   public void tearDown() {
+    sqlContext.sparkContext().stop();
+    sqlContext = null;
+    sc = null;
   }
 
   @SuppressWarnings("unchecked")
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
index 2706e01bd28af..6f9e7f68dc39c 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
@@ -21,13 +21,14 @@
 import java.io.IOException;
 import java.util.*;
 
+import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
+import org.apache.spark.SparkContext;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.sql.test.TestSQLContext$;
 import org.apache.spark.sql.*;
 import org.apache.spark.sql.types.DataTypes;
 import org.apache.spark.sql.types.StructField;
@@ -52,8 +53,9 @@ private void checkAnswer(DataFrame actual, List<Row> expected) {
 
   @Before
   public void setUp() throws IOException {
-    sqlContext = TestSQLContext$.MODULE$;
-    sc = new JavaSparkContext(sqlContext.sparkContext());
+    SparkContext _sc = new SparkContext("local[*]", "testing");
+    sqlContext = new SQLContext(_sc);
+    sc = new JavaSparkContext(_sc);
 
     originalDefaultSource = sqlContext.conf().defaultDataSourceName();
     path =
@@ -71,6 +73,13 @@ public void setUp() throws IOException {
     df.registerTempTable("jsonTable");
   }
 
+  @After
+  public void tearDown() {
+    sqlContext.sparkContext().stop();
+    sqlContext = null;
+    sc = null;
+  }
+
   @Test
   public void saveAndLoad() {
     Map<String, String> options = new HashMap<String, String>();
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index a88df91b1001c..af7590c3d3c17 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -18,24 +18,20 @@
 package org.apache.spark.sql
 
 import scala.concurrent.duration._
-import scala.language.{implicitConversions, postfixOps}
+import scala.language.postfixOps
 
 import org.scalatest.concurrent.Eventually._
 
 import org.apache.spark.Accumulators
-import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.columnar._
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.storage.{StorageLevel, RDDBlockId}
 
-case class BigData(s: String)
+private case class BigData(s: String)
 
-class CachedTableSuite extends QueryTest {
-  TestData // Load test tables.
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-  import ctx.sql
+class CachedTableSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   def rddIdOf(tableName: String): Int = {
     val executedPlan = ctx.table(tableName).queryExecution.executedPlan
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 6a09a3b72c081..ee74e3e83da5a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -21,16 +21,20 @@ import org.scalatest.Matchers._
 
 import org.apache.spark.sql.execution.{Project, TungstenProject}
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.test.SQLTestUtils
 
-class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
-  import org.apache.spark.sql.TestData._
+class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-
-  override def sqlContext(): SQLContext = ctx
+  private lazy val booleanData = {
+    ctx.createDataFrame(ctx.sparkContext.parallelize(
+      Row(false, false) ::
+      Row(false, true) ::
+      Row(true, false) ::
+      Row(true, true) :: Nil),
+      StructType(Seq(StructField("a", BooleanType), StructField("b", BooleanType))))
+  }
 
   test("column names with space") {
     val df = Seq((1, "a")).toDF("name with space", "name.with.dot")
@@ -258,7 +262,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
       nullStrings.collect().toSeq.filter(r => r.getString(1) eq null))
 
     checkAnswer(
-      ctx.sql("select isnull(null), isnull(1)"),
+      sql("select isnull(null), isnull(1)"),
       Row(true, false))
   }
 
@@ -268,7 +272,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
       nullStrings.collect().toSeq.filter(r => r.getString(1) ne null))
 
     checkAnswer(
-      ctx.sql("select isnotnull(null), isnotnull('a')"),
+      sql("select isnotnull(null), isnotnull('a')"),
       Row(false, true))
   }
 
@@ -289,7 +293,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
       Row(true, true) :: Row(true, true) :: Row(false, false) :: Row(false, false) :: Nil)
 
     checkAnswer(
-      ctx.sql("select isnan(15), isnan('invalid')"),
+      sql("select isnan(15), isnan('invalid')"),
       Row(false, false))
   }
 
@@ -309,7 +313,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
     )
     testData.registerTempTable("t")
     checkAnswer(
-      ctx.sql(
+      sql(
         "select nanvl(a, 5), nanvl(b, 10), nanvl(10, b), nanvl(c, null), nanvl(d, 10), " +
           " nanvl(b, e), nanvl(e, f) from t"),
       Row(null, 3.0, 10.0, null, Double.PositiveInfinity, 3.0, 1.0)
@@ -433,13 +437,6 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
     }
   }
 
-  val booleanData = ctx.createDataFrame(ctx.sparkContext.parallelize(
-    Row(false, false) ::
-      Row(false, true) ::
-      Row(true, false) ::
-      Row(true, true) :: Nil),
-    StructType(Seq(StructField("a", BooleanType), StructField("b", BooleanType))))
-
   test("&&") {
     checkAnswer(
       booleanData.filter($"a" && true),
@@ -523,7 +520,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
     )
 
     checkAnswer(
-      ctx.sql("SELECT upper('aB'), ucase('cDe')"),
+      sql("SELECT upper('aB'), ucase('cDe')"),
       Row("AB", "CDE"))
   }
 
@@ -544,7 +541,7 @@ class ColumnExpressionSuite extends QueryTest with SQLTestUtils {
     )
 
     checkAnswer(
-      ctx.sql("SELECT lower('aB'), lcase('cDe')"),
+      sql("SELECT lower('aB'), lcase('cDe')"),
       Row("ab", "cde"))
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
index f9cff7440a76e..72cf7aab0b977 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
@@ -17,15 +17,13 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.{BinaryType, DecimalType}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types.DecimalType
 
 
-class DataFrameAggregateSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class DataFrameAggregateSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("groupBy") {
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 03116a374f3be..9d965258e389d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -17,17 +17,15 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 /**
  * Test suite for functions in [[org.apache.spark.sql.functions]].
  */
-class DataFrameFunctionsSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class DataFrameFunctionsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("array with column name") {
     val df = Seq((0, 1)).toDF("a", "b")
@@ -119,11 +117,11 @@ class DataFrameFunctionsSuite extends QueryTest {
 
   test("constant functions") {
     checkAnswer(
-      ctx.sql("SELECT E()"),
+      sql("SELECT E()"),
       Row(scala.math.E)
     )
     checkAnswer(
-      ctx.sql("SELECT PI()"),
+      sql("SELECT PI()"),
       Row(scala.math.Pi)
     )
   }
@@ -153,7 +151,7 @@ class DataFrameFunctionsSuite extends QueryTest {
 
   test("nvl function") {
     checkAnswer(
-      ctx.sql("SELECT nvl(null, 'x'), nvl('y', 'x'), nvl(null, null)"),
+      sql("SELECT nvl(null, 'x'), nvl('y', 'x'), nvl(null, null)"),
       Row("x", "y", null))
   }
 
@@ -222,7 +220,7 @@ class DataFrameFunctionsSuite extends QueryTest {
       Row(-1)
     )
     checkAnswer(
-      ctx.sql("SELECT least(a, 2) as l from testData2 order by l"),
+      sql("SELECT least(a, 2) as l from testData2 order by l"),
       Seq(Row(1), Row(1), Row(2), Row(2), Row(2), Row(2))
     )
   }
@@ -233,7 +231,7 @@ class DataFrameFunctionsSuite extends QueryTest {
       Row(3)
     )
     checkAnswer(
-      ctx.sql("SELECT greatest(a, 2) as g from testData2 order by g"),
+      sql("SELECT greatest(a, 2) as g from testData2 order by g"),
       Seq(Row(2), Row(2), Row(2), Row(2), Row(3), Row(3))
     )
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
index fbb30706a4943..e5d7d63441a6b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.sql
 
-class DataFrameImplicitsSuite extends QueryTest {
+import org.apache.spark.sql.test.SharedSQLContext
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class DataFrameImplicitsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("RDD of tuples") {
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
index e1c6c706242d2..e2716d7841d85 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameJoinSuite.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.execution.joins.BroadcastHashJoin
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 
-class DataFrameJoinSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class DataFrameJoinSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("join - join using") {
     val df = Seq(1, 2, 3).map(i => (i, i.toString)).toDF("int", "str")
@@ -59,7 +57,7 @@ class DataFrameJoinSuite extends QueryTest {
 
     checkAnswer(
       df1.join(df2, $"df1.key" === $"df2.key"),
-      ctx.sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key")
+      sql("SELECT a.key, b.key FROM testData a JOIN testData b ON a.key = b.key")
         .collect().toSeq)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
index dbe3b44ee2c79..cdaa14ac80785 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
@@ -19,11 +19,11 @@ package org.apache.spark.sql
 
 import scala.collection.JavaConversions._
 
+import org.apache.spark.sql.test.SharedSQLContext
 
-class DataFrameNaFunctionsSuite extends QueryTest {
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class DataFrameNaFunctionsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   def createDF(): DataFrame = {
     Seq[(String, java.lang.Integer, java.lang.Double)](
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 8f5984e4a8ce2..28bdd6f83b687 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -19,20 +19,17 @@ package org.apache.spark.sql
 
 import java.util.Random
 
-import org.scalatest.Matchers._
-
 import org.apache.spark.sql.functions.col
+import org.apache.spark.sql.test.SharedSQLContext
 
-class DataFrameStatSuite extends QueryTest {
-
-  private val sqlCtx = org.apache.spark.sql.test.TestSQLContext
-  import sqlCtx.implicits._
+class DataFrameStatSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   private def toLetter(i: Int): String = (i + 97).toChar.toString
 
   test("sample with replacement") {
     val n = 100
-    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
     checkAnswer(
       data.sample(withReplacement = true, 0.05, seed = 13),
       Seq(5, 10, 52, 73).map(Row(_))
@@ -41,7 +38,7 @@ class DataFrameStatSuite extends QueryTest {
 
   test("sample without replacement") {
     val n = 100
-    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
     checkAnswer(
       data.sample(withReplacement = false, 0.05, seed = 13),
       Seq(16, 23, 88, 100).map(Row(_))
@@ -50,7 +47,7 @@ class DataFrameStatSuite extends QueryTest {
 
   test("randomSplit") {
     val n = 600
-    val data = sqlCtx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
     for (seed <- 1 to 5) {
       val splits = data.randomSplit(Array[Double](1, 2, 3), seed)
       assert(splits.length == 3, "wrong number of splits")
@@ -167,7 +164,7 @@ class DataFrameStatSuite extends QueryTest {
   }
 
   test("Frequent Items 2") {
-    val rows = sqlCtx.sparkContext.parallelize(Seq.empty[Int], 4)
+    val rows = ctx.sparkContext.parallelize(Seq.empty[Int], 4)
     // this is a regression test, where when merging partitions, we omitted values with higher
     // counts than those that existed in the map when the map was full. This test should also fail
     // if anything like SPARK-9614 is observed once again
@@ -185,7 +182,7 @@ class DataFrameStatSuite extends QueryTest {
   }
 
   test("sampleBy") {
-    val df = sqlCtx.range(0, 100).select((col("id") % 3).as("key"))
+    val df = ctx.range(0, 100).select((col("id") % 3).as("key"))
     val sampled = df.stat.sampleBy("key", Map(0 -> 0.1, 1 -> 0.2), 0L)
     checkAnswer(
       sampled.groupBy("key").count().orderBy("key"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 2feec29955bc8..10bfa9b64f00d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -23,18 +23,12 @@ import scala.language.postfixOps
 import scala.util.Random
 
 import org.apache.spark.sql.catalyst.plans.logical.OneRowRelation
-import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.execution.datasources.json.JSONRelation
-import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, SQLTestUtils}
+import org.apache.spark.sql.test.{ExamplePointUDT, ExamplePoint, SharedSQLContext}
 
-class DataFrameSuite extends QueryTest with SQLTestUtils {
-  import org.apache.spark.sql.TestData._
-
-  lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.implicits._
+class DataFrameSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("analysis error should be eagerly reported") {
     // Eager analysis.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
index bf8ef9a97bc60..77907e91363ec 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql
 
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 /**
@@ -27,10 +27,8 @@ import org.apache.spark.sql.types._
  * This is here for now so I can make sure Tungsten project is tested without refactoring existing
  * end-to-end test infra. In the long run this should just go away.
  */
-class DataFrameTungstenSuite extends QueryTest with SQLTestUtils {
-
-  override lazy val sqlContext: SQLContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.implicits._
+class DataFrameTungstenSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("test simple types") {
     withSQLConf(SQLConf.UNSAFE_ENABLED.key -> "true") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
index 17897caf952a3..9080c53c491ac 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DateFunctionsSuite.scala
@@ -22,19 +22,18 @@ import java.text.SimpleDateFormat
 
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.unsafe.types.CalendarInterval
 
-class DateFunctionsSuite extends QueryTest {
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-
-  import ctx.implicits._
+class DateFunctionsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("function current_date") {
     val df1 = Seq((1, 2), (3, 1)).toDF("a", "b")
     val d0 = DateTimeUtils.millisToDays(System.currentTimeMillis())
     val d1 = DateTimeUtils.fromJavaDate(df1.select(current_date()).collect().head.getDate(0))
     val d2 = DateTimeUtils.fromJavaDate(
-      ctx.sql("""SELECT CURRENT_DATE()""").collect().head.getDate(0))
+      sql("""SELECT CURRENT_DATE()""").collect().head.getDate(0))
     val d3 = DateTimeUtils.millisToDays(System.currentTimeMillis())
     assert(d0 <= d1 && d1 <= d2 && d2 <= d3 && d3 - d0 <= 1)
   }
@@ -44,9 +43,9 @@ class DateFunctionsSuite extends QueryTest {
     val df1 = Seq((1, 2), (3, 1)).toDF("a", "b")
     checkAnswer(df1.select(countDistinct(current_timestamp())), Row(1))
     // Execution in one query should return the same value
-    checkAnswer(ctx.sql("""SELECT CURRENT_TIMESTAMP() = CURRENT_TIMESTAMP()"""),
+    checkAnswer(sql("""SELECT CURRENT_TIMESTAMP() = CURRENT_TIMESTAMP()"""),
       Row(true))
-    assert(math.abs(ctx.sql("""SELECT CURRENT_TIMESTAMP()""").collect().head.getTimestamp(
+    assert(math.abs(sql("""SELECT CURRENT_TIMESTAMP()""").collect().head.getTimestamp(
       0).getTime - System.currentTimeMillis()) < 5000)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index ae07eaf91c872..f5c5046a8ed88 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -17,22 +17,15 @@
 
 package org.apache.spark.sql
 
-import org.scalatest.BeforeAndAfterEach
-
-import org.apache.spark.sql.TestData._
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
 import org.apache.spark.sql.execution.joins._
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.test.SharedSQLContext
 
 
-class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
-  // Ensures tables are loaded.
-  TestData
+class JoinSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
-  override def sqlContext: SQLContext = org.apache.spark.sql.test.TestSQLContext
-  lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-  import ctx.logicalPlanToSparkQuery
+  setupTestData()
 
   test("equi-join is hash-join") {
     val x = testData2.as("x")
@@ -43,7 +36,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
   }
 
   def assertJoin(sqlString: String, c: Class[_]): Any = {
-    val df = ctx.sql(sqlString)
+    val df = sql(sqlString)
     val physical = df.queryExecution.sparkPlan
     val operators = physical.collect {
       case j: ShuffledHashJoin => j
@@ -126,7 +119,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
 
   test("broadcasted hash join operator selection") {
     ctx.cacheManager.clearCache()
-    ctx.sql("CACHE TABLE testData")
+    sql("CACHE TABLE testData")
     for (sortMergeJoinEnabled <- Seq(true, false)) {
       withClue(s"sortMergeJoinEnabled=$sortMergeJoinEnabled") {
         withSQLConf(SQLConf.SORTMERGE_JOIN.key -> s"$sortMergeJoinEnabled") {
@@ -141,12 +134,12 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
         }
       }
     }
-    ctx.sql("UNCACHE TABLE testData")
+    sql("UNCACHE TABLE testData")
   }
 
   test("broadcasted hash outer join operator selection") {
     ctx.cacheManager.clearCache()
-    ctx.sql("CACHE TABLE testData")
+    sql("CACHE TABLE testData")
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
       Seq(
         ("SELECT * FROM testData LEFT JOIN testData2 ON key = a",
@@ -167,7 +160,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
           classOf[BroadcastHashOuterJoin])
       ).foreach { case (query, joinClass) => assertJoin(query, joinClass) }
     }
-    ctx.sql("UNCACHE TABLE testData")
+    sql("UNCACHE TABLE testData")
   }
 
   test("multiple-key equi-join is hash-join") {
@@ -279,7 +272,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
     // Make sure we are choosing left.outputPartitioning as the
     // outputPartitioning for the outer join operator.
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT l.N, count(*)
           |FROM upperCaseData l LEFT OUTER JOIN allNulls r ON (l.N = r.a)
@@ -293,7 +286,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
         Row(6, 1) :: Nil)
 
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT r.a, count(*)
           |FROM upperCaseData l LEFT OUTER JOIN allNulls r ON (l.N = r.a)
@@ -339,7 +332,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
     // Make sure we are choosing right.outputPartitioning as the
     // outputPartitioning for the outer join operator.
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT l.a, count(*)
           |FROM allNulls l RIGHT OUTER JOIN upperCaseData r ON (l.a = r.N)
@@ -348,7 +341,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
       Row(null, 6))
 
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT r.N, count(*)
           |FROM allNulls l RIGHT OUTER JOIN upperCaseData r ON (l.a = r.N)
@@ -400,7 +393,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
 
     // Make sure we are UnknownPartitioning as the outputPartitioning for the outer join operator.
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT l.a, count(*)
           |FROM allNulls l FULL OUTER JOIN upperCaseData r ON (l.a = r.N)
@@ -409,7 +402,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
       Row(null, 10))
 
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT r.N, count(*)
           |FROM allNulls l FULL OUTER JOIN upperCaseData r ON (l.a = r.N)
@@ -424,7 +417,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
         Row(null, 4) :: Nil)
 
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT l.N, count(*)
           |FROM upperCaseData l FULL OUTER JOIN allNulls r ON (l.N = r.a)
@@ -439,7 +432,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
         Row(null, 4) :: Nil)
 
     checkAnswer(
-      ctx.sql(
+      sql(
         """
           |SELECT r.a, count(*)
           |FROM upperCaseData l FULL OUTER JOIN allNulls r ON (l.N = r.a)
@@ -450,7 +443,7 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
 
   test("broadcasted left semi join operator selection") {
     ctx.cacheManager.clearCache()
-    ctx.sql("CACHE TABLE testData")
+    sql("CACHE TABLE testData")
 
     withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "1000000000") {
       Seq(
@@ -469,11 +462,11 @@ class JoinSuite extends QueryTest with SQLTestUtils with BeforeAndAfterEach {
       }
     }
 
-    ctx.sql("UNCACHE TABLE testData")
+    sql("UNCACHE TABLE testData")
   }
 
   test("left semi join") {
-    val df = ctx.sql("SELECT * FROM testData2 LEFT SEMI JOIN testData ON key = a")
+    val df = sql("SELECT * FROM testData2 LEFT SEMI JOIN testData ON key = a")
     checkAnswer(df,
       Row(1, 1) ::
         Row(1, 2) ::
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
index 71c26a6f8d367..045fea82e4c89 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JsonFunctionsSuite.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.sql
 
-class JsonFunctionsSuite extends QueryTest {
+import org.apache.spark.sql.test.SharedSQLContext
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class JsonFunctionsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("function get_json_object") {
     val df: DataFrame = Seq(("""{"name": "alice", "age": 5}""", "")).toDF("a", "b")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
index 2089660c52bf7..babf8835d2545 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
@@ -19,12 +19,11 @@ package org.apache.spark.sql
 
 import org.scalatest.BeforeAndAfter
 
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.{BooleanType, StringType, StructField, StructType}
 
-class ListTablesSuite extends QueryTest with BeforeAndAfter {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class ListTablesSuite extends QueryTest with BeforeAndAfter with SharedSQLContext {
+  import testImplicits._
 
   private lazy val df = (1 to 10).map(i => (i, s"str$i")).toDF("key", "value")
 
@@ -42,7 +41,7 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter {
       Row("ListTablesSuiteTable", true))
 
     checkAnswer(
-      ctx.sql("SHOW tables").filter("tableName = 'ListTablesSuiteTable'"),
+      sql("SHOW tables").filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
     ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
@@ -55,7 +54,7 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter {
       Row("ListTablesSuiteTable", true))
 
     checkAnswer(
-      ctx.sql("show TABLES in DB").filter("tableName = 'ListTablesSuiteTable'"),
+      sql("show TABLES in DB").filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
     ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
@@ -67,13 +66,13 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter {
       StructField("tableName", StringType, false) ::
       StructField("isTemporary", BooleanType, false) :: Nil)
 
-    Seq(ctx.tables(), ctx.sql("SHOW TABLes")).foreach {
+    Seq(ctx.tables(), sql("SHOW TABLes")).foreach {
       case tableDF =>
         assert(expectedSchema === tableDF.schema)
 
         tableDF.registerTempTable("tables")
         checkAnswer(
-          ctx.sql(
+          sql(
             "SELECT isTemporary, tableName from tables WHERE tableName = 'ListTablesSuiteTable'"),
           Row(true, "ListTablesSuiteTable")
         )
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
index 8cf2ef5957d8d..30289c3c1d097 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/MathExpressionsSuite.scala
@@ -19,18 +19,16 @@ package org.apache.spark.sql
 
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.functions.{log => logarithm}
+import org.apache.spark.sql.test.SharedSQLContext
 
 private object MathExpressionsTestData {
   case class DoubleData(a: java.lang.Double, b: java.lang.Double)
   case class NullDoubles(a: java.lang.Double)
 }
 
-class MathExpressionsSuite extends QueryTest {
-
+class MathExpressionsSuite extends QueryTest with SharedSQLContext {
   import MathExpressionsTestData._
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+  import testImplicits._
 
   private lazy val doubleData = (1 to 10).map(i => DoubleData(i * 0.2 - 1, i * -0.2 + 1)).toDF()
 
@@ -149,7 +147,7 @@ class MathExpressionsSuite extends QueryTest {
   test("toDegrees") {
     testOneToOneMathFunction(toDegrees, math.toDegrees)
     checkAnswer(
-      ctx.sql("SELECT degrees(0), degrees(1), degrees(1.5)"),
+      sql("SELECT degrees(0), degrees(1), degrees(1.5)"),
       Seq((1, 2)).toDF().select(toDegrees(lit(0)), toDegrees(lit(1)), toDegrees(lit(1.5)))
     )
   }
@@ -157,7 +155,7 @@ class MathExpressionsSuite extends QueryTest {
   test("toRadians") {
     testOneToOneMathFunction(toRadians, math.toRadians)
     checkAnswer(
-      ctx.sql("SELECT radians(0), radians(1), radians(1.5)"),
+      sql("SELECT radians(0), radians(1), radians(1.5)"),
       Seq((1, 2)).toDF().select(toRadians(lit(0)), toRadians(lit(1)), toRadians(lit(1.5)))
     )
   }
@@ -169,7 +167,7 @@ class MathExpressionsSuite extends QueryTest {
   test("ceil and ceiling") {
     testOneToOneMathFunction(ceil, math.ceil)
     checkAnswer(
-      ctx.sql("SELECT ceiling(0), ceiling(1), ceiling(1.5)"),
+      sql("SELECT ceiling(0), ceiling(1), ceiling(1.5)"),
       Row(0.0, 1.0, 2.0))
   }
 
@@ -214,7 +212,7 @@ class MathExpressionsSuite extends QueryTest {
 
     val pi = 3.1415
     checkAnswer(
-      ctx.sql(s"SELECT round($pi, -3), round($pi, -2), round($pi, -1), " +
+      sql(s"SELECT round($pi, -3), round($pi, -2), round($pi, -1), " +
         s"round($pi, 0), round($pi, 1), round($pi, 2), round($pi, 3)"),
       Seq(Row(BigDecimal("0E3"), BigDecimal("0E2"), BigDecimal("0E1"), BigDecimal(3),
         BigDecimal("3.1"), BigDecimal("3.14"), BigDecimal("3.142")))
@@ -233,7 +231,7 @@ class MathExpressionsSuite extends QueryTest {
     testOneToOneMathFunction[Double](signum, math.signum)
 
     checkAnswer(
-      ctx.sql("SELECT sign(10), signum(-11)"),
+      sql("SELECT sign(10), signum(-11)"),
       Row(1, -1))
   }
 
@@ -241,7 +239,7 @@ class MathExpressionsSuite extends QueryTest {
     testTwoToOneMathFunction(pow, pow, math.pow)
 
     checkAnswer(
-      ctx.sql("SELECT pow(1, 2), power(2, 1)"),
+      sql("SELECT pow(1, 2), power(2, 1)"),
       Seq((1, 2)).toDF().select(pow(lit(1), lit(2)), pow(lit(2), lit(1)))
     )
   }
@@ -280,7 +278,7 @@ class MathExpressionsSuite extends QueryTest {
   test("log / ln") {
     testOneToOneNonNegativeMathFunction(org.apache.spark.sql.functions.log, math.log)
     checkAnswer(
-      ctx.sql("SELECT ln(0), ln(1), ln(1.5)"),
+      sql("SELECT ln(0), ln(1), ln(1.5)"),
       Seq((1, 2)).toDF().select(logarithm(lit(0)), logarithm(lit(1)), logarithm(lit(1.5)))
     )
   }
@@ -375,7 +373,7 @@ class MathExpressionsSuite extends QueryTest {
       df.select(log2("b") + log2("a")),
       Row(1))
 
-    checkAnswer(ctx.sql("SELECT LOG2(8), LOG2(null)"), Row(3, null))
+    checkAnswer(sql("SELECT LOG2(8), LOG2(null)"), Row(3, null))
   }
 
   test("sqrt") {
@@ -384,13 +382,13 @@ class MathExpressionsSuite extends QueryTest {
       df.select(sqrt("a"), sqrt("b")),
       Row(1.0, 2.0))
 
-    checkAnswer(ctx.sql("SELECT SQRT(4.0), SQRT(null)"), Row(2.0, null))
+    checkAnswer(sql("SELECT SQRT(4.0), SQRT(null)"), Row(2.0, null))
     checkAnswer(df.selectExpr("sqrt(a)", "sqrt(b)", "sqrt(null)"), Row(1.0, 2.0, null))
   }
 
   test("negative") {
     checkAnswer(
-      ctx.sql("SELECT negative(1), negative(0), negative(-1)"),
+      sql("SELECT negative(1), negative(0), negative(-1)"),
       Row(-1, 0, 1))
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
index 98ba3c99283a1..4adcefb7dc4b1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
@@ -71,12 +71,6 @@ class QueryTest extends PlanTest {
     checkAnswer(df, expectedAnswer.collect())
   }
 
-  def sqlTest(sqlString: String, expectedAnswer: Seq[Row])(implicit sqlContext: SQLContext) {
-    test(sqlString) {
-      checkAnswer(sqlContext.sql(sqlString), expectedAnswer)
-    }
-  }
-
   /**
    * Asserts that a given [[DataFrame]] will be executed using the given number of cached results.
    */
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index 8a679c7865d6a..795d4e983f27e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -20,13 +20,12 @@ package org.apache.spark.sql
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.execution.SparkSqlSerializer
 import org.apache.spark.sql.catalyst.expressions.{GenericMutableRow, SpecificMutableRow}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
-class RowSuite extends SparkFunSuite {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class RowSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
   test("create row") {
     val expected = new GenericMutableRow(4)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 75791e9d53c20..7699adadd9cc8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.test.SharedSQLContext
 
-class SQLConfSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
 
+class SQLConfSuite extends QueryTest with SharedSQLContext {
   private val testKey = "test.key.0"
   private val testVal = "test.val.0"
 
@@ -52,21 +51,21 @@ class SQLConfSuite extends QueryTest {
 
   test("parse SQL set commands") {
     ctx.conf.clear()
-    ctx.sql(s"set $testKey=$testVal")
+    sql(s"set $testKey=$testVal")
     assert(ctx.getConf(testKey, testVal + "_") === testVal)
     assert(ctx.getConf(testKey, testVal + "_") === testVal)
 
-    ctx.sql("set some.property=20")
+    sql("set some.property=20")
     assert(ctx.getConf("some.property", "0") === "20")
-    ctx.sql("set some.property = 40")
+    sql("set some.property = 40")
     assert(ctx.getConf("some.property", "0") === "40")
 
     val key = "spark.sql.key"
     val vs = "val0,val_1,val2.3,my_table"
-    ctx.sql(s"set $key=$vs")
+    sql(s"set $key=$vs")
     assert(ctx.getConf(key, "0") === vs)
 
-    ctx.sql(s"set $key=")
+    sql(s"set $key=")
     assert(ctx.getConf(key, "0") === "")
 
     ctx.conf.clear()
@@ -74,14 +73,14 @@ class SQLConfSuite extends QueryTest {
 
   test("deprecated property") {
     ctx.conf.clear()
-    ctx.sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
+    sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
     assert(ctx.conf.numShufflePartitions === 10)
   }
 
   test("invalid conf value") {
     ctx.conf.clear()
     val e = intercept[IllegalArgumentException] {
-      ctx.sql(s"set ${SQLConf.CASE_SENSITIVE.key}=10")
+      sql(s"set ${SQLConf.CASE_SENSITIVE.key}=10")
     }
     assert(e.getMessage === s"${SQLConf.CASE_SENSITIVE.key} should be boolean, but was 10")
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
index c8d8796568a41..007be12950774 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
@@ -17,16 +17,17 @@
 
 package org.apache.spark.sql
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.test.SharedSQLContext
 
-class SQLContextSuite extends SparkFunSuite with BeforeAndAfterAll {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+class SQLContextSuite extends SparkFunSuite with SharedSQLContext {
 
   override def afterAll(): Unit = {
-    SQLContext.setLastInstantiatedContext(ctx)
+    try {
+      SQLContext.setLastInstantiatedContext(ctx)
+    } finally {
+      super.afterAll()
+    }
   }
 
   test("getOrCreate instantiates SQLContext") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index b14ef9bab90cb..8c2c328f8191c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -19,28 +19,23 @@ package org.apache.spark.sql
 
 import java.sql.Timestamp
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.AccumulatorSuite
 import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.TestData._
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.SQLTestData._
 import org.apache.spark.sql.types._
 
 /** A SQL Dialect for testing purpose, and it can not be nested type */
 class MyDialect extends DefaultParserDialect
 
-class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
-  // Make sure the tables are loaded.
-  TestData
+class SQLQuerySuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
-  val sqlContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.implicits._
-  import sqlContext.sql
+  setupTestData()
 
   test("having clause") {
     Seq(("one", 1), ("two", 2), ("three", 3), ("one", 5)).toDF("k", "v").registerTempTable("hav")
@@ -60,7 +55,8 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
   }
 
   test("show functions") {
-    checkAnswer(sql("SHOW functions"), FunctionRegistry.builtin.listFunction().sorted.map(Row(_)))
+    checkAnswer(sql("SHOW functions"),
+      FunctionRegistry.builtin.listFunction().sorted.map(Row(_)))
   }
 
   test("describe functions") {
@@ -178,7 +174,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
 
     val df = Seq(Tuple1(1), Tuple1(2), Tuple1(3)).toDF("index")
     // we except the id is materialized once
-    val idUDF = udf(() => UUID.randomUUID().toString)
+    val idUDF = org.apache.spark.sql.functions.udf(() => UUID.randomUUID().toString)
 
     val dfWithId = df.withColumn("id", idUDF())
     // Make a new DataFrame (actually the same reference to the old one)
@@ -712,9 +708,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
 
     checkAnswer(
       sql(
-        """
-          |SELECT COUNT(a), COUNT(b), COUNT(1), COUNT(DISTINCT a), COUNT(DISTINCT b) FROM testData3
-        """.stripMargin),
+        "SELECT COUNT(a), COUNT(b), COUNT(1), COUNT(DISTINCT a), COUNT(DISTINCT b) FROM testData3"),
       Row(2, 1, 2, 2, 1))
   }
 
@@ -1161,7 +1155,8 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
     validateMetadata(sql("SELECT * FROM personWithMeta"))
     validateMetadata(sql("SELECT id, name FROM personWithMeta"))
     validateMetadata(sql("SELECT * FROM personWithMeta JOIN salary ON id = personId"))
-    validateMetadata(sql("SELECT name, salary FROM personWithMeta JOIN salary ON id = personId"))
+    validateMetadata(sql(
+      "SELECT name, salary FROM personWithMeta JOIN salary ON id = personId"))
   }
 
   test("SPARK-3371 Renaming a function expression with group by gives error") {
@@ -1627,7 +1622,7 @@ class SQLQuerySuite extends QueryTest with BeforeAndAfterAll with SQLTestUtils {
       .toDF("num", "str")
     df.registerTempTable("1one")
 
-    checkAnswer(sqlContext.sql("select count(num) from 1one"), Row(10))
+    checkAnswer(sql("select count(num) from 1one"), Row(10))
 
     sqlContext.dropTempTable("1one")
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
index ab6d3dd96d271..295f02f9a7b5d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ScalaReflectionRelationSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql
 import java.sql.{Date, Timestamp}
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.test.SharedSQLContext
 
 case class ReflectData(
     stringField: String,
@@ -71,17 +72,15 @@ case class ComplexReflectData(
     mapFieldContainsNull: Map[Int, Option[Long]],
     dataField: Data)
 
-class ScalaReflectionRelationSuite extends SparkFunSuite {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class ScalaReflectionRelationSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
   test("query case class RDD") {
     val data = ReflectData("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true,
       new java.math.BigDecimal(1), Date.valueOf("1970-01-01"), new Timestamp(12345), Seq(1, 2, 3))
     Seq(data).toDF().registerTempTable("reflectData")
 
-    assert(ctx.sql("SELECT * FROM reflectData").collect().head ===
+    assert(sql("SELECT * FROM reflectData").collect().head ===
       Row("a", 1, 1L, 1.toFloat, 1.toDouble, 1.toShort, 1.toByte, true,
         new java.math.BigDecimal(1), Date.valueOf("1970-01-01"),
         new Timestamp(12345), Seq(1, 2, 3)))
@@ -91,7 +90,7 @@ class ScalaReflectionRelationSuite extends SparkFunSuite {
     val data = NullReflectData(null, null, null, null, null, null, null)
     Seq(data).toDF().registerTempTable("reflectNullData")
 
-    assert(ctx.sql("SELECT * FROM reflectNullData").collect().head ===
+    assert(sql("SELECT * FROM reflectNullData").collect().head ===
       Row.fromSeq(Seq.fill(7)(null)))
   }
 
@@ -99,7 +98,7 @@ class ScalaReflectionRelationSuite extends SparkFunSuite {
     val data = OptionalReflectData(None, None, None, None, None, None, None)
     Seq(data).toDF().registerTempTable("reflectOptionalData")
 
-    assert(ctx.sql("SELECT * FROM reflectOptionalData").collect().head ===
+    assert(sql("SELECT * FROM reflectOptionalData").collect().head ===
       Row.fromSeq(Seq.fill(7)(null)))
   }
 
@@ -107,7 +106,7 @@ class ScalaReflectionRelationSuite extends SparkFunSuite {
   test("query binary data") {
     Seq(ReflectBinary(Array[Byte](1))).toDF().registerTempTable("reflectBinary")
 
-    val result = ctx.sql("SELECT data FROM reflectBinary")
+    val result = sql("SELECT data FROM reflectBinary")
       .collect().head(0).asInstanceOf[Array[Byte]]
     assert(result.toSeq === Seq[Byte](1))
   }
@@ -126,7 +125,7 @@ class ScalaReflectionRelationSuite extends SparkFunSuite {
         Nested(None, "abc")))
 
     Seq(data).toDF().registerTempTable("reflectComplexData")
-    assert(ctx.sql("SELECT * FROM reflectComplexData").collect().head ===
+    assert(sql("SELECT * FROM reflectComplexData").collect().head ===
       Row(
         Seq(1, 2, 3),
         Seq(1, 2, null),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
index e55c9e460b791..45d0ee4a8e749 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
@@ -19,13 +19,12 @@ package org.apache.spark.sql
 
 import org.apache.spark.{SparkConf, SparkFunSuite}
 import org.apache.spark.serializer.JavaSerializer
+import org.apache.spark.sql.test.SharedSQLContext
 
-class SerializationSuite extends SparkFunSuite {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
+class SerializationSuite extends SparkFunSuite with SharedSQLContext {
 
   test("[SPARK-5235] SQLContext should be serializable") {
-    val sqlContext = new SQLContext(ctx.sparkContext)
-    new JavaSerializer(new SparkConf()).newInstance().serialize(sqlContext)
+    val _sqlContext = new SQLContext(sqlContext.sparkContext)
+    new JavaSerializer(new SparkConf()).newInstance().serialize(_sqlContext)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index ca298b2434410..cc95eede005d7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -18,13 +18,12 @@
 package org.apache.spark.sql
 
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.Decimal
 
 
-class StringFunctionsSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class StringFunctionsSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("string concat") {
     val df = Seq[(String, String, String)](("a", "b", null)).toDF("a", "b", "c")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
deleted file mode 100644
index bd9729c431f30..0000000000000
--- a/sql/core/src/test/scala/org/apache/spark/sql/TestData.scala
+++ /dev/null
@@ -1,197 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql
-
-import org.apache.spark.sql.test.TestSQLContext.implicits._
-import org.apache.spark.sql.test._
-
-
-case class TestData(key: Int, value: String)
-
-object TestData {
-  val testData = TestSQLContext.sparkContext.parallelize(
-    (1 to 100).map(i => TestData(i, i.toString))).toDF()
-  testData.registerTempTable("testData")
-
-  val negativeData = TestSQLContext.sparkContext.parallelize(
-    (1 to 100).map(i => TestData(-i, (-i).toString))).toDF()
-  negativeData.registerTempTable("negativeData")
-
-  case class LargeAndSmallInts(a: Int, b: Int)
-  val largeAndSmallInts =
-    TestSQLContext.sparkContext.parallelize(
-      LargeAndSmallInts(2147483644, 1) ::
-      LargeAndSmallInts(1, 2) ::
-      LargeAndSmallInts(2147483645, 1) ::
-      LargeAndSmallInts(2, 2) ::
-      LargeAndSmallInts(2147483646, 1) ::
-      LargeAndSmallInts(3, 2) :: Nil).toDF()
-  largeAndSmallInts.registerTempTable("largeAndSmallInts")
-
-  case class TestData2(a: Int, b: Int)
-  val testData2 =
-    TestSQLContext.sparkContext.parallelize(
-      TestData2(1, 1) ::
-      TestData2(1, 2) ::
-      TestData2(2, 1) ::
-      TestData2(2, 2) ::
-      TestData2(3, 1) ::
-      TestData2(3, 2) :: Nil, 2).toDF()
-  testData2.registerTempTable("testData2")
-
-  case class DecimalData(a: BigDecimal, b: BigDecimal)
-
-  val decimalData =
-    TestSQLContext.sparkContext.parallelize(
-      DecimalData(1, 1) ::
-      DecimalData(1, 2) ::
-      DecimalData(2, 1) ::
-      DecimalData(2, 2) ::
-      DecimalData(3, 1) ::
-      DecimalData(3, 2) :: Nil).toDF()
-  decimalData.registerTempTable("decimalData")
-
-  case class BinaryData(a: Array[Byte], b: Int)
-  val binaryData =
-    TestSQLContext.sparkContext.parallelize(
-      BinaryData("12".getBytes(), 1) ::
-      BinaryData("22".getBytes(), 5) ::
-      BinaryData("122".getBytes(), 3) ::
-      BinaryData("121".getBytes(), 2) ::
-      BinaryData("123".getBytes(), 4) :: Nil).toDF()
-  binaryData.registerTempTable("binaryData")
-
-  case class TestData3(a: Int, b: Option[Int])
-  val testData3 =
-    TestSQLContext.sparkContext.parallelize(
-      TestData3(1, None) ::
-      TestData3(2, Some(2)) :: Nil).toDF()
-  testData3.registerTempTable("testData3")
-
-  case class UpperCaseData(N: Int, L: String)
-  val upperCaseData =
-    TestSQLContext.sparkContext.parallelize(
-      UpperCaseData(1, "A") ::
-      UpperCaseData(2, "B") ::
-      UpperCaseData(3, "C") ::
-      UpperCaseData(4, "D") ::
-      UpperCaseData(5, "E") ::
-      UpperCaseData(6, "F") :: Nil).toDF()
-  upperCaseData.registerTempTable("upperCaseData")
-
-  case class LowerCaseData(n: Int, l: String)
-  val lowerCaseData =
-    TestSQLContext.sparkContext.parallelize(
-      LowerCaseData(1, "a") ::
-      LowerCaseData(2, "b") ::
-      LowerCaseData(3, "c") ::
-      LowerCaseData(4, "d") :: Nil).toDF()
-  lowerCaseData.registerTempTable("lowerCaseData")
-
-  case class ArrayData(data: Seq[Int], nestedData: Seq[Seq[Int]])
-  val arrayData =
-    TestSQLContext.sparkContext.parallelize(
-      ArrayData(Seq(1, 2, 3), Seq(Seq(1, 2, 3))) ::
-      ArrayData(Seq(2, 3, 4), Seq(Seq(2, 3, 4))) :: Nil)
-  arrayData.toDF().registerTempTable("arrayData")
-
-  case class MapData(data: scala.collection.Map[Int, String])
-  val mapData =
-    TestSQLContext.sparkContext.parallelize(
-      MapData(Map(1 -> "a1", 2 -> "b1", 3 -> "c1", 4 -> "d1", 5 -> "e1")) ::
-      MapData(Map(1 -> "a2", 2 -> "b2", 3 -> "c2", 4 -> "d2")) ::
-      MapData(Map(1 -> "a3", 2 -> "b3", 3 -> "c3")) ::
-      MapData(Map(1 -> "a4", 2 -> "b4")) ::
-      MapData(Map(1 -> "a5")) :: Nil)
-  mapData.toDF().registerTempTable("mapData")
-
-  case class StringData(s: String)
-  val repeatedData =
-    TestSQLContext.sparkContext.parallelize(List.fill(2)(StringData("test")))
-  repeatedData.toDF().registerTempTable("repeatedData")
-
-  val nullableRepeatedData =
-    TestSQLContext.sparkContext.parallelize(
-      List.fill(2)(StringData(null)) ++
-      List.fill(2)(StringData("test")))
-  nullableRepeatedData.toDF().registerTempTable("nullableRepeatedData")
-
-  case class NullInts(a: Integer)
-  val nullInts =
-    TestSQLContext.sparkContext.parallelize(
-      NullInts(1) ::
-      NullInts(2) ::
-      NullInts(3) ::
-      NullInts(null) :: Nil
-    ).toDF()
-  nullInts.registerTempTable("nullInts")
-
-  val allNulls =
-    TestSQLContext.sparkContext.parallelize(
-      NullInts(null) ::
-      NullInts(null) ::
-      NullInts(null) ::
-      NullInts(null) :: Nil).toDF()
-  allNulls.registerTempTable("allNulls")
-
-  case class NullStrings(n: Int, s: String)
-  val nullStrings =
-    TestSQLContext.sparkContext.parallelize(
-      NullStrings(1, "abc") ::
-      NullStrings(2, "ABC") ::
-      NullStrings(3, null) :: Nil).toDF()
-  nullStrings.registerTempTable("nullStrings")
-
-  case class TableName(tableName: String)
-  TestSQLContext
-    .sparkContext
-    .parallelize(TableName("test") :: Nil)
-    .toDF()
-    .registerTempTable("tableName")
-
-  val unparsedStrings =
-    TestSQLContext.sparkContext.parallelize(
-      "1, A1, true, null" ::
-      "2, B2, false, null" ::
-      "3, C3, true, null" ::
-      "4, D4, true, 2147483644" :: Nil)
-
-  case class IntField(i: Int)
-  // An RDD with 4 elements and 8 partitions
-  val withEmptyParts = TestSQLContext.sparkContext.parallelize((1 to 4).map(IntField), 8)
-  withEmptyParts.toDF().registerTempTable("withEmptyParts")
-
-  case class Person(id: Int, name: String, age: Int)
-  case class Salary(personId: Int, salary: Double)
-  val person = TestSQLContext.sparkContext.parallelize(
-    Person(0, "mike", 30) ::
-    Person(1, "jim", 20) :: Nil).toDF()
-  person.registerTempTable("person")
-  val salary = TestSQLContext.sparkContext.parallelize(
-    Salary(0, 2000.0) ::
-    Salary(1, 1000.0) :: Nil).toDF()
-  salary.registerTempTable("salary")
-
-  case class ComplexData(m: Map[String, Int], s: TestData, a: Seq[Int], b: Boolean)
-  val complexData =
-    TestSQLContext.sparkContext.parallelize(
-      ComplexData(Map("1" -> 1), TestData(1, "1"), Seq(1, 1, 1), true)
-        :: ComplexData(Map("2" -> 2), TestData(2, "2"), Seq(2, 2, 2), false)
-        :: Nil).toDF()
-  complexData.registerTempTable("complexData")
-}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
index 183dc3407b3ab..eb275af101e2f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
@@ -17,16 +17,13 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.SQLTestData._
 
-case class FunctionResult(f1: String, f2: String)
+private case class FunctionResult(f1: String, f2: String)
 
-class UDFSuite extends QueryTest with SQLTestUtils {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-
-  override def sqlContext(): SQLContext = ctx
+class UDFSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("built-in fixed arity expressions") {
     val df = ctx.emptyDataFrame
@@ -57,7 +54,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
   test("SPARK-8003 spark_partition_id") {
     val df = Seq((1, "Tearing down the walls that divide us")).toDF("id", "saying")
     df.registerTempTable("tmp_table")
-    checkAnswer(ctx.sql("select spark_partition_id() from tmp_table").toDF(), Row(0))
+    checkAnswer(sql("select spark_partition_id() from tmp_table").toDF(), Row(0))
     ctx.dropTempTable("tmp_table")
   }
 
@@ -66,9 +63,9 @@ class UDFSuite extends QueryTest with SQLTestUtils {
       val data = ctx.sparkContext.parallelize(0 to 10, 2).toDF("id")
       data.write.parquet(dir.getCanonicalPath)
       ctx.read.parquet(dir.getCanonicalPath).registerTempTable("test_table")
-      val answer = ctx.sql("select input_file_name() from test_table").head().getString(0)
+      val answer = sql("select input_file_name() from test_table").head().getString(0)
       assert(answer.contains(dir.getCanonicalPath))
-      assert(ctx.sql("select input_file_name() from test_table").distinct().collect().length >= 2)
+      assert(sql("select input_file_name() from test_table").distinct().collect().length >= 2)
       ctx.dropTempTable("test_table")
     }
   }
@@ -91,17 +88,17 @@ class UDFSuite extends QueryTest with SQLTestUtils {
 
   test("Simple UDF") {
     ctx.udf.register("strLenScala", (_: String).length)
-    assert(ctx.sql("SELECT strLenScala('test')").head().getInt(0) === 4)
+    assert(sql("SELECT strLenScala('test')").head().getInt(0) === 4)
   }
 
   test("ZeroArgument UDF") {
     ctx.udf.register("random0", () => { Math.random()})
-    assert(ctx.sql("SELECT random0()").head().getDouble(0) >= 0.0)
+    assert(sql("SELECT random0()").head().getDouble(0) >= 0.0)
   }
 
   test("TwoArgument UDF") {
     ctx.udf.register("strLenScala", (_: String).length + (_: Int))
-    assert(ctx.sql("SELECT strLenScala('test', 1)").head().getInt(0) === 5)
+    assert(sql("SELECT strLenScala('test', 1)").head().getInt(0) === 5)
   }
 
   test("UDF in a WHERE") {
@@ -112,7 +109,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
     df.registerTempTable("integerData")
 
     val result =
-      ctx.sql("SELECT * FROM integerData WHERE oneArgFilter(key)")
+      sql("SELECT * FROM integerData WHERE oneArgFilter(key)")
     assert(result.count() === 20)
   }
 
@@ -124,7 +121,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
     df.registerTempTable("groupData")
 
     val result =
-      ctx.sql(
+      sql(
         """
          | SELECT g, SUM(v) as s
          | FROM groupData
@@ -143,7 +140,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
     df.registerTempTable("groupData")
 
     val result =
-      ctx.sql(
+      sql(
         """
          | SELECT SUM(v)
          | FROM groupData
@@ -163,7 +160,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
     df.registerTempTable("groupData")
 
     val result =
-      ctx.sql(
+      sql(
         """
          | SELECT timesHundred(SUM(v)) as v100
          | FROM groupData
@@ -178,7 +175,7 @@ class UDFSuite extends QueryTest with SQLTestUtils {
     ctx.udf.register("returnStruct", (f1: String, f2: String) => FunctionResult(f1, f2))
 
     val result =
-      ctx.sql("SELECT returnStruct('test', 'test2') as ret")
+      sql("SELECT returnStruct('test', 'test2') as ret")
         .select($"ret.f1").head().getString(0)
     assert(result === "test")
   }
@@ -186,12 +183,12 @@ class UDFSuite extends QueryTest with SQLTestUtils {
   test("udf that is transformed") {
     ctx.udf.register("makeStruct", (x: Int, y: Int) => (x, y))
     // 1 + 1 is constant folded causing a transformation.
-    assert(ctx.sql("SELECT makeStruct(1 + 1, 2)").first().getAs[Row](0) === Row(2, 2))
+    assert(sql("SELECT makeStruct(1 + 1, 2)").first().getAs[Row](0) === Row(2, 2))
   }
 
   test("type coercion for udf inputs") {
     ctx.udf.register("intExpected", (x: Int) => x)
     // pass a decimal to intExpected.
-    assert(ctx.sql("SELECT intExpected(1.0)").head().getInt(0) === 1)
+    assert(sql("SELECT intExpected(1.0)").head().getInt(0) === 1)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
index 9181222f6922b..b6d279ae47268 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
@@ -24,6 +24,7 @@ import com.clearspring.analytics.stream.cardinality.HyperLogLog
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.expressions.{OpenHashSetUDT, HyperLogLogUDT}
 import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 import org.apache.spark.util.collection.OpenHashSet
@@ -66,10 +67,8 @@ private[sql] class MyDenseVectorUDT extends UserDefinedType[MyDenseVector] {
   private[spark] override def asNullable: MyDenseVectorUDT = this
 }
 
-class UserDefinedTypeSuite extends QueryTest {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class UserDefinedTypeSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   private lazy val pointsRDD = Seq(
     MyLabeledPoint(1.0, new MyDenseVector(Array(0.1, 1.0))),
@@ -94,7 +93,7 @@ class UserDefinedTypeSuite extends QueryTest {
     ctx.udf.register("testType", (d: MyDenseVector) => d.isInstanceOf[MyDenseVector])
     pointsRDD.registerTempTable("points")
     checkAnswer(
-      ctx.sql("SELECT testType(features) from points"),
+      sql("SELECT testType(features) from points"),
       Seq(Row(true), Row(true)))
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 9bca4e7e660d6..952637c5f9cb8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -19,18 +19,16 @@ package org.apache.spark.sql.columnar
 
 import java.sql.{Date, Timestamp}
 
-import org.apache.spark.sql.TestData._
+import org.apache.spark.sql.{QueryTest, Row}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.SQLTestData._
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{QueryTest, Row, TestData}
 import org.apache.spark.storage.StorageLevel.MEMORY_ONLY
 
-class InMemoryColumnarQuerySuite extends QueryTest {
-  // Make sure the tables are loaded.
-  TestData
+class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-  import ctx.{logicalPlanToSparkQuery, sql}
+  setupTestData()
 
   test("simple columnar query") {
     val plan = ctx.executePlan(testData.logicalPlan).executedPlan
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
index 2c0879927a129..ab2644eb4581d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
@@ -17,20 +17,19 @@
 
 package org.apache.spark.sql.columnar
 
-import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll}
-
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql._
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.SQLTestData._
 
-class PartitionBatchPruningSuite extends SparkFunSuite with BeforeAndAfterAll with BeforeAndAfter {
-
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
+class PartitionBatchPruningSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
   private lazy val originalColumnBatchSize = ctx.conf.columnBatchSize
   private lazy val originalInMemoryPartitionPruning = ctx.conf.inMemoryPartitionPruning
 
   override protected def beforeAll(): Unit = {
+    super.beforeAll()
     // Make a table with 5 partitions, 2 batches per partition, 10 elements per batch
     ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, 10)
 
@@ -44,19 +43,17 @@ class PartitionBatchPruningSuite extends SparkFunSuite with BeforeAndAfterAll wi
     ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, true)
     // Enable in-memory table scan accumulators
     ctx.setConf("spark.sql.inMemoryTableScanStatistics.enable", "true")
-  }
-
-  override protected def afterAll(): Unit = {
-    ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize)
-    ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning)
-  }
-
-  before {
     ctx.cacheTable("pruningData")
   }
 
-  after {
-    ctx.uncacheTable("pruningData")
+  override protected def afterAll(): Unit = {
+    try {
+      ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize)
+      ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning)
+      ctx.uncacheTable("pruningData")
+    } finally {
+      super.afterAll()
+    }
   }
 
   // Comparisons
@@ -110,7 +107,7 @@ class PartitionBatchPruningSuite extends SparkFunSuite with BeforeAndAfterAll wi
       expectedQueryResult: => Seq[Int]): Unit = {
 
     test(query) {
-      val df = ctx.sql(query)
+      val df = sql(query)
       val queryExecution = df.queryExecution
 
       assertResult(expectedQueryResult.toArray, s"Wrong query result: $queryExecution") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
index 79e903c2bbd40..8998f5111124c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
@@ -19,8 +19,9 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.plans.physical.SinglePartition
+import org.apache.spark.sql.test.SharedSQLContext
 
-class ExchangeSuite extends SparkPlanTest {
+class ExchangeSuite extends SparkPlanTest with SharedSQLContext {
   test("shuffling UnsafeRows in exchange") {
     val input = (1 to 1000).map(Tuple1.apply)
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 5582caa0d366e..937a108543531 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.TestData._
+import org.apache.spark.sql.{execution, Row, SQLConf}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Ascending, Attribute, Literal, SortOrder}
 import org.apache.spark.sql.catalyst.plans._
@@ -27,19 +27,18 @@ import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.plans.physical._
 import org.apache.spark.sql.execution.joins.{BroadcastHashJoin, ShuffledHashJoin}
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.test.{SQLTestUtils, TestSQLContext}
-import org.apache.spark.sql.test.TestSQLContext._
-import org.apache.spark.sql.test.TestSQLContext.implicits._
-import org.apache.spark.sql.test.TestSQLContext.planner._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{SQLContext, Row, SQLConf, execution}
 
 
-class PlannerSuite extends SparkFunSuite with SQLTestUtils {
+class PlannerSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
-  override def sqlContext: SQLContext = TestSQLContext
+  setupTestData()
 
   private def testPartialAggregationPlan(query: LogicalPlan): Unit = {
+    val _ctx = ctx
+    import _ctx.planner._
     val plannedOption = HashAggregation(query).headOption.orElse(Aggregation(query).headOption)
     val planned =
       plannedOption.getOrElse(
@@ -54,6 +53,8 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
   }
 
   test("unions are collapsed") {
+    val _ctx = ctx
+    import _ctx.planner._
     val query = testData.unionAll(testData).unionAll(testData).logicalPlan
     val planned = BasicOperators(query).head
     val logicalUnions = query collect { case u: logical.Union => u }
@@ -81,14 +82,14 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
 
   test("sizeInBytes estimation of limit operator for broadcast hash join optimization") {
     def checkPlan(fieldTypes: Seq[DataType], newThreshold: Int): Unit = {
-      setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, newThreshold)
+      ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, newThreshold)
       val fields = fieldTypes.zipWithIndex.map {
         case (dataType, index) => StructField(s"c${index}", dataType, true)
       } :+ StructField("key", IntegerType, true)
       val schema = StructType(fields)
       val row = Row.fromSeq(Seq.fill(fields.size)(null))
-      val rowRDD = org.apache.spark.sql.test.TestSQLContext.sparkContext.parallelize(row :: Nil)
-      createDataFrame(rowRDD, schema).registerTempTable("testLimit")
+      val rowRDD = ctx.sparkContext.parallelize(row :: Nil)
+      ctx.createDataFrame(rowRDD, schema).registerTempTable("testLimit")
 
       val planned = sql(
         """
@@ -102,10 +103,10 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
       assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
       assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
 
-      dropTempTable("testLimit")
+      ctx.dropTempTable("testLimit")
     }
 
-    val origThreshold = conf.autoBroadcastJoinThreshold
+    val origThreshold = ctx.conf.autoBroadcastJoinThreshold
 
     val simpleTypes =
       NullType ::
@@ -137,18 +138,18 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
 
     checkPlan(complexTypes, newThreshold = 901617)
 
-    setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
+    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
   }
 
   test("InMemoryRelation statistics propagation") {
-    val origThreshold = conf.autoBroadcastJoinThreshold
-    setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, 81920)
+    val origThreshold = ctx.conf.autoBroadcastJoinThreshold
+    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, 81920)
 
     testData.limit(3).registerTempTable("tiny")
     sql("CACHE TABLE tiny")
 
     val a = testData.as("a")
-    val b = table("tiny").as("b")
+    val b = ctx.table("tiny").as("b")
     val planned = a.join(b, $"a.key" === $"b.key").queryExecution.executedPlan
 
     val broadcastHashJoins = planned.collect { case join: BroadcastHashJoin => join }
@@ -157,12 +158,12 @@ class PlannerSuite extends SparkFunSuite with SQLTestUtils {
     assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
     assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
 
-    setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
+    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
   }
 
   test("efficient limit -> project -> sort") {
     val query = testData.sort('key).select('value).limit(2).logicalPlan
-    val planned = planner.TakeOrderedAndProject(query)
+    val planned = ctx.planner.TakeOrderedAndProject(query)
     assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index dd08e9025a927..ef6ad59b71fb3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -21,11 +21,11 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{AttributeReference, Attribute, Literal, IsNull}
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.types.{GenericArrayData, ArrayType, StructType, StringType}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types.{GenericArrayData, ArrayType, StringType}
 import org.apache.spark.unsafe.types.UTF8String
 
-class RowFormatConvertersSuite extends SparkPlanTest {
+class RowFormatConvertersSuite extends SparkPlanTest with SharedSQLContext {
 
   private def getConverters(plan: SparkPlan): Seq[SparkPlan] = plan.collect {
     case c: ConvertToUnsafe => c
@@ -39,20 +39,20 @@ class RowFormatConvertersSuite extends SparkPlanTest {
 
   test("planner should insert unsafe->safe conversions when required") {
     val plan = Limit(10, outputsUnsafe)
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(preparedPlan.children.head.isInstanceOf[ConvertToSafe])
   }
 
   test("filter can process unsafe rows") {
     val plan = Filter(IsNull(IsNull(Literal(1))), outputsUnsafe)
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(getConverters(preparedPlan).size === 1)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("filter can process safe rows") {
     val plan = Filter(IsNull(IsNull(Literal(1))), outputsSafe)
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(getConverters(preparedPlan).isEmpty)
     assert(!preparedPlan.outputsUnsafeRows)
   }
@@ -67,33 +67,33 @@ class RowFormatConvertersSuite extends SparkPlanTest {
   test("union requires all of its input rows' formats to agree") {
     val plan = Union(Seq(outputsSafe, outputsUnsafe))
     assert(plan.canProcessSafeRows && plan.canProcessUnsafeRows)
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("union can process safe rows") {
     val plan = Union(Seq(outputsSafe, outputsSafe))
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(!preparedPlan.outputsUnsafeRows)
   }
 
   test("union can process unsafe rows") {
     val plan = Union(Seq(outputsUnsafe, outputsUnsafe))
-    val preparedPlan = TestSQLContext.prepareForExecution.execute(plan)
+    val preparedPlan = ctx.prepareForExecution.execute(plan)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("round trip with ConvertToUnsafe and ConvertToSafe") {
     val input = Seq(("hello", 1), ("world", 2))
     checkAnswer(
-      TestSQLContext.createDataFrame(input),
+      ctx.createDataFrame(input),
       plan => ConvertToSafe(ConvertToUnsafe(plan)),
       input.map(Row.fromTuple)
     )
   }
 
   test("SPARK-9683: copy UTF8String when convert unsafe array/map to safe") {
-    SparkPlan.currentContext.set(TestSQLContext)
+    SparkPlan.currentContext.set(ctx)
     val schema = ArrayType(StringType)
     val rows = (1 to 100).map { i =>
       InternalRow(new GenericArrayData(Array[Any](UTF8String.fromString(i.toString))))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
index a2c10fdaf6cdb..8fa77b0fcb7b7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
@@ -19,8 +19,9 @@ package org.apache.spark.sql.execution
 
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.test.SharedSQLContext
 
-class SortSuite extends SparkPlanTest {
+class SortSuite extends SparkPlanTest with SharedSQLContext {
 
   // This test was originally added as an example of how to use [[SparkPlanTest]];
   // it's not designed to be a comprehensive test of ExternalSort.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
index f46855edfe0de..3a87f374d94b0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
@@ -17,29 +17,27 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.util._
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.{SQLContext, DataFrame, DataFrameHolder, Row}
-
 import scala.language.implicitConversions
 import scala.reflect.runtime.universe.TypeTag
 import scala.util.control.NonFatal
 
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.{DataFrame, DataFrameHolder, Row, SQLContext}
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.util._
+
 /**
  * Base class for writing tests for individual physical operators. For an example of how this
  * class's test helper methods can be used, see [[SortSuite]].
  */
-class SparkPlanTest extends SparkFunSuite {
-
-  protected def sqlContext: SQLContext = TestSQLContext
+private[sql] abstract class SparkPlanTest extends SparkFunSuite {
+  protected def _sqlContext: SQLContext
 
   /**
    * Creates a DataFrame from a local Seq of Product.
    */
   implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder = {
-    sqlContext.implicits.localSeqToDataFrameHolder(data)
+    _sqlContext.implicits.localSeqToDataFrameHolder(data)
   }
 
   /**
@@ -100,7 +98,7 @@ class SparkPlanTest extends SparkFunSuite {
       planFunction: Seq[SparkPlan] => SparkPlan,
       expectedAnswer: Seq[Row],
       sortAnswers: Boolean = true): Unit = {
-    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer, sortAnswers, sqlContext) match {
+    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer, sortAnswers, _sqlContext) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
@@ -124,7 +122,7 @@ class SparkPlanTest extends SparkFunSuite {
       expectedPlanFunction: SparkPlan => SparkPlan,
       sortAnswers: Boolean = true): Unit = {
     SparkPlanTest.checkAnswer(
-        input, planFunction, expectedPlanFunction, sortAnswers, sqlContext) match {
+        input, planFunction, expectedPlanFunction, sortAnswers, _sqlContext) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
@@ -151,13 +149,13 @@ object SparkPlanTest {
       planFunction: SparkPlan => SparkPlan,
       expectedPlanFunction: SparkPlan => SparkPlan,
       sortAnswers: Boolean,
-      sqlContext: SQLContext): Option[String] = {
+      _sqlContext: SQLContext): Option[String] = {
 
     val outputPlan = planFunction(input.queryExecution.sparkPlan)
     val expectedOutputPlan = expectedPlanFunction(input.queryExecution.sparkPlan)
 
     val expectedAnswer: Seq[Row] = try {
-      executePlan(expectedOutputPlan, sqlContext)
+      executePlan(expectedOutputPlan, _sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -172,7 +170,7 @@ object SparkPlanTest {
     }
 
     val actualAnswer: Seq[Row] = try {
-      executePlan(outputPlan, sqlContext)
+      executePlan(outputPlan, _sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -212,12 +210,12 @@ object SparkPlanTest {
       planFunction: Seq[SparkPlan] => SparkPlan,
       expectedAnswer: Seq[Row],
       sortAnswers: Boolean,
-      sqlContext: SQLContext): Option[String] = {
+      _sqlContext: SQLContext): Option[String] = {
 
     val outputPlan = planFunction(input.map(_.queryExecution.sparkPlan))
 
     val sparkAnswer: Seq[Row] = try {
-      executePlan(outputPlan, sqlContext)
+      executePlan(outputPlan, _sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -280,10 +278,10 @@ object SparkPlanTest {
     }
   }
 
-  private def executePlan(outputPlan: SparkPlan, sqlContext: SQLContext): Seq[Row] = {
+  private def executePlan(outputPlan: SparkPlan, _sqlContext: SQLContext): Seq[Row] = {
     // A very simple resolver to make writing tests easier. In contrast to the real resolver
     // this is always case sensitive and does not try to handle scoping or complex type resolution.
-    val resolvedPlan = sqlContext.prepareForExecution.execute(
+    val resolvedPlan = _sqlContext.prepareForExecution.execute(
       outputPlan transform {
         case plan: SparkPlan =>
           val inputMap = plan.children.flatMap(_.output).map(a => (a.name, a)).toMap
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
index 88bce0e319f9e..3158458edb831 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
@@ -19,25 +19,28 @@ package org.apache.spark.sql.execution
 
 import scala.util.Random
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.AccumulatorSuite
 import org.apache.spark.sql.{RandomDataGenerator, Row, SQLConf}
 import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 /**
  * A test suite that generates randomized data to test the [[TungstenSort]] operator.
  */
-class TungstenSortSuite extends SparkPlanTest with BeforeAndAfterAll {
+class TungstenSortSuite extends SparkPlanTest with SharedSQLContext {
 
   override def beforeAll(): Unit = {
-    TestSQLContext.conf.setConf(SQLConf.CODEGEN_ENABLED, true)
+    super.beforeAll()
+    ctx.conf.setConf(SQLConf.CODEGEN_ENABLED, true)
   }
 
   override def afterAll(): Unit = {
-    TestSQLContext.conf.setConf(SQLConf.CODEGEN_ENABLED, SQLConf.CODEGEN_ENABLED.defaultValue.get)
+    try {
+      ctx.conf.setConf(SQLConf.CODEGEN_ENABLED, SQLConf.CODEGEN_ENABLED.defaultValue.get)
+    } finally {
+      super.afterAll()
+    }
   }
 
   test("sort followed by limit") {
@@ -61,7 +64,7 @@ class TungstenSortSuite extends SparkPlanTest with BeforeAndAfterAll {
   }
 
   test("sorting updates peak execution memory") {
-    val sc = TestSQLContext.sparkContext
+    val sc = ctx.sparkContext
     AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "unsafe external sort") {
       checkThatPlansAgree(
         (1 to 100).map(v => Tuple1(v)).toDF("a"),
@@ -80,8 +83,8 @@ class TungstenSortSuite extends SparkPlanTest with BeforeAndAfterAll {
   ) {
     test(s"sorting on $dataType with nullable=$nullable, sortOrder=$sortOrder") {
       val inputData = Seq.fill(1000)(randomDataGenerator())
-      val inputDf = TestSQLContext.createDataFrame(
-        TestSQLContext.sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
+      val inputDf = ctx.createDataFrame(
+        ctx.sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
         StructType(StructField("a", dataType, nullable = true) :: Nil)
       )
       assert(TungstenSort.supportsSchema(inputDf.schema))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
index e03473041c3e9..d1f0b2b1fc52f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeFixedWidthAggregationMapSuite.scala
@@ -26,7 +26,7 @@ import org.scalatest.Matchers
 import org.apache.spark.sql.catalyst.expressions.{UnsafeRow, UnsafeProjection}
 import org.apache.spark.{TaskContextImpl, TaskContext, SparkFunSuite}
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 import org.apache.spark.unsafe.types.UTF8String
@@ -36,7 +36,10 @@ import org.apache.spark.unsafe.types.UTF8String
  *
  * Use [[testWithMemoryLeakDetection]] rather than [[test]] to construct test cases.
  */
-class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
+class UnsafeFixedWidthAggregationMapSuite
+  extends SparkFunSuite
+  with Matchers
+  with SharedSQLContext {
 
   import UnsafeFixedWidthAggregationMap._
 
@@ -171,9 +174,6 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
   }
 
   testWithMemoryLeakDetection("test external sorting") {
-    // Calling this make sure we have block manager and everything else setup.
-    TestSQLContext
-
     // Memory consumption in the beginning of the task.
     val initialMemoryConsumption = shuffleMemoryManager.getMemoryConsumptionForThisTask()
 
@@ -233,8 +233,6 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
   }
 
   testWithMemoryLeakDetection("test external sorting with an empty map") {
-    // Calling this make sure we have block manager and everything else setup.
-    TestSQLContext
 
     val map = new UnsafeFixedWidthAggregationMap(
       emptyAggregationBuffer,
@@ -282,8 +280,6 @@ class UnsafeFixedWidthAggregationMapSuite extends SparkFunSuite with Matchers {
   }
 
   testWithMemoryLeakDetection("test external sorting with empty records") {
-    // Calling this make sure we have block manager and everything else setup.
-    TestSQLContext
 
     // Memory consumption in the beginning of the task.
     val initialMemoryConsumption = shuffleMemoryManager.getMemoryConsumptionForThisTask()
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
index a9515a03acf2c..d3be568a8758c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeKVExternalSorterSuite.scala
@@ -23,15 +23,14 @@ import org.apache.spark._
 import org.apache.spark.sql.{RandomDataGenerator, Row}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.{InterpretedOrdering, UnsafeRow, UnsafeProjection}
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.memory.{ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
 
 /**
  * Test suite for [[UnsafeKVExternalSorter]], with randomly generated test data.
  */
-class UnsafeKVExternalSorterSuite extends SparkFunSuite {
-
+class UnsafeKVExternalSorterSuite extends SparkFunSuite with SharedSQLContext {
   private val keyTypes = Seq(IntegerType, FloatType, DoubleType, StringType)
   private val valueTypes = Seq(IntegerType, FloatType, DoubleType, StringType)
 
@@ -109,8 +108,6 @@ class UnsafeKVExternalSorterSuite extends SparkFunSuite {
       inputData: Seq[(InternalRow, InternalRow)],
       pageSize: Long,
       spill: Boolean): Unit = {
-    // Calling this make sure we have block manager and everything else setup.
-    TestSQLContext
 
     val taskMemMgr = new TaskMemoryManager(new ExecutorMemoryManager(MemoryAllocator.HEAP))
     val shuffleMemMgr = new TestShuffleMemoryManager
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
index ac22c2f3c0a58..5fdb82b067728 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
@@ -21,15 +21,12 @@ import org.apache.spark._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection
 import org.apache.spark.sql.execution.metric.SQLMetrics
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.unsafe.memory.TaskMemoryManager
 
-class TungstenAggregationIteratorSuite extends SparkFunSuite {
+class TungstenAggregationIteratorSuite extends SparkFunSuite with SharedSQLContext {
 
   test("memory acquired on construction") {
-    // set up environment
-    val ctx = TestSQLContext
-
     val taskMemoryManager = new TaskMemoryManager(SparkEnv.get.executorMemoryManager)
     val taskContext = new TaskContextImpl(0, 0, 0, 0, taskMemoryManager, null, Seq.empty)
     TaskContext.setTaskContext(taskContext)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
index 73d5621897819..1174b27732f22 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
@@ -24,22 +24,16 @@ import com.fasterxml.jackson.core.JsonFactory
 import org.apache.spark.rdd.RDD
 import org.scalactic.Tolerance._
 
-import org.apache.spark.sql.{SQLContext, QueryTest, Row, SQLConf}
-import org.apache.spark.sql.TestData._
+import org.apache.spark.sql.{QueryTest, Row, SQLConf}
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.execution.datasources.{ResolvedDataSource, LogicalRelation}
 import org.apache.spark.sql.execution.datasources.json.InferSchema.compatibleType
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.util.Utils
 
-class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
-
-  protected lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  override def sqlContext: SQLContext = ctx // used by SQLTestUtils
-
-  import ctx.sql
-  import ctx.implicits._
+class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
+  import testImplicits._
 
   test("Type promotion") {
     def checkTypePromotion(expected: Any, actual: Any) {
@@ -596,7 +590,8 @@ class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
 
     val schema = StructType(StructField("a", LongType, true) :: Nil)
     val logicalRelation =
-      ctx.read.schema(schema).json(path).queryExecution.analyzed.asInstanceOf[LogicalRelation]
+      ctx.read.schema(schema).json(path)
+        .queryExecution.analyzed.asInstanceOf[LogicalRelation]
     val relationWithSchema = logicalRelation.relation.asInstanceOf[JSONRelation]
     assert(relationWithSchema.paths === Array(path))
     assert(relationWithSchema.schema === schema)
@@ -1040,31 +1035,29 @@ class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
   }
 
   test("JSONRelation equality test") {
-    val context = org.apache.spark.sql.test.TestSQLContext
-
     val relation0 = new JSONRelation(
       Some(empty),
       1.0,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(context)
+      None, None)(ctx)
     val logicalRelation0 = LogicalRelation(relation0)
     val relation1 = new JSONRelation(
       Some(singleRow),
       1.0,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(context)
+      None, None)(ctx)
     val logicalRelation1 = LogicalRelation(relation1)
     val relation2 = new JSONRelation(
       Some(singleRow),
       0.5,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(context)
+      None, None)(ctx)
     val logicalRelation2 = LogicalRelation(relation2)
     val relation3 = new JSONRelation(
       Some(singleRow),
       1.0,
       Some(StructType(StructField("b", IntegerType, true) :: Nil)),
-      None, None)(context)
+      None, None)(ctx)
     val logicalRelation3 = LogicalRelation(relation3)
 
     assert(relation0 !== relation1)
@@ -1089,14 +1082,14 @@ class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
         .map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path)
 
       val d1 = ResolvedDataSource(
-        context,
+        ctx,
         userSpecifiedSchema = None,
         partitionColumns = Array.empty[String],
         provider = classOf[DefaultSource].getCanonicalName,
         options = Map("path" -> path))
 
       val d2 = ResolvedDataSource(
-        context,
+        ctx,
         userSpecifiedSchema = None,
         partitionColumns = Array.empty[String],
         provider = classOf[DefaultSource].getCanonicalName,
@@ -1162,11 +1155,12 @@ class JsonSuite extends QueryTest with SQLTestUtils with TestJsonData {
         "abd")
 
         ctx.read.json(root.getAbsolutePath).registerTempTable("test_myjson_with_part")
-        checkAnswer(
-          sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abc'"), Row(4))
-        checkAnswer(
-          sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abd'"), Row(5))
-        checkAnswer(sql("SELECT count(a) FROM test_myjson_with_part where d1 = 1"), Row(9))
+        checkAnswer(sql(
+          "SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abc'"), Row(4))
+        checkAnswer(sql(
+          "SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abd'"), Row(5))
+        checkAnswer(sql(
+          "SELECT count(a) FROM test_myjson_with_part where d1 = 1"), Row(9))
     })
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
index 6b62c9a003df6..2864181cf91d5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
@@ -20,12 +20,11 @@ package org.apache.spark.sql.execution.datasources.json
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SQLContext
 
-trait TestJsonData {
-
-  protected def ctx: SQLContext
+private[json] trait TestJsonData {
+  protected def _sqlContext: SQLContext
 
   def primitiveFieldAndType: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"string":"this is a simple string.",
           "integer":10,
           "long":21474836470,
@@ -36,7 +35,7 @@ trait TestJsonData {
       }"""  :: Nil)
 
   def primitiveFieldValueTypeConflict: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"num_num_1":11, "num_num_2":null, "num_num_3": 1.1,
           "num_bool":true, "num_str":13.1, "str_bool":"str1"}""" ::
       """{"num_num_1":null, "num_num_2":21474836470.9, "num_num_3": null,
@@ -47,14 +46,14 @@ trait TestJsonData {
           "num_bool":null, "num_str":92233720368547758070, "str_bool":null}""" :: Nil)
 
   def jsonNullStruct: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"nullstr":"","ip":"27.31.100.29","headers":{"Host":"1.abc.com","Charset":"UTF-8"}}""" ::
         """{"nullstr":"","ip":"27.31.100.29","headers":{}}""" ::
         """{"nullstr":"","ip":"27.31.100.29","headers":""}""" ::
         """{"nullstr":null,"ip":"27.31.100.29","headers":null}""" :: Nil)
 
   def complexFieldValueTypeConflict: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"num_struct":11, "str_array":[1, 2, 3],
           "array":[], "struct_array":[], "struct": {}}""" ::
       """{"num_struct":{"field":false}, "str_array":null,
@@ -65,14 +64,14 @@ trait TestJsonData {
           "array":[7], "struct_array":{"field": true}, "struct": {"field": "str"}}""" :: Nil)
 
   def arrayElementTypeConflict: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"array1": [1, 1.1, true, null, [], {}, [2,3,4], {"field":"str"}],
           "array2": [{"field":214748364700}, {"field":1}]}""" ::
       """{"array3": [{"field":"str"}, {"field":1}]}""" ::
       """{"array3": [1, 2, 3]}""" :: Nil)
 
   def missingFields: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"a":true}""" ::
       """{"b":21474836470}""" ::
       """{"c":[33, 44]}""" ::
@@ -80,7 +79,7 @@ trait TestJsonData {
       """{"e":"str"}""" :: Nil)
 
   def complexFieldAndType1: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"struct":{"field1": true, "field2": 92233720368547758070},
           "structWithArrayFields":{"field1":[4, 5, 6], "field2":["str1", "str2"]},
           "arrayOfString":["str1", "str2"],
@@ -96,7 +95,7 @@ trait TestJsonData {
          }"""  :: Nil)
 
   def complexFieldAndType2: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"arrayOfStruct":[{"field1": true, "field2": "str1"}, {"field1": false}, {"field3": null}],
           "complexArrayOfStruct": [
           {
@@ -150,7 +149,7 @@ trait TestJsonData {
       }""" :: Nil)
 
   def mapType1: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"map": {"a": 1}}""" ::
       """{"map": {"b": 2}}""" ::
       """{"map": {"c": 3}}""" ::
@@ -158,7 +157,7 @@ trait TestJsonData {
       """{"map": {"e": null}}""" :: Nil)
 
   def mapType2: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"map": {"a": {"field1": [1, 2, 3, null]}}}""" ::
       """{"map": {"b": {"field2": 2}}}""" ::
       """{"map": {"c": {"field1": [], "field2": 4}}}""" ::
@@ -167,21 +166,21 @@ trait TestJsonData {
       """{"map": {"f": {"field1": null}}}""" :: Nil)
 
   def nullsInArrays: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{"field1":[[null], [[["Test"]]]]}""" ::
       """{"field2":[null, [{"Test":1}]]}""" ::
       """{"field3":[[null], [{"Test":"2"}]]}""" ::
       """{"field4":[[null, [1,2,3]]]}""" :: Nil)
 
   def jsonArray: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """[{"a":"str_a_1"}]""" ::
       """[{"a":"str_a_2"}, {"b":"str_b_3"}]""" ::
       """{"b":"str_b_4", "a":"str_a_4", "c":"str_c_4"}""" ::
       """[]""" :: Nil)
 
   def corruptRecords: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{""" ::
       """""" ::
       """{"a":1, b:2}""" ::
@@ -190,7 +189,7 @@ trait TestJsonData {
       """]""" :: Nil)
 
   def emptyRecords: RDD[String] =
-    ctx.sparkContext.parallelize(
+    _sqlContext.sparkContext.parallelize(
       """{""" ::
         """""" ::
         """{"a": {}}""" ::
@@ -198,9 +197,8 @@ trait TestJsonData {
         """{"b": [{"c": {}}]}""" ::
         """]""" :: Nil)
 
-  lazy val singleRow: RDD[String] =
-    ctx.sparkContext.parallelize(
-      """{"a":123}""" :: Nil)
 
-  def empty: RDD[String] = ctx.sparkContext.parallelize(Seq[String]())
+  lazy val singleRow: RDD[String] = _sqlContext.sparkContext.parallelize("""{"a":123}""" :: Nil)
+
+  def empty: RDD[String] = _sqlContext.sparkContext.parallelize(Seq[String]())
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index 866a975ad5404..82d40e2b61a10 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -27,18 +27,16 @@ import org.apache.avro.generic.IndexedRecord
 import org.apache.hadoop.fs.Path
 import org.apache.parquet.avro.AvroParquetWriter
 
-import org.apache.spark.sql.execution.datasources.parquet.test.avro.{Nested, ParquetAvroCompat, ParquetEnum, Suit}
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.execution.datasources.parquet.test.avro._
+import org.apache.spark.sql.test.SharedSQLContext
 
-class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest {
+class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest with SharedSQLContext {
   import ParquetCompatibilityTest._
 
-  override val sqlContext: SQLContext = TestSQLContext
-
   private def withWriter[T <: IndexedRecord]
       (path: String, schema: Schema)
-      (f: AvroParquetWriter[T] => Unit) = {
+      (f: AvroParquetWriter[T] => Unit): Unit = {
     val writer = new AvroParquetWriter[T](new Path(path), schema)
     try f(writer) finally writer.close()
   }
@@ -129,7 +127,7 @@ class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest {
   }
 
   test("SPARK-9407 Don't push down predicates involving Parquet ENUM columns") {
-    import sqlContext.implicits._
+    import testImplicits._
 
     withTempPath { dir =>
       val path = dir.getCanonicalPath
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index 0ea64aa2a509b..b3406729fcc5e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -22,16 +22,18 @@ import scala.collection.JavaConversions._
 import org.apache.hadoop.fs.{Path, PathFilter}
 import org.apache.parquet.hadoop.ParquetFileReader
 import org.apache.parquet.schema.MessageType
-import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql.QueryTest
 
-abstract class ParquetCompatibilityTest extends QueryTest with ParquetTest with BeforeAndAfterAll {
-  def readParquetSchema(path: String): MessageType = {
+/**
+ * Helper class for testing Parquet compatibility.
+ */
+private[sql] abstract class ParquetCompatibilityTest extends QueryTest with ParquetTest {
+  protected def readParquetSchema(path: String): MessageType = {
     readParquetSchema(path, { path => !path.getName.startsWith("_") })
   }
 
-  def readParquetSchema(path: String, pathFilter: Path => Boolean): MessageType = {
+  protected def readParquetSchema(path: String, pathFilter: Path => Boolean): MessageType = {
     val fsPath = new Path(path)
     val fs = fsPath.getFileSystem(configuration)
     val parquetFiles = fs.listStatus(fsPath, new PathFilter {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
index 7dd9680d8cd65..5b4e568bb9838 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
@@ -20,12 +20,13 @@ package org.apache.spark.sql.execution.datasources.parquet
 import org.apache.parquet.filter2.predicate.Operators._
 import org.apache.parquet.filter2.predicate.{FilterPredicate, Operators}
 
+import org.apache.spark.sql.{Column, DataFrame, QueryTest, Row, SQLConf}
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.execution.datasources.LogicalRelation
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.{Column, DataFrame, QueryTest, Row, SQLConf}
 
 /**
  * A test suite that tests Parquet filter2 API based filter pushdown optimization.
@@ -39,8 +40,7 @@ import org.apache.spark.sql.{Column, DataFrame, QueryTest, Row, SQLConf}
  * 2. `Tuple1(Option(x))` is used together with `AnyVal` types like `Int` to ensure the inferred
  *    data type is nullable.
  */
-class ParquetFilterSuite extends QueryTest with ParquetTest {
-  lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
+class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContext {
 
   private def checkFilterPredicate(
       df: DataFrame,
@@ -301,7 +301,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest {
   }
 
   test("SPARK-6554: don't push down predicates which reference partition columns") {
-    import sqlContext.implicits._
+    import testImplicits._
 
     withSQLConf(SQLConf.PARQUET_FILTER_PUSHDOWN_ENABLED.key -> "true") {
       withTempPath { dir =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index cb166349fdb26..d819f3ab5e6ab 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -37,6 +37,7 @@ import org.apache.spark.SparkException
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 // Write support class for nested groups: ParquetWriter initializes GroupWriteSupport
@@ -62,9 +63,8 @@ private[parquet] class TestGroupWriteSupport(schema: MessageType) extends WriteS
 /**
  * A test suite that tests basic Parquet I/O.
  */
-class ParquetIOSuite extends QueryTest with ParquetTest {
-  lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.implicits._
+class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
+  import testImplicits._
 
   /**
    * Writes `data` to a Parquet file, reads it back and check file contents.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
index 73152de244759..ed8bafb10c60b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
@@ -26,13 +26,13 @@ import scala.collection.mutable.ArrayBuffer
 import com.google.common.io.Files
 import org.apache.hadoop.fs.Path
 
+import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Literal
 import org.apache.spark.sql.execution.datasources.{LogicalRelation, PartitionSpec, Partition, PartitioningUtils}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
-import org.apache.spark.sql._
 import org.apache.spark.unsafe.types.UTF8String
-import PartitioningUtils._
 
 // The data where the partitioning key exists only in the directory structure.
 case class ParquetData(intField: Int, stringField: String)
@@ -40,11 +40,9 @@ case class ParquetData(intField: Int, stringField: String)
 // The data that also includes the partitioning key
 case class ParquetDataWithKey(intField: Int, pi: Int, stringField: String, ps: String)
 
-class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest {
-
-  override lazy val sqlContext: SQLContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.implicits._
-  import sqlContext.sql
+class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest with SharedSQLContext {
+  import PartitioningUtils._
+  import testImplicits._
 
   val defaultPartitionName = "__HIVE_DEFAULT_PARTITION__"
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala
index 981334cf771cf..b290429c2a021 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetProtobufCompatibilitySuite.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.test.SharedSQLContext
 
-class ParquetProtobufCompatibilitySuite extends ParquetCompatibilityTest {
-  override def sqlContext: SQLContext = TestSQLContext
+class ParquetProtobufCompatibilitySuite extends ParquetCompatibilityTest with SharedSQLContext {
 
   private def readParquetProtobufFile(name: String): DataFrame = {
     val url = Thread.currentThread().getContextClassLoader.getResource(name)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index 5e6d9c1cd44a8..e2f2a8c744783 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -21,16 +21,15 @@ import java.io.File
 
 import org.apache.hadoop.fs.Path
 
-import org.apache.spark.sql.types._
 import org.apache.spark.sql.{QueryTest, Row, SQLConf}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
 /**
  * A test suite that tests various Parquet queries.
  */
-class ParquetQuerySuite extends QueryTest with ParquetTest {
-  lazy val sqlContext = org.apache.spark.sql.test.TestSQLContext
-  import sqlContext.sql
+class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext {
 
   test("simple select queries") {
     withParquetTable((0 until 10).map(i => (i, i.toString)), "t") {
@@ -41,22 +40,22 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
 
   test("appending") {
     val data = (0 until 10).map(i => (i, i.toString))
-    sqlContext.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
+    ctx.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
     withParquetTable(data, "t") {
       sql("INSERT INTO TABLE t SELECT * FROM tmp")
-      checkAnswer(sqlContext.table("t"), (data ++ data).map(Row.fromTuple))
+      checkAnswer(ctx.table("t"), (data ++ data).map(Row.fromTuple))
     }
-    sqlContext.catalog.unregisterTable(Seq("tmp"))
+    ctx.catalog.unregisterTable(Seq("tmp"))
   }
 
   test("overwriting") {
     val data = (0 until 10).map(i => (i, i.toString))
-    sqlContext.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
+    ctx.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
     withParquetTable(data, "t") {
       sql("INSERT OVERWRITE TABLE t SELECT * FROM tmp")
-      checkAnswer(sqlContext.table("t"), data.map(Row.fromTuple))
+      checkAnswer(ctx.table("t"), data.map(Row.fromTuple))
     }
-    sqlContext.catalog.unregisterTable(Seq("tmp"))
+    ctx.catalog.unregisterTable(Seq("tmp"))
   }
 
   test("self-join") {
@@ -119,9 +118,9 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
     val schema = StructType(List(StructField("d", DecimalType(18, 0), false),
       StructField("time", TimestampType, false)).toArray)
     withTempPath { file =>
-      val df = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(data), schema)
+      val df = ctx.createDataFrame(ctx.sparkContext.parallelize(data), schema)
       df.write.parquet(file.getCanonicalPath)
-      val df2 = sqlContext.read.parquet(file.getCanonicalPath)
+      val df2 = ctx.read.parquet(file.getCanonicalPath)
       checkAnswer(df2, df.collect().toSeq)
     }
   }
@@ -130,12 +129,12 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
     def testSchemaMerging(expectedColumnNumber: Int): Unit = {
       withTempDir { dir =>
         val basePath = dir.getCanonicalPath
-        sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-        sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
+        ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+        ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
         // delete summary files, so if we don't merge part-files, one column will not be included.
         Utils.deleteRecursively(new File(basePath + "/foo=1/_metadata"))
         Utils.deleteRecursively(new File(basePath + "/foo=1/_common_metadata"))
-        assert(sqlContext.read.parquet(basePath).columns.length === expectedColumnNumber)
+        assert(ctx.read.parquet(basePath).columns.length === expectedColumnNumber)
       }
     }
 
@@ -154,9 +153,9 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
     def testSchemaMerging(expectedColumnNumber: Int): Unit = {
       withTempDir { dir =>
         val basePath = dir.getCanonicalPath
-        sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-        sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
-        assert(sqlContext.read.parquet(basePath).columns.length === expectedColumnNumber)
+        ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+        ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
+        assert(ctx.read.parquet(basePath).columns.length === expectedColumnNumber)
       }
     }
 
@@ -172,19 +171,19 @@ class ParquetQuerySuite extends QueryTest with ParquetTest {
   test("SPARK-8990 DataFrameReader.parquet() should respect user specified options") {
     withTempPath { dir =>
       val basePath = dir.getCanonicalPath
-      sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-      sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=a").toString)
+      ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+      ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=a").toString)
 
       // Disables the global SQL option for schema merging
       withSQLConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED.key -> "false") {
         assertResult(2) {
           // Disables schema merging via data source option
-          sqlContext.read.option("mergeSchema", "false").parquet(basePath).columns.length
+          ctx.read.option("mergeSchema", "false").parquet(basePath).columns.length
         }
 
         assertResult(3) {
           // Enables schema merging via data source option
-          sqlContext.read.option("mergeSchema", "true").parquet(basePath).columns.length
+          ctx.read.option("mergeSchema", "true").parquet(basePath).columns.length
         }
       }
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 971f71e27bfc6..9dcbc1a047bea 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -22,13 +22,11 @@ import scala.reflect.runtime.universe.TypeTag
 
 import org.apache.parquet.schema.MessageTypeParser
 
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.ScalaReflection
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
-abstract class ParquetSchemaTest extends SparkFunSuite with ParquetTest {
-  val sqlContext = TestSQLContext
+abstract class ParquetSchemaTest extends ParquetTest with SharedSQLContext {
 
   /**
    * Checks whether the reflected Parquet message type for product type `T` conforms `messageType`.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
index 3c6e54db4bca7..5dbc7d1630f27 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
@@ -22,9 +22,8 @@ import java.io.File
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.test.SQLTestUtils
-import org.apache.spark.sql.{DataFrame, SaveMode}
+import org.apache.spark.sql.{DataFrame, SaveMode, SQLContext}
 
 /**
  * A helper trait that provides convenient facilities for Parquet testing.
@@ -33,7 +32,9 @@ import org.apache.spark.sql.{DataFrame, SaveMode}
  * convenient to use tuples rather than special case classes when writing test cases/suites.
  * Especially, `Tuple1.apply` can be used to easily wrap a single type/value.
  */
-private[sql] trait ParquetTest extends SQLTestUtils { this: SparkFunSuite =>
+private[sql] trait ParquetTest extends SQLTestUtils {
+  protected def _sqlContext: SQLContext
+
   /**
    * Writes `data` to a Parquet file, which is then passed to `f` and will be deleted after `f`
    * returns.
@@ -42,7 +43,7 @@ private[sql] trait ParquetTest extends SQLTestUtils { this: SparkFunSuite =>
       (data: Seq[T])
       (f: String => Unit): Unit = {
     withTempPath { file =>
-      sqlContext.createDataFrame(data).write.parquet(file.getCanonicalPath)
+      _sqlContext.createDataFrame(data).write.parquet(file.getCanonicalPath)
       f(file.getCanonicalPath)
     }
   }
@@ -54,7 +55,7 @@ private[sql] trait ParquetTest extends SQLTestUtils { this: SparkFunSuite =>
   protected def withParquetDataFrame[T <: Product: ClassTag: TypeTag]
       (data: Seq[T])
       (f: DataFrame => Unit): Unit = {
-    withParquetFile(data)(path => f(sqlContext.read.parquet(path)))
+    withParquetFile(data)(path => f(_sqlContext.read.parquet(path)))
   }
 
   /**
@@ -66,14 +67,14 @@ private[sql] trait ParquetTest extends SQLTestUtils { this: SparkFunSuite =>
       (data: Seq[T], tableName: String)
       (f: => Unit): Unit = {
     withParquetDataFrame(data) { df =>
-      sqlContext.registerDataFrameAsTable(df, tableName)
+      _sqlContext.registerDataFrameAsTable(df, tableName)
       withTempTable(tableName)(f)
     }
   }
 
   protected def makeParquetFile[T <: Product: ClassTag: TypeTag](
       data: Seq[T], path: File): Unit = {
-    sqlContext.createDataFrame(data).write.mode(SaveMode.Overwrite).parquet(path.getCanonicalPath)
+    _sqlContext.createDataFrame(data).write.mode(SaveMode.Overwrite).parquet(path.getCanonicalPath)
   }
 
   protected def makeParquetFile[T <: Product: ClassTag: TypeTag](
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
index 92b1d822172d5..b789c5a106e56 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import org.apache.spark.sql.test.TestSQLContext
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.test.SharedSQLContext
 
-class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest {
+class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest with SharedSQLContext {
   import ParquetCompatibilityTest._
 
-  override val sqlContext: SQLContext = TestSQLContext
-
   private val parquetFilePath =
     Thread.currentThread().getContextClassLoader.getResource("parquet-thrift-compat.snappy.parquet")
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
index 239deb7973845..22189477d277d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/debug/DebuggingSuite.scala
@@ -18,10 +18,10 @@
 package org.apache.spark.sql.execution.debug
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.TestData._
-import org.apache.spark.sql.test.TestSQLContext._
+import org.apache.spark.sql.test.SharedSQLContext
+
+class DebuggingSuite extends SparkFunSuite with SharedSQLContext {
 
-class DebuggingSuite extends SparkFunSuite {
   test("DataFrame.debug()") {
     testData.debug()
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index d33a967093ca5..4c9187a9a7106 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -23,12 +23,12 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.metric.SQLMetrics
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 import org.apache.spark.util.collection.CompactBuffer
 
 
-class HashedRelationSuite extends SparkFunSuite {
+class HashedRelationSuite extends SparkFunSuite with SharedSQLContext {
 
   // Key is simply the record itself
   private val keyProjection = new Projection {
@@ -37,7 +37,7 @@ class HashedRelationSuite extends SparkFunSuite {
 
   test("GeneralHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
     val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[GeneralHashedRelation])
 
@@ -53,7 +53,7 @@ class HashedRelationSuite extends SparkFunSuite {
 
   test("UniqueKeyHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
     val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[UniqueKeyHashedRelation])
 
@@ -73,7 +73,7 @@ class HashedRelationSuite extends SparkFunSuite {
   test("UnsafeHashedRelation") {
     val schema = StructType(StructField("a", IntegerType, true) :: Nil)
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
     val toUnsafe = UnsafeProjection.create(schema)
     val unsafeData = data.map(toUnsafe(_).copy()).toArray
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
index ddff7cebcc17d..cc649b9bd4c45 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
@@ -17,97 +17,19 @@
 
 package org.apache.spark.sql.execution.joins
 
+import org.apache.spark.sql.{DataFrame, execution, Row, SQLConf}
+import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
 import org.apache.spark.sql.catalyst.plans.Inner
 import org.apache.spark.sql.catalyst.plans.logical.Join
-import org.apache.spark.sql.test.SQLTestUtils
-import org.apache.spark.sql.types.{IntegerType, StringType, StructType}
-import org.apache.spark.sql.{SQLConf, execution, Row, DataFrame}
-import org.apache.spark.sql.catalyst.expressions.Expression
 import org.apache.spark.sql.execution._
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types.{IntegerType, StringType, StructType}
 
-class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
-
-  private def testInnerJoin(
-      testName: String,
-      leftRows: DataFrame,
-      rightRows: DataFrame,
-      condition: Expression,
-      expectedAnswer: Seq[Product]): Unit = {
-    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
-    ExtractEquiJoinKeys.unapply(join).foreach {
-      case (joinType, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
-
-        def makeBroadcastHashJoin(left: SparkPlan, right: SparkPlan, side: BuildSide) = {
-          val broadcastHashJoin =
-            execution.joins.BroadcastHashJoin(leftKeys, rightKeys, side, left, right)
-          boundCondition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin)
-        }
-
-        def makeShuffledHashJoin(left: SparkPlan, right: SparkPlan, side: BuildSide) = {
-          val shuffledHashJoin =
-            execution.joins.ShuffledHashJoin(leftKeys, rightKeys, side, left, right)
-          val filteredJoin =
-            boundCondition.map(Filter(_, shuffledHashJoin)).getOrElse(shuffledHashJoin)
-          EnsureRequirements(sqlContext).apply(filteredJoin)
-        }
-
-        def makeSortMergeJoin(left: SparkPlan, right: SparkPlan) = {
-          val sortMergeJoin =
-            execution.joins.SortMergeJoin(leftKeys, rightKeys, left, right)
-          val filteredJoin = boundCondition.map(Filter(_, sortMergeJoin)).getOrElse(sortMergeJoin)
-          EnsureRequirements(sqlContext).apply(filteredJoin)
-        }
-
-        test(s"$testName using BroadcastHashJoin (build=left)") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              makeBroadcastHashJoin(left, right, joins.BuildLeft),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
-        }
-
-        test(s"$testName using BroadcastHashJoin (build=right)") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              makeBroadcastHashJoin(left, right, joins.BuildRight),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
-        }
-
-        test(s"$testName using ShuffledHashJoin (build=left)") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              makeShuffledHashJoin(left, right, joins.BuildLeft),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
-        }
-
-        test(s"$testName using ShuffledHashJoin (build=right)") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              makeShuffledHashJoin(left, right, joins.BuildRight),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
-        }
+class InnerJoinSuite extends SparkPlanTest with SharedSQLContext {
 
-        test(s"$testName using SortMergeJoin") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              makeSortMergeJoin(left, right),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
-        }
-    }
-  }
-
-  {
-    val upperCaseData = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+  private lazy val myUpperCaseData = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
       Row(1, "A"),
       Row(2, "B"),
       Row(3, "C"),
@@ -117,7 +39,8 @@ class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
       Row(null, "G")
     )), new StructType().add("N", IntegerType).add("L", StringType))
 
-    val lowerCaseData = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
+  private lazy val myLowerCaseData = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
       Row(1, "a"),
       Row(2, "b"),
       Row(3, "c"),
@@ -125,21 +48,7 @@ class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
       Row(null, "e")
     )), new StructType().add("n", IntegerType).add("l", StringType))
 
-    testInnerJoin(
-      "inner join, one match per row",
-      upperCaseData,
-      lowerCaseData,
-      (upperCaseData.col("N") === lowerCaseData.col("n")).expr,
-      Seq(
-        (1, "A", 1, "a"),
-        (2, "B", 2, "b"),
-        (3, "C", 3, "c"),
-        (4, "D", 4, "d")
-      )
-    )
-  }
-
-  private val testData2 = Seq(
+  private lazy val myTestData = Seq(
     (1, 1),
     (1, 2),
     (2, 1),
@@ -148,14 +57,139 @@ class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
     (3, 2)
   ).toDF("a", "b")
 
+  // Note: the input dataframes and expression must be evaluated lazily because
+  // the SQLContext should be used only within a test to keep SQL tests stable
+  private def testInnerJoin(
+      testName: String,
+      leftRows: => DataFrame,
+      rightRows: => DataFrame,
+      condition: () => Expression,
+      expectedAnswer: Seq[Product]): Unit = {
+
+    def extractJoinParts(): Option[ExtractEquiJoinKeys.ReturnType] = {
+      val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition()))
+      ExtractEquiJoinKeys.unapply(join)
+    }
+
+    def makeBroadcastHashJoin(
+        leftKeys: Seq[Expression],
+        rightKeys: Seq[Expression],
+        boundCondition: Option[Expression],
+        leftPlan: SparkPlan,
+        rightPlan: SparkPlan,
+        side: BuildSide) = {
+      val broadcastHashJoin =
+        execution.joins.BroadcastHashJoin(leftKeys, rightKeys, side, leftPlan, rightPlan)
+      boundCondition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin)
+    }
+
+    def makeShuffledHashJoin(
+        leftKeys: Seq[Expression],
+        rightKeys: Seq[Expression],
+        boundCondition: Option[Expression],
+        leftPlan: SparkPlan,
+        rightPlan: SparkPlan,
+        side: BuildSide) = {
+      val shuffledHashJoin =
+        execution.joins.ShuffledHashJoin(leftKeys, rightKeys, side, leftPlan, rightPlan)
+      val filteredJoin =
+        boundCondition.map(Filter(_, shuffledHashJoin)).getOrElse(shuffledHashJoin)
+      EnsureRequirements(sqlContext).apply(filteredJoin)
+    }
+
+    def makeSortMergeJoin(
+        leftKeys: Seq[Expression],
+        rightKeys: Seq[Expression],
+        boundCondition: Option[Expression],
+        leftPlan: SparkPlan,
+        rightPlan: SparkPlan) = {
+      val sortMergeJoin =
+        execution.joins.SortMergeJoin(leftKeys, rightKeys, leftPlan, rightPlan)
+      val filteredJoin = boundCondition.map(Filter(_, sortMergeJoin)).getOrElse(sortMergeJoin)
+      EnsureRequirements(sqlContext).apply(filteredJoin)
+    }
+
+    test(s"$testName using BroadcastHashJoin (build=left)") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (leftPlan: SparkPlan, rightPlan: SparkPlan) =>
+            makeBroadcastHashJoin(
+              leftKeys, rightKeys, boundCondition, leftPlan, rightPlan, joins.BuildLeft),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
+      }
+    }
+
+    test(s"$testName using BroadcastHashJoin (build=right)") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (leftPlan: SparkPlan, rightPlan: SparkPlan) =>
+            makeBroadcastHashJoin(
+              leftKeys, rightKeys, boundCondition, leftPlan, rightPlan, joins.BuildRight),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
+      }
+    }
+
+    test(s"$testName using ShuffledHashJoin (build=left)") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (leftPlan: SparkPlan, rightPlan: SparkPlan) =>
+            makeShuffledHashJoin(
+              leftKeys, rightKeys, boundCondition, leftPlan, rightPlan, joins.BuildLeft),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
+      }
+    }
+
+    test(s"$testName using ShuffledHashJoin (build=right)") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (leftPlan: SparkPlan, rightPlan: SparkPlan) =>
+            makeShuffledHashJoin(
+              leftKeys, rightKeys, boundCondition, leftPlan, rightPlan, joins.BuildRight),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
+      }
+    }
+
+    test(s"$testName using SortMergeJoin") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (leftPlan: SparkPlan, rightPlan: SparkPlan) =>
+            makeSortMergeJoin(leftKeys, rightKeys, boundCondition, leftPlan, rightPlan),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
+      }
+    }
+  }
+
+  testInnerJoin(
+    "inner join, one match per row",
+    myUpperCaseData,
+    myLowerCaseData,
+    () => (myUpperCaseData.col("N") === myLowerCaseData.col("n")).expr,
+    Seq(
+      (1, "A", 1, "a"),
+      (2, "B", 2, "b"),
+      (3, "C", 3, "c"),
+      (4, "D", 4, "d")
+    )
+  )
+
   {
-    val left = testData2.where("a = 1")
-    val right = testData2.where("a = 1")
+    lazy val left = myTestData.where("a = 1")
+    lazy val right = myTestData.where("a = 1")
     testInnerJoin(
       "inner join, multiple matches",
       left,
       right,
-      (left.col("a") === right.col("a")).expr,
+      () => (left.col("a") === right.col("a")).expr,
       Seq(
         (1, 1, 1, 1),
         (1, 1, 1, 2),
@@ -166,13 +200,13 @@ class InnerJoinSuite extends SparkPlanTest with SQLTestUtils {
   }
 
   {
-    val left = testData2.where("a = 1")
-    val right = testData2.where("a = 2")
+    lazy val left = myTestData.where("a = 1")
+    lazy val right = myTestData.where("a = 2")
     testInnerJoin(
       "inner join, no matches",
       left,
       right,
-      (left.col("a") === right.col("a")).expr,
+      () => (left.col("a") === right.col("a")).expr,
       Seq.empty
     )
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
index e16f5e39aa2f4..a1a617d7b7398 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
@@ -17,28 +17,65 @@
 
 package org.apache.spark.sql.execution.joins
 
+import org.apache.spark.sql.{DataFrame, Row, SQLConf}
 import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
+import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical.Join
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.catalyst.expressions.{And, Expression, LessThan}
+import org.apache.spark.sql.execution.{EnsureRequirements, SparkPlan, SparkPlanTest}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.{IntegerType, DoubleType, StructType}
-import org.apache.spark.sql.{SQLConf, DataFrame, Row}
-import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans._
-import org.apache.spark.sql.execution.{EnsureRequirements, joins, SparkPlan, SparkPlanTest}
 
-class OuterJoinSuite extends SparkPlanTest with SQLTestUtils {
+class OuterJoinSuite extends SparkPlanTest with SharedSQLContext {
+
+  private lazy val left = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
+      Row(1, 2.0),
+      Row(2, 100.0),
+      Row(2, 1.0), // This row is duplicated to ensure that we will have multiple buffered matches
+      Row(2, 1.0),
+      Row(3, 3.0),
+      Row(5, 1.0),
+      Row(6, 6.0),
+      Row(null, null)
+    )), new StructType().add("a", IntegerType).add("b", DoubleType))
+
+  private lazy val right = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
+      Row(0, 0.0),
+      Row(2, 3.0), // This row is duplicated to ensure that we will have multiple buffered matches
+      Row(2, -1.0),
+      Row(2, -1.0),
+      Row(2, 3.0),
+      Row(3, 2.0),
+      Row(4, 1.0),
+      Row(5, 3.0),
+      Row(7, 7.0),
+      Row(null, null)
+    )), new StructType().add("c", IntegerType).add("d", DoubleType))
+
+  private lazy val condition = {
+    And((left.col("a") === right.col("c")).expr,
+      LessThan(left.col("b").expr, right.col("d").expr))
+  }
 
+  // Note: the input dataframes and expression must be evaluated lazily because
+  // the SQLContext should be used only within a test to keep SQL tests stable
   private def testOuterJoin(
       testName: String,
-      leftRows: DataFrame,
-      rightRows: DataFrame,
+      leftRows: => DataFrame,
+      rightRows: => DataFrame,
       joinType: JoinType,
-      condition: Expression,
+      condition: => Expression,
       expectedAnswer: Seq[Product]): Unit = {
-    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
-    ExtractEquiJoinKeys.unapply(join).foreach {
-      case (_, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
-        test(s"$testName using ShuffledHashOuterJoin") {
+
+    def extractJoinParts(): Option[ExtractEquiJoinKeys.ReturnType] = {
+      val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
+      ExtractEquiJoinKeys.unapply(join)
+    }
+
+    test(s"$testName using ShuffledHashOuterJoin") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
           withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
             checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
               EnsureRequirements(sqlContext).apply(
@@ -46,19 +83,23 @@ class OuterJoinSuite extends SparkPlanTest with SQLTestUtils {
               expectedAnswer.map(Row.fromTuple),
               sortAnswers = true)
           }
-        }
+      }
+    }
 
-        if (joinType != FullOuter) {
-          test(s"$testName using BroadcastHashOuterJoin") {
+    if (joinType != FullOuter) {
+      test(s"$testName using BroadcastHashOuterJoin") {
+        extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
             withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
               checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
                 BroadcastHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right),
                 expectedAnswer.map(Row.fromTuple),
                 sortAnswers = true)
             }
-          }
+        }
+      }
 
-          test(s"$testName using SortMergeOuterJoin") {
+      test(s"$testName using SortMergeOuterJoin") {
+        extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
             withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
               checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
                 EnsureRequirements(sqlContext).apply(
@@ -66,57 +107,9 @@ class OuterJoinSuite extends SparkPlanTest with SQLTestUtils {
                 expectedAnswer.map(Row.fromTuple),
                 sortAnswers = false)
             }
-          }
         }
-    }
-
-    test(s"$testName using BroadcastNestedLoopJoin (build=left)") {
-      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-          joins.BroadcastNestedLoopJoin(left, right, joins.BuildLeft, joinType, Some(condition)),
-          expectedAnswer.map(Row.fromTuple),
-          sortAnswers = true)
       }
     }
-
-    test(s"$testName using BroadcastNestedLoopJoin (build=right)") {
-      withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-        checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-          joins.BroadcastNestedLoopJoin(left, right, joins.BuildRight, joinType, Some(condition)),
-          expectedAnswer.map(Row.fromTuple),
-          sortAnswers = true)
-      }
-    }
-  }
-
-  val left = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
-    Row(1, 2.0),
-    Row(2, 100.0),
-    Row(2, 1.0), // This row is duplicated to ensure that we will have multiple buffered matches
-    Row(2, 1.0),
-    Row(3, 3.0),
-    Row(5, 1.0),
-    Row(6, 6.0),
-    Row(null, null)
-  )), new StructType().add("a", IntegerType).add("b", DoubleType))
-
-  val right = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
-    Row(0, 0.0),
-    Row(2, 3.0), // This row is duplicated to ensure that we will have multiple buffered matches
-    Row(2, -1.0),
-    Row(2, -1.0),
-    Row(2, 3.0),
-    Row(3, 2.0),
-    Row(4, 1.0),
-    Row(5, 3.0),
-    Row(7, 7.0),
-    Row(null, null)
-  )), new StructType().add("c", IntegerType).add("d", DoubleType))
-
-  val condition = {
-    And(
-      (left.col("a") === right.col("c")).expr,
-      LessThan(left.col("b").expr, right.col("d").expr))
   }
 
   // --- Basic outer joins ------------------------------------------------------------------------
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
index 4503ed251fcb1..baa86e320d986 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
@@ -17,44 +17,80 @@
 
 package org.apache.spark.sql.execution.joins
 
+import org.apache.spark.sql.{SQLConf, DataFrame, Row}
 import org.apache.spark.sql.catalyst.planning.ExtractEquiJoinKeys
 import org.apache.spark.sql.catalyst.plans.Inner
 import org.apache.spark.sql.catalyst.plans.logical.Join
-import org.apache.spark.sql.test.SQLTestUtils
-import org.apache.spark.sql.types.{DoubleType, IntegerType, StructType}
-import org.apache.spark.sql.{SQLConf, DataFrame, Row}
 import org.apache.spark.sql.catalyst.expressions.{And, LessThan, Expression}
 import org.apache.spark.sql.execution.{EnsureRequirements, SparkPlan, SparkPlanTest}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.types.{DoubleType, IntegerType, StructType}
+
+class SemiJoinSuite extends SparkPlanTest with SharedSQLContext {
 
-class SemiJoinSuite extends SparkPlanTest with SQLTestUtils {
+  private lazy val left = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
+      Row(1, 2.0),
+      Row(1, 2.0),
+      Row(2, 1.0),
+      Row(2, 1.0),
+      Row(3, 3.0),
+      Row(null, null),
+      Row(null, 5.0),
+      Row(6, null)
+    )), new StructType().add("a", IntegerType).add("b", DoubleType))
 
+  private lazy val right = ctx.createDataFrame(
+    ctx.sparkContext.parallelize(Seq(
+      Row(2, 3.0),
+      Row(2, 3.0),
+      Row(3, 2.0),
+      Row(4, 1.0),
+      Row(null, null),
+      Row(null, 5.0),
+      Row(6, null)
+    )), new StructType().add("c", IntegerType).add("d", DoubleType))
+
+  private lazy val condition = {
+    And((left.col("a") === right.col("c")).expr,
+      LessThan(left.col("b").expr, right.col("d").expr))
+  }
+
+  // Note: the input dataframes and expression must be evaluated lazily because
+  // the SQLContext should be used only within a test to keep SQL tests stable
   private def testLeftSemiJoin(
       testName: String,
-      leftRows: DataFrame,
-      rightRows: DataFrame,
-      condition: Expression,
+      leftRows: => DataFrame,
+      rightRows: => DataFrame,
+      condition: => Expression,
       expectedAnswer: Seq[Product]): Unit = {
-    val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
-    ExtractEquiJoinKeys.unapply(join).foreach {
-      case (joinType, leftKeys, rightKeys, boundCondition, leftChild, rightChild) =>
-        test(s"$testName using LeftSemiJoinHash") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              EnsureRequirements(left.sqlContext).apply(
-                LeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition)),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
+
+    def extractJoinParts(): Option[ExtractEquiJoinKeys.ReturnType] = {
+      val join = Join(leftRows.logicalPlan, rightRows.logicalPlan, Inner, Some(condition))
+      ExtractEquiJoinKeys.unapply(join)
+    }
+
+    test(s"$testName using LeftSemiJoinHash") {
+      extractJoinParts().foreach { case (joinType, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+            EnsureRequirements(left.sqlContext).apply(
+              LeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition)),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
         }
+      }
+    }
 
-        test(s"$testName using BroadcastLeftSemiJoinHash") {
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              BroadcastLeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
+    test(s"$testName using BroadcastLeftSemiJoinHash") {
+      extractJoinParts().foreach { case (joinType, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+            BroadcastLeftSemiJoinHash(leftKeys, rightKeys, left, right, boundCondition),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
         }
+      }
     }
 
     test(s"$testName using LeftSemiJoinBNL") {
@@ -67,33 +103,6 @@ class SemiJoinSuite extends SparkPlanTest with SQLTestUtils {
     }
   }
 
-  val left = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
-    Row(1, 2.0),
-    Row(1, 2.0),
-    Row(2, 1.0),
-    Row(2, 1.0),
-    Row(3, 3.0),
-    Row(null, null),
-    Row(null, 5.0),
-    Row(6, null)
-  )), new StructType().add("a", IntegerType).add("b", DoubleType))
-
-  val right = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(Seq(
-    Row(2, 3.0),
-    Row(2, 3.0),
-    Row(3, 2.0),
-    Row(4, 1.0),
-    Row(null, null),
-    Row(null, 5.0),
-    Row(6, null)
-  )), new StructType().add("c", IntegerType).add("d", DoubleType))
-
-  val condition = {
-    And(
-      (left.col("a") === right.col("c")).expr,
-      LessThan(left.col("b").expr, right.col("d").expr))
-  }
-
   testLeftSemiJoin(
     "basic test",
     left,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
index 7383d3f8fe024..80006bf077fe8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
@@ -28,17 +28,15 @@ import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.ui.SparkPlanGraph
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.test.{SQLTestUtils, TestSQLContext}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
-class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
-  override val sqlContext = TestSQLContext
-
-  import sqlContext.implicits._
+class SQLMetricsSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
   test("LongSQLMetric should not box Long") {
-    val l = SQLMetrics.createLongMetric(TestSQLContext.sparkContext, "long")
+    val l = SQLMetrics.createLongMetric(ctx.sparkContext, "long")
     val f = () => {
       l += 1L
       l.add(1L)
@@ -52,7 +50,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
   test("Normal accumulator should do boxing") {
     // We need this test to make sure BoxingFinder works.
-    val l = TestSQLContext.sparkContext.accumulator(0L)
+    val l = ctx.sparkContext.accumulator(0L)
     val f = () => { l += 1L }
     BoxingFinder.getClassReader(f.getClass).foreach { cl =>
       val boxingFinder = new BoxingFinder()
@@ -73,19 +71,19 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       df: DataFrame,
       expectedNumOfJobs: Int,
       expectedMetrics: Map[Long, (String, Map[String, Any])]): Unit = {
-    val previousExecutionIds = TestSQLContext.listener.executionIdToData.keySet
+    val previousExecutionIds = ctx.listener.executionIdToData.keySet
     df.collect()
-    TestSQLContext.sparkContext.listenerBus.waitUntilEmpty(10000)
-    val executionIds = TestSQLContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
+    ctx.sparkContext.listenerBus.waitUntilEmpty(10000)
+    val executionIds = ctx.listener.executionIdToData.keySet.diff(previousExecutionIds)
     assert(executionIds.size === 1)
     val executionId = executionIds.head
-    val jobs = TestSQLContext.listener.getExecution(executionId).get.jobs
+    val jobs = ctx.listener.getExecution(executionId).get.jobs
     // Use "<=" because there is a race condition that we may miss some jobs
     // TODO Change it to "=" once we fix the race condition that missing the JobStarted event.
     assert(jobs.size <= expectedNumOfJobs)
     if (jobs.size == expectedNumOfJobs) {
       // If we can track all jobs, check the metric values
-      val metricValues = TestSQLContext.listener.getExecutionMetrics(executionId)
+      val metricValues = ctx.listener.getExecutionMetrics(executionId)
       val actualMetrics = SparkPlanGraph(df.queryExecution.executedPlan).nodes.filter { node =>
         expectedMetrics.contains(node.id)
       }.map { node =>
@@ -111,7 +109,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       SQLConf.TUNGSTEN_ENABLED.key -> "false") {
       // Assume the execution plan is
       // PhysicalRDD(nodeId = 1) -> Project(nodeId = 0)
-      val df = TestData.person.select('name)
+      val df = person.select('name)
       testSparkPlanMetrics(df, 1, Map(
         0L ->("Project", Map(
           "number of rows" -> 2L)))
@@ -126,7 +124,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       SQLConf.TUNGSTEN_ENABLED.key -> "true") {
       // Assume the execution plan is
       // PhysicalRDD(nodeId = 1) -> TungstenProject(nodeId = 0)
-      val df = TestData.person.select('name)
+      val df = person.select('name)
       testSparkPlanMetrics(df, 1, Map(
         0L ->("TungstenProject", Map(
           "number of rows" -> 2L)))
@@ -137,7 +135,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
   test("Filter metrics") {
     // Assume the execution plan is
     // PhysicalRDD(nodeId = 1) -> Filter(nodeId = 0)
-    val df = TestData.person.filter('age < 25)
+    val df = person.filter('age < 25)
     testSparkPlanMetrics(df, 1, Map(
       0L -> ("Filter", Map(
         "number of input rows" -> 2L,
@@ -152,7 +150,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       SQLConf.TUNGSTEN_ENABLED.key -> "false") {
       // Assume the execution plan is
       // ... -> Aggregate(nodeId = 2) -> TungstenExchange(nodeId = 1) -> Aggregate(nodeId = 0)
-      val df = TestData.testData2.groupBy().count() // 2 partitions
+      val df = testData2.groupBy().count() // 2 partitions
       testSparkPlanMetrics(df, 1, Map(
         2L -> ("Aggregate", Map(
           "number of input rows" -> 6L,
@@ -163,7 +161,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       )
 
       // 2 partitions and each partition contains 2 keys
-      val df2 = TestData.testData2.groupBy('a).count()
+      val df2 = testData2.groupBy('a).count()
       testSparkPlanMetrics(df2, 1, Map(
         2L -> ("Aggregate", Map(
           "number of input rows" -> 6L,
@@ -185,7 +183,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       // Assume the execution plan is
       // ... -> SortBasedAggregate(nodeId = 2) -> TungstenExchange(nodeId = 1) ->
       // SortBasedAggregate(nodeId = 0)
-      val df = TestData.testData2.groupBy().count() // 2 partitions
+      val df = testData2.groupBy().count() // 2 partitions
       testSparkPlanMetrics(df, 1, Map(
         2L -> ("SortBasedAggregate", Map(
           "number of input rows" -> 6L,
@@ -199,7 +197,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       // ... -> SortBasedAggregate(nodeId = 3) -> TungstenExchange(nodeId = 2)
       // -> ExternalSort(nodeId = 1)-> SortBasedAggregate(nodeId = 0)
       // 2 partitions and each partition contains 2 keys
-      val df2 = TestData.testData2.groupBy('a).count()
+      val df2 = testData2.groupBy('a).count()
       testSparkPlanMetrics(df2, 1, Map(
         3L -> ("SortBasedAggregate", Map(
           "number of input rows" -> 6L,
@@ -219,7 +217,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       // Assume the execution plan is
       // ... -> TungstenAggregate(nodeId = 2) -> Exchange(nodeId = 1)
       // -> TungstenAggregate(nodeId = 0)
-      val df = TestData.testData2.groupBy().count() // 2 partitions
+      val df = testData2.groupBy().count() // 2 partitions
       testSparkPlanMetrics(df, 1, Map(
         2L -> ("TungstenAggregate", Map(
           "number of input rows" -> 6L,
@@ -230,7 +228,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
       )
 
       // 2 partitions and each partition contains 2 keys
-      val df2 = TestData.testData2.groupBy('a).count()
+      val df2 = testData2.groupBy('a).count()
       testSparkPlanMetrics(df2, 1, Map(
         2L -> ("TungstenAggregate", Map(
           "number of input rows" -> 6L,
@@ -246,7 +244,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
     // Because SortMergeJoin may skip different rows if the number of partitions is different, this
     // test should use the deterministic number of partitions.
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
-      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      val testDataForJoin = testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
       testDataForJoin.registerTempTable("testDataForJoin")
       withTempTable("testDataForJoin") {
         // Assume the execution plan is
@@ -268,7 +266,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
     // Because SortMergeOuterJoin may skip different rows if the number of partitions is different,
     // this test should use the deterministic number of partitions.
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
-      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      val testDataForJoin = testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
       testDataForJoin.registerTempTable("testDataForJoin")
       withTempTable("testDataForJoin") {
         // Assume the execution plan is
@@ -314,7 +312,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
   test("ShuffledHashJoin metrics") {
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "false") {
-      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      val testDataForJoin = testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
       testDataForJoin.registerTempTable("testDataForJoin")
       withTempTable("testDataForJoin") {
         // Assume the execution plan is
@@ -390,7 +388,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
   test("BroadcastNestedLoopJoin metrics") {
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
-      val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+      val testDataForJoin = testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
       testDataForJoin.registerTempTable("testDataForJoin")
       withTempTable("testDataForJoin") {
         // Assume the execution plan is
@@ -458,7 +456,7 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
   }
 
   test("CartesianProduct metrics") {
-    val testDataForJoin = TestData.testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
+    val testDataForJoin = testData2.filter('a < 2) // TestData2(1, 1) :: TestData2(1, 2)
     testDataForJoin.registerTempTable("testDataForJoin")
     withTempTable("testDataForJoin") {
       // Assume the execution plan is
@@ -476,19 +474,19 @@ class SQLMetricsSuite extends SparkFunSuite with SQLTestUtils {
 
   test("save metrics") {
     withTempPath { file =>
-      val previousExecutionIds = TestSQLContext.listener.executionIdToData.keySet
+      val previousExecutionIds = ctx.listener.executionIdToData.keySet
       // Assume the execution plan is
       // PhysicalRDD(nodeId = 0)
-      TestData.person.select('name).write.format("json").save(file.getAbsolutePath)
-      TestSQLContext.sparkContext.listenerBus.waitUntilEmpty(10000)
-      val executionIds = TestSQLContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
+      person.select('name).write.format("json").save(file.getAbsolutePath)
+      ctx.sparkContext.listenerBus.waitUntilEmpty(10000)
+      val executionIds = ctx.listener.executionIdToData.keySet.diff(previousExecutionIds)
       assert(executionIds.size === 1)
       val executionId = executionIds.head
-      val jobs = TestSQLContext.listener.getExecution(executionId).get.jobs
+      val jobs = ctx.listener.getExecution(executionId).get.jobs
       // Use "<=" because there is a race condition that we may miss some jobs
       // TODO Change "<=" to "=" once we fix the race condition that missing the JobStarted event.
       assert(jobs.size <= 1)
-      val metricValues = TestSQLContext.listener.getExecutionMetrics(executionId)
+      val metricValues = ctx.listener.getExecutionMetrics(executionId)
       // Because "save" will create a new DataFrame internally, we cannot get the real metric id.
       // However, we still can check the value.
       assert(metricValues.values.toSeq === Seq(2L))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
index 41dd1896c15df..80d1e88956949 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
@@ -25,12 +25,12 @@ import org.apache.spark.sql.execution.metric.LongSQLMetricValue
 import org.apache.spark.scheduler._
 import org.apache.spark.sql.{DataFrame, SQLContext}
 import org.apache.spark.sql.execution.SQLExecution
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 
-class SQLListenerSuite extends SparkFunSuite {
+class SQLListenerSuite extends SparkFunSuite with SharedSQLContext {
+  import testImplicits._
 
   private def createTestDataFrame: DataFrame = {
-    import TestSQLContext.implicits._
     Seq(
       (1, 1),
       (2, 2)
@@ -74,7 +74,7 @@ class SQLListenerSuite extends SparkFunSuite {
   }
 
   test("basic") {
-    val listener = new SQLListener(TestSQLContext)
+    val listener = new SQLListener(ctx)
     val executionId = 0
     val df = createTestDataFrame
     val accumulatorIds =
@@ -212,7 +212,7 @@ class SQLListenerSuite extends SparkFunSuite {
   }
 
   test("onExecutionEnd happens before onJobEnd(JobSucceeded)") {
-    val listener = new SQLListener(TestSQLContext)
+    val listener = new SQLListener(ctx)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
@@ -241,7 +241,7 @@ class SQLListenerSuite extends SparkFunSuite {
   }
 
   test("onExecutionEnd happens before multiple onJobEnd(JobSucceeded)s") {
-    val listener = new SQLListener(TestSQLContext)
+    val listener = new SQLListener(ctx)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
@@ -281,7 +281,7 @@ class SQLListenerSuite extends SparkFunSuite {
   }
 
   test("onExecutionEnd happens before onJobEnd(JobFailed)") {
-    val listener = new SQLListener(TestSQLContext)
+    val listener = new SQLListener(ctx)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index e4dcf4c75d208..0edac0848c3bb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -25,10 +25,13 @@ import org.h2.jdbc.JdbcSQLException
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
-class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
+class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext {
+  import testImplicits._
+
   val url = "jdbc:h2:mem:testdb0"
   val urlWithUserAndPass = "jdbc:h2:mem:testdb0;user=testUser;password=testPass"
   var conn: java.sql.Connection = null
@@ -42,10 +45,6 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter {
       Some(StringType)
   }
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-  import ctx.sql
-
   before {
     Utils.classForName("org.h2.Driver")
     // Extra properties that will be specified for our database. We need these to test
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
index 84b52ca2c733c..5dc3a2c07b8c7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
@@ -23,11 +23,13 @@ import java.util.Properties
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.{SaveMode, Row}
+import org.apache.spark.sql.{Row, SaveMode}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
-class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter {
+class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext {
+
   val url = "jdbc:h2:mem:testdb2"
   var conn: java.sql.Connection = null
   val url1 = "jdbc:h2:mem:testdb3"
@@ -37,10 +39,6 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter {
   properties.setProperty("password", "testPass")
   properties.setProperty("rowId", "false")
 
-  private lazy val ctx = org.apache.spark.sql.test.TestSQLContext
-  import ctx.implicits._
-  import ctx.sql
-
   before {
     Utils.classForName("org.h2.Driver")
     conn = DriverManager.getConnection(url)
@@ -58,14 +56,14 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter {
       "create table test.people1 (name TEXT(32) NOT NULL, theid INTEGER NOT NULL)").executeUpdate()
     conn1.commit()
 
-    ctx.sql(
+    sql(
       s"""
         |CREATE TEMPORARY TABLE PEOPLE
         |USING org.apache.spark.sql.jdbc
         |OPTIONS (url '$url1', dbtable 'TEST.PEOPLE', user 'testUser', password 'testPass')
       """.stripMargin.replaceAll("\n", " "))
 
-    ctx.sql(
+    sql(
       s"""
         |CREATE TEMPORARY TABLE PEOPLE1
         |USING org.apache.spark.sql.jdbc
@@ -144,14 +142,14 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter {
   }
 
   test("INSERT to JDBC Datasource") {
-    ctx.sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
+    sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
     assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
     assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
   }
 
   test("INSERT to JDBC Datasource with overwrite") {
-    ctx.sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
-    ctx.sql("INSERT OVERWRITE TABLE PEOPLE1 SELECT * FROM PEOPLE")
+    sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
+    sql("INSERT OVERWRITE TABLE PEOPLE1 SELECT * FROM PEOPLE")
     assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
     assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
index 562c279067048..9bc3f6bcf6fce 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
@@ -19,28 +19,32 @@ package org.apache.spark.sql.sources
 
 import java.io.{File, IOException}
 
-import org.scalatest.BeforeAndAfterAll
+import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.execution.datasources.DDLException
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
-class CreateTableAsSelectSuite extends DataSourceTest with BeforeAndAfterAll {
-
-  import caseInsensitiveContext.sql
 
+class CreateTableAsSelectSuite extends DataSourceTest with SharedSQLContext with BeforeAndAfter {
+  protected override lazy val sql = caseInsensitiveContext.sql _
   private lazy val sparkContext = caseInsensitiveContext.sparkContext
-
-  var path: File = null
+  private var path: File = null
 
   override def beforeAll(): Unit = {
+    super.beforeAll()
     path = Utils.createTempDir()
     val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str${i}"}"""))
     caseInsensitiveContext.read.json(rdd).registerTempTable("jt")
   }
 
   override def afterAll(): Unit = {
-    caseInsensitiveContext.dropTempTable("jt")
+    try {
+      caseInsensitiveContext.dropTempTable("jt")
+    } finally {
+      super.afterAll()
+    }
   }
 
   after {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
index 392da0b0826b5..853707c036c9a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLSourceLoadSuite.scala
@@ -18,11 +18,12 @@
 package org.apache.spark.sql.sources
 
 import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.{StringType, StructField, StructType}
 
 
 // please note that the META-INF/services had to be modified for the test directory for this to work
-class DDLSourceLoadSuite extends DataSourceTest {
+class DDLSourceLoadSuite extends DataSourceTest with SharedSQLContext {
 
   test("data sources with the same name") {
     intercept[RuntimeException] {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
index 84855ce45e918..5f8514e1a2411 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DDLTestSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.sources
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -68,10 +69,12 @@ case class SimpleDDLScan(from: Int, to: Int, table: String)(@transient val sqlCo
   }
 }
 
-class DDLTestSuite extends DataSourceTest {
+class DDLTestSuite extends DataSourceTest with SharedSQLContext {
+  protected override lazy val sql = caseInsensitiveContext.sql _
 
-  before {
-    caseInsensitiveContext.sql(
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    sql(
       """
       |CREATE TEMPORARY TABLE ddlPeople
       |USING org.apache.spark.sql.sources.DDLScanSource
@@ -105,7 +108,7 @@ class DDLTestSuite extends DataSourceTest {
       ))
 
   test("SPARK-7686 DescribeCommand should have correct physical plan output attributes") {
-    val attributes = caseInsensitiveContext.sql("describe ddlPeople")
+    val attributes = sql("describe ddlPeople")
       .queryExecution.executedPlan.output
     assert(attributes.map(_.name) === Seq("col_name", "data_type", "comment"))
     assert(attributes.map(_.dataType).toSet === Set(StringType))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
index 00cc7d5ea580f..d74d29fb0beb0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
@@ -17,18 +17,23 @@
 
 package org.apache.spark.sql.sources
 
-import org.scalatest.BeforeAndAfter
-
 import org.apache.spark.sql._
-import org.apache.spark.sql.test.TestSQLContext
 
 
-abstract class DataSourceTest extends QueryTest with BeforeAndAfter {
+private[sql] abstract class DataSourceTest extends QueryTest {
+  protected def _sqlContext: SQLContext
+
   // We want to test some edge cases.
-  protected implicit lazy val caseInsensitiveContext = {
-    val ctx = new SQLContext(TestSQLContext.sparkContext)
+  protected lazy val caseInsensitiveContext: SQLContext = {
+    val ctx = new SQLContext(_sqlContext.sparkContext)
     ctx.setConf(SQLConf.CASE_SENSITIVE, false)
     ctx
   }
 
+  protected def sqlTest(sqlString: String, expectedAnswer: Seq[Row]) {
+    test(sqlString) {
+      checkAnswer(caseInsensitiveContext.sql(sqlString), expectedAnswer)
+    }
+  }
+
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
index 5ef365797eace..c81c3d3982805 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
@@ -21,6 +21,7 @@ import scala.language.existentials
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 
@@ -96,11 +97,11 @@ object FiltersPushed {
   var list: Seq[Filter] = Nil
 }
 
-class FilteredScanSuite extends DataSourceTest {
+class FilteredScanSuite extends DataSourceTest with SharedSQLContext {
+  protected override lazy val sql = caseInsensitiveContext.sql _
 
-  import caseInsensitiveContext.sql
-
-  before {
+  override def beforeAll(): Unit = {
+    super.beforeAll()
     sql(
       """
         |CREATE TEMPORARY TABLE oneToTenFiltered
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
index cdbfaf6455fe4..78bd3e5582964 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
@@ -19,20 +19,17 @@ package org.apache.spark.sql.sources
 
 import java.io.File
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.sql.{SaveMode, AnalysisException, Row}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
-class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
-
-  import caseInsensitiveContext.sql
-
+class InsertSuite extends DataSourceTest with SharedSQLContext {
+  protected override lazy val sql = caseInsensitiveContext.sql _
   private lazy val sparkContext = caseInsensitiveContext.sparkContext
-
-  var path: File = null
+  private var path: File = null
 
   override def beforeAll(): Unit = {
+    super.beforeAll()
     path = Utils.createTempDir()
     val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""))
     caseInsensitiveContext.read.json(rdd).registerTempTable("jt")
@@ -47,9 +44,13 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
   }
 
   override def afterAll(): Unit = {
-    caseInsensitiveContext.dropTempTable("jsonTable")
-    caseInsensitiveContext.dropTempTable("jt")
-    Utils.deleteRecursively(path)
+    try {
+      caseInsensitiveContext.dropTempTable("jsonTable")
+      caseInsensitiveContext.dropTempTable("jt")
+      Utils.deleteRecursively(path)
+    } finally {
+      super.afterAll()
+    }
   }
 
   test("Simple INSERT OVERWRITE a JSONRelation") {
@@ -221,9 +222,10 @@ class InsertSuite extends DataSourceTest with BeforeAndAfterAll {
       sql("SELECT a * 2 FROM jsonTable"),
       (1 to 10).map(i => Row(i * 2)).toSeq)
 
-    assertCached(sql("SELECT x.a, y.a FROM jsonTable x JOIN jsonTable y ON x.a = y.a + 1"), 2)
-    checkAnswer(
-      sql("SELECT x.a, y.a FROM jsonTable x JOIN jsonTable y ON x.a = y.a + 1"),
+    assertCached(sql(
+      "SELECT x.a, y.a FROM jsonTable x JOIN jsonTable y ON x.a = y.a + 1"), 2)
+    checkAnswer(sql(
+      "SELECT x.a, y.a FROM jsonTable x JOIN jsonTable y ON x.a = y.a + 1"),
       (2 to 10).map(i => Row(i, i - 1)).toSeq)
 
     // Insert overwrite and keep the same schema.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
index c86ddd7c83e53..79b6e9b45c009 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
@@ -19,21 +19,21 @@ package org.apache.spark.sql.sources
 
 import org.apache.spark.sql.{Row, QueryTest}
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.test.TestSQLContext
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
-class PartitionedWriteSuite extends QueryTest {
-  import TestSQLContext.implicits._
+class PartitionedWriteSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
 
   test("write many partitions") {
     val path = Utils.createTempDir()
     path.delete()
 
-    val df = TestSQLContext.range(100).select($"id", lit(1).as("data"))
+    val df = ctx.range(100).select($"id", lit(1).as("data"))
     df.write.partitionBy("id").save(path.getCanonicalPath)
 
     checkAnswer(
-      TestSQLContext.read.load(path.getCanonicalPath),
+      ctx.read.load(path.getCanonicalPath),
       (0 to 99).map(Row(1, _)).toSeq)
 
     Utils.deleteRecursively(path)
@@ -43,12 +43,12 @@ class PartitionedWriteSuite extends QueryTest {
     val path = Utils.createTempDir()
     path.delete()
 
-    val base = TestSQLContext.range(100)
+    val base = ctx.range(100)
     val df = base.unionAll(base).select($"id", lit(1).as("data"))
     df.write.partitionBy("id").save(path.getCanonicalPath)
 
     checkAnswer(
-      TestSQLContext.read.load(path.getCanonicalPath),
+      ctx.read.load(path.getCanonicalPath),
       (0 to 99).map(Row(1, _)).toSeq ++ (0 to 99).map(Row(1, _)).toSeq)
 
     Utils.deleteRecursively(path)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala
index 0d5183444af78..a89c5f8007e78 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PrunedScanSuite.scala
@@ -21,6 +21,7 @@ import scala.language.existentials
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 class PrunedScanSource extends RelationProvider {
@@ -51,10 +52,12 @@ case class SimplePrunedScan(from: Int, to: Int)(@transient val sqlContext: SQLCo
   }
 }
 
-class PrunedScanSuite extends DataSourceTest {
+class PrunedScanSuite extends DataSourceTest with SharedSQLContext {
+  protected override lazy val sql = caseInsensitiveContext.sql _
 
-  before {
-    caseInsensitiveContext.sql(
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    sql(
       """
         |CREATE TEMPORARY TABLE oneToTenPruned
         |USING org.apache.spark.sql.sources.PrunedScanSource
@@ -114,7 +117,7 @@ class PrunedScanSuite extends DataSourceTest {
 
   def testPruning(sqlString: String, expectedColumns: String*): Unit = {
     test(s"Columns output ${expectedColumns.mkString(",")}: $sqlString") {
-      val queryExecution = caseInsensitiveContext.sql(sqlString).queryExecution
+      val queryExecution = sql(sqlString).queryExecution
       val rawPlan = queryExecution.executedPlan.collect {
         case p: execution.PhysicalRDD => p
       } match {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
index 31730a3d3f8d3..f18546b4c2d9b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
@@ -19,25 +19,22 @@ package org.apache.spark.sql.sources
 
 import java.io.File
 
-import org.scalatest.BeforeAndAfterAll
+import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.{AnalysisException, SaveMode, SQLConf, DataFrame}
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
-class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll {
-
-  import caseInsensitiveContext.sql
-
+class SaveLoadSuite extends DataSourceTest with SharedSQLContext with BeforeAndAfter {
+  protected override lazy val sql = caseInsensitiveContext.sql _
   private lazy val sparkContext = caseInsensitiveContext.sparkContext
-
-  var originalDefaultSource: String = null
-
-  var path: File = null
-
-  var df: DataFrame = null
+  private var originalDefaultSource: String = null
+  private var path: File = null
+  private var df: DataFrame = null
 
   override def beforeAll(): Unit = {
+    super.beforeAll()
     originalDefaultSource = caseInsensitiveContext.conf.defaultDataSourceName
 
     path = Utils.createTempDir()
@@ -49,11 +46,14 @@ class SaveLoadSuite extends DataSourceTest with BeforeAndAfterAll {
   }
 
   override def afterAll(): Unit = {
-    caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource)
+    try {
+      caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource)
+    } finally {
+      super.afterAll()
+    }
   }
 
   after {
-    caseInsensitiveContext.conf.setConf(SQLConf.DEFAULT_DATA_SOURCE_NAME, originalDefaultSource)
     Utils.deleteRecursively(path)
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
index e34e0956d1fdd..12af8068c398f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala
@@ -22,6 +22,7 @@ import java.sql.{Date, Timestamp}
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 class DefaultSource extends SimpleScanSource
@@ -95,8 +96,8 @@ case class AllDataTypesScan(
   }
 }
 
-class TableScanSuite extends DataSourceTest {
-  import caseInsensitiveContext.sql
+class TableScanSuite extends DataSourceTest with SharedSQLContext {
+  protected override lazy val sql = caseInsensitiveContext.sql _
 
   private lazy val tableWithSchemaExpected = (1 to 10).map { i =>
     Row(
@@ -122,7 +123,8 @@ class TableScanSuite extends DataSourceTest {
       Row(Seq(s"str_$i", s"str_${i + 1}"), Row(Seq(Date.valueOf(s"1970-01-${i + 1}")))))
   }.toSeq
 
-  before {
+  override def beforeAll(): Unit = {
+    super.beforeAll()
     sql(
       """
         |CREATE TEMPORARY TABLE oneToTen
@@ -303,9 +305,10 @@ class TableScanSuite extends DataSourceTest {
       sql("SELECT i * 2 FROM oneToTen"),
       (1 to 10).map(i => Row(i * 2)).toSeq)
 
-    assertCached(sql("SELECT a.i, b.i FROM oneToTen a JOIN oneToTen b ON a.i = b.i + 1"), 2)
-    checkAnswer(
-      sql("SELECT a.i, b.i FROM oneToTen a JOIN oneToTen b ON a.i = b.i + 1"),
+    assertCached(sql(
+      "SELECT a.i, b.i FROM oneToTen a JOIN oneToTen b ON a.i = b.i + 1"), 2)
+    checkAnswer(sql(
+      "SELECT a.i, b.i FROM oneToTen a JOIN oneToTen b ON a.i = b.i + 1"),
       (2 to 10).map(i => Row(i, i - 1)).toSeq)
 
     // Verify uncaching
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
new file mode 100644
index 0000000000000..1374a97476ca1
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
@@ -0,0 +1,290 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.test
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{DataFrame, SQLContext, SQLImplicits}
+
+/**
+ * A collection of sample data used in SQL tests.
+ */
+private[sql] trait SQLTestData { self =>
+  protected def _sqlContext: SQLContext
+
+  // Helper object to import SQL implicits without a concrete SQLContext
+  private object internalImplicits extends SQLImplicits {
+    protected override def _sqlContext: SQLContext = self._sqlContext
+  }
+
+  import internalImplicits._
+  import SQLTestData._
+
+  // Note: all test data should be lazy because the SQLContext is not set up yet.
+
+  protected lazy val testData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      (1 to 100).map(i => TestData(i, i.toString))).toDF()
+    df.registerTempTable("testData")
+    df
+  }
+
+  protected lazy val testData2: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      TestData2(1, 1) ::
+      TestData2(1, 2) ::
+      TestData2(2, 1) ::
+      TestData2(2, 2) ::
+      TestData2(3, 1) ::
+      TestData2(3, 2) :: Nil, 2).toDF()
+    df.registerTempTable("testData2")
+    df
+  }
+
+  protected lazy val testData3: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      TestData3(1, None) ::
+      TestData3(2, Some(2)) :: Nil).toDF()
+    df.registerTempTable("testData3")
+    df
+  }
+
+  protected lazy val negativeData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      (1 to 100).map(i => TestData(-i, (-i).toString))).toDF()
+    df.registerTempTable("negativeData")
+    df
+  }
+
+  protected lazy val largeAndSmallInts: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      LargeAndSmallInts(2147483644, 1) ::
+      LargeAndSmallInts(1, 2) ::
+      LargeAndSmallInts(2147483645, 1) ::
+      LargeAndSmallInts(2, 2) ::
+      LargeAndSmallInts(2147483646, 1) ::
+      LargeAndSmallInts(3, 2) :: Nil).toDF()
+    df.registerTempTable("largeAndSmallInts")
+    df
+  }
+
+  protected lazy val decimalData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      DecimalData(1, 1) ::
+      DecimalData(1, 2) ::
+      DecimalData(2, 1) ::
+      DecimalData(2, 2) ::
+      DecimalData(3, 1) ::
+      DecimalData(3, 2) :: Nil).toDF()
+    df.registerTempTable("decimalData")
+    df
+  }
+
+  protected lazy val binaryData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      BinaryData("12".getBytes, 1) ::
+      BinaryData("22".getBytes, 5) ::
+      BinaryData("122".getBytes, 3) ::
+      BinaryData("121".getBytes, 2) ::
+      BinaryData("123".getBytes, 4) :: Nil).toDF()
+    df.registerTempTable("binaryData")
+    df
+  }
+
+  protected lazy val upperCaseData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      UpperCaseData(1, "A") ::
+      UpperCaseData(2, "B") ::
+      UpperCaseData(3, "C") ::
+      UpperCaseData(4, "D") ::
+      UpperCaseData(5, "E") ::
+      UpperCaseData(6, "F") :: Nil).toDF()
+    df.registerTempTable("upperCaseData")
+    df
+  }
+
+  protected lazy val lowerCaseData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      LowerCaseData(1, "a") ::
+      LowerCaseData(2, "b") ::
+      LowerCaseData(3, "c") ::
+      LowerCaseData(4, "d") :: Nil).toDF()
+    df.registerTempTable("lowerCaseData")
+    df
+  }
+
+  protected lazy val arrayData: RDD[ArrayData] = {
+    val rdd = _sqlContext.sparkContext.parallelize(
+      ArrayData(Seq(1, 2, 3), Seq(Seq(1, 2, 3))) ::
+      ArrayData(Seq(2, 3, 4), Seq(Seq(2, 3, 4))) :: Nil)
+    rdd.toDF().registerTempTable("arrayData")
+    rdd
+  }
+
+  protected lazy val mapData: RDD[MapData] = {
+    val rdd = _sqlContext.sparkContext.parallelize(
+      MapData(Map(1 -> "a1", 2 -> "b1", 3 -> "c1", 4 -> "d1", 5 -> "e1")) ::
+      MapData(Map(1 -> "a2", 2 -> "b2", 3 -> "c2", 4 -> "d2")) ::
+      MapData(Map(1 -> "a3", 2 -> "b3", 3 -> "c3")) ::
+      MapData(Map(1 -> "a4", 2 -> "b4")) ::
+      MapData(Map(1 -> "a5")) :: Nil)
+    rdd.toDF().registerTempTable("mapData")
+    rdd
+  }
+
+  protected lazy val repeatedData: RDD[StringData] = {
+    val rdd = _sqlContext.sparkContext.parallelize(List.fill(2)(StringData("test")))
+    rdd.toDF().registerTempTable("repeatedData")
+    rdd
+  }
+
+  protected lazy val nullableRepeatedData: RDD[StringData] = {
+    val rdd = _sqlContext.sparkContext.parallelize(
+      List.fill(2)(StringData(null)) ++
+      List.fill(2)(StringData("test")))
+    rdd.toDF().registerTempTable("nullableRepeatedData")
+    rdd
+  }
+
+  protected lazy val nullInts: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      NullInts(1) ::
+      NullInts(2) ::
+      NullInts(3) ::
+      NullInts(null) :: Nil).toDF()
+    df.registerTempTable("nullInts")
+    df
+  }
+
+  protected lazy val allNulls: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      NullInts(null) ::
+      NullInts(null) ::
+      NullInts(null) ::
+      NullInts(null) :: Nil).toDF()
+    df.registerTempTable("allNulls")
+    df
+  }
+
+  protected lazy val nullStrings: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      NullStrings(1, "abc") ::
+      NullStrings(2, "ABC") ::
+      NullStrings(3, null) :: Nil).toDF()
+    df.registerTempTable("nullStrings")
+    df
+  }
+
+  protected lazy val tableName: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(TableName("test") :: Nil).toDF()
+    df.registerTempTable("tableName")
+    df
+  }
+
+  protected lazy val unparsedStrings: RDD[String] = {
+    _sqlContext.sparkContext.parallelize(
+      "1, A1, true, null" ::
+      "2, B2, false, null" ::
+      "3, C3, true, null" ::
+      "4, D4, true, 2147483644" :: Nil)
+  }
+
+  // An RDD with 4 elements and 8 partitions
+  protected lazy val withEmptyParts: RDD[IntField] = {
+    val rdd = _sqlContext.sparkContext.parallelize((1 to 4).map(IntField), 8)
+    rdd.toDF().registerTempTable("withEmptyParts")
+    rdd
+  }
+
+  protected lazy val person: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      Person(0, "mike", 30) ::
+      Person(1, "jim", 20) :: Nil).toDF()
+    df.registerTempTable("person")
+    df
+  }
+
+  protected lazy val salary: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      Salary(0, 2000.0) ::
+      Salary(1, 1000.0) :: Nil).toDF()
+    df.registerTempTable("salary")
+    df
+  }
+
+  protected lazy val complexData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      ComplexData(Map("1" -> 1), TestData(1, "1"), Seq(1, 1, 1), true) ::
+      ComplexData(Map("2" -> 2), TestData(2, "2"), Seq(2, 2, 2), false) ::
+      Nil).toDF()
+    df.registerTempTable("complexData")
+    df
+  }
+
+  /**
+   * Initialize all test data such that all temp tables are properly registered.
+   */
+  def loadTestData(): Unit = {
+    assert(_sqlContext != null, "attempted to initialize test data before SQLContext.")
+    testData
+    testData2
+    testData3
+    negativeData
+    largeAndSmallInts
+    decimalData
+    binaryData
+    upperCaseData
+    lowerCaseData
+    arrayData
+    mapData
+    repeatedData
+    nullableRepeatedData
+    nullInts
+    allNulls
+    nullStrings
+    tableName
+    unparsedStrings
+    withEmptyParts
+    person
+    salary
+    complexData
+  }
+}
+
+/**
+ * Case classes used in test data.
+ */
+private[sql] object SQLTestData {
+  case class TestData(key: Int, value: String)
+  case class TestData2(a: Int, b: Int)
+  case class TestData3(a: Int, b: Option[Int])
+  case class LargeAndSmallInts(a: Int, b: Int)
+  case class DecimalData(a: BigDecimal, b: BigDecimal)
+  case class BinaryData(a: Array[Byte], b: Int)
+  case class UpperCaseData(N: Int, L: String)
+  case class LowerCaseData(n: Int, l: String)
+  case class ArrayData(data: Seq[Int], nestedData: Seq[Seq[Int]])
+  case class MapData(data: scala.collection.Map[Int, String])
+  case class StringData(s: String)
+  case class IntField(i: Int)
+  case class NullInts(a: Integer)
+  case class NullStrings(n: Int, s: String)
+  case class TableName(tableName: String)
+  case class Person(id: Int, name: String, age: Int)
+  case class Salary(personId: Int, salary: Double)
+  case class ComplexData(m: Map[String, Int], s: TestData, a: Seq[Int], b: Boolean)
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
index 1066695589778..cdd691e035897 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
@@ -21,15 +21,71 @@ import java.io.File
 import java.util.UUID
 
 import scala.util.Try
+import scala.language.implicitConversions
+
+import org.apache.hadoop.conf.Configuration
+import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.{DataFrame, SQLContext, SQLImplicits}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.util.Utils
 
-trait SQLTestUtils { this: SparkFunSuite =>
-  protected def sqlContext: SQLContext
+/**
+ * Helper trait that should be extended by all SQL test suites.
+ *
+ * This allows subclasses to plugin a custom [[SQLContext]]. It comes with test data
+ * prepared in advance as well as all implicit conversions used extensively by dataframes.
+ * To use implicit methods, import `testImplicits._` instead of through the [[SQLContext]].
+ *
+ * Subclasses should *not* create [[SQLContext]]s in the test suite constructor, which is
+ * prone to leaving multiple overlapping [[org.apache.spark.SparkContext]]s in the same JVM.
+ */
+private[sql] trait SQLTestUtils
+  extends SparkFunSuite
+  with BeforeAndAfterAll
+  with SQLTestData { self =>
+
+  protected def _sqlContext: SQLContext
+
+  // Whether to materialize all test data before the first test is run
+  private var loadTestDataBeforeTests = false
+
+  // Shorthand for running a query using our SQLContext
+  protected lazy val sql = _sqlContext.sql _
+
+  /**
+   * A helper object for importing SQL implicits.
+   *
+   * Note that the alternative of importing `sqlContext.implicits._` is not possible here.
+   * This is because we create the [[SQLContext]] immediately before the first test is run,
+   * but the implicits import is needed in the constructor.
+   */
+  protected object testImplicits extends SQLImplicits {
+    protected override def _sqlContext: SQLContext = self._sqlContext
+  }
+
+  /**
+   * Materialize the test data immediately after the [[SQLContext]] is set up.
+   * This is necessary if the data is accessed by name but not through direct reference.
+   */
+  protected def setupTestData(): Unit = {
+    loadTestDataBeforeTests = true
+  }
 
-  protected def configuration = sqlContext.sparkContext.hadoopConfiguration
+  protected override def beforeAll(): Unit = {
+    super.beforeAll()
+    if (loadTestDataBeforeTests) {
+      loadTestData()
+    }
+  }
+
+  /**
+   * The Hadoop configuration used by the active [[SQLContext]].
+   */
+  protected def configuration: Configuration = {
+    _sqlContext.sparkContext.hadoopConfiguration
+  }
 
   /**
    * Sets all SQL configurations specified in `pairs`, calls `f`, and then restore all SQL
@@ -39,12 +95,12 @@ trait SQLTestUtils { this: SparkFunSuite =>
    */
   protected def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
     val (keys, values) = pairs.unzip
-    val currentValues = keys.map(key => Try(sqlContext.conf.getConfString(key)).toOption)
-    (keys, values).zipped.foreach(sqlContext.conf.setConfString)
+    val currentValues = keys.map(key => Try(_sqlContext.conf.getConfString(key)).toOption)
+    (keys, values).zipped.foreach(_sqlContext.conf.setConfString)
     try f finally {
       keys.zip(currentValues).foreach {
-        case (key, Some(value)) => sqlContext.conf.setConfString(key, value)
-        case (key, None) => sqlContext.conf.unsetConf(key)
+        case (key, Some(value)) => _sqlContext.conf.setConfString(key, value)
+        case (key, None) => _sqlContext.conf.unsetConf(key)
       }
     }
   }
@@ -76,7 +132,7 @@ trait SQLTestUtils { this: SparkFunSuite =>
    * Drops temporary table `tableName` after calling `f`.
    */
   protected def withTempTable(tableNames: String*)(f: => Unit): Unit = {
-    try f finally tableNames.foreach(sqlContext.dropTempTable)
+    try f finally tableNames.foreach(_sqlContext.dropTempTable)
   }
 
   /**
@@ -85,7 +141,7 @@ trait SQLTestUtils { this: SparkFunSuite =>
   protected def withTable(tableNames: String*)(f: => Unit): Unit = {
     try f finally {
       tableNames.foreach { name =>
-        sqlContext.sql(s"DROP TABLE IF EXISTS $name")
+        _sqlContext.sql(s"DROP TABLE IF EXISTS $name")
       }
     }
   }
@@ -98,12 +154,12 @@ trait SQLTestUtils { this: SparkFunSuite =>
     val dbName = s"db_${UUID.randomUUID().toString.replace('-', '_')}"
 
     try {
-      sqlContext.sql(s"CREATE DATABASE $dbName")
+      _sqlContext.sql(s"CREATE DATABASE $dbName")
     } catch { case cause: Throwable =>
       fail("Failed to create temporary database", cause)
     }
 
-    try f(dbName) finally sqlContext.sql(s"DROP DATABASE $dbName CASCADE")
+    try f(dbName) finally _sqlContext.sql(s"DROP DATABASE $dbName CASCADE")
   }
 
   /**
@@ -111,7 +167,15 @@ trait SQLTestUtils { this: SparkFunSuite =>
    * `f` returns.
    */
   protected def activateDatabase(db: String)(f: => Unit): Unit = {
-    sqlContext.sql(s"USE $db")
-    try f finally sqlContext.sql(s"USE default")
+    _sqlContext.sql(s"USE $db")
+    try f finally _sqlContext.sql(s"USE default")
+  }
+
+  /**
+   * Turn a logical plan into a [[DataFrame]]. This should be removed once we have an easier
+   * way to construct [[DataFrame]] directly out of local data without relying on implicits.
+   */
+  protected implicit def logicalPlanToSparkQuery(plan: LogicalPlan): DataFrame = {
+    DataFrame(_sqlContext, plan)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
new file mode 100644
index 0000000000000..3cfd822e2a747
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
@@ -0,0 +1,68 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.test
+
+import org.apache.spark.sql.SQLContext
+
+
+/**
+ * Helper trait for SQL test suites where all tests share a single [[TestSQLContext]].
+ */
+private[sql] trait SharedSQLContext extends SQLTestUtils {
+
+  /**
+   * The [[TestSQLContext]] to use for all tests in this suite.
+   *
+   * By default, the underlying [[org.apache.spark.SparkContext]] will be run in local
+   * mode with the default test configurations.
+   */
+  private var _ctx: TestSQLContext = null
+
+  /**
+   * The [[TestSQLContext]] to use for all tests in this suite.
+   */
+  protected def ctx: TestSQLContext = _ctx
+  protected def sqlContext: TestSQLContext = _ctx
+  protected override def _sqlContext: SQLContext = _ctx
+
+  /**
+   * Initialize the [[TestSQLContext]].
+   */
+  protected override def beforeAll(): Unit = {
+    if (_ctx == null) {
+      _ctx = new TestSQLContext
+    }
+    // Ensure we have initialized the context before calling parent code
+    super.beforeAll()
+  }
+
+  /**
+   * Stop the underlying [[org.apache.spark.SparkContext]], if any.
+   */
+  protected override def afterAll(): Unit = {
+    try {
+      if (_ctx != null) {
+        _ctx.sparkContext.stop()
+        _ctx = null
+      }
+    } finally {
+      super.afterAll()
+    }
+  }
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
similarity index 54%
rename from sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala
rename to sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
index b3a4231da91c2..92ef2f7d74ba1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/test/TestSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
@@ -17,40 +17,36 @@
 
 package org.apache.spark.sql.test
 
-import scala.language.implicitConversions
-
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.sql.{DataFrame, SQLConf, SQLContext}
-import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
-
-/** A SQLContext that can be used for local testing. */
-class LocalSQLContext
-  extends SQLContext(
-    new SparkContext("local[2]", "TestSQLContext", new SparkConf()
-      .set("spark.sql.testkey", "true")
-      // SPARK-8910
-      .set("spark.ui.enabled", "false"))) {
-
-  override protected[sql] def createSession(): SQLSession = {
-    new this.SQLSession()
+import org.apache.spark.sql.{SQLConf, SQLContext}
+
+
+/**
+ * A special [[SQLContext]] prepared for testing.
+ */
+private[sql] class TestSQLContext(sc: SparkContext) extends SQLContext(sc) { self =>
+
+  def this() {
+    this(new SparkContext("local[2]", "test-sql-context",
+      new SparkConf().set("spark.sql.testkey", "true")))
   }
 
+  // Use fewer partitions to speed up testing
+  protected[sql] override def createSession(): SQLSession = new this.SQLSession()
+
+  /** A special [[SQLSession]] that uses fewer shuffle partitions than normal. */
   protected[sql] class SQLSession extends super.SQLSession {
     protected[sql] override lazy val conf: SQLConf = new SQLConf {
-      /** Fewer partitions to speed up testing. */
       override def numShufflePartitions: Int = this.getConf(SQLConf.SHUFFLE_PARTITIONS, 5)
     }
   }
 
-  /**
-   * Turn a logical plan into a [[DataFrame]]. This should be removed once we have an easier way to
-   * construct [[DataFrame]] directly out of local data without relying on implicits.
-   */
-  protected[sql] implicit def logicalPlanToSparkQuery(plan: LogicalPlan): DataFrame = {
-    DataFrame(this, plan)
+  // Needed for Java tests
+  def loadTestData(): Unit = {
+    testData.loadTestData()
   }
 
+  private object testData extends SQLTestData {
+    protected override def _sqlContext: SQLContext = self
+  }
 }
-
-object TestSQLContext extends LocalSQLContext
-
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala
index 806240e6de458..bf431cd6b0260 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/UISeleniumSuite.scala
@@ -27,7 +27,6 @@ import org.scalatest.concurrent.Eventually._
 import org.scalatest.selenium.WebBrowser
 import org.scalatest.time.SpanSugar._
 
-import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.ui.SparkUICssErrorHandler
 
 class UISeleniumSuite
@@ -36,7 +35,6 @@ class UISeleniumSuite
 
   implicit var webDriver: WebDriver = _
   var server: HiveThriftServer2 = _
-  var hc: HiveContext = _
   val uiPort = 20000 + Random.nextInt(10000)
   override def mode: ServerMode.Value = ServerMode.binary
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
index 59e65ff97b8e0..574624d501f22 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
@@ -26,7 +26,7 @@ import org.apache.spark.sql.hive.test.TestHive.implicits._
 import org.apache.spark.sql.sources.DataSourceTest
 import org.apache.spark.sql.test.{ExamplePointUDT, SQLTestUtils}
 import org.apache.spark.sql.types.{DecimalType, StringType, StructType}
-import org.apache.spark.sql.{Row, SaveMode}
+import org.apache.spark.sql.{Row, SaveMode, SQLContext}
 import org.apache.spark.{Logging, SparkFunSuite}
 
 
@@ -53,7 +53,8 @@ class HiveMetastoreCatalogSuite extends SparkFunSuite with Logging {
 }
 
 class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTestUtils {
-  override val sqlContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  import testImplicits._
 
   private val testDF = range(1, 3).select(
     ('id + 0.1) cast DecimalType(10, 3) as 'd1,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
index 1fa005d5f9a15..fe0db5228de16 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
@@ -19,14 +19,13 @@ package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.execution.datasources.parquet.ParquetTest
-import org.apache.spark.sql.{QueryTest, Row}
+import org.apache.spark.sql.{QueryTest, Row, SQLContext}
 
 case class Cases(lower: String, UPPER: String)
 
 class HiveParquetSuite extends QueryTest with ParquetTest {
-  val sqlContext = TestHive
-
-  import sqlContext._
+  private val ctx = TestHive
+  override def _sqlContext: SQLContext = ctx
 
   test("Case insensitive attribute names") {
     withParquetTable((1 to 4).map(i => Cases(i.toString, i.toString)), "cases") {
@@ -54,7 +53,7 @@ class HiveParquetSuite extends QueryTest with ParquetTest {
   test("Converting Hive to Parquet Table via saveAsParquetFile") {
     withTempPath { dir =>
       sql("SELECT * FROM src").write.parquet(dir.getCanonicalPath)
-      read.parquet(dir.getCanonicalPath).registerTempTable("p")
+      ctx.read.parquet(dir.getCanonicalPath).registerTempTable("p")
       withTempTable("p") {
         checkAnswer(
           sql("SELECT * FROM src ORDER BY key"),
@@ -67,7 +66,7 @@ class HiveParquetSuite extends QueryTest with ParquetTest {
     withParquetTable((1 to 10).map(i => (i, s"val_$i")), "t") {
       withTempPath { file =>
         sql("SELECT * FROM t LIMIT 1").write.parquet(file.getCanonicalPath)
-        read.parquet(file.getCanonicalPath).registerTempTable("p")
+        ctx.read.parquet(file.getCanonicalPath).registerTempTable("p")
         withTempTable("p") {
           // let's do three overwrites for good measure
           sql("INSERT OVERWRITE TABLE p SELECT * FROM t")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index 7f36a483a3965..20a50586d5201 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -22,7 +22,6 @@ import java.io.{IOException, File}
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.hadoop.fs.Path
-import org.apache.hadoop.mapred.InvalidInputException
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.Logging
@@ -42,7 +41,8 @@ import org.apache.spark.util.Utils
  */
 class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll
   with Logging {
-  override val sqlContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   var jsonFilePath: String = _
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
index 73852f13ad20d..417e8b07917cc 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
@@ -22,9 +22,8 @@ import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.{QueryTest, SQLContext, SaveMode}
 
 class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
-  override val sqlContext: SQLContext = TestHive
-
-  import sqlContext.sql
+  override val _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   private val df = sqlContext.range(10).coalesce(1)
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index 251e0324bfa5f..13452e71a1b3b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -26,7 +26,8 @@ import org.apache.spark.sql.{Row, SQLConf, SQLContext}
 class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
   import ParquetCompatibilityTest.makeNullable
 
-  override val sqlContext: SQLContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   /**
    * Set the staging directory (and hence path to ignore Parquet files under)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
index 9b3ede43ee2d1..7ee1c8d13aa3f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.sql.{Row, QueryTest}
+import org.apache.spark.sql.QueryTest
 
 case class FunctionResult(f1: String, f2: String)
 
 class UDFSuite extends QueryTest {
-
   private lazy val ctx = org.apache.spark.sql.hive.test.TestHive
-  import ctx.implicits._
 
   test("UDF case insensitive") {
     ctx.udf.register("random0", () => { Math.random() })
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index 7b5aa4763fd9e..a312f84958248 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -17,17 +17,18 @@
 
 package org.apache.spark.sql.hive.execution
 
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.sql._
 import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
-import org.apache.spark.sql._
-import org.scalatest.BeforeAndAfterAll
 import _root_.test.org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
 
 abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
-
-  override val sqlContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  protected val sqlContext = _sqlContext
   import sqlContext.implicits._
 
   var originalUseAggregate2: Boolean = _
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
index 44c5b80392fa5..11d7a872dff09 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
@@ -26,8 +26,8 @@ import org.apache.spark.sql.test.SQLTestUtils
  * A set of tests that validates support for Hive Explain command.
  */
 class HiveExplainSuite extends QueryTest with SQLTestUtils {
-
-  def sqlContext: SQLContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   test("explain extended command") {
     checkExistence(sql(" explain   select * from src where key=123 "), true,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 79a136ae6f619..8b8f520776e70 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -66,7 +66,8 @@ class MyDialect extends DefaultParserDialect
  * valid, but Hive currently cannot execute it.
  */
 class SQLQuerySuite extends QueryTest with SQLTestUtils {
-  override def sqlContext: SQLContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   test("UDTF") {
     sql(s"ADD JAR ${TestHive.getHiveFile("TestUDTF.jar").getCanonicalPath()}")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
index 0875232aede3e..9aca40f15ac15 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
@@ -31,7 +31,8 @@ import org.apache.spark.sql.types.StringType
 
 class ScriptTransformationSuite extends SparkPlanTest {
 
-  override def sqlContext: SQLContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   private val noSerdeIOSchema = HiveScriptIOSchema(
     inputRowFormat = Seq.empty,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
index 145965388da01..f7ba20ff41d8d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
@@ -27,8 +27,8 @@ import org.apache.spark.sql._
 import org.apache.spark.sql.test.SQLTestUtils
 
 private[sql] trait OrcTest extends SQLTestUtils { this: SparkFunSuite =>
-  lazy val sqlContext = org.apache.spark.sql.hive.test.TestHive
-
+  protected override def _sqlContext: SQLContext = org.apache.spark.sql.hive.test.TestHive
+  protected val sqlContext = _sqlContext
   import sqlContext.implicits._
   import sqlContext.sparkContext
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
index 50f02432dacce..34d3434569f58 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
@@ -685,7 +685,8 @@ class ParquetSourceSuite extends ParquetPartitioningTest {
  * A collection of tests for parquet data with various forms of partitioning.
  */
 abstract class ParquetPartitioningTest extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
-  override def sqlContext: SQLContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  protected val sqlContext = _sqlContext
 
   var partitionedTableDir: File = null
   var normalTableDir: File = null
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
index e976125b3706d..b4640b1616281 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
@@ -18,14 +18,16 @@
 package org.apache.spark.sql.sources
 
 import org.apache.hadoop.fs.Path
-import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 
 
 class CommitFailureTestRelationSuite extends SparkFunSuite with SQLTestUtils {
-  override val sqlContext = TestHive
+  override def _sqlContext: SQLContext = TestHive
+  private val sqlContext = _sqlContext
 
   // When committing a task, `CommitFailureTestSource` throws an exception for testing purpose.
   val dataSourceName: String = classOf[CommitFailureTestSource].getCanonicalName
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 2a69d331b6e52..af445626fbe4d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -34,9 +34,8 @@ import org.apache.spark.sql.types._
 
 
 abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
-  override lazy val sqlContext: SQLContext = TestHive
-
-  import sqlContext.sql
+  override def _sqlContext: SQLContext = TestHive
+  protected val sqlContext = _sqlContext
   import sqlContext.implicits._
 
   val dataSourceName: String

From bd35385d53a6b039e0241e3e73092b8b0a8e455a Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 13 Aug 2015 21:12:59 -0700
Subject: [PATCH 239/633] [SPARK-9945] [SQL] pageSize should be calculated from
 executor.memory

Currently, pageSize of TungstenSort is calculated from driver.memory, it should use executor.memory instead.

Also, in the worst case, the safeFactor could be 4 (because of rounding), increase it to 16.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #8175 from davies/page_size.
---
 .../org/apache/spark/shuffle/ShuffleMemoryManager.scala     | 4 +++-
 .../main/scala/org/apache/spark/sql/execution/sort.scala    | 6 +++---
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
index 8c3a72644c38a..a0d8abc2eecb3 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/ShuffleMemoryManager.scala
@@ -175,7 +175,9 @@ private[spark] object ShuffleMemoryManager {
     val minPageSize = 1L * 1024 * 1024   // 1MB
     val maxPageSize = 64L * minPageSize  // 64MB
     val cores = if (numCores > 0) numCores else Runtime.getRuntime.availableProcessors()
-    val safetyFactor = 8
+    // Because of rounding to next power of 2, we may have safetyFactor as 8 in worst case
+    val safetyFactor = 16
+    // TODO(davies): don't round to next power of 2
     val size = ByteArrayMethods.nextPowerOf2(maxMemory / cores / safetyFactor)
     val default = math.min(maxPageSize, math.max(minPageSize, size))
     conf.getSizeAsBytes("spark.buffer.pageSize", default)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
index e316930470127..40ef7c3b53530 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/sort.scala
@@ -17,15 +17,15 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.{SparkEnv, InternalAccumulator, TaskContext}
 import org.apache.spark.rdd.{MapPartitionsWithPreparationRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.errors._
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.physical.{UnspecifiedDistribution, OrderedDistribution, Distribution}
+import org.apache.spark.sql.catalyst.plans.physical.{Distribution, OrderedDistribution, UnspecifiedDistribution}
 import org.apache.spark.sql.types.StructType
 import org.apache.spark.util.CompletionIterator
 import org.apache.spark.util.collection.ExternalSorter
+import org.apache.spark.{SparkEnv, InternalAccumulator, TaskContext}
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 // This file defines various sort operators.
@@ -122,7 +122,6 @@ case class TungstenSort(
   protected override def doExecute(): RDD[InternalRow] = {
     val schema = child.schema
     val childOutput = child.output
-    val pageSize = SparkEnv.get.shuffleMemoryManager.pageSizeBytes
 
     /**
      * Set up the sorter in each partition before computing the parent partition.
@@ -143,6 +142,7 @@ case class TungstenSort(
         }
       }
 
+      val pageSize = SparkEnv.get.shuffleMemoryManager.pageSizeBytes
       val sorter = new UnsafeExternalRowSorter(
         schema, ordering, prefixComparator, prefixComputer, pageSize)
       if (testSpillFrequency > 0) {

From 7c7c7529a16c0e79778e522a3df82a0f1c3762a3 Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Thu, 13 Aug 2015 22:06:09 -0700
Subject: [PATCH 240/633] [MINOR] [SQL] Remove canEqual in Row

As `InternalRow` does not extend `Row` now, I think we can remove it.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #8170 from viirya/remove_canequal.
---
 .../main/scala/org/apache/spark/sql/Row.scala | 21 -------------------
 1 file changed, 21 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index 40159aaf14d34..ec895af9c3037 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -364,31 +364,10 @@ trait Row extends Serializable {
     false
   }
 
-  /**
-   * Returns true if we can check equality for these 2 rows.
-   * Equality check between external row and internal row is not allowed.
-   * Here we do this check to prevent call `equals` on external row with internal row.
-   */
-  protected def canEqual(other: Row) = {
-    // Note that `Row` is not only the interface of external row but also the parent
-    // of `InternalRow`, so we have to ensure `other` is not a internal row here to prevent
-    // call `equals` on external row with internal row.
-    // `InternalRow` overrides canEqual, and these two canEquals together makes sure that
-    // equality check between external Row and InternalRow will always fail.
-    // In the future, InternalRow should not extend Row. In that case, we can remove these
-    // canEqual methods.
-    !other.isInstanceOf[InternalRow]
-  }
-
   override def equals(o: Any): Boolean = {
     if (!o.isInstanceOf[Row]) return false
     val other = o.asInstanceOf[Row]
 
-    if (!canEqual(other)) {
-      throw new UnsupportedOperationException(
-        "cannot check equality between external and internal rows")
-    }
-
     if (other eq null) return false
 
     if (length != other.length) {

From c8677d73666850b37ff937520e538650632ce304 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 14 Aug 2015 14:41:53 +0800
Subject: [PATCH 241/633] [SPARK-9958] [SQL] Make HiveThriftServer2Listener
 thread-safe and update the tab name to "JDBC/ODBC Server"

This PR fixed the thread-safe issue of HiveThriftServer2Listener, and also changed the tab name to "JDBC/ODBC Server" since it's conflict with the new SQL tab.

<img width="1377" alt="thriftserver" src="https://cloud.githubusercontent.com/assets/1000778/9265707/c46f3f2c-4269-11e5-8d7e-888c9113ab4f.png">

Author: zsxwing <zsxwing@gmail.com>

Closes #8185 from zsxwing/SPARK-9958.
---
 .../hive/thriftserver/HiveThriftServer2.scala | 64 +++++++++++--------
 .../thriftserver/ui/ThriftServerPage.scala    | 32 +++++-----
 .../ui/ThriftServerSessionPage.scala          | 38 +++++------
 .../thriftserver/ui/ThriftServerTab.scala     |  4 +-
 4 files changed, 78 insertions(+), 60 deletions(-)

diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
index 2c9fa595b2dad..dd9fef9206d0b 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2.scala
@@ -152,16 +152,26 @@ object HiveThriftServer2 extends Logging {
     override def onApplicationEnd(applicationEnd: SparkListenerApplicationEnd): Unit = {
       server.stop()
     }
-    var onlineSessionNum: Int = 0
-    val sessionList = new mutable.LinkedHashMap[String, SessionInfo]
-    val executionList = new mutable.LinkedHashMap[String, ExecutionInfo]
-    val retainedStatements =
-      conf.getConf(SQLConf.THRIFTSERVER_UI_STATEMENT_LIMIT)
-    val retainedSessions =
-      conf.getConf(SQLConf.THRIFTSERVER_UI_SESSION_LIMIT)
-    var totalRunning = 0
-
-    override def onJobStart(jobStart: SparkListenerJobStart): Unit = {
+    private var onlineSessionNum: Int = 0
+    private val sessionList = new mutable.LinkedHashMap[String, SessionInfo]
+    private val executionList = new mutable.LinkedHashMap[String, ExecutionInfo]
+    private val retainedStatements = conf.getConf(SQLConf.THRIFTSERVER_UI_STATEMENT_LIMIT)
+    private val retainedSessions = conf.getConf(SQLConf.THRIFTSERVER_UI_SESSION_LIMIT)
+    private var totalRunning = 0
+
+    def getOnlineSessionNum: Int = synchronized { onlineSessionNum }
+
+    def getTotalRunning: Int = synchronized { totalRunning }
+
+    def getSessionList: Seq[SessionInfo] = synchronized { sessionList.values.toSeq }
+
+    def getSession(sessionId: String): Option[SessionInfo] = synchronized {
+      sessionList.get(sessionId)
+    }
+
+    def getExecutionList: Seq[ExecutionInfo] = synchronized { executionList.values.toSeq }
+
+    override def onJobStart(jobStart: SparkListenerJobStart): Unit = synchronized {
       for {
         props <- Option(jobStart.properties)
         groupId <- Option(props.getProperty(SparkContext.SPARK_JOB_GROUP_ID))
@@ -173,13 +183,15 @@ object HiveThriftServer2 extends Logging {
     }
 
     def onSessionCreated(ip: String, sessionId: String, userName: String = "UNKNOWN"): Unit = {
-      val info = new SessionInfo(sessionId, System.currentTimeMillis, ip, userName)
-      sessionList.put(sessionId, info)
-      onlineSessionNum += 1
-      trimSessionIfNecessary()
+      synchronized {
+        val info = new SessionInfo(sessionId, System.currentTimeMillis, ip, userName)
+        sessionList.put(sessionId, info)
+        onlineSessionNum += 1
+        trimSessionIfNecessary()
+      }
     }
 
-    def onSessionClosed(sessionId: String): Unit = {
+    def onSessionClosed(sessionId: String): Unit = synchronized {
       sessionList(sessionId).finishTimestamp = System.currentTimeMillis
       onlineSessionNum -= 1
       trimSessionIfNecessary()
@@ -190,7 +202,7 @@ object HiveThriftServer2 extends Logging {
         sessionId: String,
         statement: String,
         groupId: String,
-        userName: String = "UNKNOWN"): Unit = {
+        userName: String = "UNKNOWN"): Unit = synchronized {
       val info = new ExecutionInfo(statement, sessionId, System.currentTimeMillis, userName)
       info.state = ExecutionState.STARTED
       executionList.put(id, info)
@@ -200,27 +212,29 @@ object HiveThriftServer2 extends Logging {
       totalRunning += 1
     }
 
-    def onStatementParsed(id: String, executionPlan: String): Unit = {
+    def onStatementParsed(id: String, executionPlan: String): Unit = synchronized {
       executionList(id).executePlan = executionPlan
       executionList(id).state = ExecutionState.COMPILED
     }
 
     def onStatementError(id: String, errorMessage: String, errorTrace: String): Unit = {
-      executionList(id).finishTimestamp = System.currentTimeMillis
-      executionList(id).detail = errorMessage
-      executionList(id).state = ExecutionState.FAILED
-      totalRunning -= 1
-      trimExecutionIfNecessary()
+      synchronized {
+        executionList(id).finishTimestamp = System.currentTimeMillis
+        executionList(id).detail = errorMessage
+        executionList(id).state = ExecutionState.FAILED
+        totalRunning -= 1
+        trimExecutionIfNecessary()
+      }
     }
 
-    def onStatementFinish(id: String): Unit = {
+    def onStatementFinish(id: String): Unit = synchronized {
       executionList(id).finishTimestamp = System.currentTimeMillis
       executionList(id).state = ExecutionState.FINISHED
       totalRunning -= 1
       trimExecutionIfNecessary()
     }
 
-    private def trimExecutionIfNecessary() = synchronized {
+    private def trimExecutionIfNecessary() = {
       if (executionList.size > retainedStatements) {
         val toRemove = math.max(retainedStatements / 10, 1)
         executionList.filter(_._2.finishTimestamp != 0).take(toRemove).foreach { s =>
@@ -229,7 +243,7 @@ object HiveThriftServer2 extends Logging {
       }
     }
 
-    private def trimSessionIfNecessary() = synchronized {
+    private def trimSessionIfNecessary() = {
       if (sessionList.size > retainedSessions) {
         val toRemove = math.max(retainedSessions / 10, 1)
         sessionList.filter(_._2.finishTimestamp != 0).take(toRemove).foreach { s =>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala
index 10c83d8b27a2a..e990bd06011ff 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerPage.scala
@@ -39,14 +39,16 @@ private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage(""
   /** Render the page */
   def render(request: HttpServletRequest): Seq[Node] = {
     val content =
-      generateBasicStats() ++
-      <br/> ++
-      <h4>
-        {listener.onlineSessionNum} session(s) are online,
-        running {listener.totalRunning} SQL statement(s)
-      </h4> ++
-      generateSessionStatsTable() ++
-      generateSQLStatsTable()
+      listener.synchronized { // make sure all parts in this page are consistent
+        generateBasicStats() ++
+        <br/> ++
+        <h4>
+        {listener.getOnlineSessionNum} session(s) are online,
+        running {listener.getTotalRunning} SQL statement(s)
+        </h4> ++
+        generateSessionStatsTable() ++
+        generateSQLStatsTable()
+      }
     UIUtils.headerSparkPage("JDBC/ODBC Server", content, parent, Some(5000))
   }
 
@@ -65,11 +67,11 @@ private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage(""
 
   /** Generate stats of batch statements of the thrift server program */
   private def generateSQLStatsTable(): Seq[Node] = {
-    val numStatement = listener.executionList.size
+    val numStatement = listener.getExecutionList.size
     val table = if (numStatement > 0) {
       val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration",
         "Statement", "State", "Detail")
-      val dataRows = listener.executionList.values
+      val dataRows = listener.getExecutionList
 
       def generateDataRow(info: ExecutionInfo): Seq[Node] = {
         val jobLink = info.jobId.map { id: String =>
@@ -136,15 +138,15 @@ private[ui] class ThriftServerPage(parent: ThriftServerTab) extends WebUIPage(""
 
   /** Generate stats of batch sessions of the thrift server program */
   private def generateSessionStatsTable(): Seq[Node] = {
-    val numBatches = listener.sessionList.size
+    val sessionList = listener.getSessionList
+    val numBatches = sessionList.size
     val table = if (numBatches > 0) {
-      val dataRows =
-        listener.sessionList.values
+      val dataRows = sessionList
       val headerRow = Seq("User", "IP", "Session ID", "Start Time", "Finish Time", "Duration",
         "Total Execute")
       def generateDataRow(session: SessionInfo): Seq[Node] = {
-        val sessionLink = "%s/sql/session?id=%s"
-          .format(UIUtils.prependBaseUri(parent.basePath), session.sessionId)
+        val sessionLink = "%s/%s/session?id=%s"
+          .format(UIUtils.prependBaseUri(parent.basePath), parent.prefix, session.sessionId)
         <tr>
           <td> {session.userName} </td>
           <td> {session.ip} </td>
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala
index 3b01afa603cea..af16cb31df187 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerSessionPage.scala
@@ -40,21 +40,22 @@ private[ui] class ThriftServerSessionPage(parent: ThriftServerTab)
   def render(request: HttpServletRequest): Seq[Node] = {
     val parameterId = request.getParameter("id")
     require(parameterId != null && parameterId.nonEmpty, "Missing id parameter")
-    val sessionStat = listener.sessionList.find(stat => {
-      stat._1 == parameterId
-    }).getOrElse(null)
-    require(sessionStat != null, "Invalid sessionID[" + parameterId + "]")
 
     val content =
-      generateBasicStats() ++
-      <br/> ++
-      <h4>
-        User {sessionStat._2.userName},
-        IP {sessionStat._2.ip},
-        Session created at {formatDate(sessionStat._2.startTimestamp)},
-        Total run {sessionStat._2.totalExecution} SQL
-      </h4> ++
-      generateSQLStatsTable(sessionStat._2.sessionId)
+      listener.synchronized { // make sure all parts in this page are consistent
+        val sessionStat = listener.getSession(parameterId).getOrElse(null)
+        require(sessionStat != null, "Invalid sessionID[" + parameterId + "]")
+
+        generateBasicStats() ++
+        <br/> ++
+        <h4>
+        User {sessionStat.userName},
+        IP {sessionStat.ip},
+        Session created at {formatDate(sessionStat.startTimestamp)},
+        Total run {sessionStat.totalExecution} SQL
+        </h4> ++
+        generateSQLStatsTable(sessionStat.sessionId)
+      }
     UIUtils.headerSparkPage("JDBC/ODBC Session", content, parent, Some(5000))
   }
 
@@ -73,13 +74,13 @@ private[ui] class ThriftServerSessionPage(parent: ThriftServerTab)
 
   /** Generate stats of batch statements of the thrift server program */
   private def generateSQLStatsTable(sessionID: String): Seq[Node] = {
-    val executionList = listener.executionList
-      .filter(_._2.sessionId == sessionID)
+    val executionList = listener.getExecutionList
+      .filter(_.sessionId == sessionID)
     val numStatement = executionList.size
     val table = if (numStatement > 0) {
       val headerRow = Seq("User", "JobID", "GroupID", "Start Time", "Finish Time", "Duration",
         "Statement", "State", "Detail")
-      val dataRows = executionList.values.toSeq.sortBy(_.startTimestamp).reverse
+      val dataRows = executionList.sortBy(_.startTimestamp).reverse
 
       def generateDataRow(info: ExecutionInfo): Seq[Node] = {
         val jobLink = info.jobId.map { id: String =>
@@ -146,10 +147,11 @@ private[ui] class ThriftServerSessionPage(parent: ThriftServerTab)
 
   /** Generate stats of batch sessions of the thrift server program */
   private def generateSessionStatsTable(): Seq[Node] = {
-    val numBatches = listener.sessionList.size
+    val sessionList = listener.getSessionList
+    val numBatches = sessionList.size
     val table = if (numBatches > 0) {
       val dataRows =
-        listener.sessionList.values.toSeq.sortBy(_.startTimestamp).reverse.map ( session =>
+        sessionList.sortBy(_.startTimestamp).reverse.map ( session =>
         Seq(
           session.userName,
           session.ip,
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala
index 94fd8a6bb60b9..4eabeaa6735e6 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/ui/ThriftServerTab.scala
@@ -27,9 +27,9 @@ import org.apache.spark.{SparkContext, Logging, SparkException}
  * This assumes the given SparkContext has enabled its SparkUI.
  */
 private[thriftserver] class ThriftServerTab(sparkContext: SparkContext)
-  extends SparkUITab(getSparkUI(sparkContext), "sql") with Logging {
+  extends SparkUITab(getSparkUI(sparkContext), "sqlserver") with Logging {
 
-  override val name = "SQL"
+  override val name = "JDBC/ODBC Server"
 
   val parent = getSparkUI(sparkContext)
   val listener = HiveThriftServer2.listener

From a0e1abbd010b9e73d472ce12ff1d987678005d32 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Fri, 14 Aug 2015 10:25:11 -0700
Subject: [PATCH 242/633] [SPARK-9661] [MLLIB] minor clean-up of SPARK-9661

Some minor clean-ups after SPARK-9661. See my inline comments. MechCoder jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8190 from mengxr/SPARK-9661-fix.
---
 .../spark/mllib/clustering/LDAModel.scala     |  5 +--
 .../apache/spark/mllib/stat/Statistics.scala  |  6 +--
 .../spark/mllib/clustering/JavaLDASuite.java  | 40 +++++++++++--------
 .../spark/mllib/clustering/LDASuite.scala     |  2 +-
 4 files changed, 28 insertions(+), 25 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index f31949f13a4cf..82f05e4a18cee 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -674,10 +674,9 @@ class DistributedLDAModel private[clustering] (
   }
 
   /** Java-friendly version of [[topTopicsPerDocument]] */
-  def javaTopTopicsPerDocument(
-      k: Int): JavaRDD[(java.lang.Long, Array[Int], Array[java.lang.Double])] = {
+  def javaTopTopicsPerDocument(k: Int): JavaRDD[(java.lang.Long, Array[Int], Array[Double])] = {
     val topics = topTopicsPerDocument(k)
-    topics.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[java.lang.Double])]].toJavaRDD()
+    topics.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[Double])]].toJavaRDD()
   }
 
   // TODO:
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
index 24fe48cb8f71f..ef8d78607048f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
@@ -221,9 +221,7 @@ object Statistics {
   def kolmogorovSmirnovTest(
       data: JavaDoubleRDD,
       distName: String,
-      params: java.lang.Double*): KolmogorovSmirnovTestResult = {
-    val javaParams = params.asInstanceOf[Seq[Double]]
-    KolmogorovSmirnovTest.testOneSample(data.rdd.asInstanceOf[RDD[Double]],
-      distName, javaParams: _*)
+      params: Double*): KolmogorovSmirnovTestResult = {
+    kolmogorovSmirnovTest(data.rdd.asInstanceOf[RDD[Double]], distName, params: _*)
   }
 }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index 427be9430d820..6e91cde2eabb5 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -22,12 +22,14 @@
 import java.util.Arrays;
 
 import scala.Tuple2;
+import scala.Tuple3;
 
 import org.junit.After;
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertArrayEquals;
 import org.junit.Before;
 import org.junit.Test;
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
 
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.api.java.JavaPairRDD;
@@ -44,9 +46,9 @@ public class JavaLDASuite implements Serializable {
   public void setUp() {
     sc = new JavaSparkContext("local", "JavaLDA");
     ArrayList<Tuple2<Long, Vector>> tinyCorpus = new ArrayList<Tuple2<Long, Vector>>();
-    for (int i = 0; i < LDASuite$.MODULE$.tinyCorpus().length; i++) {
-      tinyCorpus.add(new Tuple2<Long, Vector>((Long)LDASuite$.MODULE$.tinyCorpus()[i]._1(),
-          LDASuite$.MODULE$.tinyCorpus()[i]._2()));
+    for (int i = 0; i < LDASuite.tinyCorpus().length; i++) {
+      tinyCorpus.add(new Tuple2<Long, Vector>((Long)LDASuite.tinyCorpus()[i]._1(),
+          LDASuite.tinyCorpus()[i]._2()));
     }
     JavaRDD<Tuple2<Long, Vector>> tmpCorpus = sc.parallelize(tinyCorpus, 2);
     corpus = JavaPairRDD.fromJavaRDD(tmpCorpus);
@@ -60,7 +62,7 @@ public void tearDown() {
 
   @Test
   public void localLDAModel() {
-    Matrix topics = LDASuite$.MODULE$.tinyTopics();
+    Matrix topics = LDASuite.tinyTopics();
     double[] topicConcentration = new double[topics.numRows()];
     Arrays.fill(topicConcentration, 1.0D / topics.numRows());
     LocalLDAModel model = new LocalLDAModel(topics, Vectors.dense(topicConcentration), 1D, 100D);
@@ -110,8 +112,8 @@ public void distributedLDAModel() {
     assertEquals(roundedLocalTopicSummary.length, k);
 
     // Check: log probabilities
-    assert(model.logLikelihood() < 0.0);
-    assert(model.logPrior() < 0.0);
+    assertTrue(model.logLikelihood() < 0.0);
+    assertTrue(model.logPrior() < 0.0);
 
     // Check: topic distributions
     JavaPairRDD<Long, Vector> topicDistributions = model.javaTopicDistributions();
@@ -126,8 +128,12 @@ public Boolean call(Tuple2<Long, Vector> tuple2) {
     assertEquals(topicDistributions.count(), nonEmptyCorpus.count());
 
     // Check: javaTopTopicsPerDocuments
-    JavaRDD<scala.Tuple3<java.lang.Long, int[], java.lang.Double[]>> topTopics =
-      model.javaTopTopicsPerDocument(3);
+    Tuple3<Long, int[], double[]> topTopics = model.javaTopTopicsPerDocument(3).first();
+    Long docId = topTopics._1(); // confirm doc ID type
+    int[] topicIndices = topTopics._2();
+    double[] topicWeights = topTopics._3();
+    assertEquals(3, topicIndices.length);
+    assertEquals(3, topicWeights.length);
   }
 
   @Test
@@ -177,18 +183,18 @@ public void localLdaMethods() {
 
     // check: logLikelihood.
     ArrayList<Tuple2<Long, Vector>> docsSingleWord = new ArrayList<Tuple2<Long, Vector>>();
-    docsSingleWord.add(new Tuple2<Long, Vector>(Long.valueOf(0), Vectors.dense(1.0, 0.0, 0.0)));
+    docsSingleWord.add(new Tuple2<Long, Vector>(0L, Vectors.dense(1.0, 0.0, 0.0)));
     JavaPairRDD<Long, Vector> single = JavaPairRDD.fromJavaRDD(sc.parallelize(docsSingleWord));
     double logLikelihood = toyModel.logLikelihood(single);
   }
 
-  private static int tinyK = LDASuite$.MODULE$.tinyK();
-  private static int tinyVocabSize = LDASuite$.MODULE$.tinyVocabSize();
-  private static Matrix tinyTopics = LDASuite$.MODULE$.tinyTopics();
+  private static int tinyK = LDASuite.tinyK();
+  private static int tinyVocabSize = LDASuite.tinyVocabSize();
+  private static Matrix tinyTopics = LDASuite.tinyTopics();
   private static Tuple2<int[], double[]>[] tinyTopicDescription =
-      LDASuite$.MODULE$.tinyTopicDescription();
+      LDASuite.tinyTopicDescription();
   private JavaPairRDD<Long, Vector> corpus;
-  private LocalLDAModel toyModel = LDASuite$.MODULE$.toyModel();
-  private ArrayList<Tuple2<Long, Vector>> toyData = LDASuite$.MODULE$.javaToyData();
+  private LocalLDAModel toyModel = LDASuite.toyModel();
+  private ArrayList<Tuple2<Long, Vector>> toyData = LDASuite.javaToyData();
 
 }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 926185e90bcf9..99e28499fd316 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -581,7 +581,7 @@ private[clustering] object LDASuite {
   def javaToyData: JArrayList[(java.lang.Long, Vector)] = {
     val javaData = new JArrayList[(java.lang.Long, Vector)]
     var i = 0
-    while (i < toyData.size) {
+    while (i < toyData.length) {
       javaData.add((toyData(i)._1, toyData(i)._2))
       i += 1
     }

From 7ecf0c46990c39df8aeddbd64ca33d01824bcc0a Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Fri, 14 Aug 2015 10:48:02 -0700
Subject: [PATCH 243/633] [SPARK-9956] [ML] Make trees work with one-category
 features

This modifies DecisionTreeMetadata construction to treat 1-category features as continuous, so that trees do not fail with such features.  It is important for the pipelines API, where VectorIndexer can automatically categorize certain features as categorical.

As stated in the JIRA, this is a temp fix which we can improve upon later by automatically filtering out those features. That will take longer, though, since it will require careful indexing.

Targeted for 1.5 and master

CC: manishamde  mengxr yanboliang

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8187 from jkbradley/tree-1cat.
---
 .../tree/impl/DecisionTreeMetadata.scala      | 27 ++++++++++++-------
 .../DecisionTreeClassifierSuite.scala         | 13 +++++++++
 2 files changed, 30 insertions(+), 10 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
index 9fe264656ede7..21ee49c45788c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impl/DecisionTreeMetadata.scala
@@ -144,21 +144,28 @@ private[spark] object DecisionTreeMetadata extends Logging {
       val maxCategoriesForUnorderedFeature =
         ((math.log(maxPossibleBins / 2 + 1) / math.log(2.0)) + 1).floor.toInt
       strategy.categoricalFeaturesInfo.foreach { case (featureIndex, numCategories) =>
-        // Decide if some categorical features should be treated as unordered features,
-        //  which require 2 * ((1 << numCategories - 1) - 1) bins.
-        // We do this check with log values to prevent overflows in case numCategories is large.
-        // The next check is equivalent to: 2 * ((1 << numCategories - 1) - 1) <= maxBins
-        if (numCategories <= maxCategoriesForUnorderedFeature) {
-          unorderedFeatures.add(featureIndex)
-          numBins(featureIndex) = numUnorderedBins(numCategories)
-        } else {
-          numBins(featureIndex) = numCategories
+        // Hack: If a categorical feature has only 1 category, we treat it as continuous.
+        // TODO(SPARK-9957): Handle this properly by filtering out those features.
+        if (numCategories > 1) {
+          // Decide if some categorical features should be treated as unordered features,
+          //  which require 2 * ((1 << numCategories - 1) - 1) bins.
+          // We do this check with log values to prevent overflows in case numCategories is large.
+          // The next check is equivalent to: 2 * ((1 << numCategories - 1) - 1) <= maxBins
+          if (numCategories <= maxCategoriesForUnorderedFeature) {
+            unorderedFeatures.add(featureIndex)
+            numBins(featureIndex) = numUnorderedBins(numCategories)
+          } else {
+            numBins(featureIndex) = numCategories
+          }
         }
       }
     } else {
       // Binary classification or regression
       strategy.categoricalFeaturesInfo.foreach { case (featureIndex, numCategories) =>
-        numBins(featureIndex) = numCategories
+        // If a categorical feature has only 1 category, we treat it as continuous: SPARK-9957
+        if (numCategories > 1) {
+          numBins(featureIndex) = numCategories
+        }
       }
     }
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
index 4b7c5d3f23d2c..f680d8d3c4cc2 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala
@@ -261,6 +261,19 @@ class DecisionTreeClassifierSuite extends SparkFunSuite with MLlibTestSparkConte
     }
   }
 
+  test("training with 1-category categorical feature") {
+    val data = sc.parallelize(Seq(
+      LabeledPoint(0, Vectors.dense(0, 2, 3)),
+      LabeledPoint(1, Vectors.dense(0, 3, 1)),
+      LabeledPoint(0, Vectors.dense(0, 2, 2)),
+      LabeledPoint(1, Vectors.dense(0, 3, 9)),
+      LabeledPoint(0, Vectors.dense(0, 2, 6))
+    ))
+    val df = TreeTests.setMetadata(data, Map(0 -> 1), 2)
+    val dt = new DecisionTreeClassifier().setMaxDepth(3)
+    val model = dt.fit(df)
+  }
+
   /////////////////////////////////////////////////////////////////////////////
   // Tests of model save/load
   /////////////////////////////////////////////////////////////////////////////

From a7317ccdc20d001e5b7f5277b0535923468bfbc6 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Fri, 14 Aug 2015 11:22:10 -0700
Subject: [PATCH 244/633] [SPARK-8744] [ML] Add a public constructor to
 StringIndexer

It would be helpful to allow users to pass a pre-computed index to create an indexer, rather than always going through StringIndexer to create the model.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7267 from holdenk/SPARK-8744-StringIndexerModel-should-have-public-constructor.
---
 .../scala/org/apache/spark/ml/feature/StringIndexer.scala     | 4 +++-
 .../org/apache/spark/ml/feature/StringIndexerSuite.scala      | 2 ++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 9f6e7b6b6b274..63475780a6ff9 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -102,10 +102,12 @@ class StringIndexer(override val uid: String) extends Estimator[StringIndexerMod
  * This is a temporary fix for the case when target labels do not exist during prediction.
  */
 @Experimental
-class StringIndexerModel private[ml] (
+class StringIndexerModel (
     override val uid: String,
     labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase {
 
+  def this(labels: Array[String]) = this(Identifiable.randomUID("strIdx"), labels)
+
   private val labelToIndex: OpenHashMap[String, Double] = {
     val n = labels.length
     val map = new OpenHashMap[String, Double](n)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index fa918ce64877c..0b4c8ba71ee61 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -30,7 +30,9 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
   test("params") {
     ParamsSuite.checkParams(new StringIndexer)
     val model = new StringIndexerModel("indexer", Array("a", "b"))
+    val modelWithoutUid = new StringIndexerModel(Array("a", "b"))
     ParamsSuite.checkParams(model)
+    ParamsSuite.checkParams(modelWithoutUid)
   }
 
   test("StringIndexer") {

From 34d610be854d2a975d9c1e232d87433b85add6fd Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 14 Aug 2015 12:00:01 -0700
Subject: [PATCH 245/633] [SPARK-9929] [SQL] support metadata in withColumn

in MLlib sometimes we need to set metadata for the new column, thus we will alias the new column with metadata before call `withColumn` and in `withColumn` we alias this clolumn again. Here I overloaded `withColumn` to allow user set metadata, just like what we did  for `Column.as`.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8159 from cloud-fan/withColumn.
---
 .../spark/ml/classification/OneVsRest.scala     |  6 +++---
 .../apache/spark/ml/feature/Bucketizer.scala    |  2 +-
 .../apache/spark/ml/feature/VectorIndexer.scala |  2 +-
 .../apache/spark/ml/feature/VectorSlicer.scala  |  3 +--
 .../scala/org/apache/spark/sql/DataFrame.scala  | 17 +++++++++++++++++
 5 files changed, 23 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index 1132d8046df67..c62e132f5d533 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -131,7 +131,7 @@ final class OneVsRestModel private[ml] (
 
     // output label and label metadata as prediction
     aggregatedDataset
-      .withColumn($(predictionCol), labelUDF(col(accColName)).as($(predictionCol), labelMetadata))
+      .withColumn($(predictionCol), labelUDF(col(accColName)), labelMetadata)
       .drop(accColName)
   }
 
@@ -203,8 +203,8 @@ final class OneVsRest(override val uid: String)
       // TODO: use when ... otherwise after SPARK-7321 is merged
       val newLabelMeta = BinaryAttribute.defaultAttr.withName("label").toMetadata()
       val labelColName = "mc2b$" + index
-      val labelUDFWithNewMeta = labelUDF(col($(labelCol))).as(labelColName, newLabelMeta)
-      val trainingDataset = multiclassLabeled.withColumn(labelColName, labelUDFWithNewMeta)
+      val trainingDataset =
+        multiclassLabeled.withColumn(labelColName, labelUDF(col($(labelCol))), newLabelMeta)
       val classifier = getClassifier
       val paramMap = new ParamMap()
       paramMap.put(classifier.labelCol -> labelColName)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
index cfca494dcf468..6fdf25b015b0b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
@@ -75,7 +75,7 @@ final class Bucketizer(override val uid: String)
     }
     val newCol = bucketizer(dataset($(inputCol)))
     val newField = prepOutputField(dataset.schema)
-    dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata))
+    dataset.withColumn($(outputCol), newCol, newField.metadata)
   }
 
   private def prepOutputField(schema: StructType): StructField = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
index 6875aefe065bb..61b925c0fdc07 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
@@ -341,7 +341,7 @@ class VectorIndexerModel private[ml] (
     val newField = prepOutputField(dataset.schema)
     val transformUDF = udf { (vector: Vector) => transformFunc(vector) }
     val newCol = transformUDF(dataset($(inputCol)))
-    dataset.withColumn($(outputCol), newCol.as($(outputCol), newField.metadata))
+    dataset.withColumn($(outputCol), newCol, newField.metadata)
   }
 
   override def transformSchema(schema: StructType): StructType = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
index 772bebeff214b..c5c2272270792 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
@@ -119,8 +119,7 @@ final class VectorSlicer(override val uid: String)
         case features: SparseVector => features.slice(inds)
       }
     }
-    dataset.withColumn($(outputCol),
-      slicer(dataset($(inputCol))).as($(outputCol), outputAttr.toMetadata()))
+    dataset.withColumn($(outputCol), slicer(dataset($(inputCol))), outputAttr.toMetadata())
   }
 
   /** Get the feature indices in order: indices, names */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index c466d9e6cb349..cf75e64e884b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1149,6 +1149,23 @@ class DataFrame private[sql](
     }
   }
 
+  /**
+   * Returns a new [[DataFrame]] by adding a column with metadata.
+   */
+  private[spark] def withColumn(colName: String, col: Column, metadata: Metadata): DataFrame = {
+    val resolver = sqlContext.analyzer.resolver
+    val replaced = schema.exists(f => resolver(f.name, colName))
+    if (replaced) {
+      val colNames = schema.map { field =>
+        val name = field.name
+        if (resolver(name, colName)) col.as(colName, metadata) else Column(name)
+      }
+      select(colNames : _*)
+    } else {
+      select(Column("*"), col.as(colName, metadata))
+    }
+  }
+
   /**
    * Returns a new [[DataFrame]] with a column renamed.
    * This is a no-op if schema doesn't contain existingName.

From 57c2d08800a506614b461b5a505a1dd1a28a8908 Mon Sep 17 00:00:00 2001
From: Neelesh Srinivas Salian <nsalian@cloudera.com>
Date: Fri, 14 Aug 2015 20:03:50 +0100
Subject: [PATCH 246/633] [SPARK-9923] [CORE]
 ShuffleMapStage.numAvailableOutputs should be an Int instead of Long

Modified type of ShuffleMapStage.numAvailableOutputs from Long to Int

Author: Neelesh Srinivas Salian <nsalian@cloudera.com>

Closes #8183 from nssalian/SPARK-9923.
---
 .../main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
index 66c75f325fcde..48d8d8e9c4b78 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
@@ -37,7 +37,7 @@ private[spark] class ShuffleMapStage(
 
   override def toString: String = "ShuffleMapStage " + id
 
-  var numAvailableOutputs: Long = 0
+  var numAvailableOutputs: Int = 0
 
   def isAvailable: Boolean = numAvailableOutputs == numPartitions
 

From 3bc55287220b1248e935bf817d880ff176ad4d3b Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 14 Aug 2015 12:32:35 -0700
Subject: [PATCH 247/633] [SPARK-9946] [SPARK-9589] [SQL] fix NPE and
 thread-safety in TaskMemoryManager

Currently, we access the `page.pageNumer` after it's freed, that could be modified by other thread, cause NPE.

The same TaskMemoryManager could be used by multiple threads (for example, Python UDF and TransportScript), so it should be thread safe to allocate/free memory/page. The underlying Bitset and HashSet are not thread safe, we should put them inside a synchronized block.

cc JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #8177 from davies/memory_manager.
---
 .../unsafe/memory/TaskMemoryManager.java      | 44 ++++++++++++-------
 1 file changed, 28 insertions(+), 16 deletions(-)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
index 358bb37250158..ca70d7f4a4311 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
@@ -144,14 +144,16 @@ public MemoryBlock allocatePage(long size) {
   public void freePage(MemoryBlock page) {
     assert (page.pageNumber != -1) :
       "Called freePage() on memory that wasn't allocated with allocatePage()";
-    executorMemoryManager.free(page);
+    assert(allocatedPages.get(page.pageNumber));
+    pageTable[page.pageNumber] = null;
     synchronized (this) {
       allocatedPages.clear(page.pageNumber);
     }
-    pageTable[page.pageNumber] = null;
     if (logger.isTraceEnabled()) {
       logger.trace("Freed page number {} ({} bytes)", page.pageNumber, page.size());
     }
+    // Cannot access a page once it's freed.
+    executorMemoryManager.free(page);
   }
 
   /**
@@ -166,7 +168,9 @@ public void freePage(MemoryBlock page) {
   public MemoryBlock allocate(long size) throws OutOfMemoryError {
     assert(size > 0) : "Size must be positive, but got " + size;
     final MemoryBlock memory = executorMemoryManager.allocate(size);
-    allocatedNonPageMemory.add(memory);
+    synchronized(allocatedNonPageMemory) {
+      allocatedNonPageMemory.add(memory);
+    }
     return memory;
   }
 
@@ -176,8 +180,10 @@ public MemoryBlock allocate(long size) throws OutOfMemoryError {
   public void free(MemoryBlock memory) {
     assert (memory.pageNumber == -1) : "Should call freePage() for pages, not free()";
     executorMemoryManager.free(memory);
-    final boolean wasAlreadyRemoved = !allocatedNonPageMemory.remove(memory);
-    assert (!wasAlreadyRemoved) : "Called free() on memory that was already freed!";
+    synchronized(allocatedNonPageMemory) {
+      final boolean wasAlreadyRemoved = !allocatedNonPageMemory.remove(memory);
+      assert (!wasAlreadyRemoved) : "Called free() on memory that was already freed!";
+    }
   }
 
   /**
@@ -223,9 +229,10 @@ public Object getPage(long pagePlusOffsetAddress) {
     if (inHeap) {
       final int pageNumber = decodePageNumber(pagePlusOffsetAddress);
       assert (pageNumber >= 0 && pageNumber < PAGE_TABLE_SIZE);
-      final Object page = pageTable[pageNumber].getBaseObject();
+      final MemoryBlock page = pageTable[pageNumber];
       assert (page != null);
-      return page;
+      assert (page.getBaseObject() != null);
+      return page.getBaseObject();
     } else {
       return null;
     }
@@ -244,7 +251,9 @@ public long getOffsetInPage(long pagePlusOffsetAddress) {
       // converted the absolute address into a relative address. Here, we invert that operation:
       final int pageNumber = decodePageNumber(pagePlusOffsetAddress);
       assert (pageNumber >= 0 && pageNumber < PAGE_TABLE_SIZE);
-      return pageTable[pageNumber].getBaseOffset() + offsetInPage;
+      final MemoryBlock page = pageTable[pageNumber];
+      assert (page != null);
+      return page.getBaseOffset() + offsetInPage;
     }
   }
 
@@ -260,14 +269,17 @@ public long cleanUpAllAllocatedMemory() {
         freePage(page);
       }
     }
-    final Iterator<MemoryBlock> iter = allocatedNonPageMemory.iterator();
-    while (iter.hasNext()) {
-      final MemoryBlock memory = iter.next();
-      freedBytes += memory.size();
-      // We don't call free() here because that calls Set.remove, which would lead to a
-      // ConcurrentModificationException here.
-      executorMemoryManager.free(memory);
-      iter.remove();
+
+    synchronized (allocatedNonPageMemory) {
+      final Iterator<MemoryBlock> iter = allocatedNonPageMemory.iterator();
+      while (iter.hasNext()) {
+        final MemoryBlock memory = iter.next();
+        freedBytes += memory.size();
+        // We don't call free() here because that calls Set.remove, which would lead to a
+        // ConcurrentModificationException here.
+        executorMemoryManager.free(memory);
+        iter.remove();
+      }
     }
     return freedBytes;
   }

From ece00566e4d5f38585f2810bef38e526cae7d25e Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 14 Aug 2015 12:37:21 -0700
Subject: [PATCH 248/633] [SPARK-9561] Re-enable BroadcastJoinSuite

We can do this now that SPARK-9580 is resolved.

Author: Andrew Or <andrew@databricks.com>

Closes #8208 from andrewor14/reenable-sql-tests.
---
 .../execution/joins/BroadcastJoinSuite.scala  | 145 +++++++++---------
 1 file changed, 69 insertions(+), 76 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
index 0554e11d252ba..53a0e53fd7719 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
@@ -15,80 +15,73 @@
 * limitations under the License.
 */
 
-// TODO: uncomment the test here! It is currently failing due to
-// bad interaction with org.apache.spark.sql.test.TestSQLContext.
+package org.apache.spark.sql.execution.joins
 
-// scalastyle:off
-//package org.apache.spark.sql.execution.joins
-//
-//import scala.reflect.ClassTag
-//
-//import org.scalatest.BeforeAndAfterAll
-//
-//import org.apache.spark.{AccumulatorSuite, SparkConf, SparkContext}
-//import org.apache.spark.sql.functions._
-//import org.apache.spark.sql.{SQLConf, SQLContext, QueryTest}
-//
-///**
-// * Test various broadcast join operators with unsafe enabled.
-// *
-// * This needs to be its own suite because [[org.apache.spark.sql.test.TestSQLContext]] runs
-// * in local mode, but for tests in this suite we need to run Spark in local-cluster mode.
-// * In particular, the use of [[org.apache.spark.unsafe.map.BytesToBytesMap]] in
-// * [[org.apache.spark.sql.execution.joins.UnsafeHashedRelation]] is not triggered without
-// * serializing the hashed relation, which does not happen in local mode.
-// */
-//class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
-//  private var sc: SparkContext = null
-//  private var sqlContext: SQLContext = null
-//
-//  /**
-//   * Create a new [[SQLContext]] running in local-cluster mode with unsafe and codegen enabled.
-//   */
-//  override def beforeAll(): Unit = {
-//    super.beforeAll()
-//    val conf = new SparkConf()
-//      .setMaster("local-cluster[2,1,1024]")
-//      .setAppName("testing")
-//    sc = new SparkContext(conf)
-//    sqlContext = new SQLContext(sc)
-//    sqlContext.setConf(SQLConf.UNSAFE_ENABLED, true)
-//    sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
-//  }
-//
-//  override def afterAll(): Unit = {
-//    sc.stop()
-//    sc = null
-//    sqlContext = null
-//  }
-//
-//  /**
-//   * Test whether the specified broadcast join updates the peak execution memory accumulator.
-//   */
-//  private def testBroadcastJoin[T: ClassTag](name: String, joinType: String): Unit = {
-//    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, name) {
-//      val df1 = sqlContext.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
-//      val df2 = sqlContext.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
-//      // Comparison at the end is for broadcast left semi join
-//      val joinExpression = df1("key") === df2("key") && df1("value") > df2("value")
-//      val df3 = df1.join(broadcast(df2), joinExpression, joinType)
-//      val plan = df3.queryExecution.executedPlan
-//      assert(plan.collect { case p: T => p }.size === 1)
-//      plan.executeCollect()
-//    }
-//  }
-//
-//  test("unsafe broadcast hash join updates peak execution memory") {
-//    testBroadcastJoin[BroadcastHashJoin]("unsafe broadcast hash join", "inner")
-//  }
-//
-//  test("unsafe broadcast hash outer join updates peak execution memory") {
-//    testBroadcastJoin[BroadcastHashOuterJoin]("unsafe broadcast hash outer join", "left_outer")
-//  }
-//
-//  test("unsafe broadcast left semi join updates peak execution memory") {
-//    testBroadcastJoin[BroadcastLeftSemiJoinHash]("unsafe broadcast left semi join", "leftsemi")
-//  }
-//
-//}
-// scalastyle:on
+import scala.reflect.ClassTag
+
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.{AccumulatorSuite, SparkConf, SparkContext}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.{SQLConf, SQLContext, QueryTest}
+
+/**
+ * Test various broadcast join operators with unsafe enabled.
+ *
+ * Tests in this suite we need to run Spark in local-cluster mode. In particular, the use of
+ * unsafe map in [[org.apache.spark.sql.execution.joins.UnsafeHashedRelation]] is not triggered
+ * without serializing the hashed relation, which does not happen in local mode.
+ */
+class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
+  private var sc: SparkContext = null
+  private var sqlContext: SQLContext = null
+
+  /**
+   * Create a new [[SQLContext]] running in local-cluster mode with unsafe and codegen enabled.
+   */
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    val conf = new SparkConf()
+      .setMaster("local-cluster[2,1,1024]")
+      .setAppName("testing")
+    sc = new SparkContext(conf)
+    sqlContext = new SQLContext(sc)
+    sqlContext.setConf(SQLConf.UNSAFE_ENABLED, true)
+    sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
+  }
+
+  override def afterAll(): Unit = {
+    sc.stop()
+    sc = null
+    sqlContext = null
+  }
+
+  /**
+   * Test whether the specified broadcast join updates the peak execution memory accumulator.
+   */
+  private def testBroadcastJoin[T: ClassTag](name: String, joinType: String): Unit = {
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, name) {
+      val df1 = sqlContext.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
+      val df2 = sqlContext.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
+      // Comparison at the end is for broadcast left semi join
+      val joinExpression = df1("key") === df2("key") && df1("value") > df2("value")
+      val df3 = df1.join(broadcast(df2), joinExpression, joinType)
+      val plan = df3.queryExecution.executedPlan
+      assert(plan.collect { case p: T => p }.size === 1)
+      plan.executeCollect()
+    }
+  }
+
+  test("unsafe broadcast hash join updates peak execution memory") {
+    testBroadcastJoin[BroadcastHashJoin]("unsafe broadcast hash join", "inner")
+  }
+
+  test("unsafe broadcast hash outer join updates peak execution memory") {
+    testBroadcastJoin[BroadcastHashOuterJoin]("unsafe broadcast hash outer join", "left_outer")
+  }
+
+  test("unsafe broadcast left semi join updates peak execution memory") {
+    testBroadcastJoin[BroadcastLeftSemiJoinHash]("unsafe broadcast left semi join", "leftsemi")
+  }
+
+}

From ffa05c84fe75663fc33f3d954d1cb1e084ab3280 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Fri, 14 Aug 2015 12:46:05 -0700
Subject: [PATCH 249/633] [SPARK-9828] [PYSPARK] Mutable values should not be
 default arguments

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8110 from MechCoder/spark-9828.
---
 python/pyspark/ml/evaluation.py     |  4 +++-
 python/pyspark/ml/param/__init__.py | 26 +++++++++++++++++---------
 python/pyspark/ml/pipeline.py       |  4 ++--
 python/pyspark/ml/tuning.py         |  8 ++++++--
 python/pyspark/rdd.py               |  5 ++++-
 python/pyspark/sql/readwriter.py    |  8 ++++++--
 python/pyspark/statcounter.py       |  4 +++-
 python/pyspark/streaming/kafka.py   | 12 +++++++++---
 8 files changed, 50 insertions(+), 21 deletions(-)

diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index 2734092575ad9..e23ce053baeb7 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -46,7 +46,7 @@ def _evaluate(self, dataset):
         """
         raise NotImplementedError()
 
-    def evaluate(self, dataset, params={}):
+    def evaluate(self, dataset, params=None):
         """
         Evaluates the output with optional parameters.
 
@@ -56,6 +56,8 @@ def evaluate(self, dataset, params={}):
                        params
         :return: metric
         """
+        if params is None:
+            params = dict()
         if isinstance(params, dict):
             if params:
                 return self.copy(params)._evaluate(dataset)
diff --git a/python/pyspark/ml/param/__init__.py b/python/pyspark/ml/param/__init__.py
index 7845536161e07..eeeac49b21980 100644
--- a/python/pyspark/ml/param/__init__.py
+++ b/python/pyspark/ml/param/__init__.py
@@ -60,14 +60,16 @@ class Params(Identifiable):
 
     __metaclass__ = ABCMeta
 
-    #: internal param map for user-supplied values param map
-    _paramMap = {}
+    def __init__(self):
+        super(Params, self).__init__()
+        #: internal param map for user-supplied values param map
+        self._paramMap = {}
 
-    #: internal param map for default values
-    _defaultParamMap = {}
+        #: internal param map for default values
+        self._defaultParamMap = {}
 
-    #: value returned by :py:func:`params`
-    _params = None
+        #: value returned by :py:func:`params`
+        self._params = None
 
     @property
     def params(self):
@@ -155,7 +157,7 @@ def getOrDefault(self, param):
         else:
             return self._defaultParamMap[param]
 
-    def extractParamMap(self, extra={}):
+    def extractParamMap(self, extra=None):
         """
         Extracts the embedded default param values and user-supplied
         values, and then merges them with extra values from input into
@@ -165,12 +167,14 @@ def extractParamMap(self, extra={}):
         :param extra: extra param values
         :return: merged param map
         """
+        if extra is None:
+            extra = dict()
         paramMap = self._defaultParamMap.copy()
         paramMap.update(self._paramMap)
         paramMap.update(extra)
         return paramMap
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
         """
         Creates a copy of this instance with the same uid and some
         extra params. The default implementation creates a
@@ -181,6 +185,8 @@ def copy(self, extra={}):
         :param extra: Extra parameters to copy to the new instance
         :return: Copy of this instance
         """
+        if extra is None:
+            extra = dict()
         that = copy.copy(self)
         that._paramMap = self.extractParamMap(extra)
         return that
@@ -233,7 +239,7 @@ def _setDefault(self, **kwargs):
             self._defaultParamMap[getattr(self, param)] = value
         return self
 
-    def _copyValues(self, to, extra={}):
+    def _copyValues(self, to, extra=None):
         """
         Copies param values from this instance to another instance for
         params shared by them.
@@ -241,6 +247,8 @@ def _copyValues(self, to, extra={}):
         :param extra: extra params to be copied
         :return: the target instance with param values copied
         """
+        if extra is None:
+            extra = dict()
         paramMap = self.extractParamMap(extra)
         for p in self.params:
             if p in paramMap and to.hasParam(p.name):
diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py
index 9889f56cac9e4..13cf2b0f7bbd9 100644
--- a/python/pyspark/ml/pipeline.py
+++ b/python/pyspark/ml/pipeline.py
@@ -141,7 +141,7 @@ class Pipeline(Estimator):
     @keyword_only
     def __init__(self, stages=None):
         """
-        __init__(self, stages=[])
+        __init__(self, stages=None)
         """
         if stages is None:
             stages = []
@@ -170,7 +170,7 @@ def getStages(self):
     @keyword_only
     def setParams(self, stages=None):
         """
-        setParams(self, stages=[])
+        setParams(self, stages=None)
         Sets params for Pipeline.
         """
         if stages is None:
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index 0bf988fd72f14..dcfee6a3170ab 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -227,7 +227,9 @@ def _fit(self, dataset):
         bestModel = est.fit(dataset, epm[bestIndex])
         return CrossValidatorModel(bestModel)
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
+        if extra is None:
+            extra = dict()
         newCV = Params.copy(self, extra)
         if self.isSet(self.estimator):
             newCV.setEstimator(self.getEstimator().copy(extra))
@@ -250,7 +252,7 @@ def __init__(self, bestModel):
     def _transform(self, dataset):
         return self.bestModel.transform(dataset)
 
-    def copy(self, extra={}):
+    def copy(self, extra=None):
         """
         Creates a copy of this instance with a randomly generated uid
         and some extra params. This copies the underlying bestModel,
@@ -259,6 +261,8 @@ def copy(self, extra={}):
         :param extra: Extra parameters to copy to the new instance
         :return: Copy of this instance
         """
+        if extra is None:
+            extra = dict()
         return CrossValidatorModel(self.bestModel.copy(extra))
 
 
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index fa8e0a0574a62..9ef60a7e2c84b 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -700,7 +700,7 @@ def groupBy(self, f, numPartitions=None):
         return self.map(lambda x: (f(x), x)).groupByKey(numPartitions)
 
     @ignore_unicode_prefix
-    def pipe(self, command, env={}, checkCode=False):
+    def pipe(self, command, env=None, checkCode=False):
         """
         Return an RDD created by piping elements to a forked external process.
 
@@ -709,6 +709,9 @@ def pipe(self, command, env={}, checkCode=False):
 
         :param checkCode: whether or not to check the return value of the shell command.
         """
+        if env is None:
+            env = dict()
+
         def func(iterator):
             pipe = Popen(
                 shlex.split(command), env=env, stdin=PIPE, stdout=PIPE)
diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index bf6ac084bbbf8..78247c8fa7372 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -182,7 +182,7 @@ def orc(self, path):
 
     @since(1.4)
     def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPartitions=None,
-             predicates=None, properties={}):
+             predicates=None, properties=None):
         """
         Construct a :class:`DataFrame` representing the database table accessible
         via JDBC URL `url` named `table` and connection `properties`.
@@ -208,6 +208,8 @@ def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPar
                            should be included.
         :return: a DataFrame
         """
+        if properties is None:
+            properties = dict()
         jprop = JavaClass("java.util.Properties", self._sqlContext._sc._gateway._gateway_client)()
         for k in properties:
             jprop.setProperty(k, properties[k])
@@ -427,7 +429,7 @@ def orc(self, path, mode=None, partitionBy=None):
         self._jwrite.orc(path)
 
     @since(1.4)
-    def jdbc(self, url, table, mode=None, properties={}):
+    def jdbc(self, url, table, mode=None, properties=None):
         """Saves the content of the :class:`DataFrame` to a external database table via JDBC.
 
         .. note:: Don't create too many partitions in parallel on a large cluster;\
@@ -445,6 +447,8 @@ def jdbc(self, url, table, mode=None, properties={}):
                            arbitrary string tag/value. Normally at least a
                            "user" and "password" property should be included.
         """
+        if properties is None:
+            properties = dict()
         jprop = JavaClass("java.util.Properties", self._sqlContext._sc._gateway._gateway_client)()
         for k in properties:
             jprop.setProperty(k, properties[k])
diff --git a/python/pyspark/statcounter.py b/python/pyspark/statcounter.py
index 944fa414b0c0e..0fee3b2096826 100644
--- a/python/pyspark/statcounter.py
+++ b/python/pyspark/statcounter.py
@@ -30,7 +30,9 @@
 
 class StatCounter(object):
 
-    def __init__(self, values=[]):
+    def __init__(self, values=None):
+        if values is None:
+            values = list()
         self.n = 0    # Running count of our values
         self.mu = 0.0  # Running mean of our values
         self.m2 = 0.0  # Running variance numerator (sum of (x - mean)^2)
diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py
index 33dd596335b47..dc5b7fd878aef 100644
--- a/python/pyspark/streaming/kafka.py
+++ b/python/pyspark/streaming/kafka.py
@@ -35,7 +35,7 @@ def utf8_decoder(s):
 class KafkaUtils(object):
 
     @staticmethod
-    def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={},
+    def createStream(ssc, zkQuorum, groupId, topics, kafkaParams=None,
                      storageLevel=StorageLevel.MEMORY_AND_DISK_SER_2,
                      keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
         """
@@ -52,6 +52,8 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={},
         :param valueDecoder:  A function used to decode value (default is utf8_decoder)
         :return: A DStream object
         """
+        if kafkaParams is None:
+            kafkaParams = dict()
         kafkaParams.update({
             "zookeeper.connect": zkQuorum,
             "group.id": groupId,
@@ -77,7 +79,7 @@ def createStream(ssc, zkQuorum, groupId, topics, kafkaParams={},
         return stream.map(lambda k_v: (keyDecoder(k_v[0]), valueDecoder(k_v[1])))
 
     @staticmethod
-    def createDirectStream(ssc, topics, kafkaParams, fromOffsets={},
+    def createDirectStream(ssc, topics, kafkaParams, fromOffsets=None,
                            keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
         """
         .. note:: Experimental
@@ -105,6 +107,8 @@ def createDirectStream(ssc, topics, kafkaParams, fromOffsets={},
         :param valueDecoder:  A function used to decode value (default is utf8_decoder).
         :return: A DStream object
         """
+        if fromOffsets is None:
+            fromOffsets = dict()
         if not isinstance(topics, list):
             raise TypeError("topics should be list")
         if not isinstance(kafkaParams, dict):
@@ -129,7 +133,7 @@ def createDirectStream(ssc, topics, kafkaParams, fromOffsets={},
         return KafkaDStream(stream._jdstream, ssc, stream._jrdd_deserializer)
 
     @staticmethod
-    def createRDD(sc, kafkaParams, offsetRanges, leaders={},
+    def createRDD(sc, kafkaParams, offsetRanges, leaders=None,
                   keyDecoder=utf8_decoder, valueDecoder=utf8_decoder):
         """
         .. note:: Experimental
@@ -145,6 +149,8 @@ def createRDD(sc, kafkaParams, offsetRanges, leaders={},
         :param valueDecoder:  A function used to decode value (default is utf8_decoder)
         :return: A RDD object
         """
+        if leaders is None:
+            leaders = dict()
         if not isinstance(kafkaParams, dict):
             raise TypeError("kafkaParams should be dict")
         if not isinstance(offsetRanges, list):

From 33bae585d4cb25aed2ac32e0d1248f78cc65318b Mon Sep 17 00:00:00 2001
From: Carson Wang <carson.wang@intel.com>
Date: Fri, 14 Aug 2015 13:38:25 -0700
Subject: [PATCH 250/633] [SPARK-9809] Task crashes because the internal
 accumulators are not properly initialized

When a stage failed and another stage was resubmitted with only part of partitions to compute, all the tasks failed with error message: java.util.NoSuchElementException: key not found: peakExecutionMemory.
This is because the internal accumulators are not properly initialized for this stage while other codes assume the internal accumulators always exist.

Author: Carson Wang <carson.wang@intel.com>

Closes #8090 from carsonwang/SPARK-9809.
---
 .../main/scala/org/apache/spark/scheduler/DAGScheduler.scala   | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 7ab5ccf50adb7..f1c63d08766c2 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -790,9 +790,10 @@ class DAGScheduler(
       }
     }
 
+    // Create internal accumulators if the stage has no accumulators initialized.
     // Reset internal accumulators only if this stage is not partially submitted
     // Otherwise, we may override existing accumulator values from some tasks
-    if (allPartitions == partitionsToCompute) {
+    if (stage.internalAccumulators.isEmpty || allPartitions == partitionsToCompute) {
       stage.resetInternalAccumulators()
     }
 

From 6518ef63037aa56b541927f99ad26744f91098ce Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 14 Aug 2015 13:42:53 -0700
Subject: [PATCH 251/633] [SPARK-9948] Fix flaky AccumulatorSuite - internal
 accumulators

In these tests, we use a custom listener and we assert on fields in the stage / task completion events. However, these events are posted in a separate thread so they're not guaranteed to be posted in time. This commit fixes this flakiness through a job end registration callback.

Author: Andrew Or <andrew@databricks.com>

Closes #8176 from andrewor14/fix-accumulator-suite.
---
 .../org/apache/spark/AccumulatorSuite.scala   | 153 +++++++++++-------
 1 file changed, 92 insertions(+), 61 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
index 0eb2293a9d063..5b84acf40be4e 100644
--- a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
+++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala
@@ -182,26 +182,30 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
     sc = new SparkContext("local", "test")
     sc.addSparkListener(listener)
     // Have each task add 1 to the internal accumulator
-    sc.parallelize(1 to 100, numPartitions).mapPartitions { iter =>
+    val rdd = sc.parallelize(1 to 100, numPartitions).mapPartitions { iter =>
       TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
       iter
-    }.count()
-    val stageInfos = listener.getCompletedStageInfos
-    val taskInfos = listener.getCompletedTaskInfos
-    assert(stageInfos.size === 1)
-    assert(taskInfos.size === numPartitions)
-    // The accumulator values should be merged in the stage
-    val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
-    assert(stageAccum.value.toLong === numPartitions)
-    // The accumulator should be updated locally on each task
-    val taskAccumValues = taskInfos.map { taskInfo =>
-      val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
-      assert(taskAccum.update.isDefined)
-      assert(taskAccum.update.get.toLong === 1)
-      taskAccum.value.toLong
     }
-    // Each task should keep track of the partial value on the way, i.e. 1, 2, ... numPartitions
-    assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
+    // Register asserts in job completion callback to avoid flakiness
+    listener.registerJobCompletionCallback { _ =>
+      val stageInfos = listener.getCompletedStageInfos
+      val taskInfos = listener.getCompletedTaskInfos
+      assert(stageInfos.size === 1)
+      assert(taskInfos.size === numPartitions)
+      // The accumulator values should be merged in the stage
+      val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
+      assert(stageAccum.value.toLong === numPartitions)
+      // The accumulator should be updated locally on each task
+      val taskAccumValues = taskInfos.map { taskInfo =>
+        val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
+        assert(taskAccum.update.isDefined)
+        assert(taskAccum.update.get.toLong === 1)
+        taskAccum.value.toLong
+      }
+      // Each task should keep track of the partial value on the way, i.e. 1, 2, ... numPartitions
+      assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
+    }
+    rdd.count()
   }
 
   test("internal accumulators in multiple stages") {
@@ -211,7 +215,7 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
     sc.addSparkListener(listener)
     // Each stage creates its own set of internal accumulators so the
     // values for the same metric should not be mixed up across stages
-    sc.parallelize(1 to 100, numPartitions)
+    val rdd = sc.parallelize(1 to 100, numPartitions)
       .map { i => (i, i) }
       .mapPartitions { iter =>
         TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
@@ -227,16 +231,20 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
         TaskContext.get().internalMetricsToAccumulators(TEST_ACCUMULATOR) += 100
         iter
       }
-      .count()
-    // We ran 3 stages, and the accumulator values should be distinct
-    val stageInfos = listener.getCompletedStageInfos
-    assert(stageInfos.size === 3)
-    val firstStageAccum = findAccumulableInfo(stageInfos(0).accumulables.values, TEST_ACCUMULATOR)
-    val secondStageAccum = findAccumulableInfo(stageInfos(1).accumulables.values, TEST_ACCUMULATOR)
-    val thirdStageAccum = findAccumulableInfo(stageInfos(2).accumulables.values, TEST_ACCUMULATOR)
-    assert(firstStageAccum.value.toLong === numPartitions)
-    assert(secondStageAccum.value.toLong === numPartitions * 10)
-    assert(thirdStageAccum.value.toLong === numPartitions * 2 * 100)
+    // Register asserts in job completion callback to avoid flakiness
+    listener.registerJobCompletionCallback { _ =>
+      // We ran 3 stages, and the accumulator values should be distinct
+      val stageInfos = listener.getCompletedStageInfos
+      assert(stageInfos.size === 3)
+      val (firstStageAccum, secondStageAccum, thirdStageAccum) =
+        (findAccumulableInfo(stageInfos(0).accumulables.values, TEST_ACCUMULATOR),
+        findAccumulableInfo(stageInfos(1).accumulables.values, TEST_ACCUMULATOR),
+        findAccumulableInfo(stageInfos(2).accumulables.values, TEST_ACCUMULATOR))
+      assert(firstStageAccum.value.toLong === numPartitions)
+      assert(secondStageAccum.value.toLong === numPartitions * 10)
+      assert(thirdStageAccum.value.toLong === numPartitions * 2 * 100)
+    }
+    rdd.count()
   }
 
   test("internal accumulators in fully resubmitted stages") {
@@ -268,7 +276,7 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
     // This says use 1 core and retry tasks up to 2 times
     sc = new SparkContext("local[1, 2]", "test")
     sc.addSparkListener(listener)
-    sc.parallelize(1 to 100, numPartitions).mapPartitionsWithIndex { case (i, iter) =>
+    val rdd = sc.parallelize(1 to 100, numPartitions).mapPartitionsWithIndex { case (i, iter) =>
       val taskContext = TaskContext.get()
       taskContext.internalMetricsToAccumulators(TEST_ACCUMULATOR) += 1
       // Fail the first attempts of a subset of the tasks
@@ -276,28 +284,32 @@ class AccumulatorSuite extends SparkFunSuite with Matchers with LocalSparkContex
         throw new Exception("Failing a task intentionally.")
       }
       iter
-    }.count()
-    val stageInfos = listener.getCompletedStageInfos
-    val taskInfos = listener.getCompletedTaskInfos
-    assert(stageInfos.size === 1)
-    assert(taskInfos.size === numPartitions + numFailedPartitions)
-    val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
-    // We should not double count values in the merged accumulator
-    assert(stageAccum.value.toLong === numPartitions)
-    val taskAccumValues = taskInfos.flatMap { taskInfo =>
-      if (!taskInfo.failed) {
-        // If a task succeeded, its update value should always be 1
-        val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
-        assert(taskAccum.update.isDefined)
-        assert(taskAccum.update.get.toLong === 1)
-        Some(taskAccum.value.toLong)
-      } else {
-        // If a task failed, we should not get its accumulator values
-        assert(taskInfo.accumulables.isEmpty)
-        None
+    }
+    // Register asserts in job completion callback to avoid flakiness
+    listener.registerJobCompletionCallback { _ =>
+      val stageInfos = listener.getCompletedStageInfos
+      val taskInfos = listener.getCompletedTaskInfos
+      assert(stageInfos.size === 1)
+      assert(taskInfos.size === numPartitions + numFailedPartitions)
+      val stageAccum = findAccumulableInfo(stageInfos.head.accumulables.values, TEST_ACCUMULATOR)
+      // We should not double count values in the merged accumulator
+      assert(stageAccum.value.toLong === numPartitions)
+      val taskAccumValues = taskInfos.flatMap { taskInfo =>
+        if (!taskInfo.failed) {
+          // If a task succeeded, its update value should always be 1
+          val taskAccum = findAccumulableInfo(taskInfo.accumulables, TEST_ACCUMULATOR)
+          assert(taskAccum.update.isDefined)
+          assert(taskAccum.update.get.toLong === 1)
+          Some(taskAccum.value.toLong)
+        } else {
+          // If a task failed, we should not get its accumulator values
+          assert(taskInfo.accumulables.isEmpty)
+          None
+        }
       }
+      assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
     }
-    assert(taskAccumValues.sorted === (1L to numPartitions).toSeq)
+    rdd.count()
   }
 
 }
@@ -313,20 +325,27 @@ private[spark] object AccumulatorSuite {
       testName: String)(testBody: => Unit): Unit = {
     val listener = new SaveInfoListener
     sc.addSparkListener(listener)
-    // Verify that the accumulator does not already exist
+    // Register asserts in job completion callback to avoid flakiness
+    listener.registerJobCompletionCallback { jobId =>
+      if (jobId == 0) {
+        // The first job is a dummy one to verify that the accumulator does not already exist
+        val accums = listener.getCompletedStageInfos.flatMap(_.accumulables.values)
+        assert(!accums.exists(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY))
+      } else {
+        // In the subsequent jobs, verify that peak execution memory is updated
+        val accum = listener.getCompletedStageInfos
+          .flatMap(_.accumulables.values)
+          .find(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY)
+          .getOrElse {
+          throw new TestFailedException(
+            s"peak execution memory accumulator not set in '$testName'", 0)
+        }
+        assert(accum.value.toLong > 0)
+      }
+    }
+    // Run the jobs
     sc.parallelize(1 to 10).count()
-    val accums = listener.getCompletedStageInfos.flatMap(_.accumulables.values)
-    assert(!accums.exists(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY))
     testBody
-    // Verify that peak execution memory is updated
-    val accum = listener.getCompletedStageInfos
-      .flatMap(_.accumulables.values)
-      .find(_.name == InternalAccumulator.PEAK_EXECUTION_MEMORY)
-      .getOrElse {
-        throw new TestFailedException(
-          s"peak execution memory accumulator not set in '$testName'", 0)
-      }
-    assert(accum.value.toLong > 0)
   }
 }
 
@@ -336,10 +355,22 @@ private[spark] object AccumulatorSuite {
 private class SaveInfoListener extends SparkListener {
   private val completedStageInfos: ArrayBuffer[StageInfo] = new ArrayBuffer[StageInfo]
   private val completedTaskInfos: ArrayBuffer[TaskInfo] = new ArrayBuffer[TaskInfo]
+  private var jobCompletionCallback: (Int => Unit) = null // parameter is job ID
 
   def getCompletedStageInfos: Seq[StageInfo] = completedStageInfos.toArray.toSeq
   def getCompletedTaskInfos: Seq[TaskInfo] = completedTaskInfos.toArray.toSeq
 
+  /** Register a callback to be called on job end. */
+  def registerJobCompletionCallback(callback: (Int => Unit)): Unit = {
+    jobCompletionCallback = callback
+  }
+
+  override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = {
+    if (jobCompletionCallback != null) {
+      jobCompletionCallback(jobEnd.jobId)
+    }
+  }
+
   override def onStageCompleted(stageCompleted: SparkListenerStageCompleted): Unit = {
     completedStageInfos += stageCompleted.stageInfo
   }

From 9407baa2a7c26f527f2d043715d313d75bd765bb Mon Sep 17 00:00:00 2001
From: jerryshao <sshao@hortonworks.com>
Date: Fri, 14 Aug 2015 13:44:38 -0700
Subject: [PATCH 252/633] [SPARK-9877] [CORE] Fix StandaloneRestServer NPE when
 submitting application

Detailed exception log can be seen in [SPARK-9877](https://issues.apache.org/jira/browse/SPARK-9877), the problem is when creating `StandaloneRestServer`, `self` (`masterEndpoint`) is null.  So this fix is creating `StandaloneRestServer` when `self` is available.

Author: jerryshao <sshao@hortonworks.com>

Closes #8127 from jerryshao/SPARK-9877.
---
 .../org/apache/spark/deploy/master/Master.scala  | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
index 9217202b69a66..26904d39a9bec 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala
@@ -127,14 +127,8 @@ private[deploy] class Master(
 
   // Alternative application submission gateway that is stable across Spark versions
   private val restServerEnabled = conf.getBoolean("spark.master.rest.enabled", true)
-  private val restServer =
-    if (restServerEnabled) {
-      val port = conf.getInt("spark.master.rest.port", 6066)
-      Some(new StandaloneRestServer(address.host, port, conf, self, masterUrl))
-    } else {
-      None
-    }
-  private val restServerBoundPort = restServer.map(_.start())
+  private var restServer: Option[StandaloneRestServer] = None
+  private var restServerBoundPort: Option[Int] = None
 
   override def onStart(): Unit = {
     logInfo("Starting Spark master at " + masterUrl)
@@ -148,6 +142,12 @@ private[deploy] class Master(
       }
     }, 0, WORKER_TIMEOUT_MS, TimeUnit.MILLISECONDS)
 
+    if (restServerEnabled) {
+      val port = conf.getInt("spark.master.rest.port", 6066)
+      restServer = Some(new StandaloneRestServer(address.host, port, conf, self, masterUrl))
+    }
+    restServerBoundPort = restServer.map(_.start())
+
     masterMetricsSystem.registerSource(masterSource)
     masterMetricsSystem.start()
     applicationMetricsSystem.start()

From 11ed2b180ec86523a94679a8b8132fadb911ccd5 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 14 Aug 2015 13:55:29 -0700
Subject: [PATCH 253/633] [SPARK-9978] [PYSPARK] [SQL] fix Window.orderBy and
 doc of ntile()

Author: Davies Liu <davies@databricks.com>

Closes #8213 from davies/fix_window.
---
 python/pyspark/sql/functions.py |  7 ++++---
 python/pyspark/sql/tests.py     | 23 +++++++++++++++++++++++
 python/pyspark/sql/window.py    |  2 +-
 3 files changed, 28 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index e98979533f901..41dfee9f54f7a 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -530,9 +530,10 @@ def lead(col, count=1, default=None):
 @since(1.4)
 def ntile(n):
     """
-    Window function: returns a group id from 1 to `n` (inclusive) in a round-robin fashion in
-    a window partition. Fow example, if `n` is 3, the first row will get 1, the second row will
-    get 2, the third row will get 3, and the fourth row will get 1...
+    Window function: returns the ntile group id (from 1 to `n` inclusive)
+    in an ordered window partition. Fow example, if `n` is 4, the first
+    quarter of the rows will get value 1, the second quarter will get 2,
+    the third quarter will get 3, and the last quarter will get 4.
 
     This is equivalent to the NTILE function in SQL.
 
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 38c83c427a747..9b748101b5e53 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -1124,5 +1124,28 @@ def test_window_functions(self):
         for r, ex in zip(rs, expected):
             self.assertEqual(tuple(r), ex[:len(r)])
 
+    def test_window_functions_without_partitionBy(self):
+        df = self.sqlCtx.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"])
+        w = Window.orderBy("key", df.value)
+        from pyspark.sql import functions as F
+        sel = df.select(df.value, df.key,
+                        F.max("key").over(w.rowsBetween(0, 1)),
+                        F.min("key").over(w.rowsBetween(0, 1)),
+                        F.count("key").over(w.rowsBetween(float('-inf'), float('inf'))),
+                        F.rowNumber().over(w),
+                        F.rank().over(w),
+                        F.denseRank().over(w),
+                        F.ntile(2).over(w))
+        rs = sorted(sel.collect())
+        expected = [
+            ("1", 1, 1, 1, 4, 1, 1, 1, 1),
+            ("2", 1, 1, 1, 4, 2, 2, 2, 1),
+            ("2", 1, 2, 1, 4, 3, 2, 2, 2),
+            ("2", 2, 2, 2, 4, 4, 4, 3, 2)
+        ]
+        for r, ex in zip(rs, expected):
+            self.assertEqual(tuple(r), ex[:len(r)])
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/python/pyspark/sql/window.py b/python/pyspark/sql/window.py
index c74745c726a0c..eaf4d7e98620a 100644
--- a/python/pyspark/sql/window.py
+++ b/python/pyspark/sql/window.py
@@ -64,7 +64,7 @@ def orderBy(*cols):
         Creates a :class:`WindowSpec` with the partitioning defined.
         """
         sc = SparkContext._active_spark_context
-        jspec = sc._jvm.org.apache.spark.sql.expressions.Window.partitionBy(_to_java_cols(cols))
+        jspec = sc._jvm.org.apache.spark.sql.expressions.Window.orderBy(_to_java_cols(cols))
         return WindowSpec(jspec)
 
 

From 2a6590e510aba3bfc6603d280023128b3f5ac702 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Fri, 14 Aug 2015 14:05:03 -0700
Subject: [PATCH 254/633] [SPARK-9981] [ML] Made labels public for
 StringIndexerModel

Also added unit test for integration between StringIndexerModel and IndexToString

CC: holdenk We realized we should have left in your unit test (to catch the issue with removing the inverse() method), so this adds it back.  mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8211 from jkbradley/stridx-labels.
---
 .../spark/ml/feature/StringIndexer.scala       |  5 ++++-
 .../spark/ml/feature/StringIndexerSuite.scala  | 18 ++++++++++++++++++
 2 files changed, 22 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 63475780a6ff9..24250e4c4cf92 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -97,14 +97,17 @@ class StringIndexer(override val uid: String) extends Estimator[StringIndexerMod
 /**
  * :: Experimental ::
  * Model fitted by [[StringIndexer]].
+ *
  * NOTE: During transformation, if the input column does not exist,
  * [[StringIndexerModel.transform]] would return the input dataset unmodified.
  * This is a temporary fix for the case when target labels do not exist during prediction.
+ *
+ * @param labels  Ordered list of labels, corresponding to indices to be assigned
  */
 @Experimental
 class StringIndexerModel (
     override val uid: String,
-    labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase {
+    val labels: Array[String]) extends Model[StringIndexerModel] with StringIndexerBase {
 
   def this(labels: Array[String]) = this(Identifiable.randomUID("strIdx"), labels)
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index 0b4c8ba71ee61..05e05bdc64bb1 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -147,4 +147,22 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
         assert(actual === expected)
     }
   }
+
+  test("StringIndexer, IndexToString are inverses") {
+    val data = sc.parallelize(Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")), 2)
+    val df = sqlContext.createDataFrame(data).toDF("id", "label")
+    val indexer = new StringIndexer()
+      .setInputCol("label")
+      .setOutputCol("labelIndex")
+      .fit(df)
+    val transformed = indexer.transform(df)
+    val idx2str = new IndexToString()
+      .setInputCol("labelIndex")
+      .setOutputCol("sameLabel")
+      .setLabels(indexer.labels)
+    idx2str.transform(transformed).select("label", "sameLabel").collect().foreach {
+      case Row(a: String, b: String) =>
+        assert(a === b)
+    }
+  }
 }

From 1150a19b188a075166899fdb1e107b2ba1e505d8 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 14 Aug 2015 14:09:46 -0700
Subject: [PATCH 255/633] [SPARK-8670] [SQL] Nested columns can't be referenced
 in pyspark

This bug is caused by a wrong column-exist-check in `__getitem__` of pyspark dataframe. `DataFrame.apply` accepts not only top level column names, but also nested column name like `a.b`, so we should remove that check from `__getitem__`.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8202 from cloud-fan/nested.
---
 python/pyspark/sql/dataframe.py                              | 2 --
 python/pyspark/sql/tests.py                                  | 4 +++-
 sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala | 2 ++
 3 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 09647ff6d0749..da742d7ce7d13 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -722,8 +722,6 @@ def __getitem__(self, item):
         [Row(age=5, name=u'Bob')]
         """
         if isinstance(item, basestring):
-            if item not in self.columns:
-                raise IndexError("no such column: %s" % item)
             jc = self._jdf.apply(item)
             return Column(jc)
         elif isinstance(item, Column):
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 9b748101b5e53..13cf647b66da8 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -770,7 +770,7 @@ def test_access_column(self):
         self.assertTrue(isinstance(df['key'], Column))
         self.assertTrue(isinstance(df[0], Column))
         self.assertRaises(IndexError, lambda: df[2])
-        self.assertRaises(IndexError, lambda: df["bad_key"])
+        self.assertRaises(AnalysisException, lambda: df["bad_key"])
         self.assertRaises(TypeError, lambda: df[{}])
 
     def test_column_name_with_non_ascii(self):
@@ -794,7 +794,9 @@ def test_field_accessor(self):
         df = self.sc.parallelize([Row(l=[1], r=Row(a=1, b="b"), d={"k": "v"})]).toDF()
         self.assertEqual(1, df.select(df.l[0]).first()[0])
         self.assertEqual(1, df.select(df.r["a"]).first()[0])
+        self.assertEqual(1, df.select(df["r.a"]).first()[0])
         self.assertEqual("b", df.select(df.r["b"]).first()[0])
+        self.assertEqual("b", df.select(df["r.b"]).first()[0])
         self.assertEqual("v", df.select(df.d["k"]).first()[0])
 
     def test_infer_long_type(self):
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index cf75e64e884b4..fd0ead4401193 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -634,6 +634,7 @@ class DataFrame private[sql](
 
   /**
    * Selects column based on the column name and return it as a [[Column]].
+   * Note that the column name can also reference to a nested column like `a.b`.
    * @group dfops
    * @since 1.3.0
    */
@@ -641,6 +642,7 @@ class DataFrame private[sql](
 
   /**
    * Selects column based on the column name and return it as a [[Column]].
+   * Note that the column name can also reference to a nested column like `a.b`.
    * @group dfops
    * @since 1.3.0
    */

From f3bfb711c1742d0915e43bda8230b4d1d22b4190 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Fri, 14 Aug 2015 15:10:01 -0700
Subject: [PATCH 256/633] [SPARK-9966] [STREAMING] Handle couple of corner
 cases in PIDRateEstimator

1. The rate estimator should not estimate any rate when there are no records in the batch, as there is no data to estimate the rate. In the current state, it estimates and set the rate to zero. That is incorrect.

2. The rate estimator should not never set the rate to zero under any circumstances. Otherwise the system will stop receiving data, and stop generating useful estimates (see reason 1). So the fix is to define a parameters that sets a lower bound on the estimated rate, so that the system always receives some data.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8199 from tdas/SPARK-9966 and squashes the following commits:

829f793 [Tathagata Das] Fixed unit test and added comments
3a994db [Tathagata Das] Added min rate and updated tests in PIDRateEstimator
---
 .../scheduler/rate/PIDRateEstimator.scala     | 46 ++++++++---
 .../scheduler/rate/RateEstimator.scala        |  4 +-
 .../rate/PIDRateEstimatorSuite.scala          | 79 ++++++++++++-------
 3 files changed, 87 insertions(+), 42 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala
index 6ae56a68ad88c..84a3ca9d74e58 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimator.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.streaming.scheduler.rate
 
+import org.apache.spark.Logging
+
 /**
  * Implements a proportional-integral-derivative (PID) controller which acts on
  * the speed of ingestion of elements into Spark Streaming. A PID controller works
@@ -26,7 +28,7 @@ package org.apache.spark.streaming.scheduler.rate
  *
  * @see https://en.wikipedia.org/wiki/PID_controller
  *
- * @param batchDurationMillis the batch duration, in milliseconds
+ * @param batchIntervalMillis the batch duration, in milliseconds
  * @param proportional how much the correction should depend on the current
  *        error. This term usually provides the bulk of correction and should be positive or zero.
  *        A value too large would make the controller overshoot the setpoint, while a small value
@@ -39,13 +41,17 @@ package org.apache.spark.streaming.scheduler.rate
  *        of future errors, based on current rate of change. This value should be positive or 0.
  *        This term is not used very often, as it impacts stability of the system. The default
  *        value is 0.
+ * @param minRate what is the minimum rate that can be estimated.
+ *        This must be greater than zero, so that the system always receives some data for rate
+ *        estimation to work.
  */
 private[streaming] class PIDRateEstimator(
     batchIntervalMillis: Long,
-    proportional: Double = 1D,
-    integral: Double = .2D,
-    derivative: Double = 0D)
-  extends RateEstimator {
+    proportional: Double,
+    integral: Double,
+    derivative: Double,
+    minRate: Double
+  ) extends RateEstimator with Logging {
 
   private var firstRun: Boolean = true
   private var latestTime: Long = -1L
@@ -64,16 +70,23 @@ private[streaming] class PIDRateEstimator(
   require(
     derivative >= 0,
     s"Derivative term $derivative in PIDRateEstimator should be >= 0.")
+  require(
+    minRate > 0,
+    s"Minimum rate in PIDRateEstimator should be > 0")
 
+  logInfo(s"Created PIDRateEstimator with proportional = $proportional, integral = $integral, " +
+    s"derivative = $derivative, min rate = $minRate")
 
-  def compute(time: Long, // in milliseconds
+  def compute(
+      time: Long, // in milliseconds
       numElements: Long,
       processingDelay: Long, // in milliseconds
       schedulingDelay: Long // in milliseconds
     ): Option[Double] = {
-
+    logTrace(s"\ntime = $time, # records = $numElements, " +
+      s"processing time = $processingDelay, scheduling delay = $schedulingDelay")
     this.synchronized {
-      if (time > latestTime && processingDelay > 0 && batchIntervalMillis > 0) {
+      if (time > latestTime && numElements > 0 && processingDelay > 0) {
 
         // in seconds, should be close to batchDuration
         val delaySinceUpdate = (time - latestTime).toDouble / 1000
@@ -104,21 +117,30 @@ private[streaming] class PIDRateEstimator(
 
         val newRate = (latestRate - proportional * error -
                                     integral * historicalError -
-                                    derivative * dError).max(0.0)
+                                    derivative * dError).max(minRate)
+        logTrace(s"""
+            | latestRate = $latestRate, error = $error
+            | latestError = $latestError, historicalError = $historicalError
+            | delaySinceUpdate = $delaySinceUpdate, dError = $dError
+            """.stripMargin)
+
         latestTime = time
         if (firstRun) {
           latestRate = processingRate
           latestError = 0D
           firstRun = false
-
+          logTrace("First run, rate estimation skipped")
           None
         } else {
           latestRate = newRate
           latestError = error
-
+          logTrace(s"New rate = $newRate")
           Some(newRate)
         }
-      } else None
+      } else {
+        logTrace("Rate estimation skipped")
+        None
+      }
     }
   }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
index 17ccebc1ed41b..d7210f64fcc36 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala
@@ -18,7 +18,6 @@
 package org.apache.spark.streaming.scheduler.rate
 
 import org.apache.spark.SparkConf
-import org.apache.spark.SparkException
 import org.apache.spark.streaming.Duration
 
 /**
@@ -61,7 +60,8 @@ object RateEstimator {
         val proportional = conf.getDouble("spark.streaming.backpressure.pid.proportional", 1.0)
         val integral = conf.getDouble("spark.streaming.backpressure.pid.integral", 0.2)
         val derived = conf.getDouble("spark.streaming.backpressure.pid.derived", 0.0)
-        new PIDRateEstimator(batchInterval.milliseconds, proportional, integral, derived)
+        val minRate = conf.getDouble("spark.streaming.backpressure.pid.minRate", 100)
+        new PIDRateEstimator(batchInterval.milliseconds, proportional, integral, derived, minRate)
 
       case estimator =>
         throw new IllegalArgumentException(s"Unkown rate estimator: $estimator")
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala
index 97c32d8f2d59e..a1af95be81c8e 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/rate/PIDRateEstimatorSuite.scala
@@ -36,72 +36,89 @@ class PIDRateEstimatorSuite extends SparkFunSuite with Matchers {
 
   test("estimator checks ranges") {
     intercept[IllegalArgumentException] {
-      new PIDRateEstimator(0, 1, 2, 3)
+      new PIDRateEstimator(batchIntervalMillis = 0, 1, 2, 3, 10)
     }
     intercept[IllegalArgumentException] {
-      new PIDRateEstimator(100, -1, 2, 3)
+      new PIDRateEstimator(100, proportional = -1, 2, 3, 10)
     }
     intercept[IllegalArgumentException] {
-      new PIDRateEstimator(100, 0, -1, 3)
+      new PIDRateEstimator(100, 0, integral = -1, 3, 10)
     }
     intercept[IllegalArgumentException] {
-      new PIDRateEstimator(100, 0, 0, -1)
+      new PIDRateEstimator(100, 0, 0, derivative = -1, 10)
+    }
+    intercept[IllegalArgumentException] {
+      new PIDRateEstimator(100, 0, 0, 0, minRate = 0)
+    }
+    intercept[IllegalArgumentException] {
+      new PIDRateEstimator(100, 0, 0, 0, minRate = -10)
     }
   }
 
-  private def createDefaultEstimator: PIDRateEstimator = {
-    new PIDRateEstimator(20, 1D, 0D, 0D)
-  }
-
-  test("first bound is None") {
-    val p = createDefaultEstimator
+  test("first estimate is None") {
+    val p = createDefaultEstimator()
     p.compute(0, 10, 10, 0) should equal(None)
   }
 
-  test("second bound is rate") {
-    val p = createDefaultEstimator
+  test("second estimate is not None") {
+    val p = createDefaultEstimator()
     p.compute(0, 10, 10, 0)
     // 1000 elements / s
     p.compute(10, 10, 10, 0) should equal(Some(1000))
   }
 
-  test("works even with no time between updates") {
-    val p = createDefaultEstimator
+  test("no estimate when no time difference between successive calls") {
+    val p = createDefaultEstimator()
+    p.compute(0, 10, 10, 0)
+    p.compute(time = 10, 10, 10, 0) shouldNot equal(None)
+    p.compute(time = 10, 10, 10, 0) should equal(None)
+  }
+
+  test("no estimate when no records in previous batch") {
+    val p = createDefaultEstimator()
     p.compute(0, 10, 10, 0)
-    p.compute(10, 10, 10, 0)
-    p.compute(10, 10, 10, 0) should equal(None)
+    p.compute(10, numElements = 0, 10, 0) should equal(None)
+    p.compute(20, numElements = -10, 10, 0) should equal(None)
   }
 
-  test("bound is never negative") {
-    val p = new PIDRateEstimator(20, 1D, 1D, 0D)
+  test("no estimate when there is no processing delay") {
+    val p = createDefaultEstimator()
+    p.compute(0, 10, 10, 0)
+    p.compute(10, 10, processingDelay = 0, 0) should equal(None)
+    p.compute(20, 10, processingDelay = -10, 0) should equal(None)
+  }
+
+  test("estimate is never less than min rate") {
+    val minRate = 5D
+    val p = new PIDRateEstimator(20, 1D, 1D, 0D, minRate)
     // prepare a series of batch updates, one every 20ms, 0 processed elements, 2ms of processing
     // this might point the estimator to try and decrease the bound, but we test it never
-    // goes below zero, which would be nonsensical.
+    // goes below the min rate, which would be nonsensical.
     val times = List.tabulate(50)(x => x * 20) // every 20ms
-    val elements = List.fill(50)(0) // no processing
+    val elements = List.fill(50)(1) // no processing
     val proc = List.fill(50)(20) // 20ms of processing
     val sched = List.fill(50)(100) // strictly positive accumulation
     val res = for (i <- List.range(0, 50)) yield p.compute(times(i), elements(i), proc(i), sched(i))
     res.head should equal(None)
-    res.tail should equal(List.fill(49)(Some(0D)))
+    res.tail should equal(List.fill(49)(Some(minRate)))
   }
 
   test("with no accumulated or positive error, |I| > 0, follow the processing speed") {
-    val p = new PIDRateEstimator(20, 1D, 1D, 0D)
+    val p = new PIDRateEstimator(20, 1D, 1D, 0D, 10)
     // prepare a series of batch updates, one every 20ms with an increasing number of processed
     // elements in each batch, but constant processing time, and no accumulated error. Even though
     // the integral part is non-zero, the estimated rate should follow only the proportional term
     val times = List.tabulate(50)(x => x * 20) // every 20ms
-    val elements = List.tabulate(50)(x => x * 20) // increasing
+    val elements = List.tabulate(50)(x => (x + 1) * 20) // increasing
     val proc = List.fill(50)(20) // 20ms of processing
     val sched = List.fill(50)(0)
     val res = for (i <- List.range(0, 50)) yield p.compute(times(i), elements(i), proc(i), sched(i))
     res.head should equal(None)
-    res.tail should equal(List.tabulate(50)(x => Some(x * 1000D)).tail)
+    res.tail should equal(List.tabulate(50)(x => Some((x + 1) * 1000D)).tail)
   }
 
   test("with no accumulated but some positive error, |I| > 0, follow the processing speed") {
-    val p = new PIDRateEstimator(20, 1D, 1D, 0D)
+    val p = new PIDRateEstimator(20, 1D, 1D, 0D, 10)
     // prepare a series of batch updates, one every 20ms with an decreasing number of processed
     // elements in each batch, but constant processing time, and no accumulated error. Even though
     // the integral part is non-zero, the estimated rate should follow only the proportional term,
@@ -116,13 +133,14 @@ class PIDRateEstimatorSuite extends SparkFunSuite with Matchers {
   }
 
   test("with some accumulated and some positive error, |I| > 0, stay below the processing speed") {
-    val p = new PIDRateEstimator(20, 1D, .01D, 0D)
+    val minRate = 10D
+    val p = new PIDRateEstimator(20, 1D, .01D, 0D, minRate)
     val times = List.tabulate(50)(x => x * 20) // every 20ms
     val rng = new Random()
-    val elements = List.tabulate(50)(x => rng.nextInt(1000))
+    val elements = List.tabulate(50)(x => rng.nextInt(1000) + 1000)
     val procDelayMs = 20
     val proc = List.fill(50)(procDelayMs) // 20ms of processing
-    val sched = List.tabulate(50)(x => rng.nextInt(19)) // random wait
+    val sched = List.tabulate(50)(x => rng.nextInt(19) + 1) // random wait
     val speeds = elements map ((x) => x.toDouble / procDelayMs * 1000)
 
     val res = for (i <- List.range(0, 50)) yield p.compute(times(i), elements(i), proc(i), sched(i))
@@ -131,7 +149,12 @@ class PIDRateEstimatorSuite extends SparkFunSuite with Matchers {
       res(n) should not be None
       if (res(n).get > 0 && sched(n) > 0) {
         res(n).get should be < speeds(n)
+        res(n).get should be >= minRate
       }
     }
   }
+
+  private def createDefaultEstimator(): PIDRateEstimator = {
+    new PIDRateEstimator(20, 1D, 0D, 0D, 10)
+  }
 }

From 18a761ef7a01a4dfa1dd91abe78cd68f2f8fdb67 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Fri, 14 Aug 2015 15:54:14 -0700
Subject: [PATCH 257/633] [SPARK-9968] [STREAMING] Reduced time spent within
 synchronized block to prevent lock starvation

When the rate limiter is actually limiting the rate at which data is inserted into the buffer, the synchronized block of BlockGenerator.addData stays blocked for long time. This causes the thread switching the buffer and generating blocks (synchronized with addData) to starve and not generate blocks for seconds. The correct solution is to not block on the rate limiter within the synchronized block for adding data to the buffer.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8204 from tdas/SPARK-9968 and squashes the following commits:

8cbcc1b [Tathagata Das] Removed unused val
a73b645 [Tathagata Das] Reduced time spent within synchronized block
---
 .../streaming/receiver/BlockGenerator.scala   | 40 +++++++++++++++----
 1 file changed, 32 insertions(+), 8 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
index 794dece370b2c..300e820d01777 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
@@ -155,10 +155,17 @@ private[streaming] class BlockGenerator(
   /**
    * Push a single data item into the buffer.
    */
-  def addData(data: Any): Unit = synchronized {
+  def addData(data: Any): Unit = {
     if (state == Active) {
       waitToPush()
-      currentBuffer += data
+      synchronized {
+        if (state == Active) {
+          currentBuffer += data
+        } else {
+          throw new SparkException(
+            "Cannot add data as BlockGenerator has not been started or has been stopped")
+        }
+      }
     } else {
       throw new SparkException(
         "Cannot add data as BlockGenerator has not been started or has been stopped")
@@ -169,11 +176,18 @@ private[streaming] class BlockGenerator(
    * Push a single data item into the buffer. After buffering the data, the
    * `BlockGeneratorListener.onAddData` callback will be called.
    */
-  def addDataWithCallback(data: Any, metadata: Any): Unit = synchronized {
+  def addDataWithCallback(data: Any, metadata: Any): Unit = {
     if (state == Active) {
       waitToPush()
-      currentBuffer += data
-      listener.onAddData(data, metadata)
+      synchronized {
+        if (state == Active) {
+          currentBuffer += data
+          listener.onAddData(data, metadata)
+        } else {
+          throw new SparkException(
+            "Cannot add data as BlockGenerator has not been started or has been stopped")
+        }
+      }
     } else {
       throw new SparkException(
         "Cannot add data as BlockGenerator has not been started or has been stopped")
@@ -185,13 +199,23 @@ private[streaming] class BlockGenerator(
    * `BlockGeneratorListener.onAddData` callback will be called. Note that all the data items
    * are atomically added to the buffer, and are hence guaranteed to be present in a single block.
    */
-  def addMultipleDataWithCallback(dataIterator: Iterator[Any], metadata: Any): Unit = synchronized {
+  def addMultipleDataWithCallback(dataIterator: Iterator[Any], metadata: Any): Unit = {
     if (state == Active) {
+      // Unroll iterator into a temp buffer, and wait for pushing in the process
+      val tempBuffer = new ArrayBuffer[Any]
       dataIterator.foreach { data =>
         waitToPush()
-        currentBuffer += data
+        tempBuffer += data
+      }
+      synchronized {
+        if (state == Active) {
+          currentBuffer ++= tempBuffer
+          listener.onAddData(tempBuffer, metadata)
+        } else {
+          throw new SparkException(
+            "Cannot add data as BlockGenerator has not been started or has been stopped")
+        }
       }
-      listener.onAddData(dataIterator, metadata)
     } else {
       throw new SparkException(
         "Cannot add data as BlockGenerator has not been started or has been stopped")

From 932b24fd144232fb08184f0bd0a46369ecba164e Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 14 Aug 2015 17:35:17 -0700
Subject: [PATCH 258/633] [SPARK-9949] [SQL] Fix TakeOrderedAndProject's
 output.

https://issues.apache.org/jira/browse/SPARK-9949

Author: Yin Huai <yhuai@databricks.com>

Closes #8179 from yhuai/SPARK-9949.
---
 .../spark/sql/execution/basicOperators.scala  | 12 ++++++++++-
 .../spark/sql/execution/PlannerSuite.scala    | 20 ++++++++++++++++---
 2 files changed, 28 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 247c900baae9e..77b98064a9e16 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -237,7 +237,10 @@ case class TakeOrderedAndProject(
     projectList: Option[Seq[NamedExpression]],
     child: SparkPlan) extends UnaryNode {
 
-  override def output: Seq[Attribute] = child.output
+  override def output: Seq[Attribute] = {
+    val projectOutput = projectList.map(_.map(_.toAttribute))
+    projectOutput.getOrElse(child.output)
+  }
 
   override def outputPartitioning: Partitioning = SinglePartition
 
@@ -263,6 +266,13 @@ case class TakeOrderedAndProject(
   protected override def doExecute(): RDD[InternalRow] = sparkContext.makeRDD(collectData(), 1)
 
   override def outputOrdering: Seq[SortOrder] = sortOrder
+
+  override def simpleString: String = {
+    val orderByString = sortOrder.mkString("[", ",", "]")
+    val outputString = output.mkString("[", ",", "]")
+
+    s"TakeOrderedAndProject(limit=$limit, orderBy=$orderByString, output=$outputString)"
+  }
 }
 
 /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index 937a108543531..fad93b014c237 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -162,9 +162,23 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("efficient limit -> project -> sort") {
-    val query = testData.sort('key).select('value).limit(2).logicalPlan
-    val planned = ctx.planner.TakeOrderedAndProject(query)
-    assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
+    {
+      val query =
+        testData.select('key, 'value).sort('key).limit(2).logicalPlan
+      val planned = ctx.planner.TakeOrderedAndProject(query)
+      assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
+      assert(planned.head.output === testData.select('key, 'value).logicalPlan.output)
+    }
+
+    {
+      // We need to make sure TakeOrderedAndProject's output is correct when we push a project
+      // into it.
+      val query =
+        testData.select('key, 'value).sort('key).select('value, 'key).limit(2).logicalPlan
+      val planned = ctx.planner.TakeOrderedAndProject(query)
+      assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
+      assert(planned.head.output === testData.select('value, 'key).logicalPlan.output)
+    }
   }
 
   test("PartitioningCollection") {

From e5fd60415fbfea2c5c02602f7ddbc999dd058064 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 14 Aug 2015 20:55:32 -0700
Subject: [PATCH 259/633] [SPARK-9934] Deprecate NIO ConnectionManager.

Deprecate NIO ConnectionManager in Spark 1.5.0, before removing it in Spark 1.6.0.

Author: Reynold Xin <rxin@databricks.com>

Closes #8162 from rxin/SPARK-9934.
---
 core/src/main/scala/org/apache/spark/SparkEnv.scala | 2 ++
 docs/configuration.md                               | 3 ++-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index a796e72850191..0f1e2e069568d 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -331,6 +331,8 @@ object SparkEnv extends Logging {
         case "netty" =>
           new NettyBlockTransferService(conf, securityManager, numUsableCores)
         case "nio" =>
+          logWarning("NIO-based block transfer service is deprecated, " +
+            "and will be removed in Spark 1.6.0.")
           new NioBlockTransferService(conf, securityManager)
       }
 
diff --git a/docs/configuration.md b/docs/configuration.md
index c60dd16839c02..32147098ae64a 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -389,7 +389,8 @@ Apart from these, the following properties are also available, and may be useful
     Implementation to use for transferring shuffle and cached blocks between executors. There
     are two implementations available: <code>netty</code> and <code>nio</code>. Netty-based
     block transfer is intended to be simpler but equally efficient and is the default option
-    starting in 1.2.
+    starting in 1.2, and <code>nio</code> block transfer is deprecated in Spark 1.5.0 and will
+    be removed in Spark 1.6.0.
   </td>
 </tr>
 <tr>

From 37586e5449ff8f892d41f0b6b8fa1de83dd3849e Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 14 Aug 2015 20:56:55 -0700
Subject: [PATCH 260/633] [HOTFIX] fix duplicated braces

Author: Davies Liu <davies@databricks.com>

Closes #8219 from davies/fix_typo.
---
 .../main/scala/org/apache/spark/storage/BlockManager.scala  | 2 +-
 .../scala/org/apache/spark/storage/BlockManagerMaster.scala | 6 +++---
 .../main/scala/org/apache/spark/util/ClosureCleaner.scala   | 2 +-
 core/src/main/scala/org/apache/spark/util/Utils.scala       | 2 +-
 .../scala/org/apache/spark/examples/ml/MovieLensALS.scala   | 2 +-
 .../apache/spark/examples/mllib/DecisionTreeRunner.scala    | 2 +-
 .../org/apache/spark/examples/mllib/MovieLensALS.scala      | 2 +-
 .../src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala | 2 +-
 .../spark/sql/catalyst/analysis/HiveTypeCoercion.scala      | 2 +-
 .../spark/sql/execution/datasources/jdbc/JdbcUtils.scala    | 2 +-
 .../org/apache/spark/sql/execution/ui/SQLListener.scala     | 2 +-
 .../spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala   | 2 +-
 .../apache/spark/streaming/scheduler/InputInfoTracker.scala | 2 +-
 13 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index 86493673d958d..eedb27942e841 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -222,7 +222,7 @@ private[spark] class BlockManager(
         return
       } catch {
         case e: Exception if i < MAX_ATTEMPTS =>
-          logError(s"Failed to connect to external shuffle server, will retry ${MAX_ATTEMPTS - i}}"
+          logError(s"Failed to connect to external shuffle server, will retry ${MAX_ATTEMPTS - i}"
             + s" more times after waiting $SLEEP_TIME_SECS seconds...", e)
           Thread.sleep(SLEEP_TIME_SECS * 1000)
       }
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
index f70f701494dbf..2a11f371b9d6e 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
@@ -103,7 +103,7 @@ class BlockManagerMaster(
     val future = driverEndpoint.askWithRetry[Future[Seq[Int]]](RemoveRdd(rddId))
     future.onFailure {
       case e: Exception =>
-        logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}", e)
+        logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}", e)
     }(ThreadUtils.sameThread)
     if (blocking) {
       timeout.awaitResult(future)
@@ -115,7 +115,7 @@ class BlockManagerMaster(
     val future = driverEndpoint.askWithRetry[Future[Seq[Boolean]]](RemoveShuffle(shuffleId))
     future.onFailure {
       case e: Exception =>
-        logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}", e)
+        logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}", e)
     }(ThreadUtils.sameThread)
     if (blocking) {
       timeout.awaitResult(future)
@@ -129,7 +129,7 @@ class BlockManagerMaster(
     future.onFailure {
       case e: Exception =>
         logWarning(s"Failed to remove broadcast $broadcastId" +
-          s" with removeFromMaster = $removeFromMaster - ${e.getMessage}}", e)
+          s" with removeFromMaster = $removeFromMaster - ${e.getMessage}", e)
     }(ThreadUtils.sameThread)
     if (blocking) {
       timeout.awaitResult(future)
diff --git a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
index ebead830c6466..150d82b3930ef 100644
--- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
@@ -181,7 +181,7 @@ private[spark] object ClosureCleaner extends Logging {
       return
     }
 
-    logDebug(s"+++ Cleaning closure $func (${func.getClass.getName}}) +++")
+    logDebug(s"+++ Cleaning closure $func (${func.getClass.getName}) +++")
 
     // A list of classes that represents closures enclosed in the given one
     val innerClasses = getInnerClosureClasses(func)
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index f2abf227dc129..fddc24dbfc237 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -1366,7 +1366,7 @@ private[spark] object Utils extends Logging {
           file.getAbsolutePath, effectiveStartIndex, effectiveEndIndex))
       }
       sum += fileToLength(file)
-      logDebug(s"After processing file $file, string built is ${stringBuffer.toString}}")
+      logDebug(s"After processing file $file, string built is ${stringBuffer.toString}")
     }
     stringBuffer.toString
   }
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala
index cd411397a4b9d..3ae53e57dbdb8 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/MovieLensALS.scala
@@ -76,7 +76,7 @@ object MovieLensALS {
         .text("path to a MovieLens dataset of movies")
         .action((x, c) => c.copy(movies = x))
       opt[Int]("rank")
-        .text(s"rank, default: ${defaultParams.rank}}")
+        .text(s"rank, default: ${defaultParams.rank}")
         .action((x, c) => c.copy(rank = x))
       opt[Int]("maxIter")
         .text(s"max number of iterations, default: ${defaultParams.maxIter}")
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
index 57ffe3dd2524f..cc6bce3cb7c9c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DecisionTreeRunner.scala
@@ -100,7 +100,7 @@ object DecisionTreeRunner {
         .action((x, c) => c.copy(numTrees = x))
       opt[String]("featureSubsetStrategy")
         .text(s"feature subset sampling strategy" +
-          s" (${RandomForest.supportedFeatureSubsetStrategies.mkString(", ")}}), " +
+          s" (${RandomForest.supportedFeatureSubsetStrategies.mkString(", ")}), " +
           s"default: ${defaultParams.featureSubsetStrategy}")
         .action((x, c) => c.copy(featureSubsetStrategy = x))
       opt[Double]("fracTest")
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
index e43a6f2864c73..69691ae297f64 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/MovieLensALS.scala
@@ -55,7 +55,7 @@ object MovieLensALS {
     val parser = new OptionParser[Params]("MovieLensALS") {
       head("MovieLensALS: an example app for ALS on MovieLens data.")
       opt[Int]("rank")
-        .text(s"rank, default: ${defaultParams.rank}}")
+        .text(s"rank, default: ${defaultParams.rank}")
         .action((x, c) => c.copy(rank = x))
       opt[Int]("numIterations")
         .text(s"number of iterations, default: ${defaultParams.numIterations}")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
index 9029093e0fa08..bbbcc8436b7c2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
@@ -469,7 +469,7 @@ private[spark] object BLAS extends Serializable with Logging {
     require(A.numCols == x.size,
       s"The columns of A don't match the number of elements of x. A: ${A.numCols}, x: ${x.size}")
     require(A.numRows == y.size,
-      s"The rows of A don't match the number of elements of y. A: ${A.numRows}, y:${y.size}}")
+      s"The rows of A don't match the number of elements of y. A: ${A.numRows}, y:${y.size}")
     if (alpha == 0.0) {
       logDebug("gemv: alpha is equal to 0. Returning y.")
     } else {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 970f3c8282c81..8581d6b496c15 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -164,7 +164,7 @@ object HiveTypeCoercion {
               // Leave the same if the dataTypes match.
               case Some(newType) if a.dataType == newType.dataType => a
               case Some(newType) =>
-                logDebug(s"Promoting $a to $newType in ${q.simpleString}}")
+                logDebug(s"Promoting $a to $newType in ${q.simpleString}")
                 newType
             }
         }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
index 039c13bf163ca..8ee3b8bda8fc7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
@@ -170,7 +170,7 @@ object JdbcUtils extends Logging {
             case BinaryType => "BLOB"
             case TimestampType => "TIMESTAMP"
             case DateType => "DATE"
-            case t: DecimalType => s"DECIMAL(${t.precision}},${t.scale}})"
+            case t: DecimalType => s"DECIMAL(${t.precision},${t.scale})"
             case _ => throw new IllegalArgumentException(s"Don't know how to save $field to JDBC")
           })
       val nullable = if (field.nullable) "" else "NOT NULL"
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
index 0b9bad987c488..5779c71f64e9e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/SQLListener.scala
@@ -162,7 +162,7 @@ private[sql] class SQLListener(sqlContext: SQLContext) extends SparkListener wit
           // A task of an old stage attempt. Because a new stage is submitted, we can ignore it.
         } else if (stageAttemptID > stageMetrics.stageAttemptId) {
           logWarning(s"A task should not have a higher stageAttemptID ($stageAttemptID) then " +
-            s"what we have seen (${stageMetrics.stageAttemptId}})")
+            s"what we have seen (${stageMetrics.stageAttemptId})")
         } else {
           // TODO We don't know the attemptId. Currently, what we can do is overriding the
           // accumulator updates. However, if there are two same task are running, such as
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
index 31ce8e1ec14d7..620b8a36a2baf 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
@@ -84,7 +84,7 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag](
   require(
     blockIds.length == walRecordHandles.length,
     s"Number of block Ids (${blockIds.length}) must be " +
-      s" same as number of WAL record handles (${walRecordHandles.length}})")
+      s" same as number of WAL record handles (${walRecordHandles.length})")
 
   require(
     isBlockIdValid.isEmpty || isBlockIdValid.length == blockIds.length,
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala
index 363c03d431f04..deb15d075975c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/InputInfoTracker.scala
@@ -66,7 +66,7 @@ private[streaming] class InputInfoTracker(ssc: StreamingContext) extends Logging
       new mutable.HashMap[Int, StreamInputInfo]())
 
     if (inputInfos.contains(inputInfo.inputStreamId)) {
-      throw new IllegalStateException(s"Input stream ${inputInfo.inputStreamId}} for batch" +
+      throw new IllegalStateException(s"Input stream ${inputInfo.inputStreamId} for batch" +
         s"$batchTime is already added into InputInfoTracker, this is a illegal state")
     }
     inputInfos += ((inputInfo.inputStreamId, inputInfo))

From ec29f2034a3306cc0afdc4c160b42c2eefa0897c Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 14 Aug 2015 20:59:54 -0700
Subject: [PATCH 261/633] [SPARK-9634] [SPARK-9323] [SQL] cleanup unnecessary
 Aliases in LogicalPlan at the end of analysis

Also alias the ExtractValue instead of wrapping it with UnresolvedAlias when resolve attribute in LogicalPlan, as this alias will be trimmed if it's unnecessary.

Based on #7957 without the changes to mllib, but instead maintaining earlier behavior when using `withColumn` on expressions that already have metadata.

Author: Wenchen Fan <cloud0fan@outlook.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #8215 from marmbrus/pr/7957.
---
 .../sql/catalyst/analysis/Analyzer.scala      | 75 ++++++++++++++++---
 .../expressions/complexTypeCreator.scala      |  2 -
 .../sql/catalyst/optimizer/Optimizer.scala    |  9 ++-
 .../catalyst/plans/logical/LogicalPlan.scala  |  8 +-
 .../plans/logical/basicOperators.scala        |  2 +-
 .../sql/catalyst/analysis/AnalysisSuite.scala | 17 +++++
 .../scala/org/apache/spark/sql/Column.scala   | 16 +++-
 .../spark/sql/ColumnExpressionSuite.scala     |  9 +++
 .../org/apache/spark/sql/DataFrameSuite.scala |  6 ++
 9 files changed, 120 insertions(+), 24 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index a684dbc3afa42..4bc1c1af40bf4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -82,7 +82,9 @@ class Analyzer(
       HiveTypeCoercion.typeCoercionRules ++
       extendedResolutionRules : _*),
     Batch("Nondeterministic", Once,
-      PullOutNondeterministic)
+      PullOutNondeterministic),
+    Batch("Cleanup", fixedPoint,
+      CleanupAliases)
   )
 
   /**
@@ -146,8 +148,6 @@ class Analyzer(
           child match {
             case _: UnresolvedAttribute => u
             case ne: NamedExpression => ne
-            case g: GetStructField => Alias(g, g.field.name)()
-            case g: GetArrayStructFields => Alias(g, g.field.name)()
             case g: Generator if g.resolved && g.elementTypes.size > 1 => MultiAlias(g, Nil)
             case e if !e.resolved => u
             case other => Alias(other, s"_c$i")()
@@ -384,9 +384,7 @@ class Analyzer(
           case u @ UnresolvedAttribute(nameParts) =>
             // Leave unchanged if resolution fails.  Hopefully will be resolved next round.
             val result =
-              withPosition(u) {
-                q.resolveChildren(nameParts, resolver).map(trimUnresolvedAlias).getOrElse(u)
-              }
+              withPosition(u) { q.resolveChildren(nameParts, resolver).getOrElse(u) }
             logDebug(s"Resolving $u to $result")
             result
           case UnresolvedExtractValue(child, fieldExpr) if child.resolved =>
@@ -412,11 +410,6 @@ class Analyzer(
       exprs.exists(_.collect { case _: Star => true }.nonEmpty)
   }
 
-  private def trimUnresolvedAlias(ne: NamedExpression) = ne match {
-    case UnresolvedAlias(child) => child
-    case other => other
-  }
-
   private def resolveSortOrders(ordering: Seq[SortOrder], plan: LogicalPlan, throws: Boolean) = {
     ordering.map { order =>
       // Resolve SortOrder in one round.
@@ -426,7 +419,7 @@ class Analyzer(
       try {
         val newOrder = order transformUp {
           case u @ UnresolvedAttribute(nameParts) =>
-            plan.resolve(nameParts, resolver).map(trimUnresolvedAlias).getOrElse(u)
+            plan.resolve(nameParts, resolver).getOrElse(u)
           case UnresolvedExtractValue(child, fieldName) if child.resolved =>
             ExtractValue(child, fieldName, resolver)
         }
@@ -968,3 +961,61 @@ object EliminateSubQueries extends Rule[LogicalPlan] {
     case Subquery(_, child) => child
   }
 }
+
+/**
+ * Cleans up unnecessary Aliases inside the plan. Basically we only need Alias as a top level
+ * expression in Project(project list) or Aggregate(aggregate expressions) or
+ * Window(window expressions).
+ */
+object CleanupAliases extends Rule[LogicalPlan] {
+  private def trimAliases(e: Expression): Expression = {
+    var stop = false
+    e.transformDown {
+      // CreateStruct is a special case, we need to retain its top level Aliases as they decide the
+      // name of StructField. We also need to stop transform down this expression, or the Aliases
+      // under CreateStruct will be mistakenly trimmed.
+      case c: CreateStruct if !stop =>
+        stop = true
+        c.copy(children = c.children.map(trimNonTopLevelAliases))
+      case c: CreateStructUnsafe if !stop =>
+        stop = true
+        c.copy(children = c.children.map(trimNonTopLevelAliases))
+      case Alias(child, _) if !stop => child
+    }
+  }
+
+  def trimNonTopLevelAliases(e: Expression): Expression = e match {
+    case a: Alias =>
+      Alias(trimAliases(a.child), a.name)(a.exprId, a.qualifiers, a.explicitMetadata)
+    case other => trimAliases(other)
+  }
+
+  override def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+    case Project(projectList, child) =>
+      val cleanedProjectList =
+        projectList.map(trimNonTopLevelAliases(_).asInstanceOf[NamedExpression])
+      Project(cleanedProjectList, child)
+
+    case Aggregate(grouping, aggs, child) =>
+      val cleanedAggs = aggs.map(trimNonTopLevelAliases(_).asInstanceOf[NamedExpression])
+      Aggregate(grouping.map(trimAliases), cleanedAggs, child)
+
+    case w @ Window(projectList, windowExprs, partitionSpec, orderSpec, child) =>
+      val cleanedWindowExprs =
+        windowExprs.map(e => trimNonTopLevelAliases(e).asInstanceOf[NamedExpression])
+      Window(projectList, cleanedWindowExprs, partitionSpec.map(trimAliases),
+        orderSpec.map(trimAliases(_).asInstanceOf[SortOrder]), child)
+
+    case other =>
+      var stop = false
+      other transformExpressionsDown {
+        case c: CreateStruct if !stop =>
+          stop = true
+          c.copy(children = c.children.map(trimNonTopLevelAliases))
+        case c: CreateStructUnsafe if !stop =>
+          stop = true
+          c.copy(children = c.children.map(trimNonTopLevelAliases))
+        case Alias(child, _) if !stop => child
+      }
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 4a071e663e0d1..298aee3499275 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -75,8 +75,6 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
 
   override def foldable: Boolean = children.forall(_.foldable)
 
-  override lazy val resolved: Boolean = childrenResolved
-
   override lazy val dataType: StructType = {
     val fields = children.zipWithIndex.map { case (child, idx) =>
       child match {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 4ab5ac2c61e3c..47b06cae15436 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.catalyst.optimizer
 
 import scala.collection.immutable.HashSet
-import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
+import org.apache.spark.sql.catalyst.analysis.{CleanupAliases, EliminateSubQueries}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.Inner
 import org.apache.spark.sql.catalyst.plans.FullOuter
@@ -260,8 +260,11 @@ object ProjectCollapsing extends Rule[LogicalPlan] {
         val substitutedProjection = projectList1.map(_.transform {
           case a: Attribute => aliasMap.getOrElse(a, a)
         }).asInstanceOf[Seq[NamedExpression]]
-
-        Project(substitutedProjection, child)
+        // collapse 2 projects may introduce unnecessary Aliases, trim them here.
+        val cleanedProjection = substitutedProjection.map(p =>
+          CleanupAliases.trimNonTopLevelAliases(p).asInstanceOf[NamedExpression]
+        )
+        Project(cleanedProjection, child)
       }
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index c290e6acb361c..9bb466ac2d29c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -259,13 +259,13 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
       // One match, but we also need to extract the requested nested field.
       case Seq((a, nestedFields)) =>
         // The foldLeft adds ExtractValues for every remaining parts of the identifier,
-        // and wrap it with UnresolvedAlias which will be removed later.
+        // and aliased it with the last part of the name.
         // For example, consider "a.b.c", where "a" is resolved to an existing attribute.
-        // Then this will add ExtractValue("c", ExtractValue("b", a)), and wrap it as
-        // UnresolvedAlias(ExtractValue("c", ExtractValue("b", a))).
+        // Then this will add ExtractValue("c", ExtractValue("b", a)), and alias the final
+        // expression as "c".
         val fieldExprs = nestedFields.foldLeft(a: Expression)((expr, fieldName) =>
           ExtractValue(expr, Literal(fieldName), resolver))
-        Some(UnresolvedAlias(fieldExprs))
+        Some(Alias(fieldExprs, nestedFields.last)())
 
       // No matches.
       case Seq() =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 7c404722d811c..73b8261260acb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -228,7 +228,7 @@ case class Window(
     child: LogicalPlan) extends UnaryNode {
 
   override def output: Seq[Attribute] =
-    (projectList ++ windowExpressions).map(_.toAttribute)
+    projectList ++ windowExpressions.map(_.toAttribute)
 }
 
 /**
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index c944bc69e25b0..1e0cc81dae974 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -119,4 +119,21 @@ class AnalysisSuite extends AnalysisTest {
           Project(testRelation.output :+ projected, testRelation)))
     checkAnalysis(plan, expected)
   }
+
+  test("SPARK-9634: cleanup unnecessary Aliases in LogicalPlan") {
+    val a = testRelation.output.head
+    var plan = testRelation.select(((a + 1).as("a+1") + 2).as("col"))
+    var expected = testRelation.select((a + 1 + 2).as("col"))
+    checkAnalysis(plan, expected)
+
+    plan = testRelation.groupBy(a.as("a1").as("a2"))((min(a).as("min_a") + 1).as("col"))
+    expected = testRelation.groupBy(a)((min(a) + 1).as("col"))
+    checkAnalysis(plan, expected)
+
+    // CreateStruct is a special case that we should not trim Alias for it.
+    plan = testRelation.select(CreateStruct(Seq(a, (a + 1).as("a+1"))).as("col"))
+    checkAnalysis(plan, plan)
+    plan = testRelation.select(CreateStructUnsafe(Seq(a, (a + 1).as("a+1"))).as("col"))
+    checkAnalysis(plan, plan)
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
index 27bd084847346..807bc8c30c12d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Column.scala
@@ -753,10 +753,16 @@ class Column(protected[sql] val expr: Expression) extends Logging {
    *   df.select($"colA".as("colB"))
    * }}}
    *
+   * If the current column has metadata associated with it, this metadata will be propagated
+   * to the new column.  If this not desired, use `as` with explicitly empty metadata.
+   *
    * @group expr_ops
    * @since 1.3.0
    */
-  def as(alias: String): Column = Alias(expr, alias)()
+  def as(alias: String): Column = expr match {
+    case ne: NamedExpression => Alias(expr, alias)(explicitMetadata = Some(ne.metadata))
+    case other => Alias(other, alias)()
+  }
 
   /**
    * (Scala-specific) Assigns the given aliases to the results of a table generating function.
@@ -789,10 +795,16 @@ class Column(protected[sql] val expr: Expression) extends Logging {
    *   df.select($"colA".as('colB))
    * }}}
    *
+   * If the current column has metadata associated with it, this metadata will be propagated
+   * to the new column.  If this not desired, use `as` with explicitly empty metadata.
+   *
    * @group expr_ops
    * @since 1.3.0
    */
-  def as(alias: Symbol): Column = Alias(expr, alias.name)()
+  def as(alias: Symbol): Column = expr match {
+    case ne: NamedExpression => Alias(expr, alias.name)(explicitMetadata = Some(ne.metadata))
+    case other => Alias(other, alias.name)()
+  }
 
   /**
    * Gives the column an alias with metadata.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index ee74e3e83da5a..37738ec5b3c1d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.catalyst.expressions.NamedExpression
 import org.scalatest.Matchers._
 
 import org.apache.spark.sql.execution.{Project, TungstenProject}
@@ -110,6 +111,14 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
     assert(df.select(df("a").alias("b")).columns.head === "b")
   }
 
+  test("as propagates metadata") {
+    val metadata = new MetadataBuilder
+    metadata.putString("key", "value")
+    val origCol = $"a".as("b", metadata.build())
+    val newCol = origCol.as("c")
+    assert(newCol.expr.asInstanceOf[NamedExpression].metadata.getString("key") === "value")
+  }
+
   test("single explode") {
     val df = Seq((1, Seq(1, 2, 3))).toDF("a", "intList")
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 10bfa9b64f00d..cf22797752b97 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -867,4 +867,10 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
     val actual = df.sort(rand(seed)).collect().map(_.getInt(0))
     assert(expected === actual)
   }
+
+  test("SPARK-9323: DataFrame.orderBy should support nested column name") {
+    val df = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+      """{"a": {"b": 1}}""" :: Nil))
+    checkAnswer(df.orderBy("a.b"), Row(Row(1)))
+  }
 }

From 6c4fdbec33af287d24cd0995ecbd7191545d05c9 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Fri, 14 Aug 2015 21:03:14 -0700
Subject: [PATCH 262/633] [SPARK-8887] [SQL] Explicit define which data types
 can be used as dynamic partition columns

This PR enforce dynamic partition column data type requirements by adding analysis rules.

JIRA: https://issues.apache.org/jira/browse/SPARK-8887

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8201 from yjshen/dynamic_partition_columns.
---
 .../datasources/PartitioningUtils.scala         | 13 +++++++++++++
 .../datasources/ResolvedDataSource.scala        |  5 ++++-
 .../execution/datasources/WriterContainer.scala |  2 +-
 .../spark/sql/execution/datasources/rules.scala |  8 ++++++--
 .../sql/sources/hadoopFsRelationSuites.scala    | 17 +++++++++++++++++
 5 files changed, 41 insertions(+), 4 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
index 66dfcc308ceca..0a2007e15843c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
@@ -26,6 +26,7 @@ import scala.util.Try
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.util.Shell
 
+import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
 import org.apache.spark.sql.types._
@@ -270,6 +271,18 @@ private[sql] object PartitioningUtils {
   private val upCastingOrder: Seq[DataType] =
     Seq(NullType, IntegerType, LongType, FloatType, DoubleType, StringType)
 
+  def validatePartitionColumnDataTypes(
+      schema: StructType,
+      partitionColumns: Array[String]): Unit = {
+
+    ResolvedDataSource.partitionColumnsSchema(schema, partitionColumns).foreach { field =>
+      field.dataType match {
+        case _: AtomicType => // OK
+        case _ => throw new AnalysisException(s"Cannot use ${field.dataType} for partition column")
+      }
+    }
+  }
+
   /**
    * Given a collection of [[Literal]]s, resolves possible type conflicts by up-casting "lower"
    * types.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
index 7770bbd712f04..8fbaf3a3059db 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
@@ -143,7 +143,7 @@ object ResolvedDataSource extends Logging {
     new ResolvedDataSource(clazz, relation)
   }
 
-  private def partitionColumnsSchema(
+  def partitionColumnsSchema(
       schema: StructType,
       partitionColumns: Array[String]): StructType = {
     StructType(partitionColumns.map { col =>
@@ -179,6 +179,9 @@ object ResolvedDataSource extends Logging {
           val fs = path.getFileSystem(sqlContext.sparkContext.hadoopConfiguration)
           path.makeQualified(fs.getUri, fs.getWorkingDirectory)
         }
+
+        PartitioningUtils.validatePartitionColumnDataTypes(data.schema, partitionColumns)
+
         val dataSchema = StructType(data.schema.filterNot(f => partitionColumns.contains(f.name)))
         val r = dataSource.createRelation(
           sqlContext,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index 2f11f40422402..d36197e50d448 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -287,7 +287,7 @@ private[sql] class DynamicPartitionWriterContainer(
           PartitioningUtils.escapePathName _, StringType, Seq(Cast(c, StringType)), Seq(StringType))
       val str = If(IsNull(c), Literal(defaultPartitionName), escaped)
       val partitionName = Literal(c.name + "=") :: str :: Nil
-      if (i == 0) partitionName else Literal(Path.SEPARATOR_CHAR.toString) :: partitionName
+      if (i == 0) partitionName else Literal(Path.SEPARATOR) :: partitionName
     }
 
     // Returns the partition path given a partition key.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
index 40ca8bf4095d8..9d3d35692ffcc 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
@@ -116,6 +116,8 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           // OK
         }
 
+        PartitioningUtils.validatePartitionColumnDataTypes(r.schema, part.keySet.toArray)
+
         // Get all input data source relations of the query.
         val srcRelations = query.collect {
           case LogicalRelation(src: BaseRelation) => src
@@ -138,10 +140,10 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           // OK
         }
 
-      case CreateTableUsingAsSelect(tableName, _, _, _, SaveMode.Overwrite, _, query) =>
+      case CreateTableUsingAsSelect(tableName, _, _, partitionColumns, mode, _, query) =>
         // When the SaveMode is Overwrite, we need to check if the table is an input table of
         // the query. If so, we will throw an AnalysisException to let users know it is not allowed.
-        if (catalog.tableExists(Seq(tableName))) {
+        if (mode == SaveMode.Overwrite && catalog.tableExists(Seq(tableName))) {
           // Need to remove SubQuery operator.
           EliminateSubQueries(catalog.lookupRelation(Seq(tableName))) match {
             // Only do the check if the table is a data source table
@@ -164,6 +166,8 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           // OK
         }
 
+        PartitioningUtils.validatePartitionColumnDataTypes(query.schema, partitionColumns)
+
       case _ => // OK
     }
   }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index af445626fbe4d..8d0d9218ddd6a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.sources
 
+import java.sql.Date
+
 import scala.collection.JavaConversions._
 
 import org.apache.hadoop.conf.Configuration
@@ -553,6 +555,21 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
       clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
     }
   }
+
+  test("SPARK-8887: Explicitly define which data types can be used as dynamic partition columns") {
+    val df = Seq(
+      (1, "v1", Array(1, 2, 3), Map("k1" -> "v1"), Tuple2(1, "4")),
+      (2, "v2", Array(4, 5, 6), Map("k2" -> "v2"), Tuple2(2, "5")),
+      (3, "v3", Array(7, 8, 9), Map("k3" -> "v3"), Tuple2(3, "6"))).toDF("a", "b", "c", "d", "e")
+    withTempDir { file =>
+      intercept[AnalysisException] {
+        df.write.format(dataSourceName).partitionBy("c", "d", "e").save(file.getCanonicalPath)
+      }
+    }
+    intercept[AnalysisException] {
+      df.write.format(dataSourceName).partitionBy("c", "d", "e").saveAsTable("t")
+    }
+  }
 }
 
 // This class is used to test SPARK-8578. We should not use any custom output committer when

From 609ce3c07d4962a9242e488ad0ed48c183896802 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Fri, 14 Aug 2015 21:12:11 -0700
Subject: [PATCH 263/633] [SPARK-9984] [SQL] Create local physical operator
 interface.

This pull request creates a new operator interface that is more similar to traditional database query iterators (with open/close/next/get).

These local operators are not currently used anywhere, but will become the basis for SPARK-9983 (local physical operators for query execution).

cc zsxwing

Author: Reynold Xin <rxin@databricks.com>

Closes #8212 from rxin/SPARK-9984.
---
 .../sql/execution/local/FilterNode.scala      | 47 ++++++++++
 .../spark/sql/execution/local/LocalNode.scala | 86 +++++++++++++++++++
 .../sql/execution/local/ProjectNode.scala     | 42 +++++++++
 .../sql/execution/local/SeqScanNode.scala     | 49 +++++++++++
 4 files changed, 224 insertions(+)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
new file mode 100644
index 0000000000000..a485a1a1d7ae4
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
@@ -0,0 +1,47 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
+import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate
+
+
+case class FilterNode(condition: Expression, child: LocalNode) extends UnaryLocalNode {
+
+  private[this] var predicate: (InternalRow) => Boolean = _
+
+  override def output: Seq[Attribute] = child.output
+
+  override def open(): Unit = {
+    child.open()
+    predicate = GeneratePredicate.generate(condition, child.output)
+  }
+
+  override def next(): Boolean = {
+    var found = false
+    while (child.next() && !found) {
+      found = predicate.apply(child.get())
+    }
+    found
+  }
+
+  override def get(): InternalRow = child.get()
+
+  override def close(): Unit = child.close()
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
new file mode 100644
index 0000000000000..341c81438e6d6
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -0,0 +1,86 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.trees.TreeNode
+import org.apache.spark.sql.types.StructType
+
+/**
+ * A local physical operator, in the form of an iterator.
+ *
+ * Before consuming the iterator, open function must be called.
+ * After consuming the iterator, close function must be called.
+ */
+abstract class LocalNode extends TreeNode[LocalNode] {
+
+  def output: Seq[Attribute]
+
+  /**
+   * Initializes the iterator state. Must be called before calling `next()`.
+   *
+   * Implementations of this must also call the `open()` function of its children.
+   */
+  def open(): Unit
+
+  /**
+   * Advances the iterator to the next tuple. Returns true if there is at least one more tuple.
+   */
+  def next(): Boolean
+
+  /**
+   * Returns the current tuple.
+   */
+  def get(): InternalRow
+
+  /**
+   * Closes the iterator and releases all resources.
+   *
+   * Implementations of this must also call the `close()` function of its children.
+   */
+  def close(): Unit
+
+  /**
+   * Returns the content of the iterator from the beginning to the end in the form of a Scala Seq.
+   */
+  def collect(): Seq[Row] = {
+    val converter = CatalystTypeConverters.createToScalaConverter(StructType.fromAttributes(output))
+    val result = new scala.collection.mutable.ArrayBuffer[Row]
+    open()
+    while (next()) {
+      result += converter.apply(get()).asInstanceOf[Row]
+    }
+    close()
+    result
+  }
+}
+
+
+abstract class LeafLocalNode extends LocalNode {
+  override def children: Seq[LocalNode] = Seq.empty
+}
+
+
+abstract class UnaryLocalNode extends LocalNode {
+
+  def child: LocalNode
+
+  override def children: Seq[LocalNode] = Seq(child)
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
new file mode 100644
index 0000000000000..e574d1473cdcb
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
@@ -0,0 +1,42 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, Attribute, NamedExpression}
+
+
+case class ProjectNode(projectList: Seq[NamedExpression], child: LocalNode) extends UnaryLocalNode {
+
+  private[this] var project: UnsafeProjection = _
+
+  override def output: Seq[Attribute] = projectList.map(_.toAttribute)
+
+  override def open(): Unit = {
+    project = UnsafeProjection.create(projectList, child.output)
+    child.open()
+  }
+
+  override def next(): Boolean = child.next()
+
+  override def get(): InternalRow = {
+    project.apply(child.get())
+  }
+
+  override def close(): Unit = child.close()
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
new file mode 100644
index 0000000000000..994de8afa9a02
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
@@ -0,0 +1,49 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+
+/**
+ * An operator that scans some local data collection in the form of Scala Seq.
+ */
+case class SeqScanNode(output: Seq[Attribute], data: Seq[InternalRow]) extends LeafLocalNode {
+
+  private[this] var iterator: Iterator[InternalRow] = _
+  private[this] var currentRow: InternalRow = _
+
+  override def open(): Unit = {
+    iterator = data.iterator
+  }
+
+  override def next(): Boolean = {
+    if (iterator.hasNext) {
+      currentRow = iterator.next()
+      true
+    } else {
+      false
+    }
+  }
+
+  override def get(): InternalRow = currentRow
+
+  override def close(): Unit = {
+    // Do nothing
+  }
+}

From 71a3af8a94f900a26ac7094f22ec1216cab62e15 Mon Sep 17 00:00:00 2001
From: zc he <farseer90718@gmail.com>
Date: Fri, 14 Aug 2015 21:28:50 -0700
Subject: [PATCH 264/633] [SPARK-9960] [GRAPHX] sendMessage type fix in
 LabelPropagation.scala

Author: zc he <farseer90718@gmail.com>

Closes #8188 from farseer90718/farseer-patch-1.
---
 .../scala/org/apache/spark/graphx/lib/LabelPropagation.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
index 2bcf8684b8b8e..a3ad6bed1c998 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/lib/LabelPropagation.scala
@@ -43,7 +43,7 @@ object LabelPropagation {
    */
   def run[VD, ED: ClassTag](graph: Graph[VD, ED], maxSteps: Int): Graph[VertexId, ED] = {
     val lpaGraph = graph.mapVertices { case (vid, _) => vid }
-    def sendMessage(e: EdgeTriplet[VertexId, ED]): Iterator[(VertexId, Map[VertexId, VertexId])] = {
+    def sendMessage(e: EdgeTriplet[VertexId, ED]): Iterator[(VertexId, Map[VertexId, Long])] = {
       Iterator((e.srcId, Map(e.dstAttr -> 1L)), (e.dstId, Map(e.srcAttr -> 1L)))
     }
     def mergeMessage(count1: Map[VertexId, Long], count2: Map[VertexId, Long])

From 7c1e56825b716a7d703dff38254b4739755ac0c4 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 14 Aug 2015 22:30:35 -0700
Subject: [PATCH 265/633] [SPARK-9725] [SQL] fix serialization of UTF8String
 across different JVM

The BYTE_ARRAY_OFFSET could be different in JVM with different configurations (for example, different heap size, 24 if heap > 32G, otherwise 16), so offset of UTF8String is not portable, we should handler that during serialization.

Author: Davies Liu <davies@databricks.com>

Closes #8210 from davies/serialize_utf8string.
---
 .../apache/spark/unsafe/types/UTF8String.java | 31 +++++++++++++++----
 1 file changed, 25 insertions(+), 6 deletions(-)

diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index 667c00900f2c5..cbcab958c05a9 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -18,8 +18,7 @@
 package org.apache.spark.unsafe.types;
 
 import javax.annotation.Nonnull;
-import java.io.Serializable;
-import java.io.UnsupportedEncodingException;
+import java.io.*;
 import java.nio.ByteOrder;
 import java.util.Arrays;
 import java.util.Map;
@@ -38,12 +37,13 @@
  * <p>
  * Note: This is not designed for general use cases, should not be used outside SQL.
  */
-public final class UTF8String implements Comparable<UTF8String>, Serializable {
+public final class UTF8String implements Comparable<UTF8String>, Externalizable {
 
+  // These are only updated by readExternal()
   @Nonnull
-  private final Object base;
-  private final long offset;
-  private final int numBytes;
+  private Object base;
+  private long offset;
+  private int numBytes;
 
   public Object getBaseObject() { return base; }
   public long getBaseOffset() { return offset; }
@@ -127,6 +127,11 @@ protected UTF8String(Object base, long offset, int numBytes) {
     this.numBytes = numBytes;
   }
 
+  // for serialization
+  public UTF8String() {
+    this(null, 0, 0);
+  }
+
   /**
    * Writes the content of this string into a memory address, identified by an object and an offset.
    * The target memory address must already been allocated, and have enough space to hold all the
@@ -978,4 +983,18 @@ public UTF8String soundex() {
     }
     return UTF8String.fromBytes(sx);
   }
+
+  public void writeExternal(ObjectOutput out) throws IOException {
+    byte[] bytes = getBytes();
+    out.writeInt(bytes.length);
+    out.write(bytes);
+  }
+
+  public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException {
+    offset = BYTE_ARRAY_OFFSET;
+    numBytes = in.readInt();
+    base = new byte[numBytes];
+    in.readFully((byte[]) base);
+  }
+
 }

From a85fb6c07fdda5c74d53d6373910dcf5db3ff111 Mon Sep 17 00:00:00 2001
From: Herman van Hovell <hvanhovell@questtec.nl>
Date: Sat, 15 Aug 2015 10:46:04 +0100
Subject: [PATCH 266/633] [SPARK-9980] [BUILD] Fix SBT publishLocal error due
 to invalid characters in doc

Tiny modification to a few comments ```sbt publishLocal``` work again.

Author: Herman van Hovell <hvanhovell@questtec.nl>

Closes #8209 from hvanhovell/SPARK-9980.
---
 .../java/org/apache/spark/unsafe/map/BytesToBytesMap.java | 6 +++---
 .../apache/spark/examples/ml/JavaDeveloperApiExample.java | 4 ++--
 .../examples/streaming/JavaStatefulNetworkWordCount.java  | 2 +-
 .../src/main/java/org/apache/spark/launcher/Main.java     | 4 ++--
 .../apache/spark/launcher/SparkClassCommandBuilder.java   | 2 +-
 .../java/org/apache/spark/launcher/SparkLauncher.java     | 6 +++---
 .../apache/spark/launcher/SparkSubmitCommandBuilder.java  | 4 ++--
 .../apache/spark/launcher/SparkSubmitOptionParser.java    | 8 ++++----
 .../org/apache/spark/unsafe/memory/TaskMemoryManager.java | 2 +-
 9 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
index 5f3a4fcf4d585..b24eed3952fd6 100644
--- a/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
+++ b/core/src/main/java/org/apache/spark/unsafe/map/BytesToBytesMap.java
@@ -92,9 +92,9 @@ public final class BytesToBytesMap {
 
   /**
    * The maximum number of keys that BytesToBytesMap supports. The hash table has to be
-   * power-of-2-sized and its backing Java array can contain at most (1 << 30) elements, since
-   * that's the largest power-of-2 that's less than Integer.MAX_VALUE. We need two long array
-   * entries per key, giving us a maximum capacity of (1 << 29).
+   * power-of-2-sized and its backing Java array can contain at most (1 &lt;&lt; 30) elements,
+   * since that's the largest power-of-2 that's less than Integer.MAX_VALUE. We need two long array
+   * entries per key, giving us a maximum capacity of (1 &lt;&lt; 29).
    */
   @VisibleForTesting
   static final int MAX_CAPACITY = (1 << 29);
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
index 3f1fe900b0008..a377694507d29 100644
--- a/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaDeveloperApiExample.java
@@ -124,7 +124,7 @@ public String uid() {
 
   /**
    * Param for max number of iterations
-   * <p/>
+   * <p>
    * NOTE: The usual way to add a parameter to a model or algorithm is to include:
    * - val myParamName: ParamType
    * - def getMyParamName
@@ -222,7 +222,7 @@ public Vector predictRaw(Vector features) {
   /**
    * Create a copy of the model.
    * The copy is shallow, except for the embedded paramMap, which gets a deep copy.
-   * <p/>
+   * <p>
    * This is used for the defaul implementation of [[transform()]].
    *
    * In Java, we have to make this method public since Java does not understand Scala's protected
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
index 02f58f48b07ab..99b63a2590ae2 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaStatefulNetworkWordCount.java
@@ -45,7 +45,7 @@
  * Usage: JavaStatefulNetworkWordCount <hostname> <port>
  * <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
  * data.
- * <p/>
+ * <p>
  * To run this on your local machine, you need to first run a Netcat server
  * `$ nc -lk 9999`
  * and then run the example
diff --git a/launcher/src/main/java/org/apache/spark/launcher/Main.java b/launcher/src/main/java/org/apache/spark/launcher/Main.java
index 62492f9baf3bb..a4e3acc674f36 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/Main.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/Main.java
@@ -32,7 +32,7 @@ class Main {
 
   /**
    * Usage: Main [class] [class args]
-   * <p/>
+   * <p>
    * This CLI works in two different modes:
    * <ul>
    *   <li>"spark-submit": if <i>class</i> is "org.apache.spark.deploy.SparkSubmit", the
@@ -42,7 +42,7 @@ class Main {
    *
    * This class works in tandem with the "bin/spark-class" script on Unix-like systems, and
    * "bin/spark-class2.cmd" batch script on Windows to execute the final command.
-   * <p/>
+   * <p>
    * On Unix-like systems, the output is a list of command arguments, separated by the NULL
    * character. On Windows, the output is a command line suitable for direct execution from the
    * script.
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java
index 5f95e2c74f902..931a24cfd4b1d 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkClassCommandBuilder.java
@@ -28,7 +28,7 @@
 
 /**
  * Command builder for internal Spark classes.
- * <p/>
+ * <p>
  * This class handles building the command to launch all internal Spark classes except for
  * SparkSubmit (which is handled by {@link SparkSubmitCommandBuilder} class.
  */
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
index 03c9358bc865d..57993405e47be 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkLauncher.java
@@ -193,7 +193,7 @@ public SparkLauncher setMainClass(String mainClass) {
    * Adds a no-value argument to the Spark invocation. If the argument is known, this method
    * validates whether the argument is indeed a no-value argument, and throws an exception
    * otherwise.
-   * <p/>
+   * <p>
    * Use this method with caution. It is possible to create an invalid Spark command by passing
    * unknown arguments to this method, since those are allowed for forward compatibility.
    *
@@ -211,10 +211,10 @@ public SparkLauncher addSparkArg(String arg) {
    * Adds an argument with a value to the Spark invocation. If the argument name corresponds to
    * a known argument, the code validates that the argument actually expects a value, and throws
    * an exception otherwise.
-   * <p/>
+   * <p>
    * It is safe to add arguments modified by other methods in this class (such as
    * {@link #setMaster(String)} - the last invocation will be the one to take effect.
-   * <p/>
+   * <p>
    * Use this method with caution. It is possible to create an invalid Spark command by passing
    * unknown arguments to this method, since those are allowed for forward compatibility.
    *
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
index 4f354cedee66f..fc87814a59ed5 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitCommandBuilder.java
@@ -25,11 +25,11 @@
 
 /**
  * Special command builder for handling a CLI invocation of SparkSubmit.
- * <p/>
+ * <p>
  * This builder adds command line parsing compatible with SparkSubmit. It handles setting
  * driver-side options and special parsing behavior needed for the special-casing certain internal
  * Spark applications.
- * <p/>
+ * <p>
  * This class has also some special features to aid launching pyspark.
  */
 class SparkSubmitCommandBuilder extends AbstractCommandBuilder {
diff --git a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
index 5779eb3fc0f78..6767cc5079649 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/SparkSubmitOptionParser.java
@@ -23,7 +23,7 @@
 
 /**
  * Parser for spark-submit command line options.
- * <p/>
+ * <p>
  * This class encapsulates the parsing code for spark-submit command line options, so that there
  * is a single list of options that needs to be maintained (well, sort of, but it makes it harder
  * to break things).
@@ -80,10 +80,10 @@ class SparkSubmitOptionParser {
    * This is the canonical list of spark-submit options. Each entry in the array contains the
    * different aliases for the same option; the first element of each entry is the "official"
    * name of the option, passed to {@link #handle(String, String)}.
-   * <p/>
+   * <p>
    * Options not listed here nor in the "switch" list below will result in a call to
    * {@link $#handleUnknown(String)}.
-   * <p/>
+   * <p>
    * These two arrays are visible for tests.
    */
   final String[][] opts = {
@@ -130,7 +130,7 @@ class SparkSubmitOptionParser {
 
   /**
    * Parse a list of spark-submit command line options.
-   * <p/>
+   * <p>
    * See SparkSubmitArguments.scala for a more formal description of available options.
    *
    * @throws IllegalArgumentException If an error is found during parsing.
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
index ca70d7f4a4311..97b2c93f0dc37 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/memory/TaskMemoryManager.java
@@ -60,7 +60,7 @@ public class TaskMemoryManager {
 
   /**
    * Maximum supported data page size (in bytes). In principle, the maximum addressable page size is
-   * (1L << OFFSET_BITS) bytes, which is 2+ petabytes. However, the on-heap allocator's maximum page
+   * (1L &lt;&lt; OFFSET_BITS) bytes, which is 2+ petabytes. However, the on-heap allocator's maximum page
    * size is limited by the maximum amount of data that can be stored in a  long[] array, which is
    * (2^32 - 1) * 8 bytes (or 16 gigabytes). Therefore, we cap this at 16 gigabytes.
    */

From 570567258b5839c1e0e28b5182f4c29b119ed4c4 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Sat, 15 Aug 2015 14:13:12 -0700
Subject: [PATCH 267/633] [SPARK-9955] [SQL] correct error message for
 aggregate

We should skip unresolved `LogicalPlan`s for `PullOutNondeterministic`, as calling `output` on unresolved `LogicalPlan` will produce confusing error message.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8203 from cloud-fan/error-msg and squashes the following commits:

1c67ca7 [Wenchen Fan] move test
7593080 [Wenchen Fan] correct error message for aggregate
---
 .../spark/sql/catalyst/analysis/Analyzer.scala      |  1 +
 .../sql/catalyst/analysis/AnalysisErrorSuite.scala  |  5 +++++
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala  | 13 ++++++-------
 3 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 4bc1c1af40bf4..d0eb9c2c90bdf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -924,6 +924,7 @@ class Analyzer(
    */
   object PullOutNondeterministic extends Rule[LogicalPlan] {
     override def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
+      case p if !p.resolved => p // Skip unresolved nodes.
       case p: Project => p
       case f: Filter => f
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index f60d11c988ef8..7065adce04bf8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -145,6 +145,11 @@ class AnalysisErrorSuite extends AnalysisTest {
     UnresolvedTestPlan(),
     "unresolved" :: Nil)
 
+  errorTest(
+    "SPARK-9955: correct error message for aggregate",
+    // When parse SQL string, we will wrap aggregate expressions with UnresolvedAlias.
+    testRelation2.where('bad_column > 1).groupBy('a)(UnresolvedAlias(max('b))),
+    "cannot resolve 'bad_column'" :: Nil)
 
   test("SPARK-6452 regression test") {
     // CheckAnalysis should throw AnalysisException when Aggregate contains missing attribute(s)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 8c2c328f8191c..c329fdb2a6bb1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1618,12 +1618,11 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-9511: error with table starting with number") {
-    val df = sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
-      .toDF("num", "str")
-    df.registerTempTable("1one")
-
-    checkAnswer(sql("select count(num) from 1one"), Row(10))
-
-    sqlContext.dropTempTable("1one")
+    withTempTable("1one") {
+      sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
+        .toDF("num", "str")
+        .registerTempTable("1one")
+      checkAnswer(sql("select count(num) from 1one"), Row(10))
+    }
   }
 }

From 1db7179fae672fcec7b8de12c374dd384ce51c67 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Sat, 15 Aug 2015 18:48:20 -0700
Subject: [PATCH 268/633] [SPARK-9805] [MLLIB] [PYTHON] [STREAMING] Added
 _eventually for ml streaming pyspark tests

Recently, PySpark ML streaming tests have been flaky, most likely because of the batches not being processed in time.  Proposal: Replace the use of _ssc_wait (which waits for a fixed amount of time) with a method which waits for a fixed amount of time but can terminate early based on a termination condition method.  With this, we can extend the waiting period (to make tests less flaky) but also stop early when possible (making tests faster on average, which I verified locally).

CC: mengxr tdas freeman-lab

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8087 from jkbradley/streaming-ml-tests.
---
 python/pyspark/mllib/tests.py | 177 +++++++++++++++++++++++++---------
 1 file changed, 129 insertions(+), 48 deletions(-)

diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 3f5a02af12e39..5097c5e8ba4cd 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -32,6 +32,9 @@
 
 from py4j.protocol import Py4JJavaError
 
+if sys.version > '3':
+    basestring = str
+
 if sys.version_info[:2] <= (2, 6):
     try:
         import unittest2 as unittest
@@ -86,9 +89,42 @@ def tearDown(self):
         self.ssc.stop(False)
 
     @staticmethod
-    def _ssc_wait(start_time, end_time, sleep_time):
-        while time() - start_time < end_time:
+    def _eventually(condition, timeout=30.0, catch_assertions=False):
+        """
+        Wait a given amount of time for a condition to pass, else fail with an error.
+        This is a helper utility for streaming ML tests.
+        :param condition: Function that checks for termination conditions.
+                          condition() can return:
+                           - True: Conditions met. Return without error.
+                           - other value: Conditions not met yet. Continue. Upon timeout,
+                                          include last such value in error message.
+                          Note that this method may be called at any time during
+                          streaming execution (e.g., even before any results
+                          have been created).
+        :param timeout: Number of seconds to wait.  Default 30 seconds.
+        :param catch_assertions: If False (default), do not catch AssertionErrors.
+                                 If True, catch AssertionErrors; continue, but save
+                                 error to throw upon timeout.
+        """
+        start_time = time()
+        lastValue = None
+        while time() - start_time < timeout:
+            if catch_assertions:
+                try:
+                    lastValue = condition()
+                except AssertionError as e:
+                    lastValue = e
+            else:
+                lastValue = condition()
+            if lastValue is True:
+                return
             sleep(0.01)
+        if isinstance(lastValue, AssertionError):
+            raise lastValue
+        else:
+            raise AssertionError(
+                "Test failed due to timeout after %g sec, with last condition returning: %s"
+                % (timeout, lastValue))
 
 
 def _squared_distance(a, b):
@@ -999,10 +1035,13 @@ def test_accuracy_for_single_center(self):
             [self.sc.parallelize(batch, 1) for batch in batches])
         stkm.trainOn(input_stream)
 
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 10.0, 0.01)
-        self.assertEquals(stkm.latestModel().clusterWeights, [25.0])
+
+        def condition():
+            self.assertEquals(stkm.latestModel().clusterWeights, [25.0])
+            return True
+        self._eventually(condition, catch_assertions=True)
+
         realCenters = array_sum(array(centers), axis=0)
         for i in range(5):
             modelCenters = stkm.latestModel().centers[0][i]
@@ -1027,7 +1066,7 @@ def test_trainOn_model(self):
         stkm.setInitialCenters(
             centers=initCenters, weights=[1.0, 1.0, 1.0, 1.0])
 
-        # Create a toy dataset by setting a tiny offest for each point.
+        # Create a toy dataset by setting a tiny offset for each point.
         offsets = [[0, 0.1], [0, -0.1], [0.1, 0], [-0.1, 0]]
         batches = []
         for offset in offsets:
@@ -1037,14 +1076,15 @@ def test_trainOn_model(self):
         batches = [self.sc.parallelize(batch, 1) for batch in batches]
         input_stream = self.ssc.queueStream(batches)
         stkm.trainOn(input_stream)
-        t = time()
         self.ssc.start()
 
         # Give enough time to train the model.
-        self._ssc_wait(t, 6.0, 0.01)
-        finalModel = stkm.latestModel()
-        self.assertTrue(all(finalModel.centers == array(initCenters)))
-        self.assertEquals(finalModel.clusterWeights, [5.0, 5.0, 5.0, 5.0])
+        def condition():
+            finalModel = stkm.latestModel()
+            self.assertTrue(all(finalModel.centers == array(initCenters)))
+            self.assertEquals(finalModel.clusterWeights, [5.0, 5.0, 5.0, 5.0])
+            return True
+        self._eventually(condition, catch_assertions=True)
 
     def test_predictOn_model(self):
         """Test that the model predicts correctly on toy data."""
@@ -1066,10 +1106,13 @@ def update(rdd):
                 result.append(rdd_collect)
 
         predict_val.foreachRDD(update)
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 6.0, 0.01)
-        self.assertEquals(result, [[0], [1], [2], [3]])
+
+        def condition():
+            self.assertEquals(result, [[0], [1], [2], [3]])
+            return True
+
+        self._eventually(condition, catch_assertions=True)
 
     def test_trainOn_predictOn(self):
         """Test that prediction happens on the updated model."""
@@ -1095,10 +1138,13 @@ def collect(rdd):
         predict_stream = stkm.predictOn(input_stream)
         predict_stream.foreachRDD(collect)
 
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 6.0, 0.01)
-        self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]])
+
+        def condition():
+            self.assertEqual(predict_results, [[0, 1, 1], [1, 0, 1]])
+            return True
+
+        self._eventually(condition, catch_assertions=True)
 
 
 class LinearDataGeneratorTests(MLlibTestCase):
@@ -1156,11 +1202,14 @@ def test_parameter_accuracy(self):
         slr.setInitialWeights([0.0])
         slr.trainOn(input_stream)
 
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 20.0, 0.01)
-        rel = (1.5 - slr.latestModel().weights.array[0]) / 1.5
-        self.assertAlmostEqual(rel, 0.1, 1)
+
+        def condition():
+            rel = (1.5 - slr.latestModel().weights.array[0]) / 1.5
+            self.assertAlmostEqual(rel, 0.1, 1)
+            return True
+
+        self._eventually(condition, catch_assertions=True)
 
     def test_convergence(self):
         """
@@ -1179,13 +1228,18 @@ def test_convergence(self):
         input_stream.foreachRDD(
             lambda x: models.append(slr.latestModel().weights[0]))
 
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 15.0, 0.01)
+
+        def condition():
+            self.assertEquals(len(models), len(input_batches))
+            return True
+
+        # We want all batches to finish for this test.
+        self._eventually(condition, 60.0, catch_assertions=True)
+
         t_models = array(models)
         diff = t_models[1:] - t_models[:-1]
-
-        # Test that weights improve with a small tolerance,
+        # Test that weights improve with a small tolerance
         self.assertTrue(all(diff >= -0.1))
         self.assertTrue(array_sum(diff > 0) > 1)
 
@@ -1208,9 +1262,13 @@ def test_predictions(self):
         predict_stream = slr.predictOnValues(input_stream)
         true_predicted = []
         predict_stream.foreachRDD(lambda x: true_predicted.append(x.collect()))
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 5.0, 0.01)
+
+        def condition():
+            self.assertEquals(len(true_predicted), len(input_batches))
+            return True
+
+        self._eventually(condition, catch_assertions=True)
 
         # Test that the accuracy error is no more than 0.4 on each batch.
         for batch in true_predicted:
@@ -1242,12 +1300,17 @@ def collect_errors(rdd):
         ps = slr.predictOnValues(predict_stream)
         ps.foreachRDD(lambda x: collect_errors(x))
 
-        t = time()
         self.ssc.start()
-        self._ssc_wait(t, 20.0, 0.01)
 
-        # Test that the improvement in error is atleast 0.3
-        self.assertTrue(errors[1] - errors[-1] > 0.3)
+        def condition():
+            # Test that the improvement in error is > 0.3
+            if len(errors) == len(predict_batches):
+                self.assertGreater(errors[1] - errors[-1], 0.3)
+            if len(errors) >= 3 and errors[1] - errors[-1] > 0.3:
+                return True
+            return "Latest errors: " + ", ".join(map(lambda x: str(x), errors))
+
+        self._eventually(condition)
 
 
 class StreamingLinearRegressionWithTests(MLLibStreamingTestCase):
@@ -1274,13 +1337,16 @@ def test_parameter_accuracy(self):
             batches.append(sc.parallelize(batch))
 
         input_stream = self.ssc.queueStream(batches)
-        t = time()
         slr.trainOn(input_stream)
         self.ssc.start()
-        self._ssc_wait(t, 10, 0.01)
-        self.assertArrayAlmostEqual(
-            slr.latestModel().weights.array, [10., 10.], 1)
-        self.assertAlmostEqual(slr.latestModel().intercept, 0.0, 1)
+
+        def condition():
+            self.assertArrayAlmostEqual(
+                slr.latestModel().weights.array, [10., 10.], 1)
+            self.assertAlmostEqual(slr.latestModel().intercept, 0.0, 1)
+            return True
+
+        self._eventually(condition, catch_assertions=True)
 
     def test_parameter_convergence(self):
         """Test that the model parameters improve with streaming data."""
@@ -1298,13 +1364,18 @@ def test_parameter_convergence(self):
         input_stream = self.ssc.queueStream(batches)
         input_stream.foreachRDD(
             lambda x: model_weights.append(slr.latestModel().weights[0]))
-        t = time()
         slr.trainOn(input_stream)
         self.ssc.start()
-        self._ssc_wait(t, 10, 0.01)
 
-        model_weights = array(model_weights)
-        diff = model_weights[1:] - model_weights[:-1]
+        def condition():
+            self.assertEquals(len(model_weights), len(batches))
+            return True
+
+        # We want all batches to finish for this test.
+        self._eventually(condition, catch_assertions=True)
+
+        w = array(model_weights)
+        diff = w[1:] - w[:-1]
         self.assertTrue(all(diff >= -0.1))
 
     def test_prediction(self):
@@ -1323,13 +1394,18 @@ def test_prediction(self):
                 sc.parallelize(batch).map(lambda lp: (lp.label, lp.features)))
 
         input_stream = self.ssc.queueStream(batches)
-        t = time()
         output_stream = slr.predictOnValues(input_stream)
         samples = []
         output_stream.foreachRDD(lambda x: samples.append(x.collect()))
 
         self.ssc.start()
-        self._ssc_wait(t, 5, 0.01)
+
+        def condition():
+            self.assertEquals(len(samples), len(batches))
+            return True
+
+        # We want all batches to finish for this test.
+        self._eventually(condition, catch_assertions=True)
 
         # Test that mean absolute error on each batch is less than 0.1
         for batch in samples:
@@ -1350,22 +1426,27 @@ def test_train_prediction(self):
 
         predict_batches = [
             b.map(lambda lp: (lp.label, lp.features)) for b in batches]
-        mean_absolute_errors = []
+        errors = []
 
         def func(rdd):
             true, predicted = zip(*rdd.collect())
-            mean_absolute_errors.append(mean(abs(true) - abs(predicted)))
+            errors.append(mean(abs(true) - abs(predicted)))
 
-        model_weights = []
         input_stream = self.ssc.queueStream(batches)
         output_stream = self.ssc.queueStream(predict_batches)
-        t = time()
         slr.trainOn(input_stream)
         output_stream = slr.predictOnValues(output_stream)
         output_stream.foreachRDD(func)
         self.ssc.start()
-        self._ssc_wait(t, 10, 0.01)
-        self.assertTrue(mean_absolute_errors[1] - mean_absolute_errors[-1] > 2)
+
+        def condition():
+            if len(errors) == len(predict_batches):
+                self.assertGreater(errors[1] - errors[-1], 2)
+            if len(errors) >= 3 and errors[1] - errors[-1] > 2:
+                return True
+            return "Latest errors: " + ", ".join(map(lambda x: str(x), errors))
+
+        self._eventually(condition)
 
 
 class MLUtilsTests(MLlibTestCase):

From 182f9b7a6d3a3ee7ec7de6abc24e296aa794e4e8 Mon Sep 17 00:00:00 2001
From: Kun Xu <viper_kun@163.com>
Date: Sun, 16 Aug 2015 14:44:23 +0800
Subject: [PATCH 269/633] [SPARK-9973] [SQL] Correct in-memory columnar buffer
 size

The `initialSize` argument of `ColumnBuilder.initialize()` should be the
number of rows rather than bytes.  However `InMemoryColumnarTableScan`
passes in a byte size, which makes Spark SQL allocate more memory than
necessary when building in-memory columnar buffers.

Author: Kun Xu <viper_kun@163.com>

Closes #8189 from viper-kun/errorSize.
---
 .../apache/spark/sql/columnar/InMemoryColumnarTableScan.scala  | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index d553bb6169ecc..45f15fd04d4e2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -121,8 +121,7 @@ private[sql] case class InMemoryRelation(
         def next(): CachedBatch = {
           val columnBuilders = output.map { attribute =>
             val columnType = ColumnType(attribute.dataType)
-            val initialBufferSize = columnType.defaultSize * batchSize
-            ColumnBuilder(attribute.dataType, initialBufferSize, attribute.name, useCompression)
+            ColumnBuilder(attribute.dataType, batchSize, attribute.name, useCompression)
           }.toArray
 
           var rowCount = 0

From 5f9ce738fe6bab3f0caffad0df1d3876178cf469 Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Sun, 16 Aug 2015 00:30:02 -0700
Subject: [PATCH 270/633] [SPARK-8844] [SPARKR] head/collect is broken in
 SparkR.

This is a WIP patch for SPARK-8844  for collecting reviews.

This bug is about reading an empty DataFrame. in readCol(),
      lapply(1:numRows, function(x) {
does not take into consideration the case where numRows = 0.

Will add unit test case.

Author: Sun Rui <rui.sun@intel.com>

Closes #7419 from sun-rui/SPARK-8844.
---
 R/pkg/R/deserialize.R            | 16 ++++++++++------
 R/pkg/inst/tests/test_sparkSQL.R | 20 ++++++++++++++++++++
 2 files changed, 30 insertions(+), 6 deletions(-)

diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index 6d364f77be7ee..33bf13ec9e784 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -176,10 +176,14 @@ readRow <- function(inputCon) {
 
 # Take a single column as Array[Byte] and deserialize it into an atomic vector
 readCol <- function(inputCon, numRows) {
-  # sapply can not work with POSIXlt
-  do.call(c, lapply(1:numRows, function(x) {
-    value <- readObject(inputCon)
-    # Replace NULL with NA so we can coerce to vectors
-    if (is.null(value)) NA else value
-  }))
+  if (numRows > 0) {
+    # sapply can not work with POSIXlt
+    do.call(c, lapply(1:numRows, function(x) {
+      value <- readObject(inputCon)
+      # Replace NULL with NA so we can coerce to vectors
+      if (is.null(value)) NA else value
+    }))
+  } else {
+    vector()
+  }
 }
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index e6d3b21ff825b..c77f633135856 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -408,6 +408,14 @@ test_that("collect() returns a data.frame", {
   expect_equal(names(rdf)[1], "age")
   expect_equal(nrow(rdf), 3)
   expect_equal(ncol(rdf), 2)
+
+  # collect() returns data correctly from a DataFrame with 0 row
+  df0 <- limit(df, 0)
+  rdf <- collect(df0)
+  expect_true(is.data.frame(rdf))
+  expect_equal(names(rdf)[1], "age")
+  expect_equal(nrow(rdf), 0)
+  expect_equal(ncol(rdf), 2)
 })
 
 test_that("limit() returns DataFrame with the correct number of rows", {
@@ -492,6 +500,18 @@ test_that("head() and first() return the correct data", {
 
   testFirst <- first(df)
   expect_equal(nrow(testFirst), 1)
+
+  # head() and first() return the correct data on
+  # a DataFrame with 0 row
+  df0 <- limit(df, 0)
+
+  testHead <- head(df0)
+  expect_equal(nrow(testHead), 0)
+  expect_equal(ncol(testHead), 2)
+
+  testFirst <- first(df0)
+  expect_equal(nrow(testFirst), 0)
+  expect_equal(ncol(testFirst), 2)
 })
 
 test_that("distinct() and unique on DataFrames", {

From cf016075a006034c24c5b758edb279f3e151d25d Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Sun, 16 Aug 2015 00:34:58 -0700
Subject: [PATCH 271/633] [SPARK-10008] Ensure shuffle locality doesn't take
 precedence over narrow deps

The shuffle locality patch made the DAGScheduler aware of shuffle data,
but for RDDs that have both narrow and shuffle dependencies, it can
cause them to place tasks based on the shuffle dependency instead of the
narrow one. This case is common in iterative join-based algorithms like
PageRank and ALS, where one RDD is hash-partitioned and one isn't.

Author: Matei Zaharia <matei@databricks.com>

Closes #8220 from mateiz/shuffle-loc-fix.
---
 .../apache/spark/scheduler/DAGScheduler.scala | 37 ++++++++++---------
 .../spark/scheduler/DAGSchedulerSuite.scala   | 26 ++++++++++++-
 2 files changed, 44 insertions(+), 19 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index f1c63d08766c2..dadf83a38296d 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -1383,33 +1383,36 @@ class DAGScheduler(
       return rddPrefs.map(TaskLocation(_))
     }
 
+    // If the RDD has narrow dependencies, pick the first partition of the first narrow dependency
+    // that has any placement preferences. Ideally we would choose based on transfer sizes,
+    // but this will do for now.
     rdd.dependencies.foreach {
       case n: NarrowDependency[_] =>
-        // If the RDD has narrow dependencies, pick the first partition of the first narrow dep
-        // that has any placement preferences. Ideally we would choose based on transfer sizes,
-        // but this will do for now.
         for (inPart <- n.getParents(partition)) {
           val locs = getPreferredLocsInternal(n.rdd, inPart, visited)
           if (locs != Nil) {
             return locs
           }
         }
-      case s: ShuffleDependency[_, _, _] =>
-        // For shuffle dependencies, pick locations which have at least REDUCER_PREF_LOCS_FRACTION
-        // of data as preferred locations
-        if (shuffleLocalityEnabled &&
-            rdd.partitions.size < SHUFFLE_PREF_REDUCE_THRESHOLD &&
-            s.rdd.partitions.size < SHUFFLE_PREF_MAP_THRESHOLD) {
-          // Get the preferred map output locations for this reducer
-          val topLocsForReducer = mapOutputTracker.getLocationsWithLargestOutputs(s.shuffleId,
-            partition, rdd.partitions.size, REDUCER_PREF_LOCS_FRACTION)
-          if (topLocsForReducer.nonEmpty) {
-            return topLocsForReducer.get.map(loc => TaskLocation(loc.host, loc.executorId))
-          }
-        }
-
       case _ =>
     }
+
+    // If the RDD has shuffle dependencies and shuffle locality is enabled, pick locations that
+    // have at least REDUCER_PREF_LOCS_FRACTION of data as preferred locations
+    if (shuffleLocalityEnabled && rdd.partitions.length < SHUFFLE_PREF_REDUCE_THRESHOLD) {
+      rdd.dependencies.foreach {
+        case s: ShuffleDependency[_, _, _] =>
+          if (s.rdd.partitions.length < SHUFFLE_PREF_MAP_THRESHOLD) {
+            // Get the preferred map output locations for this reducer
+            val topLocsForReducer = mapOutputTracker.getLocationsWithLargestOutputs(s.shuffleId,
+              partition, rdd.partitions.length, REDUCER_PREF_LOCS_FRACTION)
+            if (topLocsForReducer.nonEmpty) {
+              return topLocsForReducer.get.map(loc => TaskLocation(loc.host, loc.executorId))
+            }
+          }
+        case _ =>
+      }
+    }
     Nil
   }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index b0ca49cbea4f7..a063596d3eae2 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -926,7 +926,7 @@ class DAGSchedulerSuite
     assertLocations(reduceTaskSet, Seq(Seq("hostA")))
     complete(reduceTaskSet, Seq((Success, 42)))
     assert(results === Map(0 -> 42))
-    assertDataStructuresEmpty
+    assertDataStructuresEmpty()
   }
 
   test("reduce task locality preferences should only include machines with largest map outputs") {
@@ -950,7 +950,29 @@ class DAGSchedulerSuite
     assertLocations(reduceTaskSet, Seq(hosts))
     complete(reduceTaskSet, Seq((Success, 42)))
     assert(results === Map(0 -> 42))
-    assertDataStructuresEmpty
+    assertDataStructuresEmpty()
+  }
+
+  test("stages with both narrow and shuffle dependencies use narrow ones for locality") {
+    // Create an RDD that has both a shuffle dependency and a narrow dependency (e.g. for a join)
+    val rdd1 = new MyRDD(sc, 1, Nil)
+    val rdd2 = new MyRDD(sc, 1, Nil, locations = Seq(Seq("hostB")))
+    val shuffleDep = new ShuffleDependency(rdd1, null)
+    val narrowDep = new OneToOneDependency(rdd2)
+    val shuffleId = shuffleDep.shuffleId
+    val reduceRdd = new MyRDD(sc, 1, List(shuffleDep, narrowDep))
+    submit(reduceRdd, Array(0))
+    complete(taskSets(0), Seq(
+      (Success, makeMapStatus("hostA", 1))))
+    assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1).toSet ===
+      HashSet(makeBlockManagerId("hostA")))
+
+    // Reducer should run where RDD 2 has preferences, even though though it also has a shuffle dep
+    val reduceTaskSet = taskSets(1)
+    assertLocations(reduceTaskSet, Seq(Seq("hostB")))
+    complete(reduceTaskSet, Seq((Success, 42)))
+    assert(results === Map(0 -> 42))
+    assertDataStructuresEmpty()
   }
 
   test("Spark exceptions should include call site in stack trace") {

From ae2370e72f93db8a28b262e8252c55fe1fc9873c Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sun, 16 Aug 2015 10:17:58 -0700
Subject: [PATCH 272/633] [SPARK-10005] [SQL] Fixes schema merging for nested
 structs

In case of schema merging, we only handled first level fields when converting Parquet groups to `InternalRow`s. Nested struct fields are not properly handled.

For example, the schema of a Parquet file to be read can be:

```
message individual {
  required group f1 {
    optional binary f11 (utf8);
  }
}
```

while the global schema is:

```
message global {
  required group f1 {
    optional binary f11 (utf8);
    optional int32 f12;
  }
}
```

This PR fixes this issue by padding missing fields when creating actual converters.

Author: Cheng Lian <lian@databricks.com>

Closes #8228 from liancheng/spark-10005/nested-schema-merging.
---
 .../parquet/CatalystReadSupport.scala         | 19 +++--
 .../parquet/CatalystRowConverter.scala        | 70 +++++++++++++++++--
 .../parquet/CatalystSchemaConverter.scala     | 15 +---
 .../parquet/ParquetQuerySuite.scala           | 30 +++++++-
 4 files changed, 112 insertions(+), 22 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index 4049795ed3bad..a4679bb2f6389 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -32,6 +32,7 @@ import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types.StructType
 
 private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with Logging {
+  // Called after `init()` when initializing Parquet record reader.
   override def prepareForRead(
       conf: Configuration,
       keyValueMetaData: JMap[String, String],
@@ -51,19 +52,30 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
           // available if the target file is written by Spark SQL.
           .orElse(metadata.get(CatalystReadSupport.SPARK_METADATA_KEY))
       }.map(StructType.fromString).getOrElse {
-        logDebug("Catalyst schema not available, falling back to Parquet schema")
+        logInfo("Catalyst schema not available, falling back to Parquet schema")
         toCatalyst.convert(parquetRequestedSchema)
       }
 
-    logDebug(s"Catalyst schema used to read Parquet files: $catalystRequestedSchema")
+    logInfo {
+      s"""Going to read the following fields from the Parquet file:
+         |
+         |Parquet form:
+         |$parquetRequestedSchema
+         |
+         |Catalyst form:
+         |$catalystRequestedSchema
+       """.stripMargin
+    }
+
     new CatalystRecordMaterializer(parquetRequestedSchema, catalystRequestedSchema)
   }
 
+  // Called before `prepareForRead()` when initializing Parquet record reader.
   override def init(context: InitContext): ReadContext = {
     val conf = context.getConfiguration
 
     // If the target file was written by Spark SQL, we should be able to find a serialized Catalyst
-    // schema of this file from its the metadata.
+    // schema of this file from its metadata.
     val maybeRowSchema = Option(conf.get(RowWriteSupport.SPARK_ROW_SCHEMA))
 
     // Optional schema of requested columns, in the form of a string serialized from a Catalyst
@@ -141,7 +153,6 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
         maybeRequestedSchema.map(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA -> _) ++
         maybeRowSchema.map(RowWriteSupport.SPARK_ROW_SCHEMA -> _)
 
-    logInfo(s"Going to read Parquet file with these requested columns: $parquetRequestedSchema")
     new ReadContext(parquetRequestedSchema, metadata)
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index ab5a6ddd41cfc..18c5b500209e6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -25,9 +25,10 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.parquet.column.Dictionary
 import org.apache.parquet.io.api.{Binary, Converter, GroupConverter, PrimitiveConverter}
-import org.apache.parquet.schema.OriginalType.LIST
+import org.apache.parquet.schema.OriginalType.{LIST, INT_32, UTF8}
+import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName.DOUBLE
 import org.apache.parquet.schema.Type.Repetition
-import org.apache.parquet.schema.{GroupType, PrimitiveType, Type}
+import org.apache.parquet.schema.{GroupType, MessageType, PrimitiveType, Type}
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
@@ -88,12 +89,54 @@ private[parquet] class CatalystPrimitiveConverter(val updater: ParentContainerUp
 }
 
 /**
- * A [[CatalystRowConverter]] is used to convert Parquet "structs" into Spark SQL [[InternalRow]]s.
- * Since any Parquet record is also a struct, this converter can also be used as root converter.
+ * A [[CatalystRowConverter]] is used to convert Parquet records into Catalyst [[InternalRow]]s.
+ * Since Catalyst `StructType` is also a Parquet record, this converter can be used as root
+ * converter.  Take the following Parquet type as an example:
+ * {{{
+ *   message root {
+ *     required int32 f1;
+ *     optional group f2 {
+ *       required double f21;
+ *       optional binary f22 (utf8);
+ *     }
+ *   }
+ * }}}
+ * 5 converters will be created:
+ *
+ * - a root [[CatalystRowConverter]] for [[MessageType]] `root`, which contains:
+ *   - a [[CatalystPrimitiveConverter]] for required [[INT_32]] field `f1`, and
+ *   - a nested [[CatalystRowConverter]] for optional [[GroupType]] `f2`, which contains:
+ *     - a [[CatalystPrimitiveConverter]] for required [[DOUBLE]] field `f21`, and
+ *     - a [[CatalystStringConverter]] for optional [[UTF8]] string field `f22`
  *
  * When used as a root converter, [[NoopUpdater]] should be used since root converters don't have
  * any "parent" container.
  *
+ * @note Constructor argument [[parquetType]] refers to requested fields of the actual schema of the
+ *       Parquet file being read, while constructor argument [[catalystType]] refers to requested
+ *       fields of the global schema.  The key difference is that, in case of schema merging,
+ *       [[parquetType]] can be a subset of [[catalystType]].  For example, it's possible to have
+ *       the following [[catalystType]]:
+ *       {{{
+ *         new StructType()
+ *           .add("f1", IntegerType, nullable = false)
+ *           .add("f2", StringType, nullable = true)
+ *           .add("f3", new StructType()
+ *             .add("f31", DoubleType, nullable = false)
+ *             .add("f32", IntegerType, nullable = true)
+ *             .add("f33", StringType, nullable = true), nullable = false)
+ *       }}}
+ *       and the following [[parquetType]] (`f2` and `f32` are missing):
+ *       {{{
+ *         message root {
+ *           required int32 f1;
+ *           required group f3 {
+ *             required double f31;
+ *             optional binary f33 (utf8);
+ *           }
+ *         }
+ *       }}}
+ *
  * @param parquetType Parquet schema of Parquet records
  * @param catalystType Spark SQL schema that corresponds to the Parquet record type
  * @param updater An updater which propagates converted field values to the parent container
@@ -126,7 +169,24 @@ private[parquet] class CatalystRowConverter(
 
   // Converters for each field.
   private val fieldConverters: Array[Converter with HasParentContainerUpdater] = {
-    parquetType.getFields.zip(catalystType).zipWithIndex.map {
+    // In case of schema merging, `parquetType` can be a subset of `catalystType`.  We need to pad
+    // those missing fields and create converters for them, although values of these fields are
+    // always null.
+    val paddedParquetFields = {
+      val parquetFields = parquetType.getFields
+      val parquetFieldNames = parquetFields.map(_.getName).toSet
+      val missingFields = catalystType.filterNot(f => parquetFieldNames.contains(f.name))
+
+      // We don't need to worry about feature flag arguments like `assumeBinaryIsString` when
+      // creating the schema converter here, since values of missing fields are always null.
+      val toParquet = new CatalystSchemaConverter()
+
+      (parquetFields ++ missingFields.map(toParquet.convertField)).sortBy { f =>
+        catalystType.indexWhere(_.name == f.getName)
+      }
+    }
+
+    paddedParquetFields.zip(catalystType).zipWithIndex.map {
       case ((parquetFieldType, catalystField), ordinal) =>
         // Converted field value should be set to the `ordinal`-th cell of `currentRow`
         newConverter(parquetFieldType, catalystField.dataType, new RowUpdater(currentRow, ordinal))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index 275646e8181ad..535f0684e97f9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -72,18 +72,9 @@ private[parquet] class CatalystSchemaConverter(
     followParquetFormatSpec = conf.followParquetFormatSpec)
 
   def this(conf: Configuration) = this(
-    assumeBinaryIsString =
-      conf.getBoolean(
-        SQLConf.PARQUET_BINARY_AS_STRING.key,
-        SQLConf.PARQUET_BINARY_AS_STRING.defaultValue.get),
-    assumeInt96IsTimestamp =
-      conf.getBoolean(
-        SQLConf.PARQUET_INT96_AS_TIMESTAMP.key,
-        SQLConf.PARQUET_INT96_AS_TIMESTAMP.defaultValue.get),
-    followParquetFormatSpec =
-      conf.getBoolean(
-        SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key,
-        SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.defaultValue.get))
+    assumeBinaryIsString = conf.get(SQLConf.PARQUET_BINARY_AS_STRING.key).toBoolean,
+    assumeInt96IsTimestamp = conf.get(SQLConf.PARQUET_INT96_AS_TIMESTAMP.key).toBoolean,
+    followParquetFormatSpec = conf.get(SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key).toBoolean)
 
   /**
    * Converts Parquet [[MessageType]] `parquetSchema` to a Spark SQL [[StructType]].
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index e2f2a8c744783..b7b70c2bbbd5c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -21,7 +21,7 @@ import java.io.File
 
 import org.apache.hadoop.fs.Path
 
-import org.apache.spark.sql.{QueryTest, Row, SQLConf}
+import org.apache.spark.sql._
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
@@ -201,4 +201,32 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
       assert(Decimal("67123.45") === Decimal(decimal))
     }
   }
+
+  test("SPARK-10005 Schema merging for nested struct") {
+    val sqlContext = _sqlContext
+    import sqlContext.implicits._
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      def append(df: DataFrame): Unit = {
+        df.write.mode(SaveMode.Append).parquet(path)
+      }
+
+      // Note that both the following two DataFrames contain a single struct column with multiple
+      // nested fields.
+      append((1 to 2).map(i => Tuple1((i, i))).toDF())
+      append((1 to 2).map(i => Tuple1((i, i, i))).toDF())
+
+      withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING.key -> "true") {
+        checkAnswer(
+          sqlContext.read.option("mergeSchema", "true").parquet(path),
+          Seq(
+            Row(Row(1, 1, null)),
+            Row(Row(2, 2, null)),
+            Row(Row(1, 1, 1)),
+            Row(Row(2, 2, 2))))
+      }
+    }
+  }
 }

From 26e760581fdf7ca913da93fa80e73b7ddabcedf6 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Sun, 16 Aug 2015 23:33:20 -0700
Subject: [PATCH 273/633] [SPARK-9871] [SPARKR] Add expression functions into
 SparkR which have a variable parameter

### Summary

- Add `lit` function
- Add `concat`, `greatest`, `least` functions

I think we need to improve `collect` function in order to implement `struct` function. Since `collect` doesn't work with arguments which includes a nested `list` variable. It seems that a list against `struct` still has `jobj` classes. So it would be better to solve this problem on another issue.

### JIRA
[[SPARK-9871] Add expression functions into SparkR which have a variable parameter - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9871)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8194 from yu-iskw/SPARK-9856.
---
 R/pkg/NAMESPACE                  |  4 +++
 R/pkg/R/functions.R              | 42 ++++++++++++++++++++++++++++++++
 R/pkg/R/generics.R               | 16 ++++++++++++
 R/pkg/inst/tests/test_sparkSQL.R | 13 ++++++++++
 4 files changed, 75 insertions(+)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index b2d92bdf4840e..fd9dfdf60edb3 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -98,6 +98,7 @@ exportMethods("abs",
               "contains",
               "cos",
               "cosh",
+              "concat",
               "countDistinct",
               "desc",
               "endsWith",
@@ -106,10 +107,13 @@ exportMethods("abs",
               "floor",
               "getField",
               "getItem",
+              "greatest",
               "hypot",
               "isNotNull",
               "isNull",
+              "lit",
               "last",
+              "least",
               "like",
               "log",
               "log10",
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index a15d2d5da534e..6eef4d638ee9a 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -67,6 +67,14 @@ createFunctions <- function() {
 
 createFunctions()
 
+#' @rdname functions
+#' @return Creates a Column class of literal value.
+setMethod("lit", signature("ANY"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "lit", ifelse(class(x) == "Column", x@jc, x))
+            column(jc)
+          })
+
 #' Approx Count Distinct
 #'
 #' @rdname functions
@@ -93,6 +101,40 @@ setMethod("countDistinct",
             column(jc)
           })
 
+#' @rdname functions
+#' @return Concatenates multiple input string columns together into a single string column.
+setMethod("concat",
+          signature(x = "Column"),
+          function(x, ...) {
+            jcols <- lapply(list(x, ...), function(x) { x@jc })
+            jc <- callJStatic("org.apache.spark.sql.functions", "concat", listToSeq(jcols))
+            column(jc)
+          })
+
+#' @rdname functions
+#' @return Returns the greatest value of the list of column names, skipping null values.
+#'         This function takes at least 2 parameters. It will return null if all parameters are null.
+setMethod("greatest",
+          signature(x = "Column"),
+          function(x, ...) {
+            stopifnot(length(list(...)) > 0)
+            jcols <- lapply(list(x, ...), function(x) { x@jc })
+            jc <- callJStatic("org.apache.spark.sql.functions", "greatest", listToSeq(jcols))
+            column(jc)
+          })
+
+#' @rdname functions
+#' @return Returns the least value of the list of column names, skipping null values.
+#'         This function takes at least 2 parameters. It will return null iff all parameters are null.
+setMethod("least",
+          signature(x = "Column"),
+          function(x, ...) {
+            stopifnot(length(list(...)) > 0)
+            jcols <- lapply(list(x, ...), function(x) { x@jc })
+            jc <- callJStatic("org.apache.spark.sql.functions", "least", listToSeq(jcols))
+            column(jc)
+          })
+
 #' @rdname functions
 #' @aliases ceil
 setMethod("ceiling",
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index f11e7fcb6a07c..5c1cc98fd9e80 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -682,6 +682,10 @@ setGeneric("cbrt", function(x) { standardGeneric("cbrt") })
 #' @export
 setGeneric("ceil", function(x) { standardGeneric("ceil") })
 
+#' @rdname functions
+#' @export
+setGeneric("concat", function(x, ...) { standardGeneric("concat") })
+
 #' @rdname functions
 #' @export
 setGeneric("crc32", function(x) { standardGeneric("crc32") })
@@ -702,6 +706,10 @@ setGeneric("dayofyear", function(x) { standardGeneric("dayofyear") })
 #' @export
 setGeneric("explode", function(x) { standardGeneric("explode") })
 
+#' @rdname functions
+#' @export
+setGeneric("greatest", function(x, ...) { standardGeneric("greatest") })
+
 #' @rdname functions
 #' @export
 setGeneric("hex", function(x) { standardGeneric("hex") })
@@ -722,10 +730,18 @@ setGeneric("isNaN", function(x) { standardGeneric("isNaN") })
 #' @export
 setGeneric("last_day", function(x) { standardGeneric("last_day") })
 
+#' @rdname functions
+#' @export
+setGeneric("least", function(x, ...) { standardGeneric("least") })
+
 #' @rdname functions
 #' @export
 setGeneric("levenshtein", function(y, x) { standardGeneric("levenshtein") })
 
+#' @rdname functions
+#' @export
+setGeneric("lit", function(x) { standardGeneric("lit") })
+
 #' @rdname functions
 #' @export
 setGeneric("lower", function(x) { standardGeneric("lower") })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index c77f633135856..83caba8b5b964 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -580,6 +580,11 @@ test_that("select with column", {
   df2 <- select(df, df$age)
   expect_equal(columns(df2), c("age"))
   expect_equal(count(df2), 3)
+
+  df3 <- select(df, lit("x"))
+  expect_equal(columns(df3), c("x"))
+  expect_equal(count(df3), 3)
+  expect_equal(collect(select(df3, "x"))[[1, 1]], "x")
 })
 
 test_that("selectExpr() on a DataFrame", {
@@ -712,6 +717,14 @@ test_that("string operators", {
   expect_equal(count(where(df, startsWith(df$name, "A"))), 1)
   expect_equal(first(select(df, substr(df$name, 1, 2)))[[1]], "Mi")
   expect_equal(collect(select(df, cast(df$age, "string")))[[2, 1]], "30")
+  expect_equal(collect(select(df, concat(df$name, lit(":"), df$age)))[[2, 1]], "Andy:30")
+})
+
+test_that("greatest() and least() on a DataFrame", {
+  l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
+  df <- createDataFrame(sqlContext, l)
+  expect_equal(collect(select(df, greatest(df$a, df$b)))[, 1], c(2, 4))
+  expect_equal(collect(select(df, least(df$a, df$b)))[, 1], c(1, 3))
 })
 
 test_that("group by", {

From 3ff81ad2dedc7cc3defb6e418d7c5fb415d56026 Mon Sep 17 00:00:00 2001
From: Calvin Jia <jia.calvin@gmail.com>
Date: Mon, 17 Aug 2015 08:28:16 +0100
Subject: [PATCH 274/633] [SPARK-9199] [CORE] Upgrade Tachyon version from
 0.7.0 -> 0.7.1.

Updates the tachyon-client version to the latest release.

The main difference between 0.7.0 and 0.7.1 on the client side is to support running Tachyon on local file system by default.

No new non-Tachyon dependencies are added, and no code changes are required since the client API has not changed.

Author: Calvin Jia <jia.calvin@gmail.com>

Closes #8235 from calvinjia/spark-9199-master.
---
 core/pom.xml         | 2 +-
 make-distribution.sh | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/core/pom.xml b/core/pom.xml
index 0e53a79fd2235..4f79d71bf85fa 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -266,7 +266,7 @@
     <dependency>
       <groupId>org.tachyonproject</groupId>
       <artifactId>tachyon-client</artifactId>
-      <version>0.7.0</version>
+      <version>0.7.1</version>
       <exclusions>
         <exclusion>
           <groupId>org.apache.hadoop</groupId>
diff --git a/make-distribution.sh b/make-distribution.sh
index 247a81341e4a4..04ad0052eb24c 100755
--- a/make-distribution.sh
+++ b/make-distribution.sh
@@ -33,7 +33,7 @@ SPARK_HOME="$(cd "`dirname "$0"`"; pwd)"
 DISTDIR="$SPARK_HOME/dist"
 
 SPARK_TACHYON=false
-TACHYON_VERSION="0.7.0"
+TACHYON_VERSION="0.7.1"
 TACHYON_TGZ="tachyon-${TACHYON_VERSION}-bin.tar.gz"
 TACHYON_URL="https://github.com/amplab/tachyon/releases/download/v${TACHYON_VERSION}/${TACHYON_TGZ}"
 

From f7efda3975d46a8ce4fd720b3730127ea482560b Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 17 Aug 2015 09:58:34 -0700
Subject: [PATCH 275/633] [SPARK-9959] [MLLIB] Association Rules Java
 Compatibility

mengxr

Author: Feynman Liang <fliang@databricks.com>

Closes #8206 from feynmanliang/SPARK-9959-arules-java.
---
 .../spark/mllib/fpm/AssociationRules.scala    | 30 +++++++++++++++++--
 1 file changed, 28 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
index 72d0ea0c12e1e..7f4de77044994 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -16,6 +16,7 @@
  */
 package org.apache.spark.mllib.fpm
 
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
@@ -95,8 +96,10 @@ object AssociationRules {
    * :: Experimental ::
    *
    * An association rule between sets of items.
-   * @param antecedent hypotheses of the rule
-   * @param consequent conclusion of the rule
+   * @param antecedent hypotheses of the rule. Java users should call [[Rule#javaAntecedent]]
+   *                   instead.
+   * @param consequent conclusion of the rule. Java users should call [[Rule#javaConsequent]]
+   *                   instead.
    * @tparam Item item type
    *
    * @since 1.5.0
@@ -108,6 +111,11 @@ object AssociationRules {
       freqUnion: Double,
       freqAntecedent: Double) extends Serializable {
 
+    /**
+     * Returns the confidence of the rule.
+     *
+     * @since 1.5.0
+     */
     def confidence: Double = freqUnion.toDouble / freqAntecedent
 
     require(antecedent.toSet.intersect(consequent.toSet).isEmpty, {
@@ -115,5 +123,23 @@ object AssociationRules {
       s"A valid association rule must have disjoint antecedent and " +
         s"consequent but ${sharedItems} is present in both."
     })
+
+    /**
+     * Returns antecedent in a Java List.
+     *
+     * @since 1.5.0
+     */
+    def javaAntecedent: java.util.List[Item] = {
+      antecedent.toList.asJava
+    }
+
+    /**
+     * Returns consequent in a Java List.
+     *
+     * @since 1.5.0
+     */
+    def javaConsequent: java.util.List[Item] = {
+      consequent.toList.asJava
+    }
   }
 }

From 76c155dd4483d58499e5cb66e5e9373bb771dbeb Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Tue, 18 Aug 2015 00:59:05 +0800
Subject: [PATCH 276/633] [SPARK-7837] [SQL] Avoids double closing output
 writers when commitTask() fails

When inserting data into a `HadoopFsRelation`, if `commitTask()` of the writer container fails, `abortTask()` will be invoked. However, both `commitTask()` and `abortTask()` try to close the output writer(s). The problem is that, closing underlying writers may not be an idempotent operation. E.g., `ParquetRecordWriter.close()` throws NPE when called twice.

Author: Cheng Lian <lian@databricks.com>

Closes #8236 from liancheng/spark-7837/double-closing.
---
 .../datasources/WriterContainer.scala         | 21 +++++++--
 .../datasources/parquet/ParquetIOSuite.scala  | 46 ++++++++++++++++++-
 2 files changed, 61 insertions(+), 6 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index d36197e50d448..e0147079e6997 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -217,6 +217,8 @@ private[sql] class DefaultWriterContainer(
     val writer = outputWriterFactory.newInstance(getWorkPath, dataSchema, taskAttemptContext)
     writer.initConverter(dataSchema)
 
+    var writerClosed = false
+
     // If anything below fails, we should abort the task.
     try {
       while (iterator.hasNext) {
@@ -235,7 +237,10 @@ private[sql] class DefaultWriterContainer(
     def commitTask(): Unit = {
       try {
         assert(writer != null, "OutputWriter instance should have been initialized")
-        writer.close()
+        if (!writerClosed) {
+          writer.close()
+          writerClosed = true
+        }
         super.commitTask()
       } catch {
         case cause: Throwable =>
@@ -247,7 +252,10 @@ private[sql] class DefaultWriterContainer(
 
     def abortTask(): Unit = {
       try {
-        writer.close()
+        if (!writerClosed) {
+          writer.close()
+          writerClosed = true
+        }
       } finally {
         super.abortTask()
       }
@@ -275,6 +283,8 @@ private[sql] class DynamicPartitionWriterContainer(
     val outputWriters = new java.util.HashMap[InternalRow, OutputWriter]
     executorSideSetup(taskContext)
 
+    var outputWritersCleared = false
+
     // Returns the partition key given an input row
     val getPartitionKey = UnsafeProjection.create(partitionColumns, inputSchema)
     // Returns the data columns to be written given an input row
@@ -379,8 +389,11 @@ private[sql] class DynamicPartitionWriterContainer(
     }
 
     def clearOutputWriters(): Unit = {
-      outputWriters.asScala.values.foreach(_.close())
-      outputWriters.clear()
+      if (!outputWritersCleared) {
+        outputWriters.asScala.values.foreach(_.close())
+        outputWriters.clear()
+        outputWritersCleared = true
+      }
     }
 
     def commitTask(): Unit = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index d819f3ab5e6ab..e6b0a2ea95e38 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -424,7 +424,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
 
       configuration.set(
         "spark.sql.parquet.output.committer.class",
-        classOf[BogusParquetOutputCommitter].getCanonicalName)
+        classOf[JobCommitFailureParquetOutputCommitter].getCanonicalName)
 
       try {
         val message = intercept[SparkException] {
@@ -450,12 +450,54 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
     }.toString
     assert(errorMessage.contains("UnknownHostException"))
   }
+
+  test("SPARK-7837 Do not close output writer twice when commitTask() fails") {
+    val clonedConf = new Configuration(configuration)
+
+    // Using a output committer that always fail when committing a task, so that both
+    // `commitTask()` and `abortTask()` are invoked.
+    configuration.set(
+      "spark.sql.parquet.output.committer.class",
+      classOf[TaskCommitFailureParquetOutputCommitter].getCanonicalName)
+
+    try {
+      // Before fixing SPARK-7837, the following code results in an NPE because both
+      // `commitTask()` and `abortTask()` try to close output writers.
+
+      withTempPath { dir =>
+        val m1 = intercept[SparkException] {
+          sqlContext.range(1).coalesce(1).write.parquet(dir.getCanonicalPath)
+        }.getCause.getMessage
+        assert(m1.contains("Intentional exception for testing purposes"))
+      }
+
+      withTempPath { dir =>
+        val m2 = intercept[SparkException] {
+          val df = sqlContext.range(1).select('id as 'a, 'id as 'b).coalesce(1)
+          df.write.partitionBy("a").parquet(dir.getCanonicalPath)
+        }.getCause.getMessage
+        assert(m2.contains("Intentional exception for testing purposes"))
+      }
+    } finally {
+      // Hadoop 1 doesn't have `Configuration.unset`
+      configuration.clear()
+      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+    }
+  }
 }
 
-class BogusParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext)
+class JobCommitFailureParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext)
   extends ParquetOutputCommitter(outputPath, context) {
 
   override def commitJob(jobContext: JobContext): Unit = {
     sys.error("Intentional exception for testing purposes")
   }
 }
+
+class TaskCommitFailureParquetOutputCommitter(outputPath: Path, context: TaskAttemptContext)
+  extends ParquetOutputCommitter(outputPath, context) {
+
+  override def commitTask(context: TaskAttemptContext): Unit = {
+    sys.error("Intentional exception for testing purposes")
+  }
+}

From ed092a06c28dfa8204b473a5c964a9ef9a6b655e Mon Sep 17 00:00:00 2001
From: Rohit Agarwal <rohita@qubole.com>
Date: Mon, 17 Aug 2015 10:31:57 -0700
Subject: [PATCH 277/633] [SPARK-9924] [WEB UI] Don't schedule checkForLogs
 while some of them are already running.

Author: Rohit Agarwal <rohita@qubole.com>

Closes #8153 from mindprince/SPARK-9924.
---
 .../deploy/history/FsHistoryProvider.scala    | 28 ++++++++++++++-----
 1 file changed, 21 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index 53c18ca3ff50c..e573ff16c50a3 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -126,11 +126,11 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
     // Disable the background thread during tests.
     if (!conf.contains("spark.testing")) {
       // A task that periodically checks for event log updates on disk.
-      pool.scheduleAtFixedRate(getRunner(checkForLogs), 0, UPDATE_INTERVAL_S, TimeUnit.SECONDS)
+      pool.scheduleWithFixedDelay(getRunner(checkForLogs), 0, UPDATE_INTERVAL_S, TimeUnit.SECONDS)
 
       if (conf.getBoolean("spark.history.fs.cleaner.enabled", false)) {
         // A task that periodically cleans event logs on disk.
-        pool.scheduleAtFixedRate(getRunner(cleanLogs), 0, CLEAN_INTERVAL_S, TimeUnit.SECONDS)
+        pool.scheduleWithFixedDelay(getRunner(cleanLogs), 0, CLEAN_INTERVAL_S, TimeUnit.SECONDS)
       }
     }
   }
@@ -204,11 +204,25 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
           mod1 >= mod2
       }
 
-      logInfos.sliding(20, 20).foreach { batch =>
-        replayExecutor.submit(new Runnable {
-          override def run(): Unit = mergeApplicationListing(batch)
-        })
-      }
+      logInfos.grouped(20)
+        .map { batch =>
+          replayExecutor.submit(new Runnable {
+            override def run(): Unit = mergeApplicationListing(batch)
+          })
+        }
+        .foreach { task =>
+          try {
+            // Wait for all tasks to finish. This makes sure that checkForLogs
+            // is not scheduled again while some tasks are already running in
+            // the replayExecutor.
+            task.get()
+          } catch {
+            case e: InterruptedException =>
+              throw e
+            case e: Exception =>
+              logError("Exception while merging application listings", e)
+          }
+        }
 
       lastModifiedTime = newLastModifiedTime
     } catch {

From f68d024096c90936f9aa4e325141b39f08c72476 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Mon, 17 Aug 2015 10:34:22 -0700
Subject: [PATCH 278/633] [SPARK-7736] [CORE] [YARN] Make pyspark fail YARN app
 on failure.

The YARN backend doesn't like when user code calls `System.exit`,
since it cannot know the exit status and thus cannot set an
appropriate final status for the application.

So, for pyspark, avoid that call and instead throw an exception with
the exit code. SparkSubmit handles that exception and exits with
the given exit code, while YARN uses the exit code as the failure
code for the Spark app.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7751 from vanzin/SPARK-9416.
---
 .../org/apache/spark/SparkException.scala     |  7 ++++++
 .../apache/spark/deploy/PythonRunner.scala    | 23 +++++++++++++++----
 .../org/apache/spark/deploy/SparkSubmit.scala | 10 ++++++--
 .../spark/deploy/yarn/ApplicationMaster.scala |  8 +++++--
 4 files changed, 40 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkException.scala b/core/src/main/scala/org/apache/spark/SparkException.scala
index 2ebd7a7151a59..977a27bdfe1bf 100644
--- a/core/src/main/scala/org/apache/spark/SparkException.scala
+++ b/core/src/main/scala/org/apache/spark/SparkException.scala
@@ -30,3 +30,10 @@ class SparkException(message: String, cause: Throwable)
  */
 private[spark] class SparkDriverExecutionException(cause: Throwable)
   extends SparkException("Execution error", cause)
+
+/**
+ * Exception thrown when the main user code is run as a child process (e.g. pyspark) and we want
+ * the parent SparkSubmit process to exit with the same exit code.
+ */
+private[spark] case class SparkUserAppException(exitCode: Int)
+  extends SparkException(s"User application exited with $exitCode")
diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index c2ed43a5397d6..4277ac2ad13ea 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -24,6 +24,7 @@ import scala.collection.mutable.ArrayBuffer
 import scala.collection.JavaConversions._
 import scala.util.Try
 
+import org.apache.spark.SparkUserAppException
 import org.apache.spark.api.python.PythonUtils
 import org.apache.spark.util.{RedirectThread, Utils}
 
@@ -46,7 +47,14 @@ object PythonRunner {
     // Launch a Py4J gateway server for the process to connect to; this will let it see our
     // Java system properties and such
     val gatewayServer = new py4j.GatewayServer(null, 0)
-    gatewayServer.start()
+    val thread = new Thread(new Runnable() {
+      override def run(): Unit = Utils.logUncaughtExceptions {
+        gatewayServer.start()
+      }
+    })
+    thread.setName("py4j-gateway")
+    thread.setDaemon(true)
+    thread.start()
 
     // Build up a PYTHONPATH that includes the Spark assembly JAR (where this class is), the
     // python directories in SPARK_HOME (if set), and any files in the pyFiles argument
@@ -64,11 +72,18 @@ object PythonRunner {
     env.put("PYTHONUNBUFFERED", "YES") // value is needed to be set to a non-empty string
     env.put("PYSPARK_GATEWAY_PORT", "" + gatewayServer.getListeningPort)
     builder.redirectErrorStream(true) // Ugly but needed for stdout and stderr to synchronize
-    val process = builder.start()
+    try {
+      val process = builder.start()
 
-    new RedirectThread(process.getInputStream, System.out, "redirect output").start()
+      new RedirectThread(process.getInputStream, System.out, "redirect output").start()
 
-    System.exit(process.waitFor())
+      val exitCode = process.waitFor()
+      if (exitCode != 0) {
+        throw new SparkUserAppException(exitCode)
+      }
+    } finally {
+      gatewayServer.shutdown()
+    }
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 02fa3088eded0..86fcf942c2c4e 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -39,8 +39,8 @@ import org.apache.ivy.plugins.matcher.GlobPatternMatcher
 import org.apache.ivy.plugins.repository.file.FileRepository
 import org.apache.ivy.plugins.resolver.{FileSystemResolver, ChainResolver, IBiblioResolver}
 
+import org.apache.spark.{SparkUserAppException, SPARK_VERSION}
 import org.apache.spark.api.r.RUtils
-import org.apache.spark.SPARK_VERSION
 import org.apache.spark.deploy.rest._
 import org.apache.spark.util.{ChildFirstURLClassLoader, MutableURLClassLoader, Utils}
 
@@ -672,7 +672,13 @@ object SparkSubmit {
       mainMethod.invoke(null, childArgs.toArray)
     } catch {
       case t: Throwable =>
-        throw findCause(t)
+        findCause(t) match {
+          case SparkUserAppException(exitCode) =>
+            System.exit(exitCode)
+
+          case t: Throwable =>
+            throw t
+        }
     }
   }
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 6a8ddb37b29e8..991b5cec00bd8 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -30,8 +30,8 @@ import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.conf.YarnConfiguration
 
 import org.apache.spark.rpc._
-import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkEnv}
-import org.apache.spark.SparkException
+import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkContext, SparkEnv,
+  SparkException, SparkUserAppException}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.deploy.history.HistoryServer
 import org.apache.spark.scheduler.cluster.{CoarseGrainedSchedulerBackend, YarnSchedulerBackend}
@@ -530,6 +530,10 @@ private[spark] class ApplicationMaster(
             e.getCause match {
               case _: InterruptedException =>
                 // Reporter thread can interrupt to stop user class
+              case SparkUserAppException(exitCode) =>
+                val msg = s"User application exited with status $exitCode"
+                logError(msg)
+                finish(FinalApplicationStatus.FAILED, exitCode, msg)
               case cause: Throwable =>
                 logError("User class threw exception: " + cause, cause)
                 finish(FinalApplicationStatus.FAILED,

From a4acdabb103f6d04603163c9555c1ddc413c3b80 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 17 Aug 2015 11:36:18 -0700
Subject: [PATCH 279/633] [SPARK-9950] [SQL] Wrong Analysis Error for
 grouping/aggregating on struct fields

This issue has been fixed by https://github.com/apache/spark/pull/8215, this PR added regression test for it.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8222 from cloud-fan/minor and squashes the following commits:

0bbfb1c [Wenchen Fan] fix style...
7e2d8d9 [Wenchen Fan] add test
---
 .../src/test/scala/org/apache/spark/sql/DataFrameSuite.scala | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index cf22797752b97..1e2aaae52c9e8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -873,4 +873,9 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
       """{"a": {"b": 1}}""" :: Nil))
     checkAnswer(df.orderBy("a.b"), Row(Row(1)))
   }
+
+  test("SPARK-9950: correctly analyze grouping/aggregating on struct fields") {
+    val df = Seq(("x", (1, 1)), ("y", (2, 2))).toDF("a", "b")
+    checkAnswer(df.groupBy("b._1").agg(sum("b._2")), Row(1, 1) :: Row(2, 2) :: Nil)
+  }
 }

From f10660fe7b809be2059da4f9781a5743f117a35a Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 17 Aug 2015 11:53:33 -0700
Subject: [PATCH 280/633] [SPARK-10036] [SQL] Load JDBC driver in
 DataFrameReader.jdbc and DataFrameWriter.jdbc

This PR uses `JDBCRDD.getConnector` to load JDBC driver before creating connection in `DataFrameReader.jdbc` and `DataFrameWriter.jdbc`.

Author: zsxwing <zsxwing@gmail.com>

Closes #8232 from zsxwing/SPARK-10036 and squashes the following commits:

adf75de [zsxwing] Add extraOptions to the connection properties
57f59d4 [zsxwing] Load JDBC driver in DataFrameReader.jdbc and DataFrameWriter.jdbc
---
 .../scala/org/apache/spark/sql/DataFrameReader.scala   |  8 +++++++-
 .../scala/org/apache/spark/sql/DataFrameWriter.scala   | 10 ++++++++--
 .../spark/sql/execution/datasources/jdbc/JDBCRDD.scala |  5 +++--
 .../sql/execution/datasources/jdbc/JdbcUtils.scala     |  4 ++--
 4 files changed, 20 insertions(+), 7 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 9ea955b010017..6dc7bfe333498 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -197,7 +197,13 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
       table: String,
       parts: Array[Partition],
       connectionProperties: Properties): DataFrame = {
-    val relation = JDBCRelation(url, table, parts, connectionProperties)(sqlContext)
+    val props = new Properties()
+    extraOptions.foreach { case (key, value) =>
+      props.put(key, value)
+    }
+    // connectionProperties should override settings in extraOptions
+    props.putAll(connectionProperties)
+    val relation = JDBCRelation(url, table, parts, props)(sqlContext)
     sqlContext.baseRelationToDataFrame(relation)
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index 5fa11da4c38cd..f0bf1be506411 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -244,7 +244,13 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    *                             should be included.
    */
   def jdbc(url: String, table: String, connectionProperties: Properties): Unit = {
-    val conn = JdbcUtils.createConnection(url, connectionProperties)
+    val props = new Properties()
+    extraOptions.foreach { case (key, value) =>
+      props.put(key, value)
+    }
+    // connectionProperties should override settings in extraOptions
+    props.putAll(connectionProperties)
+    val conn = JdbcUtils.createConnection(url, props)
 
     try {
       var tableExists = JdbcUtils.tableExists(conn, table)
@@ -272,7 +278,7 @@ final class DataFrameWriter private[sql](df: DataFrame) {
       conn.close()
     }
 
-    JdbcUtils.saveTable(df, url, table, connectionProperties)
+    JdbcUtils.saveTable(df, url, table, props)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
index 8eab6a0adccc4..e537d631f4559 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
@@ -118,7 +118,7 @@ private[sql] object JDBCRDD extends Logging {
    */
   def resolveTable(url: String, table: String, properties: Properties): StructType = {
     val dialect = JdbcDialects.get(url)
-    val conn: Connection = DriverManager.getConnection(url, properties)
+    val conn: Connection = getConnector(properties.getProperty("driver"), url, properties)()
     try {
       val rs = conn.prepareStatement(s"SELECT * FROM $table WHERE 1=0").executeQuery()
       try {
@@ -171,7 +171,8 @@ private[sql] object JDBCRDD extends Logging {
    * getConnector is run on the driver code, while the function it returns
    * is run on the executor.
    *
-   * @param driver - The class name of the JDBC driver for the given url.
+   * @param driver - The class name of the JDBC driver for the given url, or null if the class name
+   *                 is not necessary.
    * @param url - The JDBC url to connect to.
    *
    * @return A function that loads the driver and connects to the url.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
index 8ee3b8bda8fc7..2d0e736ee4766 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution.datasources.jdbc
 
-import java.sql.{Connection, DriverManager, PreparedStatement}
+import java.sql.{Connection, PreparedStatement}
 import java.util.Properties
 
 import scala.util.Try
@@ -36,7 +36,7 @@ object JdbcUtils extends Logging {
    * Establishes a JDBC connection.
    */
   def createConnection(url: String, connectionProperties: Properties): Connection = {
-    DriverManager.getConnection(url, connectionProperties)
+    JDBCRDD.getConnector(connectionProperties.getProperty("driver"), url, connectionProperties)()
   }
 
   /**

From b265e282b62954548740a5767e97ab1678c65194 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Mon, 17 Aug 2015 14:10:19 -0700
Subject: [PATCH 281/633] [SPARK-9526] [SQL] Utilize randomized tests to reveal
 potential bugs in sql expressions

JIRA: https://issues.apache.org/jira/browse/SPARK-9526

This PR is a follow up of #7830, aiming at utilizing randomized tests to reveal more potential bugs in sql expression.

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #7855 from yjshen/property_check.
---
 .../ArithmeticExpressionSuite.scala           |  42 ++++++
 .../expressions/BitwiseFunctionsSuite.scala   |  17 ++-
 .../ConditionalExpressionSuite.scala          |  13 +-
 .../expressions/DateExpressionsSuite.scala    |  17 +++
 .../expressions/ExpressionEvalHelper.scala    | 112 ++++++++++++++-
 .../expressions/LiteralGenerator.scala        | 128 ++++++++++++++++++
 .../expressions/MathFunctionsSuite.scala      |  45 +++++-
 .../expressions/MiscFunctionsSuite.scala      |   4 +-
 .../catalyst/expressions/PredicateSuite.scala |  19 +++
 .../spark/sql/types/DataTypeTestUtils.scala   |  19 +++
 10 files changed, 410 insertions(+), 6 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralGenerator.scala

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
index a1f15e4f0f25a..72285c6a24199 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ArithmeticExpressionSuite.scala
@@ -52,6 +52,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Add(positiveShortLit, negativeShortLit), -1.toShort)
     checkEvaluation(Add(positiveIntLit, negativeIntLit), -1)
     checkEvaluation(Add(positiveLongLit, negativeLongLit), -1L)
+
+    DataTypeTestUtils.numericAndInterval.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(Add, tpe, tpe)
+    }
   }
 
   test("- (UnaryMinus)") {
@@ -71,6 +75,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(UnaryMinus(negativeIntLit), - negativeInt)
     checkEvaluation(UnaryMinus(positiveLongLit), - positiveLong)
     checkEvaluation(UnaryMinus(negativeLongLit), - negativeLong)
+
+    DataTypeTestUtils.numericAndInterval.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(UnaryMinus, tpe)
+    }
   }
 
   test("- (Minus)") {
@@ -85,6 +93,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       (positiveShort - negativeShort).toShort)
     checkEvaluation(Subtract(positiveIntLit, negativeIntLit), positiveInt - negativeInt)
     checkEvaluation(Subtract(positiveLongLit, negativeLongLit), positiveLong - negativeLong)
+
+    DataTypeTestUtils.numericAndInterval.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(Subtract, tpe, tpe)
+    }
   }
 
   test("* (Multiply)") {
@@ -99,6 +111,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       (positiveShort * negativeShort).toShort)
     checkEvaluation(Multiply(positiveIntLit, negativeIntLit), positiveInt * negativeInt)
     checkEvaluation(Multiply(positiveLongLit, negativeLongLit), positiveLong * negativeLong)
+
+    DataTypeTestUtils.numericTypeWithoutDecimal.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(Multiply, tpe, tpe)
+    }
   }
 
   test("/ (Divide) basic") {
@@ -111,6 +127,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
       checkEvaluation(Divide(left, Literal.create(null, right.dataType)), null)
       checkEvaluation(Divide(left, Literal(convert(0))), null)  // divide by zero
     }
+
+    DataTypeTestUtils.numericTypeWithoutDecimal.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(Divide, tpe, tpe)
+    }
   }
 
   test("/ (Divide) for integral type") {
@@ -144,6 +164,12 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Remainder(negativeIntLit, negativeIntLit), 0)
     checkEvaluation(Remainder(positiveLongLit, positiveLongLit), 0L)
     checkEvaluation(Remainder(negativeLongLit, negativeLongLit), 0L)
+
+    // TODO: the following lines would fail the test due to inconsistency result of interpret
+    // and codegen for remainder between giant values, seems like a numeric stability issue
+    // DataTypeTestUtils.numericTypeWithoutDecimal.foreach { tpe =>
+    //  checkConsistencyBetweenInterpretedAndCodegen(Remainder, tpe, tpe)
+    // }
   }
 
   test("Abs") {
@@ -161,6 +187,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Abs(negativeIntLit), - negativeInt)
     checkEvaluation(Abs(positiveLongLit), positiveLong)
     checkEvaluation(Abs(negativeLongLit), - negativeLong)
+
+    DataTypeTestUtils.numericTypeWithoutDecimal.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(Abs, tpe)
+    }
   }
 
   test("MaxOf basic") {
@@ -175,6 +205,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(MaxOf(positiveShortLit, negativeShortLit), (positiveShort).toShort)
     checkEvaluation(MaxOf(positiveIntLit, negativeIntLit), positiveInt)
     checkEvaluation(MaxOf(positiveLongLit, negativeLongLit), positiveLong)
+
+    DataTypeTestUtils.ordered.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(MaxOf, tpe, tpe)
+    }
   }
 
   test("MaxOf for atomic type") {
@@ -196,6 +230,10 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(MinOf(positiveShortLit, negativeShortLit), (negativeShort).toShort)
     checkEvaluation(MinOf(positiveIntLit, negativeIntLit), negativeInt)
     checkEvaluation(MinOf(positiveLongLit, negativeLongLit), negativeLong)
+
+    DataTypeTestUtils.ordered.foreach { tpe =>
+      checkConsistencyBetweenInterpretedAndCodegen(MinOf, tpe, tpe)
+    }
   }
 
   test("MinOf for atomic type") {
@@ -222,4 +260,8 @@ class ArithmeticExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     checkEvaluation(Pmod(positiveInt, negativeInt), positiveInt)
     checkEvaluation(Pmod(positiveLong, negativeLong), positiveLong)
   }
+
+  DataTypeTestUtils.numericTypeWithoutDecimal.foreach { tpe =>
+    checkConsistencyBetweenInterpretedAndCodegen(MinOf, tpe, tpe)
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
index 4fc1c06153595..3a310c0e9a7a6 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/BitwiseFunctionsSuite.scala
@@ -45,6 +45,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(BitwiseNot(negativeIntLit), ~negativeInt)
     checkEvaluation(BitwiseNot(positiveLongLit), ~positiveLong)
     checkEvaluation(BitwiseNot(negativeLongLit), ~negativeLong)
+
+    DataTypeTestUtils.integralType.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(BitwiseNot, dt)
+    }
   }
 
   test("BitwiseAnd") {
@@ -68,6 +72,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       (positiveShort & negativeShort).toShort)
     checkEvaluation(BitwiseAnd(positiveIntLit, negativeIntLit), positiveInt & negativeInt)
     checkEvaluation(BitwiseAnd(positiveLongLit, negativeLongLit), positiveLong & negativeLong)
+
+    DataTypeTestUtils.integralType.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(BitwiseAnd, dt, dt)
+    }
   }
 
   test("BitwiseOr") {
@@ -91,6 +99,10 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       (positiveShort | negativeShort).toShort)
     checkEvaluation(BitwiseOr(positiveIntLit, negativeIntLit), positiveInt | negativeInt)
     checkEvaluation(BitwiseOr(positiveLongLit, negativeLongLit), positiveLong | negativeLong)
+
+    DataTypeTestUtils.integralType.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(BitwiseOr, dt, dt)
+    }
   }
 
   test("BitwiseXor") {
@@ -110,10 +122,13 @@ class BitwiseFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(BitwiseXor(nullLit, Literal(1)), null)
     checkEvaluation(BitwiseXor(Literal(1), nullLit), null)
     checkEvaluation(BitwiseXor(nullLit, nullLit), null)
-
     checkEvaluation(BitwiseXor(positiveShortLit, negativeShortLit),
       (positiveShort ^ negativeShort).toShort)
     checkEvaluation(BitwiseXor(positiveIntLit, negativeIntLit), positiveInt ^ negativeInt)
     checkEvaluation(BitwiseXor(positiveLongLit, negativeLongLit), positiveLong ^ negativeLong)
+
+    DataTypeTestUtils.integralType.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(BitwiseXor, dt, dt)
+    }
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
index d26bcdb2902ab..0df673bb9fa02 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala
@@ -66,6 +66,10 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
     testIf(_.toLong, TimestampType)
 
     testIf(_.toString, StringType)
+
+    DataTypeTestUtils.propertyCheckSupported.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(If, BooleanType, dt, dt)
+    }
   }
 
   test("case when") {
@@ -176,6 +180,10 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
         Literal(Timestamp.valueOf("2015-07-01 08:00:00")),
         Literal(Timestamp.valueOf("2015-07-01 10:00:00")))),
       Timestamp.valueOf("2015-07-01 08:00:00"), InternalRow.empty)
+
+    DataTypeTestUtils.ordered.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(Least, dt, 2)
+    }
   }
 
   test("function greatest") {
@@ -218,6 +226,9 @@ class ConditionalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper
         Literal(Timestamp.valueOf("2015-07-01 08:00:00")),
         Literal(Timestamp.valueOf("2015-07-01 10:00:00")))),
       Timestamp.valueOf("2015-07-01 10:00:00"), InternalRow.empty)
-  }
 
+    DataTypeTestUtils.ordered.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(Greatest, dt, 2)
+    }
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
index f9b73f1a75e73..610d39e8493cd 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DateExpressionsSuite.scala
@@ -60,6 +60,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       }
     }
     checkEvaluation(DayOfYear(Literal.create(null, DateType)), null)
+    checkConsistencyBetweenInterpretedAndCodegen(DayOfYear, DateType)
   }
 
   test("Year") {
@@ -79,6 +80,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         }
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Year, DateType)
   }
 
   test("Quarter") {
@@ -98,6 +100,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         }
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Quarter, DateType)
   }
 
   test("Month") {
@@ -117,6 +120,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         }
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Month, DateType)
   }
 
   test("Day / DayOfMonth") {
@@ -135,6 +139,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           c.get(Calendar.DAY_OF_MONTH))
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(DayOfMonth, DateType)
   }
 
   test("Seconds") {
@@ -149,6 +154,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       checkEvaluation(Second(Literal(new Timestamp(c.getTimeInMillis))),
         c.get(Calendar.SECOND))
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Second, TimestampType)
   }
 
   test("WeekOfYear") {
@@ -157,6 +163,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(WeekOfYear(Cast(Literal(sdfDate.format(d)), DateType)), 15)
     checkEvaluation(WeekOfYear(Cast(Literal(ts), DateType)), 45)
     checkEvaluation(WeekOfYear(Cast(Literal("2011-05-06"), DateType)), 18)
+    checkConsistencyBetweenInterpretedAndCodegen(WeekOfYear, DateType)
   }
 
   test("DateFormat") {
@@ -184,6 +191,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
         }
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Hour, TimestampType)
   }
 
   test("Minute") {
@@ -200,6 +208,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
           c.get(Calendar.MINUTE))
       }
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Minute, TimestampType)
   }
 
   test("date_add") {
@@ -218,6 +227,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       DateAdd(Literal(Date.valueOf("2016-02-28")), positiveIntLit), 49627)
     checkEvaluation(
       DateAdd(Literal(Date.valueOf("2016-02-28")), negativeIntLit), -15910)
+    checkConsistencyBetweenInterpretedAndCodegen(DateAdd, DateType, IntegerType)
   }
 
   test("date_sub") {
@@ -236,6 +246,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       DateSub(Literal(Date.valueOf("2016-02-28")), positiveIntLit), -15909)
     checkEvaluation(
       DateSub(Literal(Date.valueOf("2016-02-28")), negativeIntLit), 49628)
+    checkConsistencyBetweenInterpretedAndCodegen(DateSub, DateType, IntegerType)
   }
 
   test("time_add") {
@@ -254,6 +265,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       TimeAdd(Literal.create(null, TimestampType), Literal.create(null, CalendarIntervalType)),
       null)
+    checkConsistencyBetweenInterpretedAndCodegen(TimeAdd, TimestampType, CalendarIntervalType)
   }
 
   test("time_sub") {
@@ -277,6 +289,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(
       TimeSub(Literal.create(null, TimestampType), Literal.create(null, CalendarIntervalType)),
       null)
+    checkConsistencyBetweenInterpretedAndCodegen(TimeSub, TimestampType, CalendarIntervalType)
   }
 
   test("add_months") {
@@ -296,6 +309,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       AddMonths(Literal(Date.valueOf("2016-02-28")), positiveIntLit), 1014213)
     checkEvaluation(
       AddMonths(Literal(Date.valueOf("2016-02-28")), negativeIntLit), -980528)
+    checkConsistencyBetweenInterpretedAndCodegen(AddMonths, DateType, IntegerType)
   }
 
   test("months_between") {
@@ -320,6 +334,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(MonthsBetween(t, tnull), null)
     checkEvaluation(MonthsBetween(tnull, t), null)
     checkEvaluation(MonthsBetween(tnull, tnull), null)
+    checkConsistencyBetweenInterpretedAndCodegen(MonthsBetween, TimestampType, TimestampType)
   }
 
   test("last_day") {
@@ -337,6 +352,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(LastDay(Literal(Date.valueOf("2016-01-06"))), Date.valueOf("2016-01-31"))
     checkEvaluation(LastDay(Literal(Date.valueOf("2016-02-07"))), Date.valueOf("2016-02-29"))
     checkEvaluation(LastDay(Literal.create(null, DateType)), null)
+    checkConsistencyBetweenInterpretedAndCodegen(LastDay, DateType)
   }
 
   test("next_day") {
@@ -370,6 +386,7 @@ class DateExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       ToDate(Literal(Date.valueOf("2015-07-22"))),
       DateTimeUtils.fromJavaDate(Date.valueOf("2015-07-22")))
     checkEvaluation(ToDate(Literal.create(null, DateType)), null)
+    checkConsistencyBetweenInterpretedAndCodegen(ToDate, DateType)
   }
 
   test("function trunc") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
index a41185b4d8754..465f7d08aa142 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvalHelper.scala
@@ -17,18 +17,21 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
+import org.scalacheck.Gen
 import org.scalactic.TripleEqualsSupport.Spread
+import org.scalatest.prop.GeneratorDrivenPropertyChecks
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
 import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
+import org.apache.spark.sql.types.DataType
 
 /**
  * A few helper functions for expression evaluation testing. Mixin this trait to use them.
  */
-trait ExpressionEvalHelper {
+trait ExpressionEvalHelper extends GeneratorDrivenPropertyChecks {
   self: SparkFunSuite =>
 
   protected def create_row(values: Any*): InternalRow = {
@@ -211,4 +214,111 @@ trait ExpressionEvalHelper {
       plan(inputRow)).get(0, expression.dataType)
     assert(checkResult(actual, expected))
   }
+
+  /**
+   * Test evaluation results between Interpreted mode and Codegen mode, making sure we have
+   * consistent result regardless of the evaluation method we use.
+   *
+   * This method test against unary expressions by feeding them arbitrary literals of `dataType`.
+   */
+  def checkConsistencyBetweenInterpretedAndCodegen(
+      c: Expression => Expression,
+      dataType: DataType): Unit = {
+    forAll (LiteralGenerator.randomGen(dataType)) { (l: Literal) =>
+      cmpInterpretWithCodegen(EmptyRow, c(l))
+    }
+  }
+
+  /**
+   * Test evaluation results between Interpreted mode and Codegen mode, making sure we have
+   * consistent result regardless of the evaluation method we use.
+   *
+   * This method test against binary expressions by feeding them arbitrary literals of `dataType1`
+   * and `dataType2`.
+   */
+  def checkConsistencyBetweenInterpretedAndCodegen(
+      c: (Expression, Expression) => Expression,
+      dataType1: DataType,
+      dataType2: DataType): Unit = {
+    forAll (
+      LiteralGenerator.randomGen(dataType1),
+      LiteralGenerator.randomGen(dataType2)
+    ) { (l1: Literal, l2: Literal) =>
+      cmpInterpretWithCodegen(EmptyRow, c(l1, l2))
+    }
+  }
+
+  /**
+   * Test evaluation results between Interpreted mode and Codegen mode, making sure we have
+   * consistent result regardless of the evaluation method we use.
+   *
+   * This method test against ternary expressions by feeding them arbitrary literals of `dataType1`,
+   * `dataType2` and `dataType3`.
+   */
+  def checkConsistencyBetweenInterpretedAndCodegen(
+      c: (Expression, Expression, Expression) => Expression,
+      dataType1: DataType,
+      dataType2: DataType,
+      dataType3: DataType): Unit = {
+    forAll (
+      LiteralGenerator.randomGen(dataType1),
+      LiteralGenerator.randomGen(dataType2),
+      LiteralGenerator.randomGen(dataType3)
+    ) { (l1: Literal, l2: Literal, l3: Literal) =>
+      cmpInterpretWithCodegen(EmptyRow, c(l1, l2, l3))
+    }
+  }
+
+  /**
+   * Test evaluation results between Interpreted mode and Codegen mode, making sure we have
+   * consistent result regardless of the evaluation method we use.
+   *
+   * This method test against expressions take Seq[Expression] as input by feeding them
+   * arbitrary length Seq of arbitrary literal of `dataType`.
+   */
+  def checkConsistencyBetweenInterpretedAndCodegen(
+      c: Seq[Expression] => Expression,
+      dataType: DataType,
+      minNumElements: Int = 0): Unit = {
+    forAll (Gen.listOf(LiteralGenerator.randomGen(dataType))) { (literals: Seq[Literal]) =>
+      whenever(literals.size >= minNumElements) {
+        cmpInterpretWithCodegen(EmptyRow, c(literals))
+      }
+    }
+  }
+
+  private def cmpInterpretWithCodegen(inputRow: InternalRow, expr: Expression): Unit = {
+    val interpret = try {
+      evaluate(expr, inputRow)
+    } catch {
+      case e: Exception => fail(s"Exception evaluating $expr", e)
+    }
+
+    val plan = generateProject(
+      GenerateMutableProjection.generate(Alias(expr, s"Optimized($expr)")() :: Nil)(),
+      expr)
+    val codegen = plan(inputRow).get(0, expr.dataType)
+
+    if (!compareResults(interpret, codegen)) {
+      fail(s"Incorrect evaluation: $expr, interpret: $interpret, codegen: $codegen")
+    }
+  }
+
+  /**
+   * Check the equality between result of expression and expected value, it will handle
+   * Array[Byte] and Spread[Double].
+   */
+  private[this] def compareResults(result: Any, expected: Any): Boolean = {
+    (result, expected) match {
+      case (result: Array[Byte], expected: Array[Byte]) =>
+        java.util.Arrays.equals(result, expected)
+      case (result: Double, expected: Spread[Double]) =>
+        expected.isWithin(result)
+      case (result: Double, expected: Double) if result.isNaN && expected.isNaN =>
+        true
+      case (result: Float, expected: Float) if result.isNaN && expected.isNaN =>
+        true
+      case _ => result == expected
+    }
+  }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralGenerator.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralGenerator.scala
new file mode 100644
index 0000000000000..ee6d25157fc08
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralGenerator.scala
@@ -0,0 +1,128 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import java.sql.{Date, Timestamp}
+
+import org.scalacheck.{Arbitrary, Gen}
+import org.scalatest.Matchers
+import org.scalatest.prop.GeneratorDrivenPropertyChecks
+
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.CalendarInterval
+
+/**
+ * Property is a high-level specification of behavior that should hold for a range of data points.
+ *
+ * For example, while we are evaluating a deterministic expression for some input, we should always
+ * hold the property that the result never changes, regardless of how we get the result,
+ * via interpreted or codegen.
+ *
+ * In ScalaTest, properties are specified as functions and the data points used to check properties
+ * can be supplied by either tables or generators.
+ *
+ * Generator-driven property checks are performed via integration with ScalaCheck.
+ *
+ * @example {{{
+ *   def toTest(i: Int): Boolean = if (i % 2 == 0) true else false
+ *
+ *   import org.scalacheck.Gen
+ *
+ *   test ("true if param is even") {
+ *     val evenInts = for (n <- Gen.choose(-1000, 1000)) yield 2 * n
+ *     forAll(evenInts) { (i: Int) =>
+ *       assert (toTest(i) === true)
+ *     }
+ *   }
+ * }}}
+ *
+ */
+object LiteralGenerator {
+
+  lazy val byteLiteralGen: Gen[Literal] =
+    for { b <- Arbitrary.arbByte.arbitrary } yield Literal.create(b, ByteType)
+
+  lazy val shortLiteralGen: Gen[Literal] =
+    for { s <- Arbitrary.arbShort.arbitrary } yield Literal.create(s, ShortType)
+
+  lazy val integerLiteralGen: Gen[Literal] =
+    for { i <- Arbitrary.arbInt.arbitrary } yield Literal.create(i, IntegerType)
+
+  lazy val longLiteralGen: Gen[Literal] =
+    for { l <- Arbitrary.arbLong.arbitrary } yield Literal.create(l, LongType)
+
+  lazy val floatLiteralGen: Gen[Literal] =
+    for {
+      f <- Gen.chooseNum(Float.MinValue / 2, Float.MaxValue / 2,
+        Float.NaN, Float.PositiveInfinity, Float.NegativeInfinity)
+    } yield Literal.create(f, FloatType)
+
+  lazy val doubleLiteralGen: Gen[Literal] =
+    for {
+      f <- Gen.chooseNum(Double.MinValue / 2, Double.MaxValue / 2,
+        Double.NaN, Double.PositiveInfinity, Double.NegativeInfinity)
+    } yield Literal.create(f, DoubleType)
+
+  // TODO: decimal type
+
+  lazy val stringLiteralGen: Gen[Literal] =
+    for { s <- Arbitrary.arbString.arbitrary } yield Literal.create(s, StringType)
+
+  lazy val binaryLiteralGen: Gen[Literal] =
+    for { ab <- Gen.listOf[Byte](Arbitrary.arbByte.arbitrary) }
+      yield Literal.create(ab.toArray, BinaryType)
+
+  lazy val booleanLiteralGen: Gen[Literal] =
+    for { b <- Arbitrary.arbBool.arbitrary } yield Literal.create(b, BooleanType)
+
+  lazy val dateLiteralGen: Gen[Literal] =
+    for { d <- Arbitrary.arbInt.arbitrary } yield Literal.create(new Date(d), DateType)
+
+  lazy val timestampLiteralGen: Gen[Literal] =
+    for { t <- Arbitrary.arbLong.arbitrary } yield Literal.create(new Timestamp(t), TimestampType)
+
+  lazy val calendarIntervalLiterGen: Gen[Literal] =
+    for { m <- Arbitrary.arbInt.arbitrary; s <- Arbitrary.arbLong.arbitrary}
+      yield Literal.create(new CalendarInterval(m, s), CalendarIntervalType)
+
+
+  // Sometimes, it would be quite expensive when unlimited value is used,
+  // for example, the `times` arguments for StringRepeat would hang the test 'forever'
+  // if it's tested against Int.MaxValue by ScalaCheck, therefore, use values from a limited
+  // range is more reasonable
+  lazy val limitedIntegerLiteralGen: Gen[Literal] =
+    for { i <- Gen.choose(-100, 100) } yield Literal.create(i, IntegerType)
+
+  def randomGen(dt: DataType): Gen[Literal] = {
+    dt match {
+      case ByteType => byteLiteralGen
+      case ShortType => shortLiteralGen
+      case IntegerType => integerLiteralGen
+      case LongType => longLiteralGen
+      case DoubleType => doubleLiteralGen
+      case FloatType => floatLiteralGen
+      case DateType => dateLiteralGen
+      case TimestampType => timestampLiteralGen
+      case BooleanType => booleanLiteralGen
+      case StringType => stringLiteralGen
+      case BinaryType => binaryLiteralGen
+      case CalendarIntervalType => calendarIntervalLiterGen
+      case dt => throw new IllegalArgumentException(s"not supported type $dt")
+    }
+  }
+}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
index 033792eee6c0f..90c59f240b542 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MathFunctionsSuite.scala
@@ -27,7 +27,6 @@ import org.apache.spark.sql.catalyst.optimizer.DefaultOptimizer
 import org.apache.spark.sql.catalyst.plans.logical.{OneRowRelation, Project}
 import org.apache.spark.sql.types._
 
-
 class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   import IntegralLiteralTestUtils._
@@ -184,60 +183,74 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("sin") {
     testUnary(Sin, math.sin)
+    checkConsistencyBetweenInterpretedAndCodegen(Sin, DoubleType)
   }
 
   test("asin") {
     testUnary(Asin, math.asin, (-10 to 10).map(_ * 0.1))
     testUnary(Asin, math.asin, (11 to 20).map(_ * 0.1), expectNaN = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Asin, DoubleType)
   }
 
   test("sinh") {
     testUnary(Sinh, math.sinh)
+    checkConsistencyBetweenInterpretedAndCodegen(Sinh, DoubleType)
   }
 
   test("cos") {
     testUnary(Cos, math.cos)
+    checkConsistencyBetweenInterpretedAndCodegen(Cos, DoubleType)
   }
 
   test("acos") {
     testUnary(Acos, math.acos, (-10 to 10).map(_ * 0.1))
     testUnary(Acos, math.acos, (11 to 20).map(_ * 0.1), expectNaN = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Acos, DoubleType)
   }
 
   test("cosh") {
     testUnary(Cosh, math.cosh)
+    checkConsistencyBetweenInterpretedAndCodegen(Cosh, DoubleType)
   }
 
   test("tan") {
     testUnary(Tan, math.tan)
+    checkConsistencyBetweenInterpretedAndCodegen(Tan, DoubleType)
   }
 
   test("atan") {
     testUnary(Atan, math.atan)
+    checkConsistencyBetweenInterpretedAndCodegen(Atan, DoubleType)
   }
 
   test("tanh") {
     testUnary(Tanh, math.tanh)
+    checkConsistencyBetweenInterpretedAndCodegen(Tanh, DoubleType)
   }
 
   test("toDegrees") {
     testUnary(ToDegrees, math.toDegrees)
+    checkConsistencyBetweenInterpretedAndCodegen(Acos, DoubleType)
   }
 
   test("toRadians") {
     testUnary(ToRadians, math.toRadians)
+    checkConsistencyBetweenInterpretedAndCodegen(ToRadians, DoubleType)
   }
 
   test("cbrt") {
     testUnary(Cbrt, math.cbrt)
+    checkConsistencyBetweenInterpretedAndCodegen(Cbrt, DoubleType)
   }
 
   test("ceil") {
     testUnary(Ceil, math.ceil)
+    checkConsistencyBetweenInterpretedAndCodegen(Ceil, DoubleType)
   }
 
   test("floor") {
     testUnary(Floor, math.floor)
+    checkConsistencyBetweenInterpretedAndCodegen(Floor, DoubleType)
   }
 
   test("factorial") {
@@ -247,37 +260,45 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Literal.create(null, IntegerType), null, create_row(null))
     checkEvaluation(Factorial(Literal(20)), 2432902008176640000L, EmptyRow)
     checkEvaluation(Factorial(Literal(21)), null, EmptyRow)
+    checkConsistencyBetweenInterpretedAndCodegen(Factorial.apply _, IntegerType)
   }
 
   test("rint") {
     testUnary(Rint, math.rint)
+    checkConsistencyBetweenInterpretedAndCodegen(Rint, DoubleType)
   }
 
   test("exp") {
     testUnary(Exp, math.exp)
+    checkConsistencyBetweenInterpretedAndCodegen(Exp, DoubleType)
   }
 
   test("expm1") {
     testUnary(Expm1, math.expm1)
+    checkConsistencyBetweenInterpretedAndCodegen(Expm1, DoubleType)
   }
 
   test("signum") {
     testUnary[Double, Double](Signum, math.signum)
+    checkConsistencyBetweenInterpretedAndCodegen(Signum, DoubleType)
   }
 
   test("log") {
     testUnary(Log, math.log, (1 to 20).map(_ * 0.1))
     testUnary(Log, math.log, (-5 to 0).map(_ * 0.1), expectNull = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Log, DoubleType)
   }
 
   test("log10") {
     testUnary(Log10, math.log10, (1 to 20).map(_ * 0.1))
     testUnary(Log10, math.log10, (-5 to 0).map(_ * 0.1), expectNull = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Log10, DoubleType)
   }
 
   test("log1p") {
     testUnary(Log1p, math.log1p, (0 to 20).map(_ * 0.1))
     testUnary(Log1p, math.log1p, (-10 to -1).map(_ * 1.0), expectNull = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Log1p, DoubleType)
   }
 
   test("bin") {
@@ -298,12 +319,15 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
 
     checkEvaluation(Bin(positiveLongLit), java.lang.Long.toBinaryString(positiveLong))
     checkEvaluation(Bin(negativeLongLit), java.lang.Long.toBinaryString(negativeLong))
+
+    checkConsistencyBetweenInterpretedAndCodegen(Bin, LongType)
   }
 
   test("log2") {
     def f: (Double) => Double = (x: Double) => math.log(x) / math.log(2)
     testUnary(Log2, f, (1 to 20).map(_ * 0.1))
     testUnary(Log2, f, (-5 to 0).map(_ * 1.0), expectNull = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Log2, DoubleType)
   }
 
   test("sqrt") {
@@ -313,11 +337,13 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Sqrt(Literal.create(null, DoubleType)), null, create_row(null))
     checkNaN(Sqrt(Literal(-1.0)), EmptyRow)
     checkNaN(Sqrt(Literal(-1.5)), EmptyRow)
+    checkConsistencyBetweenInterpretedAndCodegen(Sqrt, DoubleType)
   }
 
   test("pow") {
     testBinary(Pow, math.pow, (-5 to 5).map(v => (v * 1.0, v * 1.0)))
     testBinary(Pow, math.pow, Seq((-1.0, 0.9), (-2.2, 1.7), (-2.2, -1.7)), expectNaN = true)
+    checkConsistencyBetweenInterpretedAndCodegen(Pow, DoubleType, DoubleType)
   }
 
   test("shift left") {
@@ -338,6 +364,9 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(ShiftLeft(positiveLongLit, negativeIntLit), positiveLong << negativeInt)
     checkEvaluation(ShiftLeft(negativeLongLit, positiveIntLit), negativeLong << positiveInt)
     checkEvaluation(ShiftLeft(negativeLongLit, negativeIntLit), negativeLong << negativeInt)
+
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftLeft, IntegerType, IntegerType)
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftLeft, LongType, IntegerType)
   }
 
   test("shift right") {
@@ -358,6 +387,9 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(ShiftRight(positiveLongLit, negativeIntLit), positiveLong >> negativeInt)
     checkEvaluation(ShiftRight(negativeLongLit, positiveIntLit), negativeLong >> positiveInt)
     checkEvaluation(ShiftRight(negativeLongLit, negativeIntLit), negativeLong >> negativeInt)
+
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftRight, IntegerType, IntegerType)
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftRight, LongType, IntegerType)
   }
 
   test("shift right unsigned") {
@@ -386,6 +418,9 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       negativeLong >>> positiveInt)
     checkEvaluation(ShiftRightUnsigned(negativeLongLit, negativeIntLit),
       negativeLong >>> negativeInt)
+
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftRightUnsigned, IntegerType, IntegerType)
+    checkConsistencyBetweenInterpretedAndCodegen(ShiftRightUnsigned, LongType, IntegerType)
   }
 
   test("hex") {
@@ -400,6 +435,9 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     // Turn off scala style for non-ascii chars
     checkEvaluation(Hex(Literal("三重的".getBytes("UTF8"))), "E4B889E9878DE79A84")
     // scalastyle:on
+    Seq(LongType, BinaryType, StringType).foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(Hex.apply _, dt)
+    }
   }
 
   test("unhex") {
@@ -413,16 +451,18 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     // Turn off scala style for non-ascii chars
     checkEvaluation(Unhex(Literal("E4B889E9878DE79A84")), "三重的".getBytes("UTF-8"))
     checkEvaluation(Unhex(Literal("三重的")), null)
-
     // scalastyle:on
+    checkConsistencyBetweenInterpretedAndCodegen(Unhex, StringType)
   }
 
   test("hypot") {
     testBinary(Hypot, math.hypot)
+    checkConsistencyBetweenInterpretedAndCodegen(Hypot, DoubleType, DoubleType)
   }
 
   test("atan2") {
     testBinary(Atan2, math.atan2)
+    checkConsistencyBetweenInterpretedAndCodegen(Atan2, DoubleType, DoubleType)
   }
 
   test("binary log") {
@@ -454,6 +494,7 @@ class MathFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       Logarithm(Literal(1.0), Literal(-1.0)),
       null,
       create_row(null))
+    checkConsistencyBetweenInterpretedAndCodegen(Logarithm, DoubleType, DoubleType)
   }
 
   test("round") {
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
index b524d0af14a67..75d17417e5a02 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/MiscFunctionsSuite.scala
@@ -29,6 +29,7 @@ class MiscFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Md5(Literal.create(Array[Byte](1, 2, 3, 4, 5, 6), BinaryType)),
       "6ac1e56bc78f031059be7be854522c4c")
     checkEvaluation(Md5(Literal.create(null, BinaryType)), null)
+    checkConsistencyBetweenInterpretedAndCodegen(Md5, BinaryType)
   }
 
   test("sha1") {
@@ -37,6 +38,7 @@ class MiscFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
       "5d211bad8f4ee70e16c7d343a838fc344a1ed961")
     checkEvaluation(Sha1(Literal.create(null, BinaryType)), null)
     checkEvaluation(Sha1(Literal("".getBytes)), "da39a3ee5e6b4b0d3255bfef95601890afd80709")
+    checkConsistencyBetweenInterpretedAndCodegen(Sha1, BinaryType)
   }
 
   test("sha2") {
@@ -55,6 +57,6 @@ class MiscFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Crc32(Literal.create(Array[Byte](1, 2, 3, 4, 5, 6), BinaryType)),
       2180413220L)
     checkEvaluation(Crc32(Literal.create(null, BinaryType)), null)
+    checkConsistencyBetweenInterpretedAndCodegen(Crc32, BinaryType)
   }
-
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index 7beef71845e43..54c04faddb477 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -73,6 +73,16 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     notTrueTable.foreach { case (v, answer) =>
       checkEvaluation(Not(Literal.create(v, BooleanType)), answer)
     }
+    checkConsistencyBetweenInterpretedAndCodegen(Not, BooleanType)
+  }
+
+  test("AND, OR, EqualTo, EqualNullSafe consistency check") {
+    checkConsistencyBetweenInterpretedAndCodegen(And, BooleanType, BooleanType)
+    checkConsistencyBetweenInterpretedAndCodegen(Or, BooleanType, BooleanType)
+    DataTypeTestUtils.propertyCheckSupported.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(EqualTo, dt, dt)
+      checkConsistencyBetweenInterpretedAndCodegen(EqualNullSafe, dt, dt)
+    }
   }
 
   booleanLogicTest("AND", And,
@@ -180,6 +190,15 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
   private val equalValues2 =
     Seq(1, Decimal(1), Array(1.toByte), "a", Float.NaN, Double.NaN, true).map(Literal(_))
 
+  test("BinaryComparison consistency check") {
+    DataTypeTestUtils.ordered.foreach { dt =>
+      checkConsistencyBetweenInterpretedAndCodegen(LessThan, dt, dt)
+      checkConsistencyBetweenInterpretedAndCodegen(LessThanOrEqual, dt, dt)
+      checkConsistencyBetweenInterpretedAndCodegen(GreaterThan, dt, dt)
+      checkConsistencyBetweenInterpretedAndCodegen(GreaterThanOrEqual, dt, dt)
+    }
+  }
+
   test("BinaryComparison: lessThan") {
     for (i <- 0 until smallValues.length) {
       checkEvaluation(LessThan(smallValues(i), largeValues(i)), true)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
index 417df006ab7c2..ed2c641d63e25 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeTestUtils.scala
@@ -46,6 +46,25 @@ object DataTypeTestUtils {
    */
   val numericTypes: Set[NumericType] = integralType ++ fractionalTypes
 
+  // TODO: remove this once we find out how to handle decimal properly in property check
+  val numericTypeWithoutDecimal: Set[DataType] = integralType ++ Set(DoubleType, FloatType)
+
+  /**
+   * Instances of all [[NumericType]]s and [[CalendarIntervalType]]
+   */
+  val numericAndInterval: Set[DataType] = numericTypeWithoutDecimal + CalendarIntervalType
+
+  /**
+   * All the types that support ordering
+   */
+  val ordered: Set[DataType] =
+    numericTypeWithoutDecimal + BooleanType + TimestampType + DateType + StringType + BinaryType
+
+  /**
+   * All the types that we can use in a property check
+   */
+  val propertyCheckSupported: Set[DataType] = ordered
+
   /**
    * Instances of all [[AtomicType]]s.
    */

From 772e7c18fb1a79c0f080408cb43307fe89a4fa04 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 17 Aug 2015 15:30:50 -0700
Subject: [PATCH 282/633] [SPARK-9592] [SQL] Fix Last function implemented
 based on AggregateExpression1.

https://issues.apache.org/jira/browse/SPARK-9592

#8113 has the fundamental fix. But, if we want to minimize the number of changed lines, we can go with this one. Then, in 1.6, we merge #8113.

Author: Yin Huai <yhuai@databricks.com>

Closes #8172 from yhuai/lastFix and squashes the following commits:

b28c42a [Yin Huai] Regression test.
af87086 [Yin Huai] Fix last.
---
 .../sql/catalyst/expressions/aggregates.scala     |  9 +++++++--
 .../hive/execution/AggregationQuerySuite.scala    | 15 +++++++++++++++
 2 files changed, 22 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 2cf8312ea59aa..5e8298aaaa9cb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -650,6 +650,7 @@ case class FirstFunction(expr: Expression, base: AggregateExpression1) extends A
   var result: Any = null
 
   override def update(input: InternalRow): Unit = {
+    // We ignore null values.
     if (result == null) {
       result = expr.eval(input)
     }
@@ -679,10 +680,14 @@ case class LastFunction(expr: Expression, base: AggregateExpression1) extends Ag
   var result: Any = null
 
   override def update(input: InternalRow): Unit = {
-    result = input
+    val value = expr.eval(input)
+    // We ignore null values.
+    if (value != null) {
+      result = value
+    }
   }
 
   override def eval(input: InternalRow): Any = {
-    if (result != null) expr.eval(result.asInstanceOf[InternalRow]) else null
+    result
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index a312f84958248..119663af1887a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -480,6 +480,21 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
         Row(0, null, 1, 1, null, 0) :: Nil)
   }
 
+  test("test Last implemented based on AggregateExpression1") {
+    // TODO: Remove this test once we remove AggregateExpression1.
+    import org.apache.spark.sql.functions._
+    val df = Seq((1, 1), (2, 2), (3, 3)).toDF("i", "j").repartition(1)
+    withSQLConf(
+      SQLConf.SHUFFLE_PARTITIONS.key -> "1",
+      SQLConf.USE_SQL_AGGREGATE2.key -> "false") {
+
+      checkAnswer(
+        df.groupBy("i").agg(last("j")),
+        df
+      )
+    }
+  }
+
   test("error handling") {
     withSQLConf("spark.sql.useAggregate2" -> "false") {
       val errorMessage = intercept[AnalysisException] {

From fdaf17f63f751f02623414fbc7d0a2f545364050 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 17 Aug 2015 15:42:14 -0700
Subject: [PATCH 283/633] [SPARK-10068] [MLLIB] Adds links to MLlib types,
 algos, utilities listing

mengxr jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8255 from feynmanliang/SPARK-10068.
---
 docs/mllib-guide.md | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index eea864eacf7c4..e8000ff478300 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -23,19 +23,19 @@ This lists functionality included in `spark.mllib`, the main MLlib API.
 
 * [Data types](mllib-data-types.html)
 * [Basic statistics](mllib-statistics.html)
-  * summary statistics
-  * correlations
-  * stratified sampling
-  * hypothesis testing
-  * random data generation  
+  * [summary statistics](mllib-statistics.html#summary-statistics)
+  * [correlations](mllib-statistics.html#correlations)
+  * [stratified sampling](mllib-statistics.html#stratified-sampling)
+  * [hypothesis testing](mllib-statistics.html#hypothesis-testing)
+  * [random data generation](mllib-statistics.html#random-data-generation)
 * [Classification and regression](mllib-classification-regression.html)
   * [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html)
   * [naive Bayes](mllib-naive-bayes.html)
   * [decision trees](mllib-decision-tree.html)
-  * [ensembles of trees](mllib-ensembles.html) (Random Forests and Gradient-Boosted Trees)
+  * [ensembles of trees (Random Forests and Gradient-Boosted Trees)](mllib-ensembles.html)
   * [isotonic regression](mllib-isotonic-regression.html)
 * [Collaborative filtering](mllib-collaborative-filtering.html)
-  * alternating least squares (ALS)
+  * [alternating least squares (ALS)](mllib-collaborative-filtering.html#collaborative-filtering)
 * [Clustering](mllib-clustering.html)
   * [k-means](mllib-clustering.html#k-means)
   * [Gaussian mixture](mllib-clustering.html#gaussian-mixture)
@@ -43,19 +43,19 @@ This lists functionality included in `spark.mllib`, the main MLlib API.
   * [latent Dirichlet allocation (LDA)](mllib-clustering.html#latent-dirichlet-allocation-lda)
   * [streaming k-means](mllib-clustering.html#streaming-k-means)
 * [Dimensionality reduction](mllib-dimensionality-reduction.html)
-  * singular value decomposition (SVD)
-  * principal component analysis (PCA)
+  * [singular value decomposition (SVD)](mllib-dimensionality-reduction.html#singular-value-decomposition-svd)
+  * [principal component analysis (PCA)](mllib-dimensionality-reduction.html#principal-component-analysis-pca)
 * [Feature extraction and transformation](mllib-feature-extraction.html)
 * [Frequent pattern mining](mllib-frequent-pattern-mining.html)
-  * FP-growth
+  * [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
 * [Evaluation Metrics](mllib-evaluation-metrics.html)
 * [Optimization (developer)](mllib-optimization.html)
-  * stochastic gradient descent
-  * limited-memory BFGS (L-BFGS)
+  * [stochastic gradient descent](mllib-optimization.html#stochastic-gradient-descent-sgd)
+  * [limited-memory BFGS (L-BFGS)](mllib-optimization.html#limited-memory-bfgs-l-bfgs)
 * [PMML model export](mllib-pmml-model-export.html)
 
 MLlib is under active development.
-The APIs marked `Experimental`/`DeveloperApi` may change in future releases, 
+The APIs marked `Experimental`/`DeveloperApi` may change in future releases,
 and the migration guide below will explain all changes between releases.
 
 # spark.ml: high-level APIs for ML pipelines

From 088b11ec5949e135cb3db2a1ce136837e046c288 Mon Sep 17 00:00:00 2001
From: Sameer Abhyankar <sabhyankar@sabhyankar-MBP.Samavihome>
Date: Mon, 17 Aug 2015 16:00:23 -0700
Subject: [PATCH 284/633] [SPARK-8920] [MLLIB] Add @since tags to mllib.linalg

Author: Sameer Abhyankar <sabhyankar@sabhyankar-MBP.Samavihome>
Author: Sameer Abhyankar <sabhyankar@sabhyankar-MBP.local>

Closes #7729 from sabhyankar/branch_8920.
---
 .../apache/spark/mllib/linalg/Matrices.scala  | 63 +++++++++++++++++++
 .../linalg/SingularValueDecomposition.scala   |  1 +
 .../apache/spark/mllib/linalg/Vectors.scala   | 60 ++++++++++++++++++
 .../linalg/distributed/BlockMatrix.scala      | 43 +++++++++++--
 .../linalg/distributed/CoordinateMatrix.scala | 28 +++++++--
 .../distributed/DistributedMatrix.scala       |  1 +
 .../linalg/distributed/IndexedRowMatrix.scala | 24 ++++++-
 .../mllib/linalg/distributed/RowMatrix.scala  | 24 ++++++-
 8 files changed, 227 insertions(+), 17 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 1139ce36d50b8..dfa8910fcbc8c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -227,6 +227,7 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
  * @param values matrix entries in column major if not transposed or in row major otherwise
  * @param isTransposed whether the matrix is transposed. If true, `values` stores the matrix in
  *                     row major.
+ * @since 1.0.0
  */
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
 class DenseMatrix(
@@ -252,6 +253,7 @@ class DenseMatrix(
    * @param numRows number of rows
    * @param numCols number of columns
    * @param values matrix entries in column major
+   * @since 1.3.0
    */
   def this(numRows: Int, numCols: Int, values: Array[Double]) =
     this(numRows, numCols, values, false)
@@ -276,6 +278,9 @@ class DenseMatrix(
 
   private[mllib] def apply(i: Int): Double = values(i)
 
+  /**
+   * @since 1.3.0
+   */
   override def apply(i: Int, j: Int): Double = values(index(i, j))
 
   private[mllib] def index(i: Int, j: Int): Int = {
@@ -286,6 +291,9 @@ class DenseMatrix(
     values(index(i, j)) = v
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def copy: DenseMatrix = new DenseMatrix(numRows, numCols, values.clone())
 
   private[spark] def map(f: Double => Double) = new DenseMatrix(numRows, numCols, values.map(f),
@@ -301,6 +309,9 @@ class DenseMatrix(
     this
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def transpose: DenseMatrix = new DenseMatrix(numCols, numRows, values, !isTransposed)
 
   private[spark] override def foreachActive(f: (Int, Int, Double) => Unit): Unit = {
@@ -331,13 +342,20 @@ class DenseMatrix(
     }
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def numNonzeros: Int = values.count(_ != 0)
 
+  /**
+   * @since 1.5.0
+   */
   override def numActives: Int = values.length
 
   /**
    * Generate a `SparseMatrix` from the given `DenseMatrix`. The new matrix will have isTransposed
    * set to false.
+   * @since 1.3.0
    */
   def toSparse: SparseMatrix = {
     val spVals: MArrayBuilder[Double] = new MArrayBuilder.ofDouble
@@ -365,6 +383,7 @@ class DenseMatrix(
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.DenseMatrix]].
+ * @since 1.3.0
  */
 object DenseMatrix {
 
@@ -373,6 +392,7 @@ object DenseMatrix {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `DenseMatrix` with size `numRows` x `numCols` and values of zeros
+   * @since 1.3.0
    */
   def zeros(numRows: Int, numCols: Int): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
@@ -385,6 +405,7 @@ object DenseMatrix {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `DenseMatrix` with size `numRows` x `numCols` and values of ones
+   * @since 1.3.0
    */
   def ones(numRows: Int, numCols: Int): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
@@ -396,6 +417,7 @@ object DenseMatrix {
    * Generate an Identity Matrix in `DenseMatrix` format.
    * @param n number of rows and columns of the matrix
    * @return `DenseMatrix` with size `n` x `n` and values of ones on the diagonal
+   * @since 1.3.0
    */
   def eye(n: Int): DenseMatrix = {
     val identity = DenseMatrix.zeros(n, n)
@@ -413,6 +435,7 @@ object DenseMatrix {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `DenseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
+   * @since 1.3.0
    */
   def rand(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
@@ -426,6 +449,7 @@ object DenseMatrix {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `DenseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
+   * @since 1.3.0
    */
   def randn(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
@@ -438,6 +462,7 @@ object DenseMatrix {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `DenseMatrix` with size `values.length` x `values.length` and `values`
    *         on the diagonal
+   * @since 1.3.0
    */
   def diag(vector: Vector): DenseMatrix = {
     val n = vector.size
@@ -473,6 +498,7 @@ object DenseMatrix {
  * @param isTransposed whether the matrix is transposed. If true, the matrix can be considered
  *                     Compressed Sparse Row (CSR) format, where `colPtrs` behaves as rowPtrs,
  *                     and `rowIndices` behave as colIndices, and `values` are stored in row major.
+ * @since 1.2.0
  */
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
 class SparseMatrix(
@@ -510,6 +536,7 @@ class SparseMatrix(
    * @param rowIndices the row index of the entry. They must be in strictly increasing
    *                   order for each column
    * @param values non-zero matrix entries in column major
+   * @since 1.3.0
    */
   def this(
       numRows: Int,
@@ -532,6 +559,9 @@ class SparseMatrix(
      }
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def apply(i: Int, j: Int): Double = {
     val ind = index(i, j)
     if (ind < 0) 0.0 else values(ind)
@@ -555,6 +585,9 @@ class SparseMatrix(
     }
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def copy: SparseMatrix = {
     new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.clone())
   }
@@ -572,6 +605,9 @@ class SparseMatrix(
     this
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def transpose: SparseMatrix =
     new SparseMatrix(numCols, numRows, colPtrs, rowIndices, values, !isTransposed)
 
@@ -605,19 +641,27 @@ class SparseMatrix(
   /**
    * Generate a `DenseMatrix` from the given `SparseMatrix`. The new matrix will have isTransposed
    * set to false.
+   * @since 1.3.0
    */
   def toDense: DenseMatrix = {
     new DenseMatrix(numRows, numCols, toArray)
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def numNonzeros: Int = values.count(_ != 0)
 
+  /**
+   * @since 1.5.0
+   */
   override def numActives: Int = values.length
 
 }
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.SparseMatrix]].
+ * @since 1.3.0
  */
 object SparseMatrix {
 
@@ -629,6 +673,7 @@ object SparseMatrix {
    * @param numCols number of columns of the matrix
    * @param entries Array of (i, j, value) tuples
    * @return The corresponding `SparseMatrix`
+   * @since 1.3.0
    */
   def fromCOO(numRows: Int, numCols: Int, entries: Iterable[(Int, Int, Double)]): SparseMatrix = {
     val sortedEntries = entries.toSeq.sortBy(v => (v._2, v._1))
@@ -677,6 +722,7 @@ object SparseMatrix {
    * Generate an Identity Matrix in `SparseMatrix` format.
    * @param n number of rows and columns of the matrix
    * @return `SparseMatrix` with size `n` x `n` and values of ones on the diagonal
+   * @since 1.3.0
    */
   def speye(n: Int): SparseMatrix = {
     new SparseMatrix(n, n, (0 to n).toArray, (0 until n).toArray, Array.fill(n)(1.0))
@@ -746,6 +792,7 @@ object SparseMatrix {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `SparseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
+   * @since 1.3.0
    */
   def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
     val mat = genRandMatrix(numRows, numCols, density, rng)
@@ -759,6 +806,7 @@ object SparseMatrix {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `SparseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
+   * @since 1.3.0
    */
   def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
     val mat = genRandMatrix(numRows, numCols, density, rng)
@@ -770,6 +818,7 @@ object SparseMatrix {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `SparseMatrix` with size `values.length` x `values.length` and non-zero
    *         `values` on the diagonal
+   * @since 1.3.0
    */
   def spdiag(vector: Vector): SparseMatrix = {
     val n = vector.size
@@ -786,6 +835,7 @@ object SparseMatrix {
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.Matrix]].
+ * @since 1.0.0
  */
 object Matrices {
 
@@ -795,6 +845,7 @@ object Matrices {
    * @param numRows number of rows
    * @param numCols number of columns
    * @param values matrix entries in column major
+   * @since 1.0.0
    */
   def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix = {
     new DenseMatrix(numRows, numCols, values)
@@ -808,6 +859,7 @@ object Matrices {
    * @param colPtrs the index corresponding to the start of a new column
    * @param rowIndices the row index of the entry
    * @param values non-zero matrix entries in column major
+   * @since 1.2.0
    */
   def sparse(
      numRows: Int,
@@ -841,6 +893,7 @@ object Matrices {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `Matrix` with size `numRows` x `numCols` and values of zeros
+   * @since 1.2.0
    */
   def zeros(numRows: Int, numCols: Int): Matrix = DenseMatrix.zeros(numRows, numCols)
 
@@ -849,6 +902,7 @@ object Matrices {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `Matrix` with size `numRows` x `numCols` and values of ones
+   * @since 1.2.0
    */
   def ones(numRows: Int, numCols: Int): Matrix = DenseMatrix.ones(numRows, numCols)
 
@@ -856,6 +910,7 @@ object Matrices {
    * Generate a dense Identity Matrix in `Matrix` format.
    * @param n number of rows and columns of the matrix
    * @return `Matrix` with size `n` x `n` and values of ones on the diagonal
+   * @since 1.2.0
    */
   def eye(n: Int): Matrix = DenseMatrix.eye(n)
 
@@ -863,6 +918,7 @@ object Matrices {
    * Generate a sparse Identity Matrix in `Matrix` format.
    * @param n number of rows and columns of the matrix
    * @return `Matrix` with size `n` x `n` and values of ones on the diagonal
+   * @since 1.3.0
    */
   def speye(n: Int): Matrix = SparseMatrix.speye(n)
 
@@ -872,6 +928,7 @@ object Matrices {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
+   * @since 1.2.0
    */
   def rand(numRows: Int, numCols: Int, rng: Random): Matrix =
     DenseMatrix.rand(numRows, numCols, rng)
@@ -883,6 +940,7 @@ object Matrices {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
+   * @since 1.3.0
    */
   def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
     SparseMatrix.sprand(numRows, numCols, density, rng)
@@ -893,6 +951,7 @@ object Matrices {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
+   * @since 1.2.0
    */
   def randn(numRows: Int, numCols: Int, rng: Random): Matrix =
     DenseMatrix.randn(numRows, numCols, rng)
@@ -904,6 +963,7 @@ object Matrices {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
+   * @since 1.3.0
    */
   def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
     SparseMatrix.sprandn(numRows, numCols, density, rng)
@@ -913,6 +973,7 @@ object Matrices {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `Matrix` with size `values.length` x `values.length` and `values`
    *         on the diagonal
+   * @since 1.2.0
    */
   def diag(vector: Vector): Matrix = DenseMatrix.diag(vector)
 
@@ -922,6 +983,7 @@ object Matrices {
    * a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
    * @param matrices array of matrices
    * @return a single `Matrix` composed of the matrices that were horizontally concatenated
+   * @since 1.3.0
    */
   def horzcat(matrices: Array[Matrix]): Matrix = {
     if (matrices.isEmpty) {
@@ -980,6 +1042,7 @@ object Matrices {
    * a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
    * @param matrices array of matrices
    * @return a single `Matrix` composed of the matrices that were vertically concatenated
+   * @since 1.3.0
    */
   def vertcat(matrices: Array[Matrix]): Matrix = {
     if (matrices.isEmpty) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index cff5dbeee3e57..8f504f6984cb0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -22,6 +22,7 @@ import org.apache.spark.annotation.Experimental
 /**
  * :: Experimental ::
  * Represents singular value decomposition (SVD) factors.
+ * @since 1.0.0
  */
 @Experimental
 case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index df15d985c814c..52ef7be3b38be 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -240,11 +240,13 @@ class VectorUDT extends UserDefinedType[Vector] {
  * Factory methods for [[org.apache.spark.mllib.linalg.Vector]].
  * We don't use the name `Vector` because Scala imports
  * [[scala.collection.immutable.Vector]] by default.
+ * @since 1.0.0
  */
 object Vectors {
 
   /**
    * Creates a dense vector from its values.
+   * @since 1.0.0
    */
   @varargs
   def dense(firstValue: Double, otherValues: Double*): Vector =
@@ -253,6 +255,7 @@ object Vectors {
   // A dummy implicit is used to avoid signature collision with the one generated by @varargs.
   /**
    * Creates a dense vector from a double array.
+   * @since 1.0.0
    */
   def dense(values: Array[Double]): Vector = new DenseVector(values)
 
@@ -262,6 +265,7 @@ object Vectors {
    * @param size vector size.
    * @param indices index array, must be strictly increasing.
    * @param values value array, must have the same length as indices.
+   * @since 1.0.0
    */
   def sparse(size: Int, indices: Array[Int], values: Array[Double]): Vector =
     new SparseVector(size, indices, values)
@@ -271,6 +275,7 @@ object Vectors {
    *
    * @param size vector size.
    * @param elements vector elements in (index, value) pairs.
+   * @since 1.0.0
    */
   def sparse(size: Int, elements: Seq[(Int, Double)]): Vector = {
     require(size > 0, "The size of the requested sparse vector must be greater than 0.")
@@ -292,6 +297,7 @@ object Vectors {
    *
    * @param size vector size.
    * @param elements vector elements in (index, value) pairs.
+   * @since 1.0.0
    */
   def sparse(size: Int, elements: JavaIterable[(JavaInteger, JavaDouble)]): Vector = {
     sparse(size, elements.asScala.map { case (i, x) =>
@@ -304,6 +310,7 @@ object Vectors {
    *
    * @param size vector size
    * @return a zero vector
+   * @since 1.1.0
    */
   def zeros(size: Int): Vector = {
     new DenseVector(new Array[Double](size))
@@ -311,6 +318,7 @@ object Vectors {
 
   /**
    * Parses a string resulted from [[Vector.toString]] into a [[Vector]].
+   * @since 1.1.0
    */
   def parse(s: String): Vector = {
     parseNumeric(NumericParser.parse(s))
@@ -354,6 +362,7 @@ object Vectors {
    * @param vector input vector.
    * @param p norm.
    * @return norm in L^p^ space.
+   * @since 1.3.0
    */
   def norm(vector: Vector, p: Double): Double = {
     require(p >= 1.0, "To compute the p-norm of the vector, we require that you specify a p>=1. " +
@@ -406,6 +415,7 @@ object Vectors {
    * @param v1 first Vector.
    * @param v2 second Vector.
    * @return squared distance between two Vectors.
+   * @since 1.3.0
    */
   def sqdist(v1: Vector, v2: Vector): Double = {
     require(v1.size == v2.size, s"Vector dimensions do not match: Dim(v1)=${v1.size} and Dim(v2)" +
@@ -519,20 +529,33 @@ object Vectors {
 
 /**
  * A dense vector represented by a value array.
+ * @since 1.0.0
  */
 @SQLUserDefinedType(udt = classOf[VectorUDT])
 class DenseVector(val values: Array[Double]) extends Vector {
 
+  /**
+   * @since 1.0.0
+   */
   override def size: Int = values.length
 
   override def toString: String = values.mkString("[", ",", "]")
 
+  /**
+   * @since 1.0.0
+   */
   override def toArray: Array[Double] = values
 
   private[spark] override def toBreeze: BV[Double] = new BDV[Double](values)
 
+  /**
+   * @since 1.0.0
+   */
   override def apply(i: Int): Double = values(i)
 
+  /**
+   * @since 1.1.0
+   */
   override def copy: DenseVector = {
     new DenseVector(values.clone())
   }
@@ -564,8 +587,14 @@ class DenseVector(val values: Array[Double]) extends Vector {
     result
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def numActives: Int = size
 
+  /**
+   * @since 1.4.0
+   */
   override def numNonzeros: Int = {
     // same as values.count(_ != 0.0) but faster
     var nnz = 0
@@ -577,6 +606,9 @@ class DenseVector(val values: Array[Double]) extends Vector {
     nnz
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def toSparse: SparseVector = {
     val nnz = numNonzeros
     val ii = new Array[Int](nnz)
@@ -592,6 +624,9 @@ class DenseVector(val values: Array[Double]) extends Vector {
     new SparseVector(size, ii, vv)
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def argmax: Int = {
     if (size == 0) {
       -1
@@ -611,6 +646,9 @@ class DenseVector(val values: Array[Double]) extends Vector {
   }
 }
 
+/**
+ * @since 1.3.0
+ */
 object DenseVector {
   /** Extracts the value array from a dense vector. */
   def unapply(dv: DenseVector): Option[Array[Double]] = Some(dv.values)
@@ -622,6 +660,7 @@ object DenseVector {
  * @param size size of the vector.
  * @param indices index array, assume to be strictly increasing.
  * @param values value array, must have the same length as the index array.
+ * @since 1.0.0
  */
 @SQLUserDefinedType(udt = classOf[VectorUDT])
 class SparseVector(
@@ -638,6 +677,9 @@ class SparseVector(
   override def toString: String =
     s"($size,${indices.mkString("[", ",", "]")},${values.mkString("[", ",", "]")})"
 
+  /**
+   * @since 1.0.0
+   */
   override def toArray: Array[Double] = {
     val data = new Array[Double](size)
     var i = 0
@@ -649,6 +691,9 @@ class SparseVector(
     data
   }
 
+  /**
+   * @since 1.1.0
+   */
   override def copy: SparseVector = {
     new SparseVector(size, indices.clone(), values.clone())
   }
@@ -689,8 +734,14 @@ class SparseVector(
     result
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def numActives: Int = values.length
 
+  /**
+   * @since 1.4.0
+   */
   override def numNonzeros: Int = {
     var nnz = 0
     values.foreach { v =>
@@ -701,6 +752,9 @@ class SparseVector(
     nnz
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def toSparse: SparseVector = {
     val nnz = numNonzeros
     if (nnz == numActives) {
@@ -720,6 +774,9 @@ class SparseVector(
     }
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def argmax: Int = {
     if (size == 0) {
       -1
@@ -790,6 +847,9 @@ class SparseVector(
   }
 }
 
+/**
+ * @since 1.3.0
+ */
 object SparseVector {
   def unapply(sv: SparseVector): Option[(Int, Array[Int], Array[Double])] =
     Some((sv.size, sv.indices, sv.values))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
index 3323ae7b1fba0..cfb6680a18b34 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
@@ -128,6 +128,8 @@ private[mllib] object GridPartitioner {
  *              the number of rows will be calculated when `numRows` is invoked.
  * @param nCols Number of columns of this matrix. If the supplied value is less than or equal to
  *              zero, the number of columns will be calculated when `numCols` is invoked.
+ * @since 1.3.0
+ *
  */
 @Experimental
 class BlockMatrix(
@@ -149,6 +151,9 @@ class BlockMatrix(
    *                     rows are not required to have the given number of rows
    * @param colsPerBlock Number of columns that make up each block. The blocks forming the final
    *                     columns are not required to have the given number of columns
+   *
+   * @since 1.3.0
+   *
    */
   def this(
       blocks: RDD[((Int, Int), Matrix)],
@@ -157,11 +162,20 @@ class BlockMatrix(
     this(blocks, rowsPerBlock, colsPerBlock, 0L, 0L)
   }
 
+  /**
+   * @since 1.3.0
+   * */
+
   override def numRows(): Long = {
     if (nRows <= 0L) estimateDim()
     nRows
   }
 
+  /**
+   *
+   * @since 1.3.0
+   */
+
   override def numCols(): Long = {
     if (nCols <= 0L) estimateDim()
     nCols
@@ -192,6 +206,7 @@ class BlockMatrix(
   /**
    * Validates the block matrix info against the matrix data (`blocks`) and throws an exception if
    * any error is found.
+   * @since 1.3.0
    */
   def validate(): Unit = {
     logDebug("Validating BlockMatrix...")
@@ -228,19 +243,25 @@ class BlockMatrix(
     logDebug("BlockMatrix is valid!")
   }
 
-  /** Caches the underlying RDD. */
+  /** Caches the underlying RDD.
+    * @since 1.3.0
+    * */
   def cache(): this.type = {
     blocks.cache()
     this
   }
 
-  /** Persists the underlying RDD with the specified storage level. */
+  /** Persists the underlying RDD with the specified storage level.
+    * @since 1.3.0
+    * */
   def persist(storageLevel: StorageLevel): this.type = {
     blocks.persist(storageLevel)
     this
   }
 
-  /** Converts to CoordinateMatrix. */
+  /** Converts to CoordinateMatrix.
+    * @since 1.3.0
+    * */
   def toCoordinateMatrix(): CoordinateMatrix = {
     val entryRDD = blocks.flatMap { case ((blockRowIndex, blockColIndex), mat) =>
       val rowStart = blockRowIndex.toLong * rowsPerBlock
@@ -254,7 +275,9 @@ class BlockMatrix(
     new CoordinateMatrix(entryRDD, numRows(), numCols())
   }
 
-  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range. */
+  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range.
+    * @since 1.3.0
+    * */
   def toIndexedRowMatrix(): IndexedRowMatrix = {
     require(numCols() < Int.MaxValue, "The number of columns must be within the integer range. " +
       s"numCols: ${numCols()}")
@@ -262,7 +285,9 @@ class BlockMatrix(
     toCoordinateMatrix().toIndexedRowMatrix()
   }
 
-  /** Collect the distributed matrix on the driver as a `DenseMatrix`. */
+  /** Collect the distributed matrix on the driver as a `DenseMatrix`.
+    * @since 1.3.0
+    * */
   def toLocalMatrix(): Matrix = {
     require(numRows() < Int.MaxValue, "The number of rows of this matrix should be less than " +
       s"Int.MaxValue. Currently numRows: ${numRows()}")
@@ -288,7 +313,10 @@ class BlockMatrix(
   }
 
   /** Transpose this `BlockMatrix`. Returns a new `BlockMatrix` instance sharing the
-    * same underlying data. Is a lazy operation. */
+    * same underlying data. Is a lazy operation.
+    * @since 1.3.0
+    *
+    * */
   def transpose: BlockMatrix = {
     val transposedBlocks = blocks.map { case ((blockRowIndex, blockColIndex), mat) =>
       ((blockColIndex, blockRowIndex), mat.transpose)
@@ -307,6 +335,7 @@ class BlockMatrix(
     * instances of [[SparseMatrix]], the resulting sub matrix will also be a [[SparseMatrix]], even
     * if it is being added to a [[DenseMatrix]]. If two dense matrices are added, the output will
     * also be a [[DenseMatrix]].
+    * @since 1.3.0
     */
   def add(other: BlockMatrix): BlockMatrix = {
     require(numRows() == other.numRows(), "Both matrices must have the same number of rows. " +
@@ -340,6 +369,8 @@ class BlockMatrix(
     * [[SparseMatrix]], they will have to be converted to a [[DenseMatrix]]. The output
     * [[BlockMatrix]] will only consist of blocks of [[DenseMatrix]]. This may cause
     * some performance issues until support for multiplying two sparse matrices is added.
+    *
+    * @since 1.3.0
     */
   def multiply(other: BlockMatrix): BlockMatrix = {
     require(numCols() == other.numRows(), "The number of columns of A and the number of rows " +
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
index 078d1fac44443..2b751e45dd76c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
@@ -29,6 +29,7 @@ import org.apache.spark.mllib.linalg.{Matrix, SparseMatrix, Vectors}
  * @param i row index
  * @param j column index
  * @param value value of the entry
+ * @since 1.0.0
  */
 @Experimental
 case class MatrixEntry(i: Long, j: Long, value: Double)
@@ -42,6 +43,7 @@ case class MatrixEntry(i: Long, j: Long, value: Double)
  *              be determined by the max row index plus one.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the max column index plus one.
+ * @since 1.0.0
  */
 @Experimental
 class CoordinateMatrix(
@@ -49,10 +51,14 @@ class CoordinateMatrix(
     private var nRows: Long,
     private var nCols: Long) extends DistributedMatrix {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically.
+    * @since 1.0.0
+    * */
   def this(entries: RDD[MatrixEntry]) = this(entries, 0L, 0L)
 
-  /** Gets or computes the number of columns. */
+  /** Gets or computes the number of columns.
+    * @since 1.0.0
+    * */
   override def numCols(): Long = {
     if (nCols <= 0L) {
       computeSize()
@@ -60,7 +66,9 @@ class CoordinateMatrix(
     nCols
   }
 
-  /** Gets or computes the number of rows. */
+  /** Gets or computes the number of rows.
+    * @since 1.0.0
+    * */
   override def numRows(): Long = {
     if (nRows <= 0L) {
       computeSize()
@@ -68,12 +76,16 @@ class CoordinateMatrix(
     nRows
   }
 
-  /** Transposes this CoordinateMatrix. */
+  /** Transposes this CoordinateMatrix.
+    * @since 1.3.0
+    * */
   def transpose(): CoordinateMatrix = {
     new CoordinateMatrix(entries.map(x => MatrixEntry(x.j, x.i, x.value)), numCols(), numRows())
   }
 
-  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range. */
+  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range.
+    * @since 1.0.0
+    * */
   def toIndexedRowMatrix(): IndexedRowMatrix = {
     val nl = numCols()
     if (nl > Int.MaxValue) {
@@ -92,12 +104,15 @@ class CoordinateMatrix(
   /**
    * Converts to RowMatrix, dropping row indices after grouping by row index.
    * The number of columns must be within the integer range.
+   * @since 1.0.0
    */
   def toRowMatrix(): RowMatrix = {
     toIndexedRowMatrix().toRowMatrix()
   }
 
-  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024. */
+  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024.
+    * @since 1.3.0
+    * */
   def toBlockMatrix(): BlockMatrix = {
     toBlockMatrix(1024, 1024)
   }
@@ -109,6 +124,7 @@ class CoordinateMatrix(
    * @param colsPerBlock The number of columns of each block. The blocks at the right edge may have
    *                     a smaller value. Must be an integer value greater than 0.
    * @return a [[BlockMatrix]]
+   * @since 1.3.0
    */
   def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix = {
     require(rowsPerBlock > 0,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
index a0e26ce3bc465..98e90af84abac 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
@@ -21,6 +21,7 @@ import breeze.linalg.{DenseMatrix => BDM}
 
 /**
  * Represents a distributively stored matrix backed by one or more RDDs.
+ * @since 1.0.0
  */
 trait DistributedMatrix extends Serializable {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index 1c33b43ea7a8a..a09f88ce28e58 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -27,6 +27,7 @@ import org.apache.spark.mllib.linalg.SingularValueDecomposition
 /**
  * :: Experimental ::
  * Represents a row of [[org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix]].
+ * @since 1.0.0
  */
 @Experimental
 case class IndexedRow(index: Long, vector: Vector)
@@ -41,6 +42,7 @@ case class IndexedRow(index: Long, vector: Vector)
  *              be determined by the max row index plus one.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the size of the first row.
+ * @since 1.0.0
  */
 @Experimental
 class IndexedRowMatrix(
@@ -48,9 +50,15 @@ class IndexedRowMatrix(
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically.
+    * @since 1.0.0
+    * */
   def this(rows: RDD[IndexedRow]) = this(rows, 0L, 0)
 
+  /**
+   *
+   * @since 1.0.0
+   */
   override def numCols(): Long = {
     if (nCols <= 0) {
       // Calling `first` will throw an exception if `rows` is empty.
@@ -59,6 +67,10 @@ class IndexedRowMatrix(
     nCols
   }
 
+  /**
+   *
+   * @since 1.0.0
+   */
   override def numRows(): Long = {
     if (nRows <= 0L) {
       // Reduce will throw an exception if `rows` is empty.
@@ -70,12 +82,15 @@ class IndexedRowMatrix(
   /**
    * Drops row indices and converts this matrix to a
    * [[org.apache.spark.mllib.linalg.distributed.RowMatrix]].
+   * @since 1.0.0
    */
   def toRowMatrix(): RowMatrix = {
     new RowMatrix(rows.map(_.vector), 0L, nCols)
   }
 
-  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024. */
+  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024.
+    * @since 1.3.0
+    * */
   def toBlockMatrix(): BlockMatrix = {
     toBlockMatrix(1024, 1024)
   }
@@ -87,6 +102,7 @@ class IndexedRowMatrix(
    * @param colsPerBlock The number of columns of each block. The blocks at the right edge may have
    *                     a smaller value. Must be an integer value greater than 0.
    * @return a [[BlockMatrix]]
+   * @since 1.3.0
    */
   def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix = {
     // TODO: This implementation may be optimized
@@ -96,6 +112,7 @@ class IndexedRowMatrix(
   /**
    * Converts this matrix to a
    * [[org.apache.spark.mllib.linalg.distributed.CoordinateMatrix]].
+   * @since 1.3.0
    */
   def toCoordinateMatrix(): CoordinateMatrix = {
     val entries = rows.flatMap { row =>
@@ -132,6 +149,7 @@ class IndexedRowMatrix(
    * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
    *              are treated as zero, where sigma(0) is the largest singular value.
    * @return SingularValueDecomposition(U, s, V)
+   * @since 1.0.0
    */
   def computeSVD(
       k: Int,
@@ -158,6 +176,7 @@ class IndexedRowMatrix(
    *
    * @param B a local matrix whose number of rows must match the number of columns of this matrix
    * @return an IndexedRowMatrix representing the product, which preserves partitioning
+   * @since 1.0.0
    */
   def multiply(B: Matrix): IndexedRowMatrix = {
     val mat = toRowMatrix().multiply(B)
@@ -169,6 +188,7 @@ class IndexedRowMatrix(
 
   /**
    * Computes the Gramian matrix `A^T A`.
+   * @since 1.0.0
    */
   def computeGramianMatrix(): Matrix = {
     toRowMatrix().computeGramianMatrix()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index bfc90c9ef8527..b2e94f2dd6201 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -44,6 +44,7 @@ import org.apache.spark.storage.StorageLevel
  *              be determined by the number of records in the RDD `rows`.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the size of the first row.
+ * @since 1.0.0
  */
 @Experimental
 class RowMatrix(
@@ -51,10 +52,14 @@ class RowMatrix(
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix with Logging {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically.
+    * @since 1.0.0
+    * */
   def this(rows: RDD[Vector]) = this(rows, 0L, 0)
 
-  /** Gets or computes the number of columns. */
+  /** Gets or computes the number of columns.
+    * @since 1.0.0
+    * */
   override def numCols(): Long = {
     if (nCols <= 0) {
       try {
@@ -69,7 +74,9 @@ class RowMatrix(
     nCols
   }
 
-  /** Gets or computes the number of rows. */
+  /** Gets or computes the number of rows.
+    * @since 1.0.0
+    * */
   override def numRows(): Long = {
     if (nRows <= 0L) {
       nRows = rows.count()
@@ -107,6 +114,7 @@ class RowMatrix(
 
   /**
    * Computes the Gramian matrix `A^T A`.
+   * @since 1.0.0
    */
   def computeGramianMatrix(): Matrix = {
     val n = numCols().toInt
@@ -177,6 +185,7 @@ class RowMatrix(
    * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
    *              are treated as zero, where sigma(0) is the largest singular value.
    * @return SingularValueDecomposition(U, s, V). U = null if computeU = false.
+   * @since 1.0.0
    */
   def computeSVD(
       k: Int,
@@ -317,6 +326,7 @@ class RowMatrix(
   /**
    * Computes the covariance matrix, treating each row as an observation.
    * @return a local dense matrix of size n x n
+   * @since 1.0.0
    */
   def computeCovariance(): Matrix = {
     val n = numCols().toInt
@@ -370,6 +380,7 @@ class RowMatrix(
    *
    * @param k number of top principal components.
    * @return a matrix of size n-by-k, whose columns are principal components
+   * @since 1.0.0
    */
   def computePrincipalComponents(k: Int): Matrix = {
     val n = numCols().toInt
@@ -388,6 +399,7 @@ class RowMatrix(
 
   /**
    * Computes column-wise summary statistics.
+   * @since 1.0.0
    */
   def computeColumnSummaryStatistics(): MultivariateStatisticalSummary = {
     val summary = rows.treeAggregate(new MultivariateOnlineSummarizer)(
@@ -403,6 +415,7 @@ class RowMatrix(
    * @param B a local matrix whose number of rows must match the number of columns of this matrix
    * @return a [[org.apache.spark.mllib.linalg.distributed.RowMatrix]] representing the product,
    *         which preserves partitioning
+   * @since 1.0.0
    */
   def multiply(B: Matrix): RowMatrix = {
     val n = numCols().toInt
@@ -435,6 +448,7 @@ class RowMatrix(
    *
    * @return An n x n sparse upper-triangular matrix of cosine similarities between
    *         columns of this matrix.
+   * @since 1.2.0
    */
   def columnSimilarities(): CoordinateMatrix = {
     columnSimilarities(0.0)
@@ -478,6 +492,7 @@ class RowMatrix(
    *                  with the cost vs estimate quality trade-off described above.
    * @return An n x n sparse upper-triangular matrix of cosine similarities
    *         between columns of this matrix.
+   * @since 1.2.0
    */
   def columnSimilarities(threshold: Double): CoordinateMatrix = {
     require(threshold >= 0, s"Threshold cannot be negative: $threshold")
@@ -656,6 +671,9 @@ class RowMatrix(
   }
 }
 
+/**
+ * @since 1.0.0
+ */
 @Experimental
 object RowMatrix {
 

From 52ae952574f5d641a398dd185e09e5a79318c8a9 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 17 Aug 2015 17:25:14 -0700
Subject: [PATCH 285/633] [SPARK-9974] [BUILD] [SQL] Makes sure
 com.twitter:parquet-hadoop-bundle:1.6.0 is in SBT assembly jar

PR #7967 enables Spark SQL to persist Parquet tables in Hive compatible format when possible. One of the consequence is that, we have to set input/output classes to `MapredParquetInputFormat`/`MapredParquetOutputFormat`, which rely on com.twitter:parquet-hadoop:1.6.0 bundled with Hive 1.2.1.

When loading such a table in Spark SQL, `o.a.h.h.ql.metadata.Table` first loads these input/output format classes, and thus classes in com.twitter:parquet-hadoop:1.6.0.  However, the scope of this dependency is defined as "runtime", and is not packaged into Spark assembly jar.  This results in a `ClassNotFoundException`.

This issue can be worked around by asking users to add parquet-hadoop 1.6.0 via the `--driver-class-path` option.  However, considering Maven build is immune to this problem, I feel it can be confusing and inconvenient for users.

So this PR fixes this issue by changing scope of parquet-hadoop 1.6.0 to "compile".

Author: Cheng Lian <lian@databricks.com>

Closes #8198 from liancheng/spark-9974/bundle-parquet-1.6.0.
---
 pom.xml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pom.xml b/pom.xml
index cfd7d32563f2a..9bfca1c4170bb 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1598,7 +1598,7 @@
         <groupId>com.twitter</groupId>
         <artifactId>parquet-hadoop-bundle</artifactId>
         <version>${hive.parquet.version}</version>
-        <scope>runtime</scope>
+        <scope>compile</scope>
       </dependency>
       <dependency>
         <groupId>org.apache.flume</groupId>

From 0076e8212334c613599dcbc2ac23f49e9e50cc44 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 17 Aug 2015 17:25:41 -0700
Subject: [PATCH 286/633] [SPARK-9768] [PYSPARK] [ML] Add Python API and user
 guide for ml.feature.ElementwiseProduct

Add Python API, user guide and example for ml.feature.ElementwiseProduct.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8061 from yanboliang/SPARK-9768.
---
 docs/ml-features.md          | 23 ++++++++++---
 python/pyspark/ml/feature.py | 67 +++++++++++++++++++++++++++++++++---
 2 files changed, 81 insertions(+), 9 deletions(-)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index cec2cbe673407..6b2e36b3536dc 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1212,7 +1212,7 @@ v_N
 This example below demonstrates how to transform vectors using a transforming vector value.
 
 <div class="codetabs">
-<div data-lang="scala">
+<div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.ml.feature.ElementwiseProduct
 import org.apache.spark.mllib.linalg.Vectors
@@ -1229,12 +1229,12 @@ val transformer = new ElementwiseProduct()
   .setOutputCol("transformedVector")
 
 // Batch transform the vectors to create new column:
-val transformedData = transformer.transform(dataFrame)
+transformer.transform(dataFrame).show()
 
 {% endhighlight %}
 </div>
 
-<div data-lang="java">
+<div data-lang="java" markdown="1">
 {% highlight java %}
 import com.google.common.collect.Lists;
 
@@ -1267,10 +1267,25 @@ ElementwiseProduct transformer = new ElementwiseProduct()
   .setInputCol("vector")
   .setOutputCol("transformedVector");
 // Batch transform the vectors to create new column:
-DataFrame transformedData = transformer.transform(dataFrame);
+transformer.transform(dataFrame).show();
 
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+{% highlight python %}
+from pyspark.ml.feature import ElementwiseProduct
+from pyspark.mllib.linalg import Vectors
+
+data = [(Vectors.dense([1.0, 2.0, 3.0]),), (Vectors.dense([4.0, 5.0, 6.0]),)]
+df = sqlContext.createDataFrame(data, ["vector"])
+transformer = ElementwiseProduct(scalingVec=Vectors.dense([0.0, 1.0, 2.0]), 
+                                 inputCol="vector", outputCol="transformedVector")
+transformer.transform(df).show()
+
+{% endhighlight %}
+</div>
+
 </div>
 
 ## VectorAssembler
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 535d55326646c..04b2b2ccc9e55 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -26,11 +26,11 @@
 from pyspark.mllib.common import inherit_doc
 from pyspark.mllib.linalg import _convert_to_vector
 
-__all__ = ['Binarizer', 'Bucketizer', 'HashingTF', 'IDF', 'IDFModel', 'NGram', 'Normalizer',
-           'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer', 'StandardScaler',
-           'StandardScalerModel', 'StringIndexer', 'StringIndexerModel', 'Tokenizer',
-           'VectorAssembler', 'VectorIndexer', 'Word2Vec', 'Word2VecModel', 'PCA',
-           'PCAModel', 'RFormula', 'RFormulaModel']
+__all__ = ['Binarizer', 'Bucketizer', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
+           'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer',
+           'StandardScaler', 'StandardScalerModel', 'StringIndexer', 'StringIndexerModel',
+           'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec', 'Word2VecModel',
+           'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
 
 
 @inherit_doc
@@ -166,6 +166,63 @@ def getSplits(self):
         return self.getOrDefault(self.splits)
 
 
+@inherit_doc
+class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    Outputs the Hadamard product (i.e., the element-wise product) of each input vector
+    with a provided "weight" vector. In other words, it scales each column of the dataset
+    by a scalar multiplier.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([(Vectors.dense([2.0, 1.0, 3.0]),)], ["values"])
+    >>> ep = ElementwiseProduct(scalingVec=Vectors.dense([1.0, 2.0, 3.0]),
+    ...     inputCol="values", outputCol="eprod")
+    >>> ep.transform(df).head().eprod
+    DenseVector([2.0, 2.0, 9.0])
+    >>> ep.setParams(scalingVec=Vectors.dense([2.0, 3.0, 5.0])).transform(df).head().eprod
+    DenseVector([4.0, 3.0, 15.0])
+    """
+
+    # a placeholder to make it appear in the generated doc
+    scalingVec = Param(Params._dummy(), "scalingVec", "vector for hadamard product, " +
+                       "it must be MLlib Vector type.")
+
+    @keyword_only
+    def __init__(self, scalingVec=None, inputCol=None, outputCol=None):
+        """
+        __init__(self, scalingVec=None, inputCol=None, outputCol=None)
+        """
+        super(ElementwiseProduct, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.ElementwiseProduct",
+                                            self.uid)
+        self.scalingVec = Param(self, "scalingVec", "vector for hadamard product, " +
+                                "it must be MLlib Vector type.")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, scalingVec=None, inputCol=None, outputCol=None):
+        """
+        setParams(self, scalingVec=None, inputCol=None, outputCol=None)
+        Sets params for this ElementwiseProduct.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setScalingVec(self, value):
+        """
+        Sets the value of :py:attr:`scalingVec`.
+        """
+        self._paramMap[self.scalingVec] = value
+        return self
+
+    def getScalingVec(self):
+        """
+        Gets the value of scalingVec or its default value.
+        """
+        return self.getOrDefault(self.scalingVec)
+
+
 @inherit_doc
 class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures):
     """

From 18523c130548f0438dff8d1f25531fd2ed36e517 Mon Sep 17 00:00:00 2001
From: Prayag Chandran <prayagchandran@gmail.com>
Date: Mon, 17 Aug 2015 17:26:08 -0700
Subject: [PATCH 287/633] SPARK-8916 [Documentation, MLlib] Add @since tags to
 mllib.regression

Added since tags to mllib.regression

Author: Prayag Chandran <prayagchandran@gmail.com>

Closes #7518 from prayagchandran/sinceTags and squashes the following commits:

fa4dda2 [Prayag Chandran] Re-formatting
6c6d584 [Prayag Chandran] Corrected a few tags. Removed few unnecessary tags
1a0365f [Prayag Chandran] Reformating and adding a few more tags
89fdb66 [Prayag Chandran] SPARK-8916 [Documentation, MLlib] Add @since tags to mllib.regression
---
 .../GeneralizedLinearAlgorithm.scala          | 26 ++++++++++++++-
 .../mllib/regression/IsotonicRegression.scala | 23 ++++++++++++-
 .../spark/mllib/regression/LabeledPoint.scala |  6 ++++
 .../apache/spark/mllib/regression/Lasso.scala | 21 ++++++++++++
 .../mllib/regression/LinearRegression.scala   | 21 ++++++++++++
 .../mllib/regression/RegressionModel.scala    |  9 ++++++
 .../mllib/regression/RidgeRegression.scala    | 21 ++++++++++++
 .../regression/StreamingLinearAlgorithm.scala | 32 ++++++++++++++++---
 .../StreamingLinearRegressionWithSGD.scala    | 21 ++++++++----
 9 files changed, 168 insertions(+), 12 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 6709bd79bc820..2980b94de35b0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -34,6 +34,8 @@ import org.apache.spark.storage.StorageLevel
  *
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
+ *
+ * @since 0.8.0
  */
 @DeveloperApi
 abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double)
@@ -53,6 +55,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    *
    * @param testData RDD representing data points to be predicted
    * @return RDD[Double] where each entry contains the corresponding prediction
+   *
+   * @since 1.0.0
    */
   def predict(testData: RDD[Vector]): RDD[Double] = {
     // A small optimization to avoid serializing the entire model. Only the weightsMatrix
@@ -71,6 +75,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    *
    * @param testData array representing a single data point
    * @return Double prediction from the trained model
+   *
+   * @since 1.0.0
    */
   def predict(testData: Vector): Double = {
     predictPoint(testData, weights, intercept)
@@ -88,6 +94,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
  * :: DeveloperApi ::
  * GeneralizedLinearAlgorithm implements methods to train a Generalized Linear Model (GLM).
  * This class should be extended with an Optimizer to create a new GLM.
+ *
+ * @since 0.8.0
  */
 @DeveloperApi
 abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
@@ -95,7 +103,11 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
   protected val validators: Seq[RDD[LabeledPoint] => Boolean] = List()
 
-  /** The optimizer to solve the problem. */
+  /**
+   * The optimizer to solve the problem.
+   *
+   * @since 1.0.0
+   */
   def optimizer: Optimizer
 
   /** Whether to add intercept (default: false). */
@@ -130,6 +142,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
   /**
    * The dimension of training features.
+   *
+   * @since 1.4.0
    */
   def getNumFeatures: Int = this.numFeatures
 
@@ -153,12 +167,16 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
   /**
    * Get if the algorithm uses addIntercept
+   *
+   * @since 1.4.0
    */
   def isAddIntercept: Boolean = this.addIntercept
 
   /**
    * Set if the algorithm should add an intercept. Default false.
    * We set the default to false because adding the intercept will cause memory allocation.
+   *
+   * @since 0.8.0
    */
   def setIntercept(addIntercept: Boolean): this.type = {
     this.addIntercept = addIntercept
@@ -167,6 +185,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
 
   /**
    * Set if the algorithm should validate data before training. Default true.
+   *
+   * @since 0.8.0
    */
   def setValidateData(validateData: Boolean): this.type = {
     this.validateData = validateData
@@ -176,6 +196,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * Run the algorithm with the configured parameters on an input
    * RDD of LabeledPoint entries.
+   *
+   * @since 0.8.0
    */
   def run(input: RDD[LabeledPoint]): M = {
     if (numFeatures < 0) {
@@ -208,6 +230,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * Run the algorithm with the configured parameters on an input RDD
    * of LabeledPoint entries starting from the initial weights provided.
+   *
+   * @since 1.0.0
    */
   def run(input: RDD[LabeledPoint], initialWeights: Vector): M = {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
index f3b46c75c05f3..8995591d9e8ce 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
@@ -46,6 +46,8 @@ import org.apache.spark.sql.SQLContext
  * @param predictions Array of predictions associated to the boundaries at the same index.
  *                    Results of isotonic regression and therefore monotone.
  * @param isotonic indicates whether this is isotonic or antitonic.
+ *
+ * @since 1.3.0
  */
 @Experimental
 class IsotonicRegressionModel (
@@ -59,7 +61,11 @@ class IsotonicRegressionModel (
   assertOrdered(boundaries)
   assertOrdered(predictions)(predictionOrd)
 
-  /** A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter. */
+  /**
+   * A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter.
+   *
+   * @since 1.4.0
+   */
   def this(boundaries: java.lang.Iterable[Double],
       predictions: java.lang.Iterable[Double],
       isotonic: java.lang.Boolean) = {
@@ -83,6 +89,8 @@ class IsotonicRegressionModel (
    *
    * @param testData Features to be labeled.
    * @return Predicted labels.
+   *
+   * @since 1.3.0
    */
   def predict(testData: RDD[Double]): RDD[Double] = {
     testData.map(predict)
@@ -94,6 +102,8 @@ class IsotonicRegressionModel (
    *
    * @param testData Features to be labeled.
    * @return Predicted labels.
+   *
+   * @since 1.3.0
    */
   def predict(testData: JavaDoubleRDD): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(predict(testData.rdd.retag.asInstanceOf[RDD[Double]]))
@@ -114,6 +124,8 @@ class IsotonicRegressionModel (
    *         3) If testData falls between two values in boundary array then prediction is treated
    *           as piecewise linear function and interpolated value is returned. In case there are
    *           multiple values with the same boundary then the same rules as in 2) are used.
+   *
+   * @since 1.3.0
    */
   def predict(testData: Double): Double = {
 
@@ -148,6 +160,9 @@ class IsotonicRegressionModel (
   /** A convenient method for boundaries called by the Python API. */
   private[mllib] def predictionVector: Vector = Vectors.dense(predictions)
 
+  /**
+   * @since 1.4.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     IsotonicRegressionModel.SaveLoadV1_0.save(sc, path, boundaries, predictions, isotonic)
   }
@@ -155,6 +170,9 @@ class IsotonicRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.4.0
+ */
 object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
 
   import org.apache.spark.mllib.util.Loader._
@@ -200,6 +218,9 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
     }
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def load(sc: SparkContext, path: String): IsotonicRegressionModel = {
     implicit val formats = DefaultFormats
     val (loadedClassName, version, metadata) = loadMetadata(sc, path)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
index d5fea822ad77b..8b51011eeb297 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
@@ -28,6 +28,8 @@ import org.apache.spark.SparkException
  *
  * @param label Label for this data point.
  * @param features List of features for this data point.
+ *
+ * @since 0.8.0
  */
 @BeanInfo
 case class LabeledPoint(label: Double, features: Vector) {
@@ -38,11 +40,15 @@ case class LabeledPoint(label: Double, features: Vector) {
 
 /**
  * Parser for [[org.apache.spark.mllib.regression.LabeledPoint]].
+ *
+ * @since 1.1.0
  */
 object LabeledPoint {
   /**
    * Parses a string resulted from `LabeledPoint#toString` into
    * an [[org.apache.spark.mllib.regression.LabeledPoint]].
+   *
+   * @since 1.1.0
    */
   def parse(s: String): LabeledPoint = {
     if (s.startsWith("(")) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index 4f482384f0f38..03eb589b05a0e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -30,6 +30,8 @@ import org.apache.spark.rdd.RDD
  *
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
+ *
+ * @since 0.8.0
  */
 class LassoModel (
     override val weights: Vector,
@@ -44,6 +46,9 @@ class LassoModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -51,8 +56,14 @@ class LassoModel (
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.3.0
+ */
 object LassoModel extends Loader[LassoModel] {
 
+  /**
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): LassoModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -106,6 +117,8 @@ class LassoWithSGD private (
 
 /**
  * Top-level methods for calling Lasso.
+ *
+ * @since 0.8.0
  */
 object LassoWithSGD {
 
@@ -123,6 +136,8 @@ object LassoWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
+   *
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -146,6 +161,8 @@ object LassoWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -167,6 +184,8 @@ object LassoWithSGD {
    * @param regParam Regularization parameter.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LassoModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -185,6 +204,8 @@ object LassoWithSGD {
    *              matrix A as well as the corresponding right hand side label y
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LassoModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index 9453c4f66c216..fb5c220daaedb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -30,6 +30,8 @@ import org.apache.spark.rdd.RDD
  *
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
+ *
+ * @since 0.8.0
  */
 class LinearRegressionModel (
     override val weights: Vector,
@@ -44,6 +46,9 @@ class LinearRegressionModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -51,8 +56,14 @@ class LinearRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.3.0
+ */
 object LinearRegressionModel extends Loader[LinearRegressionModel] {
 
+  /**
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): LinearRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -105,6 +116,8 @@ class LinearRegressionWithSGD private[mllib] (
 
 /**
  * Top-level methods for calling LinearRegression.
+ *
+ * @since 0.8.0
  */
 object LinearRegressionWithSGD {
 
@@ -121,6 +134,8 @@ object LinearRegressionWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
+   *
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -142,6 +157,8 @@ object LinearRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param miniBatchFraction Fraction of data to be used per iteration.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -161,6 +178,8 @@ object LinearRegressionWithSGD {
    * @param stepSize Step size to be used for each iteration of Gradient Descent.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LinearRegressionModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -178,6 +197,8 @@ object LinearRegressionWithSGD {
    *              matrix A as well as the corresponding right hand side label y
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LinearRegressionModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
index 214ac4d0ed7dd..b097fd38fdd82 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
@@ -24,6 +24,9 @@ import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
 
+/**
+ * @since 0.8.0
+ */
 @Experimental
 trait RegressionModel extends Serializable {
   /**
@@ -31,6 +34,8 @@ trait RegressionModel extends Serializable {
    *
    * @param testData RDD representing data points to be predicted
    * @return RDD[Double] where each entry contains the corresponding prediction
+   *
+   * @since 1.0.0
    */
   def predict(testData: RDD[Vector]): RDD[Double]
 
@@ -39,6 +44,8 @@ trait RegressionModel extends Serializable {
    *
    * @param testData array representing a single data point
    * @return Double prediction from the trained model
+   *
+   * @since 1.0.0
    */
   def predict(testData: Vector): Double
 
@@ -46,6 +53,8 @@ trait RegressionModel extends Serializable {
    * Predict values for examples stored in a JavaRDD.
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
+   *
+   * @since 1.0.0
    */
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index 7d28ffad45c92..5bced6b4b7b53 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -31,6 +31,8 @@ import org.apache.spark.rdd.RDD
  *
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
+ *
+ * @since 0.8.0
  */
 class RidgeRegressionModel (
     override val weights: Vector,
@@ -45,6 +47,9 @@ class RidgeRegressionModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -52,8 +57,14 @@ class RidgeRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.3.0
+ */
 object RidgeRegressionModel extends Loader[RidgeRegressionModel] {
 
+  /**
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): RidgeRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -108,6 +119,8 @@ class RidgeRegressionWithSGD private (
 
 /**
  * Top-level methods for calling RidgeRegression.
+ *
+ * @since 0.8.0
  */
 object RidgeRegressionWithSGD {
 
@@ -124,6 +137,8 @@ object RidgeRegressionWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -146,6 +161,8 @@ object RidgeRegressionWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -166,6 +183,8 @@ object RidgeRegressionWithSGD {
    * @param regParam Regularization parameter.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a RidgeRegressionModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -183,6 +202,8 @@ object RidgeRegressionWithSGD {
    * @param input RDD of (label, array of features) pairs.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a RidgeRegressionModel which has the weights and offset from training.
+   *
+   * @since 0.8.0
    */
   def train(
       input: RDD[LabeledPoint],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
index 141052ba813ee..a2ab95c474765 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
@@ -53,6 +53,8 @@ import org.apache.spark.streaming.dstream.DStream
  * It is also ok to call trainOn on different streams; this will update
  * the model using each of the different sources, in sequence.
  *
+ *
+ * @since 1.1.0
  */
 @DeveloperApi
 abstract class StreamingLinearAlgorithm[
@@ -65,7 +67,11 @@ abstract class StreamingLinearAlgorithm[
   /** The algorithm to use for updating. */
   protected val algorithm: A
 
-  /** Return the latest model. */
+  /**
+   * Return the latest model.
+   *
+   * @since 1.1.0
+   */
   def latestModel(): M = {
     model.get
   }
@@ -77,6 +83,8 @@ abstract class StreamingLinearAlgorithm[
    * batch of data from the stream.
    *
    * @param data DStream containing labeled data
+   *
+   * @since 1.3.0
    */
   def trainOn(data: DStream[LabeledPoint]): Unit = {
     if (model.isEmpty) {
@@ -95,7 +103,11 @@ abstract class StreamingLinearAlgorithm[
     }
   }
 
-  /** Java-friendly version of `trainOn`. */
+  /**
+   * Java-friendly version of `trainOn`.
+   *
+   * @since 1.3.0
+   */
   def trainOn(data: JavaDStream[LabeledPoint]): Unit = trainOn(data.dstream)
 
   /**
@@ -103,6 +115,8 @@ abstract class StreamingLinearAlgorithm[
    *
    * @param data DStream containing feature vectors
    * @return DStream containing predictions
+   *
+   * @since 1.1.0
    */
   def predictOn(data: DStream[Vector]): DStream[Double] = {
     if (model.isEmpty) {
@@ -111,7 +125,11 @@ abstract class StreamingLinearAlgorithm[
     data.map{x => model.get.predict(x)}
   }
 
-  /** Java-friendly version of `predictOn`. */
+  /**
+   * Java-friendly version of `predictOn`.
+   *
+   * @since 1.1.0
+   */
   def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Double] = {
     JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Double]])
   }
@@ -121,6 +139,8 @@ abstract class StreamingLinearAlgorithm[
    * @param data DStream containing feature vectors
    * @tparam K key type
    * @return DStream containing the input keys and the predictions as values
+   *
+   * @since 1.1.0
    */
   def predictOnValues[K: ClassTag](data: DStream[(K, Vector)]): DStream[(K, Double)] = {
     if (model.isEmpty) {
@@ -130,7 +150,11 @@ abstract class StreamingLinearAlgorithm[
   }
 
 
-  /** Java-friendly version of `predictOnValues`. */
+  /**
+   * Java-friendly version of `predictOnValues`.
+   *
+   * @since 1.3.0
+   */
   def predictOnValues[K](data: JavaPairDStream[K, Vector]): JavaPairDStream[K, java.lang.Double] = {
     implicit val tag = fakeClassTag[K]
     JavaPairDStream.fromPairDStream(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
index c6d04464a12ba..537a05274eec2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
@@ -39,7 +39,6 @@ import org.apache.spark.mllib.linalg.Vector
  *    .setNumIterations(10)
  *    .setInitialWeights(Vectors.dense(...))
  *    .trainOn(DStream)
- *
  */
 @Experimental
 class StreamingLinearRegressionWithSGD private[mllib] (
@@ -61,31 +60,41 @@ class StreamingLinearRegressionWithSGD private[mllib] (
 
   protected var model: Option[LinearRegressionModel] = None
 
-  /** Set the step size for gradient descent. Default: 0.1. */
+  /**
+   * Set the step size for gradient descent. Default: 0.1.
+   */
   def setStepSize(stepSize: Double): this.type = {
     this.algorithm.optimizer.setStepSize(stepSize)
     this
   }
 
-  /** Set the number of iterations of gradient descent to run per update. Default: 50. */
+  /**
+   * Set the number of iterations of gradient descent to run per update. Default: 50.
+   */
   def setNumIterations(numIterations: Int): this.type = {
     this.algorithm.optimizer.setNumIterations(numIterations)
     this
   }
 
-  /** Set the fraction of each batch to use for updates. Default: 1.0. */
+  /**
+   * Set the fraction of each batch to use for updates. Default: 1.0.
+   */
   def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
     this.algorithm.optimizer.setMiniBatchFraction(miniBatchFraction)
     this
   }
 
-  /** Set the initial weights. */
+  /**
+   * Set the initial weights.
+   */
   def setInitialWeights(initialWeights: Vector): this.type = {
     this.model = Some(algorithm.createModel(initialWeights, 0.0))
     this
   }
 
-  /** Set the convergence tolerance. */
+  /**
+   * Set the convergence tolerance.
+   */
   def setConvergenceTol(tolerance: Double): this.type = {
     this.algorithm.optimizer.setConvergenceTol(tolerance)
     this

From 0b6b01761370629ce387c143a25d41f3a334ff28 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 17 Aug 2015 17:53:24 -0700
Subject: [PATCH 288/633] [SPARK-9898] [MLLIB] Prefix Span user guide

Adds user guide for `PrefixSpan`, including Scala and Java example code.

mengxr zhangjiajin

Author: Feynman Liang <fliang@databricks.com>

Closes #8253 from feynmanliang/SPARK-9898.
---
 docs/mllib-frequent-pattern-mining.md | 96 +++++++++++++++++++++++++++
 docs/mllib-guide.md                   |  1 +
 2 files changed, 97 insertions(+)

diff --git a/docs/mllib-frequent-pattern-mining.md b/docs/mllib-frequent-pattern-mining.md
index bcc066a185526..8ea4389266484 100644
--- a/docs/mllib-frequent-pattern-mining.md
+++ b/docs/mllib-frequent-pattern-mining.md
@@ -96,3 +96,99 @@ for (FPGrowth.FreqItemset<String> itemset: model.freqItemsets().toJavaRDD().coll
 
 </div>
 </div>
+
+## PrefixSpan
+
+PrefixSpan is a sequential pattern mining algorithm described in
+[Pei et al., Mining Sequential Patterns by Pattern-Growth: The
+PrefixSpan Approach](http://dx.doi.org/10.1109%2FTKDE.2004.77). We refer
+the reader to the referenced paper for formalizing the sequential
+pattern mining problem.
+
+MLlib's PrefixSpan implementation takes the following parameters:
+
+* `minSupport`: the minimum support required to be considered a frequent
+  sequential pattern.
+* `maxPatternLength`: the maximum length of a frequent sequential
+  pattern. Any frequent pattern exceeding this length will not be
+  included in the results.
+* `maxLocalProjDBSize`: the maximum number of items allowed in a
+  prefix-projected database before local iterative processing of the
+  projected databse begins. This parameter should be tuned with respect
+  to the size of your executors.
+
+**Examples**
+
+The following example illustrates PrefixSpan running on the sequences
+(using same notation as Pei et al):
+
+~~~
+  <(12)3>
+  <1(32)(12)>
+  <(12)5>
+  <6>
+~~~
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+[`PrefixSpan`](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpan) implements the
+PrefixSpan algorithm.
+Calling `PrefixSpan.run` returns a
+[`PrefixSpanModel`](api/scala/index.html#org.apache.spark.mllib.fpm.PrefixSpanModel)
+that stores the frequent sequences with their frequencies.
+
+{% highlight scala %}
+import org.apache.spark.mllib.fpm.PrefixSpan
+
+val sequences = sc.parallelize(Seq(
+    Array(Array(1, 2), Array(3)),
+    Array(Array(1), Array(3, 2), Array(1, 2)),
+    Array(Array(1, 2), Array(5)),
+    Array(Array(6))
+  ), 2).cache()
+val prefixSpan = new PrefixSpan()
+  .setMinSupport(0.5)
+  .setMaxPatternLength(5)
+val model = prefixSpan.run(sequences)
+model.freqSequences.collect().foreach { freqSequence =>
+println(
+  freqSequence.sequence.map(_.mkString("[", ", ", "]")).mkString("[", ", ", "]") + ", " + freqSequence.freq)
+}
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`PrefixSpan`](api/java/org/apache/spark/mllib/fpm/PrefixSpan.html) implements the
+PrefixSpan algorithm.
+Calling `PrefixSpan.run` returns a
+[`PrefixSpanModel`](api/java/org/apache/spark/mllib/fpm/PrefixSpanModel.html)
+that stores the frequent sequences with their frequencies.
+
+{% highlight java %}
+import java.util.Arrays;
+import java.util.List;
+
+import org.apache.spark.mllib.fpm.PrefixSpan;
+import org.apache.spark.mllib.fpm.PrefixSpanModel;
+
+JavaRDD<List<List<Integer>>> sequences = sc.parallelize(Arrays.asList(
+  Arrays.asList(Arrays.asList(1, 2), Arrays.asList(3)),
+  Arrays.asList(Arrays.asList(1), Arrays.asList(3, 2), Arrays.asList(1, 2)),
+  Arrays.asList(Arrays.asList(1, 2), Arrays.asList(5)),
+  Arrays.asList(Arrays.asList(6))
+), 2);
+PrefixSpan prefixSpan = new PrefixSpan()
+  .setMinSupport(0.5)
+  .setMaxPatternLength(5);
+PrefixSpanModel<Integer> model = prefixSpan.run(sequences);
+for (PrefixSpan.FreqSequence<Integer> freqSeq: model.freqSequences().toJavaRDD().collect()) {
+  System.out.println(freqSeq.javaSequence() + ", " + freqSeq.freq());
+}
+{% endhighlight %}
+
+</div>
+</div>
+
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index e8000ff478300..7851175b98230 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -48,6 +48,7 @@ This lists functionality included in `spark.mllib`, the main MLlib API.
 * [Feature extraction and transformation](mllib-feature-extraction.html)
 * [Frequent pattern mining](mllib-frequent-pattern-mining.html)
   * [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
+  * [PrefixSpan](mllib-frequent-pattern-mining.html#prefix-span)
 * [Evaluation Metrics](mllib-evaluation-metrics.html)
 * [Optimization (developer)](mllib-optimization.html)
   * [stochastic gradient descent](mllib-optimization.html#stochastic-gradient-descent-sgd)

From f9d1a92aa1bac4494022d78559b871149579e6e8 Mon Sep 17 00:00:00 2001
From: Sandy Ryza <sandy@cloudera.com>
Date: Mon, 17 Aug 2015 17:57:51 -0700
Subject: [PATCH 289/633] [SPARK-7707] User guide and example code for
 KernelDensity

Author: Sandy Ryza <sandy@cloudera.com>

Closes #8230 from sryza/sandy-spark-7707.
---
 docs/mllib-statistics.md | 77 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)

diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md
index be04d0b4b53a8..80a9d064c0fe5 100644
--- a/docs/mllib-statistics.md
+++ b/docs/mllib-statistics.md
@@ -528,5 +528,82 @@ u = RandomRDDs.uniformRDD(sc, 1000000L, 10)
 v = u.map(lambda x: 1.0 + 2.0 * x)
 {% endhighlight %}
 </div>
+</div>
+
+## Kernel density estimation
+
+[Kernel density estimation](https://en.wikipedia.org/wiki/Kernel_density_estimation) is a technique
+useful for visualizing empirical probability distributions without requiring assumptions about the
+particular distribution that the observed samples are drawn from. It computes an estimate of the
+probability density function of a random variables, evaluated at a given set of points. It achieves
+this estimate by expressing the PDF of the empirical distribution at a particular point as the the
+mean of PDFs of normal distributions centered around each of the samples.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+[`KernelDensity`](api/scala/index.html#org.apache.spark.mllib.stat.KernelDensity) provides methods
+to compute kernel density estimates from an RDD of samples. The following example demonstrates how
+to do so.
+
+{% highlight scala %}
+import org.apache.spark.mllib.stat.KernelDensity
+import org.apache.spark.rdd.RDD
+
+val data: RDD[Double] = ... // an RDD of sample data
+
+// Construct the density estimator with the sample data and a standard deviation for the Gaussian
+// kernels
+val kd = new KernelDensity()
+  .setSample(data)
+  .setBandwidth(3.0)
+
+// Find density estimates for the given values
+val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+[`KernelDensity`](api/java/index.html#org.apache.spark.mllib.stat.KernelDensity) provides methods
+to compute kernel density estimates from an RDD of samples. The following example demonstrates how
+to do so.
+
+{% highlight java %}
+import org.apache.spark.mllib.stat.KernelDensity;
+import org.apache.spark.rdd.RDD;
+
+RDD<Double> data = ... // an RDD of sample data
+
+// Construct the density estimator with the sample data and a standard deviation for the Gaussian
+// kernels
+KernelDensity kd = new KernelDensity()
+  .setSample(data)
+  .setBandwidth(3.0);
+
+// Find density estimates for the given values
+double[] densities = kd.estimate(new double[] {-1.0, 2.0, 5.0});
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+[`KernelDensity`](api/python/pyspark.mllib.html#pyspark.mllib.stat.KernelDensity) provides methods
+to compute kernel density estimates from an RDD of samples. The following example demonstrates how
+to do so.
+
+{% highlight python %}
+from pyspark.mllib.stat import KernelDensity
+
+data = ... # an RDD of sample data
+
+# Construct the density estimator with the sample data and a standard deviation for the Gaussian
+# kernels
+kd = KernelDensity()
+kd.setSample(data)
+kd.setBandwidth(3.0)
+
+# Find density estimates for the given values
+densities = kd.estimate([-1.0, 2.0, 5.0])
+{% endhighlight %}
+</div>
 
 </div>

From c90c605dc6a876aef3cc204ac15cd65bab9743ad Mon Sep 17 00:00:00 2001
From: "jose.cambronero" <jose.cambronero@cloudera.com>
Date: Mon, 17 Aug 2015 19:09:45 -0700
Subject: [PATCH 290/633] [SPARK-9902] [MLLIB] Add Java and Python examples to
 user guide for 1-sample KS test

added doc examples for python.

Author: jose.cambronero <jose.cambronero@cloudera.com>

Closes #8154 from josepablocam/spark_9902.
---
 docs/mllib-statistics.md | 51 ++++++++++++++++++++++++++++++++++++----
 1 file changed, 47 insertions(+), 4 deletions(-)

diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md
index 80a9d064c0fe5..6acfc71d7b014 100644
--- a/docs/mllib-statistics.md
+++ b/docs/mllib-statistics.md
@@ -438,22 +438,65 @@ run a 1-sample, 2-sided Kolmogorov-Smirnov test. The following example demonstra
 and interpret the hypothesis tests.
 
 {% highlight scala %}
-import org.apache.spark.SparkContext
-import org.apache.spark.mllib.stat.Statistics._
+import org.apache.spark.mllib.stat.Statistics
 
 val data: RDD[Double] = ... // an RDD of sample data
 
 // run a KS test for the sample versus a standard normal distribution
 val testResult = Statistics.kolmogorovSmirnovTest(data, "norm", 0, 1)
 println(testResult) // summary of the test including the p-value, test statistic,
-                      // and null hypothesis
-                      // if our p-value indicates significance, we can reject the null hypothesis
+                    // and null hypothesis
+                    // if our p-value indicates significance, we can reject the null hypothesis
 
 // perform a KS test using a cumulative distribution function of our making
 val myCDF: Double => Double = ...
 val testResult2 = Statistics.kolmogorovSmirnovTest(data, myCDF)
 {% endhighlight %}
 </div>
+
+<div data-lang="java" markdown="1">
+[`Statistics`](api/java/org/apache/spark/mllib/stat/Statistics.html) provides methods to
+run a 1-sample, 2-sided Kolmogorov-Smirnov test. The following example demonstrates how to run
+and interpret the hypothesis tests.
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaDoubleRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+
+import org.apache.spark.mllib.stat.Statistics;
+import org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult;
+
+JavaSparkContext jsc = ...
+JavaDoubleRDD data = jsc.parallelizeDoubles(Arrays.asList(0.2, 1.0, ...));
+KolmogorovSmirnovTestResult testResult = Statistics.kolmogorovSmirnovTest(data, "norm", 0.0, 1.0);
+// summary of the test including the p-value, test statistic,
+// and null hypothesis
+// if our p-value indicates significance, we can reject the null hypothesis
+System.out.println(testResult);
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+[`Statistics`](api/python/pyspark.mllib.html#pyspark.mllib.stat.Statistics) provides methods to
+run a 1-sample, 2-sided Kolmogorov-Smirnov test. The following example demonstrates how to run
+and interpret the hypothesis tests.
+
+{% highlight python %}
+from pyspark.mllib.stat import Statistics
+
+parallelData = sc.parallelize([1.0, 2.0, ... ])
+
+# run a KS test for the sample versus a standard normal distribution
+testResult = Statistics.kolmogorovSmirnovTest(parallelData, "norm", 0, 1)
+print(testResult) # summary of the test including the p-value, test statistic,
+                  # and null hypothesis
+                  # if our p-value indicates significance, we can reject the null hypothesis
+# Note that the Scala functionality of calling Statistics.kolmogorovSmirnovTest with
+# a lambda to calculate the CDF is not made available in the Python API
+{% endhighlight %}
+</div>
 </div>
 
 

From ee093c8b927e8d488aeb76115c7fb0de96af7720 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Mon, 17 Aug 2015 19:35:35 -0700
Subject: [PATCH 291/633] [SPARK-10059] [YARN] Explicitly add JSP dependencies
 for tests.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8251 from vanzin/SPARK-10059.
---
 yarn/pom.xml | 22 +++++++++++++++++++---
 1 file changed, 19 insertions(+), 3 deletions(-)

diff --git a/yarn/pom.xml b/yarn/pom.xml
index 49360c48256ea..15db54e4e7909 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -92,12 +92,28 @@
       <artifactId>jetty-servlet</artifactId>
     </dependency>
     <!-- End of shaded deps. -->
-    
+
+    <!--
+      SPARK-10059: Explicitly add JSP dependencies for tests since the MiniYARN cluster needs them.
+    -->
+    <dependency>
+      <groupId>org.eclipse.jetty.orbit</groupId>
+      <artifactId>javax.servlet.jsp</artifactId>
+      <version>2.2.0.v201112011158</version>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.eclipse.jetty.orbit</groupId>
+      <artifactId>javax.servlet.jsp.jstl</artifactId>
+      <version>1.2.0.v201105211821</version>
+      <scope>test</scope>
+    </dependency>
+
      <!--
     See SPARK-3710. hadoop-yarn-server-tests in Hadoop 2.2 fails to pull some needed
     dependencies, so they need to be added manually for the tests to work.
     -->
-     
+
     <dependency>
       <groupId>org.apache.hadoop</groupId>
       <artifactId>hadoop-yarn-server-tests</artifactId>
@@ -137,7 +153,7 @@
        <scope>test</scope>
      </dependency>
   </dependencies>
-  
+
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
     <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory>

From e290029a356222bddf4da1be0525a221a5a1630b Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 17 Aug 2015 19:40:51 -0700
Subject: [PATCH 292/633] [SPARK-7808] [ML] add package doc for ml.feature

This PR adds a short description of `ml.feature` package with code example. The Java package doc will come in a separate PR. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8260 from mengxr/SPARK-7808.
---
 .../org/apache/spark/ml/feature/package.scala | 89 +++++++++++++++++++
 1 file changed, 89 insertions(+)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/package.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala
new file mode 100644
index 0000000000000..4571ab26800c0
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/package.scala
@@ -0,0 +1,89 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml
+
+import org.apache.spark.ml.feature.{HashingTF, IDF, IDFModel, VectorAssembler}
+import org.apache.spark.sql.DataFrame
+
+/**
+ * == Feature transformers ==
+ *
+ * The `ml.feature` package provides common feature transformers that help convert raw data or
+ * features into more suitable forms for model fitting.
+ * Most feature transformers are implemented as [[Transformer]]s, which transform one [[DataFrame]]
+ * into another, e.g., [[HashingTF]].
+ * Some feature transformers are implemented as [[Estimator]]s, because the transformation requires
+ * some aggregated information of the dataset, e.g., document frequencies in [[IDF]].
+ * For those feature transformers, calling [[Estimator!.fit]] is required to obtain the model first,
+ * e.g., [[IDFModel]], in order to apply transformation.
+ * The transformation is usually done by appending new columns to the input [[DataFrame]], so all
+ * input columns are carried over.
+ *
+ * We try to make each transformer minimal, so it becomes flexible to assemble feature
+ * transformation pipelines.
+ * [[Pipeline]] can be used to chain feature transformers, and [[VectorAssembler]] can be used to
+ * combine multiple feature transformations, for example:
+ *
+ * {{{
+ *   import org.apache.spark.ml.feature._
+ *   import org.apache.spark.ml.Pipeline
+ *
+ *   // a DataFrame with three columns: id (integer), text (string), and rating (double).
+ *   val df = sqlContext.createDataFrame(Seq(
+ *     (0, "Hi I heard about Spark", 3.0),
+ *     (1, "I wish Java could use case classes", 4.0),
+ *     (2, "Logistic regression models are neat", 4.0)
+ *   )).toDF("id", "text", "rating")
+ *
+ *   // define feature transformers
+ *   val tok = new RegexTokenizer()
+ *     .setInputCol("text")
+ *     .setOutputCol("words")
+ *   val sw = new StopWordsRemover()
+ *     .setInputCol("words")
+ *     .setOutputCol("filtered_words")
+ *   val tf = new HashingTF()
+ *     .setInputCol("filtered_words")
+ *     .setOutputCol("tf")
+ *     .setNumFeatures(10000)
+ *   val idf = new IDF()
+ *     .setInputCol("tf")
+ *     .setOutputCol("tf_idf")
+ *   val assembler = new VectorAssembler()
+ *     .setInputCols(Array("tf_idf", "rating"))
+ *     .setOutputCol("features")
+ *
+ *   // assemble and fit the feature transformation pipeline
+ *   val pipeline = new Pipeline()
+ *     .setStages(Array(tok, sw, tf, idf, assembler))
+ *   val model = pipeline.fit(df)
+ *
+ *   // save transformed features with raw data
+ *   model.transform(df)
+ *     .select("id", "text", "rating", "features")
+ *     .write.format("parquet").save("/output/path")
+ * }}}
+ *
+ * Some feature transformers implemented in MLlib are inspired by those implemented in scikit-learn.
+ * The major difference is that most scikit-learn feature transformers operate eagerly on the entire
+ * input dataset, while MLlib's feature transformers operate lazily on individual columns,
+ * which is more efficient and flexible to handle large and complex datasets.
+ *
+ * @see [[http://scikit-learn.org/stable/modules/preprocessing.html scikit-learn.preprocessing]]
+ */
+package object feature

From a0910315dae88b033e38a1de07f39ca21f6552ad Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Mon, 17 Aug 2015 23:27:11 -0700
Subject: [PATCH 293/633] [MINOR] Format the comment of `translate` at
 `functions.scala`

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8265 from yu-iskw/minor-translate-comment.
---
 .../scala/org/apache/spark/sql/functions.scala  | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 79c5f596661d4..435e6319a64c4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -1863,14 +1863,15 @@ object functions {
   def substring_index(str: Column, delim: String, count: Int): Column =
     SubstringIndex(str.expr, lit(delim).expr, lit(count).expr)
 
-  /* Translate any character in the src by a character in replaceString.
-  * The characters in replaceString is corresponding to the characters in matchingString.
-  * The translate will happen when any character in the string matching with the character
-  * in the matchingString.
-  *
-  * @group string_funcs
-  * @since 1.5.0
-  */
+  /**
+   * Translate any character in the src by a character in replaceString.
+   * The characters in replaceString is corresponding to the characters in matchingString.
+   * The translate will happen when any character in the string matching with the character
+   * in the matchingString.
+   *
+   * @group string_funcs
+   * @since 1.5.0
+   */
   def translate(src: Column, matchingString: String, replaceString: String): Column =
     StringTranslate(src.expr, lit(matchingString).expr, lit(replaceString).expr)
 

From 5af3838d2e59ed83766f85634e26918baa53819f Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 17 Aug 2015 23:27:55 -0700
Subject: [PATCH 294/633] [SPARK-10038] [SQL] fix bug in generated unsafe
 projection when there is binary in ArrayData

The type for array of array in Java is slightly different than array of others.

cc cloud-fan

Author: Davies Liu <davies@databricks.com>

Closes #8250 from davies/array_binary.
---
 .../codegen/GenerateUnsafeProjection.scala    | 12 ++++++++---
 .../codegen/GeneratedProjectionSuite.scala    | 21 ++++++++++++++++++-
 2 files changed, 29 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index b2fb913850794..b570fe86db1aa 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -224,7 +224,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     // go through the input array to calculate how many bytes we need.
     val calculateNumBytes = elementType match {
-      case _ if (ctx.isPrimitiveType(elementType)) =>
+      case _ if ctx.isPrimitiveType(elementType) =>
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
@@ -237,6 +237,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       case _ =>
         val writer = getWriter(elementType)
         val elementSize = s"$writer.getSize($elements[$index])"
+        // TODO(davies): avoid the copy
         val unsafeType = elementType match {
           case _: StructType => "UnsafeRow"
           case _: ArrayType => "UnsafeArrayData"
@@ -249,8 +250,13 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           case _ => ""
         }
 
+        val newElements = if (elementType == BinaryType) {
+          s"new byte[$numElements][]"
+        } else {
+          s"new $unsafeType[$numElements]"
+        }
         s"""
-          final $unsafeType[] $elements = new $unsafeType[$numElements];
+          final $unsafeType[] $elements = $newElements;
           for (int $index = 0; $index < $numElements; $index++) {
             ${convertedElement.code}
             if (!${convertedElement.isNull}) {
@@ -262,7 +268,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     }
 
     val writeElement = elementType match {
-      case _ if (ctx.isPrimitiveType(elementType)) =>
+      case _ if ctx.isPrimitiveType(elementType) =>
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala
index 8c7ee8720f7bb..098944a9f4fc5 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/codegen/GeneratedProjectionSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.expressions.codegen
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.types.{StringType, IntegerType, StructField, StructType}
+import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
 /**
@@ -79,4 +79,23 @@ class GeneratedProjectionSuite extends SparkFunSuite {
     val row2 = mutableProj(result)
     assert(result === row2)
   }
+
+  test("generated unsafe projection with array of binary") {
+    val row = InternalRow(
+      Array[Byte](1, 2),
+      new GenericArrayData(Array(Array[Byte](1, 2), null, Array[Byte](3, 4))))
+    val fields = (BinaryType :: ArrayType(BinaryType) :: Nil).toArray[DataType]
+
+    val unsafeProj = UnsafeProjection.create(fields)
+    val unsafeRow: UnsafeRow = unsafeProj(row)
+    assert(java.util.Arrays.equals(unsafeRow.getBinary(0), Array[Byte](1, 2)))
+    assert(java.util.Arrays.equals(unsafeRow.getArray(1).getBinary(0), Array[Byte](1, 2)))
+    assert(unsafeRow.getArray(1).isNullAt(1))
+    assert(unsafeRow.getArray(1).getBinary(1) === null)
+    assert(java.util.Arrays.equals(unsafeRow.getArray(1).getBinary(2), Array[Byte](3, 4)))
+
+    val safeProj = FromUnsafeProjection(fields)
+    val row2 = safeProj(unsafeRow)
+    assert(row2 === row)
+  }
 }

From dd0614fd618ad28cb77aecfbd49bb319b98fdba0 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 17 Aug 2015 23:57:02 -0700
Subject: [PATCH 295/633] [SPARK-10076] [ML] make
 MultilayerPerceptronClassifier layers and weights public

Fix the issue that ```layers``` and ```weights``` should be public variables of ```MultilayerPerceptronClassificationModel```. Users can not get ```layers``` and ```weights``` from a ```MultilayerPerceptronClassificationModel``` currently.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8263 from yanboliang/mlp-public.
---
 .../ml/classification/MultilayerPerceptronClassifier.scala    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index c154561886585..ccca4ecc004c3 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -172,8 +172,8 @@ class MultilayerPerceptronClassifier(override val uid: String)
 @Experimental
 class MultilayerPerceptronClassificationModel private[ml] (
     override val uid: String,
-    layers: Array[Int],
-    weights: Vector)
+    val layers: Array[Int],
+    val weights: Vector)
   extends PredictionModel[Vector, MultilayerPerceptronClassificationModel]
   with Serializable {
 

From c34e9ff0eac2032283b959fe63b47cc30f28d21c Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Tue, 18 Aug 2015 10:31:11 +0100
Subject: [PATCH 296/633] [MINOR] fix the comments in IndexShuffleBlockResolver

it might be a typo  introduced at the first moment or some leftover after some renaming......

the name of the method accessing the index file is called `getBlockData` now (not `getBlockLocation` as indicated in the comments)

Author: CodingCat <zhunansjtu@gmail.com>

Closes #8238 from CodingCat/minor_1.
---
 .../org/apache/spark/shuffle/IndexShuffleBlockResolver.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala
index fae69551e7330..d0163d326dba7 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/IndexShuffleBlockResolver.scala
@@ -71,7 +71,7 @@ private[spark] class IndexShuffleBlockResolver(conf: SparkConf) extends ShuffleB
 
   /**
    * Write an index file with the offsets of each block, plus a final offset at the end for the
-   * end of the output file. This will be used by getBlockLocation to figure out where each block
+   * end of the output file. This will be used by getBlockData to figure out where each block
    * begins and ends.
    * */
   def writeIndexFile(shuffleId: Int, mapId: Int, lengths: Array[Long]): Unit = {

From 5723d26d7e677b89383de3fcf2c9a821b68a65b7 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Tue, 18 Aug 2015 20:15:33 +0800
Subject: [PATCH 297/633] [SPARK-8118] [SQL] Redirects Parquet JUL logger via
 SLF4J

Parquet hard coded a JUL logger which always writes to stdout. This PR redirects it via SLF4j JUL bridge handler, so that we can control Parquet logs via `log4j.properties`.

This solution is inspired by https://github.com/Parquet/parquet-mr/issues/390#issuecomment-46064909.

Author: Cheng Lian <lian@databricks.com>

Closes #8196 from liancheng/spark-8118/redirect-parquet-jul.
---
 conf/log4j.properties.template                |  2 +
 .../datasources/parquet/ParquetRelation.scala | 77 ++++++++++---------
 .../parquet/ParquetTableSupport.scala         |  1 -
 .../parquet/ParquetTypesConverter.scala       |  3 -
 sql/hive/src/test/resources/log4j.properties  |  7 +-
 5 files changed, 47 insertions(+), 43 deletions(-)

diff --git a/conf/log4j.properties.template b/conf/log4j.properties.template
index 27006e45e932b..74c5cea94403a 100644
--- a/conf/log4j.properties.template
+++ b/conf/log4j.properties.template
@@ -10,6 +10,8 @@ log4j.logger.org.spark-project.jetty=WARN
 log4j.logger.org.spark-project.jetty.util.component.AbstractLifeCycle=ERROR
 log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
 log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO
+log4j.logger.org.apache.parquet=ERROR
+log4j.logger.parquet=ERROR
 
 # SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
 log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index 52fac18ba187a..68169d48ac57c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.execution.datasources.parquet
 
 import java.net.URI
-import java.util.logging.{Level, Logger => JLogger}
+import java.util.logging.{Logger => JLogger}
 import java.util.{List => JList}
 
 import scala.collection.JavaConversions._
@@ -31,22 +31,22 @@ import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.parquet.filter2.predicate.FilterApi
+import org.apache.parquet.hadoop._
 import org.apache.parquet.hadoop.metadata.CompressionCodecName
 import org.apache.parquet.hadoop.util.ContextUtil
-import org.apache.parquet.hadoop.{ParquetOutputCommitter, ParquetRecordReader, _}
 import org.apache.parquet.schema.MessageType
-import org.apache.parquet.{Log => ParquetLog}
+import org.apache.parquet.{Log => ApacheParquetLog}
+import org.slf4j.bridge.SLF4JBridgeHandler
 
-import org.apache.spark.{Logging, Partition => SparkPartition, SparkException}
 import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.{SqlNewHadoopPartition, SqlNewHadoopRDD, RDD}
-import org.apache.spark.rdd.RDD._
+import org.apache.spark.rdd.{RDD, SqlNewHadoopPartition, SqlNewHadoopRDD}
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.execution.datasources.PartitionSpec
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.{DataType, StructType}
 import org.apache.spark.util.{SerializableConfiguration, Utils}
+import org.apache.spark.{Logging, Partition => SparkPartition, SparkException}
 
 
 private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
@@ -759,38 +759,39 @@ private[sql] object ParquetRelation extends Logging {
     }.toOption
   }
 
-  def enableLogForwarding() {
-    // Note: the org.apache.parquet.Log class has a static initializer that
-    // sets the java.util.logging Logger for "org.apache.parquet". This
-    // checks first to see if there's any handlers already set
-    // and if not it creates them. If this method executes prior
-    // to that class being loaded then:
-    //  1) there's no handlers installed so there's none to
-    // remove. But when it IS finally loaded the desired affect
-    // of removing them is circumvented.
-    //  2) The parquet.Log static initializer calls setUseParentHandlers(false)
-    // undoing the attempt to override the logging here.
-    //
-    // Therefore we need to force the class to be loaded.
-    // This should really be resolved by Parquet.
-    Utils.classForName(classOf[ParquetLog].getName)
-
-    // Note: Logger.getLogger("parquet") has a default logger
-    // that appends to Console which needs to be cleared.
-    val parquetLogger = JLogger.getLogger(classOf[ParquetLog].getPackage.getName)
-    parquetLogger.getHandlers.foreach(parquetLogger.removeHandler)
-    parquetLogger.setUseParentHandlers(true)
-
-    // Disables a WARN log message in ParquetOutputCommitter.  We first ensure that
-    // ParquetOutputCommitter is loaded and the static LOG field gets initialized.
-    // See https://issues.apache.org/jira/browse/SPARK-5968 for details
-    Utils.classForName(classOf[ParquetOutputCommitter].getName)
-    JLogger.getLogger(classOf[ParquetOutputCommitter].getName).setLevel(Level.OFF)
-
-    // Similar as above, disables a unnecessary WARN log message in ParquetRecordReader.
-    // See https://issues.apache.org/jira/browse/PARQUET-220 for details
-    Utils.classForName(classOf[ParquetRecordReader[_]].getName)
-    JLogger.getLogger(classOf[ParquetRecordReader[_]].getName).setLevel(Level.OFF)
+  // JUL loggers must be held by a strong reference, otherwise they may get destroyed by GC.
+  // However, the root JUL logger used by Parquet isn't properly referenced.  Here we keep
+  // references to loggers in both parquet-mr <= 1.6 and >= 1.7
+  val apacheParquetLogger: JLogger = JLogger.getLogger(classOf[ApacheParquetLog].getPackage.getName)
+  val parquetLogger: JLogger = JLogger.getLogger("parquet")
+
+  // Parquet initializes its own JUL logger in a static block which always prints to stdout.  Here
+  // we redirect the JUL logger via SLF4J JUL bridge handler.
+  val redirectParquetLogsViaSLF4J: Unit = {
+    def redirect(logger: JLogger): Unit = {
+      logger.getHandlers.foreach(logger.removeHandler)
+      logger.setUseParentHandlers(false)
+      logger.addHandler(new SLF4JBridgeHandler)
+    }
+
+    // For parquet-mr 1.7.0 and above versions, which are under `org.apache.parquet` namespace.
+    // scalastyle:off classforname
+    Class.forName(classOf[ApacheParquetLog].getName)
+    // scalastyle:on classforname
+    redirect(JLogger.getLogger(classOf[ApacheParquetLog].getPackage.getName))
+
+    // For parquet-mr 1.6.0 and lower versions bundled with Hive, which are under `parquet`
+    // namespace.
+    try {
+      // scalastyle:off classforname
+      Class.forName("parquet.Log")
+      // scalastyle:on classforname
+      redirect(JLogger.getLogger("parquet"))
+    } catch { case _: Throwable =>
+      // SPARK-9974: com.twitter:parquet-hadoop-bundle:1.6.0 is not packaged into the assembly jar
+      // when Spark is built with SBT. So `parquet.Log` may not be found.  This try/catch block
+      // should be removed after this issue is fixed.
+    }
   }
 
   // The parquet compression short names
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
index 3191cf3d121bb..ed89aa27aa1f0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTableSupport.scala
@@ -52,7 +52,6 @@ private[parquet] class RowWriteSupport extends WriteSupport[InternalRow] with Lo
     }
 
     log.debug(s"write support initialized for requested schema $attributes")
-    ParquetRelation.enableLogForwarding()
     new WriteSupport.WriteContext(ParquetTypesConverter.convertFromAttributes(attributes), metadata)
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
index 019db34fc666d..42376ef7a9c1f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
@@ -104,7 +104,6 @@ private[parquet] object ParquetTypesConverter extends Logging {
       extraMetadata,
       "Spark")
 
-    ParquetRelation.enableLogForwarding()
     ParquetFileWriter.writeMetadataFile(
       conf,
       path,
@@ -140,8 +139,6 @@ private[parquet] object ParquetTypesConverter extends Logging {
           (name(0) == '.' || name(0) == '_') && name != ParquetFileWriter.PARQUET_METADATA_FILE
         }
 
-    ParquetRelation.enableLogForwarding()
-
     // NOTE (lian): Parquet "_metadata" file can be very slow if the file consists of lots of row
     // groups. Since Parquet schema is replicated among all row groups, we only need to touch a
     // single row group to read schema related metadata. Notice that we are making assumptions that
diff --git a/sql/hive/src/test/resources/log4j.properties b/sql/hive/src/test/resources/log4j.properties
index 92eaf1f2795ba..fea3404769d9d 100644
--- a/sql/hive/src/test/resources/log4j.properties
+++ b/sql/hive/src/test/resources/log4j.properties
@@ -48,9 +48,14 @@ log4j.logger.hive.log=OFF
 log4j.additivity.parquet.hadoop.ParquetRecordReader=false
 log4j.logger.parquet.hadoop.ParquetRecordReader=OFF
 
+log4j.additivity.org.apache.parquet.hadoop.ParquetRecordReader=false
+log4j.logger.org.apache.parquet.hadoop.ParquetRecordReader=OFF
+
+log4j.additivity.org.apache.parquet.hadoop.ParquetOutputCommitter=false
+log4j.logger.org.apache.parquet.hadoop.ParquetOutputCommitter=OFF
+
 log4j.additivity.hive.ql.metadata.Hive=false
 log4j.logger.hive.ql.metadata.Hive=OFF
 
 log4j.additivity.org.apache.hadoop.hive.ql.io.RCFile=false
 log4j.logger.org.apache.hadoop.hive.ql.io.RCFile=ERROR
-

From 1968276af0f681fe51328b7dd795bd21724a5441 Mon Sep 17 00:00:00 2001
From: Yuu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Tue, 18 Aug 2015 09:10:59 -0700
Subject: [PATCH 298/633] [SPARK-10007] [SPARKR] Update `NAMESPACE` file in
 SparkR for simple parameters functions

### JIRA
[[SPARK-10007] Update `NAMESPACE` file in SparkR for simple parameters functions - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10007)

Author: Yuu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8277 from yu-iskw/SPARK-10007.
---
 R/pkg/NAMESPACE | 50 ++++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 47 insertions(+), 3 deletions(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index fd9dfdf60edb3..607aef26115d5 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -87,48 +87,86 @@ exportMethods("abs",
               "alias",
               "approxCountDistinct",
               "asc",
+              "ascii",
               "asin",
               "atan",
               "atan2",
               "avg",
+              "base64",
               "between",
+              "bin",
+              "bitwiseNOT",
               "cast",
               "cbrt",
+              "ceil",
               "ceiling",
+              "concat",
               "contains",
               "cos",
               "cosh",
-              "concat",
+              "count",
               "countDistinct",
+              "crc32",
+              "datediff",
+              "dayofmonth",
+              "dayofyear",
               "desc",
               "endsWith",
               "exp",
+              "explode",
               "expm1",
+              "factorial",
+              "first",
               "floor",
               "getField",
               "getItem",
               "greatest",
+              "hex",
+              "hour",
               "hypot",
+              "initcap",
+              "isNaN",
               "isNotNull",
               "isNull",
-              "lit",
               "last",
+              "last_day",
               "least",
+              "length",
+              "levenshtein",
               "like",
+              "lit",
               "log",
               "log10",
               "log1p",
+              "log2",
               "lower",
+              "ltrim",
               "max",
+              "md5",
               "mean",
               "min",
+              "minute",
+              "month",
+              "months_between",
               "n",
               "n_distinct",
+              "nanvl",
+              "negate",
+              "pmod",
+              "quarter",
+              "reverse",
               "rint",
               "rlike",
+              "round",
+              "rtrim",
+              "second",
+              "sha1",
               "sign",
+              "signum",
               "sin",
               "sinh",
+              "size",
+              "soundex",
               "sqrt",
               "startsWith",
               "substr",
@@ -138,7 +176,13 @@ exportMethods("abs",
               "tanh",
               "toDegrees",
               "toRadians",
-              "upper")
+              "to_date",
+              "trim",
+              "unbase64",
+              "unhex",
+              "upper",
+              "weekofyear",
+              "year")
 
 exportClasses("GroupedData")
 exportMethods("agg")

From 354f4582b637fa25d3892ec2b12869db50ed83c9 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Tue, 18 Aug 2015 11:00:09 -0700
Subject: [PATCH 299/633] [SPARK-9028] [ML] Add CountVectorizer as an estimator
 to generate CountVectorizerModel

jira: https://issues.apache.org/jira/browse/SPARK-9028

Add an estimator for CountVectorizerModel. The estimator will extract a vocabulary from document collections according to the term frequency.

I changed the meaning of minCount as a filter across the corpus. This aligns with Word2Vec and the similar parameter in SKlearn.

Author: Yuhao Yang <hhbyyh@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #7388 from hhbyyh/cvEstimator.
---
 .../spark/ml/feature/CountVectorizer.scala    | 235 ++++++++++++++++++
 .../ml/feature/CountVectorizerModel.scala     |  82 ------
 .../ml/feature/CountVectorizerSuite.scala     | 167 +++++++++++++
 .../ml/feature/CountVectorizorSuite.scala     |  73 ------
 4 files changed, 402 insertions(+), 155 deletions(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
 delete mode 100644 mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
 delete mode 100644 mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
new file mode 100644
index 0000000000000..49028e4b85064
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizer.scala
@@ -0,0 +1,235 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.ml.feature
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.mllib.linalg.{VectorUDT, Vectors}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.util.collection.OpenHashMap
+
+/**
+ * Params for [[CountVectorizer]] and [[CountVectorizerModel]].
+ */
+private[feature] trait CountVectorizerParams extends Params with HasInputCol with HasOutputCol {
+
+  /**
+   * Max size of the vocabulary.
+   * CountVectorizer will build a vocabulary that only considers the top
+   * vocabSize terms ordered by term frequency across the corpus.
+   *
+   * Default: 2^18^
+   * @group param
+   */
+  val vocabSize: IntParam =
+    new IntParam(this, "vocabSize", "max size of the vocabulary", ParamValidators.gt(0))
+
+  /** @group getParam */
+  def getVocabSize: Int = $(vocabSize)
+
+  /**
+   * Specifies the minimum number of different documents a term must appear in to be included
+   * in the vocabulary.
+   * If this is an integer >= 1, this specifies the number of documents the term must appear in;
+   * if this is a double in [0,1), then this specifies the fraction of documents.
+   *
+   * Default: 1
+   * @group param
+   */
+  val minDF: DoubleParam = new DoubleParam(this, "minDF", "Specifies the minimum number of" +
+    " different documents a term must appear in to be included in the vocabulary." +
+    " If this is an integer >= 1, this specifies the number of documents the term must" +
+    " appear in; if this is a double in [0,1), then this specifies the fraction of documents.",
+    ParamValidators.gtEq(0.0))
+
+  /** @group getParam */
+  def getMinDF: Double = $(minDF)
+
+  /** Validates and transforms the input schema. */
+  protected def validateAndTransformSchema(schema: StructType): StructType = {
+    SchemaUtils.checkColumnType(schema, $(inputCol), new ArrayType(StringType, true))
+    SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
+  }
+
+  /**
+   * Filter to ignore rare words in a document. For each document, terms with
+   * frequency/count less than the given threshold are ignored.
+   * If this is an integer >= 1, then this specifies a count (of times the term must appear
+   * in the document);
+   * if this is a double in [0,1), then this specifies a fraction (out of the document's token
+   * count).
+   *
+   * Note that the parameter is only used in transform of [[CountVectorizerModel]] and does not
+   * affect fitting.
+   *
+   * Default: 1
+   * @group param
+   */
+  val minTF: DoubleParam = new DoubleParam(this, "minTF", "Filter to ignore rare words in" +
+    " a document. For each document, terms with frequency/count less than the given threshold are" +
+    " ignored. If this is an integer >= 1, then this specifies a count (of times the term must" +
+    " appear in the document); if this is a double in [0,1), then this specifies a fraction (out" +
+    " of the document's token count). Note that the parameter is only used in transform of" +
+    " CountVectorizerModel and does not affect fitting.", ParamValidators.gtEq(0.0))
+
+  setDefault(minTF -> 1)
+
+  /** @group getParam */
+  def getMinTF: Double = $(minTF)
+}
+
+/**
+ * :: Experimental ::
+ * Extracts a vocabulary from document collections and generates a [[CountVectorizerModel]].
+ */
+@Experimental
+class CountVectorizer(override val uid: String)
+  extends Estimator[CountVectorizerModel] with CountVectorizerParams {
+
+  def this() = this(Identifiable.randomUID("cntVec"))
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  /** @group setParam */
+  def setVocabSize(value: Int): this.type = set(vocabSize, value)
+
+  /** @group setParam */
+  def setMinDF(value: Double): this.type = set(minDF, value)
+
+  /** @group setParam */
+  def setMinTF(value: Double): this.type = set(minTF, value)
+
+  setDefault(vocabSize -> (1 << 18), minDF -> 1)
+
+  override def fit(dataset: DataFrame): CountVectorizerModel = {
+    transformSchema(dataset.schema, logging = true)
+    val vocSize = $(vocabSize)
+    val input = dataset.select($(inputCol)).map(_.getAs[Seq[String]](0))
+    val minDf = if ($(minDF) >= 1.0) {
+      $(minDF)
+    } else {
+      $(minDF) * input.cache().count()
+    }
+    val wordCounts: RDD[(String, Long)] = input.flatMap { case (tokens) =>
+      val wc = new OpenHashMap[String, Long]
+      tokens.foreach { w =>
+        wc.changeValue(w, 1L, _ + 1L)
+      }
+      wc.map { case (word, count) => (word, (count, 1)) }
+    }.reduceByKey { case ((wc1, df1), (wc2, df2)) =>
+      (wc1 + wc2, df1 + df2)
+    }.filter { case (word, (wc, df)) =>
+      df >= minDf
+    }.map { case (word, (count, dfCount)) =>
+      (word, count)
+    }.cache()
+    val fullVocabSize = wordCounts.count()
+    val vocab: Array[String] = {
+      val tmpSortedWC: Array[(String, Long)] = if (fullVocabSize <= vocSize) {
+        // Use all terms
+        wordCounts.collect().sortBy(-_._2)
+      } else {
+        // Sort terms to select vocab
+        wordCounts.sortBy(_._2, ascending = false).take(vocSize)
+      }
+      tmpSortedWC.map(_._1)
+    }
+
+    require(vocab.length > 0, "The vocabulary size should be > 0. Lower minDF as necessary.")
+    copyValues(new CountVectorizerModel(uid, vocab).setParent(this))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+
+  override def copy(extra: ParamMap): CountVectorizer = defaultCopy(extra)
+}
+
+/**
+ * :: Experimental ::
+ * Converts a text document to a sparse vector of token counts.
+ * @param vocabulary An Array over terms. Only the terms in the vocabulary will be counted.
+ */
+@Experimental
+class CountVectorizerModel(override val uid: String, val vocabulary: Array[String])
+  extends Model[CountVectorizerModel] with CountVectorizerParams {
+
+  def this(vocabulary: Array[String]) = {
+    this(Identifiable.randomUID("cntVecModel"), vocabulary)
+    set(vocabSize, vocabulary.length)
+  }
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  /** @group setParam */
+  def setMinTF(value: Double): this.type = set(minTF, value)
+
+  /** Dictionary created from [[vocabulary]] and its indices, broadcast once for [[transform()]] */
+  private var broadcastDict: Option[Broadcast[Map[String, Int]]] = None
+
+  override def transform(dataset: DataFrame): DataFrame = {
+    if (broadcastDict.isEmpty) {
+      val dict = vocabulary.zipWithIndex.toMap
+      broadcastDict = Some(dataset.sqlContext.sparkContext.broadcast(dict))
+    }
+    val dictBr = broadcastDict.get
+    val minTf = $(minTF)
+    val vectorizer = udf { (document: Seq[String]) =>
+      val termCounts = new OpenHashMap[Int, Double]
+      var tokenCount = 0L
+      document.foreach { term =>
+        dictBr.value.get(term) match {
+          case Some(index) => termCounts.changeValue(index, 1.0, _ + 1.0)
+          case None => // ignore terms not in the vocabulary
+        }
+        tokenCount += 1
+      }
+      val effectiveMinTF = if (minTf >= 1.0) {
+        minTf
+      } else {
+        tokenCount * minTf
+      }
+      Vectors.sparse(dictBr.value.size, termCounts.filter(_._2 >= effectiveMinTF).toSeq)
+    }
+    dataset.withColumn($(outputCol), vectorizer(col($(inputCol))))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+
+  override def copy(extra: ParamMap): CountVectorizerModel = {
+    val copied = new CountVectorizerModel(uid, vocabulary).setParent(parent)
+    copyValues(copied, extra)
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala
deleted file mode 100644
index 6b77de89a0330..0000000000000
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/CountVectorizerModel.scala
+++ /dev/null
@@ -1,82 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.spark.ml.feature
-
-import scala.collection.mutable
-
-import org.apache.spark.annotation.Experimental
-import org.apache.spark.ml.UnaryTransformer
-import org.apache.spark.ml.param.{ParamMap, ParamValidators, IntParam}
-import org.apache.spark.ml.util.Identifiable
-import org.apache.spark.mllib.linalg.{Vectors, VectorUDT, Vector}
-import org.apache.spark.sql.types.{StringType, ArrayType, DataType}
-
-/**
- * :: Experimental ::
- * Converts a text document to a sparse vector of token counts.
- * @param vocabulary An Array over terms. Only the terms in the vocabulary will be counted.
- */
-@Experimental
-class CountVectorizerModel (override val uid: String, val vocabulary: Array[String])
-  extends UnaryTransformer[Seq[String], Vector, CountVectorizerModel] {
-
-  def this(vocabulary: Array[String]) =
-    this(Identifiable.randomUID("cntVec"), vocabulary)
-
-  /**
-   * Corpus-specific filter to ignore scarce words in a document. For each document, terms with
-   * frequency (count) less than the given threshold are ignored.
-   * Default: 1
-   * @group param
-   */
-  val minTermFreq: IntParam = new IntParam(this, "minTermFreq",
-    "minimum frequency (count) filter used to neglect scarce words (>= 1). For each document, " +
-      "terms with frequency less than the given threshold are ignored.", ParamValidators.gtEq(1))
-
-  /** @group setParam */
-  def setMinTermFreq(value: Int): this.type = set(minTermFreq, value)
-
-  /** @group getParam */
-  def getMinTermFreq: Int = $(minTermFreq)
-
-  setDefault(minTermFreq -> 1)
-
-  override protected def createTransformFunc: Seq[String] => Vector = {
-    val dict = vocabulary.zipWithIndex.toMap
-    document =>
-      val termCounts = mutable.HashMap.empty[Int, Double]
-      document.foreach { term =>
-        dict.get(term) match {
-          case Some(index) => termCounts.put(index, termCounts.getOrElse(index, 0.0) + 1.0)
-          case None => // ignore terms not in the vocabulary
-        }
-      }
-      Vectors.sparse(dict.size, termCounts.filter(_._2 >= $(minTermFreq)).toSeq)
-  }
-
-  override protected def validateInputType(inputType: DataType): Unit = {
-    require(inputType.sameType(ArrayType(StringType)),
-      s"Input type must be ArrayType(StringType) but got $inputType.")
-  }
-
-  override protected def outputDataType: DataType = new VectorUDT()
-
-  override def copy(extra: ParamMap): CountVectorizerModel = {
-    val copied = new CountVectorizerModel(uid, vocabulary)
-    copyValues(copied, extra)
-  }
-}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
new file mode 100644
index 0000000000000..e192fa4850af0
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizerSuite.scala
@@ -0,0 +1,167 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.ml.feature
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.sql.Row
+
+class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  test("params") {
+    ParamsSuite.checkParams(new CountVectorizerModel(Array("empty")))
+  }
+
+  private def split(s: String): Seq[String] = s.split("\\s+")
+
+  test("CountVectorizerModel common cases") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, split("a b c d"),
+        Vectors.sparse(4, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0)))),
+      (1, split("a b b c d  a"),
+        Vectors.sparse(4, Seq((0, 2.0), (1, 2.0), (2, 1.0), (3, 1.0)))),
+      (2, split("a"), Vectors.sparse(4, Seq((0, 1.0)))),
+      (3, split(""), Vectors.sparse(4, Seq())), // empty string
+      (4, split("a notInDict d"),
+        Vectors.sparse(4, Seq((0, 1.0), (3, 1.0))))  // with words not in vocabulary
+    )).toDF("id", "words", "expected")
+    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
+      .setInputCol("words")
+      .setOutputCol("features")
+    cv.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+  }
+
+  test("CountVectorizer common cases") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, split("a b c d e"),
+        Vectors.sparse(5, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0), (4, 1.0)))),
+      (1, split("a a a a a a"), Vectors.sparse(5, Seq((0, 6.0)))),
+      (2, split("c"), Vectors.sparse(5, Seq((2, 1.0)))),
+      (3, split("b b b b b"), Vectors.sparse(5, Seq((1, 5.0)))))
+    ).toDF("id", "words", "expected")
+    val cv = new CountVectorizer()
+      .setInputCol("words")
+      .setOutputCol("features")
+      .fit(df)
+    assert(cv.vocabulary === Array("a", "b", "c", "d", "e"))
+
+    cv.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+  }
+
+  test("CountVectorizer vocabSize and minDF") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, split("a b c d"), Vectors.sparse(3, Seq((0, 1.0), (1, 1.0)))),
+      (1, split("a b c"), Vectors.sparse(3, Seq((0, 1.0), (1, 1.0)))),
+      (2, split("a b"), Vectors.sparse(3, Seq((0, 1.0), (1, 1.0)))),
+      (3, split("a"), Vectors.sparse(3, Seq((0, 1.0)))))
+    ).toDF("id", "words", "expected")
+    val cvModel = new CountVectorizer()
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setVocabSize(3)  // limit vocab size to 3
+      .fit(df)
+    assert(cvModel.vocabulary === Array("a", "b", "c"))
+
+    // minDF: ignore terms with count less than 3
+    val cvModel2 = new CountVectorizer()
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setMinDF(3)
+      .fit(df)
+    assert(cvModel2.vocabulary === Array("a", "b"))
+
+    cvModel2.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+
+    // minDF: ignore terms with freq < 0.75
+    val cvModel3 = new CountVectorizer()
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setMinDF(3.0 / df.count())
+      .fit(df)
+    assert(cvModel3.vocabulary === Array("a", "b"))
+
+    cvModel3.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+  }
+
+  test("CountVectorizer throws exception when vocab is empty") {
+    intercept[IllegalArgumentException] {
+      val df = sqlContext.createDataFrame(Seq(
+        (0, split("a a b b c c")),
+        (1, split("aa bb cc")))
+      ).toDF("id", "words")
+      val cvModel = new CountVectorizer()
+        .setInputCol("words")
+        .setOutputCol("features")
+        .setVocabSize(3) // limit vocab size to 3
+        .setMinDF(3)
+        .fit(df)
+    }
+  }
+
+  test("CountVectorizerModel with minTF count") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, split("a a a b b c c c d "), Vectors.sparse(4, Seq((0, 3.0), (2, 3.0)))),
+      (1, split("c c c c c c"), Vectors.sparse(4, Seq((2, 6.0)))),
+      (2, split("a"), Vectors.sparse(4, Seq())),
+      (3, split("e e e e e"), Vectors.sparse(4, Seq())))
+    ).toDF("id", "words", "expected")
+
+    // minTF: count
+    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setMinTF(3)
+    cv.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+  }
+
+  test("CountVectorizerModel with minTF freq") {
+    val df = sqlContext.createDataFrame(Seq(
+      (0, split("a a a b b c c c d "), Vectors.sparse(4, Seq((0, 3.0), (2, 3.0)))),
+      (1, split("c c c c c c"), Vectors.sparse(4, Seq((2, 6.0)))),
+      (2, split("a"), Vectors.sparse(4, Seq((0, 1.0)))),
+      (3, split("e e e e e"), Vectors.sparse(4, Seq())))
+    ).toDF("id", "words", "expected")
+
+    // minTF: count
+    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
+      .setInputCol("words")
+      .setOutputCol("features")
+      .setMinTF(0.3)
+    cv.transform(df).select("features", "expected").collect().foreach {
+      case Row(features: Vector, expected: Vector) =>
+        assert(features ~== expected absTol 1e-14)
+    }
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala
deleted file mode 100644
index e90d9d4ef21ff..0000000000000
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/CountVectorizorSuite.scala
+++ /dev/null
@@ -1,73 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-package org.apache.spark.ml.feature
-
-import org.apache.spark.SparkFunSuite
-import org.apache.spark.ml.param.ParamsSuite
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.util.MLlibTestSparkContext
-import org.apache.spark.mllib.util.TestingUtils._
-
-class CountVectorizerSuite extends SparkFunSuite with MLlibTestSparkContext {
-
-  test("params") {
-    ParamsSuite.checkParams(new CountVectorizerModel(Array("empty")))
-  }
-
-  test("CountVectorizerModel common cases") {
-    val df = sqlContext.createDataFrame(Seq(
-      (0, "a b c d".split(" ").toSeq,
-        Vectors.sparse(4, Seq((0, 1.0), (1, 1.0), (2, 1.0), (3, 1.0)))),
-      (1, "a b b c d  a".split(" ").toSeq,
-        Vectors.sparse(4, Seq((0, 2.0), (1, 2.0), (2, 1.0), (3, 1.0)))),
-      (2, "a".split(" ").toSeq, Vectors.sparse(4, Seq((0, 1.0)))),
-      (3, "".split(" ").toSeq, Vectors.sparse(4, Seq())), // empty string
-      (4, "a notInDict d".split(" ").toSeq,
-        Vectors.sparse(4, Seq((0, 1.0), (3, 1.0))))  // with words not in vocabulary
-    )).toDF("id", "words", "expected")
-    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
-      .setInputCol("words")
-      .setOutputCol("features")
-    val output = cv.transform(df).collect()
-    output.foreach { p =>
-      val features = p.getAs[Vector]("features")
-      val expected = p.getAs[Vector]("expected")
-      assert(features ~== expected absTol 1e-14)
-    }
-  }
-
-  test("CountVectorizerModel with minTermFreq") {
-    val df = sqlContext.createDataFrame(Seq(
-      (0, "a a a b b c c c d ".split(" ").toSeq, Vectors.sparse(4, Seq((0, 3.0), (2, 3.0)))),
-      (1, "c c c c c c".split(" ").toSeq, Vectors.sparse(4, Seq((2, 6.0)))),
-      (2, "a".split(" ").toSeq, Vectors.sparse(4, Seq())),
-      (3, "e e e e e".split(" ").toSeq, Vectors.sparse(4, Seq())))
-    ).toDF("id", "words", "expected")
-    val cv = new CountVectorizerModel(Array("a", "b", "c", "d"))
-      .setInputCol("words")
-      .setOutputCol("features")
-      .setMinTermFreq(3)
-    val output = cv.transform(df).collect()
-    output.foreach { p =>
-      val features = p.getAs[Vector]("features")
-      val expected = p.getAs[Vector]("expected")
-      assert(features ~== expected absTol 1e-14)
-    }
-  }
-}
-
-

From c1840a862eb548bc4306e53ee7e9f26986b31832 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 18 Aug 2015 11:36:36 -0700
Subject: [PATCH 300/633] [SPARK-7736] [CORE] Fix a race introduced in
 PythonRunner.

The fix for SPARK-7736 introduced a race where a port value of "-1"
could be passed down to the pyspark process, causing it to fail to
connect back to the JVM. This change adds code to fix that race.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8258 from vanzin/SPARK-7736.
---
 .../main/scala/org/apache/spark/deploy/PythonRunner.scala | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index 4277ac2ad13ea..23d01e9cbb9f9 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -52,10 +52,16 @@ object PythonRunner {
         gatewayServer.start()
       }
     })
-    thread.setName("py4j-gateway")
+    thread.setName("py4j-gateway-init")
     thread.setDaemon(true)
     thread.start()
 
+    // Wait until the gateway server has started, so that we know which port is it bound to.
+    // `gatewayServer.start()` will start a new thread and run the server code there, after
+    // initializing the socket, so the thread started above will end as soon as the server is
+    // ready to serve connections.
+    thread.join()
+
     // Build up a PYTHONPATH that includes the Spark assembly JAR (where this class is), the
     // python directories in SPARK_HOME (if set), and any files in the pyFiles argument
     val pathElements = new ArrayBuffer[String]

From f5ea3912900ccdf23e2eb419a342bfe3c0c0b61b Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 18 Aug 2015 12:53:57 -0700
Subject: [PATCH 301/633] [SPARK-9900] [MLLIB] User guide for Association Rules

Updates FPM user guide to include Association Rules.

Author: Feynman Liang <fliang@databricks.com>

Closes #8207 from feynmanliang/SPARK-9900-arules.
---
 docs/mllib-frequent-pattern-mining.md         | 130 ++++++++++++++++--
 docs/mllib-guide.md                           |   1 +
 .../mllib/fpm/JavaAssociationRulesSuite.java  |   2 +-
 3 files changed, 118 insertions(+), 15 deletions(-)

diff --git a/docs/mllib-frequent-pattern-mining.md b/docs/mllib-frequent-pattern-mining.md
index 8ea4389266484..6c06550703d46 100644
--- a/docs/mllib-frequent-pattern-mining.md
+++ b/docs/mllib-frequent-pattern-mining.md
@@ -39,18 +39,30 @@ MLlib's FP-growth implementation takes the following (hyper-)parameters:
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-[`FPGrowth`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth) implements the
-FP-growth algorithm.
-It take a `JavaRDD` of transactions, where each transaction is an `Iterable` of items of a generic type.
+[`FPGrowth`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth)
+implements the FP-growth algorithm.  It take an `RDD` of transactions,
+where each transaction is an `Iterable` of items of a generic type.
 Calling `FPGrowth.run` with transactions returns an
 [`FPGrowthModel`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowthModel)
-that stores the frequent itemsets with their frequencies.
+that stores the frequent itemsets with their frequencies.  The following
+example illustrates how to mine frequent itemsets and association rules
+(see [Association
+Rules](mllib-frequent-pattern-mining.html#association-rules) for
+details) from `transactions`.
+
 
 {% highlight scala %}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel}
 
-val transactions: RDD[Array[String]] = ...
+val transactions: RDD[Array[String]] = sc.parallelize(Seq(
+  "r z h k p",
+  "z y x w v u t s",
+  "s x o n r",
+  "x z y m t s q e",
+  "z",
+  "x z y r q t p")
+  .map(_.split(" ")))
 
 val fpg = new FPGrowth()
   .setMinSupport(0.2)
@@ -60,29 +72,48 @@ val model = fpg.run(transactions)
 model.freqItemsets.collect().foreach { itemset =>
   println(itemset.items.mkString("[", ",", "]") + ", " + itemset.freq)
 }
+
+val minConfidence = 0.8
+model.generateAssociationRules(minConfidence).collect().foreach { rule =>
+  println(
+    rule.antecedent.mkString("[", ",", "]")
+      + " => " + rule.consequent .mkString("[", ",", "]")
+      + ", " + rule.confidence)
+}
 {% endhighlight %}
 
 </div>
 
 <div data-lang="java" markdown="1">
 
-[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the
-FP-growth algorithm.
-It take an `RDD` of transactions, where each transaction is an `Array` of items of a generic type.
-Calling `FPGrowth.run` with transactions returns an
+[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html)
+implements the FP-growth algorithm.  It take a `JavaRDD` of
+transactions, where each transaction is an `Array` of items of a generic
+type.  Calling `FPGrowth.run` with transactions returns an
 [`FPGrowthModel`](api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html)
-that stores the frequent itemsets with their frequencies.
+that stores the frequent itemsets with their frequencies.  The following
+example illustrates how to mine frequent itemsets and association rules
+(see [Association
+Rules](mllib-frequent-pattern-mining.html#association-rules) for
+details) from `transactions`.
 
 {% highlight java %}
+import java.util.Arrays;
 import java.util.List;
 
-import com.google.common.base.Joiner;
-
 import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.fpm.AssociationRules;
 import org.apache.spark.mllib.fpm.FPGrowth;
 import org.apache.spark.mllib.fpm.FPGrowthModel;
 
-JavaRDD<List<String>> transactions = ...
+JavaRDD<List<String>> transactions = sc.parallelize(Arrays.asList(
+  Arrays.asList("r z h k p".split(" ")),
+  Arrays.asList("z y x w v u t s".split(" ")),
+  Arrays.asList("s x o n r".split(" ")),
+  Arrays.asList("x z y m t s q e".split(" ")),
+  Arrays.asList("z".split(" ")),
+  Arrays.asList("x z y r q t p".split(" "))), 2);
 
 FPGrowth fpg = new FPGrowth()
   .setMinSupport(0.2)
@@ -90,7 +121,78 @@ FPGrowth fpg = new FPGrowth()
 FPGrowthModel<String> model = fpg.run(transactions);
 
 for (FPGrowth.FreqItemset<String> itemset: model.freqItemsets().toJavaRDD().collect()) {
-   System.out.println("[" + Joiner.on(",").join(s.javaItems()) + "], " + s.freq());
+  System.out.println("[" + itemset.javaItems() + "], " + itemset.freq());
+}
+
+double minConfidence = 0.8;
+for (AssociationRules.Rule<String> rule
+    : model.generateAssociationRules(minConfidence).toJavaRDD().collect()) {
+  System.out.println(
+    rule.javaAntecedent() + " => " + rule.javaConsequent() + ", " + rule.confidence());
+}
+{% endhighlight %}
+
+</div>
+</div>
+
+## Association Rules
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+[AssociationRules](api/scala/index.html#org.apache.spark.mllib.fpm.AssociationRules)
+implements a parallel rule generation algorithm for constructing rules
+that have a single item as the consequent.
+
+{% highlight scala %}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.mllib.fpm.AssociationRules
+import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset
+
+val freqItemsets = sc.parallelize(Seq(
+  new FreqItemset(Array("a"), 15L),
+  new FreqItemset(Array("b"), 35L),
+  new FreqItemset(Array("a", "b"), 12L)
+));
+
+val ar = new AssociationRules()
+  .setMinConfidence(0.8)
+val results = ar.run(freqItemsets)
+
+results.collect().foreach { rule =>
+  println("[" + rule.antecedent.mkString(",")
+    + "=>"
+    + rule.consequent.mkString(",") + "]," + rule.confidence)
+}
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+[AssociationRules](api/java/org/apache/spark/mllib/fpm/AssociationRules.html)
+implements a parallel rule generation algorithm for constructing rules
+that have a single item as the consequent.
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.fpm.AssociationRules;
+import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset;
+
+JavaRDD<FPGrowth.FreqItemset<String>> freqItemsets = sc.parallelize(Arrays.asList(
+  new FreqItemset<String>(new String[] {"a"}, 15L),
+  new FreqItemset<String>(new String[] {"b"}, 35L),
+  new FreqItemset<String>(new String[] {"a", "b"}, 12L)
+));
+
+AssociationRules arules = new AssociationRules()
+  .setMinConfidence(0.8);
+JavaRDD<AssociationRules.Rule<String>> results = arules.run(freqItemsets);
+
+for (AssociationRules.Rule<String> rule: results.collect()) {
+  System.out.println(
+    rule.javaAntecedent() + " => " + rule.javaConsequent() + ", " + rule.confidence());
 }
 {% endhighlight %}
 
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 7851175b98230..0573e5d4bc5ff 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -48,6 +48,7 @@ This lists functionality included in `spark.mllib`, the main MLlib API.
 * [Feature extraction and transformation](mllib-feature-extraction.html)
 * [Frequent pattern mining](mllib-frequent-pattern-mining.html)
   * [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
+  * [association rules](mllib-frequent-pattern-mining.html#association-rules)
   * [PrefixSpan](mllib-frequent-pattern-mining.html#prefix-span)
 * [Evaluation Metrics](mllib-evaluation-metrics.html)
 * [Optimization (developer)](mllib-optimization.html)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
index b3815ae6039c0..d7c2cb3ae2067 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
@@ -49,7 +49,7 @@ public void runAssociationRules() {
     JavaRDD<FPGrowth.FreqItemset<String>> freqItemsets = sc.parallelize(Lists.newArrayList(
       new FreqItemset<String>(new String[] {"a"}, 15L),
       new FreqItemset<String>(new String[] {"b"}, 35L),
-      new FreqItemset<String>(new String[] {"a", "b"}, 18L)
+      new FreqItemset<String>(new String[] {"a", "b"}, 12L)
     ));
 
     JavaRDD<AssociationRules.Rule<String>> results = (new AssociationRules()).run(freqItemsets);

From f4fa61effe34dae2f0eab0bef57b2dee220cf92f Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 18 Aug 2015 12:55:36 -0700
Subject: [PATCH 302/633] [SPARK-10029] [MLLIB] [DOC] Add Python examples for
 mllib IsotonicRegression user guide

Add Python examples for mllib IsotonicRegression user guide

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8225 from yanboliang/spark-10029.
---
 docs/mllib-isotonic-regression.md | 35 +++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/docs/mllib-isotonic-regression.md b/docs/mllib-isotonic-regression.md
index 5732bc4c7e79e..6aa881f749185 100644
--- a/docs/mllib-isotonic-regression.md
+++ b/docs/mllib-isotonic-regression.md
@@ -160,4 +160,39 @@ model.save(sc.sc(), "myModelPath");
 IsotonicRegressionModel sameModel = IsotonicRegressionModel.load(sc.sc(), "myModelPath");
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+Data are read from a file where each line has a format label,feature
+i.e. 4710.28,500.00. The data are split to training and testing set.
+Model is created using the training set and a mean squared error is calculated from the predicted
+labels and real labels in the test set.
+
+{% highlight python %}
+import math
+from pyspark.mllib.regression import IsotonicRegression, IsotonicRegressionModel
+
+data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt")
+
+# Create label, feature, weight tuples from input data with weight set to default value 1.0.
+parsedData = data.map(lambda line: tuple([float(x) for x in line.split(',')]) + (1.0,))
+
+# Split data into training (60%) and test (40%) sets.
+training, test = parsedData.randomSplit([0.6, 0.4], 11)
+
+# Create isotonic regression model from training data.
+# Isotonic parameter defaults to true so it is only shown for demonstration
+model = IsotonicRegression.train(training)
+
+# Create tuples of predicted and real labels.
+predictionAndLabel = test.map(lambda p: (model.predict(p[1]), p[0]))
+
+# Calculate mean squared error between predicted and real labels.
+meanSquaredError = predictionAndLabel.map(lambda pl: math.pow((pl[0] - pl[1]), 2)).mean()
+print("Mean Squared Error = " + str(meanSquaredError))
+
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = IsotonicRegressionModel.load(sc, "myModelPath")
+{% endhighlight %}
+</div>
 </div>

From 747c2ba8006d5b86f3be8dfa9ace639042a35628 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 18 Aug 2015 12:56:36 -0700
Subject: [PATCH 303/633] [SPARK-10032] [PYSPARK] [DOC] Add Python example for
 mllib LDAModel user guide

Add Python example for mllib LDAModel user guide

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8227 from yanboliang/spark-10032.
---
 docs/mllib-clustering.md | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index bb875ae2ae6cb..fd9ab258e196d 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -564,6 +564,34 @@ public class JavaLDAExample {
 {% endhighlight %}
 </div>
 
+<div data-lang="python" markdown="1">
+{% highlight python %}
+from pyspark.mllib.clustering import LDA, LDAModel
+from pyspark.mllib.linalg import Vectors
+
+# Load and parse the data
+data = sc.textFile("data/mllib/sample_lda_data.txt")
+parsedData = data.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
+# Index documents with unique IDs
+corpus = parsedData.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
+
+# Cluster the documents into three topics using LDA
+ldaModel = LDA.train(corpus, k=3)
+
+# Output topics. Each is a distribution over words (matching word count vectors)
+print("Learned topics (as distributions over vocab of " + str(ldaModel.vocabSize()) + " words):")
+topics = ldaModel.topicsMatrix()
+for topic in range(3):
+    print("Topic " + str(topic) + ":")
+    for word in range(0, ldaModel.vocabSize()):
+        print(" " + str(topics[word][topic]))
+		
+# Save and load model
+model.save(sc, "myModelPath")
+sameModel = LDAModel.load(sc, "myModelPath")
+{% endhighlight %}
+</div>
+
 </div>
 
 ## Streaming k-means

From 8bae9015b7e7b4528ca2bc5180771cb95d2aac13 Mon Sep 17 00:00:00 2001
From: Piotr Migdal <pmigdal@gmail.com>
Date: Tue, 18 Aug 2015 12:59:28 -0700
Subject: [PATCH 304/633] [SPARK-10085] [MLLIB] [DOCS] removed unnecessary
 numpy array import

See https://issues.apache.org/jira/browse/SPARK-10085

Author: Piotr Migdal <pmigdal@gmail.com>

Closes #8284 from stared/spark-10085.
---
 docs/mllib-linear-methods.md | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md
index 07655baa414b5..e9b2d276cd389 100644
--- a/docs/mllib-linear-methods.md
+++ b/docs/mllib-linear-methods.md
@@ -504,7 +504,6 @@ will in the future.
 {% highlight python %}
 from pyspark.mllib.classification import LogisticRegressionWithLBFGS, LogisticRegressionModel
 from pyspark.mllib.regression import LabeledPoint
-from numpy import array
 
 # Load and parse the data
 def parsePoint(line):
@@ -676,7 +675,6 @@ Note that the Python API does not yet support model save/load but will in the fu
 
 {% highlight python %}
 from pyspark.mllib.regression import LabeledPoint, LinearRegressionWithSGD, LinearRegressionModel
-from numpy import array
 
 # Load and parse the data
 def parsePoint(line):

From bf1d6614dcb8f5974e62e406d9c0f8aac52556d3 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 18 Aug 2015 13:35:45 -0700
Subject: [PATCH 305/633] [SPARK-9574] [STREAMING] Remove unnecessary contents
 of spark-streaming-XXX-assembly jars

Removed contents already included in Spark assembly jar from spark-streaming-XXX-assembly jars.

Author: zsxwing <zsxwing@gmail.com>

Closes #8069 from zsxwing/SPARK-9574.
---
 external/flume-assembly/pom.xml     | 11 ++++
 external/kafka-assembly/pom.xml     | 84 +++++++++++++++++++++++++++++
 external/mqtt-assembly/pom.xml      | 74 +++++++++++++++++++++++++
 extras/kinesis-asl-assembly/pom.xml | 79 +++++++++++++++++++++++++++
 pom.xml                             |  2 +-
 5 files changed, 249 insertions(+), 1 deletion(-)

diff --git a/external/flume-assembly/pom.xml b/external/flume-assembly/pom.xml
index 13189595d1d6c..e05e4318969ce 100644
--- a/external/flume-assembly/pom.xml
+++ b/external/flume-assembly/pom.xml
@@ -68,6 +68,11 @@
       <artifactId>commons-codec</artifactId>
       <scope>provided</scope>
     </dependency>
+    <dependency>
+      <groupId>commons-lang</groupId>
+      <artifactId>commons-lang</artifactId>
+      <scope>provided</scope>
+    </dependency>
     <dependency>
       <groupId>commons-net</groupId>
       <artifactId>commons-net</artifactId>
@@ -88,6 +93,12 @@
       <artifactId>avro-ipc</artifactId>
       <scope>provided</scope>
     </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-mapred</artifactId>
+      <classifier>${avro.mapred.classifier}</classifier>
+      <scope>provided</scope>
+    </dependency>
     <dependency>
       <groupId>org.scala-lang</groupId>
       <artifactId>scala-library</artifactId>
diff --git a/external/kafka-assembly/pom.xml b/external/kafka-assembly/pom.xml
index 977514fa5a1ec..36342f37bb2ea 100644
--- a/external/kafka-assembly/pom.xml
+++ b/external/kafka-assembly/pom.xml
@@ -47,6 +47,90 @@
       <version>${project.version}</version>
       <scope>provided</scope>
     </dependency>
+    <!--
+      Demote already included in the Spark assembly.
+    -->
+    <dependency>
+      <groupId>commons-codec</groupId>
+      <artifactId>commons-codec</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>commons-lang</groupId>
+      <artifactId>commons-lang</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.google.protobuf</groupId>
+      <artifactId>protobuf-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-server</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-core</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>net.jpountz.lz4</groupId>
+      <artifactId>lz4</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-client</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-mapred</artifactId>
+      <classifier>${avro.mapred.classifier}</classifier>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.curator</groupId>
+      <artifactId>curator-recipes</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.zookeeper</groupId>
+      <artifactId>zookeeper</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>log4j</groupId>
+      <artifactId>log4j</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>net.java.dev.jets3t</groupId>
+      <artifactId>jets3t</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-library</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-api</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.xerial.snappy</groupId>
+      <artifactId>snappy-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
   </dependencies>
 
   <build>
diff --git a/external/mqtt-assembly/pom.xml b/external/mqtt-assembly/pom.xml
index 9c94473053d96..f3e3f93e7ed50 100644
--- a/external/mqtt-assembly/pom.xml
+++ b/external/mqtt-assembly/pom.xml
@@ -47,6 +47,80 @@
       <version>${project.version}</version>
       <scope>provided</scope>
     </dependency>
+    <!--
+      Demote already included in the Spark assembly.
+    -->
+    <dependency>
+      <groupId>commons-lang</groupId>
+      <artifactId>commons-lang</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.google.protobuf</groupId>
+      <artifactId>protobuf-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-server</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-core</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-client</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-mapred</artifactId>
+      <classifier>${avro.mapred.classifier}</classifier>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.curator</groupId>
+      <artifactId>curator-recipes</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.zookeeper</groupId>
+      <artifactId>zookeeper</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>log4j</groupId>
+      <artifactId>log4j</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>net.java.dev.jets3t</groupId>
+      <artifactId>jets3t</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.scala-lang</groupId>
+      <artifactId>scala-library</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-api</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.xerial.snappy</groupId>
+      <artifactId>snappy-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
   </dependencies>
 
   <build>
diff --git a/extras/kinesis-asl-assembly/pom.xml b/extras/kinesis-asl-assembly/pom.xml
index 70d2c9c58f54e..3ca538608f694 100644
--- a/extras/kinesis-asl-assembly/pom.xml
+++ b/extras/kinesis-asl-assembly/pom.xml
@@ -47,6 +47,85 @@
       <version>${project.version}</version>
       <scope>provided</scope>
     </dependency>
+    <!--
+      Demote already included in the Spark assembly.
+    -->
+    <dependency>
+      <groupId>com.fasterxml.jackson.core</groupId>
+      <artifactId>jackson-databind</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>commons-lang</groupId>
+      <artifactId>commons-lang</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.google.protobuf</groupId>
+      <artifactId>protobuf-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-server</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.sun.jersey</groupId>
+      <artifactId>jersey-core</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>log4j</groupId>
+      <artifactId>log4j</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>net.java.dev.jets3t</groupId>
+      <artifactId>jets3t</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-client</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-ipc</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.avro</groupId>
+      <artifactId>avro-mapred</artifactId>
+      <classifier>${avro.mapred.classifier}</classifier>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.curator</groupId>
+      <artifactId>curator-recipes</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.zookeeper</groupId>
+      <artifactId>zookeeper</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-api</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.slf4j</groupId>
+      <artifactId>slf4j-log4j12</artifactId>
+      <scope>provided</scope>
+    </dependency>
+    <dependency>
+      <groupId>org.xerial.snappy</groupId>
+      <artifactId>snappy-java</artifactId>
+      <scope>provided</scope>
+    </dependency>
   </dependencies>
 
   <build>
diff --git a/pom.xml b/pom.xml
index 9bfca1c4170bb..ccfa1ea19b21e 100644
--- a/pom.xml
+++ b/pom.xml
@@ -432,7 +432,7 @@
         <version>${commons-lang3.version}</version>
       </dependency>
       <dependency>
-        <groupId>org.apache.commons</groupId>
+        <groupId>commons-lang</groupId>
         <artifactId>commons-lang</artifactId>
         <version>${commons-lang2.version}</version>
       </dependency>

From 80cb25b228e821a80256546a2f03f73a45cf7645 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 18 Aug 2015 13:50:51 -0700
Subject: [PATCH 306/633] [SPARK-10080] [SQL] Fix binary incompatibility for $
 column interpolation

Turns out that inner classes of inner objects are referenced directly, and thus moving it will break binary compatibility.

Author: Michael Armbrust <michael@databricks.com>

Closes #8281 from marmbrus/binaryCompat.
---
 .../main/scala/org/apache/spark/sql/SQLContext.scala | 11 +++++++++++
 .../scala/org/apache/spark/sql/SQLImplicits.scala    | 10 ----------
 .../org/apache/spark/sql/test/SharedSQLContext.scala | 12 +++++++++++-
 3 files changed, 22 insertions(+), 11 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 53de10d5fa9da..58fe75b59f418 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -334,6 +334,17 @@ class SQLContext(@transient val sparkContext: SparkContext)
   @Experimental
   object implicits extends SQLImplicits with Serializable {
     protected override def _sqlContext: SQLContext = self
+
+    /**
+     * Converts $"col name" into an [[Column]].
+     * @since 1.3.0
+     */
+    // This must live here to preserve binary compatibility with Spark < 1.5.
+    implicit class StringToColumn(val sc: StringContext) {
+      def $(args: Any*): ColumnName = {
+        new ColumnName(sc.s(args: _*))
+      }
+    }
   }
   // scalastyle:on
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
index 5f82372700f2c..47b6f80bed483 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
@@ -33,16 +33,6 @@ import org.apache.spark.unsafe.types.UTF8String
 private[sql] abstract class SQLImplicits {
   protected def _sqlContext: SQLContext
 
-  /**
-   * Converts $"col name" into an [[Column]].
-   * @since 1.3.0
-   */
-  implicit class StringToColumn(val sc: StringContext) {
-    def $(args: Any*): ColumnName = {
-      new ColumnName(sc.s(args: _*))
-    }
-  }
-
   /**
    * An implicit conversion that turns a Scala `Symbol` into a [[Column]].
    * @since 1.3.0
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
index 3cfd822e2a747..8a061b6bc690d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.test
 
-import org.apache.spark.sql.SQLContext
+import org.apache.spark.sql.{ColumnName, SQLContext}
 
 
 /**
@@ -65,4 +65,14 @@ private[sql] trait SharedSQLContext extends SQLTestUtils {
     }
   }
 
+  /**
+   * Converts $"col name" into an [[Column]].
+   * @since 1.3.0
+   */
+  // This must be duplicated here to preserve binary compatibility with Spark < 1.5.
+  implicit class StringToColumn(val sc: StringContext) {
+    def $(args: Any*): ColumnName = {
+      new ColumnName(sc.s(args: _*))
+    }
+  }
 }

From 9b731fad2b43ca18f3c5274062d4c7bc2622ab72 Mon Sep 17 00:00:00 2001
From: Dennis Huo <dhuo@google.com>
Date: Tue, 18 Aug 2015 14:34:20 -0700
Subject: [PATCH 307/633] [SPARK-9782] [YARN] Support YARN application tags via
 SparkConf

Add a new test case in yarn/ClientSuite which checks how the various SparkConf
and ClientArguments propagate into the ApplicationSubmissionContext.

Author: Dennis Huo <dhuo@google.com>

Closes #8072 from dennishuo/dhuo-yarn-application-tags.
---
 docs/running-on-yarn.md                       |  8 +++++
 .../org/apache/spark/deploy/yarn/Client.scala | 21 +++++++++++
 .../spark/deploy/yarn/ClientSuite.scala       | 36 +++++++++++++++++++
 3 files changed, 65 insertions(+)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index ec32c419b7c51..8ac26e98da635 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -319,6 +319,14 @@ If you need a reference to the proper location to put log files in the YARN so t
   running against earlier versions, this property will be ignored.
   </td>
 </tr>
+<tr>
+  <td><code>spark.yarn.tags</code></td>
+  <td>(none)</td>
+  <td>
+  Comma-separated list of strings to pass through as YARN application tags appearing
+  in YARN ApplicationReports, which can be used for filtering when querying YARN apps.
+  </td>
+</tr>
 <tr>
   <td><code>spark.yarn.keytab</code></td>
   <td>(none)</td>
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 6d63ddaf15852..5c6a716863f66 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -163,6 +163,23 @@ private[spark] class Client(
     appContext.setQueue(args.amQueue)
     appContext.setAMContainerSpec(containerContext)
     appContext.setApplicationType("SPARK")
+    sparkConf.getOption(CONF_SPARK_YARN_APPLICATION_TAGS)
+      .map(StringUtils.getTrimmedStringCollection(_))
+      .filter(!_.isEmpty())
+      .foreach { tagCollection =>
+        try {
+          // The setApplicationTags method was only introduced in Hadoop 2.4+, so we need to use
+          // reflection to set it, printing a warning if a tag was specified but the YARN version
+          // doesn't support it.
+          val method = appContext.getClass().getMethod(
+            "setApplicationTags", classOf[java.util.Set[String]])
+          method.invoke(appContext, new java.util.HashSet[String](tagCollection))
+        } catch {
+          case e: NoSuchMethodException =>
+            logWarning(s"Ignoring $CONF_SPARK_YARN_APPLICATION_TAGS because this version of " +
+              "YARN does not support it")
+        }
+      }
     sparkConf.getOption("spark.yarn.maxAppAttempts").map(_.toInt) match {
       case Some(v) => appContext.setMaxAppAttempts(v)
       case None => logDebug("spark.yarn.maxAppAttempts is not set. " +
@@ -987,6 +1004,10 @@ object Client extends Logging {
   // of the executors
   val CONF_SPARK_YARN_SECONDARY_JARS = "spark.yarn.secondary.jars"
 
+  // Comma-separated list of strings to pass through as YARN application tags appearing
+  // in YARN ApplicationReports, which can be used for filtering when querying YARN.
+  val CONF_SPARK_YARN_APPLICATION_TAGS = "spark.yarn.tags"
+
   // Staging directory is private! -> rwx--------
   val STAGING_DIR_PERMISSION: FsPermission =
     FsPermission.createImmutable(Integer.parseInt("700", 8).toShort)
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
index 837f8d3fa55a7..0a5402c89e764 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
@@ -29,8 +29,11 @@ import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce.MRJobConfig
 import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
+import org.apache.hadoop.yarn.api.protocolrecords.GetNewApplicationResponse
 import org.apache.hadoop.yarn.api.records._
+import org.apache.hadoop.yarn.client.api.YarnClientApplication
 import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.apache.hadoop.yarn.util.Records
 import org.mockito.Matchers._
 import org.mockito.Mockito._
 import org.scalatest.{BeforeAndAfterAll, Matchers}
@@ -170,6 +173,39 @@ class ClientSuite extends SparkFunSuite with Matchers with BeforeAndAfterAll {
     cp should contain ("/remotePath/my1.jar")
   }
 
+  test("configuration and args propagate through createApplicationSubmissionContext") {
+    val conf = new Configuration()
+    // When parsing tags, duplicates and leading/trailing whitespace should be removed.
+    // Spaces between non-comma strings should be preserved as single tags. Empty strings may or
+    // may not be removed depending on the version of Hadoop being used.
+    val sparkConf = new SparkConf()
+      .set(Client.CONF_SPARK_YARN_APPLICATION_TAGS, ",tag1, dup,tag2 , ,multi word , dup")
+      .set("spark.yarn.maxAppAttempts", "42")
+    val args = new ClientArguments(Array(
+      "--name", "foo-test-app",
+      "--queue", "staging-queue"), sparkConf)
+
+    val appContext = Records.newRecord(classOf[ApplicationSubmissionContext])
+    val getNewApplicationResponse = Records.newRecord(classOf[GetNewApplicationResponse])
+    val containerLaunchContext = Records.newRecord(classOf[ContainerLaunchContext])
+
+    val client = new Client(args, conf, sparkConf)
+    client.createApplicationSubmissionContext(
+      new YarnClientApplication(getNewApplicationResponse, appContext),
+      containerLaunchContext)
+
+    appContext.getApplicationName should be ("foo-test-app")
+    appContext.getQueue should be ("staging-queue")
+    appContext.getAMContainerSpec should be (containerLaunchContext)
+    appContext.getApplicationType should be ("SPARK")
+    appContext.getClass.getMethods.filter(_.getName.equals("getApplicationTags")).foreach{ method =>
+      val tags = method.invoke(appContext).asInstanceOf[java.util.Set[String]]
+      tags should contain allOf ("tag1", "dup", "tag2", "multi word")
+      tags.filter(!_.isEmpty).size should be (4)
+    }
+    appContext.getMaxAppAttempts should be (42)
+  }
+
   object Fixtures {
 
     val knownDefYarnAppCP: Seq[String] =

From fa41e0242f075843beff7dc600d1a6bac004bdc7 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 18 Aug 2015 14:43:05 -0700
Subject: [PATCH 308/633] [SPARK-10089] [SQL] Add missing golden files.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8283 from vanzin/SPARK-10089.
---
 ...ry test-0-515e406ffb23f6fd0d8cd34c2b25fbe6 |   3 +
 ...ry test-0-eabbebd5c1d127b1605bfec52d7b7f3f | 500 ++++++++++++++++++
 2 files changed, 503 insertions(+)
 create mode 100644 sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-515e406ffb23f6fd0d8cd34c2b25fbe6
 create mode 100644 sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-eabbebd5c1d127b1605bfec52d7b7f3f

diff --git a/sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-515e406ffb23f6fd0d8cd34c2b25fbe6 b/sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-515e406ffb23f6fd0d8cd34c2b25fbe6
new file mode 100644
index 0000000000000..9a276bc794c0d
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/Column pruning - non-trivial top project with aliases - query test-0-515e406ffb23f6fd0d8cd34c2b25fbe6	
@@ -0,0 +1,3 @@
+476
+172
+622
diff --git a/sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-eabbebd5c1d127b1605bfec52d7b7f3f b/sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-eabbebd5c1d127b1605bfec52d7b7f3f
new file mode 100644
index 0000000000000..444039e75fbac
--- /dev/null
+++ b/sql/hive/src/test/resources/golden/Partition pruning - non-partitioned, non-trivial project - query test-0-eabbebd5c1d127b1605bfec52d7b7f3f	
@@ -0,0 +1,500 @@
+476
+172
+622
+54
+330
+818
+510
+556
+196
+968
+530
+386
+802
+300
+546
+448
+738
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+256
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+748
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+938
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+834
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+834
+978
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+938
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+918
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+664
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+768
+978
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+168
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+822
+460
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+48
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+992
+0
+644
+394
+936
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+398
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+836
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+52
+330
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+460
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+262
+102
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+86
+872
+312
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+962
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+170
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+0
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+612
+448
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+778
+654
+484
+738
+784
+544
+662
+802
+484
+904
+354
+452
+10
+994
+804
+792
+634
+790
+116
+70
+672
+190
+22
+336
+68
+458
+466
+286
+944
+644
+996
+320
+390
+84
+642
+860
+238
+978
+916
+156
+152
+82
+446
+984
+298
+898
+436
+456
+276
+906
+60
+418
+128
+936
+152
+148
+684
+138
+460
+66
+736
+206
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+226
+432
+734
+688
+334
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+438
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+970
+232
+446
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+526
+140
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+960
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+256
+934
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+404
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+938
+926
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+4
+70
+566
+662
+470
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+88
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+966
+106
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+380
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+228
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+180
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+524
+696
+848
+24
+792
+402
+434
+328
+862
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+956
+596
+250
+862
+328
+848
+374
+764
+10
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+794
+960
+582
+48
+702
+510
+208
+140
+326
+876
+238
+828
+400
+982
+474
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+720
+496
+958
+610
+834
+398
+888
+240
+858
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+886
+646
+650
+554
+460
+956
+356
+936
+620
+634
+666
+986
+920
+414
+498
+530
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+166
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+706
+344
+428
+924
+466
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+266
+350
+378
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+842
+814
+768
+512
+52
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+758
+36
+924
+984
+200
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+18
+682
+996
+292
+916
+724
+372
+570
+696
+334
+36
+546
+366
+562
+688
+194
+938
+630
+168
+56
+74
+896
+304
+696
+614
+388
+828
+954
+444
+252
+180
+338
+806
+800
+400
+194

From 492ac1facbc79ee251d45cff315598ec9935a0e2 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 18 Aug 2015 14:45:19 -0700
Subject: [PATCH 309/633] [SPARK-10088] [SQL] Add support for "stored as avro"
 in HiveQL parser.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8282 from vanzin/SPARK-10088.
---
 .../scala/org/apache/spark/sql/hive/HiveQl.scala     | 11 +++++++++++
 .../org/apache/spark/sql/hive/test/TestHive.scala    | 12 ++----------
 2 files changed, 13 insertions(+), 10 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index c3f29350101d3..ad33dee555dd2 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -729,6 +729,17 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
                 inputFormat = Option("org.apache.hadoop.mapred.SequenceFileInputFormat"),
                 outputFormat = Option("org.apache.hadoop.mapred.SequenceFileOutputFormat"))
 
+            case "avro" =>
+              tableDesc = tableDesc.copy(
+                inputFormat =
+                  Option("org.apache.hadoop.hive.ql.io.avro.AvroContainerInputFormat"),
+                outputFormat =
+                  Option("org.apache.hadoop.hive.ql.io.avro.AvroContainerOutputFormat"))
+              if (tableDesc.serde.isEmpty) {
+                tableDesc = tableDesc.copy(
+                  serde = Option("org.apache.hadoop.hive.serde2.avro.AvroSerDe"))
+              }
+
             case _ =>
               throw new SemanticException(
                 s"Unrecognized file format in STORED AS clause: ${child.getText}")
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 4eae699ac3b51..4da86636ac100 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -25,10 +25,8 @@ import scala.language.implicitConversions
 
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.ql.exec.FunctionRegistry
-import org.apache.hadoop.hive.ql.io.avro.{AvroContainerInputFormat, AvroContainerOutputFormat}
 import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe
-import org.apache.hadoop.hive.serde2.avro.AvroSerDe
 
 import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.analysis._
@@ -276,10 +274,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
       "INSERT OVERWRITE TABLE serdeins SELECT * FROM src".cmd),
     TestTable("episodes",
       s"""CREATE TABLE episodes (title STRING, air_date STRING, doctor INT)
-         |ROW FORMAT SERDE '${classOf[AvroSerDe].getCanonicalName}'
-         |STORED AS
-         |INPUTFORMAT '${classOf[AvroContainerInputFormat].getCanonicalName}'
-         |OUTPUTFORMAT '${classOf[AvroContainerOutputFormat].getCanonicalName}'
+         |STORED AS avro
          |TBLPROPERTIES (
          |  'avro.schema.literal'='{
          |    "type": "record",
@@ -312,10 +307,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
     TestTable("episodes_part",
       s"""CREATE TABLE episodes_part (title STRING, air_date STRING, doctor INT)
          |PARTITIONED BY (doctor_pt INT)
-         |ROW FORMAT SERDE '${classOf[AvroSerDe].getCanonicalName}'
-         |STORED AS
-         |INPUTFORMAT '${classOf[AvroContainerInputFormat].getCanonicalName}'
-         |OUTPUTFORMAT '${classOf[AvroContainerOutputFormat].getCanonicalName}'
+         |STORED AS avro
          |TBLPROPERTIES (
          |  'avro.schema.literal'='{
          |    "type": "record",

From 1dbffba37a84c62202befd3911d25888f958191d Mon Sep 17 00:00:00 2001
From: Bryan Cutler <bjcutler@us.ibm.com>
Date: Tue, 18 Aug 2015 14:58:30 -0700
Subject: [PATCH 310/633] [SPARK-8924] [MLLIB, DOCUMENTATION] Added @since tags
 to mllib.tree

Added since tags to mllib.tree

Author: Bryan Cutler <bjcutler@us.ibm.com>

Closes #7380 from BryanCutler/sinceTag-mllibTree-8924.
---
 .../spark/mllib/tree/DecisionTree.scala       | 13 +++++++
 .../mllib/tree/GradientBoostedTrees.scala     | 10 +++++
 .../spark/mllib/tree/RandomForest.scala       | 10 +++++
 .../spark/mllib/tree/configuration/Algo.scala |  1 +
 .../tree/configuration/BoostingStrategy.scala |  6 +++
 .../tree/configuration/FeatureType.scala      |  1 +
 .../tree/configuration/QuantileStrategy.scala |  1 +
 .../mllib/tree/configuration/Strategy.scala   | 20 +++++++++-
 .../spark/mllib/tree/impurity/Entropy.scala   |  4 ++
 .../spark/mllib/tree/impurity/Gini.scala      |  4 ++
 .../spark/mllib/tree/impurity/Impurity.scala  |  3 ++
 .../spark/mllib/tree/impurity/Variance.scala  |  4 ++
 .../spark/mllib/tree/loss/AbsoluteError.scala |  2 +
 .../spark/mllib/tree/loss/LogLoss.scala       |  2 +
 .../apache/spark/mllib/tree/loss/Loss.scala   |  3 ++
 .../apache/spark/mllib/tree/loss/Losses.scala |  6 +++
 .../spark/mllib/tree/loss/SquaredError.scala  |  2 +
 .../mllib/tree/model/DecisionTreeModel.scala  | 22 +++++++++++
 .../tree/model/InformationGainStats.scala     |  1 +
 .../apache/spark/mllib/tree/model/Node.scala  |  3 ++
 .../spark/mllib/tree/model/Predict.scala      |  1 +
 .../apache/spark/mllib/tree/model/Split.scala |  1 +
 .../mllib/tree/model/treeEnsembleModels.scala | 37 +++++++++++++++++++
 .../org/apache/spark/mllib/tree/package.scala |  1 +
 24 files changed, 157 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index cecd1fed896d5..e5200b86fddd4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -43,6 +43,7 @@ import org.apache.spark.util.random.XORShiftRandom
  * @param strategy The configuration parameters for the tree algorithm which specify the type
  *                 of algorithm (classification, regression, etc.), feature type (continuous,
  *                 categorical), depth of the tree, quantile calculation strategy, etc.
+ * @since 1.0.0
  */
 @Experimental
 class DecisionTree (private val strategy: Strategy) extends Serializable with Logging {
@@ -53,6 +54,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
    * Method to train a decision tree model over an RDD
    * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.2.0
    */
   def run(input: RDD[LabeledPoint]): DecisionTreeModel = {
     // Note: random seed will not be used since numTrees = 1.
@@ -62,6 +64,9 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
   }
 }
 
+/**
+ * @since 1.0.0
+ */
 object DecisionTree extends Serializable with Logging {
 
   /**
@@ -79,6 +84,7 @@ object DecisionTree extends Serializable with Logging {
    *                 of algorithm (classification, regression, etc.), feature type (continuous,
    *                 categorical), depth of the tree, quantile calculation strategy, etc.
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.0.0
   */
   def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel = {
     new DecisionTree(strategy).run(input)
@@ -100,6 +106,7 @@ object DecisionTree extends Serializable with Logging {
    * @param maxDepth Maximum depth of the tree.
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -127,6 +134,7 @@ object DecisionTree extends Serializable with Logging {
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
    * @param numClasses number of classes for classification. Default value of 2.
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.2.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -160,6 +168,7 @@ object DecisionTree extends Serializable with Logging {
    *                                E.g., an entry (n -> k) indicates that feature n is categorical
    *                                with k categories indexed from 0: {0, 1, ..., k-1}.
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.0.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -192,6 +201,7 @@ object DecisionTree extends Serializable with Logging {
    * @param maxBins maximum number of bins used for splitting features
    *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.1.0
    */
   def trainClassifier(
       input: RDD[LabeledPoint],
@@ -207,6 +217,7 @@ object DecisionTree extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]]
+   * @since 1.1.0
    */
   def trainClassifier(
       input: JavaRDD[LabeledPoint],
@@ -236,6 +247,7 @@ object DecisionTree extends Serializable with Logging {
    * @param maxBins maximum number of bins used for splitting features
    *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
+   * @since 1.1.0
    */
   def trainRegressor(
       input: RDD[LabeledPoint],
@@ -249,6 +261,7 @@ object DecisionTree extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]]
+   * @since 1.1.0
    */
   def trainRegressor(
       input: JavaRDD[LabeledPoint],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
index 9ce6faa137c41..143617098637a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
@@ -48,6 +48,7 @@ import org.apache.spark.storage.StorageLevel
  *       for other loss functions.
  *
  * @param boostingStrategy Parameters for the gradient boosting algorithm.
+ * @since 1.2.0
  */
 @Experimental
 class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
@@ -57,6 +58,7 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
    * Method to train a gradient boosting model
    * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    * @return a gradient boosted trees model that can be used for prediction
+   * @since 1.2.0
    */
   def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel = {
     val algo = boostingStrategy.treeStrategy.algo
@@ -74,6 +76,7 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees!#run]].
+   * @since 1.2.0
    */
   def run(input: JavaRDD[LabeledPoint]): GradientBoostedTreesModel = {
     run(input.rdd)
@@ -88,6 +91,7 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
    *                        E.g., these two datasets could be created from an original dataset
    *                        by using [[org.apache.spark.rdd.RDD.randomSplit()]]
    * @return a gradient boosted trees model that can be used for prediction
+   * @since 1.4.0
    */
   def runWithValidation(
       input: RDD[LabeledPoint],
@@ -111,6 +115,7 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees!#runWithValidation]].
+   * @since 1.4.0
    */
   def runWithValidation(
       input: JavaRDD[LabeledPoint],
@@ -119,6 +124,9 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
   }
 }
 
+/**
+ * @since 1.2.0
+ */
 object GradientBoostedTrees extends Logging {
 
   /**
@@ -129,6 +137,7 @@ object GradientBoostedTrees extends Logging {
    *              For regression, labels are real numbers.
    * @param boostingStrategy Configuration options for the boosting algorithm.
    * @return a gradient boosted trees model that can be used for prediction
+   * @since 1.2.0
    */
   def train(
       input: RDD[LabeledPoint],
@@ -138,6 +147,7 @@ object GradientBoostedTrees extends Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees$#train]]
+   * @since 1.2.0
    */
   def train(
       input: JavaRDD[LabeledPoint],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
index 069959976a188..9f3230656acc5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
@@ -260,6 +260,9 @@ private class RandomForest (
 
 }
 
+/**
+ * @since 1.2.0
+ */
 object RandomForest extends Serializable with Logging {
 
   /**
@@ -276,6 +279,7 @@ object RandomForest extends Serializable with Logging {
    *                                if numTrees > 1 (forest) set to "sqrt".
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
+   * @since 1.2.0
    */
   def trainClassifier(
       input: RDD[LabeledPoint],
@@ -313,6 +317,7 @@ object RandomForest extends Serializable with Logging {
    *                 (suggested value: 100)
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model  that can be used for prediction
+   * @since 1.2.0
    */
   def trainClassifier(
       input: RDD[LabeledPoint],
@@ -332,6 +337,7 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.RandomForest$#trainClassifier]]
+   * @since 1.2.0
    */
   def trainClassifier(
       input: JavaRDD[LabeledPoint],
@@ -362,6 +368,7 @@ object RandomForest extends Serializable with Logging {
    *                                if numTrees > 1 (forest) set to "onethird".
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
+   * @since 1.2.0
    */
   def trainRegressor(
       input: RDD[LabeledPoint],
@@ -398,6 +405,7 @@ object RandomForest extends Serializable with Logging {
    *                 (suggested value: 100)
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
+   * @since 1.2.0
    */
   def trainRegressor(
       input: RDD[LabeledPoint],
@@ -416,6 +424,7 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.RandomForest$#trainRegressor]]
+   * @since 1.2.0
    */
   def trainRegressor(
       input: JavaRDD[LabeledPoint],
@@ -433,6 +442,7 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * List of supported feature subset sampling strategies.
+   * @since 1.2.0
    */
   val supportedFeatureSubsetStrategies: Array[String] =
     Array("auto", "all", "sqrt", "log2", "onethird")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
index b6099259971b7..d9a49aa71fcfb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
@@ -22,6 +22,7 @@ import org.apache.spark.annotation.Experimental
 /**
  * :: Experimental ::
  * Enum to select the algorithm for the decision tree
+ * @since 1.0.0
  */
 @Experimental
 object Algo extends Enumeration {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index 50fe2ac53da9d..88e5f57e9ab32 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -38,6 +38,7 @@ import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
  *                      validation input between two iterations is less than the validationTol
  *                      then stop.  Ignored when
  *                      [[org.apache.spark.mllib.tree.GradientBoostedTrees.run()]] is used.
+ * @since 1.2.0
  */
 @Experimental
 case class BoostingStrategy(
@@ -70,6 +71,9 @@ case class BoostingStrategy(
   }
 }
 
+/**
+ * @since 1.2.0
+ */
 @Experimental
 object BoostingStrategy {
 
@@ -77,6 +81,7 @@ object BoostingStrategy {
    * Returns default configuration for the boosting algorithm
    * @param algo Learning goal.  Supported: "Classification" or "Regression"
    * @return Configuration for boosting algorithm
+   * @since 1.2.0
    */
   def defaultParams(algo: String): BoostingStrategy = {
     defaultParams(Algo.fromString(algo))
@@ -88,6 +93,7 @@ object BoostingStrategy {
    *             [[org.apache.spark.mllib.tree.configuration.Algo.Classification]],
    *             [[org.apache.spark.mllib.tree.configuration.Algo.Regression]]
    * @return Configuration for boosting algorithm
+   * @since 1.3.0
    */
   def defaultParams(algo: Algo): BoostingStrategy = {
     val treeStrategy = Strategy.defaultStrategy(algo)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
index f4c877232750f..0684cafa486bd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
@@ -22,6 +22,7 @@ import org.apache.spark.annotation.Experimental
 /**
  * :: Experimental ::
  * Enum to describe whether a feature is "continuous" or "categorical"
+ * @since 1.0.0
  */
 @Experimental
 object FeatureType extends Enumeration {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
index 7da976e55a722..2daa63c4d2771 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
@@ -22,6 +22,7 @@ import org.apache.spark.annotation.Experimental
 /**
  * :: Experimental ::
  * Enum for selecting the quantile calculation strategy
+ * @since 1.0.0
  */
 @Experimental
 object QuantileStrategy extends Enumeration {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index de2c784809443..7ae25a88bf500 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -66,6 +66,7 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                           E.g. 10 means that the cache will get checkpointed every 10 updates. If
  *                           the checkpoint directory is not set in
  *                           [[org.apache.spark.SparkContext]], this setting is ignored.
+ * @since 1.0.0
  */
 @Experimental
 class Strategy (
@@ -83,16 +84,23 @@ class Strategy (
     @BeanProperty var useNodeIdCache: Boolean = false,
     @BeanProperty var checkpointInterval: Int = 10) extends Serializable {
 
+  /**
+   * @since 1.2.0
+   */
   def isMulticlassClassification: Boolean = {
     algo == Classification && numClasses > 2
   }
 
+  /**
+   * @since 1.2.0
+   */
   def isMulticlassWithCategoricalFeatures: Boolean = {
     isMulticlassClassification && (categoricalFeaturesInfo.size > 0)
   }
 
   /**
    * Java-friendly constructor for [[org.apache.spark.mllib.tree.configuration.Strategy]]
+   * @since 1.1.0
    */
   def this(
       algo: Algo,
@@ -107,6 +115,7 @@ class Strategy (
 
   /**
    * Sets Algorithm using a String.
+   * @since 1.2.0
    */
   def setAlgo(algo: String): Unit = algo match {
     case "Classification" => setAlgo(Classification)
@@ -115,6 +124,7 @@ class Strategy (
 
   /**
    * Sets categoricalFeaturesInfo using a Java Map.
+   * @since 1.2.0
    */
   def setCategoricalFeaturesInfo(
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer]): Unit = {
@@ -162,7 +172,10 @@ class Strategy (
       s"$subsamplingRate")
   }
 
-  /** Returns a shallow copy of this instance. */
+  /**
+   * Returns a shallow copy of this instance.
+   * @since 1.2.0
+   */
   def copy: Strategy = {
     new Strategy(algo, impurity, maxDepth, numClasses, maxBins,
       quantileCalculationStrategy, categoricalFeaturesInfo, minInstancesPerNode, minInfoGain,
@@ -170,12 +183,16 @@ class Strategy (
   }
 }
 
+/**
+ * @since 1.2.0
+ */
 @Experimental
 object Strategy {
 
   /**
    * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
    * @param algo  "Classification" or "Regression"
+   * @since 1.2.0
    */
   def defaultStrategy(algo: String): Strategy = {
     defaultStrategy(Algo.fromString(algo))
@@ -184,6 +201,7 @@ object Strategy {
   /**
    * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
    * @param algo Algo.Classification or Algo.Regression
+   * @since 1.3.0
    */
   def defaultStrategy(algo: Algo): Strategy = algo match {
     case Algo.Classification =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
index 0768204c33914..0b6c7266dee05 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
@@ -23,6 +23,7 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
  * :: Experimental ::
  * Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during
  * binary classification.
+ * @since 1.0.0
  */
 @Experimental
 object Entropy extends Impurity {
@@ -35,6 +36,7 @@ object Entropy extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
+   * @since 1.1.0
    */
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double = {
@@ -62,6 +64,7 @@ object Entropy extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
+   * @since 1.0.0
    */
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
@@ -70,6 +73,7 @@ object Entropy extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
+   * @since 1.1.0
    */
   def instance: this.type = this
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
index d0077db6832e3..3b0be428833ae 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
@@ -24,6 +24,7 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
  * Class for calculating the
  * [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]]
  * during binary classification.
+ * @since 1.0.0
  */
 @Experimental
 object Gini extends Impurity {
@@ -34,6 +35,7 @@ object Gini extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
+   * @since 1.1.0
    */
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double = {
@@ -58,6 +60,7 @@ object Gini extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
+   * @since 1.0.0
    */
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
@@ -66,6 +69,7 @@ object Gini extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
+   * @since 1.1.0
    */
   def instance: this.type = this
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
index 86cee7e430b0a..dd297400058d2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
@@ -25,6 +25,7 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
  * This trait is used for
  *  (a) setting the impurity parameter in [[org.apache.spark.mllib.tree.configuration.Strategy]]
  *  (b) calculating impurity values from sufficient statistics.
+ *  @since 1.0.0
  */
 @Experimental
 trait Impurity extends Serializable {
@@ -35,6 +36,7 @@ trait Impurity extends Serializable {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
+   * @since 1.1.0
    */
   @DeveloperApi
   def calculate(counts: Array[Double], totalCount: Double): Double
@@ -46,6 +48,7 @@ trait Impurity extends Serializable {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
+   * @since 1.0.0
    */
   @DeveloperApi
   def calculate(count: Double, sum: Double, sumSquares: Double): Double
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
index 04d0cd24e6632..adbe05811f286 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
@@ -22,6 +22,7 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
 /**
  * :: Experimental ::
  * Class for calculating variance during regression
+ * @since 1.0.0
  */
 @Experimental
 object Variance extends Impurity {
@@ -32,6 +33,7 @@ object Variance extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
+   * @since 1.1.0
    */
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double =
@@ -44,6 +46,7 @@ object Variance extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
+   * @since 1.0.0
    */
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double = {
@@ -57,6 +60,7 @@ object Variance extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
+   * @since 1.0.0
    */
   def instance: this.type = this
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
index 2bdef73c4a8f1..c6e3d0d824dd7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
@@ -29,6 +29,7 @@ import org.apache.spark.mllib.tree.model.TreeEnsembleModel
  * The absolute (L1) error is defined as:
  *  |y - F(x)|
  * where y is the label and F(x) is the model prediction for features x.
+ * @since 1.2.0
  */
 @DeveloperApi
 object AbsoluteError extends Loss {
@@ -40,6 +41,7 @@ object AbsoluteError extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
+   * @since 1.2.0
    */
   override def gradient(prediction: Double, label: Double): Double = {
     if (label - prediction < 0) 1.0 else -1.0
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
index 778c24526de70..eee58445a1ec1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
@@ -31,6 +31,7 @@ import org.apache.spark.mllib.util.MLUtils
  * The log loss is defined as:
  *   2 log(1 + exp(-2 y F(x)))
  * where y is a label in {-1, 1} and F(x) is the model prediction for features x.
+ * @since 1.2.0
  */
 @DeveloperApi
 object LogLoss extends Loss {
@@ -42,6 +43,7 @@ object LogLoss extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
+   * @since 1.2.0
    */
   override def gradient(prediction: Double, label: Double): Double = {
     - 4.0 * label / (1.0 + math.exp(2.0 * label * prediction))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
index 64ffccbce073f..7c9fb924645c8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
@@ -26,6 +26,7 @@ import org.apache.spark.rdd.RDD
 /**
  * :: DeveloperApi ::
  * Trait for adding "pluggable" loss functions for the gradient boosting algorithm.
+ * @since 1.2.0
  */
 @DeveloperApi
 trait Loss extends Serializable {
@@ -35,6 +36,7 @@ trait Loss extends Serializable {
    * @param prediction Predicted feature
    * @param label true label.
    * @return Loss gradient.
+   * @since 1.2.0
    */
   def gradient(prediction: Double, label: Double): Double
 
@@ -45,6 +47,7 @@ trait Loss extends Serializable {
    * @param model Model of the weak learner.
    * @param data Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    * @return Measure of model error on data
+   * @since 1.2.0
    */
   def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double = {
     data.map(point => computeError(model.predict(point.features), point.label)).mean()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
index 42c9ead9884b4..47dc94cde7e03 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
@@ -17,8 +17,14 @@
 
 package org.apache.spark.mllib.tree.loss
 
+/**
+ * @since 1.2.0
+ */
 object Losses {
 
+  /**
+   * @since 1.2.0
+   */
   def fromString(name: String): Loss = name match {
     case "leastSquaresError" => SquaredError
     case "leastAbsoluteError" => AbsoluteError
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
index 011a5d57422f7..ff8903d6956bd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
@@ -29,6 +29,7 @@ import org.apache.spark.mllib.tree.model.TreeEnsembleModel
  * The squared (L2) error is defined as:
  *   (y - F(x))**2
  * where y is the label and F(x) is the model prediction for features x.
+ * @since 1.2.0
  */
 @DeveloperApi
 object SquaredError extends Loss {
@@ -40,6 +41,7 @@ object SquaredError extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
+   * @since 1.2.0
    */
   override def gradient(prediction: Double, label: Double): Double = {
     - 2.0 * (label - prediction)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
index f2c78bbabff0b..0f386a26601ce 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
@@ -40,6 +40,7 @@ import org.apache.spark.util.Utils
  * This model stores the decision tree structure and parameters.
  * @param topNode root node
  * @param algo algorithm type -- classification or regression
+ * @since 1.0.0
  */
 @Experimental
 class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable with Saveable {
@@ -49,6 +50,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features array representing a single data point
    * @return Double prediction from the trained model
+   * @since 1.0.0
    */
   def predict(features: Vector): Double = {
     topNode.predict(features)
@@ -59,6 +61,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features RDD representing data points to be predicted
    * @return RDD of predictions for each of the given data points
+   * @since 1.0.0
    */
   def predict(features: RDD[Vector]): RDD[Double] = {
     features.map(x => predict(x))
@@ -69,6 +72,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features JavaRDD representing data points to be predicted
    * @return JavaRDD of predictions for each of the given data points
+   * @since 1.2.0
    */
   def predict(features: JavaRDD[Vector]): JavaRDD[Double] = {
     predict(features.rdd)
@@ -76,6 +80,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
 
   /**
    * Get number of nodes in tree, including leaf nodes.
+   * @since 1.1.0
    */
   def numNodes: Int = {
     1 + topNode.numDescendants
@@ -84,6 +89,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
   /**
    * Get depth of tree.
    * E.g.: Depth 0 means 1 leaf node.  Depth 1 means 1 internal node and 2 leaf nodes.
+   * @since 1.1.0
    */
   def depth: Int = {
     topNode.subtreeDepth
@@ -109,6 +115,12 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
     header + topNode.subtreeToString(2)
   }
 
+  /**
+   * @param sc  Spark context used to save model data.
+   * @param path  Path specifying the directory in which to save this model.
+   *              If the directory already exists, this method throws an exception.
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     DecisionTreeModel.SaveLoadV1_0.save(sc, path, this)
   }
@@ -116,6 +128,9 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
   override protected def formatVersion: String = DecisionTreeModel.formatVersion
 }
 
+/**
+ * @since 1.3.0
+ */
 object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
 
   private[spark] def formatVersion: String = "1.0"
@@ -297,6 +312,13 @@ object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
     }
   }
 
+  /**
+   *
+   * @param sc  Spark context used for loading model files.
+   * @param path  Path specifying the directory to which the model was saved.
+   * @return  Model instance
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): DecisionTreeModel = {
     implicit val formats = DefaultFormats
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
index 508bf9c1bdb47..23f0363639120 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
@@ -29,6 +29,7 @@ import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
  * @param rightImpurity right node impurity
  * @param leftPredict left node predict
  * @param rightPredict right node predict
+ * @since 1.0.0
  */
 @DeveloperApi
 class InformationGainStats(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
index a6d1398fc267b..aca3350c2e535 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -38,6 +38,7 @@ import org.apache.spark.mllib.linalg.Vector
  * @param leftNode  left child
  * @param rightNode right child
  * @param stats information gain stats
+ * @since 1.0.0
  */
 @DeveloperApi
 class Node (
@@ -58,6 +59,7 @@ class Node (
   /**
    * build the left node and right nodes if not leaf
    * @param nodes array of nodes
+   * @since 1.0.0
    */
   @deprecated("build should no longer be used since trees are constructed on-the-fly in training",
     "1.2.0")
@@ -79,6 +81,7 @@ class Node (
    * predict value if node is not leaf
    * @param features feature value
    * @return predicted value
+   * @since 1.1.0
    */
   def predict(features: Vector) : Double = {
     if (isLeaf) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
index 5cbe7c280dbee..be819b59e7048 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
@@ -23,6 +23,7 @@ import org.apache.spark.annotation.DeveloperApi
  * Predicted value for a node
  * @param predict predicted value
  * @param prob probability of the label (classification only)
+ * @since 1.2.0
  */
 @DeveloperApi
 class Predict(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
index be6c9b3de5479..18d40530aee1e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
@@ -30,6 +30,7 @@ import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
  *                  Split left if feature <= threshold, else right.
  * @param featureType type of feature -- categorical or continuous
  * @param categories Split left if categorical feature value is in this set, else right.
+ * @since 1.0.0
  */
 @DeveloperApi
 case class Split(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
index 905c5fb42bd44..0c629b12a84df 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
@@ -45,6 +45,7 @@ import org.apache.spark.util.Utils
  *
  * @param algo algorithm for the ensemble model, either Classification or Regression
  * @param trees tree ensembles
+ * @since 1.2.0
  */
 @Experimental
 class RandomForestModel(override val algo: Algo, override val trees: Array[DecisionTreeModel])
@@ -54,6 +55,13 @@ class RandomForestModel(override val algo: Algo, override val trees: Array[Decis
 
   require(trees.forall(_.algo == algo))
 
+  /**
+   *
+   * @param sc  Spark context used to save model data.
+   * @param path  Path specifying the directory in which to save this model.
+   *              If the directory already exists, this method throws an exception.
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     TreeEnsembleModel.SaveLoadV1_0.save(sc, path, this,
       RandomForestModel.SaveLoadV1_0.thisClassName)
@@ -62,10 +70,20 @@ class RandomForestModel(override val algo: Algo, override val trees: Array[Decis
   override protected def formatVersion: String = RandomForestModel.formatVersion
 }
 
+/**
+ * @since 1.3.0
+ */
 object RandomForestModel extends Loader[RandomForestModel] {
 
   private[mllib] def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion
 
+  /**
+   *
+   * @param sc  Spark context used for loading model files.
+   * @param path  Path specifying the directory to which the model was saved.
+   * @return  Model instance
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): RandomForestModel = {
     val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName
@@ -96,6 +114,7 @@ object RandomForestModel extends Loader[RandomForestModel] {
  * @param algo algorithm for the ensemble model, either Classification or Regression
  * @param trees tree ensembles
  * @param treeWeights tree ensemble weights
+ * @since 1.2.0
  */
 @Experimental
 class GradientBoostedTreesModel(
@@ -107,6 +126,12 @@ class GradientBoostedTreesModel(
 
   require(trees.length == treeWeights.length)
 
+  /**
+   * @param sc  Spark context used to save model data.
+   * @param path  Path specifying the directory in which to save this model.
+   *              If the directory already exists, this method throws an exception.
+   * @since 1.3.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     TreeEnsembleModel.SaveLoadV1_0.save(sc, path, this,
       GradientBoostedTreesModel.SaveLoadV1_0.thisClassName)
@@ -118,6 +143,7 @@ class GradientBoostedTreesModel(
    * @param loss evaluation metric.
    * @return an array with index i having the losses or errors for the ensemble
    *         containing the first i+1 trees
+   * @since 1.4.0
    */
   def evaluateEachIteration(
       data: RDD[LabeledPoint],
@@ -159,6 +185,9 @@ class GradientBoostedTreesModel(
   override protected def formatVersion: String = GradientBoostedTreesModel.formatVersion
 }
 
+/**
+ * @since 1.3.0
+ */
 object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
 
   /**
@@ -170,6 +199,7 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
    * @param loss: evaluation metric.
    * @return a RDD with each element being a zip of the prediction and error
    *         corresponding to every sample.
+   * @since 1.4.0
    */
   def computeInitialPredictionAndError(
       data: RDD[LabeledPoint],
@@ -193,6 +223,7 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
    * @param loss: evaluation metric.
    * @return a RDD with each element being a zip of the prediction and error
    *         corresponding to each sample.
+   * @since 1.4.0
    */
   def updatePredictionError(
     data: RDD[LabeledPoint],
@@ -213,6 +244,12 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
 
   private[mllib] def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion
 
+  /**
+   * @param sc  Spark context used for loading model files.
+   * @param path  Path specifying the directory to which the model was saved.
+   * @return  Model instance
+   * @since 1.3.0
+   */
   override def load(sc: SparkContext, path: String): GradientBoostedTreesModel = {
     val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
index bcaacc1b1f191..f520b3a1b7c72 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
@@ -24,6 +24,7 @@ package org.apache.spark.mllib
  *  - information loss calculation with entropy and Gini for classification and
  *    variance for regression,
  *  - both continuous and categorical features.
+ *  @since 1.0.0
  */
 package object tree {
 }

From c635a16f64c939182196b46725ef2d00ed107cca Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Tue, 18 Aug 2015 15:30:23 -0700
Subject: [PATCH 311/633] [SPARK-10012] [ML] Missing test case for
 Params#arrayLengthGt

Currently there is no test case for `Params#arrayLengthGt`.

Author: lewuathe <lewuathe@me.com>

Closes #8223 from Lewuathe/SPARK-10012.
---
 .../src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
index be95638d81686..2c878f8372a47 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
@@ -199,6 +199,9 @@ class ParamsSuite extends SparkFunSuite {
 
     val inArray = ParamValidators.inArray[Int](Array(1, 2))
     assert(inArray(1) && inArray(2) && !inArray(0))
+
+    val arrayLengthGt = ParamValidators.arrayLengthGt[Int](2.0)
+    assert(arrayLengthGt(Array(0, 1, 2)) && !arrayLengthGt(Array(0, 1)))
   }
 
   test("Params.copyValues") {

From 9108eff74a2815986fd067b273c2a344b6315405 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 18 Aug 2015 17:00:13 -0700
Subject: [PATCH 312/633] [SPARK-10098] [STREAMING] [TEST] Cleanup active
 context after test in FailureSuite

Failures in streaming.FailureSuite can leak StreamingContext and SparkContext which fails all subsequent tests

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8289 from tdas/SPARK-10098.
---
 .../apache/spark/streaming/FailureSuite.scala | 27 ++++++++++++-------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala
index 0c4c06534a693..e82c2fa4e72ad 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/FailureSuite.scala
@@ -17,25 +17,32 @@
 
 package org.apache.spark.streaming
 
-import org.apache.spark.Logging
+import java.io.File
+
+import org.scalatest.BeforeAndAfter
+
+import org.apache.spark.{SparkFunSuite, Logging}
 import org.apache.spark.util.Utils
 
 /**
  * This testsuite tests master failures at random times while the stream is running using
  * the real clock.
  */
-class FailureSuite extends TestSuiteBase with Logging {
+class FailureSuite extends SparkFunSuite with BeforeAndAfter with Logging {
 
-  val directory = Utils.createTempDir()
-  val numBatches = 30
+  private val batchDuration: Duration = Milliseconds(1000)
+  private val numBatches = 30
+  private var directory: File = null
 
-  override def batchDuration: Duration = Milliseconds(1000)
-
-  override def useManualClock: Boolean = false
+  before {
+    directory = Utils.createTempDir()
+  }
 
-  override def afterFunction() {
-    Utils.deleteRecursively(directory)
-    super.afterFunction()
+  after {
+    if (directory != null) {
+      Utils.deleteRecursively(directory)
+    }
+    StreamingContext.getActive().foreach { _.stop() }
   }
 
   test("multiple failures with map") {

From badf7fa650f9801c70515907fcc26b58d7ec3143 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 18 Aug 2015 17:54:49 -0700
Subject: [PATCH 313/633] [SPARK-8473] [SPARK-9889] [ML] User guide and example
 code for DCT

mengxr jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8184 from feynmanliang/SPARK-9889-DCT-docs.
---
 docs/ml-features.md | 71 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 6b2e36b3536dc..28a61933f8238 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -649,6 +649,77 @@ for expanded in polyDF.select("polyFeatures").take(3):
 </div>
 </div>
 
+## Discrete Cosine Transform (DCT)
+
+The [Discrete Cosine
+Transform](https://en.wikipedia.org/wiki/Discrete_cosine_transform)
+transforms a length $N$ real-valued sequence in the time domain into
+another length $N$ real-valued sequence in the frequency domain. A
+[DCT](api/scala/index.html#org.apache.spark.ml.feature.DCT) class
+provides this functionality, implementing the
+[DCT-II](https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II)
+and scaling the result by $1/\sqrt{2}$ such that the representing matrix
+for the transform is unitary. No shift is applied to the transformed
+sequence (e.g. the $0$th element of the transformed sequence is the
+$0$th DCT coefficient and _not_ the $N/2$th).
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.feature.DCT
+import org.apache.spark.mllib.linalg.Vectors
+
+val data = Seq(
+  Vectors.dense(0.0, 1.0, -2.0, 3.0),
+  Vectors.dense(-1.0, 2.0, 4.0, -7.0),
+  Vectors.dense(14.0, -2.0, -5.0, 1.0))
+val df = sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features")
+val dct = new DCT()
+  .setInputCol("features")
+  .setOutputCol("featuresDCT")
+  .setInverse(false)
+val dctDf = dct.transform(df)
+dctDf.select("featuresDCT").show(3)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.feature.DCT;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.VectorUDT;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
+  RowFactory.create(Vectors.dense(0.0, 1.0, -2.0, 3.0)),
+  RowFactory.create(Vectors.dense(-1.0, 2.0, 4.0, -7.0)),
+  RowFactory.create(Vectors.dense(14.0, -2.0, -5.0, 1.0))
+));
+StructType schema = new StructType(new StructField[] {
+  new StructField("features", new VectorUDT(), false, Metadata.empty()),
+});
+DataFrame df = jsql.createDataFrame(data, schema);
+DCT dct = new DCT()
+  .setInputCol("features")
+  .setOutputCol("featuresDCT")
+  .setInverse(false);
+DataFrame dctDf = dct.transform(df);
+dctDf.select("featuresDCT").show(3);
+{% endhighlight %}
+</div>
+</div>
+
 ## StringIndexer
 
 `StringIndexer` encodes a string column of labels to a column of label indices.

From 04e0fea79b9acfa3a3cb81dbacb08f9d287b42c3 Mon Sep 17 00:00:00 2001
From: Hossein <hossein@databricks.com>
Date: Tue, 18 Aug 2015 18:02:22 -0700
Subject: [PATCH 314/633] Bump SparkR version string to 1.5.0

This patch is against master, but we need to apply it to 1.5 branch as well.

cc shivaram  and rxin

Author: Hossein <hossein@databricks.com>

Closes #8291 from falaki/SparkRVersion1.5.
---
 R/pkg/DESCRIPTION | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index 83e64897216b1..d0d7201f004a2 100644
--- a/R/pkg/DESCRIPTION
+++ b/R/pkg/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: SparkR
 Type: Package
 Title: R frontend for Spark
-Version: 1.4.0
+Version: 1.5.0
 Date: 2013-09-09
 Author: The Apache Software Foundation
 Maintainer: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

From 1f8902964d8af7853c389b3b013b3a9b002c6c06 Mon Sep 17 00:00:00 2001
From: jerryshao <sshao@hortonworks.com>
Date: Tue, 18 Aug 2015 18:53:19 -0700
Subject: [PATCH 315/633] [SPARK-9969] [YARN] Remove old MR classpath API
 support

Here propose to remove old MRJobConfig#DEFAULT_APPLICATION_CLASSPATH support, since we now move to Yarn stable API.

vanzin and sryza , any opinion on this? If we still want to support old API, I can close it. But as far as I know now major Hadoop releases has moved to stable API.

Author: jerryshao <sshao@hortonworks.com>

Closes #8192 from jerryshao/SPARK-9969.
---
 .../scala/org/apache/spark/deploy/yarn/Client.scala  | 12 +-----------
 1 file changed, 1 insertion(+), 11 deletions(-)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 5c6a716863f66..8672ef601784b 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -1098,20 +1098,10 @@ object Client extends Logging {
     triedDefault.toOption
   }
 
-  /**
-   * In Hadoop 0.23, the MR application classpath comes with the YARN application
-   * classpath. In Hadoop 2.0, it's an array of Strings, and in 2.2+ it's a String.
-   * So we need to use reflection to retrieve it.
-   */
   private[yarn] def getDefaultMRApplicationClasspath: Option[Seq[String]] = {
     val triedDefault = Try[Seq[String]] {
       val field = classOf[MRJobConfig].getField("DEFAULT_MAPREDUCE_APPLICATION_CLASSPATH")
-      val value = if (field.getType == classOf[String]) {
-        StringUtils.getStrings(field.get(null).asInstanceOf[String]).toArray
-      } else {
-        field.get(null).asInstanceOf[Array[String]]
-      }
-      value.toSeq
+      StringUtils.getStrings(field.get(null).asInstanceOf[String]).toSeq
     } recoverWith {
       case e: NoSuchFieldException => Success(Seq.empty[String])
     }

From b4b35f133aecaf84f04e8e444b660a33c6b7894a Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Tue, 18 Aug 2015 19:18:05 -0700
Subject: [PATCH 316/633] [SPARKR] [MINOR] Get rid of a long line warning

```
R/functions.R:74:1: style: lines should not be more than 100 characters.
            jc <- callJStatic("org.apache.spark.sql.functions", "lit", ifelse(class(x) == "Column", xjc, x))
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
```

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8297 from yu-iskw/minor-lint-r.
---
 R/pkg/R/functions.R | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index 6eef4d638ee9a..e606b20570f53 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -71,7 +71,9 @@ createFunctions()
 #' @return Creates a Column class of literal value.
 setMethod("lit", signature("ANY"),
           function(x) {
-            jc <- callJStatic("org.apache.spark.sql.functions", "lit", ifelse(class(x) == "Column", x@jc, x))
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "lit",
+                              ifelse(class(x) == "Column", x@jc, x))
             column(jc)
           })
 

From 1aeae05bb20f01ab7ccaa62fe905a63e020074b5 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 18 Aug 2015 19:26:38 -0700
Subject: [PATCH 317/633] [SPARK-10072] [STREAMING] BlockGenerator can deadlock
 when the queue of generate blocks fills up to capacity

Generated blocks are inserted into an ArrayBlockingQueue, and another thread pulls stuff from the ArrayBlockingQueue and pushes it into BlockManager. Now if that queue fills up to capacity (default is 10 blocks), then the inserting into queue (done in the function updateCurrentBuffer) get blocked inside a synchronized block. However, the thread that is pulling blocks from the queue uses the same lock to check the current (active or stopped) while pulling from the queue. Since the block generating threads is blocked (as the queue is full) on the lock, this thread that is supposed to drain the queue gets blocked. Ergo, deadlock.

Solution: Moved blocking call to ArrayBlockingQueue outside the synchronized to prevent deadlock.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8257 from tdas/SPARK-10072.
---
 .../streaming/receiver/BlockGenerator.scala   | 29 ++++++++++++-------
 1 file changed, 19 insertions(+), 10 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
index 300e820d01777..421d60ae359f8 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala
@@ -227,16 +227,21 @@ private[streaming] class BlockGenerator(
   def isStopped(): Boolean = state == StoppedAll
 
   /** Change the buffer to which single records are added to. */
-  private def updateCurrentBuffer(time: Long): Unit = synchronized {
+  private def updateCurrentBuffer(time: Long): Unit = {
     try {
-      val newBlockBuffer = currentBuffer
-      currentBuffer = new ArrayBuffer[Any]
-      if (newBlockBuffer.size > 0) {
-        val blockId = StreamBlockId(receiverId, time - blockIntervalMs)
-        val newBlock = new Block(blockId, newBlockBuffer)
-        listener.onGenerateBlock(blockId)
+      var newBlock: Block = null
+      synchronized {
+        if (currentBuffer.nonEmpty) {
+          val newBlockBuffer = currentBuffer
+          currentBuffer = new ArrayBuffer[Any]
+          val blockId = StreamBlockId(receiverId, time - blockIntervalMs)
+          listener.onGenerateBlock(blockId)
+          newBlock = new Block(blockId, newBlockBuffer)
+        }
+      }
+
+      if (newBlock != null) {
         blocksForPushing.put(newBlock)  // put is blocking when queue is full
-        logDebug("Last element in " + blockId + " is " + newBlockBuffer.last)
       }
     } catch {
       case ie: InterruptedException =>
@@ -250,9 +255,13 @@ private[streaming] class BlockGenerator(
   private def keepPushingBlocks() {
     logInfo("Started block pushing thread")
 
-    def isGeneratingBlocks = synchronized { state == Active || state == StoppedAddingData }
+    def areBlocksBeingGenerated: Boolean = synchronized {
+      state != StoppedGeneratingBlocks
+    }
+
     try {
-      while (isGeneratingBlocks) {
+      // While blocks are being generated, keep polling for to-be-pushed blocks and push them.
+      while (areBlocksBeingGenerated) {
         Option(blocksForPushing.poll(10, TimeUnit.MILLISECONDS)) match {
           case Some(block) => pushBlock(block)
           case None =>

From 90273eff9604439a5a5853077e232d34555c67d7 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 18 Aug 2015 20:15:54 -0700
Subject: [PATCH 318/633] [SPARK-10102] [STREAMING] Fix a race condition that
 startReceiver may happen before setting trackerState to Started

Test failure: https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-Maven-with-YARN/HADOOP_PROFILE=hadoop-2.4,label=spark-test/3305/testReport/junit/org.apache.spark.streaming/StreamingContextSuite/stop_gracefully/

There is a race condition that setting `trackerState` to `Started` could happen after calling `startReceiver`. Then `startReceiver` won't start the receivers because it uses `! isTrackerStarted` to check if ReceiverTracker is stopping or stopped. But actually, `trackerState` is `Initialized` and will be changed to `Started` soon.

Therefore, we should use `isTrackerStopping || isTrackerStopped`.

Author: zsxwing <zsxwing@gmail.com>

Closes #8294 from zsxwing/SPARK-9504.
---
 .../spark/streaming/scheduler/ReceiverTracker.scala   | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index e076fb5ea174b..aae3acf7aba3e 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -468,8 +468,13 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
      * Start a receiver along with its scheduled executors
      */
     private def startReceiver(receiver: Receiver[_], scheduledExecutors: Seq[String]): Unit = {
+      def shouldStartReceiver: Boolean = {
+        // It's okay to start when trackerState is Initialized or Started
+        !(isTrackerStopping || isTrackerStopped)
+      }
+
       val receiverId = receiver.streamId
-      if (!isTrackerStarted) {
+      if (!shouldStartReceiver) {
         onReceiverJobFinish(receiverId)
         return
       }
@@ -494,14 +499,14 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
       // We will keep restarting the receiver job until ReceiverTracker is stopped
       future.onComplete {
         case Success(_) =>
-          if (!isTrackerStarted) {
+          if (!shouldStartReceiver) {
             onReceiverJobFinish(receiverId)
           } else {
             logInfo(s"Restarting Receiver $receiverId")
             self.send(RestartReceiver(receiver))
           }
         case Failure(e) =>
-          if (!isTrackerStarted) {
+          if (!shouldStartReceiver) {
             onReceiverJobFinish(receiverId)
           } else {
             logError("Receiver has been stopped. Try to restart it.", e)

From a5b5b936596ceb45f5f5b68bf1d6368534fb9470 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 19 Aug 2015 11:21:46 +0800
Subject: [PATCH 319/633] [SPARK-9939] [SQL] Resorts to Java process API in
 CliSuite, HiveSparkSubmitSuite and HiveThriftServer2 test suites

Scala process API has a known bug ([SI-8768] [1]), which may be the reason why several test suites which fork sub-processes are flaky.

This PR replaces Scala process API with Java process API in `CliSuite`, `HiveSparkSubmitSuite`, and `HiveThriftServer2` related test suites to see whether it fix these flaky tests.

[1]: https://issues.scala-lang.org/browse/SI-8768

Author: Cheng Lian <lian@databricks.com>

Closes #8168 from liancheng/spark-9939/use-java-process-api.
---
 .../spark/sql/test/ProcessTestUtils.scala     | 37 +++++++++
 sql/hive-thriftserver/pom.xml                 |  7 ++
 .../sql/hive/thriftserver/CliSuite.scala      | 64 ++++++--------
 .../HiveThriftServer2Suites.scala             | 83 ++++++++++---------
 .../spark/sql/hive/HiveSparkSubmitSuite.scala | 49 +++++++----
 5 files changed, 149 insertions(+), 91 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/test/ProcessTestUtils.scala

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/ProcessTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/ProcessTestUtils.scala
new file mode 100644
index 0000000000000..152c9c8459de9
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/ProcessTestUtils.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.test
+
+import java.io.{IOException, InputStream}
+
+import scala.sys.process.BasicIO
+
+object ProcessTestUtils {
+  class ProcessOutputCapturer(stream: InputStream, capture: String => Unit) extends Thread {
+    this.setDaemon(true)
+
+    override def run(): Unit = {
+      try {
+        BasicIO.processFully(capture)(stream)
+      } catch { case _: IOException =>
+        // Ignores the IOException thrown when the process termination, which closes the input
+        // stream abruptly.
+      }
+    }
+  }
+}
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
index 2dfbcb2425a37..3566c87dd248c 100644
--- a/sql/hive-thriftserver/pom.xml
+++ b/sql/hive-thriftserver/pom.xml
@@ -86,6 +86,13 @@
       <artifactId>selenium-java</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-sql_${scala.binary.version}</artifactId>
+      <type>test-jar</type>
+      <version>${project.version}</version>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
index 121b3e077f71f..e59a14ec00d5c 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/CliSuite.scala
@@ -18,18 +18,19 @@
 package org.apache.spark.sql.hive.thriftserver
 
 import java.io._
+import java.sql.Timestamp
+import java.util.Date
 
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.duration._
 import scala.concurrent.{Await, Promise}
-import scala.sys.process.{Process, ProcessLogger}
-import scala.util.Failure
+import org.apache.spark.sql.test.ProcessTestUtils.ProcessOutputCapturer
 
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.scalatest.BeforeAndAfter
 
-import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.util.Utils
+import org.apache.spark.{Logging, SparkFunSuite}
 
 /**
  * A test suite for the `spark-sql` CLI tool.  Note that all test cases share the same temporary
@@ -70,6 +71,9 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
       queriesAndExpectedAnswers: (String, String)*): Unit = {
 
     val (queries, expectedAnswers) = queriesAndExpectedAnswers.unzip
+    // Explicitly adds ENTER for each statement to make sure they are actually entered into the CLI.
+    val queriesString = queries.map(_ + "\n").mkString
+
     val command = {
       val cliScript = "../../bin/spark-sql".split("/").mkString(File.separator)
       val jdbcUrl = s"jdbc:derby:;databaseName=$metastorePath;create=true"
@@ -83,13 +87,14 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
 
     var next = 0
     val foundAllExpectedAnswers = Promise.apply[Unit]()
-    // Explicitly adds ENTER for each statement to make sure they are actually entered into the CLI.
-    val queryStream = new ByteArrayInputStream(queries.map(_ + "\n").mkString.getBytes)
     val buffer = new ArrayBuffer[String]()
     val lock = new Object
 
     def captureOutput(source: String)(line: String): Unit = lock.synchronized {
-      buffer += s"$source> $line"
+      // This test suite sometimes gets extremely slow out of unknown reason on Jenkins.  Here we
+      // add a timestamp to provide more diagnosis information.
+      buffer += s"${new Timestamp(new Date().getTime)} - $source> $line"
+
       // If we haven't found all expected answers and another expected answer comes up...
       if (next < expectedAnswers.size && line.startsWith(expectedAnswers(next))) {
         next += 1
@@ -98,48 +103,27 @@ class CliSuite extends SparkFunSuite with BeforeAndAfter with Logging {
           foundAllExpectedAnswers.trySuccess(())
         }
       } else {
-        errorResponses.foreach( r => {
+        errorResponses.foreach { r =>
           if (line.startsWith(r)) {
             foundAllExpectedAnswers.tryFailure(
               new RuntimeException(s"Failed with error line '$line'"))
-          }})
-      }
-    }
-
-    // Searching expected output line from both stdout and stderr of the CLI process
-    val process = (Process(command, None) #< queryStream).run(
-      ProcessLogger(captureOutput("stdout"), captureOutput("stderr")))
-
-    // catch the output value
-    class exitCodeCatcher extends Runnable {
-      var exitValue = 0
-
-      override def run(): Unit = {
-        try {
-          exitValue = process.exitValue()
-        } catch {
-          case rte: RuntimeException =>
-            // ignored as it will get triggered when the process gets destroyed
-            logDebug("Ignoring exception while waiting for exit code", rte)
-        }
-        if (exitValue != 0) {
-          // process exited: fail fast
-          foundAllExpectedAnswers.tryFailure(
-            new RuntimeException(s"Failed with exit code $exitValue"))
+          }
         }
       }
     }
-    // spin off the code catche thread. No attempt is made to kill this
-    // as it will exit once the launched process terminates.
-    val codeCatcherThread = new Thread(new exitCodeCatcher())
-    codeCatcherThread.start()
+
+    val process = new ProcessBuilder(command: _*).start()
+
+    val stdinWriter = new OutputStreamWriter(process.getOutputStream)
+    stdinWriter.write(queriesString)
+    stdinWriter.flush()
+    stdinWriter.close()
+
+    new ProcessOutputCapturer(process.getInputStream, captureOutput("stdout")).start()
+    new ProcessOutputCapturer(process.getErrorStream, captureOutput("stderr")).start()
 
     try {
-      Await.ready(foundAllExpectedAnswers.future, timeout)
-      foundAllExpectedAnswers.future.value match {
-        case Some(Failure(t)) => throw t
-        case _ =>
-      }
+      Await.result(foundAllExpectedAnswers.future, timeout)
     } catch { case cause: Throwable =>
       val message =
         s"""
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
index 17e7044c46ecb..ded42bca9971e 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
@@ -22,10 +22,9 @@ import java.net.URL
 import java.sql.{Date, DriverManager, SQLException, Statement}
 
 import scala.collection.mutable.ArrayBuffer
+import scala.concurrent.ExecutionContext.Implicits.global
 import scala.concurrent.duration._
 import scala.concurrent.{Await, Promise, future}
-import scala.concurrent.ExecutionContext.Implicits.global
-import scala.sys.process.{Process, ProcessLogger}
 import scala.util.{Random, Try}
 
 import com.google.common.base.Charsets.UTF_8
@@ -38,11 +37,12 @@ import org.apache.hive.service.cli.thrift.TCLIService.Client
 import org.apache.hive.service.cli.thrift.ThriftCLIServiceClient
 import org.apache.thrift.protocol.TBinaryProtocol
 import org.apache.thrift.transport.TSocket
-import org.scalatest.{Ignore, BeforeAndAfterAll}
+import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.sql.hive.HiveContext
+import org.apache.spark.sql.test.ProcessTestUtils.ProcessOutputCapturer
 import org.apache.spark.util.Utils
+import org.apache.spark.{Logging, SparkFunSuite}
 
 object TestData {
   def getTestDataFilePath(name: String): URL = {
@@ -53,7 +53,6 @@ object TestData {
   val smallKvWithNull = getTestDataFilePath("small_kv_with_null.txt")
 }
 
-@Ignore // SPARK-9606
 class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest {
   override def mode: ServerMode.Value = ServerMode.binary
 
@@ -380,7 +379,6 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest {
   }
 }
 
-@Ignore // SPARK-9606
 class HiveThriftHttpServerSuite extends HiveThriftJdbcTest {
   override def mode: ServerMode.Value = ServerMode.http
 
@@ -484,7 +482,7 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
       val tempLog4jConf = Utils.createTempDir().getCanonicalPath
 
       Files.write(
-        """log4j.rootCategory=INFO, console
+        """log4j.rootCategory=DEBUG, console
           |log4j.appender.console=org.apache.log4j.ConsoleAppender
           |log4j.appender.console.target=System.err
           |log4j.appender.console.layout=org.apache.log4j.PatternLayout
@@ -493,7 +491,7 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
         new File(s"$tempLog4jConf/log4j.properties"),
         UTF_8)
 
-      tempLog4jConf // + File.pathSeparator + sys.props("java.class.path")
+      tempLog4jConf
     }
 
     s"""$startScript
@@ -521,7 +519,7 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
    */
   val THRIFT_HTTP_SERVICE_LIVE = "Started ThriftHttpCLIService in http"
 
-  val SERVER_STARTUP_TIMEOUT = 1.minute
+  val SERVER_STARTUP_TIMEOUT = 3.minutes
 
   private def startThriftServer(port: Int, attempt: Int) = {
     warehousePath = Utils.createTempDir()
@@ -543,17 +541,22 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
 
     logInfo(s"Trying to start HiveThriftServer2: port=$port, mode=$mode, attempt=$attempt")
 
-    val env = Seq(
-      // Disables SPARK_TESTING to exclude log4j.properties in test directories.
-      "SPARK_TESTING" -> "0",
-      // Points SPARK_PID_DIR to SPARK_HOME, otherwise only 1 Thrift server instance can be started
-      // at a time, which is not Jenkins friendly.
-      "SPARK_PID_DIR" -> pidDir.getCanonicalPath)
-
-    logPath = Process(command, None, env: _*).lines.collectFirst {
-      case line if line.contains(LOG_FILE_MARK) => new File(line.drop(LOG_FILE_MARK.length))
-    }.getOrElse {
-      throw new RuntimeException("Failed to find HiveThriftServer2 log file.")
+    logPath = {
+      val lines = Utils.executeAndGetOutput(
+        command = command,
+        extraEnvironment = Map(
+          // Disables SPARK_TESTING to exclude log4j.properties in test directories.
+          "SPARK_TESTING" -> "0",
+          // Points SPARK_PID_DIR to SPARK_HOME, otherwise only 1 Thrift server instance can be
+          // started at a time, which is not Jenkins friendly.
+          "SPARK_PID_DIR" -> pidDir.getCanonicalPath),
+        redirectStderr = true)
+
+      lines.split("\n").collectFirst {
+        case line if line.contains(LOG_FILE_MARK) => new File(line.drop(LOG_FILE_MARK.length))
+      }.getOrElse {
+        throw new RuntimeException("Failed to find HiveThriftServer2 log file.")
+      }
     }
 
     val serverStarted = Promise[Unit]()
@@ -561,30 +564,36 @@ abstract class HiveThriftServer2Test extends SparkFunSuite with BeforeAndAfterAl
     // Ensures that the following "tail" command won't fail.
     logPath.createNewFile()
     val successLines = Seq(THRIFT_BINARY_SERVICE_LIVE, THRIFT_HTTP_SERVICE_LIVE)
-    val failureLines = Seq("HiveServer2 is stopped", "Exception in thread", "Error:")
-    logTailingProcess =
+
+    logTailingProcess = {
+      val command = s"/usr/bin/env tail -n +0 -f ${logPath.getCanonicalPath}".split(" ")
       // Using "-n +0" to make sure all lines in the log file are checked.
-      Process(s"/usr/bin/env tail -n +0 -f ${logPath.getCanonicalPath}").run(ProcessLogger(
-        (line: String) => {
-          diagnosisBuffer += line
-          successLines.foreach(r => {
-            if (line.contains(r)) {
-              serverStarted.trySuccess(())
-            }
-          })
-          failureLines.foreach(r => {
-            if (line.contains(r)) {
-              serverStarted.tryFailure(new RuntimeException(s"Failed with output '$line'"))
-            }
-          })
-        }))
+      val builder = new ProcessBuilder(command: _*)
+      val captureOutput = (line: String) => diagnosisBuffer.synchronized {
+        diagnosisBuffer += line
+
+        successLines.foreach { r =>
+          if (line.contains(r)) {
+            serverStarted.trySuccess(())
+          }
+        }
+      }
+
+        val process = builder.start()
+
+      new ProcessOutputCapturer(process.getInputStream, captureOutput).start()
+      new ProcessOutputCapturer(process.getErrorStream, captureOutput).start()
+      process
+    }
 
     Await.result(serverStarted.future, SERVER_STARTUP_TIMEOUT)
   }
 
   private def stopThriftServer(): Unit = {
     // The `spark-daemon.sh' script uses kill, which is not synchronous, have to wait for a while.
-    Process(stopScript, None, "SPARK_PID_DIR" -> pidDir.getCanonicalPath).run().exitValue()
+    Utils.executeAndGetOutput(
+      command = Seq(stopScript),
+      extraEnvironment = Map("SPARK_PID_DIR" -> pidDir.getCanonicalPath))
     Thread.sleep(3.seconds.toMillis)
 
     warehousePath.delete()
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index 0c29646114465..dc2d85f48624c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -18,9 +18,10 @@
 package org.apache.spark.sql.hive
 
 import java.io.File
+import java.sql.Timestamp
+import java.util.Date
 
 import scala.collection.mutable.ArrayBuffer
-import scala.sys.process.{Process, ProcessLogger}
 
 import org.scalatest.Matchers
 import org.scalatest.concurrent.Timeouts
@@ -30,6 +31,7 @@ import org.scalatest.time.SpanSugar._
 import org.apache.spark._
 import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.hive.test.{TestHive, TestHiveContext}
+import org.apache.spark.sql.test.ProcessTestUtils.ProcessOutputCapturer
 import org.apache.spark.sql.types.DecimalType
 import org.apache.spark.util.{ResetSystemProperties, Utils}
 
@@ -39,6 +41,8 @@ import org.apache.spark.util.{ResetSystemProperties, Utils}
 class HiveSparkSubmitSuite
   extends SparkFunSuite
   with Matchers
+  // This test suite sometimes gets extremely slow out of unknown reason on Jenkins.  Here we
+  // add a timestamp to provide more diagnosis information.
   with ResetSystemProperties
   with Timeouts {
 
@@ -110,28 +114,44 @@ class HiveSparkSubmitSuite
     val history = ArrayBuffer.empty[String]
     val commands = Seq("./bin/spark-submit") ++ args
     val commandLine = commands.mkString("'", "' '", "'")
-    val process = Process(
-      commands,
-      new File(sparkHome),
-      "SPARK_TESTING" -> "1",
-      "SPARK_HOME" -> sparkHome
-    ).run(ProcessLogger(
+
+    val builder = new ProcessBuilder(commands: _*).directory(new File(sparkHome))
+    val env = builder.environment()
+    env.put("SPARK_TESTING", "1")
+    env.put("SPARK_HOME", sparkHome)
+
+    def captureOutput(source: String)(line: String): Unit = {
+      // This test suite has some weird behaviors when executed on Jenkins:
+      //
+      // 1. Sometimes it gets extremely slow out of unknown reason on Jenkins.  Here we add a
+      //    timestamp to provide more diagnosis information.
+      // 2. Log lines are not correctly redirected to unit-tests.log as expected, so here we print
+      //    them out for debugging purposes.
+      val logLine = s"${new Timestamp(new Date().getTime)} - $source> $line"
       // scalastyle:off println
-      (line: String) => { println(s"stdout> $line"); history += s"out> $line"},
-      (line: String) => { println(s"stderr> $line"); history += s"err> $line" }
+      println(logLine)
       // scalastyle:on println
-    ))
+      history += logLine
+    }
+
+    val process = builder.start()
+    new ProcessOutputCapturer(process.getInputStream, captureOutput("stdout")).start()
+    new ProcessOutputCapturer(process.getErrorStream, captureOutput("stderr")).start()
 
     try {
-      val exitCode = failAfter(180.seconds) { process.exitValue() }
+      val exitCode = failAfter(180.seconds) { process.waitFor() }
       if (exitCode != 0) {
         // include logs in output. Note that logging is async and may not have completed
         // at the time this exception is raised
         Thread.sleep(1000)
         val historyLog = history.mkString("\n")
-        fail(s"$commandLine returned with exit code $exitCode." +
-            s" See the log4j logs for more detail." +
-            s"\n$historyLog")
+        fail {
+          s"""spark-submit returned with exit code $exitCode.
+             |Command line: $commandLine
+             |
+             |$historyLog
+           """.stripMargin
+        }
       }
     } catch {
       case to: TestFailedDueToTimeoutException =>
@@ -263,6 +283,7 @@ object SPARK_9757 extends QueryTest with Logging {
 
     val hiveContext = new TestHiveContext(sparkContext)
     import hiveContext.implicits._
+
     import org.apache.spark.sql.functions._
 
     val dir = Utils.createTempDir()

From bf32c1f7f47dd907d787469f979c5859e02ce5e6 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Tue, 18 Aug 2015 20:27:36 -0700
Subject: [PATCH 320/633] [SPARK-10075] [SPARKR] Add `when` expressino function
 in SparkR

- Add `when` and `otherwise` as `Column` methods
- Add `When` as an expression function
- Add `%otherwise%` infix as an alias of `otherwise`

Since R doesn't support a feature like method chaining, `otherwise(when(condition, value), value)` style is a little annoying for me. If `%otherwise%` looks strange for shivaram, I can remove it. What do you think?

### JIRA
[[SPARK-10075] Add `when` expressino function in SparkR - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10075)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8266 from yu-iskw/SPARK-10075.
---
 R/pkg/NAMESPACE                  |  2 ++
 R/pkg/R/column.R                 | 14 ++++++++++++++
 R/pkg/R/functions.R              | 14 ++++++++++++++
 R/pkg/R/generics.R               |  8 ++++++++
 R/pkg/inst/tests/test_sparkSQL.R |  7 +++++++
 5 files changed, 45 insertions(+)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 607aef26115d5..8fa12d5adef8a 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -152,6 +152,7 @@ exportMethods("abs",
               "n_distinct",
               "nanvl",
               "negate",
+              "otherwise",
               "pmod",
               "quarter",
               "reverse",
@@ -182,6 +183,7 @@ exportMethods("abs",
               "unhex",
               "upper",
               "weekofyear",
+              "when",
               "year")
 
 exportClasses("GroupedData")
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index 328f595d0805f..5a07ebd308296 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -203,3 +203,17 @@ setMethod("%in%",
             jc <- callJMethod(x@jc, "in", table)
             return(column(jc))
           })
+
+#' otherwise
+#'
+#' If values in the specified column are null, returns the value. 
+#' Can be used in conjunction with `when` to specify a default value for expressions.
+#'
+#' @rdname column
+setMethod("otherwise",
+          signature(x = "Column", value = "ANY"),
+          function(x, value) {
+            value <- ifelse(class(value) == "Column", value@jc, value)
+            jc <- callJMethod(x@jc, "otherwise", value)
+            column(jc)
+          })
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index e606b20570f53..366c230e1e85b 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -165,3 +165,17 @@ setMethod("n", signature(x = "Column"),
           function(x) {
             count(x)
           })
+
+#' when
+#'
+#' Evaluates a list of conditions and returns one of multiple possible result expressions.
+#' For unmatched expressions null is returned.
+#'
+#' @rdname column
+setMethod("when", signature(condition = "Column", value = "ANY"),
+          function(condition, value) {
+              condition <- condition@jc
+              value <- ifelse(class(value) == "Column", value@jc, value)
+              jc <- callJStatic("org.apache.spark.sql.functions", "when", condition, value)
+              column(jc)
+          })
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 5c1cc98fd9e80..338b32e648078 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -651,6 +651,14 @@ setGeneric("rlike", function(x, ...) { standardGeneric("rlike") })
 #' @export
 setGeneric("startsWith", function(x, ...) { standardGeneric("startsWith") })
 
+#' @rdname column
+#' @export
+setGeneric("when", function(condition, value) { standardGeneric("when") })
+
+#' @rdname column
+#' @export
+setGeneric("otherwise", function(x, value) { standardGeneric("otherwise") })
+
 
 ###################### Expression Function Methods ##########################
 
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 83caba8b5b964..841de657df37e 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -727,6 +727,13 @@ test_that("greatest() and least() on a DataFrame", {
   expect_equal(collect(select(df, least(df$a, df$b)))[, 1], c(1, 3))
 })
 
+test_that("when() and otherwise() on a DataFrame", {
+  l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
+  df <- createDataFrame(sqlContext, l)
+  expect_equal(collect(select(df, when(df$a > 1 & df$b > 2, 1)))[, 1], c(NA, 1))
+  expect_equal(collect(select(df, otherwise(when(df$a > 1, 1), 0)))[, 1], c(0, 1))
+})
+
 test_that("group by", {
   df <- jsonFile(sqlContext, jsonPath)
   df1 <- agg(df, name = "max", age = "sum")

From 270ee677750a1f2adaf24b5816857194e61782ff Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 18 Aug 2015 20:39:59 -0700
Subject: [PATCH 321/633] [SPARK-10095] [SQL] use public API of BigInteger

In UnsafeRow, we use the private field of BigInteger for better performance, but it actually didn't contribute much (3% in one benchmark) to end-to-end runtime, and make it not portable (may fail on other JVM implementations).

So we should use the public API instead.

cc rxin

Author: Davies Liu <davies@databricks.com>

Closes #8286 from davies/portable_decimal.
---
 .../sql/catalyst/expressions/UnsafeRow.java   | 29 +++++--------------
 .../expressions/UnsafeRowWriters.java         |  9 ++----
 .../org/apache/spark/unsafe/Platform.java     | 18 ------------
 3 files changed, 11 insertions(+), 45 deletions(-)

diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
index 7fd94772090df..6c020045c311a 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRow.java
@@ -273,14 +273,13 @@ public void setDecimal(int ordinal, Decimal value, int precision) {
       } else {
 
         final BigInteger integer = value.toJavaBigDecimal().unscaledValue();
-        final int[] mag = (int[]) Platform.getObjectVolatile(integer,
-          Platform.BIG_INTEGER_MAG_OFFSET);
-        assert(mag.length <= 4);
+        byte[] bytes = integer.toByteArray();
+        assert(bytes.length <= 16);
 
         // Write the bytes to the variable length portion.
         Platform.copyMemory(
-          mag, Platform.INT_ARRAY_OFFSET, baseObject, baseOffset + cursor, mag.length * 4);
-        setLong(ordinal, (cursor << 32) | ((long) (((integer.signum() + 1) << 8) + mag.length)));
+          bytes, Platform.BYTE_ARRAY_OFFSET, baseObject, baseOffset + cursor, bytes.length);
+        setLong(ordinal, (cursor << 32) | ((long) bytes.length));
       }
     }
   }
@@ -375,8 +374,6 @@ public double getDouble(int ordinal) {
     return Platform.getDouble(baseObject, getFieldOffset(ordinal));
   }
 
-  private static byte[] EMPTY = new byte[0];
-
   @Override
   public Decimal getDecimal(int ordinal, int precision, int scale) {
     if (isNullAt(ordinal)) {
@@ -385,20 +382,10 @@ public Decimal getDecimal(int ordinal, int precision, int scale) {
     if (precision <= Decimal.MAX_LONG_DIGITS()) {
       return Decimal.apply(getLong(ordinal), precision, scale);
     } else {
-      long offsetAndSize = getLong(ordinal);
-      long offset = offsetAndSize >>> 32;
-      int signum = ((int) (offsetAndSize & 0xfff) >> 8);
-      assert signum >=0 && signum <= 2 : "invalid signum " + signum;
-      int size = (int) (offsetAndSize & 0xff);
-      int[] mag = new int[size];
-      Platform.copyMemory(
-        baseObject, baseOffset + offset, mag, Platform.INT_ARRAY_OFFSET, size * 4);
-
-      // create a BigInteger using signum and mag
-      BigInteger v = new BigInteger(0, EMPTY);  // create the initial object
-      Platform.putInt(v, Platform.BIG_INTEGER_SIGNUM_OFFSET, signum - 1);
-      Platform.putObjectVolatile(v, Platform.BIG_INTEGER_MAG_OFFSET, mag);
-      return Decimal.apply(new BigDecimal(v, scale), precision, scale);
+      byte[] bytes = getBinary(ordinal);
+      BigInteger bigInteger = new BigInteger(bytes);
+      BigDecimal javaDecimal = new BigDecimal(bigInteger, scale);
+      return Decimal.apply(javaDecimal, precision, scale);
     }
   }
 
diff --git a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
index 005351f0883e5..2f43db68a750e 100644
--- a/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
+++ b/sql/catalyst/src/main/java/org/apache/spark/sql/catalyst/expressions/UnsafeRowWriters.java
@@ -71,16 +71,13 @@ public static int write(UnsafeRow target, int ordinal, int cursor, Decimal input
       }
 
       final BigInteger integer = input.toJavaBigDecimal().unscaledValue();
-      int signum = integer.signum() + 1;
-      final int[] mag = (int[]) Platform.getObjectVolatile(
-        integer, Platform.BIG_INTEGER_MAG_OFFSET);
-      assert(mag.length <= 4);
+      byte[] bytes = integer.toByteArray();
 
       // Write the bytes to the variable length portion.
       Platform.copyMemory(
-        mag, Platform.INT_ARRAY_OFFSET, base, target.getBaseOffset() + cursor, mag.length * 4);
+        bytes, Platform.BYTE_ARRAY_OFFSET, base, target.getBaseOffset() + cursor, bytes.length);
       // Set the fixed length portion.
-      target.setLong(ordinal, (((long) cursor) << 32) | ((long) ((signum << 8) + mag.length)));
+      target.setLong(ordinal, (((long) cursor) << 32) | (long) bytes.length);
 
       return SIZE;
     }
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java b/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
index 18343efdc3437..1c16da982923b 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/Platform.java
@@ -18,7 +18,6 @@
 package org.apache.spark.unsafe;
 
 import java.lang.reflect.Field;
-import java.math.BigInteger;
 
 import sun.misc.Unsafe;
 
@@ -34,10 +33,6 @@ public final class Platform {
 
   public static final int DOUBLE_ARRAY_OFFSET;
 
-  // Support for resetting final fields while deserializing
-  public static final long BIG_INTEGER_SIGNUM_OFFSET;
-  public static final long BIG_INTEGER_MAG_OFFSET;
-
   public static int getInt(Object object, long offset) {
     return _UNSAFE.getInt(object, offset);
   }
@@ -150,24 +145,11 @@ public static void throwException(Throwable t) {
       INT_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(int[].class);
       LONG_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(long[].class);
       DOUBLE_ARRAY_OFFSET = _UNSAFE.arrayBaseOffset(double[].class);
-
-      long signumOffset = 0;
-      long magOffset = 0;
-      try {
-        signumOffset = _UNSAFE.objectFieldOffset(BigInteger.class.getDeclaredField("signum"));
-        magOffset = _UNSAFE.objectFieldOffset(BigInteger.class.getDeclaredField("mag"));
-      } catch (Exception ex) {
-        // should not happen
-      }
-      BIG_INTEGER_SIGNUM_OFFSET = signumOffset;
-      BIG_INTEGER_MAG_OFFSET = magOffset;
     } else {
       BYTE_ARRAY_OFFSET = 0;
       INT_ARRAY_OFFSET = 0;
       LONG_ARRAY_OFFSET = 0;
       DOUBLE_ARRAY_OFFSET = 0;
-      BIG_INTEGER_SIGNUM_OFFSET = 0;
-      BIG_INTEGER_MAG_OFFSET = 0;
     }
   }
 }

From 1ff0580eda90f9247a5233809667f5cebaea290e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 18 Aug 2015 22:08:15 -0700
Subject: [PATCH 322/633] [SPARK-10093] [SPARK-10096] [SQL] Avoid
 transformation on executors & fix UDFs on complex types

This is kind of a weird case, but given a sufficiently complex query plan (in this case a TungstenProject with an Exchange underneath), we could have NPEs on the executors due to the time when we were calling transformAllExpressions

In general we should ensure that all transformations occur on the driver and not on the executors. Some reasons for avoid executor side transformations include:

* (this case) Some operator constructors require state such as access to the Spark/SQL conf so doing a makeCopy on the executor can fail.
* (unrelated reason for avoid executor transformations) ExprIds are calculated using an atomic integer, so you can violate their uniqueness constraint by constructing them anywhere other than the driver.

This subsumes #8285.

Author: Reynold Xin <rxin@databricks.com>
Author: Michael Armbrust <michael@databricks.com>

Closes #8295 from rxin/SPARK-10096.
---
 .../expressions/complexTypeCreator.scala      |  8 +++-
 .../spark/sql/execution/basicOperators.scala  | 12 +++--
 .../spark/sql/DataFrameComplexTypeSuite.scala | 46 +++++++++++++++++++
 .../org/apache/spark/sql/DataFrameSuite.scala |  9 ++++
 4 files changed, 68 insertions(+), 7 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 298aee3499275..1c546719730b7 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -206,7 +206,9 @@ case class CreateStructUnsafe(children: Seq[Expression]) extends Expression {
 
   override def nullable: Boolean = false
 
-  override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
+  override def eval(input: InternalRow): Any = {
+    InternalRow(children.map(_.eval(input)): _*)
+  }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val eval = GenerateUnsafeProjection.createCode(ctx, children)
@@ -244,7 +246,9 @@ case class CreateNamedStructUnsafe(children: Seq[Expression]) extends Expression
 
   override def nullable: Boolean = false
 
-  override def eval(input: InternalRow): Any = throw new UnsupportedOperationException
+  override def eval(input: InternalRow): Any = {
+    InternalRow(valExprs.map(_.eval(input)): _*)
+  }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val eval = GenerateUnsafeProjection.createCode(ctx, valExprs)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 77b98064a9e16..3f68b05a24f44 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -75,14 +75,16 @@ case class TungstenProject(projectList: Seq[NamedExpression], child: SparkPlan)
 
   override def output: Seq[Attribute] = projectList.map(_.toAttribute)
 
+  /** Rewrite the project list to use unsafe expressions as needed. */
+  protected val unsafeProjectList = projectList.map(_ transform {
+    case CreateStruct(children) => CreateStructUnsafe(children)
+    case CreateNamedStruct(children) => CreateNamedStructUnsafe(children)
+  })
+
   protected override def doExecute(): RDD[InternalRow] = {
     val numRows = longMetric("numRows")
     child.execute().mapPartitions { iter =>
-      this.transformAllExpressions {
-        case CreateStruct(children) => CreateStructUnsafe(children)
-        case CreateNamedStruct(children) => CreateNamedStructUnsafe(children)
-      }
-      val project = UnsafeProjection.create(projectList, child.output)
+      val project = UnsafeProjection.create(unsafeProjectList, child.output)
       iter.map { row =>
         numRows += 1
         project(row)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala
new file mode 100644
index 0000000000000..3c359dd840ab7
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala
@@ -0,0 +1,46 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql
+
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.test.SharedSQLContext
+
+/**
+ * A test suite to test DataFrame/SQL functionalities with complex types (i.e. array, struct, map).
+ */
+class DataFrameComplexTypeSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
+
+  test("UDF on struct") {
+    val f = udf((a: String) => a)
+    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    df.select(struct($"a").as("s")).select(f($"s.a")).collect()
+  }
+
+  test("UDF on named_struct") {
+    val f = udf((a: String) => a)
+    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    df.selectExpr("named_struct('a', a) s").select(f($"s.a")).collect()
+  }
+
+  test("UDF on array") {
+    val f = udf((a: String) => a)
+    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    df.select(array($"a").as("s")).select(f(expr("s[0]"))).collect()
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 1e2aaae52c9e8..284fff184085a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -878,4 +878,13 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
     val df = Seq(("x", (1, 1)), ("y", (2, 2))).toDF("a", "b")
     checkAnswer(df.groupBy("b._1").agg(sum("b._2")), Row(1, 1) :: Row(2, 2) :: Nil)
   }
+
+  test("SPARK-10093: Avoid transformations on executors") {
+    val df = Seq((1, 1)).toDF("a", "b")
+    df.where($"a" === 1)
+      .select($"a", $"b", struct($"b"))
+      .orderBy("a")
+      .select(struct($"b"))
+      .collect()
+  }
 }

From de3223872a217c5224ba7136604f6b7753b29108 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 18 Aug 2015 22:11:27 -0700
Subject: [PATCH 323/633] [SPARK-9705] [DOC] fix docs about Python version

cc JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #8245 from davies/python_doc.
---
 docs/configuration.md     |  6 +++++-
 docs/programming-guide.md | 12 ++++++++++--
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index 32147098ae64a..4a6e4dd05b661 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1561,7 +1561,11 @@ The following variables can be set in `spark-env.sh`:
   </tr>
   <tr>
     <td><code>PYSPARK_PYTHON</code></td>
-    <td>Python binary executable to use for PySpark.</td>
+    <td>Python binary executable to use for PySpark in both driver and workers (default is `python`).</td>
+  </tr>
+  <tr>
+    <td><code>PYSPARK_DRIVER_PYTHON</code></td>
+    <td>Python binary executable to use for PySpark in driver only (default is PYSPARK_PYTHON).</td>
   </tr>
   <tr>
     <td><code>SPARK_LOCAL_IP</code></td>
diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index ae712d62746f6..982c5eabe652b 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -85,8 +85,8 @@ import org.apache.spark.SparkConf
 
 <div data-lang="python"  markdown="1">
 
-Spark {{site.SPARK_VERSION}} works with Python 2.6 or higher (but not Python 3). It uses the standard CPython interpreter,
-so C libraries like NumPy can be used.
+Spark {{site.SPARK_VERSION}} works with Python 2.6+ or Python 3.4+. It can use the standard CPython interpreter,
+so C libraries like NumPy can be used. It also works with PyPy 2.3+.
 
 To run Spark applications in Python, use the `bin/spark-submit` script located in the Spark directory.
 This script will load Spark's Java/Scala libraries and allow you to submit applications to a cluster.
@@ -104,6 +104,14 @@ Finally, you need to import some Spark classes into your program. Add the follow
 from pyspark import SparkContext, SparkConf
 {% endhighlight %}
 
+PySpark requires the same minor version of Python in both driver and workers. It uses the default python version in PATH,
+you can specify which version of Python you want to use by `PYSPARK_PYTHON`, for example:
+
+{% highlight bash %}
+$ PYSPARK_PYTHON=python3.4 bin/pyspark
+$ PYSPARK_PYTHON=/opt/pypy-2.5/bin/pypy bin/spark-submit examples/src/main/python/pi.py
+{% endhighlight %}
+
 </div>
 
 </div>

From 1c843e284818004f16c3f1101c33b510f80722e3 Mon Sep 17 00:00:00 2001
From: Alexander Ulanov <nashb@yandex.ru>
Date: Tue, 18 Aug 2015 22:13:52 -0700
Subject: [PATCH 324/633] [SPARK-9508] GraphX Pregel docs update with new
 Pregel code

SPARK-9436 simplifies the Pregel code. graphx-programming-guide needs to be modified accordingly since it lists the old Pregel code

Author: Alexander Ulanov <nashb@yandex.ru>

Closes #7831 from avulanov/SPARK-9508-pregel-doc2.
---
 docs/graphx-programming-guide.md | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/docs/graphx-programming-guide.md b/docs/graphx-programming-guide.md
index 99f8c827f767f..c861a763d6222 100644
--- a/docs/graphx-programming-guide.md
+++ b/docs/graphx-programming-guide.md
@@ -768,16 +768,14 @@ class GraphOps[VD, ED] {
     // Loop until no messages remain or maxIterations is achieved
     var i = 0
     while (activeMessages > 0 && i < maxIterations) {
-      // Receive the messages: -----------------------------------------------------------------------
-      // Run the vertex program on all vertices that receive messages
-      val newVerts = g.vertices.innerJoin(messages)(vprog).cache()
-      // Merge the new vertex values back into the graph
-      g = g.outerJoinVertices(newVerts) { (vid, old, newOpt) => newOpt.getOrElse(old) }.cache()
-      // Send Messages: ------------------------------------------------------------------------------
-      // Vertices that didn't receive a message above don't appear in newVerts and therefore don't
-      // get to send messages.  More precisely the map phase of mapReduceTriplets is only invoked
-      // on edges in the activeDir of vertices in newVerts
-      messages = g.mapReduceTriplets(sendMsg, mergeMsg, Some((newVerts, activeDir))).cache()
+      // Receive the messages and update the vertices.
+      g = g.joinVertices(messages)(vprog).cache()
+      val oldMessages = messages
+      // Send new messages, skipping edges where neither side received a message. We must cache
+      // messages so it can be materialized on the next line, allowing us to uncache the previous
+      // iteration.
+      messages = g.mapReduceTriplets(
+        sendMsg, mergeMsg, Some((oldMessages, activeDirection))).cache()
       activeMessages = messages.count()
       i += 1
     }

From 010b03ed52f35fd4d426d522f8a9927ddc579209 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 18 Aug 2015 22:30:13 -0700
Subject: [PATCH 325/633] [SPARK-9952] Fix N^2 loop when
 DAGScheduler.getPreferredLocsInternal accesses cacheLocs

In Scala, `Seq.fill` always seems to return a List. Accessing a list by index is an O(N) operation. Thus, the following code will be really slow (~10 seconds on my machine):

```scala
val numItems = 100000
val s = Seq.fill(numItems)(1)
for (i <- 0 until numItems) s(i)
```

It turns out that we had a loop like this in DAGScheduler code, although it's a little tricky to spot. In `getPreferredLocsInternal`, there's a call to `getCacheLocs(rdd)(partition)`.  The `getCacheLocs` call returns a Seq. If this Seq is a List and the RDD contains many partitions, then indexing into this list will cost O(partitions). Thus, when we loop over our tasks to compute their individual preferred locations we implicitly perform an N^2 loop, reducing scheduling throughput.

This patch fixes this by replacing `Seq` with `Array`.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8178 from JoshRosen/dagscheduler-perf.
---
 .../apache/spark/scheduler/DAGScheduler.scala | 22 +++++++++----------
 .../spark/storage/BlockManagerMaster.scala    |  5 +++--
 .../storage/BlockManagerMasterEndpoint.scala  |  3 ++-
 .../spark/scheduler/DAGSchedulerSuite.scala   |  4 ++--
 4 files changed, 18 insertions(+), 16 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index dadf83a38296d..684db6646765f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -111,7 +111,7 @@ class DAGScheduler(
    *
    * All accesses to this map should be guarded by synchronizing on it (see SPARK-4454).
    */
-  private val cacheLocs = new HashMap[Int, Seq[Seq[TaskLocation]]]
+  private val cacheLocs = new HashMap[Int, IndexedSeq[Seq[TaskLocation]]]
 
   // For tracking failed nodes, we use the MapOutputTracker's epoch number, which is sent with
   // every task. When we detect a node failing, we note the current epoch number and failed
@@ -205,12 +205,12 @@ class DAGScheduler(
   }
 
   private[scheduler]
-  def getCacheLocs(rdd: RDD[_]): Seq[Seq[TaskLocation]] = cacheLocs.synchronized {
+  def getCacheLocs(rdd: RDD[_]): IndexedSeq[Seq[TaskLocation]] = cacheLocs.synchronized {
     // Note: this doesn't use `getOrElse()` because this method is called O(num tasks) times
     if (!cacheLocs.contains(rdd.id)) {
       // Note: if the storage level is NONE, we don't need to get locations from block manager.
-      val locs: Seq[Seq[TaskLocation]] = if (rdd.getStorageLevel == StorageLevel.NONE) {
-        Seq.fill(rdd.partitions.size)(Nil)
+      val locs: IndexedSeq[Seq[TaskLocation]] = if (rdd.getStorageLevel == StorageLevel.NONE) {
+        IndexedSeq.fill(rdd.partitions.length)(Nil)
       } else {
         val blockIds =
           rdd.partitions.indices.map(index => RDDBlockId(rdd.id, index)).toArray[BlockId]
@@ -302,12 +302,12 @@ class DAGScheduler(
       shuffleDep: ShuffleDependency[_, _, _],
       firstJobId: Int): ShuffleMapStage = {
     val rdd = shuffleDep.rdd
-    val numTasks = rdd.partitions.size
+    val numTasks = rdd.partitions.length
     val stage = newShuffleMapStage(rdd, numTasks, shuffleDep, firstJobId, rdd.creationSite)
     if (mapOutputTracker.containsShuffle(shuffleDep.shuffleId)) {
       val serLocs = mapOutputTracker.getSerializedMapOutputStatuses(shuffleDep.shuffleId)
       val locs = MapOutputTracker.deserializeMapStatuses(serLocs)
-      for (i <- 0 until locs.size) {
+      for (i <- 0 until locs.length) {
         stage.outputLocs(i) = Option(locs(i)).toList // locs(i) will be null if missing
       }
       stage.numAvailableOutputs = locs.count(_ != null)
@@ -315,7 +315,7 @@ class DAGScheduler(
       // Kind of ugly: need to register RDDs with the cache and map output tracker here
       // since we can't do it in the RDD constructor because # of partitions is unknown
       logInfo("Registering RDD " + rdd.id + " (" + rdd.getCreationSite + ")")
-      mapOutputTracker.registerShuffle(shuffleDep.shuffleId, rdd.partitions.size)
+      mapOutputTracker.registerShuffle(shuffleDep.shuffleId, rdd.partitions.length)
     }
     stage
   }
@@ -566,7 +566,7 @@ class DAGScheduler(
       properties: Properties): PartialResult[R] = {
     val listener = new ApproximateActionListener(rdd, func, evaluator, timeout)
     val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
-    val partitions = (0 until rdd.partitions.size).toArray
+    val partitions = (0 until rdd.partitions.length).toArray
     val jobId = nextJobId.getAndIncrement()
     eventProcessLoop.post(JobSubmitted(
       jobId, rdd, func2, partitions, callSite, listener, SerializationUtils.clone(properties)))
@@ -718,7 +718,7 @@ class DAGScheduler(
     try {
       // New stage creation may throw an exception if, for example, jobs are run on a
       // HadoopRDD whose underlying HDFS files have been deleted.
-      finalStage = newResultStage(finalRDD, partitions.size, jobId, callSite)
+      finalStage = newResultStage(finalRDD, partitions.length, jobId, callSite)
     } catch {
       case e: Exception =>
         logWarning("Creating new stage failed due to exception - job: " + jobId, e)
@@ -1039,7 +1039,7 @@ class DAGScheduler(
               //       we registered these map outputs.
               mapOutputTracker.registerMapOutputs(
                 shuffleStage.shuffleDep.shuffleId,
-                shuffleStage.outputLocs.map(list => if (list.isEmpty) null else list.head).toArray,
+                shuffleStage.outputLocs.map(list => if (list.isEmpty) null else list.head),
                 changeEpoch = true)
 
               clearCacheLocs()
@@ -1169,7 +1169,7 @@ class DAGScheduler(
         // TODO: This will be really slow if we keep accumulating shuffle map stages
         for ((shuffleId, stage) <- shuffleToMapStage) {
           stage.removeOutputsOnExecutor(execId)
-          val locs = stage.outputLocs.map(list => if (list.isEmpty) null else list.head).toArray
+          val locs = stage.outputLocs.map(list => if (list.isEmpty) null else list.head)
           mapOutputTracker.registerMapOutputs(shuffleId, locs, changeEpoch = true)
         }
         if (shuffleToMapStage.isEmpty) {
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
index 2a11f371b9d6e..f45bff34d4dbc 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala
@@ -69,8 +69,9 @@ class BlockManagerMaster(
   }
 
   /** Get locations of multiple blockIds from the driver */
-  def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = {
-    driverEndpoint.askWithRetry[Seq[Seq[BlockManagerId]]](GetLocationsMultipleBlockIds(blockIds))
+  def getLocations(blockIds: Array[BlockId]): IndexedSeq[Seq[BlockManagerId]] = {
+    driverEndpoint.askWithRetry[IndexedSeq[Seq[BlockManagerId]]](
+      GetLocationsMultipleBlockIds(blockIds))
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
index 5dc0c537cbb62..6fec5240707a6 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
@@ -372,7 +372,8 @@ class BlockManagerMasterEndpoint(
     if (blockLocations.containsKey(blockId)) blockLocations.get(blockId).toSeq else Seq.empty
   }
 
-  private def getLocationsMultipleBlockIds(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = {
+  private def getLocationsMultipleBlockIds(
+      blockIds: Array[BlockId]): IndexedSeq[Seq[BlockManagerId]] = {
     blockIds.map(blockId => getLocations(blockId))
   }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index a063596d3eae2..2e8688cf41d99 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -133,11 +133,11 @@ class DAGSchedulerSuite
   val cacheLocations = new HashMap[(Int, Int), Seq[BlockManagerId]]
   // stub out BlockManagerMaster.getLocations to use our cacheLocations
   val blockManagerMaster = new BlockManagerMaster(null, conf, true) {
-      override def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = {
+      override def getLocations(blockIds: Array[BlockId]): IndexedSeq[Seq[BlockManagerId]] = {
         blockIds.map {
           _.asRDDId.map(id => (id.rddId -> id.splitIndex)).flatMap(key => cacheLocations.get(key)).
             getOrElse(Seq())
-        }.toSeq
+        }.toIndexedSeq
       }
       override def removeExecutor(execId: String) {
         // don't need to propagate to the driver, which we don't have

From bc9a0e03235865d2ec33372f6400dec8c770778a Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 18 Aug 2015 23:37:57 -0700
Subject: [PATCH 326/633] [SPARK-9967] [SPARK-10099] [STREAMING] Renamed conf
 spark.streaming.backpressure.{enable-->enabled} and fixed deprecated
 annotations

Small changes
- Renamed conf spark.streaming.backpressure.{enable --> enabled}
- Change Java Deprecated annotations to Scala deprecated annotation with more information.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8299 from tdas/SPARK-9967.
---
 .../scala/org/apache/spark/api/java/JavaRDDLike.scala     | 2 +-
 .../scala/org/apache/spark/deploy/SparkHadoopUtil.scala   | 2 +-
 .../apache/spark/streaming/api/java/JavaDStreamLike.scala | 4 ++--
 .../apache/spark/streaming/scheduler/RateController.scala | 8 ++++----
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index 829fae1d1d9bf..c582488f16fe7 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -354,7 +354,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * Return an array that contains all of the elements in this RDD.
    * @deprecated As of Spark 1.0.0, toArray() is deprecated, use {@link #collect()} instead
    */
-  @Deprecated
+  @deprecated("use collect()", "1.0.0")
   def toArray(): JList[T] = collect()
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index 7e9dba42bebd8..dda4216c7efe2 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -76,7 +76,7 @@ class SparkHadoopUtil extends Logging {
     }
   }
 
-  @Deprecated
+  @deprecated("use newConfiguration with SparkConf argument", "1.2.0")
   def newConfiguration(): Configuration = newConfiguration(null)
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index 808dcc174cf9a..214cd80108b9b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -291,7 +291,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    *
    * @deprecated  As of release 0.9.0, replaced by foreachRDD
    */
-  @Deprecated
+  @deprecated("Use foreachRDD", "0.9.0")
   def foreach(foreachFunc: JFunction[R, Void]) {
     foreachRDD(foreachFunc)
   }
@@ -302,7 +302,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    *
    * @deprecated  As of release 0.9.0, replaced by foreachRDD
    */
-  @Deprecated
+  @deprecated("Use foreachRDD", "0.9.0")
   def foreach(foreachFunc: JFunction2[R, Time, Void]) {
     foreachRDD(foreachFunc)
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala
index 882ca0676b6ad..a46c0c1b25e74 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala
@@ -76,9 +76,9 @@ private[streaming] abstract class RateController(val streamUID: Int, rateEstimat
     val elements = batchCompleted.batchInfo.streamIdToInputInfo
 
     for {
-      processingEnd <- batchCompleted.batchInfo.processingEndTime;
-      workDelay <- batchCompleted.batchInfo.processingDelay;
-      waitDelay <- batchCompleted.batchInfo.schedulingDelay;
+      processingEnd <- batchCompleted.batchInfo.processingEndTime
+      workDelay <- batchCompleted.batchInfo.processingDelay
+      waitDelay <- batchCompleted.batchInfo.schedulingDelay
       elems <- elements.get(streamUID).map(_.numRecords)
     } computeAndPublish(processingEnd, elems, workDelay, waitDelay)
   }
@@ -86,5 +86,5 @@ private[streaming] abstract class RateController(val streamUID: Int, rateEstimat
 
 object RateController {
   def isBackPressureEnabled(conf: SparkConf): Boolean =
-    conf.getBoolean("spark.streaming.backpressure.enable", false)
+    conf.getBoolean("spark.streaming.backpressure.enabled", false)
 }

From b23c4d3ffc36e47c057360c611d8ab1a13877699 Mon Sep 17 00:00:00 2001
From: Bill Chambers <wchambers@ischool.berkeley.edu>
Date: Wed, 19 Aug 2015 00:05:01 -0700
Subject: [PATCH 327/633] Fix Broken Link

Link was broken because it included tick marks.

Author: Bill Chambers <wchambers@ischool.berkeley.edu>

Closes #8302 from anabranch/patch-1.
---
 docs/sql-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 6c317175d3278..33e7893d7bd0a 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -28,7 +28,7 @@ All of the examples on this page use sample data included in the Spark distribut
 <div data-lang="scala"  markdown="1">
 
 The entry point into all functionality in Spark SQL is the
-[`SQLContext`](api/scala/index.html#org.apache.spark.sql.`SQLContext`) class, or one of its
+[`SQLContext`](api/scala/index.html#org.apache.spark.sql.SQLContext) class, or one of its
 descendants.  To create a basic `SQLContext`, all you need is a SparkContext.
 
 {% highlight scala %}

From f141efeafb42b14b5fcfd9aa8c5275162042349f Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Wed, 19 Aug 2015 09:41:09 +0100
Subject: [PATCH 328/633] [SPARK-10070] [DOCS] Remove Guava dependencies in
 user guides

`Lists.newArrayList` -> `Arrays.asList`

CC jkbradley feynmanliang

Anybody into replacing usages of `Lists.newArrayList` in the examples / source code too? this method isn't useful in Java 7 and beyond.

Author: Sean Owen <sowen@cloudera.com>

Closes #8272 from srowen/SPARK-10070.
---
 docs/ml-features.md | 52 ++++++++++++++++++++++-----------------------
 docs/ml-guide.md    | 21 ++++++++++--------
 2 files changed, 38 insertions(+), 35 deletions(-)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 28a61933f8238..d82c85ee75a0e 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -55,7 +55,7 @@ rescaledData.select("features", "label").take(3).foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.HashingTF;
@@ -70,7 +70,7 @@ import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
   RowFactory.create(0, "Hi I heard about Spark"),
   RowFactory.create(0, "I wish Java could use case classes"),
   RowFactory.create(1, "Logistic regression models are neat")
@@ -153,7 +153,7 @@ result.select("result").take(3).foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -167,10 +167,10 @@ JavaSparkContext jsc = ...
 SQLContext sqlContext = ...
 
 // Input data: Each row is a bag of words from a sentence or document.
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
-  RowFactory.create(Lists.newArrayList("Hi I heard about Spark".split(" "))),
-  RowFactory.create(Lists.newArrayList("I wish Java could use case classes".split(" "))),
-  RowFactory.create(Lists.newArrayList("Logistic regression models are neat".split(" ")))
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))),
+  RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
+  RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" ")))
 ));
 StructType schema = new StructType(new StructField[]{
   new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
@@ -248,7 +248,7 @@ regexTokenized.select("words", "label").take(3).foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.RegexTokenizer;
@@ -262,7 +262,7 @@ import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
   RowFactory.create(0, "Hi I heard about Spark"),
   RowFactory.create(1, "I wish Java could use case classes"),
   RowFactory.create(2, "Logistic,regression,models,are,neat")
@@ -341,7 +341,7 @@ ngramDataFrame.take(3).map(_.getAs[Stream[String]]("ngrams").toList).foreach(pri
 [`NGram`](api/java/org/apache/spark/ml/feature/NGram.html) takes an input column name, an output column name, and an optional length parameter n (n=2 by default).
 
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.NGram;
@@ -354,10 +354,10 @@ import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
-  RowFactory.create(0D, Lists.newArrayList("Hi", "I", "heard", "about", "Spark")),
-  RowFactory.create(1D, Lists.newArrayList("I", "wish", "Java", "could", "use", "case", "classes")),
-  RowFactory.create(2D, Lists.newArrayList("Logistic", "regression", "models", "are", "neat"))
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(0.0, Arrays.asList("Hi", "I", "heard", "about", "Spark")),
+  RowFactory.create(1.0, Arrays.asList("I", "wish", "Java", "could", "use", "case", "classes")),
+  RowFactory.create(2.0, Arrays.asList("Logistic", "regression", "models", "are", "neat"))
 ));
 StructType schema = new StructType(new StructField[]{
   new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
@@ -427,7 +427,7 @@ binarizedFeatures.collect().foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.Binarizer;
@@ -439,7 +439,7 @@ import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
   RowFactory.create(0, 0.1),
   RowFactory.create(1, 0.8),
   RowFactory.create(2, 0.2)
@@ -511,7 +511,7 @@ result.show()
 <div data-lang="java" markdown="1">
 See the [Java API documentation](api/java/org/apache/spark/ml/feature/PCA.html) for API details.
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -529,7 +529,7 @@ import org.apache.spark.sql.types.StructType;
 
 JavaSparkContext jsc = ...
 SQLContext jsql = ...
-JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
   RowFactory.create(Vectors.sparse(5, new int[]{1, 3}, new double[]{1.0, 7.0})),
   RowFactory.create(Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0)),
   RowFactory.create(Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0))
@@ -593,7 +593,7 @@ polyDF.select("polyFeatures").take(3).foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -614,7 +614,7 @@ PolynomialExpansion polyExpansion = new PolynomialExpansion()
   .setInputCol("features")
   .setOutputCol("polyFeatures")
   .setDegree(3);
-JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
   RowFactory.create(Vectors.dense(-2.0, 2.3)),
   RowFactory.create(Vectors.dense(0.0, 0.0)),
   RowFactory.create(Vectors.dense(0.6, -1.1))
@@ -869,7 +869,7 @@ encoded.select("id", "categoryVec").foreach(println)
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.OneHotEncoder;
@@ -883,7 +883,7 @@ import org.apache.spark.sql.types.Metadata;
 import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
     RowFactory.create(0, "a"),
     RowFactory.create(1, "b"),
     RowFactory.create(2, "c"),
@@ -1206,7 +1206,7 @@ val bucketedData = bucketizer.transform(dataFrame)
 
 <div data-lang="java">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.Row;
@@ -1218,7 +1218,7 @@ import org.apache.spark.sql.types.StructType;
 
 double[] splits = {Double.NEGATIVE_INFINITY, -0.5, 0.0, 0.5, Double.POSITIVE_INFINITY};
 
-JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
   RowFactory.create(-0.5),
   RowFactory.create(-0.3),
   RowFactory.create(0.0),
@@ -1307,7 +1307,7 @@ transformer.transform(dataFrame).show()
 
 <div data-lang="java" markdown="1">
 {% highlight java %}
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.ElementwiseProduct;
@@ -1323,7 +1323,7 @@ import org.apache.spark.sql.types.StructField;
 import org.apache.spark.sql.types.StructType;
 
 // Create some vector data; also works for sparse vectors
-JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
   RowFactory.create("a", Vectors.dense(1.0, 2.0, 3.0)),
   RowFactory.create("b", Vectors.dense(4.0, 5.0, 6.0))
 ));
diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index a03ab4356a413..4fe0ea78bbe13 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -274,8 +274,9 @@ sc.stop()
 
 <div data-lang="java">
 {% highlight java %}
+import java.util.Arrays;
 import java.util.List;
-import com.google.common.collect.Lists;
+
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.classification.LogisticRegressionModel;
@@ -294,7 +295,7 @@ SQLContext jsql = new SQLContext(jsc);
 // Prepare training data.
 // We use LabeledPoint, which is a JavaBean.  Spark SQL can convert RDDs of JavaBeans
 // into DataFrames, where it uses the bean metadata to infer the schema.
-List<LabeledPoint> localTraining = Lists.newArrayList(
+List<LabeledPoint> localTraining = Arrays.asList(
   new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
   new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
   new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
@@ -335,7 +336,7 @@ LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
 System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
 
 // Prepare test documents.
-List<LabeledPoint> localTest = Lists.newArrayList(
+List<LabeledPoint> localTest = Arrays.asList(
     new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
     new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
     new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)));
@@ -496,8 +497,9 @@ sc.stop()
 
 <div data-lang="java">
 {% highlight java %}
+import java.util.Arrays;
 import java.util.List;
-import com.google.common.collect.Lists;
+
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.Pipeline;
@@ -546,7 +548,7 @@ JavaSparkContext jsc = new JavaSparkContext(conf);
 SQLContext jsql = new SQLContext(jsc);
 
 // Prepare training documents, which are labeled.
-List<LabeledDocument> localTraining = Lists.newArrayList(
+List<LabeledDocument> localTraining = Arrays.asList(
   new LabeledDocument(0L, "a b c d e spark", 1.0),
   new LabeledDocument(1L, "b d", 0.0),
   new LabeledDocument(2L, "spark f g h", 1.0),
@@ -571,7 +573,7 @@ Pipeline pipeline = new Pipeline()
 PipelineModel model = pipeline.fit(training);
 
 // Prepare test documents, which are unlabeled.
-List<Document> localTest = Lists.newArrayList(
+List<Document> localTest = Arrays.asList(
   new Document(4L, "spark i j k"),
   new Document(5L, "l m n"),
   new Document(6L, "mapreduce spark"),
@@ -747,8 +749,9 @@ sc.stop()
 
 <div data-lang="java">
 {% highlight java %}
+import java.util.Arrays;
 import java.util.List;
-import com.google.common.collect.Lists;
+
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.Pipeline;
@@ -800,7 +803,7 @@ JavaSparkContext jsc = new JavaSparkContext(conf);
 SQLContext jsql = new SQLContext(jsc);
 
 // Prepare training documents, which are labeled.
-List<LabeledDocument> localTraining = Lists.newArrayList(
+List<LabeledDocument> localTraining = Arrays.asList(
   new LabeledDocument(0L, "a b c d e spark", 1.0),
   new LabeledDocument(1L, "b d", 0.0),
   new LabeledDocument(2L, "spark f g h", 1.0),
@@ -849,7 +852,7 @@ crossval.setNumFolds(2); // Use 3+ in practice
 CrossValidatorModel cvModel = crossval.fit(training);
 
 // Prepare test documents, which are unlabeled.
-List<Document> localTest = Lists.newArrayList(
+List<Document> localTest = Arrays.asList(
   new Document(4L, "spark i j k"),
   new Document(5L, "l m n"),
   new Document(6L, "mapreduce spark"),

From 865a3df3d578c0442c97d749c81f554b560da406 Mon Sep 17 00:00:00 2001
From: Moussa Taifi <moutai10@gmail.com>
Date: Wed, 19 Aug 2015 09:42:41 +0100
Subject: [PATCH 329/633] [DOCS] [SQL] [PYSPARK] Fix typo in ntile function

Fix typo in ntile function.

Author: Moussa Taifi <moutai10@gmail.com>

Closes #8261 from moutai/patch-2.
---
 python/pyspark/sql/functions.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 41dfee9f54f7a..4b74a501521a5 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -531,7 +531,7 @@ def lead(col, count=1, default=None):
 def ntile(n):
     """
     Window function: returns the ntile group id (from 1 to `n` inclusive)
-    in an ordered window partition. Fow example, if `n` is 4, the first
+    in an ordered window partition. For example, if `n` is 4, the first
     quarter of the rows will get value 1, the second quarter will get 2,
     the third quarter will get 3, and the last quarter will get 4.
 

From ba2a07e2b6c5a39597b64041cd5bf342ef9631f5 Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Wed, 19 Aug 2015 09:54:03 +0100
Subject: [PATCH 330/633] [SPARK-9977] [DOCS] Update documentation for
 StringIndexer

By using `StringIndexer`, we can obtain indexed label on new column. So a following estimator should use this new column through pipeline if it wants to use string indexed label.
I think it is better to make it explicit on documentation.

Author: lewuathe <lewuathe@me.com>

Closes #8205 from Lewuathe/SPARK-9977.
---
 docs/ml-features.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index d82c85ee75a0e..8d56dc32ca3c0 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -725,7 +725,11 @@ dctDf.select("featuresDCT").show(3);
 `StringIndexer` encodes a string column of labels to a column of label indices.
 The indices are in `[0, numLabels)`, ordered by label frequencies.
 So the most frequent label gets index `0`.
-If the input column is numeric, we cast it to string and index the string values.
+If the input column is numeric, we cast it to string and index the string 
+values. When downstream pipeline components such as `Estimator` or 
+`Transformer` make use of this string-indexed label, you must set the input 
+column of the component to this string-indexed column name. In many cases, 
+you can set the input column with `setInputCol`.
 
 **Examples**
 

From 3d16a545007922ee6fa36e5f5c3959406cb46484 Mon Sep 17 00:00:00 2001
From: Han JU <ju.han.felix@gmail.com>
Date: Wed, 19 Aug 2015 13:04:16 +0100
Subject: [PATCH 331/633] [SPARK-8949] Print warnings when using preferred
 locations feature

Add warnings according to SPARK-8949 in `SparkContext`
  - warnings in scaladoc
  - log warnings when preferred locations feature is used through `SparkContext`'s constructor

However I didn't found any documentation reference of this feature. Please direct me if you know any reference to this feature.

Author: Han JU <ju.han.felix@gmail.com>

Closes #7874 from darkjh/SPARK-8949.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 2e01a9a18c784..1ddaca8a5ba8c 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -118,9 +118,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * Can be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
    * from a list of input files or InputFormats for the application.
    */
+  @deprecated("Passing in preferred locations has no effect at all, see SPARK-8949", "1.5.0")
   @DeveloperApi
   def this(config: SparkConf, preferredNodeLocationData: Map[String, Set[SplitInfo]]) = {
     this(config)
+    logWarning("Passing in preferred locations has no effect at all, see SPARK-8949")
     this.preferredNodeLocationData = preferredNodeLocationData
   }
 
@@ -153,6 +155,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
       preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map()) =
   {
     this(SparkContext.updatedConf(new SparkConf(), master, appName, sparkHome, jars, environment))
+    if (preferredNodeLocationData.nonEmpty) {
+      logWarning("Passing in preferred locations has no effect at all, see SPARK-8949")
+    }
     this.preferredNodeLocationData = preferredNodeLocationData
   }
 

From 39e4ebd521defdb68a0787bcd3bde6bc855f5198 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Wed, 19 Aug 2015 07:38:27 -0700
Subject: [PATCH 332/633] [SPARK-10060] [ML] [DOC] spark.ml DecisionTree user
 guide

New user guide section ml-decision-tree.md, including code examples.

I have run all examples, including the Java ones.

CC: manishamde yanboliang mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8244 from jkbradley/ml-dt-docs.
---
 docs/ml-decision-tree.md | 510 +++++++++++++++++++++++++++++++++++++++
 docs/ml-features.md      |   2 -
 docs/ml-guide.md         |   9 +-
 docs/mllib-ensembles.md  |   2 +-
 docs/mllib-guide.md      |   9 +-
 5 files changed, 519 insertions(+), 13 deletions(-)
 create mode 100644 docs/ml-decision-tree.md

diff --git a/docs/ml-decision-tree.md b/docs/ml-decision-tree.md
new file mode 100644
index 0000000000000..958c6f5e4716c
--- /dev/null
+++ b/docs/ml-decision-tree.md
@@ -0,0 +1,510 @@
+---
+layout: global
+title: Decision Trees - SparkML
+displayTitle: <a href="ml-guide.html">ML</a> - Decision Trees
+---
+
+**Table of Contents**
+
+* This will become a table of contents (this text will be scraped).
+{:toc}
+
+
+# Overview
+
+[Decision trees](http://en.wikipedia.org/wiki/Decision_tree_learning)
+and their ensembles are popular methods for the machine learning tasks of
+classification and regression. Decision trees are widely used since they are easy to interpret,
+handle categorical features, extend to the multiclass classification setting, do not require
+feature scaling, and are able to capture non-linearities and feature interactions. Tree ensemble
+algorithms such as random forests and boosting are among the top performers for classification and
+regression tasks.
+
+MLlib supports decision trees for binary and multiclass classification and for regression,
+using both continuous and categorical features. The implementation partitions data by rows,
+allowing distributed training with millions or even billions of instances.
+
+Users can find more information about the decision tree algorithm in the [MLlib Decision Tree guide](mllib-decision-tree.html).  In this section, we demonstrate the Pipelines API for Decision Trees.
+
+The Pipelines API for Decision Trees offers a bit more functionality than the original API.  In particular, for classification, users can get the predicted probability of each class (a.k.a. class conditional probabilities).
+
+Ensembles of trees (Random Forests and Gradient-Boosted Trees) are described in the [Ensembles guide](ml-ensembles.html).
+
+# Inputs and Outputs (Predictions)
+
+We list the input and output (prediction) column types here.
+All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.
+
+## Input Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>labelCol</td>
+      <td>Double</td>
+      <td>"label"</td>
+      <td>Label to predict</td>
+    </tr>
+    <tr>
+      <td>featuresCol</td>
+      <td>Vector</td>
+      <td>"features"</td>
+      <td>Feature vector</td>
+    </tr>
+  </tbody>
+</table>
+
+## Output Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+      <th align="left">Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>predictionCol</td>
+      <td>Double</td>
+      <td>"prediction"</td>
+      <td>Predicted label</td>
+      <td></td>
+    </tr>
+    <tr>
+      <td>rawPredictionCol</td>
+      <td>Vector</td>
+      <td>"rawPrediction"</td>
+      <td>Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction</td>
+      <td>Classification only</td>
+    </tr>
+    <tr>
+      <td>probabilityCol</td>
+      <td>Vector</td>
+      <td>"probability"</td>
+      <td>Vector of length # classes equal to rawPrediction normalized to a multinomial distribution</td>
+      <td>Classification only</td>
+    </tr>
+  </tbody>
+</table>
+
+# Examples
+
+The below examples demonstrate the Pipelines API for Decision Trees. The main differences between this API and the [original MLlib Decision Tree API](mllib-decision-tree.html) are:
+
+* support for ML Pipelines
+* separation of Decision Trees for classification vs. regression
+* use of DataFrame metadata to distinguish continuous and categorical features
+
+
+## Classification
+
+The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the `DataFrame` which the Decision Tree algorithm can recognize.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.classification.DecisionTreeClassifier).
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.classification.DecisionTreeClassifier
+import org.apache.spark.ml.classification.DecisionTreeClassificationModel
+import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+val labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data)
+// Automatically identify categorical features, and index them.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4) // features with > 4 distinct values are treated as continuous
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a DecisionTree model.
+val dt = new DecisionTreeClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures")
+
+// Convert indexed labels back to original labels.
+val labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels)
+
+// Chain indexers and tree in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(labelIndexer, featureIndexer, dt, labelConverter))
+
+// Train model.  This also runs the indexers.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision")
+val accuracy = evaluator.evaluate(predictions)
+println("Test Error = " + (1.0 - accuracy))
+
+val treeModel = model.stages(2).asInstanceOf[DecisionTreeClassificationModel]
+println("Learned classification tree model:\n" + treeModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/classification/DecisionTreeClassifier.html).
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.classification.DecisionTreeClassifier;
+import org.apache.spark.ml.classification.DecisionTreeClassificationModel;
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
+import org.apache.spark.ml.feature.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+StringIndexerModel labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data);
+// Automatically identify categorical features, and index them.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4) // features with > 4 distinct values are treated as continuous
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a DecisionTree model.
+DecisionTreeClassifier dt = new DecisionTreeClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures");
+
+// Convert indexed labels back to original labels.
+IndexToString labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels());
+
+// Chain indexers and tree in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});
+
+// Train model.  This also runs the indexers.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision");
+double accuracy = evaluator.evaluate(predictions);
+System.out.println("Test Error = " + (1.0 - accuracy));
+
+DecisionTreeClassificationModel treeModel =
+  (DecisionTreeClassificationModel)(model.stages()[2]);
+System.out.println("Learned classification tree model:\n" + treeModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.classification.DecisionTreeClassifier).
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.classification import DecisionTreeClassifier
+from pyspark.ml.feature import StringIndexer, VectorIndexer
+from pyspark.ml.evaluation import MulticlassClassificationEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Index labels, adding metadata to the label column.
+# Fit on whole dataset to include all labels in index.
+labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
+# Automatically identify categorical features, and index them.
+# We specify maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a DecisionTree model.
+dt = DecisionTreeClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures")
+
+# Chain indexers and tree in a Pipeline
+pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt])
+
+# Train model.  This also runs the indexers.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "indexedLabel", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = MulticlassClassificationEvaluator(
+    labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
+accuracy = evaluator.evaluate(predictions)
+print "Test Error = %g" % (1.0 - accuracy)
+
+treeModel = model.stages[2]
+print treeModel # summary only
+{% endhighlight %}
+</div>
+
+</div>
+
+
+## Regression
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.classification.DecisionTreeClassifier).
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.regression.DecisionTreeRegressor
+import org.apache.spark.ml.regression.DecisionTreeRegressionModel
+import org.apache.spark.ml.feature.VectorIndexer
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Automatically identify categorical features, and index them.
+// Here, we treat features with > 4 distinct values as continuous.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a DecisionTree model.
+val dt = new DecisionTreeRegressor()
+  .setLabelCol("label")
+  .setFeaturesCol("indexedFeatures")
+
+// Chain indexers and tree in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(featureIndexer, dt))
+
+// Train model.  This also runs the indexer.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("prediction", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new RegressionEvaluator()
+  .setLabelCol("label")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse")
+// We negate the RMSE value since RegressionEvalutor returns negated RMSE
+// (since evaluation metrics are meant to be maximized by CrossValidator).
+val rmse = - evaluator.evaluate(predictions)
+println("Root Mean Squared Error (RMSE) on test data = " + rmse)
+
+val treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel]
+println("Learned regression tree model:\n" + treeModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/classification/DecisionTreeClassifier.html).
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.feature.*;
+import org.apache.spark.ml.regression.DecisionTreeRegressionModel;
+import org.apache.spark.ml.regression.DecisionTreeRegressor;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+StringIndexerModel labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data);
+// Automatically identify categorical features, and index them.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4) // features with > 4 distinct values are treated as continuous
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a DecisionTree model.
+DecisionTreeRegressor dt = new DecisionTreeRegressor()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures");
+
+// Convert indexed labels back to original labels.
+IndexToString labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels());
+
+// Chain indexers and tree in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});
+
+// Train model.  This also runs the indexers.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+RegressionEvaluator evaluator = new RegressionEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse");
+// We negate the RMSE value since RegressionEvalutor returns negated RMSE
+// (since evaluation metrics are meant to be maximized by CrossValidator).
+double rmse = - evaluator.evaluate(predictions);
+System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse);
+
+DecisionTreeRegressionModel treeModel =
+  (DecisionTreeRegressionModel)(model.stages()[2]);
+System.out.println("Learned regression tree model:\n" + treeModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.classification.DecisionTreeClassifier).
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.regression import DecisionTreeRegressor
+from pyspark.ml.feature import StringIndexer, VectorIndexer
+from pyspark.ml.evaluation import RegressionEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Index labels, adding metadata to the label column.
+# Fit on whole dataset to include all labels in index.
+labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
+# Automatically identify categorical features, and index them.
+# We specify maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a DecisionTree model.
+dt = DecisionTreeRegressor(labelCol="indexedLabel", featuresCol="indexedFeatures")
+
+# Chain indexers and tree in a Pipeline
+pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt])
+
+# Train model.  This also runs the indexers.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "indexedLabel", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = RegressionEvaluator(
+    labelCol="indexedLabel", predictionCol="prediction", metricName="rmse")
+# We negate the RMSE value since RegressionEvalutor returns negated RMSE
+# (since evaluation metrics are meant to be maximized by CrossValidator).
+rmse = -evaluator.evaluate(predictions)
+print "Root Mean Squared Error (RMSE) on test data = %g" % rmse
+
+treeModel = model.stages[1]
+print treeModel # summary only
+{% endhighlight %}
+</div>
+
+</div>
diff --git a/docs/ml-features.md b/docs/ml-features.md
index 8d56dc32ca3c0..d0e8eeb7a757e 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1477,5 +1477,3 @@ print(output.select("features", "clicked").first())
 </div>
 </div>
 
-# Feature Selectors
-
diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 4fe0ea78bbe13..c64fff7c0315a 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -32,10 +32,7 @@ Users should be comfortable using `spark.mllib` features and expect more feature
 Developers should contribute new algorithms to `spark.mllib` and can optionally contribute
 to `spark.ml`.
 
-Guides for sub-packages of `spark.ml` include:
-
-* [Feature Extraction, Transformation, and Selection](ml-features.html): Details on transformers supported in the Pipelines API, including a few not in the lower-level `spark.mllib` API
-* [Ensembles](ml-ensembles.html): Details on ensemble learning methods in the Pipelines API
+See the [Algorithm Guides section](#algorithm-guides) below for guides on sub-packages of `spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.
 
 
 **Table of Contents**
@@ -179,10 +176,8 @@ There are now several algorithms in the Pipelines API which are not in the lower
 **Pipelines API Algorithm Guides**
 
 * [Feature Extraction, Transformation, and Selection](ml-features.html)
+* [Decision Trees for Classification and Regression](ml-decision-tree.html)
 * [Ensembles](ml-ensembles.html)
-
-**Algorithms in `spark.ml`**
-
 * [Linear methods with elastic net regularization](ml-linear-methods.html)
 
 # Code Examples
diff --git a/docs/mllib-ensembles.md b/docs/mllib-ensembles.md
index 7521fb14a7bd6..1e00b2083ed73 100644
--- a/docs/mllib-ensembles.md
+++ b/docs/mllib-ensembles.md
@@ -9,7 +9,7 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Ensembles
 
 An [ensemble method](http://en.wikipedia.org/wiki/Ensemble_learning)
 is a learning algorithm which creates a model composed of a set of other base models.
-MLlib supports two major ensemble algorithms: [`GradientBoostedTrees`](api/scala/index.html#org.apache.spark.mllib.tree.GradientBosotedTrees) and [`RandomForest`](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest).
+MLlib supports two major ensemble algorithms: [`GradientBoostedTrees`](api/scala/index.html#org.apache.spark.mllib.tree.GradientBoostedTrees) and [`RandomForest`](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest).
 Both use [decision trees](mllib-decision-tree.html) as their base models.
 
 ## Gradient-Boosted Trees vs. Random Forests
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 0573e5d4bc5ff..6330c977552d1 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -73,11 +73,14 @@ Users should be comfortable using `spark.mllib` features and expect more feature
 Developers should contribute new algorithms to `spark.mllib` and can optionally contribute
 to `spark.ml`.
 
-More detailed guides for `spark.ml` include:
+Guides for `spark.ml` include:
 
 * **[spark.ml programming guide](ml-guide.html)**: overview of the Pipelines API and major concepts
-* [Feature transformers](ml-features.html): Details on transformers supported in the Pipelines API, including a few not in the lower-level `spark.mllib` API
-* [Ensembles](ml-ensembles.html): Details on ensemble learning methods in the Pipelines API
+* Guides on using algorithms within the Pipelines API:
+  * [Feature transformers](ml-features.html), including a few not in the lower-level `spark.mllib` API
+  * [Decision trees](ml-decision-tree.html)
+  * [Ensembles](ml-ensembles.html)
+  * [Linear methods](ml-linear-methods.html)
 
 # Dependencies
 

From 802b5b8791fc2c892810981b2479a04175aa3dcd Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Wed, 19 Aug 2015 08:53:34 -0700
Subject: [PATCH 333/633] [SPARK-10084] [MLLIB] [DOC] Add Python example for
 mllib FP-growth user guide

1, Add Python example for mllib FP-growth user guide.
2, Correct mistakes of Scala and Java examples.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8279 from yanboliang/spark-10084.
---
 docs/mllib-frequent-pattern-mining.md | 73 ++++++++++++++++++---------
 1 file changed, 50 insertions(+), 23 deletions(-)

diff --git a/docs/mllib-frequent-pattern-mining.md b/docs/mllib-frequent-pattern-mining.md
index 6c06550703d46..4d4f5cfdc564e 100644
--- a/docs/mllib-frequent-pattern-mining.md
+++ b/docs/mllib-frequent-pattern-mining.md
@@ -39,9 +39,9 @@ MLlib's FP-growth implementation takes the following (hyper-)parameters:
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-[`FPGrowth`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth)
-implements the FP-growth algorithm.  It take an `RDD` of transactions,
-where each transaction is an `Iterable` of items of a generic type.
+[`FPGrowth`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowth) implements the
+FP-growth algorithm.
+It take a `RDD` of transactions, where each transaction is an `Array` of items of a generic type.
 Calling `FPGrowth.run` with transactions returns an
 [`FPGrowthModel`](api/scala/index.html#org.apache.spark.mllib.fpm.FPGrowthModel)
 that stores the frequent itemsets with their frequencies.  The following
@@ -53,16 +53,11 @@ details) from `transactions`.
 
 {% highlight scala %}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.mllib.fpm.{FPGrowth, FPGrowthModel}
+import org.apache.spark.mllib.fpm.FPGrowth
 
-val transactions: RDD[Array[String]] = sc.parallelize(Seq(
-  "r z h k p",
-  "z y x w v u t s",
-  "s x o n r",
-  "x z y m t s q e",
-  "z",
-  "x z y r q t p")
-  .map(_.split(" ")))
+val data = sc.textFile("data/mllib/sample_fpgrowth.txt")
+
+val transactions: RDD[Array[String]] = data.map(s => s.trim.split(' '))
 
 val fpg = new FPGrowth()
   .setMinSupport(0.2)
@@ -86,10 +81,10 @@ model.generateAssociationRules(minConfidence).collect().foreach { rule =>
 
 <div data-lang="java" markdown="1">
 
-[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html)
-implements the FP-growth algorithm.  It take a `JavaRDD` of
-transactions, where each transaction is an `Array` of items of a generic
-type.  Calling `FPGrowth.run` with transactions returns an
+[`FPGrowth`](api/java/org/apache/spark/mllib/fpm/FPGrowth.html) implements the
+FP-growth algorithm.
+It take an `JavaRDD` of transactions, where each transaction is an `Iterable` of items of a generic type.
+Calling `FPGrowth.run` with transactions returns an
 [`FPGrowthModel`](api/java/org/apache/spark/mllib/fpm/FPGrowthModel.html)
 that stores the frequent itemsets with their frequencies.  The following
 example illustrates how to mine frequent itemsets and association rules
@@ -107,13 +102,19 @@ import org.apache.spark.mllib.fpm.AssociationRules;
 import org.apache.spark.mllib.fpm.FPGrowth;
 import org.apache.spark.mllib.fpm.FPGrowthModel;
 
-JavaRDD<List<String>> transactions = sc.parallelize(Arrays.asList(
-  Arrays.asList("r z h k p".split(" ")),
-  Arrays.asList("z y x w v u t s".split(" ")),
-  Arrays.asList("s x o n r".split(" ")),
-  Arrays.asList("x z y m t s q e".split(" ")),
-  Arrays.asList("z".split(" ")),
-  Arrays.asList("x z y r q t p".split(" "))), 2);
+SparkConf conf = new SparkConf().setAppName("FP-growth Example");
+JavaSparkContext sc = new JavaSparkContext(conf);
+
+JavaRDD<String> data = sc.textFile("data/mllib/sample_fpgrowth.txt");
+
+JavaRDD<List<String>> transactions = data.map(
+  new Function<String, List<String>>() {
+    public List<String> call(String line) {
+      String[] parts = line.split(" ");
+      return Arrays.asList(parts);
+    }
+  }
+);
 
 FPGrowth fpg = new FPGrowth()
   .setMinSupport(0.2)
@@ -133,6 +134,32 @@ for (AssociationRules.Rule<String> rule
 {% endhighlight %}
 
 </div>
+
+<div data-lang="python" markdown="1">
+
+[`FPGrowth`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowth) implements the
+FP-growth algorithm.
+It take an `RDD` of transactions, where each transaction is an `List` of items of a generic type.
+Calling `FPGrowth.train` with transactions returns an
+[`FPGrowthModel`](api/python/pyspark.mllib.html#pyspark.mllib.fpm.FPGrowthModel)
+that stores the frequent itemsets with their frequencies.
+
+{% highlight python %}
+from pyspark.mllib.fpm import FPGrowth
+
+data = sc.textFile("data/mllib/sample_fpgrowth.txt")
+
+transactions = data.map(lambda line: line.strip().split(' '))
+
+model = FPGrowth.train(transactions, minSupport=0.2, numPartitions=10)
+
+result = model.freqItemsets().collect()
+for fi in result:
+    print(fi)
+{% endhighlight %}
+
+</div>
+
 </div>
 
 ## Association Rules

From f3e177917dd093873dca286d2419704a561ba44b Mon Sep 17 00:00:00 2001
From: Carsten Blank <blank@cncengine.com>
Date: Wed, 19 Aug 2015 10:19:24 -0700
Subject: [PATCH 334/633] [SPARK-5754] [YARN] Spark/Yarn/Windows
 driver/executor escaping Fix

This is my retry to suggest a fix for using Spark on Yarn on Windows. The former request lacked coding style which I hope to have learned to do better, and wasn't a true solution as I didn't really understand where the problem came from. Albeit being still a bit obscure, I can name the "players" and have come up with a better explaination of why I am suggesting this fix.

I also used vanzin and srowen input to *try* to give a more elegant solution. I am not so sure if that worked out though.

I still hope that this PR is a lot more useful than the last. Also do I hope that this is a _solution_ to the problem that Spark doesn't work on Yarn on Windows. With these changes it works (and I can also explain why!).

I still believe that a Unit Test should be included, kind of like the one I committed the last time. But that was premature, as I want to get the principal 'Go' from vanzin and srowen.

Thanks for your time both of you.

Author: Carsten Blank <blank@cncengine.com>
Author: cbvoxel <blank@cncengine.com>

Closes #8053 from cbvoxel/master.
---
 .../org/apache/spark/deploy/yarn/Client.scala |  2 +-
 .../spark/deploy/yarn/ExecutorRunnable.scala  |  2 +-
 .../deploy/yarn/YarnSparkHadoopUtil.scala     | 56 +++++++++++++++----
 .../launcher/YarnCommandBuilderUtils.scala    | 27 +++++++++
 4 files changed, 75 insertions(+), 12 deletions(-)
 create mode 100644 yarn/src/main/scala/org/apache/spark/launcher/YarnCommandBuilderUtils.scala

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 8672ef601784b..262c6a8d6397f 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -431,7 +431,7 @@ private[spark] class Client(
     }
 
     // Distribute an archive with Hadoop and Spark configuration for the AM.
-    val (_, confLocalizedPath) = distribute(createConfArchive().getAbsolutePath(),
+    val (_, confLocalizedPath) = distribute(createConfArchive().toURI().getPath(),
       resType = LocalResourceType.ARCHIVE,
       destName = Some(LOCALIZED_CONF_DIR),
       appMasterOnly = true)
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
index 52580deb372c2..4cc50483a17ff 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
@@ -217,7 +217,7 @@ class ExecutorRunnable(
       // an inconsistent state.
       // TODO: If the OOM is not recoverable by rescheduling it on different node, then do
       // 'something' to fail job ... akin to blacklisting trackers in mapred ?
-      "-XX:OnOutOfMemoryError='kill %p'") ++
+      YarnSparkHadoopUtil.getOutOfMemoryErrorArgument) ++
       javaOpts ++
       Seq("org.apache.spark.executor.CoarseGrainedExecutorBackend",
         "--driver-url", masterAddress.toString,
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
index 68d01c17ef720..445d3dcd266db 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
@@ -37,6 +37,7 @@ import org.apache.hadoop.yarn.api.records.{ApplicationAccessType, ContainerId, P
 import org.apache.hadoop.yarn.util.ConverterUtils
 
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.launcher.YarnCommandBuilderUtils
 import org.apache.spark.{SecurityManager, SparkConf, SparkException}
 import org.apache.spark.util.Utils
 
@@ -219,26 +220,61 @@ object YarnSparkHadoopUtil {
     }
   }
 
+  /**
+   * The handler if an OOM Exception is thrown by the JVM must be configured on Windows
+   * differently: the 'taskkill' command should be used, whereas Unix-based systems use 'kill'.
+   *
+   * As the JVM interprets both %p and %%p as the same, we can use either of them. However,
+   * some tests on Windows computers suggest, that the JVM only accepts '%%p'.
+   *
+   * Furthermore, the behavior of the character '%' on the Windows command line differs from
+   * the behavior of '%' in a .cmd file: it gets interpreted as an incomplete environment
+   * variable. Windows .cmd files escape a '%' by '%%'. Thus, the correct way of writing
+   * '%%p' in an escaped way is '%%%%p'.
+   *
+   * @return The correct OOM Error handler JVM option, platform dependent.
+   */
+  def getOutOfMemoryErrorArgument : String = {
+    if (Utils.isWindows) {
+      escapeForShell("-XX:OnOutOfMemoryError=taskkill /F /PID %%%%p")
+    } else {
+      "-XX:OnOutOfMemoryError='kill %p'"
+    }
+  }
+
   /**
    * Escapes a string for inclusion in a command line executed by Yarn. Yarn executes commands
-   * using `bash -c "command arg1 arg2"` and that means plain quoting doesn't really work. The
-   * argument is enclosed in single quotes and some key characters are escaped.
+   * using either
+   *
+   * (Unix-based) `bash -c "command arg1 arg2"` and that means plain quoting doesn't really work.
+   * The argument is enclosed in single quotes and some key characters are escaped.
+   *
+   * (Windows-based) part of a .cmd file in which case windows escaping for each argument must be
+   * applied. Windows is quite lenient, however it is usually Java that causes trouble, needing to
+   * distinguish between arguments starting with '-' and class names. If arguments are surrounded
+   * by ' java takes the following string as is, hence an argument is mistakenly taken as a class
+   * name which happens to start with a '-'. The way to avoid this, is to surround nothing with
+   * a ', but instead with a ".
    *
    * @param arg A single argument.
    * @return Argument quoted for execution via Yarn's generated shell script.
    */
   def escapeForShell(arg: String): String = {
     if (arg != null) {
-      val escaped = new StringBuilder("'")
-      for (i <- 0 to arg.length() - 1) {
-        arg.charAt(i) match {
-          case '$' => escaped.append("\\$")
-          case '"' => escaped.append("\\\"")
-          case '\'' => escaped.append("'\\''")
-          case c => escaped.append(c)
+      if (Utils.isWindows) {
+        YarnCommandBuilderUtils.quoteForBatchScript(arg)
+      } else {
+        val escaped = new StringBuilder("'")
+        for (i <- 0 to arg.length() - 1) {
+          arg.charAt(i) match {
+            case '$' => escaped.append("\\$")
+            case '"' => escaped.append("\\\"")
+            case '\'' => escaped.append("'\\''")
+            case c => escaped.append(c)
+          }
         }
+        escaped.append("'").toString()
       }
-      escaped.append("'").toString()
     } else {
       arg
     }
diff --git a/yarn/src/main/scala/org/apache/spark/launcher/YarnCommandBuilderUtils.scala b/yarn/src/main/scala/org/apache/spark/launcher/YarnCommandBuilderUtils.scala
new file mode 100644
index 0000000000000..3ac36ef0a1c3f
--- /dev/null
+++ b/yarn/src/main/scala/org/apache/spark/launcher/YarnCommandBuilderUtils.scala
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.launcher
+
+/**
+ * Exposes needed methods
+ */
+private[spark] object YarnCommandBuilderUtils {
+  def quoteForBatchScript(arg: String) : String = {
+    CommandBuilderUtils.quoteForBatchScript(arg)
+  }
+}

From 2fcb9cb9552dac1d78dcca5d4d5032b4fa6c985c Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Wed, 19 Aug 2015 10:41:14 -0700
Subject: [PATCH 335/633] [SPARK-9856] [SPARKR] Add expression functions into
 SparkR whose params are complicated

I added lots of Column functinos into SparkR. And I also added `rand(seed: Int)` and `randn(seed: Int)` in Scala. Since we need such APIs for R integer type.

### JIRA
[[SPARK-9856] Add expression functions into SparkR whose params are complicated - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-9856)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8264 from yu-iskw/SPARK-9856-3.
---
 R/pkg/NAMESPACE                               |  28 ++
 R/pkg/R/functions.R                           | 415 ++++++++++++++++++
 R/pkg/R/generics.R                            | 113 +++++
 R/pkg/inst/tests/test_sparkSQL.R              |  98 ++++-
 .../apache/spark/api/r/RBackendHandler.scala  |   1 +
 5 files changed, 649 insertions(+), 6 deletions(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 8fa12d5adef8a..111a2dc30db73 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -84,6 +84,7 @@ exportClasses("Column")
 
 exportMethods("abs",
               "acos",
+              "add_months",
               "alias",
               "approxCountDistinct",
               "asc",
@@ -101,12 +102,17 @@ exportMethods("abs",
               "ceil",
               "ceiling",
               "concat",
+              "concat_ws",
               "contains",
+              "conv",
               "cos",
               "cosh",
               "count",
               "countDistinct",
               "crc32",
+              "date_add",
+              "date_format",
+              "date_sub",
               "datediff",
               "dayofmonth",
               "dayofyear",
@@ -115,9 +121,14 @@ exportMethods("abs",
               "exp",
               "explode",
               "expm1",
+              "expr",
               "factorial",
               "first",
               "floor",
+              "format_number",
+              "format_string",
+              "from_unixtime",
+              "from_utc_timestamp",
               "getField",
               "getItem",
               "greatest",
@@ -125,6 +136,7 @@ exportMethods("abs",
               "hour",
               "hypot",
               "initcap",
+              "instr",
               "isNaN",
               "isNotNull",
               "isNull",
@@ -135,11 +147,13 @@ exportMethods("abs",
               "levenshtein",
               "like",
               "lit",
+              "locate",
               "log",
               "log10",
               "log1p",
               "log2",
               "lower",
+              "lpad",
               "ltrim",
               "max",
               "md5",
@@ -152,16 +166,26 @@ exportMethods("abs",
               "n_distinct",
               "nanvl",
               "negate",
+              "next_day",
               "otherwise",
               "pmod",
               "quarter",
+              "rand",
+              "randn",
+              "regexp_extract",
+              "regexp_replace",
               "reverse",
               "rint",
               "rlike",
               "round",
+              "rpad",
               "rtrim",
               "second",
               "sha1",
+              "sha2",
+              "shiftLeft",
+              "shiftRight",
+              "shiftRightUnsigned",
               "sign",
               "signum",
               "sin",
@@ -171,6 +195,7 @@ exportMethods("abs",
               "sqrt",
               "startsWith",
               "substr",
+              "substring_index",
               "sum",
               "sumDistinct",
               "tan",
@@ -178,9 +203,12 @@ exportMethods("abs",
               "toDegrees",
               "toRadians",
               "to_date",
+              "to_utc_timestamp",
+              "translate",
               "trim",
               "unbase64",
               "unhex",
+              "unix_timestamp",
               "upper",
               "weekofyear",
               "when",
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index 366c230e1e85b..5dba0887d150d 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -166,6 +166,421 @@ setMethod("n", signature(x = "Column"),
             count(x)
           })
 
+#' date_format
+#'
+#' Converts a date/timestamp/string to a value of string in the format specified by the date
+#' format given by the second argument.
+#'
+#' A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
+#' pattern letters of `java.text.SimpleDateFormat` can be used.
+#'
+#' NOTE: Use when ever possible specialized functions like `year`. These benefit from a
+#' specialized implementation.
+#'
+#' @rdname functions
+setMethod("date_format", signature(y = "Column", x = "character"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "date_format", y@jc, x)
+            column(jc)
+          })
+
+#' from_utc_timestamp
+#'
+#' Assumes given timestamp is UTC and converts to given timezone.
+#'
+#' @rdname functions
+setMethod("from_utc_timestamp", signature(y = "Column", x = "character"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "from_utc_timestamp", y@jc, x)
+            column(jc)
+          })
+
+#' instr
+#'
+#' Locate the position of the first occurrence of substr column in the given string.
+#' Returns null if either of the arguments are null.
+#'
+#' NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+#' could not be found in str.
+#'
+#' @rdname functions
+setMethod("instr", signature(y = "Column", x = "character"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "instr", y@jc, x)
+            column(jc)
+          })
+
+#' next_day
+#'
+#' Given a date column, returns the first date which is later than the value of the date column
+#' that is on the specified day of the week.
+#'
+#' For example, `next <- day('2015-07-27', "Sunday")` returns 2015-08-02 because that is the first
+#' Sunday after 2015-07-27.
+#'
+#' Day of the week parameter is case insensitive, and accepts:
+#' "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun".
+#'
+#' @rdname functions
+setMethod("next_day", signature(y = "Column", x = "character"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "next_day", y@jc, x)
+            column(jc)
+          })
+
+#' to_utc_timestamp
+#'
+#' Assumes given timestamp is in given timezone and converts to UTC.
+#'
+#' @rdname functions
+setMethod("to_utc_timestamp", signature(y = "Column", x = "character"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "to_utc_timestamp", y@jc, x)
+            column(jc)
+          })
+
+#' add_months
+#'
+#' Returns the date that is numMonths after startDate.
+#'
+#' @rdname functions
+setMethod("add_months", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "add_months", y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' date_add
+#'
+#' Returns the date that is `days` days after `start`
+#'
+#' @rdname functions
+setMethod("date_add", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "date_add", y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' date_sub
+#'
+#' Returns the date that is `days` days before `start`
+#'
+#' @rdname functions
+setMethod("date_sub", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "date_sub", y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' format_number
+#'
+#' Formats numeric column x to a format like '#,###,###.##', rounded to d decimal places,
+#' and returns the result as a string column.
+#'
+#' If d is 0, the result has no decimal point or fractional part.
+#' If d < 0, the result will be null.'
+#'
+#' @rdname functions
+setMethod("format_number", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "format_number",
+                              y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' sha2
+#'
+#' Calculates the SHA-2 family of hash functions of a binary column and
+#' returns the value as a hex string.
+#'
+#' @rdname functions
+#' @param y column to compute SHA-2 on.
+#' @param x one of 224, 256, 384, or 512.
+setMethod("sha2", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sha2", y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' shiftLeft
+#'
+#' Shift the the given value numBits left. If the given value is a long value, this function
+#' will return a long value else it will return an integer value.
+#'
+#' @rdname functions
+setMethod("shiftLeft", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "shiftLeft",
+                              y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' shiftRight
+#'
+#' Shift the the given value numBits right. If the given value is a long value, it will return
+#' a long value else it will return an integer value.
+#'
+#' @rdname functions
+setMethod("shiftRight", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "shiftRight",
+                              y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' shiftRightUnsigned
+#'
+#' Unsigned shift the the given value numBits right. If the given value is a long value,
+#' it will return a long value else it will return an integer value.
+#'
+#' @rdname functions
+setMethod("shiftRightUnsigned", signature(y = "Column", x = "numeric"),
+          function(y, x) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "shiftRightUnsigned",
+                              y@jc, as.integer(x))
+            column(jc)
+          })
+
+#' concat_ws
+#'
+#' Concatenates multiple input string columns together into a single string column,
+#' using the given separator.
+#'
+#' @rdname functions
+setMethod("concat_ws", signature(sep = "character", x = "Column"),
+          function(sep, x, ...) {
+            jcols <- listToSeq(lapply(list(x, ...), function(x) { x@jc }))
+            jc <- callJStatic("org.apache.spark.sql.functions", "concat_ws", sep, jcols)
+            column(jc)
+          })
+
+#' conv
+#'
+#' Convert a number in a string column from one base to another.
+#'
+#' @rdname functions
+setMethod("conv", signature(x = "Column", fromBase = "numeric", toBase = "numeric"),
+          function(x, fromBase, toBase) {
+            fromBase <- as.integer(fromBase)
+            toBase <- as.integer(toBase)
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "conv",
+                              x@jc, fromBase, toBase)
+            column(jc)
+          })
+
+#' expr
+#'
+#' Parses the expression string into the column that it represents, similar to
+#' DataFrame.selectExpr
+#'
+#' @rdname functions
+setMethod("expr", signature(x = "character"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "expr", x)
+            column(jc)
+          })
+
+#' format_string
+#'
+#' Formats the arguments in printf-style and returns the result as a string column.
+#'
+#' @rdname functions
+setMethod("format_string", signature(format = "character", x = "Column"),
+          function(format, x, ...) {
+            jcols <- listToSeq(lapply(list(x, ...), function(arg) { arg@jc }))
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "format_string",
+                              format, jcols)
+            column(jc)
+          })
+
+#' from_unixtime
+#'
+#' Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
+#' representing the timestamp of that moment in the current system time zone in the given
+#' format.
+#'
+#' @rdname functions
+setMethod("from_unixtime", signature(x = "Column"),
+          function(x, format = "yyyy-MM-dd HH:mm:ss") {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "from_unixtime",
+                              x@jc, format)
+            column(jc)
+          })
+
+#' locate
+#'
+#' Locate the position of the first occurrence of substr.
+#' NOTE: The position is not zero based, but 1 based index, returns 0 if substr
+#' could not be found in str.
+#'
+#' @rdname functions
+setMethod("locate", signature(substr = "character", str = "Column"),
+          function(substr, str, pos = 0) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "locate",
+                              substr, str@jc, as.integer(pos))
+            column(jc)
+          })
+
+#' lpad
+#'
+#' Left-pad the string column with
+#'
+#' @rdname functions
+setMethod("lpad", signature(x = "Column", len = "numeric", pad = "character"),
+          function(x, len, pad) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "lpad",
+                              x@jc, as.integer(len), pad)
+            column(jc)
+          })
+
+#' rand
+#'
+#' Generate a random column with i.i.d. samples from U[0.0, 1.0].
+#'
+#' @rdname functions
+setMethod("rand", signature(seed = "missing"),
+          function(seed) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "rand")
+            column(jc)
+          })
+setMethod("rand", signature(seed = "numeric"),
+          function(seed) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "rand", as.integer(seed))
+            column(jc)
+          })
+
+#' randn
+#'
+#' Generate a column with i.i.d. samples from the standard normal distribution.
+#'
+#' @rdname functions
+setMethod("randn", signature(seed = "missing"),
+          function(seed) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "randn")
+            column(jc)
+          })
+setMethod("randn", signature(seed = "numeric"),
+          function(seed) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "randn", as.integer(seed))
+            column(jc)
+          })
+
+#' regexp_extract
+#'
+#' Extract a specific(idx) group identified by a java regex, from the specified string column.
+#'
+#' @rdname functions
+setMethod("regexp_extract",
+          signature(x = "Column", pattern = "character", idx = "numeric"),
+          function(x, pattern, idx) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "regexp_extract",
+                              x@jc, pattern, as.integer(idx))
+            column(jc)
+          })
+
+#' regexp_replace
+#'
+#' Replace all substrings of the specified string value that match regexp with rep.
+#'
+#' @rdname functions
+setMethod("regexp_replace",
+          signature(x = "Column", pattern = "character", replacement = "character"),
+          function(x, pattern, replacement) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "regexp_replace",
+                              x@jc, pattern, replacement)
+            column(jc)
+          })
+
+#' rpad
+#'
+#' Right-padded with pad to a length of len.
+#'
+#' @rdname functions
+setMethod("rpad", signature(x = "Column", len = "numeric", pad = "character"),
+          function(x, len, pad) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "rpad",
+                              x@jc, as.integer(len), pad)
+            column(jc)
+          })
+
+#' substring_index
+#'
+#' Returns the substring from string str before count occurrences of the delimiter delim.
+#' If count is positive, everything the left of the final delimiter (counting from left) is
+#' returned. If count is negative, every to the right of the final delimiter (counting from the
+#' right) is returned. substring <- index performs a case-sensitive match when searching for delim.
+#'
+#' @rdname functions
+setMethod("substring_index",
+          signature(x = "Column", delim = "character", count = "numeric"),
+          function(x, delim, count) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "substring_index",
+                              x@jc, delim, as.integer(count))
+            column(jc)
+          })
+
+#' translate
+#'
+#' Translate any character in the src by a character in replaceString.
+#' The characters in replaceString is corresponding to the characters in matchingString.
+#' The translate will happen when any character in the string matching with the character
+#' in the matchingString.
+#'
+#' @rdname functions
+setMethod("translate",
+          signature(x = "Column", matchingString = "character", replaceString = "character"),
+          function(x, matchingString, replaceString) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "translate", x@jc, matchingString, replaceString)
+            column(jc)
+          })
+
+#' unix_timestamp
+#'
+#' Gets current Unix timestamp in seconds.
+#'
+#' @rdname functions
+setMethod("unix_timestamp", signature(x = "missing", format = "missing"),
+          function(x, format) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp")
+            column(jc)
+          })
+#' unix_timestamp
+#'
+#' Converts time string in format yyyy-MM-dd HH:mm:ss to Unix timestamp (in seconds),
+#' using the default timezone and the default locale, return null if fail.
+#'
+#' @rdname functions
+setMethod("unix_timestamp", signature(x = "Column", format = "missing"),
+          function(x, format) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp", x@jc)
+            column(jc)
+          })
+#' unix_timestamp
+#'
+#' Convert time string with given pattern
+#' (see [http://docs.oracle.com/javase/tutorial/i18n/format/simpleDateFormat.html])
+#' to Unix time stamp (in seconds), return null if fail.
+#'
+#' @rdname functions
+setMethod("unix_timestamp", signature(x = "Column", format = "character"),
+          function(x, format = "yyyy-MM-dd HH:mm:ss") {
+            jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp", x@jc, format)
+            column(jc)
+          })
 #' when
 #'
 #' Evaluates a list of conditions and returns one of multiple possible result expressions.
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 338b32e648078..84cb8dfdaa2dd 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -662,6 +662,10 @@ setGeneric("otherwise", function(x, value) { standardGeneric("otherwise") })
 
 ###################### Expression Function Methods ##########################
 
+#' @rdname functions
+#' @export
+setGeneric("add_months", function(y, x) { standardGeneric("add_months") })
+
 #' @rdname functions
 #' @export
 setGeneric("ascii", function(x) { standardGeneric("ascii") })
@@ -694,6 +698,14 @@ setGeneric("ceil", function(x) { standardGeneric("ceil") })
 #' @export
 setGeneric("concat", function(x, ...) { standardGeneric("concat") })
 
+#' @rdname functions
+#' @export
+setGeneric("concat_ws", function(sep, x, ...) { standardGeneric("concat_ws") })
+
+#' @rdname functions
+#' @export
+setGeneric("conv", function(x, fromBase, toBase) { standardGeneric("conv") })
+
 #' @rdname functions
 #' @export
 setGeneric("crc32", function(x) { standardGeneric("crc32") })
@@ -702,6 +714,18 @@ setGeneric("crc32", function(x) { standardGeneric("crc32") })
 #' @export
 setGeneric("datediff", function(y, x) { standardGeneric("datediff") })
 
+#' @rdname functions
+#' @export
+setGeneric("date_add", function(y, x) { standardGeneric("date_add") })
+
+#' @rdname functions
+#' @export
+setGeneric("date_format", function(y, x) { standardGeneric("date_format") })
+
+#' @rdname functions
+#' @export
+setGeneric("date_sub", function(y, x) { standardGeneric("date_sub") })
+
 #' @rdname functions
 #' @export
 setGeneric("dayofmonth", function(x) { standardGeneric("dayofmonth") })
@@ -714,6 +738,26 @@ setGeneric("dayofyear", function(x) { standardGeneric("dayofyear") })
 #' @export
 setGeneric("explode", function(x) { standardGeneric("explode") })
 
+#' @rdname functions
+#' @export
+setGeneric("expr", function(x) { standardGeneric("expr") })
+
+#' @rdname functions
+#' @export
+setGeneric("from_utc_timestamp", function(y, x) { standardGeneric("from_utc_timestamp") })
+
+#' @rdname functions
+#' @export
+setGeneric("format_number", function(y, x) { standardGeneric("format_number") })
+
+#' @rdname functions
+#' @export
+setGeneric("format_string", function(format, x, ...) { standardGeneric("format_string") })
+
+#' @rdname functions
+#' @export
+setGeneric("from_unixtime", function(x, ...) { standardGeneric("from_unixtime") })
+
 #' @rdname functions
 #' @export
 setGeneric("greatest", function(x, ...) { standardGeneric("greatest") })
@@ -730,6 +774,10 @@ setGeneric("hour", function(x) { standardGeneric("hour") })
 #' @export
 setGeneric("initcap", function(x) { standardGeneric("initcap") })
 
+#' @rdname functions
+#' @export
+setGeneric("instr", function(y, x) { standardGeneric("instr") })
+
 #' @rdname functions
 #' @export
 setGeneric("isNaN", function(x) { standardGeneric("isNaN") })
@@ -750,10 +798,18 @@ setGeneric("levenshtein", function(y, x) { standardGeneric("levenshtein") })
 #' @export
 setGeneric("lit", function(x) { standardGeneric("lit") })
 
+#' @rdname functions
+#' @export
+setGeneric("locate", function(substr, str, ...) { standardGeneric("locate") })
+
 #' @rdname functions
 #' @export
 setGeneric("lower", function(x) { standardGeneric("lower") })
 
+#' @rdname functions
+#' @export
+setGeneric("lpad", function(x, len, pad) { standardGeneric("lpad") })
+
 #' @rdname functions
 #' @export
 setGeneric("ltrim", function(x) { standardGeneric("ltrim") })
@@ -782,6 +838,10 @@ setGeneric("nanvl", function(y, x) { standardGeneric("nanvl") })
 #' @export
 setGeneric("negate", function(x) { standardGeneric("negate") })
 
+#' @rdname functions
+#' @export
+setGeneric("next_day", function(y, x) { standardGeneric("next_day") })
+
 #' @rdname functions
 #' @export
 setGeneric("pmod", function(y, x) { standardGeneric("pmod") })
@@ -790,10 +850,31 @@ setGeneric("pmod", function(y, x) { standardGeneric("pmod") })
 #' @export
 setGeneric("quarter", function(x) { standardGeneric("quarter") })
 
+#' @rdname functions
+#' @export
+setGeneric("rand", function(seed) { standardGeneric("rand") })
+
+#' @rdname functions
+#' @export
+setGeneric("randn", function(seed) { standardGeneric("randn") })
+
+#' @rdname functions
+#' @export
+setGeneric("regexp_extract", function(x, pattern, idx) { standardGeneric("regexp_extract") })
+
+#' @rdname functions
+#' @export
+setGeneric("regexp_replace",
+           function(x, pattern, replacement) { standardGeneric("regexp_replace") })
+
 #' @rdname functions
 #' @export
 setGeneric("reverse", function(x) { standardGeneric("reverse") })
 
+#' @rdname functions
+#' @export
+setGeneric("rpad", function(x, len, pad) { standardGeneric("rpad") })
+
 #' @rdname functions
 #' @export
 setGeneric("rtrim", function(x) { standardGeneric("rtrim") })
@@ -806,6 +887,22 @@ setGeneric("second", function(x) { standardGeneric("second") })
 #' @export
 setGeneric("sha1", function(x) { standardGeneric("sha1") })
 
+#' @rdname functions
+#' @export
+setGeneric("sha2", function(y, x) { standardGeneric("sha2") })
+
+#' @rdname functions
+#' @export
+setGeneric("shiftLeft", function(y, x) { standardGeneric("shiftLeft") })
+
+#' @rdname functions
+#' @export
+setGeneric("shiftRight", function(y, x) { standardGeneric("shiftRight") })
+
+#' @rdname functions
+#' @export
+setGeneric("shiftRightUnsigned", function(y, x) { standardGeneric("shiftRightUnsigned") })
+
 #' @rdname functions
 #' @export
 setGeneric("signum", function(x) { standardGeneric("signum") })
@@ -818,6 +915,10 @@ setGeneric("size", function(x) { standardGeneric("size") })
 #' @export
 setGeneric("soundex", function(x) { standardGeneric("soundex") })
 
+#' @rdname functions
+#' @export
+setGeneric("substring_index", function(x, delim, count) { standardGeneric("substring_index") })
+
 #' @rdname functions
 #' @export
 setGeneric("sumDistinct", function(x) { standardGeneric("sumDistinct") })
@@ -834,6 +935,14 @@ setGeneric("toRadians", function(x) { standardGeneric("toRadians") })
 #' @export
 setGeneric("to_date", function(x) { standardGeneric("to_date") })
 
+#' @rdname functions
+#' @export
+setGeneric("to_utc_timestamp", function(y, x) { standardGeneric("to_utc_timestamp") })
+
+#' @rdname functions
+#' @export
+setGeneric("translate", function(x, matchingString, replaceString) { standardGeneric("translate") })
+
 #' @rdname functions
 #' @export
 setGeneric("trim", function(x) { standardGeneric("trim") })
@@ -846,6 +955,10 @@ setGeneric("unbase64", function(x) { standardGeneric("unbase64") })
 #' @export
 setGeneric("unhex", function(x) { standardGeneric("unhex") })
 
+#' @rdname functions
+#' @export
+setGeneric("unix_timestamp", function(x, format) { standardGeneric("unix_timestamp") })
+
 #' @rdname functions
 #' @export
 setGeneric("upper", function(x) { standardGeneric("upper") })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 841de657df37e..670017ed34765 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -598,6 +598,11 @@ test_that("selectExpr() on a DataFrame", {
   expect_equal(count(selected2), 3)
 })
 
+test_that("expr() on a DataFrame", {
+  df <- jsonFile(sqlContext, jsonPath)
+  expect_equal(collect(select(df, expr("abs(-123)")))[1, 1], 123)
+})
+
 test_that("column calculation", {
   df <- jsonFile(sqlContext, jsonPath)
   d <- collect(select(df, alias(df$age + 1, "age2")))
@@ -667,16 +672,15 @@ test_that("column functions", {
   c <- SparkR:::col("a")
   c1 <- abs(c) + acos(c) + approxCountDistinct(c) + ascii(c) + asin(c) + atan(c)
   c2 <- avg(c) + base64(c) + bin(c) + bitwiseNOT(c) + cbrt(c) + ceil(c) + cos(c)
-  c3 <- cosh(c) + count(c) + crc32(c) + dayofmonth(c) + dayofyear(c) + exp(c)
+  c3 <- cosh(c) + count(c) + crc32(c) + exp(c)
   c4 <- explode(c) + expm1(c) + factorial(c) + first(c) + floor(c) + hex(c)
   c5 <- hour(c) + initcap(c) + isNaN(c) + last(c) + last_day(c) + length(c)
   c6 <- log(c) + (c) + log1p(c) + log2(c) + lower(c) + ltrim(c) + max(c) + md5(c)
-  c7 <- mean(c) + min(c) + minute(c) + month(c) + negate(c) + quarter(c)
-  c8 <- reverse(c) + rint(c) + round(c) + rtrim(c) + second(c) + sha1(c)
+  c7 <- mean(c) + min(c) + month(c) + negate(c) + quarter(c)
+  c8 <- reverse(c) + rint(c) + round(c) + rtrim(c) + sha1(c)
   c9 <- signum(c) + sin(c) + sinh(c) + size(c) + soundex(c) + sqrt(c) + sum(c)
   c10 <- sumDistinct(c) + tan(c) + tanh(c) + toDegrees(c) + toRadians(c)
-  c11 <- to_date(c) + trim(c) + unbase64(c) + unhex(c) + upper(c) + weekofyear(c)
-  c12 <- year(c)
+  c11 <- to_date(c) + trim(c) + unbase64(c) + unhex(c) + upper(c)
 
   df <- jsonFile(sqlContext, jsonPath)
   df2 <- select(df, between(df$age, c(20, 30)), between(df$age, c(10, 20)))
@@ -689,8 +693,11 @@ test_that("column functions", {
   expect_equal(collect(df3)[[1, 1]], TRUE)
   expect_equal(collect(df3)[[2, 1]], FALSE)
   expect_equal(collect(df3)[[3, 1]], TRUE)
-})
 
+  df4 <- createDataFrame(sqlContext, list(list(a = "010101")))
+  expect_equal(collect(select(df4, conv(df4$a, 2, 16)))[1, 1], "15")
+})
+#
 test_that("column binary mathfunctions", {
   lines <- c("{\"a\":1, \"b\":5}",
              "{\"a\":2, \"b\":6}",
@@ -709,6 +716,13 @@ test_that("column binary mathfunctions", {
   expect_equal(collect(select(df, hypot(df$a, df$b)))[3, "HYPOT(a, b)"], sqrt(3^2 + 7^2))
   expect_equal(collect(select(df, hypot(df$a, df$b)))[4, "HYPOT(a, b)"], sqrt(4^2 + 8^2))
   ## nolint end
+  expect_equal(collect(select(df, shiftLeft(df$b, 1)))[4, 1], 16)
+  expect_equal(collect(select(df, shiftRight(df$b, 1)))[4, 1], 4)
+  expect_equal(collect(select(df, shiftRightUnsigned(df$b, 1)))[4, 1], 4)
+  expect_equal(class(collect(select(df, rand()))[2, 1]), "numeric")
+  expect_equal(collect(select(df, rand(1)))[1, 1], 0.45, tolerance = 0.01)
+  expect_equal(class(collect(select(df, randn()))[2, 1]), "numeric")
+  expect_equal(collect(select(df, randn(1)))[1, 1], -0.0111, tolerance = 0.01)
 })
 
 test_that("string operators", {
@@ -718,6 +732,78 @@ test_that("string operators", {
   expect_equal(first(select(df, substr(df$name, 1, 2)))[[1]], "Mi")
   expect_equal(collect(select(df, cast(df$age, "string")))[[2, 1]], "30")
   expect_equal(collect(select(df, concat(df$name, lit(":"), df$age)))[[2, 1]], "Andy:30")
+  expect_equal(collect(select(df, concat_ws(":", df$name)))[[2, 1]], "Andy")
+  expect_equal(collect(select(df, concat_ws(":", df$name, df$age)))[[2, 1]], "Andy:30")
+  expect_equal(collect(select(df, instr(df$name, "i")))[, 1], c(2, 0, 5))
+  expect_equal(collect(select(df, format_number(df$age, 2)))[2, 1], "30.00")
+  expect_equal(collect(select(df, sha1(df$name)))[2, 1],
+               "ab5a000e88b5d9d0fa2575f5c6263eb93452405d")
+  expect_equal(collect(select(df, sha2(df$name, 256)))[2, 1],
+               "80f2aed3c618c423ddf05a2891229fba44942d907173152442cf6591441ed6dc")
+  expect_equal(collect(select(df, format_string("Name:%s", df$name)))[2, 1], "Name:Andy")
+  expect_equal(collect(select(df, format_string("%s, %d", df$name, df$age)))[2, 1], "Andy, 30")
+  expect_equal(collect(select(df, regexp_extract(df$name, "(n.y)", 1)))[2, 1], "ndy")
+  expect_equal(collect(select(df, regexp_replace(df$name, "(n.y)", "ydn")))[2, 1], "Aydn")
+
+  l2 <- list(list(a = "aaads"))
+  df2 <- createDataFrame(sqlContext, l2)
+  expect_equal(collect(select(df2, locate("aa", df2$a)))[1, 1], 1)
+  expect_equal(collect(select(df2, locate("aa", df2$a, 1)))[1, 1], 2)
+  expect_equal(collect(select(df2, lpad(df2$a, 8, "#")))[1, 1], "###aaads")
+  expect_equal(collect(select(df2, rpad(df2$a, 8, "#")))[1, 1], "aaads###")
+
+  l3 <- list(list(a = "a.b.c.d"))
+  df3 <- createDataFrame(sqlContext, l3)
+  expect_equal(collect(select(df3, substring_index(df3$a, ".", 2)))[1, 1], "a.b")
+  expect_equal(collect(select(df3, substring_index(df3$a, ".", -3)))[1, 1], "b.c.d")
+  expect_equal(collect(select(df3, translate(df3$a, "bc", "12")))[1, 1], "a.1.2.d")
+})
+
+test_that("date functions on a DataFrame", {
+  .originalTimeZone <- Sys.getenv("TZ")
+  Sys.setenv(TZ = "UTC")
+  l <- list(list(a = 1L, b = as.Date("2012-12-13")),
+            list(a = 2L, b = as.Date("2013-12-14")),
+            list(a = 3L, b = as.Date("2014-12-15")))
+  df <- createDataFrame(sqlContext, l)
+  expect_equal(collect(select(df, dayofmonth(df$b)))[, 1], c(13, 14, 15))
+  expect_equal(collect(select(df, dayofyear(df$b)))[, 1], c(348, 348, 349))
+  expect_equal(collect(select(df, weekofyear(df$b)))[, 1], c(50, 50, 51))
+  expect_equal(collect(select(df, year(df$b)))[, 1], c(2012, 2013, 2014))
+  expect_equal(collect(select(df, month(df$b)))[, 1], c(12, 12, 12))
+  expect_equal(collect(select(df, last_day(df$b)))[, 1],
+               c(as.Date("2012-12-31"), as.Date("2013-12-31"), as.Date("2014-12-31")))
+  expect_equal(collect(select(df, next_day(df$b, "MONDAY")))[, 1],
+               c(as.Date("2012-12-17"), as.Date("2013-12-16"), as.Date("2014-12-22")))
+  expect_equal(collect(select(df, date_format(df$b, "y")))[, 1], c("2012", "2013", "2014"))
+  expect_equal(collect(select(df, add_months(df$b, 3)))[, 1],
+               c(as.Date("2013-03-13"), as.Date("2014-03-14"), as.Date("2015-03-15")))
+  expect_equal(collect(select(df, date_add(df$b, 1)))[, 1],
+               c(as.Date("2012-12-14"), as.Date("2013-12-15"), as.Date("2014-12-16")))
+  expect_equal(collect(select(df, date_sub(df$b, 1)))[, 1],
+               c(as.Date("2012-12-12"), as.Date("2013-12-13"), as.Date("2014-12-14")))
+
+  l2 <- list(list(a = 1L, b = as.POSIXlt("2012-12-13 12:34:00", tz = "UTC")),
+            list(a = 2L, b = as.POSIXlt("2014-12-15 01:24:34", tz = "UTC")))
+  df2 <- createDataFrame(sqlContext, l2)
+  expect_equal(collect(select(df2, minute(df2$b)))[, 1], c(34, 24))
+  expect_equal(collect(select(df2, second(df2$b)))[, 1], c(0, 34))
+  expect_equal(collect(select(df2, from_utc_timestamp(df2$b, "JST")))[, 1],
+               c(as.POSIXlt("2012-12-13 21:34:00 UTC"), as.POSIXlt("2014-12-15 10:24:34 UTC")))
+  expect_equal(collect(select(df2, to_utc_timestamp(df2$b, "JST")))[, 1],
+               c(as.POSIXlt("2012-12-13 03:34:00 UTC"), as.POSIXlt("2014-12-14 16:24:34 UTC")))
+  expect_more_than(collect(select(df2, unix_timestamp()))[1, 1], 0)
+  expect_more_than(collect(select(df2, unix_timestamp(df2$b)))[1, 1], 0)
+  expect_more_than(collect(select(df2, unix_timestamp(lit("2015-01-01"), "yyyy-MM-dd")))[1, 1], 0)
+
+  l3 <- list(list(a = 1000), list(a = -1000))
+  df3 <- createDataFrame(sqlContext, l3)
+  result31 <- collect(select(df3, from_unixtime(df3$a)))
+  expect_equal(grep("\\d{4}-\\d{2}-\\d{2} \\d{2}:\\d{2}:\\d{2}", result31[, 1], perl = TRUE),
+               c(1, 2))
+  result32 <- collect(select(df3, from_unixtime(df3$a, "yyyy")))
+  expect_equal(grep("\\d{4}", result32[, 1]), c(1, 2))
+  Sys.setenv(TZ = .originalTimeZone)
 })
 
 test_that("greatest() and least() on a DataFrame", {
diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
index 14dac4ed28ce3..6ce02e2ea336a 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
@@ -182,6 +182,7 @@ private[r] class RBackendHandler(server: RBackend)
       if (parameterType.isPrimitive) {
         parameterWrapperType = parameterType match {
           case java.lang.Integer.TYPE => classOf[java.lang.Integer]
+          case java.lang.Long.TYPE => classOf[java.lang.Integer]
           case java.lang.Double.TYPE => classOf[java.lang.Double]
           case java.lang.Boolean.TYPE => classOf[java.lang.Boolean]
           case _ => parameterType

From 5fd53c64bb01de74ae57a7068de85b34adc856cf Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 19 Aug 2015 10:51:59 -0700
Subject: [PATCH 336/633] [SPARK-9833] [YARN] Add options to disable delegation
 token retrieval.

This allows skipping the code that tries to talk to Hive and HBase to
fetch delegation tokens, in case that somehow conflicts with the application
being run.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8134 from vanzin/SPARK-9833.
---
 docs/running-on-yarn.md                       | 12 +++++++++
 .../org/apache/spark/deploy/yarn/Client.scala | 27 ++++++++++++++-----
 2 files changed, 33 insertions(+), 6 deletions(-)

diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md
index 8ac26e98da635..5159ef9e3394e 100644
--- a/docs/running-on-yarn.md
+++ b/docs/running-on-yarn.md
@@ -369,6 +369,18 @@ If you need a reference to the proper location to put log files in the YARN so t
   See <code>spark.yarn.config.gatewayPath</code>.
   </td>
 </tr>
+<tr>
+  <td><code>spark.yarn.security.tokens.${service}.enabled</code></td>
+  <td>true</td>
+  <td>
+  Controls whether to retrieve delegation tokens for non-HDFS services when security is enabled.
+  By default, delegation tokens for all supported services are retrieved when those services are
+  configured, but it's possible to disable that behavior if it somehow conflicts with the
+  application being run.
+  <p/>
+  Currently supported services are: hive, hbase
+  </td>
+</tr>
 </table>
 
 # Important notes
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index 262c6a8d6397f..bff585b46cbbe 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -285,8 +285,8 @@ private[spark] class Client(
     // multiple times, YARN will fail to launch containers for the app with an internal
     // error.
     val distributedUris = new HashSet[String]
-    obtainTokenForHiveMetastore(hadoopConf, credentials)
-    obtainTokenForHBase(hadoopConf, credentials)
+    obtainTokenForHiveMetastore(sparkConf, hadoopConf, credentials)
+    obtainTokenForHBase(sparkConf, hadoopConf, credentials)
 
     val replication = sparkConf.getInt("spark.yarn.submit.file.replication",
       fs.getDefaultReplication(dst)).toShort
@@ -1239,8 +1239,11 @@ object Client extends Logging {
   /**
    * Obtains token for the Hive metastore and adds them to the credentials.
    */
-  private def obtainTokenForHiveMetastore(conf: Configuration, credentials: Credentials) {
-    if (UserGroupInformation.isSecurityEnabled) {
+  private def obtainTokenForHiveMetastore(
+      sparkConf: SparkConf,
+      conf: Configuration,
+      credentials: Credentials) {
+    if (shouldGetTokens(sparkConf, "hive") && UserGroupInformation.isSecurityEnabled) {
       val mirror = universe.runtimeMirror(getClass.getClassLoader)
 
       try {
@@ -1297,8 +1300,11 @@ object Client extends Logging {
   /**
    * Obtain security token for HBase.
    */
-  def obtainTokenForHBase(conf: Configuration, credentials: Credentials): Unit = {
-    if (UserGroupInformation.isSecurityEnabled) {
+  def obtainTokenForHBase(
+      sparkConf: SparkConf,
+      conf: Configuration,
+      credentials: Credentials): Unit = {
+    if (shouldGetTokens(sparkConf, "hbase") && UserGroupInformation.isSecurityEnabled) {
       val mirror = universe.runtimeMirror(getClass.getClassLoader)
 
       try {
@@ -1394,4 +1400,13 @@ object Client extends Logging {
     components.mkString(Path.SEPARATOR)
   }
 
+  /**
+   * Return whether delegation tokens should be retrieved for the given service when security is
+   * enabled. By default, tokens are retrieved, but that behavior can be changed by setting
+   * a service-specific configuration.
+   */
+  def shouldGetTokens(conf: SparkConf, service: String): Boolean = {
+    conf.getBoolean(s"spark.yarn.security.tokens.${service}.enabled", true)
+  }
+
 }

From 28a98464ea65aa7b35e24fca5ddaa60c2e5d53ee Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Wed, 19 Aug 2015 11:35:05 -0700
Subject: [PATCH 337/633] [SPARK-10097] Adds `shouldMaximize` flag to
 `ml.evaluation.Evaluator`

Previously, users of evaluator (`CrossValidator` and `TrainValidationSplit`) would only maximize the metric in evaluator, leading to a hacky solution which negated metrics to be minimized and caused erroneous negative values to be reported to the user.

This PR adds a `isLargerBetter` attribute to the `Evaluator` base class, instructing users of `Evaluator` on whether the chosen metric should be maximized or minimized.

CC jkbradley

Author: Feynman Liang <fliang@databricks.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8290 from feynmanliang/SPARK-10097.
---
 .../BinaryClassificationEvaluator.scala       | 20 +++++++++++--------
 .../spark/ml/evaluation/Evaluator.scala       |  7 +++++++
 .../MulticlassClassificationEvaluator.scala   |  8 ++++++++
 .../ml/evaluation/RegressionEvaluator.scala   | 19 ++++++++++--------
 .../spark/ml/tuning/CrossValidator.scala      |  4 +++-
 .../ml/tuning/TrainValidationSplit.scala      |  4 +++-
 .../evaluation/RegressionEvaluatorSuite.scala |  4 ++--
 .../spark/ml/tuning/CrossValidatorSuite.scala |  2 ++
 .../ml/tuning/TrainValidationSplitSuite.scala |  2 ++
 python/pyspark/ml/evaluation.py               |  4 ++--
 10 files changed, 52 insertions(+), 22 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
index 5d5cb7e94f45b..56419a0a15952 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -40,8 +40,11 @@ class BinaryClassificationEvaluator(override val uid: String)
    * param for metric name in evaluation
    * @group param
    */
-  val metricName: Param[String] = new Param(this, "metricName",
-    "metric name in evaluation (areaUnderROC|areaUnderPR)")
+  val metricName: Param[String] = {
+    val allowedParams = ParamValidators.inArray(Array("areaUnderROC", "areaUnderPR"))
+    new Param(
+      this, "metricName", "metric name in evaluation (areaUnderROC|areaUnderPR)", allowedParams)
+  }
 
   /** @group getParam */
   def getMetricName: String = $(metricName)
@@ -76,16 +79,17 @@ class BinaryClassificationEvaluator(override val uid: String)
       }
     val metrics = new BinaryClassificationMetrics(scoreAndLabels)
     val metric = $(metricName) match {
-      case "areaUnderROC" =>
-        metrics.areaUnderROC()
-      case "areaUnderPR" =>
-        metrics.areaUnderPR()
-      case other =>
-        throw new IllegalArgumentException(s"Does not support metric $other.")
+      case "areaUnderROC" => metrics.areaUnderROC()
+      case "areaUnderPR" => metrics.areaUnderPR()
     }
     metrics.unpersist()
     metric
   }
 
+  override def isLargerBetter: Boolean = $(metricName) match {
+    case "areaUnderROC" => true
+    case "areaUnderPR" => true
+  }
+
   override def copy(extra: ParamMap): BinaryClassificationEvaluator = defaultCopy(extra)
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala
index e56c946a063e8..13bd3307f8a2f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/Evaluator.scala
@@ -46,5 +46,12 @@ abstract class Evaluator extends Params {
    */
   def evaluate(dataset: DataFrame): Double
 
+  /**
+   * Indicates whether the metric returned by [[evaluate()]] should be maximized (true, default)
+   * or minimized (false).
+   * A given evaluator may support multiple metrics which may be maximized or minimized.
+   */
+  def isLargerBetter: Boolean = true
+
   override def copy(extra: ParamMap): Evaluator
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
index 44f779c1908d7..f73d2345078e6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/MulticlassClassificationEvaluator.scala
@@ -81,5 +81,13 @@ class MulticlassClassificationEvaluator (override val uid: String)
     metric
   }
 
+  override def isLargerBetter: Boolean = $(metricName) match {
+    case "f1" => true
+    case "precision" => true
+    case "recall" => true
+    case "weightedPrecision" => true
+    case "weightedRecall" => true
+  }
+
   override def copy(extra: ParamMap): MulticlassClassificationEvaluator = defaultCopy(extra)
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala
index 01c000b47514c..d21c88ab9b109 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/RegressionEvaluator.scala
@@ -73,17 +73,20 @@ final class RegressionEvaluator(override val uid: String)
       }
     val metrics = new RegressionMetrics(predictionAndLabels)
     val metric = $(metricName) match {
-      case "rmse" =>
-        -metrics.rootMeanSquaredError
-      case "mse" =>
-        -metrics.meanSquaredError
-      case "r2" =>
-        metrics.r2
-      case "mae" =>
-        -metrics.meanAbsoluteError
+      case "rmse" => metrics.rootMeanSquaredError
+      case "mse" => metrics.meanSquaredError
+      case "r2" => metrics.r2
+      case "mae" => metrics.meanAbsoluteError
     }
     metric
   }
 
+  override def isLargerBetter: Boolean = $(metricName) match {
+    case "rmse" => false
+    case "mse" => false
+    case "r2" => true
+    case "mae" => false
+  }
+
   override def copy(extra: ParamMap): RegressionEvaluator = defaultCopy(extra)
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
index 4792eb0f0a288..0679bfd0f3ffe 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala
@@ -100,7 +100,9 @@ class CrossValidator(override val uid: String) extends Estimator[CrossValidatorM
     }
     f2jBLAS.dscal(numModels, 1.0 / $(numFolds), metrics, 1)
     logInfo(s"Average cross-validation metrics: ${metrics.toSeq}")
-    val (bestMetric, bestIndex) = metrics.zipWithIndex.maxBy(_._1)
+    val (bestMetric, bestIndex) =
+      if (eval.isLargerBetter) metrics.zipWithIndex.maxBy(_._1)
+      else metrics.zipWithIndex.minBy(_._1)
     logInfo(s"Best set of parameters:\n${epm(bestIndex)}")
     logInfo(s"Best cross-validation metric: $bestMetric.")
     val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]]
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
index c0edc730b6fd6..73a14b8310157 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala
@@ -99,7 +99,9 @@ class TrainValidationSplit(override val uid: String) extends Estimator[TrainVali
     validationDataset.unpersist()
 
     logInfo(s"Train validation split metrics: ${metrics.toSeq}")
-    val (bestMetric, bestIndex) = metrics.zipWithIndex.maxBy(_._1)
+    val (bestMetric, bestIndex) =
+      if (eval.isLargerBetter) metrics.zipWithIndex.maxBy(_._1)
+      else metrics.zipWithIndex.minBy(_._1)
     logInfo(s"Best set of parameters:\n${epm(bestIndex)}")
     logInfo(s"Best train validation split metric: $bestMetric.")
     val bestModel = est.fit(dataset, epm(bestIndex)).asInstanceOf[Model[_]]
diff --git a/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala
index 5b203784559e2..aa722da323935 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/evaluation/RegressionEvaluatorSuite.scala
@@ -63,7 +63,7 @@ class RegressionEvaluatorSuite extends SparkFunSuite with MLlibTestSparkContext
 
     // default = rmse
     val evaluator = new RegressionEvaluator()
-    assert(evaluator.evaluate(predictions) ~== -0.1019382 absTol 0.001)
+    assert(evaluator.evaluate(predictions) ~== 0.1019382 absTol 0.001)
 
     // r2 score
     evaluator.setMetricName("r2")
@@ -71,6 +71,6 @@ class RegressionEvaluatorSuite extends SparkFunSuite with MLlibTestSparkContext
 
     // mae
     evaluator.setMetricName("mae")
-    assert(evaluator.evaluate(predictions) ~== -0.08036075 absTol 0.001)
+    assert(evaluator.evaluate(predictions) ~== 0.08036075 absTol 0.001)
   }
 }
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
index aaca08bb61a45..fde02e0c84bc0 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala
@@ -143,6 +143,8 @@ object CrossValidatorSuite {
       throw new UnsupportedOperationException
     }
 
+    override def isLargerBetter: Boolean = true
+
     override val uid: String = "eval"
 
     override def copy(extra: ParamMap): MyEvaluator = defaultCopy(extra)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
index c8e58f216cceb..ef24e6fb6b80f 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
@@ -132,6 +132,8 @@ object TrainValidationSplitSuite {
       throw new UnsupportedOperationException
     }
 
+    override def isLargerBetter: Boolean = true
+
     override val uid: String = "eval"
 
     override def copy(extra: ParamMap): MyEvaluator = defaultCopy(extra)
diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index e23ce053baeb7..6b0a9ffde9f42 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -163,11 +163,11 @@ class RegressionEvaluator(JavaEvaluator, HasLabelCol, HasPredictionCol):
     ...
     >>> evaluator = RegressionEvaluator(predictionCol="raw")
     >>> evaluator.evaluate(dataset)
-    -2.842...
+    2.842...
     >>> evaluator.evaluate(dataset, {evaluator.metricName: "r2"})
     0.993...
     >>> evaluator.evaluate(dataset, {evaluator.metricName: "mae"})
-    -2.649...
+    2.649...
     """
     # Because we will maximize evaluation value (ref: `CrossValidator`),
     # when we evaluate a metric that is needed to minimize (e.g., `"rmse"`, `"mse"`, `"mae"`),

From d898c33f774b9a3db2fb6aa8f0cb2c2ac6004b58 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Wed, 19 Aug 2015 12:39:37 -0700
Subject: [PATCH 338/633] [SPARK-10106] [SPARKR] Add `ifelse` Column function
 to SparkR

### JIRA
[[SPARK-10106] Add `ifelse` Column function to SparkR - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10106)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8303 from yu-iskw/SPARK-10106.
---
 R/pkg/NAMESPACE                  |  1 +
 R/pkg/R/functions.R              | 19 +++++++++++++++++++
 R/pkg/inst/tests/test_sparkSQL.R |  3 ++-
 3 files changed, 22 insertions(+), 1 deletion(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 111a2dc30db73..3e5c89d779b7b 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -135,6 +135,7 @@ exportMethods("abs",
               "hex",
               "hour",
               "hypot",
+              "ifelse",
               "initcap",
               "instr",
               "isNaN",
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index 5dba0887d150d..b5879bd9ad553 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -594,3 +594,22 @@ setMethod("when", signature(condition = "Column", value = "ANY"),
               jc <- callJStatic("org.apache.spark.sql.functions", "when", condition, value)
               column(jc)
           })
+
+#' ifelse
+#'
+#' Evaluates a list of conditions and returns `yes` if the conditions are satisfied.
+#' Otherwise `no` is returned for unmatched conditions.
+#'
+#' @rdname column
+setMethod("ifelse",
+          signature(test = "Column", yes = "ANY", no = "ANY"),
+          function(test, yes, no) {
+              test <- test@jc
+              yes <- ifelse(class(yes) == "Column", yes@jc, yes)
+              no <- ifelse(class(no) == "Column", no@jc, no)
+              jc <- callJMethod(callJStatic("org.apache.spark.sql.functions",
+                                            "when",
+                                            test, yes),
+                                "otherwise", no)
+              column(jc)
+          })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 670017ed34765..556b8c5447054 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -813,11 +813,12 @@ test_that("greatest() and least() on a DataFrame", {
   expect_equal(collect(select(df, least(df$a, df$b)))[, 1], c(1, 3))
 })
 
-test_that("when() and otherwise() on a DataFrame", {
+test_that("when(), otherwise() and ifelse() on a DataFrame", {
   l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
   df <- createDataFrame(sqlContext, l)
   expect_equal(collect(select(df, when(df$a > 1 & df$b > 2, 1)))[, 1], c(NA, 1))
   expect_equal(collect(select(df, otherwise(when(df$a > 1, 1), 0)))[, 1], c(0, 1))
+  expect_equal(collect(select(df, ifelse(df$a > 1 & df$b > 2, 0, 1)))[, 1], c(1, 0))
 })
 
 test_that("group by", {

From 5b62bef8cbf73f910513ef3b1f557aa94b384854 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 19 Aug 2015 13:17:26 -0700
Subject: [PATCH 339/633] [SPARK-8918] [MLLIB] [DOC] Add @since tags to
 mllib.clustering

This continues the work from #8256. I removed `since` tags from private/protected/local methods/variables (see https://github.com/apache/spark/commit/72fdeb64630470f6f46cf3eed8ffbfe83a7c4659). MechCoder

Closes #8256

Author: Xiangrui Meng <meng@databricks.com>
Author: Xiaoqing Wang <spark445@126.com>
Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8288 from mengxr/SPARK-8918.
---
 .../mllib/clustering/GaussianMixture.scala    | 56 +++++++++++----
 .../clustering/GaussianMixtureModel.scala     | 32 +++++++--
 .../spark/mllib/clustering/KMeans.scala       | 36 +++++++++-
 .../spark/mllib/clustering/KMeansModel.scala  | 37 ++++++++--
 .../apache/spark/mllib/clustering/LDA.scala   | 71 ++++++++++++++++---
 .../spark/mllib/clustering/LDAModel.scala     | 64 +++++++++++++++--
 .../spark/mllib/clustering/LDAOptimizer.scala | 12 +++-
 .../clustering/PowerIterationClustering.scala | 29 +++++++-
 .../mllib/clustering/StreamingKMeans.scala    | 53 +++++++++++---
 9 files changed, 338 insertions(+), 52 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index e459367333d26..bc27b1fe7390b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -62,6 +62,7 @@ class GaussianMixture private (
   /**
    * Constructs a default instance. The default parameters are {k: 2, convergenceTol: 0.01,
    * maxIterations: 100, seed: random}.
+   * @since 1.3.0
    */
   def this() = this(2, 0.01, 100, Utils.random.nextLong())
 
@@ -72,9 +73,11 @@ class GaussianMixture private (
   // default random starting point
   private var initialModel: Option[GaussianMixtureModel] = None
 
-  /** Set the initial GMM starting point, bypassing the random initialization.
-   *  You must call setK() prior to calling this method, and the condition
-   *  (model.k == this.k) must be met; failure will result in an IllegalArgumentException
+  /**
+   * Set the initial GMM starting point, bypassing the random initialization.
+   * You must call setK() prior to calling this method, and the condition
+   * (model.k == this.k) must be met; failure will result in an IllegalArgumentException
+   * @since 1.3.0
    */
   def setInitialModel(model: GaussianMixtureModel): this.type = {
     if (model.k == k) {
@@ -85,30 +88,46 @@ class GaussianMixture private (
     this
   }
 
-  /** Return the user supplied initial GMM, if supplied */
+  /**
+   * Return the user supplied initial GMM, if supplied
+   * @since 1.3.0
+   */
   def getInitialModel: Option[GaussianMixtureModel] = initialModel
 
-  /** Set the number of Gaussians in the mixture model.  Default: 2 */
+  /**
+   * Set the number of Gaussians in the mixture model.  Default: 2
+   * @since 1.3.0
+   */
   def setK(k: Int): this.type = {
     this.k = k
     this
   }
 
-  /** Return the number of Gaussians in the mixture model */
+  /**
+   * Return the number of Gaussians in the mixture model
+   * @since 1.3.0
+   */
   def getK: Int = k
 
-  /** Set the maximum number of iterations to run. Default: 100 */
+  /**
+   * Set the maximum number of iterations to run. Default: 100
+   * @since 1.3.0
+   */
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
   }
 
-  /** Return the maximum number of iterations to run */
+  /**
+   * Return the maximum number of iterations to run
+   * @since 1.3.0
+   */
   def getMaxIterations: Int = maxIterations
 
   /**
    * Set the largest change in log-likelihood at which convergence is
    * considered to have occurred.
+   * @since 1.3.0
    */
   def setConvergenceTol(convergenceTol: Double): this.type = {
     this.convergenceTol = convergenceTol
@@ -118,19 +137,29 @@ class GaussianMixture private (
   /**
    * Return the largest change in log-likelihood at which convergence is
    * considered to have occurred.
+   * @since 1.3.0
    */
   def getConvergenceTol: Double = convergenceTol
 
-  /** Set the random seed */
+  /**
+   * Set the random seed
+   * @since 1.3.0
+   */
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
   }
 
-  /** Return the random seed */
+  /**
+   * Return the random seed
+   * @since 1.3.0
+   */
   def getSeed: Long = seed
 
-  /** Perform expectation maximization */
+  /**
+   * Perform expectation maximization
+   * @since 1.3.0
+   */
   def run(data: RDD[Vector]): GaussianMixtureModel = {
     val sc = data.sparkContext
 
@@ -204,7 +233,10 @@ class GaussianMixture private (
     new GaussianMixtureModel(weights, gaussians)
   }
 
-  /** Java-friendly version of [[run()]] */
+  /**
+   * Java-friendly version of [[run()]]
+   * @since 1.3.0
+   */
   def run(data: JavaRDD[Vector]): GaussianMixtureModel = run(data.rdd)
 
   private def updateWeightsAndGaussians(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index 76aeebd703d4e..2fa0473737aae 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -43,6 +43,7 @@ import org.apache.spark.sql.{SQLContext, Row}
  *                the weight for Gaussian i, and weights.sum == 1
  * @param gaussians Array of MultivariateGaussian where gaussians(i) represents
  *                  the Multivariate Gaussian (Normal) Distribution for Gaussian i
+ * @since 1.3.0
  */
 @Experimental
 class GaussianMixtureModel(
@@ -53,32 +54,48 @@ class GaussianMixtureModel(
 
   override protected def formatVersion = "1.0"
 
+  /**
+   * @since 1.4.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     GaussianMixtureModel.SaveLoadV1_0.save(sc, path, weights, gaussians)
   }
 
-  /** Number of gaussians in mixture */
+  /**
+   * Number of gaussians in mixture
+   * @since 1.3.0
+   */
   def k: Int = weights.length
 
-  /** Maps given points to their cluster indices. */
+  /**
+   * Maps given points to their cluster indices.
+   * @since 1.3.0
+   */
   def predict(points: RDD[Vector]): RDD[Int] = {
     val responsibilityMatrix = predictSoft(points)
     responsibilityMatrix.map(r => r.indexOf(r.max))
   }
 
-  /** Maps given point to its cluster index. */
+  /**
+   * Maps given point to its cluster index.
+   * @since 1.5.0
+   */
   def predict(point: Vector): Int = {
     val r = computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
     r.indexOf(r.max)
   }
 
-  /** Java-friendly version of [[predict()]] */
+  /**
+   * Java-friendly version of [[predict()]]
+   * @since 1.4.0
+   */
   def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
     predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
 
   /**
    * Given the input vectors, return the membership value of each vector
    * to all mixture components.
+   * @since 1.3.0
    */
   def predictSoft(points: RDD[Vector]): RDD[Array[Double]] = {
     val sc = points.sparkContext
@@ -91,6 +108,7 @@ class GaussianMixtureModel(
 
   /**
    * Given the input vector, return the membership values to all mixture components.
+   * @since 1.4.0
    */
   def predictSoft(point: Vector): Array[Double] = {
     computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
@@ -115,6 +133,9 @@ class GaussianMixtureModel(
   }
 }
 
+/**
+ * @since 1.4.0
+ */
 @Experimental
 object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
 
@@ -165,6 +186,9 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
     }
   }
 
+  /**
+   * @since 1.4.0
+   */
   override def load(sc: SparkContext, path: String) : GaussianMixtureModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 0a65403f4ec95..9ef6834e5ea8d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -49,15 +49,20 @@ class KMeans private (
   /**
    * Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, runs: 1,
    * initializationMode: "k-means||", initializationSteps: 5, epsilon: 1e-4, seed: random}.
+   * @since 0.8.0
    */
   def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4, Utils.random.nextLong())
 
   /**
    * Number of clusters to create (k).
+   * @since 1.4.0
    */
   def getK: Int = k
 
-  /** Set the number of clusters to create (k). Default: 2. */
+  /**
+   * Set the number of clusters to create (k). Default: 2.
+   * @since 0.8.0
+   */
   def setK(k: Int): this.type = {
     this.k = k
     this
@@ -65,10 +70,14 @@ class KMeans private (
 
   /**
    * Maximum number of iterations to run.
+   * @since 1.4.0
    */
   def getMaxIterations: Int = maxIterations
 
-  /** Set maximum number of iterations to run. Default: 20. */
+  /**
+   * Set maximum number of iterations to run. Default: 20.
+   * @since 0.8.0
+   */
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
@@ -76,6 +85,7 @@ class KMeans private (
 
   /**
    * The initialization algorithm. This can be either "random" or "k-means||".
+   * @since 1.4.0
    */
   def getInitializationMode: String = initializationMode
 
@@ -83,6 +93,7 @@ class KMeans private (
    * Set the initialization algorithm. This can be either "random" to choose random points as
    * initial cluster centers, or "k-means||" to use a parallel variant of k-means++
    * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
+   * @since 0.8.0
    */
   def setInitializationMode(initializationMode: String): this.type = {
     KMeans.validateInitMode(initializationMode)
@@ -93,6 +104,7 @@ class KMeans private (
   /**
    * :: Experimental ::
    * Number of runs of the algorithm to execute in parallel.
+   * @since 1.4.0
    */
   @Experimental
   def getRuns: Int = runs
@@ -102,6 +114,7 @@ class KMeans private (
    * Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm
    * this many times with random starting conditions (configured by the initialization mode), then
    * return the best clustering found over any run. Default: 1.
+   * @since 0.8.0
    */
   @Experimental
   def setRuns(runs: Int): this.type = {
@@ -114,12 +127,14 @@ class KMeans private (
 
   /**
    * Number of steps for the k-means|| initialization mode
+   * @since 1.4.0
    */
   def getInitializationSteps: Int = initializationSteps
 
   /**
    * Set the number of steps for the k-means|| initialization mode. This is an advanced
    * setting -- the default of 5 is almost always enough. Default: 5.
+   * @since 0.8.0
    */
   def setInitializationSteps(initializationSteps: Int): this.type = {
     if (initializationSteps <= 0) {
@@ -131,12 +146,14 @@ class KMeans private (
 
   /**
    * The distance threshold within which we've consider centers to have converged.
+   * @since 1.4.0
    */
   def getEpsilon: Double = epsilon
 
   /**
    * Set the distance threshold within which we've consider centers to have converged.
    * If all centers move less than this Euclidean distance, we stop iterating one run.
+   * @since 0.8.0
    */
   def setEpsilon(epsilon: Double): this.type = {
     this.epsilon = epsilon
@@ -145,10 +162,14 @@ class KMeans private (
 
   /**
    * The random seed for cluster initialization.
+   * @since 1.4.0
    */
   def getSeed: Long = seed
 
-  /** Set the random seed for cluster initialization. */
+  /**
+   * Set the random seed for cluster initialization.
+   * @since 1.4.0
+   */
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -162,6 +183,7 @@ class KMeans private (
    * Set the initial starting point, bypassing the random initialization or k-means||
    * The condition model.k == this.k must be met, failure results
    * in an IllegalArgumentException.
+   * @since 1.4.0
    */
   def setInitialModel(model: KMeansModel): this.type = {
     require(model.k == k, "mismatched cluster count")
@@ -172,6 +194,7 @@ class KMeans private (
   /**
    * Train a K-means model on the given set of points; `data` should be cached for high
    * performance, because this is an iterative algorithm.
+   * @since 0.8.0
    */
   def run(data: RDD[Vector]): KMeansModel = {
 
@@ -430,11 +453,14 @@ class KMeans private (
 
 /**
  * Top-level methods for calling K-means clustering.
+ * @since 0.8.0
  */
 object KMeans {
 
   // Initialization mode names
+  /** @since 0.8.0 */
   val RANDOM = "random"
+  /** @since 0.8.0 */
   val K_MEANS_PARALLEL = "k-means||"
 
   /**
@@ -446,6 +472,7 @@ object KMeans {
    * @param runs number of parallel runs, defaults to 1. The best model is returned.
    * @param initializationMode initialization model, either "random" or "k-means||" (default).
    * @param seed random seed value for cluster initialization
+   * @since 1.3.0
    */
   def train(
       data: RDD[Vector],
@@ -470,6 +497,7 @@ object KMeans {
    * @param maxIterations max number of iterations
    * @param runs number of parallel runs, defaults to 1. The best model is returned.
    * @param initializationMode initialization model, either "random" or "k-means||" (default).
+   * @since 0.8.0
    */
   def train(
       data: RDD[Vector],
@@ -486,6 +514,7 @@ object KMeans {
 
   /**
    * Trains a k-means model using specified parameters and the default values for unspecified.
+   * @since 0.8.0
    */
   def train(
       data: RDD[Vector],
@@ -496,6 +525,7 @@ object KMeans {
 
   /**
    * Trains a k-means model using specified parameters and the default values for unspecified.
+   * @since 0.8.0
    */
   def train(
       data: RDD[Vector],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index 96359024fa228..8de2087ceb4df 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -34,35 +34,52 @@ import org.apache.spark.sql.Row
 
 /**
  * A clustering model for K-means. Each point belongs to the cluster with the closest center.
+ * @since 0.8.0
  */
 class KMeansModel (
     val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable {
 
-  /** A Java-friendly constructor that takes an Iterable of Vectors. */
+  /**
+   * A Java-friendly constructor that takes an Iterable of Vectors.
+   * @since 1.4.0
+   */
   def this(centers: java.lang.Iterable[Vector]) = this(centers.asScala.toArray)
 
-  /** Total number of clusters. */
+  /**
+   * Total number of clusters.
+   * @since 0.8.0
+   */
   def k: Int = clusterCenters.length
 
-  /** Returns the cluster index that a given point belongs to. */
+  /**
+   * Returns the cluster index that a given point belongs to.
+   * @since 0.8.0
+   */
   def predict(point: Vector): Int = {
     KMeans.findClosest(clusterCentersWithNorm, new VectorWithNorm(point))._1
   }
 
-  /** Maps given points to their cluster indices. */
+  /**
+   * Maps given points to their cluster indices.
+   * @since 1.0.0
+   */
   def predict(points: RDD[Vector]): RDD[Int] = {
     val centersWithNorm = clusterCentersWithNorm
     val bcCentersWithNorm = points.context.broadcast(centersWithNorm)
     points.map(p => KMeans.findClosest(bcCentersWithNorm.value, new VectorWithNorm(p))._1)
   }
 
-  /** Maps given points to their cluster indices. */
+  /**
+   * Maps given points to their cluster indices.
+   * @since 1.0.0
+   */
   def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
     predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
 
   /**
    * Return the K-means cost (sum of squared distances of points to their nearest center) for this
    * model on the given data.
+   * @since 0.8.0
    */
   def computeCost(data: RDD[Vector]): Double = {
     val centersWithNorm = clusterCentersWithNorm
@@ -73,6 +90,9 @@ class KMeansModel (
   private def clusterCentersWithNorm: Iterable[VectorWithNorm] =
     clusterCenters.map(new VectorWithNorm(_))
 
+  /**
+   * @since 1.4.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     KMeansModel.SaveLoadV1_0.save(sc, this, path)
   }
@@ -80,7 +100,14 @@ class KMeansModel (
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.4.0
+ */
 object KMeansModel extends Loader[KMeansModel] {
+
+  /**
+   * @since 1.4.0
+   */
   override def load(sc: SparkContext, path: String): KMeansModel = {
     KMeansModel.SaveLoadV1_0.load(sc, path)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index 0fc9b1ac4d716..2a8c6acbaec61 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -43,6 +43,7 @@ import org.apache.spark.util.Utils
  *
  * @see [[http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation Latent Dirichlet allocation
  *       (Wikipedia)]]
+ * @since 1.3.0
  */
 @Experimental
 class LDA private (
@@ -54,18 +55,25 @@ class LDA private (
     private var checkpointInterval: Int,
     private var ldaOptimizer: LDAOptimizer) extends Logging {
 
+  /**
+   * Constructs a LDA instance with default parameters.
+   * @since 1.3.0
+   */
   def this() = this(k = 10, maxIterations = 20, docConcentration = Vectors.dense(-1),
     topicConcentration = -1, seed = Utils.random.nextLong(), checkpointInterval = 10,
     ldaOptimizer = new EMLDAOptimizer)
 
   /**
    * Number of topics to infer.  I.e., the number of soft cluster centers.
+   *
+   * @since 1.3.0
    */
   def getK: Int = k
 
   /**
    * Number of topics to infer.  I.e., the number of soft cluster centers.
    * (default = 10)
+   * @since 1.3.0
    */
   def setK(k: Int): this.type = {
     require(k > 0, s"LDA k (number of clusters) must be > 0, but was set to $k")
@@ -78,6 +86,7 @@ class LDA private (
    * distributions over topics ("theta").
    *
    * This is the parameter to a Dirichlet distribution.
+   * @since 1.5.0
    */
   def getAsymmetricDocConcentration: Vector = this.docConcentration
 
@@ -87,6 +96,7 @@ class LDA private (
    *
    * This method assumes the Dirichlet distribution is symmetric and can be described by a single
    * [[Double]] parameter. It should fail if docConcentration is asymmetric.
+   * @since 1.3.0
    */
   def getDocConcentration: Double = {
     val parameter = docConcentration(0)
@@ -121,6 +131,7 @@ class LDA private (
    *     - Values should be >= 0
    *     - default = uniformly (1.0 / k), following the implementation from
    *       [[https://github.com/Blei-Lab/onlineldavb]].
+   * @since 1.5.0
    */
   def setDocConcentration(docConcentration: Vector): this.type = {
     require(docConcentration.size > 0, "docConcentration must have > 0 elements")
@@ -128,22 +139,37 @@ class LDA private (
     this
   }
 
-  /** Replicates a [[Double]] docConcentration to create a symmetric prior. */
+  /**
+   * Replicates a [[Double]] docConcentration to create a symmetric prior.
+   * @since 1.3.0
+   */
   def setDocConcentration(docConcentration: Double): this.type = {
     this.docConcentration = Vectors.dense(docConcentration)
     this
   }
 
-  /** Alias for [[getAsymmetricDocConcentration]] */
+  /**
+   * Alias for [[getAsymmetricDocConcentration]]
+   * @since 1.5.0
+   */
   def getAsymmetricAlpha: Vector = getAsymmetricDocConcentration
 
-  /** Alias for [[getDocConcentration]] */
+  /**
+   * Alias for [[getDocConcentration]]
+   * @since 1.3.0
+   */
   def getAlpha: Double = getDocConcentration
 
-  /** Alias for [[setDocConcentration()]] */
+  /**
+   * Alias for [[setDocConcentration()]]
+   * @since 1.5.0
+   */
   def setAlpha(alpha: Vector): this.type = setDocConcentration(alpha)
 
-  /** Alias for [[setDocConcentration()]] */
+  /**
+   * Alias for [[setDocConcentration()]]
+   * @since 1.3.0
+   */
   def setAlpha(alpha: Double): this.type = setDocConcentration(alpha)
 
   /**
@@ -154,6 +180,7 @@ class LDA private (
    *
    * Note: The topics' distributions over terms are called "beta" in the original LDA paper
    * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.
+   * @since 1.3.0
    */
   def getTopicConcentration: Double = this.topicConcentration
 
@@ -178,36 +205,51 @@ class LDA private (
    *     - Value should be >= 0
    *     - default = (1.0 / k), following the implementation from
    *       [[https://github.com/Blei-Lab/onlineldavb]].
+   * @since 1.3.0
    */
   def setTopicConcentration(topicConcentration: Double): this.type = {
     this.topicConcentration = topicConcentration
     this
   }
 
-  /** Alias for [[getTopicConcentration]] */
+  /**
+   * Alias for [[getTopicConcentration]]
+   * @since 1.3.0
+   */
   def getBeta: Double = getTopicConcentration
 
-  /** Alias for [[setTopicConcentration()]] */
+  /**
+   * Alias for [[setTopicConcentration()]]
+   * @since 1.3.0
+   */
   def setBeta(beta: Double): this.type = setTopicConcentration(beta)
 
   /**
    * Maximum number of iterations for learning.
+   * @since 1.3.0
    */
   def getMaxIterations: Int = maxIterations
 
   /**
    * Maximum number of iterations for learning.
    * (default = 20)
+   * @since 1.3.0
    */
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
   }
 
-  /** Random seed */
+  /**
+   * Random seed
+   * @since 1.3.0
+   */
   def getSeed: Long = seed
 
-  /** Random seed */
+  /**
+   * Random seed
+   * @since 1.3.0
+   */
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -215,6 +257,7 @@ class LDA private (
 
   /**
    * Period (in iterations) between checkpoints.
+   * @since 1.3.0
    */
   def getCheckpointInterval: Int = checkpointInterval
 
@@ -225,6 +268,7 @@ class LDA private (
    * [[org.apache.spark.SparkContext]], this setting is ignored.
    *
    * @see [[org.apache.spark.SparkContext#setCheckpointDir]]
+   * @since 1.3.0
    */
   def setCheckpointInterval(checkpointInterval: Int): this.type = {
     this.checkpointInterval = checkpointInterval
@@ -236,6 +280,7 @@ class LDA private (
    * :: DeveloperApi ::
    *
    * LDAOptimizer used to perform the actual calculation
+   * @since 1.4.0
    */
   @DeveloperApi
   def getOptimizer: LDAOptimizer = ldaOptimizer
@@ -244,6 +289,7 @@ class LDA private (
    * :: DeveloperApi ::
    *
    * LDAOptimizer used to perform the actual calculation (default = EMLDAOptimizer)
+   * @since 1.4.0
    */
   @DeveloperApi
   def setOptimizer(optimizer: LDAOptimizer): this.type = {
@@ -254,6 +300,7 @@ class LDA private (
   /**
    * Set the LDAOptimizer used to perform the actual calculation by algorithm name.
    * Currently "em", "online" are supported.
+   * @since 1.4.0
    */
   def setOptimizer(optimizerName: String): this.type = {
     this.ldaOptimizer =
@@ -274,6 +321,7 @@ class LDA private (
    *                   (where the vocabulary size is the length of the vector).
    *                   Document IDs must be unique and >= 0.
    * @return  Inferred LDA model
+   * @since 1.3.0
    */
   def run(documents: RDD[(Long, Vector)]): LDAModel = {
     val state = ldaOptimizer.initialize(documents, this)
@@ -289,7 +337,10 @@ class LDA private (
     state.getLDAModel(iterationTimes)
   }
 
-  /** Java-friendly version of [[run()]] */
+  /**
+   * Java-friendly version of [[run()]]
+   * @since 1.3.0
+   */
   def run(documents: JavaPairRDD[java.lang.Long, Vector]): LDAModel = {
     run(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 82f05e4a18cee..b70e380c0393e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -192,12 +192,24 @@ class LocalLDAModel private[clustering] (
     override protected[clustering] val gammaShape: Double = 100)
   extends LDAModel with Serializable {
 
+  /**
+   * @since 1.3.0
+   */
   override def k: Int = topics.numCols
 
+  /**
+   * @since 1.3.0
+   */
   override def vocabSize: Int = topics.numRows
 
+  /**
+   * @since 1.3.0
+   */
   override def topicsMatrix: Matrix = topics
 
+  /**
+   * @since 1.3.0
+   */
   override def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])] = {
     val brzTopics = topics.toBreeze.toDenseMatrix
     Range(0, k).map { topicIndex =>
@@ -210,6 +222,9 @@ class LocalLDAModel private[clustering] (
 
   override protected def formatVersion = "1.0"
 
+  /**
+   * @since 1.5.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     LocalLDAModel.SaveLoadV1_0.save(sc, path, topicsMatrix, docConcentration, topicConcentration,
       gammaShape)
@@ -223,12 +238,16 @@ class LocalLDAModel private[clustering] (
    *
    * @param documents test corpus to use for calculating log likelihood
    * @return variational lower bound on the log likelihood of the entire corpus
+   * @since 1.5.0
    */
   def logLikelihood(documents: RDD[(Long, Vector)]): Double = logLikelihoodBound(documents,
     docConcentration, topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k,
     vocabSize)
 
-  /** Java-friendly version of [[logLikelihood]] */
+  /**
+   * Java-friendly version of [[logLikelihood]]
+   * @since 1.5.0
+   */
   def logLikelihood(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
     logLikelihood(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
@@ -239,6 +258,7 @@ class LocalLDAModel private[clustering] (
    *
    * @param documents test corpus to use for calculating perplexity
    * @return Variational upper bound on log perplexity per token.
+   * @since 1.5.0
    */
   def logPerplexity(documents: RDD[(Long, Vector)]): Double = {
     val corpusTokenCount = documents
@@ -247,7 +267,9 @@ class LocalLDAModel private[clustering] (
     -logLikelihood(documents) / corpusTokenCount
   }
 
-  /** Java-friendly version of [[logPerplexity]] */
+  /** Java-friendly version of [[logPerplexity]]
+   *  @since 1.5.0
+   */
   def logPerplexity(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
     logPerplexity(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
@@ -325,6 +347,7 @@ class LocalLDAModel private[clustering] (
    * for each document.
    * @param documents documents to predict topic mixture distributions for
    * @return An RDD of (document ID, topic mixture distribution for document)
+   * @since 1.3.0
    */
   // TODO: declare in LDAModel and override once implemented in DistributedLDAModel
   def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = {
@@ -351,7 +374,10 @@ class LocalLDAModel private[clustering] (
     }
   }
 
-  /** Java-friendly version of [[topicDistributions]] */
+  /**
+   * Java-friendly version of [[topicDistributions]]
+   * @since 1.4.1
+   */
   def topicDistributions(
       documents: JavaPairRDD[java.lang.Long, Vector]): JavaPairRDD[java.lang.Long, Vector] = {
     val distributions = topicDistributions(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
@@ -425,6 +451,9 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
     }
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def load(sc: SparkContext, path: String): LocalLDAModel = {
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
@@ -481,6 +510,7 @@ class DistributedLDAModel private[clustering] (
    * Convert model to a local model.
    * The local model stores the inferred topics but not the topic distributions for training
    * documents.
+   * @since 1.3.0
    */
   def toLocal: LocalLDAModel = new LocalLDAModel(topicsMatrix, docConcentration, topicConcentration,
     gammaShape)
@@ -491,6 +521,7 @@ class DistributedLDAModel private[clustering] (
    * No guarantees are given about the ordering of the topics.
    *
    * WARNING: This matrix is collected from an RDD. Beware memory usage when vocabSize, k are large.
+   * @since 1.3.0
    */
   override lazy val topicsMatrix: Matrix = {
     // Collect row-major topics
@@ -510,6 +541,9 @@ class DistributedLDAModel private[clustering] (
     Matrices.fromBreeze(brzTopics)
   }
 
+  /**
+   * @since 1.3.0
+   */
   override def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])] = {
     val numTopics = k
     // Note: N_k is not needed to find the top terms, but it is needed to normalize weights
@@ -548,6 +582,7 @@ class DistributedLDAModel private[clustering] (
    * @return  Array over topics.  Each element represent as a pair of matching arrays:
    *          (IDs for the documents, weights of the topic in these documents).
    *          For each topic, documents are sorted in order of decreasing topic weights.
+   * @since 1.5.0
    */
   def topDocumentsPerTopic(maxDocumentsPerTopic: Int): Array[(Array[Long], Array[Double])] = {
     val numTopics = k
@@ -587,6 +622,7 @@ class DistributedLDAModel private[clustering] (
    *  - This excludes the prior; for that, use [[logPrior]].
    *  - Even with [[logPrior]], this is NOT the same as the data log likelihood given the
    *    hyperparameters.
+   * @since 1.3.0
    */
   lazy val logLikelihood: Double = {
     // TODO: generalize this for asymmetric (non-scalar) alpha
@@ -612,7 +648,8 @@ class DistributedLDAModel private[clustering] (
 
   /**
    * Log probability of the current parameter estimate:
-   *  log P(topics, topic distributions for docs | alpha, eta)
+   * log P(topics, topic distributions for docs | alpha, eta)
+   * @since 1.3.0
    */
   lazy val logPrior: Double = {
     // TODO: generalize this for asymmetric (non-scalar) alpha
@@ -644,6 +681,7 @@ class DistributedLDAModel private[clustering] (
    * ("theta_doc").
    *
    * @return  RDD of (document ID, topic distribution) pairs
+   * @since 1.3.0
    */
   def topicDistributions: RDD[(Long, Vector)] = {
     graph.vertices.filter(LDA.isDocumentVertex).map { case (docID, topicCounts) =>
@@ -651,7 +689,10 @@ class DistributedLDAModel private[clustering] (
     }
   }
 
-  /** Java-friendly version of [[topicDistributions]] */
+  /**
+   * Java-friendly version of [[topicDistributions]]
+   * @since 1.4.1
+   */
   def javaTopicDistributions: JavaPairRDD[java.lang.Long, Vector] = {
     JavaPairRDD.fromRDD(topicDistributions.asInstanceOf[RDD[(java.lang.Long, Vector)]])
   }
@@ -659,6 +700,7 @@ class DistributedLDAModel private[clustering] (
   /**
    * For each document, return the top k weighted topics for that document and their weights.
    * @return RDD of (doc ID, topic indices, topic weights)
+   * @since 1.5.0
    */
   def topTopicsPerDocument(k: Int): RDD[(Long, Array[Int], Array[Double])] = {
     graph.vertices.filter(LDA.isDocumentVertex).map { case (docID, topicCounts) =>
@@ -673,7 +715,10 @@ class DistributedLDAModel private[clustering] (
     }
   }
 
-  /** Java-friendly version of [[topTopicsPerDocument]] */
+  /**
+   * Java-friendly version of [[topTopicsPerDocument]]
+   * @since 1.5.0
+   */
   def javaTopTopicsPerDocument(k: Int): JavaRDD[(java.lang.Long, Array[Int], Array[Double])] = {
     val topics = topTopicsPerDocument(k)
     topics.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[Double])]].toJavaRDD()
@@ -684,6 +729,10 @@ class DistributedLDAModel private[clustering] (
 
   override protected def formatVersion = "1.0"
 
+  /**
+   * Java-friendly version of [[topicDistributions]]
+   * @since 1.5.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     DistributedLDAModel.SaveLoadV1_0.save(
       sc, path, graph, globalTopicTotals, k, vocabSize, docConcentration, topicConcentration,
@@ -784,6 +833,9 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
 
   }
 
+  /**
+   * @since 1.5.0
+   */
   override def load(sc: SparkContext, path: String): DistributedLDAModel = {
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index a0008f9c99ad7..360241c8081ac 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -35,6 +35,7 @@ import org.apache.spark.rdd.RDD
  *
  * An LDAOptimizer specifies which optimization/learning/inference algorithm to use, and it can
  * hold optimizer-specific parameters for users to set.
+ * @since 1.4.0
  */
 @DeveloperApi
 sealed trait LDAOptimizer {
@@ -73,7 +74,7 @@ sealed trait LDAOptimizer {
  *  - Paper which clearly explains several algorithms, including EM:
  *    Asuncion, Welling, Smyth, and Teh.
  *    "On Smoothing and Inference for Topic Models."  UAI, 2009.
- *
+ * @since 1.4.0
  */
 @DeveloperApi
 final class EMLDAOptimizer extends LDAOptimizer {
@@ -225,6 +226,7 @@ final class EMLDAOptimizer extends LDAOptimizer {
  *
  * Original Online LDA paper:
  *   Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010.
+ * @since 1.4.0
  */
 @DeveloperApi
 final class OnlineLDAOptimizer extends LDAOptimizer {
@@ -274,6 +276,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   /**
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
+   * @since 1.4.0
    */
   def getTau0: Double = this.tau0
 
@@ -281,6 +284,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
    * Default: 1024, following the original Online LDA paper.
+   * @since 1.4.0
    */
   def setTau0(tau0: Double): this.type = {
     require(tau0 > 0, s"LDA tau0 must be positive, but was set to $tau0")
@@ -290,6 +294,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 
   /**
    * Learning rate: exponential decay rate
+   * @since 1.4.0
    */
   def getKappa: Double = this.kappa
 
@@ -297,6 +302,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
    * Learning rate: exponential decay rate---should be between
    * (0.5, 1.0] to guarantee asymptotic convergence.
    * Default: 0.51, based on the original Online LDA paper.
+   * @since 1.4.0
    */
   def setKappa(kappa: Double): this.type = {
     require(kappa >= 0, s"Online LDA kappa must be nonnegative, but was set to $kappa")
@@ -306,6 +312,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 
   /**
    * Mini-batch fraction, which sets the fraction of document sampled and used in each iteration
+   * @since 1.4.0
    */
   def getMiniBatchFraction: Double = this.miniBatchFraction
 
@@ -318,6 +325,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
    * maxIterations * miniBatchFraction >= 1.
    *
    * Default: 0.05, i.e., 5% of total documents.
+   * @since 1.4.0
    */
   def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
     require(miniBatchFraction > 0.0 && miniBatchFraction <= 1.0,
@@ -329,6 +337,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   /**
    * Optimize alpha, indicates whether alpha (Dirichlet parameter for document-topic distribution)
    * will be optimized during training.
+   * @since 1.5.0
    */
   def getOptimzeAlpha: Boolean = this.optimizeAlpha
 
@@ -336,6 +345,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
    * Sets whether to optimize alpha parameter during training.
    *
    * Default: false
+   * @since 1.5.0
    */
   def setOptimzeAlpha(optimizeAlpha: Boolean): this.type = {
     this.optimizeAlpha = optimizeAlpha
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
index 407e43a024a2e..b4733ca975152 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
@@ -39,12 +39,16 @@ import org.apache.spark.{Logging, SparkContext, SparkException}
  *
  * @param k number of clusters
  * @param assignments an RDD of clustering [[PowerIterationClustering#Assignment]]s
+ * @since 1.3.0
  */
 @Experimental
 class PowerIterationClusteringModel(
     val k: Int,
     val assignments: RDD[PowerIterationClustering.Assignment]) extends Saveable with Serializable {
 
+  /**
+   * @since 1.4.0
+   */
   override def save(sc: SparkContext, path: String): Unit = {
     PowerIterationClusteringModel.SaveLoadV1_0.save(sc, this, path)
   }
@@ -52,6 +56,9 @@ class PowerIterationClusteringModel(
   override protected def formatVersion: String = "1.0"
 }
 
+/**
+ * @since 1.4.0
+ */
 object PowerIterationClusteringModel extends Loader[PowerIterationClusteringModel] {
   override def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
     PowerIterationClusteringModel.SaveLoadV1_0.load(sc, path)
@@ -65,6 +72,9 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
     private[clustering]
     val thisClassName = "org.apache.spark.mllib.clustering.PowerIterationClusteringModel"
 
+    /**
+     * @since 1.4.0
+     */
     def save(sc: SparkContext, model: PowerIterationClusteringModel, path: String): Unit = {
       val sqlContext = new SQLContext(sc)
       import sqlContext.implicits._
@@ -77,6 +87,9 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
       dataRDD.write.parquet(Loader.dataPath(path))
     }
 
+    /**
+     * @since 1.4.0
+     */
     def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
       implicit val formats = DefaultFormats
       val sqlContext = new SQLContext(sc)
@@ -120,13 +133,16 @@ class PowerIterationClustering private[clustering] (
 
   import org.apache.spark.mllib.clustering.PowerIterationClustering._
 
-  /** Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100,
-   *  initMode: "random"}.
+  /**
+   * Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100,
+   * initMode: "random"}.
+   * @since 1.3.0
    */
   def this() = this(k = 2, maxIterations = 100, initMode = "random")
 
   /**
    * Set the number of clusters.
+   * @since 1.3.0
    */
   def setK(k: Int): this.type = {
     this.k = k
@@ -135,6 +151,7 @@ class PowerIterationClustering private[clustering] (
 
   /**
    * Set maximum number of iterations of the power iteration loop
+   * @since 1.3.0
    */
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
@@ -144,6 +161,7 @@ class PowerIterationClustering private[clustering] (
   /**
    * Set the initialization mode. This can be either "random" to use a random vector
    * as vertex properties, or "degree" to use normalized sum similarities. Default: random.
+   * @since 1.3.0
    */
   def setInitializationMode(mode: String): this.type = {
     this.initMode = mode match {
@@ -164,6 +182,7 @@ class PowerIterationClustering private[clustering] (
    *              assume s,,ij,, = 0.0.
    *
    * @return a [[PowerIterationClusteringModel]] that contains the clustering result
+   * @since 1.5.0
    */
   def run(graph: Graph[Double, Double]): PowerIterationClusteringModel = {
     val w = normalize(graph)
@@ -185,6 +204,7 @@ class PowerIterationClustering private[clustering] (
    *                     assume s,,ij,, = 0.0.
    *
    * @return a [[PowerIterationClusteringModel]] that contains the clustering result
+   * @since 1.3.0
    */
   def run(similarities: RDD[(Long, Long, Double)]): PowerIterationClusteringModel = {
     val w = normalize(similarities)
@@ -197,6 +217,7 @@ class PowerIterationClustering private[clustering] (
 
   /**
    * A Java-friendly version of [[PowerIterationClustering.run]].
+   * @since 1.3.0
    */
   def run(similarities: JavaRDD[(java.lang.Long, java.lang.Long, java.lang.Double)])
     : PowerIterationClusteringModel = {
@@ -221,6 +242,9 @@ class PowerIterationClustering private[clustering] (
   }
 }
 
+/**
+ * @since 1.3.0
+ */
 @Experimental
 object PowerIterationClustering extends Logging {
 
@@ -229,6 +253,7 @@ object PowerIterationClustering extends Logging {
    * Cluster assignment.
    * @param id node id
    * @param cluster assigned cluster id
+   * @since 1.3.0
    */
   @Experimental
   case class Assignment(id: Long, cluster: Int)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
index d9b34cec64894..a915804b02c90 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
@@ -63,6 +63,7 @@ import org.apache.spark.util.random.XORShiftRandom
  * such that at time t + h the discount applied to the data from t is 0.5.
  * The definition remains the same whether the time unit is given
  * as batches or points.
+ * @since 1.2.0
  *
  */
 @Experimental
@@ -70,7 +71,10 @@ class StreamingKMeansModel(
     override val clusterCenters: Array[Vector],
     val clusterWeights: Array[Double]) extends KMeansModel(clusterCenters) with Logging {
 
-  /** Perform a k-means update on a batch of data. */
+  /**
+   * Perform a k-means update on a batch of data.
+   * @since 1.2.0
+   */
   def update(data: RDD[Vector], decayFactor: Double, timeUnit: String): StreamingKMeansModel = {
 
     // find nearest cluster to each point
@@ -82,6 +86,7 @@ class StreamingKMeansModel(
       (p1._1, p1._2 + p2._2)
     }
     val dim = clusterCenters(0).size
+
     val pointStats: Array[(Int, (Vector, Long))] = closest
       .aggregateByKey((Vectors.zeros(dim), 0L))(mergeContribs, mergeContribs)
       .collect()
@@ -161,6 +166,7 @@ class StreamingKMeansModel(
  *    .setRandomCenters(5, 100.0)
  *    .trainOn(DStream)
  * }}}
+ * @since 1.2.0
  */
 @Experimental
 class StreamingKMeans(
@@ -168,23 +174,33 @@ class StreamingKMeans(
     var decayFactor: Double,
     var timeUnit: String) extends Logging with Serializable {
 
+  /** @since 1.2.0 */
   def this() = this(2, 1.0, StreamingKMeans.BATCHES)
 
   protected var model: StreamingKMeansModel = new StreamingKMeansModel(null, null)
 
-  /** Set the number of clusters. */
+  /**
+   * Set the number of clusters.
+   * @since 1.2.0
+   */
   def setK(k: Int): this.type = {
     this.k = k
     this
   }
 
-  /** Set the decay factor directly (for forgetful algorithms). */
+  /**
+   * Set the decay factor directly (for forgetful algorithms).
+   * @since 1.2.0
+   */
   def setDecayFactor(a: Double): this.type = {
     this.decayFactor = a
     this
   }
 
-  /** Set the half life and time unit ("batches" or "points") for forgetful algorithms. */
+  /**
+   * Set the half life and time unit ("batches" or "points") for forgetful algorithms.
+   * @since 1.2.0
+   */
   def setHalfLife(halfLife: Double, timeUnit: String): this.type = {
     if (timeUnit != StreamingKMeans.BATCHES && timeUnit != StreamingKMeans.POINTS) {
       throw new IllegalArgumentException("Invalid time unit for decay: " + timeUnit)
@@ -195,7 +211,10 @@ class StreamingKMeans(
     this
   }
 
-  /** Specify initial centers directly. */
+  /**
+   * Specify initial centers directly.
+   * @since 1.2.0
+   */
   def setInitialCenters(centers: Array[Vector], weights: Array[Double]): this.type = {
     model = new StreamingKMeansModel(centers, weights)
     this
@@ -207,6 +226,7 @@ class StreamingKMeans(
    * @param dim Number of dimensions
    * @param weight Weight for each center
    * @param seed Random seed
+   * @since 1.2.0
    */
   def setRandomCenters(dim: Int, weight: Double, seed: Long = Utils.random.nextLong): this.type = {
     val random = new XORShiftRandom(seed)
@@ -216,7 +236,10 @@ class StreamingKMeans(
     this
   }
 
-  /** Return the latest model. */
+  /**
+   * Return the latest model.
+   * @since 1.2.0
+   */
   def latestModel(): StreamingKMeansModel = {
     model
   }
@@ -228,6 +251,7 @@ class StreamingKMeans(
    * and updates the model using each batch of data from the stream.
    *
    * @param data DStream containing vector data
+   * @since 1.2.0
    */
   def trainOn(data: DStream[Vector]) {
     assertInitialized()
@@ -236,7 +260,10 @@ class StreamingKMeans(
     }
   }
 
-  /** Java-friendly version of `trainOn`. */
+  /**
+   * Java-friendly version of `trainOn`.
+   * @since 1.4.0
+   */
   def trainOn(data: JavaDStream[Vector]): Unit = trainOn(data.dstream)
 
   /**
@@ -244,13 +271,17 @@ class StreamingKMeans(
    *
    * @param data DStream containing vector data
    * @return DStream containing predictions
+   * @since 1.2.0
    */
   def predictOn(data: DStream[Vector]): DStream[Int] = {
     assertInitialized()
     data.map(model.predict)
   }
 
-  /** Java-friendly version of `predictOn`. */
+  /**
+   * Java-friendly version of `predictOn`.
+   * @since 1.4.0
+   */
   def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Integer] = {
     JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Integer]])
   }
@@ -261,13 +292,17 @@ class StreamingKMeans(
    * @param data DStream containing (key, feature vector) pairs
    * @tparam K key type
    * @return DStream containing the input keys and the predictions as values
+   * @since 1.2.0
    */
   def predictOnValues[K: ClassTag](data: DStream[(K, Vector)]): DStream[(K, Int)] = {
     assertInitialized()
     data.mapValues(model.predict)
   }
 
-  /** Java-friendly version of `predictOnValues`. */
+  /**
+   * Java-friendly version of `predictOnValues`.
+   * @since 1.4.0
+   */
   def predictOnValues[K](
       data: JavaPairDStream[K, Vector]): JavaPairDStream[K, java.lang.Integer] = {
     implicit val tag = fakeClassTag[K]

From f3391ff2b8b9c1f1308755dc223776692e3b7725 Mon Sep 17 00:00:00 2001
From: Joshi <rekhajoshm@gmail.com>
Date: Wed, 19 Aug 2015 21:23:02 +0100
Subject: [PATCH 340/633] [SPARK-8889] [CORE] Fix for OOM for graph creation

Fix for OOM for graph creation

Author: Joshi <rekhajoshm@gmail.com>
Author: Rekha Joshi <rekhajoshm@gmail.com>

Closes #7602 from rekhajoshm/SPARK-8889.
---
 .../spark/ui/scope/RDDOperationGraph.scala    | 23 +++++------
 .../org/apache/spark/ui/UISeleniumSuite.scala | 39 +++++++++++++++++++
 2 files changed, 51 insertions(+), 11 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
index ffea9817c0b08..81f168a447ead 100644
--- a/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
+++ b/core/src/main/scala/org/apache/spark/ui/scope/RDDOperationGraph.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.ui.scope
 
 import scala.collection.mutable
-import scala.collection.mutable.ListBuffer
+import scala.collection.mutable.{StringBuilder, ListBuffer}
 
 import org.apache.spark.Logging
 import org.apache.spark.scheduler.StageInfo
@@ -167,7 +167,7 @@ private[ui] object RDDOperationGraph extends Logging {
   def makeDotFile(graph: RDDOperationGraph): String = {
     val dotFile = new StringBuilder
     dotFile.append("digraph G {\n")
-    dotFile.append(makeDotSubgraph(graph.rootCluster, indent = "  "))
+    makeDotSubgraph(dotFile, graph.rootCluster, indent = "  ")
     graph.edges.foreach { edge => dotFile.append(s"""  ${edge.fromId}->${edge.toId};\n""") }
     dotFile.append("}")
     val result = dotFile.toString()
@@ -180,18 +180,19 @@ private[ui] object RDDOperationGraph extends Logging {
     s"""${node.id} [label="${node.name} [${node.id}]"]"""
   }
 
-  /** Return the dot representation of a subgraph in an RDDOperationGraph. */
-  private def makeDotSubgraph(cluster: RDDOperationCluster, indent: String): String = {
-    val subgraph = new StringBuilder
-    subgraph.append(indent + s"subgraph cluster${cluster.id} {\n")
-    subgraph.append(indent + s"""  label="${cluster.name}";\n""")
+  /** Update the dot representation of the RDDOperationGraph in cluster to subgraph. */
+  private def makeDotSubgraph(
+      subgraph: StringBuilder,
+      cluster: RDDOperationCluster,
+      indent: String): Unit = {
+    subgraph.append(indent).append(s"subgraph cluster${cluster.id} {\n")
+    subgraph.append(indent).append(s"""  label="${cluster.name}";\n""")
     cluster.childNodes.foreach { node =>
-      subgraph.append(indent + s"  ${makeDotNode(node)};\n")
+      subgraph.append(indent).append(s"  ${makeDotNode(node)};\n")
     }
     cluster.childClusters.foreach { cscope =>
-      subgraph.append(makeDotSubgraph(cscope, indent + "  "))
+      makeDotSubgraph(subgraph, cscope, indent + "  ")
     }
-    subgraph.append(indent + "}\n")
-    subgraph.toString()
+    subgraph.append(indent).append("}\n")
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
index 3aa672f8b713c..69888b2694bae 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.ui
 import java.net.{HttpURLConnection, URL}
 import javax.servlet.http.{HttpServletResponse, HttpServletRequest}
 
+import scala.io.Source
 import scala.collection.JavaConversions._
 import scala.xml.Node
 
@@ -603,6 +604,44 @@ class UISeleniumSuite extends SparkFunSuite with WebBrowser with Matchers with B
     }
   }
 
+  test("job stages should have expected dotfile under DAG visualization") {
+    withSpark(newSparkContext()) { sc =>
+      // Create a multi-stage job
+      val rdd =
+        sc.parallelize(Seq(1, 2, 3)).map(identity).groupBy(identity).map(identity).groupBy(identity)
+      rdd.count()
+
+      val stage0 = Source.fromURL(sc.ui.get.appUIAddress +
+        "/stages/stage/?id=0&attempt=0&expandDagViz=true").mkString
+      assert(stage0.contains("digraph G {\n  subgraph clusterstage_0 {\n    " +
+        "label=&quot;Stage 0&quot;;\n    subgraph "))
+      assert(stage0.contains("{\n      label=&quot;parallelize&quot;;\n      " +
+        "0 [label=&quot;ParallelCollectionRDD [0]&quot;];\n    }"))
+      assert(stage0.contains("{\n      label=&quot;map&quot;;\n      " +
+        "1 [label=&quot;MapPartitionsRDD [1]&quot;];\n    }"))
+      assert(stage0.contains("{\n      label=&quot;groupBy&quot;;\n      " +
+        "2 [label=&quot;MapPartitionsRDD [2]&quot;];\n    }"))
+
+      val stage1 = Source.fromURL(sc.ui.get.appUIAddress +
+        "/stages/stage/?id=1&attempt=0&expandDagViz=true").mkString
+      assert(stage1.contains("digraph G {\n  subgraph clusterstage_1 {\n    " +
+        "label=&quot;Stage 1&quot;;\n    subgraph "))
+      assert(stage1.contains("{\n      label=&quot;groupBy&quot;;\n      " +
+        "3 [label=&quot;ShuffledRDD [3]&quot;];\n    }"))
+      assert(stage1.contains("{\n      label=&quot;map&quot;;\n      " +
+        "4 [label=&quot;MapPartitionsRDD [4]&quot;];\n    }"))
+      assert(stage1.contains("{\n      label=&quot;groupBy&quot;;\n      " +
+        "5 [label=&quot;MapPartitionsRDD [5]&quot;];\n    }"))
+
+      val stage2 = Source.fromURL(sc.ui.get.appUIAddress +
+        "/stages/stage/?id=2&attempt=0&expandDagViz=true").mkString
+      assert(stage2.contains("digraph G {\n  subgraph clusterstage_2 {\n    " +
+        "label=&quot;Stage 2&quot;;\n    subgraph "))
+      assert(stage2.contains("{\n      label=&quot;groupBy&quot;;\n      " +
+        "6 [label=&quot;ShuffledRDD [6]&quot;];\n    }"))
+    }
+  }
+
   def goToUi(sc: SparkContext, path: String): Unit = {
     goToUi(sc.ui.get, path)
   }

From e05da5cb5ea253e6372f648fc8203204f2a8df8d Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 19 Aug 2015 13:43:04 -0700
Subject: [PATCH 341/633] [SPARK-10107] [SQL] fix NPE in format_number

Author: Davies Liu <davies@databricks.com>

Closes #8305 from davies/format_number.
---
 .../spark/sql/catalyst/expressions/stringOperations.scala     | 2 +-
 .../scala/org/apache/spark/sql/StringFunctionsSuite.scala     | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
index 134f1aa2af9a8..ca044d3e95e38 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
@@ -1306,8 +1306,8 @@ case class FormatNumber(x: Expression, d: Expression)
             $df $dFormat = new $df($pattern.toString());
             $lastDValue = $d;
             $numberFormat.applyPattern($dFormat.toPattern());
-            ${ev.primitive} = UTF8String.fromString($numberFormat.format(${typeHelper(num)}));
           }
+          ${ev.primitive} = UTF8String.fromString($numberFormat.format(${typeHelper(num)}));
         } else {
           ${ev.primitive} = null;
           ${ev.isNull} = true;
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index cc95eede005d7..b91438baea06f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -348,9 +348,9 @@ class StringFunctionsSuite extends QueryTest with SharedSQLContext {
     // it will still use the interpretProjection if projection follows by a LocalRelation,
     // hence we add a filter operator.
     // See the optimizer rule `ConvertToLocalRelation`
-    val df2 = Seq((5L, 4), (4L, 3), (3L, 2)).toDF("a", "b")
+    val df2 = Seq((5L, 4), (4L, 3), (4L, 3), (4L, 3), (3L, 2)).toDF("a", "b")
     checkAnswer(
       df2.filter("b>0").selectExpr("format_number(a, b)"),
-      Row("5.0000") :: Row("4.000") :: Row("3.00") :: Nil)
+      Row("5.0000") :: Row("4.000") :: Row("4.000") :: Row("4.000") :: Row("3.00") :: Nil)
   }
 }

From 08887369c890e0dd87eb8b34e8c32bb03307bf24 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 19 Aug 2015 13:56:40 -0700
Subject: [PATCH 342/633] [SPARK-10073] [SQL] Python withColumn should replace
 the old column

DataFrame.withColumn in Python should be consistent with the Scala one (replacing the existing column  that has the same name).

cc marmbrus

Author: Davies Liu <davies@databricks.com>

Closes #8300 from davies/with_column.
---
 python/pyspark/sql/dataframe.py                      | 12 ++++++------
 python/pyspark/sql/tests.py                          |  4 ++++
 .../main/scala/org/apache/spark/sql/DataFrame.scala  |  3 ++-
 3 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index da742d7ce7d13..025811f519293 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -1202,7 +1202,9 @@ def freqItems(self, cols, support=None):
     @ignore_unicode_prefix
     @since(1.3)
     def withColumn(self, colName, col):
-        """Returns a new :class:`DataFrame` by adding a column.
+        """
+        Returns a new :class:`DataFrame` by adding a column or replacing the
+        existing column that has the same name.
 
         :param colName: string, name of the new column.
         :param col: a :class:`Column` expression for the new column.
@@ -1210,7 +1212,8 @@ def withColumn(self, colName, col):
         >>> df.withColumn('age2', df.age + 2).collect()
         [Row(age=2, name=u'Alice', age2=4), Row(age=5, name=u'Bob', age2=7)]
         """
-        return self.select('*', col.alias(colName))
+        assert isinstance(col, Column), "col should be Column"
+        return DataFrame(self._jdf.withColumn(colName, col._jc), self.sql_ctx)
 
     @ignore_unicode_prefix
     @since(1.3)
@@ -1223,10 +1226,7 @@ def withColumnRenamed(self, existing, new):
         >>> df.withColumnRenamed('age', 'age2').collect()
         [Row(age2=2, name=u'Alice'), Row(age2=5, name=u'Bob')]
         """
-        cols = [Column(_to_java_column(c)).alias(new)
-                if c == existing else c
-                for c in self.columns]
-        return self.select(*cols)
+        return DataFrame(self._jdf.withColumnRenamed(existing, new), self.sql_ctx)
 
     @since(1.4)
     @ignore_unicode_prefix
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 13cf647b66da8..aacfb34c77618 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -1035,6 +1035,10 @@ def test_capture_illegalargument_exception(self):
         self.assertRaisesRegexp(IllegalArgumentException, "1024 is not in the permitted values",
                                 lambda: df.select(sha2(df.a, 1024)).collect())
 
+    def test_with_column_with_existing_name(self):
+        keys = self.df.withColumn("key", self.df.key).select("key").collect()
+        self.assertEqual([r.key for r in keys], list(range(100)))
+
 
 class HiveContextSQLTests(ReusedPySparkTestCase):
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index fd0ead4401193..d6688b24ae7d6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1133,7 +1133,8 @@ class DataFrame private[sql](
   /////////////////////////////////////////////////////////////////////////////
 
   /**
-   * Returns a new [[DataFrame]] by adding a column.
+   * Returns a new [[DataFrame]] by adding a column or replacing the existing column that has
+   * the same name.
    * @group dfops
    * @since 1.3.0
    */

From 21bdbe9fe69be47be562de24216a469e5ee64c7b Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 19 Aug 2015 13:57:52 -0700
Subject: [PATCH 343/633] [SPARK-9627] [SQL] Stops using Scala runtime
 reflection in DictionaryEncoding

`DictionaryEncoding` uses Scala runtime reflection to avoid boxing costs while building the directory array. However, this code path may hit [SI-6240] [1] and throw exception.

[1]: https://issues.scala-lang.org/browse/SI-6240

Author: Cheng Lian <lian@databricks.com>

Closes #8306 from liancheng/spark-9627/in-memory-cache-scala-reflection.
---
 .../sql/columnar/InMemoryColumnarTableScan.scala  |  1 -
 .../columnar/compression/compressionSchemes.scala | 15 ++++-----------
 2 files changed, 4 insertions(+), 12 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
index 45f15fd04d4e2..66d429bc06198 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala
@@ -120,7 +120,6 @@ private[sql] case class InMemoryRelation(
       new Iterator[CachedBatch] {
         def next(): CachedBatch = {
           val columnBuilders = output.map { attribute =>
-            val columnType = ColumnType(attribute.dataType)
             ColumnBuilder(attribute.dataType, batchSize, attribute.name, useCompression)
           }.toArray
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
index c91d960a0932b..ca910a99db082 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/compression/compressionSchemes.scala
@@ -270,20 +270,13 @@ private[sql] case object DictionaryEncoding extends CompressionScheme {
   class Decoder[T <: AtomicType](buffer: ByteBuffer, columnType: NativeColumnType[T])
     extends compression.Decoder[T] {
 
-    private val dictionary = {
-      // TODO Can we clean up this mess? Maybe move this to `DataType`?
-      implicit val classTag = {
-        val mirror = runtimeMirror(Utils.getSparkClassLoader)
-        ClassTag[T#InternalType](mirror.runtimeClass(columnType.scalaTag.tpe))
-      }
-
-      Array.fill(buffer.getInt()) {
-        columnType.extract(buffer)
-      }
+    private val dictionary: Array[Any] = {
+      val elementNum = buffer.getInt()
+      Array.fill[Any](elementNum)(columnType.extract(buffer).asInstanceOf[Any])
     }
 
     override def next(row: MutableRow, ordinal: Int): Unit = {
-      columnType.setField(row, ordinal, dictionary(buffer.getShort()))
+      columnType.setField(row, ordinal, dictionary(buffer.getShort()).asInstanceOf[T#InternalType])
     }
 
     override def hasNext: Boolean = buffer.hasRemaining

From 1f4c4fe6dfd8cc52b5fddfd67a31a77edbb1a036 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 19 Aug 2015 14:03:47 -0700
Subject: [PATCH 344/633] [SPARK-10090] [SQL] fix decimal scale of division

We should rounding the result of multiply/division of decimal to expected precision/scale, also check overflow.

Author: Davies Liu <davies@databricks.com>

Closes #8287 from davies/decimal_division.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 28 +++++----
 .../spark/sql/catalyst/expressions/Cast.scala | 32 +++++-----
 .../expressions/decimalFunctions.scala        | 38 ++++++++++-
 .../org/apache/spark/sql/types/Decimal.scala  |  4 +-
 .../expressions/DecimalExpressionSuite.scala  | 63 +++++++++++++++++++
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 23 ++++++-
 6 files changed, 157 insertions(+), 31 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DecimalExpressionSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 8581d6b496c15..62c27ee0b9ee0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -371,8 +371,8 @@ object HiveTypeCoercion {
       DecimalType.bounded(range + scale, scale)
     }
 
-    private def changePrecision(e: Expression, dataType: DataType): Expression = {
-      ChangeDecimalPrecision(Cast(e, dataType))
+    private def promotePrecision(e: Expression, dataType: DataType): Expression = {
+      PromotePrecision(Cast(e, dataType))
     }
 
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
@@ -383,36 +383,42 @@ object HiveTypeCoercion {
         case e if !e.childrenResolved => e
 
         // Skip nodes who is already promoted
-        case e: BinaryArithmetic if e.left.isInstanceOf[ChangeDecimalPrecision] => e
+        case e: BinaryArithmetic if e.left.isInstanceOf[PromotePrecision] => e
 
         case Add(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
           val dt = DecimalType.bounded(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2))
-          Add(changePrecision(e1, dt), changePrecision(e2, dt))
+          CheckOverflow(Add(promotePrecision(e1, dt), promotePrecision(e2, dt)), dt)
 
         case Subtract(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
           val dt = DecimalType.bounded(max(s1, s2) + max(p1 - s1, p2 - s2) + 1, max(s1, s2))
-          Subtract(changePrecision(e1, dt), changePrecision(e2, dt))
+          CheckOverflow(Subtract(promotePrecision(e1, dt), promotePrecision(e2, dt)), dt)
 
         case Multiply(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          val dt = DecimalType.bounded(p1 + p2 + 1, s1 + s2)
-          Multiply(changePrecision(e1, dt), changePrecision(e2, dt))
+          val resultType = DecimalType.bounded(p1 + p2 + 1, s1 + s2)
+          val widerType = widerDecimalType(p1, s1, p2, s2)
+          CheckOverflow(Multiply(promotePrecision(e1, widerType), promotePrecision(e2, widerType)),
+            resultType)
 
         case Divide(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          val dt = DecimalType.bounded(p1 - s1 + s2 + max(6, s1 + p2 + 1), max(6, s1 + p2 + 1))
-          Divide(changePrecision(e1, dt), changePrecision(e2, dt))
+          val resultType = DecimalType.bounded(p1 - s1 + s2 + max(6, s1 + p2 + 1),
+            max(6, s1 + p2 + 1))
+          val widerType = widerDecimalType(p1, s1, p2, s2)
+          CheckOverflow(Divide(promotePrecision(e1, widerType), promotePrecision(e2, widerType)),
+            resultType)
 
         case Remainder(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
           val resultType = DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2))
           // resultType may have lower precision, so we cast them into wider type first.
           val widerType = widerDecimalType(p1, s1, p2, s2)
-          Cast(Remainder(changePrecision(e1, widerType), changePrecision(e2, widerType)),
+          CheckOverflow(Remainder(promotePrecision(e1, widerType), promotePrecision(e2, widerType)),
             resultType)
 
         case Pmod(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
           val resultType = DecimalType.bounded(min(p1 - s1, p2 - s2) + max(s1, s2), max(s1, s2))
           // resultType may have lower precision, so we cast them into wider type first.
           val widerType = widerDecimalType(p1, s1, p2, s2)
-          Cast(Pmod(changePrecision(e1, widerType), changePrecision(e2, widerType)), resultType)
+          CheckOverflow(Pmod(promotePrecision(e1, widerType), promotePrecision(e2, widerType)),
+            resultType)
 
         case b @ BinaryComparison(e1 @ DecimalType.Expression(p1, s1),
                                   e2 @ DecimalType.Expression(p2, s2)) if p1 != p2 || s1 != s2 =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 616b9e0e65b78..2db954257be35 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -447,7 +447,7 @@ case class Cast(child: Expression, dataType: DataType)
     case StringType => castToStringCode(from, ctx)
     case BinaryType => castToBinaryCode(from)
     case DateType => castToDateCode(from, ctx)
-    case decimal: DecimalType => castToDecimalCode(from, decimal)
+    case decimal: DecimalType => castToDecimalCode(from, decimal, ctx)
     case TimestampType => castToTimestampCode(from, ctx)
     case CalendarIntervalType => castToIntervalCode(from)
     case BooleanType => castToBooleanCode(from)
@@ -528,14 +528,18 @@ case class Cast(child: Expression, dataType: DataType)
       }
     """
 
-  private[this] def castToDecimalCode(from: DataType, target: DecimalType): CastFunction = {
+  private[this] def castToDecimalCode(
+      from: DataType,
+      target: DecimalType,
+      ctx: CodeGenContext): CastFunction = {
+    val tmp = ctx.freshName("tmpDecimal")
     from match {
       case StringType =>
         (c, evPrim, evNull) =>
           s"""
             try {
-              Decimal tmpDecimal = Decimal.apply(new java.math.BigDecimal($c.toString()));
-              ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+              Decimal $tmp = Decimal.apply(new java.math.BigDecimal($c.toString()));
+              ${changePrecision(tmp, target, evPrim, evNull)}
             } catch (java.lang.NumberFormatException e) {
               $evNull = true;
             }
@@ -543,8 +547,8 @@ case class Cast(child: Expression, dataType: DataType)
       case BooleanType =>
         (c, evPrim, evNull) =>
           s"""
-            Decimal tmpDecimal = $c ? Decimal.apply(1) : Decimal.apply(0);
-            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+            Decimal $tmp = $c ? Decimal.apply(1) : Decimal.apply(0);
+            ${changePrecision(tmp, target, evPrim, evNull)}
           """
       case DateType =>
         // date can't cast to decimal in Hive
@@ -553,29 +557,29 @@ case class Cast(child: Expression, dataType: DataType)
         // Note that we lose precision here.
         (c, evPrim, evNull) =>
           s"""
-            Decimal tmpDecimal = Decimal.apply(
+            Decimal $tmp = Decimal.apply(
               scala.math.BigDecimal.valueOf(${timestampToDoubleCode(c)}));
-            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+            ${changePrecision(tmp, target, evPrim, evNull)}
           """
       case DecimalType() =>
         (c, evPrim, evNull) =>
           s"""
-            Decimal tmpDecimal = $c.clone();
-            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+            Decimal $tmp = $c.clone();
+            ${changePrecision(tmp, target, evPrim, evNull)}
           """
       case x: IntegralType =>
         (c, evPrim, evNull) =>
           s"""
-            Decimal tmpDecimal = Decimal.apply((long) $c);
-            ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+            Decimal $tmp = Decimal.apply((long) $c);
+            ${changePrecision(tmp, target, evPrim, evNull)}
           """
       case x: FractionalType =>
         // All other numeric types can be represented precisely as Doubles
         (c, evPrim, evNull) =>
           s"""
             try {
-              Decimal tmpDecimal = Decimal.apply(scala.math.BigDecimal.valueOf((double) $c));
-              ${changePrecision("tmpDecimal", target, evPrim, evNull)}
+              Decimal $tmp = Decimal.apply(scala.math.BigDecimal.valueOf((double) $c));
+              ${changePrecision(tmp, target, evPrim, evNull)}
             } catch (java.lang.NumberFormatException e) {
               $evNull = true;
             }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
index adb33e4c8d4a1..b7be12f7aa741 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
@@ -66,10 +66,44 @@ case class MakeDecimal(child: Expression, precision: Int, scale: Int) extends Un
  * An expression used to wrap the children when promote the precision of DecimalType to avoid
  * promote multiple times.
  */
-case class ChangeDecimalPrecision(child: Expression) extends UnaryExpression {
+case class PromotePrecision(child: Expression) extends UnaryExpression {
   override def dataType: DataType = child.dataType
   override def eval(input: InternalRow): Any = child.eval(input)
   override def gen(ctx: CodeGenContext): GeneratedExpressionCode = child.gen(ctx)
   override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = ""
-  override def prettyName: String = "change_decimal_precision"
+  override def prettyName: String = "promote_precision"
+}
+
+/**
+ * Rounds the decimal to given scale and check whether the decimal can fit in provided precision
+ * or not, returns null if not.
+ */
+case class CheckOverflow(child: Expression, dataType: DecimalType) extends UnaryExpression {
+
+  override def nullable: Boolean = true
+
+  override def nullSafeEval(input: Any): Any = {
+    val d = input.asInstanceOf[Decimal].clone()
+    if (d.changePrecision(dataType.precision, dataType.scale)) {
+      d
+    } else {
+      null
+    }
+  }
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    nullSafeCodeGen(ctx, ev, eval => {
+      val tmp = ctx.freshName("tmp")
+      s"""
+         | Decimal $tmp = $eval.clone();
+         | if ($tmp.changePrecision(${dataType.precision}, ${dataType.scale})) {
+         |   ${ev.primitive} = $tmp;
+         | } else {
+         |   ${ev.isNull} = true;
+         | }
+       """.stripMargin
+    })
+  }
+
+  override def toString: String = s"CheckOverflow($child, $dataType)"
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
index d95805c24521c..c988f1d1b972e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/Decimal.scala
@@ -267,7 +267,7 @@ final class Decimal extends Ordered[Decimal] with Serializable {
     if (decimalVal.eq(null) && that.decimalVal.eq(null) && scale == that.scale) {
       Decimal(longVal + that.longVal, Math.max(precision, that.precision), scale)
     } else {
-      Decimal(toBigDecimal + that.toBigDecimal, precision, scale)
+      Decimal(toBigDecimal + that.toBigDecimal)
     }
   }
 
@@ -275,7 +275,7 @@ final class Decimal extends Ordered[Decimal] with Serializable {
     if (decimalVal.eq(null) && that.decimalVal.eq(null) && scale == that.scale) {
       Decimal(longVal - that.longVal, Math.max(precision, that.precision), scale)
     } else {
-      Decimal(toBigDecimal - that.toBigDecimal, precision, scale)
+      Decimal(toBigDecimal - that.toBigDecimal)
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DecimalExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DecimalExpressionSuite.scala
new file mode 100644
index 0000000000000..511f0307901df
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/DecimalExpressionSuite.scala
@@ -0,0 +1,63 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.types.{LongType, DecimalType, Decimal}
+
+
+class DecimalExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
+
+  test("UnscaledValue") {
+    val d1 = Decimal("10.1")
+    checkEvaluation(UnscaledValue(Literal(d1)), 101L)
+    val d2 = Decimal(101, 3, 1)
+    checkEvaluation(UnscaledValue(Literal(d2)), 101L)
+    checkEvaluation(UnscaledValue(Literal.create(null, DecimalType(2, 1))), null)
+  }
+
+  test("MakeDecimal") {
+    checkEvaluation(MakeDecimal(Literal(101L), 3, 1), Decimal("10.1"))
+    checkEvaluation(MakeDecimal(Literal.create(null, LongType), 3, 1), null)
+  }
+
+  test("PromotePrecision") {
+    val d1 = Decimal("10.1")
+    checkEvaluation(PromotePrecision(Literal(d1)), d1)
+    val d2 = Decimal(101, 3, 1)
+    checkEvaluation(PromotePrecision(Literal(d2)), d2)
+    checkEvaluation(PromotePrecision(Literal.create(null, DecimalType(2, 1))), null)
+  }
+
+  test("CheckOverflow") {
+    val d1 = Decimal("10.1")
+    checkEvaluation(CheckOverflow(Literal(d1), DecimalType(4, 0)), Decimal("10"))
+    checkEvaluation(CheckOverflow(Literal(d1), DecimalType(4, 1)), d1)
+    checkEvaluation(CheckOverflow(Literal(d1), DecimalType(4, 2)), d1)
+    checkEvaluation(CheckOverflow(Literal(d1), DecimalType(4, 3)), null)
+
+    val d2 = Decimal(101, 3, 1)
+    checkEvaluation(CheckOverflow(Literal(d2), DecimalType(4, 0)), Decimal("10"))
+    checkEvaluation(CheckOverflow(Literal(d2), DecimalType(4, 1)), d2)
+    checkEvaluation(CheckOverflow(Literal(d2), DecimalType(4, 2)), d2)
+    checkEvaluation(CheckOverflow(Literal(d2), DecimalType(4, 3)), null)
+
+    checkEvaluation(CheckOverflow(Literal.create(null, DecimalType(2, 1)), DecimalType(3, 2)), null)
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index c329fdb2a6bb1..141468ca00d67 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -17,16 +17,17 @@
 
 package org.apache.spark.sql
 
+import java.math.MathContext
 import java.sql.Timestamp
 
 import org.apache.spark.AccumulatorSuite
-import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.DefaultParserDialect
+import org.apache.spark.sql.catalyst.analysis.FunctionRegistry
 import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.test.SQLTestData._
+import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 /** A SQL Dialect for testing purpose, and it can not be nested type */
@@ -1608,6 +1609,24 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     }
   }
 
+  test("decimal precision with multiply/division") {
+    checkAnswer(sql("select 10.3 * 3.0"), Row(BigDecimal("30.90")))
+    checkAnswer(sql("select 10.3000 * 3.0"), Row(BigDecimal("30.90000")))
+    checkAnswer(sql("select 10.30000 * 30.0"), Row(BigDecimal("309.000000")))
+    checkAnswer(sql("select 10.300000000000000000 * 3.000000000000000000"),
+      Row(BigDecimal("30.900000000000000000000000000000000000", new MathContext(38))))
+    checkAnswer(sql("select 10.300000000000000000 * 3.0000000000000000000"),
+      Row(null))
+
+    checkAnswer(sql("select 10.3 / 3.0"), Row(BigDecimal("3.433333")))
+    checkAnswer(sql("select 10.3000 / 3.0"), Row(BigDecimal("3.4333333")))
+    checkAnswer(sql("select 10.30000 / 30.0"), Row(BigDecimal("0.343333333")))
+    checkAnswer(sql("select 10.300000000000000000 / 3.00000000000000000"),
+      Row(BigDecimal("3.4333333333333333333333333333333333333", new MathContext(38))))
+    checkAnswer(sql("select 10.3000000000000000000 / 3.00000000000000000"),
+      Row(null))
+  }
+
   test("external sorting updates peak execution memory") {
     withSQLConf((SQLConf.EXTERNAL_SORT.key, "true")) {
       val sc = sqlContext.sparkContext

From f3ff4c41d2e32bd0f2419d1c9c68fcd0c2593e41 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 19 Aug 2015 14:15:28 -0700
Subject: [PATCH 345/633] [SPARK-9899] [SQL] Disables customized output
 committer when speculation is on

Speculation hates direct output committer, as there are multiple corner cases that may cause data corruption and/or data loss.

Please see this [PR comment] [1] for more details.

[1]: https://github.com/apache/spark/pull/8191#issuecomment-131598385

Author: Cheng Lian <lian@databricks.com>

Closes #8317 from liancheng/spark-9899/speculation-hates-direct-output-committer.
---
 .../datasources/WriterContainer.scala         | 16 ++++++++-
 .../sql/sources/hadoopFsRelationSuites.scala  | 34 +++++++++++++++++++
 2 files changed, 49 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index e0147079e6997..78f48a5cd72c7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -58,6 +58,9 @@ private[sql] abstract class BaseWriterContainer(
   // This is only used on driver side.
   @transient private val jobContext: JobContext = job
 
+  private val speculationEnabled: Boolean =
+    relation.sqlContext.sparkContext.conf.getBoolean("spark.speculation", defaultValue = false)
+
   // The following fields are initialized and used on both driver and executor side.
   @transient protected var outputCommitter: OutputCommitter = _
   @transient private var jobId: JobID = _
@@ -126,10 +129,21 @@ private[sql] abstract class BaseWriterContainer(
       // associated with the file output format since it is not safe to use a custom
       // committer for appending. For example, in S3, direct parquet output committer may
       // leave partial data in the destination dir when the the appending job fails.
+      //
+      // See SPARK-8578 for more details
       logInfo(
-        s"Using output committer class ${defaultOutputCommitter.getClass.getCanonicalName} " +
+        s"Using default output committer ${defaultOutputCommitter.getClass.getCanonicalName} " +
           "for appending.")
       defaultOutputCommitter
+    } else if (speculationEnabled) {
+      // When speculation is enabled, it's not safe to use customized output committer classes,
+      // especially direct output committers (e.g. `DirectParquetOutputCommitter`).
+      //
+      // See SPARK-9899 for more details.
+      logInfo(
+        s"Using default output committer ${defaultOutputCommitter.getClass.getCanonicalName} " +
+          "because spark.speculation is configured to be true.")
+      defaultOutputCommitter
     } else {
       val committerClass = context.getConfiguration.getClass(
         SQLConf.OUTPUT_COMMITTER_CLASS.key, null, classOf[OutputCommitter])
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 8d0d9218ddd6a..5bbca14bad320 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -570,6 +570,40 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
       df.write.format(dataSourceName).partitionBy("c", "d", "e").saveAsTable("t")
     }
   }
+
+  test("SPARK-9899 Disable customized output committer when speculation is on") {
+    val clonedConf = new Configuration(configuration)
+    val speculationEnabled =
+      sqlContext.sparkContext.conf.getBoolean("spark.speculation", defaultValue = false)
+
+    try {
+      withTempPath { dir =>
+        // Enables task speculation
+        sqlContext.sparkContext.conf.set("spark.speculation", "true")
+
+        // Uses a customized output committer which always fails
+        configuration.set(
+          SQLConf.OUTPUT_COMMITTER_CLASS.key,
+          classOf[AlwaysFailOutputCommitter].getName)
+
+        // Code below shouldn't throw since customized output committer should be disabled.
+        val df = sqlContext.range(10).coalesce(1)
+        df.write.format(dataSourceName).save(dir.getCanonicalPath)
+        checkAnswer(
+          sqlContext
+            .read
+            .format(dataSourceName)
+            .option("dataSchema", df.schema.json)
+            .load(dir.getCanonicalPath),
+          df)
+      }
+    } finally {
+      // Hadoop 1 doesn't have `Configuration.unset`
+      configuration.clear()
+      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      sqlContext.sparkContext.conf.set("spark.speculation", speculationEnabled.toString)
+    }
+  }
 }
 
 // This class is used to test SPARK-8578. We should not use any custom output committer when

From 373a376c04320aab228b5c385e2b788809877d3e Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Wed, 19 Aug 2015 14:31:51 -0700
Subject: [PATCH 346/633] [SPARK-10083] [SQL] CaseWhen should support type
 coercion of DecimalType and FractionalType

create t1 (a decimal(7, 2), b long);
select case when 1=1 then a else 1.0 end from t1;
select case when 1=1 then a else b end from t1;

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #8270 from adrian-wang/casewhenfractional.
---
 .../sql/catalyst/analysis/HiveTypeCoercion.scala      |  4 ++--
 .../sql/catalyst/analysis/HiveTypeCoercionSuite.scala | 11 +++++++++++
 2 files changed, 13 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 62c27ee0b9ee0..f2f2ba2f96552 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -605,7 +605,7 @@ object HiveTypeCoercion {
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
       case c: CaseWhenLike if c.childrenResolved && !c.valueTypesEqual =>
         logDebug(s"Input values for null casting ${c.valueTypes.mkString(",")}")
-        val maybeCommonType = findTightestCommonTypeAndPromoteToString(c.valueTypes)
+        val maybeCommonType = findWiderCommonType(c.valueTypes)
         maybeCommonType.map { commonType =>
           val castedBranches = c.branches.grouped(2).map {
             case Seq(when, value) if value.dataType != commonType =>
@@ -622,7 +622,7 @@ object HiveTypeCoercion {
 
       case c: CaseKeyWhen if c.childrenResolved && !c.resolved =>
         val maybeCommonType =
-          findTightestCommonTypeAndPromoteToString((c.key +: c.whenList).map(_.dataType))
+          findWiderCommonType((c.key +: c.whenList).map(_.dataType))
         maybeCommonType.map { commonType =>
           val castedBranches = c.branches.grouped(2).map {
             case Seq(whenExpr, thenExpr) if whenExpr.dataType != commonType =>
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
index cbdf453f600ab..6f33ab733b615 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercionSuite.scala
@@ -285,6 +285,17 @@ class HiveTypeCoercionSuite extends PlanTest {
       CaseKeyWhen(Literal(true), Seq(Literal(1), Literal("a"))),
       CaseKeyWhen(Literal(true), Seq(Literal(1), Literal("a")))
     )
+    ruleTest(HiveTypeCoercion.CaseWhenCoercion,
+      CaseWhen(Seq(Literal(true), Literal(1.2), Literal.create(1, DecimalType(7, 2)))),
+      CaseWhen(Seq(
+        Literal(true), Literal(1.2), Cast(Literal.create(1, DecimalType(7, 2)), DoubleType)))
+    )
+    ruleTest(HiveTypeCoercion.CaseWhenCoercion,
+      CaseWhen(Seq(Literal(true), Literal(100L), Literal.create(1, DecimalType(7, 2)))),
+      CaseWhen(Seq(
+        Literal(true), Cast(Literal(100L), DecimalType(22, 2)),
+        Cast(Literal.create(1, DecimalType(7, 2)), DecimalType(22, 2))))
+    )
   }
 
   test("type coercion simplification for equal to") {

From e0dd1309ac248375f429639801923570f14de18d Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 19 Aug 2015 14:33:32 -0700
Subject: [PATCH 347/633] [SPARK-10119] [CORE] Fix isDynamicAllocationEnabled
 when config is expliticly disabled.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8316 from vanzin/SPARK-10119.
---
 .../main/scala/org/apache/spark/util/Utils.scala   |  2 +-
 .../scala/org/apache/spark/util/UtilsSuite.scala   | 14 ++++++++++++++
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index fddc24dbfc237..8313312226713 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -2141,7 +2141,7 @@ private[spark] object Utils extends Logging {
    * the latter should override the former (SPARK-9092).
    */
   def isDynamicAllocationEnabled(conf: SparkConf): Boolean = {
-    conf.contains("spark.dynamicAllocation.enabled") &&
+    conf.getBoolean("spark.dynamicAllocation.enabled", false) &&
       conf.getInt("spark.executor.instances", 0) == 0
   }
 
diff --git a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
index 8f7e402d5f2a6..1fb81ad565b41 100644
--- a/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/UtilsSuite.scala
@@ -720,4 +720,18 @@ class UtilsSuite extends SparkFunSuite with ResetSystemProperties with Logging {
     assert(Utils.nanSafeCompareFloats(Float.PositiveInfinity, Float.NaN) === -1)
     assert(Utils.nanSafeCompareFloats(Float.NegativeInfinity, Float.NaN) === -1)
   }
+
+  test("isDynamicAllocationEnabled") {
+    val conf = new SparkConf()
+    assert(Utils.isDynamicAllocationEnabled(conf) === false)
+    assert(Utils.isDynamicAllocationEnabled(
+      conf.set("spark.dynamicAllocation.enabled", "false")) === false)
+    assert(Utils.isDynamicAllocationEnabled(
+      conf.set("spark.dynamicAllocation.enabled", "true")) === true)
+    assert(Utils.isDynamicAllocationEnabled(
+      conf.set("spark.executor.instances", "1")) === false)
+    assert(Utils.isDynamicAllocationEnabled(
+      conf.set("spark.executor.instances", "0")) === true)
+  }
+
 }

From b0dbaec4f942a47afde3490b9339ad3bd187024d Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 19 Aug 2015 15:04:56 -0700
Subject: [PATCH 348/633] [SPARK-6489] [SQL] add column pruning for Generate

This PR takes over https://github.com/apache/spark/pull/5358

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8268 from cloud-fan/6489.
---
 .../sql/catalyst/expressions/generators.scala |  2 -
 .../sql/catalyst/optimizer/Optimizer.scala    | 16 ++++
 .../optimizer/ColumnPruningSuite.scala        | 84 +++++++++++++++++++
 3 files changed, 100 insertions(+), 2 deletions(-)
 create mode 100644 sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
index d474853355e5b..c0845e1a0102f 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/generators.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import scala.collection.Map
-
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 47b06cae15436..42457d5318b48 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -165,6 +165,7 @@ object SetOperationPushDown extends Rule[LogicalPlan] {
  *
  *  - Inserting Projections beneath the following operators:
  *   - Aggregate
+ *   - Generate
  *   - Project <- Join
  *   - LeftSemiJoin
  */
@@ -178,6 +179,21 @@ object ColumnPruning extends Rule[LogicalPlan] {
     case a @ Aggregate(_, _, child) if (child.outputSet -- a.references).nonEmpty =>
       a.copy(child = Project(a.references.toSeq, child))
 
+    // Eliminate attributes that are not needed to calculate the Generate.
+    case g: Generate if !g.join && (g.child.outputSet -- g.references).nonEmpty =>
+      g.copy(child = Project(g.references.toSeq, g.child))
+
+    case p @ Project(_, g: Generate) if g.join && p.references.subsetOf(g.generatedSet) =>
+      p.copy(child = g.copy(join = false))
+
+    case p @ Project(projectList, g: Generate) if g.join =>
+      val neededChildOutput = p.references -- g.generatorOutput ++ g.references
+      if (neededChildOutput == g.child.outputSet) {
+        p
+      } else {
+        Project(projectList, g.copy(child = Project(neededChildOutput.toSeq, g.child)))
+      }
+
     case p @ Project(projectList, a @ Aggregate(groupingExpressions, aggregateExpressions, child))
         if (a.outputSet -- p.references).nonEmpty =>
       Project(
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala
new file mode 100644
index 0000000000000..dbebcb86809de
--- /dev/null
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ColumnPruningSuite.scala
@@ -0,0 +1,84 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.optimizer
+
+import org.apache.spark.sql.catalyst.expressions.Explode
+import org.apache.spark.sql.catalyst.plans.PlanTest
+import org.apache.spark.sql.catalyst.plans.logical.{Project, LocalRelation, Generate, LogicalPlan}
+import org.apache.spark.sql.catalyst.rules.RuleExecutor
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.dsl.plans._
+import org.apache.spark.sql.types.StringType
+
+class ColumnPruningSuite extends PlanTest {
+
+  object Optimize extends RuleExecutor[LogicalPlan] {
+    val batches = Batch("Column pruning", FixedPoint(100),
+      ColumnPruning) :: Nil
+  }
+
+  test("Column pruning for Generate when Generate.join = false") {
+    val input = LocalRelation('a.int, 'b.array(StringType))
+
+    val query = Generate(Explode('b), false, false, None, 's.string :: Nil, input).analyze
+    val optimized = Optimize.execute(query)
+
+    val correctAnswer =
+      Generate(Explode('b), false, false, None, 's.string :: Nil,
+        Project('b.attr :: Nil, input)).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("Column pruning for Generate when Generate.join = true") {
+    val input = LocalRelation('a.int, 'b.int, 'c.array(StringType))
+
+    val query =
+      Project(Seq('a, 's),
+        Generate(Explode('c), true, false, None, 's.string :: Nil,
+          input)).analyze
+    val optimized = Optimize.execute(query)
+
+    val correctAnswer =
+      Project(Seq('a, 's),
+        Generate(Explode('c), true, false, None, 's.string :: Nil,
+          Project(Seq('a, 'c),
+            input))).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  test("Turn Generate.join to false if possible") {
+    val input = LocalRelation('b.array(StringType))
+
+    val query =
+      Project(('s + 1).as("s+1") :: Nil,
+        Generate(Explode('b), true, false, None, 's.string :: Nil,
+          input)).analyze
+    val optimized = Optimize.execute(query)
+
+    val correctAnswer =
+      Project(('s + 1).as("s+1") :: Nil,
+        Generate(Explode('b), false, false, None, 's.string :: Nil,
+          input)).analyze
+
+    comparePlans(optimized, correctAnswer)
+  }
+
+  // todo: add more tests for column pruning
+}

From 8e0a072f78b4902d5f7ccc6b15232ed202a117f9 Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Wed, 19 Aug 2015 15:43:08 -0700
Subject: [PATCH 349/633] [SPARK-9895] User Guide for RFormula Feature
 Transformer

mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #8293 from ericl/docs-2.
---
 docs/ml-features.md                           | 108 ++++++++++++++++++
 .../apache/spark/ml/feature/RFormula.scala    |   4 +-
 2 files changed, 110 insertions(+), 2 deletions(-)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index d0e8eeb7a757e..6309db97be4d0 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1477,3 +1477,111 @@ print(output.select("features", "clicked").first())
 </div>
 </div>
 
+## RFormula
+
+`RFormula` selects columns specified by an [R model formula](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/formula.html). It produces a vector column of features and a double column of labels. Like when formulas are used in R for linear regression, string input columns will be one-hot encoded, and numeric columns will be cast to doubles. If not already present in the DataFrame, the output label column will be created from the specified response variable in the formula.
+
+**Examples**
+
+Assume that we have a DataFrame with the columns `id`, `country`, `hour`, and `clicked`:
+
+~~~
+id | country | hour | clicked
+---|---------|------|---------
+ 7 | "US"    | 18   | 1.0
+ 8 | "CA"    | 12   | 0.0
+ 9 | "NZ"    | 15   | 0.0
+~~~
+
+If we use `RFormula` with a formula string of `clicked ~ country + hour`, which indicates that we want to
+predict `clicked` based on `country` and `hour`, after transformation we should get the following DataFrame:
+
+~~~
+id | country | hour | clicked | features         | label
+---|---------|------|---------|------------------|-------
+ 7 | "US"    | 18   | 1.0     | [0.0, 0.0, 18.0] | 1.0
+ 8 | "CA"    | 12   | 0.0     | [0.0, 1.0, 12.0] | 0.0
+ 9 | "NZ"    | 15   | 0.0     | [1.0, 0.0, 15.0] | 0.0
+~~~
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+[`RFormula`](api/scala/index.html#org.apache.spark.ml.feature.RFormula) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight scala %}
+import org.apache.spark.ml.feature.RFormula
+
+val dataset = sqlContext.createDataFrame(Seq(
+  (7, "US", 18, 1.0),
+  (8, "CA", 12, 0.0),
+  (9, "NZ", 15, 0.0)
+)).toDF("id", "country", "hour", "clicked")
+val formula = new RFormula()
+  .setFormula("clicked ~ country + hour")
+  .setFeaturesCol("features")
+  .setLabelCol("label")
+val output = formula.fit(dataset).transform(dataset)
+output.select("features", "label").show()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`RFormula`](api/java/org/apache/spark/ml/feature/RFormula.html) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.RFormula;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.*;
+import static org.apache.spark.sql.types.DataTypes.*;
+
+StructType schema = createStructType(new StructField[] {
+  createStructField("id", IntegerType, false),
+  createStructField("country", StringType, false),
+  createStructField("hour", IntegerType, false),
+  createStructField("clicked", DoubleType, false)
+});
+JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(7, "US", 18, 1.0),
+  RowFactory.create(8, "CA", 12, 0.0),
+  RowFactory.create(9, "NZ", 15, 0.0)
+));
+DataFrame dataset = sqlContext.createDataFrame(rdd, schema);
+
+RFormula formula = new RFormula()
+  .setFormula("clicked ~ country + hour")
+  .setFeaturesCol("features")
+  .setLabelCol("label");
+
+DataFrame output = formula.fit(dataset).transform(dataset);
+output.select("features", "label").show();
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+[`RFormula`](api/python/pyspark.ml.html#pyspark.ml.feature.RFormula) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight python %}
+from pyspark.ml.feature import RFormula
+
+dataset = sqlContext.createDataFrame(
+    [(7, "US", 18, 1.0),
+     (8, "CA", 12, 0.0),
+     (9, "NZ", 15, 0.0)],
+    ["id", "country", "hour", "clicked"])
+formula = RFormula(
+    formula="clicked ~ country + hour",
+    featuresCol="features",
+    labelCol="label")
+output = formula.fit(dataset).transform(dataset)
+output.select("features", "label").show()
+{% endhighlight %}
+</div>
+</div>
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index a752dacd72d95..a7fa50444209b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -42,8 +42,8 @@ private[feature] trait RFormulaBase extends HasFeaturesCol with HasLabelCol {
 /**
  * :: Experimental ::
  * Implements the transforms required for fitting a dataset against an R model formula. Currently
- * we support a limited subset of the R operators, including '~' and '+'. Also see the R formula
- * docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
+ * we support a limited subset of the R operators, including '.', '~', '+', and '-'. Also see the
+ * R formula docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
  */
 @Experimental
 class RFormula(override val uid: String) extends Estimator[RFormulaModel] with RFormulaBase {

From ba5f7e1842f2c5852b5309910c0d39926643da69 Mon Sep 17 00:00:00 2001
From: hyukjinkwon <gurwls223@gmail.com>
Date: Thu, 20 Aug 2015 08:13:25 +0800
Subject: [PATCH 350/633] [SPARK-10035] [SQL] Parquet filters does not process
 EqualNullSafe filter.
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

As I talked with Lian,

1. I added EquelNullSafe to ParquetFilters
 - It uses the same equality comparison filter with EqualTo since the Parquet filter performs actually null-safe equality comparison.

2. Updated the test code (ParquetFilterSuite)
 - Convert catalyst.Expression to sources.Filter
 - Removed Cast since only Literal is picked up as a proper Filter in DataSourceStrategy
 - Added EquelNullSafe comparison

3. Removed deprecated createFilter for catalyst.Expression

Author: hyukjinkwon <gurwls223@gmail.com>
Author: 권혁진 <gurwls223@gmail.com>

Closes #8275 from HyukjinKwon/master.
---
 .../datasources/parquet/ParquetFilters.scala  | 113 +++---------------
 .../parquet/ParquetFilterSuite.scala          |  63 ++++------
 2 files changed, 37 insertions(+), 139 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
index 63915e0a28655..c74c8388632f5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
@@ -22,8 +22,6 @@ import java.nio.ByteBuffer
 
 import com.google.common.io.BaseEncoding
 import org.apache.hadoop.conf.Configuration
-import org.apache.parquet.filter2.compat.FilterCompat
-import org.apache.parquet.filter2.compat.FilterCompat._
 import org.apache.parquet.filter2.predicate.FilterApi._
 import org.apache.parquet.filter2.predicate._
 import org.apache.parquet.io.api.Binary
@@ -39,12 +37,6 @@ import org.apache.spark.unsafe.types.UTF8String
 private[sql] object ParquetFilters {
   val PARQUET_FILTER_DATA = "org.apache.spark.sql.parquet.row.filter"
 
-  def createRecordFilter(filterExpressions: Seq[Expression]): Option[Filter] = {
-    filterExpressions.flatMap { filter =>
-      createFilter(filter)
-    }.reduceOption(FilterApi.and).map(FilterCompat.get)
-  }
-
   case class SetInFilter[T <: Comparable[T]](
     valueSet: Set[T]) extends UserDefinedPredicate[T] with Serializable {
 
@@ -205,6 +197,16 @@ private[sql] object ParquetFilters {
     // For any comparison operator `cmp`, both `a cmp NULL` and `NULL cmp a` evaluate to `NULL`,
     // which can be casted to `false` implicitly. Please refer to the `eval` method of these
     // operators and the `SimplifyFilters` rule for details.
+
+    // Hyukjin:
+    // I added [[EqualNullSafe]] with [[org.apache.parquet.filter2.predicate.Operators.Eq]].
+    // So, it performs equality comparison identically when given [[sources.Filter]] is [[EqualTo]].
+    // The reason why I did this is, that the actual Parquet filter checks null-safe equality
+    // comparison.
+    // So I added this and maybe [[EqualTo]] should be changed. It still seems fine though, because
+    // physical planning does not set `NULL` to [[EqualTo]] but changes it to [[IsNull]] and etc.
+    // Probably I missed something and obviously this should be changed.
+
     predicate match {
       case sources.IsNull(name) =>
         makeEq.lift(dataTypeOf(name)).map(_(name, null))
@@ -216,6 +218,11 @@ private[sql] object ParquetFilters {
       case sources.Not(sources.EqualTo(name, value)) =>
         makeNotEq.lift(dataTypeOf(name)).map(_(name, value))
 
+      case sources.EqualNullSafe(name, value) =>
+        makeEq.lift(dataTypeOf(name)).map(_(name, value))
+      case sources.Not(sources.EqualNullSafe(name, value)) =>
+        makeNotEq.lift(dataTypeOf(name)).map(_(name, value))
+
       case sources.LessThan(name, value) =>
         makeLt.lift(dataTypeOf(name)).map(_(name, value))
       case sources.LessThanOrEqual(name, value) =>
@@ -273,96 +280,6 @@ private[sql] object ParquetFilters {
     addMethod.invoke(null, classOf[Binary], enumTypeDescriptor)
   }
 
-  /**
-   * Converts Catalyst predicate expressions to Parquet filter predicates.
-   *
-   * @todo This can be removed once we get rid of the old Parquet support.
-   */
-  def createFilter(predicate: Expression): Option[FilterPredicate] = {
-    // NOTE:
-    //
-    // For any comparison operator `cmp`, both `a cmp NULL` and `NULL cmp a` evaluate to `NULL`,
-    // which can be casted to `false` implicitly. Please refer to the `eval` method of these
-    // operators and the `SimplifyFilters` rule for details.
-    predicate match {
-      case IsNull(NamedExpression(name, dataType)) =>
-        makeEq.lift(dataType).map(_(name, null))
-      case IsNotNull(NamedExpression(name, dataType)) =>
-        makeNotEq.lift(dataType).map(_(name, null))
-
-      case EqualTo(NamedExpression(name, _), NonNullLiteral(value, dataType)) =>
-        makeEq.lift(dataType).map(_(name, value))
-      case EqualTo(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) =>
-        makeEq.lift(dataType).map(_(name, value))
-      case EqualTo(NonNullLiteral(value, dataType), NamedExpression(name, _)) =>
-        makeEq.lift(dataType).map(_(name, value))
-      case EqualTo(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) =>
-        makeEq.lift(dataType).map(_(name, value))
-
-      case Not(EqualTo(NamedExpression(name, _), NonNullLiteral(value, dataType))) =>
-        makeNotEq.lift(dataType).map(_(name, value))
-      case Not(EqualTo(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _))) =>
-        makeNotEq.lift(dataType).map(_(name, value))
-      case Not(EqualTo(NonNullLiteral(value, dataType), NamedExpression(name, _))) =>
-        makeNotEq.lift(dataType).map(_(name, value))
-      case Not(EqualTo(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType))) =>
-        makeNotEq.lift(dataType).map(_(name, value))
-
-      case LessThan(NamedExpression(name, _), NonNullLiteral(value, dataType)) =>
-        makeLt.lift(dataType).map(_(name, value))
-      case LessThan(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) =>
-        makeLt.lift(dataType).map(_(name, value))
-      case LessThan(NonNullLiteral(value, dataType), NamedExpression(name, _)) =>
-        makeGt.lift(dataType).map(_(name, value))
-      case LessThan(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) =>
-        makeGt.lift(dataType).map(_(name, value))
-
-      case LessThanOrEqual(NamedExpression(name, _), NonNullLiteral(value, dataType)) =>
-        makeLtEq.lift(dataType).map(_(name, value))
-      case LessThanOrEqual(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) =>
-        makeLtEq.lift(dataType).map(_(name, value))
-      case LessThanOrEqual(NonNullLiteral(value, dataType), NamedExpression(name, _)) =>
-        makeGtEq.lift(dataType).map(_(name, value))
-      case LessThanOrEqual(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) =>
-        makeGtEq.lift(dataType).map(_(name, value))
-
-      case GreaterThan(NamedExpression(name, _), NonNullLiteral(value, dataType)) =>
-        makeGt.lift(dataType).map(_(name, value))
-      case GreaterThan(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) =>
-        makeGt.lift(dataType).map(_(name, value))
-      case GreaterThan(NonNullLiteral(value, dataType), NamedExpression(name, _)) =>
-        makeLt.lift(dataType).map(_(name, value))
-      case GreaterThan(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) =>
-        makeLt.lift(dataType).map(_(name, value))
-
-      case GreaterThanOrEqual(NamedExpression(name, _), NonNullLiteral(value, dataType)) =>
-        makeGtEq.lift(dataType).map(_(name, value))
-      case GreaterThanOrEqual(Cast(NamedExpression(name, _), dataType), NonNullLiteral(value, _)) =>
-        makeGtEq.lift(dataType).map(_(name, value))
-      case GreaterThanOrEqual(NonNullLiteral(value, dataType), NamedExpression(name, _)) =>
-        makeLtEq.lift(dataType).map(_(name, value))
-      case GreaterThanOrEqual(NonNullLiteral(value, _), Cast(NamedExpression(name, _), dataType)) =>
-        makeLtEq.lift(dataType).map(_(name, value))
-
-      case And(lhs, rhs) =>
-        (createFilter(lhs) ++ createFilter(rhs)).reduceOption(FilterApi.and)
-
-      case Or(lhs, rhs) =>
-        for {
-          lhsFilter <- createFilter(lhs)
-          rhsFilter <- createFilter(rhs)
-        } yield FilterApi.or(lhsFilter, rhsFilter)
-
-      case Not(pred) =>
-        createFilter(pred).map(FilterApi.not)
-
-      case InSet(NamedExpression(name, dataType), valueSet) =>
-        makeInSet.lift(dataType).map(_(name, valueSet))
-
-      case _ => None
-    }
-  }
-
   /**
    * Note: Inside the Hadoop API we only have access to `Configuration`, not to
    * [[org.apache.spark.SparkContext]], so we cannot use broadcasts to convey
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
index 5b4e568bb9838..f067112cfca95 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilterSuite.scala
@@ -24,9 +24,8 @@ import org.apache.spark.sql.{Column, DataFrame, QueryTest, Row, SQLConf}
 import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
-import org.apache.spark.sql.execution.datasources.LogicalRelation
+import org.apache.spark.sql.execution.datasources.{DataSourceStrategy, LogicalRelation}
 import org.apache.spark.sql.test.SharedSQLContext
-import org.apache.spark.sql.types._
 
 /**
  * A test suite that tests Parquet filter2 API based filter pushdown optimization.
@@ -55,20 +54,22 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
         .select(output.map(e => Column(e)): _*)
         .where(Column(predicate))
 
-      val maybeAnalyzedPredicate = query.queryExecution.optimizedPlan.collect {
+      val analyzedPredicate = query.queryExecution.optimizedPlan.collect {
         case PhysicalOperation(_, filters, LogicalRelation(_: ParquetRelation)) => filters
-      }.flatten.reduceOption(_ && _)
+      }.flatten
+      assert(analyzedPredicate.nonEmpty)
 
-      assert(maybeAnalyzedPredicate.isDefined)
-      maybeAnalyzedPredicate.foreach { pred =>
-        val maybeFilter = ParquetFilters.createFilter(pred)
+      val selectedFilters = DataSourceStrategy.selectFilters(analyzedPredicate)
+      assert(selectedFilters.nonEmpty)
+
+      selectedFilters.foreach { pred =>
+        val maybeFilter = ParquetFilters.createFilter(df.schema, pred)
         assert(maybeFilter.isDefined, s"Couldn't generate filter predicate for $pred")
         maybeFilter.foreach { f =>
           // Doesn't bother checking type parameters here (e.g. `Eq[Integer]`)
           assert(f.getClass === filterClass)
         }
       }
-
       checker(query, expected)
     }
   }
@@ -109,43 +110,18 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], Seq(Row(true), Row(false)))
 
       checkFilterPredicate('_1 === true, classOf[Eq[_]], true)
+      checkFilterPredicate('_1 <=> true, classOf[Eq[_]], true)
       checkFilterPredicate('_1 !== true, classOf[NotEq[_]], false)
     }
   }
 
-  test("filter pushdown - short") {
-    withParquetDataFrame((1 to 4).map(i => Tuple1(Option(i.toShort)))) { implicit df =>
-      checkFilterPredicate(Cast('_1, IntegerType) === 1, classOf[Eq[_]], 1)
-      checkFilterPredicate(
-        Cast('_1, IntegerType) !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_)))
-
-      checkFilterPredicate(Cast('_1, IntegerType) < 2, classOf[Lt[_]], 1)
-      checkFilterPredicate(Cast('_1, IntegerType) > 3, classOf[Gt[_]], 4)
-      checkFilterPredicate(Cast('_1, IntegerType) <= 1, classOf[LtEq[_]], 1)
-      checkFilterPredicate(Cast('_1, IntegerType) >= 4, classOf[GtEq[_]], 4)
-
-      checkFilterPredicate(Literal(1) === Cast('_1, IntegerType), classOf[Eq[_]], 1)
-      checkFilterPredicate(Literal(2) > Cast('_1, IntegerType), classOf[Lt[_]], 1)
-      checkFilterPredicate(Literal(3) < Cast('_1, IntegerType), classOf[Gt[_]], 4)
-      checkFilterPredicate(Literal(1) >= Cast('_1, IntegerType), classOf[LtEq[_]], 1)
-      checkFilterPredicate(Literal(4) <= Cast('_1, IntegerType), classOf[GtEq[_]], 4)
-
-      checkFilterPredicate(!(Cast('_1, IntegerType) < 4), classOf[GtEq[_]], 4)
-      checkFilterPredicate(
-        Cast('_1, IntegerType) > 2 && Cast('_1, IntegerType) < 4, classOf[Operators.And], 3)
-      checkFilterPredicate(
-        Cast('_1, IntegerType) < 2 || Cast('_1, IntegerType) > 3,
-        classOf[Operators.Or],
-        Seq(Row(1), Row(4)))
-    }
-  }
-
   test("filter pushdown - integer") {
     withParquetDataFrame((1 to 4).map(i => Tuple1(Option(i)))) { implicit df =>
       checkFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row])
       checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1)
+      checkFilterPredicate('_1 <=> 1, classOf[Eq[_]], 1)
       checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1)
@@ -154,13 +130,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1)
+      checkFilterPredicate(Literal(1) <=> '_1, classOf[Eq[_]], 1)
       checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1)
       checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4)
       checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1)
       checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4)
-      checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3)
       checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4)))
     }
   }
@@ -171,6 +147,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1)
+      checkFilterPredicate('_1 <=> 1, classOf[Eq[_]], 1)
       checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1)
@@ -179,13 +156,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1)
+      checkFilterPredicate(Literal(1) <=> '_1, classOf[Eq[_]], 1)
       checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1)
       checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4)
       checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1)
       checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4)
-      checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3)
       checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4)))
     }
   }
@@ -196,6 +173,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1)
+      checkFilterPredicate('_1 <=> 1, classOf[Eq[_]], 1)
       checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1)
@@ -204,13 +182,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1)
+      checkFilterPredicate(Literal(1) <=> '_1, classOf[Eq[_]], 1)
       checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1)
       checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4)
       checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1)
       checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4)
-      checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3)
       checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4)))
     }
   }
@@ -221,6 +199,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 === 1, classOf[Eq[_]], 1)
+      checkFilterPredicate('_1 <=> 1, classOf[Eq[_]], 1)
       checkFilterPredicate('_1 !== 1, classOf[NotEq[_]], (2 to 4).map(Row.apply(_)))
 
       checkFilterPredicate('_1 < 2, classOf[Lt[_]], 1)
@@ -229,13 +208,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1 >= 4, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(Literal(1) === '_1, classOf[Eq[_]], 1)
+      checkFilterPredicate(Literal(1) <=> '_1, classOf[Eq[_]], 1)
       checkFilterPredicate(Literal(2) > '_1, classOf[Lt[_]], 1)
       checkFilterPredicate(Literal(3) < '_1, classOf[Gt[_]], 4)
       checkFilterPredicate(Literal(1) >= '_1, classOf[LtEq[_]], 1)
       checkFilterPredicate(Literal(4) <= '_1, classOf[GtEq[_]], 4)
 
       checkFilterPredicate(!('_1 < 4), classOf[GtEq[_]], 4)
-      checkFilterPredicate('_1 > 2 && '_1 < 4, classOf[Operators.And], 3)
       checkFilterPredicate('_1 < 2 || '_1 > 3, classOf[Operators.Or], Seq(Row(1), Row(4)))
     }
   }
@@ -247,6 +226,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
         '_1.isNotNull, classOf[NotEq[_]], (1 to 4).map(i => Row.apply(i.toString)))
 
       checkFilterPredicate('_1 === "1", classOf[Eq[_]], "1")
+      checkFilterPredicate('_1 <=> "1", classOf[Eq[_]], "1")
       checkFilterPredicate(
         '_1 !== "1", classOf[NotEq[_]], (2 to 4).map(i => Row.apply(i.toString)))
 
@@ -256,13 +236,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkFilterPredicate('_1 >= "4", classOf[GtEq[_]], "4")
 
       checkFilterPredicate(Literal("1") === '_1, classOf[Eq[_]], "1")
+      checkFilterPredicate(Literal("1") <=> '_1, classOf[Eq[_]], "1")
       checkFilterPredicate(Literal("2") > '_1, classOf[Lt[_]], "1")
       checkFilterPredicate(Literal("3") < '_1, classOf[Gt[_]], "4")
       checkFilterPredicate(Literal("1") >= '_1, classOf[LtEq[_]], "1")
       checkFilterPredicate(Literal("4") <= '_1, classOf[GtEq[_]], "4")
 
       checkFilterPredicate(!('_1 < "4"), classOf[GtEq[_]], "4")
-      checkFilterPredicate('_1 > "2" && '_1 < "4", classOf[Operators.And], "3")
       checkFilterPredicate('_1 < "2" || '_1 > "3", classOf[Operators.Or], Seq(Row("1"), Row("4")))
     }
   }
@@ -274,6 +254,7 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
 
     withParquetDataFrame((1 to 4).map(i => Tuple1(i.b))) { implicit df =>
       checkBinaryFilterPredicate('_1 === 1.b, classOf[Eq[_]], 1.b)
+      checkBinaryFilterPredicate('_1 <=> 1.b, classOf[Eq[_]], 1.b)
 
       checkBinaryFilterPredicate('_1.isNull, classOf[Eq[_]], Seq.empty[Row])
       checkBinaryFilterPredicate(
@@ -288,13 +269,13 @@ class ParquetFilterSuite extends QueryTest with ParquetTest with SharedSQLContex
       checkBinaryFilterPredicate('_1 >= 4.b, classOf[GtEq[_]], 4.b)
 
       checkBinaryFilterPredicate(Literal(1.b) === '_1, classOf[Eq[_]], 1.b)
+      checkBinaryFilterPredicate(Literal(1.b) <=> '_1, classOf[Eq[_]], 1.b)
       checkBinaryFilterPredicate(Literal(2.b) > '_1, classOf[Lt[_]], 1.b)
       checkBinaryFilterPredicate(Literal(3.b) < '_1, classOf[Gt[_]], 4.b)
       checkBinaryFilterPredicate(Literal(1.b) >= '_1, classOf[LtEq[_]], 1.b)
       checkBinaryFilterPredicate(Literal(4.b) <= '_1, classOf[GtEq[_]], 4.b)
 
       checkBinaryFilterPredicate(!('_1 < 4.b), classOf[GtEq[_]], 4.b)
-      checkBinaryFilterPredicate('_1 > 2.b && '_1 < 4.b, classOf[Operators.And], 3.b)
       checkBinaryFilterPredicate(
         '_1 < 2.b || '_1 > 3.b, classOf[Operators.Or], Seq(Row(1.b), Row(4.b)))
     }

From 2f2686a73f5a2a53ca5b1023e0d7e0e6c9be5896 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 19 Aug 2015 17:35:41 -0700
Subject: [PATCH 351/633] [SPARK-9242] [SQL] Audit UDAF interface.

A few minor changes:

1. Improved documentation
2. Rename apply(distinct....) to distinct.
3. Changed MutableAggregationBuffer from a trait to an abstract class.
4. Renamed returnDataType to dataType to be more consistent with other expressions.

And unrelated to UDAFs:

1. Renamed file names in expressions to use suffix "Expressions" to be more consistent.
2. Moved regexp related expressions out to its own file.
3. Renamed StringComparison => StringPredicate.

Author: Reynold Xin <rxin@databricks.com>

Closes #8321 from rxin/SPARK-9242.
---
 ...bitwise.scala => bitwiseExpressions.scala} |   0
 ...als.scala => conditionalExpressions.scala} |   0
 ...ctions.scala => datetimeExpressions.scala} |   0
 ...nctions.scala => decimalExpressions.scala} |   0
 ...nFunctions.scala => jsonExpressions.scala} |   0
 .../{math.scala => mathExpressions.scala}     |   0
 ...lFunctions.scala => nullExpressions.scala} |   0
 .../{random.scala => randomExpressions.scala} |   0
 .../expressions/regexpExpressions.scala       | 346 ++++++++++++++++++
 ...erations.scala => stringExpressions.scala} | 332 +----------------
 .../sql/catalyst/optimizer/Optimizer.scala    |   2 +-
 .../expressions/StringExpressionsSuite.scala  |   2 +-
 .../apache/spark/sql/UDFRegistration.scala    |   1 +
 .../spark/sql/execution/aggregate/udaf.scala  |   2 +-
 .../apache/spark/sql/expressions/udaf.scala   |  44 ++-
 .../spark/sql/hive/JavaDataFrameSuite.java    |   2 +-
 .../spark/sql/hive/aggregate/MyDoubleAvg.java |   2 +-
 .../spark/sql/hive/aggregate/MyDoubleSum.java |   2 +-
 18 files changed, 386 insertions(+), 349 deletions(-)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{bitwise.scala => bitwiseExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{conditionals.scala => conditionalExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{datetimeFunctions.scala => datetimeExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{decimalFunctions.scala => decimalExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{jsonFunctions.scala => jsonExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{math.scala => mathExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{nullFunctions.scala => nullExpressions.scala} (100%)
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{random.scala => randomExpressions.scala} (100%)
 create mode 100644 sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/regexpExpressions.scala
 rename sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/{stringOperations.scala => stringExpressions.scala} (74%)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwiseExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwise.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/bitwiseExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionalExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionals.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/conditionalExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeFunctions.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalFunctions.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/decimalExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonFunctions.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/jsonExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/math.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullFunctions.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/nullExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/randomExpressions.scala
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/randomExpressions.scala
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/regexpExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/regexpExpressions.scala
new file mode 100644
index 0000000000000..6dff28a7cde46
--- /dev/null
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/regexpExpressions.scala
@@ -0,0 +1,346 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.expressions
+
+import java.util.regex.{MatchResult, Pattern}
+
+import org.apache.commons.lang3.StringEscapeUtils
+
+import org.apache.spark.sql.catalyst.expressions.codegen._
+import org.apache.spark.sql.catalyst.util.StringUtils
+import org.apache.spark.sql.types._
+import org.apache.spark.unsafe.types.UTF8String
+
+
+trait StringRegexExpression extends ImplicitCastInputTypes {
+  self: BinaryExpression =>
+
+  def escape(v: String): String
+  def matches(regex: Pattern, str: String): Boolean
+
+  override def dataType: DataType = BooleanType
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
+
+  // try cache the pattern for Literal
+  private lazy val cache: Pattern = right match {
+    case x @ Literal(value: String, StringType) => compile(value)
+    case _ => null
+  }
+
+  protected def compile(str: String): Pattern = if (str == null) {
+    null
+  } else {
+    // Let it raise exception if couldn't compile the regex string
+    Pattern.compile(escape(str))
+  }
+
+  protected def pattern(str: String) = if (cache == null) compile(str) else cache
+
+  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
+    val regex = pattern(input2.asInstanceOf[UTF8String].toString)
+    if(regex == null) {
+      null
+    } else {
+      matches(regex, input1.asInstanceOf[UTF8String].toString)
+    }
+  }
+}
+
+
+/**
+ * Simple RegEx pattern matching function
+ */
+case class Like(left: Expression, right: Expression)
+  extends BinaryExpression with StringRegexExpression with CodegenFallback {
+
+  override def escape(v: String): String = StringUtils.escapeLikeRegex(v)
+
+  override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).matches()
+
+  override def toString: String = s"$left LIKE $right"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val patternClass = classOf[Pattern].getName
+    val escapeFunc = StringUtils.getClass.getName.stripSuffix("$") + ".escapeLikeRegex"
+    val pattern = ctx.freshName("pattern")
+
+    if (right.foldable) {
+      val rVal = right.eval()
+      if (rVal != null) {
+        val regexStr =
+          StringEscapeUtils.escapeJava(escape(rVal.asInstanceOf[UTF8String].toString()))
+        ctx.addMutableState(patternClass, pattern,
+          s"""$pattern = ${patternClass}.compile("$regexStr");""")
+
+        // We don't use nullSafeCodeGen here because we don't want to re-evaluate right again.
+        val eval = left.gen(ctx)
+        s"""
+          ${eval.code}
+          boolean ${ev.isNull} = ${eval.isNull};
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (!${ev.isNull}) {
+            ${ev.primitive} = $pattern.matcher(${eval.primitive}.toString()).matches();
+          }
+        """
+      } else {
+        s"""
+          boolean ${ev.isNull} = true;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+        """
+      }
+    } else {
+      nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
+        s"""
+          String rightStr = ${eval2}.toString();
+          ${patternClass} $pattern = ${patternClass}.compile($escapeFunc(rightStr));
+          ${ev.primitive} = $pattern.matcher(${eval1}.toString()).matches();
+        """
+      })
+    }
+  }
+}
+
+
+case class RLike(left: Expression, right: Expression)
+  extends BinaryExpression with StringRegexExpression with CodegenFallback {
+
+  override def escape(v: String): String = v
+  override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
+  override def toString: String = s"$left RLIKE $right"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val patternClass = classOf[Pattern].getName
+    val pattern = ctx.freshName("pattern")
+
+    if (right.foldable) {
+      val rVal = right.eval()
+      if (rVal != null) {
+        val regexStr =
+          StringEscapeUtils.escapeJava(rVal.asInstanceOf[UTF8String].toString())
+        ctx.addMutableState(patternClass, pattern,
+          s"""$pattern = ${patternClass}.compile("$regexStr");""")
+
+        // We don't use nullSafeCodeGen here because we don't want to re-evaluate right again.
+        val eval = left.gen(ctx)
+        s"""
+          ${eval.code}
+          boolean ${ev.isNull} = ${eval.isNull};
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+          if (!${ev.isNull}) {
+            ${ev.primitive} = $pattern.matcher(${eval.primitive}.toString()).find(0);
+          }
+        """
+      } else {
+        s"""
+          boolean ${ev.isNull} = true;
+          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
+        """
+      }
+    } else {
+      nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
+        s"""
+          String rightStr = ${eval2}.toString();
+          ${patternClass} $pattern = ${patternClass}.compile(rightStr);
+          ${ev.primitive} = $pattern.matcher(${eval1}.toString()).find(0);
+        """
+      })
+    }
+  }
+}
+
+
+/**
+ * Splits str around pat (pattern is a regular expression).
+ */
+case class StringSplit(str: Expression, pattern: Expression)
+  extends BinaryExpression with ImplicitCastInputTypes {
+
+  override def left: Expression = str
+  override def right: Expression = pattern
+  override def dataType: DataType = ArrayType(StringType)
+  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
+
+  override def nullSafeEval(string: Any, regex: Any): Any = {
+    val strings = string.asInstanceOf[UTF8String].split(regex.asInstanceOf[UTF8String], -1)
+    new GenericArrayData(strings.asInstanceOf[Array[Any]])
+  }
+
+  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val arrayClass = classOf[GenericArrayData].getName
+    nullSafeCodeGen(ctx, ev, (str, pattern) =>
+      // Array in java is covariant, so we don't need to cast UTF8String[] to Object[].
+      s"""${ev.primitive} = new $arrayClass($str.split($pattern, -1));""")
+  }
+
+  override def prettyName: String = "split"
+}
+
+
+/**
+ * Replace all substrings of str that match regexp with rep.
+ *
+ * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
+ */
+case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expression)
+  extends TernaryExpression with ImplicitCastInputTypes {
+
+  // last regex in string, we will update the pattern iff regexp value changed.
+  @transient private var lastRegex: UTF8String = _
+  // last regex pattern, we cache it for performance concern
+  @transient private var pattern: Pattern = _
+  // last replacement string, we don't want to convert a UTF8String => java.langString every time.
+  @transient private var lastReplacement: String = _
+  @transient private var lastReplacementInUTF8: UTF8String = _
+  // result buffer write by Matcher
+  @transient private val result: StringBuffer = new StringBuffer
+
+  override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
+    if (!p.equals(lastRegex)) {
+      // regex value changed
+      lastRegex = p.asInstanceOf[UTF8String].clone()
+      pattern = Pattern.compile(lastRegex.toString)
+    }
+    if (!r.equals(lastReplacementInUTF8)) {
+      // replacement string changed
+      lastReplacementInUTF8 = r.asInstanceOf[UTF8String].clone()
+      lastReplacement = lastReplacementInUTF8.toString
+    }
+    val m = pattern.matcher(s.toString())
+    result.delete(0, result.length())
+
+    while (m.find) {
+      m.appendReplacement(result, lastReplacement)
+    }
+    m.appendTail(result)
+
+    UTF8String.fromString(result.toString)
+  }
+
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType, StringType)
+  override def children: Seq[Expression] = subject :: regexp :: rep :: Nil
+  override def prettyName: String = "regexp_replace"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val termLastRegex = ctx.freshName("lastRegex")
+    val termPattern = ctx.freshName("pattern")
+
+    val termLastReplacement = ctx.freshName("lastReplacement")
+    val termLastReplacementInUTF8 = ctx.freshName("lastReplacementInUTF8")
+
+    val termResult = ctx.freshName("result")
+
+    val classNamePattern = classOf[Pattern].getCanonicalName
+    val classNameStringBuffer = classOf[java.lang.StringBuffer].getCanonicalName
+
+    ctx.addMutableState("UTF8String", termLastRegex, s"${termLastRegex} = null;")
+    ctx.addMutableState(classNamePattern, termPattern, s"${termPattern} = null;")
+    ctx.addMutableState("String", termLastReplacement, s"${termLastReplacement} = null;")
+    ctx.addMutableState("UTF8String",
+      termLastReplacementInUTF8, s"${termLastReplacementInUTF8} = null;")
+    ctx.addMutableState(classNameStringBuffer,
+      termResult, s"${termResult} = new $classNameStringBuffer();")
+
+    nullSafeCodeGen(ctx, ev, (subject, regexp, rep) => {
+    s"""
+      if (!$regexp.equals(${termLastRegex})) {
+        // regex value changed
+        ${termLastRegex} = $regexp.clone();
+        ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
+      }
+      if (!$rep.equals(${termLastReplacementInUTF8})) {
+        // replacement string changed
+        ${termLastReplacementInUTF8} = $rep.clone();
+        ${termLastReplacement} = ${termLastReplacementInUTF8}.toString();
+      }
+      ${termResult}.delete(0, ${termResult}.length());
+      java.util.regex.Matcher m = ${termPattern}.matcher($subject.toString());
+
+      while (m.find()) {
+        m.appendReplacement(${termResult}, ${termLastReplacement});
+      }
+      m.appendTail(${termResult});
+      ${ev.primitive} = UTF8String.fromString(${termResult}.toString());
+      ${ev.isNull} = false;
+    """
+    })
+  }
+}
+
+/**
+ * Extract a specific(idx) group identified by a Java regex.
+ *
+ * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
+ */
+case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expression)
+  extends TernaryExpression with ImplicitCastInputTypes {
+  def this(s: Expression, r: Expression) = this(s, r, Literal(1))
+
+  // last regex in string, we will update the pattern iff regexp value changed.
+  @transient private var lastRegex: UTF8String = _
+  // last regex pattern, we cache it for performance concern
+  @transient private var pattern: Pattern = _
+
+  override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
+    if (!p.equals(lastRegex)) {
+      // regex value changed
+      lastRegex = p.asInstanceOf[UTF8String].clone()
+      pattern = Pattern.compile(lastRegex.toString)
+    }
+    val m = pattern.matcher(s.toString)
+    if (m.find) {
+      val mr: MatchResult = m.toMatchResult
+      UTF8String.fromString(mr.group(r.asInstanceOf[Int]))
+    } else {
+      UTF8String.EMPTY_UTF8
+    }
+  }
+
+  override def dataType: DataType = StringType
+  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType, IntegerType)
+  override def children: Seq[Expression] = subject :: regexp :: idx :: Nil
+  override def prettyName: String = "regexp_extract"
+
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val termLastRegex = ctx.freshName("lastRegex")
+    val termPattern = ctx.freshName("pattern")
+    val classNamePattern = classOf[Pattern].getCanonicalName
+
+    ctx.addMutableState("UTF8String", termLastRegex, s"${termLastRegex} = null;")
+    ctx.addMutableState(classNamePattern, termPattern, s"${termPattern} = null;")
+
+    nullSafeCodeGen(ctx, ev, (subject, regexp, idx) => {
+      s"""
+      if (!$regexp.equals(${termLastRegex})) {
+        // regex value changed
+        ${termLastRegex} = $regexp.clone();
+        ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
+      }
+      java.util.regex.Matcher m =
+        ${termPattern}.matcher($subject.toString());
+      if (m.find()) {
+        java.util.regex.MatchResult mr = m.toMatchResult();
+        ${ev.primitive} = UTF8String.fromString(mr.group($idx));
+        ${ev.isNull} = false;
+      } else {
+        ${ev.primitive} = UTF8String.EMPTY_UTF8;
+        ${ev.isNull} = false;
+      }"""
+    })
+  }
+}
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
similarity index 74%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
rename to sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
index ca044d3e95e38..3c23f2ecfb57c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
@@ -21,13 +21,9 @@ import java.text.DecimalFormat
 import java.util.Arrays
 import java.util.{Map => JMap, HashMap}
 import java.util.Locale
-import java.util.regex.{MatchResult, Pattern}
-
-import org.apache.commons.lang3.StringEscapeUtils
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.catalyst.util.StringUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
 
@@ -124,143 +120,6 @@ case class ConcatWs(children: Seq[Expression])
   }
 }
 
-
-trait StringRegexExpression extends ImplicitCastInputTypes {
-  self: BinaryExpression =>
-
-  def escape(v: String): String
-  def matches(regex: Pattern, str: String): Boolean
-
-  override def dataType: DataType = BooleanType
-  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
-
-  // try cache the pattern for Literal
-  private lazy val cache: Pattern = right match {
-    case x @ Literal(value: String, StringType) => compile(value)
-    case _ => null
-  }
-
-  protected def compile(str: String): Pattern = if (str == null) {
-    null
-  } else {
-    // Let it raise exception if couldn't compile the regex string
-    Pattern.compile(escape(str))
-  }
-
-  protected def pattern(str: String) = if (cache == null) compile(str) else cache
-
-  protected override def nullSafeEval(input1: Any, input2: Any): Any = {
-    val regex = pattern(input2.asInstanceOf[UTF8String].toString())
-    if(regex == null) {
-      null
-    } else {
-      matches(regex, input1.asInstanceOf[UTF8String].toString())
-    }
-  }
-}
-
-/**
- * Simple RegEx pattern matching function
- */
-case class Like(left: Expression, right: Expression)
-  extends BinaryExpression with StringRegexExpression with CodegenFallback {
-
-  override def escape(v: String): String = StringUtils.escapeLikeRegex(v)
-
-  override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).matches()
-
-  override def toString: String = s"$left LIKE $right"
-
-  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val patternClass = classOf[Pattern].getName
-    val escapeFunc = StringUtils.getClass.getName.stripSuffix("$") + ".escapeLikeRegex"
-    val pattern = ctx.freshName("pattern")
-
-    if (right.foldable) {
-      val rVal = right.eval()
-      if (rVal != null) {
-        val regexStr =
-          StringEscapeUtils.escapeJava(escape(rVal.asInstanceOf[UTF8String].toString()))
-        ctx.addMutableState(patternClass, pattern,
-          s"""$pattern = ${patternClass}.compile("$regexStr");""")
-
-        // We don't use nullSafeCodeGen here because we don't want to re-evaluate right again.
-        val eval = left.gen(ctx)
-        s"""
-          ${eval.code}
-          boolean ${ev.isNull} = ${eval.isNull};
-          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-          if (!${ev.isNull}) {
-            ${ev.primitive} = $pattern.matcher(${eval.primitive}.toString()).matches();
-          }
-        """
-      } else {
-        s"""
-          boolean ${ev.isNull} = true;
-          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-        """
-      }
-    } else {
-      nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
-        s"""
-          String rightStr = ${eval2}.toString();
-          ${patternClass} $pattern = ${patternClass}.compile($escapeFunc(rightStr));
-          ${ev.primitive} = $pattern.matcher(${eval1}.toString()).matches();
-        """
-      })
-    }
-  }
-}
-
-
-case class RLike(left: Expression, right: Expression)
-  extends BinaryExpression with StringRegexExpression with CodegenFallback {
-
-  override def escape(v: String): String = v
-  override def matches(regex: Pattern, str: String): Boolean = regex.matcher(str).find(0)
-  override def toString: String = s"$left RLIKE $right"
-
-  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val patternClass = classOf[Pattern].getName
-    val pattern = ctx.freshName("pattern")
-
-    if (right.foldable) {
-      val rVal = right.eval()
-      if (rVal != null) {
-        val regexStr =
-          StringEscapeUtils.escapeJava(rVal.asInstanceOf[UTF8String].toString())
-        ctx.addMutableState(patternClass, pattern,
-          s"""$pattern = ${patternClass}.compile("$regexStr");""")
-
-        // We don't use nullSafeCodeGen here because we don't want to re-evaluate right again.
-        val eval = left.gen(ctx)
-        s"""
-          ${eval.code}
-          boolean ${ev.isNull} = ${eval.isNull};
-          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-          if (!${ev.isNull}) {
-            ${ev.primitive} = $pattern.matcher(${eval.primitive}.toString()).find(0);
-          }
-        """
-      } else {
-        s"""
-          boolean ${ev.isNull} = true;
-          ${ctx.javaType(dataType)} ${ev.primitive} = ${ctx.defaultValue(dataType)};
-        """
-      }
-    } else {
-      nullSafeCodeGen(ctx, ev, (eval1, eval2) => {
-        s"""
-          String rightStr = ${eval2}.toString();
-          ${patternClass} $pattern = ${patternClass}.compile(rightStr);
-          ${ev.primitive} = $pattern.matcher(${eval1}.toString()).find(0);
-        """
-      })
-    }
-  }
-}
-
-
 trait String2StringExpression extends ImplicitCastInputTypes {
   self: UnaryExpression =>
 
@@ -305,7 +164,7 @@ case class Lower(child: Expression) extends UnaryExpression with String2StringEx
 }
 
 /** A base trait for functions that compare two strings, returning a boolean. */
-trait StringComparison extends ImplicitCastInputTypes {
+trait StringPredicate extends Predicate with ImplicitCastInputTypes {
   self: BinaryExpression =>
 
   def compare(l: UTF8String, r: UTF8String): Boolean
@@ -322,7 +181,7 @@ trait StringComparison extends ImplicitCastInputTypes {
  * A function that returns true if the string `left` contains the string `right`.
  */
 case class Contains(left: Expression, right: Expression)
-    extends BinaryExpression with Predicate with StringComparison {
+    extends BinaryExpression with StringPredicate {
   override def compare(l: UTF8String, r: UTF8String): Boolean = l.contains(r)
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, (c1, c2) => s"($c1).contains($c2)")
@@ -333,7 +192,7 @@ case class Contains(left: Expression, right: Expression)
  * A function that returns true if the string `left` starts with the string `right`.
  */
 case class StartsWith(left: Expression, right: Expression)
-    extends BinaryExpression with Predicate with StringComparison {
+    extends BinaryExpression with StringPredicate {
   override def compare(l: UTF8String, r: UTF8String): Boolean = l.startsWith(r)
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, (c1, c2) => s"($c1).startsWith($c2)")
@@ -344,7 +203,7 @@ case class StartsWith(left: Expression, right: Expression)
  * A function that returns true if the string `left` ends with the string `right`.
  */
 case class EndsWith(left: Expression, right: Expression)
-    extends BinaryExpression with Predicate with StringComparison {
+    extends BinaryExpression with StringPredicate {
   override def compare(l: UTF8String, r: UTF8String): Boolean = l.endsWith(r)
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     defineCodeGen(ctx, ev, (c1, c2) => s"($c1).endsWith($c2)")
@@ -769,32 +628,6 @@ case class StringSpace(child: Expression)
   override def prettyName: String = "space"
 }
 
-/**
- * Splits str around pat (pattern is a regular expression).
- */
-case class StringSplit(str: Expression, pattern: Expression)
-  extends BinaryExpression with ImplicitCastInputTypes {
-
-  override def left: Expression = str
-  override def right: Expression = pattern
-  override def dataType: DataType = ArrayType(StringType)
-  override def inputTypes: Seq[DataType] = Seq(StringType, StringType)
-
-  override def nullSafeEval(string: Any, regex: Any): Any = {
-    val strings = string.asInstanceOf[UTF8String].split(regex.asInstanceOf[UTF8String], -1)
-    new GenericArrayData(strings.asInstanceOf[Array[Any]])
-  }
-
-  override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val arrayClass = classOf[GenericArrayData].getName
-    nullSafeCodeGen(ctx, ev, (str, pattern) =>
-      // Array in java is covariant, so we don't need to cast UTF8String[] to Object[].
-      s"""${ev.primitive} = new $arrayClass($str.split($pattern, -1));""")
-  }
-
-  override def prettyName: String = "split"
-}
-
 object Substring {
   def subStringBinarySQL(bytes: Array[Byte], pos: Int, len: Int): Array[Byte] = {
     if (pos > bytes.length) {
@@ -1048,163 +881,6 @@ case class Encode(value: Expression, charset: Expression)
   }
 }
 
-/**
- * Replace all substrings of str that match regexp with rep.
- *
- * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
- */
-case class RegExpReplace(subject: Expression, regexp: Expression, rep: Expression)
-  extends TernaryExpression with ImplicitCastInputTypes {
-
-  // last regex in string, we will update the pattern iff regexp value changed.
-  @transient private var lastRegex: UTF8String = _
-  // last regex pattern, we cache it for performance concern
-  @transient private var pattern: Pattern = _
-  // last replacement string, we don't want to convert a UTF8String => java.langString every time.
-  @transient private var lastReplacement: String = _
-  @transient private var lastReplacementInUTF8: UTF8String = _
-  // result buffer write by Matcher
-  @transient private val result: StringBuffer = new StringBuffer
-
-  override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
-    if (!p.equals(lastRegex)) {
-      // regex value changed
-      lastRegex = p.asInstanceOf[UTF8String].clone()
-      pattern = Pattern.compile(lastRegex.toString)
-    }
-    if (!r.equals(lastReplacementInUTF8)) {
-      // replacement string changed
-      lastReplacementInUTF8 = r.asInstanceOf[UTF8String].clone()
-      lastReplacement = lastReplacementInUTF8.toString
-    }
-    val m = pattern.matcher(s.toString())
-    result.delete(0, result.length())
-
-    while (m.find) {
-      m.appendReplacement(result, lastReplacement)
-    }
-    m.appendTail(result)
-
-    UTF8String.fromString(result.toString)
-  }
-
-  override def dataType: DataType = StringType
-  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType, StringType)
-  override def children: Seq[Expression] = subject :: regexp :: rep :: Nil
-  override def prettyName: String = "regexp_replace"
-
-  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val termLastRegex = ctx.freshName("lastRegex")
-    val termPattern = ctx.freshName("pattern")
-
-    val termLastReplacement = ctx.freshName("lastReplacement")
-    val termLastReplacementInUTF8 = ctx.freshName("lastReplacementInUTF8")
-
-    val termResult = ctx.freshName("result")
-
-    val classNamePattern = classOf[Pattern].getCanonicalName
-    val classNameStringBuffer = classOf[java.lang.StringBuffer].getCanonicalName
-
-    ctx.addMutableState("UTF8String",
-      termLastRegex, s"${termLastRegex} = null;")
-    ctx.addMutableState(classNamePattern,
-      termPattern, s"${termPattern} = null;")
-    ctx.addMutableState("String",
-      termLastReplacement, s"${termLastReplacement} = null;")
-    ctx.addMutableState("UTF8String",
-      termLastReplacementInUTF8, s"${termLastReplacementInUTF8} = null;")
-    ctx.addMutableState(classNameStringBuffer,
-      termResult, s"${termResult} = new $classNameStringBuffer();")
-
-    nullSafeCodeGen(ctx, ev, (subject, regexp, rep) => {
-    s"""
-      if (!$regexp.equals(${termLastRegex})) {
-        // regex value changed
-        ${termLastRegex} = $regexp.clone();
-        ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
-      }
-      if (!$rep.equals(${termLastReplacementInUTF8})) {
-        // replacement string changed
-        ${termLastReplacementInUTF8} = $rep.clone();
-        ${termLastReplacement} = ${termLastReplacementInUTF8}.toString();
-      }
-      ${termResult}.delete(0, ${termResult}.length());
-      java.util.regex.Matcher m = ${termPattern}.matcher($subject.toString());
-
-      while (m.find()) {
-        m.appendReplacement(${termResult}, ${termLastReplacement});
-      }
-      m.appendTail(${termResult});
-      ${ev.primitive} = UTF8String.fromString(${termResult}.toString());
-      ${ev.isNull} = false;
-    """
-    })
-  }
-}
-
-/**
- * Extract a specific(idx) group identified by a Java regex.
- *
- * NOTE: this expression is not THREAD-SAFE, as it has some internal mutable status.
- */
-case class RegExpExtract(subject: Expression, regexp: Expression, idx: Expression)
-  extends TernaryExpression with ImplicitCastInputTypes {
-  def this(s: Expression, r: Expression) = this(s, r, Literal(1))
-
-  // last regex in string, we will update the pattern iff regexp value changed.
-  @transient private var lastRegex: UTF8String = _
-  // last regex pattern, we cache it for performance concern
-  @transient private var pattern: Pattern = _
-
-  override def nullSafeEval(s: Any, p: Any, r: Any): Any = {
-    if (!p.equals(lastRegex)) {
-      // regex value changed
-      lastRegex = p.asInstanceOf[UTF8String].clone()
-      pattern = Pattern.compile(lastRegex.toString)
-    }
-    val m = pattern.matcher(s.toString())
-    if (m.find) {
-      val mr: MatchResult = m.toMatchResult
-      UTF8String.fromString(mr.group(r.asInstanceOf[Int]))
-    } else {
-      UTF8String.EMPTY_UTF8
-    }
-  }
-
-  override def dataType: DataType = StringType
-  override def inputTypes: Seq[AbstractDataType] = Seq(StringType, StringType, IntegerType)
-  override def children: Seq[Expression] = subject :: regexp :: idx :: Nil
-  override def prettyName: String = "regexp_extract"
-
-  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val termLastRegex = ctx.freshName("lastRegex")
-    val termPattern = ctx.freshName("pattern")
-    val classNamePattern = classOf[Pattern].getCanonicalName
-
-    ctx.addMutableState("UTF8String", termLastRegex, s"${termLastRegex} = null;")
-    ctx.addMutableState(classNamePattern, termPattern, s"${termPattern} = null;")
-
-    nullSafeCodeGen(ctx, ev, (subject, regexp, idx) => {
-      s"""
-      if (!$regexp.equals(${termLastRegex})) {
-        // regex value changed
-        ${termLastRegex} = $regexp.clone();
-        ${termPattern} = ${classNamePattern}.compile(${termLastRegex}.toString());
-      }
-      java.util.regex.Matcher m =
-        ${termPattern}.matcher($subject.toString());
-      if (m.find()) {
-        java.util.regex.MatchResult mr = m.toMatchResult();
-        ${ev.primitive} = UTF8String.fromString(mr.group($idx));
-        ${ev.isNull} = false;
-      } else {
-        ${ev.primitive} = UTF8String.EMPTY_UTF8;
-        ${ev.isNull} = false;
-      }"""
-    })
-  }
-}
-
 /**
  * Formats the number X to a format like '#,###,###.##', rounded to D decimal places,
  * and returns the result as a string. If D is 0, the result has no decimal point or
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 42457d5318b48..854463dd11c74 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -372,7 +372,7 @@ object NullPropagation extends Rule[LogicalPlan] {
         case _ => e
       }
 
-      case e: StringComparison => e.children match {
+      case e: StringPredicate => e.children match {
         case Literal(null, _) :: right :: Nil => Literal.create(null, e.dataType)
         case left :: Literal(null, _) :: Nil => Literal.create(null, e.dataType)
         case _ => e
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
index 426dc272471ae..99e3b13ce8c97 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/StringExpressionsSuite.scala
@@ -673,7 +673,7 @@ class StringExpressionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(Length(Literal.create(null, BinaryType)), null, create_row(bytes))
   }
 
-  test("number format") {
+  test("format_number / FormatNumber") {
     checkEvaluation(FormatNumber(Literal(4.asInstanceOf[Byte]), Literal(3)), "4.000")
     checkEvaluation(FormatNumber(Literal(4.asInstanceOf[Short]), Literal(3)), "4.000")
     checkEvaluation(FormatNumber(Literal(4.0f), Literal(3)), "4.000")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
index 1f270560d7bc1..fc4d0938c533a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/UDFRegistration.scala
@@ -56,6 +56,7 @@ class UDFRegistration private[sql] (sqlContext: SQLContext) extends Logging {
 
   /**
    * Register a user-defined aggregate function (UDAF).
+   *
    * @param name the name of the UDAF.
    * @param udaf the UDAF needs to be registered.
    * @return the registered UDAF.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
index 7619f3ec9f0a7..d43d3dd9ffaae 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/udaf.scala
@@ -304,7 +304,7 @@ private[sql] case class ScalaUDAF(
 
   override def nullable: Boolean = true
 
-  override def dataType: DataType = udaf.returnDataType
+  override def dataType: DataType = udaf.dataType
 
   override def deterministic: Boolean = udaf.deterministic
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala b/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
index 5180871585f25..258afadc76951 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/expressions/udaf.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.expressions
 
-import org.apache.spark.sql.catalyst.expressions.ScalaUDF
 import org.apache.spark.sql.catalyst.expressions.aggregate.{Complete, AggregateExpression2}
 import org.apache.spark.sql.execution.aggregate.ScalaUDAF
 import org.apache.spark.sql.{Column, Row}
@@ -26,7 +25,7 @@ import org.apache.spark.annotation.Experimental
 
 /**
  * :: Experimental ::
- * The abstract class for implementing user-defined aggregate functions.
+ * The base class for implementing user-defined aggregate functions (UDAF).
  */
 @Experimental
 abstract class UserDefinedAggregateFunction extends Serializable {
@@ -67,22 +66,35 @@ abstract class UserDefinedAggregateFunction extends Serializable {
   /**
    * The [[DataType]] of the returned value of this [[UserDefinedAggregateFunction]].
    */
-  def returnDataType: DataType
+  def dataType: DataType
 
-  /** Indicates if this function is deterministic. */
+  /**
+   * Returns true iff this function is deterministic, i.e. given the same input,
+   * always return the same output.
+   */
   def deterministic: Boolean
 
   /**
-   *  Initializes the given aggregation buffer. Initial values set by this method should satisfy
-   *  the condition that when merging two buffers with initial values, the new buffer
-   *  still store initial values.
+   * Initializes the given aggregation buffer, i.e. the zero value of the aggregation buffer.
+   *
+   * The contract should be that applying the merge function on two initial buffers should just
+   * return the initial buffer itself, i.e.
+   * `merge(initialBuffer, initialBuffer)` should equal `initialBuffer`.
    */
   def initialize(buffer: MutableAggregationBuffer): Unit
 
-  /** Updates the given aggregation buffer `buffer` with new input data from `input`. */
+  /**
+   * Updates the given aggregation buffer `buffer` with new input data from `input`.
+   *
+   * This is called once per input row.
+   */
   def update(buffer: MutableAggregationBuffer, input: Row): Unit
 
-  /** Merges two aggregation buffers and stores the updated buffer values back to `buffer1`. */
+  /**
+   * Merges two aggregation buffers and stores the updated buffer values back to `buffer1`.
+   *
+   * This is called when we merge two partially aggregated data together.
+   */
   def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit
 
   /**
@@ -92,7 +104,7 @@ abstract class UserDefinedAggregateFunction extends Serializable {
   def evaluate(buffer: Row): Any
 
   /**
-   * Creates a [[Column]] for this UDAF with given [[Column]]s as arguments.
+   * Creates a [[Column]] for this UDAF using given [[Column]]s as input arguments.
    */
   @scala.annotation.varargs
   def apply(exprs: Column*): Column = {
@@ -105,16 +117,16 @@ abstract class UserDefinedAggregateFunction extends Serializable {
   }
 
   /**
-   * Creates a [[Column]] for this UDAF with given [[Column]]s as arguments.
-   * If `isDistinct` is true, this UDAF is working on distinct input values.
+   * Creates a [[Column]] for this UDAF using the distinct values of the given
+   * [[Column]]s as input arguments.
    */
   @scala.annotation.varargs
-  def apply(isDistinct: Boolean, exprs: Column*): Column = {
+  def distinct(exprs: Column*): Column = {
     val aggregateExpression =
       AggregateExpression2(
         ScalaUDAF(exprs.map(_.expr), this),
         Complete,
-        isDistinct = isDistinct)
+        isDistinct = true)
     Column(aggregateExpression)
   }
 }
@@ -122,9 +134,11 @@ abstract class UserDefinedAggregateFunction extends Serializable {
 /**
  * :: Experimental ::
  * A [[Row]] representing an mutable aggregation buffer.
+ *
+ * This is not meant to be extended outside of Spark.
  */
 @Experimental
-trait MutableAggregationBuffer extends Row {
+abstract class MutableAggregationBuffer extends Row {
 
   /** Update the ith value of this buffer. */
   def update(i: Int, value: Any): Unit
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
index 21b053f07a3ba..a30dfa554eabc 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
@@ -92,7 +92,7 @@ public void testUDAF() {
     DataFrame aggregatedDF =
       df.groupBy()
         .agg(
-          udaf.apply(true, col("value")),
+          udaf.distinct(col("value")),
           udaf.apply(col("value")),
           registeredUDAF.apply(col("value")),
           callUDF("mydoublesum", col("value")));
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
index a2247e3da1554..2961b803f14aa 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
@@ -65,7 +65,7 @@ public MyDoubleAvg() {
     return _bufferSchema;
   }
 
-  @Override public DataType returnDataType() {
+  @Override public DataType dataType() {
     return _returnDataType;
   }
 
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
index da29e24d267dd..c71882a6e7bed 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
+++ b/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
@@ -60,7 +60,7 @@ public MyDoubleSum() {
     return _bufferSchema;
   }
 
-  @Override public DataType returnDataType() {
+  @Override public DataType dataType() {
     return _returnDataType;
   }
 

From 1f29d502e7ecd6faa185d70dc714f9ea3922fb6d Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 19 Aug 2015 18:36:01 -0700
Subject: [PATCH 352/633] [SPARK-9812] [STREAMING] Fix Python 3 compatibility
 issue in PySpark Streaming and some docs

This PR includes the following fixes:
1. Use `range` instead of `xrange` in `queue_stream.py` to support Python 3.
2. Fix the issue that `utf8_decoder` will return `bytes` rather than `str` when receiving an empty `bytes` in Python 3.
3. Fix the commands in docs so that the user can copy them directly to the command line. The previous commands was broken in the middle of a path, so when copying to the command line, the path would be split to two parts by the extra spaces, which forces the user to fix it manually.

Author: zsxwing <zsxwing@gmail.com>

Closes #8315 from zsxwing/SPARK-9812.
---
 .../src/main/python/streaming/direct_kafka_wordcount.py     | 6 +++---
 examples/src/main/python/streaming/flume_wordcount.py       | 5 +++--
 examples/src/main/python/streaming/kafka_wordcount.py       | 5 +++--
 examples/src/main/python/streaming/mqtt_wordcount.py        | 5 +++--
 examples/src/main/python/streaming/queue_stream.py          | 4 ++--
 python/pyspark/streaming/flume.py                           | 4 +++-
 python/pyspark/streaming/kafka.py                           | 4 +++-
 python/pyspark/streaming/kinesis.py                         | 4 +++-
 8 files changed, 23 insertions(+), 14 deletions(-)

diff --git a/examples/src/main/python/streaming/direct_kafka_wordcount.py b/examples/src/main/python/streaming/direct_kafka_wordcount.py
index 6ef188a220c51..ea20678b9acad 100644
--- a/examples/src/main/python/streaming/direct_kafka_wordcount.py
+++ b/examples/src/main/python/streaming/direct_kafka_wordcount.py
@@ -23,8 +23,8 @@
  http://kafka.apache.org/documentation.html#quickstart
 
  and then run the example
-    `$ bin/spark-submit --jars external/kafka-assembly/target/scala-*/\
-      spark-streaming-kafka-assembly-*.jar \
+    `$ bin/spark-submit --jars \
+      external/kafka-assembly/target/scala-*/spark-streaming-kafka-assembly-*.jar \
       examples/src/main/python/streaming/direct_kafka_wordcount.py \
       localhost:9092 test`
 """
@@ -37,7 +37,7 @@
 
 if __name__ == "__main__":
     if len(sys.argv) != 3:
-        print >> sys.stderr, "Usage: direct_kafka_wordcount.py <broker_list> <topic>"
+        print("Usage: direct_kafka_wordcount.py <broker_list> <topic>", file=sys.stderr)
         exit(-1)
 
     sc = SparkContext(appName="PythonStreamingDirectKafkaWordCount")
diff --git a/examples/src/main/python/streaming/flume_wordcount.py b/examples/src/main/python/streaming/flume_wordcount.py
index 091b64d8c4af4..d75bc6daac138 100644
--- a/examples/src/main/python/streaming/flume_wordcount.py
+++ b/examples/src/main/python/streaming/flume_wordcount.py
@@ -23,8 +23,9 @@
  https://flume.apache.org/documentation.html
 
  and then run the example
-    `$ bin/spark-submit --jars external/flume-assembly/target/scala-*/\
-      spark-streaming-flume-assembly-*.jar examples/src/main/python/streaming/flume_wordcount.py \
+    `$ bin/spark-submit --jars \
+      external/flume-assembly/target/scala-*/spark-streaming-flume-assembly-*.jar \
+      examples/src/main/python/streaming/flume_wordcount.py \
       localhost 12345
 """
 from __future__ import print_function
diff --git a/examples/src/main/python/streaming/kafka_wordcount.py b/examples/src/main/python/streaming/kafka_wordcount.py
index b178e7899b5e1..8d697f620f467 100644
--- a/examples/src/main/python/streaming/kafka_wordcount.py
+++ b/examples/src/main/python/streaming/kafka_wordcount.py
@@ -23,8 +23,9 @@
  http://kafka.apache.org/documentation.html#quickstart
 
  and then run the example
-    `$ bin/spark-submit --jars external/kafka-assembly/target/scala-*/\
-      spark-streaming-kafka-assembly-*.jar examples/src/main/python/streaming/kafka_wordcount.py \
+    `$ bin/spark-submit --jars \
+      external/kafka-assembly/target/scala-*/spark-streaming-kafka-assembly-*.jar \
+      examples/src/main/python/streaming/kafka_wordcount.py \
       localhost:2181 test`
 """
 from __future__ import print_function
diff --git a/examples/src/main/python/streaming/mqtt_wordcount.py b/examples/src/main/python/streaming/mqtt_wordcount.py
index 617ce5ea6775e..abf9c0e21d307 100644
--- a/examples/src/main/python/streaming/mqtt_wordcount.py
+++ b/examples/src/main/python/streaming/mqtt_wordcount.py
@@ -26,8 +26,9 @@
  http://www.eclipse.org/paho/#getting-started
 
  and then run the example
-    `$ bin/spark-submit --jars external/mqtt-assembly/target/scala-*/\
-      spark-streaming-mqtt-assembly-*.jar examples/src/main/python/streaming/mqtt_wordcount.py \
+    `$ bin/spark-submit --jars \
+      external/mqtt-assembly/target/scala-*/spark-streaming-mqtt-assembly-*.jar \
+      examples/src/main/python/streaming/mqtt_wordcount.py \
       tcp://localhost:1883 foo`
 """
 
diff --git a/examples/src/main/python/streaming/queue_stream.py b/examples/src/main/python/streaming/queue_stream.py
index dcd6a0fc6ff91..b3808907f74a6 100644
--- a/examples/src/main/python/streaming/queue_stream.py
+++ b/examples/src/main/python/streaming/queue_stream.py
@@ -36,8 +36,8 @@
     # Create the queue through which RDDs can be pushed to
     # a QueueInputDStream
     rddQueue = []
-    for i in xrange(5):
-        rddQueue += [ssc.sparkContext.parallelize([j for j in xrange(1, 1001)], 10)]
+    for i in range(5):
+        rddQueue += [ssc.sparkContext.parallelize([j for j in range(1, 1001)], 10)]
 
     # Create the QueueInputDStream and use it do some processing
     inputStream = ssc.queueStream(rddQueue)
diff --git a/python/pyspark/streaming/flume.py b/python/pyspark/streaming/flume.py
index cbb573f226bbe..c0cdc50d8d423 100644
--- a/python/pyspark/streaming/flume.py
+++ b/python/pyspark/streaming/flume.py
@@ -31,7 +31,9 @@
 
 def utf8_decoder(s):
     """ Decode the unicode as UTF-8 """
-    return s and s.decode('utf-8')
+    if s is None:
+        return None
+    return s.decode('utf-8')
 
 
 class FlumeUtils(object):
diff --git a/python/pyspark/streaming/kafka.py b/python/pyspark/streaming/kafka.py
index dc5b7fd878aef..8a814c64c0423 100644
--- a/python/pyspark/streaming/kafka.py
+++ b/python/pyspark/streaming/kafka.py
@@ -29,7 +29,9 @@
 
 def utf8_decoder(s):
     """ Decode the unicode as UTF-8 """
-    return s and s.decode('utf-8')
+    if s is None:
+        return None
+    return s.decode('utf-8')
 
 
 class KafkaUtils(object):
diff --git a/python/pyspark/streaming/kinesis.py b/python/pyspark/streaming/kinesis.py
index bcfe2703fecf9..34be5880e1708 100644
--- a/python/pyspark/streaming/kinesis.py
+++ b/python/pyspark/streaming/kinesis.py
@@ -26,7 +26,9 @@
 
 def utf8_decoder(s):
     """ Decode the unicode as UTF-8 """
-    return s and s.decode('utf-8')
+    if s is None:
+        return None
+    return s.decode('utf-8')
 
 
 class KinesisUtils(object):

From affc8a887ede9fdc2ca6051833954cd10918c869 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 19 Aug 2015 19:43:09 -0700
Subject: [PATCH 353/633] [SPARK-10125] [STREAMING] Fix a potential deadlock in
 JobGenerator.stop

Because `lazy val` uses `this` lock, if JobGenerator.stop and JobGenerator.doCheckpoint (JobGenerator.shouldCheckpoint has not yet been initialized) run at the same time, it may hang.

Here are the stack traces for the deadlock:

```Java
"pool-1-thread-1-ScalaTest-running-StreamingListenerSuite" #11 prio=5 os_prio=31 tid=0x00007fd35d094800 nid=0x5703 in Object.wait() [0x000000012ecaf000]
   java.lang.Thread.State: WAITING (on object monitor)
        at java.lang.Object.wait(Native Method)
        at java.lang.Thread.join(Thread.java:1245)
        - locked <0x00000007b5d8d7f8> (a org.apache.spark.util.EventLoop$$anon$1)
        at java.lang.Thread.join(Thread.java:1319)
        at org.apache.spark.util.EventLoop.stop(EventLoop.scala:81)
        at org.apache.spark.streaming.scheduler.JobGenerator.stop(JobGenerator.scala:155)
        - locked <0x00000007b5d8cea0> (a org.apache.spark.streaming.scheduler.JobGenerator)
        at org.apache.spark.streaming.scheduler.JobScheduler.stop(JobScheduler.scala:95)
        - locked <0x00000007b5d8ced8> (a org.apache.spark.streaming.scheduler.JobScheduler)
        at org.apache.spark.streaming.StreamingContext.stop(StreamingContext.scala:687)

"JobGenerator" #67 daemon prio=5 os_prio=31 tid=0x00007fd35c3b9800 nid=0x9f03 waiting for monitor entry [0x0000000139e4a000]
   java.lang.Thread.State: BLOCKED (on object monitor)
        at org.apache.spark.streaming.scheduler.JobGenerator.shouldCheckpoint$lzycompute(JobGenerator.scala:63)
        - waiting to lock <0x00000007b5d8cea0> (a org.apache.spark.streaming.scheduler.JobGenerator)
        at org.apache.spark.streaming.scheduler.JobGenerator.shouldCheckpoint(JobGenerator.scala:63)
        at org.apache.spark.streaming.scheduler.JobGenerator.doCheckpoint(JobGenerator.scala:290)
        at org.apache.spark.streaming.scheduler.JobGenerator.org$apache$spark$streaming$scheduler$JobGenerator$$processEvent(JobGenerator.scala:182)
        at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:83)
        at org.apache.spark.streaming.scheduler.JobGenerator$$anon$1.onReceive(JobGenerator.scala:82)
        at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
```

I can use this patch to produce this deadlock: https://github.com/zsxwing/spark/commit/8a88f28d1331003a65fabef48ae3d22a7c21f05f

And a timeout build in Jenkins due to this deadlock: https://amplab.cs.berkeley.edu/jenkins/job/NewSparkPullRequestBuilder/1654/

This PR initializes `checkpointWriter` before `eventLoop` uses it to avoid this deadlock.

Author: zsxwing <zsxwing@gmail.com>

Closes #8326 from zsxwing/SPARK-10125.
---
 .../org/apache/spark/streaming/scheduler/JobGenerator.scala   | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
index 9f2117ada61c0..2de035d166e7b 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
@@ -79,6 +79,10 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging {
   def start(): Unit = synchronized {
     if (eventLoop != null) return // generator has already been started
 
+    // Call checkpointWriter here to initialize it before eventLoop uses it to avoid a deadlock.
+    // See SPARK-10125
+    checkpointWriter
+
     eventLoop = new EventLoop[JobGeneratorEvent]("JobGenerator") {
       override protected def onReceive(event: JobGeneratorEvent): Unit = processEvent(event)
 

From 73431d8afb41b93888d2642a1ce2d011f03fb740 Mon Sep 17 00:00:00 2001
From: Timothy Chen <tnachen@gmail.com>
Date: Wed, 19 Aug 2015 19:43:26 -0700
Subject: [PATCH 354/633] [SPARK-10124] [MESOS] Fix removing queued driver in
 mesos cluster mode.

Currently the spark applications can be queued to the Mesos cluster dispatcher, but when multiple jobs are in queue we don't handle removing jobs from the buffer correctly while iterating and causes null pointer exception.

This patch copies the buffer before iterating them, so exceptions aren't thrown when the jobs are removed.

Author: Timothy Chen <tnachen@gmail.com>

Closes #8322 from tnachen/fix_cluster_mode.
---
 .../cluster/mesos/MesosClusterScheduler.scala | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
index 64ec2b8e3db15..1206f184fbc82 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
@@ -507,14 +507,16 @@ private[spark] class MesosClusterScheduler(
       val driversToRetry = pendingRetryDrivers.filter { d =>
         d.retryState.get.nextRetry.before(currentTime)
       }
+
       scheduleTasks(
-        driversToRetry,
+        copyBuffer(driversToRetry),
         removeFromPendingRetryDrivers,
         currentOffers,
         tasks)
+
       // Then we walk through the queued drivers and try to schedule them.
       scheduleTasks(
-        queuedDrivers,
+        copyBuffer(queuedDrivers),
         removeFromQueuedDrivers,
         currentOffers,
         tasks)
@@ -527,13 +529,14 @@ private[spark] class MesosClusterScheduler(
       .foreach(o => driver.declineOffer(o.getId))
   }
 
+  private def copyBuffer(
+      buffer: ArrayBuffer[MesosDriverDescription]): ArrayBuffer[MesosDriverDescription] = {
+    val newBuffer = new ArrayBuffer[MesosDriverDescription](buffer.size)
+    buffer.copyToBuffer(newBuffer)
+    newBuffer
+  }
+
   def getSchedulerState(): MesosClusterSchedulerState = {
-    def copyBuffer(
-        buffer: ArrayBuffer[MesosDriverDescription]): ArrayBuffer[MesosDriverDescription] = {
-      val newBuffer = new ArrayBuffer[MesosDriverDescription](buffer.size)
-      buffer.copyToBuffer(newBuffer)
-      newBuffer
-    }
     stateLock.synchronized {
       new MesosClusterSchedulerState(
         frameworkId,

From b762f9920f7587d3c08493c49dd2fede62110b88 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed, 19 Aug 2015 21:15:58 -0700
Subject: [PATCH 355/633] [SPARK-10128] [STREAMING] Used correct classloader to
 deserialize WAL data

Recovering Kinesis sequence numbers from WAL leads to classnotfoundexception because the ObjectInputStream does not use the correct classloader and the SequenceNumberRanges class (in streaming-kinesis-asl package) cannot be found (added through spark-submit) while deserializing. The solution is to use `Thread.currentThread().getContextClassLoader` while deserializing.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8328 from tdas/SPARK-10128 and squashes the following commits:

f19b1c2 [Tathagata Das] Used correct classloader to deserialize WAL data
---
 .../spark/streaming/scheduler/ReceivedBlockTracker.scala     | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
index 7720259a5d794..53b96d51c9180 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.streaming.Time
 import org.apache.spark.streaming.util.{WriteAheadLog, WriteAheadLogUtils}
 import org.apache.spark.util.{Clock, Utils}
-import org.apache.spark.{Logging, SparkConf, SparkException}
+import org.apache.spark.{Logging, SparkConf}
 
 /** Trait representing any event in the ReceivedBlockTracker that updates its state. */
 private[streaming] sealed trait ReceivedBlockTrackerLogEvent
@@ -199,7 +199,8 @@ private[streaming] class ReceivedBlockTracker(
       import scala.collection.JavaConversions._
       writeAheadLog.readAll().foreach { byteBuffer =>
         logTrace("Recovering record " + byteBuffer)
-        Utils.deserialize[ReceivedBlockTrackerLogEvent](byteBuffer.array) match {
+        Utils.deserialize[ReceivedBlockTrackerLogEvent](
+          byteBuffer.array, Thread.currentThread().getContextClassLoader) match {
           case BlockAdditionEvent(receivedBlockInfo) =>
             insertAddedBlock(receivedBlockInfo)
           case BatchAllocationEvent(time, allocatedBlocks) =>

From 43e0135421b2262cbb0e06aae53523f663b4f959 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 20 Aug 2015 15:30:31 +0800
Subject: [PATCH 356/633] [SPARK-10092] [SQL] Multi-DB support follow up.

https://issues.apache.org/jira/browse/SPARK-10092

This pr is a follow-up one for Multi-DB support. It has the following changes:

* `HiveContext.refreshTable` now accepts `dbName.tableName`.
* `HiveContext.analyze` now accepts `dbName.tableName`.
* `CreateTableUsing`, `CreateTableUsingAsSelect`, `CreateTempTableUsing`, `CreateTempTableUsingAsSelect`, `CreateMetastoreDataSource`, and `CreateMetastoreDataSourceAsSelect` all take `TableIdentifier` instead of the string representation of table name.
* When you call `saveAsTable` with a specified database, the data will be saved to the correct location.
* Explicitly do not allow users to create a temporary with a specified database name (users cannot do it before).
* When we save table to metastore, we also check if db name and table name can be accepted by hive (using `MetaStoreUtils.validateName`).

Author: Yin Huai <yhuai@databricks.com>

Closes #8324 from yhuai/saveAsTableDB.
---
 .../spark/sql/catalyst/TableIdentifier.scala  |   4 +-
 .../spark/sql/catalyst/analysis/Catalog.scala |  63 +++++--
 .../apache/spark/sql/DataFrameWriter.scala    |   2 +-
 .../org/apache/spark/sql/SQLContext.scala     |  15 +-
 .../spark/sql/execution/SparkStrategies.scala |  10 +-
 .../sql/execution/datasources/DDLParser.scala |  32 ++--
 .../spark/sql/execution/datasources/ddl.scala |  22 +--
 .../sql/execution/datasources/rules.scala     |   8 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  35 ++++
 .../apache/spark/sql/hive/HiveContext.scala   |  14 +-
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  22 ++-
 .../spark/sql/hive/HiveStrategies.scala       |  12 +-
 .../spark/sql/hive/execution/commands.scala   |  54 ++++--
 .../spark/sql/hive/ListTablesSuite.scala      |   6 -
 .../spark/sql/hive/MultiDatabaseSuite.scala   | 158 +++++++++++++++++-
 .../sql/hive/execution/SQLQuerySuite.scala    |  35 ++++
 16 files changed, 398 insertions(+), 94 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala
index aebcdeb9d070f..d701559bf2d9b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/TableIdentifier.scala
@@ -25,7 +25,9 @@ private[sql] case class TableIdentifier(table: String, database: Option[String]
 
   def toSeq: Seq[String] = database.toSeq :+ table
 
-  override def toString: String = toSeq.map("`" + _ + "`").mkString(".")
+  override def toString: String = quotedString
+
+  def quotedString: String = toSeq.map("`" + _ + "`").mkString(".")
 
   def unquotedString: String = toSeq.mkString(".")
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
index 5766e6a2dd51a..503c4f4b20f38 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
@@ -23,6 +23,7 @@ import scala.collection.JavaConversions._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
+import org.apache.spark.sql.AnalysisException
 import org.apache.spark.sql.catalyst.{TableIdentifier, CatalystConf, EmptyConf}
 import org.apache.spark.sql.catalyst.plans.logical.{LogicalPlan, Subquery}
 
@@ -55,12 +56,15 @@ trait Catalog {
 
   def refreshTable(tableIdent: TableIdentifier): Unit
 
+  // TODO: Refactor it in the work of SPARK-10104
   def registerTable(tableIdentifier: Seq[String], plan: LogicalPlan): Unit
 
+  // TODO: Refactor it in the work of SPARK-10104
   def unregisterTable(tableIdentifier: Seq[String]): Unit
 
   def unregisterAllTables(): Unit
 
+  // TODO: Refactor it in the work of SPARK-10104
   protected def processTableIdentifier(tableIdentifier: Seq[String]): Seq[String] = {
     if (conf.caseSensitiveAnalysis) {
       tableIdentifier
@@ -69,6 +73,7 @@ trait Catalog {
     }
   }
 
+  // TODO: Refactor it in the work of SPARK-10104
   protected def getDbTableName(tableIdent: Seq[String]): String = {
     val size = tableIdent.size
     if (size <= 2) {
@@ -78,9 +83,22 @@ trait Catalog {
     }
   }
 
+  // TODO: Refactor it in the work of SPARK-10104
   protected def getDBTable(tableIdent: Seq[String]) : (Option[String], String) = {
     (tableIdent.lift(tableIdent.size - 2), tableIdent.last)
   }
+
+  /**
+   * It is not allowed to specifiy database name for tables stored in [[SimpleCatalog]].
+   * We use this method to check it.
+   */
+  protected def checkTableIdentifier(tableIdentifier: Seq[String]): Unit = {
+    if (tableIdentifier.length > 1) {
+      throw new AnalysisException("Specifying database name or other qualifiers are not allowed " +
+        "for temporary tables. If the table name has dots (.) in it, please quote the " +
+        "table name with backticks (`).")
+    }
+  }
 }
 
 class SimpleCatalog(val conf: CatalystConf) extends Catalog {
@@ -89,11 +107,13 @@ class SimpleCatalog(val conf: CatalystConf) extends Catalog {
   override def registerTable(
       tableIdentifier: Seq[String],
       plan: LogicalPlan): Unit = {
+    checkTableIdentifier(tableIdentifier)
     val tableIdent = processTableIdentifier(tableIdentifier)
     tables.put(getDbTableName(tableIdent), plan)
   }
 
   override def unregisterTable(tableIdentifier: Seq[String]): Unit = {
+    checkTableIdentifier(tableIdentifier)
     val tableIdent = processTableIdentifier(tableIdentifier)
     tables.remove(getDbTableName(tableIdent))
   }
@@ -103,6 +123,7 @@ class SimpleCatalog(val conf: CatalystConf) extends Catalog {
   }
 
   override def tableExists(tableIdentifier: Seq[String]): Boolean = {
+    checkTableIdentifier(tableIdentifier)
     val tableIdent = processTableIdentifier(tableIdentifier)
     tables.containsKey(getDbTableName(tableIdent))
   }
@@ -110,6 +131,7 @@ class SimpleCatalog(val conf: CatalystConf) extends Catalog {
   override def lookupRelation(
       tableIdentifier: Seq[String],
       alias: Option[String] = None): LogicalPlan = {
+    checkTableIdentifier(tableIdentifier)
     val tableIdent = processTableIdentifier(tableIdentifier)
     val tableFullName = getDbTableName(tableIdent)
     val table = tables.get(tableFullName)
@@ -149,7 +171,13 @@ trait OverrideCatalog extends Catalog {
 
   abstract override def tableExists(tableIdentifier: Seq[String]): Boolean = {
     val tableIdent = processTableIdentifier(tableIdentifier)
-    overrides.get(getDBTable(tableIdent)) match {
+    // A temporary tables only has a single part in the tableIdentifier.
+    val overriddenTable = if (tableIdentifier.length > 1) {
+      None: Option[LogicalPlan]
+    } else {
+      overrides.get(getDBTable(tableIdent))
+    }
+    overriddenTable match {
       case Some(_) => true
       case None => super.tableExists(tableIdentifier)
     }
@@ -159,7 +187,12 @@ trait OverrideCatalog extends Catalog {
       tableIdentifier: Seq[String],
       alias: Option[String] = None): LogicalPlan = {
     val tableIdent = processTableIdentifier(tableIdentifier)
-    val overriddenTable = overrides.get(getDBTable(tableIdent))
+    // A temporary tables only has a single part in the tableIdentifier.
+    val overriddenTable = if (tableIdentifier.length > 1) {
+      None: Option[LogicalPlan]
+    } else {
+      overrides.get(getDBTable(tableIdent))
+    }
     val tableWithQualifers = overriddenTable.map(r => Subquery(tableIdent.last, r))
 
     // If an alias was specified by the lookup, wrap the plan in a subquery so that attributes are
@@ -171,20 +204,8 @@ trait OverrideCatalog extends Catalog {
   }
 
   abstract override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = {
-    val dbName = if (conf.caseSensitiveAnalysis) {
-      databaseName
-    } else {
-      if (databaseName.isDefined) Some(databaseName.get.toLowerCase) else None
-    }
-
-    val temporaryTables = overrides.filter {
-      // If a temporary table does not have an associated database, we should return its name.
-      case ((None, _), _) => true
-      // If a temporary table does have an associated database, we should return it if the database
-      // matches the given database name.
-      case ((db: Some[String], _), _) if db == dbName => true
-      case _ => false
-    }.map {
+    // We always return all temporary tables.
+    val temporaryTables = overrides.map {
       case ((_, tableName), _) => (tableName, true)
     }.toSeq
 
@@ -194,13 +215,19 @@ trait OverrideCatalog extends Catalog {
   override def registerTable(
       tableIdentifier: Seq[String],
       plan: LogicalPlan): Unit = {
+    checkTableIdentifier(tableIdentifier)
     val tableIdent = processTableIdentifier(tableIdentifier)
     overrides.put(getDBTable(tableIdent), plan)
   }
 
   override def unregisterTable(tableIdentifier: Seq[String]): Unit = {
-    val tableIdent = processTableIdentifier(tableIdentifier)
-    overrides.remove(getDBTable(tableIdent))
+    // A temporary tables only has a single part in the tableIdentifier.
+    // If tableIdentifier has more than one parts, it is not a temporary table
+    // and we do not need to do anything at here.
+    if (tableIdentifier.length == 1) {
+      val tableIdent = processTableIdentifier(tableIdentifier)
+      overrides.remove(getDBTable(tableIdent))
+    }
   }
 
   override def unregisterAllTables(): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index f0bf1be506411..ce8744b53175b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -218,7 +218,7 @@ final class DataFrameWriter private[sql](df: DataFrame) {
       case _ =>
         val cmd =
           CreateTableUsingAsSelect(
-            tableIdent.unquotedString,
+            tableIdent,
             source,
             temporary = false,
             partitioningColumns.map(_.toArray).getOrElse(Array.empty[String]),
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 58fe75b59f418..126c9c6f839c7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -584,9 +584,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
       tableName: String,
       source: String,
       options: Map[String, String]): DataFrame = {
+    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
     val cmd =
       CreateTableUsing(
-        tableName,
+        tableIdent,
         userSpecifiedSchema = None,
         source,
         temporary = false,
@@ -594,7 +595,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
         allowExisting = false,
         managedIfNoPath = false)
     executePlan(cmd).toRdd
-    table(tableName)
+    table(tableIdent)
   }
 
   /**
@@ -629,9 +630,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
       source: String,
       schema: StructType,
       options: Map[String, String]): DataFrame = {
+    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
     val cmd =
       CreateTableUsing(
-        tableName,
+        tableIdent,
         userSpecifiedSchema = Some(schema),
         source,
         temporary = false,
@@ -639,7 +641,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
         allowExisting = false,
         managedIfNoPath = false)
     executePlan(cmd).toRdd
-    table(tableName)
+    table(tableIdent)
   }
 
   /**
@@ -724,7 +726,10 @@ class SQLContext(@transient val sparkContext: SparkContext)
    * @since 1.3.0
    */
   def table(tableName: String): DataFrame = {
-    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
+    table(new SqlParser().parseTableIdentifier(tableName))
+  }
+
+  private def table(tableIdent: TableIdentifier): DataFrame = {
     DataFrame(this, catalog.lookupRelation(tableIdent.toSeq))
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 1fc870d44b578..4df53687a0731 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -395,22 +395,22 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
 
   object DDLStrategy extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
-      case CreateTableUsing(tableName, userSpecifiedSchema, provider, true, opts, false, _) =>
+      case CreateTableUsing(tableIdent, userSpecifiedSchema, provider, true, opts, false, _) =>
         ExecutedCommand(
           CreateTempTableUsing(
-            tableName, userSpecifiedSchema, provider, opts)) :: Nil
+            tableIdent, userSpecifiedSchema, provider, opts)) :: Nil
       case c: CreateTableUsing if !c.temporary =>
         sys.error("Tables created with SQLContext must be TEMPORARY. Use a HiveContext instead.")
       case c: CreateTableUsing if c.temporary && c.allowExisting =>
         sys.error("allowExisting should be set to false when creating a temporary table.")
 
-      case CreateTableUsingAsSelect(tableName, provider, true, partitionsCols, mode, opts, query)
+      case CreateTableUsingAsSelect(tableIdent, provider, true, partitionsCols, mode, opts, query)
           if partitionsCols.nonEmpty =>
         sys.error("Cannot create temporary partitioned table.")
 
-      case CreateTableUsingAsSelect(tableName, provider, true, _, mode, opts, query) =>
+      case CreateTableUsingAsSelect(tableIdent, provider, true, _, mode, opts, query) =>
         val cmd = CreateTempTableUsingAsSelect(
-          tableName, provider, Array.empty[String], mode, opts, query)
+          tableIdent, provider, Array.empty[String], mode, opts, query)
         ExecutedCommand(cmd) :: Nil
       case c: CreateTableUsingAsSelect if !c.temporary =>
         sys.error("Tables created with SQLContext must be TEMPORARY. Use a HiveContext instead.")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
index 6c462fa30461b..f7a88b98c0b48 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DDLParser.scala
@@ -80,9 +80,9 @@ class DDLParser(parseQuery: String => LogicalPlan)
    */
   protected lazy val createTable: Parser[LogicalPlan] = {
     // TODO: Support database.table.
-    (CREATE ~> TEMPORARY.? <~ TABLE) ~ (IF ~> NOT <~ EXISTS).? ~ ident ~
+    (CREATE ~> TEMPORARY.? <~ TABLE) ~ (IF ~> NOT <~ EXISTS).? ~ tableIdentifier ~
       tableCols.? ~ (USING ~> className) ~ (OPTIONS ~> options).? ~ (AS ~> restInput).? ^^ {
-      case temp ~ allowExisting ~ tableName ~ columns ~ provider ~ opts ~ query =>
+      case temp ~ allowExisting ~ tableIdent ~ columns ~ provider ~ opts ~ query =>
         if (temp.isDefined && allowExisting.isDefined) {
           throw new DDLException(
             "a CREATE TEMPORARY TABLE statement does not allow IF NOT EXISTS clause.")
@@ -104,7 +104,7 @@ class DDLParser(parseQuery: String => LogicalPlan)
           }
 
           val queryPlan = parseQuery(query.get)
-          CreateTableUsingAsSelect(tableName,
+          CreateTableUsingAsSelect(tableIdent,
             provider,
             temp.isDefined,
             Array.empty[String],
@@ -114,7 +114,7 @@ class DDLParser(parseQuery: String => LogicalPlan)
         } else {
           val userSpecifiedSchema = columns.flatMap(fields => Some(StructType(fields)))
           CreateTableUsing(
-            tableName,
+            tableIdent,
             userSpecifiedSchema,
             provider,
             temp.isDefined,
@@ -125,6 +125,12 @@ class DDLParser(parseQuery: String => LogicalPlan)
     }
   }
 
+  // This is the same as tableIdentifier in SqlParser.
+  protected lazy val tableIdentifier: Parser[TableIdentifier] =
+    (ident <~ ".").? ~ ident ^^ {
+      case maybeDbName ~ tableName => TableIdentifier(tableName, maybeDbName)
+    }
+
   protected lazy val tableCols: Parser[Seq[StructField]] = "(" ~> repsep(column, ",") <~ ")"
 
   /*
@@ -132,21 +138,15 @@ class DDLParser(parseQuery: String => LogicalPlan)
    * This will display all columns of table `avroTable` includes column_name,column_type,comment
    */
   protected lazy val describeTable: Parser[LogicalPlan] =
-    (DESCRIBE ~> opt(EXTENDED)) ~ (ident <~ ".").? ~ ident  ^^ {
-      case e ~ db ~ tbl =>
-        val tblIdentifier = db match {
-          case Some(dbName) =>
-            Seq(dbName, tbl)
-          case None =>
-            Seq(tbl)
-        }
-        DescribeCommand(UnresolvedRelation(tblIdentifier, None), e.isDefined)
+    (DESCRIBE ~> opt(EXTENDED)) ~ tableIdentifier ^^ {
+      case e ~ tableIdent =>
+        DescribeCommand(UnresolvedRelation(tableIdent.toSeq, None), e.isDefined)
     }
 
   protected lazy val refreshTable: Parser[LogicalPlan] =
-    REFRESH ~> TABLE ~> (ident <~ ".").? ~ ident ^^ {
-      case maybeDatabaseName ~ tableName =>
-        RefreshTable(TableIdentifier(tableName, maybeDatabaseName))
+    REFRESH ~> TABLE ~> tableIdentifier ^^ {
+      case tableIndet =>
+        RefreshTable(tableIndet)
     }
 
   protected lazy val options: Parser[Map[String, String]] =
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
index ecd304c30cdee..31d6b75e13477 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ddl.scala
@@ -53,7 +53,7 @@ case class DescribeCommand(
   *                      If it is false, an exception will be thrown
   */
 case class CreateTableUsing(
-    tableName: String,
+    tableIdent: TableIdentifier,
     userSpecifiedSchema: Option[StructType],
     provider: String,
     temporary: Boolean,
@@ -71,8 +71,9 @@ case class CreateTableUsing(
  * can analyze the logical plan that will be used to populate the table.
  * So, [[PreWriteCheck]] can detect cases that are not allowed.
  */
+// TODO: Use TableIdentifier instead of String for tableName (SPARK-10104).
 case class CreateTableUsingAsSelect(
-    tableName: String,
+    tableIdent: TableIdentifier,
     provider: String,
     temporary: Boolean,
     partitionColumns: Array[String],
@@ -80,12 +81,10 @@ case class CreateTableUsingAsSelect(
     options: Map[String, String],
     child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = Seq.empty[Attribute]
-  // TODO: Override resolved after we support databaseName.
-  // override lazy val resolved = databaseName != None && childrenResolved
 }
 
 case class CreateTempTableUsing(
-    tableName: String,
+    tableIdent: TableIdentifier,
     userSpecifiedSchema: Option[StructType],
     provider: String,
     options: Map[String, String]) extends RunnableCommand {
@@ -93,14 +92,16 @@ case class CreateTempTableUsing(
   def run(sqlContext: SQLContext): Seq[Row] = {
     val resolved = ResolvedDataSource(
       sqlContext, userSpecifiedSchema, Array.empty[String], provider, options)
-    sqlContext.registerDataFrameAsTable(
-      DataFrame(sqlContext, LogicalRelation(resolved.relation)), tableName)
+    sqlContext.catalog.registerTable(
+      tableIdent.toSeq,
+      DataFrame(sqlContext, LogicalRelation(resolved.relation)).logicalPlan)
+
     Seq.empty[Row]
   }
 }
 
 case class CreateTempTableUsingAsSelect(
-    tableName: String,
+    tableIdent: TableIdentifier,
     provider: String,
     partitionColumns: Array[String],
     mode: SaveMode,
@@ -110,8 +111,9 @@ case class CreateTempTableUsingAsSelect(
   override def run(sqlContext: SQLContext): Seq[Row] = {
     val df = DataFrame(sqlContext, query)
     val resolved = ResolvedDataSource(sqlContext, provider, partitionColumns, mode, options, df)
-    sqlContext.registerDataFrameAsTable(
-      DataFrame(sqlContext, LogicalRelation(resolved.relation)), tableName)
+    sqlContext.catalog.registerTable(
+      tableIdent.toSeq,
+      DataFrame(sqlContext, LogicalRelation(resolved.relation)).logicalPlan)
 
     Seq.empty[Row]
   }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
index 9d3d35692ffcc..16c9138419fa2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/rules.scala
@@ -140,12 +140,12 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
           // OK
         }
 
-      case CreateTableUsingAsSelect(tableName, _, _, partitionColumns, mode, _, query) =>
+      case CreateTableUsingAsSelect(tableIdent, _, _, partitionColumns, mode, _, query) =>
         // When the SaveMode is Overwrite, we need to check if the table is an input table of
         // the query. If so, we will throw an AnalysisException to let users know it is not allowed.
-        if (mode == SaveMode.Overwrite && catalog.tableExists(Seq(tableName))) {
+        if (mode == SaveMode.Overwrite && catalog.tableExists(tableIdent.toSeq)) {
           // Need to remove SubQuery operator.
-          EliminateSubQueries(catalog.lookupRelation(Seq(tableName))) match {
+          EliminateSubQueries(catalog.lookupRelation(tableIdent.toSeq)) match {
             // Only do the check if the table is a data source table
             // (the relation is a BaseRelation).
             case l @ LogicalRelation(dest: BaseRelation) =>
@@ -155,7 +155,7 @@ private[sql] case class PreWriteCheck(catalog: Catalog) extends (LogicalPlan =>
               }
               if (srcRelations.contains(dest)) {
                 failAnalysis(
-                  s"Cannot overwrite table $tableName that is also being read from.")
+                  s"Cannot overwrite table $tableIdent that is also being read from.")
               } else {
                 // OK
               }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 141468ca00d67..da50aec17c89e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1644,4 +1644,39 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
       checkAnswer(sql("select count(num) from 1one"), Row(10))
     }
   }
+
+  test("specifying database name for a temporary table is not allowed") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df =
+        sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
+      df
+        .write
+        .format("parquet")
+        .save(path)
+
+      val message = intercept[AnalysisException] {
+        sqlContext.sql(
+          s"""
+          |CREATE TEMPORARY TABLE db.t
+          |USING parquet
+          |OPTIONS (
+          |  path '$path'
+          |)
+        """.stripMargin)
+      }.getMessage
+      assert(message.contains("Specifying database name or other qualifiers are not allowed"))
+
+      // If you use backticks to quote the name of a temporary table having dot in it.
+      sqlContext.sql(
+        s"""
+          |CREATE TEMPORARY TABLE `db.t`
+          |USING parquet
+          |OPTIONS (
+          |  path '$path'
+          |)
+        """.stripMargin)
+      checkAnswer(sqlContext.table("`db.t`"), df)
+    }
+  }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 17762649fd70d..17cc83087fb1d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -43,7 +43,7 @@ import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql._
 import org.apache.spark.sql.SQLConf.SQLConfEntry
 import org.apache.spark.sql.SQLConf.SQLConfEntry._
-import org.apache.spark.sql.catalyst.{TableIdentifier, ParserDialect}
+import org.apache.spark.sql.catalyst.{SqlParser, TableIdentifier, ParserDialect}
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.execution.{ExecutedCommand, ExtractPythonUDFs, SetCommand}
@@ -189,6 +189,10 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
     // We instantiate a HiveConf here to read in the hive-site.xml file and then pass the options
     // into the isolated client loader
     val metadataConf = new HiveConf()
+
+    val defaltWarehouseLocation = metadataConf.get("hive.metastore.warehouse.dir")
+    logInfo("defalt warehouse location is " + defaltWarehouseLocation)
+
     // `configure` goes second to override other settings.
     val allConfig = metadataConf.iterator.map(e => e.getKey -> e.getValue).toMap ++ configure
 
@@ -288,12 +292,13 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
    * @since 1.3.0
    */
   def refreshTable(tableName: String): Unit = {
-    val tableIdent = TableIdentifier(tableName).withDatabase(catalog.client.currentDatabase)
+    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
     catalog.refreshTable(tableIdent)
   }
 
   protected[hive] def invalidateTable(tableName: String): Unit = {
-    catalog.invalidateTable(catalog.client.currentDatabase, tableName)
+    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
+    catalog.invalidateTable(tableIdent)
   }
 
   /**
@@ -307,7 +312,8 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
    */
   @Experimental
   def analyze(tableName: String) {
-    val relation = EliminateSubQueries(catalog.lookupRelation(Seq(tableName)))
+    val tableIdent = new SqlParser().parseTableIdentifier(tableName)
+    val relation = EliminateSubQueries(catalog.lookupRelation(tableIdent.toSeq))
 
     relation match {
       case relation: MetastoreRelation =>
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 6770462bb0ad3..bbe8c1911bf86 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -174,10 +174,13 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
     // it is better at here to invalidate the cache to avoid confusing waring logs from the
     // cache loader (e.g. cannot find data source provider, which is only defined for
     // data source table.).
-    invalidateTable(tableIdent.database.getOrElse(client.currentDatabase), tableIdent.table)
+    invalidateTable(tableIdent)
   }
 
-  def invalidateTable(databaseName: String, tableName: String): Unit = {
+  def invalidateTable(tableIdent: TableIdentifier): Unit = {
+    val databaseName = tableIdent.database.getOrElse(client.currentDatabase)
+    val tableName = tableIdent.table
+
     cachedDataSourceTables.invalidate(QualifiedTableName(databaseName, tableName).toLowerCase)
   }
 
@@ -187,6 +190,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
    * Creates a data source table (a table created with USING clause) in Hive's metastore.
    * Returns true when the table has been created. Otherwise, false.
    */
+  // TODO: Remove this in SPARK-10104.
   def createDataSourceTable(
       tableName: String,
       userSpecifiedSchema: Option[StructType],
@@ -203,7 +207,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       isExternal)
   }
 
-  private def createDataSourceTable(
+  def createDataSourceTable(
       tableIdent: TableIdentifier,
       userSpecifiedSchema: Option[StructType],
       partitionColumns: Array[String],
@@ -371,10 +375,16 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
   }
 
   def hiveDefaultTableFilePath(tableName: String): String = {
+    hiveDefaultTableFilePath(new SqlParser().parseTableIdentifier(tableName))
+  }
+
+  def hiveDefaultTableFilePath(tableIdent: TableIdentifier): String = {
     // Code based on: hiveWarehouse.getTablePath(currentDatabase, tableName)
+    val database = tableIdent.database.getOrElse(client.currentDatabase)
+
     new Path(
-      new Path(client.getDatabase(client.currentDatabase).location),
-      tableName.toLowerCase).toString
+      new Path(client.getDatabase(database).location),
+      tableIdent.table.toLowerCase).toString
   }
 
   def tableExists(tableIdentifier: Seq[String]): Boolean = {
@@ -635,7 +645,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
 
           val mode = if (allowExisting) SaveMode.Ignore else SaveMode.ErrorIfExists
           CreateTableUsingAsSelect(
-            desc.name,
+            TableIdentifier(desc.name),
             hive.conf.defaultDataSourceName,
             temporary = false,
             Array.empty[String],
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
index cd6cd322c94ed..d38ad9127327d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala
@@ -83,14 +83,16 @@ private[hive] trait HiveStrategies {
   object HiveDDLStrategy extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case CreateTableUsing(
-          tableName, userSpecifiedSchema, provider, false, opts, allowExisting, managedIfNoPath) =>
-        ExecutedCommand(
+        tableIdent, userSpecifiedSchema, provider, false, opts, allowExisting, managedIfNoPath) =>
+        val cmd =
           CreateMetastoreDataSource(
-            tableName, userSpecifiedSchema, provider, opts, allowExisting, managedIfNoPath)) :: Nil
+            tableIdent, userSpecifiedSchema, provider, opts, allowExisting, managedIfNoPath)
+        ExecutedCommand(cmd) :: Nil
 
-      case CreateTableUsingAsSelect(tableName, provider, false, partitionCols, mode, opts, query) =>
+      case CreateTableUsingAsSelect(
+        tableIdent, provider, false, partitionCols, mode, opts, query) =>
         val cmd =
-          CreateMetastoreDataSourceAsSelect(tableName, provider, partitionCols, mode, opts, query)
+          CreateMetastoreDataSourceAsSelect(tableIdent, provider, partitionCols, mode, opts, query)
         ExecutedCommand(cmd) :: Nil
 
       case _ => Nil
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
index 05a78930afe3d..d1699dd536817 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/commands.scala
@@ -17,7 +17,9 @@
 
 package org.apache.spark.sql.hive.execution
 
+import org.apache.hadoop.hive.metastore.MetaStoreUtils
 import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.{TableIdentifier, SqlParser}
 import org.apache.spark.sql.catalyst.analysis.EliminateSubQueries
 import org.apache.spark.sql.catalyst.expressions.Attribute
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
@@ -120,9 +122,10 @@ case class AddFile(path: String) extends RunnableCommand {
   }
 }
 
+// TODO: Use TableIdentifier instead of String for tableName (SPARK-10104).
 private[hive]
 case class CreateMetastoreDataSource(
-    tableName: String,
+    tableIdent: TableIdentifier,
     userSpecifiedSchema: Option[StructType],
     provider: String,
     options: Map[String, String],
@@ -130,9 +133,24 @@ case class CreateMetastoreDataSource(
     managedIfNoPath: Boolean) extends RunnableCommand {
 
   override def run(sqlContext: SQLContext): Seq[Row] = {
+    // Since we are saving metadata to metastore, we need to check if metastore supports
+    // the table name and database name we have for this query. MetaStoreUtils.validateName
+    // is the method used by Hive to check if a table name or a database name is valid for
+    // the metastore.
+    if (!MetaStoreUtils.validateName(tableIdent.table)) {
+      throw new AnalysisException(s"Table name ${tableIdent.table} is not a valid name for " +
+        s"metastore. Metastore only accepts table name containing characters, numbers and _.")
+    }
+    if (tableIdent.database.isDefined && !MetaStoreUtils.validateName(tableIdent.database.get)) {
+      throw new AnalysisException(s"Database name ${tableIdent.database.get} is not a valid name " +
+        s"for metastore. Metastore only accepts database name containing " +
+        s"characters, numbers and _.")
+    }
+
+    val tableName = tableIdent.unquotedString
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
 
-    if (hiveContext.catalog.tableExists(tableName :: Nil)) {
+    if (hiveContext.catalog.tableExists(tableIdent.toSeq)) {
       if (allowExisting) {
         return Seq.empty[Row]
       } else {
@@ -144,13 +162,13 @@ case class CreateMetastoreDataSource(
     val optionsWithPath =
       if (!options.contains("path") && managedIfNoPath) {
         isExternal = false
-        options + ("path" -> hiveContext.catalog.hiveDefaultTableFilePath(tableName))
+        options + ("path" -> hiveContext.catalog.hiveDefaultTableFilePath(tableIdent))
       } else {
         options
       }
 
     hiveContext.catalog.createDataSourceTable(
-      tableName,
+      tableIdent,
       userSpecifiedSchema,
       Array.empty[String],
       provider,
@@ -161,9 +179,10 @@ case class CreateMetastoreDataSource(
   }
 }
 
+// TODO: Use TableIdentifier instead of String for tableName (SPARK-10104).
 private[hive]
 case class CreateMetastoreDataSourceAsSelect(
-    tableName: String,
+    tableIdent: TableIdentifier,
     provider: String,
     partitionColumns: Array[String],
     mode: SaveMode,
@@ -171,19 +190,34 @@ case class CreateMetastoreDataSourceAsSelect(
     query: LogicalPlan) extends RunnableCommand {
 
   override def run(sqlContext: SQLContext): Seq[Row] = {
+    // Since we are saving metadata to metastore, we need to check if metastore supports
+    // the table name and database name we have for this query. MetaStoreUtils.validateName
+    // is the method used by Hive to check if a table name or a database name is valid for
+    // the metastore.
+    if (!MetaStoreUtils.validateName(tableIdent.table)) {
+      throw new AnalysisException(s"Table name ${tableIdent.table} is not a valid name for " +
+        s"metastore. Metastore only accepts table name containing characters, numbers and _.")
+    }
+    if (tableIdent.database.isDefined && !MetaStoreUtils.validateName(tableIdent.database.get)) {
+      throw new AnalysisException(s"Database name ${tableIdent.database.get} is not a valid name " +
+        s"for metastore. Metastore only accepts database name containing " +
+        s"characters, numbers and _.")
+    }
+
+    val tableName = tableIdent.unquotedString
     val hiveContext = sqlContext.asInstanceOf[HiveContext]
     var createMetastoreTable = false
     var isExternal = true
     val optionsWithPath =
       if (!options.contains("path")) {
         isExternal = false
-        options + ("path" -> hiveContext.catalog.hiveDefaultTableFilePath(tableName))
+        options + ("path" -> hiveContext.catalog.hiveDefaultTableFilePath(tableIdent))
       } else {
         options
       }
 
     var existingSchema = None: Option[StructType]
-    if (sqlContext.catalog.tableExists(Seq(tableName))) {
+    if (sqlContext.catalog.tableExists(tableIdent.toSeq)) {
       // Check if we need to throw an exception or just return.
       mode match {
         case SaveMode.ErrorIfExists =>
@@ -200,7 +234,7 @@ case class CreateMetastoreDataSourceAsSelect(
           val resolved = ResolvedDataSource(
             sqlContext, Some(query.schema.asNullable), partitionColumns, provider, optionsWithPath)
           val createdRelation = LogicalRelation(resolved.relation)
-          EliminateSubQueries(sqlContext.table(tableName).logicalPlan) match {
+          EliminateSubQueries(sqlContext.catalog.lookupRelation(tableIdent.toSeq)) match {
             case l @ LogicalRelation(_: InsertableRelation | _: HadoopFsRelation) =>
               if (l.relation != createdRelation.relation) {
                 val errorDescription =
@@ -249,7 +283,7 @@ case class CreateMetastoreDataSourceAsSelect(
       // the schema of df). It is important since the nullability may be changed by the relation
       // provider (for example, see org.apache.spark.sql.parquet.DefaultSource).
       hiveContext.catalog.createDataSourceTable(
-        tableName,
+        tableIdent,
         Some(resolved.relation.schema),
         partitionColumns,
         provider,
@@ -258,7 +292,7 @@ case class CreateMetastoreDataSourceAsSelect(
     }
 
     // Refresh the cache of the table in the catalog.
-    hiveContext.refreshTable(tableName)
+    hiveContext.catalog.refreshTable(tableIdent)
     Seq.empty[Row]
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
index 1c15997ea8e6d..d3388a9429e41 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
@@ -34,7 +34,6 @@ class ListTablesSuite extends QueryTest with BeforeAndAfterAll {
   override def beforeAll(): Unit = {
     // The catalog in HiveContext is a case insensitive one.
     catalog.registerTable(Seq("ListTablesSuiteTable"), df.logicalPlan)
-    catalog.registerTable(Seq("ListTablesSuiteDB", "InDBListTablesSuiteTable"), df.logicalPlan)
     sql("CREATE TABLE HiveListTablesSuiteTable (key int, value string)")
     sql("CREATE DATABASE IF NOT EXISTS ListTablesSuiteDB")
     sql("CREATE TABLE ListTablesSuiteDB.HiveInDBListTablesSuiteTable (key int, value string)")
@@ -42,7 +41,6 @@ class ListTablesSuite extends QueryTest with BeforeAndAfterAll {
 
   override def afterAll(): Unit = {
     catalog.unregisterTable(Seq("ListTablesSuiteTable"))
-    catalog.unregisterTable(Seq("ListTablesSuiteDB", "InDBListTablesSuiteTable"))
     sql("DROP TABLE IF EXISTS HiveListTablesSuiteTable")
     sql("DROP TABLE IF EXISTS ListTablesSuiteDB.HiveInDBListTablesSuiteTable")
     sql("DROP DATABASE IF EXISTS ListTablesSuiteDB")
@@ -55,7 +53,6 @@ class ListTablesSuite extends QueryTest with BeforeAndAfterAll {
         checkAnswer(
           allTables.filter("tableName = 'listtablessuitetable'"),
           Row("listtablessuitetable", true))
-        assert(allTables.filter("tableName = 'indblisttablessuitetable'").count() === 0)
         checkAnswer(
           allTables.filter("tableName = 'hivelisttablessuitetable'"),
           Row("hivelisttablessuitetable", false))
@@ -69,9 +66,6 @@ class ListTablesSuite extends QueryTest with BeforeAndAfterAll {
         checkAnswer(
           allTables.filter("tableName = 'listtablessuitetable'"),
           Row("listtablessuitetable", true))
-        checkAnswer(
-          allTables.filter("tableName = 'indblisttablessuitetable'"),
-          Row("indblisttablessuitetable", true))
         assert(allTables.filter("tableName = 'hivelisttablessuitetable'").count() === 0)
         checkAnswer(
           allTables.filter("tableName = 'hiveindblisttablessuitetable'"),
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
index 417e8b07917cc..997c667ec0d1b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
@@ -19,14 +19,22 @@ package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
-import org.apache.spark.sql.{QueryTest, SQLContext, SaveMode}
+import org.apache.spark.sql.{AnalysisException, QueryTest, SQLContext, SaveMode}
 
 class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
-  override val _sqlContext: SQLContext = TestHive
+  override val _sqlContext: HiveContext = TestHive
   private val sqlContext = _sqlContext
 
   private val df = sqlContext.range(10).coalesce(1)
 
+  private def checkTablePath(dbName: String, tableName: String): Unit = {
+    // val hiveContext = sqlContext.asInstanceOf[HiveContext]
+    val metastoreTable = sqlContext.catalog.client.getTable(dbName, tableName)
+    val expectedPath = sqlContext.catalog.client.getDatabase(dbName).location + "/" + tableName
+
+    assert(metastoreTable.serdeProperties("path") === expectedPath)
+  }
+
   test(s"saveAsTable() to non-default database - with USE - Overwrite") {
     withTempDatabase { db =>
       activateDatabase(db) {
@@ -37,6 +45,8 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
 
       assert(sqlContext.tableNames(db).contains("t"))
       checkAnswer(sqlContext.table(s"$db.t"), df)
+
+      checkTablePath(db, "t")
     }
   }
 
@@ -45,6 +55,58 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
       df.write.mode(SaveMode.Overwrite).saveAsTable(s"$db.t")
       assert(sqlContext.tableNames(db).contains("t"))
       checkAnswer(sqlContext.table(s"$db.t"), df)
+
+      checkTablePath(db, "t")
+    }
+  }
+
+  test(s"createExternalTable() to non-default database - with USE") {
+    withTempDatabase { db =>
+      activateDatabase(db) {
+        withTempPath { dir =>
+          val path = dir.getCanonicalPath
+          df.write.format("parquet").mode(SaveMode.Overwrite).save(path)
+
+          sqlContext.createExternalTable("t", path, "parquet")
+          assert(sqlContext.tableNames(db).contains("t"))
+          checkAnswer(sqlContext.table("t"), df)
+
+          sql(
+            s"""
+              |CREATE TABLE t1
+              |USING parquet
+              |OPTIONS (
+              |  path '$path'
+              |)
+            """.stripMargin)
+          assert(sqlContext.tableNames(db).contains("t1"))
+          checkAnswer(sqlContext.table("t1"), df)
+        }
+      }
+    }
+  }
+
+  test(s"createExternalTable() to non-default database - without USE") {
+    withTempDatabase { db =>
+      withTempPath { dir =>
+        val path = dir.getCanonicalPath
+        df.write.format("parquet").mode(SaveMode.Overwrite).save(path)
+        sqlContext.createExternalTable(s"$db.t", path, "parquet")
+
+        assert(sqlContext.tableNames(db).contains("t"))
+        checkAnswer(sqlContext.table(s"$db.t"), df)
+
+        sql(
+          s"""
+              |CREATE TABLE $db.t1
+              |USING parquet
+              |OPTIONS (
+              |  path '$path'
+              |)
+            """.stripMargin)
+        assert(sqlContext.tableNames(db).contains("t1"))
+        checkAnswer(sqlContext.table(s"$db.t1"), df)
+      }
     }
   }
 
@@ -59,6 +121,8 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
 
       assert(sqlContext.tableNames(db).contains("t"))
       checkAnswer(sqlContext.table(s"$db.t"), df.unionAll(df))
+
+      checkTablePath(db, "t")
     }
   }
 
@@ -68,6 +132,8 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
       df.write.mode(SaveMode.Append).saveAsTable(s"$db.t")
       assert(sqlContext.tableNames(db).contains("t"))
       checkAnswer(sqlContext.table(s"$db.t"), df.unionAll(df))
+
+      checkTablePath(db, "t")
     }
   }
 
@@ -130,7 +196,7 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
     }
   }
 
-  test("Refreshes a table in a non-default database") {
+  test("Refreshes a table in a non-default database - with USE") {
     import org.apache.spark.sql.functions.lit
 
     withTempDatabase { db =>
@@ -151,8 +217,94 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
           sql("ALTER TABLE t ADD PARTITION (p=1)")
           sql("REFRESH TABLE t")
           checkAnswer(sqlContext.table("t"), df.withColumn("p", lit(1)))
+
+          df.write.parquet(s"$path/p=2")
+          sql("ALTER TABLE t ADD PARTITION (p=2)")
+          sqlContext.refreshTable("t")
+          checkAnswer(
+            sqlContext.table("t"),
+            df.withColumn("p", lit(1)).unionAll(df.withColumn("p", lit(2))))
         }
       }
     }
   }
+
+  test("Refreshes a table in a non-default database - without USE") {
+    import org.apache.spark.sql.functions.lit
+
+    withTempDatabase { db =>
+      withTempPath { dir =>
+        val path = dir.getCanonicalPath
+
+        sql(
+          s"""CREATE EXTERNAL TABLE $db.t (id BIGINT)
+               |PARTITIONED BY (p INT)
+               |STORED AS PARQUET
+               |LOCATION '$path'
+             """.stripMargin)
+
+        checkAnswer(sqlContext.table(s"$db.t"), sqlContext.emptyDataFrame)
+
+        df.write.parquet(s"$path/p=1")
+        sql(s"ALTER TABLE $db.t ADD PARTITION (p=1)")
+        sql(s"REFRESH TABLE $db.t")
+        checkAnswer(sqlContext.table(s"$db.t"), df.withColumn("p", lit(1)))
+
+        df.write.parquet(s"$path/p=2")
+        sql(s"ALTER TABLE $db.t ADD PARTITION (p=2)")
+        sqlContext.refreshTable(s"$db.t")
+        checkAnswer(
+          sqlContext.table(s"$db.t"),
+          df.withColumn("p", lit(1)).unionAll(df.withColumn("p", lit(2))))
+      }
+    }
+  }
+
+  test("invalid database name and table names") {
+    {
+      val message = intercept[AnalysisException] {
+        df.write.format("parquet").saveAsTable("`d:b`.`t:a`")
+      }.getMessage
+      assert(message.contains("is not a valid name for metastore"))
+    }
+
+    {
+      val message = intercept[AnalysisException] {
+        df.write.format("parquet").saveAsTable("`d:b`.`table`")
+      }.getMessage
+      assert(message.contains("is not a valid name for metastore"))
+    }
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      {
+        val message = intercept[AnalysisException] {
+          sql(
+            s"""
+            |CREATE TABLE `d:b`.`t:a` (a int)
+            |USING parquet
+            |OPTIONS (
+            |  path '$path'
+            |)
+            """.stripMargin)
+        }.getMessage
+        assert(message.contains("is not a valid name for metastore"))
+      }
+
+      {
+        val message = intercept[AnalysisException] {
+          sql(
+            s"""
+              |CREATE TABLE `d:b`.`table` (a int)
+              |USING parquet
+              |OPTIONS (
+              |  path '$path'
+              |)
+              """.stripMargin)
+        }.getMessage
+        assert(message.contains("is not a valid name for metastore"))
+      }
+    }
+  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 8b8f520776e70..55ecbd5b5f21d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -1138,4 +1138,39 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
       Row(CalendarInterval.fromString(
         "interval 4 minutes 59 seconds 889 milliseconds 987 microseconds")))
   }
+
+  test("specifying database name for a temporary table is not allowed") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df =
+        sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
+      df
+        .write
+        .format("parquet")
+        .save(path)
+
+      val message = intercept[AnalysisException] {
+        sqlContext.sql(
+          s"""
+          |CREATE TEMPORARY TABLE db.t
+          |USING parquet
+          |OPTIONS (
+          |  path '$path'
+          |)
+        """.stripMargin)
+      }.getMessage
+      assert(message.contains("Specifying database name or other qualifiers are not allowed"))
+
+      // If you use backticks to quote the name of a temporary table having dot in it.
+      sqlContext.sql(
+        s"""
+          |CREATE TEMPORARY TABLE `db.t`
+          |USING parquet
+          |OPTIONS (
+          |  path '$path'
+          |)
+        """.stripMargin)
+      checkAnswer(sqlContext.table("`db.t`"), df)
+    }
+  }
 }

From b4f4e91c395cb69ced61d9ff1492d1b814f96828 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Thu, 20 Aug 2015 07:53:27 -0700
Subject: [PATCH 357/633] [SPARK-10100] [SQL] Eliminate hash table lookup if
 there is no grouping key in aggregation.

This improves performance by ~ 20 - 30% in one of my local test and should fix the performance regression from 1.4 to 1.5 on ss_max.

Author: Reynold Xin <rxin@databricks.com>

Closes #8332 from rxin/SPARK-10100.
---
 .../aggregate/TungstenAggregate.scala         |  2 +-
 .../TungstenAggregationIterator.scala         | 30 +++++++++++++------
 2 files changed, 22 insertions(+), 10 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
index 99f51ba5b6935..ba379d358d206 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
@@ -104,7 +104,7 @@ case class TungstenAggregate(
         } else {
           // This is a grouped aggregate and the input iterator is empty,
           // so return an empty iterator.
-          Iterator[UnsafeRow]()
+          Iterator.empty
         }
       } else {
         aggregationIterator.start(parentIterator)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
index af7e0fcedbe4e..26fdbc83ef50b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIterator.scala
@@ -357,18 +357,30 @@ class TungstenAggregationIterator(
   // sort-based aggregation (by calling switchToSortBasedAggregation).
   private def processInputs(): Unit = {
     assert(inputIter != null, "attempted to process input when iterator was null")
-    while (!sortBased && inputIter.hasNext) {
-      val newInput = inputIter.next()
-      numInputRows += 1
-      val groupingKey = groupProjection.apply(newInput)
+    if (groupingExpressions.isEmpty) {
+      // If there is no grouping expressions, we can just reuse the same buffer over and over again.
+      // Note that it would be better to eliminate the hash map entirely in the future.
+      val groupingKey = groupProjection.apply(null)
       val buffer: UnsafeRow = hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
-      if (buffer == null) {
-        // buffer == null means that we could not allocate more memory.
-        // Now, we need to spill the map and switch to sort-based aggregation.
-        switchToSortBasedAggregation(groupingKey, newInput)
-      } else {
+      while (inputIter.hasNext) {
+        val newInput = inputIter.next()
+        numInputRows += 1
         processRow(buffer, newInput)
       }
+    } else {
+      while (!sortBased && inputIter.hasNext) {
+        val newInput = inputIter.next()
+        numInputRows += 1
+        val groupingKey = groupProjection.apply(newInput)
+        val buffer: UnsafeRow = hashMap.getAggregationBufferFromUnsafeRow(groupingKey)
+        if (buffer == null) {
+          // buffer == null means that we could not allocate more memory.
+          // Now, we need to spill the map and switch to sort-based aggregation.
+          switchToSortBasedAggregation(groupingKey, newInput)
+        } else {
+          processRow(buffer, newInput)
+        }
+      }
     }
   }
 

From 52c60537a274af5414f6b0340a4bd7488ef35280 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 20 Aug 2015 10:05:31 -0700
Subject: [PATCH 358/633] [MINOR] [SQL] Fix sphinx warnings in PySpark SQL

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8171 from MechCoder/sql_sphinx.
---
 python/pyspark/context.py   | 8 ++++----
 python/pyspark/sql/types.py | 4 +++-
 2 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index eb5b0bbbdac4b..1b2a52ad64114 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -302,10 +302,10 @@ def applicationId(self):
         """
         A unique identifier for the Spark application.
         Its format depends on the scheduler implementation.
-        (i.e.
-            in case of local spark app something like 'local-1433865536131'
-            in case of YARN something like 'application_1433865536131_34483'
-        )
+
+        * in case of local spark app something like 'local-1433865536131'
+        * in case of YARN something like 'application_1433865536131_34483'
+
         >>> sc.applicationId  # doctest: +ELLIPSIS
         u'local-...'
         """
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index c083bf89905bf..ed4e5b594bd61 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -467,9 +467,11 @@ def add(self, field, data_type=None, nullable=True, metadata=None):
         """
         Construct a StructType by adding new elements to it to define the schema. The method accepts
         either:
+
             a) A single parameter which is a StructField object.
             b) Between 2 and 4 parameters as (name, data_type, nullable (optional),
-             metadata(optional). The data_type parameter may be either a String or a DataType object
+               metadata(optional). The data_type parameter may be either a String or a
+               DataType object.
 
         >>> struct1 = StructType().add("f1", StringType(), True).add("f2", StringType(), True, None)
         >>> struct2 = StructType([StructField("f1", StringType(), True),\

From 39e91fe2fd43044cc734d55625a3c03284b69f09 Mon Sep 17 00:00:00 2001
From: Alex Shkurenko <ashkurenko@enova.com>
Date: Thu, 20 Aug 2015 10:16:38 -0700
Subject: [PATCH 359/633] [SPARK-9982] [SPARKR] SparkR DataFrame fail to return
 data of Decimal type

Author: Alex Shkurenko <ashkurenko@enova.com>

Closes #8239 from ashkurenko/master.
---
 core/src/main/scala/org/apache/spark/api/r/SerDe.scala | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index d5b4260bf4529..3c89f24473744 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -181,6 +181,7 @@ private[spark] object SerDe {
   // Boolean -> logical
   // Float -> double
   // Double -> double
+  // Decimal -> double
   // Long -> double
   // Array[Byte] -> raw
   // Date -> Date
@@ -219,6 +220,10 @@ private[spark] object SerDe {
         case "float" | "java.lang.Float" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Float].toDouble)
+        case "decimal" | "java.math.BigDecimal" =>
+          writeType(dos, "double")
+          val javaDecimal = value.asInstanceOf[java.math.BigDecimal]
+          writeDouble(dos, scala.math.BigDecimal(javaDecimal).toDouble)
         case "double" | "java.lang.Double" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Double])

From 85f9a61357994da5023b08b0a8a2eb09388ce7f8 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 20 Aug 2015 11:00:24 -0700
Subject: [PATCH 360/633] [SPARK-10136] [SQL] Fixes Parquet support for Avro
 array of primitive array

I caught SPARK-10136 while adding more test cases to `ParquetAvroCompatibilitySuite`. Actual bug fix code lies in `CatalystRowConverter.scala`.

Author: Cheng Lian <lian@databricks.com>

Closes #8341 from liancheng/spark-10136/parquet-avro-nested-primitive-array.
---
 .../parquet/CatalystReadSupport.scala         |   1 -
 .../parquet/CatalystRowConverter.scala        |  24 +-
 sql/core/src/test/avro/parquet-compat.avdl    |  19 +-
 sql/core/src/test/avro/parquet-compat.avpr    |  54 +-
 .../parquet/test/avro/AvroArrayOfArray.java   | 142 +++
 .../parquet/test/avro/AvroMapOfArray.java     | 142 +++
 .../test/avro/AvroNonNullableArrays.java      | 196 +++++
 .../test/avro/AvroOptionalPrimitives.java     | 466 ++++++++++
 .../parquet/test/avro/AvroPrimitives.java     | 461 ++++++++++
 .../parquet/test/avro/CompatibilityTest.java  |   2 +-
 .../parquet/test/avro/ParquetAvroCompat.java  | 821 +-----------------
 .../ParquetAvroCompatibilitySuite.scala       | 227 +++--
 .../parquet/ParquetCompatibilityTest.scala    |   7 +
 13 files changed, 1718 insertions(+), 844 deletions(-)
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroArrayOfArray.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroMapOfArray.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroNonNullableArrays.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroOptionalPrimitives.java
 create mode 100644 sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroPrimitives.java

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index a4679bb2f6389..3f8353af6e2ad 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -61,7 +61,6 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
          |
          |Parquet form:
          |$parquetRequestedSchema
-         |
          |Catalyst form:
          |$catalystRequestedSchema
        """.stripMargin
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index 18c5b500209e6..d2c2db51769ba 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -25,11 +25,12 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.parquet.column.Dictionary
 import org.apache.parquet.io.api.{Binary, Converter, GroupConverter, PrimitiveConverter}
-import org.apache.parquet.schema.OriginalType.{LIST, INT_32, UTF8}
+import org.apache.parquet.schema.OriginalType.{INT_32, LIST, UTF8}
 import org.apache.parquet.schema.PrimitiveType.PrimitiveTypeName.DOUBLE
 import org.apache.parquet.schema.Type.Repetition
 import org.apache.parquet.schema.{GroupType, MessageType, PrimitiveType, Type}
 
+import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.util.DateTimeUtils
@@ -145,7 +146,16 @@ private[parquet] class CatalystRowConverter(
     parquetType: GroupType,
     catalystType: StructType,
     updater: ParentContainerUpdater)
-  extends CatalystGroupConverter(updater) {
+  extends CatalystGroupConverter(updater) with Logging {
+
+  logDebug(
+    s"""Building row converter for the following schema:
+       |
+       |Parquet form:
+       |$parquetType
+       |Catalyst form:
+       |${catalystType.prettyJson}
+     """.stripMargin)
 
   /**
    * Updater used together with field converters within a [[CatalystRowConverter]].  It propagates
@@ -464,9 +474,15 @@ private[parquet] class CatalystRowConverter(
 
       override def getConverter(fieldIndex: Int): Converter = converter
 
-      override def end(): Unit = currentArray += currentElement
+      override def end(): Unit = {
+        converter.updater.end()
+        currentArray += currentElement
+      }
 
-      override def start(): Unit = currentElement = null
+      override def start(): Unit = {
+        converter.updater.start()
+        currentElement = null
+      }
     }
   }
 
diff --git a/sql/core/src/test/avro/parquet-compat.avdl b/sql/core/src/test/avro/parquet-compat.avdl
index 8070d0a9170a3..c5eb5b5164cf4 100644
--- a/sql/core/src/test/avro/parquet-compat.avdl
+++ b/sql/core/src/test/avro/parquet-compat.avdl
@@ -34,7 +34,7 @@ protocol CompatibilityTest {
         string nested_string_column;
     }
 
-    record ParquetAvroCompat {
+    record AvroPrimitives {
         boolean bool_column;
         int int_column;
         long long_column;
@@ -42,7 +42,9 @@ protocol CompatibilityTest {
         double double_column;
         bytes binary_column;
         string string_column;
+    }
 
+    record AvroOptionalPrimitives {
         union { null, boolean } maybe_bool_column;
         union { null, int } maybe_int_column;
         union { null, long } maybe_long_column;
@@ -50,7 +52,22 @@ protocol CompatibilityTest {
         union { null, double } maybe_double_column;
         union { null, bytes } maybe_binary_column;
         union { null, string } maybe_string_column;
+    }
+
+    record AvroNonNullableArrays {
+        array<string> strings_column;
+        union { null, array<int> } maybe_ints_column;
+    }
 
+    record AvroArrayOfArray {
+        array<array<int>> int_arrays_column;
+    }
+
+    record AvroMapOfArray {
+        map<array<int>> string_to_ints_column;
+    }
+
+    record ParquetAvroCompat {
         array<string> strings_column;
         map<int> string_to_int_column;
         map<array<Nested>> complex_column;
diff --git a/sql/core/src/test/avro/parquet-compat.avpr b/sql/core/src/test/avro/parquet-compat.avpr
index 060391765034b..9ad315b74fb41 100644
--- a/sql/core/src/test/avro/parquet-compat.avpr
+++ b/sql/core/src/test/avro/parquet-compat.avpr
@@ -27,7 +27,7 @@
     } ]
   }, {
     "type" : "record",
-    "name" : "ParquetAvroCompat",
+    "name" : "AvroPrimitives",
     "fields" : [ {
       "name" : "bool_column",
       "type" : "boolean"
@@ -49,7 +49,11 @@
     }, {
       "name" : "string_column",
       "type" : "string"
-    }, {
+    } ]
+  }, {
+    "type" : "record",
+    "name" : "AvroOptionalPrimitives",
+    "fields" : [ {
       "name" : "maybe_bool_column",
       "type" : [ "null", "boolean" ]
     }, {
@@ -70,7 +74,53 @@
     }, {
       "name" : "maybe_string_column",
       "type" : [ "null", "string" ]
+    } ]
+  }, {
+    "type" : "record",
+    "name" : "AvroNonNullableArrays",
+    "fields" : [ {
+      "name" : "strings_column",
+      "type" : {
+        "type" : "array",
+        "items" : "string"
+      }
     }, {
+      "name" : "maybe_ints_column",
+      "type" : [ "null", {
+        "type" : "array",
+        "items" : "int"
+      } ]
+    } ]
+  }, {
+    "type" : "record",
+    "name" : "AvroArrayOfArray",
+    "fields" : [ {
+      "name" : "int_arrays_column",
+      "type" : {
+        "type" : "array",
+        "items" : {
+          "type" : "array",
+          "items" : "int"
+        }
+      }
+    } ]
+  }, {
+    "type" : "record",
+    "name" : "AvroMapOfArray",
+    "fields" : [ {
+      "name" : "string_to_ints_column",
+      "type" : {
+        "type" : "map",
+        "values" : {
+          "type" : "array",
+          "items" : "int"
+        }
+      }
+    } ]
+  }, {
+    "type" : "record",
+    "name" : "ParquetAvroCompat",
+    "fields" : [ {
       "name" : "strings_column",
       "type" : {
         "type" : "array",
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroArrayOfArray.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroArrayOfArray.java
new file mode 100644
index 0000000000000..ee327827903e5
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroArrayOfArray.java
@@ -0,0 +1,142 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class AvroArrayOfArray extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"AvroArrayOfArray\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"int_arrays_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"array\",\"items\":\"int\"}}}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public java.util.List<java.util.List<java.lang.Integer>> int_arrays_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public AvroArrayOfArray() {}
+
+  /**
+   * All-args constructor.
+   */
+  public AvroArrayOfArray(java.util.List<java.util.List<java.lang.Integer>> int_arrays_column) {
+    this.int_arrays_column = int_arrays_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return int_arrays_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: int_arrays_column = (java.util.List<java.util.List<java.lang.Integer>>)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'int_arrays_column' field.
+   */
+  public java.util.List<java.util.List<java.lang.Integer>> getIntArraysColumn() {
+    return int_arrays_column;
+  }
+
+  /**
+   * Sets the value of the 'int_arrays_column' field.
+   * @param value the value to set.
+   */
+  public void setIntArraysColumn(java.util.List<java.util.List<java.lang.Integer>> value) {
+    this.int_arrays_column = value;
+  }
+
+  /** Creates a new AvroArrayOfArray RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder();
+  }
+  
+  /** Creates a new AvroArrayOfArray RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder(other);
+  }
+  
+  /** Creates a new AvroArrayOfArray RecordBuilder by copying an existing AvroArrayOfArray instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for AvroArrayOfArray instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<AvroArrayOfArray>
+    implements org.apache.avro.data.RecordBuilder<AvroArrayOfArray> {
+
+    private java.util.List<java.util.List<java.lang.Integer>> int_arrays_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.int_arrays_column)) {
+        this.int_arrays_column = data().deepCopy(fields()[0].schema(), other.int_arrays_column);
+        fieldSetFlags()[0] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing AvroArrayOfArray instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.SCHEMA$);
+      if (isValidValue(fields()[0], other.int_arrays_column)) {
+        this.int_arrays_column = data().deepCopy(fields()[0].schema(), other.int_arrays_column);
+        fieldSetFlags()[0] = true;
+      }
+    }
+
+    /** Gets the value of the 'int_arrays_column' field */
+    public java.util.List<java.util.List<java.lang.Integer>> getIntArraysColumn() {
+      return int_arrays_column;
+    }
+    
+    /** Sets the value of the 'int_arrays_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder setIntArraysColumn(java.util.List<java.util.List<java.lang.Integer>> value) {
+      validate(fields()[0], value);
+      this.int_arrays_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'int_arrays_column' field has been set */
+    public boolean hasIntArraysColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'int_arrays_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroArrayOfArray.Builder clearIntArraysColumn() {
+      int_arrays_column = null;
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    @Override
+    public AvroArrayOfArray build() {
+      try {
+        AvroArrayOfArray record = new AvroArrayOfArray();
+        record.int_arrays_column = fieldSetFlags()[0] ? this.int_arrays_column : (java.util.List<java.util.List<java.lang.Integer>>) defaultValue(fields()[0]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroMapOfArray.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroMapOfArray.java
new file mode 100644
index 0000000000000..727f6a7bf733e
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroMapOfArray.java
@@ -0,0 +1,142 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class AvroMapOfArray extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"AvroMapOfArray\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"string_to_ints_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"int\"},\"avro.java.string\":\"String\"}}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> string_to_ints_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public AvroMapOfArray() {}
+
+  /**
+   * All-args constructor.
+   */
+  public AvroMapOfArray(java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> string_to_ints_column) {
+    this.string_to_ints_column = string_to_ints_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return string_to_ints_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: string_to_ints_column = (java.util.Map<java.lang.String,java.util.List<java.lang.Integer>>)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'string_to_ints_column' field.
+   */
+  public java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> getStringToIntsColumn() {
+    return string_to_ints_column;
+  }
+
+  /**
+   * Sets the value of the 'string_to_ints_column' field.
+   * @param value the value to set.
+   */
+  public void setStringToIntsColumn(java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> value) {
+    this.string_to_ints_column = value;
+  }
+
+  /** Creates a new AvroMapOfArray RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder();
+  }
+  
+  /** Creates a new AvroMapOfArray RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder(other);
+  }
+  
+  /** Creates a new AvroMapOfArray RecordBuilder by copying an existing AvroMapOfArray instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for AvroMapOfArray instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<AvroMapOfArray>
+    implements org.apache.avro.data.RecordBuilder<AvroMapOfArray> {
+
+    private java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> string_to_ints_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.string_to_ints_column)) {
+        this.string_to_ints_column = data().deepCopy(fields()[0].schema(), other.string_to_ints_column);
+        fieldSetFlags()[0] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing AvroMapOfArray instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.SCHEMA$);
+      if (isValidValue(fields()[0], other.string_to_ints_column)) {
+        this.string_to_ints_column = data().deepCopy(fields()[0].schema(), other.string_to_ints_column);
+        fieldSetFlags()[0] = true;
+      }
+    }
+
+    /** Gets the value of the 'string_to_ints_column' field */
+    public java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> getStringToIntsColumn() {
+      return string_to_ints_column;
+    }
+    
+    /** Sets the value of the 'string_to_ints_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder setStringToIntsColumn(java.util.Map<java.lang.String,java.util.List<java.lang.Integer>> value) {
+      validate(fields()[0], value);
+      this.string_to_ints_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'string_to_ints_column' field has been set */
+    public boolean hasStringToIntsColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'string_to_ints_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroMapOfArray.Builder clearStringToIntsColumn() {
+      string_to_ints_column = null;
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    @Override
+    public AvroMapOfArray build() {
+      try {
+        AvroMapOfArray record = new AvroMapOfArray();
+        record.string_to_ints_column = fieldSetFlags()[0] ? this.string_to_ints_column : (java.util.Map<java.lang.String,java.util.List<java.lang.Integer>>) defaultValue(fields()[0]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroNonNullableArrays.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroNonNullableArrays.java
new file mode 100644
index 0000000000000..934793f42f9c9
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroNonNullableArrays.java
@@ -0,0 +1,196 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class AvroNonNullableArrays extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"AvroNonNullableArrays\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"maybe_ints_column\",\"type\":[\"null\",{\"type\":\"array\",\"items\":\"int\"}]}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public java.util.List<java.lang.String> strings_column;
+  @Deprecated public java.util.List<java.lang.Integer> maybe_ints_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public AvroNonNullableArrays() {}
+
+  /**
+   * All-args constructor.
+   */
+  public AvroNonNullableArrays(java.util.List<java.lang.String> strings_column, java.util.List<java.lang.Integer> maybe_ints_column) {
+    this.strings_column = strings_column;
+    this.maybe_ints_column = maybe_ints_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return strings_column;
+    case 1: return maybe_ints_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: strings_column = (java.util.List<java.lang.String>)value$; break;
+    case 1: maybe_ints_column = (java.util.List<java.lang.Integer>)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'strings_column' field.
+   */
+  public java.util.List<java.lang.String> getStringsColumn() {
+    return strings_column;
+  }
+
+  /**
+   * Sets the value of the 'strings_column' field.
+   * @param value the value to set.
+   */
+  public void setStringsColumn(java.util.List<java.lang.String> value) {
+    this.strings_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_ints_column' field.
+   */
+  public java.util.List<java.lang.Integer> getMaybeIntsColumn() {
+    return maybe_ints_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_ints_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeIntsColumn(java.util.List<java.lang.Integer> value) {
+    this.maybe_ints_column = value;
+  }
+
+  /** Creates a new AvroNonNullableArrays RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder();
+  }
+  
+  /** Creates a new AvroNonNullableArrays RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder(other);
+  }
+  
+  /** Creates a new AvroNonNullableArrays RecordBuilder by copying an existing AvroNonNullableArrays instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for AvroNonNullableArrays instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<AvroNonNullableArrays>
+    implements org.apache.avro.data.RecordBuilder<AvroNonNullableArrays> {
+
+    private java.util.List<java.lang.String> strings_column;
+    private java.util.List<java.lang.Integer> maybe_ints_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.strings_column)) {
+        this.strings_column = data().deepCopy(fields()[0].schema(), other.strings_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.maybe_ints_column)) {
+        this.maybe_ints_column = data().deepCopy(fields()[1].schema(), other.maybe_ints_column);
+        fieldSetFlags()[1] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing AvroNonNullableArrays instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.SCHEMA$);
+      if (isValidValue(fields()[0], other.strings_column)) {
+        this.strings_column = data().deepCopy(fields()[0].schema(), other.strings_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.maybe_ints_column)) {
+        this.maybe_ints_column = data().deepCopy(fields()[1].schema(), other.maybe_ints_column);
+        fieldSetFlags()[1] = true;
+      }
+    }
+
+    /** Gets the value of the 'strings_column' field */
+    public java.util.List<java.lang.String> getStringsColumn() {
+      return strings_column;
+    }
+    
+    /** Sets the value of the 'strings_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder setStringsColumn(java.util.List<java.lang.String> value) {
+      validate(fields()[0], value);
+      this.strings_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'strings_column' field has been set */
+    public boolean hasStringsColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'strings_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder clearStringsColumn() {
+      strings_column = null;
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_ints_column' field */
+    public java.util.List<java.lang.Integer> getMaybeIntsColumn() {
+      return maybe_ints_column;
+    }
+    
+    /** Sets the value of the 'maybe_ints_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder setMaybeIntsColumn(java.util.List<java.lang.Integer> value) {
+      validate(fields()[1], value);
+      this.maybe_ints_column = value;
+      fieldSetFlags()[1] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_ints_column' field has been set */
+    public boolean hasMaybeIntsColumn() {
+      return fieldSetFlags()[1];
+    }
+    
+    /** Clears the value of the 'maybe_ints_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroNonNullableArrays.Builder clearMaybeIntsColumn() {
+      maybe_ints_column = null;
+      fieldSetFlags()[1] = false;
+      return this;
+    }
+
+    @Override
+    public AvroNonNullableArrays build() {
+      try {
+        AvroNonNullableArrays record = new AvroNonNullableArrays();
+        record.strings_column = fieldSetFlags()[0] ? this.strings_column : (java.util.List<java.lang.String>) defaultValue(fields()[0]);
+        record.maybe_ints_column = fieldSetFlags()[1] ? this.maybe_ints_column : (java.util.List<java.lang.Integer>) defaultValue(fields()[1]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroOptionalPrimitives.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroOptionalPrimitives.java
new file mode 100644
index 0000000000000..e4d1ead8dd15f
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroOptionalPrimitives.java
@@ -0,0 +1,466 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class AvroOptionalPrimitives extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"AvroOptionalPrimitives\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public java.lang.Boolean maybe_bool_column;
+  @Deprecated public java.lang.Integer maybe_int_column;
+  @Deprecated public java.lang.Long maybe_long_column;
+  @Deprecated public java.lang.Float maybe_float_column;
+  @Deprecated public java.lang.Double maybe_double_column;
+  @Deprecated public java.nio.ByteBuffer maybe_binary_column;
+  @Deprecated public java.lang.String maybe_string_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public AvroOptionalPrimitives() {}
+
+  /**
+   * All-args constructor.
+   */
+  public AvroOptionalPrimitives(java.lang.Boolean maybe_bool_column, java.lang.Integer maybe_int_column, java.lang.Long maybe_long_column, java.lang.Float maybe_float_column, java.lang.Double maybe_double_column, java.nio.ByteBuffer maybe_binary_column, java.lang.String maybe_string_column) {
+    this.maybe_bool_column = maybe_bool_column;
+    this.maybe_int_column = maybe_int_column;
+    this.maybe_long_column = maybe_long_column;
+    this.maybe_float_column = maybe_float_column;
+    this.maybe_double_column = maybe_double_column;
+    this.maybe_binary_column = maybe_binary_column;
+    this.maybe_string_column = maybe_string_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return maybe_bool_column;
+    case 1: return maybe_int_column;
+    case 2: return maybe_long_column;
+    case 3: return maybe_float_column;
+    case 4: return maybe_double_column;
+    case 5: return maybe_binary_column;
+    case 6: return maybe_string_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: maybe_bool_column = (java.lang.Boolean)value$; break;
+    case 1: maybe_int_column = (java.lang.Integer)value$; break;
+    case 2: maybe_long_column = (java.lang.Long)value$; break;
+    case 3: maybe_float_column = (java.lang.Float)value$; break;
+    case 4: maybe_double_column = (java.lang.Double)value$; break;
+    case 5: maybe_binary_column = (java.nio.ByteBuffer)value$; break;
+    case 6: maybe_string_column = (java.lang.String)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'maybe_bool_column' field.
+   */
+  public java.lang.Boolean getMaybeBoolColumn() {
+    return maybe_bool_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_bool_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeBoolColumn(java.lang.Boolean value) {
+    this.maybe_bool_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_int_column' field.
+   */
+  public java.lang.Integer getMaybeIntColumn() {
+    return maybe_int_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_int_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeIntColumn(java.lang.Integer value) {
+    this.maybe_int_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_long_column' field.
+   */
+  public java.lang.Long getMaybeLongColumn() {
+    return maybe_long_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_long_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeLongColumn(java.lang.Long value) {
+    this.maybe_long_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_float_column' field.
+   */
+  public java.lang.Float getMaybeFloatColumn() {
+    return maybe_float_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_float_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeFloatColumn(java.lang.Float value) {
+    this.maybe_float_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_double_column' field.
+   */
+  public java.lang.Double getMaybeDoubleColumn() {
+    return maybe_double_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_double_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeDoubleColumn(java.lang.Double value) {
+    this.maybe_double_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_binary_column' field.
+   */
+  public java.nio.ByteBuffer getMaybeBinaryColumn() {
+    return maybe_binary_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_binary_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeBinaryColumn(java.nio.ByteBuffer value) {
+    this.maybe_binary_column = value;
+  }
+
+  /**
+   * Gets the value of the 'maybe_string_column' field.
+   */
+  public java.lang.String getMaybeStringColumn() {
+    return maybe_string_column;
+  }
+
+  /**
+   * Sets the value of the 'maybe_string_column' field.
+   * @param value the value to set.
+   */
+  public void setMaybeStringColumn(java.lang.String value) {
+    this.maybe_string_column = value;
+  }
+
+  /** Creates a new AvroOptionalPrimitives RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder();
+  }
+  
+  /** Creates a new AvroOptionalPrimitives RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder(other);
+  }
+  
+  /** Creates a new AvroOptionalPrimitives RecordBuilder by copying an existing AvroOptionalPrimitives instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for AvroOptionalPrimitives instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<AvroOptionalPrimitives>
+    implements org.apache.avro.data.RecordBuilder<AvroOptionalPrimitives> {
+
+    private java.lang.Boolean maybe_bool_column;
+    private java.lang.Integer maybe_int_column;
+    private java.lang.Long maybe_long_column;
+    private java.lang.Float maybe_float_column;
+    private java.lang.Double maybe_double_column;
+    private java.nio.ByteBuffer maybe_binary_column;
+    private java.lang.String maybe_string_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.maybe_bool_column)) {
+        this.maybe_bool_column = data().deepCopy(fields()[0].schema(), other.maybe_bool_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.maybe_int_column)) {
+        this.maybe_int_column = data().deepCopy(fields()[1].schema(), other.maybe_int_column);
+        fieldSetFlags()[1] = true;
+      }
+      if (isValidValue(fields()[2], other.maybe_long_column)) {
+        this.maybe_long_column = data().deepCopy(fields()[2].schema(), other.maybe_long_column);
+        fieldSetFlags()[2] = true;
+      }
+      if (isValidValue(fields()[3], other.maybe_float_column)) {
+        this.maybe_float_column = data().deepCopy(fields()[3].schema(), other.maybe_float_column);
+        fieldSetFlags()[3] = true;
+      }
+      if (isValidValue(fields()[4], other.maybe_double_column)) {
+        this.maybe_double_column = data().deepCopy(fields()[4].schema(), other.maybe_double_column);
+        fieldSetFlags()[4] = true;
+      }
+      if (isValidValue(fields()[5], other.maybe_binary_column)) {
+        this.maybe_binary_column = data().deepCopy(fields()[5].schema(), other.maybe_binary_column);
+        fieldSetFlags()[5] = true;
+      }
+      if (isValidValue(fields()[6], other.maybe_string_column)) {
+        this.maybe_string_column = data().deepCopy(fields()[6].schema(), other.maybe_string_column);
+        fieldSetFlags()[6] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing AvroOptionalPrimitives instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.SCHEMA$);
+      if (isValidValue(fields()[0], other.maybe_bool_column)) {
+        this.maybe_bool_column = data().deepCopy(fields()[0].schema(), other.maybe_bool_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.maybe_int_column)) {
+        this.maybe_int_column = data().deepCopy(fields()[1].schema(), other.maybe_int_column);
+        fieldSetFlags()[1] = true;
+      }
+      if (isValidValue(fields()[2], other.maybe_long_column)) {
+        this.maybe_long_column = data().deepCopy(fields()[2].schema(), other.maybe_long_column);
+        fieldSetFlags()[2] = true;
+      }
+      if (isValidValue(fields()[3], other.maybe_float_column)) {
+        this.maybe_float_column = data().deepCopy(fields()[3].schema(), other.maybe_float_column);
+        fieldSetFlags()[3] = true;
+      }
+      if (isValidValue(fields()[4], other.maybe_double_column)) {
+        this.maybe_double_column = data().deepCopy(fields()[4].schema(), other.maybe_double_column);
+        fieldSetFlags()[4] = true;
+      }
+      if (isValidValue(fields()[5], other.maybe_binary_column)) {
+        this.maybe_binary_column = data().deepCopy(fields()[5].schema(), other.maybe_binary_column);
+        fieldSetFlags()[5] = true;
+      }
+      if (isValidValue(fields()[6], other.maybe_string_column)) {
+        this.maybe_string_column = data().deepCopy(fields()[6].schema(), other.maybe_string_column);
+        fieldSetFlags()[6] = true;
+      }
+    }
+
+    /** Gets the value of the 'maybe_bool_column' field */
+    public java.lang.Boolean getMaybeBoolColumn() {
+      return maybe_bool_column;
+    }
+    
+    /** Sets the value of the 'maybe_bool_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeBoolColumn(java.lang.Boolean value) {
+      validate(fields()[0], value);
+      this.maybe_bool_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_bool_column' field has been set */
+    public boolean hasMaybeBoolColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'maybe_bool_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeBoolColumn() {
+      maybe_bool_column = null;
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_int_column' field */
+    public java.lang.Integer getMaybeIntColumn() {
+      return maybe_int_column;
+    }
+    
+    /** Sets the value of the 'maybe_int_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeIntColumn(java.lang.Integer value) {
+      validate(fields()[1], value);
+      this.maybe_int_column = value;
+      fieldSetFlags()[1] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_int_column' field has been set */
+    public boolean hasMaybeIntColumn() {
+      return fieldSetFlags()[1];
+    }
+    
+    /** Clears the value of the 'maybe_int_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeIntColumn() {
+      maybe_int_column = null;
+      fieldSetFlags()[1] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_long_column' field */
+    public java.lang.Long getMaybeLongColumn() {
+      return maybe_long_column;
+    }
+    
+    /** Sets the value of the 'maybe_long_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeLongColumn(java.lang.Long value) {
+      validate(fields()[2], value);
+      this.maybe_long_column = value;
+      fieldSetFlags()[2] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_long_column' field has been set */
+    public boolean hasMaybeLongColumn() {
+      return fieldSetFlags()[2];
+    }
+    
+    /** Clears the value of the 'maybe_long_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeLongColumn() {
+      maybe_long_column = null;
+      fieldSetFlags()[2] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_float_column' field */
+    public java.lang.Float getMaybeFloatColumn() {
+      return maybe_float_column;
+    }
+    
+    /** Sets the value of the 'maybe_float_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeFloatColumn(java.lang.Float value) {
+      validate(fields()[3], value);
+      this.maybe_float_column = value;
+      fieldSetFlags()[3] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_float_column' field has been set */
+    public boolean hasMaybeFloatColumn() {
+      return fieldSetFlags()[3];
+    }
+    
+    /** Clears the value of the 'maybe_float_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeFloatColumn() {
+      maybe_float_column = null;
+      fieldSetFlags()[3] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_double_column' field */
+    public java.lang.Double getMaybeDoubleColumn() {
+      return maybe_double_column;
+    }
+    
+    /** Sets the value of the 'maybe_double_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeDoubleColumn(java.lang.Double value) {
+      validate(fields()[4], value);
+      this.maybe_double_column = value;
+      fieldSetFlags()[4] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_double_column' field has been set */
+    public boolean hasMaybeDoubleColumn() {
+      return fieldSetFlags()[4];
+    }
+    
+    /** Clears the value of the 'maybe_double_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeDoubleColumn() {
+      maybe_double_column = null;
+      fieldSetFlags()[4] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_binary_column' field */
+    public java.nio.ByteBuffer getMaybeBinaryColumn() {
+      return maybe_binary_column;
+    }
+    
+    /** Sets the value of the 'maybe_binary_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeBinaryColumn(java.nio.ByteBuffer value) {
+      validate(fields()[5], value);
+      this.maybe_binary_column = value;
+      fieldSetFlags()[5] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_binary_column' field has been set */
+    public boolean hasMaybeBinaryColumn() {
+      return fieldSetFlags()[5];
+    }
+    
+    /** Clears the value of the 'maybe_binary_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeBinaryColumn() {
+      maybe_binary_column = null;
+      fieldSetFlags()[5] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'maybe_string_column' field */
+    public java.lang.String getMaybeStringColumn() {
+      return maybe_string_column;
+    }
+    
+    /** Sets the value of the 'maybe_string_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder setMaybeStringColumn(java.lang.String value) {
+      validate(fields()[6], value);
+      this.maybe_string_column = value;
+      fieldSetFlags()[6] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'maybe_string_column' field has been set */
+    public boolean hasMaybeStringColumn() {
+      return fieldSetFlags()[6];
+    }
+    
+    /** Clears the value of the 'maybe_string_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroOptionalPrimitives.Builder clearMaybeStringColumn() {
+      maybe_string_column = null;
+      fieldSetFlags()[6] = false;
+      return this;
+    }
+
+    @Override
+    public AvroOptionalPrimitives build() {
+      try {
+        AvroOptionalPrimitives record = new AvroOptionalPrimitives();
+        record.maybe_bool_column = fieldSetFlags()[0] ? this.maybe_bool_column : (java.lang.Boolean) defaultValue(fields()[0]);
+        record.maybe_int_column = fieldSetFlags()[1] ? this.maybe_int_column : (java.lang.Integer) defaultValue(fields()[1]);
+        record.maybe_long_column = fieldSetFlags()[2] ? this.maybe_long_column : (java.lang.Long) defaultValue(fields()[2]);
+        record.maybe_float_column = fieldSetFlags()[3] ? this.maybe_float_column : (java.lang.Float) defaultValue(fields()[3]);
+        record.maybe_double_column = fieldSetFlags()[4] ? this.maybe_double_column : (java.lang.Double) defaultValue(fields()[4]);
+        record.maybe_binary_column = fieldSetFlags()[5] ? this.maybe_binary_column : (java.nio.ByteBuffer) defaultValue(fields()[5]);
+        record.maybe_string_column = fieldSetFlags()[6] ? this.maybe_string_column : (java.lang.String) defaultValue(fields()[6]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroPrimitives.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroPrimitives.java
new file mode 100644
index 0000000000000..1c2afed16781e
--- /dev/null
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/AvroPrimitives.java
@@ -0,0 +1,461 @@
+/**
+ * Autogenerated by Avro
+ * 
+ * DO NOT EDIT DIRECTLY
+ */
+package org.apache.spark.sql.execution.datasources.parquet.test.avro;  
+@SuppressWarnings("all")
+@org.apache.avro.specific.AvroGenerated
+public class AvroPrimitives extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"AvroPrimitives\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}");
+  public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
+  @Deprecated public boolean bool_column;
+  @Deprecated public int int_column;
+  @Deprecated public long long_column;
+  @Deprecated public float float_column;
+  @Deprecated public double double_column;
+  @Deprecated public java.nio.ByteBuffer binary_column;
+  @Deprecated public java.lang.String string_column;
+
+  /**
+   * Default constructor.  Note that this does not initialize fields
+   * to their default values from the schema.  If that is desired then
+   * one should use <code>newBuilder()</code>. 
+   */
+  public AvroPrimitives() {}
+
+  /**
+   * All-args constructor.
+   */
+  public AvroPrimitives(java.lang.Boolean bool_column, java.lang.Integer int_column, java.lang.Long long_column, java.lang.Float float_column, java.lang.Double double_column, java.nio.ByteBuffer binary_column, java.lang.String string_column) {
+    this.bool_column = bool_column;
+    this.int_column = int_column;
+    this.long_column = long_column;
+    this.float_column = float_column;
+    this.double_column = double_column;
+    this.binary_column = binary_column;
+    this.string_column = string_column;
+  }
+
+  public org.apache.avro.Schema getSchema() { return SCHEMA$; }
+  // Used by DatumWriter.  Applications should not call. 
+  public java.lang.Object get(int field$) {
+    switch (field$) {
+    case 0: return bool_column;
+    case 1: return int_column;
+    case 2: return long_column;
+    case 3: return float_column;
+    case 4: return double_column;
+    case 5: return binary_column;
+    case 6: return string_column;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+  // Used by DatumReader.  Applications should not call. 
+  @SuppressWarnings(value="unchecked")
+  public void put(int field$, java.lang.Object value$) {
+    switch (field$) {
+    case 0: bool_column = (java.lang.Boolean)value$; break;
+    case 1: int_column = (java.lang.Integer)value$; break;
+    case 2: long_column = (java.lang.Long)value$; break;
+    case 3: float_column = (java.lang.Float)value$; break;
+    case 4: double_column = (java.lang.Double)value$; break;
+    case 5: binary_column = (java.nio.ByteBuffer)value$; break;
+    case 6: string_column = (java.lang.String)value$; break;
+    default: throw new org.apache.avro.AvroRuntimeException("Bad index");
+    }
+  }
+
+  /**
+   * Gets the value of the 'bool_column' field.
+   */
+  public java.lang.Boolean getBoolColumn() {
+    return bool_column;
+  }
+
+  /**
+   * Sets the value of the 'bool_column' field.
+   * @param value the value to set.
+   */
+  public void setBoolColumn(java.lang.Boolean value) {
+    this.bool_column = value;
+  }
+
+  /**
+   * Gets the value of the 'int_column' field.
+   */
+  public java.lang.Integer getIntColumn() {
+    return int_column;
+  }
+
+  /**
+   * Sets the value of the 'int_column' field.
+   * @param value the value to set.
+   */
+  public void setIntColumn(java.lang.Integer value) {
+    this.int_column = value;
+  }
+
+  /**
+   * Gets the value of the 'long_column' field.
+   */
+  public java.lang.Long getLongColumn() {
+    return long_column;
+  }
+
+  /**
+   * Sets the value of the 'long_column' field.
+   * @param value the value to set.
+   */
+  public void setLongColumn(java.lang.Long value) {
+    this.long_column = value;
+  }
+
+  /**
+   * Gets the value of the 'float_column' field.
+   */
+  public java.lang.Float getFloatColumn() {
+    return float_column;
+  }
+
+  /**
+   * Sets the value of the 'float_column' field.
+   * @param value the value to set.
+   */
+  public void setFloatColumn(java.lang.Float value) {
+    this.float_column = value;
+  }
+
+  /**
+   * Gets the value of the 'double_column' field.
+   */
+  public java.lang.Double getDoubleColumn() {
+    return double_column;
+  }
+
+  /**
+   * Sets the value of the 'double_column' field.
+   * @param value the value to set.
+   */
+  public void setDoubleColumn(java.lang.Double value) {
+    this.double_column = value;
+  }
+
+  /**
+   * Gets the value of the 'binary_column' field.
+   */
+  public java.nio.ByteBuffer getBinaryColumn() {
+    return binary_column;
+  }
+
+  /**
+   * Sets the value of the 'binary_column' field.
+   * @param value the value to set.
+   */
+  public void setBinaryColumn(java.nio.ByteBuffer value) {
+    this.binary_column = value;
+  }
+
+  /**
+   * Gets the value of the 'string_column' field.
+   */
+  public java.lang.String getStringColumn() {
+    return string_column;
+  }
+
+  /**
+   * Sets the value of the 'string_column' field.
+   * @param value the value to set.
+   */
+  public void setStringColumn(java.lang.String value) {
+    this.string_column = value;
+  }
+
+  /** Creates a new AvroPrimitives RecordBuilder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder newBuilder() {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder();
+  }
+  
+  /** Creates a new AvroPrimitives RecordBuilder by copying an existing Builder */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder(other);
+  }
+  
+  /** Creates a new AvroPrimitives RecordBuilder by copying an existing AvroPrimitives instance */
+  public static org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder newBuilder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives other) {
+    return new org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder(other);
+  }
+  
+  /**
+   * RecordBuilder for AvroPrimitives instances.
+   */
+  public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<AvroPrimitives>
+    implements org.apache.avro.data.RecordBuilder<AvroPrimitives> {
+
+    private boolean bool_column;
+    private int int_column;
+    private long long_column;
+    private float float_column;
+    private double double_column;
+    private java.nio.ByteBuffer binary_column;
+    private java.lang.String string_column;
+
+    /** Creates a new Builder */
+    private Builder() {
+      super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.SCHEMA$);
+    }
+    
+    /** Creates a Builder by copying an existing Builder */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder other) {
+      super(other);
+      if (isValidValue(fields()[0], other.bool_column)) {
+        this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.int_column)) {
+        this.int_column = data().deepCopy(fields()[1].schema(), other.int_column);
+        fieldSetFlags()[1] = true;
+      }
+      if (isValidValue(fields()[2], other.long_column)) {
+        this.long_column = data().deepCopy(fields()[2].schema(), other.long_column);
+        fieldSetFlags()[2] = true;
+      }
+      if (isValidValue(fields()[3], other.float_column)) {
+        this.float_column = data().deepCopy(fields()[3].schema(), other.float_column);
+        fieldSetFlags()[3] = true;
+      }
+      if (isValidValue(fields()[4], other.double_column)) {
+        this.double_column = data().deepCopy(fields()[4].schema(), other.double_column);
+        fieldSetFlags()[4] = true;
+      }
+      if (isValidValue(fields()[5], other.binary_column)) {
+        this.binary_column = data().deepCopy(fields()[5].schema(), other.binary_column);
+        fieldSetFlags()[5] = true;
+      }
+      if (isValidValue(fields()[6], other.string_column)) {
+        this.string_column = data().deepCopy(fields()[6].schema(), other.string_column);
+        fieldSetFlags()[6] = true;
+      }
+    }
+    
+    /** Creates a Builder by copying an existing AvroPrimitives instance */
+    private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives other) {
+            super(org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.SCHEMA$);
+      if (isValidValue(fields()[0], other.bool_column)) {
+        this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
+        fieldSetFlags()[0] = true;
+      }
+      if (isValidValue(fields()[1], other.int_column)) {
+        this.int_column = data().deepCopy(fields()[1].schema(), other.int_column);
+        fieldSetFlags()[1] = true;
+      }
+      if (isValidValue(fields()[2], other.long_column)) {
+        this.long_column = data().deepCopy(fields()[2].schema(), other.long_column);
+        fieldSetFlags()[2] = true;
+      }
+      if (isValidValue(fields()[3], other.float_column)) {
+        this.float_column = data().deepCopy(fields()[3].schema(), other.float_column);
+        fieldSetFlags()[3] = true;
+      }
+      if (isValidValue(fields()[4], other.double_column)) {
+        this.double_column = data().deepCopy(fields()[4].schema(), other.double_column);
+        fieldSetFlags()[4] = true;
+      }
+      if (isValidValue(fields()[5], other.binary_column)) {
+        this.binary_column = data().deepCopy(fields()[5].schema(), other.binary_column);
+        fieldSetFlags()[5] = true;
+      }
+      if (isValidValue(fields()[6], other.string_column)) {
+        this.string_column = data().deepCopy(fields()[6].schema(), other.string_column);
+        fieldSetFlags()[6] = true;
+      }
+    }
+
+    /** Gets the value of the 'bool_column' field */
+    public java.lang.Boolean getBoolColumn() {
+      return bool_column;
+    }
+    
+    /** Sets the value of the 'bool_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setBoolColumn(boolean value) {
+      validate(fields()[0], value);
+      this.bool_column = value;
+      fieldSetFlags()[0] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'bool_column' field has been set */
+    public boolean hasBoolColumn() {
+      return fieldSetFlags()[0];
+    }
+    
+    /** Clears the value of the 'bool_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearBoolColumn() {
+      fieldSetFlags()[0] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'int_column' field */
+    public java.lang.Integer getIntColumn() {
+      return int_column;
+    }
+    
+    /** Sets the value of the 'int_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setIntColumn(int value) {
+      validate(fields()[1], value);
+      this.int_column = value;
+      fieldSetFlags()[1] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'int_column' field has been set */
+    public boolean hasIntColumn() {
+      return fieldSetFlags()[1];
+    }
+    
+    /** Clears the value of the 'int_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearIntColumn() {
+      fieldSetFlags()[1] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'long_column' field */
+    public java.lang.Long getLongColumn() {
+      return long_column;
+    }
+    
+    /** Sets the value of the 'long_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setLongColumn(long value) {
+      validate(fields()[2], value);
+      this.long_column = value;
+      fieldSetFlags()[2] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'long_column' field has been set */
+    public boolean hasLongColumn() {
+      return fieldSetFlags()[2];
+    }
+    
+    /** Clears the value of the 'long_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearLongColumn() {
+      fieldSetFlags()[2] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'float_column' field */
+    public java.lang.Float getFloatColumn() {
+      return float_column;
+    }
+    
+    /** Sets the value of the 'float_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setFloatColumn(float value) {
+      validate(fields()[3], value);
+      this.float_column = value;
+      fieldSetFlags()[3] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'float_column' field has been set */
+    public boolean hasFloatColumn() {
+      return fieldSetFlags()[3];
+    }
+    
+    /** Clears the value of the 'float_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearFloatColumn() {
+      fieldSetFlags()[3] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'double_column' field */
+    public java.lang.Double getDoubleColumn() {
+      return double_column;
+    }
+    
+    /** Sets the value of the 'double_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setDoubleColumn(double value) {
+      validate(fields()[4], value);
+      this.double_column = value;
+      fieldSetFlags()[4] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'double_column' field has been set */
+    public boolean hasDoubleColumn() {
+      return fieldSetFlags()[4];
+    }
+    
+    /** Clears the value of the 'double_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearDoubleColumn() {
+      fieldSetFlags()[4] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'binary_column' field */
+    public java.nio.ByteBuffer getBinaryColumn() {
+      return binary_column;
+    }
+    
+    /** Sets the value of the 'binary_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setBinaryColumn(java.nio.ByteBuffer value) {
+      validate(fields()[5], value);
+      this.binary_column = value;
+      fieldSetFlags()[5] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'binary_column' field has been set */
+    public boolean hasBinaryColumn() {
+      return fieldSetFlags()[5];
+    }
+    
+    /** Clears the value of the 'binary_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearBinaryColumn() {
+      binary_column = null;
+      fieldSetFlags()[5] = false;
+      return this;
+    }
+
+    /** Gets the value of the 'string_column' field */
+    public java.lang.String getStringColumn() {
+      return string_column;
+    }
+    
+    /** Sets the value of the 'string_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder setStringColumn(java.lang.String value) {
+      validate(fields()[6], value);
+      this.string_column = value;
+      fieldSetFlags()[6] = true;
+      return this; 
+    }
+    
+    /** Checks whether the 'string_column' field has been set */
+    public boolean hasStringColumn() {
+      return fieldSetFlags()[6];
+    }
+    
+    /** Clears the value of the 'string_column' field */
+    public org.apache.spark.sql.execution.datasources.parquet.test.avro.AvroPrimitives.Builder clearStringColumn() {
+      string_column = null;
+      fieldSetFlags()[6] = false;
+      return this;
+    }
+
+    @Override
+    public AvroPrimitives build() {
+      try {
+        AvroPrimitives record = new AvroPrimitives();
+        record.bool_column = fieldSetFlags()[0] ? this.bool_column : (java.lang.Boolean) defaultValue(fields()[0]);
+        record.int_column = fieldSetFlags()[1] ? this.int_column : (java.lang.Integer) defaultValue(fields()[1]);
+        record.long_column = fieldSetFlags()[2] ? this.long_column : (java.lang.Long) defaultValue(fields()[2]);
+        record.float_column = fieldSetFlags()[3] ? this.float_column : (java.lang.Float) defaultValue(fields()[3]);
+        record.double_column = fieldSetFlags()[4] ? this.double_column : (java.lang.Double) defaultValue(fields()[4]);
+        record.binary_column = fieldSetFlags()[5] ? this.binary_column : (java.nio.ByteBuffer) defaultValue(fields()[5]);
+        record.string_column = fieldSetFlags()[6] ? this.string_column : (java.lang.String) defaultValue(fields()[6]);
+        return record;
+      } catch (Exception e) {
+        throw new org.apache.avro.AvroRuntimeException(e);
+      }
+    }
+  }
+}
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
index 2368323cb36b9..28fdc1dfb911c 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/CompatibilityTest.java
@@ -8,7 +8,7 @@
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public interface CompatibilityTest {
-  public static final org.apache.avro.Protocol PROTOCOL = org.apache.avro.Protocol.parse("{\"protocol\":\"CompatibilityTest\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"types\":[{\"type\":\"enum\",\"name\":\"Suit\",\"symbols\":[\"SPADES\",\"HEARTS\",\"DIAMONDS\",\"CLUBS\"]},{\"type\":\"record\",\"name\":\"ParquetEnum\",\"fields\":[{\"name\":\"suit\",\"type\":\"Suit\"}]},{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"Nested\"},\"avro.java.string\":\"String\"}}]}],\"messages\":{}}");
+  public static final org.apache.avro.Protocol PROTOCOL = org.apache.avro.Protocol.parse("{\"protocol\":\"CompatibilityTest\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"types\":[{\"type\":\"enum\",\"name\":\"Suit\",\"symbols\":[\"SPADES\",\"HEARTS\",\"DIAMONDS\",\"CLUBS\"]},{\"type\":\"record\",\"name\":\"ParquetEnum\",\"fields\":[{\"name\":\"suit\",\"type\":\"Suit\"}]},{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"AvroPrimitives\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"AvroOptionalPrimitives\",\"fields\":[{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]}]},{\"type\":\"record\",\"name\":\"AvroNonNullableArrays\",\"fields\":[{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"maybe_ints_column\",\"type\":[\"null\",{\"type\":\"array\",\"items\":\"int\"}]}]},{\"type\":\"record\",\"name\":\"AvroArrayOfArray\",\"fields\":[{\"name\":\"int_arrays_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"array\",\"items\":\"int\"}}}]},{\"type\":\"record\",\"name\":\"AvroMapOfArray\",\"fields\":[{\"name\":\"string_to_ints_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"int\"},\"avro.java.string\":\"String\"}}]},{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"fields\":[{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":\"Nested\"},\"avro.java.string\":\"String\"}}]}],\"messages\":{}}");
 
   @SuppressWarnings("all")
   public interface Callback extends CompatibilityTest {
diff --git a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
index 681cacbd12c7c..ef12d193f916c 100644
--- a/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
+++ b/sql/core/src/test/gen-java/org/apache/spark/sql/execution/datasources/parquet/test/avro/ParquetAvroCompat.java
@@ -7,22 +7,8 @@
 @SuppressWarnings("all")
 @org.apache.avro.specific.AvroGenerated
 public class ParquetAvroCompat extends org.apache.avro.specific.SpecificRecordBase implements org.apache.avro.specific.SpecificRecord {
-  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"bool_column\",\"type\":\"boolean\"},{\"name\":\"int_column\",\"type\":\"int\"},{\"name\":\"long_column\",\"type\":\"long\"},{\"name\":\"float_column\",\"type\":\"float\"},{\"name\":\"double_column\",\"type\":\"double\"},{\"name\":\"binary_column\",\"type\":\"bytes\"},{\"name\":\"string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}},{\"name\":\"maybe_bool_column\",\"type\":[\"null\",\"boolean\"]},{\"name\":\"maybe_int_column\",\"type\":[\"null\",\"int\"]},{\"name\":\"maybe_long_column\",\"type\":[\"null\",\"long\"]},{\"name\":\"maybe_float_column\",\"type\":[\"null\",\"float\"]},{\"name\":\"maybe_double_column\",\"type\":[\"null\",\"double\"]},{\"name\":\"maybe_binary_column\",\"type\":[\"null\",\"bytes\"]},{\"name\":\"maybe_string_column\",\"type\":[\"null\",{\"type\":\"string\",\"avro.java.string\":\"String\"}]},{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}},\"avro.java.string\":\"String\"}}]}");
+  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"record\",\"name\":\"ParquetAvroCompat\",\"namespace\":\"org.apache.spark.sql.execution.datasources.parquet.test.avro\",\"fields\":[{\"name\":\"strings_column\",\"type\":{\"type\":\"array\",\"items\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}},{\"name\":\"string_to_int_column\",\"type\":{\"type\":\"map\",\"values\":\"int\",\"avro.java.string\":\"String\"}},{\"name\":\"complex_column\",\"type\":{\"type\":\"map\",\"values\":{\"type\":\"array\",\"items\":{\"type\":\"record\",\"name\":\"Nested\",\"fields\":[{\"name\":\"nested_ints_column\",\"type\":{\"type\":\"array\",\"items\":\"int\"}},{\"name\":\"nested_string_column\",\"type\":{\"type\":\"string\",\"avro.java.string\":\"String\"}}]}},\"avro.java.string\":\"String\"}}]}");
   public static org.apache.avro.Schema getClassSchema() { return SCHEMA$; }
-  @Deprecated public boolean bool_column;
-  @Deprecated public int int_column;
-  @Deprecated public long long_column;
-  @Deprecated public float float_column;
-  @Deprecated public double double_column;
-  @Deprecated public java.nio.ByteBuffer binary_column;
-  @Deprecated public java.lang.String string_column;
-  @Deprecated public java.lang.Boolean maybe_bool_column;
-  @Deprecated public java.lang.Integer maybe_int_column;
-  @Deprecated public java.lang.Long maybe_long_column;
-  @Deprecated public java.lang.Float maybe_float_column;
-  @Deprecated public java.lang.Double maybe_double_column;
-  @Deprecated public java.nio.ByteBuffer maybe_binary_column;
-  @Deprecated public java.lang.String maybe_string_column;
   @Deprecated public java.util.List<java.lang.String> strings_column;
   @Deprecated public java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column;
   @Deprecated public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column;
@@ -37,21 +23,7 @@ public ParquetAvroCompat() {}
   /**
    * All-args constructor.
    */
-  public ParquetAvroCompat(java.lang.Boolean bool_column, java.lang.Integer int_column, java.lang.Long long_column, java.lang.Float float_column, java.lang.Double double_column, java.nio.ByteBuffer binary_column, java.lang.String string_column, java.lang.Boolean maybe_bool_column, java.lang.Integer maybe_int_column, java.lang.Long maybe_long_column, java.lang.Float maybe_float_column, java.lang.Double maybe_double_column, java.nio.ByteBuffer maybe_binary_column, java.lang.String maybe_string_column, java.util.List<java.lang.String> strings_column, java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column, java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column) {
-    this.bool_column = bool_column;
-    this.int_column = int_column;
-    this.long_column = long_column;
-    this.float_column = float_column;
-    this.double_column = double_column;
-    this.binary_column = binary_column;
-    this.string_column = string_column;
-    this.maybe_bool_column = maybe_bool_column;
-    this.maybe_int_column = maybe_int_column;
-    this.maybe_long_column = maybe_long_column;
-    this.maybe_float_column = maybe_float_column;
-    this.maybe_double_column = maybe_double_column;
-    this.maybe_binary_column = maybe_binary_column;
-    this.maybe_string_column = maybe_string_column;
+  public ParquetAvroCompat(java.util.List<java.lang.String> strings_column, java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column, java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column) {
     this.strings_column = strings_column;
     this.string_to_int_column = string_to_int_column;
     this.complex_column = complex_column;
@@ -61,23 +33,9 @@ public ParquetAvroCompat(java.lang.Boolean bool_column, java.lang.Integer int_co
   // Used by DatumWriter.  Applications should not call. 
   public java.lang.Object get(int field$) {
     switch (field$) {
-    case 0: return bool_column;
-    case 1: return int_column;
-    case 2: return long_column;
-    case 3: return float_column;
-    case 4: return double_column;
-    case 5: return binary_column;
-    case 6: return string_column;
-    case 7: return maybe_bool_column;
-    case 8: return maybe_int_column;
-    case 9: return maybe_long_column;
-    case 10: return maybe_float_column;
-    case 11: return maybe_double_column;
-    case 12: return maybe_binary_column;
-    case 13: return maybe_string_column;
-    case 14: return strings_column;
-    case 15: return string_to_int_column;
-    case 16: return complex_column;
+    case 0: return strings_column;
+    case 1: return string_to_int_column;
+    case 2: return complex_column;
     default: throw new org.apache.avro.AvroRuntimeException("Bad index");
     }
   }
@@ -85,237 +43,13 @@ public java.lang.Object get(int field$) {
   @SuppressWarnings(value="unchecked")
   public void put(int field$, java.lang.Object value$) {
     switch (field$) {
-    case 0: bool_column = (java.lang.Boolean)value$; break;
-    case 1: int_column = (java.lang.Integer)value$; break;
-    case 2: long_column = (java.lang.Long)value$; break;
-    case 3: float_column = (java.lang.Float)value$; break;
-    case 4: double_column = (java.lang.Double)value$; break;
-    case 5: binary_column = (java.nio.ByteBuffer)value$; break;
-    case 6: string_column = (java.lang.String)value$; break;
-    case 7: maybe_bool_column = (java.lang.Boolean)value$; break;
-    case 8: maybe_int_column = (java.lang.Integer)value$; break;
-    case 9: maybe_long_column = (java.lang.Long)value$; break;
-    case 10: maybe_float_column = (java.lang.Float)value$; break;
-    case 11: maybe_double_column = (java.lang.Double)value$; break;
-    case 12: maybe_binary_column = (java.nio.ByteBuffer)value$; break;
-    case 13: maybe_string_column = (java.lang.String)value$; break;
-    case 14: strings_column = (java.util.List<java.lang.String>)value$; break;
-    case 15: string_to_int_column = (java.util.Map<java.lang.String,java.lang.Integer>)value$; break;
-    case 16: complex_column = (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>)value$; break;
+    case 0: strings_column = (java.util.List<java.lang.String>)value$; break;
+    case 1: string_to_int_column = (java.util.Map<java.lang.String,java.lang.Integer>)value$; break;
+    case 2: complex_column = (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>)value$; break;
     default: throw new org.apache.avro.AvroRuntimeException("Bad index");
     }
   }
 
-  /**
-   * Gets the value of the 'bool_column' field.
-   */
-  public java.lang.Boolean getBoolColumn() {
-    return bool_column;
-  }
-
-  /**
-   * Sets the value of the 'bool_column' field.
-   * @param value the value to set.
-   */
-  public void setBoolColumn(java.lang.Boolean value) {
-    this.bool_column = value;
-  }
-
-  /**
-   * Gets the value of the 'int_column' field.
-   */
-  public java.lang.Integer getIntColumn() {
-    return int_column;
-  }
-
-  /**
-   * Sets the value of the 'int_column' field.
-   * @param value the value to set.
-   */
-  public void setIntColumn(java.lang.Integer value) {
-    this.int_column = value;
-  }
-
-  /**
-   * Gets the value of the 'long_column' field.
-   */
-  public java.lang.Long getLongColumn() {
-    return long_column;
-  }
-
-  /**
-   * Sets the value of the 'long_column' field.
-   * @param value the value to set.
-   */
-  public void setLongColumn(java.lang.Long value) {
-    this.long_column = value;
-  }
-
-  /**
-   * Gets the value of the 'float_column' field.
-   */
-  public java.lang.Float getFloatColumn() {
-    return float_column;
-  }
-
-  /**
-   * Sets the value of the 'float_column' field.
-   * @param value the value to set.
-   */
-  public void setFloatColumn(java.lang.Float value) {
-    this.float_column = value;
-  }
-
-  /**
-   * Gets the value of the 'double_column' field.
-   */
-  public java.lang.Double getDoubleColumn() {
-    return double_column;
-  }
-
-  /**
-   * Sets the value of the 'double_column' field.
-   * @param value the value to set.
-   */
-  public void setDoubleColumn(java.lang.Double value) {
-    this.double_column = value;
-  }
-
-  /**
-   * Gets the value of the 'binary_column' field.
-   */
-  public java.nio.ByteBuffer getBinaryColumn() {
-    return binary_column;
-  }
-
-  /**
-   * Sets the value of the 'binary_column' field.
-   * @param value the value to set.
-   */
-  public void setBinaryColumn(java.nio.ByteBuffer value) {
-    this.binary_column = value;
-  }
-
-  /**
-   * Gets the value of the 'string_column' field.
-   */
-  public java.lang.String getStringColumn() {
-    return string_column;
-  }
-
-  /**
-   * Sets the value of the 'string_column' field.
-   * @param value the value to set.
-   */
-  public void setStringColumn(java.lang.String value) {
-    this.string_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_bool_column' field.
-   */
-  public java.lang.Boolean getMaybeBoolColumn() {
-    return maybe_bool_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_bool_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeBoolColumn(java.lang.Boolean value) {
-    this.maybe_bool_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_int_column' field.
-   */
-  public java.lang.Integer getMaybeIntColumn() {
-    return maybe_int_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_int_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeIntColumn(java.lang.Integer value) {
-    this.maybe_int_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_long_column' field.
-   */
-  public java.lang.Long getMaybeLongColumn() {
-    return maybe_long_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_long_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeLongColumn(java.lang.Long value) {
-    this.maybe_long_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_float_column' field.
-   */
-  public java.lang.Float getMaybeFloatColumn() {
-    return maybe_float_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_float_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeFloatColumn(java.lang.Float value) {
-    this.maybe_float_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_double_column' field.
-   */
-  public java.lang.Double getMaybeDoubleColumn() {
-    return maybe_double_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_double_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeDoubleColumn(java.lang.Double value) {
-    this.maybe_double_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_binary_column' field.
-   */
-  public java.nio.ByteBuffer getMaybeBinaryColumn() {
-    return maybe_binary_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_binary_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeBinaryColumn(java.nio.ByteBuffer value) {
-    this.maybe_binary_column = value;
-  }
-
-  /**
-   * Gets the value of the 'maybe_string_column' field.
-   */
-  public java.lang.String getMaybeStringColumn() {
-    return maybe_string_column;
-  }
-
-  /**
-   * Sets the value of the 'maybe_string_column' field.
-   * @param value the value to set.
-   */
-  public void setMaybeStringColumn(java.lang.String value) {
-    this.maybe_string_column = value;
-  }
-
   /**
    * Gets the value of the 'strings_column' field.
    */
@@ -382,20 +116,6 @@ public static org.apache.spark.sql.execution.datasources.parquet.test.avro.Parqu
   public static class Builder extends org.apache.avro.specific.SpecificRecordBuilderBase<ParquetAvroCompat>
     implements org.apache.avro.data.RecordBuilder<ParquetAvroCompat> {
 
-    private boolean bool_column;
-    private int int_column;
-    private long long_column;
-    private float float_column;
-    private double double_column;
-    private java.nio.ByteBuffer binary_column;
-    private java.lang.String string_column;
-    private java.lang.Boolean maybe_bool_column;
-    private java.lang.Integer maybe_int_column;
-    private java.lang.Long maybe_long_column;
-    private java.lang.Float maybe_float_column;
-    private java.lang.Double maybe_double_column;
-    private java.nio.ByteBuffer maybe_binary_column;
-    private java.lang.String maybe_string_column;
     private java.util.List<java.lang.String> strings_column;
     private java.util.Map<java.lang.String,java.lang.Integer> string_to_int_column;
     private java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> complex_column;
@@ -408,492 +128,35 @@ private Builder() {
     /** Creates a Builder by copying an existing Builder */
     private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder other) {
       super(other);
-      if (isValidValue(fields()[0], other.bool_column)) {
-        this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
+      if (isValidValue(fields()[0], other.strings_column)) {
+        this.strings_column = data().deepCopy(fields()[0].schema(), other.strings_column);
         fieldSetFlags()[0] = true;
       }
-      if (isValidValue(fields()[1], other.int_column)) {
-        this.int_column = data().deepCopy(fields()[1].schema(), other.int_column);
+      if (isValidValue(fields()[1], other.string_to_int_column)) {
+        this.string_to_int_column = data().deepCopy(fields()[1].schema(), other.string_to_int_column);
         fieldSetFlags()[1] = true;
       }
-      if (isValidValue(fields()[2], other.long_column)) {
-        this.long_column = data().deepCopy(fields()[2].schema(), other.long_column);
+      if (isValidValue(fields()[2], other.complex_column)) {
+        this.complex_column = data().deepCopy(fields()[2].schema(), other.complex_column);
         fieldSetFlags()[2] = true;
       }
-      if (isValidValue(fields()[3], other.float_column)) {
-        this.float_column = data().deepCopy(fields()[3].schema(), other.float_column);
-        fieldSetFlags()[3] = true;
-      }
-      if (isValidValue(fields()[4], other.double_column)) {
-        this.double_column = data().deepCopy(fields()[4].schema(), other.double_column);
-        fieldSetFlags()[4] = true;
-      }
-      if (isValidValue(fields()[5], other.binary_column)) {
-        this.binary_column = data().deepCopy(fields()[5].schema(), other.binary_column);
-        fieldSetFlags()[5] = true;
-      }
-      if (isValidValue(fields()[6], other.string_column)) {
-        this.string_column = data().deepCopy(fields()[6].schema(), other.string_column);
-        fieldSetFlags()[6] = true;
-      }
-      if (isValidValue(fields()[7], other.maybe_bool_column)) {
-        this.maybe_bool_column = data().deepCopy(fields()[7].schema(), other.maybe_bool_column);
-        fieldSetFlags()[7] = true;
-      }
-      if (isValidValue(fields()[8], other.maybe_int_column)) {
-        this.maybe_int_column = data().deepCopy(fields()[8].schema(), other.maybe_int_column);
-        fieldSetFlags()[8] = true;
-      }
-      if (isValidValue(fields()[9], other.maybe_long_column)) {
-        this.maybe_long_column = data().deepCopy(fields()[9].schema(), other.maybe_long_column);
-        fieldSetFlags()[9] = true;
-      }
-      if (isValidValue(fields()[10], other.maybe_float_column)) {
-        this.maybe_float_column = data().deepCopy(fields()[10].schema(), other.maybe_float_column);
-        fieldSetFlags()[10] = true;
-      }
-      if (isValidValue(fields()[11], other.maybe_double_column)) {
-        this.maybe_double_column = data().deepCopy(fields()[11].schema(), other.maybe_double_column);
-        fieldSetFlags()[11] = true;
-      }
-      if (isValidValue(fields()[12], other.maybe_binary_column)) {
-        this.maybe_binary_column = data().deepCopy(fields()[12].schema(), other.maybe_binary_column);
-        fieldSetFlags()[12] = true;
-      }
-      if (isValidValue(fields()[13], other.maybe_string_column)) {
-        this.maybe_string_column = data().deepCopy(fields()[13].schema(), other.maybe_string_column);
-        fieldSetFlags()[13] = true;
-      }
-      if (isValidValue(fields()[14], other.strings_column)) {
-        this.strings_column = data().deepCopy(fields()[14].schema(), other.strings_column);
-        fieldSetFlags()[14] = true;
-      }
-      if (isValidValue(fields()[15], other.string_to_int_column)) {
-        this.string_to_int_column = data().deepCopy(fields()[15].schema(), other.string_to_int_column);
-        fieldSetFlags()[15] = true;
-      }
-      if (isValidValue(fields()[16], other.complex_column)) {
-        this.complex_column = data().deepCopy(fields()[16].schema(), other.complex_column);
-        fieldSetFlags()[16] = true;
-      }
     }
     
     /** Creates a Builder by copying an existing ParquetAvroCompat instance */
     private Builder(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat other) {
             super(org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.SCHEMA$);
-      if (isValidValue(fields()[0], other.bool_column)) {
-        this.bool_column = data().deepCopy(fields()[0].schema(), other.bool_column);
+      if (isValidValue(fields()[0], other.strings_column)) {
+        this.strings_column = data().deepCopy(fields()[0].schema(), other.strings_column);
         fieldSetFlags()[0] = true;
       }
-      if (isValidValue(fields()[1], other.int_column)) {
-        this.int_column = data().deepCopy(fields()[1].schema(), other.int_column);
+      if (isValidValue(fields()[1], other.string_to_int_column)) {
+        this.string_to_int_column = data().deepCopy(fields()[1].schema(), other.string_to_int_column);
         fieldSetFlags()[1] = true;
       }
-      if (isValidValue(fields()[2], other.long_column)) {
-        this.long_column = data().deepCopy(fields()[2].schema(), other.long_column);
+      if (isValidValue(fields()[2], other.complex_column)) {
+        this.complex_column = data().deepCopy(fields()[2].schema(), other.complex_column);
         fieldSetFlags()[2] = true;
       }
-      if (isValidValue(fields()[3], other.float_column)) {
-        this.float_column = data().deepCopy(fields()[3].schema(), other.float_column);
-        fieldSetFlags()[3] = true;
-      }
-      if (isValidValue(fields()[4], other.double_column)) {
-        this.double_column = data().deepCopy(fields()[4].schema(), other.double_column);
-        fieldSetFlags()[4] = true;
-      }
-      if (isValidValue(fields()[5], other.binary_column)) {
-        this.binary_column = data().deepCopy(fields()[5].schema(), other.binary_column);
-        fieldSetFlags()[5] = true;
-      }
-      if (isValidValue(fields()[6], other.string_column)) {
-        this.string_column = data().deepCopy(fields()[6].schema(), other.string_column);
-        fieldSetFlags()[6] = true;
-      }
-      if (isValidValue(fields()[7], other.maybe_bool_column)) {
-        this.maybe_bool_column = data().deepCopy(fields()[7].schema(), other.maybe_bool_column);
-        fieldSetFlags()[7] = true;
-      }
-      if (isValidValue(fields()[8], other.maybe_int_column)) {
-        this.maybe_int_column = data().deepCopy(fields()[8].schema(), other.maybe_int_column);
-        fieldSetFlags()[8] = true;
-      }
-      if (isValidValue(fields()[9], other.maybe_long_column)) {
-        this.maybe_long_column = data().deepCopy(fields()[9].schema(), other.maybe_long_column);
-        fieldSetFlags()[9] = true;
-      }
-      if (isValidValue(fields()[10], other.maybe_float_column)) {
-        this.maybe_float_column = data().deepCopy(fields()[10].schema(), other.maybe_float_column);
-        fieldSetFlags()[10] = true;
-      }
-      if (isValidValue(fields()[11], other.maybe_double_column)) {
-        this.maybe_double_column = data().deepCopy(fields()[11].schema(), other.maybe_double_column);
-        fieldSetFlags()[11] = true;
-      }
-      if (isValidValue(fields()[12], other.maybe_binary_column)) {
-        this.maybe_binary_column = data().deepCopy(fields()[12].schema(), other.maybe_binary_column);
-        fieldSetFlags()[12] = true;
-      }
-      if (isValidValue(fields()[13], other.maybe_string_column)) {
-        this.maybe_string_column = data().deepCopy(fields()[13].schema(), other.maybe_string_column);
-        fieldSetFlags()[13] = true;
-      }
-      if (isValidValue(fields()[14], other.strings_column)) {
-        this.strings_column = data().deepCopy(fields()[14].schema(), other.strings_column);
-        fieldSetFlags()[14] = true;
-      }
-      if (isValidValue(fields()[15], other.string_to_int_column)) {
-        this.string_to_int_column = data().deepCopy(fields()[15].schema(), other.string_to_int_column);
-        fieldSetFlags()[15] = true;
-      }
-      if (isValidValue(fields()[16], other.complex_column)) {
-        this.complex_column = data().deepCopy(fields()[16].schema(), other.complex_column);
-        fieldSetFlags()[16] = true;
-      }
-    }
-
-    /** Gets the value of the 'bool_column' field */
-    public java.lang.Boolean getBoolColumn() {
-      return bool_column;
-    }
-    
-    /** Sets the value of the 'bool_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setBoolColumn(boolean value) {
-      validate(fields()[0], value);
-      this.bool_column = value;
-      fieldSetFlags()[0] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'bool_column' field has been set */
-    public boolean hasBoolColumn() {
-      return fieldSetFlags()[0];
-    }
-    
-    /** Clears the value of the 'bool_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearBoolColumn() {
-      fieldSetFlags()[0] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'int_column' field */
-    public java.lang.Integer getIntColumn() {
-      return int_column;
-    }
-    
-    /** Sets the value of the 'int_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setIntColumn(int value) {
-      validate(fields()[1], value);
-      this.int_column = value;
-      fieldSetFlags()[1] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'int_column' field has been set */
-    public boolean hasIntColumn() {
-      return fieldSetFlags()[1];
-    }
-    
-    /** Clears the value of the 'int_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearIntColumn() {
-      fieldSetFlags()[1] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'long_column' field */
-    public java.lang.Long getLongColumn() {
-      return long_column;
-    }
-    
-    /** Sets the value of the 'long_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setLongColumn(long value) {
-      validate(fields()[2], value);
-      this.long_column = value;
-      fieldSetFlags()[2] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'long_column' field has been set */
-    public boolean hasLongColumn() {
-      return fieldSetFlags()[2];
-    }
-    
-    /** Clears the value of the 'long_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearLongColumn() {
-      fieldSetFlags()[2] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'float_column' field */
-    public java.lang.Float getFloatColumn() {
-      return float_column;
-    }
-    
-    /** Sets the value of the 'float_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setFloatColumn(float value) {
-      validate(fields()[3], value);
-      this.float_column = value;
-      fieldSetFlags()[3] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'float_column' field has been set */
-    public boolean hasFloatColumn() {
-      return fieldSetFlags()[3];
-    }
-    
-    /** Clears the value of the 'float_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearFloatColumn() {
-      fieldSetFlags()[3] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'double_column' field */
-    public java.lang.Double getDoubleColumn() {
-      return double_column;
-    }
-    
-    /** Sets the value of the 'double_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setDoubleColumn(double value) {
-      validate(fields()[4], value);
-      this.double_column = value;
-      fieldSetFlags()[4] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'double_column' field has been set */
-    public boolean hasDoubleColumn() {
-      return fieldSetFlags()[4];
-    }
-    
-    /** Clears the value of the 'double_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearDoubleColumn() {
-      fieldSetFlags()[4] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'binary_column' field */
-    public java.nio.ByteBuffer getBinaryColumn() {
-      return binary_column;
-    }
-    
-    /** Sets the value of the 'binary_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setBinaryColumn(java.nio.ByteBuffer value) {
-      validate(fields()[5], value);
-      this.binary_column = value;
-      fieldSetFlags()[5] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'binary_column' field has been set */
-    public boolean hasBinaryColumn() {
-      return fieldSetFlags()[5];
-    }
-    
-    /** Clears the value of the 'binary_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearBinaryColumn() {
-      binary_column = null;
-      fieldSetFlags()[5] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'string_column' field */
-    public java.lang.String getStringColumn() {
-      return string_column;
-    }
-    
-    /** Sets the value of the 'string_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringColumn(java.lang.String value) {
-      validate(fields()[6], value);
-      this.string_column = value;
-      fieldSetFlags()[6] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'string_column' field has been set */
-    public boolean hasStringColumn() {
-      return fieldSetFlags()[6];
-    }
-    
-    /** Clears the value of the 'string_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringColumn() {
-      string_column = null;
-      fieldSetFlags()[6] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_bool_column' field */
-    public java.lang.Boolean getMaybeBoolColumn() {
-      return maybe_bool_column;
-    }
-    
-    /** Sets the value of the 'maybe_bool_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBoolColumn(java.lang.Boolean value) {
-      validate(fields()[7], value);
-      this.maybe_bool_column = value;
-      fieldSetFlags()[7] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_bool_column' field has been set */
-    public boolean hasMaybeBoolColumn() {
-      return fieldSetFlags()[7];
-    }
-    
-    /** Clears the value of the 'maybe_bool_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBoolColumn() {
-      maybe_bool_column = null;
-      fieldSetFlags()[7] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_int_column' field */
-    public java.lang.Integer getMaybeIntColumn() {
-      return maybe_int_column;
-    }
-    
-    /** Sets the value of the 'maybe_int_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeIntColumn(java.lang.Integer value) {
-      validate(fields()[8], value);
-      this.maybe_int_column = value;
-      fieldSetFlags()[8] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_int_column' field has been set */
-    public boolean hasMaybeIntColumn() {
-      return fieldSetFlags()[8];
-    }
-    
-    /** Clears the value of the 'maybe_int_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeIntColumn() {
-      maybe_int_column = null;
-      fieldSetFlags()[8] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_long_column' field */
-    public java.lang.Long getMaybeLongColumn() {
-      return maybe_long_column;
-    }
-    
-    /** Sets the value of the 'maybe_long_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeLongColumn(java.lang.Long value) {
-      validate(fields()[9], value);
-      this.maybe_long_column = value;
-      fieldSetFlags()[9] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_long_column' field has been set */
-    public boolean hasMaybeLongColumn() {
-      return fieldSetFlags()[9];
-    }
-    
-    /** Clears the value of the 'maybe_long_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeLongColumn() {
-      maybe_long_column = null;
-      fieldSetFlags()[9] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_float_column' field */
-    public java.lang.Float getMaybeFloatColumn() {
-      return maybe_float_column;
-    }
-    
-    /** Sets the value of the 'maybe_float_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeFloatColumn(java.lang.Float value) {
-      validate(fields()[10], value);
-      this.maybe_float_column = value;
-      fieldSetFlags()[10] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_float_column' field has been set */
-    public boolean hasMaybeFloatColumn() {
-      return fieldSetFlags()[10];
-    }
-    
-    /** Clears the value of the 'maybe_float_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeFloatColumn() {
-      maybe_float_column = null;
-      fieldSetFlags()[10] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_double_column' field */
-    public java.lang.Double getMaybeDoubleColumn() {
-      return maybe_double_column;
-    }
-    
-    /** Sets the value of the 'maybe_double_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeDoubleColumn(java.lang.Double value) {
-      validate(fields()[11], value);
-      this.maybe_double_column = value;
-      fieldSetFlags()[11] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_double_column' field has been set */
-    public boolean hasMaybeDoubleColumn() {
-      return fieldSetFlags()[11];
-    }
-    
-    /** Clears the value of the 'maybe_double_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeDoubleColumn() {
-      maybe_double_column = null;
-      fieldSetFlags()[11] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_binary_column' field */
-    public java.nio.ByteBuffer getMaybeBinaryColumn() {
-      return maybe_binary_column;
-    }
-    
-    /** Sets the value of the 'maybe_binary_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeBinaryColumn(java.nio.ByteBuffer value) {
-      validate(fields()[12], value);
-      this.maybe_binary_column = value;
-      fieldSetFlags()[12] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_binary_column' field has been set */
-    public boolean hasMaybeBinaryColumn() {
-      return fieldSetFlags()[12];
-    }
-    
-    /** Clears the value of the 'maybe_binary_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeBinaryColumn() {
-      maybe_binary_column = null;
-      fieldSetFlags()[12] = false;
-      return this;
-    }
-
-    /** Gets the value of the 'maybe_string_column' field */
-    public java.lang.String getMaybeStringColumn() {
-      return maybe_string_column;
-    }
-    
-    /** Sets the value of the 'maybe_string_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setMaybeStringColumn(java.lang.String value) {
-      validate(fields()[13], value);
-      this.maybe_string_column = value;
-      fieldSetFlags()[13] = true;
-      return this; 
-    }
-    
-    /** Checks whether the 'maybe_string_column' field has been set */
-    public boolean hasMaybeStringColumn() {
-      return fieldSetFlags()[13];
-    }
-    
-    /** Clears the value of the 'maybe_string_column' field */
-    public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearMaybeStringColumn() {
-      maybe_string_column = null;
-      fieldSetFlags()[13] = false;
-      return this;
     }
 
     /** Gets the value of the 'strings_column' field */
@@ -903,21 +166,21 @@ public java.util.List<java.lang.String> getStringsColumn() {
     
     /** Sets the value of the 'strings_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringsColumn(java.util.List<java.lang.String> value) {
-      validate(fields()[14], value);
+      validate(fields()[0], value);
       this.strings_column = value;
-      fieldSetFlags()[14] = true;
+      fieldSetFlags()[0] = true;
       return this; 
     }
     
     /** Checks whether the 'strings_column' field has been set */
     public boolean hasStringsColumn() {
-      return fieldSetFlags()[14];
+      return fieldSetFlags()[0];
     }
     
     /** Clears the value of the 'strings_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringsColumn() {
       strings_column = null;
-      fieldSetFlags()[14] = false;
+      fieldSetFlags()[0] = false;
       return this;
     }
 
@@ -928,21 +191,21 @@ public java.util.Map<java.lang.String,java.lang.Integer> getStringToIntColumn()
     
     /** Sets the value of the 'string_to_int_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setStringToIntColumn(java.util.Map<java.lang.String,java.lang.Integer> value) {
-      validate(fields()[15], value);
+      validate(fields()[1], value);
       this.string_to_int_column = value;
-      fieldSetFlags()[15] = true;
+      fieldSetFlags()[1] = true;
       return this; 
     }
     
     /** Checks whether the 'string_to_int_column' field has been set */
     public boolean hasStringToIntColumn() {
-      return fieldSetFlags()[15];
+      return fieldSetFlags()[1];
     }
     
     /** Clears the value of the 'string_to_int_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearStringToIntColumn() {
       string_to_int_column = null;
-      fieldSetFlags()[15] = false;
+      fieldSetFlags()[1] = false;
       return this;
     }
 
@@ -953,21 +216,21 @@ public java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execut
     
     /** Sets the value of the 'complex_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder setComplexColumn(java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>> value) {
-      validate(fields()[16], value);
+      validate(fields()[2], value);
       this.complex_column = value;
-      fieldSetFlags()[16] = true;
+      fieldSetFlags()[2] = true;
       return this; 
     }
     
     /** Checks whether the 'complex_column' field has been set */
     public boolean hasComplexColumn() {
-      return fieldSetFlags()[16];
+      return fieldSetFlags()[2];
     }
     
     /** Clears the value of the 'complex_column' field */
     public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroCompat.Builder clearComplexColumn() {
       complex_column = null;
-      fieldSetFlags()[16] = false;
+      fieldSetFlags()[2] = false;
       return this;
     }
 
@@ -975,23 +238,9 @@ public org.apache.spark.sql.execution.datasources.parquet.test.avro.ParquetAvroC
     public ParquetAvroCompat build() {
       try {
         ParquetAvroCompat record = new ParquetAvroCompat();
-        record.bool_column = fieldSetFlags()[0] ? this.bool_column : (java.lang.Boolean) defaultValue(fields()[0]);
-        record.int_column = fieldSetFlags()[1] ? this.int_column : (java.lang.Integer) defaultValue(fields()[1]);
-        record.long_column = fieldSetFlags()[2] ? this.long_column : (java.lang.Long) defaultValue(fields()[2]);
-        record.float_column = fieldSetFlags()[3] ? this.float_column : (java.lang.Float) defaultValue(fields()[3]);
-        record.double_column = fieldSetFlags()[4] ? this.double_column : (java.lang.Double) defaultValue(fields()[4]);
-        record.binary_column = fieldSetFlags()[5] ? this.binary_column : (java.nio.ByteBuffer) defaultValue(fields()[5]);
-        record.string_column = fieldSetFlags()[6] ? this.string_column : (java.lang.String) defaultValue(fields()[6]);
-        record.maybe_bool_column = fieldSetFlags()[7] ? this.maybe_bool_column : (java.lang.Boolean) defaultValue(fields()[7]);
-        record.maybe_int_column = fieldSetFlags()[8] ? this.maybe_int_column : (java.lang.Integer) defaultValue(fields()[8]);
-        record.maybe_long_column = fieldSetFlags()[9] ? this.maybe_long_column : (java.lang.Long) defaultValue(fields()[9]);
-        record.maybe_float_column = fieldSetFlags()[10] ? this.maybe_float_column : (java.lang.Float) defaultValue(fields()[10]);
-        record.maybe_double_column = fieldSetFlags()[11] ? this.maybe_double_column : (java.lang.Double) defaultValue(fields()[11]);
-        record.maybe_binary_column = fieldSetFlags()[12] ? this.maybe_binary_column : (java.nio.ByteBuffer) defaultValue(fields()[12]);
-        record.maybe_string_column = fieldSetFlags()[13] ? this.maybe_string_column : (java.lang.String) defaultValue(fields()[13]);
-        record.strings_column = fieldSetFlags()[14] ? this.strings_column : (java.util.List<java.lang.String>) defaultValue(fields()[14]);
-        record.string_to_int_column = fieldSetFlags()[15] ? this.string_to_int_column : (java.util.Map<java.lang.String,java.lang.Integer>) defaultValue(fields()[15]);
-        record.complex_column = fieldSetFlags()[16] ? this.complex_column : (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>) defaultValue(fields()[16]);
+        record.strings_column = fieldSetFlags()[0] ? this.strings_column : (java.util.List<java.lang.String>) defaultValue(fields()[0]);
+        record.string_to_int_column = fieldSetFlags()[1] ? this.string_to_int_column : (java.util.Map<java.lang.String,java.lang.Integer>) defaultValue(fields()[1]);
+        record.complex_column = fieldSetFlags()[2] ? this.complex_column : (java.util.Map<java.lang.String,java.util.List<org.apache.spark.sql.execution.datasources.parquet.test.avro.Nested>>) defaultValue(fields()[2]);
         return record;
       } catch (Exception e) {
         throw new org.apache.avro.AvroRuntimeException(e);
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index 82d40e2b61a10..45db619567a22 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -20,7 +20,8 @@ package org.apache.spark.sql.execution.datasources.parquet
 import java.nio.ByteBuffer
 import java.util.{List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters.seqAsJavaListConverter
+import scala.collection.JavaConverters.mapAsJavaMapConverter
 
 import org.apache.avro.Schema
 import org.apache.avro.generic.IndexedRecord
@@ -32,48 +33,196 @@ import org.apache.spark.sql.execution.datasources.parquet.test.avro._
 import org.apache.spark.sql.test.SharedSQLContext
 
 class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest with SharedSQLContext {
-  import ParquetCompatibilityTest._
-
   private def withWriter[T <: IndexedRecord]
       (path: String, schema: Schema)
       (f: AvroParquetWriter[T] => Unit): Unit = {
+    logInfo(
+      s"""Writing Avro records with the following Avro schema into Parquet file:
+         |
+         |${schema.toString(true)}
+       """.stripMargin)
+
     val writer = new AvroParquetWriter[T](new Path(path), schema)
     try f(writer) finally writer.close()
   }
 
-  test("Read Parquet file generated by parquet-avro") {
+  test("required primitives") {
     withTempPath { dir =>
       val path = dir.getCanonicalPath
 
-      withWriter[ParquetAvroCompat](path, ParquetAvroCompat.getClassSchema) { writer =>
-        (0 until 10).foreach(i => writer.write(makeParquetAvroCompat(i)))
+      withWriter[AvroPrimitives](path, AvroPrimitives.getClassSchema) { writer =>
+        (0 until 10).foreach { i =>
+          writer.write(
+            AvroPrimitives.newBuilder()
+              .setBoolColumn(i % 2 == 0)
+              .setIntColumn(i)
+              .setLongColumn(i.toLong * 10)
+              .setFloatColumn(i.toFloat + 0.1f)
+              .setDoubleColumn(i.toDouble + 0.2d)
+              .setBinaryColumn(ByteBuffer.wrap(s"val_$i".getBytes("UTF-8")))
+              .setStringColumn(s"val_$i")
+              .build())
+        }
       }
 
-      logInfo(
-        s"""Schema of the Parquet file written by parquet-avro:
-           |${readParquetSchema(path)}
-       """.stripMargin)
+      logParquetSchema(path)
 
       checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
-        def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
-
         Row(
           i % 2 == 0,
           i,
           i.toLong * 10,
           i.toFloat + 0.1f,
           i.toDouble + 0.2d,
-          s"val_$i".getBytes,
-          s"val_$i",
+          s"val_$i".getBytes("UTF-8"),
+          s"val_$i")
+      })
+    }
+  }
+
+  test("optional primitives") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[AvroOptionalPrimitives](path, AvroOptionalPrimitives.getClassSchema) { writer =>
+        (0 until 10).foreach { i =>
+          val record = if (i % 3 == 0) {
+            AvroOptionalPrimitives.newBuilder()
+              .setMaybeBoolColumn(null)
+              .setMaybeIntColumn(null)
+              .setMaybeLongColumn(null)
+              .setMaybeFloatColumn(null)
+              .setMaybeDoubleColumn(null)
+              .setMaybeBinaryColumn(null)
+              .setMaybeStringColumn(null)
+              .build()
+          } else {
+            AvroOptionalPrimitives.newBuilder()
+              .setMaybeBoolColumn(i % 2 == 0)
+              .setMaybeIntColumn(i)
+              .setMaybeLongColumn(i.toLong * 10)
+              .setMaybeFloatColumn(i.toFloat + 0.1f)
+              .setMaybeDoubleColumn(i.toDouble + 0.2d)
+              .setMaybeBinaryColumn(ByteBuffer.wrap(s"val_$i".getBytes("UTF-8")))
+              .setMaybeStringColumn(s"val_$i")
+              .build()
+          }
+
+          writer.write(record)
+        }
+      }
+
+      logParquetSchema(path)
+
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        if (i % 3 == 0) {
+          Row.apply(Seq.fill(7)(null): _*)
+        } else {
+          Row(
+            i % 2 == 0,
+            i,
+            i.toLong * 10,
+            i.toFloat + 0.1f,
+            i.toDouble + 0.2d,
+            s"val_$i".getBytes("UTF-8"),
+            s"val_$i")
+        }
+      })
+    }
+  }
+
+  test("non-nullable arrays") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[AvroNonNullableArrays](path, AvroNonNullableArrays.getClassSchema) { writer =>
+        (0 until 10).foreach { i =>
+          val record = {
+            val builder =
+              AvroNonNullableArrays.newBuilder()
+                .setStringsColumn(Seq.tabulate(3)(i => s"val_$i").asJava)
+
+            if (i % 3 == 0) {
+              builder.setMaybeIntsColumn(null).build()
+            } else {
+              builder.setMaybeIntsColumn(Seq.tabulate(3)(Int.box).asJava).build()
+            }
+          }
+
+          writer.write(record)
+        }
+      }
+
+      logParquetSchema(path)
+
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        Row(
+          Seq.tabulate(3)(i => s"val_$i"),
+          if (i % 3 == 0) null else Seq.tabulate(3)(identity))
+      })
+    }
+  }
+
+  ignore("nullable arrays (parquet-avro 1.7.0 does not properly support this)") {
+    // TODO Complete this test case after upgrading to parquet-mr 1.8+
+  }
+
+  test("SPARK-10136 array of primitive array") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[AvroArrayOfArray](path, AvroArrayOfArray.getClassSchema) { writer =>
+        (0 until 10).foreach { i =>
+          writer.write(AvroArrayOfArray.newBuilder()
+            .setIntArraysColumn(
+              Seq.tabulate(3, 3)((i, j) => i * 3 + j: Integer).map(_.asJava).asJava)
+            .build())
+        }
+      }
 
-          nullable(i % 2 == 0: java.lang.Boolean),
-          nullable(i: Integer),
-          nullable(i.toLong: java.lang.Long),
-          nullable(i.toFloat + 0.1f: java.lang.Float),
-          nullable(i.toDouble + 0.2d: java.lang.Double),
-          nullable(s"val_$i".getBytes),
-          nullable(s"val_$i"),
+      logParquetSchema(path)
 
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        Row(Seq.tabulate(3, 3)((i, j) => i * 3 + j))
+      })
+    }
+  }
+
+  test("map of primitive array") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[AvroMapOfArray](path, AvroMapOfArray.getClassSchema) { writer =>
+        (0 until 10).foreach { i =>
+          writer.write(AvroMapOfArray.newBuilder()
+            .setStringToIntsColumn(
+              Seq.tabulate(3) { i =>
+                i.toString -> Seq.tabulate(3)(j => i + j: Integer).asJava
+              }.toMap.asJava)
+            .build())
+        }
+      }
+
+      logParquetSchema(path)
+
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        Row(Seq.tabulate(3)(i => i.toString -> Seq.tabulate(3)(j => i + j)).toMap)
+      })
+    }
+  }
+
+  test("various complex types") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      withWriter[ParquetAvroCompat](path, ParquetAvroCompat.getClassSchema) { writer =>
+        (0 until 10).foreach(i => writer.write(makeParquetAvroCompat(i)))
+      }
+
+      logParquetSchema(path)
+
+      checkAnswer(sqlContext.read.parquet(path), (0 until 10).map { i =>
+        Row(
           Seq.tabulate(3)(n => s"arr_${i + n}"),
           Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap,
           Seq.tabulate(3) { n =>
@@ -86,47 +235,27 @@ class ParquetAvroCompatibilitySuite extends ParquetCompatibilityTest with Shared
   }
 
   def makeParquetAvroCompat(i: Int): ParquetAvroCompat = {
-    def nullable[T <: AnyRef] = makeNullable[T](i) _
-
     def makeComplexColumn(i: Int): JMap[String, JList[Nested]] = {
-      mapAsJavaMap(Seq.tabulate(3) { n =>
-        (i + n).toString -> seqAsJavaList(Seq.tabulate(3) { m =>
+      Seq.tabulate(3) { n =>
+        (i + n).toString -> Seq.tabulate(3) { m =>
           Nested
             .newBuilder()
-            .setNestedIntsColumn(seqAsJavaList(Seq.tabulate(3)(j => i + j + m)))
+            .setNestedIntsColumn(Seq.tabulate(3)(j => i + j + m: Integer).asJava)
             .setNestedStringColumn(s"val_${i + m}")
             .build()
-        })
-      }.toMap)
+        }.asJava
+      }.toMap.asJava
     }
 
     ParquetAvroCompat
       .newBuilder()
-      .setBoolColumn(i % 2 == 0)
-      .setIntColumn(i)
-      .setLongColumn(i.toLong * 10)
-      .setFloatColumn(i.toFloat + 0.1f)
-      .setDoubleColumn(i.toDouble + 0.2d)
-      .setBinaryColumn(ByteBuffer.wrap(s"val_$i".getBytes))
-      .setStringColumn(s"val_$i")
-
-      .setMaybeBoolColumn(nullable(i % 2 == 0: java.lang.Boolean))
-      .setMaybeIntColumn(nullable(i: Integer))
-      .setMaybeLongColumn(nullable(i.toLong: java.lang.Long))
-      .setMaybeFloatColumn(nullable(i.toFloat + 0.1f: java.lang.Float))
-      .setMaybeDoubleColumn(nullable(i.toDouble + 0.2d: java.lang.Double))
-      .setMaybeBinaryColumn(nullable(ByteBuffer.wrap(s"val_$i".getBytes)))
-      .setMaybeStringColumn(nullable(s"val_$i"))
-
-      .setStringsColumn(Seq.tabulate(3)(n => s"arr_${i + n}"))
-      .setStringToIntColumn(
-        mapAsJavaMap(Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap))
+      .setStringsColumn(Seq.tabulate(3)(n => s"arr_${i + n}").asJava)
+      .setStringToIntColumn(Seq.tabulate(3)(n => n.toString -> (i + n: Integer)).toMap.asJava)
       .setComplexColumn(makeComplexColumn(i))
-
       .build()
   }
 
-  test("SPARK-9407 Don't push down predicates involving Parquet ENUM columns") {
+  test("SPARK-9407 Push down predicates involving Parquet ENUM columns") {
     import testImplicits._
 
     withTempPath { dir =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index b3406729fcc5e..d85c564e3e8d1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -43,6 +43,13 @@ private[sql] abstract class ParquetCompatibilityTest extends QueryTest with Parq
     val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
     footers.head.getParquetMetadata.getFileMetaData.getSchema
   }
+
+  protected def logParquetSchema(path: String): Unit = {
+    logInfo(
+      s"""Schema of the Parquet file written by parquet-avro:
+         |${readParquetSchema(path)}
+       """.stripMargin)
+  }
 }
 
 object ParquetCompatibilityTest {

From 12de348332108f8c0c5bdad1d4cfac89b952b0f8 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Thu, 20 Aug 2015 11:31:03 -0700
Subject: [PATCH 361/633] [SPARK-10126] [PROJECT INFRA] Fix typo in
 release-build.sh which broke snapshot publishing for Scala 2.11

The current `release-build.sh` has a typo which breaks snapshot publication for Scala 2.11. We should change the Scala version to 2.11 and clean before building a 2.11 snapshot.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8325 from JoshRosen/fix-2.11-snapshots.
---
 dev/create-release/release-build.sh | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dev/create-release/release-build.sh b/dev/create-release/release-build.sh
index 399c73e7bf6bc..d0b3a54dde1dc 100755
--- a/dev/create-release/release-build.sh
+++ b/dev/create-release/release-build.sh
@@ -225,9 +225,9 @@ if [[ "$1" == "publish-snapshot" ]]; then
 
   $MVN -DzincPort=$ZINC_PORT --settings $tmp_settings -DskipTests $PUBLISH_PROFILES \
     -Phive-thriftserver deploy
-  ./dev/change-scala-version.sh 2.10
+  ./dev/change-scala-version.sh 2.11
   $MVN -DzincPort=$ZINC_PORT -Dscala-2.11 --settings $tmp_settings \
-    -DskipTests $PUBLISH_PROFILES deploy
+    -DskipTests $PUBLISH_PROFILES clean deploy
 
   # Clean-up Zinc nailgun process
   /usr/sbin/lsof -P |grep $ZINC_PORT | grep LISTEN | awk '{ print $2; }' | xargs kill

From 907df2fce00d2cbc9fae371344f05f800e0d2726 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 20 Aug 2015 13:51:54 -0700
Subject: [PATCH 362/633] [SQL] [MINOR] remove unnecessary class

This class is identical to `org.apache.spark.sql.execution.datasources.jdbc. DefaultSource` and is not needed.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8334 from cloud-fan/minor.
---
 .../execution/datasources/DefaultSource.scala | 64 -------------------
 1 file changed, 64 deletions(-)
 delete mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala
deleted file mode 100644
index 6e4cc4de7f651..0000000000000
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DefaultSource.scala
+++ /dev/null
@@ -1,64 +0,0 @@
-/*
-* Licensed to the Apache Software Foundation (ASF) under one or more
-* contributor license agreements.  See the NOTICE file distributed with
-* this work for additional information regarding copyright ownership.
-* The ASF licenses this file to You under the Apache License, Version 2.0
-* (the "License"); you may not use this file except in compliance with
-* the License.  You may obtain a copy of the License at
-*
-*    http://www.apache.org/licenses/LICENSE-2.0
-*
-* Unless required by applicable law or agreed to in writing, software
-* distributed under the License is distributed on an "AS IS" BASIS,
-* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-* See the License for the specific language governing permissions and
-* limitations under the License.
-*/
-
-package org.apache.spark.sql.execution.datasources
-
-import java.util.Properties
-
-import org.apache.spark.sql.SQLContext
-import org.apache.spark.sql.execution.datasources.jdbc.{JDBCRelation, JDBCPartitioningInfo, DriverRegistry}
-import org.apache.spark.sql.sources.{BaseRelation, DataSourceRegister, RelationProvider}
-
-
-class DefaultSource extends RelationProvider with DataSourceRegister {
-
-  override def shortName(): String = "jdbc"
-
-  /** Returns a new base relation with the given parameters. */
-  override def createRelation(
-      sqlContext: SQLContext,
-      parameters: Map[String, String]): BaseRelation = {
-    val url = parameters.getOrElse("url", sys.error("Option 'url' not specified"))
-    val driver = parameters.getOrElse("driver", null)
-    val table = parameters.getOrElse("dbtable", sys.error("Option 'dbtable' not specified"))
-    val partitionColumn = parameters.getOrElse("partitionColumn", null)
-    val lowerBound = parameters.getOrElse("lowerBound", null)
-    val upperBound = parameters.getOrElse("upperBound", null)
-    val numPartitions = parameters.getOrElse("numPartitions", null)
-
-    if (driver != null) DriverRegistry.register(driver)
-
-    if (partitionColumn != null
-      && (lowerBound == null || upperBound == null || numPartitions == null)) {
-      sys.error("Partitioning incompletely specified")
-    }
-
-    val partitionInfo = if (partitionColumn == null) {
-      null
-    } else {
-      JDBCPartitioningInfo(
-        partitionColumn,
-        lowerBound.toLong,
-        upperBound.toLong,
-        numPartitions.toInt)
-    }
-    val parts = JDBCRelation.columnPartition(partitionInfo)
-    val properties = new Properties() // Additional properties that we will pass to getConnection
-    parameters.foreach(kv => properties.setProperty(kv._1, kv._2))
-    JDBCRelation(url, table, parts, properties)(sqlContext)
-  }
-}

From 2a3d98aae285aba39786e9809f96de412a130f39 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 20 Aug 2015 14:47:04 -0700
Subject: [PATCH 363/633] [SPARK-10138] [ML] move setters to
 MultilayerPerceptronClassifier and add Java test suite

Otherwise, setters do not return self type. jkbradley avulanov

Author: Xiangrui Meng <meng@databricks.com>

Closes #8342 from mengxr/SPARK-10138.
---
 .../MultilayerPerceptronClassifier.scala      | 54 +++++++-------
 ...vaMultilayerPerceptronClassifierSuite.java | 74 +++++++++++++++++++
 2 files changed, 101 insertions(+), 27 deletions(-)
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index ccca4ecc004c3..1e5b0bc4453e4 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -42,9 +42,6 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
     ParamValidators.arrayLengthGt(1)
   )
 
-  /** @group setParam */
-  def setLayers(value: Array[Int]): this.type = set(layers, value)
-
   /** @group getParam */
   final def getLayers: Array[Int] = $(layers)
 
@@ -61,33 +58,9 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
       "it is adjusted to the size of this data. Recommended size is between 10 and 1000",
     ParamValidators.gt(0))
 
-  /** @group setParam */
-  def setBlockSize(value: Int): this.type = set(blockSize, value)
-
   /** @group getParam */
   final def getBlockSize: Int = $(blockSize)
 
-  /**
-   * Set the maximum number of iterations.
-   * Default is 100.
-   * @group setParam
-   */
-  def setMaxIter(value: Int): this.type = set(maxIter, value)
-
-  /**
-   * Set the convergence tolerance of iterations.
-   * Smaller value will lead to higher accuracy with the cost of more iterations.
-   * Default is 1E-4.
-   * @group setParam
-   */
-  def setTol(value: Double): this.type = set(tol, value)
-
-  /**
-   * Set the seed for weights initialization.
-   * @group setParam
-   */
-  def setSeed(value: Long): this.type = set(seed, value)
-
   setDefault(maxIter -> 100, tol -> 1e-4, layers -> Array(1, 1), blockSize -> 128)
 }
 
@@ -136,6 +109,33 @@ class MultilayerPerceptronClassifier(override val uid: String)
 
   def this() = this(Identifiable.randomUID("mlpc"))
 
+  /** @group setParam */
+  def setLayers(value: Array[Int]): this.type = set(layers, value)
+
+  /** @group setParam */
+  def setBlockSize(value: Int): this.type = set(blockSize, value)
+
+  /**
+   * Set the maximum number of iterations.
+   * Default is 100.
+   * @group setParam
+   */
+  def setMaxIter(value: Int): this.type = set(maxIter, value)
+
+  /**
+   * Set the convergence tolerance of iterations.
+   * Smaller value will lead to higher accuracy with the cost of more iterations.
+   * Default is 1E-4.
+   * @group setParam
+   */
+  def setTol(value: Double): this.type = set(tol, value)
+
+  /**
+   * Set the seed for weights initialization.
+   * @group setParam
+   */
+  def setSeed(value: Long): this.type = set(seed, value)
+
   override def copy(extra: ParamMap): MultilayerPerceptronClassifier = defaultCopy(extra)
 
   /**
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java
new file mode 100644
index 0000000000000..ec6b4bf3c0f8c
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaMultilayerPerceptronClassifierSuite.java
@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.classification;
+
+import java.io.Serializable;
+import java.util.Arrays;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SQLContext;
+
+public class JavaMultilayerPerceptronClassifierSuite implements Serializable {
+
+  private transient JavaSparkContext jsc;
+  private transient SQLContext sqlContext;
+
+  @Before
+  public void setUp() {
+    jsc = new JavaSparkContext("local", "JavaLogisticRegressionSuite");
+    sqlContext = new SQLContext(jsc);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+    sqlContext = null;
+  }
+
+  @Test
+  public void testMLPC() {
+    DataFrame dataFrame = sqlContext.createDataFrame(
+      jsc.parallelize(Arrays.asList(
+        new LabeledPoint(0.0, Vectors.dense(0.0, 0.0)),
+        new LabeledPoint(1.0, Vectors.dense(0.0, 1.0)),
+        new LabeledPoint(1.0, Vectors.dense(1.0, 0.0)),
+        new LabeledPoint(0.0, Vectors.dense(1.0, 1.0)))),
+      LabeledPoint.class);
+    MultilayerPerceptronClassifier mlpc = new MultilayerPerceptronClassifier()
+      .setLayers(new int[] {2, 5, 2})
+      .setBlockSize(1)
+      .setSeed(11L)
+      .setMaxIter(100);
+    MultilayerPerceptronClassificationModel model = mlpc.fit(dataFrame);
+    DataFrame result = model.transform(dataFrame);
+    Row[] predictionAndLabels = result.select("prediction", "label").collect();
+    for (Row r: predictionAndLabels) {
+      Assert.assertEquals((int) r.getDouble(0), (int) r.getDouble(1));
+    }
+  }
+}

From 7cfc0750e14f2c1b3847e4720cc02150253525a9 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 20 Aug 2015 14:56:08 -0700
Subject: [PATCH 364/633] [SPARK-10108] Add since tags to mllib.feature

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8309 from MechCoder/tags_feature.
---
 .../spark/mllib/feature/ChiSqSelector.scala   | 12 +++++++++---
 .../mllib/feature/ElementwiseProduct.scala    |  4 +++-
 .../spark/mllib/feature/HashingTF.scala       | 11 ++++++++++-
 .../org/apache/spark/mllib/feature/IDF.scala  |  8 +++++++-
 .../spark/mllib/feature/Normalizer.scala      |  5 ++++-
 .../org/apache/spark/mllib/feature/PCA.scala  |  9 ++++++++-
 .../spark/mllib/feature/StandardScaler.scala  | 13 ++++++++++++-
 .../mllib/feature/VectorTransformer.scala     |  6 +++++-
 .../apache/spark/mllib/feature/Word2Vec.scala | 19 ++++++++++++++++++-
 9 files changed, 76 insertions(+), 11 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
index 5f8c1dea237b4..fdd974d7a391e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.feature
 
 import scala.collection.mutable.ArrayBuilder
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.stat.Statistics
@@ -31,8 +31,10 @@ import org.apache.spark.rdd.RDD
  *
  * @param selectedFeatures list of indices to select (filter). Must be ordered asc
  */
+@Since("1.3.0")
 @Experimental
-class ChiSqSelectorModel (val selectedFeatures: Array[Int]) extends VectorTransformer {
+class ChiSqSelectorModel (
+  @Since("1.3.0") val selectedFeatures: Array[Int]) extends VectorTransformer {
 
   require(isSorted(selectedFeatures), "Array has to be sorted asc")
 
@@ -52,6 +54,7 @@ class ChiSqSelectorModel (val selectedFeatures: Array[Int]) extends VectorTransf
    * @param vector vector to be transformed.
    * @return transformed vector.
    */
+  @Since("1.3.0")
   override def transform(vector: Vector): Vector = {
     compress(vector, selectedFeatures)
   }
@@ -107,8 +110,10 @@ class ChiSqSelectorModel (val selectedFeatures: Array[Int]) extends VectorTransf
  * @param numTopFeatures number of features that selector will select
  *                       (ordered by statistic value descending)
  */
+@Since("1.3.0")
 @Experimental
-class ChiSqSelector (val numTopFeatures: Int) extends Serializable {
+class ChiSqSelector (
+  @Since("1.3.0") val numTopFeatures: Int) extends Serializable {
 
   /**
    * Returns a ChiSquared feature selector.
@@ -117,6 +122,7 @@ class ChiSqSelector (val numTopFeatures: Int) extends Serializable {
    *             Real-valued features will be treated as categorical for each distinct value.
    *             Apply feature discretizer before using this function.
    */
+  @Since("1.3.0")
   def fit(data: RDD[LabeledPoint]): ChiSqSelectorModel = {
     val indices = Statistics.chiSqTest(data)
       .zipWithIndex.sortBy { case (res, _) => -res.statistic }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
index d67fe6c3ee4f8..33e2d17bb472e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.feature
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg._
 
 /**
@@ -27,6 +27,7 @@ import org.apache.spark.mllib.linalg._
  * multiplier.
  * @param scalingVec The values used to scale the reference vector's individual components.
  */
+@Since("1.4.0")
 @Experimental
 class ElementwiseProduct(val scalingVec: Vector) extends VectorTransformer {
 
@@ -36,6 +37,7 @@ class ElementwiseProduct(val scalingVec: Vector) extends VectorTransformer {
    * @param vector vector to be transformed.
    * @return transformed vector.
    */
+  @Since("1.4.0")
   override def transform(vector: Vector): Vector = {
     require(vector.size == scalingVec.size,
       s"vector sizes do not match: Expected ${scalingVec.size} but found ${vector.size}")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
index c53475818395f..e47d524b61623 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/HashingTF.scala
@@ -22,7 +22,7 @@ import java.lang.{Iterable => JavaIterable}
 import scala.collection.JavaConverters._
 import scala.collection.mutable
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.rdd.RDD
@@ -34,19 +34,25 @@ import org.apache.spark.util.Utils
  *
  * @param numFeatures number of features (default: 2^20^)
  */
+@Since("1.1.0")
 @Experimental
 class HashingTF(val numFeatures: Int) extends Serializable {
 
+  /**
+   */
+  @Since("1.1.0")
   def this() = this(1 << 20)
 
   /**
    * Returns the index of the input term.
    */
+  @Since("1.1.0")
   def indexOf(term: Any): Int = Utils.nonNegativeMod(term.##, numFeatures)
 
   /**
    * Transforms the input document into a sparse term frequency vector.
    */
+  @Since("1.1.0")
   def transform(document: Iterable[_]): Vector = {
     val termFrequencies = mutable.HashMap.empty[Int, Double]
     document.foreach { term =>
@@ -59,6 +65,7 @@ class HashingTF(val numFeatures: Int) extends Serializable {
   /**
    * Transforms the input document into a sparse term frequency vector (Java version).
    */
+  @Since("1.1.0")
   def transform(document: JavaIterable[_]): Vector = {
     transform(document.asScala)
   }
@@ -66,6 +73,7 @@ class HashingTF(val numFeatures: Int) extends Serializable {
   /**
    * Transforms the input document to term frequency vectors.
    */
+  @Since("1.1.0")
   def transform[D <: Iterable[_]](dataset: RDD[D]): RDD[Vector] = {
     dataset.map(this.transform)
   }
@@ -73,6 +81,7 @@ class HashingTF(val numFeatures: Int) extends Serializable {
   /**
    * Transforms the input document to term frequency vectors (Java version).
    */
+  @Since("1.1.0")
   def transform[D <: JavaIterable[_]](dataset: JavaRDD[D]): JavaRDD[Vector] = {
     dataset.rdd.map(this.transform).toJavaRDD()
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
index 3fab7ea79befc..d5353ddd972e0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.feature
 
 import breeze.linalg.{DenseVector => BDV}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.rdd.RDD
@@ -37,6 +37,7 @@ import org.apache.spark.rdd.RDD
  * @param minDocFreq minimum of documents in which a term
  *                   should appear for filtering
  */
+@Since("1.1.0")
 @Experimental
 class IDF(val minDocFreq: Int) {
 
@@ -48,6 +49,7 @@ class IDF(val minDocFreq: Int) {
    * Computes the inverse document frequency.
    * @param dataset an RDD of term frequency vectors
    */
+  @Since("1.1.0")
   def fit(dataset: RDD[Vector]): IDFModel = {
     val idf = dataset.treeAggregate(new IDF.DocumentFrequencyAggregator(
           minDocFreq = minDocFreq))(
@@ -61,6 +63,7 @@ class IDF(val minDocFreq: Int) {
    * Computes the inverse document frequency.
    * @param dataset a JavaRDD of term frequency vectors
    */
+  @Since("1.1.0")
   def fit(dataset: JavaRDD[Vector]): IDFModel = {
     fit(dataset.rdd)
   }
@@ -171,6 +174,7 @@ class IDFModel private[spark] (val idf: Vector) extends Serializable {
    * @param dataset an RDD of term frequency vectors
    * @return an RDD of TF-IDF vectors
    */
+  @Since("1.1.0")
   def transform(dataset: RDD[Vector]): RDD[Vector] = {
     val bcIdf = dataset.context.broadcast(idf)
     dataset.mapPartitions(iter => iter.map(v => IDFModel.transform(bcIdf.value, v)))
@@ -182,6 +186,7 @@ class IDFModel private[spark] (val idf: Vector) extends Serializable {
    * @param v a term frequency vector
    * @return a TF-IDF vector
    */
+  @Since("1.3.0")
   def transform(v: Vector): Vector = IDFModel.transform(idf, v)
 
   /**
@@ -189,6 +194,7 @@ class IDFModel private[spark] (val idf: Vector) extends Serializable {
    * @param dataset a JavaRDD of term frequency vectors
    * @return a JavaRDD of TF-IDF vectors
    */
+  @Since("1.1.0")
   def transform(dataset: JavaRDD[Vector]): JavaRDD[Vector] = {
     transform(dataset.rdd).toJavaRDD()
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
index 32848e039eb81..0e070257d9fb2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.feature
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 
 /**
@@ -31,9 +31,11 @@ import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors
  *
  * @param p Normalization in L^p^ space, p = 2 by default.
  */
+@Since("1.1.0")
 @Experimental
 class Normalizer(p: Double) extends VectorTransformer {
 
+  @Since("1.1.0")
   def this() = this(2)
 
   require(p >= 1.0)
@@ -44,6 +46,7 @@ class Normalizer(p: Double) extends VectorTransformer {
    * @param vector vector to be normalized.
    * @return normalized vector. If the norm of the input is zero, it will return the input vector.
    */
+  @Since("1.1.0")
   override def transform(vector: Vector): Vector = {
     val norm = Vectors.norm(vector, p)
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
index 2a66263d8b7d6..a48b7bba665d7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.feature
 
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
@@ -27,6 +28,7 @@ import org.apache.spark.rdd.RDD
  *
  * @param k number of principal components
  */
+@Since("1.4.0")
 class PCA(val k: Int) {
   require(k >= 1, s"PCA requires a number of principal components k >= 1 but was given $k")
 
@@ -35,6 +37,7 @@ class PCA(val k: Int) {
    *
    * @param sources source vectors
    */
+  @Since("1.4.0")
   def fit(sources: RDD[Vector]): PCAModel = {
     require(k <= sources.first().size,
       s"source vector size is ${sources.first().size} must be greater than k=$k")
@@ -58,7 +61,10 @@ class PCA(val k: Int) {
     new PCAModel(k, pc)
   }
 
-  /** Java-friendly version of [[fit()]] */
+  /**
+   * Java-friendly version of [[fit()]]
+   */
+  @Since("1.4.0")
   def fit(sources: JavaRDD[Vector]): PCAModel = fit(sources.rdd)
 }
 
@@ -76,6 +82,7 @@ class PCAModel private[spark] (val k: Int, val pc: DenseMatrix) extends VectorTr
    *               Vector must be the same length as the source vectors given to [[PCA.fit()]].
    * @return transformed vector. Vector will be of length k.
    */
+  @Since("1.4.0")
   override def transform(vector: Vector): Vector = {
     vector match {
       case dv: DenseVector =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
index c73b8f258060d..b95d5a899001e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.feature
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.rdd.RDD
@@ -32,9 +32,11 @@ import org.apache.spark.rdd.RDD
  *                 dense output, so this does not work on sparse input and will raise an exception.
  * @param withStd True by default. Scales the data to unit standard deviation.
  */
+@Since("1.1.0")
 @Experimental
 class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
 
+  @Since("1.1.0")
   def this() = this(false, true)
 
   if (!(withMean || withStd)) {
@@ -47,6 +49,7 @@ class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
    * @param data The data used to compute the mean and variance to build the transformation model.
    * @return a StandardScalarModel
    */
+  @Since("1.1.0")
   def fit(data: RDD[Vector]): StandardScalerModel = {
     // TODO: skip computation if both withMean and withStd are false
     val summary = data.treeAggregate(new MultivariateOnlineSummarizer)(
@@ -69,6 +72,7 @@ class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
  * @param withStd whether to scale the data to have unit standard deviation
  * @param withMean whether to center the data before scaling
  */
+@Since("1.1.0")
 @Experimental
 class StandardScalerModel (
     val std: Vector,
@@ -76,6 +80,9 @@ class StandardScalerModel (
     var withStd: Boolean,
     var withMean: Boolean) extends VectorTransformer {
 
+  /**
+   */
+  @Since("1.3.0")
   def this(std: Vector, mean: Vector) {
     this(std, mean, withStd = std != null, withMean = mean != null)
     require(this.withStd || this.withMean,
@@ -86,8 +93,10 @@ class StandardScalerModel (
     }
   }
 
+  @Since("1.3.0")
   def this(std: Vector) = this(std, null)
 
+  @Since("1.3.0")
   @DeveloperApi
   def setWithMean(withMean: Boolean): this.type = {
     require(!(withMean && this.mean == null), "cannot set withMean to true while mean is null")
@@ -95,6 +104,7 @@ class StandardScalerModel (
     this
   }
 
+  @Since("1.3.0")
   @DeveloperApi
   def setWithStd(withStd: Boolean): this.type = {
     require(!(withStd && this.std == null),
@@ -115,6 +125,7 @@ class StandardScalerModel (
    * @return Standardized vector. If the std of a column is zero, it will return default `0.0`
    *         for the column with zero std.
    */
+  @Since("1.1.0")
   override def transform(vector: Vector): Vector = {
     require(mean.size == vector.size)
     if (withMean) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala
index 7358c1c84f79c..5778fd1d09254 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/VectorTransformer.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.feature
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
@@ -26,6 +26,7 @@ import org.apache.spark.rdd.RDD
  * :: DeveloperApi ::
  * Trait for transformation of a vector
  */
+@Since("1.1.0")
 @DeveloperApi
 trait VectorTransformer extends Serializable {
 
@@ -35,6 +36,7 @@ trait VectorTransformer extends Serializable {
    * @param vector vector to be transformed.
    * @return transformed vector.
    */
+  @Since("1.1.0")
   def transform(vector: Vector): Vector
 
   /**
@@ -43,6 +45,7 @@ trait VectorTransformer extends Serializable {
    * @param data RDD[Vector] to be transformed.
    * @return transformed RDD[Vector].
    */
+  @Since("1.1.0")
   def transform(data: RDD[Vector]): RDD[Vector] = {
     // Later in #1498 , all RDD objects are sent via broadcasting instead of akka.
     // So it should be no longer necessary to explicitly broadcast `this` object.
@@ -55,6 +58,7 @@ trait VectorTransformer extends Serializable {
    * @param data JavaRDD[Vector] to be transformed.
    * @return transformed JavaRDD[Vector].
    */
+  @Since("1.1.0")
   def transform(data: JavaRDD[Vector]): JavaRDD[Vector] = {
     transform(data.rdd)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index cbbd2b0c8d060..e6f45ae4b01d5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -32,7 +32,7 @@ import org.json4s.jackson.JsonMethods._
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext
 import org.apache.spark.SparkContext._
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{Vector, Vectors, DenseMatrix, BLAS, DenseVector}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -70,6 +70,7 @@ private case class VocabWord(
  * and
  * Distributed Representations of Words and Phrases and their Compositionality.
  */
+@Since("1.1.0")
 @Experimental
 class Word2Vec extends Serializable with Logging {
 
@@ -83,6 +84,7 @@ class Word2Vec extends Serializable with Logging {
   /**
    * Sets vector size (default: 100).
    */
+  @Since("1.1.0")
   def setVectorSize(vectorSize: Int): this.type = {
     this.vectorSize = vectorSize
     this
@@ -91,6 +93,7 @@ class Word2Vec extends Serializable with Logging {
   /**
    * Sets initial learning rate (default: 0.025).
    */
+  @Since("1.1.0")
   def setLearningRate(learningRate: Double): this.type = {
     this.learningRate = learningRate
     this
@@ -99,6 +102,7 @@ class Word2Vec extends Serializable with Logging {
   /**
    * Sets number of partitions (default: 1). Use a small number for accuracy.
    */
+  @Since("1.1.0")
   def setNumPartitions(numPartitions: Int): this.type = {
     require(numPartitions > 0, s"numPartitions must be greater than 0 but got $numPartitions")
     this.numPartitions = numPartitions
@@ -109,6 +113,7 @@ class Word2Vec extends Serializable with Logging {
    * Sets number of iterations (default: 1), which should be smaller than or equal to number of
    * partitions.
    */
+  @Since("1.1.0")
   def setNumIterations(numIterations: Int): this.type = {
     this.numIterations = numIterations
     this
@@ -117,6 +122,7 @@ class Word2Vec extends Serializable with Logging {
   /**
    * Sets random seed (default: a random long integer).
    */
+  @Since("1.1.0")
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -126,6 +132,7 @@ class Word2Vec extends Serializable with Logging {
    * Sets minCount, the minimum number of times a token must appear to be included in the word2vec
    * model's vocabulary (default: 5).
    */
+  @Since("1.3.0")
   def setMinCount(minCount: Int): this.type = {
     this.minCount = minCount
     this
@@ -263,6 +270,7 @@ class Word2Vec extends Serializable with Logging {
    * @param dataset an RDD of words
    * @return a Word2VecModel
    */
+  @Since("1.1.0")
   def fit[S <: Iterable[String]](dataset: RDD[S]): Word2VecModel = {
 
     val words = dataset.flatMap(x => x)
@@ -412,6 +420,7 @@ class Word2Vec extends Serializable with Logging {
    * @param dataset a JavaRDD of words
    * @return a Word2VecModel
    */
+  @Since("1.1.0")
   def fit[S <: JavaIterable[String]](dataset: JavaRDD[S]): Word2VecModel = {
     fit(dataset.rdd.map(_.asScala))
   }
@@ -454,6 +463,7 @@ class Word2VecModel private[mllib] (
     wordVecNorms
   }
 
+  @Since("1.5.0")
   def this(model: Map[String, Array[Float]]) = {
     this(Word2VecModel.buildWordIndex(model), Word2VecModel.buildWordVectors(model))
   }
@@ -469,6 +479,7 @@ class Word2VecModel private[mllib] (
 
   override protected def formatVersion = "1.0"
 
+  @Since("1.4.0")
   def save(sc: SparkContext, path: String): Unit = {
     Word2VecModel.SaveLoadV1_0.save(sc, path, getVectors)
   }
@@ -478,6 +489,7 @@ class Word2VecModel private[mllib] (
    * @param word a word
    * @return vector representation of word
    */
+  @Since("1.1.0")
   def transform(word: String): Vector = {
     wordIndex.get(word) match {
       case Some(ind) =>
@@ -494,6 +506,7 @@ class Word2VecModel private[mllib] (
    * @param num number of synonyms to find
    * @return array of (word, cosineSimilarity)
    */
+  @Since("1.1.0")
   def findSynonyms(word: String, num: Int): Array[(String, Double)] = {
     val vector = transform(word)
     findSynonyms(vector, num)
@@ -505,6 +518,7 @@ class Word2VecModel private[mllib] (
    * @param num number of synonyms to find
    * @return array of (word, cosineSimilarity)
    */
+  @Since("1.1.0")
   def findSynonyms(vector: Vector, num: Int): Array[(String, Double)] = {
     require(num > 0, "Number of similar words should > 0")
     // TODO: optimize top-k
@@ -534,6 +548,7 @@ class Word2VecModel private[mllib] (
   /**
    * Returns a map of words to their vector representations.
    */
+  @Since("1.2.0")
   def getVectors: Map[String, Array[Float]] = {
     wordIndex.map { case (word, ind) =>
       (word, wordVectors.slice(vectorSize * ind, vectorSize * ind + vectorSize))
@@ -541,6 +556,7 @@ class Word2VecModel private[mllib] (
   }
 }
 
+@Since("1.4.0")
 @Experimental
 object Word2VecModel extends Loader[Word2VecModel] {
 
@@ -600,6 +616,7 @@ object Word2VecModel extends Loader[Word2VecModel] {
     }
   }
 
+  @Since("1.4.0")
   override def load(sc: SparkContext, path: String): Word2VecModel = {
 
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)

From eaafe139f881d6105996373c9b11f2ccd91b5b3e Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Thu, 20 Aug 2015 15:01:31 -0700
Subject: [PATCH 365/633] [SPARK-9245] [MLLIB] LDA topic assignments

For each (document, term) pair, return top topic.  Note that instances of (doc, term) pairs within a document (a.k.a. "tokens") are exchangeable, so we should provide an estimate per document-term, rather than per token.

CC: rotationsymmetry mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8329 from jkbradley/lda-topic-assignments.
---
 .../spark/mllib/clustering/LDAModel.scala     | 51 +++++++++++++++++--
 .../spark/mllib/clustering/LDAOptimizer.scala |  2 +-
 .../spark/mllib/clustering/JavaLDASuite.java  |  7 +++
 .../spark/mllib/clustering/LDASuite.scala     | 21 +++++++-
 4 files changed, 74 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index b70e380c0393e..6bc68a4c18b99 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.clustering
 
-import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argtopk, normalize, sum}
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, argmax, argtopk, normalize, sum}
 import breeze.numerics.{exp, lgamma}
 import org.apache.hadoop.fs.Path
 import org.json4s.DefaultFormats
@@ -438,7 +438,7 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
       Loader.checkSchema[Data](dataFrame.schema)
       val topics = dataFrame.collect()
       val vocabSize = topics(0).getAs[Vector](0).size
-      val k = topics.size
+      val k = topics.length
 
       val brzTopics = BDM.zeros[Double](vocabSize, k)
       topics.foreach { case Row(vec: Vector, ind: Int) =>
@@ -610,6 +610,50 @@ class DistributedLDAModel private[clustering] (
     }
   }
 
+  /**
+   * Return the top topic for each (doc, term) pair.  I.e., for each document, what is the most
+   * likely topic generating each term?
+   *
+   * @return RDD of (doc ID, assignment of top topic index for each term),
+   *         where the assignment is specified via a pair of zippable arrays
+   *         (term indices, topic indices).  Note that terms will be omitted if not present in
+   *         the document.
+   */
+  lazy val topicAssignments: RDD[(Long, Array[Int], Array[Int])] = {
+    // For reference, compare the below code with the core part of EMLDAOptimizer.next().
+    val eta = topicConcentration
+    val W = vocabSize
+    val alpha = docConcentration(0)
+    val N_k = globalTopicTotals
+    val sendMsg: EdgeContext[TopicCounts, TokenCount, (Array[Int], Array[Int])] => Unit =
+      (edgeContext) => {
+        // E-STEP: Compute gamma_{wjk} (smoothed topic distributions).
+        val scaledTopicDistribution: TopicCounts =
+          computePTopic(edgeContext.srcAttr, edgeContext.dstAttr, N_k, W, eta, alpha)
+        // For this (doc j, term w), send top topic k to doc vertex.
+        val topTopic: Int = argmax(scaledTopicDistribution)
+        val term: Int = index2term(edgeContext.dstId)
+        edgeContext.sendToSrc((Array(term), Array(topTopic)))
+      }
+    val mergeMsg: ((Array[Int], Array[Int]), (Array[Int], Array[Int])) => (Array[Int], Array[Int]) =
+      (terms_topics0, terms_topics1) => {
+        (terms_topics0._1 ++ terms_topics1._1, terms_topics0._2 ++ terms_topics1._2)
+      }
+    // M-STEP: Aggregation computes new N_{kj}, N_{wk} counts.
+    val perDocAssignments =
+      graph.aggregateMessages[(Array[Int], Array[Int])](sendMsg, mergeMsg).filter(isDocumentVertex)
+    perDocAssignments.map { case (docID: Long, (terms: Array[Int], topics: Array[Int])) =>
+      // TODO: Avoid zip, which is inefficient.
+      val (sortedTerms, sortedTopics) = terms.zip(topics).sortBy(_._1).unzip
+      (docID, sortedTerms.toArray, sortedTopics.toArray)
+    }
+  }
+
+  /** Java-friendly version of [[topicAssignments]] */
+  lazy val javaTopicAssignments: JavaRDD[(java.lang.Long, Array[Int], Array[Int])] = {
+    topicAssignments.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[Int])]].toJavaRDD()
+  }
+
   // TODO
   // override def logLikelihood(documents: RDD[(Long, Vector)]): Double = ???
 
@@ -849,10 +893,9 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
     val classNameV1_0 = SaveLoadV1_0.thisClassName
 
     val model = (loadedClassName, loadedVersion) match {
-      case (className, "1.0") if className == classNameV1_0 => {
+      case (className, "1.0") if className == classNameV1_0 =>
         DistributedLDAModel.SaveLoadV1_0.load(sc, path, vocabSize, docConcentration,
           topicConcentration, iterationTimes.toArray, gammaShape)
-      }
       case _ => throw new Exception(
         s"DistributedLDAModel.load did not recognize model with (className, format version):" +
           s"($loadedClassName, $loadedVersion).  Supported: ($classNameV1_0, 1.0)")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 360241c8081ac..cb517f9689ade 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -167,7 +167,7 @@ final class EMLDAOptimizer extends LDAOptimizer {
         edgeContext.sendToDst((false, scaledTopicDistribution))
         edgeContext.sendToSrc((false, scaledTopicDistribution))
       }
-    // This is a hack to detect whether we could modify the values in-place.
+    // The Boolean is a hack to detect whether we could modify the values in-place.
     // TODO: Add zero/seqOp/combOp option to aggregateMessages. (SPARK-5438)
     val mergeMsg: ((Boolean, TopicCounts), (Boolean, TopicCounts)) => (Boolean, TopicCounts) =
       (m0, m1) => {
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
index 6e91cde2eabb5..3fea359a3b46c 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaLDASuite.java
@@ -134,6 +134,13 @@ public Boolean call(Tuple2<Long, Vector> tuple2) {
     double[] topicWeights = topTopics._3();
     assertEquals(3, topicIndices.length);
     assertEquals(3, topicWeights.length);
+
+    // Check: topTopicAssignments
+    Tuple3<Long, int[], int[]> topicAssignment = model.javaTopicAssignments().first();
+    Long docId2 = topicAssignment._1();
+    int[] termIndices2 = topicAssignment._2();
+    int[] topicIndices2 = topicAssignment._3();
+    assertEquals(termIndices2.length, topicIndices2.length);
   }
 
   @Test
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 99e28499fd316..8a714f9b79e02 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -135,17 +135,34 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     }
 
     // Top 3 documents per topic
-    model.topDocumentsPerTopic(3).zip(topDocsByTopicDistributions(3)).foreach {case (t1, t2) =>
+    model.topDocumentsPerTopic(3).zip(topDocsByTopicDistributions(3)).foreach { case (t1, t2) =>
       assert(t1._1 === t2._1)
       assert(t1._2 === t2._2)
     }
 
     // All documents per topic
     val q = tinyCorpus.length
-    model.topDocumentsPerTopic(q).zip(topDocsByTopicDistributions(q)).foreach {case (t1, t2) =>
+    model.topDocumentsPerTopic(q).zip(topDocsByTopicDistributions(q)).foreach { case (t1, t2) =>
       assert(t1._1 === t2._1)
       assert(t1._2 === t2._2)
     }
+
+    // Check: topTopicAssignments
+    // Make sure it assigns a topic to each term appearing in each doc.
+    val topTopicAssignments: Map[Long, (Array[Int], Array[Int])] =
+      model.topicAssignments.collect().map(x => x._1 -> (x._2, x._3)).toMap
+    assert(topTopicAssignments.keys.max < tinyCorpus.length)
+    tinyCorpus.foreach { case (docID: Long, doc: Vector) =>
+      if (topTopicAssignments.contains(docID)) {
+        val (inds, vals) = topTopicAssignments(docID)
+        assert(inds.length === doc.numNonzeros)
+        // For "term" in actual doc,
+        // check that it has a topic assigned.
+        doc.foreachActive((term, wcnt) => assert(wcnt === 0 || inds.contains(term)))
+      } else {
+        assert(doc.numNonzeros === 0)
+      }
+    }
   }
 
   test("vertex indexing") {

From afe9f03fd964d1e8604d02feee8d6970efbe6009 Mon Sep 17 00:00:00 2001
From: Tarek Auel <tarek.auel@googlemail.com>
Date: Thu, 20 Aug 2015 15:10:13 -0700
Subject: [PATCH 366/633] [SPARK-9400] [SQL] codegen for StringLocate

This is based on #7779 , thanks to tarekauel . Fix the conflict and nullability.

Closes #7779 and #8274 .

Author: Tarek Auel <tarek.auel@googlemail.com>
Author: Davies Liu <davies@databricks.com>

Closes #8330 from davies/stringLocate.
---
 .../expressions/stringExpressions.scala       | 28 ++++++++++++++++++-
 1 file changed, 27 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
index 3c23f2ecfb57c..b60d318534a41 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
@@ -409,13 +409,14 @@ case class SubstringIndex(strExpr: Expression, delimExpr: Expression, countExpr:
  * in given string after position pos.
  */
 case class StringLocate(substr: Expression, str: Expression, start: Expression)
-  extends TernaryExpression with ImplicitCastInputTypes with CodegenFallback {
+  extends TernaryExpression with ImplicitCastInputTypes {
 
   def this(substr: Expression, str: Expression) = {
     this(substr, str, Literal(0))
   }
 
   override def children: Seq[Expression] = substr :: str :: start :: Nil
+  override def nullable: Boolean = substr.nullable || str.nullable
   override def dataType: DataType = IntegerType
   override def inputTypes: Seq[DataType] = Seq(StringType, StringType, IntegerType)
 
@@ -441,6 +442,31 @@ case class StringLocate(substr: Expression, str: Expression, start: Expression)
     }
   }
 
+  override protected def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
+    val substrGen = substr.gen(ctx)
+    val strGen = str.gen(ctx)
+    val startGen = start.gen(ctx)
+    s"""
+      int ${ev.primitive} = 0;
+      boolean ${ev.isNull} = false;
+      ${startGen.code}
+      if (!${startGen.isNull}) {
+        ${substrGen.code}
+        if (!${substrGen.isNull}) {
+          ${strGen.code}
+          if (!${strGen.isNull}) {
+            ${ev.primitive} = ${strGen.primitive}.indexOf(${substrGen.primitive},
+              ${startGen.primitive}) + 1;
+          } else {
+            ${ev.isNull} = true;
+          }
+        } else {
+          ${ev.isNull} = true;
+        }
+      }
+     """
+  }
+
   override def prettyName: String = "locate"
 }
 

From cdd9a2bb10e20556003843a0f7aaa33acd55f6d2 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Thu, 20 Aug 2015 20:01:13 -0700
Subject: [PATCH 367/633] [SPARK-10140] [DOC] add target fields to @Since

so constructors parameters and public fields can be annotated. rxin MechCoder

Author: Xiangrui Meng <meng@databricks.com>

Closes #8344 from mengxr/SPARK-10140.2.
---
 core/src/main/scala/org/apache/spark/annotation/Since.scala | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/annotation/Since.scala b/core/src/main/scala/org/apache/spark/annotation/Since.scala
index fa59393c22476..af483e361e339 100644
--- a/core/src/main/scala/org/apache/spark/annotation/Since.scala
+++ b/core/src/main/scala/org/apache/spark/annotation/Since.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.annotation
 
 import scala.annotation.StaticAnnotation
+import scala.annotation.meta._
 
 /**
  * A Scala annotation that specifies the Spark version when a definition was added.
@@ -25,4 +26,5 @@ import scala.annotation.StaticAnnotation
  * hence works for overridden methods that inherit API documentation directly from parents.
  * The limitation is that it does not show up in the generated Java API documentation.
  */
+@param @field @getter @setter @beanGetter @beanSetter
 private[spark] class Since(version: String) extends StaticAnnotation

From dcfe0c5cde953b31c5bfeb6e41d1fc9b333241eb Mon Sep 17 00:00:00 2001
From: Alexander Ulanov <nashb@yandex.ru>
Date: Thu, 20 Aug 2015 20:02:27 -0700
Subject: [PATCH 368/633] [SPARK-9846] [DOCS] User guide for Multilayer
 Perceptron Classifier

Added user guide for multilayer perceptron classifier:
  - Simplified description of the multilayer perceptron classifier
  - Example code for Scala and Java

Author: Alexander Ulanov <nashb@yandex.ru>

Closes #8262 from avulanov/SPARK-9846-mlpc-docs.
---
 docs/ml-ann.md   | 123 +++++++++++++++++++++++++++++++++++++++++++++++
 docs/ml-guide.md |   1 +
 2 files changed, 124 insertions(+)
 create mode 100644 docs/ml-ann.md

diff --git a/docs/ml-ann.md b/docs/ml-ann.md
new file mode 100644
index 0000000000000..d5ddd92af1e96
--- /dev/null
+++ b/docs/ml-ann.md
@@ -0,0 +1,123 @@
+---
+layout: global
+title: Multilayer perceptron classifier - ML
+displayTitle: <a href="ml-guide.html">ML</a> - Multilayer perceptron classifier
+---
+
+
+`\[
+\newcommand{\R}{\mathbb{R}}
+\newcommand{\E}{\mathbb{E}}
+\newcommand{\x}{\mathbf{x}}
+\newcommand{\y}{\mathbf{y}}
+\newcommand{\wv}{\mathbf{w}}
+\newcommand{\av}{\mathbf{\alpha}}
+\newcommand{\bv}{\mathbf{b}}
+\newcommand{\N}{\mathbb{N}}
+\newcommand{\id}{\mathbf{I}}
+\newcommand{\ind}{\mathbf{1}}
+\newcommand{\0}{\mathbf{0}}
+\newcommand{\unit}{\mathbf{e}}
+\newcommand{\one}{\mathbf{1}}
+\newcommand{\zero}{\mathbf{0}}
+\]`
+
+
+Multilayer perceptron classifier (MLPC) is a classifier based on the [feedforward artificial neural network](https://en.wikipedia.org/wiki/Feedforward_neural_network). 
+MLPC consists of multiple layers of nodes. 
+Each layer is fully connected to the next layer in the network. Nodes in the input layer represent the input data. All other nodes maps inputs to the outputs 
+by performing linear combination of the inputs with the node's weights `$\wv$` and bias `$\bv$` and applying an activation function. 
+It can be written in matrix form for MLPC with `$K+1$` layers as follows:
+`\[
+\mathrm{y}(\x) = \mathrm{f_K}(...\mathrm{f_2}(\wv_2^T\mathrm{f_1}(\wv_1^T \x+b_1)+b_2)...+b_K)
+\]`
+Nodes in intermediate layers use sigmoid (logistic) function:
+`\[
+\mathrm{f}(z_i) = \frac{1}{1 + e^{-z_i}}
+\]`
+Nodes in the output layer use softmax function:
+`\[
+\mathrm{f}(z_i) = \frac{e^{z_i}}{\sum_{k=1}^N e^{z_k}}
+\]`
+The number of nodes `$N$` in the output layer corresponds to the number of classes. 
+
+MLPC employes backpropagation for learning the model. We use logistic loss function for optimization and L-BFGS as optimization routine.
+
+**Examples**
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+{% highlight scala %}
+import org.apache.spark.ml.classification.MultilayerPerceptronClassifier
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.sql.Row
+
+// Load training data
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_multiclass_classification_data.txt").toDF()
+// Split the data into train and test
+val splits = data.randomSplit(Array(0.6, 0.4), seed = 1234L)
+val train = splits(0)
+val test = splits(1)
+// specify layers for the neural network: 
+// input layer of size 4 (features), two intermediate of size 5 and 4 and output of size 3 (classes)
+val layers = Array[Int](4, 5, 4, 3)
+// create the trainer and set its parameters
+val trainer = new MultilayerPerceptronClassifier()
+  .setLayers(layers)
+  .setBlockSize(128)
+  .setSeed(1234L)
+  .setMaxIter(100)
+// train the model
+val model = trainer.fit(train)
+// compute precision on the test set
+val result = model.transform(test)
+val predictionAndLabels = result.select("prediction", "label")
+val evaluator = new MulticlassClassificationEvaluator()
+  .setMetricName("precision")
+println("Precision:" + evaluator.evaluate(predictionAndLabels))
+{% endhighlight %}
+
+</div>
+
+<div data-lang="java" markdown="1">
+
+{% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.classification.MultilayerPerceptronClassificationModel;
+import org.apache.spark.ml.classification.MultilayerPerceptronClassifier;
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+
+// Load training data
+String path = "data/mllib/sample_multiclass_classification_data.txt";
+JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD();
+DataFrame dataFrame = sqlContext.createDataFrame(data, LabeledPoint.class);
+// Split the data into train and test
+DataFrame[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L);
+DataFrame train = splits[0];
+DataFrame test = splits[1];
+// specify layers for the neural network:
+// input layer of size 4 (features), two intermediate of size 5 and 4 and output of size 3 (classes)
+int[] layers = new int[] {4, 5, 4, 3};
+// create the trainer and set its parameters
+MultilayerPerceptronClassifier trainer = new MultilayerPerceptronClassifier()
+  .setLayers(layers)
+  .setBlockSize(128)
+  .setSeed(1234L)
+  .setMaxIter(100);
+// train the model
+MultilayerPerceptronClassificationModel model = trainer.fit(train);
+// compute precision on the test set
+DataFrame result = model.transform(test);
+DataFrame predictionAndLabels = result.select("prediction", "label");
+MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
+  .setMetricName("precision");
+System.out.println("Precision = " + evaluator.evaluate(predictionAndLabels));
+{% endhighlight %}
+</div>
+
+</div>
diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index c64fff7c0315a..de8fead3529e4 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -179,6 +179,7 @@ There are now several algorithms in the Pipelines API which are not in the lower
 * [Decision Trees for Classification and Regression](ml-decision-tree.html)
 * [Ensembles](ml-ensembles.html)
 * [Linear methods with elastic net regularization](ml-linear-methods.html)
+* [Multilayer perceptron classifier](ml-ann.html)
 
 # Code Examples
 

From bb220f6570aa0b95598b30524224a3e82c1effbc Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Fri, 21 Aug 2015 01:43:49 -0700
Subject: [PATCH 369/633] [SPARK-10040] [SQL] Use batch insert for JDBC writing

JIRA: https://issues.apache.org/jira/browse/SPARK-10040

We should use batch insert instead of single row in JDBC.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #8273 from viirya/jdbc-insert-batch.
---
 .../execution/datasources/jdbc/JdbcUtils.scala  | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
index 2d0e736ee4766..26788b2a4fd69 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
@@ -88,13 +88,15 @@ object JdbcUtils extends Logging {
       table: String,
       iterator: Iterator[Row],
       rddSchema: StructType,
-      nullTypes: Array[Int]): Iterator[Byte] = {
+      nullTypes: Array[Int],
+      batchSize: Int): Iterator[Byte] = {
     val conn = getConnection()
     var committed = false
     try {
       conn.setAutoCommit(false) // Everything in the same db transaction.
       val stmt = insertStatement(conn, table, rddSchema)
       try {
+        var rowCount = 0
         while (iterator.hasNext) {
           val row = iterator.next()
           val numFields = rddSchema.fields.length
@@ -122,7 +124,15 @@ object JdbcUtils extends Logging {
             }
             i = i + 1
           }
-          stmt.executeUpdate()
+          stmt.addBatch()
+          rowCount += 1
+          if (rowCount % batchSize == 0) {
+            stmt.executeBatch()
+            rowCount = 0
+          }
+        }
+        if (rowCount > 0) {
+          stmt.executeBatch()
         }
       } finally {
         stmt.close()
@@ -211,8 +221,9 @@ object JdbcUtils extends Logging {
     val rddSchema = df.schema
     val driver: String = DriverRegistry.getDriverClassName(url)
     val getConnection: () => Connection = JDBCRDD.getConnector(driver, url, properties)
+    val batchSize = properties.getProperty("batchsize", "1000").toInt
     df.foreachPartition { iterator =>
-      savePartition(getConnection, table, iterator, rddSchema, nullTypes)
+      savePartition(getConnection, table, iterator, rddSchema, nullTypes, batchSize)
     }
   }
 

From 708036c1de52d674ceff30ac465e1dcedeb8dde8 Mon Sep 17 00:00:00 2001
From: Imran Rashid <irashid@cloudera.com>
Date: Fri, 21 Aug 2015 08:41:36 -0500
Subject: [PATCH 370/633] [SPARK-9439] [YARN] External shuffle service robust
 to NM restarts using leveldb

https://issues.apache.org/jira/browse/SPARK-9439

In general, Yarn apps should be robust to NodeManager restarts.  However, if you run spark with the external shuffle service on, after a NM restart all shuffles fail, b/c the shuffle service has lost some state with info on each executor.  (Note the shuffle data is perfectly fine on disk across a NM restart, the problem is we've lost the small bit of state that lets us *find* those files.)

The solution proposed here is that the external shuffle service can write out its state to leveldb (backed by a local file) every time an executor is added.  When running with yarn, that file is in the NM's local dir.  Whenever the service is started, it looks for that file, and if it exists, it reads the file and re-registers all executors there.

Nothing is changed in non-yarn modes with this patch.  The service is not given a place to save the state to, so it operates the same as before.  This should make it easy to update other cluster managers as well, by just supplying the right file & the equivalent of yarn's `initializeApplication` -- I'm not familiar enough with those modes to know how to do that.

Author: Imran Rashid <irashid@cloudera.com>

Closes #7943 from squito/leveldb_external_shuffle_service_NM_restart and squashes the following commits:

0d285d3 [Imran Rashid] review feedback
70951d6 [Imran Rashid] Merge branch 'master' into leveldb_external_shuffle_service_NM_restart
5c71c8c [Imran Rashid] save executor to db before registering; style
2499c8c [Imran Rashid] explicit dependency on jackson-annotations
795d28f [Imran Rashid] review feedback
81f80e2 [Imran Rashid] Merge branch 'master' into leveldb_external_shuffle_service_NM_restart
594d520 [Imran Rashid] use json to serialize application executor info
1a7980b [Imran Rashid] version
8267d2a [Imran Rashid] style
e9f99e8 [Imran Rashid] cleanup the handling of bad dbs a little
9378ba3 [Imran Rashid] fail gracefully on corrupt leveldb files
acedb62 [Imran Rashid] switch to writing out one record per executor
79922b7 [Imran Rashid] rely on yarn to call stopApplication; assorted cleanup
12b6a35 [Imran Rashid] save registered executors when apps are removed; add tests
c878fbe [Imran Rashid] better explanation of shuffle service port handling
694934c [Imran Rashid] only open leveldb connection once per service
d596410 [Imran Rashid] store executor data in leveldb
59800b7 [Imran Rashid] Files.move in case renaming is unsupported
32fe5ae [Imran Rashid] Merge branch 'master' into external_shuffle_service_NM_restart
d7450f0 [Imran Rashid] style
f729e2b [Imran Rashid] debugging
4492835 [Imran Rashid] lol, dont use a PrintWriter b/c of scalastyle checks
0a39b98 [Imran Rashid] Merge branch 'master' into external_shuffle_service_NM_restart
55f49fc [Imran Rashid] make sure the service doesnt die if the registered executor file is corrupt; add tests
245db19 [Imran Rashid] style
62586a6 [Imran Rashid] just serialize the whole executors map
bdbbf0d [Imran Rashid] comments, remove some unnecessary changes
857331a [Imran Rashid] better tests & comments
bb9d1e6 [Imran Rashid] formatting
bdc4b32 [Imran Rashid] rename
86e0cb9 [Imran Rashid] for tests, shuffle service finds an open port
23994ff [Imran Rashid] style
7504de8 [Imran Rashid] style
a36729c [Imran Rashid] cleanup
efb6195 [Imran Rashid] proper unit test, and no longer leak if apps stop during NM restart
dd93dc0 [Imran Rashid] test for shuffle service w/ NM restarts
d596969 [Imran Rashid] cleanup imports
0e9d69b [Imran Rashid] better names
9eae119 [Imran Rashid] cleanup lots of duplication
1136f44 [Imran Rashid] test needs to have an actual shuffle
0b588bd [Imran Rashid] more fixes ...
ad122ef [Imran Rashid] more fixes
5e5a7c3 [Imran Rashid] fix build
c69f46b [Imran Rashid] maybe working version, needs tests & cleanup ...
bb3ba49 [Imran Rashid] minor cleanup
36127d3 [Imran Rashid] wip
b9d2ced [Imran Rashid] incomplete setup for external shuffle service tests
---
 .../spark/deploy/ExternalShuffleService.scala |   2 +-
 .../mesos/MesosExternalShuffleService.scala   |   2 +-
 .../apache/spark/storage/BlockManager.scala   |  14 +-
 .../spark/ExternalShuffleServiceSuite.scala   |   2 +-
 network/shuffle/pom.xml                       |  16 ++
 .../shuffle/ExternalShuffleBlockHandler.java  |  37 ++-
 .../shuffle/ExternalShuffleBlockResolver.java | 225 +++++++++++++++--
 .../shuffle/protocol/ExecutorShuffleInfo.java |   8 +-
 .../ExternalShuffleBlockResolverSuite.java    |  35 ++-
 .../shuffle/ExternalShuffleCleanupSuite.java  |   9 +-
 .../ExternalShuffleIntegrationSuite.java      |   2 +-
 .../shuffle/ExternalShuffleSecuritySuite.java |   5 +-
 .../network/yarn/YarnShuffleService.java      |  62 ++++-
 pom.xml                                       |   5 +
 yarn/pom.xml                                  |   6 +
 .../deploy/yarn/BaseYarnClusterSuite.scala    | 193 +++++++++++++++
 .../spark/deploy/yarn/YarnClusterSuite.scala  | 173 +------------
 .../yarn/YarnShuffleIntegrationSuite.scala    | 109 ++++++++
 .../network/shuffle/ShuffleTestAccessor.scala |  71 ++++++
 .../yarn/YarnShuffleServiceSuite.scala        | 233 ++++++++++++++++++
 .../spark/network/yarn/YarnTestAccessor.scala |  37 +++
 21 files changed, 1031 insertions(+), 215 deletions(-)
 create mode 100644 yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
 create mode 100644 yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
 create mode 100644 yarn/src/test/scala/org/apache/spark/network/shuffle/ShuffleTestAccessor.scala
 create mode 100644 yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
 create mode 100644 yarn/src/test/scala/org/apache/spark/network/yarn/YarnTestAccessor.scala

diff --git a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
index 20a9faa1784b7..22ef701d833b2 100644
--- a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
@@ -53,7 +53,7 @@ class ExternalShuffleService(sparkConf: SparkConf, securityManager: SecurityMana
 
   /** Create a new shuffle block handler. Factored out for subclasses to override. */
   protected def newShuffleBlockHandler(conf: TransportConf): ExternalShuffleBlockHandler = {
-    new ExternalShuffleBlockHandler(conf)
+    new ExternalShuffleBlockHandler(conf, null)
   }
 
   /** Starts the external shuffle service if the user has configured us to. */
diff --git a/core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala
index 061857476a8a0..12337a940a414 100644
--- a/core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/mesos/MesosExternalShuffleService.scala
@@ -34,7 +34,7 @@ import org.apache.spark.network.util.TransportConf
  * It detects driver termination and calls the cleanup callback to [[ExternalShuffleService]].
  */
 private[mesos] class MesosExternalShuffleBlockHandler(transportConf: TransportConf)
-  extends ExternalShuffleBlockHandler(transportConf) with Logging {
+  extends ExternalShuffleBlockHandler(transportConf, null) with Logging {
 
   // Stores a map of driver socket addresses to app ids
   private val connectedApps = new mutable.HashMap[SocketAddress, String]
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index eedb27942e841..fefaef0ab82c8 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -93,8 +93,17 @@ private[spark] class BlockManager(
 
   // Port used by the external shuffle service. In Yarn mode, this may be already be
   // set through the Hadoop configuration as the server is launched in the Yarn NM.
-  private val externalShuffleServicePort =
-    Utils.getSparkOrYarnConfig(conf, "spark.shuffle.service.port", "7337").toInt
+  private val externalShuffleServicePort = {
+    val tmpPort = Utils.getSparkOrYarnConfig(conf, "spark.shuffle.service.port", "7337").toInt
+    if (tmpPort == 0) {
+      // for testing, we set "spark.shuffle.service.port" to 0 in the yarn config, so yarn finds
+      // an open port.  But we still need to tell our spark apps the right port to use.  So
+      // only if the yarn config has the port set to 0, we prefer the value in the spark config
+      conf.get("spark.shuffle.service.port").toInt
+    } else {
+      tmpPort
+    }
+  }
 
   // Check that we're not using external shuffle service with consolidated shuffle files.
   if (externalShuffleServiceEnabled
@@ -191,6 +200,7 @@ private[spark] class BlockManager(
       executorId, blockTransferService.hostName, blockTransferService.port)
 
     shuffleServerId = if (externalShuffleServiceEnabled) {
+      logInfo(s"external shuffle service port = $externalShuffleServicePort")
       BlockManagerId(executorId, blockTransferService.hostName, externalShuffleServicePort)
     } else {
       blockManagerId
diff --git a/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala b/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala
index c38d70252add1..e846a72c888c6 100644
--- a/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ExternalShuffleServiceSuite.scala
@@ -36,7 +36,7 @@ class ExternalShuffleServiceSuite extends ShuffleSuite with BeforeAndAfterAll {
 
   override def beforeAll() {
     val transportConf = SparkTransportConf.fromSparkConf(conf, numUsableCores = 2)
-    rpcHandler = new ExternalShuffleBlockHandler(transportConf)
+    rpcHandler = new ExternalShuffleBlockHandler(transportConf, null)
     val transportContext = new TransportContext(transportConf, rpcHandler)
     server = transportContext.createServer()
 
diff --git a/network/shuffle/pom.xml b/network/shuffle/pom.xml
index 532463e96fbb7..3d2edf9d94515 100644
--- a/network/shuffle/pom.xml
+++ b/network/shuffle/pom.xml
@@ -43,6 +43,22 @@
       <version>${project.version}</version>
     </dependency>
 
+    <dependency>
+      <groupId>org.fusesource.leveldbjni</groupId>
+      <artifactId>leveldbjni-all</artifactId>
+      <version>1.8</version>
+    </dependency>
+
+    <dependency>
+      <groupId>com.fasterxml.jackson.core</groupId>
+      <artifactId>jackson-databind</artifactId>
+    </dependency>
+
+    <dependency>
+      <groupId>com.fasterxml.jackson.core</groupId>
+      <artifactId>jackson-annotations</artifactId>
+    </dependency>
+
     <!-- Provided dependencies -->
     <dependency>
       <groupId>org.slf4j</groupId>
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
index db9dc4f17cee9..0df1dd621f6e9 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
@@ -17,11 +17,12 @@
 
 package org.apache.spark.network.shuffle;
 
+import java.io.File;
+import java.io.IOException;
 import java.util.List;
 
 import com.google.common.annotations.VisibleForTesting;
 import com.google.common.collect.Lists;
-import org.apache.spark.network.util.TransportConf;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -31,10 +32,10 @@
 import org.apache.spark.network.server.OneForOneStreamManager;
 import org.apache.spark.network.server.RpcHandler;
 import org.apache.spark.network.server.StreamManager;
-import org.apache.spark.network.shuffle.protocol.BlockTransferMessage;
-import org.apache.spark.network.shuffle.protocol.OpenBlocks;
-import org.apache.spark.network.shuffle.protocol.RegisterExecutor;
-import org.apache.spark.network.shuffle.protocol.StreamHandle;
+import org.apache.spark.network.shuffle.ExternalShuffleBlockResolver.AppExecId;
+import org.apache.spark.network.shuffle.protocol.*;
+import org.apache.spark.network.util.TransportConf;
+
 
 /**
  * RPC Handler for a server which can serve shuffle blocks from outside of an Executor process.
@@ -46,11 +47,13 @@
 public class ExternalShuffleBlockHandler extends RpcHandler {
   private final Logger logger = LoggerFactory.getLogger(ExternalShuffleBlockHandler.class);
 
-  private final ExternalShuffleBlockResolver blockManager;
+  @VisibleForTesting
+  final ExternalShuffleBlockResolver blockManager;
   private final OneForOneStreamManager streamManager;
 
-  public ExternalShuffleBlockHandler(TransportConf conf) {
-    this(new OneForOneStreamManager(), new ExternalShuffleBlockResolver(conf));
+  public ExternalShuffleBlockHandler(TransportConf conf, File registeredExecutorFile) throws IOException {
+    this(new OneForOneStreamManager(),
+      new ExternalShuffleBlockResolver(conf, registeredExecutorFile));
   }
 
   /** Enables mocking out the StreamManager and BlockManager. */
@@ -105,4 +108,22 @@ public StreamManager getStreamManager() {
   public void applicationRemoved(String appId, boolean cleanupLocalDirs) {
     blockManager.applicationRemoved(appId, cleanupLocalDirs);
   }
+
+  /**
+   * Register an (application, executor) with the given shuffle info.
+   *
+   * The "re-" is meant to highlight the intended use of this method -- when this service is
+   * restarted, this is used to restore the state of executors from before the restart.  Normal
+   * registration will happen via a message handled in receive()
+   *
+   * @param appExecId
+   * @param executorInfo
+   */
+  public void reregisterExecutor(AppExecId appExecId, ExecutorShuffleInfo executorInfo) {
+    blockManager.registerExecutor(appExecId.appId, appExecId.execId, executorInfo);
+  }
+
+  public void close() {
+    blockManager.close();
+  }
 }
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java
index 022ed88a16480..79beec4429a99 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolver.java
@@ -17,19 +17,24 @@
 
 package org.apache.spark.network.shuffle;
 
-import java.io.DataInputStream;
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.IOException;
-import java.util.Iterator;
-import java.util.Map;
+import java.io.*;
+import java.util.*;
 import java.util.concurrent.ConcurrentMap;
 import java.util.concurrent.Executor;
 import java.util.concurrent.Executors;
 
+import com.fasterxml.jackson.annotation.JsonCreator;
+import com.fasterxml.jackson.annotation.JsonProperty;
+import com.fasterxml.jackson.databind.ObjectMapper;
 import com.google.common.annotations.VisibleForTesting;
+import com.google.common.base.Charsets;
 import com.google.common.base.Objects;
 import com.google.common.collect.Maps;
+import org.fusesource.leveldbjni.JniDBFactory;
+import org.fusesource.leveldbjni.internal.NativeDB;
+import org.iq80.leveldb.DB;
+import org.iq80.leveldb.DBIterator;
+import org.iq80.leveldb.Options;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -52,25 +57,87 @@
 public class ExternalShuffleBlockResolver {
   private static final Logger logger = LoggerFactory.getLogger(ExternalShuffleBlockResolver.class);
 
+  private static final ObjectMapper mapper = new ObjectMapper();
+  /**
+   * This a common prefix to the key for each app registration we stick in leveldb, so they
+   * are easy to find, since leveldb lets you search based on prefix.
+   */
+  private static final String APP_KEY_PREFIX = "AppExecShuffleInfo";
+  private static final StoreVersion CURRENT_VERSION = new StoreVersion(1, 0);
+
   // Map containing all registered executors' metadata.
-  private final ConcurrentMap<AppExecId, ExecutorShuffleInfo> executors;
+  @VisibleForTesting
+  final ConcurrentMap<AppExecId, ExecutorShuffleInfo> executors;
 
   // Single-threaded Java executor used to perform expensive recursive directory deletion.
   private final Executor directoryCleaner;
 
   private final TransportConf conf;
 
-  public ExternalShuffleBlockResolver(TransportConf conf) {
-    this(conf, Executors.newSingleThreadExecutor(
+  @VisibleForTesting
+  final File registeredExecutorFile;
+  @VisibleForTesting
+  final DB db;
+
+  public ExternalShuffleBlockResolver(TransportConf conf, File registeredExecutorFile)
+      throws IOException {
+    this(conf, registeredExecutorFile, Executors.newSingleThreadExecutor(
         // Add `spark` prefix because it will run in NM in Yarn mode.
         NettyUtils.createThreadFactory("spark-shuffle-directory-cleaner")));
   }
 
   // Allows tests to have more control over when directories are cleaned up.
   @VisibleForTesting
-  ExternalShuffleBlockResolver(TransportConf conf, Executor directoryCleaner) {
+  ExternalShuffleBlockResolver(
+      TransportConf conf,
+      File registeredExecutorFile,
+      Executor directoryCleaner) throws IOException {
     this.conf = conf;
-    this.executors = Maps.newConcurrentMap();
+    this.registeredExecutorFile = registeredExecutorFile;
+    if (registeredExecutorFile != null) {
+      Options options = new Options();
+      options.createIfMissing(false);
+      options.logger(new LevelDBLogger());
+      DB tmpDb;
+      try {
+        tmpDb = JniDBFactory.factory.open(registeredExecutorFile, options);
+      } catch (NativeDB.DBException e) {
+        if (e.isNotFound() || e.getMessage().contains(" does not exist ")) {
+          logger.info("Creating state database at " + registeredExecutorFile);
+          options.createIfMissing(true);
+          try {
+            tmpDb = JniDBFactory.factory.open(registeredExecutorFile, options);
+          } catch (NativeDB.DBException dbExc) {
+            throw new IOException("Unable to create state store", dbExc);
+          }
+        } else {
+          // the leveldb file seems to be corrupt somehow.  Lets just blow it away and create a new
+          // one, so we can keep processing new apps
+          logger.error("error opening leveldb file {}.  Creating new file, will not be able to " +
+            "recover state for existing applications", registeredExecutorFile, e);
+          if (registeredExecutorFile.isDirectory()) {
+            for (File f : registeredExecutorFile.listFiles()) {
+              f.delete();
+            }
+          }
+          registeredExecutorFile.delete();
+          options.createIfMissing(true);
+          try {
+            tmpDb = JniDBFactory.factory.open(registeredExecutorFile, options);
+          } catch (NativeDB.DBException dbExc) {
+            throw new IOException("Unable to create state store", dbExc);
+          }
+
+        }
+      }
+      // if there is a version mismatch, we throw an exception, which means the service is unusable
+      checkVersion(tmpDb);
+      executors = reloadRegisteredExecutors(tmpDb);
+      db = tmpDb;
+    } else {
+      db = null;
+      executors = Maps.newConcurrentMap();
+    }
     this.directoryCleaner = directoryCleaner;
   }
 
@@ -81,6 +148,15 @@ public void registerExecutor(
       ExecutorShuffleInfo executorInfo) {
     AppExecId fullId = new AppExecId(appId, execId);
     logger.info("Registered executor {} with {}", fullId, executorInfo);
+    try {
+      if (db != null) {
+        byte[] key = dbAppExecKey(fullId);
+        byte[] value = mapper.writeValueAsString(executorInfo).getBytes(Charsets.UTF_8);
+        db.put(key, value);
+      }
+    } catch (Exception e) {
+      logger.error("Error saving registered executors", e);
+    }
     executors.put(fullId, executorInfo);
   }
 
@@ -136,6 +212,13 @@ public void applicationRemoved(String appId, boolean cleanupLocalDirs) {
       // Only touch executors associated with the appId that was removed.
       if (appId.equals(fullId.appId)) {
         it.remove();
+        if (db != null) {
+          try {
+            db.delete(dbAppExecKey(fullId));
+          } catch (IOException e) {
+            logger.error("Error deleting {} from executor state db", appId, e);
+          }
+        }
 
         if (cleanupLocalDirs) {
           logger.info("Cleaning up executor {}'s {} local dirs", fullId, executor.localDirs.length);
@@ -220,12 +303,23 @@ static File getFile(String[] localDirs, int subDirsPerLocalDir, String filename)
     return new File(new File(localDir, String.format("%02x", subDirId)), filename);
   }
 
+  void close() {
+    if (db != null) {
+      try {
+        db.close();
+      } catch (IOException e) {
+        logger.error("Exception closing leveldb with registered executors", e);
+      }
+    }
+  }
+
   /** Simply encodes an executor's full ID, which is appId + execId. */
-  private static class AppExecId {
-    final String appId;
-    final String execId;
+  public static class AppExecId {
+    public final String appId;
+    public final String execId;
 
-    private AppExecId(String appId, String execId) {
+    @JsonCreator
+    public AppExecId(@JsonProperty("appId") String appId, @JsonProperty("execId") String execId) {
       this.appId = appId;
       this.execId = execId;
     }
@@ -252,4 +346,105 @@ public String toString() {
         .toString();
     }
   }
+
+  private static byte[] dbAppExecKey(AppExecId appExecId) throws IOException {
+    // we stick a common prefix on all the keys so we can find them in the DB
+    String appExecJson = mapper.writeValueAsString(appExecId);
+    String key = (APP_KEY_PREFIX + ";" + appExecJson);
+    return key.getBytes(Charsets.UTF_8);
+  }
+
+  private static AppExecId parseDbAppExecKey(String s) throws IOException {
+    if (!s.startsWith(APP_KEY_PREFIX)) {
+      throw new IllegalArgumentException("expected a string starting with " + APP_KEY_PREFIX);
+    }
+    String json = s.substring(APP_KEY_PREFIX.length() + 1);
+    AppExecId parsed = mapper.readValue(json, AppExecId.class);
+    return parsed;
+  }
+
+  @VisibleForTesting
+  static ConcurrentMap<AppExecId, ExecutorShuffleInfo> reloadRegisteredExecutors(DB db)
+      throws IOException {
+    ConcurrentMap<AppExecId, ExecutorShuffleInfo> registeredExecutors = Maps.newConcurrentMap();
+    if (db != null) {
+      DBIterator itr = db.iterator();
+      itr.seek(APP_KEY_PREFIX.getBytes(Charsets.UTF_8));
+      while (itr.hasNext()) {
+        Map.Entry<byte[], byte[]> e = itr.next();
+        String key = new String(e.getKey(), Charsets.UTF_8);
+        if (!key.startsWith(APP_KEY_PREFIX)) {
+          break;
+        }
+        AppExecId id = parseDbAppExecKey(key);
+        ExecutorShuffleInfo shuffleInfo = mapper.readValue(e.getValue(), ExecutorShuffleInfo.class);
+        registeredExecutors.put(id, shuffleInfo);
+      }
+    }
+    return registeredExecutors;
+  }
+
+  private static class LevelDBLogger implements org.iq80.leveldb.Logger {
+    private static final Logger LOG = LoggerFactory.getLogger(LevelDBLogger.class);
+
+    @Override
+    public void log(String message) {
+      LOG.info(message);
+    }
+  }
+
+  /**
+   * Simple major.minor versioning scheme.  Any incompatible changes should be across major
+   * versions.  Minor version differences are allowed -- meaning we should be able to read
+   * dbs that are either earlier *or* later on the minor version.
+   */
+  private static void checkVersion(DB db) throws IOException {
+    byte[] bytes = db.get(StoreVersion.KEY);
+    if (bytes == null) {
+      storeVersion(db);
+    } else {
+      StoreVersion version = mapper.readValue(bytes, StoreVersion.class);
+      if (version.major != CURRENT_VERSION.major) {
+        throw new IOException("cannot read state DB with version " + version + ", incompatible " +
+          "with current version " + CURRENT_VERSION);
+      }
+      storeVersion(db);
+    }
+  }
+
+  private static void storeVersion(DB db) throws IOException {
+    db.put(StoreVersion.KEY, mapper.writeValueAsBytes(CURRENT_VERSION));
+  }
+
+
+  public static class StoreVersion {
+
+    final static byte[] KEY = "StoreVersion".getBytes(Charsets.UTF_8);
+
+    public final int major;
+    public final int minor;
+
+    @JsonCreator public StoreVersion(@JsonProperty("major") int major, @JsonProperty("minor") int minor) {
+      this.major = major;
+      this.minor = minor;
+    }
+
+    @Override
+    public boolean equals(Object o) {
+      if (this == o) return true;
+      if (o == null || getClass() != o.getClass()) return false;
+
+      StoreVersion that = (StoreVersion) o;
+
+      return major == that.major && minor == that.minor;
+    }
+
+    @Override
+    public int hashCode() {
+      int result = major;
+      result = 31 * result + minor;
+      return result;
+    }
+  }
+
 }
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/ExecutorShuffleInfo.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/ExecutorShuffleInfo.java
index cadc8e8369c6a..102d4efb8bf3b 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/ExecutorShuffleInfo.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/ExecutorShuffleInfo.java
@@ -19,6 +19,8 @@
 
 import java.util.Arrays;
 
+import com.fasterxml.jackson.annotation.JsonCreator;
+import com.fasterxml.jackson.annotation.JsonProperty;
 import com.google.common.base.Objects;
 import io.netty.buffer.ByteBuf;
 
@@ -34,7 +36,11 @@ public class ExecutorShuffleInfo implements Encodable {
   /** Shuffle manager (SortShuffleManager or HashShuffleManager) that the executor is using. */
   public final String shuffleManager;
 
-  public ExecutorShuffleInfo(String[] localDirs, int subDirsPerLocalDir, String shuffleManager) {
+  @JsonCreator
+  public ExecutorShuffleInfo(
+      @JsonProperty("localDirs") String[] localDirs,
+      @JsonProperty("subDirsPerLocalDir") int subDirsPerLocalDir,
+      @JsonProperty("shuffleManager") String shuffleManager) {
     this.localDirs = localDirs;
     this.subDirsPerLocalDir = subDirsPerLocalDir;
     this.shuffleManager = shuffleManager;
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolverSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolverSuite.java
index d02f4f0fdb682..3c6cb367dea46 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolverSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockResolverSuite.java
@@ -21,9 +21,12 @@
 import java.io.InputStream;
 import java.io.InputStreamReader;
 
+import com.fasterxml.jackson.databind.ObjectMapper;
 import com.google.common.io.CharStreams;
+import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo;
 import org.apache.spark.network.util.SystemPropertyConfigProvider;
 import org.apache.spark.network.util.TransportConf;
+import org.apache.spark.network.shuffle.ExternalShuffleBlockResolver.AppExecId;
 import org.junit.AfterClass;
 import org.junit.BeforeClass;
 import org.junit.Test;
@@ -59,8 +62,8 @@ public static void afterAll() {
   }
 
   @Test
-  public void testBadRequests() {
-    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf);
+  public void testBadRequests() throws IOException {
+    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf, null);
     // Unregistered executor
     try {
       resolver.getBlockData("app0", "exec1", "shuffle_1_1_0");
@@ -91,7 +94,7 @@ public void testBadRequests() {
 
   @Test
   public void testSortShuffleBlocks() throws IOException {
-    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf);
+    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf, null);
     resolver.registerExecutor("app0", "exec0",
       dataContext.createExecutorInfo("org.apache.spark.shuffle.sort.SortShuffleManager"));
 
@@ -110,7 +113,7 @@ public void testSortShuffleBlocks() throws IOException {
 
   @Test
   public void testHashShuffleBlocks() throws IOException {
-    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf);
+    ExternalShuffleBlockResolver resolver = new ExternalShuffleBlockResolver(conf, null);
     resolver.registerExecutor("app0", "exec0",
       dataContext.createExecutorInfo("org.apache.spark.shuffle.hash.HashShuffleManager"));
 
@@ -126,4 +129,28 @@ public void testHashShuffleBlocks() throws IOException {
     block1Stream.close();
     assertEquals(hashBlock1, block1);
   }
+
+  @Test
+  public void jsonSerializationOfExecutorRegistration() throws IOException {
+    ObjectMapper mapper = new ObjectMapper();
+    AppExecId appId = new AppExecId("foo", "bar");
+    String appIdJson = mapper.writeValueAsString(appId);
+    AppExecId parsedAppId = mapper.readValue(appIdJson, AppExecId.class);
+    assertEquals(parsedAppId, appId);
+
+    ExecutorShuffleInfo shuffleInfo =
+      new ExecutorShuffleInfo(new String[]{"/bippy", "/flippy"}, 7, "hash");
+    String shuffleJson = mapper.writeValueAsString(shuffleInfo);
+    ExecutorShuffleInfo parsedShuffleInfo =
+      mapper.readValue(shuffleJson, ExecutorShuffleInfo.class);
+    assertEquals(parsedShuffleInfo, shuffleInfo);
+
+    // Intentionally keep these hard-coded strings in here, to check backwards-compatability.
+    // its not legacy yet, but keeping this here in case anybody changes it
+    String legacyAppIdJson = "{\"appId\":\"foo\", \"execId\":\"bar\"}";
+    assertEquals(appId, mapper.readValue(legacyAppIdJson, AppExecId.class));
+    String legacyShuffleJson = "{\"localDirs\": [\"/bippy\", \"/flippy\"], " +
+      "\"subDirsPerLocalDir\": 7, \"shuffleManager\": \"hash\"}";
+    assertEquals(shuffleInfo, mapper.readValue(legacyShuffleJson, ExecutorShuffleInfo.class));
+  }
 }
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java
index d9d9c1bf2f17a..2f4f1d0df478b 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleCleanupSuite.java
@@ -42,7 +42,7 @@ public void noCleanupAndCleanup() throws IOException {
     TestShuffleDataContext dataContext = createSomeData();
 
     ExternalShuffleBlockResolver resolver =
-      new ExternalShuffleBlockResolver(conf, sameThreadExecutor);
+      new ExternalShuffleBlockResolver(conf, null, sameThreadExecutor);
     resolver.registerExecutor("app", "exec0", dataContext.createExecutorInfo("shuffleMgr"));
     resolver.applicationRemoved("app", false /* cleanup */);
 
@@ -65,7 +65,8 @@ public void cleanupUsesExecutor() throws IOException {
       @Override public void execute(Runnable runnable) { cleanupCalled.set(true); }
     };
 
-    ExternalShuffleBlockResolver manager = new ExternalShuffleBlockResolver(conf, noThreadExecutor);
+    ExternalShuffleBlockResolver manager =
+      new ExternalShuffleBlockResolver(conf, null, noThreadExecutor);
 
     manager.registerExecutor("app", "exec0", dataContext.createExecutorInfo("shuffleMgr"));
     manager.applicationRemoved("app", true);
@@ -83,7 +84,7 @@ public void cleanupMultipleExecutors() throws IOException {
     TestShuffleDataContext dataContext1 = createSomeData();
 
     ExternalShuffleBlockResolver resolver =
-      new ExternalShuffleBlockResolver(conf, sameThreadExecutor);
+      new ExternalShuffleBlockResolver(conf, null, sameThreadExecutor);
 
     resolver.registerExecutor("app", "exec0", dataContext0.createExecutorInfo("shuffleMgr"));
     resolver.registerExecutor("app", "exec1", dataContext1.createExecutorInfo("shuffleMgr"));
@@ -99,7 +100,7 @@ public void cleanupOnlyRemovedApp() throws IOException {
     TestShuffleDataContext dataContext1 = createSomeData();
 
     ExternalShuffleBlockResolver resolver =
-      new ExternalShuffleBlockResolver(conf, sameThreadExecutor);
+      new ExternalShuffleBlockResolver(conf, null, sameThreadExecutor);
 
     resolver.registerExecutor("app-0", "exec0", dataContext0.createExecutorInfo("shuffleMgr"));
     resolver.registerExecutor("app-1", "exec0", dataContext1.createExecutorInfo("shuffleMgr"));
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java
index 39aa49911d9cb..a3f9a38b1aeb9 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleIntegrationSuite.java
@@ -92,7 +92,7 @@ public static void beforeAll() throws IOException {
     dataContext1.insertHashShuffleData(1, 0, exec1Blocks);
 
     conf = new TransportConf(new SystemPropertyConfigProvider());
-    handler = new ExternalShuffleBlockHandler(conf);
+    handler = new ExternalShuffleBlockHandler(conf, null);
     TransportContext transportContext = new TransportContext(conf, handler);
     server = transportContext.createServer();
   }
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java
index d4ec1956c1e29..aa99efda94948 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleSecuritySuite.java
@@ -43,8 +43,9 @@ public class ExternalShuffleSecuritySuite {
   TransportServer server;
 
   @Before
-  public void beforeEach() {
-    TransportContext context = new TransportContext(conf, new ExternalShuffleBlockHandler(conf));
+  public void beforeEach() throws IOException {
+    TransportContext context =
+      new TransportContext(conf, new ExternalShuffleBlockHandler(conf, null));
     TransportServerBootstrap bootstrap = new SaslServerBootstrap(conf,
         new TestSecretKeyHolder("my-app-id", "secret"));
     this.server = context.createServer(Arrays.asList(bootstrap));
diff --git a/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java b/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java
index 463f99ef3352d..11ea7f3fd3cfe 100644
--- a/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java
+++ b/network/yarn/src/main/java/org/apache/spark/network/yarn/YarnShuffleService.java
@@ -17,25 +17,21 @@
 
 package org.apache.spark.network.yarn;
 
+import java.io.File;
 import java.nio.ByteBuffer;
 import java.util.List;
 
+import com.google.common.annotations.VisibleForTesting;
 import com.google.common.collect.Lists;
 import org.apache.hadoop.conf.Configuration;
-import org.apache.hadoop.yarn.api.records.ApplicationId;
 import org.apache.hadoop.yarn.api.records.ContainerId;
-import org.apache.hadoop.yarn.server.api.AuxiliaryService;
-import org.apache.hadoop.yarn.server.api.ApplicationInitializationContext;
-import org.apache.hadoop.yarn.server.api.ApplicationTerminationContext;
-import org.apache.hadoop.yarn.server.api.ContainerInitializationContext;
-import org.apache.hadoop.yarn.server.api.ContainerTerminationContext;
+import org.apache.hadoop.yarn.server.api.*;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 import org.apache.spark.network.TransportContext;
 import org.apache.spark.network.sasl.SaslServerBootstrap;
 import org.apache.spark.network.sasl.ShuffleSecretManager;
-import org.apache.spark.network.server.RpcHandler;
 import org.apache.spark.network.server.TransportServer;
 import org.apache.spark.network.server.TransportServerBootstrap;
 import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler;
@@ -79,11 +75,26 @@ public class YarnShuffleService extends AuxiliaryService {
   private TransportServer shuffleServer = null;
 
   // Handles registering executors and opening shuffle blocks
-  private ExternalShuffleBlockHandler blockHandler;
+  @VisibleForTesting
+  ExternalShuffleBlockHandler blockHandler;
+
+  // Where to store & reload executor info for recovering state after an NM restart
+  @VisibleForTesting
+  File registeredExecutorFile;
+
+  // just for testing when you want to find an open port
+  @VisibleForTesting
+  static int boundPort = -1;
+
+  // just for integration tests that want to look at this file -- in general not sensible as
+  // a static
+  @VisibleForTesting
+  static YarnShuffleService instance;
 
   public YarnShuffleService() {
     super("spark_shuffle");
     logger.info("Initializing YARN shuffle service for Spark");
+    instance = this;
   }
 
   /**
@@ -100,11 +111,24 @@ private boolean isAuthenticationEnabled() {
    */
   @Override
   protected void serviceInit(Configuration conf) {
+
+    // In case this NM was killed while there were running spark applications, we need to restore
+    // lost state for the existing executors.  We look for an existing file in the NM's local dirs.
+    // If we don't find one, then we choose a file to use to save the state next time.  Even if
+    // an application was stopped while the NM was down, we expect yarn to call stopApplication()
+    // when it comes back
+    registeredExecutorFile =
+      findRegisteredExecutorFile(conf.getStrings("yarn.nodemanager.local-dirs"));
+
     TransportConf transportConf = new TransportConf(new HadoopConfigProvider(conf));
     // If authentication is enabled, set up the shuffle server to use a
     // special RPC handler that filters out unauthenticated fetch requests
     boolean authEnabled = conf.getBoolean(SPARK_AUTHENTICATE_KEY, DEFAULT_SPARK_AUTHENTICATE);
-    blockHandler = new ExternalShuffleBlockHandler(transportConf);
+    try {
+      blockHandler = new ExternalShuffleBlockHandler(transportConf, registeredExecutorFile);
+    } catch (Exception e) {
+      logger.error("Failed to initialize external shuffle service", e);
+    }
 
     List<TransportServerBootstrap> bootstraps = Lists.newArrayList();
     if (authEnabled) {
@@ -116,9 +140,13 @@ protected void serviceInit(Configuration conf) {
       SPARK_SHUFFLE_SERVICE_PORT_KEY, DEFAULT_SPARK_SHUFFLE_SERVICE_PORT);
     TransportContext transportContext = new TransportContext(transportConf, blockHandler);
     shuffleServer = transportContext.createServer(port, bootstraps);
+    // the port should normally be fixed, but for tests its useful to find an open port
+    port = shuffleServer.getPort();
+    boundPort = port;
     String authEnabledString = authEnabled ? "enabled" : "not enabled";
     logger.info("Started YARN shuffle service for Spark on port {}. " +
-      "Authentication is {}.", port, authEnabledString);
+      "Authentication is {}.  Registered executor file is {}", port, authEnabledString,
+      registeredExecutorFile);
   }
 
   @Override
@@ -161,6 +189,16 @@ public void stopContainer(ContainerTerminationContext context) {
     logger.info("Stopping container {}", containerId);
   }
 
+  private File findRegisteredExecutorFile(String[] localDirs) {
+    for (String dir: localDirs) {
+      File f = new File(dir, "registeredExecutors.ldb");
+      if (f.exists()) {
+        return f;
+      }
+    }
+    return new File(localDirs[0], "registeredExecutors.ldb");
+  }
+
   /**
    * Close the shuffle server to clean up any associated state.
    */
@@ -170,6 +208,9 @@ protected void serviceStop() {
       if (shuffleServer != null) {
         shuffleServer.close();
       }
+      if (blockHandler != null) {
+        blockHandler.close();
+      }
     } catch (Exception e) {
       logger.error("Exception when stopping service", e);
     }
@@ -180,5 +221,4 @@ protected void serviceStop() {
   public ByteBuffer getMetaData() {
     return ByteBuffer.allocate(0);
   }
-
 }
diff --git a/pom.xml b/pom.xml
index ccfa1ea19b21e..d5945f2546d38 100644
--- a/pom.xml
+++ b/pom.xml
@@ -655,6 +655,11 @@
         <artifactId>jackson-databind</artifactId>
         <version>${fasterxml.jackson.version}</version>
       </dependency>
+      <dependency>
+        <groupId>com.fasterxml.jackson.core</groupId>
+        <artifactId>jackson-annotations</artifactId>
+        <version>${fasterxml.jackson.version}</version>
+      </dependency>
       <!-- Guava is excluded because of SPARK-6149.  The Guava version referenced in this module is
            15.0, which causes runtime incompatibility issues. -->
       <dependency>
diff --git a/yarn/pom.xml b/yarn/pom.xml
index 15db54e4e7909..f6737695307a2 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -38,6 +38,12 @@
       <artifactId>spark-core_${scala.binary.version}</artifactId>
       <version>${project.version}</version>
     </dependency>
+    <dependency>
+      <groupId>org.apache.spark</groupId>
+      <artifactId>spark-network-yarn_${scala.binary.version}</artifactId>
+      <version>${project.version}</version>
+      <scope>test</scope>
+    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-core_${scala.binary.version}</artifactId>
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
new file mode 100644
index 0000000000000..128e996b71fe5
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
@@ -0,0 +1,193 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn
+
+import java.io.{File, FileOutputStream, OutputStreamWriter}
+import java.util.Properties
+import java.util.concurrent.TimeUnit
+
+import scala.collection.JavaConversions._
+
+import com.google.common.base.Charsets.UTF_8
+import com.google.common.io.Files
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.apache.hadoop.yarn.server.MiniYARNCluster
+import org.scalatest.{BeforeAndAfterAll, Matchers}
+
+import org.apache.spark._
+import org.apache.spark.util.Utils
+
+abstract class BaseYarnClusterSuite
+  extends SparkFunSuite with BeforeAndAfterAll with Matchers with Logging {
+
+  // log4j configuration for the YARN containers, so that their output is collected
+  // by YARN instead of trying to overwrite unit-tests.log.
+  protected val LOG4J_CONF = """
+    |log4j.rootCategory=DEBUG, console
+    |log4j.appender.console=org.apache.log4j.ConsoleAppender
+    |log4j.appender.console.target=System.err
+    |log4j.appender.console.layout=org.apache.log4j.PatternLayout
+    |log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
+    """.stripMargin
+
+  private var yarnCluster: MiniYARNCluster = _
+  protected var tempDir: File = _
+  private var fakeSparkJar: File = _
+  private var hadoopConfDir: File = _
+  private var logConfDir: File = _
+
+
+  def yarnConfig: YarnConfiguration
+
+  override def beforeAll() {
+    super.beforeAll()
+
+    tempDir = Utils.createTempDir()
+    logConfDir = new File(tempDir, "log4j")
+    logConfDir.mkdir()
+    System.setProperty("SPARK_YARN_MODE", "true")
+
+    val logConfFile = new File(logConfDir, "log4j.properties")
+    Files.write(LOG4J_CONF, logConfFile, UTF_8)
+
+    yarnCluster = new MiniYARNCluster(getClass().getName(), 1, 1, 1)
+    yarnCluster.init(yarnConfig)
+    yarnCluster.start()
+
+    // There's a race in MiniYARNCluster in which start() may return before the RM has updated
+    // its address in the configuration. You can see this in the logs by noticing that when
+    // MiniYARNCluster prints the address, it still has port "0" assigned, although later the
+    // test works sometimes:
+    //
+    //    INFO MiniYARNCluster: MiniYARN ResourceManager address: blah:0
+    //
+    // That log message prints the contents of the RM_ADDRESS config variable. If you check it
+    // later on, it looks something like this:
+    //
+    //    INFO YarnClusterSuite: RM address in configuration is blah:42631
+    //
+    // This hack loops for a bit waiting for the port to change, and fails the test if it hasn't
+    // done so in a timely manner (defined to be 10 seconds).
+    val config = yarnCluster.getConfig()
+    val deadline = System.currentTimeMillis() + TimeUnit.SECONDS.toMillis(10)
+    while (config.get(YarnConfiguration.RM_ADDRESS).split(":")(1) == "0") {
+      if (System.currentTimeMillis() > deadline) {
+        throw new IllegalStateException("Timed out waiting for RM to come up.")
+      }
+      logDebug("RM address still not set in configuration, waiting...")
+      TimeUnit.MILLISECONDS.sleep(100)
+    }
+
+    logInfo(s"RM address in configuration is ${config.get(YarnConfiguration.RM_ADDRESS)}")
+
+    fakeSparkJar = File.createTempFile("sparkJar", null, tempDir)
+    hadoopConfDir = new File(tempDir, Client.LOCALIZED_CONF_DIR)
+    assert(hadoopConfDir.mkdir())
+    File.createTempFile("token", ".txt", hadoopConfDir)
+  }
+
+  override def afterAll() {
+    yarnCluster.stop()
+    System.clearProperty("SPARK_YARN_MODE")
+    super.afterAll()
+  }
+
+  protected def runSpark(
+      clientMode: Boolean,
+      klass: String,
+      appArgs: Seq[String] = Nil,
+      sparkArgs: Seq[String] = Nil,
+      extraClassPath: Seq[String] = Nil,
+      extraJars: Seq[String] = Nil,
+      extraConf: Map[String, String] = Map()): Unit = {
+    val master = if (clientMode) "yarn-client" else "yarn-cluster"
+    val props = new Properties()
+
+    props.setProperty("spark.yarn.jar", "local:" + fakeSparkJar.getAbsolutePath())
+
+    val childClasspath = logConfDir.getAbsolutePath() +
+      File.pathSeparator +
+      sys.props("java.class.path") +
+      File.pathSeparator +
+      extraClassPath.mkString(File.pathSeparator)
+    props.setProperty("spark.driver.extraClassPath", childClasspath)
+    props.setProperty("spark.executor.extraClassPath", childClasspath)
+
+    // SPARK-4267: make sure java options are propagated correctly.
+    props.setProperty("spark.driver.extraJavaOptions", "-Dfoo=\"one two three\"")
+    props.setProperty("spark.executor.extraJavaOptions", "-Dfoo=\"one two three\"")
+
+    yarnCluster.getConfig().foreach { e =>
+      props.setProperty("spark.hadoop." + e.getKey(), e.getValue())
+    }
+
+    sys.props.foreach { case (k, v) =>
+      if (k.startsWith("spark.")) {
+        props.setProperty(k, v)
+      }
+    }
+
+    extraConf.foreach { case (k, v) => props.setProperty(k, v) }
+
+    val propsFile = File.createTempFile("spark", ".properties", tempDir)
+    val writer = new OutputStreamWriter(new FileOutputStream(propsFile), UTF_8)
+    props.store(writer, "Spark properties.")
+    writer.close()
+
+    val extraJarArgs = if (!extraJars.isEmpty()) Seq("--jars", extraJars.mkString(",")) else Nil
+    val mainArgs =
+      if (klass.endsWith(".py")) {
+        Seq(klass)
+      } else {
+        Seq("--class", klass, fakeSparkJar.getAbsolutePath())
+      }
+    val argv =
+      Seq(
+        new File(sys.props("spark.test.home"), "bin/spark-submit").getAbsolutePath(),
+        "--master", master,
+        "--num-executors", "1",
+        "--properties-file", propsFile.getAbsolutePath()) ++
+      extraJarArgs ++
+      sparkArgs ++
+      mainArgs ++
+      appArgs
+
+    Utils.executeAndGetOutput(argv,
+      extraEnvironment = Map("YARN_CONF_DIR" -> hadoopConfDir.getAbsolutePath()))
+  }
+
+  /**
+   * This is a workaround for an issue with yarn-cluster mode: the Client class will not provide
+   * any sort of error when the job process finishes successfully, but the job itself fails. So
+   * the tests enforce that something is written to a file after everything is ok to indicate
+   * that the job succeeded.
+   */
+  protected def checkResult(result: File): Unit = {
+    checkResult(result, "success")
+  }
+
+  protected def checkResult(result: File, expected: String): Unit = {
+    val resultString = Files.toString(result, UTF_8)
+    resultString should be (expected)
+  }
+
+  protected def mainClassName(klass: Class[_]): String = {
+    klass.getName().stripSuffix("$")
+  }
+
+}
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index eb6e1fd370620..128350b648992 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -17,25 +17,20 @@
 
 package org.apache.spark.deploy.yarn
 
-import java.io.{File, FileOutputStream, OutputStreamWriter}
+import java.io.File
 import java.net.URL
-import java.util.Properties
-import java.util.concurrent.TimeUnit
 
-import scala.collection.JavaConversions._
 import scala.collection.mutable
+import scala.collection.JavaConversions._
 
 import com.google.common.base.Charsets.UTF_8
-import com.google.common.io.ByteStreams
-import com.google.common.io.Files
+import com.google.common.io.{ByteStreams, Files}
 import org.apache.hadoop.yarn.conf.YarnConfiguration
-import org.apache.hadoop.yarn.server.MiniYARNCluster
-import org.scalatest.{BeforeAndAfterAll, Matchers}
+import org.scalatest.Matchers
 
 import org.apache.spark._
+import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationStart, SparkListenerExecutorAdded}
 import org.apache.spark.scheduler.cluster.ExecutorInfo
-import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationStart,
-  SparkListenerExecutorAdded}
 import org.apache.spark.util.Utils
 
 /**
@@ -43,17 +38,9 @@ import org.apache.spark.util.Utils
  * applications, and require the Spark assembly to be built before they can be successfully
  * run.
  */
-class YarnClusterSuite extends SparkFunSuite with BeforeAndAfterAll with Matchers with Logging {
-
-  // log4j configuration for the YARN containers, so that their output is collected
-  // by YARN instead of trying to overwrite unit-tests.log.
-  private val LOG4J_CONF = """
-    |log4j.rootCategory=DEBUG, console
-    |log4j.appender.console=org.apache.log4j.ConsoleAppender
-    |log4j.appender.console.target=System.err
-    |log4j.appender.console.layout=org.apache.log4j.PatternLayout
-    |log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
-    """.stripMargin
+class YarnClusterSuite extends BaseYarnClusterSuite {
+
+  override def yarnConfig: YarnConfiguration = new YarnConfiguration()
 
   private val TEST_PYFILE = """
     |import mod1, mod2
@@ -82,65 +69,6 @@ class YarnClusterSuite extends SparkFunSuite with BeforeAndAfterAll with Matcher
     |    return 42
     """.stripMargin
 
-  private var yarnCluster: MiniYARNCluster = _
-  private var tempDir: File = _
-  private var fakeSparkJar: File = _
-  private var hadoopConfDir: File = _
-  private var logConfDir: File = _
-
-  override def beforeAll() {
-    super.beforeAll()
-
-    tempDir = Utils.createTempDir()
-    logConfDir = new File(tempDir, "log4j")
-    logConfDir.mkdir()
-    System.setProperty("SPARK_YARN_MODE", "true")
-
-    val logConfFile = new File(logConfDir, "log4j.properties")
-    Files.write(LOG4J_CONF, logConfFile, UTF_8)
-
-    yarnCluster = new MiniYARNCluster(getClass().getName(), 1, 1, 1)
-    yarnCluster.init(new YarnConfiguration())
-    yarnCluster.start()
-
-    // There's a race in MiniYARNCluster in which start() may return before the RM has updated
-    // its address in the configuration. You can see this in the logs by noticing that when
-    // MiniYARNCluster prints the address, it still has port "0" assigned, although later the
-    // test works sometimes:
-    //
-    //    INFO MiniYARNCluster: MiniYARN ResourceManager address: blah:0
-    //
-    // That log message prints the contents of the RM_ADDRESS config variable. If you check it
-    // later on, it looks something like this:
-    //
-    //    INFO YarnClusterSuite: RM address in configuration is blah:42631
-    //
-    // This hack loops for a bit waiting for the port to change, and fails the test if it hasn't
-    // done so in a timely manner (defined to be 10 seconds).
-    val config = yarnCluster.getConfig()
-    val deadline = System.currentTimeMillis() + TimeUnit.SECONDS.toMillis(10)
-    while (config.get(YarnConfiguration.RM_ADDRESS).split(":")(1) == "0") {
-      if (System.currentTimeMillis() > deadline) {
-        throw new IllegalStateException("Timed out waiting for RM to come up.")
-      }
-      logDebug("RM address still not set in configuration, waiting...")
-      TimeUnit.MILLISECONDS.sleep(100)
-    }
-
-    logInfo(s"RM address in configuration is ${config.get(YarnConfiguration.RM_ADDRESS)}")
-
-    fakeSparkJar = File.createTempFile("sparkJar", null, tempDir)
-    hadoopConfDir = new File(tempDir, Client.LOCALIZED_CONF_DIR)
-    assert(hadoopConfDir.mkdir())
-    File.createTempFile("token", ".txt", hadoopConfDir)
-  }
-
-  override def afterAll() {
-    yarnCluster.stop()
-    System.clearProperty("SPARK_YARN_MODE")
-    super.afterAll()
-  }
-
   test("run Spark in yarn-client mode") {
     testBasicYarnApp(true)
   }
@@ -174,7 +102,7 @@ class YarnClusterSuite extends SparkFunSuite with BeforeAndAfterAll with Matcher
   }
 
   private def testBasicYarnApp(clientMode: Boolean): Unit = {
-    var result = File.createTempFile("result", null, tempDir)
+    val result = File.createTempFile("result", null, tempDir)
     runSpark(clientMode, mainClassName(YarnClusterDriver.getClass),
       appArgs = Seq(result.getAbsolutePath()))
     checkResult(result)
@@ -224,89 +152,6 @@ class YarnClusterSuite extends SparkFunSuite with BeforeAndAfterAll with Matcher
     checkResult(executorResult, "OVERRIDDEN")
   }
 
-  private def runSpark(
-      clientMode: Boolean,
-      klass: String,
-      appArgs: Seq[String] = Nil,
-      sparkArgs: Seq[String] = Nil,
-      extraClassPath: Seq[String] = Nil,
-      extraJars: Seq[String] = Nil,
-      extraConf: Map[String, String] = Map()): Unit = {
-    val master = if (clientMode) "yarn-client" else "yarn-cluster"
-    val props = new Properties()
-
-    props.setProperty("spark.yarn.jar", "local:" + fakeSparkJar.getAbsolutePath())
-
-    val childClasspath = logConfDir.getAbsolutePath() +
-      File.pathSeparator +
-      sys.props("java.class.path") +
-      File.pathSeparator +
-      extraClassPath.mkString(File.pathSeparator)
-    props.setProperty("spark.driver.extraClassPath", childClasspath)
-    props.setProperty("spark.executor.extraClassPath", childClasspath)
-
-    // SPARK-4267: make sure java options are propagated correctly.
-    props.setProperty("spark.driver.extraJavaOptions", "-Dfoo=\"one two three\"")
-    props.setProperty("spark.executor.extraJavaOptions", "-Dfoo=\"one two three\"")
-
-    yarnCluster.getConfig().foreach { e =>
-      props.setProperty("spark.hadoop." + e.getKey(), e.getValue())
-    }
-
-    sys.props.foreach { case (k, v) =>
-      if (k.startsWith("spark.")) {
-        props.setProperty(k, v)
-      }
-    }
-
-    extraConf.foreach { case (k, v) => props.setProperty(k, v) }
-
-    val propsFile = File.createTempFile("spark", ".properties", tempDir)
-    val writer = new OutputStreamWriter(new FileOutputStream(propsFile), UTF_8)
-    props.store(writer, "Spark properties.")
-    writer.close()
-
-    val extraJarArgs = if (!extraJars.isEmpty()) Seq("--jars", extraJars.mkString(",")) else Nil
-    val mainArgs =
-      if (klass.endsWith(".py")) {
-        Seq(klass)
-      } else {
-        Seq("--class", klass, fakeSparkJar.getAbsolutePath())
-      }
-    val argv =
-      Seq(
-        new File(sys.props("spark.test.home"), "bin/spark-submit").getAbsolutePath(),
-        "--master", master,
-        "--num-executors", "1",
-        "--properties-file", propsFile.getAbsolutePath()) ++
-      extraJarArgs ++
-      sparkArgs ++
-      mainArgs ++
-      appArgs
-
-    Utils.executeAndGetOutput(argv,
-      extraEnvironment = Map("YARN_CONF_DIR" -> hadoopConfDir.getAbsolutePath()))
-  }
-
-  /**
-   * This is a workaround for an issue with yarn-cluster mode: the Client class will not provide
-   * any sort of error when the job process finishes successfully, but the job itself fails. So
-   * the tests enforce that something is written to a file after everything is ok to indicate
-   * that the job succeeded.
-   */
-  private def checkResult(result: File): Unit = {
-    checkResult(result, "success")
-  }
-
-  private def checkResult(result: File, expected: String): Unit = {
-    var resultString = Files.toString(result, UTF_8)
-    resultString should be (expected)
-  }
-
-  private def mainClassName(klass: Class[_]): String = {
-    klass.getName().stripSuffix("$")
-  }
-
 }
 
 private[spark] class SaveExecutorInfo extends SparkListener {
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
new file mode 100644
index 0000000000000..5e8238822b90a
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
@@ -0,0 +1,109 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.deploy.yarn
+
+import java.io.File
+
+import com.google.common.base.Charsets.UTF_8
+import com.google.common.io.Files
+import org.apache.commons.io.FileUtils
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.scalatest.Matchers
+
+import org.apache.spark._
+import org.apache.spark.network.shuffle.ShuffleTestAccessor
+import org.apache.spark.network.yarn.{YarnShuffleService, YarnTestAccessor}
+
+/**
+ * Integration test for the external shuffle service with a yarn mini-cluster
+ */
+class YarnShuffleIntegrationSuite extends BaseYarnClusterSuite {
+
+  override def yarnConfig: YarnConfiguration = {
+    val yarnConfig = new YarnConfiguration()
+    yarnConfig.set(YarnConfiguration.NM_AUX_SERVICES, "spark_shuffle")
+    yarnConfig.set(YarnConfiguration.NM_AUX_SERVICE_FMT.format("spark_shuffle"),
+      classOf[YarnShuffleService].getCanonicalName)
+    yarnConfig.set("spark.shuffle.service.port", "0")
+    yarnConfig
+  }
+
+  test("external shuffle service") {
+    val shuffleServicePort = YarnTestAccessor.getShuffleServicePort
+    val shuffleService = YarnTestAccessor.getShuffleServiceInstance
+
+    val registeredExecFile = YarnTestAccessor.getRegisteredExecutorFile(shuffleService)
+
+    logInfo("Shuffle service port = " + shuffleServicePort)
+    val result = File.createTempFile("result", null, tempDir)
+    runSpark(
+      false,
+      mainClassName(YarnExternalShuffleDriver.getClass),
+      appArgs = Seq(result.getAbsolutePath(), registeredExecFile.getAbsolutePath),
+      extraConf = Map(
+        "spark.shuffle.service.enabled" -> "true",
+        "spark.shuffle.service.port" -> shuffleServicePort.toString
+      )
+    )
+    checkResult(result)
+    assert(YarnTestAccessor.getRegisteredExecutorFile(shuffleService).exists())
+  }
+}
+
+private object YarnExternalShuffleDriver extends Logging with Matchers {
+
+  val WAIT_TIMEOUT_MILLIS = 10000
+
+  def main(args: Array[String]): Unit = {
+    if (args.length != 2) {
+      // scalastyle:off println
+      System.err.println(
+        s"""
+        |Invalid command line: ${args.mkString(" ")}
+        |
+        |Usage: ExternalShuffleDriver [result file] [registed exec file]
+        """.stripMargin)
+      // scalastyle:on println
+      System.exit(1)
+    }
+
+    val sc = new SparkContext(new SparkConf()
+      .setAppName("External Shuffle Test"))
+    val conf = sc.getConf
+    val status = new File(args(0))
+    val registeredExecFile = new File(args(1))
+    logInfo("shuffle service executor file = " + registeredExecFile)
+    var result = "failure"
+    val execStateCopy = new File(registeredExecFile.getAbsolutePath + "_dup")
+    try {
+      val data = sc.parallelize(0 until 100, 10).map { x => (x % 10) -> x }.reduceByKey{ _ + _ }.
+        collect().toSet
+      sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)
+      data should be ((0 until 10).map{x => x -> (x * 10 + 450)}.toSet)
+      result = "success"
+      // only one process can open a leveldb file at a time, so we copy the files
+      FileUtils.copyDirectory(registeredExecFile, execStateCopy)
+      assert(!ShuffleTestAccessor.reloadRegisteredExecutors(execStateCopy).isEmpty)
+    } finally {
+      sc.stop()
+      FileUtils.deleteDirectory(execStateCopy)
+      Files.write(result, status, UTF_8)
+    }
+  }
+
+}
diff --git a/yarn/src/test/scala/org/apache/spark/network/shuffle/ShuffleTestAccessor.scala b/yarn/src/test/scala/org/apache/spark/network/shuffle/ShuffleTestAccessor.scala
new file mode 100644
index 0000000000000..aa46ec5100f0e
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/network/shuffle/ShuffleTestAccessor.scala
@@ -0,0 +1,71 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.network.shuffle
+
+import java.io.{IOException, File}
+import java.util.concurrent.ConcurrentMap
+
+import com.google.common.annotations.VisibleForTesting
+import org.apache.hadoop.yarn.api.records.ApplicationId
+import org.fusesource.leveldbjni.JniDBFactory
+import org.iq80.leveldb.{DB, Options}
+
+import org.apache.spark.network.shuffle.ExternalShuffleBlockResolver.AppExecId
+import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo
+
+/**
+ * just a cheat to get package-visible members in tests
+ */
+object ShuffleTestAccessor {
+
+  def getBlockResolver(handler: ExternalShuffleBlockHandler): ExternalShuffleBlockResolver = {
+    handler.blockManager
+  }
+
+  def getExecutorInfo(
+      appId: ApplicationId,
+      execId: String,
+      resolver: ExternalShuffleBlockResolver
+  ): Option[ExecutorShuffleInfo] = {
+    val id = new AppExecId(appId.toString, execId)
+    Option(resolver.executors.get(id))
+  }
+
+  def registeredExecutorFile(resolver: ExternalShuffleBlockResolver): File = {
+    resolver.registeredExecutorFile
+  }
+
+  def shuffleServiceLevelDB(resolver: ExternalShuffleBlockResolver): DB = {
+    resolver.db
+  }
+
+  def reloadRegisteredExecutors(
+    file: File): ConcurrentMap[ExternalShuffleBlockResolver.AppExecId, ExecutorShuffleInfo] = {
+    val options: Options = new Options
+    options.createIfMissing(true)
+    val factory = new JniDBFactory
+    val db = factory.open(file, options)
+    val result = ExternalShuffleBlockResolver.reloadRegisteredExecutors(db)
+    db.close()
+    result
+  }
+
+  def reloadRegisteredExecutors(
+      db: DB): ConcurrentMap[ExternalShuffleBlockResolver.AppExecId, ExecutorShuffleInfo] = {
+    ExternalShuffleBlockResolver.reloadRegisteredExecutors(db)
+  }
+}
diff --git a/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
new file mode 100644
index 0000000000000..2f22cbdbeac37
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
@@ -0,0 +1,233 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.network.yarn
+
+import java.io.{DataOutputStream, File, FileOutputStream}
+
+import scala.annotation.tailrec
+
+import org.apache.commons.io.FileUtils
+import org.apache.hadoop.yarn.api.records.ApplicationId
+import org.apache.hadoop.yarn.conf.YarnConfiguration
+import org.apache.hadoop.yarn.server.api.{ApplicationInitializationContext, ApplicationTerminationContext}
+import org.scalatest.{BeforeAndAfterEach, Matchers}
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.network.shuffle.ShuffleTestAccessor
+import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo
+
+class YarnShuffleServiceSuite extends SparkFunSuite with Matchers with BeforeAndAfterEach {
+  private[yarn] var yarnConfig: YarnConfiguration = new YarnConfiguration
+
+  override def beforeEach(): Unit = {
+    yarnConfig.set(YarnConfiguration.NM_AUX_SERVICES, "spark_shuffle")
+    yarnConfig.set(YarnConfiguration.NM_AUX_SERVICE_FMT.format("spark_shuffle"),
+      classOf[YarnShuffleService].getCanonicalName)
+
+    yarnConfig.get("yarn.nodemanager.local-dirs").split(",").foreach { dir =>
+      val d = new File(dir)
+      if (d.exists()) {
+        FileUtils.deleteDirectory(d)
+      }
+      FileUtils.forceMkdir(d)
+      logInfo(s"creating yarn.nodemanager.local-dirs: $d")
+    }
+  }
+
+  var s1: YarnShuffleService = null
+  var s2: YarnShuffleService = null
+  var s3: YarnShuffleService = null
+
+  override def afterEach(): Unit = {
+    if (s1 != null) {
+      s1.stop()
+      s1 = null
+    }
+    if (s2 != null) {
+      s2.stop()
+      s2 = null
+    }
+    if (s3 != null) {
+      s3.stop()
+      s3 = null
+    }
+  }
+
+  test("executor state kept across NM restart") {
+    s1 = new YarnShuffleService
+    s1.init(yarnConfig)
+    val app1Id = ApplicationId.newInstance(0, 1)
+    val app1Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app1Id, null)
+    s1.initializeApplication(app1Data)
+    val app2Id = ApplicationId.newInstance(0, 2)
+    val app2Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app2Id, null)
+    s1.initializeApplication(app2Data)
+
+    val execStateFile = s1.registeredExecutorFile
+    execStateFile should not be (null)
+    val shuffleInfo1 = new ExecutorShuffleInfo(Array("/foo", "/bar"), 3, "sort")
+    val shuffleInfo2 = new ExecutorShuffleInfo(Array("/bippy"), 5, "hash")
+
+    val blockHandler = s1.blockHandler
+    val blockResolver = ShuffleTestAccessor.getBlockResolver(blockHandler)
+    ShuffleTestAccessor.registeredExecutorFile(blockResolver) should be (execStateFile)
+
+    blockResolver.registerExecutor(app1Id.toString, "exec-1", shuffleInfo1)
+    blockResolver.registerExecutor(app2Id.toString, "exec-2", shuffleInfo2)
+    ShuffleTestAccessor.getExecutorInfo(app1Id, "exec-1", blockResolver) should
+      be (Some(shuffleInfo1))
+    ShuffleTestAccessor.getExecutorInfo(app2Id, "exec-2", blockResolver) should
+      be (Some(shuffleInfo2))
+
+    if (!execStateFile.exists()) {
+      @tailrec def findExistingParent(file: File): File = {
+        if (file == null) file
+        else if (file.exists()) file
+        else findExistingParent(file.getParentFile())
+      }
+      val existingParent = findExistingParent(execStateFile)
+      assert(false, s"$execStateFile does not exist -- closest existing parent is $existingParent")
+    }
+    assert(execStateFile.exists(), s"$execStateFile did not exist")
+
+    // now we pretend the shuffle service goes down, and comes back up
+    s1.stop()
+    s2 = new YarnShuffleService
+    s2.init(yarnConfig)
+    s2.registeredExecutorFile should be (execStateFile)
+
+    val handler2 = s2.blockHandler
+    val resolver2 = ShuffleTestAccessor.getBlockResolver(handler2)
+
+    // now we reinitialize only one of the apps, and expect yarn to tell us that app2 was stopped
+    // during the restart
+    s2.initializeApplication(app1Data)
+    s2.stopApplication(new ApplicationTerminationContext(app2Id))
+    ShuffleTestAccessor.getExecutorInfo(app1Id, "exec-1", resolver2) should be (Some(shuffleInfo1))
+    ShuffleTestAccessor.getExecutorInfo(app2Id, "exec-2", resolver2) should be (None)
+
+    // Act like the NM restarts one more time
+    s2.stop()
+    s3 = new YarnShuffleService
+    s3.init(yarnConfig)
+    s3.registeredExecutorFile should be (execStateFile)
+
+    val handler3 = s3.blockHandler
+    val resolver3 = ShuffleTestAccessor.getBlockResolver(handler3)
+
+    // app1 is still running
+    s3.initializeApplication(app1Data)
+    ShuffleTestAccessor.getExecutorInfo(app1Id, "exec-1", resolver3) should be (Some(shuffleInfo1))
+    ShuffleTestAccessor.getExecutorInfo(app2Id, "exec-2", resolver3) should be (None)
+    s3.stop()
+  }
+
+  test("removed applications should not be in registered executor file") {
+    s1 = new YarnShuffleService
+    s1.init(yarnConfig)
+    val app1Id = ApplicationId.newInstance(0, 1)
+    val app1Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app1Id, null)
+    s1.initializeApplication(app1Data)
+    val app2Id = ApplicationId.newInstance(0, 2)
+    val app2Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app2Id, null)
+    s1.initializeApplication(app2Data)
+
+    val execStateFile = s1.registeredExecutorFile
+    execStateFile should not be (null)
+    val shuffleInfo1 = new ExecutorShuffleInfo(Array("/foo", "/bar"), 3, "sort")
+    val shuffleInfo2 = new ExecutorShuffleInfo(Array("/bippy"), 5, "hash")
+
+    val blockHandler = s1.blockHandler
+    val blockResolver = ShuffleTestAccessor.getBlockResolver(blockHandler)
+    ShuffleTestAccessor.registeredExecutorFile(blockResolver) should be (execStateFile)
+
+    blockResolver.registerExecutor(app1Id.toString, "exec-1", shuffleInfo1)
+    blockResolver.registerExecutor(app2Id.toString, "exec-2", shuffleInfo2)
+
+    val db = ShuffleTestAccessor.shuffleServiceLevelDB(blockResolver)
+    ShuffleTestAccessor.reloadRegisteredExecutors(db) should not be empty
+
+    s1.stopApplication(new ApplicationTerminationContext(app1Id))
+    ShuffleTestAccessor.reloadRegisteredExecutors(db) should not be empty
+    s1.stopApplication(new ApplicationTerminationContext(app2Id))
+    ShuffleTestAccessor.reloadRegisteredExecutors(db) shouldBe empty
+  }
+
+  test("shuffle service should be robust to corrupt registered executor file") {
+    s1 = new YarnShuffleService
+    s1.init(yarnConfig)
+    val app1Id = ApplicationId.newInstance(0, 1)
+    val app1Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app1Id, null)
+    s1.initializeApplication(app1Data)
+
+    val execStateFile = s1.registeredExecutorFile
+    val shuffleInfo1 = new ExecutorShuffleInfo(Array("/foo", "/bar"), 3, "sort")
+
+    val blockHandler = s1.blockHandler
+    val blockResolver = ShuffleTestAccessor.getBlockResolver(blockHandler)
+    ShuffleTestAccessor.registeredExecutorFile(blockResolver) should be (execStateFile)
+
+    blockResolver.registerExecutor(app1Id.toString, "exec-1", shuffleInfo1)
+
+    // now we pretend the shuffle service goes down, and comes back up.  But we'll also
+    // make a corrupt registeredExecutor File
+    s1.stop()
+
+    execStateFile.listFiles().foreach{_.delete()}
+
+    val out = new DataOutputStream(new FileOutputStream(execStateFile + "/CURRENT"))
+    out.writeInt(42)
+    out.close()
+
+    s2 = new YarnShuffleService
+    s2.init(yarnConfig)
+    s2.registeredExecutorFile should be (execStateFile)
+
+    val handler2 = s2.blockHandler
+    val resolver2 = ShuffleTestAccessor.getBlockResolver(handler2)
+
+    // we re-initialize app1, but since the file was corrupt there is nothing we can do about it ...
+    s2.initializeApplication(app1Data)
+    // however, when we initialize a totally new app2, everything is still happy
+    val app2Id = ApplicationId.newInstance(0, 2)
+    val app2Data: ApplicationInitializationContext =
+      new ApplicationInitializationContext("user", app2Id, null)
+    s2.initializeApplication(app2Data)
+    val shuffleInfo2 = new ExecutorShuffleInfo(Array("/bippy"), 5, "hash")
+    resolver2.registerExecutor(app2Id.toString, "exec-2", shuffleInfo2)
+    ShuffleTestAccessor.getExecutorInfo(app2Id, "exec-2", resolver2) should be (Some(shuffleInfo2))
+    s2.stop()
+
+    // another stop & restart should be fine though (eg., we recover from previous corruption)
+    s3 = new YarnShuffleService
+    s3.init(yarnConfig)
+    s3.registeredExecutorFile should be (execStateFile)
+    val handler3 = s3.blockHandler
+    val resolver3 = ShuffleTestAccessor.getBlockResolver(handler3)
+
+    s3.initializeApplication(app2Data)
+    ShuffleTestAccessor.getExecutorInfo(app2Id, "exec-2", resolver3) should be (Some(shuffleInfo2))
+    s3.stop()
+
+  }
+
+}
diff --git a/yarn/src/test/scala/org/apache/spark/network/yarn/YarnTestAccessor.scala b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnTestAccessor.scala
new file mode 100644
index 0000000000000..db322cd18e150
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnTestAccessor.scala
@@ -0,0 +1,37 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.network.yarn
+
+import java.io.File
+
+/**
+ * just a cheat to get package-visible members in tests
+ */
+object YarnTestAccessor {
+  def getShuffleServicePort: Int = {
+    YarnShuffleService.boundPort
+  }
+
+  def getShuffleServiceInstance: YarnShuffleService = {
+    YarnShuffleService.instance
+  }
+
+  def getRegisteredExecutorFile(service: YarnShuffleService): File = {
+    service.registeredExecutorFile
+  }
+
+}

From 3c462f5d87a9654c5a68fd658a40f5062029fd9a Mon Sep 17 00:00:00 2001
From: Daoyuan Wang <daoyuan.wang@intel.com>
Date: Fri, 21 Aug 2015 12:21:51 -0700
Subject: [PATCH 371/633] [SPARK-10130] [SQL] type coercion for IF should have
 children resolved first

Type coercion for IF should have children resolved first, or we could meet unresolved exception.

Author: Daoyuan Wang <daoyuan.wang@intel.com>

Closes #8331 from adrian-wang/spark10130.
---
 .../spark/sql/catalyst/analysis/HiveTypeCoercion.scala     | 1 +
 .../test/scala/org/apache/spark/sql/SQLQuerySuite.scala    | 7 +++++++
 2 files changed, 8 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index f2f2ba2f96552..2cb067f4aac91 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -639,6 +639,7 @@ object HiveTypeCoercion {
    */
   object IfCoercion extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveExpressions {
+      case e if !e.childrenResolved => e
       // Find tightest common type for If, if the true value and false value have different types.
       case i @ If(pred, left, right) if left.dataType != right.dataType =>
         findTightestCommonTypeToString(left.dataType, right.dataType).map { widestType =>
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index da50aec17c89e..dcb4e83710982 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1679,4 +1679,11 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
       checkAnswer(sqlContext.table("`db.t`"), df)
     }
   }
+
+  test("SPARK-10130 type coercion for IF should have children resolved first") {
+    val df = Seq((1, 1), (-1, 1)).toDF("key", "value")
+    df.registerTempTable("src")
+    checkAnswer(
+      sql("SELECT IF(a > 0, a, 0) FROM (SELECT key a FROM src) temp"), Seq(Row(1), Row(0)))
+  }
 }

From d89cc38b33815e7b99fb3389b5038a543527065d Mon Sep 17 00:00:00 2001
From: jerryshao <sshao@hortonworks.com>
Date: Fri, 21 Aug 2015 13:10:11 -0700
Subject: [PATCH 372/633] [SPARK-10122] [PYSPARK] [STREAMING] Fix
 getOffsetRanges bug in PySpark-Streaming transform function

Details of the bug and explanations can be seen in [SPARK-10122](https://issues.apache.org/jira/browse/SPARK-10122).

tdas , please help to review.

Author: jerryshao <sshao@hortonworks.com>

Closes #8347 from jerryshao/SPARK-10122 and squashes the following commits:

4039b16 [jerryshao] Fix getOffsetRanges in transform() bug
---
 python/pyspark/streaming/dstream.py | 5 ++++-
 python/pyspark/streaming/tests.py   | 4 +++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/streaming/dstream.py b/python/pyspark/streaming/dstream.py
index 8dcb9645cdc6b..698336cfce18d 100644
--- a/python/pyspark/streaming/dstream.py
+++ b/python/pyspark/streaming/dstream.py
@@ -610,7 +610,10 @@ def __init__(self, prev, func):
         self.is_checkpointed = False
         self._jdstream_val = None
 
-        if (isinstance(prev, TransformedDStream) and
+        # Using type() to avoid folding the functions and compacting the DStreams which is not
+        # not strictly a object of TransformedDStream.
+        # Changed here is to avoid bug in KafkaTransformedDStream when calling offsetRanges().
+        if (type(prev) is TransformedDStream and
                 not prev.is_cached and not prev.is_checkpointed):
             prev_func = prev.func
             self.func = lambda t, rdd: func(t, prev_func(t, rdd))
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 6108c845c1efe..214d5be439003 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -850,7 +850,9 @@ def transformWithOffsetRanges(rdd):
                 offsetRanges.append(o)
             return rdd
 
-        stream.transform(transformWithOffsetRanges).foreachRDD(lambda rdd: rdd.count())
+        # Test whether it is ok mixing KafkaTransformedDStream and TransformedDStream together,
+        # only the TransformedDstreams can be folded together.
+        stream.transform(transformWithOffsetRanges).map(lambda kv: kv[1]).count().pprint()
         self.ssc.start()
         self.wait_for(offsetRanges, 1)
 

From f5b028ed2f1ad6de43c8b50ebf480e1b6c047035 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Fri, 21 Aug 2015 14:19:24 -0700
Subject: [PATCH 373/633] [SPARK-9864] [DOC] [MLlib] [SQL] Replace since in
 scaladoc to Since annotation

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8352 from MechCoder/since.
---
 .../classification/ClassificationModel.scala  |   8 +-
 .../classification/LogisticRegression.scala   |  30 ++---
 .../mllib/classification/NaiveBayes.scala     |   7 +-
 .../spark/mllib/classification/SVM.scala      |  28 ++---
 .../mllib/clustering/GaussianMixture.scala    |  28 ++---
 .../clustering/GaussianMixtureModel.scala     |  28 ++---
 .../spark/mllib/clustering/KMeans.scala       |  50 ++++-----
 .../spark/mllib/clustering/KMeansModel.scala  |  27 ++---
 .../apache/spark/mllib/clustering/LDA.scala   |  56 ++++-----
 .../spark/mllib/clustering/LDAModel.scala     |  69 +++++-------
 .../spark/mllib/clustering/LDAOptimizer.scala |  24 ++--
 .../clustering/PowerIterationClustering.scala |  38 +++----
 .../mllib/clustering/StreamingKMeans.scala    |  35 +++---
 .../BinaryClassificationMetrics.scala         |  26 ++---
 .../mllib/evaluation/MulticlassMetrics.scala  |  20 ++--
 .../mllib/evaluation/MultilabelMetrics.scala  |   9 +-
 .../mllib/evaluation/RankingMetrics.scala     |  10 +-
 .../mllib/evaluation/RegressionMetrics.scala  |  14 +--
 .../spark/mllib/fpm/AssociationRules.scala    |  20 ++--
 .../org/apache/spark/mllib/fpm/FPGrowth.scala |  22 ++--
 .../apache/spark/mllib/linalg/Matrices.scala  | 106 ++++++++----------
 .../linalg/SingularValueDecomposition.scala   |   4 +-
 .../apache/spark/mllib/linalg/Vectors.scala   |  90 +++++----------
 .../linalg/distributed/BlockMatrix.scala      |  88 ++++++---------
 .../linalg/distributed/CoordinateMatrix.scala |  40 +++----
 .../distributed/DistributedMatrix.scala       |   4 +-
 .../linalg/distributed/IndexedRowMatrix.scala |  38 +++----
 .../mllib/linalg/distributed/RowMatrix.scala  |  39 +++----
 .../spark/mllib/recommendation/ALS.scala      |  22 ++--
 .../MatrixFactorizationModel.scala            |  28 +++--
 .../GeneralizedLinearAlgorithm.scala          |  24 ++--
 .../mllib/regression/IsotonicRegression.scala |  22 ++--
 .../spark/mllib/regression/LabeledPoint.scala |   7 +-
 .../apache/spark/mllib/regression/Lasso.scala |  25 ++---
 .../mllib/regression/LinearRegression.scala   |  25 ++---
 .../mllib/regression/RegressionModel.scala    |  12 +-
 .../mllib/regression/RidgeRegression.scala    |  25 ++---
 .../regression/StreamingLinearAlgorithm.scala |  18 +--
 .../spark/mllib/stat/KernelDensity.scala      |  12 +-
 .../stat/MultivariateOnlineSummarizer.scala   |  24 ++--
 .../stat/MultivariateStatisticalSummary.scala |  19 ++--
 .../apache/spark/mllib/stat/Statistics.scala  |  30 ++---
 .../distribution/MultivariateGaussian.scala   |   8 +-
 .../spark/mllib/tree/DecisionTree.scala       |  28 +++--
 .../mllib/tree/GradientBoostedTrees.scala     |  20 ++--
 .../spark/mllib/tree/RandomForest.scala       |  20 ++--
 .../spark/mllib/tree/configuration/Algo.scala |   4 +-
 .../tree/configuration/BoostingStrategy.scala |  12 +-
 .../tree/configuration/FeatureType.scala      |   4 +-
 .../tree/configuration/QuantileStrategy.scala |   4 +-
 .../mllib/tree/configuration/Strategy.scala   |  24 ++--
 .../spark/mllib/tree/impurity/Entropy.scala   |  10 +-
 .../spark/mllib/tree/impurity/Gini.scala      |  10 +-
 .../spark/mllib/tree/impurity/Impurity.scala  |   8 +-
 .../spark/mllib/tree/impurity/Variance.scala  |  10 +-
 .../spark/mllib/tree/loss/AbsoluteError.scala |   6 +-
 .../spark/mllib/tree/loss/LogLoss.scala       |   6 +-
 .../apache/spark/mllib/tree/loss/Loss.scala   |   8 +-
 .../apache/spark/mllib/tree/loss/Losses.scala |  10 +-
 .../spark/mllib/tree/loss/SquaredError.scala  |   6 +-
 .../mllib/tree/model/DecisionTreeModel.scala  |  22 ++--
 .../tree/model/InformationGainStats.scala     |   4 +-
 .../apache/spark/mllib/tree/model/Node.scala  |   8 +-
 .../spark/mllib/tree/model/Predict.scala      |   4 +-
 .../apache/spark/mllib/tree/model/Split.scala |   4 +-
 .../mllib/tree/model/treeEnsembleModels.scala |  26 ++---
 .../org/apache/spark/mllib/tree/package.scala |   1 -
 .../org/apache/spark/mllib/util/MLUtils.scala |  36 +++---
 68 files changed, 692 insertions(+), 862 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
index ba73024e3c04d..a29b425a71fd6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.classification
 
 import org.json4s.{DefaultFormats, JValue}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
@@ -36,8 +36,8 @@ trait ClassificationModel extends Serializable {
    *
    * @param testData RDD representing data points to be predicted
    * @return an RDD[Double] where each entry contains the corresponding prediction
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def predict(testData: RDD[Vector]): RDD[Double]
 
   /**
@@ -45,16 +45,16 @@ trait ClassificationModel extends Serializable {
    *
    * @param testData array representing a single data point
    * @return predicted category from the trained model
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def predict(testData: Vector): Double
 
   /**
    * Predict values for examples stored in a JavaRDD.
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index 268642ac6a2f6..e03e662227d14 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.classification
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.classification.impl.GLMClassificationModel
 import org.apache.spark.mllib.linalg.BLAS.dot
 import org.apache.spark.mllib.linalg.{DenseVector, Vector}
@@ -85,8 +85,8 @@ class LogisticRegressionModel (
    * in Binary Logistic Regression. An example with prediction score greater than or equal to
    * this threshold is identified as an positive, and negative otherwise. The default value is 0.5.
    * It is only used for binary classification.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @Experimental
   def setThreshold(threshold: Double): this.type = {
     this.threshold = Some(threshold)
@@ -97,8 +97,8 @@ class LogisticRegressionModel (
    * :: Experimental ::
    * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
    * It is only used for binary classification.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   @Experimental
   def getThreshold: Option[Double] = threshold
 
@@ -106,8 +106,8 @@ class LogisticRegressionModel (
    * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
    * It is only used for binary classification.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @Experimental
   def clearThreshold(): this.type = {
     threshold = None
@@ -158,9 +158,7 @@ class LogisticRegressionModel (
     }
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMClassificationModel.SaveLoadV1_0.save(sc, path, this.getClass.getName,
       numFeatures, numClasses, weights, intercept, threshold)
@@ -168,9 +166,7 @@ class LogisticRegressionModel (
 
   override protected def formatVersion: String = "1.0"
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def toString: String = {
     s"${super.toString}, numClasses = ${numClasses}, threshold = ${threshold.getOrElse("None")}"
   }
@@ -178,9 +174,7 @@ class LogisticRegressionModel (
 
 object LogisticRegressionModel extends Loader[LogisticRegressionModel] {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): LogisticRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -261,8 +255,8 @@ object LogisticRegressionWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -284,8 +278,8 @@ object LogisticRegressionWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
 
    * @param miniBatchFraction Fraction of data to be used per iteration.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -306,8 +300,8 @@ object LogisticRegressionWithSGD {
 
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LogisticRegressionModel which has the weights and offset from training.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -324,8 +318,8 @@ object LogisticRegressionWithSGD {
    * @param input RDD of (label, array of features) pairs.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LogisticRegressionModel which has the weights and offset from training.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int): LogisticRegressionModel = {
@@ -361,8 +355,8 @@ class LogisticRegressionWithLBFGS
    * Set the number of possible outcomes for k classes classification problem in
    * Multinomial Logistic Regression.
    * By default, it is binary logistic regression so k will be set to 2.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   @Experimental
   def setNumClasses(numClasses: Int): this.type = {
     require(numClasses > 1)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index 2df91c09421e9..dab369207cc9a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -25,6 +25,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkContext, SparkException}
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, DenseVector, SparseVector, Vector}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -444,8 +445,8 @@ object NaiveBayes {
    *
    * @param input RDD of `(label, array of features)` pairs.  Every vector should be a frequency
    *              vector or a count vector.
-   * @since 0.9.0
    */
+  @Since("0.9.0")
   def train(input: RDD[LabeledPoint]): NaiveBayesModel = {
     new NaiveBayes().run(input)
   }
@@ -460,8 +461,8 @@ object NaiveBayes {
    * @param input RDD of `(label, array of features)` pairs.  Every vector should be a frequency
    *              vector or a count vector.
    * @param lambda The smoothing parameter
-   * @since 0.9.0
    */
+  @Since("0.9.0")
   def train(input: RDD[LabeledPoint], lambda: Double): NaiveBayesModel = {
     new NaiveBayes(lambda, Multinomial).run(input)
   }
@@ -483,8 +484,8 @@ object NaiveBayes {
    *
    * @param modelType The type of NB model to fit from the enumeration NaiveBayesModels, can be
    *              multinomial or bernoulli
-   * @since 0.9.0
    */
+  @Since("0.9.0")
   def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = {
     require(supportedModelTypes.contains(modelType),
       s"NaiveBayes was created with an unknown modelType: $modelType.")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index 5b54feeb10467..5f87269863572 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.classification
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.classification.impl.GLMClassificationModel
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
@@ -46,8 +46,8 @@ class SVMModel (
    * Sets the threshold that separates positive predictions from negative predictions. An example
    * with prediction score greater than or equal to this threshold is identified as an positive,
    * and negative otherwise. The default value is 0.0.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   @Experimental
   def setThreshold(threshold: Double): this.type = {
     this.threshold = Some(threshold)
@@ -57,16 +57,16 @@ class SVMModel (
   /**
    * :: Experimental ::
    * Returns the threshold (if any) used for converting raw prediction scores into 0/1 predictions.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   @Experimental
   def getThreshold: Option[Double] = threshold
 
   /**
    * :: Experimental ::
    * Clears the threshold so that `predict` will output raw prediction scores.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @Experimental
   def clearThreshold(): this.type = {
     threshold = None
@@ -84,9 +84,7 @@ class SVMModel (
     }
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMClassificationModel.SaveLoadV1_0.save(sc, path, this.getClass.getName,
       numFeatures = weights.size, numClasses = 2, weights, intercept, threshold)
@@ -94,9 +92,7 @@ class SVMModel (
 
   override protected def formatVersion: String = "1.0"
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def toString: String = {
     s"${super.toString}, numClasses = 2, threshold = ${threshold.getOrElse("None")}"
   }
@@ -104,9 +100,7 @@ class SVMModel (
 
 object SVMModel extends Loader[SVMModel] {
 
-   /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): SVMModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -185,8 +179,8 @@ object SVMWithSGD {
    * @param miniBatchFraction Fraction of data to be used per iteration.
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -209,8 +203,8 @@ object SVMWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -231,8 +225,8 @@ object SVMWithSGD {
    * @param regParam Regularization parameter.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a SVMModel which has the weights and offset from training.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -250,8 +244,8 @@ object SVMWithSGD {
    * @param input RDD of (label, array of features) pairs.
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a SVMModel which has the weights and offset from training.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(input: RDD[LabeledPoint], numIterations: Int): SVMModel = {
     train(input, numIterations, 1.0, 0.01, 1.0)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index bc27b1fe7390b..fcc9dfecac54f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -21,7 +21,7 @@ import scala.collection.mutable.IndexedSeq
 
 import breeze.linalg.{diag, DenseMatrix => BreezeMatrix, DenseVector => BDV, Vector => BV}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix, Matrices, Vector, Vectors}
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
@@ -62,8 +62,8 @@ class GaussianMixture private (
   /**
    * Constructs a default instance. The default parameters are {k: 2, convergenceTol: 0.01,
    * maxIterations: 100, seed: random}.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this() = this(2, 0.01, 100, Utils.random.nextLong())
 
   // number of samples per cluster to use when initializing Gaussians
@@ -77,8 +77,8 @@ class GaussianMixture private (
    * Set the initial GMM starting point, bypassing the random initialization.
    * You must call setK() prior to calling this method, and the condition
    * (model.k == this.k) must be met; failure will result in an IllegalArgumentException
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setInitialModel(model: GaussianMixtureModel): this.type = {
     if (model.k == k) {
       initialModel = Some(model)
@@ -90,14 +90,14 @@ class GaussianMixture private (
 
   /**
    * Return the user supplied initial GMM, if supplied
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getInitialModel: Option[GaussianMixtureModel] = initialModel
 
   /**
    * Set the number of Gaussians in the mixture model.  Default: 2
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setK(k: Int): this.type = {
     this.k = k
     this
@@ -105,14 +105,14 @@ class GaussianMixture private (
 
   /**
    * Return the number of Gaussians in the mixture model
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getK: Int = k
 
   /**
    * Set the maximum number of iterations to run. Default: 100
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
@@ -120,15 +120,15 @@ class GaussianMixture private (
 
   /**
    * Return the maximum number of iterations to run
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getMaxIterations: Int = maxIterations
 
   /**
    * Set the largest change in log-likelihood at which convergence is
    * considered to have occurred.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setConvergenceTol(convergenceTol: Double): this.type = {
     this.convergenceTol = convergenceTol
     this
@@ -137,14 +137,14 @@ class GaussianMixture private (
   /**
    * Return the largest change in log-likelihood at which convergence is
    * considered to have occurred.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getConvergenceTol: Double = convergenceTol
 
   /**
    * Set the random seed
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -152,14 +152,14 @@ class GaussianMixture private (
 
   /**
    * Return the random seed
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getSeed: Long = seed
 
   /**
    * Perform expectation maximization
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(data: RDD[Vector]): GaussianMixtureModel = {
     val sc = data.sparkContext
 
@@ -235,8 +235,8 @@ class GaussianMixture private (
 
   /**
    * Java-friendly version of [[run()]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(data: JavaRDD[Vector]): GaussianMixtureModel = run(data.rdd)
 
   private def updateWeightsAndGaussians(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index 2fa0473737aae..1a10a8b624218 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -24,7 +24,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.{Vector, Matrices, Matrix}
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
@@ -43,8 +43,8 @@ import org.apache.spark.sql.{SQLContext, Row}
  *                the weight for Gaussian i, and weights.sum == 1
  * @param gaussians Array of MultivariateGaussian where gaussians(i) represents
  *                  the Multivariate Gaussian (Normal) Distribution for Gaussian i
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class GaussianMixtureModel(
   val weights: Array[Double],
@@ -54,23 +54,21 @@ class GaussianMixtureModel(
 
   override protected def formatVersion = "1.0"
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GaussianMixtureModel.SaveLoadV1_0.save(sc, path, weights, gaussians)
   }
 
   /**
    * Number of gaussians in mixture
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def k: Int = weights.length
 
   /**
    * Maps given points to their cluster indices.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predict(points: RDD[Vector]): RDD[Int] = {
     val responsibilityMatrix = predictSoft(points)
     responsibilityMatrix.map(r => r.indexOf(r.max))
@@ -78,8 +76,8 @@ class GaussianMixtureModel(
 
   /**
    * Maps given point to its cluster index.
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def predict(point: Vector): Int = {
     val r = computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
     r.indexOf(r.max)
@@ -87,16 +85,16 @@ class GaussianMixtureModel(
 
   /**
    * Java-friendly version of [[predict()]]
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
     predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
 
   /**
    * Given the input vectors, return the membership value of each vector
    * to all mixture components.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predictSoft(points: RDD[Vector]): RDD[Array[Double]] = {
     val sc = points.sparkContext
     val bcDists = sc.broadcast(gaussians)
@@ -108,8 +106,8 @@ class GaussianMixtureModel(
 
   /**
    * Given the input vector, return the membership values to all mixture components.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def predictSoft(point: Vector): Array[Double] = {
     computeSoftAssignments(point.toBreeze.toDenseVector, gaussians, weights, k)
   }
@@ -133,9 +131,7 @@ class GaussianMixtureModel(
   }
 }
 
-/**
- * @since 1.4.0
- */
+@Since("1.4.0")
 @Experimental
 object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
 
@@ -186,9 +182,7 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
     }
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def load(sc: SparkContext, path: String) : GaussianMixtureModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 9ef6834e5ea8d..3e9545a74bef3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.linalg.BLAS.{axpy, scal}
 import org.apache.spark.mllib.util.MLUtils
@@ -49,20 +49,20 @@ class KMeans private (
   /**
    * Constructs a KMeans instance with default parameters: {k: 2, maxIterations: 20, runs: 1,
    * initializationMode: "k-means||", initializationSteps: 5, epsilon: 1e-4, seed: random}.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def this() = this(2, 20, 1, KMeans.K_MEANS_PARALLEL, 5, 1e-4, Utils.random.nextLong())
 
   /**
    * Number of clusters to create (k).
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getK: Int = k
 
   /**
    * Set the number of clusters to create (k). Default: 2.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setK(k: Int): this.type = {
     this.k = k
     this
@@ -70,14 +70,14 @@ class KMeans private (
 
   /**
    * Maximum number of iterations to run.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getMaxIterations: Int = maxIterations
 
   /**
    * Set maximum number of iterations to run. Default: 20.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
@@ -85,16 +85,16 @@ class KMeans private (
 
   /**
    * The initialization algorithm. This can be either "random" or "k-means||".
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getInitializationMode: String = initializationMode
 
   /**
    * Set the initialization algorithm. This can be either "random" to choose random points as
    * initial cluster centers, or "k-means||" to use a parallel variant of k-means++
    * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setInitializationMode(initializationMode: String): this.type = {
     KMeans.validateInitMode(initializationMode)
     this.initializationMode = initializationMode
@@ -104,8 +104,8 @@ class KMeans private (
   /**
    * :: Experimental ::
    * Number of runs of the algorithm to execute in parallel.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   @Experimental
   def getRuns: Int = runs
 
@@ -114,8 +114,8 @@ class KMeans private (
    * Set the number of runs of the algorithm to execute in parallel. We initialize the algorithm
    * this many times with random starting conditions (configured by the initialization mode), then
    * return the best clustering found over any run. Default: 1.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   @Experimental
   def setRuns(runs: Int): this.type = {
     if (runs <= 0) {
@@ -127,15 +127,15 @@ class KMeans private (
 
   /**
    * Number of steps for the k-means|| initialization mode
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getInitializationSteps: Int = initializationSteps
 
   /**
    * Set the number of steps for the k-means|| initialization mode. This is an advanced
    * setting -- the default of 5 is almost always enough. Default: 5.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setInitializationSteps(initializationSteps: Int): this.type = {
     if (initializationSteps <= 0) {
       throw new IllegalArgumentException("Number of initialization steps must be positive")
@@ -146,15 +146,15 @@ class KMeans private (
 
   /**
    * The distance threshold within which we've consider centers to have converged.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getEpsilon: Double = epsilon
 
   /**
    * Set the distance threshold within which we've consider centers to have converged.
    * If all centers move less than this Euclidean distance, we stop iterating one run.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setEpsilon(epsilon: Double): this.type = {
     this.epsilon = epsilon
     this
@@ -162,14 +162,14 @@ class KMeans private (
 
   /**
    * The random seed for cluster initialization.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getSeed: Long = seed
 
   /**
    * Set the random seed for cluster initialization.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -183,8 +183,8 @@ class KMeans private (
    * Set the initial starting point, bypassing the random initialization or k-means||
    * The condition model.k == this.k must be met, failure results
    * in an IllegalArgumentException.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setInitialModel(model: KMeansModel): this.type = {
     require(model.k == k, "mismatched cluster count")
     initialModel = Some(model)
@@ -194,8 +194,8 @@ class KMeans private (
   /**
    * Train a K-means model on the given set of points; `data` should be cached for high
    * performance, because this is an iterative algorithm.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def run(data: RDD[Vector]): KMeansModel = {
 
     if (data.getStorageLevel == StorageLevel.NONE) {
@@ -453,14 +453,14 @@ class KMeans private (
 
 /**
  * Top-level methods for calling K-means clustering.
- * @since 0.8.0
  */
+@Since("0.8.0")
 object KMeans {
 
   // Initialization mode names
-  /** @since 0.8.0 */
+  @Since("0.8.0")
   val RANDOM = "random"
-  /** @since 0.8.0 */
+  @Since("0.8.0")
   val K_MEANS_PARALLEL = "k-means||"
 
   /**
@@ -472,8 +472,8 @@ object KMeans {
    * @param runs number of parallel runs, defaults to 1. The best model is returned.
    * @param initializationMode initialization model, either "random" or "k-means||" (default).
    * @param seed random seed value for cluster initialization
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def train(
       data: RDD[Vector],
       k: Int,
@@ -497,8 +497,8 @@ object KMeans {
    * @param maxIterations max number of iterations
    * @param runs number of parallel runs, defaults to 1. The best model is returned.
    * @param initializationMode initialization model, either "random" or "k-means||" (default).
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       data: RDD[Vector],
       k: Int,
@@ -514,8 +514,8 @@ object KMeans {
 
   /**
    * Trains a k-means model using specified parameters and the default values for unspecified.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       data: RDD[Vector],
       k: Int,
@@ -525,8 +525,8 @@ object KMeans {
 
   /**
    * Trains a k-means model using specified parameters and the default values for unspecified.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       data: RDD[Vector],
       k: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index 8de2087ceb4df..e425ecdd481c6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -23,6 +23,7 @@ import org.json4s._
 import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.pmml.PMMLExportable
@@ -34,35 +35,35 @@ import org.apache.spark.sql.Row
 
 /**
  * A clustering model for K-means. Each point belongs to the cluster with the closest center.
- * @since 0.8.0
  */
+@Since("0.8.0")
 class KMeansModel (
     val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable {
 
   /**
    * A Java-friendly constructor that takes an Iterable of Vectors.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def this(centers: java.lang.Iterable[Vector]) = this(centers.asScala.toArray)
 
   /**
    * Total number of clusters.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def k: Int = clusterCenters.length
 
   /**
    * Returns the cluster index that a given point belongs to.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def predict(point: Vector): Int = {
     KMeans.findClosest(clusterCentersWithNorm, new VectorWithNorm(point))._1
   }
 
   /**
    * Maps given points to their cluster indices.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(points: RDD[Vector]): RDD[Int] = {
     val centersWithNorm = clusterCentersWithNorm
     val bcCentersWithNorm = points.context.broadcast(centersWithNorm)
@@ -71,16 +72,16 @@ class KMeansModel (
 
   /**
    * Maps given points to their cluster indices.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(points: JavaRDD[Vector]): JavaRDD[java.lang.Integer] =
     predict(points.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Integer]]
 
   /**
    * Return the K-means cost (sum of squared distances of points to their nearest center) for this
    * model on the given data.
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def computeCost(data: RDD[Vector]): Double = {
     val centersWithNorm = clusterCentersWithNorm
     val bcCentersWithNorm = data.context.broadcast(centersWithNorm)
@@ -90,9 +91,7 @@ class KMeansModel (
   private def clusterCentersWithNorm: Iterable[VectorWithNorm] =
     clusterCenters.map(new VectorWithNorm(_))
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def save(sc: SparkContext, path: String): Unit = {
     KMeansModel.SaveLoadV1_0.save(sc, this, path)
   }
@@ -100,14 +99,10 @@ class KMeansModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.4.0
- */
+@Since("1.4.0")
 object KMeansModel extends Loader[KMeansModel] {
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def load(sc: SparkContext, path: String): KMeansModel = {
     KMeansModel.SaveLoadV1_0.load(sc, path)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
index 2a8c6acbaec61..92a321afb0ca3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDA.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import breeze.linalg.{DenseVector => BDV}
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 import org.apache.spark.api.java.JavaPairRDD
 import org.apache.spark.graphx._
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
@@ -43,8 +43,8 @@ import org.apache.spark.util.Utils
  *
  * @see [[http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation Latent Dirichlet allocation
  *       (Wikipedia)]]
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class LDA private (
     private var k: Int,
@@ -57,8 +57,8 @@ class LDA private (
 
   /**
    * Constructs a LDA instance with default parameters.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this() = this(k = 10, maxIterations = 20, docConcentration = Vectors.dense(-1),
     topicConcentration = -1, seed = Utils.random.nextLong(), checkpointInterval = 10,
     ldaOptimizer = new EMLDAOptimizer)
@@ -66,15 +66,15 @@ class LDA private (
   /**
    * Number of topics to infer.  I.e., the number of soft cluster centers.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getK: Int = k
 
   /**
    * Number of topics to infer.  I.e., the number of soft cluster centers.
    * (default = 10)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setK(k: Int): this.type = {
     require(k > 0, s"LDA k (number of clusters) must be > 0, but was set to $k")
     this.k = k
@@ -86,8 +86,8 @@ class LDA private (
    * distributions over topics ("theta").
    *
    * This is the parameter to a Dirichlet distribution.
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def getAsymmetricDocConcentration: Vector = this.docConcentration
 
   /**
@@ -96,8 +96,8 @@ class LDA private (
    *
    * This method assumes the Dirichlet distribution is symmetric and can be described by a single
    * [[Double]] parameter. It should fail if docConcentration is asymmetric.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getDocConcentration: Double = {
     val parameter = docConcentration(0)
     if (docConcentration.size == 1) {
@@ -131,8 +131,8 @@ class LDA private (
    *     - Values should be >= 0
    *     - default = uniformly (1.0 / k), following the implementation from
    *       [[https://github.com/Blei-Lab/onlineldavb]].
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def setDocConcentration(docConcentration: Vector): this.type = {
     require(docConcentration.size > 0, "docConcentration must have > 0 elements")
     this.docConcentration = docConcentration
@@ -141,8 +141,8 @@ class LDA private (
 
   /**
    * Replicates a [[Double]] docConcentration to create a symmetric prior.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setDocConcentration(docConcentration: Double): this.type = {
     this.docConcentration = Vectors.dense(docConcentration)
     this
@@ -150,26 +150,26 @@ class LDA private (
 
   /**
    * Alias for [[getAsymmetricDocConcentration]]
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def getAsymmetricAlpha: Vector = getAsymmetricDocConcentration
 
   /**
    * Alias for [[getDocConcentration]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getAlpha: Double = getDocConcentration
 
   /**
    * Alias for [[setDocConcentration()]]
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def setAlpha(alpha: Vector): this.type = setDocConcentration(alpha)
 
   /**
    * Alias for [[setDocConcentration()]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setAlpha(alpha: Double): this.type = setDocConcentration(alpha)
 
   /**
@@ -180,8 +180,8 @@ class LDA private (
    *
    * Note: The topics' distributions over terms are called "beta" in the original LDA paper
    * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getTopicConcentration: Double = this.topicConcentration
 
   /**
@@ -205,8 +205,8 @@ class LDA private (
    *     - Value should be >= 0
    *     - default = (1.0 / k), following the implementation from
    *       [[https://github.com/Blei-Lab/onlineldavb]].
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setTopicConcentration(topicConcentration: Double): this.type = {
     this.topicConcentration = topicConcentration
     this
@@ -214,27 +214,27 @@ class LDA private (
 
   /**
    * Alias for [[getTopicConcentration]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getBeta: Double = getTopicConcentration
 
   /**
    * Alias for [[setTopicConcentration()]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setBeta(beta: Double): this.type = setTopicConcentration(beta)
 
   /**
    * Maximum number of iterations for learning.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getMaxIterations: Int = maxIterations
 
   /**
    * Maximum number of iterations for learning.
    * (default = 20)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
@@ -242,14 +242,14 @@ class LDA private (
 
   /**
    * Random seed
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getSeed: Long = seed
 
   /**
    * Random seed
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -257,8 +257,8 @@ class LDA private (
 
   /**
    * Period (in iterations) between checkpoints.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def getCheckpointInterval: Int = checkpointInterval
 
   /**
@@ -268,8 +268,8 @@ class LDA private (
    * [[org.apache.spark.SparkContext]], this setting is ignored.
    *
    * @see [[org.apache.spark.SparkContext#setCheckpointDir]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setCheckpointInterval(checkpointInterval: Int): this.type = {
     this.checkpointInterval = checkpointInterval
     this
@@ -280,8 +280,8 @@ class LDA private (
    * :: DeveloperApi ::
    *
    * LDAOptimizer used to perform the actual calculation
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   @DeveloperApi
   def getOptimizer: LDAOptimizer = ldaOptimizer
 
@@ -289,8 +289,8 @@ class LDA private (
    * :: DeveloperApi ::
    *
    * LDAOptimizer used to perform the actual calculation (default = EMLDAOptimizer)
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   @DeveloperApi
   def setOptimizer(optimizer: LDAOptimizer): this.type = {
     this.ldaOptimizer = optimizer
@@ -300,8 +300,8 @@ class LDA private (
   /**
    * Set the LDAOptimizer used to perform the actual calculation by algorithm name.
    * Currently "em", "online" are supported.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setOptimizer(optimizerName: String): this.type = {
     this.ldaOptimizer =
       optimizerName.toLowerCase match {
@@ -321,8 +321,8 @@ class LDA private (
    *                   (where the vocabulary size is the length of the vector).
    *                   Document IDs must be unique and >= 0.
    * @return  Inferred LDA model
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(documents: RDD[(Long, Vector)]): LDAModel = {
     val state = ldaOptimizer.initialize(documents, this)
     var iter = 0
@@ -339,8 +339,8 @@ class LDA private (
 
   /**
    * Java-friendly version of [[run()]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(documents: JavaPairRDD[java.lang.Long, Vector]): LDAModel = {
     run(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 6bc68a4c18b99..667374a2bc418 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -25,7 +25,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.{JavaPairRDD, JavaRDD}
 import org.apache.spark.graphx.{Edge, EdgeContext, Graph, VertexId}
 import org.apache.spark.mllib.linalg.{Matrices, Matrix, Vector, Vectors}
@@ -192,24 +192,16 @@ class LocalLDAModel private[clustering] (
     override protected[clustering] val gammaShape: Double = 100)
   extends LDAModel with Serializable {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def k: Int = topics.numCols
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def vocabSize: Int = topics.numRows
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def topicsMatrix: Matrix = topics
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])] = {
     val brzTopics = topics.toBreeze.toDenseMatrix
     Range(0, k).map { topicIndex =>
@@ -222,9 +214,7 @@ class LocalLDAModel private[clustering] (
 
   override protected def formatVersion = "1.0"
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def save(sc: SparkContext, path: String): Unit = {
     LocalLDAModel.SaveLoadV1_0.save(sc, path, topicsMatrix, docConcentration, topicConcentration,
       gammaShape)
@@ -238,16 +228,16 @@ class LocalLDAModel private[clustering] (
    *
    * @param documents test corpus to use for calculating log likelihood
    * @return variational lower bound on the log likelihood of the entire corpus
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def logLikelihood(documents: RDD[(Long, Vector)]): Double = logLikelihoodBound(documents,
     docConcentration, topicConcentration, topicsMatrix.toBreeze.toDenseMatrix, gammaShape, k,
     vocabSize)
 
   /**
    * Java-friendly version of [[logLikelihood]]
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def logLikelihood(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
     logLikelihood(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
@@ -258,8 +248,8 @@ class LocalLDAModel private[clustering] (
    *
    * @param documents test corpus to use for calculating perplexity
    * @return Variational upper bound on log perplexity per token.
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def logPerplexity(documents: RDD[(Long, Vector)]): Double = {
     val corpusTokenCount = documents
       .map { case (_, termCounts) => termCounts.toArray.sum }
@@ -267,9 +257,8 @@ class LocalLDAModel private[clustering] (
     -logLikelihood(documents) / corpusTokenCount
   }
 
-  /** Java-friendly version of [[logPerplexity]]
-   *  @since 1.5.0
-   */
+  /** Java-friendly version of [[logPerplexity]] */
+  @Since("1.5.0")
   def logPerplexity(documents: JavaPairRDD[java.lang.Long, Vector]): Double = {
     logPerplexity(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
   }
@@ -347,8 +336,8 @@ class LocalLDAModel private[clustering] (
    * for each document.
    * @param documents documents to predict topic mixture distributions for
    * @return An RDD of (document ID, topic mixture distribution for document)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   // TODO: declare in LDAModel and override once implemented in DistributedLDAModel
   def topicDistributions(documents: RDD[(Long, Vector)]): RDD[(Long, Vector)] = {
     // Double transpose because dirichletExpectation normalizes by row and we need to normalize
@@ -376,8 +365,8 @@ class LocalLDAModel private[clustering] (
 
   /**
    * Java-friendly version of [[topicDistributions]]
-   * @since 1.4.1
    */
+  @Since("1.4.1")
   def topicDistributions(
       documents: JavaPairRDD[java.lang.Long, Vector]): JavaPairRDD[java.lang.Long, Vector] = {
     val distributions = topicDistributions(documents.rdd.asInstanceOf[RDD[(Long, Vector)]])
@@ -451,9 +440,7 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
     }
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def load(sc: SparkContext, path: String): LocalLDAModel = {
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
@@ -510,8 +497,8 @@ class DistributedLDAModel private[clustering] (
    * Convert model to a local model.
    * The local model stores the inferred topics but not the topic distributions for training
    * documents.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toLocal: LocalLDAModel = new LocalLDAModel(topicsMatrix, docConcentration, topicConcentration,
     gammaShape)
 
@@ -521,8 +508,8 @@ class DistributedLDAModel private[clustering] (
    * No guarantees are given about the ordering of the topics.
    *
    * WARNING: This matrix is collected from an RDD. Beware memory usage when vocabSize, k are large.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override lazy val topicsMatrix: Matrix = {
     // Collect row-major topics
     val termTopicCounts: Array[(Int, TopicCounts)] =
@@ -541,9 +528,7 @@ class DistributedLDAModel private[clustering] (
     Matrices.fromBreeze(brzTopics)
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])] = {
     val numTopics = k
     // Note: N_k is not needed to find the top terms, but it is needed to normalize weights
@@ -582,8 +567,8 @@ class DistributedLDAModel private[clustering] (
    * @return  Array over topics.  Each element represent as a pair of matching arrays:
    *          (IDs for the documents, weights of the topic in these documents).
    *          For each topic, documents are sorted in order of decreasing topic weights.
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def topDocumentsPerTopic(maxDocumentsPerTopic: Int): Array[(Array[Long], Array[Double])] = {
     val numTopics = k
     val topicsInQueues: Array[BoundedPriorityQueue[(Double, Long)]] =
@@ -666,8 +651,8 @@ class DistributedLDAModel private[clustering] (
    *  - This excludes the prior; for that, use [[logPrior]].
    *  - Even with [[logPrior]], this is NOT the same as the data log likelihood given the
    *    hyperparameters.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   lazy val logLikelihood: Double = {
     // TODO: generalize this for asymmetric (non-scalar) alpha
     val alpha = this.docConcentration(0) // To avoid closure capture of enclosing object
@@ -693,8 +678,8 @@ class DistributedLDAModel private[clustering] (
   /**
    * Log probability of the current parameter estimate:
    * log P(topics, topic distributions for docs | alpha, eta)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   lazy val logPrior: Double = {
     // TODO: generalize this for asymmetric (non-scalar) alpha
     val alpha = this.docConcentration(0) // To avoid closure capture of enclosing object
@@ -725,8 +710,8 @@ class DistributedLDAModel private[clustering] (
    * ("theta_doc").
    *
    * @return  RDD of (document ID, topic distribution) pairs
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def topicDistributions: RDD[(Long, Vector)] = {
     graph.vertices.filter(LDA.isDocumentVertex).map { case (docID, topicCounts) =>
       (docID.toLong, Vectors.fromBreeze(normalize(topicCounts, 1.0)))
@@ -735,8 +720,8 @@ class DistributedLDAModel private[clustering] (
 
   /**
    * Java-friendly version of [[topicDistributions]]
-   * @since 1.4.1
    */
+  @Since("1.4.1")
   def javaTopicDistributions: JavaPairRDD[java.lang.Long, Vector] = {
     JavaPairRDD.fromRDD(topicDistributions.asInstanceOf[RDD[(java.lang.Long, Vector)]])
   }
@@ -744,8 +729,8 @@ class DistributedLDAModel private[clustering] (
   /**
    * For each document, return the top k weighted topics for that document and their weights.
    * @return RDD of (doc ID, topic indices, topic weights)
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def topTopicsPerDocument(k: Int): RDD[(Long, Array[Int], Array[Double])] = {
     graph.vertices.filter(LDA.isDocumentVertex).map { case (docID, topicCounts) =>
       val topIndices = argtopk(topicCounts, k)
@@ -761,8 +746,8 @@ class DistributedLDAModel private[clustering] (
 
   /**
    * Java-friendly version of [[topTopicsPerDocument]]
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def javaTopTopicsPerDocument(k: Int): JavaRDD[(java.lang.Long, Array[Int], Array[Double])] = {
     val topics = topTopicsPerDocument(k)
     topics.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[Double])]].toJavaRDD()
@@ -775,8 +760,8 @@ class DistributedLDAModel private[clustering] (
 
   /**
    * Java-friendly version of [[topicDistributions]]
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   override def save(sc: SparkContext, path: String): Unit = {
     DistributedLDAModel.SaveLoadV1_0.save(
       sc, path, graph, globalTopicTotals, k, vocabSize, docConcentration, topicConcentration,
@@ -877,9 +862,7 @@ object DistributedLDAModel extends Loader[DistributedLDAModel] {
 
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def load(sc: SparkContext, path: String): DistributedLDAModel = {
     val (loadedClassName, loadedVersion, metadata) = Loader.loadMetadata(sc, path)
     implicit val formats = DefaultFormats
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index cb517f9689ade..5c2aae6403bea 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -23,7 +23,7 @@ import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, all, normalize, su
 import breeze.numerics.{trigamma, abs, exp}
 import breeze.stats.distributions.{Gamma, RandBasis}
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl
 import org.apache.spark.mllib.impl.PeriodicGraphCheckpointer
@@ -35,8 +35,8 @@ import org.apache.spark.rdd.RDD
  *
  * An LDAOptimizer specifies which optimization/learning/inference algorithm to use, and it can
  * hold optimizer-specific parameters for users to set.
- * @since 1.4.0
  */
+@Since("1.4.0")
 @DeveloperApi
 sealed trait LDAOptimizer {
 
@@ -74,8 +74,8 @@ sealed trait LDAOptimizer {
  *  - Paper which clearly explains several algorithms, including EM:
  *    Asuncion, Welling, Smyth, and Teh.
  *    "On Smoothing and Inference for Topic Models."  UAI, 2009.
- * @since 1.4.0
  */
+@Since("1.4.0")
 @DeveloperApi
 final class EMLDAOptimizer extends LDAOptimizer {
 
@@ -226,8 +226,8 @@ final class EMLDAOptimizer extends LDAOptimizer {
  *
  * Original Online LDA paper:
  *   Hoffman, Blei and Bach, "Online Learning for Latent Dirichlet Allocation." NIPS, 2010.
- * @since 1.4.0
  */
+@Since("1.4.0")
 @DeveloperApi
 final class OnlineLDAOptimizer extends LDAOptimizer {
 
@@ -276,16 +276,16 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   /**
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getTau0: Double = this.tau0
 
   /**
    * A (positive) learning parameter that downweights early iterations. Larger values make early
    * iterations count less.
    * Default: 1024, following the original Online LDA paper.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setTau0(tau0: Double): this.type = {
     require(tau0 > 0, s"LDA tau0 must be positive, but was set to $tau0")
     this.tau0 = tau0
@@ -294,16 +294,16 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 
   /**
    * Learning rate: exponential decay rate
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getKappa: Double = this.kappa
 
   /**
    * Learning rate: exponential decay rate---should be between
    * (0.5, 1.0] to guarantee asymptotic convergence.
    * Default: 0.51, based on the original Online LDA paper.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setKappa(kappa: Double): this.type = {
     require(kappa >= 0, s"Online LDA kappa must be nonnegative, but was set to $kappa")
     this.kappa = kappa
@@ -312,8 +312,8 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 
   /**
    * Mini-batch fraction, which sets the fraction of document sampled and used in each iteration
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getMiniBatchFraction: Double = this.miniBatchFraction
 
   /**
@@ -325,8 +325,8 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
    * maxIterations * miniBatchFraction >= 1.
    *
    * Default: 0.05, i.e., 5% of total documents.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
     require(miniBatchFraction > 0.0 && miniBatchFraction <= 1.0,
       s"Online LDA miniBatchFraction must be in range (0,1], but was set to $miniBatchFraction")
@@ -337,16 +337,16 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   /**
    * Optimize alpha, indicates whether alpha (Dirichlet parameter for document-topic distribution)
    * will be optimized during training.
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def getOptimzeAlpha: Boolean = this.optimizeAlpha
 
   /**
    * Sets whether to optimize alpha parameter during training.
    *
    * Default: false
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def setOptimzeAlpha(optimizeAlpha: Boolean): this.type = {
     this.optimizeAlpha = optimizeAlpha
     this
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
index b4733ca975152..396b36f2f6454 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
@@ -21,7 +21,7 @@ import org.json4s.JsonDSL._
 import org.json4s._
 import org.json4s.jackson.JsonMethods._
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.graphx._
 import org.apache.spark.graphx.impl.GraphImpl
@@ -39,16 +39,14 @@ import org.apache.spark.{Logging, SparkContext, SparkException}
  *
  * @param k number of clusters
  * @param assignments an RDD of clustering [[PowerIterationClustering#Assignment]]s
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class PowerIterationClusteringModel(
     val k: Int,
     val assignments: RDD[PowerIterationClustering.Assignment]) extends Saveable with Serializable {
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def save(sc: SparkContext, path: String): Unit = {
     PowerIterationClusteringModel.SaveLoadV1_0.save(sc, this, path)
   }
@@ -56,9 +54,7 @@ class PowerIterationClusteringModel(
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.4.0
- */
+@Since("1.4.0")
 object PowerIterationClusteringModel extends Loader[PowerIterationClusteringModel] {
   override def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
     PowerIterationClusteringModel.SaveLoadV1_0.load(sc, path)
@@ -73,8 +69,8 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
     val thisClassName = "org.apache.spark.mllib.clustering.PowerIterationClusteringModel"
 
     /**
-     * @since 1.4.0
      */
+    @Since("1.4.0")
     def save(sc: SparkContext, model: PowerIterationClusteringModel, path: String): Unit = {
       val sqlContext = new SQLContext(sc)
       import sqlContext.implicits._
@@ -87,9 +83,7 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
       dataRDD.write.parquet(Loader.dataPath(path))
     }
 
-    /**
-     * @since 1.4.0
-     */
+    @Since("1.4.0")
     def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
       implicit val formats = DefaultFormats
       val sqlContext = new SQLContext(sc)
@@ -136,14 +130,14 @@ class PowerIterationClustering private[clustering] (
   /**
    * Constructs a PIC instance with default parameters: {k: 2, maxIterations: 100,
    * initMode: "random"}.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this() = this(k = 2, maxIterations = 100, initMode = "random")
 
   /**
    * Set the number of clusters.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setK(k: Int): this.type = {
     this.k = k
     this
@@ -151,8 +145,8 @@ class PowerIterationClustering private[clustering] (
 
   /**
    * Set maximum number of iterations of the power iteration loop
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setMaxIterations(maxIterations: Int): this.type = {
     this.maxIterations = maxIterations
     this
@@ -161,8 +155,8 @@ class PowerIterationClustering private[clustering] (
   /**
    * Set the initialization mode. This can be either "random" to use a random vector
    * as vertex properties, or "degree" to use normalized sum similarities. Default: random.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setInitializationMode(mode: String): this.type = {
     this.initMode = mode match {
       case "random" | "degree" => mode
@@ -182,8 +176,8 @@ class PowerIterationClustering private[clustering] (
    *              assume s,,ij,, = 0.0.
    *
    * @return a [[PowerIterationClusteringModel]] that contains the clustering result
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def run(graph: Graph[Double, Double]): PowerIterationClusteringModel = {
     val w = normalize(graph)
     val w0 = initMode match {
@@ -204,8 +198,8 @@ class PowerIterationClustering private[clustering] (
    *                     assume s,,ij,, = 0.0.
    *
    * @return a [[PowerIterationClusteringModel]] that contains the clustering result
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(similarities: RDD[(Long, Long, Double)]): PowerIterationClusteringModel = {
     val w = normalize(similarities)
     val w0 = initMode match {
@@ -217,8 +211,8 @@ class PowerIterationClustering private[clustering] (
 
   /**
    * A Java-friendly version of [[PowerIterationClustering.run]].
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run(similarities: JavaRDD[(java.lang.Long, java.lang.Long, java.lang.Double)])
     : PowerIterationClusteringModel = {
     run(similarities.rdd.asInstanceOf[RDD[(Long, Long, Double)]])
@@ -242,9 +236,7 @@ class PowerIterationClustering private[clustering] (
   }
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 @Experimental
 object PowerIterationClustering extends Logging {
 
@@ -253,8 +245,8 @@ object PowerIterationClustering extends Logging {
    * Cluster assignment.
    * @param id node id
    * @param cluster assigned cluster id
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   @Experimental
   case class Assignment(id: Long, cluster: Int)
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
index a915804b02c90..41f2668ec6a7d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaSparkContext._
 import org.apache.spark.mllib.linalg.{BLAS, Vector, Vectors}
 import org.apache.spark.rdd.RDD
@@ -63,9 +63,8 @@ import org.apache.spark.util.random.XORShiftRandom
  * such that at time t + h the discount applied to the data from t is 0.5.
  * The definition remains the same whether the time unit is given
  * as batches or points.
- * @since 1.2.0
- *
  */
+@Since("1.2.0")
 @Experimental
 class StreamingKMeansModel(
     override val clusterCenters: Array[Vector],
@@ -73,8 +72,8 @@ class StreamingKMeansModel(
 
   /**
    * Perform a k-means update on a batch of data.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def update(data: RDD[Vector], decayFactor: Double, timeUnit: String): StreamingKMeansModel = {
 
     // find nearest cluster to each point
@@ -166,23 +165,23 @@ class StreamingKMeansModel(
  *    .setRandomCenters(5, 100.0)
  *    .trainOn(DStream)
  * }}}
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class StreamingKMeans(
     var k: Int,
     var decayFactor: Double,
     var timeUnit: String) extends Logging with Serializable {
 
-  /** @since 1.2.0 */
+  @Since("1.2.0")
   def this() = this(2, 1.0, StreamingKMeans.BATCHES)
 
   protected var model: StreamingKMeansModel = new StreamingKMeansModel(null, null)
 
   /**
    * Set the number of clusters.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setK(k: Int): this.type = {
     this.k = k
     this
@@ -190,8 +189,8 @@ class StreamingKMeans(
 
   /**
    * Set the decay factor directly (for forgetful algorithms).
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setDecayFactor(a: Double): this.type = {
     this.decayFactor = a
     this
@@ -199,8 +198,8 @@ class StreamingKMeans(
 
   /**
    * Set the half life and time unit ("batches" or "points") for forgetful algorithms.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setHalfLife(halfLife: Double, timeUnit: String): this.type = {
     if (timeUnit != StreamingKMeans.BATCHES && timeUnit != StreamingKMeans.POINTS) {
       throw new IllegalArgumentException("Invalid time unit for decay: " + timeUnit)
@@ -213,8 +212,8 @@ class StreamingKMeans(
 
   /**
    * Specify initial centers directly.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setInitialCenters(centers: Array[Vector], weights: Array[Double]): this.type = {
     model = new StreamingKMeansModel(centers, weights)
     this
@@ -226,8 +225,8 @@ class StreamingKMeans(
    * @param dim Number of dimensions
    * @param weight Weight for each center
    * @param seed Random seed
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setRandomCenters(dim: Int, weight: Double, seed: Long = Utils.random.nextLong): this.type = {
     val random = new XORShiftRandom(seed)
     val centers = Array.fill(k)(Vectors.dense(Array.fill(dim)(random.nextGaussian())))
@@ -238,8 +237,8 @@ class StreamingKMeans(
 
   /**
    * Return the latest model.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def latestModel(): StreamingKMeansModel = {
     model
   }
@@ -251,8 +250,8 @@ class StreamingKMeans(
    * and updates the model using each batch of data from the stream.
    *
    * @param data DStream containing vector data
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainOn(data: DStream[Vector]) {
     assertInitialized()
     data.foreachRDD { (rdd, time) =>
@@ -262,8 +261,8 @@ class StreamingKMeans(
 
   /**
    * Java-friendly version of `trainOn`.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def trainOn(data: JavaDStream[Vector]): Unit = trainOn(data.dstream)
 
   /**
@@ -271,8 +270,8 @@ class StreamingKMeans(
    *
    * @param data DStream containing vector data
    * @return DStream containing predictions
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def predictOn(data: DStream[Vector]): DStream[Int] = {
     assertInitialized()
     data.map(model.predict)
@@ -280,8 +279,8 @@ class StreamingKMeans(
 
   /**
    * Java-friendly version of `predictOn`.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Integer] = {
     JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Integer]])
   }
@@ -292,8 +291,8 @@ class StreamingKMeans(
    * @param data DStream containing (key, feature vector) pairs
    * @tparam K key type
    * @return DStream containing the input keys and the predictions as values
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def predictOnValues[K: ClassTag](data: DStream[(K, Vector)]): DStream[(K, Int)] = {
     assertInitialized()
     data.mapValues(model.predict)
@@ -301,8 +300,8 @@ class StreamingKMeans(
 
   /**
    * Java-friendly version of `predictOnValues`.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def predictOnValues[K](
       data: JavaPairDStream[K, Vector]): JavaPairDStream[K, java.lang.Integer] = {
     implicit val tag = fakeClassTag[K]
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
index 486741edd6f5a..76ae847921f44 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.evaluation
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.evaluation.binary._
@@ -41,8 +41,8 @@ import org.apache.spark.sql.DataFrame
  *                of bins may not exactly equal numBins. The last bin in each partition may
  *                be smaller as a result, meaning there may be an extra sample at
  *                partition boundaries.
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class BinaryClassificationMetrics(
     val scoreAndLabels: RDD[(Double, Double)],
@@ -52,8 +52,8 @@ class BinaryClassificationMetrics(
 
   /**
    * Defaults `numBins` to 0.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def this(scoreAndLabels: RDD[(Double, Double)]) = this(scoreAndLabels, 0)
 
   /**
@@ -65,16 +65,16 @@ class BinaryClassificationMetrics(
 
   /**
    * Unpersist intermediate RDDs used in the computation.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def unpersist() {
     cumulativeCounts.unpersist()
   }
 
   /**
    * Returns thresholds in descending order.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def thresholds(): RDD[Double] = cumulativeCounts.map(_._1)
 
   /**
@@ -82,8 +82,8 @@ class BinaryClassificationMetrics(
    * which is an RDD of (false positive rate, true positive rate)
    * with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.
    * @see http://en.wikipedia.org/wiki/Receiver_operating_characteristic
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def roc(): RDD[(Double, Double)] = {
     val rocCurve = createCurve(FalsePositiveRate, Recall)
     val sc = confusions.context
@@ -94,16 +94,16 @@ class BinaryClassificationMetrics(
 
   /**
    * Computes the area under the receiver operating characteristic (ROC) curve.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def areaUnderROC(): Double = AreaUnderCurve.of(roc())
 
   /**
    * Returns the precision-recall curve, which is an RDD of (recall, precision),
    * NOT (precision, recall), with (0.0, 1.0) prepended to it.
    * @see http://en.wikipedia.org/wiki/Precision_and_recall
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def pr(): RDD[(Double, Double)] = {
     val prCurve = createCurve(Recall, Precision)
     val sc = confusions.context
@@ -113,8 +113,8 @@ class BinaryClassificationMetrics(
 
   /**
    * Computes the area under the precision-recall curve.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def areaUnderPR(): Double = AreaUnderCurve.of(pr())
 
   /**
@@ -122,26 +122,26 @@ class BinaryClassificationMetrics(
    * @param beta the beta factor in F-Measure computation.
    * @return an RDD of (threshold, F-Measure) pairs.
    * @see http://en.wikipedia.org/wiki/F1_score
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def fMeasureByThreshold(beta: Double): RDD[(Double, Double)] = createCurve(FMeasure(beta))
 
   /**
    * Returns the (threshold, F-Measure) curve with beta = 1.0.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def fMeasureByThreshold(): RDD[(Double, Double)] = fMeasureByThreshold(1.0)
 
   /**
    * Returns the (threshold, precision) curve.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def precisionByThreshold(): RDD[(Double, Double)] = createCurve(Precision)
 
   /**
    * Returns the (threshold, recall) curve.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def recallByThreshold(): RDD[(Double, Double)] = createCurve(Recall)
 
   private lazy val (
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
index dddfa3ea5b800..02e89d921033c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.evaluation
 import scala.collection.Map
 
 import org.apache.spark.SparkContext._
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.{Matrices, Matrix}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.DataFrame
@@ -30,8 +30,8 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for multiclass classification.
  *
  * @param predictionAndLabels an RDD of (prediction, label) pairs.
- * @since 1.1.0
  */
+@Since("1.1.0")
 @Experimental
 class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
 
@@ -65,8 +65,8 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
    * predicted classes are in columns,
    * they are ordered by class label ascending,
    * as in "labels"
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def confusionMatrix: Matrix = {
     val n = labels.size
     val values = Array.ofDim[Double](n * n)
@@ -85,15 +85,15 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns true positive rate for a given label (category)
    * @param label the label.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def truePositiveRate(label: Double): Double = recall(label)
 
   /**
    * Returns false positive rate for a given label (category)
    * @param label the label.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def falsePositiveRate(label: Double): Double = {
     val fp = fpByClass.getOrElse(label, 0)
     fp.toDouble / (labelCount - labelCountByClass(label))
@@ -102,8 +102,8 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns precision for a given label (category)
    * @param label the label.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def precision(label: Double): Double = {
     val tp = tpByClass(label)
     val fp = fpByClass.getOrElse(label, 0)
@@ -113,16 +113,16 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns recall for a given label (category)
    * @param label the label.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def recall(label: Double): Double = tpByClass(label).toDouble / labelCountByClass(label)
 
   /**
    * Returns f-measure for a given label (category)
    * @param label the label.
    * @param beta the beta parameter.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def fMeasure(label: Double, beta: Double): Double = {
     val p = precision(label)
     val r = recall(label)
@@ -133,8 +133,8 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns f1-measure for a given label (category)
    * @param label the label.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def fMeasure(label: Double): Double = fMeasure(label, 1.0)
 
   /**
@@ -187,8 +187,8 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns weighted averaged f-measure
    * @param beta the beta parameter.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def weightedFMeasure(beta: Double): Double = labelCountByClass.map { case (category, count) =>
     fMeasure(category, beta) * count.toDouble / labelCount
   }.sum
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
index 77cb1e09bdbb5..a0a8d9c56847b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.mllib.evaluation
 
+import org.apache.spark.annotation.Since
 import org.apache.spark.rdd.RDD
 import org.apache.spark.SparkContext._
 import org.apache.spark.sql.DataFrame
@@ -25,8 +26,8 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for multilabel classification.
  * @param predictionAndLabels an RDD of (predictions, labels) pairs,
  * both are non-null Arrays, each with unique elements.
- * @since 1.2.0
  */
+@Since("1.2.0")
 class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) {
 
   /**
@@ -104,8 +105,8 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns precision for a given label (category)
    * @param label the label.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def precision(label: Double): Double = {
     val tp = tpPerClass(label)
     val fp = fpPerClass.getOrElse(label, 0L)
@@ -115,8 +116,8 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns recall for a given label (category)
    * @param label the label.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def recall(label: Double): Double = {
     val tp = tpPerClass(label)
     val fn = fnPerClass.getOrElse(label, 0L)
@@ -126,8 +127,8 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns f1-measure for a given label (category)
    * @param label the label.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def f1Measure(label: Double): Double = {
     val p = precision(label)
     val r = recall(label)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
index 063fbed8cdeea..a7f43f0b110f5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RankingMetrics.scala
@@ -23,7 +23,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.{JavaSparkContext, JavaRDD}
 import org.apache.spark.rdd.RDD
 
@@ -34,8 +34,8 @@ import org.apache.spark.rdd.RDD
  * Java users should use [[RankingMetrics$.of]] to create a [[RankingMetrics]] instance.
  *
  * @param predictionAndLabels an RDD of (predicted ranking, ground truth set) pairs.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])])
   extends Logging with Serializable {
@@ -56,8 +56,8 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
    *
    * @param k the position to compute the truncated precision, must be positive
    * @return the average precision at the first k ranking positions
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def precisionAt(k: Int): Double = {
     require(k > 0, "ranking position k should be positive")
     predictionAndLabels.map { case (pred, lab) =>
@@ -126,8 +126,8 @@ class RankingMetrics[T: ClassTag](predictionAndLabels: RDD[(Array[T], Array[T])]
    *
    * @param k the position to compute the truncated ndcg, must be positive
    * @return the average ndcg at the first k ranking positions
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def ndcgAt(k: Int): Double = {
     require(k > 0, "ranking position k should be positive")
     predictionAndLabels.map { case (pred, lab) =>
@@ -165,8 +165,8 @@ object RankingMetrics {
   /**
    * Creates a [[RankingMetrics]] instance (for Java users).
    * @param predictionAndLabels a JavaRDD of (predicted ranking, ground truth set) pairs
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def of[E, T <: jl.Iterable[E]](predictionAndLabels: JavaRDD[(T, T)]): RankingMetrics[E] = {
     implicit val tag = JavaSparkContext.fakeClassTag[E]
     val rdd = predictionAndLabels.rdd.map { case (predictions, labels) =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
index 54dfd8c099494..36a6c357c3897 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.evaluation
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.Logging
 import org.apache.spark.mllib.linalg.Vectors
@@ -29,8 +29,8 @@ import org.apache.spark.sql.DataFrame
  * Evaluator for regression.
  *
  * @param predictionAndObservations an RDD of (prediction, observation) pairs.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extends Logging {
 
@@ -67,8 +67,8 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
    * Returns the variance explained by regression.
    * explainedVariance = \sum_i (\hat{y_i} - \bar{y})^2 / n
    * @see [[https://en.wikipedia.org/wiki/Fraction_of_variance_unexplained]]
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def explainedVariance: Double = {
     SSreg / summary.count
   }
@@ -76,8 +76,8 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the mean absolute error, which is a risk function corresponding to the
    * expected value of the absolute error loss or l1-norm loss.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def meanAbsoluteError: Double = {
     summary.normL1(1) / summary.count
   }
@@ -85,8 +85,8 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the mean squared error, which is a risk function corresponding to the
    * expected value of the squared error loss or quadratic loss.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def meanSquaredError: Double = {
     SSerr / summary.count
   }
@@ -94,8 +94,8 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns the root mean squared error, which is defined as the square root of
    * the mean squared error.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def rootMeanSquaredError: Double = {
     math.sqrt(this.meanSquaredError)
   }
@@ -103,8 +103,8 @@ class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extend
   /**
    * Returns R^2^, the unadjusted coefficient of determination.
    * @see [[http://en.wikipedia.org/wiki/Coefficient_of_determination]]
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def r2: Double = {
     1 - SSerr / SStot
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
index 7f4de77044994..ba3b447a83398 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -20,7 +20,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.mllib.fpm.AssociationRules.Rule
@@ -33,24 +33,22 @@ import org.apache.spark.rdd.RDD
  * Generates association rules from a [[RDD[FreqItemset[Item]]]. This method only generates
  * association rules which have a single item as the consequent.
  *
- * @since 1.5.0
  */
+@Since("1.5.0")
 @Experimental
 class AssociationRules private[fpm] (
     private var minConfidence: Double) extends Logging with Serializable {
 
   /**
    * Constructs a default instance with default parameters {minConfidence = 0.8}.
-   *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def this() = this(0.8)
 
   /**
    * Sets the minimal confidence (default: `0.8`).
-   *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def setMinConfidence(minConfidence: Double): this.type = {
     require(minConfidence >= 0.0 && minConfidence <= 1.0)
     this.minConfidence = minConfidence
@@ -62,8 +60,8 @@ class AssociationRules private[fpm] (
    * @param freqItemsets frequent itemset model obtained from [[FPGrowth]]
    * @return a [[Set[Rule[Item]]] containing the assocation rules.
    *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def run[Item: ClassTag](freqItemsets: RDD[FreqItemset[Item]]): RDD[Rule[Item]] = {
     // For candidate rule X => Y, generate (X, (Y, freq(X union Y)))
     val candidates = freqItemsets.flatMap { itemset =>
@@ -102,8 +100,8 @@ object AssociationRules {
    *                   instead.
    * @tparam Item item type
    *
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   @Experimental
   class Rule[Item] private[fpm] (
       val antecedent: Array[Item],
@@ -114,8 +112,8 @@ object AssociationRules {
     /**
      * Returns the confidence of the rule.
      *
-     * @since 1.5.0
      */
+    @Since("1.5.0")
     def confidence: Double = freqUnion.toDouble / freqAntecedent
 
     require(antecedent.toSet.intersect(consequent.toSet).isEmpty, {
@@ -127,8 +125,8 @@ object AssociationRules {
     /**
      * Returns antecedent in a Java List.
      *
-     * @since 1.5.0
      */
+    @Since("1.5.0")
     def javaAntecedent: java.util.List[Item] = {
       antecedent.toList.asJava
     }
@@ -136,8 +134,8 @@ object AssociationRules {
     /**
      * Returns consequent in a Java List.
      *
-     * @since 1.5.0
      */
+    @Since("1.5.0")
     def javaConsequent: java.util.List[Item] = {
       consequent.toList.asJava
     }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index e2370a52f4930..e37f806271680 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -25,7 +25,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.{HashPartitioner, Logging, Partitioner, SparkException}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.mllib.fpm.FPGrowth._
@@ -39,15 +39,15 @@ import org.apache.spark.storage.StorageLevel
  * @param freqItemsets frequent itemset, which is an RDD of [[FreqItemset]]
  * @tparam Item item type
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable {
   /**
    * Generates association rules for the [[Item]]s in [[freqItemsets]].
    * @param confidence minimal confidence of the rules produced
-   * @since 1.5.0
    */
+  @Since("1.5.0")
   def generateAssociationRules(confidence: Double): RDD[AssociationRules.Rule[Item]] = {
     val associationRules = new AssociationRules(confidence)
     associationRules.run(freqItemsets)
@@ -71,8 +71,8 @@ class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) ex
  * @see [[http://en.wikipedia.org/wiki/Association_rule_learning Association rule learning
  *       (Wikipedia)]]
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class FPGrowth private (
     private var minSupport: Double,
@@ -82,15 +82,15 @@ class FPGrowth private (
    * Constructs a default instance with default parameters {minSupport: `0.3`, numPartitions: same
    * as the input data}.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this() = this(0.3, -1)
 
   /**
    * Sets the minimal support level (default: `0.3`).
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setMinSupport(minSupport: Double): this.type = {
     this.minSupport = minSupport
     this
@@ -99,8 +99,8 @@ class FPGrowth private (
   /**
    * Sets the number of partitions used by parallel FP-growth (default: same as input data).
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def setNumPartitions(numPartitions: Int): this.type = {
     this.numPartitions = numPartitions
     this
@@ -111,8 +111,8 @@ class FPGrowth private (
    * @param data input data set, each element contains a transaction
    * @return an [[FPGrowthModel]]
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def run[Item: ClassTag](data: RDD[Array[Item]]): FPGrowthModel[Item] = {
     if (data.getStorageLevel == StorageLevel.NONE) {
       logWarning("Input data is not cached.")
@@ -213,8 +213,8 @@ class FPGrowth private (
 /**
  * :: Experimental ::
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 object FPGrowth {
 
@@ -224,15 +224,15 @@ object FPGrowth {
    * @param freq frequency
    * @tparam Item item type
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   class FreqItemset[Item](val items: Array[Item], val freq: Long) extends Serializable {
 
     /**
      * Returns items in a Java List.
      *
-     * @since 1.3.0
      */
+    @Since("1.3.0")
     def javaItems: java.util.List[Item] = {
       items.toList.asJava
     }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index dfa8910fcbc8c..28b5b4637bf17 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -23,7 +23,7 @@ import scala.collection.mutable.{ArrayBuilder => MArrayBuilder, HashSet => MHash
 
 import breeze.linalg.{CSCMatrix => BSM, DenseMatrix => BDM, Matrix => BM}
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.types._
@@ -227,8 +227,8 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
  * @param values matrix entries in column major if not transposed or in row major otherwise
  * @param isTransposed whether the matrix is transposed. If true, `values` stores the matrix in
  *                     row major.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
 class DenseMatrix(
     val numRows: Int,
@@ -253,8 +253,8 @@ class DenseMatrix(
    * @param numRows number of rows
    * @param numCols number of columns
    * @param values matrix entries in column major
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this(numRows: Int, numCols: Int, values: Array[Double]) =
     this(numRows, numCols, values, false)
 
@@ -278,9 +278,7 @@ class DenseMatrix(
 
   private[mllib] def apply(i: Int): Double = values(i)
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def apply(i: Int, j: Int): Double = values(index(i, j))
 
   private[mllib] def index(i: Int, j: Int): Int = {
@@ -291,9 +289,7 @@ class DenseMatrix(
     values(index(i, j)) = v
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def copy: DenseMatrix = new DenseMatrix(numRows, numCols, values.clone())
 
   private[spark] def map(f: Double => Double) = new DenseMatrix(numRows, numCols, values.map(f),
@@ -309,9 +305,7 @@ class DenseMatrix(
     this
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def transpose: DenseMatrix = new DenseMatrix(numCols, numRows, values, !isTransposed)
 
   private[spark] override def foreachActive(f: (Int, Int, Double) => Unit): Unit = {
@@ -342,21 +336,17 @@ class DenseMatrix(
     }
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numNonzeros: Int = values.count(_ != 0)
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numActives: Int = values.length
 
   /**
    * Generate a `SparseMatrix` from the given `DenseMatrix`. The new matrix will have isTransposed
    * set to false.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toSparse: SparseMatrix = {
     val spVals: MArrayBuilder[Double] = new MArrayBuilder.ofDouble
     val colPtrs: Array[Int] = new Array[Int](numCols + 1)
@@ -383,8 +373,8 @@ class DenseMatrix(
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.DenseMatrix]].
- * @since 1.3.0
  */
+@Since("1.3.0")
 object DenseMatrix {
 
   /**
@@ -392,8 +382,8 @@ object DenseMatrix {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `DenseMatrix` with size `numRows` x `numCols` and values of zeros
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def zeros(numRows: Int, numCols: Int): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -405,8 +395,8 @@ object DenseMatrix {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `DenseMatrix` with size `numRows` x `numCols` and values of ones
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def ones(numRows: Int, numCols: Int): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -417,8 +407,8 @@ object DenseMatrix {
    * Generate an Identity Matrix in `DenseMatrix` format.
    * @param n number of rows and columns of the matrix
    * @return `DenseMatrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def eye(n: Int): DenseMatrix = {
     val identity = DenseMatrix.zeros(n, n)
     var i = 0
@@ -435,8 +425,8 @@ object DenseMatrix {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `DenseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def rand(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -449,8 +439,8 @@ object DenseMatrix {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `DenseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def randn(numRows: Int, numCols: Int, rng: Random): DenseMatrix = {
     require(numRows.toLong * numCols <= Int.MaxValue,
             s"$numRows x $numCols dense matrix is too large to allocate")
@@ -462,8 +452,8 @@ object DenseMatrix {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `DenseMatrix` with size `values.length` x `values.length` and `values`
    *         on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def diag(vector: Vector): DenseMatrix = {
     val n = vector.size
     val matrix = DenseMatrix.zeros(n, n)
@@ -498,8 +488,8 @@ object DenseMatrix {
  * @param isTransposed whether the matrix is transposed. If true, the matrix can be considered
  *                     Compressed Sparse Row (CSR) format, where `colPtrs` behaves as rowPtrs,
  *                     and `rowIndices` behave as colIndices, and `values` are stored in row major.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
 class SparseMatrix(
     val numRows: Int,
@@ -536,8 +526,8 @@ class SparseMatrix(
    * @param rowIndices the row index of the entry. They must be in strictly increasing
    *                   order for each column
    * @param values non-zero matrix entries in column major
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def this(
       numRows: Int,
       numCols: Int,
@@ -560,8 +550,8 @@ class SparseMatrix(
   }
 
   /**
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def apply(i: Int, j: Int): Double = {
     val ind = index(i, j)
     if (ind < 0) 0.0 else values(ind)
@@ -585,9 +575,7 @@ class SparseMatrix(
     }
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def copy: SparseMatrix = {
     new SparseMatrix(numRows, numCols, colPtrs, rowIndices, values.clone())
   }
@@ -605,9 +593,7 @@ class SparseMatrix(
     this
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def transpose: SparseMatrix =
     new SparseMatrix(numCols, numRows, colPtrs, rowIndices, values, !isTransposed)
 
@@ -641,28 +627,24 @@ class SparseMatrix(
   /**
    * Generate a `DenseMatrix` from the given `SparseMatrix`. The new matrix will have isTransposed
    * set to false.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toDense: DenseMatrix = {
     new DenseMatrix(numRows, numCols, toArray)
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numNonzeros: Int = values.count(_ != 0)
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def numActives: Int = values.length
 
 }
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.SparseMatrix]].
- * @since 1.3.0
  */
+@Since("1.3.0")
 object SparseMatrix {
 
   /**
@@ -673,8 +655,8 @@ object SparseMatrix {
    * @param numCols number of columns of the matrix
    * @param entries Array of (i, j, value) tuples
    * @return The corresponding `SparseMatrix`
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def fromCOO(numRows: Int, numCols: Int, entries: Iterable[(Int, Int, Double)]): SparseMatrix = {
     val sortedEntries = entries.toSeq.sortBy(v => (v._2, v._1))
     val numEntries = sortedEntries.size
@@ -722,8 +704,8 @@ object SparseMatrix {
    * Generate an Identity Matrix in `SparseMatrix` format.
    * @param n number of rows and columns of the matrix
    * @return `SparseMatrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def speye(n: Int): SparseMatrix = {
     new SparseMatrix(n, n, (0 to n).toArray, (0 until n).toArray, Array.fill(n)(1.0))
   }
@@ -792,8 +774,8 @@ object SparseMatrix {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `SparseMatrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
     val mat = genRandMatrix(numRows, numCols, density, rng)
     mat.update(i => rng.nextDouble())
@@ -806,8 +788,8 @@ object SparseMatrix {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `SparseMatrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix = {
     val mat = genRandMatrix(numRows, numCols, density, rng)
     mat.update(i => rng.nextGaussian())
@@ -818,8 +800,8 @@ object SparseMatrix {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `SparseMatrix` with size `values.length` x `values.length` and non-zero
    *         `values` on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def spdiag(vector: Vector): SparseMatrix = {
     val n = vector.size
     vector match {
@@ -835,8 +817,8 @@ object SparseMatrix {
 
 /**
  * Factory methods for [[org.apache.spark.mllib.linalg.Matrix]].
- * @since 1.0.0
  */
+@Since("1.0.0")
 object Matrices {
 
   /**
@@ -845,8 +827,8 @@ object Matrices {
    * @param numRows number of rows
    * @param numCols number of columns
    * @param values matrix entries in column major
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix = {
     new DenseMatrix(numRows, numCols, values)
   }
@@ -859,8 +841,8 @@ object Matrices {
    * @param colPtrs the index corresponding to the start of a new column
    * @param rowIndices the row index of the entry
    * @param values non-zero matrix entries in column major
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def sparse(
      numRows: Int,
      numCols: Int,
@@ -893,8 +875,8 @@ object Matrices {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `Matrix` with size `numRows` x `numCols` and values of zeros
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def zeros(numRows: Int, numCols: Int): Matrix = DenseMatrix.zeros(numRows, numCols)
 
   /**
@@ -902,24 +884,24 @@ object Matrices {
    * @param numRows number of rows of the matrix
    * @param numCols number of columns of the matrix
    * @return `Matrix` with size `numRows` x `numCols` and values of ones
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def ones(numRows: Int, numCols: Int): Matrix = DenseMatrix.ones(numRows, numCols)
 
   /**
    * Generate a dense Identity Matrix in `Matrix` format.
    * @param n number of rows and columns of the matrix
    * @return `Matrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def eye(n: Int): Matrix = DenseMatrix.eye(n)
 
   /**
    * Generate a sparse Identity Matrix in `Matrix` format.
    * @param n number of rows and columns of the matrix
    * @return `Matrix` with size `n` x `n` and values of ones on the diagonal
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def speye(n: Int): Matrix = SparseMatrix.speye(n)
 
   /**
@@ -928,8 +910,8 @@ object Matrices {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def rand(numRows: Int, numCols: Int, rng: Random): Matrix =
     DenseMatrix.rand(numRows, numCols, rng)
 
@@ -940,8 +922,8 @@ object Matrices {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in U(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
     SparseMatrix.sprand(numRows, numCols, density, rng)
 
@@ -951,8 +933,8 @@ object Matrices {
    * @param numCols number of columns of the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def randn(numRows: Int, numCols: Int, rng: Random): Matrix =
     DenseMatrix.randn(numRows, numCols, rng)
 
@@ -963,8 +945,8 @@ object Matrices {
    * @param density the desired density for the matrix
    * @param rng a random number generator
    * @return `Matrix` with size `numRows` x `numCols` and values in N(0, 1)
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): Matrix =
     SparseMatrix.sprandn(numRows, numCols, density, rng)
 
@@ -973,8 +955,8 @@ object Matrices {
    * @param vector a `Vector` that will form the values on the diagonal of the matrix
    * @return Square `Matrix` with size `values.length` x `values.length` and `values`
    *         on the diagonal
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def diag(vector: Vector): Matrix = DenseMatrix.diag(vector)
 
   /**
@@ -983,8 +965,8 @@ object Matrices {
    * a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
    * @param matrices array of matrices
    * @return a single `Matrix` composed of the matrices that were horizontally concatenated
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def horzcat(matrices: Array[Matrix]): Matrix = {
     if (matrices.isEmpty) {
       return new DenseMatrix(0, 0, Array[Double]())
@@ -1042,8 +1024,8 @@ object Matrices {
    * a sparse matrix. If the Array is empty, an empty `DenseMatrix` will be returned.
    * @param matrices array of matrices
    * @return a single `Matrix` composed of the matrices that were vertically concatenated
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def vertcat(matrices: Array[Matrix]): Matrix = {
     if (matrices.isEmpty) {
       return new DenseMatrix(0, 0, Array[Double]())
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index 8f504f6984cb0..a37aca99d5e72 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.mllib.linalg
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 
 /**
  * :: Experimental ::
  * Represents singular value decomposition (SVD) factors.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VType)
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 52ef7be3b38be..3d577edbe23e1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -26,7 +26,7 @@ import scala.collection.JavaConverters._
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV, Vector => BV}
 
 import org.apache.spark.SparkException
-import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.annotation.{AlphaComponent, Since}
 import org.apache.spark.mllib.util.NumericParser
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
@@ -240,14 +240,14 @@ class VectorUDT extends UserDefinedType[Vector] {
  * Factory methods for [[org.apache.spark.mllib.linalg.Vector]].
  * We don't use the name `Vector` because Scala imports
  * [[scala.collection.immutable.Vector]] by default.
- * @since 1.0.0
  */
+@Since("1.0.0")
 object Vectors {
 
   /**
    * Creates a dense vector from its values.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @varargs
   def dense(firstValue: Double, otherValues: Double*): Vector =
     new DenseVector((firstValue +: otherValues).toArray)
@@ -255,8 +255,8 @@ object Vectors {
   // A dummy implicit is used to avoid signature collision with the one generated by @varargs.
   /**
    * Creates a dense vector from a double array.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def dense(values: Array[Double]): Vector = new DenseVector(values)
 
   /**
@@ -265,8 +265,8 @@ object Vectors {
    * @param size vector size.
    * @param indices index array, must be strictly increasing.
    * @param values value array, must have the same length as indices.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def sparse(size: Int, indices: Array[Int], values: Array[Double]): Vector =
     new SparseVector(size, indices, values)
 
@@ -275,8 +275,8 @@ object Vectors {
    *
    * @param size vector size.
    * @param elements vector elements in (index, value) pairs.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def sparse(size: Int, elements: Seq[(Int, Double)]): Vector = {
     require(size > 0, "The size of the requested sparse vector must be greater than 0.")
 
@@ -297,8 +297,8 @@ object Vectors {
    *
    * @param size vector size.
    * @param elements vector elements in (index, value) pairs.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def sparse(size: Int, elements: JavaIterable[(JavaInteger, JavaDouble)]): Vector = {
     sparse(size, elements.asScala.map { case (i, x) =>
       (i.intValue(), x.doubleValue())
@@ -310,16 +310,16 @@ object Vectors {
    *
    * @param size vector size
    * @return a zero vector
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def zeros(size: Int): Vector = {
     new DenseVector(new Array[Double](size))
   }
 
   /**
    * Parses a string resulted from [[Vector.toString]] into a [[Vector]].
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def parse(s: String): Vector = {
     parseNumeric(NumericParser.parse(s))
   }
@@ -362,8 +362,8 @@ object Vectors {
    * @param vector input vector.
    * @param p norm.
    * @return norm in L^p^ space.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def norm(vector: Vector, p: Double): Double = {
     require(p >= 1.0, "To compute the p-norm of the vector, we require that you specify a p>=1. " +
       s"You specified p=$p.")
@@ -415,8 +415,8 @@ object Vectors {
    * @param v1 first Vector.
    * @param v2 second Vector.
    * @return squared distance between two Vectors.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def sqdist(v1: Vector, v2: Vector): Double = {
     require(v1.size == v2.size, s"Vector dimensions do not match: Dim(v1)=${v1.size} and Dim(v2)" +
       s"=${v2.size}.")
@@ -529,33 +529,25 @@ object Vectors {
 
 /**
  * A dense vector represented by a value array.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[VectorUDT])
 class DenseVector(val values: Array[Double]) extends Vector {
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def size: Int = values.length
 
   override def toString: String = values.mkString("[", ",", "]")
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def toArray: Array[Double] = values
 
   private[spark] override def toBreeze: BV[Double] = new BDV[Double](values)
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def apply(i: Int): Double = values(i)
 
-  /**
-   * @since 1.1.0
-   */
+  @Since("1.1.0")
   override def copy: DenseVector = {
     new DenseVector(values.clone())
   }
@@ -587,14 +579,10 @@ class DenseVector(val values: Array[Double]) extends Vector {
     result
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numActives: Int = size
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numNonzeros: Int = {
     // same as values.count(_ != 0.0) but faster
     var nnz = 0
@@ -606,9 +594,7 @@ class DenseVector(val values: Array[Double]) extends Vector {
     nnz
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def toSparse: SparseVector = {
     val nnz = numNonzeros
     val ii = new Array[Int](nnz)
@@ -624,9 +610,7 @@ class DenseVector(val values: Array[Double]) extends Vector {
     new SparseVector(size, ii, vv)
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def argmax: Int = {
     if (size == 0) {
       -1
@@ -646,9 +630,7 @@ class DenseVector(val values: Array[Double]) extends Vector {
   }
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object DenseVector {
   /** Extracts the value array from a dense vector. */
   def unapply(dv: DenseVector): Option[Array[Double]] = Some(dv.values)
@@ -660,8 +642,8 @@ object DenseVector {
  * @param size size of the vector.
  * @param indices index array, assume to be strictly increasing.
  * @param values value array, must have the same length as the index array.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[VectorUDT])
 class SparseVector(
     override val size: Int,
@@ -677,9 +659,7 @@ class SparseVector(
   override def toString: String =
     s"($size,${indices.mkString("[", ",", "]")},${values.mkString("[", ",", "]")})"
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def toArray: Array[Double] = {
     val data = new Array[Double](size)
     var i = 0
@@ -691,9 +671,7 @@ class SparseVector(
     data
   }
 
-  /**
-   * @since 1.1.0
-   */
+  @Since("1.1.0")
   override def copy: SparseVector = {
     new SparseVector(size, indices.clone(), values.clone())
   }
@@ -734,14 +712,10 @@ class SparseVector(
     result
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numActives: Int = values.length
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def numNonzeros: Int = {
     var nnz = 0
     values.foreach { v =>
@@ -752,9 +726,7 @@ class SparseVector(
     nnz
   }
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def toSparse: SparseVector = {
     val nnz = numNonzeros
     if (nnz == numActives) {
@@ -774,9 +746,7 @@ class SparseVector(
     }
   }
 
-  /**
-   * @since 1.5.0
-   */
+  @Since("1.5.0")
   override def argmax: Int = {
     if (size == 0) {
       -1
@@ -847,9 +817,7 @@ class SparseVector(
   }
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object SparseVector {
   def unapply(sv: SparseVector): Option[(Int, Array[Int], Array[Double])] =
     Some((sv.size, sv.indices, sv.values))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
index cfb6680a18b34..94376c24a7ac6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable.ArrayBuffer
 import breeze.linalg.{DenseMatrix => BDM}
 
 import org.apache.spark.{Logging, Partitioner, SparkException}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.{DenseMatrix, Matrices, Matrix, SparseMatrix}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
@@ -128,9 +128,8 @@ private[mllib] object GridPartitioner {
  *              the number of rows will be calculated when `numRows` is invoked.
  * @param nCols Number of columns of this matrix. If the supplied value is less than or equal to
  *              zero, the number of columns will be calculated when `numCols` is invoked.
- * @since 1.3.0
- *
  */
+@Since("1.3.0")
 @Experimental
 class BlockMatrix(
     val blocks: RDD[((Int, Int), Matrix)],
@@ -151,10 +150,8 @@ class BlockMatrix(
    *                     rows are not required to have the given number of rows
    * @param colsPerBlock Number of columns that make up each block. The blocks forming the final
    *                     columns are not required to have the given number of columns
-   *
-   * @since 1.3.0
-   *
    */
+  @Since("1.3.0")
   def this(
       blocks: RDD[((Int, Int), Matrix)],
       rowsPerBlock: Int,
@@ -162,20 +159,13 @@ class BlockMatrix(
     this(blocks, rowsPerBlock, colsPerBlock, 0L, 0L)
   }
 
-  /**
-   * @since 1.3.0
-   * */
-
+  @Since("1.3.0")
   override def numRows(): Long = {
     if (nRows <= 0L) estimateDim()
     nRows
   }
 
-  /**
-   *
-   * @since 1.3.0
-   */
-
+  @Since("1.3.0")
   override def numCols(): Long = {
     if (nCols <= 0L) estimateDim()
     nCols
@@ -206,8 +196,8 @@ class BlockMatrix(
   /**
    * Validates the block matrix info against the matrix data (`blocks`) and throws an exception if
    * any error is found.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def validate(): Unit = {
     logDebug("Validating BlockMatrix...")
     // check if the matrix is larger than the claimed dimensions
@@ -243,25 +233,22 @@ class BlockMatrix(
     logDebug("BlockMatrix is valid!")
   }
 
-  /** Caches the underlying RDD.
-    * @since 1.3.0
-    * */
+  /** Caches the underlying RDD. */
+  @Since("1.3.0")
   def cache(): this.type = {
     blocks.cache()
     this
   }
 
-  /** Persists the underlying RDD with the specified storage level.
-    * @since 1.3.0
-    * */
+  /** Persists the underlying RDD with the specified storage level. */
+  @Since("1.3.0")
   def persist(storageLevel: StorageLevel): this.type = {
     blocks.persist(storageLevel)
     this
   }
 
-  /** Converts to CoordinateMatrix.
-    * @since 1.3.0
-    * */
+  /** Converts to CoordinateMatrix. */
+  @Since("1.3.0")
   def toCoordinateMatrix(): CoordinateMatrix = {
     val entryRDD = blocks.flatMap { case ((blockRowIndex, blockColIndex), mat) =>
       val rowStart = blockRowIndex.toLong * rowsPerBlock
@@ -275,9 +262,8 @@ class BlockMatrix(
     new CoordinateMatrix(entryRDD, numRows(), numCols())
   }
 
-  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range.
-    * @since 1.3.0
-    * */
+  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range. */
+  @Since("1.3.0")
   def toIndexedRowMatrix(): IndexedRowMatrix = {
     require(numCols() < Int.MaxValue, "The number of columns must be within the integer range. " +
       s"numCols: ${numCols()}")
@@ -285,9 +271,8 @@ class BlockMatrix(
     toCoordinateMatrix().toIndexedRowMatrix()
   }
 
-  /** Collect the distributed matrix on the driver as a `DenseMatrix`.
-    * @since 1.3.0
-    * */
+  /** Collect the distributed matrix on the driver as a `DenseMatrix`. */
+  @Since("1.3.0")
   def toLocalMatrix(): Matrix = {
     require(numRows() < Int.MaxValue, "The number of rows of this matrix should be less than " +
       s"Int.MaxValue. Currently numRows: ${numRows()}")
@@ -312,11 +297,11 @@ class BlockMatrix(
     new DenseMatrix(m, n, values)
   }
 
-  /** Transpose this `BlockMatrix`. Returns a new `BlockMatrix` instance sharing the
-    * same underlying data. Is a lazy operation.
-    * @since 1.3.0
-    *
-    * */
+  /**
+   * Transpose this `BlockMatrix`. Returns a new `BlockMatrix` instance sharing the
+   * same underlying data. Is a lazy operation.
+   */
+  @Since("1.3.0")
   def transpose: BlockMatrix = {
     val transposedBlocks = blocks.map { case ((blockRowIndex, blockColIndex), mat) =>
       ((blockColIndex, blockRowIndex), mat.transpose)
@@ -330,13 +315,14 @@ class BlockMatrix(
     new BDM[Double](localMat.numRows, localMat.numCols, localMat.toArray)
   }
 
-  /** Adds two block matrices together. The matrices must have the same size and matching
-    * `rowsPerBlock` and `colsPerBlock` values. If one of the blocks that are being added are
-    * instances of [[SparseMatrix]], the resulting sub matrix will also be a [[SparseMatrix]], even
-    * if it is being added to a [[DenseMatrix]]. If two dense matrices are added, the output will
-    * also be a [[DenseMatrix]].
-    * @since 1.3.0
-    */
+  /**
+   * Adds two block matrices together. The matrices must have the same size and matching
+   * `rowsPerBlock` and `colsPerBlock` values. If one of the blocks that are being added are
+   * instances of [[SparseMatrix]], the resulting sub matrix will also be a [[SparseMatrix]], even
+   * if it is being added to a [[DenseMatrix]]. If two dense matrices are added, the output will
+   * also be a [[DenseMatrix]].
+   */
+  @Since("1.3.0")
   def add(other: BlockMatrix): BlockMatrix = {
     require(numRows() == other.numRows(), "Both matrices must have the same number of rows. " +
       s"A.numRows: ${numRows()}, B.numRows: ${other.numRows()}")
@@ -364,14 +350,14 @@ class BlockMatrix(
     }
   }
 
-  /** Left multiplies this [[BlockMatrix]] to `other`, another [[BlockMatrix]]. The `colsPerBlock`
-    * of this matrix must equal the `rowsPerBlock` of `other`. If `other` contains
-    * [[SparseMatrix]], they will have to be converted to a [[DenseMatrix]]. The output
-    * [[BlockMatrix]] will only consist of blocks of [[DenseMatrix]]. This may cause
-    * some performance issues until support for multiplying two sparse matrices is added.
-    *
-    * @since 1.3.0
-    */
+  /**
+   * Left multiplies this [[BlockMatrix]] to `other`, another [[BlockMatrix]]. The `colsPerBlock`
+   * of this matrix must equal the `rowsPerBlock` of `other`. If `other` contains
+   * [[SparseMatrix]], they will have to be converted to a [[DenseMatrix]]. The output
+   * [[BlockMatrix]] will only consist of blocks of [[DenseMatrix]]. This may cause
+   * some performance issues until support for multiplying two sparse matrices is added.
+   */
+  @Since("1.3.0")
   def multiply(other: BlockMatrix): BlockMatrix = {
     require(numCols() == other.numRows(), "The number of columns of A and the number of rows " +
       s"of B must be equal. A.numCols: ${numCols()}, B.numRows: ${other.numRows()}. If you " +
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
index 2b751e45dd76c..4bb27ec840902 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg.{Matrix, SparseMatrix, Vectors}
 
@@ -29,8 +29,8 @@ import org.apache.spark.mllib.linalg.{Matrix, SparseMatrix, Vectors}
  * @param i row index
  * @param j column index
  * @param value value of the entry
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 case class MatrixEntry(i: Long, j: Long, value: Double)
 
@@ -43,22 +43,20 @@ case class MatrixEntry(i: Long, j: Long, value: Double)
  *              be determined by the max row index plus one.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the max column index plus one.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class CoordinateMatrix(
     val entries: RDD[MatrixEntry],
     private var nRows: Long,
     private var nCols: Long) extends DistributedMatrix {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically.
-    * @since 1.0.0
-    * */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  @Since("1.0.0")
   def this(entries: RDD[MatrixEntry]) = this(entries, 0L, 0L)
 
-  /** Gets or computes the number of columns.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of columns. */
+  @Since("1.0.0")
   override def numCols(): Long = {
     if (nCols <= 0L) {
       computeSize()
@@ -66,9 +64,8 @@ class CoordinateMatrix(
     nCols
   }
 
-  /** Gets or computes the number of rows.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of rows. */
+  @Since("1.0.0")
   override def numRows(): Long = {
     if (nRows <= 0L) {
       computeSize()
@@ -76,16 +73,14 @@ class CoordinateMatrix(
     nRows
   }
 
-  /** Transposes this CoordinateMatrix.
-    * @since 1.3.0
-    * */
+  /** Transposes this CoordinateMatrix. */
+  @Since("1.3.0")
   def transpose(): CoordinateMatrix = {
     new CoordinateMatrix(entries.map(x => MatrixEntry(x.j, x.i, x.value)), numCols(), numRows())
   }
 
-  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range.
-    * @since 1.0.0
-    * */
+  /** Converts to IndexedRowMatrix. The number of columns must be within the integer range. */
+  @Since("1.0.0")
   def toIndexedRowMatrix(): IndexedRowMatrix = {
     val nl = numCols()
     if (nl > Int.MaxValue) {
@@ -104,15 +99,14 @@ class CoordinateMatrix(
   /**
    * Converts to RowMatrix, dropping row indices after grouping by row index.
    * The number of columns must be within the integer range.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def toRowMatrix(): RowMatrix = {
     toIndexedRowMatrix().toRowMatrix()
   }
 
-  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024.
-    * @since 1.3.0
-    * */
+  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024. */
+  @Since("1.3.0")
   def toBlockMatrix(): BlockMatrix = {
     toBlockMatrix(1024, 1024)
   }
@@ -124,8 +118,8 @@ class CoordinateMatrix(
    * @param colsPerBlock The number of columns of each block. The blocks at the right edge may have
    *                     a smaller value. Must be an integer value greater than 0.
    * @return a [[BlockMatrix]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix = {
     require(rowsPerBlock > 0,
       s"rowsPerBlock needs to be greater than 0. rowsPerBlock: $rowsPerBlock")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
index 98e90af84abac..e51327ebb7b58 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
@@ -19,10 +19,12 @@ package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
 
+import org.apache.spark.annotation.Since
+
 /**
  * Represents a distributively stored matrix backed by one or more RDDs.
- * @since 1.0.0
  */
+@Since("1.0.0")
 trait DistributedMatrix extends Serializable {
 
   /** Gets or computes the number of rows. */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index a09f88ce28e58..6d2c05a47d049 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.linalg.distributed
 
 import breeze.linalg.{DenseMatrix => BDM}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.linalg.SingularValueDecomposition
@@ -27,8 +27,8 @@ import org.apache.spark.mllib.linalg.SingularValueDecomposition
 /**
  * :: Experimental ::
  * Represents a row of [[org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix]].
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 case class IndexedRow(index: Long, vector: Vector)
 
@@ -42,23 +42,19 @@ case class IndexedRow(index: Long, vector: Vector)
  *              be determined by the max row index plus one.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the size of the first row.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class IndexedRowMatrix(
     val rows: RDD[IndexedRow],
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically.
-    * @since 1.0.0
-    * */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  @Since("1.0.0")
   def this(rows: RDD[IndexedRow]) = this(rows, 0L, 0)
 
-  /**
-   *
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def numCols(): Long = {
     if (nCols <= 0) {
       // Calling `first` will throw an exception if `rows` is empty.
@@ -67,10 +63,7 @@ class IndexedRowMatrix(
     nCols
   }
 
-  /**
-   *
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   override def numRows(): Long = {
     if (nRows <= 0L) {
       // Reduce will throw an exception if `rows` is empty.
@@ -82,15 +75,14 @@ class IndexedRowMatrix(
   /**
    * Drops row indices and converts this matrix to a
    * [[org.apache.spark.mllib.linalg.distributed.RowMatrix]].
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def toRowMatrix(): RowMatrix = {
     new RowMatrix(rows.map(_.vector), 0L, nCols)
   }
 
-  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024.
-    * @since 1.3.0
-    * */
+  /** Converts to BlockMatrix. Creates blocks of [[SparseMatrix]] with size 1024 x 1024. */
+  @Since("1.3.0")
   def toBlockMatrix(): BlockMatrix = {
     toBlockMatrix(1024, 1024)
   }
@@ -102,8 +94,8 @@ class IndexedRowMatrix(
    * @param colsPerBlock The number of columns of each block. The blocks at the right edge may have
    *                     a smaller value. Must be an integer value greater than 0.
    * @return a [[BlockMatrix]]
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix = {
     // TODO: This implementation may be optimized
     toCoordinateMatrix().toBlockMatrix(rowsPerBlock, colsPerBlock)
@@ -112,8 +104,8 @@ class IndexedRowMatrix(
   /**
    * Converts this matrix to a
    * [[org.apache.spark.mllib.linalg.distributed.CoordinateMatrix]].
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def toCoordinateMatrix(): CoordinateMatrix = {
     val entries = rows.flatMap { row =>
       val rowIndex = row.index
@@ -149,8 +141,8 @@ class IndexedRowMatrix(
    * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
    *              are treated as zero, where sigma(0) is the largest singular value.
    * @return SingularValueDecomposition(U, s, V)
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeSVD(
       k: Int,
       computeU: Boolean = false,
@@ -176,8 +168,8 @@ class IndexedRowMatrix(
    *
    * @param B a local matrix whose number of rows must match the number of columns of this matrix
    * @return an IndexedRowMatrix representing the product, which preserves partitioning
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def multiply(B: Matrix): IndexedRowMatrix = {
     val mat = toRowMatrix().multiply(B)
     val indexedRows = rows.map(_.index).zip(mat.rows).map { case (i, v) =>
@@ -188,8 +180,8 @@ class IndexedRowMatrix(
 
   /**
    * Computes the Gramian matrix `A^T A`.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeGramianMatrix(): Matrix = {
     toRowMatrix().computeGramianMatrix()
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index b2e94f2dd6201..78036eba5c3e6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -28,7 +28,7 @@ import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.stat.{MultivariateOnlineSummarizer, MultivariateStatisticalSummary}
 import org.apache.spark.rdd.RDD
@@ -44,22 +44,20 @@ import org.apache.spark.storage.StorageLevel
  *              be determined by the number of records in the RDD `rows`.
  * @param nCols number of columns. A non-positive value means unknown, and then the number of
  *              columns will be determined by the size of the first row.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class RowMatrix(
     val rows: RDD[Vector],
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix with Logging {
 
-  /** Alternative constructor leaving matrix dimensions to be determined automatically.
-    * @since 1.0.0
-    * */
+  /** Alternative constructor leaving matrix dimensions to be determined automatically. */
+  @Since("1.0.0")
   def this(rows: RDD[Vector]) = this(rows, 0L, 0)
 
-  /** Gets or computes the number of columns.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of columns. */
+  @Since("1.0.0")
   override def numCols(): Long = {
     if (nCols <= 0) {
       try {
@@ -74,9 +72,8 @@ class RowMatrix(
     nCols
   }
 
-  /** Gets or computes the number of rows.
-    * @since 1.0.0
-    * */
+  /** Gets or computes the number of rows. */
+  @Since("1.0.0")
   override def numRows(): Long = {
     if (nRows <= 0L) {
       nRows = rows.count()
@@ -114,8 +111,8 @@ class RowMatrix(
 
   /**
    * Computes the Gramian matrix `A^T A`.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeGramianMatrix(): Matrix = {
     val n = numCols().toInt
     checkNumColumns(n)
@@ -185,8 +182,8 @@ class RowMatrix(
    * @param rCond the reciprocal condition number. All singular values smaller than rCond * sigma(0)
    *              are treated as zero, where sigma(0) is the largest singular value.
    * @return SingularValueDecomposition(U, s, V). U = null if computeU = false.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeSVD(
       k: Int,
       computeU: Boolean = false,
@@ -326,8 +323,8 @@ class RowMatrix(
   /**
    * Computes the covariance matrix, treating each row as an observation.
    * @return a local dense matrix of size n x n
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeCovariance(): Matrix = {
     val n = numCols().toInt
     checkNumColumns(n)
@@ -380,8 +377,8 @@ class RowMatrix(
    *
    * @param k number of top principal components.
    * @return a matrix of size n-by-k, whose columns are principal components
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computePrincipalComponents(k: Int): Matrix = {
     val n = numCols().toInt
     require(k > 0 && k <= n, s"k = $k out of range (0, n = $n]")
@@ -399,8 +396,8 @@ class RowMatrix(
 
   /**
    * Computes column-wise summary statistics.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def computeColumnSummaryStatistics(): MultivariateStatisticalSummary = {
     val summary = rows.treeAggregate(new MultivariateOnlineSummarizer)(
       (aggregator, data) => aggregator.add(data),
@@ -415,8 +412,8 @@ class RowMatrix(
    * @param B a local matrix whose number of rows must match the number of columns of this matrix
    * @return a [[org.apache.spark.mllib.linalg.distributed.RowMatrix]] representing the product,
    *         which preserves partitioning
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def multiply(B: Matrix): RowMatrix = {
     val n = numCols().toInt
     val k = B.numCols
@@ -448,8 +445,8 @@ class RowMatrix(
    *
    * @return An n x n sparse upper-triangular matrix of cosine similarities between
    *         columns of this matrix.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def columnSimilarities(): CoordinateMatrix = {
     columnSimilarities(0.0)
   }
@@ -492,8 +489,8 @@ class RowMatrix(
    *                  with the cost vs estimate quality trade-off described above.
    * @return An n x n sparse upper-triangular matrix of cosine similarities
    *         between columns of this matrix.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def columnSimilarities(threshold: Double): CoordinateMatrix = {
     require(threshold >= 0, s"Threshold cannot be negative: $threshold")
 
@@ -671,9 +668,7 @@ class RowMatrix(
   }
 }
 
-/**
- * @since 1.0.0
- */
+@Since("1.0.0")
 @Experimental
 object RowMatrix {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index 56c549ef99cb7..b27ef1b949e2e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.recommendation
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.ml.recommendation.{ALS => NewALS}
 import org.apache.spark.rdd.RDD
@@ -26,8 +26,8 @@ import org.apache.spark.storage.StorageLevel
 
 /**
  * A more compact class to represent a rating than Tuple3[Int, Int, Double].
- * @since 0.8.0
  */
+@Since("0.8.0")
 case class Rating(user: Int, product: Int, rating: Double)
 
 /**
@@ -255,8 +255,8 @@ class ALS private (
 
 /**
  * Top-level methods for calling Alternating Least Squares (ALS) matrix factorization.
- * @since 0.8.0
  */
+@Since("0.8.0")
 object ALS {
   /**
    * Train a matrix factorization model given an RDD of ratings given by users to some products,
@@ -271,8 +271,8 @@ object ALS {
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
    * @param seed       random seed
-   * @since 0.9.1
    */
+  @Since("0.9.1")
   def train(
       ratings: RDD[Rating],
       rank: Int,
@@ -296,8 +296,8 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       ratings: RDD[Rating],
       rank: Int,
@@ -319,8 +319,8 @@ object ALS {
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double)
     : MatrixFactorizationModel = {
     train(ratings, rank, iterations, lambda, -1)
@@ -336,8 +336,8 @@ object ALS {
    * @param ratings    RDD of (userID, productID, rating) pairs
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(ratings: RDD[Rating], rank: Int, iterations: Int)
     : MatrixFactorizationModel = {
     train(ratings, rank, iterations, 0.01, -1)
@@ -357,8 +357,8 @@ object ALS {
    * @param blocks     level of parallelism to split computation into
    * @param alpha      confidence parameter
    * @param seed       random seed
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(
       ratings: RDD[Rating],
       rank: Int,
@@ -384,8 +384,8 @@ object ALS {
    * @param lambda     regularization factor (recommended: 0.01)
    * @param blocks     level of parallelism to split computation into
    * @param alpha      confidence parameter
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(
       ratings: RDD[Rating],
       rank: Int,
@@ -409,8 +409,8 @@ object ALS {
    * @param iterations number of iterations of ALS (recommended: 10-20)
    * @param lambda     regularization factor (recommended: 0.01)
    * @param alpha      confidence parameter
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double)
     : MatrixFactorizationModel = {
     trainImplicit(ratings, rank, iterations, lambda, -1, alpha)
@@ -427,8 +427,8 @@ object ALS {
    * @param ratings    RDD of (userID, productID, rating) pairs
    * @param rank       number of features to use
    * @param iterations number of iterations of ALS (recommended: 10-20)
-   * @since 0.8.1
    */
+  @Since("0.8.1")
   def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int)
     : MatrixFactorizationModel = {
     trainImplicit(ratings, rank, iterations, 0.01, -1, 1.0)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index 261ca9cef0c5b..ba4cfdcd9f1dd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -30,6 +30,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkContext}
+import org.apache.spark.annotation.Since
 import org.apache.spark.api.java.{JavaPairRDD, JavaRDD}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.rdd.MLPairRDDFunctions._
@@ -49,8 +50,8 @@ import org.apache.spark.storage.StorageLevel
  *                     the features computed for this user.
  * @param productFeatures RDD of tuples where each tuple represents the productId
  *                        and the features computed for this product.
- * @since 0.8.0
  */
+@Since("0.8.0")
 class MatrixFactorizationModel(
     val rank: Int,
     val userFeatures: RDD[(Int, Array[Double])],
@@ -74,9 +75,8 @@ class MatrixFactorizationModel(
     }
   }
 
-  /** Predict the rating of one user for one product.
-   * @since 0.8.0
-   */
+  /** Predict the rating of one user for one product. */
+  @Since("0.8.0")
   def predict(user: Int, product: Int): Double = {
     val userVector = userFeatures.lookup(user).head
     val productVector = productFeatures.lookup(product).head
@@ -114,8 +114,8 @@ class MatrixFactorizationModel(
    *
    * @param usersProducts  RDD of (user, product) pairs.
    * @return RDD of Ratings.
-   * @since 0.9.0
    */
+  @Since("0.9.0")
   def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
     // Previously the partitions of ratings are only based on the given products.
     // So if the usersProducts given for prediction contains only few products or
@@ -146,8 +146,8 @@ class MatrixFactorizationModel(
 
   /**
    * Java-friendly version of [[MatrixFactorizationModel.predict]].
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def predict(usersProducts: JavaPairRDD[JavaInteger, JavaInteger]): JavaRDD[Rating] = {
     predict(usersProducts.rdd.asInstanceOf[RDD[(Int, Int)]]).toJavaRDD()
   }
@@ -162,8 +162,8 @@ class MatrixFactorizationModel(
    *  by score, decreasing. The first returned is the one predicted to be most strongly
    *  recommended to the user. The score is an opaque value that indicates how strongly
    *  recommended the product is.
-   *  @since 1.1.0
    */
+  @Since("1.1.0")
   def recommendProducts(user: Int, num: Int): Array[Rating] =
     MatrixFactorizationModel.recommend(userFeatures.lookup(user).head, productFeatures, num)
       .map(t => Rating(user, t._1, t._2))
@@ -179,8 +179,8 @@ class MatrixFactorizationModel(
    *  by score, decreasing. The first returned is the one predicted to be most strongly
    *  recommended to the product. The score is an opaque value that indicates how strongly
    *  recommended the user is.
-   *  @since 1.1.0
    */
+  @Since("1.1.0")
   def recommendUsers(product: Int, num: Int): Array[Rating] =
     MatrixFactorizationModel.recommend(productFeatures.lookup(product).head, userFeatures, num)
       .map(t => Rating(t._1, product, t._2))
@@ -199,8 +199,8 @@ class MatrixFactorizationModel(
    * @param sc  Spark context used to save model data.
    * @param path  Path specifying the directory in which to save this model.
    *              If the directory already exists, this method throws an exception.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     MatrixFactorizationModel.SaveLoadV1_0.save(this, path)
   }
@@ -212,8 +212,8 @@ class MatrixFactorizationModel(
    * @return [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of
    * rating objects which contains the same userId, recommended productID and a "score" in the
    * rating field. Semantics of score is same as recommendProducts API
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])] = {
     MatrixFactorizationModel.recommendForAll(rank, userFeatures, productFeatures, num).map {
       case (user, top) =>
@@ -230,8 +230,8 @@ class MatrixFactorizationModel(
    * @return [(Int, Array[Rating])] objects, where every tuple contains a productID and an array
    * of rating objects which contains the recommended userId, same productID and a "score" in the
    * rating field. Semantics of score is same as recommendUsers API
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])] = {
     MatrixFactorizationModel.recommendForAll(rank, productFeatures, userFeatures, num).map {
       case (product, top) =>
@@ -241,9 +241,7 @@ class MatrixFactorizationModel(
   }
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
 
   import org.apache.spark.mllib.util.Loader._
@@ -326,8 +324,8 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
    * @param sc  Spark context used for loading model files.
    * @param path  Path specifying the directory to which the model was saved.
    * @return  Model instance
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): MatrixFactorizationModel = {
     val (loadedClassName, formatVersion, _) = loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 2980b94de35b0..509f6a2d169c4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.feature.StandardScaler
 import org.apache.spark.{Logging, SparkException}
 import org.apache.spark.rdd.RDD
@@ -35,8 +35,8 @@ import org.apache.spark.storage.StorageLevel
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 @DeveloperApi
 abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double)
   extends Serializable {
@@ -56,8 +56,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    * @param testData RDD representing data points to be predicted
    * @return RDD[Double] where each entry contains the corresponding prediction
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: RDD[Vector]): RDD[Double] = {
     // A small optimization to avoid serializing the entire model. Only the weightsMatrix
     // and intercept is needed.
@@ -76,8 +76,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    * @param testData array representing a single data point
    * @return Double prediction from the trained model
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: Vector): Double = {
     predictPoint(testData, weights, intercept)
   }
@@ -95,8 +95,8 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
  * GeneralizedLinearAlgorithm implements methods to train a Generalized Linear Model (GLM).
  * This class should be extended with an Optimizer to create a new GLM.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 @DeveloperApi
 abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   extends Logging with Serializable {
@@ -106,8 +106,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * The optimizer to solve the problem.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def optimizer: Optimizer
 
   /** Whether to add intercept (default: false). */
@@ -143,8 +143,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * The dimension of training features.
    *
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def getNumFeatures: Int = this.numFeatures
 
   /**
@@ -168,16 +168,16 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * Get if the algorithm uses addIntercept
    *
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def isAddIntercept: Boolean = this.addIntercept
 
   /**
    * Set if the algorithm should add an intercept. Default false.
    * We set the default to false because adding the intercept will cause memory allocation.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setIntercept(addIntercept: Boolean): this.type = {
     this.addIntercept = addIntercept
     this
@@ -186,8 +186,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
   /**
    * Set if the algorithm should validate data before training. Default true.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def setValidateData(validateData: Boolean): this.type = {
     this.validateData = validateData
     this
@@ -197,8 +197,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    * Run the algorithm with the configured parameters on an input
    * RDD of LabeledPoint entries.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def run(input: RDD[LabeledPoint]): M = {
     if (numFeatures < 0) {
       numFeatures = input.map(_.features.size).first()
@@ -231,8 +231,8 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    * Run the algorithm with the configured parameters on an input RDD
    * of LabeledPoint entries starting from the initial weights provided.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def run(input: RDD[LabeledPoint], initialWeights: Vector): M = {
 
     if (numFeatures < 0) {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
index 8995591d9e8ce..31ca7c2f207d9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
@@ -29,7 +29,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.{Loader, Saveable}
@@ -47,8 +47,8 @@ import org.apache.spark.sql.SQLContext
  *                    Results of isotonic regression and therefore monotone.
  * @param isotonic indicates whether this is isotonic or antitonic.
  *
- * @since 1.3.0
  */
+@Since("1.3.0")
 @Experimental
 class IsotonicRegressionModel (
     val boundaries: Array[Double],
@@ -64,8 +64,8 @@ class IsotonicRegressionModel (
   /**
    * A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter.
    *
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def this(boundaries: java.lang.Iterable[Double],
       predictions: java.lang.Iterable[Double],
       isotonic: java.lang.Boolean) = {
@@ -90,8 +90,8 @@ class IsotonicRegressionModel (
    * @param testData Features to be labeled.
    * @return Predicted labels.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predict(testData: RDD[Double]): RDD[Double] = {
     testData.map(predict)
   }
@@ -103,8 +103,8 @@ class IsotonicRegressionModel (
    * @param testData Features to be labeled.
    * @return Predicted labels.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predict(testData: JavaDoubleRDD): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(predict(testData.rdd.retag.asInstanceOf[RDD[Double]]))
   }
@@ -125,8 +125,8 @@ class IsotonicRegressionModel (
    *           as piecewise linear function and interpolated value is returned. In case there are
    *           multiple values with the same boundary then the same rules as in 2) are used.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predict(testData: Double): Double = {
 
     def linearInterpolation(x1: Double, y1: Double, x2: Double, y2: Double, x: Double): Double = {
@@ -160,9 +160,7 @@ class IsotonicRegressionModel (
   /** A convenient method for boundaries called by the Python API. */
   private[mllib] def predictionVector: Vector = Vectors.dense(predictions)
 
-  /**
-   * @since 1.4.0
-   */
+  @Since("1.4.0")
   override def save(sc: SparkContext, path: String): Unit = {
     IsotonicRegressionModel.SaveLoadV1_0.save(sc, path, boundaries, predictions, isotonic)
   }
@@ -170,9 +168,7 @@ class IsotonicRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.4.0
- */
+@Since("1.4.0")
 object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
 
   import org.apache.spark.mllib.util.Loader._
@@ -219,8 +215,8 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
   }
 
   /**
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   override def load(sc: SparkContext, path: String): IsotonicRegressionModel = {
     implicit val formats = DefaultFormats
     val (loadedClassName, version, metadata) = loadMetadata(sc, path)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
index 8b51011eeb297..f7fe1b7b21fca 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
@@ -19,6 +19,7 @@ package org.apache.spark.mllib.regression
 
 import scala.beans.BeanInfo
 
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 import org.apache.spark.mllib.util.NumericParser
 import org.apache.spark.SparkException
@@ -29,8 +30,8 @@ import org.apache.spark.SparkException
  * @param label Label for this data point.
  * @param features List of features for this data point.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 @BeanInfo
 case class LabeledPoint(label: Double, features: Vector) {
   override def toString: String = {
@@ -41,15 +42,15 @@ case class LabeledPoint(label: Double, features: Vector) {
 /**
  * Parser for [[org.apache.spark.mllib.regression.LabeledPoint]].
  *
- * @since 1.1.0
  */
+@Since("1.1.0")
 object LabeledPoint {
   /**
    * Parses a string resulted from `LabeledPoint#toString` into
    * an [[org.apache.spark.mllib.regression.LabeledPoint]].
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def parse(s: String): LabeledPoint = {
     if (s.startsWith("(")) {
       NumericParser.parse(s) match {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index 03eb589b05a0e..556411a366bd2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.pmml.PMMLExportable
@@ -31,8 +32,8 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 class LassoModel (
     override val weights: Vector,
     override val intercept: Double)
@@ -46,9 +47,7 @@ class LassoModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -56,14 +55,10 @@ class LassoModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object LassoModel extends Loader[LassoModel] {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): LassoModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -118,8 +113,8 @@ class LassoWithSGD private (
 /**
  * Top-level methods for calling Lasso.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 object LassoWithSGD {
 
   /**
@@ -137,8 +132,8 @@ object LassoWithSGD {
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -162,8 +157,8 @@ object LassoWithSGD {
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -185,8 +180,8 @@ object LassoWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LassoModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -205,8 +200,8 @@ object LassoWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LassoModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int): LassoModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index fb5c220daaedb..00ab06e3ba738 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.pmml.PMMLExportable
@@ -31,8 +32,8 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 class LinearRegressionModel (
     override val weights: Vector,
     override val intercept: Double)
@@ -46,9 +47,7 @@ class LinearRegressionModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -56,14 +55,10 @@ class LinearRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object LinearRegressionModel extends Loader[LinearRegressionModel] {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): LinearRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -117,8 +112,8 @@ class LinearRegressionWithSGD private[mllib] (
 /**
  * Top-level methods for calling LinearRegression.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 object LinearRegressionWithSGD {
 
   /**
@@ -135,8 +130,8 @@ object LinearRegressionWithSGD {
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -158,8 +153,8 @@ object LinearRegressionWithSGD {
    * @param stepSize Step size to be used for each iteration of gradient descent.
    * @param miniBatchFraction Fraction of data to be used per iteration.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -179,8 +174,8 @@ object LinearRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LinearRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -198,8 +193,8 @@ object LinearRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a LinearRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int): LinearRegressionModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
index b097fd38fdd82..0e72d6591ce83 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RegressionModel.scala
@@ -19,14 +19,12 @@ package org.apache.spark.mllib.regression
 
 import org.json4s.{DefaultFormats, JValue}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
 
-/**
- * @since 0.8.0
- */
+@Since("0.8.0")
 @Experimental
 trait RegressionModel extends Serializable {
   /**
@@ -35,8 +33,8 @@ trait RegressionModel extends Serializable {
    * @param testData RDD representing data points to be predicted
    * @return RDD[Double] where each entry contains the corresponding prediction
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: RDD[Vector]): RDD[Double]
 
   /**
@@ -45,8 +43,8 @@ trait RegressionModel extends Serializable {
    * @param testData array representing a single data point
    * @return Double prediction from the trained model
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: Vector): Double
 
   /**
@@ -54,8 +52,8 @@ trait RegressionModel extends Serializable {
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
    *
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index 5bced6b4b7b53..21a791d98b2cb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.regression
 
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.pmml.PMMLExportable
@@ -32,8 +33,8 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 class RidgeRegressionModel (
     override val weights: Vector,
     override val intercept: Double)
@@ -47,9 +48,7 @@ class RidgeRegressionModel (
     weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
   }
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
   }
@@ -57,14 +56,10 @@ class RidgeRegressionModel (
   override protected def formatVersion: String = "1.0"
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object RidgeRegressionModel extends Loader[RidgeRegressionModel] {
 
-  /**
-   * @since 1.3.0
-   */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): RidgeRegressionModel = {
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
     // Hard-code class name string in case it changes in the future
@@ -120,8 +115,8 @@ class RidgeRegressionWithSGD private (
 /**
  * Top-level methods for calling RidgeRegression.
  *
- * @since 0.8.0
  */
+@Since("0.8.0")
 object RidgeRegressionWithSGD {
 
   /**
@@ -138,8 +133,8 @@ object RidgeRegressionWithSGD {
    * @param initialWeights Initial set of weights to be used. Array should be equal in size to
    *        the number of features in the data.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -162,8 +157,8 @@ object RidgeRegressionWithSGD {
    * @param regParam Regularization parameter.
    * @param miniBatchFraction Fraction of data to be used per iteration.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -184,8 +179,8 @@ object RidgeRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a RidgeRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
@@ -203,8 +198,8 @@ object RidgeRegressionWithSGD {
    * @param numIterations Number of iterations of gradient descent to run.
    * @return a RidgeRegressionModel which has the weights and offset from training.
    *
-   * @since 0.8.0
    */
+  @Since("0.8.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int): RidgeRegressionModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
index a2ab95c474765..cd3ed8a1549db 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.regression
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.streaming.api.java.{JavaDStream, JavaPairDStream}
@@ -54,8 +54,8 @@ import org.apache.spark.streaming.dstream.DStream
  * the model using each of the different sources, in sequence.
  *
  *
- * @since 1.1.0
  */
+@Since("1.1.0")
 @DeveloperApi
 abstract class StreamingLinearAlgorithm[
     M <: GeneralizedLinearModel,
@@ -70,8 +70,8 @@ abstract class StreamingLinearAlgorithm[
   /**
    * Return the latest model.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def latestModel(): M = {
     model.get
   }
@@ -84,8 +84,8 @@ abstract class StreamingLinearAlgorithm[
    *
    * @param data DStream containing labeled data
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def trainOn(data: DStream[LabeledPoint]): Unit = {
     if (model.isEmpty) {
       throw new IllegalArgumentException("Model must be initialized before starting training.")
@@ -106,8 +106,8 @@ abstract class StreamingLinearAlgorithm[
   /**
    * Java-friendly version of `trainOn`.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def trainOn(data: JavaDStream[LabeledPoint]): Unit = trainOn(data.dstream)
 
   /**
@@ -116,8 +116,8 @@ abstract class StreamingLinearAlgorithm[
    * @param data DStream containing feature vectors
    * @return DStream containing predictions
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def predictOn(data: DStream[Vector]): DStream[Double] = {
     if (model.isEmpty) {
       throw new IllegalArgumentException("Model must be initialized before starting prediction.")
@@ -128,8 +128,8 @@ abstract class StreamingLinearAlgorithm[
   /**
    * Java-friendly version of `predictOn`.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Double] = {
     JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Double]])
   }
@@ -140,8 +140,8 @@ abstract class StreamingLinearAlgorithm[
    * @tparam K key type
    * @return DStream containing the input keys and the predictions as values
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def predictOnValues[K: ClassTag](data: DStream[(K, Vector)]): DStream[(K, Double)] = {
     if (model.isEmpty) {
       throw new IllegalArgumentException("Model must be initialized before starting prediction")
@@ -153,8 +153,8 @@ abstract class StreamingLinearAlgorithm[
   /**
    * Java-friendly version of `predictOnValues`.
    *
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def predictOnValues[K](data: JavaPairDStream[K, Vector]): JavaPairDStream[K, java.lang.Double] = {
     implicit val tag = fakeClassTag[K]
     JavaPairDStream.fromPairDStream(
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
index 93a6753efd4d9..4a856f7f3434a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/KernelDensity.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.stat
 
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.rdd.RDD
 
@@ -37,8 +37,8 @@ import org.apache.spark.rdd.RDD
  *   .setBandwidth(3.0)
  * val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
  * }}}
- * @since 1.4.0
  */
+@Since("1.4.0")
 @Experimental
 class KernelDensity extends Serializable {
 
@@ -52,8 +52,8 @@ class KernelDensity extends Serializable {
 
   /**
    * Sets the bandwidth (standard deviation) of the Gaussian kernel (default: `1.0`).
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setBandwidth(bandwidth: Double): this.type = {
     require(bandwidth > 0, s"Bandwidth must be positive, but got $bandwidth.")
     this.bandwidth = bandwidth
@@ -62,8 +62,8 @@ class KernelDensity extends Serializable {
 
   /**
    * Sets the sample to use for density estimation.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setSample(sample: RDD[Double]): this.type = {
     this.sample = sample
     this
@@ -71,8 +71,8 @@ class KernelDensity extends Serializable {
 
   /**
    * Sets the sample to use for density estimation (for Java users).
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def setSample(sample: JavaRDD[java.lang.Double]): this.type = {
     this.sample = sample.rdd.asInstanceOf[RDD[Double]]
     this
@@ -80,8 +80,8 @@ class KernelDensity extends Serializable {
 
   /**
    * Estimates probability density function at the given array of points.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def estimate(points: Array[Double]): Array[Double] = {
     val sample = this.sample
     val bandwidth = this.bandwidth
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
index 64e4be0ebb97e..51b713e263e0c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.stat
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.linalg.{Vectors, Vector}
 
 /**
@@ -33,8 +33,8 @@ import org.apache.spark.mllib.linalg.{Vectors, Vector}
  * Reference: [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]]
  * Zero elements (including explicit zero values) are skipped when calling add(),
  * to have time complexity O(nnz) instead of O(n) for each column.
- * @since 1.1.0
  */
+@Since("1.1.0")
 @DeveloperApi
 class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with Serializable {
 
@@ -53,8 +53,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    *
    * @param sample The sample in dense/sparse vector format to be added into this summarizer.
    * @return This MultivariateOnlineSummarizer object.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def add(sample: Vector): this.type = {
     if (n == 0) {
       require(sample.size > 0, s"Vector should have dimension larger than zero.")
@@ -109,8 +109,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
    *
    * @param other The other MultivariateOnlineSummarizer to be merged.
    * @return This MultivariateOnlineSummarizer object.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def merge(other: MultivariateOnlineSummarizer): this.type = {
     if (this.totalCnt != 0 && other.totalCnt != 0) {
       require(n == other.n, s"Dimensions mismatch when merging with another summarizer. " +
@@ -155,8 +155,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * Sample mean of each dimension.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def mean: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -172,8 +172,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * Sample variance of each dimension.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def variance: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -199,15 +199,15 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * Sample size.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def count: Long = totalCnt
 
   /**
    * Number of nonzero elements in each dimension.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def numNonzeros: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -217,8 +217,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * Maximum value of each dimension.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def max: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -233,8 +233,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * Minimum value of each dimension.
    *
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   override def min: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -249,8 +249,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * L2 (Euclidian) norm of each dimension.
    *
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   override def normL2: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
@@ -268,8 +268,8 @@ class MultivariateOnlineSummarizer extends MultivariateStatisticalSummary with S
   /**
    * L1 norm of each dimension.
    *
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   override def normL1: Vector = {
     require(totalCnt > 0, s"Nothing has been added to this summarizer.")
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala
index 3bb49f12289e1..39a16fb743d64 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.scala
@@ -17,59 +17,60 @@
 
 package org.apache.spark.mllib.stat
 
+import org.apache.spark.annotation.Since
 import org.apache.spark.mllib.linalg.Vector
 
 /**
  * Trait for multivariate statistical summary of a data matrix.
- * @since 1.0.0
  */
+@Since("1.0.0")
 trait MultivariateStatisticalSummary {
 
   /**
    * Sample mean vector.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def mean: Vector
 
   /**
    * Sample variance vector. Should return a zero vector if the sample size is 1.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def variance: Vector
 
   /**
    * Sample size.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def count: Long
 
   /**
    * Number of nonzero elements (including explicitly presented zero values) in each column.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def numNonzeros: Vector
 
   /**
    * Maximum value of each column.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def max: Vector
 
   /**
    * Minimum value of each column.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def min: Vector
 
   /**
    * Euclidean magnitude of each column
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def normL2: Vector
 
   /**
    * L1 norm of each column
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def normL1: Vector
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
index ef8d78607048f..84d64a5bfb38e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.stat
 
 import scala.annotation.varargs
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.{JavaRDD, JavaDoubleRDD}
 import org.apache.spark.mllib.linalg.distributed.RowMatrix
 import org.apache.spark.mllib.linalg.{Matrix, Vector}
@@ -32,8 +32,8 @@ import org.apache.spark.rdd.RDD
 /**
  * :: Experimental ::
  * API for statistical functions in MLlib.
- * @since 1.1.0
  */
+@Since("1.1.0")
 @Experimental
 object Statistics {
 
@@ -42,8 +42,8 @@ object Statistics {
    *
    * @param X an RDD[Vector] for which column-wise summary statistics are to be computed.
    * @return [[MultivariateStatisticalSummary]] object containing column-wise summary statistics.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def colStats(X: RDD[Vector]): MultivariateStatisticalSummary = {
     new RowMatrix(X).computeColumnSummaryStatistics()
   }
@@ -54,8 +54,8 @@ object Statistics {
    *
    * @param X an RDD[Vector] for which the correlation matrix is to be computed.
    * @return Pearson correlation matrix comparing columns in X.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def corr(X: RDD[Vector]): Matrix = Correlations.corrMatrix(X)
 
   /**
@@ -71,8 +71,8 @@ object Statistics {
    * @param method String specifying the method to use for computing correlation.
    *               Supported: `pearson` (default), `spearman`
    * @return Correlation matrix comparing columns in X.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def corr(X: RDD[Vector], method: String): Matrix = Correlations.corrMatrix(X, method)
 
   /**
@@ -85,14 +85,14 @@ object Statistics {
    * @param x RDD[Double] of the same cardinality as y.
    * @param y RDD[Double] of the same cardinality as x.
    * @return A Double containing the Pearson correlation between the two input RDD[Double]s
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def corr(x: RDD[Double], y: RDD[Double]): Double = Correlations.corr(x, y)
 
   /**
    * Java-friendly version of [[corr()]]
-   * @since 1.4.1
    */
+  @Since("1.4.1")
   def corr(x: JavaRDD[java.lang.Double], y: JavaRDD[java.lang.Double]): Double =
     corr(x.rdd.asInstanceOf[RDD[Double]], y.rdd.asInstanceOf[RDD[Double]])
 
@@ -109,14 +109,14 @@ object Statistics {
    *               Supported: `pearson` (default), `spearman`
    * @return A Double containing the correlation between the two input RDD[Double]s using the
    *         specified method.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def corr(x: RDD[Double], y: RDD[Double], method: String): Double = Correlations.corr(x, y, method)
 
   /**
    * Java-friendly version of [[corr()]]
-   * @since 1.4.1
    */
+  @Since("1.4.1")
   def corr(x: JavaRDD[java.lang.Double], y: JavaRDD[java.lang.Double], method: String): Double =
     corr(x.rdd.asInstanceOf[RDD[Double]], y.rdd.asInstanceOf[RDD[Double]], method)
 
@@ -133,8 +133,8 @@ object Statistics {
    *                 `expected` is rescaled if the `expected` sum differs from the `observed` sum.
    * @return ChiSquaredTest object containing the test statistic, degrees of freedom, p-value,
    *         the method used, and the null hypothesis.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def chiSqTest(observed: Vector, expected: Vector): ChiSqTestResult = {
     ChiSqTest.chiSquared(observed, expected)
   }
@@ -148,8 +148,8 @@ object Statistics {
    * @param observed Vector containing the observed categorical counts/relative frequencies.
    * @return ChiSquaredTest object containing the test statistic, degrees of freedom, p-value,
    *         the method used, and the null hypothesis.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def chiSqTest(observed: Vector): ChiSqTestResult = ChiSqTest.chiSquared(observed)
 
   /**
@@ -159,8 +159,8 @@ object Statistics {
    * @param observed The contingency matrix (containing either counts or relative frequencies).
    * @return ChiSquaredTest object containing the test statistic, degrees of freedom, p-value,
    *         the method used, and the null hypothesis.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def chiSqTest(observed: Matrix): ChiSqTestResult = ChiSqTest.chiSquaredMatrix(observed)
 
   /**
@@ -172,13 +172,14 @@ object Statistics {
    *             Real-valued features will be treated as categorical for each distinct value.
    * @return an array containing the ChiSquaredTestResult for every feature against the label.
    *         The order of the elements in the returned array reflects the order of input features.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def chiSqTest(data: RDD[LabeledPoint]): Array[ChiSqTestResult] = {
     ChiSqTest.chiSquaredFeatures(data)
   }
 
   /** Java-friendly version of [[chiSqTest()]] */
+  @Since("1.5.0")
   def chiSqTest(data: JavaRDD[LabeledPoint]): Array[ChiSqTestResult] = chiSqTest(data.rdd)
 
   /**
@@ -194,6 +195,7 @@ object Statistics {
    * @return [[org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult]] object containing test
    *        statistic, p-value, and null hypothesis.
    */
+  @Since("1.5.0")
   def kolmogorovSmirnovTest(data: RDD[Double], cdf: Double => Double)
     : KolmogorovSmirnovTestResult = {
     KolmogorovSmirnovTest.testOneSample(data, cdf)
@@ -210,6 +212,7 @@ object Statistics {
    * @return [[org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult]] object containing test
    *        statistic, p-value, and null hypothesis.
    */
+  @Since("1.5.0")
   @varargs
   def kolmogorovSmirnovTest(data: RDD[Double], distName: String, params: Double*)
     : KolmogorovSmirnovTestResult = {
@@ -217,6 +220,7 @@ object Statistics {
   }
 
   /** Java-friendly version of [[kolmogorovSmirnovTest()]] */
+  @Since("1.5.0")
   @varargs
   def kolmogorovSmirnovTest(
       data: JavaDoubleRDD,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
index 9aa7763d7890d..bd4d81390bfae 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.stat.distribution
 
 import breeze.linalg.{DenseVector => DBV, DenseMatrix => DBM, diag, max, eigSym, Vector => BV}
 
-import org.apache.spark.annotation.DeveloperApi;
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.linalg.{Vectors, Vector, Matrices, Matrix}
 import org.apache.spark.mllib.util.MLUtils
 
@@ -32,8 +32,8 @@ import org.apache.spark.mllib.util.MLUtils
  *
  * @param mu The mean vector of the distribution
  * @param sigma The covariance matrix of the distribution
- * @since 1.3.0
  */
+@Since("1.3.0")
 @DeveloperApi
 class MultivariateGaussian (
     val mu: Vector,
@@ -62,15 +62,15 @@ class MultivariateGaussian (
   private val (rootSigmaInv: DBM[Double], u: Double) = calculateCovarianceConstants
 
   /** Returns density of this multivariate Gaussian at given point, x
-    * @since 1.3.0
     */
+   @Since("1.3.0")
   def pdf(x: Vector): Double = {
     pdf(x.toBreeze)
   }
 
   /** Returns the log-density of this multivariate Gaussian at given point, x
-    * @since 1.3.0
     */
+   @Since("1.3.0")
   def logpdf(x: Vector): Double = {
     logpdf(x.toBreeze)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index e5200b86fddd4..972841015d4f0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -22,7 +22,7 @@ import scala.collection.mutable
 import scala.collection.mutable.ArrayBuilder
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.RandomForest.NodeIndexInfo
@@ -43,8 +43,8 @@ import org.apache.spark.util.random.XORShiftRandom
  * @param strategy The configuration parameters for the tree algorithm which specify the type
  *                 of algorithm (classification, regression, etc.), feature type (continuous,
  *                 categorical), depth of the tree, quantile calculation strategy, etc.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class DecisionTree (private val strategy: Strategy) extends Serializable with Logging {
 
@@ -54,8 +54,8 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
    * Method to train a decision tree model over an RDD
    * @param input Training data: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]]
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def run(input: RDD[LabeledPoint]): DecisionTreeModel = {
     // Note: random seed will not be used since numTrees = 1.
     val rf = new RandomForest(strategy, numTrees = 1, featureSubsetStrategy = "all", seed = 0)
@@ -64,9 +64,7 @@ class DecisionTree (private val strategy: Strategy) extends Serializable with Lo
   }
 }
 
-/**
- * @since 1.0.0
- */
+@Since("1.0.0")
 object DecisionTree extends Serializable with Logging {
 
   /**
@@ -84,8 +82,8 @@ object DecisionTree extends Serializable with Logging {
    *                 of algorithm (classification, regression, etc.), feature type (continuous,
    *                 categorical), depth of the tree, quantile calculation strategy, etc.
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.0.0
-  */
+   */
+ @Since("1.0.0")
   def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel = {
     new DecisionTree(strategy).run(input)
   }
@@ -106,8 +104,8 @@ object DecisionTree extends Serializable with Logging {
    * @param maxDepth Maximum depth of the tree.
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       algo: Algo,
@@ -134,8 +132,8 @@ object DecisionTree extends Serializable with Logging {
    *                 E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.
    * @param numClasses number of classes for classification. Default value of 2.
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def train(
       input: RDD[LabeledPoint],
       algo: Algo,
@@ -168,8 +166,8 @@ object DecisionTree extends Serializable with Logging {
    *                                E.g., an entry (n -> k) indicates that feature n is categorical
    *                                with k categories indexed from 0: {0, 1, ..., k-1}.
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       algo: Algo,
@@ -201,8 +199,8 @@ object DecisionTree extends Serializable with Logging {
    * @param maxBins maximum number of bins used for splitting features
    *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def trainClassifier(
       input: RDD[LabeledPoint],
       numClasses: Int,
@@ -217,8 +215,8 @@ object DecisionTree extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.DecisionTree$#trainClassifier]]
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def trainClassifier(
       input: JavaRDD[LabeledPoint],
       numClasses: Int,
@@ -247,8 +245,8 @@ object DecisionTree extends Serializable with Logging {
    * @param maxBins maximum number of bins used for splitting features
    *                 (suggested value: 32)
    * @return DecisionTreeModel that can be used for prediction
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def trainRegressor(
       input: RDD[LabeledPoint],
       categoricalFeaturesInfo: Map[Int, Int],
@@ -261,8 +259,8 @@ object DecisionTree extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.DecisionTree$#trainRegressor]]
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def trainRegressor(
       input: JavaRDD[LabeledPoint],
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer],
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
index 143617098637a..e750408600c33 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.tree
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.impl.PeriodicRDDCheckpointer
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -48,8 +48,8 @@ import org.apache.spark.storage.StorageLevel
  *       for other loss functions.
  *
  * @param boostingStrategy Parameters for the gradient boosting algorithm.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
   extends Serializable with Logging {
@@ -58,8 +58,8 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
    * Method to train a gradient boosting model
    * @param input Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    * @return a gradient boosted trees model that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def run(input: RDD[LabeledPoint]): GradientBoostedTreesModel = {
     val algo = boostingStrategy.treeStrategy.algo
     algo match {
@@ -76,8 +76,8 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees!#run]].
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def run(input: JavaRDD[LabeledPoint]): GradientBoostedTreesModel = {
     run(input.rdd)
   }
@@ -91,8 +91,8 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
    *                        E.g., these two datasets could be created from an original dataset
    *                        by using [[org.apache.spark.rdd.RDD.randomSplit()]]
    * @return a gradient boosted trees model that can be used for prediction
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def runWithValidation(
       input: RDD[LabeledPoint],
       validationInput: RDD[LabeledPoint]): GradientBoostedTreesModel = {
@@ -115,8 +115,8 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees!#runWithValidation]].
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def runWithValidation(
       input: JavaRDD[LabeledPoint],
       validationInput: JavaRDD[LabeledPoint]): GradientBoostedTreesModel = {
@@ -124,9 +124,7 @@ class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
   }
 }
 
-/**
- * @since 1.2.0
- */
+@Since("1.2.0")
 object GradientBoostedTrees extends Logging {
 
   /**
@@ -137,8 +135,8 @@ object GradientBoostedTrees extends Logging {
    *              For regression, labels are real numbers.
    * @param boostingStrategy Configuration options for the boosting algorithm.
    * @return a gradient boosted trees model that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def train(
       input: RDD[LabeledPoint],
       boostingStrategy: BoostingStrategy): GradientBoostedTreesModel = {
@@ -147,8 +145,8 @@ object GradientBoostedTrees extends Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.GradientBoostedTrees$#train]]
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def train(
       input: JavaRDD[LabeledPoint],
       boostingStrategy: BoostingStrategy): GradientBoostedTreesModel = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
index 9f3230656acc5..63a902f3eb51e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/RandomForest.scala
@@ -23,7 +23,7 @@ import scala.collection.mutable
 import scala.collection.JavaConverters._
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.configuration.Strategy
@@ -260,9 +260,7 @@ private class RandomForest (
 
 }
 
-/**
- * @since 1.2.0
- */
+@Since("1.2.0")
 object RandomForest extends Serializable with Logging {
 
   /**
@@ -279,8 +277,8 @@ object RandomForest extends Serializable with Logging {
    *                                if numTrees > 1 (forest) set to "sqrt".
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainClassifier(
       input: RDD[LabeledPoint],
       strategy: Strategy,
@@ -317,8 +315,8 @@ object RandomForest extends Serializable with Logging {
    *                 (suggested value: 100)
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model  that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainClassifier(
       input: RDD[LabeledPoint],
       numClasses: Int,
@@ -337,8 +335,8 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.RandomForest$#trainClassifier]]
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainClassifier(
       input: JavaRDD[LabeledPoint],
       numClasses: Int,
@@ -368,8 +366,8 @@ object RandomForest extends Serializable with Logging {
    *                                if numTrees > 1 (forest) set to "onethird".
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainRegressor(
       input: RDD[LabeledPoint],
       strategy: Strategy,
@@ -405,8 +403,8 @@ object RandomForest extends Serializable with Logging {
    *                 (suggested value: 100)
    * @param seed  Random seed for bootstrapping and choosing feature subsets.
    * @return a random forest model that can be used for prediction
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainRegressor(
       input: RDD[LabeledPoint],
       categoricalFeaturesInfo: Map[Int, Int],
@@ -424,8 +422,8 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * Java-friendly API for [[org.apache.spark.mllib.tree.RandomForest$#trainRegressor]]
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def trainRegressor(
       input: JavaRDD[LabeledPoint],
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer],
@@ -442,8 +440,8 @@ object RandomForest extends Serializable with Logging {
 
   /**
    * List of supported feature subset sampling strategies.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   val supportedFeatureSubsetStrategies: Array[String] =
     Array("auto", "all", "sqrt", "log2", "onethird")
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
index d9a49aa71fcfb..8301ad160836b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.mllib.tree.configuration
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 
 /**
  * :: Experimental ::
  * Enum to select the algorithm for the decision tree
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object Algo extends Enumeration {
   type Algo = Value
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index 88e5f57e9ab32..7c569981977b4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.tree.configuration
 
 import scala.beans.BeanProperty
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
 
@@ -38,8 +38,8 @@ import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
  *                      validation input between two iterations is less than the validationTol
  *                      then stop.  Ignored when
  *                      [[org.apache.spark.mllib.tree.GradientBoostedTrees.run()]] is used.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 case class BoostingStrategy(
     // Required boosting parameters
@@ -71,9 +71,7 @@ case class BoostingStrategy(
   }
 }
 
-/**
- * @since 1.2.0
- */
+@Since("1.2.0")
 @Experimental
 object BoostingStrategy {
 
@@ -81,8 +79,8 @@ object BoostingStrategy {
    * Returns default configuration for the boosting algorithm
    * @param algo Learning goal.  Supported: "Classification" or "Regression"
    * @return Configuration for boosting algorithm
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def defaultParams(algo: String): BoostingStrategy = {
     defaultParams(Algo.fromString(algo))
   }
@@ -93,8 +91,8 @@ object BoostingStrategy {
    *             [[org.apache.spark.mllib.tree.configuration.Algo.Classification]],
    *             [[org.apache.spark.mllib.tree.configuration.Algo.Regression]]
    * @return Configuration for boosting algorithm
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def defaultParams(algo: Algo): BoostingStrategy = {
     val treeStrategy = Strategy.defaultStrategy(algo)
     treeStrategy.maxDepth = 3
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
index 0684cafa486bd..bb7c7ee4f964f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.mllib.tree.configuration
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 
 /**
  * :: Experimental ::
  * Enum to describe whether a feature is "continuous" or "categorical"
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object FeatureType extends Enumeration {
   type FeatureType = Value
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
index 2daa63c4d2771..904e42deebb5f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.mllib.tree.configuration
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 
 /**
  * :: Experimental ::
  * Enum for selecting the quantile calculation strategy
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object QuantileStrategy extends Enumeration {
   type QuantileStrategy = Value
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index 7ae25a88bf500..a58f01ba8544e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.tree.configuration
 import scala.beans.BeanProperty
 import scala.collection.JavaConverters._
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.tree.impurity.{Variance, Entropy, Gini, Impurity}
 import org.apache.spark.mllib.tree.configuration.Algo._
 import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
@@ -66,8 +66,8 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  *                           E.g. 10 means that the cache will get checkpointed every 10 updates. If
  *                           the checkpoint directory is not set in
  *                           [[org.apache.spark.SparkContext]], this setting is ignored.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class Strategy (
     @BeanProperty var algo: Algo,
@@ -85,23 +85,23 @@ class Strategy (
     @BeanProperty var checkpointInterval: Int = 10) extends Serializable {
 
   /**
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def isMulticlassClassification: Boolean = {
     algo == Classification && numClasses > 2
   }
 
   /**
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def isMulticlassWithCategoricalFeatures: Boolean = {
     isMulticlassClassification && (categoricalFeaturesInfo.size > 0)
   }
 
   /**
    * Java-friendly constructor for [[org.apache.spark.mllib.tree.configuration.Strategy]]
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def this(
       algo: Algo,
       impurity: Impurity,
@@ -115,8 +115,8 @@ class Strategy (
 
   /**
    * Sets Algorithm using a String.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setAlgo(algo: String): Unit = algo match {
     case "Classification" => setAlgo(Classification)
     case "Regression" => setAlgo(Regression)
@@ -124,8 +124,8 @@ class Strategy (
 
   /**
    * Sets categoricalFeaturesInfo using a Java Map.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def setCategoricalFeaturesInfo(
       categoricalFeaturesInfo: java.util.Map[java.lang.Integer, java.lang.Integer]): Unit = {
     this.categoricalFeaturesInfo =
@@ -174,8 +174,8 @@ class Strategy (
 
   /**
    * Returns a shallow copy of this instance.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def copy: Strategy = {
     new Strategy(algo, impurity, maxDepth, numClasses, maxBins,
       quantileCalculationStrategy, categoricalFeaturesInfo, minInstancesPerNode, minInfoGain,
@@ -183,17 +183,15 @@ class Strategy (
   }
 }
 
-/**
- * @since 1.2.0
- */
+@Since("1.2.0")
 @Experimental
 object Strategy {
 
   /**
    * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
    * @param algo  "Classification" or "Regression"
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def defaultStrategy(algo: String): Strategy = {
     defaultStrategy(Algo.fromString(algo))
   }
@@ -201,8 +199,8 @@ object Strategy {
   /**
    * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
    * @param algo Algo.Classification or Algo.Regression
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   def defaultStrategy(algo: Algo): Strategy = algo match {
     case Algo.Classification =>
       new Strategy(algo = Classification, impurity = Gini, maxDepth = 10,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
index 0b6c7266dee05..73df6b054a8ce 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Entropy.scala
@@ -17,14 +17,14 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 
 /**
  * :: Experimental ::
  * Class for calculating [[http://en.wikipedia.org/wiki/Binary_entropy_function entropy]] during
  * binary classification.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object Entropy extends Impurity {
 
@@ -36,8 +36,8 @@ object Entropy extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double = {
     if (totalCount == 0) {
@@ -64,8 +64,8 @@ object Entropy extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
     throw new UnsupportedOperationException("Entropy.calculate")
@@ -73,8 +73,8 @@ object Entropy extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def instance: this.type = this
 
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
index 3b0be428833ae..f21845b21a802 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Gini.scala
@@ -17,15 +17,15 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 
 /**
  * :: Experimental ::
  * Class for calculating the
  * [[http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity Gini impurity]]
  * during binary classification.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object Gini extends Impurity {
 
@@ -35,8 +35,8 @@ object Gini extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double = {
     if (totalCount == 0) {
@@ -60,8 +60,8 @@ object Gini extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double =
     throw new UnsupportedOperationException("Gini.calculate")
@@ -69,8 +69,8 @@ object Gini extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def instance: this.type = this
 
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
index dd297400058d2..4637dcceea7f8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Impurity.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 
 /**
  * :: Experimental ::
@@ -25,8 +25,8 @@ import org.apache.spark.annotation.{DeveloperApi, Experimental}
  * This trait is used for
  *  (a) setting the impurity parameter in [[org.apache.spark.mllib.tree.configuration.Strategy]]
  *  (b) calculating impurity values from sufficient statistics.
- *  @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 trait Impurity extends Serializable {
 
@@ -36,8 +36,8 @@ trait Impurity extends Serializable {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   @DeveloperApi
   def calculate(counts: Array[Double], totalCount: Double): Double
 
@@ -48,8 +48,8 @@ trait Impurity extends Serializable {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @DeveloperApi
   def calculate(count: Double, sum: Double, sumSquares: Double): Double
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
index adbe05811f286..a74197278d6f7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/impurity/Variance.scala
@@ -17,13 +17,13 @@
 
 package org.apache.spark.mllib.tree.impurity
 
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 
 /**
  * :: Experimental ::
  * Class for calculating variance during regression
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 object Variance extends Impurity {
 
@@ -33,8 +33,8 @@ object Variance extends Impurity {
    * @param counts Array[Double] with counts for each label
    * @param totalCount sum of counts for all labels
    * @return information value, or 0 if totalCount = 0
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   @DeveloperApi
   override def calculate(counts: Array[Double], totalCount: Double): Double =
      throw new UnsupportedOperationException("Variance.calculate")
@@ -46,8 +46,8 @@ object Variance extends Impurity {
    * @param sum sum of labels
    * @param sumSquares summation of squares of the labels
    * @return information value, or 0 if count = 0
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @DeveloperApi
   override def calculate(count: Double, sum: Double, sumSquares: Double): Double = {
     if (count == 0) {
@@ -60,8 +60,8 @@ object Variance extends Impurity {
   /**
    * Get this impurity instance.
    * This is useful for passing impurity parameters to a Strategy in Java.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def instance: this.type = this
 
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
index c6e3d0d824dd7..bab7b8c6cadf2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/AbsoluteError.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.loss
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.model.TreeEnsembleModel
 
@@ -29,8 +29,8 @@ import org.apache.spark.mllib.tree.model.TreeEnsembleModel
  * The absolute (L1) error is defined as:
  *  |y - F(x)|
  * where y is the label and F(x) is the model prediction for features x.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @DeveloperApi
 object AbsoluteError extends Loss {
 
@@ -41,8 +41,8 @@ object AbsoluteError extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   override def gradient(prediction: Double, label: Double): Double = {
     if (label - prediction < 0) 1.0 else -1.0
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
index eee58445a1ec1..b2b4594712f0d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/LogLoss.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.loss
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.model.TreeEnsembleModel
 import org.apache.spark.mllib.util.MLUtils
@@ -31,8 +31,8 @@ import org.apache.spark.mllib.util.MLUtils
  * The log loss is defined as:
  *   2 log(1 + exp(-2 y F(x)))
  * where y is a label in {-1, 1} and F(x) is the model prediction for features x.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @DeveloperApi
 object LogLoss extends Loss {
 
@@ -43,8 +43,8 @@ object LogLoss extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   override def gradient(prediction: Double, label: Double): Double = {
     - 4.0 * label / (1.0 + math.exp(2.0 * label * prediction))
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
index 7c9fb924645c8..687cde325ffed 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Loss.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.loss
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.model.TreeEnsembleModel
 import org.apache.spark.rdd.RDD
@@ -26,8 +26,8 @@ import org.apache.spark.rdd.RDD
 /**
  * :: DeveloperApi ::
  * Trait for adding "pluggable" loss functions for the gradient boosting algorithm.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @DeveloperApi
 trait Loss extends Serializable {
 
@@ -36,8 +36,8 @@ trait Loss extends Serializable {
    * @param prediction Predicted feature
    * @param label true label.
    * @return Loss gradient.
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def gradient(prediction: Double, label: Double): Double
 
   /**
@@ -47,8 +47,8 @@ trait Loss extends Serializable {
    * @param model Model of the weak learner.
    * @param data Training dataset: RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    * @return Measure of model error on data
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def computeError(model: TreeEnsembleModel, data: RDD[LabeledPoint]): Double = {
     data.map(point => computeError(model.predict(point.features), point.label)).mean()
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
index 47dc94cde7e03..2b112fbe12202 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/Losses.scala
@@ -17,14 +17,12 @@
 
 package org.apache.spark.mllib.tree.loss
 
-/**
- * @since 1.2.0
- */
+import org.apache.spark.annotation.Since
+
+@Since("1.2.0")
 object Losses {
 
-  /**
-   * @since 1.2.0
-   */
+  @Since("1.2.0")
   def fromString(name: String): Loss = name match {
     case "leastSquaresError" => SquaredError
     case "leastAbsoluteError" => AbsoluteError
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
index ff8903d6956bd..3f7d3d38be16c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/loss/SquaredError.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.loss
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.tree.model.TreeEnsembleModel
 
@@ -29,8 +29,8 @@ import org.apache.spark.mllib.tree.model.TreeEnsembleModel
  * The squared (L2) error is defined as:
  *   (y - F(x))**2
  * where y is the label and F(x) is the model prediction for features x.
- * @since 1.2.0
  */
+@Since("1.2.0")
 @DeveloperApi
 object SquaredError extends Loss {
 
@@ -41,8 +41,8 @@ object SquaredError extends Loss {
    * @param prediction Predicted label.
    * @param label True label.
    * @return Loss gradient
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   override def gradient(prediction: Double, label: Double): Double = {
     - 2.0 * (label - prediction)
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
index 0f386a26601ce..3eefd135f7836 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
@@ -24,7 +24,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkContext}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.tree.configuration.{Algo, FeatureType}
@@ -40,8 +40,8 @@ import org.apache.spark.util.Utils
  * This model stores the decision tree structure and parameters.
  * @param topNode root node
  * @param algo algorithm type -- classification or regression
- * @since 1.0.0
  */
+@Since("1.0.0")
 @Experimental
 class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable with Saveable {
 
@@ -50,8 +50,8 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features array representing a single data point
    * @return Double prediction from the trained model
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(features: Vector): Double = {
     topNode.predict(features)
   }
@@ -61,8 +61,8 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features RDD representing data points to be predicted
    * @return RDD of predictions for each of the given data points
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def predict(features: RDD[Vector]): RDD[Double] = {
     features.map(x => predict(x))
   }
@@ -72,16 +72,16 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    *
    * @param features JavaRDD representing data points to be predicted
    * @return JavaRDD of predictions for each of the given data points
-   * @since 1.2.0
    */
+  @Since("1.2.0")
   def predict(features: JavaRDD[Vector]): JavaRDD[Double] = {
     predict(features.rdd)
   }
 
   /**
    * Get number of nodes in tree, including leaf nodes.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def numNodes: Int = {
     1 + topNode.numDescendants
   }
@@ -89,8 +89,8 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
   /**
    * Get depth of tree.
    * E.g.: Depth 0 means 1 leaf node.  Depth 1 means 1 internal node and 2 leaf nodes.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def depth: Int = {
     topNode.subtreeDepth
   }
@@ -119,8 +119,8 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
    * @param sc  Spark context used to save model data.
    * @param path  Path specifying the directory in which to save this model.
    *              If the directory already exists, this method throws an exception.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     DecisionTreeModel.SaveLoadV1_0.save(sc, path, this)
   }
@@ -128,9 +128,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
   override protected def formatVersion: String = DecisionTreeModel.formatVersion
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
 
   private[spark] def formatVersion: String = "1.0"
@@ -317,8 +315,8 @@ object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
    * @param sc  Spark context used for loading model files.
    * @param path  Path specifying the directory to which the model was saved.
    * @return  Model instance
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): DecisionTreeModel = {
     implicit val formats = DefaultFormats
     val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
index 23f0363639120..091a0462c204f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/InformationGainStats.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.model
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
 
 /**
@@ -29,8 +29,8 @@ import org.apache.spark.mllib.tree.impurity.ImpurityCalculator
  * @param rightImpurity right node impurity
  * @param leftPredict left node predict
  * @param rightPredict right node predict
- * @since 1.0.0
  */
+@Since("1.0.0")
 @DeveloperApi
 class InformationGainStats(
     val gain: Double,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
index aca3350c2e535..8c54c55107233 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.model
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.Logging
 import org.apache.spark.mllib.tree.configuration.FeatureType._
 import org.apache.spark.mllib.linalg.Vector
@@ -38,8 +38,8 @@ import org.apache.spark.mllib.linalg.Vector
  * @param leftNode  left child
  * @param rightNode right child
  * @param stats information gain stats
- * @since 1.0.0
  */
+@Since("1.0.0")
 @DeveloperApi
 class Node (
     val id: Int,
@@ -59,8 +59,8 @@ class Node (
   /**
    * build the left node and right nodes if not leaf
    * @param nodes array of nodes
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @deprecated("build should no longer be used since trees are constructed on-the-fly in training",
     "1.2.0")
   def build(nodes: Array[Node]): Unit = {
@@ -81,8 +81,8 @@ class Node (
    * predict value if node is not leaf
    * @param features feature value
    * @return predicted value
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def predict(features: Vector) : Double = {
     if (isLeaf) {
       predict.predict
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
index be819b59e7048..965784051ede5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
@@ -17,14 +17,14 @@
 
 package org.apache.spark.mllib.tree.model
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 
 /**
  * Predicted value for a node
  * @param predict predicted value
  * @param prob probability of the label (classification only)
- * @since 1.2.0
  */
+@Since("1.2.0")
 @DeveloperApi
 class Predict(
     val predict: Double,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
index 18d40530aee1e..45db83ae3a1f3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.tree.model
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
 import org.apache.spark.mllib.tree.configuration.FeatureType
 import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
@@ -30,8 +30,8 @@ import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
  *                  Split left if feature <= threshold, else right.
  * @param featureType type of feature -- categorical or continuous
  * @param categories Split left if categorical feature value is in this set, else right.
- * @since 1.0.0
  */
+@Since("1.0.0")
 @DeveloperApi
 case class Split(
     feature: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
index 0c629b12a84df..19571447a2c56 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
@@ -25,7 +25,7 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.{Logging, SparkContext}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -45,8 +45,8 @@ import org.apache.spark.util.Utils
  *
  * @param algo algorithm for the ensemble model, either Classification or Regression
  * @param trees tree ensembles
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class RandomForestModel(override val algo: Algo, override val trees: Array[DecisionTreeModel])
   extends TreeEnsembleModel(algo, trees, Array.fill(trees.length)(1.0),
@@ -60,8 +60,8 @@ class RandomForestModel(override val algo: Algo, override val trees: Array[Decis
    * @param sc  Spark context used to save model data.
    * @param path  Path specifying the directory in which to save this model.
    *              If the directory already exists, this method throws an exception.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     TreeEnsembleModel.SaveLoadV1_0.save(sc, path, this,
       RandomForestModel.SaveLoadV1_0.thisClassName)
@@ -70,9 +70,7 @@ class RandomForestModel(override val algo: Algo, override val trees: Array[Decis
   override protected def formatVersion: String = RandomForestModel.formatVersion
 }
 
-/**
- * @since 1.3.0
- */
+@Since("1.3.0")
 object RandomForestModel extends Loader[RandomForestModel] {
 
   private[mllib] def formatVersion: String = TreeEnsembleModel.SaveLoadV1_0.thisFormatVersion
@@ -82,8 +80,8 @@ object RandomForestModel extends Loader[RandomForestModel] {
    * @param sc  Spark context used for loading model files.
    * @param path  Path specifying the directory to which the model was saved.
    * @return  Model instance
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): RandomForestModel = {
     val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName
@@ -114,8 +112,8 @@ object RandomForestModel extends Loader[RandomForestModel] {
  * @param algo algorithm for the ensemble model, either Classification or Regression
  * @param trees tree ensembles
  * @param treeWeights tree ensemble weights
- * @since 1.2.0
  */
+@Since("1.2.0")
 @Experimental
 class GradientBoostedTreesModel(
     override val algo: Algo,
@@ -130,8 +128,8 @@ class GradientBoostedTreesModel(
    * @param sc  Spark context used to save model data.
    * @param path  Path specifying the directory in which to save this model.
    *              If the directory already exists, this method throws an exception.
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     TreeEnsembleModel.SaveLoadV1_0.save(sc, path, this,
       GradientBoostedTreesModel.SaveLoadV1_0.thisClassName)
@@ -143,8 +141,8 @@ class GradientBoostedTreesModel(
    * @param loss evaluation metric.
    * @return an array with index i having the losses or errors for the ensemble
    *         containing the first i+1 trees
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def evaluateEachIteration(
       data: RDD[LabeledPoint],
       loss: Loss): Array[Double] = {
@@ -186,8 +184,8 @@ class GradientBoostedTreesModel(
 }
 
 /**
- * @since 1.3.0
  */
+@Since("1.3.0")
 object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
 
   /**
@@ -199,8 +197,8 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
    * @param loss: evaluation metric.
    * @return a RDD with each element being a zip of the prediction and error
    *         corresponding to every sample.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def computeInitialPredictionAndError(
       data: RDD[LabeledPoint],
       initTreeWeight: Double,
@@ -223,8 +221,8 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
    * @param loss: evaluation metric.
    * @return a RDD with each element being a zip of the prediction and error
    *         corresponding to each sample.
-   * @since 1.4.0
    */
+  @Since("1.4.0")
   def updatePredictionError(
     data: RDD[LabeledPoint],
     predictionAndError: RDD[(Double, Double)],
@@ -248,8 +246,8 @@ object GradientBoostedTreesModel extends Loader[GradientBoostedTreesModel] {
    * @param sc  Spark context used for loading model files.
    * @param path  Path specifying the directory to which the model was saved.
    * @return  Model instance
-   * @since 1.3.0
    */
+  @Since("1.3.0")
   override def load(sc: SparkContext, path: String): GradientBoostedTreesModel = {
     val (loadedClassName, version, jsonMetadata) = Loader.loadMetadata(sc, path)
     val classNameV1_0 = SaveLoadV1_0.thisClassName
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
index f520b3a1b7c72..bcaacc1b1f191 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/package.scala
@@ -24,7 +24,6 @@ package org.apache.spark.mllib
  *  - information loss calculation with entropy and Gini for classification and
  *    variance for regression,
  *  - both continuous and categorical features.
- *  @since 1.0.0
  */
 package object tree {
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index 11ed23176fc12..4940974bf4f41 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -21,7 +21,7 @@ import scala.reflect.ClassTag
 
 import breeze.linalg.{DenseVector => BDV, SparseVector => BSV}
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.rdd.PartitionwiseSampledRDD
@@ -64,8 +64,8 @@ object MLUtils {
    *                    feature dimensions.
    * @param minPartitions min number of partitions
    * @return labeled data stored as an RDD[LabeledPoint]
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def loadLibSVMFile(
       sc: SparkContext,
       path: String,
@@ -115,9 +115,7 @@ object MLUtils {
 
   // Convenient methods for `loadLibSVMFile`.
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -130,17 +128,15 @@ object MLUtils {
   /**
    * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of
    * partitions.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def loadLibSVMFile(
       sc: SparkContext,
       path: String,
       numFeatures: Int): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, numFeatures, sc.defaultMinPartitions)
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -149,9 +145,7 @@ object MLUtils {
       numFeatures: Int): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, numFeatures)
 
-  /**
-   * @since 1.0.0
-   */
+  @Since("1.0.0")
   @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
   def loadLibSVMFile(
       sc: SparkContext,
@@ -162,8 +156,8 @@ object MLUtils {
   /**
    * Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of
    * features determined automatically and the default number of partitions.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def loadLibSVMFile(sc: SparkContext, path: String): RDD[LabeledPoint] =
     loadLibSVMFile(sc, path, -1)
 
@@ -193,15 +187,15 @@ object MLUtils {
    * @param path file or directory path in any Hadoop-supported file system URI
    * @param minPartitions min number of partitions
    * @return vectors stored as an RDD[Vector]
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def loadVectors(sc: SparkContext, path: String, minPartitions: Int): RDD[Vector] =
     sc.textFile(path, minPartitions).map(Vectors.parse)
 
   /**
    * Loads vectors saved using `RDD[Vector].saveAsTextFile` with the default number of partitions.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def loadVectors(sc: SparkContext, path: String): RDD[Vector] =
     sc.textFile(path, sc.defaultMinPartitions).map(Vectors.parse)
 
@@ -211,16 +205,16 @@ object MLUtils {
    * @param path file or directory path in any Hadoop-supported file system URI
    * @param minPartitions min number of partitions
    * @return labeled points stored as an RDD[LabeledPoint]
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def loadLabeledPoints(sc: SparkContext, path: String, minPartitions: Int): RDD[LabeledPoint] =
     sc.textFile(path, minPartitions).map(LabeledPoint.parse)
 
   /**
    * Loads labeled points saved using `RDD[LabeledPoint].saveAsTextFile` with the default number of
    * partitions.
-   * @since 1.1.0
    */
+  @Since("1.1.0")
   def loadLabeledPoints(sc: SparkContext, dir: String): RDD[LabeledPoint] =
     loadLabeledPoints(sc, dir, sc.defaultMinPartitions)
 
@@ -236,8 +230,8 @@ object MLUtils {
    *
    * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
    *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @deprecated("Should use MLUtils.loadLabeledPoints instead.", "1.0.1")
   def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
     sc.textFile(dir).map { line =>
@@ -258,8 +252,8 @@ object MLUtils {
    *
    * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
    *            [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @deprecated("Should use RDD[LabeledPoint].saveAsTextFile instead.", "1.0.1")
   def saveLabeledData(data: RDD[LabeledPoint], dir: String) {
     val dataStr = data.map(x => x.label + "," + x.features.toArray.mkString(" "))
@@ -271,8 +265,8 @@ object MLUtils {
    * Return a k element array of pairs of RDDs with the first element of each pair
    * containing the training data, a complement of the validation data and the second
    * element, the validation data, containing a unique 1/kth of the data. Where k=numFolds.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   @Experimental
   def kFold[T: ClassTag](rdd: RDD[T], numFolds: Int, seed: Int): Array[(RDD[T], RDD[T])] = {
     val numFoldsF = numFolds.toFloat
@@ -287,8 +281,8 @@ object MLUtils {
 
   /**
    * Returns a new vector with `1.0` (bias) appended to the input vector.
-   * @since 1.0.0
    */
+  @Since("1.0.0")
   def appendBias(vector: Vector): Vector = {
     vector match {
       case dv: DenseVector =>

From e3355090d4030daffed5efb0959bf1d724c13c13 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 21 Aug 2015 14:30:00 -0700
Subject: [PATCH 374/633] [SPARK-10143] [SQL] Use parquet's block size (row
 group size) setting as the min split size if necessary.

https://issues.apache.org/jira/browse/SPARK-10143

With this PR, we will set min split size to parquet's block size (row group size) set in the conf if the min split size is smaller. So, we can avoid have too many tasks and even useless tasks for reading parquet data.

I tested it locally. The table I have has 343MB and it is in my local FS. Because I did not set any min/max split size, the default split size was 32MB and the map stage had 11 tasks. But there were only three tasks that actually read data. With my PR, there were only three tasks in the map stage. Here is the difference.

Without this PR:
![image](https://cloud.githubusercontent.com/assets/2072857/9399179/8587dba6-4765-11e5-9189-7ebba52a2b6d.png)

With this PR:
![image](https://cloud.githubusercontent.com/assets/2072857/9399185/a4735d74-4765-11e5-8848-1f1e361a6b4b.png)

Even if the block size setting does match the actual block size of parquet file, I think it is still generally good to use parquet's block size setting if min split size is smaller than this block size.

Tested it on a cluster using
```
val count = sqlContext.table("""store_sales""").groupBy().count().queryExecution.executedPlan(3).execute().count
```
Basically, it reads 0 column of table `store_sales`. My table has 1824 parquet files with size from 80MB to 280MB (1 to 3 row group sizes). Without this patch, in a 16 worker cluster, the job had 5023 tasks and spent 102s. With this patch, the job had 2893 tasks and spent 64s. It is still not as good as using one mapper per file (1824 tasks and 42s), but it is much better than our master.

Author: Yin Huai <yhuai@databricks.com>

Closes #8346 from yhuai/parquetMinSplit.
---
 .../datasources/parquet/ParquetRelation.scala | 41 ++++++++++++++++++-
 1 file changed, 39 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index 68169d48ac57c..bbf682aec0f9d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -26,6 +26,7 @@ import scala.collection.mutable
 import scala.util.{Failure, Try}
 
 import com.google.common.base.Objects
+import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, Path}
 import org.apache.hadoop.io.Writable
 import org.apache.hadoop.mapreduce._
@@ -281,12 +282,18 @@ private[sql] class ParquetRelation(
     val assumeInt96IsTimestamp = sqlContext.conf.isParquetINT96AsTimestamp
     val followParquetFormatSpec = sqlContext.conf.followParquetFormatSpec
 
+    // Parquet row group size. We will use this value as the value for
+    // mapreduce.input.fileinputformat.split.minsize and mapred.min.split.size if the value
+    // of these flags are smaller than the parquet row group size.
+    val parquetBlockSize = ParquetOutputFormat.getLongBlockSize(broadcastedConf.value.value)
+
     // Create the function to set variable Parquet confs at both driver and executor side.
     val initLocalJobFuncOpt =
       ParquetRelation.initializeLocalJobFunc(
         requiredColumns,
         filters,
         dataSchema,
+        parquetBlockSize,
         useMetadataCache,
         parquetFilterPushDown,
         assumeBinaryIsString,
@@ -294,7 +301,8 @@ private[sql] class ParquetRelation(
         followParquetFormatSpec) _
 
     // Create the function to set input paths at the driver side.
-    val setInputPaths = ParquetRelation.initializeDriverSideJobFunc(inputFiles) _
+    val setInputPaths =
+      ParquetRelation.initializeDriverSideJobFunc(inputFiles, parquetBlockSize) _
 
     Utils.withDummyCallSite(sqlContext.sparkContext) {
       new SqlNewHadoopRDD(
@@ -482,11 +490,35 @@ private[sql] object ParquetRelation extends Logging {
   // internally.
   private[sql] val METASTORE_SCHEMA = "metastoreSchema"
 
+  /**
+   * If parquet's block size (row group size) setting is larger than the min split size,
+   * we use parquet's block size setting as the min split size. Otherwise, we will create
+   * tasks processing nothing (because a split does not cover the starting point of a
+   * parquet block). See https://issues.apache.org/jira/browse/SPARK-10143 for more information.
+   */
+  private def overrideMinSplitSize(parquetBlockSize: Long, conf: Configuration): Unit = {
+    val minSplitSize =
+      math.max(
+        conf.getLong("mapred.min.split.size", 0L),
+        conf.getLong("mapreduce.input.fileinputformat.split.minsize", 0L))
+    if (parquetBlockSize > minSplitSize) {
+      val message =
+        s"Parquet's block size (row group size) is larger than " +
+          s"mapred.min.split.size/mapreduce.input.fileinputformat.split.minsize. Setting " +
+          s"mapred.min.split.size and mapreduce.input.fileinputformat.split.minsize to " +
+          s"$parquetBlockSize."
+      logDebug(message)
+      conf.set("mapred.min.split.size", parquetBlockSize.toString)
+      conf.set("mapreduce.input.fileinputformat.split.minsize", parquetBlockSize.toString)
+    }
+  }
+
   /** This closure sets various Parquet configurations at both driver side and executor side. */
   private[parquet] def initializeLocalJobFunc(
       requiredColumns: Array[String],
       filters: Array[Filter],
       dataSchema: StructType,
+      parquetBlockSize: Long,
       useMetadataCache: Boolean,
       parquetFilterPushDown: Boolean,
       assumeBinaryIsString: Boolean,
@@ -522,16 +554,21 @@ private[sql] object ParquetRelation extends Logging {
     conf.setBoolean(SQLConf.PARQUET_BINARY_AS_STRING.key, assumeBinaryIsString)
     conf.setBoolean(SQLConf.PARQUET_INT96_AS_TIMESTAMP.key, assumeInt96IsTimestamp)
     conf.setBoolean(SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.key, followParquetFormatSpec)
+
+    overrideMinSplitSize(parquetBlockSize, conf)
   }
 
   /** This closure sets input paths at the driver side. */
   private[parquet] def initializeDriverSideJobFunc(
-      inputFiles: Array[FileStatus])(job: Job): Unit = {
+      inputFiles: Array[FileStatus],
+      parquetBlockSize: Long)(job: Job): Unit = {
     // We side the input paths at the driver side.
     logInfo(s"Reading Parquet file(s) from ${inputFiles.map(_.getPath).mkString(", ")}")
     if (inputFiles.nonEmpty) {
       FileInputFormat.setInputPaths(job, inputFiles.map(_.getPath): _*)
     }
+
+    overrideMinSplitSize(parquetBlockSize, job.getConfiguration)
   }
 
   private[parquet] def readSchema(

From f01c4220d2b791f470fa6596ffe11baa51517fbe Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Fri, 21 Aug 2015 16:28:00 -0700
Subject: [PATCH 375/633] [SPARK-10163] [ML] Allow single-category features for
 GBT models

Removed categorical feature info validation since no longer needed

This is needed to make the ML user guide examples work (in another current PR).

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8367 from jkbradley/gbt-single-cat.
---
 .../org/apache/spark/mllib/tree/configuration/Strategy.scala | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index a58f01ba8544e..b74e3f1f46523 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -158,11 +158,6 @@ class Strategy (
       s"  Valid values are integers >= 0.")
     require(maxBins >= 2, s"DecisionTree Strategy given invalid maxBins parameter: $maxBins." +
       s"  Valid values are integers >= 2.")
-    categoricalFeaturesInfo.foreach { case (feature, arity) =>
-      require(arity >= 2,
-        s"DecisionTree Strategy given invalid categoricalFeaturesInfo setting:" +
-        s" feature $feature has $arity categories.  The number of categories should be >= 2.")
-    }
     require(minInstancesPerNode >= 1,
       s"DecisionTree Strategy requires minInstancesPerNode >= 1 but was given $minInstancesPerNode")
     require(maxMemoryInMB <= 10240,

From 630a994e6a9785d1704f8e7fb604f32f5dea24f8 Mon Sep 17 00:00:00 2001
From: Xusen Yin <yinxusen@gmail.com>
Date: Fri, 21 Aug 2015 16:30:12 -0700
Subject: [PATCH 376/633] [SPARK-9893] User guide with Java test suite for
 VectorSlicer

Add user guide for `VectorSlicer`, with Java test suite and Python version VectorSlicer.

Note that Python version does not support selecting by names now.

Author: Xusen Yin <yinxusen@gmail.com>

Closes #8267 from yinxusen/SPARK-9893.
---
 docs/ml-features.md                           | 133 ++++++++++++++++++
 .../ml/feature/JavaVectorSlicerSuite.java     |  85 +++++++++++
 2 files changed, 218 insertions(+)
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 6309db97be4d0..642a4b4c53183 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1477,6 +1477,139 @@ print(output.select("features", "clicked").first())
 </div>
 </div>
 
+# Feature Selectors
+
+## VectorSlicer
+
+`VectorSlicer` is a transformer that takes a feature vector and outputs a new feature vector with a
+sub-array of the original features. It is useful for extracting features from a vector column.
+
+`VectorSlicer` accepts a vector column with a specified indices, then outputs a new vector column
+whose values are selected via those indices. There are two types of indices, 
+
+ 1. Integer indices that represents the indices into the vector, `setIndices()`;
+
+ 2. String indices that represents the names of features into the vector, `setNames()`. 
+ *This requires the vector column to have an `AttributeGroup` since the implementation matches on
+ the name field of an `Attribute`.*
+
+Specification by integer and string are both acceptable. Moreover, you can use integer index and 
+string name simultaneously. At least one feature must be selected. Duplicate features are not
+allowed, so there can be no overlap between selected indices and names. Note that if names of
+features are selected, an exception will be threw out when encountering with empty input attributes.
+
+The output vector will order features with the selected indices first (in the order given),
+followed by the selected names (in the order given).
+
+**Examples**
+
+Suppose that we have a DataFrame with the column `userFeatures`:
+
+~~~
+ userFeatures     
+------------------
+ [0.0, 10.0, 0.5] 
+~~~
+
+`userFeatures` is a vector column that contains three user features. Assuming that the first column
+of `userFeatures` are all zeros, so we want to remove it and only the last two columns are selected.
+The `VectorSlicer` selects the last two elements with `setIndices(1, 2)` then produces a new vector
+column named `features`:
+
+~~~
+ userFeatures     | features
+------------------|-----------------------------
+ [0.0, 10.0, 0.5] | [10.0, 0.5]
+~~~
+
+Suppose also that we have a potential input attributes for the `userFeatures`, i.e. 
+`["f1", "f2", "f3"]`, then we can use `setNames("f2", "f3")` to select them.
+
+~~~
+ userFeatures     | features
+------------------|-----------------------------
+ [0.0, 10.0, 0.5] | [10.0, 0.5]
+ ["f1", "f2", "f3"] | ["f2", "f3"]
+~~~
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+[`VectorSlicer`](api/scala/index.html#org.apache.spark.ml.feature.VectorSlicer) takes an input
+column name with specified indices or names and an output column name.
+
+{% highlight scala %}
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute}
+import org.apache.spark.ml.feature.VectorSlicer
+import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.{DataFrame, Row, SQLContext}
+
+val data = Array(
+  Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))),
+  Vectors.dense(-2.0, 2.3, 0.0)
+)
+
+val defaultAttr = NumericAttribute.defaultAttr
+val attrs = Array("f1", "f2", "f3").map(defaultAttr.withName)
+val attrGroup = new AttributeGroup("userFeatures", attrs.asInstanceOf[Array[Attribute]])
+
+val dataRDD = sc.parallelize(data).map(Row.apply)
+val dataset = sqlContext.createDataFrame(dataRDD, StructType(attrGroup.toStructField()))
+
+val slicer = new VectorSlicer().setInputCol("userFeatures").setOutputCol("features")
+
+slicer.setIndices(1).setNames("f3")
+// or slicer.setIndices(Array(1, 2)), or slicer.setNames(Array("f2", "f3"))
+
+val output = slicer.transform(dataset)
+println(output.select("userFeatures", "features").first())
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`VectorSlicer`](api/java/org/apache/spark/ml/feature/VectorSlicer.html) takes an input column name
+with specified indices or names and an output column name.
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.*;
+import static org.apache.spark.sql.types.DataTypes.*;
+
+Attribute[] attrs = new Attribute[]{
+  NumericAttribute.defaultAttr().withName("f1"),
+  NumericAttribute.defaultAttr().withName("f2"),
+  NumericAttribute.defaultAttr().withName("f3")
+};
+AttributeGroup group = new AttributeGroup("userFeatures", attrs);
+
+JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+  RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})),
+  RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0))
+));
+
+DataFrame dataset = jsql.createDataFrame(jrdd, (new StructType()).add(group.toStructField()));
+
+VectorSlicer vectorSlicer = new VectorSlicer()
+  .setInputCol("userFeatures").setOutputCol("features");
+
+vectorSlicer.setIndices(new int[]{1}).setNames(new String[]{"f3"});
+// or slicer.setIndices(new int[]{1, 2}), or slicer.setNames(new String[]{"f2", "f3"})
+
+DataFrame output = vectorSlicer.transform(dataset);
+
+System.out.println(output.select("userFeatures", "features").first());
+{% endhighlight %}
+</div>
+</div>
+
 ## RFormula
 
 `RFormula` selects columns specified by an [R model formula](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/formula.html). It produces a vector column of features and a double column of labels. Like when formulas are used in R for linear regression, string input columns will be one-hot encoded, and numeric columns will be cast to doubles. If not already present in the DataFrame, the output label column will be created from the specified response variable in the formula.
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
new file mode 100644
index 0000000000000..56988b9fb29cb
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
@@ -0,0 +1,85 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature;
+
+import com.google.common.collect.Lists;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.attribute.Attribute;
+import org.apache.spark.ml.attribute.AttributeGroup;
+import org.apache.spark.ml.attribute.NumericAttribute;
+import org.apache.spark.mllib.linalg.Vector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.StructType;
+
+
+public class JavaVectorSlicerSuite {
+  private transient JavaSparkContext jsc;
+  private transient SQLContext jsql;
+
+  @Before
+  public void setUp() {
+    jsc = new JavaSparkContext("local", "JavaVectorSlicerSuite");
+    jsql = new SQLContext(jsc);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+  }
+
+  @Test
+  public void vectorSlice() {
+    Attribute[] attrs = new Attribute[]{
+      NumericAttribute.defaultAttr().withName("f1"),
+      NumericAttribute.defaultAttr().withName("f2"),
+      NumericAttribute.defaultAttr().withName("f3")
+    };
+    AttributeGroup group = new AttributeGroup("userFeatures", attrs);
+
+    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+      RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})),
+      RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0))
+    ));
+
+    DataFrame dataset = jsql.createDataFrame(jrdd, (new StructType()).add(group.toStructField()));
+
+    VectorSlicer vectorSlicer = new VectorSlicer()
+      .setInputCol("userFeatures").setOutputCol("features");
+
+    vectorSlicer.setIndices(new int[]{1}).setNames(new String[]{"f3"});
+
+    DataFrame output = vectorSlicer.transform(dataset);
+
+    for (Row r : output.select("userFeatures", "features").take(2)) {
+      Vector features = r.getAs(1);
+      Assert.assertEquals(features.size(), 2);
+    }
+  }
+}

From 46fcb9e0dbb2b28110f68a3d9f6c0c47bfd197b1 Mon Sep 17 00:00:00 2001
From: Keiji Yoshida <yoshida.keiji.84@gmail.com>
Date: Sat, 22 Aug 2015 02:38:10 -0700
Subject: [PATCH 377/633] Update programming-guide.md

Update `lineLengths.persist();` to `lineLengths.persist(StorageLevel.MEMORY_ONLY());` because `JavaRDD#persist` needs a parameter of `StorageLevel`.

Author: Keiji Yoshida <yoshida.keiji.84@gmail.com>

Closes #8372 from yosssi/patch-1.
---
 docs/programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/programming-guide.md b/docs/programming-guide.md
index 982c5eabe652b..4cf83bb392636 100644
--- a/docs/programming-guide.md
+++ b/docs/programming-guide.md
@@ -549,7 +549,7 @@ returning only its answer to the driver program.
 If we also wanted to use `lineLengths` again later, we could add:
 
 {% highlight java %}
-lineLengths.persist();
+lineLengths.persist(StorageLevel.MEMORY_ONLY());
 {% endhighlight %}
 
 before the `reduce`, which would cause `lineLengths` to be saved in memory after the first time it is computed.

From 90cb9f05655a25b95b8f9fe81da14e5b9c8bcf44 Mon Sep 17 00:00:00 2001
From: Yijie Shen <henry.yijieshen@gmail.com>
Date: Sat, 22 Aug 2015 10:16:35 -0700
Subject: [PATCH 378/633] [SPARK-9401] [SQL] Fully implement code generation
 for ConcatWs

This PR adds full codegen support for ConcatWs, is a substitute of #7782

JIRA: https://issues.apache.org/jira/browse/SPARK-9401

cc davies

Author: Yijie Shen <henry.yijieshen@gmail.com>

Closes #8353 from yjshen/concatws.
---
 .../expressions/stringExpressions.scala       | 42 +++++++++++++++++--
 1 file changed, 39 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
index b60d318534a41..48d02bb534501 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
@@ -72,7 +72,7 @@ case class Concat(children: Seq[Expression]) extends Expression with ImplicitCas
  * Returns null if the separator is null. Otherwise, concat_ws skips all null values.
  */
 case class ConcatWs(children: Seq[Expression])
-  extends Expression with ImplicitCastInputTypes with CodegenFallback {
+  extends Expression with ImplicitCastInputTypes {
 
   require(children.nonEmpty, s"$prettyName requires at least one argument.")
 
@@ -114,8 +114,44 @@ case class ConcatWs(children: Seq[Expression])
         boolean ${ev.isNull} = ${ev.primitive} == null;
       """
     } else {
-      // Contains a mix of strings and array<string>s. Fall back to interpreted mode for now.
-      super.genCode(ctx, ev)
+      val array = ctx.freshName("array")
+      val varargNum = ctx.freshName("varargNum")
+      val idxInVararg = ctx.freshName("idxInVararg")
+
+      val evals = children.map(_.gen(ctx))
+      val (varargCount, varargBuild) = children.tail.zip(evals.tail).map { case (child, eval) =>
+        child.dataType match {
+          case StringType =>
+            ("", // we count all the StringType arguments num at once below.
+              s"$array[$idxInVararg ++] = ${eval.isNull} ? (UTF8String) null : ${eval.primitive};")
+          case _: ArrayType =>
+            val size = ctx.freshName("n")
+            (s"""
+              if (!${eval.isNull}) {
+                $varargNum += ${eval.primitive}.numElements();
+              }
+            """,
+            s"""
+            if (!${eval.isNull}) {
+              final int $size = ${eval.primitive}.numElements();
+              for (int j = 0; j < $size; j ++) {
+                $array[$idxInVararg ++] = ${ctx.getValue(eval.primitive, StringType, "j")};
+              }
+            }
+            """)
+        }
+      }.unzip
+
+      evals.map(_.code).mkString("\n") +
+      s"""
+        int $varargNum = ${children.count(_.dataType == StringType) - 1};
+        int $idxInVararg = 0;
+        ${varargCount.mkString("\n")}
+        UTF8String[] $array = new UTF8String[$varargNum];
+        ${varargBuild.mkString("\n")}
+        UTF8String ${ev.primitive} = UTF8String.concatWs(${evals.head.primitive}, $array);
+        boolean ${ev.isNull} = ${ev.primitive} == null;
+      """
     }
   }
 }

From 623c675fde7a3a39957a62c7af26a54f4b01f8ce Mon Sep 17 00:00:00 2001
From: Keiji Yoshida <yoshida.keiji.84@gmail.com>
Date: Sun, 23 Aug 2015 11:04:29 +0100
Subject: [PATCH 379/633] Update streaming-programming-guide.md

Update `See the Scala example` to `See the Java example`.

Author: Keiji Yoshida <yoshida.keiji.84@gmail.com>

Closes #8376 from yosssi/patch-1.
---
 docs/streaming-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index c59d936b43c88..118ced298f4b0 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -1702,7 +1702,7 @@ context.awaitTermination();
 If the `checkpointDirectory` exists, then the context will be recreated from the checkpoint data.
 If the directory does not exist (i.e., running for the first time),
 then the function `contextFactory` will be called to create a new
-context and set up the DStreams. See the Scala example
+context and set up the DStreams. See the Java example
 [JavaRecoverableNetworkWordCount]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples/streaming/JavaRecoverableNetworkWordCount.java).
 This example appends the word counts of network data into a file.
 

From c6df5f66d9a8b9760f2cd46fcd930f977650c9c5 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Sun, 23 Aug 2015 17:41:49 -0700
Subject: [PATCH 380/633] [SPARK-10148] [STREAMING] Display active and inactive
 receiver numbers in Streaming page

Added the active and inactive receiver numbers in the summary section of Streaming page.

<img width="1074" alt="screen shot 2015-08-21 at 2 08 54 pm" src="https://cloud.githubusercontent.com/assets/1000778/9402437/ff2806a2-480f-11e5-8f8e-efdf8e5d514d.png">

Author: zsxwing <zsxwing@gmail.com>

Closes #8351 from zsxwing/receiver-number.
---
 .../spark/streaming/ui/StreamingJobProgressListener.scala | 8 ++++++++
 .../org/apache/spark/streaming/ui/StreamingPage.scala     | 6 ++++++
 2 files changed, 14 insertions(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
index b77c555c68b8b..78aeb004e18b1 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingJobProgressListener.scala
@@ -148,6 +148,14 @@ private[streaming] class StreamingJobProgressListener(ssc: StreamingContext)
     receiverInfos.size
   }
 
+  def numActiveReceivers: Int = synchronized {
+    receiverInfos.count(_._2.active)
+  }
+
+  def numInactiveReceivers: Int = {
+    ssc.graph.getReceiverInputStreams().size - numActiveReceivers
+  }
+
   def numTotalCompletedBatches: Long = synchronized {
     totalCompletedBatches
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
index 87af902428ec8..96d943e75d272 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/StreamingPage.scala
@@ -303,6 +303,7 @@ private[ui] class StreamingPage(parent: StreamingTab)
 
     val numCompletedBatches = listener.retainedCompletedBatches.size
     val numActiveBatches = batchTimes.length - numCompletedBatches
+    val numReceivers = listener.numInactiveReceivers + listener.numActiveReceivers
     val table =
       // scalastyle:off
       <table id="stat-table" class="table table-bordered" style="width: auto">
@@ -330,6 +331,11 @@ private[ui] class StreamingPage(parent: StreamingTab)
                 }
               }
               </div>
+              {
+                if (numReceivers > 0) {
+                  <div>Receivers: {listener.numActiveReceivers} / {numReceivers} active</div>
+                }
+              }
               <div>Avg: {eventRateForAllStreams.formattedAvg} events/sec</div>
             </div>
           </td>

From b963c19a803c5a26c9b65655d40ca6621acf8bd4 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Sun, 23 Aug 2015 18:34:07 -0700
Subject: [PATCH 381/633] [SPARK-10164] [MLLIB] Fixed GMM distributed
 decomposition bug

GaussianMixture now distributes matrix decompositions for certain problem sizes. Distributed computation actually fails, but this was not tested in unit tests.

This PR adds a unit test which checks this.  It failed previously but works with this fix.

CC: mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8370 from jkbradley/gmm-fix.
---
 .../mllib/clustering/GaussianMixture.scala    | 22 +++++++++++++------
 .../clustering/GaussianMixtureSuite.scala     | 22 +++++++++++++++++--
 2 files changed, 35 insertions(+), 9 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index fcc9dfecac54f..daa947e81d44d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -169,9 +169,7 @@ class GaussianMixture private (
     // Get length of the input vectors
     val d = breezeData.first().length
 
-    // Heuristic to distribute the computation of the [[MultivariateGaussian]]s, approximately when
-    // d > 25 except for when k is very small
-    val distributeGaussians = ((k - 1.0) / k) * d > 25
+    val shouldDistributeGaussians = GaussianMixture.shouldDistributeGaussians(k, d)
 
     // Determine initial weights and corresponding Gaussians.
     // If the user supplied an initial GMM, we use those values, otherwise
@@ -205,15 +203,15 @@ class GaussianMixture private (
       // (often referred to as the "M" step in literature)
       val sumWeights = sums.weights.sum
 
-      if (distributeGaussians) {
+      if (shouldDistributeGaussians) {
         val numPartitions = math.min(k, 1024)
         val tuples =
           Seq.tabulate(k)(i => (sums.means(i), sums.sigmas(i), sums.weights(i)))
         val (ws, gs) = sc.parallelize(tuples, numPartitions).map { case (mean, sigma, weight) =>
           updateWeightsAndGaussians(mean, sigma, weight, sumWeights)
-        }.collect.unzip
-        Array.copy(ws, 0, weights, 0, ws.length)
-        Array.copy(gs, 0, gaussians, 0, gs.length)
+        }.collect().unzip
+        Array.copy(ws.toArray, 0, weights, 0, ws.length)
+        Array.copy(gs.toArray, 0, gaussians, 0, gs.length)
       } else {
         var i = 0
         while (i < k) {
@@ -271,6 +269,16 @@ class GaussianMixture private (
   }
 }
 
+private[clustering] object GaussianMixture {
+  /**
+   * Heuristic to distribute the computation of the [[MultivariateGaussian]]s, approximately when
+   * d > 25 except for when k is very small.
+   * @param k  Number of topics
+   * @param d  Number of features
+   */
+  def shouldDistributeGaussians(k: Int, d: Int): Boolean = ((k - 1.0) / k) * d > 25
+}
+
 // companion class to provide zero constructor for ExpectationSum
 private object ExpectationSum {
   def zero(k: Int, d: Int): ExpectationSum = {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
index b636d02f786e6..a72723eb00daf 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/GaussianMixtureSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.clustering
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.mllib.linalg.{Vectors, Matrices}
+import org.apache.spark.mllib.linalg.{Vector, Vectors, Matrices}
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.mllib.util.TestingUtils._
@@ -76,6 +76,20 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(gmm.gaussians(1).sigma ~== Esigma(1) absTol 1E-3)
   }
 
+  test("two clusters with distributed decompositions") {
+    val data = sc.parallelize(GaussianTestData.data2, 2)
+
+    val k = 5
+    val d = data.first().size
+    assert(GaussianMixture.shouldDistributeGaussians(k, d))
+
+    val gmm = new GaussianMixture()
+      .setK(k)
+      .run(data)
+
+    assert(gmm.k === k)
+  }
+
   test("single cluster with sparse data") {
     val data = sc.parallelize(Array(
       Vectors.sparse(3, Array(0, 2), Array(4.0, 2.0)),
@@ -116,7 +130,7 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext {
     val sparseGMM = new GaussianMixture()
       .setK(2)
       .setInitialModel(initialGmm)
-      .run(data)
+      .run(sparseData)
 
     assert(sparseGMM.weights(0) ~== Ew(0) absTol 1E-3)
     assert(sparseGMM.weights(1) ~== Ew(1) absTol 1E-3)
@@ -168,5 +182,9 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext {
       Vectors.dense( 4.5605), Vectors.dense( 5.2043), Vectors.dense( 6.2734)
     )
 
+    val data2: Array[Vector] = Array.tabulate(25){ i: Int =>
+      Vectors.dense(Array.tabulate(50)(i + _.toDouble))
+    }
+
   }
 }

From 053d94fcf32268369b5a40837271f15d6af41aa4 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Sun, 23 Aug 2015 19:24:32 -0700
Subject: [PATCH 382/633] [SPARK-10142] [STREAMING] Made python checkpoint
 recovery handle non-local checkpoint paths and existing SparkContexts

The current code only checks checkpoint files in local filesystem, and always tries to create a new Python SparkContext (even if one already exists). The solution is to do the following:
1. Use the same code path as Java to check whether a valid checkpoint exists
2. Create a new Python SparkContext only if there no active one.

There is not test for the path as its hard to test with distributed filesystem paths in a local unit test. I am going to test it with a distributed file system manually to verify that this patch works.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8366 from tdas/SPARK-10142 and squashes the following commits:

3afa666 [Tathagata Das] Added tests
2dd4ae5 [Tathagata Das] Added the check to not create a context if one already exists
9bf151b [Tathagata Das] Made python checkpoint recovery use java to find the checkpoint files
---
 python/pyspark/streaming/context.py           | 22 ++++++----
 python/pyspark/streaming/tests.py             | 43 ++++++++++++++++---
 .../apache/spark/streaming/Checkpoint.scala   |  9 ++++
 3 files changed, 58 insertions(+), 16 deletions(-)

diff --git a/python/pyspark/streaming/context.py b/python/pyspark/streaming/context.py
index e3ba70e4e5e88..4069d7a149986 100644
--- a/python/pyspark/streaming/context.py
+++ b/python/pyspark/streaming/context.py
@@ -150,26 +150,30 @@ def getOrCreate(cls, checkpointPath, setupFunc):
         @param checkpointPath: Checkpoint directory used in an earlier streaming program
         @param setupFunc:      Function to create a new context and setup DStreams
         """
-        # TODO: support checkpoint in HDFS
-        if not os.path.exists(checkpointPath) or not os.listdir(checkpointPath):
+        cls._ensure_initialized()
+        gw = SparkContext._gateway
+
+        # Check whether valid checkpoint information exists in the given path
+        if gw.jvm.CheckpointReader.read(checkpointPath).isEmpty():
             ssc = setupFunc()
             ssc.checkpoint(checkpointPath)
             return ssc
 
-        cls._ensure_initialized()
-        gw = SparkContext._gateway
-
         try:
             jssc = gw.jvm.JavaStreamingContext(checkpointPath)
         except Exception:
             print("failed to load StreamingContext from checkpoint", file=sys.stderr)
             raise
 
-        jsc = jssc.sparkContext()
-        conf = SparkConf(_jconf=jsc.getConf())
-        sc = SparkContext(conf=conf, gateway=gw, jsc=jsc)
+        # If there is already an active instance of Python SparkContext use it, or create a new one
+        if not SparkContext._active_spark_context:
+            jsc = jssc.sparkContext()
+            conf = SparkConf(_jconf=jsc.getConf())
+            SparkContext(conf=conf, gateway=gw, jsc=jsc)
+
+        sc = SparkContext._active_spark_context
+
         # update ctx in serializer
-        SparkContext._active_spark_context = sc
         cls._transformerSerializer.ctx = sc
         return StreamingContext(sc, None, jssc)
 
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 214d5be439003..510a4f2b3e472 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -603,6 +603,10 @@ def tearDownClass():
     def tearDown(self):
         if self.ssc is not None:
             self.ssc.stop(True)
+        if self.sc is not None:
+            self.sc.stop()
+        if self.cpd is not None:
+            shutil.rmtree(self.cpd)
 
     def test_get_or_create_and_get_active_or_create(self):
         inputd = tempfile.mkdtemp()
@@ -622,8 +626,12 @@ def setup():
             self.setupCalled = True
             return ssc
 
-        cpd = tempfile.mkdtemp("test_streaming_cps")
-        self.ssc = StreamingContext.getOrCreate(cpd, setup)
+        # Verify that getOrCreate() calls setup() in absence of checkpoint files
+        self.cpd = tempfile.mkdtemp("test_streaming_cps")
+        self.setupCalled = False
+        self.ssc = StreamingContext.getOrCreate(self.cpd, setup)
+        self.assertFalse(self.setupCalled)
+
         self.ssc.start()
 
         def check_output(n):
@@ -660,31 +668,52 @@ def check_output(n):
         self.ssc.stop(True, True)
         time.sleep(1)
         self.setupCalled = False
-        self.ssc = StreamingContext.getOrCreate(cpd, setup)
+        self.ssc = StreamingContext.getOrCreate(self.cpd, setup)
         self.assertFalse(self.setupCalled)
         self.ssc.start()
         check_output(3)
 
+        # Verify that getOrCreate() uses existing SparkContext
+        self.ssc.stop(True, True)
+        time.sleep(1)
+        sc = SparkContext(SparkConf())
+        self.setupCalled = False
+        self.ssc = StreamingContext.getOrCreate(self.cpd, setup)
+        self.assertFalse(self.setupCalled)
+        self.assertTrue(self.ssc.sparkContext == sc)
+
         # Verify the getActiveOrCreate() recovers from checkpoint files
         self.ssc.stop(True, True)
         time.sleep(1)
         self.setupCalled = False
-        self.ssc = StreamingContext.getActiveOrCreate(cpd, setup)
+        self.ssc = StreamingContext.getActiveOrCreate(self.cpd, setup)
         self.assertFalse(self.setupCalled)
         self.ssc.start()
         check_output(4)
 
         # Verify that getActiveOrCreate() returns active context
         self.setupCalled = False
-        self.assertEquals(StreamingContext.getActiveOrCreate(cpd, setup), self.ssc)
+        self.assertEquals(StreamingContext.getActiveOrCreate(self.cpd, setup), self.ssc)
         self.assertFalse(self.setupCalled)
 
+        # Verify that getActiveOrCreate() uses existing SparkContext
+        self.ssc.stop(True, True)
+        time.sleep(1)
+        self.sc = SparkContext(SparkConf())
+        self.setupCalled = False
+        self.ssc = StreamingContext.getActiveOrCreate(self.cpd, setup)
+        self.assertFalse(self.setupCalled)
+        self.assertTrue(self.ssc.sparkContext == sc)
+
         # Verify that getActiveOrCreate() calls setup() in absence of checkpoint files
         self.ssc.stop(True, True)
-        shutil.rmtree(cpd)  # delete checkpoint directory
+        shutil.rmtree(self.cpd)  # delete checkpoint directory
+        time.sleep(1)
         self.setupCalled = False
-        self.ssc = StreamingContext.getActiveOrCreate(cpd, setup)
+        self.ssc = StreamingContext.getActiveOrCreate(self.cpd, setup)
         self.assertTrue(self.setupCalled)
+
+        # Stop everything
         self.ssc.stop(True, True)
 
 
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index 6f6b449accc3c..cd5d960369c05 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -286,6 +286,15 @@ class CheckpointWriter(
 private[streaming]
 object CheckpointReader extends Logging {
 
+  /**
+   * Read checkpoint files present in the given checkpoint directory. If there are no checkpoint
+   * files, then return None, else try to return the latest valid checkpoint object. If no
+   * checkpoint files could be read correctly, then return None.
+   */
+  def read(checkpointDir: String): Option[Checkpoint] = {
+    read(checkpointDir, new SparkConf(), SparkHadoopUtil.get.conf, ignoreReadError = true)
+  }
+
   /**
    * Read checkpoint files present in the given checkpoint directory. If there are no checkpoint
    * files, then return None, else try to return the latest valid checkpoint object. If no

From 4e0395ddb764d092b5b38447af49e196e590e0f0 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 24 Aug 2015 12:38:01 -0700
Subject: [PATCH 383/633] [SPARK-10168] [STREAMING] Fix the issue that maven
 publishes wrong artifact jars

This PR removed the `outputFile` configuration from pom.xml and updated `tests.py` to search jars for both sbt build and maven build.

I ran ` mvn -Pkinesis-asl -DskipTests clean install` locally, and verified the jars in my local repository were correct. I also checked Python tests for maven build, and it passed all tests.

Author: zsxwing <zsxwing@gmail.com>

Closes #8373 from zsxwing/SPARK-10168 and squashes the following commits:

e0b5818 [zsxwing] Fix the sbt build
c697627 [zsxwing] Add the jar pathes to the exception message
be1d8a5 [zsxwing] Fix the issue that maven publishes wrong artifact jars
---
 external/flume-assembly/pom.xml     |  1 -
 external/kafka-assembly/pom.xml     |  1 -
 external/mqtt-assembly/pom.xml      |  1 -
 extras/kinesis-asl-assembly/pom.xml |  1 -
 python/pyspark/streaming/tests.py   | 47 ++++++++++++++++-------------
 5 files changed, 26 insertions(+), 25 deletions(-)

diff --git a/external/flume-assembly/pom.xml b/external/flume-assembly/pom.xml
index e05e4318969ce..561ed4babe5d0 100644
--- a/external/flume-assembly/pom.xml
+++ b/external/flume-assembly/pom.xml
@@ -115,7 +115,6 @@
         <artifactId>maven-shade-plugin</artifactId>
         <configuration>
           <shadedArtifactAttached>false</shadedArtifactAttached>
-          <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-flume-assembly-${project.version}.jar</outputFile>
           <artifactSet>
             <includes>
               <include>*:*</include>
diff --git a/external/kafka-assembly/pom.xml b/external/kafka-assembly/pom.xml
index 36342f37bb2ea..6f4e2a89e9af7 100644
--- a/external/kafka-assembly/pom.xml
+++ b/external/kafka-assembly/pom.xml
@@ -142,7 +142,6 @@
       <artifactId>maven-shade-plugin</artifactId>
       <configuration>
         <shadedArtifactAttached>false</shadedArtifactAttached>
-        <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-kafka-assembly-${project.version}.jar</outputFile>
         <artifactSet>
           <includes>
             <include>*:*</include>
diff --git a/external/mqtt-assembly/pom.xml b/external/mqtt-assembly/pom.xml
index f3e3f93e7ed50..8412600633734 100644
--- a/external/mqtt-assembly/pom.xml
+++ b/external/mqtt-assembly/pom.xml
@@ -132,7 +132,6 @@
         <artifactId>maven-shade-plugin</artifactId>
         <configuration>
           <shadedArtifactAttached>false</shadedArtifactAttached>
-          <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-mqtt-assembly-${project.version}.jar</outputFile>
           <artifactSet>
             <includes>
               <include>*:*</include>
diff --git a/extras/kinesis-asl-assembly/pom.xml b/extras/kinesis-asl-assembly/pom.xml
index 3ca538608f694..51af3e6f2225f 100644
--- a/extras/kinesis-asl-assembly/pom.xml
+++ b/extras/kinesis-asl-assembly/pom.xml
@@ -137,7 +137,6 @@
       <artifactId>maven-shade-plugin</artifactId>
       <configuration>
         <shadedArtifactAttached>false</shadedArtifactAttached>
-        <outputFile>${project.build.directory}/scala-${scala.binary.version}/spark-streaming-kinesis-asl-assembly-${project.version}.jar</outputFile>
         <artifactSet>
           <includes>
             <include>*:*</include>
diff --git a/python/pyspark/streaming/tests.py b/python/pyspark/streaming/tests.py
index 510a4f2b3e472..cfea95b0dec71 100644
--- a/python/pyspark/streaming/tests.py
+++ b/python/pyspark/streaming/tests.py
@@ -1162,11 +1162,20 @@ def get_output(_, rdd):
             kinesisTestUtils.deleteDynamoDBTable(kinesisAppName)
 
 
+# Search jar in the project dir using the jar name_prefix for both sbt build and maven build because
+# the artifact jars are in different directories.
+def search_jar(dir, name_prefix):
+    # We should ignore the following jars
+    ignored_jar_suffixes = ("javadoc.jar", "sources.jar", "test-sources.jar", "tests.jar")
+    jars = (glob.glob(os.path.join(dir, "target/scala-*/" + name_prefix + "-*.jar")) +  # sbt build
+            glob.glob(os.path.join(dir, "target/" + name_prefix + "_*.jar")))  # maven build
+    return [jar for jar in jars if not jar.endswith(ignored_jar_suffixes)]
+
+
 def search_kafka_assembly_jar():
     SPARK_HOME = os.environ["SPARK_HOME"]
     kafka_assembly_dir = os.path.join(SPARK_HOME, "external/kafka-assembly")
-    jars = glob.glob(
-        os.path.join(kafka_assembly_dir, "target/scala-*/spark-streaming-kafka-assembly-*.jar"))
+    jars = search_jar(kafka_assembly_dir, "spark-streaming-kafka-assembly")
     if not jars:
         raise Exception(
             ("Failed to find Spark Streaming kafka assembly jar in %s. " % kafka_assembly_dir) +
@@ -1174,8 +1183,8 @@ def search_kafka_assembly_jar():
             "'build/sbt assembly/assembly streaming-kafka-assembly/assembly' or "
             "'build/mvn package' before running this test.")
     elif len(jars) > 1:
-        raise Exception(("Found multiple Spark Streaming Kafka assembly JARs in %s; please "
-                         "remove all but one") % kafka_assembly_dir)
+        raise Exception(("Found multiple Spark Streaming Kafka assembly JARs: %s; please "
+                         "remove all but one") % (", ".join(jars)))
     else:
         return jars[0]
 
@@ -1183,8 +1192,7 @@ def search_kafka_assembly_jar():
 def search_flume_assembly_jar():
     SPARK_HOME = os.environ["SPARK_HOME"]
     flume_assembly_dir = os.path.join(SPARK_HOME, "external/flume-assembly")
-    jars = glob.glob(
-        os.path.join(flume_assembly_dir, "target/scala-*/spark-streaming-flume-assembly-*.jar"))
+    jars = search_jar(flume_assembly_dir, "spark-streaming-flume-assembly")
     if not jars:
         raise Exception(
             ("Failed to find Spark Streaming Flume assembly jar in %s. " % flume_assembly_dir) +
@@ -1192,8 +1200,8 @@ def search_flume_assembly_jar():
             "'build/sbt assembly/assembly streaming-flume-assembly/assembly' or "
             "'build/mvn package' before running this test.")
     elif len(jars) > 1:
-        raise Exception(("Found multiple Spark Streaming Flume assembly JARs in %s; please "
-                        "remove all but one") % flume_assembly_dir)
+        raise Exception(("Found multiple Spark Streaming Flume assembly JARs: %s; please "
+                        "remove all but one") % (", ".join(jars)))
     else:
         return jars[0]
 
@@ -1201,8 +1209,7 @@ def search_flume_assembly_jar():
 def search_mqtt_assembly_jar():
     SPARK_HOME = os.environ["SPARK_HOME"]
     mqtt_assembly_dir = os.path.join(SPARK_HOME, "external/mqtt-assembly")
-    jars = glob.glob(
-        os.path.join(mqtt_assembly_dir, "target/scala-*/spark-streaming-mqtt-assembly-*.jar"))
+    jars = search_jar(mqtt_assembly_dir, "spark-streaming-mqtt-assembly")
     if not jars:
         raise Exception(
             ("Failed to find Spark Streaming MQTT assembly jar in %s. " % mqtt_assembly_dir) +
@@ -1210,8 +1217,8 @@ def search_mqtt_assembly_jar():
             "'build/sbt assembly/assembly streaming-mqtt-assembly/assembly' or "
             "'build/mvn package' before running this test")
     elif len(jars) > 1:
-        raise Exception(("Found multiple Spark Streaming MQTT assembly JARs in %s; please "
-                         "remove all but one") % mqtt_assembly_dir)
+        raise Exception(("Found multiple Spark Streaming MQTT assembly JARs: %s; please "
+                         "remove all but one") % (", ".join(jars)))
     else:
         return jars[0]
 
@@ -1227,8 +1234,8 @@ def search_mqtt_test_jar():
             "You need to build Spark with "
             "'build/sbt assembly/assembly streaming-mqtt/test:assembly'")
     elif len(jars) > 1:
-        raise Exception(("Found multiple Spark Streaming MQTT test JARs in %s; please "
-                         "remove all but one") % mqtt_test_dir)
+        raise Exception(("Found multiple Spark Streaming MQTT test JARs: %s; please "
+                         "remove all but one") % (", ".join(jars)))
     else:
         return jars[0]
 
@@ -1236,14 +1243,12 @@ def search_mqtt_test_jar():
 def search_kinesis_asl_assembly_jar():
     SPARK_HOME = os.environ["SPARK_HOME"]
     kinesis_asl_assembly_dir = os.path.join(SPARK_HOME, "extras/kinesis-asl-assembly")
-    jars = glob.glob(
-        os.path.join(kinesis_asl_assembly_dir,
-                     "target/scala-*/spark-streaming-kinesis-asl-assembly-*.jar"))
+    jars = search_jar(kinesis_asl_assembly_dir, "spark-streaming-kinesis-asl-assembly")
     if not jars:
         return None
     elif len(jars) > 1:
-        raise Exception(("Found multiple Spark Streaming Kinesis ASL assembly JARs in %s; please "
-                         "remove all but one") % kinesis_asl_assembly_dir)
+        raise Exception(("Found multiple Spark Streaming Kinesis ASL assembly JARs: %s; please "
+                         "remove all but one") % (", ".join(jars)))
     else:
         return jars[0]
 
@@ -1269,8 +1274,8 @@ def search_kinesis_asl_assembly_jar():
                                    mqtt_test_jar, kinesis_asl_assembly_jar)
 
     os.environ["PYSPARK_SUBMIT_ARGS"] = "--jars %s pyspark-shell" % jars
-    testcases = [BasicOperationTests, WindowFunctionTests, StreamingContextTests,
-                 CheckpointTests, KafkaStreamTests, FlumeStreamTests, FlumePollingStreamTests]
+    testcases = [BasicOperationTests, WindowFunctionTests, StreamingContextTests, CheckpointTests,
+                 KafkaStreamTests, FlumeStreamTests, FlumePollingStreamTests, MQTTStreamTests]
 
     if kinesis_jar_present is True:
         testcases.append(KinesisStreamTests)

From 7478c8b66d6a2b1179f20c38b49e27e37b0caec3 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Mon, 24 Aug 2015 12:40:09 -0700
Subject: [PATCH 384/633] [SPARK-9791] [PACKAGE] Change private class to
 private class to prevent unnecessary classes from showing up in the docs

In addition, some random cleanup of import ordering

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8387 from tdas/SPARK-9791 and squashes the following commits:

67f3ee9 [Tathagata Das] Change private class to private[package] class to prevent them from showing up in the docs
---
 .../spark/streaming/flume/FlumeUtils.scala    |  2 +-
 .../apache/spark/streaming/kafka/Broker.scala |  6 ++--
 .../streaming/kafka/KafkaTestUtils.scala      | 10 +++---
 .../spark/streaming/kafka/KafkaUtils.scala    | 36 +++++--------------
 .../spark/streaming/kafka/OffsetRange.scala   |  8 -----
 .../spark/streaming/mqtt/MQTTUtils.scala      |  6 ++--
 .../spark/streaming/mqtt/MQTTTestUtils.scala  |  2 +-
 .../streaming/kinesis/KinesisTestUtils.scala  |  2 +-
 .../spark/streaming/util/WriteAheadLog.java   |  2 ++
 .../util/WriteAheadLogRecordHandle.java       |  2 ++
 .../receiver/ReceivedBlockHandler.scala       |  2 +-
 .../streaming/scheduler/ReceiverTracker.scala |  2 +-
 .../apache/spark/streaming/ui/BatchPage.scala |  2 +-
 13 files changed, 28 insertions(+), 54 deletions(-)

diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
index 095bfb0c73a9a..a65a9b921aafa 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
@@ -247,7 +247,7 @@ object FlumeUtils {
  * This is a helper class that wraps the methods in FlumeUtils into more Python-friendly class and
  * function so that it can be easily instantiated and called from Python's FlumeUtils.
  */
-private class FlumeUtilsPythonHelper {
+private[flume] class FlumeUtilsPythonHelper {
 
   def createStream(
       jssc: JavaStreamingContext,
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Broker.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Broker.scala
index 5a74febb4bd46..9159051ba06e4 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Broker.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/Broker.scala
@@ -20,11 +20,9 @@ package org.apache.spark.streaming.kafka
 import org.apache.spark.annotation.Experimental
 
 /**
- * :: Experimental ::
- * Represent the host and port info for a Kafka broker.
- * Differs from the Kafka project's internal kafka.cluster.Broker, which contains a server ID
+ * Represents the host and port info for a Kafka broker.
+ * Differs from the Kafka project's internal kafka.cluster.Broker, which contains a server ID.
  */
-@Experimental
 final class Broker private(
     /** Broker's hostname */
     val host: String,
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
index b608b75952721..79a9db4291bef 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
@@ -20,9 +20,8 @@ package org.apache.spark.streaming.kafka
 import java.io.File
 import java.lang.{Integer => JInt}
 import java.net.InetSocketAddress
-import java.util.{Map => JMap}
-import java.util.Properties
 import java.util.concurrent.TimeoutException
+import java.util.{Map => JMap, Properties}
 
 import scala.annotation.tailrec
 import scala.language.postfixOps
@@ -30,17 +29,16 @@ import scala.util.control.NonFatal
 
 import kafka.admin.AdminUtils
 import kafka.api.Request
-import kafka.common.TopicAndPartition
 import kafka.producer.{KeyedMessage, Producer, ProducerConfig}
 import kafka.serializer.StringEncoder
 import kafka.server.{KafkaConfig, KafkaServer}
 import kafka.utils.{ZKStringSerializer, ZkUtils}
-import org.apache.zookeeper.server.{NIOServerCnxnFactory, ZooKeeperServer}
 import org.I0Itec.zkclient.ZkClient
+import org.apache.zookeeper.server.{NIOServerCnxnFactory, ZooKeeperServer}
 
-import org.apache.spark.{Logging, SparkConf}
 import org.apache.spark.streaming.Time
 import org.apache.spark.util.Utils
+import org.apache.spark.{Logging, SparkConf}
 
 /**
  * This is a helper class for Kafka test suites. This has the functionality to set up
@@ -48,7 +46,7 @@ import org.apache.spark.util.Utils
  *
  * The reason to put Kafka test utility class in src is to test Python related Kafka APIs.
  */
-private class KafkaTestUtils extends Logging {
+private[kafka] class KafkaTestUtils extends Logging {
 
   // Zookeeper related configurations
   private val zkHost = "localhost"
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
index f3b01bd60b178..388dbb8184106 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
@@ -17,29 +17,25 @@
 
 package org.apache.spark.streaming.kafka
 
-import java.lang.{Integer => JInt}
-import java.lang.{Long => JLong}
-import java.util.{Map => JMap}
-import java.util.{Set => JSet}
-import java.util.{List => JList}
+import java.lang.{Integer => JInt, Long => JLong}
+import java.util.{List => JList, Map => JMap, Set => JSet}
 
-import scala.reflect.ClassTag
 import scala.collection.JavaConversions._
+import scala.reflect.ClassTag
 
 import kafka.common.TopicAndPartition
 import kafka.message.MessageAndMetadata
-import kafka.serializer.{DefaultDecoder, Decoder, StringDecoder}
+import kafka.serializer.{Decoder, DefaultDecoder, StringDecoder}
 
 import org.apache.spark.api.java.function.{Function => JFunction}
-import org.apache.spark.streaming.util.WriteAheadLogUtils
-import org.apache.spark.{SparkContext, SparkException}
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.StreamingContext
-import org.apache.spark.streaming.api.java.{JavaPairInputDStream, JavaInputDStream, JavaPairReceiverInputDStream, JavaStreamingContext}
+import org.apache.spark.streaming.api.java.{JavaInputDStream, JavaPairInputDStream, JavaPairReceiverInputDStream, JavaStreamingContext}
 import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream}
-import org.apache.spark.api.java.{JavaSparkContext, JavaPairRDD, JavaRDD}
+import org.apache.spark.streaming.util.WriteAheadLogUtils
+import org.apache.spark.{SparkContext, SparkException}
 
 object KafkaUtils {
   /**
@@ -196,7 +192,6 @@ object KafkaUtils {
    * @param offsetRanges Each OffsetRange in the batch corresponds to a
    *   range of offsets for a given Kafka topic/partition
    */
-  @Experimental
   def createRDD[
     K: ClassTag,
     V: ClassTag,
@@ -214,7 +209,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create a RDD from Kafka using offset ranges for each topic and partition. This allows you
    * specify the Kafka leader to connect to (to optimize fetching) and access the message as well
    * as the metadata.
@@ -230,7 +224,6 @@ object KafkaUtils {
    *   in which case leaders will be looked up on the driver.
    * @param messageHandler Function for translating each message and metadata into the desired type
    */
-  @Experimental
   def createRDD[
     K: ClassTag,
     V: ClassTag,
@@ -268,7 +261,6 @@ object KafkaUtils {
    * @param offsetRanges Each OffsetRange in the batch corresponds to a
    *   range of offsets for a given Kafka topic/partition
    */
-  @Experimental
   def createRDD[K, V, KD <: Decoder[K], VD <: Decoder[V]](
       jsc: JavaSparkContext,
       keyClass: Class[K],
@@ -287,7 +279,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create a RDD from Kafka using offset ranges for each topic and partition. This allows you
    * specify the Kafka leader to connect to (to optimize fetching) and access the message as well
    * as the metadata.
@@ -303,7 +294,6 @@ object KafkaUtils {
    *   in which case leaders will be looked up on the driver.
    * @param messageHandler Function for translating each message and metadata into the desired type
    */
-  @Experimental
   def createRDD[K, V, KD <: Decoder[K], VD <: Decoder[V], R](
       jsc: JavaSparkContext,
       keyClass: Class[K],
@@ -327,7 +317,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create an input stream that directly pulls messages from Kafka Brokers
    * without using any receiver. This stream can guarantee that each message
    * from Kafka is included in transformations exactly once (see points below).
@@ -357,7 +346,6 @@ object KafkaUtils {
    *    starting point of the stream
    * @param messageHandler Function for translating each message and metadata into the desired type
    */
-  @Experimental
   def createDirectStream[
     K: ClassTag,
     V: ClassTag,
@@ -375,7 +363,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create an input stream that directly pulls messages from Kafka Brokers
    * without using any receiver. This stream can guarantee that each message
    * from Kafka is included in transformations exactly once (see points below).
@@ -405,7 +392,6 @@ object KafkaUtils {
    *   to determine where the stream starts (defaults to "largest")
    * @param topics Names of the topics to consume
    */
-  @Experimental
   def createDirectStream[
     K: ClassTag,
     V: ClassTag,
@@ -437,7 +423,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create an input stream that directly pulls messages from Kafka Brokers
    * without using any receiver. This stream can guarantee that each message
    * from Kafka is included in transformations exactly once (see points below).
@@ -472,7 +457,6 @@ object KafkaUtils {
    *    starting point of the stream
    * @param messageHandler Function for translating each message and metadata into the desired type
    */
-  @Experimental
   def createDirectStream[K, V, KD <: Decoder[K], VD <: Decoder[V], R](
       jssc: JavaStreamingContext,
       keyClass: Class[K],
@@ -499,7 +483,6 @@ object KafkaUtils {
   }
 
   /**
-   * :: Experimental ::
    * Create an input stream that directly pulls messages from Kafka Brokers
    * without using any receiver. This stream can guarantee that each message
    * from Kafka is included in transformations exactly once (see points below).
@@ -533,7 +516,6 @@ object KafkaUtils {
    *   to determine where the stream starts (defaults to "largest")
    * @param topics Names of the topics to consume
    */
-  @Experimental
   def createDirectStream[K, V, KD <: Decoder[K], VD <: Decoder[V]](
       jssc: JavaStreamingContext,
       keyClass: Class[K],
@@ -564,7 +546,7 @@ object KafkaUtils {
  * classOf[KafkaUtilsPythonHelper].newInstance(), and the createStream()
  * takes care of known parameters instead of passing them from Python
  */
-private class KafkaUtilsPythonHelper {
+private[kafka] class KafkaUtilsPythonHelper {
   def createStream(
       jssc: JavaStreamingContext,
       kafkaParams: JMap[String, String],
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
index 2f8981d4898bd..8a5f371494511 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/OffsetRange.scala
@@ -19,10 +19,7 @@ package org.apache.spark.streaming.kafka
 
 import kafka.common.TopicAndPartition
 
-import org.apache.spark.annotation.Experimental
-
 /**
- * :: Experimental ::
  * Represents any object that has a collection of [[OffsetRange]]s. This can be used access the
  * offset ranges in RDDs generated by the direct Kafka DStream (see
  * [[KafkaUtils.createDirectStream()]]).
@@ -33,13 +30,11 @@ import org.apache.spark.annotation.Experimental
  *   }
  * }}}
  */
-@Experimental
 trait HasOffsetRanges {
   def offsetRanges: Array[OffsetRange]
 }
 
 /**
- * :: Experimental ::
  * Represents a range of offsets from a single Kafka TopicAndPartition. Instances of this class
  * can be created with `OffsetRange.create()`.
  * @param topic Kafka topic name
@@ -47,7 +42,6 @@ trait HasOffsetRanges {
  * @param fromOffset Inclusive starting offset
  * @param untilOffset Exclusive ending offset
  */
-@Experimental
 final class OffsetRange private(
     val topic: String,
     val partition: Int,
@@ -84,10 +78,8 @@ final class OffsetRange private(
 }
 
 /**
- * :: Experimental ::
  * Companion object the provides methods to create instances of [[OffsetRange]].
  */
-@Experimental
 object OffsetRange {
   def create(topic: String, partition: Int, fromOffset: Long, untilOffset: Long): OffsetRange =
     new OffsetRange(topic, partition, fromOffset, untilOffset)
diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
index 38a1114863d15..7b8d56d6faf2d 100644
--- a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
+++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala
@@ -21,8 +21,8 @@ import scala.reflect.ClassTag
 
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.StreamingContext
-import org.apache.spark.streaming.api.java.{JavaReceiverInputDStream, JavaStreamingContext, JavaDStream}
-import org.apache.spark.streaming.dstream.{ReceiverInputDStream, DStream}
+import org.apache.spark.streaming.api.java.{JavaDStream, JavaReceiverInputDStream, JavaStreamingContext}
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
 
 object MQTTUtils {
   /**
@@ -79,7 +79,7 @@ object MQTTUtils {
  * This is a helper class that wraps the methods in MQTTUtils into more Python-friendly class and
  * function so that it can be easily instantiated and called from Python's MQTTUtils.
  */
-private class MQTTUtilsPythonHelper {
+private[mqtt] class MQTTUtilsPythonHelper {
 
   def createStream(
       jssc: JavaStreamingContext,
diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
index 1a371b7008824..1618e2c088b70 100644
--- a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
+++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTTestUtils.scala
@@ -33,7 +33,7 @@ import org.apache.spark.{Logging, SparkConf}
 /**
  * Share codes for Scala and Python unit tests
  */
-private class MQTTTestUtils extends Logging {
+private[mqtt] class MQTTTestUtils extends Logging {
 
   private val persistenceDir = Utils.createTempDir()
   private val brokerHost = "localhost"
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
index 711aade182945..c8eec13ec7dc7 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
@@ -36,7 +36,7 @@ import org.apache.spark.Logging
 /**
  * Shared utility methods for performing Kinesis tests that actually transfer data
  */
-private class KinesisTestUtils extends Logging {
+private[kinesis] class KinesisTestUtils extends Logging {
 
   val endpointUrl = KinesisTestUtils.endpointUrl
   val regionName = RegionUtils.getRegionByEndpoint(endpointUrl).getName()
diff --git a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java
index 8c0fdfa9c7478..3738fc1a235c2 100644
--- a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java
+++ b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLog.java
@@ -21,6 +21,8 @@
 import java.util.Iterator;
 
 /**
+ * :: DeveloperApi ::
+ *
  * This abstract class represents a write ahead log (aka journal) that is used by Spark Streaming
  * to save the received data (by receivers) and associated metadata to a reliable storage, so that
  * they can be recovered after driver failures. See the Spark documentation for more information
diff --git a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java
index 02324189b7822..662889e779fb2 100644
--- a/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java
+++ b/streaming/src/main/java/org/apache/spark/streaming/util/WriteAheadLogRecordHandle.java
@@ -18,6 +18,8 @@
 package org.apache.spark.streaming.util;
 
 /**
+ * :: DeveloperApi ::
+ *
  * This abstract class represents a handle that refers to a record written in a
  * {@link org.apache.spark.streaming.util.WriteAheadLog WriteAheadLog}.
  * It must contain all the information necessary for the record to be read and returned by
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala
index c8dd6e06812dc..5f6c5b024085c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/ReceivedBlockHandler.scala
@@ -222,7 +222,7 @@ private[streaming] object WriteAheadLogBasedBlockHandler {
 /**
  * A utility that will wrap the Iterator to get the count
  */
-private class CountingIterator[T](iterator: Iterator[T]) extends Iterator[T] {
+private[streaming] class CountingIterator[T](iterator: Iterator[T]) extends Iterator[T] {
    private var _count = 0
 
    private def isFullyConsumed: Boolean = !iterator.hasNext
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index aae3acf7aba3e..30d25a64e307a 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -546,7 +546,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
  * Function to start the receiver on the worker node. Use a class instead of closure to avoid
  * the serialization issue.
  */
-private class StartReceiverFunc(
+private[streaming] class StartReceiverFunc(
     checkpointDirOption: Option[String],
     serializableHadoopConf: SerializableConfiguration)
   extends (Iterator[Receiver[_]] => Unit) with Serializable {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
index 0c891662c264f..90d1b0fadecfc 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/ui/BatchPage.scala
@@ -28,7 +28,7 @@ import org.apache.spark.ui.{UIUtils => SparkUIUtils, WebUIPage}
 import org.apache.spark.streaming.ui.StreamingJobProgressListener.{SparkJobId, OutputOpId}
 import org.apache.spark.ui.jobs.UIData.JobUIData
 
-private case class SparkJobIdWithUIData(sparkJobId: SparkJobId, jobUIData: Option[JobUIData])
+private[ui] case class SparkJobIdWithUIData(sparkJobId: SparkJobId, jobUIData: Option[JobUIData])
 
 private[ui] class BatchPage(parent: StreamingTab) extends WebUIPage("batch") {
   private val streamingListener = parent.listener

From 9ce0c7ad333f4a3c01207e5e9ed42bcafb99d894 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Mon, 24 Aug 2015 13:48:01 -0700
Subject: [PATCH 385/633] [SPARK-7710] [SPARK-7998] [DOCS] Docs for
 DataFrameStatFunctions

This PR contains examples on how to use some of the Stat Functions available for DataFrames under `df.stat`.

rxin

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #8378 from brkyvz/update-sql-docs.
---
 .../org/apache/spark/sql/DataFrame.scala      |   2 +-
 .../spark/sql/DataFrameStatFunctions.scala    | 101 ++++++++++++++++++
 2 files changed, 102 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index d6688b24ae7d6..791c10c3d7ce7 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -684,7 +684,7 @@ class DataFrame private[sql](
       // make it a NamedExpression.
       case Column(u: UnresolvedAttribute) => UnresolvedAlias(u)
       case Column(expr: NamedExpression) => expr
-      // Leave an unaliased explode with an empty list of names since the analzyer will generate the
+      // Leave an unaliased explode with an empty list of names since the analyzer will generate the
       // correct defaults after the nested expression's type has been resolved.
       case Column(explode: Explode) => MultiAlias(explode, Nil)
       case Column(expr: Expression) => Alias(expr, expr.prettyString)()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
index 2e68e358f2f1f..69c984717526d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameStatFunctions.scala
@@ -39,6 +39,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the second column
    * @return the covariance of the two columns.
    *
+   * {{{
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.cov("rand1", "rand2")
+   *    res1: Double = 0.065...
+   * }}}
+   *
    * @since 1.4.0
    */
   def cov(col1: String, col2: String): Double = {
@@ -54,6 +61,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the column to calculate the correlation against
    * @return The Pearson Correlation Coefficient as a Double.
    *
+   * {{{
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.corr("rand1", "rand2")
+   *    res1: Double = 0.613...
+   * }}}
+   *
    * @since 1.4.0
    */
   def corr(col1: String, col2: String, method: String): Double = {
@@ -69,6 +83,13 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param col2 the name of the column to calculate the correlation against
    * @return The Pearson Correlation Coefficient as a Double.
    *
+   * {{{
+   *    val df = sc.parallelize(0 until 10).toDF("id").withColumn("rand1", rand(seed=10))
+   *      .withColumn("rand2", rand(seed=27))
+   *    df.stat.corr("rand1", "rand2", "pearson")
+   *    res1: Double = 0.613...
+   * }}}
+   *
    * @since 1.4.0
    */
   def corr(col1: String, col2: String): Double = {
@@ -92,6 +113,20 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    *             of the DataFrame.
    * @return A DataFrame containing for the contingency table.
    *
+   * {{{
+   *    val df = sqlContext.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+   *      (3, 3))).toDF("key", "value")
+   *    val ct = df.stat.crosstab("key", "value")
+   *    ct.show()
+   *    +---------+---+---+---+
+   *    |key_value|  1|  2|  3|
+   *    +---------+---+---+---+
+   *    |        2|  2|  0|  1|
+   *    |        1|  1|  1|  0|
+   *    |        3|  0|  1|  1|
+   *    +---------+---+---+---+
+   * }}}
+   *
    * @since 1.4.0
    */
   def crosstab(col1: String, col2: String): DataFrame = {
@@ -112,6 +147,32 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    *                than 1e-4.
    * @return A Local DataFrame with the Array of frequent items for each column.
    *
+   * {{{
+   *    val rows = Seq.tabulate(100) { i =>
+   *      if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+   *    }
+   *    val df = sqlContext.createDataFrame(rows).toDF("a", "b")
+   *    // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+   *    // "a" and "b"
+   *    val freqSingles = df.stat.freqItems(Array("a", "b"), 0.4)
+   *    freqSingles.show()
+   *    +-----------+-------------+
+   *    |a_freqItems|  b_freqItems|
+   *    +-----------+-------------+
+   *    |    [1, 99]|[-1.0, -99.0]|
+   *    +-----------+-------------+
+   *    // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+   *    val pairDf = df.select(struct("a", "b").as("a-b"))
+   *    val freqPairs = pairDf.stat.freqItems(Array("a-b"), 0.1)
+   *    freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+   *    +----------+
+   *    |   freq_ab|
+   *    +----------+
+   *    |  [1,-1.0]|
+   *    |   ...    |
+   *    +----------+
+   * }}}
+   *
    * @since 1.4.0
    */
   def freqItems(cols: Array[String], support: Double): DataFrame = {
@@ -147,6 +208,32 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @param cols the names of the columns to search frequent items in.
    * @return A Local DataFrame with the Array of frequent items for each column.
    *
+   * {{{
+   *    val rows = Seq.tabulate(100) { i =>
+   *      if (i % 2 == 0) (1, -1.0) else (i, i * -1.0)
+   *    }
+   *    val df = sqlContext.createDataFrame(rows).toDF("a", "b")
+   *    // find the items with a frequency greater than 0.4 (observed 40% of the time) for columns
+   *    // "a" and "b"
+   *    val freqSingles = df.stat.freqItems(Seq("a", "b"), 0.4)
+   *    freqSingles.show()
+   *    +-----------+-------------+
+   *    |a_freqItems|  b_freqItems|
+   *    +-----------+-------------+
+   *    |    [1, 99]|[-1.0, -99.0]|
+   *    +-----------+-------------+
+   *    // find the pair of items with a frequency greater than 0.1 in columns "a" and "b"
+   *    val pairDf = df.select(struct("a", "b").as("a-b"))
+   *    val freqPairs = pairDf.stat.freqItems(Seq("a-b"), 0.1)
+   *    freqPairs.select(explode($"a-b_freqItems").as("freq_ab")).show()
+   *    +----------+
+   *    |   freq_ab|
+   *    +----------+
+   *    |  [1,-1.0]|
+   *    |   ...    |
+   *    +----------+
+   * }}}
+   *
    * @since 1.4.0
    */
   def freqItems(cols: Seq[String], support: Double): DataFrame = {
@@ -180,6 +267,20 @@ final class DataFrameStatFunctions private[sql](df: DataFrame) {
    * @tparam T stratum type
    * @return a new [[DataFrame]] that represents the stratified sample
    *
+   * {{{
+   *    val df = sqlContext.createDataFrame(Seq((1, 1), (1, 2), (2, 1), (2, 1), (2, 3), (3, 2),
+   *      (3, 3))).toDF("key", "value")
+   *    val fractions = Map(1 -> 1.0, 3 -> 0.5)
+   *    df.stat.sampleBy("key", fractions, 36L).show()
+   *    +---+-----+
+   *    |key|value|
+   *    +---+-----+
+   *    |  1|    1|
+   *    |  1|    2|
+   *    |  3|    2|
+   *    +---+-----+
+   * }}}
+   *
    * @since 1.5.0
    */
   def sampleBy[T](col: String, fractions: Map[T, Double], seed: Long): DataFrame = {

From 662bb9667669cb07cf6d2ccee0d8e76bb561cd89 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 24 Aug 2015 14:10:50 -0700
Subject: [PATCH 386/633] [SPARK-10144] [UI] Actually show peak execution
 memory by default

The peak execution memory metric was introduced in SPARK-8735. That was before Tungsten was enabled by default, so it assumed that `spark.sql.unsafe.enabled` must be explicitly set to true. The result is that the memory is not displayed by default.

Author: Andrew Or <andrew@databricks.com>

Closes #8345 from andrewor14/show-memory-default.
---
 .../main/scala/org/apache/spark/ui/jobs/StagePage.scala   | 6 ++----
 .../test/scala/org/apache/spark/ui/StagePageSuite.scala   | 8 ++++++--
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index fb4556b836859..4adc6596ba21c 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -68,8 +68,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
   // if we find that it's okay.
   private val MAX_TIMELINE_TASKS = parent.conf.getInt("spark.ui.timeline.tasks.maximum", 1000)
 
-  private val displayPeakExecutionMemory =
-    parent.conf.getOption("spark.sql.unsafe.enabled").exists(_.toBoolean)
+  private val displayPeakExecutionMemory = parent.conf.getBoolean("spark.sql.unsafe.enabled", true)
 
   def render(request: HttpServletRequest): Seq[Node] = {
     progressListener.synchronized {
@@ -1193,8 +1192,7 @@ private[ui] class TaskPagedTable(
     desc: Boolean) extends PagedTable[TaskTableRowData] {
 
   // We only track peak memory used for unsafe operators
-  private val displayPeakExecutionMemory =
-    conf.getOption("spark.sql.unsafe.enabled").exists(_.toBoolean)
+  private val displayPeakExecutionMemory = conf.getBoolean("spark.sql.unsafe.enabled", true)
 
   override def tableId: String = "task-table"
 
diff --git a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
index 98f9314f31dff..3388c6dca81f1 100644
--- a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
@@ -33,14 +33,18 @@ class StagePageSuite extends SparkFunSuite with LocalSparkContext {
 
   test("peak execution memory only displayed if unsafe is enabled") {
     val unsafeConf = "spark.sql.unsafe.enabled"
-    val conf = new SparkConf().set(unsafeConf, "true")
+    val conf = new SparkConf(false).set(unsafeConf, "true")
     val html = renderStagePage(conf).toString().toLowerCase
     val targetString = "peak execution memory"
     assert(html.contains(targetString))
     // Disable unsafe and make sure it's not there
-    val conf2 = new SparkConf().set(unsafeConf, "false")
+    val conf2 = new SparkConf(false).set(unsafeConf, "false")
     val html2 = renderStagePage(conf2).toString().toLowerCase
     assert(!html2.contains(targetString))
+    // Avoid setting anything; it should be displayed by default
+    val conf3 = new SparkConf(false)
+    val html3 = renderStagePage(conf3).toString().toLowerCase
+    assert(html3.contains(targetString))
   }
 
   /**

From a2f4cdceba32aaa0df59df335ca0ce1ac73fc6c2 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Mon, 24 Aug 2015 14:11:19 -0700
Subject: [PATCH 387/633] [SPARK-8580] [SQL] Refactors
 ParquetHiveCompatibilitySuite and adds more test cases

This PR refactors `ParquetHiveCompatibilitySuite` so that it's easier to add new test cases.

Hit two bugs, SPARK-10177 and HIVE-11625, while working on this, added test cases for them and marked as ignored for now. SPARK-10177 will be addressed in a separate PR.

Author: Cheng Lian <lian@databricks.com>

Closes #8392 from liancheng/spark-8580/parquet-hive-compat-tests.
---
 .../hive/ParquetHiveCompatibilitySuite.scala  | 132 ++++++++++++------
 1 file changed, 93 insertions(+), 39 deletions(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index 13452e71a1b3b..bc30180cf0917 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -17,15 +17,17 @@
 
 package org.apache.spark.sql.hive
 
+import java.sql.Timestamp
+import java.util.{Locale, TimeZone}
+
 import org.apache.hadoop.hive.conf.HiveConf
+import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.execution.datasources.parquet.ParquetCompatibilityTest
+import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.{Row, SQLConf, SQLContext}
 
-class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
-  import ParquetCompatibilityTest.makeNullable
-
+class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest with BeforeAndAfterAll {
   override def _sqlContext: SQLContext = TestHive
   private val sqlContext = _sqlContext
 
@@ -35,69 +37,121 @@ class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest {
    */
   private val stagingDir = new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR)
 
-  test("Read Parquet file generated by parquet-hive") {
+  private val originalTimeZone = TimeZone.getDefault
+  private val originalLocale = Locale.getDefault
+
+  protected override def beforeAll(): Unit = {
+    TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
+    Locale.setDefault(Locale.US)
+  }
+
+  override protected def afterAll(): Unit = {
+    TimeZone.setDefault(originalTimeZone)
+    Locale.setDefault(originalLocale)
+  }
+
+  override protected def logParquetSchema(path: String): Unit = {
+    val schema = readParquetSchema(path, { path =>
+      !path.getName.startsWith("_") && !path.getName.startsWith(stagingDir)
+    })
+
+    logInfo(
+      s"""Schema of the Parquet file written by parquet-avro:
+         |$schema
+       """.stripMargin)
+  }
+
+  private def testParquetHiveCompatibility(row: Row, hiveTypes: String*): Unit = {
     withTable("parquet_compat") {
       withTempPath { dir =>
         val path = dir.getCanonicalPath
 
+        // Hive columns are always nullable, so here we append a all-null row.
+        val rows = row :: Row(Seq.fill(row.length)(null): _*) :: Nil
+
+        // Don't convert Hive metastore Parquet tables to let Hive write those Parquet files.
         withSQLConf(HiveContext.CONVERT_METASTORE_PARQUET.key -> "false") {
           withTempTable("data") {
-            sqlContext.sql(
+            val fields = hiveTypes.zipWithIndex.map { case (typ, index) => s"  col_$index $typ" }
+
+            val ddl =
               s"""CREATE TABLE parquet_compat(
-                 |  bool_column BOOLEAN,
-                 |  byte_column TINYINT,
-                 |  short_column SMALLINT,
-                 |  int_column INT,
-                 |  long_column BIGINT,
-                 |  float_column FLOAT,
-                 |  double_column DOUBLE,
-                 |
-                 |  strings_column ARRAY<STRING>,
-                 |  int_to_string_column MAP<INT, STRING>
+                 |${fields.mkString(",\n")}
                  |)
                  |STORED AS PARQUET
                  |LOCATION '$path'
+               """.stripMargin
+
+            logInfo(
+              s"""Creating testing Parquet table with the following DDL:
+                 |$ddl
                """.stripMargin)
 
+            sqlContext.sql(ddl)
+
             val schema = sqlContext.table("parquet_compat").schema
-            val rowRDD = sqlContext.sparkContext.parallelize(makeRows).coalesce(1)
+            val rowRDD = sqlContext.sparkContext.parallelize(rows).coalesce(1)
             sqlContext.createDataFrame(rowRDD, schema).registerTempTable("data")
             sqlContext.sql("INSERT INTO TABLE parquet_compat SELECT * FROM data")
           }
         }
 
-        val schema = readParquetSchema(path, { path =>
-          !path.getName.startsWith("_") && !path.getName.startsWith(stagingDir)
-        })
-
-        logInfo(
-          s"""Schema of the Parquet file written by parquet-hive:
-             |$schema
-           """.stripMargin)
+        logParquetSchema(path)
 
         // Unfortunately parquet-hive doesn't add `UTF8` annotation to BINARY when writing strings.
         // Have to assume all BINARY values are strings here.
         withSQLConf(SQLConf.PARQUET_BINARY_AS_STRING.key -> "true") {
-          checkAnswer(sqlContext.read.parquet(path), makeRows)
+          checkAnswer(sqlContext.read.parquet(path), rows)
         }
       }
     }
   }
 
-  def makeRows: Seq[Row] = {
-    (0 until 10).map { i =>
-      def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
+  test("simple primitives") {
+    testParquetHiveCompatibility(
+      Row(true, 1.toByte, 2.toShort, 3, 4.toLong, 5.1f, 6.1d, "foo"),
+      "BOOLEAN", "TINYINT", "SMALLINT", "INT", "BIGINT", "FLOAT", "DOUBLE", "STRING")
+  }
 
+  ignore("SPARK-10177 timestamp") {
+    testParquetHiveCompatibility(Row(Timestamp.valueOf("2015-08-24 00:31:00")), "TIMESTAMP")
+  }
+
+  test("array") {
+    testParquetHiveCompatibility(
       Row(
-        nullable(i % 2 == 0: java.lang.Boolean),
-        nullable(i.toByte: java.lang.Byte),
-        nullable((i + 1).toShort: java.lang.Short),
-        nullable(i + 2: Integer),
-        nullable(i.toLong * 10: java.lang.Long),
-        nullable(i.toFloat + 0.1f: java.lang.Float),
-        nullable(i.toDouble + 0.2d: java.lang.Double),
-        nullable(Seq.tabulate(3)(n => s"arr_${i + n}")),
-        nullable(Seq.tabulate(3)(n => (i + n: Integer) -> s"val_${i + n}").toMap))
-    }
+        Seq[Integer](1: Integer, null, 2: Integer, null),
+        Seq[String]("foo", null, "bar", null),
+        Seq[Seq[Integer]](
+          Seq[Integer](1: Integer, null),
+          Seq[Integer](2: Integer, null))),
+      "ARRAY<INT>",
+      "ARRAY<STRING>",
+      "ARRAY<ARRAY<INT>>")
+  }
+
+  test("map") {
+    testParquetHiveCompatibility(
+      Row(
+        Map[Integer, String](
+          (1: Integer) -> "foo",
+          (2: Integer) -> null)),
+      "MAP<INT, STRING>")
+  }
+
+  // HIVE-11625: Parquet map entries with null keys are dropped by Hive
+  ignore("map entries with null keys") {
+    testParquetHiveCompatibility(
+      Row(
+        Map[Integer, String](
+          null.asInstanceOf[Integer] -> "bar",
+          null.asInstanceOf[Integer] -> null)),
+      "MAP<INT, STRING>")
+  }
+
+  test("struct") {
+    testParquetHiveCompatibility(
+      Row(Row(1, Seq("foo", "bar", null))),
+      "STRUCT<f0: INT, f1: ARRAY<STRING>>")
   }
 }

From cb2d2e15844d7ae34b5dd7028b55e11586ed93fa Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Mon, 24 Aug 2015 22:35:21 +0100
Subject: [PATCH 388/633] [SPARK-9758] [TEST] [SQL] Compilation issue for hive
 test / wrong package?

Move `test.org.apache.spark.sql.hive` package tests to apparent intended `org.apache.spark.sql.hive` as they don't intend to test behavior from outside org.apache.spark.*

Alternate take, per discussion at https://github.com/apache/spark/pull/8051
I think this is what vanzin and I had in mind but also CC rxin to cross-check, as this does indeed depend on whether these tests were accidentally in this package or not. Testing from a `test.org.apache.spark` package is legitimate but didn't seem to be the intent here.

Author: Sean Owen <sowen@cloudera.com>

Closes #8307 from srowen/SPARK-9758.
---
 .../org/apache/spark/sql/hive/JavaDataFrameSuite.java       | 6 ++----
 .../spark/sql/hive/JavaMetastoreDataSourcesSuite.java       | 3 +--
 .../org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java    | 2 +-
 .../org/apache/spark/sql/hive/aggregate/MyDoubleSum.java    | 2 +-
 .../apache/spark/sql/hive/execution/UDFIntegerToString.java | 0
 .../org/apache/spark/sql/hive/execution/UDFListListInt.java | 0
 .../org/apache/spark/sql/hive/execution/UDFListString.java  | 0
 .../apache/spark/sql/hive/execution/UDFStringString.java    | 0
 .../org/apache/spark/sql/hive/execution/UDFTwoListList.java | 0
 .../spark/sql/hive/execution/AggregationQuerySuite.scala    | 2 +-
 10 files changed, 6 insertions(+), 9 deletions(-)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/JavaDataFrameSuite.java (94%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java (98%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java (98%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java (98%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/execution/UDFIntegerToString.java (100%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/execution/UDFListListInt.java (100%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/execution/UDFListString.java (100%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/execution/UDFStringString.java (100%)
 rename sql/hive/src/test/java/{test => }/org/apache/spark/sql/hive/execution/UDFTwoListList.java (100%)

diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
similarity index 94%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
index a30dfa554eabc..019d8a30266e2 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaDataFrameSuite.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package test.org.apache.spark.sql.hive;
+package org.apache.spark.sql.hive;
 
 import java.io.IOException;
 import java.util.ArrayList;
@@ -31,10 +31,8 @@
 import org.apache.spark.sql.expressions.Window;
 import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
 import static org.apache.spark.sql.functions.*;
-import org.apache.spark.sql.hive.HiveContext;
 import org.apache.spark.sql.hive.test.TestHive$;
-import org.apache.spark.sql.expressions.UserDefinedAggregateFunction;
-import test.org.apache.spark.sql.hive.aggregate.MyDoubleSum;
+import org.apache.spark.sql.hive.aggregate.MyDoubleSum;
 
 public class JavaDataFrameSuite {
   private transient JavaSparkContext sc;
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
similarity index 98%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
index 15c2c3deb0d83..4192155975c47 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package test.org.apache.spark.sql.hive;
+package org.apache.spark.sql.hive;
 
 import java.io.File;
 import java.io.IOException;
@@ -37,7 +37,6 @@
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.QueryTest$;
 import org.apache.spark.sql.Row;
-import org.apache.spark.sql.hive.HiveContext;
 import org.apache.spark.sql.hive.test.TestHive$;
 import org.apache.spark.sql.types.DataTypes;
 import org.apache.spark.sql.types.StructField;
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
similarity index 98%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
index 2961b803f14aa..5a167edd89592 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleAvg.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package test.org.apache.spark.sql.hive.aggregate;
+package org.apache.spark.sql.hive.aggregate;
 
 import java.util.ArrayList;
 import java.util.List;
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
similarity index 98%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
index c71882a6e7bed..c3b7768e71bf8 100644
--- a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/aggregate/MyDoubleSum.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package test.org.apache.spark.sql.hive.aggregate;
+package org.apache.spark.sql.hive.aggregate;
 
 import java.util.ArrayList;
 import java.util.List;
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFIntegerToString.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFIntegerToString.java
similarity index 100%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFIntegerToString.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFIntegerToString.java
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListListInt.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListListInt.java
similarity index 100%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListListInt.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListListInt.java
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListString.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListString.java
similarity index 100%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFListString.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFListString.java
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFStringString.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFStringString.java
similarity index 100%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFStringString.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFStringString.java
diff --git a/sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFTwoListList.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFTwoListList.java
similarity index 100%
rename from sql/hive/src/test/java/test/org/apache/spark/sql/hive/execution/UDFTwoListList.java
rename to sql/hive/src/test/java/org/apache/spark/sql/hive/execution/UDFTwoListList.java
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index 119663af1887a..4886a85948367 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
-import _root_.test.org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
+import org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
 
 abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
   override def _sqlContext: SQLContext = TestHive

From 13db11cb08eb90eb0ea3402c9fe0270aa282f247 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Mon, 24 Aug 2015 15:38:54 -0700
Subject: [PATCH 389/633] [SPARK-10061] [DOC] ML ensemble docs

User guide for spark.ml GBTs and Random Forests.
The examples are copied from the decision tree guide and modified to run.

I caught some issues I had somehow missed in the tree guide as well.

I have run all examples, including Java ones.  (Of course, I thought I had previously as well...)

CC: mengxr manishamde yanboliang

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8369 from jkbradley/ml-ensemble-docs.
---
 docs/ml-decision-tree.md |  75 ++-
 docs/ml-ensembles.md     | 952 ++++++++++++++++++++++++++++++++++++++-
 2 files changed, 976 insertions(+), 51 deletions(-)

diff --git a/docs/ml-decision-tree.md b/docs/ml-decision-tree.md
index 958c6f5e4716c..542819e93e6dc 100644
--- a/docs/ml-decision-tree.md
+++ b/docs/ml-decision-tree.md
@@ -30,7 +30,7 @@ The Pipelines API for Decision Trees offers a bit more functionality than the or
 
 Ensembles of trees (Random Forests and Gradient-Boosted Trees) are described in the [Ensembles guide](ml-ensembles.html).
 
-# Inputs and Outputs (Predictions)
+# Inputs and Outputs
 
 We list the input and output (prediction) column types here.
 All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.
@@ -234,7 +234,7 @@ IndexToString labelConverter = new IndexToString()
 
 // Chain indexers and tree in a Pipeline
 Pipeline pipeline = new Pipeline()
-  .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});
+  .setStages(new PipelineStage[] {labelIndexer, featureIndexer, dt, labelConverter});
 
 // Train model.  This also runs the indexers.
 PipelineModel model = pipeline.fit(trainingData);
@@ -315,10 +315,13 @@ print treeModel # summary only
 
 ## Regression
 
+The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use a feature transformer to index categorical features, adding metadata to the `DataFrame` which the Decision Tree algorithm can recognize.
+
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.classification.DecisionTreeClassifier).
+More details on parameters can be found in the [Scala API documentation](api/scala/index.html#org.apache.spark.ml.regression.DecisionTreeRegressor).
 
 {% highlight scala %}
 import org.apache.spark.ml.Pipeline
@@ -347,7 +350,7 @@ val dt = new DecisionTreeRegressor()
   .setLabelCol("label")
   .setFeaturesCol("indexedFeatures")
 
-// Chain indexers and tree in a Pipeline
+// Chain indexer and tree in a Pipeline
 val pipeline = new Pipeline()
   .setStages(Array(featureIndexer, dt))
 
@@ -365,9 +368,7 @@ val evaluator = new RegressionEvaluator()
   .setLabelCol("label")
   .setPredictionCol("prediction")
   .setMetricName("rmse")
-// We negate the RMSE value since RegressionEvalutor returns negated RMSE
-// (since evaluation metrics are meant to be maximized by CrossValidator).
-val rmse = - evaluator.evaluate(predictions)
+val rmse = evaluator.evaluate(predictions)
 println("Root Mean Squared Error (RMSE) on test data = " + rmse)
 
 val treeModel = model.stages(1).asInstanceOf[DecisionTreeRegressionModel]
@@ -377,14 +378,15 @@ println("Learned regression tree model:\n" + treeModel.toDebugString)
 
 <div data-lang="java" markdown="1">
 
-More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/classification/DecisionTreeClassifier.html).
+More details on parameters can be found in the [Java API documentation](api/java/org/apache/spark/ml/regression/DecisionTreeRegressor.html).
 
 {% highlight java %}
 import org.apache.spark.ml.Pipeline;
 import org.apache.spark.ml.PipelineModel;
 import org.apache.spark.ml.PipelineStage;
 import org.apache.spark.ml.evaluation.RegressionEvaluator;
-import org.apache.spark.ml.feature.*;
+import org.apache.spark.ml.feature.VectorIndexer;
+import org.apache.spark.ml.feature.VectorIndexerModel;
 import org.apache.spark.ml.regression.DecisionTreeRegressionModel;
 import org.apache.spark.ml.regression.DecisionTreeRegressor;
 import org.apache.spark.mllib.regression.LabeledPoint;
@@ -396,17 +398,12 @@ import org.apache.spark.sql.DataFrame;
 RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
 DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
 
-// Index labels, adding metadata to the label column.
-// Fit on whole dataset to include all labels in index.
-StringIndexerModel labelIndexer = new StringIndexer()
-  .setInputCol("label")
-  .setOutputCol("indexedLabel")
-  .fit(data);
 // Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
 VectorIndexerModel featureIndexer = new VectorIndexer()
   .setInputCol("features")
   .setOutputCol("indexedFeatures")
-  .setMaxCategories(4) // features with > 4 distinct values are treated as continuous
+  .setMaxCategories(4)
   .fit(data);
 
 // Split the data into training and test sets (30% held out for testing)
@@ -416,61 +413,49 @@ DataFrame testData = splits[1];
 
 // Train a DecisionTree model.
 DecisionTreeRegressor dt = new DecisionTreeRegressor()
-  .setLabelCol("indexedLabel")
   .setFeaturesCol("indexedFeatures");
 
-// Convert indexed labels back to original labels.
-IndexToString labelConverter = new IndexToString()
-  .setInputCol("prediction")
-  .setOutputCol("predictedLabel")
-  .setLabels(labelIndexer.labels());
-
-// Chain indexers and tree in a Pipeline
+// Chain indexer and tree in a Pipeline
 Pipeline pipeline = new Pipeline()
-  .setStages(new PipelineStage[]{labelIndexer, featureIndexer, dt, labelConverter});
+  .setStages(new PipelineStage[] {featureIndexer, dt});
 
-// Train model.  This also runs the indexers.
+// Train model.  This also runs the indexer.
 PipelineModel model = pipeline.fit(trainingData);
 
 // Make predictions.
 DataFrame predictions = model.transform(testData);
 
 // Select example rows to display.
-predictions.select("predictedLabel", "label", "features").show(5);
+predictions.select("label", "features").show(5);
 
 // Select (prediction, true label) and compute test error
 RegressionEvaluator evaluator = new RegressionEvaluator()
-  .setLabelCol("indexedLabel")
+  .setLabelCol("label")
   .setPredictionCol("prediction")
   .setMetricName("rmse");
-// We negate the RMSE value since RegressionEvalutor returns negated RMSE
-// (since evaluation metrics are meant to be maximized by CrossValidator).
-double rmse = - evaluator.evaluate(predictions);
+double rmse = evaluator.evaluate(predictions);
 System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse);
 
 DecisionTreeRegressionModel treeModel =
-  (DecisionTreeRegressionModel)(model.stages()[2]);
+  (DecisionTreeRegressionModel)(model.stages()[1]);
 System.out.println("Learned regression tree model:\n" + treeModel.toDebugString());
 {% endhighlight %}
 </div>
 
 <div data-lang="python" markdown="1">
 
-More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.classification.DecisionTreeClassifier).
+More details on parameters can be found in the [Python API documentation](api/python/pyspark.ml.html#pyspark.ml.regression.DecisionTreeRegressor).
 
 {% highlight python %}
 from pyspark.ml import Pipeline
 from pyspark.ml.regression import DecisionTreeRegressor
-from pyspark.ml.feature import StringIndexer, VectorIndexer
+from pyspark.ml.feature import VectorIndexer
 from pyspark.ml.evaluation import RegressionEvaluator
 from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
 data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
 
-# Index labels, adding metadata to the label column.
-# Fit on whole dataset to include all labels in index.
-labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
 # Automatically identify categorical features, and index them.
 # We specify maxCategories so features with > 4 distinct values are treated as continuous.
 featureIndexer =\
@@ -480,26 +465,24 @@ featureIndexer =\
 (trainingData, testData) = data.randomSplit([0.7, 0.3])
 
 # Train a DecisionTree model.
-dt = DecisionTreeRegressor(labelCol="indexedLabel", featuresCol="indexedFeatures")
+dt = DecisionTreeRegressor(featuresCol="indexedFeatures")
 
-# Chain indexers and tree in a Pipeline
-pipeline = Pipeline(stages=[labelIndexer, featureIndexer, dt])
+# Chain indexer and tree in a Pipeline
+pipeline = Pipeline(stages=[featureIndexer, dt])
 
-# Train model.  This also runs the indexers.
+# Train model.  This also runs the indexer.
 model = pipeline.fit(trainingData)
 
 # Make predictions.
 predictions = model.transform(testData)
 
 # Select example rows to display.
-predictions.select("prediction", "indexedLabel", "features").show(5)
+predictions.select("prediction", "label", "features").show(5)
 
 # Select (prediction, true label) and compute test error
 evaluator = RegressionEvaluator(
-    labelCol="indexedLabel", predictionCol="prediction", metricName="rmse")
-# We negate the RMSE value since RegressionEvalutor returns negated RMSE
-# (since evaluation metrics are meant to be maximized by CrossValidator).
-rmse = -evaluator.evaluate(predictions)
+    labelCol="label", predictionCol="prediction", metricName="rmse")
+rmse = evaluator.evaluate(predictions)
 print "Root Mean Squared Error (RMSE) on test data = %g" % rmse
 
 treeModel = model.stages[1]
diff --git a/docs/ml-ensembles.md b/docs/ml-ensembles.md
index 9ff50e95fc479..62749909e01dc 100644
--- a/docs/ml-ensembles.md
+++ b/docs/ml-ensembles.md
@@ -11,11 +11,947 @@ displayTitle: <a href="ml-guide.html">ML</a> - Ensembles
 
 An [ensemble method](http://en.wikipedia.org/wiki/Ensemble_learning)
 is a learning algorithm which creates a model composed of a set of other base models.
-The Pipelines API supports the following ensemble algorithms: [`OneVsRest`](api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest)
 
-## OneVsRest
+## Tree Ensembles
 
-[OneVsRest](http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest) is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently.
+The Pipelines API supports two major tree ensemble algorithms: [Random Forests](http://en.wikipedia.org/wiki/Random_forest) and [Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting).
+Both use [MLlib decision trees](ml-decision-tree.html) as their base models.
+
+Users can find more information about ensemble algorithms in the [MLlib Ensemble guide](mllib-ensembles.html).  In this section, we demonstrate the Pipelines API for ensembles.
+
+The main differences between this API and the [original MLlib ensembles API](mllib-ensembles.html) are:
+* support for ML Pipelines
+* separation of classification vs. regression
+* use of DataFrame metadata to distinguish continuous and categorical features
+* a bit more functionality for random forests: estimates of feature importance, as well as the predicted probability of each class (a.k.a. class conditional probabilities) for classification.
+
+### Random Forests
+
+[Random forests](http://en.wikipedia.org/wiki/Random_forest)
+are ensembles of [decision trees](ml-decision-tree.html).
+Random forests combine many decision trees in order to reduce the risk of overfitting.
+MLlib supports random forests for binary and multiclass classification and for regression,
+using both continuous and categorical features.
+
+This section gives examples of using random forests with the Pipelines API.
+For more information on the algorithm, please see the [main MLlib docs on random forests](mllib-ensembles.html).
+
+#### Inputs and Outputs
+
+We list the input and output (prediction) column types here.
+All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.
+
+##### Input Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>labelCol</td>
+      <td>Double</td>
+      <td>"label"</td>
+      <td>Label to predict</td>
+    </tr>
+    <tr>
+      <td>featuresCol</td>
+      <td>Vector</td>
+      <td>"features"</td>
+      <td>Feature vector</td>
+    </tr>
+  </tbody>
+</table>
+
+##### Output Columns (Predictions)
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+      <th align="left">Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>predictionCol</td>
+      <td>Double</td>
+      <td>"prediction"</td>
+      <td>Predicted label</td>
+      <td></td>
+    </tr>
+    <tr>
+      <td>rawPredictionCol</td>
+      <td>Vector</td>
+      <td>"rawPrediction"</td>
+      <td>Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction</td>
+      <td>Classification only</td>
+    </tr>
+    <tr>
+      <td>probabilityCol</td>
+      <td>Vector</td>
+      <td>"probability"</td>
+      <td>Vector of length # classes equal to rawPrediction normalized to a multinomial distribution</td>
+      <td>Classification only</td>
+    </tr>
+  </tbody>
+</table>
+
+#### Example: Classification
+
+The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.RandomForestClassifier) for more details.
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.classification.RandomForestClassifier
+import org.apache.spark.ml.classification.RandomForestClassificationModel
+import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+val labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data)
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a RandomForest model.
+val rf = new RandomForestClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures")
+  .setNumTrees(10)
+
+// Convert indexed labels back to original labels.
+val labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels)
+
+// Chain indexers and forest in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(labelIndexer, featureIndexer, rf, labelConverter))
+
+// Train model.  This also runs the indexers.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision")
+val accuracy = evaluator.evaluate(predictions)
+println("Test Error = " + (1.0 - accuracy))
+
+val rfModel = model.stages(2).asInstanceOf[RandomForestClassificationModel]
+println("Learned classification forest model:\n" + rfModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/RandomForestClassifier.html) for more details.
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.classification.RandomForestClassifier;
+import org.apache.spark.ml.classification.RandomForestClassificationModel;
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
+import org.apache.spark.ml.feature.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+StringIndexerModel labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data);
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a RandomForest model.
+RandomForestClassifier rf = new RandomForestClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures");
+
+// Convert indexed labels back to original labels.
+IndexToString labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels());
+
+// Chain indexers and forest in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[] {labelIndexer, featureIndexer, rf, labelConverter});
+
+// Train model.  This also runs the indexers.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision");
+double accuracy = evaluator.evaluate(predictions);
+System.out.println("Test Error = " + (1.0 - accuracy));
+
+RandomForestClassificationModel rfModel =
+  (RandomForestClassificationModel)(model.stages()[2]);
+System.out.println("Learned classification forest model:\n" + rfModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.RandomForestClassifier) for more details.
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.classification import RandomForestClassifier
+from pyspark.ml.feature import StringIndexer, VectorIndexer
+from pyspark.ml.evaluation import MulticlassClassificationEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Index labels, adding metadata to the label column.
+# Fit on whole dataset to include all labels in index.
+labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
+# Automatically identify categorical features, and index them.
+# Set maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a RandomForest model.
+rf = RandomForestClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures")
+
+# Chain indexers and forest in a Pipeline
+pipeline = Pipeline(stages=[labelIndexer, featureIndexer, rf])
+
+# Train model.  This also runs the indexers.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "indexedLabel", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = MulticlassClassificationEvaluator(
+    labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
+accuracy = evaluator.evaluate(predictions)
+print "Test Error = %g" % (1.0 - accuracy)
+
+rfModel = model.stages[2]
+print rfModel # summary only
+{% endhighlight %}
+</div>
+</div>
+
+#### Example: Regression
+
+The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use a feature transformer to index categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.RandomForestRegressor) for more details.
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.regression.RandomForestRegressor
+import org.apache.spark.ml.regression.RandomForestRegressionModel
+import org.apache.spark.ml.feature.VectorIndexer
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a RandomForest model.
+val rf = new RandomForestRegressor()
+  .setLabelCol("label")
+  .setFeaturesCol("indexedFeatures")
+
+// Chain indexer and forest in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(featureIndexer, rf))
+
+// Train model.  This also runs the indexer.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("prediction", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new RegressionEvaluator()
+  .setLabelCol("label")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse")
+val rmse = evaluator.evaluate(predictions)
+println("Root Mean Squared Error (RMSE) on test data = " + rmse)
+
+val rfModel = model.stages(1).asInstanceOf[RandomForestRegressionModel]
+println("Learned regression forest model:\n" + rfModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/RandomForestRegressor.html) for more details.
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.feature.VectorIndexer;
+import org.apache.spark.ml.feature.VectorIndexerModel;
+import org.apache.spark.ml.regression.RandomForestRegressionModel;
+import org.apache.spark.ml.regression.RandomForestRegressor;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a RandomForest model.
+RandomForestRegressor rf = new RandomForestRegressor()
+  .setLabelCol("label")
+  .setFeaturesCol("indexedFeatures");
+
+// Chain indexer and forest in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[] {featureIndexer, rf});
+
+// Train model.  This also runs the indexer.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("prediction", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+RegressionEvaluator evaluator = new RegressionEvaluator()
+  .setLabelCol("label")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse");
+double rmse = evaluator.evaluate(predictions);
+System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse);
+
+RandomForestRegressionModel rfModel =
+  (RandomForestRegressionModel)(model.stages()[1]);
+System.out.println("Learned regression forest model:\n" + rfModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor) for more details.
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.regression import RandomForestRegressor
+from pyspark.ml.feature import VectorIndexer
+from pyspark.ml.evaluation import RegressionEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Automatically identify categorical features, and index them.
+# Set maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a RandomForest model.
+rf = RandomForestRegressor(featuresCol="indexedFeatures")
+
+# Chain indexer and forest in a Pipeline
+pipeline = Pipeline(stages=[featureIndexer, rf])
+
+# Train model.  This also runs the indexer.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "label", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = RegressionEvaluator(
+    labelCol="label", predictionCol="prediction", metricName="rmse")
+rmse = evaluator.evaluate(predictions)
+print "Root Mean Squared Error (RMSE) on test data = %g" % rmse
+
+rfModel = model.stages[1]
+print rfModel # summary only
+{% endhighlight %}
+</div>
+</div>
+
+### Gradient-Boosted Trees (GBTs)
+
+[Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting)
+are ensembles of [decision trees](ml-decision-tree.html).
+GBTs iteratively train decision trees in order to minimize a loss function.
+MLlib supports GBTs for binary classification and for regression,
+using both continuous and categorical features.
+
+This section gives examples of using GBTs with the Pipelines API.
+For more information on the algorithm, please see the [main MLlib docs on GBTs](mllib-ensembles.html).
+
+#### Inputs and Outputs
+
+We list the input and output (prediction) column types here.
+All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.
+
+##### Input Columns
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>labelCol</td>
+      <td>Double</td>
+      <td>"label"</td>
+      <td>Label to predict</td>
+    </tr>
+    <tr>
+      <td>featuresCol</td>
+      <td>Vector</td>
+      <td>"features"</td>
+      <td>Feature vector</td>
+    </tr>
+  </tbody>
+</table>
+
+Note that `GBTClassifier` currently only supports binary labels.
+
+##### Output Columns (Predictions)
+
+<table class="table">
+  <thead>
+    <tr>
+      <th align="left">Param name</th>
+      <th align="left">Type(s)</th>
+      <th align="left">Default</th>
+      <th align="left">Description</th>
+      <th align="left">Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>predictionCol</td>
+      <td>Double</td>
+      <td>"prediction"</td>
+      <td>Predicted label</td>
+      <td></td>
+    </tr>
+  </tbody>
+</table>
+
+In the future, `GBTClassifier` will also output columns for `rawPrediction` and `probability`, just as `RandomForestClassifier` does.
+
+#### Example: Classification
+
+The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set.
+We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the `DataFrame` which the tree-based algorithms can recognize.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.GBTClassifier) for more details.
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.classification.GBTClassifier
+import org.apache.spark.ml.classification.GBTClassificationModel
+import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+val labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data)
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a GBT model.
+val gbt = new GBTClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures")
+  .setMaxIter(10)
+
+// Convert indexed labels back to original labels.
+val labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels)
+
+// Chain indexers and GBT in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(labelIndexer, featureIndexer, gbt, labelConverter))
+
+// Train model.  This also runs the indexers.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision")
+val accuracy = evaluator.evaluate(predictions)
+println("Test Error = " + (1.0 - accuracy))
+
+val gbtModel = model.stages(2).asInstanceOf[GBTClassificationModel]
+println("Learned classification GBT model:\n" + gbtModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/GBTClassifier.html) for more details.
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.classification.GBTClassifier;
+import org.apache.spark.ml.classification.GBTClassificationModel;
+import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
+import org.apache.spark.ml.feature.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Index labels, adding metadata to the label column.
+// Fit on whole dataset to include all labels in index.
+StringIndexerModel labelIndexer = new StringIndexer()
+  .setInputCol("label")
+  .setOutputCol("indexedLabel")
+  .fit(data);
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a GBT model.
+GBTClassifier gbt = new GBTClassifier()
+  .setLabelCol("indexedLabel")
+  .setFeaturesCol("indexedFeatures")
+  .setMaxIter(10);
+
+// Convert indexed labels back to original labels.
+IndexToString labelConverter = new IndexToString()
+  .setInputCol("prediction")
+  .setOutputCol("predictedLabel")
+  .setLabels(labelIndexer.labels());
+
+// Chain indexers and GBT in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[] {labelIndexer, featureIndexer, gbt, labelConverter});
+
+// Train model.  This also runs the indexers.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("predictedLabel", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
+  .setLabelCol("indexedLabel")
+  .setPredictionCol("prediction")
+  .setMetricName("precision");
+double accuracy = evaluator.evaluate(predictions);
+System.out.println("Test Error = " + (1.0 - accuracy));
+
+GBTClassificationModel gbtModel =
+  (GBTClassificationModel)(model.stages()[2]);
+System.out.println("Learned classification GBT model:\n" + gbtModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.GBTClassifier) for more details.
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.classification import GBTClassifier
+from pyspark.ml.feature import StringIndexer, VectorIndexer
+from pyspark.ml.evaluation import MulticlassClassificationEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Index labels, adding metadata to the label column.
+# Fit on whole dataset to include all labels in index.
+labelIndexer = StringIndexer(inputCol="label", outputCol="indexedLabel").fit(data)
+# Automatically identify categorical features, and index them.
+# Set maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a GBT model.
+gbt = GBTClassifier(labelCol="indexedLabel", featuresCol="indexedFeatures", maxIter=10)
+
+# Chain indexers and GBT in a Pipeline
+pipeline = Pipeline(stages=[labelIndexer, featureIndexer, gbt])
+
+# Train model.  This also runs the indexers.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "indexedLabel", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = MulticlassClassificationEvaluator(
+    labelCol="indexedLabel", predictionCol="prediction", metricName="precision")
+accuracy = evaluator.evaluate(predictions)
+print "Test Error = %g" % (1.0 - accuracy)
+
+gbtModel = model.stages[2]
+print gbtModel # summary only
+{% endhighlight %}
+</div>
+</div>
+
+#### Example: Regression
+
+Note: For this example dataset, `GBTRegressor` actually only needs 1 iteration, but that will not
+be true in general.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.regression.GBTRegressor) for more details.
+
+{% highlight scala %}
+import org.apache.spark.ml.Pipeline
+import org.apache.spark.ml.regression.GBTRegressor
+import org.apache.spark.ml.regression.GBTRegressionModel
+import org.apache.spark.ml.feature.VectorIndexer
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.mllib.util.MLUtils
+
+// Load and parse the data file, converting it to a DataFrame.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+val featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data)
+
+// Split the data into training and test sets (30% held out for testing)
+val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
+
+// Train a GBT model.
+val gbt = new GBTRegressor()
+  .setLabelCol("label")
+  .setFeaturesCol("indexedFeatures")
+  .setMaxIter(10)
+
+// Chain indexer and GBT in a Pipeline
+val pipeline = new Pipeline()
+  .setStages(Array(featureIndexer, gbt))
+
+// Train model.  This also runs the indexer.
+val model = pipeline.fit(trainingData)
+
+// Make predictions.
+val predictions = model.transform(testData)
+
+// Select example rows to display.
+predictions.select("prediction", "label", "features").show(5)
+
+// Select (prediction, true label) and compute test error
+val evaluator = new RegressionEvaluator()
+  .setLabelCol("label")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse")
+val rmse = evaluator.evaluate(predictions)
+println("Root Mean Squared Error (RMSE) on test data = " + rmse)
+
+val gbtModel = model.stages(1).asInstanceOf[GBTRegressionModel]
+println("Learned regression GBT model:\n" + gbtModel.toDebugString)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [Java API docs](api/java/org/apache/spark/ml/regression/GBTRegressor.html) for more details.
+
+{% highlight java %}
+import org.apache.spark.ml.Pipeline;
+import org.apache.spark.ml.PipelineModel;
+import org.apache.spark.ml.PipelineStage;
+import org.apache.spark.ml.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.feature.VectorIndexer;
+import org.apache.spark.ml.feature.VectorIndexerModel;
+import org.apache.spark.ml.regression.GBTRegressionModel;
+import org.apache.spark.ml.regression.GBTRegressor;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+// Load and parse the data file, converting it to a DataFrame.
+RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
+DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+
+// Automatically identify categorical features, and index them.
+// Set maxCategories so features with > 4 distinct values are treated as continuous.
+VectorIndexerModel featureIndexer = new VectorIndexer()
+  .setInputCol("features")
+  .setOutputCol("indexedFeatures")
+  .setMaxCategories(4)
+  .fit(data);
+
+// Split the data into training and test sets (30% held out for testing)
+DataFrame[] splits = data.randomSplit(new double[] {0.7, 0.3});
+DataFrame trainingData = splits[0];
+DataFrame testData = splits[1];
+
+// Train a GBT model.
+GBTRegressor gbt = new GBTRegressor()
+  .setLabelCol("label")
+  .setFeaturesCol("indexedFeatures")
+  .setMaxIter(10);
+
+// Chain indexer and GBT in a Pipeline
+Pipeline pipeline = new Pipeline()
+  .setStages(new PipelineStage[] {featureIndexer, gbt});
+
+// Train model.  This also runs the indexer.
+PipelineModel model = pipeline.fit(trainingData);
+
+// Make predictions.
+DataFrame predictions = model.transform(testData);
+
+// Select example rows to display.
+predictions.select("prediction", "label", "features").show(5);
+
+// Select (prediction, true label) and compute test error
+RegressionEvaluator evaluator = new RegressionEvaluator()
+  .setLabelCol("label")
+  .setPredictionCol("prediction")
+  .setMetricName("rmse");
+double rmse = evaluator.evaluate(predictions);
+System.out.println("Root Mean Squared Error (RMSE) on test data = " + rmse);
+
+GBTRegressionModel gbtModel =
+  (GBTRegressionModel)(model.stages()[1]);
+System.out.println("Learned regression GBT model:\n" + gbtModel.toDebugString());
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.regression.GBTRegressor) for more details.
+
+{% highlight python %}
+from pyspark.ml import Pipeline
+from pyspark.ml.regression import GBTRegressor
+from pyspark.ml.feature import VectorIndexer
+from pyspark.ml.evaluation import RegressionEvaluator
+from pyspark.mllib.util import MLUtils
+
+# Load and parse the data file, converting it to a DataFrame.
+data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+# Automatically identify categorical features, and index them.
+# Set maxCategories so features with > 4 distinct values are treated as continuous.
+featureIndexer =\
+    VectorIndexer(inputCol="features", outputCol="indexedFeatures", maxCategories=4).fit(data)
+
+# Split the data into training and test sets (30% held out for testing)
+(trainingData, testData) = data.randomSplit([0.7, 0.3])
+
+# Train a GBT model.
+gbt = GBTRegressor(featuresCol="indexedFeatures", maxIter=10)
+
+# Chain indexer and GBT in a Pipeline
+pipeline = Pipeline(stages=[featureIndexer, gbt])
+
+# Train model.  This also runs the indexer.
+model = pipeline.fit(trainingData)
+
+# Make predictions.
+predictions = model.transform(testData)
+
+# Select example rows to display.
+predictions.select("prediction", "label", "features").show(5)
+
+# Select (prediction, true label) and compute test error
+evaluator = RegressionEvaluator(
+    labelCol="label", predictionCol="prediction", metricName="rmse")
+rmse = evaluator.evaluate(predictions)
+print "Root Mean Squared Error (RMSE) on test data = %g" % rmse
+
+gbtModel = model.stages[1]
+print gbtModel # summary only
+{% endhighlight %}
+</div>
+</div>
+
+
+## One-vs-Rest (a.k.a. One-vs-All)
+
+[OneVsRest](http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest) is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently.  It is also known as "One-vs-All."
 
 `OneVsRest` is implemented as an `Estimator`. For the base classifier it takes instances of `Classifier` and creates a binary classification problem for each of the k classes. The classifier for class i is trained to predict whether the label is i or not, distinguishing class i from all other classes.
 
@@ -28,6 +964,9 @@ The example below demonstrates how to load the
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
+
+Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest) for more details.
+
 {% highlight scala %}
 import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest}
 import org.apache.spark.mllib.evaluation.MulticlassMetrics
@@ -64,9 +1003,12 @@ println("label\tfpr\n")
 }
 {% endhighlight %}
 </div>
+
 <div data-lang="java" markdown="1">
-{% highlight java %}
 
+Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/OneVsRest.html) for more details.
+
+{% highlight java %}
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.classification.LogisticRegression;
@@ -88,7 +1030,7 @@ RDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(),
   "data/mllib/sample_multiclass_classification_data.txt");
 
 DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
-DataFrame[] splits = dataFrame.randomSplit(new double[]{0.7, 0.3}, 12345);
+DataFrame[] splits = dataFrame.randomSplit(new double[] {0.7, 0.3}, 12345);
 DataFrame train = splits[0];
 DataFrame test = splits[1];
 

From d7b4c095271c36fcc7f9ded267ecf5ec66fac803 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Mon, 24 Aug 2015 16:17:45 -0700
Subject: [PATCH 390/633] [SPARK-10190] Fix NPE in CatalystTypeConverters
 Decimal toScala converter

This adds a missing null check to the Decimal `toScala` converter in `CatalystTypeConverters`, fixing an NPE.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8401 from JoshRosen/SPARK-10190.
---
 .../apache/spark/sql/catalyst/CatalystTypeConverters.scala   | 5 ++++-
 .../spark/sql/catalyst/CatalystTypeConvertersSuite.scala     | 4 +++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
index 8d0c64eae4774..966623ed017ba 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala
@@ -329,7 +329,10 @@ object CatalystTypeConverters {
         null
       }
     }
-    override def toScala(catalystValue: Decimal): JavaBigDecimal = catalystValue.toJavaBigDecimal
+    override def toScala(catalystValue: Decimal): JavaBigDecimal = {
+      if (catalystValue == null) null
+      else catalystValue.toJavaBigDecimal
+    }
     override def toScalaImpl(row: InternalRow, column: Int): JavaBigDecimal =
       row.getDecimal(column, dataType.precision, dataType.scale).toJavaBigDecimal
   }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/CatalystTypeConvertersSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/CatalystTypeConvertersSuite.scala
index df0f04563edcf..03bb102c67fe7 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/CatalystTypeConvertersSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/CatalystTypeConvertersSuite.scala
@@ -32,7 +32,9 @@ class CatalystTypeConvertersSuite extends SparkFunSuite {
     IntegerType,
     LongType,
     FloatType,
-    DoubleType)
+    DoubleType,
+    DecimalType.SYSTEM_DEFAULT,
+    DecimalType.USER_DEFAULT)
 
   test("null handling in rows") {
     val schema = StructType(simpleTypes.map(t => StructField(t.getClass.getName, t)))

From 2bf338c626e9d97ccc033cfadae8b36a82c66fd1 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Mon, 24 Aug 2015 18:10:51 -0700
Subject: [PATCH 391/633] [SPARK-10165] [SQL] Await child resolution in
 ResolveFunctions

Currently, we eagerly attempt to resolve functions, even before their children are resolved.  However, this is not valid in cases where we need to know the types of the input arguments (i.e. when resolving Hive UDFs).

As a fix, this PR delays function resolution until the functions children are resolved.  This change also necessitates a change to the way we resolve aggregate expressions that are not in aggregate operators (e.g., in `HAVING` or `ORDER BY` clauses).  Specifically, we can't assume that these misplaced functions will be resolved, allowing us to differentiate aggregate functions from normal functions.  To compensate for this change we now attempt to resolve these unresolved expressions in the context of the aggregate operator, before checking to see if any aggregate expressions are present.

Author: Michael Armbrust <michael@databricks.com>

Closes #8371 from marmbrus/hiveUDFResolution.
---
 .../sql/catalyst/analysis/Analyzer.scala      | 116 +++++++++++-------
 .../sql/hive/execution/HiveUDFSuite.scala     |   5 +
 2 files changed, 77 insertions(+), 44 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index d0eb9c2c90bdf..1a5de15c61f86 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -78,7 +78,7 @@ class Analyzer(
       ResolveAliases ::
       ExtractWindowExpressions ::
       GlobalAggregates ::
-      UnresolvedHavingClauseAttributes ::
+      ResolveAggregateFunctions ::
       HiveTypeCoercion.typeCoercionRules ++
       extendedResolutionRules : _*),
     Batch("Nondeterministic", Once,
@@ -452,37 +452,6 @@ class Analyzer(
           logDebug(s"Failed to find $missing in ${p.output.mkString(", ")}")
           s // Nothing we can do here. Return original plan.
         }
-      case s @ Sort(ordering, global, a @ Aggregate(grouping, aggs, child))
-          if !s.resolved && a.resolved =>
-        // A small hack to create an object that will allow us to resolve any references that
-        // refer to named expressions that are present in the grouping expressions.
-        val groupingRelation = LocalRelation(
-          grouping.collect { case ne: NamedExpression => ne.toAttribute }
-        )
-
-        // Find sort attributes that are projected away so we can temporarily add them back in.
-        val (newOrdering, missingAttr) = resolveAndFindMissing(ordering, a, groupingRelation)
-
-        // Find aggregate expressions and evaluate them early, since they can't be evaluated in a
-        // Sort.
-        val (withAggsRemoved, aliasedAggregateList) = newOrdering.map {
-          case aggOrdering if aggOrdering.collect { case a: AggregateExpression => a }.nonEmpty =>
-            val aliased = Alias(aggOrdering.child, "_aggOrdering")()
-            (aggOrdering.copy(child = aliased.toAttribute), Some(aliased))
-
-          case other => (other, None)
-        }.unzip
-
-        val missing = missingAttr ++ aliasedAggregateList.flatten
-
-        if (missing.nonEmpty) {
-          // Add missing grouping exprs and then project them away after the sort.
-          Project(a.output,
-            Sort(withAggsRemoved, global,
-              Aggregate(grouping, aggs ++ missing, child)))
-        } else {
-          s // Nothing we can do here. Return original plan.
-        }
     }
 
     /**
@@ -515,6 +484,7 @@ class Analyzer(
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case q: LogicalPlan =>
         q transformExpressions {
+          case u if !u.childrenResolved => u // Skip until children are resolved.
           case u @ UnresolvedFunction(name, children, isDistinct) =>
             withPosition(u) {
               registry.lookupFunction(name, children) match {
@@ -559,21 +529,79 @@ class Analyzer(
   }
 
   /**
-   * This rule finds expressions in HAVING clause filters that depend on
-   * unresolved attributes.  It pushes these expressions down to the underlying
-   * aggregates and then projects them away above the filter.
+   * This rule finds aggregate expressions that are not in an aggregate operator.  For example,
+   * those in a HAVING clause or ORDER BY clause.  These expressions are pushed down to the
+   * underlying aggregate operator and then projected away after the original operator.
    */
-  object UnresolvedHavingClauseAttributes extends Rule[LogicalPlan] {
+  object ResolveAggregateFunctions extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
-      case filter @ Filter(havingCondition, aggregate @ Aggregate(_, originalAggExprs, _))
-          if aggregate.resolved && containsAggregate(havingCondition) =>
-
-        val evaluatedCondition = Alias(havingCondition, "havingCondition")()
-        val aggExprsWithHaving = evaluatedCondition +: originalAggExprs
+      case filter @ Filter(havingCondition,
+             aggregate @ Aggregate(grouping, originalAggExprs, child))
+          if aggregate.resolved && !filter.resolved =>
+
+        // Try resolving the condition of the filter as though it is in the aggregate clause
+        val aggregatedCondition =
+          Aggregate(grouping, Alias(havingCondition, "havingCondition")() :: Nil, child)
+        val resolvedOperator = execute(aggregatedCondition)
+        def resolvedAggregateFilter =
+          resolvedOperator
+            .asInstanceOf[Aggregate]
+            .aggregateExpressions.head
+
+        // If resolution was successful and we see the filter has an aggregate in it, add it to
+        // the original aggregate operator.
+        if (resolvedOperator.resolved && containsAggregate(resolvedAggregateFilter)) {
+          val aggExprsWithHaving = resolvedAggregateFilter +: originalAggExprs
+
+          Project(aggregate.output,
+            Filter(resolvedAggregateFilter.toAttribute,
+              aggregate.copy(aggregateExpressions = aggExprsWithHaving)))
+        } else {
+          filter
+        }
 
-        Project(aggregate.output,
-          Filter(evaluatedCondition.toAttribute,
-            aggregate.copy(aggregateExpressions = aggExprsWithHaving)))
+      case sort @ Sort(sortOrder, global,
+             aggregate @ Aggregate(grouping, originalAggExprs, child))
+        if aggregate.resolved && !sort.resolved =>
+
+        // Try resolving the ordering as though it is in the aggregate clause.
+        try {
+          val aliasedOrder = sortOrder.map(o => Alias(o.child, "aggOrder")())
+          val aggregatedOrdering = Aggregate(grouping, aliasedOrder, child)
+          val resolvedOperator: Aggregate = execute(aggregatedOrdering).asInstanceOf[Aggregate]
+          def resolvedAggregateOrdering = resolvedOperator.aggregateExpressions
+
+          // Expressions that have an aggregate can be pushed down.
+          val needsAggregate = resolvedAggregateOrdering.exists(containsAggregate)
+
+          // Attribute references, that are missing from the order but are present in the grouping
+          // expressions can also be pushed down.
+          val requiredAttributes = resolvedAggregateOrdering.map(_.references).reduce(_ ++ _)
+          val missingAttributes = requiredAttributes -- aggregate.outputSet
+          val validPushdownAttributes =
+            missingAttributes.filter(a => grouping.exists(a.semanticEquals))
+
+          // If resolution was successful and we see the ordering either has an aggregate in it or
+          // it is missing something that is projected away by the aggregate, add the ordering
+          // the original aggregate operator.
+          if (resolvedOperator.resolved && (needsAggregate || validPushdownAttributes.nonEmpty)) {
+            val evaluatedOrderings: Seq[SortOrder] = sortOrder.zip(resolvedAggregateOrdering).map {
+              case (order, evaluated) => order.copy(child = evaluated.toAttribute)
+            }
+            val aggExprsWithOrdering: Seq[NamedExpression] =
+              resolvedAggregateOrdering ++ originalAggExprs
+
+            Project(aggregate.output,
+              Sort(evaluatedOrderings, global,
+                aggregate.copy(aggregateExpressions = aggExprsWithOrdering)))
+          } else {
+            sort
+          }
+        } catch {
+          // Attempting to resolve in the aggregate can result in ambiguity.  When this happens,
+          // just return the original plan.
+          case ae: AnalysisException => sort
+        }
     }
 
     protected def containsAggregate(condition: Expression): Boolean = {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index 10f2902e5eef0..b03a35132325d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -276,6 +276,11 @@ class HiveUDFSuite extends QueryTest {
     checkAnswer(
       sql("SELECT testStringStringUDF(\"hello\", s) FROM stringTable"),
       Seq(Row("hello world"), Row("hello goodbye")))
+
+    checkAnswer(
+      sql("SELECT testStringStringUDF(\"\", testStringStringUDF(\"hello\", s)) FROM stringTable"),
+      Seq(Row(" hello world"), Row(" hello goodbye")))
+
     sql("DROP TEMPORARY FUNCTION IF EXISTS testStringStringUDF")
 
     TestHive.reset()

From 6511bf559b736d8e23ae398901c8d78938e66869 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Mon, 24 Aug 2015 18:17:51 -0700
Subject: [PATCH 392/633] [SPARK-10118] [SPARKR] [DOCS] Improve SparkR API docs
 for 1.5 release

cc: shivaram

## Summary

- Modify `tdname` of expression functions. i.e. `ascii`: `rdname functions` => `rdname ascii`
- Replace the dynamical function definitions to the static ones because of thir documentations.

## Generated PDF File
https://drive.google.com/file/d/0B9biIZIU47lLX2t6ZjRoRnBTSEU/view?usp=sharing

## JIRA
[[SPARK-10118] Improve SparkR API docs for 1.5 release - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10118)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Author: Yuu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8386 from yu-iskw/SPARK-10118.
---
 R/create-docs.sh    |    2 +-
 R/pkg/R/column.R    |    5 +-
 R/pkg/R/functions.R | 1603 +++++++++++++++++++++++++++++++++++++++----
 R/pkg/R/generics.R  |  214 +++---
 4 files changed, 1596 insertions(+), 228 deletions(-)

diff --git a/R/create-docs.sh b/R/create-docs.sh
index 6a4687b06ecb9..d2ae160b50021 100755
--- a/R/create-docs.sh
+++ b/R/create-docs.sh
@@ -39,7 +39,7 @@ pushd $FWDIR
 mkdir -p pkg/html
 pushd pkg/html
 
-Rscript -e 'library(SparkR, lib.loc="../../lib"); library(knitr); knit_rd("SparkR")'
+Rscript -e 'libDir <- "../../lib"; library(SparkR, lib.loc=libDir); library(knitr); knit_rd("SparkR", links = tools::findHTMLlinks(paste(libDir, "SparkR", sep="/")))'
 
 popd
 
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index 5a07ebd308296..a1f50c383367c 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -169,8 +169,7 @@ setMethod("between", signature(x = "Column"),
 #'
 #' @rdname column
 #'
-#' @examples
-#' \dontrun{
+#' @examples \dontrun{
 #'   cast(df$age, "string")
 #'   cast(df$name, list(type="array", elementType="byte", containsNull = TRUE))
 #' }
@@ -192,7 +191,7 @@ setMethod("cast",
 #'
 #' @rdname column
 #' @return a matched values as a result of comparing with given values.
-#' \dontrun{
+#' @examples \dontrun{
 #'   filter(df, "age in (10, 30)")
 #'   where(df, df$age %in% c(10, 30))
 #' }
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index b5879bd9ad553..d848730e70433 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -18,69 +18,1298 @@
 #' @include generics.R column.R
 NULL
 
-#' @title S4 expression functions for DataFrame column(s)
-#' @description These are expression functions on DataFrame columns
-
-functions1 <- c(
-  "abs", "acos", "approxCountDistinct", "ascii", "asin", "atan",
-  "avg", "base64", "bin", "bitwiseNOT", "cbrt", "ceil", "cos", "cosh", "count",
-  "crc32", "dayofmonth", "dayofyear", "exp", "explode", "expm1", "factorial",
-  "first", "floor", "hex", "hour", "initcap", "isNaN", "last", "last_day",
-  "length", "log", "log10", "log1p", "log2", "lower", "ltrim", "max", "md5",
-  "mean", "min", "minute", "month", "negate", "quarter", "reverse",
-  "rint", "round", "rtrim", "second", "sha1", "signum", "sin", "sinh", "size",
-  "soundex", "sqrt", "sum", "sumDistinct", "tan", "tanh", "toDegrees",
-  "toRadians", "to_date", "trim", "unbase64", "unhex", "upper", "weekofyear",
-  "year")
-functions2 <- c(
-  "atan2", "datediff", "hypot", "levenshtein", "months_between", "nanvl", "pmod")
-
-createFunction1 <- function(name) {
-  setMethod(name,
-            signature(x = "Column"),
-            function(x) {
-              jc <- callJStatic("org.apache.spark.sql.functions", name, x@jc)
-              column(jc)
-            })
-}
-
-createFunction2 <- function(name) {
-  setMethod(name,
-            signature(y = "Column"),
-            function(y, x) {
-              if (class(x) == "Column") {
-                x <- x@jc
-              }
-              jc <- callJStatic("org.apache.spark.sql.functions", name, y@jc, x)
-              column(jc)
-            })
-}
+#' Creates a \code{Column} of literal value.
+#'
+#' The passed in object is returned directly if it is already a \linkS4class{Column}.
+#' If the object is a Scala Symbol, it is converted into a \linkS4class{Column} also.
+#' Otherwise, a new \linkS4class{Column} is created to represent the literal value.
+#'
+#' @family normal_funcs
+#' @rdname lit
+#' @name lit
+#' @export
+setMethod("lit", signature("ANY"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions",
+                              "lit",
+                              ifelse(class(x) == "Column", x@jc, x))
+            column(jc)
+          })
+
+#' abs
+#'
+#' Computes the absolute value.
+#'
+#' @rdname abs
+#' @name abs
+#' @family normal_funcs
+#' @export
+#' @examples \dontrun{abs(df$c)}
+setMethod("abs",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "abs", x@jc)
+            column(jc)
+          })
+
+#' acos
+#'
+#' Computes the cosine inverse of the given value; the returned angle is in the range
+#' 0.0 through pi.
+#'
+#' @rdname acos
+#' @name acos
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{acos(df$c)}
+setMethod("acos",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "acos", x@jc)
+            column(jc)
+          })
+
+#' approxCountDistinct
+#'
+#' Aggregate function: returns the approximate number of distinct items in a group.
+#'
+#' @rdname approxCountDistinct
+#' @name approxCountDistinct
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{approxCountDistinct(df$c)}
+setMethod("approxCountDistinct",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "approxCountDistinct", x@jc)
+            column(jc)
+          })
+
+#' ascii
+#'
+#' Computes the numeric value of the first character of the string column, and returns the
+#' result as a int column.
+#'
+#' @rdname ascii
+#' @name ascii
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{\dontrun{ascii(df$c)}}
+setMethod("ascii",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "ascii", x@jc)
+            column(jc)
+          })
+
+#' asin
+#'
+#' Computes the sine inverse of the given value; the returned angle is in the range
+#' -pi/2 through pi/2.
+#'
+#' @rdname asin
+#' @name asin
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{asin(df$c)}
+setMethod("asin",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "asin", x@jc)
+            column(jc)
+          })
+
+#' atan
+#'
+#' Computes the tangent inverse of the given value.
+#'
+#' @rdname atan
+#' @name atan
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{atan(df$c)}
+setMethod("atan",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "atan", x@jc)
+            column(jc)
+          })
+
+#' avg
+#'
+#' Aggregate function: returns the average of the values in a group.
+#'
+#' @rdname avg
+#' @name avg
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{avg(df$c)}
+setMethod("avg",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "avg", x@jc)
+            column(jc)
+          })
+
+#' base64
+#'
+#' Computes the BASE64 encoding of a binary column and returns it as a string column.
+#' This is the reverse of unbase64.
+#'
+#' @rdname base64
+#' @name base64
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{base64(df$c)}
+setMethod("base64",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "base64", x@jc)
+            column(jc)
+          })
+
+#' bin
+#'
+#' An expression that returns the string representation of the binary value of the given long
+#' column. For example, bin("12") returns "1100".
+#'
+#' @rdname bin
+#' @name bin
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{bin(df$c)}
+setMethod("bin",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "bin", x@jc)
+            column(jc)
+          })
+
+#' bitwiseNOT
+#'
+#' Computes bitwise NOT.
+#'
+#' @rdname bitwiseNOT
+#' @name bitwiseNOT
+#' @family normal_funcs
+#' @export
+#' @examples \dontrun{bitwiseNOT(df$c)}
+setMethod("bitwiseNOT",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "bitwiseNOT", x@jc)
+            column(jc)
+          })
+
+#' cbrt
+#'
+#' Computes the cube-root of the given value.
+#'
+#' @rdname cbrt
+#' @name cbrt
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{cbrt(df$c)}
+setMethod("cbrt",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "cbrt", x@jc)
+            column(jc)
+          })
+
+#' ceil
+#'
+#' Computes the ceiling of the given value.
+#'
+#' @rdname ceil
+#' @name ceil
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{ceil(df$c)}
+setMethod("ceil",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "ceil", x@jc)
+            column(jc)
+          })
+
+#' cos
+#'
+#' Computes the cosine of the given value.
+#'
+#' @rdname cos
+#' @name cos
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{cos(df$c)}
+setMethod("cos",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "cos", x@jc)
+            column(jc)
+          })
+
+#' cosh
+#'
+#' Computes the hyperbolic cosine of the given value.
+#'
+#' @rdname cosh
+#' @name cosh
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{cosh(df$c)}
+setMethod("cosh",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "cosh", x@jc)
+            column(jc)
+          })
+
+#' count
+#'
+#' Aggregate function: returns the number of items in a group.
+#'
+#' @rdname count
+#' @name count
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{count(df$c)}
+setMethod("count",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "count", x@jc)
+            column(jc)
+          })
+
+#' crc32
+#'
+#' Calculates the cyclic redundancy check value  (CRC32) of a binary column and
+#' returns the value as a bigint.
+#'
+#' @rdname crc32
+#' @name crc32
+#' @family misc_funcs
+#' @export
+#' @examples \dontrun{crc32(df$c)}
+setMethod("crc32",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "crc32", x@jc)
+            column(jc)
+          })
+
+#' dayofmonth
+#'
+#' Extracts the day of the month as an integer from a given date/timestamp/string.
+#'
+#' @rdname dayofmonth
+#' @name dayofmonth
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{dayofmonth(df$c)}
+setMethod("dayofmonth",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "dayofmonth", x@jc)
+            column(jc)
+          })
+
+#' dayofyear
+#'
+#' Extracts the day of the year as an integer from a given date/timestamp/string.
+#'
+#' @rdname dayofyear
+#' @name dayofyear
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{dayofyear(df$c)}
+setMethod("dayofyear",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "dayofyear", x@jc)
+            column(jc)
+          })
+
+#' exp
+#'
+#' Computes the exponential of the given value.
+#'
+#' @rdname exp
+#' @name exp
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{exp(df$c)}
+setMethod("exp",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "exp", x@jc)
+            column(jc)
+          })
+
+#' explode
+#'
+#' Creates a new row for each element in the given array or map column.
+#'
+#' @rdname explode
+#' @name explode
+#' @family collection_funcs
+#' @export
+#' @examples \dontrun{explode(df$c)}
+setMethod("explode",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "explode", x@jc)
+            column(jc)
+          })
+
+#' expm1
+#'
+#' Computes the exponential of the given value minus one.
+#'
+#' @rdname expm1
+#' @name expm1
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{expm1(df$c)}
+setMethod("expm1",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "expm1", x@jc)
+            column(jc)
+          })
+
+#' factorial
+#'
+#' Computes the factorial of the given value.
+#'
+#' @rdname factorial
+#' @name factorial
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{factorial(df$c)}
+setMethod("factorial",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "factorial", x@jc)
+            column(jc)
+          })
+
+#' first
+#'
+#' Aggregate function: returns the first value in a group.
+#'
+#' @rdname first
+#' @name first
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{first(df$c)}
+setMethod("first",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "first", x@jc)
+            column(jc)
+          })
+
+#' floor
+#'
+#' Computes the floor of the given value.
+#'
+#' @rdname floor
+#' @name floor
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{floor(df$c)}
+setMethod("floor",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "floor", x@jc)
+            column(jc)
+          })
+
+#' hex
+#'
+#' Computes hex value of the given column.
+#'
+#' @rdname hex
+#' @name hex
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{hex(df$c)}
+setMethod("hex",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "hex", x@jc)
+            column(jc)
+          })
+
+#' hour
+#'
+#' Extracts the hours as an integer from a given date/timestamp/string.
+#'
+#' @rdname hour
+#' @name hour
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{hour(df$c)}
+setMethod("hour",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "hour", x@jc)
+            column(jc)
+          })
+
+#' initcap
+#'
+#' Returns a new string column by converting the first letter of each word to uppercase.
+#' Words are delimited by whitespace.
+#'
+#' For example, "hello world" will become "Hello World".
+#'
+#' @rdname initcap
+#' @name initcap
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{initcap(df$c)}
+setMethod("initcap",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "initcap", x@jc)
+            column(jc)
+          })
+
+#' isNaN
+#'
+#' Return true iff the column is NaN.
+#'
+#' @rdname isNaN
+#' @name isNaN
+#' @family normal_funcs
+#' @export
+#' @examples \dontrun{isNaN(df$c)}
+setMethod("isNaN",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "isNaN", x@jc)
+            column(jc)
+          })
+
+#' last
+#'
+#' Aggregate function: returns the last value in a group.
+#'
+#' @rdname last
+#' @name last
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{last(df$c)}
+setMethod("last",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "last", x@jc)
+            column(jc)
+          })
+
+#' last_day
+#'
+#' Given a date column, returns the last day of the month which the given date belongs to.
+#' For example, input "2015-07-27" returns "2015-07-31" since July 31 is the last day of the
+#' month in July 2015.
+#'
+#' @rdname last_day
+#' @name last_day
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{last_day(df$c)}
+setMethod("last_day",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "last_day", x@jc)
+            column(jc)
+          })
+
+#' length
+#'
+#' Computes the length of a given string or binary column.
+#'
+#' @rdname length
+#' @name length
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{length(df$c)}
+setMethod("length",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "length", x@jc)
+            column(jc)
+          })
+
+#' log
+#'
+#' Computes the natural logarithm of the given value.
+#'
+#' @rdname log
+#' @name log
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{log(df$c)}
+setMethod("log",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "log", x@jc)
+            column(jc)
+          })
+
+#' log10
+#'
+#' Computes the logarithm of the given value in base 10.
+#'
+#' @rdname log10
+#' @name log10
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{log10(df$c)}
+setMethod("log10",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "log10", x@jc)
+            column(jc)
+          })
+
+#' log1p
+#'
+#' Computes the natural logarithm of the given value plus one.
+#'
+#' @rdname log1p
+#' @name log1p
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{log1p(df$c)}
+setMethod("log1p",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "log1p", x@jc)
+            column(jc)
+          })
+
+#' log2
+#'
+#' Computes the logarithm of the given column in base 2.
+#'
+#' @rdname log2
+#' @name log2
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{log2(df$c)}
+setMethod("log2",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "log2", x@jc)
+            column(jc)
+          })
+
+#' lower
+#'
+#' Converts a string column to lower case.
+#'
+#' @rdname lower
+#' @name lower
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{lower(df$c)}
+setMethod("lower",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "lower", x@jc)
+            column(jc)
+          })
+
+#' ltrim
+#'
+#' Trim the spaces from left end for the specified string value.
+#'
+#' @rdname ltrim
+#' @name ltrim
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{ltrim(df$c)}
+setMethod("ltrim",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "ltrim", x@jc)
+            column(jc)
+          })
+
+#' max
+#'
+#' Aggregate function: returns the maximum value of the expression in a group.
+#'
+#' @rdname max
+#' @name max
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{max(df$c)}
+setMethod("max",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "max", x@jc)
+            column(jc)
+          })
+
+#' md5
+#'
+#' Calculates the MD5 digest of a binary column and returns the value
+#' as a 32 character hex string.
+#'
+#' @rdname md5
+#' @name md5
+#' @family misc_funcs
+#' @export
+#' @examples \dontrun{md5(df$c)}
+setMethod("md5",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "md5", x@jc)
+            column(jc)
+          })
+
+#' mean
+#'
+#' Aggregate function: returns the average of the values in a group.
+#' Alias for avg.
+#'
+#' @rdname mean
+#' @name mean
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{mean(df$c)}
+setMethod("mean",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "mean", x@jc)
+            column(jc)
+          })
+
+#' min
+#'
+#' Aggregate function: returns the minimum value of the expression in a group.
+#'
+#' @rdname min
+#' @name min
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{min(df$c)}
+setMethod("min",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "min", x@jc)
+            column(jc)
+          })
+
+#' minute
+#'
+#' Extracts the minutes as an integer from a given date/timestamp/string.
+#'
+#' @rdname minute
+#' @name minute
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{minute(df$c)}
+setMethod("minute",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "minute", x@jc)
+            column(jc)
+          })
+
+#' month
+#'
+#' Extracts the month as an integer from a given date/timestamp/string.
+#'
+#' @rdname month
+#' @name month
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{month(df$c)}
+setMethod("month",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "month", x@jc)
+            column(jc)
+          })
+
+#' negate
+#'
+#' Unary minus, i.e. negate the expression.
+#'
+#' @rdname negate
+#' @name negate
+#' @family normal_funcs
+#' @export
+#' @examples \dontrun{negate(df$c)}
+setMethod("negate",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "negate", x@jc)
+            column(jc)
+          })
 
-createFunctions <- function() {
-  for (name in functions1) {
-    createFunction1(name)
-  }
-  for (name in functions2) {
-    createFunction2(name)
-  }
-}
+#' quarter
+#'
+#' Extracts the quarter as an integer from a given date/timestamp/string.
+#'
+#' @rdname quarter
+#' @name quarter
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{quarter(df$c)}
+setMethod("quarter",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "quarter", x@jc)
+            column(jc)
+          })
 
-createFunctions()
+#' reverse
+#'
+#' Reverses the string column and returns it as a new string column.
+#'
+#' @rdname reverse
+#' @name reverse
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{reverse(df$c)}
+setMethod("reverse",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "reverse", x@jc)
+            column(jc)
+          })
 
-#' @rdname functions
-#' @return Creates a Column class of literal value.
-setMethod("lit", signature("ANY"),
+#' rint
+#'
+#' Returns the double value that is closest in value to the argument and
+#' is equal to a mathematical integer.
+#'
+#' @rdname rint
+#' @name rint
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{rint(df$c)}
+setMethod("rint",
+          signature(x = "Column"),
           function(x) {
-            jc <- callJStatic("org.apache.spark.sql.functions",
-                              "lit",
-                              ifelse(class(x) == "Column", x@jc, x))
+            jc <- callJStatic("org.apache.spark.sql.functions", "rint", x@jc)
+            column(jc)
+          })
+
+#' round
+#'
+#' Returns the value of the column `e` rounded to 0 decimal places.
+#'
+#' @rdname round
+#' @name round
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{round(df$c)}
+setMethod("round",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "round", x@jc)
+            column(jc)
+          })
+
+#' rtrim
+#'
+#' Trim the spaces from right end for the specified string value.
+#'
+#' @rdname rtrim
+#' @name rtrim
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{rtrim(df$c)}
+setMethod("rtrim",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "rtrim", x@jc)
+            column(jc)
+          })
+
+#' second
+#'
+#' Extracts the seconds as an integer from a given date/timestamp/string.
+#'
+#' @rdname second
+#' @name second
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{second(df$c)}
+setMethod("second",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "second", x@jc)
+            column(jc)
+          })
+
+#' sha1
+#'
+#' Calculates the SHA-1 digest of a binary column and returns the value
+#' as a 40 character hex string.
+#'
+#' @rdname sha1
+#' @name sha1
+#' @family misc_funcs
+#' @export
+#' @examples \dontrun{sha1(df$c)}
+setMethod("sha1",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sha1", x@jc)
+            column(jc)
+          })
+
+#' signum
+#'
+#' Computes the signum of the given value.
+#'
+#' @rdname signum
+#' @name signum
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{signum(df$c)}
+setMethod("signum",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "signum", x@jc)
+            column(jc)
+          })
+
+#' sin
+#'
+#' Computes the sine of the given value.
+#'
+#' @rdname sin
+#' @name sin
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{sin(df$c)}
+setMethod("sin",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sin", x@jc)
+            column(jc)
+          })
+
+#' sinh
+#'
+#' Computes the hyperbolic sine of the given value.
+#'
+#' @rdname sinh
+#' @name sinh
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{sinh(df$c)}
+setMethod("sinh",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sinh", x@jc)
+            column(jc)
+          })
+
+#' size
+#'
+#' Returns length of array or map.
+#'
+#' @rdname size
+#' @name size
+#' @family collection_funcs
+#' @export
+#' @examples \dontrun{size(df$c)}
+setMethod("size",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "size", x@jc)
+            column(jc)
+          })
+
+#' soundex
+#'
+#' Return the soundex code for the specified expression.
+#'
+#' @rdname soundex
+#' @name soundex
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{soundex(df$c)}
+setMethod("soundex",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "soundex", x@jc)
+            column(jc)
+          })
+
+#' sqrt
+#'
+#' Computes the square root of the specified float value.
+#'
+#' @rdname sqrt
+#' @name sqrt
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{sqrt(df$c)}
+setMethod("sqrt",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sqrt", x@jc)
+            column(jc)
+          })
+
+#' sum
+#'
+#' Aggregate function: returns the sum of all values in the expression.
+#'
+#' @rdname sum
+#' @name sum
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{sum(df$c)}
+setMethod("sum",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sum", x@jc)
+            column(jc)
+          })
+
+#' sumDistinct
+#'
+#' Aggregate function: returns the sum of distinct values in the expression.
+#'
+#' @rdname sumDistinct
+#' @name sumDistinct
+#' @family agg_funcs
+#' @export
+#' @examples \dontrun{sumDistinct(df$c)}
+setMethod("sumDistinct",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "sumDistinct", x@jc)
+            column(jc)
+          })
+
+#' tan
+#'
+#' Computes the tangent of the given value.
+#'
+#' @rdname tan
+#' @name tan
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{tan(df$c)}
+setMethod("tan",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "tan", x@jc)
+            column(jc)
+          })
+
+#' tanh
+#'
+#' Computes the hyperbolic tangent of the given value.
+#'
+#' @rdname tanh
+#' @name tanh
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{tanh(df$c)}
+setMethod("tanh",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "tanh", x@jc)
+            column(jc)
+          })
+
+#' toDegrees
+#'
+#' Converts an angle measured in radians to an approximately equivalent angle measured in degrees.
+#'
+#' @rdname toDegrees
+#' @name toDegrees
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{toDegrees(df$c)}
+setMethod("toDegrees",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "toDegrees", x@jc)
+            column(jc)
+          })
+
+#' toRadians
+#'
+#' Converts an angle measured in degrees to an approximately equivalent angle measured in radians.
+#'
+#' @rdname toRadians
+#' @name toRadians
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{toRadians(df$c)}
+setMethod("toRadians",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "toRadians", x@jc)
+            column(jc)
+          })
+
+#' to_date
+#'
+#' Converts the column into DateType.
+#'
+#' @rdname to_date
+#' @name to_date
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{to_date(df$c)}
+setMethod("to_date",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "to_date", x@jc)
+            column(jc)
+          })
+
+#' trim
+#'
+#' Trim the spaces from both ends for the specified string column.
+#'
+#' @rdname trim
+#' @name trim
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{trim(df$c)}
+setMethod("trim",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "trim", x@jc)
+            column(jc)
+          })
+
+#' unbase64
+#'
+#' Decodes a BASE64 encoded string column and returns it as a binary column.
+#' This is the reverse of base64.
+#'
+#' @rdname unbase64
+#' @name unbase64
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{unbase64(df$c)}
+setMethod("unbase64",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "unbase64", x@jc)
+            column(jc)
+          })
+
+#' unhex
+#'
+#' Inverse of hex. Interprets each pair of characters as a hexadecimal number
+#' and converts to the byte representation of number.
+#'
+#' @rdname unhex
+#' @name unhex
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{unhex(df$c)}
+setMethod("unhex",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "unhex", x@jc)
+            column(jc)
+          })
+
+#' upper
+#'
+#' Converts a string column to upper case.
+#'
+#' @rdname upper
+#' @name upper
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{upper(df$c)}
+setMethod("upper",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "upper", x@jc)
+            column(jc)
+          })
+
+#' weekofyear
+#'
+#' Extracts the week number as an integer from a given date/timestamp/string.
+#'
+#' @rdname weekofyear
+#' @name weekofyear
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{weekofyear(df$c)}
+setMethod("weekofyear",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "weekofyear", x@jc)
+            column(jc)
+          })
+
+#' year
+#'
+#' Extracts the year as an integer from a given date/timestamp/string.
+#'
+#' @rdname year
+#' @name year
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{year(df$c)}
+setMethod("year",
+          signature(x = "Column"),
+          function(x) {
+            jc <- callJStatic("org.apache.spark.sql.functions", "year", x@jc)
             column(jc)
           })
 
+#' atan2
+#'
+#' Returns the angle theta from the conversion of rectangular coordinates (x, y) to
+#' polar coordinates (r, theta).
+#'
+#' @rdname atan2
+#' @name atan2
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{atan2(df$c, x)}
+setMethod("atan2", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "atan2", y@jc, x)
+            column(jc)
+          })
+
+#' datediff
+#'
+#' Returns the number of days from `start` to `end`.
+#'
+#' @rdname datediff
+#' @name datediff
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{datediff(df$c, x)}
+setMethod("datediff", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "datediff", y@jc, x)
+            column(jc)
+          })
+
+#' hypot
+#'
+#' Computes `sqrt(a^2^ + b^2^)` without intermediate overflow or underflow.
+#'
+#' @rdname hypot
+#' @name hypot
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{hypot(df$c, x)}
+setMethod("hypot", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "hypot", y@jc, x)
+            column(jc)
+          })
+
+#' levenshtein
+#'
+#' Computes the Levenshtein distance of the two given string columns.
+#'
+#' @rdname levenshtein
+#' @name levenshtein
+#' @family string_funcs
+#' @export
+#' @examples \dontrun{levenshtein(df$c, x)}
+setMethod("levenshtein", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "levenshtein", y@jc, x)
+            column(jc)
+          })
+
+#' months_between
+#'
+#' Returns number of months between dates `date1` and `date2`.
+#'
+#' @rdname months_between
+#' @name months_between
+#' @family datetime_funcs
+#' @export
+#' @examples \dontrun{months_between(df$c, x)}
+setMethod("months_between", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "months_between", y@jc, x)
+            column(jc)
+          })
+
+#' nanvl
+#'
+#' Returns col1 if it is not NaN, or col2 if col1 is NaN.
+#' hhBoth inputs should be floating point columns (DoubleType or FloatType).
+#'
+#' @rdname nanvl
+#' @name nanvl
+#' @family normal_funcs
+#' @export
+#' @examples \dontrun{nanvl(df$c, x)}
+setMethod("nanvl", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "nanvl", y@jc, x)
+            column(jc)
+          })
+
+#' pmod
+#'
+#' Returns the positive value of dividend mod divisor.
+#'
+#' @rdname pmod
+#' @name pmod
+#' @docType methods
+#' @family math_funcs
+#' @export
+#' @examples \dontrun{pmod(df$c, x)}
+setMethod("pmod", signature(y = "Column"),
+          function(y, x) {
+            if (class(x) == "Column") {
+              x <- x@jc
+            }
+            jc <- callJStatic("org.apache.spark.sql.functions", "pmod", y@jc, x)
+            column(jc)
+          })
+
+
 #' Approx Count Distinct
 #'
-#' @rdname functions
+#' @family agg_funcs
+#' @rdname approxCountDistinct
+#' @name approxCountDistinct
 #' @return the approximate number of distinct items in a group.
+#' @export
 setMethod("approxCountDistinct",
           signature(x = "Column"),
           function(x, rsd = 0.95) {
@@ -90,8 +1319,11 @@ setMethod("approxCountDistinct",
 
 #' Count Distinct
 #'
-#' @rdname functions
+#' @family agg_funcs
+#' @rdname countDistinct
+#' @name countDistinct
 #' @return the number of distinct items in a group.
+#' @export
 setMethod("countDistinct",
           signature(x = "Column"),
           function(x, ...) {
@@ -103,8 +1335,15 @@ setMethod("countDistinct",
             column(jc)
           })
 
-#' @rdname functions
-#' @return Concatenates multiple input string columns together into a single string column.
+
+#' concat
+#'
+#' Concatenates multiple input string columns together into a single string column.
+#'
+#' @family string_funcs
+#' @rdname concat
+#' @name concat
+#' @export
 setMethod("concat",
           signature(x = "Column"),
           function(x, ...) {
@@ -113,9 +1352,15 @@ setMethod("concat",
             column(jc)
           })
 
-#' @rdname functions
-#' @return Returns the greatest value of the list of column names, skipping null values.
-#'         This function takes at least 2 parameters. It will return null if all parameters are null.
+#' greatest
+#'
+#' Returns the greatest value of the list of column names, skipping null values.
+#' This function takes at least 2 parameters. It will return null if all parameters are null.
+#'
+#' @family normal_funcs
+#' @rdname greatest
+#' @name greatest
+#' @export
 setMethod("greatest",
           signature(x = "Column"),
           function(x, ...) {
@@ -125,9 +1370,15 @@ setMethod("greatest",
             column(jc)
           })
 
-#' @rdname functions
-#' @return Returns the least value of the list of column names, skipping null values.
-#'         This function takes at least 2 parameters. It will return null iff all parameters are null.
+#' least
+#'
+#' Returns the least value of the list of column names, skipping null values.
+#' This function takes at least 2 parameters. It will return null iff all parameters are null.
+#'
+#' @family normal_funcs
+#' @rdname least
+#' @name least
+#' @export
 setMethod("least",
           signature(x = "Column"),
           function(x, ...) {
@@ -137,30 +1388,58 @@ setMethod("least",
             column(jc)
           })
 
-#' @rdname functions
+#' ceiling
+#'
+#' Computes the ceiling of the given value.
+#'
+#' @family math_funcs
+#' @rdname ceil
+#' @name ceil
 #' @aliases ceil
+#' @export
 setMethod("ceiling",
           signature(x = "Column"),
           function(x) {
             ceil(x)
           })
 
-#' @rdname functions
+#' sign
+#'
+#' Computes the signum of the given value.
+#'
+#' @family math_funcs
+#' @rdname signum
+#' @name signum
 #' @aliases signum
+#' @export
 setMethod("sign", signature(x = "Column"),
           function(x) {
             signum(x)
           })
 
-#' @rdname functions
+#' n_distinct
+#'
+#' Aggregate function: returns the number of distinct items in a group.
+#'
+#' @family agg_funcs
+#' @rdname countDistinct
+#' @name countDistinct
 #' @aliases countDistinct
+#' @export
 setMethod("n_distinct", signature(x = "Column"),
           function(x, ...) {
             countDistinct(x, ...)
           })
 
-#' @rdname functions
+#' n
+#'
+#' Aggregate function: returns the number of items in a group.
+#'
+#' @family agg_funcs
+#' @rdname count
+#' @name count
 #' @aliases count
+#' @export
 setMethod("n", signature(x = "Column"),
           function(x) {
             count(x)
@@ -171,13 +1450,16 @@ setMethod("n", signature(x = "Column"),
 #' Converts a date/timestamp/string to a value of string in the format specified by the date
 #' format given by the second argument.
 #'
-#' A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
-#' pattern letters of `java.text.SimpleDateFormat` can be used.
+#' A pattern could be for instance \preformatted{dd.MM.yyyy} and could return a string like '18.03.1993'. All
+#' pattern letters of \code{java.text.SimpleDateFormat} can be used.
 #'
-#' NOTE: Use when ever possible specialized functions like `year`. These benefit from a
+#' NOTE: Use when ever possible specialized functions like \code{year}. These benefit from a
 #' specialized implementation.
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname date_format
+#' @name date_format
+#' @export
 setMethod("date_format", signature(y = "Column", x = "character"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "date_format", y@jc, x)
@@ -188,7 +1470,10 @@ setMethod("date_format", signature(y = "Column", x = "character"),
 #'
 #' Assumes given timestamp is UTC and converts to given timezone.
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname from_utc_timestamp
+#' @name from_utc_timestamp
+#' @export
 setMethod("from_utc_timestamp", signature(y = "Column", x = "character"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "from_utc_timestamp", y@jc, x)
@@ -203,7 +1488,10 @@ setMethod("from_utc_timestamp", signature(y = "Column", x = "character"),
 #' NOTE: The position is not zero based, but 1 based index, returns 0 if substr
 #' could not be found in str.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname instr
+#' @name instr
+#' @export
 setMethod("instr", signature(y = "Column", x = "character"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "instr", y@jc, x)
@@ -215,13 +1503,16 @@ setMethod("instr", signature(y = "Column", x = "character"),
 #' Given a date column, returns the first date which is later than the value of the date column
 #' that is on the specified day of the week.
 #'
-#' For example, `next <- day('2015-07-27', "Sunday")` returns 2015-08-02 because that is the first
+#' For example, \code{next_day('2015-07-27', "Sunday")} returns 2015-08-02 because that is the first
 #' Sunday after 2015-07-27.
 #'
 #' Day of the week parameter is case insensitive, and accepts:
 #' "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun".
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname next_day
+#' @name next_day
+#' @export
 setMethod("next_day", signature(y = "Column", x = "character"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "next_day", y@jc, x)
@@ -232,7 +1523,10 @@ setMethod("next_day", signature(y = "Column", x = "character"),
 #'
 #' Assumes given timestamp is in given timezone and converts to UTC.
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname to_utc_timestamp
+#' @name to_utc_timestamp
+#' @export
 setMethod("to_utc_timestamp", signature(y = "Column", x = "character"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "to_utc_timestamp", y@jc, x)
@@ -243,7 +1537,11 @@ setMethod("to_utc_timestamp", signature(y = "Column", x = "character"),
 #'
 #' Returns the date that is numMonths after startDate.
 #'
-#' @rdname functions
+#' @name add_months
+#' @family datetime_funcs
+#' @rdname add_months
+#' @name add_months
+#' @export
 setMethod("add_months", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "add_months", y@jc, as.integer(x))
@@ -254,7 +1552,10 @@ setMethod("add_months", signature(y = "Column", x = "numeric"),
 #'
 #' Returns the date that is `days` days after `start`
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname date_add
+#' @name date_add
+#' @export
 setMethod("date_add", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "date_add", y@jc, as.integer(x))
@@ -265,7 +1566,10 @@ setMethod("date_add", signature(y = "Column", x = "numeric"),
 #'
 #' Returns the date that is `days` days before `start`
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname date_sub
+#' @name date_sub
+#' @export
 setMethod("date_sub", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "date_sub", y@jc, as.integer(x))
@@ -280,7 +1584,10 @@ setMethod("date_sub", signature(y = "Column", x = "numeric"),
 #' If d is 0, the result has no decimal point or fractional part.
 #' If d < 0, the result will be null.'
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname format_number
+#' @name format_number
+#' @export
 setMethod("format_number", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -294,9 +1601,12 @@ setMethod("format_number", signature(y = "Column", x = "numeric"),
 #' Calculates the SHA-2 family of hash functions of a binary column and
 #' returns the value as a hex string.
 #'
-#' @rdname functions
 #' @param y column to compute SHA-2 on.
 #' @param x one of 224, 256, 384, or 512.
+#' @family misc_funcs
+#' @rdname sha2
+#' @name sha2
+#' @export
 setMethod("sha2", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "sha2", y@jc, as.integer(x))
@@ -308,7 +1618,10 @@ setMethod("sha2", signature(y = "Column", x = "numeric"),
 #' Shift the the given value numBits left. If the given value is a long value, this function
 #' will return a long value else it will return an integer value.
 #'
-#' @rdname functions
+#' @family math_funcs
+#' @rdname shiftLeft
+#' @name shiftLeft
+#' @export
 setMethod("shiftLeft", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -322,7 +1635,10 @@ setMethod("shiftLeft", signature(y = "Column", x = "numeric"),
 #' Shift the the given value numBits right. If the given value is a long value, it will return
 #' a long value else it will return an integer value.
 #'
-#' @rdname functions
+#' @family math_funcs
+#' @rdname shiftRight
+#' @name shiftRight
+#' @export
 setMethod("shiftRight", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -336,7 +1652,10 @@ setMethod("shiftRight", signature(y = "Column", x = "numeric"),
 #' Unsigned shift the the given value numBits right. If the given value is a long value,
 #' it will return a long value else it will return an integer value.
 #'
-#' @rdname functions
+#' @family math_funcs
+#' @rdname shiftRightUnsigned
+#' @name shiftRightUnsigned
+#' @export
 setMethod("shiftRightUnsigned", signature(y = "Column", x = "numeric"),
           function(y, x) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -350,7 +1669,10 @@ setMethod("shiftRightUnsigned", signature(y = "Column", x = "numeric"),
 #' Concatenates multiple input string columns together into a single string column,
 #' using the given separator.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname concat_ws
+#' @name concat_ws
+#' @export
 setMethod("concat_ws", signature(sep = "character", x = "Column"),
           function(sep, x, ...) {
             jcols <- listToSeq(lapply(list(x, ...), function(x) { x@jc }))
@@ -362,7 +1684,10 @@ setMethod("concat_ws", signature(sep = "character", x = "Column"),
 #'
 #' Convert a number in a string column from one base to another.
 #'
-#' @rdname functions
+#' @family math_funcs
+#' @rdname conv
+#' @name conv
+#' @export
 setMethod("conv", signature(x = "Column", fromBase = "numeric", toBase = "numeric"),
           function(x, fromBase, toBase) {
             fromBase <- as.integer(fromBase)
@@ -378,7 +1703,10 @@ setMethod("conv", signature(x = "Column", fromBase = "numeric", toBase = "numeri
 #' Parses the expression string into the column that it represents, similar to
 #' DataFrame.selectExpr
 #'
-#' @rdname functions
+#' @family normal_funcs
+#' @rdname expr
+#' @name expr
+#' @export
 setMethod("expr", signature(x = "character"),
           function(x) {
             jc <- callJStatic("org.apache.spark.sql.functions", "expr", x)
@@ -389,7 +1717,10 @@ setMethod("expr", signature(x = "character"),
 #'
 #' Formats the arguments in printf-style and returns the result as a string column.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname format_string
+#' @name format_string
+#' @export
 setMethod("format_string", signature(format = "character", x = "Column"),
           function(format, x, ...) {
             jcols <- listToSeq(lapply(list(x, ...), function(arg) { arg@jc }))
@@ -405,7 +1736,10 @@ setMethod("format_string", signature(format = "character", x = "Column"),
 #' representing the timestamp of that moment in the current system time zone in the given
 #' format.
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname from_unixtime
+#' @name from_unixtime
+#' @export
 setMethod("from_unixtime", signature(x = "Column"),
           function(x, format = "yyyy-MM-dd HH:mm:ss") {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -420,7 +1754,10 @@ setMethod("from_unixtime", signature(x = "Column"),
 #' NOTE: The position is not zero based, but 1 based index, returns 0 if substr
 #' could not be found in str.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname locate
+#' @name locate
+#' @export
 setMethod("locate", signature(substr = "character", str = "Column"),
           function(substr, str, pos = 0) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -433,7 +1770,10 @@ setMethod("locate", signature(substr = "character", str = "Column"),
 #'
 #' Left-pad the string column with
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname lpad
+#' @name lpad
+#' @export
 setMethod("lpad", signature(x = "Column", len = "numeric", pad = "character"),
           function(x, len, pad) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -446,12 +1786,19 @@ setMethod("lpad", signature(x = "Column", len = "numeric", pad = "character"),
 #'
 #' Generate a random column with i.i.d. samples from U[0.0, 1.0].
 #'
-#' @rdname functions
+#' @family normal_funcs
+#' @rdname rand
+#' @name rand
+#' @export
 setMethod("rand", signature(seed = "missing"),
           function(seed) {
             jc <- callJStatic("org.apache.spark.sql.functions", "rand")
             column(jc)
           })
+#' @family normal_funcs
+#' @rdname rand
+#' @name rand
+#' @export
 setMethod("rand", signature(seed = "numeric"),
           function(seed) {
             jc <- callJStatic("org.apache.spark.sql.functions", "rand", as.integer(seed))
@@ -462,12 +1809,19 @@ setMethod("rand", signature(seed = "numeric"),
 #'
 #' Generate a column with i.i.d. samples from the standard normal distribution.
 #'
-#' @rdname functions
+#' @family normal_funcs
+#' @rdname randn
+#' @name randn
+#' @export
 setMethod("randn", signature(seed = "missing"),
           function(seed) {
             jc <- callJStatic("org.apache.spark.sql.functions", "randn")
             column(jc)
           })
+#' @family normal_funcs
+#' @rdname randn
+#' @name randn
+#' @export
 setMethod("randn", signature(seed = "numeric"),
           function(seed) {
             jc <- callJStatic("org.apache.spark.sql.functions", "randn", as.integer(seed))
@@ -478,7 +1832,10 @@ setMethod("randn", signature(seed = "numeric"),
 #'
 #' Extract a specific(idx) group identified by a java regex, from the specified string column.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname regexp_extract
+#' @name regexp_extract
+#' @export
 setMethod("regexp_extract",
           signature(x = "Column", pattern = "character", idx = "numeric"),
           function(x, pattern, idx) {
@@ -492,7 +1849,10 @@ setMethod("regexp_extract",
 #'
 #' Replace all substrings of the specified string value that match regexp with rep.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname regexp_replace
+#' @name regexp_replace
+#' @export
 setMethod("regexp_replace",
           signature(x = "Column", pattern = "character", replacement = "character"),
           function(x, pattern, replacement) {
@@ -506,7 +1866,10 @@ setMethod("regexp_replace",
 #'
 #' Right-padded with pad to a length of len.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname rpad
+#' @name rpad
+#' @export
 setMethod("rpad", signature(x = "Column", len = "numeric", pad = "character"),
           function(x, len, pad) {
             jc <- callJStatic("org.apache.spark.sql.functions",
@@ -522,7 +1885,10 @@ setMethod("rpad", signature(x = "Column", len = "numeric", pad = "character"),
 #' returned. If count is negative, every to the right of the final delimiter (counting from the
 #' right) is returned. substring <- index performs a case-sensitive match when searching for delim.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname substring_index
+#' @name substring_index
+#' @export
 setMethod("substring_index",
           signature(x = "Column", delim = "character", count = "numeric"),
           function(x, delim, count) {
@@ -539,7 +1905,10 @@ setMethod("substring_index",
 #' The translate will happen when any character in the string matching with the character
 #' in the matchingString.
 #'
-#' @rdname functions
+#' @family string_funcs
+#' @rdname translate
+#' @name translate
+#' @export
 setMethod("translate",
           signature(x = "Column", matchingString = "character", replaceString = "character"),
           function(x, matchingString, replaceString) {
@@ -552,30 +1921,28 @@ setMethod("translate",
 #'
 #' Gets current Unix timestamp in seconds.
 #'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname unix_timestamp
+#' @name unix_timestamp
+#' @export
 setMethod("unix_timestamp", signature(x = "missing", format = "missing"),
           function(x, format) {
             jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp")
             column(jc)
           })
-#' unix_timestamp
-#'
-#' Converts time string in format yyyy-MM-dd HH:mm:ss to Unix timestamp (in seconds),
-#' using the default timezone and the default locale, return null if fail.
-#'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname unix_timestamp
+#' @name unix_timestamp
+#' @export
 setMethod("unix_timestamp", signature(x = "Column", format = "missing"),
           function(x, format) {
             jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp", x@jc)
             column(jc)
           })
-#' unix_timestamp
-#'
-#' Convert time string with given pattern
-#' (see [http://docs.oracle.com/javase/tutorial/i18n/format/simpleDateFormat.html])
-#' to Unix time stamp (in seconds), return null if fail.
-#'
-#' @rdname functions
+#' @family datetime_funcs
+#' @rdname unix_timestamp
+#' @name unix_timestamp
+#' @export
 setMethod("unix_timestamp", signature(x = "Column", format = "character"),
           function(x, format = "yyyy-MM-dd HH:mm:ss") {
             jc <- callJStatic("org.apache.spark.sql.functions", "unix_timestamp", x@jc, format)
@@ -586,7 +1953,10 @@ setMethod("unix_timestamp", signature(x = "Column", format = "character"),
 #' Evaluates a list of conditions and returns one of multiple possible result expressions.
 #' For unmatched expressions null is returned.
 #'
-#' @rdname column
+#' @family normal_funcs
+#' @rdname when
+#' @name when
+#' @export
 setMethod("when", signature(condition = "Column", value = "ANY"),
           function(condition, value) {
               condition <- condition@jc
@@ -597,10 +1967,13 @@ setMethod("when", signature(condition = "Column", value = "ANY"),
 
 #' ifelse
 #'
-#' Evaluates a list of conditions and returns `yes` if the conditions are satisfied.
-#' Otherwise `no` is returned for unmatched conditions.
+#' Evaluates a list of conditions and returns \code{yes} if the conditions are satisfied.
+#' Otherwise \code{no} is returned for unmatched conditions.
 #'
-#' @rdname column
+#' @family normal_funcs
+#' @rdname ifelse
+#' @name ifelse
+#' @export
 setMethod("ifelse",
           signature(test = "Column", yes = "ANY", no = "ANY"),
           function(test, yes, no) {
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 84cb8dfdaa2dd..610a8c31223cd 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -567,10 +567,6 @@ setGeneric("withColumnRenamed",
 
 ###################### Column Methods ##########################
 
-#' @rdname column
-#' @export
-setGeneric("approxCountDistinct", function(x, ...) { standardGeneric("approxCountDistinct") })
-
 #' @rdname column
 #' @export
 setGeneric("asc", function(x) { standardGeneric("asc") })
@@ -587,10 +583,6 @@ setGeneric("cast", function(x, dataType) { standardGeneric("cast") })
 #' @export
 setGeneric("contains", function(x, ...) { standardGeneric("contains") })
 
-#' @rdname column
-#' @export
-setGeneric("countDistinct", function(x, ...) { standardGeneric("countDistinct") })
-
 #' @rdname column
 #' @export
 setGeneric("desc", function(x) { standardGeneric("desc") })
@@ -607,10 +599,6 @@ setGeneric("getField", function(x, ...) { standardGeneric("getField") })
 #' @export
 setGeneric("getItem", function(x, ...) { standardGeneric("getItem") })
 
-#' @rdname column
-#' @export
-setGeneric("hypot", function(y, x) { standardGeneric("hypot") })
-
 #' @rdname column
 #' @export
 setGeneric("isNull", function(x) { standardGeneric("isNull") })
@@ -619,30 +607,10 @@ setGeneric("isNull", function(x) { standardGeneric("isNull") })
 #' @export
 setGeneric("isNotNull", function(x) { standardGeneric("isNotNull") })
 
-#' @rdname column
-#' @export
-setGeneric("last", function(x) { standardGeneric("last") })
-
 #' @rdname column
 #' @export
 setGeneric("like", function(x, ...) { standardGeneric("like") })
 
-#' @rdname column
-#' @export
-setGeneric("lower", function(x) { standardGeneric("lower") })
-
-#' @rdname column
-#' @export
-setGeneric("n", function(x) { standardGeneric("n") })
-
-#' @rdname column
-#' @export
-setGeneric("n_distinct", function(x, ...) { standardGeneric("n_distinct") })
-
-#' @rdname column
-#' @export
-setGeneric("rint", function(x, ...) { standardGeneric("rint") })
-
 #' @rdname column
 #' @export
 setGeneric("rlike", function(x, ...) { standardGeneric("rlike") })
@@ -662,312 +630,340 @@ setGeneric("otherwise", function(x, value) { standardGeneric("otherwise") })
 
 ###################### Expression Function Methods ##########################
 
-#' @rdname functions
+#' @rdname add_months
 #' @export
 setGeneric("add_months", function(y, x) { standardGeneric("add_months") })
 
-#' @rdname functions
+#' @rdname approxCountDistinct
+#' @export
+setGeneric("approxCountDistinct", function(x, ...) { standardGeneric("approxCountDistinct") })
+
+#' @rdname ascii
 #' @export
 setGeneric("ascii", function(x) { standardGeneric("ascii") })
 
-#' @rdname functions
+#' @rdname avg
 #' @export
 setGeneric("avg", function(x, ...) { standardGeneric("avg") })
 
-#' @rdname functions
+#' @rdname base64
 #' @export
 setGeneric("base64", function(x) { standardGeneric("base64") })
 
-#' @rdname functions
+#' @rdname bin
 #' @export
 setGeneric("bin", function(x) { standardGeneric("bin") })
 
-#' @rdname functions
+#' @rdname bitwiseNOT
 #' @export
 setGeneric("bitwiseNOT", function(x) { standardGeneric("bitwiseNOT") })
 
-#' @rdname functions
+#' @rdname cbrt
 #' @export
 setGeneric("cbrt", function(x) { standardGeneric("cbrt") })
 
-#' @rdname functions
+#' @rdname ceil
 #' @export
 setGeneric("ceil", function(x) { standardGeneric("ceil") })
 
-#' @rdname functions
+#' @rdname concat
 #' @export
 setGeneric("concat", function(x, ...) { standardGeneric("concat") })
 
-#' @rdname functions
+#' @rdname concat_ws
 #' @export
 setGeneric("concat_ws", function(sep, x, ...) { standardGeneric("concat_ws") })
 
-#' @rdname functions
+#' @rdname conv
 #' @export
 setGeneric("conv", function(x, fromBase, toBase) { standardGeneric("conv") })
 
-#' @rdname functions
+#' @rdname countDistinct
+#' @export
+setGeneric("countDistinct", function(x, ...) { standardGeneric("countDistinct") })
+
+#' @rdname crc32
 #' @export
 setGeneric("crc32", function(x) { standardGeneric("crc32") })
 
-#' @rdname functions
+#' @rdname datediff
 #' @export
 setGeneric("datediff", function(y, x) { standardGeneric("datediff") })
 
-#' @rdname functions
+#' @rdname date_add
 #' @export
 setGeneric("date_add", function(y, x) { standardGeneric("date_add") })
 
-#' @rdname functions
+#' @rdname date_format
 #' @export
 setGeneric("date_format", function(y, x) { standardGeneric("date_format") })
 
-#' @rdname functions
+#' @rdname date_sub
 #' @export
 setGeneric("date_sub", function(y, x) { standardGeneric("date_sub") })
 
-#' @rdname functions
+#' @rdname dayofmonth
 #' @export
 setGeneric("dayofmonth", function(x) { standardGeneric("dayofmonth") })
 
-#' @rdname functions
+#' @rdname dayofyear
 #' @export
 setGeneric("dayofyear", function(x) { standardGeneric("dayofyear") })
 
-#' @rdname functions
+#' @rdname explode
 #' @export
 setGeneric("explode", function(x) { standardGeneric("explode") })
 
-#' @rdname functions
+#' @rdname expr
 #' @export
 setGeneric("expr", function(x) { standardGeneric("expr") })
 
-#' @rdname functions
+#' @rdname from_utc_timestamp
 #' @export
 setGeneric("from_utc_timestamp", function(y, x) { standardGeneric("from_utc_timestamp") })
 
-#' @rdname functions
+#' @rdname format_number
 #' @export
 setGeneric("format_number", function(y, x) { standardGeneric("format_number") })
 
-#' @rdname functions
+#' @rdname format_string
 #' @export
 setGeneric("format_string", function(format, x, ...) { standardGeneric("format_string") })
 
-#' @rdname functions
+#' @rdname from_unixtime
 #' @export
 setGeneric("from_unixtime", function(x, ...) { standardGeneric("from_unixtime") })
 
-#' @rdname functions
+#' @rdname greatest
 #' @export
 setGeneric("greatest", function(x, ...) { standardGeneric("greatest") })
 
-#' @rdname functions
+#' @rdname hex
 #' @export
 setGeneric("hex", function(x) { standardGeneric("hex") })
 
-#' @rdname functions
+#' @rdname hour
 #' @export
 setGeneric("hour", function(x) { standardGeneric("hour") })
 
-#' @rdname functions
+#' @rdname hypot
+#' @export
+setGeneric("hypot", function(y, x) { standardGeneric("hypot") })
+
+#' @rdname initcap
 #' @export
 setGeneric("initcap", function(x) { standardGeneric("initcap") })
 
-#' @rdname functions
+#' @rdname instr
 #' @export
 setGeneric("instr", function(y, x) { standardGeneric("instr") })
 
-#' @rdname functions
+#' @rdname isNaN
 #' @export
 setGeneric("isNaN", function(x) { standardGeneric("isNaN") })
 
-#' @rdname functions
+#' @rdname last
+#' @export
+setGeneric("last", function(x) { standardGeneric("last") })
+
+#' @rdname last_day
 #' @export
 setGeneric("last_day", function(x) { standardGeneric("last_day") })
 
-#' @rdname functions
+#' @rdname least
 #' @export
 setGeneric("least", function(x, ...) { standardGeneric("least") })
 
-#' @rdname functions
+#' @rdname levenshtein
 #' @export
 setGeneric("levenshtein", function(y, x) { standardGeneric("levenshtein") })
 
-#' @rdname functions
+#' @rdname lit
 #' @export
 setGeneric("lit", function(x) { standardGeneric("lit") })
 
-#' @rdname functions
+#' @rdname locate
 #' @export
 setGeneric("locate", function(substr, str, ...) { standardGeneric("locate") })
 
-#' @rdname functions
+#' @rdname lower
 #' @export
 setGeneric("lower", function(x) { standardGeneric("lower") })
 
-#' @rdname functions
+#' @rdname lpad
 #' @export
 setGeneric("lpad", function(x, len, pad) { standardGeneric("lpad") })
 
-#' @rdname functions
+#' @rdname ltrim
 #' @export
 setGeneric("ltrim", function(x) { standardGeneric("ltrim") })
 
-#' @rdname functions
+#' @rdname md5
 #' @export
 setGeneric("md5", function(x) { standardGeneric("md5") })
 
-#' @rdname functions
+#' @rdname minute
 #' @export
 setGeneric("minute", function(x) { standardGeneric("minute") })
 
-#' @rdname functions
+#' @rdname month
 #' @export
 setGeneric("month", function(x) { standardGeneric("month") })
 
-#' @rdname functions
+#' @rdname months_between
 #' @export
 setGeneric("months_between", function(y, x) { standardGeneric("months_between") })
 
-#' @rdname functions
+#' @rdname count
+#' @export
+setGeneric("n", function(x) { standardGeneric("n") })
+
+#' @rdname nanvl
 #' @export
 setGeneric("nanvl", function(y, x) { standardGeneric("nanvl") })
 
-#' @rdname functions
+#' @rdname negate
 #' @export
 setGeneric("negate", function(x) { standardGeneric("negate") })
 
-#' @rdname functions
+#' @rdname next_day
 #' @export
 setGeneric("next_day", function(y, x) { standardGeneric("next_day") })
 
-#' @rdname functions
+#' @rdname countDistinct
+#' @export
+setGeneric("n_distinct", function(x, ...) { standardGeneric("n_distinct") })
+
+#' @rdname pmod
 #' @export
 setGeneric("pmod", function(y, x) { standardGeneric("pmod") })
 
-#' @rdname functions
+#' @rdname quarter
 #' @export
 setGeneric("quarter", function(x) { standardGeneric("quarter") })
 
-#' @rdname functions
+#' @rdname rand
 #' @export
 setGeneric("rand", function(seed) { standardGeneric("rand") })
 
-#' @rdname functions
+#' @rdname randn
 #' @export
 setGeneric("randn", function(seed) { standardGeneric("randn") })
 
-#' @rdname functions
+#' @rdname regexp_extract
 #' @export
 setGeneric("regexp_extract", function(x, pattern, idx) { standardGeneric("regexp_extract") })
 
-#' @rdname functions
+#' @rdname regexp_replace
 #' @export
 setGeneric("regexp_replace",
            function(x, pattern, replacement) { standardGeneric("regexp_replace") })
 
-#' @rdname functions
+#' @rdname reverse
 #' @export
 setGeneric("reverse", function(x) { standardGeneric("reverse") })
 
-#' @rdname functions
+#' @rdname rint
+#' @export
+setGeneric("rint", function(x, ...) { standardGeneric("rint") })
+
+#' @rdname rpad
 #' @export
 setGeneric("rpad", function(x, len, pad) { standardGeneric("rpad") })
 
-#' @rdname functions
+#' @rdname rtrim
 #' @export
 setGeneric("rtrim", function(x) { standardGeneric("rtrim") })
 
-#' @rdname functions
+#' @rdname second
 #' @export
 setGeneric("second", function(x) { standardGeneric("second") })
 
-#' @rdname functions
+#' @rdname sha1
 #' @export
 setGeneric("sha1", function(x) { standardGeneric("sha1") })
 
-#' @rdname functions
+#' @rdname sha2
 #' @export
 setGeneric("sha2", function(y, x) { standardGeneric("sha2") })
 
-#' @rdname functions
+#' @rdname shiftLeft
 #' @export
 setGeneric("shiftLeft", function(y, x) { standardGeneric("shiftLeft") })
 
-#' @rdname functions
+#' @rdname shiftRight
 #' @export
 setGeneric("shiftRight", function(y, x) { standardGeneric("shiftRight") })
 
-#' @rdname functions
+#' @rdname shiftRightUnsigned
 #' @export
 setGeneric("shiftRightUnsigned", function(y, x) { standardGeneric("shiftRightUnsigned") })
 
-#' @rdname functions
+#' @rdname signum
 #' @export
 setGeneric("signum", function(x) { standardGeneric("signum") })
 
-#' @rdname functions
+#' @rdname size
 #' @export
 setGeneric("size", function(x) { standardGeneric("size") })
 
-#' @rdname functions
+#' @rdname soundex
 #' @export
 setGeneric("soundex", function(x) { standardGeneric("soundex") })
 
-#' @rdname functions
+#' @rdname substring_index
 #' @export
 setGeneric("substring_index", function(x, delim, count) { standardGeneric("substring_index") })
 
-#' @rdname functions
+#' @rdname sumDistinct
 #' @export
 setGeneric("sumDistinct", function(x) { standardGeneric("sumDistinct") })
 
-#' @rdname functions
+#' @rdname toDegrees
 #' @export
 setGeneric("toDegrees", function(x) { standardGeneric("toDegrees") })
 
-#' @rdname functions
+#' @rdname toRadians
 #' @export
 setGeneric("toRadians", function(x) { standardGeneric("toRadians") })
 
-#' @rdname functions
+#' @rdname to_date
 #' @export
 setGeneric("to_date", function(x) { standardGeneric("to_date") })
 
-#' @rdname functions
+#' @rdname to_utc_timestamp
 #' @export
 setGeneric("to_utc_timestamp", function(y, x) { standardGeneric("to_utc_timestamp") })
 
-#' @rdname functions
+#' @rdname translate
 #' @export
 setGeneric("translate", function(x, matchingString, replaceString) { standardGeneric("translate") })
 
-#' @rdname functions
+#' @rdname trim
 #' @export
 setGeneric("trim", function(x) { standardGeneric("trim") })
 
-#' @rdname functions
+#' @rdname unbase64
 #' @export
 setGeneric("unbase64", function(x) { standardGeneric("unbase64") })
 
-#' @rdname functions
+#' @rdname unhex
 #' @export
 setGeneric("unhex", function(x) { standardGeneric("unhex") })
 
-#' @rdname functions
+#' @rdname unix_timestamp
 #' @export
 setGeneric("unix_timestamp", function(x, format) { standardGeneric("unix_timestamp") })
 
-#' @rdname functions
+#' @rdname upper
 #' @export
 setGeneric("upper", function(x) { standardGeneric("upper") })
 
-#' @rdname functions
+#' @rdname weekofyear
 #' @export
 setGeneric("weekofyear", function(x) { standardGeneric("weekofyear") })
 
-#' @rdname functions
+#' @rdname year
 #' @export
 setGeneric("year", function(x) { standardGeneric("year") })
 

From 642c43c81c835139e3f35dfd6a215d668a474203 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Mon, 24 Aug 2015 19:45:41 -0700
Subject: [PATCH 393/633] [SQL] [MINOR] [DOC] Clarify docs for inferring
 DataFrame from RDD of Products

 * Makes `SQLImplicits.rddToDataFrameHolder` scaladoc consistent with `SQLContext.createDataFrame[A <: Product](rdd: RDD[A])` since the former is essentially a wrapper for the latter
 * Clarifies `createDataFrame[A <: Product]` scaladoc to apply for any `RDD[Product]`, not just case classes

Author: Feynman Liang <fliang@databricks.com>

Closes #8406 from feynmanliang/sql-doc-fixes.
---
 sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala   | 2 +-
 sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 126c9c6f839c7..a1eea09e0477b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -350,7 +350,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   /**
    * :: Experimental ::
-   * Creates a DataFrame from an RDD of case classes.
+   * Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).
    *
    * @group dataframes
    * @since 1.3.0
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
index 47b6f80bed483..bf03c61088426 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLImplicits.scala
@@ -40,7 +40,7 @@ private[sql] abstract class SQLImplicits {
   implicit def symbolToColumn(s: Symbol): ColumnName = new ColumnName(s.name)
 
   /**
-   * Creates a DataFrame from an RDD of case classes or tuples.
+   * Creates a DataFrame from an RDD of Product (e.g. case classes, tuples).
    * @since 1.3.0
    */
   implicit def rddToDataFrameHolder[A <: Product : TypeTag](rdd: RDD[A]): DataFrameHolder = {

From a0c0aae1defe5e1e57704065631d201f8e3f6bac Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Tue, 25 Aug 2015 12:49:50 +0800
Subject: [PATCH 394/633] [SPARK-10121] [SQL] Thrift server always use the
 latest class loader provided by the conf of executionHive's state

https://issues.apache.org/jira/browse/SPARK-10121

Looks like the problem is that if we add a jar through another thread, the thread handling the JDBC session will not get the latest classloader.

Author: Yin Huai <yhuai@databricks.com>

Closes #8368 from yhuai/SPARK-10121.
---
 .../SparkExecuteStatementOperation.scala      |  6 +++
 .../HiveThriftServer2Suites.scala             | 54 +++++++++++++++++++
 2 files changed, 60 insertions(+)

diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
index 833bf62d47d07..02cc7e5efa521 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
@@ -159,6 +159,12 @@ private[hive] class SparkExecuteStatementOperation(
 
               // User information is part of the metastore client member in Hive
               hiveContext.setSession(currentSqlSession)
+              // Always use the latest class loader provided by executionHive's state.
+              val executionHiveClassLoader =
+                hiveContext.executionHive.state.getConf.getClassLoader
+              sessionHive.getConf.setClassLoader(executionHiveClassLoader)
+              parentSessionState.getConf.setClassLoader(executionHiveClassLoader)
+
               Hive.set(sessionHive)
               SessionState.setCurrentSessionState(parentSessionState)
               try {
diff --git a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
index ded42bca9971e..b72249b3bf8c0 100644
--- a/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
+++ b/sql/hive-thriftserver/src/test/scala/org/apache/spark/sql/hive/thriftserver/HiveThriftServer2Suites.scala
@@ -377,6 +377,60 @@ class HiveThriftBinaryServerSuite extends HiveThriftJdbcTest {
       rs2.close()
     }
   }
+
+  test("test add jar") {
+    withMultipleConnectionJdbcStatement(
+      {
+        statement =>
+          val jarFile =
+            "../hive/src/test/resources/hive-hcatalog-core-0.13.1.jar"
+              .split("/")
+              .mkString(File.separator)
+
+          statement.executeQuery(s"ADD JAR $jarFile")
+      },
+
+      {
+        statement =>
+          val queries = Seq(
+            "DROP TABLE IF EXISTS smallKV",
+            "CREATE TABLE smallKV(key INT, val STRING)",
+            s"LOAD DATA LOCAL INPATH '${TestData.smallKv}' OVERWRITE INTO TABLE smallKV",
+            "DROP TABLE IF EXISTS addJar",
+            """CREATE TABLE addJar(key string)
+              |ROW FORMAT SERDE 'org.apache.hive.hcatalog.data.JsonSerDe'
+            """.stripMargin)
+
+          queries.foreach(statement.execute)
+
+          statement.executeQuery(
+            """
+              |INSERT INTO TABLE addJar SELECT 'k1' as key FROM smallKV limit 1
+            """.stripMargin)
+
+          val actualResult =
+            statement.executeQuery("SELECT key FROM addJar")
+          val actualResultBuffer = new collection.mutable.ArrayBuffer[String]()
+          while (actualResult.next()) {
+            actualResultBuffer += actualResult.getString(1)
+          }
+          actualResult.close()
+
+          val expectedResult =
+            statement.executeQuery("SELECT 'k1'")
+          val expectedResultBuffer = new collection.mutable.ArrayBuffer[String]()
+          while (expectedResult.next()) {
+            expectedResultBuffer += expectedResult.getString(1)
+          }
+          expectedResult.close()
+
+          assert(expectedResultBuffer === actualResultBuffer)
+
+          statement.executeQuery("DROP TABLE IF EXISTS addJar")
+          statement.executeQuery("DROP TABLE IF EXISTS smallKV")
+      }
+    )
+  }
 }
 
 class HiveThriftHttpServerSuite extends HiveThriftJdbcTest {

From 5175ca0c85b10045d12c3fb57b1e52278a413ecf Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Mon, 24 Aug 2015 23:15:27 -0700
Subject: [PATCH 395/633] [SPARK-10178] [SQL] HiveComparisionTest should print
 out dependent tables

In `HiveComparisionTest`s it is possible to fail a query of the form `SELECT * FROM dest1`, where `dest1` is the query that is actually computing the incorrect results.  To aid debugging this patch improves the harness to also print these query plans and their results.

Author: Michael Armbrust <michael@databricks.com>

Closes #8388 from marmbrus/generatedTables.
---
 .../hive/execution/HiveComparisonTest.scala   | 36 +++++++++++++++++++
 1 file changed, 36 insertions(+)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 2bdb0e11878e5..4d45249d9c6b8 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.hive.execution
 
 import java.io._
 
+import scala.util.control.NonFatal
+
 import org.scalatest.{BeforeAndAfterAll, GivenWhenThen}
 
 import org.apache.spark.{Logging, SparkFunSuite}
@@ -386,11 +388,45 @@ abstract class HiveComparisonTest
                 hiveCacheFiles.foreach(_.delete())
               }
 
+              // If this query is reading other tables that were created during this test run
+              // also print out the query plans and results for those.
+              val computedTablesMessages: String = try {
+                val tablesRead = new TestHive.QueryExecution(query).executedPlan.collect {
+                  case ts: HiveTableScan => ts.relation.tableName
+                }.toSet
+
+                TestHive.reset()
+                val executions = queryList.map(new TestHive.QueryExecution(_))
+                executions.foreach(_.toRdd)
+                val tablesGenerated = queryList.zip(executions).flatMap {
+                  case (q, e) => e.executedPlan.collect {
+                    case i: InsertIntoHiveTable if tablesRead contains i.table.tableName =>
+                      (q, e, i)
+                  }
+                }
+
+                tablesGenerated.map { case (hiveql, execution, insert) =>
+                  s"""
+                     |=== Generated Table ===
+                     |$hiveql
+                     |$execution
+                     |== Results ==
+                     |${insert.child.execute().collect().mkString("\n")}
+                   """.stripMargin
+                }.mkString("\n")
+
+              } catch {
+                case NonFatal(e) =>
+                  logError("Failed to compute generated tables", e)
+                  s"Couldn't compute dependent tables: $e"
+              }
+
               val errorMessage =
                 s"""
                   |Results do not match for $testCaseName:
                   |$hiveQuery\n${hiveQuery.analyzed.output.map(_.name).mkString("\t")}
                   |$resultComparison
+                  |$computedTablesMessages
                 """.stripMargin
 
               stringToFile(new File(wrongDirectory, testCaseName), errorMessage + consoleTestCase)

From d9c25dec87e6da7d66a47ff94e7eefa008081b9d Mon Sep 17 00:00:00 2001
From: cody koeninger <cody@koeninger.org>
Date: Mon, 24 Aug 2015 23:26:14 -0700
Subject: [PATCH 396/633] =?UTF-8?q?[SPARK-9786]=20[STREAMING]=20[KAFKA]=20?=
 =?UTF-8?q?fix=20backpressure=20so=20it=20works=20with=20defa=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…ult maxRatePerPartition setting of 0

Author: cody koeninger <cody@koeninger.org>

Closes #8413 from koeninger/backpressure-testing-master.
---
 .../spark/streaming/kafka/DirectKafkaInputDStream.scala  | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
index 8a177077775c6..1000094e93cb3 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
@@ -95,8 +95,13 @@ class DirectKafkaInputDStream[
 
     val effectiveRateLimitPerPartition = estimatedRateLimit
       .filter(_ > 0)
-      .map(limit => Math.min(maxRateLimitPerPartition, (limit / numPartitions)))
-      .getOrElse(maxRateLimitPerPartition)
+      .map { limit =>
+        if (maxRateLimitPerPartition > 0) {
+          Math.min(maxRateLimitPerPartition, (limit / numPartitions))
+        } else {
+          limit / numPartitions
+        }
+      }.getOrElse(maxRateLimitPerPartition)
 
     if (effectiveRateLimitPerPartition > 0) {
       val secsPerBatch = context.graph.batchDuration.milliseconds.toDouble / 1000

From f023aa2fcc1d1dbb82aee568be0a8f2457c309ae Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 24 Aug 2015 23:34:50 -0700
Subject: [PATCH 397/633] [SPARK-10137] [STREAMING] Avoid to restart receivers
 if scheduleReceivers returns balanced results

This PR fixes the following cases for `ReceiverSchedulingPolicy`.

1) Assume there are 4 executors: host1, host2, host3, host4, and 5 receivers: r1, r2, r3, r4, r5. Then `ReceiverSchedulingPolicy.scheduleReceivers` will return (r1 -> host1, r2 -> host2, r3 -> host3, r4 -> host4, r5 -> host1).
Let's assume r1 starts at first on `host1` as `scheduleReceivers` suggested,  and try to register with ReceiverTracker. But the previous `ReceiverSchedulingPolicy.rescheduleReceiver` will return (host2, host3, host4) according to the current executor weights (host1 -> 1.0, host2 -> 0.5, host3 -> 0.5, host4 -> 0.5), so ReceiverTracker will reject `r1`. This is unexpected since r1 is starting exactly where `scheduleReceivers` suggested.

This case can be fixed by ignoring the information of the receiver that is rescheduling in `receiverTrackingInfoMap`.

2) Assume there are 3 executors (host1, host2, host3) and each executors has 3 cores, and 3 receivers: r1, r2, r3. Assume r1 is running on host1. Now r2 is restarting, the previous `ReceiverSchedulingPolicy.rescheduleReceiver` will always return (host1, host2, host3). So it's possible that r2 will be scheduled to host1 by TaskScheduler. r3 is similar. Then at last, it's possible that there are 3 receivers running on host1, while host2 and host3 are idle.

This issue can be fixed by returning only executors that have the minimum wight rather than returning at least 3 executors.

Author: zsxwing <zsxwing@gmail.com>

Closes #8340 from zsxwing/fix-receiver-scheduling.
---
 .../scheduler/ReceiverSchedulingPolicy.scala  |  58 +++++++---
 .../streaming/scheduler/ReceiverTracker.scala | 106 ++++++++++++------
 .../ReceiverSchedulingPolicySuite.scala       |  13 ++-
 3 files changed, 120 insertions(+), 57 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala
index ef5b687b5831a..10b5a7f57a802 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicy.scala
@@ -22,6 +22,36 @@ import scala.collection.mutable
 
 import org.apache.spark.streaming.receiver.Receiver
 
+/**
+ * A class that tries to schedule receivers with evenly distributed. There are two phases for
+ * scheduling receivers.
+ *
+ * - The first phase is global scheduling when ReceiverTracker is starting and we need to schedule
+ *   all receivers at the same time. ReceiverTracker will call `scheduleReceivers` at this phase.
+ *   It will try to schedule receivers with evenly distributed. ReceiverTracker should update its
+ *   receiverTrackingInfoMap according to the results of `scheduleReceivers`.
+ *   `ReceiverTrackingInfo.scheduledExecutors` for each receiver will set to an executor list that
+ *   contains the scheduled locations. Then when a receiver is starting, it will send a register
+ *   request and `ReceiverTracker.registerReceiver` will be called. In
+ *   `ReceiverTracker.registerReceiver`, if a receiver's scheduled executors is set, it should check
+ *   if the location of this receiver is one of the scheduled executors, if not, the register will
+ *   be rejected.
+ * - The second phase is local scheduling when a receiver is restarting. There are two cases of
+ *   receiver restarting:
+ *   - If a receiver is restarting because it's rejected due to the real location and the scheduled
+ *     executors mismatching, in other words, it fails to start in one of the locations that
+ *     `scheduleReceivers` suggested, `ReceiverTracker` should firstly choose the executors that are
+ *     still alive in the list of scheduled executors, then use them to launch the receiver job.
+ *   - If a receiver is restarting without a scheduled executors list, or the executors in the list
+ *     are dead, `ReceiverTracker` should call `rescheduleReceiver`. If so, `ReceiverTracker` should
+ *     not set `ReceiverTrackingInfo.scheduledExecutors` for this executor, instead, it should clear
+ *     it. Then when this receiver is registering, we can know this is a local scheduling, and
+ *     `ReceiverTrackingInfo` should call `rescheduleReceiver` again to check if the launching
+ *     location is matching.
+ *
+ * In conclusion, we should make a global schedule, try to achieve that exactly as long as possible,
+ * otherwise do local scheduling.
+ */
 private[streaming] class ReceiverSchedulingPolicy {
 
   /**
@@ -102,8 +132,7 @@ private[streaming] class ReceiverSchedulingPolicy {
 
   /**
    * Return a list of candidate executors to run the receiver. If the list is empty, the caller can
-   * run this receiver in arbitrary executor. The caller can use `preferredNumExecutors` to require
-   * returning `preferredNumExecutors` executors if possible.
+   * run this receiver in arbitrary executor.
    *
    * This method tries to balance executors' load. Here is the approach to schedule executors
    * for a receiver.
@@ -122,9 +151,8 @@ private[streaming] class ReceiverSchedulingPolicy {
    *         If a receiver is scheduled to an executor but has not yet run, it contributes
    *         `1.0 / #candidate_executors_of_this_receiver` to the executor's weight.</li>
    *     </ul>
-   *     At last, if there are more than `preferredNumExecutors` idle executors (weight = 0),
-   *     returns all idle executors. Otherwise, we only return `preferredNumExecutors` best options
-   *     according to the weights.
+   *     At last, if there are any idle executors (weight = 0), returns all idle executors.
+   *     Otherwise, returns the executors that have the minimum weight.
    *   </li>
    * </ol>
    *
@@ -134,8 +162,7 @@ private[streaming] class ReceiverSchedulingPolicy {
       receiverId: Int,
       preferredLocation: Option[String],
       receiverTrackingInfoMap: Map[Int, ReceiverTrackingInfo],
-      executors: Seq[String],
-      preferredNumExecutors: Int = 3): Seq[String] = {
+      executors: Seq[String]): Seq[String] = {
     if (executors.isEmpty) {
       return Seq.empty
     }
@@ -156,15 +183,18 @@ private[streaming] class ReceiverSchedulingPolicy {
       }
     }.groupBy(_._1).mapValues(_.map(_._2).sum) // Sum weights for each executor
 
-    val idleExecutors = (executors.toSet -- executorWeights.keys).toSeq
-    if (idleExecutors.size >= preferredNumExecutors) {
-      // If there are more than `preferredNumExecutors` idle executors, return all of them
+    val idleExecutors = executors.toSet -- executorWeights.keys
+    if (idleExecutors.nonEmpty) {
       scheduledExecutors ++= idleExecutors
     } else {
-      // If there are less than `preferredNumExecutors` idle executors, return 3 best options
-      scheduledExecutors ++= idleExecutors
-      val sortedExecutors = executorWeights.toSeq.sortBy(_._2).map(_._1)
-      scheduledExecutors ++= (idleExecutors ++ sortedExecutors).take(preferredNumExecutors)
+      // There is no idle executor. So select all executors that have the minimum weight.
+      val sortedExecutors = executorWeights.toSeq.sortBy(_._2)
+      if (sortedExecutors.nonEmpty) {
+        val minWeight = sortedExecutors(0)._2
+        scheduledExecutors ++= sortedExecutors.takeWhile(_._2 == minWeight).map(_._1)
+      } else {
+        // This should not happen since "executors" is not empty
+      }
     }
     scheduledExecutors.toSeq
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index 30d25a64e307a..3d532a675db02 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -244,8 +244,21 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
     }
 
     if (isTrackerStopping || isTrackerStopped) {
-      false
-    } else if (!scheduleReceiver(streamId).contains(hostPort)) {
+      return false
+    }
+
+    val scheduledExecutors = receiverTrackingInfos(streamId).scheduledExecutors
+    val accetableExecutors = if (scheduledExecutors.nonEmpty) {
+        // This receiver is registering and it's scheduled by
+        // ReceiverSchedulingPolicy.scheduleReceivers. So use "scheduledExecutors" to check it.
+        scheduledExecutors.get
+      } else {
+        // This receiver is scheduled by "ReceiverSchedulingPolicy.rescheduleReceiver", so calling
+        // "ReceiverSchedulingPolicy.rescheduleReceiver" again to check it.
+        scheduleReceiver(streamId)
+      }
+
+    if (!accetableExecutors.contains(hostPort)) {
       // Refuse it since it's scheduled to a wrong executor
       false
     } else {
@@ -426,12 +439,25 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
           startReceiver(receiver, executors)
         }
       case RestartReceiver(receiver) =>
-        val scheduledExecutors = schedulingPolicy.rescheduleReceiver(
-          receiver.streamId,
-          receiver.preferredLocation,
-          receiverTrackingInfos,
-          getExecutors)
-        updateReceiverScheduledExecutors(receiver.streamId, scheduledExecutors)
+        // Old scheduled executors minus the ones that are not active any more
+        val oldScheduledExecutors = getStoredScheduledExecutors(receiver.streamId)
+        val scheduledExecutors = if (oldScheduledExecutors.nonEmpty) {
+            // Try global scheduling again
+            oldScheduledExecutors
+          } else {
+            val oldReceiverInfo = receiverTrackingInfos(receiver.streamId)
+            // Clear "scheduledExecutors" to indicate we are going to do local scheduling
+            val newReceiverInfo = oldReceiverInfo.copy(
+              state = ReceiverState.INACTIVE, scheduledExecutors = None)
+            receiverTrackingInfos(receiver.streamId) = newReceiverInfo
+            schedulingPolicy.rescheduleReceiver(
+              receiver.streamId,
+              receiver.preferredLocation,
+              receiverTrackingInfos,
+              getExecutors)
+          }
+        // Assume there is one receiver restarting at one time, so we don't need to update
+        // receiverTrackingInfos
         startReceiver(receiver, scheduledExecutors)
       case c: CleanupOldBlocks =>
         receiverTrackingInfos.values.flatMap(_.endpoint).foreach(_.send(c))
@@ -464,6 +490,24 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         context.reply(true)
     }
 
+    /**
+     * Return the stored scheduled executors that are still alive.
+     */
+    private def getStoredScheduledExecutors(receiverId: Int): Seq[String] = {
+      if (receiverTrackingInfos.contains(receiverId)) {
+        val scheduledExecutors = receiverTrackingInfos(receiverId).scheduledExecutors
+        if (scheduledExecutors.nonEmpty) {
+          val executors = getExecutors.toSet
+          // Only return the alive executors
+          scheduledExecutors.get.filter(executors)
+        } else {
+          Nil
+        }
+      } else {
+        Nil
+      }
+    }
+
     /**
      * Start a receiver along with its scheduled executors
      */
@@ -484,7 +528,23 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         new SerializableConfiguration(ssc.sparkContext.hadoopConfiguration)
 
       // Function to start the receiver on the worker node
-      val startReceiverFunc = new StartReceiverFunc(checkpointDirOption, serializableHadoopConf)
+      val startReceiverFunc: Iterator[Receiver[_]] => Unit =
+        (iterator: Iterator[Receiver[_]]) => {
+          if (!iterator.hasNext) {
+            throw new SparkException(
+              "Could not start receiver as object not found.")
+          }
+          if (TaskContext.get().attemptNumber() == 0) {
+            val receiver = iterator.next()
+            assert(iterator.hasNext == false)
+            val supervisor = new ReceiverSupervisorImpl(
+              receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
+            supervisor.start()
+            supervisor.awaitTermination()
+          } else {
+            // It's restarted by TaskScheduler, but we want to reschedule it again. So exit it.
+          }
+        }
 
       // Create the RDD using the scheduledExecutors to run the receiver in a Spark job
       val receiverRDD: RDD[Receiver[_]] =
@@ -541,31 +601,3 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
   }
 
 }
-
-/**
- * Function to start the receiver on the worker node. Use a class instead of closure to avoid
- * the serialization issue.
- */
-private[streaming] class StartReceiverFunc(
-    checkpointDirOption: Option[String],
-    serializableHadoopConf: SerializableConfiguration)
-  extends (Iterator[Receiver[_]] => Unit) with Serializable {
-
-  override def apply(iterator: Iterator[Receiver[_]]): Unit = {
-    if (!iterator.hasNext) {
-      throw new SparkException(
-        "Could not start receiver as object not found.")
-    }
-    if (TaskContext.get().attemptNumber() == 0) {
-      val receiver = iterator.next()
-      assert(iterator.hasNext == false)
-      val supervisor = new ReceiverSupervisorImpl(
-        receiver, SparkEnv.get, serializableHadoopConf.value, checkpointDirOption)
-      supervisor.start()
-      supervisor.awaitTermination()
-    } else {
-      // It's restarted by TaskScheduler, but we want to reschedule it again. So exit it.
-    }
-  }
-
-}
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala
index 0418d776ecc9a..b2a51d72bac2b 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverSchedulingPolicySuite.scala
@@ -39,7 +39,7 @@ class ReceiverSchedulingPolicySuite extends SparkFunSuite {
     assert(scheduledExecutors.toSet === Set("host1", "host2"))
   }
 
-  test("rescheduleReceiver: return all idle executors if more than 3 idle executors") {
+  test("rescheduleReceiver: return all idle executors if there are any idle executors") {
     val executors = Seq("host1", "host2", "host3", "host4", "host5")
     // host3 is idle
     val receiverTrackingInfoMap = Map(
@@ -49,16 +49,16 @@ class ReceiverSchedulingPolicySuite extends SparkFunSuite {
     assert(scheduledExecutors.toSet === Set("host2", "host3", "host4", "host5"))
   }
 
-  test("rescheduleReceiver: return 3 best options if less than 3 idle executors") {
+  test("rescheduleReceiver: return all executors that have minimum weight if no idle executors") {
     val executors = Seq("host1", "host2", "host3", "host4", "host5")
-    // Weights: host1 = 1.5, host2 = 0.5, host3 = 1.0
-    // host4 and host5 are idle
+    // Weights: host1 = 1.5, host2 = 0.5, host3 = 1.0, host4 = 0.5, host5 = 0.5
     val receiverTrackingInfoMap = Map(
       0 -> ReceiverTrackingInfo(0, ReceiverState.ACTIVE, None, Some("host1")),
       1 -> ReceiverTrackingInfo(1, ReceiverState.SCHEDULED, Some(Seq("host2", "host3")), None),
-      2 -> ReceiverTrackingInfo(1, ReceiverState.SCHEDULED, Some(Seq("host1", "host3")), None))
+      2 -> ReceiverTrackingInfo(2, ReceiverState.SCHEDULED, Some(Seq("host1", "host3")), None),
+      3 -> ReceiverTrackingInfo(4, ReceiverState.SCHEDULED, Some(Seq("host4", "host5")), None))
     val scheduledExecutors = receiverSchedulingPolicy.rescheduleReceiver(
-      3, None, receiverTrackingInfoMap, executors)
+      4, None, receiverTrackingInfoMap, executors)
     assert(scheduledExecutors.toSet === Set("host2", "host4", "host5"))
   }
 
@@ -127,4 +127,5 @@ class ReceiverSchedulingPolicySuite extends SparkFunSuite {
       assert(executors.isEmpty)
     }
   }
+
 }

From df7041d02d3fd44b08a859f5d77bf6fb726895f0 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Mon, 24 Aug 2015 23:38:32 -0700
Subject: [PATCH 398/633] [SPARK-10196] [SQL] Correctly saving decimals in
 internal rows to JSON.

https://issues.apache.org/jira/browse/SPARK-10196

Author: Yin Huai <yhuai@databricks.com>

Closes #8408 from yhuai/DecimalJsonSPARK-10196.
---
 .../datasources/json/JacksonGenerator.scala   |  2 +-
 .../sources/JsonHadoopFsRelationSuite.scala   | 27 +++++++++++++++++++
 2 files changed, 28 insertions(+), 1 deletion(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
index 99ac7730bd1c9..330ba907b2ef9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
@@ -95,7 +95,7 @@ private[sql] object JacksonGenerator {
       case (FloatType, v: Float) => gen.writeNumber(v)
       case (DoubleType, v: Double) => gen.writeNumber(v)
       case (LongType, v: Long) => gen.writeNumber(v)
-      case (DecimalType(), v: java.math.BigDecimal) => gen.writeNumber(v)
+      case (DecimalType(), v: Decimal) => gen.writeNumber(v.toJavaBigDecimal)
       case (ByteType, v: Byte) => gen.writeNumber(v.toInt)
       case (BinaryType, v: Array[Byte]) => gen.writeBinary(v)
       case (BooleanType, v: Boolean) => gen.writeBoolean(v)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
index ed6d512ab36fe..8ca3a17085194 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.sources
 
+import java.math.BigDecimal
+
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
@@ -75,4 +77,29 @@ class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
       )
     }
   }
+
+  test("SPARK-10196: save decimal type to JSON") {
+    withTempDir { file =>
+      file.delete()
+
+      val schema =
+        new StructType()
+          .add("decimal", DecimalType(7, 2))
+
+      val data =
+        Row(new BigDecimal("10.02")) ::
+          Row(new BigDecimal("20000.99")) ::
+          Row(new BigDecimal("10000")) :: Nil
+      val df = createDataFrame(sparkContext.parallelize(data), schema)
+
+      // Write the data out.
+      df.write.format(dataSourceName).save(file.getCanonicalPath)
+
+      // Read it back and check the result.
+      checkAnswer(
+        read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
+        df
+      )
+    }
+  }
 }

From bf03fe68d62f33dda70dff45c3bda1f57b032dfc Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Tue, 25 Aug 2015 14:58:42 +0800
Subject: [PATCH 399/633] [SPARK-10136] [SQL] A more robust fix for SPARK-10136

PR #8341 is a valid fix for SPARK-10136, but it didn't catch the real root cause.  The real problem can be rather tricky to explain, and requires audiences to be pretty familiar with parquet-format spec, especially details of `LIST` backwards-compatibility rules.  Let me have a try to give an explanation here.

The structure of the problematic Parquet schema generated by parquet-avro is something like this:

```
message m {
  <repetition> group f (LIST) {         // Level 1
    repeated group array (LIST) {       // Level 2
      repeated <primitive-type> array;  // Level 3
    }
  }
}
```

(The schema generated by parquet-thrift is structurally similar, just replace the `array` at level 2 with `f_tuple`, and the other one at level 3 with `f_tuple_tuple`.)

This structure consists of two nested legacy 2-level `LIST`-like structures:

1. The repeated group type at level 2 is the element type of the outer array defined at level 1

   This group should map to an `CatalystArrayConverter.ElementConverter` when building converters.

2. The repeated primitive type at level 3 is the element type of the inner array defined at level 2

   This group should also map to an `CatalystArrayConverter.ElementConverter`.

The root cause of SPARK-10136 is that, the group at level 2 isn't properly recognized as the element type of level 1.  Thus, according to parquet-format spec, the repeated primitive at level 3 is left as a so called "unannotated repeated primitive type", and is recognized as a required list of required primitive type, thus a `RepeatedPrimitiveConverter` instead of a `CatalystArrayConverter.ElementConverter` is created for it.

According to  parquet-format spec, unannotated repeated type shouldn't appear in a `LIST`- or `MAP`-annotated group.  PR #8341 fixed this issue by allowing such unannotated repeated type appear in `LIST`-annotated groups, which is a non-standard, hacky, but valid fix.  (I didn't realize this when authoring #8341 though.)

As for the reason why level 2 isn't recognized as a list element type, it's because of the following `LIST` backwards-compatibility rule defined in the parquet-format spec:

> If the repeated field is a group with one field and is named either `array` or uses the `LIST`-annotated group's name with `_tuple` appended then the repeated type is the element type and elements are required.

(The `array` part is for parquet-avro compatibility, while the `_tuple` part is for parquet-thrift.)

This rule is implemented in [`CatalystSchemaConverter.isElementType`] [1], but neglected in [`CatalystRowConverter.isElementType`] [2].  This PR delivers a more robust fix by adding this rule in the latter method.

Note that parquet-avro 1.7.0 also suffers from this issue. Details can be found at [PARQUET-364] [3].

[1]: https://github.com/apache/spark/blob/85f9a61357994da5023b08b0a8a2eb09388ce7f8/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala#L259-L305
[2]: https://github.com/apache/spark/blob/85f9a61357994da5023b08b0a8a2eb09388ce7f8/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala#L456-L463
[3]: https://issues.apache.org/jira/browse/PARQUET-364

Author: Cheng Lian <lian@databricks.com>

Closes #8361 from liancheng/spark-10136/proper-version.
---
 .../parquet/CatalystRowConverter.scala         | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index d2c2db51769ba..cbf0704c4a9a4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -415,8 +415,9 @@ private[parquet] class CatalystRowConverter(
     private val elementConverter: Converter = {
       val repeatedType = parquetSchema.getType(0)
       val elementType = catalystSchema.elementType
+      val parentName = parquetSchema.getName
 
-      if (isElementType(repeatedType, elementType)) {
+      if (isElementType(repeatedType, elementType, parentName)) {
         newConverter(repeatedType, elementType, new ParentContainerUpdater {
           override def set(value: Any): Unit = currentArray += value
         })
@@ -453,10 +454,13 @@ private[parquet] class CatalystRowConverter(
      * @see https://github.com/apache/parquet-format/blob/master/LogicalTypes.md#backward-compatibility-rules
      */
     // scalastyle:on
-    private def isElementType(parquetRepeatedType: Type, catalystElementType: DataType): Boolean = {
+    private def isElementType(
+        parquetRepeatedType: Type, catalystElementType: DataType, parentName: String): Boolean = {
       (parquetRepeatedType, catalystElementType) match {
         case (t: PrimitiveType, _) => true
         case (t: GroupType, _) if t.getFieldCount > 1 => true
+        case (t: GroupType, _) if t.getFieldCount == 1 && t.getName == "array" => true
+        case (t: GroupType, _) if t.getFieldCount == 1 && t.getName == parentName + "_tuple" => true
         case (t: GroupType, StructType(Array(f))) if f.name == t.getFieldName(0) => true
         case _ => false
       }
@@ -474,15 +478,9 @@ private[parquet] class CatalystRowConverter(
 
       override def getConverter(fieldIndex: Int): Converter = converter
 
-      override def end(): Unit = {
-        converter.updater.end()
-        currentArray += currentElement
-      }
+      override def end(): Unit = currentArray += currentElement
 
-      override def start(): Unit = {
-        converter.updater.start()
-        currentElement = null
-      }
+      override def start(): Unit = currentElement = null
     }
   }
 

From 82268f07abfa658869df2354ae72f8d6ddd119e8 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 25 Aug 2015 00:04:10 -0700
Subject: [PATCH 400/633] [SPARK-9293] [SPARK-9813] Analysis should check that
 set operations are only performed on tables with equal numbers of columns

This patch adds an analyzer rule to ensure that set operations (union, intersect, and except) are only applied to tables with the same number of columns. Without this rule, there are scenarios where invalid queries can return incorrect results instead of failing with error messages; SPARK-9813 provides one example of this problem. In other cases, the invalid query can crash at runtime with extremely confusing exceptions.

I also performed a bit of cleanup to refactor some of those logical operators' code into a common `SetOperation` base class.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #7631 from JoshRosen/SPARK-9293.
---
 .../sql/catalyst/analysis/CheckAnalysis.scala |  6 +++
 .../catalyst/analysis/HiveTypeCoercion.scala  | 14 +++----
 .../plans/logical/basicOperators.scala        | 38 +++++++++----------
 .../analysis/AnalysisErrorSuite.scala         | 18 +++++++++
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  2 +-
 .../hive/execution/InsertIntoHiveTable.scala  |  2 +-
 6 files changed, 48 insertions(+), 32 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
index 39f554c137c98..7701fd0451041 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/CheckAnalysis.scala
@@ -137,6 +137,12 @@ trait CheckAnalysis {
               }
             }
 
+          case s @ SetOperation(left, right) if left.output.length != right.output.length =>
+            failAnalysis(
+              s"${s.nodeName} can only be performed on tables with the same number of columns, " +
+               s"but the left table has ${left.output.length} columns and the right has " +
+               s"${right.output.length}")
+
           case _ => // Fallbacks to the following checks
         }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 2cb067f4aac91..a1aa2a2b2c680 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -203,6 +203,7 @@ object HiveTypeCoercion {
         planName: String,
         left: LogicalPlan,
         right: LogicalPlan): (LogicalPlan, LogicalPlan) = {
+      require(left.output.length == right.output.length)
 
       val castedTypes = left.output.zip(right.output).map {
         case (lhs, rhs) if lhs.dataType != rhs.dataType =>
@@ -229,15 +230,10 @@ object HiveTypeCoercion {
     def apply(plan: LogicalPlan): LogicalPlan = plan resolveOperators {
       case p if p.analyzed => p
 
-      case u @ Union(left, right) if u.childrenResolved && !u.resolved =>
-        val (newLeft, newRight) = widenOutputTypes(u.nodeName, left, right)
-        Union(newLeft, newRight)
-      case e @ Except(left, right) if e.childrenResolved && !e.resolved =>
-        val (newLeft, newRight) = widenOutputTypes(e.nodeName, left, right)
-        Except(newLeft, newRight)
-      case i @ Intersect(left, right) if i.childrenResolved && !i.resolved =>
-        val (newLeft, newRight) = widenOutputTypes(i.nodeName, left, right)
-        Intersect(newLeft, newRight)
+      case s @ SetOperation(left, right) if s.childrenResolved
+          && left.output.length == right.output.length && !s.resolved =>
+        val (newLeft, newRight) = widenOutputTypes(s.nodeName, left, right)
+        s.makeCopy(Array(newLeft, newRight))
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
index 73b8261260acb..722f69cdca827 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicOperators.scala
@@ -89,13 +89,21 @@ case class Filter(condition: Expression, child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 }
 
-case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
+abstract class SetOperation(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
   // TODO: These aren't really the same attributes as nullability etc might change.
-  override def output: Seq[Attribute] = left.output
+  final override def output: Seq[Attribute] = left.output
 
-  override lazy val resolved: Boolean =
+  final override lazy val resolved: Boolean =
     childrenResolved &&
-    left.output.zip(right.output).forall { case (l, r) => l.dataType == r.dataType }
+      left.output.length == right.output.length &&
+      left.output.zip(right.output).forall { case (l, r) => l.dataType == r.dataType }
+}
+
+private[sql] object SetOperation {
+  def unapply(p: SetOperation): Option[(LogicalPlan, LogicalPlan)] = Some((p.left, p.right))
+}
+
+case class Union(left: LogicalPlan, right: LogicalPlan) extends SetOperation(left, right) {
 
   override def statistics: Statistics = {
     val sizeInBytes = left.statistics.sizeInBytes + right.statistics.sizeInBytes
@@ -103,6 +111,10 @@ case class Union(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
   }
 }
 
+case class Intersect(left: LogicalPlan, right: LogicalPlan) extends SetOperation(left, right)
+
+case class Except(left: LogicalPlan, right: LogicalPlan) extends SetOperation(left, right)
+
 case class Join(
   left: LogicalPlan,
   right: LogicalPlan,
@@ -142,15 +154,6 @@ case class BroadcastHint(child: LogicalPlan) extends UnaryNode {
   override def output: Seq[Attribute] = child.output
 }
 
-
-case class Except(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
-  override def output: Seq[Attribute] = left.output
-
-  override lazy val resolved: Boolean =
-    childrenResolved &&
-      left.output.zip(right.output).forall { case (l, r) => l.dataType == r.dataType }
-}
-
 case class InsertIntoTable(
     table: LogicalPlan,
     partition: Map[String, Option[String]],
@@ -160,7 +163,7 @@ case class InsertIntoTable(
   extends LogicalPlan {
 
   override def children: Seq[LogicalPlan] = child :: Nil
-  override def output: Seq[Attribute] = child.output
+  override def output: Seq[Attribute] = Seq.empty
 
   assert(overwrite || !ifNotExists)
   override lazy val resolved: Boolean = childrenResolved && child.output.zip(table.output).forall {
@@ -440,10 +443,3 @@ case object OneRowRelation extends LeafNode {
   override def statistics: Statistics = Statistics(sizeInBytes = 1)
 }
 
-case class Intersect(left: LogicalPlan, right: LogicalPlan) extends BinaryNode {
-  override def output: Seq[Attribute] = left.output
-
-  override lazy val resolved: Boolean =
-    childrenResolved &&
-      left.output.zip(right.output).forall { case (l, r) => l.dataType == r.dataType }
-}
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
index 7065adce04bf8..fbdd3a7776f50 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisErrorSuite.scala
@@ -145,6 +145,24 @@ class AnalysisErrorSuite extends AnalysisTest {
     UnresolvedTestPlan(),
     "unresolved" :: Nil)
 
+  errorTest(
+    "union with unequal number of columns",
+    testRelation.unionAll(testRelation2),
+    "union" :: "number of columns" :: testRelation2.output.length.toString ::
+      testRelation.output.length.toString :: Nil)
+
+  errorTest(
+    "intersect with unequal number of columns",
+    testRelation.intersect(testRelation2),
+    "intersect" :: "number of columns" :: testRelation2.output.length.toString ::
+      testRelation.output.length.toString :: Nil)
+
+  errorTest(
+    "except with unequal number of columns",
+    testRelation.except(testRelation2),
+    "except" :: "number of columns" :: testRelation2.output.length.toString ::
+      testRelation.output.length.toString :: Nil)
+
   errorTest(
     "SPARK-9955: correct error message for aggregate",
     // When parse SQL string, we will wrap aggregate expressions with UnresolvedAlias.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index bbe8c1911bf86..98d21aa76d64e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -751,7 +751,7 @@ private[hive] case class InsertIntoHiveTable(
   extends LogicalPlan {
 
   override def children: Seq[LogicalPlan] = child :: Nil
-  override def output: Seq[Attribute] = child.output
+  override def output: Seq[Attribute] = Seq.empty
 
   val numDynamicPartitions = partition.values.count(_.isEmpty)
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 12c667e6e92da..62efda613a176 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -61,7 +61,7 @@ case class InsertIntoHiveTable(
     serializer
   }
 
-  def output: Seq[Attribute] = child.output
+  def output: Seq[Attribute] = Seq.empty
 
   def saveAsHiveFile(
       rdd: RDD[InternalRow],

From d4549fe58fa0d781e0e891bceff893420cb1d598 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Tue, 25 Aug 2015 00:28:51 -0700
Subject: [PATCH 401/633] [SPARK-10214] [SPARKR] [DOCS] Improve SparkR Column,
 DataFrame API docs

cc: shivaram

## Summary

- Add name tags to each methods in DataFrame.R and column.R
- Replace `rdname column` with `rdname {each_func}`. i.e. alias method : `rdname column` =>  `rdname alias`

## Generated PDF File
https://drive.google.com/file/d/0B9biIZIU47lLNHN2aFpnQXlSeGs/view?usp=sharing

## JIRA
[[SPARK-10214] Improve SparkR Column, DataFrame API docs - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10214)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8414 from yu-iskw/SPARK-10214.
---
 R/pkg/R/DataFrame.R | 101 +++++++++++++++++++++++++++++++++++---------
 R/pkg/R/column.R    |  40 ++++++++++++------
 R/pkg/R/generics.R  |   2 +-
 3 files changed, 109 insertions(+), 34 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 895603235011e..10f3c4ea59864 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -27,9 +27,10 @@ setOldClass("jobj")
 #'              \code{jsonFile}, \code{table} etc.
 #' @rdname DataFrame
 #' @seealso jsonFile, table
+#' @docType class
 #'
-#' @param env An R environment that stores bookkeeping states of the DataFrame
-#' @param sdf A Java object reference to the backing Scala DataFrame
+#' @slot env An R environment that stores bookkeeping states of the DataFrame
+#' @slot sdf A Java object reference to the backing Scala DataFrame
 #' @export
 setClass("DataFrame",
          slots = list(env = "environment",
@@ -61,6 +62,7 @@ dataFrame <- function(sdf, isCached = FALSE) {
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname printSchema
+#' @name printSchema
 #' @export
 #' @examples
 #'\dontrun{
@@ -84,6 +86,7 @@ setMethod("printSchema",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname schema
+#' @name schema
 #' @export
 #' @examples
 #'\dontrun{
@@ -106,6 +109,7 @@ setMethod("schema",
 #' @param x A SparkSQL DataFrame
 #' @param extended Logical. If extended is False, explain() only prints the physical plan.
 #' @rdname explain
+#' @name explain
 #' @export
 #' @examples
 #'\dontrun{
@@ -135,6 +139,7 @@ setMethod("explain",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname isLocal
+#' @name isLocal
 #' @export
 #' @examples
 #'\dontrun{
@@ -158,6 +163,7 @@ setMethod("isLocal",
 #' @param numRows The number of rows to print. Defaults to 20.
 #'
 #' @rdname showDF
+#' @name showDF
 #' @export
 #' @examples
 #'\dontrun{
@@ -181,6 +187,7 @@ setMethod("showDF",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname show
+#' @name show
 #' @export
 #' @examples
 #'\dontrun{
@@ -206,6 +213,7 @@ setMethod("show", "DataFrame",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname dtypes
+#' @name dtypes
 #' @export
 #' @examples
 #'\dontrun{
@@ -230,6 +238,8 @@ setMethod("dtypes",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname columns
+#' @name columns
+#' @aliases names
 #' @export
 #' @examples
 #'\dontrun{
@@ -248,7 +258,7 @@ setMethod("columns",
           })
 
 #' @rdname columns
-#' @aliases names,DataFrame,function-method
+#' @name names
 setMethod("names",
           signature(x = "DataFrame"),
           function(x) {
@@ -256,6 +266,7 @@ setMethod("names",
           })
 
 #' @rdname columns
+#' @name names<-
 setMethod("names<-",
           signature(x = "DataFrame"),
           function(x, value) {
@@ -273,6 +284,7 @@ setMethod("names<-",
 #' @param tableName A character vector containing the name of the table
 #'
 #' @rdname registerTempTable
+#' @name registerTempTable
 #' @export
 #' @examples
 #'\dontrun{
@@ -299,6 +311,7 @@ setMethod("registerTempTable",
 #' the existing rows in the table.
 #'
 #' @rdname insertInto
+#' @name insertInto
 #' @export
 #' @examples
 #'\dontrun{
@@ -321,7 +334,8 @@ setMethod("insertInto",
 #'
 #' @param x A SparkSQL DataFrame
 #'
-#' @rdname cache-methods
+#' @rdname cache
+#' @name cache
 #' @export
 #' @examples
 #'\dontrun{
@@ -347,6 +361,7 @@ setMethod("cache",
 #'
 #' @param x The DataFrame to persist
 #' @rdname persist
+#' @name persist
 #' @export
 #' @examples
 #'\dontrun{
@@ -372,6 +387,7 @@ setMethod("persist",
 #' @param x The DataFrame to unpersist
 #' @param blocking Whether to block until all blocks are deleted
 #' @rdname unpersist-methods
+#' @name unpersist
 #' @export
 #' @examples
 #'\dontrun{
@@ -397,6 +413,7 @@ setMethod("unpersist",
 #' @param x A SparkSQL DataFrame
 #' @param numPartitions The number of partitions to use.
 #' @rdname repartition
+#' @name repartition
 #' @export
 #' @examples
 #'\dontrun{
@@ -446,6 +463,7 @@ setMethod("toJSON",
 #' @param x A SparkSQL DataFrame
 #' @param path The directory where the file is saved
 #' @rdname saveAsParquetFile
+#' @name saveAsParquetFile
 #' @export
 #' @examples
 #'\dontrun{
@@ -467,6 +485,7 @@ setMethod("saveAsParquetFile",
 #'
 #' @param x A SparkSQL DataFrame
 #' @rdname distinct
+#' @name distinct
 #' @export
 #' @examples
 #'\dontrun{
@@ -488,7 +507,8 @@ setMethod("distinct",
 #' @description Returns a new DataFrame containing distinct rows in this DataFrame
 #'
 #' @rdname unique
-#' @aliases unique
+#' @name unique
+#' @aliases distinct
 setMethod("unique",
           signature(x = "DataFrame"),
           function(x) {
@@ -526,7 +546,7 @@ setMethod("sample",
           })
 
 #' @rdname sample
-#' @aliases sample
+#' @name sample_frac
 setMethod("sample_frac",
           signature(x = "DataFrame", withReplacement = "logical",
                     fraction = "numeric"),
@@ -541,6 +561,8 @@ setMethod("sample_frac",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname count
+#' @name count
+#' @aliases nrow
 #' @export
 #' @examples
 #'\dontrun{
@@ -574,6 +596,7 @@ setMethod("nrow",
 #' @param x a SparkSQL DataFrame
 #'
 #' @rdname ncol
+#' @name ncol
 #' @export
 #' @examples
 #'\dontrun{
@@ -593,6 +616,7 @@ setMethod("ncol",
 #' @param x a SparkSQL DataFrame
 #'
 #' @rdname dim
+#' @name dim
 #' @export
 #' @examples
 #'\dontrun{
@@ -613,8 +637,8 @@ setMethod("dim",
 #' @param x A SparkSQL DataFrame
 #' @param stringsAsFactors (Optional) A logical indicating whether or not string columns
 #' should be converted to factors. FALSE by default.
-
-#' @rdname collect-methods
+#' @rdname collect
+#' @name collect
 #' @export
 #' @examples
 #'\dontrun{
@@ -650,6 +674,7 @@ setMethod("collect",
 #' @return A new DataFrame containing the number of rows specified.
 #'
 #' @rdname limit
+#' @name limit
 #' @export
 #' @examples
 #' \dontrun{
@@ -669,6 +694,7 @@ setMethod("limit",
 #' Take the first NUM rows of a DataFrame and return a the results as a data.frame
 #'
 #' @rdname take
+#' @name take
 #' @export
 #' @examples
 #'\dontrun{
@@ -696,6 +722,7 @@ setMethod("take",
 #' @return A data.frame
 #'
 #' @rdname head
+#' @name head
 #' @export
 #' @examples
 #'\dontrun{
@@ -717,6 +744,7 @@ setMethod("head",
 #' @param x A SparkSQL DataFrame
 #'
 #' @rdname first
+#' @name first
 #' @export
 #' @examples
 #'\dontrun{
@@ -732,7 +760,7 @@ setMethod("first",
             take(x, 1)
           })
 
-# toRDD()
+# toRDD
 #
 # Converts a Spark DataFrame to an RDD while preserving column names.
 #
@@ -769,6 +797,7 @@ setMethod("toRDD",
 #' @seealso GroupedData
 #' @aliases group_by
 #' @rdname groupBy
+#' @name groupBy
 #' @export
 #' @examples
 #' \dontrun{
@@ -792,7 +821,7 @@ setMethod("groupBy",
            })
 
 #' @rdname groupBy
-#' @aliases group_by
+#' @name group_by
 setMethod("group_by",
           signature(x = "DataFrame"),
           function(x, ...) {
@@ -804,7 +833,8 @@ setMethod("group_by",
 #' Compute aggregates by specifying a list of columns
 #'
 #' @param x a DataFrame
-#' @rdname DataFrame
+#' @rdname agg
+#' @name agg
 #' @aliases summarize
 #' @export
 setMethod("agg",
@@ -813,8 +843,8 @@ setMethod("agg",
             agg(groupBy(x), ...)
           })
 
-#' @rdname DataFrame
-#' @aliases agg
+#' @rdname agg
+#' @name summarize
 setMethod("summarize",
           signature(x = "DataFrame"),
           function(x, ...) {
@@ -890,12 +920,14 @@ getColumn <- function(x, c) {
 }
 
 #' @rdname select
+#' @name $
 setMethod("$", signature(x = "DataFrame"),
           function(x, name) {
             getColumn(x, name)
           })
 
 #' @rdname select
+#' @name $<-
 setMethod("$<-", signature(x = "DataFrame"),
           function(x, name, value) {
             stopifnot(class(value) == "Column" || is.null(value))
@@ -923,6 +955,7 @@ setMethod("$<-", signature(x = "DataFrame"),
           })
 
 #' @rdname select
+#' @name [[
 setMethod("[[", signature(x = "DataFrame"),
           function(x, i) {
             if (is.numeric(i)) {
@@ -933,6 +966,7 @@ setMethod("[[", signature(x = "DataFrame"),
           })
 
 #' @rdname select
+#' @name [
 setMethod("[", signature(x = "DataFrame", i = "missing"),
           function(x, i, j, ...) {
             if (is.numeric(j)) {
@@ -1008,6 +1042,7 @@ setMethod("select",
 #' @param ... Additional expressions
 #' @return A DataFrame
 #' @rdname selectExpr
+#' @name selectExpr
 #' @export
 #' @examples
 #'\dontrun{
@@ -1034,6 +1069,8 @@ setMethod("selectExpr",
 #' @param col A Column expression.
 #' @return A DataFrame with the new column added.
 #' @rdname withColumn
+#' @name withColumn
+#' @aliases mutate
 #' @export
 #' @examples
 #'\dontrun{
@@ -1057,7 +1094,7 @@ setMethod("withColumn",
 #' @param col a named argument of the form name = col
 #' @return A new DataFrame with the new columns added.
 #' @rdname withColumn
-#' @aliases withColumn
+#' @name mutate
 #' @export
 #' @examples
 #'\dontrun{
@@ -1094,6 +1131,7 @@ setMethod("mutate",
 #' @param newCol The new column name.
 #' @return A DataFrame with the column name changed.
 #' @rdname withColumnRenamed
+#' @name withColumnRenamed
 #' @export
 #' @examples
 #'\dontrun{
@@ -1124,6 +1162,7 @@ setMethod("withColumnRenamed",
 #' @param newCol A named pair of the form new_column_name = existing_column
 #' @return A DataFrame with the column name changed.
 #' @rdname withColumnRenamed
+#' @name rename
 #' @aliases withColumnRenamed
 #' @export
 #' @examples
@@ -1165,6 +1204,8 @@ setClassUnion("characterOrColumn", c("character", "Column"))
 #' @param ... Additional sorting fields
 #' @return A DataFrame where all elements are sorted.
 #' @rdname arrange
+#' @name arrange
+#' @aliases orderby
 #' @export
 #' @examples
 #'\dontrun{
@@ -1191,7 +1232,7 @@ setMethod("arrange",
           })
 
 #' @rdname arrange
-#' @aliases orderBy,DataFrame,function-method
+#' @name orderby
 setMethod("orderBy",
           signature(x = "DataFrame", col = "characterOrColumn"),
           function(x, col) {
@@ -1207,6 +1248,7 @@ setMethod("orderBy",
 #' or a string containing a SQL statement
 #' @return A DataFrame containing only the rows that meet the condition.
 #' @rdname filter
+#' @name filter
 #' @export
 #' @examples
 #'\dontrun{
@@ -1228,7 +1270,7 @@ setMethod("filter",
           })
 
 #' @rdname filter
-#' @aliases where,DataFrame,function-method
+#' @name where
 setMethod("where",
           signature(x = "DataFrame", condition = "characterOrColumn"),
           function(x, condition) {
@@ -1247,6 +1289,7 @@ setMethod("where",
 #' 'inner', 'outer', 'left_outer', 'right_outer', 'semijoin'. The default joinType is "inner".
 #' @return A DataFrame containing the result of the join operation.
 #' @rdname join
+#' @name join
 #' @export
 #' @examples
 #'\dontrun{
@@ -1279,8 +1322,9 @@ setMethod("join",
             dataFrame(sdf)
           })
 
-#' rdname merge
-#' aliases join
+#' @rdname merge
+#' @name merge
+#' @aliases join
 setMethod("merge",
           signature(x = "DataFrame", y = "DataFrame"),
           function(x, y, joinExpr = NULL, joinType = NULL, ...) {
@@ -1298,6 +1342,7 @@ setMethod("merge",
 #' @param y A Spark DataFrame
 #' @return A DataFrame containing the result of the union.
 #' @rdname unionAll
+#' @name unionAll
 #' @export
 #' @examples
 #'\dontrun{
@@ -1319,6 +1364,7 @@ setMethod("unionAll",
 #' @description Returns a new DataFrame containing rows of all parameters.
 #
 #' @rdname rbind
+#' @name rbind
 #' @aliases unionAll
 setMethod("rbind",
           signature(... = "DataFrame"),
@@ -1339,6 +1385,7 @@ setMethod("rbind",
 #' @param y A Spark DataFrame
 #' @return A DataFrame containing the result of the intersect.
 #' @rdname intersect
+#' @name intersect
 #' @export
 #' @examples
 #'\dontrun{
@@ -1364,6 +1411,7 @@ setMethod("intersect",
 #' @param y A Spark DataFrame
 #' @return A DataFrame containing the result of the except operation.
 #' @rdname except
+#' @name except
 #' @export
 #' @examples
 #'\dontrun{
@@ -1403,6 +1451,8 @@ setMethod("except",
 #' @param mode One of 'append', 'overwrite', 'error', 'ignore'
 #'
 #' @rdname write.df
+#' @name write.df
+#' @aliases saveDF
 #' @export
 #' @examples
 #'\dontrun{
@@ -1435,7 +1485,7 @@ setMethod("write.df",
           })
 
 #' @rdname write.df
-#' @aliases saveDF
+#' @name saveDF
 #' @export
 setMethod("saveDF",
           signature(df = "DataFrame", path = "character"),
@@ -1466,6 +1516,7 @@ setMethod("saveDF",
 #' @param mode One of 'append', 'overwrite', 'error', 'ignore'
 #'
 #' @rdname saveAsTable
+#' @name saveAsTable
 #' @export
 #' @examples
 #'\dontrun{
@@ -1505,6 +1556,8 @@ setMethod("saveAsTable",
 #' @param ... Additional expressions
 #' @return A DataFrame
 #' @rdname describe
+#' @name describe
+#' @aliases summary
 #' @export
 #' @examples
 #'\dontrun{
@@ -1525,6 +1578,7 @@ setMethod("describe",
           })
 
 #' @rdname describe
+#' @name describe
 setMethod("describe",
           signature(x = "DataFrame"),
           function(x) {
@@ -1538,7 +1592,7 @@ setMethod("describe",
 #' @description Computes statistics for numeric columns of the DataFrame
 #'
 #' @rdname summary
-#' @aliases describe
+#' @name summary
 setMethod("summary",
           signature(x = "DataFrame"),
           function(x) {
@@ -1562,6 +1616,8 @@ setMethod("summary",
 #' @return A DataFrame
 #'
 #' @rdname nafunctions
+#' @name dropna
+#' @aliases na.omit
 #' @export
 #' @examples
 #'\dontrun{
@@ -1588,7 +1644,8 @@ setMethod("dropna",
             dataFrame(sdf)
           })
 
-#' @aliases dropna
+#' @rdname nafunctions
+#' @name na.omit
 #' @export
 setMethod("na.omit",
           signature(x = "DataFrame"),
@@ -1615,6 +1672,7 @@ setMethod("na.omit",
 #' @return A DataFrame
 #'
 #' @rdname nafunctions
+#' @name fillna
 #' @export
 #' @examples
 #'\dontrun{
@@ -1685,6 +1743,7 @@ setMethod("fillna",
 #'         occurrences will have zero as their counts.
 #'
 #' @rdname statfunctions
+#' @name crosstab
 #' @export
 #' @examples
 #' \dontrun{
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index a1f50c383367c..4805096f3f9c5 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -24,10 +24,9 @@ setOldClass("jobj")
 
 #' @title S4 class that represents a DataFrame column
 #' @description The column class supports unary, binary operations on DataFrame columns
-
 #' @rdname column
 #'
-#' @param jc reference to JVM DataFrame column
+#' @slot jc reference to JVM DataFrame column
 #' @export
 setClass("Column",
          slots = list(jc = "jobj"))
@@ -46,6 +45,7 @@ col <- function(x) {
 }
 
 #' @rdname show
+#' @name show
 setMethod("show", "Column",
           function(object) {
             cat("Column", callJMethod(object@jc, "toString"), "\n")
@@ -122,8 +122,11 @@ createMethods()
 #' alias
 #'
 #' Set a new name for a column
-
-#' @rdname column
+#'
+#' @rdname alias
+#' @name alias
+#' @family colum_func
+#' @export
 setMethod("alias",
           signature(object = "Column"),
           function(object, data) {
@@ -138,7 +141,9 @@ setMethod("alias",
 #'
 #' An expression that returns a substring.
 #'
-#' @rdname column
+#' @rdname substr
+#' @name substr
+#' @family colum_func
 #'
 #' @param start starting position
 #' @param stop ending position
@@ -152,7 +157,9 @@ setMethod("substr", signature(x = "Column"),
 #'
 #' Test if the column is between the lower bound and upper bound, inclusive.
 #'
-#' @rdname column
+#' @rdname between
+#' @name between
+#' @family colum_func
 #'
 #' @param bounds lower and upper bounds
 setMethod("between", signature(x = "Column"),
@@ -167,7 +174,9 @@ setMethod("between", signature(x = "Column"),
 
 #' Casts the column to a different data type.
 #'
-#' @rdname column
+#' @rdname cast
+#' @name cast
+#' @family colum_func
 #'
 #' @examples \dontrun{
 #'   cast(df$age, "string")
@@ -189,11 +198,15 @@ setMethod("cast",
 
 #' Match a column with given values.
 #'
-#' @rdname column
+#' @rdname match
+#' @name %in%
+#' @aliases %in%
 #' @return a matched values as a result of comparing with given values.
-#' @examples \dontrun{
-#'   filter(df, "age in (10, 30)")
-#'   where(df, df$age %in% c(10, 30))
+#' @export
+#' @examples
+#' \dontrun{
+#' filter(df, "age in (10, 30)")
+#' where(df, df$age %in% c(10, 30))
 #' }
 setMethod("%in%",
           signature(x = "Column"),
@@ -208,7 +221,10 @@ setMethod("%in%",
 #' If values in the specified column are null, returns the value. 
 #' Can be used in conjunction with `when` to specify a default value for expressions.
 #'
-#' @rdname column
+#' @rdname otherwise
+#' @name otherwise
+#' @family colum_func
+#' @export
 setMethod("otherwise",
           signature(x = "Column", value = "ANY"),
           function(x, value) {
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index 610a8c31223cd..a829d46c1894c 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -441,7 +441,7 @@ setGeneric("filter", function(x, condition) { standardGeneric("filter") })
 #' @export
 setGeneric("group_by", function(x, ...) { standardGeneric("group_by") })
 
-#' @rdname DataFrame
+#' @rdname groupBy
 #' @export
 setGeneric("groupBy", function(x, ...) { standardGeneric("groupBy") })
 

From 57b960bf3706728513f9e089455a533f0244312e Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 25 Aug 2015 08:32:20 +0100
Subject: [PATCH 402/633] [SPARK-6196] [BUILD] Remove MapR profiles in favor of
 hadoop-provided

Follow up to https://github.com/apache/spark/pull/7047

pwendell mentioned that MapR should use `hadoop-provided` now, and indeed the new build script does not produce `mapr3`/`mapr4` artifacts anymore. Hence the action seems to be to remove the profiles, which are now not used.

CC trystanleftwich

Author: Sean Owen <sowen@cloudera.com>

Closes #8338 from srowen/SPARK-6196.
---
 pom.xml | 38 --------------------------------------
 1 file changed, 38 deletions(-)

diff --git a/pom.xml b/pom.xml
index d5945f2546d38..0716016523ee1 100644
--- a/pom.xml
+++ b/pom.xml
@@ -2386,44 +2386,6 @@
       </modules>
     </profile>
 
-    <profile>
-      <id>mapr3</id>
-      <properties>
-        <hadoop.version>1.0.3-mapr-3.0.3</hadoop.version>
-        <yarn.version>2.4.1-mapr-1408</yarn.version>
-        <hbase.version>0.98.4-mapr-1408</hbase.version>
-        <zookeeper.version>3.4.5-mapr-1406</zookeeper.version>
-      </properties>
-    </profile>
-
-    <profile>
-      <id>mapr4</id>
-      <properties>
-        <hadoop.version>2.4.1-mapr-1408</hadoop.version>
-        <yarn.version>2.4.1-mapr-1408</yarn.version>
-        <hbase.version>0.98.4-mapr-1408</hbase.version>
-        <zookeeper.version>3.4.5-mapr-1406</zookeeper.version>
-      </properties>
-      <dependencies>
-        <dependency>
-          <groupId>org.apache.curator</groupId>
-          <artifactId>curator-recipes</artifactId>
-          <version>${curator.version}</version>
-          <exclusions>
-            <exclusion>
-              <groupId>org.apache.zookeeper</groupId>
-              <artifactId>zookeeper</artifactId>
-            </exclusion>
-          </exclusions>
-        </dependency>
-        <dependency>
-          <groupId>org.apache.zookeeper</groupId>
-          <artifactId>zookeeper</artifactId>
-          <version>3.4.5-mapr-1406</version>
-        </dependency>
-      </dependencies>
-    </profile>
-
     <profile>
       <id>hive-thriftserver</id>
       <modules>

From 1fc37581a52530bac5d555dbf14927a5780c3b75 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 25 Aug 2015 00:35:51 -0700
Subject: [PATCH 403/633] [SPARK-10210] [STREAMING] Filter out non-existent
 blocks before creating BlockRDD

When write ahead log is not enabled, a recovered streaming driver still tries to run jobs using pre-failure block ids, and fails as the block do not exists in-memory any more (and cannot be recovered as receiver WAL is not enabled).

This occurs because the driver-side WAL of ReceivedBlockTracker is recovers that past block information, and ReceiveInputDStream creates BlockRDDs even if those blocks do not exist.

The solution in this PR is to filter out block ids that do not exist before creating the BlockRDD. In addition, it adds unit tests to verify other logic in ReceiverInputDStream.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8405 from tdas/SPARK-10210.
---
 .../dstream/ReceiverInputDStream.scala        |  10 +-
 .../rdd/WriteAheadLogBackedBlockRDD.scala     |   2 +-
 .../streaming/ReceiverInputDStreamSuite.scala | 156 ++++++++++++++++++
 3 files changed, 166 insertions(+), 2 deletions(-)
 create mode 100644 streaming/src/test/scala/org/apache/spark/streaming/ReceiverInputDStreamSuite.scala

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index a15800917c6f4..6c139f32da31d 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -116,7 +116,15 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont
             logWarning("Some blocks have Write Ahead Log information; this is unexpected")
           }
         }
-        new BlockRDD[T](ssc.sc, blockIds)
+        val validBlockIds = blockIds.filter { id =>
+          ssc.sparkContext.env.blockManager.master.contains(id)
+        }
+        if (validBlockIds.size != blockIds.size) {
+          logWarning("Some blocks could not be recovered as they were not found in memory. " +
+            "To prevent such data loss, enabled Write Ahead Log (see programming guide " +
+            "for more details.")
+        }
+        new BlockRDD[T](ssc.sc, validBlockIds)
       }
     } else {
       // If no block is ready now, creating WriteAheadLogBackedBlockRDD or BlockRDD
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
index 620b8a36a2baf..e081ffe46f502 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
@@ -75,7 +75,7 @@ private[streaming]
 class WriteAheadLogBackedBlockRDD[T: ClassTag](
     @transient sc: SparkContext,
     @transient blockIds: Array[BlockId],
-    @transient walRecordHandles: Array[WriteAheadLogRecordHandle],
+    @transient val walRecordHandles: Array[WriteAheadLogRecordHandle],
     @transient isBlockIdValid: Array[Boolean] = Array.empty,
     storeInBlockManager: Boolean = false,
     storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER)
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceiverInputDStreamSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverInputDStreamSuite.scala
new file mode 100644
index 0000000000000..6d388d9624d92
--- /dev/null
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceiverInputDStreamSuite.scala
@@ -0,0 +1,156 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.streaming
+
+import scala.util.Random
+
+import org.scalatest.BeforeAndAfterAll
+
+import org.apache.spark.rdd.BlockRDD
+import org.apache.spark.storage.{StorageLevel, StreamBlockId}
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
+import org.apache.spark.streaming.rdd.WriteAheadLogBackedBlockRDD
+import org.apache.spark.streaming.receiver.{BlockManagerBasedStoreResult, Receiver, WriteAheadLogBasedStoreResult}
+import org.apache.spark.streaming.scheduler.ReceivedBlockInfo
+import org.apache.spark.streaming.util.{WriteAheadLogRecordHandle, WriteAheadLogUtils}
+import org.apache.spark.{SparkConf, SparkEnv}
+
+class ReceiverInputDStreamSuite extends TestSuiteBase with BeforeAndAfterAll {
+
+  override def afterAll(): Unit = {
+    StreamingContext.getActive().map { _.stop() }
+  }
+
+  testWithoutWAL("createBlockRDD creates empty BlockRDD when no block info") { receiverStream =>
+    val rdd = receiverStream.createBlockRDD(Time(0), Seq.empty)
+    assert(rdd.isInstanceOf[BlockRDD[_]])
+    assert(!rdd.isInstanceOf[WriteAheadLogBackedBlockRDD[_]])
+    assert(rdd.isEmpty())
+  }
+
+  testWithoutWAL("createBlockRDD creates correct BlockRDD with block info") { receiverStream =>
+    val blockInfos = Seq.fill(5) { createBlockInfo(withWALInfo = false) }
+    val blockIds = blockInfos.map(_.blockId)
+
+    // Verify that there are some blocks that are present, and some that are not
+    require(blockIds.forall(blockId => SparkEnv.get.blockManager.master.contains(blockId)))
+
+    val rdd = receiverStream.createBlockRDD(Time(0), blockInfos)
+    assert(rdd.isInstanceOf[BlockRDD[_]])
+    assert(!rdd.isInstanceOf[WriteAheadLogBackedBlockRDD[_]])
+    val blockRDD = rdd.asInstanceOf[BlockRDD[_]]
+    assert(blockRDD.blockIds.toSeq === blockIds)
+  }
+
+  testWithoutWAL("createBlockRDD filters non-existent blocks before creating BlockRDD") {
+    receiverStream =>
+      val presentBlockInfos = Seq.fill(2)(createBlockInfo(withWALInfo = false, createBlock = true))
+      val absentBlockInfos = Seq.fill(3)(createBlockInfo(withWALInfo = false, createBlock = false))
+      val blockInfos = presentBlockInfos ++ absentBlockInfos
+      val blockIds = blockInfos.map(_.blockId)
+
+      // Verify that there are some blocks that are present, and some that are not
+      require(blockIds.exists(blockId => SparkEnv.get.blockManager.master.contains(blockId)))
+      require(blockIds.exists(blockId => !SparkEnv.get.blockManager.master.contains(blockId)))
+
+      val rdd = receiverStream.createBlockRDD(Time(0), blockInfos)
+      assert(rdd.isInstanceOf[BlockRDD[_]])
+      val blockRDD = rdd.asInstanceOf[BlockRDD[_]]
+      assert(blockRDD.blockIds.toSeq === presentBlockInfos.map { _.blockId})
+  }
+
+  testWithWAL("createBlockRDD creates empty WALBackedBlockRDD when no block info") {
+    receiverStream =>
+      val rdd = receiverStream.createBlockRDD(Time(0), Seq.empty)
+      assert(rdd.isInstanceOf[WriteAheadLogBackedBlockRDD[_]])
+      assert(rdd.isEmpty())
+  }
+
+  testWithWAL(
+    "createBlockRDD creates correct WALBackedBlockRDD with all block info having WAL info") {
+    receiverStream =>
+      val blockInfos = Seq.fill(5) { createBlockInfo(withWALInfo = true) }
+      val blockIds = blockInfos.map(_.blockId)
+      val rdd = receiverStream.createBlockRDD(Time(0), blockInfos)
+      assert(rdd.isInstanceOf[WriteAheadLogBackedBlockRDD[_]])
+      val blockRDD = rdd.asInstanceOf[WriteAheadLogBackedBlockRDD[_]]
+      assert(blockRDD.blockIds.toSeq === blockIds)
+      assert(blockRDD.walRecordHandles.toSeq === blockInfos.map { _.walRecordHandleOption.get })
+  }
+
+  testWithWAL("createBlockRDD creates BlockRDD when some block info dont have WAL info") {
+    receiverStream =>
+      val blockInfos1 = Seq.fill(2) { createBlockInfo(withWALInfo = true) }
+      val blockInfos2 = Seq.fill(3) { createBlockInfo(withWALInfo = false) }
+      val blockInfos = blockInfos1 ++ blockInfos2
+      val blockIds = blockInfos.map(_.blockId)
+      val rdd = receiverStream.createBlockRDD(Time(0), blockInfos)
+      assert(rdd.isInstanceOf[BlockRDD[_]])
+      val blockRDD = rdd.asInstanceOf[BlockRDD[_]]
+      assert(blockRDD.blockIds.toSeq === blockIds)
+  }
+
+
+  private def testWithoutWAL(msg: String)(body: ReceiverInputDStream[_] => Unit): Unit = {
+    test(s"Without WAL enabled: $msg") {
+      runTest(enableWAL = false, body)
+    }
+  }
+
+  private def testWithWAL(msg: String)(body: ReceiverInputDStream[_] => Unit): Unit = {
+    test(s"With WAL enabled: $msg") {
+      runTest(enableWAL = true, body)
+    }
+  }
+
+  private def runTest(enableWAL: Boolean, body: ReceiverInputDStream[_] => Unit): Unit = {
+    val conf = new SparkConf()
+    conf.setMaster("local[4]").setAppName("ReceiverInputDStreamSuite")
+    conf.set(WriteAheadLogUtils.RECEIVER_WAL_ENABLE_CONF_KEY, enableWAL.toString)
+    require(WriteAheadLogUtils.enableReceiverLog(conf) === enableWAL)
+    val ssc = new StreamingContext(conf, Seconds(1))
+    val receiverStream = new ReceiverInputDStream[Int](ssc) {
+      override def getReceiver(): Receiver[Int] = null
+    }
+    withStreamingContext(ssc) { ssc =>
+      body(receiverStream)
+    }
+  }
+
+  /**
+   * Create a block info for input to the ReceiverInputDStream.createBlockRDD
+   * @param withWALInfo Create block with WAL info in it
+   * @param createBlock Actually create the block in the BlockManager
+   * @return
+   */
+  private def createBlockInfo(
+      withWALInfo: Boolean,
+      createBlock: Boolean = true): ReceivedBlockInfo = {
+    val blockId = new StreamBlockId(0, Random.nextLong())
+    if (createBlock) {
+      SparkEnv.get.blockManager.putSingle(blockId, 1, StorageLevel.MEMORY_ONLY, tellMaster = true)
+      require(SparkEnv.get.blockManager.master.contains(blockId))
+    }
+    val storeResult = if (withWALInfo) {
+      new WriteAheadLogBasedStoreResult(blockId, None, new WriteAheadLogRecordHandle { })
+    } else {
+      new BlockManagerBasedStoreResult(blockId, None)
+    }
+    new ReceivedBlockInfo(0, None, None, storeResult)
+  }
+}

From 2f493f7e3924b769160a16f73cccbebf21973b91 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 25 Aug 2015 16:00:44 +0800
Subject: [PATCH 404/633] [SPARK-10177] [SQL] fix reading Timestamp in parquet
 from Hive

We misunderstood the Julian days and nanoseconds of the day in parquet (as TimestampType) from Hive/Impala, they are overlapped, so can't be added together directly.

In order to avoid the confusing rounding when do the converting, we use `2440588` as the Julian Day of epoch of unix timestamp (which should be 2440587.5).

Author: Davies Liu <davies@databricks.com>
Author: Cheng Lian <lian@databricks.com>

Closes #8400 from davies/timestamp_parquet.
---
 .../spark/sql/catalyst/util/DateTimeUtils.scala     |  7 ++++---
 .../sql/catalyst/util/DateTimeUtilsSuite.scala      | 13 +++++++++----
 .../sql/hive/ParquetHiveCompatibilitySuite.scala    |  2 +-
 3 files changed, 14 insertions(+), 8 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index 672620460c3c5..d652fce3fd9b6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -37,7 +37,8 @@ object DateTimeUtils {
   type SQLTimestamp = Long
 
   // see http://stackoverflow.com/questions/466321/convert-unix-timestamp-to-julian
-  final val JULIAN_DAY_OF_EPOCH = 2440587  // and .5
+  // it's 2440587.5, rounding up to compatible with Hive
+  final val JULIAN_DAY_OF_EPOCH = 2440588
   final val SECONDS_PER_DAY = 60 * 60 * 24L
   final val MICROS_PER_SECOND = 1000L * 1000L
   final val NANOS_PER_SECOND = MICROS_PER_SECOND * 1000L
@@ -183,7 +184,7 @@ object DateTimeUtils {
    */
   def fromJulianDay(day: Int, nanoseconds: Long): SQLTimestamp = {
     // use Long to avoid rounding errors
-    val seconds = (day - JULIAN_DAY_OF_EPOCH).toLong * SECONDS_PER_DAY - SECONDS_PER_DAY / 2
+    val seconds = (day - JULIAN_DAY_OF_EPOCH).toLong * SECONDS_PER_DAY
     seconds * MICROS_PER_SECOND + nanoseconds / 1000L
   }
 
@@ -191,7 +192,7 @@ object DateTimeUtils {
    * Returns Julian day and nanoseconds in a day from the number of microseconds
    */
   def toJulianDay(us: SQLTimestamp): (Int, Long) = {
-    val seconds = us / MICROS_PER_SECOND + SECONDS_PER_DAY / 2
+    val seconds = us / MICROS_PER_SECOND
     val day = seconds / SECONDS_PER_DAY + JULIAN_DAY_OF_EPOCH
     val secondsInDay = seconds % SECONDS_PER_DAY
     val nanos = (us % MICROS_PER_SECOND) * 1000L
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index d18fa4df13355..1596bb79fa94b 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -49,13 +49,18 @@ class DateTimeUtilsSuite extends SparkFunSuite {
   test("us and julian day") {
     val (d, ns) = toJulianDay(0)
     assert(d === JULIAN_DAY_OF_EPOCH)
-    assert(ns === SECONDS_PER_DAY / 2 * NANOS_PER_SECOND)
+    assert(ns === 0)
     assert(fromJulianDay(d, ns) == 0L)
 
-    val t = new Timestamp(61394778610000L) // (2015, 6, 11, 10, 10, 10, 100)
+    val t = Timestamp.valueOf("2015-06-11 10:10:10.100")
     val (d1, ns1) = toJulianDay(fromJavaTimestamp(t))
-    val t2 = toJavaTimestamp(fromJulianDay(d1, ns1))
-    assert(t.equals(t2))
+    val t1 = toJavaTimestamp(fromJulianDay(d1, ns1))
+    assert(t.equals(t1))
+
+    val t2 = Timestamp.valueOf("2015-06-11 20:10:10.100")
+    val (d2, ns2) = toJulianDay(fromJavaTimestamp(t2))
+    val t22 = toJavaTimestamp(fromJulianDay(d2, ns2))
+    assert(t2.equals(t22))
   }
 
   test("SPARK-6785: java date conversion before and after epoch") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index bc30180cf0917..91d7a48208e8d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -113,7 +113,7 @@ class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest with Before
       "BOOLEAN", "TINYINT", "SMALLINT", "INT", "BIGINT", "FLOAT", "DOUBLE", "STRING")
   }
 
-  ignore("SPARK-10177 timestamp") {
+  test("SPARK-10177 timestamp") {
     testParquetHiveCompatibility(Row(Timestamp.valueOf("2015-08-24 00:31:00")), "TIMESTAMP")
   }
 

From 7bc9a8c6249300ded31ea931c463d0a8f798e193 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Tue, 25 Aug 2015 01:06:36 -0700
Subject: [PATCH 405/633] [SPARK-10195] [SQL] Data sources Filter should not
 expose internal types

Spark SQL's data sources API exposes Catalyst's internal types through its Filter interfaces. This is a problem because types like UTF8String are not stable developer APIs and should not be exposed to third-parties.

This issue caused incompatibilities when upgrading our `spark-redshift` library to work against Spark 1.5.0.  To avoid these issues in the future we should only expose public types through these Filter objects. This patch accomplishes this by using CatalystTypeConverters to add the appropriate conversions.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8403 from JoshRosen/datasources-internal-vs-external-types.
---
 .../datasources/DataSourceStrategy.scala      | 67 ++++++++++---------
 .../execution/datasources/jdbc/JDBCRDD.scala  |  2 +-
 .../datasources/parquet/ParquetFilters.scala  | 19 +++---
 .../spark/sql/sources/FilteredScanSuite.scala |  7 ++
 4 files changed, 54 insertions(+), 41 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 2a4c40db8bb66..6c1ef6a6df887 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -20,7 +20,8 @@ package org.apache.spark.sql.execution.datasources
 import org.apache.spark.{Logging, TaskContext}
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.{MapPartitionsRDD, RDD, UnionRDD}
-import org.apache.spark.sql.catalyst.{InternalRow, expressions}
+import org.apache.spark.sql.catalyst.CatalystTypeConverters.convertToScala
+import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow, expressions}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
@@ -344,45 +345,47 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
    */
   protected[sql] def selectFilters(filters: Seq[Expression]) = {
     def translate(predicate: Expression): Option[Filter] = predicate match {
-      case expressions.EqualTo(a: Attribute, Literal(v, _)) =>
-        Some(sources.EqualTo(a.name, v))
-      case expressions.EqualTo(Literal(v, _), a: Attribute) =>
-        Some(sources.EqualTo(a.name, v))
-
-      case expressions.EqualNullSafe(a: Attribute, Literal(v, _)) =>
-        Some(sources.EqualNullSafe(a.name, v))
-      case expressions.EqualNullSafe(Literal(v, _), a: Attribute) =>
-        Some(sources.EqualNullSafe(a.name, v))
-
-      case expressions.GreaterThan(a: Attribute, Literal(v, _)) =>
-        Some(sources.GreaterThan(a.name, v))
-      case expressions.GreaterThan(Literal(v, _), a: Attribute) =>
-        Some(sources.LessThan(a.name, v))
-
-      case expressions.LessThan(a: Attribute, Literal(v, _)) =>
-        Some(sources.LessThan(a.name, v))
-      case expressions.LessThan(Literal(v, _), a: Attribute) =>
-        Some(sources.GreaterThan(a.name, v))
-
-      case expressions.GreaterThanOrEqual(a: Attribute, Literal(v, _)) =>
-        Some(sources.GreaterThanOrEqual(a.name, v))
-      case expressions.GreaterThanOrEqual(Literal(v, _), a: Attribute) =>
-        Some(sources.LessThanOrEqual(a.name, v))
-
-      case expressions.LessThanOrEqual(a: Attribute, Literal(v, _)) =>
-        Some(sources.LessThanOrEqual(a.name, v))
-      case expressions.LessThanOrEqual(Literal(v, _), a: Attribute) =>
-        Some(sources.GreaterThanOrEqual(a.name, v))
+      case expressions.EqualTo(a: Attribute, Literal(v, t)) =>
+        Some(sources.EqualTo(a.name, convertToScala(v, t)))
+      case expressions.EqualTo(Literal(v, t), a: Attribute) =>
+        Some(sources.EqualTo(a.name, convertToScala(v, t)))
+
+      case expressions.EqualNullSafe(a: Attribute, Literal(v, t)) =>
+        Some(sources.EqualNullSafe(a.name, convertToScala(v, t)))
+      case expressions.EqualNullSafe(Literal(v, t), a: Attribute) =>
+        Some(sources.EqualNullSafe(a.name, convertToScala(v, t)))
+
+      case expressions.GreaterThan(a: Attribute, Literal(v, t)) =>
+        Some(sources.GreaterThan(a.name, convertToScala(v, t)))
+      case expressions.GreaterThan(Literal(v, t), a: Attribute) =>
+        Some(sources.LessThan(a.name, convertToScala(v, t)))
+
+      case expressions.LessThan(a: Attribute, Literal(v, t)) =>
+        Some(sources.LessThan(a.name, convertToScala(v, t)))
+      case expressions.LessThan(Literal(v, t), a: Attribute) =>
+        Some(sources.GreaterThan(a.name, convertToScala(v, t)))
+
+      case expressions.GreaterThanOrEqual(a: Attribute, Literal(v, t)) =>
+        Some(sources.GreaterThanOrEqual(a.name, convertToScala(v, t)))
+      case expressions.GreaterThanOrEqual(Literal(v, t), a: Attribute) =>
+        Some(sources.LessThanOrEqual(a.name, convertToScala(v, t)))
+
+      case expressions.LessThanOrEqual(a: Attribute, Literal(v, t)) =>
+        Some(sources.LessThanOrEqual(a.name, convertToScala(v, t)))
+      case expressions.LessThanOrEqual(Literal(v, t), a: Attribute) =>
+        Some(sources.GreaterThanOrEqual(a.name, convertToScala(v, t)))
 
       case expressions.InSet(a: Attribute, set) =>
-        Some(sources.In(a.name, set.toArray))
+        val toScala = CatalystTypeConverters.createToScalaConverter(a.dataType)
+        Some(sources.In(a.name, set.toArray.map(toScala)))
 
       // Because we only convert In to InSet in Optimizer when there are more than certain
       // items. So it is possible we still get an In expression here that needs to be pushed
       // down.
       case expressions.In(a: Attribute, list) if !list.exists(!_.isInstanceOf[Literal]) =>
         val hSet = list.map(e => e.eval(EmptyRow))
-        Some(sources.In(a.name, hSet.toArray))
+        val toScala = CatalystTypeConverters.createToScalaConverter(a.dataType)
+        Some(sources.In(a.name, hSet.toArray.map(toScala)))
 
       case expressions.IsNull(a: Attribute) =>
         Some(sources.IsNull(a.name))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
index e537d631f4559..730d88b024cb1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala
@@ -262,7 +262,7 @@ private[sql] class JDBCRDD(
    * Converts value to SQL expression.
    */
   private def compileValue(value: Any): Any = value match {
-    case stringValue: UTF8String => s"'${escapeSql(stringValue.toString)}'"
+    case stringValue: String => s"'${escapeSql(stringValue)}'"
     case _ => value
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
index c74c8388632f5..c6b3fe7900da8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetFilters.scala
@@ -32,7 +32,6 @@ import org.apache.spark.SparkEnv
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.sources
 import org.apache.spark.sql.types._
-import org.apache.spark.unsafe.types.UTF8String
 
 private[sql] object ParquetFilters {
   val PARQUET_FILTER_DATA = "org.apache.spark.sql.parquet.row.filter"
@@ -65,7 +64,7 @@ private[sql] object ParquetFilters {
     case StringType =>
       (n: String, v: Any) => FilterApi.eq(
         binaryColumn(n),
-        Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull)
+        Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[String].getBytes("utf-8"))).orNull)
     case BinaryType =>
       (n: String, v: Any) => FilterApi.eq(
         binaryColumn(n),
@@ -86,7 +85,7 @@ private[sql] object ParquetFilters {
     case StringType =>
       (n: String, v: Any) => FilterApi.notEq(
         binaryColumn(n),
-        Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[UTF8String].getBytes)).orNull)
+        Option(v).map(s => Binary.fromByteArray(s.asInstanceOf[String].getBytes("utf-8"))).orNull)
     case BinaryType =>
       (n: String, v: Any) => FilterApi.notEq(
         binaryColumn(n),
@@ -104,7 +103,8 @@ private[sql] object ParquetFilters {
       (n: String, v: Any) => FilterApi.lt(doubleColumn(n), v.asInstanceOf[java.lang.Double])
     case StringType =>
       (n: String, v: Any) =>
-        FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes))
+        FilterApi.lt(binaryColumn(n),
+          Binary.fromByteArray(v.asInstanceOf[String].getBytes("utf-8")))
     case BinaryType =>
       (n: String, v: Any) =>
         FilterApi.lt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]]))
@@ -121,7 +121,8 @@ private[sql] object ParquetFilters {
       (n: String, v: Any) => FilterApi.ltEq(doubleColumn(n), v.asInstanceOf[java.lang.Double])
     case StringType =>
       (n: String, v: Any) =>
-        FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes))
+        FilterApi.ltEq(binaryColumn(n),
+          Binary.fromByteArray(v.asInstanceOf[String].getBytes("utf-8")))
     case BinaryType =>
       (n: String, v: Any) =>
         FilterApi.ltEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]]))
@@ -138,7 +139,8 @@ private[sql] object ParquetFilters {
       (n: String, v: Any) => FilterApi.gt(doubleColumn(n), v.asInstanceOf[java.lang.Double])
     case StringType =>
       (n: String, v: Any) =>
-        FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes))
+        FilterApi.gt(binaryColumn(n),
+          Binary.fromByteArray(v.asInstanceOf[String].getBytes("utf-8")))
     case BinaryType =>
       (n: String, v: Any) =>
         FilterApi.gt(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]]))
@@ -155,7 +157,8 @@ private[sql] object ParquetFilters {
       (n: String, v: Any) => FilterApi.gtEq(doubleColumn(n), v.asInstanceOf[java.lang.Double])
     case StringType =>
       (n: String, v: Any) =>
-        FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[UTF8String].getBytes))
+        FilterApi.gtEq(binaryColumn(n),
+          Binary.fromByteArray(v.asInstanceOf[String].getBytes("utf-8")))
     case BinaryType =>
       (n: String, v: Any) =>
         FilterApi.gtEq(binaryColumn(n), Binary.fromByteArray(v.asInstanceOf[Array[Byte]]))
@@ -177,7 +180,7 @@ private[sql] object ParquetFilters {
     case StringType =>
       (n: String, v: Set[Any]) =>
         FilterApi.userDefined(binaryColumn(n),
-          SetInFilter(v.map(e => Binary.fromByteArray(e.asInstanceOf[UTF8String].getBytes))))
+          SetInFilter(v.map(s => Binary.fromByteArray(s.asInstanceOf[String].getBytes("utf-8")))))
     case BinaryType =>
       (n: String, v: Set[Any]) =>
         FilterApi.userDefined(binaryColumn(n),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
index c81c3d3982805..68ce37c00077e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/FilteredScanSuite.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.sources
 import scala.language.existentials
 
 import org.apache.spark.rdd.RDD
+import org.apache.spark.unsafe.types.UTF8String
 import org.apache.spark.sql._
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
@@ -78,6 +79,9 @@ case class SimpleFilteredScan(from: Int, to: Int)(@transient val sqlContext: SQL
       case StringStartsWith("c", v) => _.startsWith(v)
       case StringEndsWith("c", v) => _.endsWith(v)
       case StringContains("c", v) => _.contains(v)
+      case EqualTo("c", v: String) => _.equals(v)
+      case EqualTo("c", v: UTF8String) => sys.error("UTF8String should not appear in filters")
+      case In("c", values) => (s: String) => values.map(_.asInstanceOf[String]).toSet.contains(s)
       case _ => (c: String) => true
     }
 
@@ -237,6 +241,9 @@ class FilteredScanSuite extends DataSourceTest with SharedSQLContext {
   testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%eE%'", 1)
   testPushDown("SELECT a, b, c FROM oneToTenFiltered WHERE c like '%Ee%'", 0)
 
+  testPushDown("SELECT c FROM oneToTenFiltered WHERE c = 'aaaaaAAAAA'", 1)
+  testPushDown("SELECT c FROM oneToTenFiltered WHERE c IN ('aaaaaAAAAA', 'foo')", 1)
+
   def testPushDown(sqlString: String, expectedCount: Int): Unit = {
     test(s"PushDown Returns $expectedCount: $sqlString") {
       val queryExecution = sql(sqlString).queryExecution

From 0e6368ffaec1965d0c7f89420e04a974675c7f6e Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Tue, 25 Aug 2015 16:19:34 +0800
Subject: [PATCH 406/633] [SPARK-10197] [SQL] Add null check in wrapperFor
 (inside HiveInspectors).

https://issues.apache.org/jira/browse/SPARK-10197

Author: Yin Huai <yhuai@databricks.com>

Closes #8407 from yhuai/ORCSPARK-10197.
---
 .../spark/sql/hive/HiveInspectors.scala       | 29 +++++++++++++++----
 .../spark/sql/hive/orc/OrcSourceSuite.scala   | 29 +++++++++++++++++++
 2 files changed, 53 insertions(+), 5 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 9824dad239596..64fffdbf9b020 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -370,17 +370,36 @@ private[hive] trait HiveInspectors {
   protected def wrapperFor(oi: ObjectInspector, dataType: DataType): Any => Any = oi match {
     case _: JavaHiveVarcharObjectInspector =>
       (o: Any) =>
-        val s = o.asInstanceOf[UTF8String].toString
-        new HiveVarchar(s, s.size)
+        if (o != null) {
+          val s = o.asInstanceOf[UTF8String].toString
+          new HiveVarchar(s, s.size)
+        } else {
+          null
+        }
 
     case _: JavaHiveDecimalObjectInspector =>
-      (o: Any) => HiveDecimal.create(o.asInstanceOf[Decimal].toJavaBigDecimal)
+      (o: Any) =>
+        if (o != null) {
+          HiveDecimal.create(o.asInstanceOf[Decimal].toJavaBigDecimal)
+        } else {
+          null
+        }
 
     case _: JavaDateObjectInspector =>
-      (o: Any) => DateTimeUtils.toJavaDate(o.asInstanceOf[Int])
+      (o: Any) =>
+        if (o != null) {
+          DateTimeUtils.toJavaDate(o.asInstanceOf[Int])
+        } else {
+          null
+        }
 
     case _: JavaTimestampObjectInspector =>
-      (o: Any) => DateTimeUtils.toJavaTimestamp(o.asInstanceOf[Long])
+      (o: Any) =>
+        if (o != null) {
+          DateTimeUtils.toJavaTimestamp(o.asInstanceOf[Long])
+        } else {
+          null
+        }
 
     case soi: StandardStructObjectInspector =>
       val schema = dataType.asInstanceOf[StructType]
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
index 82e08caf46457..80c38084f293d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
@@ -121,6 +121,35 @@ abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
       sql("SELECT * FROM normal_orc_as_source"),
       (6 to 10).map(i => Row(i, s"part-$i")))
   }
+
+  test("write null values") {
+    sql("DROP TABLE IF EXISTS orcNullValues")
+
+    val df = sql(
+      """
+        |SELECT
+        |  CAST(null as TINYINT),
+        |  CAST(null as SMALLINT),
+        |  CAST(null as INT),
+        |  CAST(null as BIGINT),
+        |  CAST(null as FLOAT),
+        |  CAST(null as DOUBLE),
+        |  CAST(null as DECIMAL(7,2)),
+        |  CAST(null as TIMESTAMP),
+        |  CAST(null as DATE),
+        |  CAST(null as STRING),
+        |  CAST(null as VARCHAR(10))
+        |FROM orc_temp_table limit 1
+      """.stripMargin)
+
+    df.write.format("orc").saveAsTable("orcNullValues")
+
+    checkAnswer(
+      sql("SELECT * FROM orcNullValues"),
+      Row.fromSeq(Seq.fill(11)(null)))
+
+    sql("DROP TABLE IF EXISTS orcNullValues")
+  }
 }
 
 class OrcSourceSuite extends OrcSuite {

From 5c14890159a5711072bf395f662b2433a389edf9 Mon Sep 17 00:00:00 2001
From: "Zhang, Liye" <liye.zhang@intel.com>
Date: Tue, 25 Aug 2015 11:48:55 +0100
Subject: [PATCH 407/633] [DOC] add missing parameters in SparkContext.scala
 for scala doc

Author: Zhang, Liye <liye.zhang@intel.com>

Closes #8412 from liyezhang556520/minorDoc.
---
 .../scala/org/apache/spark/SparkContext.scala     | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 1ddaca8a5ba8c..9849aff85d72e 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -114,6 +114,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * :: DeveloperApi ::
    * Alternative constructor for setting preferred locations where Spark will create executors.
    *
+   * @param config a [[org.apache.spark.SparkConf]] object specifying other Spark parameters
    * @param preferredNodeLocationData used in YARN mode to select nodes to launch containers on.
    * Can be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
    * from a list of input files or InputFormats for the application.
@@ -145,6 +146,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * @param jars Collection of JARs to send to the cluster. These can be paths on the local file
    *             system or HDFS, HTTP, HTTPS, or FTP URLs.
    * @param environment Environment variables to set on worker nodes.
+   * @param preferredNodeLocationData used in YARN mode to select nodes to launch containers on.
+   * Can be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]]
+   * from a list of input files or InputFormats for the application.
    */
   def this(
       master: String,
@@ -841,6 +845,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * @note Small files are preferred, large file is also allowable, but may cause bad performance.
    * @note On some filesystems, `.../path/&#42;` can be a more efficient way to read all files
    *       in a directory rather than `.../path/` or `.../path`
+   *
+   * @param path Directory to the input data files, the path can be comma separated paths as the
+   *             list of inputs.
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
   def wholeTextFiles(
@@ -889,6 +896,9 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * @note Small files are preferred; very large files may cause bad performance.
    * @note On some filesystems, `.../path/&#42;` can be a more efficient way to read all files
    *       in a directory rather than `.../path/` or `.../path`
+   *
+   * @param path Directory to the input data files, the path can be comma separated paths as the
+   *             list of inputs.
    * @param minPartitions A suggestion value of the minimal splitting number for input data.
    */
   @Experimental
@@ -918,8 +928,11 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * '''Note:''' We ensure that the byte array for each record in the resulting RDD
    * has the provided record length.
    *
-   * @param path Directory to the input data files
+   * @param path Directory to the input data files, the path can be comma separated paths as the
+   *             list of inputs.
    * @param recordLength The length at which to split the records
+   * @param conf Configuration for setting up the dataset.
+   *
    * @return An RDD of data with values, represented as byte arrays
    */
   @Experimental

From 7f1e507bf7e82bff323c5dec3c1ee044687c4173 Mon Sep 17 00:00:00 2001
From: ehnalis <zoltan.zvara@gmail.com>
Date: Tue, 25 Aug 2015 12:30:06 +0100
Subject: [PATCH 408/633] Fixed a typo in DAGScheduler.

Author: ehnalis <zoltan.zvara@gmail.com>

Closes #8308 from ehnalis/master.
---
 .../apache/spark/scheduler/DAGScheduler.scala | 27 ++++++++++++++-----
 1 file changed, 20 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 684db6646765f..daf9b0f95273e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -152,17 +152,24 @@ class DAGScheduler(
   // may lead to more delay in scheduling if those locations are busy.
   private[scheduler] val REDUCER_PREF_LOCS_FRACTION = 0.2
 
-  // Called by TaskScheduler to report task's starting.
+  /**
+   * Called by the TaskSetManager to report task's starting.
+   */
   def taskStarted(task: Task[_], taskInfo: TaskInfo) {
     eventProcessLoop.post(BeginEvent(task, taskInfo))
   }
 
-  // Called to report that a task has completed and results are being fetched remotely.
+  /**
+   * Called by the TaskSetManager to report that a task has completed
+   * and results are being fetched remotely.
+   */
   def taskGettingResult(taskInfo: TaskInfo) {
     eventProcessLoop.post(GettingResultEvent(taskInfo))
   }
 
-  // Called by TaskScheduler to report task completions or failures.
+  /**
+   * Called by the TaskSetManager to report task completions or failures.
+   */
   def taskEnded(
       task: Task[_],
       reason: TaskEndReason,
@@ -188,18 +195,24 @@ class DAGScheduler(
       BlockManagerHeartbeat(blockManagerId), new RpcTimeout(600 seconds, "BlockManagerHeartbeat"))
   }
 
-  // Called by TaskScheduler when an executor fails.
+  /**
+   * Called by TaskScheduler implementation when an executor fails.
+   */
   def executorLost(execId: String): Unit = {
     eventProcessLoop.post(ExecutorLost(execId))
   }
 
-  // Called by TaskScheduler when a host is added
+  /**
+   * Called by TaskScheduler implementation when a host is added.
+   */
   def executorAdded(execId: String, host: String): Unit = {
     eventProcessLoop.post(ExecutorAdded(execId, host))
   }
 
-  // Called by TaskScheduler to cancel an entire TaskSet due to either repeated failures or
-  // cancellation of the job itself.
+  /**
+   * Called by the TaskSetManager to cancel an entire TaskSet due to either repeated failures or
+   * cancellation of the job itself.
+   */
   def taskSetFailed(taskSet: TaskSet, reason: String, exception: Option[Throwable]): Unit = {
     eventProcessLoop.post(TaskSetFailed(taskSet, reason, exception))
   }

From 69c9c177160e32a2fbc9b36ecc52156077fca6fc Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 25 Aug 2015 12:33:13 +0100
Subject: [PATCH 409/633] [SPARK-9613] [CORE] Ban use of JavaConversions and
 migrate all existing uses to JavaConverters

Replace `JavaConversions` implicits with `JavaConverters`

Most occurrences I've seen so far are necessary conversions; a few have been avoidable. None are in critical code as far as I see, yet.

Author: Sean Owen <sowen@cloudera.com>

Closes #8033 from srowen/SPARK-9613.
---
 .../shuffle/unsafe/UnsafeShuffleWriter.java   |   4 +-
 .../org/apache/spark/MapOutputTracker.scala   |   4 +-
 .../scala/org/apache/spark/SSLOptions.scala   |  11 +-
 .../scala/org/apache/spark/SparkContext.scala |   4 +-
 .../scala/org/apache/spark/TestUtils.scala    |   9 +-
 .../apache/spark/api/java/JavaHadoopRDD.scala |   4 +-
 .../spark/api/java/JavaNewHadoopRDD.scala     |   4 +-
 .../apache/spark/api/java/JavaPairRDD.scala   |  19 ++-
 .../apache/spark/api/java/JavaRDDLike.scala   |  75 +++++-------
 .../spark/api/java/JavaSparkContext.scala     |  20 ++--
 .../spark/api/python/PythonHadoopUtil.scala   |  28 ++---
 .../apache/spark/api/python/PythonRDD.scala   |  26 ++---
 .../apache/spark/api/python/PythonUtils.scala |  15 ++-
 .../api/python/PythonWorkerFactory.scala      |  11 +-
 .../apache/spark/api/python/SerDeUtil.scala   |   3 +-
 .../WriteInputFormatTestDataGenerator.scala   |   8 +-
 .../scala/org/apache/spark/api/r/RRDD.scala   |  13 ++-
 .../scala/org/apache/spark/api/r/RUtils.scala |   5 +-
 .../scala/org/apache/spark/api/r/SerDe.scala  |   4 +-
 .../spark/broadcast/TorrentBroadcast.scala    |   4 +-
 .../spark/deploy/ExternalShuffleService.scala |   8 +-
 .../apache/spark/deploy/PythonRunner.scala    |   4 +-
 .../apache/spark/deploy/RPackageUtils.scala   |   4 +-
 .../org/apache/spark/deploy/RRunner.scala     |   4 +-
 .../spark/deploy/SparkCuratorUtil.scala       |   4 +-
 .../apache/spark/deploy/SparkHadoopUtil.scala |  19 +--
 .../spark/deploy/SparkSubmitArguments.scala   |   6 +-
 .../master/ZooKeeperPersistenceEngine.scala   |   6 +-
 .../spark/deploy/worker/CommandUtils.scala    |   5 +-
 .../spark/deploy/worker/DriverRunner.scala    |   8 +-
 .../spark/deploy/worker/ExecutorRunner.scala  |   7 +-
 .../apache/spark/deploy/worker/Worker.scala   |   1 -
 .../org/apache/spark/executor/Executor.scala  |   6 +-
 .../spark/executor/ExecutorSource.scala       |   4 +-
 .../spark/executor/MesosExecutorBackend.scala |   6 +-
 .../spark/input/PortableDataStream.scala      |  11 +-
 .../input/WholeTextFileInputFormat.scala      |   8 +-
 .../spark/launcher/WorkerCommandBuilder.scala |   4 +-
 .../apache/spark/metrics/MetricsConfig.scala  |  22 ++--
 .../network/netty/NettyBlockRpcServer.scala   |   4 +-
 .../netty/NettyBlockTransferService.scala     |   6 +-
 .../apache/spark/network/nio/Connection.scala |   4 +-
 .../spark/partial/GroupedCountEvaluator.scala |  10 +-
 .../spark/partial/GroupedMeanEvaluator.scala  |  10 +-
 .../spark/partial/GroupedSumEvaluator.scala   |  10 +-
 .../apache/spark/rdd/PairRDDFunctions.scala   |   6 +-
 .../scala/org/apache/spark/rdd/PipedRDD.scala |   6 +-
 .../org/apache/spark/rdd/SubtractedRDD.scala  |   4 +-
 .../spark/scheduler/InputFormatInfo.scala     |   4 +-
 .../org/apache/spark/scheduler/Pool.scala     |  10 +-
 .../mesos/CoarseMesosSchedulerBackend.scala   |  20 ++--
 .../mesos/MesosClusterPersistenceEngine.scala |   4 +-
 .../cluster/mesos/MesosClusterScheduler.scala |  14 +--
 .../cluster/mesos/MesosSchedulerBackend.scala |  22 ++--
 .../cluster/mesos/MesosSchedulerUtils.scala   |  25 ++--
 .../spark/serializer/KryoSerializer.scala     |  10 +-
 .../shuffle/FileShuffleBlockResolver.scala    |   8 +-
 .../storage/BlockManagerMasterEndpoint.scala  |   8 +-
 .../org/apache/spark/util/AkkaUtils.scala     |   4 +-
 .../org/apache/spark/util/ListenerBus.scala   |   7 +-
 .../spark/util/MutableURLClassLoader.scala    |   2 -
 .../spark/util/TimeStampedHashMap.scala       |  10 +-
 .../spark/util/TimeStampedHashSet.scala       |   4 +-
 .../scala/org/apache/spark/util/Utils.scala   |  20 ++--
 .../apache/spark/util/collection/Utils.scala  |   4 +-
 .../java/org/apache/spark/JavaAPISuite.java   |   6 +-
 .../org/apache/spark/SparkConfSuite.scala     |   7 +-
 .../spark/deploy/LogUrlsStandaloneSuite.scala |   1 -
 .../spark/deploy/RPackageUtilsSuite.scala     |   8 +-
 .../deploy/worker/ExecutorRunnerTest.scala    |   5 +-
 .../spark/scheduler/SparkListenerSuite.scala  |   9 +-
 .../mesos/MesosSchedulerBackendSuite.scala    |  15 +--
 .../serializer/KryoSerializerSuite.scala      |   3 +-
 .../org/apache/spark/ui/UISeleniumSuite.scala |  21 ++--
 .../spark/examples/CassandraCQLTest.scala     |  15 +--
 .../apache/spark/examples/CassandraTest.scala |   6 +-
 .../spark/examples/DriverSubmissionTest.scala |   6 +-
 .../pythonconverters/AvroConverters.scala     |  16 +--
 .../CassandraConverters.scala                 |  14 ++-
 .../pythonconverters/HBaseConverters.scala    |   5 +-
 .../streaming/flume/sink/SparkSinkSuite.scala |   4 +-
 .../streaming/flume/EventTransformer.scala    |   4 +-
 .../streaming/flume/FlumeBatchFetcher.scala   |   3 +-
 .../streaming/flume/FlumeInputDStream.scala   |   7 +-
 .../flume/FlumePollingInputDStream.scala      |   6 +-
 .../streaming/flume/FlumeTestUtils.scala      |  10 +-
 .../spark/streaming/flume/FlumeUtils.scala    |   8 +-
 .../flume/PollingFlumeTestUtils.scala         |  16 ++-
 .../flume/FlumePollingStreamSuite.scala       |   8 +-
 .../streaming/flume/FlumeStreamSuite.scala    |   2 +-
 .../streaming/kafka/KafkaTestUtils.scala      |   4 +-
 .../spark/streaming/kafka/KafkaUtils.scala    |  35 +++---
 .../spark/streaming/zeromq/ZeroMQUtils.scala  |  15 ++-
 .../kinesis/KinesisBackedBlockRDD.scala       |   4 +-
 .../streaming/kinesis/KinesisReceiver.scala   |   4 +-
 .../streaming/kinesis/KinesisTestUtils.scala  |   3 +-
 .../kinesis/KinesisReceiverSuite.scala        |  12 +-
 .../mllib/util/LinearDataGenerator.scala      |   4 +-
 .../ml/classification/JavaOneVsRestSuite.java |   7 +-
 .../LogisticRegressionSuite.scala             |   4 +-
 .../spark/mllib/classification/SVMSuite.scala |   4 +-
 .../optimization/GradientDescentSuite.scala   |   4 +-
 .../spark/mllib/recommendation/ALSSuite.scala |   4 +-
 project/SparkBuild.scala                      |   8 +-
 python/pyspark/sql/column.py                  |  12 ++
 python/pyspark/sql/dataframe.py               |   4 +-
 scalastyle-config.xml                         |   7 ++
 .../main/scala/org/apache/spark/sql/Row.scala |  12 +-
 .../spark/sql/catalyst/analysis/Catalog.scala |   4 +-
 .../spark/sql/DataFrameNaFunctions.scala      |   8 +-
 .../apache/spark/sql/DataFrameReader.scala    |   4 +-
 .../apache/spark/sql/DataFrameWriter.scala    |   4 +-
 .../org/apache/spark/sql/GroupedData.scala    |   4 +-
 .../scala/org/apache/spark/sql/SQLConf.scala  |  13 ++-
 .../org/apache/spark/sql/SQLContext.scala     |   8 +-
 .../datasources/ResolvedDataSource.scala      |   4 +-
 .../parquet/CatalystReadSupport.scala         |   8 +-
 .../parquet/CatalystRowConverter.scala        |   4 +-
 .../parquet/CatalystSchemaConverter.scala     |   4 +-
 .../datasources/parquet/ParquetRelation.scala |  13 ++-
 .../parquet/ParquetTypesConverter.scala       |   4 +-
 .../joins/ShuffledHashOuterJoin.scala         |   6 +-
 .../spark/sql/execution/pythonUDFs.scala      |  11 +-
 .../apache/spark/sql/JavaDataFrameSuite.java  |   8 +-
 .../spark/sql/DataFrameNaFunctionsSuite.scala |   6 +-
 .../org/apache/spark/sql/QueryTest.scala      |   4 +-
 .../ParquetAvroCompatibilitySuite.scala       |   3 +-
 .../parquet/ParquetCompatibilityTest.scala    |   7 +-
 .../datasources/parquet/ParquetIOSuite.scala  |  25 ++--
 .../SparkExecuteStatementOperation.scala      |  10 +-
 .../hive/thriftserver/SparkSQLCLIDriver.scala |  16 +--
 .../thriftserver/SparkSQLCLIService.scala     |   6 +-
 .../hive/thriftserver/SparkSQLDriver.scala    |  14 +--
 .../sql/hive/thriftserver/SparkSQLEnv.scala   |   4 +-
 .../apache/spark/sql/hive/HiveContext.scala   |   4 +-
 .../spark/sql/hive/HiveInspectors.scala       |  40 +++----
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  12 +-
 .../org/apache/spark/sql/hive/HiveQl.scala    | 110 ++++++++++--------
 .../org/apache/spark/sql/hive/HiveShim.scala  |   5 +-
 .../spark/sql/hive/client/ClientWrapper.scala |  27 ++---
 .../spark/sql/hive/client/HiveShim.scala      |  14 +--
 .../execution/DescribeHiveTableCommand.scala  |   8 +-
 .../sql/hive/execution/HiveTableScan.scala    |   9 +-
 .../hive/execution/InsertIntoHiveTable.scala  |  12 +-
 .../hive/execution/ScriptTransformation.scala |  12 +-
 .../org/apache/spark/sql/hive/hiveUDFs.scala  |   9 +-
 .../spark/sql/hive/orc/OrcRelation.scala      |   8 +-
 .../apache/spark/sql/hive/test/TestHive.scala |  11 +-
 .../spark/sql/hive/client/FiltersSuite.scala  |   4 +-
 .../sql/hive/execution/HiveUDFSuite.scala     |  29 +++--
 .../sql/hive/execution/PruningSuite.scala     |   7 +-
 .../sql/hive/execution/SQLQuerySuite.scala    |   4 +-
 .../sql/sources/hadoopFsRelationSuites.scala  |   8 +-
 .../streaming/api/java/JavaDStreamLike.scala  |  12 +-
 .../streaming/api/java/JavaPairDStream.scala  |  28 ++---
 .../api/java/JavaStreamingContext.scala       |  32 ++---
 .../streaming/api/python/PythonDStream.scala  |   5 +-
 .../spark/streaming/receiver/Receiver.scala   |   6 +-
 .../streaming/scheduler/JobScheduler.scala    |   4 +-
 .../scheduler/ReceivedBlockTracker.scala      |   4 +-
 .../util/FileBasedWriteAheadLog.scala         |   4 +-
 .../spark/streaming/JavaTestUtils.scala       |  24 ++--
 .../streaming/util/WriteAheadLogSuite.scala   |   4 +-
 .../spark/tools/GenerateMIMAIgnore.scala      |   6 +-
 .../org/apache/spark/deploy/yarn/Client.scala |  13 ++-
 .../spark/deploy/yarn/ExecutorRunnable.scala  |  24 ++--
 .../spark/deploy/yarn/YarnAllocator.scala     |  19 ++-
 .../spark/deploy/yarn/YarnRMClient.scala      |   8 +-
 .../deploy/yarn/BaseYarnClusterSuite.scala    |   6 +-
 .../spark/deploy/yarn/ClientSuite.scala       |   8 +-
 .../spark/deploy/yarn/YarnClusterSuite.scala  |   5 +-
 171 files changed, 863 insertions(+), 880 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
index 2389c28b28395..fdb309e365f69 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriter.java
@@ -24,7 +24,7 @@
 
 import scala.Option;
 import scala.Product2;
-import scala.collection.JavaConversions;
+import scala.collection.JavaConverters;
 import scala.collection.immutable.Map;
 import scala.reflect.ClassTag;
 import scala.reflect.ClassTag$;
@@ -160,7 +160,7 @@ public long getPeakMemoryUsedBytes() {
    */
   @VisibleForTesting
   public void write(Iterator<Product2<K, V>> records) throws IOException {
-    write(JavaConversions.asScalaIterator(records));
+    write(JavaConverters.asScalaIteratorConverter(records).asScala());
   }
 
   @Override
diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
index 92218832d256f..a387592783850 100644
--- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
+++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
@@ -21,8 +21,8 @@ import java.io._
 import java.util.concurrent.ConcurrentHashMap
 import java.util.zip.{GZIPInputStream, GZIPOutputStream}
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, Map}
-import scala.collection.JavaConversions._
 import scala.reflect.ClassTag
 
 import org.apache.spark.rpc.{RpcEndpointRef, RpcEnv, RpcCallContext, RpcEndpoint}
@@ -398,7 +398,7 @@ private[spark] class MapOutputTrackerMaster(conf: SparkConf)
  */
 private[spark] class MapOutputTrackerWorker(conf: SparkConf) extends MapOutputTracker(conf) {
   protected val mapStatuses: Map[Int, Array[MapStatus]] =
-    new ConcurrentHashMap[Int, Array[MapStatus]]
+    new ConcurrentHashMap[Int, Array[MapStatus]]().asScala
 }
 
 private[spark] object MapOutputTracker extends Logging {
diff --git a/core/src/main/scala/org/apache/spark/SSLOptions.scala b/core/src/main/scala/org/apache/spark/SSLOptions.scala
index 32df42d57dbd6..3b9c885bf97a7 100644
--- a/core/src/main/scala/org/apache/spark/SSLOptions.scala
+++ b/core/src/main/scala/org/apache/spark/SSLOptions.scala
@@ -17,9 +17,11 @@
 
 package org.apache.spark
 
-import java.io.{File, FileInputStream}
-import java.security.{KeyStore, NoSuchAlgorithmException}
-import javax.net.ssl.{KeyManager, KeyManagerFactory, SSLContext, TrustManager, TrustManagerFactory}
+import java.io.File
+import java.security.NoSuchAlgorithmException
+import javax.net.ssl.SSLContext
+
+import scala.collection.JavaConverters._
 
 import com.typesafe.config.{Config, ConfigFactory, ConfigValueFactory}
 import org.eclipse.jetty.util.ssl.SslContextFactory
@@ -79,7 +81,6 @@ private[spark] case class SSLOptions(
    * object. It can be used then to compose the ultimate Akka configuration.
    */
   def createAkkaConfig: Option[Config] = {
-    import scala.collection.JavaConversions._
     if (enabled) {
       Some(ConfigFactory.empty()
         .withValue("akka.remote.netty.tcp.security.key-store",
@@ -97,7 +98,7 @@ private[spark] case class SSLOptions(
         .withValue("akka.remote.netty.tcp.security.protocol",
           ConfigValueFactory.fromAnyRef(protocol.getOrElse("")))
         .withValue("akka.remote.netty.tcp.security.enabled-algorithms",
-          ConfigValueFactory.fromIterable(supportedAlgorithms.toSeq))
+          ConfigValueFactory.fromIterable(supportedAlgorithms.asJava))
         .withValue("akka.remote.netty.tcp.enable-ssl",
           ConfigValueFactory.fromAnyRef(true)))
     } else {
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 9849aff85d72e..f3da04a7f55d0 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -26,8 +26,8 @@ import java.util.{Arrays, Properties, UUID}
 import java.util.concurrent.atomic.{AtomicReference, AtomicBoolean, AtomicInteger}
 import java.util.UUID.randomUUID
 
+import scala.collection.JavaConverters._
 import scala.collection.{Map, Set}
-import scala.collection.JavaConversions._
 import scala.collection.generic.Growable
 import scala.collection.mutable.HashMap
 import scala.reflect.{ClassTag, classTag}
@@ -1546,7 +1546,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
   def getAllPools: Seq[Schedulable] = {
     assertNotStopped()
     // TODO(xiajunluan): We should take nested pools into account
-    taskScheduler.rootPool.schedulableQueue.toSeq
+    taskScheduler.rootPool.schedulableQueue.asScala.toSeq
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/TestUtils.scala b/core/src/main/scala/org/apache/spark/TestUtils.scala
index a1ebbecf93b7b..888763a3e8ebf 100644
--- a/core/src/main/scala/org/apache/spark/TestUtils.scala
+++ b/core/src/main/scala/org/apache/spark/TestUtils.scala
@@ -19,11 +19,12 @@ package org.apache.spark
 
 import java.io.{ByteArrayInputStream, File, FileInputStream, FileOutputStream}
 import java.net.{URI, URL}
+import java.nio.charset.StandardCharsets
+import java.util.Arrays
 import java.util.jar.{JarEntry, JarOutputStream}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
-import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.{ByteStreams, Files}
 import javax.tools.{JavaFileObject, SimpleJavaFileObject, ToolProvider}
 
@@ -71,7 +72,7 @@ private[spark] object TestUtils {
     files.foreach { case (k, v) =>
       val entry = new JarEntry(k)
       jarStream.putNextEntry(entry)
-      ByteStreams.copy(new ByteArrayInputStream(v.getBytes(UTF_8)), jarStream)
+      ByteStreams.copy(new ByteArrayInputStream(v.getBytes(StandardCharsets.UTF_8)), jarStream)
     }
     jarStream.close()
     jarFile.toURI.toURL
@@ -125,7 +126,7 @@ private[spark] object TestUtils {
     } else {
       Seq()
     }
-    compiler.getTask(null, null, null, options, null, Seq(sourceFile)).call()
+    compiler.getTask(null, null, null, options.asJava, null, Arrays.asList(sourceFile)).call()
 
     val fileName = className + ".class"
     val result = new File(fileName)
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaHadoopRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaHadoopRDD.scala
index 0ae0b4ec042e2..891bcddeac286 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaHadoopRDD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.api.java
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.hadoop.mapred.InputSplit
@@ -37,7 +37,7 @@ class JavaHadoopRDD[K, V](rdd: HadoopRDD[K, V])
   def mapPartitionsWithInputSplit[R](
       f: JFunction2[InputSplit, java.util.Iterator[(K, V)], java.util.Iterator[R]],
       preservesPartitioning: Boolean = false): JavaRDD[R] = {
-    new JavaRDD(rdd.mapPartitionsWithInputSplit((a, b) => f.call(a, asJavaIterator(b)),
+    new JavaRDD(rdd.mapPartitionsWithInputSplit((a, b) => f.call(a, b.asJava).asScala,
       preservesPartitioning)(fakeClassTag))(fakeClassTag)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaNewHadoopRDD.scala
index ec4f3964d75e0..0f49279f3e647 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaNewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaNewHadoopRDD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.api.java
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.hadoop.mapreduce.InputSplit
@@ -37,7 +37,7 @@ class JavaNewHadoopRDD[K, V](rdd: NewHadoopRDD[K, V])
   def mapPartitionsWithInputSplit[R](
       f: JFunction2[InputSplit, java.util.Iterator[(K, V)], java.util.Iterator[R]],
       preservesPartitioning: Boolean = false): JavaRDD[R] = {
-    new JavaRDD(rdd.mapPartitionsWithInputSplit((a, b) => f.call(a, asJavaIterator(b)),
+    new JavaRDD(rdd.mapPartitionsWithInputSplit((a, b) => f.call(a, b.asJava).asScala,
       preservesPartitioning)(fakeClassTag))(fakeClassTag)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index 8441bb3a3047e..fb787979c1820 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -20,7 +20,7 @@ package org.apache.spark.api.java
 import java.util.{Comparator, List => JList, Map => JMap}
 import java.lang.{Iterable => JIterable}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
@@ -142,7 +142,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   def sampleByKey(withReplacement: Boolean,
       fractions: JMap[K, Double],
       seed: Long): JavaPairRDD[K, V] =
-    new JavaPairRDD[K, V](rdd.sampleByKey(withReplacement, fractions, seed))
+    new JavaPairRDD[K, V](rdd.sampleByKey(withReplacement, fractions.asScala, seed))
 
   /**
    * Return a subset of this RDD sampled by key (via stratified sampling).
@@ -173,7 +173,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
   def sampleByKeyExact(withReplacement: Boolean,
       fractions: JMap[K, Double],
       seed: Long): JavaPairRDD[K, V] =
-    new JavaPairRDD[K, V](rdd.sampleByKeyExact(withReplacement, fractions, seed))
+    new JavaPairRDD[K, V](rdd.sampleByKeyExact(withReplacement, fractions.asScala, seed))
 
   /**
    * ::Experimental::
@@ -768,7 +768,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
    * Return the list of values in the RDD for key `key`. This operation is done efficiently if the
    * RDD has a known partitioner by only searching the partition that the key maps to.
    */
-  def lookup(key: K): JList[V] = seqAsJavaList(rdd.lookup(key))
+  def lookup(key: K): JList[V] = rdd.lookup(key).asJava
 
   /** Output the RDD to any Hadoop-supported file system. */
   def saveAsHadoopFile[F <: OutputFormat[_, _]](
@@ -987,30 +987,27 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
 object JavaPairRDD {
   private[spark]
   def groupByResultToJava[K: ClassTag, T](rdd: RDD[(K, Iterable[T])]): RDD[(K, JIterable[T])] = {
-    rddToPairRDDFunctions(rdd).mapValues(asJavaIterable)
+    rddToPairRDDFunctions(rdd).mapValues(_.asJava)
   }
 
   private[spark]
   def cogroupResultToJava[K: ClassTag, V, W](
       rdd: RDD[(K, (Iterable[V], Iterable[W]))]): RDD[(K, (JIterable[V], JIterable[W]))] = {
-    rddToPairRDDFunctions(rdd).mapValues(x => (asJavaIterable(x._1), asJavaIterable(x._2)))
+    rddToPairRDDFunctions(rdd).mapValues(x => (x._1.asJava, x._2.asJava))
   }
 
   private[spark]
   def cogroupResult2ToJava[K: ClassTag, V, W1, W2](
       rdd: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2]))])
       : RDD[(K, (JIterable[V], JIterable[W1], JIterable[W2]))] = {
-    rddToPairRDDFunctions(rdd)
-      .mapValues(x => (asJavaIterable(x._1), asJavaIterable(x._2), asJavaIterable(x._3)))
+    rddToPairRDDFunctions(rdd).mapValues(x => (x._1.asJava, x._2.asJava, x._3.asJava))
   }
 
   private[spark]
   def cogroupResult3ToJava[K: ClassTag, V, W1, W2, W3](
       rdd: RDD[(K, (Iterable[V], Iterable[W1], Iterable[W2], Iterable[W3]))])
   : RDD[(K, (JIterable[V], JIterable[W1], JIterable[W2], JIterable[W3]))] = {
-    rddToPairRDDFunctions(rdd)
-      .mapValues(x =>
-        (asJavaIterable(x._1), asJavaIterable(x._2), asJavaIterable(x._3), asJavaIterable(x._4)))
+    rddToPairRDDFunctions(rdd).mapValues(x => (x._1.asJava, x._2.asJava, x._3.asJava, x._4.asJava))
   }
 
   def fromRDD[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]): JavaPairRDD[K, V] = {
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
index c582488f16fe7..fc817cdd6a3f8 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala
@@ -21,7 +21,6 @@ import java.{lang => jl}
 import java.lang.{Iterable => JIterable, Long => JLong}
 import java.util.{Comparator, List => JList, Iterator => JIterator}
 
-import scala.collection.JavaConversions._
 import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
@@ -59,10 +58,10 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def rdd: RDD[T]
 
   @deprecated("Use partitions() instead.", "1.1.0")
-  def splits: JList[Partition] = new java.util.ArrayList(rdd.partitions.toSeq)
+  def splits: JList[Partition] = rdd.partitions.toSeq.asJava
 
   /** Set of partitions in this RDD. */
-  def partitions: JList[Partition] = new java.util.ArrayList(rdd.partitions.toSeq)
+  def partitions: JList[Partition] = rdd.partitions.toSeq.asJava
 
   /** The partitioner of this RDD. */
   def partitioner: Optional[Partitioner] = JavaUtils.optionToOptional(rdd.partitioner)
@@ -82,7 +81,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * subclasses of RDD.
    */
   def iterator(split: Partition, taskContext: TaskContext): java.util.Iterator[T] =
-    asJavaIterator(rdd.iterator(split, taskContext))
+    rdd.iterator(split, taskContext).asJava
 
   // Transformations (return a new RDD)
 
@@ -99,7 +98,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def mapPartitionsWithIndex[R](
       f: JFunction2[jl.Integer, java.util.Iterator[T], java.util.Iterator[R]],
       preservesPartitioning: Boolean = false): JavaRDD[R] =
-    new JavaRDD(rdd.mapPartitionsWithIndex(((a, b) => f(a, asJavaIterator(b))),
+    new JavaRDD(rdd.mapPartitionsWithIndex((a, b) => f.call(a, b.asJava).asScala,
         preservesPartitioning)(fakeClassTag))(fakeClassTag)
 
   /**
@@ -153,7 +152,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    */
   def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U]): JavaRDD[U] = {
     def fn: (Iterator[T]) => Iterator[U] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     JavaRDD.fromRDD(rdd.mapPartitions(fn)(fakeClassTag[U]))(fakeClassTag[U])
   }
@@ -164,7 +163,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U],
       preservesPartitioning: Boolean): JavaRDD[U] = {
     def fn: (Iterator[T]) => Iterator[U] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     JavaRDD.fromRDD(
       rdd.mapPartitions(fn, preservesPartitioning)(fakeClassTag[U]))(fakeClassTag[U])
@@ -175,7 +174,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    */
   def mapPartitionsToDouble(f: DoubleFlatMapFunction[java.util.Iterator[T]]): JavaDoubleRDD = {
     def fn: (Iterator[T]) => Iterator[jl.Double] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     new JavaDoubleRDD(rdd.mapPartitions(fn).map((x: jl.Double) => x.doubleValue()))
   }
@@ -186,7 +185,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2]):
   JavaPairRDD[K2, V2] = {
     def fn: (Iterator[T]) => Iterator[(K2, V2)] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     JavaPairRDD.fromRDD(rdd.mapPartitions(fn))(fakeClassTag[K2], fakeClassTag[V2])
   }
@@ -197,7 +196,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def mapPartitionsToDouble(f: DoubleFlatMapFunction[java.util.Iterator[T]],
       preservesPartitioning: Boolean): JavaDoubleRDD = {
     def fn: (Iterator[T]) => Iterator[jl.Double] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     new JavaDoubleRDD(rdd.mapPartitions(fn, preservesPartitioning)
       .map(x => x.doubleValue()))
@@ -209,7 +208,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2],
       preservesPartitioning: Boolean): JavaPairRDD[K2, V2] = {
     def fn: (Iterator[T]) => Iterator[(K2, V2)] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     JavaPairRDD.fromRDD(
       rdd.mapPartitions(fn, preservesPartitioning))(fakeClassTag[K2], fakeClassTag[V2])
@@ -219,14 +218,14 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * Applies a function f to each partition of this RDD.
    */
   def foreachPartition(f: VoidFunction[java.util.Iterator[T]]) {
-    rdd.foreachPartition((x => f.call(asJavaIterator(x))))
+    rdd.foreachPartition((x => f.call(x.asJava)))
   }
 
   /**
    * Return an RDD created by coalescing all elements within each partition into an array.
    */
   def glom(): JavaRDD[JList[T]] =
-    new JavaRDD(rdd.glom().map(x => new java.util.ArrayList[T](x.toSeq)))
+    new JavaRDD(rdd.glom().map(_.toSeq.asJava))
 
   /**
    * Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of
@@ -266,13 +265,13 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * Return an RDD created by piping elements to a forked external process.
    */
   def pipe(command: JList[String]): JavaRDD[String] =
-    rdd.pipe(asScalaBuffer(command))
+    rdd.pipe(command.asScala)
 
   /**
    * Return an RDD created by piping elements to a forked external process.
    */
   def pipe(command: JList[String], env: java.util.Map[String, String]): JavaRDD[String] =
-    rdd.pipe(asScalaBuffer(command), mapAsScalaMap(env))
+    rdd.pipe(command.asScala, env.asScala)
 
   /**
    * Zips this RDD with another one, returning key-value pairs with the first element in each RDD,
@@ -294,8 +293,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
       other: JavaRDDLike[U, _],
       f: FlatMapFunction2[java.util.Iterator[T], java.util.Iterator[U], V]): JavaRDD[V] = {
     def fn: (Iterator[T], Iterator[U]) => Iterator[V] = {
-      (x: Iterator[T], y: Iterator[U]) => asScalaIterator(
-        f.call(asJavaIterator(x), asJavaIterator(y)).iterator())
+      (x: Iterator[T], y: Iterator[U]) => f.call(x.asJava, y.asJava).iterator().asScala
     }
     JavaRDD.fromRDD(
       rdd.zipPartitions(other.rdd)(fn)(other.classTag, fakeClassTag[V]))(fakeClassTag[V])
@@ -333,22 +331,16 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   /**
    * Return an array that contains all of the elements in this RDD.
    */
-  def collect(): JList[T] = {
-    import scala.collection.JavaConversions._
-    val arr: java.util.Collection[T] = rdd.collect().toSeq
-    new java.util.ArrayList(arr)
-  }
+  def collect(): JList[T] =
+    rdd.collect().toSeq.asJava
 
   /**
    * Return an iterator that contains all of the elements in this RDD.
    *
    * The iterator will consume as much memory as the largest partition in this RDD.
    */
-  def toLocalIterator(): JIterator[T] = {
-     import scala.collection.JavaConversions._
-     rdd.toLocalIterator
-  }
-
+  def toLocalIterator(): JIterator[T] =
+     asJavaIteratorConverter(rdd.toLocalIterator).asJava
 
   /**
    * Return an array that contains all of the elements in this RDD.
@@ -363,9 +355,8 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
   def collectPartitions(partitionIds: Array[Int]): Array[JList[T]] = {
     // This is useful for implementing `take` from other language frontends
     // like Python where the data is serialized.
-    import scala.collection.JavaConversions._
     val res = context.runJob(rdd, (it: Iterator[T]) => it.toArray, partitionIds)
-    res.map(x => new java.util.ArrayList(x.toSeq)).toArray
+    res.map(_.toSeq.asJava)
   }
 
   /**
@@ -489,20 +480,14 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * it will be slow if a lot of partitions are required. In that case, use collect() to get the
    * whole RDD instead.
    */
-  def take(num: Int): JList[T] = {
-    import scala.collection.JavaConversions._
-    val arr: java.util.Collection[T] = rdd.take(num).toSeq
-    new java.util.ArrayList(arr)
-  }
+  def take(num: Int): JList[T] =
+    rdd.take(num).toSeq.asJava
 
   def takeSample(withReplacement: Boolean, num: Int): JList[T] =
     takeSample(withReplacement, num, Utils.random.nextLong)
 
-  def takeSample(withReplacement: Boolean, num: Int, seed: Long): JList[T] = {
-    import scala.collection.JavaConversions._
-    val arr: java.util.Collection[T] = rdd.takeSample(withReplacement, num, seed).toSeq
-    new java.util.ArrayList(arr)
-  }
+  def takeSample(withReplacement: Boolean, num: Int, seed: Long): JList[T] =
+    rdd.takeSample(withReplacement, num, seed).toSeq.asJava
 
   /**
    * Return the first element in this RDD.
@@ -582,10 +567,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * @return an array of top elements
    */
   def top(num: Int, comp: Comparator[T]): JList[T] = {
-    import scala.collection.JavaConversions._
-    val topElems = rdd.top(num)(Ordering.comparatorToOrdering(comp))
-    val arr: java.util.Collection[T] = topElems.toSeq
-    new java.util.ArrayList(arr)
+    rdd.top(num)(Ordering.comparatorToOrdering(comp)).toSeq.asJava
   }
 
   /**
@@ -607,10 +589,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * @return an array of top elements
    */
   def takeOrdered(num: Int, comp: Comparator[T]): JList[T] = {
-    import scala.collection.JavaConversions._
-    val topElems = rdd.takeOrdered(num)(Ordering.comparatorToOrdering(comp))
-    val arr: java.util.Collection[T] = topElems.toSeq
-    new java.util.ArrayList(arr)
+    rdd.takeOrdered(num)(Ordering.comparatorToOrdering(comp)).toSeq.asJava
   }
 
   /**
@@ -696,7 +675,7 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
    * applies a function f to each partition of this RDD.
    */
   def foreachPartitionAsync(f: VoidFunction[java.util.Iterator[T]]): JavaFutureAction[Void] = {
-    new JavaFutureActionWrapper[Unit, Void](rdd.foreachPartitionAsync(x => f.call(x)),
+    new JavaFutureActionWrapper[Unit, Void](rdd.foreachPartitionAsync(x => f.call(x.asJava)),
       { x => null.asInstanceOf[Void] })
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
index 02e49a853c5f7..609496ccdfef1 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala
@@ -21,8 +21,7 @@ import java.io.Closeable
 import java.util
 import java.util.{Map => JMap}
 
-import scala.collection.JavaConversions
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
@@ -104,7 +103,7 @@ class JavaSparkContext(val sc: SparkContext)
    */
   def this(master: String, appName: String, sparkHome: String, jars: Array[String],
       environment: JMap[String, String]) =
-    this(new SparkContext(master, appName, sparkHome, jars.toSeq, environment, Map()))
+    this(new SparkContext(master, appName, sparkHome, jars.toSeq, environment.asScala, Map()))
 
   private[spark] val env = sc.env
 
@@ -118,7 +117,7 @@ class JavaSparkContext(val sc: SparkContext)
 
   def appName: String = sc.appName
 
-  def jars: util.List[String] = sc.jars
+  def jars: util.List[String] = sc.jars.asJava
 
   def startTime: java.lang.Long = sc.startTime
 
@@ -142,7 +141,7 @@ class JavaSparkContext(val sc: SparkContext)
   /** Distribute a local Scala collection to form an RDD. */
   def parallelize[T](list: java.util.List[T], numSlices: Int): JavaRDD[T] = {
     implicit val ctag: ClassTag[T] = fakeClassTag
-    sc.parallelize(JavaConversions.asScalaBuffer(list), numSlices)
+    sc.parallelize(list.asScala, numSlices)
   }
 
   /** Get an RDD that has no partitions or elements. */
@@ -161,7 +160,7 @@ class JavaSparkContext(val sc: SparkContext)
   : JavaPairRDD[K, V] = {
     implicit val ctagK: ClassTag[K] = fakeClassTag
     implicit val ctagV: ClassTag[V] = fakeClassTag
-    JavaPairRDD.fromRDD(sc.parallelize(JavaConversions.asScalaBuffer(list), numSlices))
+    JavaPairRDD.fromRDD(sc.parallelize(list.asScala, numSlices))
   }
 
   /** Distribute a local Scala collection to form an RDD. */
@@ -170,8 +169,7 @@ class JavaSparkContext(val sc: SparkContext)
 
   /** Distribute a local Scala collection to form an RDD. */
   def parallelizeDoubles(list: java.util.List[java.lang.Double], numSlices: Int): JavaDoubleRDD =
-    JavaDoubleRDD.fromRDD(sc.parallelize(JavaConversions.asScalaBuffer(list).map(_.doubleValue()),
-      numSlices))
+    JavaDoubleRDD.fromRDD(sc.parallelize(list.asScala.map(_.doubleValue()), numSlices))
 
   /** Distribute a local Scala collection to form an RDD. */
   def parallelizeDoubles(list: java.util.List[java.lang.Double]): JavaDoubleRDD =
@@ -519,7 +517,7 @@ class JavaSparkContext(val sc: SparkContext)
 
   /** Build the union of two or more RDDs. */
   override def union[T](first: JavaRDD[T], rest: java.util.List[JavaRDD[T]]): JavaRDD[T] = {
-    val rdds: Seq[RDD[T]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.rdd)
+    val rdds: Seq[RDD[T]] = (Seq(first) ++ rest.asScala).map(_.rdd)
     implicit val ctag: ClassTag[T] = first.classTag
     sc.union(rdds)
   }
@@ -527,7 +525,7 @@ class JavaSparkContext(val sc: SparkContext)
   /** Build the union of two or more RDDs. */
   override def union[K, V](first: JavaPairRDD[K, V], rest: java.util.List[JavaPairRDD[K, V]])
       : JavaPairRDD[K, V] = {
-    val rdds: Seq[RDD[(K, V)]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.rdd)
+    val rdds: Seq[RDD[(K, V)]] = (Seq(first) ++ rest.asScala).map(_.rdd)
     implicit val ctag: ClassTag[(K, V)] = first.classTag
     implicit val ctagK: ClassTag[K] = first.kClassTag
     implicit val ctagV: ClassTag[V] = first.vClassTag
@@ -536,7 +534,7 @@ class JavaSparkContext(val sc: SparkContext)
 
   /** Build the union of two or more RDDs. */
   override def union(first: JavaDoubleRDD, rest: java.util.List[JavaDoubleRDD]): JavaDoubleRDD = {
-    val rdds: Seq[RDD[Double]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.srdd)
+    val rdds: Seq[RDD[Double]] = (Seq(first) ++ rest.asScala).map(_.srdd)
     new JavaDoubleRDD(sc.union(rdds))
   }
 
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala b/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala
index b959b683d1674..a7dfa1d257cf2 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonHadoopUtil.scala
@@ -17,15 +17,17 @@
 
 package org.apache.spark.api.python
 
-import org.apache.spark.broadcast.Broadcast
-import org.apache.spark.rdd.RDD
-import org.apache.spark.util.{SerializableConfiguration, Utils}
-import org.apache.spark.{Logging, SparkException}
+import scala.collection.JavaConverters._
+import scala.util.{Failure, Success, Try}
+
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.io._
-import scala.util.{Failure, Success, Try}
-import org.apache.spark.annotation.Experimental
 
+import org.apache.spark.{Logging, SparkException}
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.rdd.RDD
+import org.apache.spark.util.{SerializableConfiguration, Utils}
 
 /**
  * :: Experimental ::
@@ -68,7 +70,6 @@ private[python] class WritableToJavaConverter(
    * object representation
    */
   private def convertWritable(writable: Writable): Any = {
-    import collection.JavaConversions._
     writable match {
       case iw: IntWritable => iw.get()
       case dw: DoubleWritable => dw.get()
@@ -89,9 +90,7 @@ private[python] class WritableToJavaConverter(
         aw.get().map(convertWritable(_))
       case mw: MapWritable =>
         val map = new java.util.HashMap[Any, Any]()
-        mw.foreach { case (k, v) =>
-          map.put(convertWritable(k), convertWritable(v))
-        }
+        mw.asScala.foreach { case (k, v) => map.put(convertWritable(k), convertWritable(v)) }
         map
       case w: Writable => WritableUtils.clone(w, conf.value.value)
       case other => other
@@ -122,7 +121,6 @@ private[python] class JavaToWritableConverter extends Converter[Any, Writable] {
    * supported out-of-the-box.
    */
   private def convertToWritable(obj: Any): Writable = {
-    import collection.JavaConversions._
     obj match {
       case i: java.lang.Integer => new IntWritable(i)
       case d: java.lang.Double => new DoubleWritable(d)
@@ -134,7 +132,7 @@ private[python] class JavaToWritableConverter extends Converter[Any, Writable] {
       case null => NullWritable.get()
       case map: java.util.Map[_, _] =>
         val mapWritable = new MapWritable()
-        map.foreach { case (k, v) =>
+        map.asScala.foreach { case (k, v) =>
           mapWritable.put(convertToWritable(k), convertToWritable(v))
         }
         mapWritable
@@ -161,9 +159,8 @@ private[python] object PythonHadoopUtil {
    * Convert a [[java.util.Map]] of properties to a [[org.apache.hadoop.conf.Configuration]]
    */
   def mapToConf(map: java.util.Map[String, String]): Configuration = {
-    import collection.JavaConversions._
     val conf = new Configuration()
-    map.foreach{ case (k, v) => conf.set(k, v) }
+    map.asScala.foreach { case (k, v) => conf.set(k, v) }
     conf
   }
 
@@ -172,9 +169,8 @@ private[python] object PythonHadoopUtil {
    * any matching keys in left
    */
   def mergeConfs(left: Configuration, right: Configuration): Configuration = {
-    import collection.JavaConversions._
     val copy = new Configuration(left)
-    right.iterator().foreach(entry => copy.set(entry.getKey, entry.getValue))
+    right.asScala.foreach(entry => copy.set(entry.getKey, entry.getValue))
     copy
   }
 
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index 2a56bf28d7027..b4d152b336602 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -21,7 +21,7 @@ import java.io._
 import java.net._
 import java.util.{Collections, ArrayList => JArrayList, List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.language.existentials
 
@@ -66,11 +66,11 @@ private[spark] class PythonRDD(
     val env = SparkEnv.get
     val localdir = env.blockManager.diskBlockManager.localDirs.map(
       f => f.getPath()).mkString(",")
-    envVars += ("SPARK_LOCAL_DIRS" -> localdir) // it's also used in monitor thread
+    envVars.put("SPARK_LOCAL_DIRS", localdir) // it's also used in monitor thread
     if (reuse_worker) {
-      envVars += ("SPARK_REUSE_WORKER" -> "1")
+      envVars.put("SPARK_REUSE_WORKER", "1")
     }
-    val worker: Socket = env.createPythonWorker(pythonExec, envVars.toMap)
+    val worker: Socket = env.createPythonWorker(pythonExec, envVars.asScala.toMap)
     // Whether is the worker released into idle pool
     @volatile var released = false
 
@@ -150,7 +150,7 @@ private[spark] class PythonRDD(
               // Check whether the worker is ready to be re-used.
               if (stream.readInt() == SpecialLengths.END_OF_STREAM) {
                 if (reuse_worker) {
-                  env.releasePythonWorker(pythonExec, envVars.toMap, worker)
+                  env.releasePythonWorker(pythonExec, envVars.asScala.toMap, worker)
                   released = true
                 }
               }
@@ -217,13 +217,13 @@ private[spark] class PythonRDD(
         // sparkFilesDir
         PythonRDD.writeUTF(SparkFiles.getRootDirectory, dataOut)
         // Python includes (*.zip and *.egg files)
-        dataOut.writeInt(pythonIncludes.length)
-        for (include <- pythonIncludes) {
+        dataOut.writeInt(pythonIncludes.size())
+        for (include <- pythonIncludes.asScala) {
           PythonRDD.writeUTF(include, dataOut)
         }
         // Broadcast variables
         val oldBids = PythonRDD.getWorkerBroadcasts(worker)
-        val newBids = broadcastVars.map(_.id).toSet
+        val newBids = broadcastVars.asScala.map(_.id).toSet
         // number of different broadcasts
         val toRemove = oldBids.diff(newBids)
         val cnt = toRemove.size + newBids.diff(oldBids).size
@@ -233,7 +233,7 @@ private[spark] class PythonRDD(
           dataOut.writeLong(- bid - 1)  // bid >= 0
           oldBids.remove(bid)
         }
-        for (broadcast <- broadcastVars) {
+        for (broadcast <- broadcastVars.asScala) {
           if (!oldBids.contains(broadcast.id)) {
             // send new broadcast
             dataOut.writeLong(broadcast.id)
@@ -287,7 +287,7 @@ private[spark] class PythonRDD(
       if (!context.isCompleted) {
         try {
           logWarning("Incomplete task interrupted: Attempting to kill Python Worker")
-          env.destroyPythonWorker(pythonExec, envVars.toMap, worker)
+          env.destroyPythonWorker(pythonExec, envVars.asScala.toMap, worker)
         } catch {
           case e: Exception =>
             logError("Exception when trying to kill worker", e)
@@ -358,10 +358,10 @@ private[spark] object PythonRDD extends Logging {
     type ByteArray = Array[Byte]
     type UnrolledPartition = Array[ByteArray]
     val allPartitions: Array[UnrolledPartition] =
-      sc.runJob(rdd, (x: Iterator[ByteArray]) => x.toArray, partitions)
+      sc.runJob(rdd, (x: Iterator[ByteArray]) => x.toArray, partitions.asScala)
     val flattenedPartition: UnrolledPartition = Array.concat(allPartitions: _*)
     serveIterator(flattenedPartition.iterator,
-      s"serve RDD ${rdd.id} with partitions ${partitions.mkString(",")}")
+      s"serve RDD ${rdd.id} with partitions ${partitions.asScala.mkString(",")}")
   }
 
   /**
@@ -819,7 +819,7 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort:
       val in = socket.getInputStream
       val out = new DataOutputStream(new BufferedOutputStream(socket.getOutputStream, bufferSize))
       out.writeInt(val2.size)
-      for (array <- val2) {
+      for (array <- val2.asScala) {
         out.writeInt(array.length)
         out.write(array)
       }
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
index 90dacaeb93429..31e534f160eeb 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonUtils.scala
@@ -17,10 +17,10 @@
 
 package org.apache.spark.api.python
 
-import java.io.{File}
+import java.io.File
 import java.util.{List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.SparkContext
@@ -51,7 +51,14 @@ private[spark] object PythonUtils {
    * Convert list of T into seq of T (for calling API with varargs)
    */
   def toSeq[T](vs: JList[T]): Seq[T] = {
-    vs.toList.toSeq
+    vs.asScala
+  }
+
+  /**
+   * Convert list of T into a (Scala) List of T
+   */
+  def toList[T](vs: JList[T]): List[T] = {
+    vs.asScala.toList
   }
 
   /**
@@ -65,6 +72,6 @@ private[spark] object PythonUtils {
    * Convert java map of K, V into Map of K, V (for calling API with varargs)
    */
   def toScalaMap[K, V](jm: java.util.Map[K, V]): Map[K, V] = {
-    jm.toMap
+    jm.asScala.toMap
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
index e314408c067e9..7039b734d2e40 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonWorkerFactory.scala
@@ -19,9 +19,10 @@ package org.apache.spark.api.python
 
 import java.io.{DataOutputStream, DataInputStream, InputStream, OutputStreamWriter}
 import java.net.{InetAddress, ServerSocket, Socket, SocketException}
+import java.util.Arrays
 
 import scala.collection.mutable
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark._
 import org.apache.spark.util.{RedirectThread, Utils}
@@ -108,9 +109,9 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
       serverSocket = new ServerSocket(0, 1, InetAddress.getByAddress(Array(127, 0, 0, 1)))
 
       // Create and start the worker
-      val pb = new ProcessBuilder(Seq(pythonExec, "-m", "pyspark.worker"))
+      val pb = new ProcessBuilder(Arrays.asList(pythonExec, "-m", "pyspark.worker"))
       val workerEnv = pb.environment()
-      workerEnv.putAll(envVars)
+      workerEnv.putAll(envVars.asJava)
       workerEnv.put("PYTHONPATH", pythonPath)
       // This is equivalent to setting the -u flag; we use it because ipython doesn't support -u:
       workerEnv.put("PYTHONUNBUFFERED", "YES")
@@ -151,9 +152,9 @@ private[spark] class PythonWorkerFactory(pythonExec: String, envVars: Map[String
 
       try {
         // Create and start the daemon
-        val pb = new ProcessBuilder(Seq(pythonExec, "-m", "pyspark.daemon"))
+        val pb = new ProcessBuilder(Arrays.asList(pythonExec, "-m", "pyspark.daemon"))
         val workerEnv = pb.environment()
-        workerEnv.putAll(envVars)
+        workerEnv.putAll(envVars.asJava)
         workerEnv.put("PYTHONPATH", pythonPath)
         // This is equivalent to setting the -u flag; we use it because ipython doesn't support -u:
         workerEnv.put("PYTHONUNBUFFERED", "YES")
diff --git a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
index 1f1debcf84ad4..fd27276e70bfe 100644
--- a/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/SerDeUtil.scala
@@ -22,7 +22,6 @@ import java.util.{ArrayList => JArrayList}
 
 import org.apache.spark.api.java.JavaRDD
 
-import scala.collection.JavaConversions._
 import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.util.Failure
@@ -214,7 +213,7 @@ private[spark] object SerDeUtil extends Logging {
         new AutoBatchedPickler(cleaned)
       } else {
         val pickle = new Pickler
-        cleaned.grouped(batchSize).map(batched => pickle.dumps(seqAsJavaList(batched)))
+        cleaned.grouped(batchSize).map(batched => pickle.dumps(batched.asJava))
       }
     }
   }
diff --git a/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala b/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala
index 8f30ff9202c83..ee1fb056f0d96 100644
--- a/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/WriteInputFormatTestDataGenerator.scala
@@ -20,6 +20,8 @@ package org.apache.spark.api.python
 import java.io.{DataOutput, DataInput}
 import java.{util => ju}
 
+import scala.collection.JavaConverters._
+
 import com.google.common.base.Charsets.UTF_8
 
 import org.apache.hadoop.io._
@@ -62,10 +64,9 @@ private[python] class TestInputKeyConverter extends Converter[Any, Any] {
 }
 
 private[python] class TestInputValueConverter extends Converter[Any, Any] {
-  import collection.JavaConversions._
   override def convert(obj: Any): ju.List[Double] = {
     val m = obj.asInstanceOf[MapWritable]
-    seqAsJavaList(m.keySet.map(w => w.asInstanceOf[DoubleWritable].get()).toSeq)
+    m.keySet.asScala.map(_.asInstanceOf[DoubleWritable].get()).toSeq.asJava
   }
 }
 
@@ -76,9 +77,8 @@ private[python] class TestOutputKeyConverter extends Converter[Any, Any] {
 }
 
 private[python] class TestOutputValueConverter extends Converter[Any, Any] {
-  import collection.JavaConversions._
   override def convert(obj: Any): DoubleWritable = {
-    new DoubleWritable(obj.asInstanceOf[java.util.Map[Double, _]].keySet().head)
+    new DoubleWritable(obj.asInstanceOf[java.util.Map[Double, _]].keySet().iterator().next())
   }
 }
 
diff --git a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
index 1cf2824f862ee..9d5bbb5d609f3 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RRDD.scala
@@ -19,9 +19,10 @@ package org.apache.spark.api.r
 
 import java.io._
 import java.net.{InetAddress, ServerSocket}
+import java.util.Arrays
 import java.util.{Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.io.Source
 import scala.reflect.ClassTag
 import scala.util.Try
@@ -365,11 +366,11 @@ private[r] object RRDD {
       sparkConf.setIfMissing("spark.master", "local")
     }
 
-    for ((name, value) <- sparkEnvirMap) {
-      sparkConf.set(name.asInstanceOf[String], value.asInstanceOf[String])
+    for ((name, value) <- sparkEnvirMap.asScala) {
+      sparkConf.set(name.toString, value.toString)
     }
-    for ((name, value) <- sparkExecutorEnvMap) {
-      sparkConf.setExecutorEnv(name.asInstanceOf[String], value.asInstanceOf[String])
+    for ((name, value) <- sparkExecutorEnvMap.asScala) {
+      sparkConf.setExecutorEnv(name.toString, value.toString)
     }
 
     val jsc = new JavaSparkContext(sparkConf)
@@ -395,7 +396,7 @@ private[r] object RRDD {
     val rOptions = "--vanilla"
     val rLibDir = RUtils.sparkRPackagePath(isDriver = false)
     val rExecScript = rLibDir + "/SparkR/worker/" + script
-    val pb = new ProcessBuilder(List(rCommand, rOptions, rExecScript))
+    val pb = new ProcessBuilder(Arrays.asList(rCommand, rOptions, rExecScript))
     // Unset the R_TESTS environment variable for workers.
     // This is set by R CMD check as startup.Rs
     // (http://svn.r-project.org/R/trunk/src/library/tools/R/testing.R)
diff --git a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
index 427b2bc7cbcbb..9e807cc52f18c 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RUtils.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.api.r
 
 import java.io.File
-
-import scala.collection.JavaConversions._
+import java.util.Arrays
 
 import org.apache.spark.{SparkEnv, SparkException}
 
@@ -68,7 +67,7 @@ private[spark] object RUtils {
   /** Check if R is installed before running tests that use R commands. */
   def isRInstalled: Boolean = {
     try {
-      val builder = new ProcessBuilder(Seq("R", "--version"))
+      val builder = new ProcessBuilder(Arrays.asList("R", "--version"))
       builder.start().waitFor() == 0
     } catch {
       case e: Exception => false
diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index 3c89f24473744..dbbbcf40c1e96 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -20,7 +20,7 @@ package org.apache.spark.api.r
 import java.io.{DataInputStream, DataOutputStream}
 import java.sql.{Timestamp, Date, Time}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 /**
  * Utility functions to serialize, deserialize objects to / from R
@@ -165,7 +165,7 @@ private[spark] object SerDe {
         val valueType = readObjectType(in)
         readTypedObject(in, valueType)
       })
-      mapAsJavaMap(keys.zip(values).toMap)
+      keys.zip(values).toMap.asJava
     } else {
       new java.util.HashMap[Object, Object]()
     }
diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
index a0c9b5e63c744..7e3764d802fe1 100644
--- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
+++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala
@@ -20,7 +20,7 @@ package org.apache.spark.broadcast
 import java.io._
 import java.nio.ByteBuffer
 
-import scala.collection.JavaConversions.asJavaEnumeration
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 import scala.util.Random
 
@@ -210,7 +210,7 @@ private object TorrentBroadcast extends Logging {
       compressionCodec: Option[CompressionCodec]): T = {
     require(blocks.nonEmpty, "Cannot unblockify an empty array of blocks")
     val is = new SequenceInputStream(
-      asJavaEnumeration(blocks.iterator.map(block => new ByteBufferInputStream(block))))
+      blocks.iterator.map(new ByteBufferInputStream(_)).asJavaEnumeration)
     val in: InputStream = compressionCodec.map(c => c.compressedInputStream(is)).getOrElse(is)
     val ser = serializer.newInstance()
     val serIn = ser.deserializeStream(in)
diff --git a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
index 22ef701d833b2..6840a3ae831f0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/ExternalShuffleService.scala
@@ -19,13 +19,13 @@ package org.apache.spark.deploy
 
 import java.util.concurrent.CountDownLatch
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.{Logging, SparkConf, SecurityManager}
 import org.apache.spark.network.TransportContext
 import org.apache.spark.network.netty.SparkTransportConf
 import org.apache.spark.network.sasl.SaslServerBootstrap
-import org.apache.spark.network.server.TransportServer
+import org.apache.spark.network.server.{TransportServerBootstrap, TransportServer}
 import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler
 import org.apache.spark.network.util.TransportConf
 import org.apache.spark.util.Utils
@@ -67,13 +67,13 @@ class ExternalShuffleService(sparkConf: SparkConf, securityManager: SecurityMana
   def start() {
     require(server == null, "Shuffle server already started")
     logInfo(s"Starting shuffle service on port $port with useSasl = $useSasl")
-    val bootstraps =
+    val bootstraps: Seq[TransportServerBootstrap] =
       if (useSasl) {
         Seq(new SaslServerBootstrap(transportConf, securityManager))
       } else {
         Nil
       }
-    server = transportContext.createServer(port, bootstraps)
+    server = transportContext.createServer(port, bootstraps.asJava)
   }
 
   /** Clean up all shuffle files associated with an application that has exited. */
diff --git a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
index 23d01e9cbb9f9..d85327603f64d 100644
--- a/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala
@@ -21,7 +21,7 @@ import java.net.URI
 import java.io.File
 
 import scala.collection.mutable.ArrayBuffer
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Try
 
 import org.apache.spark.SparkUserAppException
@@ -71,7 +71,7 @@ object PythonRunner {
     val pythonPath = PythonUtils.mergePythonPaths(pathElements: _*)
 
     // Launch Python process
-    val builder = new ProcessBuilder(Seq(pythonExec, formattedPythonFile) ++ otherArgs)
+    val builder = new ProcessBuilder((Seq(pythonExec, formattedPythonFile) ++ otherArgs).asJava)
     val env = builder.environment()
     env.put("PYTHONPATH", pythonPath)
     // This is equivalent to setting the -u flag; we use it because ipython doesn't support -u:
diff --git a/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala b/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
index ed1e972955679..4b28866dcaa7c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/RPackageUtils.scala
@@ -22,7 +22,7 @@ import java.util.jar.JarFile
 import java.util.logging.Level
 import java.util.zip.{ZipEntry, ZipOutputStream}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.io.{ByteStreams, Files}
 
@@ -110,7 +110,7 @@ private[deploy] object RPackageUtils extends Logging {
       print(s"Building R package with the command: $installCmd", printStream)
     }
     try {
-      val builder = new ProcessBuilder(installCmd)
+      val builder = new ProcessBuilder(installCmd.asJava)
       builder.redirectErrorStream(true)
       val env = builder.environment()
       env.clear()
diff --git a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
index c0cab22fa8252..05b954ce36998 100644
--- a/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/RRunner.scala
@@ -20,7 +20,7 @@ package org.apache.spark.deploy
 import java.io._
 import java.util.concurrent.{Semaphore, TimeUnit}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.fs.Path
 
@@ -68,7 +68,7 @@ object RRunner {
     if (initialized.tryAcquire(backendTimeout, TimeUnit.SECONDS)) {
       // Launch R
       val returnCode = try {
-        val builder = new ProcessBuilder(Seq(rCommand, rFileNormalized) ++ otherArgs)
+        val builder = new ProcessBuilder((Seq(rCommand, rFileNormalized) ++ otherArgs).asJava)
         val env = builder.environment()
         env.put("EXISTING_SPARKR_BACKEND_PORT", sparkRBackendPort.toString)
         val rPackageDir = RUtils.sparkRPackagePath(isDriver = true)
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkCuratorUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkCuratorUtil.scala
index b8d3993540220..8d5e716e6aea4 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkCuratorUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkCuratorUtil.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.deploy
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.curator.framework.{CuratorFramework, CuratorFrameworkFactory}
 import org.apache.curator.retry.ExponentialBackoffRetry
@@ -57,7 +57,7 @@ private[spark] object SparkCuratorUtil extends Logging {
 
   def deleteRecursive(zk: CuratorFramework, path: String) {
     if (zk.checkExists().forPath(path) != null) {
-      for (child <- zk.getChildren.forPath(path)) {
+      for (child <- zk.getChildren.forPath(path).asScala) {
         zk.delete().forPath(path + "/" + child)
       }
       zk.delete().forPath(path)
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index dda4216c7efe2..f7723ef5bde4c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -22,7 +22,7 @@ import java.lang.reflect.Method
 import java.security.PrivilegedExceptionAction
 import java.util.{Arrays, Comparator}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.concurrent.duration._
 import scala.language.postfixOps
 import scala.util.control.NonFatal
@@ -71,7 +71,7 @@ class SparkHadoopUtil extends Logging {
   }
 
   def transferCredentials(source: UserGroupInformation, dest: UserGroupInformation) {
-    for (token <- source.getTokens()) {
+    for (token <- source.getTokens.asScala) {
       dest.addToken(token)
     }
   }
@@ -175,8 +175,8 @@ class SparkHadoopUtil extends Logging {
   }
 
   private def getFileSystemThreadStatistics(): Seq[AnyRef] = {
-    val stats = FileSystem.getAllStatistics()
-    stats.map(Utils.invoke(classOf[Statistics], _, "getThreadStatistics"))
+    FileSystem.getAllStatistics.asScala.map(
+      Utils.invoke(classOf[Statistics], _, "getThreadStatistics"))
   }
 
   private def getFileSystemThreadStatisticsMethod(methodName: String): Method = {
@@ -306,12 +306,13 @@ class SparkHadoopUtil extends Logging {
     val renewalInterval =
       sparkConf.getLong("spark.yarn.token.renewal.interval", (24 hours).toMillis)
 
-    credentials.getAllTokens.filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND)
+    credentials.getAllTokens.asScala
+      .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND)
       .map { t =>
-      val identifier = new DelegationTokenIdentifier()
-      identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier)))
-      (identifier.getIssueDate + fraction * renewalInterval).toLong - now
-    }.foldLeft(0L)(math.max)
+        val identifier = new DelegationTokenIdentifier()
+        identifier.readFields(new DataInputStream(new ByteArrayInputStream(t.getIdentifier)))
+        (identifier.getIssueDate + fraction * renewalInterval).toLong - now
+      }.foldLeft(0L)(math.max)
   }
 
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
index 3f3c6627c21fb..18a1c52ae53fb 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala
@@ -23,7 +23,7 @@ import java.net.URI
 import java.util.{List => JList}
 import java.util.jar.JarFile
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 import scala.io.Source
 
@@ -94,7 +94,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
 
   // Set parameters from command line arguments
   try {
-    parse(args.toList)
+    parse(args.asJava)
   } catch {
     case e: IllegalArgumentException =>
       SparkSubmit.printErrorAndExit(e.getMessage())
@@ -458,7 +458,7 @@ private[deploy] class SparkSubmitArguments(args: Seq[String], env: Map[String, S
   }
 
   override protected def handleExtraArgs(extra: JList[String]): Unit = {
-    childArgs ++= extra
+    childArgs ++= extra.asScala
   }
 
   private def printUsageAndExit(exitCode: Int, unknownParam: Any = null): Unit = {
diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala
index 563831cc6b8dd..540e802420ce0 100644
--- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala
@@ -19,7 +19,7 @@ package org.apache.spark.deploy.master
 
 import java.nio.ByteBuffer
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.curator.framework.CuratorFramework
@@ -49,8 +49,8 @@ private[master] class ZooKeeperPersistenceEngine(conf: SparkConf, val serializer
   }
 
   override def read[T: ClassTag](prefix: String): Seq[T] = {
-    val file = zk.getChildren.forPath(WORKING_DIR).filter(_.startsWith(prefix))
-    file.map(deserializeFromFile[T]).flatten
+    zk.getChildren.forPath(WORKING_DIR).asScala
+      .filter(_.startsWith(prefix)).map(deserializeFromFile[T]).flatten
   }
 
   override def close() {
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
index 45a3f43045437..ce02ee203a4bd 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala
@@ -18,9 +18,8 @@
 package org.apache.spark.deploy.worker
 
 import java.io.{File, FileOutputStream, InputStream, IOException}
-import java.lang.System._
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.Map
 
 import org.apache.spark.Logging
@@ -62,7 +61,7 @@ object CommandUtils extends Logging {
     // SPARK-698: do not call the run.cmd script, as process.destroy()
     // fails to kill a process tree on Windows
     val cmd = new WorkerCommandBuilder(sparkHome, memory, command).buildCommand()
-    cmd.toSeq ++ Seq(command.mainClass) ++ command.arguments
+    cmd.asScala ++ Seq(command.mainClass) ++ command.arguments
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
index ec51c3d935d8e..89159ff5e2b3c 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala
@@ -19,7 +19,7 @@ package org.apache.spark.deploy.worker
 
 import java.io._
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.Files
@@ -172,8 +172,8 @@ private[deploy] class DriverRunner(
       CommandUtils.redirectStream(process.getInputStream, stdout)
 
       val stderr = new File(baseDir, "stderr")
-      val header = "Launch Command: %s\n%s\n\n".format(
-        builder.command.mkString("\"", "\" \"", "\""), "=" * 40)
+      val formattedCommand = builder.command.asScala.mkString("\"", "\" \"", "\"")
+      val header = "Launch Command: %s\n%s\n\n".format(formattedCommand, "=" * 40)
       Files.append(header, stderr, UTF_8)
       CommandUtils.redirectStream(process.getErrorStream, stderr)
     }
@@ -229,6 +229,6 @@ private[deploy] trait ProcessBuilderLike {
 private[deploy] object ProcessBuilderLike {
   def apply(processBuilder: ProcessBuilder): ProcessBuilderLike = new ProcessBuilderLike {
     override def start(): Process = processBuilder.start()
-    override def command: Seq[String] = processBuilder.command()
+    override def command: Seq[String] = processBuilder.command().asScala
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
index ab3fea475c2a5..3aef0515cbf6e 100644
--- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala
@@ -19,7 +19,7 @@ package org.apache.spark.deploy.worker
 
 import java.io._
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.Files
@@ -129,7 +129,8 @@ private[deploy] class ExecutorRunner(
       val builder = CommandUtils.buildProcessBuilder(appDesc.command, new SecurityManager(conf),
         memory, sparkHome.getAbsolutePath, substituteVariables)
       val command = builder.command()
-      logInfo("Launch command: " + command.mkString("\"", "\" \"", "\""))
+      val formattedCommand = command.asScala.mkString("\"", "\" \"", "\"")
+      logInfo(s"Launch command: $formattedCommand")
 
       builder.directory(executorDir)
       builder.environment.put("SPARK_EXECUTOR_DIRS", appLocalDirs.mkString(File.pathSeparator))
@@ -145,7 +146,7 @@ private[deploy] class ExecutorRunner(
 
       process = builder.start()
       val header = "Spark Executor Command: %s\n%s\n\n".format(
-        command.mkString("\"", "\" \"", "\""), "=" * 40)
+        formattedCommand, "=" * 40)
 
       // Redirect its stdout and stderr to files
       val stdout = new File(executorDir, "stdout")
diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
index 79b1536d94016..770927c80f7a4 100755
--- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala
@@ -24,7 +24,6 @@ import java.util.{UUID, Date}
 import java.util.concurrent._
 import java.util.concurrent.{Future => JFuture, ScheduledFuture => JScheduledFuture}
 
-import scala.collection.JavaConversions._
 import scala.collection.mutable.{HashMap, HashSet, LinkedHashMap}
 import scala.concurrent.ExecutionContext
 import scala.util.Random
diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala
index 42a85e42ea2b6..c3491bb8b1cf3 100644
--- a/core/src/main/scala/org/apache/spark/executor/Executor.scala
+++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala
@@ -23,7 +23,7 @@ import java.net.URL
 import java.nio.ByteBuffer
 import java.util.concurrent.{ConcurrentHashMap, TimeUnit}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 import scala.util.control.NonFatal
 
@@ -147,7 +147,7 @@ private[spark] class Executor(
 
   /** Returns the total amount of time this JVM process has spent in garbage collection. */
   private def computeTotalGcTime(): Long = {
-    ManagementFactory.getGarbageCollectorMXBeans.map(_.getCollectionTime).sum
+    ManagementFactory.getGarbageCollectorMXBeans.asScala.map(_.getCollectionTime).sum
   }
 
   class TaskRunner(
@@ -425,7 +425,7 @@ private[spark] class Executor(
     val tasksMetrics = new ArrayBuffer[(Long, TaskMetrics)]()
     val curGCTime = computeTotalGcTime()
 
-    for (taskRunner <- runningTasks.values()) {
+    for (taskRunner <- runningTasks.values().asScala) {
       if (taskRunner.task != null) {
         taskRunner.task.metrics.foreach { metrics =>
           metrics.updateShuffleReadMetrics()
diff --git a/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala b/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala
index 293c512f8b70c..d16f4a1fc4e3b 100644
--- a/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala
+++ b/core/src/main/scala/org/apache/spark/executor/ExecutorSource.scala
@@ -19,7 +19,7 @@ package org.apache.spark.executor
 
 import java.util.concurrent.ThreadPoolExecutor
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.codahale.metrics.{Gauge, MetricRegistry}
 import org.apache.hadoop.fs.FileSystem
@@ -30,7 +30,7 @@ private[spark]
 class ExecutorSource(threadPool: ThreadPoolExecutor, executorId: String) extends Source {
 
   private def fileStats(scheme: String) : Option[FileSystem.Statistics] =
-    FileSystem.getAllStatistics().find(s => s.getScheme.equals(scheme))
+    FileSystem.getAllStatistics.asScala.find(s => s.getScheme.equals(scheme))
 
   private def registerFileSystemStat[T](
         scheme: String, name: String, f: FileSystem.Statistics => T, defaultValue: T) = {
diff --git a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
index cfd672e1d8a97..0474fd2ccc12e 100644
--- a/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
+++ b/core/src/main/scala/org/apache/spark/executor/MesosExecutorBackend.scala
@@ -19,7 +19,7 @@ package org.apache.spark.executor
 
 import java.nio.ByteBuffer
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.mesos.protobuf.ByteString
 import org.apache.mesos.{Executor => MesosExecutor, ExecutorDriver, MesosExecutorDriver}
@@ -28,7 +28,7 @@ import org.apache.mesos.Protos.{TaskStatus => MesosTaskStatus, _}
 import org.apache.spark.{Logging, TaskState, SparkConf, SparkEnv}
 import org.apache.spark.TaskState.TaskState
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.scheduler.cluster.mesos.{MesosTaskLaunchData}
+import org.apache.spark.scheduler.cluster.mesos.MesosTaskLaunchData
 import org.apache.spark.util.{SignalLogger, Utils}
 
 private[spark] class MesosExecutorBackend
@@ -55,7 +55,7 @@ private[spark] class MesosExecutorBackend
       slaveInfo: SlaveInfo) {
 
     // Get num cores for this task from ExecutorInfo, created in MesosSchedulerBackend.
-    val cpusPerTask = executorInfo.getResourcesList
+    val cpusPerTask = executorInfo.getResourcesList.asScala
       .find(_.getName == "cpus")
       .map(_.getScalar.getValue.toInt)
       .getOrElse(0)
diff --git a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
index 6cda7772f77bc..a5ad47293f1c2 100644
--- a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
+++ b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
@@ -19,7 +19,7 @@ package org.apache.spark.input
 
 import java.io.{ByteArrayInputStream, ByteArrayOutputStream, DataInputStream, DataOutputStream}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.io.ByteStreams
 import org.apache.hadoop.conf.Configuration
@@ -44,12 +44,9 @@ private[spark] abstract class StreamFileInputFormat[T]
    * which is set through setMaxSplitSize
    */
   def setMinPartitions(context: JobContext, minPartitions: Int) {
-    val files = listStatus(context)
-    val totalLen = files.map { file =>
-      if (file.isDir) 0L else file.getLen
-    }.sum
-
-    val maxSplitSize = Math.ceil(totalLen * 1.0 / files.length).toLong
+    val files = listStatus(context).asScala
+    val totalLen = files.map(file => if (file.isDir) 0L else file.getLen).sum
+    val maxSplitSize = Math.ceil(totalLen * 1.0 / files.size).toLong
     super.setMaxSplitSize(maxSplitSize)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala b/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala
index aaef7c74eea33..1ba34a11414a2 100644
--- a/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala
+++ b/core/src/main/scala/org/apache/spark/input/WholeTextFileInputFormat.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.input
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce.InputSplit
@@ -52,10 +52,8 @@ private[spark] class WholeTextFileInputFormat
    * which is set through setMaxSplitSize
    */
   def setMinPartitions(context: JobContext, minPartitions: Int) {
-    val files = listStatus(context)
-    val totalLen = files.map { file =>
-      if (file.isDir) 0L else file.getLen
-    }.sum
+    val files = listStatus(context).asScala
+    val totalLen = files.map(file => if (file.isDir) 0L else file.getLen).sum
     val maxSplitSize = Math.ceil(totalLen * 1.0 /
       (if (minPartitions == 0) 1 else minPartitions)).toLong
     super.setMaxSplitSize(maxSplitSize)
diff --git a/core/src/main/scala/org/apache/spark/launcher/WorkerCommandBuilder.scala b/core/src/main/scala/org/apache/spark/launcher/WorkerCommandBuilder.scala
index 9be98723aed14..0c096656f9236 100644
--- a/core/src/main/scala/org/apache/spark/launcher/WorkerCommandBuilder.scala
+++ b/core/src/main/scala/org/apache/spark/launcher/WorkerCommandBuilder.scala
@@ -20,7 +20,7 @@ package org.apache.spark.launcher
 import java.io.File
 import java.util.{HashMap => JHashMap, List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.deploy.Command
 
@@ -32,7 +32,7 @@ import org.apache.spark.deploy.Command
 private[spark] class WorkerCommandBuilder(sparkHome: String, memoryMb: Int, command: Command)
     extends AbstractCommandBuilder {
 
-  childEnv.putAll(command.environment)
+  childEnv.putAll(command.environment.asJava)
   childEnv.put(CommandBuilderUtils.ENV_SPARK_HOME, sparkHome)
 
   override def buildCommand(env: JMap[String, String]): JList[String] = {
diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala
index d7495551ad233..dd2d325d87034 100644
--- a/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala
+++ b/core/src/main/scala/org/apache/spark/metrics/MetricsConfig.scala
@@ -20,6 +20,7 @@ package org.apache.spark.metrics
 import java.io.{FileInputStream, InputStream}
 import java.util.Properties
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.util.matching.Regex
 
@@ -58,25 +59,20 @@ private[spark] class MetricsConfig(conf: SparkConf) extends Logging {
 
     propertyCategories = subProperties(properties, INSTANCE_REGEX)
     if (propertyCategories.contains(DEFAULT_PREFIX)) {
-      import scala.collection.JavaConversions._
-
-      val defaultProperty = propertyCategories(DEFAULT_PREFIX)
-      for { (inst, prop) <- propertyCategories
-            if (inst != DEFAULT_PREFIX)
-            (k, v) <- defaultProperty
-            if (prop.getProperty(k) == null) } {
-        prop.setProperty(k, v)
+      val defaultProperty = propertyCategories(DEFAULT_PREFIX).asScala
+      for((inst, prop) <- propertyCategories if (inst != DEFAULT_PREFIX);
+          (k, v) <- defaultProperty if (prop.get(k) == null)) {
+        prop.put(k, v)
       }
     }
   }
 
   def subProperties(prop: Properties, regex: Regex): mutable.HashMap[String, Properties] = {
     val subProperties = new mutable.HashMap[String, Properties]
-    import scala.collection.JavaConversions._
-    prop.foreach { kv =>
-      if (regex.findPrefixOf(kv._1).isDefined) {
-        val regex(prefix, suffix) = kv._1
-        subProperties.getOrElseUpdate(prefix, new Properties).setProperty(suffix, kv._2)
+    prop.asScala.foreach { kv =>
+      if (regex.findPrefixOf(kv._1.toString).isDefined) {
+        val regex(prefix, suffix) = kv._1.toString
+        subProperties.getOrElseUpdate(prefix, new Properties).setProperty(suffix, kv._2.toString)
       }
     }
     subProperties
diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
index b089da8596e2b..7c170a742fb64 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
@@ -19,7 +19,7 @@ package org.apache.spark.network.netty
 
 import java.nio.ByteBuffer
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.Logging
 import org.apache.spark.network.BlockDataManager
@@ -55,7 +55,7 @@ class NettyBlockRpcServer(
       case openBlocks: OpenBlocks =>
         val blocks: Seq[ManagedBuffer] =
           openBlocks.blockIds.map(BlockId.apply).map(blockManager.getBlockData)
-        val streamId = streamManager.registerStream(blocks.iterator)
+        val streamId = streamManager.registerStream(blocks.iterator.asJava)
         logTrace(s"Registered streamId $streamId with ${blocks.size} buffers")
         responseContext.onSuccess(new StreamHandle(streamId, blocks.size).toByteArray)
 
diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
index d650d5fe73087..ff8aae9ebe9f0 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.network.netty
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.concurrent.{Future, Promise}
 
 import org.apache.spark.{SecurityManager, SparkConf}
@@ -58,7 +58,7 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage
         securityManager.isSaslEncryptionEnabled()))
     }
     transportContext = new TransportContext(transportConf, rpcHandler)
-    clientFactory = transportContext.createClientFactory(clientBootstrap.toList)
+    clientFactory = transportContext.createClientFactory(clientBootstrap.toSeq.asJava)
     server = createServer(serverBootstrap.toList)
     appId = conf.getAppId
     logInfo("Server created on " + server.getPort)
@@ -67,7 +67,7 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage
   /** Creates and binds the TransportServer, possibly trying multiple ports. */
   private def createServer(bootstraps: List[TransportServerBootstrap]): TransportServer = {
     def startService(port: Int): (TransportServer, Int) = {
-      val server = transportContext.createServer(port, bootstraps)
+      val server = transportContext.createServer(port, bootstraps.asJava)
       (server, server.getPort)
     }
 
diff --git a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
index 1499da07bb83b..8d9ebadaf79d4 100644
--- a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
+++ b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
@@ -23,7 +23,7 @@ import java.nio.channels._
 import java.util.concurrent.ConcurrentLinkedQueue
 import java.util.LinkedList
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 import scala.util.control.NonFatal
 
@@ -145,7 +145,7 @@ abstract class Connection(val channel: SocketChannel, val selector: Selector,
   }
 
   def callOnExceptionCallbacks(e: Throwable) {
-    onExceptionCallbacks foreach {
+    onExceptionCallbacks.asScala.foreach {
       callback =>
         try {
           callback(this, e)
diff --git a/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala b/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala
index 91b07ce3af1b6..5afce75680f94 100644
--- a/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala
+++ b/core/src/main/scala/org/apache/spark/partial/GroupedCountEvaluator.scala
@@ -19,7 +19,7 @@ package org.apache.spark.partial
 
 import java.util.{HashMap => JHashMap}
 
-import scala.collection.JavaConversions.mapAsScalaMap
+import scala.collection.JavaConverters._
 import scala.collection.Map
 import scala.collection.mutable.HashMap
 import scala.reflect.ClassTag
@@ -48,9 +48,9 @@ private[spark] class GroupedCountEvaluator[T : ClassTag](totalOutputs: Int, conf
     if (outputsMerged == totalOutputs) {
       val result = new JHashMap[T, BoundedDouble](sums.size)
       sums.foreach { case (key, sum) =>
-        result(key) = new BoundedDouble(sum, 1.0, sum, sum)
+        result.put(key, new BoundedDouble(sum, 1.0, sum, sum))
       }
-      result
+      result.asScala
     } else if (outputsMerged == 0) {
       new HashMap[T, BoundedDouble]
     } else {
@@ -64,9 +64,9 @@ private[spark] class GroupedCountEvaluator[T : ClassTag](totalOutputs: Int, conf
         val stdev = math.sqrt(variance)
         val low = mean - confFactor * stdev
         val high = mean + confFactor * stdev
-        result(key) = new BoundedDouble(mean, confidence, low, high)
+        result.put(key, new BoundedDouble(mean, confidence, low, high))
       }
-      result
+      result.asScala
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala b/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala
index af26c3d59ac02..a164040684803 100644
--- a/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala
+++ b/core/src/main/scala/org/apache/spark/partial/GroupedMeanEvaluator.scala
@@ -19,7 +19,7 @@ package org.apache.spark.partial
 
 import java.util.{HashMap => JHashMap}
 
-import scala.collection.JavaConversions.mapAsScalaMap
+import scala.collection.JavaConverters._
 import scala.collection.Map
 import scala.collection.mutable.HashMap
 
@@ -55,9 +55,9 @@ private[spark] class GroupedMeanEvaluator[T](totalOutputs: Int, confidence: Doub
       while (iter.hasNext) {
         val entry = iter.next()
         val mean = entry.getValue.mean
-        result(entry.getKey) = new BoundedDouble(mean, 1.0, mean, mean)
+        result.put(entry.getKey, new BoundedDouble(mean, 1.0, mean, mean))
       }
-      result
+      result.asScala
     } else if (outputsMerged == 0) {
       new HashMap[T, BoundedDouble]
     } else {
@@ -72,9 +72,9 @@ private[spark] class GroupedMeanEvaluator[T](totalOutputs: Int, confidence: Doub
         val confFactor = studentTCacher.get(counter.count)
         val low = mean - confFactor * stdev
         val high = mean + confFactor * stdev
-        result(entry.getKey) = new BoundedDouble(mean, confidence, low, high)
+        result.put(entry.getKey, new BoundedDouble(mean, confidence, low, high))
       }
-      result
+      result.asScala
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/partial/GroupedSumEvaluator.scala b/core/src/main/scala/org/apache/spark/partial/GroupedSumEvaluator.scala
index 442fb86227d86..54a1beab3514b 100644
--- a/core/src/main/scala/org/apache/spark/partial/GroupedSumEvaluator.scala
+++ b/core/src/main/scala/org/apache/spark/partial/GroupedSumEvaluator.scala
@@ -19,7 +19,7 @@ package org.apache.spark.partial
 
 import java.util.{HashMap => JHashMap}
 
-import scala.collection.JavaConversions.mapAsScalaMap
+import scala.collection.JavaConverters._
 import scala.collection.Map
 import scala.collection.mutable.HashMap
 
@@ -55,9 +55,9 @@ private[spark] class GroupedSumEvaluator[T](totalOutputs: Int, confidence: Doubl
       while (iter.hasNext) {
         val entry = iter.next()
         val sum = entry.getValue.sum
-        result(entry.getKey) = new BoundedDouble(sum, 1.0, sum, sum)
+        result.put(entry.getKey, new BoundedDouble(sum, 1.0, sum, sum))
       }
-      result
+      result.asScala
     } else if (outputsMerged == 0) {
       new HashMap[T, BoundedDouble]
     } else {
@@ -80,9 +80,9 @@ private[spark] class GroupedSumEvaluator[T](totalOutputs: Int, confidence: Doubl
         val confFactor = studentTCacher.get(counter.count)
         val low = sumEstimate - confFactor * sumStdev
         val high = sumEstimate + confFactor * sumStdev
-        result(entry.getKey) = new BoundedDouble(sumEstimate, confidence, low, high)
+        result.put(entry.getKey, new BoundedDouble(sumEstimate, confidence, low, high))
       }
-      result
+      result.asScala
     }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 326fafb230a40..4e5f2e8a5d467 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -22,7 +22,7 @@ import java.text.SimpleDateFormat
 import java.util.{Date, HashMap => JHashMap}
 
 import scala.collection.{Map, mutable}
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 import scala.util.DynamicVariable
@@ -312,14 +312,14 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     } : Iterator[JHashMap[K, V]]
 
     val mergeMaps = (m1: JHashMap[K, V], m2: JHashMap[K, V]) => {
-      m2.foreach { pair =>
+      m2.asScala.foreach { pair =>
         val old = m1.get(pair._1)
         m1.put(pair._1, if (old == null) pair._2 else cleanedF(old, pair._2))
       }
       m1
     } : JHashMap[K, V]
 
-    self.mapPartitions(reducePartition).reduce(mergeMaps)
+    self.mapPartitions(reducePartition).reduce(mergeMaps).asScala
   }
 
   /** Alias for reduceByKeyLocally */
diff --git a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
index 3bb9998e1db44..afbe566b76566 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PipedRDD.scala
@@ -23,7 +23,7 @@ import java.io.IOException
 import java.io.PrintWriter
 import java.util.StringTokenizer
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.Map
 import scala.collection.mutable.ArrayBuffer
 import scala.io.Source
@@ -72,7 +72,7 @@ private[spark] class PipedRDD[T: ClassTag](
   }
 
   override def compute(split: Partition, context: TaskContext): Iterator[String] = {
-    val pb = new ProcessBuilder(command)
+    val pb = new ProcessBuilder(command.asJava)
     // Add the environmental variables to the process.
     val currentEnvVars = pb.environment()
     envVars.foreach { case (variable, value) => currentEnvVars.put(variable, value) }
@@ -81,7 +81,7 @@ private[spark] class PipedRDD[T: ClassTag](
     // so the user code can access the input filename
     if (split.isInstanceOf[HadoopPartition]) {
       val hadoopSplit = split.asInstanceOf[HadoopPartition]
-      currentEnvVars.putAll(hadoopSplit.getPipeEnvVars())
+      currentEnvVars.putAll(hadoopSplit.getPipeEnvVars().asJava)
     }
 
     // When spark.worker.separated.working.directory option is turned on, each
diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
index f7cb1791d4ac6..9a4fa301b06e3 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
@@ -19,7 +19,7 @@ package org.apache.spark.rdd
 
 import java.util.{HashMap => JHashMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 
@@ -125,7 +125,7 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag](
     integrate(0, t => getSeq(t._1) += t._2)
     // the second dep is rdd2; remove all of its keys
     integrate(1, t => map.remove(t._1))
-    map.iterator.map { t => t._2.iterator.map { (t._1, _) } }.flatten
+    map.asScala.iterator.map(t => t._2.iterator.map((t._1, _))).flatten
   }
 
   override def clearDependencies() {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala
index bac37bfdaa23f..0e438ab4366d9 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.scheduler
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.immutable.Set
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
@@ -107,7 +107,7 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl
 
     val retval = new ArrayBuffer[SplitInfo]()
     val list = instance.getSplits(job)
-    for (split <- list) {
+    for (split <- list.asScala) {
       retval ++= SplitInfo.toSplitInfo(inputFormatClazz, path, split)
     }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
index 174b73221afc0..5821afea98982 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
@@ -19,7 +19,7 @@ package org.apache.spark.scheduler
 
 import java.util.concurrent.{ConcurrentHashMap, ConcurrentLinkedQueue}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.Logging
@@ -74,7 +74,7 @@ private[spark] class Pool(
     if (schedulableNameToSchedulable.containsKey(schedulableName)) {
       return schedulableNameToSchedulable.get(schedulableName)
     }
-    for (schedulable <- schedulableQueue) {
+    for (schedulable <- schedulableQueue.asScala) {
       val sched = schedulable.getSchedulableByName(schedulableName)
       if (sched != null) {
         return sched
@@ -84,12 +84,12 @@ private[spark] class Pool(
   }
 
   override def executorLost(executorId: String, host: String) {
-    schedulableQueue.foreach(_.executorLost(executorId, host))
+    schedulableQueue.asScala.foreach(_.executorLost(executorId, host))
   }
 
   override def checkSpeculatableTasks(): Boolean = {
     var shouldRevive = false
-    for (schedulable <- schedulableQueue) {
+    for (schedulable <- schedulableQueue.asScala) {
       shouldRevive |= schedulable.checkSpeculatableTasks()
     }
     shouldRevive
@@ -98,7 +98,7 @@ private[spark] class Pool(
   override def getSortedTaskSetQueue: ArrayBuffer[TaskSetManager] = {
     var sortedTaskSetQueue = new ArrayBuffer[TaskSetManager]
     val sortedSchedulableQueue =
-      schedulableQueue.toSeq.sortWith(taskSetSchedulingAlgorithm.comparator)
+      schedulableQueue.asScala.toSeq.sortWith(taskSetSchedulingAlgorithm.comparator)
     for (schedulable <- sortedSchedulableQueue) {
       sortedTaskSetQueue ++= schedulable.getSortedTaskSetQueue
     }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index d6e1e9e5bebc2..452c32d5411cd 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -21,7 +21,7 @@ import java.io.File
 import java.util.concurrent.locks.ReentrantLock
 import java.util.{Collections, List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{HashMap, HashSet}
 
 import com.google.common.collect.HashBiMap
@@ -233,7 +233,7 @@ private[spark] class CoarseMesosSchedulerBackend(
   override def resourceOffers(d: SchedulerDriver, offers: JList[Offer]) {
     stateLock.synchronized {
       val filters = Filters.newBuilder().setRefuseSeconds(5).build()
-      for (offer <- offers) {
+      for (offer <- offers.asScala) {
         val offerAttributes = toAttributeMap(offer.getAttributesList)
         val meetsConstraints = matchesAttributeRequirements(slaveOfferConstraints, offerAttributes)
         val slaveId = offer.getSlaveId.getValue
@@ -251,21 +251,21 @@ private[spark] class CoarseMesosSchedulerBackend(
           val cpusToUse = math.min(cpus, maxCores - totalCoresAcquired)
           totalCoresAcquired += cpusToUse
           val taskId = newMesosTaskId()
-          taskIdToSlaveId(taskId) = slaveId
+          taskIdToSlaveId.put(taskId, slaveId)
           slaveIdsWithExecutors += slaveId
           coresByTaskId(taskId) = cpusToUse
           // Gather cpu resources from the available resources and use them in the task.
           val (remainingResources, cpuResourcesToUse) =
             partitionResources(offer.getResourcesList, "cpus", cpusToUse)
           val (_, memResourcesToUse) =
-            partitionResources(remainingResources, "mem", calculateTotalMemory(sc))
+            partitionResources(remainingResources.asJava, "mem", calculateTotalMemory(sc))
           val taskBuilder = MesosTaskInfo.newBuilder()
             .setTaskId(TaskID.newBuilder().setValue(taskId.toString).build())
             .setSlaveId(offer.getSlaveId)
             .setCommand(createCommand(offer, cpusToUse + extraCoresPerSlave, taskId))
             .setName("Task " + taskId)
-            .addAllResources(cpuResourcesToUse)
-            .addAllResources(memResourcesToUse)
+            .addAllResources(cpuResourcesToUse.asJava)
+            .addAllResources(memResourcesToUse.asJava)
 
           sc.conf.getOption("spark.mesos.executor.docker.image").foreach { image =>
             MesosSchedulerBackendUtil
@@ -314,9 +314,9 @@ private[spark] class CoarseMesosSchedulerBackend(
       }
 
       if (TaskState.isFinished(TaskState.fromMesos(state))) {
-        val slaveId = taskIdToSlaveId(taskId)
+        val slaveId = taskIdToSlaveId.get(taskId)
         slaveIdsWithExecutors -= slaveId
-        taskIdToSlaveId -= taskId
+        taskIdToSlaveId.remove(taskId)
         // Remove the cores we have remembered for this task, if it's in the hashmap
         for (cores <- coresByTaskId.get(taskId)) {
           totalCoresAcquired -= cores
@@ -361,7 +361,7 @@ private[spark] class CoarseMesosSchedulerBackend(
     stateLock.synchronized {
       if (slaveIdsWithExecutors.contains(slaveId)) {
         val slaveIdToTaskId = taskIdToSlaveId.inverse()
-        if (slaveIdToTaskId.contains(slaveId)) {
+        if (slaveIdToTaskId.containsKey(slaveId)) {
           val taskId: Int = slaveIdToTaskId.get(slaveId)
           taskIdToSlaveId.remove(taskId)
           removeExecutor(sparkExecutorId(slaveId, taskId.toString), reason)
@@ -411,7 +411,7 @@ private[spark] class CoarseMesosSchedulerBackend(
     val slaveIdToTaskId = taskIdToSlaveId.inverse()
     for (executorId <- executorIds) {
       val slaveId = executorId.split("/")(0)
-      if (slaveIdToTaskId.contains(slaveId)) {
+      if (slaveIdToTaskId.containsKey(slaveId)) {
         mesosDriver.killTask(
           TaskID.newBuilder().setValue(slaveIdToTaskId.get(slaveId).toString).build())
         pendingRemovedSlaveIds += slaveId
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala
index 3efc536f1456c..e0c547dce6d07 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterPersistenceEngine.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.scheduler.cluster.mesos
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.curator.framework.CuratorFramework
 import org.apache.zookeeper.CreateMode
@@ -129,6 +129,6 @@ private[spark] class ZookeeperMesosClusterPersistenceEngine(
   }
 
   override def fetchAll[T](): Iterable[T] = {
-    zk.getChildren.forPath(WORKING_DIR).map(fetch[T]).flatten
+    zk.getChildren.forPath(WORKING_DIR).asScala.flatMap(fetch[T])
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
index 1206f184fbc82..07da9242b9922 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
@@ -21,7 +21,7 @@ import java.io.File
 import java.util.concurrent.locks.ReentrantLock
 import java.util.{Collections, Date, List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
@@ -350,7 +350,7 @@ private[spark] class MesosClusterScheduler(
         }
         // TODO: Page the status updates to avoid trying to reconcile
         // a large amount of tasks at once.
-        driver.reconcileTasks(statuses)
+        driver.reconcileTasks(statuses.toSeq.asJava)
       }
     }
   }
@@ -493,10 +493,10 @@ private[spark] class MesosClusterScheduler(
   }
 
   override def resourceOffers(driver: SchedulerDriver, offers: JList[Offer]): Unit = {
-    val currentOffers = offers.map { o =>
+    val currentOffers = offers.asScala.map(o =>
       new ResourceOffer(
         o, getResource(o.getResourcesList, "cpus"), getResource(o.getResourcesList, "mem"))
-    }.toList
+    ).toList
     logTrace(s"Received offers from Mesos: \n${currentOffers.mkString("\n")}")
     val tasks = new mutable.HashMap[OfferID, ArrayBuffer[TaskInfo]]()
     val currentTime = new Date()
@@ -521,10 +521,10 @@ private[spark] class MesosClusterScheduler(
         currentOffers,
         tasks)
     }
-    tasks.foreach { case (offerId, tasks) =>
-      driver.launchTasks(Collections.singleton(offerId), tasks)
+    tasks.foreach { case (offerId, taskInfos) =>
+      driver.launchTasks(Collections.singleton(offerId), taskInfos.asJava)
     }
-    offers
+    offers.asScala
       .filter(o => !tasks.keySet.contains(o.getId))
       .foreach(o => driver.declineOffer(o.getId))
   }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index 5c20606d58715..2e424054be785 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -20,7 +20,7 @@ package org.apache.spark.scheduler.cluster.mesos
 import java.io.File
 import java.util.{ArrayList => JArrayList, Collections, List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{HashMap, HashSet}
 
 import org.apache.mesos.{Scheduler => MScheduler, _}
@@ -129,14 +129,12 @@ private[spark] class MesosSchedulerBackend(
     val (resourcesAfterCpu, usedCpuResources) =
       partitionResources(availableResources, "cpus", scheduler.CPUS_PER_TASK)
     val (resourcesAfterMem, usedMemResources) =
-      partitionResources(resourcesAfterCpu, "mem", calculateTotalMemory(sc))
+      partitionResources(resourcesAfterCpu.asJava, "mem", calculateTotalMemory(sc))
 
-    builder.addAllResources(usedCpuResources)
-    builder.addAllResources(usedMemResources)
+    builder.addAllResources(usedCpuResources.asJava)
+    builder.addAllResources(usedMemResources.asJava)
 
-    sc.conf.getOption("spark.mesos.uris").map { uris =>
-      setupUris(uris, command)
-    }
+    sc.conf.getOption("spark.mesos.uris").foreach(setupUris(_, command))
 
     val executorInfo = builder
       .setExecutorId(ExecutorID.newBuilder().setValue(execId).build())
@@ -148,7 +146,7 @@ private[spark] class MesosSchedulerBackend(
         .setupContainerBuilderDockerInfo(image, sc.conf, executorInfo.getContainerBuilder())
     }
 
-    (executorInfo.build(), resourcesAfterMem)
+    (executorInfo.build(), resourcesAfterMem.asJava)
   }
 
   /**
@@ -193,7 +191,7 @@ private[spark] class MesosSchedulerBackend(
 
   private def getTasksSummary(tasks: JArrayList[MesosTaskInfo]): String = {
     val builder = new StringBuilder
-    tasks.foreach { t =>
+    tasks.asScala.foreach { t =>
       builder.append("Task id: ").append(t.getTaskId.getValue).append("\n")
         .append("Slave id: ").append(t.getSlaveId.getValue).append("\n")
         .append("Task resources: ").append(t.getResourcesList).append("\n")
@@ -211,7 +209,7 @@ private[spark] class MesosSchedulerBackend(
   override def resourceOffers(d: SchedulerDriver, offers: JList[Offer]) {
     inClassLoader() {
       // Fail-fast on offers we know will be rejected
-      val (usableOffers, unUsableOffers) = offers.partition { o =>
+      val (usableOffers, unUsableOffers) = offers.asScala.partition { o =>
         val mem = getResource(o.getResourcesList, "mem")
         val cpus = getResource(o.getResourcesList, "cpus")
         val slaveId = o.getSlaveId.getValue
@@ -323,10 +321,10 @@ private[spark] class MesosSchedulerBackend(
       .setSlaveId(SlaveID.newBuilder().setValue(slaveId).build())
       .setExecutor(executorInfo)
       .setName(task.name)
-      .addAllResources(cpuResources)
+      .addAllResources(cpuResources.asJava)
       .setData(MesosTaskLaunchData(task.serializedTask, task.attemptNumber).toByteString)
       .build()
-    (taskInfo, finalResources)
+    (taskInfo, finalResources.asJava)
   }
 
   override def statusUpdate(d: SchedulerDriver, status: TaskStatus) {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
index 5b854aa5c2754..860c8e097b3b9 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala
@@ -20,7 +20,7 @@ package org.apache.spark.scheduler.cluster.mesos
 import java.util.{List => JList}
 import java.util.concurrent.CountDownLatch
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 import scala.util.control.NonFatal
 
@@ -137,7 +137,7 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
   protected def getResource(res: JList[Resource], name: String): Double = {
     // A resource can have multiple values in the offer since it can either be from
     // a specific role or wildcard.
-    res.filter(_.getName == name).map(_.getScalar.getValue).sum
+    res.asScala.filter(_.getName == name).map(_.getScalar.getValue).sum
   }
 
   protected def markRegistered(): Unit = {
@@ -169,7 +169,7 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
       amountToUse: Double): (List[Resource], List[Resource]) = {
     var remain = amountToUse
     var requestedResources = new ArrayBuffer[Resource]
-    val remainingResources = resources.map {
+    val remainingResources = resources.asScala.map {
       case r => {
         if (remain > 0 &&
           r.getType == Value.Type.SCALAR &&
@@ -214,7 +214,7 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
    * @return
    */
   protected def toAttributeMap(offerAttributes: JList[Attribute]): Map[String, GeneratedMessage] = {
-    offerAttributes.map(attr => {
+    offerAttributes.asScala.map(attr => {
       val attrValue = attr.getType match {
         case Value.Type.SCALAR => attr.getScalar
         case Value.Type.RANGES => attr.getRanges
@@ -253,7 +253,7 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
             requiredValues.map(_.toLong).exists(offerRange.contains(_))
           case Some(offeredValue: Value.Set) =>
             // check if the specified required values is a subset of offered set
-            requiredValues.subsetOf(offeredValue.getItemList.toSet)
+            requiredValues.subsetOf(offeredValue.getItemList.asScala.toSet)
           case Some(textValue: Value.Text) =>
             // check if the specified value is equal, if multiple values are specified
             // we succeed if any of them match.
@@ -299,14 +299,13 @@ private[mesos] trait MesosSchedulerUtils extends Logging {
       Map()
     } else {
       try {
-        Map() ++ mapAsScalaMap(splitter.split(constraintsVal)).map {
-          case (k, v) =>
-            if (v == null || v.isEmpty) {
-              (k, Set[String]())
-            } else {
-              (k, v.split(',').toSet)
-            }
-        }
+        splitter.split(constraintsVal).asScala.toMap.mapValues(v =>
+          if (v == null || v.isEmpty) {
+            Set[String]()
+          } else {
+            v.split(',').toSet
+          }
+        )
       } catch {
         case NonFatal(e) =>
           throw new IllegalArgumentException(s"Bad constraint string: $constraintsVal", e)
diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index 0ff7562e912ca..048a938507277 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -21,6 +21,7 @@ import java.io.{EOFException, IOException, InputStream, OutputStream}
 import java.nio.ByteBuffer
 import javax.annotation.Nullable
 
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import com.esotericsoftware.kryo.{Kryo, KryoException}
@@ -373,16 +374,15 @@ private class JavaIterableWrapperSerializer
   override def read(kryo: Kryo, in: KryoInput, clz: Class[java.lang.Iterable[_]])
     : java.lang.Iterable[_] = {
     kryo.readClassAndObject(in) match {
-      case scalaIterable: Iterable[_] =>
-        scala.collection.JavaConversions.asJavaIterable(scalaIterable)
-      case javaIterable: java.lang.Iterable[_] =>
-        javaIterable
+      case scalaIterable: Iterable[_] => scalaIterable.asJava
+      case javaIterable: java.lang.Iterable[_] => javaIterable
     }
   }
 }
 
 private object JavaIterableWrapperSerializer extends Logging {
-  // The class returned by asJavaIterable (scala.collection.convert.Wrappers$IterableWrapper).
+  // The class returned by JavaConverters.asJava
+  // (scala.collection.convert.Wrappers$IterableWrapper).
   val wrapperClass =
     scala.collection.convert.WrapAsJava.asJavaIterable(Seq(1)).getClass
 
diff --git a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
index f6a96d81e7aa9..c057de9b3f4df 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/FileShuffleBlockResolver.scala
@@ -21,7 +21,7 @@ import java.io.File
 import java.util.concurrent.ConcurrentLinkedQueue
 import java.util.concurrent.atomic.AtomicInteger
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.{Logging, SparkConf, SparkEnv}
 import org.apache.spark.executor.ShuffleWriteMetrics
@@ -210,11 +210,13 @@ private[spark] class FileShuffleBlockResolver(conf: SparkConf)
     shuffleStates.get(shuffleId) match {
       case Some(state) =>
         if (consolidateShuffleFiles) {
-          for (fileGroup <- state.allFileGroups; file <- fileGroup.files) {
+          for (fileGroup <- state.allFileGroups.asScala;
+               file <- fileGroup.files) {
             file.delete()
           }
         } else {
-          for (mapId <- state.completedMapTasks; reduceId <- 0 until state.numBuckets) {
+          for (mapId <- state.completedMapTasks.asScala;
+               reduceId <- 0 until state.numBuckets) {
             val blockId = new ShuffleBlockId(shuffleId, mapId, reduceId)
             blockManager.diskBlockManager.getFile(blockId).delete()
           }
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
index 6fec5240707a6..7db6035553ae6 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterEndpoint.scala
@@ -21,7 +21,7 @@ import java.util.{HashMap => JHashMap}
 
 import scala.collection.immutable.HashSet
 import scala.collection.mutable
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.concurrent.{ExecutionContext, Future}
 
 import org.apache.spark.rpc.{RpcEndpointRef, RpcEnv, RpcCallContext, ThreadSafeRpcEndpoint}
@@ -133,7 +133,7 @@ class BlockManagerMasterEndpoint(
 
     // Find all blocks for the given RDD, remove the block from both blockLocations and
     // the blockManagerInfo that is tracking the blocks.
-    val blocks = blockLocations.keys.flatMap(_.asRDDId).filter(_.rddId == rddId)
+    val blocks = blockLocations.asScala.keys.flatMap(_.asRDDId).filter(_.rddId == rddId)
     blocks.foreach { blockId =>
       val bms: mutable.HashSet[BlockManagerId] = blockLocations.get(blockId)
       bms.foreach(bm => blockManagerInfo.get(bm).foreach(_.removeBlock(blockId)))
@@ -242,7 +242,7 @@ class BlockManagerMasterEndpoint(
 
   private def storageStatus: Array[StorageStatus] = {
     blockManagerInfo.map { case (blockManagerId, info) =>
-      new StorageStatus(blockManagerId, info.maxMem, info.blocks)
+      new StorageStatus(blockManagerId, info.maxMem, info.blocks.asScala)
     }.toArray
   }
 
@@ -292,7 +292,7 @@ class BlockManagerMasterEndpoint(
           if (askSlaves) {
             info.slaveEndpoint.ask[Seq[BlockId]](getMatchingBlockIds)
           } else {
-            Future { info.blocks.keys.filter(filter).toSeq }
+            Future { info.blocks.asScala.keys.filter(filter).toSeq }
           }
         future
       }
diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
index 78e7ddc27d1c7..1738258a0c794 100644
--- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
+++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.util
 
-import scala.collection.JavaConversions.mapAsJavaMap
+import scala.collection.JavaConverters._
 
 import akka.actor.{ActorRef, ActorSystem, ExtendedActorSystem}
 import akka.pattern.ask
@@ -92,7 +92,7 @@ private[spark] object AkkaUtils extends Logging {
     val akkaSslConfig = securityManager.akkaSSLOptions.createAkkaConfig
         .getOrElse(ConfigFactory.empty())
 
-    val akkaConf = ConfigFactory.parseMap(conf.getAkkaConf.toMap[String, String])
+    val akkaConf = ConfigFactory.parseMap(conf.getAkkaConf.toMap.asJava)
       .withFallback(akkaSslConfig).withFallback(ConfigFactory.parseString(
       s"""
       |akka.daemonic = on
diff --git a/core/src/main/scala/org/apache/spark/util/ListenerBus.scala b/core/src/main/scala/org/apache/spark/util/ListenerBus.scala
index a725767d08cc2..13cb516b583e9 100644
--- a/core/src/main/scala/org/apache/spark/util/ListenerBus.scala
+++ b/core/src/main/scala/org/apache/spark/util/ListenerBus.scala
@@ -19,12 +19,11 @@ package org.apache.spark.util
 
 import java.util.concurrent.CopyOnWriteArrayList
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 import scala.util.control.NonFatal
 
 import org.apache.spark.Logging
-import org.apache.spark.scheduler.SparkListener
 
 /**
  * An event bus which posts events to its listeners.
@@ -46,7 +45,7 @@ private[spark] trait ListenerBus[L <: AnyRef, E] extends Logging {
    * `postToAll` in the same thread for all events.
    */
   final def postToAll(event: E): Unit = {
-    // JavaConversions will create a JIterableWrapper if we use some Scala collection functions.
+    // JavaConverters can create a JIterableWrapper if we use asScala.
     // However, this method will be called frequently. To avoid the wrapper cost, here ewe use
     // Java Iterator directly.
     val iter = listeners.iterator
@@ -69,7 +68,7 @@ private[spark] trait ListenerBus[L <: AnyRef, E] extends Logging {
 
   private[spark] def findListenersByClass[T <: L : ClassTag](): Seq[T] = {
     val c = implicitly[ClassTag[T]].runtimeClass
-    listeners.filter(_.getClass == c).map(_.asInstanceOf[T]).toSeq
+    listeners.asScala.filter(_.getClass == c).map(_.asInstanceOf[T]).toSeq
   }
 
 }
diff --git a/core/src/main/scala/org/apache/spark/util/MutableURLClassLoader.scala b/core/src/main/scala/org/apache/spark/util/MutableURLClassLoader.scala
index 169489df6c1ea..a1c33212cdb2b 100644
--- a/core/src/main/scala/org/apache/spark/util/MutableURLClassLoader.scala
+++ b/core/src/main/scala/org/apache/spark/util/MutableURLClassLoader.scala
@@ -21,8 +21,6 @@ import java.net.{URLClassLoader, URL}
 import java.util.Enumeration
 import java.util.concurrent.ConcurrentHashMap
 
-import scala.collection.JavaConversions._
-
 /**
  * URL class loader that exposes the `addURL` and `getURLs` methods in URLClassLoader.
  */
diff --git a/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala b/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala
index 8de75ba9a9c92..d7e5143c30953 100644
--- a/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala
+++ b/core/src/main/scala/org/apache/spark/util/TimeStampedHashMap.scala
@@ -21,7 +21,8 @@ import java.util.Set
 import java.util.Map.Entry
 import java.util.concurrent.ConcurrentHashMap
 
-import scala.collection.{JavaConversions, mutable}
+import scala.collection.JavaConverters._
+import scala.collection.mutable
 
 import org.apache.spark.Logging
 
@@ -50,8 +51,7 @@ private[spark] class TimeStampedHashMap[A, B](updateTimeStampOnGet: Boolean = fa
   }
 
   def iterator: Iterator[(A, B)] = {
-    val jIterator = getEntrySet.iterator
-    JavaConversions.asScalaIterator(jIterator).map(kv => (kv.getKey, kv.getValue.value))
+    getEntrySet.iterator.asScala.map(kv => (kv.getKey, kv.getValue.value))
   }
 
   def getEntrySet: Set[Entry[A, TimeStampedValue[B]]] = internalMap.entrySet
@@ -90,9 +90,7 @@ private[spark] class TimeStampedHashMap[A, B](updateTimeStampOnGet: Boolean = fa
   }
 
   override def filter(p: ((A, B)) => Boolean): mutable.Map[A, B] = {
-    JavaConversions.mapAsScalaConcurrentMap(internalMap)
-      .map { case (k, TimeStampedValue(v, t)) => (k, v) }
-      .filter(p)
+    internalMap.asScala.map { case (k, TimeStampedValue(v, t)) => (k, v) }.filter(p)
   }
 
   override def empty: mutable.Map[A, B] = new TimeStampedHashMap[A, B]()
diff --git a/core/src/main/scala/org/apache/spark/util/TimeStampedHashSet.scala b/core/src/main/scala/org/apache/spark/util/TimeStampedHashSet.scala
index 7cd8f28b12dd6..65efeb1f4c19c 100644
--- a/core/src/main/scala/org/apache/spark/util/TimeStampedHashSet.scala
+++ b/core/src/main/scala/org/apache/spark/util/TimeStampedHashSet.scala
@@ -19,7 +19,7 @@ package org.apache.spark.util
 
 import java.util.concurrent.ConcurrentHashMap
 
-import scala.collection.JavaConversions
+import scala.collection.JavaConverters._
 import scala.collection.mutable.Set
 
 private[spark] class TimeStampedHashSet[A] extends Set[A] {
@@ -31,7 +31,7 @@ private[spark] class TimeStampedHashSet[A] extends Set[A] {
 
   def iterator: Iterator[A] = {
     val jIterator = internalMap.entrySet().iterator()
-    JavaConversions.asScalaIterator(jIterator).map(_.getKey)
+    jIterator.asScala.map(_.getKey)
   }
 
   override def + (elem: A): Set[A] = {
diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala
index 8313312226713..2bab4af2e73ab 100644
--- a/core/src/main/scala/org/apache/spark/util/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/Utils.scala
@@ -25,7 +25,7 @@ import java.util.{Properties, Locale, Random, UUID}
 import java.util.concurrent._
 import javax.net.ssl.HttpsURLConnection
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.Map
 import scala.collection.mutable.ArrayBuffer
 import scala.io.Source
@@ -748,12 +748,12 @@ private[spark] object Utils extends Logging {
         // getNetworkInterfaces returns ifs in reverse order compared to ifconfig output order
         // on unix-like system. On windows, it returns in index order.
         // It's more proper to pick ip address following system output order.
-        val activeNetworkIFs = NetworkInterface.getNetworkInterfaces.toList
+        val activeNetworkIFs = NetworkInterface.getNetworkInterfaces.asScala.toSeq
         val reOrderedNetworkIFs = if (isWindows) activeNetworkIFs else activeNetworkIFs.reverse
 
         for (ni <- reOrderedNetworkIFs) {
-          val addresses = ni.getInetAddresses.toList
-            .filterNot(addr => addr.isLinkLocalAddress || addr.isLoopbackAddress)
+          val addresses = ni.getInetAddresses.asScala
+            .filterNot(addr => addr.isLinkLocalAddress || addr.isLoopbackAddress).toSeq
           if (addresses.nonEmpty) {
             val addr = addresses.find(_.isInstanceOf[Inet4Address]).getOrElse(addresses.head)
             // because of Inet6Address.toHostName may add interface at the end if it knows about it
@@ -1498,10 +1498,8 @@ private[spark] object Utils extends Logging {
     * properties which have been set explicitly, as well as those for which only a default value
     * has been defined. */
   def getSystemProperties: Map[String, String] = {
-    val sysProps = for (key <- System.getProperties.stringPropertyNames()) yield
-      (key, System.getProperty(key))
-
-    sysProps.toMap
+    System.getProperties.stringPropertyNames().asScala
+      .map(key => (key, System.getProperty(key))).toMap
   }
 
   /**
@@ -1812,7 +1810,8 @@ private[spark] object Utils extends Logging {
     try {
       val properties = new Properties()
       properties.load(inReader)
-      properties.stringPropertyNames().map(k => (k, properties(k).trim)).toMap
+      properties.stringPropertyNames().asScala.map(
+        k => (k, properties.getProperty(k).trim)).toMap
     } catch {
       case e: IOException =>
         throw new SparkException(s"Failed when loading Spark properties from $filename", e)
@@ -1941,7 +1940,8 @@ private[spark] object Utils extends Logging {
           return true
         }
         isBindCollision(e.getCause)
-      case e: MultiException => e.getThrowables.exists(isBindCollision)
+      case e: MultiException =>
+        e.getThrowables.asScala.exists(isBindCollision)
       case e: Exception => isBindCollision(e.getCause)
       case _ => false
     }
diff --git a/core/src/main/scala/org/apache/spark/util/collection/Utils.scala b/core/src/main/scala/org/apache/spark/util/collection/Utils.scala
index bdbca00a00622..4939b600dbfbd 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/Utils.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/Utils.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.util.collection
 
-import scala.collection.JavaConversions.{collectionAsScalaIterable, asJavaIterator}
+import scala.collection.JavaConverters._
 
 import com.google.common.collect.{Ordering => GuavaOrdering}
 
@@ -34,6 +34,6 @@ private[spark] object Utils {
     val ordering = new GuavaOrdering[T] {
       override def compare(l: T, r: T): Int = ord.compare(l, r)
     }
-    collectionAsScalaIterable(ordering.leastOf(asJavaIterator(input), num)).iterator
+    ordering.leastOf(input.asJava, num).iterator.asScala
   }
 }
diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index ffe4b4baffb2a..ebd3d61ae7324 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -24,10 +24,10 @@
 import java.util.*;
 import java.util.concurrent.*;
 
-import scala.collection.JavaConversions;
 import scala.Tuple2;
 import scala.Tuple3;
 import scala.Tuple4;
+import scala.collection.JavaConverters;
 
 import com.google.common.collect.ImmutableMap;
 import com.google.common.collect.Iterables;
@@ -1473,7 +1473,9 @@ public Integer call(Integer v1, Integer v2) throws Exception {
     Assert.assertEquals(expected, results);
 
     Partitioner defaultPartitioner = Partitioner.defaultPartitioner(
-        combinedRDD.rdd(), JavaConversions.asScalaBuffer(Lists.<RDD<?>>newArrayList()));
+        combinedRDD.rdd(),
+        JavaConverters.collectionAsScalaIterableConverter(
+            Collections.<RDD<?>>emptyList()).asScala().toSeq());
     combinedRDD = originalRDD.keyBy(keyFunction)
         .combineByKey(
              createCombinerFunction,
diff --git a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala
index 90cb7da94e88a..ff9a92cc0a421 100644
--- a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark
 
 import java.util.concurrent.{TimeUnit, Executors}
 
+import scala.collection.JavaConverters._
 import scala.concurrent.duration._
 import scala.language.postfixOps
 import scala.util.{Try, Random}
@@ -148,7 +149,6 @@ class SparkConfSuite extends SparkFunSuite with LocalSparkContext with ResetSyst
   }
 
   test("Thread safeness - SPARK-5425") {
-    import scala.collection.JavaConversions._
     val executor = Executors.newSingleThreadScheduledExecutor()
     val sf = executor.scheduleAtFixedRate(new Runnable {
       override def run(): Unit =
@@ -163,8 +163,9 @@ class SparkConfSuite extends SparkFunSuite with LocalSparkContext with ResetSyst
       }
     } finally {
       executor.shutdownNow()
-      for (key <- System.getProperties.stringPropertyNames() if key.startsWith("spark.5425."))
-        System.getProperties.remove(key)
+      val sysProps = System.getProperties
+      for (key <- sysProps.stringPropertyNames().asScala if key.startsWith("spark.5425."))
+        sysProps.remove(key)
     }
   }
 
diff --git a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala
index cbd2aee10c0e2..86eb41dd7e5d7 100644
--- a/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/LogUrlsStandaloneSuite.scala
@@ -19,7 +19,6 @@ package org.apache.spark.deploy
 
 import java.net.URL
 
-import scala.collection.JavaConversions._
 import scala.collection.mutable
 import scala.io.Source
 
diff --git a/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala b/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala
index 47a64081e297e..1ed4bae3ca21e 100644
--- a/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/RPackageUtilsSuite.scala
@@ -21,14 +21,14 @@ import java.io.{PrintStream, OutputStream, File}
 import java.net.URI
 import java.util.jar.Attributes.Name
 import java.util.jar.{JarFile, Manifest}
-import java.util.zip.{ZipEntry, ZipFile}
+import java.util.zip.ZipFile
 
-import org.scalatest.BeforeAndAfterEach
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 
 import com.google.common.io.Files
 import org.apache.commons.io.FileUtils
+import org.scalatest.BeforeAndAfterEach
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.api.r.RUtils
@@ -142,7 +142,7 @@ class RPackageUtilsSuite extends SparkFunSuite with BeforeAndAfterEach {
       IvyTestUtils.writeFile(fakePackageDir, "DESCRIPTION", "abc")
       val finalZip = RPackageUtils.zipRLibraries(tempDir, "sparkr.zip")
       assert(finalZip.exists())
-      val entries = new ZipFile(finalZip).entries().toSeq.map(_.getName)
+      val entries = new ZipFile(finalZip).entries().asScala.map(_.getName).toSeq
       assert(entries.contains("/test.R"))
       assert(entries.contains("/SparkR/abc.R"))
       assert(entries.contains("/SparkR/DESCRIPTION"))
diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
index bed6f3ea61241..98664dc1101e6 100644
--- a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala
@@ -19,8 +19,6 @@ package org.apache.spark.deploy.worker
 
 import java.io.File
 
-import scala.collection.JavaConversions._
-
 import org.apache.spark.deploy.{ApplicationDescription, Command, ExecutorState}
 import org.apache.spark.{SecurityManager, SparkConf, SparkFunSuite}
 
@@ -36,6 +34,7 @@ class ExecutorRunnerTest extends SparkFunSuite {
       ExecutorState.RUNNING)
     val builder = CommandUtils.buildProcessBuilder(
       appDesc.command, new SecurityManager(conf), 512, sparkHome, er.substituteVariables)
-    assert(builder.command().last === appId)
+    val builderCommand = builder.command()
+    assert(builderCommand.get(builderCommand.size() - 1) === appId)
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
index 730535ece7878..a9652d7e7d0b0 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala
@@ -20,7 +20,7 @@ package org.apache.spark.scheduler
 import java.util.concurrent.Semaphore
 
 import scala.collection.mutable
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.scalatest.Matchers
 
@@ -365,10 +365,9 @@ class SparkListenerSuite extends SparkFunSuite with LocalSparkContext with Match
       .set("spark.extraListeners", classOf[ListenerThatAcceptsSparkConf].getName + "," +
         classOf[BasicJobCounter].getName)
     sc = new SparkContext(conf)
-    sc.listenerBus.listeners.collect { case x: BasicJobCounter => x}.size should be (1)
-    sc.listenerBus.listeners.collect {
-      case x: ListenerThatAcceptsSparkConf => x
-    }.size should be (1)
+    sc.listenerBus.listeners.asScala.count(_.isInstanceOf[BasicJobCounter]) should be (1)
+    sc.listenerBus.listeners.asScala
+      .count(_.isInstanceOf[ListenerThatAcceptsSparkConf]) should be (1)
   }
 
   /**
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
index 5ed30f64d705f..319b3173e7a6e 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
@@ -18,10 +18,11 @@
 package org.apache.spark.scheduler.cluster.mesos
 
 import java.nio.ByteBuffer
-import java.util
+import java.util.Arrays
+import java.util.Collection
 import java.util.Collections
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
@@ -61,7 +62,7 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
 
     val mesosSchedulerBackend = new MesosSchedulerBackend(taskScheduler, sc, "master")
 
-    val resources = List(
+    val resources = Arrays.asList(
       mesosSchedulerBackend.createResource("cpus", 4),
       mesosSchedulerBackend.createResource("mem", 1024))
     // uri is null.
@@ -98,7 +99,7 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     val backend = new MesosSchedulerBackend(taskScheduler, sc, "master")
 
     val (execInfo, _) = backend.createExecutorInfo(
-      List(backend.createResource("cpus", 4)), "mockExecutor")
+      Arrays.asList(backend.createResource("cpus", 4)), "mockExecutor")
     assert(execInfo.getContainer.getDocker.getImage.equals("spark/mock"))
     val portmaps = execInfo.getContainer.getDocker.getPortMappingsList
     assert(portmaps.get(0).getHostPort.equals(80))
@@ -179,7 +180,7 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     when(taskScheduler.resourceOffers(expectedWorkerOffers)).thenReturn(Seq(Seq(taskDesc)))
     when(taskScheduler.CPUS_PER_TASK).thenReturn(2)
 
-    val capture = ArgumentCaptor.forClass(classOf[util.Collection[TaskInfo]])
+    val capture = ArgumentCaptor.forClass(classOf[Collection[TaskInfo]])
     when(
       driver.launchTasks(
         Matchers.eq(Collections.singleton(mesosOffers.get(0).getId)),
@@ -279,7 +280,7 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     when(taskScheduler.resourceOffers(expectedWorkerOffers)).thenReturn(Seq(Seq(taskDesc)))
     when(taskScheduler.CPUS_PER_TASK).thenReturn(1)
 
-    val capture = ArgumentCaptor.forClass(classOf[util.Collection[TaskInfo]])
+    val capture = ArgumentCaptor.forClass(classOf[Collection[TaskInfo]])
     when(
       driver.launchTasks(
         Matchers.eq(Collections.singleton(mesosOffers.get(0).getId)),
@@ -304,7 +305,7 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
     assert(cpusDev.getName.equals("cpus"))
     assert(cpusDev.getScalar.getValue.equals(1.0))
     assert(cpusDev.getRole.equals("dev"))
-    val executorResources = taskInfo.getExecutor.getResourcesList
+    val executorResources = taskInfo.getExecutor.getResourcesList.asScala
     assert(executorResources.exists { r =>
       r.getName.equals("mem") && r.getScalar.getValue.equals(484.0) && r.getRole.equals("prod")
     })
diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
index 23a1fdb0f5009..8d1c9d17e977e 100644
--- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.serializer
 
 import java.io.{ByteArrayInputStream, ByteArrayOutputStream}
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.reflect.ClassTag
 
@@ -173,7 +174,7 @@ class KryoSerializerSuite extends SparkFunSuite with SharedSparkContext {
   test("asJavaIterable") {
     // Serialize a collection wrapped by asJavaIterable
     val ser = new KryoSerializer(conf).newInstance()
-    val a = ser.serialize(scala.collection.convert.WrapAsJava.asJavaIterable(Seq(12345)))
+    val a = ser.serialize(Seq(12345).asJava)
     val b = ser.deserialize[java.lang.Iterable[Int]](a)
     assert(b.iterator().next() === 12345)
 
diff --git a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
index 69888b2694bae..22e30ecaf0533 100644
--- a/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala
@@ -21,7 +21,6 @@ import java.net.{HttpURLConnection, URL}
 import javax.servlet.http.{HttpServletResponse, HttpServletRequest}
 
 import scala.io.Source
-import scala.collection.JavaConversions._
 import scala.xml.Node
 
 import com.gargoylesoftware.htmlunit.DefaultCssErrorHandler
@@ -341,15 +340,15 @@ class UISeleniumSuite extends SparkFunSuite with WebBrowser with Matchers with B
         // The completed jobs table should have two rows. The first row will be the most recent job:
         val firstRow = find(cssSelector("tbody tr")).get.underlying
         val firstRowColumns = firstRow.findElements(By.tagName("td"))
-        firstRowColumns(0).getText should be ("1")
-        firstRowColumns(4).getText should be ("1/1 (2 skipped)")
-        firstRowColumns(5).getText should be ("8/8 (16 skipped)")
+        firstRowColumns.get(0).getText should be ("1")
+        firstRowColumns.get(4).getText should be ("1/1 (2 skipped)")
+        firstRowColumns.get(5).getText should be ("8/8 (16 skipped)")
         // The second row is the first run of the job, where nothing was skipped:
         val secondRow = findAll(cssSelector("tbody tr")).toSeq(1).underlying
         val secondRowColumns = secondRow.findElements(By.tagName("td"))
-        secondRowColumns(0).getText should be ("0")
-        secondRowColumns(4).getText should be ("3/3")
-        secondRowColumns(5).getText should be ("24/24")
+        secondRowColumns.get(0).getText should be ("0")
+        secondRowColumns.get(4).getText should be ("3/3")
+        secondRowColumns.get(5).getText should be ("24/24")
       }
     }
   }
@@ -502,8 +501,8 @@ class UISeleniumSuite extends SparkFunSuite with WebBrowser with Matchers with B
         for {
           (row, idx) <- rows.zipWithIndex
           columns = row.findElements(By.tagName("td"))
-          id = columns(0).getText()
-          description = columns(1).getText()
+          id = columns.get(0).getText()
+          description = columns.get(1).getText()
         } {
           id should be (expJobInfo(idx)._1)
           description should include (expJobInfo(idx)._2)
@@ -547,8 +546,8 @@ class UISeleniumSuite extends SparkFunSuite with WebBrowser with Matchers with B
         for {
           (row, idx) <- rows.zipWithIndex
           columns = row.findElements(By.tagName("td"))
-          id = columns(0).getText()
-          description = columns(1).getText()
+          id = columns.get(0).getText()
+          description = columns.get(1).getText()
         } {
           id should be (expStageInfo(idx)._1)
           description should include (expStageInfo(idx)._2)
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
index 36832f51d2ad4..fa07c1e5017cd 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
@@ -19,10 +19,7 @@
 package org.apache.spark.examples
 
 import java.nio.ByteBuffer
-
-import scala.collection.JavaConversions._
-import scala.collection.mutable.ListBuffer
-import scala.collection.immutable.Map
+import java.util.Collections
 
 import org.apache.cassandra.hadoop.ConfigHelper
 import org.apache.cassandra.hadoop.cql3.CqlPagingInputFormat
@@ -32,7 +29,6 @@ import org.apache.cassandra.utils.ByteBufferUtil
 import org.apache.hadoop.mapreduce.Job
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.SparkContext._
 
 
 /*
@@ -121,12 +117,9 @@ object CassandraCQLTest {
 
     val casoutputCF = aggregatedRDD.map {
       case (productId, saleCount) => {
-        val outColFamKey = Map("prod_id" -> ByteBufferUtil.bytes(productId))
-        val outKey: java.util.Map[String, ByteBuffer] = outColFamKey
-        var outColFamVal = new ListBuffer[ByteBuffer]
-        outColFamVal += ByteBufferUtil.bytes(saleCount)
-        val outVal: java.util.List[ByteBuffer] = outColFamVal
-       (outKey, outVal)
+        val outKey = Collections.singletonMap("prod_id", ByteBufferUtil.bytes(productId))
+        val outVal = Collections.singletonList(ByteBufferUtil.bytes(saleCount))
+        (outKey, outVal)
       }
     }
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
index 96ef3e198e380..2e56d24c60c33 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
@@ -19,10 +19,9 @@
 package org.apache.spark.examples
 
 import java.nio.ByteBuffer
+import java.util.Arrays
 import java.util.SortedMap
 
-import scala.collection.JavaConversions._
-
 import org.apache.cassandra.db.IColumn
 import org.apache.cassandra.hadoop.ColumnFamilyOutputFormat
 import org.apache.cassandra.hadoop.ConfigHelper
@@ -32,7 +31,6 @@ import org.apache.cassandra.utils.ByteBufferUtil
 import org.apache.hadoop.mapreduce.Job
 
 import org.apache.spark.{SparkConf, SparkContext}
-import org.apache.spark.SparkContext._
 
 /*
  * This example demonstrates using Spark with Cassandra with the New Hadoop API and Cassandra
@@ -118,7 +116,7 @@ object CassandraTest {
 
         val outputkey = ByteBufferUtil.bytes(word + "-COUNT-" + System.currentTimeMillis)
 
-        val mutations: java.util.List[Mutation] = new Mutation() :: new Mutation() :: Nil
+        val mutations = Arrays.asList(new Mutation(), new Mutation())
         mutations.get(0).setColumn_or_supercolumn(new ColumnOrSuperColumn())
         mutations.get(0).column_or_supercolumn.setColumn(colWord)
         mutations.get(1).setColumn_or_supercolumn(new ColumnOrSuperColumn())
diff --git a/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala b/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala
index c42df2b8845d2..bec61f3cd4296 100644
--- a/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala
@@ -18,7 +18,7 @@
 // scalastyle:off println
 package org.apache.spark.examples
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.util.Utils
 
@@ -36,10 +36,10 @@ object DriverSubmissionTest {
     val properties = Utils.getSystemProperties
 
     println("Environment variables containing SPARK_TEST:")
-    env.filter{case (k, v) => k.contains("SPARK_TEST")}.foreach(println)
+    env.asScala.filter { case (k, _) => k.contains("SPARK_TEST")}.foreach(println)
 
     println("System properties containing spark.test:")
-    properties.filter{case (k, v) => k.toString.contains("spark.test")}.foreach(println)
+    properties.filter { case (k, _) => k.toString.contains("spark.test") }.foreach(println)
 
     for (i <- 1 until numSecondsToSleep) {
       println(s"Alive for $i out of $numSecondsToSleep seconds")
diff --git a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala
index 3ebb112fc069e..805184e740f06 100644
--- a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/AvroConverters.scala
@@ -19,7 +19,7 @@ package org.apache.spark.examples.pythonconverters
 
 import java.util.{Collection => JCollection, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.avro.generic.{GenericFixed, IndexedRecord}
 import org.apache.avro.mapred.AvroWrapper
@@ -58,7 +58,7 @@ object AvroConversionUtil extends Serializable {
     val map = new java.util.HashMap[String, Any]
     obj match {
       case record: IndexedRecord =>
-        record.getSchema.getFields.zipWithIndex.foreach { case (f, i) =>
+        record.getSchema.getFields.asScala.zipWithIndex.foreach { case (f, i) =>
           map.put(f.name, fromAvro(record.get(i), f.schema))
         }
       case other => throw new SparkException(
@@ -68,9 +68,9 @@ object AvroConversionUtil extends Serializable {
   }
 
   def unpackMap(obj: Any, schema: Schema): JMap[String, Any] = {
-    obj.asInstanceOf[JMap[_, _]].map { case (key, value) =>
+    obj.asInstanceOf[JMap[_, _]].asScala.map { case (key, value) =>
       (key.toString, fromAvro(value, schema.getValueType))
-    }
+    }.asJava
   }
 
   def unpackFixed(obj: Any, schema: Schema): Array[Byte] = {
@@ -91,17 +91,17 @@ object AvroConversionUtil extends Serializable {
 
   def unpackArray(obj: Any, schema: Schema): JCollection[Any] = obj match {
     case c: JCollection[_] =>
-      c.map(fromAvro(_, schema.getElementType))
+      c.asScala.map(fromAvro(_, schema.getElementType)).toSeq.asJava
     case arr: Array[_] if arr.getClass.getComponentType.isPrimitive =>
-      arr.toSeq
+      arr.toSeq.asJava.asInstanceOf[JCollection[Any]]
     case arr: Array[_] =>
-      arr.map(fromAvro(_, schema.getElementType)).toSeq
+      arr.map(fromAvro(_, schema.getElementType)).toSeq.asJava
     case other => throw new SparkException(
       s"Unknown ARRAY type ${other.getClass.getName}")
   }
 
   def unpackUnion(obj: Any, schema: Schema): Any = {
-    schema.getTypes.toList match {
+    schema.getTypes.asScala.toList match {
       case List(s) => fromAvro(obj, s)
       case List(n, s) if n.getType == NULL => fromAvro(obj, s)
       case List(s, n) if n.getType == NULL => fromAvro(obj, s)
diff --git a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala
index 83feb5703b908..00ce47af4813d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/CassandraConverters.scala
@@ -17,11 +17,13 @@
 
 package org.apache.spark.examples.pythonconverters
 
-import org.apache.spark.api.python.Converter
 import java.nio.ByteBuffer
+
+import scala.collection.JavaConverters._
+
 import org.apache.cassandra.utils.ByteBufferUtil
-import collection.JavaConversions._
 
+import org.apache.spark.api.python.Converter
 
 /**
  * Implementation of [[org.apache.spark.api.python.Converter]] that converts Cassandra
@@ -30,7 +32,7 @@ import collection.JavaConversions._
 class CassandraCQLKeyConverter extends Converter[Any, java.util.Map[String, Int]] {
   override def convert(obj: Any): java.util.Map[String, Int] = {
     val result = obj.asInstanceOf[java.util.Map[String, ByteBuffer]]
-    mapAsJavaMap(result.mapValues(bb => ByteBufferUtil.toInt(bb)))
+    result.asScala.mapValues(ByteBufferUtil.toInt).asJava
   }
 }
 
@@ -41,7 +43,7 @@ class CassandraCQLKeyConverter extends Converter[Any, java.util.Map[String, Int]
 class CassandraCQLValueConverter extends Converter[Any, java.util.Map[String, String]] {
   override def convert(obj: Any): java.util.Map[String, String] = {
     val result = obj.asInstanceOf[java.util.Map[String, ByteBuffer]]
-    mapAsJavaMap(result.mapValues(bb => ByteBufferUtil.string(bb)))
+    result.asScala.mapValues(ByteBufferUtil.string).asJava
   }
 }
 
@@ -52,7 +54,7 @@ class CassandraCQLValueConverter extends Converter[Any, java.util.Map[String, St
 class ToCassandraCQLKeyConverter extends Converter[Any, java.util.Map[String, ByteBuffer]] {
   override def convert(obj: Any): java.util.Map[String, ByteBuffer] = {
     val input = obj.asInstanceOf[java.util.Map[String, Int]]
-    mapAsJavaMap(input.mapValues(i => ByteBufferUtil.bytes(i)))
+    input.asScala.mapValues(ByteBufferUtil.bytes).asJava
   }
 }
 
@@ -63,6 +65,6 @@ class ToCassandraCQLKeyConverter extends Converter[Any, java.util.Map[String, By
 class ToCassandraCQLValueConverter extends Converter[Any, java.util.List[ByteBuffer]] {
   override def convert(obj: Any): java.util.List[ByteBuffer] = {
     val input = obj.asInstanceOf[java.util.List[String]]
-    seqAsJavaList(input.map(s => ByteBufferUtil.bytes(s)))
+    input.asScala.map(ByteBufferUtil.bytes).asJava
   }
 }
diff --git a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala
index 90d48a64106c7..0a25ee7ae56f4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/pythonconverters/HBaseConverters.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.examples.pythonconverters
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.parsing.json.JSONObject
 
 import org.apache.spark.api.python.Converter
@@ -33,7 +33,6 @@ import org.apache.hadoop.hbase.CellUtil
  */
 class HBaseResultToStringConverter extends Converter[Any, String] {
   override def convert(obj: Any): String = {
-    import collection.JavaConverters._
     val result = obj.asInstanceOf[Result]
     val output = result.listCells.asScala.map(cell =>
         Map(
@@ -77,7 +76,7 @@ class StringToImmutableBytesWritableConverter extends Converter[Any, ImmutableBy
  */
 class StringListToPutConverter extends Converter[Any, Put] {
   override def convert(obj: Any): Put = {
-    val output = obj.asInstanceOf[java.util.ArrayList[String]].map(Bytes.toBytes(_)).toArray
+    val output = obj.asInstanceOf[java.util.ArrayList[String]].asScala.map(Bytes.toBytes).toArray
     val put = new Put(output(0))
     put.add(output(1), output(2), output(3))
   }
diff --git a/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala b/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala
index fa43629d49771..d2654700ea729 100644
--- a/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala
+++ b/external/flume-sink/src/test/scala/org/apache/spark/streaming/flume/sink/SparkSinkSuite.scala
@@ -20,7 +20,7 @@ import java.net.InetSocketAddress
 import java.util.concurrent.atomic.AtomicInteger
 import java.util.concurrent.{TimeUnit, CountDownLatch, Executors}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.concurrent.{ExecutionContext, Future}
 import scala.util.{Failure, Success}
 
@@ -166,7 +166,7 @@ class SparkSinkSuite extends FunSuite {
     channelContext.put("capacity", channelCapacity.toString)
     channelContext.put("transactionCapacity", 1000.toString)
     channelContext.put("keep-alive", 0.toString)
-    channelContext.putAll(overrides)
+    channelContext.putAll(overrides.asJava)
     channel.setName(scala.util.Random.nextString(10))
     channel.configure(channelContext)
 
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala
index 65c49c131518b..48df27b26867f 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/EventTransformer.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming.flume
 
 import java.io.{ObjectOutput, ObjectInput}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.util.Utils
 import org.apache.spark.Logging
@@ -60,7 +60,7 @@ private[streaming] object EventTransformer extends Logging {
     out.write(body)
     val numHeaders = headers.size()
     out.writeInt(numHeaders)
-    for ((k, v) <- headers) {
+    for ((k, v) <- headers.asScala) {
       val keyBuff = Utils.serialize(k.toString)
       out.writeInt(keyBuff.length)
       out.write(keyBuff)
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala
index 88cc2aa3bf022..b9d4e762ca05d 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeBatchFetcher.scala
@@ -16,7 +16,6 @@
  */
 package org.apache.spark.streaming.flume
 
-import scala.collection.JavaConversions._
 import scala.collection.mutable.ArrayBuffer
 
 import com.google.common.base.Throwables
@@ -155,7 +154,7 @@ private[flume] class FlumeBatchFetcher(receiver: FlumePollingReceiver) extends R
     val buffer = new ArrayBuffer[SparkFlumeEvent](events.size())
     var j = 0
     while (j < events.size()) {
-      val event = events(j)
+      val event = events.get(j)
       val sparkFlumeEvent = new SparkFlumeEvent()
       sparkFlumeEvent.event.setBody(event.getBody)
       sparkFlumeEvent.event.setHeaders(event.getHeaders)
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
index 1e32a365a1eee..2bf99cb3cba1f 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
@@ -22,7 +22,7 @@ import java.io.{ObjectInput, ObjectOutput, Externalizable}
 import java.nio.ByteBuffer
 import java.util.concurrent.Executors
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.flume.source.avro.AvroSourceProtocol
@@ -99,7 +99,7 @@ class SparkFlumeEvent() extends Externalizable {
 
     val numHeaders = event.getHeaders.size()
     out.writeInt(numHeaders)
-    for ((k, v) <- event.getHeaders) {
+    for ((k, v) <- event.getHeaders.asScala) {
       val keyBuff = Utils.serialize(k.toString)
       out.writeInt(keyBuff.length)
       out.write(keyBuff)
@@ -127,8 +127,7 @@ class FlumeEventServer(receiver : FlumeReceiver) extends AvroSourceProtocol {
   }
 
   override def appendBatch(events : java.util.List[AvroFlumeEvent]) : Status = {
-    events.foreach (event =>
-      receiver.store(SparkFlumeEvent.fromAvroFlumeEvent(event)))
+    events.asScala.foreach(event => receiver.store(SparkFlumeEvent.fromAvroFlumeEvent(event)))
     Status.OK
   }
 }
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
index 583e7dca317ad..0bc46209b8369 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
@@ -20,7 +20,7 @@ package org.apache.spark.streaming.flume
 import java.net.InetSocketAddress
 import java.util.concurrent.{LinkedBlockingQueue, Executors}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import com.google.common.util.concurrent.ThreadFactoryBuilder
@@ -94,9 +94,7 @@ private[streaming] class FlumePollingReceiver(
   override def onStop(): Unit = {
     logInfo("Shutting down Flume Polling Receiver")
     receiverExecutor.shutdownNow()
-    connections.foreach(connection => {
-      connection.transceiver.close()
-    })
+    connections.asScala.foreach(_.transceiver.close())
     channelFactory.releaseExternalResources()
   }
 
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala
index 9d9c3b189415f..70018c86f92be 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeTestUtils.scala
@@ -19,9 +19,9 @@ package org.apache.spark.streaming.flume
 
 import java.net.{InetSocketAddress, ServerSocket}
 import java.nio.ByteBuffer
-import java.util.{List => JList}
+import java.util.Collections
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.base.Charsets.UTF_8
 import org.apache.avro.ipc.NettyTransceiver
@@ -59,13 +59,13 @@ private[flume] class FlumeTestUtils {
   }
 
   /** Send data to the flume receiver */
-  def writeInput(input: JList[String], enableCompression: Boolean): Unit = {
+  def writeInput(input: Seq[String], enableCompression: Boolean): Unit = {
     val testAddress = new InetSocketAddress("localhost", testPort)
 
     val inputEvents = input.map { item =>
       val event = new AvroFlumeEvent
       event.setBody(ByteBuffer.wrap(item.getBytes(UTF_8)))
-      event.setHeaders(Map[CharSequence, CharSequence]("test" -> "header"))
+      event.setHeaders(Collections.singletonMap("test", "header"))
       event
     }
 
@@ -88,7 +88,7 @@ private[flume] class FlumeTestUtils {
     }
 
     // Send data
-    val status = client.appendBatch(inputEvents.toList)
+    val status = client.appendBatch(inputEvents.asJava)
     if (status != avro.Status.OK) {
       throw new AssertionError("Sent events unsuccessfully")
     }
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
index a65a9b921aafa..c719b80aca7ed 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala
@@ -21,7 +21,7 @@ import java.net.InetSocketAddress
 import java.io.{DataOutputStream, ByteArrayOutputStream}
 import java.util.{List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.api.java.function.PairFunction
 import org.apache.spark.api.python.PythonRDD
@@ -268,8 +268,8 @@ private[flume] class FlumeUtilsPythonHelper {
       maxBatchSize: Int,
       parallelism: Int
     ): JavaPairDStream[Array[Byte], Array[Byte]] = {
-    assert(hosts.length == ports.length)
-    val addresses = hosts.zip(ports).map {
+    assert(hosts.size() == ports.size())
+    val addresses = hosts.asScala.zip(ports.asScala).map {
       case (host, port) => new InetSocketAddress(host, port)
     }
     val dstream = FlumeUtils.createPollingStream(
@@ -286,7 +286,7 @@ private object FlumeUtilsPythonHelper {
     val output = new DataOutputStream(byteStream)
     try {
       output.writeInt(map.size)
-      map.foreach { kv =>
+      map.asScala.foreach { kv =>
         PythonRDD.writeUTF(kv._1.toString, output)
         PythonRDD.writeUTF(kv._2.toString, output)
       }
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala
index 91d63d49dbec3..a2ab320957db3 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/PollingFlumeTestUtils.scala
@@ -18,9 +18,8 @@
 package org.apache.spark.streaming.flume
 
 import java.util.concurrent._
-import java.util.{List => JList, Map => JMap}
+import java.util.{Map => JMap, Collections}
 
-import scala.collection.JavaConversions._
 import scala.collection.mutable.ArrayBuffer
 
 import com.google.common.base.Charsets.UTF_8
@@ -77,7 +76,7 @@ private[flume] class PollingFlumeTestUtils {
   /**
    * Start 2 sinks and return the ports
    */
-  def startMultipleSinks(): JList[Int] = {
+  def startMultipleSinks(): Seq[Int] = {
     channels.clear()
     sinks.clear()
 
@@ -138,8 +137,7 @@ private[flume] class PollingFlumeTestUtils {
   /**
    * A Python-friendly method to assert the output
    */
-  def assertOutput(
-      outputHeaders: JList[JMap[String, String]], outputBodies: JList[String]): Unit = {
+  def assertOutput(outputHeaders: Seq[JMap[String, String]], outputBodies: Seq[String]): Unit = {
     require(outputHeaders.size == outputBodies.size)
     val eventSize = outputHeaders.size
     if (eventSize != totalEventsPerChannel * channels.size) {
@@ -149,12 +147,12 @@ private[flume] class PollingFlumeTestUtils {
     var counter = 0
     for (k <- 0 until channels.size; i <- 0 until totalEventsPerChannel) {
       val eventBodyToVerify = s"${channels(k).getName}-$i"
-      val eventHeaderToVerify: JMap[String, String] = Map[String, String](s"test-$i" -> "header")
+      val eventHeaderToVerify: JMap[String, String] = Collections.singletonMap(s"test-$i", "header")
       var found = false
       var j = 0
       while (j < eventSize && !found) {
-        if (eventBodyToVerify == outputBodies.get(j) &&
-          eventHeaderToVerify == outputHeaders.get(j)) {
+        if (eventBodyToVerify == outputBodies(j) &&
+          eventHeaderToVerify == outputHeaders(j)) {
           found = true
           counter += 1
         }
@@ -195,7 +193,7 @@ private[flume] class PollingFlumeTestUtils {
         tx.begin()
         for (j <- 0 until eventsPerBatch) {
           channel.put(EventBuilder.withBody(s"${channel.getName}-$t".getBytes(UTF_8),
-            Map[String, String](s"test-$t" -> "header")))
+            Collections.singletonMap(s"test-$t", "header")))
           t += 1
         }
         tx.commit()
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
index d5f9a0aa38f9f..ff2fb8eed204c 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumePollingStreamSuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming.flume
 
 import java.net.InetSocketAddress
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
 import scala.concurrent.duration._
 import scala.language.postfixOps
@@ -116,9 +116,9 @@ class FlumePollingStreamSuite extends SparkFunSuite with BeforeAndAfter with Log
       // The eventually is required to ensure that all data in the batch has been processed.
       eventually(timeout(10 seconds), interval(100 milliseconds)) {
         val flattenOutputBuffer = outputBuffer.flatten
-        val headers = flattenOutputBuffer.map(_.event.getHeaders.map {
-          case kv => (kv._1.toString, kv._2.toString)
-        }).map(mapAsJavaMap)
+        val headers = flattenOutputBuffer.map(_.event.getHeaders.asScala.map {
+          case (key, value) => (key.toString, value.toString)
+        }).map(_.asJava)
         val bodies = flattenOutputBuffer.map(e => new String(e.event.getBody.array(), UTF_8))
         utils.assertOutput(headers, bodies)
       }
diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
index 5bc4cdf65306c..5ffb60bd602f9 100644
--- a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
+++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.streaming.flume
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer}
 import scala.concurrent.duration._
 import scala.language.postfixOps
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
index 79a9db4291bef..c9fd715d3d554 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaTestUtils.scala
@@ -24,6 +24,7 @@ import java.util.concurrent.TimeoutException
 import java.util.{Map => JMap, Properties}
 
 import scala.annotation.tailrec
+import scala.collection.JavaConverters._
 import scala.language.postfixOps
 import scala.util.control.NonFatal
 
@@ -159,8 +160,7 @@ private[kafka] class KafkaTestUtils extends Logging {
 
   /** Java-friendly function for sending messages to the Kafka broker */
   def sendMessages(topic: String, messageToFreq: JMap[String, JInt]): Unit = {
-    import scala.collection.JavaConversions._
-    sendMessages(topic, Map(messageToFreq.mapValues(_.intValue()).toSeq: _*))
+    sendMessages(topic, Map(messageToFreq.asScala.mapValues(_.intValue()).toSeq: _*))
   }
 
   /** Send the messages to the Kafka broker */
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
index 388dbb8184106..3128222077537 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala
@@ -20,7 +20,7 @@ package org.apache.spark.streaming.kafka
 import java.lang.{Integer => JInt, Long => JLong}
 import java.util.{List => JList, Map => JMap, Set => JSet}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import kafka.common.TopicAndPartition
@@ -96,7 +96,7 @@ object KafkaUtils {
       groupId: String,
       topics: JMap[String, JInt]
     ): JavaPairReceiverInputDStream[String, String] = {
-    createStream(jssc.ssc, zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*))
+    createStream(jssc.ssc, zkQuorum, groupId, Map(topics.asScala.mapValues(_.intValue()).toSeq: _*))
   }
 
   /**
@@ -115,7 +115,7 @@ object KafkaUtils {
       topics: JMap[String, JInt],
       storageLevel: StorageLevel
     ): JavaPairReceiverInputDStream[String, String] = {
-    createStream(jssc.ssc, zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*),
+    createStream(jssc.ssc, zkQuorum, groupId, Map(topics.asScala.mapValues(_.intValue()).toSeq: _*),
       storageLevel)
   }
 
@@ -149,7 +149,10 @@ object KafkaUtils {
     implicit val valueCmd: ClassTag[T] = ClassTag(valueDecoderClass)
 
     createStream[K, V, U, T](
-      jssc.ssc, kafkaParams.toMap, Map(topics.mapValues(_.intValue()).toSeq: _*), storageLevel)
+      jssc.ssc,
+      kafkaParams.asScala.toMap,
+      Map(topics.asScala.mapValues(_.intValue()).toSeq: _*),
+      storageLevel)
   }
 
   /** get leaders for the given offset ranges, or throw an exception */
@@ -275,7 +278,7 @@ object KafkaUtils {
     implicit val keyDecoderCmt: ClassTag[KD] = ClassTag(keyDecoderClass)
     implicit val valueDecoderCmt: ClassTag[VD] = ClassTag(valueDecoderClass)
     new JavaPairRDD(createRDD[K, V, KD, VD](
-      jsc.sc, Map(kafkaParams.toSeq: _*), offsetRanges))
+      jsc.sc, Map(kafkaParams.asScala.toSeq: _*), offsetRanges))
   }
 
   /**
@@ -311,9 +314,9 @@ object KafkaUtils {
     implicit val keyDecoderCmt: ClassTag[KD] = ClassTag(keyDecoderClass)
     implicit val valueDecoderCmt: ClassTag[VD] = ClassTag(valueDecoderClass)
     implicit val recordCmt: ClassTag[R] = ClassTag(recordClass)
-    val leaderMap = Map(leaders.toSeq: _*)
+    val leaderMap = Map(leaders.asScala.toSeq: _*)
     createRDD[K, V, KD, VD, R](
-      jsc.sc, Map(kafkaParams.toSeq: _*), offsetRanges, leaderMap, messageHandler.call _)
+      jsc.sc, Map(kafkaParams.asScala.toSeq: _*), offsetRanges, leaderMap, messageHandler.call(_))
   }
 
   /**
@@ -476,8 +479,8 @@ object KafkaUtils {
     val cleanedHandler = jssc.sparkContext.clean(messageHandler.call _)
     createDirectStream[K, V, KD, VD, R](
       jssc.ssc,
-      Map(kafkaParams.toSeq: _*),
-      Map(fromOffsets.mapValues { _.longValue() }.toSeq: _*),
+      Map(kafkaParams.asScala.toSeq: _*),
+      Map(fromOffsets.asScala.mapValues(_.longValue()).toSeq: _*),
       cleanedHandler
     )
   }
@@ -531,8 +534,8 @@ object KafkaUtils {
     implicit val valueDecoderCmt: ClassTag[VD] = ClassTag(valueDecoderClass)
     createDirectStream[K, V, KD, VD](
       jssc.ssc,
-      Map(kafkaParams.toSeq: _*),
-      Set(topics.toSeq: _*)
+      Map(kafkaParams.asScala.toSeq: _*),
+      Set(topics.asScala.toSeq: _*)
     )
   }
 }
@@ -602,10 +605,10 @@ private[kafka] class KafkaUtilsPythonHelper {
     ): JavaPairInputDStream[Array[Byte], Array[Byte]] = {
 
     if (!fromOffsets.isEmpty) {
-      import scala.collection.JavaConversions._
-      val topicsFromOffsets = fromOffsets.keySet().map(_.topic)
-      if (topicsFromOffsets != topics.toSet) {
-        throw new IllegalStateException(s"The specified topics: ${topics.toSet.mkString(" ")} " +
+      val topicsFromOffsets = fromOffsets.keySet().asScala.map(_.topic)
+      if (topicsFromOffsets != topics.asScala.toSet) {
+        throw new IllegalStateException(
+          s"The specified topics: ${topics.asScala.toSet.mkString(" ")} " +
           s"do not equal to the topic from offsets: ${topicsFromOffsets.mkString(" ")}")
       }
     }
@@ -663,6 +666,6 @@ private[kafka] class KafkaUtilsPythonHelper {
         "with this RDD, please call this method only on a Kafka RDD.")
 
     val kafkaRDD = kafkaRDDs.head.asInstanceOf[KafkaRDD[_, _, _, _, _]]
-    kafkaRDD.offsetRanges.toSeq
+    kafkaRDD.offsetRanges.toSeq.asJava
   }
 }
diff --git a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala
index 0469d0af8864a..4ea218eaa4de1 100644
--- a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala
+++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala
@@ -18,15 +18,17 @@
 package org.apache.spark.streaming.zeromq
 
 import scala.reflect.ClassTag
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
+
 import akka.actor.{Props, SupervisorStrategy}
 import akka.util.ByteString
 import akka.zeromq.Subscribe
+
 import org.apache.spark.api.java.function.{Function => JFunction}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.StreamingContext
 import org.apache.spark.streaming.api.java.{JavaReceiverInputDStream, JavaStreamingContext}
-import org.apache.spark.streaming.dstream.{ReceiverInputDStream}
+import org.apache.spark.streaming.dstream.ReceiverInputDStream
 import org.apache.spark.streaming.receiver.ActorSupervisorStrategy
 
 object ZeroMQUtils {
@@ -75,7 +77,8 @@ object ZeroMQUtils {
     ): JavaReceiverInputDStream[T] = {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
-    val fn = (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).toIterator
+    val fn =
+      (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).iterator().asScala
     createStream[T](jssc.ssc, publisherUrl, subscribe, fn, storageLevel, supervisorStrategy)
   }
 
@@ -99,7 +102,8 @@ object ZeroMQUtils {
     ): JavaReceiverInputDStream[T] = {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
-    val fn = (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).toIterator
+    val fn =
+      (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).iterator().asScala
     createStream[T](jssc.ssc, publisherUrl, subscribe, fn, storageLevel)
   }
 
@@ -122,7 +126,8 @@ object ZeroMQUtils {
     ): JavaReceiverInputDStream[T] = {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
-    val fn = (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).toIterator
+    val fn =
+      (x: Seq[ByteString]) => bytesToObjects.call(x.map(_.toArray).toArray).iterator().asScala
     createStream[T](jssc.ssc, publisherUrl, subscribe, fn)
   }
 }
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
index a003ddf325e6e..5d32fa699ae5b 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisBackedBlockRDD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.streaming.kinesis
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.control.NonFatal
 
 import com.amazonaws.auth.{AWSCredentials, DefaultAWSCredentialsProviderChain}
@@ -213,7 +213,7 @@ class KinesisSequenceRangeIterator(
       s"getting records using shard iterator") {
         client.getRecords(getRecordsRequest)
       }
-    (getRecordsResult.getRecords.iterator(), getRecordsResult.getNextShardIterator)
+    (getRecordsResult.getRecords.iterator().asScala, getRecordsResult.getNextShardIterator)
   }
 
   /**
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
index 22324e821ce94..6e0988c1af8a1 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisReceiver.scala
@@ -18,7 +18,7 @@ package org.apache.spark.streaming.kinesis
 
 import java.util.UUID
 
-import scala.collection.JavaConversions.asScalaIterator
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.util.control.NonFatal
 
@@ -202,7 +202,7 @@ private[kinesis] class KinesisReceiver(
   /** Add records of the given shard to the current block being generated */
   private[kinesis] def addRecords(shardId: String, records: java.util.List[Record]): Unit = {
     if (records.size > 0) {
-      val dataIterator = records.iterator().map { record =>
+      val dataIterator = records.iterator().asScala.map { record =>
         val byteBuffer = record.getData()
         val byteArray = new Array[Byte](byteBuffer.remaining())
         byteBuffer.get(byteArray)
diff --git a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
index c8eec13ec7dc7..634bf94521079 100644
--- a/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
+++ b/extras/kinesis-asl/src/main/scala/org/apache/spark/streaming/kinesis/KinesisTestUtils.scala
@@ -20,6 +20,7 @@ package org.apache.spark.streaming.kinesis
 import java.nio.ByteBuffer
 import java.util.concurrent.TimeUnit
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 import scala.util.{Failure, Random, Success, Try}
@@ -115,7 +116,7 @@ private[kinesis] class KinesisTestUtils extends Logging {
    * Expose a Python friendly API.
    */
   def pushData(testData: java.util.List[Int]): Unit = {
-    pushData(scala.collection.JavaConversions.asScalaBuffer(testData))
+    pushData(testData.asScala)
   }
 
   def deleteStream(): Unit = {
diff --git a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
index ceb135e0651aa..3d136aec2e702 100644
--- a/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
+++ b/extras/kinesis-asl/src/test/scala/org/apache/spark/streaming/kinesis/KinesisReceiverSuite.scala
@@ -17,10 +17,10 @@
 package org.apache.spark.streaming.kinesis
 
 import java.nio.ByteBuffer
+import java.nio.charset.StandardCharsets
+import java.util.Arrays
 
-import scala.collection.JavaConversions.seqAsJavaList
-
-import com.amazonaws.services.kinesis.clientlibrary.exceptions.{InvalidStateException, KinesisClientLibDependencyException, ShutdownException, ThrottlingException}
+import com.amazonaws.services.kinesis.clientlibrary.exceptions._
 import com.amazonaws.services.kinesis.clientlibrary.interfaces.IRecordProcessorCheckpointer
 import com.amazonaws.services.kinesis.clientlibrary.types.ShutdownReason
 import com.amazonaws.services.kinesis.model.Record
@@ -47,10 +47,10 @@ class KinesisReceiverSuite extends TestSuiteBase with Matchers with BeforeAndAft
   val someSeqNum = Some(seqNum)
 
   val record1 = new Record()
-  record1.setData(ByteBuffer.wrap("Spark In Action".getBytes()))
+  record1.setData(ByteBuffer.wrap("Spark In Action".getBytes(StandardCharsets.UTF_8)))
   val record2 = new Record()
-  record2.setData(ByteBuffer.wrap("Learning Spark".getBytes()))
-  val batch = List[Record](record1, record2)
+  record2.setData(ByteBuffer.wrap("Learning Spark".getBytes(StandardCharsets.UTF_8)))
+  val batch = Arrays.asList(record1, record2)
 
   var receiverMock: KinesisReceiver = _
   var checkpointerMock: IRecordProcessorCheckpointer = _
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
index 87eeb5db05d26..7a1c7796065ee 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.util
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Random
 
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
@@ -52,7 +52,7 @@ object LinearDataGenerator {
       nPoints: Int,
       seed: Int,
       eps: Double): java.util.List[LabeledPoint] = {
-    seqAsJavaList(generateLinearInput(intercept, weights, nPoints, seed, eps))
+    generateLinearInput(intercept, weights, nPoints, seed, eps).asJava
   }
 
   /**
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
index a1ee554152372..2744e020e9e49 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
@@ -20,7 +20,7 @@
 import java.io.Serializable;
 import java.util.List;
 
-import static scala.collection.JavaConversions.seqAsJavaList;
+import scala.collection.JavaConverters;
 
 import org.junit.After;
 import org.junit.Assert;
@@ -55,8 +55,9 @@ public void setUp() {
 
         double[] xMean = {5.843, 3.057, 3.758, 1.199};
         double[] xVariance = {0.6856, 0.1899, 3.116, 0.581};
-        List<LabeledPoint> points = seqAsJavaList(generateMultinomialLogisticInput(
-                weights, xMean, xVariance, true, nPoints, 42));
+        List<LabeledPoint> points = JavaConverters.asJavaListConverter(
+            generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42)
+        ).asJava();
         datasetRDD = jsc.parallelize(points, 2);
         dataset = jsql.createDataFrame(datasetRDD, LabeledPoint.class);
     }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index 2473510e13514..8d14bb6572155 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.classification
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Random
 import scala.util.control.Breaks._
 
@@ -38,7 +38,7 @@ object LogisticRegressionSuite {
     scale: Double,
     nPoints: Int,
     seed: Int): java.util.List[LabeledPoint] = {
-    seqAsJavaList(generateLogisticInput(offset, scale, nPoints, seed))
+    generateLogisticInput(offset, scale, nPoints, seed).asJava
   }
 
   // Generate input of the form Y = logistic(offset + scale*X)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
index b1d78cba9e3dc..ee3c85d09a463 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/SVMSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.classification
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Random
 
 import org.jblas.DoubleMatrix
@@ -35,7 +35,7 @@ object SVMSuite {
     weights: Array[Double],
     nPoints: Int,
     seed: Int): java.util.List[LabeledPoint] = {
-    seqAsJavaList(generateSVMInput(intercept, weights, nPoints, seed))
+    generateSVMInput(intercept, weights, nPoints, seed).asJava
   }
 
   // Generate noisy input of the form Y = signum(x.dot(weights) + intercept + noise)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
index 13b754a03943a..36ac7d267243d 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.optimization
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Random
 
 import org.scalatest.Matchers
@@ -35,7 +35,7 @@ object GradientDescentSuite {
       scale: Double,
       nPoints: Int,
       seed: Int): java.util.List[LabeledPoint] = {
-    seqAsJavaList(generateGDInput(offset, scale, nPoints, seed))
+    generateGDInput(offset, scale, nPoints, seed).asJava
   }
 
   // Generate input of the form Y = logistic(offset + scale * X)
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
index 05b87728d6fdb..045135f7f8d60 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.recommendation
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.math.abs
 import scala.util.Random
 
@@ -38,7 +38,7 @@ object ALSSuite {
       negativeWeights: Boolean): (java.util.List[Rating], DoubleMatrix, DoubleMatrix) = {
     val (sampledRatings, trueRatings, truePrefs) =
       generateRatings(users, products, features, samplingRate, implicitPrefs)
-    (seqAsJavaList(sampledRatings), trueRatings, truePrefs)
+    (sampledRatings.asJava, trueRatings, truePrefs)
   }
 
   def generateRatings(
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 04e0d49b178cf..ea52bfd67944a 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -18,13 +18,13 @@
 import java.io._
 
 import scala.util.Properties
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import sbt._
 import sbt.Classpaths.publishTask
 import sbt.Keys._
 import sbtunidoc.Plugin.UnidocKeys.unidocGenjavadocVersion
-import com.typesafe.sbt.pom.{loadEffectivePom, PomBuild, SbtPomKeys}
+import com.typesafe.sbt.pom.{PomBuild, SbtPomKeys}
 import net.virtualvoid.sbt.graph.Plugin.graphSettings
 
 import spray.revolver.RevolverPlugin._
@@ -120,7 +120,7 @@ object SparkBuild extends PomBuild {
     case _ =>
   }
 
-  override val userPropertiesMap = System.getProperties.toMap
+  override val userPropertiesMap = System.getProperties.asScala.toMap
 
   lazy val MavenCompile = config("m2r") extend(Compile)
   lazy val publishLocalBoth = TaskKey[Unit]("publish-local", "publish local for m2 and ivy")
@@ -559,7 +559,7 @@ object TestSettings {
     javaOptions in Test += "-Dspark.unsafe.exceptionOnMemoryLeak=true",
     javaOptions in Test += "-Dsun.io.serialization.extendedDebugInfo=true",
     javaOptions in Test += "-Dderby.system.durability=test",
-    javaOptions in Test ++= System.getProperties.filter(_._1 startsWith "spark")
+    javaOptions in Test ++= System.getProperties.asScala.filter(_._1.startsWith("spark"))
       .map { case (k,v) => s"-D$k=$v" }.toSeq,
     javaOptions in Test += "-ea",
     javaOptions in Test ++= "-Xmx3g -Xss4096k -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g"
diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 8af8637cf948d..0948f9b27cd38 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -61,6 +61,18 @@ def _to_seq(sc, cols, converter=None):
     return sc._jvm.PythonUtils.toSeq(cols)
 
 
+def _to_list(sc, cols, converter=None):
+    """
+    Convert a list of Column (or names) into a JVM (Scala) List of Column.
+
+    An optional `converter` could be used to convert items in `cols`
+    into JVM Column objects.
+    """
+    if converter:
+        cols = [converter(c) for c in cols]
+    return sc._jvm.PythonUtils.toList(cols)
+
+
 def _unary_op(name, doc="unary operator"):
     """ Create a method for given unary operator """
     def _(self):
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index 025811f519293..e269ef4304f3f 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -32,7 +32,7 @@
 from pyspark.traceback_utils import SCCallSiteSync
 from pyspark.sql import since
 from pyspark.sql.types import _parse_datatype_json_string
-from pyspark.sql.column import Column, _to_seq, _to_java_column
+from pyspark.sql.column import Column, _to_seq, _to_list, _to_java_column
 from pyspark.sql.readwriter import DataFrameWriter
 from pyspark.sql.types import *
 
@@ -494,7 +494,7 @@ def randomSplit(self, weights, seed=None):
             if w < 0.0:
                 raise ValueError("Weights must be positive. Found weight value: %s" % w)
         seed = seed if seed is not None else random.randint(0, sys.maxsize)
-        rdd_array = self._jdf.randomSplit(_to_seq(self.sql_ctx._sc, weights), long(seed))
+        rdd_array = self._jdf.randomSplit(_to_list(self.sql_ctx._sc, weights), long(seed))
         return [DataFrame(rdd, self.sql_ctx) for rdd in rdd_array]
 
     @property
diff --git a/scalastyle-config.xml b/scalastyle-config.xml
index b5e2e882d2254..68fdb4141cf27 100644
--- a/scalastyle-config.xml
+++ b/scalastyle-config.xml
@@ -161,6 +161,13 @@ This file is divided into 3 sections:
     ]]></customMessage>
   </check>
 
+  <!-- As of SPARK-9613 JavaConversions should be replaced with JavaConverters -->
+  <check customId="javaconversions" level="error" class="org.scalastyle.scalariform.TokenChecker" enabled="true">
+    <parameters><parameter name="regex">JavaConversions</parameter></parameters>
+    <customMessage>Instead of importing implicits in scala.collection.JavaConversions._, import
+    scala.collection.JavaConverters._ and use .asScala / .asJava methods</customMessage>
+  </check>
+
   <!-- ================================================================================ -->
   <!--       rules we'd like to enforce, but haven't cleaned up the codebase yet        -->
   <!-- ================================================================================ -->
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index ec895af9c3037..cfd9cb0e62598 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql
 
+import scala.collection.JavaConverters._
+
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.GenericRow
 import org.apache.spark.sql.types.StructType
@@ -280,9 +282,8 @@ trait Row extends Serializable {
    *
    * @throws ClassCastException when data type does not match.
    */
-  def getList[T](i: Int): java.util.List[T] = {
-    scala.collection.JavaConversions.seqAsJavaList(getSeq[T](i))
-  }
+  def getList[T](i: Int): java.util.List[T] =
+    getSeq[T](i).asJava
 
   /**
    * Returns the value at position i of map type as a Scala Map.
@@ -296,9 +297,8 @@ trait Row extends Serializable {
    *
    * @throws ClassCastException when data type does not match.
    */
-  def getJavaMap[K, V](i: Int): java.util.Map[K, V] = {
-    scala.collection.JavaConversions.mapAsJavaMap(getMap[K, V](i))
-  }
+  def getJavaMap[K, V](i: Int): java.util.Map[K, V] =
+    getMap[K, V](i).asJava
 
   /**
    * Returns the value at position i of struct type as an [[Row]] object.
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
index 503c4f4b20f38..4cc9a5520a085 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Catalog.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.catalyst.analysis
 
 import java.util.concurrent.ConcurrentHashMap
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.collection.mutable.ArrayBuffer
 
@@ -147,7 +147,7 @@ class SimpleCatalog(val conf: CatalystConf) extends Catalog {
 
   override def getTables(databaseName: Option[String]): Seq[(String, Boolean)] = {
     val result = ArrayBuffer.empty[(String, Boolean)]
-    for (name <- tables.keySet()) {
+    for (name <- tables.keySet().asScala) {
       result += ((name, true))
     }
     result
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
index a4fd4cf3b330b..77a42c0873a6b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameNaFunctions.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql
 
 import java.{lang => jl}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.expressions._
@@ -209,7 +209,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    *
    * @since 1.3.1
    */
-  def fill(valueMap: java.util.Map[String, Any]): DataFrame = fill0(valueMap.toSeq)
+  def fill(valueMap: java.util.Map[String, Any]): DataFrame = fill0(valueMap.asScala.toSeq)
 
   /**
    * (Scala-specific) Returns a new [[DataFrame]] that replaces null values.
@@ -254,7 +254,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * @since 1.3.1
    */
   def replace[T](col: String, replacement: java.util.Map[T, T]): DataFrame = {
-    replace[T](col, replacement.toMap : Map[T, T])
+    replace[T](col, replacement.asScala.toMap)
   }
 
   /**
@@ -277,7 +277,7 @@ final class DataFrameNaFunctions private[sql](df: DataFrame) {
    * @since 1.3.1
    */
   def replace[T](cols: Array[String], replacement: java.util.Map[T, T]): DataFrame = {
-    replace(cols.toSeq, replacement.toMap)
+    replace(cols.toSeq, replacement.asScala.toMap)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
index 6dc7bfe333498..97a8b6518a832 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql
 
 import java.util.Properties
 
+import scala.collection.JavaConverters._
+
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.annotation.Experimental
@@ -90,7 +92,7 @@ class DataFrameReader private[sql](sqlContext: SQLContext) extends Logging {
    * @since 1.4.0
    */
   def options(options: java.util.Map[String, String]): DataFrameReader = {
-    this.options(scala.collection.JavaConversions.mapAsScalaMap(options))
+    this.options(options.asScala)
     this
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
index ce8744b53175b..b2a66dd417b4c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql
 
 import java.util.Properties
 
+import scala.collection.JavaConverters._
+
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.sql.catalyst.{SqlParser, TableIdentifier}
 import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
@@ -109,7 +111,7 @@ final class DataFrameWriter private[sql](df: DataFrame) {
    * @since 1.4.0
    */
   def options(options: java.util.Map[String, String]): DataFrameWriter = {
-    this.options(scala.collection.JavaConversions.mapAsScalaMap(options))
+    this.options(options.asScala)
     this
   }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
index 99d557b03a033..ee31d83cce42c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 
 import org.apache.spark.annotation.Experimental
@@ -188,7 +188,7 @@ class GroupedData protected[sql](
    * @since 1.3.0
    */
   def agg(exprs: java.util.Map[String, String]): DataFrame = {
-    agg(exprs.toMap)
+    agg(exprs.asScala.toMap)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index e9de14f025502..e6f7619519e6a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql
 import java.util.Properties
 
 import scala.collection.immutable
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.parquet.hadoop.ParquetOutputCommitter
 
@@ -531,7 +531,7 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
 
   /** Set Spark SQL configuration properties. */
   def setConf(props: Properties): Unit = settings.synchronized {
-    props.foreach { case (k, v) => setConfString(k, v) }
+    props.asScala.foreach { case (k, v) => setConfString(k, v) }
   }
 
   /** Set the given Spark SQL configuration property using a `string` value. */
@@ -601,24 +601,25 @@ private[sql] class SQLConf extends Serializable with CatalystConf {
    * Return all the configuration properties that have been set (i.e. not the default).
    * This creates a new copy of the config properties in the form of a Map.
    */
-  def getAllConfs: immutable.Map[String, String] = settings.synchronized { settings.toMap }
+  def getAllConfs: immutable.Map[String, String] =
+    settings.synchronized { settings.asScala.toMap }
 
   /**
    * Return all the configuration definitions that have been defined in [[SQLConf]]. Each
    * definition contains key, defaultValue and doc.
    */
   def getAllDefinedConfs: Seq[(String, String, String)] = sqlConfEntries.synchronized {
-    sqlConfEntries.values.filter(_.isPublic).map { entry =>
+    sqlConfEntries.values.asScala.filter(_.isPublic).map { entry =>
       (entry.key, entry.defaultValueString, entry.doc)
     }.toSeq
   }
 
   private[spark] def unsetConf(key: String): Unit = {
-    settings -= key
+    settings.remove(key)
   }
 
   private[spark] def unsetConf(entry: SQLConfEntry[_]): Unit = {
-    settings -= entry.key
+    settings.remove(entry.key)
   }
 
   private[spark] def clear(): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index a1eea09e0477b..4e8414af50b44 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -21,7 +21,7 @@ import java.beans.Introspector
 import java.util.Properties
 import java.util.concurrent.atomic.AtomicReference
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.immutable
 import scala.reflect.runtime.universe.TypeTag
 import scala.util.control.NonFatal
@@ -225,7 +225,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     conf.setConf(properties)
     // After we have populated SQLConf, we call setConf to populate other confs in the subclass
     // (e.g. hiveconf in HiveContext).
-    properties.foreach {
+    properties.asScala.foreach {
       case (key, value) => setConf(key, value)
     }
   }
@@ -567,7 +567,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       tableName: String,
       source: String,
       options: java.util.Map[String, String]): DataFrame = {
-    createExternalTable(tableName, source, options.toMap)
+    createExternalTable(tableName, source, options.asScala.toMap)
   }
 
   /**
@@ -612,7 +612,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
       source: String,
       schema: StructType,
       options: java.util.Map[String, String]): DataFrame = {
-    createExternalTable(tableName, source, schema, options.toMap)
+    createExternalTable(tableName, source, schema, options.asScala.toMap)
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
index 8fbaf3a3059db..011724436621d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/ResolvedDataSource.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.datasources
 
 import java.util.ServiceLoader
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.{existentials, implicitConversions}
 import scala.util.{Success, Failure, Try}
 
@@ -55,7 +55,7 @@ object ResolvedDataSource extends Logging {
     val loader = Utils.getContextOrSparkClassLoader
     val serviceLoader = ServiceLoader.load(classOf[DataSourceRegister], loader)
 
-    serviceLoader.iterator().filter(_.shortName().equalsIgnoreCase(provider)).toList match {
+    serviceLoader.asScala.filter(_.shortName().equalsIgnoreCase(provider)).toList match {
       /** the provider format did not match any given registered aliases */
       case Nil => Try(loader.loadClass(provider)).orElse(Try(loader.loadClass(provider2))) match {
         case Success(dataSource) => dataSource
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index 3f8353af6e2ad..0a6bb44445f6e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.datasources.parquet
 
 import java.util.{Map => JMap}
 
-import scala.collection.JavaConversions.{iterableAsScalaIterable, mapAsJavaMap, mapAsScalaMap}
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.parquet.hadoop.api.ReadSupport.ReadContext
@@ -44,7 +44,7 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
     val parquetRequestedSchema = readContext.getRequestedSchema
 
     val catalystRequestedSchema =
-      Option(readContext.getReadSupportMetadata).map(_.toMap).flatMap { metadata =>
+      Option(readContext.getReadSupportMetadata).map(_.asScala).flatMap { metadata =>
         metadata
           // First tries to read requested schema, which may result from projections
           .get(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA)
@@ -123,7 +123,7 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
       maybeRequestedSchema.fold(context.getFileSchema) { schemaString =>
         val toParquet = new CatalystSchemaConverter(conf)
         val fileSchema = context.getFileSchema.asGroupType()
-        val fileFieldNames = fileSchema.getFields.map(_.getName).toSet
+        val fileFieldNames = fileSchema.getFields.asScala.map(_.getName).toSet
 
         StructType
           // Deserializes the Catalyst schema of requested columns
@@ -152,7 +152,7 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
         maybeRequestedSchema.map(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA -> _) ++
         maybeRowSchema.map(RowWriteSupport.SPARK_ROW_SCHEMA -> _)
 
-    new ReadContext(parquetRequestedSchema, metadata)
+    new ReadContext(parquetRequestedSchema, metadata.asJava)
   }
 }
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index cbf0704c4a9a4..f682ca0d8ff4f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.execution.datasources.parquet
 import java.math.{BigDecimal, BigInteger}
 import java.nio.ByteOrder
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.parquet.column.Dictionary
@@ -183,7 +183,7 @@ private[parquet] class CatalystRowConverter(
     // those missing fields and create converters for them, although values of these fields are
     // always null.
     val paddedParquetFields = {
-      val parquetFields = parquetType.getFields
+      val parquetFields = parquetType.getFields.asScala
       val parquetFieldNames = parquetFields.map(_.getName).toSet
       val missingFields = catalystType.filterNot(f => parquetFieldNames.contains(f.name))
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index 535f0684e97f9..be6c0545f5a0a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.parquet.schema.OriginalType._
@@ -82,7 +82,7 @@ private[parquet] class CatalystSchemaConverter(
   def convert(parquetSchema: MessageType): StructType = convert(parquetSchema.asGroupType())
 
   private def convert(parquetSchema: GroupType): StructType = {
-    val fields = parquetSchema.getFields.map { field =>
+    val fields = parquetSchema.getFields.asScala.map { field =>
       field.getRepetition match {
         case OPTIONAL =>
           StructField(field.getName, convertField(field), nullable = true)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index bbf682aec0f9d..64982f37cf872 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -21,7 +21,7 @@ import java.net.URI
 import java.util.logging.{Logger => JLogger}
 import java.util.{List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.util.{Failure, Try}
 
@@ -336,7 +336,7 @@ private[sql] class ParquetRelation(
         override def getPartitions: Array[SparkPartition] = {
           val inputFormat = new ParquetInputFormat[InternalRow] {
             override def listStatus(jobContext: JobContext): JList[FileStatus] = {
-              if (cacheMetadata) cachedStatuses else super.listStatus(jobContext)
+              if (cacheMetadata) cachedStatuses.asJava else super.listStatus(jobContext)
             }
           }
 
@@ -344,7 +344,8 @@ private[sql] class ParquetRelation(
           val rawSplits = inputFormat.getSplits(jobContext)
 
           Array.tabulate[SparkPartition](rawSplits.size) { i =>
-            new SqlNewHadoopPartition(id, i, rawSplits(i).asInstanceOf[InputSplit with Writable])
+            new SqlNewHadoopPartition(
+              id, i, rawSplits.get(i).asInstanceOf[InputSplit with Writable])
           }
         }
       }.asInstanceOf[RDD[Row]]  // type erasure hack to pass RDD[InternalRow] as RDD[Row]
@@ -588,7 +589,7 @@ private[sql] object ParquetRelation extends Logging {
       val metadata = footer.getParquetMetadata.getFileMetaData
       val serializedSchema = metadata
         .getKeyValueMetaData
-        .toMap
+        .asScala.toMap
         .get(CatalystReadSupport.SPARK_METADATA_KEY)
       if (serializedSchema.isEmpty) {
         // Falls back to Parquet schema if no Spark SQL schema found.
@@ -745,7 +746,7 @@ private[sql] object ParquetRelation extends Logging {
           // Reads footers in multi-threaded manner within each task
           val footers =
             ParquetFileReader.readAllFootersInParallel(
-              serializedConf.value, fakeFileStatuses, skipRowGroups)
+              serializedConf.value, fakeFileStatuses.asJava, skipRowGroups).asScala
 
           // Converter used to convert Parquet `MessageType` to Spark SQL `StructType`
           val converter =
@@ -772,7 +773,7 @@ private[sql] object ParquetRelation extends Logging {
     val fileMetaData = footer.getParquetMetadata.getFileMetaData
     fileMetaData
       .getKeyValueMetaData
-      .toMap
+      .asScala.toMap
       .get(CatalystReadSupport.SPARK_METADATA_KEY)
       .flatMap(deserializeSchemaString)
       .getOrElse(converter.convert(fileMetaData.getSchema))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
index 42376ef7a9c1f..142301fe87cb6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
@@ -18,8 +18,8 @@
 package org.apache.spark.sql.execution.datasources.parquet
 
 import java.io.IOException
+import java.util.{Collections, Arrays}
 
-import scala.collection.JavaConversions._
 import scala.util.Try
 
 import org.apache.hadoop.conf.Configuration
@@ -107,7 +107,7 @@ private[parquet] object ParquetTypesConverter extends Logging {
     ParquetFileWriter.writeMetadataFile(
       conf,
       path,
-      new Footer(path, new ParquetMetadata(metaData, Nil)) :: Nil)
+      Arrays.asList(new Footer(path, new ParquetMetadata(metaData, Collections.emptyList()))))
   }
 
   /**
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
index ed282f98b7d71..d800c7456bdac 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/ShuffledHashOuterJoin.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution.joins
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
@@ -92,9 +92,9 @@ case class ShuffledHashOuterJoin(
         case FullOuter =>
           // TODO(davies): use UnsafeRow
           val leftHashTable =
-            buildHashTable(leftIter, numLeftRows, newProjection(leftKeys, left.output))
+            buildHashTable(leftIter, numLeftRows, newProjection(leftKeys, left.output)).asScala
           val rightHashTable =
-            buildHashTable(rightIter, numRightRows, newProjection(rightKeys, right.output))
+            buildHashTable(rightIter, numRightRows, newProjection(rightKeys, right.output)).asScala
           (leftHashTable.keySet ++ rightHashTable.keySet).iterator.flatMap { key =>
             fullOuterIterator(key,
               leftHashTable.getOrElse(key, EMPTY_LIST),
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
index 59f8b079ab333..5a58d846ad80b 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/pythonUDFs.scala
@@ -20,7 +20,7 @@ package org.apache.spark.sql.execution
 import java.io.OutputStream
 import java.util.{List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import net.razorvine.pickle._
 
@@ -196,14 +196,15 @@ object EvaluatePython {
     case (c, BinaryType) if c.getClass.isArray && c.getClass.getComponentType.getName == "byte" => c
 
     case (c: java.util.List[_], ArrayType(elementType, _)) =>
-      new GenericArrayData(c.map { e => fromJava(e, elementType)}.toArray)
+      new GenericArrayData(c.asScala.map { e => fromJava(e, elementType)}.toArray)
 
     case (c, ArrayType(elementType, _)) if c.getClass.isArray =>
       new GenericArrayData(c.asInstanceOf[Array[_]].map(e => fromJava(e, elementType)))
 
     case (c: java.util.Map[_, _], MapType(keyType, valueType, _)) =>
-      val keys = c.keysIterator.map(fromJava(_, keyType)).toArray
-      val values = c.valuesIterator.map(fromJava(_, valueType)).toArray
+      val keyValues = c.asScala.toSeq
+      val keys = keyValues.map(kv => fromJava(kv._1, keyType)).toArray
+      val values = keyValues.map(kv => fromJava(kv._2, valueType)).toArray
       ArrayBasedMapData(keys, values)
 
     case (c, StructType(fields)) if c.getClass.isArray =>
@@ -367,7 +368,7 @@ case class BatchPythonEvaluation(udf: PythonUDF, output: Seq[Attribute], child:
       val pickle = new Unpickler
       iter.flatMap { pickedResult =>
         val unpickledBatch = pickle.loads(pickedResult)
-        unpickledBatch.asInstanceOf[java.util.ArrayList[Any]]
+        unpickledBatch.asInstanceOf[java.util.ArrayList[Any]].asScala
       }
     }.mapPartitions { iter =>
       val row = new GenericMutableRow(1)
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 7abdd3db80341..4867cebf5328c 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -23,7 +23,7 @@
 import java.util.List;
 import java.util.Map;
 
-import scala.collection.JavaConversions;
+import scala.collection.JavaConverters;
 import scala.collection.Seq;
 
 import com.google.common.collect.ImmutableMap;
@@ -96,7 +96,7 @@ public void testVarargMethods() {
     df.groupBy().agg(countDistinct("key", "value"));
     df.groupBy().agg(countDistinct(col("key"), col("value")));
     df.select(coalesce(col("key")));
-    
+
     // Varargs with mathfunctions
     DataFrame df2 = context.table("testData2");
     df2.select(exp("a"), exp("b"));
@@ -172,7 +172,7 @@ public void testCreateDataFrameFromJavaBeans() {
     Seq<Integer> outputBuffer = (Seq<Integer>) first.getJavaMap(2).get("hello");
     Assert.assertArrayEquals(
       bean.getC().get("hello"),
-      Ints.toArray(JavaConversions.seqAsJavaList(outputBuffer)));
+      Ints.toArray(JavaConverters.seqAsJavaListConverter(outputBuffer).asJava()));
     Seq<String> d = first.getAs(3);
     Assert.assertEquals(bean.getD().size(), d.length());
     for (int i = 0; i < d.length(); i++) {
@@ -206,7 +206,7 @@ public void testCrosstab() {
       count++;
     }
   }
-  
+
   @Test
   public void testFrequentItems() {
     DataFrame df = context.table("testData2");
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
index cdaa14ac80785..329ffb66083b1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameNaFunctionsSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.sql.test.SharedSQLContext
 
@@ -153,11 +153,11 @@ class DataFrameNaFunctionsSuite extends QueryTest with SharedSQLContext {
 
     // Test Java version
     checkAnswer(
-      df.na.fill(mapAsJavaMap(Map(
+      df.na.fill(Map(
         "a" -> "test",
         "c" -> 1,
         "d" -> 2.2
-      ))),
+      ).asJava),
       Row("test", null, 1, 2.2))
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
index 4adcefb7dc4b1..3649c2a97b5ef 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql
 
 import java.util.{Locale, TimeZone}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.util._
@@ -145,7 +145,7 @@ object QueryTest {
   }
 
   def checkAnswer(df: DataFrame, expectedAnswer: java.util.List[Row]): String = {
-    checkAnswer(df, expectedAnswer.toSeq) match {
+    checkAnswer(df, expectedAnswer.asScala) match {
       case Some(errorMessage) => errorMessage
       case None => null
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index 45db619567a22..bd7cf8c10abef 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -20,8 +20,7 @@ package org.apache.spark.sql.execution.datasources.parquet
 import java.nio.ByteBuffer
 import java.util.{List => JList, Map => JMap}
 
-import scala.collection.JavaConverters.seqAsJavaListConverter
-import scala.collection.JavaConverters.mapAsJavaMapConverter
+import scala.collection.JavaConverters._
 
 import org.apache.avro.Schema
 import org.apache.avro.generic.IndexedRecord
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index d85c564e3e8d1..df68432faeeb3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.fs.{Path, PathFilter}
 import org.apache.parquet.hadoop.ParquetFileReader
@@ -40,8 +40,9 @@ private[sql] abstract class ParquetCompatibilityTest extends QueryTest with Parq
       override def accept(path: Path): Boolean = pathFilter(path)
     }).toSeq
 
-    val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
-    footers.head.getParquetMetadata.getFileMetaData.getSchema
+    val footers =
+      ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles.asJava, true)
+    footers.iterator().next().getParquetMetadata.getFileMetaData.getSchema
   }
 
   protected def logParquetSchema(path: String): Unit = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index e6b0a2ea95e38..08d2b9dee99b0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -17,7 +17,9 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import scala.collection.JavaConversions._
+import java.util.Collections
+
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
@@ -28,7 +30,7 @@ import org.apache.parquet.example.data.simple.SimpleGroup
 import org.apache.parquet.example.data.{Group, GroupWriter}
 import org.apache.parquet.hadoop.api.WriteSupport
 import org.apache.parquet.hadoop.api.WriteSupport.WriteContext
-import org.apache.parquet.hadoop.metadata.{CompressionCodecName, FileMetaData, ParquetMetadata}
+import org.apache.parquet.hadoop.metadata.{BlockMetaData, CompressionCodecName, FileMetaData, ParquetMetadata}
 import org.apache.parquet.hadoop.{Footer, ParquetFileWriter, ParquetOutputCommitter, ParquetWriter}
 import org.apache.parquet.io.api.RecordConsumer
 import org.apache.parquet.schema.{MessageType, MessageTypeParser}
@@ -205,9 +207,8 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
   test("compression codec") {
     def compressionCodecFor(path: String): String = {
       val codecs = ParquetTypesConverter
-        .readMetaData(new Path(path), Some(configuration))
-        .getBlocks
-        .flatMap(_.getColumns)
+        .readMetaData(new Path(path), Some(configuration)).getBlocks.asScala
+        .flatMap(_.getColumns.asScala)
         .map(_.getCodec.name())
         .distinct
 
@@ -348,14 +349,16 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
       """.stripMargin)
 
     withTempPath { location =>
-      val extraMetadata = Map(CatalystReadSupport.SPARK_METADATA_KEY -> sparkSchema.toString)
+      val extraMetadata = Collections.singletonMap(
+        CatalystReadSupport.SPARK_METADATA_KEY, sparkSchema.toString)
       val fileMetadata = new FileMetaData(parquetSchema, extraMetadata, "Spark")
       val path = new Path(location.getCanonicalPath)
 
       ParquetFileWriter.writeMetadataFile(
         sqlContext.sparkContext.hadoopConfiguration,
         path,
-        new Footer(path, new ParquetMetadata(fileMetadata, Nil)) :: Nil)
+        Collections.singletonList(
+          new Footer(path, new ParquetMetadata(fileMetadata, Collections.emptyList()))))
 
       assertResult(sqlContext.read.parquet(path.toString).schema) {
         StructType(
@@ -386,7 +389,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
       configuration.clear()
-      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
     }
   }
 
@@ -410,7 +413,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
       configuration.clear()
-      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
     }
   }
 
@@ -434,7 +437,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
       } finally {
         // Hadoop 1 doesn't have `Configuration.unset`
         configuration.clear()
-        clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+        clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
       }
     }
   }
@@ -481,7 +484,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
       configuration.clear()
-      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
     }
   }
 }
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
index 02cc7e5efa521..306f98bcb5344 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkExecuteStatementOperation.scala
@@ -20,9 +20,9 @@ package org.apache.spark.sql.hive.thriftserver
 import java.security.PrivilegedExceptionAction
 import java.sql.{Date, Timestamp}
 import java.util.concurrent.RejectedExecutionException
-import java.util.{Map => JMap, UUID}
+import java.util.{Arrays, Map => JMap, UUID}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, Map => SMap}
 import scala.util.control.NonFatal
 
@@ -126,13 +126,13 @@ private[hive] class SparkExecuteStatementOperation(
 
   def getResultSetSchema: TableSchema = {
     if (result == null || result.queryExecution.analyzed.output.size == 0) {
-      new TableSchema(new FieldSchema("Result", "string", "") :: Nil)
+      new TableSchema(Arrays.asList(new FieldSchema("Result", "string", "")))
     } else {
       logInfo(s"Result Schema: ${result.queryExecution.analyzed.output}")
       val schema = result.queryExecution.analyzed.output.map { attr =>
         new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
       }
-      new TableSchema(schema)
+      new TableSchema(schema.asJava)
     }
   }
 
@@ -298,7 +298,7 @@ private[hive] class SparkExecuteStatementOperation(
       sqlOperationConf = new HiveConf(sqlOperationConf)
 
       // apply overlay query specific settings, if any
-      getConfOverlay().foreach { case (k, v) =>
+      getConfOverlay().asScala.foreach { case (k, v) =>
         try {
           sqlOperationConf.verifyAndSet(k, v)
         } catch {
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
index 7799704c819d9..a29df567983b1 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -17,11 +17,11 @@
 
 package org.apache.spark.sql.hive.thriftserver
 
-import scala.collection.JavaConversions._
-
 import java.io._
 import java.util.{ArrayList => JArrayList, Locale}
 
+import scala.collection.JavaConverters._
+
 import jline.console.ConsoleReader
 import jline.console.history.FileHistory
 
@@ -101,9 +101,9 @@ private[hive] object SparkSQLCLIDriver extends Logging {
 
     // Set all properties specified via command line.
     val conf: HiveConf = sessionState.getConf
-    sessionState.cmdProperties.entrySet().foreach { item =>
-      val key = item.getKey.asInstanceOf[String]
-      val value = item.getValue.asInstanceOf[String]
+    sessionState.cmdProperties.entrySet().asScala.foreach { item =>
+      val key = item.getKey.toString
+      val value = item.getValue.toString
       // We do not propagate metastore options to the execution copy of hive.
       if (key != "javax.jdo.option.ConnectionURL") {
         conf.set(key, value)
@@ -316,15 +316,15 @@ private[hive] class SparkSQLCLIDriver extends CliDriver with Logging {
 
           if (HiveConf.getBoolVar(conf, HiveConf.ConfVars.HIVE_CLI_PRINT_HEADER)) {
             // Print the column names.
-            Option(driver.getSchema.getFieldSchemas).map { fields =>
-              out.println(fields.map(_.getName).mkString("\t"))
+            Option(driver.getSchema.getFieldSchemas).foreach { fields =>
+              out.println(fields.asScala.map(_.getName).mkString("\t"))
             }
           }
 
           var counter = 0
           try {
             while (!out.checkError() && driver.getResults(res)) {
-              res.foreach{ l =>
+              res.asScala.foreach { l =>
                 counter += 1
                 out.println(l)
               }
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
index 644165acf70a7..5ad8c54f296d5 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIService.scala
@@ -21,6 +21,8 @@ import java.io.IOException
 import java.util.{List => JList}
 import javax.security.auth.login.LoginException
 
+import scala.collection.JavaConverters._
+
 import org.apache.commons.logging.Log
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.shims.Utils
@@ -34,8 +36,6 @@ import org.apache.hive.service.{AbstractService, Service, ServiceException}
 import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.sql.hive.thriftserver.ReflectionUtils._
 
-import scala.collection.JavaConversions._
-
 private[hive] class SparkSQLCLIService(hiveServer: HiveServer2, hiveContext: HiveContext)
   extends CLIService(hiveServer)
   with ReflectedCompositeService {
@@ -76,7 +76,7 @@ private[thriftserver] trait ReflectedCompositeService { this: AbstractService =>
   def initCompositeService(hiveConf: HiveConf) {
     // Emulating `CompositeService.init(hiveConf)`
     val serviceList = getAncestorField[JList[Service]](this, 2, "serviceList")
-    serviceList.foreach(_.init(hiveConf))
+    serviceList.asScala.foreach(_.init(hiveConf))
 
     // Emulating `AbstractService.init(hiveConf)`
     invoke(classOf[AbstractService], this, "ensureCurrentState", classOf[STATE] -> STATE.NOTINITED)
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
index 77272aecf2835..2619286afc148 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLDriver.scala
@@ -17,7 +17,9 @@
 
 package org.apache.spark.sql.hive.thriftserver
 
-import java.util.{ArrayList => JArrayList, List => JList}
+import java.util.{Arrays, ArrayList => JArrayList, List => JList}
+
+import scala.collection.JavaConverters._
 
 import org.apache.commons.lang3.exception.ExceptionUtils
 import org.apache.hadoop.hive.metastore.api.{FieldSchema, Schema}
@@ -27,8 +29,6 @@ import org.apache.hadoop.hive.ql.processors.CommandProcessorResponse
 import org.apache.spark.Logging
 import org.apache.spark.sql.hive.{HiveContext, HiveMetastoreTypes}
 
-import scala.collection.JavaConversions._
-
 private[hive] class SparkSQLDriver(
     val context: HiveContext = SparkSQLEnv.hiveContext)
   extends Driver
@@ -43,14 +43,14 @@ private[hive] class SparkSQLDriver(
   private def getResultSetSchema(query: context.QueryExecution): Schema = {
     val analyzed = query.analyzed
     logDebug(s"Result Schema: ${analyzed.output}")
-    if (analyzed.output.size == 0) {
-      new Schema(new FieldSchema("Response code", "string", "") :: Nil, null)
+    if (analyzed.output.isEmpty) {
+      new Schema(Arrays.asList(new FieldSchema("Response code", "string", "")), null)
     } else {
       val fieldSchemas = analyzed.output.map { attr =>
         new FieldSchema(attr.name, HiveMetastoreTypes.toMetastoreType(attr.dataType), "")
       }
 
-      new Schema(fieldSchemas, null)
+      new Schema(fieldSchemas.asJava, null)
     }
   }
 
@@ -79,7 +79,7 @@ private[hive] class SparkSQLDriver(
     if (hiveResponse == null) {
       false
     } else {
-      res.asInstanceOf[JArrayList[String]].addAll(hiveResponse)
+      res.asInstanceOf[JArrayList[String]].addAll(hiveResponse.asJava)
       hiveResponse = null
       true
     }
diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
index 1d41c46131828..bacf6cc458fd5 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLEnv.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.thriftserver
 
 import java.io.PrintStream
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.scheduler.StatsReportListener
 import org.apache.spark.sql.hive.HiveContext
@@ -64,7 +64,7 @@ private[hive] object SparkSQLEnv extends Logging {
       hiveContext.setConf("spark.sql.hive.version", HiveContext.hiveExecutionVersion)
 
       if (log.isDebugEnabled) {
-        hiveContext.hiveconf.getAllProperties.toSeq.sorted.foreach { case (k, v) =>
+        hiveContext.hiveconf.getAllProperties.asScala.toSeq.sorted.foreach { case (k, v) =>
           logDebug(s"HiveConf var: $k=$v")
         }
       }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 17cc83087fb1d..c0a458fa9ab8d 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -22,7 +22,7 @@ import java.net.{URL, URLClassLoader}
 import java.sql.Timestamp
 import java.util.concurrent.TimeUnit
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.HashMap
 import scala.language.implicitConversions
 import scala.concurrent.duration._
@@ -194,7 +194,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
     logInfo("defalt warehouse location is " + defaltWarehouseLocation)
 
     // `configure` goes second to override other settings.
-    val allConfig = metadataConf.iterator.map(e => e.getKey -> e.getValue).toMap ++ configure
+    val allConfig = metadataConf.asScala.map(e => e.getKey -> e.getValue).toMap ++ configure
 
     val isolatedLoader = if (hiveMetastoreJars == "builtin") {
       if (hiveExecutionVersion != hiveMetastoreVersion) {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
index 64fffdbf9b020..cfe2bb05ad89e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveInspectors.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql.hive
 
+import scala.collection.JavaConverters._
+
 import org.apache.hadoop.hive.common.`type`.{HiveDecimal, HiveVarchar}
 import org.apache.hadoop.hive.serde2.objectinspector.primitive._
 import org.apache.hadoop.hive.serde2.objectinspector.{StructField => HiveStructField, _}
@@ -31,9 +33,6 @@ import org.apache.spark.sql.types._
 import org.apache.spark.sql.{AnalysisException, types}
 import org.apache.spark.unsafe.types.UTF8String
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
-
 /**
  * 1. The Underlying data type in catalyst and in Hive
  * In catalyst:
@@ -290,13 +289,13 @@ private[hive] trait HiveInspectors {
       DateTimeUtils.fromJavaDate(poi.getWritableConstantValue.get())
     case mi: StandardConstantMapObjectInspector =>
       // take the value from the map inspector object, rather than the input data
-      val map = mi.getWritableConstantValue
-      val keys = map.keysIterator.map(unwrap(_, mi.getMapKeyObjectInspector)).toArray
-      val values = map.valuesIterator.map(unwrap(_, mi.getMapValueObjectInspector)).toArray
+      val keyValues = mi.getWritableConstantValue.asScala.toSeq
+      val keys = keyValues.map(kv => unwrap(kv._1, mi.getMapKeyObjectInspector)).toArray
+      val values = keyValues.map(kv => unwrap(kv._2, mi.getMapValueObjectInspector)).toArray
       ArrayBasedMapData(keys, values)
     case li: StandardConstantListObjectInspector =>
       // take the value from the list inspector object, rather than the input data
-      val values = li.getWritableConstantValue
+      val values = li.getWritableConstantValue.asScala
         .map(unwrap(_, li.getListElementObjectInspector))
         .toArray
       new GenericArrayData(values)
@@ -342,7 +341,7 @@ private[hive] trait HiveInspectors {
     case li: ListObjectInspector =>
       Option(li.getList(data))
         .map { l =>
-          val values = l.map(unwrap(_, li.getListElementObjectInspector)).toArray
+          val values = l.asScala.map(unwrap(_, li.getListElementObjectInspector)).toArray
           new GenericArrayData(values)
         }
         .orNull
@@ -351,15 +350,16 @@ private[hive] trait HiveInspectors {
       if (map == null) {
         null
       } else {
-        val keys = map.keysIterator.map(unwrap(_, mi.getMapKeyObjectInspector)).toArray
-        val values = map.valuesIterator.map(unwrap(_, mi.getMapValueObjectInspector)).toArray
+        val keyValues = map.asScala.toSeq
+        val keys = keyValues.map(kv => unwrap(kv._1, mi.getMapKeyObjectInspector)).toArray
+        val values = keyValues.map(kv => unwrap(kv._2, mi.getMapValueObjectInspector)).toArray
         ArrayBasedMapData(keys, values)
       }
     // currently, hive doesn't provide the ConstantStructObjectInspector
     case si: StructObjectInspector =>
       val allRefs = si.getAllStructFieldRefs
-      InternalRow.fromSeq(
-        allRefs.map(r => unwrap(si.getStructFieldData(data, r), r.getFieldObjectInspector)))
+      InternalRow.fromSeq(allRefs.asScala.map(
+        r => unwrap(si.getStructFieldData(data, r), r.getFieldObjectInspector)))
   }
 
 
@@ -403,14 +403,14 @@ private[hive] trait HiveInspectors {
 
     case soi: StandardStructObjectInspector =>
       val schema = dataType.asInstanceOf[StructType]
-      val wrappers = soi.getAllStructFieldRefs.zip(schema.fields).map { case (ref, field) =>
-        wrapperFor(ref.getFieldObjectInspector, field.dataType)
+      val wrappers = soi.getAllStructFieldRefs.asScala.zip(schema.fields).map {
+        case (ref, field) => wrapperFor(ref.getFieldObjectInspector, field.dataType)
       }
       (o: Any) => {
         if (o != null) {
           val struct = soi.create()
           val row = o.asInstanceOf[InternalRow]
-          soi.getAllStructFieldRefs.zip(wrappers).zipWithIndex.foreach {
+          soi.getAllStructFieldRefs.asScala.zip(wrappers).zipWithIndex.foreach {
             case ((field, wrapper), i) =>
               soi.setStructFieldData(struct, field, wrapper(row.get(i, schema(i).dataType)))
           }
@@ -537,7 +537,7 @@ private[hive] trait HiveInspectors {
       // 1. create the pojo (most likely) object
       val result = x.create()
       var i = 0
-      while (i < fieldRefs.length) {
+      while (i < fieldRefs.size) {
         // 2. set the property for the pojo
         val tpe = structType(i).dataType
         x.setStructFieldData(
@@ -552,9 +552,9 @@ private[hive] trait HiveInspectors {
       val fieldRefs = x.getAllStructFieldRefs
       val structType = dataType.asInstanceOf[StructType]
       val row = a.asInstanceOf[InternalRow]
-      val result = new java.util.ArrayList[AnyRef](fieldRefs.length)
+      val result = new java.util.ArrayList[AnyRef](fieldRefs.size)
       var i = 0
-      while (i < fieldRefs.length) {
+      while (i < fieldRefs.size) {
         val tpe = structType(i).dataType
         result.add(wrap(row.get(i, tpe), fieldRefs.get(i).getFieldObjectInspector, tpe))
         i += 1
@@ -712,10 +712,10 @@ private[hive] trait HiveInspectors {
 
   def inspectorToDataType(inspector: ObjectInspector): DataType = inspector match {
     case s: StructObjectInspector =>
-      StructType(s.getAllStructFieldRefs.map(f => {
+      StructType(s.getAllStructFieldRefs.asScala.map(f =>
         types.StructField(
           f.getFieldName, inspectorToDataType(f.getFieldObjectInspector), nullable = true)
-      }))
+      ))
     case l: ListObjectInspector => ArrayType(inspectorToDataType(l.getListElementObjectInspector))
     case m: MapObjectInspector =>
       MapType(
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index 98d21aa76d64e..b8da0840ae569 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 
 import com.google.common.base.Objects
@@ -483,7 +483,7 @@ private[hive] class HiveMetastoreCatalog(val client: ClientInterface, hive: Hive
       // are empty.
       val partitions = metastoreRelation.getHiveQlPartitions().map { p =>
         val location = p.getLocation
-        val values = InternalRow.fromSeq(p.getValues.zip(partitionColumnDataTypes).map {
+        val values = InternalRow.fromSeq(p.getValues.asScala.zip(partitionColumnDataTypes).map {
           case (rawValue, dataType) => Cast(Literal(rawValue), dataType).eval(null)
         })
         ParquetPartition(values, location)
@@ -798,9 +798,9 @@ private[hive] case class MetastoreRelation
 
     val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
     tTable.setSd(sd)
-    sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+    sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)).asJava)
     tTable.setPartitionKeys(
-      table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+      table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment)).asJava)
 
     table.location.foreach(sd.setLocation)
     table.inputFormat.foreach(sd.setInputFormat)
@@ -852,11 +852,11 @@ private[hive] case class MetastoreRelation
       val tPartition = new org.apache.hadoop.hive.metastore.api.Partition
       tPartition.setDbName(databaseName)
       tPartition.setTableName(tableName)
-      tPartition.setValues(p.values)
+      tPartition.setValues(p.values.asJava)
 
       val sd = new org.apache.hadoop.hive.metastore.api.StorageDescriptor()
       tPartition.setSd(sd)
-      sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+      sd.setCols(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)).asJava)
 
       sd.setLocation(p.storage.location)
       sd.setInputFormat(p.storage.inputFormat)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
index ad33dee555dd2..d5cd7e98b5267 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveQl.scala
@@ -20,6 +20,9 @@ package org.apache.spark.sql.hive
 import java.sql.Date
 import java.util.Locale
 
+import scala.collection.JavaConverters._
+import scala.collection.mutable.ArrayBuffer
+
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
 import org.apache.hadoop.hive.serde.serdeConstants
@@ -48,10 +51,6 @@ import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.CalendarInterval
 import org.apache.spark.util.random.RandomSampler
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
-import scala.collection.mutable.ArrayBuffer
-
 /**
  * Used when we need to start parsing the AST before deciding that we are going to pass the command
  * back for Hive to execute natively.  Will be replaced with a native command that contains the
@@ -202,7 +201,7 @@ private[hive] object HiveQl extends Logging {
      * Returns a scala.Seq equivalent to [s] or Nil if [s] is null.
      */
     private def nilIfEmpty[A](s: java.util.List[A]): Seq[A] =
-      Option(s).map(_.toSeq).getOrElse(Nil)
+      Option(s).map(_.asScala).getOrElse(Nil)
 
     /**
      * Returns this ASTNode with the text changed to `newText`.
@@ -217,7 +216,7 @@ private[hive] object HiveQl extends Logging {
      */
     def withChildren(newChildren: Seq[ASTNode]): ASTNode = {
       (1 to n.getChildCount).foreach(_ => n.deleteChild(0))
-      n.addChildren(newChildren)
+      n.addChildren(newChildren.asJava)
       n
     }
 
@@ -323,11 +322,11 @@ private[hive] object HiveQl extends Logging {
     assert(tree.asInstanceOf[ASTNode].getText == "TOK_CREATETABLE", "Only CREATE TABLE supported.")
     val tableOps = tree.getChildren
     val colList =
-      tableOps
+      tableOps.asScala
         .find(_.asInstanceOf[ASTNode].getText == "TOK_TABCOLLIST")
         .getOrElse(sys.error("No columnList!")).getChildren
 
-    colList.map(nodeToAttribute)
+    colList.asScala.map(nodeToAttribute)
   }
 
   /** Extractor for matching Hive's AST Tokens. */
@@ -337,7 +336,7 @@ private[hive] object HiveQl extends Logging {
       case t: ASTNode =>
         CurrentOrigin.setPosition(t.getLine, t.getCharPositionInLine)
         Some((t.getText,
-          Option(t.getChildren).map(_.toList).getOrElse(Nil).asInstanceOf[Seq[ASTNode]]))
+          Option(t.getChildren).map(_.asScala.toList).getOrElse(Nil).asInstanceOf[Seq[ASTNode]]))
       case _ => None
     }
   }
@@ -424,7 +423,9 @@ private[hive] object HiveQl extends Logging {
 
   protected def extractDbNameTableName(tableNameParts: Node): (Option[String], String) = {
     val (db, tableName) =
-      tableNameParts.getChildren.map { case Token(part, Nil) => cleanIdentifier(part) } match {
+      tableNameParts.getChildren.asScala.map {
+        case Token(part, Nil) => cleanIdentifier(part)
+      } match {
         case Seq(tableOnly) => (None, tableOnly)
         case Seq(databaseName, table) => (Some(databaseName), table)
       }
@@ -433,7 +434,9 @@ private[hive] object HiveQl extends Logging {
   }
 
   protected def extractTableIdent(tableNameParts: Node): Seq[String] = {
-    tableNameParts.getChildren.map { case Token(part, Nil) => cleanIdentifier(part) } match {
+    tableNameParts.getChildren.asScala.map {
+      case Token(part, Nil) => cleanIdentifier(part)
+    } match {
       case Seq(tableOnly) => Seq(tableOnly)
       case Seq(databaseName, table) => Seq(databaseName, table)
       case other => sys.error("Hive only supports tables names like 'tableName' " +
@@ -624,7 +627,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
           val cols = BaseSemanticAnalyzer.getColumns(list, true)
           if (cols != null) {
             tableDesc = tableDesc.copy(
-              schema = cols.map { field =>
+              schema = cols.asScala.map { field =>
                 HiveColumn(field.getName, field.getType, field.getComment)
               })
           }
@@ -636,7 +639,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
           val cols = BaseSemanticAnalyzer.getColumns(list(0), false)
           if (cols != null) {
             tableDesc = tableDesc.copy(
-              partitionColumns = cols.map { field =>
+              partitionColumns = cols.asScala.map { field =>
                 HiveColumn(field.getName, field.getType, field.getComment)
               })
           }
@@ -672,7 +675,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             case _ => assert(false)
           }
           tableDesc = tableDesc.copy(
-            serdeProperties = tableDesc.serdeProperties ++ serdeParams)
+            serdeProperties = tableDesc.serdeProperties ++ serdeParams.asScala)
         case Token("TOK_TABLELOCATION", child :: Nil) =>
           var location = BaseSemanticAnalyzer.unescapeSQLString(child.getText)
           location = EximUtil.relativeToAbsolutePath(hiveConf, location)
@@ -684,7 +687,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             val serdeParams = new java.util.HashMap[String, String]()
             BaseSemanticAnalyzer.readProps(
               (child.getChild(1).getChild(0)).asInstanceOf[ASTNode], serdeParams)
-            tableDesc = tableDesc.copy(serdeProperties = tableDesc.serdeProperties ++ serdeParams)
+            tableDesc = tableDesc.copy(
+              serdeProperties = tableDesc.serdeProperties ++ serdeParams.asScala)
           }
         case Token("TOK_FILEFORMAT_GENERIC", child :: Nil) =>
           child.getText().toLowerCase(Locale.ENGLISH) match {
@@ -847,7 +851,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         }
 
         val withWhere = whereClause.map { whereNode =>
-          val Seq(whereExpr) = whereNode.getChildren.toSeq
+          val Seq(whereExpr) = whereNode.getChildren.asScala
           Filter(nodeToExpr(whereExpr), relations)
         }.getOrElse(relations)
 
@@ -856,7 +860,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
         // Script transformations are expressed as a select clause with a single expression of type
         // TOK_TRANSFORM
-        val transformation = select.getChildren.head match {
+        val transformation = select.getChildren.iterator().next() match {
           case Token("TOK_SELEXPR",
                  Token("TOK_TRANSFORM",
                    Token("TOK_EXPLIST", inputExprs) ::
@@ -925,10 +929,10 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
         val withLateralView = lateralViewClause.map { lv =>
           val Token("TOK_SELECT",
-          Token("TOK_SELEXPR", clauses) :: Nil) = lv.getChildren.head
+          Token("TOK_SELEXPR", clauses) :: Nil) = lv.getChildren.iterator().next()
 
-          val alias =
-            getClause("TOK_TABALIAS", clauses).getChildren.head.asInstanceOf[ASTNode].getText
+          val alias = getClause("TOK_TABALIAS", clauses).getChildren.iterator().next()
+            .asInstanceOf[ASTNode].getText
 
           val (generator, attributes) = nodesToGenerator(clauses)
             Generate(
@@ -944,7 +948,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         // (if there is a group by) or a script transformation.
         val withProject: LogicalPlan = transformation.getOrElse {
           val selectExpressions =
-            select.getChildren.flatMap(selExprNodeToExpr).map(UnresolvedAlias(_)).toSeq
+            select.getChildren.asScala.flatMap(selExprNodeToExpr).map(UnresolvedAlias)
           Seq(
             groupByClause.map(e => e match {
               case Token("TOK_GROUPBY", children) =>
@@ -973,7 +977,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
         // Handle HAVING clause.
         val withHaving = havingClause.map { h =>
-          val havingExpr = h.getChildren.toSeq match { case Seq(hexpr) => nodeToExpr(hexpr) }
+          val havingExpr = h.getChildren.asScala match { case Seq(hexpr) => nodeToExpr(hexpr) }
           // Note that we added a cast to boolean. If the expression itself is already boolean,
           // the optimizer will get rid of the unnecessary cast.
           Filter(Cast(havingExpr, BooleanType), withProject)
@@ -983,32 +987,42 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         val withDistinct =
           if (selectDistinctClause.isDefined) Distinct(withHaving) else withHaving
 
-        // Handle ORDER BY, SORT BY, DISTRIBETU BY, and CLUSTER BY clause.
+        // Handle ORDER BY, SORT BY, DISTRIBUTE BY, and CLUSTER BY clause.
         val withSort =
           (orderByClause, sortByClause, distributeByClause, clusterByClause) match {
             case (Some(totalOrdering), None, None, None) =>
-              Sort(totalOrdering.getChildren.map(nodeToSortOrder), true, withDistinct)
+              Sort(totalOrdering.getChildren.asScala.map(nodeToSortOrder), true, withDistinct)
             case (None, Some(perPartitionOrdering), None, None) =>
-              Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false, withDistinct)
+              Sort(
+                perPartitionOrdering.getChildren.asScala.map(nodeToSortOrder),
+                false, withDistinct)
             case (None, None, Some(partitionExprs), None) =>
-              RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withDistinct)
+              RepartitionByExpression(
+                partitionExprs.getChildren.asScala.map(nodeToExpr), withDistinct)
             case (None, Some(perPartitionOrdering), Some(partitionExprs), None) =>
-              Sort(perPartitionOrdering.getChildren.map(nodeToSortOrder), false,
-                RepartitionByExpression(partitionExprs.getChildren.map(nodeToExpr), withDistinct))
+              Sort(
+                perPartitionOrdering.getChildren.asScala.map(nodeToSortOrder), false,
+                RepartitionByExpression(
+                  partitionExprs.getChildren.asScala.map(nodeToExpr),
+                  withDistinct))
             case (None, None, None, Some(clusterExprs)) =>
-              Sort(clusterExprs.getChildren.map(nodeToExpr).map(SortOrder(_, Ascending)), false,
-                RepartitionByExpression(clusterExprs.getChildren.map(nodeToExpr), withDistinct))
+              Sort(
+                clusterExprs.getChildren.asScala.map(nodeToExpr).map(SortOrder(_, Ascending)),
+                false,
+                RepartitionByExpression(
+                  clusterExprs.getChildren.asScala.map(nodeToExpr),
+                  withDistinct))
             case (None, None, None, None) => withDistinct
             case _ => sys.error("Unsupported set of ordering / distribution clauses.")
           }
 
         val withLimit =
-          limitClause.map(l => nodeToExpr(l.getChildren.head))
+          limitClause.map(l => nodeToExpr(l.getChildren.iterator().next()))
             .map(Limit(_, withSort))
             .getOrElse(withSort)
 
         // Collect all window specifications defined in the WINDOW clause.
-        val windowDefinitions = windowClause.map(_.getChildren.toSeq.collect {
+        val windowDefinitions = windowClause.map(_.getChildren.asScala.collect {
           case Token("TOK_WINDOWDEF",
           Token(windowName, Nil) :: Token("TOK_WINDOWSPEC", spec) :: Nil) =>
             windowName -> nodesToWindowSpecification(spec)
@@ -1063,7 +1077,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       val Token("TOK_SELECT",
             Token("TOK_SELEXPR", clauses) :: Nil) = selectClause
 
-      val alias = getClause("TOK_TABALIAS", clauses).getChildren.head.asInstanceOf[ASTNode].getText
+      val alias = getClause("TOK_TABALIAS", clauses).getChildren.iterator().next()
+        .asInstanceOf[ASTNode].getText
 
       val (generator, attributes) = nodesToGenerator(clauses)
         Generate(
@@ -1092,7 +1107,9 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       }
 
       val tableIdent =
-        tableNameParts.getChildren.map{ case Token(part, Nil) => cleanIdentifier(part)} match {
+        tableNameParts.getChildren.asScala.map {
+          case Token(part, Nil) => cleanIdentifier(part)
+        } match {
           case Seq(tableOnly) => Seq(tableOnly)
           case Seq(databaseName, table) => Seq(databaseName, table)
           case other => sys.error("Hive only supports tables names like 'tableName' " +
@@ -1139,7 +1156,8 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
       val isPreserved = tableOrdinals.map(i => (i - 1 < 0) || joinArgs(i - 1).getText == "PRESERVE")
       val tables = tableOrdinals.map(i => nodeToRelation(joinArgs(i)))
-      val joinExpressions = tableOrdinals.map(i => joinArgs(i + 1).getChildren.map(nodeToExpr))
+      val joinExpressions =
+        tableOrdinals.map(i => joinArgs(i + 1).getChildren.asScala.map(nodeToExpr))
 
       val joinConditions = joinExpressions.sliding(2).map {
         case Seq(c1, c2) =>
@@ -1164,7 +1182,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
             joinType = joinType.remove(joinType.length - 1))
       }
 
-      val groups = (0 until joinExpressions.head.size).map(i => Coalesce(joinExpressions.map(_(i))))
+      val groups = joinExpressions.head.indices.map(i => Coalesce(joinExpressions.map(_(i))))
 
       // Unique join is not really the same as an outer join so we must group together results where
       // the joinExpressions are the same, taking the First of each value is only okay because the
@@ -1229,7 +1247,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
       val tableIdent = extractTableIdent(tableNameParts)
 
-      val partitionKeys = partitionClause.map(_.getChildren.map {
+      val partitionKeys = partitionClause.map(_.getChildren.asScala.map {
         // Parse partitions. We also make keys case insensitive.
         case Token("TOK_PARTVAL", Token(key, Nil) :: Token(value, Nil) :: Nil) =>
           cleanIdentifier(key.toLowerCase) -> Some(PlanUtils.stripQuotes(value))
@@ -1249,7 +1267,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
 
       val tableIdent = extractTableIdent(tableNameParts)
 
-      val partitionKeys = partitionClause.map(_.getChildren.map {
+      val partitionKeys = partitionClause.map(_.getChildren.asScala.map {
         // Parse partitions. We also make keys case insensitive.
         case Token("TOK_PARTVAL", Token(key, Nil) :: Token(value, Nil) :: Nil) =>
           cleanIdentifier(key.toLowerCase) -> Some(PlanUtils.stripQuotes(value))
@@ -1590,18 +1608,18 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
         val (partitionByClause :: orderByClause :: sortByClause :: clusterByClause :: Nil) =
           getClauses(
             Seq("TOK_DISTRIBUTEBY", "TOK_ORDERBY", "TOK_SORTBY", "TOK_CLUSTERBY"),
-            partitionAndOrdering.getChildren.toSeq.asInstanceOf[Seq[ASTNode]])
+            partitionAndOrdering.getChildren.asScala.asInstanceOf[Seq[ASTNode]])
 
         (partitionByClause, orderByClause.orElse(sortByClause), clusterByClause) match {
           case (Some(partitionByExpr), Some(orderByExpr), None) =>
-            (partitionByExpr.getChildren.map(nodeToExpr),
-              orderByExpr.getChildren.map(nodeToSortOrder))
+            (partitionByExpr.getChildren.asScala.map(nodeToExpr),
+              orderByExpr.getChildren.asScala.map(nodeToSortOrder))
           case (Some(partitionByExpr), None, None) =>
-            (partitionByExpr.getChildren.map(nodeToExpr), Nil)
+            (partitionByExpr.getChildren.asScala.map(nodeToExpr), Nil)
           case (None, Some(orderByExpr), None) =>
-            (Nil, orderByExpr.getChildren.map(nodeToSortOrder))
+            (Nil, orderByExpr.getChildren.asScala.map(nodeToSortOrder))
           case (None, None, Some(clusterByExpr)) =>
-            val expressions = clusterByExpr.getChildren.map(nodeToExpr)
+            val expressions = clusterByExpr.getChildren.asScala.map(nodeToExpr)
             (expressions, expressions.map(SortOrder(_, Ascending)))
           case _ =>
             throw new NotImplementedError(
@@ -1639,7 +1657,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
           }
 
           rowFrame.orElse(rangeFrame).map { frame =>
-            frame.getChildren.toList match {
+            frame.getChildren.asScala.toList match {
               case precedingNode :: followingNode :: Nil =>
                 SpecifiedWindowFrame(
                   frameType,
@@ -1701,7 +1719,7 @@ https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C
       case other => sys.error(s"Non ASTNode encountered: $other")
     }
 
-    Option(node.getChildren).map(_.toList).getOrElse(Nil).foreach(dumpTree(_, builder, indent + 1))
+    Option(node.getChildren).map(_.asScala).getOrElse(Nil).foreach(dumpTree(_, builder, indent + 1))
     builder
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
index 267074f3ad102..004805f3aed0b 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveShim.scala
@@ -22,8 +22,7 @@ import java.rmi.server.UID
 
 import org.apache.avro.Schema
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
@@ -73,7 +72,7 @@ private[hive] object HiveShim {
    */
   def appendReadColumns(conf: Configuration, ids: Seq[Integer], names: Seq[String]) {
     if (ids != null && ids.nonEmpty) {
-      ColumnProjectionUtils.appendReadColumns(conf, ids)
+      ColumnProjectionUtils.appendReadColumns(conf, ids.asJava)
     }
     if (names != null && names.nonEmpty) {
       appendReadColumnNames(conf, names)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
index f49c97de8ff4e..4d1e3ed9198e6 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/ClientWrapper.scala
@@ -21,7 +21,7 @@ import java.io.{File, PrintStream}
 import java.util.{Map => JMap}
 import javax.annotation.concurrent.GuardedBy
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.reflectiveCalls
 
 import org.apache.hadoop.fs.Path
@@ -305,10 +305,11 @@ private[hive] class ClientWrapper(
       HiveTable(
         name = h.getTableName,
         specifiedDatabase = Option(h.getDbName),
-        schema = h.getCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)),
-        partitionColumns = h.getPartCols.map(f => HiveColumn(f.getName, f.getType, f.getComment)),
-        properties = h.getParameters.toMap,
-        serdeProperties = h.getTTable.getSd.getSerdeInfo.getParameters.toMap,
+        schema = h.getCols.asScala.map(f => HiveColumn(f.getName, f.getType, f.getComment)),
+        partitionColumns = h.getPartCols.asScala.map(f =>
+          HiveColumn(f.getName, f.getType, f.getComment)),
+        properties = h.getParameters.asScala.toMap,
+        serdeProperties = h.getTTable.getSd.getSerdeInfo.getParameters.asScala.toMap,
         tableType = h.getTableType match {
           case HTableType.MANAGED_TABLE => ManagedTable
           case HTableType.EXTERNAL_TABLE => ExternalTable
@@ -334,9 +335,9 @@ private[hive] class ClientWrapper(
   private def toQlTable(table: HiveTable): metadata.Table = {
     val qlTable = new metadata.Table(table.database, table.name)
 
-    qlTable.setFields(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+    qlTable.setFields(table.schema.map(c => new FieldSchema(c.name, c.hiveType, c.comment)).asJava)
     qlTable.setPartCols(
-      table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment)))
+      table.partitionColumns.map(c => new FieldSchema(c.name, c.hiveType, c.comment)).asJava)
     table.properties.foreach { case (k, v) => qlTable.setProperty(k, v) }
     table.serdeProperties.foreach { case (k, v) => qlTable.setSerdeParam(k, v) }
 
@@ -366,13 +367,13 @@ private[hive] class ClientWrapper(
   private def toHivePartition(partition: metadata.Partition): HivePartition = {
     val apiPartition = partition.getTPartition
     HivePartition(
-      values = Option(apiPartition.getValues).map(_.toSeq).getOrElse(Seq.empty),
+      values = Option(apiPartition.getValues).map(_.asScala).getOrElse(Seq.empty),
       storage = HiveStorageDescriptor(
         location = apiPartition.getSd.getLocation,
         inputFormat = apiPartition.getSd.getInputFormat,
         outputFormat = apiPartition.getSd.getOutputFormat,
         serde = apiPartition.getSd.getSerdeInfo.getSerializationLib,
-        serdeProperties = apiPartition.getSd.getSerdeInfo.getParameters.toMap))
+        serdeProperties = apiPartition.getSd.getSerdeInfo.getParameters.asScala.toMap))
   }
 
   override def getPartitionOption(
@@ -397,7 +398,7 @@ private[hive] class ClientWrapper(
   }
 
   override def listTables(dbName: String): Seq[String] = withHiveState {
-    client.getAllTables(dbName)
+    client.getAllTables(dbName).asScala
   }
 
   /**
@@ -514,17 +515,17 @@ private[hive] class ClientWrapper(
   }
 
   def reset(): Unit = withHiveState {
-    client.getAllTables("default").foreach { t =>
+    client.getAllTables("default").asScala.foreach { t =>
         logDebug(s"Deleting table $t")
         val table = client.getTable("default", t)
-        client.getIndexes("default", t, 255).foreach { index =>
+        client.getIndexes("default", t, 255).asScala.foreach { index =>
           shim.dropIndex(client, "default", t, index.getIndexName)
         }
         if (!table.isIndexTable) {
           client.dropTable("default", t)
         }
       }
-      client.getAllDatabases.filterNot(_ == "default").foreach { db =>
+      client.getAllDatabases.asScala.filterNot(_ == "default").foreach { db =>
         logDebug(s"Dropping Database: $db")
         client.dropDatabase(db, true, false, true)
       }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
index 8fc8935b1dc3c..48bbb21e6c1de 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/HiveShim.scala
@@ -23,7 +23,7 @@ import java.net.URI
 import java.util.{ArrayList => JArrayList, List => JList, Map => JMap, Set => JSet}
 import java.util.concurrent.TimeUnit
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.fs.{FileSystem, Path}
 import org.apache.hadoop.hive.conf.HiveConf
@@ -201,7 +201,7 @@ private[client] class Shim_v0_12 extends Shim with Logging {
     setDataLocationMethod.invoke(table, new URI(loc))
 
   override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] =
-    getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq
+    getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].asScala.toSeq
 
   override def getPartitionsByFilter(
       hive: Hive,
@@ -220,7 +220,7 @@ private[client] class Shim_v0_12 extends Shim with Logging {
   override def getDriverResults(driver: Driver): Seq[String] = {
     val res = new JArrayList[String]()
     getDriverResultsMethod.invoke(driver, res)
-    res.toSeq
+    res.asScala
   }
 
   override def getMetastoreClientConnectRetryDelayMillis(conf: HiveConf): Long = {
@@ -310,7 +310,7 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
     setDataLocationMethod.invoke(table, new Path(loc))
 
   override def getAllPartitions(hive: Hive, table: Table): Seq[Partition] =
-    getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].toSeq
+    getAllPartitionsMethod.invoke(hive, table).asInstanceOf[JSet[Partition]].asScala.toSeq
 
   /**
    * Converts catalyst expression to the format that Hive's getPartitionsByFilter() expects, i.e.
@@ -320,7 +320,7 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
    */
   def convertFilters(table: Table, filters: Seq[Expression]): String = {
     // hive varchar is treated as catalyst string, but hive varchar can't be pushed down.
-    val varcharKeys = table.getPartitionKeys
+    val varcharKeys = table.getPartitionKeys.asScala
       .filter(col => col.getType.startsWith(serdeConstants.VARCHAR_TYPE_NAME))
       .map(col => col.getName).toSet
 
@@ -354,7 +354,7 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
         getPartitionsByFilterMethod.invoke(hive, table, filter).asInstanceOf[JArrayList[Partition]]
       }
 
-    partitions.toSeq
+    partitions.asScala.toSeq
   }
 
   override def getCommandProcessor(token: String, conf: HiveConf): CommandProcessor =
@@ -363,7 +363,7 @@ private[client] class Shim_v0_13 extends Shim_v0_12 {
   override def getDriverResults(driver: Driver): Seq[String] = {
     val res = new JArrayList[Object]()
     getDriverResultsMethod.invoke(driver, res)
-    res.map { r =>
+    res.asScala.map { r =>
       r match {
         case s: String => s
         case a: Array[Object] => a(0).asInstanceOf[String]
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
index 5f0ed5393d191..441b6b6033e1f 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/DescribeHiveTableCommand.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive.execution
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.hive.metastore.api.FieldSchema
 
@@ -39,8 +39,8 @@ case class DescribeHiveTableCommand(
     // Trying to mimic the format of Hive's output. But not exactly the same.
     var results: Seq[(String, String, String)] = Nil
 
-    val columns: Seq[FieldSchema] = table.hiveQlTable.getCols
-    val partitionColumns: Seq[FieldSchema] = table.hiveQlTable.getPartCols
+    val columns: Seq[FieldSchema] = table.hiveQlTable.getCols.asScala
+    val partitionColumns: Seq[FieldSchema] = table.hiveQlTable.getPartCols.asScala
     results ++= columns.map(field => (field.getName, field.getType, field.getComment))
     if (partitionColumns.nonEmpty) {
       val partColumnInfo =
@@ -48,7 +48,7 @@ case class DescribeHiveTableCommand(
       results ++=
         partColumnInfo ++
           Seq(("# Partition Information", "", "")) ++
-          Seq((s"# ${output.get(0).name}", output.get(1).name, output.get(2).name)) ++
+          Seq((s"# ${output(0).name}", output(1).name, output(2).name)) ++
           partColumnInfo
     }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
index ba7eb15a1c0c6..806d2b9b0b7d4 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScan.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive.execution
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.ql.metadata.{Partition => HivePartition}
@@ -98,7 +98,7 @@ case class HiveTableScan(
       .asInstanceOf[StructObjectInspector]
 
     val columnTypeNames = structOI
-      .getAllStructFieldRefs
+      .getAllStructFieldRefs.asScala
       .map(_.getFieldObjectInspector)
       .map(TypeInfoUtils.getTypeInfoFromObjectInspector(_).getTypeName)
       .mkString(",")
@@ -118,9 +118,8 @@ case class HiveTableScan(
       case None => partitions
       case Some(shouldKeep) => partitions.filter { part =>
         val dataTypes = relation.partitionKeys.map(_.dataType)
-        val castedValues = for ((value, dataType) <- part.getValues.zip(dataTypes)) yield {
-          castFromString(value, dataType)
-        }
+        val castedValues = part.getValues.asScala.zip(dataTypes)
+          .map { case (value, dataType) => castFromString(value, dataType) }
 
         // Only partitioned values are needed here, since the predicate has already been bound to
         // partition key attribute references.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 62efda613a176..58f7fa640e8a9 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -19,9 +19,10 @@ package org.apache.spark.sql.hive.execution
 
 import java.util
 
+import scala.collection.JavaConverters._
+
 import org.apache.hadoop.hive.conf.HiveConf
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
-import org.apache.hadoop.hive.metastore.MetaStoreUtils
 import org.apache.hadoop.hive.ql.plan.TableDesc
 import org.apache.hadoop.hive.ql.{Context, ErrorMsg}
 import org.apache.hadoop.hive.serde2.Serializer
@@ -38,8 +39,6 @@ import org.apache.spark.sql.hive.HiveShim.{ShimFileSinkDesc => FileSinkDesc}
 import org.apache.spark.sql.hive._
 import org.apache.spark.sql.types.DataType
 import org.apache.spark.{SparkException, TaskContext}
-
-import scala.collection.JavaConversions._
 import org.apache.spark.util.SerializableJobConf
 
 private[hive]
@@ -94,7 +93,8 @@ case class InsertIntoHiveTable(
           ObjectInspectorCopyOption.JAVA)
         .asInstanceOf[StructObjectInspector]
 
-      val fieldOIs = standardOI.getAllStructFieldRefs.map(_.getFieldObjectInspector).toArray
+      val fieldOIs = standardOI.getAllStructFieldRefs.asScala
+        .map(_.getFieldObjectInspector).toArray
       val dataTypes: Array[DataType] = child.output.map(_.dataType).toArray
       val wrappers = fieldOIs.zip(dataTypes).map { case (f, dt) => wrapperFor(f, dt)}
       val outputData = new Array[Any](fieldOIs.length)
@@ -198,7 +198,7 @@ case class InsertIntoHiveTable(
 
       // loadPartition call orders directories created on the iteration order of the this map
       val orderedPartitionSpec = new util.LinkedHashMap[String, String]()
-      table.hiveQlTable.getPartCols().foreach { entry =>
+      table.hiveQlTable.getPartCols.asScala.foreach { entry =>
         orderedPartitionSpec.put(entry.getName, partitionSpec.get(entry.getName).getOrElse(""))
       }
 
@@ -226,7 +226,7 @@ case class InsertIntoHiveTable(
         val oldPart =
           catalog.client.getPartitionOption(
             catalog.client.getTable(table.databaseName, table.tableName),
-            partitionSpec)
+            partitionSpec.asJava)
 
         if (oldPart.isEmpty || !ifNotExists) {
             catalog.client.loadPartition(
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index ade27454b9d29..c7651daffe36e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -21,7 +21,7 @@ import java.io._
 import java.util.Properties
 import javax.annotation.Nullable
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.control.NonFatal
 
 import org.apache.hadoop.hive.serde.serdeConstants
@@ -61,7 +61,7 @@ case class ScriptTransformation(
   protected override def doExecute(): RDD[InternalRow] = {
     def processIterator(inputIterator: Iterator[InternalRow]): Iterator[InternalRow] = {
       val cmd = List("/bin/bash", "-c", script)
-      val builder = new ProcessBuilder(cmd)
+      val builder = new ProcessBuilder(cmd.asJava)
 
       val proc = builder.start()
       val inputStream = proc.getInputStream
@@ -172,10 +172,10 @@ case class ScriptTransformation(
             val fieldList = outputSoi.getAllStructFieldRefs()
             var i = 0
             while (i < dataList.size()) {
-              if (dataList(i) == null) {
+              if (dataList.get(i) == null) {
                 mutableRow.setNullAt(i)
               } else {
-                mutableRow(i) = unwrap(dataList(i), fieldList(i).getFieldObjectInspector)
+                mutableRow(i) = unwrap(dataList.get(i), fieldList.get(i).getFieldObjectInspector)
               }
               i += 1
             }
@@ -307,7 +307,7 @@ case class HiveScriptIOSchema (
       val serde = initSerDe(serdeClass, columns, columnTypes, inputSerdeProps)
       val fieldObjectInspectors = columnTypes.map(toInspector)
       val objectInspector = ObjectInspectorFactory
-        .getStandardStructObjectInspector(columns, fieldObjectInspectors)
+        .getStandardStructObjectInspector(columns.asJava, fieldObjectInspectors.asJava)
         .asInstanceOf[ObjectInspector]
       (serde, objectInspector)
     }
@@ -342,7 +342,7 @@ case class HiveScriptIOSchema (
     propsMap = propsMap + (serdeConstants.LIST_COLUMN_TYPES -> columnTypesNames)
 
     val properties = new Properties()
-    properties.putAll(propsMap)
+    properties.putAll(propsMap.asJava)
     serde.initialize(null, properties)
 
     serde
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
index 7182246e466a4..cad02373e5ba1 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveUDFs.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.sql.hive
 
 import scala.collection.mutable.ArrayBuffer
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.Try
 
 import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ConstantObjectInspector}
@@ -81,8 +81,7 @@ private[hive] class HiveFunctionRegistry(underlying: analysis.FunctionRegistry)
 
   /* List all of the registered function names. */
   override def listFunction(): Seq[String] = {
-    val a = FunctionRegistry.getFunctionNames ++ underlying.listFunction()
-    a.toList.sorted
+    (FunctionRegistry.getFunctionNames.asScala ++ underlying.listFunction()).toList.sorted
   }
 
   /* Get the class of the registered function by specified name. */
@@ -116,7 +115,7 @@ private[hive] case class HiveSimpleUDF(funcWrapper: HiveFunctionWrapper, childre
 
   @transient
   private lazy val method =
-    function.getResolver.getEvalMethod(children.map(_.dataType.toTypeInfo))
+    function.getResolver.getEvalMethod(children.map(_.dataType.toTypeInfo).asJava)
 
   @transient
   private lazy val arguments = children.map(toInspector).toArray
@@ -541,7 +540,7 @@ private[hive] case class HiveGenericUDTF(
   @transient
   protected lazy val collector = new UDTFCollector
 
-  lazy val elementTypes = outputInspector.getAllStructFieldRefs.map {
+  lazy val elementTypes = outputInspector.getAllStructFieldRefs.asScala.map {
     field => (inspectorToDataType(field.getFieldObjectInspector), true)
   }
 
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 9f4f8b5789afe..1cff5cf9c3543 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.hive.orc
 
 import java.util.Properties
 
+import scala.collection.JavaConverters._
+
 import com.google.common.base.Objects
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, Path}
@@ -43,9 +45,6 @@ import org.apache.spark.sql.types.StructType
 import org.apache.spark.sql.{Row, SQLContext}
 import org.apache.spark.util.SerializableConfiguration
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
-
 private[sql] class DefaultSource extends HadoopFsRelationProvider with DataSourceRegister {
 
   override def shortName(): String = "orc"
@@ -97,7 +96,8 @@ private[orc] class OrcOutputWriter(
   private val reusableOutputBuffer = new Array[Any](dataSchema.length)
 
   // Used to convert Catalyst values into Hadoop `Writable`s.
-  private val wrappers = structOI.getAllStructFieldRefs.zip(dataSchema.fields.map(_.dataType))
+  private val wrappers = structOI.getAllStructFieldRefs.asScala
+    .zip(dataSchema.fields.map(_.dataType))
     .map { case (ref, dt) =>
       wrapperFor(ref.getFieldObjectInspector, dt)
     }.toArray
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 4da86636ac100..572eaebe81ac2 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -20,6 +20,7 @@ package org.apache.spark.sql.hive.test
 import java.io.File
 import java.util.{Set => JavaSet}
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.language.implicitConversions
 
@@ -37,9 +38,6 @@ import org.apache.spark.sql.hive.execution.HiveNativeCommand
 import org.apache.spark.util.{ShutdownHookManager, Utils}
 import org.apache.spark.{SparkConf, SparkContext}
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
-
 // SPARK-3729: Test key required to check for initialization errors with config.
 object TestHive
   extends TestHiveContext(
@@ -405,7 +403,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
   def reset() {
     try {
       // HACK: Hive is too noisy by default.
-      org.apache.log4j.LogManager.getCurrentLoggers.foreach { log =>
+      org.apache.log4j.LogManager.getCurrentLoggers.asScala.foreach { log =>
         log.asInstanceOf[org.apache.log4j.Logger].setLevel(org.apache.log4j.Level.WARN)
       }
 
@@ -415,9 +413,8 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
       catalog.client.reset()
       catalog.unregisterAllTables()
 
-      FunctionRegistry.getFunctionNames.filterNot(originalUDFs.contains(_)).foreach { udfName =>
-        FunctionRegistry.unregisterTemporaryUDF(udfName)
-      }
+      FunctionRegistry.getFunctionNames.asScala.filterNot(originalUDFs.contains(_)).
+        foreach { udfName => FunctionRegistry.unregisterTemporaryUDF(udfName) }
 
       // Some tests corrupt this value on purpose, which breaks the RESET call below.
       hiveconf.set("fs.default.name", new File(".").toURI.toString)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
index 0efcf80bd4ea7..5e7b93d457106 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/FiltersSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.hive.client
 
-import scala.collection.JavaConversions._
+import java.util.Collections
 
 import org.apache.hadoop.hive.metastore.api.FieldSchema
 import org.apache.hadoop.hive.serde.serdeConstants
@@ -38,7 +38,7 @@ class FiltersSuite extends SparkFunSuite with Logging {
   private val varCharCol = new FieldSchema()
   varCharCol.setName("varchar")
   varCharCol.setType(serdeConstants.VARCHAR_TYPE_NAME)
-  testTable.setPartCols(varCharCol :: Nil)
+  testTable.setPartCols(Collections.singletonList(varCharCol))
 
   filterTest("string filter",
     (a("stringcol", StringType) > Literal("test")) :: Nil,
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index b03a35132325d..9c10ffe1113dc 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -18,8 +18,7 @@
 package org.apache.spark.sql.hive.execution
 
 import java.io.{DataInput, DataOutput}
-import java.util
-import java.util.Properties
+import java.util.{ArrayList, Arrays, Properties}
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.hive.ql.udf.generic.{GenericUDAFAverage, GenericUDF}
@@ -33,8 +32,6 @@ import org.apache.spark.sql.hive.test.TestHive
 
 import org.apache.spark.util.Utils
 
-import scala.collection.JavaConversions._
-
 case class Fields(f1: Int, f2: Int, f3: Int, f4: Int, f5: Int)
 
 // Case classes for the custom UDF's.
@@ -326,11 +323,11 @@ class PairSerDe extends AbstractSerDe {
   override def getObjectInspector: ObjectInspector = {
     ObjectInspectorFactory
       .getStandardStructObjectInspector(
-        Seq("pair"),
-        Seq(ObjectInspectorFactory.getStandardStructObjectInspector(
-          Seq("id", "value"),
-          Seq(PrimitiveObjectInspectorFactory.javaIntObjectInspector,
-              PrimitiveObjectInspectorFactory.javaIntObjectInspector))
+        Arrays.asList("pair"),
+        Arrays.asList(ObjectInspectorFactory.getStandardStructObjectInspector(
+          Arrays.asList("id", "value"),
+          Arrays.asList(PrimitiveObjectInspectorFactory.javaIntObjectInspector,
+                        PrimitiveObjectInspectorFactory.javaIntObjectInspector))
     ))
   }
 
@@ -343,10 +340,10 @@ class PairSerDe extends AbstractSerDe {
   override def deserialize(value: Writable): AnyRef = {
     val pair = value.asInstanceOf[TestPair]
 
-    val row = new util.ArrayList[util.ArrayList[AnyRef]]
-    row.add(new util.ArrayList[AnyRef](2))
-    row(0).add(Integer.valueOf(pair.entry._1))
-    row(0).add(Integer.valueOf(pair.entry._2))
+    val row = new ArrayList[ArrayList[AnyRef]]
+    row.add(new ArrayList[AnyRef](2))
+    row.get(0).add(Integer.valueOf(pair.entry._1))
+    row.get(0).add(Integer.valueOf(pair.entry._2))
 
     row
   }
@@ -355,9 +352,9 @@ class PairSerDe extends AbstractSerDe {
 class PairUDF extends GenericUDF {
   override def initialize(p1: Array[ObjectInspector]): ObjectInspector =
     ObjectInspectorFactory.getStandardStructObjectInspector(
-      Seq("id", "value"),
-      Seq(PrimitiveObjectInspectorFactory.javaIntObjectInspector,
-        PrimitiveObjectInspectorFactory.javaIntObjectInspector)
+      Arrays.asList("id", "value"),
+      Arrays.asList(PrimitiveObjectInspectorFactory.javaIntObjectInspector,
+                    PrimitiveObjectInspectorFactory.javaIntObjectInspector)
   )
 
   override def evaluate(args: Array[DeferredObject]): AnyRef = {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
index 3bf8f3ac20480..210d566745415 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/PruningSuite.scala
@@ -17,13 +17,12 @@
 
 package org.apache.spark.sql.hive.execution
 
+import scala.collection.JavaConverters._
+
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.hive.test.TestHive
 
-/* Implicit conversions */
-import scala.collection.JavaConversions._
-
 /**
  * A set of test cases that validate partition and column pruning.
  */
@@ -161,7 +160,7 @@ class PruningSuite extends HiveComparisonTest with BeforeAndAfter {
       assert(actualOutputColumns === expectedOutputColumns, "Output columns mismatch")
       assert(actualScannedColumns === expectedScannedColumns, "Scanned columns mismatch")
 
-      val actualPartitions = actualPartValues.map(_.toSeq.mkString(",")).sorted
+      val actualPartitions = actualPartValues.map(_.asScala.mkString(",")).sorted
       val expectedPartitions = expectedPartValues.map(_.mkString(",")).sorted
 
       assert(actualPartitions === expectedPartitions, "Partitions selected do not match")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 55ecbd5b5f21d..1ff1d9a2934cc 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -19,7 +19,7 @@ package org.apache.spark.sql.hive.execution
 
 import java.sql.{Date, Timestamp}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.DefaultParserDialect
@@ -164,7 +164,7 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
   test("show functions") {
     val allFunctions =
       (FunctionRegistry.builtin.listFunction().toSet[String] ++
-        org.apache.hadoop.hive.ql.exec.FunctionRegistry.getFunctionNames).toList.sorted
+        org.apache.hadoop.hive.ql.exec.FunctionRegistry.getFunctionNames.asScala).toList.sorted
     checkAnswer(sql("SHOW functions"), allFunctions.map(Row(_)))
     checkAnswer(sql("SHOW functions abs"), Row("abs"))
     checkAnswer(sql("SHOW functions 'abs'"), Row("abs"))
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 5bbca14bad320..7966b43596e75 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -17,9 +17,7 @@
 
 package org.apache.spark.sql.sources
 
-import java.sql.Date
-
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.Path
@@ -552,7 +550,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
       configuration.clear()
-      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
     }
   }
 
@@ -600,7 +598,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
       configuration.clear()
-      clonedConf.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
       sqlContext.sparkContext.conf.set("spark.speculation", speculationEnabled.toString)
     }
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
index 214cd80108b9b..edfa474677f15 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaDStreamLike.scala
@@ -17,11 +17,10 @@
 
 package org.apache.spark.streaming.api.java
 
-import java.util
 import java.lang.{Long => JLong}
 import java.util.{List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
@@ -145,8 +144,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    * an array.
    */
   def glom(): JavaDStream[JList[T]] =
-    new JavaDStream(dstream.glom().map(x => new java.util.ArrayList[T](x.toSeq)))
-
+    new JavaDStream(dstream.glom().map(_.toSeq.asJava))
 
 
   /** Return the [[org.apache.spark.streaming.StreamingContext]] associated with this DStream */
@@ -191,7 +189,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    */
   def mapPartitions[U](f: FlatMapFunction[java.util.Iterator[T], U]): JavaDStream[U] = {
     def fn: (Iterator[T]) => Iterator[U] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     new JavaDStream(dstream.mapPartitions(fn)(fakeClassTag[U]))(fakeClassTag[U])
   }
@@ -204,7 +202,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
   def mapPartitionsToPair[K2, V2](f: PairFlatMapFunction[java.util.Iterator[T], K2, V2])
   : JavaPairDStream[K2, V2] = {
     def fn: (Iterator[T]) => Iterator[(K2, V2)] = {
-      (x: Iterator[T]) => asScalaIterator(f.call(asJavaIterator(x)).iterator())
+      (x: Iterator[T]) => f.call(x.asJava).iterator().asScala
     }
     new JavaPairDStream(dstream.mapPartitions(fn))(fakeClassTag[K2], fakeClassTag[V2])
   }
@@ -282,7 +280,7 @@ trait JavaDStreamLike[T, This <: JavaDStreamLike[T, This, R], R <: JavaRDDLike[T
    * Return all the RDDs between 'fromDuration' to 'toDuration' (both included)
    */
   def slice(fromTime: Time, toTime: Time): JList[R] = {
-    new util.ArrayList(dstream.slice(fromTime, toTime).map(wrapRDD(_)).toSeq)
+    dstream.slice(fromTime, toTime).map(wrapRDD).asJava
   }
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
index 26383e420101e..e2aec6c2f63e7 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala
@@ -20,7 +20,7 @@ package org.apache.spark.streaming.api.java
 import java.lang.{Long => JLong, Iterable => JIterable}
 import java.util.{List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.language.implicitConversions
 import scala.reflect.ClassTag
 
@@ -116,14 +116,14 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    * generate the RDDs with Spark's default number of partitions.
    */
   def groupByKey(): JavaPairDStream[K, JIterable[V]] =
-    dstream.groupByKey().mapValues(asJavaIterable _)
+    dstream.groupByKey().mapValues(_.asJava)
 
   /**
    * Return a new DStream by applying `groupByKey` to each RDD. Hash partitioning is used to
    * generate the RDDs with `numPartitions` partitions.
    */
   def groupByKey(numPartitions: Int): JavaPairDStream[K, JIterable[V]] =
-    dstream.groupByKey(numPartitions).mapValues(asJavaIterable _)
+    dstream.groupByKey(numPartitions).mapValues(_.asJava)
 
   /**
    * Return a new DStream by applying `groupByKey` on each RDD of `this` DStream.
@@ -132,7 +132,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    * is used to control the partitioning of each RDD.
    */
   def groupByKey(partitioner: Partitioner): JavaPairDStream[K, JIterable[V]] =
-    dstream.groupByKey(partitioner).mapValues(asJavaIterable _)
+    dstream.groupByKey(partitioner).mapValues(_.asJava)
 
   /**
    * Return a new DStream by applying `reduceByKey` to each RDD. The values for each key are
@@ -197,7 +197,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    *                       batching interval
    */
   def groupByKeyAndWindow(windowDuration: Duration): JavaPairDStream[K, JIterable[V]] = {
-    dstream.groupByKeyAndWindow(windowDuration).mapValues(asJavaIterable _)
+    dstream.groupByKeyAndWindow(windowDuration).mapValues(_.asJava)
   }
 
   /**
@@ -212,7 +212,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    */
   def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration)
   : JavaPairDStream[K, JIterable[V]] = {
-    dstream.groupByKeyAndWindow(windowDuration, slideDuration).mapValues(asJavaIterable _)
+    dstream.groupByKeyAndWindow(windowDuration, slideDuration).mapValues(_.asJava)
   }
 
   /**
@@ -228,8 +228,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    */
   def groupByKeyAndWindow(windowDuration: Duration, slideDuration: Duration, numPartitions: Int)
     : JavaPairDStream[K, JIterable[V]] = {
-    dstream.groupByKeyAndWindow(windowDuration, slideDuration, numPartitions)
-      .mapValues(asJavaIterable _)
+    dstream.groupByKeyAndWindow(windowDuration, slideDuration, numPartitions).mapValues(_.asJava)
   }
 
   /**
@@ -248,8 +247,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
       slideDuration: Duration,
       partitioner: Partitioner
     ): JavaPairDStream[K, JIterable[V]] = {
-    dstream.groupByKeyAndWindow(windowDuration, slideDuration, partitioner)
-      .mapValues(asJavaIterable _)
+    dstream.groupByKeyAndWindow(windowDuration, slideDuration, partitioner).mapValues(_.asJava)
   }
 
   /**
@@ -431,7 +429,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
   private def convertUpdateStateFunction[S](in: JFunction2[JList[V], Optional[S], Optional[S]]):
   (Seq[V], Option[S]) => Option[S] = {
     val scalaFunc: (Seq[V], Option[S]) => Option[S] = (values, state) => {
-      val list: JList[V] = values
+      val list: JList[V] = values.asJava
       val scalaState: Optional[S] = JavaUtils.optionToOptional(state)
       val result: Optional[S] = in.apply(list, scalaState)
       result.isPresent match {
@@ -539,7 +537,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
    */
   def cogroup[W](other: JavaPairDStream[K, W]): JavaPairDStream[K, (JIterable[V], JIterable[W])] = {
     implicit val cm: ClassTag[W] = fakeClassTag
-    dstream.cogroup(other.dstream).mapValues(t => (asJavaIterable(t._1), asJavaIterable((t._2))))
+    dstream.cogroup(other.dstream).mapValues(t => (t._1.asJava, t._2.asJava))
   }
 
   /**
@@ -551,8 +549,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
       numPartitions: Int
     ): JavaPairDStream[K, (JIterable[V], JIterable[W])] = {
     implicit val cm: ClassTag[W] = fakeClassTag
-    dstream.cogroup(other.dstream, numPartitions)
-           .mapValues(t => (asJavaIterable(t._1), asJavaIterable((t._2))))
+    dstream.cogroup(other.dstream, numPartitions).mapValues(t => (t._1.asJava, t._2.asJava))
   }
 
   /**
@@ -564,8 +561,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])(
       partitioner: Partitioner
     ): JavaPairDStream[K, (JIterable[V], JIterable[W])] = {
     implicit val cm: ClassTag[W] = fakeClassTag
-    dstream.cogroup(other.dstream, partitioner)
-           .mapValues(t => (asJavaIterable(t._1), asJavaIterable((t._2))))
+    dstream.cogroup(other.dstream, partitioner).mapValues(t => (t._1.asJava, t._2.asJava))
   }
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index 35cc3ce5cf468..13f371f29603a 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
@@ -21,7 +21,7 @@ import java.lang.{Boolean => JBoolean}
 import java.io.{Closeable, InputStream}
 import java.util.{List => JList, Map => JMap}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import akka.actor.{Props, SupervisorStrategy}
@@ -115,7 +115,13 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
     sparkHome: String,
     jars: Array[String],
     environment: JMap[String, String]) =
-    this(new StreamingContext(master, appName, batchDuration, sparkHome, jars, environment))
+    this(new StreamingContext(
+      master,
+      appName,
+      batchDuration,
+      sparkHome,
+      jars,
+      environment.asScala))
 
   /**
    * Create a JavaStreamingContext using an existing JavaSparkContext.
@@ -197,7 +203,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
       converter: JFunction[InputStream, java.lang.Iterable[T]],
       storageLevel: StorageLevel)
   : JavaReceiverInputDStream[T] = {
-    def fn: (InputStream) => Iterator[T] = (x: InputStream) => converter.call(x).toIterator
+    def fn: (InputStream) => Iterator[T] = (x: InputStream) => converter.call(x).iterator().asScala
     implicit val cmt: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
     ssc.socketStream(hostname, port, fn, storageLevel)
@@ -432,7 +438,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
     val sQueue = new scala.collection.mutable.Queue[RDD[T]]
-    sQueue.enqueue(queue.map(_.rdd).toSeq: _*)
+    sQueue.enqueue(queue.asScala.map(_.rdd).toSeq: _*)
     ssc.queueStream(sQueue)
   }
 
@@ -456,7 +462,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
     val sQueue = new scala.collection.mutable.Queue[RDD[T]]
-    sQueue.enqueue(queue.map(_.rdd).toSeq: _*)
+    sQueue.enqueue(queue.asScala.map(_.rdd).toSeq: _*)
     ssc.queueStream(sQueue, oneAtATime)
   }
 
@@ -481,7 +487,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
     val sQueue = new scala.collection.mutable.Queue[RDD[T]]
-    sQueue.enqueue(queue.map(_.rdd).toSeq: _*)
+    sQueue.enqueue(queue.asScala.map(_.rdd).toSeq: _*)
     ssc.queueStream(sQueue, oneAtATime, defaultRDD.rdd)
   }
 
@@ -500,7 +506,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
    * Create a unified DStream from multiple DStreams of the same type and same slide duration.
    */
   def union[T](first: JavaDStream[T], rest: JList[JavaDStream[T]]): JavaDStream[T] = {
-    val dstreams: Seq[DStream[T]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.dstream)
+    val dstreams: Seq[DStream[T]] = (Seq(first) ++ rest.asScala).map(_.dstream)
     implicit val cm: ClassTag[T] = first.classTag
     ssc.union(dstreams)(cm)
   }
@@ -512,7 +518,7 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
       first: JavaPairDStream[K, V],
       rest: JList[JavaPairDStream[K, V]]
     ): JavaPairDStream[K, V] = {
-    val dstreams: Seq[DStream[(K, V)]] = (Seq(first) ++ asScalaBuffer(rest)).map(_.dstream)
+    val dstreams: Seq[DStream[(K, V)]] = (Seq(first) ++ rest.asScala).map(_.dstream)
     implicit val cm: ClassTag[(K, V)] = first.classTag
     implicit val kcm: ClassTag[K] = first.kManifest
     implicit val vcm: ClassTag[V] = first.vManifest
@@ -534,12 +540,11 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
     ): JavaDStream[T] = {
     implicit val cmt: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
-    val scalaDStreams = dstreams.map(_.dstream).toSeq
     val scalaTransformFunc = (rdds: Seq[RDD[_]], time: Time) => {
-      val jrdds = rdds.map(rdd => JavaRDD.fromRDD[AnyRef](rdd.asInstanceOf[RDD[AnyRef]])).toList
+      val jrdds = rdds.map(JavaRDD.fromRDD(_)).asJava
       transformFunc.call(jrdds, time).rdd
     }
-    ssc.transform(scalaDStreams, scalaTransformFunc)
+    ssc.transform(dstreams.asScala.map(_.dstream).toSeq, scalaTransformFunc)
   }
 
   /**
@@ -559,12 +564,11 @@ class JavaStreamingContext(val ssc: StreamingContext) extends Closeable {
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]]
     implicit val cmv: ClassTag[V] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]]
-    val scalaDStreams = dstreams.map(_.dstream).toSeq
     val scalaTransformFunc = (rdds: Seq[RDD[_]], time: Time) => {
-      val jrdds = rdds.map(rdd => JavaRDD.fromRDD[AnyRef](rdd.asInstanceOf[RDD[AnyRef]])).toList
+      val jrdds = rdds.map(JavaRDD.fromRDD(_)).asJava
       transformFunc.call(jrdds, time).rdd
     }
-    ssc.transform(scalaDStreams, scalaTransformFunc)
+    ssc.transform(dstreams.asScala.map(_.dstream).toSeq, scalaTransformFunc)
   }
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index d06401245ff17..2c373640d2fd9 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -20,14 +20,13 @@ package org.apache.spark.streaming.api.python
 import java.io.{ObjectInputStream, ObjectOutputStream}
 import java.lang.reflect.Proxy
 import java.util.{ArrayList => JArrayList, List => JList}
-import scala.collection.JavaConversions._
+
 import scala.collection.JavaConverters._
 import scala.language.existentials
 
 import py4j.GatewayServer
 
 import org.apache.spark.api.java._
-import org.apache.spark.api.python._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Interval, Duration, Time}
@@ -161,7 +160,7 @@ private[python] object PythonDStream {
    */
   def toRDDQueue(rdds: JArrayList[JavaRDD[Array[Byte]]]): java.util.Queue[JavaRDD[Array[Byte]]] = {
     val queue = new java.util.LinkedList[JavaRDD[Array[Byte]]]
-    rdds.forall(queue.add(_))
+    rdds.asScala.foreach(queue.add)
     queue
   }
 }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
index 554aae0117b24..2252e28f22af8 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala
@@ -20,7 +20,7 @@ package org.apache.spark.streaming.receiver
 import java.nio.ByteBuffer
 
 import scala.collection.mutable.ArrayBuffer
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.annotation.DeveloperApi
@@ -144,12 +144,12 @@ abstract class Receiver[T](val storageLevel: StorageLevel) extends Serializable
    * for being used in the corresponding InputDStream.
    */
   def store(dataIterator: java.util.Iterator[T], metadata: Any) {
-    supervisor.pushIterator(dataIterator, Some(metadata), None)
+    supervisor.pushIterator(dataIterator.asScala, Some(metadata), None)
   }
 
   /** Store an iterator of received data as a data block into Spark's memory. */
   def store(dataIterator: java.util.Iterator[T]) {
-    supervisor.pushIterator(dataIterator, None, None)
+    supervisor.pushIterator(dataIterator.asScala, None, None)
   }
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
index 6d4cdc4aa6b10..0cd39594ee923 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala
@@ -19,7 +19,7 @@ package org.apache.spark.streaming.scheduler
 
 import java.util.concurrent.{ConcurrentHashMap, TimeUnit}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.util.{Failure, Success}
 
 import org.apache.spark.Logging
@@ -128,7 +128,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging {
   }
 
   def getPendingTimes(): Seq[Time] = {
-    jobSets.keySet.toSeq
+    jobSets.asScala.keys.toSeq
   }
 
   def reportError(msg: String, e: Throwable) {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
index 53b96d51c9180..f2711d1355e60 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceivedBlockTracker.scala
@@ -19,6 +19,7 @@ package org.apache.spark.streaming.scheduler
 
 import java.nio.ByteBuffer
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable
 import scala.language.implicitConversions
 
@@ -196,8 +197,7 @@ private[streaming] class ReceivedBlockTracker(
 
     writeAheadLogOption.foreach { writeAheadLog =>
       logInfo(s"Recovering from write ahead logs in ${checkpointDirOption.get}")
-      import scala.collection.JavaConversions._
-      writeAheadLog.readAll().foreach { byteBuffer =>
+      writeAheadLog.readAll().asScala.foreach { byteBuffer =>
         logTrace("Recovering record " + byteBuffer)
         Utils.deserialize[ReceivedBlockTrackerLogEvent](
           byteBuffer.array, Thread.currentThread().getContextClassLoader) match {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala
index fe6328b1ce727..9f4a4d6806ab5 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/util/FileBasedWriteAheadLog.scala
@@ -19,6 +19,7 @@ package org.apache.spark.streaming.util
 import java.nio.ByteBuffer
 import java.util.{Iterator => JIterator}
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.{Await, ExecutionContext, Future}
 import scala.language.postfixOps
@@ -118,7 +119,6 @@ private[streaming] class FileBasedWriteAheadLog(
    * hence the implementation is kept simple.
    */
   def readAll(): JIterator[ByteBuffer] = synchronized {
-    import scala.collection.JavaConversions._
     val logFilesToRead = pastLogs.map{ _.path} ++ currentLogPath
     logInfo("Reading from the logs: " + logFilesToRead.mkString("\n"))
 
@@ -126,7 +126,7 @@ private[streaming] class FileBasedWriteAheadLog(
       logDebug(s"Creating log reader with $file")
       val reader = new FileBasedWriteAheadLogReader(file, hadoopConf)
       CompletionIterator[ByteBuffer, Iterator[ByteBuffer]](reader, reader.close _)
-    } flatMap { x => x }
+    }.flatten.asJava
   }
 
   /**
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala b/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala
index bb80bff6dc2e6..57b50bdfd6520 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaTestUtils.scala
@@ -17,16 +17,13 @@
 
 package org.apache.spark.streaming
 
-import scala.collection.mutable.{SynchronizedBuffer, ArrayBuffer}
+import java.util.{List => JList}
+
+import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
-import java.util.{List => JList}
-import org.apache.spark.streaming.api.java.{JavaPairDStream, JavaDStreamLike, JavaDStream, JavaStreamingContext}
-import org.apache.spark.streaming._
-import java.util.ArrayList
-import collection.JavaConversions._
 import org.apache.spark.api.java.JavaRDDLike
-import org.apache.spark.streaming.dstream.DStream
+import org.apache.spark.streaming.api.java.{JavaDStreamLike, JavaDStream, JavaStreamingContext}
 
 /** Exposes streaming test functionality in a Java-friendly way. */
 trait JavaTestBase extends TestSuiteBase {
@@ -39,7 +36,7 @@ trait JavaTestBase extends TestSuiteBase {
       ssc: JavaStreamingContext,
       data: JList[JList[T]],
       numPartitions: Int) = {
-    val seqData = data.map(Seq(_:_*))
+    val seqData = data.asScala.map(_.asScala)
 
     implicit val cm: ClassTag[T] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]]
@@ -72,9 +69,7 @@ trait JavaTestBase extends TestSuiteBase {
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]]
     ssc.getState()
     val res = runStreams[V](ssc.ssc, numBatches, numExpectedOutput)
-    val out = new ArrayList[JList[V]]()
-    res.map(entry => out.append(new ArrayList[V](entry)))
-    out
+    res.map(_.asJava).asJava
   }
 
   /**
@@ -90,12 +85,7 @@ trait JavaTestBase extends TestSuiteBase {
     implicit val cm: ClassTag[V] =
       implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]]
     val res = runStreamsWithPartitions[V](ssc.ssc, numBatches, numExpectedOutput)
-    val out = new ArrayList[JList[JList[V]]]()
-    res.map{entry =>
-      val lists = entry.map(new ArrayList[V](_))
-      out.append(new ArrayList[JList[V]](lists))
-    }
-    out
+    res.map(entry => entry.map(_.asJava).asJava).asJava
   }
 }
 
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala
index 325ff7c74c39d..5e49fd00769ad 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/util/WriteAheadLogSuite.scala
@@ -20,6 +20,7 @@ import java.io._
 import java.nio.ByteBuffer
 import java.util
 
+import scala.collection.JavaConverters._
 import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.duration._
 import scala.language.{implicitConversions, postfixOps}
@@ -417,9 +418,8 @@ object WriteAheadLogSuite {
 
   /** Read all the data in the log file in a directory using the WriteAheadLog class. */
   def readDataUsingWriteAheadLog(logDirectory: String): Seq[String] = {
-    import scala.collection.JavaConversions._
     val wal = new FileBasedWriteAheadLog(new SparkConf(), logDirectory, hadoopConf, 1, 1)
-    val data = wal.readAll().map(byteBufferToString).toSeq
+    val data = wal.readAll().asScala.map(byteBufferToString).toSeq
     wal.close()
     data
   }
diff --git a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
index 9418beb6b3e3a..a0524cabff2d4 100644
--- a/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
+++ b/tools/src/main/scala/org/apache/spark/tools/GenerateMIMAIgnore.scala
@@ -22,7 +22,7 @@ import java.io.File
 import java.util.jar.JarFile
 
 import scala.collection.mutable
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.reflect.runtime.universe.runtimeMirror
 import scala.reflect.runtime.{universe => unv}
 import scala.util.Try
@@ -161,7 +161,7 @@ object GenerateMIMAIgnore {
     val path = packageName.replace('.', '/')
     val resources = classLoader.getResources(path)
 
-    val jars = resources.filter(x => x.getProtocol == "jar")
+    val jars = resources.asScala.filter(_.getProtocol == "jar")
       .map(_.getFile.split(":")(1).split("!")(0)).toSeq
 
     jars.flatMap(getClassesFromJar(_, path))
@@ -175,7 +175,7 @@ object GenerateMIMAIgnore {
   private def getClassesFromJar(jarPath: String, packageName: String) = {
     import scala.collection.mutable
     val jar = new JarFile(new File(jarPath))
-    val enums = jar.entries().map(_.getName).filter(_.startsWith(packageName))
+    val enums = jar.entries().asScala.map(_.getName).filter(_.startsWith(packageName))
     val classes = mutable.HashSet[Class[_]]()
     for (entry <- enums if entry.endsWith(".class")) {
       try {
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index bff585b46cbbe..e9a02baafd28e 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -25,7 +25,7 @@ import java.security.PrivilegedExceptionAction
 import java.util.{Properties, UUID}
 import java.util.zip.{ZipEntry, ZipOutputStream}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, ListBuffer, Map}
 import scala.reflect.runtime.universe
 import scala.util.{Try, Success, Failure}
@@ -511,7 +511,7 @@ private[spark] class Client(
     val nns = YarnSparkHadoopUtil.get.getNameNodesToAccess(sparkConf) + stagingDirPath
     YarnSparkHadoopUtil.get.obtainTokensForNamenodes(
       nns, hadoopConf, creds, Some(sparkConf.get("spark.yarn.principal")))
-    val t = creds.getAllTokens
+    val t = creds.getAllTokens.asScala
       .filter(_.getKind == DelegationTokenIdentifier.HDFS_DELEGATION_KIND)
       .head
     val newExpiration = t.renew(hadoopConf)
@@ -650,8 +650,8 @@ private[spark] class Client(
     distCacheMgr.setDistArchivesEnv(launchEnv)
 
     val amContainer = Records.newRecord(classOf[ContainerLaunchContext])
-    amContainer.setLocalResources(localResources)
-    amContainer.setEnvironment(launchEnv)
+    amContainer.setLocalResources(localResources.asJava)
+    amContainer.setEnvironment(launchEnv.asJava)
 
     val javaOpts = ListBuffer[String]()
 
@@ -782,7 +782,7 @@ private[spark] class Client(
 
     // TODO: it would be nicer to just make sure there are no null commands here
     val printableCommands = commands.map(s => if (s == null) "null" else s).toList
-    amContainer.setCommands(printableCommands)
+    amContainer.setCommands(printableCommands.asJava)
 
     logDebug("===============================================================================")
     logDebug("YARN AM launch context:")
@@ -797,7 +797,8 @@ private[spark] class Client(
 
     // send the acl settings into YARN to control who has access via YARN interfaces
     val securityManager = new SecurityManager(sparkConf)
-    amContainer.setApplicationACLs(YarnSparkHadoopUtil.getApplicationAclsForYarn(securityManager))
+    amContainer.setApplicationACLs(
+      YarnSparkHadoopUtil.getApplicationAclsForYarn(securityManager).asJava)
     setupSecurityToken(amContainer)
     UserGroupInformation.getCurrentUser().addCredentials(credentials)
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
index 4cc50483a17ff..9abd09b3cc7a5 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ExecutorRunnable.scala
@@ -20,14 +20,13 @@ package org.apache.spark.deploy.yarn
 import java.io.File
 import java.net.URI
 import java.nio.ByteBuffer
+import java.util.Collections
 
-import org.apache.hadoop.fs.Path
-import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
-import org.apache.spark.util.Utils
-
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{HashMap, ListBuffer}
 
+import org.apache.hadoop.fs.Path
+import org.apache.hadoop.yarn.api.ApplicationConstants.Environment
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.io.DataOutputBuffer
 import org.apache.hadoop.security.UserGroupInformation
@@ -40,6 +39,7 @@ import org.apache.hadoop.yarn.util.{ConverterUtils, Records}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
 import org.apache.spark.network.util.JavaUtils
+import org.apache.spark.util.Utils
 
 class ExecutorRunnable(
     container: Container,
@@ -74,9 +74,9 @@ class ExecutorRunnable(
       .asInstanceOf[ContainerLaunchContext]
 
     val localResources = prepareLocalResources
-    ctx.setLocalResources(localResources)
+    ctx.setLocalResources(localResources.asJava)
 
-    ctx.setEnvironment(env)
+    ctx.setEnvironment(env.asJava)
 
     val credentials = UserGroupInformation.getCurrentUser().getCredentials()
     val dob = new DataOutputBuffer()
@@ -96,8 +96,9 @@ class ExecutorRunnable(
       |===============================================================================
       """.stripMargin)
 
-    ctx.setCommands(commands)
-    ctx.setApplicationACLs(YarnSparkHadoopUtil.getApplicationAclsForYarn(securityMgr))
+    ctx.setCommands(commands.asJava)
+    ctx.setApplicationACLs(
+      YarnSparkHadoopUtil.getApplicationAclsForYarn(securityMgr).asJava)
 
     // If external shuffle service is enabled, register with the Yarn shuffle service already
     // started on the NodeManager and, if authentication is enabled, provide it with our secret
@@ -112,7 +113,7 @@ class ExecutorRunnable(
           // Authentication is not enabled, so just provide dummy metadata
           ByteBuffer.allocate(0)
         }
-      ctx.setServiceData(Map[String, ByteBuffer]("spark_shuffle" -> secretBytes))
+      ctx.setServiceData(Collections.singletonMap("spark_shuffle", secretBytes))
     }
 
     // Send the start request to the ContainerManager
@@ -314,7 +315,8 @@ class ExecutorRunnable(
       env("SPARK_LOG_URL_STDOUT") = s"$baseUrl/stdout?start=-4096"
     }
 
-    System.getenv().filterKeys(_.startsWith("SPARK")).foreach { case (k, v) => env(k) = v }
+    System.getenv().asScala.filterKeys(_.startsWith("SPARK"))
+      .foreach { case (k, v) => env(k) = v }
     env
   }
 }
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index ccf753e69f4b6..5f897cbcb4e9f 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -21,9 +21,7 @@ import java.util.Collections
 import java.util.concurrent._
 import java.util.regex.Pattern
 
-import org.apache.spark.util.Utils
-
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
 
 import com.google.common.util.concurrent.ThreadFactoryBuilder
@@ -39,8 +37,8 @@ import org.apache.log4j.{Level, Logger}
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._
 import org.apache.spark.rpc.RpcEndpointRef
-import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
+import org.apache.spark.util.Utils
 
 /**
  * YarnAllocator is charged with requesting containers from the YARN ResourceManager and deciding
@@ -164,7 +162,7 @@ private[yarn] class YarnAllocator(
    * Number of container requests at the given location that have not yet been fulfilled.
    */
   private def getNumPendingAtLocation(location: String): Int =
-    amClient.getMatchingRequests(RM_REQUEST_PRIORITY, location, resource).map(_.size).sum
+    amClient.getMatchingRequests(RM_REQUEST_PRIORITY, location, resource).asScala.map(_.size).sum
 
   /**
    * Request as many executors from the ResourceManager as needed to reach the desired total. If
@@ -231,14 +229,14 @@ private[yarn] class YarnAllocator(
           numExecutorsRunning,
           allocateResponse.getAvailableResources))
 
-      handleAllocatedContainers(allocatedContainers)
+      handleAllocatedContainers(allocatedContainers.asScala)
     }
 
     val completedContainers = allocateResponse.getCompletedContainersStatuses()
     if (completedContainers.size > 0) {
       logDebug("Completed %d containers".format(completedContainers.size))
 
-      processCompletedContainers(completedContainers)
+      processCompletedContainers(completedContainers.asScala)
 
       logDebug("Finished processing %d completed containers. Current running executor count: %d."
         .format(completedContainers.size, numExecutorsRunning))
@@ -271,7 +269,7 @@ private[yarn] class YarnAllocator(
         val request = createContainerRequest(resource, locality.nodes, locality.racks)
         amClient.addContainerRequest(request)
         val nodes = request.getNodes
-        val hostStr = if (nodes == null || nodes.isEmpty) "Any" else nodes.last
+        val hostStr = if (nodes == null || nodes.isEmpty) "Any" else nodes.asScala.last
         logInfo(s"Container request (host: $hostStr, capability: $resource)")
       }
     } else if (missing < 0) {
@@ -280,7 +278,8 @@ private[yarn] class YarnAllocator(
 
       val matchingRequests = amClient.getMatchingRequests(RM_REQUEST_PRIORITY, ANY_HOST, resource)
       if (!matchingRequests.isEmpty) {
-        matchingRequests.head.take(numToCancel).foreach(amClient.removeContainerRequest)
+        matchingRequests.iterator().next().asScala
+          .take(numToCancel).foreach(amClient.removeContainerRequest)
       } else {
         logWarning("Expected to find pending requests, but found none.")
       }
@@ -459,7 +458,7 @@ private[yarn] class YarnAllocator(
         }
       }
 
-      if (allocatedContainerToHostMap.containsKey(containerId)) {
+      if (allocatedContainerToHostMap.contains(containerId)) {
         val host = allocatedContainerToHostMap.get(containerId).get
         val containerSet = allocatedHostToContainersMap.get(host).get
 
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
index 4999f9c06210a..df042bf291de7 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnRMClient.scala
@@ -19,17 +19,15 @@ package org.apache.spark.deploy.yarn
 
 import java.util.{List => JList}
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 import scala.collection.{Map, Set}
 import scala.util.Try
 
 import org.apache.hadoop.conf.Configuration
-import org.apache.hadoop.yarn.api.ApplicationConstants
 import org.apache.hadoop.yarn.api.records._
 import org.apache.hadoop.yarn.client.api.AMRMClient
 import org.apache.hadoop.yarn.client.api.AMRMClient.ContainerRequest
 import org.apache.hadoop.yarn.conf.YarnConfiguration
-import org.apache.hadoop.yarn.util.ConverterUtils
 import org.apache.hadoop.yarn.webapp.util.WebAppUtils
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
@@ -108,8 +106,8 @@ private[spark] class YarnRMClient(args: ApplicationMasterArguments) extends Logg
       val method = classOf[WebAppUtils].getMethod("getProxyHostsAndPortsForAmFilter",
         classOf[Configuration])
       val proxies = method.invoke(null, conf).asInstanceOf[JList[String]]
-      val hosts = proxies.map { proxy => proxy.split(":")(0) }
-      val uriBases = proxies.map { proxy => prefix + proxy + proxyBase }
+      val hosts = proxies.asScala.map { proxy => proxy.split(":")(0) }
+      val uriBases = proxies.asScala.map { proxy => prefix + proxy + proxyBase }
       Map("PROXY_HOSTS" -> hosts.mkString(","), "PROXY_URI_BASES" -> uriBases.mkString(","))
     } catch {
       case e: NoSuchMethodException =>
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
index 128e996b71fe5..b4f8049bff577 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
@@ -21,7 +21,7 @@ import java.io.{File, FileOutputStream, OutputStreamWriter}
 import java.util.Properties
 import java.util.concurrent.TimeUnit
 
-import scala.collection.JavaConversions._
+import scala.collection.JavaConverters._
 
 import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.Files
@@ -132,7 +132,7 @@ abstract class BaseYarnClusterSuite
     props.setProperty("spark.driver.extraJavaOptions", "-Dfoo=\"one two three\"")
     props.setProperty("spark.executor.extraJavaOptions", "-Dfoo=\"one two three\"")
 
-    yarnCluster.getConfig().foreach { e =>
+    yarnCluster.getConfig.asScala.foreach { e =>
       props.setProperty("spark.hadoop." + e.getKey(), e.getValue())
     }
 
@@ -149,7 +149,7 @@ abstract class BaseYarnClusterSuite
     props.store(writer, "Spark properties.")
     writer.close()
 
-    val extraJarArgs = if (!extraJars.isEmpty()) Seq("--jars", extraJars.mkString(",")) else Nil
+    val extraJarArgs = if (extraJars.nonEmpty) Seq("--jars", extraJars.mkString(",")) else Nil
     val mainArgs =
       if (klass.endsWith(".py")) {
         Seq(klass)
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
index 0a5402c89e764..e7f2501e7899f 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/ClientSuite.scala
@@ -20,8 +20,8 @@ package org.apache.spark.deploy.yarn
 import java.io.File
 import java.net.URI
 
-import scala.collection.JavaConversions._
-import scala.collection.mutable.{ HashMap => MutableHashMap }
+import scala.collection.JavaConverters._
+import scala.collection.mutable.{HashMap => MutableHashMap}
 import scala.reflect.ClassTag
 import scala.util.Try
 
@@ -38,7 +38,7 @@ import org.mockito.Matchers._
 import org.mockito.Mockito._
 import org.scalatest.{BeforeAndAfterAll, Matchers}
 
-import org.apache.spark.{SparkConf, SparkException, SparkFunSuite}
+import org.apache.spark.{SparkConf, SparkFunSuite}
 import org.apache.spark.util.Utils
 
 class ClientSuite extends SparkFunSuite with Matchers with BeforeAndAfterAll {
@@ -201,7 +201,7 @@ class ClientSuite extends SparkFunSuite with Matchers with BeforeAndAfterAll {
     appContext.getClass.getMethods.filter(_.getName.equals("getApplicationTags")).foreach{ method =>
       val tags = method.invoke(appContext).asInstanceOf[java.util.Set[String]]
       tags should contain allOf ("tag1", "dup", "tag2", "multi word")
-      tags.filter(!_.isEmpty).size should be (4)
+      tags.asScala.filter(_.nonEmpty).size should be (4)
     }
     appContext.getMaxAppAttempts should be (42)
   }
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index 128350b648992..5a4ea2ea2f4ff 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -21,7 +21,6 @@ import java.io.File
 import java.net.URL
 
 import scala.collection.mutable
-import scala.collection.JavaConversions._
 
 import com.google.common.base.Charsets.UTF_8
 import com.google.common.io.{ByteStreams, Files}
@@ -216,8 +215,8 @@ private object YarnClusterDriver extends Logging with Matchers {
       assert(listener.driverLogs.nonEmpty)
       val driverLogs = listener.driverLogs.get
       assert(driverLogs.size === 2)
-      assert(driverLogs.containsKey("stderr"))
-      assert(driverLogs.containsKey("stdout"))
+      assert(driverLogs.contains("stderr"))
+      assert(driverLogs.contains("stdout"))
       val urlStr = driverLogs("stderr")
       // Ensure that this is a valid URL, else this will throw an exception
       new URL(urlStr)

From 5c08c86bfa43462fb2ca5f7c5980ddfb44dd57f8 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 25 Aug 2015 10:22:54 -0700
Subject: [PATCH 410/633] [SPARK-10198] [SQL] Turn off partition verification
 by default

Author: Michael Armbrust <michael@databricks.com>

Closes #8404 from marmbrus/turnOffPartitionVerification.
---
 .../scala/org/apache/spark/sql/SQLConf.scala  |  2 +-
 .../spark/sql/hive/QueryPartitionSuite.scala  | 64 ++++++++++---------
 2 files changed, 35 insertions(+), 31 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
index e6f7619519e6a..9de75f4c4d084 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLConf.scala
@@ -312,7 +312,7 @@ private[spark] object SQLConf {
     doc = "When true, enable filter pushdown for ORC files.")
 
   val HIVE_VERIFY_PARTITION_PATH = booleanConf("spark.sql.hive.verifyPartitionPath",
-    defaultValue = Some(true),
+    defaultValue = Some(false),
     doc = "<TODO>")
 
   val HIVE_METASTORE_PARTITION_PRUNING = booleanConf("spark.sql.hive.metastorePartitionPruning",
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
index 017bc2adc103b..1cc8a93e83411 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
@@ -18,50 +18,54 @@
 package org.apache.spark.sql.hive
 
 import com.google.common.io.Files
+import org.apache.spark.sql.test.SQLTestUtils
 
 import org.apache.spark.sql.{QueryTest, _}
 import org.apache.spark.util.Utils
 
 
-class QueryPartitionSuite extends QueryTest {
+class QueryPartitionSuite extends QueryTest with SQLTestUtils {
 
   private lazy val ctx = org.apache.spark.sql.hive.test.TestHive
   import ctx.implicits._
-  import ctx.sql
+
+  protected def _sqlContext = ctx
 
   test("SPARK-5068: query data when path doesn't exist"){
-    val testData = ctx.sparkContext.parallelize(
-      (1 to 10).map(i => TestData(i, i.toString))).toDF()
-    testData.registerTempTable("testData")
+    withSQLConf((SQLConf.HIVE_VERIFY_PARTITION_PATH.key, "true")) {
+      val testData = ctx.sparkContext.parallelize(
+        (1 to 10).map(i => TestData(i, i.toString))).toDF()
+      testData.registerTempTable("testData")
 
-    val tmpDir = Files.createTempDir()
-    // create the table for test
-    sql(s"CREATE TABLE table_with_partition(key int,value string) " +
-      s"PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' ")
-    sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='1') " +
-      "SELECT key,value FROM testData")
-    sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='2') " +
-      "SELECT key,value FROM testData")
-    sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='3') " +
-      "SELECT key,value FROM testData")
-    sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='4') " +
-      "SELECT key,value FROM testData")
+      val tmpDir = Files.createTempDir()
+      // create the table for test
+      sql(s"CREATE TABLE table_with_partition(key int,value string) " +
+        s"PARTITIONED by (ds string) location '${tmpDir.toURI.toString}' ")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='1') " +
+        "SELECT key,value FROM testData")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='2') " +
+        "SELECT key,value FROM testData")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='3') " +
+        "SELECT key,value FROM testData")
+      sql("INSERT OVERWRITE TABLE table_with_partition  partition (ds='4') " +
+        "SELECT key,value FROM testData")
 
-    // test for the exist path
-    checkAnswer(sql("select key,value from table_with_partition"),
-      testData.toDF.collect ++ testData.toDF.collect
-        ++ testData.toDF.collect ++ testData.toDF.collect)
+      // test for the exist path
+      checkAnswer(sql("select key,value from table_with_partition"),
+        testData.toDF.collect ++ testData.toDF.collect
+          ++ testData.toDF.collect ++ testData.toDF.collect)
 
-    // delete the path of one partition
-    tmpDir.listFiles
-      .find { f => f.isDirectory && f.getName().startsWith("ds=") }
-      .foreach { f => Utils.deleteRecursively(f) }
+      // delete the path of one partition
+      tmpDir.listFiles
+        .find { f => f.isDirectory && f.getName().startsWith("ds=") }
+        .foreach { f => Utils.deleteRecursively(f) }
 
-    // test for after delete the path
-    checkAnswer(sql("select key,value from table_with_partition"),
-      testData.toDF.collect ++ testData.toDF.collect ++ testData.toDF.collect)
+      // test for after delete the path
+      checkAnswer(sql("select key,value from table_with_partition"),
+        testData.toDF.collect ++ testData.toDF.collect ++ testData.toDF.collect)
 
-    sql("DROP TABLE table_with_partition")
-    sql("DROP TABLE createAndInsertTest")
+      sql("DROP TABLE table_with_partition")
+      sql("DROP TABLE createAndInsertTest")
+    }
   }
 }

From b37f0cc1b4c064d6f09edb161250fa8b783de52a Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Tue, 25 Aug 2015 10:54:03 -0700
Subject: [PATCH 411/633] [SPARK-8531] [ML] Update ML user guide for
 MinMaxScaler

jira: https://issues.apache.org/jira/browse/SPARK-8531

Update ML user guide for MinMaxScaler

Author: Yuhao Yang <hhbyyh@gmail.com>
Author: unknown <yuhaoyan@yuhaoyan-MOBL1.ccr.corp.intel.com>

Closes #7211 from hhbyyh/minmaxdoc.
---
 docs/ml-features.md | 71 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 642a4b4c53183..62de4838981cb 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1133,6 +1133,7 @@ val scaledData = scalerModel.transform(dataFrame)
 {% highlight java %}
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.StandardScaler;
+import org.apache.spark.ml.feature.StandardScalerModel;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
@@ -1173,6 +1174,76 @@ scaledData = scalerModel.transform(dataFrame)
 </div>
 </div>
 
+## MinMaxScaler
+
+`MinMaxScaler` transforms a dataset of `Vector` rows, rescaling each feature to a specific range (often [0, 1]).  It takes parameters:
+
+* `min`: 0.0 by default. Lower bound after transformation, shared by all features.
+* `max`: 1.0 by default. Upper bound after transformation, shared by all features.
+
+`MinMaxScaler` computes summary statistics on a data set and produces a `MinMaxScalerModel`. The model can then transform each feature individually such that it is in the given range.
+
+The rescaled value for a feature E is calculated as,
+`\begin{equation}
+  Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min
+\end{equation}`
+For the case `E_{max} == E_{min}`, `Rescaled(e_i) = 0.5 * (max + min)`
+
+Note that since zero values will probably be transformed to non-zero values, output of the transformer will be DenseVector even for sparse input.
+
+The following example demonstrates how to load a dataset in libsvm format and then rescale each feature to [0, 1].
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+More details can be found in the API docs for
+[MinMaxScaler](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScaler) and
+[MinMaxScalerModel](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScalerModel).
+{% highlight scala %}
+import org.apache.spark.ml.feature.MinMaxScaler
+import org.apache.spark.mllib.util.MLUtils
+
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
+val dataFrame = sqlContext.createDataFrame(data)
+val scaler = new MinMaxScaler()
+  .setInputCol("features")
+  .setOutputCol("scaledFeatures")
+
+// Compute summary statistics and generate MinMaxScalerModel
+val scalerModel = scaler.fit(dataFrame)
+
+// rescale each feature to range [min, max].
+val scaledData = scalerModel.transform(dataFrame)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+More details can be found in the API docs for
+[MinMaxScaler](api/java/org/apache/spark/ml/feature/MinMaxScaler.html) and
+[MinMaxScalerModel](api/java/org/apache/spark/ml/feature/MinMaxScalerModel.html).
+{% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.MinMaxScaler;
+import org.apache.spark.ml.feature.MinMaxScalerModel;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.sql.DataFrame;
+
+JavaRDD<LabeledPoint> data =
+  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
+DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+MinMaxScaler scaler = new MinMaxScaler()
+  .setInputCol("features")
+  .setOutputCol("scaledFeatures");
+
+// Compute summary statistics and generate MinMaxScalerModel
+MinMaxScalerModel scalerModel = scaler.fit(dataFrame);
+
+// rescale each feature to range [min, max].
+DataFrame scaledData = scalerModel.transform(dataFrame);
+{% endhighlight %}
+</div>
+</div>
+
 ## Bucketizer
 
 `Bucketizer` transforms a column of continuous features to a column of feature buckets, where the buckets are specified by users. It takes a parameter:

From 881208a8e849facf54166bdd69d3634407f952e7 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 25 Aug 2015 11:58:47 -0700
Subject: [PATCH 412/633] [SPARK-10230] [MLLIB] Rename optimizeAlpha to
 optimizeDocConcentration

See [discussion](https://github.com/apache/spark/pull/8254#discussion_r37837770)

CC jkbradley

Author: Feynman Liang <fliang@databricks.com>

Closes #8422 from feynmanliang/SPARK-10230.
---
 .../spark/mllib/clustering/LDAOptimizer.scala    | 16 ++++++++--------
 .../apache/spark/mllib/clustering/LDASuite.scala |  2 +-
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
index 5c2aae6403bea..38486e949bbcf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
@@ -258,7 +258,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   private var tau0: Double = 1024
   private var kappa: Double = 0.51
   private var miniBatchFraction: Double = 0.05
-  private var optimizeAlpha: Boolean = false
+  private var optimizeDocConcentration: Boolean = false
 
   // internal data structure
   private var docs: RDD[(Long, Vector)] = null
@@ -335,20 +335,20 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
   }
 
   /**
-   * Optimize alpha, indicates whether alpha (Dirichlet parameter for document-topic distribution)
-   * will be optimized during training.
+   * Optimize docConcentration, indicates whether docConcentration (Dirichlet parameter for
+   * document-topic distribution) will be optimized during training.
    */
   @Since("1.5.0")
-  def getOptimzeAlpha: Boolean = this.optimizeAlpha
+  def getOptimizeDocConcentration: Boolean = this.optimizeDocConcentration
 
   /**
-   * Sets whether to optimize alpha parameter during training.
+   * Sets whether to optimize docConcentration parameter during training.
    *
    * Default: false
    */
   @Since("1.5.0")
-  def setOptimzeAlpha(optimizeAlpha: Boolean): this.type = {
-    this.optimizeAlpha = optimizeAlpha
+  def setOptimizeDocConcentration(optimizeDocConcentration: Boolean): this.type = {
+    this.optimizeDocConcentration = optimizeDocConcentration
     this
   }
 
@@ -458,7 +458,7 @@ final class OnlineLDAOptimizer extends LDAOptimizer {
 
     // Note that this is an optimization to avoid batch.count
     updateLambda(batchResult, (miniBatchFraction * corpusSize).ceil.toInt)
-    if (optimizeAlpha) updateAlpha(gammat)
+    if (optimizeDocConcentration) updateAlpha(gammat)
     this
   }
 
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 8a714f9b79e02..746a76a7e5fa1 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -423,7 +423,7 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     val k = 2
     val docs = sc.parallelize(toyData)
     val op = new OnlineLDAOptimizer().setMiniBatchFraction(1).setTau0(1024).setKappa(0.51)
-      .setGammaShape(100).setOptimzeAlpha(true).setSampleWithReplacement(false)
+      .setGammaShape(100).setOptimizeDocConcentration(true).setSampleWithReplacement(false)
     val lda = new LDA().setK(k)
       .setDocConcentration(1D / k)
       .setTopicConcentration(0.01)

From 16a2be1a84c0a274a60c0a584faaf58b55d4942b Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 12:16:23 -0700
Subject: [PATCH 413/633] [SPARK-10231] [MLLIB] update @Since annotation for
 mllib.classification

Update `Since` annotation in `mllib.classification`:

1. add version to classes, objects, constructors, and public variables declared in constructors
2. correct some versions
3. remove `Since` on `toString`

MechCoder dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8421 from mengxr/SPARK-10231 and squashes the following commits:

b2dce80 [Xiangrui Meng] update @Since annotation for mllib.classification
---
 .../classification/ClassificationModel.scala  |  7 +++--
 .../classification/LogisticRegression.scala   | 20 +++++++++----
 .../mllib/classification/NaiveBayes.scala     | 28 +++++++++++++++----
 .../spark/mllib/classification/SVM.scala      | 15 ++++++----
 .../StreamingLogisticRegressionWithSGD.scala  |  9 +++++-
 5 files changed, 58 insertions(+), 21 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
index a29b425a71fd6..85a413243b049 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/ClassificationModel.scala
@@ -30,6 +30,7 @@ import org.apache.spark.rdd.RDD
  * belongs. The categories are represented by double values: 0.0, 1.0, 2.0, etc.
  */
 @Experimental
+@Since("0.8.0")
 trait ClassificationModel extends Serializable {
   /**
    * Predict values for the given data set using the model trained.
@@ -37,7 +38,7 @@ trait ClassificationModel extends Serializable {
    * @param testData RDD representing data points to be predicted
    * @return an RDD[Double] where each entry contains the corresponding prediction
    */
-  @Since("0.8.0")
+  @Since("1.0.0")
   def predict(testData: RDD[Vector]): RDD[Double]
 
   /**
@@ -46,7 +47,7 @@ trait ClassificationModel extends Serializable {
    * @param testData array representing a single data point
    * @return predicted category from the trained model
    */
-  @Since("0.8.0")
+  @Since("1.0.0")
   def predict(testData: Vector): Double
 
   /**
@@ -54,7 +55,7 @@ trait ClassificationModel extends Serializable {
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
    */
-  @Since("0.8.0")
+  @Since("1.0.0")
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
index e03e662227d14..5ceff5b2259ea 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
@@ -41,11 +41,12 @@ import org.apache.spark.rdd.RDD
  *                   Multinomial Logistic Regression. By default, it is binary logistic regression
  *                   so numClasses will be set to 2.
  */
-class LogisticRegressionModel (
-    override val weights: Vector,
-    override val intercept: Double,
-    val numFeatures: Int,
-    val numClasses: Int)
+@Since("0.8.0")
+class LogisticRegressionModel @Since("1.3.0") (
+    @Since("1.0.0") override val weights: Vector,
+    @Since("1.0.0") override val intercept: Double,
+    @Since("1.3.0") val numFeatures: Int,
+    @Since("1.3.0") val numClasses: Int)
   extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable
   with Saveable with PMMLExportable {
 
@@ -75,6 +76,7 @@ class LogisticRegressionModel (
   /**
    * Constructs a [[LogisticRegressionModel]] with weights and intercept for binary classification.
    */
+  @Since("1.0.0")
   def this(weights: Vector, intercept: Double) = this(weights, intercept, weights.size, 2)
 
   private var threshold: Option[Double] = Some(0.5)
@@ -166,12 +168,12 @@ class LogisticRegressionModel (
 
   override protected def formatVersion: String = "1.0"
 
-  @Since("1.4.0")
   override def toString: String = {
     s"${super.toString}, numClasses = ${numClasses}, threshold = ${threshold.getOrElse("None")}"
   }
 }
 
+@Since("1.3.0")
 object LogisticRegressionModel extends Loader[LogisticRegressionModel] {
 
   @Since("1.3.0")
@@ -207,6 +209,7 @@ object LogisticRegressionModel extends Loader[LogisticRegressionModel] {
  * for k classes multi-label classification problem.
  * Using [[LogisticRegressionWithLBFGS]] is recommended over this.
  */
+@Since("0.8.0")
 class LogisticRegressionWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -216,6 +219,7 @@ class LogisticRegressionWithSGD private[mllib] (
 
   private val gradient = new LogisticGradient()
   private val updater = new SquaredL2Updater()
+  @Since("0.8.0")
   override val optimizer = new GradientDescent(gradient, updater)
     .setStepSize(stepSize)
     .setNumIterations(numIterations)
@@ -227,6 +231,7 @@ class LogisticRegressionWithSGD private[mllib] (
    * Construct a LogisticRegression object with default parameters: {stepSize: 1.0,
    * numIterations: 100, regParm: 0.01, miniBatchFraction: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(1.0, 100, 0.01, 1.0)
 
   override protected[mllib] def createModel(weights: Vector, intercept: Double) = {
@@ -238,6 +243,7 @@ class LogisticRegressionWithSGD private[mllib] (
  * Top-level methods for calling Logistic Regression using Stochastic Gradient Descent.
  * NOTE: Labels used in Logistic Regression should be {0, 1}
  */
+@Since("0.8.0")
 object LogisticRegressionWithSGD {
   // NOTE(shivaram): We use multiple train methods instead of default arguments to support
   // Java programs.
@@ -333,11 +339,13 @@ object LogisticRegressionWithSGD {
  * NOTE: Labels used in Logistic Regression should be {0, 1, ..., k - 1}
  * for k classes multi-label classification problem.
  */
+@Since("1.1.0")
 class LogisticRegressionWithLBFGS
   extends GeneralizedLinearAlgorithm[LogisticRegressionModel] with Serializable {
 
   this.setFeatureScaling(true)
 
+  @Since("1.1.0")
   override val optimizer = new LBFGS(new LogisticGradient, new SquaredL2Updater)
 
   override protected val validators = List(multiLabelValidator)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
index dab369207cc9a..a956084ae06e8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
@@ -41,11 +41,12 @@ import org.apache.spark.sql.{DataFrame, SQLContext}
  *              where D is number of features
  * @param modelType The type of NB model to fit  can be "multinomial" or "bernoulli"
  */
+@Since("0.9.0")
 class NaiveBayesModel private[spark] (
-    val labels: Array[Double],
-    val pi: Array[Double],
-    val theta: Array[Array[Double]],
-    val modelType: String)
+    @Since("1.0.0") val labels: Array[Double],
+    @Since("0.9.0") val pi: Array[Double],
+    @Since("0.9.0") val theta: Array[Array[Double]],
+    @Since("1.4.0") val modelType: String)
   extends ClassificationModel with Serializable with Saveable {
 
   import NaiveBayes.{Bernoulli, Multinomial, supportedModelTypes}
@@ -83,6 +84,7 @@ class NaiveBayesModel private[spark] (
       throw new UnknownError(s"Invalid modelType: $modelType.")
   }
 
+  @Since("1.0.0")
   override def predict(testData: RDD[Vector]): RDD[Double] = {
     val bcModel = testData.context.broadcast(this)
     testData.mapPartitions { iter =>
@@ -91,6 +93,7 @@ class NaiveBayesModel private[spark] (
     }
   }
 
+  @Since("1.0.0")
   override def predict(testData: Vector): Double = {
     modelType match {
       case Multinomial =>
@@ -107,6 +110,7 @@ class NaiveBayesModel private[spark] (
    * @return an RDD[Vector] where each entry contains the predicted posterior class probabilities,
    *         in the same order as class labels
    */
+  @Since("1.5.0")
   def predictProbabilities(testData: RDD[Vector]): RDD[Vector] = {
     val bcModel = testData.context.broadcast(this)
     testData.mapPartitions { iter =>
@@ -122,6 +126,7 @@ class NaiveBayesModel private[spark] (
    * @return predicted posterior class probabilities from the trained model,
    *         in the same order as class labels
    */
+  @Since("1.5.0")
   def predictProbabilities(testData: Vector): Vector = {
     modelType match {
       case Multinomial =>
@@ -158,6 +163,7 @@ class NaiveBayesModel private[spark] (
     new DenseVector(scaledProbs.map(_ / probSum))
   }
 
+  @Since("1.3.0")
   override def save(sc: SparkContext, path: String): Unit = {
     val data = NaiveBayesModel.SaveLoadV2_0.Data(labels, pi, theta, modelType)
     NaiveBayesModel.SaveLoadV2_0.save(sc, path, data)
@@ -166,6 +172,7 @@ class NaiveBayesModel private[spark] (
   override protected def formatVersion: String = "2.0"
 }
 
+@Since("1.3.0")
 object NaiveBayesModel extends Loader[NaiveBayesModel] {
 
   import org.apache.spark.mllib.util.Loader._
@@ -199,6 +206,7 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] {
       dataRDD.write.parquet(dataPath(path))
     }
 
+    @Since("1.3.0")
     def load(sc: SparkContext, path: String): NaiveBayesModel = {
       val sqlContext = new SQLContext(sc)
       // Load Parquet data.
@@ -301,30 +309,35 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] {
  * document classification.  By making every vector a 0-1 vector, it can also be used as
  * Bernoulli NB ([[http://tinyurl.com/p7c96j6]]). The input feature values must be nonnegative.
  */
-
+@Since("0.9.0")
 class NaiveBayes private (
     private var lambda: Double,
     private var modelType: String) extends Serializable with Logging {
 
   import NaiveBayes.{Bernoulli, Multinomial}
 
+  @Since("1.4.0")
   def this(lambda: Double) = this(lambda, NaiveBayes.Multinomial)
 
+  @Since("0.9.0")
   def this() = this(1.0, NaiveBayes.Multinomial)
 
   /** Set the smoothing parameter. Default: 1.0. */
+  @Since("0.9.0")
   def setLambda(lambda: Double): NaiveBayes = {
     this.lambda = lambda
     this
   }
 
   /** Get the smoothing parameter. */
+  @Since("1.4.0")
   def getLambda: Double = lambda
 
   /**
    * Set the model type using a string (case-sensitive).
    * Supported options: "multinomial" (default) and "bernoulli".
    */
+  @Since("1.4.0")
   def setModelType(modelType: String): NaiveBayes = {
     require(NaiveBayes.supportedModelTypes.contains(modelType),
       s"NaiveBayes was created with an unknown modelType: $modelType.")
@@ -333,6 +346,7 @@ class NaiveBayes private (
   }
 
   /** Get the model type. */
+  @Since("1.4.0")
   def getModelType: String = this.modelType
 
   /**
@@ -340,6 +354,7 @@ class NaiveBayes private (
    *
    * @param data RDD of [[org.apache.spark.mllib.regression.LabeledPoint]].
    */
+  @Since("0.9.0")
   def run(data: RDD[LabeledPoint]): NaiveBayesModel = {
     val requireNonnegativeValues: Vector => Unit = (v: Vector) => {
       val values = v match {
@@ -423,6 +438,7 @@ class NaiveBayes private (
 /**
  * Top-level methods for calling naive Bayes.
  */
+@Since("0.9.0")
 object NaiveBayes {
 
   /** String name for multinomial model type. */
@@ -485,7 +501,7 @@ object NaiveBayes {
    * @param modelType The type of NB model to fit from the enumeration NaiveBayesModels, can be
    *              multinomial or bernoulli
    */
-  @Since("0.9.0")
+  @Since("1.4.0")
   def train(input: RDD[LabeledPoint], lambda: Double, modelType: String): NaiveBayesModel = {
     require(supportedModelTypes.contains(modelType),
       s"NaiveBayes was created with an unknown modelType: $modelType.")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
index 5f87269863572..896565cd90e89 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala
@@ -33,9 +33,10 @@ import org.apache.spark.rdd.RDD
  * @param weights Weights computed for every feature.
  * @param intercept Intercept computed for this model.
  */
-class SVMModel (
-    override val weights: Vector,
-    override val intercept: Double)
+@Since("0.8.0")
+class SVMModel @Since("1.1.0") (
+    @Since("1.0.0") override val weights: Vector,
+    @Since("0.8.0") override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable
   with Saveable with PMMLExportable {
 
@@ -47,7 +48,7 @@ class SVMModel (
    * with prediction score greater than or equal to this threshold is identified as an positive,
    * and negative otherwise. The default value is 0.0.
    */
-  @Since("1.3.0")
+  @Since("1.0.0")
   @Experimental
   def setThreshold(threshold: Double): this.type = {
     this.threshold = Some(threshold)
@@ -92,12 +93,12 @@ class SVMModel (
 
   override protected def formatVersion: String = "1.0"
 
-  @Since("1.4.0")
   override def toString: String = {
     s"${super.toString}, numClasses = 2, threshold = ${threshold.getOrElse("None")}"
   }
 }
 
+@Since("1.3.0")
 object SVMModel extends Loader[SVMModel] {
 
   @Since("1.3.0")
@@ -132,6 +133,7 @@ object SVMModel extends Loader[SVMModel] {
  * regularization is used, which can be changed via [[SVMWithSGD.optimizer]].
  * NOTE: Labels used in SVM should be {0, 1}.
  */
+@Since("0.8.0")
 class SVMWithSGD private (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -141,6 +143,7 @@ class SVMWithSGD private (
 
   private val gradient = new HingeGradient()
   private val updater = new SquaredL2Updater()
+  @Since("0.8.0")
   override val optimizer = new GradientDescent(gradient, updater)
     .setStepSize(stepSize)
     .setNumIterations(numIterations)
@@ -152,6 +155,7 @@ class SVMWithSGD private (
    * Construct a SVM object with default parameters: {stepSize: 1.0, numIterations: 100,
    * regParm: 0.01, miniBatchFraction: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(1.0, 100, 0.01, 1.0)
 
   override protected def createModel(weights: Vector, intercept: Double) = {
@@ -162,6 +166,7 @@ class SVMWithSGD private (
 /**
  * Top-level methods for calling SVM. NOTE: Labels used in SVM should be {0, 1}.
  */
+@Since("0.8.0")
 object SVMWithSGD {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
index 7d33df3221fbf..75630054d1368 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionWithSGD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.classification
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.regression.StreamingLinearAlgorithm
 
@@ -44,6 +44,7 @@ import org.apache.spark.mllib.regression.StreamingLinearAlgorithm
  * }}}
  */
 @Experimental
+@Since("1.3.0")
 class StreamingLogisticRegressionWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -58,6 +59,7 @@ class StreamingLogisticRegressionWithSGD private[mllib] (
    * Initial weights must be set before using trainOn or predictOn
    * (see `StreamingLinearAlgorithm`)
    */
+  @Since("1.3.0")
   def this() = this(0.1, 50, 1.0, 0.0)
 
   protected val algorithm = new LogisticRegressionWithSGD(
@@ -66,30 +68,35 @@ class StreamingLogisticRegressionWithSGD private[mllib] (
   protected var model: Option[LogisticRegressionModel] = None
 
   /** Set the step size for gradient descent. Default: 0.1. */
+  @Since("1.3.0")
   def setStepSize(stepSize: Double): this.type = {
     this.algorithm.optimizer.setStepSize(stepSize)
     this
   }
 
   /** Set the number of iterations of gradient descent to run per update. Default: 50. */
+  @Since("1.3.0")
   def setNumIterations(numIterations: Int): this.type = {
     this.algorithm.optimizer.setNumIterations(numIterations)
     this
   }
 
   /** Set the fraction of each batch to use for updates. Default: 1.0. */
+  @Since("1.3.0")
   def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
     this.algorithm.optimizer.setMiniBatchFraction(miniBatchFraction)
     this
   }
 
   /** Set the regularization parameter. Default: 0.0. */
+  @Since("1.3.0")
   def setRegParam(regParam: Double): this.type = {
     this.algorithm.optimizer.setRegParam(regParam)
     this
   }
 
   /** Set the initial weights. Default: [0.0, 0.0]. */
+  @Since("1.3.0")
   def setInitialWeights(initialWeights: Vector): this.type = {
     this.model = Some(algorithm.createModel(initialWeights, 0.0))
     this

From 71a138cd0e0a14e8426f97877e3b52a562bbd02c Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Tue, 25 Aug 2015 13:14:10 -0700
Subject: [PATCH 414/633] [SPARK-10048] [SPARKR] Support arbitrary nested Java
 array in serde.

This PR:
1. supports transferring arbitrary nested array from JVM to R side in SerDe;
2. based on 1, collect() implemenation is improved. Now it can support collecting data of complex types
   from a DataFrame.

Author: Sun Rui <rui.sun@intel.com>

Closes #8276 from sun-rui/SPARK-10048.
---
 R/pkg/R/DataFrame.R                           | 55 +++++++++---
 R/pkg/R/deserialize.R                         | 72 +++++++---------
 R/pkg/R/serialize.R                           | 10 +--
 R/pkg/inst/tests/test_Serde.R                 | 77 +++++++++++++++++
 R/pkg/inst/worker/worker.R                    |  4 +-
 .../apache/spark/api/r/RBackendHandler.scala  |  7 ++
 .../scala/org/apache/spark/api/r/SerDe.scala  | 86 +++++++++++--------
 .../org/apache/spark/sql/api/r/SQLUtils.scala | 32 +------
 8 files changed, 216 insertions(+), 127 deletions(-)
 create mode 100644 R/pkg/inst/tests/test_Serde.R

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 10f3c4ea59864..ae1d912cf6da1 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -652,18 +652,49 @@ setMethod("dim",
 setMethod("collect",
           signature(x = "DataFrame"),
           function(x, stringsAsFactors = FALSE) {
-            # listCols is a list of raw vectors, one per column
-            listCols <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "dfToCols", x@sdf)
-            cols <- lapply(listCols, function(col) {
-              objRaw <- rawConnection(col)
-              numRows <- readInt(objRaw)
-              col <- readCol(objRaw, numRows)
-              close(objRaw)
-              col
-            })
-            names(cols) <- columns(x)
-            do.call(cbind.data.frame, list(cols, stringsAsFactors = stringsAsFactors))
-          })
+            names <- columns(x)
+            ncol <- length(names)
+            if (ncol <= 0) {
+              # empty data.frame with 0 columns and 0 rows
+              data.frame()
+            } else {
+              # listCols is a list of columns
+              listCols <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "dfToCols", x@sdf)
+              stopifnot(length(listCols) == ncol)
+              
+              # An empty data.frame with 0 columns and number of rows as collected
+              nrow <- length(listCols[[1]])
+              if (nrow <= 0) {
+                df <- data.frame()
+              } else {
+                df <- data.frame(row.names = 1 : nrow)                
+              }
+              
+              # Append columns one by one
+              for (colIndex in 1 : ncol) {
+                # Note: appending a column of list type into a data.frame so that
+                # data of complex type can be held. But getting a cell from a column
+                # of list type returns a list instead of a vector. So for columns of
+                # non-complex type, append them as vector.
+                col <- listCols[[colIndex]]
+                if (length(col) <= 0) {
+                  df[[names[colIndex]]] <- col
+                } else {
+                  # TODO: more robust check on column of primitive types
+                  vec <- do.call(c, col)
+                  if (class(vec) != "list") {
+                    df[[names[colIndex]]] <- vec                  
+                  } else {
+                    # For columns of complex type, be careful to access them.
+                    # Get a column of complex type returns a list.
+                    # Get a cell from a column of complex type returns a list instead of a vector.
+                    df[[names[colIndex]]] <- col
+                 }
+              }
+            }
+            df
+          }
+        })
 
 #' Limit
 #'
diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index 33bf13ec9e784..6cf628e3007de 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -48,6 +48,7 @@ readTypedObject <- function(con, type) {
     "r" = readRaw(con),
     "D" = readDate(con),
     "t" = readTime(con),
+    "a" = readArray(con),
     "l" = readList(con),
     "n" = NULL,
     "j" = getJobj(readString(con)),
@@ -85,8 +86,7 @@ readTime <- function(con) {
   as.POSIXct(t, origin = "1970-01-01")
 }
 
-# We only support lists where all elements are of same type
-readList <- function(con) {
+readArray <- function(con) {
   type <- readType(con)
   len <- readInt(con)
   if (len > 0) {
@@ -100,6 +100,25 @@ readList <- function(con) {
   }
 }
 
+# Read a list. Types of each element may be different.
+# Null objects are read as NA.
+readList <- function(con) {
+  len <- readInt(con)
+  if (len > 0) {
+    l <- vector("list", len)
+    for (i in 1:len) {
+      elem <- readObject(con)
+      if (is.null(elem)) {
+        elem <- NA
+      }
+      l[[i]] <- elem
+    }
+    l
+  } else {
+    list()
+  }
+}
+
 readRaw <- function(con) {
   dataLen <- readInt(con)
   readBin(con, raw(), as.integer(dataLen), endian = "big")
@@ -132,18 +151,19 @@ readDeserialize <- function(con) {
   }
 }
 
-readDeserializeRows <- function(inputCon) {
-  # readDeserializeRows will deserialize a DataOutputStream composed of
-  # a list of lists. Since the DOS is one continuous stream and
-  # the number of rows varies, we put the readRow function in a while loop
-  # that termintates when the next row is empty.
+readMultipleObjects <- function(inputCon) {
+  # readMultipleObjects will read multiple continuous objects from
+  # a DataOutputStream. There is no preceding field telling the count
+  # of the objects, so the number of objects varies, we try to read
+  # all objects in a loop until the end of the stream.
   data <- list()
   while(TRUE) {
-    row <- readRow(inputCon)
-    if (length(row) == 0) {
+    # If reaching the end of the stream, type returned should be "".
+    type <- readType(inputCon)
+    if (type == "") {
       break
     }
-    data[[length(data) + 1L]] <- row
+    data[[length(data) + 1L]] <- readTypedObject(inputCon, type)
   }
   data # this is a list of named lists now
 }
@@ -155,35 +175,5 @@ readRowList <- function(obj) {
   # deserialize the row.
   rawObj <- rawConnection(obj, "r+")
   on.exit(close(rawObj))
-  readRow(rawObj)
-}
-
-readRow <- function(inputCon) {
-  numCols <- readInt(inputCon)
-  if (length(numCols) > 0 && numCols > 0) {
-    lapply(1:numCols, function(x) {
-      obj <- readObject(inputCon)
-      if (is.null(obj)) {
-        NA
-      } else {
-        obj
-      }
-    }) # each row is a list now
-  } else {
-    list()
-  }
-}
-
-# Take a single column as Array[Byte] and deserialize it into an atomic vector
-readCol <- function(inputCon, numRows) {
-  if (numRows > 0) {
-    # sapply can not work with POSIXlt
-    do.call(c, lapply(1:numRows, function(x) {
-      value <- readObject(inputCon)
-      # Replace NULL with NA so we can coerce to vectors
-      if (is.null(value)) NA else value
-    }))
-  } else {
-    vector()
-  }
+  readObject(rawObj)
 }
diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R
index 311021e5d8473..e3676f57f907f 100644
--- a/R/pkg/R/serialize.R
+++ b/R/pkg/R/serialize.R
@@ -110,18 +110,10 @@ writeRowSerialize <- function(outputCon, rows) {
 serializeRow <- function(row) {
   rawObj <- rawConnection(raw(0), "wb")
   on.exit(close(rawObj))
-  writeRow(rawObj, row)
+  writeGenericList(rawObj, row)
   rawConnectionValue(rawObj)
 }
 
-writeRow <- function(con, row) {
-  numCols <- length(row)
-  writeInt(con, numCols)
-  for (i in 1:numCols) {
-    writeObject(con, row[[i]])
-  }
-}
-
 writeRaw <- function(con, batch) {
   writeInt(con, length(batch))
   writeBin(batch, con, endian = "big")
diff --git a/R/pkg/inst/tests/test_Serde.R b/R/pkg/inst/tests/test_Serde.R
new file mode 100644
index 0000000000000..009db85da2beb
--- /dev/null
+++ b/R/pkg/inst/tests/test_Serde.R
@@ -0,0 +1,77 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+context("SerDe functionality")
+
+sc <- sparkR.init()
+
+test_that("SerDe of primitive types", {
+  x <- callJStatic("SparkRHandler", "echo", 1L)
+  expect_equal(x, 1L)
+  expect_equal(class(x), "integer")
+  
+  x <- callJStatic("SparkRHandler", "echo", 1)
+  expect_equal(x, 1)
+  expect_equal(class(x), "numeric")
+
+  x <- callJStatic("SparkRHandler", "echo", TRUE)
+  expect_true(x)
+  expect_equal(class(x), "logical")
+  
+  x <- callJStatic("SparkRHandler", "echo", "abc")
+  expect_equal(x, "abc")
+  expect_equal(class(x), "character")  
+})
+
+test_that("SerDe of list of primitive types", {
+  x <- list(1L, 2L, 3L)
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+  expect_equal(class(y[[1]]), "integer")
+
+  x <- list(1, 2, 3)
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+  expect_equal(class(y[[1]]), "numeric")
+  
+  x <- list(TRUE, FALSE)
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+  expect_equal(class(y[[1]]), "logical")
+  
+  x <- list("a", "b", "c")
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+  expect_equal(class(y[[1]]), "character")
+  
+  # Empty list
+  x <- list()
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+})
+
+test_that("SerDe of list of lists", {
+  x <- list(list(1L, 2L, 3L), list(1, 2, 3),
+            list(TRUE, FALSE), list("a", "b", "c"))
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+
+  # List of empty lists
+  x <- list(list(), list())
+  y <- callJStatic("SparkRHandler", "echo", x)
+  expect_equal(x, y)
+})
diff --git a/R/pkg/inst/worker/worker.R b/R/pkg/inst/worker/worker.R
index 7e3b5fc403b25..0c3b0d1f4be20 100644
--- a/R/pkg/inst/worker/worker.R
+++ b/R/pkg/inst/worker/worker.R
@@ -94,7 +94,7 @@ if (isEmpty != 0) {
     } else if (deserializer == "string") {
       data <- as.list(readLines(inputCon))
     } else if (deserializer == "row") {
-      data <- SparkR:::readDeserializeRows(inputCon)
+      data <- SparkR:::readMultipleObjects(inputCon)
     }
     # Timing reading input data for execution
     inputElap <- elapsedSecs()
@@ -120,7 +120,7 @@ if (isEmpty != 0) {
     } else if (deserializer == "string") {
       data <- readLines(inputCon)
     } else if (deserializer == "row") {
-      data <- SparkR:::readDeserializeRows(inputCon)
+      data <- SparkR:::readMultipleObjects(inputCon)
     }
     # Timing reading input data for execution
     inputElap <- elapsedSecs()
diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
index 6ce02e2ea336a..bb82f3285f1d9 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
@@ -53,6 +53,13 @@ private[r] class RBackendHandler(server: RBackend)
 
     if (objId == "SparkRHandler") {
       methodName match {
+        // This function is for test-purpose only
+        case "echo" =>
+          val args = readArgs(numArgs, dis)
+          assert(numArgs == 1)
+
+          writeInt(dos, 0)
+          writeObject(dos, args(0))
         case "stopBackend" =>
           writeInt(dos, 0)
           writeType(dos, "void")
diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index dbbbcf40c1e96..26ad4f1d4697e 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -149,6 +149,10 @@ private[spark] object SerDe {
       case 'b' => readBooleanArr(dis)
       case 'j' => readStringArr(dis).map(x => JVMObjectTracker.getObject(x))
       case 'r' => readBytesArr(dis)
+      case 'l' => {
+        val len = readInt(dis)
+        (0 until len).map(_ => readList(dis)).toArray
+      }
       case _ => throw new IllegalArgumentException(s"Invalid array type $arrType")
     }
   }
@@ -200,6 +204,9 @@ private[spark] object SerDe {
       case "date" => dos.writeByte('D')
       case "time" => dos.writeByte('t')
       case "raw" => dos.writeByte('r')
+      // Array of primitive types
+      case "array" => dos.writeByte('a')
+      // Array of objects
       case "list" => dos.writeByte('l')
       case "jobj" => dos.writeByte('j')
       case _ => throw new IllegalArgumentException(s"Invalid type $typeStr")
@@ -211,26 +218,35 @@ private[spark] object SerDe {
       writeType(dos, "void")
     } else {
       value.getClass.getName match {
+        case "java.lang.Character" =>
+          writeType(dos, "character")
+          writeString(dos, value.asInstanceOf[Character].toString)
         case "java.lang.String" =>
           writeType(dos, "character")
           writeString(dos, value.asInstanceOf[String])
-        case "long" | "java.lang.Long" =>
+        case "java.lang.Long" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Long].toDouble)
-        case "float" | "java.lang.Float" =>
+        case "java.lang.Float" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Float].toDouble)
-        case "decimal" | "java.math.BigDecimal" =>
+        case "java.math.BigDecimal" =>
           writeType(dos, "double")
           val javaDecimal = value.asInstanceOf[java.math.BigDecimal]
           writeDouble(dos, scala.math.BigDecimal(javaDecimal).toDouble)
-        case "double" | "java.lang.Double" =>
+        case "java.lang.Double" =>
           writeType(dos, "double")
           writeDouble(dos, value.asInstanceOf[Double])
-        case "int" | "java.lang.Integer" =>
+        case "java.lang.Byte" =>
+          writeType(dos, "integer")
+          writeInt(dos, value.asInstanceOf[Byte].toInt)
+        case "java.lang.Short" =>
+          writeType(dos, "integer")
+          writeInt(dos, value.asInstanceOf[Short].toInt)
+        case "java.lang.Integer" =>
           writeType(dos, "integer")
           writeInt(dos, value.asInstanceOf[Int])
-        case "boolean" | "java.lang.Boolean" =>
+        case "java.lang.Boolean" =>
           writeType(dos, "logical")
           writeBoolean(dos, value.asInstanceOf[Boolean])
         case "java.sql.Date" =>
@@ -242,43 +258,48 @@ private[spark] object SerDe {
         case "java.sql.Timestamp" =>
           writeType(dos, "time")
           writeTime(dos, value.asInstanceOf[Timestamp])
+
+        // Handle arrays
+
+        // Array of primitive types
+
+        // Special handling for byte array
         case "[B" =>
           writeType(dos, "raw")
           writeBytes(dos, value.asInstanceOf[Array[Byte]])
-        // TODO: Types not handled right now include
-        // byte, char, short, float
 
-        // Handle arrays
-        case "[Ljava.lang.String;" =>
-          writeType(dos, "list")
-          writeStringArr(dos, value.asInstanceOf[Array[String]])
+        case "[C" =>
+          writeType(dos, "array")
+          writeStringArr(dos, value.asInstanceOf[Array[Char]].map(_.toString))
+        case "[S" =>
+          writeType(dos, "array")
+          writeIntArr(dos, value.asInstanceOf[Array[Short]].map(_.toInt))
         case "[I" =>
-          writeType(dos, "list")
+          writeType(dos, "array")
           writeIntArr(dos, value.asInstanceOf[Array[Int]])
         case "[J" =>
-          writeType(dos, "list")
+          writeType(dos, "array")
           writeDoubleArr(dos, value.asInstanceOf[Array[Long]].map(_.toDouble))
+        case "[F" =>
+          writeType(dos, "array")
+          writeDoubleArr(dos, value.asInstanceOf[Array[Float]].map(_.toDouble))
         case "[D" =>
-          writeType(dos, "list")
+          writeType(dos, "array")
           writeDoubleArr(dos, value.asInstanceOf[Array[Double]])
         case "[Z" =>
-          writeType(dos, "list")
+          writeType(dos, "array")
           writeBooleanArr(dos, value.asInstanceOf[Array[Boolean]])
-        case "[[B" =>
+
+        // Array of objects, null objects use "void" type
+        case c if c.startsWith("[") =>
           writeType(dos, "list")
-          writeBytesArr(dos, value.asInstanceOf[Array[Array[Byte]]])
-        case otherName =>
-          // Handle array of objects
-          if (otherName.startsWith("[L")) {
-            val objArr = value.asInstanceOf[Array[Object]]
-            writeType(dos, "list")
-            writeType(dos, "jobj")
-            dos.writeInt(objArr.length)
-            objArr.foreach(o => writeJObj(dos, o))
-          } else {
-            writeType(dos, "jobj")
-            writeJObj(dos, value)
-          }
+          val array = value.asInstanceOf[Array[Object]]
+          writeInt(dos, array.length)
+          array.foreach(elem => writeObject(dos, elem))
+
+        case _ =>
+          writeType(dos, "jobj")
+          writeJObj(dos, value)
       }
     }
   }
@@ -350,11 +371,6 @@ private[spark] object SerDe {
     value.foreach(v => writeString(out, v))
   }
 
-  def writeBytesArr(out: DataOutputStream, value: Array[Array[Byte]]): Unit = {
-    writeType(out, "raw")
-    out.writeInt(value.length)
-    value.foreach(v => writeBytes(out, v))
-  }
 }
 
 private[r] object SerializationFormats {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
index 92861ab038f19..7f3defec3d42e 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
@@ -98,27 +98,17 @@ private[r] object SQLUtils {
     val bos = new ByteArrayOutputStream()
     val dos = new DataOutputStream(bos)
 
-    SerDe.writeInt(dos, row.length)
-    (0 until row.length).map { idx =>
-      val obj: Object = row(idx).asInstanceOf[Object]
-      SerDe.writeObject(dos, obj)
-    }
+    val cols = (0 until row.length).map(row(_).asInstanceOf[Object]).toArray
+    SerDe.writeObject(dos, cols)
     bos.toByteArray()
   }
 
-  def dfToCols(df: DataFrame): Array[Array[Byte]] = {
+  def dfToCols(df: DataFrame): Array[Array[Any]] = {
     // localDF is Array[Row]
     val localDF = df.collect()
     val numCols = df.columns.length
-    // dfCols is Array[Array[Any]]
-    val dfCols = convertRowsToColumns(localDF, numCols)
-
-    dfCols.map { col =>
-      colToRBytes(col)
-    }
-  }
 
-  def convertRowsToColumns(localDF: Array[Row], numCols: Int): Array[Array[Any]] = {
+    // result is Array[Array[Any]]
     (0 until numCols).map { colIdx =>
       localDF.map { row =>
         row(colIdx)
@@ -126,20 +116,6 @@ private[r] object SQLUtils {
     }.toArray
   }
 
-  def colToRBytes(col: Array[Any]): Array[Byte] = {
-    val numRows = col.length
-    val bos = new ByteArrayOutputStream()
-    val dos = new DataOutputStream(bos)
-
-    SerDe.writeInt(dos, numRows)
-
-    col.map { item =>
-      val obj: Object = item.asInstanceOf[Object]
-      SerDe.writeObject(dos, obj)
-    }
-    bos.toByteArray()
-  }
-
   def saveMode(mode: String): SaveMode = {
     mode match {
       case "append" => SaveMode.Append

From c0e9ff1588b4d9313cc6ec6e00e5c7663eb67910 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 25 Aug 2015 13:21:05 -0700
Subject: [PATCH 415/633] [SPARK-9800] Adds docs for
 GradientDescent$.runMiniBatchSGD alias

* Adds doc for alias of runMIniBatchSGD documenting default value for convergeTol
* Cleans up a note in code

Author: Feynman Liang <fliang@databricks.com>

Closes #8425 from feynmanliang/SPARK-9800.
---
 .../apache/spark/mllib/optimization/GradientDescent.scala    | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
index 8f0d1e4aa010a..3b663b5defb03 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
@@ -235,7 +235,7 @@ object GradientDescent extends Logging {
 
       if (miniBatchSize > 0) {
         /**
-         * NOTE(Xinghao): lossSum is computed using the weights from the previous iteration
+         * lossSum is computed using the weights from the previous iteration
          * and regVal is the regularization value computed in the previous iteration as well.
          */
         stochasticLossHistory.append(lossSum / miniBatchSize + regVal)
@@ -264,6 +264,9 @@ object GradientDescent extends Logging {
 
   }
 
+  /**
+   * Alias of [[runMiniBatchSGD]] with convergenceTol set to default value of 0.001.
+   */
   def runMiniBatchSGD(
       data: RDD[(Double, Vector)],
       gradient: Gradient,

From c619c7552f22d28cfa321ce671fc9ca854dd655f Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 13:22:38 -0700
Subject: [PATCH 416/633] [SPARK-10237] [MLLIB] update since versions in
 mllib.fpm

Same as #8421 but for `mllib.fpm`.

cc feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8429 from mengxr/SPARK-10237.
---
 .../spark/mllib/fpm/AssociationRules.scala    |  7 ++++--
 .../org/apache/spark/mllib/fpm/FPGrowth.scala |  9 ++++++--
 .../apache/spark/mllib/fpm/PrefixSpan.scala   | 23 ++++++++++++++++---
 3 files changed, 32 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
index ba3b447a83398..95c688c86a7e4 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/AssociationRules.scala
@@ -82,12 +82,15 @@ class AssociationRules private[fpm] (
     }.filter(_.confidence >= minConfidence)
   }
 
+  /** Java-friendly version of [[run]]. */
+  @Since("1.5.0")
   def run[Item](freqItemsets: JavaRDD[FreqItemset[Item]]): JavaRDD[Rule[Item]] = {
     val tag = fakeClassTag[Item]
     run(freqItemsets.rdd)(tag)
   }
 }
 
+@Since("1.5.0")
 object AssociationRules {
 
   /**
@@ -104,8 +107,8 @@ object AssociationRules {
   @Since("1.5.0")
   @Experimental
   class Rule[Item] private[fpm] (
-      val antecedent: Array[Item],
-      val consequent: Array[Item],
+      @Since("1.5.0") val antecedent: Array[Item],
+      @Since("1.5.0") val consequent: Array[Item],
       freqUnion: Double,
       freqAntecedent: Double) extends Serializable {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
index e37f806271680..aea5c4f8a8a7d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala
@@ -42,7 +42,8 @@ import org.apache.spark.storage.StorageLevel
  */
 @Since("1.3.0")
 @Experimental
-class FPGrowthModel[Item: ClassTag](val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable {
+class FPGrowthModel[Item: ClassTag] @Since("1.3.0") (
+    @Since("1.3.0") val freqItemsets: RDD[FreqItemset[Item]]) extends Serializable {
   /**
    * Generates association rules for the [[Item]]s in [[freqItemsets]].
    * @param confidence minimal confidence of the rules produced
@@ -126,6 +127,8 @@ class FPGrowth private (
     new FPGrowthModel(freqItemsets)
   }
 
+  /** Java-friendly version of [[run]]. */
+  @Since("1.3.0")
   def run[Item, Basket <: JavaIterable[Item]](data: JavaRDD[Basket]): FPGrowthModel[Item] = {
     implicit val tag = fakeClassTag[Item]
     run(data.rdd.map(_.asScala.toArray))
@@ -226,7 +229,9 @@ object FPGrowth {
    *
    */
   @Since("1.3.0")
-  class FreqItemset[Item](val items: Array[Item], val freq: Long) extends Serializable {
+  class FreqItemset[Item] @Since("1.3.0") (
+      @Since("1.3.0") val items: Array[Item],
+      @Since("1.3.0") val freq: Long) extends Serializable {
 
     /**
      * Returns items in a Java List.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
index dc4ae1d0b69ed..97916daa2e9ad 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala
@@ -25,7 +25,7 @@ import scala.collection.JavaConverters._
 import scala.reflect.ClassTag
 
 import org.apache.spark.Logging
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.api.java.JavaRDD
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
 import org.apache.spark.rdd.RDD
@@ -51,6 +51,7 @@ import org.apache.spark.storage.StorageLevel
  *       (Wikipedia)]]
  */
 @Experimental
+@Since("1.5.0")
 class PrefixSpan private (
     private var minSupport: Double,
     private var maxPatternLength: Int,
@@ -61,17 +62,20 @@ class PrefixSpan private (
    * Constructs a default instance with default parameters
    * {minSupport: `0.1`, maxPatternLength: `10`, maxLocalProjDBSize: `32000000L`}.
    */
+  @Since("1.5.0")
   def this() = this(0.1, 10, 32000000L)
 
   /**
    * Get the minimal support (i.e. the frequency of occurrence before a pattern is considered
    * frequent).
    */
+  @Since("1.5.0")
   def getMinSupport: Double = minSupport
 
   /**
    * Sets the minimal support level (default: `0.1`).
    */
+  @Since("1.5.0")
   def setMinSupport(minSupport: Double): this.type = {
     require(minSupport >= 0 && minSupport <= 1,
       s"The minimum support value must be in [0, 1], but got $minSupport.")
@@ -82,11 +86,13 @@ class PrefixSpan private (
   /**
    * Gets the maximal pattern length (i.e. the length of the longest sequential pattern to consider.
    */
+  @Since("1.5.0")
   def getMaxPatternLength: Int = maxPatternLength
 
   /**
    * Sets maximal pattern length (default: `10`).
    */
+  @Since("1.5.0")
   def setMaxPatternLength(maxPatternLength: Int): this.type = {
     // TODO: support unbounded pattern length when maxPatternLength = 0
     require(maxPatternLength >= 1,
@@ -98,12 +104,14 @@ class PrefixSpan private (
   /**
    * Gets the maximum number of items allowed in a projected database before local processing.
    */
+  @Since("1.5.0")
   def getMaxLocalProjDBSize: Long = maxLocalProjDBSize
 
   /**
    * Sets the maximum number of items (including delimiters used in the internal storage format)
    * allowed in a projected database before local processing (default: `32000000L`).
    */
+  @Since("1.5.0")
   def setMaxLocalProjDBSize(maxLocalProjDBSize: Long): this.type = {
     require(maxLocalProjDBSize >= 0L,
       s"The maximum local projected database size must be nonnegative, but got $maxLocalProjDBSize")
@@ -116,6 +124,7 @@ class PrefixSpan private (
    * @param data sequences of itemsets.
    * @return a [[PrefixSpanModel]] that contains the frequent patterns
    */
+  @Since("1.5.0")
   def run[Item: ClassTag](data: RDD[Array[Array[Item]]]): PrefixSpanModel[Item] = {
     if (data.getStorageLevel == StorageLevel.NONE) {
       logWarning("Input data is not cached.")
@@ -202,6 +211,7 @@ class PrefixSpan private (
    * @tparam Sequence sequence type, which is an Iterable of Itemsets
    * @return a [[PrefixSpanModel]] that contains the frequent sequential patterns
    */
+  @Since("1.5.0")
   def run[Item, Itemset <: jl.Iterable[Item], Sequence <: jl.Iterable[Itemset]](
       data: JavaRDD[Sequence]): PrefixSpanModel[Item] = {
     implicit val tag = fakeClassTag[Item]
@@ -211,6 +221,7 @@ class PrefixSpan private (
 }
 
 @Experimental
+@Since("1.5.0")
 object PrefixSpan extends Logging {
 
   /**
@@ -535,10 +546,14 @@ object PrefixSpan extends Logging {
    * @param freq frequency
    * @tparam Item item type
    */
-  class FreqSequence[Item](val sequence: Array[Array[Item]], val freq: Long) extends Serializable {
+  @Since("1.5.0")
+  class FreqSequence[Item] @Since("1.5.0") (
+      @Since("1.5.0") val sequence: Array[Array[Item]],
+      @Since("1.5.0") val freq: Long) extends Serializable {
     /**
      * Returns sequence as a Java List of lists for Java users.
      */
+    @Since("1.5.0")
     def javaSequence: ju.List[ju.List[Item]] = sequence.map(_.toList.asJava).toList.asJava
   }
 }
@@ -548,5 +563,7 @@ object PrefixSpan extends Logging {
  * @param freqSequences frequent sequences
  * @tparam Item item type
  */
-class PrefixSpanModel[Item](val freqSequences: RDD[PrefixSpan.FreqSequence[Item]])
+@Since("1.5.0")
+class PrefixSpanModel[Item] @Since("1.5.0") (
+    @Since("1.5.0") val freqSequences: RDD[PrefixSpan.FreqSequence[Item]])
   extends Serializable

From 9205907876cf65695e56c2a94bedd83df3675c03 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 25 Aug 2015 13:23:15 -0700
Subject: [PATCH 417/633] [SPARK-9797] [MLLIB] [DOC]
 StreamingLinearRegressionWithSGD.setConvergenceTol default value

Adds default convergence tolerance (0.001, set in `GradientDescent.convergenceTol`) to `setConvergenceTol`'s scaladoc

Author: Feynman Liang <fliang@databricks.com>

Closes #8424 from feynmanliang/SPARK-9797.
---
 .../mllib/regression/StreamingLinearRegressionWithSGD.scala     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
index 537a05274eec2..26654e4a06838 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
@@ -93,7 +93,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   }
 
   /**
-   * Set the convergence tolerance.
+   * Set the convergence tolerance. Default: 0.001.
    */
   def setConvergenceTol(tolerance: Double): this.type = {
     this.algorithm.optimizer.setConvergenceTol(tolerance)

From 00ae4be97f7b205432db2967ba6d506286ef2ca6 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 14:11:38 -0700
Subject: [PATCH 418/633] [SPARK-10239] [SPARK-10244] [MLLIB] update since
 versions in mllib.pmml and mllib.util

Same as #8421 but for `mllib.pmml` and `mllib.util`.

cc dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8430 from mengxr/SPARK-10239 and squashes the following commits:

a189acf [Xiangrui Meng] update since versions in mllib.pmml and mllib.util
---
 .../org/apache/spark/mllib/pmml/PMMLExportable.scala   |  7 ++++++-
 .../org/apache/spark/mllib/util/DataValidators.scala   |  7 +++++--
 .../apache/spark/mllib/util/KMeansDataGenerator.scala  |  5 ++++-
 .../apache/spark/mllib/util/LinearDataGenerator.scala  | 10 ++++++++--
 .../mllib/util/LogisticRegressionDataGenerator.scala   |  5 ++++-
 .../org/apache/spark/mllib/util/MFDataGenerator.scala  |  4 +++-
 .../scala/org/apache/spark/mllib/util/MLUtils.scala    |  2 ++
 .../org/apache/spark/mllib/util/SVMDataGenerator.scala |  6 ++++--
 .../org/apache/spark/mllib/util/modelSaveLoad.scala    |  6 +++++-
 9 files changed, 41 insertions(+), 11 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala b/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala
index 5e882d4ebb10b..274ac7c99553b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/pmml/PMMLExportable.scala
@@ -23,7 +23,7 @@ import javax.xml.transform.stream.StreamResult
 import org.jpmml.model.JAXBUtil
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 import org.apache.spark.mllib.pmml.export.PMMLModelExportFactory
 
 /**
@@ -33,6 +33,7 @@ import org.apache.spark.mllib.pmml.export.PMMLModelExportFactory
  * developed by the Data Mining Group (www.dmg.org).
  */
 @DeveloperApi
+@Since("1.4.0")
 trait PMMLExportable {
 
   /**
@@ -48,6 +49,7 @@ trait PMMLExportable {
    * Export the model to a local file in PMML format
    */
   @Experimental
+  @Since("1.4.0")
   def toPMML(localPath: String): Unit = {
     toPMML(new StreamResult(new File(localPath)))
   }
@@ -57,6 +59,7 @@ trait PMMLExportable {
    * Export the model to a directory on a distributed file system in PMML format
    */
   @Experimental
+  @Since("1.4.0")
   def toPMML(sc: SparkContext, path: String): Unit = {
     val pmml = toPMML()
     sc.parallelize(Array(pmml), 1).saveAsTextFile(path)
@@ -67,6 +70,7 @@ trait PMMLExportable {
    * Export the model to the OutputStream in PMML format
    */
   @Experimental
+  @Since("1.4.0")
   def toPMML(outputStream: OutputStream): Unit = {
     toPMML(new StreamResult(outputStream))
   }
@@ -76,6 +80,7 @@ trait PMMLExportable {
    * Export the model to a String in PMML format
    */
   @Experimental
+  @Since("1.4.0")
   def toPMML(): String = {
     val writer = new StringWriter
     toPMML(new StreamResult(writer))
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala
index be335a1aca58a..dffe6e78939e8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/DataValidators.scala
@@ -17,16 +17,17 @@
 
 package org.apache.spark.mllib.util
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.Logging
-import org.apache.spark.rdd.RDD
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
 
 /**
  * :: DeveloperApi ::
  * A collection of methods used to validate data before applying ML algorithms.
  */
 @DeveloperApi
+@Since("0.8.0")
 object DataValidators extends Logging {
 
   /**
@@ -34,6 +35,7 @@ object DataValidators extends Logging {
    *
    * @return True if labels are all zero or one, false otherwise.
    */
+  @Since("1.0.0")
   val binaryLabelValidator: RDD[LabeledPoint] => Boolean = { data =>
     val numInvalid = data.filter(x => x.label != 1.0 && x.label != 0.0).count()
     if (numInvalid != 0) {
@@ -48,6 +50,7 @@ object DataValidators extends Logging {
    *
    * @return True if labels are all in the range of {0, 1, ..., k-1}, false otherwise.
    */
+  @Since("1.3.0")
   def multiLabelValidator(k: Int): RDD[LabeledPoint] => Boolean = { data =>
     val numInvalid = data.filter(x =>
       x.label - x.label.toInt != 0.0 || x.label < 0 || x.label > k - 1).count()
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala
index e6bcff48b022c..00fd1606a369c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/KMeansDataGenerator.scala
@@ -19,8 +19,8 @@ package org.apache.spark.mllib.util
 
 import scala.util.Random
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.SparkContext
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.rdd.RDD
 
 /**
@@ -30,6 +30,7 @@ import org.apache.spark.rdd.RDD
  * cluster with scale 1 around each center.
  */
 @DeveloperApi
+@Since("0.8.0")
 object KMeansDataGenerator {
 
   /**
@@ -42,6 +43,7 @@ object KMeansDataGenerator {
    * @param r Scaling factor for the distribution of the initial centers
    * @param numPartitions Number of partitions of the generated RDD; default 2
    */
+  @Since("0.8.0")
   def generateKMeansRDD(
       sc: SparkContext,
       numPoints: Int,
@@ -62,6 +64,7 @@ object KMeansDataGenerator {
     }
   }
 
+  @Since("0.8.0")
   def main(args: Array[String]) {
     if (args.length < 6) {
       // scalastyle:off println
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
index 7a1c7796065ee..d0ba454f379a9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala
@@ -22,11 +22,11 @@ import scala.util.Random
 
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.SparkContext
-import org.apache.spark.rdd.RDD
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
 
 /**
  * :: DeveloperApi ::
@@ -35,6 +35,7 @@ import org.apache.spark.mllib.regression.LabeledPoint
  * response variable `Y`.
  */
 @DeveloperApi
+@Since("0.8.0")
 object LinearDataGenerator {
 
   /**
@@ -46,6 +47,7 @@ object LinearDataGenerator {
    * @param seed Random seed
    * @return Java List of input.
    */
+  @Since("0.8.0")
   def generateLinearInputAsList(
       intercept: Double,
       weights: Array[Double],
@@ -68,6 +70,7 @@ object LinearDataGenerator {
    * @param eps Epsilon scaling factor.
    * @return Seq of input.
    */
+  @Since("0.8.0")
   def generateLinearInput(
       intercept: Double,
       weights: Array[Double],
@@ -92,6 +95,7 @@ object LinearDataGenerator {
    * @param eps Epsilon scaling factor.
    * @return Seq of input.
    */
+  @Since("0.8.0")
   def generateLinearInput(
       intercept: Double,
       weights: Array[Double],
@@ -132,6 +136,7 @@ object LinearDataGenerator {
    *
    * @return RDD of LabeledPoint containing sample data.
    */
+  @Since("0.8.0")
   def generateLinearRDD(
       sc: SparkContext,
       nexamples: Int,
@@ -151,6 +156,7 @@ object LinearDataGenerator {
     data
   }
 
+  @Since("0.8.0")
   def main(args: Array[String]) {
     if (args.length < 2) {
       // scalastyle:off println
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
index c09cbe69bb971..33477ee20ebbd 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/LogisticRegressionDataGenerator.scala
@@ -19,7 +19,7 @@ package org.apache.spark.mllib.util
 
 import scala.util.Random
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{Since, DeveloperApi}
 import org.apache.spark.SparkContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -31,6 +31,7 @@ import org.apache.spark.mllib.linalg.Vectors
  * with probability `probOne` and scales features for positive examples by `eps`.
  */
 @DeveloperApi
+@Since("0.8.0")
 object LogisticRegressionDataGenerator {
 
   /**
@@ -43,6 +44,7 @@ object LogisticRegressionDataGenerator {
    * @param nparts Number of partitions of the generated RDD. Default value is 2.
    * @param probOne Probability that a label is 1 (and not 0). Default value is 0.5.
    */
+  @Since("0.8.0")
   def generateLogisticRDD(
     sc: SparkContext,
     nexamples: Int,
@@ -62,6 +64,7 @@ object LogisticRegressionDataGenerator {
     data
   }
 
+  @Since("0.8.0")
   def main(args: Array[String]) {
     if (args.length != 5) {
       // scalastyle:off println
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala
index 16f430599a515..906bd30563bd0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MFDataGenerator.scala
@@ -23,7 +23,7 @@ import scala.language.postfixOps
 import scala.util.Random
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{Since, DeveloperApi}
 import org.apache.spark.mllib.linalg.{BLAS, DenseMatrix}
 import org.apache.spark.rdd.RDD
 
@@ -52,7 +52,9 @@ import org.apache.spark.rdd.RDD
  *   testSampFact   (Double) Percentage of training data to use as test data.
  */
 @DeveloperApi
+@Since("0.8.0")
 object MFDataGenerator {
+  @Since("0.8.0")
   def main(args: Array[String]) {
     if (args.length < 2) {
       // scalastyle:off println
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index 4940974bf4f41..81c2f0ce6e12c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -36,6 +36,7 @@ import org.apache.spark.streaming.dstream.DStream
 /**
  * Helper methods to load, save and pre-process data used in ML Lib.
  */
+@Since("0.8.0")
 object MLUtils {
 
   private[mllib] lazy val EPSILON = {
@@ -168,6 +169,7 @@ object MLUtils {
    *
    * @see [[org.apache.spark.mllib.util.MLUtils#loadLibSVMFile]]
    */
+  @Since("1.0.0")
   def saveAsLibSVMFile(data: RDD[LabeledPoint], dir: String) {
     // TODO: allow to specify label precision and feature precision.
     val dataStr = data.map { case LabeledPoint(label, features) =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
index ad20b7694a779..cde5979396178 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/SVMDataGenerator.scala
@@ -21,11 +21,11 @@ import scala.util.Random
 
 import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.SparkContext
-import org.apache.spark.rdd.RDD
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.rdd.RDD
 
 /**
  * :: DeveloperApi ::
@@ -33,8 +33,10 @@ import org.apache.spark.mllib.regression.LabeledPoint
  * for the features and adds Gaussian noise with weight 0.1 to generate labels.
  */
 @DeveloperApi
+@Since("0.8.0")
 object SVMDataGenerator {
 
+  @Since("0.8.0")
   def main(args: Array[String]) {
     if (args.length < 2) {
       // scalastyle:off println
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala
index 30d642c754b7c..4d71d534a0774 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/modelSaveLoad.scala
@@ -24,7 +24,7 @@ import org.json4s._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.types.{DataType, StructField, StructType}
 
@@ -35,6 +35,7 @@ import org.apache.spark.sql.types.{DataType, StructField, StructType}
  * This should be inherited by the class which implements model instances.
  */
 @DeveloperApi
+@Since("1.3.0")
 trait Saveable {
 
   /**
@@ -50,6 +51,7 @@ trait Saveable {
    * @param path  Path specifying the directory in which to save this model.
    *              If the directory already exists, this method throws an exception.
    */
+  @Since("1.3.0")
   def save(sc: SparkContext, path: String): Unit
 
   /** Current version of model save/load format. */
@@ -64,6 +66,7 @@ trait Saveable {
  * This should be inherited by an object paired with the model class.
  */
 @DeveloperApi
+@Since("1.3.0")
 trait Loader[M <: Saveable] {
 
   /**
@@ -75,6 +78,7 @@ trait Loader[M <: Saveable] {
    * @param path  Path specifying the directory to which the model was saved.
    * @return  Model instance
    */
+  @Since("1.3.0")
   def load(sc: SparkContext, path: String): M
 
 }

From ec89bd840a6862751999d612f586a962cae63f6d Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 25 Aug 2015 14:55:34 -0700
Subject: [PATCH 419/633] [SPARK-10245] [SQL] Fix decimal literals with
 precision < scale

In BigDecimal or java.math.BigDecimal, the precision could be smaller than scale, for example, BigDecimal("0.001") has precision = 1 and scale = 3. But DecimalType require that the precision should be larger than scale, so we should use the maximum of precision and scale when inferring the schema from decimal literal.

Author: Davies Liu <davies@databricks.com>

Closes #8428 from davies/smaller_decimal.
---
 .../spark/sql/catalyst/expressions/literals.scala      |  7 ++++---
 .../catalyst/expressions/LiteralExpressionSuite.scala  |  8 +++++---
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala     | 10 ++++++++++
 3 files changed, 19 insertions(+), 6 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
index 34bad23802ba4..8c0c5d5b1e31e 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/literals.scala
@@ -36,9 +36,10 @@ object Literal {
     case s: Short => Literal(s, ShortType)
     case s: String => Literal(UTF8String.fromString(s), StringType)
     case b: Boolean => Literal(b, BooleanType)
-    case d: BigDecimal => Literal(Decimal(d), DecimalType(d.precision, d.scale))
-    case d: java.math.BigDecimal => Literal(Decimal(d), DecimalType(d.precision(), d.scale()))
-    case d: Decimal => Literal(d, DecimalType(d.precision, d.scale))
+    case d: BigDecimal => Literal(Decimal(d), DecimalType(Math.max(d.precision, d.scale), d.scale))
+    case d: java.math.BigDecimal =>
+      Literal(Decimal(d), DecimalType(Math.max(d.precision, d.scale), d.scale()))
+    case d: Decimal => Literal(d, DecimalType(Math.max(d.precision, d.scale), d.scale))
     case t: Timestamp => Literal(DateTimeUtils.fromJavaTimestamp(t), TimestampType)
     case d: Date => Literal(DateTimeUtils.fromJavaDate(d), DateType)
     case a: Array[Byte] => Literal(a, BinaryType)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
index f6404d21611e5..015eb1897fb8c 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/LiteralExpressionSuite.scala
@@ -83,12 +83,14 @@ class LiteralExpressionSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("decimal") {
-    List(0.0, 1.2, 1.1111, 5).foreach { d =>
+    List(-0.0001, 0.0, 0.001, 1.2, 1.1111, 5).foreach { d =>
       checkEvaluation(Literal(Decimal(d)), Decimal(d))
       checkEvaluation(Literal(Decimal(d.toInt)), Decimal(d.toInt))
       checkEvaluation(Literal(Decimal(d.toLong)), Decimal(d.toLong))
-      checkEvaluation(Literal(Decimal((d * 1000L).toLong, 10, 1)),
-        Decimal((d * 1000L).toLong, 10, 1))
+      checkEvaluation(Literal(Decimal((d * 1000L).toLong, 10, 3)),
+        Decimal((d * 1000L).toLong, 10, 3))
+      checkEvaluation(Literal(BigDecimal(d.toString)), Decimal(d))
+      checkEvaluation(Literal(new java.math.BigDecimal(d.toString)), Decimal(d))
     }
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index dcb4e83710982..aa07665c6b705 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1627,6 +1627,16 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
       Row(null))
   }
 
+  test("precision smaller than scale") {
+    checkAnswer(sql("select 10.00"), Row(BigDecimal("10.00")))
+    checkAnswer(sql("select 1.00"), Row(BigDecimal("1.00")))
+    checkAnswer(sql("select 0.10"), Row(BigDecimal("0.10")))
+    checkAnswer(sql("select 0.01"), Row(BigDecimal("0.01")))
+    checkAnswer(sql("select 0.001"), Row(BigDecimal("0.001")))
+    checkAnswer(sql("select -0.01"), Row(BigDecimal("-0.01")))
+    checkAnswer(sql("select -0.001"), Row(BigDecimal("-0.001")))
+  }
+
   test("external sorting updates peak execution memory") {
     withSQLConf((SQLConf.EXTERNAL_SORT.key, "true")) {
       val sc = sqlContext.sparkContext

From 7467b52ed07f174d93dfc4cb544dc4b69a2c2826 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 25 Aug 2015 15:19:41 -0700
Subject: [PATCH 420/633] [SPARK-10215] [SQL] Fix precision of division (follow
 the rule in Hive)

Follow the rule in Hive for decimal division. see https://github.com/apache/hive/blob/ac755ebe26361a4647d53db2a28500f71697b276/ql/src/java/org/apache/hadoop/hive/ql/udf/generic/GenericUDFOPDivide.java#L113

cc chenghao-intel

Author: Davies Liu <davies@databricks.com>

Closes #8415 from davies/decimal_div2.
---
 .../catalyst/analysis/HiveTypeCoercion.scala  | 10 ++++++--
 .../sql/catalyst/analysis/AnalysisSuite.scala |  9 +++----
 .../analysis/DecimalPrecisionSuite.scala      |  8 +++---
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 25 +++++++++++++++++--
 4 files changed, 39 insertions(+), 13 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index a1aa2a2b2c680..87c11abbad490 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -396,8 +396,14 @@ object HiveTypeCoercion {
             resultType)
 
         case Divide(e1 @ DecimalType.Expression(p1, s1), e2 @ DecimalType.Expression(p2, s2)) =>
-          val resultType = DecimalType.bounded(p1 - s1 + s2 + max(6, s1 + p2 + 1),
-            max(6, s1 + p2 + 1))
+          var intDig = min(DecimalType.MAX_SCALE, p1 - s1 + s2)
+          var decDig = min(DecimalType.MAX_SCALE, max(6, s1 + p2 + 1))
+          val diff = (intDig + decDig) - DecimalType.MAX_SCALE
+          if (diff > 0) {
+            decDig -= diff / 2 + 1
+            intDig = DecimalType.MAX_SCALE - decDig
+          }
+          val resultType = DecimalType.bounded(intDig + decDig, decDig)
           val widerType = widerDecimalType(p1, s1, p2, s2)
           CheckOverflow(Divide(promotePrecision(e1, widerType), promotePrecision(e2, widerType)),
             resultType)
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
index 1e0cc81dae974..820b336aac759 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala
@@ -17,15 +17,14 @@
 
 package org.apache.spark.sql.catalyst.analysis
 
+import org.apache.spark.sql.catalyst.dsl.expressions._
+import org.apache.spark.sql.catalyst.dsl.plans._
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.types._
-import org.apache.spark.sql.catalyst.SimpleCatalystConf
-import org.apache.spark.sql.catalyst.dsl.expressions._
-import org.apache.spark.sql.catalyst.dsl.plans._
 
 class AnalysisSuite extends AnalysisTest {
-  import TestRelations._
+  import org.apache.spark.sql.catalyst.analysis.TestRelations._
 
   test("union project *") {
     val plan = (1 to 100)
@@ -96,7 +95,7 @@ class AnalysisSuite extends AnalysisTest {
     assert(pl(1).dataType == DoubleType)
     assert(pl(2).dataType == DoubleType)
     // StringType will be promoted into Decimal(38, 18)
-    assert(pl(3).dataType == DecimalType(38, 29))
+    assert(pl(3).dataType == DecimalType(38, 22))
     assert(pl(4).dataType == DoubleType)
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
index fc11627da6fd1..b4ad618c23e39 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/DecimalPrecisionSuite.scala
@@ -136,10 +136,10 @@ class DecimalPrecisionSuite extends SparkFunSuite with BeforeAndAfter {
     checkType(Multiply(i, u), DecimalType(38, 18))
     checkType(Multiply(u, u), DecimalType(38, 36))
 
-    checkType(Divide(u, d1), DecimalType(38, 21))
-    checkType(Divide(u, d2), DecimalType(38, 24))
-    checkType(Divide(u, i), DecimalType(38, 29))
-    checkType(Divide(u, u), DecimalType(38, 38))
+    checkType(Divide(u, d1), DecimalType(38, 18))
+    checkType(Divide(u, d2), DecimalType(38, 19))
+    checkType(Divide(u, i), DecimalType(38, 23))
+    checkType(Divide(u, u), DecimalType(38, 18))
 
     checkType(Remainder(d1, u), DecimalType(19, 18))
     checkType(Remainder(d2, u), DecimalType(21, 18))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index aa07665c6b705..9e172b2c264cb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1622,9 +1622,30 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     checkAnswer(sql("select 10.3000 / 3.0"), Row(BigDecimal("3.4333333")))
     checkAnswer(sql("select 10.30000 / 30.0"), Row(BigDecimal("0.343333333")))
     checkAnswer(sql("select 10.300000000000000000 / 3.00000000000000000"),
-      Row(BigDecimal("3.4333333333333333333333333333333333333", new MathContext(38))))
+      Row(BigDecimal("3.433333333333333333333333333", new MathContext(38))))
     checkAnswer(sql("select 10.3000000000000000000 / 3.00000000000000000"),
-      Row(null))
+      Row(BigDecimal("3.4333333333333333333333333333", new MathContext(38))))
+  }
+
+  test("SPARK-10215 Div of Decimal returns null") {
+    val d = Decimal(1.12321)
+    val df = Seq((d, 1)).toDF("a", "b")
+
+    checkAnswer(
+      df.selectExpr("b * a / b"),
+      Seq(Row(d.toBigDecimal)))
+    checkAnswer(
+      df.selectExpr("b * a / b / b"),
+      Seq(Row(d.toBigDecimal)))
+    checkAnswer(
+      df.selectExpr("b * a + b"),
+      Seq(Row(BigDecimal(2.12321))))
+    checkAnswer(
+      df.selectExpr("b * a - b"),
+      Seq(Row(BigDecimal(0.12321))))
+    checkAnswer(
+      df.selectExpr("b * a * b"),
+      Seq(Row(d.toBigDecimal)))
   }
 
   test("precision smaller than scale") {

From 125205cdb35530cdb4a8fff3e1ee49cf4a299583 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Tue, 25 Aug 2015 17:39:20 -0700
Subject: [PATCH 421/633] [SPARK-9888] [MLLIB] User guide for new LDA features

 * Adds two new sections to LDA's user guide; one for each optimizer/model
 * Documents new features added to LDA (e.g. topXXXperXXX, asymmetric priors, hyperpam optimization)
 * Cleans up a TODO and sets a default parameter in LDA code

jkbradley hhbyyh

Author: Feynman Liang <fliang@databricks.com>

Closes #8254 from feynmanliang/SPARK-9888.
---
 docs/mllib-clustering.md                      | 135 +++++++++++++++---
 .../spark/mllib/clustering/LDAModel.scala     |   1 -
 .../spark/mllib/clustering/LDASuite.scala     |   1 +
 3 files changed, 117 insertions(+), 20 deletions(-)

diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index fd9ab258e196d..3fb35d3c50b06 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -438,28 +438,125 @@ sameModel = PowerIterationClusteringModel.load(sc, "myModelPath")
 is a topic model which infers topics from a collection of text documents.
 LDA can be thought of as a clustering algorithm as follows:
 
-* Topics correspond to cluster centers, and documents correspond to examples (rows) in a dataset.
-* Topics and documents both exist in a feature space, where feature vectors are vectors of word counts.
-* Rather than estimating a clustering using a traditional distance, LDA uses a function based
- on a statistical model of how text documents are generated.
-
-LDA takes in a collection of documents as vectors of word counts.
-It supports different inference algorithms via `setOptimizer` function. EMLDAOptimizer learns clustering using [expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
-on the likelihood function and yields comprehensive results, while OnlineLDAOptimizer uses iterative mini-batch sampling for [online variational inference](https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf) and is generally memory friendly. After fitting on the documents, LDA provides:
-
-* Topics: Inferred topics, each of which is a probability distribution over terms (words).
-* Topic distributions for documents: For each non empty document in the training set, LDA gives a probability distribution over topics. (EM only). Note that for empty documents, we don't create the topic distributions. (EM only)
+* Topics correspond to cluster centers, and documents correspond to
+examples (rows) in a dataset.
+* Topics and documents both exist in a feature space, where feature
+vectors are vectors of word counts (bag of words).
+* Rather than estimating a clustering using a traditional distance, LDA
+uses a function based on a statistical model of how text documents are
+generated.
+
+LDA supports different inference algorithms via `setOptimizer` function.
+`EMLDAOptimizer` learns clustering using
+[expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
+on the likelihood function and yields comprehensive results, while
+`OnlineLDAOptimizer` uses iterative mini-batch sampling for [online
+variational
+inference](https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf)
+and is generally memory friendly.
 
-LDA takes the following parameters:
+LDA takes in a collection of documents as vectors of word counts and the
+following parameters (set using the builder pattern):
 
 * `k`: Number of topics (i.e., cluster centers)
-* `maxIterations`: Limit on the number of iterations of EM used for learning
-* `docConcentration`: Hyperparameter for prior over documents' distributions over topics. Currently must be > 1, where larger values encourage smoother inferred distributions.
-* `topicConcentration`: Hyperparameter for prior over topics' distributions over terms (words). Currently must be > 1, where larger values encourage smoother inferred distributions.
-* `checkpointInterval`: If using checkpointing (set in the Spark configuration), this parameter specifies the frequency with which checkpoints will be created.  If `maxIterations` is large, using checkpointing can help reduce shuffle file sizes on disk and help with failure recovery.
-
-*Note*: LDA is a new feature with some missing functionality.  In particular, it does not yet
-support prediction on new documents, and it does not have a Python API.  These will be added in the future.
+* `optimizer`: Optimizer to use for learning the LDA model, either
+`EMLDAOptimizer` or `OnlineLDAOptimizer`
+* `docConcentration`: Dirichlet parameter for prior over documents'
+distributions over topics. Larger values encourage smoother inferred
+distributions.
+* `topicConcentration`: Dirichlet parameter for prior over topics'
+distributions over terms (words). Larger values encourage smoother
+inferred distributions.
+* `maxIterations`: Limit on the number of iterations.
+* `checkpointInterval`: If using checkpointing (set in the Spark
+configuration), this parameter specifies the frequency with which
+checkpoints will be created.  If `maxIterations` is large, using
+checkpointing can help reduce shuffle file sizes on disk and help with
+failure recovery.
+
+
+All of MLlib's LDA models support:
+
+* `describeTopics`: Returns topics as arrays of most important terms and
+term weights
+* `topicsMatrix`: Returns a `vocabSize` by `k` matrix where each column
+is a topic
+
+*Note*: LDA is still an experimental feature under active development.
+As a result, certain features are only available in one of the two
+optimizers / models generated by the optimizer. Currently, a distributed
+model can be converted into a local model, but not vice-versa.
+
+The following discussion will describe each optimizer/model pair
+separately.
+
+**Expectation Maximization**
+
+Implemented in
+[`EMLDAOptimizer`](api/scala/index.html#org.apache.spark.mllib.clustering.EMLDAOptimizer)
+and
+[`DistributedLDAModel`](api/scala/index.html#org.apache.spark.mllib.clustering.DistributedLDAModel).
+
+For the parameters provided to `LDA`:
+
+* `docConcentration`: Only symmetric priors are supported, so all values
+in the provided `k`-dimensional vector must be identical. All values
+must also be $> 1.0$. Providing `Vector(-1)` results in default behavior
+(uniform `k` dimensional vector with value $(50 / k) + 1$
+* `topicConcentration`: Only symmetric priors supported. Values must be
+$> 1.0$. Providing `-1` results in defaulting to a value of $0.1 + 1$.
+* `maxIterations`: The maximum number of EM iterations.
+
+`EMLDAOptimizer` produces a `DistributedLDAModel`, which stores not only
+the inferred topics but also the full training corpus and topic
+distributions for each document in the training corpus. A
+`DistributedLDAModel` supports:
+
+ * `topTopicsPerDocument`: The top topics and their weights for
+ each document in the training corpus
+ * `topDocumentsPerTopic`: The top documents for each topic and
+ the corresponding weight of the topic in the documents.
+ * `logPrior`: log probability of the estimated topics and
+ document-topic distributions given the hyperparameters
+ `docConcentration` and `topicConcentration`
+ * `logLikelihood`: log likelihood of the training corpus, given the
+ inferred topics and document-topic distributions
+
+**Online Variational Bayes**
+
+Implemented in
+[`OnlineLDAOptimizer`](api/scala/org/apache/spark/mllib/clustering/OnlineLDAOptimizer.html)
+and
+[`LocalLDAModel`](api/scala/org/apache/spark/mllib/clustering/LocalLDAModel.html).
+
+For the parameters provided to `LDA`:
+
+* `docConcentration`: Asymmetric priors can be used by passing in a
+vector with values equal to the Dirichlet parameter in each of the `k`
+dimensions. Values should be $>= 0$. Providing `Vector(-1)` results in
+default behavior (uniform `k` dimensional vector with value $(1.0 / k)$)
+* `topicConcentration`: Only symmetric priors supported. Values must be
+$>= 0$. Providing `-1` results in defaulting to a value of $(1.0 / k)$.
+* `maxIterations`: Maximum number of minibatches to submit.
+
+In addition, `OnlineLDAOptimizer` accepts the following parameters:
+
+* `miniBatchFraction`: Fraction of corpus sampled and used at each
+iteration
+* `optimizeDocConcentration`: If set to true, performs maximum-likelihood
+estimation of the hyperparameter `docConcentration` (aka `alpha`)
+after each minibatch and sets the optimized `docConcentration` in the
+returned `LocalLDAModel`
+* `tau0` and `kappa`: Used for learning-rate decay, which is computed by
+$(\tau_0 + iter)^{-\kappa}$ where $iter$ is the current number of iterations.
+
+`OnlineLDAOptimizer` produces a `LocalLDAModel`, which only stores the
+inferred topics. A `LocalLDAModel` supports:
+
+* `logLikelihood(documents)`: Calculates a lower bound on the provided
+`documents` given the inferred topics.
+* `logPerplexity(documents)`: Calculates an upper bound on the
+perplexity of the provided `documents` given the inferred topics.
 
 **Examples**
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 667374a2bc418..432bbedc8d6f8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -435,7 +435,6 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
       }
       val topicsMat = Matrices.fromBreeze(brzTopics)
 
-      // TODO: initialize with docConcentration, topicConcentration, and gammaShape after SPARK-9940
       new LocalLDAModel(topicsMat, docConcentration, topicConcentration, gammaShape)
     }
   }
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
index 746a76a7e5fa1..37fb69d68f6be 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/LDASuite.scala
@@ -68,6 +68,7 @@ class LDASuite extends SparkFunSuite with MLlibTestSparkContext {
     // Train a model
     val lda = new LDA()
     lda.setK(k)
+      .setOptimizer(new EMLDAOptimizer)
       .setDocConcentration(topicSmoothing)
       .setTopicConcentration(termSmoothing)
       .setMaxIterations(5)

From 8668ead2e7097b9591069599fbfccf67c53db659 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 18:17:54 -0700
Subject: [PATCH 422/633] [SPARK-10233] [MLLIB] update since version in
 mllib.evaluation

Same as #8421 but for `mllib.evaluation`.

cc avulanov

Author: Xiangrui Meng <meng@databricks.com>

Closes #8423 from mengxr/SPARK-10233.
---
 .../evaluation/BinaryClassificationMetrics.scala     |  8 ++++----
 .../spark/mllib/evaluation/MulticlassMetrics.scala   | 11 ++++++++++-
 .../spark/mllib/evaluation/MultilabelMetrics.scala   | 12 +++++++++++-
 .../spark/mllib/evaluation/RegressionMetrics.scala   |  3 ++-
 4 files changed, 27 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
index 76ae847921f44..508fe532b1306 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetrics.scala
@@ -42,11 +42,11 @@ import org.apache.spark.sql.DataFrame
  *                be smaller as a result, meaning there may be an extra sample at
  *                partition boundaries.
  */
-@Since("1.3.0")
+@Since("1.0.0")
 @Experimental
-class BinaryClassificationMetrics(
-    val scoreAndLabels: RDD[(Double, Double)],
-    val numBins: Int) extends Logging {
+class BinaryClassificationMetrics @Since("1.3.0") (
+    @Since("1.3.0") val scoreAndLabels: RDD[(Double, Double)],
+    @Since("1.3.0") val numBins: Int) extends Logging {
 
   require(numBins >= 0, "numBins must be nonnegative")
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
index 02e89d921033c..00e837661dfc2 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MulticlassMetrics.scala
@@ -33,7 +33,7 @@ import org.apache.spark.sql.DataFrame
  */
 @Since("1.1.0")
 @Experimental
-class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
+class MulticlassMetrics @Since("1.1.0") (predictionAndLabels: RDD[(Double, Double)]) {
 
   /**
    * An auxiliary constructor taking a DataFrame.
@@ -140,6 +140,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns precision
    */
+  @Since("1.1.0")
   lazy val precision: Double = tpByClass.values.sum.toDouble / labelCount
 
   /**
@@ -148,23 +149,27 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
    * because sum of all false positives is equal to sum
    * of all false negatives)
    */
+  @Since("1.1.0")
   lazy val recall: Double = precision
 
   /**
    * Returns f-measure
    * (equals to precision and recall because precision equals recall)
    */
+  @Since("1.1.0")
   lazy val fMeasure: Double = precision
 
   /**
    * Returns weighted true positive rate
    * (equals to precision, recall and f-measure)
    */
+  @Since("1.1.0")
   lazy val weightedTruePositiveRate: Double = weightedRecall
 
   /**
    * Returns weighted false positive rate
    */
+  @Since("1.1.0")
   lazy val weightedFalsePositiveRate: Double = labelCountByClass.map { case (category, count) =>
     falsePositiveRate(category) * count.toDouble / labelCount
   }.sum
@@ -173,6 +178,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
    * Returns weighted averaged recall
    * (equals to precision, recall and f-measure)
    */
+  @Since("1.1.0")
   lazy val weightedRecall: Double = labelCountByClass.map { case (category, count) =>
     recall(category) * count.toDouble / labelCount
   }.sum
@@ -180,6 +186,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns weighted averaged precision
    */
+  @Since("1.1.0")
   lazy val weightedPrecision: Double = labelCountByClass.map { case (category, count) =>
     precision(category) * count.toDouble / labelCount
   }.sum
@@ -196,6 +203,7 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns weighted averaged f1-measure
    */
+  @Since("1.1.0")
   lazy val weightedFMeasure: Double = labelCountByClass.map { case (category, count) =>
     fMeasure(category, 1.0) * count.toDouble / labelCount
   }.sum
@@ -203,5 +211,6 @@ class MulticlassMetrics(predictionAndLabels: RDD[(Double, Double)]) {
   /**
    * Returns the sequence of labels in ascending order
    */
+  @Since("1.1.0")
   lazy val labels: Array[Double] = tpByClass.keys.toArray.sorted
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
index a0a8d9c56847b..c100b3c9ec14a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/MultilabelMetrics.scala
@@ -28,7 +28,7 @@ import org.apache.spark.sql.DataFrame
  * both are non-null Arrays, each with unique elements.
  */
 @Since("1.2.0")
-class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) {
+class MultilabelMetrics @Since("1.2.0") (predictionAndLabels: RDD[(Array[Double], Array[Double])]) {
 
   /**
    * An auxiliary constructor taking a DataFrame.
@@ -46,6 +46,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
    * Returns subset accuracy
    * (for equal sets of labels)
    */
+  @Since("1.2.0")
   lazy val subsetAccuracy: Double = predictionAndLabels.filter { case (predictions, labels) =>
     predictions.deep == labels.deep
   }.count().toDouble / numDocs
@@ -53,6 +54,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns accuracy
    */
+  @Since("1.2.0")
   lazy val accuracy: Double = predictionAndLabels.map { case (predictions, labels) =>
     labels.intersect(predictions).size.toDouble /
       (labels.size + predictions.size - labels.intersect(predictions).size)}.sum / numDocs
@@ -61,6 +63,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns Hamming-loss
    */
+  @Since("1.2.0")
   lazy val hammingLoss: Double = predictionAndLabels.map { case (predictions, labels) =>
     labels.size + predictions.size - 2 * labels.intersect(predictions).size
   }.sum / (numDocs * numLabels)
@@ -68,6 +71,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns document-based precision averaged by the number of documents
    */
+  @Since("1.2.0")
   lazy val precision: Double = predictionAndLabels.map { case (predictions, labels) =>
     if (predictions.size > 0) {
       predictions.intersect(labels).size.toDouble / predictions.size
@@ -79,6 +83,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns document-based recall averaged by the number of documents
    */
+  @Since("1.2.0")
   lazy val recall: Double = predictionAndLabels.map { case (predictions, labels) =>
     labels.intersect(predictions).size.toDouble / labels.size
   }.sum / numDocs
@@ -86,6 +91,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
   /**
    * Returns document-based f1-measure averaged by the number of documents
    */
+  @Since("1.2.0")
   lazy val f1Measure: Double = predictionAndLabels.map { case (predictions, labels) =>
     2.0 * predictions.intersect(labels).size / (predictions.size + labels.size)
   }.sum / numDocs
@@ -143,6 +149,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
    * Returns micro-averaged label-based precision
    * (equals to micro-averaged document-based precision)
    */
+  @Since("1.2.0")
   lazy val microPrecision: Double = {
     val sumFp = fpPerClass.foldLeft(0L){ case(cum, (_, fp)) => cum + fp}
     sumTp.toDouble / (sumTp + sumFp)
@@ -152,6 +159,7 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
    * Returns micro-averaged label-based recall
    * (equals to micro-averaged document-based recall)
    */
+  @Since("1.2.0")
   lazy val microRecall: Double = {
     val sumFn = fnPerClass.foldLeft(0.0){ case(cum, (_, fn)) => cum + fn}
     sumTp.toDouble / (sumTp + sumFn)
@@ -161,10 +169,12 @@ class MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]
    * Returns micro-averaged label-based f1-measure
    * (equals to micro-averaged document-based f1-measure)
    */
+  @Since("1.2.0")
   lazy val microF1Measure: Double = 2.0 * sumTp / (2 * sumTp + sumFnClass + sumFpClass)
 
   /**
    * Returns the sequence of labels in ascending order
    */
+  @Since("1.2.0")
   lazy val labels: Array[Double] = tpPerClass.keys.toArray.sorted
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
index 36a6c357c3897..799ebb980ef01 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala
@@ -32,7 +32,8 @@ import org.apache.spark.sql.DataFrame
  */
 @Since("1.2.0")
 @Experimental
-class RegressionMetrics(predictionAndObservations: RDD[(Double, Double)]) extends Logging {
+class RegressionMetrics @Since("1.2.0") (
+    predictionAndObservations: RDD[(Double, Double)]) extends Logging {
 
   /**
    * An auxiliary constructor taking a DataFrame.

From ab431f8a970b85fba34ccb506c0f8815e55c63bf Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 20:07:56 -0700
Subject: [PATCH 423/633] [SPARK-10238] [MLLIB] update since versions in
 mllib.linalg

Same as #8421 but for `mllib.linalg`.

cc dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8440 from mengxr/SPARK-10238 and squashes the following commits:

b38437e [Xiangrui Meng] update since versions in mllib.linalg
---
 .../apache/spark/mllib/linalg/Matrices.scala  | 44 ++++++++++++-------
 .../linalg/SingularValueDecomposition.scala   |  1 +
 .../apache/spark/mllib/linalg/Vectors.scala   | 25 ++++++++---
 .../linalg/distributed/BlockMatrix.scala      | 10 +++--
 .../linalg/distributed/CoordinateMatrix.scala |  4 +-
 .../distributed/DistributedMatrix.scala       |  2 +
 .../linalg/distributed/IndexedRowMatrix.scala |  4 +-
 .../mllib/linalg/distributed/RowMatrix.scala  |  5 ++-
 8 files changed, 64 insertions(+), 31 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
index 28b5b4637bf17..c02ba426fcc3a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Matrices.scala
@@ -32,18 +32,23 @@ import org.apache.spark.sql.types._
  * Trait for a local matrix.
  */
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
+@Since("1.0.0")
 sealed trait Matrix extends Serializable {
 
   /** Number of rows. */
+  @Since("1.0.0")
   def numRows: Int
 
   /** Number of columns. */
+  @Since("1.0.0")
   def numCols: Int
 
   /** Flag that keeps track whether the matrix is transposed or not. False by default. */
+  @Since("1.3.0")
   val isTransposed: Boolean = false
 
   /** Converts to a dense array in column major. */
+  @Since("1.0.0")
   def toArray: Array[Double] = {
     val newArray = new Array[Double](numRows * numCols)
     foreachActive { (i, j, v) =>
@@ -56,6 +61,7 @@ sealed trait Matrix extends Serializable {
   private[mllib] def toBreeze: BM[Double]
 
   /** Gets the (i, j)-th element. */
+  @Since("1.3.0")
   def apply(i: Int, j: Int): Double
 
   /** Return the index for the (i, j)-th element in the backing array. */
@@ -65,12 +71,15 @@ sealed trait Matrix extends Serializable {
   private[mllib] def update(i: Int, j: Int, v: Double): Unit
 
   /** Get a deep copy of the matrix. */
+  @Since("1.2.0")
   def copy: Matrix
 
   /** Transpose the Matrix. Returns a new `Matrix` instance sharing the same underlying data. */
+  @Since("1.3.0")
   def transpose: Matrix
 
   /** Convenience method for `Matrix`-`DenseMatrix` multiplication. */
+  @Since("1.2.0")
   def multiply(y: DenseMatrix): DenseMatrix = {
     val C: DenseMatrix = DenseMatrix.zeros(numRows, y.numCols)
     BLAS.gemm(1.0, this, y, 0.0, C)
@@ -78,11 +87,13 @@ sealed trait Matrix extends Serializable {
   }
 
   /** Convenience method for `Matrix`-`DenseVector` multiplication. For binary compatibility. */
+  @Since("1.2.0")
   def multiply(y: DenseVector): DenseVector = {
     multiply(y.asInstanceOf[Vector])
   }
 
   /** Convenience method for `Matrix`-`Vector` multiplication. */
+  @Since("1.4.0")
   def multiply(y: Vector): DenseVector = {
     val output = new DenseVector(new Array[Double](numRows))
     BLAS.gemv(1.0, this, y, 0.0, output)
@@ -93,6 +104,7 @@ sealed trait Matrix extends Serializable {
   override def toString: String = toBreeze.toString()
 
   /** A human readable representation of the matrix with maximum lines and width */
+  @Since("1.4.0")
   def toString(maxLines: Int, maxLineWidth: Int): String = toBreeze.toString(maxLines, maxLineWidth)
 
   /** Map the values of this matrix using a function. Generates a new matrix. Performs the
@@ -118,11 +130,13 @@ sealed trait Matrix extends Serializable {
   /**
    * Find the number of non-zero active values.
    */
+  @Since("1.5.0")
   def numNonzeros: Int
 
   /**
    * Find the number of values stored explicitly. These values can be zero as well.
    */
+  @Since("1.5.0")
   def numActives: Int
 }
 
@@ -230,11 +244,11 @@ private[spark] class MatrixUDT extends UserDefinedType[Matrix] {
  */
 @Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
-class DenseMatrix(
-    val numRows: Int,
-    val numCols: Int,
-    val values: Array[Double],
-    override val isTransposed: Boolean) extends Matrix {
+class DenseMatrix @Since("1.3.0") (
+    @Since("1.0.0") val numRows: Int,
+    @Since("1.0.0") val numCols: Int,
+    @Since("1.0.0") val values: Array[Double],
+    @Since("1.3.0") override val isTransposed: Boolean) extends Matrix {
 
   require(values.length == numRows * numCols, "The number of values supplied doesn't match the " +
     s"size of the matrix! values.length: ${values.length}, numRows * numCols: ${numRows * numCols}")
@@ -254,7 +268,7 @@ class DenseMatrix(
    * @param numCols number of columns
    * @param values matrix entries in column major
    */
-  @Since("1.3.0")
+  @Since("1.0.0")
   def this(numRows: Int, numCols: Int, values: Array[Double]) =
     this(numRows, numCols, values, false)
 
@@ -491,13 +505,13 @@ object DenseMatrix {
  */
 @Since("1.2.0")
 @SQLUserDefinedType(udt = classOf[MatrixUDT])
-class SparseMatrix(
-    val numRows: Int,
-    val numCols: Int,
-    val colPtrs: Array[Int],
-    val rowIndices: Array[Int],
-    val values: Array[Double],
-    override val isTransposed: Boolean) extends Matrix {
+class SparseMatrix @Since("1.3.0") (
+    @Since("1.2.0") val numRows: Int,
+    @Since("1.2.0") val numCols: Int,
+    @Since("1.2.0") val colPtrs: Array[Int],
+    @Since("1.2.0") val rowIndices: Array[Int],
+    @Since("1.2.0") val values: Array[Double],
+    @Since("1.3.0") override val isTransposed: Boolean) extends Matrix {
 
   require(values.length == rowIndices.length, "The number of row indices and values don't match! " +
     s"values.length: ${values.length}, rowIndices.length: ${rowIndices.length}")
@@ -527,7 +541,7 @@ class SparseMatrix(
    *                   order for each column
    * @param values non-zero matrix entries in column major
    */
-  @Since("1.3.0")
+  @Since("1.2.0")
   def this(
       numRows: Int,
       numCols: Int,
@@ -549,8 +563,6 @@ class SparseMatrix(
      }
   }
 
-  /**
-   */
   @Since("1.3.0")
   override def apply(i: Int, j: Int): Double = {
     val ind = index(i, j)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
index a37aca99d5e72..4dcf8f28f2023 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/SingularValueDecomposition.scala
@@ -31,6 +31,7 @@ case class SingularValueDecomposition[UType, VType](U: UType, s: Vector, V: VTyp
  * :: Experimental ::
  * Represents QR factors.
  */
+@Since("1.5.0")
 @Experimental
 case class QRDecomposition[QType, RType](Q: QType, R: RType)
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 3d577edbe23e1..06ebb15869909 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -38,16 +38,19 @@ import org.apache.spark.sql.types._
  * Note: Users should not implement this interface.
  */
 @SQLUserDefinedType(udt = classOf[VectorUDT])
+@Since("1.0.0")
 sealed trait Vector extends Serializable {
 
   /**
    * Size of the vector.
    */
+  @Since("1.0.0")
   def size: Int
 
   /**
    * Converts the instance to a double array.
    */
+  @Since("1.0.0")
   def toArray: Array[Double]
 
   override def equals(other: Any): Boolean = {
@@ -99,11 +102,13 @@ sealed trait Vector extends Serializable {
    * Gets the value of the ith element.
    * @param i index
    */
+  @Since("1.1.0")
   def apply(i: Int): Double = toBreeze(i)
 
   /**
    * Makes a deep copy of this vector.
    */
+  @Since("1.1.0")
   def copy: Vector = {
     throw new NotImplementedError(s"copy is not implemented for ${this.getClass}.")
   }
@@ -121,26 +126,31 @@ sealed trait Vector extends Serializable {
    * Number of active entries.  An "active entry" is an element which is explicitly stored,
    * regardless of its value.  Note that inactive entries have value 0.
    */
+  @Since("1.4.0")
   def numActives: Int
 
   /**
    * Number of nonzero elements. This scans all active values and count nonzeros.
    */
+  @Since("1.4.0")
   def numNonzeros: Int
 
   /**
    * Converts this vector to a sparse vector with all explicit zeros removed.
    */
+  @Since("1.4.0")
   def toSparse: SparseVector
 
   /**
    * Converts this vector to a dense vector.
    */
+  @Since("1.4.0")
   def toDense: DenseVector = new DenseVector(this.toArray)
 
   /**
    * Returns a vector in either dense or sparse format, whichever uses less storage.
    */
+  @Since("1.4.0")
   def compressed: Vector = {
     val nnz = numNonzeros
     // A dense vector needs 8 * size + 8 bytes, while a sparse vector needs 12 * nnz + 20 bytes.
@@ -155,6 +165,7 @@ sealed trait Vector extends Serializable {
    * Find the index of a maximal element.  Returns the first maximal element in case of a tie.
    * Returns -1 if vector has length 0.
    */
+  @Since("1.5.0")
   def argmax: Int
 }
 
@@ -532,7 +543,8 @@ object Vectors {
  */
 @Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[VectorUDT])
-class DenseVector(val values: Array[Double]) extends Vector {
+class DenseVector @Since("1.0.0") (
+    @Since("1.0.0") val values: Array[Double]) extends Vector {
 
   @Since("1.0.0")
   override def size: Int = values.length
@@ -632,7 +644,9 @@ class DenseVector(val values: Array[Double]) extends Vector {
 
 @Since("1.3.0")
 object DenseVector {
+
   /** Extracts the value array from a dense vector. */
+  @Since("1.3.0")
   def unapply(dv: DenseVector): Option[Array[Double]] = Some(dv.values)
 }
 
@@ -645,10 +659,10 @@ object DenseVector {
  */
 @Since("1.0.0")
 @SQLUserDefinedType(udt = classOf[VectorUDT])
-class SparseVector(
-    override val size: Int,
-    val indices: Array[Int],
-    val values: Array[Double]) extends Vector {
+class SparseVector @Since("1.0.0") (
+    @Since("1.0.0") override val size: Int,
+    @Since("1.0.0") val indices: Array[Int],
+    @Since("1.0.0") val values: Array[Double]) extends Vector {
 
   require(indices.length == values.length, "Sparse vectors require that the dimension of the" +
     s" indices match the dimension of the values. You provided ${indices.length} indices and " +
@@ -819,6 +833,7 @@ class SparseVector(
 
 @Since("1.3.0")
 object SparseVector {
+  @Since("1.3.0")
   def unapply(sv: SparseVector): Option[(Int, Array[Int], Array[Double])] =
     Some((sv.size, sv.indices, sv.values))
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
index 94376c24a7ac6..a33b6137cf9cc 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
@@ -131,10 +131,10 @@ private[mllib] object GridPartitioner {
  */
 @Since("1.3.0")
 @Experimental
-class BlockMatrix(
-    val blocks: RDD[((Int, Int), Matrix)],
-    val rowsPerBlock: Int,
-    val colsPerBlock: Int,
+class BlockMatrix @Since("1.3.0") (
+    @Since("1.3.0") val blocks: RDD[((Int, Int), Matrix)],
+    @Since("1.3.0") val rowsPerBlock: Int,
+    @Since("1.3.0") val colsPerBlock: Int,
     private var nRows: Long,
     private var nCols: Long) extends DistributedMatrix with Logging {
 
@@ -171,7 +171,9 @@ class BlockMatrix(
     nCols
   }
 
+  @Since("1.3.0")
   val numRowBlocks = math.ceil(numRows() * 1.0 / rowsPerBlock).toInt
+  @Since("1.3.0")
   val numColBlocks = math.ceil(numCols() * 1.0 / colsPerBlock).toInt
 
   private[mllib] def createPartitioner(): GridPartitioner =
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
index 4bb27ec840902..644f293d88a75 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.scala
@@ -46,8 +46,8 @@ case class MatrixEntry(i: Long, j: Long, value: Double)
  */
 @Since("1.0.0")
 @Experimental
-class CoordinateMatrix(
-    val entries: RDD[MatrixEntry],
+class CoordinateMatrix @Since("1.0.0") (
+    @Since("1.0.0") val entries: RDD[MatrixEntry],
     private var nRows: Long,
     private var nCols: Long) extends DistributedMatrix {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
index e51327ebb7b58..db3433a5e2456 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/DistributedMatrix.scala
@@ -28,9 +28,11 @@ import org.apache.spark.annotation.Since
 trait DistributedMatrix extends Serializable {
 
   /** Gets or computes the number of rows. */
+  @Since("1.0.0")
   def numRows(): Long
 
   /** Gets or computes the number of columns. */
+  @Since("1.0.0")
   def numCols(): Long
 
   /** Collects data and assembles a local dense breeze matrix (for test only). */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
index 6d2c05a47d049..b20ea0dc50da5 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.scala
@@ -45,8 +45,8 @@ case class IndexedRow(index: Long, vector: Vector)
  */
 @Since("1.0.0")
 @Experimental
-class IndexedRowMatrix(
-    val rows: RDD[IndexedRow],
+class IndexedRowMatrix @Since("1.0.0") (
+    @Since("1.0.0") val rows: RDD[IndexedRow],
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix {
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 78036eba5c3e6..9a423ddafdc09 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -47,8 +47,8 @@ import org.apache.spark.storage.StorageLevel
  */
 @Since("1.0.0")
 @Experimental
-class RowMatrix(
-    val rows: RDD[Vector],
+class RowMatrix @Since("1.0.0") (
+    @Since("1.0.0") val rows: RDD[Vector],
     private var nRows: Long,
     private var nCols: Int) extends DistributedMatrix with Logging {
 
@@ -519,6 +519,7 @@ class RowMatrix(
    * @param computeQ whether to computeQ
    * @return QRDecomposition(Q, R), Q = null if computeQ = false.
    */
+  @Since("1.5.0")
   def tallSkinnyQR(computeQ: Boolean = false): QRDecomposition[RowMatrix, Matrix] = {
     val col = numCols().toInt
     // split rows horizontally into smaller matrices, and compute QR for each of them

From c3a54843c0c8a14059da4e6716c1ad45c69bbe6c Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 22:31:23 -0700
Subject: [PATCH 424/633] [SPARK-10240] [SPARK-10242] [MLLIB] update since
 versions in mlilb.random and mllib.stat

The same as #8241 but for `mllib.stat` and `mllib.random`.

cc feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8439 from mengxr/SPARK-10242.
---
 .../mllib/random/RandomDataGenerator.scala    | 43 ++++++++++--
 .../spark/mllib/random/RandomRDDs.scala       | 69 ++++++++++++++++---
 .../distribution/MultivariateGaussian.scala   |  6 +-
 .../spark/mllib/stat/test/TestResult.scala    | 24 ++++---
 4 files changed, 117 insertions(+), 25 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
index 9349ecaa13f56..a2d85a68cd327 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.random
 import org.apache.commons.math3.distribution.{ExponentialDistribution,
   GammaDistribution, LogNormalDistribution, PoissonDistribution}
 
-import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.annotation.{Since, DeveloperApi}
 import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom}
 
 /**
@@ -28,17 +28,20 @@ import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom}
  * Trait for random data generators that generate i.i.d. data.
  */
 @DeveloperApi
+@Since("1.1.0")
 trait RandomDataGenerator[T] extends Pseudorandom with Serializable {
 
   /**
    * Returns an i.i.d. sample as a generic type from an underlying distribution.
    */
+  @Since("1.1.0")
   def nextValue(): T
 
   /**
    * Returns a copy of the RandomDataGenerator with a new instance of the rng object used in the
    * class when applicable for non-locking concurrent usage.
    */
+  @Since("1.1.0")
   def copy(): RandomDataGenerator[T]
 }
 
@@ -47,17 +50,21 @@ trait RandomDataGenerator[T] extends Pseudorandom with Serializable {
  * Generates i.i.d. samples from U[0.0, 1.0]
  */
 @DeveloperApi
+@Since("1.1.0")
 class UniformGenerator extends RandomDataGenerator[Double] {
 
   // XORShiftRandom for better performance. Thread safety isn't necessary here.
   private val random = new XORShiftRandom()
 
+  @Since("1.1.0")
   override def nextValue(): Double = {
     random.nextDouble()
   }
 
+  @Since("1.1.0")
   override def setSeed(seed: Long): Unit = random.setSeed(seed)
 
+  @Since("1.1.0")
   override def copy(): UniformGenerator = new UniformGenerator()
 }
 
@@ -66,17 +73,21 @@ class UniformGenerator extends RandomDataGenerator[Double] {
  * Generates i.i.d. samples from the standard normal distribution.
  */
 @DeveloperApi
+@Since("1.1.0")
 class StandardNormalGenerator extends RandomDataGenerator[Double] {
 
   // XORShiftRandom for better performance. Thread safety isn't necessary here.
   private val random = new XORShiftRandom()
 
+  @Since("1.1.0")
   override def nextValue(): Double = {
       random.nextGaussian()
   }
 
+  @Since("1.1.0")
   override def setSeed(seed: Long): Unit = random.setSeed(seed)
 
+  @Since("1.1.0")
   override def copy(): StandardNormalGenerator = new StandardNormalGenerator()
 }
 
@@ -87,16 +98,21 @@ class StandardNormalGenerator extends RandomDataGenerator[Double] {
  * @param mean mean for the Poisson distribution.
  */
 @DeveloperApi
-class PoissonGenerator(val mean: Double) extends RandomDataGenerator[Double] {
+@Since("1.1.0")
+class PoissonGenerator @Since("1.1.0") (
+    @Since("1.1.0") val mean: Double) extends RandomDataGenerator[Double] {
 
   private val rng = new PoissonDistribution(mean)
 
+  @Since("1.1.0")
   override def nextValue(): Double = rng.sample()
 
+  @Since("1.1.0")
   override def setSeed(seed: Long) {
     rng.reseedRandomGenerator(seed)
   }
 
+  @Since("1.1.0")
   override def copy(): PoissonGenerator = new PoissonGenerator(mean)
 }
 
@@ -107,16 +123,21 @@ class PoissonGenerator(val mean: Double) extends RandomDataGenerator[Double] {
  * @param mean mean for the exponential distribution.
  */
 @DeveloperApi
-class ExponentialGenerator(val mean: Double) extends RandomDataGenerator[Double] {
+@Since("1.3.0")
+class ExponentialGenerator @Since("1.3.0") (
+    @Since("1.3.0") val mean: Double) extends RandomDataGenerator[Double] {
 
   private val rng = new ExponentialDistribution(mean)
 
+  @Since("1.3.0")
   override def nextValue(): Double = rng.sample()
 
+  @Since("1.3.0")
   override def setSeed(seed: Long) {
     rng.reseedRandomGenerator(seed)
   }
 
+  @Since("1.3.0")
   override def copy(): ExponentialGenerator = new ExponentialGenerator(mean)
 }
 
@@ -128,16 +149,22 @@ class ExponentialGenerator(val mean: Double) extends RandomDataGenerator[Double]
  * @param scale scale for the gamma distribution
  */
 @DeveloperApi
-class GammaGenerator(val shape: Double, val scale: Double) extends RandomDataGenerator[Double] {
+@Since("1.3.0")
+class GammaGenerator @Since("1.3.0") (
+    @Since("1.3.0") val shape: Double,
+    @Since("1.3.0") val scale: Double) extends RandomDataGenerator[Double] {
 
   private val rng = new GammaDistribution(shape, scale)
 
+  @Since("1.3.0")
   override def nextValue(): Double = rng.sample()
 
+  @Since("1.3.0")
   override def setSeed(seed: Long) {
     rng.reseedRandomGenerator(seed)
   }
 
+  @Since("1.3.0")
   override def copy(): GammaGenerator = new GammaGenerator(shape, scale)
 }
 
@@ -150,15 +177,21 @@ class GammaGenerator(val shape: Double, val scale: Double) extends RandomDataGen
  * @param std standard deviation for the log normal distribution
  */
 @DeveloperApi
-class LogNormalGenerator(val mean: Double, val std: Double) extends RandomDataGenerator[Double] {
+@Since("1.3.0")
+class LogNormalGenerator @Since("1.3.0") (
+    @Since("1.3.0") val mean: Double,
+    @Since("1.3.0") val std: Double) extends RandomDataGenerator[Double] {
 
   private val rng = new LogNormalDistribution(mean, std)
 
+  @Since("1.3.0")
   override def nextValue(): Double = rng.sample()
 
+  @Since("1.3.0")
   override def setSeed(seed: Long) {
     rng.reseedRandomGenerator(seed)
   }
 
+  @Since("1.3.0")
   override def copy(): LogNormalGenerator = new LogNormalGenerator(mean, std)
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
index 174d5e0f6c9f0..4dd5ea214d678 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomRDDs.scala
@@ -20,7 +20,7 @@ package org.apache.spark.mllib.random
 import scala.reflect.ClassTag
 
 import org.apache.spark.SparkContext
-import org.apache.spark.annotation.{DeveloperApi, Experimental}
+import org.apache.spark.annotation.{DeveloperApi, Experimental, Since}
 import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD, JavaSparkContext}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.rdd.{RandomRDD, RandomVectorRDD}
@@ -32,6 +32,7 @@ import org.apache.spark.util.Utils
  * Generator methods for creating RDDs comprised of `i.i.d.` samples from some distribution.
  */
 @Experimental
+@Since("1.1.0")
 object RandomRDDs {
 
   /**
@@ -46,6 +47,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ `U(0.0, 1.0)`.
    */
+  @Since("1.1.0")
   def uniformRDD(
       sc: SparkContext,
       size: Long,
@@ -58,6 +60,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#uniformRDD]].
    */
+  @Since("1.1.0")
   def uniformJavaRDD(
       jsc: JavaSparkContext,
       size: Long,
@@ -69,6 +72,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#uniformJavaRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def uniformJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size, numPartitions))
   }
@@ -76,6 +80,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#uniformJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def uniformJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(uniformRDD(jsc.sc, size))
   }
@@ -92,6 +97,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ N(0.0, 1.0).
    */
+  @Since("1.1.0")
   def normalRDD(
       sc: SparkContext,
       size: Long,
@@ -104,6 +110,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#normalRDD]].
    */
+  @Since("1.1.0")
   def normalJavaRDD(
       jsc: JavaSparkContext,
       size: Long,
@@ -115,6 +122,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#normalJavaRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def normalJavaRDD(jsc: JavaSparkContext, size: Long, numPartitions: Int): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size, numPartitions))
   }
@@ -122,6 +130,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#normalJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def normalJavaRDD(jsc: JavaSparkContext, size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(normalRDD(jsc.sc, size))
   }
@@ -137,6 +146,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
    */
+  @Since("1.1.0")
   def poissonRDD(
       sc: SparkContext,
       mean: Double,
@@ -150,6 +160,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#poissonRDD]].
    */
+  @Since("1.1.0")
   def poissonJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -162,6 +173,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#poissonJavaRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def poissonJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -173,6 +185,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#poissonJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def poissonJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(poissonRDD(jsc.sc, mean, size))
   }
@@ -188,6 +201,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
    */
+  @Since("1.3.0")
   def exponentialRDD(
       sc: SparkContext,
       mean: Double,
@@ -201,6 +215,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#exponentialRDD]].
    */
+  @Since("1.3.0")
   def exponentialJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -213,6 +228,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#exponentialJavaRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def exponentialJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -224,6 +240,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#exponentialJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.3.0")
   def exponentialJavaRDD(jsc: JavaSparkContext, mean: Double, size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(exponentialRDD(jsc.sc, mean, size))
   }
@@ -240,6 +257,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
    */
+  @Since("1.3.0")
   def gammaRDD(
       sc: SparkContext,
       shape: Double,
@@ -254,6 +272,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#gammaRDD]].
    */
+  @Since("1.3.0")
   def gammaJavaRDD(
       jsc: JavaSparkContext,
       shape: Double,
@@ -267,6 +286,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#gammaJavaRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def gammaJavaRDD(
       jsc: JavaSparkContext,
       shape: Double,
@@ -279,11 +299,12 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#gammaJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.3.0")
   def gammaJavaRDD(
-    jsc: JavaSparkContext,
-    shape: Double,
-    scale: Double,
-    size: Long): JavaDoubleRDD = {
+      jsc: JavaSparkContext,
+      shape: Double,
+      scale: Double,
+      size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(gammaRDD(jsc.sc, shape, scale, size))
   }
 
@@ -299,6 +320,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Double] comprised of `i.i.d.` samples ~ Pois(mean).
    */
+  @Since("1.3.0")
   def logNormalRDD(
       sc: SparkContext,
       mean: Double,
@@ -313,6 +335,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#logNormalRDD]].
    */
+  @Since("1.3.0")
   def logNormalJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -326,6 +349,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#logNormalJavaRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def logNormalJavaRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -338,11 +362,12 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#logNormalJavaRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.3.0")
   def logNormalJavaRDD(
-    jsc: JavaSparkContext,
-    mean: Double,
-    std: Double,
-    size: Long): JavaDoubleRDD = {
+      jsc: JavaSparkContext,
+      mean: Double,
+      std: Double,
+      size: Long): JavaDoubleRDD = {
     JavaDoubleRDD.fromRDD(logNormalRDD(jsc.sc, mean, std, size))
   }
 
@@ -359,6 +384,7 @@ object RandomRDDs {
    * @return RDD[Double] comprised of `i.i.d.` samples produced by generator.
    */
   @DeveloperApi
+  @Since("1.1.0")
   def randomRDD[T: ClassTag](
       sc: SparkContext,
       generator: RandomDataGenerator[T],
@@ -381,6 +407,7 @@ object RandomRDDs {
    * @param seed Seed for the RNG that generates the seed for the generator in each partition.
    * @return RDD[Vector] with vectors containing i.i.d samples ~ `U(0.0, 1.0)`.
    */
+  @Since("1.1.0")
   def uniformVectorRDD(
       sc: SparkContext,
       numRows: Long,
@@ -394,6 +421,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#uniformVectorRDD]].
    */
+  @Since("1.1.0")
   def uniformJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -406,6 +434,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#uniformJavaVectorRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def uniformJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -417,6 +446,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#uniformJavaVectorRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def uniformJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -435,6 +465,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ `N(0.0, 1.0)`.
    */
+  @Since("1.1.0")
   def normalVectorRDD(
       sc: SparkContext,
       numRows: Long,
@@ -448,6 +479,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#normalVectorRDD]].
    */
+  @Since("1.1.0")
   def normalJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -460,6 +492,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#normalJavaVectorRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def normalJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -471,6 +504,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#normalJavaVectorRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def normalJavaVectorRDD(
       jsc: JavaSparkContext,
       numRows: Long,
@@ -491,6 +525,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing `i.i.d.` samples.
    */
+  @Since("1.3.0")
   def logNormalVectorRDD(
       sc: SparkContext,
       mean: Double,
@@ -507,6 +542,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#logNormalVectorRDD]].
    */
+  @Since("1.3.0")
   def logNormalJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -521,6 +557,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#logNormalJavaVectorRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def logNormalJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -535,6 +572,7 @@ object RandomRDDs {
    * [[RandomRDDs#logNormalJavaVectorRDD]] with the default number of partitions and
    * the default seed.
    */
+  @Since("1.3.0")
   def logNormalJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -556,6 +594,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Pois(mean).
    */
+  @Since("1.1.0")
   def poissonVectorRDD(
       sc: SparkContext,
       mean: Double,
@@ -570,6 +609,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#poissonVectorRDD]].
    */
+  @Since("1.1.0")
   def poissonJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -583,6 +623,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#poissonJavaVectorRDD]] with the default seed.
    */
+  @Since("1.1.0")
   def poissonJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -595,6 +636,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#poissonJavaVectorRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.1.0")
   def poissonJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -615,6 +657,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Exp(mean).
    */
+  @Since("1.3.0")
   def exponentialVectorRDD(
       sc: SparkContext,
       mean: Double,
@@ -630,6 +673,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#exponentialVectorRDD]].
    */
+  @Since("1.3.0")
   def exponentialJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -643,6 +687,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#exponentialJavaVectorRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def exponentialJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -656,6 +701,7 @@ object RandomRDDs {
    * [[RandomRDDs#exponentialJavaVectorRDD]] with the default number of partitions
    * and the default seed.
    */
+  @Since("1.3.0")
   def exponentialJavaVectorRDD(
       jsc: JavaSparkContext,
       mean: Double,
@@ -678,6 +724,7 @@ object RandomRDDs {
    * @param seed Random seed (default: a random long integer).
    * @return RDD[Vector] with vectors containing `i.i.d.` samples ~ Exp(mean).
    */
+  @Since("1.3.0")
   def gammaVectorRDD(
       sc: SparkContext,
       shape: Double,
@@ -693,6 +740,7 @@ object RandomRDDs {
   /**
    * Java-friendly version of [[RandomRDDs#gammaVectorRDD]].
    */
+  @Since("1.3.0")
   def gammaJavaVectorRDD(
       jsc: JavaSparkContext,
       shape: Double,
@@ -707,6 +755,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#gammaJavaVectorRDD]] with the default seed.
    */
+  @Since("1.3.0")
   def gammaJavaVectorRDD(
       jsc: JavaSparkContext,
       shape: Double,
@@ -720,6 +769,7 @@ object RandomRDDs {
   /**
    * [[RandomRDDs#gammaJavaVectorRDD]] with the default number of partitions and the default seed.
    */
+  @Since("1.3.0")
   def gammaJavaVectorRDD(
       jsc: JavaSparkContext,
       shape: Double,
@@ -744,6 +794,7 @@ object RandomRDDs {
    * @return RDD[Vector] with vectors containing `i.i.d.` samples produced by generator.
    */
   @DeveloperApi
+  @Since("1.1.0")
   def randomVectorRDD(sc: SparkContext,
       generator: RandomDataGenerator[Double],
       numRows: Long,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
index bd4d81390bfae..92a5af708d04b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala
@@ -35,9 +35,9 @@ import org.apache.spark.mllib.util.MLUtils
  */
 @Since("1.3.0")
 @DeveloperApi
-class MultivariateGaussian (
-    val mu: Vector,
-    val sigma: Matrix) extends Serializable {
+class MultivariateGaussian @Since("1.3.0") (
+    @Since("1.3.0") val mu: Vector,
+    @Since("1.3.0") val sigma: Matrix) extends Serializable {
 
   require(sigma.numCols == sigma.numRows, "Covariance matrix must be square")
   require(mu.size == sigma.numCols, "Mean vector length must match covariance matrix size")
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
index f44be13706695..d01b3707be944 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/test/TestResult.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.stat.test
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 
 /**
  * :: Experimental ::
@@ -25,28 +25,33 @@ import org.apache.spark.annotation.Experimental
  * @tparam DF Return type of `degreesOfFreedom`.
  */
 @Experimental
+@Since("1.1.0")
 trait TestResult[DF] {
 
   /**
    * The probability of obtaining a test statistic result at least as extreme as the one that was
    * actually observed, assuming that the null hypothesis is true.
    */
+  @Since("1.1.0")
   def pValue: Double
 
   /**
    * Returns the degree(s) of freedom of the hypothesis test.
    * Return type should be Number(e.g. Int, Double) or tuples of Numbers for toString compatibility.
    */
+  @Since("1.1.0")
   def degreesOfFreedom: DF
 
   /**
    * Test statistic.
    */
+  @Since("1.1.0")
   def statistic: Double
 
   /**
    * Null hypothesis of the test.
    */
+  @Since("1.1.0")
   def nullHypothesis: String
 
   /**
@@ -78,11 +83,12 @@ trait TestResult[DF] {
  * Object containing the test results for the chi-squared hypothesis test.
  */
 @Experimental
+@Since("1.1.0")
 class ChiSqTestResult private[stat] (override val pValue: Double,
-    override val degreesOfFreedom: Int,
-    override val statistic: Double,
-    val method: String,
-    override val nullHypothesis: String) extends TestResult[Int] {
+    @Since("1.1.0") override val degreesOfFreedom: Int,
+    @Since("1.1.0") override val statistic: Double,
+    @Since("1.1.0") val method: String,
+    @Since("1.1.0") override val nullHypothesis: String) extends TestResult[Int] {
 
   override def toString: String = {
     "Chi squared test summary:\n" +
@@ -96,11 +102,13 @@ class ChiSqTestResult private[stat] (override val pValue: Double,
  * Object containing the test results for the Kolmogorov-Smirnov test.
  */
 @Experimental
+@Since("1.5.0")
 class KolmogorovSmirnovTestResult private[stat] (
-    override val pValue: Double,
-    override val statistic: Double,
-    override val nullHypothesis: String) extends TestResult[Int] {
+    @Since("1.5.0") override val pValue: Double,
+    @Since("1.5.0") override val statistic: Double,
+    @Since("1.5.0") override val nullHypothesis: String) extends TestResult[Int] {
 
+  @Since("1.5.0")
   override val degreesOfFreedom = 0
 
   override def toString: String = {

From d703372f86d6a59383ba8569fcd9d379849cffbf Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 22:33:48 -0700
Subject: [PATCH 425/633] [SPARK-10234] [MLLIB] update since version in
 mllib.clustering

Same as #8421 but for `mllib.clustering`.

cc feynmanliang yu-iskw

Author: Xiangrui Meng <meng@databricks.com>

Closes #8435 from mengxr/SPARK-10234.
---
 .../mllib/clustering/GaussianMixture.scala    |  1 +
 .../clustering/GaussianMixtureModel.scala     |  8 +++---
 .../spark/mllib/clustering/KMeans.scala       |  1 +
 .../spark/mllib/clustering/KMeansModel.scala  |  4 +--
 .../spark/mllib/clustering/LDAModel.scala     | 28 ++++++++++++++-----
 .../clustering/PowerIterationClustering.scala | 10 +++++--
 .../mllib/clustering/StreamingKMeans.scala    | 15 +++++-----
 7 files changed, 44 insertions(+), 23 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
index daa947e81d44d..f82bd82c20371 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala
@@ -53,6 +53,7 @@ import org.apache.spark.util.Utils
  * @param maxIterations The maximum number of iterations to perform
  */
 @Experimental
+@Since("1.3.0")
 class GaussianMixture private (
     private var k: Int,
     private var convergenceTol: Double,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index 1a10a8b624218..7f6163e04bf17 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -46,9 +46,9 @@ import org.apache.spark.sql.{SQLContext, Row}
  */
 @Since("1.3.0")
 @Experimental
-class GaussianMixtureModel(
-  val weights: Array[Double],
-  val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable {
+class GaussianMixtureModel @Since("1.3.0") (
+  @Since("1.3.0") val weights: Array[Double],
+  @Since("1.3.0") val gaussians: Array[MultivariateGaussian]) extends Serializable with Saveable {
 
   require(weights.length == gaussians.length, "Length of weight and Gaussian arrays must match")
 
@@ -178,7 +178,7 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
           (weight, new MultivariateGaussian(mu, sigma))
       }.unzip
 
-      return new GaussianMixtureModel(weights.toArray, gaussians.toArray)
+      new GaussianMixtureModel(weights.toArray, gaussians.toArray)
     }
   }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 3e9545a74bef3..46920fffe6e1a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -37,6 +37,7 @@ import org.apache.spark.util.random.XORShiftRandom
  * This is an iterative algorithm that will make multiple passes over the data, so any RDDs given
  * to it should be cached by the user.
  */
+@Since("0.8.0")
 class KMeans private (
     private var k: Int,
     private var maxIterations: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
index e425ecdd481c6..a741584982725 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
@@ -37,8 +37,8 @@ import org.apache.spark.sql.Row
  * A clustering model for K-means. Each point belongs to the cluster with the closest center.
  */
 @Since("0.8.0")
-class KMeansModel (
-    val clusterCenters: Array[Vector]) extends Saveable with Serializable with PMMLExportable {
+class KMeansModel @Since("1.1.0") (@Since("1.0.0") val clusterCenters: Array[Vector])
+  extends Saveable with Serializable with PMMLExportable {
 
   /**
    * A Java-friendly constructor that takes an Iterable of Vectors.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
index 432bbedc8d6f8..15129e0dd5a91 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
@@ -43,12 +43,15 @@ import org.apache.spark.util.BoundedPriorityQueue
  * including local and distributed data structures.
  */
 @Experimental
+@Since("1.3.0")
 abstract class LDAModel private[clustering] extends Saveable {
 
   /** Number of topics */
+  @Since("1.3.0")
   def k: Int
 
   /** Vocabulary size (number of terms or terms in the vocabulary) */
+  @Since("1.3.0")
   def vocabSize: Int
 
   /**
@@ -57,6 +60,7 @@ abstract class LDAModel private[clustering] extends Saveable {
    *
    * This is the parameter to a Dirichlet distribution.
    */
+  @Since("1.5.0")
   def docConcentration: Vector
 
   /**
@@ -68,6 +72,7 @@ abstract class LDAModel private[clustering] extends Saveable {
    * Note: The topics' distributions over terms are called "beta" in the original LDA paper
    * by Blei et al., but are called "phi" in many later papers such as Asuncion et al., 2009.
    */
+  @Since("1.5.0")
   def topicConcentration: Double
 
   /**
@@ -81,6 +86,7 @@ abstract class LDAModel private[clustering] extends Saveable {
    * This is a matrix of size vocabSize x k, where each column is a topic.
    * No guarantees are given about the ordering of the topics.
    */
+  @Since("1.3.0")
   def topicsMatrix: Matrix
 
   /**
@@ -91,6 +97,7 @@ abstract class LDAModel private[clustering] extends Saveable {
    *          (term indices, term weights in topic).
    *          Each topic's terms are sorted in order of decreasing weight.
    */
+  @Since("1.3.0")
   def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])]
 
   /**
@@ -102,6 +109,7 @@ abstract class LDAModel private[clustering] extends Saveable {
    *          (term indices, term weights in topic).
    *          Each topic's terms are sorted in order of decreasing weight.
    */
+  @Since("1.3.0")
   def describeTopics(): Array[(Array[Int], Array[Double])] = describeTopics(vocabSize)
 
   /* TODO (once LDA can be trained with Strings or given a dictionary)
@@ -185,10 +193,11 @@ abstract class LDAModel private[clustering] extends Saveable {
  * @param topics Inferred topics (vocabSize x k matrix).
  */
 @Experimental
+@Since("1.3.0")
 class LocalLDAModel private[clustering] (
-    val topics: Matrix,
-    override val docConcentration: Vector,
-    override val topicConcentration: Double,
+    @Since("1.3.0") val topics: Matrix,
+    @Since("1.5.0") override val docConcentration: Vector,
+    @Since("1.5.0") override val topicConcentration: Double,
     override protected[clustering] val gammaShape: Double = 100)
   extends LDAModel with Serializable {
 
@@ -376,6 +385,7 @@ class LocalLDAModel private[clustering] (
 }
 
 @Experimental
+@Since("1.5.0")
 object LocalLDAModel extends Loader[LocalLDAModel] {
 
   private object SaveLoadV1_0 {
@@ -479,13 +489,14 @@ object LocalLDAModel extends Loader[LocalLDAModel] {
  * than the [[LocalLDAModel]].
  */
 @Experimental
+@Since("1.3.0")
 class DistributedLDAModel private[clustering] (
     private[clustering] val graph: Graph[LDA.TopicCounts, LDA.TokenCount],
     private[clustering] val globalTopicTotals: LDA.TopicCounts,
-    val k: Int,
-    val vocabSize: Int,
-    override val docConcentration: Vector,
-    override val topicConcentration: Double,
+    @Since("1.3.0") val k: Int,
+    @Since("1.3.0") val vocabSize: Int,
+    @Since("1.5.0") override val docConcentration: Vector,
+    @Since("1.5.0") override val topicConcentration: Double,
     private[spark] val iterationTimes: Array[Double],
     override protected[clustering] val gammaShape: Double = 100)
   extends LDAModel {
@@ -603,6 +614,7 @@ class DistributedLDAModel private[clustering] (
    *         (term indices, topic indices).  Note that terms will be omitted if not present in
    *         the document.
    */
+  @Since("1.5.0")
   lazy val topicAssignments: RDD[(Long, Array[Int], Array[Int])] = {
     // For reference, compare the below code with the core part of EMLDAOptimizer.next().
     val eta = topicConcentration
@@ -634,6 +646,7 @@ class DistributedLDAModel private[clustering] (
   }
 
   /** Java-friendly version of [[topicAssignments]] */
+  @Since("1.5.0")
   lazy val javaTopicAssignments: JavaRDD[(java.lang.Long, Array[Int], Array[Int])] = {
     topicAssignments.asInstanceOf[RDD[(java.lang.Long, Array[Int], Array[Int])]].toJavaRDD()
   }
@@ -770,6 +783,7 @@ class DistributedLDAModel private[clustering] (
 
 
 @Experimental
+@Since("1.5.0")
 object DistributedLDAModel extends Loader[DistributedLDAModel] {
 
   private object SaveLoadV1_0 {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
index 396b36f2f6454..da234bdbb29e6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
@@ -42,9 +42,10 @@ import org.apache.spark.{Logging, SparkContext, SparkException}
  */
 @Since("1.3.0")
 @Experimental
-class PowerIterationClusteringModel(
-    val k: Int,
-    val assignments: RDD[PowerIterationClustering.Assignment]) extends Saveable with Serializable {
+class PowerIterationClusteringModel @Since("1.3.0") (
+    @Since("1.3.0") val k: Int,
+    @Since("1.3.0") val assignments: RDD[PowerIterationClustering.Assignment])
+  extends Saveable with Serializable {
 
   @Since("1.4.0")
   override def save(sc: SparkContext, path: String): Unit = {
@@ -56,6 +57,8 @@ class PowerIterationClusteringModel(
 
 @Since("1.4.0")
 object PowerIterationClusteringModel extends Loader[PowerIterationClusteringModel] {
+
+  @Since("1.4.0")
   override def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
     PowerIterationClusteringModel.SaveLoadV1_0.load(sc, path)
   }
@@ -120,6 +123,7 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
  * @see [[http://en.wikipedia.org/wiki/Spectral_clustering Spectral clustering (Wikipedia)]]
  */
 @Experimental
+@Since("1.3.0")
 class PowerIterationClustering private[clustering] (
     private var k: Int,
     private var maxIterations: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
index 41f2668ec6a7d..1d50ffec96faf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
@@ -66,9 +66,10 @@ import org.apache.spark.util.random.XORShiftRandom
  */
 @Since("1.2.0")
 @Experimental
-class StreamingKMeansModel(
-    override val clusterCenters: Array[Vector],
-    val clusterWeights: Array[Double]) extends KMeansModel(clusterCenters) with Logging {
+class StreamingKMeansModel @Since("1.2.0") (
+    @Since("1.2.0") override val clusterCenters: Array[Vector],
+    @Since("1.2.0") val clusterWeights: Array[Double])
+  extends KMeansModel(clusterCenters) with Logging {
 
   /**
    * Perform a k-means update on a batch of data.
@@ -168,10 +169,10 @@ class StreamingKMeansModel(
  */
 @Since("1.2.0")
 @Experimental
-class StreamingKMeans(
-    var k: Int,
-    var decayFactor: Double,
-    var timeUnit: String) extends Logging with Serializable {
+class StreamingKMeans @Since("1.2.0") (
+    @Since("1.2.0") var k: Int,
+    @Since("1.2.0") var decayFactor: Double,
+    @Since("1.2.0") var timeUnit: String) extends Logging with Serializable {
 
   @Since("1.2.0")
   def this() = this(2, 1.0, StreamingKMeans.BATCHES)

From fb7e12fe2e14af8de4c206ca8096b2e8113bfddc Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 22:35:49 -0700
Subject: [PATCH 426/633] [SPARK-10243] [MLLIB] update since versions in
 mllib.tree

Same as #8421 but for `mllib.tree`.

cc jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8442 from mengxr/SPARK-10236.
---
 .../spark/mllib/tree/DecisionTree.scala       |  3 +-
 .../mllib/tree/GradientBoostedTrees.scala     |  2 +-
 .../spark/mllib/tree/configuration/Algo.scala |  2 ++
 .../tree/configuration/BoostingStrategy.scala | 12 ++++----
 .../tree/configuration/FeatureType.scala      |  2 ++
 .../tree/configuration/QuantileStrategy.scala |  2 ++
 .../mllib/tree/configuration/Strategy.scala   | 29 ++++++++++---------
 .../mllib/tree/model/DecisionTreeModel.scala  |  5 +++-
 .../apache/spark/mllib/tree/model/Node.scala  | 18 ++++++------
 .../spark/mllib/tree/model/Predict.scala      |  6 ++--
 .../apache/spark/mllib/tree/model/Split.scala |  8 ++---
 .../mllib/tree/model/treeEnsembleModels.scala | 12 ++++----
 12 files changed, 57 insertions(+), 44 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
index 972841015d4f0..4a77d4adcd865 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/DecisionTree.scala
@@ -46,7 +46,8 @@ import org.apache.spark.util.random.XORShiftRandom
  */
 @Since("1.0.0")
 @Experimental
-class DecisionTree (private val strategy: Strategy) extends Serializable with Logging {
+class DecisionTree @Since("1.0.0") (private val strategy: Strategy)
+  extends Serializable with Logging {
 
   strategy.assertValid()
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
index e750408600c33..95ed48cea6716 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala
@@ -51,7 +51,7 @@ import org.apache.spark.storage.StorageLevel
  */
 @Since("1.2.0")
 @Experimental
-class GradientBoostedTrees(private val boostingStrategy: BoostingStrategy)
+class GradientBoostedTrees @Since("1.2.0") (private val boostingStrategy: BoostingStrategy)
   extends Serializable with Logging {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
index 8301ad160836b..853c7319ec44d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Algo.scala
@@ -26,7 +26,9 @@ import org.apache.spark.annotation.{Experimental, Since}
 @Since("1.0.0")
 @Experimental
 object Algo extends Enumeration {
+  @Since("1.0.0")
   type Algo = Value
+  @Since("1.0.0")
   val Classification, Regression = Value
 
   private[mllib] def fromString(name: String): Algo = name match {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index 7c569981977b4..b5c72fba3ede1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -41,14 +41,14 @@ import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
  */
 @Since("1.2.0")
 @Experimental
-case class BoostingStrategy(
+case class BoostingStrategy @Since("1.4.0") (
     // Required boosting parameters
-    @BeanProperty var treeStrategy: Strategy,
-    @BeanProperty var loss: Loss,
+    @Since("1.2.0") @BeanProperty var treeStrategy: Strategy,
+    @Since("1.2.0") @BeanProperty var loss: Loss,
     // Optional boosting parameters
-    @BeanProperty var numIterations: Int = 100,
-    @BeanProperty var learningRate: Double = 0.1,
-    @BeanProperty var validationTol: Double = 1e-5) extends Serializable {
+    @Since("1.2.0") @BeanProperty var numIterations: Int = 100,
+    @Since("1.2.0") @BeanProperty var learningRate: Double = 0.1,
+    @Since("1.4.0") @BeanProperty var validationTol: Double = 1e-5) extends Serializable {
 
   /**
    * Check validity of parameters.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
index bb7c7ee4f964f..4e0cd473def06 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/FeatureType.scala
@@ -26,6 +26,8 @@ import org.apache.spark.annotation.{Experimental, Since}
 @Since("1.0.0")
 @Experimental
 object FeatureType extends Enumeration {
+  @Since("1.0.0")
   type FeatureType = Value
+  @Since("1.0.0")
   val Continuous, Categorical = Value
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
index 904e42deebb5f..8262db8a4f111 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/QuantileStrategy.scala
@@ -26,6 +26,8 @@ import org.apache.spark.annotation.{Experimental, Since}
 @Since("1.0.0")
 @Experimental
 object QuantileStrategy extends Enumeration {
+  @Since("1.0.0")
   type QuantileStrategy = Value
+  @Since("1.0.0")
   val Sort, MinMax, ApproxHist = Value
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index b74e3f1f46523..89cc13b7c06cf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -69,20 +69,20 @@ import org.apache.spark.mllib.tree.configuration.QuantileStrategy._
  */
 @Since("1.0.0")
 @Experimental
-class Strategy (
-    @BeanProperty var algo: Algo,
-    @BeanProperty var impurity: Impurity,
-    @BeanProperty var maxDepth: Int,
-    @BeanProperty var numClasses: Int = 2,
-    @BeanProperty var maxBins: Int = 32,
-    @BeanProperty var quantileCalculationStrategy: QuantileStrategy = Sort,
-    @BeanProperty var categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
-    @BeanProperty var minInstancesPerNode: Int = 1,
-    @BeanProperty var minInfoGain: Double = 0.0,
-    @BeanProperty var maxMemoryInMB: Int = 256,
-    @BeanProperty var subsamplingRate: Double = 1,
-    @BeanProperty var useNodeIdCache: Boolean = false,
-    @BeanProperty var checkpointInterval: Int = 10) extends Serializable {
+class Strategy @Since("1.3.0") (
+    @Since("1.0.0") @BeanProperty var algo: Algo,
+    @Since("1.0.0") @BeanProperty var impurity: Impurity,
+    @Since("1.0.0") @BeanProperty var maxDepth: Int,
+    @Since("1.2.0") @BeanProperty var numClasses: Int = 2,
+    @Since("1.0.0") @BeanProperty var maxBins: Int = 32,
+    @Since("1.0.0") @BeanProperty var quantileCalculationStrategy: QuantileStrategy = Sort,
+    @Since("1.0.0") @BeanProperty var categoricalFeaturesInfo: Map[Int, Int] = Map[Int, Int](),
+    @Since("1.2.0") @BeanProperty var minInstancesPerNode: Int = 1,
+    @Since("1.2.0") @BeanProperty var minInfoGain: Double = 0.0,
+    @Since("1.0.0") @BeanProperty var maxMemoryInMB: Int = 256,
+    @Since("1.2.0") @BeanProperty var subsamplingRate: Double = 1,
+    @Since("1.2.0") @BeanProperty var useNodeIdCache: Boolean = false,
+    @Since("1.2.0") @BeanProperty var checkpointInterval: Int = 10) extends Serializable {
 
   /**
    */
@@ -206,6 +206,7 @@ object Strategy {
   }
 
   @deprecated("Use Strategy.defaultStrategy instead.", "1.5.0")
+  @Since("1.2.0")
   def defaultStategy(algo: Algo): Strategy = defaultStrategy(algo)
 
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
index 3eefd135f7836..e1bf23f4c34bb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
@@ -43,7 +43,9 @@ import org.apache.spark.util.Utils
  */
 @Since("1.0.0")
 @Experimental
-class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable with Saveable {
+class DecisionTreeModel @Since("1.0.0") (
+    @Since("1.0.0") val topNode: Node,
+    @Since("1.0.0") val algo: Algo) extends Serializable with Saveable {
 
   /**
    * Predict values for a single data point using the model trained.
@@ -110,6 +112,7 @@ class DecisionTreeModel(val topNode: Node, val algo: Algo) extends Serializable
   /**
    * Print the full model to a string.
    */
+  @Since("1.2.0")
   def toDebugString: String = {
     val header = toString + "\n"
     header + topNode.subtreeToString(2)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
index 8c54c55107233..ea6e5aa5d94e7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Node.scala
@@ -41,15 +41,15 @@ import org.apache.spark.mllib.linalg.Vector
  */
 @Since("1.0.0")
 @DeveloperApi
-class Node (
-    val id: Int,
-    var predict: Predict,
-    var impurity: Double,
-    var isLeaf: Boolean,
-    var split: Option[Split],
-    var leftNode: Option[Node],
-    var rightNode: Option[Node],
-    var stats: Option[InformationGainStats]) extends Serializable with Logging {
+class Node @Since("1.2.0") (
+    @Since("1.0.0") val id: Int,
+    @Since("1.0.0") var predict: Predict,
+    @Since("1.2.0") var impurity: Double,
+    @Since("1.0.0") var isLeaf: Boolean,
+    @Since("1.0.0") var split: Option[Split],
+    @Since("1.0.0") var leftNode: Option[Node],
+    @Since("1.0.0") var rightNode: Option[Node],
+    @Since("1.0.0") var stats: Option[InformationGainStats]) extends Serializable with Logging {
 
   override def toString: String = {
     s"id = $id, isLeaf = $isLeaf, predict = $predict, impurity = $impurity, " +
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
index 965784051ede5..06ceff19d8633 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Predict.scala
@@ -26,9 +26,9 @@ import org.apache.spark.annotation.{DeveloperApi, Since}
  */
 @Since("1.2.0")
 @DeveloperApi
-class Predict(
-    val predict: Double,
-    val prob: Double = 0.0) extends Serializable {
+class Predict @Since("1.2.0") (
+    @Since("1.2.0") val predict: Double,
+    @Since("1.2.0") val prob: Double = 0.0) extends Serializable {
 
   override def toString: String = s"$predict (prob = $prob)"
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
index 45db83ae3a1f3..b85a66c05a81d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/Split.scala
@@ -34,10 +34,10 @@ import org.apache.spark.mllib.tree.configuration.FeatureType.FeatureType
 @Since("1.0.0")
 @DeveloperApi
 case class Split(
-    feature: Int,
-    threshold: Double,
-    featureType: FeatureType,
-    categories: List[Double]) {
+    @Since("1.0.0") feature: Int,
+    @Since("1.0.0") threshold: Double,
+    @Since("1.0.0") featureType: FeatureType,
+    @Since("1.0.0") categories: List[Double]) {
 
   override def toString: String = {
     s"Feature = $feature, threshold = $threshold, featureType = $featureType, " +
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
index 19571447a2c56..df5b8feab5d5d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
@@ -48,7 +48,9 @@ import org.apache.spark.util.Utils
  */
 @Since("1.2.0")
 @Experimental
-class RandomForestModel(override val algo: Algo, override val trees: Array[DecisionTreeModel])
+class RandomForestModel @Since("1.2.0") (
+    @Since("1.2.0") override val algo: Algo,
+    @Since("1.2.0") override val trees: Array[DecisionTreeModel])
   extends TreeEnsembleModel(algo, trees, Array.fill(trees.length)(1.0),
     combiningStrategy = if (algo == Classification) Vote else Average)
   with Saveable {
@@ -115,10 +117,10 @@ object RandomForestModel extends Loader[RandomForestModel] {
  */
 @Since("1.2.0")
 @Experimental
-class GradientBoostedTreesModel(
-    override val algo: Algo,
-    override val trees: Array[DecisionTreeModel],
-    override val treeWeights: Array[Double])
+class GradientBoostedTreesModel @Since("1.2.0") (
+    @Since("1.2.0") override val algo: Algo,
+    @Since("1.2.0") override val trees: Array[DecisionTreeModel],
+    @Since("1.2.0") override val treeWeights: Array[Double])
   extends TreeEnsembleModel(algo, trees, treeWeights, combiningStrategy = Sum)
   with Saveable {
 

From 4657fa1f37d41dd4c7240a960342b68c7c591f48 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 22:49:33 -0700
Subject: [PATCH 427/633] [SPARK-10235] [MLLIB] update since versions in
 mllib.regression

Same as #8421 but for `mllib.regression`.

cc freeman-lab dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8426 from mengxr/SPARK-10235 and squashes the following commits:

6cd28e4 [Xiangrui Meng] update since versions in mllib.regression
---
 .../regression/GeneralizedLinearAlgorithm.scala  |  6 ++++--
 .../mllib/regression/IsotonicRegression.scala    | 16 +++++++++-------
 .../spark/mllib/regression/LabeledPoint.scala    |  5 +++--
 .../apache/spark/mllib/regression/Lasso.scala    |  9 ++++++---
 .../mllib/regression/LinearRegression.scala      |  9 ++++++---
 .../spark/mllib/regression/RidgeRegression.scala | 12 +++++++-----
 .../regression/StreamingLinearAlgorithm.scala    |  8 +++-----
 .../StreamingLinearRegressionWithSGD.scala       | 11 +++++++++--
 8 files changed, 47 insertions(+), 29 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 509f6a2d169c4..7e3b4d5648fe3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -38,7 +38,9 @@ import org.apache.spark.storage.StorageLevel
  */
 @Since("0.8.0")
 @DeveloperApi
-abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double)
+abstract class GeneralizedLinearModel @Since("1.0.0") (
+    @Since("1.0.0") val weights: Vector,
+    @Since("0.8.0") val intercept: Double)
   extends Serializable {
 
   /**
@@ -107,7 +109,7 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    * The optimizer to solve the problem.
    *
    */
-  @Since("1.0.0")
+  @Since("0.8.0")
   def optimizer: Optimizer
 
   /** Whether to add intercept (default: false). */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
index 31ca7c2f207d9..877d31ba41303 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
@@ -50,10 +50,10 @@ import org.apache.spark.sql.SQLContext
  */
 @Since("1.3.0")
 @Experimental
-class IsotonicRegressionModel (
-    val boundaries: Array[Double],
-    val predictions: Array[Double],
-    val isotonic: Boolean) extends Serializable with Saveable {
+class IsotonicRegressionModel @Since("1.3.0") (
+    @Since("1.3.0") val boundaries: Array[Double],
+    @Since("1.3.0") val predictions: Array[Double],
+    @Since("1.3.0") val isotonic: Boolean) extends Serializable with Saveable {
 
   private val predictionOrd = if (isotonic) Ordering[Double] else Ordering[Double].reverse
 
@@ -63,7 +63,6 @@ class IsotonicRegressionModel (
 
   /**
    * A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter.
-   *
    */
   @Since("1.4.0")
   def this(boundaries: java.lang.Iterable[Double],
@@ -214,8 +213,6 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
     }
   }
 
-  /**
-   */
   @Since("1.4.0")
   override def load(sc: SparkContext, path: String): IsotonicRegressionModel = {
     implicit val formats = DefaultFormats
@@ -256,6 +253,7 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
  * @see [[http://en.wikipedia.org/wiki/Isotonic_regression Isotonic regression (Wikipedia)]]
  */
 @Experimental
+@Since("1.3.0")
 class IsotonicRegression private (private var isotonic: Boolean) extends Serializable {
 
   /**
@@ -263,6 +261,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali
    *
    * @return New instance of IsotonicRegression.
    */
+  @Since("1.3.0")
   def this() = this(true)
 
   /**
@@ -271,6 +270,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali
    * @param isotonic Isotonic (increasing) or antitonic (decreasing) sequence.
    * @return This instance of IsotonicRegression.
    */
+  @Since("1.3.0")
   def setIsotonic(isotonic: Boolean): this.type = {
     this.isotonic = isotonic
     this
@@ -286,6 +286,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali
    *              the algorithm is executed.
    * @return Isotonic regression model.
    */
+  @Since("1.3.0")
   def run(input: RDD[(Double, Double, Double)]): IsotonicRegressionModel = {
     val preprocessedInput = if (isotonic) {
       input
@@ -311,6 +312,7 @@ class IsotonicRegression private (private var isotonic: Boolean) extends Seriali
    *              the algorithm is executed.
    * @return Isotonic regression model.
    */
+  @Since("1.3.0")
   def run(input: JavaRDD[(JDouble, JDouble, JDouble)]): IsotonicRegressionModel = {
     run(input.rdd.retag.asInstanceOf[RDD[(Double, Double, Double)]])
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
index f7fe1b7b21fca..c284ad2325374 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LabeledPoint.scala
@@ -29,11 +29,12 @@ import org.apache.spark.SparkException
  *
  * @param label Label for this data point.
  * @param features List of features for this data point.
- *
  */
 @Since("0.8.0")
 @BeanInfo
-case class LabeledPoint(label: Double, features: Vector) {
+case class LabeledPoint @Since("1.0.0") (
+    @Since("0.8.0") label: Double,
+    @Since("1.0.0") features: Vector) {
   override def toString: String = {
     s"($label,$features)"
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
index 556411a366bd2..a9aba173fa0e3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala
@@ -34,9 +34,9 @@ import org.apache.spark.rdd.RDD
  *
  */
 @Since("0.8.0")
-class LassoModel (
-    override val weights: Vector,
-    override val intercept: Double)
+class LassoModel @Since("1.1.0") (
+    @Since("1.0.0") override val weights: Vector,
+    @Since("0.8.0") override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept)
   with RegressionModel with Serializable with Saveable with PMMLExportable {
 
@@ -84,6 +84,7 @@ object LassoModel extends Loader[LassoModel] {
  * its corresponding right hand side label y.
  * See also the documentation for the precise formulation.
  */
+@Since("0.8.0")
 class LassoWithSGD private (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -93,6 +94,7 @@ class LassoWithSGD private (
 
   private val gradient = new LeastSquaresGradient()
   private val updater = new L1Updater()
+  @Since("0.8.0")
   override val optimizer = new GradientDescent(gradient, updater)
     .setStepSize(stepSize)
     .setNumIterations(numIterations)
@@ -103,6 +105,7 @@ class LassoWithSGD private (
    * Construct a Lasso object with default parameters: {stepSize: 1.0, numIterations: 100,
    * regParam: 0.01, miniBatchFraction: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(1.0, 100, 0.01, 1.0)
 
   override protected def createModel(weights: Vector, intercept: Double) = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
index 00ab06e3ba738..4996ace5df85d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala
@@ -34,9 +34,9 @@ import org.apache.spark.rdd.RDD
  *
  */
 @Since("0.8.0")
-class LinearRegressionModel (
-    override val weights: Vector,
-    override val intercept: Double)
+class LinearRegressionModel @Since("1.1.0") (
+    @Since("1.0.0") override val weights: Vector,
+    @Since("0.8.0") override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable
   with Saveable with PMMLExportable {
 
@@ -85,6 +85,7 @@ object LinearRegressionModel extends Loader[LinearRegressionModel] {
  * its corresponding right hand side label y.
  * See also the documentation for the precise formulation.
  */
+@Since("0.8.0")
 class LinearRegressionWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -93,6 +94,7 @@ class LinearRegressionWithSGD private[mllib] (
 
   private val gradient = new LeastSquaresGradient()
   private val updater = new SimpleUpdater()
+  @Since("0.8.0")
   override val optimizer = new GradientDescent(gradient, updater)
     .setStepSize(stepSize)
     .setNumIterations(numIterations)
@@ -102,6 +104,7 @@ class LinearRegressionWithSGD private[mllib] (
    * Construct a LinearRegression object with default parameters: {stepSize: 1.0,
    * numIterations: 100, miniBatchFraction: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(1.0, 100, 1.0)
 
   override protected[mllib] def createModel(weights: Vector, intercept: Double) = {
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
index 21a791d98b2cb..0a44ff559d55b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala
@@ -35,9 +35,9 @@ import org.apache.spark.rdd.RDD
  *
  */
 @Since("0.8.0")
-class RidgeRegressionModel (
-    override val weights: Vector,
-    override val intercept: Double)
+class RidgeRegressionModel @Since("1.1.0") (
+    @Since("1.0.0") override val weights: Vector,
+    @Since("0.8.0") override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept)
   with RegressionModel with Serializable with Saveable with PMMLExportable {
 
@@ -85,6 +85,7 @@ object RidgeRegressionModel extends Loader[RidgeRegressionModel] {
  * its corresponding right hand side label y.
  * See also the documentation for the precise formulation.
  */
+@Since("0.8.0")
 class RidgeRegressionWithSGD private (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -94,7 +95,7 @@ class RidgeRegressionWithSGD private (
 
   private val gradient = new LeastSquaresGradient()
   private val updater = new SquaredL2Updater()
-
+  @Since("0.8.0")
   override val optimizer = new GradientDescent(gradient, updater)
     .setStepSize(stepSize)
     .setNumIterations(numIterations)
@@ -105,6 +106,7 @@ class RidgeRegressionWithSGD private (
    * Construct a RidgeRegression object with default parameters: {stepSize: 1.0, numIterations: 100,
    * regParam: 0.01, miniBatchFraction: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(1.0, 100, 0.01, 1.0)
 
   override protected def createModel(weights: Vector, intercept: Double) = {
@@ -134,7 +136,7 @@ object RidgeRegressionWithSGD {
    *        the number of features in the data.
    *
    */
-  @Since("0.8.0")
+  @Since("1.0.0")
   def train(
       input: RDD[LabeledPoint],
       numIterations: Int,
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
index cd3ed8a1549db..73948b2d9851a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
@@ -22,7 +22,7 @@ import scala.reflect.ClassTag
 import org.apache.spark.Logging
 import org.apache.spark.annotation.{DeveloperApi, Since}
 import org.apache.spark.api.java.JavaSparkContext.fakeClassTag
-import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.streaming.api.java.{JavaDStream, JavaPairDStream}
 import org.apache.spark.streaming.dstream.DStream
 
@@ -83,9 +83,8 @@ abstract class StreamingLinearAlgorithm[
    * batch of data from the stream.
    *
    * @param data DStream containing labeled data
-   *
    */
-  @Since("1.3.0")
+  @Since("1.1.0")
   def trainOn(data: DStream[LabeledPoint]): Unit = {
     if (model.isEmpty) {
       throw new IllegalArgumentException("Model must be initialized before starting training.")
@@ -105,7 +104,6 @@ abstract class StreamingLinearAlgorithm[
 
   /**
    * Java-friendly version of `trainOn`.
-   *
    */
   @Since("1.3.0")
   def trainOn(data: JavaDStream[LabeledPoint]): Unit = trainOn(data.dstream)
@@ -129,7 +127,7 @@ abstract class StreamingLinearAlgorithm[
    * Java-friendly version of `predictOn`.
    *
    */
-  @Since("1.1.0")
+  @Since("1.3.0")
   def predictOn(data: JavaDStream[Vector]): JavaDStream[java.lang.Double] = {
     JavaDStream.fromDStream(predictOn(data.dstream).asInstanceOf[DStream[java.lang.Double]])
   }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
index 26654e4a06838..fe1d487cdd078 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.regression
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Experimental, Since}
 import org.apache.spark.mllib.linalg.Vector
 
 /**
@@ -41,6 +41,7 @@ import org.apache.spark.mllib.linalg.Vector
  *    .trainOn(DStream)
  */
 @Experimental
+@Since("1.1.0")
 class StreamingLinearRegressionWithSGD private[mllib] (
     private var stepSize: Double,
     private var numIterations: Int,
@@ -54,8 +55,10 @@ class StreamingLinearRegressionWithSGD private[mllib] (
    * Initial weights must be set before using trainOn or predictOn
    * (see `StreamingLinearAlgorithm`)
    */
+  @Since("1.1.0")
   def this() = this(0.1, 50, 1.0)
 
+  @Since("1.1.0")
   val algorithm = new LinearRegressionWithSGD(stepSize, numIterations, miniBatchFraction)
 
   protected var model: Option[LinearRegressionModel] = None
@@ -63,6 +66,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   /**
    * Set the step size for gradient descent. Default: 0.1.
    */
+  @Since("1.1.0")
   def setStepSize(stepSize: Double): this.type = {
     this.algorithm.optimizer.setStepSize(stepSize)
     this
@@ -71,6 +75,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   /**
    * Set the number of iterations of gradient descent to run per update. Default: 50.
    */
+  @Since("1.1.0")
   def setNumIterations(numIterations: Int): this.type = {
     this.algorithm.optimizer.setNumIterations(numIterations)
     this
@@ -79,6 +84,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   /**
    * Set the fraction of each batch to use for updates. Default: 1.0.
    */
+  @Since("1.1.0")
   def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
     this.algorithm.optimizer.setMiniBatchFraction(miniBatchFraction)
     this
@@ -87,6 +93,7 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   /**
    * Set the initial weights.
    */
+  @Since("1.1.0")
   def setInitialWeights(initialWeights: Vector): this.type = {
     this.model = Some(algorithm.createModel(initialWeights, 0.0))
     this
@@ -95,9 +102,9 @@ class StreamingLinearRegressionWithSGD private[mllib] (
   /**
    * Set the convergence tolerance. Default: 0.001.
    */
+  @Since("1.5.0")
   def setConvergenceTol(tolerance: Double): this.type = {
     this.algorithm.optimizer.setConvergenceTol(tolerance)
     this
   }
-
 }

From 321d7759691bed9867b1f0470f12eab2faa50aff Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 25 Aug 2015 23:45:41 -0700
Subject: [PATCH 428/633] [SPARK-10236] [MLLIB] update since versions in
 mllib.feature

Same as #8421 but for `mllib.feature`.

cc dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8449 from mengxr/SPARK-10236.feature and squashes the following commits:

0e8d658 [Xiangrui Meng] remove unnecessary comment
ad70b03 [Xiangrui Meng] update since versions in mllib.feature
---
 .../mllib/clustering/PowerIterationClustering.scala  |  2 --
 .../apache/spark/mllib/feature/ChiSqSelector.scala   |  4 ++--
 .../spark/mllib/feature/ElementwiseProduct.scala     |  3 ++-
 .../scala/org/apache/spark/mllib/feature/IDF.scala   |  6 ++++--
 .../org/apache/spark/mllib/feature/Normalizer.scala  |  2 +-
 .../scala/org/apache/spark/mllib/feature/PCA.scala   |  7 +++++--
 .../apache/spark/mllib/feature/StandardScaler.scala  | 12 ++++++------
 .../org/apache/spark/mllib/feature/Word2Vec.scala    |  1 +
 8 files changed, 21 insertions(+), 16 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
index da234bdbb29e6..6c76e26fd1626 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
@@ -71,8 +71,6 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
     private[clustering]
     val thisClassName = "org.apache.spark.mllib.clustering.PowerIterationClusteringModel"
 
-    /**
-     */
     @Since("1.4.0")
     def save(sc: SparkContext, model: PowerIterationClusteringModel, path: String): Unit = {
       val sqlContext = new SQLContext(sc)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
index fdd974d7a391e..4743cfd1a2c3f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ChiSqSelector.scala
@@ -33,7 +33,7 @@ import org.apache.spark.rdd.RDD
  */
 @Since("1.3.0")
 @Experimental
-class ChiSqSelectorModel (
+class ChiSqSelectorModel @Since("1.3.0") (
   @Since("1.3.0") val selectedFeatures: Array[Int]) extends VectorTransformer {
 
   require(isSorted(selectedFeatures), "Array has to be sorted asc")
@@ -112,7 +112,7 @@ class ChiSqSelectorModel (
  */
 @Since("1.3.0")
 @Experimental
-class ChiSqSelector (
+class ChiSqSelector @Since("1.3.0") (
   @Since("1.3.0") val numTopFeatures: Int) extends Serializable {
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
index 33e2d17bb472e..d0a6cf61687a8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala
@@ -29,7 +29,8 @@ import org.apache.spark.mllib.linalg._
  */
 @Since("1.4.0")
 @Experimental
-class ElementwiseProduct(val scalingVec: Vector) extends VectorTransformer {
+class ElementwiseProduct @Since("1.4.0") (
+    @Since("1.4.0") val scalingVec: Vector) extends VectorTransformer {
 
   /**
    * Does the hadamard product transformation.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
index d5353ddd972e0..68078ccfa3d60 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala
@@ -39,8 +39,9 @@ import org.apache.spark.rdd.RDD
  */
 @Since("1.1.0")
 @Experimental
-class IDF(val minDocFreq: Int) {
+class IDF @Since("1.2.0") (@Since("1.2.0") val minDocFreq: Int) {
 
+  @Since("1.1.0")
   def this() = this(0)
 
   // TODO: Allow different IDF formulations.
@@ -162,7 +163,8 @@ private object IDF {
  * Represents an IDF model that can transform term frequency vectors.
  */
 @Experimental
-class IDFModel private[spark] (val idf: Vector) extends Serializable {
+@Since("1.1.0")
+class IDFModel private[spark] (@Since("1.1.0") val idf: Vector) extends Serializable {
 
   /**
    * Transforms term frequency (TF) vectors to TF-IDF vectors.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
index 0e070257d9fb2..8d5a22520d6b8 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
@@ -33,7 +33,7 @@ import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vector, Vectors
  */
 @Since("1.1.0")
 @Experimental
-class Normalizer(p: Double) extends VectorTransformer {
+class Normalizer @Since("1.1.0") (p: Double) extends VectorTransformer {
 
   @Since("1.1.0")
   def this() = this(2)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
index a48b7bba665d7..ecb3c1e6c1c83 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/PCA.scala
@@ -29,7 +29,7 @@ import org.apache.spark.rdd.RDD
  * @param k number of principal components
  */
 @Since("1.4.0")
-class PCA(val k: Int) {
+class PCA @Since("1.4.0") (@Since("1.4.0") val k: Int) {
   require(k >= 1, s"PCA requires a number of principal components k >= 1 but was given $k")
 
   /**
@@ -74,7 +74,10 @@ class PCA(val k: Int) {
  * @param k number of principal components.
  * @param pc a principal components Matrix. Each column is one principal component.
  */
-class PCAModel private[spark] (val k: Int, val pc: DenseMatrix) extends VectorTransformer {
+@Since("1.4.0")
+class PCAModel private[spark] (
+    @Since("1.4.0") val k: Int,
+    @Since("1.4.0") val pc: DenseMatrix) extends VectorTransformer {
   /**
    * Transform a vector by computed Principal Components.
    *
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
index b95d5a899001e..f018b453bae7e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/StandardScaler.scala
@@ -34,7 +34,7 @@ import org.apache.spark.rdd.RDD
  */
 @Since("1.1.0")
 @Experimental
-class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
+class StandardScaler @Since("1.1.0") (withMean: Boolean, withStd: Boolean) extends Logging {
 
   @Since("1.1.0")
   def this() = this(false, true)
@@ -74,11 +74,11 @@ class StandardScaler(withMean: Boolean, withStd: Boolean) extends Logging {
  */
 @Since("1.1.0")
 @Experimental
-class StandardScalerModel (
-    val std: Vector,
-    val mean: Vector,
-    var withStd: Boolean,
-    var withMean: Boolean) extends VectorTransformer {
+class StandardScalerModel @Since("1.3.0") (
+    @Since("1.3.0") val std: Vector,
+    @Since("1.1.0") val mean: Vector,
+    @Since("1.3.0") var withStd: Boolean,
+    @Since("1.3.0") var withMean: Boolean) extends VectorTransformer {
 
   /**
    */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index e6f45ae4b01d5..36b124c5d2966 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -436,6 +436,7 @@ class Word2Vec extends Serializable with Logging {
  *                    (i * vectorSize, i * vectorSize + vectorSize)
  */
 @Experimental
+@Since("1.1.0")
 class Word2VecModel private[mllib] (
     private val wordIndex: Map[String, Int],
     private val wordVectors: Array[Float]) extends Serializable with Saveable {

From 75d4773aa50e24972c533e8b48697fde586429eb Mon Sep 17 00:00:00 2001
From: felixcheung <felixcheung_m@hotmail.com>
Date: Tue, 25 Aug 2015 23:48:16 -0700
Subject: [PATCH 429/633] [SPARK-9316] [SPARKR] Add support for filtering using
 `[` (synonym for filter / select)

Add support for
```
   df[df$name == "Smith", c(1,2)]
   df[df$age %in% c(19, 30), 1:2]
```

shivaram

Author: felixcheung <felixcheung_m@hotmail.com>

Closes #8394 from felixcheung/rsubset.
---
 R/pkg/R/DataFrame.R              | 22 +++++++++++++++++++++-
 R/pkg/inst/tests/test_sparkSQL.R | 27 +++++++++++++++++++++++++++
 2 files changed, 48 insertions(+), 1 deletion(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index ae1d912cf6da1..a5162de705f8f 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -985,9 +985,11 @@ setMethod("$<-", signature(x = "DataFrame"),
             x
           })
 
+setClassUnion("numericOrcharacter", c("numeric", "character"))
+
 #' @rdname select
 #' @name [[
-setMethod("[[", signature(x = "DataFrame"),
+setMethod("[[", signature(x = "DataFrame", i = "numericOrcharacter"),
           function(x, i) {
             if (is.numeric(i)) {
               cols <- columns(x)
@@ -1010,6 +1012,20 @@ setMethod("[", signature(x = "DataFrame", i = "missing"),
             select(x, j)
           })
 
+#' @rdname select
+#' @name [
+setMethod("[", signature(x = "DataFrame", i = "Column"),
+          function(x, i, j, ...) {
+            # It could handle i as "character" but it seems confusing and not required
+            # https://stat.ethz.ch/R-manual/R-devel/library/base/html/Extract.data.frame.html
+            filtered <- filter(x, i)
+            if (!missing(j)) {
+              filtered[, j]
+            } else {
+              filtered
+            }
+          })
+
 #' Select
 #'
 #' Selects a set of columns with names or Column expressions.
@@ -1028,8 +1044,12 @@ setMethod("[", signature(x = "DataFrame", i = "missing"),
 #'   # Columns can also be selected using `[[` and `[`
 #'   df[[2]] == df[["age"]]
 #'   df[,2] == df[,"age"]
+#'   df[,c("name", "age")]
 #'   # Similar to R data frames columns can also be selected using `$`
 #'   df$age
+#'   # It can also be subset on rows and Columns
+#'   df[df$name == "Smith", c(1,2)]
+#'   df[df$age %in% c(19, 30), 1:2]
 #' }
 setMethod("select", signature(x = "DataFrame", col = "character"),
           function(x, col, ...) {
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 556b8c5447054..ee48a3dc0cc05 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -587,6 +587,33 @@ test_that("select with column", {
   expect_equal(collect(select(df3, "x"))[[1, 1]], "x")
 })
 
+test_that("subsetting", {
+  # jsonFile returns columns in random order
+  df <- select(jsonFile(sqlContext, jsonPath), "name", "age")
+  filtered <- df[df$age > 20,]
+  expect_equal(count(filtered), 1)
+  expect_equal(columns(filtered), c("name", "age"))
+  expect_equal(collect(filtered)$name, "Andy")
+
+  df2 <- df[df$age == 19, 1]
+  expect_is(df2, "DataFrame")
+  expect_equal(count(df2), 1)
+  expect_equal(columns(df2), c("name"))
+  expect_equal(collect(df2)$name, "Justin")
+
+  df3 <- df[df$age > 20, 2]
+  expect_equal(count(df3), 1)
+  expect_equal(columns(df3), c("age"))
+
+  df4 <- df[df$age %in% c(19, 30), 1:2]
+  expect_equal(count(df4), 2)
+  expect_equal(columns(df4), c("name", "age"))
+  
+  df5 <- df[df$age %in% c(19), c(1,2)]
+  expect_equal(count(df5), 1)
+  expect_equal(columns(df5), c("name", "age"))
+})
+
 test_that("selectExpr() on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
   selected <- selectExpr(df, "age * 2")

From bb1640529725c6c38103b95af004f8bd90eeee5c Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Wed, 26 Aug 2015 00:37:04 -0700
Subject: [PATCH 430/633] Closes #8443


From 6519fd06cc8175c9182ef16cf8a37d7f255eb846 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 26 Aug 2015 11:47:05 -0700
Subject: [PATCH 431/633] [SPARK-9665] [MLLIB] audit MLlib API annotations

I only found `ml.NaiveBayes` missing `Experimental` annotation. This PR doesn't cover Python APIs.

cc jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8452 from mengxr/SPARK-9665.
---
 .../apache/spark/ml/classification/NaiveBayes.scala  | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index 97cbaf1fa8761..69cb88a7e6718 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -18,11 +18,11 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.SparkException
-import org.apache.spark.ml.{PredictorParams, PredictionModel, Predictor}
-import org.apache.spark.ml.param.{ParamMap, ParamValidators, Param, DoubleParam}
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.PredictorParams
+import org.apache.spark.ml.param.{DoubleParam, Param, ParamMap, ParamValidators}
 import org.apache.spark.ml.util.Identifiable
-import org.apache.spark.mllib.classification.{NaiveBayes => OldNaiveBayes}
-import org.apache.spark.mllib.classification.{NaiveBayesModel => OldNaiveBayesModel}
+import org.apache.spark.mllib.classification.{NaiveBayes => OldNaiveBayes, NaiveBayesModel => OldNaiveBayesModel}
 import org.apache.spark.mllib.linalg._
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.rdd.RDD
@@ -59,6 +59,7 @@ private[ml] trait NaiveBayesParams extends PredictorParams {
 }
 
 /**
+ * :: Experimental ::
  * Naive Bayes Classifiers.
  * It supports both Multinomial NB
  * ([[http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html]])
@@ -68,6 +69,7 @@ private[ml] trait NaiveBayesParams extends PredictorParams {
  * ([[http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html]]).
  * The input feature values must be nonnegative.
  */
+@Experimental
 class NaiveBayes(override val uid: String)
   extends ProbabilisticClassifier[Vector, NaiveBayes, NaiveBayesModel]
   with NaiveBayesParams {
@@ -101,11 +103,13 @@ class NaiveBayes(override val uid: String)
 }
 
 /**
+ * :: Experimental ::
  * Model produced by [[NaiveBayes]]
  * @param pi log of class priors, whose dimension is C (number of classes)
  * @param theta log of class conditional probabilities, whose dimension is C (number of classes)
  *              by D (number of features)
  */
+@Experimental
 class NaiveBayesModel private[ml] (
     override val uid: String,
     val pi: Vector,

From de7209c256aaf79a0978cfcf6e98bb013267b93a Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Wed, 26 Aug 2015 12:19:36 -0700
Subject: [PATCH 432/633] HOTFIX: Increase PRB timeout

---
 dev/run-tests-jenkins | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins
index c4d39d95d5890..f144c053046c5 100755
--- a/dev/run-tests-jenkins
+++ b/dev/run-tests-jenkins
@@ -48,8 +48,8 @@ COMMIT_URL="https://github.com/apache/spark/commit/${ghprbActualCommit}"
 SHORT_COMMIT_HASH="${ghprbActualCommit:0:7}"
 
 # format: http://linux.die.net/man/1/timeout
-# must be less than the timeout configured on Jenkins (currently 180m)
-TESTS_TIMEOUT="175m"
+# must be less than the timeout configured on Jenkins (currently 300m)
+TESTS_TIMEOUT="250m"
 
 # Array to capture all tests to run on the pull request. These tests are held under the
 #+ dev/tests/ directory.

From 086d4681df3ebfccfc04188262c10482f44553b0 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Wed, 26 Aug 2015 14:02:19 -0700
Subject: [PATCH 433/633] [SPARK-10241] [MLLIB] update since versions in
 mllib.recommendation

Same as #8421 but for `mllib.recommendation`.

cc srowen coderxiang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8432 from mengxr/SPARK-10241.
---
 .../spark/mllib/recommendation/ALS.scala      | 22 ++++++++++++++++++-
 .../MatrixFactorizationModel.scala            |  8 +++----
 2 files changed, 25 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
index b27ef1b949e2e..33aaf853e599d 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
@@ -28,7 +28,10 @@ import org.apache.spark.storage.StorageLevel
  * A more compact class to represent a rating than Tuple3[Int, Int, Double].
  */
 @Since("0.8.0")
-case class Rating(user: Int, product: Int, rating: Double)
+case class Rating @Since("0.8.0") (
+    @Since("0.8.0") user: Int,
+    @Since("0.8.0") product: Int,
+    @Since("0.8.0") rating: Double)
 
 /**
  * Alternating Least Squares matrix factorization.
@@ -59,6 +62,7 @@ case class Rating(user: Int, product: Int, rating: Double)
  * indicated user
  * preferences rather than explicit ratings given to items.
  */
+@Since("0.8.0")
 class ALS private (
     private var numUserBlocks: Int,
     private var numProductBlocks: Int,
@@ -74,6 +78,7 @@ class ALS private (
    * Constructs an ALS instance with default parameters: {numBlocks: -1, rank: 10, iterations: 10,
    * lambda: 0.01, implicitPrefs: false, alpha: 1.0}.
    */
+  @Since("0.8.0")
   def this() = this(-1, -1, 10, 10, 0.01, false, 1.0)
 
   /** If true, do alternating nonnegative least squares. */
@@ -90,6 +95,7 @@ class ALS private (
    * Set the number of blocks for both user blocks and product blocks to parallelize the computation
    * into; pass -1 for an auto-configured number of blocks. Default: -1.
    */
+  @Since("0.8.0")
   def setBlocks(numBlocks: Int): this.type = {
     this.numUserBlocks = numBlocks
     this.numProductBlocks = numBlocks
@@ -99,6 +105,7 @@ class ALS private (
   /**
    * Set the number of user blocks to parallelize the computation.
    */
+  @Since("1.1.0")
   def setUserBlocks(numUserBlocks: Int): this.type = {
     this.numUserBlocks = numUserBlocks
     this
@@ -107,30 +114,35 @@ class ALS private (
   /**
    * Set the number of product blocks to parallelize the computation.
    */
+  @Since("1.1.0")
   def setProductBlocks(numProductBlocks: Int): this.type = {
     this.numProductBlocks = numProductBlocks
     this
   }
 
   /** Set the rank of the feature matrices computed (number of features). Default: 10. */
+  @Since("0.8.0")
   def setRank(rank: Int): this.type = {
     this.rank = rank
     this
   }
 
   /** Set the number of iterations to run. Default: 10. */
+  @Since("0.8.0")
   def setIterations(iterations: Int): this.type = {
     this.iterations = iterations
     this
   }
 
   /** Set the regularization parameter, lambda. Default: 0.01. */
+  @Since("0.8.0")
   def setLambda(lambda: Double): this.type = {
     this.lambda = lambda
     this
   }
 
   /** Sets whether to use implicit preference. Default: false. */
+  @Since("0.8.1")
   def setImplicitPrefs(implicitPrefs: Boolean): this.type = {
     this.implicitPrefs = implicitPrefs
     this
@@ -139,12 +151,14 @@ class ALS private (
   /**
    * Sets the constant used in computing confidence in implicit ALS. Default: 1.0.
    */
+  @Since("0.8.1")
   def setAlpha(alpha: Double): this.type = {
     this.alpha = alpha
     this
   }
 
   /** Sets a random seed to have deterministic results. */
+  @Since("1.0.0")
   def setSeed(seed: Long): this.type = {
     this.seed = seed
     this
@@ -154,6 +168,7 @@ class ALS private (
    * Set whether the least-squares problems solved at each iteration should have
    * nonnegativity constraints.
    */
+  @Since("1.1.0")
   def setNonnegative(b: Boolean): this.type = {
     this.nonnegative = b
     this
@@ -166,6 +181,7 @@ class ALS private (
    * set `spark.rdd.compress` to `true` to reduce the space requirement, at the cost of speed.
    */
   @DeveloperApi
+  @Since("1.1.0")
   def setIntermediateRDDStorageLevel(storageLevel: StorageLevel): this.type = {
     require(storageLevel != StorageLevel.NONE,
       "ALS is not designed to run without persisting intermediate RDDs.")
@@ -181,6 +197,7 @@ class ALS private (
    * at the cost of speed.
    */
   @DeveloperApi
+  @Since("1.3.0")
   def setFinalRDDStorageLevel(storageLevel: StorageLevel): this.type = {
     this.finalRDDStorageLevel = storageLevel
     this
@@ -194,6 +211,7 @@ class ALS private (
    * this setting is ignored.
    */
   @DeveloperApi
+  @Since("1.4.0")
   def setCheckpointInterval(checkpointInterval: Int): this.type = {
     this.checkpointInterval = checkpointInterval
     this
@@ -203,6 +221,7 @@ class ALS private (
    * Run ALS with the configured parameters on an input RDD of (user, product, rating) triples.
    * Returns a MatrixFactorizationModel with feature vectors for each user and product.
    */
+  @Since("0.8.0")
   def run(ratings: RDD[Rating]): MatrixFactorizationModel = {
     val sc = ratings.context
 
@@ -250,6 +269,7 @@ class ALS private (
   /**
    * Java-friendly version of [[ALS.run]].
    */
+  @Since("1.3.0")
   def run(ratings: JavaRDD[Rating]): MatrixFactorizationModel = run(ratings.rdd)
 }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
index ba4cfdcd9f1dd..46562eb2ad0f7 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
@@ -52,10 +52,10 @@ import org.apache.spark.storage.StorageLevel
  *                        and the features computed for this product.
  */
 @Since("0.8.0")
-class MatrixFactorizationModel(
-    val rank: Int,
-    val userFeatures: RDD[(Int, Array[Double])],
-    val productFeatures: RDD[(Int, Array[Double])])
+class MatrixFactorizationModel @Since("0.8.0") (
+    @Since("0.8.0") val rank: Int,
+    @Since("0.8.0") val userFeatures: RDD[(Int, Array[Double])],
+    @Since("0.8.0") val productFeatures: RDD[(Int, Array[Double])])
   extends Saveable with Serializable with Logging {
 
   require(rank > 0)

From d41d6c48207159490c1e1d9cc54015725cfa41b2 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 26 Aug 2015 16:04:44 -0700
Subject: [PATCH 434/633] [SPARK-10305] [SQL] fix create DataFrame from Python
 class

cc jkbradley

Author: Davies Liu <davies@databricks.com>

Closes #8470 from davies/fix_create_df.
---
 python/pyspark/sql/tests.py | 12 ++++++++++++
 python/pyspark/sql/types.py |  6 ++++++
 2 files changed, 18 insertions(+)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index aacfb34c77618..cd32e26c64f22 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -145,6 +145,12 @@ class PythonOnlyPoint(ExamplePoint):
     __UDT__ = PythonOnlyUDT()
 
 
+class MyObject(object):
+    def __init__(self, key, value):
+        self.key = key
+        self.value = value
+
+
 class DataTypeTests(unittest.TestCase):
     # regression test for SPARK-6055
     def test_data_type_eq(self):
@@ -383,6 +389,12 @@ def test_infer_nested_schema(self):
         df = self.sqlCtx.inferSchema(rdd)
         self.assertEquals(Row(field1=1, field2=u'row1'), df.first())
 
+    def test_create_dataframe_from_objects(self):
+        data = [MyObject(1, "1"), MyObject(2, "2")]
+        df = self.sqlCtx.createDataFrame(data)
+        self.assertEqual(df.dtypes, [("key", "bigint"), ("value", "string")])
+        self.assertEqual(df.first(), Row(key=1, value="1"))
+
     def test_select_null_literal(self):
         df = self.sqlCtx.sql("select null as col")
         self.assertEquals(Row(col=None), df.first())
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index ed4e5b594bd61..94e581a78364c 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -537,6 +537,9 @@ def toInternal(self, obj):
                 return tuple(f.toInternal(obj.get(n)) for n, f in zip(self.names, self.fields))
             elif isinstance(obj, (tuple, list)):
                 return tuple(f.toInternal(v) for f, v in zip(self.fields, obj))
+            elif hasattr(obj, "__dict__"):
+                d = obj.__dict__
+                return tuple(f.toInternal(d.get(n)) for n, f in zip(self.names, self.fields))
             else:
                 raise ValueError("Unexpected tuple %r with StructType" % obj)
         else:
@@ -544,6 +547,9 @@ def toInternal(self, obj):
                 return tuple(obj.get(n) for n in self.names)
             elif isinstance(obj, (list, tuple)):
                 return tuple(obj)
+            elif hasattr(obj, "__dict__"):
+                d = obj.__dict__
+                return tuple(d.get(n) for n in self.names)
             else:
                 raise ValueError("Unexpected tuple %r with StructType" % obj)
 

From ad7f0f160be096c0fdae6e6cf7e3b6ba4a606de7 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Wed, 26 Aug 2015 18:13:07 -0700
Subject: [PATCH 435/633] [SPARK-10308] [SPARKR] Add %in% to the exported
 namespace

I also checked all the other functions defined in column.R, functions.R and DataFrame.R and everything else looked fine.

cc yu-iskw

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #8473 from shivaram/in-namespace.
---
 R/pkg/NAMESPACE | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 3e5c89d779b7b..5286c01986204 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -47,12 +47,12 @@ exportMethods("arrange",
               "join",
               "limit",
               "merge",
+              "mutate",
+              "na.omit",
               "names",
               "ncol",
               "nrow",
               "orderBy",
-              "mutate",
-              "names",
               "persist",
               "printSchema",
               "rbind",
@@ -82,7 +82,8 @@ exportMethods("arrange",
 
 exportClasses("Column")
 
-exportMethods("abs",
+exportMethods("%in%",
+              "abs",
               "acos",
               "add_months",
               "alias",

From 773ca037a43d464ce7f16fe693ca6034f09a35b7 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Wed, 26 Aug 2015 18:14:32 -0700
Subject: [PATCH 436/633] [MINOR] [SPARKR] Fix some validation problems in
 SparkR

Getting rid of some validation problems in SparkR
https://github.com/apache/spark/pull/7883

cc shivaram

```
inst/tests/test_Serde.R:26:1: style: Trailing whitespace is superfluous.

^~
inst/tests/test_Serde.R:34:1: style: Trailing whitespace is superfluous.

^~
inst/tests/test_Serde.R:37:38: style: Trailing whitespace is superfluous.
  expect_equal(class(x), "character")
                                     ^~
inst/tests/test_Serde.R:50:1: style: Trailing whitespace is superfluous.

^~
inst/tests/test_Serde.R:55:1: style: Trailing whitespace is superfluous.

^~
inst/tests/test_Serde.R:60:1: style: Trailing whitespace is superfluous.

^~
inst/tests/test_sparkSQL.R:611:1: style: Trailing whitespace is superfluous.

^~
R/DataFrame.R:664:1: style: Trailing whitespace is superfluous.

^~~~~~~~~~~~~~
R/DataFrame.R:670:55: style: Trailing whitespace is superfluous.
                df <- data.frame(row.names = 1 : nrow)
                                                      ^~~~~~~~~~~~~~~~
R/DataFrame.R:672:1: style: Trailing whitespace is superfluous.

^~~~~~~~~~~~~~
R/DataFrame.R:686:49: style: Trailing whitespace is superfluous.
                    df[[names[colIndex]]] <- vec
                                                ^~~~~~~~~~~~~~~~~~
```

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8474 from yu-iskw/minor-fix-sparkr.
---
 R/pkg/R/DataFrame.R              |  8 ++++----
 R/pkg/inst/tests/test_Serde.R    | 12 ++++++------
 R/pkg/inst/tests/test_sparkSQL.R |  2 +-
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index a5162de705f8f..dd8126aebf467 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -661,15 +661,15 @@ setMethod("collect",
               # listCols is a list of columns
               listCols <- callJStatic("org.apache.spark.sql.api.r.SQLUtils", "dfToCols", x@sdf)
               stopifnot(length(listCols) == ncol)
-              
+
               # An empty data.frame with 0 columns and number of rows as collected
               nrow <- length(listCols[[1]])
               if (nrow <= 0) {
                 df <- data.frame()
               } else {
-                df <- data.frame(row.names = 1 : nrow)                
+                df <- data.frame(row.names = 1 : nrow)
               }
-              
+
               # Append columns one by one
               for (colIndex in 1 : ncol) {
                 # Note: appending a column of list type into a data.frame so that
@@ -683,7 +683,7 @@ setMethod("collect",
                   # TODO: more robust check on column of primitive types
                   vec <- do.call(c, col)
                   if (class(vec) != "list") {
-                    df[[names[colIndex]]] <- vec                  
+                    df[[names[colIndex]]] <- vec
                   } else {
                     # For columns of complex type, be careful to access them.
                     # Get a column of complex type returns a list.
diff --git a/R/pkg/inst/tests/test_Serde.R b/R/pkg/inst/tests/test_Serde.R
index 009db85da2beb..dddce54d70443 100644
--- a/R/pkg/inst/tests/test_Serde.R
+++ b/R/pkg/inst/tests/test_Serde.R
@@ -23,7 +23,7 @@ test_that("SerDe of primitive types", {
   x <- callJStatic("SparkRHandler", "echo", 1L)
   expect_equal(x, 1L)
   expect_equal(class(x), "integer")
-  
+
   x <- callJStatic("SparkRHandler", "echo", 1)
   expect_equal(x, 1)
   expect_equal(class(x), "numeric")
@@ -31,10 +31,10 @@ test_that("SerDe of primitive types", {
   x <- callJStatic("SparkRHandler", "echo", TRUE)
   expect_true(x)
   expect_equal(class(x), "logical")
-  
+
   x <- callJStatic("SparkRHandler", "echo", "abc")
   expect_equal(x, "abc")
-  expect_equal(class(x), "character")  
+  expect_equal(class(x), "character")
 })
 
 test_that("SerDe of list of primitive types", {
@@ -47,17 +47,17 @@ test_that("SerDe of list of primitive types", {
   y <- callJStatic("SparkRHandler", "echo", x)
   expect_equal(x, y)
   expect_equal(class(y[[1]]), "numeric")
-  
+
   x <- list(TRUE, FALSE)
   y <- callJStatic("SparkRHandler", "echo", x)
   expect_equal(x, y)
   expect_equal(class(y[[1]]), "logical")
-  
+
   x <- list("a", "b", "c")
   y <- callJStatic("SparkRHandler", "echo", x)
   expect_equal(x, y)
   expect_equal(class(y[[1]]), "character")
-  
+
   # Empty list
   x <- list()
   y <- callJStatic("SparkRHandler", "echo", x)
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index ee48a3dc0cc05..8e22c56824b16 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -608,7 +608,7 @@ test_that("subsetting", {
   df4 <- df[df$age %in% c(19, 30), 1:2]
   expect_equal(count(df4), 2)
   expect_equal(columns(df4), c("name", "age"))
-  
+
   df5 <- df[df$age %in% c(19), c(1,2)]
   expect_equal(count(df5), 1)
   expect_equal(columns(df5), c("name", "age"))

From 0fac144f6bd835395059154532d72cdb5dc7ef8d Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 26 Aug 2015 18:14:54 -0700
Subject: [PATCH 437/633] [SPARK-9424] [SQL] Parquet programming guide updates
 for 1.5

Author: Cheng Lian <lian@databricks.com>

Closes #8467 from liancheng/spark-9424/parquet-docs-for-1.5.
---
 docs/sql-programming-guide.md | 45 ++++++++++++++++++++++++++++-------
 1 file changed, 37 insertions(+), 8 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 33e7893d7bd0a..e64190b9b209d 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1124,6 +1124,13 @@ a simple schema, and gradually add more columns to the schema as needed.  In thi
 up with multiple Parquet files with different but mutually compatible schemas.  The Parquet data
 source is now able to automatically detect this case and merge schemas of all these files.
 
+Since schema merging is a relatively expensive operation, and is not a necessity in most cases, we
+turned it off by default starting from 1.5.0.  You may enable it by
+
+1. setting data source option `mergeSchema` to `true` when reading Parquet files (as shown in the
+   examples below), or
+2. setting the global SQL option `spark.sql.parquet.mergeSchema` to `true`.
+
 <div class="codetabs">
 
 <div data-lang="scala"  markdown="1">
@@ -1143,7 +1150,7 @@ val df2 = sc.makeRDD(6 to 10).map(i => (i, i * 3)).toDF("single", "triple")
 df2.write.parquet("data/test_table/key=2")
 
 // Read the partitioned table
-val df3 = sqlContext.read.parquet("data/test_table")
+val df3 = sqlContext.read.option("mergeSchema", "true").parquet("data/test_table")
 df3.printSchema()
 
 // The final schema consists of all 3 columns in the Parquet files together
@@ -1165,16 +1172,16 @@ df3.printSchema()
 # Create a simple DataFrame, stored into a partition directory
 df1 = sqlContext.createDataFrame(sc.parallelize(range(1, 6))\
                                    .map(lambda i: Row(single=i, double=i * 2)))
-df1.save("data/test_table/key=1", "parquet")
+df1.write.parquet("data/test_table/key=1")
 
 # Create another DataFrame in a new partition directory,
 # adding a new column and dropping an existing column
 df2 = sqlContext.createDataFrame(sc.parallelize(range(6, 11))
                                    .map(lambda i: Row(single=i, triple=i * 3)))
-df2.save("data/test_table/key=2", "parquet")
+df2.write.parquet("data/test_table/key=2")
 
 # Read the partitioned table
-df3 = sqlContext.load("data/test_table", "parquet")
+df3 = sqlContext.read.option("mergeSchema", "true").parquet("data/test_table")
 df3.printSchema()
 
 # The final schema consists of all 3 columns in the Parquet files together
@@ -1201,7 +1208,7 @@ saveDF(df1, "data/test_table/key=1", "parquet", "overwrite")
 saveDF(df2, "data/test_table/key=2", "parquet", "overwrite")
 
 # Read the partitioned table
-df3 <- loadDF(sqlContext, "data/test_table", "parquet")
+df3 <- loadDF(sqlContext, "data/test_table", "parquet", mergeSchema="true")
 printSchema(df3)
 
 # The final schema consists of all 3 columns in the Parquet files together
@@ -1301,7 +1308,7 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
   <td><code>spark.sql.parquet.binaryAsString</code></td>
   <td>false</td>
   <td>
-    Some other Parquet-producing systems, in particular Impala and older versions of Spark SQL, do
+    Some other Parquet-producing systems, in particular Impala, Hive, and older versions of Spark SQL, do
     not differentiate between binary data and strings when writing out the Parquet schema. This
     flag tells Spark SQL to interpret binary data as a string to provide compatibility with these systems.
   </td>
@@ -1310,8 +1317,7 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
   <td><code>spark.sql.parquet.int96AsTimestamp</code></td>
   <td>true</td>
   <td>
-    Some Parquet-producing systems, in particular Impala, store Timestamp into INT96. Spark would also
-    store Timestamp as INT96 because we need to avoid precision lost of the nanoseconds field. This
+    Some Parquet-producing systems, in particular Impala and Hive, store Timestamp into INT96.  This
     flag tells Spark SQL to interpret INT96 data as a timestamp to provide compatibility with these systems.
   </td>
 </tr>
@@ -1355,6 +1361,9 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
     <p>
       <b>Note:</b>
       <ul>
+        <li>
+          This option is automatically ignored if <code>spark.speculation</code> is turned on.
+        </li>
         <li>
           This option must be set via Hadoop <code>Configuration</code> rather than Spark
           <code>SQLConf</code>.
@@ -1371,6 +1380,26 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
     </p>
   </td>
 </tr>
+<tr>
+  <td><code>spark.sql.parquet.mergeSchema</code></td>
+  <td><code>false</code></td>
+  <td>
+    <p>
+      When true, the Parquet data source merges schemas collected from all data files, otherwise the
+      schema is picked from the summary file or a random data file if no summary file is available.
+    </p>
+  </td>
+</tr>
+<tr>
+  <td><code>spark.sql.parquet.mergeSchema</code></td>
+  <td><code>false</code></td>
+  <td>
+    <p>
+      When true, the Parquet data source merges schemas collected from all data files, otherwise the
+      schema is picked from the summary file or a random data file if no summary file is available.
+    </p>
+  </td>
+</tr>
 </table>
 
 ## JSON Datasets

From ce97834dc0cc55eece0e909a4061ca6f2123f60d Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Wed, 26 Aug 2015 22:19:11 -0700
Subject: [PATCH 438/633] [SPARK-9964] [PYSPARK] [SQL] PySpark DataFrameReader
 accept RDD of String for JSON

PySpark DataFrameReader should could accept an RDD of Strings (like the Scala version does) for JSON, rather than only taking a path.
If this PR is merged, it should be duplicated to cover the other input types (not just JSON).

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8444 from yanboliang/spark-9964.
---
 python/pyspark/sql/readwriter.py | 28 ++++++++++++++++++++++------
 1 file changed, 22 insertions(+), 6 deletions(-)

diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index 78247c8fa7372..3fa6895880a97 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -15,8 +15,14 @@
 # limitations under the License.
 #
 
+import sys
+
+if sys.version >= '3':
+    basestring = unicode = str
+
 from py4j.java_gateway import JavaClass
 
+from pyspark import RDD
 from pyspark.sql import since
 from pyspark.sql.column import _to_seq
 from pyspark.sql.types import *
@@ -125,23 +131,33 @@ def load(self, path=None, format=None, schema=None, **options):
     @since(1.4)
     def json(self, path, schema=None):
         """
-        Loads a JSON file (one object per line) and returns the result as
-        a :class`DataFrame`.
+        Loads a JSON file (one object per line) or an RDD of Strings storing JSON objects
+        (one object per record) and returns the result as a :class`DataFrame`.
 
         If the ``schema`` parameter is not specified, this function goes
         through the input once to determine the input schema.
 
-        :param path: string, path to the JSON dataset.
+        :param path: string represents path to the JSON dataset,
+                     or RDD of Strings storing JSON objects.
         :param schema: an optional :class:`StructType` for the input schema.
 
-        >>> df = sqlContext.read.json('python/test_support/sql/people.json')
-        >>> df.dtypes
+        >>> df1 = sqlContext.read.json('python/test_support/sql/people.json')
+        >>> df1.dtypes
+        [('age', 'bigint'), ('name', 'string')]
+        >>> rdd = sc.textFile('python/test_support/sql/people.json')
+        >>> df2 = sqlContext.read.json(rdd)
+        >>> df2.dtypes
         [('age', 'bigint'), ('name', 'string')]
 
         """
         if schema is not None:
             self.schema(schema)
-        return self._df(self._jreader.json(path))
+        if isinstance(path, basestring):
+            return self._df(self._jreader.json(path))
+        elif isinstance(path, RDD):
+            return self._df(self._jreader.json(path._jrdd))
+        else:
+            raise TypeError("path can be only string or RDD")
 
     @since(1.4)
     def table(self, tableName):

From e936cf8088a06d6aefce44305f3904bbeb17b432 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Wed, 26 Aug 2015 22:27:31 -0700
Subject: [PATCH 439/633] [SPARK-10219] [SPARKR] Fix varargsToEnv and add test
 case

cc sun-rui davies

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #8475 from shivaram/varargs-fix.
---
 R/pkg/R/utils.R                  | 3 ++-
 R/pkg/inst/tests/test_sparkSQL.R | 6 ++++++
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R
index 4f9f4d9cad2a8..3babcb519378e 100644
--- a/R/pkg/R/utils.R
+++ b/R/pkg/R/utils.R
@@ -314,7 +314,8 @@ convertEnvsToList <- function(keys, vals) {
 
 # Utility function to capture the varargs into environment object
 varargsToEnv <- function(...) {
-  pairs <- as.list(substitute(list(...)))[-1L]
+  # Based on http://stackoverflow.com/a/3057419/4577954
+  pairs <- list(...)
   env <- new.env()
   for (name in names(pairs)) {
     env[[name]] <- pairs[[name]]
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 8e22c56824b16..4b672e115f924 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -1060,6 +1060,12 @@ test_that("parquetFile works with multiple input paths", {
   parquetDF <- parquetFile(sqlContext, parquetPath, parquetPath2)
   expect_is(parquetDF, "DataFrame")
   expect_equal(count(parquetDF), count(df) * 2)
+
+  # Test if varargs works with variables
+  saveMode <- "overwrite"
+  mergeSchema <- "true"
+  parquetPath3 <- tempfile(pattern = "parquetPath3", fileext = ".parquet")
+  write.df(df, parquetPath2, "parquet", mode = saveMode, mergeSchema = mergeSchema)
 })
 
 test_that("describe() and summarize() on a DataFrame", {

From de0278286cf6db8df53b0b68918ea114f2c77f1f Mon Sep 17 00:00:00 2001
From: Ram Sriharsha <rsriharsha@hw11853.local>
Date: Wed, 26 Aug 2015 23:12:55 -0700
Subject: [PATCH 440/633] =?UTF-8?q?[SPARK-10251]=20[CORE]=20some=20common?=
 =?UTF-8?q?=20types=20are=20not=20registered=20for=20Kryo=20Serializat?=
 =?UTF-8?q?=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

…ion by default

Author: Ram Sriharsha <rsriharsha@hw11853.local>

Closes #8465 from harsha2010/SPARK-10251.
---
 .../spark/serializer/KryoSerializer.scala     | 35 ++++++++++++++++++-
 .../serializer/KryoSerializerSuite.scala      | 30 ++++++++++++++++
 2 files changed, 64 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index 048a938507277..b977711e7d5ad 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -22,6 +22,7 @@ import java.nio.ByteBuffer
 import javax.annotation.Nullable
 
 import scala.collection.JavaConverters._
+import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 
 import com.esotericsoftware.kryo.{Kryo, KryoException}
@@ -38,7 +39,7 @@ import org.apache.spark.network.nio.{GetBlock, GotBlock, PutBlock}
 import org.apache.spark.network.util.ByteUnit
 import org.apache.spark.scheduler.{CompressedMapStatus, HighlyCompressedMapStatus}
 import org.apache.spark.storage._
-import org.apache.spark.util.{BoundedPriorityQueue, SerializableConfiguration, SerializableJobConf}
+import org.apache.spark.util.{Utils, BoundedPriorityQueue, SerializableConfiguration, SerializableJobConf}
 import org.apache.spark.util.collection.CompactBuffer
 
 /**
@@ -131,6 +132,38 @@ class KryoSerializer(conf: SparkConf)
     // our code override the generic serializers in Chill for things like Seq
     new AllScalaRegistrar().apply(kryo)
 
+    // Register types missed by Chill.
+    // scalastyle:off
+    kryo.register(classOf[Array[Tuple1[Any]]])
+    kryo.register(classOf[Array[Tuple2[Any, Any]]])
+    kryo.register(classOf[Array[Tuple3[Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple4[Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple5[Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple6[Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple7[Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple8[Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple9[Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple10[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple11[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple12[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple13[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple14[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple15[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple16[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple17[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple18[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple19[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple20[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple21[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+    kryo.register(classOf[Array[Tuple22[Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any]]])
+
+    // scalastyle:on
+
+    kryo.register(None.getClass)
+    kryo.register(Nil.getClass)
+    kryo.register(Utils.classForName("scala.collection.immutable.$colon$colon"))
+    kryo.register(classOf[ArrayBuffer[Any]])
+
     kryo.setClassLoader(classLoader)
     kryo
   }
diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
index 8d1c9d17e977e..e428414cf6e85 100644
--- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala
@@ -150,6 +150,36 @@ class KryoSerializerSuite extends SparkFunSuite with SharedSparkContext {
       mutable.HashMap(1->"one", 2->"two", 3->"three")))
   }
 
+  test("Bug: SPARK-10251") {
+    val ser = new KryoSerializer(conf.clone.set("spark.kryo.registrationRequired", "true"))
+      .newInstance()
+    def check[T: ClassTag](t: T) {
+      assert(ser.deserialize[T](ser.serialize(t)) === t)
+    }
+    check((1, 3))
+    check(Array((1, 3)))
+    check(List((1, 3)))
+    check(List[Int]())
+    check(List[Int](1, 2, 3))
+    check(List[String]())
+    check(List[String]("x", "y", "z"))
+    check(None)
+    check(Some(1))
+    check(Some("hi"))
+    check(1 -> 1)
+    check(mutable.ArrayBuffer(1, 2, 3))
+    check(mutable.ArrayBuffer("1", "2", "3"))
+    check(mutable.Map())
+    check(mutable.Map(1 -> "one", 2 -> "two"))
+    check(mutable.Map("one" -> 1, "two" -> 2))
+    check(mutable.HashMap(1 -> "one", 2 -> "two"))
+    check(mutable.HashMap("one" -> 1, "two" -> 2))
+    check(List(Some(mutable.HashMap(1->1, 2->2)), None, Some(mutable.HashMap(3->4))))
+    check(List(
+      mutable.HashMap("one" -> 1, "two" -> 2),
+      mutable.HashMap(1->"one", 2->"two", 3->"three")))
+  }
+
   test("ranges") {
     val ser = new KryoSerializer(conf).newInstance()
     def check[T: ClassTag](t: T) {

From 9625d13d575c97bbff264f6a94838aae72c9202d Mon Sep 17 00:00:00 2001
From: Moussa Taifi <moutai10@gmail.com>
Date: Thu, 27 Aug 2015 10:34:47 +0100
Subject: [PATCH 441/633] [DOCS] [STREAMING] [KAFKA] Fix typo in exactly once
 semantics

Fix Typo in exactly once semantics
[Semantics of output operations] link

Author: Moussa Taifi <moutai10@gmail.com>

Closes #8468 from moutai/patch-3.
---
 docs/streaming-kafka-integration.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/streaming-kafka-integration.md b/docs/streaming-kafka-integration.md
index 7571e22575efd..5db39ae54a274 100644
--- a/docs/streaming-kafka-integration.md
+++ b/docs/streaming-kafka-integration.md
@@ -82,7 +82,7 @@ This approach has the following advantages over the receiver-based approach (i.e
 
 - *Efficiency:* Achieving zero-data loss in the first approach required the data to be stored in a Write Ahead Log, which further replicated the data. This is actually inefficient as the data effectively gets replicated twice - once by Kafka, and a second time by the Write Ahead Log. This second approach eliminates the problem as there is no receiver, and hence no need for Write Ahead Logs. As long as you have sufficient Kafka retention, messages can be recovered from Kafka.
 
-- *Exactly-once semantics:* The first approach uses Kafka's high level API to store consumed offsets in Zookeeper. This is traditionally the way to consume data from Kafka. While this approach (in combination with write ahead logs) can ensure zero data loss (i.e. at-least once semantics), there is a small chance some records may get consumed twice under some failures. This occurs because of inconsistencies between data reliably received by Spark Streaming and offsets tracked by Zookeeper. Hence, in this second approach, we use simple Kafka API that does not use Zookeeper. Offsets are tracked by Spark Streaming within its checkpoints. This eliminates inconsistencies between Spark Streaming and Zookeeper/Kafka, and so each record is received by Spark Streaming effectively exactly once despite failures. In order to achieve exactly-once semantics for output of your results, your output operation that saves the data to an external data store must be either idempotent, or an atomic transaction that saves results and offsets (see [Semanitcs of output operations](streaming-programming-guide.html#semantics-of-output-operations) in the main programming guide for further information).
+- *Exactly-once semantics:* The first approach uses Kafka's high level API to store consumed offsets in Zookeeper. This is traditionally the way to consume data from Kafka. While this approach (in combination with write ahead logs) can ensure zero data loss (i.e. at-least once semantics), there is a small chance some records may get consumed twice under some failures. This occurs because of inconsistencies between data reliably received by Spark Streaming and offsets tracked by Zookeeper. Hence, in this second approach, we use simple Kafka API that does not use Zookeeper. Offsets are tracked by Spark Streaming within its checkpoints. This eliminates inconsistencies between Spark Streaming and Zookeeper/Kafka, and so each record is received by Spark Streaming effectively exactly once despite failures. In order to achieve exactly-once semantics for output of your results, your output operation that saves the data to an external data store must be either idempotent, or an atomic transaction that saves results and offsets (see [Semantics of output operations](streaming-programming-guide.html#semantics-of-output-operations) in the main programming guide for further information).
 
 Note that one disadvantage of this approach is that it does not update offsets in Zookeeper, hence Zookeeper-based Kafka monitoring tools will not show progress. However, you can access the offsets processed by this approach in each batch and update Zookeeper yourself (see below).
 

From 1650f6f56ed4b7f1a7f645c9e8d5ac533464bd78 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 10:44:44 +0100
Subject: [PATCH 442/633] [SPARK-10254] [ML] Removes Guava dependencies in
 spark.ml.feature JavaTests

* Replaces `com.google.common` dependencies with `java.util.Arrays`
* Small clean up in `JavaNormalizerSuite`

Author: Feynman Liang <fliang@databricks.com>

Closes #8445 from feynmanliang/SPARK-10254.
---
 .../apache/spark/ml/feature/JavaBucketizerSuite.java  |  5 +++--
 .../org/apache/spark/ml/feature/JavaDCTSuite.java     |  5 +++--
 .../apache/spark/ml/feature/JavaHashingTFSuite.java   |  5 +++--
 .../apache/spark/ml/feature/JavaNormalizerSuite.java  | 11 +++++------
 .../org/apache/spark/ml/feature/JavaPCASuite.java     |  4 ++--
 .../ml/feature/JavaPolynomialExpansionSuite.java      |  5 +++--
 .../spark/ml/feature/JavaStandardScalerSuite.java     |  4 ++--
 .../apache/spark/ml/feature/JavaTokenizerSuite.java   |  6 ++++--
 .../spark/ml/feature/JavaVectorIndexerSuite.java      |  5 ++---
 .../spark/ml/feature/JavaVectorSlicerSuite.java       |  4 ++--
 .../apache/spark/ml/feature/JavaWord2VecSuite.java    | 11 ++++++-----
 11 files changed, 35 insertions(+), 30 deletions(-)

diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java
index d5bd230a957a1..47d68de599da2 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaBucketizerSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -54,7 +55,7 @@ public void tearDown() {
   public void bucketizerTest() {
     double[] splits = {-0.5, 0.0, 0.5};
 
-    JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
       RowFactory.create(-0.5),
       RowFactory.create(-0.3),
       RowFactory.create(0.0),
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
index 845eed61c45c6..0f6ec64d97d36 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaDCTSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import edu.emory.mathcs.jtransforms.dct.DoubleDCT_1D;
 import org.junit.After;
 import org.junit.Assert;
@@ -56,7 +57,7 @@ public void tearDown() {
   @Test
   public void javaCompatibilityTest() {
     double[] input = new double[] {1D, 2D, 3D, 4D};
-    JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
       RowFactory.create(Vectors.dense(input))
     ));
     DataFrame dataset = jsql.createDataFrame(data, new StructType(new StructField[]{
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
index 599e9cfd23ad4..03dd5369bddf7 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaHashingTFSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -54,7 +55,7 @@ public void tearDown() {
 
   @Test
   public void hashingTF() {
-    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
       RowFactory.create(0.0, "Hi I heard about Spark"),
       RowFactory.create(0.0, "I wish Java could use case classes"),
       RowFactory.create(1.0, "Logistic regression models are neat")
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java
index d82f3b7e8c076..e17d549c5059b 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaNormalizerSuite.java
@@ -17,15 +17,15 @@
 
 package org.apache.spark.ml.feature;
 
-import java.util.List;
+import java.util.Arrays;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
 
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 
@@ -48,13 +48,12 @@ public void tearDown() {
   @Test
   public void normalizer() {
     // The tests are to check Java compatibility.
-    List<VectorIndexerSuite.FeatureData> points = Lists.newArrayList(
+    JavaRDD<VectorIndexerSuite.FeatureData> points = jsc.parallelize(Arrays.asList(
       new VectorIndexerSuite.FeatureData(Vectors.dense(0.0, -2.0)),
       new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 3.0)),
       new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 4.0))
-    );
-    DataFrame dataFrame = jsql.createDataFrame(jsc.parallelize(points, 2),
-      VectorIndexerSuite.FeatureData.class);
+    ));
+    DataFrame dataFrame = jsql.createDataFrame(points, VectorIndexerSuite.FeatureData.class);
     Normalizer normalizer = new Normalizer()
       .setInputCol("features")
       .setOutputCol("normFeatures");
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java
index 5cf43fec6f29e..e8f329f9cf29e 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPCASuite.java
@@ -18,11 +18,11 @@
 package org.apache.spark.ml.feature;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -78,7 +78,7 @@ public Vector getExpected() {
 
   @Test
   public void testPCA() {
-    List<Vector> points = Lists.newArrayList(
+    List<Vector> points = Arrays.asList(
       Vectors.sparse(5, new int[]{1, 3}, new double[]{1.0, 7.0}),
       Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
       Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java
index 5e8211c2c5118..834fedbb59e1b 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaPolynomialExpansionSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -59,7 +60,7 @@ public void polynomialExpansionTest() {
       .setOutputCol("polyFeatures")
       .setDegree(3);
 
-    JavaRDD<Row> data = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> data = jsc.parallelize(Arrays.asList(
       RowFactory.create(
         Vectors.dense(-2.0, 2.3),
         Vectors.dense(-2.0, 4.0, -8.0, 2.3, -4.6, 9.2, 5.29, -10.58, 12.17)
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java
index 74eb2733f06ef..ed74363f59e34 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStandardScalerSuite.java
@@ -17,9 +17,9 @@
 
 package org.apache.spark.ml.feature;
 
+import java.util.Arrays;
 import java.util.List;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -48,7 +48,7 @@ public void tearDown() {
   @Test
   public void standardScaler() {
     // The tests are to check Java compatibility.
-    List<VectorIndexerSuite.FeatureData> points = Lists.newArrayList(
+    List<VectorIndexerSuite.FeatureData> points = Arrays.asList(
       new VectorIndexerSuite.FeatureData(Vectors.dense(0.0, -2.0)),
       new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 3.0)),
       new VectorIndexerSuite.FeatureData(Vectors.dense(1.0, 4.0))
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java
index 3806f650025b2..02309ce63219a 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaTokenizerSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -54,7 +55,8 @@ public void regexTokenizer() {
       .setGaps(true)
       .setMinTokenLength(3);
 
-    JavaRDD<TokenizerTestData> rdd = jsc.parallelize(Lists.newArrayList(
+
+    JavaRDD<TokenizerTestData> rdd = jsc.parallelize(Arrays.asList(
       new TokenizerTestData("Test of tok.", new String[] {"Test", "tok."}),
       new TokenizerTestData("Te,st.  punct", new String[] {"Te,st.", "punct"})
     ));
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java
index c7ae5468b9429..bfcca62fa1c98 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorIndexerSuite.java
@@ -18,6 +18,7 @@
 package org.apache.spark.ml.feature;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 import java.util.Map;
 
@@ -26,8 +27,6 @@
 import org.junit.Before;
 import org.junit.Test;
 
-import com.google.common.collect.Lists;
-
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.feature.VectorIndexerSuite.FeatureData;
 import org.apache.spark.mllib.linalg.Vectors;
@@ -52,7 +51,7 @@ public void tearDown() {
   @Test
   public void vectorIndexerAPI() {
     // The tests are to check Java compatibility.
-    List<FeatureData> points = Lists.newArrayList(
+    List<FeatureData> points = Arrays.asList(
       new FeatureData(Vectors.dense(0.0, -2.0)),
       new FeatureData(Vectors.dense(1.0, 3.0)),
       new FeatureData(Vectors.dense(1.0, 4.0))
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
index 56988b9fb29cb..f953361427586 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaVectorSlicerSuite.java
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
 
 import org.junit.After;
 import org.junit.Assert;
@@ -63,7 +63,7 @@ public void vectorSlice() {
     };
     AttributeGroup group = new AttributeGroup("userFeatures", attrs);
 
-    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
       RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})),
       RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0))
     ));
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java
index 39c70157f83c0..70f5ad9432212 100644
--- a/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaWord2VecSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.feature;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -50,10 +51,10 @@ public void tearDown() {
 
   @Test
   public void testJavaWord2Vec() {
-    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
-      RowFactory.create(Lists.newArrayList("Hi I heard about Spark".split(" "))),
-      RowFactory.create(Lists.newArrayList("I wish Java could use case classes".split(" "))),
-      RowFactory.create(Lists.newArrayList("Logistic regression models are neat".split(" ")))
+    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
+      RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))),
+      RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
+      RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" ")))
     ));
     StructType schema = new StructType(new StructField[]{
       new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())

From 75d62307946283b03bec6aaf1bdd4f2b08c93915 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 10:45:35 +0100
Subject: [PATCH 443/633] [SPARK-10255] [ML] Removes Guava dependencies from
 spark.ml.param JavaTests

Author: Feynman Liang <fliang@databricks.com>

Closes #8446 from feynmanliang/SPARK-10255.
---
 .../java/org/apache/spark/ml/param/JavaParamsSuite.java    | 7 ++++---
 .../java/org/apache/spark/ml/param/JavaTestParams.java     | 5 ++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java
index 9890155e9f865..fa777f3d42a9a 100644
--- a/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaParamsSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.ml.param;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -61,7 +62,7 @@ public void testParamValidate() {
     ParamValidators.ltEq(1.0);
     ParamValidators.inRange(0, 1, true, false);
     ParamValidators.inRange(0, 1);
-    ParamValidators.inArray(Lists.newArrayList(0, 1, 3));
-    ParamValidators.inArray(Lists.newArrayList("a", "b"));
+    ParamValidators.inArray(Arrays.asList(0, 1, 3));
+    ParamValidators.inArray(Arrays.asList("a", "b"));
   }
 }
diff --git a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
index dc6ce8061f62b..65841182df9b4 100644
--- a/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
+++ b/mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java
@@ -17,10 +17,9 @@
 
 package org.apache.spark.ml.param;
 
+import java.util.Arrays;
 import java.util.List;
 
-import com.google.common.collect.Lists;
-
 import org.apache.spark.ml.util.Identifiable$;
 
 /**
@@ -89,7 +88,7 @@ private void init() {
     myIntParam_ = new IntParam(this, "myIntParam", "this is an int param", ParamValidators.gt(0));
     myDoubleParam_ = new DoubleParam(this, "myDoubleParam", "this is a double param",
       ParamValidators.inRange(0.0, 1.0));
-    List<String> validStrings = Lists.newArrayList("a", "b");
+    List<String> validStrings = Arrays.asList("a", "b");
     myStringParam_ = new Param<String>(this, "myStringParam", "this is a string param",
       ParamValidators.inArray(validStrings));
     myDoubleArrayParam_ =

From 1a446f75b6cac46caea0217a66abeb226946ac71 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 10:46:18 +0100
Subject: [PATCH 444/633] [SPARK-10256] [ML] Removes guava dependency from
 spark.ml.classification JavaTests

Author: Feynman Liang <fliang@databricks.com>

Closes #8447 from feynmanliang/SPARK-10256.
---
 .../apache/spark/ml/classification/JavaNaiveBayesSuite.java   | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
index a700c9cddb206..8fd7bf55a2e5d 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
@@ -18,8 +18,8 @@
 package org.apache.spark.ml.classification;
 
 import java.io.Serializable;
+import java.util.Arrays;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -74,7 +74,7 @@ public void naiveBayesDefaultParams() {
 
   @Test
   public void testNaiveBayes() {
-    JavaRDD<Row> jrdd = jsc.parallelize(Lists.newArrayList(
+    JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
       RowFactory.create(0.0, Vectors.dense(1.0, 0.0, 0.0)),
       RowFactory.create(0.0, Vectors.dense(2.0, 0.0, 0.0)),
       RowFactory.create(1.0, Vectors.dense(0.0, 1.0, 0.0)),

From b02e8187225d1765f67ce38864dfaca487be8a44 Mon Sep 17 00:00:00 2001
From: Jacek Laskowski <jacek@japila.pl>
Date: Thu, 27 Aug 2015 11:07:37 +0100
Subject: [PATCH 445/633] [SPARK-9613] [HOTFIX] Fix usage of JavaConverters
 removed in Scala 2.11

Fix for [JavaConverters.asJavaListConverter](http://www.scala-lang.org/api/2.10.5/index.html#scala.collection.JavaConverters$) being removed in 2.11.7 and hence the build fails with the 2.11 profile enabled. Tested with the default 2.10 and 2.11 profiles. BUILD SUCCESS in both cases.

Build for 2.10:

    ./build/mvn -Pyarn -Phadoop-2.6 -Dhadoop.version=2.7.1 -DskipTests clean install

and 2.11:

    ./dev/change-scala-version.sh 2.11
    ./build/mvn -Pyarn -Phadoop-2.6 -Dhadoop.version=2.7.1 -Dscala-2.11 -DskipTests clean install

Author: Jacek Laskowski <jacek@japila.pl>

Closes #8479 from jaceklaskowski/SPARK-9613-hotfix.
---
 .../org/apache/spark/ml/classification/JavaOneVsRestSuite.java  | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
index 2744e020e9e49..253cabf0133d0 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
@@ -55,7 +55,7 @@ public void setUp() {
 
         double[] xMean = {5.843, 3.057, 3.758, 1.199};
         double[] xVariance = {0.6856, 0.1899, 3.116, 0.581};
-        List<LabeledPoint> points = JavaConverters.asJavaListConverter(
+        List<LabeledPoint> points = JavaConverters.seqAsJavaListConverter(
             generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42)
         ).asJava();
         datasetRDD = jsc.parallelize(points, 2);

From e1f4de4a7d15d4ca4b5c64ff929ac3980f5d706f Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 18:46:41 +0100
Subject: [PATCH 446/633] [SPARK-10257] [MLLIB] Removes Guava from all
 spark.mllib Java tests

* Replaces instances of `Lists.newArrayList` with `Arrays.asList`
* Replaces `commons.lang.StringUtils` over `com.google.collections.Strings`
* Replaces `List` interface over `ArrayList` implementations

This PR along with #8445 #8446 #8447 completely removes all `com.google.collections.Lists` dependencies within mllib's Java tests.

Author: Feynman Liang <fliang@databricks.com>

Closes #8451 from feynmanliang/SPARK-10257.
---
 .../JavaStreamingLogisticRegressionSuite.java | 10 +++----
 .../clustering/JavaGaussianMixtureSuite.java  |  4 +--
 .../mllib/clustering/JavaKMeansSuite.java     |  9 +++----
 .../clustering/JavaStreamingKMeansSuite.java  | 10 +++----
 .../spark/mllib/feature/JavaTfIdfSuite.java   | 19 +++++++------
 .../mllib/feature/JavaWord2VecSuite.java      |  6 ++---
 .../mllib/fpm/JavaAssociationRulesSuite.java  |  5 ++--
 .../spark/mllib/fpm/JavaFPGrowthSuite.java    | 17 ++++++------
 .../spark/mllib/linalg/JavaVectorsSuite.java  |  5 ++--
 .../mllib/random/JavaRandomRDDsSuite.java     | 27 ++++++++++---------
 .../mllib/recommendation/JavaALSSuite.java    |  5 ++--
 .../JavaIsotonicRegressionSuite.java          |  7 ++---
 .../JavaStreamingLinearRegressionSuite.java   | 10 +++----
 .../spark/mllib/stat/JavaStatisticsSuite.java | 11 ++++----
 14 files changed, 71 insertions(+), 74 deletions(-)

diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java
index 55787f8606d48..c9e5ee22f3273 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaStreamingLogisticRegressionSuite.java
@@ -18,11 +18,11 @@
 package org.apache.spark.mllib.classification;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -60,16 +60,16 @@ public void tearDown() {
   @Test
   @SuppressWarnings("unchecked")
   public void javaAPI() {
-    List<LabeledPoint> trainingBatch = Lists.newArrayList(
+    List<LabeledPoint> trainingBatch = Arrays.asList(
       new LabeledPoint(1.0, Vectors.dense(1.0)),
       new LabeledPoint(0.0, Vectors.dense(0.0)));
     JavaDStream<LabeledPoint> training =
-      attachTestInputStream(ssc, Lists.newArrayList(trainingBatch, trainingBatch), 2);
-    List<Tuple2<Integer, Vector>> testBatch = Lists.newArrayList(
+      attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2);
+    List<Tuple2<Integer, Vector>> testBatch = Arrays.asList(
       new Tuple2<Integer, Vector>(10, Vectors.dense(1.0)),
       new Tuple2<Integer, Vector>(11, Vectors.dense(0.0)));
     JavaPairDStream<Integer, Vector> test = JavaPairDStream.fromJavaDStream(
-      attachTestInputStream(ssc, Lists.newArrayList(testBatch, testBatch), 2));
+      attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2));
     StreamingLogisticRegressionWithSGD slr = new StreamingLogisticRegressionWithSGD()
       .setNumIterations(2)
       .setInitialWeights(Vectors.dense(0.0));
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java
index 467a7a69e8f30..123f78da54e34 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaGaussianMixtureSuite.java
@@ -18,9 +18,9 @@
 package org.apache.spark.mllib.clustering;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -48,7 +48,7 @@ public void tearDown() {
 
   @Test
   public void runGaussianMixture() {
-    List<Vector> points = Lists.newArrayList(
+    List<Vector> points = Arrays.asList(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
       Vectors.dense(1.0, 4.0, 6.0)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
index 31676e64025d0..ad06676c72ac6 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaKMeansSuite.java
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.clustering;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import org.junit.After;
@@ -25,8 +26,6 @@
 import org.junit.Test;
 import static org.junit.Assert.*;
 
-import com.google.common.collect.Lists;
-
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.mllib.linalg.Vector;
@@ -48,7 +47,7 @@ public void tearDown() {
 
   @Test
   public void runKMeansUsingStaticMethods() {
-    List<Vector> points = Lists.newArrayList(
+    List<Vector> points = Arrays.asList(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
       Vectors.dense(1.0, 4.0, 6.0)
@@ -67,7 +66,7 @@ public void runKMeansUsingStaticMethods() {
 
   @Test
   public void runKMeansUsingConstructor() {
-    List<Vector> points = Lists.newArrayList(
+    List<Vector> points = Arrays.asList(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
       Vectors.dense(1.0, 4.0, 6.0)
@@ -90,7 +89,7 @@ public void runKMeansUsingConstructor() {
 
   @Test
   public void testPredictJavaRDD() {
-    List<Vector> points = Lists.newArrayList(
+    List<Vector> points = Arrays.asList(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
       Vectors.dense(1.0, 4.0, 6.0)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java
index 3b0e879eec77f..d644766d1e54d 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/clustering/JavaStreamingKMeansSuite.java
@@ -18,11 +18,11 @@
 package org.apache.spark.mllib.clustering;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -60,16 +60,16 @@ public void tearDown() {
   @Test
   @SuppressWarnings("unchecked")
   public void javaAPI() {
-    List<Vector> trainingBatch = Lists.newArrayList(
+    List<Vector> trainingBatch = Arrays.asList(
       Vectors.dense(1.0),
       Vectors.dense(0.0));
     JavaDStream<Vector> training =
-      attachTestInputStream(ssc, Lists.newArrayList(trainingBatch, trainingBatch), 2);
-    List<Tuple2<Integer, Vector>> testBatch = Lists.newArrayList(
+      attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2);
+    List<Tuple2<Integer, Vector>> testBatch = Arrays.asList(
       new Tuple2<Integer, Vector>(10, Vectors.dense(1.0)),
       new Tuple2<Integer, Vector>(11, Vectors.dense(0.0)));
     JavaPairDStream<Integer, Vector> test = JavaPairDStream.fromJavaDStream(
-      attachTestInputStream(ssc, Lists.newArrayList(testBatch, testBatch), 2));
+      attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2));
     StreamingKMeans skmeans = new StreamingKMeans()
       .setK(1)
       .setDecayFactor(1.0)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java
index fbc26167ce66f..8a320afa4b13d 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaTfIdfSuite.java
@@ -18,14 +18,13 @@
 package org.apache.spark.mllib.feature;
 
 import java.io.Serializable;
-import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
-import com.google.common.collect.Lists;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -50,10 +49,10 @@ public void tfIdf() {
     // The tests are to check Java compatibility.
     HashingTF tf = new HashingTF();
     @SuppressWarnings("unchecked")
-    JavaRDD<ArrayList<String>> documents = sc.parallelize(Lists.newArrayList(
-      Lists.newArrayList("this is a sentence".split(" ")),
-      Lists.newArrayList("this is another sentence".split(" ")),
-      Lists.newArrayList("this is still a sentence".split(" "))), 2);
+    JavaRDD<List<String>> documents = sc.parallelize(Arrays.asList(
+      Arrays.asList("this is a sentence".split(" ")),
+      Arrays.asList("this is another sentence".split(" ")),
+      Arrays.asList("this is still a sentence".split(" "))), 2);
     JavaRDD<Vector> termFreqs = tf.transform(documents);
     termFreqs.collect();
     IDF idf = new IDF();
@@ -70,10 +69,10 @@ public void tfIdfMinimumDocumentFrequency() {
     // The tests are to check Java compatibility.
     HashingTF tf = new HashingTF();
     @SuppressWarnings("unchecked")
-    JavaRDD<ArrayList<String>> documents = sc.parallelize(Lists.newArrayList(
-      Lists.newArrayList("this is a sentence".split(" ")),
-      Lists.newArrayList("this is another sentence".split(" ")),
-      Lists.newArrayList("this is still a sentence".split(" "))), 2);
+    JavaRDD<List<String>> documents = sc.parallelize(Arrays.asList(
+      Arrays.asList("this is a sentence".split(" ")),
+      Arrays.asList("this is another sentence".split(" ")),
+      Arrays.asList("this is still a sentence".split(" "))), 2);
     JavaRDD<Vector> termFreqs = tf.transform(documents);
     termFreqs.collect();
     IDF idf = new IDF(2);
diff --git a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java
index fb7afe8c6434b..e13ed07e283dd 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/feature/JavaWord2VecSuite.java
@@ -18,11 +18,11 @@
 package org.apache.spark.mllib.feature;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
 import com.google.common.base.Strings;
 import org.junit.After;
 import org.junit.Assert;
@@ -51,8 +51,8 @@ public void tearDown() {
   public void word2Vec() {
     // The tests are to check Java compatibility.
     String sentence = Strings.repeat("a b ", 100) + Strings.repeat("a c ", 10);
-    List<String> words = Lists.newArrayList(sentence.split(" "));
-    List<List<String>> localDoc = Lists.newArrayList(words, words);
+    List<String> words = Arrays.asList(sentence.split(" "));
+    List<List<String>> localDoc = Arrays.asList(words, words);
     JavaRDD<List<String>> doc = sc.parallelize(localDoc);
     Word2Vec word2vec = new Word2Vec()
       .setVectorSize(10)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
index d7c2cb3ae2067..2bef7a8609757 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaAssociationRulesSuite.java
@@ -17,17 +17,16 @@
 package org.apache.spark.mllib.fpm;
 
 import java.io.Serializable;
+import java.util.Arrays;
 
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
-import com.google.common.collect.Lists;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.mllib.fpm.FPGrowth.FreqItemset;
 
-
 public class JavaAssociationRulesSuite implements Serializable {
   private transient JavaSparkContext sc;
 
@@ -46,7 +45,7 @@ public void tearDown() {
   public void runAssociationRules() {
 
     @SuppressWarnings("unchecked")
-    JavaRDD<FPGrowth.FreqItemset<String>> freqItemsets = sc.parallelize(Lists.newArrayList(
+    JavaRDD<FPGrowth.FreqItemset<String>> freqItemsets = sc.parallelize(Arrays.asList(
       new FreqItemset<String>(new String[] {"a"}, 15L),
       new FreqItemset<String>(new String[] {"b"}, 35L),
       new FreqItemset<String>(new String[] {"a", "b"}, 12L)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java
index 9ce2c52dca8b6..154f75d75e4a6 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/fpm/JavaFPGrowthSuite.java
@@ -18,13 +18,12 @@
 package org.apache.spark.mllib.fpm;
 
 import java.io.Serializable;
-import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
-import com.google.common.collect.Lists;
 import static org.junit.Assert.*;
 
 import org.apache.spark.api.java.JavaRDD;
@@ -48,13 +47,13 @@ public void tearDown() {
   public void runFPGrowth() {
 
     @SuppressWarnings("unchecked")
-    JavaRDD<ArrayList<String>> rdd = sc.parallelize(Lists.newArrayList(
-      Lists.newArrayList("r z h k p".split(" ")),
-      Lists.newArrayList("z y x w v u t s".split(" ")),
-      Lists.newArrayList("s x o n r".split(" ")),
-      Lists.newArrayList("x z y m t s q e".split(" ")),
-      Lists.newArrayList("z".split(" ")),
-      Lists.newArrayList("x z y r q t p".split(" "))), 2);
+    JavaRDD<List<String>> rdd = sc.parallelize(Arrays.asList(
+      Arrays.asList("r z h k p".split(" ")),
+      Arrays.asList("z y x w v u t s".split(" ")),
+      Arrays.asList("s x o n r".split(" ")),
+      Arrays.asList("x z y m t s q e".split(" ")),
+      Arrays.asList("z".split(" ")),
+      Arrays.asList("x z y r q t p".split(" "))), 2);
 
     FPGrowthModel<String> model = new FPGrowth()
       .setMinSupport(0.5)
diff --git a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java
index 1421067dc61ed..77c8c6274f374 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaVectorsSuite.java
@@ -18,11 +18,10 @@
 package org.apache.spark.mllib.linalg;
 
 import java.io.Serializable;
+import java.util.Arrays;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
-
 import org.junit.Test;
 import static org.junit.Assert.*;
 
@@ -37,7 +36,7 @@ public void denseArrayConstruction() {
   @Test
   public void sparseArrayConstruction() {
     @SuppressWarnings("unchecked")
-    Vector v = Vectors.sparse(3, Lists.<Tuple2<Integer, Double>>newArrayList(
+    Vector v = Vectors.sparse(3, Arrays.asList(
         new Tuple2<Integer, Double>(0, 2.0),
         new Tuple2<Integer, Double>(2, 3.0)));
     assertArrayEquals(new double[]{2.0, 0.0, 3.0}, v.toArray(), 0.0);
diff --git a/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java
index fcc13c00cbdc5..33d81b1e9592b 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/random/JavaRandomRDDsSuite.java
@@ -17,7 +17,8 @@
 
 package org.apache.spark.mllib.random;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.apache.spark.api.java.JavaRDD;
 import org.junit.Assert;
 import org.junit.After;
@@ -51,7 +52,7 @@ public void testUniformRDD() {
     JavaDoubleRDD rdd1 = uniformJavaRDD(sc, m);
     JavaDoubleRDD rdd2 = uniformJavaRDD(sc, m, p);
     JavaDoubleRDD rdd3 = uniformJavaRDD(sc, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -64,7 +65,7 @@ public void testNormalRDD() {
     JavaDoubleRDD rdd1 = normalJavaRDD(sc, m);
     JavaDoubleRDD rdd2 = normalJavaRDD(sc, m, p);
     JavaDoubleRDD rdd3 = normalJavaRDD(sc, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -79,7 +80,7 @@ public void testLNormalRDD() {
     JavaDoubleRDD rdd1 = logNormalJavaRDD(sc, mean, std, m);
     JavaDoubleRDD rdd2 = logNormalJavaRDD(sc, mean, std, m, p);
     JavaDoubleRDD rdd3 = logNormalJavaRDD(sc, mean, std, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -93,7 +94,7 @@ public void testPoissonRDD() {
     JavaDoubleRDD rdd1 = poissonJavaRDD(sc, mean, m);
     JavaDoubleRDD rdd2 = poissonJavaRDD(sc, mean, m, p);
     JavaDoubleRDD rdd3 = poissonJavaRDD(sc, mean, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -107,7 +108,7 @@ public void testExponentialRDD() {
     JavaDoubleRDD rdd1 = exponentialJavaRDD(sc, mean, m);
     JavaDoubleRDD rdd2 = exponentialJavaRDD(sc, mean, m, p);
     JavaDoubleRDD rdd3 = exponentialJavaRDD(sc, mean, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -122,7 +123,7 @@ public void testGammaRDD() {
     JavaDoubleRDD rdd1 = gammaJavaRDD(sc, shape, scale, m);
     JavaDoubleRDD rdd2 = gammaJavaRDD(sc, shape, scale, m, p);
     JavaDoubleRDD rdd3 = gammaJavaRDD(sc, shape, scale, m, p, seed);
-    for (JavaDoubleRDD rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaDoubleRDD rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
     }
   }
@@ -138,7 +139,7 @@ public void testUniformVectorRDD() {
     JavaRDD<Vector> rdd1 = uniformJavaVectorRDD(sc, m, n);
     JavaRDD<Vector> rdd2 = uniformJavaVectorRDD(sc, m, n, p);
     JavaRDD<Vector> rdd3 = uniformJavaVectorRDD(sc, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
@@ -154,7 +155,7 @@ public void testNormalVectorRDD() {
     JavaRDD<Vector> rdd1 = normalJavaVectorRDD(sc, m, n);
     JavaRDD<Vector> rdd2 = normalJavaVectorRDD(sc, m, n, p);
     JavaRDD<Vector> rdd3 = normalJavaVectorRDD(sc, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
@@ -172,7 +173,7 @@ public void testLogNormalVectorRDD() {
     JavaRDD<Vector> rdd1 = logNormalJavaVectorRDD(sc, mean, std, m, n);
     JavaRDD<Vector> rdd2 = logNormalJavaVectorRDD(sc, mean, std, m, n, p);
     JavaRDD<Vector> rdd3 = logNormalJavaVectorRDD(sc, mean, std, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
@@ -189,7 +190,7 @@ public void testPoissonVectorRDD() {
     JavaRDD<Vector> rdd1 = poissonJavaVectorRDD(sc, mean, m, n);
     JavaRDD<Vector> rdd2 = poissonJavaVectorRDD(sc, mean, m, n, p);
     JavaRDD<Vector> rdd3 = poissonJavaVectorRDD(sc, mean, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
@@ -206,7 +207,7 @@ public void testExponentialVectorRDD() {
     JavaRDD<Vector> rdd1 = exponentialJavaVectorRDD(sc, mean, m, n);
     JavaRDD<Vector> rdd2 = exponentialJavaVectorRDD(sc, mean, m, n, p);
     JavaRDD<Vector> rdd3 = exponentialJavaVectorRDD(sc, mean, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
@@ -224,7 +225,7 @@ public void testGammaVectorRDD() {
     JavaRDD<Vector> rdd1 = gammaJavaVectorRDD(sc, shape, scale, m, n);
     JavaRDD<Vector> rdd2 = gammaJavaVectorRDD(sc, shape, scale, m, n, p);
     JavaRDD<Vector> rdd3 = gammaJavaVectorRDD(sc, shape, scale, m, n, p, seed);
-    for (JavaRDD<Vector> rdd: Lists.newArrayList(rdd1, rdd2, rdd3)) {
+    for (JavaRDD<Vector> rdd: Arrays.asList(rdd1, rdd2, rdd3)) {
       Assert.assertEquals(m, rdd.count());
       Assert.assertEquals(n, rdd.first().size());
     }
diff --git a/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java b/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java
index af688c504cf1e..271dda4662e0d 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/recommendation/JavaALSSuite.java
@@ -18,12 +18,12 @@
 package org.apache.spark.mllib.recommendation;
 
 import java.io.Serializable;
+import java.util.ArrayList;
 import java.util.List;
 
 import scala.Tuple2;
 import scala.Tuple3;
 
-import com.google.common.collect.Lists;
 import org.jblas.DoubleMatrix;
 import org.junit.After;
 import org.junit.Assert;
@@ -56,8 +56,7 @@ void validatePrediction(
       double matchThreshold,
       boolean implicitPrefs,
       DoubleMatrix truePrefs) {
-    List<Tuple2<Integer, Integer>> localUsersProducts =
-      Lists.newArrayListWithCapacity(users * products);
+    List<Tuple2<Integer, Integer>> localUsersProducts = new ArrayList(users * products);
     for (int u=0; u < users; ++u) {
       for (int p=0; p < products; ++p) {
         localUsersProducts.add(new Tuple2<Integer, Integer>(u, p));
diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java
index d38fc91ace3cf..32c2f4f3395b7 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaIsotonicRegressionSuite.java
@@ -18,11 +18,12 @@
 package org.apache.spark.mllib.regression;
 
 import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple3;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Assert;
 import org.junit.Before;
@@ -36,7 +37,7 @@ public class JavaIsotonicRegressionSuite implements Serializable {
   private transient JavaSparkContext sc;
 
   private List<Tuple3<Double, Double, Double>> generateIsotonicInput(double[] labels) {
-    List<Tuple3<Double, Double, Double>> input = Lists.newArrayList();
+    ArrayList<Tuple3<Double, Double, Double>> input = new ArrayList(labels.length);
 
     for (int i = 1; i <= labels.length; i++) {
       input.add(new Tuple3<Double, Double, Double>(labels[i-1], (double) i, 1d));
@@ -77,7 +78,7 @@ public void testIsotonicRegressionPredictionsJavaRDD() {
     IsotonicRegressionModel model =
       runIsotonicRegression(new double[]{1, 2, 3, 3, 1, 6, 7, 8, 11, 9, 10, 12});
 
-    JavaDoubleRDD testRDD = sc.parallelizeDoubles(Lists.newArrayList(0.0, 1.0, 9.5, 12.0, 13.0));
+    JavaDoubleRDD testRDD = sc.parallelizeDoubles(Arrays.asList(0.0, 1.0, 9.5, 12.0, 13.0));
     List<Double> predictions = model.predict(testRDD).collect();
 
     Assert.assertTrue(predictions.get(0) == 1d);
diff --git a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java
index 899c4ea607869..dbf6488d41085 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/regression/JavaStreamingLinearRegressionSuite.java
@@ -18,11 +18,11 @@
 package org.apache.spark.mllib.regression;
 
 import java.io.Serializable;
+import java.util.Arrays;
 import java.util.List;
 
 import scala.Tuple2;
 
-import com.google.common.collect.Lists;
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -59,16 +59,16 @@ public void tearDown() {
   @Test
   @SuppressWarnings("unchecked")
   public void javaAPI() {
-    List<LabeledPoint> trainingBatch = Lists.newArrayList(
+    List<LabeledPoint> trainingBatch = Arrays.asList(
       new LabeledPoint(1.0, Vectors.dense(1.0)),
       new LabeledPoint(0.0, Vectors.dense(0.0)));
     JavaDStream<LabeledPoint> training =
-      attachTestInputStream(ssc, Lists.newArrayList(trainingBatch, trainingBatch), 2);
-    List<Tuple2<Integer, Vector>> testBatch = Lists.newArrayList(
+      attachTestInputStream(ssc, Arrays.asList(trainingBatch, trainingBatch), 2);
+    List<Tuple2<Integer, Vector>> testBatch = Arrays.asList(
       new Tuple2<Integer, Vector>(10, Vectors.dense(1.0)),
       new Tuple2<Integer, Vector>(11, Vectors.dense(0.0)));
     JavaPairDStream<Integer, Vector> test = JavaPairDStream.fromJavaDStream(
-      attachTestInputStream(ssc, Lists.newArrayList(testBatch, testBatch), 2));
+      attachTestInputStream(ssc, Arrays.asList(testBatch, testBatch), 2));
     StreamingLinearRegressionWithSGD slr = new StreamingLinearRegressionWithSGD()
       .setNumIterations(2)
       .setInitialWeights(Vectors.dense(0.0));
diff --git a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
index eb4e3698624bc..4795809e47a46 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/stat/JavaStatisticsSuite.java
@@ -19,7 +19,8 @@
 
 import java.io.Serializable;
 
-import com.google.common.collect.Lists;
+import java.util.Arrays;
+
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
@@ -50,8 +51,8 @@ public void tearDown() {
 
   @Test
   public void testCorr() {
-    JavaRDD<Double> x = sc.parallelize(Lists.newArrayList(1.0, 2.0, 3.0, 4.0));
-    JavaRDD<Double> y = sc.parallelize(Lists.newArrayList(1.1, 2.2, 3.1, 4.3));
+    JavaRDD<Double> x = sc.parallelize(Arrays.asList(1.0, 2.0, 3.0, 4.0));
+    JavaRDD<Double> y = sc.parallelize(Arrays.asList(1.1, 2.2, 3.1, 4.3));
 
     Double corr1 = Statistics.corr(x, y);
     Double corr2 = Statistics.corr(x, y, "pearson");
@@ -61,7 +62,7 @@ public void testCorr() {
 
   @Test
   public void kolmogorovSmirnovTest() {
-    JavaDoubleRDD data = sc.parallelizeDoubles(Lists.newArrayList(0.2, 1.0, -1.0, 2.0));
+    JavaDoubleRDD data = sc.parallelizeDoubles(Arrays.asList(0.2, 1.0, -1.0, 2.0));
     KolmogorovSmirnovTestResult testResult1 = Statistics.kolmogorovSmirnovTest(data, "norm");
     KolmogorovSmirnovTestResult testResult2 = Statistics.kolmogorovSmirnovTest(
       data, "norm", 0.0, 1.0);
@@ -69,7 +70,7 @@ public void kolmogorovSmirnovTest() {
 
   @Test
   public void chiSqTest() {
-    JavaRDD<LabeledPoint> data = sc.parallelize(Lists.newArrayList(
+    JavaRDD<LabeledPoint> data = sc.parallelize(Arrays.asList(
       new LabeledPoint(0.0, Vectors.dense(0.1, 2.3)),
       new LabeledPoint(1.0, Vectors.dense(1.5, 5.1)),
       new LabeledPoint(0.0, Vectors.dense(2.4, 8.1))));

From fdd466bed7a7151dd066d732ef98d225f4acda4a Mon Sep 17 00:00:00 2001
From: Vyacheslav Baranov <slavik.baranov@gmail.com>
Date: Thu, 27 Aug 2015 18:56:18 +0100
Subject: [PATCH 447/633] [SPARK-10182] [MLLIB] GeneralizedLinearModel doesn't
 unpersist cached data

`GeneralizedLinearModel` creates a cached RDD when building a model. It's inconvenient, since these RDDs flood the memory when building several models in a row, so useful data might get evicted from the cache.

The proposed solution is to always cache the dataset & remove the warning. There's a caveat though: input dataset gets evaluated twice, in line 270 when fitting `StandardScaler` for the first time, and when running optimizer for the second time. So, it might worth to return removed warning.

Another possible solution is to disable caching entirely & return removed warning. I don't really know what approach is better.

Author: Vyacheslav Baranov <slavik.baranov@gmail.com>

Closes #8395 from SlavikBaranov/SPARK-10182.
---
 .../spark/mllib/regression/GeneralizedLinearAlgorithm.scala  | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 7e3b4d5648fe3..8f657bfb9c730 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -359,6 +359,11 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
         + " parent RDDs are also uncached.")
     }
 
+    // Unpersist cached data
+    if (data.getStorageLevel != StorageLevel.NONE) {
+      data.unpersist(false)
+    }
+
     createModel(weights, intercept)
   }
 }

From dc86a227e4fc8a9d8c3e8c68da8dff9298447fd0 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Thu, 27 Aug 2015 11:45:15 -0700
Subject: [PATCH 448/633] [SPARK-9148] [SPARK-10252] [SQL] Update SQL
 Programming Guide

Author: Michael Armbrust <michael@databricks.com>

Closes #8441 from marmbrus/documentation.
---
 docs/sql-programming-guide.md | 92 +++++++++++++++++++++++++++--------
 1 file changed, 73 insertions(+), 19 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index e64190b9b209d..99fec6c7785af 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -11,7 +11,7 @@ title: Spark SQL and DataFrames
 
 Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrames and can also act as distributed SQL query engine.
 
-For how to enable Hive support, please refer to the [Hive Tables](#hive-tables) section.
+Spark SQL can also be used to read data from an existing Hive installation.  For more on how to configure this feature, please refer to the [Hive Tables](#hive-tables) section.
 
 # DataFrames
 
@@ -213,6 +213,11 @@ df.groupBy("age").count().show()
 // 30   1
 {% endhighlight %}
 
+For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/scala/index.html#org.apache.spark.sql.DataFrame).
+
+In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more.  The complete list is available in the [DataFrame Function Reference](api/scala/index.html#org.apache.spark.sql.DataFrame).
+
+
 </div>
 
 <div data-lang="java" markdown="1">
@@ -263,6 +268,10 @@ df.groupBy("age").count().show();
 // 30   1
 {% endhighlight %}
 
+For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/java/org/apache/spark/sql/DataFrame.html).
+
+In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more.  The complete list is available in the [DataFrame Function Reference](api/java/org/apache/spark/sql/functions.html).
+
 </div>
 
 <div data-lang="python"  markdown="1">
@@ -320,6 +329,10 @@ df.groupBy("age").count().show()
 
 {% endhighlight %}
 
+For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/python/pyspark.sql.html#pyspark.sql.DataFrame).
+
+In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more.  The complete list is available in the [DataFrame Function Reference](api/python/pyspark.sql.html#module-pyspark.sql.functions).
+
 </div>
 
 <div data-lang="r"  markdown="1">
@@ -370,10 +383,13 @@ showDF(count(groupBy(df, "age")))
 
 {% endhighlight %}
 
-</div>
+For a complete list of the types of operations that can be performed on a DataFrame refer to the [API Documentation](api/R/index.html).
+
+In addition to simple column references and expressions, DataFrames also have a rich library of functions including string manipulation, date arithmetic, common math operations and more.  The complete list is available in the [DataFrame Function Reference](api/R/index.html).
 
 </div>
 
+</div>
 
 ## Running SQL Queries Programmatically
 
@@ -870,12 +886,11 @@ saveDF(select(df, "name", "age"), "namesAndAges.parquet", "parquet")
 
 Save operations can optionally take a `SaveMode`, that specifies how to handle existing data if
 present.  It is important to realize that these save modes do not utilize any locking and are not
-atomic.  Thus, it is not safe to have multiple writers attempting to write to the same location.
-Additionally, when performing a `Overwrite`, the data will be deleted before writing out the
+atomic.  Additionally, when performing a `Overwrite`, the data will be deleted before writing out the
 new data.
 
 <table class="table">
-<tr><th>Scala/Java</th><th>Python</th><th>Meaning</th></tr>
+<tr><th>Scala/Java</th><th>Any Language</th><th>Meaning</th></tr>
 <tr>
   <td><code>SaveMode.ErrorIfExists</code> (default)</td>
   <td><code>"error"</code> (default)</td>
@@ -1671,12 +1686,12 @@ results <- collect(sql(sqlContext, "FROM src SELECT key, value"))
 ### Interacting with Different Versions of Hive Metastore
 
 One of the most important pieces of Spark SQL's Hive support is interaction with Hive metastore,
-which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below.
+which enables Spark SQL to access metadata of Hive tables. Starting from Spark 1.4.0, a single binary 
+build of Spark SQL can be used to query different versions of Hive metastores, using the configuration described below.
+Note that independent of the version of Hive that is being used to talk to the metastore, internally Spark SQL
+will compile against Hive 1.2.1 and use those classes for internal execution (serdes, UDFs, UDAFs, etc).
 
-Internally, Spark SQL uses two Hive clients, one for executing native Hive commands like `SET`
-and `DESCRIBE`, the other dedicated for communicating with Hive metastore. The former uses Hive
-jars of version 0.13.1, which are bundled with Spark 1.4.0. The latter uses Hive jars of the
-version specified by users. An isolated classloader is used here to avoid dependency conflicts.
+The following options can be used to configure the version of Hive that is used to retrieve metadata:
 
 <table class="table">
   <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
@@ -1685,7 +1700,7 @@ version specified by users. An isolated classloader is used here to avoid depend
     <td><code>0.13.1</code></td>
     <td>
       Version of the Hive metastore. Available
-      options are <code>0.12.0</code> and <code>0.13.1</code>. Support for more versions is coming in the future.
+      options are <code>0.12.0</code> through <code>1.2.1</code>.
     </td>
   </tr>
   <tr>
@@ -1696,12 +1711,16 @@ version specified by users. An isolated classloader is used here to avoid depend
       property can be one of three options:
       <ol>
         <li><code>builtin</code></li>
-        Use Hive 0.13.1, which is bundled with the Spark assembly jar when <code>-Phive</code> is
+        Use Hive 1.2.1, which is bundled with the Spark assembly jar when <code>-Phive</code> is
         enabled. When this option is chosen, <code>spark.sql.hive.metastore.version</code> must be
-        either <code>0.13.1</code> or not defined.
+        either <code>1.2.1</code> or not defined.
         <li><code>maven</code></li>
-        Use Hive jars of specified version downloaded from Maven repositories.
-        <li>A classpath in the standard format for both Hive and Hadoop.</li>
+        Use Hive jars of specified version downloaded from Maven repositories.  This configuration
+        is not generally recommended for production deployments. 
+        <li>A classpath in the standard format for the JVM.  This classpath must include all of Hive 
+        and its dependencies, including the correct version of Hadoop.  These jars only need to be
+        present on the driver, but if you are running in yarn cluster mode then you must ensure
+        they are packaged with you application.</li>
       </ol>
     </td>
   </tr>
@@ -2017,6 +2036,28 @@ options.
 
 # Migration Guide
 
+## Upgrading From Spark SQL 1.4 to 1.5
+
+ - Optimized execution using manually managed memory (Tungsten) is now enabled by default, along with
+   code generation for expression evaluation.  These features can both be disabled by setting
+   `spark.sql.tungsten.enabled` to `false.
+ - Parquet schema merging is no longer enabled by default.  It can be re-enabled by setting 
+   `spark.sql.parquet.mergeSchema` to `true`.
+ - Resolution of strings to columns in python now supports using dots (`.`) to qualify the column or 
+   access nested values.  For example `df['table.column.nestedField']`.  However, this means that if 
+   your column name contains any dots you must now escape them using backticks (e.g., ``table.`column.with.dots`.nested``).   
+ - In-memory columnar storage partition pruning is on by default. It can be disabled by setting
+   `spark.sql.inMemoryColumnarStorage.partitionPruning` to `false`.
+ - Unlimited precision decimal columns are no longer supported, instead Spark SQL enforces a maximum
+   precision of 38.  When inferring schema from `BigDecimal` objects, a precision of (38, 18) is now
+   used. When no precision is specified in DDL then the default remains `Decimal(10, 0)`.
+ - Timestamps are now stored at a precision of 1us, rather than 1ns
+ - In the `sql` dialect, floating point numbers are now parsed as decimal.  HiveQL parsing remains
+   unchanged.
+ - The canonical name of SQL/DataFrame functions are now lower case (e.g. sum vs SUM).
+ - It has been determined that using the DirectOutputCommitter when speculation is enabled is unsafe
+   and thus this output committer will not be used when speculation is on, independent of configuration.
+
 ## Upgrading from Spark SQL 1.3 to 1.4
 
 #### DataFrame data reader/writer interface
@@ -2038,7 +2079,8 @@ See the API docs for `SQLContext.read` (
 
 #### DataFrame.groupBy retains grouping columns
 
-Based on user feedback, we changed the default behavior of `DataFrame.groupBy().agg()` to retain the grouping columns in the resulting `DataFrame`. To keep the behavior in 1.3, set `spark.sql.retainGroupColumns` to `false`.
+Based on user feedback, we changed the default behavior of `DataFrame.groupBy().agg()` to retain the
+grouping columns in the resulting `DataFrame`. To keep the behavior in 1.3, set `spark.sql.retainGroupColumns` to `false`.
 
 <div class="codetabs">
 <div data-lang="scala"  markdown="1">
@@ -2175,7 +2217,7 @@ Python UDF registration is unchanged.
 When using DataTypes in Python you will need to construct them (i.e. `StringType()`) instead of
 referencing a singleton.
 
-## Migration Guide for Shark User
+## Migration Guide for Shark Users
 
 ### Scheduling
 To set a [Fair Scheduler](job-scheduling.html#fair-scheduler-pools) pool for a JDBC client session,
@@ -2251,6 +2293,7 @@ Spark SQL supports the vast majority of Hive features, such as:
 * User defined functions (UDF)
 * User defined aggregation functions (UDAF)
 * User defined serialization formats (SerDes)
+* Window functions
 * Joins
   * `JOIN`
   * `{LEFT|RIGHT|FULL} OUTER JOIN`
@@ -2261,7 +2304,7 @@ Spark SQL supports the vast majority of Hive features, such as:
   * `SELECT col FROM ( SELECT a + b AS col from t1) t2`
 * Sampling
 * Explain
-* Partitioned tables
+* Partitioned tables including dynamic partition insertion
 * View
 * All Hive DDL Functions, including:
   * `CREATE TABLE`
@@ -2323,8 +2366,9 @@ releases of Spark SQL.
   Hive can optionally merge the small files into fewer large files to avoid overflowing the HDFS
   metadata. Spark SQL does not support that.
 
+# Reference
 
-# Data Types
+## Data Types
 
 Spark SQL and DataFrames support the following data types:
 
@@ -2937,3 +2981,13 @@ from pyspark.sql.types import *
 
 </div>
 
+## NaN Semantics
+
+There is specially handling for not-a-number (NaN) when dealing with `float` or `double` types that
+does not exactly match standard floating point semantics.
+Specifically:
+
+ - NaN = NaN returns true.
+ - In aggregations all NaN values are grouped together.
+ - NaN is treated as a normal value in join keys.
+ - NaN values go last when in ascending order, larger than any other numeric value.

From 84baa5e9b5edc8c55871fbed5057324450bf097f Mon Sep 17 00:00:00 2001
From: CodingCat <zhunansjtu@gmail.com>
Date: Thu, 27 Aug 2015 20:19:09 +0100
Subject: [PATCH 449/633] [SPARK-10315] remove document on
 spark.akka.failure-detector.threshold

https://issues.apache.org/jira/browse/SPARK-10315

this parameter is not used any longer and there is some mistake in the current document , should be 'akka.remote.watch-failure-detector.threshold'

Author: CodingCat <zhunansjtu@gmail.com>

Closes #8483 from CodingCat/SPARK_10315.
---
 docs/configuration.md | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index 4a6e4dd05b661..77c5cbc7b3196 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -906,16 +906,6 @@ Apart from these, the following properties are also available, and may be useful
 #### Networking
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
-<tr>
-  <td><code>spark.akka.failure-detector.threshold</code></td>
-  <td>300.0</td>
-  <td>
-     This is set to a larger value to disable failure detector that comes inbuilt akka. It can be
-     enabled again, if you plan to use this feature (Not recommended). This maps to akka's
-     `akka.remote.transport-failure-detector.threshold`. Tune this in combination of
-     `spark.akka.heartbeat.pauses` and `spark.akka.heartbeat.interval` if you need to.
-  </td>
-</tr>
 <tr>
   <td><code>spark.akka.frameSize</code></td>
   <td>128</td>

From 6185cdd2afcd492b77ff225b477b3624e3bc7bb2 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Thu, 27 Aug 2015 13:57:20 -0700
Subject: [PATCH 450/633] [SPARK-9901] User guide for RowMatrix Tall-and-skinny
 QR

jira: https://issues.apache.org/jira/browse/SPARK-9901

The jira covers only the document update. I can further provide example code for QR (like the ones for SVD and PCA) in a separate PR.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #8462 from hhbyyh/qrDoc.
---
 docs/mllib-data-types.md | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md
index f0e8d5495675d..065bf4727624f 100644
--- a/docs/mllib-data-types.md
+++ b/docs/mllib-data-types.md
@@ -337,7 +337,10 @@ limited by the integer range but it should be much smaller in practice.
 <div data-lang="scala" markdown="1">
 
 A [`RowMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) can be
-created from an `RDD[Vector]` instance.  Then we can compute its column summary statistics.
+created from an `RDD[Vector]` instance.  Then we can compute its column summary statistics and decompositions.
+[QR decomposition](https://en.wikipedia.org/wiki/QR_decomposition) is of the form A = QR where Q is an orthogonal matrix and R is an upper triangular matrix.
+For [singular value decomposition (SVD)](https://en.wikipedia.org/wiki/Singular_value_decomposition) and [principal component analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis), please refer to [Dimensionality reduction](mllib-dimensionality-reduction.html).
+
 
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.Vector
@@ -350,6 +353,9 @@ val mat: RowMatrix = new RowMatrix(rows)
 // Get its size.
 val m = mat.numRows()
 val n = mat.numCols()
+
+// QR decomposition 
+val qrResult = mat.tallSkinnyQR(true)
 {% endhighlight %}
 </div>
 
@@ -370,6 +376,9 @@ RowMatrix mat = new RowMatrix(rows.rdd());
 // Get its size.
 long m = mat.numRows();
 long n = mat.numCols();
+
+// QR decomposition 
+QRDecomposition<RowMatrix, Matrix> result = mat.tallSkinnyQR(true);
 {% endhighlight %}
 </div>
 

From c94ecdfc5b3c0fe6c38a170dc2af9259354dc9e3 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 27 Aug 2015 15:33:43 -0700
Subject: [PATCH 451/633] [SPARK-9906] [ML] User guide for
 LogisticRegressionSummary

User guide for LogisticRegression summaries

Author: MechCoder <manojkumarsivaraj334@gmail.com>
Author: Manoj Kumar <mks542@nyu.edu>
Author: Feynman Liang <fliang@databricks.com>

Closes #8197 from MechCoder/log_summary_user_guide.
---
 docs/ml-linear-methods.md | 149 ++++++++++++++++++++++++++++++++++----
 1 file changed, 133 insertions(+), 16 deletions(-)

diff --git a/docs/ml-linear-methods.md b/docs/ml-linear-methods.md
index 1ac83d94c9e81..2761aeb789621 100644
--- a/docs/ml-linear-methods.md
+++ b/docs/ml-linear-methods.md
@@ -23,20 +23,41 @@ displayTitle: <a href="ml-guide.html">ML</a> - Linear Methods
 \]`
 
 
-In MLlib, we implement popular linear methods such as logistic regression and linear least squares with L1 or L2 regularization. Refer to [the linear methods in mllib](mllib-linear-methods.html) for details. In `spark.ml`, we also include Pipelines API for [Elastic net](http://en.wikipedia.org/wiki/Elastic_net_regularization), a hybrid of L1 and L2 regularization proposed in [this paper](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf). Mathematically it is defined as a linear combination of the L1-norm and the L2-norm:
+In MLlib, we implement popular linear methods such as logistic
+regression and linear least squares with $L_1$ or $L_2$ regularization.
+Refer to [the linear methods in mllib](mllib-linear-methods.html) for
+details.  In `spark.ml`, we also include Pipelines API for [Elastic
+net](http://en.wikipedia.org/wiki/Elastic_net_regularization), a hybrid
+of $L_1$ and $L_2$ regularization proposed in [Zou et al, Regularization
+and variable selection via the elastic
+net](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf).
+Mathematically, it is defined as a convex combination of the $L_1$ and
+the $L_2$ regularization terms:
 `\[
-\alpha \|\wv\|_1 + (1-\alpha) \frac{1}{2}\|\wv\|_2^2, \alpha \in [0, 1].
+\alpha~\lambda \|\wv\|_1 + (1-\alpha) \frac{\lambda}{2}\|\wv\|_2^2, \alpha \in [0, 1], \lambda \geq 0.
 \]`
-By setting $\alpha$ properly, it contains both L1 and L2 regularization as special cases. For example, if a [linear regression](https://en.wikipedia.org/wiki/Linear_regression) model is trained with the elastic net parameter $\alpha$ set to $1$, it is equivalent to a [Lasso](http://en.wikipedia.org/wiki/Least_squares#Lasso_method) model. On the other hand, if $\alpha$ is set to $0$, the trained model reduces to a [ridge regression](http://en.wikipedia.org/wiki/Tikhonov_regularization) model. We implement Pipelines API for both linear regression and logistic regression with elastic net regularization.
-
-**Examples**
+By setting $\alpha$ properly, elastic net contains both $L_1$ and $L_2$
+regularization as special cases. For example, if a [linear
+regression](https://en.wikipedia.org/wiki/Linear_regression) model is
+trained with the elastic net parameter $\alpha$ set to $1$, it is
+equivalent to a
+[Lasso](http://en.wikipedia.org/wiki/Least_squares#Lasso_method) model.
+On the other hand, if $\alpha$ is set to $0$, the trained model reduces
+to a [ridge
+regression](http://en.wikipedia.org/wiki/Tikhonov_regularization) model.
+We implement Pipelines API for both linear regression and logistic
+regression with elastic net regularization.
+
+## Example: Logistic Regression
+
+The following example shows how to train a logistic regression model
+with elastic net regularization. `elasticNetParam` corresponds to
+$\alpha$ and `regParam` corresponds to $\lambda$.
 
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
-
 {% highlight scala %}
-
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.mllib.util.MLUtils
 
@@ -53,15 +74,11 @@ val lrModel = lr.fit(training)
 
 // Print the weights and intercept for logistic regression
 println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
-
 {% endhighlight %}
-
 </div>
 
 <div data-lang="java" markdown="1">
-
 {% highlight java %}
-
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.ml.classification.LogisticRegressionModel;
 import org.apache.spark.mllib.regression.LabeledPoint;
@@ -99,9 +116,7 @@ public class LogisticRegressionWithElasticNetExample {
 </div>
 
 <div data-lang="python" markdown="1">
-
 {% highlight python %}
-
 from pyspark.ml.classification import LogisticRegression
 from pyspark.mllib.regression import LabeledPoint
 from pyspark.mllib.util import MLUtils
@@ -118,12 +133,114 @@ lrModel = lr.fit(training)
 print("Weights: " + str(lrModel.weights))
 print("Intercept: " + str(lrModel.intercept))
 {% endhighlight %}
+</div>
 
 </div>
 
+The `spark.ml` implementation of logistic regression also supports
+extracting a summary of the model over the training set. Note that the
+predictions and metrics which are stored as `Dataframe` in
+`BinaryLogisticRegressionSummary` are annotated `@transient` and hence
+only available on the driver.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+[`LogisticRegressionTrainingSummary`](api/scala/index.html#org.apache.spark.ml.classification.LogisticRegressionTrainingSummary)
+provides a summary for a
+[`LogisticRegressionModel`](api/scala/index.html#org.apache.spark.ml.classification.LogisticRegressionModel).
+Currently, only binary classification is supported and the
+summary must be explicitly cast to
+[`BinaryLogisticRegressionTrainingSummary`](api/scala/index.html#org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary).
+This will likely change when multiclass classification is supported.
+
+Continuing the earlier example:
+
+{% highlight scala %}
+// Extract the summary from the returned LogisticRegressionModel instance trained in the earlier example
+val trainingSummary = lrModel.summary
+
+// Obtain the loss per iteration.
+val objectiveHistory = trainingSummary.objectiveHistory
+objectiveHistory.foreach(loss => println(loss))
+
+// Obtain the metrics useful to judge performance on test data.
+// We cast the summary to a BinaryLogisticRegressionSummary since the problem is a
+// binary classification problem.
+val binarySummary = trainingSummary.asInstanceOf[BinaryLogisticRegressionSummary]
+
+// Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
+val roc = binarySummary.roc
+roc.show()
+roc.select("FPR").show()
+println(binarySummary.areaUnderROC)
+
+// Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
+// this selected threshold.
+val fMeasure = binarySummary.fMeasureByThreshold
+val maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0)
+val bestThreshold = fMeasure.where($"F-Measure" === maxFMeasure).
+  select("threshold").head().getDouble(0)
+logReg.setThreshold(bestThreshold)
+logReg.fit(logRegDataFrame)
+{% endhighlight %}
 </div>
 
-### Optimization
+<div data-lang="java" markdown="1">
+[`LogisticRegressionTrainingSummary`](api/java/org/apache/spark/ml/classification/LogisticRegressionTrainingSummary.html)
+provides a summary for a
+[`LogisticRegressionModel`](api/java/org/apache/spark/ml/classification/LogisticRegressionModel.html).
+Currently, only binary classification is supported and the
+summary must be explicitly cast to
+[`BinaryLogisticRegressionTrainingSummary`](api/java/org/apache/spark/ml/classification/BinaryLogisticRegressionTrainingSummary.html).
+This will likely change when multiclass classification is supported.
+
+Continuing the earlier example:
+
+{% highlight java %}
+// Extract the summary from the returned LogisticRegressionModel instance trained in the earlier example
+LogisticRegressionTrainingSummary trainingSummary = logRegModel.summary();
+
+// Obtain the loss per iteration.
+double[] objectiveHistory = trainingSummary.objectiveHistory();
+for (double lossPerIteration : objectiveHistory) {
+  System.out.println(lossPerIteration);
+}
+
+// Obtain the metrics useful to judge performance on test data.
+// We cast the summary to a BinaryLogisticRegressionSummary since the problem is a
+// binary classification problem.
+BinaryLogisticRegressionSummary binarySummary = (BinaryLogisticRegressionSummary) trainingSummary;
+
+// Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
+DataFrame roc = binarySummary.roc();
+roc.show();
+roc.select("FPR").show();
+System.out.println(binarySummary.areaUnderROC());
+
+// Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
+// this selected threshold.
+DataFrame fMeasure = binarySummary.fMeasureByThreshold();
+double maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0);
+double bestThreshold = fMeasure.where(fMeasure.col("F-Measure").equalTo(maxFMeasure)).
+  select("threshold").head().getDouble(0);
+logReg.setThreshold(bestThreshold);
+logReg.fit(logRegDataFrame);
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+Logistic regression model summary is not yet supported in Python.
+</div>
+
+</div>
+
+# Optimization
+
+The optimization algorithm underlying the implementation is called
+[Orthant-Wise Limited-memory
+QuasiNewton](http://research-srv.microsoft.com/en-us/um/people/jfgao/paper/icml07scalable.pdf)
+(OWL-QN). It is an extension of L-BFGS that can effectively handle L1
+regularization and elastic net.
 
-The optimization algorithm underlies the implementation is called [Orthant-Wise Limited-memory QuasiNewton](http://research-srv.microsoft.com/en-us/um/people/jfgao/paper/icml07scalable.pdf)
-(OWL-QN). It is an extension of L-BFGS that can effectively handle L1 regularization and elastic net.

From 5bfe9e1111d9862084586549a7dc79476f67bab9 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 16:10:37 -0700
Subject: [PATCH 452/633] [SPARK-9680] [MLLIB] [DOC] StopWordsRemovers user
 guide and Java compatibility test

* Adds user guide for ml.feature.StopWordsRemovers, ran code examples on my machine
* Cleans up scaladocs for public methods
* Adds test for Java compatibility
* Follow up Python user guide code example is tracked by SPARK-10249

Author: Feynman Liang <fliang@databricks.com>

Closes #8436 from feynmanliang/SPARK-10230.
---
 docs/ml-features.md                           | 102 +++++++++++++++++-
 .../ml/feature/JavaStopWordsRemoverSuite.java |  72 +++++++++++++
 2 files changed, 171 insertions(+), 3 deletions(-)
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 62de4838981cb..89a9bad570446 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -306,15 +306,111 @@ regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern=
 </div>
 </div>
 
+## StopWordsRemover
+[Stop words](https://en.wikipedia.org/wiki/Stop_words) are words which
+should be excluded from the input, typically because the words appear
+frequently and don't carry as much meaning.
+
+`StopWordsRemover` takes as input a sequence of strings (e.g. the output
+of a [Tokenizer](ml-features.html#tokenizer)) and drops all the stop
+words from the input sequences. The list of stopwords is specified by
+the `stopWords` parameter.  We provide [a list of stop
+words](http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words) by
+default, accessible by calling `getStopWords` on a newly instantiated
+`StopWordsRemover` instance.
 
-## $n$-gram
+**Examples**
 
-An [n-gram](https://en.wikipedia.org/wiki/N-gram) is a sequence of $n$ tokens (typically words) for some integer $n$. The `NGram` class can be used to transform input features into $n$-grams.
+Assume that we have the following DataFrame with columns `id` and `raw`:
 
-`NGram` takes as input a sequence of strings (e.g. the output of a [Tokenizer](ml-features.html#tokenizer).  The parameter `n` is used to determine the number of terms in each $n$-gram. The output will consist of a sequence of $n$-grams where each $n$-gram is represented by a space-delimited string of $n$ consecutive words.  If the input sequence contains fewer than `n` strings, no output is produced.
+~~~~
+ id | raw
+----|----------
+ 0  | [I, saw, the, red, baloon]
+ 1  | [Mary, had, a, little, lamb]
+~~~~
+
+Applying `StopWordsRemover` with `raw` as the input column and `filtered` as the output
+column, we should get the following:
+
+~~~~
+ id | raw                         | filtered
+----|-----------------------------|--------------------
+ 0  | [I, saw, the, red, baloon]  |  [saw, red, baloon]
+ 1  | [Mary, had, a, little, lamb]|[Mary, little, lamb]
+~~~~
+
+In `filtered`, the stop words "I", "the", "had", and "a" have been
+filtered out.
 
 <div class="codetabs">
+
 <div data-lang="scala" markdown="1">
+
+[`StopWordsRemover`](api/scala/index.html#org.apache.spark.ml.feature.StopWordsRemover)
+takes an input column name, an output column name, a list of stop words,
+and a boolean indicating if the matches should be case sensitive (false
+by default).
+
+{% highlight scala %}
+import org.apache.spark.ml.feature.StopWordsRemover
+
+val remover = new StopWordsRemover()
+  .setInputCol("raw")
+  .setOutputCol("filtered")
+val dataSet = sqlContext.createDataFrame(Seq(
+  (0, Seq("I", "saw", "the", "red", "baloon")),
+  (1, Seq("Mary", "had", "a", "little", "lamb"))
+)).toDF("id", "raw")
+
+remover.transform(dataSet).show()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`StopWordsRemover`](api/java/org/apache/spark/ml/feature/StopWordsRemover.html)
+takes an input column name, an output column name, a list of stop words,
+and a boolean indicating if the matches should be case sensitive (false
+by default).
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.StopWordsRemover;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+StopWordsRemover remover = new StopWordsRemover()
+  .setInputCol("raw")
+  .setOutputCol("filtered");
+
+JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")),
+  RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb"))
+));
+StructType schema = new StructType(new StructField[] {
+  new StructField("raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
+});
+DataFrame dataset = jsql.createDataFrame(rdd, schema);
+
+remover.transform(dataset).show();
+{% endhighlight %}
+</div>
+</div>
+
+## $n$-gram
+
+An [n-gram](https://en.wikipedia.org/wiki/N-gram) is a sequence of $n$ tokens (typically words) for some integer $n$. The `NGram` class can be used to transform input features into $n$-grams.
+
+`NGram` takes as input a sequence of strings (e.g. the output of a [Tokenizer](ml-features.html#tokenizer)).  The parameter `n` is used to determine the number of terms in each $n$-gram. The output will consist of a sequence of $n$-grams where each $n$-gram is represented by a space-delimited string of $n$ consecutive words.  If the input sequence contains fewer than `n` strings, no output is produced.
+
 <div class="codetabs">
 
 <div data-lang="scala" markdown="1">
diff --git a/mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java
new file mode 100644
index 0000000000000..76cdd0fae84ab
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/feature/JavaStopWordsRemoverSuite.java
@@ -0,0 +1,72 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature;
+
+import java.util.Arrays;
+
+import org.junit.After;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+
+
+public class JavaStopWordsRemoverSuite {
+
+  private transient JavaSparkContext jsc;
+  private transient SQLContext jsql;
+
+  @Before
+  public void setUp() {
+    jsc = new JavaSparkContext("local", "JavaStopWordsRemoverSuite");
+    jsql = new SQLContext(jsc);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+  }
+
+  @Test
+  public void javaCompatibilityTest() {
+    StopWordsRemover remover = new StopWordsRemover()
+      .setInputCol("raw")
+      .setOutputCol("filtered");
+
+    JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(
+      RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")),
+      RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb"))
+    ));
+    StructType schema = new StructType(new StructField[] {
+      new StructField("raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
+    });
+    DataFrame dataset = jsql.createDataFrame(rdd, schema);
+
+    remover.transform(dataset).collect();
+  }
+}

From b3dd569ad40905f8861a547a1e25ed3ca8e1d272 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Thu, 27 Aug 2015 16:11:25 -0700
Subject: [PATCH 453/633] [SPARK-10287] [SQL] Fixes JSONRelation refreshing on
 read path

https://issues.apache.org/jira/browse/SPARK-10287

After porting json to HadoopFsRelation, it seems hard to keep the behavior of picking up new files automatically for JSON. This PR removes this behavior, so JSON is consistent with others (ORC and Parquet).

Author: Yin Huai <yhuai@databricks.com>

Closes #8469 from yhuai/jsonRefresh.
---
 docs/sql-programming-guide.md                     |  6 ++++++
 .../execution/datasources/json/JSONRelation.scala |  9 ---------
 .../org/apache/spark/sql/sources/interfaces.scala |  2 +-
 .../apache/spark/sql/sources/InsertSuite.scala    | 15 ---------------
 4 files changed, 7 insertions(+), 25 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 99fec6c7785af..e8eb88488ee24 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -2057,6 +2057,12 @@ options.
  - The canonical name of SQL/DataFrame functions are now lower case (e.g. sum vs SUM).
  - It has been determined that using the DirectOutputCommitter when speculation is enabled is unsafe
    and thus this output committer will not be used when speculation is on, independent of configuration.
+ - JSON data source will not automatically load new files that are created by other applications
+   (i.e. files that are not inserted to the dataset through Spark SQL).
+   For a JSON persistent table (i.e. the metadata of the table is stored in Hive Metastore),
+   users can use `REFRESH TABLE` SQL command or `HiveContext`'s `refreshTable` method
+   to include those new files to the table. For a DataFrame representing a JSON dataset, users need to recreate
+   the DataFrame and the new DataFrame will include new files.
 
 ## Upgrading from Spark SQL 1.3 to 1.4
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
index 114c8b211891e..ab8ca5f748f24 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
@@ -111,15 +111,6 @@ private[sql] class JSONRelation(
     jsonSchema
   }
 
-  override private[sql] def buildScan(
-      requiredColumns: Array[String],
-      filters: Array[Filter],
-      inputPaths: Array[String],
-      broadcastedConf: Broadcast[SerializableConfiguration]): RDD[Row] = {
-    refresh()
-    super.buildScan(requiredColumns, filters, inputPaths, broadcastedConf)
-  }
-
   override def buildScan(
       requiredColumns: Array[String],
       filters: Array[Filter],
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index b3b326fe612c7..dff726b33fc74 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -562,7 +562,7 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
     })
   }
 
-  private[sql] def buildScan(
+  final private[sql] def buildScan(
       requiredColumns: Array[String],
       filters: Array[Filter],
       inputPaths: Array[String],
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
index 78bd3e5582964..084d83f6e9bff 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
@@ -167,21 +167,6 @@ class InsertSuite extends DataSourceTest with SharedSQLContext {
     )
   }
 
-  test("save directly to the path of a JSON table") {
-    caseInsensitiveContext.table("jt").selectExpr("a * 5 as a", "b")
-      .write.mode(SaveMode.Overwrite).json(path.toString)
-    checkAnswer(
-      sql("SELECT a, b FROM jsonTable"),
-      (1 to 10).map(i => Row(i * 5, s"str$i"))
-    )
-
-    caseInsensitiveContext.table("jt").write.mode(SaveMode.Overwrite).json(path.toString)
-    checkAnswer(
-      sql("SELECT a, b FROM jsonTable"),
-      (1 to 10).map(i => Row(i, s"str$i"))
-    )
-  }
-
   test("it is not allowed to write to a table while querying it.") {
     val message = intercept[AnalysisException] {
       sql(

From 54cda0deb6bebf1470f16ba5bcc6c4fb842bdac1 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Thu, 27 Aug 2015 16:38:00 -0700
Subject: [PATCH 454/633] [SPARK-10321] sizeInBytes in HadoopFsRelation

Having sizeInBytes in HadoopFsRelation to enable broadcast join.

cc marmbrus

Author: Davies Liu <davies@databricks.com>

Closes #8490 from davies/sizeInByte.
---
 .../main/scala/org/apache/spark/sql/sources/interfaces.scala    | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
index dff726b33fc74..7b030b7d73bd5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala
@@ -518,6 +518,8 @@ abstract class HadoopFsRelation private[sql](maybePartitionSpec: Option[Partitio
 
   override def inputFiles: Array[String] = cachedLeafStatuses().map(_.getPath.toString).toArray
 
+  override def sizeInBytes: Long = cachedLeafStatuses().map(_.getLen).sum
+
   /**
    * Partition columns.  Can be either defined by [[userDefinedPartitionColumns]] or automatically
    * discovered.  Note that they should always be nullable.

From 1f90c5e2198bcf49e115d97ec300c17c1be4dcb4 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Thu, 27 Aug 2015 19:38:53 -0700
Subject: [PATCH 455/633] [SPARK-8505] [SPARKR] Add settings to kick `lint-r`
 from `./dev/run-test.py`

JoshRosen we'd like to check the SparkR source code with the `dev/lint-r` script on the Jenkins. I tried to incorporate the script into `dev/run-test.py`. Could you review it when you have time?

shivaram I modified `dev/lint-r` and `dev/lint-r.R` to install lintr package into a local directory(`R/lib/`) and to exit with a lint status. Could you review it?

- [[SPARK-8505] Add settings to kick `lint-r` from `./dev/run-test.py` - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8505)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #7883 from yu-iskw/SPARK-8505.
---
 dev/lint-r             | 11 +++++++++++
 dev/lint-r.R           | 12 +++++++-----
 dev/run-tests-codes.sh | 13 +++++++------
 dev/run-tests-jenkins  |  2 ++
 dev/run-tests.py       | 21 ++++++++++++++++++++-
 5 files changed, 47 insertions(+), 12 deletions(-)

diff --git a/dev/lint-r b/dev/lint-r
index 7d5f4cd31153d..c15d57aad86da 100755
--- a/dev/lint-r
+++ b/dev/lint-r
@@ -28,3 +28,14 @@ if ! type "Rscript" > /dev/null; then
 fi
 
 `which Rscript` --vanilla "$SPARK_ROOT_DIR/dev/lint-r.R" "$SPARK_ROOT_DIR" | tee "$LINT_R_REPORT_FILE_NAME"
+
+NUM_LINES=`wc -l < "$LINT_R_REPORT_FILE_NAME"`
+if [ "$NUM_LINES" = "0" ] ; then
+  lint_status=0
+  echo "lintr checks passed."
+else
+  lint_status=1
+  echo "lintr checks failed."
+fi
+
+exit "$lint_status"
diff --git a/dev/lint-r.R b/dev/lint-r.R
index 48bd6246096ae..999eef571b824 100644
--- a/dev/lint-r.R
+++ b/dev/lint-r.R
@@ -17,8 +17,14 @@
 
 argv <- commandArgs(TRUE)
 SPARK_ROOT_DIR <- as.character(argv[1])
+LOCAL_LIB_LOC <- file.path(SPARK_ROOT_DIR, "R", "lib")
 
-# Installs lintr from Github.
+# Checks if SparkR is installed in a local directory.
+if (! library(SparkR, lib.loc = LOCAL_LIB_LOC, logical.return = TRUE)) {
+  stop("You should install SparkR in a local directory with `R/install-dev.sh`.")
+}
+
+# Installs lintr from Github in a local directory.
 # NOTE: The CRAN's version is too old to adapt to our rules.
 if ("lintr" %in% row.names(installed.packages())  == FALSE) {
   devtools::install_github("jimhester/lintr")
@@ -27,9 +33,5 @@ if ("lintr" %in% row.names(installed.packages())  == FALSE) {
 library(lintr)
 library(methods)
 library(testthat)
-if (! library(SparkR, lib.loc = file.path(SPARK_ROOT_DIR, "R", "lib"), logical.return = TRUE)) {
-  stop("You should install SparkR in a local directory with `R/install-dev.sh`.")
-}
-
 path.to.package <- file.path(SPARK_ROOT_DIR, "R", "pkg")
 lint_package(path.to.package, cache = FALSE)
diff --git a/dev/run-tests-codes.sh b/dev/run-tests-codes.sh
index f4b238e1b78a7..1f16790522e76 100644
--- a/dev/run-tests-codes.sh
+++ b/dev/run-tests-codes.sh
@@ -21,9 +21,10 @@ readonly BLOCK_GENERAL=10
 readonly BLOCK_RAT=11
 readonly BLOCK_SCALA_STYLE=12
 readonly BLOCK_PYTHON_STYLE=13
-readonly BLOCK_DOCUMENTATION=14
-readonly BLOCK_BUILD=15
-readonly BLOCK_MIMA=16
-readonly BLOCK_SPARK_UNIT_TESTS=17
-readonly BLOCK_PYSPARK_UNIT_TESTS=18
-readonly BLOCK_SPARKR_UNIT_TESTS=19
+readonly BLOCK_R_STYLE=14
+readonly BLOCK_DOCUMENTATION=15
+readonly BLOCK_BUILD=16
+readonly BLOCK_MIMA=17
+readonly BLOCK_SPARK_UNIT_TESTS=18
+readonly BLOCK_PYSPARK_UNIT_TESTS=19
+readonly BLOCK_SPARKR_UNIT_TESTS=20
diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins
index f144c053046c5..39cf54f78104c 100755
--- a/dev/run-tests-jenkins
+++ b/dev/run-tests-jenkins
@@ -210,6 +210,8 @@ done
       failing_test="Scala style tests"
     elif [ "$test_result" -eq "$BLOCK_PYTHON_STYLE" ]; then
       failing_test="Python style tests"
+    elif [ "$test_result" -eq "$BLOCK_R_STYLE" ]; then
+      failing_test="R style tests"
     elif [ "$test_result" -eq "$BLOCK_DOCUMENTATION" ]; then
       failing_test="to generate documentation"
     elif [ "$test_result" -eq "$BLOCK_BUILD" ]; then
diff --git a/dev/run-tests.py b/dev/run-tests.py
index f689425ee40b6..4fd703a7c219f 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -209,6 +209,18 @@ def run_python_style_checks():
     run_cmd([os.path.join(SPARK_HOME, "dev", "lint-python")])
 
 
+def run_sparkr_style_checks():
+    set_title_and_block("Running R style checks", "BLOCK_R_STYLE")
+
+    if which("R"):
+        # R style check should be executed after `install-dev.sh`.
+        # Since warnings about `no visible global function definition` appear
+        # without the installation. SEE ALSO: SPARK-9121.
+        run_cmd([os.path.join(SPARK_HOME, "dev", "lint-r")])
+    else:
+        print("Ignoring SparkR style check as R was not found in PATH")
+
+
 def build_spark_documentation():
     set_title_and_block("Building Spark Documentation", "BLOCK_DOCUMENTATION")
     os.environ["PRODUCTION"] = "1 jekyll build"
@@ -387,7 +399,6 @@ def run_sparkr_tests():
     set_title_and_block("Running SparkR tests", "BLOCK_SPARKR_UNIT_TESTS")
 
     if which("R"):
-        run_cmd([os.path.join(SPARK_HOME, "R", "install-dev.sh")])
         run_cmd([os.path.join(SPARK_HOME, "R", "run-tests.sh")])
     else:
         print("Ignoring SparkR tests as R was not found in PATH")
@@ -438,6 +449,12 @@ def main():
     if java_version.minor < 8:
         print("[warn] Java 8 tests will not run because JDK version is < 1.8.")
 
+    # install SparkR
+    if which("R"):
+        run_cmd([os.path.join(SPARK_HOME, "R", "install-dev.sh")])
+    else:
+        print("Can't install SparkR as R is was not found in PATH")
+
     if os.environ.get("AMPLAB_JENKINS"):
         # if we're on the Amplab Jenkins build servers setup variables
         # to reflect the environment settings
@@ -485,6 +502,8 @@ def main():
         run_scala_style_checks()
     if not changed_files or any(f.endswith(".py") for f in changed_files):
         run_python_style_checks()
+    if not changed_files or any(f.endswith(".R") for f in changed_files):
+        run_sparkr_style_checks()
 
     # determine if docs were changed and if we're inside the amplab environment
     # note - the below commented out until *all* Jenkins workers can get `jekyll` installed

From 30734d45fbbb269437c062241a9161e198805a76 Mon Sep 17 00:00:00 2001
From: MechCoder <manojkumarsivaraj334@gmail.com>
Date: Thu, 27 Aug 2015 21:44:06 -0700
Subject: [PATCH 456/633] [SPARK-9911] [DOC] [ML] Update Userguide for
 Evaluator

I added a small note about the different types of evaluator and the metrics used.

Author: MechCoder <manojkumarsivaraj334@gmail.com>

Closes #8304 from MechCoder/multiclass_evaluator.
---
 docs/ml-guide.md | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index de8fead3529e4..01bf5ee18e328 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -643,6 +643,13 @@ An important task in ML is *model selection*, or using data to find the best mod
 Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator) class, which takes an `Estimator`, a set of `ParamMap`s, and an [`Evaluator`](api/scala/index.html#org.apache.spark.ml.Evaluator).
 `CrossValidator` begins by splitting the dataset into a set of *folds* which are used as separate training and test datasets; e.g., with `$k=3$` folds, `CrossValidator` will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.
 `CrossValidator` iterates through the set of `ParamMap`s. For each `ParamMap`, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
+
+The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.RegressionEvaluator)
+for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.BinaryClassificationEvaluator)
+for binary data or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.MultiClassClassificationEvaluator)
+for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the setMetric
+method in each of these evaluators.
+
 The `ParamMap` which produces the best evaluation metric (averaged over the `$k$` folds) is selected as the best model.
 `CrossValidator` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.
 
@@ -708,9 +715,12 @@ val pipeline = new Pipeline()
 // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
 // This will allow us to jointly choose parameters for all Pipeline stages.
 // A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+// Note that the evaluator here is a BinaryClassificationEvaluator and the default metric
+// used is areaUnderROC.
 val crossval = new CrossValidator()
   .setEstimator(pipeline)
   .setEvaluator(new BinaryClassificationEvaluator)
+
 // We use a ParamGridBuilder to construct a grid of parameters to search over.
 // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
 // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
@@ -831,9 +841,12 @@ Pipeline pipeline = new Pipeline()
 // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
 // This will allow us to jointly choose parameters for all Pipeline stages.
 // A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+// Note that the evaluator here is a BinaryClassificationEvaluator and the default metric
+// used is areaUnderROC.
 CrossValidator crossval = new CrossValidator()
     .setEstimator(pipeline)
     .setEvaluator(new BinaryClassificationEvaluator());
+
 // We use a ParamGridBuilder to construct a grid of parameters to search over.
 // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
 // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.

From af0e1249b1c881c0fa7a921fd21fd2c27214b980 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Thu, 27 Aug 2015 21:55:20 -0700
Subject: [PATCH 457/633] [SPARK-9905] [ML] [DOC] Adds LinearRegressionSummary
 user guide

* Adds user guide for `LinearRegressionSummary`
* Fixes unresolved issues in  #8197

CC jkbradley mengxr

Author: Feynman Liang <fliang@databricks.com>

Closes #8491 from feynmanliang/SPARK-9905.
---
 docs/ml-linear-methods.md | 140 ++++++++++++++++++++++++++++++++++----
 1 file changed, 127 insertions(+), 13 deletions(-)

diff --git a/docs/ml-linear-methods.md b/docs/ml-linear-methods.md
index 2761aeb789621..cdd9d4999fa1b 100644
--- a/docs/ml-linear-methods.md
+++ b/docs/ml-linear-methods.md
@@ -34,7 +34,7 @@ net](http://users.stat.umn.edu/~zouxx019/Papers/elasticnet.pdf).
 Mathematically, it is defined as a convex combination of the $L_1$ and
 the $L_2$ regularization terms:
 `\[
-\alpha~\lambda \|\wv\|_1 + (1-\alpha) \frac{\lambda}{2}\|\wv\|_2^2, \alpha \in [0, 1], \lambda \geq 0.
+\alpha \left( \lambda \|\wv\|_1 \right) + (1-\alpha) \left( \frac{\lambda}{2}\|\wv\|_2^2 \right) , \alpha \in [0, 1], \lambda \geq 0
 \]`
 By setting $\alpha$ properly, elastic net contains both $L_1$ and $L_2$
 regularization as special cases. For example, if a [linear
@@ -95,7 +95,7 @@ public class LogisticRegressionWithElasticNetExample {
 
     SparkContext sc = new SparkContext(conf);
     SQLContext sql = new SQLContext(sc);
-    String path = "sample_libsvm_data.txt";
+    String path = "data/mllib/sample_libsvm_data.txt";
 
     // Load training data
     DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
@@ -103,7 +103,7 @@ public class LogisticRegressionWithElasticNetExample {
     LogisticRegression lr = new LogisticRegression()
       .setMaxIter(10)
       .setRegParam(0.3)
-      .setElasticNetParam(0.8)
+      .setElasticNetParam(0.8);
 
     // Fit the model
     LogisticRegressionModel lrModel = lr.fit(training);
@@ -158,10 +158,12 @@ This will likely change when multiclass classification is supported.
 Continuing the earlier example:
 
 {% highlight scala %}
+import org.apache.spark.ml.classification.BinaryLogisticRegressionSummary
+
 // Extract the summary from the returned LogisticRegressionModel instance trained in the earlier example
 val trainingSummary = lrModel.summary
 
-// Obtain the loss per iteration.
+// Obtain the objective per iteration.
 val objectiveHistory = trainingSummary.objectiveHistory
 objectiveHistory.foreach(loss => println(loss))
 
@@ -173,17 +175,14 @@ val binarySummary = trainingSummary.asInstanceOf[BinaryLogisticRegressionSummary
 // Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
 val roc = binarySummary.roc
 roc.show()
-roc.select("FPR").show()
 println(binarySummary.areaUnderROC)
 
-// Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
-// this selected threshold.
+// Set the model threshold to maximize F-Measure
 val fMeasure = binarySummary.fMeasureByThreshold
 val maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0)
 val bestThreshold = fMeasure.where($"F-Measure" === maxFMeasure).
   select("threshold").head().getDouble(0)
-logReg.setThreshold(bestThreshold)
-logReg.fit(logRegDataFrame)
+lrModel.setThreshold(bestThreshold)
 {% endhighlight %}
 </div>
 
@@ -199,8 +198,12 @@ This will likely change when multiclass classification is supported.
 Continuing the earlier example:
 
 {% highlight java %}
+import org.apache.spark.ml.classification.LogisticRegressionTrainingSummary;
+import org.apache.spark.ml.classification.BinaryLogisticRegressionSummary;
+import org.apache.spark.sql.functions;
+
 // Extract the summary from the returned LogisticRegressionModel instance trained in the earlier example
-LogisticRegressionTrainingSummary trainingSummary = logRegModel.summary();
+LogisticRegressionTrainingSummary trainingSummary = lrModel.summary();
 
 // Obtain the loss per iteration.
 double[] objectiveHistory = trainingSummary.objectiveHistory();
@@ -222,20 +225,131 @@ System.out.println(binarySummary.areaUnderROC());
 // Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
 // this selected threshold.
 DataFrame fMeasure = binarySummary.fMeasureByThreshold();
-double maxFMeasure = fMeasure.select(max("F-Measure")).head().getDouble(0);
+double maxFMeasure = fMeasure.select(functions.max("F-Measure")).head().getDouble(0);
 double bestThreshold = fMeasure.where(fMeasure.col("F-Measure").equalTo(maxFMeasure)).
   select("threshold").head().getDouble(0);
-logReg.setThreshold(bestThreshold);
-logReg.fit(logRegDataFrame);
+lrModel.setThreshold(bestThreshold);
 {% endhighlight %}
 </div>
 
+<!--- TODO: Add python model summaries once implemented -->
 <div data-lang="python" markdown="1">
 Logistic regression model summary is not yet supported in Python.
 </div>
 
 </div>
 
+## Example: Linear Regression
+
+The interface for working with linear regression models and model
+summaries is similar to the logistic regression case. The following
+example demonstrates training an elastic net regularized linear
+regression model and extracting model summary statistics.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.mllib.util.MLUtils
+
+// Load training data
+val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+val lr = new LinearRegression()
+  .setMaxIter(10)
+  .setRegParam(0.3)
+  .setElasticNetParam(0.8)
+
+// Fit the model
+val lrModel = lr.fit(training)
+
+// Print the weights and intercept for linear regression
+println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
+
+// Summarize the model over the training set and print out some metrics
+val trainingSummary = lrModel.summary
+println(s"numIterations: ${trainingSummary.totalIterations}")
+println(s"objectiveHistory: ${trainingSummary.objectiveHistory.toList}")
+trainingSummary.residuals.show()
+println(s"RMSE: ${trainingSummary.rootMeanSquaredError}")
+println(s"r2: ${trainingSummary.r2}")
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+import org.apache.spark.ml.regression.LinearRegression;
+import org.apache.spark.ml.regression.LinearRegressionModel;
+import org.apache.spark.ml.regression.LinearRegressionTrainingSummary;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+import org.apache.spark.SparkContext;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.SQLContext;
+
+public class LinearRegressionWithElasticNetExample {
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf()
+      .setAppName("Linear Regression with Elastic Net Example");
+
+    SparkContext sc = new SparkContext(conf);
+    SQLContext sql = new SQLContext(sc);
+    String path = "data/mllib/sample_libsvm_data.txt";
+
+    // Load training data
+    DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
+
+    LinearRegression lr = new LinearRegression()
+      .setMaxIter(10)
+      .setRegParam(0.3)
+      .setElasticNetParam(0.8);
+
+    // Fit the model
+    LinearRegressionModel lrModel = lr.fit(training);
+
+    // Print the weights and intercept for linear regression
+    System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept());
+
+    // Summarize the model over the training set and print out some metrics
+    LinearRegressionTrainingSummary trainingSummary = lrModel.summary();
+    System.out.println("numIterations: " + trainingSummary.totalIterations());
+    System.out.println("objectiveHistory: " + Vectors.dense(trainingSummary.objectiveHistory()));
+    trainingSummary.residuals().show();
+    System.out.println("RMSE: " + trainingSummary.rootMeanSquaredError());
+    System.out.println("r2: " + trainingSummary.r2());
+  }
+}
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+<!--- TODO: Add python model summaries once implemented -->
+{% highlight python %}
+from pyspark.ml.regression import LinearRegression
+from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.util import MLUtils
+
+# Load training data
+training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+
+lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
+
+# Fit the model
+lrModel = lr.fit(training)
+
+# Print the weights and intercept for linear regression
+print("Weights: " + str(lrModel.weights))
+print("Intercept: " + str(lrModel.intercept))
+
+# Linear regression model summary is not yet supported in Python.
+{% endhighlight %}
+</div>
+
+</div>
+
 # Optimization
 
 The optimization algorithm underlying the implementation is called

From 89b943438512fcfb239c268b43431397de46cbcf Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 27 Aug 2015 22:30:01 -0700
Subject: [PATCH 458/633] [SPARK-SQL] [MINOR] Fixes some typos in HiveContext

Author: Cheng Lian <lian@databricks.com>

Closes #8481 from liancheng/hive-context-typo.
---
 .../scala/org/apache/spark/sql/hive/HiveContext.scala     | 8 ++++----
 .../scala/org/apache/spark/sql/hive/test/TestHive.scala   | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index c0a458fa9ab8d..2e791cea96b41 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -171,11 +171,11 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
    * Overrides default Hive configurations to avoid breaking changes to Spark SQL users.
    *  - allow SQL11 keywords to be used as identifiers
    */
-  private[sql] def defaultOverides() = {
+  private[sql] def defaultOverrides() = {
     setConf(ConfVars.HIVE_SUPPORT_SQL11_RESERVED_KEYWORDS.varname, "false")
   }
 
-  defaultOverides()
+  defaultOverrides()
 
   /**
    * The copy of the Hive client that is used to retrieve metadata from the Hive MetaStore.
@@ -190,8 +190,8 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
     // into the isolated client loader
     val metadataConf = new HiveConf()
 
-    val defaltWarehouseLocation = metadataConf.get("hive.metastore.warehouse.dir")
-    logInfo("defalt warehouse location is " + defaltWarehouseLocation)
+    val defaultWarehouseLocation = metadataConf.get("hive.metastore.warehouse.dir")
+    logInfo("default warehouse location is " + defaultWarehouseLocation)
 
     // `configure` goes second to override other settings.
     val allConfig = metadataConf.asScala.map(e => e.getKey -> e.getValue).toMap ++ configure
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 572eaebe81ac2..57fea5d8db343 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -434,7 +434,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
         case (k, v) =>
           metadataHive.runSqlHive(s"SET $k=$v")
       }
-      defaultOverides()
+      defaultOverrides()
 
       runSqlHive("USE default")
 

From 7583681e6b0824d7eed471dc4d8fa0b2addf9ffc Mon Sep 17 00:00:00 2001
From: noelsmith <mail@noelsmith.com>
Date: Thu, 27 Aug 2015 23:59:30 -0700
Subject: [PATCH 459/633] [SPARK-10188] [PYSPARK] Pyspark CrossValidator with
 RMSE selects incorrect model

* Added isLargerBetter() method to Pyspark Evaluator to match the Scala version.
* JavaEvaluator delegates isLargerBetter() to underlying Scala object.
* Added check for isLargerBetter() in CrossValidator to determine whether to use argmin or argmax.
* Added test cases for where smaller is better (RMSE) and larger is better (R-Squared).

(This contribution is my original work and that I license the work to the project under Sparks' open source license)

Author: noelsmith <mail@noelsmith.com>

Closes #8399 from noel-smith/pyspark-rmse-xval-fix.
---
 python/pyspark/ml/evaluation.py | 12 +++++
 python/pyspark/ml/tests.py      | 87 +++++++++++++++++++++++++++++++++
 python/pyspark/ml/tuning.py     |  6 ++-
 3 files changed, 104 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/ml/evaluation.py b/python/pyspark/ml/evaluation.py
index 6b0a9ffde9f42..cb3b07947e488 100644
--- a/python/pyspark/ml/evaluation.py
+++ b/python/pyspark/ml/evaluation.py
@@ -66,6 +66,14 @@ def evaluate(self, dataset, params=None):
         else:
             raise ValueError("Params must be a param map but got %s." % type(params))
 
+    def isLargerBetter(self):
+        """
+        Indicates whether the metric returned by :py:meth:`evaluate` should be maximized
+        (True, default) or minimized (False).
+        A given evaluator may support multiple metrics which may be maximized or minimized.
+        """
+        return True
+
 
 @inherit_doc
 class JavaEvaluator(Evaluator, JavaWrapper):
@@ -85,6 +93,10 @@ def _evaluate(self, dataset):
         self._transfer_params_to_java()
         return self._java_obj.evaluate(dataset._jdf)
 
+    def isLargerBetter(self):
+        self._transfer_params_to_java()
+        return self._java_obj.isLargerBetter()
+
 
 @inherit_doc
 class BinaryClassificationEvaluator(JavaEvaluator, HasLabelCol, HasRawPredictionCol):
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index c151d21fd661a..60e4237293adc 100644
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -32,11 +32,14 @@
 
 from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
 from pyspark.sql import DataFrame, SQLContext
+from pyspark.sql.functions import rand
+from pyspark.ml.evaluation import RegressionEvaluator
 from pyspark.ml.param import Param, Params
 from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed
 from pyspark.ml.util import keyword_only
 from pyspark.ml import Estimator, Model, Pipeline, Transformer
 from pyspark.ml.feature import *
+from pyspark.ml.tuning import ParamGridBuilder, CrossValidator, CrossValidatorModel
 from pyspark.mllib.linalg import DenseVector
 
 
@@ -264,5 +267,89 @@ def test_ngram(self):
         self.assertEquals(transformedDF.head().output, ["a b c d", "b c d e"])
 
 
+class HasInducedError(Params):
+
+    def __init__(self):
+        super(HasInducedError, self).__init__()
+        self.inducedError = Param(self, "inducedError",
+                                  "Uniformly-distributed error added to feature")
+
+    def getInducedError(self):
+        return self.getOrDefault(self.inducedError)
+
+
+class InducedErrorModel(Model, HasInducedError):
+
+    def __init__(self):
+        super(InducedErrorModel, self).__init__()
+
+    def _transform(self, dataset):
+        return dataset.withColumn("prediction",
+                                  dataset.feature + (rand(0) * self.getInducedError()))
+
+
+class InducedErrorEstimator(Estimator, HasInducedError):
+
+    def __init__(self, inducedError=1.0):
+        super(InducedErrorEstimator, self).__init__()
+        self._set(inducedError=inducedError)
+
+    def _fit(self, dataset):
+        model = InducedErrorModel()
+        self._copyValues(model)
+        return model
+
+
+class CrossValidatorTests(PySparkTestCase):
+
+    def test_fit_minimize_metric(self):
+        sqlContext = SQLContext(self.sc)
+        dataset = sqlContext.createDataFrame([
+            (10, 10.0),
+            (50, 50.0),
+            (100, 100.0),
+            (500, 500.0)] * 10,
+            ["feature", "label"])
+
+        iee = InducedErrorEstimator()
+        evaluator = RegressionEvaluator(metricName="rmse")
+
+        grid = (ParamGridBuilder()
+                .addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
+                .build())
+        cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
+        cvModel = cv.fit(dataset)
+        bestModel = cvModel.bestModel
+        bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
+
+        self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
+                         "Best model should have zero induced error")
+        self.assertEqual(0.0, bestModelMetric, "Best model has RMSE of 0")
+
+    def test_fit_maximize_metric(self):
+        sqlContext = SQLContext(self.sc)
+        dataset = sqlContext.createDataFrame([
+            (10, 10.0),
+            (50, 50.0),
+            (100, 100.0),
+            (500, 500.0)] * 10,
+            ["feature", "label"])
+
+        iee = InducedErrorEstimator()
+        evaluator = RegressionEvaluator(metricName="r2")
+
+        grid = (ParamGridBuilder()
+                .addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
+                .build())
+        cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
+        cvModel = cv.fit(dataset)
+        bestModel = cvModel.bestModel
+        bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
+
+        self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
+                         "Best model should have zero induced error")
+        self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index dcfee6a3170ab..cae778869e9c5 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -223,7 +223,11 @@ def _fit(self, dataset):
                 # TODO: duplicate evaluator to take extra params from input
                 metric = eva.evaluate(model.transform(validation, epm[j]))
                 metrics[j] += metric
-        bestIndex = np.argmax(metrics)
+
+        if eva.isLargerBetter():
+            bestIndex = np.argmax(metrics)
+        else:
+            bestIndex = np.argmin(metrics)
         bestModel = est.fit(dataset, epm[bestIndex])
         return CrossValidatorModel(bestModel)
 

From 2f99c37273c1d82e2ba39476e4429ea4aaba7ec6 Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Fri, 28 Aug 2015 00:37:50 -0700
Subject: [PATCH 460/633] [SPARK-10328] [SPARKR] Fix generic for na.omit

S3 function is at https://stat.ethz.ch/R-manual/R-patched/library/stats/html/na.fail.html

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Author: Shivaram Venkataraman <shivaram.venkataraman@gmail.com>
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8495 from shivaram/na-omit-fix.
---
 R/pkg/R/DataFrame.R              |  6 +++---
 R/pkg/R/generics.R               |  2 +-
 R/pkg/inst/tests/test_sparkSQL.R | 23 ++++++++++++++++++++++-
 dev/lint-r                       |  2 +-
 4 files changed, 27 insertions(+), 6 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index dd8126aebf467..74de7c81e35a6 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -1699,9 +1699,9 @@ setMethod("dropna",
 #' @name na.omit
 #' @export
 setMethod("na.omit",
-          signature(x = "DataFrame"),
-          function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) {
-            dropna(x, how, minNonNulls, cols)
+          signature(object = "DataFrame"),
+          function(object, how = c("any", "all"), minNonNulls = NULL, cols = NULL) {
+            dropna(object, how, minNonNulls, cols)
           })
 
 #' fillna
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index a829d46c1894c..b578b8789d2c5 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -413,7 +413,7 @@ setGeneric("dropna",
 #' @rdname nafunctions
 #' @export
 setGeneric("na.omit",
-           function(x, how = c("any", "all"), minNonNulls = NULL, cols = NULL) {
+           function(object, ...) {
              standardGeneric("na.omit")
            })
 
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 4b672e115f924..933b11c8ee7e2 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -1083,7 +1083,7 @@ test_that("describe() and summarize() on a DataFrame", {
   expect_equal(collect(stats2)[5, "age"], "30")
 })
 
-test_that("dropna() on a DataFrame", {
+test_that("dropna() and na.omit() on a DataFrame", {
   df <- jsonFile(sqlContext, jsonPathNa)
   rows <- collect(df)
 
@@ -1092,6 +1092,8 @@ test_that("dropna() on a DataFrame", {
   expected <- rows[!is.na(rows$name),]
   actual <- collect(dropna(df, cols = "name"))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, cols = "name"))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age),]
   actual <- collect(dropna(df, cols = "age"))
@@ -1101,48 +1103,67 @@ test_that("dropna() on a DataFrame", {
   expect_identical(expected$age, actual$age)
   expect_identical(expected$height, actual$height)
   expect_identical(expected$name, actual$name)
+  actual <- collect(na.omit(df, cols = "age"))
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height),]
   actual <- collect(dropna(df, cols = c("age", "height")))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, cols = c("age", "height")))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
   actual <- collect(dropna(df))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df))
+  expect_identical(expected, actual)
 
   # drop with how
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
   actual <- collect(dropna(df))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) | !is.na(rows$height) | !is.na(rows$name),]
   actual <- collect(dropna(df, "all"))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, "all"))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height) & !is.na(rows$name),]
   actual <- collect(dropna(df, "any"))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, "any"))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) & !is.na(rows$height),]
   actual <- collect(dropna(df, "any", cols = c("age", "height")))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, "any", cols = c("age", "height")))
+  expect_identical(expected, actual)
 
   expected <- rows[!is.na(rows$age) | !is.na(rows$height),]
   actual <- collect(dropna(df, "all", cols = c("age", "height")))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, "all", cols = c("age", "height")))
+  expect_identical(expected, actual)
 
   # drop with threshold
 
   expected <- rows[as.integer(!is.na(rows$age)) + as.integer(!is.na(rows$height)) >= 2,]
   actual <- collect(dropna(df, minNonNulls = 2, cols = c("age", "height")))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, minNonNulls = 2, cols = c("age", "height")))
+  expect_identical(expected, actual)
 
   expected <- rows[as.integer(!is.na(rows$age)) +
                    as.integer(!is.na(rows$height)) +
                    as.integer(!is.na(rows$name)) >= 3,]
   actual <- collect(dropna(df, minNonNulls = 3, cols = c("name", "age", "height")))
   expect_identical(expected, actual)
+  actual <- collect(na.omit(df, minNonNulls = 3, cols = c("name", "age", "height")))
+  expect_identical(expected, actual)
 })
 
 test_that("fillna() on a DataFrame", {
diff --git a/dev/lint-r b/dev/lint-r
index c15d57aad86da..bfda0bca15eb7 100755
--- a/dev/lint-r
+++ b/dev/lint-r
@@ -29,7 +29,7 @@ fi
 
 `which Rscript` --vanilla "$SPARK_ROOT_DIR/dev/lint-r.R" "$SPARK_ROOT_DIR" | tee "$LINT_R_REPORT_FILE_NAME"
 
-NUM_LINES=`wc -l < "$LINT_R_REPORT_FILE_NAME"`
+NUM_LINES=`wc -l < "$LINT_R_REPORT_FILE_NAME" | awk '{print $1}'`
 if [ "$NUM_LINES" = "0" ] ; then
   lint_status=0
   echo "lintr checks passed."

From 4eeda8d472498acd40ef54723d1be9924a273d76 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Fri, 28 Aug 2015 00:50:26 -0700
Subject: [PATCH 461/633] [SPARK-10260] [ML] Add @Since annotation to
 ml.clustering

### JIRA
[[SPARK-10260] Add Since annotation to ml.clustering - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-10260)

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8455 from yu-iskw/SPARK-10260.
---
 .../apache/spark/ml/clustering/KMeans.scala   | 32 +++++++++++++++++--
 1 file changed, 29 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
index 47a18cdb31b53..f40ab71fb22a6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.ml.clustering
 
-import org.apache.spark.annotation.Experimental
+import org.apache.spark.annotation.{Since, Experimental}
 import org.apache.spark.ml.param.{Param, Params, IntParam, ParamMap}
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
@@ -39,9 +39,11 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
    * Set the number of clusters to create (k). Must be > 1. Default: 2.
    * @group param
    */
+  @Since("1.5.0")
   final val k = new IntParam(this, "k", "number of clusters to create", (x: Int) => x > 1)
 
   /** @group getParam */
+  @Since("1.5.0")
   def getK: Int = $(k)
 
   /**
@@ -50,10 +52,12 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
    * (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
    * @group expertParam
    */
+  @Since("1.5.0")
   final val initMode = new Param[String](this, "initMode", "initialization algorithm",
     (value: String) => MLlibKMeans.validateInitMode(value))
 
   /** @group expertGetParam */
+  @Since("1.5.0")
   def getInitMode: String = $(initMode)
 
   /**
@@ -61,10 +65,12 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
    * setting -- the default of 5 is almost always enough. Must be > 0. Default: 5.
    * @group expertParam
    */
+  @Since("1.5.0")
   final val initSteps = new IntParam(this, "initSteps", "number of steps for k-means||",
     (value: Int) => value > 0)
 
   /** @group expertGetParam */
+  @Since("1.5.0")
   def getInitSteps: Int = $(initSteps)
 
   /**
@@ -84,27 +90,32 @@ private[clustering] trait KMeansParams extends Params with HasMaxIter with HasFe
  *
  * @param parentModel a model trained by spark.mllib.clustering.KMeans.
  */
+@Since("1.5.0")
 @Experimental
 class KMeansModel private[ml] (
-    override val uid: String,
+    @Since("1.5.0") override val uid: String,
     private val parentModel: MLlibKMeansModel) extends Model[KMeansModel] with KMeansParams {
 
+  @Since("1.5.0")
   override def copy(extra: ParamMap): KMeansModel = {
     val copied = new KMeansModel(uid, parentModel)
     copyValues(copied, extra)
   }
 
+  @Since("1.5.0")
   override def transform(dataset: DataFrame): DataFrame = {
     val predictUDF = udf((vector: Vector) => predict(vector))
     dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol))))
   }
 
+  @Since("1.5.0")
   override def transformSchema(schema: StructType): StructType = {
     validateAndTransformSchema(schema)
   }
 
   private[clustering] def predict(features: Vector): Int = parentModel.predict(features)
 
+  @Since("1.5.0")
   def clusterCenters: Array[Vector] = parentModel.clusterCenters
 }
 
@@ -114,8 +125,11 @@ class KMeansModel private[ml] (
  *
  * @see [[http://dx.doi.org/10.14778/2180912.2180915 Bahmani et al., Scalable k-means++.]]
  */
+@Since("1.5.0")
 @Experimental
-class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMeansParams {
+class KMeans @Since("1.5.0") (
+    @Since("1.5.0") override val uid: String)
+  extends Estimator[KMeansModel] with KMeansParams {
 
   setDefault(
     k -> 2,
@@ -124,34 +138,45 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
     initSteps -> 5,
     tol -> 1e-4)
 
+  @Since("1.5.0")
   override def copy(extra: ParamMap): KMeans = defaultCopy(extra)
 
+  @Since("1.5.0")
   def this() = this(Identifiable.randomUID("kmeans"))
 
   /** @group setParam */
+  @Since("1.5.0")
   def setFeaturesCol(value: String): this.type = set(featuresCol, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setPredictionCol(value: String): this.type = set(predictionCol, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setK(value: Int): this.type = set(k, value)
 
   /** @group expertSetParam */
+  @Since("1.5.0")
   def setInitMode(value: String): this.type = set(initMode, value)
 
   /** @group expertSetParam */
+  @Since("1.5.0")
   def setInitSteps(value: Int): this.type = set(initSteps, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setMaxIter(value: Int): this.type = set(maxIter, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setTol(value: Double): this.type = set(tol, value)
 
   /** @group setParam */
+  @Since("1.5.0")
   def setSeed(value: Long): this.type = set(seed, value)
 
+  @Since("1.5.0")
   override def fit(dataset: DataFrame): KMeansModel = {
     val rdd = dataset.select(col($(featuresCol))).map { case Row(point: Vector) => point }
 
@@ -167,6 +192,7 @@ class KMeans(override val uid: String) extends Estimator[KMeansModel] with KMean
     copyValues(model)
   }
 
+  @Since("1.5.0")
   override def transformSchema(schema: StructType): StructType = {
     validateAndTransformSchema(schema)
   }

From cc39803062119c1d14611dc227b9ed0ed1284d38 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Fri, 28 Aug 2015 09:32:23 +0100
Subject: [PATCH 462/633] [SPARK-10295] [CORE] Dynamic allocation in Mesos does
 not release when RDDs are cached

Remove obsolete warning about dynamic allocation not working with cached RDDs

See discussion in https://issues.apache.org/jira/browse/SPARK-10295

Author: Sean Owen <sowen@cloudera.com>

Closes #8489 from srowen/SPARK-10295.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 5 -----
 1 file changed, 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index f3da04a7f55d0..738887076b0d1 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1590,11 +1590,6 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    * Register an RDD to be persisted in memory and/or disk storage
    */
   private[spark] def persistRDD(rdd: RDD[_]) {
-    _executorAllocationManager.foreach { _ =>
-      logWarning(
-        s"Dynamic allocation currently does not support cached RDDs. Cached data for RDD " +
-        s"${rdd.id} will be lost when executors are removed.")
-    }
     persistentRdds(rdd.id) = rdd
   }
 

From 18294cd8710427076caa86bfac596de67089d57e Mon Sep 17 00:00:00 2001
From: Keiji Yoshida <yoshida.keiji.84@gmail.com>
Date: Fri, 28 Aug 2015 09:36:50 +0100
Subject: [PATCH 463/633] Fix DynamodDB/DynamoDB typo in Kinesis Integration
 doc

Fix DynamodDB/DynamoDB typo in Kinesis Integration doc

Author: Keiji Yoshida <yoshida.keiji.84@gmail.com>

Closes #8501 from yosssi/patch-1.
---
 docs/streaming-kinesis-integration.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/streaming-kinesis-integration.md b/docs/streaming-kinesis-integration.md
index a7bcaec6fcd84..238a911a9199f 100644
--- a/docs/streaming-kinesis-integration.md
+++ b/docs/streaming-kinesis-integration.md
@@ -91,7 +91,7 @@ A Kinesis stream can be set up at one of the valid Kinesis endpoints with 1 or m
 
 	- Kinesis data processing is ordered per partition and occurs at-least once per message.
 
-	- Multiple applications can read from the same Kinesis stream.  Kinesis will maintain the application-specific shard and checkpoint info in DynamodDB.
+	- Multiple applications can read from the same Kinesis stream.  Kinesis will maintain the application-specific shard and checkpoint info in DynamoDB.
 
 	- A single Kinesis stream shard is processed by one input DStream at a time.
 

From 71a077f6c16c8816eae13341f645ba50d997f63d Mon Sep 17 00:00:00 2001
From: Dharmesh Kakadia <dharmeshkakadia@users.noreply.github.com>
Date: Fri, 28 Aug 2015 09:38:35 +0100
Subject: [PATCH 464/633] typo in comment

Author: Dharmesh Kakadia <dharmeshkakadia@users.noreply.github.com>

Closes #8497 from dharmeshkakadia/patch-2.
---
 .../apache/spark/network/shuffle/protocol/RegisterExecutor.java | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java
index cca8b17c4f129..167ef33104227 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/protocol/RegisterExecutor.java
@@ -27,7 +27,7 @@
 
 /**
  * Initial registration message between an executor and its local shuffle server.
- * Returns nothing (empty bye array).
+ * Returns nothing (empty byte array).
  */
 public class RegisterExecutor extends BlockTransferMessage {
   public final String appId;

From 1502a0f6c5d2f85a331b29d3bf50002911ea393e Mon Sep 17 00:00:00 2001
From: jerryshao <sshao@hortonworks.com>
Date: Fri, 28 Aug 2015 09:32:52 -0500
Subject: [PATCH 465/633] [YARN] [MINOR] Avoid hard code port number in
 YarnShuffleService test

Current port number is fixed as default (7337) in test, this will introduce port contention exception, better to change to a random number in unit test.

squito , seems you're author of this unit test, mind taking a look at this fix? Thanks a lot.

```
[info] - executor state kept across NM restart *** FAILED *** (597 milliseconds)
[info]   org.apache.hadoop.service.ServiceStateException: java.net.BindException: Address already in use
[info]   at org.apache.hadoop.service.ServiceStateException.convert(ServiceStateException.java:59)
[info]   at org.apache.hadoop.service.AbstractService.init(AbstractService.java:172)
[info]   at org.apache.spark.network.yarn.YarnShuffleServiceSuite$$anonfun$1.apply$mcV$sp(YarnShuffleServiceSuite.scala:72)
[info]   at org.apache.spark.network.yarn.YarnShuffleServiceSuite$$anonfun$1.apply(YarnShuffleServiceSuite.scala:70)
[info]   at org.apache.spark.network.yarn.YarnShuffleServiceSuite$$anonfun$1.apply(YarnShuffleServiceSuite.scala:70)
[info]   at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
[info]   at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
[info]   at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
[info]   at org.scalatest.Transformer.apply(Transformer.scala:22)
[info]   at org.scalatest.Transformer.apply(Transformer.scala:20)
[info]   at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
[info]   at org.apache.spark.SparkFunSuite.withFixture(SparkFunSuite.scala:42)
...
```

Author: jerryshao <sshao@hortonworks.com>

Closes #8502 from jerryshao/avoid-hardcode-port.
---
 .../org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala  | 1 +
 1 file changed, 1 insertion(+)

diff --git a/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
index 2f22cbdbeac37..6aa8c814cd4f0 100644
--- a/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/network/yarn/YarnShuffleServiceSuite.scala
@@ -37,6 +37,7 @@ class YarnShuffleServiceSuite extends SparkFunSuite with Matchers with BeforeAnd
     yarnConfig.set(YarnConfiguration.NM_AUX_SERVICES, "spark_shuffle")
     yarnConfig.set(YarnConfiguration.NM_AUX_SERVICE_FMT.format("spark_shuffle"),
       classOf[YarnShuffleService].getCanonicalName)
+    yarnConfig.setInt("spark.shuffle.service.port", 0)
 
     yarnConfig.get("yarn.nodemanager.local-dirs").split(",").foreach { dir =>
       val d = new File(dir)

From e2a843090cb031f6aa774f6d9c031a7f0f732ee1 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Fri, 28 Aug 2015 08:00:44 -0700
Subject: [PATCH 466/633] [SPARK-9890] [DOC] [ML] User guide for
 CountVectorizer

jira: https://issues.apache.org/jira/browse/SPARK-9890

document with Scala and java examples

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #8487 from hhbyyh/cvDoc.
---
 docs/ml-features.md | 109 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 109 insertions(+)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 89a9bad570446..90654d1e5a248 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -211,6 +211,115 @@ for feature in result.select("result").take(3):
 </div>
 </div>
 
+## CountVectorizer
+
+`CountVectorizer` and `CountVectorizerModel` aim to help convert a collection of text documents
+ to vectors of token counts. When an a-priori dictionary is not available, `CountVectorizer` can
+ be used as an `Estimator` to extract the vocabulary and generates a `CountVectorizerModel`. The
+ model produces sparse representations for the documents over the vocabulary, which can then be
+ passed to other algorithms like LDA.
+
+ During the fitting process, `CountVectorizer` will select the top `vocabSize` words ordered by
+ term frequency across the corpus. An optional parameter "minDF" also affect the fitting process
+ by specifying the minimum number (or fraction if < 1.0) of documents a term must appear in to be
+ included in the vocabulary.
+
+**Examples**
+
+Assume that we have the following DataFrame with columns `id` and `texts`:
+
+~~~~
+ id | texts
+----|----------
+ 0  | Array("a", "b", "c")
+ 1  | Array("a", "b", "b", "c", "a")
+~~~~
+
+each row in`texts` is a document of type Array[String].
+Invoking fit of `CountVectorizer` produces a `CountVectorizerModel` with vocabulary (a, b, c),
+then the output column "vector" after transformation contains:
+
+~~~~
+ id | texts                           | vector
+----|---------------------------------|---------------
+ 0  | Array("a", "b", "c")            | (3,[0,1,2],[1.0,1.0,1.0])
+ 1  | Array("a", "b", "b", "c", "a")  | (3,[0,1,2],[2.0,2.0,1.0])
+~~~~
+
+each vector represents the token counts of the document over the vocabulary.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+More details can be found in the API docs for
+[CountVectorizer](api/scala/index.html#org.apache.spark.ml.feature.CountVectorizer) and
+[CountVectorizerModel](api/scala/index.html#org.apache.spark.ml.feature.CountVectorizerModel).
+{% highlight scala %}
+import org.apache.spark.ml.feature.CountVectorizer
+import org.apache.spark.mllib.util.CountVectorizerModel
+
+val df = sqlContext.createDataFrame(Seq(
+  (0, Array("a", "b", "c")),
+  (1, Array("a", "b", "b", "c", "a"))
+)).toDF("id", "words")
+
+// fit a CountVectorizerModel from the corpus
+val cvModel: CountVectorizerModel = new CountVectorizer()
+  .setInputCol("words")
+  .setOutputCol("features")
+  .setVocabSize(3)
+  .setMinDF(2) // a term must appear in more or equal to 2 documents to be included in the vocabulary
+  .fit(df)
+
+// alternatively, define CountVectorizerModel with a-priori vocabulary
+val cvm = new CountVectorizerModel(Array("a", "b", "c"))
+  .setInputCol("words")
+  .setOutputCol("features")
+
+cvModel.transform(df).select("features").show()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+More details can be found in the API docs for
+[CountVectorizer](api/java/org/apache/spark/ml/feature/CountVectorizer.html) and
+[CountVectorizerModel](api/java/org/apache/spark/ml/feature/CountVectorizerModel.html).
+{% highlight java %}
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.CountVectorizer;
+import org.apache.spark.ml.feature.CountVectorizerModel;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.*;
+
+// Input data: Each row is a bag of words from a sentence or document.
+JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(Arrays.asList("a", "b", "c")),
+  RowFactory.create(Arrays.asList("a", "b", "b", "c", "a"))
+));
+StructType schema = new StructType(new StructField [] {
+  new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
+});
+DataFrame df = sqlContext.createDataFrame(jrdd, schema);
+
+// fit a CountVectorizerModel from the corpus
+CountVectorizerModel cvModel = new CountVectorizer()
+  .setInputCol("text")
+  .setOutputCol("feature")
+  .setVocabSize(3)
+  .setMinDF(2) // a term must appear in more or equal to 2 documents to be included in the vocabulary
+  .fit(df);
+
+// alternatively, define CountVectorizerModel with a-priori vocabulary
+CountVectorizerModel cvm = new CountVectorizerModel(new String[]{"a", "b", "c"})
+  .setInputCol("text")
+  .setOutputCol("feature");
+
+cvModel.transform(df).show();
+{% endhighlight %}
+</div>
+</div>
+
 # Feature Transformers
 
 ## Tokenizer

From 499e8e154bdcc9d7b2f685b159e0ddb4eae48fe4 Mon Sep 17 00:00:00 2001
From: Luciano Resende <lresende@apache.org>
Date: Fri, 28 Aug 2015 09:13:21 -0700
Subject: [PATCH 467/633] [SPARK-8952] [SPARKR] - Wrap normalizePath calls with
 suppressWarnings

This is based on davies comment on SPARK-8952 which suggests to only call normalizePath() when path starts with '~'

Author: Luciano Resende <lresende@apache.org>

Closes #8343 from lresende/SPARK-8952.
---
 R/pkg/R/SQLContext.R | 4 ++--
 R/pkg/R/sparkR.R     | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R
index 110117a18ccbc..1bc6445311473 100644
--- a/R/pkg/R/SQLContext.R
+++ b/R/pkg/R/SQLContext.R
@@ -201,7 +201,7 @@ setMethod("toDF", signature(x = "RDD"),
 
 jsonFile <- function(sqlContext, path) {
   # Allow the user to have a more flexible definiton of the text file path
-  path <- normalizePath(path)
+  path <- suppressWarnings(normalizePath(path))
   # Convert a string vector of paths to a string containing comma separated paths
   path <- paste(path, collapse = ",")
   sdf <- callJMethod(sqlContext, "jsonFile", path)
@@ -251,7 +251,7 @@ jsonRDD <- function(sqlContext, rdd, schema = NULL, samplingRatio = 1.0) {
 # TODO: Implement saveasParquetFile and write examples for both
 parquetFile <- function(sqlContext, ...) {
   # Allow the user to have a more flexible definiton of the text file path
-  paths <- lapply(list(...), normalizePath)
+  paths <- lapply(list(...), function(x) suppressWarnings(normalizePath(x)))
   sdf <- callJMethod(sqlContext, "parquetFile", paths)
   dataFrame(sdf)
 }
diff --git a/R/pkg/R/sparkR.R b/R/pkg/R/sparkR.R
index e83104f116422..3c57a44db257d 100644
--- a/R/pkg/R/sparkR.R
+++ b/R/pkg/R/sparkR.R
@@ -160,7 +160,7 @@ sparkR.init <- function(
   })
 
   if (nchar(sparkHome) != 0) {
-    sparkHome <- normalizePath(sparkHome)
+    sparkHome <- suppressWarnings(normalizePath(sparkHome))
   }
 
   sparkEnvirMap <- new.env()

From d3f87dc39480f075170817bbd00142967a938078 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Fri, 28 Aug 2015 11:51:42 -0700
Subject: [PATCH 468/633] [SPARK-10325] Override hashCode() for public Row

This commit fixes an issue where the public SQL `Row` class did not override `hashCode`, causing it to violate the hashCode() + equals() contract. To fix this, I simply ported the `hashCode` implementation from the 1.4.x version of `Row`.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8500 from JoshRosen/SPARK-10325 and squashes the following commits:

51ffea1 [Josh Rosen] Override hashCode() for public Row.
---
 .../src/main/scala/org/apache/spark/sql/Row.scala   | 13 +++++++++++++
 .../test/scala/org/apache/spark/sql/RowSuite.scala  |  9 +++++++++
 2 files changed, 22 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
index cfd9cb0e62598..ed2fdf9f2f7cf 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/Row.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql
 
 import scala.collection.JavaConverters._
+import scala.util.hashing.MurmurHash3
 
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.GenericRow
@@ -410,6 +411,18 @@ trait Row extends Serializable {
     true
   }
 
+  override def hashCode: Int = {
+    // Using Scala's Seq hash code implementation.
+    var n = 0
+    var h = MurmurHash3.seqSeed
+    val len = length
+    while (n < len) {
+      h = MurmurHash3.mix(h, apply(n).##)
+      n += 1
+    }
+    MurmurHash3.finalizeHash(h, n)
+  }
+
   /* ---------------------- utility methods for Scala ---------------------- */
 
   /**
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index 795d4e983f27e..77ccd6f775e50 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -85,4 +85,13 @@ class RowSuite extends SparkFunSuite with SharedSQLContext {
     val r2 = Row(Double.NaN)
     assert(r1 === r2)
   }
+
+  test("equals and hashCode") {
+    val r1 = Row("Hello")
+    val r2 = Row("Hello")
+    assert(r1 === r2)
+    assert(r1.hashCode() === r2.hashCode())
+    val r3 = Row("World")
+    assert(r3.hashCode() != r1.hashCode())
+  }
 }

From c53c902fa9c458200245f919067b41dde9cd9418 Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Fri, 28 Aug 2015 12:33:40 -0700
Subject: [PATCH 469/633] [SPARK-9284] [TESTS] Allow all tests to run without
 an assembly.

This change aims at speeding up the dev cycle a little bit, by making
sure that all tests behave the same w.r.t. where the code to be tested
is loaded from. Namely, that means that tests don't rely on the assembly
anymore, rather loading all needed classes from the build directories.

The main change is to make sure all build directories (classes and test-classes)
are added to the classpath of child processes when running tests.

YarnClusterSuite required some custom code since the executors are run
differently (i.e. not through the launcher library, like standalone and
Mesos do).

I also found a couple of tests that could leak a SparkContext on failure,
and added code to handle those.

With this patch, it's possible to run the following command from a clean
source directory and have all tests pass:

  mvn -Pyarn -Phadoop-2.4 -Phive-thriftserver install

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #7629 from vanzin/SPARK-9284.
---
 bin/spark-class                               | 16 ++++----
 .../spark/launcher/SparkLauncherSuite.java    |  0
 .../spark/broadcast/BroadcastSuite.scala      | 10 ++++-
 .../launcher/AbstractCommandBuilder.java      | 28 ++++++++------
 pom.xml                                       |  8 ++++
 project/SparkBuild.scala                      |  2 +
 .../deploy/yarn/BaseYarnClusterSuite.scala    | 37 +++++++++++++------
 .../spark/deploy/yarn/YarnClusterSuite.scala  | 20 ++++++++--
 .../yarn/YarnShuffleIntegrationSuite.scala    |  2 +-
 .../spark/launcher/TestClasspathBuilder.scala | 36 ++++++++++++++++++
 10 files changed, 122 insertions(+), 37 deletions(-)
 rename {launcher => core}/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java (100%)
 create mode 100644 yarn/src/test/scala/org/apache/spark/launcher/TestClasspathBuilder.scala

diff --git a/bin/spark-class b/bin/spark-class
index 2b59e5df5736f..e38e08dec40e4 100755
--- a/bin/spark-class
+++ b/bin/spark-class
@@ -43,17 +43,19 @@ else
 fi
 
 num_jars="$(ls -1 "$ASSEMBLY_DIR" | grep "^spark-assembly.*hadoop.*\.jar$" | wc -l)"
-if [ "$num_jars" -eq "0" -a -z "$SPARK_ASSEMBLY_JAR" ]; then
+if [ "$num_jars" -eq "0" -a -z "$SPARK_ASSEMBLY_JAR" -a "$SPARK_PREPEND_CLASSES" != "1" ]; then
   echo "Failed to find Spark assembly in $ASSEMBLY_DIR." 1>&2
   echo "You need to build Spark before running this program." 1>&2
   exit 1
 fi
-ASSEMBLY_JARS="$(ls -1 "$ASSEMBLY_DIR" | grep "^spark-assembly.*hadoop.*\.jar$" || true)"
-if [ "$num_jars" -gt "1" ]; then
-  echo "Found multiple Spark assembly jars in $ASSEMBLY_DIR:" 1>&2
-  echo "$ASSEMBLY_JARS" 1>&2
-  echo "Please remove all but one jar." 1>&2
-  exit 1
+if [ -d "$ASSEMBLY_DIR" ]; then
+  ASSEMBLY_JARS="$(ls -1 "$ASSEMBLY_DIR" | grep "^spark-assembly.*hadoop.*\.jar$" || true)"
+  if [ "$num_jars" -gt "1" ]; then
+    echo "Found multiple Spark assembly jars in $ASSEMBLY_DIR:" 1>&2
+    echo "$ASSEMBLY_JARS" 1>&2
+    echo "Please remove all but one jar." 1>&2
+    exit 1
+  fi
 fi
 
 SPARK_ASSEMBLY_JAR="${ASSEMBLY_DIR}/${ASSEMBLY_JARS}"
diff --git a/launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java b/core/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
similarity index 100%
rename from launcher/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
rename to core/src/test/java/org/apache/spark/launcher/SparkLauncherSuite.java
diff --git a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
index 48e74f06f79b1..fb7a8ae3f9d41 100644
--- a/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
+++ b/core/src/test/scala/org/apache/spark/broadcast/BroadcastSuite.scala
@@ -310,8 +310,14 @@ class BroadcastSuite extends SparkFunSuite with LocalSparkContext {
       val _sc =
         new SparkContext("local-cluster[%d, 1, 1024]".format(numSlaves), "test", broadcastConf)
       // Wait until all salves are up
-      _sc.jobProgressListener.waitUntilExecutorsUp(numSlaves, 10000)
-      _sc
+      try {
+        _sc.jobProgressListener.waitUntilExecutorsUp(numSlaves, 10000)
+        _sc
+      } catch {
+        case e: Throwable =>
+          _sc.stop()
+          throw e
+      }
     } else {
       new SparkContext("local", "test", broadcastConf)
     }
diff --git a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java
index 5e793a5c48775..0a237ee73b670 100644
--- a/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java
+++ b/launcher/src/main/java/org/apache/spark/launcher/AbstractCommandBuilder.java
@@ -169,9 +169,11 @@ List<String> buildClassPath(String appClassPath) throws IOException {
         "streaming", "tools", "sql/catalyst", "sql/core", "sql/hive", "sql/hive-thriftserver",
         "yarn", "launcher");
       if (prependClasses) {
-        System.err.println(
-          "NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark classes ahead of " +
-          "assembly.");
+        if (!isTesting) {
+          System.err.println(
+            "NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark classes ahead of " +
+            "assembly.");
+        }
         for (String project : projects) {
           addToClassPath(cp, String.format("%s/%s/target/scala-%s/classes", sparkHome, project,
             scala));
@@ -200,7 +202,7 @@ List<String> buildClassPath(String appClassPath) throws IOException {
     // For the user code case, we fall back to looking for the Spark assembly under SPARK_HOME.
     // That duplicates some of the code in the shell scripts that look for the assembly, though.
     String assembly = getenv(ENV_SPARK_ASSEMBLY);
-    if (assembly == null && isEmpty(getenv("SPARK_TESTING"))) {
+    if (assembly == null && !isTesting) {
       assembly = findAssembly();
     }
     addToClassPath(cp, assembly);
@@ -215,12 +217,14 @@ List<String> buildClassPath(String appClassPath) throws IOException {
       libdir = new File(sparkHome, "lib_managed/jars");
     }
 
-    checkState(libdir.isDirectory(), "Library directory '%s' does not exist.",
-      libdir.getAbsolutePath());
-    for (File jar : libdir.listFiles()) {
-      if (jar.getName().startsWith("datanucleus-")) {
-        addToClassPath(cp, jar.getAbsolutePath());
+    if (libdir.isDirectory()) {
+      for (File jar : libdir.listFiles()) {
+        if (jar.getName().startsWith("datanucleus-")) {
+          addToClassPath(cp, jar.getAbsolutePath());
+        }
       }
+    } else {
+      checkState(isTesting, "Library directory '%s' does not exist.", libdir.getAbsolutePath());
     }
 
     addToClassPath(cp, getenv("HADOOP_CONF_DIR"));
@@ -256,15 +260,15 @@ String getScalaVersion() {
       return scala;
     }
     String sparkHome = getSparkHome();
-    File scala210 = new File(sparkHome, "assembly/target/scala-2.10");
-    File scala211 = new File(sparkHome, "assembly/target/scala-2.11");
+    File scala210 = new File(sparkHome, "launcher/target/scala-2.10");
+    File scala211 = new File(sparkHome, "launcher/target/scala-2.11");
     checkState(!scala210.isDirectory() || !scala211.isDirectory(),
       "Presence of build for both scala versions (2.10 and 2.11) detected.\n" +
       "Either clean one of them or set SPARK_SCALA_VERSION in your environment.");
     if (scala210.isDirectory()) {
       return "2.10";
     } else {
-      checkState(scala211.isDirectory(), "Cannot find any assembly build directories.");
+      checkState(scala211.isDirectory(), "Cannot find any build directories.");
       return "2.11";
     }
   }
diff --git a/pom.xml b/pom.xml
index 0716016523ee1..88ebceca769e9 100644
--- a/pom.xml
+++ b/pom.xml
@@ -1421,6 +1421,10 @@
             <groupId>org.apache.thrift</groupId>
             <artifactId>libthrift</artifactId>
           </exclusion>
+          <exclusion>
+            <groupId>org.mortbay.jetty</groupId>
+            <artifactId>servlet-api</artifactId>
+          </exclusion>
           <exclusion>
             <groupId>com.google.guava</groupId>
             <artifactId>guava</artifactId>
@@ -1892,6 +1896,8 @@
                 launched by the tests have access to the correct test-time classpath.
               -->
               <SPARK_DIST_CLASSPATH>${test_classpath}</SPARK_DIST_CLASSPATH>
+              <SPARK_PREPEND_CLASSES>1</SPARK_PREPEND_CLASSES>
+              <SPARK_TESTING>1</SPARK_TESTING>
               <JAVA_HOME>${test.java.home}</JAVA_HOME>
             </environmentVariables>
             <systemProperties>
@@ -1929,6 +1935,8 @@
                 launched by the tests have access to the correct test-time classpath.
               -->
               <SPARK_DIST_CLASSPATH>${test_classpath}</SPARK_DIST_CLASSPATH>
+              <SPARK_PREPEND_CLASSES>1</SPARK_PREPEND_CLASSES>
+              <SPARK_TESTING>1</SPARK_TESTING>
               <JAVA_HOME>${test.java.home}</JAVA_HOME>
             </environmentVariables>
             <systemProperties>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index ea52bfd67944a..901cfa538d23e 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -547,6 +547,8 @@ object TestSettings {
     envVars in Test ++= Map(
       "SPARK_DIST_CLASSPATH" ->
         (fullClasspath in Test).value.files.map(_.getAbsolutePath).mkString(":").stripSuffix(":"),
+      "SPARK_PREPEND_CLASSES" -> "1",
+      "SPARK_TESTING" -> "1",
       "JAVA_HOME" -> sys.env.get("JAVA_HOME").getOrElse(sys.props("java.home"))),
     javaOptions in Test += s"-Djava.io.tmpdir=$testTempDir",
     javaOptions in Test += "-Dspark.test.home=" + sparkHome,
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
index b4f8049bff577..17c59ff06e0c1 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/BaseYarnClusterSuite.scala
@@ -30,6 +30,7 @@ import org.apache.hadoop.yarn.server.MiniYARNCluster
 import org.scalatest.{BeforeAndAfterAll, Matchers}
 
 import org.apache.spark._
+import org.apache.spark.launcher.TestClasspathBuilder
 import org.apache.spark.util.Utils
 
 abstract class BaseYarnClusterSuite
@@ -43,6 +44,9 @@ abstract class BaseYarnClusterSuite
     |log4j.appender.console.target=System.err
     |log4j.appender.console.layout=org.apache.log4j.PatternLayout
     |log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n
+    |log4j.logger.org.apache.hadoop=WARN
+    |log4j.logger.org.eclipse.jetty=WARN
+    |log4j.logger.org.spark-project.jetty=WARN
     """.stripMargin
 
   private var yarnCluster: MiniYARNCluster = _
@@ -51,8 +55,7 @@ abstract class BaseYarnClusterSuite
   private var hadoopConfDir: File = _
   private var logConfDir: File = _
 
-
-  def yarnConfig: YarnConfiguration
+  def newYarnConfig(): YarnConfiguration
 
   override def beforeAll() {
     super.beforeAll()
@@ -65,8 +68,14 @@ abstract class BaseYarnClusterSuite
     val logConfFile = new File(logConfDir, "log4j.properties")
     Files.write(LOG4J_CONF, logConfFile, UTF_8)
 
+    // Disable the disk utilization check to avoid the test hanging when people's disks are
+    // getting full.
+    val yarnConf = newYarnConfig()
+    yarnConf.set("yarn.nodemanager.disk-health-checker.max-disk-utilization-per-disk-percentage",
+      "100.0")
+
     yarnCluster = new MiniYARNCluster(getClass().getName(), 1, 1, 1)
-    yarnCluster.init(yarnConfig)
+    yarnCluster.init(yarnConf)
     yarnCluster.start()
 
     // There's a race in MiniYARNCluster in which start() may return before the RM has updated
@@ -114,19 +123,23 @@ abstract class BaseYarnClusterSuite
       sparkArgs: Seq[String] = Nil,
       extraClassPath: Seq[String] = Nil,
       extraJars: Seq[String] = Nil,
-      extraConf: Map[String, String] = Map()): Unit = {
+      extraConf: Map[String, String] = Map(),
+      extraEnv: Map[String, String] = Map()): Unit = {
     val master = if (clientMode) "yarn-client" else "yarn-cluster"
     val props = new Properties()
 
     props.setProperty("spark.yarn.jar", "local:" + fakeSparkJar.getAbsolutePath())
 
-    val childClasspath = logConfDir.getAbsolutePath() +
-      File.pathSeparator +
-      sys.props("java.class.path") +
-      File.pathSeparator +
-      extraClassPath.mkString(File.pathSeparator)
-    props.setProperty("spark.driver.extraClassPath", childClasspath)
-    props.setProperty("spark.executor.extraClassPath", childClasspath)
+    val testClasspath = new TestClasspathBuilder()
+      .buildClassPath(
+        logConfDir.getAbsolutePath() +
+        File.pathSeparator +
+        extraClassPath.mkString(File.pathSeparator))
+      .asScala
+      .mkString(File.pathSeparator)
+
+    props.setProperty("spark.driver.extraClassPath", testClasspath)
+    props.setProperty("spark.executor.extraClassPath", testClasspath)
 
     // SPARK-4267: make sure java options are propagated correctly.
     props.setProperty("spark.driver.extraJavaOptions", "-Dfoo=\"one two three\"")
@@ -168,7 +181,7 @@ abstract class BaseYarnClusterSuite
       appArgs
 
     Utils.executeAndGetOutput(argv,
-      extraEnvironment = Map("YARN_CONF_DIR" -> hadoopConfDir.getAbsolutePath()))
+      extraEnvironment = Map("YARN_CONF_DIR" -> hadoopConfDir.getAbsolutePath()) ++ extraEnv)
   }
 
   /**
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index 5a4ea2ea2f4ff..b5a42fd6afd98 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -28,7 +28,9 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration
 import org.scalatest.Matchers
 
 import org.apache.spark._
-import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationStart, SparkListenerExecutorAdded}
+import org.apache.spark.launcher.TestClasspathBuilder
+import org.apache.spark.scheduler.{SparkListener, SparkListenerApplicationStart,
+  SparkListenerExecutorAdded}
 import org.apache.spark.scheduler.cluster.ExecutorInfo
 import org.apache.spark.util.Utils
 
@@ -39,7 +41,7 @@ import org.apache.spark.util.Utils
  */
 class YarnClusterSuite extends BaseYarnClusterSuite {
 
-  override def yarnConfig: YarnConfiguration = new YarnConfiguration()
+  override def newYarnConfig(): YarnConfiguration = new YarnConfiguration()
 
   private val TEST_PYFILE = """
     |import mod1, mod2
@@ -111,6 +113,17 @@ class YarnClusterSuite extends BaseYarnClusterSuite {
     val primaryPyFile = new File(tempDir, "test.py")
     Files.write(TEST_PYFILE, primaryPyFile, UTF_8)
 
+    // When running tests, let's not assume the user has built the assembly module, which also
+    // creates the pyspark archive. Instead, let's use PYSPARK_ARCHIVES_PATH to point at the
+    // needed locations.
+    val sparkHome = sys.props("spark.test.home");
+    val pythonPath = Seq(
+        s"$sparkHome/python/lib/py4j-0.8.2.1-src.zip",
+        s"$sparkHome/python")
+    val extraEnv = Map(
+      "PYSPARK_ARCHIVES_PATH" -> pythonPath.map("local:" + _).mkString(File.pathSeparator),
+      "PYTHONPATH" -> pythonPath.mkString(File.pathSeparator))
+
     val moduleDir =
       if (clientMode) {
         // In client-mode, .py files added with --py-files are not visible in the driver.
@@ -130,7 +143,8 @@ class YarnClusterSuite extends BaseYarnClusterSuite {
 
     runSpark(clientMode, primaryPyFile.getAbsolutePath(),
       sparkArgs = Seq("--py-files", pyFiles),
-      appArgs = Seq(result.getAbsolutePath()))
+      appArgs = Seq(result.getAbsolutePath()),
+      extraEnv = extraEnv)
     checkResult(result)
   }
 
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
index 5e8238822b90a..8d9c9b3004eda 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
@@ -34,7 +34,7 @@ import org.apache.spark.network.yarn.{YarnShuffleService, YarnTestAccessor}
  */
 class YarnShuffleIntegrationSuite extends BaseYarnClusterSuite {
 
-  override def yarnConfig: YarnConfiguration = {
+  override def newYarnConfig(): YarnConfiguration = {
     val yarnConfig = new YarnConfiguration()
     yarnConfig.set(YarnConfiguration.NM_AUX_SERVICES, "spark_shuffle")
     yarnConfig.set(YarnConfiguration.NM_AUX_SERVICE_FMT.format("spark_shuffle"),
diff --git a/yarn/src/test/scala/org/apache/spark/launcher/TestClasspathBuilder.scala b/yarn/src/test/scala/org/apache/spark/launcher/TestClasspathBuilder.scala
new file mode 100644
index 0000000000000..da9e8e21a26ae
--- /dev/null
+++ b/yarn/src/test/scala/org/apache/spark/launcher/TestClasspathBuilder.scala
@@ -0,0 +1,36 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.launcher
+
+import java.util.{List => JList, Map => JMap}
+
+/**
+ * Exposes AbstractCommandBuilder to the YARN tests, so that they can build classpaths the same
+ * way other cluster managers do.
+ */
+private[spark] class TestClasspathBuilder extends AbstractCommandBuilder {
+
+  childEnv.put(CommandBuilderUtils.ENV_SPARK_HOME, sys.props("spark.test.home"))
+
+  override def buildClassPath(extraCp: String): JList[String] = super.buildClassPath(extraCp)
+
+  /** Not used by the YARN tests. */
+  override def buildCommand(env: JMap[String, String]): JList[String] =
+    throw new UnsupportedOperationException()
+
+}

From 45723214e694b9a440723e9504c562e6393709f3 Mon Sep 17 00:00:00 2001
From: Shuo Xiang <sxiang@pinterest.com>
Date: Fri, 28 Aug 2015 13:09:13 -0700
Subject: [PATCH 470/633] [SPARK-10336][example] fix not being able to set
 intercept in LR example

`fitIntercept` is a command line option but not set in the main program.

dbtsai

Author: Shuo Xiang <sxiang@pinterest.com>

Closes #8510 from coderxiang/intercept and squashes the following commits:

57c9b7d [Shuo Xiang] fix not being able to set intercept in LR example
---
 .../org/apache/spark/examples/ml/LogisticRegressionExample.scala | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
index 7682557127b51..8e3760ddb50a9 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
@@ -136,6 +136,7 @@ object LogisticRegressionExample {
       .setElasticNetParam(params.elasticNetParam)
       .setMaxIter(params.maxIter)
       .setTol(params.tol)
+      .setFitIntercept(params.fitIntercept)
 
     stages += lor
     val pipeline = new Pipeline().setStages(stages.toArray)

From 88032ecaf0455886aed7a66b30af80dae7f6cff7 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Fri, 28 Aug 2015 13:53:31 -0700
Subject: [PATCH 471/633] [SPARK-9671] [MLLIB] re-org user guide and add
 migration guide

This PR updates the MLlib user guide and adds migration guide for 1.4->1.5.

* merge migration guide for `spark.mllib` and `spark.ml` packages
* remove dependency section from `spark.ml` guide
* move the paragraph about `spark.mllib` and `spark.ml` to the top and recommend `spark.ml`
* move Sam's talk to footnote to make the section focus on dependencies

Minor changes to code examples and other wording will be in a separate PR.

jkbradley srowen feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8498 from mengxr/SPARK-9671.
---
 docs/ml-guide.md               |  52 ++------------
 docs/mllib-guide.md            | 119 ++++++++++++++++-----------------
 docs/mllib-migration-guides.md |  30 +++++++++
 3 files changed, 95 insertions(+), 106 deletions(-)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 01bf5ee18e328..ce53400b6ee56 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -21,19 +21,11 @@ title: Spark ML Programming Guide
 \]`
 
 
-Spark 1.2 introduced a new package called `spark.ml`, which aims to provide a uniform set of
-high-level APIs that help users create and tune practical machine learning pipelines.
-
-*Graduated from Alpha!*  The Pipelines API is no longer an alpha component, although many elements of it are still `Experimental` or `DeveloperApi`.
-
-Note that we will keep supporting and adding features to `spark.mllib` along with the
-development of `spark.ml`.
-Users should be comfortable using `spark.mllib` features and expect more features coming.
-Developers should contribute new algorithms to `spark.mllib` and can optionally contribute
-to `spark.ml`.
-
-See the [Algorithm Guides section](#algorithm-guides) below for guides on sub-packages of `spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.
-
+The `spark.ml` package aims to provide a uniform set of high-level APIs built on top of
+[DataFrames](sql-programming-guide.html#dataframes) that help users create and tune practical
+machine learning pipelines.
+See the [Algorithm Guides section](#algorithm-guides) below for guides on sub-packages of
+`spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.
 
 **Table of Contents**
 
@@ -171,7 +163,7 @@ This is useful if there are two algorithms with the `maxIter` parameter in a `Pi
 
 # Algorithm Guides
 
-There are now several algorithms in the Pipelines API which are not in the lower-level MLlib API, so we link to documentation for them here.  These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines.
+There are now several algorithms in the Pipelines API which are not in the `spark.mllib` API, so we link to documentation for them here.  These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines.
 
 **Pipelines API Algorithm Guides**
 
@@ -880,35 +872,3 @@ jsc.stop();
 </div>
 
 </div>
-
-# Dependencies
-
-Spark ML currently depends on MLlib and has the same dependencies.
-Please see the [MLlib Dependencies guide](mllib-guide.html#dependencies) for more info.
-
-Spark ML also depends upon Spark SQL, but the relevant parts of Spark SQL do not bring additional dependencies.
-
-# Migration Guide
-
-## From 1.3 to 1.4
-
-Several major API changes occurred, including:
-* `Param` and other APIs for specifying parameters
-* `uid` unique IDs for Pipeline components
-* Reorganization of certain classes
-Since the `spark.ml` API was an Alpha Component in Spark 1.3, we do not list all changes here.
-
-However, now that `spark.ml` is no longer an Alpha Component, we will provide details on any API changes for future releases.
-
-## From 1.2 to 1.3
-
-The main API changes are from Spark SQL.  We list the most important changes here:
-
-* The old [SchemaRDD](http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD) has been replaced with [DataFrame](api/scala/index.html#org.apache.spark.sql.DataFrame) with a somewhat modified API.  All algorithms in Spark ML which used to use SchemaRDD now use DataFrame.
-* In Spark 1.2, we used implicit conversions from `RDD`s of `LabeledPoint` into `SchemaRDD`s by calling `import sqlContext._` where `sqlContext` was an instance of `SQLContext`.  These implicits have been moved, so we now call `import sqlContext.implicits._`.
-* Java APIs for SQL have also changed accordingly.  Please see the examples above and the [Spark SQL Programming Guide](sql-programming-guide.html) for details.
-
-Other changes were in `LogisticRegression`:
-
-* The `scoreCol` output column (with default value "score") was renamed to be `probabilityCol` (with default value "probability").  The type was originally `Double` (for the probability of class 1.0), but it is now `Vector` (for the probability of each class, to support multiclass classification in the future).
-* In Spark 1.2, `LogisticRegressionModel` did not include an intercept.  In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for [spark.mllib.LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS).  The option to use an intercept will be added in the future.
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 6330c977552d1..876dcfd40ed7b 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -5,21 +5,28 @@ displayTitle: Machine Learning Library (MLlib) Guide
 description: MLlib machine learning library overview for Spark SPARK_VERSION_SHORT
 ---
 
-MLlib is Spark's scalable machine learning library consisting of common learning algorithms and utilities,
-including classification, regression, clustering, collaborative
-filtering, dimensionality reduction, as well as underlying optimization primitives.
-Guides for individual algorithms are listed below.
+MLlib is Spark's machine learning (ML) library.
+Its goal is to make practical machine learning scalable and easy.
+It consists of common learning algorithms and utilities, including classification, regression,
+clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization
+primitives and higher-level pipeline APIs.
 
-The API is divided into 2 parts:
+It divides into two packages:
 
-* [The original `spark.mllib` API](mllib-guide.html#mllib-types-algorithms-and-utilities) is the primary API.
-* [The "Pipelines" `spark.ml` API](mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines) is a higher-level API for constructing ML workflows.
+* [`spark.mllib`](mllib-guide.html#mllib-types-algorithms-and-utilities) contains the original API
+  built on top of RDDs.
+* [`spark.ml`](mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines) provides higher-level API
+  built on top of DataFrames for constructing ML pipelines.
 
-We list major functionality from both below, with links to detailed guides.
+Using `spark.ml` is recommended because with DataFrames the API is more versatile and flexible.
+But we will keep supporting `spark.mllib` along with the development of `spark.ml`.
+Users should be comfortable using `spark.mllib` features and expect more features coming.
+Developers should contribute new algorithms to `spark.ml` if they fit the ML pipeline concept well,
+e.g., feature extractors and transformers.
 
-# MLlib types, algorithms and utilities
+We list major functionality from both below, with links to detailed guides.
 
-This lists functionality included in `spark.mllib`, the main MLlib API.
+# spark.mllib: data types, algorithms, and utilities
 
 * [Data types](mllib-data-types.html)
 * [Basic statistics](mllib-statistics.html)
@@ -56,71 +63,63 @@ This lists functionality included in `spark.mllib`, the main MLlib API.
   * [limited-memory BFGS (L-BFGS)](mllib-optimization.html#limited-memory-bfgs-l-bfgs)
 * [PMML model export](mllib-pmml-model-export.html)
 
-MLlib is under active development.
-The APIs marked `Experimental`/`DeveloperApi` may change in future releases,
-and the migration guide below will explain all changes between releases.
-
 # spark.ml: high-level APIs for ML pipelines
 
-Spark 1.2 introduced a new package called `spark.ml`, which aims to provide a uniform set of
-high-level APIs that help users create and tune practical machine learning pipelines.
-
-*Graduated from Alpha!*  The Pipelines API is no longer an alpha component, although many elements of it are still `Experimental` or `DeveloperApi`.
-
-Note that we will keep supporting and adding features to `spark.mllib` along with the
-development of `spark.ml`.
-Users should be comfortable using `spark.mllib` features and expect more features coming.
-Developers should contribute new algorithms to `spark.mllib` and can optionally contribute
-to `spark.ml`.
-
-Guides for `spark.ml` include:
+**[spark.ml programming guide](ml-guide.html)** provides an overview of the Pipelines API and major
+concepts. It also contains sections on using algorithms within the Pipelines API, for example:
 
-* **[spark.ml programming guide](ml-guide.html)**: overview of the Pipelines API and major concepts
-* Guides on using algorithms within the Pipelines API:
-  * [Feature transformers](ml-features.html), including a few not in the lower-level `spark.mllib` API
-  * [Decision trees](ml-decision-tree.html)
-  * [Ensembles](ml-ensembles.html)
-  * [Linear methods](ml-linear-methods.html)
+* [Feature Extraction, Transformation, and Selection](ml-features.html)
+* [Decision Trees for Classification and Regression](ml-decision-tree.html)
+* [Ensembles](ml-ensembles.html)
+* [Linear methods with elastic net regularization](ml-linear-methods.html)
+* [Multilayer perceptron classifier](ml-ann.html)
 
 # Dependencies
 
-MLlib uses the linear algebra package
-[Breeze](http://www.scalanlp.org/), which depends on
-[netlib-java](https://github.com/fommil/netlib-java) for optimised
-numerical processing. If natives are not available at runtime, you
-will see a warning message and a pure JVM implementation will be used
-instead.
+MLlib uses the linear algebra package [Breeze](http://www.scalanlp.org/), which depends on
+[netlib-java](https://github.com/fommil/netlib-java) for optimised numerical processing.
+If natives libraries[^1] are not available at runtime, you will see a warning message and a pure JVM
+implementation will be used instead.
 
-To learn more about the benefits and background of system optimised
-natives, you may wish to watch Sam Halliday's ScalaX talk on
-[High Performance Linear Algebra in Scala](http://fommil.github.io/scalax14/#/)).
+Due to licensing issues with runtime proprietary binaries, we do not include `netlib-java`'s native
+proxies by default.
+To configure `netlib-java` / Breeze to use system optimised binaries, include
+`com.github.fommil.netlib:all:1.1.2` (or build Spark with `-Pnetlib-lgpl`) as a dependency of your
+project and read the [netlib-java](https://github.com/fommil/netlib-java) documentation for your
+platform's additional installation instructions.
 
-Due to licensing issues with runtime proprietary binaries, we do not
-include `netlib-java`'s native proxies by default. To configure
-`netlib-java` / Breeze to use system optimised binaries, include
-`com.github.fommil.netlib:all:1.1.2` (or build Spark with
-`-Pnetlib-lgpl`) as a dependency of your project and read the
-[netlib-java](https://github.com/fommil/netlib-java) documentation for
-your platform's additional installation instructions.
+To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4 or newer.
 
-To use MLlib in Python, you will need [NumPy](http://www.numpy.org)
-version 1.4 or newer.
+[^1]: To learn more about the benefits and background of system optimised natives, you may wish to
+    watch Sam Halliday's ScalaX talk on [High Performance Linear Algebra in Scala](http://fommil.github.io/scalax14/#/).
 
----
+# Migration guide
 
-# Migration Guide
+MLlib is under active development.
+The APIs marked `Experimental`/`DeveloperApi` may change in future releases,
+and the migration guide below will explain all changes between releases.
+
+## From 1.4 to 1.5
 
-For the `spark.ml` package, please see the [spark.ml Migration Guide](ml-guide.html#migration-guide).
+In the `spark.mllib` package, there are no break API changes but several behavior changes:
 
-## From 1.3 to 1.4
+* [SPARK-9005](https://issues.apache.org/jira/browse/SPARK-9005):
+  `RegressionMetrics.explainedVariance` returns the average regression sum of squares.
+* [SPARK-8600](https://issues.apache.org/jira/browse/SPARK-8600): `NaiveBayesModel.labels` become
+  sorted.
+* [SPARK-3382](https://issues.apache.org/jira/browse/SPARK-3382): `GradientDescent` has a default
+  convergence tolerance `1e-3`, and hence iterations might end earlier than 1.4.
 
-In the `spark.mllib` package, there were several breaking changes, but all in `DeveloperApi` or `Experimental` APIs:
+In the `spark.ml` package, there exists one break API change and one behavior change:
 
-* Gradient-Boosted Trees
-    * *(Breaking change)* The signature of the [`Loss.gradient`](api/scala/index.html#org.apache.spark.mllib.tree.loss.Loss) method was changed.  This is only an issues for users who wrote their own losses for GBTs.
-    * *(Breaking change)* The `apply` and `copy` methods for the case class [`BoostingStrategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.BoostingStrategy) have been changed because of a modification to the case class fields.  This could be an issue for users who use `BoostingStrategy` to set GBT parameters.
-* *(Breaking change)* The return value of [`LDA.run`](api/scala/index.html#org.apache.spark.mllib.clustering.LDA) has changed.  It now returns an abstract class `LDAModel` instead of the concrete class `DistributedLDAModel`.  The object of type `LDAModel` can still be cast to the appropriate concrete type, which depends on the optimization algorithm.
+* [SPARK-9268](https://issues.apache.org/jira/browse/SPARK-9268): Java's varargs support is removed
+  from `Params.setDefault` due to a
+  [Scala compiler bug](https://issues.scala-lang.org/browse/SI-9013).
+* [SPARK-10097](https://issues.apache.org/jira/browse/SPARK-10097): `Evaluator.isLargerBetter` is
+  added to indicate metric ordering. Metrics like RMSE no longer flip signs as in 1.4.
 
-## Previous Spark Versions
+## Previous Spark versions
 
 Earlier migration guides are archived [on this page](mllib-migration-guides.html).
+
+---
diff --git a/docs/mllib-migration-guides.md b/docs/mllib-migration-guides.md
index 8df68d81f3c78..774b85d1f773a 100644
--- a/docs/mllib-migration-guides.md
+++ b/docs/mllib-migration-guides.md
@@ -7,6 +7,25 @@ description: MLlib migration guides from before Spark SPARK_VERSION_SHORT
 
 The migration guide for the current Spark version is kept on the [MLlib Programming Guide main page](mllib-guide.html#migration-guide).
 
+## From 1.3 to 1.4
+
+In the `spark.mllib` package, there were several breaking changes, but all in `DeveloperApi` or `Experimental` APIs:
+
+* Gradient-Boosted Trees
+    * *(Breaking change)* The signature of the [`Loss.gradient`](api/scala/index.html#org.apache.spark.mllib.tree.loss.Loss) method was changed.  This is only an issues for users who wrote their own losses for GBTs.
+    * *(Breaking change)* The `apply` and `copy` methods for the case class [`BoostingStrategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.BoostingStrategy) have been changed because of a modification to the case class fields.  This could be an issue for users who use `BoostingStrategy` to set GBT parameters.
+* *(Breaking change)* The return value of [`LDA.run`](api/scala/index.html#org.apache.spark.mllib.clustering.LDA) has changed.  It now returns an abstract class `LDAModel` instead of the concrete class `DistributedLDAModel`.  The object of type `LDAModel` can still be cast to the appropriate concrete type, which depends on the optimization algorithm.
+
+In the `spark.ml` package, several major API changes occurred, including:
+
+* `Param` and other APIs for specifying parameters
+* `uid` unique IDs for Pipeline components
+* Reorganization of certain classes
+
+Since the `spark.ml` API was an alpha component in Spark 1.3, we do not list all changes here.
+However, since 1.4 `spark.ml` is no longer an alpha component, we will provide details on any API
+changes for future releases.
+
 ## From 1.2 to 1.3
 
 In the `spark.mllib` package, there were several breaking changes.  The first change (in `ALS`) is the only one in a component not marked as Alpha or Experimental.
@@ -23,6 +42,17 @@ In the `spark.mllib` package, there were several breaking changes.  The first ch
 * In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2.
   So in order to produce the same result as in 1.2, the regularization parameter needs to be divided by 2 and the step size needs to be multiplied by 2.
 
+In the `spark.ml` package, the main API changes are from Spark SQL.  We list the most important changes here:
+
+* The old [SchemaRDD](http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD) has been replaced with [DataFrame](api/scala/index.html#org.apache.spark.sql.DataFrame) with a somewhat modified API.  All algorithms in Spark ML which used to use SchemaRDD now use DataFrame.
+* In Spark 1.2, we used implicit conversions from `RDD`s of `LabeledPoint` into `SchemaRDD`s by calling `import sqlContext._` where `sqlContext` was an instance of `SQLContext`.  These implicits have been moved, so we now call `import sqlContext.implicits._`.
+* Java APIs for SQL have also changed accordingly.  Please see the examples above and the [Spark SQL Programming Guide](sql-programming-guide.html) for details.
+
+Other changes were in `LogisticRegression`:
+
+* The `scoreCol` output column (with default value "score") was renamed to be `probabilityCol` (with default value "probability").  The type was originally `Double` (for the probability of class 1.0), but it is now `Vector` (for the probability of each class, to support multiclass classification in the future).
+* In Spark 1.2, `LogisticRegressionModel` did not include an intercept.  In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for [spark.mllib.LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS).  The option to use an intercept will be added in the future.
+
 ## From 1.1 to 1.2
 
 The only API changes in MLlib v1.2 are in

From bb7f35239385ec74b5ee69631b5480fbcee253e4 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 28 Aug 2015 14:38:20 -0700
Subject: [PATCH 472/633] [SPARK-10323] [SQL] fix nullability of
 In/InSet/ArrayContain

After this PR, In/InSet/ArrayContain will return null if value is null, instead of false. They also will return null even if there is a null in the set/array.

Author: Davies Liu <davies@databricks.com>

Closes #8492 from davies/fix_in.
---
 .../expressions/collectionOperations.scala    | 62 ++++++++--------
 .../sql/catalyst/expressions/predicates.scala | 71 +++++++++++++++----
 .../sql/catalyst/optimizer/Optimizer.scala    |  6 --
 .../CollectionFunctionsSuite.scala            | 12 +++-
 .../catalyst/expressions/PredicateSuite.scala | 49 +++++++++----
 .../optimizer/ConstantFoldingSuite.scala      | 21 +-----
 .../spark/sql/DataFrameFunctionsSuite.scala   | 14 ++--
 7 files changed, 138 insertions(+), 97 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
index 646afa4047d84..7b8c5b723ded4 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
@@ -19,9 +19,7 @@ package org.apache.spark.sql.catalyst.expressions
 import java.util.Comparator
 
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen.{
-  CodegenFallback, CodeGenContext, GeneratedExpressionCode}
-import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, CodegenFallback, GeneratedExpressionCode}
 import org.apache.spark.sql.types._
 
 /**
@@ -145,46 +143,42 @@ case class ArrayContains(left: Expression, right: Expression)
     }
   }
 
-  override def nullable: Boolean = false
+  override def nullable: Boolean = {
+    left.nullable || right.nullable || left.dataType.asInstanceOf[ArrayType].containsNull
+  }
 
-  override def eval(input: InternalRow): Boolean = {
-    val arr = left.eval(input)
-    if (arr == null) {
-      false
-    } else {
-      val value = right.eval(input)
-      if (value == null) {
-        false
-      } else {
-        arr.asInstanceOf[ArrayData].foreach(right.dataType, (i, v) =>
-          if (v == value) return true
-        )
-        false
+  override def nullSafeEval(arr: Any, value: Any): Any = {
+    var hasNull = false
+    arr.asInstanceOf[ArrayData].foreach(right.dataType, (i, v) =>
+      if (v == null) {
+        hasNull = true
+      } else if (v == value) {
+        return true
       }
+    )
+    if (hasNull) {
+      null
+    } else {
+      false
     }
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val arrGen = left.gen(ctx)
-    val elementGen = right.gen(ctx)
-    val i = ctx.freshName("i")
-    val getValue = ctx.getValue(arrGen.primitive, right.dataType, i)
-    s"""
-      ${arrGen.code}
-      boolean ${ev.isNull} = false;
-      boolean ${ev.primitive} = false;
-      if (!${arrGen.isNull}) {
-        ${elementGen.code}
-        if (!${elementGen.isNull}) {
-          for (int $i = 0; $i < ${arrGen.primitive}.numElements(); $i ++) {
-            if (${ctx.genEqual(right.dataType, elementGen.primitive, getValue)}) {
-              ${ev.primitive} = true;
-              break;
-            }
-          }
+    nullSafeCodeGen(ctx, ev, (arr, value) => {
+      val i = ctx.freshName("i")
+      val getValue = ctx.getValue(arr, right.dataType, i)
+      s"""
+      for (int $i = 0; $i < $arr.numElements(); $i ++) {
+        if ($arr.isNullAt($i)) {
+          ${ev.isNull} = true;
+        } else if (${ctx.genEqual(right.dataType, value, getValue)}) {
+          ${ev.isNull} = false;
+          ${ev.primitive} = true;
+          break;
         }
       }
      """
+    })
   }
 
   override def prettyName: String = "array_contains"
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index fe7dffb815987..65706dba7d975 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -17,11 +17,9 @@
 
 package org.apache.spark.sql.catalyst.expressions
 
-import scala.collection.mutable
-
-import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
-import org.apache.spark.sql.catalyst.expressions.codegen.{CodegenFallback, GeneratedExpressionCode, CodeGenContext}
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
+import org.apache.spark.sql.catalyst.expressions.codegen.{CodeGenContext, GeneratedExpressionCode}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.util.TypeUtils
 import org.apache.spark.sql.types._
@@ -103,6 +101,8 @@ case class Not(child: Expression)
 case class In(value: Expression, list: Seq[Expression]) extends Predicate
     with ImplicitCastInputTypes {
 
+  require(list != null, "list should not be null")
+
   override def inputTypes: Seq[AbstractDataType] = value.dataType +: list.map(_.dataType)
 
   override def checkInputDataTypes(): TypeCheckResult = {
@@ -116,12 +116,31 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate
 
   override def children: Seq[Expression] = value +: list
 
-  override def nullable: Boolean = false // TODO: Figure out correct nullability semantics of IN.
+  override def nullable: Boolean = children.exists(_.nullable)
+  override def foldable: Boolean = children.forall(_.foldable)
+
   override def toString: String = s"$value IN ${list.mkString("(", ",", ")")}"
 
   override def eval(input: InternalRow): Any = {
     val evaluatedValue = value.eval(input)
-    list.exists(e => e.eval(input) == evaluatedValue)
+    if (evaluatedValue == null) {
+      null
+    } else {
+      var hasNull = false
+      list.foreach { e =>
+        val v = e.eval(input)
+        if (v == evaluatedValue) {
+          return true
+        } else if (v == null) {
+          hasNull = true
+        }
+      }
+      if (hasNull) {
+        null
+      } else {
+        false
+      }
+    }
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
@@ -131,7 +150,10 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate
       s"""
         if (!${ev.primitive}) {
           ${x.code}
-          if (${ctx.genEqual(value.dataType, valueGen.primitive, x.primitive)}) {
+          if (${x.isNull}) {
+            ${ev.isNull} = true;
+          } else if (${ctx.genEqual(value.dataType, valueGen.primitive, x.primitive)}) {
+            ${ev.isNull} = false;
             ${ev.primitive} = true;
           }
         }
@@ -139,8 +161,10 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate
     s"""
       ${valueGen.code}
       boolean ${ev.primitive} = false;
-      boolean ${ev.isNull} = false;
-      $listCode
+      boolean ${ev.isNull} = ${valueGen.isNull};
+      if (!${ev.isNull}) {
+        $listCode
+      }
     """
   }
 }
@@ -151,11 +175,22 @@ case class In(value: Expression, list: Seq[Expression]) extends Predicate
  */
 case class InSet(child: Expression, hset: Set[Any]) extends UnaryExpression with Predicate {
 
-  override def nullable: Boolean = false // TODO: Figure out correct nullability semantics of IN.
+  require(hset != null, "hset could not be null")
+
   override def toString: String = s"$child INSET ${hset.mkString("(", ",", ")")}"
 
-  override def eval(input: InternalRow): Any = {
-    hset.contains(child.eval(input))
+  @transient private[this] lazy val hasNull: Boolean = hset.contains(null)
+
+  override def nullable: Boolean = child.nullable || hasNull
+
+  protected override def nullSafeEval(value: Any): Any = {
+    if (hset.contains(value)) {
+      true
+    } else if (hasNull) {
+      null
+    } else {
+      false
+    }
   }
 
   def getHSet(): Set[Any] = hset
@@ -166,12 +201,20 @@ case class InSet(child: Expression, hset: Set[Any]) extends UnaryExpression with
     val childGen = child.gen(ctx)
     ctx.references += this
     val hsetTerm = ctx.freshName("hset")
+    val hasNullTerm = ctx.freshName("hasNull")
     ctx.addMutableState(setName, hsetTerm,
       s"$hsetTerm = (($InSetName)expressions[${ctx.references.size - 1}]).getHSet();")
+    ctx.addMutableState("boolean", hasNullTerm, s"$hasNullTerm = $hsetTerm.contains(null);")
     s"""
       ${childGen.code}
-      boolean ${ev.isNull} = false;
-      boolean ${ev.primitive} = $hsetTerm.contains(${childGen.primitive});
+      boolean ${ev.isNull} = ${childGen.isNull};
+      boolean ${ev.primitive} = false;
+      if (!${ev.isNull}) {
+        ${ev.primitive} = $hsetTerm.contains(${childGen.primitive});
+        if (!${ev.primitive} && $hasNullTerm) {
+          ${ev.isNull} = true;
+        }
+      }
      """
   }
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index 854463dd11c74..a430000bef653 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -395,12 +395,6 @@ object ConstantFolding extends Rule[LogicalPlan] {
 
       // Fold expressions that are foldable.
       case e if e.foldable => Literal.create(e.eval(EmptyRow), e.dataType)
-
-      // Fold "literal in (item1, item2, ..., literal, ...)" into true directly.
-      case In(Literal(v, _), list) if list.exists {
-          case Literal(candidate, _) if candidate == v => true
-          case _ => false
-        } => Literal.create(true, BooleanType)
     }
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
index 95f0e38212a1a..a3e81888dfd0d 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CollectionFunctionsSuite.scala
@@ -70,14 +70,20 @@ class CollectionFunctionsSuite extends SparkFunSuite with ExpressionEvalHelper {
     val a0 = Literal.create(Seq(1, 2, 3), ArrayType(IntegerType))
     val a1 = Literal.create(Seq[String](null, ""), ArrayType(StringType))
     val a2 = Literal.create(Seq(null), ArrayType(LongType))
+    val a3 = Literal.create(null, ArrayType(StringType))
 
     checkEvaluation(ArrayContains(a0, Literal(1)), true)
     checkEvaluation(ArrayContains(a0, Literal(0)), false)
-    checkEvaluation(ArrayContains(a0, Literal(null)), false)
+    checkEvaluation(ArrayContains(a0, Literal.create(null, IntegerType)), null)
 
     checkEvaluation(ArrayContains(a1, Literal("")), true)
-    checkEvaluation(ArrayContains(a1, Literal(null)), false)
+    checkEvaluation(ArrayContains(a1, Literal("a")), null)
+    checkEvaluation(ArrayContains(a1, Literal.create(null, StringType)), null)
 
-    checkEvaluation(ArrayContains(a2, Literal(null)), false)
+    checkEvaluation(ArrayContains(a2, Literal(1L)), null)
+    checkEvaluation(ArrayContains(a2, Literal.create(null, LongType)), null)
+
+    checkEvaluation(ArrayContains(a3, Literal("")), null)
+    checkEvaluation(ArrayContains(a3, Literal.create(null, StringType)), null)
   }
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
index 54c04faddb477..03e7611fce8ff 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/PredicateSuite.scala
@@ -20,7 +20,6 @@ package org.apache.spark.sql.catalyst.expressions
 import scala.collection.immutable.HashSet
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.RandomDataGenerator
 import org.apache.spark.sql.types._
 
@@ -119,6 +118,12 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
       (null, null, null) :: Nil)
 
   test("IN") {
+    checkEvaluation(In(Literal.create(null, IntegerType), Seq(Literal(1), Literal(2))), null)
+    checkEvaluation(In(Literal.create(null, IntegerType), Seq(Literal.create(null, IntegerType))),
+      null)
+    checkEvaluation(In(Literal(1), Seq(Literal.create(null, IntegerType))), null)
+    checkEvaluation(In(Literal(1), Seq(Literal(1), Literal.create(null, IntegerType))), true)
+    checkEvaluation(In(Literal(2), Seq(Literal(1), Literal.create(null, IntegerType))), null)
     checkEvaluation(In(Literal(1), Seq(Literal(1), Literal(2))), true)
     checkEvaluation(In(Literal(2), Seq(Literal(1), Literal(2))), true)
     checkEvaluation(In(Literal(3), Seq(Literal(1), Literal(2))), false)
@@ -126,14 +131,18 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
       And(In(Literal(1), Seq(Literal(1), Literal(2))), In(Literal(2), Seq(Literal(1), Literal(2)))),
       true)
 
-    checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("^Ba*n"))), true)
+    val ns = Literal.create(null, StringType)
+    checkEvaluation(In(ns, Seq(Literal("1"), Literal("2"))), null)
+    checkEvaluation(In(ns, Seq(ns)), null)
+    checkEvaluation(In(Literal("a"), Seq(ns)), null)
+    checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("^Ba*n"), ns)), true)
     checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("aa"), Literal("^Ba*n"))), true)
     checkEvaluation(In(Literal("^Ba*n"), Seq(Literal("aa"), Literal("^n"))), false)
 
     val primitiveTypes = Seq(IntegerType, FloatType, DoubleType, StringType, ByteType, ShortType,
       LongType, BinaryType, BooleanType, DecimalType.USER_DEFAULT, TimestampType)
     primitiveTypes.map { t =>
-      val dataGen = RandomDataGenerator.forType(t, nullable = false).get
+      val dataGen = RandomDataGenerator.forType(t, nullable = true).get
       val inputData = Seq.fill(10) {
         val value = dataGen.apply()
         value match {
@@ -142,9 +151,17 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
           case _ => value
         }
       }
-      val input = inputData.map(Literal(_))
-      checkEvaluation(In(input(0), input.slice(1, 10)),
-        inputData.slice(1, 10).contains(inputData(0)))
+      val input = inputData.map(Literal.create(_, t))
+      val expected = if (inputData(0) == null) {
+        null
+      } else if (inputData.slice(1, 10).contains(inputData(0))) {
+        true
+      } else if (inputData.slice(1, 10).contains(null)) {
+        null
+      } else {
+        false
+      }
+      checkEvaluation(In(input(0), input.slice(1, 10)), expected)
     }
   }
 
@@ -158,15 +175,15 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
     checkEvaluation(InSet(one, hS), true)
     checkEvaluation(InSet(two, hS), true)
     checkEvaluation(InSet(two, nS), true)
-    checkEvaluation(InSet(nl, nS), true)
     checkEvaluation(InSet(three, hS), false)
-    checkEvaluation(InSet(three, nS), false)
-    checkEvaluation(And(InSet(one, hS), InSet(two, hS)), true)
+    checkEvaluation(InSet(three, nS), null)
+    checkEvaluation(InSet(nl, hS), null)
+    checkEvaluation(InSet(nl, nS), null)
 
     val primitiveTypes = Seq(IntegerType, FloatType, DoubleType, StringType, ByteType, ShortType,
       LongType, BinaryType, BooleanType, DecimalType.USER_DEFAULT, TimestampType)
     primitiveTypes.map { t =>
-      val dataGen = RandomDataGenerator.forType(t, nullable = false).get
+      val dataGen = RandomDataGenerator.forType(t, nullable = true).get
       val inputData = Seq.fill(10) {
         val value = dataGen.apply()
         value match {
@@ -176,8 +193,16 @@ class PredicateSuite extends SparkFunSuite with ExpressionEvalHelper {
         }
       }
       val input = inputData.map(Literal(_))
-      checkEvaluation(InSet(input(0), inputData.slice(1, 10).toSet),
-        inputData.slice(1, 10).contains(inputData(0)))
+      val expected = if (inputData(0) == null) {
+        null
+      } else if (inputData.slice(1, 10).contains(inputData(0))) {
+        true
+      } else if (inputData.slice(1, 10).contains(null)) {
+        null
+      } else {
+        false
+      }
+      checkEvaluation(InSet(input(0), inputData.slice(1, 10).toSet), expected)
     }
   }
 
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
index ec3b2f1edfa05..e67606288f514 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala
@@ -250,29 +250,14 @@ class ConstantFoldingSuite extends PlanTest {
   }
 
   test("Constant folding test: Fold In(v, list) into true or false") {
-    var originalQuery =
+    val originalQuery =
       testRelation
         .select('a)
         .where(In(Literal(1), Seq(Literal(1), Literal(2))))
 
-    var optimized = Optimize.execute(originalQuery.analyze)
-
-    var correctAnswer =
-      testRelation
-        .select('a)
-        .where(Literal(true))
-        .analyze
-
-    comparePlans(optimized, correctAnswer)
-
-    originalQuery =
-      testRelation
-        .select('a)
-        .where(In(Literal(1), Seq(Literal(1), 'a.attr)))
-
-    optimized = Optimize.execute(originalQuery.analyze)
+    val optimized = Optimize.execute(originalQuery.analyze)
 
-    correctAnswer =
+    val correctAnswer =
       testRelation
         .select('a)
         .where(Literal(true))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
index 9d965258e389d..3a3f19af1473b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameFunctionsSuite.scala
@@ -366,10 +366,6 @@ class DataFrameFunctionsSuite extends QueryTest with SharedSQLContext {
       df.selectExpr("array_contains(a, 1)"),
       Seq(Row(true), Row(false))
     )
-    checkAnswer(
-      df.select(array_contains(array(lit(2), lit(null)), 1)),
-      Seq(Row(false), Row(false))
-    )
 
     // In hive, this errors because null has no type information
     intercept[AnalysisException] {
@@ -382,15 +378,13 @@ class DataFrameFunctionsSuite extends QueryTest with SharedSQLContext {
       df.selectExpr("array_contains(null, 1)")
     }
 
-    // In hive, if either argument has a matching type has a null value, return false, even if
-    // the first argument array contains a null and the second argument is null
     checkAnswer(
-      df.selectExpr("array_contains(array(array(1), null)[1], 1)"),
-      Seq(Row(false), Row(false))
+      df.selectExpr("array_contains(array(array(1), null)[0], 1)"),
+      Seq(Row(true), Row(true))
     )
     checkAnswer(
-      df.selectExpr("array_contains(array(0, null), array(1, null)[1])"),
-      Seq(Row(false), Row(false))
+      df.selectExpr("array_contains(array(1, null), array(1, null)[0])"),
+      Seq(Row(true), Row(true))
     )
   }
 }

From 2a4e00ca4d4e7a148b4ff8ce0ad1c6d517cee55f Mon Sep 17 00:00:00 2001
From: felixcheung <felixcheung_m@hotmail.com>
Date: Fri, 28 Aug 2015 18:35:01 -0700
Subject: [PATCH 473/633] [SPARK-9803] [SPARKR] Add subset and transform +
 tests

Add subset and transform
Also reorganize `[` & `[[` to subset instead of select

Note: for transform, transform is very similar to mutate. Spark doesn't seem to replace existing column with the name in mutate (ie. `mutate(df, age = df$age + 2)` - returned DataFrame has 2 columns with the same name 'age'), so therefore not doing that for now in transform.
Though it is clearly stated it should replace column with matching name (should I open a JIRA for mutate/transform?)

Author: felixcheung <felixcheung_m@hotmail.com>

Closes #8503 from felixcheung/rsubset_transform.
---
 R/pkg/NAMESPACE                  |  2 +
 R/pkg/R/DataFrame.R              | 70 +++++++++++++++++++++++++-------
 R/pkg/R/generics.R               | 10 ++++-
 R/pkg/inst/tests/test_sparkSQL.R | 20 ++++++++-
 4 files changed, 85 insertions(+), 17 deletions(-)

diff --git a/R/pkg/NAMESPACE b/R/pkg/NAMESPACE
index 5286c01986204..9d39630706436 100644
--- a/R/pkg/NAMESPACE
+++ b/R/pkg/NAMESPACE
@@ -69,9 +69,11 @@ exportMethods("arrange",
               "selectExpr",
               "show",
               "showDF",
+              "subset",
               "summarize",
               "summary",
               "take",
+              "transform",
               "unionAll",
               "unique",
               "unpersist",
diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 74de7c81e35a6..8a00238b41d60 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -987,7 +987,7 @@ setMethod("$<-", signature(x = "DataFrame"),
 
 setClassUnion("numericOrcharacter", c("numeric", "character"))
 
-#' @rdname select
+#' @rdname subset
 #' @name [[
 setMethod("[[", signature(x = "DataFrame", i = "numericOrcharacter"),
           function(x, i) {
@@ -998,7 +998,7 @@ setMethod("[[", signature(x = "DataFrame", i = "numericOrcharacter"),
             getColumn(x, i)
           })
 
-#' @rdname select
+#' @rdname subset
 #' @name [
 setMethod("[", signature(x = "DataFrame", i = "missing"),
           function(x, i, j, ...) {
@@ -1012,7 +1012,7 @@ setMethod("[", signature(x = "DataFrame", i = "missing"),
             select(x, j)
           })
 
-#' @rdname select
+#' @rdname subset
 #' @name [
 setMethod("[", signature(x = "DataFrame", i = "Column"),
           function(x, i, j, ...) {
@@ -1020,12 +1020,43 @@ setMethod("[", signature(x = "DataFrame", i = "Column"),
             # https://stat.ethz.ch/R-manual/R-devel/library/base/html/Extract.data.frame.html
             filtered <- filter(x, i)
             if (!missing(j)) {
-              filtered[, j]
+              filtered[, j, ...]
             } else {
               filtered
             }
           })
 
+#' Subset
+#'
+#' Return subsets of DataFrame according to given conditions
+#' @param x A DataFrame
+#' @param subset A logical expression to filter on rows
+#' @param select expression for the single Column or a list of columns to select from the DataFrame
+#' @return A new DataFrame containing only the rows that meet the condition with selected columns
+#' @export
+#' @rdname subset
+#' @name subset
+#' @aliases [
+#' @family subsetting functions
+#' @examples
+#' \dontrun{
+#'   # Columns can be selected using `[[` and `[`
+#'   df[[2]] == df[["age"]]
+#'   df[,2] == df[,"age"]
+#'   df[,c("name", "age")]
+#'   # Or to filter rows
+#'   df[df$age > 20,]
+#'   # DataFrame can be subset on both rows and Columns
+#'   df[df$name == "Smith", c(1,2)]
+#'   df[df$age %in% c(19, 30), 1:2]
+#'   subset(df, df$age %in% c(19, 30), 1:2)
+#'   subset(df, df$age %in% c(19), select = c(1,2))
+#' }
+setMethod("subset", signature(x = "DataFrame"),
+          function(x, subset, select, ...) {
+            x[subset, select, ...]
+          })
+
 #' Select
 #'
 #' Selects a set of columns with names or Column expressions.
@@ -1034,6 +1065,8 @@ setMethod("[", signature(x = "DataFrame", i = "Column"),
 #' @return A new DataFrame with selected columns
 #' @export
 #' @rdname select
+#' @name select
+#' @family subsetting functions
 #' @examples
 #' \dontrun{
 #'   select(df, "*")
@@ -1041,15 +1074,8 @@ setMethod("[", signature(x = "DataFrame", i = "Column"),
 #'   select(df, df$name, df$age + 1)
 #'   select(df, c("col1", "col2"))
 #'   select(df, list(df$name, df$age + 1))
-#'   # Columns can also be selected using `[[` and `[`
-#'   df[[2]] == df[["age"]]
-#'   df[,2] == df[,"age"]
-#'   df[,c("name", "age")]
 #'   # Similar to R data frames columns can also be selected using `$`
 #'   df$age
-#'   # It can also be subset on rows and Columns
-#'   df[df$name == "Smith", c(1,2)]
-#'   df[df$age %in% c(19, 30), 1:2]
 #' }
 setMethod("select", signature(x = "DataFrame", col = "character"),
           function(x, col, ...) {
@@ -1121,7 +1147,7 @@ setMethod("selectExpr",
 #' @return A DataFrame with the new column added.
 #' @rdname withColumn
 #' @name withColumn
-#' @aliases mutate
+#' @aliases mutate transform
 #' @export
 #' @examples
 #'\dontrun{
@@ -1141,11 +1167,12 @@ setMethod("withColumn",
 #'
 #' Return a new DataFrame with the specified columns added.
 #'
-#' @param x A DataFrame
+#' @param .data A DataFrame
 #' @param col a named argument of the form name = col
 #' @return A new DataFrame with the new columns added.
 #' @rdname withColumn
 #' @name mutate
+#' @aliases withColumn transform
 #' @export
 #' @examples
 #'\dontrun{
@@ -1155,10 +1182,12 @@ setMethod("withColumn",
 #' df <- jsonFile(sqlContext, path)
 #' newDF <- mutate(df, newCol = df$col1 * 5, newCol2 = df$col1 * 2)
 #' names(newDF) # Will contain newCol, newCol2
+#' newDF2 <- transform(df, newCol = df$col1 / 5, newCol2 = df$col1 * 2)
 #' }
 setMethod("mutate",
-          signature(x = "DataFrame"),
-          function(x, ...) {
+          signature(.data = "DataFrame"),
+          function(.data, ...) {
+            x <- .data
             cols <- list(...)
             stopifnot(length(cols) > 0)
             stopifnot(class(cols[[1]]) == "Column")
@@ -1173,6 +1202,16 @@ setMethod("mutate",
             do.call(select, c(x, x$"*", cols))
           })
 
+#' @export
+#' @rdname withColumn
+#' @name transform
+#' @aliases withColumn mutate
+setMethod("transform",
+          signature(`_data` = "DataFrame"),
+          function(`_data`, ...) {
+            mutate(`_data`, ...)
+          })
+
 #' WithColumnRenamed
 #'
 #' Rename an existing column in a DataFrame.
@@ -1300,6 +1339,7 @@ setMethod("orderBy",
 #' @return A DataFrame containing only the rows that meet the condition.
 #' @rdname filter
 #' @name filter
+#' @family subsetting functions
 #' @export
 #' @examples
 #'\dontrun{
diff --git a/R/pkg/R/generics.R b/R/pkg/R/generics.R
index b578b8789d2c5..43dd8d283ab6b 100644
--- a/R/pkg/R/generics.R
+++ b/R/pkg/R/generics.R
@@ -467,7 +467,7 @@ setGeneric("merge")
 
 #' @rdname withColumn
 #' @export
-setGeneric("mutate", function(x, ...) {standardGeneric("mutate") })
+setGeneric("mutate", function(.data, ...) {standardGeneric("mutate") })
 
 #' @rdname arrange
 #' @export
@@ -507,6 +507,10 @@ setGeneric("saveAsTable", function(df, tableName, source, mode, ...) {
   standardGeneric("saveAsTable")
 })
 
+#' @rdname withColumn
+#' @export
+setGeneric("transform", function(`_data`, ...) {standardGeneric("transform") })
+
 #' @rdname write.df
 #' @export
 setGeneric("write.df", function(df, path, ...) { standardGeneric("write.df") })
@@ -531,6 +535,10 @@ setGeneric("selectExpr", function(x, expr, ...) { standardGeneric("selectExpr")
 #' @export
 setGeneric("showDF", function(x,...) { standardGeneric("showDF") })
 
+# @rdname subset
+# @export
+setGeneric("subset", function(x, subset, select, ...) { standardGeneric("subset") })
+
 #' @rdname agg
 #' @export
 setGeneric("summarize", function(x,...) { standardGeneric("summarize") })
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 933b11c8ee7e2..0da5e38654732 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -612,6 +612,10 @@ test_that("subsetting", {
   df5 <- df[df$age %in% c(19), c(1,2)]
   expect_equal(count(df5), 1)
   expect_equal(columns(df5), c("name", "age"))
+
+  df6 <- subset(df, df$age %in% c(30), c(1,2))
+  expect_equal(count(df6), 1)
+  expect_equal(columns(df6), c("name", "age"))
 })
 
 test_that("selectExpr() on a DataFrame", {
@@ -1028,7 +1032,7 @@ test_that("withColumn() and withColumnRenamed()", {
   expect_equal(columns(newDF2)[1], "newerAge")
 })
 
-test_that("mutate(), rename() and names()", {
+test_that("mutate(), transform(), rename() and names()", {
   df <- jsonFile(sqlContext, jsonPath)
   newDF <- mutate(df, newAge = df$age + 2)
   expect_equal(length(columns(newDF)), 3)
@@ -1042,6 +1046,20 @@ test_that("mutate(), rename() and names()", {
   names(newDF2) <- c("newerName", "evenNewerAge")
   expect_equal(length(names(newDF2)), 2)
   expect_equal(names(newDF2)[1], "newerName")
+
+  transformedDF <- transform(df, newAge = -df$age, newAge2 = df$age / 2)
+  expect_equal(length(columns(transformedDF)), 4)
+  expect_equal(columns(transformedDF)[3], "newAge")
+  expect_equal(columns(transformedDF)[4], "newAge2")
+  expect_equal(first(filter(transformedDF, transformedDF$name == "Andy"))$newAge, -30)
+
+  # test if transform on local data frames works
+  # ensure the proper signature is used - otherwise this will fail to run
+  attach(airquality)
+  result <- transform(Ozone, logOzone = log(Ozone))
+  expect_equal(nrow(result), 153)
+  expect_equal(ncol(result), 2)
+  detach(airquality)
 })
 
 test_that("write.df() on DataFrame and works with parquetFile", {

From e8ea5bafee9ca734edf62021145d0c2d5491cba8 Mon Sep 17 00:00:00 2001
From: martinzapletal <zapletal-martin@email.cz>
Date: Fri, 28 Aug 2015 21:03:48 -0700
Subject: [PATCH 474/633] [SPARK-9910] [ML] User guide for train validation
 split

Author: martinzapletal <zapletal-martin@email.cz>

Closes #8377 from zapletal-martin/SPARK-9910.
---
 docs/ml-guide.md                              | 117 ++++++++++++++++++
 .../ml/JavaTrainValidationSplitExample.java   |  90 ++++++++++++++
 .../ml/TrainValidationSplitExample.scala      |  80 ++++++++++++
 3 files changed, 287 insertions(+)
 create mode 100644 examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java
 create mode 100644 examples/src/main/scala/org/apache/spark/examples/ml/TrainValidationSplitExample.scala

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index ce53400b6ee56..a92a285f3af85 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -872,3 +872,120 @@ jsc.stop();
 </div>
 
 </div>
+
+## Example: Model Selection via Train Validation Split
+In addition to  `CrossValidator` Spark also offers `TrainValidationSplit` for hyper-parameter tuning.
+`TrainValidationSplit` only evaluates each combination of parameters once as opposed to k times in
+ case of `CrossValidator`. It is therefore less expensive,
+ but will not produce as reliable results when the training dataset is not sufficiently large..
+
+`TrainValidationSplit` takes an `Estimator`, a set of `ParamMap`s provided in the `estimatorParamMaps` parameter,
+and an `Evaluator`.
+It begins by splitting the dataset into two parts using `trainRatio` parameter
+which are used as separate training and test datasets. For example with `$trainRatio=0.75$` (default),
+`TrainValidationSplit` will generate a training and test dataset pair where 75% of the data is used for training and 25% for validation.
+Similar to `CrossValidator`, `TrainValidationSplit` also iterates through the set of `ParamMap`s.
+For each combination of parameters, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
+The `ParamMap` which produces the best evaluation metric is selected as the best option.
+`TrainValidationSplit` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+{% highlight scala %}
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.ml.tuning.{ParamGridBuilder, TrainValidationSplit}
+import org.apache.spark.mllib.util.MLUtils
+
+// Prepare training and test data.
+val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val Array(training, test) = data.randomSplit(Array(0.9, 0.1), seed = 12345)
+
+val lr = new LinearRegression()
+
+// We use a ParamGridBuilder to construct a grid of parameters to search over.
+// TrainValidationSplit will try all combinations of values and determine best model using
+// the evaluator.
+val paramGrid = new ParamGridBuilder()
+  .addGrid(lr.regParam, Array(0.1, 0.01))
+  .addGrid(lr.fitIntercept, Array(true, false))
+  .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
+  .build()
+
+// In this case the estimator is simply the linear regression.
+// A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+val trainValidationSplit = new TrainValidationSplit()
+  .setEstimator(lr)
+  .setEvaluator(new RegressionEvaluator)
+  .setEstimatorParamMaps(paramGrid)
+
+// 80% of the data will be used for training and the remaining 20% for validation.
+trainValidationSplit.setTrainRatio(0.8)
+
+// Run train validation split, and choose the best set of parameters.
+val model = trainValidationSplit.fit(training)
+
+// Make predictions on test data. model is the model with combination of parameters
+// that performed best.
+model.transform(test)
+  .select("features", "label", "prediction")
+  .show()
+
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+{% highlight java %}
+import org.apache.spark.ml.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.param.ParamMap;
+import org.apache.spark.ml.regression.LinearRegression;
+import org.apache.spark.ml.tuning.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.rdd.RDD;
+import org.apache.spark.sql.DataFrame;
+
+DataFrame data = jsql.createDataFrame(
+  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt"),
+  LabeledPoint.class);
+
+// Prepare training and test data.
+DataFrame[] splits = data.randomSplit(new double [] {0.9, 0.1}, 12345);
+DataFrame training = splits[0];
+DataFrame test = splits[1];
+
+LinearRegression lr = new LinearRegression();
+
+// We use a ParamGridBuilder to construct a grid of parameters to search over.
+// TrainValidationSplit will try all combinations of values and determine best model using
+// the evaluator.
+ParamMap[] paramGrid = new ParamGridBuilder()
+  .addGrid(lr.regParam(), new double[] {0.1, 0.01})
+  .addGrid(lr.fitIntercept())
+  .addGrid(lr.elasticNetParam(), new double[] {0.0, 0.5, 1.0})
+  .build();
+
+// In this case the estimator is simply the linear regression.
+// A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+TrainValidationSplit trainValidationSplit = new TrainValidationSplit()
+  .setEstimator(lr)
+  .setEvaluator(new RegressionEvaluator())
+  .setEstimatorParamMaps(paramGrid);
+
+// 80% of the data will be used for training and the remaining 20% for validation.
+trainValidationSplit.setTrainRatio(0.8);
+
+// Run train validation split, and choose the best set of parameters.
+TrainValidationSplitModel model = trainValidationSplit.fit(training);
+
+// Make predictions on test data. model is the model with combination of parameters
+// that performed best.
+model.transform(test)
+  .select("features", "label", "prediction")
+  .show();
+
+{% endhighlight %}
+</div>
+
+</div>
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java
new file mode 100644
index 0000000000000..23f834ab4332b
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaTrainValidationSplitExample.java
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.ml;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.ml.evaluation.RegressionEvaluator;
+import org.apache.spark.ml.param.ParamMap;
+import org.apache.spark.ml.regression.LinearRegression;
+import org.apache.spark.ml.tuning.*;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.SQLContext;
+
+/**
+ * A simple example demonstrating model selection using TrainValidationSplit.
+ *
+ * The example is based on {@link org.apache.spark.examples.ml.JavaSimpleParamsExample}
+ * using linear regression.
+ *
+ * Run with
+ * {{{
+ * bin/run-example ml.JavaTrainValidationSplitExample
+ * }}}
+ */
+public class JavaTrainValidationSplitExample {
+
+  public static void main(String[] args) {
+    SparkConf conf = new SparkConf().setAppName("JavaTrainValidationSplitExample");
+    JavaSparkContext jsc = new JavaSparkContext(conf);
+    SQLContext jsql = new SQLContext(jsc);
+
+    DataFrame data = jsql.createDataFrame(
+      MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt"),
+      LabeledPoint.class);
+
+    // Prepare training and test data.
+    DataFrame[] splits = data.randomSplit(new double [] {0.9, 0.1}, 12345);
+    DataFrame training = splits[0];
+    DataFrame test = splits[1];
+
+    LinearRegression lr = new LinearRegression();
+
+    // We use a ParamGridBuilder to construct a grid of parameters to search over.
+    // TrainValidationSplit will try all combinations of values and determine best model using
+    // the evaluator.
+    ParamMap[] paramGrid = new ParamGridBuilder()
+      .addGrid(lr.regParam(), new double[] {0.1, 0.01})
+      .addGrid(lr.fitIntercept())
+      .addGrid(lr.elasticNetParam(), new double[] {0.0, 0.5, 1.0})
+      .build();
+
+    // In this case the estimator is simply the linear regression.
+    // A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+    TrainValidationSplit trainValidationSplit = new TrainValidationSplit()
+      .setEstimator(lr)
+      .setEvaluator(new RegressionEvaluator())
+      .setEstimatorParamMaps(paramGrid);
+
+    // 80% of the data will be used for training and the remaining 20% for validation.
+    trainValidationSplit.setTrainRatio(0.8);
+
+    // Run train validation split, and choose the best set of parameters.
+    TrainValidationSplitModel model = trainValidationSplit.fit(training);
+
+    // Make predictions on test data. model is the model with combination of parameters
+    // that performed best.
+    model.transform(test)
+      .select("features", "label", "prediction")
+      .show();
+
+    jsc.stop();
+  }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/TrainValidationSplitExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/TrainValidationSplitExample.scala
new file mode 100644
index 0000000000000..1abdf219b1c00
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/TrainValidationSplitExample.scala
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.ml
+
+import org.apache.spark.ml.evaluation.RegressionEvaluator
+import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.ml.tuning.{ParamGridBuilder, TrainValidationSplit}
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.{SparkConf, SparkContext}
+
+/**
+ * A simple example demonstrating model selection using TrainValidationSplit.
+ *
+ * The example is based on [[SimpleParamsExample]] using linear regression.
+ * Run with
+ * {{{
+ * bin/run-example ml.TrainValidationSplitExample
+ * }}}
+ */
+object TrainValidationSplitExample {
+
+  def main(args: Array[String]): Unit = {
+    val conf = new SparkConf().setAppName("TrainValidationSplitExample")
+    val sc = new SparkContext(conf)
+    val sqlContext = new SQLContext(sc)
+    import sqlContext.implicits._
+
+    // Prepare training and test data.
+    val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+    val Array(training, test) = data.randomSplit(Array(0.9, 0.1), seed = 12345)
+
+    val lr = new LinearRegression()
+
+    // We use a ParamGridBuilder to construct a grid of parameters to search over.
+    // TrainValidationSplit will try all combinations of values and determine best model using
+    // the evaluator.
+    val paramGrid = new ParamGridBuilder()
+      .addGrid(lr.regParam, Array(0.1, 0.01))
+      .addGrid(lr.fitIntercept, Array(true, false))
+      .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
+      .build()
+
+    // In this case the estimator is simply the linear regression.
+    // A TrainValidationSplit requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+    val trainValidationSplit = new TrainValidationSplit()
+      .setEstimator(lr)
+      .setEvaluator(new RegressionEvaluator)
+      .setEstimatorParamMaps(paramGrid)
+
+    // 80% of the data will be used for training and the remaining 20% for validation.
+    trainValidationSplit.setTrainRatio(0.8)
+
+    // Run train validation split, and choose the best set of parameters.
+    val model = trainValidationSplit.fit(training)
+
+    // Make predictions on test data. model is the model with combination of parameters
+    // that performed best.
+    model.transform(test)
+      .select("features", "label", "prediction")
+      .show()
+
+    sc.stop()
+  }
+}

From 5369be806848f43cb87c76504258c4e7de930c90 Mon Sep 17 00:00:00 2001
From: GuoQiang Li <witgo@qq.com>
Date: Sat, 29 Aug 2015 13:20:22 -0700
Subject: [PATCH 475/633] [SPARK-10350] [DOC] [SQL] Removed duplicated option
 description from SQL guide

Author: GuoQiang Li <witgo@qq.com>

Closes #8520 from witgo/SPARK-10350.
---
 docs/sql-programming-guide.md | 10 ----------
 1 file changed, 10 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index e8eb88488ee24..6a1b0fbfa1eb3 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1405,16 +1405,6 @@ Configuration of Parquet can be done using the `setConf` method on `SQLContext`
     </p>
   </td>
 </tr>
-<tr>
-  <td><code>spark.sql.parquet.mergeSchema</code></td>
-  <td><code>false</code></td>
-  <td>
-    <p>
-      When true, the Parquet data source merges schemas collected from all data files, otherwise the
-      schema is picked from the summary file or a random data file if no summary file is available.
-    </p>
-  </td>
-</tr>
 </table>
 
 ## JSON Datasets

From 24ffa85c002a095ffb270175ec838995d3ed5469 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sat, 29 Aug 2015 13:24:32 -0700
Subject: [PATCH 476/633] [SPARK-10289] [SQL] A direct write API for testing
 Parquet

This PR introduces a direct write API for testing Parquet. It's a DSL flavored version of the [`writeDirect` method] [1] comes with parquet-avro testing code. With this API, it's much easier to construct arbitrary Parquet structures. It's especially useful when adding regression tests for various compatibility corner cases.

Sample usage of this API can be found in the new test case added in `ParquetThriftCompatibilitySuite`.

[1]: https://github.com/apache/parquet-mr/blob/apache-parquet-1.8.1/parquet-avro/src/test/java/org/apache/parquet/avro/TestArrayCompatibility.java#L945-L972

Author: Cheng Lian <lian@databricks.com>

Closes #8454 from liancheng/spark-10289/parquet-testing-direct-write-api.
---
 .../parquet/ParquetCompatibilityTest.scala    |  84 +++++++++++++--
 .../ParquetThriftCompatibilitySuite.scala     | 100 +++++++++++++++---
 2 files changed, 160 insertions(+), 24 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index df68432faeeb3..91f3ce4d34c8b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -17,11 +17,15 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
-import scala.collection.JavaConverters._
+import scala.collection.JavaConverters.{collectionAsScalaIterableConverter, mapAsJavaMapConverter, seqAsJavaListConverter}
 
+import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{Path, PathFilter}
-import org.apache.parquet.hadoop.ParquetFileReader
-import org.apache.parquet.schema.MessageType
+import org.apache.parquet.hadoop.api.WriteSupport
+import org.apache.parquet.hadoop.api.WriteSupport.WriteContext
+import org.apache.parquet.hadoop.{ParquetFileReader, ParquetWriter}
+import org.apache.parquet.io.api.RecordConsumer
+import org.apache.parquet.schema.{MessageType, MessageTypeParser}
 
 import org.apache.spark.sql.QueryTest
 
@@ -38,11 +42,10 @@ private[sql] abstract class ParquetCompatibilityTest extends QueryTest with Parq
     val fs = fsPath.getFileSystem(configuration)
     val parquetFiles = fs.listStatus(fsPath, new PathFilter {
       override def accept(path: Path): Boolean = pathFilter(path)
-    }).toSeq
+    }).toSeq.asJava
 
-    val footers =
-      ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles.asJava, true)
-    footers.iterator().next().getParquetMetadata.getFileMetaData.getSchema
+    val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
+    footers.asScala.head.getParquetMetadata.getFileMetaData.getSchema
   }
 
   protected def logParquetSchema(path: String): Unit = {
@@ -53,8 +56,69 @@ private[sql] abstract class ParquetCompatibilityTest extends QueryTest with Parq
   }
 }
 
-object ParquetCompatibilityTest {
-  def makeNullable[T <: AnyRef](i: Int)(f: => T): T = {
-    if (i % 3 == 0) null.asInstanceOf[T] else f
+private[sql] object ParquetCompatibilityTest {
+  implicit class RecordConsumerDSL(consumer: RecordConsumer) {
+    def message(f: => Unit): Unit = {
+      consumer.startMessage()
+      f
+      consumer.endMessage()
+    }
+
+    def group(f: => Unit): Unit = {
+      consumer.startGroup()
+      f
+      consumer.endGroup()
+    }
+
+    def field(name: String, index: Int)(f: => Unit): Unit = {
+      consumer.startField(name, index)
+      f
+      consumer.endField(name, index)
+    }
+  }
+
+  /**
+   * A testing Parquet [[WriteSupport]] implementation used to write manually constructed Parquet
+   * records with arbitrary structures.
+   */
+  private class DirectWriteSupport(schema: MessageType, metadata: Map[String, String])
+    extends WriteSupport[RecordConsumer => Unit] {
+
+    private var recordConsumer: RecordConsumer = _
+
+    override def init(configuration: Configuration): WriteContext = {
+      new WriteContext(schema, metadata.asJava)
+    }
+
+    override def write(recordWriter: RecordConsumer => Unit): Unit = {
+      recordWriter.apply(recordConsumer)
+    }
+
+    override def prepareForWrite(recordConsumer: RecordConsumer): Unit = {
+      this.recordConsumer = recordConsumer
+    }
+  }
+
+  /**
+   * Writes arbitrary messages conforming to a given `schema` to a Parquet file located by `path`.
+   * Records are produced by `recordWriters`.
+   */
+  def writeDirect(path: String, schema: String, recordWriters: (RecordConsumer => Unit)*): Unit = {
+    writeDirect(path, schema, Map.empty[String, String], recordWriters: _*)
+  }
+
+  /**
+   * Writes arbitrary messages conforming to a given `schema` to a Parquet file located by `path`
+   * with given user-defined key-value `metadata`. Records are produced by `recordWriters`.
+   */
+  def writeDirect(
+      path: String,
+      schema: String,
+      metadata: Map[String, String],
+      recordWriters: (RecordConsumer => Unit)*): Unit = {
+    val messageType = MessageTypeParser.parseMessageType(schema)
+    val writeSupport = new DirectWriteSupport(messageType, metadata)
+    val parquetWriter = new ParquetWriter[RecordConsumer => Unit](new Path(path), writeSupport)
+    try recordWriters.foreach(parquetWriter.write) finally parquetWriter.close()
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
index b789c5a106e56..88a3d878f97fe 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetThriftCompatibilitySuite.scala
@@ -33,11 +33,9 @@ class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest with Shar
        """.stripMargin)
 
     checkAnswer(sqlContext.read.parquet(parquetFilePath.toString), (0 until 10).map { i =>
-      def nullable[T <: AnyRef]: ( => T) => T = makeNullable[T](i)
-
       val suits = Array("SPADES", "HEARTS", "DIAMONDS", "CLUBS")
 
-      Row(
+      val nonNullablePrimitiveValues = Seq(
         i % 2 == 0,
         i.toByte,
         (i + 1).toShort,
@@ -50,18 +48,15 @@ class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest with Shar
         s"val_$i",
         s"val_$i",
         // Thrift ENUM values are converted to Parquet binaries containing UTF-8 strings
-        suits(i % 4),
-
-        nullable(i % 2 == 0: java.lang.Boolean),
-        nullable(i.toByte: java.lang.Byte),
-        nullable((i + 1).toShort: java.lang.Short),
-        nullable(i + 2: Integer),
-        nullable((i * 10).toLong: java.lang.Long),
-        nullable(i.toDouble + 0.2d: java.lang.Double),
-        nullable(s"val_$i"),
-        nullable(s"val_$i"),
-        nullable(suits(i % 4)),
+        suits(i % 4))
+
+      val nullablePrimitiveValues = if (i % 3 == 0) {
+        Seq.fill(nonNullablePrimitiveValues.length)(null)
+      } else {
+        nonNullablePrimitiveValues
+      }
 
+      val complexValues = Seq(
         Seq.tabulate(3)(n => s"arr_${i + n}"),
         // Thrift `SET`s are converted to Parquet `LIST`s
         Seq(i),
@@ -71,6 +66,83 @@ class ParquetThriftCompatibilitySuite extends ParquetCompatibilityTest with Shar
             Row(Seq.tabulate(3)(j => i + j + m), s"val_${i + m}")
           }
         }.toMap)
+
+      Row(nonNullablePrimitiveValues ++ nullablePrimitiveValues ++ complexValues: _*)
     })
   }
+
+  test("SPARK-10136 list of primitive list") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      // This Parquet schema is translated from the following Thrift schema:
+      //
+      //   struct ListOfPrimitiveList {
+      //     1: list<list<i32>> f;
+      //   }
+      val schema =
+        s"""message ListOfPrimitiveList {
+           |  required group f (LIST) {
+           |    repeated group f_tuple (LIST) {
+           |      repeated int32 f_tuple_tuple;
+           |    }
+           |  }
+           |}
+         """.stripMargin
+
+      writeDirect(path, schema, { rc =>
+        rc.message {
+          rc.field("f", 0) {
+            rc.group {
+              rc.field("f_tuple", 0) {
+                rc.group {
+                  rc.field("f_tuple_tuple", 0) {
+                    rc.addInteger(0)
+                    rc.addInteger(1)
+                  }
+                }
+
+                rc.group {
+                  rc.field("f_tuple_tuple", 0) {
+                    rc.addInteger(2)
+                    rc.addInteger(3)
+                  }
+                }
+              }
+            }
+          }
+        }
+      }, { rc =>
+        rc.message {
+          rc.field("f", 0) {
+            rc.group {
+              rc.field("f_tuple", 0) {
+                rc.group {
+                  rc.field("f_tuple_tuple", 0) {
+                    rc.addInteger(4)
+                    rc.addInteger(5)
+                  }
+                }
+
+                rc.group {
+                  rc.field("f_tuple_tuple", 0) {
+                    rc.addInteger(6)
+                    rc.addInteger(7)
+                  }
+                }
+              }
+            }
+          }
+        }
+      })
+
+      logParquetSchema(path)
+
+      checkAnswer(
+        sqlContext.read.parquet(path),
+        Seq(
+          Row(Seq(Seq(0, 1), Seq(2, 3))),
+          Row(Seq(Seq(4, 5), Seq(6, 7)))))
+    }
+  }
 }

From 5c3d16a9b91bb9a458d3ba141f7bef525cf3d285 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Sat, 29 Aug 2015 13:26:01 -0700
Subject: [PATCH 477/633] [SPARK-10344] [SQL] Add tests for extraStrategies

Actually using this API requires access to a lot of classes that we might make private by accident.  I've added some tests to prevent this.

Author: Michael Armbrust <michael@databricks.com>

Closes #8516 from marmbrus/extraStrategiesTests.
---
 .../spark/sql/ExtraStrategiesSuite.scala      | 67 +++++++++++++++++++
 .../spark/sql/test/SharedSQLContext.scala     |  2 +-
 2 files changed, 68 insertions(+), 1 deletion(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala
new file mode 100644
index 0000000000000..8d2f45d70308b
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package test.org.apache.spark.sql
+
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Literal, GenericInternalRow, Attribute}
+import org.apache.spark.sql.catalyst.plans.logical.{Project, LogicalPlan}
+import org.apache.spark.sql.execution.SparkPlan
+import org.apache.spark.sql.{Row, Strategy, QueryTest}
+import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.unsafe.types.UTF8String
+
+case class FastOperator(output: Seq[Attribute]) extends SparkPlan {
+
+  override protected def doExecute(): RDD[InternalRow] = {
+    val str = Literal("so fast").value
+    val row = new GenericInternalRow(Array[Any](str))
+    sparkContext.parallelize(Seq(row))
+  }
+
+  override def children: Seq[SparkPlan] = Nil
+}
+
+object TestStrategy extends Strategy {
+  def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+    case Project(Seq(attr), _) if attr.name == "a" =>
+      FastOperator(attr.toAttribute :: Nil) :: Nil
+    case _ => Nil
+  }
+}
+
+class ExtraStrategiesSuite extends QueryTest with SharedSQLContext {
+  import testImplicits._
+
+  test("insert an extraStrategy") {
+    try {
+      sqlContext.experimental.extraStrategies = TestStrategy :: Nil
+
+      val df = sqlContext.sparkContext.parallelize(Seq(("so slow", 1))).toDF("a", "b")
+      checkAnswer(
+        df.select("a"),
+        Row("so fast"))
+
+      checkAnswer(
+        df.select("a", "b"),
+        Row("so slow", 1))
+    } finally {
+      sqlContext.experimental.extraStrategies = Nil
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
index 8a061b6bc690d..d23c6a0732669 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
@@ -23,7 +23,7 @@ import org.apache.spark.sql.{ColumnName, SQLContext}
 /**
  * Helper trait for SQL test suites where all tests share a single [[TestSQLContext]].
  */
-private[sql] trait SharedSQLContext extends SQLTestUtils {
+trait SharedSQLContext extends SQLTestUtils {
 
   /**
    * The [[TestSQLContext]] to use for all tests in this suite.

From 277148b285748e863f2b9fdf6cf12963977f91ca Mon Sep 17 00:00:00 2001
From: wangwei <wangwei82@huawei.com>
Date: Sat, 29 Aug 2015 13:29:50 -0700
Subject: [PATCH 478/633] [SPARK-10226] [SQL] Fix exclamation mark issue in
 SparkSQL

When I tested the latest version of spark with exclamation mark, I got some errors. Then I reseted the spark version and found that commit id "a2409d1c8e8ddec04b529ac6f6a12b5993f0eeda" brought the bug. With jline version changing from 0.9.94 to 2.12 after this commit, exclamation mark would be treated as a special character in ConsoleReader.

Author: wangwei <wangwei82@huawei.com>

Closes #8420 from small-wang/jline-SPARK-10226.
---
 .../apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala   | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
index a29df567983b1..b5073961a1c84 100644
--- a/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
+++ b/sql/hive-thriftserver/src/main/scala/org/apache/spark/sql/hive/thriftserver/SparkSQLCLIDriver.scala
@@ -171,6 +171,7 @@ private[hive] object SparkSQLCLIDriver extends Logging {
 
     val reader = new ConsoleReader()
     reader.setBellEnabled(false)
+    reader.setExpandEvents(false)
     // reader.setDebug(new PrintWriter(new FileWriter("writer.debug", true)))
     CliDriver.getCommandCompleter.foreach((e) => reader.addCompleter(e))
 

From 6a6f3c91ee1f63dd464eb03d156d02c1a5887d88 Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sat, 29 Aug 2015 13:36:25 -0700
Subject: [PATCH 479/633] [SPARK-10330] Use SparkHadoopUtil TaskAttemptContext
 reflection methods in more places

SparkHadoopUtil contains methods that use reflection to work around TaskAttemptContext binary incompatibilities between Hadoop 1.x and 2.x. We should use these methods in more places.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8499 from JoshRosen/use-hadoop-reflection-in-more-places.
---
 .../sql/execution/datasources/WriterContainer.scala    | 10 +++++++---
 .../sql/execution/datasources/json/JSONRelation.scala  |  7 +++++--
 .../datasources/parquet/ParquetRelation.scala          |  7 +++++--
 .../org/apache/spark/sql/hive/orc/OrcRelation.scala    |  9 ++++++---
 .../apache/spark/sql/sources/SimpleTextRelation.scala  |  7 +++++--
 5 files changed, 28 insertions(+), 12 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index 78f48a5cd72c7..879fd69863211 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -25,6 +25,7 @@ import org.apache.hadoop.fs.Path
 import org.apache.hadoop.mapreduce._
 import org.apache.hadoop.mapreduce.lib.output.{FileOutputCommitter => MapReduceFileOutputCommitter}
 import org.apache.spark._
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.mapreduce.SparkHadoopMapReduceUtil
 import org.apache.spark.sql._
@@ -145,7 +146,8 @@ private[sql] abstract class BaseWriterContainer(
           "because spark.speculation is configured to be true.")
       defaultOutputCommitter
     } else {
-      val committerClass = context.getConfiguration.getClass(
+      val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
+      val committerClass = configuration.getClass(
         SQLConf.OUTPUT_COMMITTER_CLASS.key, null, classOf[OutputCommitter])
 
       Option(committerClass).map { clazz =>
@@ -227,7 +229,8 @@ private[sql] class DefaultWriterContainer(
 
   def writeRows(taskContext: TaskContext, iterator: Iterator[InternalRow]): Unit = {
     executorSideSetup(taskContext)
-    taskAttemptContext.getConfiguration.set("spark.sql.sources.output.path", outputPath)
+    val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(taskAttemptContext)
+    configuration.set("spark.sql.sources.output.path", outputPath)
     val writer = outputWriterFactory.newInstance(getWorkPath, dataSchema, taskAttemptContext)
     writer.initConverter(dataSchema)
 
@@ -395,7 +398,8 @@ private[sql] class DynamicPartitionWriterContainer(
     def newOutputWriter(key: InternalRow): OutputWriter = {
       val partitionPath = getPartitionString(key).getString(0)
       val path = new Path(getWorkPath, partitionPath)
-      taskAttemptContext.getConfiguration.set(
+      val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(taskAttemptContext)
+      configuration.set(
         "spark.sql.sources.output.path", new Path(outputPath, partitionPath).toString)
       val newWriter = outputWriterFactory.newInstance(path.toString, dataSchema, taskAttemptContext)
       newWriter.initConverter(dataSchema)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
index ab8ca5f748f24..7a49157d9e72c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
@@ -30,6 +30,7 @@ import org.apache.hadoop.mapreduce.{Job, RecordWriter, TaskAttemptContext}
 
 import org.apache.spark.Logging
 import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -169,8 +170,10 @@ private[json] class JsonOutputWriter(
   private val recordWriter: RecordWriter[NullWritable, Text] = {
     new TextOutputFormat[NullWritable, Text]() {
       override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
-        val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
-        val split = context.getTaskAttemptID.getTaskID.getId
+        val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
+        val uniqueWriteJobId = configuration.get("spark.sql.sources.writeJobUUID")
+        val taskAttemptId = SparkHadoopUtil.get.getTaskAttemptIDFromTaskAttemptContext(context)
+        val split = taskAttemptId.getTaskID.getId
         new Path(path, f"part-r-$split%05d-$uniqueWriteJobId$extension")
       }
     }.getRecordWriter(context)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index 64982f37cf872..c6bbc392cad4c 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -40,6 +40,7 @@ import org.apache.parquet.{Log => ApacheParquetLog}
 import org.slf4j.bridge.SLF4JBridgeHandler
 
 import org.apache.spark.broadcast.Broadcast
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.rdd.{RDD, SqlNewHadoopPartition, SqlNewHadoopRDD}
 import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.InternalRow
@@ -81,8 +82,10 @@ private[sql] class ParquetOutputWriter(path: String, context: TaskAttemptContext
         //     `FileOutputCommitter.getWorkPath()`, which points to the base directory of all
         //     partitions in the case of dynamic partitioning.
         override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
-          val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
-          val split = context.getTaskAttemptID.getTaskID.getId
+          val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
+          val uniqueWriteJobId = configuration.get("spark.sql.sources.writeJobUUID")
+          val taskAttemptId = SparkHadoopUtil.get.getTaskAttemptIDFromTaskAttemptContext(context)
+          val split = taskAttemptId.getTaskID.getId
           new Path(path, f"part-r-$split%05d-$uniqueWriteJobId$extension")
         }
       }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 1cff5cf9c3543..4eeca9aec12bd 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -34,6 +34,7 @@ import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
 import org.apache.hadoop.mapreduce.{Job, TaskAttemptContext}
 
 import org.apache.spark.Logging
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
 import org.apache.spark.rdd.{HadoopRDD, RDD}
 import org.apache.spark.sql.catalyst.InternalRow
@@ -77,7 +78,8 @@ private[orc] class OrcOutputWriter(
     }.mkString(":"))
 
     val serde = new OrcSerde
-    serde.initialize(context.getConfiguration, table)
+    val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
+    serde.initialize(configuration, table)
     serde
   }
 
@@ -109,9 +111,10 @@ private[orc] class OrcOutputWriter(
   private lazy val recordWriter: RecordWriter[NullWritable, Writable] = {
     recordWriterInstantiated = true
 
-    val conf = context.getConfiguration
+    val conf = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
     val uniqueWriteJobId = conf.get("spark.sql.sources.writeJobUUID")
-    val partition = context.getTaskAttemptID.getTaskID.getId
+    val taskAttemptId = SparkHadoopUtil.get.getTaskAttemptIDFromTaskAttemptContext(context)
+    val partition = taskAttemptId.getTaskID.getId
     val filename = f"part-r-$partition%05d-$uniqueWriteJobId.orc"
 
     new OrcOutputFormat().getRecordWriter(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
index e8141923a9b5c..527ca7a81cad8 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
@@ -27,6 +27,7 @@ import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat, TextOutputForma
 import org.apache.hadoop.mapreduce.{Job, RecordWriter, TaskAttemptContext}
 
 import org.apache.spark.rdd.RDD
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql.catalyst.CatalystTypeConverters
 import org.apache.spark.sql.catalyst.expressions.{Cast, Literal}
 import org.apache.spark.sql.types.{DataType, StructType}
@@ -53,8 +54,10 @@ class AppendingTextOutputFormat(outputFile: Path) extends TextOutputFormat[NullW
   numberFormat.setGroupingUsed(false)
 
   override def getDefaultWorkFile(context: TaskAttemptContext, extension: String): Path = {
-    val uniqueWriteJobId = context.getConfiguration.get("spark.sql.sources.writeJobUUID")
-    val split = context.getTaskAttemptID.getTaskID.getId
+    val configuration = SparkHadoopUtil.get.getConfigurationFromJobContext(context)
+    val uniqueWriteJobId = configuration.get("spark.sql.sources.writeJobUUID")
+    val taskAttemptId = SparkHadoopUtil.get.getTaskAttemptIDFromTaskAttemptContext(context)
+    val split = taskAttemptId.getTaskID.getId
     val name = FileOutputFormat.getOutputName(context)
     new Path(outputFile, s"$name-${numberFormat.format(split)}-$uniqueWriteJobId")
   }

From 097a7e36e0bf7290b1879331375bacc905583bd3 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Sat, 29 Aug 2015 16:39:40 -0700
Subject: [PATCH 480/633] [SPARK-10339] [SPARK-10334] [SPARK-10301] [SQL]
 Partitioned table scan can OOM driver and throw a better error message when
 users need to enable parquet schema merging

This fixes the problem that scanning partitioned table causes driver have a high memory pressure and takes down the cluster. Also, with this fix, we will be able to correctly show the query plan of a query consuming partitioned tables.

https://issues.apache.org/jira/browse/SPARK-10339
https://issues.apache.org/jira/browse/SPARK-10334

Finally, this PR squeeze in a "quick fix" for SPARK-10301. It is not a real fix, but it just throw a better error message to let user know what to do.

Author: Yin Huai <yhuai@databricks.com>

Closes #8515 from yhuai/partitionedTableScan.
---
 .../datasources/DataSourceStrategy.scala      | 85 ++++++++++---------
 .../parquet/CatalystRowConverter.scala        |  7 ++
 .../ParquetHadoopFsRelationSuite.scala        | 15 +++-
 3 files changed, 65 insertions(+), 42 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
index 6c1ef6a6df887..c58213155daa8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceStrategy.scala
@@ -26,6 +26,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.execution.SparkPlan
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.types.{StringType, StructType}
 import org.apache.spark.sql.{SaveMode, Strategy, execution, sources, _}
@@ -121,7 +122,7 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
       projections: Seq[NamedExpression],
       filters: Seq[Expression],
       partitionColumns: StructType,
-      partitions: Array[Partition]) = {
+      partitions: Array[Partition]): SparkPlan = {
     val relation = logicalRelation.relation.asInstanceOf[HadoopFsRelation]
 
     // Because we are creating one RDD per partition, we need to have a shared HadoopConf.
@@ -130,49 +131,51 @@ private[sql] object DataSourceStrategy extends Strategy with Logging {
     val confBroadcast =
       relation.sqlContext.sparkContext.broadcast(new SerializableConfiguration(sharedHadoopConf))
 
-    // Builds RDD[Row]s for each selected partition.
-    val perPartitionRows = partitions.map { case Partition(partitionValues, dir) =>
-      // The table scan operator (PhysicalRDD) which retrieves required columns from data files.
-      // Notice that the schema of data files, represented by `relation.dataSchema`, may contain
-      // some partition column(s).
-      val scan =
-        pruneFilterProject(
-          logicalRelation,
-          projections,
-          filters,
-          (columns: Seq[Attribute], filters) => {
-            val partitionColNames = partitionColumns.fieldNames
-
-            // Don't scan any partition columns to save I/O.  Here we are being optimistic and
-            // assuming partition columns data stored in data files are always consistent with those
-            // partition values encoded in partition directory paths.
-            val needed = columns.filterNot(a => partitionColNames.contains(a.name))
-            val dataRows =
-              relation.buildScan(needed.map(_.name).toArray, filters, Array(dir), confBroadcast)
-
-            // Merges data values with partition values.
-            mergeWithPartitionValues(
-              relation.schema,
-              columns.map(_.name).toArray,
-              partitionColNames,
-              partitionValues,
-              toCatalystRDD(logicalRelation, needed, dataRows))
-          })
-
-      scan.execute()
-    }
+    // Now, we create a scan builder, which will be used by pruneFilterProject. This scan builder
+    // will union all partitions and attach partition values if needed.
+    val scanBuilder = {
+      (columns: Seq[Attribute], filters: Array[Filter]) => {
+        // Builds RDD[Row]s for each selected partition.
+        val perPartitionRows = partitions.map { case Partition(partitionValues, dir) =>
+          val partitionColNames = partitionColumns.fieldNames
+
+          // Don't scan any partition columns to save I/O.  Here we are being optimistic and
+          // assuming partition columns data stored in data files are always consistent with those
+          // partition values encoded in partition directory paths.
+          val needed = columns.filterNot(a => partitionColNames.contains(a.name))
+          val dataRows =
+            relation.buildScan(needed.map(_.name).toArray, filters, Array(dir), confBroadcast)
+
+          // Merges data values with partition values.
+          mergeWithPartitionValues(
+            relation.schema,
+            columns.map(_.name).toArray,
+            partitionColNames,
+            partitionValues,
+            toCatalystRDD(logicalRelation, needed, dataRows))
+        }
+
+        val unionedRows =
+          if (perPartitionRows.length == 0) {
+            relation.sqlContext.emptyResult
+          } else {
+            new UnionRDD(relation.sqlContext.sparkContext, perPartitionRows)
+          }
 
-    val unionedRows =
-      if (perPartitionRows.length == 0) {
-        relation.sqlContext.emptyResult
-      } else {
-        new UnionRDD(relation.sqlContext.sparkContext, perPartitionRows)
+        unionedRows
       }
+    }
+
+    // Create the scan operator. If needed, add Filter and/or Project on top of the scan.
+    // The added Filter/Project is on top of the unioned RDD. We do not want to create
+    // one Filter/Project for every partition.
+    val sparkPlan = pruneFilterProject(
+      logicalRelation,
+      projections,
+      filters,
+      scanBuilder)
 
-    execution.PhysicalRDD.createFromDataSource(
-      projections.map(_.toAttribute),
-      unionedRows,
-      logicalRelation.relation)
+    sparkPlan
   }
 
   // TODO: refactor this thing. It is very complicated because it does projection internally.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index f682ca0d8ff4f..fe13dfbbed385 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -196,6 +196,13 @@ private[parquet] class CatalystRowConverter(
       }
     }
 
+    if (paddedParquetFields.length != catalystType.length) {
+      throw new UnsupportedOperationException(
+        "A Parquet file's schema has different number of fields with the table schema. " +
+          "Please enable schema merging by setting \"mergeSchema\" to true when load " +
+          "a Parquet dataset or set spark.sql.parquet.mergeSchema to true in SQLConf.")
+    }
+
     paddedParquetFields.zip(catalystType).zipWithIndex.map {
       case ((parquetFieldType, catalystField), ordinal) =>
         // Converted field value should be set to the `ordinal`-th cell of `currentRow`
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
index cb4cedddbfddd..06dadbb5feab0 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
@@ -23,7 +23,7 @@ import com.google.common.io.Files
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.{AnalysisException, SaveMode}
+import org.apache.spark.sql.{execution, AnalysisException, SaveMode}
 import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 
 
@@ -136,4 +136,17 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
       assert(fs.exists(commonSummaryPath))
     }
   }
+
+  test("SPARK-10334 Projections and filters should be kept in physical plan") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      sqlContext.range(2).select('id as 'a, 'id as 'b).write.partitionBy("b").parquet(path)
+      val df = sqlContext.read.parquet(path).filter('a === 0).select('b)
+      val physicalPlan = df.queryExecution.executedPlan
+
+      assert(physicalPlan.collect { case p: execution.Project => p }.length === 1)
+      assert(physicalPlan.collect { case p: execution.Filter => p }.length === 1)
+    }
+  }
 }

From 13f5f8ec97c6886346641b73bd99004e0d70836c Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Sat, 29 Aug 2015 18:10:44 -0700
Subject: [PATCH 481/633] [SPARK-9986] [SPARK-9991] [SPARK-9993] [SQL] Create a
 simple test framework for local operators

This PR includes the following changes:
- Add `LocalNodeTest` for local operator tests and add unit tests for FilterNode and ProjectNode.
- Add `LimitNode` and `UnionNode` and their unit tests to show how to use `LocalNodeTest`. (SPARK-9991, SPARK-9993)

Author: zsxwing <zsxwing@gmail.com>

Closes #8464 from zsxwing/local-execution.
---
 .../sql/execution/local/FilterNode.scala      |   6 +-
 .../spark/sql/execution/local/LimitNode.scala |  45 ++++++
 .../spark/sql/execution/local/LocalNode.scala |  13 +-
 .../sql/execution/local/ProjectNode.scala     |   4 +-
 .../sql/execution/local/SeqScanNode.scala     |   2 +-
 .../spark/sql/execution/local/UnionNode.scala |  72 +++++++++
 .../spark/sql/execution/SparkPlanTest.scala   |  46 +-----
 .../sql/execution/local/FilterNodeSuite.scala |  41 +++++
 .../sql/execution/local/LimitNodeSuite.scala  |  39 +++++
 .../sql/execution/local/LocalNodeTest.scala   | 146 ++++++++++++++++++
 .../execution/local/ProjectNodeSuite.scala    |  44 ++++++
 .../sql/execution/local/UnionNodeSuite.scala  |  52 +++++++
 .../apache/spark/sql/test/SQLTestData.scala   |   8 +
 .../apache/spark/sql/test/SQLTestUtils.scala  |  46 +++++-
 14 files changed, 509 insertions(+), 55 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
index a485a1a1d7ae4..81dd37c7da733 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
@@ -35,13 +35,13 @@ case class FilterNode(condition: Expression, child: LocalNode) extends UnaryLoca
 
   override def next(): Boolean = {
     var found = false
-    while (child.next() && !found) {
-      found = predicate.apply(child.get())
+    while (!found && child.next()) {
+      found = predicate.apply(child.fetch())
     }
     found
   }
 
-  override def get(): InternalRow = child.get()
+  override def fetch(): InternalRow = child.fetch()
 
   override def close(): Unit = child.close()
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
new file mode 100644
index 0000000000000..fffc52abf6dd5
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
@@ -0,0 +1,45 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+
+
+case class LimitNode(limit: Int, child: LocalNode) extends UnaryLocalNode {
+
+  private[this] var count = 0
+
+  override def output: Seq[Attribute] = child.output
+
+  override def open(): Unit = child.open()
+
+  override def close(): Unit = child.close()
+
+  override def fetch(): InternalRow = child.fetch()
+
+  override def next(): Boolean = {
+    if (count < limit) {
+      count += 1
+      child.next()
+    } else {
+      false
+    }
+  }
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index 341c81438e6d6..1c4469acbf264 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -48,10 +48,10 @@ abstract class LocalNode extends TreeNode[LocalNode] {
   /**
    * Returns the current tuple.
    */
-  def get(): InternalRow
+  def fetch(): InternalRow
 
   /**
-   * Closes the iterator and releases all resources.
+   * Closes the iterator and releases all resources. It should be idempotent.
    *
    * Implementations of this must also call the `close()` function of its children.
    */
@@ -64,10 +64,13 @@ abstract class LocalNode extends TreeNode[LocalNode] {
     val converter = CatalystTypeConverters.createToScalaConverter(StructType.fromAttributes(output))
     val result = new scala.collection.mutable.ArrayBuffer[Row]
     open()
-    while (next()) {
-      result += converter.apply(get()).asInstanceOf[Row]
+    try {
+      while (next()) {
+        result += converter.apply(fetch()).asInstanceOf[Row]
+      }
+    } finally {
+      close()
     }
-    close()
     result
   }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
index e574d1473cdcb..9b8a4fe493026 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
@@ -34,8 +34,8 @@ case class ProjectNode(projectList: Seq[NamedExpression], child: LocalNode) exte
 
   override def next(): Boolean = child.next()
 
-  override def get(): InternalRow = {
-    project.apply(child.get())
+  override def fetch(): InternalRow = {
+    project.apply(child.fetch())
   }
 
   override def close(): Unit = child.close()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
index 994de8afa9a02..242cb66e07b7f 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
@@ -41,7 +41,7 @@ case class SeqScanNode(output: Seq[Attribute], data: Seq[InternalRow]) extends L
     }
   }
 
-  override def get(): InternalRow = currentRow
+  override def fetch(): InternalRow = currentRow
 
   override def close(): Unit = {
     // Do nothing
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
new file mode 100644
index 0000000000000..ba4aa7671aebd
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
@@ -0,0 +1,72 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+
+case class UnionNode(children: Seq[LocalNode]) extends LocalNode {
+
+  override def output: Seq[Attribute] = children.head.output
+
+  private[this] var currentChild: LocalNode = _
+
+  private[this] var nextChildIndex: Int = _
+
+  override def open(): Unit = {
+    currentChild = children.head
+    currentChild.open()
+    nextChildIndex = 1
+  }
+
+  private def advanceToNextChild(): Boolean = {
+    var found = false
+    var exit = false
+    while (!exit && !found) {
+      if (currentChild != null) {
+        currentChild.close()
+      }
+      if (nextChildIndex >= children.size) {
+        found = false
+        exit = true
+      } else {
+        currentChild = children(nextChildIndex)
+        nextChildIndex += 1
+        currentChild.open()
+        found = currentChild.next()
+      }
+    }
+    found
+  }
+
+  override def close(): Unit = {
+    if (currentChild != null) {
+      currentChild.close()
+    }
+  }
+
+  override def fetch(): InternalRow = currentChild.fetch()
+
+  override def next(): Boolean = {
+    if (currentChild.next()) {
+      true
+    } else {
+      advanceToNextChild()
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
index 3a87f374d94b0..5ab8f44faebf6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
@@ -24,7 +24,7 @@ import scala.util.control.NonFatal
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.{DataFrame, DataFrameHolder, Row, SQLContext}
 import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
-import org.apache.spark.sql.catalyst.util._
+import org.apache.spark.sql.test.SQLTestUtils
 
 /**
  * Base class for writing tests for individual physical operators. For an example of how this
@@ -184,7 +184,7 @@ object SparkPlanTest {
         return Some(errorMessage)
     }
 
-    compareAnswers(actualAnswer, expectedAnswer, sortAnswers).map { errorMessage =>
+    SQLTestUtils.compareAnswers(actualAnswer, expectedAnswer, sortAnswers).map { errorMessage =>
       s"""
          | Results do not match.
          | Actual result Spark plan:
@@ -229,7 +229,7 @@ object SparkPlanTest {
         return Some(errorMessage)
     }
 
-    compareAnswers(sparkAnswer, expectedAnswer, sortAnswers).map { errorMessage =>
+    SQLTestUtils.compareAnswers(sparkAnswer, expectedAnswer, sortAnswers).map { errorMessage =>
       s"""
          | Results do not match for Spark plan:
          | $outputPlan
@@ -238,46 +238,6 @@ object SparkPlanTest {
     }
   }
 
-  private def compareAnswers(
-      sparkAnswer: Seq[Row],
-      expectedAnswer: Seq[Row],
-      sort: Boolean): Option[String] = {
-    def prepareAnswer(answer: Seq[Row]): Seq[Row] = {
-      // Converts data to types that we can do equality comparison using Scala collections.
-      // For BigDecimal type, the Scala type has a better definition of equality test (similar to
-      // Java's java.math.BigDecimal.compareTo).
-      // For binary arrays, we convert it to Seq to avoid of calling java.util.Arrays.equals for
-      // equality test.
-      // This function is copied from Catalyst's QueryTest
-      val converted: Seq[Row] = answer.map { s =>
-        Row.fromSeq(s.toSeq.map {
-          case d: java.math.BigDecimal => BigDecimal(d)
-          case b: Array[Byte] => b.toSeq
-          case o => o
-        })
-      }
-      if (sort) {
-        converted.sortBy(_.toString())
-      } else {
-        converted
-      }
-    }
-    if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) {
-      val errorMessage =
-        s"""
-           | == Results ==
-           | ${sideBySide(
-              s"== Expected Answer - ${expectedAnswer.size} ==" +:
-              prepareAnswer(expectedAnswer).map(_.toString()),
-              s"== Actual Answer - ${sparkAnswer.size} ==" +:
-              prepareAnswer(sparkAnswer).map(_.toString())).mkString("\n")}
-      """.stripMargin
-      Some(errorMessage)
-    } else {
-      None
-    }
-  }
-
   private def executePlan(outputPlan: SparkPlan, _sqlContext: SQLContext): Seq[Row] = {
     // A very simple resolver to make writing tests easier. In contrast to the real resolver
     // this is always case sensitive and does not try to handle scoping or complex type resolution.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
new file mode 100644
index 0000000000000..07209f3779248
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
@@ -0,0 +1,41 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.test.SharedSQLContext
+
+class FilterNodeSuite extends LocalNodeTest with SharedSQLContext {
+
+  test("basic") {
+    val condition = (testData.col("key") % 2) === 0
+    checkAnswer(
+      testData,
+      node => FilterNode(condition.expr, node),
+      testData.filter(condition).collect()
+    )
+  }
+
+  test("empty") {
+    val condition = (emptyTestData.col("key") % 2) === 0
+    checkAnswer(
+      emptyTestData,
+      node => FilterNode(condition.expr, node),
+      emptyTestData.filter(condition).collect()
+    )
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
new file mode 100644
index 0000000000000..523c02f4a6014
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
@@ -0,0 +1,39 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.test.SharedSQLContext
+
+class LimitNodeSuite extends LocalNodeTest with SharedSQLContext {
+
+  test("basic") {
+    checkAnswer(
+      testData,
+      node => LimitNode(10, node),
+      testData.limit(10).collect()
+    )
+  }
+
+  test("empty") {
+    checkAnswer(
+      emptyTestData,
+      node => LimitNode(10, node),
+      emptyTestData.limit(10).collect()
+    )
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
new file mode 100644
index 0000000000000..95f06081bd0a8
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
@@ -0,0 +1,146 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import scala.util.control.NonFatal
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.{DataFrame, Row}
+import org.apache.spark.sql.test.SQLTestUtils
+
+class LocalNodeTest extends SparkFunSuite {
+
+  /**
+   * Runs the LocalNode and makes sure the answer matches the expected result.
+   * @param input the input data to be used.
+   * @param nodeFunction a function which accepts the input LocalNode and uses it to instantiate
+   *                     the local physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
+   */
+  protected def checkAnswer(
+      input: DataFrame,
+      nodeFunction: LocalNode => LocalNode,
+      expectedAnswer: Seq[Row],
+      sortAnswers: Boolean = true): Unit = {
+    doCheckAnswer(
+      input :: Nil,
+      nodes => nodeFunction(nodes.head),
+      expectedAnswer,
+      sortAnswers)
+  }
+
+  /**
+   * Runs the LocalNode and makes sure the answer matches the expected result.
+   * @param left the left input data to be used.
+   * @param right the right input data to be used.
+   * @param nodeFunction a function which accepts the input LocalNode and uses it to instantiate
+   *                     the local physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
+   */
+  protected def checkAnswer2(
+      left: DataFrame,
+      right: DataFrame,
+      nodeFunction: (LocalNode, LocalNode) => LocalNode,
+      expectedAnswer: Seq[Row],
+      sortAnswers: Boolean = true): Unit = {
+    doCheckAnswer(
+      left :: right :: Nil,
+      nodes => nodeFunction(nodes(0), nodes(1)),
+      expectedAnswer,
+      sortAnswers)
+  }
+
+  /**
+   * Runs the `LocalNode`s and makes sure the answer matches the expected result.
+   * @param input the input data to be used.
+   * @param nodeFunction a function which accepts a sequence of input `LocalNode`s and uses them to
+   *                     instantiate the local physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
+   */
+  protected def doCheckAnswer(
+    input: Seq[DataFrame],
+    nodeFunction: Seq[LocalNode] => LocalNode,
+    expectedAnswer: Seq[Row],
+    sortAnswers: Boolean = true): Unit = {
+    LocalNodeTest.checkAnswer(
+      input.map(dataFrameToSeqScanNode), nodeFunction, expectedAnswer, sortAnswers) match {
+      case Some(errorMessage) => fail(errorMessage)
+      case None =>
+    }
+  }
+
+  protected def dataFrameToSeqScanNode(df: DataFrame): SeqScanNode = {
+    new SeqScanNode(
+      df.queryExecution.sparkPlan.output,
+      df.queryExecution.toRdd.map(_.copy()).collect())
+  }
+
+}
+
+/**
+ * Helper methods for writing tests of individual local physical operators.
+ */
+object LocalNodeTest {
+
+  /**
+   * Runs the `LocalNode`s and makes sure the answer matches the expected result.
+   * @param input the input data to be used.
+   * @param nodeFunction a function which accepts the input `LocalNode`s and uses them to
+   *                     instantiate the local physical operator that's being tested.
+   * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
+   * @param sortAnswers if true, the answers will be sorted by their toString representations prior
+   *                    to being compared.
+   */
+  def checkAnswer(
+    input: Seq[SeqScanNode],
+    nodeFunction: Seq[LocalNode] => LocalNode,
+    expectedAnswer: Seq[Row],
+    sortAnswers: Boolean): Option[String] = {
+
+    val outputNode = nodeFunction(input)
+
+    val outputResult: Seq[Row] = try {
+      outputNode.collect()
+    } catch {
+      case NonFatal(e) =>
+        val errorMessage =
+          s"""
+              | Exception thrown while executing local plan:
+              | $outputNode
+              | == Exception ==
+              | $e
+              | ${org.apache.spark.sql.catalyst.util.stackTraceToString(e)}
+          """.stripMargin
+        return Some(errorMessage)
+    }
+
+    SQLTestUtils.compareAnswers(outputResult, expectedAnswer, sortAnswers).map { errorMessage =>
+      s"""
+          | Results do not match for local plan:
+          | $outputNode
+          | $errorMessage
+       """.stripMargin
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
new file mode 100644
index 0000000000000..ffcf092e2c66a
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
@@ -0,0 +1,44 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.test.SharedSQLContext
+
+class ProjectNodeSuite extends LocalNodeTest with SharedSQLContext {
+
+  test("basic") {
+    val output = testData.queryExecution.sparkPlan.output
+    val columns = Seq(output(1), output(0))
+    checkAnswer(
+      testData,
+      node => ProjectNode(columns, node),
+      testData.select("value", "key").collect()
+    )
+  }
+
+  test("empty") {
+    val output = emptyTestData.queryExecution.sparkPlan.output
+    val columns = Seq(output(1), output(0))
+    checkAnswer(
+      emptyTestData,
+      node => ProjectNode(columns, node),
+      emptyTestData.select("value", "key").collect()
+    )
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
new file mode 100644
index 0000000000000..34670287c3e1d
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
@@ -0,0 +1,52 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.test.SharedSQLContext
+
+class UnionNodeSuite extends LocalNodeTest with SharedSQLContext {
+
+  test("basic") {
+    checkAnswer2(
+      testData,
+      testData,
+      (node1, node2) => UnionNode(Seq(node1, node2)),
+      testData.unionAll(testData).collect()
+    )
+  }
+
+  test("empty") {
+    checkAnswer2(
+      emptyTestData,
+      emptyTestData,
+      (node1, node2) => UnionNode(Seq(node1, node2)),
+      emptyTestData.unionAll(emptyTestData).collect()
+    )
+  }
+
+  test("complicated union") {
+    val dfs = Seq(testData, emptyTestData, emptyTestData, testData, testData, emptyTestData,
+      emptyTestData, emptyTestData, testData, emptyTestData)
+    doCheckAnswer(
+      dfs,
+      nodes => UnionNode(nodes),
+      dfs.reduce(_.unionAll(_)).collect()
+    )
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
index 1374a97476ca1..3fc02df954e23 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
@@ -36,6 +36,13 @@ private[sql] trait SQLTestData { self =>
 
   // Note: all test data should be lazy because the SQLContext is not set up yet.
 
+  protected lazy val emptyTestData: DataFrame = {
+    val df = _sqlContext.sparkContext.parallelize(
+      Seq.empty[Int].map(i => TestData(i, i.toString))).toDF()
+    df.registerTempTable("emptyTestData")
+    df
+  }
+
   protected lazy val testData: DataFrame = {
     val df = _sqlContext.sparkContext.parallelize(
       (1 to 100).map(i => TestData(i, i.toString))).toDF()
@@ -240,6 +247,7 @@ private[sql] trait SQLTestData { self =>
    */
   def loadTestData(): Unit = {
     assert(_sqlContext != null, "attempted to initialize test data before SQLContext.")
+    emptyTestData
     testData
     testData2
     testData3
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
index cdd691e035897..dc08306ad9cb4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
@@ -27,8 +27,9 @@ import org.apache.hadoop.conf.Configuration
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.{DataFrame, SQLContext, SQLImplicits}
+import org.apache.spark.sql.{DataFrame, Row, SQLContext, SQLImplicits}
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.util.Utils
 
 /**
@@ -179,3 +180,46 @@ private[sql] trait SQLTestUtils
     DataFrame(_sqlContext, plan)
   }
 }
+
+private[sql] object SQLTestUtils {
+
+  def compareAnswers(
+      sparkAnswer: Seq[Row],
+      expectedAnswer: Seq[Row],
+      sort: Boolean): Option[String] = {
+    def prepareAnswer(answer: Seq[Row]): Seq[Row] = {
+      // Converts data to types that we can do equality comparison using Scala collections.
+      // For BigDecimal type, the Scala type has a better definition of equality test (similar to
+      // Java's java.math.BigDecimal.compareTo).
+      // For binary arrays, we convert it to Seq to avoid of calling java.util.Arrays.equals for
+      // equality test.
+      // This function is copied from Catalyst's QueryTest
+      val converted: Seq[Row] = answer.map { s =>
+        Row.fromSeq(s.toSeq.map {
+          case d: java.math.BigDecimal => BigDecimal(d)
+          case b: Array[Byte] => b.toSeq
+          case o => o
+        })
+      }
+      if (sort) {
+        converted.sortBy(_.toString())
+      } else {
+        converted
+      }
+    }
+    if (prepareAnswer(expectedAnswer) != prepareAnswer(sparkAnswer)) {
+      val errorMessage =
+        s"""
+           | == Results ==
+           | ${sideBySide(
+          s"== Expected Answer - ${expectedAnswer.size} ==" +:
+            prepareAnswer(expectedAnswer).map(_.toString()),
+          s"== Actual Answer - ${sparkAnswer.size} ==" +:
+            prepareAnswer(sparkAnswer).map(_.toString())).mkString("\n")}
+      """.stripMargin
+      Some(errorMessage)
+    } else {
+      None
+    }
+  }
+}

From 905fbe498bdd29116468628e6a2a553c1fd57165 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Sat, 29 Aug 2015 23:26:23 -0700
Subject: [PATCH 482/633] [SPARK-10348] [MLLIB] updates ml-guide

* replace `ML Dataset` by `DataFrame` to unify the abstraction
* ML algorithms -> pipeline components to describe the main concept
* remove Scala API doc links from the main guide
* `Section Title` -> `Section tile` to be consistent with other section titles in MLlib guide
* modified lines break at 100 chars or periods

jkbradley feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8517 from mengxr/SPARK-10348.
---
 docs/ml-guide.md    | 118 +++++++++++++++++++++++++++-----------------
 docs/mllib-guide.md |  12 ++---
 2 files changed, 78 insertions(+), 52 deletions(-)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index a92a285f3af85..4ba07542bfb40 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -24,61 +24,74 @@ title: Spark ML Programming Guide
 The `spark.ml` package aims to provide a uniform set of high-level APIs built on top of
 [DataFrames](sql-programming-guide.html#dataframes) that help users create and tune practical
 machine learning pipelines.
-See the [Algorithm Guides section](#algorithm-guides) below for guides on sub-packages of
+See the [algorithm guides](#algorithm-guides) section below for guides on sub-packages of
 `spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.
 
-**Table of Contents**
+**Table of contents**
 
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-# Main Concepts
+# Main concepts
 
-Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple algorithms into a single pipeline, or workflow.  This section covers the key concepts introduced by the Spark ML API.
+Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple
+algorithms into a single pipeline, or workflow.
+This section covers the key concepts introduced by the Spark ML API, where the pipeline concept is
+mostly inspired by the [scikit-learn](http://scikit-learn.org/) project.
 
-* **[ML Dataset](ml-guide.html#ml-dataset)**: Spark ML uses the [`DataFrame`](api/scala/index.html#org.apache.spark.sql.DataFrame) from Spark SQL as a dataset which can hold a variety of data types.
-E.g., a dataset could have different columns storing text, feature vectors, true labels, and predictions.
+* **[`DataFrame`](ml-guide.html#dataframe)**: Spark ML uses `DataFrame` from Spark SQL as an ML
+  dataset, which can hold a variety of data types.
+  E.g., a `DataFrame` could have different columns storing text, feature vectors, true labels, and predictions.
 
 * **[`Transformer`](ml-guide.html#transformers)**: A `Transformer` is an algorithm which can transform one `DataFrame` into another `DataFrame`.
-E.g., an ML model is a `Transformer` which transforms an RDD with features into an RDD with predictions.
+E.g., an ML model is a `Transformer` which transforms `DataFrame` with features into a `DataFrame` with predictions.
 
 * **[`Estimator`](ml-guide.html#estimators)**: An `Estimator` is an algorithm which can be fit on a `DataFrame` to produce a `Transformer`.
-E.g., a learning algorithm is an `Estimator` which trains on a dataset and produces a model.
+E.g., a learning algorithm is an `Estimator` which trains on a `DataFrame` and produces a model.
 
 * **[`Pipeline`](ml-guide.html#pipeline)**: A `Pipeline` chains multiple `Transformer`s and `Estimator`s together to specify an ML workflow.
 
-* **[`Param`](ml-guide.html#parameters)**: All `Transformer`s and `Estimator`s now share a common API for specifying parameters.
+* **[`Parameter`](ml-guide.html#parameters)**: All `Transformer`s and `Estimator`s now share a common API for specifying parameters.
 
-## ML Dataset
+## DataFrame
 
 Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data.
-Spark ML adopts the [`DataFrame`](api/scala/index.html#org.apache.spark.sql.DataFrame) from Spark SQL in order to support a variety of data types under a unified Dataset concept.
+Spark ML adopts the `DataFrame` from Spark SQL in order to support a variety of data types.
 
 `DataFrame` supports many basic and structured types; see the [Spark SQL datatype reference](sql-programming-guide.html#spark-sql-datatype-reference) for a list of supported types.
-In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [`Vector`](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) types.
+In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [`Vector`](mllib-data-types.html#local-vector) types.
 
 A `DataFrame` can be created either implicitly or explicitly from a regular `RDD`.  See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples.
 
 Columns in a `DataFrame` are named.  The code examples below use names such as "text," "features," and "label."
 
-## ML Algorithms
+## Pipeline components
 
 ### Transformers
 
-A [`Transformer`](api/scala/index.html#org.apache.spark.ml.Transformer) is an abstraction which includes feature transformers and learned models.  Technically, a `Transformer` implements a method `transform()` which converts one `DataFrame` into another, generally by appending one or more columns.
+A `Transformer` is an abstraction that includes feature transformers and learned models.
+Technically, a `Transformer` implements a method `transform()`, which converts one `DataFrame` into
+another, generally by appending one or more columns.
 For example:
 
-* A feature transformer might take a dataset, read a column (e.g., text), convert it into a new column (e.g., feature vectors), append the new column to the dataset, and output the updated dataset.
-* A learning model might take a dataset, read the column containing feature vectors, predict the label for each feature vector, append the labels as a new column, and output the updated dataset.
+* A feature transformer might take a `DataFrame`, read a column (e.g., text), map it into a new
+  column (e.g., feature vectors), and output a new `DataFrame` with the mapped column appended.
+* A learning model might take a `DataFrame`, read the column containing feature vectors, predict the
+  label for each feature vector, and output a new `DataFrame` with predicted labels appended as a
+  column.
 
 ### Estimators
 
-An [`Estimator`](api/scala/index.html#org.apache.spark.ml.Estimator) abstracts the concept of a learning algorithm or any algorithm which fits or trains on data.  Technically, an `Estimator` implements a method `fit()` which accepts a `DataFrame` and produces a `Transformer`.
-For example, a learning algorithm such as `LogisticRegression` is an `Estimator`, and calling `fit()` trains a `LogisticRegressionModel`, which is a `Transformer`.
+An `Estimator` abstracts the concept of a learning algorithm or any algorithm that fits or trains on
+data.
+Technically, an `Estimator` implements a method `fit()`, which accepts a `DataFrame` and produces a
+`Model`, which is a `Transformer`.
+For example, a learning algorithm such as `LogisticRegression` is an `Estimator`, and calling
+`fit()` trains a `LogisticRegressionModel`, which is a `Model` and hence a `Transformer`.
 
-### Properties of ML Algorithms
+### Properties of pipeline components
 
-`Transformer`s and `Estimator`s are both stateless.  In the future, stateful algorithms may be supported via alternative concepts.
+`Transformer.transform()`s and `Estimator.fit()`s are both stateless.  In the future, stateful algorithms may be supported via alternative concepts.
 
 Each instance of a `Transformer` or `Estimator` has a unique ID, which is useful in specifying parameters (discussed below).
 
@@ -91,15 +104,16 @@ E.g., a simple text document processing workflow might include several stages:
 * Convert each document's words into a numerical feature vector.
 * Learn a prediction model using the feature vectors and labels.
 
-Spark ML represents such a workflow as a [`Pipeline`](api/scala/index.html#org.apache.spark.ml.Pipeline),
-which consists of a sequence of [`PipelineStage`s](api/scala/index.html#org.apache.spark.ml.PipelineStage) (`Transformer`s and `Estimator`s) to be run in a specific order.  We will use this simple workflow as a running example in this section.
+Spark ML represents such a workflow as a `Pipeline`, which consists of a sequence of
+`PipelineStage`s (`Transformer`s and `Estimator`s) to be run in a specific order.
+We will use this simple workflow as a running example in this section.
 
-### How It Works
+### How it works
 
 A `Pipeline` is specified as a sequence of stages, and each stage is either a `Transformer` or an `Estimator`.
-These stages are run in order, and the input dataset is modified as it passes through each stage.
-For `Transformer` stages, the `transform()` method is called on the dataset.
-For `Estimator` stages, the `fit()` method is called to produce a `Transformer` (which becomes part of the `PipelineModel`, or fitted `Pipeline`), and that `Transformer`'s `transform()` method is called on the dataset.
+These stages are run in order, and the input `DataFrame` is transformed as it passes through each stage.
+For `Transformer` stages, the `transform()` method is called on the `DataFrame`.
+For `Estimator` stages, the `fit()` method is called to produce a `Transformer` (which becomes part of the `PipelineModel`, or fitted `Pipeline`), and that `Transformer`'s `transform()` method is called on the `DataFrame`.
 
 We illustrate this for the simple text document workflow.  The figure below is for the *training time* usage of a `Pipeline`.
 
@@ -115,14 +129,17 @@ We illustrate this for the simple text document workflow.  The figure below is f
 Above, the top row represents a `Pipeline` with three stages.
 The first two (`Tokenizer` and `HashingTF`) are `Transformer`s (blue), and the third (`LogisticRegression`) is an `Estimator` (red).
 The bottom row represents data flowing through the pipeline, where cylinders indicate `DataFrame`s.
-The `Pipeline.fit()` method is called on the original dataset which has raw text documents and labels.
-The `Tokenizer.transform()` method splits the raw text documents into words, adding a new column with words into the dataset.
-The `HashingTF.transform()` method converts the words column into feature vectors, adding a new column with those vectors to the dataset.
+The `Pipeline.fit()` method is called on the original `DataFrame`, which has raw text documents and labels.
+The `Tokenizer.transform()` method splits the raw text documents into words, adding a new column with words to the `DataFrame`.
+The `HashingTF.transform()` method converts the words column into feature vectors, adding a new column with those vectors to the `DataFrame`.
 Now, since `LogisticRegression` is an `Estimator`, the `Pipeline` first calls `LogisticRegression.fit()` to produce a `LogisticRegressionModel`.
-If the `Pipeline` had more stages, it would call the `LogisticRegressionModel`'s `transform()` method on the dataset before passing the dataset to the next stage.
+If the `Pipeline` had more stages, it would call the `LogisticRegressionModel`'s `transform()`
+method on the `DataFrame` before passing the `DataFrame` to the next stage.
 
 A `Pipeline` is an `Estimator`.
-Thus, after a `Pipeline`'s `fit()` method runs, it produces a `PipelineModel` which is a `Transformer`.  This `PipelineModel` is used at *test time*; the figure below illustrates this usage.
+Thus, after a `Pipeline`'s `fit()` method runs, it produces a `PipelineModel`, which is a
+`Transformer`.
+This `PipelineModel` is used at *test time*; the figure below illustrates this usage.
 
 <p style="text-align: center;">
   <img
@@ -134,7 +151,8 @@ Thus, after a `Pipeline`'s `fit()` method runs, it produces a `PipelineModel` wh
 </p>
 
 In the figure above, the `PipelineModel` has the same number of stages as the original `Pipeline`, but all `Estimator`s in the original `Pipeline` have become `Transformer`s.
-When the `PipelineModel`'s `transform()` method is called on a test dataset, the data are passed through the `Pipeline` in order.
+When the `PipelineModel`'s `transform()` method is called on a test dataset, the data are passed
+through the fitted pipeline in order.
 Each stage's `transform()` method updates the dataset and passes it to the next stage.
 
 `Pipeline`s and `PipelineModel`s help to ensure that training and test data go through identical feature processing steps.
@@ -143,40 +161,48 @@ Each stage's `transform()` method updates the dataset and passes it to the next
 
 *DAG `Pipeline`s*: A `Pipeline`'s stages are specified as an ordered array.  The examples given here are all for linear `Pipeline`s, i.e., `Pipeline`s in which each stage uses data produced by the previous stage.  It is possible to create non-linear `Pipeline`s as long as the data flow graph forms a Directed Acyclic Graph (DAG).  This graph is currently specified implicitly based on the input and output column names of each stage (generally specified as parameters).  If the `Pipeline` forms a DAG, then the stages must be specified in topological order.
 
-*Runtime checking*: Since `Pipeline`s can operate on datasets with varied types, they cannot use compile-time type checking.  `Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`.  This type checking is done using the dataset *schema*, a description of the data types of columns in the `DataFrame`.
+*Runtime checking*: Since `Pipeline`s can operate on `DataFrame`s with varied types, they cannot use
+compile-time type checking.
+`Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`.
+This type checking is done using the `DataFrame` *schema*, a description of the data types of columns in the `DataFrame`.
 
 ## Parameters
 
 Spark ML `Estimator`s and `Transformer`s use a uniform API for specifying parameters.
 
-A [`Param`](api/scala/index.html#org.apache.spark.ml.param.Param) is a named parameter with self-contained documentation.
-A [`ParamMap`](api/scala/index.html#org.apache.spark.ml.param.ParamMap) is a set of (parameter, value) pairs.
+A `Param` is a named parameter with self-contained documentation.
+A `ParamMap` is a set of (parameter, value) pairs.
 
 There are two main ways to pass parameters to an algorithm:
 
-1. Set parameters for an instance.  E.g., if `lr` is an instance of `LogisticRegression`, one could call `lr.setMaxIter(10)` to make `lr.fit()` use at most 10 iterations.  This API resembles the API used in MLlib.
+1. Set parameters for an instance.  E.g., if `lr` is an instance of `LogisticRegression`, one could
+   call `lr.setMaxIter(10)` to make `lr.fit()` use at most 10 iterations.
+   This API resembles the API used in `spark.mllib` package.
 2. Pass a `ParamMap` to `fit()` or `transform()`.  Any parameters in the `ParamMap` will override parameters previously specified via setter methods.
 
 Parameters belong to specific instances of `Estimator`s and `Transformer`s.
 For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
 This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`.
 
-# Algorithm Guides
+# Algorithm guides
 
 There are now several algorithms in the Pipelines API which are not in the `spark.mllib` API, so we link to documentation for them here.  These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines.
 
-**Pipelines API Algorithm Guides**
-
-* [Feature Extraction, Transformation, and Selection](ml-features.html)
-* [Decision Trees for Classification and Regression](ml-decision-tree.html)
+* [Feature extraction, transformation, and selection](ml-features.html)
+* [Decision Trees for classification and regression](ml-decision-tree.html)
 * [Ensembles](ml-ensembles.html)
 * [Linear methods with elastic net regularization](ml-linear-methods.html)
 * [Multilayer perceptron classifier](ml-ann.html)
 
-# Code Examples
+# Code examples
 
 This section gives code examples illustrating the functionality discussed above.
-There is not yet documentation for specific algorithms in Spark ML.  For more info, please refer to the [API Documentation](api/scala/index.html#org.apache.spark.ml.package).  Spark ML algorithms are currently wrappers for MLlib algorithms, and the [MLlib programming guide](mllib-guide.html) has details on specific algorithms.
+For more info, please refer to the API documentation
+([Scala](api/scala/index.html#org.apache.spark.ml.package),
+[Java](api/java/org/apache/spark/ml/package-summary.html),
+and [Python](api/python/pyspark.ml.html)).
+Some Spark ML algorithms are wrappers for `spark.mllib` algorithms, and the
+[MLlib programming guide](mllib-guide.html) has details on specific algorithms.
 
 ## Example: Estimator, Transformer, and Param
 
@@ -627,7 +653,7 @@ sc.stop()
 
 </div>
 
-## Example: Model Selection via Cross-Validation
+## Example: model selection via cross-validation
 
 An important task in ML is *model selection*, or using data to find the best model or parameters for a given task.  This is also called *tuning*.
 `Pipeline`s facilitate model selection by making it easy to tune an entire `Pipeline` at once, rather than tuning each element in the `Pipeline` separately.
@@ -873,11 +899,11 @@ jsc.stop();
 
 </div>
 
-## Example: Model Selection via Train Validation Split
+## Example: model selection via train validation split
 In addition to  `CrossValidator` Spark also offers `TrainValidationSplit` for hyper-parameter tuning.
 `TrainValidationSplit` only evaluates each combination of parameters once as opposed to k times in
  case of `CrossValidator`. It is therefore less expensive,
- but will not produce as reliable results when the training dataset is not sufficiently large..
+ but will not produce as reliable results when the training dataset is not sufficiently large.
 
 `TrainValidationSplit` takes an `Estimator`, a set of `ParamMap`s provided in the `estimatorParamMaps` parameter,
 and an `Evaluator`.
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 876dcfd40ed7b..257f7cc7603fa 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -14,9 +14,9 @@ primitives and higher-level pipeline APIs.
 It divides into two packages:
 
 * [`spark.mllib`](mllib-guide.html#mllib-types-algorithms-and-utilities) contains the original API
-  built on top of RDDs.
+  built on top of [RDDs](programming-guide.html#resilient-distributed-datasets-rdds).
 * [`spark.ml`](mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines) provides higher-level API
-  built on top of DataFrames for constructing ML pipelines.
+  built on top of [DataFrames](sql-programming-guide.html#dataframes) for constructing ML pipelines.
 
 Using `spark.ml` is recommended because with DataFrames the API is more versatile and flexible.
 But we will keep supporting `spark.mllib` along with the development of `spark.ml`.
@@ -57,19 +57,19 @@ We list major functionality from both below, with links to detailed guides.
   * [FP-growth](mllib-frequent-pattern-mining.html#fp-growth)
   * [association rules](mllib-frequent-pattern-mining.html#association-rules)
   * [PrefixSpan](mllib-frequent-pattern-mining.html#prefix-span)
-* [Evaluation Metrics](mllib-evaluation-metrics.html)
+* [Evaluation metrics](mllib-evaluation-metrics.html)
+* [PMML model export](mllib-pmml-model-export.html)
 * [Optimization (developer)](mllib-optimization.html)
   * [stochastic gradient descent](mllib-optimization.html#stochastic-gradient-descent-sgd)
   * [limited-memory BFGS (L-BFGS)](mllib-optimization.html#limited-memory-bfgs-l-bfgs)
-* [PMML model export](mllib-pmml-model-export.html)
 
 # spark.ml: high-level APIs for ML pipelines
 
 **[spark.ml programming guide](ml-guide.html)** provides an overview of the Pipelines API and major
 concepts. It also contains sections on using algorithms within the Pipelines API, for example:
 
-* [Feature Extraction, Transformation, and Selection](ml-features.html)
-* [Decision Trees for Classification and Regression](ml-decision-tree.html)
+* [Feature extraction, transformation, and selection](ml-features.html)
+* [Decision trees for classification and regression](ml-decision-tree.html)
 * [Ensembles](ml-ensembles.html)
 * [Linear methods with elastic net regularization](ml-linear-methods.html)
 * [Multilayer perceptron classifier](ml-ann.html)

From ca69fc8efda8a3e5442ffa16692a2b1eb86b7673 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Sat, 29 Aug 2015 23:57:09 -0700
Subject: [PATCH 483/633] [SPARK-10331] [MLLIB] Update example code in ml-guide

* The example code was added in 1.2, before `createDataFrame`. This PR switches to `createDataFrame`. Java code still uses JavaBean.
* assume `sqlContext` is available
* fix some minor issues from previous code review

jkbradley srowen feynmanliang

Author: Xiangrui Meng <meng@databricks.com>

Closes #8518 from mengxr/SPARK-10331.
---
 docs/ml-guide.md | 362 +++++++++++++++++++----------------------------
 1 file changed, 147 insertions(+), 215 deletions(-)

diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 4ba07542bfb40..78c93a95c7807 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -212,26 +212,18 @@ This example covers the concepts of `Estimator`, `Transformer`, and `Param`.
 
 <div data-lang="scala">
 {% highlight scala %}
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
-import org.apache.spark.mllib.regression.LabeledPoint
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.Row
 
-val conf = new SparkConf().setAppName("SimpleParamsExample")
-val sc = new SparkContext(conf)
-val sqlContext = new SQLContext(sc)
-import sqlContext.implicits._
-
-// Prepare training data.
-// We use LabeledPoint, which is a case class.  Spark SQL can convert RDDs of case classes
-// into DataFrames, where it uses the case class metadata to infer the schema.
-val training = sc.parallelize(Seq(
-  LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
-  LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
-  LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
-  LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))))
+// Prepare training data from a list of (label, features) tuples.
+val training = sqlContext.createDataFrame(Seq(
+  (1.0, Vectors.dense(0.0, 1.1, 0.1)),
+  (0.0, Vectors.dense(2.0, 1.0, -1.0)),
+  (0.0, Vectors.dense(2.0, 1.3, 1.0)),
+  (1.0, Vectors.dense(0.0, 1.2, -0.5))
+)).toDF("label", "features")
 
 // Create a LogisticRegression instance.  This instance is an Estimator.
 val lr = new LogisticRegression()
@@ -243,7 +235,7 @@ lr.setMaxIter(10)
   .setRegParam(0.01)
 
 // Learn a LogisticRegression model.  This uses the parameters stored in lr.
-val model1 = lr.fit(training.toDF)
+val model1 = lr.fit(training)
 // Since model1 is a Model (i.e., a Transformer produced by an Estimator),
 // we can view the parameters it used during fit().
 // This prints the parameter (name: value) pairs, where names are unique IDs for this
@@ -253,8 +245,8 @@ println("Model 1 was fit using parameters: " + model1.parent.extractParamMap)
 // We may alternatively specify parameters using a ParamMap,
 // which supports several methods for specifying parameters.
 val paramMap = ParamMap(lr.maxIter -> 20)
-paramMap.put(lr.maxIter, 30) // Specify 1 Param.  This overwrites the original maxIter.
-paramMap.put(lr.regParam -> 0.1, lr.threshold -> 0.55) // Specify multiple Params.
+  .put(lr.maxIter, 30) // Specify 1 Param.  This overwrites the original maxIter.
+  .put(lr.regParam -> 0.1, lr.threshold -> 0.55) // Specify multiple Params.
 
 // One can also combine ParamMaps.
 val paramMap2 = ParamMap(lr.probabilityCol -> "myProbability") // Change output column name
@@ -262,27 +254,27 @@ val paramMapCombined = paramMap ++ paramMap2
 
 // Now learn a new model using the paramMapCombined parameters.
 // paramMapCombined overrides all parameters set earlier via lr.set* methods.
-val model2 = lr.fit(training.toDF, paramMapCombined)
+val model2 = lr.fit(training, paramMapCombined)
 println("Model 2 was fit using parameters: " + model2.parent.extractParamMap)
 
 // Prepare test data.
-val test = sc.parallelize(Seq(
-  LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
-  LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
-  LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))))
+val test = sqlContext.createDataFrame(Seq(
+  (1.0, Vectors.dense(-1.0, 1.5, 1.3)),
+  (0.0, Vectors.dense(3.0, 2.0, -0.1)),
+  (1.0, Vectors.dense(0.0, 2.2, -1.5))
+)).toDF("label", "features")
 
 // Make predictions on test data using the Transformer.transform() method.
 // LogisticRegression.transform will only use the 'features' column.
 // Note that model2.transform() outputs a 'myProbability' column instead of the usual
 // 'probability' column since we renamed the lr.probabilityCol parameter previously.
-model2.transform(test.toDF)
+model2.transform(test)
   .select("features", "label", "myProbability", "prediction")
   .collect()
   .foreach { case Row(features: Vector, label: Double, prob: Vector, prediction: Double) =>
     println(s"($features, $label) -> prob=$prob, prediction=$prediction")
   }
 
-sc.stop()
 {% endhighlight %}
 </div>
 
@@ -291,30 +283,23 @@ sc.stop()
 import java.util.Arrays;
 import java.util.List;
 
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.classification.LogisticRegressionModel;
 import org.apache.spark.ml.param.ParamMap;
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.mllib.linalg.Vectors;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.sql.DataFrame;
-import org.apache.spark.sql.SQLContext;
 import org.apache.spark.sql.Row;
 
-SparkConf conf = new SparkConf().setAppName("JavaSimpleParamsExample");
-JavaSparkContext jsc = new JavaSparkContext(conf);
-SQLContext jsql = new SQLContext(jsc);
-
 // Prepare training data.
 // We use LabeledPoint, which is a JavaBean.  Spark SQL can convert RDDs of JavaBeans
 // into DataFrames, where it uses the bean metadata to infer the schema.
-List<LabeledPoint> localTraining = Arrays.asList(
+DataFrame training = sqlContext.createDataFrame(Arrays.asList(
   new LabeledPoint(1.0, Vectors.dense(0.0, 1.1, 0.1)),
   new LabeledPoint(0.0, Vectors.dense(2.0, 1.0, -1.0)),
   new LabeledPoint(0.0, Vectors.dense(2.0, 1.3, 1.0)),
-  new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5)));
-DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledPoint.class);
+  new LabeledPoint(1.0, Vectors.dense(0.0, 1.2, -0.5))
+), LabeledPoint.class);
 
 // Create a LogisticRegression instance.  This instance is an Estimator.
 LogisticRegression lr = new LogisticRegression();
@@ -334,14 +319,14 @@ LogisticRegressionModel model1 = lr.fit(training);
 System.out.println("Model 1 was fit using parameters: " + model1.parent().extractParamMap());
 
 // We may alternatively specify parameters using a ParamMap.
-ParamMap paramMap = new ParamMap();
-paramMap.put(lr.maxIter().w(20)); // Specify 1 Param.
-paramMap.put(lr.maxIter(), 30); // This overwrites the original maxIter.
-paramMap.put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params.
+ParamMap paramMap = new ParamMap()
+  .put(lr.maxIter().w(20)) // Specify 1 Param.
+  .put(lr.maxIter(), 30) // This overwrites the original maxIter.
+  .put(lr.regParam().w(0.1), lr.threshold().w(0.55)); // Specify multiple Params.
 
 // One can also combine ParamMaps.
-ParamMap paramMap2 = new ParamMap();
-paramMap2.put(lr.probabilityCol().w("myProbability")); // Change output column name
+ParamMap paramMap2 = new ParamMap()
+  .put(lr.probabilityCol().w("myProbability")); // Change output column name
 ParamMap paramMapCombined = paramMap.$plus$plus(paramMap2);
 
 // Now learn a new model using the paramMapCombined parameters.
@@ -350,11 +335,11 @@ LogisticRegressionModel model2 = lr.fit(training, paramMapCombined);
 System.out.println("Model 2 was fit using parameters: " + model2.parent().extractParamMap());
 
 // Prepare test documents.
-List<LabeledPoint> localTest = Arrays.asList(
-    new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
-    new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
-    new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5)));
-DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), LabeledPoint.class);
+DataFrame test = sqlContext.createDataFrame(Arrays.asList(
+  new LabeledPoint(1.0, Vectors.dense(-1.0, 1.5, 1.3)),
+  new LabeledPoint(0.0, Vectors.dense(3.0, 2.0, -0.1)),
+  new LabeledPoint(1.0, Vectors.dense(0.0, 2.2, -1.5))
+), LabeledPoint.class);
 
 // Make predictions on test documents using the Transformer.transform() method.
 // LogisticRegression.transform will only use the 'features' column.
@@ -366,28 +351,21 @@ for (Row r: results.select("features", "label", "myProbability", "prediction").c
       + ", prediction=" + r.get(3));
 }
 
-jsc.stop();
 {% endhighlight %}
 </div>
 
 <div data-lang="python">
 {% highlight python %}
-from pyspark import SparkContext
-from pyspark.mllib.regression import LabeledPoint
+from pyspark.mllib.linalg import Vectors
 from pyspark.ml.classification import LogisticRegression
 from pyspark.ml.param import Param, Params
-from pyspark.sql import Row, SQLContext
 
-sc = SparkContext(appName="SimpleParamsExample")
-sqlContext = SQLContext(sc)
-
-# Prepare training data.
-# We use LabeledPoint.
-# Spark SQL can convert RDDs of LabeledPoints into DataFrames.
-training = sc.parallelize([LabeledPoint(1.0, [0.0, 1.1,  0.1]),
-                           LabeledPoint(0.0, [2.0, 1.0, -1.0]),
-                           LabeledPoint(0.0, [2.0, 1.3,  1.0]),
-                           LabeledPoint(1.0, [0.0, 1.2, -0.5])])
+# Prepare training data from a list of (label, features) tuples.
+training = sqlContext.createDataFrame([
+    (1.0, Vectors.dense([0.0, 1.1, 0.1])),
+    (0.0, Vectors.dense([2.0, 1.0, -1.0])),
+    (0.0, Vectors.dense([2.0, 1.3, 1.0])),
+    (1.0, Vectors.dense([0.0, 1.2, -0.5]))], ["label", "features"])
 
 # Create a LogisticRegression instance. This instance is an Estimator.
 lr = LogisticRegression(maxIter=10, regParam=0.01)
@@ -395,7 +373,7 @@ lr = LogisticRegression(maxIter=10, regParam=0.01)
 print "LogisticRegression parameters:\n" + lr.explainParams() + "\n"
 
 # Learn a LogisticRegression model. This uses the parameters stored in lr.
-model1 = lr.fit(training.toDF())
+model1 = lr.fit(training)
 
 # Since model1 is a Model (i.e., a transformer produced by an Estimator),
 # we can view the parameters it used during fit().
@@ -416,25 +394,25 @@ paramMapCombined.update(paramMap2)
 
 # Now learn a new model using the paramMapCombined parameters.
 # paramMapCombined overrides all parameters set earlier via lr.set* methods.
-model2 = lr.fit(training.toDF(), paramMapCombined)
+model2 = lr.fit(training, paramMapCombined)
 print "Model 2 was fit using parameters: "
 print model2.extractParamMap()
 
 # Prepare test data
-test = sc.parallelize([LabeledPoint(1.0, [-1.0, 1.5,  1.3]),
-                       LabeledPoint(0.0, [ 3.0, 2.0, -0.1]),
-                       LabeledPoint(1.0, [ 0.0, 2.2, -1.5])])
+test = sqlContext.createDataFrame([
+    (1.0, Vectors.dense([-1.0, 1.5, 1.3])),
+    (0.0, Vectors.dense([3.0, 2.0, -0.1])),
+    (1.0, Vectors.dense([0.0, 2.2, -1.5]))], ["label", "features"])
 
 # Make predictions on test data using the Transformer.transform() method.
 # LogisticRegression.transform will only use the 'features' column.
 # Note that model2.transform() outputs a "myProbability" column instead of the usual
 # 'probability' column since we renamed the lr.probabilityCol parameter previously.
-prediction = model2.transform(test.toDF())
+prediction = model2.transform(test)
 selected = prediction.select("features", "label", "myProbability", "prediction")
 for row in selected.collect():
     print row
 
-sc.stop()
 {% endhighlight %}
 </div>
 
@@ -448,30 +426,19 @@ This example follows the simple text document `Pipeline` illustrated in the figu
 
 <div data-lang="scala">
 {% highlight scala %}
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
 import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.sql.{Row, SQLContext}
-
-// Labeled and unlabeled instance types.
-// Spark SQL can infer schema from case classes.
-case class LabeledDocument(id: Long, text: String, label: Double)
-case class Document(id: Long, text: String)
+import org.apache.spark.sql.Row
 
-// Set up contexts.  Import implicit conversions to DataFrame from sqlContext.
-val conf = new SparkConf().setAppName("SimpleTextClassificationPipeline")
-val sc = new SparkContext(conf)
-val sqlContext = new SQLContext(sc)
-import sqlContext.implicits._
-
-// Prepare training documents, which are labeled.
-val training = sc.parallelize(Seq(
-  LabeledDocument(0L, "a b c d e spark", 1.0),
-  LabeledDocument(1L, "b d", 0.0),
-  LabeledDocument(2L, "spark f g h", 1.0),
-  LabeledDocument(3L, "hadoop mapreduce", 0.0)))
+// Prepare training documents from a list of (id, text, label) tuples.
+val training = sqlContext.createDataFrame(Seq(
+  (0L, "a b c d e spark", 1.0),
+  (1L, "b d", 0.0),
+  (2L, "spark f g h", 1.0),
+  (3L, "hadoop mapreduce", 0.0)
+)).toDF("id", "text", "label")
 
 // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
 val tokenizer = new Tokenizer()
@@ -488,14 +455,15 @@ val pipeline = new Pipeline()
   .setStages(Array(tokenizer, hashingTF, lr))
 
 // Fit the pipeline to training documents.
-val model = pipeline.fit(training.toDF)
+val model = pipeline.fit(training)
 
-// Prepare test documents, which are unlabeled.
-val test = sc.parallelize(Seq(
-  Document(4L, "spark i j k"),
-  Document(5L, "l m n"),
-  Document(6L, "mapreduce spark"),
-  Document(7L, "apache hadoop")))
+// Prepare test documents, which are unlabeled (id, text) tuples.
+val test = sqlContext.createDataFrame(Seq(
+  (4L, "spark i j k"),
+  (5L, "l m n"),
+  (6L, "mapreduce spark"),
+  (7L, "apache hadoop")
+)).toDF("id", "text")
 
 // Make predictions on test documents.
 model.transform(test.toDF)
@@ -505,7 +473,6 @@ model.transform(test.toDF)
     println(s"($id, $text) --> prob=$prob, prediction=$prediction")
   }
 
-sc.stop()
 {% endhighlight %}
 </div>
 
@@ -514,8 +481,6 @@ sc.stop()
 import java.util.Arrays;
 import java.util.List;
 
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.Pipeline;
 import org.apache.spark.ml.PipelineModel;
 import org.apache.spark.ml.PipelineStage;
@@ -524,7 +489,6 @@ import org.apache.spark.ml.feature.HashingTF;
 import org.apache.spark.ml.feature.Tokenizer;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.Row;
-import org.apache.spark.sql.SQLContext;
 
 // Labeled and unlabeled instance types.
 // Spark SQL can infer schema from Java Beans.
@@ -556,18 +520,13 @@ public class LabeledDocument extends Document implements Serializable {
   public void setLabel(double label) { this.label = label; }
 }
 
-// Set up contexts.
-SparkConf conf = new SparkConf().setAppName("JavaSimpleTextClassificationPipeline");
-JavaSparkContext jsc = new JavaSparkContext(conf);
-SQLContext jsql = new SQLContext(jsc);
-
 // Prepare training documents, which are labeled.
-List<LabeledDocument> localTraining = Arrays.asList(
+DataFrame training = sqlContext.createDataFrame(Arrays.asList(
   new LabeledDocument(0L, "a b c d e spark", 1.0),
   new LabeledDocument(1L, "b d", 0.0),
   new LabeledDocument(2L, "spark f g h", 1.0),
-  new LabeledDocument(3L, "hadoop mapreduce", 0.0));
-DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class);
+  new LabeledDocument(3L, "hadoop mapreduce", 0.0)
+), LabeledDocument.class);
 
 // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
 Tokenizer tokenizer = new Tokenizer()
@@ -587,12 +546,12 @@ Pipeline pipeline = new Pipeline()
 PipelineModel model = pipeline.fit(training);
 
 // Prepare test documents, which are unlabeled.
-List<Document> localTest = Arrays.asList(
+DataFrame test = sqlContext.createDataFrame(Arrays.asList(
   new Document(4L, "spark i j k"),
   new Document(5L, "l m n"),
   new Document(6L, "mapreduce spark"),
-  new Document(7L, "apache hadoop"));
-DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class);
+  new Document(7L, "apache hadoop")
+), Document.class);
 
 // Make predictions on test documents.
 DataFrame predictions = model.transform(test);
@@ -601,28 +560,23 @@ for (Row r: predictions.select("id", "text", "probability", "prediction").collec
       + ", prediction=" + r.get(3));
 }
 
-jsc.stop();
 {% endhighlight %}
 </div>
 
 <div data-lang="python">
 {% highlight python %}
-from pyspark import SparkContext
 from pyspark.ml import Pipeline
 from pyspark.ml.classification import LogisticRegression
 from pyspark.ml.feature import HashingTF, Tokenizer
-from pyspark.sql import Row, SQLContext
-
-sc = SparkContext(appName="SimpleTextClassificationPipeline")
-sqlContext = SQLContext(sc)
+from pyspark.sql import Row
 
-# Prepare training documents, which are labeled.
+# Prepare training documents from a list of (id, text, label) tuples.
 LabeledDocument = Row("id", "text", "label")
-training = sc.parallelize([(0L, "a b c d e spark", 1.0),
-                           (1L, "b d", 0.0),
-                           (2L, "spark f g h", 1.0),
-                           (3L, "hadoop mapreduce", 0.0)]) \
-    .map(lambda x: LabeledDocument(*x)).toDF()
+training = sqlContext.createDataFrame([
+    (0L, "a b c d e spark", 1.0),
+    (1L, "b d", 0.0),
+    (2L, "spark f g h", 1.0),
+    (3L, "hadoop mapreduce", 0.0)], ["id", "text", "label"])
 
 # Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.
 tokenizer = Tokenizer(inputCol="text", outputCol="words")
@@ -633,13 +587,12 @@ pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
 # Fit the pipeline to training documents.
 model = pipeline.fit(training)
 
-# Prepare test documents, which are unlabeled.
-Document = Row("id", "text")
-test = sc.parallelize([(4L, "spark i j k"),
-                       (5L, "l m n"),
-                       (6L, "mapreduce spark"),
-                       (7L, "apache hadoop")]) \
-    .map(lambda x: Document(*x)).toDF()
+# Prepare test documents, which are unlabeled (id, text) tuples.
+test = sqlContext.createDataFrame([
+    (4L, "spark i j k"),
+    (5L, "l m n"),
+    (6L, "mapreduce spark"),
+    (7L, "apache hadoop")], ["id", "text"])
 
 # Make predictions on test documents and print columns of interest.
 prediction = model.transform(test)
@@ -647,7 +600,6 @@ selected = prediction.select("id", "text", "prediction")
 for row in selected.collect():
     print(row)
 
-sc.stop()
 {% endhighlight %}
 </div>
 
@@ -664,8 +616,8 @@ Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/
 
 The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.RegressionEvaluator)
 for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.BinaryClassificationEvaluator)
-for binary data or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.MultiClassClassificationEvaluator)
-for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the setMetric
+for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.MultiClassClassificationEvaluator)
+for multiclass problems. The default metric used to choose the best `ParamMap` can be overriden by the `setMetric`
 method in each of these evaluators.
 
 The `ParamMap` which produces the best evaluation metric (averaged over the `$k$` folds) is selected as the best model.
@@ -684,39 +636,29 @@ However, it is also a well-established method for choosing parameters which is m
 
 <div data-lang="scala">
 {% highlight scala %}
-import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.classification.LogisticRegression
 import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
 import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
 import org.apache.spark.ml.tuning.{ParamGridBuilder, CrossValidator}
 import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.sql.{Row, SQLContext}
-
-// Labeled and unlabeled instance types.
-// Spark SQL can infer schema from case classes.
-case class LabeledDocument(id: Long, text: String, label: Double)
-case class Document(id: Long, text: String)
-
-val conf = new SparkConf().setAppName("CrossValidatorExample")
-val sc = new SparkContext(conf)
-val sqlContext = new SQLContext(sc)
-import sqlContext.implicits._
-
-// Prepare training documents, which are labeled.
-val training = sc.parallelize(Seq(
-  LabeledDocument(0L, "a b c d e spark", 1.0),
-  LabeledDocument(1L, "b d", 0.0),
-  LabeledDocument(2L, "spark f g h", 1.0),
-  LabeledDocument(3L, "hadoop mapreduce", 0.0),
-  LabeledDocument(4L, "b spark who", 1.0),
-  LabeledDocument(5L, "g d a y", 0.0),
-  LabeledDocument(6L, "spark fly", 1.0),
-  LabeledDocument(7L, "was mapreduce", 0.0),
-  LabeledDocument(8L, "e spark program", 1.0),
-  LabeledDocument(9L, "a e c l", 0.0),
-  LabeledDocument(10L, "spark compile", 1.0),
-  LabeledDocument(11L, "hadoop software", 0.0)))
+import org.apache.spark.sql.Row
+
+// Prepare training data from a list of (id, text, label) tuples.
+val training = sqlContext.createDataFrame(Seq(
+  (0L, "a b c d e spark", 1.0),
+  (1L, "b d", 0.0),
+  (2L, "spark f g h", 1.0),
+  (3L, "hadoop mapreduce", 0.0),
+  (4L, "b spark who", 1.0),
+  (5L, "g d a y", 0.0),
+  (6L, "spark fly", 1.0),
+  (7L, "was mapreduce", 0.0),
+  (8L, "e spark program", 1.0),
+  (9L, "a e c l", 0.0),
+  (10L, "spark compile", 1.0),
+  (11L, "hadoop software", 0.0)
+)).toDF("id", "text", "label")
 
 // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
 val tokenizer = new Tokenizer()
@@ -730,15 +672,6 @@ val lr = new LogisticRegression()
 val pipeline = new Pipeline()
   .setStages(Array(tokenizer, hashingTF, lr))
 
-// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
-// This will allow us to jointly choose parameters for all Pipeline stages.
-// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
-// Note that the evaluator here is a BinaryClassificationEvaluator and the default metric
-// used is areaUnderROC.
-val crossval = new CrossValidator()
-  .setEstimator(pipeline)
-  .setEvaluator(new BinaryClassificationEvaluator)
-
 // We use a ParamGridBuilder to construct a grid of parameters to search over.
 // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
 // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
@@ -746,28 +679,37 @@ val paramGrid = new ParamGridBuilder()
   .addGrid(hashingTF.numFeatures, Array(10, 100, 1000))
   .addGrid(lr.regParam, Array(0.1, 0.01))
   .build()
-crossval.setEstimatorParamMaps(paramGrid)
-crossval.setNumFolds(2) // Use 3+ in practice
+
+// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
+// This will allow us to jointly choose parameters for all Pipeline stages.
+// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+// Note that the evaluator here is a BinaryClassificationEvaluator and its default metric
+// is areaUnderROC.
+val cv = new CrossValidator()
+  .setEstimator(pipeline)
+  .setEvaluator(new BinaryClassificationEvaluator)
+  .setEstimatorParamMaps(paramGrid)
+  .setNumFolds(2) // Use 3+ in practice
 
 // Run cross-validation, and choose the best set of parameters.
-val cvModel = crossval.fit(training.toDF)
+val cvModel = cv.fit(training)
 
-// Prepare test documents, which are unlabeled.
-val test = sc.parallelize(Seq(
-  Document(4L, "spark i j k"),
-  Document(5L, "l m n"),
-  Document(6L, "mapreduce spark"),
-  Document(7L, "apache hadoop")))
+// Prepare test documents, which are unlabeled (id, text) tuples.
+val test = sqlContext.createDataFrame(Seq(
+  (4L, "spark i j k"),
+  (5L, "l m n"),
+  (6L, "mapreduce spark"),
+  (7L, "apache hadoop")
+)).toDF("id", "text")
 
 // Make predictions on test documents. cvModel uses the best model found (lrModel).
-cvModel.transform(test.toDF)
+cvModel.transform(test)
   .select("id", "text", "probability", "prediction")
   .collect()
   .foreach { case Row(id: Long, text: String, prob: Vector, prediction: Double) =>
-  println(s"($id, $text) --> prob=$prob, prediction=$prediction")
-}
+    println(s"($id, $text) --> prob=$prob, prediction=$prediction")
+  }
 
-sc.stop()
 {% endhighlight %}
 </div>
 
@@ -776,8 +718,6 @@ sc.stop()
 import java.util.Arrays;
 import java.util.List;
 
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.ml.Pipeline;
 import org.apache.spark.ml.PipelineStage;
 import org.apache.spark.ml.classification.LogisticRegression;
@@ -790,7 +730,6 @@ import org.apache.spark.ml.tuning.CrossValidatorModel;
 import org.apache.spark.ml.tuning.ParamGridBuilder;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.Row;
-import org.apache.spark.sql.SQLContext;
 
 // Labeled and unlabeled instance types.
 // Spark SQL can infer schema from Java Beans.
@@ -822,12 +761,9 @@ public class LabeledDocument extends Document implements Serializable {
   public void setLabel(double label) { this.label = label; }
 }
 
-SparkConf conf = new SparkConf().setAppName("JavaCrossValidatorExample");
-JavaSparkContext jsc = new JavaSparkContext(conf);
-SQLContext jsql = new SQLContext(jsc);
 
 // Prepare training documents, which are labeled.
-List<LabeledDocument> localTraining = Arrays.asList(
+DataFrame training = sqlContext.createDataFrame(Arrays.asList(
   new LabeledDocument(0L, "a b c d e spark", 1.0),
   new LabeledDocument(1L, "b d", 0.0),
   new LabeledDocument(2L, "spark f g h", 1.0),
@@ -839,8 +775,8 @@ List<LabeledDocument> localTraining = Arrays.asList(
   new LabeledDocument(8L, "e spark program", 1.0),
   new LabeledDocument(9L, "a e c l", 0.0),
   new LabeledDocument(10L, "spark compile", 1.0),
-  new LabeledDocument(11L, "hadoop software", 0.0));
-DataFrame training = jsql.createDataFrame(jsc.parallelize(localTraining), LabeledDocument.class);
+  new LabeledDocument(11L, "hadoop software", 0.0)
+), LabeledDocument.class);
 
 // Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.
 Tokenizer tokenizer = new Tokenizer()
@@ -856,15 +792,6 @@ LogisticRegression lr = new LogisticRegression()
 Pipeline pipeline = new Pipeline()
   .setStages(new PipelineStage[] {tokenizer, hashingTF, lr});
 
-// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
-// This will allow us to jointly choose parameters for all Pipeline stages.
-// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
-// Note that the evaluator here is a BinaryClassificationEvaluator and the default metric
-// used is areaUnderROC.
-CrossValidator crossval = new CrossValidator()
-    .setEstimator(pipeline)
-    .setEvaluator(new BinaryClassificationEvaluator());
-
 // We use a ParamGridBuilder to construct a grid of parameters to search over.
 // With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,
 // this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.
@@ -872,19 +799,28 @@ ParamMap[] paramGrid = new ParamGridBuilder()
     .addGrid(hashingTF.numFeatures(), new int[]{10, 100, 1000})
     .addGrid(lr.regParam(), new double[]{0.1, 0.01})
     .build();
-crossval.setEstimatorParamMaps(paramGrid);
-crossval.setNumFolds(2); // Use 3+ in practice
+
+// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.
+// This will allow us to jointly choose parameters for all Pipeline stages.
+// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.
+// Note that the evaluator here is a BinaryClassificationEvaluator and its default metric
+// is areaUnderROC.
+CrossValidator cv = new CrossValidator()
+  .setEstimator(pipeline)
+  .setEvaluator(new BinaryClassificationEvaluator())
+  .setEstimatorParamMaps(paramGrid)
+  .setNumFolds(2); // Use 3+ in practice
 
 // Run cross-validation, and choose the best set of parameters.
-CrossValidatorModel cvModel = crossval.fit(training);
+CrossValidatorModel cvModel = cv.fit(training);
 
 // Prepare test documents, which are unlabeled.
-List<Document> localTest = Arrays.asList(
+DataFrame test = sqlContext.createDataFrame(Arrays.asList(
   new Document(4L, "spark i j k"),
   new Document(5L, "l m n"),
   new Document(6L, "mapreduce spark"),
-  new Document(7L, "apache hadoop"));
-DataFrame test = jsql.createDataFrame(jsc.parallelize(localTest), Document.class);
+  new Document(7L, "apache hadoop")
+), Document.class);
 
 // Make predictions on test documents. cvModel uses the best model found (lrModel).
 DataFrame predictions = cvModel.transform(test);
@@ -893,7 +829,6 @@ for (Row r: predictions.select("id", "text", "probability", "prediction").collec
       + ", prediction=" + r.get(3));
 }
 
-jsc.stop();
 {% endhighlight %}
 </div>
 
@@ -935,7 +870,7 @@ val lr = new LinearRegression()
 // the evaluator.
 val paramGrid = new ParamGridBuilder()
   .addGrid(lr.regParam, Array(0.1, 0.01))
-  .addGrid(lr.fitIntercept, Array(true, false))
+  .addGrid(lr.fitIntercept)
   .addGrid(lr.elasticNetParam, Array(0.0, 0.5, 1.0))
   .build()
 
@@ -945,9 +880,8 @@ val trainValidationSplit = new TrainValidationSplit()
   .setEstimator(lr)
   .setEvaluator(new RegressionEvaluator)
   .setEstimatorParamMaps(paramGrid)
-
-// 80% of the data will be used for training and the remaining 20% for validation.
-trainValidationSplit.setTrainRatio(0.8)
+  // 80% of the data will be used for training and the remaining 20% for validation.
+  .setTrainRatio(0.8)
 
 // Run train validation split, and choose the best set of parameters.
 val model = trainValidationSplit.fit(training)
@@ -972,12 +906,12 @@ import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
-DataFrame data = jsql.createDataFrame(
+DataFrame data = sqlContext.createDataFrame(
   MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt"),
   LabeledPoint.class);
 
 // Prepare training and test data.
-DataFrame[] splits = data.randomSplit(new double [] {0.9, 0.1}, 12345);
+DataFrame[] splits = data.randomSplit(new double[] {0.9, 0.1}, 12345);
 DataFrame training = splits[0];
 DataFrame test = splits[1];
 
@@ -997,10 +931,8 @@ ParamMap[] paramGrid = new ParamGridBuilder()
 TrainValidationSplit trainValidationSplit = new TrainValidationSplit()
   .setEstimator(lr)
   .setEvaluator(new RegressionEvaluator())
-  .setEstimatorParamMaps(paramGrid);
-
-// 80% of the data will be used for training and the remaining 20% for validation.
-trainValidationSplit.setTrainRatio(0.8);
+  .setEstimatorParamMaps(paramGrid)
+  .setTrainRatio(0.8); // 80% for training and the remaining 20% for validation
 
 // Run train validation split, and choose the best set of parameters.
 TrainValidationSplitModel model = trainValidationSplit.fit(training);

From 1bfd9347822df65e76201c4c471a26488d722319 Mon Sep 17 00:00:00 2001
From: ihainan <ihainan72@gmail.com>
Date: Sun, 30 Aug 2015 08:26:14 +0100
Subject: [PATCH 484/633] [SPARK-10184] [CORE] Optimization for bounds
 determination in RangePartitioner

JIRA Issue: https://issues.apache.org/jira/browse/SPARK-10184

Change `cumWeight > target` to `cumWeight >= target` in `RangePartitioner.determineBounds` method to make the output partitions more balanced.

Author: ihainan <ihainan72@gmail.com>

Closes #8397 from ihainan/opt_for_rangepartitioner.
---
 core/src/main/scala/org/apache/spark/Partitioner.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index 4b9d59975bdc2..29e581bb57cbc 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -291,7 +291,7 @@ private[spark] object RangePartitioner {
     while ((i < numCandidates) && (j < partitions - 1)) {
       val (key, weight) = ordered(i)
       cumWeight += weight
-      if (cumWeight > target) {
+      if (cumWeight >= target) {
         // Skip duplicate values.
         if (previousBound.isEmpty || ordering.gt(key, previousBound.get)) {
           bounds += key

From 8d2ab75d3b71b632f2394f2453af32f417cb45e5 Mon Sep 17 00:00:00 2001
From: Burak Yavuz <brkyvz@gmail.com>
Date: Sun, 30 Aug 2015 12:21:15 -0700
Subject: [PATCH 485/633] [SPARK-10353] [MLLIB] BLAS gemm not scaling when beta
 = 0.0 for some subset of matrix multiplications

mengxr jkbradley rxin

It would be great if this fix made it into RC3!

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #8525 from brkyvz/blas-scaling.
---
 .../org/apache/spark/mllib/linalg/BLAS.scala  | 26 +++++++------------
 .../apache/spark/mllib/linalg/BLASSuite.scala |  5 ++++
 2 files changed, 15 insertions(+), 16 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
index bbbcc8436b7c2..ab475af264dd3 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
@@ -305,6 +305,8 @@ private[spark] object BLAS extends Serializable with Logging {
       "The matrix C cannot be the product of a transpose() call. C.isTransposed must be false.")
     if (alpha == 0.0 && beta == 1.0) {
       logDebug("gemm: alpha is equal to 0 and beta is equal to 1. Returning C.")
+    } else if (alpha == 0.0) {
+      f2jBLAS.dscal(C.values.length, beta, C.values, 1)
     } else {
       A match {
         case sparse: SparseMatrix => gemm(alpha, sparse, B, beta, C)
@@ -408,8 +410,8 @@ private[spark] object BLAS extends Serializable with Logging {
         }
       }
     } else {
-      // Scale matrix first if `beta` is not equal to 0.0
-      if (beta != 0.0) {
+      // Scale matrix first if `beta` is not equal to 1.0
+      if (beta != 1.0) {
         f2jBLAS.dscal(C.values.length, beta, C.values, 1)
       }
       // Perform matrix multiplication and add to C. The rows of A are multiplied by the columns of
@@ -470,8 +472,10 @@ private[spark] object BLAS extends Serializable with Logging {
       s"The columns of A don't match the number of elements of x. A: ${A.numCols}, x: ${x.size}")
     require(A.numRows == y.size,
       s"The rows of A don't match the number of elements of y. A: ${A.numRows}, y:${y.size}")
-    if (alpha == 0.0) {
-      logDebug("gemv: alpha is equal to 0. Returning y.")
+    if (alpha == 0.0 && beta == 1.0) {
+      logDebug("gemv: alpha is equal to 0 and beta is equal to 1. Returning y.")
+    } else if (alpha == 0.0) {
+      scal(beta, y)
     } else {
       (A, x) match {
         case (smA: SparseMatrix, dvx: DenseVector) =>
@@ -526,11 +530,6 @@ private[spark] object BLAS extends Serializable with Logging {
     val xValues = x.values
     val yValues = y.values
 
-    if (alpha == 0.0) {
-      scal(beta, y)
-      return
-    }
-
     if (A.isTransposed) {
       var rowCounterForA = 0
       while (rowCounterForA < mA) {
@@ -581,11 +580,6 @@ private[spark] object BLAS extends Serializable with Logging {
     val Arows = if (!A.isTransposed) A.rowIndices else A.colPtrs
     val Acols = if (!A.isTransposed) A.colPtrs else A.rowIndices
 
-    if (alpha == 0.0) {
-      scal(beta, y)
-      return
-    }
-
     if (A.isTransposed) {
       var rowCounter = 0
       while (rowCounter < mA) {
@@ -604,7 +598,7 @@ private[spark] object BLAS extends Serializable with Logging {
         rowCounter += 1
       }
     } else {
-      scal(beta, y)
+      if (beta != 1.0) scal(beta, y)
 
       var colCounterForA = 0
       var k = 0
@@ -659,7 +653,7 @@ private[spark] object BLAS extends Serializable with Logging {
         rowCounter += 1
       }
     } else {
-      scal(beta, y)
+      if (beta != 1.0) scal(beta, y)
       // Perform matrix-vector multiplication and add to y
       var colCounterForA = 0
       while (colCounterForA < nA) {
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
index d119e0b50a393..8db5c8424abe9 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
@@ -204,6 +204,7 @@ class BLASSuite extends SparkFunSuite {
     val C14 = C1.copy
     val C15 = C1.copy
     val C16 = C1.copy
+    val C17 = C1.copy
     val expected2 = new DenseMatrix(4, 2, Array(2.0, 1.0, 4.0, 2.0, 4.0, 0.0, 4.0, 3.0))
     val expected3 = new DenseMatrix(4, 2, Array(2.0, 2.0, 4.0, 2.0, 8.0, 0.0, 6.0, 6.0))
     val expected4 = new DenseMatrix(4, 2, Array(5.0, 0.0, 10.0, 5.0, 0.0, 0.0, 5.0, 0.0))
@@ -217,6 +218,10 @@ class BLASSuite extends SparkFunSuite {
     assert(C2 ~== expected2 absTol 1e-15)
     assert(C3 ~== expected3 absTol 1e-15)
     assert(C4 ~== expected3 absTol 1e-15)
+    gemm(1.0, dA, B, 0.0, C17)
+    assert(C17 ~== expected absTol 1e-15)
+    gemm(1.0, sA, B, 0.0, C17)
+    assert(C17 ~== expected absTol 1e-15)
 
     withClue("columns of A don't match the rows of B") {
       intercept[Exception] {

From 35e896a79bb5e72d63b82b047f46f4f6fa2e1970 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <patrick@databricks.com>
Date: Sun, 30 Aug 2015 21:39:16 -0700
Subject: [PATCH 486/633] SPARK-9545, SPARK-9547: Use Maven in PRB if title
 contains "[test-maven]"

This is just some small glue code to actually make use of the
AMPLAB_JENKINS_BUILD_TOOL switch. As far as I can tell, we actually
don't currently use the Maven support in the tool even though it exists.
This patch switches to Maven when the PR title contains "test-maven".

There are a few small other pieces of cleanup in the patch as well.

Author: Patrick Wendell <patrick@databricks.com>

Closes #7878 from pwendell/maven-tests.
---
 dev/run-tests-jenkins | 18 ++++++++++++++++--
 dev/run-tests.py      | 28 ++++++++++++++++++++++++++--
 2 files changed, 42 insertions(+), 4 deletions(-)

diff --git a/dev/run-tests-jenkins b/dev/run-tests-jenkins
index 39cf54f78104c..3be78575e70f1 100755
--- a/dev/run-tests-jenkins
+++ b/dev/run-tests-jenkins
@@ -164,8 +164,9 @@ pr_message=""
 current_pr_head="`git rev-parse HEAD`"
 
 echo "HEAD:  `git rev-parse HEAD`"
-echo "GHPRB: $ghprbActualCommit"
-echo "SHA1:  $sha1"
+echo "\$ghprbActualCommit: $ghprbActualCommit"
+echo "\$sha1:  $sha1"
+echo "\$ghprbPullTitle: $ghprbPullTitle"
 
 # Run pull request tests
 for t in "${PR_TESTS[@]}"; do
@@ -189,6 +190,19 @@ done
 {
   # Marks this build is a pull request build.
   export AMP_JENKINS_PRB=true
+  if [[ $ghprbPullTitle == *"test-maven"* ]]; then
+    export AMPLAB_JENKINS_BUILD_TOOL="maven"
+  fi
+  if [[ $ghprbPullTitle == *"test-hadoop1.0"* ]]; then
+    export AMPLAB_JENKINS_BUILD_PROFILE="hadoop1.0"
+  elif [[ $ghprbPullTitle == *"test-hadoop2.0"* ]]; then
+    export AMPLAB_JENKINS_BUILD_PROFILE="hadoop2.0"
+  elif [[ $ghprbPullTitle == *"test-hadoop2.2"* ]]; then
+    export AMPLAB_JENKINS_BUILD_PROFILE="hadoop2.2"
+  elif [[ $ghprbPullTitle == *"test-hadoop2.3"* ]]; then
+    export AMPLAB_JENKINS_BUILD_PROFILE="hadoop2.3"
+  fi
+
   timeout "${TESTS_TIMEOUT}" ./dev/run-tests
   test_result="$?"
 
diff --git a/dev/run-tests.py b/dev/run-tests.py
index 4fd703a7c219f..d8b22e1665e7b 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -21,6 +21,7 @@
 import itertools
 from optparse import OptionParser
 import os
+import random
 import re
 import sys
 import subprocess
@@ -239,11 +240,32 @@ def build_spark_documentation():
     os.chdir(SPARK_HOME)
 
 
+def get_zinc_port():
+    """
+    Get a randomized port on which to start Zinc
+    """
+    return random.randrange(3030, 4030)
+
+
+def kill_zinc_on_port(zinc_port):
+    """
+    Kill the Zinc process running on the given port, if one exists.
+    """
+    cmd = ("/usr/sbin/lsof -P |grep %s | grep LISTEN "
+           "| awk '{ print $2; }' | xargs kill") % zinc_port
+    subprocess.check_call(cmd, shell=True)
+
+
 def exec_maven(mvn_args=()):
     """Will call Maven in the current directory with the list of mvn_args passed
     in and returns the subprocess for any further processing"""
 
-    run_cmd([os.path.join(SPARK_HOME, "build", "mvn")] + mvn_args)
+    zinc_port = get_zinc_port()
+    os.environ["ZINC_PORT"] = "%s" % zinc_port
+    zinc_flag = "-DzincPort=%s" % zinc_port
+    flags = [os.path.join(SPARK_HOME, "build", "mvn"), "--force", zinc_flag]
+    run_cmd(flags + mvn_args)
+    kill_zinc_on_port(zinc_port)
 
 
 def exec_sbt(sbt_args=()):
@@ -514,7 +536,9 @@ def main():
     build_apache_spark(build_tool, hadoop_version)
 
     # backwards compatibility checks
-    detect_binary_inop_with_mima()
+    if build_tool == "sbt":
+        # Note: compatiblity tests only supported in sbt for now
+        detect_binary_inop_with_mima()
 
     # run the test suites
     run_scala_tests(build_tool, hadoop_version, test_modules)

From 8694c3ad7dcafca9563649e93b7a08076748d6f2 Mon Sep 17 00:00:00 2001
From: Feynman Liang <fliang@databricks.com>
Date: Sun, 30 Aug 2015 23:12:56 -0700
Subject: [PATCH 487/633] [SPARK-10351] [SQL] Fixes UTF8String.fromAddress to
 handle off-heap memory

CC rxin marmbrus

Author: Feynman Liang <fliang@databricks.com>

Closes #8523 from feynmanliang/SPARK-10351.
---
 .../test/scala/org/apache/spark/sql/UnsafeRowSuite.scala | 9 +++++----
 .../java/org/apache/spark/unsafe/types/UTF8String.java   | 6 +-----
 2 files changed, 6 insertions(+), 9 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
index 219435dff5bc8..2476b10e3cf9e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UnsafeRowSuite.scala
@@ -43,12 +43,12 @@ class UnsafeRowSuite extends SparkFunSuite {
     val arrayBackedUnsafeRow: UnsafeRow =
       UnsafeProjection.create(Array[DataType](StringType, StringType, IntegerType)).apply(row)
     assert(arrayBackedUnsafeRow.getBaseObject.isInstanceOf[Array[Byte]])
-    val bytesFromArrayBackedRow: Array[Byte] = {
+    val (bytesFromArrayBackedRow, field0StringFromArrayBackedRow): (Array[Byte], String) = {
       val baos = new ByteArrayOutputStream()
       arrayBackedUnsafeRow.writeToStream(baos, null)
-      baos.toByteArray
+      (baos.toByteArray, arrayBackedUnsafeRow.getString(0))
     }
-    val bytesFromOffheapRow: Array[Byte] = {
+    val (bytesFromOffheapRow, field0StringFromOffheapRow): (Array[Byte], String) = {
       val offheapRowPage = MemoryAllocator.UNSAFE.allocate(arrayBackedUnsafeRow.getSizeInBytes)
       try {
         Platform.copyMemory(
@@ -69,13 +69,14 @@ class UnsafeRowSuite extends SparkFunSuite {
         val baos = new ByteArrayOutputStream()
         val writeBuffer = new Array[Byte](1024)
         offheapUnsafeRow.writeToStream(baos, writeBuffer)
-        baos.toByteArray
+        (baos.toByteArray, offheapUnsafeRow.getString(0))
       } finally {
         MemoryAllocator.UNSAFE.free(offheapRowPage)
       }
     }
 
     assert(bytesFromArrayBackedRow === bytesFromOffheapRow)
+    assert(field0StringFromArrayBackedRow === field0StringFromOffheapRow)
   }
 
   test("calling getDouble() and getFloat() on null columns") {
diff --git a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
index cbcab958c05a9..216aeea60d1c8 100644
--- a/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
+++ b/unsafe/src/main/java/org/apache/spark/unsafe/types/UTF8String.java
@@ -90,11 +90,7 @@ public static UTF8String fromBytes(byte[] bytes, int offset, int numBytes) {
    * Creates an UTF8String from given address (base and offset) and length.
    */
   public static UTF8String fromAddress(Object base, long offset, int numBytes) {
-    if (base != null) {
-      return new UTF8String(base, offset, numBytes);
-    } else {
-      return null;
-    }
+    return new UTF8String(base, offset, numBytes);
   }
 
   /**

From f0f563a3c43fc9683e6920890cce44611c0c5f4b Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Sun, 30 Aug 2015 23:20:03 -0700
Subject: [PATCH 488/633] [SPARK-100354] [MLLIB] fix some apparent memory
 issues in k-means|| initializaiton

* do not cache first cost RDD
* change following cost RDD cache level to MEMORY_AND_DISK
* remove Vector wrapper to save a object per instance

Further improvements will be addressed in SPARK-10329

cc: yu-iskw HuJiayin

Author: Xiangrui Meng <meng@databricks.com>

Closes #8526 from mengxr/SPARK-10354.
---
 .../spark/mllib/clustering/KMeans.scala       | 21 ++++++++++++-------
 1 file changed, 14 insertions(+), 7 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
index 46920fffe6e1a..7168aac32c997 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala
@@ -369,7 +369,7 @@ class KMeans private (
   : Array[Array[VectorWithNorm]] = {
     // Initialize empty centers and point costs.
     val centers = Array.tabulate(runs)(r => ArrayBuffer.empty[VectorWithNorm])
-    var costs = data.map(_ => Vectors.dense(Array.fill(runs)(Double.PositiveInfinity))).cache()
+    var costs = data.map(_ => Array.fill(runs)(Double.PositiveInfinity))
 
     // Initialize each run's first center to a random point.
     val seed = new XORShiftRandom(this.seed).nextInt()
@@ -394,21 +394,28 @@ class KMeans private (
       val bcNewCenters = data.context.broadcast(newCenters)
       val preCosts = costs
       costs = data.zip(preCosts).map { case (point, cost) =>
-        Vectors.dense(
           Array.tabulate(runs) { r =>
             math.min(KMeans.pointCost(bcNewCenters.value(r), point), cost(r))
-          })
-      }.cache()
+          }
+        }.persist(StorageLevel.MEMORY_AND_DISK)
       val sumCosts = costs
-        .aggregate(Vectors.zeros(runs))(
+        .aggregate(new Array[Double](runs))(
           seqOp = (s, v) => {
             // s += v
-            axpy(1.0, v, s)
+            var r = 0
+            while (r < runs) {
+              s(r) += v(r)
+              r += 1
+            }
             s
           },
           combOp = (s0, s1) => {
             // s0 += s1
-            axpy(1.0, s1, s0)
+            var r = 0
+            while (r < runs) {
+              s0(r) += s1(r)
+              r += 1
+            }
             s0
           }
         )

From 72f6dbf7b0c8b271f5f9c762374422c69c8ab43d Mon Sep 17 00:00:00 2001
From: EugenCepoi <cepoi.eugen@gmail.com>
Date: Mon, 31 Aug 2015 13:24:35 -0500
Subject: [PATCH 489/633] [SPARK-8730] Fixes - Deser objects containing a
 primitive class attribute

Author: EugenCepoi <cepoi.eugen@gmail.com>

Closes #7122 from EugenCepoi/master.
---
 .../spark/serializer/JavaSerializer.scala     | 27 +++++++++++++++----
 .../serializer/JavaSerializerSuite.scala      | 18 +++++++++++++
 2 files changed, 40 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
index 4a5274b46b7a0..b463a71d5bd7d 100644
--- a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala
@@ -62,17 +62,34 @@ private[spark] class JavaDeserializationStream(in: InputStream, loader: ClassLoa
   extends DeserializationStream {
 
   private val objIn = new ObjectInputStream(in) {
-    override def resolveClass(desc: ObjectStreamClass): Class[_] = {
-      // scalastyle:off classforname
-      Class.forName(desc.getName, false, loader)
-      // scalastyle:on classforname
-    }
+    override def resolveClass(desc: ObjectStreamClass): Class[_] =
+      try {
+        // scalastyle:off classforname
+        Class.forName(desc.getName, false, loader)
+        // scalastyle:on classforname
+      } catch {
+        case e: ClassNotFoundException =>
+          JavaDeserializationStream.primitiveMappings.get(desc.getName).getOrElse(throw e)
+      }
   }
 
   def readObject[T: ClassTag](): T = objIn.readObject().asInstanceOf[T]
   def close() { objIn.close() }
 }
 
+private object JavaDeserializationStream {
+  val primitiveMappings = Map[String, Class[_]](
+    "boolean" -> classOf[Boolean],
+    "byte" -> classOf[Byte],
+    "char" -> classOf[Char],
+    "short" -> classOf[Short],
+    "int" -> classOf[Int],
+    "long" -> classOf[Long],
+    "float" -> classOf[Float],
+    "double" -> classOf[Double],
+    "void" -> classOf[Void]
+  )
+}
 
 private[spark] class JavaSerializerInstance(
     counterReset: Int, extraDebugInfo: Boolean, defaultClassLoader: ClassLoader)
diff --git a/core/src/test/scala/org/apache/spark/serializer/JavaSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/JavaSerializerSuite.scala
index 329a2b6dad831..20f45670bc2ba 100644
--- a/core/src/test/scala/org/apache/spark/serializer/JavaSerializerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/serializer/JavaSerializerSuite.scala
@@ -25,4 +25,22 @@ class JavaSerializerSuite extends SparkFunSuite {
     val instance = serializer.newInstance()
     instance.deserialize[JavaSerializer](instance.serialize(serializer))
   }
+
+  test("Deserialize object containing a primitive Class as attribute") {
+    val serializer = new JavaSerializer(new SparkConf())
+    val instance = serializer.newInstance()
+    instance.deserialize[JavaSerializer](instance.serialize(new ContainsPrimitiveClass()))
+  }
+}
+
+private class ContainsPrimitiveClass extends Serializable {
+  val intClass = classOf[Int]
+  val longClass = classOf[Long]
+  val shortClass = classOf[Short]
+  val charClass = classOf[Char]
+  val doubleClass = classOf[Double]
+  val floatClass = classOf[Float]
+  val booleanClass = classOf[Boolean]
+  val byteClass = classOf[Byte]
+  val voidClass = classOf[Void]
 }

From 4a5fe091658b1d06f427e404a11a84fc84f953c5 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 31 Aug 2015 12:19:11 -0700
Subject: [PATCH 490/633] [SPARK-10369] [STREAMING] Don't remove
 ReceiverTrackingInfo when deregisterReceivering since we may reuse it later

`deregisterReceiver` should not remove `ReceiverTrackingInfo`. Otherwise, it will throw `java.util.NoSuchElementException: key not found` when restarting it.

Author: zsxwing <zsxwing@gmail.com>

Closes #8538 from zsxwing/SPARK-10369.
---
 .../streaming/scheduler/ReceiverTracker.scala |  4 +-
 .../scheduler/ReceiverTrackerSuite.scala      | 51 +++++++++++++++++++
 2 files changed, 53 insertions(+), 2 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
index 3d532a675db02..f86fd44b48719 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala
@@ -291,7 +291,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         ReceiverTrackingInfo(
           streamId, ReceiverState.INACTIVE, None, None, None, None, Some(errorInfo))
     }
-    receiverTrackingInfos -= streamId
+    receiverTrackingInfos(streamId) = newReceiverTrackingInfo
     listenerBus.post(StreamingListenerReceiverStopped(newReceiverTrackingInfo.toReceiverInfo))
     val messageWithError = if (error != null && !error.isEmpty) {
       s"$message - $error"
@@ -483,7 +483,7 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false
         context.reply(true)
       // Local messages
       case AllReceiverIds =>
-        context.reply(receiverTrackingInfos.keys.toSeq)
+        context.reply(receiverTrackingInfos.filter(_._2.state != ReceiverState.INACTIVE).keys.toSeq)
       case StopAllReceivers =>
         assert(isTrackerStopping || isTrackerStopped)
         stopReceivers()
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
index dd292ba4dd949..45138b748ecab 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala
@@ -60,6 +60,26 @@ class ReceiverTrackerSuite extends TestSuiteBase {
       }
     }
   }
+
+  test("should restart receiver after stopping it") {
+    withStreamingContext(new StreamingContext(conf, Milliseconds(100))) { ssc =>
+      @volatile var startTimes = 0
+      ssc.addStreamingListener(new StreamingListener {
+        override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted): Unit = {
+          startTimes += 1
+        }
+      })
+      val input = ssc.receiverStream(new StoppableReceiver)
+      val output = new TestOutputStream(input)
+      output.register()
+      ssc.start()
+      StoppableReceiver.shouldStop = true
+      eventually(timeout(10 seconds), interval(10 millis)) {
+        // The receiver is stopped once, so if it's restarted, it should be started twice.
+        assert(startTimes === 2)
+      }
+    }
+  }
 }
 
 /** An input DStream with for testing rate controlling */
@@ -132,3 +152,34 @@ private[streaming] object RateTestReceiver {
 
   def getActive(): Option[RateTestReceiver] = Option(activeReceiver)
 }
+
+/**
+ * A custom receiver that could be stopped via StoppableReceiver.shouldStop
+ */
+class StoppableReceiver extends Receiver[Int](StorageLevel.MEMORY_ONLY) {
+
+  var receivingThreadOption: Option[Thread] = None
+
+  def onStart() {
+    val thread = new Thread() {
+      override def run() {
+        while (!StoppableReceiver.shouldStop) {
+          Thread.sleep(10)
+        }
+        StoppableReceiver.this.stop("stop")
+      }
+    }
+    thread.start()
+  }
+
+  def onStop() {
+    StoppableReceiver.shouldStop = true
+    receivingThreadOption.foreach(_.join())
+    // Reset it so as to restart it
+    StoppableReceiver.shouldStop = false
+  }
+}
+
+object StoppableReceiver {
+  @volatile var shouldStop = false
+}

From a2d5c72091b1c602694dbca823a7b26f86b02864 Mon Sep 17 00:00:00 2001
From: sureshthalamati <suresh.thalamati@gmail.com>
Date: Mon, 31 Aug 2015 12:39:58 -0700
Subject: [PATCH 491/633] [SPARK-10170] [SQL] Add DB2 JDBC dialect support.

Data frame write to DB2 database is failing because by default JDBC data source implementation is generating a table schema with DB2 unsupported data types TEXT for String, and BIT1(1) for Boolean.

This patch registers DB2 JDBC Dialect that maps String, Boolean to valid DB2 data types.

Author: sureshthalamati <suresh.thalamati@gmail.com>

Closes #8393 from sureshthalamati/db2_dialect_spark-10170.
---
 .../apache/spark/sql/jdbc/JdbcDialects.scala   | 18 ++++++++++++++++++
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala  |  7 +++++++
 2 files changed, 25 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala
index 8849fc2f1f0ef..c6d05c9b83b98 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/jdbc/JdbcDialects.scala
@@ -125,6 +125,7 @@ object JdbcDialects {
 
   registerDialect(MySQLDialect)
   registerDialect(PostgresDialect)
+  registerDialect(DB2Dialect)
 
   /**
    * Fetch the JdbcDialect class corresponding to a given database url.
@@ -222,3 +223,20 @@ case object MySQLDialect extends JdbcDialect {
     s"`$colName`"
   }
 }
+
+/**
+ * :: DeveloperApi ::
+ * Default DB2 dialect, mapping string/boolean on write to valid DB2 types.
+ * By default string, and boolean gets mapped to db2 invalid types TEXT, and BIT(1).
+ */
+@DeveloperApi
+case object DB2Dialect extends JdbcDialect {
+
+  override def canHandle(url: String): Boolean = url.startsWith("jdbc:db2")
+
+  override def getJDBCType(dt: DataType): Option[JdbcType] = dt match {
+    case StringType => Some(JdbcType("CLOB", java.sql.Types.CLOB))
+    case BooleanType => Some(JdbcType("CHAR(1)", java.sql.Types.CHAR))
+    case _ => None
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index 0edac0848c3bb..d8c9a08d84c61 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -407,6 +407,7 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
   test("Default jdbc dialect registration") {
     assert(JdbcDialects.get("jdbc:mysql://127.0.0.1/db") == MySQLDialect)
     assert(JdbcDialects.get("jdbc:postgresql://127.0.0.1/db") == PostgresDialect)
+    assert(JdbcDialects.get("jdbc:db2://127.0.0.1/db") == DB2Dialect)
     assert(JdbcDialects.get("test.invalid") == NoopDialect)
   }
 
@@ -443,4 +444,10 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
     assert(agg.getCatalystType(0, "", 1, null) === Some(LongType))
     assert(agg.getCatalystType(1, "", 1, null) === Some(StringType))
   }
+
+  test("DB2Dialect type mapping") {
+    val db2Dialect = JdbcDialects.get("jdbc:db2://127.0.0.1/db")
+    assert(db2Dialect.getJDBCType(StringType).map(_.databaseTypeDefinition).get == "CLOB")
+    assert(db2Dialect.getJDBCType(BooleanType).map(_.databaseTypeDefinition).get == "CHAR(1)")
+  }
 }

From 23e39cc7b1bb7f1087c4706234c9b5165a571357 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Mon, 31 Aug 2015 15:49:25 -0700
Subject: [PATCH 492/633] [SPARK-9954] [MLLIB] use first 128 nonzeros to
 compute Vector.hashCode

This could help reduce hash collisions, e.g., in `RDD[Vector].repartition`. jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #8182 from mengxr/SPARK-9954.
---
 .../apache/spark/mllib/linalg/Vectors.scala   | 38 ++++++++++---------
 1 file changed, 21 insertions(+), 17 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
index 06ebb15869909..3642e9286504f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/Vectors.scala
@@ -71,20 +71,22 @@ sealed trait Vector extends Serializable {
   }
 
   /**
-   * Returns a hash code value for the vector. The hash code is based on its size and its nonzeros
-   * in the first 16 entries, using a hash algorithm similar to [[java.util.Arrays.hashCode]].
+   * Returns a hash code value for the vector. The hash code is based on its size and its first 128
+   * nonzero entries, using a hash algorithm similar to [[java.util.Arrays.hashCode]].
    */
   override def hashCode(): Int = {
     // This is a reference implementation. It calls return in foreachActive, which is slow.
     // Subclasses should override it with optimized implementation.
     var result: Int = 31 + size
+    var nnz = 0
     this.foreachActive { (index, value) =>
-      if (index < 16) {
+      if (nnz < Vectors.MAX_HASH_NNZ) {
         // ignore explicit 0 for comparison between sparse and dense
         if (value != 0) {
           result = 31 * result + index
           val bits = java.lang.Double.doubleToLongBits(value)
           result = 31 * result + (bits ^ (bits >>> 32)).toInt
+          nnz += 1
         }
       } else {
         return result
@@ -536,6 +538,9 @@ object Vectors {
     }
     allEqual
   }
+
+  /** Max number of nonzero entries used in computing hash code. */
+  private[linalg] val MAX_HASH_NNZ = 128
 }
 
 /**
@@ -578,13 +583,15 @@ class DenseVector @Since("1.0.0") (
   override def hashCode(): Int = {
     var result: Int = 31 + size
     var i = 0
-    val end = math.min(values.length, 16)
-    while (i < end) {
+    val end = values.length
+    var nnz = 0
+    while (i < end && nnz < Vectors.MAX_HASH_NNZ) {
       val v = values(i)
       if (v != 0.0) {
         result = 31 * result + i
         val bits = java.lang.Double.doubleToLongBits(values(i))
         result = 31 * result + (bits ^ (bits >>> 32)).toInt
+        nnz += 1
       }
       i += 1
     }
@@ -707,19 +714,16 @@ class SparseVector @Since("1.0.0") (
   override def hashCode(): Int = {
     var result: Int = 31 + size
     val end = values.length
-    var continue = true
     var k = 0
-    while ((k < end) & continue) {
-      val i = indices(k)
-      if (i < 16) {
-        val v = values(k)
-        if (v != 0.0) {
-          result = 31 * result + i
-          val bits = java.lang.Double.doubleToLongBits(v)
-          result = 31 * result + (bits ^ (bits >>> 32)).toInt
-        }
-      } else {
-        continue = false
+    var nnz = 0
+    while (k < end && nnz < Vectors.MAX_HASH_NNZ) {
+      val v = values(k)
+      if (v != 0.0) {
+        val i = indices(k)
+        result = 31 * result + i
+        val bits = java.lang.Double.doubleToLongBits(v)
+        result = 31 * result + (bits ^ (bits >>> 32)).toInt
+        nnz += 1
       }
       k += 1
     }

From 5b3245d6dff65972fc39c73f90d5cbdf84d19129 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 31 Aug 2015 15:50:41 -0700
Subject: [PATCH 493/633] [SPARK-8472] [ML] [PySpark] Python API for DCT

Add Python API for ml.feature.DCT.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8485 from yanboliang/spark-8472.
---
 python/pyspark/ml/feature.py | 65 +++++++++++++++++++++++++++++++++++-
 1 file changed, 64 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 04b2b2ccc9e55..59300a607815b 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -26,7 +26,7 @@
 from pyspark.mllib.common import inherit_doc
 from pyspark.mllib.linalg import _convert_to_vector
 
-__all__ = ['Binarizer', 'Bucketizer', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
+__all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
            'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer',
            'StandardScaler', 'StandardScalerModel', 'StringIndexer', 'StringIndexerModel',
            'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec', 'Word2VecModel',
@@ -166,6 +166,69 @@ def getSplits(self):
         return self.getOrDefault(self.splits)
 
 
+@inherit_doc
+class DCT(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    A feature transformer that takes the 1D discrete cosine transform
+    of a real vector. No zero padding is performed on the input vector.
+    It returns a real vector of the same length representing the DCT.
+    The return vector is scaled such that the transform matrix is
+    unitary (aka scaled DCT-II).
+
+    More information on
+    `https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II Wikipedia`.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df1 = sqlContext.createDataFrame([(Vectors.dense([5.0, 8.0, 6.0]),)], ["vec"])
+    >>> dct = DCT(inverse=False, inputCol="vec", outputCol="resultVec")
+    >>> df2 = dct.transform(df1)
+    >>> df2.head().resultVec
+    DenseVector([10.969..., -0.707..., -2.041...])
+    >>> df3 = DCT(inverse=True, inputCol="resultVec", outputCol="origVec").transform(df2)
+    >>> df3.head().origVec
+    DenseVector([5.0, 8.0, 6.0])
+    """
+
+    # a placeholder to make it appear in the generated doc
+    inverse = Param(Params._dummy(), "inverse", "Set transformer to perform inverse DCT, " +
+                    "default False.")
+
+    @keyword_only
+    def __init__(self, inverse=False, inputCol=None, outputCol=None):
+        """
+        __init__(self, inverse=False, inputCol=None, outputCol=None)
+        """
+        super(DCT, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.DCT", self.uid)
+        self.inverse = Param(self, "inverse", "Set transformer to perform inverse DCT, " +
+                             "default False.")
+        self._setDefault(inverse=False)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inverse=False, inputCol=None, outputCol=None):
+        """
+        setParams(self, inverse=False, inputCol=None, outputCol=None)
+        Sets params for this DCT.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setInverse(self, value):
+        """
+        Sets the value of :py:attr:`inverse`.
+        """
+        self._paramMap[self.inverse] = value
+        return self
+
+    def getInverse(self):
+        """
+        Gets the value of inverse or its default value.
+        """
+        return self.getOrDefault(self.inverse)
+
+
 @inherit_doc
 class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol):
     """

From 540bdee93103a73736d282b95db6a8cda8f6a2b1 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 31 Aug 2015 15:55:22 -0700
Subject: [PATCH 494/633] [SPARK-10341] [SQL] fix memory starving in unsafe SMJ

In SMJ, the first ExternalSorter could consume all the memory before spilling, then the second can not even acquire the first page.

Before we have a better memory allocator, SMJ should call prepare() before call any compute() of it's children.

cc rxin JoshRosen

Author: Davies Liu <davies@databricks.com>

Closes #8511 from davies/smj_memory.
---
 .../rdd/MapPartitionsWithPreparationRDD.scala | 21 +++++++++++++++++--
 .../spark/rdd/ZippedPartitionsRDD.scala       | 13 ++++++++++++
 ...MapPartitionsWithPreparationRDDSuite.scala | 14 +++++++++----
 3 files changed, 42 insertions(+), 6 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
index b475bd8d79f85..1f2213d0c4346 100644
--- a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.rdd
 
+import scala.collection.mutable.ArrayBuffer
 import scala.reflect.ClassTag
 
 import org.apache.spark.{Partition, Partitioner, TaskContext}
@@ -38,12 +39,28 @@ private[spark] class MapPartitionsWithPreparationRDD[U: ClassTag, T: ClassTag, M
 
   override def getPartitions: Array[Partition] = firstParent[T].partitions
 
+  // In certain join operations, prepare can be called on the same partition multiple times.
+  // In this case, we need to ensure that each call to compute gets a separate prepare argument.
+  private[this] var preparedArguments: ArrayBuffer[M] = new ArrayBuffer[M]
+
+  /**
+   * Prepare a partition for a single call to compute.
+   */
+  def prepare(): Unit = {
+    preparedArguments += preparePartition()
+  }
+
   /**
    * Prepare a partition before computing it from its parent.
    */
   override def compute(partition: Partition, context: TaskContext): Iterator[U] = {
-    val preparedArgument = preparePartition()
+    val prepared =
+      if (preparedArguments.isEmpty) {
+        preparePartition()
+      } else {
+        preparedArguments.remove(0)
+      }
     val parentIterator = firstParent[T].iterator(partition, context)
-    executePartition(context, partition.index, preparedArgument, parentIterator)
+    executePartition(context, partition.index, prepared, parentIterator)
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
index 81f40ad33aa5d..b3c64394abc76 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
@@ -73,6 +73,16 @@ private[spark] abstract class ZippedPartitionsBaseRDD[V: ClassTag](
     super.clearDependencies()
     rdds = null
   }
+
+  /**
+   * Call the prepare method of every parent that has one.
+   * This is needed for reserving execution memory in advance.
+   */
+  protected def tryPrepareParents(): Unit = {
+    rdds.collect {
+      case rdd: MapPartitionsWithPreparationRDD[_, _, _] => rdd.prepare()
+    }
+  }
 }
 
 private[spark] class ZippedPartitionsRDD2[A: ClassTag, B: ClassTag, V: ClassTag](
@@ -84,6 +94,7 @@ private[spark] class ZippedPartitionsRDD2[A: ClassTag, B: ClassTag, V: ClassTag]
   extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2), preservesPartitioning) {
 
   override def compute(s: Partition, context: TaskContext): Iterator[V] = {
+    tryPrepareParents()
     val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions
     f(rdd1.iterator(partitions(0), context), rdd2.iterator(partitions(1), context))
   }
@@ -107,6 +118,7 @@ private[spark] class ZippedPartitionsRDD3
   extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2, rdd3), preservesPartitioning) {
 
   override def compute(s: Partition, context: TaskContext): Iterator[V] = {
+    tryPrepareParents()
     val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions
     f(rdd1.iterator(partitions(0), context),
       rdd2.iterator(partitions(1), context),
@@ -134,6 +146,7 @@ private[spark] class ZippedPartitionsRDD4
   extends ZippedPartitionsBaseRDD[V](sc, List(rdd1, rdd2, rdd3, rdd4), preservesPartitioning) {
 
   override def compute(s: Partition, context: TaskContext): Iterator[V] = {
+    tryPrepareParents()
     val partitions = s.asInstanceOf[ZippedPartitionsPartition].partitions
     f(rdd1.iterator(partitions(0), context),
       rdd2.iterator(partitions(1), context),
diff --git a/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala
index c16930e7d6491..e281e817e493d 100644
--- a/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDDSuite.scala
@@ -46,11 +46,17 @@ class MapPartitionsWithPreparationRDDSuite extends SparkFunSuite with LocalSpark
     }
 
     // Verify that the numbers are pushed in the order expected
-    val result = {
-      new MapPartitionsWithPreparationRDD[Int, Int, Unit](
-        parent, preparePartition, executePartition).collect()
-    }
+    val rdd = new MapPartitionsWithPreparationRDD[Int, Int, Unit](
+      parent, preparePartition, executePartition)
+    val result = rdd.collect()
     assert(result === Array(10, 20, 30))
+
+    TestObject.things.clear()
+    // Zip two of these RDDs, both should be prepared before the parent is executed
+    val rdd2 = new MapPartitionsWithPreparationRDD[Int, Int, Unit](
+      parent, preparePartition, executePartition)
+    val result2 = rdd.zipPartitions(rdd2)((a, b) => a).collect()
+    assert(result2 === Array(10, 10, 20, 30, 20, 30))
   }
 
 }

From fe16fd0b8b717f01151bc659ec3299dab091c97a Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 31 Aug 2015 16:06:38 -0700
Subject: [PATCH 495/633] [SPARK-10349] [ML] OneVsRest use 'when ... otherwise'
 not UDF to generate new label at binary reduction

Currently OneVsRest use UDF to generate new binary label during training.
Considering that [SPARK-7321](https://issues.apache.org/jira/browse/SPARK-7321) has been merged, we can use ```when ... otherwise``` which will be more efficiency.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8519 from yanboliang/spark-10349.
---
 .../org/apache/spark/ml/classification/OneVsRest.scala | 10 ++--------
 1 file changed, 2 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index c62e132f5d533..debc164bf2432 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -91,7 +91,6 @@ final class OneVsRestModel private[ml] (
     // add an accumulator column to store predictions of all the models
     val accColName = "mbc$acc" + UUID.randomUUID().toString
     val initUDF = udf { () => Map[Int, Double]() }
-    val mapType = MapType(IntegerType, DoubleType, valueContainsNull = false)
     val newDataset = dataset.withColumn(accColName, initUDF())
 
     // persist if underlying dataset is not persistent.
@@ -195,16 +194,11 @@ final class OneVsRest(override val uid: String)
 
     // create k columns, one for each binary classifier.
     val models = Range(0, numClasses).par.map { index =>
-      val labelUDF = udf { (label: Double) =>
-        if (label.toInt == index) 1.0 else 0.0
-      }
-
       // generate new label metadata for the binary problem.
-      // TODO: use when ... otherwise after SPARK-7321 is merged
       val newLabelMeta = BinaryAttribute.defaultAttr.withName("label").toMetadata()
       val labelColName = "mc2b$" + index
-      val trainingDataset =
-        multiclassLabeled.withColumn(labelColName, labelUDF(col($(labelCol))), newLabelMeta)
+      val trainingDataset = multiclassLabeled.withColumn(
+        labelColName, when(col($(labelCol)) === index.toDouble, 1.0).otherwise(0.0), newLabelMeta)
       val classifier = getClassifier
       val paramMap = new ParamMap()
       paramMap.put(classifier.labelCol -> labelColName)

From 52ea399e6ee37b7c44aae7709863e006fca88906 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 31 Aug 2015 16:11:27 -0700
Subject: [PATCH 496/633] [SPARK-10355] [ML] [PySpark] Add Python API for
 SQLTransformer

Add Python API for SQLTransformer

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8527 from yanboliang/spark-10355.
---
 python/pyspark/ml/feature.py | 57 ++++++++++++++++++++++++++++++++++--
 1 file changed, 54 insertions(+), 3 deletions(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 59300a607815b..0626281e200a1 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -28,9 +28,9 @@
 
 __all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
            'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer',
-           'StandardScaler', 'StandardScalerModel', 'StringIndexer', 'StringIndexerModel',
-           'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec', 'Word2VecModel',
-           'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
+           'SQLTransformer', 'StandardScaler', 'StandardScalerModel', 'StringIndexer',
+           'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec',
+           'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
 
 
 @inherit_doc
@@ -743,6 +743,57 @@ def getPattern(self):
         return self.getOrDefault(self.pattern)
 
 
+@inherit_doc
+class SQLTransformer(JavaTransformer):
+    """
+    Implements the transforms which are defined by SQL statement.
+    Currently we only support SQL syntax like 'SELECT ... FROM __THIS__'
+    where '__THIS__' represents the underlying table of the input dataset.
+
+    >>> df = sqlContext.createDataFrame([(0, 1.0, 3.0), (2, 2.0, 5.0)], ["id", "v1", "v2"])
+    >>> sqlTrans = SQLTransformer(
+    ...     statement="SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__")
+    >>> sqlTrans.transform(df).head()
+    Row(id=0, v1=1.0, v2=3.0, v3=4.0, v4=3.0)
+    """
+
+    # a placeholder to make it appear in the generated doc
+    statement = Param(Params._dummy(), "statement", "SQL statement")
+
+    @keyword_only
+    def __init__(self, statement=None):
+        """
+        __init__(self, statement=None)
+        """
+        super(SQLTransformer, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.SQLTransformer", self.uid)
+        self.statement = Param(self, "statement", "SQL statement")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, statement=None):
+        """
+        setParams(self, statement=None)
+        Sets params for this SQLTransformer.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setStatement(self, value):
+        """
+        Sets the value of :py:attr:`statement`.
+        """
+        self._paramMap[self.statement] = value
+        return self
+
+    def getStatement(self):
+        """
+        Gets the value of statement or its default value.
+        """
+        return self.getOrDefault(self.statement)
+
+
 @inherit_doc
 class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol):
     """

From d65656c455d19b83c6412571873586b458aa355e Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 31 Aug 2015 18:09:24 -0700
Subject: [PATCH 497/633] [SPARK-10378][SQL][Test] Remove
 HashJoinCompatibilitySuite.

They don't bring much value since we now have better unit test coverage for hash joins. This will also help reduce the test time.

Author: Reynold Xin <rxin@databricks.com>

Closes #8542 from rxin/SPARK-10378.
---
 .../HashJoinCompatibilitySuite.scala          | 169 ------------------
 1 file changed, 169 deletions(-)
 delete mode 100644 sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HashJoinCompatibilitySuite.scala

diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HashJoinCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HashJoinCompatibilitySuite.scala
deleted file mode 100644
index 1a5ba20404c4e..0000000000000
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HashJoinCompatibilitySuite.scala
+++ /dev/null
@@ -1,169 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.sql.hive.execution
-
-import java.io.File
-
-import org.apache.spark.sql.SQLConf
-import org.apache.spark.sql.hive.test.TestHive
-
-/**
- * Runs the test cases that are included in the hive distribution with hash joins.
- */
-class HashJoinCompatibilitySuite extends HiveCompatibilitySuite {
-  override def beforeAll() {
-    super.beforeAll()
-    TestHive.setConf(SQLConf.SORTMERGE_JOIN, false)
-  }
-
-  override def afterAll() {
-    TestHive.setConf(SQLConf.SORTMERGE_JOIN, true)
-    super.afterAll()
-  }
-
-  override def whiteList = Seq(
-    "auto_join0",
-    "auto_join1",
-    "auto_join10",
-    "auto_join11",
-    "auto_join12",
-    "auto_join13",
-    "auto_join14",
-    "auto_join14_hadoop20",
-    "auto_join15",
-    "auto_join17",
-    "auto_join18",
-    "auto_join19",
-    "auto_join2",
-    "auto_join20",
-    "auto_join21",
-    "auto_join22",
-    "auto_join23",
-    "auto_join24",
-    "auto_join25",
-    "auto_join26",
-    "auto_join27",
-    "auto_join28",
-    "auto_join3",
-    "auto_join30",
-    "auto_join31",
-    "auto_join32",
-    "auto_join4",
-    "auto_join5",
-    "auto_join6",
-    "auto_join7",
-    "auto_join8",
-    "auto_join9",
-    "auto_join_filters",
-    "auto_join_nulls",
-    "auto_join_reordering_values",
-    "auto_smb_mapjoin_14",
-    "auto_sortmerge_join_1",
-    "auto_sortmerge_join_10",
-    "auto_sortmerge_join_11",
-    "auto_sortmerge_join_12",
-    "auto_sortmerge_join_13",
-    "auto_sortmerge_join_14",
-    "auto_sortmerge_join_15",
-    "auto_sortmerge_join_16",
-    "auto_sortmerge_join_2",
-    "auto_sortmerge_join_3",
-    "auto_sortmerge_join_4",
-    "auto_sortmerge_join_5",
-    "auto_sortmerge_join_6",
-    "auto_sortmerge_join_7",
-    "auto_sortmerge_join_8",
-    "auto_sortmerge_join_9",
-    "correlationoptimizer1",
-    "correlationoptimizer10",
-    "correlationoptimizer11",
-    "correlationoptimizer13",
-    "correlationoptimizer14",
-    "correlationoptimizer15",
-    "correlationoptimizer2",
-    "correlationoptimizer3",
-    "correlationoptimizer4",
-    "correlationoptimizer6",
-    "correlationoptimizer7",
-    "correlationoptimizer8",
-    "correlationoptimizer9",
-    "join0",
-    "join1",
-    "join10",
-    "join11",
-    "join12",
-    "join13",
-    "join14",
-    "join14_hadoop20",
-    "join15",
-    "join16",
-    "join17",
-    "join18",
-    "join19",
-    "join2",
-    "join20",
-    "join21",
-    "join22",
-    "join23",
-    "join24",
-    "join25",
-    "join26",
-    "join27",
-    "join28",
-    "join29",
-    "join3",
-    "join30",
-    "join31",
-    "join32",
-    "join32_lessSize",
-    "join33",
-    "join34",
-    "join35",
-    "join36",
-    "join37",
-    "join38",
-    "join39",
-    "join4",
-    "join40",
-    "join41",
-    "join5",
-    "join6",
-    "join7",
-    "join8",
-    "join9",
-    "join_1to1",
-    "join_array",
-    "join_casesensitive",
-    "join_empty",
-    "join_filters",
-    "join_hive_626",
-    "join_map_ppr",
-    "join_nulls",
-    "join_nullsafe",
-    "join_rc",
-    "join_reorder2",
-    "join_reorder3",
-    "join_reorder4",
-    "join_star"
-  )
-
-  // Only run those query tests in the realWhileList (do not try other ignored query files).
-  override def testCases: Seq[(String, File)] = super.testCases.filter {
-    case (name, _) => realWhiteList.contains(name)
-  }
-}

From 391e6be0ae883f3ea0fab79463eb8b618af79afb Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Tue, 1 Sep 2015 16:52:59 +0800
Subject: [PATCH 498/633] [SPARK-10301] [SQL] Fixes schema merging for nested
 structs

This PR can be quite challenging to review.  I'm trying to give a detailed description of the problem as well as its solution here.

When reading Parquet files, we need to specify a potentially nested Parquet schema (of type `MessageType`) as requested schema for column pruning.  This Parquet schema is translated from a Catalyst schema (of type `StructType`), which is generated by the query planner and represents all requested columns.  However, this translation can be fairly complicated because of several reasons:

1.  Requested schema must conform to the real schema of the physical file to be read.

    This means we have to tailor the actual file schema of every individual physical Parquet file to be read according to the given Catalyst schema.  Fortunately we are already doing this in Spark 1.5 by pushing request schema conversion to executor side in PR #7231.

1.  Support for schema merging.

    A single Parquet dataset may consist of multiple physical Parquet files come with different but compatible schemas.  This means we may request for a column path that doesn't exist in a physical Parquet file.  All requested column paths can be nested.  For example, for a Parquet file schema

    ```
    message root {
      required group f0 {
        required group f00 {
          required int32 f000;
          required binary f001 (UTF8);
        }
      }
    }
    ```

    we may request for column paths defined in the following schema:

    ```
    message root {
      required group f0 {
        required group f00 {
          required binary f001 (UTF8);
          required float f002;
        }
      }

      optional double f1;
    }
    ```

    Notice that we pruned column path `f0.f00.f000`, but added `f0.f00.f002` and `f1`.

    The good news is that Parquet handles non-existing column paths properly and always returns null for them.

1.  The map from `StructType` to `MessageType` is a one-to-many map.

    This is the most unfortunate part.

    Due to historical reasons (dark histories!), schemas of Parquet files generated by different libraries have different "flavors".  For example, to handle a schema with a single non-nullable column, whose type is an array of non-nullable integers, parquet-protobuf generates the following Parquet schema:

    ```
    message m0 {
      repeated int32 f;
    }
    ```

    while parquet-avro generates another version:

    ```
    message m1 {
      required group f (LIST) {
        repeated int32 array;
      }
    }
    ```

    and parquet-thrift spills this:

    ```
    message m1 {
      required group f (LIST) {
        repeated int32 f_tuple;
      }
    }
    ```

    All of them can be mapped to the following _unique_ Catalyst schema:

    ```
    StructType(
      StructField(
        "f",
        ArrayType(IntegerType, containsNull = false),
        nullable = false))
    ```

    This greatly complicates Parquet requested schema construction, since the path of a given column varies in different cases.  To read the array elements from files with the above schemas, we must use `f` for `m0`, `f.array` for `m1`, and `f.f_tuple` for `m2`.

In earlier Spark versions, we didn't try to fix this issue properly.  Spark 1.4 and prior versions simply translate the Catalyst schema in a way more or less compatible with parquet-hive and parquet-avro, but is broken in many other cases.  Earlier revisions of Spark 1.5 only try to tailor the Parquet file schema at the first level, and ignore nested ones.  This caused [SPARK-10301] [spark-10301] as well as [SPARK-10005] [spark-10005].  In PR #8228, I tried to avoid the hard part of the problem and made a minimum change in `CatalystRowConverter` to fix SPARK-10005.  However, when taking SPARK-10301 into consideration, keeping hacking `CatalystRowConverter` doesn't seem to be a good idea.  So this PR is an attempt to fix the problem in a proper way.

For a given physical Parquet file with schema `ps` and a compatible Catalyst requested schema `cs`, we use the following algorithm to tailor `ps` to get the result Parquet requested schema `ps'`:

For a leaf column path `c` in `cs`:

- if `c` exists in `cs` and a corresponding Parquet column path `c'` can be found in `ps`, `c'` should be included in `ps'`;
- otherwise, we convert `c` to a Parquet column path `c"` using `CatalystSchemaConverter`, and include `c"` in `ps'`;
- no other column paths should exist in `ps'`.

Then comes the most tedious part:

> Given `cs`, `ps`, and `c`, how to locate `c'` in `ps`?

Unfortunately, there's no quick answer, and we have to enumerate all possible structures defined in parquet-format spec.  They are:

1.  the standard structure of nested types, and
1.  cases defined in all backwards-compatibility rules for `LIST` and `MAP`.

The core part of this PR is `CatalystReadSupport.clipParquetType()`, which tailors a given Parquet file schema according to a requested schema in its Catalyst form.  Backwards-compatibility rules of `LIST` and `MAP` are covered in `clipParquetListType()` and `clipParquetMapType()` respectively.  The column path selection algorithm is implemented in `clipParquetGroupFields()`.

With this PR, we no longer need to do schema tailoring in `CatalystReadSupport` and `CatalystRowConverter`.  Another benefit is that, now we can also read Parquet datasets consist of files with different physical Parquet schema but share the same logical schema, for example, files generated by different Parquet libraries.  This situation is illustrated by [this test case] [test-case].

[spark-10301]: https://issues.apache.org/jira/browse/SPARK-10301
[spark-10005]: https://issues.apache.org/jira/browse/SPARK-10005
[test-case]: https://github.com/liancheng/spark/commit/38644d8a45175cbdf20d2ace021c2c2544a50ab3#diff-a9b98e28ce3ae30641829dffd1173be2R26

Author: Cheng Lian <lian@databricks.com>

Closes #8509 from liancheng/spark-10301/fix-parquet-requested-schema.
---
 .../parquet/CatalystReadSupport.scala         | 235 +++++++++----
 .../parquet/CatalystRowConverter.scala        |  51 +--
 .../parquet/CatalystSchemaConverter.scala     |  14 +-
 .../ParquetAvroCompatibilitySuite.scala       |   1 +
 .../ParquetInteroperabilitySuite.scala        |  90 +++++
 .../parquet/ParquetQuerySuite.scala           |  77 +++++
 .../parquet/ParquetSchemaSuite.scala          | 310 ++++++++++++++++++
 7 files changed, 653 insertions(+), 125 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index 0a6bb44445f6e..dc4ff06df6f22 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -19,17 +19,18 @@ package org.apache.spark.sql.execution.datasources.parquet
 
 import java.util.{Map => JMap}
 
-import scala.collection.JavaConverters._
+import scala.collection.JavaConverters.{collectionAsScalaIterableConverter, mapAsJavaMapConverter, mapAsScalaMapConverter}
 
 import org.apache.hadoop.conf.Configuration
 import org.apache.parquet.hadoop.api.ReadSupport.ReadContext
 import org.apache.parquet.hadoop.api.{InitContext, ReadSupport}
 import org.apache.parquet.io.api.RecordMaterializer
-import org.apache.parquet.schema.MessageType
+import org.apache.parquet.schema.Type.Repetition
+import org.apache.parquet.schema._
 
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.types.StructType
+import org.apache.spark.sql.types._
 
 private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with Logging {
   // Called after `init()` when initializing Parquet record reader.
@@ -81,70 +82,10 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
     // `StructType` containing all requested columns.
     val maybeRequestedSchema = Option(conf.get(CatalystReadSupport.SPARK_ROW_REQUESTED_SCHEMA))
 
-    // Below we construct a Parquet schema containing all requested columns.  This schema tells
-    // Parquet which columns to read.
-    //
-    // If `maybeRequestedSchema` is defined, we assemble an equivalent Parquet schema.  Otherwise,
-    // we have to fallback to the full file schema which contains all columns in the file.
-    // Obviously this may waste IO bandwidth since it may read more columns than requested.
-    //
-    // Two things to note:
-    //
-    // 1. It's possible that some requested columns don't exist in the target Parquet file.  For
-    //    example, in the case of schema merging, the globally merged schema may contain extra
-    //    columns gathered from other Parquet files.  These columns will be simply filled with nulls
-    //    when actually reading the target Parquet file.
-    //
-    // 2. When `maybeRequestedSchema` is available, we can't simply convert the Catalyst schema to
-    //    Parquet schema using `CatalystSchemaConverter`, because the mapping is not unique due to
-    //    non-standard behaviors of some Parquet libraries/tools.  For example, a Parquet file
-    //    containing a single integer array field `f1` may have the following legacy 2-level
-    //    structure:
-    //
-    //      message root {
-    //        optional group f1 (LIST) {
-    //          required INT32 element;
-    //        }
-    //      }
-    //
-    //    while `CatalystSchemaConverter` may generate a standard 3-level structure:
-    //
-    //      message root {
-    //        optional group f1 (LIST) {
-    //          repeated group list {
-    //            required INT32 element;
-    //          }
-    //        }
-    //      }
-    //
-    //    Apparently, we can't use the 2nd schema to read the target Parquet file as they have
-    //    different physical structures.
     val parquetRequestedSchema =
       maybeRequestedSchema.fold(context.getFileSchema) { schemaString =>
-        val toParquet = new CatalystSchemaConverter(conf)
-        val fileSchema = context.getFileSchema.asGroupType()
-        val fileFieldNames = fileSchema.getFields.asScala.map(_.getName).toSet
-
-        StructType
-          // Deserializes the Catalyst schema of requested columns
-          .fromString(schemaString)
-          .map { field =>
-            if (fileFieldNames.contains(field.name)) {
-              // If the field exists in the target Parquet file, extracts the field type from the
-              // full file schema and makes a single-field Parquet schema
-              new MessageType("root", fileSchema.getType(field.name))
-            } else {
-              // Otherwise, just resorts to `CatalystSchemaConverter`
-              toParquet.convert(StructType(Array(field)))
-            }
-          }
-          // Merges all single-field Parquet schemas to form a complete schema for all requested
-          // columns.  Note that it's possible that no columns are requested at all (e.g., count
-          // some partition column of a partitioned Parquet table). That's why `fold` is used here
-          // and always fallback to an empty Parquet schema.
-          .fold(new MessageType("root")) {
-            _ union _
-          }
+        val catalystRequestedSchema = StructType.fromString(schemaString)
+        CatalystReadSupport.clipParquetSchema(context.getFileSchema, catalystRequestedSchema)
       }
 
     val metadata =
@@ -160,4 +101,168 @@ private[parquet] object CatalystReadSupport {
   val SPARK_ROW_REQUESTED_SCHEMA = "org.apache.spark.sql.parquet.row.requested_schema"
 
   val SPARK_METADATA_KEY = "org.apache.spark.sql.parquet.row.metadata"
+
+  /**
+   * Tailors `parquetSchema` according to `catalystSchema` by removing column paths don't exist
+   * in `catalystSchema`, and adding those only exist in `catalystSchema`.
+   */
+  def clipParquetSchema(parquetSchema: MessageType, catalystSchema: StructType): MessageType = {
+    val clippedParquetFields = clipParquetGroupFields(parquetSchema.asGroupType(), catalystSchema)
+    Types.buildMessage().addFields(clippedParquetFields: _*).named("root")
+  }
+
+  private def clipParquetType(parquetType: Type, catalystType: DataType): Type = {
+    catalystType match {
+      case t: ArrayType if !isPrimitiveCatalystType(t.elementType) =>
+        // Only clips array types with nested type as element type.
+        clipParquetListType(parquetType.asGroupType(), t.elementType)
+
+      case t: MapType if !isPrimitiveCatalystType(t.valueType) =>
+        // Only clips map types with nested type as value type.
+        clipParquetMapType(parquetType.asGroupType(), t.keyType, t.valueType)
+
+      case t: StructType =>
+        clipParquetGroup(parquetType.asGroupType(), t)
+
+      case _ =>
+        parquetType
+    }
+  }
+
+  /**
+   * Whether a Catalyst [[DataType]] is primitive.  Primitive [[DataType]] is not equivalent to
+   * [[AtomicType]].  For example, [[CalendarIntervalType]] is primitive, but it's not an
+   * [[AtomicType]].
+   */
+  private def isPrimitiveCatalystType(dataType: DataType): Boolean = {
+    dataType match {
+      case _: ArrayType | _: MapType | _: StructType => false
+      case _ => true
+    }
+  }
+
+  /**
+   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[ArrayType]].  The element type
+   * of the [[ArrayType]] should also be a nested type, namely an [[ArrayType]], a [[MapType]], or a
+   * [[StructType]].
+   */
+  private def clipParquetListType(parquetList: GroupType, elementType: DataType): Type = {
+    // Precondition of this method, should only be called for lists with nested element types.
+    assert(!isPrimitiveCatalystType(elementType))
+
+    // Unannotated repeated group should be interpreted as required list of required element, so
+    // list element type is just the group itself.  Clip it.
+    if (parquetList.getOriginalType == null && parquetList.isRepetition(Repetition.REPEATED)) {
+      clipParquetType(parquetList, elementType)
+    } else {
+      assert(
+        parquetList.getOriginalType == OriginalType.LIST,
+        "Invalid Parquet schema. " +
+          "Original type of annotated Parquet lists must be LIST: " +
+          parquetList.toString)
+
+      assert(
+        parquetList.getFieldCount == 1 && parquetList.getType(0).isRepetition(Repetition.REPEATED),
+        "Invalid Parquet schema. " +
+          "LIST-annotated group should only have exactly one repeated field: " +
+          parquetList)
+
+      // Precondition of this method, should only be called for lists with nested element types.
+      assert(!parquetList.getType(0).isPrimitive)
+
+      val repeatedGroup = parquetList.getType(0).asGroupType()
+
+      // If the repeated field is a group with multiple fields, or the repeated field is a group
+      // with one field and is named either "array" or uses the LIST-annotated group's name with
+      // "_tuple" appended then the repeated type is the element type and elements are required.
+      // Build a new LIST-annotated group with clipped `repeatedGroup` as element type and the
+      // only field.
+      if (
+        repeatedGroup.getFieldCount > 1 ||
+        repeatedGroup.getName == "array" ||
+        repeatedGroup.getName == parquetList.getName + "_tuple"
+      ) {
+        Types
+          .buildGroup(parquetList.getRepetition)
+          .as(OriginalType.LIST)
+          .addField(clipParquetType(repeatedGroup, elementType))
+          .named(parquetList.getName)
+      } else {
+        // Otherwise, the repeated field's type is the element type with the repeated field's
+        // repetition.
+        Types
+          .buildGroup(parquetList.getRepetition)
+          .as(OriginalType.LIST)
+          .addField(
+            Types
+              .repeatedGroup()
+              .addField(clipParquetType(repeatedGroup.getType(0), elementType))
+              .named(repeatedGroup.getName))
+          .named(parquetList.getName)
+      }
+    }
+  }
+
+  /**
+   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[MapType]].  The value type
+   * of the [[MapType]] should also be a nested type, namely an [[ArrayType]], a [[MapType]], or a
+   * [[StructType]].  Note that key type of any [[MapType]] is always a primitive type.
+   */
+  private def clipParquetMapType(
+      parquetMap: GroupType, keyType: DataType, valueType: DataType): GroupType = {
+    // Precondition of this method, should only be called for maps with nested value types.
+    assert(!isPrimitiveCatalystType(valueType))
+
+    val repeatedGroup = parquetMap.getType(0).asGroupType()
+    val parquetKeyType = repeatedGroup.getType(0)
+    val parquetValueType = repeatedGroup.getType(1)
+
+    val clippedRepeatedGroup =
+      Types
+        .repeatedGroup()
+        .as(repeatedGroup.getOriginalType)
+        .addField(parquetKeyType)
+        .addField(clipParquetType(parquetValueType, valueType))
+        .named(repeatedGroup.getName)
+
+    Types
+      .buildGroup(parquetMap.getRepetition)
+      .as(parquetMap.getOriginalType)
+      .addField(clippedRepeatedGroup)
+      .named(parquetMap.getName)
+  }
+
+  /**
+   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[StructType]].
+   *
+   * @return A clipped [[GroupType]], which has at least one field.
+   * @note Parquet doesn't allow creating empty [[GroupType]] instances except for empty
+   *       [[MessageType]].  Because it's legal to construct an empty requested schema for column
+   *       pruning.
+   */
+  private def clipParquetGroup(parquetRecord: GroupType, structType: StructType): GroupType = {
+    val clippedParquetFields = clipParquetGroupFields(parquetRecord, structType)
+    Types
+      .buildGroup(parquetRecord.getRepetition)
+      .as(parquetRecord.getOriginalType)
+      .addFields(clippedParquetFields: _*)
+      .named(parquetRecord.getName)
+  }
+
+  /**
+   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[StructType]].
+   *
+   * @return A list of clipped [[GroupType]] fields, which can be empty.
+   */
+  private def clipParquetGroupFields(
+      parquetRecord: GroupType, structType: StructType): Seq[Type] = {
+    val parquetFieldMap = parquetRecord.getFields.asScala.map(f => f.getName -> f).toMap
+    val toParquet = new CatalystSchemaConverter(followParquetFormatSpec = true)
+    structType.map { f =>
+      parquetFieldMap
+        .get(f.name)
+        .map(clipParquetType(_, f.dataType))
+        .getOrElse(toParquet.convertField(f))
+    }
+  }
 }
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index fe13dfbbed385..f17e794b76650 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -113,31 +113,6 @@ private[parquet] class CatalystPrimitiveConverter(val updater: ParentContainerUp
  * When used as a root converter, [[NoopUpdater]] should be used since root converters don't have
  * any "parent" container.
  *
- * @note Constructor argument [[parquetType]] refers to requested fields of the actual schema of the
- *       Parquet file being read, while constructor argument [[catalystType]] refers to requested
- *       fields of the global schema.  The key difference is that, in case of schema merging,
- *       [[parquetType]] can be a subset of [[catalystType]].  For example, it's possible to have
- *       the following [[catalystType]]:
- *       {{{
- *         new StructType()
- *           .add("f1", IntegerType, nullable = false)
- *           .add("f2", StringType, nullable = true)
- *           .add("f3", new StructType()
- *             .add("f31", DoubleType, nullable = false)
- *             .add("f32", IntegerType, nullable = true)
- *             .add("f33", StringType, nullable = true), nullable = false)
- *       }}}
- *       and the following [[parquetType]] (`f2` and `f32` are missing):
- *       {{{
- *         message root {
- *           required int32 f1;
- *           required group f3 {
- *             required double f31;
- *             optional binary f33 (utf8);
- *           }
- *         }
- *       }}}
- *
  * @param parquetType Parquet schema of Parquet records
  * @param catalystType Spark SQL schema that corresponds to the Parquet record type
  * @param updater An updater which propagates converted field values to the parent container
@@ -179,31 +154,7 @@ private[parquet] class CatalystRowConverter(
 
   // Converters for each field.
   private val fieldConverters: Array[Converter with HasParentContainerUpdater] = {
-    // In case of schema merging, `parquetType` can be a subset of `catalystType`.  We need to pad
-    // those missing fields and create converters for them, although values of these fields are
-    // always null.
-    val paddedParquetFields = {
-      val parquetFields = parquetType.getFields.asScala
-      val parquetFieldNames = parquetFields.map(_.getName).toSet
-      val missingFields = catalystType.filterNot(f => parquetFieldNames.contains(f.name))
-
-      // We don't need to worry about feature flag arguments like `assumeBinaryIsString` when
-      // creating the schema converter here, since values of missing fields are always null.
-      val toParquet = new CatalystSchemaConverter()
-
-      (parquetFields ++ missingFields.map(toParquet.convertField)).sortBy { f =>
-        catalystType.indexWhere(_.name == f.getName)
-      }
-    }
-
-    if (paddedParquetFields.length != catalystType.length) {
-      throw new UnsupportedOperationException(
-        "A Parquet file's schema has different number of fields with the table schema. " +
-          "Please enable schema merging by setting \"mergeSchema\" to true when load " +
-          "a Parquet dataset or set spark.sql.parquet.mergeSchema to true in SQLConf.")
-    }
-
-    paddedParquetFields.zip(catalystType).zipWithIndex.map {
+    parquetType.getFields.asScala.zip(catalystType).zipWithIndex.map {
       case ((parquetFieldType, catalystField), ordinal) =>
         // Converted field value should be set to the `ordinal`-th cell of `currentRow`
         newConverter(parquetFieldType, catalystField.dataType, new RowUpdater(currentRow, ordinal))
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index be6c0545f5a0a..a21ab1dbb25d1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -55,16 +55,10 @@ import org.apache.spark.sql.{AnalysisException, SQLConf}
  *        to old style non-standard behaviors.
  */
 private[parquet] class CatalystSchemaConverter(
-    private val assumeBinaryIsString: Boolean,
-    private val assumeInt96IsTimestamp: Boolean,
-    private val followParquetFormatSpec: Boolean) {
-
-  // Only used when constructing converter for converting Spark SQL schema to Parquet schema, in
-  // which case `assumeInt96IsTimestamp` and `assumeBinaryIsString` are irrelevant.
-  def this() = this(
-    assumeBinaryIsString = SQLConf.PARQUET_BINARY_AS_STRING.defaultValue.get,
-    assumeInt96IsTimestamp = SQLConf.PARQUET_INT96_AS_TIMESTAMP.defaultValue.get,
-    followParquetFormatSpec = SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.defaultValue.get)
+    assumeBinaryIsString: Boolean = SQLConf.PARQUET_BINARY_AS_STRING.defaultValue.get,
+    assumeInt96IsTimestamp: Boolean = SQLConf.PARQUET_INT96_AS_TIMESTAMP.defaultValue.get,
+    followParquetFormatSpec: Boolean = SQLConf.PARQUET_FOLLOW_PARQUET_FORMAT_SPEC.defaultValue.get
+) {
 
   def this(conf: SQLConf) = this(
     assumeBinaryIsString = conf.isParquetBinaryAsString,
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
index bd7cf8c10abef..36b929ee1f409 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetAvroCompatibilitySuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution.datasources.parquet
 
+import java.io.File
 import java.nio.ByteBuffer
 import java.util.{List => JList, Map => JMap}
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala
new file mode 100644
index 0000000000000..83b65fb419ed3
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala
@@ -0,0 +1,90 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.parquet
+
+import java.io.File
+
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.test.SharedSQLContext
+
+class ParquetInteroperabilitySuite extends ParquetCompatibilityTest with SharedSQLContext {
+  test("parquet files with different physical schemas but share the same logical schema") {
+    import ParquetCompatibilityTest._
+
+    // This test case writes two Parquet files, both representing the following Catalyst schema
+    //
+    //   StructType(
+    //     StructField(
+    //       "f",
+    //       ArrayType(IntegerType, containsNull = false),
+    //       nullable = false))
+    //
+    // The first Parquet file comes with parquet-avro style 2-level LIST-annotated group, while the
+    // other one comes with parquet-protobuf style 1-level unannotated primitive field.
+    withTempDir { dir =>
+      val avroStylePath = new File(dir, "avro-style").getCanonicalPath
+      val protobufStylePath = new File(dir, "protobuf-style").getCanonicalPath
+
+      val avroStyleSchema =
+        """message avro_style {
+          |  required group f (LIST) {
+          |    repeated int32 array;
+          |  }
+          |}
+        """.stripMargin
+
+      writeDirect(avroStylePath, avroStyleSchema, { rc =>
+        rc.message {
+          rc.field("f", 0) {
+            rc.group {
+              rc.field("array", 0) {
+                rc.addInteger(0)
+                rc.addInteger(1)
+              }
+            }
+          }
+        }
+      })
+
+      logParquetSchema(avroStylePath)
+
+      val protobufStyleSchema =
+        """message protobuf_style {
+          |  repeated int32 f;
+          |}
+        """.stripMargin
+
+      writeDirect(protobufStylePath, protobufStyleSchema, { rc =>
+        rc.message {
+          rc.field("f", 0) {
+            rc.addInteger(2)
+            rc.addInteger(3)
+          }
+        }
+      })
+
+      logParquetSchema(protobufStylePath)
+
+      checkAnswer(
+        sqlContext.read.parquet(dir.getCanonicalPath),
+        Seq(
+          Row(Seq(0, 1)),
+          Row(Seq(2, 3))))
+    }
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index b7b70c2bbbd5c..a379523d67f80 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -229,4 +229,81 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
       }
     }
   }
+
+  test("SPARK-10301 Clipping nested structs in requested schema") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id) AS s")
+        .coalesce(1)
+
+      df.write.mode("append").parquet(path)
+
+      val userDefinedSchema = new StructType()
+        .add("s", new StructType().add("a", LongType, nullable = true), nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(0)))
+    }
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      val df1 = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id) AS s")
+        .coalesce(1)
+
+      val df2 = sqlContext
+        .range(1, 2)
+        .selectExpr("NAMED_STRUCT('b', id, 'c', id) AS s")
+        .coalesce(1)
+
+      df1.write.parquet(path)
+      df2.write.mode(SaveMode.Append).parquet(path)
+
+      val userDefinedSchema = new StructType()
+        .add("s",
+          new StructType()
+            .add("a", LongType, nullable = true)
+            .add("c", LongType, nullable = true),
+          nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Seq(
+          Row(Row(0, null)),
+          Row(Row(null, 1))))
+    }
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      val df = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', ARRAY(NAMED_STRUCT('b', id, 'c', id))) AS s")
+        .coalesce(1)
+
+      df.write.parquet(path)
+
+      val userDefinedSchema = new StructType()
+        .add("s",
+          new StructType()
+            .add(
+              "a",
+              ArrayType(
+                new StructType()
+                  .add("b", LongType, nullable = true)
+                  .add("d", StringType, nullable = true),
+                containsNull = true),
+              nullable = true),
+          nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(Seq(Row(0, null)))))
+    }
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 9dcbc1a047bea..28c59a4abdd76 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -22,6 +22,7 @@ import scala.reflect.runtime.universe.TypeTag
 
 import org.apache.parquet.schema.MessageTypeParser
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.ScalaReflection
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
@@ -941,4 +942,313 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
       |  optional fixed_len_byte_array(8) f1 (DECIMAL(18, 3));
       |}
     """.stripMargin)
+
+  private def testSchemaClipping(
+      testName: String,
+      parquetSchema: String,
+      catalystSchema: StructType,
+      expectedSchema: String): Unit = {
+    test(s"Clipping - $testName") {
+      val expected = MessageTypeParser.parseMessageType(expectedSchema)
+      val actual = CatalystReadSupport.clipParquetSchema(
+        MessageTypeParser.parseMessageType(parquetSchema), catalystSchema)
+
+      try {
+        expected.checkContains(actual)
+        actual.checkContains(expected)
+      } catch { case cause: Throwable =>
+        fail(
+          s"""Expected clipped schema:
+             |$expected
+             |Actual clipped schema:
+             |$actual
+           """.stripMargin,
+          cause)
+      }
+    }
+  }
+
+  testSchemaClipping(
+    "simple nested struct",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    optional int32 f00;
+        |    optional int32 f01;
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f0Type = new StructType().add("f00", IntegerType, nullable = true)
+      new StructType()
+        .add("f0", f0Type, nullable = false)
+        .add("f1", IntegerType, nullable = true)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    optional int32 f00;
+        |  }
+        |  optional int32 f1;
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "parquet-protobuf style array",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    repeated binary f00 (UTF8);
+        |    repeated group f01 {
+        |      optional int32 f010;
+        |      optional double f011;
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f11Type = new StructType().add("f011", DoubleType, nullable = true)
+      val f01Type = ArrayType(StringType, containsNull = false)
+      val f0Type = new StructType()
+        .add("f00", f01Type, nullable = false)
+        .add("f01", f11Type, nullable = false)
+      val f1Type = ArrayType(IntegerType, containsNull = true)
+      new StructType()
+        .add("f0", f0Type, nullable = false)
+        .add("f1", f1Type, nullable = true)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    repeated binary f00 (UTF8);
+        |    repeated group f01 {
+        |      optional double f011;
+        |    }
+        |  }
+        |
+        |  optional group f1 (LIST) {
+        |    repeated group list {
+        |      optional int32 element;
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "parquet-thrift style array",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 {
+        |      repeated binary f00_tuple (UTF8);
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group f01_tuple {
+        |        optional int32 f010;
+        |        optional double f011;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f11ElementType = new StructType()
+        .add("f011", DoubleType, nullable = true)
+        .add("f012", LongType, nullable = true)
+
+      val f0Type = new StructType()
+        .add("f00", ArrayType(StringType, containsNull = false), nullable = false)
+        .add("f01", ArrayType(f11ElementType, containsNull = false), nullable = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 {
+        |      repeated binary f00_tuple (UTF8);
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group f01_tuple {
+        |        optional double f011;
+        |        optional int64 f012;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "parquet-avro style array",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 {
+        |      repeated binary array (UTF8);
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group array {
+        |        optional int32 f010;
+        |        optional double f011;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f11ElementType = new StructType()
+        .add("f011", DoubleType, nullable = true)
+        .add("f012", LongType, nullable = true)
+
+      val f0Type = new StructType()
+        .add("f00", ArrayType(StringType, containsNull = false), nullable = false)
+        .add("f01", ArrayType(f11ElementType, containsNull = false), nullable = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 {
+        |      repeated binary array (UTF8);
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group array {
+        |        optional double f011;
+        |        optional int64 f012;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "parquet-hive style array",
+
+    parquetSchema =
+      """message root {
+        |  optional group f0 {
+        |    optional group f00 (LIST) {
+        |      repeated group bag {
+        |        optional binary array_element;
+        |      }
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group bag {
+        |        optional group array_element {
+        |          optional int32 f010;
+        |          optional double f011;
+        |        }
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f01ElementType = new StructType()
+        .add("f011", DoubleType, nullable = true)
+        .add("f012", LongType, nullable = true)
+
+      val f0Type = new StructType()
+        .add("f00", ArrayType(StringType, containsNull = true), nullable = true)
+        .add("f01", ArrayType(f01ElementType, containsNull = true), nullable = true)
+
+      new StructType().add("f0", f0Type, nullable = true)
+    },
+
+    expectedSchema =
+      """message root {
+        |  optional group f0 {
+        |    optional group f00 (LIST) {
+        |      repeated group bag {
+        |        optional binary array_element;
+        |      }
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group bag {
+        |        optional group array_element {
+        |          optional double f011;
+        |          optional int64 f012;
+        |        }
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "2-level list of required struct",
+
+    parquetSchema =
+      s"""message root {
+         |  required group f0 {
+         |    required group f00 (LIST) {
+         |      repeated group element {
+         |        required int32 f000;
+         |        optional int64 f001;
+         |      }
+         |    }
+         |  }
+         |}
+       """.stripMargin,
+
+    catalystSchema = {
+      val f00ElementType =
+        new StructType()
+          .add("f001", LongType, nullable = true)
+          .add("f002", DoubleType, nullable = false)
+
+      val f00Type = ArrayType(f00ElementType, containsNull = false)
+      val f0Type = new StructType().add("f00", f00Type, nullable = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      s"""message root {
+         |  required group f0 {
+         |    required group f00 (LIST) {
+         |      repeated group element {
+         |        optional int64 f001;
+         |        required double f002;
+         |      }
+         |    }
+         |  }
+         |}
+       """.stripMargin)
+
+  testSchemaClipping(
+    "empty requested schema",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    required int32 f00;
+        |    required int64 f01;
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = new StructType(),
+
+    expectedSchema = "message root {}")
 }

From e6e483cc4de740c46398385b03ffe0e662edae39 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 1 Sep 2015 10:48:57 -0700
Subject: [PATCH 499/633] [SPARK-9679] [ML] [PYSPARK] Add Python API for Stop
 Words Remover

Add a python API for the Stop Words Remover.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8118 from holdenk/SPARK-9679-python-StopWordsRemover.
---
 .../spark/ml/feature/StopWordsRemover.scala   |  6 +-
 .../ml/feature/StopWordsRemoverSuite.scala    |  2 +-
 python/pyspark/ml/feature.py                  | 73 ++++++++++++++++++-
 python/pyspark/ml/tests.py                    | 20 ++++-
 4 files changed, 93 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
index 5d77ea08db657..7da430c7d16df 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
@@ -29,14 +29,14 @@ import org.apache.spark.sql.types.{ArrayType, StringType, StructField, StructTyp
 /**
  * stop words list
  */
-private object StopWords {
+private[spark] object StopWords {
 
   /**
    * Use the same default stopwords list as scikit-learn.
    * The original list can be found from "Glasgow Information Retrieval Group"
    * [[http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words]]
    */
-  val EnglishStopWords = Array( "a", "about", "above", "across", "after", "afterwards", "again",
+  val English = Array( "a", "about", "above", "across", "after", "afterwards", "again",
     "against", "all", "almost", "alone", "along", "already", "also", "although", "always",
     "am", "among", "amongst", "amoungst", "amount", "an", "and", "another",
     "any", "anyhow", "anyone", "anything", "anyway", "anywhere", "are",
@@ -121,7 +121,7 @@ class StopWordsRemover(override val uid: String)
   /** @group getParam */
   def getCaseSensitive: Boolean = $(caseSensitive)
 
-  setDefault(stopWords -> StopWords.EnglishStopWords, caseSensitive -> false)
+  setDefault(stopWords -> StopWords.English, caseSensitive -> false)
 
   override def transform(dataset: DataFrame): DataFrame = {
     val outputSchema = transformSchema(dataset.schema)
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala
index f01306f89cb5f..e0d433f566c25 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StopWordsRemoverSuite.scala
@@ -65,7 +65,7 @@ class StopWordsRemoverSuite extends SparkFunSuite with MLlibTestSparkContext {
   }
 
   test("StopWordsRemover with additional words") {
-    val stopWords = StopWords.EnglishStopWords ++ Array("python", "scala")
+    val stopWords = StopWords.English ++ Array("python", "scala")
     val remover = new StopWordsRemover()
       .setInputCol("raw")
       .setOutputCol("filtered")
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 0626281e200a1..d955307e27efd 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -22,7 +22,7 @@
 from pyspark.rdd import ignore_unicode_prefix
 from pyspark.ml.param.shared import *
 from pyspark.ml.util import keyword_only
-from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer
+from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer, _jvm
 from pyspark.mllib.common import inherit_doc
 from pyspark.mllib.linalg import _convert_to_vector
 
@@ -30,7 +30,7 @@
            'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer',
            'SQLTransformer', 'StandardScaler', 'StandardScalerModel', 'StringIndexer',
            'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec',
-           'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel']
+           'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel', 'StopWordsRemover']
 
 
 @inherit_doc
@@ -933,6 +933,75 @@ class StringIndexerModel(JavaModel):
     """
 
 
+class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    .. note:: Experimental
+
+    A feature transformer that filters out stop words from input.
+    Note: null values from input array are preserved unless adding null to stopWords explicitly.
+    """
+    # a placeholder to make the stopwords show up in generated doc
+    stopWords = Param(Params._dummy(), "stopWords", "The words to be filtered out")
+    caseSensitive = Param(Params._dummy(), "caseSensitive", "whether to do a case sensitive " +
+                          "comparison over the stop words")
+
+    @keyword_only
+    def __init__(self, inputCol=None, outputCol=None, stopWords=None,
+                 caseSensitive=False):
+        """
+        __init__(self, inputCol=None, outputCol=None, stopWords=None,\
+                 caseSensitive=false)
+        """
+        super(StopWordsRemover, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StopWordsRemover",
+                                            self.uid)
+        self.stopWords = Param(self, "stopWords", "The words to be filtered out")
+        self.caseSensitive = Param(self, "caseSensitive", "whether to do a case " +
+                                   "sensitive comparison over the stop words")
+        stopWordsObj = _jvm().org.apache.spark.ml.feature.StopWords
+        defaultStopWords = stopWordsObj.English()
+        self._setDefault(stopWords=defaultStopWords)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inputCol=None, outputCol=None, stopWords=None,
+                  caseSensitive=False):
+        """
+        setParams(self, inputCol="input", outputCol="output", stopWords=None,\
+                  caseSensitive=false)
+        Sets params for this StopWordRemover.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setStopWords(self, value):
+        """
+        Specify the stopwords to be filtered.
+        """
+        self._paramMap[self.stopWords] = value
+        return self
+
+    def getStopWords(self):
+        """
+        Get the stopwords.
+        """
+        return self.getOrDefault(self.stopWords)
+
+    def setCaseSensitive(self, value):
+        """
+        Set whether to do a case sensitive comparison over the stop words
+        """
+        self._paramMap[self.caseSensitive] = value
+        return self
+
+    def getCaseSensitive(self):
+        """
+        Get whether to do a case sensitive comparison over the stop words.
+        """
+        return self.getOrDefault(self.caseSensitive)
+
+
 @inherit_doc
 @ignore_unicode_prefix
 class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol):
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index 60e4237293adc..b892318f50bd9 100644
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -31,7 +31,7 @@
     import unittest
 
 from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
-from pyspark.sql import DataFrame, SQLContext
+from pyspark.sql import DataFrame, SQLContext, Row
 from pyspark.sql.functions import rand
 from pyspark.ml.evaluation import RegressionEvaluator
 from pyspark.ml.param import Param, Params
@@ -258,7 +258,7 @@ def test_idf(self):
     def test_ngram(self):
         sqlContext = SQLContext(self.sc)
         dataset = sqlContext.createDataFrame([
-            ([["a", "b", "c", "d", "e"]])], ["input"])
+            Row(input=["a", "b", "c", "d", "e"])])
         ngram0 = NGram(n=4, inputCol="input", outputCol="output")
         self.assertEqual(ngram0.getN(), 4)
         self.assertEqual(ngram0.getInputCol(), "input")
@@ -266,6 +266,22 @@ def test_ngram(self):
         transformedDF = ngram0.transform(dataset)
         self.assertEquals(transformedDF.head().output, ["a b c d", "b c d e"])
 
+    def test_stopwordsremover(self):
+        sqlContext = SQLContext(self.sc)
+        dataset = sqlContext.createDataFrame([Row(input=["a", "panda"])])
+        stopWordRemover = StopWordsRemover(inputCol="input", outputCol="output")
+        # Default
+        self.assertEquals(stopWordRemover.getInputCol(), "input")
+        transformedDF = stopWordRemover.transform(dataset)
+        self.assertEquals(transformedDF.head().output, ["panda"])
+        # Custom
+        stopwords = ["panda"]
+        stopWordRemover.setStopWords(stopwords)
+        self.assertEquals(stopWordRemover.getInputCol(), "input")
+        self.assertEquals(stopWordRemover.getStopWords(), stopwords)
+        transformedDF = stopWordRemover.transform(dataset)
+        self.assertEquals(transformedDF.head().output, ["a"])
+
 
 class HasInducedError(Params):
 

From 3f63bd6023edcc9af268933a235f34e10bc3d2ba Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Tue, 1 Sep 2015 20:06:01 +0100
Subject: [PATCH 500/633] [SPARK-10398] [DOCS] Migrate Spark download page to
 use new lua mirroring scripts

Migrate Apache download closer.cgi refs to new closer.lua

This is the bit of the change that affects the project docs; I'm implementing the changes to the Apache site separately.

Author: Sean Owen <sowen@cloudera.com>

Closes #8557 from srowen/SPARK-10398.
---
 docker/spark-mesos/Dockerfile | 2 +-
 docs/running-on-mesos.md      | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/docker/spark-mesos/Dockerfile b/docker/spark-mesos/Dockerfile
index b90aef3655dee..fb3f267fe5c78 100644
--- a/docker/spark-mesos/Dockerfile
+++ b/docker/spark-mesos/Dockerfile
@@ -24,7 +24,7 @@ RUN apt-get update && \
     apt-get install -y python libnss3 openjdk-7-jre-headless curl
 
 RUN mkdir /opt/spark && \
-    curl http://www.apache.org/dyn/closer.cgi/spark/spark-1.4.0/spark-1.4.0-bin-hadoop2.4.tgz \
+    curl http://www.apache.org/dyn/closer.lua/spark/spark-1.4.0/spark-1.4.0-bin-hadoop2.4.tgz \
     | tar -xzC /opt
 ENV SPARK_HOME /opt/spark
 ENV MESOS_NATIVE_JAVA_LIBRARY /usr/local/lib/libmesos.so
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index cfd219ab02e26..f36921ae30c2f 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -45,7 +45,7 @@ frameworks.  You can install Mesos either from source or using prebuilt packages
 To install Apache Mesos from source, follow these steps:
 
 1. Download a Mesos release from a
-   [mirror](http://www.apache.org/dyn/closer.cgi/mesos/{{site.MESOS_VERSION}}/)
+   [mirror](http://www.apache.org/dyn/closer.lua/mesos/{{site.MESOS_VERSION}}/)
 2. Follow the Mesos [Getting Started](http://mesos.apache.org/gettingstarted) page for compiling and
    installing Mesos
 

From ec012805337926e56343be2761a1037296446880 Mon Sep 17 00:00:00 2001
From: zhuol <zhuol@yahoo-inc.com>
Date: Tue, 1 Sep 2015 11:14:59 -1000
Subject: [PATCH 501/633] [SPARK-4223] [CORE] Support * in acls.

SPARK-4223.

Currently we support setting view and modify acls but you have to specify a list of users. It would be nice to support * meaning all users have access.

Manual tests to verify that: "*" works for any user in:
a. Spark ui: view and kill stage.     Done.
b. Spark history server.                  Done.
c. Yarn application killing.  Done.

Author: zhuol <zhuol@yahoo-inc.com>

Closes #8398 from zhuoliu/4223.
---
 .../org/apache/spark/SecurityManager.scala    | 26 ++++++++++--
 .../apache/spark/SecurityManagerSuite.scala   | 41 +++++++++++++++++++
 docs/configuration.md                         |  9 ++--
 3 files changed, 69 insertions(+), 7 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SecurityManager.scala b/core/src/main/scala/org/apache/spark/SecurityManager.scala
index 673ef49e7c1c5..746d2081d4393 100644
--- a/core/src/main/scala/org/apache/spark/SecurityManager.scala
+++ b/core/src/main/scala/org/apache/spark/SecurityManager.scala
@@ -310,7 +310,16 @@ private[spark] class SecurityManager(sparkConf: SparkConf)
     setViewAcls(Set[String](defaultUser), allowedUsers)
   }
 
-  def getViewAcls: String = viewAcls.mkString(",")
+  /**
+   * Checking the existence of "*" is necessary as YARN can't recognize the "*" in "defaultuser,*"
+   */
+  def getViewAcls: String = {
+    if (viewAcls.contains("*")) {
+      "*"
+    } else {
+      viewAcls.mkString(",")
+    }
+  }
 
   /**
    * Admin acls should be set before the view or modify acls.  If you modify the admin
@@ -321,7 +330,16 @@ private[spark] class SecurityManager(sparkConf: SparkConf)
     logInfo("Changing modify acls to: " + modifyAcls.mkString(","))
   }
 
-  def getModifyAcls: String = modifyAcls.mkString(",")
+  /**
+   * Checking the existence of "*" is necessary as YARN can't recognize the "*" in "defaultuser,*"
+   */
+  def getModifyAcls: String = {
+    if (modifyAcls.contains("*")) {
+      "*"
+    } else {
+      modifyAcls.mkString(",")
+    }
+  }
 
   /**
    * Admin acls should be set before the view or modify acls.  If you modify the admin
@@ -394,7 +412,7 @@ private[spark] class SecurityManager(sparkConf: SparkConf)
   def checkUIViewPermissions(user: String): Boolean = {
     logDebug("user=" + user + " aclsEnabled=" + aclsEnabled() + " viewAcls=" +
       viewAcls.mkString(","))
-    !aclsEnabled || user == null || viewAcls.contains(user)
+    !aclsEnabled || user == null || viewAcls.contains(user) || viewAcls.contains("*")
   }
 
   /**
@@ -409,7 +427,7 @@ private[spark] class SecurityManager(sparkConf: SparkConf)
   def checkModifyPermissions(user: String): Boolean = {
     logDebug("user=" + user + " aclsEnabled=" + aclsEnabled() + " modifyAcls=" +
       modifyAcls.mkString(","))
-    !aclsEnabled || user == null || modifyAcls.contains(user)
+    !aclsEnabled || user == null || modifyAcls.contains(user) || modifyAcls.contains("*")
   }
 
 
diff --git a/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala b/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala
index f34aefca4eb18..f29160d834082 100644
--- a/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SecurityManagerSuite.scala
@@ -125,6 +125,47 @@ class SecurityManagerSuite extends SparkFunSuite {
 
   }
 
+  test("set security with * in acls") {
+    val conf = new SparkConf
+    conf.set("spark.ui.acls.enable", "true")
+    conf.set("spark.admin.acls", "user1,user2")
+    conf.set("spark.ui.view.acls", "*")
+    conf.set("spark.modify.acls", "user4")
+
+    val securityManager = new SecurityManager(conf)
+    assert(securityManager.aclsEnabled() === true)
+
+    // check for viewAcls with *
+    assert(securityManager.checkUIViewPermissions("user1") === true)
+    assert(securityManager.checkUIViewPermissions("user5") === true)
+    assert(securityManager.checkUIViewPermissions("user6") === true)
+    assert(securityManager.checkModifyPermissions("user4") === true)
+    assert(securityManager.checkModifyPermissions("user7") === false)
+    assert(securityManager.checkModifyPermissions("user8") === false)
+
+    // check for modifyAcls with *
+    securityManager.setModifyAcls(Set("user4"), "*")
+    assert(securityManager.checkModifyPermissions("user7") === true)
+    assert(securityManager.checkModifyPermissions("user8") === true)
+
+    securityManager.setAdminAcls("user1,user2")
+    securityManager.setModifyAcls(Set("user1"), "user2")
+    securityManager.setViewAcls(Set("user1"), "user2")
+    assert(securityManager.checkUIViewPermissions("user5") === false)
+    assert(securityManager.checkUIViewPermissions("user6") === false)
+    assert(securityManager.checkModifyPermissions("user7") === false)
+    assert(securityManager.checkModifyPermissions("user8") === false)
+
+    // check for adminAcls with *
+    securityManager.setAdminAcls("user1,*")
+    securityManager.setModifyAcls(Set("user1"), "user2")
+    securityManager.setViewAcls(Set("user1"), "user2")
+    assert(securityManager.checkUIViewPermissions("user5") === true)
+    assert(securityManager.checkUIViewPermissions("user6") === true)
+    assert(securityManager.checkModifyPermissions("user7") === true)
+    assert(securityManager.checkModifyPermissions("user8") === true)
+  }
+
   test("ssl on setup") {
     val conf = SSLSampleConfigs.sparkSSLConfig()
     val expectedAlgorithms = Set(
diff --git a/docs/configuration.md b/docs/configuration.md
index 77c5cbc7b3196..fb0315ce7c3cc 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1286,7 +1286,8 @@ Apart from these, the following properties are also available, and may be useful
   <td>
     Comma separated list of users/administrators that have view and modify access to all Spark jobs.
     This can be used if you run on a shared cluster and have a set of administrators or devs who
-    help debug when things work.
+    help debug when things work. Putting a "*" in the list means any user can have the priviledge
+    of admin.
   </td>
 </tr>
 <tr>
@@ -1327,7 +1328,8 @@ Apart from these, the following properties are also available, and may be useful
   <td>Empty</td>
   <td>
     Comma separated list of users that have modify access to the Spark job. By default only the
-    user that started the Spark job has access to modify it (kill it for example).
+    user that started the Spark job has access to modify it (kill it for example). Putting a "*" in
+    the list means any user can have access to modify it.
   </td>
 </tr>
 <tr>
@@ -1349,7 +1351,8 @@ Apart from these, the following properties are also available, and may be useful
   <td>Empty</td>
   <td>
     Comma separated list of users that have view access to the Spark web ui. By default only the
-    user that started the Spark job has view access.
+    user that started the Spark job has view access. Putting a "*" in the list means any user can
+    have view access to this Spark job.
   </td>
 </tr>
 </table>

From bf550a4b551b6dd18fea3eb3f70497f9a6ad8e6c Mon Sep 17 00:00:00 2001
From: 0x0FFF <programmerag@gmail.com>
Date: Tue, 1 Sep 2015 14:34:59 -0700
Subject: [PATCH 502/633] [SPARK-10162] [SQL] Fix the timezone omitting for
 PySpark Dataframe filter function

This PR addresses [SPARK-10162](https://issues.apache.org/jira/browse/SPARK-10162)
The issue is with DataFrame filter() function, if datetime.datetime is passed to it:
* Timezone information of this datetime is ignored
* This datetime is assumed to be in local timezone, which depends on the OS timezone setting

Fix includes both code change and regression test. Problem reproduction code on master:
```python
import pytz
from datetime import datetime
from pyspark.sql import *
from pyspark.sql.types import *
sqc = SQLContext(sc)
df = sqc.createDataFrame([], StructType([StructField("dt", TimestampType())]))

m1 = pytz.timezone('UTC')
m2 = pytz.timezone('Etc/GMT+3')

df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
```
It gives the same timestamp ignoring time zone:
```
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
Filter (dt#0 > 946713600000000)
 Scan PhysicalRDD[dt#0]

>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
Filter (dt#0 > 946713600000000)
 Scan PhysicalRDD[dt#0]
```
After the fix:
```
>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m1)).explain()
Filter (dt#0 > 946684800000000)
 Scan PhysicalRDD[dt#0]

>>> df.filter(df.dt > datetime(2000, 01, 01, tzinfo=m2)).explain()
Filter (dt#0 > 946695600000000)
 Scan PhysicalRDD[dt#0]
```
PR [8536](https://github.com/apache/spark/pull/8536) was occasionally closed by me dropping the repo

Author: 0x0FFF <programmerag@gmail.com>

Closes #8555 from 0x0FFF/SPARK-10162.
---
 python/pyspark/sql/tests.py | 26 ++++++++++++++++++--------
 python/pyspark/sql/types.py |  7 +++++--
 2 files changed, 23 insertions(+), 10 deletions(-)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index cd32e26c64f22..59a891bd7c420 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -50,16 +50,17 @@
 from pyspark.sql.utils import AnalysisException, IllegalArgumentException
 
 
-class UTC(datetime.tzinfo):
-    """UTC"""
-    ZERO = datetime.timedelta(0)
+class UTCOffsetTimezone(datetime.tzinfo):
+    """
+    Specifies timezone in UTC offset
+    """
+
+    def __init__(self, offset=0):
+        self.ZERO = datetime.timedelta(hours=offset)
 
     def utcoffset(self, dt):
         return self.ZERO
 
-    def tzname(self, dt):
-        return "UTC"
-
     def dst(self, dt):
         return self.ZERO
 
@@ -841,13 +842,22 @@ def test_filter_with_datetime(self):
         self.assertEqual(0, df.filter(df.date > date).count())
         self.assertEqual(0, df.filter(df.time > time).count())
 
+    def test_filter_with_datetime_timezone(self):
+        dt1 = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000, tzinfo=UTCOffsetTimezone(0))
+        dt2 = datetime.datetime(2015, 4, 17, 23, 1, 2, 3000, tzinfo=UTCOffsetTimezone(1))
+        row = Row(date=dt1)
+        df = self.sqlCtx.createDataFrame([row])
+        self.assertEqual(0, df.filter(df.date == dt2).count())
+        self.assertEqual(1, df.filter(df.date > dt2).count())
+        self.assertEqual(0, df.filter(df.date < dt2).count())
+
     def test_time_with_timezone(self):
         day = datetime.date.today()
         now = datetime.datetime.now()
         ts = time.mktime(now.timetuple())
         # class in __main__ is not serializable
-        from pyspark.sql.tests import UTC
-        utc = UTC()
+        from pyspark.sql.tests import UTCOffsetTimezone
+        utc = UTCOffsetTimezone()
         utcnow = datetime.datetime.utcfromtimestamp(ts)  # without microseconds
         # add microseconds to utcnow (keeping year,month,day,hour,minute,second)
         utcnow = datetime.datetime(*(utcnow.timetuple()[:6] + (now.microsecond, utc)))
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 94e581a78364c..f84d08d7098ad 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -1290,8 +1290,11 @@ def can_convert(self, obj):
 
     def convert(self, obj, gateway_client):
         Timestamp = JavaClass("java.sql.Timestamp", gateway_client)
-        return Timestamp(int(time.mktime(obj.timetuple())) * 1000 + obj.microsecond // 1000)
-
+        seconds = (calendar.timegm(obj.utctimetuple()) if obj.tzinfo
+                   else time.mktime(obj.timetuple()))
+        t = Timestamp(int(seconds) * 1000)
+        t.setNanos(obj.microsecond * 1000)
+        return t
 
 # datetime is a subclass of date, we should register DatetimeConverter first
 register_input_converter(DatetimeConverter())

From 00d9af5e190475affffb8b50467fcddfc40f50dc Mon Sep 17 00:00:00 2001
From: 0x0FFF <programmerag@gmail.com>
Date: Tue, 1 Sep 2015 14:58:49 -0700
Subject: [PATCH 503/633] [SPARK-10392] [SQL] Pyspark - Wrong DateType support
 on JDBC connection

This PR addresses issue [SPARK-10392](https://issues.apache.org/jira/browse/SPARK-10392)
The problem is that for "start of epoch" date (01 Jan 1970) PySpark class DateType returns 0 instead of the `datetime.date` due to implementation of its return statement

Issue reproduction on master:
```
>>> from pyspark.sql.types import *
>>> a = DateType()
>>> a.fromInternal(0)
0
>>> a.fromInternal(1)
datetime.date(1970, 1, 2)
```

Author: 0x0FFF <programmerag@gmail.com>

Closes #8556 from 0x0FFF/SPARK-10392.
---
 python/pyspark/sql/tests.py | 5 +++++
 python/pyspark/sql/types.py | 6 ++++--
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 59a891bd7c420..fc778631d93a3 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -168,6 +168,11 @@ def test_decimal_type(self):
         t3 = DecimalType(8)
         self.assertNotEqual(t2, t3)
 
+    # regression test for SPARK-10392
+    def test_datetype_equal_zero(self):
+        dt = DateType()
+        self.assertEqual(dt.fromInternal(0), datetime.date(1970, 1, 1))
+
 
 class SQLTests(ReusedPySparkTestCase):
 
diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index f84d08d7098ad..8bd58d69eeecd 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -168,10 +168,12 @@ def needConversion(self):
         return True
 
     def toInternal(self, d):
-        return d and d.toordinal() - self.EPOCH_ORDINAL
+        if d is not None:
+            return d.toordinal() - self.EPOCH_ORDINAL
 
     def fromInternal(self, v):
-        return v and datetime.date.fromordinal(v + self.EPOCH_ORDINAL)
+        if v is not None:
+            return datetime.date.fromordinal(v + self.EPOCH_ORDINAL)
 
 
 class TimestampType(AtomicType):

From c3b881a7d7e4736f7131ff002a80e25def1f63af Mon Sep 17 00:00:00 2001
From: Chuan Shao <shaochuan@huawei.com>
Date: Wed, 2 Sep 2015 11:02:27 -0700
Subject: [PATCH 504/633] [SPARK-7336] [HISTORYSERVER] Fix bug that
 applications status incorrect on JobHistory UI.

Author: ArcherShao <shaochuan@huawei.com>

Closes #5886 from ArcherShao/SPARK-7336.
---
 .../deploy/history/FsHistoryProvider.scala    | 27 +++++++++++++++----
 1 file changed, 22 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
index e573ff16c50a3..a5755eac36396 100644
--- a/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/history/FsHistoryProvider.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.deploy.history
 
 import java.io.{BufferedInputStream, FileNotFoundException, InputStream, IOException, OutputStream}
+import java.util.UUID
 import java.util.concurrent.{ExecutorService, Executors, TimeUnit}
 import java.util.zip.{ZipEntry, ZipOutputStream}
 
@@ -73,7 +74,7 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
   // The modification time of the newest log detected during the last scan. This is used
   // to ignore logs that are older during subsequent scans, to avoid processing data that
   // is already known.
-  private var lastModifiedTime = -1L
+  private var lastScanTime = -1L
 
   // Mapping of application IDs to their metadata, in descending end time order. Apps are inserted
   // into the map in order, so the LinkedHashMap maintains the correct ordering.
@@ -179,15 +180,14 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
    */
   private[history] def checkForLogs(): Unit = {
     try {
+      val newLastScanTime = getNewLastScanTime()
       val statusList = Option(fs.listStatus(new Path(logDir))).map(_.toSeq)
         .getOrElse(Seq[FileStatus]())
-      var newLastModifiedTime = lastModifiedTime
       val logInfos: Seq[FileStatus] = statusList
         .filter { entry =>
           try {
             getModificationTime(entry).map { time =>
-              newLastModifiedTime = math.max(newLastModifiedTime, time)
-              time >= lastModifiedTime
+              time >= lastScanTime
             }.getOrElse(false)
           } catch {
             case e: AccessControlException =>
@@ -224,12 +224,29 @@ private[history] class FsHistoryProvider(conf: SparkConf, clock: Clock)
           }
         }
 
-      lastModifiedTime = newLastModifiedTime
+      lastScanTime = newLastScanTime
     } catch {
       case e: Exception => logError("Exception in checking for event log updates", e)
     }
   }
 
+  private def getNewLastScanTime(): Long = {
+    val fileName = "." + UUID.randomUUID().toString
+    val path = new Path(logDir, fileName)
+    val fos = fs.create(path)
+
+    try {
+      fos.close()
+      fs.getFileStatus(path).getModificationTime
+    } catch {
+      case e: Exception =>
+        logError("Exception encountered when attempting to update last scan time", e)
+        lastScanTime
+    } finally {
+      fs.delete(path)
+    }
+  }
+
   override def writeEventLogs(
       appId: String,
       attemptId: Option[String],

From 56c4c172e99a5e14f4bc3308e7ff36d94113b63e Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 2 Sep 2015 11:13:17 -0700
Subject: [PATCH 505/633] [SPARK-10034] [SQL] add regression test for Sort on
 Aggregate

Before #8371, there was a bug for `Sort` on `Aggregate` that we can't use aggregate expressions named `_aggOrdering` and can't use more than one ordering expressions which contains aggregate functions. The reason of this bug is that: The aggregate expression in `SortOrder` never get resolved, we alias it with `_aggOrdering` and call `toAttribute` which gives us an `UnresolvedAttribute`. So actually we are referencing aggregate expression by name, not by exprId like we thought. And if there is already an aggregate expression named `_aggOrdering` or there are more than one ordering expressions having aggregate functions, we will have conflict names and can't search by name.

However, after #8371 got merged, the `SortOrder`s are guaranteed to be resolved and we are always referencing aggregate expression by exprId. The Bug doesn't exist anymore and this PR add regression tests for it.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8231 from cloud-fan/sort-agg.
---
 .../scala/org/apache/spark/sql/DataFrameSuite.scala    |  8 ++++++++
 .../scala/org/apache/spark/sql/SQLQuerySuite.scala     | 10 ++++++++++
 2 files changed, 18 insertions(+)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index 284fff184085a..a4871e247cff7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -887,4 +887,12 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
       .select(struct($"b"))
       .collect()
   }
+
+  test("SPARK-10034: Sort on Aggregate with aggregation expression named 'aggOrdering'") {
+    val df = Seq(1 -> 2).toDF("i", "j")
+    val query = df.groupBy('i)
+      .agg(max('j).as("aggOrdering"))
+      .orderBy(sum('j))
+    checkAnswer(query, Row(1, 2))
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 9e172b2c264cb..28201073a2d7b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1490,6 +1490,16 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
         """.stripMargin),
       Row(3) :: Row(7) :: Row(11) :: Row(15) :: Nil)
 
+    checkAnswer(
+      sql(
+        """
+          |SELECT sum(b)
+          |FROM orderByData
+          |GROUP BY a
+          |ORDER BY sum(b), max(b)
+        """.stripMargin),
+      Row(3) :: Row(7) :: Row(11) :: Row(15) :: Nil)
+
     checkAnswer(
       sql(
         """

From fc48307797912dc1d53893dce741ddda8630957b Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 2 Sep 2015 11:32:27 -0700
Subject: [PATCH 506/633] [SPARK-10389] [SQL] support order by non-attribute
 grouping expression on Aggregate

For example, we can write `SELECT MAX(value) FROM src GROUP BY key + 1 ORDER BY key + 1` in PostgreSQL, and we should support this in Spark SQL.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8548 from cloud-fan/support-order-by-non-attribute.
---
 .../sql/catalyst/analysis/Analyzer.scala      | 72 ++++++++++---------
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 19 +++--
 2 files changed, 52 insertions(+), 39 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
index 1a5de15c61f86..591747b45c376 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
@@ -560,43 +560,47 @@ class Analyzer(
           filter
         }
 
-      case sort @ Sort(sortOrder, global,
-             aggregate @ Aggregate(grouping, originalAggExprs, child))
+      case sort @ Sort(sortOrder, global, aggregate: Aggregate)
         if aggregate.resolved && !sort.resolved =>
 
         // Try resolving the ordering as though it is in the aggregate clause.
         try {
-          val aliasedOrder = sortOrder.map(o => Alias(o.child, "aggOrder")())
-          val aggregatedOrdering = Aggregate(grouping, aliasedOrder, child)
-          val resolvedOperator: Aggregate = execute(aggregatedOrdering).asInstanceOf[Aggregate]
-          def resolvedAggregateOrdering = resolvedOperator.aggregateExpressions
-
-          // Expressions that have an aggregate can be pushed down.
-          val needsAggregate = resolvedAggregateOrdering.exists(containsAggregate)
-
-          // Attribute references, that are missing from the order but are present in the grouping
-          // expressions can also be pushed down.
-          val requiredAttributes = resolvedAggregateOrdering.map(_.references).reduce(_ ++ _)
-          val missingAttributes = requiredAttributes -- aggregate.outputSet
-          val validPushdownAttributes =
-            missingAttributes.filter(a => grouping.exists(a.semanticEquals))
-
-          // If resolution was successful and we see the ordering either has an aggregate in it or
-          // it is missing something that is projected away by the aggregate, add the ordering
-          // the original aggregate operator.
-          if (resolvedOperator.resolved && (needsAggregate || validPushdownAttributes.nonEmpty)) {
-            val evaluatedOrderings: Seq[SortOrder] = sortOrder.zip(resolvedAggregateOrdering).map {
-              case (order, evaluated) => order.copy(child = evaluated.toAttribute)
-            }
-            val aggExprsWithOrdering: Seq[NamedExpression] =
-              resolvedAggregateOrdering ++ originalAggExprs
-
-            Project(aggregate.output,
-              Sort(evaluatedOrderings, global,
-                aggregate.copy(aggregateExpressions = aggExprsWithOrdering)))
-          } else {
-            sort
+          val aliasedOrdering = sortOrder.map(o => Alias(o.child, "aggOrder")())
+          val aggregatedOrdering = aggregate.copy(aggregateExpressions = aliasedOrdering)
+          val resolvedAggregate: Aggregate = execute(aggregatedOrdering).asInstanceOf[Aggregate]
+          val resolvedAliasedOrdering: Seq[Alias] =
+            resolvedAggregate.aggregateExpressions.asInstanceOf[Seq[Alias]]
+
+          // If we pass the analysis check, then the ordering expressions should only reference to
+          // aggregate expressions or grouping expressions, and it's safe to push them down to
+          // Aggregate.
+          checkAnalysis(resolvedAggregate)
+
+          val originalAggExprs = aggregate.aggregateExpressions.map(
+            CleanupAliases.trimNonTopLevelAliases(_).asInstanceOf[NamedExpression])
+
+          // If the ordering expression is same with original aggregate expression, we don't need
+          // to push down this ordering expression and can reference the original aggregate
+          // expression instead.
+          val needsPushDown = ArrayBuffer.empty[NamedExpression]
+          val evaluatedOrderings = resolvedAliasedOrdering.zip(sortOrder).map {
+            case (evaluated, order) =>
+              val index = originalAggExprs.indexWhere {
+                case Alias(child, _) => child semanticEquals evaluated.child
+                case other => other semanticEquals evaluated.child
+              }
+
+              if (index == -1) {
+                needsPushDown += evaluated
+                order.copy(child = evaluated.toAttribute)
+              } else {
+                order.copy(child = originalAggExprs(index).toAttribute)
+              }
           }
+
+          Project(aggregate.output,
+            Sort(evaluatedOrderings, global,
+              aggregate.copy(aggregateExpressions = originalAggExprs ++ needsPushDown)))
         } catch {
           // Attempting to resolve in the aggregate can result in ambiguity.  When this happens,
           // just return the original plan.
@@ -605,9 +609,7 @@ class Analyzer(
     }
 
     protected def containsAggregate(condition: Expression): Boolean = {
-      condition
-        .collect { case ae: AggregateExpression => ae }
-        .nonEmpty
+      condition.find(_.isInstanceOf[AggregateExpression]).isDefined
     }
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 28201073a2d7b..0ef25fe0faef0 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -1722,9 +1722,20 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-10130 type coercion for IF should have children resolved first") {
-    val df = Seq((1, 1), (-1, 1)).toDF("key", "value")
-    df.registerTempTable("src")
-    checkAnswer(
-      sql("SELECT IF(a > 0, a, 0) FROM (SELECT key a FROM src) temp"), Seq(Row(1), Row(0)))
+    withTempTable("src") {
+      Seq((1, 1), (-1, 1)).toDF("key", "value").registerTempTable("src")
+      checkAnswer(
+        sql("SELECT IF(a > 0, a, 0) FROM (SELECT key a FROM src) temp"), Seq(Row(1), Row(0)))
+    }
+  }
+
+  test("SPARK-10389: order by non-attribute grouping expression on Aggregate") {
+    withTempTable("src") {
+      Seq((1, 1), (-1, 1)).toDF("key", "value").registerTempTable("src")
+      checkAnswer(sql("SELECT MAX(value) FROM src GROUP BY key + 1 ORDER BY key + 1"),
+        Seq(Row(1), Row(1)))
+      checkAnswer(sql("SELECT MAX(value) FROM src GROUP BY key + 1 ORDER BY (key + 1) * 2"),
+        Seq(Row(1), Row(1)))
+    }
   }
 }

From 2da3a9e98e5d129d4507b5db01bba5ee9558d28e Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Wed, 2 Sep 2015 12:53:24 -0700
Subject: [PATCH 507/633] [SPARK-10004] [SHUFFLE] Perform auth checks when
 clients read shuffle data.

To correctly isolate applications, when requests to read shuffle data
arrive at the shuffle service, proper authorization checks need to
be performed. This change makes sure that only the application that
created the shuffle data can read from it.

Such checks are only enabled when "spark.authenticate" is enabled,
otherwise there's no secure way to make sure that the client is really
who it says it is.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8218 from vanzin/SPARK-10004.
---
 .../network/netty/NettyBlockRpcServer.scala   |   3 +-
 .../netty/NettyBlockTransferService.scala     |   2 +-
 network/common/pom.xml                        |   4 +
 .../spark/network/client/TransportClient.java |  22 +++
 .../network/sasl/SaslClientBootstrap.java     |   2 +
 .../spark/network/sasl/SaslRpcHandler.java    |   1 +
 .../server/OneForOneStreamManager.java        |  31 +++-
 .../spark/network/server/StreamManager.java   |   9 +
 .../server/TransportRequestHandler.java       |   1 +
 .../shuffle/ExternalShuffleBlockHandler.java  |  16 +-
 .../network/sasl/SaslIntegrationSuite.java    | 163 +++++++++++++++---
 .../ExternalShuffleBlockHandlerSuite.java     |   2 +-
 project/MimaExcludes.scala                    |   1 +
 13 files changed, 221 insertions(+), 36 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
index 7c170a742fb64..76968249fb625 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockRpcServer.scala
@@ -38,6 +38,7 @@ import org.apache.spark.storage.{BlockId, StorageLevel}
  * is equivalent to one Spark-level shuffle block.
  */
 class NettyBlockRpcServer(
+    appId: String,
     serializer: Serializer,
     blockManager: BlockDataManager)
   extends RpcHandler with Logging {
@@ -55,7 +56,7 @@ class NettyBlockRpcServer(
       case openBlocks: OpenBlocks =>
         val blocks: Seq[ManagedBuffer] =
           openBlocks.blockIds.map(BlockId.apply).map(blockManager.getBlockData)
-        val streamId = streamManager.registerStream(blocks.iterator.asJava)
+        val streamId = streamManager.registerStream(appId, blocks.iterator.asJava)
         logTrace(s"Registered streamId $streamId with ${blocks.size} buffers")
         responseContext.onSuccess(new StreamHandle(streamId, blocks.size).toByteArray)
 
diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
index ff8aae9ebe9f0..d5ad2c9ad00e8 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
@@ -49,7 +49,7 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage
   private[this] var appId: String = _
 
   override def init(blockDataManager: BlockDataManager): Unit = {
-    val rpcHandler = new NettyBlockRpcServer(serializer, blockDataManager)
+    val rpcHandler = new NettyBlockRpcServer(conf.getAppId, serializer, blockDataManager)
     var serverBootstrap: Option[TransportServerBootstrap] = None
     var clientBootstrap: Option[TransportClientBootstrap] = None
     if (authEnabled) {
diff --git a/network/common/pom.xml b/network/common/pom.xml
index 7dc3068ab8cb7..4141fcb8267a5 100644
--- a/network/common/pom.xml
+++ b/network/common/pom.xml
@@ -48,6 +48,10 @@
       <artifactId>slf4j-api</artifactId>
       <scope>provided</scope>
     </dependency>
+    <dependency>
+      <groupId>com.google.code.findbugs</groupId>
+      <artifactId>jsr305</artifactId>
+    </dependency>
     <!--
       Promote Guava to "compile" so that maven-shade-plugin picks it up (for packaging the Optional
       class exposed in the Java API). The plugin will then remove this dependency from the published
diff --git a/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java b/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java
index e8e7f06247d3e..df841288a02e3 100644
--- a/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java
+++ b/network/common/src/main/java/org/apache/spark/network/client/TransportClient.java
@@ -23,6 +23,7 @@
 import java.util.UUID;
 import java.util.concurrent.ExecutionException;
 import java.util.concurrent.TimeUnit;
+import javax.annotation.Nullable;
 
 import com.google.common.base.Objects;
 import com.google.common.base.Preconditions;
@@ -70,6 +71,7 @@ public class TransportClient implements Closeable {
 
   private final Channel channel;
   private final TransportResponseHandler handler;
+  @Nullable private String clientId;
 
   public TransportClient(Channel channel, TransportResponseHandler handler) {
     this.channel = Preconditions.checkNotNull(channel);
@@ -84,6 +86,25 @@ public SocketAddress getSocketAddress() {
     return channel.remoteAddress();
   }
 
+  /**
+   * Returns the ID used by the client to authenticate itself when authentication is enabled.
+   *
+   * @return The client ID, or null if authentication is disabled.
+   */
+  public String getClientId() {
+    return clientId;
+  }
+
+  /**
+   * Sets the authenticated client ID. This is meant to be used by the authentication layer.
+   *
+   * Trying to set a different client ID after it's been set will result in an exception.
+   */
+  public void setClientId(String id) {
+    Preconditions.checkState(clientId == null, "Client ID has already been set.");
+    this.clientId = id;
+  }
+
   /**
    * Requests a single chunk from the remote side, from the pre-negotiated streamId.
    *
@@ -207,6 +228,7 @@ public void close() {
   public String toString() {
     return Objects.toStringHelper(this)
       .add("remoteAdress", channel.remoteAddress())
+      .add("clientId", clientId)
       .add("isActive", isActive())
       .toString();
   }
diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java
index 185ba2ef3bb1f..69923769d44b4 100644
--- a/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java
+++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslClientBootstrap.java
@@ -77,6 +77,8 @@ public void doBootstrap(TransportClient client, Channel channel) {
         payload = saslClient.response(response);
       }
 
+      client.setClientId(appId);
+
       if (encrypt) {
         if (!SparkSaslServer.QOP_AUTH_CONF.equals(saslClient.getNegotiatedProperty(Sasl.QOP))) {
           throw new RuntimeException(
diff --git a/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java b/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java
index be6165caf3c74..3f2ebe32887b8 100644
--- a/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java
+++ b/network/common/src/main/java/org/apache/spark/network/sasl/SaslRpcHandler.java
@@ -81,6 +81,7 @@ public void receive(TransportClient client, byte[] message, RpcResponseCallback
 
     if (saslServer == null) {
       // First message in the handshake, setup the necessary state.
+      client.setClientId(saslMessage.appId);
       saslServer = new SparkSaslServer(saslMessage.appId, secretKeyHolder,
         conf.saslServerAlwaysEncrypt());
     }
diff --git a/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java b/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java
index c95e64e8e2cda..e671854da1cae 100644
--- a/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java
+++ b/network/common/src/main/java/org/apache/spark/network/server/OneForOneStreamManager.java
@@ -24,13 +24,13 @@
 import java.util.concurrent.ConcurrentHashMap;
 import java.util.concurrent.atomic.AtomicLong;
 
+import com.google.common.base.Preconditions;
 import io.netty.channel.Channel;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 import org.apache.spark.network.buffer.ManagedBuffer;
-
-import com.google.common.base.Preconditions;
+import org.apache.spark.network.client.TransportClient;
 
 /**
  * StreamManager which allows registration of an Iterator&lt;ManagedBuffer&gt;, which are individually
@@ -44,6 +44,7 @@ public class OneForOneStreamManager extends StreamManager {
 
   /** State of a single stream. */
   private static class StreamState {
+    final String appId;
     final Iterator<ManagedBuffer> buffers;
 
     // The channel associated to the stream
@@ -53,7 +54,8 @@ private static class StreamState {
     // that the caller only requests each chunk one at a time, in order.
     int curChunk = 0;
 
-    StreamState(Iterator<ManagedBuffer> buffers) {
+    StreamState(String appId, Iterator<ManagedBuffer> buffers) {
+      this.appId = appId;
       this.buffers = Preconditions.checkNotNull(buffers);
     }
   }
@@ -109,15 +111,34 @@ public void connectionTerminated(Channel channel) {
     }
   }
 
+  @Override
+  public void checkAuthorization(TransportClient client, long streamId) {
+    if (client.getClientId() != null) {
+      StreamState state = streams.get(streamId);
+      Preconditions.checkArgument(state != null, "Unknown stream ID.");
+      if (!client.getClientId().equals(state.appId)) {
+        throw new SecurityException(String.format(
+          "Client %s not authorized to read stream %d (app %s).",
+          client.getClientId(),
+          streamId,
+          state.appId));
+      }
+    }
+  }
+
   /**
    * Registers a stream of ManagedBuffers which are served as individual chunks one at a time to
    * callers. Each ManagedBuffer will be release()'d after it is transferred on the wire. If a
    * client connection is closed before the iterator is fully drained, then the remaining buffers
    * will all be release()'d.
+   *
+   * If an app ID is provided, only callers who've authenticated with the given app ID will be
+   * allowed to fetch from this stream.
    */
-  public long registerStream(Iterator<ManagedBuffer> buffers) {
+  public long registerStream(String appId, Iterator<ManagedBuffer> buffers) {
     long myStreamId = nextStreamId.getAndIncrement();
-    streams.put(myStreamId, new StreamState(buffers));
+    streams.put(myStreamId, new StreamState(appId, buffers));
     return myStreamId;
   }
+
 }
diff --git a/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java b/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java
index 929f789bf9d24..aaa677c965640 100644
--- a/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java
+++ b/network/common/src/main/java/org/apache/spark/network/server/StreamManager.java
@@ -20,6 +20,7 @@
 import io.netty.channel.Channel;
 
 import org.apache.spark.network.buffer.ManagedBuffer;
+import org.apache.spark.network.client.TransportClient;
 
 /**
  * The StreamManager is used to fetch individual chunks from a stream. This is used in
@@ -60,4 +61,12 @@ public void registerChannel(Channel channel, long streamId) { }
    * to read from the associated streams again, so any state can be cleaned up.
    */
   public void connectionTerminated(Channel channel) { }
+
+  /**
+   * Verify that the client is authorized to read from the given stream.
+   *
+   * @throws SecurityException If client is not authorized.
+   */
+  public void checkAuthorization(TransportClient client, long streamId) { }
+
 }
diff --git a/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java b/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java
index e5159ab56d0d4..df6027805838d 100644
--- a/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java
+++ b/network/common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java
@@ -97,6 +97,7 @@ private void processFetchRequest(final ChunkFetchRequest req) {
 
     ManagedBuffer buf;
     try {
+      streamManager.checkAuthorization(reverseClient, req.streamChunkId.streamId);
       streamManager.registerChannel(channel, req.streamChunkId.streamId);
       buf = streamManager.getChunk(req.streamChunkId.streamId, req.streamChunkId.chunkIndex);
     } catch (Exception e) {
diff --git a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
index 0df1dd621f6e9..3ddf5c3c39189 100644
--- a/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
+++ b/network/shuffle/src/main/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandler.java
@@ -58,7 +58,7 @@ public ExternalShuffleBlockHandler(TransportConf conf, File registeredExecutorFi
 
   /** Enables mocking out the StreamManager and BlockManager. */
   @VisibleForTesting
-  ExternalShuffleBlockHandler(
+  public ExternalShuffleBlockHandler(
       OneForOneStreamManager streamManager,
       ExternalShuffleBlockResolver blockManager) {
     this.streamManager = streamManager;
@@ -77,17 +77,19 @@ protected void handleMessage(
       RpcResponseCallback callback) {
     if (msgObj instanceof OpenBlocks) {
       OpenBlocks msg = (OpenBlocks) msgObj;
-      List<ManagedBuffer> blocks = Lists.newArrayList();
+      checkAuth(client, msg.appId);
 
+      List<ManagedBuffer> blocks = Lists.newArrayList();
       for (String blockId : msg.blockIds) {
         blocks.add(blockManager.getBlockData(msg.appId, msg.execId, blockId));
       }
-      long streamId = streamManager.registerStream(blocks.iterator());
+      long streamId = streamManager.registerStream(client.getClientId(), blocks.iterator());
       logger.trace("Registered streamId {} with {} buffers", streamId, msg.blockIds.length);
       callback.onSuccess(new StreamHandle(streamId, msg.blockIds.length).toByteArray());
 
     } else if (msgObj instanceof RegisterExecutor) {
       RegisterExecutor msg = (RegisterExecutor) msgObj;
+      checkAuth(client, msg.appId);
       blockManager.registerExecutor(msg.appId, msg.execId, msg.executorInfo);
       callback.onSuccess(new byte[0]);
 
@@ -126,4 +128,12 @@ public void reregisterExecutor(AppExecId appExecId, ExecutorShuffleInfo executor
   public void close() {
     blockManager.close();
   }
+
+  private void checkAuth(TransportClient client, String appId) {
+    if (client.getClientId() != null && !client.getClientId().equals(appId)) {
+      throw new SecurityException(String.format(
+        "Client for %s not authorized for application %s.", client.getClientId(), appId));
+    }
+  }
+
 }
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java
index 382f613ecbb1b..5cb0e4d4a6458 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/sasl/SaslIntegrationSuite.java
@@ -19,6 +19,7 @@
 
 import java.io.IOException;
 import java.util.Arrays;
+import java.util.concurrent.atomic.AtomicReference;
 
 import com.google.common.collect.Lists;
 import org.junit.After;
@@ -27,9 +28,12 @@
 import org.junit.Test;
 
 import static org.junit.Assert.*;
+import static org.mockito.Mockito.*;
 
 import org.apache.spark.network.TestUtils;
 import org.apache.spark.network.TransportContext;
+import org.apache.spark.network.buffer.ManagedBuffer;
+import org.apache.spark.network.client.ChunkReceivedCallback;
 import org.apache.spark.network.client.RpcResponseCallback;
 import org.apache.spark.network.client.TransportClient;
 import org.apache.spark.network.client.TransportClientBootstrap;
@@ -39,44 +43,39 @@
 import org.apache.spark.network.server.StreamManager;
 import org.apache.spark.network.server.TransportServer;
 import org.apache.spark.network.server.TransportServerBootstrap;
+import org.apache.spark.network.shuffle.BlockFetchingListener;
 import org.apache.spark.network.shuffle.ExternalShuffleBlockHandler;
+import org.apache.spark.network.shuffle.ExternalShuffleBlockResolver;
+import org.apache.spark.network.shuffle.OneForOneBlockFetcher;
+import org.apache.spark.network.shuffle.protocol.BlockTransferMessage;
+import org.apache.spark.network.shuffle.protocol.ExecutorShuffleInfo;
+import org.apache.spark.network.shuffle.protocol.OpenBlocks;
+import org.apache.spark.network.shuffle.protocol.RegisterExecutor;
+import org.apache.spark.network.shuffle.protocol.StreamHandle;
 import org.apache.spark.network.util.SystemPropertyConfigProvider;
 import org.apache.spark.network.util.TransportConf;
 
 public class SaslIntegrationSuite {
-  static ExternalShuffleBlockHandler handler;
   static TransportServer server;
   static TransportConf conf;
   static TransportContext context;
+  static SecretKeyHolder secretKeyHolder;
 
   TransportClientFactory clientFactory;
 
-  /** Provides a secret key holder which always returns the given secret key. */
-  static class TestSecretKeyHolder implements SecretKeyHolder {
-
-    private final String secretKey;
-
-    TestSecretKeyHolder(String secretKey) {
-      this.secretKey = secretKey;
-    }
-
-    @Override
-    public String getSaslUser(String appId) {
-      return "user";
-    }
-    @Override
-    public String getSecretKey(String appId) {
-      return secretKey;
-    }
-  }
-
-
   @BeforeClass
   public static void beforeAll() throws IOException {
-    SecretKeyHolder secretKeyHolder = new TestSecretKeyHolder("good-key");
     conf = new TransportConf(new SystemPropertyConfigProvider());
     context = new TransportContext(conf, new TestRpcHandler());
 
+    secretKeyHolder = mock(SecretKeyHolder.class);
+    when(secretKeyHolder.getSaslUser(eq("app-1"))).thenReturn("app-1");
+    when(secretKeyHolder.getSecretKey(eq("app-1"))).thenReturn("app-1");
+    when(secretKeyHolder.getSaslUser(eq("app-2"))).thenReturn("app-2");
+    when(secretKeyHolder.getSecretKey(eq("app-2"))).thenReturn("app-2");
+    when(secretKeyHolder.getSaslUser(anyString())).thenReturn("other-app");
+    when(secretKeyHolder.getSecretKey(anyString())).thenReturn("correct-password");
+
     TransportServerBootstrap bootstrap = new SaslServerBootstrap(conf, secretKeyHolder);
     server = context.createServer(Arrays.asList(bootstrap));
   }
@@ -99,7 +98,7 @@ public void afterEach() {
   public void testGoodClient() throws IOException {
     clientFactory = context.createClientFactory(
       Lists.<TransportClientBootstrap>newArrayList(
-        new SaslClientBootstrap(conf, "app-id", new TestSecretKeyHolder("good-key"))));
+        new SaslClientBootstrap(conf, "app-1", secretKeyHolder)));
 
     TransportClient client = clientFactory.createClient(TestUtils.getLocalHost(), server.getPort());
     String msg = "Hello, World!";
@@ -109,13 +108,17 @@ public void testGoodClient() throws IOException {
 
   @Test
   public void testBadClient() {
+    SecretKeyHolder badKeyHolder = mock(SecretKeyHolder.class);
+    when(badKeyHolder.getSaslUser(anyString())).thenReturn("other-app");
+    when(badKeyHolder.getSecretKey(anyString())).thenReturn("wrong-password");
     clientFactory = context.createClientFactory(
       Lists.<TransportClientBootstrap>newArrayList(
-        new SaslClientBootstrap(conf, "app-id", new TestSecretKeyHolder("bad-key"))));
+        new SaslClientBootstrap(conf, "unknown-app", badKeyHolder)));
 
     try {
       // Bootstrap should fail on startup.
       clientFactory.createClient(TestUtils.getLocalHost(), server.getPort());
+      fail("Connection should have failed.");
     } catch (Exception e) {
       assertTrue(e.getMessage(), e.getMessage().contains("Mismatched response"));
     }
@@ -149,7 +152,7 @@ public void testNoSaslServer() {
     TransportContext context = new TransportContext(conf, handler);
     clientFactory = context.createClientFactory(
       Lists.<TransportClientBootstrap>newArrayList(
-        new SaslClientBootstrap(conf, "app-id", new TestSecretKeyHolder("key"))));
+        new SaslClientBootstrap(conf, "app-1", secretKeyHolder)));
     TransportServer server = context.createServer();
     try {
       clientFactory.createClient(TestUtils.getLocalHost(), server.getPort());
@@ -160,6 +163,110 @@ public void testNoSaslServer() {
     }
   }
 
+  /**
+   * This test is not actually testing SASL behavior, but testing that the shuffle service
+   * performs correct authorization checks based on the SASL authentication data.
+   */
+  @Test
+  public void testAppIsolation() throws Exception {
+    // Start a new server with the correct RPC handler to serve block data.
+    ExternalShuffleBlockResolver blockResolver = mock(ExternalShuffleBlockResolver.class);
+    ExternalShuffleBlockHandler blockHandler = new ExternalShuffleBlockHandler(
+      new OneForOneStreamManager(), blockResolver);
+    TransportServerBootstrap bootstrap = new SaslServerBootstrap(conf, secretKeyHolder);
+    TransportContext blockServerContext = new TransportContext(conf, blockHandler);
+    TransportServer blockServer = blockServerContext.createServer(Arrays.asList(bootstrap));
+
+    TransportClient client1 = null;
+    TransportClient client2 = null;
+    TransportClientFactory clientFactory2 = null;
+    try {
+      // Create a client, and make a request to fetch blocks from a different app.
+      clientFactory = blockServerContext.createClientFactory(
+        Lists.<TransportClientBootstrap>newArrayList(
+          new SaslClientBootstrap(conf, "app-1", secretKeyHolder)));
+      client1 = clientFactory.createClient(TestUtils.getLocalHost(),
+        blockServer.getPort());
+
+      final AtomicReference<Throwable> exception = new AtomicReference<>();
+
+      BlockFetchingListener listener = new BlockFetchingListener() {
+        @Override
+        public synchronized void onBlockFetchSuccess(String blockId, ManagedBuffer data) {
+          notifyAll();
+        }
+
+        @Override
+        public synchronized void onBlockFetchFailure(String blockId, Throwable t) {
+          exception.set(t);
+          notifyAll();
+        }
+      };
+
+      String[] blockIds = new String[] { "shuffle_2_3_4", "shuffle_6_7_8" };
+      OneForOneBlockFetcher fetcher = new OneForOneBlockFetcher(client1, "app-2", "0",
+        blockIds, listener);
+      synchronized (listener) {
+        fetcher.start();
+        listener.wait();
+      }
+      checkSecurityException(exception.get());
+
+      // Register an executor so that the next steps work.
+      ExecutorShuffleInfo executorInfo = new ExecutorShuffleInfo(
+        new String[] { System.getProperty("java.io.tmpdir") }, 1,
+        "org.apache.spark.shuffle.sort.SortShuffleManager");
+      RegisterExecutor regmsg = new RegisterExecutor("app-1", "0", executorInfo);
+      client1.sendRpcSync(regmsg.toByteArray(), 10000);
+
+      // Make a successful request to fetch blocks, which creates a new stream. But do not actually
+      // fetch any blocks, to keep the stream open.
+      OpenBlocks openMessage = new OpenBlocks("app-1", "0", blockIds);
+      byte[] response = client1.sendRpcSync(openMessage.toByteArray(), 10000);
+      StreamHandle stream = (StreamHandle) BlockTransferMessage.Decoder.fromByteArray(response);
+      long streamId = stream.streamId;
+
+      // Create a second client, authenticated with a different app ID, and try to read from
+      // the stream created for the previous app.
+      clientFactory2 = blockServerContext.createClientFactory(
+        Lists.<TransportClientBootstrap>newArrayList(
+          new SaslClientBootstrap(conf, "app-2", secretKeyHolder)));
+      client2 = clientFactory2.createClient(TestUtils.getLocalHost(),
+        blockServer.getPort());
+
+      ChunkReceivedCallback callback = new ChunkReceivedCallback() {
+        @Override
+        public synchronized void onSuccess(int chunkIndex, ManagedBuffer buffer) {
+          notifyAll();
+        }
+
+        @Override
+        public synchronized void onFailure(int chunkIndex, Throwable t) {
+          exception.set(t);
+          notifyAll();
+        }
+      };
+
+      exception.set(null);
+      synchronized (callback) {
+        client2.fetchChunk(streamId, 0, callback);
+        callback.wait();
+      }
+      checkSecurityException(exception.get());
+    } finally {
+      if (client1 != null) {
+        client1.close();
+      }
+      if (client2 != null) {
+        client2.close();
+      }
+      if (clientFactory2 != null) {
+        clientFactory2.close();
+      }
+      blockServer.close();
+    }
+  }
+
   /** RPC handler which simply responds with the message it received. */
   public static class TestRpcHandler extends RpcHandler {
     @Override
@@ -172,4 +279,10 @@ public StreamManager getStreamManager() {
       return new OneForOneStreamManager();
     }
   }
+
+  private void checkSecurityException(Throwable t) {
+    assertNotNull("No exception was caught.", t);
+    assertTrue("Expected SecurityException.",
+      t.getMessage().contains(SecurityException.class.getName()));
+  }
 }
diff --git a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
index 1d197497b7c8f..e61390cf57061 100644
--- a/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
+++ b/network/shuffle/src/test/java/org/apache/spark/network/shuffle/ExternalShuffleBlockHandlerSuite.java
@@ -93,7 +93,7 @@ public void testOpenShuffleBlocks() {
     @SuppressWarnings("unchecked")
     ArgumentCaptor<Iterator<ManagedBuffer>> stream = (ArgumentCaptor<Iterator<ManagedBuffer>>)
         (ArgumentCaptor<?>) ArgumentCaptor.forClass(Iterator.class);
-    verify(streamManager, times(1)).registerStream(stream.capture());
+    verify(streamManager, times(1)).registerStream(anyString(), stream.capture());
     Iterator<ManagedBuffer> buffers = stream.getValue();
     assertEquals(block0Marker, buffers.next());
     assertEquals(block1Marker, buffers.next());
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 88745dc086a04..714ce3cd9b1de 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -37,6 +37,7 @@ object MimaExcludes {
         case v if v.startsWith("1.5") =>
           Seq(
             MimaBuild.excludeSparkPackage("deploy"),
+            MimaBuild.excludeSparkPackage("network"),
             // These are needed if checking against the sbt build, since they are part of
             // the maven-generated artifacts in 1.3.
             excludePackage("org.spark-project.jetty"),

From 6cd98c1878a9c5c6475ed5974643021ab27862a7 Mon Sep 17 00:00:00 2001
From: 0x0FFF <programmerag@gmail.com>
Date: Wed, 2 Sep 2015 13:36:36 -0700
Subject: [PATCH 508/633] [SPARK-10417] [SQL] Iterating through Column results
 in infinite loop

`pyspark.sql.column.Column` object has `__getitem__` method, which makes it iterable for Python. In fact it has `__getitem__` to address the case when the column might be a list or dict, for you to be able to access certain element of it in DF API. The ability to iterate over it is just a side effect that might cause confusion for the people getting familiar with Spark DF (as you might iterate this way on Pandas DF for instance)

Issue reproduction:
```
df = sqlContext.jsonRDD(sc.parallelize(['{"name": "El Magnifico"}']))
for i in df["name"]: print i
```

Author: 0x0FFF <programmerag@gmail.com>

Closes #8574 from 0x0FFF/SPARK-10417.
---
 python/pyspark/sql/column.py | 3 +++
 python/pyspark/sql/tests.py  | 9 +++++++++
 2 files changed, 12 insertions(+)

diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 0948f9b27cd38..56e75e8caee88 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -226,6 +226,9 @@ def __getattr__(self, item):
             raise AttributeError(item)
         return self.getField(item)
 
+    def __iter__(self):
+        raise TypeError("Column is not iterable")
+
     # string methods
     rlike = _bin_op("rlike")
     like = _bin_op("like")
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index fc778631d93a3..eb449e8679fa0 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -1066,6 +1066,15 @@ def test_with_column_with_existing_name(self):
         keys = self.df.withColumn("key", self.df.key).select("key").collect()
         self.assertEqual([r.key for r in keys], list(range(100)))
 
+    # regression test for SPARK-10417
+    def test_column_iterator(self):
+
+        def foo():
+            for x in self.df.key:
+                break
+
+        self.assertRaises(TypeError, foo)
+
 
 class HiveContextSQLTests(ReusedPySparkTestCase):
 

From 03f3e91ff21707d8a1c7057a00f1b1cd8b743e3f Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Wed, 2 Sep 2015 21:00:13 -0700
Subject: [PATCH 509/633] [SPARK-10422] [SQL] String column in
 InMemoryColumnarCache needs to override clone method

https://issues.apache.org/jira/browse/SPARK-10422

Author: Yin Huai <yhuai@databricks.com>

Closes #8578 from yhuai/SPARK-10422.
---
 .../spark/sql/columnar/ColumnType.scala       |  2 ++
 .../columnar/InMemoryColumnarQuerySuite.scala | 20 +++++++++++++++++++
 2 files changed, 22 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
index 531a8244d55d1..ab482a3636121 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/columnar/ColumnType.scala
@@ -339,6 +339,8 @@ private[sql] object STRING extends NativeColumnType(StringType, 7, 8) {
   override def copyField(from: InternalRow, fromOrdinal: Int, to: MutableRow, toOrdinal: Int) {
     setField(to, toOrdinal, getField(from, fromOrdinal))
   }
+
+  override def clone(v: UTF8String): UTF8String = v.clone()
 }
 
 private[sql] object DATE extends NativeColumnType(DateType, 8, 4) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 952637c5f9cb8..83db9b6510b36 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -191,4 +191,24 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
       ctx.table("InMemoryCache_different_data_types").collect())
     ctx.dropTempTable("InMemoryCache_different_data_types")
   }
+
+  test("SPARK-10422: String column in InMemoryColumnarCache needs to override clone method") {
+    val df =
+      ctx.range(1, 100).selectExpr("id % 10 as id").rdd.map(id => Tuple1(s"str_$id")).toDF("i")
+    val cached = df.cache()
+    // count triggers the caching action. It should not throw.
+    cached.count()
+
+    // Make sure, the DataFrame is indeed cached.
+    assert(sqlContext.cacheManager.lookupCachedData(cached).nonEmpty)
+
+    // Check result.
+    checkAnswer(
+      cached,
+      ctx.range(1, 100).selectExpr("id % 10 as id").rdd.map(id => Tuple1(s"str_$id")).toDF("i")
+    )
+
+    // Drop the cache.
+    cached.unpersist()
+  }
 }

From 44948a2e9dcad5cd8d1eb749f469e49c5750b5ba Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Wed, 2 Sep 2015 21:19:42 -0700
Subject: [PATCH 510/633] [SPARK-9723] [ML] params getordefault should throw
 more useful error

Params.getOrDefault should throw a more meaningful exception than what you get from a bad key lookup.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8567 from holdenk/SPARK-9723-params-getordefault-should-throw-more-useful-error.
---
 .../scala/org/apache/spark/ml/param/params.scala  |  3 ++-
 .../org/apache/spark/ml/param/ParamsSuite.scala   | 15 ++++++++++-----
 .../org/apache/spark/ml/param/TestParams.scala    |  5 +++--
 3 files changed, 15 insertions(+), 8 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
index 91c0a5631319d..de32b7218c277 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/params.scala
@@ -461,7 +461,8 @@ trait Params extends Identifiable with Serializable {
    */
   final def getOrDefault[T](param: Param[T]): T = {
     shouldOwn(param)
-    get(param).orElse(getDefault(param)).get
+    get(param).orElse(getDefault(param)).getOrElse(
+      throw new NoSuchElementException(s"Failed to find a default value for ${param.name}"))
   }
 
   /** An alias for [[getOrDefault()]]. */
diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
index 2c878f8372a47..dfab82c8b67ad 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/param/ParamsSuite.scala
@@ -40,6 +40,10 @@ class ParamsSuite extends SparkFunSuite {
 
     assert(inputCol.toString === s"${uid}__inputCol")
 
+    intercept[java.util.NoSuchElementException] {
+      solver.getOrDefault(solver.handleInvalid)
+    }
+
     intercept[IllegalArgumentException] {
       solver.setMaxIter(-1)
     }
@@ -102,12 +106,13 @@ class ParamsSuite extends SparkFunSuite {
 
   test("params") {
     val solver = new TestParams()
-    import solver.{maxIter, inputCol}
+    import solver.{handleInvalid, maxIter, inputCol}
 
     val params = solver.params
-    assert(params.length === 2)
-    assert(params(0).eq(inputCol), "params must be ordered by name")
-    assert(params(1).eq(maxIter))
+    assert(params.length === 3)
+    assert(params(0).eq(handleInvalid), "params must be ordered by name")
+    assert(params(1).eq(inputCol), "params must be ordered by name")
+    assert(params(2).eq(maxIter))
 
     assert(!solver.isSet(maxIter))
     assert(solver.isDefined(maxIter))
@@ -122,7 +127,7 @@ class ParamsSuite extends SparkFunSuite {
     assert(solver.explainParam(maxIter) ===
       "maxIter: maximum number of iterations (>= 0) (default: 10, current: 100)")
     assert(solver.explainParams() ===
-      Seq(inputCol, maxIter).map(solver.explainParam).mkString("\n"))
+      Seq(handleInvalid, inputCol, maxIter).map(solver.explainParam).mkString("\n"))
 
     assert(solver.getParam("inputCol").eq(inputCol))
     assert(solver.getParam("maxIter").eq(maxIter))
diff --git a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala
index 2759248344531..9d23547f28447 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala
@@ -17,11 +17,12 @@
 
 package org.apache.spark.ml.param
 
-import org.apache.spark.ml.param.shared.{HasInputCol, HasMaxIter}
+import org.apache.spark.ml.param.shared.{HasHandleInvalid, HasInputCol, HasMaxIter}
 import org.apache.spark.ml.util.Identifiable
 
 /** A subclass of Params for testing. */
-class TestParams(override val uid: String) extends Params with HasMaxIter with HasInputCol {
+class TestParams(override val uid: String) extends Params with HasHandleInvalid with HasMaxIter
+    with HasInputCol {
 
   def this() = this(Identifiable.randomUID("testParams"))
 

From 4bd85d06e0334c49be18c4612b04d013b37f189c Mon Sep 17 00:00:00 2001
From: Ilya Ganelin <ilya.ganelin@capitalone.com>
Date: Wed, 2 Sep 2015 22:07:50 -0700
Subject: [PATCH 511/633] [SPARK-5945] Spark should not retry a stage
 infinitely on a FetchFailedException

The ```Stage``` class now tracks whether there were a sufficient number of consecutive failures of that stage to trigger an abort.

To avoid an infinite loop of stage retries, we abort the job completely after 4 consecutive stage failures for one stage. We still allow more than 4 consecutive stage failures if there is an intervening successful attempt for the stage, so that in very long-lived applications, where a stage may get reused many times, we don't abort the job after failures that have been recovered from successfully.

I've added test cases to exercise the most obvious scenarios.

Author: Ilya Ganelin <ilya.ganelin@capitalone.com>

Closes #5636 from ilganeli/SPARK-5945.
---
 .../apache/spark/scheduler/DAGScheduler.scala |  13 +-
 .../org/apache/spark/scheduler/Stage.scala    |  30 +-
 .../spark/scheduler/DAGSchedulerSuite.scala   | 282 +++++++++++++++++-
 3 files changed, 320 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index daf9b0f95273e..d673cb0946639 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -1101,7 +1101,6 @@ class DAGScheduler(
             s" ${task.stageAttemptId} and there is a more recent attempt for that stage " +
             s"(attempt ID ${failedStage.latestInfo.attemptId}) running")
         } else {
-
           // It is likely that we receive multiple FetchFailed for a single stage (because we have
           // multiple tasks running concurrently on different executors). In that case, it is
           // possible the fetch failure has already been handled by the scheduler.
@@ -1117,6 +1116,11 @@ class DAGScheduler(
           if (disallowStageRetryForTest) {
             abortStage(failedStage, "Fetch failure will not retry stage due to testing config",
               None)
+          } else if (failedStage.failedOnFetchAndShouldAbort(task.stageAttemptId)) {
+            abortStage(failedStage, s"$failedStage (${failedStage.name}) " +
+              s"has failed the maximum allowable number of " +
+              s"times: ${Stage.MAX_CONSECUTIVE_FETCH_FAILURES}. " +
+              s"Most recent failure reason: ${failureMessage}", None)
           } else if (failedStages.isEmpty) {
             // Don't schedule an event to resubmit failed stages if failed isn't empty, because
             // in that case the event will already have been scheduled.
@@ -1240,10 +1244,17 @@ class DAGScheduler(
     if (errorMessage.isEmpty) {
       logInfo("%s (%s) finished in %s s".format(stage, stage.name, serviceTime))
       stage.latestInfo.completionTime = Some(clock.getTimeMillis())
+
+      // Clear failure count for this stage, now that it's succeeded.
+      // We only limit consecutive failures of stage attempts,so that if a stage is
+      // re-used many times in a long-running job, unrelated failures don't eventually cause the
+      // stage to be aborted.
+      stage.clearFailures()
     } else {
       stage.latestInfo.stageFailed(errorMessage.get)
       logInfo("%s (%s) failed in %s s".format(stage, stage.name, serviceTime))
     }
+
     outputCommitCoordinator.stageEnd(stage.id)
     listenerBus.post(SparkListenerStageCompleted(stage.latestInfo))
     runningStages -= stage
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index 1cf06856ffbc2..c086535782c23 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -46,7 +46,7 @@ import org.apache.spark.util.CallSite
  * be updated for each attempt.
  *
  */
-private[spark] abstract class Stage(
+private[scheduler] abstract class Stage(
     val id: Int,
     val rdd: RDD[_],
     val numTasks: Int,
@@ -92,6 +92,29 @@ private[spark] abstract class Stage(
    */
   private var _latestInfo: StageInfo = StageInfo.fromStage(this, nextAttemptId)
 
+  /**
+   * Set of stage attempt IDs that have failed with a FetchFailure. We keep track of these
+   * failures in order to avoid endless retries if a stage keeps failing with a FetchFailure.
+   * We keep track of each attempt ID that has failed to avoid recording duplicate failures if
+   * multiple tasks from the same stage attempt fail (SPARK-5945).
+   */
+  private val fetchFailedAttemptIds = new HashSet[Int]
+
+  private[scheduler] def clearFailures() : Unit = {
+    fetchFailedAttemptIds.clear()
+  }
+
+  /**
+   * Check whether we should abort the failedStage due to multiple consecutive fetch failures.
+   *
+   * This method updates the running set of failed stage attempts and returns
+   * true if the number of failures exceeds the allowable number of failures.
+   */
+  private[scheduler] def failedOnFetchAndShouldAbort(stageAttemptId: Int): Boolean = {
+    fetchFailedAttemptIds.add(stageAttemptId)
+    fetchFailedAttemptIds.size >= Stage.MAX_CONSECUTIVE_FETCH_FAILURES
+  }
+
   /** Creates a new attempt for this stage by creating a new StageInfo with a new attempt ID. */
   def makeNewStageAttempt(
       numPartitionsToCompute: Int,
@@ -110,3 +133,8 @@ private[spark] abstract class Stage(
     case _ => false
   }
 }
+
+private[scheduler] object Stage {
+  // The number of consecutive failures allowed before a stage is aborted
+  val MAX_CONSECUTIVE_FETCH_FAILURES = 4
+}
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 2e8688cf41d99..62957c66974fb 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -26,11 +26,11 @@ import org.scalatest.concurrent.Timeouts
 import org.scalatest.time.SpanSugar._
 
 import org.apache.spark._
+import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.rdd.RDD
 import org.apache.spark.scheduler.SchedulingMode.SchedulingMode
 import org.apache.spark.storage.{BlockId, BlockManagerId, BlockManagerMaster}
 import org.apache.spark.util.CallSite
-import org.apache.spark.executor.TaskMetrics
 
 class DAGSchedulerEventProcessLoopTester(dagScheduler: DAGScheduler)
   extends DAGSchedulerEventProcessLoop(dagScheduler) {
@@ -473,6 +473,282 @@ class DAGSchedulerSuite
     assertDataStructuresEmpty()
   }
 
+
+  // Helper function to validate state when creating tests for task failures
+  private def checkStageId(stageId: Int, attempt: Int, stageAttempt: TaskSet) {
+    assert(stageAttempt.stageId === stageId)
+    assert(stageAttempt.stageAttemptId == attempt)
+  }
+
+
+  // Helper functions to extract commonly used code in Fetch Failure test cases
+  private def setupStageAbortTest(sc: SparkContext) {
+    sc.listenerBus.addListener(new EndListener())
+    ended = false
+    jobResult = null
+  }
+
+  // Create a new Listener to confirm that the listenerBus sees the JobEnd message
+  // when we abort the stage. This message will also be consumed by the EventLoggingListener
+  // so this will propagate up to the user.
+  var ended = false
+  var jobResult : JobResult = null
+
+  class EndListener extends SparkListener {
+    override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = {
+      jobResult = jobEnd.jobResult
+      ended = true
+    }
+  }
+
+  /**
+   * Common code to get the next stage attempt, confirm it's the one we expect, and complete it
+   * successfully.
+   *
+   * @param stageId - The current stageId
+   * @param attemptIdx - The current attempt count
+   * @param numShufflePartitions - The number of partitions in the next stage
+   */
+  private def completeShuffleMapStageSuccessfully(
+      stageId: Int,
+      attemptIdx: Int,
+      numShufflePartitions: Int): Unit = {
+    val stageAttempt = taskSets.last
+    checkStageId(stageId, attemptIdx, stageAttempt)
+    complete(stageAttempt, stageAttempt.tasks.zipWithIndex.map {
+      case (task, idx) =>
+        (Success, makeMapStatus("host" + ('A' + idx).toChar, numShufflePartitions))
+    }.toSeq)
+  }
+
+  /**
+   * Common code to get the next stage attempt, confirm it's the one we expect, and complete it
+   * with all FetchFailure.
+   *
+   * @param stageId - The current stageId
+   * @param attemptIdx - The current attempt count
+   * @param shuffleDep - The shuffle dependency of the stage with a fetch failure
+   */
+  private def completeNextStageWithFetchFailure(
+      stageId: Int,
+      attemptIdx: Int,
+      shuffleDep: ShuffleDependency[_, _, _]): Unit = {
+    val stageAttempt = taskSets.last
+    checkStageId(stageId, attemptIdx, stageAttempt)
+    complete(stageAttempt, stageAttempt.tasks.zipWithIndex.map { case (task, idx) =>
+      (FetchFailed(makeBlockManagerId("hostA"), shuffleDep.shuffleId, 0, idx, "ignored"), null)
+    }.toSeq)
+  }
+
+  /**
+   * Common code to get the next result stage attempt, confirm it's the one we expect, and
+   * complete it with a success where we return 42.
+   *
+   * @param stageId - The current stageId
+   * @param attemptIdx - The current attempt count
+   */
+  private def completeNextResultStageWithSuccess(stageId: Int, attemptIdx: Int): Unit = {
+    val stageAttempt = taskSets.last
+    checkStageId(stageId, attemptIdx, stageAttempt)
+    assert(scheduler.stageIdToStage(stageId).isInstanceOf[ResultStage])
+    complete(stageAttempt, stageAttempt.tasks.zipWithIndex.map(_ => (Success, 42)).toSeq)
+  }
+
+  /**
+   * In this test, we simulate a job where many tasks in the same stage fail. We want to show
+   * that many fetch failures inside a single stage attempt do not trigger an abort
+   * on their own, but only when there are enough failing stage attempts.
+   */
+  test("Single stage fetch failure should not abort the stage.") {
+    setupStageAbortTest(sc)
+
+    val parts = 8
+    val shuffleMapRdd = new MyRDD(sc, parts, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
+    val shuffleId = shuffleDep.shuffleId
+    val reduceRdd = new MyRDD(sc, parts, List(shuffleDep))
+    submit(reduceRdd, (0 until parts).toArray)
+
+    completeShuffleMapStageSuccessfully(0, 0, numShufflePartitions = parts)
+
+    completeNextStageWithFetchFailure(1, 0, shuffleDep)
+
+    // Resubmit and confirm that now all is well
+    scheduler.resubmitFailedStages()
+
+    assert(scheduler.runningStages.nonEmpty)
+    assert(!ended)
+
+    // Complete stage 0 and then stage 1 with a "42"
+    completeShuffleMapStageSuccessfully(0, 1, numShufflePartitions = parts)
+    completeNextResultStageWithSuccess(1, 1)
+
+    // Confirm job finished succesfully
+    sc.listenerBus.waitUntilEmpty(1000)
+    assert(ended === true)
+    assert(results === (0 until parts).map { idx => idx -> 42 }.toMap)
+    assertDataStructuresEmpty()
+  }
+
+  /**
+   * In this test we simulate a job failure where the first stage completes successfully and
+   * the second stage fails due to a fetch failure. Multiple successive fetch failures of a stage
+   * trigger an overall job abort to avoid endless retries.
+   */
+  test("Multiple consecutive stage fetch failures should lead to job being aborted.") {
+    setupStageAbortTest(sc)
+
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
+    val shuffleId = shuffleDep.shuffleId
+    val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
+    submit(reduceRdd, Array(0, 1))
+
+    for (attempt <- 0 until Stage.MAX_CONSECUTIVE_FETCH_FAILURES) {
+      // Complete all the tasks for the current attempt of stage 0 successfully
+      completeShuffleMapStageSuccessfully(0, attempt, numShufflePartitions = 2)
+
+      // Now we should have a new taskSet, for a new attempt of stage 1.
+      // Fail all these tasks with FetchFailure
+      completeNextStageWithFetchFailure(1, attempt, shuffleDep)
+
+      // this will trigger a resubmission of stage 0, since we've lost some of its
+      // map output, for the next iteration through the loop
+      scheduler.resubmitFailedStages()
+
+      if (attempt < Stage.MAX_CONSECUTIVE_FETCH_FAILURES - 1) {
+        assert(scheduler.runningStages.nonEmpty)
+        assert(!ended)
+      } else {
+        // Stage should have been aborted and removed from running stages
+        assertDataStructuresEmpty()
+        sc.listenerBus.waitUntilEmpty(1000)
+        assert(ended)
+        jobResult match {
+          case JobFailed(reason) =>
+            assert(reason.getMessage.contains("ResultStage 1 () has failed the maximum"))
+          case other => fail(s"expected JobFailed, not $other")
+        }
+      }
+    }
+  }
+
+  /**
+   * In this test, we create a job with two consecutive shuffles, and simulate 2 failures for each
+   * shuffle fetch. In total In total, the job has had four failures overall but not four failures
+   * for a particular stage, and as such should not be aborted.
+   */
+  test("Failures in different stages should not trigger an overall abort") {
+    setupStageAbortTest(sc)
+
+    val shuffleOneRdd = new MyRDD(sc, 2, Nil).cache()
+    val shuffleDepOne = new ShuffleDependency(shuffleOneRdd, null)
+    val shuffleTwoRdd = new MyRDD(sc, 2, List(shuffleDepOne)).cache()
+    val shuffleDepTwo = new ShuffleDependency(shuffleTwoRdd, null)
+    val finalRdd = new MyRDD(sc, 1, List(shuffleDepTwo))
+    submit(finalRdd, Array(0))
+
+    // In the first two iterations, Stage 0 succeeds and stage 1 fails. In the next two iterations,
+    // stage 2 fails.
+    for (attempt <- 0 until Stage.MAX_CONSECUTIVE_FETCH_FAILURES) {
+      // Complete all the tasks for the current attempt of stage 0 successfully
+      completeShuffleMapStageSuccessfully(0, attempt, numShufflePartitions = 2)
+
+      if (attempt < Stage.MAX_CONSECUTIVE_FETCH_FAILURES / 2) {
+        // Now we should have a new taskSet, for a new attempt of stage 1.
+        // Fail all these tasks with FetchFailure
+        completeNextStageWithFetchFailure(1, attempt, shuffleDepOne)
+      } else {
+        completeShuffleMapStageSuccessfully(1, attempt, numShufflePartitions = 1)
+
+        // Fail stage 2
+        completeNextStageWithFetchFailure(2, attempt - Stage.MAX_CONSECUTIVE_FETCH_FAILURES / 2,
+          shuffleDepTwo)
+      }
+
+      // this will trigger a resubmission of stage 0, since we've lost some of its
+      // map output, for the next iteration through the loop
+      scheduler.resubmitFailedStages()
+    }
+
+    completeShuffleMapStageSuccessfully(0, 4, numShufflePartitions = 2)
+    completeShuffleMapStageSuccessfully(1, 4, numShufflePartitions = 1)
+
+    // Succeed stage2 with a "42"
+    completeNextResultStageWithSuccess(2, Stage.MAX_CONSECUTIVE_FETCH_FAILURES/2)
+
+    assert(results === Map(0 -> 42))
+    assertDataStructuresEmpty()
+  }
+
+  /**
+   * In this test we demonstrate that only consecutive failures trigger a stage abort. A stage may
+   * fail multiple times, succeed, then fail a few more times (because its run again by downstream
+   * dependencies). The total number of failed attempts for one stage will go over the limit,
+   * but that doesn't matter, since they have successes in the middle.
+   */
+  test("Non-consecutive stage failures don't trigger abort") {
+    setupStageAbortTest(sc)
+
+    val shuffleOneRdd = new MyRDD(sc, 2, Nil).cache()
+    val shuffleDepOne = new ShuffleDependency(shuffleOneRdd, null)
+    val shuffleTwoRdd = new MyRDD(sc, 2, List(shuffleDepOne)).cache()
+    val shuffleDepTwo = new ShuffleDependency(shuffleTwoRdd, null)
+    val finalRdd = new MyRDD(sc, 1, List(shuffleDepTwo))
+    submit(finalRdd, Array(0))
+
+    // First, execute stages 0 and 1, failing stage 1 up to MAX-1 times.
+    for (attempt <- 0 until Stage.MAX_CONSECUTIVE_FETCH_FAILURES - 1) {
+      // Make each task in stage 0 success
+      completeShuffleMapStageSuccessfully(0, attempt, numShufflePartitions = 2)
+
+      // Now we should have a new taskSet, for a new attempt of stage 1.
+      // Fail these tasks with FetchFailure
+      completeNextStageWithFetchFailure(1, attempt, shuffleDepOne)
+
+      scheduler.resubmitFailedStages()
+
+      // Confirm we have not yet aborted
+      assert(scheduler.runningStages.nonEmpty)
+      assert(!ended)
+    }
+
+    // Rerun stage 0 and 1 to step through the task set
+    completeShuffleMapStageSuccessfully(0, 3, numShufflePartitions = 2)
+    completeShuffleMapStageSuccessfully(1, 3, numShufflePartitions = 1)
+
+    // Fail stage 2 so that stage 1 is resubmitted when we call scheduler.resubmitFailedStages()
+    completeNextStageWithFetchFailure(2, 0, shuffleDepTwo)
+
+    scheduler.resubmitFailedStages()
+
+    // Rerun stage 0 to step through the task set
+    completeShuffleMapStageSuccessfully(0, 4, numShufflePartitions = 2)
+
+    // Now again, fail stage 1 (up to MAX_FAILURES) but confirm that this doesn't trigger an abort
+    // since we succeeded in between.
+    completeNextStageWithFetchFailure(1, 4, shuffleDepOne)
+
+    scheduler.resubmitFailedStages()
+
+    // Confirm we have not yet aborted
+    assert(scheduler.runningStages.nonEmpty)
+    assert(!ended)
+
+    // Next, succeed all and confirm output
+    // Rerun stage 0 + 1
+    completeShuffleMapStageSuccessfully(0, 5, numShufflePartitions = 2)
+    completeShuffleMapStageSuccessfully(1, 5, numShufflePartitions = 1)
+
+    // Succeed stage 2 and verify results
+    completeNextResultStageWithSuccess(2, 1)
+
+    assertDataStructuresEmpty()
+    sc.listenerBus.waitUntilEmpty(1000)
+    assert(ended === true)
+    assert(results === Map(0 -> 42))
+  }
+
   test("trivial shuffle with multiple fetch failures") {
     val shuffleMapRdd = new MyRDD(sc, 2, Nil)
     val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
@@ -810,7 +1086,7 @@ class DAGSchedulerSuite
     submit(finalRdd, Array(0))
     cacheLocations(shuffleTwoRdd.id -> 0) = Seq(makeBlockManagerId("hostD"))
     cacheLocations(shuffleTwoRdd.id -> 1) = Seq(makeBlockManagerId("hostC"))
-    // complete stage 2
+    // complete stage 0
     complete(taskSets(0), Seq(
         (Success, makeMapStatus("hostA", 2)),
         (Success, makeMapStatus("hostB", 2))))
@@ -818,7 +1094,7 @@ class DAGSchedulerSuite
     complete(taskSets(1), Seq(
         (Success, makeMapStatus("hostA", 1)),
         (Success, makeMapStatus("hostB", 1))))
-    // pretend stage 0 failed because hostA went down
+    // pretend stage 2 failed because hostA went down
     complete(taskSets(2), Seq(
         (FetchFailed(makeBlockManagerId("hostA"), shuffleDepTwo.shuffleId, 0, 0, "ignored"), null)))
     // TODO assert this:

From 0985d2c30e031f80892987f7c3581d15dd210303 Mon Sep 17 00:00:00 2001
From: "navis.ryu" <navis@apache.org>
Date: Wed, 2 Sep 2015 22:11:11 -0700
Subject: [PATCH 512/633] [SPARK-8707] RDD#toDebugString fails if any cached
 RDD has invalid partitions

Added numPartitions(evaluate: Boolean) to RDD. With "evaluate=true" the method is same with "partitions.length". With "evaluate=false", it checks checked-out or already evaluated partitions in the RDD to get number of partition. If it's not those cases, returns -1. RDDInfo.partitionNum calls numPartition only when it's accessed.

Author: navis.ryu <navis@apache.org>

Closes #7127 from navis/SPARK-8707.
---
 core/src/main/scala/org/apache/spark/SparkContext.scala | 6 +++++-
 core/src/main/scala/org/apache/spark/rdd/RDD.scala      | 2 +-
 2 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 738887076b0d1..cbfe8bf31c3d6 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1516,8 +1516,12 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
    */
   @DeveloperApi
   def getRDDStorageInfo: Array[RDDInfo] = {
+    getRDDStorageInfo(_ => true)
+  }
+
+  private[spark] def getRDDStorageInfo(filter: RDD[_] => Boolean): Array[RDDInfo] = {
     assertNotStopped()
-    val rddInfos = persistentRdds.values.map(RDDInfo.fromRdd).toArray
+    val rddInfos = persistentRdds.values.filter(filter).map(RDDInfo.fromRdd).toArray
     StorageUtils.updateRddInfo(rddInfos, getExecutorStorageStatus)
     rddInfos.filter(_.isCached)
   }
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 081c721f23687..7dd2bc5d7cd72 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
@@ -1666,7 +1666,7 @@ abstract class RDD[T: ClassTag](
       import Utils.bytesToString
 
       val persistence = if (storageLevel != StorageLevel.NONE) storageLevel.description else ""
-      val storageInfo = rdd.context.getRDDStorageInfo.filter(_.id == rdd.id).map(info =>
+      val storageInfo = rdd.context.getRDDStorageInfo(_.id == rdd.id).map(info =>
         "    CachedPartitions: %d; MemorySize: %s; ExternalBlockStoreSize: %s; DiskSize: %s".format(
           info.numCachedPartitions, bytesToString(info.memSize),
           bytesToString(info.externalBlockStoreSize), bytesToString(info.diskSize)))

From f6c447f87592286a6f58aee5e0b2dc8dcb470d0c Mon Sep 17 00:00:00 2001
From: Evan Racah <ejracah@gmail.com>
Date: Wed, 2 Sep 2015 22:13:18 -0700
Subject: [PATCH 513/633] Removed code duplication in
 ShuffleBlockFetcherIterator

Added fetchUpToMaxBytes() to prevent having to update both code blocks when a change is made.

Author: Evan Racah <ejracah@gmail.com>

Closes #8514 from eracah/master.
---
 .../storage/ShuffleBlockFetcherIterator.scala  | 18 ++++++++++--------
 1 file changed, 10 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
index a759ceb96ec1e..0d0448feb5b06 100644
--- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
+++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala
@@ -260,10 +260,7 @@ final class ShuffleBlockFetcherIterator(
     fetchRequests ++= Utils.randomize(remoteRequests)
 
     // Send out initial requests for blocks, up to our maxBytesInFlight
-    while (fetchRequests.nonEmpty &&
-      (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) {
-      sendRequest(fetchRequests.dequeue())
-    }
+    fetchUpToMaxBytes()
 
     val numFetches = remoteRequests.size - fetchRequests.size
     logInfo("Started " + numFetches + " remote fetches in" + Utils.getUsedTimeMs(startTime))
@@ -296,10 +293,7 @@ final class ShuffleBlockFetcherIterator(
       case _ =>
     }
     // Send fetch requests up to maxBytesInFlight
-    while (fetchRequests.nonEmpty &&
-      (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) {
-      sendRequest(fetchRequests.dequeue())
-    }
+    fetchUpToMaxBytes()
 
     result match {
       case FailureFetchResult(blockId, address, e) =>
@@ -315,6 +309,14 @@ final class ShuffleBlockFetcherIterator(
     }
   }
 
+  private def fetchUpToMaxBytes(): Unit = {
+    // Send fetch requests up to maxBytesInFlight
+    while (fetchRequests.nonEmpty &&
+      (bytesInFlight == 0 || bytesInFlight + fetchRequests.front.size <= maxBytesInFlight)) {
+      sendRequest(fetchRequests.dequeue())
+    }
+  }
+
   private def throwFetchFailedException(blockId: BlockId, address: BlockManagerId, e: Throwable) = {
     blockId match {
       case ShuffleBlockId(shufId, mapId, reduceId) =>

From 3ddb9b32335154e47890a0c761e0dfea3ccaac7b Mon Sep 17 00:00:00 2001
From: Imran Rashid <irashid@cloudera.com>
Date: Wed, 2 Sep 2015 22:14:50 -0700
Subject: [PATCH 514/633] [SPARK-10247] [CORE] improve readability of a test
 case in DAGSchedulerSuite

This is pretty minor, just trying to improve the readability of `DAGSchedulerSuite`, I figure every bit helps.  Before whenever I read this test, I never knew what "should work" and "should be ignored" really meant -- this adds some asserts & updates comments to make it more clear.  Also some reformatting per a suggestion from markhamstra on https://github.com/apache/spark/pull/7699

Author: Imran Rashid <irashid@cloudera.com>

Closes #8434 from squito/SPARK-10247.
---
 .../spark/scheduler/DAGSchedulerSuite.scala   | 57 +++++++++++++++----
 1 file changed, 47 insertions(+), 10 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 62957c66974fb..80f64de44307e 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -926,27 +926,64 @@ class DAGSchedulerSuite
     val shuffleId = shuffleDep.shuffleId
     val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
     submit(reduceRdd, Array(0, 1))
+
     // pretend we were told hostA went away
     val oldEpoch = mapOutputTracker.getEpoch
     runEvent(ExecutorLost("exec-hostA"))
     val newEpoch = mapOutputTracker.getEpoch
     assert(newEpoch > oldEpoch)
+
+    // now start completing some tasks in the shuffle map stage, under different hosts
+    // and epochs, and make sure scheduler updates its state correctly
     val taskSet = taskSets(0)
+    val shuffleStage = scheduler.stageIdToStage(taskSet.stageId).asInstanceOf[ShuffleMapStage]
+    assert(shuffleStage.numAvailableOutputs === 0)
+
     // should be ignored for being too old
-    runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostA",
-      reduceRdd.partitions.size), null, createFakeTaskInfo(), null))
-    // should work because it's a non-failed host
-    runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostB",
-      reduceRdd.partitions.size), null, createFakeTaskInfo(), null))
+    runEvent(CompletionEvent(
+      taskSet.tasks(0),
+      Success,
+      makeMapStatus("hostA", reduceRdd.partitions.size),
+      null,
+      createFakeTaskInfo(),
+      null))
+    assert(shuffleStage.numAvailableOutputs === 0)
+
+    // should work because it's a non-failed host (so the available map outputs will increase)
+    runEvent(CompletionEvent(
+      taskSet.tasks(0),
+      Success,
+      makeMapStatus("hostB", reduceRdd.partitions.size),
+      null,
+      createFakeTaskInfo(),
+      null))
+    assert(shuffleStage.numAvailableOutputs === 1)
+
     // should be ignored for being too old
-    runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostA",
-      reduceRdd.partitions.size), null, createFakeTaskInfo(), null))
-    // should work because it's a new epoch
+    runEvent(CompletionEvent(
+      taskSet.tasks(0),
+      Success,
+      makeMapStatus("hostA", reduceRdd.partitions.size),
+      null,
+      createFakeTaskInfo(),
+      null))
+    assert(shuffleStage.numAvailableOutputs === 1)
+
+    // should work because it's a new epoch, which will increase the number of available map
+    // outputs, and also finish the stage
     taskSet.tasks(1).epoch = newEpoch
-    runEvent(CompletionEvent(taskSet.tasks(1), Success, makeMapStatus("hostA",
-      reduceRdd.partitions.size), null, createFakeTaskInfo(), null))
+    runEvent(CompletionEvent(
+      taskSet.tasks(1),
+      Success,
+      makeMapStatus("hostA", reduceRdd.partitions.size),
+      null,
+      createFakeTaskInfo(),
+      null))
+    assert(shuffleStage.numAvailableOutputs === 2)
     assert(mapOutputTracker.getMapSizesByExecutorId(shuffleId, 0).map(_._1).toSet ===
            HashSet(makeBlockManagerId("hostB"), makeBlockManagerId("hostA")))
+
+    // finish the next stage normally, which completes the job
     complete(taskSets(1), Seq((Success, 42), (Success, 43)))
     assert(results === Map(0 -> 42, 1 -> 43))
     assertDataStructuresEmpty()

From 62b4690d6b3016f41292b640ac28644ef31e299d Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Wed, 2 Sep 2015 22:15:54 -0700
Subject: [PATCH 515/633] [SPARK-10379] preserve first page in
 UnsafeShuffleExternalSorter

Author: Davies Liu <davies@databricks.com>

Closes #8543 from davies/preserve_page.
---
 .../spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java    | 4 ++++
 .../apache/spark/rdd/MapPartitionsWithPreparationRDD.scala   | 2 +-
 .../spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java       | 5 +++--
 3 files changed, 8 insertions(+), 3 deletions(-)

diff --git a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
index 3d1ef0c48adc5..e73ba39468828 100644
--- a/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
+++ b/core/src/main/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleExternalSorter.java
@@ -122,6 +122,10 @@ public UnsafeShuffleExternalSorter(
     this.maxRecordSizeBytes = pageSizeBytes - 4;
     this.writeMetrics = writeMetrics;
     initializeForWriting();
+
+    // preserve first page to ensure that we have at least one page to work with. Otherwise,
+    // other operators in the same task may starve this sorter (SPARK-9709).
+    acquireNewPageIfNecessary(pageSizeBytes);
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
index 1f2213d0c4346..417ff5278db2a 100644
--- a/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/MapPartitionsWithPreparationRDD.scala
@@ -41,7 +41,7 @@ private[spark] class MapPartitionsWithPreparationRDD[U: ClassTag, T: ClassTag, M
 
   // In certain join operations, prepare can be called on the same partition multiple times.
   // In this case, we need to ensure that each call to compute gets a separate prepare argument.
-  private[this] var preparedArguments: ArrayBuffer[M] = new ArrayBuffer[M]
+  private[this] val preparedArguments: ArrayBuffer[M] = new ArrayBuffer[M]
 
   /**
    * Prepare a partition for a single call to compute.
diff --git a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
index 94650be536b5f..a266b0c36e0fa 100644
--- a/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
+++ b/core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java
@@ -530,8 +530,9 @@ public void testPeakMemoryUsed() throws Exception {
       for (int i = 0; i < numRecordsPerPage * 10; i++) {
         writer.insertRecordIntoSorter(new Tuple2<Object, Object>(1, 1));
         newPeakMemory = writer.getPeakMemoryUsedBytes();
-        if (i % numRecordsPerPage == 0) {
-          // We allocated a new page for this record, so peak memory should change
+        if (i % numRecordsPerPage == 0 && i != 0) {
+          // The first page is allocated in constructor, another page will be allocated after
+          // every numRecordsPerPage records (peak memory should change).
           assertEquals(previousPeakMemory + pageSizeBytes, newPeakMemory);
         } else {
           assertEquals(previousPeakMemory, newPeakMemory);

From 0349b5b4383cf813bea4e1053bcc4e0268603743 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Wed, 2 Sep 2015 22:17:39 -0700
Subject: [PATCH 516/633] [SPARK-10411] [SQL] Move visualization above explain
 output and hide explain by default

New screenshots after this fix:

<img width="627" alt="s1" src="https://cloud.githubusercontent.com/assets/1000778/9625782/4b2dba36-518b-11e5-9104-c713ff026e3d.png">

Default:
<img width="462" alt="s2" src="https://cloud.githubusercontent.com/assets/1000778/9625817/92366e50-518b-11e5-9981-cdfb774d66b8.png">

After clicking `+details`:
<img width="377" alt="s3" src="https://cloud.githubusercontent.com/assets/1000778/9625784/4ba24342-518b-11e5-8522-846a16a95d44.png">

Author: zsxwing <zsxwing@gmail.com>

Closes #8570 from zsxwing/SPARK-10411.
---
 .../sql/execution/ui/ExecutionPage.scala      | 27 +++++++++++++++----
 1 file changed, 22 insertions(+), 5 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
index f0b56c2eb7a53..a4dbd2e1978d0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/ui/ExecutionPage.scala
@@ -74,16 +74,14 @@ private[sql] class ExecutionPage(parent: SQLTab) extends WebUIPage("execution")
                 }}
               </li>
             }}
-            <li>
-              <strong>Detail: </strong><br/>
-              <pre>{executionUIData.physicalPlanDescription}</pre>
-            </li>
           </ul>
         </div>
 
       val metrics = listener.getExecutionMetrics(executionId)
 
-      summary ++ planVisualization(metrics, executionUIData.physicalPlanGraph)
+      summary ++
+        planVisualization(metrics, executionUIData.physicalPlanGraph) ++
+        physicalPlanDescription(executionUIData.physicalPlanDescription)
     }.getOrElse {
       <div>No information to display for Plan {executionId}</div>
     }
@@ -124,4 +122,23 @@ private[sql] class ExecutionPage(parent: SQLTab) extends WebUIPage("execution")
 
   private def jobURL(jobId: Long): String =
     "%s/jobs/job?id=%s".format(UIUtils.prependBaseUri(parent.basePath), jobId)
+
+  private def physicalPlanDescription(physicalPlanDescription: String): Seq[Node] = {
+    <div>
+      <span style="cursor: pointer;" onclick="clickPhysicalPlanDetails();">
+        <span id="physical-plan-details-arrow" class="arrow-closed"></span>
+        <a>Details</a>
+      </span>
+    </div>
+    <div id="physical-plan-details" style="display: none;">
+      <pre>{physicalPlanDescription}</pre>
+    </div>
+    <script>
+      function clickPhysicalPlanDetails() {{
+        $('#physical-plan-details').toggle();
+        $('#physical-plan-details-arrow').toggleClass('arrow-open').toggleClass('arrow-closed');
+      }}
+    </script>
+    <br/>
+  }
 }

From 67580f1f574d272af3712fd91458f3c87368c2e4 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Thu, 3 Sep 2015 09:30:54 +0100
Subject: [PATCH 517/633] [SPARK-10332] [CORE] Fix yarn spark executor
 validation

From Jira:
Running spark-submit with yarn with number-executors equal to 0 when not using dynamic allocation should error out.
In spark 1.5.0 it continues and ends up hanging.
yarn.ClientArguments still has the check so something else must have changed.
spark-submit --master yarn --deploy-mode cluster --class org.apache.spark.examples.SparkPi --num-executors 0 ....
spark 1.4.1 errors with:
java.lang.IllegalArgumentException:
Number of executors was 0, but must be at least 1
(or 0 if dynamic executor allocation is enabled).

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8580 from holdenk/SPARK-10332-spark-submit-to-yarn-executors-0-message.
---
 .../scala/org/apache/spark/deploy/yarn/ClientArguments.scala   | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
index 4f42ffefa77f9..54f62e6b723ac 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala
@@ -96,6 +96,9 @@ private[spark] class ClientArguments(args: Array[String], sparkConf: SparkConf)
       }
 
       numExecutors = initialNumExecutors
+    } else {
+      val numExecutorsConf = "spark.executor.instances"
+      numExecutors = sparkConf.getInt(numExecutorsConf, numExecutors)
     }
     principal = Option(principal)
       .orElse(sparkConf.getOption("spark.yarn.principal"))

From 3abc0d512541158d11b181e2d9fa126d1371d5c0 Mon Sep 17 00:00:00 2001
From: WangTaoTheTonic <wangtao111@huawei.com>
Date: Thu, 3 Sep 2015 12:56:36 -0700
Subject: [PATCH 518/633] [SPARK-9596] [SQL] treat hadoop classes as shared one
 in IsolatedClientLoader

https://issues.apache.org/jira/browse/SPARK-9596

Author: WangTaoTheTonic <wangtao111@huawei.com>

Closes #7931 from WangTaoTheTonic/SPARK-9596.
---
 .../org/apache/spark/sql/hive/client/IsolatedClientLoader.scala  | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
index 7856037508412..1fe4cba9571f3 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/client/IsolatedClientLoader.scala
@@ -131,6 +131,7 @@ private[hive] class IsolatedClientLoader(
     name.contains("slf4j") ||
     name.contains("log4j") ||
     name.startsWith("org.apache.spark.") ||
+    (name.startsWith("org.apache.hadoop.") && !name.startsWith("org.apache.hadoop.hive.")) ||
     name.startsWith("scala.") ||
     (name.startsWith("com.google") && !name.startsWith("com.google.cloud")) ||
     name.startsWith("java.lang.") ||

From af0e3125cb1d48b1fc0e44c42b6880d67a9f1a85 Mon Sep 17 00:00:00 2001
From: CHOIJAEHONG <redrock07@naver.com>
Date: Thu, 3 Sep 2015 13:38:26 -0700
Subject: [PATCH 519/633] [SPARK-8951] [SPARKR] support Unicode characters in
 collect()

Spark gives an error message and does not show the output when a field of the result DataFrame contains characters in CJK.
I changed SerDe.scala in order that Spark support Unicode characters when writes a string to R.

Author: CHOIJAEHONG <redrock07@naver.com>

Closes #7494 from CHOIJAEHONG1/SPARK-8951.
---
 R/pkg/R/deserialize.R                         |  6 +++--
 R/pkg/R/serialize.R                           |  2 +-
 R/pkg/inst/tests/test_sparkSQL.R              | 26 +++++++++++++++++++
 .../scala/org/apache/spark/api/r/SerDe.scala  |  9 +++----
 4 files changed, 35 insertions(+), 8 deletions(-)

diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index 6cf628e3007de..88f18613fd7b1 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -57,8 +57,10 @@ readTypedObject <- function(con, type) {
 
 readString <- function(con) {
   stringLen <- readInt(con)
-  string <- readBin(con, raw(), stringLen, endian = "big")
-  rawToChar(string)
+  raw <- readBin(con, raw(), stringLen, endian = "big")
+  string <- rawToChar(raw)
+  Encoding(string) <- "UTF-8"
+  string 
 }
 
 readInt <- function(con) {
diff --git a/R/pkg/R/serialize.R b/R/pkg/R/serialize.R
index e3676f57f907f..91e6b3e5609b5 100644
--- a/R/pkg/R/serialize.R
+++ b/R/pkg/R/serialize.R
@@ -79,7 +79,7 @@ writeJobj <- function(con, value) {
 writeString <- function(con, value) {
   utfVal <- enc2utf8(value)
   writeInt(con, as.integer(nchar(utfVal, type = "bytes") + 1))
-  writeBin(utfVal, con, endian = "big")
+  writeBin(utfVal, con, endian = "big", useBytes=TRUE)
 }
 
 writeInt <- function(con, value) {
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 0da5e38654732..6d331f9883d55 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -431,6 +431,32 @@ test_that("collect() and take() on a DataFrame return the same number of rows an
   expect_equal(ncol(collect(df)), ncol(take(df, 10)))
 })
 
+test_that("collect() support Unicode characters", {
+  markUtf8 <- function(s) {
+    Encoding(s) <- "UTF-8"
+    s
+  }
+
+  lines <- c("{\"name\":\"안녕하세요\"}",
+             "{\"name\":\"您好\", \"age\":30}",
+             "{\"name\":\"こんにちは\", \"age\":19}",
+             "{\"name\":\"Xin chào\"}")
+
+  jsonPath <- tempfile(pattern="sparkr-test", fileext=".tmp")
+  writeLines(lines, jsonPath)
+
+  df <- read.df(sqlContext, jsonPath, "json")
+  rdf <- collect(df)
+  expect_true(is.data.frame(rdf))
+  expect_equal(rdf$name[1], markUtf8("안녕하세요"))
+  expect_equal(rdf$name[2], markUtf8("您好"))
+  expect_equal(rdf$name[3], markUtf8("こんにちは"))
+  expect_equal(rdf$name[4], markUtf8("Xin chào"))
+
+  df1 <- createDataFrame(sqlContext, rdf)
+  expect_equal(collect(where(df1, df1$name == markUtf8("您好")))$name, markUtf8("您好"))
+})
+
 test_that("multiple pipeline transformations result in an RDD with the correct values", {
   df <- jsonFile(sqlContext, jsonPath)
   first <- lapply(df, function(row) {
diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index 26ad4f1d4697e..190e193427af8 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -329,12 +329,11 @@ private[spark] object SerDe {
     out.writeDouble((value.getTime / 1000).toDouble + value.getNanos.toDouble / 1e9)
   }
 
-  // NOTE: Only works for ASCII right now
   def writeString(out: DataOutputStream, value: String): Unit = {
-    val len = value.length
-    out.writeInt(len + 1) // For the \0
-    out.writeBytes(value)
-    out.writeByte(0)
+    val utf8 = value.getBytes("UTF-8")
+    val len = utf8.length
+    out.writeInt(len)
+    out.write(utf8, 0, len)
   }
 
   def writeBytes(out: DataOutputStream, value: Array[Byte]): Unit = {

From 49aff7b9add4a70fb229c93f15394387bde142b8 Mon Sep 17 00:00:00 2001
From: Tom Graves <tgraves@yahoo-inc.com>
Date: Thu, 3 Sep 2015 13:46:16 -0700
Subject: [PATCH 520/633] [SPARK-10432] spark.port.maxRetries documentation is
 unclear

Author: Tom Graves <tgraves@yahoo-inc.com>

Closes #8585 from tgravescs/SPARK-10432.
---
 docs/configuration.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index fb0315ce7c3cc..29a36bd67f28b 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1015,7 +1015,11 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.port.maxRetries</code></td>
   <td>16</td>
   <td>
-    Default maximum number of retries when binding to a port before giving up.
+    Maximum number of retries when binding to a port before giving up.
+    When a port is given a specific value (non 0), each subsequent retry will
+    increment the port used in the previous attempt by 1 before retrying. This 
+    essentially allows it to try a range of ports from the start port specified 
+    to port + maxRetries.
   </td>
 </tr>
 <tr>

From d911c682f00cd5c438568c548098e03d3e7ea05c Mon Sep 17 00:00:00 2001
From: robbins <robbins@uk.ibm.com>
Date: Thu, 3 Sep 2015 13:47:22 -0700
Subject: [PATCH 521/633] [SPARK-10431] [CORE] Fix intermittent test failure.
 Wait for event queue to be clear

Author: robbins <robbins@uk.ibm.com>

Closes #8582 from robbinspg/InputOutputMetricsSuite.
---
 .../org/apache/spark/metrics/InputOutputMetricsSuite.scala    | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala
index d3218a548efc7..44eb5a0469122 100644
--- a/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala
+++ b/core/src/test/scala/org/apache/spark/metrics/InputOutputMetricsSuite.scala
@@ -286,6 +286,10 @@ class InputOutputMetricsSuite extends SparkFunSuite with SharedSparkContext
   private def runAndReturnMetrics(job: => Unit,
       collector: (SparkListenerTaskEnd) => Option[Long]): Long = {
     val taskMetrics = new ArrayBuffer[Long]()
+
+    // Avoid receiving earlier taskEnd events
+    sc.listenerBus.waitUntilEmpty(500)
+
     sc.addSparkListener(new SparkListener() {
       override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
         collector(taskEnd).foreach(taskMetrics += _)

From 754f853b02e9fd221f138c2446445fd56e3f3fb3 Mon Sep 17 00:00:00 2001
From: robbins <robbins@uk.ibm.com>
Date: Thu, 3 Sep 2015 13:48:35 -0700
Subject: [PATCH 522/633] [SPARK-9869] [STREAMING] Wait for all event
 notifications before asserting results

Author: robbins <robbins@uk.ibm.com>

Closes #8589 from robbinspg/InputStreamSuite-fix.
---
 .../scala/org/apache/spark/streaming/InputStreamsSuite.scala   | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
index ec2852d9a0206..047e38ef90998 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala
@@ -76,6 +76,9 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter {
           fail("Timeout: cannot finish all batches in 30 seconds")
         }
 
+        // Ensure progress listener has been notified of all events
+        ssc.scheduler.listenerBus.waitUntilEmpty(500)
+
         // Verify all "InputInfo"s have been reported
         assert(ssc.progressListener.numTotalReceivedRecords === input.size)
         assert(ssc.progressListener.numTotalProcessedRecords === input.size)

From e62f4a46f4396ae1e064e3d2ebfa2434f549b090 Mon Sep 17 00:00:00 2001
From: Pat Shields <yeoldefortran@gmail.com>
Date: Thu, 3 Sep 2015 13:52:47 -0700
Subject: [PATCH 523/633] =?UTF-8?q?[SPARK-9672]=20[MESOS]=20Don=E2=80=99t?=
 =?UTF-8?q?=20include=20SPARK=5FENV=5FLOADED=20when=20passing=20env=20vars?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

This contribution is my original work and I license the work to the project under the project's open source license.

Author: Pat Shields <yeoldefortran@gmail.com>

Closes #7979 from pashields/env-loading-on-driver.
---
 .../deploy/rest/RestSubmissionClient.scala      | 17 +++++++++++++----
 .../deploy/rest/StandaloneRestSubmitSuite.scala | 12 ++++++++++++
 2 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala
index 1fe956320a1b8..957a928bc402b 100644
--- a/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/rest/RestSubmissionClient.scala
@@ -392,15 +392,14 @@ private[spark] object RestSubmissionClient {
       mainClass: String,
       appArgs: Array[String],
       conf: SparkConf,
-      env: Map[String, String] = sys.env): SubmitRestProtocolResponse = {
+      env: Map[String, String] = Map()): SubmitRestProtocolResponse = {
     val master = conf.getOption("spark.master").getOrElse {
       throw new IllegalArgumentException("'spark.master' must be set.")
     }
     val sparkProperties = conf.getAll.toMap
-    val environmentVariables = env.filter { case (k, _) => k.startsWith("SPARK_") }
     val client = new RestSubmissionClient(master)
     val submitRequest = client.constructSubmitRequest(
-      appResource, mainClass, appArgs, sparkProperties, environmentVariables)
+      appResource, mainClass, appArgs, sparkProperties, env)
     client.createSubmission(submitRequest)
   }
 
@@ -413,6 +412,16 @@ private[spark] object RestSubmissionClient {
     val mainClass = args(1)
     val appArgs = args.slice(2, args.size)
     val conf = new SparkConf
-    run(appResource, mainClass, appArgs, conf)
+    val env = filterSystemEnvironment(sys.env)
+    run(appResource, mainClass, appArgs, conf, env)
+  }
+
+  /**
+   * Filter non-spark environment variables from any environment.
+   */
+  private[rest] def filterSystemEnvironment(env: Map[String, String]): Map[String, String] = {
+    env.filter { case (k, _) =>
+      (k.startsWith("SPARK_") && k != "SPARK_ENV_LOADED") || k.startsWith("MESOS_")
+    }
   }
 }
diff --git a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala
index 96e456d889ac3..9693e32bf6af6 100644
--- a/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala
+++ b/core/src/test/scala/org/apache/spark/deploy/rest/StandaloneRestSubmitSuite.scala
@@ -366,6 +366,18 @@ class StandaloneRestSubmitSuite extends SparkFunSuite with BeforeAndAfterEach {
     assert(conn3.getResponseCode === HttpServletResponse.SC_INTERNAL_SERVER_ERROR)
   }
 
+  test("client does not send 'SPARK_ENV_LOADED' env var by default") {
+    val environmentVariables = Map("SPARK_VAR" -> "1", "SPARK_ENV_LOADED" -> "1")
+    val filteredVariables = RestSubmissionClient.filterSystemEnvironment(environmentVariables)
+    assert(filteredVariables == Map("SPARK_VAR" -> "1"))
+  }
+
+  test("client includes mesos env vars") {
+    val environmentVariables = Map("SPARK_VAR" -> "1", "MESOS_VAR" -> "1", "OTHER_VAR" -> "1")
+    val filteredVariables = RestSubmissionClient.filterSystemEnvironment(environmentVariables)
+    assert(filteredVariables == Map("SPARK_VAR" -> "1", "MESOS_VAR" -> "1"))
+  }
+
   /* --------------------- *
    |     Helper methods    |
    * --------------------- */

From 11ef32c5a1fad84574733ade1e9d50a94275842b Mon Sep 17 00:00:00 2001
From: Vinod K C <vinod.kc@huawei.com>
Date: Thu, 3 Sep 2015 13:54:58 -0700
Subject: [PATCH 524/633] [SPARK-10430] [CORE] Added hashCode methods in
 AccumulableInfo and RDDOperationScope

Author: Vinod K C <vinod.kc@huawei.com>

Closes #8581 from vinodkc/fix_RDDOperationScope_Hashcode.
---
 .../scala/org/apache/spark/rdd/RDDOperationScope.scala   | 3 +++
 .../org/apache/spark/scheduler/AccumulableInfo.scala     | 8 +++++++-
 .../org/apache/spark/rdd/RDDOperationScopeSuite.scala    | 7 +++++++
 .../org/apache/spark/scheduler/DAGSchedulerSuite.scala   | 9 +++++++++
 4 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala
index 44667281c1063..540cbd688b63b 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDOperationScope.scala
@@ -23,6 +23,7 @@ import com.fasterxml.jackson.annotation.{JsonIgnore, JsonInclude, JsonPropertyOr
 import com.fasterxml.jackson.annotation.JsonInclude.Include
 import com.fasterxml.jackson.databind.ObjectMapper
 import com.fasterxml.jackson.module.scala.DefaultScalaModule
+import com.google.common.base.Objects
 
 import org.apache.spark.{Logging, SparkContext}
 
@@ -67,6 +68,8 @@ private[spark] class RDDOperationScope(
     }
   }
 
+  override def hashCode(): Int = Objects.hashCode(id, name, parent)
+
   override def toString: String = toJson
 }
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
index 11d123eec43ca..b6bff64ee368e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/AccumulableInfo.scala
@@ -34,9 +34,15 @@ class AccumulableInfo private[spark] (
   override def equals(other: Any): Boolean = other match {
     case acc: AccumulableInfo =>
       this.id == acc.id && this.name == acc.name &&
-        this.update == acc.update && this.value == acc.value
+        this.update == acc.update && this.value == acc.value &&
+        this.internal == acc.internal
     case _ => false
   }
+
+  override def hashCode(): Int = {
+    val state = Seq(id, name, update, value, internal)
+    state.map(_.hashCode).reduceLeft(31 * _ + _)
+  }
 }
 
 object AccumulableInfo {
diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala
index f65349e3e3585..16a92f54f9368 100644
--- a/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala
+++ b/core/src/test/scala/org/apache/spark/rdd/RDDOperationScopeSuite.scala
@@ -38,6 +38,13 @@ class RDDOperationScopeSuite extends SparkFunSuite with BeforeAndAfter {
     sc.stop()
   }
 
+  test("equals and hashCode") {
+    val opScope1 = new RDDOperationScope("scope1", id = "1")
+    val opScope2 = new RDDOperationScope("scope1", id = "1")
+    assert(opScope1 === opScope2)
+    assert(opScope1.hashCode() === opScope2.hashCode())
+  }
+
   test("getAllScopes") {
     assert(scope1.getAllScopes === Seq(scope1))
     assert(scope2.getAllScopes === Seq(scope1, scope2))
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 80f64de44307e..2c3aa2cf90d3c 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -285,6 +285,15 @@ class DAGSchedulerSuite
     assertDataStructuresEmpty()
   }
 
+  test("equals and hashCode AccumulableInfo") {
+    val accInfo1 = new AccumulableInfo(1, " Accumulable " + 1, Some("delta" + 1), "val" + 1, true)
+    val accInfo2 = new AccumulableInfo(1, " Accumulable " + 1, Some("delta" + 1), "val" + 1, false)
+    val accInfo3 = new AccumulableInfo(1, " Accumulable " + 1, Some("delta" + 1), "val" + 1, false)
+    assert(accInfo1 !== accInfo2)
+    assert(accInfo2 === accInfo3)
+    assert(accInfo2.hashCode() === accInfo3.hashCode())
+  }
+
   test("cache location preferences w/ dependency") {
     val baseRdd = new MyRDD(sc, 1, Nil).cache()
     val finalRdd = new MyRDD(sc, 1, List(new OneToOneDependency(baseRdd)))

From db4c130f9e8e2195c7cd8332ccb668217df55507 Mon Sep 17 00:00:00 2001
From: jeanlyn <jeanlyn92@gmail.com>
Date: Thu, 3 Sep 2015 13:56:11 -0700
Subject: [PATCH 525/633] [SPARK-9591] [CORE] Job may fail for exception during
 getting remote block

[SPARK-9591](https://issues.apache.org/jira/browse/SPARK-9591)
When we getting the broadcast variable, we can fetch the block form several location,but now when connecting the lost blockmanager(idle for enough time removed by driver when using dynamic resource allocate and so on) will cause task fail,and the worse case will cause the job fail.

Author: jeanlyn <jeanlyn92@gmail.com>

Closes #7927 from jeanlyn/catch_exception.
---
 .../spark/storage/BlockFetchException.scala   | 24 ++++++++++++
 .../apache/spark/storage/BlockManager.scala   | 21 ++++++++++-
 .../spark/storage/BlockManagerSuite.scala     | 37 +++++++++++++++++++
 3 files changed, 80 insertions(+), 2 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/storage/BlockFetchException.scala

diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetchException.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetchException.scala
new file mode 100644
index 0000000000000..f6e46ae9a481a
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/storage/BlockFetchException.scala
@@ -0,0 +1,24 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.storage
+
+import org.apache.spark.SparkException
+
+private[spark]
+case class BlockFetchException(messages: String, throwable: Throwable)
+  extends SparkException(messages, throwable)
diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
index fefaef0ab82c8..d31aa68eb6954 100644
--- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala
@@ -23,6 +23,7 @@ import java.nio.{ByteBuffer, MappedByteBuffer}
 import scala.collection.mutable.{ArrayBuffer, HashMap}
 import scala.concurrent.{ExecutionContext, Await, Future}
 import scala.concurrent.duration._
+import scala.util.control.NonFatal
 import scala.util.Random
 
 import sun.nio.ch.DirectBuffer
@@ -600,10 +601,26 @@ private[spark] class BlockManager(
   private def doGetRemote(blockId: BlockId, asBlockResult: Boolean): Option[Any] = {
     require(blockId != null, "BlockId is null")
     val locations = Random.shuffle(master.getLocations(blockId))
+    var numFetchFailures = 0
     for (loc <- locations) {
       logDebug(s"Getting remote block $blockId from $loc")
-      val data = blockTransferService.fetchBlockSync(
-        loc.host, loc.port, loc.executorId, blockId.toString).nioByteBuffer()
+      val data = try {
+        blockTransferService.fetchBlockSync(
+          loc.host, loc.port, loc.executorId, blockId.toString).nioByteBuffer()
+      } catch {
+        case NonFatal(e) =>
+          numFetchFailures += 1
+          if (numFetchFailures == locations.size) {
+            // An exception is thrown while fetching this block from all locations
+            throw new BlockFetchException(s"Failed to fetch block from" +
+              s" ${locations.size} locations. Most recent failure cause:", e)
+          } else {
+            // This location failed, so we retry fetch from a different one by returning null here
+            logWarning(s"Failed to fetch remote block $blockId " +
+              s"from $loc (failed attempt $numFetchFailures)", e)
+            null
+          }
+      }
 
       if (data != null) {
         if (asBlockResult) {
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index f480fd107a0c2..e5b54d66c8157 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -47,6 +47,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
   private val conf = new SparkConf(false)
   var store: BlockManager = null
   var store2: BlockManager = null
+  var store3: BlockManager = null
   var rpcEnv: RpcEnv = null
   var master: BlockManagerMaster = null
   conf.set("spark.authenticate", "false")
@@ -99,6 +100,10 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
       store2.stop()
       store2 = null
     }
+    if (store3 != null) {
+      store3.stop()
+      store3 = null
+    }
     rpcEnv.shutdown()
     rpcEnv.awaitTermination()
     rpcEnv = null
@@ -443,6 +448,38 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
     assert(list2DiskGet.get.readMethod === DataReadMethod.Disk)
   }
 
+  test("SPARK-9591: getRemoteBytes from another location when Exception throw") {
+    val origTimeoutOpt = conf.getOption("spark.network.timeout")
+    try {
+      conf.set("spark.network.timeout", "2s")
+      store = makeBlockManager(8000, "executor1")
+      store2 = makeBlockManager(8000, "executor2")
+      store3 = makeBlockManager(8000, "executor3")
+      val list1 = List(new Array[Byte](4000))
+      store2.putIterator("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+      store3.putIterator("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true)
+      var list1Get = store.getRemoteBytes("list1")
+      assert(list1Get.isDefined, "list1Get expected to be fetched")
+      // block manager exit
+      store2.stop()
+      store2 = null
+      list1Get = store.getRemoteBytes("list1")
+      // get `list1` block
+      assert(list1Get.isDefined, "list1Get expected to be fetched")
+      store3.stop()
+      store3 = null
+      // exception throw because there is no locations
+      intercept[BlockFetchException] {
+        list1Get = store.getRemoteBytes("list1")
+      }
+    } finally {
+      origTimeoutOpt match {
+        case Some(t) => conf.set("spark.network.timeout", t)
+        case None => conf.remove("spark.network.timeout")
+      }
+    }
+  }
+
   test("in-memory LRU storage") {
     store = makeBlockManager(12000)
     val a1 = new Array[Byte](4000)

From 08b07509746667791db7dbfa1b329b0e221044bb Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 3 Sep 2015 13:57:20 -0700
Subject: [PATCH 526/633] [SPARK-10435] Spark submit should fail fast for Mesos
 cluster mode with R

It's not supported yet so we should error with a clear message.

Author: Andrew Or <andrew@databricks.com>

Closes #8590 from andrewor14/mesos-cluster-r-guard.
---
 core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 86fcf942c2c4e..36e9750b86636 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -322,6 +322,9 @@ object SparkSubmit {
       case (MESOS, CLUSTER) if args.isPython =>
         printErrorAndExit("Cluster deploy mode is currently not supported for python " +
           "applications on Mesos clusters.")
+      case (MESOS, CLUSTER) if args.isR =>
+        printErrorAndExit("Cluster deploy mode is currently not supported for R " +
+          "applications on Mesos clusters.")
       case (STANDALONE, CLUSTER) if args.isPython =>
         printErrorAndExit("Cluster deploy mode is currently not supported for python " +
           "applications on standalone clusters.")

From 208fbca102c269c52eaf84bdde9838474ded276b Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Thu, 3 Sep 2015 15:10:30 -0700
Subject: [PATCH 527/633] [SPARK-10421] [BUILD] Exclude curator artifacts from
 tachyon dependencies.

This avoids them being mistakenly pulled instead of the newer ones that
Spark actually uses. Spark only depends on these artifacts transitively,
so sometimes maven just decides to pick tachyon's version of the
dependency for whatever reason.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8577 from vanzin/SPARK-10421.
---
 core/pom.xml | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/core/pom.xml b/core/pom.xml
index 4f79d71bf85fa..a46292c13bcc0 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -272,6 +272,14 @@
           <groupId>org.apache.hadoop</groupId>
           <artifactId>hadoop-client</artifactId>
         </exclusion>
+        <exclusion>
+          <groupId>org.apache.curator</groupId>
+          <artifactId>curator-client</artifactId>
+        </exclusion>
+        <exclusion>
+          <groupId>org.apache.curator</groupId>
+          <artifactId>curator-framework</artifactId>
+        </exclusion>
         <exclusion>
           <groupId>org.apache.curator</groupId>
           <artifactId>curator-recipes</artifactId>

From cf42138643d1d4bf464f1d700457309d9e537721 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 3 Sep 2015 17:55:10 -0700
Subject: [PATCH 528/633] [SPARK-10003] Improve readability of DAGScheduler

Note: this is not intended to be in Spark 1.5!

This patch rewrites some code in the `DAGScheduler` to make it more readable. In particular
- there were blocks of code that are unnecessary and removed for simplicity
- there were abstractions that are unnecessary and made the code hard to navigate
- other minor changes

Author: Andrew Or <andrew@databricks.com>

Closes #8217 from andrewor14/dag-scheduler-readability and squashes the following commits:

57abca3 [Andrew Or] Move comment back into if case
574fb1e [Andrew Or] Merge branch 'master' of github.com:apache/spark into dag-scheduler-readability
64a9ed2 [Andrew Or] Remove unnecessary code + minor code rewrites
---
 .../apache/spark/scheduler/DAGScheduler.scala | 46 ++++---------------
 1 file changed, 9 insertions(+), 37 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index d673cb0946639..09e963f5cdf60 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -250,11 +250,12 @@ class DAGScheduler(
       case Some(stage) => stage
       case None =>
         // We are going to register ancestor shuffle dependencies
-        registerShuffleDependencies(shuffleDep, firstJobId)
+        getAncestorShuffleDependencies(shuffleDep.rdd).foreach { dep =>
+          shuffleToMapStage(dep.shuffleId) = newOrUsedShuffleStage(dep, firstJobId)
+        }
         // Then register current shuffleDep
         val stage = newOrUsedShuffleStage(shuffleDep, firstJobId)
         shuffleToMapStage(shuffleDep.shuffleId) = stage
-
         stage
     }
   }
@@ -365,16 +366,6 @@ class DAGScheduler(
     parents.toList
   }
 
-  /** Find ancestor missing shuffle dependencies and register into shuffleToMapStage */
-  private def registerShuffleDependencies(shuffleDep: ShuffleDependency[_, _, _], firstJobId: Int) {
-    val parentsWithNoMapStage = getAncestorShuffleDependencies(shuffleDep.rdd)
-    while (parentsWithNoMapStage.nonEmpty) {
-      val currentShufDep = parentsWithNoMapStage.pop()
-      val stage = newOrUsedShuffleStage(currentShufDep, firstJobId)
-      shuffleToMapStage(currentShufDep.shuffleId) = stage
-    }
-  }
-
   /** Find ancestor shuffle dependencies that are not registered in shuffleToMapStage yet */
   private def getAncestorShuffleDependencies(rdd: RDD[_]): Stack[ShuffleDependency[_, _, _]] = {
     val parents = new Stack[ShuffleDependency[_, _, _]]
@@ -391,11 +382,9 @@ class DAGScheduler(
               if (!shuffleToMapStage.contains(shufDep.shuffleId)) {
                 parents.push(shufDep)
               }
-
-              waitingForVisit.push(shufDep.rdd)
             case _ =>
-              waitingForVisit.push(dep.rdd)
           }
+          waitingForVisit.push(dep.rdd)
         }
       }
     }
@@ -1052,10 +1041,11 @@ class DAGScheduler(
               //       we registered these map outputs.
               mapOutputTracker.registerMapOutputs(
                 shuffleStage.shuffleDep.shuffleId,
-                shuffleStage.outputLocs.map(list => if (list.isEmpty) null else list.head),
+                shuffleStage.outputLocs.map(_.headOption.orNull),
                 changeEpoch = true)
 
               clearCacheLocs()
+
               if (shuffleStage.outputLocs.contains(Nil)) {
                 // Some tasks had failed; let's resubmit this shuffleStage
                 // TODO: Lower-level scheduler should also deal with this
@@ -1064,27 +1054,9 @@ class DAGScheduler(
                   shuffleStage.outputLocs.zipWithIndex.filter(_._1.isEmpty)
                       .map(_._2).mkString(", "))
                 submitStage(shuffleStage)
-              } else {
-                val newlyRunnable = new ArrayBuffer[Stage]
-                for (shuffleStage <- waitingStages) {
-                  logInfo("Missing parents for " + shuffleStage + ": " +
-                    getMissingParentStages(shuffleStage))
-                }
-                for (shuffleStage <- waitingStages if getMissingParentStages(shuffleStage).isEmpty)
-                {
-                  newlyRunnable += shuffleStage
-                }
-                waitingStages --= newlyRunnable
-                runningStages ++= newlyRunnable
-                for {
-                  shuffleStage <- newlyRunnable.sortBy(_.id)
-                  jobId <- activeJobForStage(shuffleStage)
-                } {
-                  logInfo("Submitting " + shuffleStage + " (" +
-                    shuffleStage.rdd + "), which is now runnable")
-                  submitMissingTasks(shuffleStage, jobId)
-                }
               }
+
+              // Note: newly runnable stages will be submitted below when we submit waiting stages
             }
           }
 
@@ -1186,7 +1158,7 @@ class DAGScheduler(
         // TODO: This will be really slow if we keep accumulating shuffle map stages
         for ((shuffleId, stage) <- shuffleToMapStage) {
           stage.removeOutputsOnExecutor(execId)
-          val locs = stage.outputLocs.map(list => if (list.isEmpty) null else list.head)
+          val locs = stage.outputLocs.map(_.headOption.orNull)
           mapOutputTracker.registerMapOutputs(shuffleId, locs, changeEpoch = true)
         }
         if (shuffleToMapStage.isEmpty) {

From 143e521d1a0e16af18083524ceb4399cbe6372bc Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Fri, 4 Sep 2015 11:24:47 -0700
Subject: [PATCH 529/633] [MINOR] Minor style fix in SparkR

`dev/lintr-r` passes on my machine now

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #8601 from shivaram/sparkr-style-fix.
---
 R/pkg/R/deserialize.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/pkg/R/deserialize.R b/R/pkg/R/deserialize.R
index 88f18613fd7b1..d1858ec227b56 100644
--- a/R/pkg/R/deserialize.R
+++ b/R/pkg/R/deserialize.R
@@ -60,7 +60,7 @@ readString <- function(con) {
   raw <- readBin(con, raw(), stringLen, endian = "big")
   string <- rawToChar(raw)
   Encoding(string) <- "UTF-8"
-  string 
+  string
 }
 
 readInt <- function(con) {

From 804a0126e0cc982cc9f22cc76ba7b874ebbef5dd Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Fri, 4 Sep 2015 14:59:56 -0700
Subject: [PATCH 530/633] MAINTENANCE: Automated closing of pull requests. This
 commit exists to close the following pull requests on Github:

Closes #1890 (requested by andrewor14, JoshRosen)
Closes #3558 (requested by JoshRosen, marmbrus)
Closes #3890 (requested by marmbrus)
Closes #3895 (requested by andrewor14, marmbrus)
Closes #4055 (requested by andrewor14)
Closes #4105 (requested by andrewor14)
Closes #4812 (requested by marmbrus)
Closes #5109 (requested by andrewor14)
Closes #5178 (requested by andrewor14)
Closes #5298 (requested by marmbrus)
Closes #5393 (requested by marmbrus)
Closes #5449 (requested by andrewor14)
Closes #5468 (requested by marmbrus)
Closes #5715 (requested by marmbrus)
Closes #6192 (requested by marmbrus)
Closes #6319 (requested by marmbrus)
Closes #6326 (requested by marmbrus)
Closes #6349 (requested by marmbrus)
Closes #6380 (requested by andrewor14)
Closes #6554 (requested by marmbrus)
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Closes #6868 (requested by marmbrus)
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Closes #7914 (requested by andrewor14)
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Closes #8576 (requested by andrewor14)

From c3c0e431a6280fbcf726ac9bc4db0e1b5a862be8 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 4 Sep 2015 15:17:37 -0700
Subject: [PATCH 531/633] [SPARK-10176] [SQL] Show partially analyzed plans
 when checkAnswer fails to analyze

This PR takes over https://github.com/apache/spark/pull/8389.

This PR improves `checkAnswer` to print the partially analyzed plan in addition to the user friendly error message, in order to aid debugging failing tests.

In doing so, I ran into a conflict with the various ways that we bring a SQLContext into the tests. Depending on the trait we refer to the current context as `sqlContext`, `_sqlContext`, `ctx` or `hiveContext` with access modifiers `public`, `protected` and `private` depending on the defining class.

I propose we refactor as follows:

1. All tests should only refer to a `protected sqlContext` when testing general features, and `protected hiveContext` when it is a method that only exists on a `HiveContext`.
2. All tests should only import `testImplicits._` (i.e., don't import `TestHive.implicits._`)

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8584 from cloud-fan/cleanupTests.
---
 .../spark/sql/catalyst/plans/PlanTest.scala   |   1 -
 .../apache/spark/sql/CachedTableSuite.scala   | 156 ++++++-------
 .../spark/sql/ColumnExpressionSuite.scala     |  16 +-
 .../spark/sql/DataFrameAggregateSuite.scala   |   4 +-
 .../spark/sql/DataFrameComplexTypeSuite.scala |   6 +-
 .../spark/sql/DataFrameImplicitsSuite.scala   |   8 +-
 .../apache/spark/sql/DataFrameStatSuite.scala |  10 +-
 .../org/apache/spark/sql/DataFrameSuite.scala |  14 +-
 .../spark/sql/DataFrameTungstenSuite.scala    |   6 +-
 .../spark/sql/ExtraStrategiesSuite.scala      |   2 +-
 .../org/apache/spark/sql/JoinSuite.scala      |  12 +-
 .../apache/spark/sql/ListTablesSuite.scala    |  20 +-
 .../org/apache/spark/sql/QueryTest.scala      |  27 ++-
 .../scala/org/apache/spark/sql/RowSuite.scala |   2 +-
 .../org/apache/spark/sql/SQLConfSuite.scala   |  44 ++--
 .../apache/spark/sql/SQLContextSuite.scala    |  12 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  40 ++--
 .../apache/spark/sql/SerializationSuite.scala |   2 +-
 .../spark/sql/StringFunctionsSuite.scala      |  47 ++--
 .../scala/org/apache/spark/sql/UDFSuite.scala |  42 ++--
 .../spark/sql/UserDefinedTypeSuite.scala      |   6 +-
 .../columnar/InMemoryColumnarQuerySuite.scala |  41 ++--
 .../columnar/PartitionBatchPruningSuite.scala |  20 +-
 .../spark/sql/execution/ExchangeSuite.scala   |   2 +
 .../spark/sql/execution/PlannerSuite.scala    |  99 ++++----
 .../execution/RowFormatConvertersSuite.scala  |  16 +-
 .../spark/sql/execution/SortSuite.scala       |   1 +
 .../spark/sql/execution/SparkPlanTest.scala   |  27 +--
 .../sql/execution/TungstenSortSuite.scala     |  12 +-
 .../TungstenAggregationIteratorSuite.scala    |   2 +-
 .../datasources/json/JsonSuite.scala          | 214 +++++++++---------
 .../datasources/json/TestJsonData.scala       |  34 +--
 .../parquet/ParquetCompatibilityTest.scala    |   5 +-
 .../datasources/parquet/ParquetIOSuite.scala  |  52 ++---
 .../ParquetPartitionDiscoverySuite.scala      |   2 +-
 .../parquet/ParquetQuerySuite.scala           |  42 ++--
 .../datasources/parquet/ParquetTest.scala     |   9 +-
 .../execution/joins/BroadcastJoinSuite.scala  |  10 +-
 .../execution/joins/HashedRelationSuite.scala |   6 +-
 .../sql/execution/joins/InnerJoinSuite.scala  |   9 +-
 .../sql/execution/joins/OuterJoinSuite.scala  |   8 +-
 .../sql/execution/joins/SemiJoinSuite.scala   |   8 +-
 .../execution/metric/SQLMetricsSuite.scala    |  24 +-
 .../sql/execution/ui/SQLListenerSuite.scala   |   8 +-
 .../org/apache/spark/sql/jdbc/JDBCSuite.scala |  20 +-
 .../spark/sql/jdbc/JDBCWriteSuite.scala       |  52 ++---
 .../sources/CreateTableAsSelectSuite.scala    |   2 -
 .../spark/sql/sources/DataSourceTest.scala    |   4 +-
 .../spark/sql/sources/InsertSuite.scala       |   3 +-
 .../sql/sources/PartitionedWriteSuite.scala   |   8 +-
 .../spark/sql/sources/SaveLoadSuite.scala     |   1 -
 .../apache/spark/sql/test/SQLTestData.scala   |  52 ++---
 .../apache/spark/sql/test/SQLTestUtils.scala  |  41 ++--
 .../spark/sql/test/SharedSQLContext.scala     |  17 +-
 .../spark/sql/test/TestSQLContext.scala       |   2 +-
 .../apache/spark/sql/hive/test/TestHive.scala |   7 +-
 .../spark/sql/hive/CachedTableSuite.scala     |  20 +-
 .../spark/sql/hive/ErrorPositionSuite.scala   |   8 +-
 .../hive/HiveDataFrameAnalyticsSuite.scala    |  13 +-
 .../sql/hive/HiveDataFrameJoinSuite.scala     |   6 +-
 .../sql/hive/HiveDataFrameWindowSuite.scala   |   7 +-
 .../sql/hive/HiveMetastoreCatalogSuite.scala  |  20 +-
 .../spark/sql/hive/HiveParquetSuite.scala     |  12 +-
 .../spark/sql/hive/HiveSparkSubmitSuite.scala |  11 +-
 .../sql/hive/InsertIntoHiveTableSuite.scala   |  35 ++-
 .../spark/sql/hive/ListTablesSuite.scala      |  12 +-
 .../sql/hive/MetastoreDataSourcesSuite.scala  |  13 +-
 .../spark/sql/hive/MultiDatabaseSuite.scala   |  20 +-
 .../hive/ParquetHiveCompatibilitySuite.scala  |  24 +-
 .../spark/sql/hive/QueryPartitionSuite.scala  |  18 +-
 .../spark/sql/hive/StatisticsSuite.scala      |  42 ++--
 .../org/apache/spark/sql/hive/UDFSuite.scala  |  16 +-
 .../execution/AggregationQuerySuite.scala     |  19 +-
 .../hive/execution/HiveComparisonTest.scala   |   4 +-
 .../sql/hive/execution/HiveExplainSuite.scala |  11 +-
 .../HiveOperatorQueryableSuite.scala          |   8 +-
 .../sql/hive/execution/HivePlanTest.scala     |   8 +-
 .../sql/hive/execution/HiveUDFSuite.scala     |  54 ++---
 .../sql/hive/execution/SQLQuerySuite.scala    |  39 ++--
 .../execution/ScriptTransformationSuite.scala |  17 +-
 .../hive/orc/OrcHadoopFsRelationSuite.scala   |   7 +-
 .../hive/orc/OrcPartitionDiscoverySuite.scala |  23 +-
 .../spark/sql/hive/orc/OrcSourceSuite.scala   |  10 +-
 .../apache/spark/sql/hive/orc/OrcTest.scala   |   9 +-
 .../apache/spark/sql/hive/parquetSuites.scala |  16 +-
 .../CommitFailureTestRelationSuite.scala      |   9 +-
 .../sources/JsonHadoopFsRelationSuite.scala   |  12 +-
 .../ParquetHadoopFsRelationSuite.scala        |  15 +-
 .../SimpleTextHadoopFsRelationSuite.scala     |   4 +-
 .../sql/sources/hadoopFsRelationSuites.scala  |  28 ++-
 90 files changed, 908 insertions(+), 999 deletions(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
index 765c1e2dda99f..f76a903dcc9cf 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/plans/PlanTest.scala
@@ -26,7 +26,6 @@ import org.apache.spark.sql.catalyst.util._
  * Provides helper methods for comparing plans.
  */
 class PlanTest extends SparkFunSuite {
-
   /**
    * Since attribute references are given globally unique ids during analysis,
    * we must normalize them to check if two different queries are identical.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index af7590c3d3c17..3a3541a8429b6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -34,7 +34,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
   import testImplicits._
 
   def rddIdOf(tableName: String): Int = {
-    val executedPlan = ctx.table(tableName).queryExecution.executedPlan
+    val executedPlan = sqlContext.table(tableName).queryExecution.executedPlan
     executedPlan.collect {
       case InMemoryColumnarTableScan(_, _, relation) =>
         relation.cachedColumnBuffers.id
@@ -44,7 +44,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
   }
 
   def isMaterialized(rddId: Int): Boolean = {
-    ctx.sparkContext.env.blockManager.get(RDDBlockId(rddId, 0)).nonEmpty
+    sparkContext.env.blockManager.get(RDDBlockId(rddId, 0)).nonEmpty
   }
 
   test("withColumn doesn't invalidate cached dataframe") {
@@ -69,41 +69,41 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
   test("cache temp table") {
     testData.select('key).registerTempTable("tempTable")
     assertCached(sql("SELECT COUNT(*) FROM tempTable"), 0)
-    ctx.cacheTable("tempTable")
+    sqlContext.cacheTable("tempTable")
     assertCached(sql("SELECT COUNT(*) FROM tempTable"))
-    ctx.uncacheTable("tempTable")
+    sqlContext.uncacheTable("tempTable")
   }
 
   test("unpersist an uncached table will not raise exception") {
-    assert(None == ctx.cacheManager.lookupCachedData(testData))
+    assert(None == sqlContext.cacheManager.lookupCachedData(testData))
     testData.unpersist(blocking = true)
-    assert(None == ctx.cacheManager.lookupCachedData(testData))
+    assert(None == sqlContext.cacheManager.lookupCachedData(testData))
     testData.unpersist(blocking = false)
-    assert(None == ctx.cacheManager.lookupCachedData(testData))
+    assert(None == sqlContext.cacheManager.lookupCachedData(testData))
     testData.persist()
-    assert(None != ctx.cacheManager.lookupCachedData(testData))
+    assert(None != sqlContext.cacheManager.lookupCachedData(testData))
     testData.unpersist(blocking = true)
-    assert(None == ctx.cacheManager.lookupCachedData(testData))
+    assert(None == sqlContext.cacheManager.lookupCachedData(testData))
     testData.unpersist(blocking = false)
-    assert(None == ctx.cacheManager.lookupCachedData(testData))
+    assert(None == sqlContext.cacheManager.lookupCachedData(testData))
   }
 
   test("cache table as select") {
     sql("CACHE TABLE tempTable AS SELECT key FROM testData")
     assertCached(sql("SELECT COUNT(*) FROM tempTable"))
-    ctx.uncacheTable("tempTable")
+    sqlContext.uncacheTable("tempTable")
   }
 
   test("uncaching temp table") {
     testData.select('key).registerTempTable("tempTable1")
     testData.select('key).registerTempTable("tempTable2")
-    ctx.cacheTable("tempTable1")
+    sqlContext.cacheTable("tempTable1")
 
     assertCached(sql("SELECT COUNT(*) FROM tempTable1"))
     assertCached(sql("SELECT COUNT(*) FROM tempTable2"))
 
     // Is this valid?
-    ctx.uncacheTable("tempTable2")
+    sqlContext.uncacheTable("tempTable2")
 
     // Should this be cached?
     assertCached(sql("SELECT COUNT(*) FROM tempTable1"), 0)
@@ -111,103 +111,103 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("too big for memory") {
     val data = "*" * 1000
-    ctx.sparkContext.parallelize(1 to 200000, 1).map(_ => BigData(data)).toDF()
+    sparkContext.parallelize(1 to 200000, 1).map(_ => BigData(data)).toDF()
       .registerTempTable("bigData")
-    ctx.table("bigData").persist(StorageLevel.MEMORY_AND_DISK)
-    assert(ctx.table("bigData").count() === 200000L)
-    ctx.table("bigData").unpersist(blocking = true)
+    sqlContext.table("bigData").persist(StorageLevel.MEMORY_AND_DISK)
+    assert(sqlContext.table("bigData").count() === 200000L)
+    sqlContext.table("bigData").unpersist(blocking = true)
   }
 
   test("calling .cache() should use in-memory columnar caching") {
-    ctx.table("testData").cache()
-    assertCached(ctx.table("testData"))
-    ctx.table("testData").unpersist(blocking = true)
+    sqlContext.table("testData").cache()
+    assertCached(sqlContext.table("testData"))
+    sqlContext.table("testData").unpersist(blocking = true)
   }
 
   test("calling .unpersist() should drop in-memory columnar cache") {
-    ctx.table("testData").cache()
-    ctx.table("testData").count()
-    ctx.table("testData").unpersist(blocking = true)
-    assertCached(ctx.table("testData"), 0)
+    sqlContext.table("testData").cache()
+    sqlContext.table("testData").count()
+    sqlContext.table("testData").unpersist(blocking = true)
+    assertCached(sqlContext.table("testData"), 0)
   }
 
   test("isCached") {
-    ctx.cacheTable("testData")
+    sqlContext.cacheTable("testData")
 
-    assertCached(ctx.table("testData"))
-    assert(ctx.table("testData").queryExecution.withCachedData match {
+    assertCached(sqlContext.table("testData"))
+    assert(sqlContext.table("testData").queryExecution.withCachedData match {
       case _: InMemoryRelation => true
       case _ => false
     })
 
-    ctx.uncacheTable("testData")
-    assert(!ctx.isCached("testData"))
-    assert(ctx.table("testData").queryExecution.withCachedData match {
+    sqlContext.uncacheTable("testData")
+    assert(!sqlContext.isCached("testData"))
+    assert(sqlContext.table("testData").queryExecution.withCachedData match {
       case _: InMemoryRelation => false
       case _ => true
     })
   }
 
   test("SPARK-1669: cacheTable should be idempotent") {
-    assume(!ctx.table("testData").logicalPlan.isInstanceOf[InMemoryRelation])
+    assume(!sqlContext.table("testData").logicalPlan.isInstanceOf[InMemoryRelation])
 
-    ctx.cacheTable("testData")
-    assertCached(ctx.table("testData"))
+    sqlContext.cacheTable("testData")
+    assertCached(sqlContext.table("testData"))
 
     assertResult(1, "InMemoryRelation not found, testData should have been cached") {
-      ctx.table("testData").queryExecution.withCachedData.collect {
+      sqlContext.table("testData").queryExecution.withCachedData.collect {
         case r: InMemoryRelation => r
       }.size
     }
 
-    ctx.cacheTable("testData")
+    sqlContext.cacheTable("testData")
     assertResult(0, "Double InMemoryRelations found, cacheTable() is not idempotent") {
-      ctx.table("testData").queryExecution.withCachedData.collect {
+      sqlContext.table("testData").queryExecution.withCachedData.collect {
         case r @ InMemoryRelation(_, _, _, _, _: InMemoryColumnarTableScan, _) => r
       }.size
     }
 
-    ctx.uncacheTable("testData")
+    sqlContext.uncacheTable("testData")
   }
 
   test("read from cached table and uncache") {
-    ctx.cacheTable("testData")
-    checkAnswer(ctx.table("testData"), testData.collect().toSeq)
-    assertCached(ctx.table("testData"))
+    sqlContext.cacheTable("testData")
+    checkAnswer(sqlContext.table("testData"), testData.collect().toSeq)
+    assertCached(sqlContext.table("testData"))
 
-    ctx.uncacheTable("testData")
-    checkAnswer(ctx.table("testData"), testData.collect().toSeq)
-    assertCached(ctx.table("testData"), 0)
+    sqlContext.uncacheTable("testData")
+    checkAnswer(sqlContext.table("testData"), testData.collect().toSeq)
+    assertCached(sqlContext.table("testData"), 0)
   }
 
   test("correct error on uncache of non-cached table") {
     intercept[IllegalArgumentException] {
-      ctx.uncacheTable("testData")
+      sqlContext.uncacheTable("testData")
     }
   }
 
   test("SELECT star from cached table") {
     sql("SELECT * FROM testData").registerTempTable("selectStar")
-    ctx.cacheTable("selectStar")
+    sqlContext.cacheTable("selectStar")
     checkAnswer(
       sql("SELECT * FROM selectStar WHERE key = 1"),
       Seq(Row(1, "1")))
-    ctx.uncacheTable("selectStar")
+    sqlContext.uncacheTable("selectStar")
   }
 
   test("Self-join cached") {
     val unCachedAnswer =
       sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key").collect()
-    ctx.cacheTable("testData")
+    sqlContext.cacheTable("testData")
     checkAnswer(
       sql("SELECT * FROM testData a JOIN testData b ON a.key = b.key"),
       unCachedAnswer.toSeq)
-    ctx.uncacheTable("testData")
+    sqlContext.uncacheTable("testData")
   }
 
   test("'CACHE TABLE' and 'UNCACHE TABLE' SQL statement") {
     sql("CACHE TABLE testData")
-    assertCached(ctx.table("testData"))
+    assertCached(sqlContext.table("testData"))
 
     val rddId = rddIdOf("testData")
     assert(
@@ -215,7 +215,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
       "Eagerly cached in-memory table should have already been materialized")
 
     sql("UNCACHE TABLE testData")
-    assert(!ctx.isCached("testData"), "Table 'testData' should not be cached")
+    assert(!sqlContext.isCached("testData"), "Table 'testData' should not be cached")
 
     eventually(timeout(10 seconds)) {
       assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
@@ -224,14 +224,14 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("CACHE TABLE tableName AS SELECT * FROM anotherTable") {
     sql("CACHE TABLE testCacheTable AS SELECT * FROM testData")
-    assertCached(ctx.table("testCacheTable"))
+    assertCached(sqlContext.table("testCacheTable"))
 
     val rddId = rddIdOf("testCacheTable")
     assert(
       isMaterialized(rddId),
       "Eagerly cached in-memory table should have already been materialized")
 
-    ctx.uncacheTable("testCacheTable")
+    sqlContext.uncacheTable("testCacheTable")
     eventually(timeout(10 seconds)) {
       assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
     }
@@ -239,14 +239,14 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("CACHE TABLE tableName AS SELECT ...") {
     sql("CACHE TABLE testCacheTable AS SELECT key FROM testData LIMIT 10")
-    assertCached(ctx.table("testCacheTable"))
+    assertCached(sqlContext.table("testCacheTable"))
 
     val rddId = rddIdOf("testCacheTable")
     assert(
       isMaterialized(rddId),
       "Eagerly cached in-memory table should have already been materialized")
 
-    ctx.uncacheTable("testCacheTable")
+    sqlContext.uncacheTable("testCacheTable")
     eventually(timeout(10 seconds)) {
       assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
     }
@@ -254,7 +254,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("CACHE LAZY TABLE tableName") {
     sql("CACHE LAZY TABLE testData")
-    assertCached(ctx.table("testData"))
+    assertCached(sqlContext.table("testData"))
 
     val rddId = rddIdOf("testData")
     assert(
@@ -266,7 +266,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
       isMaterialized(rddId),
       "Lazily cached in-memory table should have been materialized")
 
-    ctx.uncacheTable("testData")
+    sqlContext.uncacheTable("testData")
     eventually(timeout(10 seconds)) {
       assert(!isMaterialized(rddId), "Uncached in-memory table should have been unpersisted")
     }
@@ -274,7 +274,7 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("InMemoryRelation statistics") {
     sql("CACHE TABLE testData")
-    ctx.table("testData").queryExecution.withCachedData.collect {
+    sqlContext.table("testData").queryExecution.withCachedData.collect {
       case cached: InMemoryRelation =>
         val actualSizeInBytes = (1 to 100).map(i => INT.defaultSize + i.toString.length + 4).sum
         assert(cached.statistics.sizeInBytes === actualSizeInBytes)
@@ -283,46 +283,48 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
   test("Drops temporary table") {
     testData.select('key).registerTempTable("t1")
-    ctx.table("t1")
-    ctx.dropTempTable("t1")
-    assert(intercept[RuntimeException](ctx.table("t1")).getMessage.startsWith("Table Not Found"))
+    sqlContext.table("t1")
+    sqlContext.dropTempTable("t1")
+    assert(
+      intercept[RuntimeException](sqlContext.table("t1")).getMessage.startsWith("Table Not Found"))
   }
 
   test("Drops cached temporary table") {
     testData.select('key).registerTempTable("t1")
     testData.select('key).registerTempTable("t2")
-    ctx.cacheTable("t1")
+    sqlContext.cacheTable("t1")
 
-    assert(ctx.isCached("t1"))
-    assert(ctx.isCached("t2"))
+    assert(sqlContext.isCached("t1"))
+    assert(sqlContext.isCached("t2"))
 
-    ctx.dropTempTable("t1")
-    assert(intercept[RuntimeException](ctx.table("t1")).getMessage.startsWith("Table Not Found"))
-    assert(!ctx.isCached("t2"))
+    sqlContext.dropTempTable("t1")
+    assert(
+      intercept[RuntimeException](sqlContext.table("t1")).getMessage.startsWith("Table Not Found"))
+    assert(!sqlContext.isCached("t2"))
   }
 
   test("Clear all cache") {
     sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1")
     sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2")
-    ctx.cacheTable("t1")
-    ctx.cacheTable("t2")
-    ctx.clearCache()
-    assert(ctx.cacheManager.isEmpty)
+    sqlContext.cacheTable("t1")
+    sqlContext.cacheTable("t2")
+    sqlContext.clearCache()
+    assert(sqlContext.cacheManager.isEmpty)
 
     sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1")
     sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2")
-    ctx.cacheTable("t1")
-    ctx.cacheTable("t2")
+    sqlContext.cacheTable("t1")
+    sqlContext.cacheTable("t2")
     sql("Clear CACHE")
-    assert(ctx.cacheManager.isEmpty)
+    assert(sqlContext.cacheManager.isEmpty)
   }
 
   test("Clear accumulators when uncacheTable to prevent memory leaking") {
     sql("SELECT key FROM testData LIMIT 10").registerTempTable("t1")
     sql("SELECT key FROM testData LIMIT 5").registerTempTable("t2")
 
-    ctx.cacheTable("t1")
-    ctx.cacheTable("t2")
+    sqlContext.cacheTable("t1")
+    sqlContext.cacheTable("t2")
 
     sql("SELECT * FROM t1").count()
     sql("SELECT * FROM t2").count()
@@ -331,8 +333,8 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
 
     Accumulators.synchronized {
       val accsSize = Accumulators.originals.size
-      ctx.uncacheTable("t1")
-      ctx.uncacheTable("t2")
+      sqlContext.uncacheTable("t1")
+      sqlContext.uncacheTable("t2")
       assert((accsSize - 2) == Accumulators.originals.size)
     }
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
index 37738ec5b3c1d..4e988f074b113 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ColumnExpressionSuite.scala
@@ -29,7 +29,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
   import testImplicits._
 
   private lazy val booleanData = {
-    ctx.createDataFrame(ctx.sparkContext.parallelize(
+    sqlContext.createDataFrame(sparkContext.parallelize(
       Row(false, false) ::
       Row(false, true) ::
       Row(true, false) ::
@@ -286,7 +286,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
   }
 
   test("isNaN") {
-    val testData = ctx.createDataFrame(ctx.sparkContext.parallelize(
+    val testData = sqlContext.createDataFrame(sparkContext.parallelize(
       Row(Double.NaN, Float.NaN) ::
       Row(math.log(-1), math.log(-3).toFloat) ::
       Row(null, null) ::
@@ -307,7 +307,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
   }
 
   test("nanvl") {
-    val testData = ctx.createDataFrame(ctx.sparkContext.parallelize(
+    val testData = sqlContext.createDataFrame(sparkContext.parallelize(
       Row(null, 3.0, Double.NaN, Double.PositiveInfinity, 1.0f, 4) :: Nil),
       StructType(Seq(StructField("a", DoubleType), StructField("b", DoubleType),
         StructField("c", DoubleType), StructField("d", DoubleType),
@@ -350,7 +350,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
   }
 
   test("!==") {
-    val nullData = ctx.createDataFrame(ctx.sparkContext.parallelize(
+    val nullData = sqlContext.createDataFrame(sparkContext.parallelize(
       Row(1, 1) ::
       Row(1, 2) ::
       Row(1, null) ::
@@ -411,7 +411,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
   }
 
   test("between") {
-    val testData = ctx.sparkContext.parallelize(
+    val testData = sparkContext.parallelize(
       (0, 1, 2) ::
       (1, 2, 3) ::
       (2, 1, 0) ::
@@ -556,7 +556,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
 
   test("monotonicallyIncreasingId") {
     // Make sure we have 2 partitions, each with 2 records.
-    val df = ctx.sparkContext.parallelize(Seq[Int](), 2).mapPartitions { _ =>
+    val df = sparkContext.parallelize(Seq[Int](), 2).mapPartitions { _ =>
       Iterator(Tuple1(1), Tuple1(2))
     }.toDF("a")
     checkAnswer(
@@ -567,7 +567,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
 
   test("sparkPartitionId") {
     // Make sure we have 2 partitions, each with 2 records.
-    val df = ctx.sparkContext.parallelize(Seq[Int](), 2).mapPartitions { _ =>
+    val df = sparkContext.parallelize(Seq[Int](), 2).mapPartitions { _ =>
       Iterator(Tuple1(1), Tuple1(2))
     }.toDF("a")
     checkAnswer(
@@ -578,7 +578,7 @@ class ColumnExpressionSuite extends QueryTest with SharedSQLContext {
 
   test("InputFileName") {
     withTempPath { dir =>
-      val data = sqlContext.sparkContext.parallelize(0 to 10).toDF("id")
+      val data = sparkContext.parallelize(0 to 10).toDF("id")
       data.write.parquet(dir.getCanonicalPath)
       val answer = sqlContext.read.parquet(dir.getCanonicalPath).select(inputFileName())
         .head.getString(0)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
index 72cf7aab0b977..c0950b09b14ad 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
@@ -66,12 +66,12 @@ class DataFrameAggregateSuite extends QueryTest with SharedSQLContext {
       Seq(Row(1, 3), Row(2, 3), Row(3, 3))
     )
 
-    ctx.conf.setConf(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS, false)
+    sqlContext.conf.setConf(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS, false)
     checkAnswer(
       testData2.groupBy("a").agg(sum($"b")),
       Seq(Row(3), Row(3), Row(3))
     )
-    ctx.conf.setConf(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS, true)
+    sqlContext.conf.setConf(SQLConf.DATAFRAME_RETAIN_GROUP_COLUMNS, true)
   }
 
   test("agg without groups") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala
index 3c359dd840ab7..09f7b507670c9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameComplexTypeSuite.scala
@@ -28,19 +28,19 @@ class DataFrameComplexTypeSuite extends QueryTest with SharedSQLContext {
 
   test("UDF on struct") {
     val f = udf((a: String) => a)
-    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    val df = sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
     df.select(struct($"a").as("s")).select(f($"s.a")).collect()
   }
 
   test("UDF on named_struct") {
     val f = udf((a: String) => a)
-    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    val df = sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
     df.selectExpr("named_struct('a', a) s").select(f($"s.a")).collect()
   }
 
   test("UDF on array") {
     val f = udf((a: String) => a)
-    val df = sqlContext.sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
+    val df = sparkContext.parallelize(Seq((1, 1))).toDF("a", "b")
     df.select(array($"a").as("s")).select(f(expr("s[0]"))).collect()
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
index e5d7d63441a6b..094efbaeadcd5 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameImplicitsSuite.scala
@@ -24,7 +24,7 @@ class DataFrameImplicitsSuite extends QueryTest with SharedSQLContext {
 
   test("RDD of tuples") {
     checkAnswer(
-      ctx.sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("intCol", "strCol"),
+      sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("intCol", "strCol"),
       (1 to 10).map(i => Row(i, i.toString)))
   }
 
@@ -36,19 +36,19 @@ class DataFrameImplicitsSuite extends QueryTest with SharedSQLContext {
 
   test("RDD[Int]") {
     checkAnswer(
-      ctx.sparkContext.parallelize(1 to 10).toDF("intCol"),
+      sparkContext.parallelize(1 to 10).toDF("intCol"),
       (1 to 10).map(i => Row(i)))
   }
 
   test("RDD[Long]") {
     checkAnswer(
-      ctx.sparkContext.parallelize(1L to 10L).toDF("longCol"),
+      sparkContext.parallelize(1L to 10L).toDF("longCol"),
       (1L to 10L).map(i => Row(i)))
   }
 
   test("RDD[String]") {
     checkAnswer(
-      ctx.sparkContext.parallelize(1 to 10).map(_.toString).toDF("stringCol"),
+      sparkContext.parallelize(1 to 10).map(_.toString).toDF("stringCol"),
       (1 to 10).map(i => Row(i.toString)))
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
index 28bdd6f83b687..6524abcf5e97f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameStatSuite.scala
@@ -29,7 +29,7 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext {
 
   test("sample with replacement") {
     val n = 100
-    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = sparkContext.parallelize(1 to n, 2).toDF("id")
     checkAnswer(
       data.sample(withReplacement = true, 0.05, seed = 13),
       Seq(5, 10, 52, 73).map(Row(_))
@@ -38,7 +38,7 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext {
 
   test("sample without replacement") {
     val n = 100
-    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = sparkContext.parallelize(1 to n, 2).toDF("id")
     checkAnswer(
       data.sample(withReplacement = false, 0.05, seed = 13),
       Seq(16, 23, 88, 100).map(Row(_))
@@ -47,7 +47,7 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext {
 
   test("randomSplit") {
     val n = 600
-    val data = ctx.sparkContext.parallelize(1 to n, 2).toDF("id")
+    val data = sparkContext.parallelize(1 to n, 2).toDF("id")
     for (seed <- 1 to 5) {
       val splits = data.randomSplit(Array[Double](1, 2, 3), seed)
       assert(splits.length == 3, "wrong number of splits")
@@ -164,7 +164,7 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext {
   }
 
   test("Frequent Items 2") {
-    val rows = ctx.sparkContext.parallelize(Seq.empty[Int], 4)
+    val rows = sparkContext.parallelize(Seq.empty[Int], 4)
     // this is a regression test, where when merging partitions, we omitted values with higher
     // counts than those that existed in the map when the map was full. This test should also fail
     // if anything like SPARK-9614 is observed once again
@@ -182,7 +182,7 @@ class DataFrameStatSuite extends QueryTest with SharedSQLContext {
   }
 
   test("sampleBy") {
-    val df = ctx.range(0, 100).select((col("id") % 3).as("key"))
+    val df = sqlContext.range(0, 100).select((col("id") % 3).as("key"))
     val sampled = df.stat.sampleBy("key", Map(0 -> 0.1, 1 -> 0.2), 0L)
     checkAnswer(
       sampled.groupBy("key").count().orderBy("key"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index a4871e247cff7..b5b9f11785074 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -345,7 +345,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
   }
 
   test("replace column using withColumn") {
-    val df2 = sqlContext.sparkContext.parallelize(Array(1, 2, 3)).toDF("x")
+    val df2 = sparkContext.parallelize(Array(1, 2, 3)).toDF("x")
     val df3 = df2.withColumn("x", df2("x") + 1)
     checkAnswer(
       df3.select("x"),
@@ -506,7 +506,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
 
   test("showString: truncate = [true, false]") {
     val longString = Array.fill(21)("1").mkString
-    val df = sqlContext.sparkContext.parallelize(Seq("1", longString)).toDF()
+    val df = sparkContext.parallelize(Seq("1", longString)).toDF()
     val expectedAnswerForFalse = """+---------------------+
                                    ||_1                   |
                                    |+---------------------+
@@ -596,7 +596,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
   }
 
   test("createDataFrame(RDD[Row], StructType) should convert UDTs (SPARK-6672)") {
-    val rowRDD = sqlContext.sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0))))
+    val rowRDD = sparkContext.parallelize(Seq(Row(new ExamplePoint(1.0, 2.0))))
     val schema = StructType(Array(StructField("point", new ExamplePointUDT(), false)))
     val df = sqlContext.createDataFrame(rowRDD, schema)
     df.rdd.collect()
@@ -619,14 +619,14 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-7551: support backticks for DataFrame attribute resolution") {
-    val df = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    val df = sqlContext.read.json(sparkContext.makeRDD(
       """{"a.b": {"c": {"d..e": {"f": 1}}}}""" :: Nil))
     checkAnswer(
       df.select(df("`a.b`.c.`d..e`.`f`")),
       Row(1)
     )
 
-    val df2 = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    val df2 = sqlContext.read.json(sparkContext.makeRDD(
       """{"a  b": {"c": {"d  e": {"f": 1}}}}""" :: Nil))
     checkAnswer(
       df2.select(df2("`a  b`.c.d  e.f")),
@@ -646,7 +646,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-7324 dropDuplicates") {
-    val testData = sqlContext.sparkContext.parallelize(
+    val testData = sparkContext.parallelize(
       (2, 1, 2) :: (1, 1, 1) ::
       (1, 2, 1) :: (2, 1, 2) ::
       (2, 2, 2) :: (2, 2, 1) ::
@@ -869,7 +869,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-9323: DataFrame.orderBy should support nested column name") {
-    val df = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    val df = sqlContext.read.json(sparkContext.makeRDD(
       """{"a": {"b": 1}}""" :: Nil))
     checkAnswer(df.orderBy("a.b"), Row(Row(1)))
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
index 77907e91363ec..7ae12a7895f7e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameTungstenSuite.scala
@@ -32,7 +32,7 @@ class DataFrameTungstenSuite extends QueryTest with SharedSQLContext {
 
   test("test simple types") {
     withSQLConf(SQLConf.UNSAFE_ENABLED.key -> "true") {
-      val df = sqlContext.sparkContext.parallelize(Seq((1, 2))).toDF("a", "b")
+      val df = sparkContext.parallelize(Seq((1, 2))).toDF("a", "b")
       assert(df.select(struct("a", "b")).first().getStruct(0) === Row(1, 2))
     }
   }
@@ -40,7 +40,7 @@ class DataFrameTungstenSuite extends QueryTest with SharedSQLContext {
   test("test struct type") {
     withSQLConf(SQLConf.UNSAFE_ENABLED.key -> "true") {
       val struct = Row(1, 2L, 3.0F, 3.0)
-      val data = sqlContext.sparkContext.parallelize(Seq(Row(1, struct)))
+      val data = sparkContext.parallelize(Seq(Row(1, struct)))
 
       val schema = new StructType()
         .add("a", IntegerType)
@@ -60,7 +60,7 @@ class DataFrameTungstenSuite extends QueryTest with SharedSQLContext {
     withSQLConf(SQLConf.UNSAFE_ENABLED.key -> "true") {
       val innerStruct = Row(1, "abcd")
       val outerStruct = Row(1, 2L, 3.0F, 3.0, innerStruct, "efg")
-      val data = sqlContext.sparkContext.parallelize(Seq(Row(1, outerStruct)))
+      val data = sparkContext.parallelize(Seq(Row(1, outerStruct)))
 
       val schema = new StructType()
         .add("a", IntegerType)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala
index 8d2f45d70308b..78a98798eff64 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ExtraStrategiesSuite.scala
@@ -52,7 +52,7 @@ class ExtraStrategiesSuite extends QueryTest with SharedSQLContext {
     try {
       sqlContext.experimental.extraStrategies = TestStrategy :: Nil
 
-      val df = sqlContext.sparkContext.parallelize(Seq(("so slow", 1))).toDF("a", "b")
+      val df = sparkContext.parallelize(Seq(("so slow", 1))).toDF("a", "b")
       checkAnswer(
         df.select("a"),
         Row("so fast"))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index f5c5046a8ed88..b05435bad5c5a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -31,7 +31,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
     val x = testData2.as("x")
     val y = testData2.as("y")
     val join = x.join(y, $"x.a" === $"y.a", "inner").queryExecution.optimizedPlan
-    val planned = ctx.planner.EquiJoinSelection(join)
+    val planned = sqlContext.planner.EquiJoinSelection(join)
     assert(planned.size === 1)
   }
 
@@ -59,7 +59,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
   }
 
   test("join operator selection") {
-    ctx.cacheManager.clearCache()
+    sqlContext.cacheManager.clearCache()
 
     Seq(
       ("SELECT * FROM testData LEFT SEMI JOIN testData2 ON key = a", classOf[LeftSemiJoinHash]),
@@ -118,7 +118,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
   }
 
   test("broadcasted hash join operator selection") {
-    ctx.cacheManager.clearCache()
+    sqlContext.cacheManager.clearCache()
     sql("CACHE TABLE testData")
     for (sortMergeJoinEnabled <- Seq(true, false)) {
       withClue(s"sortMergeJoinEnabled=$sortMergeJoinEnabled") {
@@ -138,7 +138,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
   }
 
   test("broadcasted hash outer join operator selection") {
-    ctx.cacheManager.clearCache()
+    sqlContext.cacheManager.clearCache()
     sql("CACHE TABLE testData")
     withSQLConf(SQLConf.SORTMERGE_JOIN.key -> "true") {
       Seq(
@@ -167,7 +167,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
     val x = testData2.as("x")
     val y = testData2.as("y")
     val join = x.join(y, ($"x.a" === $"y.a") && ($"x.b" === $"y.b")).queryExecution.optimizedPlan
-    val planned = ctx.planner.EquiJoinSelection(join)
+    val planned = sqlContext.planner.EquiJoinSelection(join)
     assert(planned.size === 1)
   }
 
@@ -442,7 +442,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
   }
 
   test("broadcasted left semi join operator selection") {
-    ctx.cacheManager.clearCache()
+    sqlContext.cacheManager.clearCache()
     sql("CACHE TABLE testData")
 
     withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "1000000000") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
index babf8835d2545..eab0fbb196eb6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ListTablesSuite.scala
@@ -32,33 +32,33 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter with SharedSQLContex
   }
 
   after {
-    ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
+    sqlContext.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
   }
 
   test("get all tables") {
     checkAnswer(
-      ctx.tables().filter("tableName = 'ListTablesSuiteTable'"),
+      sqlContext.tables().filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
     checkAnswer(
       sql("SHOW tables").filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
-    ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
-    assert(ctx.tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0)
+    sqlContext.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
+    assert(sqlContext.tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0)
   }
 
   test("getting all Tables with a database name has no impact on returned table names") {
     checkAnswer(
-      ctx.tables("DB").filter("tableName = 'ListTablesSuiteTable'"),
+      sqlContext.tables("DB").filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
     checkAnswer(
       sql("show TABLES in DB").filter("tableName = 'ListTablesSuiteTable'"),
       Row("ListTablesSuiteTable", true))
 
-    ctx.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
-    assert(ctx.tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0)
+    sqlContext.catalog.unregisterTable(Seq("ListTablesSuiteTable"))
+    assert(sqlContext.tables().filter("tableName = 'ListTablesSuiteTable'").count() === 0)
   }
 
   test("query the returned DataFrame of tables") {
@@ -66,7 +66,7 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter with SharedSQLContex
       StructField("tableName", StringType, false) ::
       StructField("isTemporary", BooleanType, false) :: Nil)
 
-    Seq(ctx.tables(), sql("SHOW TABLes")).foreach {
+    Seq(sqlContext.tables(), sql("SHOW TABLes")).foreach {
       case tableDF =>
         assert(expectedSchema === tableDF.schema)
 
@@ -77,9 +77,9 @@ class ListTablesSuite extends QueryTest with BeforeAndAfter with SharedSQLContex
           Row(true, "ListTablesSuiteTable")
         )
         checkAnswer(
-          ctx.tables().filter("tableName = 'tables'").select("tableName", "isTemporary"),
+          sqlContext.tables().filter("tableName = 'tables'").select("tableName", "isTemporary"),
           Row("tables", true))
-        ctx.dropTempTable("tables")
+        sqlContext.dropTempTable("tables")
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
index 3649c2a97b5ef..cada03e9ac6bb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/QueryTest.scala
@@ -25,7 +25,9 @@ import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.sql.columnar.InMemoryRelation
 
-class QueryTest extends PlanTest {
+abstract class QueryTest extends PlanTest {
+
+  protected def sqlContext: SQLContext
 
   // Timezone is fixed to America/Los_Angeles for those timezone sensitive tests (timestamp_*)
   TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
@@ -56,18 +58,33 @@ class QueryTest extends PlanTest {
    * @param df the [[DataFrame]] to be executed
    * @param expectedAnswer the expected result in a [[Seq]] of [[Row]]s.
    */
-  protected def checkAnswer(df: DataFrame, expectedAnswer: Seq[Row]): Unit = {
-    QueryTest.checkAnswer(df, expectedAnswer) match {
+  protected def checkAnswer(df: => DataFrame, expectedAnswer: Seq[Row]): Unit = {
+    val analyzedDF = try df catch {
+      case ae: AnalysisException =>
+        val currentValue = sqlContext.conf.dataFrameEagerAnalysis
+        sqlContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, false)
+        val partiallyAnalzyedPlan = df.queryExecution.analyzed
+        sqlContext.setConf(SQLConf.DATAFRAME_EAGER_ANALYSIS, currentValue)
+        fail(
+          s"""
+             |Failed to analyze query: $ae
+             |$partiallyAnalzyedPlan
+             |
+             |${stackTraceToString(ae)}
+             |""".stripMargin)
+    }
+
+    QueryTest.checkAnswer(analyzedDF, expectedAnswer) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
   }
 
-  protected def checkAnswer(df: DataFrame, expectedAnswer: Row): Unit = {
+  protected def checkAnswer(df: => DataFrame, expectedAnswer: Row): Unit = {
     checkAnswer(df, Seq(expectedAnswer))
   }
 
-  protected def checkAnswer(df: DataFrame, expectedAnswer: DataFrame): Unit = {
+  protected def checkAnswer(df: => DataFrame, expectedAnswer: DataFrame): Unit = {
     checkAnswer(df, expectedAnswer.collect())
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
index 77ccd6f775e50..3ba14d7602a62 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/RowSuite.scala
@@ -57,7 +57,7 @@ class RowSuite extends SparkFunSuite with SharedSQLContext {
 
   test("serialize w/ kryo") {
     val row = Seq((1, Seq(1), Map(1 -> 1), BigDecimal(1))).toDF().first()
-    val serializer = new SparkSqlSerializer(ctx.sparkContext.getConf)
+    val serializer = new SparkSqlSerializer(sparkContext.getConf)
     val instance = serializer.newInstance()
     val ser = instance.serialize(row)
     val de = instance.deserialize(ser).asInstanceOf[Row]
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 7699adadd9cc8..c35b31c96df48 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -27,58 +27,58 @@ class SQLConfSuite extends QueryTest with SharedSQLContext {
   test("propagate from spark conf") {
     // We create a new context here to avoid order dependence with other tests that might call
     // clear().
-    val newContext = new SQLContext(ctx.sparkContext)
+    val newContext = new SQLContext(sparkContext)
     assert(newContext.getConf("spark.sql.testkey", "false") === "true")
   }
 
   test("programmatic ways of basic setting and getting") {
-    ctx.conf.clear()
-    assert(ctx.getAllConfs.size === 0)
+    sqlContext.conf.clear()
+    assert(sqlContext.getAllConfs.size === 0)
 
-    ctx.setConf(testKey, testVal)
-    assert(ctx.getConf(testKey) === testVal)
-    assert(ctx.getConf(testKey, testVal + "_") === testVal)
-    assert(ctx.getAllConfs.contains(testKey))
+    sqlContext.setConf(testKey, testVal)
+    assert(sqlContext.getConf(testKey) === testVal)
+    assert(sqlContext.getConf(testKey, testVal + "_") === testVal)
+    assert(sqlContext.getAllConfs.contains(testKey))
 
     // Tests SQLConf as accessed from a SQLContext is mutable after
     // the latter is initialized, unlike SparkConf inside a SparkContext.
-    assert(ctx.getConf(testKey) == testVal)
-    assert(ctx.getConf(testKey, testVal + "_") === testVal)
-    assert(ctx.getAllConfs.contains(testKey))
+    assert(sqlContext.getConf(testKey) == testVal)
+    assert(sqlContext.getConf(testKey, testVal + "_") === testVal)
+    assert(sqlContext.getAllConfs.contains(testKey))
 
-    ctx.conf.clear()
+    sqlContext.conf.clear()
   }
 
   test("parse SQL set commands") {
-    ctx.conf.clear()
+    sqlContext.conf.clear()
     sql(s"set $testKey=$testVal")
-    assert(ctx.getConf(testKey, testVal + "_") === testVal)
-    assert(ctx.getConf(testKey, testVal + "_") === testVal)
+    assert(sqlContext.getConf(testKey, testVal + "_") === testVal)
+    assert(sqlContext.getConf(testKey, testVal + "_") === testVal)
 
     sql("set some.property=20")
-    assert(ctx.getConf("some.property", "0") === "20")
+    assert(sqlContext.getConf("some.property", "0") === "20")
     sql("set some.property = 40")
-    assert(ctx.getConf("some.property", "0") === "40")
+    assert(sqlContext.getConf("some.property", "0") === "40")
 
     val key = "spark.sql.key"
     val vs = "val0,val_1,val2.3,my_table"
     sql(s"set $key=$vs")
-    assert(ctx.getConf(key, "0") === vs)
+    assert(sqlContext.getConf(key, "0") === vs)
 
     sql(s"set $key=")
-    assert(ctx.getConf(key, "0") === "")
+    assert(sqlContext.getConf(key, "0") === "")
 
-    ctx.conf.clear()
+    sqlContext.conf.clear()
   }
 
   test("deprecated property") {
-    ctx.conf.clear()
+    sqlContext.conf.clear()
     sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
-    assert(ctx.conf.numShufflePartitions === 10)
+    assert(sqlContext.conf.numShufflePartitions === 10)
   }
 
   test("invalid conf value") {
-    ctx.conf.clear()
+    sqlContext.conf.clear()
     val e = intercept[IllegalArgumentException] {
       sql(s"set ${SQLConf.CASE_SENSITIVE.key}=10")
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
index 007be12950774..dd88ae3700ab9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLContextSuite.scala
@@ -24,7 +24,7 @@ class SQLContextSuite extends SparkFunSuite with SharedSQLContext {
 
   override def afterAll(): Unit = {
     try {
-      SQLContext.setLastInstantiatedContext(ctx)
+      SQLContext.setLastInstantiatedContext(sqlContext)
     } finally {
       super.afterAll()
     }
@@ -32,18 +32,18 @@ class SQLContextSuite extends SparkFunSuite with SharedSQLContext {
 
   test("getOrCreate instantiates SQLContext") {
     SQLContext.clearLastInstantiatedContext()
-    val sqlContext = SQLContext.getOrCreate(ctx.sparkContext)
+    val sqlContext = SQLContext.getOrCreate(sparkContext)
     assert(sqlContext != null, "SQLContext.getOrCreate returned null")
-    assert(SQLContext.getOrCreate(ctx.sparkContext).eq(sqlContext),
+    assert(SQLContext.getOrCreate(sparkContext).eq(sqlContext),
       "SQLContext created by SQLContext.getOrCreate not returned by SQLContext.getOrCreate")
   }
 
   test("getOrCreate gets last explicitly instantiated SQLContext") {
     SQLContext.clearLastInstantiatedContext()
-    val sqlContext = new SQLContext(ctx.sparkContext)
-    assert(SQLContext.getOrCreate(ctx.sparkContext) != null,
+    val sqlContext = new SQLContext(sparkContext)
+    assert(SQLContext.getOrCreate(sparkContext) != null,
       "SQLContext.getOrCreate after explicitly created SQLContext returned null")
-    assert(SQLContext.getOrCreate(ctx.sparkContext).eq(sqlContext),
+    assert(SQLContext.getOrCreate(sparkContext).eq(sqlContext),
       "SQLContext.getOrCreate after explicitly created SQLContext did not return the context")
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 0ef25fe0faef0..05f2000459599 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -147,14 +147,14 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SQL Dialect Switching to a new SQL parser") {
-    val newContext = new SQLContext(sqlContext.sparkContext)
+    val newContext = new SQLContext(sparkContext)
     newContext.setConf("spark.sql.dialect", classOf[MyDialect].getCanonicalName())
     assert(newContext.getSQLDialect().getClass === classOf[MyDialect])
     assert(newContext.sql("SELECT 1").collect() === Array(Row(1)))
   }
 
   test("SQL Dialect Switch to an invalid parser with alias") {
-    val newContext = new SQLContext(sqlContext.sparkContext)
+    val newContext = new SQLContext(sparkContext)
     newContext.sql("SET spark.sql.dialect=MyTestClass")
     intercept[DialectException] {
       newContext.sql("SELECT 1")
@@ -196,7 +196,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("grouping on nested fields") {
-    sqlContext.read.json(sqlContext.sparkContext.parallelize(
+    sqlContext.read.json(sparkContext.parallelize(
       """{"nested": {"attribute": 1}, "value": 2}""" :: Nil))
      .registerTempTable("rows")
 
@@ -215,7 +215,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("SPARK-6201 IN type conversion") {
     sqlContext.read.json(
-      sqlContext.sparkContext.parallelize(
+      sparkContext.parallelize(
         Seq("{\"a\": \"1\"}}", "{\"a\": \"2\"}}", "{\"a\": \"3\"}}")))
       .registerTempTable("d")
 
@@ -1342,7 +1342,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-3483 Special chars in column names") {
-    val data = sqlContext.sparkContext.parallelize(
+    val data = sparkContext.parallelize(
       Seq("""{"key?number1": "value1", "key.number2": "value2"}"""))
     sqlContext.read.json(data).registerTempTable("records")
     sql("SELECT `key?number1`, `key.number2` FROM records")
@@ -1385,13 +1385,13 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-4322 Grouping field with struct field as sub expression") {
-    sqlContext.read.json(sqlContext.sparkContext.makeRDD("""{"a": {"b": [{"c": 1}]}}""" :: Nil))
+    sqlContext.read.json(sparkContext.makeRDD("""{"a": {"b": [{"c": 1}]}}""" :: Nil))
       .registerTempTable("data")
     checkAnswer(sql("SELECT a.b[0].c FROM data GROUP BY a.b[0].c"), Row(1))
     sqlContext.dropTempTable("data")
 
     sqlContext.read.json(
-      sqlContext.sparkContext.makeRDD("""{"a": {"b": 1}}""" :: Nil)).registerTempTable("data")
+      sparkContext.makeRDD("""{"a": {"b": 1}}""" :: Nil)).registerTempTable("data")
     checkAnswer(sql("SELECT a.b + 1 FROM data GROUP BY a.b + 1"), Row(2))
     sqlContext.dropTempTable("data")
   }
@@ -1412,10 +1412,10 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("Supporting relational operator '<=>' in Spark SQL") {
     val nullCheckData1 = TestData(1, "1") :: TestData(2, null) :: Nil
-    val rdd1 = sqlContext.sparkContext.parallelize((0 to 1).map(i => nullCheckData1(i)))
+    val rdd1 = sparkContext.parallelize((0 to 1).map(i => nullCheckData1(i)))
     rdd1.toDF().registerTempTable("nulldata1")
     val nullCheckData2 = TestData(1, "1") :: TestData(2, null) :: Nil
-    val rdd2 = sqlContext.sparkContext.parallelize((0 to 1).map(i => nullCheckData2(i)))
+    val rdd2 = sparkContext.parallelize((0 to 1).map(i => nullCheckData2(i)))
     rdd2.toDF().registerTempTable("nulldata2")
     checkAnswer(sql("SELECT nulldata1.key FROM nulldata1 join " +
       "nulldata2 on nulldata1.value <=> nulldata2.value"),
@@ -1424,7 +1424,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("Multi-column COUNT(DISTINCT ...)") {
     val data = TestData(1, "val_1") :: TestData(2, "val_2") :: Nil
-    val rdd = sqlContext.sparkContext.parallelize((0 to 1).map(i => data(i)))
+    val rdd = sparkContext.parallelize((0 to 1).map(i => data(i)))
     rdd.toDF().registerTempTable("distinctData")
     checkAnswer(sql("SELECT COUNT(DISTINCT key,value) FROM distinctData"), Row(2))
   }
@@ -1432,14 +1432,14 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   test("SPARK-4699 case sensitivity SQL query") {
     sqlContext.setConf(SQLConf.CASE_SENSITIVE, false)
     val data = TestData(1, "val_1") :: TestData(2, "val_2") :: Nil
-    val rdd = sqlContext.sparkContext.parallelize((0 to 1).map(i => data(i)))
+    val rdd = sparkContext.parallelize((0 to 1).map(i => data(i)))
     rdd.toDF().registerTempTable("testTable1")
     checkAnswer(sql("SELECT VALUE FROM TESTTABLE1 where KEY = 1"), Row("val_1"))
     sqlContext.setConf(SQLConf.CASE_SENSITIVE, true)
   }
 
   test("SPARK-6145: ORDER BY test for nested fields") {
-    sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    sqlContext.read.json(sparkContext.makeRDD(
         """{"a": {"b": 1, "a": {"a": 1}}, "c": [{"d": 1}]}""" :: Nil))
       .registerTempTable("nestedOrder")
 
@@ -1452,14 +1452,14 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-6145: special cases") {
-    sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    sqlContext.read.json(sparkContext.makeRDD(
       """{"a": {"b": [1]}, "b": [{"a": 1}], "_c0": {"a": 1}}""" :: Nil)).registerTempTable("t")
     checkAnswer(sql("SELECT a.b[0] FROM t ORDER BY _c0.a"), Row(1))
     checkAnswer(sql("SELECT b[0].a FROM t ORDER BY _c0.a"), Row(1))
   }
 
   test("SPARK-6898: complete support for special chars in column names") {
-    sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+    sqlContext.read.json(sparkContext.makeRDD(
       """{"a": {"c.b": 1}, "b.$q": [{"a@!.q": 1}], "q.w": {"w.i&": [1]}}""" :: Nil))
       .registerTempTable("t")
 
@@ -1543,7 +1543,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("SPARK-7067: order by queries for complex ExtractValue chain") {
     withTempTable("t") {
-      sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+      sqlContext.read.json(sparkContext.makeRDD(
         """{"a": {"b": [{"c": 1}]}, "b": [{"d": 1}]}""" :: Nil)).registerTempTable("t")
       checkAnswer(sql("SELECT a.b FROM t ORDER BY b[0].d"), Row(Seq(Row(1))))
     }
@@ -1610,8 +1610,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("aggregation with codegen updates peak execution memory") {
     withSQLConf((SQLConf.CODEGEN_ENABLED.key, "true")) {
-      val sc = sqlContext.sparkContext
-      AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "aggregation with codegen") {
+      AccumulatorSuite.verifyPeakExecutionMemorySet(sparkContext, "aggregation with codegen") {
         testCodeGen(
           "SELECT key, count(value) FROM testData GROUP BY key",
           (1 to 100).map(i => Row(i, 1)))
@@ -1670,8 +1669,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("external sorting updates peak execution memory") {
     withSQLConf((SQLConf.EXTERNAL_SORT.key, "true")) {
-      val sc = sqlContext.sparkContext
-      AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "external sort") {
+      AccumulatorSuite.verifyPeakExecutionMemorySet(sparkContext, "external sort") {
         sortTest()
       }
     }
@@ -1679,7 +1677,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("SPARK-9511: error with table starting with number") {
     withTempTable("1one") {
-      sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
+      sparkContext.parallelize(1 to 10).map(i => (i, i.toString))
         .toDF("num", "str")
         .registerTempTable("1one")
       checkAnswer(sql("select count(num) from 1one"), Row(10))
@@ -1690,7 +1688,7 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     withTempPath { dir =>
       val path = dir.getCanonicalPath
       val df =
-        sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
+        sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
       df
         .write
         .format("parquet")
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
index 45d0ee4a8e749..ddab918629645 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SerializationSuite.scala
@@ -24,7 +24,7 @@ import org.apache.spark.sql.test.SharedSQLContext
 class SerializationSuite extends SparkFunSuite with SharedSQLContext {
 
   test("[SPARK-5235] SQLContext should be serializable") {
-    val _sqlContext = new SQLContext(sqlContext.sparkContext)
+    val _sqlContext = new SQLContext(sparkContext)
     new JavaSerializer(new SparkConf()).newInstance().serialize(_sqlContext)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
index b91438baea06f..e12e6bea30260 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/StringFunctionsSuite.scala
@@ -268,9 +268,7 @@ class StringFunctionsSuite extends QueryTest with SharedSQLContext {
       Row(3, 4))
 
     intercept[AnalysisException] {
-      checkAnswer(
-        df.selectExpr("length(c)"), // int type of the argument is unacceptable
-        Row("5.0000"))
+      df.selectExpr("length(c)") // int type of the argument is unacceptable
     }
   }
 
@@ -284,63 +282,46 @@ class StringFunctionsSuite extends QueryTest with SharedSQLContext {
   }
 
   test("number format function") {
-    val tuple =
-      ("aa", 1.asInstanceOf[Byte], 2.asInstanceOf[Short],
-        3.13223f, 4, 5L, 6.48173d, Decimal(7.128381))
-    val df =
-      Seq(tuple)
-        .toDF(
-          "a", // string "aa"
-          "b", // byte    1
-          "c", // short   2
-          "d", // float   3.13223f
-          "e", // integer 4
-          "f", // long    5L
-          "g", // double  6.48173d
-          "h") // decimal 7.128381
-
-    checkAnswer(
-      df.select(format_number($"f", 4)),
+    val df = sqlContext.range(1)
+
+    checkAnswer(
+      df.select(format_number(lit(5L), 4)),
       Row("5.0000"))
 
     checkAnswer(
-      df.selectExpr("format_number(b, e)"), // convert the 1st argument to integer
+      df.select(format_number(lit(1.toByte), 4)), // convert the 1st argument to integer
       Row("1.0000"))
 
     checkAnswer(
-      df.selectExpr("format_number(c, e)"), // convert the 1st argument to integer
+      df.select(format_number(lit(2.toShort), 4)), // convert the 1st argument to integer
       Row("2.0000"))
 
     checkAnswer(
-      df.selectExpr("format_number(d, e)"), // convert the 1st argument to double
+      df.select(format_number(lit(3.1322.toFloat), 4)), // convert the 1st argument to double
       Row("3.1322"))
 
     checkAnswer(
-      df.selectExpr("format_number(e, e)"), // not convert anything
+      df.select(format_number(lit(4), 4)), // not convert anything
       Row("4.0000"))
 
     checkAnswer(
-      df.selectExpr("format_number(f, e)"), // not convert anything
+      df.select(format_number(lit(5L), 4)), // not convert anything
       Row("5.0000"))
 
     checkAnswer(
-      df.selectExpr("format_number(g, e)"), // not convert anything
+      df.select(format_number(lit(6.48173), 4)), // not convert anything
       Row("6.4817"))
 
     checkAnswer(
-      df.selectExpr("format_number(h, e)"), // not convert anything
+      df.select(format_number(lit(BigDecimal(7.128381)), 4)), // not convert anything
       Row("7.1284"))
 
     intercept[AnalysisException] {
-      checkAnswer(
-        df.selectExpr("format_number(a, e)"), // string type of the 1st argument is unacceptable
-        Row("5.0000"))
+      df.select(format_number(lit("aa"), 4)) // string type of the 1st argument is unacceptable
     }
 
     intercept[AnalysisException] {
-      checkAnswer(
-        df.selectExpr("format_number(e, g)"), // decimal type of the 2nd argument is unacceptable
-        Row("5.0000"))
+      df.selectExpr("format_number(4, 6.48173)") // non-integral type 2nd argument is unacceptable
     }
 
     // for testing the mutable state of the expression in code gen.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
index eb275af101e2f..e0435a0dba6ad 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UDFSuite.scala
@@ -26,7 +26,7 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   import testImplicits._
 
   test("built-in fixed arity expressions") {
-    val df = ctx.emptyDataFrame
+    val df = sqlContext.emptyDataFrame
     df.selectExpr("rand()", "randn()", "rand(5)", "randn(50)")
   }
 
@@ -55,23 +55,23 @@ class UDFSuite extends QueryTest with SharedSQLContext {
     val df = Seq((1, "Tearing down the walls that divide us")).toDF("id", "saying")
     df.registerTempTable("tmp_table")
     checkAnswer(sql("select spark_partition_id() from tmp_table").toDF(), Row(0))
-    ctx.dropTempTable("tmp_table")
+    sqlContext.dropTempTable("tmp_table")
   }
 
   test("SPARK-8005 input_file_name") {
     withTempPath { dir =>
-      val data = ctx.sparkContext.parallelize(0 to 10, 2).toDF("id")
+      val data = sparkContext.parallelize(0 to 10, 2).toDF("id")
       data.write.parquet(dir.getCanonicalPath)
-      ctx.read.parquet(dir.getCanonicalPath).registerTempTable("test_table")
+      sqlContext.read.parquet(dir.getCanonicalPath).registerTempTable("test_table")
       val answer = sql("select input_file_name() from test_table").head().getString(0)
       assert(answer.contains(dir.getCanonicalPath))
       assert(sql("select input_file_name() from test_table").distinct().collect().length >= 2)
-      ctx.dropTempTable("test_table")
+      sqlContext.dropTempTable("test_table")
     }
   }
 
   test("error reporting for incorrect number of arguments") {
-    val df = ctx.emptyDataFrame
+    val df = sqlContext.emptyDataFrame
     val e = intercept[AnalysisException] {
       df.selectExpr("substr('abcd', 2, 3, 4)")
     }
@@ -79,7 +79,7 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("error reporting for undefined functions") {
-    val df = ctx.emptyDataFrame
+    val df = sqlContext.emptyDataFrame
     val e = intercept[AnalysisException] {
       df.selectExpr("a_function_that_does_not_exist()")
     }
@@ -87,24 +87,24 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("Simple UDF") {
-    ctx.udf.register("strLenScala", (_: String).length)
+    sqlContext.udf.register("strLenScala", (_: String).length)
     assert(sql("SELECT strLenScala('test')").head().getInt(0) === 4)
   }
 
   test("ZeroArgument UDF") {
-    ctx.udf.register("random0", () => { Math.random()})
+    sqlContext.udf.register("random0", () => { Math.random()})
     assert(sql("SELECT random0()").head().getDouble(0) >= 0.0)
   }
 
   test("TwoArgument UDF") {
-    ctx.udf.register("strLenScala", (_: String).length + (_: Int))
+    sqlContext.udf.register("strLenScala", (_: String).length + (_: Int))
     assert(sql("SELECT strLenScala('test', 1)").head().getInt(0) === 5)
   }
 
   test("UDF in a WHERE") {
-    ctx.udf.register("oneArgFilter", (n: Int) => { n > 80 })
+    sqlContext.udf.register("oneArgFilter", (n: Int) => { n > 80 })
 
-    val df = ctx.sparkContext.parallelize(
+    val df = sparkContext.parallelize(
       (1 to 100).map(i => TestData(i, i.toString))).toDF()
     df.registerTempTable("integerData")
 
@@ -114,7 +114,7 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("UDF in a HAVING") {
-    ctx.udf.register("havingFilter", (n: Long) => { n > 5 })
+    sqlContext.udf.register("havingFilter", (n: Long) => { n > 5 })
 
     val df = Seq(("red", 1), ("red", 2), ("blue", 10),
       ("green", 100), ("green", 200)).toDF("g", "v")
@@ -133,7 +133,7 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("UDF in a GROUP BY") {
-    ctx.udf.register("groupFunction", (n: Int) => { n > 10 })
+    sqlContext.udf.register("groupFunction", (n: Int) => { n > 10 })
 
     val df = Seq(("red", 1), ("red", 2), ("blue", 10),
       ("green", 100), ("green", 200)).toDF("g", "v")
@@ -150,10 +150,10 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("UDFs everywhere") {
-    ctx.udf.register("groupFunction", (n: Int) => { n > 10 })
-    ctx.udf.register("havingFilter", (n: Long) => { n > 2000 })
-    ctx.udf.register("whereFilter", (n: Int) => { n < 150 })
-    ctx.udf.register("timesHundred", (n: Long) => { n * 100 })
+    sqlContext.udf.register("groupFunction", (n: Int) => { n > 10 })
+    sqlContext.udf.register("havingFilter", (n: Long) => { n > 2000 })
+    sqlContext.udf.register("whereFilter", (n: Int) => { n < 150 })
+    sqlContext.udf.register("timesHundred", (n: Long) => { n * 100 })
 
     val df = Seq(("red", 1), ("red", 2), ("blue", 10),
       ("green", 100), ("green", 200)).toDF("g", "v")
@@ -172,7 +172,7 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("struct UDF") {
-    ctx.udf.register("returnStruct", (f1: String, f2: String) => FunctionResult(f1, f2))
+    sqlContext.udf.register("returnStruct", (f1: String, f2: String) => FunctionResult(f1, f2))
 
     val result =
       sql("SELECT returnStruct('test', 'test2') as ret")
@@ -181,13 +181,13 @@ class UDFSuite extends QueryTest with SharedSQLContext {
   }
 
   test("udf that is transformed") {
-    ctx.udf.register("makeStruct", (x: Int, y: Int) => (x, y))
+    sqlContext.udf.register("makeStruct", (x: Int, y: Int) => (x, y))
     // 1 + 1 is constant folded causing a transformation.
     assert(sql("SELECT makeStruct(1 + 1, 2)").first().getAs[Row](0) === Row(2, 2))
   }
 
   test("type coercion for udf inputs") {
-    ctx.udf.register("intExpected", (x: Int) => x)
+    sqlContext.udf.register("intExpected", (x: Int) => x)
     // pass a decimal to intExpected.
     assert(sql("SELECT intExpected(1.0)").head().getInt(0) === 1)
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
index b6d279ae47268..fa8f9c8e00089 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
@@ -90,7 +90,7 @@ class UserDefinedTypeSuite extends QueryTest with SharedSQLContext {
   }
 
   test("UDTs and UDFs") {
-    ctx.udf.register("testType", (d: MyDenseVector) => d.isInstanceOf[MyDenseVector])
+    sqlContext.udf.register("testType", (d: MyDenseVector) => d.isInstanceOf[MyDenseVector])
     pointsRDD.registerTempTable("points")
     checkAnswer(
       sql("SELECT testType(features) from points"),
@@ -148,8 +148,8 @@ class UserDefinedTypeSuite extends QueryTest with SharedSQLContext {
       StructField("vec", new MyDenseVectorUDT, false)
     ))
 
-    val stringRDD = ctx.sparkContext.parallelize(data)
-    val jsonRDD = ctx.read.schema(schema).json(stringRDD)
+    val stringRDD = sparkContext.parallelize(data)
+    val jsonRDD = sqlContext.read.schema(schema).json(stringRDD)
     checkAnswer(
       jsonRDD,
       Row(1, new MyDenseVector(Array(1.1, 2.2, 3.3, 4.4))) ::
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
index 83db9b6510b36..cd3644eb9c099 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/InMemoryColumnarQuerySuite.scala
@@ -31,7 +31,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
   setupTestData()
 
   test("simple columnar query") {
-    val plan = ctx.executePlan(testData.logicalPlan).executedPlan
+    val plan = sqlContext.executePlan(testData.logicalPlan).executedPlan
     val scan = InMemoryRelation(useCompression = true, 5, MEMORY_ONLY, plan, None)
 
     checkAnswer(scan, testData.collect().toSeq)
@@ -39,16 +39,16 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
 
   test("default size avoids broadcast") {
     // TODO: Improve this test when we have better statistics
-    ctx.sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString))
+    sparkContext.parallelize(1 to 10).map(i => TestData(i, i.toString))
       .toDF().registerTempTable("sizeTst")
-    ctx.cacheTable("sizeTst")
+    sqlContext.cacheTable("sizeTst")
     assert(
-      ctx.table("sizeTst").queryExecution.analyzed.statistics.sizeInBytes >
-        ctx.conf.autoBroadcastJoinThreshold)
+      sqlContext.table("sizeTst").queryExecution.analyzed.statistics.sizeInBytes >
+        sqlContext.conf.autoBroadcastJoinThreshold)
   }
 
   test("projection") {
-    val plan = ctx.executePlan(testData.select('value, 'key).logicalPlan).executedPlan
+    val plan = sqlContext.executePlan(testData.select('value, 'key).logicalPlan).executedPlan
     val scan = InMemoryRelation(useCompression = true, 5, MEMORY_ONLY, plan, None)
 
     checkAnswer(scan, testData.collect().map {
@@ -57,7 +57,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-1436 regression: in-memory columns must be able to be accessed multiple times") {
-    val plan = ctx.executePlan(testData.logicalPlan).executedPlan
+    val plan = sqlContext.executePlan(testData.logicalPlan).executedPlan
     val scan = InMemoryRelation(useCompression = true, 5, MEMORY_ONLY, plan, None)
 
     checkAnswer(scan, testData.collect().toSeq)
@@ -69,7 +69,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
       sql("SELECT * FROM repeatedData"),
       repeatedData.collect().toSeq.map(Row.fromTuple))
 
-    ctx.cacheTable("repeatedData")
+    sqlContext.cacheTable("repeatedData")
 
     checkAnswer(
       sql("SELECT * FROM repeatedData"),
@@ -81,7 +81,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
       sql("SELECT * FROM nullableRepeatedData"),
       nullableRepeatedData.collect().toSeq.map(Row.fromTuple))
 
-    ctx.cacheTable("nullableRepeatedData")
+    sqlContext.cacheTable("nullableRepeatedData")
 
     checkAnswer(
       sql("SELECT * FROM nullableRepeatedData"),
@@ -96,7 +96,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
       sql("SELECT time FROM timestamps"),
       timestamps.collect().toSeq)
 
-    ctx.cacheTable("timestamps")
+    sqlContext.cacheTable("timestamps")
 
     checkAnswer(
       sql("SELECT time FROM timestamps"),
@@ -108,7 +108,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
       sql("SELECT * FROM withEmptyParts"),
       withEmptyParts.collect().toSeq.map(Row.fromTuple))
 
-    ctx.cacheTable("withEmptyParts")
+    sqlContext.cacheTable("withEmptyParts")
 
     checkAnswer(
       sql("SELECT * FROM withEmptyParts"),
@@ -157,7 +157,7 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
 
     // Create a RDD for the schema
     val rdd =
-      ctx.sparkContext.parallelize((1 to 100), 10).map { i =>
+      sparkContext.parallelize((1 to 100), 10).map { i =>
         Row(
           s"str${i}: test cache.",
           s"binary${i}: test cache.".getBytes("UTF-8"),
@@ -177,24 +177,24 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
           (0 to i).map(j => s"map_key_$j" -> (Long.MaxValue - j)).toMap,
           Row((i - 0.25).toFloat, Seq(true, false, null)))
       }
-    ctx.createDataFrame(rdd, schema).registerTempTable("InMemoryCache_different_data_types")
+    sqlContext.createDataFrame(rdd, schema).registerTempTable("InMemoryCache_different_data_types")
     // Cache the table.
     sql("cache table InMemoryCache_different_data_types")
     // Make sure the table is indeed cached.
-    val tableScan = ctx.table("InMemoryCache_different_data_types").queryExecution.executedPlan
+    sqlContext.table("InMemoryCache_different_data_types").queryExecution.executedPlan
     assert(
-      ctx.isCached("InMemoryCache_different_data_types"),
+      sqlContext.isCached("InMemoryCache_different_data_types"),
       "InMemoryCache_different_data_types should be cached.")
     // Issue a query and check the results.
     checkAnswer(
       sql(s"SELECT DISTINCT ${allColumns} FROM InMemoryCache_different_data_types"),
-      ctx.table("InMemoryCache_different_data_types").collect())
-    ctx.dropTempTable("InMemoryCache_different_data_types")
+      sqlContext.table("InMemoryCache_different_data_types").collect())
+    sqlContext.dropTempTable("InMemoryCache_different_data_types")
   }
 
   test("SPARK-10422: String column in InMemoryColumnarCache needs to override clone method") {
-    val df =
-      ctx.range(1, 100).selectExpr("id % 10 as id").rdd.map(id => Tuple1(s"str_$id")).toDF("i")
+    val df = sqlContext.range(1, 100).selectExpr("id % 10 as id")
+      .rdd.map(id => Tuple1(s"str_$id")).toDF("i")
     val cached = df.cache()
     // count triggers the caching action. It should not throw.
     cached.count()
@@ -205,7 +205,8 @@ class InMemoryColumnarQuerySuite extends QueryTest with SharedSQLContext {
     // Check result.
     checkAnswer(
       cached,
-      ctx.range(1, 100).selectExpr("id % 10 as id").rdd.map(id => Tuple1(s"str_$id")).toDF("i")
+      sqlContext.range(1, 100).selectExpr("id % 10 as id")
+        .rdd.map(id => Tuple1(s"str_$id")).toDF("i")
     )
 
     // Drop the cache.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
index ab2644eb4581d..6b7401464f46f 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/columnar/PartitionBatchPruningSuite.scala
@@ -25,32 +25,32 @@ import org.apache.spark.sql.test.SQLTestData._
 class PartitionBatchPruningSuite extends SparkFunSuite with SharedSQLContext {
   import testImplicits._
 
-  private lazy val originalColumnBatchSize = ctx.conf.columnBatchSize
-  private lazy val originalInMemoryPartitionPruning = ctx.conf.inMemoryPartitionPruning
+  private lazy val originalColumnBatchSize = sqlContext.conf.columnBatchSize
+  private lazy val originalInMemoryPartitionPruning = sqlContext.conf.inMemoryPartitionPruning
 
   override protected def beforeAll(): Unit = {
     super.beforeAll()
     // Make a table with 5 partitions, 2 batches per partition, 10 elements per batch
-    ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, 10)
+    sqlContext.setConf(SQLConf.COLUMN_BATCH_SIZE, 10)
 
-    val pruningData = ctx.sparkContext.makeRDD((1 to 100).map { key =>
+    val pruningData = sparkContext.makeRDD((1 to 100).map { key =>
       val string = if (((key - 1) / 10) % 2 == 0) null else key.toString
       TestData(key, string)
     }, 5).toDF()
     pruningData.registerTempTable("pruningData")
 
     // Enable in-memory partition pruning
-    ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, true)
+    sqlContext.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, true)
     // Enable in-memory table scan accumulators
-    ctx.setConf("spark.sql.inMemoryTableScanStatistics.enable", "true")
-    ctx.cacheTable("pruningData")
+    sqlContext.setConf("spark.sql.inMemoryTableScanStatistics.enable", "true")
+    sqlContext.cacheTable("pruningData")
   }
 
   override protected def afterAll(): Unit = {
     try {
-      ctx.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize)
-      ctx.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning)
-      ctx.uncacheTable("pruningData")
+      sqlContext.setConf(SQLConf.COLUMN_BATCH_SIZE, originalColumnBatchSize)
+      sqlContext.setConf(SQLConf.IN_MEMORY_PARTITION_PRUNING, originalInMemoryPartitionPruning)
+      sqlContext.uncacheTable("pruningData")
     } finally {
       super.afterAll()
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
index 8998f5111124c..911d12e93e503 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ExchangeSuite.scala
@@ -22,6 +22,8 @@ import org.apache.spark.sql.catalyst.plans.physical.SinglePartition
 import org.apache.spark.sql.test.SharedSQLContext
 
 class ExchangeSuite extends SparkPlanTest with SharedSQLContext {
+  import testImplicits.localSeqToDataFrameHolder
+
   test("shuffling UnsafeRows in exchange") {
     val input = (1 to 1000).map(Tuple1.apply)
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
index fad93b014c237..cafa1d5154788 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/PlannerSuite.scala
@@ -17,7 +17,6 @@
 
 package org.apache.spark.sql.execution
 
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{execution, Row, SQLConf}
 import org.apache.spark.sql.catalyst.InternalRow
@@ -31,14 +30,14 @@ import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 
 
-class PlannerSuite extends SparkFunSuite with SharedSQLContext {
+class PlannerSuite extends SharedSQLContext {
   import testImplicits._
 
   setupTestData()
 
   private def testPartialAggregationPlan(query: LogicalPlan): Unit = {
-    val _ctx = ctx
-    import _ctx.planner._
+    val planner = sqlContext.planner
+    import planner._
     val plannedOption = HashAggregation(query).headOption.orElse(Aggregation(query).headOption)
     val planned =
       plannedOption.getOrElse(
@@ -53,8 +52,8 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("unions are collapsed") {
-    val _ctx = ctx
-    import _ctx.planner._
+    val planner = sqlContext.planner
+    import planner._
     val query = testData.unionAll(testData).unionAll(testData).logicalPlan
     val planned = BasicOperators(query).head
     val logicalUnions = query collect { case u: logical.Union => u }
@@ -81,33 +80,30 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("sizeInBytes estimation of limit operator for broadcast hash join optimization") {
-    def checkPlan(fieldTypes: Seq[DataType], newThreshold: Int): Unit = {
-      ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, newThreshold)
-      val fields = fieldTypes.zipWithIndex.map {
-        case (dataType, index) => StructField(s"c${index}", dataType, true)
-      } :+ StructField("key", IntegerType, true)
-      val schema = StructType(fields)
-      val row = Row.fromSeq(Seq.fill(fields.size)(null))
-      val rowRDD = ctx.sparkContext.parallelize(row :: Nil)
-      ctx.createDataFrame(rowRDD, schema).registerTempTable("testLimit")
-
-      val planned = sql(
-        """
-          |SELECT l.a, l.b
-          |FROM testData2 l JOIN (SELECT * FROM testLimit LIMIT 1) r ON (l.a = r.key)
-        """.stripMargin).queryExecution.executedPlan
-
-      val broadcastHashJoins = planned.collect { case join: BroadcastHashJoin => join }
-      val shuffledHashJoins = planned.collect { case join: ShuffledHashJoin => join }
-
-      assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
-      assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
-
-      ctx.dropTempTable("testLimit")
+    def checkPlan(fieldTypes: Seq[DataType]): Unit = {
+      withTempTable("testLimit") {
+        val fields = fieldTypes.zipWithIndex.map {
+          case (dataType, index) => StructField(s"c${index}", dataType, true)
+        } :+ StructField("key", IntegerType, true)
+        val schema = StructType(fields)
+        val row = Row.fromSeq(Seq.fill(fields.size)(null))
+        val rowRDD = sparkContext.parallelize(row :: Nil)
+        sqlContext.createDataFrame(rowRDD, schema).registerTempTable("testLimit")
+
+        val planned = sql(
+          """
+            |SELECT l.a, l.b
+            |FROM testData2 l JOIN (SELECT * FROM testLimit LIMIT 1) r ON (l.a = r.key)
+          """.stripMargin).queryExecution.executedPlan
+
+        val broadcastHashJoins = planned.collect { case join: BroadcastHashJoin => join }
+        val shuffledHashJoins = planned.collect { case join: ShuffledHashJoin => join }
+
+        assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
+        assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
+      }
     }
 
-    val origThreshold = ctx.conf.autoBroadcastJoinThreshold
-
     val simpleTypes =
       NullType ::
       BooleanType ::
@@ -124,7 +120,9 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
       StringType ::
       BinaryType :: Nil
 
-    checkPlan(simpleTypes, newThreshold = 16434)
+    withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "16434") {
+      checkPlan(simpleTypes)
+    }
 
     val complexTypes =
       ArrayType(DoubleType, true) ::
@@ -136,36 +134,37 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
         StructField("b", ArrayType(DoubleType), nullable = false),
         StructField("c", DoubleType, nullable = false))) :: Nil
 
-    checkPlan(complexTypes, newThreshold = 901617)
-
-    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
+    withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "901617") {
+      checkPlan(complexTypes)
+    }
   }
 
   test("InMemoryRelation statistics propagation") {
-    val origThreshold = ctx.conf.autoBroadcastJoinThreshold
-    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, 81920)
-
-    testData.limit(3).registerTempTable("tiny")
-    sql("CACHE TABLE tiny")
+    withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "81920") {
+      withTempTable("tiny") {
+        testData.limit(3).registerTempTable("tiny")
+        sql("CACHE TABLE tiny")
 
-    val a = testData.as("a")
-    val b = ctx.table("tiny").as("b")
-    val planned = a.join(b, $"a.key" === $"b.key").queryExecution.executedPlan
+        val a = testData.as("a")
+        val b = sqlContext.table("tiny").as("b")
+        val planned = a.join(b, $"a.key" === $"b.key").queryExecution.executedPlan
 
-    val broadcastHashJoins = planned.collect { case join: BroadcastHashJoin => join }
-    val shuffledHashJoins = planned.collect { case join: ShuffledHashJoin => join }
+        val broadcastHashJoins = planned.collect { case join: BroadcastHashJoin => join }
+        val shuffledHashJoins = planned.collect { case join: ShuffledHashJoin => join }
 
-    assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
-    assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
+        assert(broadcastHashJoins.size === 1, "Should use broadcast hash join")
+        assert(shuffledHashJoins.isEmpty, "Should not use shuffled hash join")
 
-    ctx.setConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD, origThreshold)
+        sqlContext.clearCache()
+      }
+    }
   }
 
   test("efficient limit -> project -> sort") {
     {
       val query =
         testData.select('key, 'value).sort('key).limit(2).logicalPlan
-      val planned = ctx.planner.TakeOrderedAndProject(query)
+      val planned = sqlContext.planner.TakeOrderedAndProject(query)
       assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
       assert(planned.head.output === testData.select('key, 'value).logicalPlan.output)
     }
@@ -175,7 +174,7 @@ class PlannerSuite extends SparkFunSuite with SharedSQLContext {
       // into it.
       val query =
         testData.select('key, 'value).sort('key).select('value, 'key).limit(2).logicalPlan
-      val planned = ctx.planner.TakeOrderedAndProject(query)
+      val planned = sqlContext.planner.TakeOrderedAndProject(query)
       assert(planned.head.isInstanceOf[execution.TakeOrderedAndProject])
       assert(planned.head.output === testData.select('value, 'key).logicalPlan.output)
     }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
index ef6ad59b71fb3..4492e37ad01ff 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/RowFormatConvertersSuite.scala
@@ -39,20 +39,20 @@ class RowFormatConvertersSuite extends SparkPlanTest with SharedSQLContext {
 
   test("planner should insert unsafe->safe conversions when required") {
     val plan = Limit(10, outputsUnsafe)
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(preparedPlan.children.head.isInstanceOf[ConvertToSafe])
   }
 
   test("filter can process unsafe rows") {
     val plan = Filter(IsNull(IsNull(Literal(1))), outputsUnsafe)
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(getConverters(preparedPlan).size === 1)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("filter can process safe rows") {
     val plan = Filter(IsNull(IsNull(Literal(1))), outputsSafe)
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(getConverters(preparedPlan).isEmpty)
     assert(!preparedPlan.outputsUnsafeRows)
   }
@@ -67,33 +67,33 @@ class RowFormatConvertersSuite extends SparkPlanTest with SharedSQLContext {
   test("union requires all of its input rows' formats to agree") {
     val plan = Union(Seq(outputsSafe, outputsUnsafe))
     assert(plan.canProcessSafeRows && plan.canProcessUnsafeRows)
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("union can process safe rows") {
     val plan = Union(Seq(outputsSafe, outputsSafe))
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(!preparedPlan.outputsUnsafeRows)
   }
 
   test("union can process unsafe rows") {
     val plan = Union(Seq(outputsUnsafe, outputsUnsafe))
-    val preparedPlan = ctx.prepareForExecution.execute(plan)
+    val preparedPlan = sqlContext.prepareForExecution.execute(plan)
     assert(preparedPlan.outputsUnsafeRows)
   }
 
   test("round trip with ConvertToUnsafe and ConvertToSafe") {
     val input = Seq(("hello", 1), ("world", 2))
     checkAnswer(
-      ctx.createDataFrame(input),
+      sqlContext.createDataFrame(input),
       plan => ConvertToSafe(ConvertToUnsafe(plan)),
       input.map(Row.fromTuple)
     )
   }
 
   test("SPARK-9683: copy UTF8String when convert unsafe array/map to safe") {
-    SparkPlan.currentContext.set(ctx)
+    SparkPlan.currentContext.set(sqlContext)
     val schema = ArrayType(StringType)
     val rows = (1 to 100).map { i =>
       InternalRow(new GenericArrayData(Array[Any](UTF8String.fromString(i.toString))))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
index 8fa77b0fcb7b7..3073d492e613b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SortSuite.scala
@@ -22,6 +22,7 @@ import org.apache.spark.sql.catalyst.dsl.expressions._
 import org.apache.spark.sql.test.SharedSQLContext
 
 class SortSuite extends SparkPlanTest with SharedSQLContext {
+  import testImplicits.localSeqToDataFrameHolder
 
   // This test was originally added as an example of how to use [[SparkPlanTest]];
   // it's not designed to be a comprehensive test of ExternalSort.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
index 5ab8f44faebf6..de45ae4635fb7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/SparkPlanTest.scala
@@ -31,14 +31,7 @@ import org.apache.spark.sql.test.SQLTestUtils
  * class's test helper methods can be used, see [[SortSuite]].
  */
 private[sql] abstract class SparkPlanTest extends SparkFunSuite {
-  protected def _sqlContext: SQLContext
-
-  /**
-   * Creates a DataFrame from a local Seq of Product.
-   */
-  implicit def localSeqToDataFrameHolder[A <: Product : TypeTag](data: Seq[A]): DataFrameHolder = {
-    _sqlContext.implicits.localSeqToDataFrameHolder(data)
-  }
+  protected def sqlContext: SQLContext
 
   /**
    * Runs the plan and makes sure the answer matches the expected result.
@@ -98,7 +91,7 @@ private[sql] abstract class SparkPlanTest extends SparkFunSuite {
       planFunction: Seq[SparkPlan] => SparkPlan,
       expectedAnswer: Seq[Row],
       sortAnswers: Boolean = true): Unit = {
-    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer, sortAnswers, _sqlContext) match {
+    SparkPlanTest.checkAnswer(input, planFunction, expectedAnswer, sortAnswers, sqlContext) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
@@ -122,7 +115,7 @@ private[sql] abstract class SparkPlanTest extends SparkFunSuite {
       expectedPlanFunction: SparkPlan => SparkPlan,
       sortAnswers: Boolean = true): Unit = {
     SparkPlanTest.checkAnswer(
-        input, planFunction, expectedPlanFunction, sortAnswers, _sqlContext) match {
+        input, planFunction, expectedPlanFunction, sortAnswers, sqlContext) match {
       case Some(errorMessage) => fail(errorMessage)
       case None =>
     }
@@ -149,13 +142,13 @@ object SparkPlanTest {
       planFunction: SparkPlan => SparkPlan,
       expectedPlanFunction: SparkPlan => SparkPlan,
       sortAnswers: Boolean,
-      _sqlContext: SQLContext): Option[String] = {
+      sqlContext: SQLContext): Option[String] = {
 
     val outputPlan = planFunction(input.queryExecution.sparkPlan)
     val expectedOutputPlan = expectedPlanFunction(input.queryExecution.sparkPlan)
 
     val expectedAnswer: Seq[Row] = try {
-      executePlan(expectedOutputPlan, _sqlContext)
+      executePlan(expectedOutputPlan, sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -170,7 +163,7 @@ object SparkPlanTest {
     }
 
     val actualAnswer: Seq[Row] = try {
-      executePlan(outputPlan, _sqlContext)
+      executePlan(outputPlan, sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -210,12 +203,12 @@ object SparkPlanTest {
       planFunction: Seq[SparkPlan] => SparkPlan,
       expectedAnswer: Seq[Row],
       sortAnswers: Boolean,
-      _sqlContext: SQLContext): Option[String] = {
+      sqlContext: SQLContext): Option[String] = {
 
     val outputPlan = planFunction(input.map(_.queryExecution.sparkPlan))
 
     val sparkAnswer: Seq[Row] = try {
-      executePlan(outputPlan, _sqlContext)
+      executePlan(outputPlan, sqlContext)
     } catch {
       case NonFatal(e) =>
         val errorMessage =
@@ -238,10 +231,10 @@ object SparkPlanTest {
     }
   }
 
-  private def executePlan(outputPlan: SparkPlan, _sqlContext: SQLContext): Seq[Row] = {
+  private def executePlan(outputPlan: SparkPlan, sqlContext: SQLContext): Seq[Row] = {
     // A very simple resolver to make writing tests easier. In contrast to the real resolver
     // this is always case sensitive and does not try to handle scoping or complex type resolution.
-    val resolvedPlan = _sqlContext.prepareForExecution.execute(
+    val resolvedPlan = sqlContext.prepareForExecution.execute(
       outputPlan transform {
         case plan: SparkPlan =>
           val inputMap = plan.children.flatMap(_.output).map(a => (a.name, a)).toMap
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
index 3158458edb831..7a0f0dfd2b7f1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/TungstenSortSuite.scala
@@ -29,15 +29,16 @@ import org.apache.spark.sql.types._
  * A test suite that generates randomized data to test the [[TungstenSort]] operator.
  */
 class TungstenSortSuite extends SparkPlanTest with SharedSQLContext {
+  import testImplicits.localSeqToDataFrameHolder
 
   override def beforeAll(): Unit = {
     super.beforeAll()
-    ctx.conf.setConf(SQLConf.CODEGEN_ENABLED, true)
+    sqlContext.conf.setConf(SQLConf.CODEGEN_ENABLED, true)
   }
 
   override def afterAll(): Unit = {
     try {
-      ctx.conf.setConf(SQLConf.CODEGEN_ENABLED, SQLConf.CODEGEN_ENABLED.defaultValue.get)
+      sqlContext.conf.unsetConf(SQLConf.CODEGEN_ENABLED)
     } finally {
       super.afterAll()
     }
@@ -64,8 +65,7 @@ class TungstenSortSuite extends SparkPlanTest with SharedSQLContext {
   }
 
   test("sorting updates peak execution memory") {
-    val sc = ctx.sparkContext
-    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, "unsafe external sort") {
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sparkContext, "unsafe external sort") {
       checkThatPlansAgree(
         (1 to 100).map(v => Tuple1(v)).toDF("a"),
         (child: SparkPlan) => TungstenSort('a.asc :: Nil, true, child),
@@ -83,8 +83,8 @@ class TungstenSortSuite extends SparkPlanTest with SharedSQLContext {
   ) {
     test(s"sorting on $dataType with nullable=$nullable, sortOrder=$sortOrder") {
       val inputData = Seq.fill(1000)(randomDataGenerator())
-      val inputDf = ctx.createDataFrame(
-        ctx.sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
+      val inputDf = sqlContext.createDataFrame(
+        sparkContext.parallelize(Random.shuffle(inputData).map(v => Row(v))),
         StructType(StructField("a", dataType, nullable = true) :: Nil)
       )
       assert(TungstenSort.supportsSchema(inputDf.schema))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
index 5fdb82b067728..afda0d29f6d91 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregationIteratorSuite.scala
@@ -37,7 +37,7 @@ class TungstenAggregationIteratorSuite extends SparkFunSuite with SharedSQLConte
       val newMutableProjection = (expr: Seq[Expression], schema: Seq[Attribute]) => {
         () => new InterpretedMutableProjection(expr, schema)
       }
-      val dummyAccum = SQLMetrics.createLongMetric(ctx.sparkContext, "dummy")
+      val dummyAccum = SQLMetrics.createLongMetric(sparkContext, "dummy")
       iter = new TungstenAggregationIterator(Seq.empty, Seq.empty, Seq.empty, 0,
         Seq.empty, newMutableProjection, Seq.empty, None, dummyAccum, dummyAccum)
       val numPages = iter.getHashMap.getNumDataPages
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
index 1174b27732f22..6a18cc6d27138 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/JsonSuite.scala
@@ -215,7 +215,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Complex field and type inferring with null in sampling") {
-    val jsonDF = ctx.read.json(jsonNullStruct)
+    val jsonDF = sqlContext.read.json(jsonNullStruct)
     val expectedSchema = StructType(
       StructField("headers", StructType(
         StructField("Charset", StringType, true) ::
@@ -234,7 +234,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Primitive field and type inferring") {
-    val jsonDF = ctx.read.json(primitiveFieldAndType)
+    val jsonDF = sqlContext.read.json(primitiveFieldAndType)
 
     val expectedSchema = StructType(
       StructField("bigInteger", DecimalType(20, 0), true) ::
@@ -262,7 +262,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Complex field and type inferring") {
-    val jsonDF = ctx.read.json(complexFieldAndType1)
+    val jsonDF = sqlContext.read.json(complexFieldAndType1)
 
     val expectedSchema = StructType(
       StructField("arrayOfArray1", ArrayType(ArrayType(StringType, true), true), true) ::
@@ -361,7 +361,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("GetField operation on complex data type") {
-    val jsonDF = ctx.read.json(complexFieldAndType1)
+    val jsonDF = sqlContext.read.json(complexFieldAndType1)
     jsonDF.registerTempTable("jsonTable")
 
     checkAnswer(
@@ -377,7 +377,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Type conflict in primitive field values") {
-    val jsonDF = ctx.read.json(primitiveFieldValueTypeConflict)
+    val jsonDF = sqlContext.read.json(primitiveFieldValueTypeConflict)
 
     val expectedSchema = StructType(
       StructField("num_bool", StringType, true) ::
@@ -449,7 +449,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   ignore("Type conflict in primitive field values (Ignored)") {
-    val jsonDF = ctx.read.json(primitiveFieldValueTypeConflict)
+    val jsonDF = sqlContext.read.json(primitiveFieldValueTypeConflict)
     jsonDF.registerTempTable("jsonTable")
 
     // Right now, the analyzer does not promote strings in a boolean expression.
@@ -502,7 +502,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Type conflict in complex field values") {
-    val jsonDF = ctx.read.json(complexFieldValueTypeConflict)
+    val jsonDF = sqlContext.read.json(complexFieldValueTypeConflict)
 
     val expectedSchema = StructType(
       StructField("array", ArrayType(LongType, true), true) ::
@@ -526,7 +526,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Type conflict in array elements") {
-    val jsonDF = ctx.read.json(arrayElementTypeConflict)
+    val jsonDF = sqlContext.read.json(arrayElementTypeConflict)
 
     val expectedSchema = StructType(
       StructField("array1", ArrayType(StringType, true), true) ::
@@ -554,7 +554,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("Handling missing fields") {
-    val jsonDF = ctx.read.json(missingFields)
+    val jsonDF = sqlContext.read.json(missingFields)
 
     val expectedSchema = StructType(
       StructField("a", BooleanType, true) ::
@@ -573,9 +573,9 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
     val dir = Utils.createTempDir()
     dir.delete()
     val path = dir.getCanonicalFile.toURI.toString
-    ctx.sparkContext.parallelize(1 to 100)
+    sparkContext.parallelize(1 to 100)
       .map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path)
-    val jsonDF = ctx.read.option("samplingRatio", "0.49").json(path)
+    val jsonDF = sqlContext.read.option("samplingRatio", "0.49").json(path)
 
     val analyzed = jsonDF.queryExecution.analyzed
     assert(
@@ -590,7 +590,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
 
     val schema = StructType(StructField("a", LongType, true) :: Nil)
     val logicalRelation =
-      ctx.read.schema(schema).json(path)
+      sqlContext.read.schema(schema).json(path)
         .queryExecution.analyzed.asInstanceOf[LogicalRelation]
     val relationWithSchema = logicalRelation.relation.asInstanceOf[JSONRelation]
     assert(relationWithSchema.paths === Array(path))
@@ -603,7 +603,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
     dir.delete()
     val path = dir.getCanonicalPath
     primitiveFieldAndType.map(record => record.replaceAll("\n", " ")).saveAsTextFile(path)
-    val jsonDF = ctx.read.json(path)
+    val jsonDF = sqlContext.read.json(path)
 
     val expectedSchema = StructType(
       StructField("bigInteger", DecimalType(20, 0), true) ::
@@ -672,7 +672,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       StructField("null", StringType, true) ::
       StructField("string", StringType, true) :: Nil)
 
-    val jsonDF1 = ctx.read.schema(schema).json(path)
+    val jsonDF1 = sqlContext.read.schema(schema).json(path)
 
     assert(schema === jsonDF1.schema)
 
@@ -689,7 +689,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       "this is a simple string.")
     )
 
-    val jsonDF2 = ctx.read.schema(schema).json(primitiveFieldAndType)
+    val jsonDF2 = sqlContext.read.schema(schema).json(primitiveFieldAndType)
 
     assert(schema === jsonDF2.schema)
 
@@ -710,7 +710,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   test("Applying schemas with MapType") {
     val schemaWithSimpleMap = StructType(
       StructField("map", MapType(StringType, IntegerType, true), false) :: Nil)
-    val jsonWithSimpleMap = ctx.read.schema(schemaWithSimpleMap).json(mapType1)
+    val jsonWithSimpleMap = sqlContext.read.schema(schemaWithSimpleMap).json(mapType1)
 
     jsonWithSimpleMap.registerTempTable("jsonWithSimpleMap")
 
@@ -738,7 +738,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
     val schemaWithComplexMap = StructType(
       StructField("map", MapType(StringType, innerStruct, true), false) :: Nil)
 
-    val jsonWithComplexMap = ctx.read.schema(schemaWithComplexMap).json(mapType2)
+    val jsonWithComplexMap = sqlContext.read.schema(schemaWithComplexMap).json(mapType2)
 
     jsonWithComplexMap.registerTempTable("jsonWithComplexMap")
 
@@ -764,7 +764,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("SPARK-2096 Correctly parse dot notations") {
-    val jsonDF = ctx.read.json(complexFieldAndType2)
+    val jsonDF = sqlContext.read.json(complexFieldAndType2)
     jsonDF.registerTempTable("jsonTable")
 
     checkAnswer(
@@ -782,7 +782,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("SPARK-3390 Complex arrays") {
-    val jsonDF = ctx.read.json(complexFieldAndType2)
+    val jsonDF = sqlContext.read.json(complexFieldAndType2)
     jsonDF.registerTempTable("jsonTable")
 
     checkAnswer(
@@ -805,7 +805,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("SPARK-3308 Read top level JSON arrays") {
-    val jsonDF = ctx.read.json(jsonArray)
+    val jsonDF = sqlContext.read.json(jsonArray)
     jsonDF.registerTempTable("jsonTable")
 
     checkAnswer(
@@ -823,64 +823,63 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
 
   test("Corrupt records") {
     // Test if we can query corrupt records.
-    val oldColumnNameOfCorruptRecord = ctx.conf.columnNameOfCorruptRecord
-    ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, "_unparsed")
-
-    val jsonDF = ctx.read.json(corruptRecords)
-    jsonDF.registerTempTable("jsonTable")
-
-    val schema = StructType(
-      StructField("_unparsed", StringType, true) ::
-      StructField("a", StringType, true) ::
-      StructField("b", StringType, true) ::
-      StructField("c", StringType, true) :: Nil)
-
-    assert(schema === jsonDF.schema)
-
-    // In HiveContext, backticks should be used to access columns starting with a underscore.
-    checkAnswer(
-      sql(
-        """
-          |SELECT a, b, c, _unparsed
-          |FROM jsonTable
-        """.stripMargin),
-      Row(null, null, null, "{") ::
-        Row(null, null, null, "") ::
-        Row(null, null, null, """{"a":1, b:2}""") ::
-        Row(null, null, null, """{"a":{, b:3}""") ::
-        Row("str_a_4", "str_b_4", "str_c_4", null) ::
-        Row(null, null, null, "]") :: Nil
-    )
-
-    checkAnswer(
-      sql(
-        """
-          |SELECT a, b, c
-          |FROM jsonTable
-          |WHERE _unparsed IS NULL
-        """.stripMargin),
-      Row("str_a_4", "str_b_4", "str_c_4")
-    )
-
-    checkAnswer(
-      sql(
-        """
-          |SELECT _unparsed
-          |FROM jsonTable
-          |WHERE _unparsed IS NOT NULL
-        """.stripMargin),
-      Row("{") ::
-        Row("") ::
-        Row("""{"a":1, b:2}""") ::
-        Row("""{"a":{, b:3}""") ::
-        Row("]") :: Nil
-    )
-
-    ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, oldColumnNameOfCorruptRecord)
+    withSQLConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD.key -> "_unparsed") {
+      withTempTable("jsonTable") {
+        val jsonDF = sqlContext.read.json(corruptRecords)
+        jsonDF.registerTempTable("jsonTable")
+
+        val schema = StructType(
+          StructField("_unparsed", StringType, true) ::
+          StructField("a", StringType, true) ::
+          StructField("b", StringType, true) ::
+          StructField("c", StringType, true) :: Nil)
+
+        assert(schema === jsonDF.schema)
+
+        // In HiveContext, backticks should be used to access columns starting with a underscore.
+        checkAnswer(
+          sql(
+            """
+              |SELECT a, b, c, _unparsed
+              |FROM jsonTable
+            """.stripMargin),
+          Row(null, null, null, "{") ::
+            Row(null, null, null, "") ::
+            Row(null, null, null, """{"a":1, b:2}""") ::
+            Row(null, null, null, """{"a":{, b:3}""") ::
+            Row("str_a_4", "str_b_4", "str_c_4", null) ::
+            Row(null, null, null, "]") :: Nil
+        )
+
+        checkAnswer(
+          sql(
+            """
+              |SELECT a, b, c
+              |FROM jsonTable
+              |WHERE _unparsed IS NULL
+            """.stripMargin),
+          Row("str_a_4", "str_b_4", "str_c_4")
+        )
+
+        checkAnswer(
+          sql(
+            """
+              |SELECT _unparsed
+              |FROM jsonTable
+              |WHERE _unparsed IS NOT NULL
+            """.stripMargin),
+          Row("{") ::
+            Row("") ::
+            Row("""{"a":1, b:2}""") ::
+            Row("""{"a":{, b:3}""") ::
+            Row("]") :: Nil
+        )
+      }
+    }
   }
 
   test("SPARK-4068: nulls in arrays") {
-    val jsonDF = ctx.read.json(nullsInArrays)
+    val jsonDF = sqlContext.read.json(nullsInArrays)
     jsonDF.registerTempTable("jsonTable")
 
     val schema = StructType(
@@ -926,7 +925,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       Row(values(0).toInt, values(1), values(2).toBoolean, r.split(",").toList, v5)
     }
 
-    val df1 = ctx.createDataFrame(rowRDD1, schema1)
+    val df1 = sqlContext.createDataFrame(rowRDD1, schema1)
     df1.registerTempTable("applySchema1")
     val df2 = df1.toDF
     val result = df2.toJSON.collect()
@@ -949,7 +948,7 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       Row(Row(values(0).toInt, values(2).toBoolean), Map(values(1) -> v4))
     }
 
-    val df3 = ctx.createDataFrame(rowRDD2, schema2)
+    val df3 = sqlContext.createDataFrame(rowRDD2, schema2)
     df3.registerTempTable("applySchema2")
     val df4 = df3.toDF
     val result2 = df4.toJSON.collect()
@@ -957,8 +956,8 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
     assert(result2(1) === "{\"f1\":{\"f11\":2,\"f12\":false},\"f2\":{\"B2\":null}}")
     assert(result2(3) === "{\"f1\":{\"f11\":4,\"f12\":true},\"f2\":{\"D4\":2147483644}}")
 
-    val jsonDF = ctx.read.json(primitiveFieldAndType)
-    val primTable = ctx.read.json(jsonDF.toJSON)
+    val jsonDF = sqlContext.read.json(primitiveFieldAndType)
+    val primTable = sqlContext.read.json(jsonDF.toJSON)
     primTable.registerTempTable("primativeTable")
     checkAnswer(
         sql("select * from primativeTable"),
@@ -970,8 +969,8 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
         "this is a simple string.")
       )
 
-    val complexJsonDF = ctx.read.json(complexFieldAndType1)
-    val compTable = ctx.read.json(complexJsonDF.toJSON)
+    val complexJsonDF = sqlContext.read.json(complexFieldAndType1)
+    val compTable = sqlContext.read.json(complexJsonDF.toJSON)
     compTable.registerTempTable("complexTable")
     // Access elements of a primitive array.
     checkAnswer(
@@ -1039,25 +1038,25 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       Some(empty),
       1.0,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(ctx)
+      None, None)(sqlContext)
     val logicalRelation0 = LogicalRelation(relation0)
     val relation1 = new JSONRelation(
       Some(singleRow),
       1.0,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(ctx)
+      None, None)(sqlContext)
     val logicalRelation1 = LogicalRelation(relation1)
     val relation2 = new JSONRelation(
       Some(singleRow),
       0.5,
       Some(StructType(StructField("a", IntegerType, true) :: Nil)),
-      None, None)(ctx)
+      None, None)(sqlContext)
     val logicalRelation2 = LogicalRelation(relation2)
     val relation3 = new JSONRelation(
       Some(singleRow),
       1.0,
       Some(StructType(StructField("b", IntegerType, true) :: Nil)),
-      None, None)(ctx)
+      None, None)(sqlContext)
     val logicalRelation3 = LogicalRelation(relation3)
 
     assert(relation0 !== relation1)
@@ -1078,18 +1077,18 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
 
     withTempPath(dir => {
       val path = dir.getCanonicalFile.toURI.toString
-      ctx.sparkContext.parallelize(1 to 100)
+      sparkContext.parallelize(1 to 100)
         .map(i => s"""{"a": 1, "b": "str$i"}""").saveAsTextFile(path)
 
       val d1 = ResolvedDataSource(
-        ctx,
+        sqlContext,
         userSpecifiedSchema = None,
         partitionColumns = Array.empty[String],
         provider = classOf[DefaultSource].getCanonicalName,
         options = Map("path" -> path))
 
       val d2 = ResolvedDataSource(
-        ctx,
+        sqlContext,
         userSpecifiedSchema = None,
         partitionColumns = Array.empty[String],
         provider = classOf[DefaultSource].getCanonicalName,
@@ -1105,24 +1104,21 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
   }
 
   test("SPARK-7565 MapType in JsonRDD") {
-    val oldColumnNameOfCorruptRecord = ctx.conf.columnNameOfCorruptRecord
-    ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, "_unparsed")
-
-    val schemaWithSimpleMap = StructType(
-      StructField("map", MapType(StringType, IntegerType, true), false) :: Nil)
-    try {
-      val temp = Utils.createTempDir().getPath
-
-      val df = ctx.read.schema(schemaWithSimpleMap).json(mapType1)
-      df.write.mode("overwrite").parquet(temp)
-      // order of MapType is not defined
-      assert(ctx.read.parquet(temp).count() == 5)
-
-      val df2 = ctx.read.json(corruptRecords)
-      df2.write.mode("overwrite").parquet(temp)
-      checkAnswer(ctx.read.parquet(temp), df2.collect())
-    } finally {
-      ctx.setConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD, oldColumnNameOfCorruptRecord)
+    withSQLConf(SQLConf.COLUMN_NAME_OF_CORRUPT_RECORD.key -> "_unparsed") {
+      withTempDir { dir =>
+        val schemaWithSimpleMap = StructType(
+          StructField("map", MapType(StringType, IntegerType, true), false) :: Nil)
+        val df = sqlContext.read.schema(schemaWithSimpleMap).json(mapType1)
+
+        val path = dir.getAbsolutePath
+        df.write.mode("overwrite").parquet(path)
+        // order of MapType is not defined
+        assert(sqlContext.read.parquet(path).count() == 5)
+
+        val df2 = sqlContext.read.json(corruptRecords)
+        df2.write.mode("overwrite").parquet(path)
+        checkAnswer(sqlContext.read.parquet(path), df2.collect())
+      }
     }
   }
 
@@ -1142,19 +1138,19 @@ class JsonSuite extends QueryTest with SharedSQLContext with TestJsonData {
       val d1 = new File(root, "d1=1")
       // root/dt=1/col1=abc
       val p1_col1 = makePartition(
-        ctx.sparkContext.parallelize(2 to 5).map(i => s"""{"a": 1, "b": "str$i"}"""),
+        sparkContext.parallelize(2 to 5).map(i => s"""{"a": 1, "b": "str$i"}"""),
         d1,
         "col1",
         "abc")
 
       // root/dt=1/col1=abd
       val p2 = makePartition(
-        ctx.sparkContext.parallelize(6 to 10).map(i => s"""{"a": 1, "b": "str$i"}"""),
+        sparkContext.parallelize(6 to 10).map(i => s"""{"a": 1, "b": "str$i"}"""),
         d1,
         "col1",
         "abd")
 
-        ctx.read.json(root.getAbsolutePath).registerTempTable("test_myjson_with_part")
+        sqlContext.read.json(root.getAbsolutePath).registerTempTable("test_myjson_with_part")
         checkAnswer(sql(
           "SELECT count(a) FROM test_myjson_with_part where d1 = 1 and col1='abc'"), Row(4))
         checkAnswer(sql(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
index 2864181cf91d5..713d1da1cb515 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/json/TestJsonData.scala
@@ -21,10 +21,10 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.SQLContext
 
 private[json] trait TestJsonData {
-  protected def _sqlContext: SQLContext
+  protected def sqlContext: SQLContext
 
   def primitiveFieldAndType: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"string":"this is a simple string.",
           "integer":10,
           "long":21474836470,
@@ -35,7 +35,7 @@ private[json] trait TestJsonData {
       }"""  :: Nil)
 
   def primitiveFieldValueTypeConflict: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"num_num_1":11, "num_num_2":null, "num_num_3": 1.1,
           "num_bool":true, "num_str":13.1, "str_bool":"str1"}""" ::
       """{"num_num_1":null, "num_num_2":21474836470.9, "num_num_3": null,
@@ -46,14 +46,14 @@ private[json] trait TestJsonData {
           "num_bool":null, "num_str":92233720368547758070, "str_bool":null}""" :: Nil)
 
   def jsonNullStruct: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"nullstr":"","ip":"27.31.100.29","headers":{"Host":"1.abc.com","Charset":"UTF-8"}}""" ::
         """{"nullstr":"","ip":"27.31.100.29","headers":{}}""" ::
         """{"nullstr":"","ip":"27.31.100.29","headers":""}""" ::
         """{"nullstr":null,"ip":"27.31.100.29","headers":null}""" :: Nil)
 
   def complexFieldValueTypeConflict: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"num_struct":11, "str_array":[1, 2, 3],
           "array":[], "struct_array":[], "struct": {}}""" ::
       """{"num_struct":{"field":false}, "str_array":null,
@@ -64,14 +64,14 @@ private[json] trait TestJsonData {
           "array":[7], "struct_array":{"field": true}, "struct": {"field": "str"}}""" :: Nil)
 
   def arrayElementTypeConflict: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"array1": [1, 1.1, true, null, [], {}, [2,3,4], {"field":"str"}],
           "array2": [{"field":214748364700}, {"field":1}]}""" ::
       """{"array3": [{"field":"str"}, {"field":1}]}""" ::
       """{"array3": [1, 2, 3]}""" :: Nil)
 
   def missingFields: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"a":true}""" ::
       """{"b":21474836470}""" ::
       """{"c":[33, 44]}""" ::
@@ -79,7 +79,7 @@ private[json] trait TestJsonData {
       """{"e":"str"}""" :: Nil)
 
   def complexFieldAndType1: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"struct":{"field1": true, "field2": 92233720368547758070},
           "structWithArrayFields":{"field1":[4, 5, 6], "field2":["str1", "str2"]},
           "arrayOfString":["str1", "str2"],
@@ -95,7 +95,7 @@ private[json] trait TestJsonData {
          }"""  :: Nil)
 
   def complexFieldAndType2: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"arrayOfStruct":[{"field1": true, "field2": "str1"}, {"field1": false}, {"field3": null}],
           "complexArrayOfStruct": [
           {
@@ -149,7 +149,7 @@ private[json] trait TestJsonData {
       }""" :: Nil)
 
   def mapType1: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"map": {"a": 1}}""" ::
       """{"map": {"b": 2}}""" ::
       """{"map": {"c": 3}}""" ::
@@ -157,7 +157,7 @@ private[json] trait TestJsonData {
       """{"map": {"e": null}}""" :: Nil)
 
   def mapType2: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"map": {"a": {"field1": [1, 2, 3, null]}}}""" ::
       """{"map": {"b": {"field2": 2}}}""" ::
       """{"map": {"c": {"field1": [], "field2": 4}}}""" ::
@@ -166,21 +166,21 @@ private[json] trait TestJsonData {
       """{"map": {"f": {"field1": null}}}""" :: Nil)
 
   def nullsInArrays: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{"field1":[[null], [[["Test"]]]]}""" ::
       """{"field2":[null, [{"Test":1}]]}""" ::
       """{"field3":[[null], [{"Test":"2"}]]}""" ::
       """{"field4":[[null, [1,2,3]]]}""" :: Nil)
 
   def jsonArray: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """[{"a":"str_a_1"}]""" ::
       """[{"a":"str_a_2"}, {"b":"str_b_3"}]""" ::
       """{"b":"str_b_4", "a":"str_a_4", "c":"str_c_4"}""" ::
       """[]""" :: Nil)
 
   def corruptRecords: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{""" ::
       """""" ::
       """{"a":1, b:2}""" ::
@@ -189,7 +189,7 @@ private[json] trait TestJsonData {
       """]""" :: Nil)
 
   def emptyRecords: RDD[String] =
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       """{""" ::
         """""" ::
         """{"a": {}}""" ::
@@ -198,7 +198,7 @@ private[json] trait TestJsonData {
         """]""" :: Nil)
 
 
-  lazy val singleRow: RDD[String] = _sqlContext.sparkContext.parallelize("""{"a":123}""" :: Nil)
+  lazy val singleRow: RDD[String] = sqlContext.sparkContext.parallelize("""{"a":123}""" :: Nil)
 
-  def empty: RDD[String] = _sqlContext.sparkContext.parallelize(Seq[String]())
+  def empty: RDD[String] = sqlContext.sparkContext.parallelize(Seq[String]())
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
index 91f3ce4d34c8b..0835bd123049b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetCompatibilityTest.scala
@@ -39,12 +39,13 @@ private[sql] abstract class ParquetCompatibilityTest extends QueryTest with Parq
 
   protected def readParquetSchema(path: String, pathFilter: Path => Boolean): MessageType = {
     val fsPath = new Path(path)
-    val fs = fsPath.getFileSystem(configuration)
+    val fs = fsPath.getFileSystem(hadoopConfiguration)
     val parquetFiles = fs.listStatus(fsPath, new PathFilter {
       override def accept(path: Path): Boolean = pathFilter(path)
     }).toSeq.asJava
 
-    val footers = ParquetFileReader.readAllFootersInParallel(configuration, parquetFiles, true)
+    val footers =
+      ParquetFileReader.readAllFootersInParallel(hadoopConfiguration, parquetFiles, true)
     footers.asScala.head.getParquetMetadata.getFileMetaData.getSchema
   }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
index 08d2b9dee99b0..cd552e83372f1 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetIOSuite.scala
@@ -101,7 +101,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
 
   test("fixed-length decimals") {
     def makeDecimalRDD(decimal: DecimalType): DataFrame =
-      sqlContext.sparkContext
+      sparkContext
         .parallelize(0 to 1000)
         .map(i => Tuple1(i / 100.0))
         .toDF()
@@ -119,7 +119,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
 
   test("date type") {
     def makeDateRDD(): DataFrame =
-      sqlContext.sparkContext
+      sparkContext
         .parallelize(0 to 1000)
         .map(i => Tuple1(DateTimeUtils.toJavaDate(i)))
         .toDF()
@@ -207,7 +207,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
   test("compression codec") {
     def compressionCodecFor(path: String): String = {
       val codecs = ParquetTypesConverter
-        .readMetaData(new Path(path), Some(configuration)).getBlocks.asScala
+        .readMetaData(new Path(path), Some(hadoopConfiguration)).getBlocks.asScala
         .flatMap(_.getColumns.asScala)
         .map(_.getCodec.name())
         .distinct
@@ -277,14 +277,14 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
   test("write metadata") {
     withTempPath { file =>
       val path = new Path(file.toURI.toString)
-      val fs = FileSystem.getLocal(configuration)
+      val fs = FileSystem.getLocal(hadoopConfiguration)
       val attributes = ScalaReflection.attributesFor[(Int, String)]
-      ParquetTypesConverter.writeMetaData(attributes, path, configuration)
+      ParquetTypesConverter.writeMetaData(attributes, path, hadoopConfiguration)
 
       assert(fs.exists(new Path(path, ParquetFileWriter.PARQUET_COMMON_METADATA_FILE)))
       assert(fs.exists(new Path(path, ParquetFileWriter.PARQUET_METADATA_FILE)))
 
-      val metaData = ParquetTypesConverter.readMetaData(path, Some(configuration))
+      val metaData = ParquetTypesConverter.readMetaData(path, Some(hadoopConfiguration))
       val actualSchema = metaData.getFileMetaData.getSchema
       val expectedSchema = ParquetTypesConverter.convertFromAttributes(attributes)
 
@@ -355,7 +355,7 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
       val path = new Path(location.getCanonicalPath)
 
       ParquetFileWriter.writeMetadataFile(
-        sqlContext.sparkContext.hadoopConfiguration,
+        sparkContext.hadoopConfiguration,
         path,
         Collections.singletonList(
           new Footer(path, new ParquetMetadata(fileMetadata, Collections.emptyList()))))
@@ -370,12 +370,12 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
   }
 
   test("SPARK-6352 DirectParquetOutputCommitter") {
-    val clonedConf = new Configuration(configuration)
+    val clonedConf = new Configuration(hadoopConfiguration)
 
     // Write to a parquet file and let it fail.
     // _temporary should be missing if direct output committer works.
     try {
-      configuration.set("spark.sql.parquet.output.committer.class",
+      hadoopConfiguration.set("spark.sql.parquet.output.committer.class",
         classOf[DirectParquetOutputCommitter].getCanonicalName)
       sqlContext.udf.register("div0", (x: Int) => x / 0)
       withTempPath { dir =>
@@ -383,23 +383,23 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
           sqlContext.sql("select div0(1)").write.parquet(dir.getCanonicalPath)
         }
         val path = new Path(dir.getCanonicalPath, "_temporary")
-        val fs = path.getFileSystem(configuration)
+        val fs = path.getFileSystem(hadoopConfiguration)
         assert(!fs.exists(path))
       }
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
-      configuration.clear()
-      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      hadoopConfiguration.clear()
+      clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
     }
   }
 
   test("SPARK-9849 DirectParquetOutputCommitter qualified name should be backward compatible") {
-    val clonedConf = new Configuration(configuration)
+    val clonedConf = new Configuration(hadoopConfiguration)
 
     // Write to a parquet file and let it fail.
     // _temporary should be missing if direct output committer works.
     try {
-      configuration.set("spark.sql.parquet.output.committer.class",
+      hadoopConfiguration.set("spark.sql.parquet.output.committer.class",
         "org.apache.spark.sql.parquet.DirectParquetOutputCommitter")
       sqlContext.udf.register("div0", (x: Int) => x / 0)
       withTempPath { dir =>
@@ -407,25 +407,25 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
           sqlContext.sql("select div0(1)").write.parquet(dir.getCanonicalPath)
         }
         val path = new Path(dir.getCanonicalPath, "_temporary")
-        val fs = path.getFileSystem(configuration)
+        val fs = path.getFileSystem(hadoopConfiguration)
         assert(!fs.exists(path))
       }
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
-      configuration.clear()
-      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      hadoopConfiguration.clear()
+      clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
     }
   }
 
 
   test("SPARK-8121: spark.sql.parquet.output.committer.class shouldn't be overridden") {
     withTempPath { dir =>
-      val clonedConf = new Configuration(configuration)
+      val clonedConf = new Configuration(hadoopConfiguration)
 
-      configuration.set(
+      hadoopConfiguration.set(
         SQLConf.OUTPUT_COMMITTER_CLASS.key, classOf[ParquetOutputCommitter].getCanonicalName)
 
-      configuration.set(
+      hadoopConfiguration.set(
         "spark.sql.parquet.output.committer.class",
         classOf[JobCommitFailureParquetOutputCommitter].getCanonicalName)
 
@@ -436,8 +436,8 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
         assert(message === "Intentional exception for testing purposes")
       } finally {
         // Hadoop 1 doesn't have `Configuration.unset`
-        configuration.clear()
-        clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+        hadoopConfiguration.clear()
+        clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
       }
     }
   }
@@ -455,11 +455,11 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
   }
 
   test("SPARK-7837 Do not close output writer twice when commitTask() fails") {
-    val clonedConf = new Configuration(configuration)
+    val clonedConf = new Configuration(hadoopConfiguration)
 
     // Using a output committer that always fail when committing a task, so that both
     // `commitTask()` and `abortTask()` are invoked.
-    configuration.set(
+    hadoopConfiguration.set(
       "spark.sql.parquet.output.committer.class",
       classOf[TaskCommitFailureParquetOutputCommitter].getCanonicalName)
 
@@ -483,8 +483,8 @@ class ParquetIOSuite extends QueryTest with ParquetTest with SharedSQLContext {
       }
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
-      configuration.clear()
-      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      hadoopConfiguration.clear()
+      clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
     }
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
index ed8bafb10c60b..7bac8609e1b91 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetPartitionDiscoverySuite.scala
@@ -517,7 +517,7 @@ class ParquetPartitionDiscoverySuite extends QueryTest with ParquetTest with Sha
     }
 
     val schema = StructType(partitionColumns :+ StructField(s"i", StringType))
-    val df = sqlContext.createDataFrame(sqlContext.sparkContext.parallelize(row :: Nil), schema)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(row :: Nil), schema)
 
     withTempPath { dir =>
       df.write.format("parquet").partitionBy(partitionColumns.map(_.name): _*).save(dir.toString)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index a379523d67f80..9edbb522684eb 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -30,6 +30,7 @@ import org.apache.spark.util.Utils
  * A test suite that tests various Parquet queries.
  */
 class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext {
+  import testImplicits._
 
   test("simple select queries") {
     withParquetTable((0 until 10).map(i => (i, i.toString)), "t") {
@@ -40,22 +41,22 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
 
   test("appending") {
     val data = (0 until 10).map(i => (i, i.toString))
-    ctx.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
+    sqlContext.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
     withParquetTable(data, "t") {
       sql("INSERT INTO TABLE t SELECT * FROM tmp")
-      checkAnswer(ctx.table("t"), (data ++ data).map(Row.fromTuple))
+      checkAnswer(sqlContext.table("t"), (data ++ data).map(Row.fromTuple))
     }
-    ctx.catalog.unregisterTable(Seq("tmp"))
+    sqlContext.catalog.unregisterTable(Seq("tmp"))
   }
 
   test("overwriting") {
     val data = (0 until 10).map(i => (i, i.toString))
-    ctx.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
+    sqlContext.createDataFrame(data).toDF("c1", "c2").registerTempTable("tmp")
     withParquetTable(data, "t") {
       sql("INSERT OVERWRITE TABLE t SELECT * FROM tmp")
-      checkAnswer(ctx.table("t"), data.map(Row.fromTuple))
+      checkAnswer(sqlContext.table("t"), data.map(Row.fromTuple))
     }
-    ctx.catalog.unregisterTable(Seq("tmp"))
+    sqlContext.catalog.unregisterTable(Seq("tmp"))
   }
 
   test("self-join") {
@@ -118,9 +119,9 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
     val schema = StructType(List(StructField("d", DecimalType(18, 0), false),
       StructField("time", TimestampType, false)).toArray)
     withTempPath { file =>
-      val df = ctx.createDataFrame(ctx.sparkContext.parallelize(data), schema)
+      val df = sqlContext.createDataFrame(sparkContext.parallelize(data), schema)
       df.write.parquet(file.getCanonicalPath)
-      val df2 = ctx.read.parquet(file.getCanonicalPath)
+      val df2 = sqlContext.read.parquet(file.getCanonicalPath)
       checkAnswer(df2, df.collect().toSeq)
     }
   }
@@ -129,12 +130,12 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
     def testSchemaMerging(expectedColumnNumber: Int): Unit = {
       withTempDir { dir =>
         val basePath = dir.getCanonicalPath
-        ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-        ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
+        sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+        sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
         // delete summary files, so if we don't merge part-files, one column will not be included.
         Utils.deleteRecursively(new File(basePath + "/foo=1/_metadata"))
         Utils.deleteRecursively(new File(basePath + "/foo=1/_common_metadata"))
-        assert(ctx.read.parquet(basePath).columns.length === expectedColumnNumber)
+        assert(sqlContext.read.parquet(basePath).columns.length === expectedColumnNumber)
       }
     }
 
@@ -153,9 +154,9 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
     def testSchemaMerging(expectedColumnNumber: Int): Unit = {
       withTempDir { dir =>
         val basePath = dir.getCanonicalPath
-        ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-        ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
-        assert(ctx.read.parquet(basePath).columns.length === expectedColumnNumber)
+        sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+        sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=2").toString)
+        assert(sqlContext.read.parquet(basePath).columns.length === expectedColumnNumber)
       }
     }
 
@@ -171,19 +172,19 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
   test("SPARK-8990 DataFrameReader.parquet() should respect user specified options") {
     withTempPath { dir =>
       val basePath = dir.getCanonicalPath
-      ctx.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
-      ctx.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=a").toString)
+      sqlContext.range(0, 10).toDF("a").write.parquet(new Path(basePath, "foo=1").toString)
+      sqlContext.range(0, 10).toDF("b").write.parquet(new Path(basePath, "foo=a").toString)
 
       // Disables the global SQL option for schema merging
       withSQLConf(SQLConf.PARQUET_SCHEMA_MERGING_ENABLED.key -> "false") {
         assertResult(2) {
           // Disables schema merging via data source option
-          ctx.read.option("mergeSchema", "false").parquet(basePath).columns.length
+          sqlContext.read.option("mergeSchema", "false").parquet(basePath).columns.length
         }
 
         assertResult(3) {
           // Enables schema merging via data source option
-          ctx.read.option("mergeSchema", "true").parquet(basePath).columns.length
+          sqlContext.read.option("mergeSchema", "true").parquet(basePath).columns.length
         }
       }
     }
@@ -193,7 +194,7 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
     withTempPath { dir =>
       val basePath = dir.getCanonicalPath
       val schema = StructType(Array(StructField("name", DecimalType(10, 5), false)))
-      val rowRDD = sqlContext.sparkContext.parallelize(Array(Row(Decimal("67123.45"))))
+      val rowRDD = sparkContext.parallelize(Array(Row(Decimal("67123.45"))))
       val df = sqlContext.createDataFrame(rowRDD, schema)
       df.write.parquet(basePath)
 
@@ -203,9 +204,6 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
   }
 
   test("SPARK-10005 Schema merging for nested struct") {
-    val sqlContext = _sqlContext
-    import sqlContext.implicits._
-
     withTempPath { dir =>
       val path = dir.getCanonicalPath
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
index 5dbc7d1630f27..442fafb12f200 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala
@@ -33,7 +33,6 @@ import org.apache.spark.sql.{DataFrame, SaveMode, SQLContext}
  * Especially, `Tuple1.apply` can be used to easily wrap a single type/value.
  */
 private[sql] trait ParquetTest extends SQLTestUtils {
-  protected def _sqlContext: SQLContext
 
   /**
    * Writes `data` to a Parquet file, which is then passed to `f` and will be deleted after `f`
@@ -43,7 +42,7 @@ private[sql] trait ParquetTest extends SQLTestUtils {
       (data: Seq[T])
       (f: String => Unit): Unit = {
     withTempPath { file =>
-      _sqlContext.createDataFrame(data).write.parquet(file.getCanonicalPath)
+      sqlContext.createDataFrame(data).write.parquet(file.getCanonicalPath)
       f(file.getCanonicalPath)
     }
   }
@@ -55,7 +54,7 @@ private[sql] trait ParquetTest extends SQLTestUtils {
   protected def withParquetDataFrame[T <: Product: ClassTag: TypeTag]
       (data: Seq[T])
       (f: DataFrame => Unit): Unit = {
-    withParquetFile(data)(path => f(_sqlContext.read.parquet(path)))
+    withParquetFile(data)(path => f(sqlContext.read.parquet(path)))
   }
 
   /**
@@ -67,14 +66,14 @@ private[sql] trait ParquetTest extends SQLTestUtils {
       (data: Seq[T], tableName: String)
       (f: => Unit): Unit = {
     withParquetDataFrame(data) { df =>
-      _sqlContext.registerDataFrameAsTable(df, tableName)
+      sqlContext.registerDataFrameAsTable(df, tableName)
       withTempTable(tableName)(f)
     }
   }
 
   protected def makeParquetFile[T <: Product: ClassTag: TypeTag](
       data: Seq[T], path: File): Unit = {
-    _sqlContext.createDataFrame(data).write.mode(SaveMode.Overwrite).parquet(path.getCanonicalPath)
+    sqlContext.createDataFrame(data).write.mode(SaveMode.Overwrite).parquet(path.getCanonicalPath)
   }
 
   protected def makeParquetFile[T <: Product: ClassTag: TypeTag](
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
index 53a0e53fd7719..dcbfdca71acb6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/BroadcastJoinSuite.scala
@@ -33,8 +33,7 @@ import org.apache.spark.sql.{SQLConf, SQLContext, QueryTest}
  * without serializing the hashed relation, which does not happen in local mode.
  */
 class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
-  private var sc: SparkContext = null
-  private var sqlContext: SQLContext = null
+  protected var sqlContext: SQLContext = null
 
   /**
    * Create a new [[SQLContext]] running in local-cluster mode with unsafe and codegen enabled.
@@ -44,15 +43,14 @@ class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
     val conf = new SparkConf()
       .setMaster("local-cluster[2,1,1024]")
       .setAppName("testing")
-    sc = new SparkContext(conf)
+    val sc = new SparkContext(conf)
     sqlContext = new SQLContext(sc)
     sqlContext.setConf(SQLConf.UNSAFE_ENABLED, true)
     sqlContext.setConf(SQLConf.CODEGEN_ENABLED, true)
   }
 
   override def afterAll(): Unit = {
-    sc.stop()
-    sc = null
+    sqlContext.sparkContext.stop()
     sqlContext = null
   }
 
@@ -60,7 +58,7 @@ class BroadcastJoinSuite extends QueryTest with BeforeAndAfterAll {
    * Test whether the specified broadcast join updates the peak execution memory accumulator.
    */
   private def testBroadcastJoin[T: ClassTag](name: String, joinType: String): Unit = {
-    AccumulatorSuite.verifyPeakExecutionMemorySet(sc, name) {
+    AccumulatorSuite.verifyPeakExecutionMemorySet(sqlContext.sparkContext, name) {
       val df1 = sqlContext.createDataFrame(Seq((1, "4"), (2, "2"))).toDF("key", "value")
       val df2 = sqlContext.createDataFrame(Seq((1, "1"), (2, "2"))).toDF("key", "value")
       // Comparison at the end is for broadcast left semi join
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
index 4c9187a9a7106..e5fd9e277fc61 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/HashedRelationSuite.scala
@@ -37,7 +37,7 @@ class HashedRelationSuite extends SparkFunSuite with SharedSQLContext {
 
   test("GeneralHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(sparkContext, "data")
     val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[GeneralHashedRelation])
 
@@ -53,7 +53,7 @@ class HashedRelationSuite extends SparkFunSuite with SharedSQLContext {
 
   test("UniqueKeyHashedRelation") {
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(sparkContext, "data")
     val hashed = HashedRelation(data.iterator, numDataRows, keyProjection)
     assert(hashed.isInstanceOf[UniqueKeyHashedRelation])
 
@@ -73,7 +73,7 @@ class HashedRelationSuite extends SparkFunSuite with SharedSQLContext {
   test("UnsafeHashedRelation") {
     val schema = StructType(StructField("a", IntegerType, true) :: Nil)
     val data = Array(InternalRow(0), InternalRow(1), InternalRow(2), InternalRow(2))
-    val numDataRows = SQLMetrics.createLongMetric(ctx.sparkContext, "data")
+    val numDataRows = SQLMetrics.createLongMetric(sparkContext, "data")
     val toUnsafe = UnsafeProjection.create(schema)
     val unsafeData = data.map(toUnsafe(_).copy()).toArray
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
index cc649b9bd4c45..4174ee055021d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/InnerJoinSuite.scala
@@ -27,9 +27,10 @@ import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.{IntegerType, StringType, StructType}
 
 class InnerJoinSuite extends SparkPlanTest with SharedSQLContext {
+  import testImplicits.localSeqToDataFrameHolder
 
-  private lazy val myUpperCaseData = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val myUpperCaseData = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(1, "A"),
       Row(2, "B"),
       Row(3, "C"),
@@ -39,8 +40,8 @@ class InnerJoinSuite extends SparkPlanTest with SharedSQLContext {
       Row(null, "G")
     )), new StructType().add("N", IntegerType).add("L", StringType))
 
-  private lazy val myLowerCaseData = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val myLowerCaseData = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(1, "a"),
       Row(2, "b"),
       Row(3, "c"),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
index a1a617d7b7398..c2e0bdac17968 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
@@ -28,8 +28,8 @@ import org.apache.spark.sql.types.{IntegerType, DoubleType, StructType}
 
 class OuterJoinSuite extends SparkPlanTest with SharedSQLContext {
 
-  private lazy val left = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val left = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(1, 2.0),
       Row(2, 100.0),
       Row(2, 1.0), // This row is duplicated to ensure that we will have multiple buffered matches
@@ -40,8 +40,8 @@ class OuterJoinSuite extends SparkPlanTest with SharedSQLContext {
       Row(null, null)
     )), new StructType().add("a", IntegerType).add("b", DoubleType))
 
-  private lazy val right = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val right = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(0, 0.0),
       Row(2, 3.0), // This row is duplicated to ensure that we will have multiple buffered matches
       Row(2, -1.0),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
index baa86e320d986..3afd762942bcf 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/SemiJoinSuite.scala
@@ -28,8 +28,8 @@ import org.apache.spark.sql.types.{DoubleType, IntegerType, StructType}
 
 class SemiJoinSuite extends SparkPlanTest with SharedSQLContext {
 
-  private lazy val left = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val left = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(1, 2.0),
       Row(1, 2.0),
       Row(2, 1.0),
@@ -40,8 +40,8 @@ class SemiJoinSuite extends SparkPlanTest with SharedSQLContext {
       Row(6, null)
     )), new StructType().add("a", IntegerType).add("b", DoubleType))
 
-  private lazy val right = ctx.createDataFrame(
-    ctx.sparkContext.parallelize(Seq(
+  private lazy val right = sqlContext.createDataFrame(
+    sparkContext.parallelize(Seq(
       Row(2, 3.0),
       Row(2, 3.0),
       Row(3, 2.0),
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
index 80006bf077fe8..6afffae161ef6 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/metric/SQLMetricsSuite.scala
@@ -36,7 +36,7 @@ class SQLMetricsSuite extends SparkFunSuite with SharedSQLContext {
   import testImplicits._
 
   test("LongSQLMetric should not box Long") {
-    val l = SQLMetrics.createLongMetric(ctx.sparkContext, "long")
+    val l = SQLMetrics.createLongMetric(sparkContext, "long")
     val f = () => {
       l += 1L
       l.add(1L)
@@ -50,7 +50,7 @@ class SQLMetricsSuite extends SparkFunSuite with SharedSQLContext {
 
   test("Normal accumulator should do boxing") {
     // We need this test to make sure BoxingFinder works.
-    val l = ctx.sparkContext.accumulator(0L)
+    val l = sparkContext.accumulator(0L)
     val f = () => { l += 1L }
     BoxingFinder.getClassReader(f.getClass).foreach { cl =>
       val boxingFinder = new BoxingFinder()
@@ -71,19 +71,19 @@ class SQLMetricsSuite extends SparkFunSuite with SharedSQLContext {
       df: DataFrame,
       expectedNumOfJobs: Int,
       expectedMetrics: Map[Long, (String, Map[String, Any])]): Unit = {
-    val previousExecutionIds = ctx.listener.executionIdToData.keySet
+    val previousExecutionIds = sqlContext.listener.executionIdToData.keySet
     df.collect()
-    ctx.sparkContext.listenerBus.waitUntilEmpty(10000)
-    val executionIds = ctx.listener.executionIdToData.keySet.diff(previousExecutionIds)
+    sparkContext.listenerBus.waitUntilEmpty(10000)
+    val executionIds = sqlContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
     assert(executionIds.size === 1)
     val executionId = executionIds.head
-    val jobs = ctx.listener.getExecution(executionId).get.jobs
+    val jobs = sqlContext.listener.getExecution(executionId).get.jobs
     // Use "<=" because there is a race condition that we may miss some jobs
     // TODO Change it to "=" once we fix the race condition that missing the JobStarted event.
     assert(jobs.size <= expectedNumOfJobs)
     if (jobs.size == expectedNumOfJobs) {
       // If we can track all jobs, check the metric values
-      val metricValues = ctx.listener.getExecutionMetrics(executionId)
+      val metricValues = sqlContext.listener.getExecutionMetrics(executionId)
       val actualMetrics = SparkPlanGraph(df.queryExecution.executedPlan).nodes.filter { node =>
         expectedMetrics.contains(node.id)
       }.map { node =>
@@ -474,19 +474,19 @@ class SQLMetricsSuite extends SparkFunSuite with SharedSQLContext {
 
   test("save metrics") {
     withTempPath { file =>
-      val previousExecutionIds = ctx.listener.executionIdToData.keySet
+      val previousExecutionIds = sqlContext.listener.executionIdToData.keySet
       // Assume the execution plan is
       // PhysicalRDD(nodeId = 0)
       person.select('name).write.format("json").save(file.getAbsolutePath)
-      ctx.sparkContext.listenerBus.waitUntilEmpty(10000)
-      val executionIds = ctx.listener.executionIdToData.keySet.diff(previousExecutionIds)
+      sparkContext.listenerBus.waitUntilEmpty(10000)
+      val executionIds = sqlContext.listener.executionIdToData.keySet.diff(previousExecutionIds)
       assert(executionIds.size === 1)
       val executionId = executionIds.head
-      val jobs = ctx.listener.getExecution(executionId).get.jobs
+      val jobs = sqlContext.listener.getExecution(executionId).get.jobs
       // Use "<=" because there is a race condition that we may miss some jobs
       // TODO Change "<=" to "=" once we fix the race condition that missing the JobStarted event.
       assert(jobs.size <= 1)
-      val metricValues = ctx.listener.getExecutionMetrics(executionId)
+      val metricValues = sqlContext.listener.getExecutionMetrics(executionId)
       // Because "save" will create a new DataFrame internally, we cannot get the real metric id.
       // However, we still can check the value.
       assert(metricValues.values.toSeq === Seq(2L))
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
index 80d1e88956949..2bbb41ca777b7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/ui/SQLListenerSuite.scala
@@ -74,7 +74,7 @@ class SQLListenerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("basic") {
-    val listener = new SQLListener(ctx)
+    val listener = new SQLListener(sqlContext)
     val executionId = 0
     val df = createTestDataFrame
     val accumulatorIds =
@@ -212,7 +212,7 @@ class SQLListenerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("onExecutionEnd happens before onJobEnd(JobSucceeded)") {
-    val listener = new SQLListener(ctx)
+    val listener = new SQLListener(sqlContext)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
@@ -241,7 +241,7 @@ class SQLListenerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("onExecutionEnd happens before multiple onJobEnd(JobSucceeded)s") {
-    val listener = new SQLListener(ctx)
+    val listener = new SQLListener(sqlContext)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
@@ -281,7 +281,7 @@ class SQLListenerSuite extends SparkFunSuite with SharedSQLContext {
   }
 
   test("onExecutionEnd happens before onJobEnd(JobFailed)") {
-    val listener = new SQLListener(ctx)
+    val listener = new SQLListener(sqlContext)
     val executionId = 0
     val df = createTestDataFrame
     listener.onExecutionStart(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
index d8c9a08d84c61..ed710689cc670 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala
@@ -255,26 +255,26 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
   }
 
   test("Basic API") {
-    assert(ctx.read.jdbc(
+    assert(sqlContext.read.jdbc(
       urlWithUserAndPass, "TEST.PEOPLE", new Properties).collect().length === 3)
   }
 
   test("Basic API with FetchSize") {
     val properties = new Properties
     properties.setProperty("fetchSize", "2")
-    assert(ctx.read.jdbc(
+    assert(sqlContext.read.jdbc(
       urlWithUserAndPass, "TEST.PEOPLE", properties).collect().length === 3)
   }
 
   test("Partitioning via JDBCPartitioningInfo API") {
     assert(
-      ctx.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", "THEID", 0, 4, 3, new Properties)
+      sqlContext.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", "THEID", 0, 4, 3, new Properties)
       .collect().length === 3)
   }
 
   test("Partitioning via list-of-where-clauses API") {
     val parts = Array[String]("THEID < 2", "THEID >= 2")
-    assert(ctx.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts, new Properties)
+    assert(sqlContext.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts, new Properties)
       .collect().length === 3)
   }
 
@@ -330,9 +330,9 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
   }
 
   test("test DATE types") {
-    val rows = ctx.read.jdbc(
+    val rows = sqlContext.read.jdbc(
       urlWithUserAndPass, "TEST.TIMETYPES", new Properties).collect()
-    val cachedRows = ctx.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties)
+    val cachedRows = sqlContext.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties)
       .cache().collect()
     assert(rows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01"))
     assert(rows(1).getAs[java.sql.Date](1) === null)
@@ -340,8 +340,8 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
   }
 
   test("test DATE types in cache") {
-    val rows = ctx.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties).collect()
-    ctx.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties)
+    val rows = sqlContext.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties).collect()
+    sqlContext.read.jdbc(urlWithUserAndPass, "TEST.TIMETYPES", new Properties)
       .cache().registerTempTable("mycached_date")
     val cachedRows = sql("select * from mycached_date").collect()
     assert(rows(0).getAs[java.sql.Date](1) === java.sql.Date.valueOf("1996-01-01"))
@@ -349,7 +349,7 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
   }
 
   test("test types for null value") {
-    val rows = ctx.read.jdbc(
+    val rows = sqlContext.read.jdbc(
       urlWithUserAndPass, "TEST.NULLTYPES", new Properties).collect()
     assert((0 to 14).forall(i => rows(0).isNullAt(i)))
   }
@@ -396,7 +396,7 @@ class JDBCSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext
 
   test("Remap types via JdbcDialects") {
     JdbcDialects.registerDialect(testH2Dialect)
-    val df = ctx.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", new Properties)
+    val df = sqlContext.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", new Properties)
     assert(df.schema.filter(_.dataType != org.apache.spark.sql.types.StringType).isEmpty)
     val rows = df.collect()
     assert(rows(0).get(0).isInstanceOf[String])
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
index 5dc3a2c07b8c7..e23ee6693133b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
@@ -22,13 +22,12 @@ import java.util.Properties
 
 import org.scalatest.BeforeAndAfter
 
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.{Row, SaveMode}
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
-class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLContext {
+class JDBCWriteSuite extends SharedSQLContext with BeforeAndAfter {
 
   val url = "jdbc:h2:mem:testdb2"
   var conn: java.sql.Connection = null
@@ -76,8 +75,6 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLCon
     conn1.close()
   }
 
-  private lazy val sc = ctx.sparkContext
-
   private lazy val arr2x2 = Array[Row](Row.apply("dave", 42), Row.apply("mary", 222))
   private lazy val arr1x2 = Array[Row](Row.apply("fred", 3))
   private lazy val schema2 = StructType(
@@ -91,49 +88,50 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLCon
       StructField("seq", IntegerType) :: Nil)
 
   test("Basic CREATE") {
-    val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
 
     df.write.jdbc(url, "TEST.BASICCREATETEST", new Properties)
-    assert(2 === ctx.read.jdbc(url, "TEST.BASICCREATETEST", new Properties).count)
-    assert(2 === ctx.read.jdbc(url, "TEST.BASICCREATETEST", new Properties).collect()(0).length)
+    assert(2 === sqlContext.read.jdbc(url, "TEST.BASICCREATETEST", new Properties).count)
+    assert(
+      2 === sqlContext.read.jdbc(url, "TEST.BASICCREATETEST", new Properties).collect()(0).length)
   }
 
   test("CREATE with overwrite") {
-    val df = ctx.createDataFrame(sc.parallelize(arr2x3), schema3)
-    val df2 = ctx.createDataFrame(sc.parallelize(arr1x2), schema2)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(arr2x3), schema3)
+    val df2 = sqlContext.createDataFrame(sparkContext.parallelize(arr1x2), schema2)
 
     df.write.jdbc(url1, "TEST.DROPTEST", properties)
-    assert(2 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).count)
-    assert(3 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).collect()(0).length)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.DROPTEST", properties).count)
+    assert(3 === sqlContext.read.jdbc(url1, "TEST.DROPTEST", properties).collect()(0).length)
 
     df2.write.mode(SaveMode.Overwrite).jdbc(url1, "TEST.DROPTEST", properties)
-    assert(1 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).count)
-    assert(2 === ctx.read.jdbc(url1, "TEST.DROPTEST", properties).collect()(0).length)
+    assert(1 === sqlContext.read.jdbc(url1, "TEST.DROPTEST", properties).count)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.DROPTEST", properties).collect()(0).length)
   }
 
   test("CREATE then INSERT to append") {
-    val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2)
-    val df2 = ctx.createDataFrame(sc.parallelize(arr1x2), schema2)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
+    val df2 = sqlContext.createDataFrame(sparkContext.parallelize(arr1x2), schema2)
 
     df.write.jdbc(url, "TEST.APPENDTEST", new Properties)
     df2.write.mode(SaveMode.Append).jdbc(url, "TEST.APPENDTEST", new Properties)
-    assert(3 === ctx.read.jdbc(url, "TEST.APPENDTEST", new Properties).count)
-    assert(2 === ctx.read.jdbc(url, "TEST.APPENDTEST", new Properties).collect()(0).length)
+    assert(3 === sqlContext.read.jdbc(url, "TEST.APPENDTEST", new Properties).count)
+    assert(2 === sqlContext.read.jdbc(url, "TEST.APPENDTEST", new Properties).collect()(0).length)
   }
 
   test("CREATE then INSERT to truncate") {
-    val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2)
-    val df2 = ctx.createDataFrame(sc.parallelize(arr1x2), schema2)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
+    val df2 = sqlContext.createDataFrame(sparkContext.parallelize(arr1x2), schema2)
 
     df.write.jdbc(url1, "TEST.TRUNCATETEST", properties)
     df2.write.mode(SaveMode.Overwrite).jdbc(url1, "TEST.TRUNCATETEST", properties)
-    assert(1 === ctx.read.jdbc(url1, "TEST.TRUNCATETEST", properties).count)
-    assert(2 === ctx.read.jdbc(url1, "TEST.TRUNCATETEST", properties).collect()(0).length)
+    assert(1 === sqlContext.read.jdbc(url1, "TEST.TRUNCATETEST", properties).count)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.TRUNCATETEST", properties).collect()(0).length)
   }
 
   test("Incompatible INSERT to append") {
-    val df = ctx.createDataFrame(sc.parallelize(arr2x2), schema2)
-    val df2 = ctx.createDataFrame(sc.parallelize(arr2x3), schema3)
+    val df = sqlContext.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
+    val df2 = sqlContext.createDataFrame(sparkContext.parallelize(arr2x3), schema3)
 
     df.write.jdbc(url, "TEST.INCOMPATIBLETEST", new Properties)
     intercept[org.apache.spark.SparkException] {
@@ -143,14 +141,14 @@ class JDBCWriteSuite extends SparkFunSuite with BeforeAndAfter with SharedSQLCon
 
   test("INSERT to JDBC Datasource") {
     sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
-    assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
-    assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
   }
 
   test("INSERT to JDBC Datasource with overwrite") {
     sql("INSERT INTO TABLE PEOPLE1 SELECT * FROM PEOPLE")
     sql("INSERT OVERWRITE TABLE PEOPLE1 SELECT * FROM PEOPLE")
-    assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
-    assert(2 === ctx.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.PEOPLE1", properties).count)
+    assert(2 === sqlContext.read.jdbc(url1, "TEST.PEOPLE1", properties).collect()(0).length)
   }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
index 9bc3f6bcf6fce..6fc9febe49707 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/CreateTableAsSelectSuite.scala
@@ -26,10 +26,8 @@ import org.apache.spark.sql.execution.datasources.DDLException
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
-
 class CreateTableAsSelectSuite extends DataSourceTest with SharedSQLContext with BeforeAndAfter {
   protected override lazy val sql = caseInsensitiveContext.sql _
-  private lazy val sparkContext = caseInsensitiveContext.sparkContext
   private var path: File = null
 
   override def beforeAll(): Unit = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
index d74d29fb0beb0..af04079ec895a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/DataSourceTest.scala
@@ -19,13 +19,11 @@ package org.apache.spark.sql.sources
 
 import org.apache.spark.sql._
 
-
 private[sql] abstract class DataSourceTest extends QueryTest {
-  protected def _sqlContext: SQLContext
 
   // We want to test some edge cases.
   protected lazy val caseInsensitiveContext: SQLContext = {
-    val ctx = new SQLContext(_sqlContext.sparkContext)
+    val ctx = new SQLContext(sqlContext.sparkContext)
     ctx.setConf(SQLConf.CASE_SENSITIVE, false)
     ctx
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
index 084d83f6e9bff..5b70d258d6ce3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/InsertSuite.scala
@@ -19,13 +19,12 @@ package org.apache.spark.sql.sources
 
 import java.io.File
 
-import org.apache.spark.sql.{SaveMode, AnalysisException, Row}
+import org.apache.spark.sql.{AnalysisException, Row}
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.util.Utils
 
 class InsertSuite extends DataSourceTest with SharedSQLContext {
   protected override lazy val sql = caseInsensitiveContext.sql _
-  private lazy val sparkContext = caseInsensitiveContext.sparkContext
   private var path: File = null
 
   override def beforeAll(): Unit = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
index 79b6e9b45c009..c9791879ec74c 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/PartitionedWriteSuite.scala
@@ -29,11 +29,11 @@ class PartitionedWriteSuite extends QueryTest with SharedSQLContext {
     val path = Utils.createTempDir()
     path.delete()
 
-    val df = ctx.range(100).select($"id", lit(1).as("data"))
+    val df = sqlContext.range(100).select($"id", lit(1).as("data"))
     df.write.partitionBy("id").save(path.getCanonicalPath)
 
     checkAnswer(
-      ctx.read.load(path.getCanonicalPath),
+      sqlContext.read.load(path.getCanonicalPath),
       (0 to 99).map(Row(1, _)).toSeq)
 
     Utils.deleteRecursively(path)
@@ -43,12 +43,12 @@ class PartitionedWriteSuite extends QueryTest with SharedSQLContext {
     val path = Utils.createTempDir()
     path.delete()
 
-    val base = ctx.range(100)
+    val base = sqlContext.range(100)
     val df = base.unionAll(base).select($"id", lit(1).as("data"))
     df.write.partitionBy("id").save(path.getCanonicalPath)
 
     checkAnswer(
-      ctx.read.load(path.getCanonicalPath),
+      sqlContext.read.load(path.getCanonicalPath),
       (0 to 99).map(Row(1, _)).toSeq ++ (0 to 99).map(Row(1, _)).toSeq)
 
     Utils.deleteRecursively(path)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
index f18546b4c2d9b..10d261368993d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/sources/SaveLoadSuite.scala
@@ -28,7 +28,6 @@ import org.apache.spark.util.Utils
 
 class SaveLoadSuite extends DataSourceTest with SharedSQLContext with BeforeAndAfter {
   protected override lazy val sql = caseInsensitiveContext.sql _
-  private lazy val sparkContext = caseInsensitiveContext.sparkContext
   private var originalDefaultSource: String = null
   private var path: File = null
   private var df: DataFrame = null
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
index 3fc02df954e23..520dea7f7dd92 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestData.scala
@@ -24,11 +24,11 @@ import org.apache.spark.sql.{DataFrame, SQLContext, SQLImplicits}
  * A collection of sample data used in SQL tests.
  */
 private[sql] trait SQLTestData { self =>
-  protected def _sqlContext: SQLContext
+  protected def sqlContext: SQLContext
 
   // Helper object to import SQL implicits without a concrete SQLContext
   private object internalImplicits extends SQLImplicits {
-    protected override def _sqlContext: SQLContext = self._sqlContext
+    protected override def _sqlContext: SQLContext = self.sqlContext
   }
 
   import internalImplicits._
@@ -37,21 +37,21 @@ private[sql] trait SQLTestData { self =>
   // Note: all test data should be lazy because the SQLContext is not set up yet.
 
   protected lazy val emptyTestData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       Seq.empty[Int].map(i => TestData(i, i.toString))).toDF()
     df.registerTempTable("emptyTestData")
     df
   }
 
   protected lazy val testData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       (1 to 100).map(i => TestData(i, i.toString))).toDF()
     df.registerTempTable("testData")
     df
   }
 
   protected lazy val testData2: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       TestData2(1, 1) ::
       TestData2(1, 2) ::
       TestData2(2, 1) ::
@@ -63,7 +63,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val testData3: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       TestData3(1, None) ::
       TestData3(2, Some(2)) :: Nil).toDF()
     df.registerTempTable("testData3")
@@ -71,14 +71,14 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val negativeData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       (1 to 100).map(i => TestData(-i, (-i).toString))).toDF()
     df.registerTempTable("negativeData")
     df
   }
 
   protected lazy val largeAndSmallInts: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       LargeAndSmallInts(2147483644, 1) ::
       LargeAndSmallInts(1, 2) ::
       LargeAndSmallInts(2147483645, 1) ::
@@ -90,7 +90,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val decimalData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       DecimalData(1, 1) ::
       DecimalData(1, 2) ::
       DecimalData(2, 1) ::
@@ -102,7 +102,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val binaryData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       BinaryData("12".getBytes, 1) ::
       BinaryData("22".getBytes, 5) ::
       BinaryData("122".getBytes, 3) ::
@@ -113,7 +113,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val upperCaseData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       UpperCaseData(1, "A") ::
       UpperCaseData(2, "B") ::
       UpperCaseData(3, "C") ::
@@ -125,7 +125,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val lowerCaseData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       LowerCaseData(1, "a") ::
       LowerCaseData(2, "b") ::
       LowerCaseData(3, "c") ::
@@ -135,7 +135,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val arrayData: RDD[ArrayData] = {
-    val rdd = _sqlContext.sparkContext.parallelize(
+    val rdd = sqlContext.sparkContext.parallelize(
       ArrayData(Seq(1, 2, 3), Seq(Seq(1, 2, 3))) ::
       ArrayData(Seq(2, 3, 4), Seq(Seq(2, 3, 4))) :: Nil)
     rdd.toDF().registerTempTable("arrayData")
@@ -143,7 +143,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val mapData: RDD[MapData] = {
-    val rdd = _sqlContext.sparkContext.parallelize(
+    val rdd = sqlContext.sparkContext.parallelize(
       MapData(Map(1 -> "a1", 2 -> "b1", 3 -> "c1", 4 -> "d1", 5 -> "e1")) ::
       MapData(Map(1 -> "a2", 2 -> "b2", 3 -> "c2", 4 -> "d2")) ::
       MapData(Map(1 -> "a3", 2 -> "b3", 3 -> "c3")) ::
@@ -154,13 +154,13 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val repeatedData: RDD[StringData] = {
-    val rdd = _sqlContext.sparkContext.parallelize(List.fill(2)(StringData("test")))
+    val rdd = sqlContext.sparkContext.parallelize(List.fill(2)(StringData("test")))
     rdd.toDF().registerTempTable("repeatedData")
     rdd
   }
 
   protected lazy val nullableRepeatedData: RDD[StringData] = {
-    val rdd = _sqlContext.sparkContext.parallelize(
+    val rdd = sqlContext.sparkContext.parallelize(
       List.fill(2)(StringData(null)) ++
       List.fill(2)(StringData("test")))
     rdd.toDF().registerTempTable("nullableRepeatedData")
@@ -168,7 +168,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val nullInts: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       NullInts(1) ::
       NullInts(2) ::
       NullInts(3) ::
@@ -178,7 +178,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val allNulls: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       NullInts(null) ::
       NullInts(null) ::
       NullInts(null) ::
@@ -188,7 +188,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val nullStrings: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       NullStrings(1, "abc") ::
       NullStrings(2, "ABC") ::
       NullStrings(3, null) :: Nil).toDF()
@@ -197,13 +197,13 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val tableName: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(TableName("test") :: Nil).toDF()
+    val df = sqlContext.sparkContext.parallelize(TableName("test") :: Nil).toDF()
     df.registerTempTable("tableName")
     df
   }
 
   protected lazy val unparsedStrings: RDD[String] = {
-    _sqlContext.sparkContext.parallelize(
+    sqlContext.sparkContext.parallelize(
       "1, A1, true, null" ::
       "2, B2, false, null" ::
       "3, C3, true, null" ::
@@ -212,13 +212,13 @@ private[sql] trait SQLTestData { self =>
 
   // An RDD with 4 elements and 8 partitions
   protected lazy val withEmptyParts: RDD[IntField] = {
-    val rdd = _sqlContext.sparkContext.parallelize((1 to 4).map(IntField), 8)
+    val rdd = sqlContext.sparkContext.parallelize((1 to 4).map(IntField), 8)
     rdd.toDF().registerTempTable("withEmptyParts")
     rdd
   }
 
   protected lazy val person: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       Person(0, "mike", 30) ::
       Person(1, "jim", 20) :: Nil).toDF()
     df.registerTempTable("person")
@@ -226,7 +226,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val salary: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       Salary(0, 2000.0) ::
       Salary(1, 1000.0) :: Nil).toDF()
     df.registerTempTable("salary")
@@ -234,7 +234,7 @@ private[sql] trait SQLTestData { self =>
   }
 
   protected lazy val complexData: DataFrame = {
-    val df = _sqlContext.sparkContext.parallelize(
+    val df = sqlContext.sparkContext.parallelize(
       ComplexData(Map("1" -> 1), TestData(1, "1"), Seq(1, 1, 1), true) ::
       ComplexData(Map("2" -> 2), TestData(2, "2"), Seq(2, 2, 2), false) ::
       Nil).toDF()
@@ -246,7 +246,7 @@ private[sql] trait SQLTestData { self =>
    * Initialize all test data such that all temp tables are properly registered.
    */
   def loadTestData(): Unit = {
-    assert(_sqlContext != null, "attempted to initialize test data before SQLContext.")
+    assert(sqlContext != null, "attempted to initialize test data before SQLContext.")
     emptyTestData
     testData
     testData2
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
index dc08306ad9cb4..9214569f18e93 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SQLTestUtils.scala
@@ -27,7 +27,7 @@ import org.apache.hadoop.conf.Configuration
 import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.{DataFrame, Row, SQLContext, SQLImplicits}
+import org.apache.spark.sql._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.catalyst.util._
 import org.apache.spark.util.Utils
@@ -47,13 +47,13 @@ private[sql] trait SQLTestUtils
   with BeforeAndAfterAll
   with SQLTestData { self =>
 
-  protected def _sqlContext: SQLContext
+  protected def sparkContext = sqlContext.sparkContext
 
   // Whether to materialize all test data before the first test is run
   private var loadTestDataBeforeTests = false
 
   // Shorthand for running a query using our SQLContext
-  protected lazy val sql = _sqlContext.sql _
+  protected lazy val sql = sqlContext.sql _
 
   /**
    * A helper object for importing SQL implicits.
@@ -63,7 +63,14 @@ private[sql] trait SQLTestUtils
    * but the implicits import is needed in the constructor.
    */
   protected object testImplicits extends SQLImplicits {
-    protected override def _sqlContext: SQLContext = self._sqlContext
+    protected override def _sqlContext: SQLContext = self.sqlContext
+
+    // This must live here to preserve binary compatibility with Spark < 1.5.
+    implicit class StringToColumn(val sc: StringContext) {
+      def $(args: Any*): ColumnName = {
+        new ColumnName(sc.s(args: _*))
+      }
+    }
   }
 
   /**
@@ -84,8 +91,8 @@ private[sql] trait SQLTestUtils
   /**
    * The Hadoop configuration used by the active [[SQLContext]].
    */
-  protected def configuration: Configuration = {
-    _sqlContext.sparkContext.hadoopConfiguration
+  protected def hadoopConfiguration: Configuration = {
+    sparkContext.hadoopConfiguration
   }
 
   /**
@@ -96,12 +103,12 @@ private[sql] trait SQLTestUtils
    */
   protected def withSQLConf(pairs: (String, String)*)(f: => Unit): Unit = {
     val (keys, values) = pairs.unzip
-    val currentValues = keys.map(key => Try(_sqlContext.conf.getConfString(key)).toOption)
-    (keys, values).zipped.foreach(_sqlContext.conf.setConfString)
+    val currentValues = keys.map(key => Try(sqlContext.conf.getConfString(key)).toOption)
+    (keys, values).zipped.foreach(sqlContext.conf.setConfString)
     try f finally {
       keys.zip(currentValues).foreach {
-        case (key, Some(value)) => _sqlContext.conf.setConfString(key, value)
-        case (key, None) => _sqlContext.conf.unsetConf(key)
+        case (key, Some(value)) => sqlContext.conf.setConfString(key, value)
+        case (key, None) => sqlContext.conf.unsetConf(key)
       }
     }
   }
@@ -133,7 +140,7 @@ private[sql] trait SQLTestUtils
    * Drops temporary table `tableName` after calling `f`.
    */
   protected def withTempTable(tableNames: String*)(f: => Unit): Unit = {
-    try f finally tableNames.foreach(_sqlContext.dropTempTable)
+    try f finally tableNames.foreach(sqlContext.dropTempTable)
   }
 
   /**
@@ -142,7 +149,7 @@ private[sql] trait SQLTestUtils
   protected def withTable(tableNames: String*)(f: => Unit): Unit = {
     try f finally {
       tableNames.foreach { name =>
-        _sqlContext.sql(s"DROP TABLE IF EXISTS $name")
+        sqlContext.sql(s"DROP TABLE IF EXISTS $name")
       }
     }
   }
@@ -155,12 +162,12 @@ private[sql] trait SQLTestUtils
     val dbName = s"db_${UUID.randomUUID().toString.replace('-', '_')}"
 
     try {
-      _sqlContext.sql(s"CREATE DATABASE $dbName")
+      sqlContext.sql(s"CREATE DATABASE $dbName")
     } catch { case cause: Throwable =>
       fail("Failed to create temporary database", cause)
     }
 
-    try f(dbName) finally _sqlContext.sql(s"DROP DATABASE $dbName CASCADE")
+    try f(dbName) finally sqlContext.sql(s"DROP DATABASE $dbName CASCADE")
   }
 
   /**
@@ -168,8 +175,8 @@ private[sql] trait SQLTestUtils
    * `f` returns.
    */
   protected def activateDatabase(db: String)(f: => Unit): Unit = {
-    _sqlContext.sql(s"USE $db")
-    try f finally _sqlContext.sql(s"USE default")
+    sqlContext.sql(s"USE $db")
+    try f finally sqlContext.sql(s"USE default")
   }
 
   /**
@@ -177,7 +184,7 @@ private[sql] trait SQLTestUtils
    * way to construct [[DataFrame]] directly out of local data without relying on implicits.
    */
   protected implicit def logicalPlanToSparkQuery(plan: LogicalPlan): DataFrame = {
-    DataFrame(_sqlContext, plan)
+    DataFrame(sqlContext, plan)
   }
 }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
index d23c6a0732669..963d10eed62ed 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/SharedSQLContext.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql.test
 
-import org.apache.spark.sql.{ColumnName, SQLContext}
+import org.apache.spark.sql.SQLContext
 
 
 /**
@@ -36,9 +36,7 @@ trait SharedSQLContext extends SQLTestUtils {
   /**
    * The [[TestSQLContext]] to use for all tests in this suite.
    */
-  protected def ctx: TestSQLContext = _ctx
-  protected def sqlContext: TestSQLContext = _ctx
-  protected override def _sqlContext: SQLContext = _ctx
+  protected def sqlContext: SQLContext = _ctx
 
   /**
    * Initialize the [[TestSQLContext]].
@@ -64,15 +62,4 @@ trait SharedSQLContext extends SQLTestUtils {
       super.afterAll()
     }
   }
-
-  /**
-   * Converts $"col name" into an [[Column]].
-   * @since 1.3.0
-   */
-  // This must be duplicated here to preserve binary compatibility with Spark < 1.5.
-  implicit class StringToColumn(val sc: StringContext) {
-    def $(args: Any*): ColumnName = {
-      new ColumnName(sc.s(args: _*))
-    }
-  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
index 92ef2f7d74ba1..d99d191ebe81e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
@@ -47,6 +47,6 @@ private[sql] class TestSQLContext(sc: SparkContext) extends SQLContext(sc) { sel
   }
 
   private object testData extends SQLTestData {
-    protected override def _sqlContext: SQLContext = self
+    protected override def sqlContext: SQLContext = self
   }
 }
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 57fea5d8db343..77f43f92705d8 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -29,7 +29,7 @@ import org.apache.hadoop.hive.ql.exec.FunctionRegistry
 import org.apache.hadoop.hive.ql.processors._
 import org.apache.hadoop.hive.serde2.`lazy`.LazySimpleSerDe
 
-import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.{SQLContext, SQLConf}
 import org.apache.spark.sql.catalyst.analysis._
 import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
 import org.apache.spark.sql.execution.CacheTableCommand
@@ -51,6 +51,11 @@ object TestHive
         // SPARK-8910
         .set("spark.ui.enabled", "false")))
 
+trait TestHiveSingleton {
+  protected val sqlContext: SQLContext = TestHive
+  protected val hiveContext: TestHiveContext = TestHive
+}
+
 /**
  * A locally running test instance of Spark's Hive execution engine.
  *
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
index 39d315aaeab57..9adb3780a2c55 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/CachedTableSuite.scala
@@ -19,14 +19,14 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
-import org.apache.spark.sql.columnar.{InMemoryColumnarTableScan, InMemoryRelation}
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.{SaveMode, AnalysisException, DataFrame, QueryTest}
+import org.apache.spark.sql.columnar.InMemoryColumnarTableScan
+import org.apache.spark.sql.hive.test.TestHiveSingleton
+import org.apache.spark.sql.{AnalysisException, QueryTest, SaveMode}
 import org.apache.spark.storage.RDDBlockId
 import org.apache.spark.util.Utils
 
-class CachedTableSuite extends QueryTest {
+class CachedTableSuite extends QueryTest with TestHiveSingleton {
+  import hiveContext._
 
   def rddIdOf(tableName: String): Int = {
     val executedPlan = table(tableName).queryExecution.executedPlan
@@ -95,18 +95,18 @@ class CachedTableSuite extends QueryTest {
 
   test("correct error on uncache of non-cached table") {
     intercept[IllegalArgumentException] {
-      TestHive.uncacheTable("src")
+      hiveContext.uncacheTable("src")
     }
   }
 
   test("'CACHE TABLE' and 'UNCACHE TABLE' HiveQL statement") {
-    TestHive.sql("CACHE TABLE src")
+    sql("CACHE TABLE src")
     assertCached(table("src"))
-    assert(TestHive.isCached("src"), "Table 'src' should be cached")
+    assert(hiveContext.isCached("src"), "Table 'src' should be cached")
 
-    TestHive.sql("UNCACHE TABLE src")
+    sql("UNCACHE TABLE src")
     assertCached(table("src"), 0)
-    assert(!TestHive.isCached("src"), "Table 'src' should not be cached")
+    assert(!hiveContext.isCached("src"), "Table 'src' should not be cached")
   }
 
   test("CACHE TABLE tableName AS SELECT * FROM anotherTable") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala
index 30f5313d2b812..cf737836939f9 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ErrorPositionSuite.scala
@@ -22,12 +22,12 @@ import scala.util.Try
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.catalyst.util.quietly
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.{AnalysisException, QueryTest}
 
 
-class ErrorPositionSuite extends QueryTest with BeforeAndAfter {
+class ErrorPositionSuite extends QueryTest with TestHiveSingleton with BeforeAndAfter {
+  import hiveContext.implicits._
 
   before {
     Seq((1, 1, 1)).toDF("a", "a", "b").registerTempTable("dupAttributes")
@@ -122,7 +122,7 @@ class ErrorPositionSuite extends QueryTest with BeforeAndAfter {
 
     test(name) {
       val error = intercept[AnalysisException] {
-        quietly(sql(query))
+        quietly(hiveContext.sql(query))
       }
 
       assert(!error.getMessage.contains("Seq("))
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameAnalyticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameAnalyticsSuite.scala
index fb10f8583da99..2e5cae415e54b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameAnalyticsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameAnalyticsSuite.scala
@@ -19,24 +19,25 @@ package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.{DataFrame, QueryTest}
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.scalatest.BeforeAndAfterAll
 
 // TODO ideally we should put the test suite into the package `sql`, as
 // `hive` package is optional in compiling, however, `SQLContext.sql` doesn't
 // support the `cube` or `rollup` yet.
-class HiveDataFrameAnalyticsSuite extends QueryTest with BeforeAndAfterAll {
+class HiveDataFrameAnalyticsSuite extends QueryTest with TestHiveSingleton with BeforeAndAfterAll {
+  import hiveContext.implicits._
+  import hiveContext.sql
+
   private var testData: DataFrame = _
 
   override def beforeAll() {
     testData = Seq((1, 2), (2, 4)).toDF("a", "b")
-    TestHive.registerDataFrameAsTable(testData, "mytable")
+    hiveContext.registerDataFrameAsTable(testData, "mytable")
   }
 
   override def afterAll(): Unit = {
-    TestHive.dropTempTable("mytable")
+    hiveContext.dropTempTable("mytable")
   }
 
   test("rollup") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala
index 52e782768cb75..f621367eb553b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameJoinSuite.scala
@@ -18,10 +18,10 @@
 package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.{Row, QueryTest}
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-
-class HiveDataFrameJoinSuite extends QueryTest {
+class HiveDataFrameJoinSuite extends QueryTest with TestHiveSingleton {
+  import hiveContext.implicits._
 
   // We should move this into SQL package if we make case sensitivity configurable in SQL.
   test("join - self join auto resolve ambiguity with case insensitivity") {
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
index c177cbdd991cf..2c98f1c3cc49c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveDataFrameWindowSuite.scala
@@ -20,10 +20,11 @@ package org.apache.spark.sql.hive
 import org.apache.spark.sql.{Row, QueryTest}
 import org.apache.spark.sql.expressions.Window
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-class HiveDataFrameWindowSuite extends QueryTest {
+class HiveDataFrameWindowSuite extends QueryTest with TestHiveSingleton {
+  import hiveContext.implicits._
+  import hiveContext.sql
 
   test("reuse window partitionBy") {
     val df = Seq((1, "1"), (2, "2"), (1, "1"), (2, "2")).toDF("key", "value")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
index 574624d501f22..107457f79ec03 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
@@ -19,18 +19,15 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.{QueryTest, Row, SaveMode}
 import org.apache.spark.sql.hive.client.{ExternalTable, ManagedTable}
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
-import org.apache.spark.sql.sources.DataSourceTest
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.test.{ExamplePointUDT, SQLTestUtils}
 import org.apache.spark.sql.types.{DecimalType, StringType, StructType}
-import org.apache.spark.sql.{Row, SaveMode, SQLContext}
-import org.apache.spark.{Logging, SparkFunSuite}
 
-
-class HiveMetastoreCatalogSuite extends SparkFunSuite with Logging {
+class HiveMetastoreCatalogSuite extends SparkFunSuite with TestHiveSingleton {
+  import hiveContext.implicits._
 
   test("struct field should accept underscore in sub-column name") {
     val hiveTypeStr = "struct<a: int, b_1: string, c: string>"
@@ -46,14 +43,15 @@ class HiveMetastoreCatalogSuite extends SparkFunSuite with Logging {
   }
 
   test("duplicated metastore relations") {
-    val df = sql("SELECT * FROM src")
+    val df = hiveContext.sql("SELECT * FROM src")
     logInfo(df.queryExecution.toString)
     df.as('a).join(df.as('b), $"a.key" === $"b.key")
   }
 }
 
-class DataSourceWithHiveMetastoreCatalogSuite extends DataSourceTest with SQLTestUtils {
-  override def _sqlContext: SQLContext = TestHive
+class DataSourceWithHiveMetastoreCatalogSuite
+  extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import hiveContext._
   import testImplicits._
 
   private val testDF = range(1, 3).select(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
index fe0db5228de16..5596ec6882ea2 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveParquetSuite.scala
@@ -17,15 +17,13 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.execution.datasources.parquet.ParquetTest
-import org.apache.spark.sql.{QueryTest, Row, SQLContext}
+import org.apache.spark.sql.hive.test.TestHiveSingleton
+import org.apache.spark.sql.{QueryTest, Row}
 
 case class Cases(lower: String, UPPER: String)
 
-class HiveParquetSuite extends QueryTest with ParquetTest {
-  private val ctx = TestHive
-  override def _sqlContext: SQLContext = ctx
+class HiveParquetSuite extends QueryTest with ParquetTest with TestHiveSingleton {
 
   test("Case insensitive attribute names") {
     withParquetTable((1 to 4).map(i => Cases(i.toString, i.toString)), "cases") {
@@ -53,7 +51,7 @@ class HiveParquetSuite extends QueryTest with ParquetTest {
   test("Converting Hive to Parquet Table via saveAsParquetFile") {
     withTempPath { dir =>
       sql("SELECT * FROM src").write.parquet(dir.getCanonicalPath)
-      ctx.read.parquet(dir.getCanonicalPath).registerTempTable("p")
+      hiveContext.read.parquet(dir.getCanonicalPath).registerTempTable("p")
       withTempTable("p") {
         checkAnswer(
           sql("SELECT * FROM src ORDER BY key"),
@@ -66,7 +64,7 @@ class HiveParquetSuite extends QueryTest with ParquetTest {
     withParquetTable((1 to 10).map(i => (i, s"val_$i")), "t") {
       withTempPath { file =>
         sql("SELECT * FROM t LIMIT 1").write.parquet(file.getCanonicalPath)
-        ctx.read.parquet(file.getCanonicalPath).registerTempTable("p")
+        hiveContext.read.parquet(file.getCanonicalPath).registerTempTable("p")
         withTempTable("p") {
           // let's do three overwrites for good measure
           sql("INSERT OVERWRITE TABLE p SELECT * FROM t")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index dc2d85f48624c..84f3db44ecd8d 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -29,7 +29,7 @@ import org.scalatest.exceptions.TestFailedDueToTimeoutException
 import org.scalatest.time.SpanSugar._
 
 import org.apache.spark._
-import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.{SQLContext, QueryTest}
 import org.apache.spark.sql.hive.test.{TestHive, TestHiveContext}
 import org.apache.spark.sql.test.ProcessTestUtils.ProcessOutputCapturer
 import org.apache.spark.sql.types.DecimalType
@@ -272,7 +272,11 @@ object SparkSQLConfTest extends Logging {
   }
 }
 
-object SPARK_9757 extends QueryTest with Logging {
+object SPARK_9757 extends QueryTest {
+  import org.apache.spark.sql.functions._
+
+  protected var sqlContext: SQLContext = _
+
   def main(args: Array[String]): Unit = {
     Utils.configTestLog4j("INFO")
 
@@ -282,10 +286,9 @@ object SPARK_9757 extends QueryTest with Logging {
         .set("spark.sql.hive.metastore.jars", "maven"))
 
     val hiveContext = new TestHiveContext(sparkContext)
+    sqlContext = hiveContext
     import hiveContext.implicits._
 
-    import org.apache.spark.sql.functions._
-
     val dir = Utils.createTempDir()
     dir.delete()
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
index d33e81227db88..80a61f82fd24f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/InsertIntoHiveTableSuite.scala
@@ -24,28 +24,25 @@ import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.execution.QueryExecutionException
 import org.apache.spark.sql.{QueryTest, _}
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
 
-/* Implicits */
-import org.apache.spark.sql.hive.test.TestHive._
-
 case class TestData(key: Int, value: String)
 
 case class ThreeCloumntable(key: Int, value: String, key1: String)
 
-class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
-  import org.apache.spark.sql.hive.test.TestHive.implicits._
-
+class InsertIntoHiveTableSuite extends QueryTest with TestHiveSingleton with BeforeAndAfter {
+  import hiveContext.implicits._
+  import hiveContext.sql
 
-  val testData = TestHive.sparkContext.parallelize(
+  val testData = hiveContext.sparkContext.parallelize(
     (1 to 100).map(i => TestData(i, i.toString))).toDF()
 
   before {
     // Since every we are doing tests for DDL statements,
     // it is better to reset before every test.
-    TestHive.reset()
+    hiveContext.reset()
     // Register the testData, which will be used in every test.
     testData.registerTempTable("testData")
   }
@@ -96,9 +93,9 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
 
   test("SPARK-4052: scala.collection.Map as value type of MapType") {
     val schema = StructType(StructField("m", MapType(StringType, StringType), true) :: Nil)
-    val rowRDD = TestHive.sparkContext.parallelize(
+    val rowRDD = hiveContext.sparkContext.parallelize(
       (1 to 100).map(i => Row(scala.collection.mutable.HashMap(s"key$i" -> s"value$i"))))
-    val df = TestHive.createDataFrame(rowRDD, schema)
+    val df = hiveContext.createDataFrame(rowRDD, schema)
     df.registerTempTable("tableWithMapValue")
     sql("CREATE TABLE hiveTableWithMapValue(m MAP <STRING, STRING>)")
     sql("INSERT OVERWRITE TABLE hiveTableWithMapValue SELECT m FROM tableWithMapValue")
@@ -169,8 +166,8 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
   test("Insert ArrayType.containsNull == false") {
     val schema = StructType(Seq(
       StructField("a", ArrayType(StringType, containsNull = false))))
-    val rowRDD = TestHive.sparkContext.parallelize((1 to 100).map(i => Row(Seq(s"value$i"))))
-    val df = TestHive.createDataFrame(rowRDD, schema)
+    val rowRDD = hiveContext.sparkContext.parallelize((1 to 100).map(i => Row(Seq(s"value$i"))))
+    val df = hiveContext.createDataFrame(rowRDD, schema)
     df.registerTempTable("tableWithArrayValue")
     sql("CREATE TABLE hiveTableWithArrayValue(a Array <STRING>)")
     sql("INSERT OVERWRITE TABLE hiveTableWithArrayValue SELECT a FROM tableWithArrayValue")
@@ -185,9 +182,9 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
   test("Insert MapType.valueContainsNull == false") {
     val schema = StructType(Seq(
       StructField("m", MapType(StringType, StringType, valueContainsNull = false))))
-    val rowRDD = TestHive.sparkContext.parallelize(
+    val rowRDD = hiveContext.sparkContext.parallelize(
       (1 to 100).map(i => Row(Map(s"key$i" -> s"value$i"))))
-    val df = TestHive.createDataFrame(rowRDD, schema)
+    val df = hiveContext.createDataFrame(rowRDD, schema)
     df.registerTempTable("tableWithMapValue")
     sql("CREATE TABLE hiveTableWithMapValue(m Map <STRING, STRING>)")
     sql("INSERT OVERWRITE TABLE hiveTableWithMapValue SELECT m FROM tableWithMapValue")
@@ -202,9 +199,9 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
   test("Insert StructType.fields.exists(_.nullable == false)") {
     val schema = StructType(Seq(
       StructField("s", StructType(Seq(StructField("f", StringType, nullable = false))))))
-    val rowRDD = TestHive.sparkContext.parallelize(
+    val rowRDD = hiveContext.sparkContext.parallelize(
       (1 to 100).map(i => Row(Row(s"value$i"))))
-    val df = TestHive.createDataFrame(rowRDD, schema)
+    val df = hiveContext.createDataFrame(rowRDD, schema)
     df.registerTempTable("tableWithStructValue")
     sql("CREATE TABLE hiveTableWithStructValue(s Struct <f: STRING>)")
     sql("INSERT OVERWRITE TABLE hiveTableWithStructValue SELECT s FROM tableWithStructValue")
@@ -217,11 +214,11 @@ class InsertIntoHiveTableSuite extends QueryTest with BeforeAndAfter {
   }
 
   test("SPARK-5498:partition schema does not match table schema") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       (1 to 10).map(i => TestData(i, i.toString))).toDF()
     testData.registerTempTable("testData")
 
-    val testDatawithNull = TestHive.sparkContext.parallelize(
+    val testDatawithNull = hiveContext.sparkContext.parallelize(
       (1 to 10).map(i => ThreeCloumntable(i, i.toString, null))).toDF()
 
     val tmpDir = Utils.createTempDir()
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
index d3388a9429e41..579631df772b5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ListTablesSuite.scala
@@ -19,17 +19,15 @@ package org.apache.spark.sql.hive
 
 import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.Row
 
-class ListTablesSuite extends QueryTest with BeforeAndAfterAll {
+class ListTablesSuite extends QueryTest with TestHiveSingleton with BeforeAndAfterAll {
+  import hiveContext._
+  import hiveContext.implicits._
 
-  import org.apache.spark.sql.hive.test.TestHive.implicits._
-
-  val df =
-    sparkContext.parallelize((1 to 10).map(i => (i, s"str$i"))).toDF("key", "value")
+  val df = sparkContext.parallelize((1 to 10).map(i => (i, s"str$i"))).toDF("key", "value")
 
   override def beforeAll(): Unit = {
     // The catalog in HiveContext is a case insensitive one.
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
index 20a50586d5201..bf0db084906cd 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MetastoreDataSourcesSuite.scala
@@ -22,15 +22,11 @@ import java.io.{IOException, File}
 import scala.collection.mutable.ArrayBuffer
 
 import org.apache.hadoop.fs.Path
-import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.Logging
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.datasources.LogicalRelation
 import org.apache.spark.sql.hive.client.{HiveTable, ManagedTable}
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
@@ -39,10 +35,9 @@ import org.apache.spark.util.Utils
 /**
  * Tests for persisting tables created though the data sources API into the metastore.
  */
-class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll
-  with Logging {
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+class MetastoreDataSourcesSuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import hiveContext._
+  import hiveContext.implicits._
 
   var jsonFilePath: String = _
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
index 997c667ec0d1b..f16c257ab5ab4 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/MultiDatabaseSuite.scala
@@ -17,20 +17,16 @@
 
 package org.apache.spark.sql.hive
 
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.test.SQLTestUtils
-import org.apache.spark.sql.{AnalysisException, QueryTest, SQLContext, SaveMode}
+import org.apache.spark.sql.{AnalysisException, QueryTest, SaveMode}
 
-class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
-  override val _sqlContext: HiveContext = TestHive
-  private val sqlContext = _sqlContext
-
-  private val df = sqlContext.range(10).coalesce(1)
+class MultiDatabaseSuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  private lazy val df = sqlContext.range(10).coalesce(1)
 
   private def checkTablePath(dbName: String, tableName: String): Unit = {
-    // val hiveContext = sqlContext.asInstanceOf[HiveContext]
-    val metastoreTable = sqlContext.catalog.client.getTable(dbName, tableName)
-    val expectedPath = sqlContext.catalog.client.getDatabase(dbName).location + "/" + tableName
+    val metastoreTable = hiveContext.catalog.client.getTable(dbName, tableName)
+    val expectedPath = hiveContext.catalog.client.getDatabase(dbName).location + "/" + tableName
 
     assert(metastoreTable.serdeProperties("path") === expectedPath)
   }
@@ -220,7 +216,7 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
 
           df.write.parquet(s"$path/p=2")
           sql("ALTER TABLE t ADD PARTITION (p=2)")
-          sqlContext.refreshTable("t")
+          hiveContext.refreshTable("t")
           checkAnswer(
             sqlContext.table("t"),
             df.withColumn("p", lit(1)).unionAll(df.withColumn("p", lit(2))))
@@ -252,7 +248,7 @@ class MultiDatabaseSuite extends QueryTest with SQLTestUtils {
 
         df.write.parquet(s"$path/p=2")
         sql(s"ALTER TABLE $db.t ADD PARTITION (p=2)")
-        sqlContext.refreshTable(s"$db.t")
+        hiveContext.refreshTable(s"$db.t")
         checkAnswer(
           sqlContext.table(s"$db.t"),
           df.withColumn("p", lit(1)).unionAll(df.withColumn("p", lit(2))))
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
index 91d7a48208e8d..49aab85cf1aaf 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/ParquetHiveCompatibilitySuite.scala
@@ -18,38 +18,20 @@
 package org.apache.spark.sql.hive
 
 import java.sql.Timestamp
-import java.util.{Locale, TimeZone}
 
 import org.apache.hadoop.hive.conf.HiveConf
-import org.scalatest.BeforeAndAfterAll
 
 import org.apache.spark.sql.execution.datasources.parquet.ParquetCompatibilityTest
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.{Row, SQLConf, SQLContext}
-
-class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest with BeforeAndAfterAll {
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+import org.apache.spark.sql.{Row, SQLConf}
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
+class ParquetHiveCompatibilitySuite extends ParquetCompatibilityTest with TestHiveSingleton {
   /**
    * Set the staging directory (and hence path to ignore Parquet files under)
    * to that set by [[HiveConf.ConfVars.STAGINGDIR]].
    */
   private val stagingDir = new HiveConf().getVar(HiveConf.ConfVars.STAGINGDIR)
 
-  private val originalTimeZone = TimeZone.getDefault
-  private val originalLocale = Locale.getDefault
-
-  protected override def beforeAll(): Unit = {
-    TimeZone.setDefault(TimeZone.getTimeZone("America/Los_Angeles"))
-    Locale.setDefault(Locale.US)
-  }
-
-  override protected def afterAll(): Unit = {
-    TimeZone.setDefault(originalTimeZone)
-    Locale.setDefault(originalLocale)
-  }
-
   override protected def logParquetSchema(path: String): Unit = {
     val schema = readParquetSchema(path, { path =>
       !path.getName.startsWith("_") && !path.getName.startsWith(stagingDir)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
index 1cc8a93e83411..f542a5a02508c 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/QueryPartitionSuite.scala
@@ -18,22 +18,18 @@
 package org.apache.spark.sql.hive
 
 import com.google.common.io.Files
-import org.apache.spark.sql.test.SQLTestUtils
 
-import org.apache.spark.sql.{QueryTest, _}
 import org.apache.spark.util.Utils
+import org.apache.spark.sql.{QueryTest, _}
+import org.apache.spark.sql.hive.test.TestHiveSingleton
+import org.apache.spark.sql.test.SQLTestUtils
 
+class QueryPartitionSuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import hiveContext.implicits._
 
-class QueryPartitionSuite extends QueryTest with SQLTestUtils {
-
-  private lazy val ctx = org.apache.spark.sql.hive.test.TestHive
-  import ctx.implicits._
-
-  protected def _sqlContext = ctx
-
-  test("SPARK-5068: query data when path doesn't exist"){
+  test("SPARK-5068: query data when path doesn't exist") {
     withSQLConf((SQLConf.HIVE_VERIFY_PARTITION_PATH.key, "true")) {
-      val testData = ctx.sparkContext.parallelize(
+      val testData = sparkContext.parallelize(
         (1 to 10).map(i => TestData(i, i.toString))).toDF()
       testData.registerTempTable("testData")
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
index e4fec7e2c8a2a..6a692d6fce562 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/StatisticsSuite.scala
@@ -17,24 +17,15 @@
 
 package org.apache.spark.sql.hive
 
-import org.scalatest.BeforeAndAfterAll
-
 import scala.reflect.ClassTag
 
 import org.apache.spark.sql.{Row, SQLConf, QueryTest}
 import org.apache.spark.sql.execution.joins._
 import org.apache.spark.sql.hive.execution._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
-
-  private lazy val ctx: HiveContext = {
-    val ctx = org.apache.spark.sql.hive.test.TestHive
-    ctx.reset()
-    ctx.cacheTables = false
-    ctx
-  }
-
-  import ctx.sql
+class StatisticsSuite extends QueryTest with TestHiveSingleton {
+  import hiveContext.sql
 
   test("parse analyze commands") {
     def assertAnalyzeCommand(analyzeCommand: String, c: Class[_]) {
@@ -54,9 +45,6 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
       }
     }
 
-    // Ensure session state is initialized.
-    ctx.parseSql("use default")
-
     assertAnalyzeCommand(
       "ANALYZE TABLE Table1 COMPUTE STATISTICS",
       classOf[HiveNativeCommand])
@@ -80,7 +68,7 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
 
   test("analyze MetastoreRelations") {
     def queryTotalSize(tableName: String): BigInt =
-      ctx.catalog.lookupRelation(Seq(tableName)).statistics.sizeInBytes
+      hiveContext.catalog.lookupRelation(Seq(tableName)).statistics.sizeInBytes
 
     // Non-partitioned table
     sql("CREATE TABLE analyzeTable (key STRING, value STRING)").collect()
@@ -114,7 +102,7 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
         |SELECT * FROM src
       """.stripMargin).collect()
 
-    assert(queryTotalSize("analyzeTable_part") === ctx.conf.defaultSizeInBytes)
+    assert(queryTotalSize("analyzeTable_part") === hiveContext.conf.defaultSizeInBytes)
 
     sql("ANALYZE TABLE analyzeTable_part COMPUTE STATISTICS noscan")
 
@@ -125,9 +113,9 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
     // Try to analyze a temp table
     sql("""SELECT * FROM src""").registerTempTable("tempTable")
     intercept[UnsupportedOperationException] {
-      ctx.analyze("tempTable")
+      hiveContext.analyze("tempTable")
     }
-    ctx.catalog.unregisterTable(Seq("tempTable"))
+    hiveContext.catalog.unregisterTable(Seq("tempTable"))
   }
 
   test("estimates the size of a test MetastoreRelation") {
@@ -155,8 +143,8 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
       val sizes = df.queryExecution.analyzed.collect {
         case r if ct.runtimeClass.isAssignableFrom(r.getClass) => r.statistics.sizeInBytes
       }
-      assert(sizes.size === 2 && sizes(0) <= ctx.conf.autoBroadcastJoinThreshold
-        && sizes(1) <= ctx.conf.autoBroadcastJoinThreshold,
+      assert(sizes.size === 2 && sizes(0) <= hiveContext.conf.autoBroadcastJoinThreshold
+        && sizes(1) <= hiveContext.conf.autoBroadcastJoinThreshold,
         s"query should contain two relations, each of which has size smaller than autoConvertSize")
 
       // Using `sparkPlan` because for relevant patterns in HashJoin to be
@@ -167,8 +155,8 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
 
       checkAnswer(df, expectedAnswer) // check correctness of output
 
-      ctx.conf.settings.synchronized {
-        val tmp = ctx.conf.autoBroadcastJoinThreshold
+      hiveContext.conf.settings.synchronized {
+        val tmp = hiveContext.conf.autoBroadcastJoinThreshold
 
         sql(s"""SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=-1""")
         df = sql(query)
@@ -211,8 +199,8 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
         .isAssignableFrom(r.getClass) =>
         r.statistics.sizeInBytes
     }
-    assert(sizes.size === 2 && sizes(1) <= ctx.conf.autoBroadcastJoinThreshold
-      && sizes(0) <= ctx.conf.autoBroadcastJoinThreshold,
+    assert(sizes.size === 2 && sizes(1) <= hiveContext.conf.autoBroadcastJoinThreshold
+      && sizes(0) <= hiveContext.conf.autoBroadcastJoinThreshold,
       s"query should contain two relations, each of which has size smaller than autoConvertSize")
 
     // Using `sparkPlan` because for relevant patterns in HashJoin to be
@@ -225,8 +213,8 @@ class StatisticsSuite extends QueryTest with BeforeAndAfterAll {
 
     checkAnswer(df, answer) // check correctness of output
 
-    ctx.conf.settings.synchronized {
-      val tmp = ctx.conf.autoBroadcastJoinThreshold
+    hiveContext.conf.settings.synchronized {
+      val tmp = hiveContext.conf.autoBroadcastJoinThreshold
 
       sql(s"SET ${SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key}=-1")
       df = sql(leftSemiJoinQuery)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
index 7ee1c8d13aa3f..3ab4576811194 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/UDFSuite.scala
@@ -18,18 +18,18 @@
 package org.apache.spark.sql.hive
 
 import org.apache.spark.sql.QueryTest
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
 case class FunctionResult(f1: String, f2: String)
 
-class UDFSuite extends QueryTest {
-  private lazy val ctx = org.apache.spark.sql.hive.test.TestHive
+class UDFSuite extends QueryTest with TestHiveSingleton {
 
   test("UDF case insensitive") {
-    ctx.udf.register("random0", () => { Math.random() })
-    ctx.udf.register("RANDOM1", () => { Math.random() })
-    ctx.udf.register("strlenScala", (_: String).length + (_: Int))
-    assert(ctx.sql("SELECT RANDOM0() FROM src LIMIT 1").head().getDouble(0) >= 0.0)
-    assert(ctx.sql("SELECT RANDOm1() FROM src LIMIT 1").head().getDouble(0) >= 0.0)
-    assert(ctx.sql("SELECT strlenscala('test', 1) FROM src LIMIT 1").head().getInt(0) === 5)
+    hiveContext.udf.register("random0", () => { Math.random() })
+    hiveContext.udf.register("RANDOM1", () => { Math.random() })
+    hiveContext.udf.register("strlenScala", (_: String).length + (_: Int))
+    assert(hiveContext.sql("SELECT RANDOM0() FROM src LIMIT 1").head().getDouble(0) >= 0.0)
+    assert(hiveContext.sql("SELECT RANDOm1() FROM src LIMIT 1").head().getDouble(0) >= 0.0)
+    assert(hiveContext.sql("SELECT strlenscala('test', 1) FROM src LIMIT 1").head().getInt(0) === 5)
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index 4886a85948367..b126ec455fc69 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -17,19 +17,15 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.aggregate
-import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
 import org.apache.spark.sql.hive.aggregate.{MyDoubleAvg, MyDoubleSum}
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
-  override def _sqlContext: SQLContext = TestHive
-  protected val sqlContext = _sqlContext
-  import sqlContext.implicits._
+abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import testImplicits._
 
   var originalUseAggregate2: Boolean = _
 
@@ -69,7 +65,7 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Be
     data2.write.saveAsTable("agg2")
 
     val emptyDF = sqlContext.createDataFrame(
-      sqlContext.sparkContext.emptyRDD[Row],
+      sparkContext.emptyRDD[Row],
       StructType(StructField("key", StringType) :: StructField("value", IntegerType) :: Nil))
     emptyDF.registerTempTable("emptyTable")
 
@@ -597,7 +593,7 @@ class TungstenAggregationQueryWithControlledFallbackSuite extends AggregationQue
     sqlContext.conf.unsetConf("spark.sql.TungstenAggregate.testFallbackStartsAt")
   }
 
-  override protected def checkAnswer(actual: DataFrame, expectedAnswer: Seq[Row]): Unit = {
+  override protected def checkAnswer(actual: => DataFrame, expectedAnswer: Seq[Row]): Unit = {
     (0 to 2).foreach { fallbackStartsAt =>
       sqlContext.setConf(
         "spark.sql.TungstenAggregate.testFallbackStartsAt",
@@ -605,6 +601,7 @@ class TungstenAggregationQueryWithControlledFallbackSuite extends AggregationQue
 
       // Create a new df to make sure its physical operator picks up
       // spark.sql.TungstenAggregate.testFallbackStartsAt.
+      // todo: remove it?
       val newActual = DataFrame(sqlContext, actual.logicalPlan)
 
       QueryTest.checkAnswer(newActual, expectedAnswer) match {
@@ -626,12 +623,12 @@ class TungstenAggregationQueryWithControlledFallbackSuite extends AggregationQue
   }
 
   // Override it to make sure we call the actually overridden checkAnswer.
-  override protected def checkAnswer(df: DataFrame, expectedAnswer: Row): Unit = {
+  override protected def checkAnswer(df: => DataFrame, expectedAnswer: Row): Unit = {
     checkAnswer(df, Seq(expectedAnswer))
   }
 
   // Override it to make sure we call the actually overridden checkAnswer.
-  override protected def checkAnswer(df: DataFrame, expectedAnswer: DataFrame): Unit = {
+  override protected def checkAnswer(df: => DataFrame, expectedAnswer: DataFrame): Unit = {
     checkAnswer(df, expectedAnswer.collect())
   }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
index 4d45249d9c6b8..aa95ba94fa873 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveComparisonTest.scala
@@ -23,7 +23,7 @@ import scala.util.control.NonFatal
 
 import org.scalatest.{BeforeAndAfterAll, GivenWhenThen}
 
-import org.apache.spark.{Logging, SparkFunSuite}
+import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.catalyst.planning.PhysicalOperation
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.util._
@@ -42,7 +42,7 @@ import org.apache.spark.sql.hive.test.TestHive
  * configured using system properties.
  */
 abstract class HiveComparisonTest
-  extends SparkFunSuite with BeforeAndAfterAll with GivenWhenThen with Logging {
+  extends SparkFunSuite with BeforeAndAfterAll with GivenWhenThen {
 
   /**
    * When set, any cache files that result in test failures will be deleted.  Used when the test
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
index 11d7a872dff09..94162da4eae1a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveExplainSuite.scala
@@ -17,17 +17,14 @@
 
 package org.apache.spark.sql.hive.execution
 
-import org.apache.spark.sql.{SQLContext, QueryTest}
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
 /**
  * A set of tests that validates support for Hive Explain command.
  */
-class HiveExplainSuite extends QueryTest with SQLTestUtils {
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+class HiveExplainSuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
 
   test("explain extended command") {
     checkExistence(sql(" explain   select * from src where key=123 "), true,
@@ -83,7 +80,7 @@ class HiveExplainSuite extends QueryTest with SQLTestUtils {
   test("SPARK-6212: The EXPLAIN output of CTAS only shows the analyzed plan") {
     withTempTable("jt") {
       val rdd = sparkContext.parallelize((1 to 10).map(i => s"""{"a":$i, "b":"str$i"}"""))
-      read.json(rdd).registerTempTable("jt")
+      hiveContext.read.json(rdd).registerTempTable("jt")
       val outputs = sql(
         s"""
            |EXPLAIN EXTENDED
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveOperatorQueryableSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveOperatorQueryableSuite.scala
index efbef68cd4447..0d4c7f86b315a 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveOperatorQueryableSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveOperatorQueryableSuite.scala
@@ -18,14 +18,16 @@
 package org.apache.spark.sql.hive.execution
 
 import org.apache.spark.sql.{Row, QueryTest}
-import org.apache.spark.sql.hive.test.TestHive._
+import org.apache.spark.sql.hive.test.{TestHive, TestHiveSingleton}
 
 /**
  * A set of tests that validates commands can also be queried by like a table
  */
-class HiveOperatorQueryableSuite extends QueryTest {
+class HiveOperatorQueryableSuite extends QueryTest with TestHiveSingleton {
+  import hiveContext._
+
   test("SPARK-5324 query result of describe command") {
-    loadTestTable("src")
+    hiveContext.loadTestTable("src")
 
     // register a describe command to be a temp table
     sql("desc src").registerTempTable("mydesc")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala
index ba56a8a6b689c..cd055f9eca37e 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HivePlanTest.scala
@@ -21,11 +21,11 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.QueryTest
 import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.expressions.Window
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-class HivePlanTest extends QueryTest {
-  import TestHive._
-  import TestHive.implicits._
+class HivePlanTest extends QueryTest with TestHiveSingleton {
+  import hiveContext.sql
+  import hiveContext.implicits._
 
   test("udf constant folding") {
     Seq.empty[Tuple1[Int]].toDF("a").registerTempTable("t")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
index 9c10ffe1113dc..d9ba895e1eceb 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveUDFSuite.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.hive.serde2.objectinspector.{ObjectInspector, ObjectIns
 import org.apache.hadoop.hive.serde2.{AbstractSerDe, SerDeStats}
 import org.apache.hadoop.io.Writable
 import org.apache.spark.sql.{AnalysisException, QueryTest, Row, SQLConf}
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
 import org.apache.spark.util.Utils
 
@@ -43,10 +43,10 @@ case class ListStringCaseClass(l: Seq[String])
 /**
  * A test suite for Hive custom UDFs.
  */
-class HiveUDFSuite extends QueryTest {
+class HiveUDFSuite extends QueryTest with TestHiveSingleton {
 
-  import TestHive.{udf, sql}
-  import TestHive.implicits._
+  import hiveContext.{udf, sql}
+  import hiveContext.implicits._
 
   test("spark sql udf test that returns a struct") {
     udf.register("getStruct", (_: Int) => Fields(1, 2, 3, 4, 5))
@@ -123,12 +123,12 @@ class HiveUDFSuite extends QueryTest {
           |           "value", value)).value FROM src
         """.stripMargin), Seq(Row("val_0")))
     }
-    val codegenDefault = TestHive.getConf(SQLConf.CODEGEN_ENABLED)
-    TestHive.setConf(SQLConf.CODEGEN_ENABLED, true)
+    val codegenDefault = hiveContext.getConf(SQLConf.CODEGEN_ENABLED)
+    hiveContext.setConf(SQLConf.CODEGEN_ENABLED, true)
     testOrderInStruct()
-    TestHive.setConf(SQLConf.CODEGEN_ENABLED, false)
+    hiveContext.setConf(SQLConf.CODEGEN_ENABLED, false)
     testOrderInStruct()
-    TestHive.setConf(SQLConf.CODEGEN_ENABLED, codegenDefault)
+    hiveContext.setConf(SQLConf.CODEGEN_ENABLED, codegenDefault)
   }
 
   test("SPARK-6409 UDAFAverage test") {
@@ -137,7 +137,7 @@ class HiveUDFSuite extends QueryTest {
       sql("SELECT test_avg(1), test_avg(substr(value,5)) FROM src"),
       Seq(Row(1.0, 260.182)))
     sql("DROP TEMPORARY FUNCTION IF EXISTS test_avg")
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("SPARK-2693 udaf aggregates test") {
@@ -157,7 +157,7 @@ class HiveUDFSuite extends QueryTest {
    }
 
   test("UDFIntegerToString") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       IntegerCaseClass(1) :: IntegerCaseClass(2) :: Nil).toDF()
     testData.registerTempTable("integerTable")
 
@@ -168,11 +168,11 @@ class HiveUDFSuite extends QueryTest {
       Seq(Row("1"), Row("2")))
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFIntegerToString")
 
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFToListString") {
-    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    val testData = hiveContext.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
     testData.registerTempTable("inputTable")
 
     sql(s"CREATE TEMPORARY FUNCTION testUDFToListString AS '${classOf[UDFToListString].getName}'")
@@ -183,11 +183,11 @@ class HiveUDFSuite extends QueryTest {
       "JVM type erasure makes spark fail to catch a component type in List<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToListString")
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFToListInt") {
-    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    val testData = hiveContext.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
     testData.registerTempTable("inputTable")
 
     sql(s"CREATE TEMPORARY FUNCTION testUDFToListInt AS '${classOf[UDFToListInt].getName}'")
@@ -198,11 +198,11 @@ class HiveUDFSuite extends QueryTest {
       "JVM type erasure makes spark fail to catch a component type in List<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToListInt")
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFToStringIntMap") {
-    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    val testData = hiveContext.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
     testData.registerTempTable("inputTable")
 
     sql(s"CREATE TEMPORARY FUNCTION testUDFToStringIntMap " +
@@ -214,11 +214,11 @@ class HiveUDFSuite extends QueryTest {
       "JVM type erasure makes spark fail to catch key and value types in Map<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToStringIntMap")
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFToIntIntMap") {
-    val testData = TestHive.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
+    val testData = hiveContext.sparkContext.parallelize(StringCaseClass("") :: Nil).toDF()
     testData.registerTempTable("inputTable")
 
     sql(s"CREATE TEMPORARY FUNCTION testUDFToIntIntMap " +
@@ -230,11 +230,11 @@ class HiveUDFSuite extends QueryTest {
       "JVM type erasure makes spark fail to catch key and value types in Map<>;")
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFToIntIntMap")
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFListListInt") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       ListListIntCaseClass(Nil) ::
       ListListIntCaseClass(Seq((1, 2, 3))) ::
       ListListIntCaseClass(Seq((4, 5, 6), (7, 8, 9))) :: Nil).toDF()
@@ -246,11 +246,11 @@ class HiveUDFSuite extends QueryTest {
       Seq(Row(0), Row(2), Row(13)))
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFListListInt")
 
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFListString") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       ListStringCaseClass(Seq("a", "b", "c")) ::
       ListStringCaseClass(Seq("d", "e")) :: Nil).toDF()
     testData.registerTempTable("listStringTable")
@@ -261,11 +261,11 @@ class HiveUDFSuite extends QueryTest {
       Seq(Row("a,b,c"), Row("d,e")))
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFListString")
 
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFStringString") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       StringCaseClass("world") :: StringCaseClass("goodbye") :: Nil).toDF()
     testData.registerTempTable("stringTable")
 
@@ -280,11 +280,11 @@ class HiveUDFSuite extends QueryTest {
 
     sql("DROP TEMPORARY FUNCTION IF EXISTS testStringStringUDF")
 
-    TestHive.reset()
+    hiveContext.reset()
   }
 
   test("UDFTwoListList") {
-    val testData = TestHive.sparkContext.parallelize(
+    val testData = hiveContext.sparkContext.parallelize(
       ListListIntCaseClass(Nil) ::
       ListListIntCaseClass(Seq((1, 2, 3))) ::
       ListListIntCaseClass(Seq((4, 5, 6), (7, 8, 9))) ::
@@ -297,7 +297,7 @@ class HiveUDFSuite extends QueryTest {
       Seq(Row("0, 0"), Row("2, 2"), Row("13, 13")))
     sql("DROP TEMPORARY FUNCTION IF EXISTS testUDFTwoListList")
 
-    TestHive.reset()
+    hiveContext.reset()
   }
 }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
index 1ff1d9a2934cc..8126d02335217 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/SQLQuerySuite.scala
@@ -26,9 +26,7 @@ import org.apache.spark.sql.catalyst.DefaultParserDialect
 import org.apache.spark.sql.catalyst.analysis.{FunctionRegistry, EliminateSubQueries}
 import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.datasources.LogicalRelation
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.hive.{HiveContext, HiveQLDialect, MetastoreRelation}
 import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
@@ -65,12 +63,12 @@ class MyDialect extends DefaultParserDialect
  * Hive to generate them (in contrast to HiveQuerySuite).  Often this is because the query is
  * valid, but Hive currently cannot execute it.
  */
-class SQLQuerySuite extends QueryTest with SQLTestUtils {
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+class SQLQuerySuite extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import hiveContext._
+  import hiveContext.implicits._
 
   test("UDTF") {
-    sql(s"ADD JAR ${TestHive.getHiveFile("TestUDTF.jar").getCanonicalPath()}")
+    sql(s"ADD JAR ${hiveContext.getHiveFile("TestUDTF.jar").getCanonicalPath()}")
     // The function source code can be found at:
     // https://cwiki.apache.org/confluence/display/Hive/DeveloperGuide+UDTF
     sql(
@@ -509,19 +507,19 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
     checkAnswer(
       sql("SELECT f1.f2.f3 FROM nested"),
       Row(1))
-    checkAnswer(sql("CREATE TABLE test_ctas_1234 AS SELECT * from nested"),
-      Seq.empty[Row])
+
+    sql("CREATE TABLE test_ctas_1234 AS SELECT * from nested")
     checkAnswer(
       sql("SELECT * FROM test_ctas_1234"),
       sql("SELECT * FROM nested").collect().toSeq)
 
     intercept[AnalysisException] {
-      sql("CREATE TABLE test_ctas_12345 AS SELECT * from notexists").collect()
+      sql("CREATE TABLE test_ctas_1234 AS SELECT * from notexists").collect()
     }
   }
 
   test("test CTAS") {
-    checkAnswer(sql("CREATE TABLE test_ctas_123 AS SELECT key, value FROM src"), Seq.empty[Row])
+    sql("CREATE TABLE test_ctas_123 AS SELECT key, value FROM src")
     checkAnswer(
       sql("SELECT key, value FROM test_ctas_123 ORDER BY key"),
       sql("SELECT key, value FROM src ORDER BY key").collect().toSeq)
@@ -614,7 +612,7 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
 
     val rowRdd = sparkContext.parallelize(row :: Nil)
 
-    TestHive.createDataFrame(rowRdd, schema).registerTempTable("testTable")
+    hiveContext.createDataFrame(rowRdd, schema).registerTempTable("testTable")
 
     sql(
       """CREATE TABLE nullValuesInInnerComplexTypes
@@ -1044,10 +1042,10 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
     val thread = new Thread {
       override def run() {
         // To make sure this test works, this jar should not be loaded in another place.
-        TestHive.sql(
-          s"ADD JAR ${TestHive.getHiveFile("hive-contrib-0.13.1.jar").getCanonicalPath()}")
+        sql(
+          s"ADD JAR ${hiveContext.getHiveFile("hive-contrib-0.13.1.jar").getCanonicalPath()}")
         try {
-          TestHive.sql(
+          sql(
             """
               |CREATE TEMPORARY FUNCTION example_max
               |AS 'org.apache.hadoop.hive.contrib.udaf.example.UDAFExampleMax'
@@ -1097,21 +1095,21 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
 
   test("SPARK-8588 HiveTypeCoercion.inConversion fires too early") {
     val df =
-      TestHive.createDataFrame(Seq((1, "2014-01-01"), (2, "2015-01-01"), (3, "2016-01-01")))
+      createDataFrame(Seq((1, "2014-01-01"), (2, "2015-01-01"), (3, "2016-01-01")))
     df.toDF("id", "datef").registerTempTable("test_SPARK8588")
     checkAnswer(
-      TestHive.sql(
+      sql(
         """
           |select id, concat(year(datef))
           |from test_SPARK8588 where concat(year(datef), ' year') in ('2015 year', '2014 year')
         """.stripMargin),
       Row(1, "2014") :: Row(2, "2015") :: Nil
     )
-    TestHive.dropTempTable("test_SPARK8588")
+    dropTempTable("test_SPARK8588")
   }
 
   test("SPARK-9371: fix the support for special chars in column names for hive context") {
-    TestHive.read.json(TestHive.sparkContext.makeRDD(
+    read.json(sparkContext.makeRDD(
       """{"a": {"c.b": 1}, "b.$q": [{"a@!.q": 1}], "q.w": {"w.i&": [1]}}""" :: Nil))
       .registerTempTable("t")
 
@@ -1142,8 +1140,7 @@ class SQLQuerySuite extends QueryTest with SQLTestUtils {
   test("specifying database name for a temporary table is not allowed") {
     withTempPath { dir =>
       val path = dir.getCanonicalPath
-      val df =
-        sqlContext.sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
+      val df = sparkContext.parallelize(1 to 10).map(i => (i, i.toString)).toDF("num", "str")
       df
         .write
         .format("parquet")
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
index 9aca40f15ac15..cb8d0fca8e693 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ScriptTransformationSuite.scala
@@ -22,17 +22,14 @@ import org.scalatest.exceptions.TestFailedException
 
 import org.apache.spark.TaskContext
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.SQLContext
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Attribute, AttributeReference}
 import org.apache.spark.sql.execution.{UnaryNode, SparkPlan, SparkPlanTest}
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.types.StringType
 
-class ScriptTransformationSuite extends SparkPlanTest {
-
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+class ScriptTransformationSuite extends SparkPlanTest with TestHiveSingleton {
+  import hiveContext.implicits._
 
   private val noSerdeIOSchema = HiveScriptIOSchema(
     inputRowFormat = Seq.empty,
@@ -59,7 +56,7 @@ class ScriptTransformationSuite extends SparkPlanTest {
         output = Seq(AttributeReference("a", StringType)()),
         child = child,
         ioschema = noSerdeIOSchema
-      )(TestHive),
+      )(hiveContext),
       rowsDf.collect())
   }
 
@@ -73,7 +70,7 @@ class ScriptTransformationSuite extends SparkPlanTest {
         output = Seq(AttributeReference("a", StringType)()),
         child = child,
         ioschema = serdeIOSchema
-      )(TestHive),
+      )(hiveContext),
       rowsDf.collect())
   }
 
@@ -88,7 +85,7 @@ class ScriptTransformationSuite extends SparkPlanTest {
           output = Seq(AttributeReference("a", StringType)()),
           child = ExceptionInjectingOperator(child),
           ioschema = noSerdeIOSchema
-        )(TestHive),
+        )(hiveContext),
         rowsDf.collect())
     }
     assert(e.getMessage().contains("intentional exception"))
@@ -105,7 +102,7 @@ class ScriptTransformationSuite extends SparkPlanTest {
           output = Seq(AttributeReference("a", StringType)()),
           child = ExceptionInjectingOperator(child),
           ioschema = serdeIOSchema
-        )(TestHive),
+        )(hiveContext),
         rowsDf.collect())
     }
     assert(e.getMessage().contains("intentional exception"))
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
index deec0048d24b8..9a299c3f9d1f3 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
@@ -24,10 +24,9 @@ import org.apache.spark.sql.sources.HadoopFsRelationTest
 import org.apache.spark.sql.types._
 
 class OrcHadoopFsRelationSuite extends HadoopFsRelationTest {
-  override val dataSourceName: String = classOf[DefaultSource].getCanonicalName
+  import testImplicits._
 
-  import sqlContext._
-  import sqlContext.implicits._
+  override val dataSourceName: String = classOf[DefaultSource].getCanonicalName
 
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
@@ -48,7 +47,7 @@ class OrcHadoopFsRelationSuite extends HadoopFsRelationTest {
         StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
 
       checkQueries(
-        read.options(Map(
+        hiveContext.read.options(Map(
           "path" -> file.getCanonicalPath,
           "dataSchema" -> dataSchemaWithPartition.json)).format(dataSourceName).load())
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
index a46ca9a2c9706..52e09f9496f05 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcPartitionDiscoverySuite.scala
@@ -18,19 +18,17 @@
 package org.apache.spark.sql.hive.orc
 
 import java.io.File
-import org.apache.hadoop.hive.conf.HiveConf.ConfVars
-import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql._
-import org.apache.spark.sql.catalyst.InternalRow
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
-import org.apache.spark.util.Utils
-import org.scalatest.BeforeAndAfterAll
 
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
+import org.scalatest.BeforeAndAfterAll
+import org.apache.hadoop.hive.conf.HiveConf.ConfVars
+
+import org.apache.spark.sql._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
+import org.apache.spark.util.Utils
+
 // The data where the partitioning key exists only in the directory structure.
 case class OrcParData(intField: Int, stringField: String)
 
@@ -38,7 +36,10 @@ case class OrcParData(intField: Int, stringField: String)
 case class OrcParDataWithKey(intField: Int, pi: Int, stringField: String, ps: String)
 
 // TODO This test suite duplicates ParquetPartitionDiscoverySuite a lot
-class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll {
+class OrcPartitionDiscoverySuite extends QueryTest with TestHiveSingleton with BeforeAndAfterAll {
+  import hiveContext._
+  import hiveContext.implicits._
+
   val defaultPartitionName = ConfVars.DEFAULTPARTITIONNAME.defaultStrVal
 
   def withTempDir(f: File => Unit): Unit = {
@@ -58,7 +59,7 @@ class OrcPartitionDiscoverySuite extends QueryTest with BeforeAndAfterAll {
   }
 
   protected def withTempTable(tableName: String)(f: => Unit): Unit = {
-    try f finally TestHive.dropTempTable(tableName)
+    try f finally hiveContext.dropTempTable(tableName)
   }
 
   protected def makePartitionDir(
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
index 80c38084f293d..7a34cf731b4c5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcSourceSuite.scala
@@ -21,12 +21,14 @@ import java.io.File
 
 import org.scalatest.BeforeAndAfterAll
 
-import org.apache.spark.sql.hive.test.TestHive._
 import org.apache.spark.sql.{QueryTest, Row}
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
 case class OrcData(intField: Int, stringField: String)
 
-abstract class OrcSuite extends QueryTest with BeforeAndAfterAll {
+abstract class OrcSuite extends QueryTest with TestHiveSingleton with BeforeAndAfterAll {
+  import hiveContext._
+
   var orcTableDir: File = null
   var orcTableAsDir: File = null
 
@@ -156,7 +158,7 @@ class OrcSourceSuite extends OrcSuite {
   override def beforeAll(): Unit = {
     super.beforeAll()
 
-    sql(
+    hiveContext.sql(
       s"""CREATE TEMPORARY TABLE normal_orc_source
          |USING org.apache.spark.sql.hive.orc
          |OPTIONS (
@@ -164,7 +166,7 @@ class OrcSourceSuite extends OrcSuite {
          |)
        """.stripMargin)
 
-    sql(
+    hiveContext.sql(
       s"""CREATE TEMPORARY TABLE normal_orc_as_source
          |USING org.apache.spark.sql.hive.orc
          |OPTIONS (
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
index f7ba20ff41d8d..88a0ed511749f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcTest.scala
@@ -22,15 +22,12 @@ import java.io.File
 import scala.reflect.ClassTag
 import scala.reflect.runtime.universe.TypeTag
 
-import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql._
 import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 
-private[sql] trait OrcTest extends SQLTestUtils { this: SparkFunSuite =>
-  protected override def _sqlContext: SQLContext = org.apache.spark.sql.hive.test.TestHive
-  protected val sqlContext = _sqlContext
-  import sqlContext.implicits._
-  import sqlContext.sparkContext
+private[sql] trait OrcTest extends SQLTestUtils with TestHiveSingleton {
+  import testImplicits._
 
   /**
    * Writes `data` to a Orc file, which is then passed to `f` and will be deleted after `f`
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
index 34d3434569f58..6842ec2b5eb37 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/parquetSuites.scala
@@ -19,15 +19,11 @@ package org.apache.spark.sql.hive
 
 import java.io.File
 
-import org.scalatest.BeforeAndAfterAll
-
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.datasources.{InsertIntoDataSource, InsertIntoHadoopFsRelation, LogicalRelation}
 import org.apache.spark.sql.execution.{ExecutedCommand, PhysicalRDD}
 import org.apache.spark.sql.hive.execution.HiveTableScan
-import org.apache.spark.sql.hive.test.TestHive
-import org.apache.spark.sql.hive.test.TestHive._
-import org.apache.spark.sql.hive.test.TestHive.implicits._
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.execution.datasources.parquet.ParquetRelation
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
@@ -58,6 +54,8 @@ case class ParquetDataWithKeyAndComplexTypes(
  * built in parquet support.
  */
 class ParquetMetastoreSuite extends ParquetPartitioningTest {
+  import hiveContext._
+
   override def beforeAll(): Unit = {
     super.beforeAll()
     dropTables("partitioned_parquet",
@@ -536,6 +534,9 @@ class ParquetMetastoreSuite extends ParquetPartitioningTest {
  * A suite of tests for the Parquet support through the data sources API.
  */
 class ParquetSourceSuite extends ParquetPartitioningTest {
+  import testImplicits._
+  import hiveContext._
+
   override def beforeAll(): Unit = {
     super.beforeAll()
     dropTables("partitioned_parquet",
@@ -684,9 +685,8 @@ class ParquetSourceSuite extends ParquetPartitioningTest {
 /**
  * A collection of tests for parquet data with various forms of partitioning.
  */
-abstract class ParquetPartitioningTest extends QueryTest with SQLTestUtils with BeforeAndAfterAll {
-  override def _sqlContext: SQLContext = TestHive
-  protected val sqlContext = _sqlContext
+abstract class ParquetPartitioningTest extends QueryTest with SQLTestUtils with TestHiveSingleton {
+  import testImplicits._
 
   var partitionedTableDir: File = null
   var normalTableDir: File = null
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
index b4640b1616281..dc0531a6d4bc5 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/CommitFailureTestRelationSuite.scala
@@ -18,16 +18,13 @@
 package org.apache.spark.sql.sources
 
 import org.apache.hadoop.fs.Path
-import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.SparkException
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.SQLContext
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.test.SQLTestUtils
 
 
-class CommitFailureTestRelationSuite extends SparkFunSuite with SQLTestUtils {
-  override def _sqlContext: SQLContext = TestHive
-  private val sqlContext = _sqlContext
+class CommitFailureTestRelationSuite extends SQLTestUtils with TestHiveSingleton  {
 
   // When committing a task, `CommitFailureTestSource` throws an exception for testing purpose.
   val dataSourceName: String = classOf[CommitFailureTestSource].getCanonicalName
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
index 8ca3a17085194..1945b15002337 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
@@ -28,8 +28,6 @@ import org.apache.spark.sql.types._
 class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String = "json"
 
-  import sqlContext._
-
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
@@ -47,7 +45,7 @@ class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
         StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
 
       checkQueries(
-        read.format(dataSourceName)
+        hiveContext.read.format(dataSourceName)
           .option("dataSchema", dataSchemaWithPartition.json)
           .load(file.getCanonicalPath))
     }
@@ -65,14 +63,14 @@ class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
       val data =
         Row(Seq(1L, 2L, 3L), Map("m1" -> Row(4L))) ::
           Row(Seq(5L, 6L, 7L), Map("m2" -> Row(10L))) :: Nil
-      val df = createDataFrame(sparkContext.parallelize(data), schema)
+      val df = hiveContext.createDataFrame(sparkContext.parallelize(data), schema)
 
       // Write the data out.
       df.write.format(dataSourceName).save(file.getCanonicalPath)
 
       // Read it back and check the result.
       checkAnswer(
-        read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
+        hiveContext.read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
         df
       )
     }
@@ -90,14 +88,14 @@ class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
         Row(new BigDecimal("10.02")) ::
           Row(new BigDecimal("20000.99")) ::
           Row(new BigDecimal("10000")) :: Nil
-      val df = createDataFrame(sparkContext.parallelize(data), schema)
+      val df = hiveContext.createDataFrame(sparkContext.parallelize(data), schema)
 
       // Write the data out.
       df.write.format(dataSourceName).save(file.getCanonicalPath)
 
       // Read it back and check the result.
       checkAnswer(
-        read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
+        hiveContext.read.format(dataSourceName).schema(schema).load(file.getCanonicalPath),
         df
       )
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
index 06dadbb5feab0..08c3c17973043 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
@@ -28,10 +28,9 @@ import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 
 
 class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
-  override val dataSourceName: String = "parquet"
+  import testImplicits._
 
-  import sqlContext._
-  import sqlContext.implicits._
+  override val dataSourceName: String = "parquet"
 
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
@@ -51,7 +50,7 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
         StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
 
       checkQueries(
-        read.format(dataSourceName)
+        hiveContext.read.format(dataSourceName)
           .option("dataSchema", dataSchemaWithPartition.json)
           .load(file.getCanonicalPath))
     }
@@ -69,7 +68,7 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
         .format("parquet")
         .save(s"${dir.getCanonicalPath}/_temporary")
 
-      checkAnswer(read.format("parquet").load(dir.getCanonicalPath), df.collect())
+      checkAnswer(hiveContext.read.format("parquet").load(dir.getCanonicalPath), df.collect())
     }
   }
 
@@ -97,7 +96,7 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
 
       // This shouldn't throw anything.
       df.write.format("parquet").mode(SaveMode.Overwrite).save(path)
-      checkAnswer(read.format("parquet").load(path), df)
+      checkAnswer(hiveContext.read.format("parquet").load(path), df)
     }
   }
 
@@ -107,7 +106,7 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
         // Parquet doesn't allow field names with spaces.  Here we are intentionally making an
         // exception thrown from the `ParquetRelation2.prepareForWriteJob()` method to trigger
         // the bug.  Please refer to spark-8079 for more details.
-        range(1, 10)
+        hiveContext.range(1, 10)
           .withColumnRenamed("id", "a b")
           .write
           .format("parquet")
@@ -125,7 +124,7 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
       val summaryPath = new Path(path, "_metadata")
       val commonSummaryPath = new Path(path, "_common_metadata")
 
-      val fs = summaryPath.getFileSystem(configuration)
+      val fs = summaryPath.getFileSystem(hadoopConfiguration)
       fs.delete(summaryPath, true)
       fs.delete(commonSummaryPath, true)
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
index e8975e5f5cd08..1125ca670107b 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -25,8 +25,6 @@ import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
 class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String = classOf[SimpleTextSource].getCanonicalName
 
-  import sqlContext._
-
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
@@ -44,7 +42,7 @@ class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
         StructType(dataSchema.fields :+ StructField("p1", IntegerType, nullable = true))
 
       checkQueries(
-        read.format(dataSourceName)
+        hiveContext.read.format(dataSourceName)
           .option("dataSchema", dataSchemaWithPartition.json)
           .load(file.getCanonicalPath))
     }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 7966b43596e75..2ad2618dfc436 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -28,14 +28,12 @@ import org.apache.parquet.hadoop.ParquetOutputCommitter
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql._
 import org.apache.spark.sql.execution.datasources.LogicalRelation
-import org.apache.spark.sql.hive.test.TestHive
+import org.apache.spark.sql.hive.test.TestHiveSingleton
 import org.apache.spark.sql.test.SQLTestUtils
 import org.apache.spark.sql.types._
 
 
-abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
-  override def _sqlContext: SQLContext = TestHive
-  protected val sqlContext = _sqlContext
+abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with TestHiveSingleton {
   import sqlContext.implicits._
 
   val dataSourceName: String
@@ -504,17 +502,17 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
   }
 
   test("SPARK-8578 specified custom output committer will not be used to append data") {
-    val clonedConf = new Configuration(configuration)
+    val clonedConf = new Configuration(hadoopConfiguration)
     try {
       val df = sqlContext.range(1, 10).toDF("i")
       withTempPath { dir =>
         df.write.mode("append").format(dataSourceName).save(dir.getCanonicalPath)
-        configuration.set(
+        hadoopConfiguration.set(
           SQLConf.OUTPUT_COMMITTER_CLASS.key,
           classOf[AlwaysFailOutputCommitter].getName)
         // Since Parquet has its own output committer setting, also set it
         // to AlwaysFailParquetOutputCommitter at here.
-        configuration.set("spark.sql.parquet.output.committer.class",
+        hadoopConfiguration.set("spark.sql.parquet.output.committer.class",
           classOf[AlwaysFailParquetOutputCommitter].getName)
         // Because there data already exists,
         // this append should succeed because we will use the output committer associated
@@ -533,12 +531,12 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
         }
       }
       withTempPath { dir =>
-        configuration.set(
+        hadoopConfiguration.set(
           SQLConf.OUTPUT_COMMITTER_CLASS.key,
           classOf[AlwaysFailOutputCommitter].getName)
         // Since Parquet has its own output committer setting, also set it
         // to AlwaysFailParquetOutputCommitter at here.
-        configuration.set("spark.sql.parquet.output.committer.class",
+        hadoopConfiguration.set("spark.sql.parquet.output.committer.class",
           classOf[AlwaysFailParquetOutputCommitter].getName)
         // Because there is no existing data,
         // this append will fail because AlwaysFailOutputCommitter is used when we do append
@@ -549,8 +547,8 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
       }
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
-      configuration.clear()
-      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      hadoopConfiguration.clear()
+      clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
     }
   }
 
@@ -570,7 +568,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
   }
 
   test("SPARK-9899 Disable customized output committer when speculation is on") {
-    val clonedConf = new Configuration(configuration)
+    val clonedConf = new Configuration(hadoopConfiguration)
     val speculationEnabled =
       sqlContext.sparkContext.conf.getBoolean("spark.speculation", defaultValue = false)
 
@@ -580,7 +578,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
         sqlContext.sparkContext.conf.set("spark.speculation", "true")
 
         // Uses a customized output committer which always fails
-        configuration.set(
+        hadoopConfiguration.set(
           SQLConf.OUTPUT_COMMITTER_CLASS.key,
           classOf[AlwaysFailOutputCommitter].getName)
 
@@ -597,8 +595,8 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils {
       }
     } finally {
       // Hadoop 1 doesn't have `Configuration.unset`
-      configuration.clear()
-      clonedConf.asScala.foreach(entry => configuration.set(entry.getKey, entry.getValue))
+      hadoopConfiguration.clear()
+      clonedConf.asScala.foreach(entry => hadoopConfiguration.set(entry.getKey, entry.getValue))
       sqlContext.sparkContext.conf.set("spark.speculation", speculationEnabled.toString)
     }
   }

From 3339e6f674264e387ab6c4f9c356bd3bc7167221 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 4 Sep 2015 15:20:20 -0700
Subject: [PATCH 532/633] [SPARK-10450] [SQL] Minor improvements to readability
 / style / typos etc.

Author: Andrew Or <andrew@databricks.com>

Closes #8603 from andrewor14/minor-sql-changes.
---
 .../sql/catalyst/expressions/predicates.scala    |  2 +-
 .../sql/catalyst/planning/QueryPlanner.scala     |  2 +-
 .../spark/sql/catalyst/planning/patterns.scala   |  8 ++++----
 .../spark/sql/execution/SparkStrategies.scala    | 16 ++++++++--------
 .../sql/execution/joins/SortMergeJoin.scala      |  2 +-
 5 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
index 65706dba7d975..daefc016bc91c 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/predicates.scala
@@ -67,7 +67,7 @@ trait PredicateHelper {
 
   /**
    * Returns true if `expr` can be evaluated using only the output of `plan`.  This method
-   * can be used to determine when is is acceptable to move expression evaluation within a query
+   * can be used to determine when it is acceptable to move expression evaluation within a query
    * plan.
    *
    * For example consider a join between two relations R(a, b) and S(c, d).
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala
index 73a21884a4710..56a3dd02f9ba3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/QueryPlanner.scala
@@ -51,7 +51,7 @@ abstract class QueryPlanner[PhysicalPlan <: TreeNode[PhysicalPlan]] {
    * filled in automatically by the QueryPlanner using the other execution strategies that are
    * available.
    */
-  protected def planLater(plan: LogicalPlan) = this.plan(plan).next()
+  protected def planLater(plan: LogicalPlan): PhysicalPlan = this.plan(plan).next()
 
   def plan(plan: LogicalPlan): Iterator[PhysicalPlan] = {
     // Obviously a lot to do here still...
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index b9ca712c1ee1c..e8abcd63f7d85 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -195,8 +195,9 @@ object ExtractEquiJoinKeys extends Logging with PredicateHelper {
       // as join keys.
       val (joinPredicates, otherPredicates) =
         condition.map(splitConjunctivePredicates).getOrElse(Nil).partition {
-          case EqualTo(l, r) if (canEvaluate(l, left) && canEvaluate(r, right)) ||
-            (canEvaluate(l, right) && canEvaluate(r, left)) => true
+          case EqualTo(l, r) =>
+            (canEvaluate(l, left) && canEvaluate(r, right)) ||
+            (canEvaluate(l, right) && canEvaluate(r, left))
           case _ => false
         }
 
@@ -204,10 +205,9 @@ object ExtractEquiJoinKeys extends Logging with PredicateHelper {
         case EqualTo(l, r) if canEvaluate(l, left) && canEvaluate(r, right) => (l, r)
         case EqualTo(l, r) if canEvaluate(l, right) && canEvaluate(r, left) => (r, l)
       }
-      val leftKeys = joinKeys.map(_._1)
-      val rightKeys = joinKeys.map(_._2)
 
       if (joinKeys.nonEmpty) {
+        val (leftKeys, rightKeys) = joinKeys.unzip
         logDebug(s"leftKeys:$leftKeys | rightKeys:$rightKeys")
         Some((joinType, leftKeys, rightKeys, otherPredicates.reduceOption(And), left, right))
       } else {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 4df53687a0731..2170bc73a0fd6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -87,7 +87,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         left: LogicalPlan,
         right: LogicalPlan,
         condition: Option[Expression],
-        side: joins.BuildSide) = {
+        side: joins.BuildSide): Seq[SparkPlan] = {
       val broadcastHashJoin = execution.joins.BroadcastHashJoin(
         leftKeys, rightKeys, side, planLater(left), planLater(right))
       condition.map(Filter(_, broadcastHashJoin)).getOrElse(broadcastHashJoin) :: Nil
@@ -123,12 +123,12 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       // --- Outer joins --------------------------------------------------------------------------
 
       case ExtractEquiJoinKeys(
-             LeftOuter, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
+          LeftOuter, leftKeys, rightKeys, condition, left, CanBroadcast(right)) =>
         joins.BroadcastHashOuterJoin(
           leftKeys, rightKeys, LeftOuter, condition, planLater(left), planLater(right)) :: Nil
 
       case ExtractEquiJoinKeys(
-             RightOuter, leftKeys, rightKeys, condition, CanBroadcast(left), right) =>
+          RightOuter, leftKeys, rightKeys, condition, CanBroadcast(left), right) =>
         joins.BroadcastHashOuterJoin(
           leftKeys, rightKeys, RightOuter, condition, planLater(left), planLater(right)) :: Nil
 
@@ -156,11 +156,11 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       // Aggregations that can be performed in two phases, before and after the shuffle.
       case PartialAggregation(
-             namedGroupingAttributes,
-             rewrittenAggregateExpressions,
-             groupingExpressions,
-             partialComputation,
-             child) if !canBeConvertedToNewAggregation(plan) =>
+          namedGroupingAttributes,
+          rewrittenAggregateExpressions,
+          groupingExpressions,
+          partialComputation,
+          child) if !canBeConvertedToNewAggregation(plan) =>
         execution.Aggregate(
           partial = false,
           namedGroupingAttributes,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
index 6b7322671d6b4..906f20d2a7289 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoin.scala
@@ -238,7 +238,7 @@ private[joins] class SortMergeJoinScanner(
    * Advances the streamed input iterator and buffers all rows from the buffered input that
    * have matching keys.
    * @return true if the streamed iterator returned a row, false otherwise. If this returns true,
-   *         then [getStreamedRow and [[getBufferedMatches]] can be called to produce the outer
+   *         then [[getStreamedRow]] and [[getBufferedMatches]] can be called to produce the outer
    *         join results.
    */
   final def findNextOuterJoinRows(): Boolean = {

From b087d23e28004f1bfdf6d2cd3ff34ae58c8132df Mon Sep 17 00:00:00 2001
From: Timothy Chen <tnachen@gmail.com>
Date: Fri, 4 Sep 2015 15:21:11 -0700
Subject: [PATCH 533/633] [SPARK-9669] [MESOS] Support PySpark on Mesos cluster
 mode.

Support running pyspark with cluster mode on Mesos!
This doesn't upload any scripts, so if running in a remote Mesos requires the user to specify the script from a available URI.

Author: Timothy Chen <tnachen@gmail.com>

Closes #8349 from tnachen/mesos_python.
---
 .../org/apache/spark/deploy/SparkSubmit.scala | 13 ++++--
 .../cluster/mesos/MesosClusterScheduler.scala | 42 +++++++++++++------
 docs/running-on-mesos.md                      |  2 +
 3 files changed, 41 insertions(+), 16 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
index 36e9750b86636..ad92f5635af35 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala
@@ -319,9 +319,6 @@ object SparkSubmit {
 
     // The following modes are not supported or applicable
     (clusterManager, deployMode) match {
-      case (MESOS, CLUSTER) if args.isPython =>
-        printErrorAndExit("Cluster deploy mode is currently not supported for python " +
-          "applications on Mesos clusters.")
       case (MESOS, CLUSTER) if args.isR =>
         printErrorAndExit("Cluster deploy mode is currently not supported for R " +
           "applications on Mesos clusters.")
@@ -554,7 +551,15 @@ object SparkSubmit {
     if (isMesosCluster) {
       assert(args.useRest, "Mesos cluster mode is only supported through the REST submission API")
       childMainClass = "org.apache.spark.deploy.rest.RestSubmissionClient"
-      childArgs += (args.primaryResource, args.mainClass)
+      if (args.isPython) {
+        // Second argument is main class
+        childArgs += (args.primaryResource, "")
+        if (args.pyFiles != null) {
+          sysProps("spark.submit.pyFiles") = args.pyFiles
+        }
+      } else {
+        childArgs += (args.primaryResource, args.mainClass)
+      }
       if (args.childArgs != null) {
         childArgs ++= args.childArgs
       }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
index 07da9242b9922..a6d9374eb9e8c 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosClusterScheduler.scala
@@ -29,7 +29,6 @@ import org.apache.mesos.Protos.Environment.Variable
 import org.apache.mesos.Protos.TaskStatus.Reason
 import org.apache.mesos.Protos.{TaskState => MesosTaskState, _}
 import org.apache.mesos.{Scheduler, SchedulerDriver}
-
 import org.apache.spark.deploy.mesos.MesosDriverDescription
 import org.apache.spark.deploy.rest.{CreateSubmissionResponse, KillSubmissionResponse, SubmissionStatusResponse}
 import org.apache.spark.metrics.MetricsSystem
@@ -375,21 +374,20 @@ private[spark] class MesosClusterScheduler(
     val executorOpts = desc.schedulerProperties.map { case (k, v) => s"-D$k=$v" }.mkString(" ")
     envBuilder.addVariables(
       Variable.newBuilder().setName("SPARK_EXECUTOR_OPTS").setValue(executorOpts))
-    val cmdOptions = generateCmdOption(desc).mkString(" ")
     val dockerDefined = desc.schedulerProperties.contains("spark.mesos.executor.docker.image")
     val executorUri = desc.schedulerProperties.get("spark.executor.uri")
       .orElse(desc.command.environment.get("SPARK_EXECUTOR_URI"))
-    val appArguments = desc.command.arguments.mkString(" ")
-    val (executable, jar) = if (dockerDefined) {
+    // Gets the path to run spark-submit, and the path to the Mesos sandbox.
+    val (executable, sandboxPath) = if (dockerDefined) {
       // Application jar is automatically downloaded in the mounted sandbox by Mesos,
       // and the path to the mounted volume is stored in $MESOS_SANDBOX env variable.
-      ("./bin/spark-submit", s"$$MESOS_SANDBOX/${desc.jarUrl.split("/").last}")
+      ("./bin/spark-submit", "$MESOS_SANDBOX")
     } else if (executorUri.isDefined) {
       builder.addUris(CommandInfo.URI.newBuilder().setValue(executorUri.get).build())
       val folderBasename = executorUri.get.split('/').last.split('.').head
       val cmdExecutable = s"cd $folderBasename*; $prefixEnv bin/spark-submit"
-      val cmdJar = s"../${desc.jarUrl.split("/").last}"
-      (cmdExecutable, cmdJar)
+      // Sandbox path points to the parent folder as we chdir into the folderBasename.
+      (cmdExecutable, "..")
     } else {
       val executorSparkHome = desc.schedulerProperties.get("spark.mesos.executor.home")
         .orElse(conf.getOption("spark.home"))
@@ -398,30 +396,50 @@ private[spark] class MesosClusterScheduler(
           throw new SparkException("Executor Spark home `spark.mesos.executor.home` is not set!")
         }
       val cmdExecutable = new File(executorSparkHome, "./bin/spark-submit").getCanonicalPath
-      val cmdJar = desc.jarUrl.split("/").last
-      (cmdExecutable, cmdJar)
+      // Sandbox points to the current directory by default with Mesos.
+      (cmdExecutable, ".")
     }
-    builder.setValue(s"$executable $cmdOptions $jar $appArguments")
+    val primaryResource = new File(sandboxPath, desc.jarUrl.split("/").last).toString()
+    val cmdOptions = generateCmdOption(desc, sandboxPath).mkString(" ")
+    val appArguments = desc.command.arguments.mkString(" ")
+    builder.setValue(s"$executable $cmdOptions $primaryResource $appArguments")
     builder.setEnvironment(envBuilder.build())
     conf.getOption("spark.mesos.uris").map { uris =>
       setupUris(uris, builder)
     }
+    desc.schedulerProperties.get("spark.mesos.uris").map { uris =>
+      setupUris(uris, builder)
+    }
+    desc.schedulerProperties.get("spark.submit.pyFiles").map { pyFiles =>
+      setupUris(pyFiles, builder)
+    }
     builder.build()
   }
 
-  private def generateCmdOption(desc: MesosDriverDescription): Seq[String] = {
+  private def generateCmdOption(desc: MesosDriverDescription, sandboxPath: String): Seq[String] = {
     var options = Seq(
       "--name", desc.schedulerProperties("spark.app.name"),
-      "--class", desc.command.mainClass,
       "--master", s"mesos://${conf.get("spark.master")}",
       "--driver-cores", desc.cores.toString,
       "--driver-memory", s"${desc.mem}M")
+
+    // Assume empty main class means we're running python
+    if (!desc.command.mainClass.equals("")) {
+      options ++= Seq("--class", desc.command.mainClass)
+    }
+
     desc.schedulerProperties.get("spark.executor.memory").map { v =>
       options ++= Seq("--executor-memory", v)
     }
     desc.schedulerProperties.get("spark.cores.max").map { v =>
       options ++= Seq("--total-executor-cores", v)
     }
+    desc.schedulerProperties.get("spark.submit.pyFiles").map { pyFiles =>
+      val formattedFiles = pyFiles.split(",")
+        .map { path => new File(sandboxPath, path.split("/").last).toString() }
+        .mkString(",")
+      options ++= Seq("--py-files", formattedFiles)
+    }
     options
   }
 
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index f36921ae30c2f..247e6ecfbdb86 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -157,6 +157,8 @@ From the client, you can submit a job to Mesos cluster by running `spark-submit`
 to the url of the MesosClusterDispatcher (e.g: mesos://dispatcher:7077). You can view driver statuses on the
 Spark cluster Web UI.
 
+Note that jars or python files that are passed to spark-submit should be URIs reachable by Mesos slaves.
+
 # Mesos Run Modes
 
 Spark can run over Mesos in two modes: "fine-grained" (default) and "coarse-grained".

From 2e1c17553d179f2d26a165805622cc01f92081b9 Mon Sep 17 00:00:00 2001
From: robbins <robbins@uk.ibm.com>
Date: Fri, 4 Sep 2015 15:23:29 -0700
Subject: [PATCH 534/633] [SPARK-10454] [SPARK CORE] wait for empty event queue

Author: robbins <robbins@uk.ibm.com>

Closes #8605 from robbinspg/DAGSchedulerSuite-fix.
---
 .../scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala     | 1 +
 1 file changed, 1 insertion(+)

diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 2c3aa2cf90d3c..1b9ff740ff530 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -812,6 +812,7 @@ class DAGSchedulerSuite
     }
 
     // The map stage should have been submitted.
+    sc.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)
     assert(countSubmittedMapStageAttempts() === 1)
 
     complete(taskSets(0), Seq(

From eafe37236c56b41ab32adf6610743e1e9a0f230f Mon Sep 17 00:00:00 2001
From: xutingjun <xutingjun@huawei.com>
Date: Fri, 4 Sep 2015 15:40:02 -0700
Subject: [PATCH 535/633] [SPARK-10311] [STREAMING] Reload appId and attemptId
 when app starts with checkpoint file in cluster mode

Author: xutingjun <xutingjun@huawei.com>

Closes #8477 from XuTingjun/streaming-attempt.
---
 .../src/main/scala/org/apache/spark/streaming/Checkpoint.scala  | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index cd5d960369c05..3985e1a3d9dfa 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -49,6 +49,8 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time)
     // Reload properties for the checkpoint application since user wants to set a reload property
     // or spark had changed its value and user wants to set it back.
     val propertiesToReload = List(
+      "spark.yarn.app.id",
+      "spark.yarn.app.attemptId",
       "spark.driver.host",
       "spark.driver.port",
       "spark.master",

From 22eab706f4a1459100f9e97fc557a7f3c88ca10e Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Fri, 4 Sep 2015 17:32:35 -0700
Subject: [PATCH 536/633] [SPARK-10402] [DOCS] [ML] Add defaults to the
 scaladoc for params in ml/

We should make sure the scaladoc for params includes their default values through the models in ml/

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8591 from holdenk/SPARK-10402-add-scaladoc-for-default-values-of-params-in-ml.
---
 .../ml/classification/MultilayerPerceptronClassifier.scala    | 2 ++
 .../spark/ml/evaluation/BinaryClassificationEvaluator.scala   | 1 +
 .../main/scala/org/apache/spark/ml/feature/Binarizer.scala    | 1 +
 mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala    | 1 +
 .../scala/org/apache/spark/ml/feature/StopWordsRemover.scala  | 2 ++
 .../scala/org/apache/spark/ml/feature/StringIndexer.scala     | 4 ++--
 .../scala/org/apache/spark/ml/feature/VectorIndexer.scala     | 1 +
 .../main/scala/org/apache/spark/ml/feature/VectorSlicer.scala | 2 ++
 .../src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala | 3 +++
 .../org/apache/spark/ml/regression/IsotonicRegression.scala   | 1 +
 10 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index 1e5b0bc4453e4..82fc80c58054f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -32,6 +32,7 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
   with HasSeed with HasMaxIter with HasTol {
   /**
    * Layer sizes including input size and output size.
+   * Default: Array(1, 1)
    * @group param
    */
   final val layers: IntArrayParam = new IntArrayParam(this, "layers",
@@ -50,6 +51,7 @@ private[ml] trait MultilayerPerceptronParams extends PredictorParams
    * Data is stacked within partitions. If block size is more than remaining data in
    * a partition then it is adjusted to the size of this data.
    * Recommended size is between 10 and 1000.
+   * Default: 128
    * @group expertParam
    */
   final val blockSize: IntParam = new IntParam(this, "blockSize",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
index 56419a0a15952..08df2919a8a87 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/evaluation/BinaryClassificationEvaluator.scala
@@ -38,6 +38,7 @@ class BinaryClassificationEvaluator(override val uid: String)
 
   /**
    * param for metric name in evaluation
+   * Default: areaUnderROC
    * @group param
    */
   val metricName: Param[String] = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
index 46314854d5e3a..edad754436455 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Binarizer.scala
@@ -41,6 +41,7 @@ final class Binarizer(override val uid: String)
    * Param for threshold used to binarize continuous features.
    * The features greater than the threshold, will be binarized to 1.0.
    * The features equal to or less than the threshold, will be binarized to 0.0.
+   * Default: 0.0
    * @group param
    */
   val threshold: DoubleParam =
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
index 938447447a0a2..4c36df75d8aa0 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/IDF.scala
@@ -35,6 +35,7 @@ private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol
 
   /**
    * The minimum of documents in which a term should appear.
+   * Default: 0
    * @group param
    */
   final val minDocFreq = new IntParam(
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
index 7da430c7d16df..2a79582625e9a 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala
@@ -98,6 +98,7 @@ class StopWordsRemover(override val uid: String)
 
   /**
    * the stop words set to be filtered out
+   * Default: [[StopWords.English]]
    * @group param
    */
   val stopWords: StringArrayParam = new StringArrayParam(this, "stopWords", "stop words")
@@ -110,6 +111,7 @@ class StopWordsRemover(override val uid: String)
 
   /**
    * whether to do a case sensitive comparison over the stop words
+   * Default: false
    * @group param
    */
   val caseSensitive: BooleanParam = new BooleanParam(this, "caseSensitive",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 24250e4c4cf92..77aeed0ab0370 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -212,8 +212,8 @@ class IndexToString private[ml] (
 
   /**
    * Param for array of labels.
-   * Optional labels to be provided by the user, if not supplied column
-   * metadata is read for labels.
+   * Optional labels to be provided by the user.
+   * Default: Empty array, in which case column metadata is used for labels.
    * @group param
    */
   final val labels: StringArrayParam = new StringArrayParam(this, "labels",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
index 61b925c0fdc07..52e0599e38d83 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorIndexer.scala
@@ -43,6 +43,7 @@ private[ml] trait VectorIndexerParams extends Params with HasInputCol with HasOu
    * Must be >= 2.
    *
    * (default = 20)
+   * @group param
    */
   val maxCategories = new IntParam(this, "maxCategories",
     "Threshold for the number of values a categorical feature can take (>= 2)." +
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
index c5c2272270792..fb3387d4aa9be 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
@@ -49,6 +49,7 @@ final class VectorSlicer(override val uid: String)
   /**
    * An array of indices to select features from a vector column.
    * There can be no overlap with [[names]].
+   * Default: Empty array
    * @group param
    */
   val indices = new IntArrayParam(this, "indices",
@@ -67,6 +68,7 @@ final class VectorSlicer(override val uid: String)
    * An array of feature names to select features from a vector column.
    * These names must be specified by ML [[org.apache.spark.ml.attribute.Attribute]]s.
    * There can be no overlap with [[indices]].
+   * Default: Empty Array
    * @group param
    */
   val names = new StringArrayParam(this, "names",
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
index 5af775a4159ad..9edab3af913ca 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Word2Vec.scala
@@ -39,6 +39,7 @@ private[feature] trait Word2VecBase extends Params
 
   /**
    * The dimension of the code that you want to transform from words.
+   * Default: 100
    * @group param
    */
   final val vectorSize = new IntParam(
@@ -50,6 +51,7 @@ private[feature] trait Word2VecBase extends Params
 
   /**
    * Number of partitions for sentences of words.
+   * Default: 1
    * @group param
    */
   final val numPartitions = new IntParam(
@@ -62,6 +64,7 @@ private[feature] trait Word2VecBase extends Params
   /**
    * The minimum number of times a token must appear to be included in the word2vec model's
    * vocabulary.
+   * Default: 5
    * @group param
    */
   final val minCount = new IntParam(this, "minCount", "the minimum number of times a token must " +
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
index 0f33bae30e622..d43a3447d3975 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
@@ -40,6 +40,7 @@ private[regression] trait IsotonicRegressionBase extends Params with HasFeatures
   /**
    * Param for whether the output sequence should be isotonic/increasing (true) or
    * antitonic/decreasing (false).
+   * Default: true
    * @group param
    */
   final val isotonic: BooleanParam =

From 47058ca5db3135a33feea9f485fcda455e430054 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 4 Sep 2015 18:58:25 -0700
Subject: [PATCH 537/633] [SPARK-9925] [SQL] [TESTS] Set
 SQLConf.SHUFFLE_PARTITIONS.key correctly for tests

This PR fix the failed test and conflict for #8155

https://issues.apache.org/jira/browse/SPARK-9925

Closes #8155

Author: Yin Huai <yhuai@databricks.com>
Author: Davies Liu <davies@databricks.com>

Closes #8602 from davies/shuffle_partitions.
---
 .../org/apache/spark/sql/SQLConfSuite.scala   | 19 ++++++++++----
 .../org/apache/spark/sql/SQLQuerySuite.scala  | 21 ++++++++++-----
 .../spark/sql/test/TestSQLContext.scala       | 26 +++++++++++++++++--
 .../apache/spark/sql/hive/test/TestHive.scala | 26 +++++++++++++++++--
 .../spark/sql/hive/HiveSparkSubmitSuite.scala |  5 +++-
 .../hive/execution/ConcurrentHiveSuite.scala  |  4 ++-
 .../sql/hive/execution/HiveQuerySuite.scala   | 10 ++++---
 7 files changed, 90 insertions(+), 21 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index c35b31c96df48..589ba86a84144 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.sql
 
-import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.{TestSQLContext, SharedSQLContext}
 
 
 class SQLConfSuite extends QueryTest with SharedSQLContext {
@@ -32,8 +32,12 @@ class SQLConfSuite extends QueryTest with SharedSQLContext {
   }
 
   test("programmatic ways of basic setting and getting") {
+    // Set a conf first.
+    sqlContext.setConf(testKey, testVal)
+    // Clear the conf.
     sqlContext.conf.clear()
-    assert(sqlContext.getAllConfs.size === 0)
+    // After clear, only overrideConfs used by unit test should be in the SQLConf.
+    assert(sqlContext.getAllConfs === TestSQLContext.overrideConfs)
 
     sqlContext.setConf(testKey, testVal)
     assert(sqlContext.getConf(testKey) === testVal)
@@ -42,7 +46,7 @@ class SQLConfSuite extends QueryTest with SharedSQLContext {
 
     // Tests SQLConf as accessed from a SQLContext is mutable after
     // the latter is initialized, unlike SparkConf inside a SparkContext.
-    assert(sqlContext.getConf(testKey) == testVal)
+    assert(sqlContext.getConf(testKey) === testVal)
     assert(sqlContext.getConf(testKey, testVal + "_") === testVal)
     assert(sqlContext.getAllConfs.contains(testKey))
 
@@ -73,8 +77,13 @@ class SQLConfSuite extends QueryTest with SharedSQLContext {
 
   test("deprecated property") {
     sqlContext.conf.clear()
-    sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
-    assert(sqlContext.conf.numShufflePartitions === 10)
+    val original = sqlContext.conf.numShufflePartitions
+    try{
+      sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
+      assert(ctx.conf.numShufflePartitions === 10)
+    } finally {
+      sql(s"set ${SQLConf.SHUFFLE_PARTITIONS}=$original")
+    }
   }
 
   test("invalid conf value") {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 05f2000459599..664b7a1512faf 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -27,7 +27,7 @@ import org.apache.spark.sql.catalyst.errors.DialectException
 import org.apache.spark.sql.execution.aggregate
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.test.SQLTestData._
-import org.apache.spark.sql.test.SharedSQLContext
+import org.apache.spark.sql.test.{SharedSQLContext, TestSQLContext}
 import org.apache.spark.sql.types._
 
 /** A SQL Dialect for testing purpose, and it can not be nested type */
@@ -991,21 +991,30 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     val nonexistentKey = "nonexistent"
 
     // "set" itself returns all config variables currently specified in SQLConf.
-    assert(sql("SET").collect().size == 0)
+    assert(sql("SET").collect().size === TestSQLContext.overrideConfs.size)
+    sql("SET").collect().foreach { row =>
+      val key = row.getString(0)
+      val value = row.getString(1)
+      assert(
+        TestSQLContext.overrideConfs.contains(key),
+        s"$key should exist in SQLConf.")
+      assert(
+        TestSQLContext.overrideConfs(key) === value,
+        s"The value of $key should be ${TestSQLContext.overrideConfs(key)} instead of $value.")
+    }
+    val overrideConfs = sql("SET").collect()
 
     // "set key=val"
     sql(s"SET $testKey=$testVal")
     checkAnswer(
       sql("SET"),
-      Row(testKey, testVal)
+      overrideConfs ++ Seq(Row(testKey, testVal))
     )
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     checkAnswer(
       sql("set"),
-      Seq(
-        Row(testKey, testVal),
-        Row(testKey + testKey, testVal + testVal))
+      overrideConfs ++ Seq(Row(testKey, testVal), Row(testKey + testKey, testVal + testVal))
     )
 
     // "set key"
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
index d99d191ebe81e..10e633f3cde46 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/test/TestSQLContext.scala
@@ -31,13 +31,24 @@ private[sql] class TestSQLContext(sc: SparkContext) extends SQLContext(sc) { sel
       new SparkConf().set("spark.sql.testkey", "true")))
   }
 
-  // Use fewer partitions to speed up testing
+  // Make sure we set those test specific confs correctly when we create
+  // the SQLConf as well as when we call clear.
   protected[sql] override def createSession(): SQLSession = new this.SQLSession()
 
   /** A special [[SQLSession]] that uses fewer shuffle partitions than normal. */
   protected[sql] class SQLSession extends super.SQLSession {
     protected[sql] override lazy val conf: SQLConf = new SQLConf {
-      override def numShufflePartitions: Int = this.getConf(SQLConf.SHUFFLE_PARTITIONS, 5)
+
+      clear()
+
+      override def clear(): Unit = {
+        super.clear()
+
+        // Make sure we start with the default test configs even after clear
+        TestSQLContext.overrideConfs.map {
+          case (key, value) => setConfString(key, value)
+        }
+      }
     }
   }
 
@@ -50,3 +61,14 @@ private[sql] class TestSQLContext(sc: SparkContext) extends SQLContext(sc) { sel
     protected override def sqlContext: SQLContext = self
   }
 }
+
+private[sql] object TestSQLContext {
+
+  /**
+   * A map used to store all confs that need to be overridden in sql/core unit tests.
+   */
+  val overrideConfs: Map[String, String] =
+    Map(
+      // Fewer shuffle partitions to speed up testing.
+      SQLConf.SHUFFLE_PARTITIONS.key -> "5")
+}
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
index 77f43f92705d8..be335a47dcabd 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/test/TestHive.scala
@@ -116,18 +116,28 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
   override def executePlan(plan: LogicalPlan): this.QueryExecution =
     new this.QueryExecution(plan)
 
+  // Make sure we set those test specific confs correctly when we create
+  // the SQLConf as well as when we call clear.
   override protected[sql] def createSession(): SQLSession = {
     new this.SQLSession()
   }
 
   protected[hive] class SQLSession extends super.SQLSession {
-    /** Fewer partitions to speed up testing. */
     protected[sql] override lazy val conf: SQLConf = new SQLConf {
-      override def numShufflePartitions: Int = getConf(SQLConf.SHUFFLE_PARTITIONS, 5)
       // TODO as in unit test, conf.clear() probably be called, all of the value will be cleared.
       // The super.getConf(SQLConf.DIALECT) is "sql" by default, we need to set it as "hiveql"
       override def dialect: String = super.getConf(SQLConf.DIALECT, "hiveql")
       override def caseSensitiveAnalysis: Boolean = getConf(SQLConf.CASE_SENSITIVE, false)
+
+      clear()
+
+      override def clear(): Unit = {
+        super.clear()
+
+        TestHiveContext.overrideConfs.map {
+          case (key, value) => setConfString(key, value)
+        }
+      }
     }
   }
 
@@ -455,3 +465,15 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
     }
   }
 }
+
+private[hive] object TestHiveContext {
+
+  /**
+   * A map used to store all confs that need to be overridden in sql/hive unit tests.
+   */
+  val overrideConfs: Map[String, String] =
+    Map(
+      // Fewer shuffle partitions to speed up testing.
+      SQLConf.SHUFFLE_PARTITIONS.key -> "5"
+    )
+}
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
index 84f3db44ecd8d..97df249bdb6d6 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveSparkSubmitSuite.scala
@@ -173,6 +173,7 @@ object SparkSubmitClassLoaderTest extends Logging {
   def main(args: Array[String]) {
     Utils.configTestLog4j("INFO")
     val conf = new SparkConf()
+    conf.set("spark.ui.enabled", "false")
     val sc = new SparkContext(conf)
     val hiveContext = new TestHiveContext(sc)
     val df = hiveContext.createDataFrame((1 to 100).map(i => (i, i))).toDF("i", "j")
@@ -264,6 +265,7 @@ object SparkSQLConfTest extends Logging {
       // For this simple test, we do not really clone this object.
       override def clone: SparkConf = this
     }
+    conf.set("spark.ui.enabled", "false")
     val sc = new SparkContext(conf)
     val hiveContext = new TestHiveContext(sc)
     // Run a simple command to make sure all lazy vals in hiveContext get instantiated.
@@ -283,7 +285,8 @@ object SPARK_9757 extends QueryTest {
     val sparkContext = new SparkContext(
       new SparkConf()
         .set("spark.sql.hive.metastore.version", "0.13.1")
-        .set("spark.sql.hive.metastore.jars", "maven"))
+        .set("spark.sql.hive.metastore.jars", "maven")
+        .set("spark.ui.enabled", "false"))
 
     val hiveContext = new TestHiveContext(sparkContext)
     sqlContext = hiveContext
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala
index b0d3dd44daedc..e38d1eb5779fe 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/ConcurrentHiveSuite.scala
@@ -25,8 +25,10 @@ class ConcurrentHiveSuite extends SparkFunSuite with BeforeAndAfterAll {
   ignore("multiple instances not supported") {
     test("Multiple Hive Instances") {
       (1 to 10).map { i =>
+        val conf = new SparkConf()
+        conf.set("spark.ui.enabled", "false")
         val ts =
-          new TestHiveContext(new SparkContext("local", s"TestSQLContext$i", new SparkConf()))
+          new TestHiveContext(new SparkContext("local", s"TestSQLContext$i", conf))
         ts.executeSql("SHOW TABLES").toRdd.collect()
         ts.executeSql("SELECT * FROM src").toRdd.collect()
         ts.executeSql("SHOW TABLES").toRdd.collect()
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
index 83f9f3eaa3a5e..fe63ad5683195 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveQuerySuite.scala
@@ -31,6 +31,7 @@ import org.apache.spark.sql.{AnalysisException, DataFrame, Row}
 import org.apache.spark.sql.catalyst.expressions.Cast
 import org.apache.spark.sql.catalyst.plans.logical.Project
 import org.apache.spark.sql.hive._
+import org.apache.spark.sql.hive.test.TestHiveContext
 import org.apache.spark.sql.hive.test.TestHive
 import org.apache.spark.sql.hive.test.TestHive._
 
@@ -1104,18 +1105,19 @@ class HiveQuerySuite extends HiveComparisonTest with BeforeAndAfter {
 
     // "SET" itself returns all config variables currently specified in SQLConf.
     // TODO: Should we be listing the default here always? probably...
-    assert(sql("SET").collect().size == 0)
+    assert(sql("SET").collect().size === TestHiveContext.overrideConfs.size)
 
+    val defaults = collectResults(sql("SET"))
     assertResult(Set(testKey -> testVal)) {
       collectResults(sql(s"SET $testKey=$testVal"))
     }
 
-    assert(hiveconf.get(testKey, "") == testVal)
-    assertResult(Set(testKey -> testVal))(collectResults(sql("SET")))
+    assert(hiveconf.get(testKey, "") === testVal)
+    assertResult(defaults ++ Set(testKey -> testVal))(collectResults(sql("SET")))
 
     sql(s"SET ${testKey + testKey}=${testVal + testVal}")
     assert(hiveconf.get(testKey + testKey, "") == testVal + testVal)
-    assertResult(Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
+    assertResult(defaults ++ Set(testKey -> testVal, (testKey + testKey) -> (testVal + testVal))) {
       collectResults(sql("SET"))
     }
 

From 6c751940ea8449b7540ba956a00ed19ba0af599b Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Fri, 4 Sep 2015 22:57:52 -1000
Subject: [PATCH 538/633] [HOTFIX] [SQL] Fixes compilation error

Jenkins master builders are currently broken by a merge conflict between PR #8584 and PR #8155.

Author: Cheng Lian <lian@databricks.com>

Closes #8614 from liancheng/hotfix/fix-pr-8155-8584-conflict.
---
 sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
index 589ba86a84144..3d2bd236ceead 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLConfSuite.scala
@@ -80,7 +80,7 @@ class SQLConfSuite extends QueryTest with SharedSQLContext {
     val original = sqlContext.conf.numShufflePartitions
     try{
       sql(s"set ${SQLConf.Deprecated.MAPRED_REDUCE_TASKS}=10")
-      assert(ctx.conf.numShufflePartitions === 10)
+      assert(sqlContext.conf.numShufflePartitions === 10)
     } finally {
       sql(s"set ${SQLConf.SHUFFLE_PARTITIONS}=$original")
     }

From 7a4f326c00fb33c384b4fb927310d687ec063329 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Fri, 4 Sep 2015 23:16:39 -1000
Subject: [PATCH 539/633] [SPARK-10440] [STREAMING] [DOCS] Update python API
 stuff in the programming guides and python docs

- Fixed information around Python API tags in streaming programming guides
- Added missing stuff in python docs

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8595 from tdas/SPARK-10440.
---
 docs/streaming-flume-integration.md |  2 --
 docs/streaming-programming-guide.md | 14 ++++----------
 python/docs/index.rst               |  8 ++++++++
 python/docs/pyspark.streaming.rst   | 21 +++++++++++++++++++++
 4 files changed, 33 insertions(+), 12 deletions(-)

diff --git a/docs/streaming-flume-integration.md b/docs/streaming-flume-integration.md
index de0461010daec..383d954409ce4 100644
--- a/docs/streaming-flume-integration.md
+++ b/docs/streaming-flume-integration.md
@@ -5,8 +5,6 @@ title: Spark Streaming + Flume Integration Guide
 
 [Apache Flume](https://flume.apache.org/) is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. Here we explain how to configure Flume and Spark Streaming to receive data from Flume. There are two approaches to this.
 
-<span class="badge" style="background-color: grey">Python API</span> Flume is not yet available in the Python API.
-
 ## Approach 1: Flume-style Push-based Approach
 Flume is designed to push data between Flume agents. In this approach, Spark Streaming essentially sets up a receiver that acts an Avro agent for Flume, to which Flume can push the data. Here are the configuration steps.
 
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 118ced298f4b0..a1acf83f75245 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -50,13 +50,7 @@ all of which are presented in this guide.
 You will find tabs throughout this guide that let you choose between code snippets of
 different languages.
 
-**Note:** Python API for Spark Streaming has been introduced in Spark 1.2. It has all the DStream
-transformations and almost all the output operations available in Scala and Java interfaces.
-However, it only has support for basic sources like text files and text data over sockets.
-APIs for additional sources, like Kafka and Flume, will be available in the future.
-Further information about available features in the Python API are mentioned throughout this
-document; look out for the tag
-<span class="badge" style="background-color: grey">Python API</span>.
+**Note:** There are a few APIs that are either different or not available in Python. Throughout this guide, you will find the tag <span class="badge" style="background-color: grey">Python API</span> highlighting these differences.
 
 ***************************************************************************************************
 
@@ -683,7 +677,7 @@ for Java, and [StreamingContext](api/python/pyspark.streaming.html#pyspark.strea
 {:.no_toc}
 
 <span class="badge" style="background-color: grey">Python API</span> As of Spark {{site.SPARK_VERSION_SHORT}},
-out of these sources, *only* Kafka, Flume and MQTT are available in the Python API. We will add more advanced sources in the Python API in future.
+out of these sources, Kafka, Kinesis, Flume and MQTT are available in the Python API.
 
 This category of sources require interfacing with external non-Spark libraries, some of them with
 complex dependencies (e.g., Kafka and Flume). Hence, to minimize issues related to version conflicts
@@ -725,9 +719,9 @@ Some of these advanced sources are as follows.
 
 - **Kafka:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Kafka 0.8.2.1. See the [Kafka Integration Guide](streaming-kafka-integration.html) for more details.
 
-- **Flume:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Flume 1.4.0. See the [Flume Integration Guide](streaming-flume-integration.html) for more details.
+- **Flume:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Flume 1.6.0. See the [Flume Integration Guide](streaming-flume-integration.html) for more details.
 
-- **Kinesis:** See the [Kinesis Integration Guide](streaming-kinesis-integration.html) for more details.
+- **Kinesis:** Spark Streaming {{site.SPARK_VERSION_SHORT}} is compatible with Kinesis Client Library 1.2.1. See the [Kinesis Integration Guide](streaming-kinesis-integration.html) for more details.
 
 - **Twitter:** Spark Streaming's TwitterUtils uses Twitter4j 3.0.3 to get the public stream of tweets using
   [Twitter's Streaming API](https://dev.twitter.com/docs/streaming-apis). Authentication information
diff --git a/python/docs/index.rst b/python/docs/index.rst
index f7eede9c3c82a..306ffdb0e0f13 100644
--- a/python/docs/index.rst
+++ b/python/docs/index.rst
@@ -29,6 +29,14 @@ Core classes:
 
     A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
 
+    :class:`pyspark.streaming.StreamingContext`
+
+    Main entry point for Spark Streaming functionality.
+
+    :class:`pyspark.streaming.DStream`
+
+    A Discretized Stream (DStream), the basic abstraction in Spark Streaming.
+
     :class:`pyspark.sql.SQLContext`
 
     Main entry point for DataFrame and SQL functionality.
diff --git a/python/docs/pyspark.streaming.rst b/python/docs/pyspark.streaming.rst
index 50822c93faba1..fc52a647543e7 100644
--- a/python/docs/pyspark.streaming.rst
+++ b/python/docs/pyspark.streaming.rst
@@ -15,3 +15,24 @@ pyspark.streaming.kafka module
     :members:
     :undoc-members:
     :show-inheritance:
+
+pyspark.streaming.kinesis module
+--------------------------------
+.. automodule:: pyspark.streaming.kinesis
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.streaming.flume.module
+------------------------------
+.. automodule:: pyspark.streaming.flume
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+pyspark.streaming.mqtt module
+-----------------------------
+.. automodule:: pyspark.streaming.mqtt
+    :members:
+    :undoc-members:
+    :show-inheritance:

From bca8c072bd710beda6cfac1533a67f32f579b134 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Sat, 5 Sep 2015 17:50:12 +0800
Subject: [PATCH 540/633] [SPARK-10434] [SQL] Fixes Parquet schema of arrays
 that may contain null

To keep full compatibility of Parquet write path with Spark 1.4, we should rename the innermost field name of arrays that may contain null from "array_element" to "array".

Please refer to [SPARK-10434] [1] for more details.

[1]: https://issues.apache.org/jira/browse/SPARK-10434

Author: Cheng Lian <lian@databricks.com>

Closes #8586 from liancheng/spark-10434/fix-parquet-array-type.
---
 .../parquet/CatalystSchemaConverter.scala           | 13 +++++++------
 .../datasources/parquet/ParquetSchemaSuite.scala    |  6 +++---
 2 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
index a21ab1dbb25d1..2d237da81c20d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystSchemaConverter.scala
@@ -426,13 +426,14 @@ private[parquet] class CatalystSchemaConverter(
       // ArrayType and MapType (for Spark versions <= 1.4.x)
       // ===================================================
 
-      // Spark 1.4.x and prior versions convert ArrayType with nullable elements into a 3-level
-      // LIST structure.  This behavior mimics parquet-hive (1.6.0rc3).  Note that this case is
-      // covered by the backwards-compatibility rules implemented in `isElementType()`.
+      // Spark 1.4.x and prior versions convert `ArrayType` with nullable elements into a 3-level
+      // `LIST` structure.  This behavior is somewhat a hybrid of parquet-hive and parquet-avro
+      // (1.6.0rc3): the 3-level structure is similar to parquet-hive while the 3rd level element
+      // field name "array" is borrowed from parquet-avro.
       case ArrayType(elementType, nullable @ true) if !followParquetFormatSpec =>
         // <list-repetition> group <name> (LIST) {
         //   optional group bag {
-        //     repeated <element-type> element;
+        //     repeated <element-type> array;
         //   }
         // }
         ConversionPatterns.listType(
@@ -441,8 +442,8 @@ private[parquet] class CatalystSchemaConverter(
           Types
             .buildGroup(REPEATED)
             // "array_element" is the name chosen by parquet-hive (1.7.0 and prior version)
-            .addField(convertField(StructField("array_element", elementType, nullable)))
-            .named(CatalystConverter.ARRAY_CONTAINS_NULL_BAG_SCHEMA_NAME))
+            .addField(convertField(StructField("array", elementType, nullable)))
+            .named("bag"))
 
       // Spark 1.4.x and prior versions convert ArrayType with non-nullable elements into a 2-level
       // LIST structure.  This behavior mimics parquet-avro (1.6.0rc3).  Note that this case is
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 28c59a4abdd76..5331d7c035236 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -197,7 +197,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
       |message root {
       |  optional group _1 (LIST) {
       |    repeated group bag {
-      |      optional int32 array_element;
+      |      optional int32 array;
       |    }
       |  }
       |}
@@ -266,7 +266,7 @@ class ParquetSchemaInferenceSuite extends ParquetSchemaTest {
       |        optional binary _1 (UTF8);
       |        optional group _2 (LIST) {
       |          repeated group bag {
-      |            optional group array_element {
+      |            optional group array {
       |              required int32 _1;
       |              required double _2;
       |            }
@@ -645,7 +645,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     """message root {
       |  optional group f1 (LIST) {
       |    repeated group bag {
-      |      optional int32 array_element;
+      |      optional int32 array;
       |    }
       |  }
       |}

From 871764c6ce531af5b1ac7ccccb32e7a903b59a2a Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Sat, 5 Sep 2015 00:04:00 -1000
Subject: [PATCH 541/633] [SPARK-10013] [ML] [JAVA] [TEST] remove java assert
 from java unit tests

From Jira: We should use assertTrue, etc. instead to make sure the asserts are not ignored in tests.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8607 from holdenk/SPARK-10013-remove-java-assert-from-java-unit-tests.
---
 .../JavaLogisticRegressionSuite.java          | 51 ++++++++++---------
 .../classification/JavaNaiveBayesSuite.java   | 13 ++---
 .../regression/JavaLinearRegressionSuite.java |  2 +-
 .../spark/mllib/linalg/JavaMatricesSuite.java | 40 +++++++--------
 4 files changed, 54 insertions(+), 52 deletions(-)

diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
index 618b95b9bd126..fd22eb6dca018 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java
@@ -22,6 +22,7 @@
 import java.util.List;
 
 import org.junit.After;
+import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
@@ -63,16 +64,16 @@ public void tearDown() {
   @Test
   public void logisticRegressionDefaultParams() {
     LogisticRegression lr = new LogisticRegression();
-    assert(lr.getLabelCol().equals("label"));
+    Assert.assertEquals(lr.getLabelCol(), "label");
     LogisticRegressionModel model = lr.fit(dataset);
     model.transform(dataset).registerTempTable("prediction");
     DataFrame predictions = jsql.sql("SELECT label, probability, prediction FROM prediction");
     predictions.collectAsList();
     // Check defaults
-    assert(model.getThreshold() == 0.5);
-    assert(model.getFeaturesCol().equals("features"));
-    assert(model.getPredictionCol().equals("prediction"));
-    assert(model.getProbabilityCol().equals("probability"));
+    Assert.assertEquals(0.5, model.getThreshold(), eps);
+    Assert.assertEquals("features", model.getFeaturesCol());
+    Assert.assertEquals("prediction", model.getPredictionCol());
+    Assert.assertEquals("probability", model.getProbabilityCol());
   }
 
   @Test
@@ -85,19 +86,19 @@ public void logisticRegressionWithSetters() {
       .setProbabilityCol("myProbability");
     LogisticRegressionModel model = lr.fit(dataset);
     LogisticRegression parent = (LogisticRegression) model.parent();
-    assert(parent.getMaxIter() == 10);
-    assert(parent.getRegParam() == 1.0);
-    assert(parent.getThresholds()[0] == 0.4);
-    assert(parent.getThresholds()[1] == 0.6);
-    assert(parent.getThreshold() == 0.6);
-    assert(model.getThreshold() == 0.6);
+    Assert.assertEquals(10, parent.getMaxIter());
+    Assert.assertEquals(1.0, parent.getRegParam(), eps);
+    Assert.assertEquals(0.4, parent.getThresholds()[0], eps);
+    Assert.assertEquals(0.6, parent.getThresholds()[1], eps);
+    Assert.assertEquals(0.6, parent.getThreshold(), eps);
+    Assert.assertEquals(0.6, model.getThreshold(), eps);
 
     // Modify model params, and check that the params worked.
     model.setThreshold(1.0);
     model.transform(dataset).registerTempTable("predAllZero");
     DataFrame predAllZero = jsql.sql("SELECT prediction, myProbability FROM predAllZero");
     for (Row r: predAllZero.collectAsList()) {
-      assert(r.getDouble(0) == 0.0);
+      Assert.assertEquals(0.0, r.getDouble(0), eps);
     }
     // Call transform with params, and check that the params worked.
     model.transform(dataset, model.threshold().w(0.0), model.probabilityCol().w("myProb"))
@@ -107,17 +108,17 @@ public void logisticRegressionWithSetters() {
     for (Row r: predNotAllZero.collectAsList()) {
       if (r.getDouble(0) != 0.0) foundNonZero = true;
     }
-    assert(foundNonZero);
+    Assert.assertTrue(foundNonZero);
 
     // Call fit() with new params, and check as many params as we can.
     LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1),
         lr.threshold().w(0.4), lr.probabilityCol().w("theProb"));
     LogisticRegression parent2 = (LogisticRegression) model2.parent();
-    assert(parent2.getMaxIter() == 5);
-    assert(parent2.getRegParam() == 0.1);
-    assert(parent2.getThreshold() == 0.4);
-    assert(model2.getThreshold() == 0.4);
-    assert(model2.getProbabilityCol().equals("theProb"));
+    Assert.assertEquals(5, parent2.getMaxIter());
+    Assert.assertEquals(0.1, parent2.getRegParam(), eps);
+    Assert.assertEquals(0.4, parent2.getThreshold(), eps);
+    Assert.assertEquals(0.4, model2.getThreshold(), eps);
+    Assert.assertEquals("theProb", model2.getProbabilityCol());
   }
 
   @SuppressWarnings("unchecked")
@@ -125,18 +126,18 @@ public void logisticRegressionWithSetters() {
   public void logisticRegressionPredictorClassifierMethods() {
     LogisticRegression lr = new LogisticRegression();
     LogisticRegressionModel model = lr.fit(dataset);
-    assert(model.numClasses() == 2);
+    Assert.assertEquals(2, model.numClasses());
 
     model.transform(dataset).registerTempTable("transformed");
     DataFrame trans1 = jsql.sql("SELECT rawPrediction, probability FROM transformed");
     for (Row row: trans1.collect()) {
       Vector raw = (Vector)row.get(0);
       Vector prob = (Vector)row.get(1);
-      assert(raw.size() == 2);
-      assert(prob.size() == 2);
+      Assert.assertEquals(raw.size(), 2);
+      Assert.assertEquals(prob.size(), 2);
       double probFromRaw1 = 1.0 / (1.0 + Math.exp(-raw.apply(1)));
-      assert(Math.abs(prob.apply(1) - probFromRaw1) < eps);
-      assert(Math.abs(prob.apply(0) - (1.0 - probFromRaw1)) < eps);
+      Assert.assertEquals(0, Math.abs(prob.apply(1) - probFromRaw1), eps);
+      Assert.assertEquals(0, Math.abs(prob.apply(0) - (1.0 - probFromRaw1)), eps);
     }
 
     DataFrame trans2 = jsql.sql("SELECT prediction, probability FROM transformed");
@@ -145,7 +146,7 @@ public void logisticRegressionPredictorClassifierMethods() {
       Vector prob = (Vector)row.get(1);
       double probOfPred = prob.apply((int)pred);
       for (int i = 0; i < prob.size(); ++i) {
-        assert(probOfPred >= prob.apply(i));
+        Assert.assertTrue(probOfPred >= prob.apply(i));
       }
     }
   }
@@ -156,6 +157,6 @@ public void logisticRegressionTrainingSummary() {
     LogisticRegressionModel model = lr.fit(dataset);
 
     LogisticRegressionTrainingSummary summary = model.summary();
-    assert(summary.totalIterations() == summary.objectiveHistory().length);
+    Assert.assertEquals(summary.totalIterations(), summary.objectiveHistory().length);
   }
 }
diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
index 8fd7bf55a2e5d..075a62c493f17 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaNaiveBayesSuite.java
@@ -23,6 +23,7 @@
 import org.junit.After;
 import org.junit.Before;
 import org.junit.Test;
+import static org.junit.Assert.assertEquals;
 
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -58,18 +59,18 @@ public void validatePrediction(DataFrame predictionAndLabels) {
     for (Row r : predictionAndLabels.collect()) {
       double prediction = r.getAs(0);
       double label = r.getAs(1);
-      assert(prediction == label);
+      assertEquals(label, prediction, 1E-5);
     }
   }
 
   @Test
   public void naiveBayesDefaultParams() {
     NaiveBayes nb = new NaiveBayes();
-    assert(nb.getLabelCol() == "label");
-    assert(nb.getFeaturesCol() == "features");
-    assert(nb.getPredictionCol() == "prediction");
-    assert(nb.getSmoothing() == 1.0);
-    assert(nb.getModelType() == "multinomial");
+    assertEquals("label", nb.getLabelCol());
+    assertEquals("features", nb.getFeaturesCol());
+    assertEquals("prediction", nb.getPredictionCol());
+    assertEquals(1.0, nb.getSmoothing(), 1E-5);
+    assertEquals("multinomial", nb.getModelType());
   }
 
   @Test
diff --git a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
index d591a456864e4..91c589d00abd5 100644
--- a/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java
@@ -60,7 +60,7 @@ public void tearDown() {
   @Test
   public void linearRegressionDefaultParams() {
     LinearRegression lr = new LinearRegression();
-    assert(lr.getLabelCol().equals("label"));
+    assertEquals("label", lr.getLabelCol());
     LinearRegressionModel model = lr.fit(dataset);
     model.transform(dataset).registerTempTable("prediction");
     DataFrame predictions = jsql.sql("SELECT label, prediction FROM prediction");
diff --git a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaMatricesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaMatricesSuite.java
index 3349c5022423a..8beea102efd01 100644
--- a/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaMatricesSuite.java
+++ b/mllib/src/test/java/org/apache/spark/mllib/linalg/JavaMatricesSuite.java
@@ -80,10 +80,10 @@ public void diagonalMatrixConstruction() {
         assertArrayEquals(sd.toArray(), s.toArray(), 0.0);
         assertArrayEquals(s.toArray(), ss.toArray(), 0.0);
         assertArrayEquals(s.values(), ss.values(), 0.0);
-        assert(s.values().length == 2);
-        assert(ss.values().length == 2);
-        assert(s.colPtrs().length == 4);
-        assert(ss.colPtrs().length == 4);
+        assertEquals(2, s.values().length);
+        assertEquals(2, ss.values().length);
+        assertEquals(4, s.colPtrs().length);
+        assertEquals(4, ss.colPtrs().length);
     }
 
     @Test
@@ -137,27 +137,27 @@ public void concatenateMatrices() {
         Matrix deHorz2 = Matrices.horzcat(new Matrix[]{spMat1, deMat2});
         Matrix deHorz3 = Matrices.horzcat(new Matrix[]{deMat1, spMat2});
 
-        assert(deHorz1.numRows() == 3);
-        assert(deHorz2.numRows() == 3);
-        assert(deHorz3.numRows() == 3);
-        assert(spHorz.numRows() == 3);
-        assert(deHorz1.numCols() == 5);
-        assert(deHorz2.numCols() == 5);
-        assert(deHorz3.numCols() == 5);
-        assert(spHorz.numCols() == 5);
+        assertEquals(3, deHorz1.numRows());
+        assertEquals(3, deHorz2.numRows());
+        assertEquals(3, deHorz3.numRows());
+        assertEquals(3, spHorz.numRows());
+        assertEquals(5, deHorz1.numCols());
+        assertEquals(5, deHorz2.numCols());
+        assertEquals(5, deHorz3.numCols());
+        assertEquals(5, spHorz.numCols());
 
         Matrix spVert = Matrices.vertcat(new Matrix[]{spMat1, spMat3});
         Matrix deVert1 = Matrices.vertcat(new Matrix[]{deMat1, deMat3});
         Matrix deVert2 = Matrices.vertcat(new Matrix[]{spMat1, deMat3});
         Matrix deVert3 = Matrices.vertcat(new Matrix[]{deMat1, spMat3});
 
-        assert(deVert1.numRows() == 5);
-        assert(deVert2.numRows() == 5);
-        assert(deVert3.numRows() == 5);
-        assert(spVert.numRows() == 5);
-        assert(deVert1.numCols() == 2);
-        assert(deVert2.numCols() == 2);
-        assert(deVert3.numCols() == 2);
-        assert(spVert.numCols() == 2);
+        assertEquals(5, deVert1.numRows());
+        assertEquals(5, deVert2.numRows());
+        assertEquals(5, deVert3.numRows());
+        assertEquals(5, spVert.numRows());
+        assertEquals(2, deVert1.numCols());
+        assertEquals(2, deVert2.numCols());
+        assertEquals(2, deVert3.numCols());
+        assertEquals(2, spVert.numCols());
     }
 }

From 5ffe752b59e468d55363f1f24b17a3677927ca8f Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 7 Sep 2015 10:42:30 -1000
Subject: [PATCH 542/633] [SPARK-9767] Remove ConnectionManager.

We introduced the Netty network module for shuffle in Spark 1.2, and has turned it on by default for 3 releases. The old ConnectionManager is difficult to maintain. If we merge the patch now, by the time it is released, it would be 1 yr for which ConnectionManager is off by default. It's time to remove it.

Author: Reynold Xin <rxin@databricks.com>

Closes #8161 from rxin/SPARK-9767.
---
 .../scala/org/apache/spark/SparkEnv.scala     |   11 +-
 .../netty/NettyBlockTransferService.scala     |    8 +-
 .../spark/network/nio/BlockMessage.scala      |  175 ---
 .../spark/network/nio/BlockMessageArray.scala |  140 --
 .../spark/network/nio/BufferMessage.scala     |  114 --
 .../apache/spark/network/nio/Connection.scala |  619 --------
 .../spark/network/nio/ConnectionId.scala      |   36 -
 .../spark/network/nio/ConnectionManager.scala | 1157 ---------------
 .../network/nio/ConnectionManagerId.scala     |   37 -
 .../apache/spark/network/nio/Message.scala    |  114 --
 .../spark/network/nio/MessageChunk.scala      |   41 -
 .../network/nio/MessageChunkHeader.scala      |   83 --
 .../network/nio/NioBlockTransferService.scala |  217 ---
 .../spark/network/nio/SecurityMessage.scala   |  160 ---
 .../spark/serializer/KryoSerializer.scala     |    4 -
 .../network/nio/ConnectionManagerSuite.scala  |  296 ----
 .../BlockManagerReplicationSuite.scala        |    6 +-
 .../spark/storage/BlockManagerSuite.scala     |   10 +-
 docs/configuration.md                         |   11 -
 project/MimaExcludes.scala                    | 1257 +++++++++--------
 .../streaming/ReceivedBlockHandlerSuite.scala |   10 +-
 21 files changed, 651 insertions(+), 3855 deletions(-)
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/Connection.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/Message.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala
 delete mode 100644 core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala
 delete mode 100644 core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala
index 0f1e2e069568d..c6fef7f91f00c 100644
--- a/core/src/main/scala/org/apache/spark/SparkEnv.scala
+++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala
@@ -33,7 +33,6 @@ import org.apache.spark.broadcast.BroadcastManager
 import org.apache.spark.metrics.MetricsSystem
 import org.apache.spark.network.BlockTransferService
 import org.apache.spark.network.netty.NettyBlockTransferService
-import org.apache.spark.network.nio.NioBlockTransferService
 import org.apache.spark.rpc.{RpcEndpointRef, RpcEndpoint, RpcEnv}
 import org.apache.spark.rpc.akka.AkkaRpcEnv
 import org.apache.spark.scheduler.{OutputCommitCoordinator, LiveListenerBus}
@@ -326,15 +325,7 @@ object SparkEnv extends Logging {
 
     val shuffleMemoryManager = ShuffleMemoryManager.create(conf, numUsableCores)
 
-    val blockTransferService =
-      conf.get("spark.shuffle.blockTransferService", "netty").toLowerCase match {
-        case "netty" =>
-          new NettyBlockTransferService(conf, securityManager, numUsableCores)
-        case "nio" =>
-          logWarning("NIO-based block transfer service is deprecated, " +
-            "and will be removed in Spark 1.6.0.")
-          new NioBlockTransferService(conf, securityManager)
-      }
+    val blockTransferService = new NettyBlockTransferService(conf, securityManager, numUsableCores)
 
     val blockManagerMaster = new BlockManagerMaster(registerOrLookupEndpoint(
       BlockManagerMaster.DRIVER_ENDPOINT_NAME,
diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
index d5ad2c9ad00e8..4b851bcb36597 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
@@ -149,7 +149,11 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage
   }
 
   override def close(): Unit = {
-    server.close()
-    clientFactory.close()
+    if (server != null) {
+      server.close()
+    }
+    if (clientFactory != null) {
+      clientFactory.close()
+    }
   }
 }
diff --git a/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
deleted file mode 100644
index 79cb0640c8672..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/BlockMessage.scala
+++ /dev/null
@@ -1,175 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import org.apache.spark.storage.{BlockId, StorageLevel, TestBlockId}
-
-import scala.collection.mutable.{ArrayBuffer, StringBuilder}
-
-// private[spark] because we need to register them in Kryo
-private[spark] case class GetBlock(id: BlockId)
-private[spark] case class GotBlock(id: BlockId, data: ByteBuffer)
-private[spark] case class PutBlock(id: BlockId, data: ByteBuffer, level: StorageLevel)
-
-private[nio] class BlockMessage() {
-  // Un-initialized: typ = 0
-  // GetBlock: typ = 1
-  // GotBlock: typ = 2
-  // PutBlock: typ = 3
-  private var typ: Int = BlockMessage.TYPE_NON_INITIALIZED
-  private var id: BlockId = null
-  private var data: ByteBuffer = null
-  private var level: StorageLevel = null
-
-  def set(getBlock: GetBlock) {
-    typ = BlockMessage.TYPE_GET_BLOCK
-    id = getBlock.id
-  }
-
-  def set(gotBlock: GotBlock) {
-    typ = BlockMessage.TYPE_GOT_BLOCK
-    id = gotBlock.id
-    data = gotBlock.data
-  }
-
-  def set(putBlock: PutBlock) {
-    typ = BlockMessage.TYPE_PUT_BLOCK
-    id = putBlock.id
-    data = putBlock.data
-    level = putBlock.level
-  }
-
-  def set(buffer: ByteBuffer) {
-    typ = buffer.getInt()
-    val idLength = buffer.getInt()
-    val idBuilder = new StringBuilder(idLength)
-    for (i <- 1 to idLength) {
-      idBuilder += buffer.getChar()
-    }
-    id = BlockId(idBuilder.toString)
-
-    if (typ == BlockMessage.TYPE_PUT_BLOCK) {
-
-      val booleanInt = buffer.getInt()
-      val replication = buffer.getInt()
-      level = StorageLevel(booleanInt, replication)
-
-      val dataLength = buffer.getInt()
-      data = ByteBuffer.allocate(dataLength)
-      if (dataLength != buffer.remaining) {
-        throw new Exception("Error parsing buffer")
-      }
-      data.put(buffer)
-      data.flip()
-    } else if (typ == BlockMessage.TYPE_GOT_BLOCK) {
-
-      val dataLength = buffer.getInt()
-      data = ByteBuffer.allocate(dataLength)
-      if (dataLength != buffer.remaining) {
-        throw new Exception("Error parsing buffer")
-      }
-      data.put(buffer)
-      data.flip()
-    }
-
-  }
-
-  def set(bufferMsg: BufferMessage) {
-    val buffer = bufferMsg.buffers.apply(0)
-    buffer.clear()
-    set(buffer)
-  }
-
-  def getType: Int = typ
-  def getId: BlockId = id
-  def getData: ByteBuffer = data
-  def getLevel: StorageLevel = level
-
-  def toBufferMessage: BufferMessage = {
-    val buffers = new ArrayBuffer[ByteBuffer]()
-    var buffer = ByteBuffer.allocate(4 + 4 + id.name.length * 2)
-    buffer.putInt(typ).putInt(id.name.length)
-    id.name.foreach((x: Char) => buffer.putChar(x))
-    buffer.flip()
-    buffers += buffer
-
-    if (typ == BlockMessage.TYPE_PUT_BLOCK) {
-      buffer = ByteBuffer.allocate(8).putInt(level.toInt).putInt(level.replication)
-      buffer.flip()
-      buffers += buffer
-
-      buffer = ByteBuffer.allocate(4).putInt(data.remaining)
-      buffer.flip()
-      buffers += buffer
-
-      buffers += data
-    } else if (typ == BlockMessage.TYPE_GOT_BLOCK) {
-      buffer = ByteBuffer.allocate(4).putInt(data.remaining)
-      buffer.flip()
-      buffers += buffer
-
-      buffers += data
-    }
-
-    Message.createBufferMessage(buffers)
-  }
-
-  override def toString: String = {
-    "BlockMessage [type = " + typ + ", id = " + id + ", level = " + level +
-    ", data = " + (if (data != null) data.remaining.toString else "null") + "]"
-  }
-}
-
-private[nio] object BlockMessage {
-  val TYPE_NON_INITIALIZED: Int = 0
-  val TYPE_GET_BLOCK: Int = 1
-  val TYPE_GOT_BLOCK: Int = 2
-  val TYPE_PUT_BLOCK: Int = 3
-
-  def fromBufferMessage(bufferMessage: BufferMessage): BlockMessage = {
-    val newBlockMessage = new BlockMessage()
-    newBlockMessage.set(bufferMessage)
-    newBlockMessage
-  }
-
-  def fromByteBuffer(buffer: ByteBuffer): BlockMessage = {
-    val newBlockMessage = new BlockMessage()
-    newBlockMessage.set(buffer)
-    newBlockMessage
-  }
-
-  def fromGetBlock(getBlock: GetBlock): BlockMessage = {
-    val newBlockMessage = new BlockMessage()
-    newBlockMessage.set(getBlock)
-    newBlockMessage
-  }
-
-  def fromGotBlock(gotBlock: GotBlock): BlockMessage = {
-    val newBlockMessage = new BlockMessage()
-    newBlockMessage.set(gotBlock)
-    newBlockMessage
-  }
-
-  def fromPutBlock(putBlock: PutBlock): BlockMessage = {
-    val newBlockMessage = new BlockMessage()
-    newBlockMessage.set(putBlock)
-    newBlockMessage
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala b/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
deleted file mode 100644
index f1c9ea8b64ca3..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/BlockMessageArray.scala
+++ /dev/null
@@ -1,140 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import org.apache.spark._
-import org.apache.spark.storage.{StorageLevel, TestBlockId}
-
-import scala.collection.mutable.ArrayBuffer
-
-private[nio]
-class BlockMessageArray(var blockMessages: Seq[BlockMessage])
-  extends Seq[BlockMessage] with Logging {
-
-  def this(bm: BlockMessage) = this(Array(bm))
-
-  def this() = this(null.asInstanceOf[Seq[BlockMessage]])
-
-  def apply(i: Int): BlockMessage = blockMessages(i)
-
-  def iterator: Iterator[BlockMessage] = blockMessages.iterator
-
-  def length: Int = blockMessages.length
-
-  def set(bufferMessage: BufferMessage) {
-    val startTime = System.currentTimeMillis
-    val newBlockMessages = new ArrayBuffer[BlockMessage]()
-    val buffer = bufferMessage.buffers(0)
-    buffer.clear()
-    while (buffer.remaining() > 0) {
-      val size = buffer.getInt()
-      logDebug("Creating block message of size " + size + " bytes")
-      val newBuffer = buffer.slice()
-      newBuffer.clear()
-      newBuffer.limit(size)
-      logDebug("Trying to convert buffer " + newBuffer + " to block message")
-      val newBlockMessage = BlockMessage.fromByteBuffer(newBuffer)
-      logDebug("Created " + newBlockMessage)
-      newBlockMessages += newBlockMessage
-      buffer.position(buffer.position() + size)
-    }
-    val finishTime = System.currentTimeMillis
-    logDebug("Converted block message array from buffer message in " +
-      (finishTime - startTime) / 1000.0  + " s")
-    this.blockMessages = newBlockMessages
-  }
-
-  def toBufferMessage: BufferMessage = {
-    val buffers = new ArrayBuffer[ByteBuffer]()
-
-    blockMessages.foreach(blockMessage => {
-      val bufferMessage = blockMessage.toBufferMessage
-      logDebug("Adding " + blockMessage)
-      val sizeBuffer = ByteBuffer.allocate(4).putInt(bufferMessage.size)
-      sizeBuffer.flip
-      buffers += sizeBuffer
-      buffers ++= bufferMessage.buffers
-      logDebug("Added " + bufferMessage)
-    })
-
-    logDebug("Buffer list:")
-    buffers.foreach((x: ByteBuffer) => logDebug("" + x))
-    Message.createBufferMessage(buffers)
-  }
-}
-
-private[nio] object BlockMessageArray extends Logging {
-
-  def fromBufferMessage(bufferMessage: BufferMessage): BlockMessageArray = {
-    val newBlockMessageArray = new BlockMessageArray()
-    newBlockMessageArray.set(bufferMessage)
-    newBlockMessageArray
-  }
-
-  def main(args: Array[String]) {
-    val blockMessages =
-      (0 until 10).map { i =>
-        if (i % 2 == 0) {
-          val buffer = ByteBuffer.allocate(100)
-          buffer.clear()
-          BlockMessage.fromPutBlock(PutBlock(TestBlockId(i.toString), buffer,
-            StorageLevel.MEMORY_ONLY_SER))
-        } else {
-          BlockMessage.fromGetBlock(GetBlock(TestBlockId(i.toString)))
-        }
-      }
-    val blockMessageArray = new BlockMessageArray(blockMessages)
-    logDebug("Block message array created")
-
-    val bufferMessage = blockMessageArray.toBufferMessage
-    logDebug("Converted to buffer message")
-
-    val totalSize = bufferMessage.size
-    val newBuffer = ByteBuffer.allocate(totalSize)
-    newBuffer.clear()
-    bufferMessage.buffers.foreach(buffer => {
-      assert (0 == buffer.position())
-      newBuffer.put(buffer)
-      buffer.rewind()
-    })
-    newBuffer.flip
-    val newBufferMessage = Message.createBufferMessage(newBuffer)
-    logDebug("Copied to new buffer message, size = " + newBufferMessage.size)
-
-    val newBlockMessageArray = BlockMessageArray.fromBufferMessage(newBufferMessage)
-    logDebug("Converted back to block message array")
-    // scalastyle:off println
-    newBlockMessageArray.foreach(blockMessage => {
-      blockMessage.getType match {
-        case BlockMessage.TYPE_PUT_BLOCK => {
-          val pB = PutBlock(blockMessage.getId, blockMessage.getData, blockMessage.getLevel)
-          println(pB)
-        }
-        case BlockMessage.TYPE_GET_BLOCK => {
-          val gB = new GetBlock(blockMessage.getId)
-          println(gB)
-        }
-      }
-    })
-    // scalastyle:on println
-  }
-}
-
-
diff --git a/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala
deleted file mode 100644
index 9a9e22b0c2366..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/BufferMessage.scala
+++ /dev/null
@@ -1,114 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import scala.collection.mutable.ArrayBuffer
-
-import org.apache.spark.storage.BlockManager
-
-
-private[nio]
-class BufferMessage(id_ : Int, val buffers: ArrayBuffer[ByteBuffer], var ackId: Int)
-  extends Message(Message.BUFFER_MESSAGE, id_) {
-
-  val initialSize = currentSize()
-  var gotChunkForSendingOnce = false
-
-  def size: Int = initialSize
-
-  def currentSize(): Int = {
-    if (buffers == null || buffers.isEmpty) {
-      0
-    } else {
-      buffers.map(_.remaining).reduceLeft(_ + _)
-    }
-  }
-
-  def getChunkForSending(maxChunkSize: Int): Option[MessageChunk] = {
-    if (maxChunkSize <= 0) {
-      throw new Exception("Max chunk size is " + maxChunkSize)
-    }
-
-    val security = if (isSecurityNeg) 1 else 0
-    if (size == 0 && !gotChunkForSendingOnce) {
-      val newChunk = new MessageChunk(
-        new MessageChunkHeader(typ, id, 0, 0, ackId, hasError, security, senderAddress), null)
-      gotChunkForSendingOnce = true
-      return Some(newChunk)
-    }
-
-    while(!buffers.isEmpty) {
-      val buffer = buffers(0)
-      if (buffer.remaining == 0) {
-        BlockManager.dispose(buffer)
-        buffers -= buffer
-      } else {
-        val newBuffer = if (buffer.remaining <= maxChunkSize) {
-          buffer.duplicate()
-        } else {
-          buffer.slice().limit(maxChunkSize).asInstanceOf[ByteBuffer]
-        }
-        buffer.position(buffer.position + newBuffer.remaining)
-        val newChunk = new MessageChunk(new MessageChunkHeader(
-          typ, id, size, newBuffer.remaining, ackId,
-          hasError, security, senderAddress), newBuffer)
-        gotChunkForSendingOnce = true
-        return Some(newChunk)
-      }
-    }
-    None
-  }
-
-  def getChunkForReceiving(chunkSize: Int): Option[MessageChunk] = {
-    // STRONG ASSUMPTION: BufferMessage created when receiving data has ONLY ONE data buffer
-    if (buffers.size > 1) {
-      throw new Exception("Attempting to get chunk from message with multiple data buffers")
-    }
-    val buffer = buffers(0)
-    val security = if (isSecurityNeg) 1 else 0
-    if (buffer.remaining > 0) {
-      if (buffer.remaining < chunkSize) {
-        throw new Exception("Not enough space in data buffer for receiving chunk")
-      }
-      val newBuffer = buffer.slice().limit(chunkSize).asInstanceOf[ByteBuffer]
-      buffer.position(buffer.position + newBuffer.remaining)
-      val newChunk = new MessageChunk(new MessageChunkHeader(
-          typ, id, size, newBuffer.remaining, ackId, hasError, security, senderAddress), newBuffer)
-      return Some(newChunk)
-    }
-    None
-  }
-
-  def flip() {
-    buffers.foreach(_.flip)
-  }
-
-  def hasAckId(): Boolean = ackId != 0
-
-  def isCompletelyReceived: Boolean = !buffers(0).hasRemaining
-
-  override def toString: String = {
-    if (hasAckId) {
-      "BufferAckMessage(aid = " + ackId + ", id = " + id + ", size = " + size + ")"
-    } else {
-      "BufferMessage(id = " + id + ", size = " + size + ")"
-    }
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala b/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
deleted file mode 100644
index 8d9ebadaf79d4..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/Connection.scala
+++ /dev/null
@@ -1,619 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.net._
-import java.nio._
-import java.nio.channels._
-import java.util.concurrent.ConcurrentLinkedQueue
-import java.util.LinkedList
-
-import scala.collection.JavaConverters._
-import scala.collection.mutable.{ArrayBuffer, HashMap}
-import scala.util.control.NonFatal
-
-import org.apache.spark._
-import org.apache.spark.network.sasl.{SparkSaslClient, SparkSaslServer}
-
-private[nio]
-abstract class Connection(val channel: SocketChannel, val selector: Selector,
-    val socketRemoteConnectionManagerId: ConnectionManagerId, val connectionId: ConnectionId,
-    val securityMgr: SecurityManager)
-  extends Logging {
-
-  var sparkSaslServer: SparkSaslServer = null
-  var sparkSaslClient: SparkSaslClient = null
-
-  def this(channel_ : SocketChannel, selector_ : Selector, id_ : ConnectionId,
-      securityMgr_ : SecurityManager) = {
-    this(channel_, selector_,
-      ConnectionManagerId.fromSocketAddress(
-        channel_.socket.getRemoteSocketAddress.asInstanceOf[InetSocketAddress]),
-        id_, securityMgr_)
-  }
-
-  channel.configureBlocking(false)
-  channel.socket.setTcpNoDelay(true)
-  channel.socket.setReuseAddress(true)
-  channel.socket.setKeepAlive(true)
-  /* channel.socket.setReceiveBufferSize(32768) */
-
-  @volatile private var closed = false
-  var onCloseCallback: Connection => Unit = null
-  val onExceptionCallbacks = new ConcurrentLinkedQueue[(Connection, Throwable) => Unit]
-  var onKeyInterestChangeCallback: (Connection, Int) => Unit = null
-
-  val remoteAddress = getRemoteAddress()
-
-  def isSaslComplete(): Boolean
-
-  def resetForceReregister(): Boolean
-
-  // Read channels typically do not register for write and write does not for read
-  // Now, we do have write registering for read too (temporarily), but this is to detect
-  // channel close NOT to actually read/consume data on it !
-  // How does this work if/when we move to SSL ?
-
-  // What is the interest to register with selector for when we want this connection to be selected
-  def registerInterest()
-
-  // What is the interest to register with selector for when we want this connection to
-  // be de-selected
-  // Traditionally, 0 - but in our case, for example, for close-detection on SendingConnection hack,
-  // it will be SelectionKey.OP_READ (until we fix it properly)
-  def unregisterInterest()
-
-  // On receiving a read event, should we change the interest for this channel or not ?
-  // Will be true for ReceivingConnection, false for SendingConnection.
-  def changeInterestForRead(): Boolean
-
-  private def disposeSasl() {
-    if (sparkSaslServer != null) {
-      sparkSaslServer.dispose()
-    }
-
-    if (sparkSaslClient != null) {
-      sparkSaslClient.dispose()
-    }
-  }
-
-  // On receiving a write event, should we change the interest for this channel or not ?
-  // Will be false for ReceivingConnection, true for SendingConnection.
-  // Actually, for now, should not get triggered for ReceivingConnection
-  def changeInterestForWrite(): Boolean
-
-  def getRemoteConnectionManagerId(): ConnectionManagerId = {
-    socketRemoteConnectionManagerId
-  }
-
-  def key(): SelectionKey = channel.keyFor(selector)
-
-  def getRemoteAddress(): InetSocketAddress = {
-    channel.socket.getRemoteSocketAddress().asInstanceOf[InetSocketAddress]
-  }
-
-  // Returns whether we have to register for further reads or not.
-  def read(): Boolean = {
-    throw new UnsupportedOperationException(
-      "Cannot read on connection of type " + this.getClass.toString)
-  }
-
-  // Returns whether we have to register for further writes or not.
-  def write(): Boolean = {
-    throw new UnsupportedOperationException(
-      "Cannot write on connection of type " + this.getClass.toString)
-  }
-
-  def close() {
-    closed = true
-    val k = key()
-    if (k != null) {
-      k.cancel()
-    }
-    channel.close()
-    disposeSasl()
-    callOnCloseCallback()
-  }
-
-  protected def isClosed: Boolean = closed
-
-  def onClose(callback: Connection => Unit) {
-    onCloseCallback = callback
-  }
-
-  def onException(callback: (Connection, Throwable) => Unit) {
-    onExceptionCallbacks.add(callback)
-  }
-
-  def onKeyInterestChange(callback: (Connection, Int) => Unit) {
-    onKeyInterestChangeCallback = callback
-  }
-
-  def callOnExceptionCallbacks(e: Throwable) {
-    onExceptionCallbacks.asScala.foreach {
-      callback =>
-        try {
-          callback(this, e)
-        } catch {
-          case NonFatal(e) => {
-            logWarning("Ignored error in onExceptionCallback", e)
-          }
-        }
-    }
-  }
-
-  def callOnCloseCallback() {
-    if (onCloseCallback != null) {
-      onCloseCallback(this)
-    } else {
-      logWarning("Connection to " + getRemoteConnectionManagerId() +
-        " closed and OnExceptionCallback not registered")
-    }
-
-  }
-
-  def changeConnectionKeyInterest(ops: Int) {
-    if (onKeyInterestChangeCallback != null) {
-      onKeyInterestChangeCallback(this, ops)
-    } else {
-      throw new Exception("OnKeyInterestChangeCallback not registered")
-    }
-  }
-
-  def printRemainingBuffer(buffer: ByteBuffer) {
-    val bytes = new Array[Byte](buffer.remaining)
-    val curPosition = buffer.position
-    buffer.get(bytes)
-    bytes.foreach(x => print(x + " "))
-    buffer.position(curPosition)
-    print(" (" + bytes.length + ")")
-  }
-
-  def printBuffer(buffer: ByteBuffer, position: Int, length: Int) {
-    val bytes = new Array[Byte](length)
-    val curPosition = buffer.position
-    buffer.position(position)
-    buffer.get(bytes)
-    bytes.foreach(x => print(x + " "))
-    print(" (" + position + ", " + length + ")")
-    buffer.position(curPosition)
-  }
-}
-
-
-private[nio]
-class SendingConnection(val address: InetSocketAddress, selector_ : Selector,
-    remoteId_ : ConnectionManagerId, id_ : ConnectionId,
-    securityMgr_ : SecurityManager)
-  extends Connection(SocketChannel.open, selector_, remoteId_, id_, securityMgr_) {
-
-  def isSaslComplete(): Boolean = {
-    if (sparkSaslClient != null) sparkSaslClient.isComplete() else false
-  }
-
-  private class Outbox {
-    val messages = new LinkedList[Message]()
-    val defaultChunkSize = 65536
-    var nextMessageToBeUsed = 0
-
-    def addMessage(message: Message) {
-      messages.synchronized {
-        messages.add(message)
-        logDebug("Added [" + message + "] to outbox for sending to " +
-          "[" + getRemoteConnectionManagerId() + "]")
-      }
-    }
-
-    def getChunk(): Option[MessageChunk] = {
-      messages.synchronized {
-        while (!messages.isEmpty) {
-          /* nextMessageToBeUsed = nextMessageToBeUsed % messages.size */
-          /* val message = messages(nextMessageToBeUsed) */
-
-          val message = if (securityMgr.isAuthenticationEnabled() && !isSaslComplete()) {
-            // only allow sending of security messages until sasl is complete
-            var pos = 0
-            var securityMsg: Message = null
-            while (pos < messages.size() && securityMsg == null) {
-              if (messages.get(pos).isSecurityNeg) {
-                securityMsg = messages.remove(pos)
-              }
-              pos = pos + 1
-            }
-            // didn't find any security messages and auth isn't completed so return
-            if (securityMsg == null) return None
-            securityMsg
-          } else {
-            messages.removeFirst()
-          }
-
-          val chunk = message.getChunkForSending(defaultChunkSize)
-          if (chunk.isDefined) {
-            messages.add(message)
-            nextMessageToBeUsed = nextMessageToBeUsed + 1
-            if (!message.started) {
-              logDebug(
-                "Starting to send [" + message + "] to [" + getRemoteConnectionManagerId() + "]")
-              message.started = true
-              message.startTime = System.currentTimeMillis
-            }
-            logTrace(
-              "Sending chunk from [" + message + "] to [" + getRemoteConnectionManagerId() + "]")
-            return chunk
-          } else {
-            message.finishTime = System.currentTimeMillis
-            logDebug("Finished sending [" + message + "] to [" + getRemoteConnectionManagerId() +
-              "] in "  + message.timeTaken )
-          }
-        }
-      }
-      None
-    }
-  }
-
-  // outbox is used as a lock - ensure that it is always used as a leaf (since methods which
-  // lock it are invoked in context of other locks)
-  private val outbox = new Outbox()
-  /*
-    This is orthogonal to whether we have pending bytes to write or not - and satisfies a slightly
-    different purpose. This flag is to see if we need to force reregister for write even when we
-    do not have any pending bytes to write to socket.
-    This can happen due to a race between adding pending buffers, and checking for existing of
-    data as detailed in https://github.com/mesos/spark/pull/791
-   */
-  private var needForceReregister = false
-
-  val currentBuffers = new ArrayBuffer[ByteBuffer]()
-
-  /* channel.socket.setSendBufferSize(256 * 1024) */
-
-  override def getRemoteAddress(): InetSocketAddress = address
-
-  val DEFAULT_INTEREST = SelectionKey.OP_READ
-
-  override def registerInterest() {
-    // Registering read too - does not really help in most cases, but for some
-    // it does - so let us keep it for now.
-    changeConnectionKeyInterest(SelectionKey.OP_WRITE | DEFAULT_INTEREST)
-  }
-
-  override def unregisterInterest() {
-    changeConnectionKeyInterest(DEFAULT_INTEREST)
-  }
-
-  def registerAfterAuth(): Unit = {
-    outbox.synchronized {
-      needForceReregister = true
-    }
-    if (channel.isConnected) {
-      registerInterest()
-    }
-  }
-
-  def send(message: Message) {
-    outbox.synchronized {
-      outbox.addMessage(message)
-      needForceReregister = true
-    }
-    if (channel.isConnected) {
-      registerInterest()
-    }
-  }
-
-  // return previous value after resetting it.
-  def resetForceReregister(): Boolean = {
-    outbox.synchronized {
-      val result = needForceReregister
-      needForceReregister = false
-      result
-    }
-  }
-
-  // MUST be called within the selector loop
-  def connect() {
-    try {
-      channel.register(selector, SelectionKey.OP_CONNECT)
-      channel.connect(address)
-      logInfo("Initiating connection to [" + address + "]")
-    } catch {
-      case e: Exception =>
-        logError("Error connecting to " + address, e)
-        callOnExceptionCallbacks(e)
-    }
-  }
-
-  def finishConnect(force: Boolean): Boolean = {
-    try {
-      // Typically, this should finish immediately since it was triggered by a connect
-      // selection - though need not necessarily always complete successfully.
-      val connected = channel.finishConnect
-      if (!force && !connected) {
-        logInfo(
-          "finish connect failed [" + address + "], " + outbox.messages.size + " messages pending")
-        return false
-      }
-
-      // Fallback to previous behavior - assume finishConnect completed
-      // This will happen only when finishConnect failed for some repeated number of times
-      // (10 or so)
-      // Is highly unlikely unless there was an unclean close of socket, etc
-      registerInterest()
-      logInfo("Connected to [" + address + "], " + outbox.messages.size + " messages pending")
-    } catch {
-      case e: Exception => {
-        logWarning("Error finishing connection to " + address, e)
-        callOnExceptionCallbacks(e)
-      }
-    }
-    true
-  }
-
-  override def write(): Boolean = {
-    try {
-      while (true) {
-        if (currentBuffers.size == 0) {
-          outbox.synchronized {
-            outbox.getChunk() match {
-              case Some(chunk) => {
-                val buffers = chunk.buffers
-                // If we have 'seen' pending messages, then reset flag - since we handle that as
-                // normal registering of event (below)
-                if (needForceReregister && buffers.exists(_.remaining() > 0)) resetForceReregister()
-
-                currentBuffers ++= buffers
-              }
-              case None => {
-                // changeConnectionKeyInterest(0)
-                /* key.interestOps(0) */
-                return false
-              }
-            }
-          }
-        }
-
-        if (currentBuffers.size > 0) {
-          val buffer = currentBuffers(0)
-          val remainingBytes = buffer.remaining
-          val writtenBytes = channel.write(buffer)
-          if (buffer.remaining == 0) {
-            currentBuffers -= buffer
-          }
-          if (writtenBytes < remainingBytes) {
-            // re-register for write.
-            return true
-          }
-        }
-      }
-    } catch {
-      case e: Exception => {
-        logWarning("Error writing in connection to " + getRemoteConnectionManagerId(), e)
-        callOnExceptionCallbacks(e)
-        close()
-        return false
-      }
-    }
-    // should not happen - to keep scala compiler happy
-    true
-  }
-
-  // This is a hack to determine if remote socket was closed or not.
-  // SendingConnection DOES NOT expect to receive any data - if it does, it is an error
-  // For a bunch of cases, read will return -1 in case remote socket is closed : hence we
-  // register for reads to determine that.
-  override def read(): Boolean = {
-    // We don't expect the other side to send anything; so, we just read to detect an error or EOF.
-    try {
-      val length = channel.read(ByteBuffer.allocate(1))
-      if (length == -1) { // EOF
-        close()
-      } else if (length > 0) {
-        logWarning(
-          "Unexpected data read from SendingConnection to " + getRemoteConnectionManagerId())
-      }
-    } catch {
-      case e: Exception =>
-        logError("Exception while reading SendingConnection to " + getRemoteConnectionManagerId(),
-          e)
-        callOnExceptionCallbacks(e)
-        close()
-    }
-
-    false
-  }
-
-  override def changeInterestForRead(): Boolean = false
-
-  override def changeInterestForWrite(): Boolean = ! isClosed
-}
-
-
-// Must be created within selector loop - else deadlock
-private[spark] class ReceivingConnection(
-    channel_ : SocketChannel,
-    selector_ : Selector,
-    id_ : ConnectionId,
-    securityMgr_ : SecurityManager)
-    extends Connection(channel_, selector_, id_, securityMgr_) {
-
-  def isSaslComplete(): Boolean = {
-    if (sparkSaslServer != null) sparkSaslServer.isComplete() else false
-  }
-
-  class Inbox() {
-    val messages = new HashMap[Int, BufferMessage]()
-
-    def getChunk(header: MessageChunkHeader): Option[MessageChunk] = {
-
-      def createNewMessage: BufferMessage = {
-        val newMessage = Message.create(header).asInstanceOf[BufferMessage]
-        newMessage.started = true
-        newMessage.startTime = System.currentTimeMillis
-        newMessage.isSecurityNeg = header.securityNeg == 1
-        logDebug(
-          "Starting to receive [" + newMessage + "] from [" + getRemoteConnectionManagerId() + "]")
-        messages += ((newMessage.id, newMessage))
-        newMessage
-      }
-
-      val message = messages.getOrElseUpdate(header.id, createNewMessage)
-      logTrace(
-        "Receiving chunk of [" + message + "] from [" + getRemoteConnectionManagerId() + "]")
-      message.getChunkForReceiving(header.chunkSize)
-    }
-
-    def getMessageForChunk(chunk: MessageChunk): Option[BufferMessage] = {
-      messages.get(chunk.header.id)
-    }
-
-    def removeMessage(message: Message) {
-      messages -= message.id
-    }
-  }
-
-  @volatile private var inferredRemoteManagerId: ConnectionManagerId = null
-
-  override def getRemoteConnectionManagerId(): ConnectionManagerId = {
-    val currId = inferredRemoteManagerId
-    if (currId != null) currId else super.getRemoteConnectionManagerId()
-  }
-
-  // The receiver's remote address is the local socket on remote side : which is NOT
-  // the connection manager id of the receiver.
-  // We infer that from the messages we receive on the receiver socket.
-  private def processConnectionManagerId(header: MessageChunkHeader) {
-    val currId = inferredRemoteManagerId
-    if (header.address == null || currId != null) return
-
-    val managerId = ConnectionManagerId.fromSocketAddress(header.address)
-
-    if (managerId != null) {
-      inferredRemoteManagerId = managerId
-    }
-  }
-
-
-  val inbox = new Inbox()
-  val headerBuffer: ByteBuffer = ByteBuffer.allocate(MessageChunkHeader.HEADER_SIZE)
-  var onReceiveCallback: (Connection, Message) => Unit = null
-  var currentChunk: MessageChunk = null
-
-  channel.register(selector, SelectionKey.OP_READ)
-
-  override def read(): Boolean = {
-    try {
-      while (true) {
-        if (currentChunk == null) {
-          val headerBytesRead = channel.read(headerBuffer)
-          if (headerBytesRead == -1) {
-            close()
-            return false
-          }
-          if (headerBuffer.remaining > 0) {
-            // re-register for read event ...
-            return true
-          }
-          headerBuffer.flip
-          if (headerBuffer.remaining != MessageChunkHeader.HEADER_SIZE) {
-            throw new Exception(
-              "Unexpected number of bytes (" + headerBuffer.remaining + ") in the header")
-          }
-          val header = MessageChunkHeader.create(headerBuffer)
-          headerBuffer.clear()
-
-          processConnectionManagerId(header)
-
-          header.typ match {
-            case Message.BUFFER_MESSAGE => {
-              if (header.totalSize == 0) {
-                if (onReceiveCallback != null) {
-                  onReceiveCallback(this, Message.create(header))
-                }
-                currentChunk = null
-                // re-register for read event ...
-                return true
-              } else {
-                currentChunk = inbox.getChunk(header).orNull
-              }
-            }
-            case _ => throw new Exception("Message of unknown type received")
-          }
-        }
-
-        if (currentChunk == null) throw new Exception("No message chunk to receive data")
-
-        val bytesRead = channel.read(currentChunk.buffer)
-        if (bytesRead == 0) {
-          // re-register for read event ...
-          return true
-        } else if (bytesRead == -1) {
-          close()
-          return false
-        }
-
-        /* logDebug("Read " + bytesRead + " bytes for the buffer") */
-
-        if (currentChunk.buffer.remaining == 0) {
-          /* println("Filled buffer at " + System.currentTimeMillis) */
-          val bufferMessage = inbox.getMessageForChunk(currentChunk).get
-          if (bufferMessage.isCompletelyReceived) {
-            bufferMessage.flip()
-            bufferMessage.finishTime = System.currentTimeMillis
-            logDebug("Finished receiving [" + bufferMessage + "] from " +
-              "[" + getRemoteConnectionManagerId() + "] in " + bufferMessage.timeTaken)
-            if (onReceiveCallback != null) {
-              onReceiveCallback(this, bufferMessage)
-            }
-            inbox.removeMessage(bufferMessage)
-          }
-          currentChunk = null
-        }
-      }
-    } catch {
-      case e: Exception => {
-        logWarning("Error reading from connection to " + getRemoteConnectionManagerId(), e)
-        callOnExceptionCallbacks(e)
-        close()
-        return false
-      }
-    }
-    // should not happen - to keep scala compiler happy
-    true
-  }
-
-  def onReceive(callback: (Connection, Message) => Unit) {onReceiveCallback = callback}
-
-  // override def changeInterestForRead(): Boolean = ! isClosed
-  override def changeInterestForRead(): Boolean = true
-
-  override def changeInterestForWrite(): Boolean = {
-    throw new IllegalStateException("Unexpected invocation right now")
-  }
-
-  override def registerInterest() {
-    // Registering read too - does not really help in most cases, but for some
-    // it does - so let us keep it for now.
-    changeConnectionKeyInterest(SelectionKey.OP_READ)
-  }
-
-  override def unregisterInterest() {
-    changeConnectionKeyInterest(0)
-  }
-
-  // For read conn, always false.
-  override def resetForceReregister(): Boolean = false
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala
deleted file mode 100644
index b3b281ff465f1..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionId.scala
+++ /dev/null
@@ -1,36 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-private[nio] case class ConnectionId(connectionManagerId: ConnectionManagerId, uniqId: Int) {
-  override def toString: String = {
-    connectionManagerId.host + "_" + connectionManagerId.port + "_" + uniqId
-  }
-}
-
-private[nio] object ConnectionId {
-
-  def createConnectionIdFromString(connectionIdString: String): ConnectionId = {
-    val res = connectionIdString.split("_").map(_.trim())
-    if (res.size != 3) {
-      throw new Exception("Error converting ConnectionId string: " + connectionIdString +
-        " to a ConnectionId Object")
-    }
-    new ConnectionId(new ConnectionManagerId(res(0), res(1).toInt), res(2).toInt)
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
deleted file mode 100644
index 9143918790381..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManager.scala
+++ /dev/null
@@ -1,1157 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.io.IOException
-import java.lang.ref.WeakReference
-import java.net._
-import java.nio._
-import java.nio.channels._
-import java.nio.channels.spi._
-import java.util.concurrent.atomic.AtomicInteger
-import java.util.concurrent.{LinkedBlockingDeque, ThreadPoolExecutor, TimeUnit}
-
-import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet, SynchronizedMap, SynchronizedQueue}
-import scala.concurrent.duration._
-import scala.concurrent.{Await, ExecutionContext, Future, Promise}
-import scala.language.postfixOps
-
-import com.google.common.base.Charsets.UTF_8
-import io.netty.util.{Timeout, TimerTask, HashedWheelTimer}
-
-import org.apache.spark._
-import org.apache.spark.network.sasl.{SparkSaslClient, SparkSaslServer}
-import org.apache.spark.util.{ThreadUtils, Utils}
-
-import scala.util.Try
-import scala.util.control.NonFatal
-
-private[nio] class ConnectionManager(
-    port: Int,
-    conf: SparkConf,
-    securityManager: SecurityManager,
-    name: String = "Connection manager")
-  extends Logging {
-
-  /**
-   * Used by sendMessageReliably to track messages being sent.
-   * @param message the message that was sent
-   * @param connectionManagerId the connection manager that sent this message
-   * @param completionHandler callback that's invoked when the send has completed or failed
-   */
-  class MessageStatus(
-      val message: Message,
-      val connectionManagerId: ConnectionManagerId,
-      completionHandler: Try[Message] => Unit) {
-
-    def success(ackMessage: Message) {
-      if (ackMessage == null) {
-        failure(new NullPointerException)
-      }
-      else {
-        completionHandler(scala.util.Success(ackMessage))
-      }
-    }
-
-    def failWithoutAck() {
-      completionHandler(scala.util.Failure(new IOException("Failed without being ACK'd")))
-    }
-
-    def failure(e: Throwable) {
-      completionHandler(scala.util.Failure(e))
-    }
-  }
-
-  private val selector = SelectorProvider.provider.openSelector()
-  private val ackTimeoutMonitor =
-    new HashedWheelTimer(ThreadUtils.namedThreadFactory("AckTimeoutMonitor"))
-
-  private val ackTimeout =
-    conf.getTimeAsSeconds("spark.core.connection.ack.wait.timeout",
-      conf.get("spark.network.timeout", "120s"))
-
-  // Get the thread counts from the Spark Configuration.
-  //
-  // Even though the ThreadPoolExecutor constructor takes both a minimum and maximum value,
-  // we only query for the minimum value because we are using LinkedBlockingDeque.
-  //
-  // The JavaDoc for ThreadPoolExecutor points out that when using a LinkedBlockingDeque (which is
-  // an unbounded queue) no more than corePoolSize threads will ever be created, so only the "min"
-  // parameter is necessary.
-  private val handlerThreadCount = conf.getInt("spark.core.connection.handler.threads.min", 20)
-  private val ioThreadCount = conf.getInt("spark.core.connection.io.threads.min", 4)
-  private val connectThreadCount = conf.getInt("spark.core.connection.connect.threads.min", 1)
-
-  private val handleMessageExecutor = new ThreadPoolExecutor(
-    handlerThreadCount,
-    handlerThreadCount,
-    conf.getInt("spark.core.connection.handler.threads.keepalive", 60), TimeUnit.SECONDS,
-    new LinkedBlockingDeque[Runnable](),
-    ThreadUtils.namedThreadFactory("handle-message-executor")) {
-
-    override def afterExecute(r: Runnable, t: Throwable): Unit = {
-      super.afterExecute(r, t)
-      if (t != null && NonFatal(t)) {
-        logError("Error in handleMessageExecutor is not handled properly", t)
-      }
-    }
-  }
-
-  private val handleReadWriteExecutor = new ThreadPoolExecutor(
-    ioThreadCount,
-    ioThreadCount,
-    conf.getInt("spark.core.connection.io.threads.keepalive", 60), TimeUnit.SECONDS,
-    new LinkedBlockingDeque[Runnable](),
-    ThreadUtils.namedThreadFactory("handle-read-write-executor")) {
-
-    override def afterExecute(r: Runnable, t: Throwable): Unit = {
-      super.afterExecute(r, t)
-      if (t != null && NonFatal(t)) {
-        logError("Error in handleReadWriteExecutor is not handled properly", t)
-      }
-    }
-  }
-
-  // Use a different, yet smaller, thread pool - infrequently used with very short lived tasks :
-  // which should be executed asap
-  private val handleConnectExecutor = new ThreadPoolExecutor(
-    connectThreadCount,
-    connectThreadCount,
-    conf.getInt("spark.core.connection.connect.threads.keepalive", 60), TimeUnit.SECONDS,
-    new LinkedBlockingDeque[Runnable](),
-    ThreadUtils.namedThreadFactory("handle-connect-executor")) {
-
-    override def afterExecute(r: Runnable, t: Throwable): Unit = {
-      super.afterExecute(r, t)
-      if (t != null && NonFatal(t)) {
-        logError("Error in handleConnectExecutor is not handled properly", t)
-      }
-    }
-  }
-
-  private val serverChannel = ServerSocketChannel.open()
-  // used to track the SendingConnections waiting to do SASL negotiation
-  private val connectionsAwaitingSasl = new HashMap[ConnectionId, SendingConnection]
-    with SynchronizedMap[ConnectionId, SendingConnection]
-  private val connectionsByKey =
-    new HashMap[SelectionKey, Connection] with SynchronizedMap[SelectionKey, Connection]
-  private val connectionsById = new HashMap[ConnectionManagerId, SendingConnection]
-    with SynchronizedMap[ConnectionManagerId, SendingConnection]
-  // Tracks sent messages for which we are awaiting acknowledgements.  Entries are added to this
-  // map when messages are sent and are removed when acknowledgement messages are received or when
-  // acknowledgement timeouts expire
-  private val messageStatuses = new HashMap[Int, MessageStatus]  // [MessageId, MessageStatus]
-  private val keyInterestChangeRequests = new SynchronizedQueue[(SelectionKey, Int)]
-  private val registerRequests = new SynchronizedQueue[SendingConnection]
-
-  implicit val futureExecContext = ExecutionContext.fromExecutor(
-    ThreadUtils.newDaemonCachedThreadPool("Connection manager future execution context"))
-
-  @volatile
-  private var onReceiveCallback: (BufferMessage, ConnectionManagerId) => Option[Message] = null
-
-  private val authEnabled = securityManager.isAuthenticationEnabled()
-
-  serverChannel.configureBlocking(false)
-  serverChannel.socket.setReuseAddress(true)
-  serverChannel.socket.setReceiveBufferSize(256 * 1024)
-
-  private def startService(port: Int): (ServerSocketChannel, Int) = {
-    serverChannel.socket.bind(new InetSocketAddress(port))
-    (serverChannel, serverChannel.socket.getLocalPort)
-  }
-  Utils.startServiceOnPort[ServerSocketChannel](port, startService, conf, name)
-  serverChannel.register(selector, SelectionKey.OP_ACCEPT)
-
-  val id = new ConnectionManagerId(Utils.localHostName, serverChannel.socket.getLocalPort)
-  logInfo("Bound socket to port " + serverChannel.socket.getLocalPort() + " with id = " + id)
-
-  // used in combination with the ConnectionManagerId to create unique Connection ids
-  // to be able to track asynchronous messages
-  private val idCount: AtomicInteger = new AtomicInteger(1)
-
-  private val writeRunnableStarted: HashSet[SelectionKey] = new HashSet[SelectionKey]()
-  private val readRunnableStarted: HashSet[SelectionKey] = new HashSet[SelectionKey]()
-
-  @volatile private var isActive = true
-  private val selectorThread = new Thread("connection-manager-thread") {
-    override def run(): Unit = ConnectionManager.this.run()
-  }
-  selectorThread.setDaemon(true)
-  // start this thread last, since it invokes run(), which accesses members above
-  selectorThread.start()
-
-  private def triggerWrite(key: SelectionKey) {
-    val conn = connectionsByKey.getOrElse(key, null)
-    if (conn == null) return
-
-    writeRunnableStarted.synchronized {
-      // So that we do not trigger more write events while processing this one.
-      // The write method will re-register when done.
-      if (conn.changeInterestForWrite()) conn.unregisterInterest()
-      if (writeRunnableStarted.contains(key)) {
-        // key.interestOps(key.interestOps() & ~ SelectionKey.OP_WRITE)
-        return
-      }
-
-      writeRunnableStarted += key
-    }
-    handleReadWriteExecutor.execute(new Runnable {
-      override def run() {
-        try {
-          var register: Boolean = false
-          try {
-            register = conn.write()
-          } finally {
-            writeRunnableStarted.synchronized {
-              writeRunnableStarted -= key
-              val needReregister = register || conn.resetForceReregister()
-              if (needReregister && conn.changeInterestForWrite()) {
-                conn.registerInterest()
-              }
-            }
-          }
-        } catch {
-          case NonFatal(e) => {
-            logError("Error when writing to " + conn.getRemoteConnectionManagerId(), e)
-            conn.callOnExceptionCallbacks(e)
-          }
-        }
-      }
-    } )
-  }
-
-
-  private def triggerRead(key: SelectionKey) {
-    val conn = connectionsByKey.getOrElse(key, null)
-    if (conn == null) return
-
-    readRunnableStarted.synchronized {
-      // So that we do not trigger more read events while processing this one.
-      // The read method will re-register when done.
-      if (conn.changeInterestForRead())conn.unregisterInterest()
-      if (readRunnableStarted.contains(key)) {
-        return
-      }
-
-      readRunnableStarted += key
-    }
-    handleReadWriteExecutor.execute(new Runnable {
-      override def run() {
-        try {
-          var register: Boolean = false
-          try {
-            register = conn.read()
-          } finally {
-            readRunnableStarted.synchronized {
-              readRunnableStarted -= key
-              if (register && conn.changeInterestForRead()) {
-                conn.registerInterest()
-              }
-            }
-          }
-        } catch {
-          case NonFatal(e) => {
-            logError("Error when reading from " + conn.getRemoteConnectionManagerId(), e)
-            conn.callOnExceptionCallbacks(e)
-          }
-        }
-      }
-    } )
-  }
-
-  private def triggerConnect(key: SelectionKey) {
-    val conn = connectionsByKey.getOrElse(key, null).asInstanceOf[SendingConnection]
-    if (conn == null) return
-
-    // prevent other events from being triggered
-    // Since we are still trying to connect, we do not need to do the additional steps in
-    // triggerWrite
-    conn.changeConnectionKeyInterest(0)
-
-    handleConnectExecutor.execute(new Runnable {
-      override def run() {
-        try {
-          var tries: Int = 10
-          while (tries >= 0) {
-            if (conn.finishConnect(false)) return
-            // Sleep ?
-            Thread.sleep(1)
-            tries -= 1
-          }
-
-          // fallback to previous behavior : we should not really come here since this method was
-          // triggered since channel became connectable : but at times, the first finishConnect need
-          // not succeed : hence the loop to retry a few 'times'.
-          conn.finishConnect(true)
-        } catch {
-          case NonFatal(e) => {
-            logError("Error when finishConnect for " + conn.getRemoteConnectionManagerId(), e)
-            conn.callOnExceptionCallbacks(e)
-          }
-        }
-      }
-    } )
-  }
-
-  // MUST be called within selector loop - else deadlock.
-  private def triggerForceCloseByException(key: SelectionKey, e: Exception) {
-    try {
-      key.interestOps(0)
-    } catch {
-      // ignore exceptions
-      case e: Exception => logDebug("Ignoring exception", e)
-    }
-
-    val conn = connectionsByKey.getOrElse(key, null)
-    if (conn == null) return
-
-    // Pushing to connect threadpool
-    handleConnectExecutor.execute(new Runnable {
-      override def run() {
-        try {
-          conn.callOnExceptionCallbacks(e)
-        } catch {
-          // ignore exceptions
-          case NonFatal(e) => logDebug("Ignoring exception", e)
-        }
-        try {
-          conn.close()
-        } catch {
-          // ignore exceptions
-          case NonFatal(e) => logDebug("Ignoring exception", e)
-        }
-      }
-    })
-  }
-
-
-  def run() {
-    try {
-      while (isActive) {
-        while (!registerRequests.isEmpty) {
-          val conn: SendingConnection = registerRequests.dequeue()
-          addListeners(conn)
-          conn.connect()
-          addConnection(conn)
-        }
-
-        while(!keyInterestChangeRequests.isEmpty) {
-          val (key, ops) = keyInterestChangeRequests.dequeue()
-
-          try {
-            if (key.isValid) {
-              val connection = connectionsByKey.getOrElse(key, null)
-              if (connection != null) {
-                val lastOps = key.interestOps()
-                key.interestOps(ops)
-
-                // hot loop - prevent materialization of string if trace not enabled.
-                if (isTraceEnabled()) {
-                  def intToOpStr(op: Int): String = {
-                    val opStrs = ArrayBuffer[String]()
-                    if ((op & SelectionKey.OP_READ) != 0) opStrs += "READ"
-                    if ((op & SelectionKey.OP_WRITE) != 0) opStrs += "WRITE"
-                    if ((op & SelectionKey.OP_CONNECT) != 0) opStrs += "CONNECT"
-                    if ((op & SelectionKey.OP_ACCEPT) != 0) opStrs += "ACCEPT"
-                    if (opStrs.size > 0) opStrs.reduceLeft(_ + " | " + _) else " "
-                  }
-
-                  logTrace("Changed key for connection to [" +
-                    connection.getRemoteConnectionManagerId()  + "] changed from [" +
-                      intToOpStr(lastOps) + "] to [" + intToOpStr(ops) + "]")
-                }
-              }
-            } else {
-              logInfo("Key not valid ? " + key)
-              throw new CancelledKeyException()
-            }
-          } catch {
-            case e: CancelledKeyException => {
-              logInfo("key already cancelled ? " + key, e)
-              triggerForceCloseByException(key, e)
-            }
-            case e: Exception => {
-              logError("Exception processing key " + key, e)
-              triggerForceCloseByException(key, e)
-            }
-          }
-        }
-
-        val selectedKeysCount =
-          try {
-            selector.select()
-          } catch {
-            // Explicitly only dealing with CancelledKeyException here since other exceptions
-            // should be dealt with differently.
-            case e: CancelledKeyException =>
-              // Some keys within the selectors list are invalid/closed. clear them.
-              val allKeys = selector.keys().iterator()
-
-              while (allKeys.hasNext) {
-                val key = allKeys.next()
-                try {
-                  if (! key.isValid) {
-                    logInfo("Key not valid ? " + key)
-                    throw new CancelledKeyException()
-                  }
-                } catch {
-                  case e: CancelledKeyException => {
-                    logInfo("key already cancelled ? " + key, e)
-                    triggerForceCloseByException(key, e)
-                  }
-                  case e: Exception => {
-                    logError("Exception processing key " + key, e)
-                    triggerForceCloseByException(key, e)
-                  }
-                }
-              }
-              0
-
-            case e: ClosedSelectorException =>
-              logDebug("Failed select() as selector is closed.", e)
-              return
-          }
-
-        if (selectedKeysCount == 0) {
-          logDebug("Selector selected " + selectedKeysCount + " of " + selector.keys.size +
-            " keys")
-        }
-        if (selectorThread.isInterrupted) {
-          logInfo("Selector thread was interrupted!")
-          return
-        }
-
-        if (0 != selectedKeysCount) {
-          val selectedKeys = selector.selectedKeys().iterator()
-          while (selectedKeys.hasNext) {
-            val key = selectedKeys.next
-            selectedKeys.remove()
-            try {
-              if (key.isValid) {
-                if (key.isAcceptable) {
-                  acceptConnection(key)
-                } else
-                if (key.isConnectable) {
-                  triggerConnect(key)
-                } else
-                if (key.isReadable) {
-                  triggerRead(key)
-                } else
-                if (key.isWritable) {
-                  triggerWrite(key)
-                }
-              } else {
-                logInfo("Key not valid ? " + key)
-                throw new CancelledKeyException()
-              }
-            } catch {
-              // weird, but we saw this happening - even though key.isValid was true,
-              // key.isAcceptable would throw CancelledKeyException.
-              case e: CancelledKeyException => {
-                logInfo("key already cancelled ? " + key, e)
-                triggerForceCloseByException(key, e)
-              }
-              case e: Exception => {
-                logError("Exception processing key " + key, e)
-                triggerForceCloseByException(key, e)
-              }
-            }
-          }
-        }
-      }
-    } catch {
-      case e: Exception => logError("Error in select loop", e)
-    }
-  }
-
-  def acceptConnection(key: SelectionKey) {
-    val serverChannel = key.channel.asInstanceOf[ServerSocketChannel]
-
-    var newChannel = serverChannel.accept()
-
-    // accept them all in a tight loop. non blocking accept with no processing, should be fine
-    while (newChannel != null) {
-      try {
-        val newConnectionId = new ConnectionId(id, idCount.getAndIncrement.intValue)
-        val newConnection = new ReceivingConnection(newChannel, selector, newConnectionId,
-          securityManager)
-        newConnection.onReceive(receiveMessage)
-        addListeners(newConnection)
-        addConnection(newConnection)
-        logInfo("Accepted connection from [" + newConnection.remoteAddress + "]")
-      } catch {
-        // might happen in case of issues with registering with selector
-        case e: Exception => logError("Error in accept loop", e)
-      }
-
-      newChannel = serverChannel.accept()
-    }
-  }
-
-  private def addListeners(connection: Connection) {
-    connection.onKeyInterestChange(changeConnectionKeyInterest)
-    connection.onException(handleConnectionError)
-    connection.onClose(removeConnection)
-  }
-
-  def addConnection(connection: Connection) {
-    connectionsByKey += ((connection.key, connection))
-  }
-
-  def removeConnection(connection: Connection) {
-    connectionsByKey -= connection.key
-
-    try {
-      connection match {
-        case sendingConnection: SendingConnection =>
-          val sendingConnectionManagerId = sendingConnection.getRemoteConnectionManagerId()
-          logInfo("Removing SendingConnection to " + sendingConnectionManagerId)
-
-          connectionsById -= sendingConnectionManagerId
-          connectionsAwaitingSasl -= connection.connectionId
-
-          messageStatuses.synchronized {
-            messageStatuses.values.filter(_.connectionManagerId == sendingConnectionManagerId)
-              .foreach(status => {
-                logInfo("Notifying " + status)
-                status.failWithoutAck()
-              })
-
-            messageStatuses.retain((i, status) => {
-              status.connectionManagerId != sendingConnectionManagerId
-            })
-          }
-        case receivingConnection: ReceivingConnection =>
-          val remoteConnectionManagerId = receivingConnection.getRemoteConnectionManagerId()
-          logInfo("Removing ReceivingConnection to " + remoteConnectionManagerId)
-
-          val sendingConnectionOpt = connectionsById.get(remoteConnectionManagerId)
-          if (!sendingConnectionOpt.isDefined) {
-            logError(s"Corresponding SendingConnection to ${remoteConnectionManagerId} not found")
-            return
-          }
-
-          val sendingConnection = sendingConnectionOpt.get
-          connectionsById -= remoteConnectionManagerId
-          sendingConnection.close()
-
-          val sendingConnectionManagerId = sendingConnection.getRemoteConnectionManagerId()
-
-          assert(sendingConnectionManagerId == remoteConnectionManagerId)
-
-          messageStatuses.synchronized {
-            for (s <- messageStatuses.values
-                 if s.connectionManagerId == sendingConnectionManagerId) {
-              logInfo("Notifying " + s)
-              s.failWithoutAck()
-            }
-
-            messageStatuses.retain((i, status) => {
-              status.connectionManagerId != sendingConnectionManagerId
-            })
-          }
-        case _ => logError("Unsupported type of connection.")
-      }
-    } finally {
-      // So that the selection keys can be removed.
-      wakeupSelector()
-    }
-  }
-
-  def handleConnectionError(connection: Connection, e: Throwable) {
-    logInfo("Handling connection error on connection to " +
-      connection.getRemoteConnectionManagerId())
-    removeConnection(connection)
-  }
-
-  def changeConnectionKeyInterest(connection: Connection, ops: Int) {
-    keyInterestChangeRequests += ((connection.key, ops))
-    // so that registrations happen !
-    wakeupSelector()
-  }
-
-  def receiveMessage(connection: Connection, message: Message) {
-    val connectionManagerId = ConnectionManagerId.fromSocketAddress(message.senderAddress)
-    logDebug("Received [" + message + "] from [" + connectionManagerId + "]")
-    val runnable = new Runnable() {
-      val creationTime = System.currentTimeMillis
-      def run() {
-        try {
-          logDebug("Handler thread delay is " + (System.currentTimeMillis - creationTime) + " ms")
-          handleMessage(connectionManagerId, message, connection)
-          logDebug("Handling delay is " + (System.currentTimeMillis - creationTime) + " ms")
-        } catch {
-          case NonFatal(e) => {
-            logError("Error when handling messages from " +
-              connection.getRemoteConnectionManagerId(), e)
-            connection.callOnExceptionCallbacks(e)
-          }
-        }
-      }
-    }
-    handleMessageExecutor.execute(runnable)
-    /* handleMessage(connection, message) */
-  }
-
-  private def handleClientAuthentication(
-      waitingConn: SendingConnection,
-      securityMsg: SecurityMessage,
-      connectionId : ConnectionId) {
-    if (waitingConn.isSaslComplete()) {
-      logDebug("Client sasl completed for id: "  + waitingConn.connectionId)
-      connectionsAwaitingSasl -= waitingConn.connectionId
-      waitingConn.registerAfterAuth()
-      wakeupSelector()
-      return
-    } else {
-      var replyToken : Array[Byte] = null
-      try {
-        replyToken = waitingConn.sparkSaslClient.response(securityMsg.getToken)
-        if (waitingConn.isSaslComplete()) {
-          logDebug("Client sasl completed after evaluate for id: " + waitingConn.connectionId)
-          connectionsAwaitingSasl -= waitingConn.connectionId
-          waitingConn.registerAfterAuth()
-          wakeupSelector()
-          return
-        }
-        val securityMsgResp = SecurityMessage.fromResponse(replyToken,
-          securityMsg.getConnectionId.toString)
-        val message = securityMsgResp.toBufferMessage
-        if (message == null) throw new IOException("Error creating security message")
-        sendSecurityMessage(waitingConn.getRemoteConnectionManagerId(), message)
-      } catch {
-        case e: Exception =>
-          logError("Error handling sasl client authentication", e)
-          waitingConn.close()
-          throw new IOException("Error evaluating sasl response: ", e)
-      }
-    }
-  }
-
-  private def handleServerAuthentication(
-      connection: Connection,
-      securityMsg: SecurityMessage,
-      connectionId: ConnectionId) {
-    if (!connection.isSaslComplete()) {
-      logDebug("saslContext not established")
-      var replyToken : Array[Byte] = null
-      try {
-        connection.synchronized {
-          if (connection.sparkSaslServer == null) {
-            logDebug("Creating sasl Server")
-            connection.sparkSaslServer = new SparkSaslServer(conf.getAppId, securityManager, false)
-          }
-        }
-        replyToken = connection.sparkSaslServer.response(securityMsg.getToken)
-        if (connection.isSaslComplete()) {
-          logDebug("Server sasl completed: " + connection.connectionId +
-            " for: " + connectionId)
-        } else {
-          logDebug("Server sasl not completed: " + connection.connectionId +
-            " for: " + connectionId)
-        }
-        if (replyToken != null) {
-          val securityMsgResp = SecurityMessage.fromResponse(replyToken,
-            securityMsg.getConnectionId)
-          val message = securityMsgResp.toBufferMessage
-          if (message == null) throw new Exception("Error creating security Message")
-          sendSecurityMessage(connection.getRemoteConnectionManagerId(), message)
-        }
-      } catch {
-        case e: Exception => {
-          logError("Error in server auth negotiation: " + e)
-          // It would probably be better to send an error message telling other side auth failed
-          // but for now just close
-          connection.close()
-        }
-      }
-    } else {
-      logDebug("connection already established for this connection id: " + connection.connectionId)
-    }
-  }
-
-
-  private def handleAuthentication(conn: Connection, bufferMessage: BufferMessage): Boolean = {
-    if (bufferMessage.isSecurityNeg) {
-      logDebug("This is security neg message")
-
-      // parse as SecurityMessage
-      val securityMsg = SecurityMessage.fromBufferMessage(bufferMessage)
-      val connectionId = ConnectionId.createConnectionIdFromString(securityMsg.getConnectionId)
-
-      connectionsAwaitingSasl.get(connectionId) match {
-        case Some(waitingConn) => {
-          // Client - this must be in response to us doing Send
-          logDebug("Client handleAuth for id: " +  waitingConn.connectionId)
-          handleClientAuthentication(waitingConn, securityMsg, connectionId)
-        }
-        case None => {
-          // Server - someone sent us something and we haven't authenticated yet
-          logDebug("Server handleAuth for id: " + connectionId)
-          handleServerAuthentication(conn, securityMsg, connectionId)
-        }
-      }
-      return true
-    } else {
-      if (!conn.isSaslComplete()) {
-        // We could handle this better and tell the client we need to do authentication
-        // negotiation, but for now just ignore them.
-        logError("message sent that is not security negotiation message on connection " +
-                 "not authenticated yet, ignoring it!!")
-        return true
-      }
-    }
-    false
-  }
-
-  private def handleMessage(
-      connectionManagerId: ConnectionManagerId,
-      message: Message,
-      connection: Connection) {
-    logDebug("Handling [" + message + "] from [" + connectionManagerId + "]")
-    message match {
-      case bufferMessage: BufferMessage => {
-        if (authEnabled) {
-          val res = handleAuthentication(connection, bufferMessage)
-          if (res) {
-            // message was security negotiation so skip the rest
-            logDebug("After handleAuth result was true, returning")
-            return
-          }
-        }
-        if (bufferMessage.hasAckId()) {
-          messageStatuses.synchronized {
-            messageStatuses.get(bufferMessage.ackId) match {
-              case Some(status) => {
-                messageStatuses -= bufferMessage.ackId
-                status.success(message)
-              }
-              case None => {
-                /**
-                 * We can fall down on this code because of following 2 cases
-                 *
-                 * (1) Invalid ack sent due to buggy code.
-                 *
-                 * (2) Late-arriving ack for a SendMessageStatus
-                 *     To avoid unwilling late-arriving ack
-                 *     caused by long pause like GC, you can set
-                 *     larger value than default to spark.core.connection.ack.wait.timeout
-                 */
-                logWarning(s"Could not find reference for received ack Message ${message.id}")
-              }
-            }
-          }
-        } else {
-          var ackMessage : Option[Message] = None
-          try {
-            ackMessage = if (onReceiveCallback != null) {
-              logDebug("Calling back")
-              onReceiveCallback(bufferMessage, connectionManagerId)
-            } else {
-              logDebug("Not calling back as callback is null")
-              None
-            }
-
-            if (ackMessage.isDefined) {
-              if (!ackMessage.get.isInstanceOf[BufferMessage]) {
-                logDebug("Response to " + bufferMessage + " is not a buffer message, it is of type "
-                  + ackMessage.get.getClass)
-              } else if (!ackMessage.get.asInstanceOf[BufferMessage].hasAckId) {
-                logDebug("Response to " + bufferMessage + " does not have ack id set")
-                ackMessage.get.asInstanceOf[BufferMessage].ackId = bufferMessage.id
-              }
-            }
-          } catch {
-            case e: Exception => {
-              logError(s"Exception was thrown while processing message", e)
-              ackMessage = Some(Message.createErrorMessage(e, bufferMessage.id))
-            }
-          } finally {
-            sendMessage(connectionManagerId, ackMessage.getOrElse {
-              Message.createBufferMessage(bufferMessage.id)
-            })
-          }
-        }
-      }
-      case _ => throw new Exception("Unknown type message received")
-    }
-  }
-
-  private def checkSendAuthFirst(connManagerId: ConnectionManagerId, conn: SendingConnection) {
-    // see if we need to do sasl before writing
-    // this should only be the first negotiation as the Client!!!
-    if (!conn.isSaslComplete()) {
-      conn.synchronized {
-        if (conn.sparkSaslClient == null) {
-          conn.sparkSaslClient = new SparkSaslClient(conf.getAppId, securityManager, false)
-          var firstResponse: Array[Byte] = null
-          try {
-            firstResponse = conn.sparkSaslClient.firstToken()
-            val securityMsg = SecurityMessage.fromResponse(firstResponse,
-              conn.connectionId.toString())
-            val message = securityMsg.toBufferMessage
-            if (message == null) throw new Exception("Error creating security message")
-            connectionsAwaitingSasl += ((conn.connectionId, conn))
-            sendSecurityMessage(connManagerId, message)
-            logDebug("adding connectionsAwaitingSasl id: " + conn.connectionId +
-              " to: " + connManagerId)
-          } catch {
-            case e: Exception => {
-              logError("Error getting first response from the SaslClient.", e)
-              conn.close()
-              throw new Exception("Error getting first response from the SaslClient")
-            }
-          }
-        }
-      }
-    } else {
-      logDebug("Sasl already established ")
-    }
-  }
-
-  // allow us to add messages to the inbox for doing sasl negotiating
-  private def sendSecurityMessage(connManagerId: ConnectionManagerId, message: Message) {
-    def startNewConnection(): SendingConnection = {
-      val inetSocketAddress = new InetSocketAddress(connManagerId.host, connManagerId.port)
-      val newConnectionId = new ConnectionId(id, idCount.getAndIncrement.intValue)
-      val newConnection = new SendingConnection(inetSocketAddress, selector, connManagerId,
-        newConnectionId, securityManager)
-      logInfo("creating new sending connection for security! " + newConnectionId )
-      registerRequests.enqueue(newConnection)
-
-      newConnection
-    }
-    // I removed the lookupKey stuff as part of merge ... should I re-add it ?
-    // We did not find it useful in our test-env ...
-    // If we do re-add it, we should consistently use it everywhere I guess ?
-    message.senderAddress = id.toSocketAddress()
-    logTrace("Sending Security [" + message + "] to [" + connManagerId + "]")
-    val connection = connectionsById.getOrElseUpdate(connManagerId, startNewConnection())
-
-    // send security message until going connection has been authenticated
-    connection.send(message)
-
-    wakeupSelector()
-  }
-
-  private def sendMessage(connectionManagerId: ConnectionManagerId, message: Message) {
-    def startNewConnection(): SendingConnection = {
-      val inetSocketAddress = new InetSocketAddress(connectionManagerId.host,
-        connectionManagerId.port)
-      val newConnectionId = new ConnectionId(id, idCount.getAndIncrement.intValue)
-      val newConnection = new SendingConnection(inetSocketAddress, selector, connectionManagerId,
-        newConnectionId, securityManager)
-      newConnection.onException {
-        case (conn, e) => {
-          logError("Exception while sending message.", e)
-          reportSendingMessageFailure(message.id, e)
-        }
-      }
-      logTrace("creating new sending connection: " + newConnectionId)
-      registerRequests.enqueue(newConnection)
-
-      newConnection
-    }
-    val connection = connectionsById.getOrElseUpdate(connectionManagerId, startNewConnection())
-
-    message.senderAddress = id.toSocketAddress()
-    logDebug("Before Sending [" + message + "] to [" + connectionManagerId + "]" + " " +
-      "connectionid: "  + connection.connectionId)
-
-    if (authEnabled) {
-      try {
-        checkSendAuthFirst(connectionManagerId, connection)
-      } catch {
-        case NonFatal(e) => {
-          reportSendingMessageFailure(message.id, e)
-        }
-      }
-    }
-    logDebug("Sending [" + message + "] to [" + connectionManagerId + "]")
-    connection.send(message)
-    wakeupSelector()
-  }
-
-  private def reportSendingMessageFailure(messageId: Int, e: Throwable): Unit = {
-    // need to tell sender it failed
-    messageStatuses.synchronized {
-      val s = messageStatuses.get(messageId)
-      s match {
-        case Some(msgStatus) => {
-          messageStatuses -= messageId
-          logInfo("Notifying " + msgStatus.connectionManagerId)
-          msgStatus.failure(e)
-        }
-        case None => {
-          logError("no messageStatus for failed message id: " + messageId)
-        }
-      }
-    }
-  }
-
-  private def wakeupSelector() {
-    selector.wakeup()
-  }
-
-  /**
-   * Send a message and block until an acknowledgment is received or an error occurs.
-   * @param connectionManagerId the message's destination
-   * @param message the message being sent
-   * @return a Future that either returns the acknowledgment message or captures an exception.
-   */
-  def sendMessageReliably(connectionManagerId: ConnectionManagerId, message: Message)
-      : Future[Message] = {
-    val promise = Promise[Message]()
-
-    // It's important that the TimerTask doesn't capture a reference to `message`, which can cause
-    // memory leaks since cancelled TimerTasks won't necessarily be garbage collected until the time
-    // at which they would originally be scheduled to run.  Therefore, extract the message id
-    // from outside of the TimerTask closure (see SPARK-4393 for more context).
-    val messageId = message.id
-    // Keep a weak reference to the promise so that the completed promise may be garbage-collected
-    val promiseReference = new WeakReference(promise)
-    val timeoutTask: TimerTask = new TimerTask {
-      override def run(timeout: Timeout): Unit = {
-        messageStatuses.synchronized {
-          messageStatuses.remove(messageId).foreach { s =>
-            val e = new IOException("sendMessageReliably failed because ack " +
-              s"was not received within $ackTimeout sec")
-            val p = promiseReference.get
-            if (p != null) {
-              // Attempt to fail the promise with a Timeout exception
-              if (!p.tryFailure(e)) {
-                // If we reach here, then someone else has already signalled success or failure
-                // on this promise, so log a warning:
-                logError("Ignore error because promise is completed", e)
-              }
-            } else {
-              // The WeakReference was empty, which should never happen because
-              // sendMessageReliably's caller should have a strong reference to promise.future;
-              logError("Promise was garbage collected; this should never happen!", e)
-            }
-          }
-        }
-      }
-    }
-
-    val timeoutTaskHandle = ackTimeoutMonitor.newTimeout(timeoutTask, ackTimeout, TimeUnit.SECONDS)
-
-    val status = new MessageStatus(message, connectionManagerId, s => {
-      timeoutTaskHandle.cancel()
-      s match {
-        case scala.util.Failure(e) =>
-          // Indicates a failure where we either never sent or never got ACK'd
-          if (!promise.tryFailure(e)) {
-            logWarning("Ignore error because promise is completed", e)
-          }
-        case scala.util.Success(ackMessage) =>
-          if (ackMessage.hasError) {
-            val errorMsgByteBuf = ackMessage.asInstanceOf[BufferMessage].buffers.head
-            val errorMsgBytes = new Array[Byte](errorMsgByteBuf.limit())
-            errorMsgByteBuf.get(errorMsgBytes)
-            val errorMsg = new String(errorMsgBytes, UTF_8)
-            val e = new IOException(
-              s"sendMessageReliably failed with ACK that signalled a remote error: $errorMsg")
-            if (!promise.tryFailure(e)) {
-              logWarning("Ignore error because promise is completed", e)
-            }
-          } else {
-            if (!promise.trySuccess(ackMessage)) {
-              logWarning("Drop ackMessage because promise is completed")
-            }
-          }
-      }
-    })
-    messageStatuses.synchronized {
-      messageStatuses += ((message.id, status))
-    }
-
-    sendMessage(connectionManagerId, message)
-    promise.future
-  }
-
-  def onReceiveMessage(callback: (Message, ConnectionManagerId) => Option[Message]) {
-    onReceiveCallback = callback
-  }
-
-  def stop() {
-    isActive = false
-    ackTimeoutMonitor.stop()
-    selector.close()
-    selectorThread.interrupt()
-    selectorThread.join()
-    val connections = connectionsByKey.values
-    connections.foreach(_.close())
-    if (connectionsByKey.size != 0) {
-      logWarning("All connections not cleaned up")
-    }
-    handleMessageExecutor.shutdown()
-    handleReadWriteExecutor.shutdown()
-    handleConnectExecutor.shutdown()
-    logInfo("ConnectionManager stopped")
-  }
-}
-
-
-private[spark] object ConnectionManager {
-  import scala.concurrent.ExecutionContext.Implicits.global
-
-  def main(args: Array[String]) {
-    val conf = new SparkConf
-    val manager = new ConnectionManager(9999, conf, new SecurityManager(conf))
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      // scalastyle:off println
-      println("Received [" + msg + "] from [" + id + "]")
-      // scalastyle:on println
-      None
-    })
-
-    /* testSequentialSending(manager) */
-    /* System.gc() */
-
-    /* testParallelSending(manager) */
-    /* System.gc() */
-
-    /* testParallelDecreasingSending(manager) */
-    /* System.gc() */
-
-    testContinuousSending(manager)
-    System.gc()
-  }
-
-  // scalastyle:off println
-  def testSequentialSending(manager: ConnectionManager) {
-    println("--------------------------")
-    println("Sequential Sending")
-    println("--------------------------")
-    val size = 10 * 1024 * 1024
-    val count = 10
-
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-
-    (0 until count).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-      Await.result(manager.sendMessageReliably(manager.id, bufferMessage), Duration.Inf)
-    })
-    println("--------------------------")
-    println()
-  }
-
-  def testParallelSending(manager: ConnectionManager) {
-    println("--------------------------")
-    println("Parallel Sending")
-    println("--------------------------")
-    val size = 10 * 1024 * 1024
-    val count = 10
-
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-
-    val startTime = System.currentTimeMillis
-    (0 until count).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-      manager.sendMessageReliably(manager.id, bufferMessage)
-    }).foreach(f => {
-      f.onFailure {
-        case e => println("Failed due to " + e)
-      }
-      Await.ready(f, 1 second)
-    })
-    val finishTime = System.currentTimeMillis
-
-    val mb = size * count / 1024.0 / 1024.0
-    val ms = finishTime - startTime
-    val tput = mb * 1000.0 / ms
-    println("--------------------------")
-    println("Started at " + startTime + ", finished at " + finishTime)
-    println("Sent " + count + " messages of size " + size + " in " + ms + " ms " +
-      "(" + tput + " MB/s)")
-    println("--------------------------")
-    println()
-  }
-
-  def testParallelDecreasingSending(manager: ConnectionManager) {
-    println("--------------------------")
-    println("Parallel Decreasing Sending")
-    println("--------------------------")
-    val size = 10 * 1024 * 1024
-    val count = 10
-    val buffers = Array.tabulate(count) { i =>
-      val bufferLen = size * (i + 1)
-      val bufferContent = Array.tabulate[Byte](bufferLen)(x => x.toByte)
-      ByteBuffer.allocate(bufferLen).put(bufferContent)
-    }
-    buffers.foreach(_.flip)
-    val mb = buffers.map(_.remaining).reduceLeft(_ + _) / 1024.0 / 1024.0
-
-    val startTime = System.currentTimeMillis
-    (0 until count).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffers(count - 1 - i).duplicate)
-      manager.sendMessageReliably(manager.id, bufferMessage)
-    }).foreach(f => {
-      f.onFailure {
-        case e => println("Failed due to " + e)
-      }
-      Await.ready(f, 1 second)
-    })
-    val finishTime = System.currentTimeMillis
-
-    val ms = finishTime - startTime
-    val tput = mb * 1000.0 / ms
-    println("--------------------------")
-    /* println("Started at " + startTime + ", finished at " + finishTime) */
-    println("Sent " + mb + " MB in " + ms + " ms (" + tput + " MB/s)")
-    println("--------------------------")
-    println()
-  }
-
-  def testContinuousSending(manager: ConnectionManager) {
-    println("--------------------------")
-    println("Continuous Sending")
-    println("--------------------------")
-    val size = 10 * 1024 * 1024
-    val count = 10
-
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-
-    val startTime = System.currentTimeMillis
-    while(true) {
-      (0 until count).map(i => {
-          val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-          manager.sendMessageReliably(manager.id, bufferMessage)
-        }).foreach(f => {
-          f.onFailure {
-            case e => println("Failed due to " + e)
-          }
-          Await.ready(f, 1 second)
-        })
-      val finishTime = System.currentTimeMillis
-      Thread.sleep(1000)
-      val mb = size * count / 1024.0 / 1024.0
-      val ms = finishTime - startTime
-      val tput = mb * 1000.0 / ms
-      println("Sent " + mb + " MB in " + ms + " ms (" + tput + " MB/s)")
-      println("--------------------------")
-      println()
-    }
-  }
-  // scalastyle:on println
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala b/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala
deleted file mode 100644
index 1cd13d887c6f6..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/ConnectionManagerId.scala
+++ /dev/null
@@ -1,37 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.net.InetSocketAddress
-
-import org.apache.spark.util.Utils
-
-private[nio] case class ConnectionManagerId(host: String, port: Int) {
-  // DEBUG code
-  Utils.checkHost(host)
-  assert (port > 0)
-
-  def toSocketAddress(): InetSocketAddress = new InetSocketAddress(host, port)
-}
-
-
-private[nio] object ConnectionManagerId {
-  def fromSocketAddress(socketAddress: InetSocketAddress): ConnectionManagerId = {
-    new ConnectionManagerId(socketAddress.getHostName, socketAddress.getPort)
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/Message.scala b/core/src/main/scala/org/apache/spark/network/nio/Message.scala
deleted file mode 100644
index 85d2fe2bf9c20..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/Message.scala
+++ /dev/null
@@ -1,114 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.net.InetSocketAddress
-import java.nio.ByteBuffer
-
-import scala.collection.mutable.ArrayBuffer
-
-import com.google.common.base.Charsets.UTF_8
-
-import org.apache.spark.util.Utils
-
-private[nio] abstract class Message(val typ: Long, val id: Int) {
-  var senderAddress: InetSocketAddress = null
-  var started = false
-  var startTime = -1L
-  var finishTime = -1L
-  var isSecurityNeg = false
-  var hasError = false
-
-  def size: Int
-
-  def getChunkForSending(maxChunkSize: Int): Option[MessageChunk]
-
-  def getChunkForReceiving(chunkSize: Int): Option[MessageChunk]
-
-  def timeTaken(): String = (finishTime - startTime).toString + " ms"
-
-  override def toString: String = {
-    this.getClass.getSimpleName + "(id = " + id + ", size = " + size + ")"
-  }
-}
-
-
-private[nio] object Message {
-  val BUFFER_MESSAGE = 1111111111L
-
-  var lastId = 1
-
-  def getNewId(): Int = synchronized {
-    lastId += 1
-    if (lastId == 0) {
-      lastId += 1
-    }
-    lastId
-  }
-
-  def createBufferMessage(dataBuffers: Seq[ByteBuffer], ackId: Int): BufferMessage = {
-    if (dataBuffers == null) {
-      return new BufferMessage(getNewId(), new ArrayBuffer[ByteBuffer], ackId)
-    }
-    if (dataBuffers.exists(_ == null)) {
-      throw new Exception("Attempting to create buffer message with null buffer")
-    }
-    new BufferMessage(getNewId(), new ArrayBuffer[ByteBuffer] ++= dataBuffers, ackId)
-  }
-
-  def createBufferMessage(dataBuffers: Seq[ByteBuffer]): BufferMessage =
-    createBufferMessage(dataBuffers, 0)
-
-  def createBufferMessage(dataBuffer: ByteBuffer, ackId: Int): BufferMessage = {
-    if (dataBuffer == null) {
-      createBufferMessage(Array(ByteBuffer.allocate(0)), ackId)
-    } else {
-      createBufferMessage(Array(dataBuffer), ackId)
-    }
-  }
-
-  def createBufferMessage(dataBuffer: ByteBuffer): BufferMessage =
-    createBufferMessage(dataBuffer, 0)
-
-  def createBufferMessage(ackId: Int): BufferMessage = {
-    createBufferMessage(new Array[ByteBuffer](0), ackId)
-  }
-
-  /**
-   * Create a "negative acknowledgment" to notify a sender that an error occurred
-   * while processing its message.  The exception's stacktrace will be formatted
-   * as a string, serialized into a byte array, and sent as the message payload.
-   */
-  def createErrorMessage(exception: Exception, ackId: Int): BufferMessage = {
-    val exceptionString = Utils.exceptionString(exception)
-    val serializedExceptionString = ByteBuffer.wrap(exceptionString.getBytes(UTF_8))
-    val errorMessage = createBufferMessage(serializedExceptionString, ackId)
-    errorMessage.hasError = true
-    errorMessage
-  }
-
-  def create(header: MessageChunkHeader): Message = {
-    val newMessage: Message = header.typ match {
-      case BUFFER_MESSAGE => new BufferMessage(header.id,
-        ArrayBuffer(ByteBuffer.allocate(header.totalSize)), header.other)
-    }
-    newMessage.hasError = header.hasError
-    newMessage.senderAddress = header.address
-    newMessage
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala b/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala
deleted file mode 100644
index a4568e849fa13..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/MessageChunk.scala
+++ /dev/null
@@ -1,41 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import scala.collection.mutable.ArrayBuffer
-
-private[nio]
-class MessageChunk(val header: MessageChunkHeader, val buffer: ByteBuffer) {
-
-  val size: Int = if (buffer == null) 0 else buffer.remaining
-
-  lazy val buffers: ArrayBuffer[ByteBuffer] = {
-    val ab = new ArrayBuffer[ByteBuffer]()
-    ab += header.buffer
-    if (buffer != null) {
-      ab += buffer
-    }
-    ab
-  }
-
-  override def toString: String = {
-    "" + this.getClass.getSimpleName + " (id = " + header.id + ", size = " + size + ")"
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala b/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala
deleted file mode 100644
index 7b3da4bb9d5ee..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/MessageChunkHeader.scala
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.net.{InetAddress, InetSocketAddress}
-import java.nio.ByteBuffer
-
-private[nio] class MessageChunkHeader(
-    val typ: Long,
-    val id: Int,
-    val totalSize: Int,
-    val chunkSize: Int,
-    val other: Int,
-    val hasError: Boolean,
-    val securityNeg: Int,
-    val address: InetSocketAddress) {
-  lazy val buffer = {
-    // No need to change this, at 'use' time, we do a reverse lookup of the hostname.
-    // Refer to network.Connection
-    val ip = address.getAddress.getAddress()
-    val port = address.getPort()
-    ByteBuffer.
-      allocate(MessageChunkHeader.HEADER_SIZE).
-      putLong(typ).
-      putInt(id).
-      putInt(totalSize).
-      putInt(chunkSize).
-      putInt(other).
-      put(if (hasError) 1.asInstanceOf[Byte] else 0.asInstanceOf[Byte]).
-      putInt(securityNeg).
-      putInt(ip.size).
-      put(ip).
-      putInt(port).
-      position(MessageChunkHeader.HEADER_SIZE).
-      flip.asInstanceOf[ByteBuffer]
-  }
-
-  override def toString: String = {
-    "" + this.getClass.getSimpleName + ":" + id + " of type " + typ +
-      " and sizes " + totalSize + " / " + chunkSize + " bytes, securityNeg: " + securityNeg
-  }
-
-}
-
-
-private[nio] object MessageChunkHeader {
-  val HEADER_SIZE = 45
-
-  def create(buffer: ByteBuffer): MessageChunkHeader = {
-    if (buffer.remaining != HEADER_SIZE) {
-      throw new IllegalArgumentException("Cannot convert buffer data to Message")
-    }
-    val typ = buffer.getLong()
-    val id = buffer.getInt()
-    val totalSize = buffer.getInt()
-    val chunkSize = buffer.getInt()
-    val other = buffer.getInt()
-    val hasError = buffer.get() != 0
-    val securityNeg = buffer.getInt()
-    val ipSize = buffer.getInt()
-    val ipBytes = new Array[Byte](ipSize)
-    buffer.get(ipBytes)
-    val ip = InetAddress.getByAddress(ipBytes)
-    val port = buffer.getInt()
-    new MessageChunkHeader(typ, id, totalSize, chunkSize, other, hasError, securityNeg,
-      new InetSocketAddress(ip, port))
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala
deleted file mode 100644
index b2aec160635c7..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/NioBlockTransferService.scala
+++ /dev/null
@@ -1,217 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import org.apache.spark.network._
-import org.apache.spark.network.buffer.{ManagedBuffer, NioManagedBuffer}
-import org.apache.spark.network.shuffle.BlockFetchingListener
-import org.apache.spark.storage.{BlockId, StorageLevel}
-import org.apache.spark.util.Utils
-import org.apache.spark.{Logging, SecurityManager, SparkConf, SparkException}
-
-import scala.concurrent.Future
-
-
-/**
- * A [[BlockTransferService]] implementation based on [[ConnectionManager]], a custom
- * implementation using Java NIO.
- */
-final class NioBlockTransferService(conf: SparkConf, securityManager: SecurityManager)
-  extends BlockTransferService with Logging {
-
-  private var cm: ConnectionManager = _
-
-  private var blockDataManager: BlockDataManager = _
-
-  /**
-   * Port number the service is listening on, available only after [[init]] is invoked.
-   */
-  override def port: Int = {
-    checkInit()
-    cm.id.port
-  }
-
-  /**
-   * Host name the service is listening on, available only after [[init]] is invoked.
-   */
-  override def hostName: String = {
-    checkInit()
-    cm.id.host
-  }
-
-  /**
-   * Initialize the transfer service by giving it the BlockDataManager that can be used to fetch
-   * local blocks or put local blocks.
-   */
-  override def init(blockDataManager: BlockDataManager): Unit = {
-    this.blockDataManager = blockDataManager
-    cm = new ConnectionManager(
-      conf.getInt("spark.blockManager.port", 0),
-      conf,
-      securityManager,
-      "Connection manager for block manager")
-    cm.onReceiveMessage(onBlockMessageReceive)
-  }
-
-  /**
-   * Tear down the transfer service.
-   */
-  override def close(): Unit = {
-    if (cm != null) {
-      cm.stop()
-    }
-  }
-
-  override def fetchBlocks(
-      host: String,
-      port: Int,
-      execId: String,
-      blockIds: Array[String],
-      listener: BlockFetchingListener): Unit = {
-    checkInit()
-
-    val cmId = new ConnectionManagerId(host, port)
-    val blockMessageArray = new BlockMessageArray(blockIds.map { blockId =>
-      BlockMessage.fromGetBlock(GetBlock(BlockId(blockId)))
-    })
-
-    val future = cm.sendMessageReliably(cmId, blockMessageArray.toBufferMessage)
-
-    // Register the listener on success/failure future callback.
-    future.onSuccess { case message =>
-      val bufferMessage = message.asInstanceOf[BufferMessage]
-      val blockMessageArray = BlockMessageArray.fromBufferMessage(bufferMessage)
-
-      // SPARK-4064: In some cases(eg. Remote block was removed) blockMessageArray may be empty.
-      if (blockMessageArray.isEmpty) {
-        blockIds.foreach { id =>
-          listener.onBlockFetchFailure(id, new SparkException(s"Received empty message from $cmId"))
-        }
-      } else {
-        for (blockMessage: BlockMessage <- blockMessageArray) {
-          val msgType = blockMessage.getType
-          if (msgType != BlockMessage.TYPE_GOT_BLOCK) {
-            if (blockMessage.getId != null) {
-              listener.onBlockFetchFailure(blockMessage.getId.toString,
-                new SparkException(s"Unexpected message $msgType received from $cmId"))
-            }
-          } else {
-            val blockId = blockMessage.getId
-            val networkSize = blockMessage.getData.limit()
-            listener.onBlockFetchSuccess(
-              blockId.toString, new NioManagedBuffer(blockMessage.getData))
-          }
-        }
-      }
-    }(cm.futureExecContext)
-
-    future.onFailure { case exception =>
-      blockIds.foreach { blockId =>
-        listener.onBlockFetchFailure(blockId, exception)
-      }
-    }(cm.futureExecContext)
-  }
-
-  /**
-   * Upload a single block to a remote node, available only after [[init]] is invoked.
-   *
-   * This call blocks until the upload completes, or throws an exception upon failures.
-   */
-  override def uploadBlock(
-      hostname: String,
-      port: Int,
-      execId: String,
-      blockId: BlockId,
-      blockData: ManagedBuffer,
-      level: StorageLevel)
-    : Future[Unit] = {
-    checkInit()
-    val msg = PutBlock(blockId, blockData.nioByteBuffer(), level)
-    val blockMessageArray = new BlockMessageArray(BlockMessage.fromPutBlock(msg))
-    val remoteCmId = new ConnectionManagerId(hostName, port)
-    val reply = cm.sendMessageReliably(remoteCmId, blockMessageArray.toBufferMessage)
-    reply.map(x => ())(cm.futureExecContext)
-  }
-
-  private def checkInit(): Unit = if (cm == null) {
-    throw new IllegalStateException(getClass.getName + " has not been initialized")
-  }
-
-  private def onBlockMessageReceive(msg: Message, id: ConnectionManagerId): Option[Message] = {
-    logDebug("Handling message " + msg)
-    msg match {
-      case bufferMessage: BufferMessage =>
-        try {
-          logDebug("Handling as a buffer message " + bufferMessage)
-          val blockMessages = BlockMessageArray.fromBufferMessage(bufferMessage)
-          logDebug("Parsed as a block message array")
-          val responseMessages = blockMessages.map(processBlockMessage).filter(_ != None).map(_.get)
-          Some(new BlockMessageArray(responseMessages).toBufferMessage)
-        } catch {
-          case e: Exception =>
-            logError("Exception handling buffer message", e)
-            Some(Message.createErrorMessage(e, msg.id))
-        }
-
-      case otherMessage: Any =>
-        val errorMsg = s"Received unknown message type: ${otherMessage.getClass.getName}"
-        logError(errorMsg)
-        Some(Message.createErrorMessage(new UnsupportedOperationException(errorMsg), msg.id))
-    }
-  }
-
-  private def processBlockMessage(blockMessage: BlockMessage): Option[BlockMessage] = {
-    blockMessage.getType match {
-      case BlockMessage.TYPE_PUT_BLOCK =>
-        val msg = PutBlock(blockMessage.getId, blockMessage.getData, blockMessage.getLevel)
-        logDebug("Received [" + msg + "]")
-        putBlock(msg.id, msg.data, msg.level)
-        None
-
-      case BlockMessage.TYPE_GET_BLOCK =>
-        val msg = new GetBlock(blockMessage.getId)
-        logDebug("Received [" + msg + "]")
-        val buffer = getBlock(msg.id)
-        if (buffer == null) {
-          return None
-        }
-        Some(BlockMessage.fromGotBlock(GotBlock(msg.id, buffer)))
-
-      case _ => None
-    }
-  }
-
-  private def putBlock(blockId: BlockId, bytes: ByteBuffer, level: StorageLevel) {
-    val startTimeMs = System.currentTimeMillis()
-    logDebug("PutBlock " + blockId + " started from " + startTimeMs + " with data: " + bytes)
-    blockDataManager.putBlockData(blockId, new NioManagedBuffer(bytes), level)
-    logDebug("PutBlock " + blockId + " used " + Utils.getUsedTimeMs(startTimeMs)
-      + " with data size: " + bytes.limit)
-  }
-
-  private def getBlock(blockId: BlockId): ByteBuffer = {
-    val startTimeMs = System.currentTimeMillis()
-    logDebug("GetBlock " + blockId + " started from " + startTimeMs)
-    val buffer = blockDataManager.getBlockData(blockId)
-    logDebug("GetBlock " + blockId + " used " + Utils.getUsedTimeMs(startTimeMs)
-      + " and got buffer " + buffer)
-    buffer.nioByteBuffer()
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala b/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala
deleted file mode 100644
index 232c552f9865d..0000000000000
--- a/core/src/main/scala/org/apache/spark/network/nio/SecurityMessage.scala
+++ /dev/null
@@ -1,160 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.nio.ByteBuffer
-
-import scala.collection.mutable.{ArrayBuffer, StringBuilder}
-
-import org.apache.spark._
-
-/**
- * SecurityMessage is class that contains the connectionId and sasl token
- * used in SASL negotiation. SecurityMessage has routines for converting
- * it to and from a BufferMessage so that it can be sent by the ConnectionManager
- * and easily consumed by users when received.
- * The api was modeled after BlockMessage.
- *
- * The connectionId is the connectionId of the client side. Since
- * message passing is asynchronous and its possible for the server side (receiving)
- * to get multiple different types of messages on the same connection the connectionId
- * is used to know which connnection the security message is intended for.
- *
- * For instance, lets say we are node_0. We need to send data to node_1. The node_0 side
- * is acting as a client and connecting to node_1. SASL negotiation has to occur
- * between node_0 and node_1 before node_1 trusts node_0 so node_0 sends a security message.
- * node_1 receives the message from node_0 but before it can process it and send a response,
- * some thread on node_1 decides it needs to send data to node_0 so it connects to node_0
- * and sends a security message of its own to authenticate as a client. Now node_0 gets
- * the message and it needs to decide if this message is in response to it being a client
- * (from the first send) or if its just node_1 trying to connect to it to send data.  This
- * is where the connectionId field is used. node_0 can lookup the connectionId to see if
- * it is in response to it being a client or if its in response to someone sending other data.
- *
- * The format of a SecurityMessage as its sent is:
- *   - Length of the ConnectionId
- *   - ConnectionId
- *   - Length of the token
- *   - Token
- */
-private[nio] class SecurityMessage extends Logging {
-
-  private var connectionId: String = null
-  private var token: Array[Byte] = null
-
-  def set(byteArr: Array[Byte], newconnectionId: String) {
-    if (byteArr == null) {
-      token = new Array[Byte](0)
-    } else {
-      token = byteArr
-    }
-    connectionId = newconnectionId
-  }
-
-  /**
-   * Read the given buffer and set the members of this class.
-   */
-  def set(buffer: ByteBuffer) {
-    val idLength = buffer.getInt()
-    val idBuilder = new StringBuilder(idLength)
-    for (i <- 1 to idLength) {
-        idBuilder += buffer.getChar()
-    }
-    connectionId = idBuilder.toString()
-
-    val tokenLength = buffer.getInt()
-    token = new Array[Byte](tokenLength)
-    if (tokenLength > 0) {
-      buffer.get(token, 0, tokenLength)
-    }
-  }
-
-  def set(bufferMsg: BufferMessage) {
-    val buffer = bufferMsg.buffers.apply(0)
-    buffer.clear()
-    set(buffer)
-  }
-
-  def getConnectionId: String = {
-    return connectionId
-  }
-
-  def getToken: Array[Byte] = {
-    return token
-  }
-
-  /**
-   * Create a BufferMessage that can be sent by the ConnectionManager containing
-   * the security information from this class.
-   * @return BufferMessage
-   */
-  def toBufferMessage: BufferMessage = {
-    val buffers = new ArrayBuffer[ByteBuffer]()
-
-    // 4 bytes for the length of the connectionId
-    // connectionId is of type char so multiple the length by 2 to get number of bytes
-    // 4 bytes for the length of token
-    // token is a byte buffer so just take the length
-    var buffer = ByteBuffer.allocate(4 + connectionId.length() * 2 + 4 + token.length)
-    buffer.putInt(connectionId.length())
-    connectionId.foreach((x: Char) => buffer.putChar(x))
-    buffer.putInt(token.length)
-
-    if (token.length > 0) {
-      buffer.put(token)
-    }
-    buffer.flip()
-    buffers += buffer
-
-    var message = Message.createBufferMessage(buffers)
-    logDebug("message total size is : " + message.size)
-    message.isSecurityNeg = true
-    return message
-  }
-
-  override def toString: String = {
-    "SecurityMessage [connId= " + connectionId + ", Token = " + token + "]"
-  }
-}
-
-private[nio] object SecurityMessage {
-
-  /**
-   * Convert the given BufferMessage to a SecurityMessage by parsing the contents
-   * of the BufferMessage and populating the SecurityMessage fields.
-   * @param bufferMessage is a BufferMessage that was received
-   * @return new SecurityMessage
-   */
-  def fromBufferMessage(bufferMessage: BufferMessage): SecurityMessage = {
-    val newSecurityMessage = new SecurityMessage()
-    newSecurityMessage.set(bufferMessage)
-    newSecurityMessage
-  }
-
-  /**
-   * Create a SecurityMessage to send from a given saslResponse.
-   * @param response is the response to a challenge from the SaslClient or Saslserver
-   * @param connectionId the client connectionId we are negotiation authentication for
-   * @return a new SecurityMessage
-   */
-  def fromResponse(response : Array[Byte], connectionId : String) : SecurityMessage = {
-    val newSecurityMessage = new SecurityMessage()
-    newSecurityMessage.set(response, connectionId)
-    newSecurityMessage
-  }
-}
diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
index b977711e7d5ad..c5195c1143a8f 100644
--- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
+++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala
@@ -35,7 +35,6 @@ import org.roaringbitmap.{ArrayContainer, BitmapContainer, RoaringArray, Roaring
 import org.apache.spark._
 import org.apache.spark.api.python.PythonBroadcast
 import org.apache.spark.broadcast.HttpBroadcast
-import org.apache.spark.network.nio.{GetBlock, GotBlock, PutBlock}
 import org.apache.spark.network.util.ByteUnit
 import org.apache.spark.scheduler.{CompressedMapStatus, HighlyCompressedMapStatus}
 import org.apache.spark.storage._
@@ -362,9 +361,6 @@ private[serializer] object KryoSerializer {
   private val toRegister: Seq[Class[_]] = Seq(
     ByteBuffer.allocate(1).getClass,
     classOf[StorageLevel],
-    classOf[PutBlock],
-    classOf[GotBlock],
-    classOf[GetBlock],
     classOf[CompressedMapStatus],
     classOf[HighlyCompressedMapStatus],
     classOf[RoaringBitmap],
diff --git a/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala b/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala
deleted file mode 100644
index 5e364cc0edeb2..0000000000000
--- a/core/src/test/scala/org/apache/spark/network/nio/ConnectionManagerSuite.scala
+++ /dev/null
@@ -1,296 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one or more
- * contributor license agreements.  See the NOTICE file distributed with
- * this work for additional information regarding copyright ownership.
- * The ASF licenses this file to You under the Apache License, Version 2.0
- * (the "License"); you may not use this file except in compliance with
- * the License.  You may obtain a copy of the License at
- *
- *    http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-package org.apache.spark.network.nio
-
-import java.io.IOException
-import java.nio._
-
-import scala.concurrent.duration._
-import scala.concurrent.{Await, TimeoutException}
-import scala.language.postfixOps
-
-import org.apache.spark.{SecurityManager, SparkConf, SparkFunSuite}
-import org.apache.spark.util.Utils
-
-/**
-  * Test the ConnectionManager with various security settings.
-  */
-class ConnectionManagerSuite extends SparkFunSuite {
-
-  test("security default off") {
-    val conf = new SparkConf
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    var receivedMessage = false
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      receivedMessage = true
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-
-    val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-    Await.result(manager.sendMessageReliably(manager.id, bufferMessage), 10 seconds)
-
-    assert(receivedMessage == true)
-
-    manager.stop()
-  }
-
-  test("security on same password") {
-    val conf = new SparkConf
-    conf.set("spark.authenticate", "true")
-    conf.set("spark.authenticate.secret", "good")
-    conf.set("spark.app.id", "app-id")
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    var numReceivedMessages = 0
-
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedMessages += 1
-      None
-    })
-    val managerServer = new ConnectionManager(0, conf, securityManager)
-    var numReceivedServerMessages = 0
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedServerMessages += 1
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val count = 10
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-
-    (0 until count).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-      Await.result(manager.sendMessageReliably(managerServer.id, bufferMessage), 10 seconds)
-    })
-
-    assert(numReceivedServerMessages == 10)
-    assert(numReceivedMessages == 0)
-
-    manager.stop()
-    managerServer.stop()
-  }
-
-  test("security mismatch password") {
-    val conf = new SparkConf
-    conf.set("spark.authenticate", "true")
-    conf.set("spark.app.id", "app-id")
-    conf.set("spark.authenticate.secret", "good")
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    var numReceivedMessages = 0
-
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedMessages += 1
-      None
-    })
-
-    val badconf = conf.clone.set("spark.authenticate.secret", "bad")
-    val badsecurityManager = new SecurityManager(badconf)
-    val managerServer = new ConnectionManager(0, badconf, badsecurityManager)
-    var numReceivedServerMessages = 0
-
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedServerMessages += 1
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-    val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-    // Expect managerServer to close connection, which we'll report as an error:
-    intercept[IOException] {
-      Await.result(manager.sendMessageReliably(managerServer.id, bufferMessage), 10 seconds)
-    }
-
-    assert(numReceivedServerMessages == 0)
-    assert(numReceivedMessages == 0)
-
-    manager.stop()
-    managerServer.stop()
-  }
-
-  test("security mismatch auth off") {
-    val conf = new SparkConf
-    conf.set("spark.authenticate", "false")
-    conf.set("spark.authenticate.secret", "good")
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    var numReceivedMessages = 0
-
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedMessages += 1
-      None
-    })
-
-    val badconf = new SparkConf
-    badconf.set("spark.authenticate", "true")
-    badconf.set("spark.authenticate.secret", "good")
-    val badsecurityManager = new SecurityManager(badconf)
-    val managerServer = new ConnectionManager(0, badconf, badsecurityManager)
-    var numReceivedServerMessages = 0
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedServerMessages += 1
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-    val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-    (0 until 1).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-      manager.sendMessageReliably(managerServer.id, bufferMessage)
-    }).foreach(f => {
-      try {
-        val g = Await.result(f, 1 second)
-        assert(false)
-      } catch {
-        case i: IOException =>
-          assert(true)
-        case e: TimeoutException => {
-          // we should timeout here since the client can't do the negotiation
-          assert(true)
-        }
-      }
-    })
-
-    assert(numReceivedServerMessages == 0)
-    assert(numReceivedMessages == 0)
-    manager.stop()
-    managerServer.stop()
-  }
-
-  test("security auth off") {
-    val conf = new SparkConf
-    conf.set("spark.authenticate", "false")
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    var numReceivedMessages = 0
-
-    manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedMessages += 1
-      None
-    })
-
-    val badconf = new SparkConf
-    badconf.set("spark.authenticate", "false")
-    val badsecurityManager = new SecurityManager(badconf)
-    val managerServer = new ConnectionManager(0, badconf, badsecurityManager)
-    var numReceivedServerMessages = 0
-
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      numReceivedServerMessages += 1
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-    val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-    (0 until 10).map(i => {
-      val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-      manager.sendMessageReliably(managerServer.id, bufferMessage)
-    }).foreach(f => {
-      try {
-        val g = Await.result(f, 1 second)
-      } catch {
-        case e: Exception => {
-          assert(false)
-        }
-      }
-    })
-    assert(numReceivedServerMessages == 10)
-    assert(numReceivedMessages == 0)
-
-    manager.stop()
-    managerServer.stop()
-  }
-
-  test("Ack error message") {
-    val conf = new SparkConf
-    conf.set("spark.authenticate", "false")
-    val securityManager = new SecurityManager(conf)
-    val manager = new ConnectionManager(0, conf, securityManager)
-    val managerServer = new ConnectionManager(0, conf, securityManager)
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      throw new Exception("Custom exception text")
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-    val bufferMessage = Message.createBufferMessage(buffer)
-
-    val future = manager.sendMessageReliably(managerServer.id, bufferMessage)
-
-    val exception = intercept[IOException] {
-      Await.result(future, 1 second)
-    }
-    assert(Utils.exceptionString(exception).contains("Custom exception text"))
-
-    manager.stop()
-    managerServer.stop()
-
-  }
-
-  test("sendMessageReliably timeout") {
-    val clientConf = new SparkConf
-    clientConf.set("spark.authenticate", "false")
-    val ackTimeoutS = 30
-    clientConf.set("spark.core.connection.ack.wait.timeout", s"${ackTimeoutS}s")
-
-    val clientSecurityManager = new SecurityManager(clientConf)
-    val manager = new ConnectionManager(0, clientConf, clientSecurityManager)
-
-    val serverConf = new SparkConf
-    serverConf.set("spark.authenticate", "false")
-    val serverSecurityManager = new SecurityManager(serverConf)
-    val managerServer = new ConnectionManager(0, serverConf, serverSecurityManager)
-    managerServer.onReceiveMessage((msg: Message, id: ConnectionManagerId) => {
-      // sleep 60 sec > ack timeout for simulating server slow down or hang up
-      Thread.sleep(ackTimeoutS * 3 * 1000)
-      None
-    })
-
-    val size = 10 * 1024 * 1024
-    val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte))
-    buffer.flip
-    val bufferMessage = Message.createBufferMessage(buffer.duplicate)
-
-    val future = manager.sendMessageReliably(managerServer.id, bufferMessage)
-
-    // Future should throw IOException in 30 sec.
-    // Otherwise TimeoutExcepton is thrown from Await.result.
-    // We expect TimeoutException is not thrown.
-    intercept[IOException] {
-      Await.result(future, (ackTimeoutS * 2) second)
-    }
-
-    manager.stop()
-    managerServer.stop()
-  }
-
-}
-
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala
index 0f5ba46f69c2f..eb5af70d57aec 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerReplicationSuite.scala
@@ -26,10 +26,10 @@ import org.mockito.Mockito.{mock, when}
 import org.scalatest.{BeforeAndAfter, Matchers}
 import org.scalatest.concurrent.Eventually._
 
+import org.apache.spark.network.netty.NettyBlockTransferService
 import org.apache.spark.rpc.RpcEnv
 import org.apache.spark._
 import org.apache.spark.network.BlockTransferService
-import org.apache.spark.network.nio.NioBlockTransferService
 import org.apache.spark.scheduler.LiveListenerBus
 import org.apache.spark.serializer.KryoSerializer
 import org.apache.spark.shuffle.hash.HashShuffleManager
@@ -38,7 +38,7 @@ import org.apache.spark.storage.StorageLevel._
 /** Testsuite that tests block replication in BlockManager */
 class BlockManagerReplicationSuite extends SparkFunSuite with Matchers with BeforeAndAfter {
 
-  private val conf = new SparkConf(false)
+  private val conf = new SparkConf(false).set("spark.app.id", "test")
   var rpcEnv: RpcEnv = null
   var master: BlockManagerMaster = null
   val securityMgr = new SecurityManager(conf)
@@ -59,7 +59,7 @@ class BlockManagerReplicationSuite extends SparkFunSuite with Matchers with Befo
   private def makeBlockManager(
       maxMem: Long,
       name: String = SparkContext.DRIVER_IDENTIFIER): BlockManager = {
-    val transfer = new NioBlockTransferService(conf, securityMgr)
+    val transfer = new NettyBlockTransferService(conf, securityMgr, numCores = 1)
     val store = new BlockManager(name, rpcEnv, master, serializer, maxMem, conf,
       mapOutputTracker, shuffleManager, transfer, securityMgr, 0)
     store.initialize("app-id")
diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
index e5b54d66c8157..34bb4952e7246 100644
--- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala
@@ -30,10 +30,10 @@ import org.scalatest._
 import org.scalatest.concurrent.Eventually._
 import org.scalatest.concurrent.Timeouts._
 
+import org.apache.spark.network.netty.NettyBlockTransferService
 import org.apache.spark.rpc.RpcEnv
 import org.apache.spark._
 import org.apache.spark.executor.DataReadMethod
-import org.apache.spark.network.nio.NioBlockTransferService
 import org.apache.spark.scheduler.LiveListenerBus
 import org.apache.spark.serializer.{JavaSerializer, KryoSerializer}
 import org.apache.spark.shuffle.hash.HashShuffleManager
@@ -44,7 +44,7 @@ import org.apache.spark.util._
 class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterEach
   with PrivateMethodTester with ResetSystemProperties {
 
-  private val conf = new SparkConf(false)
+  private val conf = new SparkConf(false).set("spark.app.id", "test")
   var store: BlockManager = null
   var store2: BlockManager = null
   var store3: BlockManager = null
@@ -66,7 +66,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
   private def makeBlockManager(
       maxMem: Long,
       name: String = SparkContext.DRIVER_IDENTIFIER): BlockManager = {
-    val transfer = new NioBlockTransferService(conf, securityMgr)
+    val transfer = new NettyBlockTransferService(conf, securityMgr, numCores = 1)
     val manager = new BlockManager(name, rpcEnv, master, serializer, maxMem, conf,
       mapOutputTracker, shuffleManager, transfer, securityMgr, 0)
     manager.initialize("app-id")
@@ -819,7 +819,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
 
   test("block store put failure") {
     // Use Java serializer so we can create an unserializable error.
-    val transfer = new NioBlockTransferService(conf, securityMgr)
+    val transfer = new NettyBlockTransferService(conf, securityMgr, numCores = 1)
     store = new BlockManager(SparkContext.DRIVER_IDENTIFIER, rpcEnv, master,
       new JavaSerializer(conf), 1200, conf, mapOutputTracker, shuffleManager, transfer, securityMgr,
       0)
@@ -833,7 +833,7 @@ class BlockManagerSuite extends SparkFunSuite with Matchers with BeforeAndAfterE
 
     // Make sure get a1 doesn't hang and returns None.
     failAfter(1 second) {
-      assert(store.getSingle("a1") == None, "a1 should not be in store")
+      assert(store.getSingle("a1").isEmpty, "a1 should not be in store")
     }
   }
 
diff --git a/docs/configuration.md b/docs/configuration.md
index 29a36bd67f28b..a2cc7a37e2240 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -382,17 +382,6 @@ Apart from these, the following properties are also available, and may be useful
     overhead per reduce task, so keep it small unless you have a large amount of memory.
   </td>
 </tr>
-<tr>
-  <td><code>spark.shuffle.blockTransferService</code></td>
-  <td>netty</td>
-  <td>
-    Implementation to use for transferring shuffle and cached blocks between executors. There
-    are two implementations available: <code>netty</code> and <code>nio</code>. Netty-based
-    block transfer is intended to be simpler but equally efficient and is the default option
-    starting in 1.2, and <code>nio</code> block transfer is deprecated in Spark 1.5.0 and will
-    be removed in Spark 1.6.0.
-  </td>
-</tr>
 <tr>
   <td><code>spark.shuffle.compress</code></td>
   <td>true</td>
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 714ce3cd9b1de..3b8b6c8ffa375 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -32,635 +32,638 @@ import com.typesafe.tools.mima.core.ProblemFilters._
  * MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap")
  */
 object MimaExcludes {
-    def excludes(version: String) =
-      version match {
-        case v if v.startsWith("1.5") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("deploy"),
-            MimaBuild.excludeSparkPackage("network"),
-            // These are needed if checking against the sbt build, since they are part of
-            // the maven-generated artifacts in 1.3.
-            excludePackage("org.spark-project.jetty"),
-            MimaBuild.excludeSparkPackage("unused"),
-            // JavaRDDLike is not meant to be extended by user programs
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.partitioner"),
-            // Modification of private static method
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.streaming.kafka.KafkaUtils.org$apache$spark$streaming$kafka$KafkaUtils$$leadersForRanges"),
-            // Mima false positive (was a private[spark] class)
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.util.collection.PairIterator"),
-            // Removing a testing method from a private class
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.kafka.KafkaTestUtils.waitUntilLeaderOffset"),
-            // While private MiMa is still not happy about the changes,
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.ml.regression.LeastSquaresAggregator.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.ml.regression.LeastSquaresCostFun.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.ml.classification.LogisticCostFun.this"),
-            // SQL execution is considered private.
-            excludePackage("org.apache.spark.sql.execution"),
-            // The old JSON RDD is removed in favor of streaming Jackson
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD"),
-            // local function inside a method
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.sql.SQLContext.org$apache$spark$sql$SQLContext$$needsConversion$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.sql.UDFRegistration.org$apache$spark$sql$UDFRegistration$$builder$24")
-          ) ++ Seq(
-            // SPARK-8479 Add numNonzeros and numActives to Matrix.
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.numNonzeros"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.numActives")
-          ) ++ Seq(
-            // SPARK-8914 Remove RDDApi
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.RDDApi")
-          ) ++ Seq(
-            // SPARK-7292 Provide operator to truncate lineage cheaply
-            ProblemFilters.exclude[AbstractClassProblem](
-              "org.apache.spark.rdd.RDDCheckpointData"),
-            ProblemFilters.exclude[AbstractClassProblem](
-              "org.apache.spark.rdd.CheckpointRDD")
-          ) ++ Seq(
-            // SPARK-8701 Add input metadata in the batch page.
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.streaming.scheduler.InputInfo$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.streaming.scheduler.InputInfo")
-          ) ++ Seq(
-            // SPARK-6797 Support YARN modes for SparkR
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.r.PairwiseRRDD.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.r.RRDD.createRWorker"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.r.RRDD.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.r.StringRRDD.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.r.BaseRRDD.this")
-          ) ++ Seq(
-            // SPARK-7422 add argmax for sparse vectors
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.argmax")
-          ) ++ Seq(
-            // SPARK-8906 Move all internal data source classes into execution.datasources
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.ResolvedDataSource"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreInsertCastAndRename$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsingAsSelect$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoDataSource$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopPartition"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$PartitionValues$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DefaultWriterContainer"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$PartitionValues"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.RefreshTable$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsing$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DynamicPartitionWriterContainer"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsingAsSelect"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreInsertCastAndRename"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.Partition$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.LogicalRelation$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.LogicalRelation"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.Partition"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.BaseWriterContainer"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreWriteCheck"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsing"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.RefreshTable"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DataSourceStrategy$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsing"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsingAsSelect$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsingAsSelect"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsing$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.ResolvedDataSource$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreWriteCheck$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoDataSource"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoHadoopFsRelation"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLParser"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CaseInsensitiveMap"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoHadoopFsRelation$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DataSourceStrategy"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLException"),
-            // SPARK-9763 Minimize exposure of internal SQL classes
-            excludePackage("org.apache.spark.sql.parquet"),
-            excludePackage("org.apache.spark.sql.json"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$JDBCConversion"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$DriverWrapper"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DefaultSource"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation")
-          ) ++ Seq(
-            // SPARK-4751 Dynamic allocation for standalone mode
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.SparkContext.supportDynamicAllocation")
-          ) ++ Seq(
-            // SPARK-9580: Remove SQL test singletons
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.test.LocalSQLContext$SQLSession"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.test.LocalSQLContext"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.test.TestSQLContext"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.test.TestSQLContext$")
-          ) ++ Seq(
-            // SPARK-9704 Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.mllib.linalg.VectorUDT.serialize")
-          )
+  def excludes(version: String) = version match {
+    case v if v.startsWith("1.6") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("network")
+        )
+    case v if v.startsWith("1.5") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("network"),
+        MimaBuild.excludeSparkPackage("deploy"),
+        // These are needed if checking against the sbt build, since they are part of
+        // the maven-generated artifacts in 1.3.
+        excludePackage("org.spark-project.jetty"),
+        MimaBuild.excludeSparkPackage("unused"),
+        // JavaRDDLike is not meant to be extended by user programs
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.partitioner"),
+        // Modification of private static method
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.streaming.kafka.KafkaUtils.org$apache$spark$streaming$kafka$KafkaUtils$$leadersForRanges"),
+        // Mima false positive (was a private[spark] class)
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.util.collection.PairIterator"),
+        // Removing a testing method from a private class
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.kafka.KafkaTestUtils.waitUntilLeaderOffset"),
+        // While private MiMa is still not happy about the changes,
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.ml.regression.LeastSquaresAggregator.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.ml.regression.LeastSquaresCostFun.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.ml.classification.LogisticCostFun.this"),
+        // SQL execution is considered private.
+        excludePackage("org.apache.spark.sql.execution"),
+        // The old JSON RDD is removed in favor of streaming Jackson
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.json.JsonRDD"),
+        // local function inside a method
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.sql.SQLContext.org$apache$spark$sql$SQLContext$$needsConversion$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.sql.UDFRegistration.org$apache$spark$sql$UDFRegistration$$builder$24")
+      ) ++ Seq(
+        // SPARK-8479 Add numNonzeros and numActives to Matrix.
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.numNonzeros"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.numActives")
+      ) ++ Seq(
+        // SPARK-8914 Remove RDDApi
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.RDDApi")
+      ) ++ Seq(
+        // SPARK-7292 Provide operator to truncate lineage cheaply
+        ProblemFilters.exclude[AbstractClassProblem](
+          "org.apache.spark.rdd.RDDCheckpointData"),
+        ProblemFilters.exclude[AbstractClassProblem](
+          "org.apache.spark.rdd.CheckpointRDD")
+      ) ++ Seq(
+        // SPARK-8701 Add input metadata in the batch page.
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.streaming.scheduler.InputInfo$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.streaming.scheduler.InputInfo")
+      ) ++ Seq(
+        // SPARK-6797 Support YARN modes for SparkR
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.r.PairwiseRRDD.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.r.RRDD.createRWorker"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.r.RRDD.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.r.StringRRDD.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.r.BaseRRDD.this")
+      ) ++ Seq(
+        // SPARK-7422 add argmax for sparse vectors
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.argmax")
+      ) ++ Seq(
+        // SPARK-8906 Move all internal data source classes into execution.datasources
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.ResolvedDataSource"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreInsertCastAndRename$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsingAsSelect$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoDataSource$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopPartition"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$PartitionValues$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DefaultWriterContainer"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$PartitionValues"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.RefreshTable$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsing$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DynamicPartitionWriterContainer"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsingAsSelect"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreInsertCastAndRename"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.Partition$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.LogicalRelation$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitioningUtils"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.LogicalRelation"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.Partition"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.BaseWriterContainer"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreWriteCheck"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsing"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.RefreshTable"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DataSourceStrategy$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsing"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsingAsSelect$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTempTableUsingAsSelect"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CreateTableUsing$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.ResolvedDataSource$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PreWriteCheck$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoDataSource"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoHadoopFsRelation"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLParser"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.CaseInsensitiveMap"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.InsertIntoHadoopFsRelation$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DataSourceStrategy"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.SqlNewHadoopRDD$NewHadoopMapPartitionsWithSplitRDD$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.PartitionSpec$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DescribeCommand"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.sources.DDLException"),
+        // SPARK-9763 Minimize exposure of internal SQL classes
+        excludePackage("org.apache.spark.sql.parquet"),
+        excludePackage("org.apache.spark.sql.json"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$DecimalConversion"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartition$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$JDBCConversion"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$DriverWrapper"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRDD"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCPartitioningInfo"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JdbcUtils"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DefaultSource"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.package$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.JDBCRelation")
+      ) ++ Seq(
+        // SPARK-4751 Dynamic allocation for standalone mode
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.SparkContext.supportDynamicAllocation")
+      ) ++ Seq(
+        // SPARK-9580: Remove SQL test singletons
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.test.LocalSQLContext$SQLSession"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.test.LocalSQLContext"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.test.TestSQLContext"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.test.TestSQLContext$")
+      ) ++ Seq(
+        // SPARK-9704 Made ProbabilisticClassifier, Identifiable, VectorUDT public APIs
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.mllib.linalg.VectorUDT.serialize")
+      )
 
-        case v if v.startsWith("1.4") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("deploy"),
-            MimaBuild.excludeSparkPackage("ml"),
-            // SPARK-7910 Adding a method to get the partioner to JavaRDD,
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitioner"),
-            // SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff"),
-            // These are needed if checking against the sbt build, since they are part of
-            // the maven-generated artifacts in 1.3.
-            excludePackage("org.spark-project.jetty"),
-            MimaBuild.excludeSparkPackage("unused"),
-            ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.rdd.JdbcRDD.compute"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.broadcast.HttpBroadcastFactory.newBroadcast"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorEndpoint")
-          ) ++ Seq(
-            // SPARK-4655 - Making Stage an Abstract class broke binary compatility even though
-            // the stage class is defined as private[spark]
-            ProblemFilters.exclude[AbstractClassProblem]("org.apache.spark.scheduler.Stage")
-          ) ++ Seq(
-            // SPARK-6510 Add a Graph#minus method acting as Set#difference
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.minus")
-          ) ++ Seq(
-            // SPARK-6492 Fix deadlock in SparkContext.stop()
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.SparkContext.org$" +
-                "apache$spark$SparkContext$$SPARK_CONTEXT_CONSTRUCTOR_LOCK")
-          )++ Seq(
-            // SPARK-6693 add tostring with max lines and width for matrix
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.toString")
-          )++ Seq(
-            // SPARK-6703 Add getOrCreate method to SparkContext
-            ProblemFilters.exclude[IncompatibleResultTypeProblem]
-                ("org.apache.spark.SparkContext.org$apache$spark$SparkContext$$activeContext")
-          )++ Seq(
-            // SPARK-7090 Introduce LDAOptimizer to LDA to further improve extensibility
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.mllib.clustering.LDA$EMOptimizer")
-          ) ++ Seq(
-            // SPARK-6756 add toSparse, toDense, numActives, numNonzeros, and compressed to Vector
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.compressed"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.toDense"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.numNonzeros"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.toSparse"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Vector.numActives"),
-            // SPARK-7681 add SparseVector support for gemv
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.multiply"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.DenseMatrix.multiply"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.SparseMatrix.multiply")
-          ) ++ Seq(
-            // Execution should never be included as its always internal.
-            MimaBuild.excludeSparkPackage("sql.execution"),
-            // This `protected[sql]` method was removed in 1.3.1
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.sql.SQLContext.checkAnalysis"),
-            // These `private[sql]` class were removed in 1.4.0:
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.execution.AddExchange"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.execution.AddExchange$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.PartitionSpec"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.PartitionSpec$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.Partition"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.Partition$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetRelation2$PartitionValues"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetRelation2$PartitionValues$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetRelation2"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetRelation2$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache"),
-            // These test support classes were moved out of src/main and into src/test:
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetTestData"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetTestData$"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.TestGroupWriteSupport"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CachedData"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CachedData$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CacheManager"),
-            // TODO: Remove the following rule once ParquetTest has been moved to src/test.
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.sql.parquet.ParquetTest")
-          ) ++ Seq(
-            // SPARK-7530 Added StreamingContext.getState()
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.StreamingContext.state_=")
-          ) ++ Seq(
-            // SPARK-7081 changed ShuffleWriter from a trait to an abstract class and removed some
-            // unnecessary type bounds in order to fix some compiler warnings that occurred when
-            // implementing this interface in Java. Note that ShuffleWriter is private[spark].
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.shuffle.ShuffleWriter")
-          ) ++ Seq(
-            // SPARK-6888 make jdbc driver handling user definable
-            // This patch renames some classes to API friendly names.
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DriverQuirks$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DriverQuirks"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.PostgresQuirks"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.NoQuirks"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.MySQLQuirks")
-          )
+    case v if v.startsWith("1.4") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("deploy"),
+        MimaBuild.excludeSparkPackage("ml"),
+        // SPARK-7910 Adding a method to get the partioner to JavaRDD,
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitioner"),
+        // SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff"),
+        // These are needed if checking against the sbt build, since they are part of
+        // the maven-generated artifacts in 1.3.
+        excludePackage("org.spark-project.jetty"),
+        MimaBuild.excludeSparkPackage("unused"),
+        ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.rdd.JdbcRDD.compute"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.broadcast.HttpBroadcastFactory.newBroadcast"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.broadcast.TorrentBroadcastFactory.newBroadcast"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.scheduler.OutputCommitCoordinator$OutputCommitCoordinatorEndpoint")
+      ) ++ Seq(
+        // SPARK-4655 - Making Stage an Abstract class broke binary compatility even though
+        // the stage class is defined as private[spark]
+        ProblemFilters.exclude[AbstractClassProblem]("org.apache.spark.scheduler.Stage")
+      ) ++ Seq(
+        // SPARK-6510 Add a Graph#minus method acting as Set#difference
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.minus")
+      ) ++ Seq(
+        // SPARK-6492 Fix deadlock in SparkContext.stop()
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.SparkContext.org$" +
+            "apache$spark$SparkContext$$SPARK_CONTEXT_CONSTRUCTOR_LOCK")
+      )++ Seq(
+        // SPARK-6693 add tostring with max lines and width for matrix
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.toString")
+      )++ Seq(
+        // SPARK-6703 Add getOrCreate method to SparkContext
+        ProblemFilters.exclude[IncompatibleResultTypeProblem]
+            ("org.apache.spark.SparkContext.org$apache$spark$SparkContext$$activeContext")
+      )++ Seq(
+        // SPARK-7090 Introduce LDAOptimizer to LDA to further improve extensibility
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.mllib.clustering.LDA$EMOptimizer")
+      ) ++ Seq(
+        // SPARK-6756 add toSparse, toDense, numActives, numNonzeros, and compressed to Vector
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.compressed"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.toDense"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.numNonzeros"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.toSparse"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Vector.numActives"),
+        // SPARK-7681 add SparseVector support for gemv
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.multiply"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.DenseMatrix.multiply"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.SparseMatrix.multiply")
+      ) ++ Seq(
+        // Execution should never be included as its always internal.
+        MimaBuild.excludeSparkPackage("sql.execution"),
+        // This `protected[sql]` method was removed in 1.3.1
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.sql.SQLContext.checkAnalysis"),
+        // These `private[sql]` class were removed in 1.4.0:
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.execution.AddExchange"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.execution.AddExchange$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.PartitionSpec"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.PartitionSpec$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.Partition"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.Partition$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetRelation2$PartitionValues"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetRelation2$PartitionValues$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetRelation2"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetRelation2$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache"),
+        // These test support classes were moved out of src/main and into src/test:
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetTestData"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetTestData$"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.TestGroupWriteSupport"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CachedData"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CachedData$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.CacheManager"),
+        // TODO: Remove the following rule once ParquetTest has been moved to src/test.
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.sql.parquet.ParquetTest")
+      ) ++ Seq(
+        // SPARK-7530 Added StreamingContext.getState()
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.StreamingContext.state_=")
+      ) ++ Seq(
+        // SPARK-7081 changed ShuffleWriter from a trait to an abstract class and removed some
+        // unnecessary type bounds in order to fix some compiler warnings that occurred when
+        // implementing this interface in Java. Note that ShuffleWriter is private[spark].
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.shuffle.ShuffleWriter")
+      ) ++ Seq(
+        // SPARK-6888 make jdbc driver handling user definable
+        // This patch renames some classes to API friendly names.
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DriverQuirks$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.DriverQuirks"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.PostgresQuirks"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.NoQuirks"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.sql.jdbc.MySQLQuirks")
+      )
 
-        case v if v.startsWith("1.3") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("deploy"),
-            MimaBuild.excludeSparkPackage("ml"),
-            // These are needed if checking against the sbt build, since they are part of
-            // the maven-generated artifacts in the 1.2 build.
-            MimaBuild.excludeSparkPackage("unused"),
-            ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional")
-          ) ++ Seq(
-            // SPARK-2321
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.SparkStageInfoImpl.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.SparkStageInfo.submissionTime")
-          ) ++ Seq(
-            // SPARK-4614
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrices.randn"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrices.rand")
-          ) ++ Seq(
-            // SPARK-5321
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.SparseMatrix.transposeMultiply"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.transpose"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.DenseMatrix.transposeMultiply"),
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.linalg.Matrix." +
-                "org$apache$spark$mllib$linalg$Matrix$_setter_$isTransposed_="),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.isTransposed"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.linalg.Matrix.foreachActive")
-          ) ++ Seq(
-            // SPARK-5540
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.solveLeastSquares"),
-            // SPARK-5536
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateBlock")
-          ) ++ Seq(
-            // SPARK-3325
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.api.java.JavaDStreamLike.print"),
-            // SPARK-2757
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.streaming.flume.sink.SparkAvroCallbackHandler." +
-                "removeAndGetProcessor")
-          ) ++ Seq(
-            // SPARK-5123 (SparkSQL data type change) - alpha component only
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.ml.feature.HashingTF.outputDataType"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.ml.feature.Tokenizer.outputDataType"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.ml.feature.Tokenizer.validateInputType"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.ml.classification.LogisticRegressionModel.validateAndTransformSchema"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.ml.classification.LogisticRegression.validateAndTransformSchema")
-          ) ++ Seq(
-            // SPARK-4014
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.TaskContext.taskAttemptId"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.TaskContext.attemptNumber")
-          ) ++ Seq(
-            // SPARK-5166 Spark SQL API stabilization
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Transformer.transform"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Estimator.fit"),
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Transformer.transform"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Pipeline.fit"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.PipelineModel.transform"),
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Estimator.fit"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Evaluator.evaluate"),
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Evaluator.evaluate"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.tuning.CrossValidator.fit"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.tuning.CrossValidatorModel.transform"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.feature.StandardScaler.fit"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.feature.StandardScalerModel.transform"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.classification.LogisticRegressionModel.transform"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.classification.LogisticRegression.fit"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.evaluation.BinaryClassificationEvaluator.evaluate")
-          ) ++ Seq(
-            // SPARK-5270
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.isEmpty")
-          ) ++ Seq(
-            // SPARK-5430
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.treeReduce"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.treeAggregate")
-          ) ++ Seq(
-            // SPARK-5297 Java FileStream do not work with custom key/values
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.api.java.JavaStreamingContext.fileStream")
-          ) ++ Seq(
-            // SPARK-5315 Spark Streaming Java API returns Scala DStream
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.api.java.JavaDStreamLike.reduceByWindow")
-          ) ++ Seq(
-            // SPARK-5461 Graph should have isCheckpointed, getCheckpointFiles methods
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.graphx.Graph.getCheckpointFiles"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.graphx.Graph.isCheckpointed")
-          ) ++ Seq(
-            // SPARK-4789 Standardize ML Prediction APIs
-            ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.linalg.VectorUDT"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.serialize"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.sqlType")
-          ) ++ Seq(
-            // SPARK-5814
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$wrapDoubleArray"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$fillFullMatrix"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$iterations"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeOutLinkBlock"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$computeYtY"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeLinkRDDs"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$alpha"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$randomFactor"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeInLinkBlock"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$dspr"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$lambda"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$implicitPrefs"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$rank")
-          ) ++ Seq(
-            // SPARK-4682
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.RealClock"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.Clock"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.TestClock")
-          ) ++ Seq(
-            // SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff")
-          )
+    case v if v.startsWith("1.3") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("deploy"),
+        MimaBuild.excludeSparkPackage("ml"),
+        // These are needed if checking against the sbt build, since they are part of
+        // the maven-generated artifacts in the 1.2 build.
+        MimaBuild.excludeSparkPackage("unused"),
+        ProblemFilters.exclude[MissingClassProblem]("com.google.common.base.Optional")
+      ) ++ Seq(
+        // SPARK-2321
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.SparkStageInfoImpl.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.SparkStageInfo.submissionTime")
+      ) ++ Seq(
+        // SPARK-4614
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrices.randn"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrices.rand")
+      ) ++ Seq(
+        // SPARK-5321
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.SparseMatrix.transposeMultiply"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.transpose"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.DenseMatrix.transposeMultiply"),
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.linalg.Matrix." +
+            "org$apache$spark$mllib$linalg$Matrix$_setter_$isTransposed_="),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.isTransposed"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.linalg.Matrix.foreachActive")
+      ) ++ Seq(
+        // SPARK-5540
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.solveLeastSquares"),
+        // SPARK-5536
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateBlock")
+      ) ++ Seq(
+        // SPARK-3325
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.api.java.JavaDStreamLike.print"),
+        // SPARK-2757
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.streaming.flume.sink.SparkAvroCallbackHandler." +
+            "removeAndGetProcessor")
+      ) ++ Seq(
+        // SPARK-5123 (SparkSQL data type change) - alpha component only
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.ml.feature.HashingTF.outputDataType"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.ml.feature.Tokenizer.outputDataType"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.ml.feature.Tokenizer.validateInputType"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.ml.classification.LogisticRegressionModel.validateAndTransformSchema"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.ml.classification.LogisticRegression.validateAndTransformSchema")
+      ) ++ Seq(
+        // SPARK-4014
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.TaskContext.taskAttemptId"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.TaskContext.attemptNumber")
+      ) ++ Seq(
+        // SPARK-5166 Spark SQL API stabilization
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Transformer.transform"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Estimator.fit"),
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Transformer.transform"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Pipeline.fit"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.PipelineModel.transform"),
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Estimator.fit"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.Evaluator.evaluate"),
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.ml.Evaluator.evaluate"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.tuning.CrossValidator.fit"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.tuning.CrossValidatorModel.transform"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.feature.StandardScaler.fit"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.feature.StandardScalerModel.transform"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.classification.LogisticRegressionModel.transform"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.classification.LogisticRegression.fit"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.ml.evaluation.BinaryClassificationEvaluator.evaluate")
+      ) ++ Seq(
+        // SPARK-5270
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.isEmpty")
+      ) ++ Seq(
+        // SPARK-5430
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.treeReduce"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.treeAggregate")
+      ) ++ Seq(
+        // SPARK-5297 Java FileStream do not work with custom key/values
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.api.java.JavaStreamingContext.fileStream")
+      ) ++ Seq(
+        // SPARK-5315 Spark Streaming Java API returns Scala DStream
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.api.java.JavaDStreamLike.reduceByWindow")
+      ) ++ Seq(
+        // SPARK-5461 Graph should have isCheckpointed, getCheckpointFiles methods
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.graphx.Graph.getCheckpointFiles"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.graphx.Graph.isCheckpointed")
+      ) ++ Seq(
+        // SPARK-4789 Standardize ML Prediction APIs
+        ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.linalg.VectorUDT"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.serialize"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem]("org.apache.spark.mllib.linalg.VectorUDT.sqlType")
+      ) ++ Seq(
+        // SPARK-5814
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$wrapDoubleArray"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$fillFullMatrix"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$iterations"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeOutLinkBlock"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$computeYtY"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeLinkRDDs"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$alpha"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$randomFactor"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$makeInLinkBlock"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$dspr"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$lambda"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$implicitPrefs"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$rank")
+      ) ++ Seq(
+        // SPARK-4682
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.RealClock"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.Clock"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.TestClock")
+      ) ++ Seq(
+        // SPARK-5922 Adding a generalized diff(other: RDD[(VertexId, VD)]) to VertexRDD
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.graphx.VertexRDD.diff")
+      )
 
-        case v if v.startsWith("1.2") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("deploy"),
-            MimaBuild.excludeSparkPackage("graphx")
-          ) ++
-          MimaBuild.excludeSparkClass("mllib.linalg.Matrix") ++
-          MimaBuild.excludeSparkClass("mllib.linalg.Vector") ++
-          Seq(
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.scheduler.TaskLocation"),
-            // Added normL1 and normL2 to trait MultivariateStatisticalSummary
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL2"),
-            // MapStatus should be private[spark]
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.scheduler.MapStatus"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.network.netty.PathResolver"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.spark.network.netty.client.BlockClientListener"),
+    case v if v.startsWith("1.2") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("deploy"),
+        MimaBuild.excludeSparkPackage("graphx")
+      ) ++
+      MimaBuild.excludeSparkClass("mllib.linalg.Matrix") ++
+      MimaBuild.excludeSparkClass("mllib.linalg.Vector") ++
+      Seq(
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.scheduler.TaskLocation"),
+        // Added normL1 and normL2 to trait MultivariateStatisticalSummary
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.stat.MultivariateStatisticalSummary.normL2"),
+        // MapStatus should be private[spark]
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.scheduler.MapStatus"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.network.netty.PathResolver"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.spark.network.netty.client.BlockClientListener"),
 
-            // TaskContext was promoted to Abstract class
-            ProblemFilters.exclude[AbstractClassProblem](
-              "org.apache.spark.TaskContext"),
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.util.collection.SortDataFormat")
-          ) ++ Seq(
-            // Adding new methods to the JavaRDDLike trait:
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.takeAsync"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.foreachPartitionAsync"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.countAsync"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.collectAsync")
-          ) ++ Seq(
-            // SPARK-3822
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.SparkContext.org$apache$spark$SparkContext$$createTaskScheduler")
-          ) ++ Seq(
-            // SPARK-1209
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.hadoop.mapreduce.SparkHadoopMapReduceUtil"),
-            ProblemFilters.exclude[MissingClassProblem](
-              "org.apache.hadoop.mapred.SparkHadoopMapRedUtil"),
-            ProblemFilters.exclude[MissingTypesProblem](
-              "org.apache.spark.rdd.PairRDDFunctions")
-          ) ++ Seq(
-            // SPARK-4062
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.kafka.KafkaReceiver#MessageHandler.this")
-          )
+        // TaskContext was promoted to Abstract class
+        ProblemFilters.exclude[AbstractClassProblem](
+          "org.apache.spark.TaskContext"),
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.util.collection.SortDataFormat")
+      ) ++ Seq(
+        // Adding new methods to the JavaRDDLike trait:
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.takeAsync"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.foreachPartitionAsync"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.countAsync"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.collectAsync")
+      ) ++ Seq(
+        // SPARK-3822
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.SparkContext.org$apache$spark$SparkContext$$createTaskScheduler")
+      ) ++ Seq(
+        // SPARK-1209
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.hadoop.mapreduce.SparkHadoopMapReduceUtil"),
+        ProblemFilters.exclude[MissingClassProblem](
+          "org.apache.hadoop.mapred.SparkHadoopMapRedUtil"),
+        ProblemFilters.exclude[MissingTypesProblem](
+          "org.apache.spark.rdd.PairRDDFunctions")
+      ) ++ Seq(
+        // SPARK-4062
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.kafka.KafkaReceiver#MessageHandler.this")
+      )
 
-        case v if v.startsWith("1.1") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("deploy"),
-            MimaBuild.excludeSparkPackage("graphx")
-          ) ++
-          Seq(
-            // Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
-            // Should probably mark this as Experimental
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
-            // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
-            // for countApproxDistinct* functions, which does not work in Java. We later removed
-            // them, and use the following to tell Mima to not care about them.
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
-            ProblemFilters.exclude[IncompatibleResultTypeProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.storage.DiskStore.getValues"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.storage.MemoryStore.Entry")
-          ) ++
-          Seq(
-            // Serializer interface change. See SPARK-3045.
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.serializer.DeserializationStream"),
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.serializer.Serializer"),
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.serializer.SerializationStream"),
-            ProblemFilters.exclude[IncompatibleTemplateDefProblem](
-              "org.apache.spark.serializer.SerializerInstance")
-          )++
-          Seq(
-            // Renamed putValues -> putArray + putIterator
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.storage.MemoryStore.putValues"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.storage.DiskStore.putValues"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.storage.TachyonStore.putValues")
-          ) ++
-          Seq(
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.streaming.flume.FlumeReceiver.this"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.streaming.kafka.KafkaUtils.createStream"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.streaming.kafka.KafkaReceiver.this")
-          ) ++
-          Seq( // Ignore some private methods in ALS.
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
-            ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
-              "org.apache.spark.mllib.recommendation.ALS.this"),
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures")
-          ) ++
-          MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
-          MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
-          MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
-          MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
-          MimaBuild.excludeSparkClass("storage.Values") ++
-          MimaBuild.excludeSparkClass("storage.Entry") ++
-          MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") ++
-          // Class was missing "@DeveloperApi" annotation in 1.0.
-          MimaBuild.excludeSparkClass("scheduler.SparkListenerApplicationStart") ++
-          Seq(
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.mllib.tree.impurity.Gini.calculate"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.mllib.tree.impurity.Entropy.calculate"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem](
-              "org.apache.spark.mllib.tree.impurity.Variance.calculate")
-          ) ++
-          Seq( // Package-private classes removed in SPARK-2341
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser$"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser"),
-            ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser$")
-          ) ++
-          Seq( // package-private classes removed in MLlib
-            ProblemFilters.exclude[MissingMethodProblem](
-              "org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.org$apache$spark$mllib$regression$GeneralizedLinearAlgorithm$$prependOne")
-          ) ++
-          Seq( // new Vector methods in MLlib (binary compatible assuming users do not implement Vector)
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.linalg.Vector.copy")
-          ) ++
-          Seq( // synthetic methods generated in LabeledPoint
-            ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.regression.LabeledPoint$"),
-            ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.mllib.regression.LabeledPoint.apply"),
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.regression.LabeledPoint.toString")
-          ) ++
-          Seq ( // Scala 2.11 compatibility fix
-            ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.StreamingContext.<init>$default$2")
-          )
-        case v if v.startsWith("1.0") =>
-          Seq(
-            MimaBuild.excludeSparkPackage("api.java"),
-            MimaBuild.excludeSparkPackage("mllib"),
-            MimaBuild.excludeSparkPackage("streaming")
-          ) ++
-          MimaBuild.excludeSparkClass("rdd.ClassTags") ++
-          MimaBuild.excludeSparkClass("util.XORShiftRandom") ++
-          MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++
-          MimaBuild.excludeSparkClass("graphx.VertexRDD") ++
-          MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++
-          MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++
-          MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
-          MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
-          MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
-          MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++
-          MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
-          MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++
-          MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
-        case _ => Seq()
-      }
-}
+    case v if v.startsWith("1.1") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("deploy"),
+        MimaBuild.excludeSparkPackage("graphx")
+      ) ++
+      Seq(
+        // Adding new method to JavaRDLike trait - we should probably mark this as a developer API.
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.api.java.JavaRDDLike.partitions"),
+        // Should probably mark this as Experimental
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.foreachAsync"),
+        // We made a mistake earlier (ed06500d3) in the Java API to use default parameter values
+        // for countApproxDistinct* functions, which does not work in Java. We later removed
+        // them, and use the following to tell Mima to not care about them.
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
+        ProblemFilters.exclude[IncompatibleResultTypeProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaPairRDD.countApproxDistinctByKey$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDD.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaRDDLike.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.api.java.JavaDoubleRDD.countApproxDistinct$default$1"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.storage.DiskStore.getValues"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.storage.MemoryStore.Entry")
+      ) ++
+      Seq(
+        // Serializer interface change. See SPARK-3045.
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.serializer.DeserializationStream"),
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.serializer.Serializer"),
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.serializer.SerializationStream"),
+        ProblemFilters.exclude[IncompatibleTemplateDefProblem](
+          "org.apache.spark.serializer.SerializerInstance")
+      )++
+      Seq(
+        // Renamed putValues -> putArray + putIterator
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.storage.MemoryStore.putValues"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.storage.DiskStore.putValues"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.storage.TachyonStore.putValues")
+      ) ++
+      Seq(
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.streaming.flume.FlumeReceiver.this"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.streaming.kafka.KafkaUtils.createStream"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.streaming.kafka.KafkaReceiver.this")
+      ) ++
+      Seq( // Ignore some private methods in ALS.
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures"),
+        ProblemFilters.exclude[MissingMethodProblem]( // The only public constructor is the one without arguments.
+          "org.apache.spark.mllib.recommendation.ALS.this"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$$<init>$default$7"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.recommendation.ALS.org$apache$spark$mllib$recommendation$ALS$^dateFeatures")
+      ) ++
+      MimaBuild.excludeSparkClass("mllib.linalg.distributed.ColumnStatisticsAggregator") ++
+      MimaBuild.excludeSparkClass("rdd.ZippedRDD") ++
+      MimaBuild.excludeSparkClass("rdd.ZippedPartition") ++
+      MimaBuild.excludeSparkClass("util.SerializableHyperLogLog") ++
+      MimaBuild.excludeSparkClass("storage.Values") ++
+      MimaBuild.excludeSparkClass("storage.Entry") ++
+      MimaBuild.excludeSparkClass("storage.MemoryStore$Entry") ++
+      // Class was missing "@DeveloperApi" annotation in 1.0.
+      MimaBuild.excludeSparkClass("scheduler.SparkListenerApplicationStart") ++
+      Seq(
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.tree.impurity.Gini.calculate"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.tree.impurity.Entropy.calculate"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem](
+          "org.apache.spark.mllib.tree.impurity.Variance.calculate")
+      ) ++
+      Seq( // Package-private classes removed in SPARK-2341
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.BinaryLabelParser$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.LabelParser$"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser"),
+        ProblemFilters.exclude[MissingClassProblem]("org.apache.spark.mllib.util.MulticlassLabelParser$")
+      ) ++
+      Seq( // package-private classes removed in MLlib
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.org$apache$spark$mllib$regression$GeneralizedLinearAlgorithm$$prependOne")
+      ) ++
+      Seq( // new Vector methods in MLlib (binary compatible assuming users do not implement Vector)
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.linalg.Vector.copy")
+      ) ++
+      Seq( // synthetic methods generated in LabeledPoint
+        ProblemFilters.exclude[MissingTypesProblem]("org.apache.spark.mllib.regression.LabeledPoint$"),
+        ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.mllib.regression.LabeledPoint.apply"),
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.mllib.regression.LabeledPoint.toString")
+      ) ++
+      Seq ( // Scala 2.11 compatibility fix
+        ProblemFilters.exclude[MissingMethodProblem]("org.apache.spark.streaming.StreamingContext.<init>$default$2")
+      )
+    case v if v.startsWith("1.0") =>
+      Seq(
+        MimaBuild.excludeSparkPackage("api.java"),
+        MimaBuild.excludeSparkPackage("mllib"),
+        MimaBuild.excludeSparkPackage("streaming")
+      ) ++
+      MimaBuild.excludeSparkClass("rdd.ClassTags") ++
+      MimaBuild.excludeSparkClass("util.XORShiftRandom") ++
+      MimaBuild.excludeSparkClass("graphx.EdgeRDD") ++
+      MimaBuild.excludeSparkClass("graphx.VertexRDD") ++
+      MimaBuild.excludeSparkClass("graphx.impl.GraphImpl") ++
+      MimaBuild.excludeSparkClass("graphx.impl.RoutingTable") ++
+      MimaBuild.excludeSparkClass("graphx.util.collection.PrimitiveKeyOpenHashMap") ++
+      MimaBuild.excludeSparkClass("graphx.util.collection.GraphXPrimitiveKeyOpenHashMap") ++
+      MimaBuild.excludeSparkClass("mllib.recommendation.MFDataGenerator") ++
+      MimaBuild.excludeSparkClass("mllib.optimization.SquaredGradient") ++
+      MimaBuild.excludeSparkClass("mllib.regression.RidgeRegressionWithSGD") ++
+      MimaBuild.excludeSparkClass("mllib.regression.LassoWithSGD") ++
+      MimaBuild.excludeSparkClass("mllib.regression.LinearRegressionWithSGD")
+    case _ => Seq()
+  }
+}
\ No newline at end of file
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala
index 6c0c926755c20..13cfe29d7b304 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/ReceivedBlockHandlerSuite.scala
@@ -29,7 +29,7 @@ import org.scalatest.{BeforeAndAfter, Matchers}
 import org.scalatest.concurrent.Eventually._
 
 import org.apache.spark._
-import org.apache.spark.network.nio.NioBlockTransferService
+import org.apache.spark.network.netty.NettyBlockTransferService
 import org.apache.spark.rpc.RpcEnv
 import org.apache.spark.scheduler.LiveListenerBus
 import org.apache.spark.serializer.KryoSerializer
@@ -47,7 +47,9 @@ class ReceivedBlockHandlerSuite
   with Matchers
   with Logging {
 
-  val conf = new SparkConf().set("spark.streaming.receiver.writeAheadLog.rollingIntervalSecs", "1")
+  val conf = new SparkConf()
+    .set("spark.streaming.receiver.writeAheadLog.rollingIntervalSecs", "1")
+    .set("spark.app.id", "streaming-test")
   val hadoopConf = new Configuration()
   val streamId = 1
   val securityMgr = new SecurityManager(conf)
@@ -184,7 +186,7 @@ class ReceivedBlockHandlerSuite
   }
 
   test("Test Block - isFullyConsumed") {
-    val sparkConf = new SparkConf()
+    val sparkConf = new SparkConf().set("spark.app.id", "streaming-test")
     sparkConf.set("spark.storage.unrollMemoryThreshold", "512")
     // spark.storage.unrollFraction set to 0.4 for BlockManager
     sparkConf.set("spark.storage.unrollFraction", "0.4")
@@ -251,7 +253,7 @@ class ReceivedBlockHandlerSuite
       maxMem: Long,
       conf: SparkConf,
       name: String = SparkContext.DRIVER_IDENTIFIER): BlockManager = {
-    val transfer = new NioBlockTransferService(conf, securityMgr)
+    val transfer = new NettyBlockTransferService(conf, securityMgr, numCores = 1)
     val manager = new BlockManager(name, rpcEnv, blockManagerMaster, serializer, maxMem, conf,
       mapOutputTracker, shuffleManager, transfer, securityMgr, 0)
     manager.initialize("app-id")

From 9d8e838d883ed21f9ef562e7e3ac074c7e4adb88 Mon Sep 17 00:00:00 2001
From: Stephen Hopper <shopper@shopper-osx.local>
Date: Tue, 8 Sep 2015 14:36:34 +0100
Subject: [PATCH 543/633] =?UTF-8?q?[DOC]=20Added=20R=20to=20the=20list=20o?=
 =?UTF-8?q?f=20languages=20with=20"high-level=20API"=20support=20in=20the?=
 =?UTF-8?q?=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

… main README.

Author: Stephen Hopper <shopper@shopper-osx.local>

Closes #8646 from enragedginger/master.
---
 README.md           |  4 ++--
 docs/quick-start.md | 16 ++++++++--------
 2 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 380422ca00dbe..76e29b4235666 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # Apache Spark
 
 Spark is a fast and general cluster computing system for Big Data. It provides
-high-level APIs in Scala, Java, and Python, and an optimized engine that
+high-level APIs in Scala, Java, Python, and R, and an optimized engine that
 supports general computation graphs for data analysis. It also supports a
 rich set of higher-level tools including Spark SQL for SQL and DataFrames,
 MLlib for machine learning, GraphX for graph processing,
@@ -94,5 +94,5 @@ distribution.
 
 ## Configuration
 
-Please refer to the [Configuration guide](http://spark.apache.org/docs/latest/configuration.html)
+Please refer to the [Configuration Guide](http://spark.apache.org/docs/latest/configuration.html)
 in the online documentation for an overview on how to configure Spark.
diff --git a/docs/quick-start.md b/docs/quick-start.md
index ce2cc9d2169cd..d481fe0ea6d70 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -126,7 +126,7 @@ scala> val wordCounts = textFile.flatMap(line => line.split(" ")).map(word => (w
 wordCounts: spark.RDD[(String, Int)] = spark.ShuffledAggregatedRDD@71f027b8
 {% endhighlight %}
 
-Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
+Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations), and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (String, Int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
 
 {% highlight scala %}
 scala> wordCounts.collect()
@@ -163,7 +163,7 @@ One common data flow pattern is MapReduce, as popularized by Hadoop. Spark can i
 >>> wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)
 {% endhighlight %}
 
-Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations) and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
+Here, we combined the [`flatMap`](programming-guide.html#transformations), [`map`](programming-guide.html#transformations), and [`reduceByKey`](programming-guide.html#transformations) transformations to compute the per-word counts in the file as an RDD of (string, int) pairs. To collect the word counts in our shell, we can use the [`collect`](programming-guide.html#actions) action:
 
 {% highlight python %}
 >>> wordCounts.collect()
@@ -217,13 +217,13 @@ a cluster, as described in the [programming guide](programming-guide.html#initia
 </div>
 
 # Self-Contained Applications
-Now say we wanted to write a self-contained application using the Spark API. We will walk through a
-simple application in both Scala (with SBT), Java (with Maven), and Python.
+Suppose we wish to write a self-contained application using the Spark API. We will walk through a
+simple application in Scala (with sbt), Java (with Maven), and Python.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
 
-We'll create a very simple Spark application in Scala. So simple, in fact, that it's
+We'll create a very simple Spark application in Scala--so simple, in fact, that it's
 named `SimpleApp.scala`:
 
 {% highlight scala %}
@@ -259,7 +259,7 @@ object which contains information about our
 application. 
 
 Our application depends on the Spark API, so we'll also include an sbt configuration file, 
-`simple.sbt` which explains that Spark is a dependency. This file also adds a repository that 
+`simple.sbt`, which explains that Spark is a dependency. This file also adds a repository that 
 Spark depends on:
 
 {% highlight scala %}
@@ -302,7 +302,7 @@ Lines with a: 46, Lines with b: 23
 
 </div>
 <div data-lang="java" markdown="1">
-This example will use Maven to compile an application jar, but any similar build system will work.
+This example will use Maven to compile an application JAR, but any similar build system will work.
 
 We'll create a very simple Spark application, `SimpleApp.java`:
 
@@ -374,7 +374,7 @@ $ find .
 Now, we can package the application using Maven and execute it with `./bin/spark-submit`.
 
 {% highlight bash %}
-# Package a jar containing your application
+# Package a JAR containing your application
 $ mvn package
 ...
 [INFO] Building jar: {..}/{..}/target/simple-project-1.0.jar

From 6ceed852ab716d8acc46ce90cba9cfcff6d3616f Mon Sep 17 00:00:00 2001
From: Jacek Laskowski <jacek@japila.pl>
Date: Tue, 8 Sep 2015 14:38:10 +0100
Subject: [PATCH 544/633] Docs small fixes

Author: Jacek Laskowski <jacek@japila.pl>

Closes #8629 from jaceklaskowski/docs-fixes.
---
 docs/building-spark.md   | 23 +++++++++++------------
 docs/cluster-overview.md | 15 ++++++++-------
 2 files changed, 19 insertions(+), 19 deletions(-)

diff --git a/docs/building-spark.md b/docs/building-spark.md
index f133eb96d9a21..4db32cfd628bc 100644
--- a/docs/building-spark.md
+++ b/docs/building-spark.md
@@ -61,12 +61,13 @@ If you don't run this, you may see errors like the following:
 You can fix this by setting the `MAVEN_OPTS` variable as discussed before.
 
 **Note:**
-* *For Java 8 and above this step is not required.*
-* *If using `build/mvn` and `MAVEN_OPTS` were not already set, the script will automate this for you.*
+
+* For Java 8 and above this step is not required.
+* If using `build/mvn` with no `MAVEN_OPTS` set, the script will automate this for you.
 
 # Specifying the Hadoop Version
 
-Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the "hadoop.version" property. If unset, Spark will build against Hadoop 2.2.0 by default. Note that certain build profiles are required for particular Hadoop versions:
+Because HDFS is not protocol-compatible across versions, if you want to read from HDFS, you'll need to build Spark against the specific HDFS version in your environment. You can do this through the `hadoop.version` property. If unset, Spark will build against Hadoop 2.2.0 by default. Note that certain build profiles are required for particular Hadoop versions:
 
 <table class="table">
   <thead>
@@ -91,7 +92,7 @@ mvn -Dhadoop.version=1.2.1 -Phadoop-1 -DskipTests clean package
 mvn -Dhadoop.version=2.0.0-mr1-cdh4.2.0 -Phadoop-1 -DskipTests clean package
 {% endhighlight %}
 
-You can enable the "yarn" profile and optionally set the "yarn.version" property if it is different from "hadoop.version". Spark only supports YARN versions 2.2.0 and later.
+You can enable the `yarn` profile and optionally set the `yarn.version` property if it is different from `hadoop.version`. Spark only supports YARN versions 2.2.0 and later.
 
 Examples:
 
@@ -125,7 +126,7 @@ mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -Phive -Phive-thriftserver -Dskip
 # Building for Scala 2.11
 To produce a Spark package compiled with Scala 2.11, use the `-Dscala-2.11` property:
 
-    dev/change-scala-version.sh 2.11
+    ./dev/change-scala-version.sh 2.11
     mvn -Pyarn -Phadoop-2.4 -Dscala-2.11 -DskipTests clean package
 
 Spark does not yet support its JDBC component for Scala 2.11.
@@ -163,11 +164,9 @@ the `spark-parent` module).
 
 Thus, the full flow for running continuous-compilation of the `core` submodule may look more like:
 
-```
- $ mvn install
- $ cd core
- $ mvn scala:cc
-```
+    $ mvn install
+    $ cd core
+    $ mvn scala:cc
 
 # Building Spark with IntelliJ IDEA or Eclipse
 
@@ -193,11 +192,11 @@ then ship it over to the cluster. We are investigating the exact cause for this.
 
 # Packaging without Hadoop Dependencies for YARN
 
-The assembly jar produced by `mvn package` will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with yarn.application.classpath.  The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself.
+The assembly jar produced by `mvn package` will, by default, include all of Spark's dependencies, including Hadoop and some of its ecosystem projects. On YARN deployments, this causes multiple versions of these to appear on executor classpaths: the version packaged in the Spark assembly and the version on each node, included with `yarn.application.classpath`.  The `hadoop-provided` profile builds the assembly without including Hadoop-ecosystem projects, like ZooKeeper and Hadoop itself.
 
 # Building with SBT
 
-Maven is the official recommendation for packaging Spark, and is the "build of reference".
+Maven is the official build tool recommended for packaging Spark, and is the *build of reference*.
 But SBT is supported for day-to-day development since it can provide much faster iterative
 compilation. More advanced developers may wish to use SBT.
 
diff --git a/docs/cluster-overview.md b/docs/cluster-overview.md
index 7079de546e2f5..faaf154d243f5 100644
--- a/docs/cluster-overview.md
+++ b/docs/cluster-overview.md
@@ -5,18 +5,19 @@ title: Cluster Mode Overview
 
 This document gives a short overview of how Spark runs on clusters, to make it easier to understand
 the components involved. Read through the [application submission guide](submitting-applications.html)
-to submit applications to a cluster.
+to learn about launching applications on a cluster.
 
 # Components
 
-Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext
+Spark applications run as independent sets of processes on a cluster, coordinated by the `SparkContext`
 object in your main program (called the _driver program_).
+
 Specifically, to run on a cluster, the SparkContext can connect to several types of _cluster managers_
-(either Spark's own standalone cluster manager or Mesos/YARN), which allocate resources across
+(either Spark's own standalone cluster manager, Mesos or YARN), which allocate resources across
 applications. Once connected, Spark acquires *executors* on nodes in the cluster, which are
 processes that run computations and store data for your application.
 Next, it sends your application code (defined by JAR or Python files passed to SparkContext) to
-the executors. Finally, SparkContext sends *tasks* for the executors to run.
+the executors. Finally, SparkContext sends *tasks* to the executors to run.
 
 <p style="text-align: center;">
   <img src="img/cluster-overview.png" title="Spark cluster components" alt="Spark cluster components" />
@@ -33,9 +34,9 @@ There are several useful things to note about this architecture:
 2. Spark is agnostic to the underlying cluster manager. As long as it can acquire executor
    processes, and these communicate with each other, it is relatively easy to run it even on a
    cluster manager that also supports other applications (e.g. Mesos/YARN).
-3. The driver program must listen for and accept incoming connections from its executors throughout 
-   its lifetime (e.g., see [spark.driver.port and spark.fileserver.port in the network config 
-   section](configuration.html#networking)). As such, the driver program must be network 
+3. The driver program must listen for and accept incoming connections from its executors throughout
+   its lifetime (e.g., see [spark.driver.port and spark.fileserver.port in the network config
+   section](configuration.html#networking)). As such, the driver program must be network
    addressable from the worker nodes.
 4. Because the driver schedules tasks on the cluster, it should be run close to the worker
    nodes, preferably on the same local area network. If you'd like to send requests to the

From 990c9f79c28db501018a0a3af446ff879962475d Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Tue, 8 Sep 2015 23:07:34 +0800
Subject: [PATCH 545/633] [SPARK-9170] [SQL] Use OrcStructInspector to be case
 preserving when writing ORC files

JIRA: https://issues.apache.org/jira/browse/SPARK-9170

`StandardStructObjectInspector` will implicitly lowercase column names. But I think Orc format doesn't have such requirement. In fact, there is a `OrcStructInspector` specified for Orc format. We should use it when serialize rows to Orc file. It can be case preserving when writing ORC files.

Author: Liang-Chi Hsieh <viirya@appier.com>

Closes #7520 from viirya/use_orcstruct.
---
 .../spark/sql/hive/orc/OrcRelation.scala      | 47 ++++++++++---------
 .../spark/sql/hive/orc/OrcQuerySuite.scala    | 14 ++++++
 2 files changed, 40 insertions(+), 21 deletions(-)

diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 4eeca9aec12bd..7e89109259955 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -25,9 +25,9 @@ import com.google.common.base.Objects
 import org.apache.hadoop.conf.Configuration
 import org.apache.hadoop.fs.{FileStatus, Path}
 import org.apache.hadoop.hive.conf.HiveConf.ConfVars
-import org.apache.hadoop.hive.ql.io.orc.{OrcInputFormat, OrcOutputFormat, OrcSerde, OrcSplit}
-import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector
-import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoUtils
+import org.apache.hadoop.hive.ql.io.orc.{OrcInputFormat, OrcOutputFormat, OrcSerde, OrcSplit, OrcStruct}
+import org.apache.hadoop.hive.serde2.objectinspector.SettableStructObjectInspector
+import org.apache.hadoop.hive.serde2.typeinfo.{TypeInfoUtils, StructTypeInfo}
 import org.apache.hadoop.io.{NullWritable, Writable}
 import org.apache.hadoop.mapred.{InputFormat => MapRedInputFormat, JobConf, OutputFormat => MapRedOutputFormat, RecordWriter, Reporter}
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat
@@ -89,21 +89,10 @@ private[orc] class OrcOutputWriter(
       TypeInfoUtils.getTypeInfoFromTypeString(
         HiveMetastoreTypes.toMetastoreType(dataSchema))
 
-    TypeInfoUtils
-      .getStandardJavaObjectInspectorFromTypeInfo(typeInfo)
-      .asInstanceOf[StructObjectInspector]
+    OrcStruct.createObjectInspector(typeInfo.asInstanceOf[StructTypeInfo])
+      .asInstanceOf[SettableStructObjectInspector]
   }
 
-  // Used to hold temporary `Writable` fields of the next row to be written.
-  private val reusableOutputBuffer = new Array[Any](dataSchema.length)
-
-  // Used to convert Catalyst values into Hadoop `Writable`s.
-  private val wrappers = structOI.getAllStructFieldRefs.asScala
-    .zip(dataSchema.fields.map(_.dataType))
-    .map { case (ref, dt) =>
-      wrapperFor(ref.getFieldObjectInspector, dt)
-    }.toArray
-
   // `OrcRecordWriter.close()` creates an empty file if no rows are written at all.  We use this
   // flag to decide whether `OrcRecordWriter.close()` needs to be called.
   private var recordWriterInstantiated = false
@@ -127,16 +116,32 @@ private[orc] class OrcOutputWriter(
 
   override def write(row: Row): Unit = throw new UnsupportedOperationException("call writeInternal")
 
-  override protected[sql] def writeInternal(row: InternalRow): Unit = {
+  private def wrapOrcStruct(
+      struct: OrcStruct,
+      oi: SettableStructObjectInspector,
+      row: InternalRow): Unit = {
+    val fieldRefs = oi.getAllStructFieldRefs
     var i = 0
-    while (i < row.numFields) {
-      reusableOutputBuffer(i) = wrappers(i)(row.get(i, dataSchema(i).dataType))
+    while (i < fieldRefs.size) {
+      oi.setStructFieldData(
+        struct,
+        fieldRefs.get(i),
+        wrap(
+          row.get(i, dataSchema(i).dataType),
+          fieldRefs.get(i).getFieldObjectInspector,
+          dataSchema(i).dataType))
       i += 1
     }
+  }
+
+  val cachedOrcStruct = structOI.create().asInstanceOf[OrcStruct]
+
+  override protected[sql] def writeInternal(row: InternalRow): Unit = {
+    wrapOrcStruct(cachedOrcStruct, structOI, row)
 
     recordWriter.write(
       NullWritable.get(),
-      serializer.serialize(reusableOutputBuffer, structOI))
+      serializer.serialize(cachedOrcStruct, structOI))
   }
 
   override def close(): Unit = {
@@ -259,7 +264,7 @@ private[orc] case class OrcTableScan(
     maybeStructOI.map { soi =>
       val (fieldRefs, fieldOrdinals) = nonPartitionKeyAttrs.map {
         case (attr, ordinal) =>
-          soi.getStructFieldRef(attr.name.toLowerCase) -> ordinal
+          soi.getStructFieldRef(attr.name) -> ordinal
       }.unzip
       val unwrappers = fieldRefs.map(unwrapperFor)
       // Map each tuple to a row object
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
index 744d462938141..8bc33fcf5d906 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcQuerySuite.scala
@@ -287,6 +287,20 @@ class OrcQuerySuite extends QueryTest with BeforeAndAfterAll with OrcTest {
     }
   }
 
+  test("SPARK-9170: Don't implicitly lowercase of user-provided columns") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      sqlContext.range(0, 10).select('id as "Acol").write.format("orc").save(path)
+      sqlContext.read.format("orc").load(path).schema("Acol")
+      intercept[IllegalArgumentException] {
+        sqlContext.read.format("orc").load(path).schema("acol")
+      }
+      checkAnswer(sqlContext.read.format("orc").load(path).select("acol").sort("acol"),
+        (0 until 10).map(Row(_)))
+    }
+  }
+
   test("SPARK-8501: Avoids discovery schema from empty ORC files") {
     withTempPath { dir =>
       val path = dir.getCanonicalPath

From 5b2192e846b843d8a0cb9427d19bb677431194a0 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 8 Sep 2015 11:11:35 -0700
Subject: [PATCH 546/633] [SPARK-10480] [ML] Fix
 ML.LinearRegressionModel.copy()

This PR fix two model ```copy()``` related issues:
[SPARK-10480](https://issues.apache.org/jira/browse/SPARK-10480)
```ML.LinearRegressionModel.copy()``` ignored argument ```extra```, it will not take effect when users setting this parameter.
[SPARK-10479](https://issues.apache.org/jira/browse/SPARK-10479)
```ML.LogisticRegressionModel.copy()``` should copy model summary if available.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8641 from yanboliang/linear-regression-copy.
---
 .../apache/spark/ml/classification/LogisticRegression.scala   | 4 +++-
 .../org/apache/spark/ml/regression/LinearRegression.scala     | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 21fbe38ca8233..a460262b87e43 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -468,7 +468,9 @@ class LogisticRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): LogisticRegressionModel = {
-    copyValues(new LogisticRegressionModel(uid, weights, intercept), extra).setParent(parent)
+    val newModel = copyValues(new LogisticRegressionModel(uid, weights, intercept), extra)
+    if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
+    newModel.setParent(parent)
   }
 
   override protected def raw2prediction(rawPrediction: Vector): Double = {
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 884003eb38524..e4602d36ccc87 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -310,7 +310,7 @@ class LinearRegressionModel private[ml] (
   }
 
   override def copy(extra: ParamMap): LinearRegressionModel = {
-    val newModel = copyValues(new LinearRegressionModel(uid, weights, intercept))
+    val newModel = copyValues(new LinearRegressionModel(uid, weights, intercept), extra)
     if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get)
     newModel.setParent(parent)
   }

From 5fd57955ef477347408f68eb1cb6ad1881fdb6e0 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Tue, 8 Sep 2015 12:05:41 -0700
Subject: [PATCH 547/633] [SPARK-10316] [SQL] respect nondeterministic
 expressions in PhysicalOperation

We did a lot of special handling for non-deterministic expressions in `Optimizer`. However, `PhysicalOperation` just collects all Projects and Filters and mess it up. We should respect the operators order caused by non-deterministic expressions in `PhysicalOperation`.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8486 from cloud-fan/fix.
---
 .../sql/catalyst/planning/patterns.scala      | 38 ++++---------------
 .../org/apache/spark/sql/DataFrameSuite.scala | 12 ++++++
 2 files changed, 20 insertions(+), 30 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
index e8abcd63f7d85..53537799517ce 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
@@ -17,35 +17,12 @@
 
 package org.apache.spark.sql.catalyst.planning
 
-import scala.annotation.tailrec
-
 import org.apache.spark.Logging
 import org.apache.spark.sql.catalyst.trees.TreeNodeRef
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans._
 import org.apache.spark.sql.catalyst.plans.logical._
 
-/**
- * A pattern that matches any number of filter operations on top of another relational operator.
- * Adjacent filter operators are collected and their conditions are broken up and returned as a
- * sequence of conjunctive predicates.
- *
- * @return A tuple containing a sequence of conjunctive predicates that should be used to filter the
- *         output and a relational operator.
- */
-object FilteredOperation extends PredicateHelper {
-  type ReturnType = (Seq[Expression], LogicalPlan)
-
-  def unapply(plan: LogicalPlan): Option[ReturnType] = Some(collectFilters(Nil, plan))
-
-  @tailrec
-  private def collectFilters(filters: Seq[Expression], plan: LogicalPlan): ReturnType = plan match {
-    case Filter(condition, child) =>
-      collectFilters(filters ++ splitConjunctivePredicates(condition), child)
-    case other => (filters, other)
-  }
-}
-
 /**
  * A pattern that matches any number of project or filter operations on top of another relational
  * operator.  All filter operators are collected and their conditions are broken up and returned
@@ -62,8 +39,9 @@ object PhysicalOperation extends PredicateHelper {
   }
 
   /**
-   * Collects projects and filters, in-lining/substituting aliases if necessary.  Here are two
-   * examples for alias in-lining/substitution.  Before:
+   * Collects all deterministic projects and filters, in-lining/substituting aliases if necessary.
+   * Here are two examples for alias in-lining/substitution.
+   * Before:
    * {{{
    *   SELECT c1 FROM (SELECT key AS c1 FROM t1) t2 WHERE c1 > 10
    *   SELECT c1 AS c2 FROM (SELECT key AS c1 FROM t1) t2 WHERE c1 > 10
@@ -74,15 +52,15 @@ object PhysicalOperation extends PredicateHelper {
    *   SELECT key AS c2 FROM t1 WHERE key > 10
    * }}}
    */
-  def collectProjectsAndFilters(plan: LogicalPlan):
+  private def collectProjectsAndFilters(plan: LogicalPlan):
       (Option[Seq[NamedExpression]], Seq[Expression], LogicalPlan, Map[Attribute, Expression]) =
     plan match {
-      case Project(fields, child) =>
+      case Project(fields, child) if fields.forall(_.deterministic) =>
         val (_, filters, other, aliases) = collectProjectsAndFilters(child)
         val substitutedFields = fields.map(substitute(aliases)).asInstanceOf[Seq[NamedExpression]]
         (Some(substitutedFields), filters, other, collectAliases(substitutedFields))
 
-      case Filter(condition, child) =>
+      case Filter(condition, child) if condition.deterministic =>
         val (fields, filters, other, aliases) = collectProjectsAndFilters(child)
         val substitutedCondition = substitute(aliases)(condition)
         (fields, filters ++ splitConjunctivePredicates(substitutedCondition), other, aliases)
@@ -91,11 +69,11 @@ object PhysicalOperation extends PredicateHelper {
         (None, Nil, other, Map.empty)
     }
 
-  def collectAliases(fields: Seq[Expression]): Map[Attribute, Expression] = fields.collect {
+  private def collectAliases(fields: Seq[Expression]): Map[Attribute, Expression] = fields.collect {
     case a @ Alias(child, _) => a.toAttribute -> child
   }.toMap
 
-  def substitute(aliases: Map[Attribute, Expression])(expr: Expression): Expression = {
+  private def substitute(aliases: Map[Attribute, Expression])(expr: Expression): Expression = {
     expr.transform {
       case a @ Alias(ref: AttributeReference, name) =>
         aliases.get(ref).map(Alias(_, name)(a.exprId, a.qualifiers)).getOrElse(a)
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index b5b9f11785074..dbed4fc247140 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -22,6 +22,8 @@ import java.io.File
 import scala.language.postfixOps
 import scala.util.Random
 
+import org.scalatest.Matchers._
+
 import org.apache.spark.sql.catalyst.plans.logical.OneRowRelation
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
@@ -895,4 +897,14 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
       .orderBy(sum('j))
     checkAnswer(query, Row(1, 2))
   }
+
+  test("SPARK-10316: respect non-deterministic expressions in PhysicalOperation") {
+    val input = sqlContext.read.json(sqlContext.sparkContext.makeRDD(
+      (1 to 10).map(i => s"""{"id": $i}""")))
+
+    val df = input.select($"id", rand(0).as('r))
+    df.as("a").join(df.filter($"r" < 0.5).as("b"), $"a.id" === $"b.id").collect().foreach { row =>
+      assert(row.getDouble(1) - row.getDouble(3) === 0.0 +- 0.001)
+    }
+  }
 }

From f7b55dbfc3343cad988e2490478fce1a11343c73 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 8 Sep 2015 12:48:21 -0700
Subject: [PATCH 548/633] [SPARK-10470] [ML] ml.IsotonicRegressionModel.copy
 should set parent

Copied model must have the same parent, but ml.IsotonicRegressionModel.copy did not set parent.
Here fix it and add test case.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8637 from yanboliang/spark-10470.
---
 .../org/apache/spark/ml/regression/IsotonicRegression.scala  | 2 +-
 .../apache/spark/ml/regression/IsotonicRegressionSuite.scala | 5 +++++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
index d43a3447d3975..2ff500f291abc 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala
@@ -203,7 +203,7 @@ class IsotonicRegressionModel private[ml] (
   def predictions: Vector = Vectors.dense(oldModel.predictions)
 
   override def copy(extra: ParamMap): IsotonicRegressionModel = {
-    copyValues(new IsotonicRegressionModel(uid, oldModel), extra)
+    copyValues(new IsotonicRegressionModel(uid, oldModel), extra).setParent(parent)
   }
 
   override def transform(dataset: DataFrame): DataFrame = {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
index c0ab00b68a2f3..59f4193abc8f0 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.sql.{DataFrame, Row}
@@ -89,6 +90,10 @@ class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(ir.getFeatureIndex === 0)
 
     val model = ir.fit(dataset)
+
+    // copied model must have the same parent.
+    MLTestingUtils.checkCopy(model)
+
     model.transform(dataset)
       .select("label", "features", "prediction", "weight")
       .collect()

From 7a9dcbc91d55dbc0cbf4812319bde65f4509b467 Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Tue, 8 Sep 2015 14:10:12 -0700
Subject: [PATCH 549/633] [SPARK-10441] [SQL] Save data correctly to json.

https://issues.apache.org/jira/browse/SPARK-10441

Author: Yin Huai <yhuai@databricks.com>

Closes #8597 from yhuai/timestampJson.
---
 .../spark/sql/RandomDataGenerator.scala       | 41 +++++++++-
 .../datasources/json/JacksonGenerator.scala   | 11 ++-
 .../datasources/json/JacksonParser.scala      | 31 ++++++++
 .../hive/orc/OrcHadoopFsRelationSuite.scala   |  8 ++
 .../sources/JsonHadoopFsRelationSuite.scala   |  8 ++
 .../ParquetHadoopFsRelationSuite.scala        |  9 ++-
 .../SimpleTextHadoopFsRelationSuite.scala     | 19 ++++-
 .../sql/sources/SimpleTextRelation.scala      |  7 +-
 .../sql/sources/hadoopFsRelationSuites.scala  | 79 +++++++++++++++++++
 9 files changed, 205 insertions(+), 8 deletions(-)

diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
index 11e0c120f4072..4025cbcec1019 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/RandomDataGenerator.scala
@@ -23,6 +23,8 @@ import java.math.MathContext
 
 import scala.util.Random
 
+import org.apache.spark.sql.catalyst.CatalystTypeConverters
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.CalendarInterval
 
@@ -84,6 +86,7 @@ object RandomDataGenerator {
    * random data generator is defined for that data type. The generated values will use an external
    * representation of the data type; for example, the random generator for [[DateType]] will return
    * instances of [[java.sql.Date]] and the generator for [[StructType]] will return a [[Row]].
+   * For a [[UserDefinedType]] for a class X, an instance of class X is returned.
    *
    * @param dataType the type to generate values for
    * @param nullable whether null values should be generated
@@ -106,7 +109,22 @@ object RandomDataGenerator {
       })
       case BooleanType => Some(() => rand.nextBoolean())
       case DateType => Some(() => new java.sql.Date(rand.nextInt()))
-      case TimestampType => Some(() => new java.sql.Timestamp(rand.nextLong()))
+      case TimestampType =>
+        val generator =
+          () => {
+            var milliseconds = rand.nextLong() % 253402329599999L
+            // -62135740800000L is the number of milliseconds before January 1, 1970, 00:00:00 GMT
+            // for "0001-01-01 00:00:00.000000". We need to find a
+            // number that is greater or equals to this number as a valid timestamp value.
+            while (milliseconds < -62135740800000L) {
+              // 253402329599999L is the the number of milliseconds since
+              // January 1, 1970, 00:00:00 GMT for "9999-12-31 23:59:59.999999".
+              milliseconds = rand.nextLong() % 253402329599999L
+            }
+            // DateTimeUtils.toJavaTimestamp takes microsecond.
+            DateTimeUtils.toJavaTimestamp(milliseconds * 1000)
+          }
+        Some(generator)
       case CalendarIntervalType => Some(() => {
         val months = rand.nextInt(1000)
         val ns = rand.nextLong()
@@ -159,6 +177,27 @@ object RandomDataGenerator {
           None
         }
       }
+      case udt: UserDefinedType[_] => {
+        val maybeSqlTypeGenerator = forType(udt.sqlType, nullable, seed)
+        // Because random data generator at here returns scala value, we need to
+        // convert it to catalyst value to call udt's deserialize.
+        val toCatalystType = CatalystTypeConverters.createToCatalystConverter(udt.sqlType)
+
+        if (maybeSqlTypeGenerator.isDefined) {
+          val sqlTypeGenerator = maybeSqlTypeGenerator.get
+          val generator = () => {
+            val generatedScalaValue = sqlTypeGenerator.apply()
+            if (generatedScalaValue == null) {
+              null
+            } else {
+              udt.deserialize(toCatalystType(generatedScalaValue))
+            }
+          }
+          Some(generator)
+        } else {
+          None
+        }
+      }
       case unsupportedType => None
     }
     // Handle nullability by wrapping the non-null value generator:
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
index 330ba907b2ef9..f65c7bbd6e29d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonGenerator.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.sql.execution.datasources.json
 
 import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.util.DateTimeUtils
 
 import scala.collection.Map
 
@@ -89,7 +90,7 @@ private[sql] object JacksonGenerator {
     def valWriter: (DataType, Any) => Unit = {
       case (_, null) | (NullType, _) => gen.writeNull()
       case (StringType, v) => gen.writeString(v.toString)
-      case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString)
+      case (TimestampType, v: Long) => gen.writeString(DateTimeUtils.toJavaTimestamp(v).toString)
       case (IntegerType, v: Int) => gen.writeNumber(v)
       case (ShortType, v: Short) => gen.writeNumber(v)
       case (FloatType, v: Float) => gen.writeNumber(v)
@@ -99,8 +100,12 @@ private[sql] object JacksonGenerator {
       case (ByteType, v: Byte) => gen.writeNumber(v.toInt)
       case (BinaryType, v: Array[Byte]) => gen.writeBinary(v)
       case (BooleanType, v: Boolean) => gen.writeBoolean(v)
-      case (DateType, v) => gen.writeString(v.toString)
-      case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, udt.serialize(v))
+      case (DateType, v: Int) => gen.writeString(DateTimeUtils.toJavaDate(v).toString)
+      // For UDT values, they should be in the SQL type's corresponding value type.
+      // We should not see values in the user-defined class at here.
+      // For example, VectorUDT's SQL type is an array of double. So, we should expect that v is
+      // an ArrayData at here, instead of a Vector.
+      case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, v)
 
       case (ArrayType(ty, _), v: ArrayData) =>
         gen.writeStartArray()
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
index cd68bd667c5c4..ff4d8c04e8eaf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JacksonParser.scala
@@ -81,9 +81,37 @@ private[sql] object JacksonParser {
       case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, FloatType) =>
         parser.getFloatValue
 
+      case (VALUE_STRING, FloatType) =>
+        // Special case handling for NaN and Infinity.
+        val value = parser.getText
+        val lowerCaseValue = value.toLowerCase()
+        if (lowerCaseValue.equals("nan") ||
+          lowerCaseValue.equals("infinity") ||
+          lowerCaseValue.equals("-infinity") ||
+          lowerCaseValue.equals("inf") ||
+          lowerCaseValue.equals("-inf")) {
+          value.toFloat
+        } else {
+          sys.error(s"Cannot parse $value as FloatType.")
+        }
+
       case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, DoubleType) =>
         parser.getDoubleValue
 
+      case (VALUE_STRING, DoubleType) =>
+        // Special case handling for NaN and Infinity.
+        val value = parser.getText
+        val lowerCaseValue = value.toLowerCase()
+        if (lowerCaseValue.equals("nan") ||
+          lowerCaseValue.equals("infinity") ||
+          lowerCaseValue.equals("-infinity") ||
+          lowerCaseValue.equals("inf") ||
+          lowerCaseValue.equals("-inf")) {
+          value.toDouble
+        } else {
+          sys.error(s"Cannot parse $value as DoubleType.")
+        }
+
       case (VALUE_NUMBER_INT | VALUE_NUMBER_FLOAT, dt: DecimalType) =>
         Decimal(parser.getDecimalValue, dt.precision, dt.scale)
 
@@ -126,6 +154,9 @@ private[sql] object JacksonParser {
 
       case (_, udt: UserDefinedType[_]) =>
         convertField(factory, parser, udt.sqlType)
+
+      case (token, dataType) =>
+        sys.error(s"Failed to parse a value for data type $dataType (current token: $token).")
     }
   }
 
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
index 9a299c3f9d1f3..92043d66c914f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcHadoopFsRelationSuite.scala
@@ -28,6 +28,14 @@ class OrcHadoopFsRelationSuite extends HadoopFsRelationTest {
 
   override val dataSourceName: String = classOf[DefaultSource].getCanonicalName
 
+  // ORC does not play well with NullType and UDT.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: NullType => false
+    case _: CalendarIntervalType => false
+    case _: UserDefinedType[_] => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
index 1945b15002337..ef37787137d07 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/JsonHadoopFsRelationSuite.scala
@@ -28,6 +28,14 @@ import org.apache.spark.sql.types._
 class JsonHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String = "json"
 
+  // JSON does not write data of NullType and does not play well with BinaryType.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: NullType => false
+    case _: BinaryType => false
+    case _: CalendarIntervalType => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
index 08c3c17973043..e2d754e806403 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/ParquetHadoopFsRelationSuite.scala
@@ -24,7 +24,7 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.sql.{execution, AnalysisException, SaveMode}
-import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
+import org.apache.spark.sql.types._
 
 
 class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
@@ -32,6 +32,13 @@ class ParquetHadoopFsRelationSuite extends HadoopFsRelationTest {
 
   override val dataSourceName: String = "parquet"
 
+  // Parquet does not play well with NullType.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: NullType => false
+    case _: CalendarIntervalType => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
index 1125ca670107b..a3a124488d983 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextHadoopFsRelationSuite.scala
@@ -20,11 +20,28 @@ package org.apache.spark.sql.sources
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.deploy.SparkHadoopUtil
-import org.apache.spark.sql.types.{IntegerType, StructField, StructType}
+import org.apache.spark.sql.types._
 
 class SimpleTextHadoopFsRelationSuite extends HadoopFsRelationTest {
   override val dataSourceName: String = classOf[SimpleTextSource].getCanonicalName
 
+  // We have a very limited number of supported types at here since it is just for a
+  // test relation and we do very basic testing at here.
+  override protected def supportsDataType(dataType: DataType): Boolean = dataType match {
+    case _: BinaryType => false
+    // We are using random data generator and the generated strings are not really valid string.
+    case _: StringType => false
+    case _: BooleanType => false // see https://issues.apache.org/jira/browse/SPARK-10442
+    case _: CalendarIntervalType => false
+    case _: DateType => false
+    case _: TimestampType => false
+    case _: ArrayType => false
+    case _: MapType => false
+    case _: StructType => false
+    case _: UserDefinedType[_] => false
+    case _ => true
+  }
+
   test("save()/load() - partitioned table - simple queries - partition columns in data") {
     withTempDir { file =>
       val basePath = new Path(file.getCanonicalPath)
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
index 527ca7a81cad8..aeaaa3e1c5220 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/SimpleTextRelation.scala
@@ -68,7 +68,9 @@ class SimpleTextOutputWriter(path: String, context: TaskAttemptContext) extends
     new AppendingTextOutputFormat(new Path(path)).getRecordWriter(context)
 
   override def write(row: Row): Unit = {
-    val serialized = row.toSeq.map(_.toString).mkString(",")
+    val serialized = row.toSeq.map { v =>
+      if (v == null) "" else v.toString
+    }.mkString(",")
     recordWriter.write(null, new Text(serialized))
   }
 
@@ -112,7 +114,8 @@ class SimpleTextRelation(
     val fields = dataSchema.map(_.dataType)
 
     sparkContext.textFile(inputStatuses.map(_.getPath).mkString(",")).map { record =>
-      Row(record.split(",").zip(fields).map { case (value, dataType) =>
+      Row(record.split(",", -1).zip(fields).map { case (v, dataType) =>
+        val value = if (v == "") null else v
         // `Cast`ed values are always of Catalyst types (i.e. UTF8String instead of String, etc.)
         val catalystValue = Cast(Literal(value), dataType).eval()
         // Here we're converting Catalyst values to Scala values to test `needsConversion`
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 2ad2618dfc436..24f43cf7c15ca 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -38,6 +38,8 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with Tes
 
   val dataSourceName: String
 
+  protected def supportsDataType(dataType: DataType): Boolean = true
+
   val dataSchema =
     StructType(
       Seq(
@@ -98,6 +100,83 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with Tes
     }
   }
 
+  test("test all data types") {
+    withTempPath { file =>
+      // Create the schema.
+      val struct =
+        StructType(
+          StructField("f1", FloatType, true) ::
+            StructField("f2", ArrayType(BooleanType), true) :: Nil)
+      // TODO: add CalendarIntervalType to here once we can save it out.
+      val dataTypes =
+        Seq(
+          StringType, BinaryType, NullType, BooleanType,
+          ByteType, ShortType, IntegerType, LongType,
+          FloatType, DoubleType, DecimalType(25, 5), DecimalType(6, 5),
+          DateType, TimestampType,
+          ArrayType(IntegerType), MapType(StringType, LongType), struct,
+          new MyDenseVectorUDT())
+      val fields = dataTypes.zipWithIndex.map { case (dataType, index) =>
+        StructField(s"col$index", dataType, nullable = true)
+      }
+      val schema = StructType(fields)
+
+      // Generate data at the driver side. We need to materialize the data first and then
+      // create RDD.
+      val maybeDataGenerator =
+        RandomDataGenerator.forType(
+          dataType = schema,
+          nullable = true,
+          seed = Some(System.nanoTime()))
+      val dataGenerator =
+        maybeDataGenerator
+          .getOrElse(fail(s"Failed to create data generator for schema $schema"))
+      val data = (1 to 10).map { i =>
+        dataGenerator.apply() match {
+          case row: Row => row
+          case null => Row.fromSeq(Seq.fill(schema.length)(null))
+          case other =>
+            fail(s"Row or null is expected to be generated, " +
+              s"but a ${other.getClass.getCanonicalName} is generated.")
+        }
+      }
+
+      // Create a DF for the schema with random data.
+      val rdd = sqlContext.sparkContext.parallelize(data, 10)
+      val df = sqlContext.createDataFrame(rdd, schema)
+
+      // All columns that have supported data types of this source.
+      val supportedColumns = schema.fields.collect {
+        case StructField(name, dataType, _, _) if supportsDataType(dataType) => name
+      }
+      val selectedColumns = util.Random.shuffle(supportedColumns.toSeq)
+
+      val dfToBeSaved = df.selectExpr(selectedColumns: _*)
+
+      // Save the data out.
+      dfToBeSaved
+        .write
+        .format(dataSourceName)
+        .option("dataSchema", dfToBeSaved.schema.json) // This option is just used by tests.
+        .save(file.getCanonicalPath)
+
+      val loadedDF =
+        sqlContext
+          .read
+          .format(dataSourceName)
+          .schema(dfToBeSaved.schema)
+          .option("dataSchema", dfToBeSaved.schema.json) // This option is just used by tests.
+          .load(file.getCanonicalPath)
+          .selectExpr(selectedColumns: _*)
+
+      // Read the data back.
+      checkAnswer(
+        loadedDF,
+        dfToBeSaved
+      )
+    }
+  }
+
   test("save()/load() - non-partitioned table - Overwrite") {
     withTempPath { file =>
       testDF.write.mode(SaveMode.Overwrite).format(dataSourceName).save(file.getCanonicalPath)

From e6f8d3686016a305a747c5bcc85f46fd4c0cbe83 Mon Sep 17 00:00:00 2001
From: Vinod K C <vinod.kc@huawei.com>
Date: Tue, 8 Sep 2015 14:44:05 -0700
Subject: [PATCH 550/633] [SPARK-10468] [ MLLIB ] Verify schema before
 Dataframe select API call

Loader.checkSchema was called to verify the schema after dataframe.select(...).
Schema verification should be done before dataframe.select(...)

Author: Vinod K C <vinod.kc@huawei.com>

Closes #8636 from vinodkc/fix_GaussianMixtureModel_load_verification.
---
 .../apache/spark/mllib/clustering/GaussianMixtureModel.scala  | 3 +--
 .../main/scala/org/apache/spark/mllib/feature/Word2Vec.scala  | 4 +---
 2 files changed, 2 insertions(+), 5 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
index 7f6163e04bf17..a5902190d4637 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
@@ -168,10 +168,9 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
       val dataPath = Loader.dataPath(path)
       val sqlContext = new SQLContext(sc)
       val dataFrame = sqlContext.read.parquet(dataPath)
-      val dataArray = dataFrame.select("weight", "mu", "sigma").collect()
-
       // Check schema explicitly since erasure makes it hard to use match-case for checking.
       Loader.checkSchema[Data](dataFrame.schema)
+      val dataArray = dataFrame.select("weight", "mu", "sigma").collect()
 
       val (weights, gaussians) = dataArray.map {
         case Row(weight: Double, mu: Vector, sigma: Matrix) =>
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
index 36b124c5d2966..58857c338f546 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
@@ -590,12 +590,10 @@ object Word2VecModel extends Loader[Word2VecModel] {
       val dataPath = Loader.dataPath(path)
       val sqlContext = new SQLContext(sc)
       val dataFrame = sqlContext.read.parquet(dataPath)
-
-      val dataArray = dataFrame.select("word", "vector").collect()
-
       // Check schema explicitly since erasure makes it hard to use match-case for checking.
       Loader.checkSchema[Data](dataFrame.schema)
 
+      val dataArray = dataFrame.select("word", "vector").collect()
       val word2VecMap = dataArray.map(i => (i.getString(0), i.getSeq[Float](1).toArray)).toMap
       new Word2VecModel(word2VecMap)
     }

From 52b24a602ad615a7f6aa427aefb1c7444c05d298 Mon Sep 17 00:00:00 2001
From: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue, 8 Sep 2015 14:54:43 -0700
Subject: [PATCH 551/633] [SPARK-10492] [STREAMING] [DOCUMENTATION] Update
 Streaming documentation about rate limiting and backpressure

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #8656 from tdas/SPARK-10492 and squashes the following commits:

986cdd6 [Tathagata Das] Added information on backpressure
---
 docs/configuration.md               | 13 +++++++++++++
 docs/streaming-programming-guide.md | 13 ++++++++++++-
 2 files changed, 25 insertions(+), 1 deletion(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index a2cc7a37e2240..e287591f3fda1 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1433,6 +1433,19 @@ Apart from these, the following properties are also available, and may be useful
 #### Spark Streaming
 <table class="table">
 <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+  <td><code>spark.streaming.backpressure.enabled</code></td>
+  <td>false</td>
+  <td>
+    Enables or disables Spark Streaming's internal backpressure mechanism (since 1.5).
+    This enables the Spark Streaming to control the receiving rate based on the 
+    current batch scheduling delays and processing times so that the system receives
+    only as fast as the system can process. Internally, this dynamically sets the 
+    maximum receiving rate of receivers. This rate is upper bounded by the values
+    `spark.streaming.receiver.maxRate` and `spark.streaming.kafka.maxRatePerPartition`
+    if they are set (see below).
+  </td>
+</tr>
 <tr>
   <td><code>spark.streaming.blockInterval</code></td>
   <td>200ms</td>
diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index a1acf83f75245..c751dbb41785a 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -1807,7 +1807,7 @@ To run a Spark Streaming applications, you need to have the following.
     + *Mesos* - [Marathon](https://github.com/mesosphere/marathon) has been used to achieve this
       with Mesos.
 
-- *[Since Spark 1.2] Configuring write ahead logs* - Since Spark 1.2,
+- *Configuring write ahead logs* - Since Spark 1.2,
   we have introduced _write ahead logs_ for achieving strong
   fault-tolerance guarantees. If enabled,  all the data received from a receiver gets written into
   a write ahead log in the configuration checkpoint directory. This prevents data loss on driver
@@ -1822,6 +1822,17 @@ To run a Spark Streaming applications, you need to have the following.
   stored in a replicated storage system. This can be done by setting the storage level for the
   input stream to `StorageLevel.MEMORY_AND_DISK_SER`.
 
+- *Setting the max receiving rate* - If the cluster resources is not large enough for the streaming
+  application to process data as fast as it is being received, the receivers can be rate limited
+  by setting a maximum rate limit in terms of records / sec.
+  See the [configuration parameters](configuration.html#spark-streaming)
+  `spark.streaming.receiver.maxRate` for receivers and `spark.streaming.kafka.maxRatePerPartition`
+  for Direct Kafka approach. In Spark 1.5, we have introduced a feature called *backpressure* that
+  eliminate the need to set this rate limit, as Spark Streaming automatically figures out the
+  rate limits and dynamically adjusts them if the processing conditions change. This backpressure
+  can be enabled by setting the [configuration parameter](configuration.html#spark-streaming)
+  `spark.streaming.backpressure.enabled` to `true`.
+
 ### Upgrading Application Code
 {:.no_toc}
 

From d637a666d5932002c8ce0bd23c06064fbfdc1c97 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Tue, 8 Sep 2015 16:16:50 -0700
Subject: [PATCH 552/633] [SPARK-10327] [SQL] Cache Table is not working while
 subquery has alias in its project list

```scala
    import org.apache.spark.sql.hive.execution.HiveTableScan
    sql("select key, value, key + 1 from src").registerTempTable("abc")
    cacheTable("abc")

    val sparkPlan = sql(
      """select a.key, b.key, c.key from
        |abc a join abc b on a.key=b.key
        |join abc c on a.key=c.key""".stripMargin).queryExecution.sparkPlan

    assert(sparkPlan.collect { case e: InMemoryColumnarTableScan => e }.size === 3) // failed
    assert(sparkPlan.collect { case e: HiveTableScan => e }.size === 0) // failed
```

The actual plan is:

```
== Parsed Logical Plan ==
'Project [unresolvedalias('a.key),unresolvedalias('b.key),unresolvedalias('c.key)]
 'Join Inner, Some(('a.key = 'c.key))
  'Join Inner, Some(('a.key = 'b.key))
   'UnresolvedRelation [abc], Some(a)
   'UnresolvedRelation [abc], Some(b)
  'UnresolvedRelation [abc], Some(c)

== Analyzed Logical Plan ==
key: int, key: int, key: int
Project [key#14,key#61,key#66]
 Join Inner, Some((key#14 = key#66))
  Join Inner, Some((key#14 = key#61))
   Subquery a
    Subquery abc
     Project [key#14,value#15,(key#14 + 1) AS _c2#16]
      MetastoreRelation default, src, None
   Subquery b
    Subquery abc
     Project [key#61,value#62,(key#61 + 1) AS _c2#58]
      MetastoreRelation default, src, None
  Subquery c
   Subquery abc
    Project [key#66,value#67,(key#66 + 1) AS _c2#63]
     MetastoreRelation default, src, None

== Optimized Logical Plan ==
Project [key#14,key#61,key#66]
 Join Inner, Some((key#14 = key#66))
  Project [key#14,key#61]
   Join Inner, Some((key#14 = key#61))
    Project [key#14]
     InMemoryRelation [key#14,value#15,_c2#16], true, 10000, StorageLevel(true, true, false, true, 1), (Project [key#14,value#15,(key#14 + 1) AS _c2#16]), Some(abc)
    Project [key#61]
     MetastoreRelation default, src, None
  Project [key#66]
   MetastoreRelation default, src, None

== Physical Plan ==
TungstenProject [key#14,key#61,key#66]
 BroadcastHashJoin [key#14], [key#66], BuildRight
  TungstenProject [key#14,key#61]
   BroadcastHashJoin [key#14], [key#61], BuildRight
    ConvertToUnsafe
     InMemoryColumnarTableScan [key#14], (InMemoryRelation [key#14,value#15,_c2#16], true, 10000, StorageLevel(true, true, false, true, 1), (Project [key#14,value#15,(key#14 + 1) AS _c2#16]), Some(abc))
    ConvertToUnsafe
     HiveTableScan [key#61], (MetastoreRelation default, src, None)
  ConvertToUnsafe
   HiveTableScan [key#66], (MetastoreRelation default, src, None)
```

Author: Cheng Hao <hao.cheng@intel.com>

Closes #8494 from chenghao-intel/weird_cache.
---
 .../sql/catalyst/plans/logical/LogicalPlan.scala | 15 ++++++++++++---
 .../org/apache/spark/sql/CachedTableSuite.scala  | 16 ++++++++++++++++
 2 files changed, 28 insertions(+), 3 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
index 9bb466ac2d29c..8f8747e105932 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/LogicalPlan.scala
@@ -135,16 +135,25 @@ abstract class LogicalPlan extends QueryPlan[LogicalPlan] with Logging {
   /** Args that have cleaned such that differences in expression id should not affect equality */
   protected lazy val cleanArgs: Seq[Any] = {
     val input = children.flatMap(_.output)
+    def cleanExpression(e: Expression) = e match {
+      case a: Alias =>
+        // As the root of the expression, Alias will always take an arbitrary exprId, we need
+        // to erase that for equality testing.
+        val cleanedExprId = Alias(a.child, a.name)(ExprId(-1), a.qualifiers)
+        BindReferences.bindReference(cleanedExprId, input, allowFailures = true)
+      case other => BindReferences.bindReference(other, input, allowFailures = true)
+    }
+
     productIterator.map {
       // Children are checked using sameResult above.
       case tn: TreeNode[_] if containsChild(tn) => null
-      case e: Expression => BindReferences.bindReference(e, input, allowFailures = true)
+      case e: Expression => cleanExpression(e)
       case s: Option[_] => s.map {
-        case e: Expression => BindReferences.bindReference(e, input, allowFailures = true)
+        case e: Expression => cleanExpression(e)
         case other => other
       }
       case s: Seq[_] => s.map {
-        case e: Expression => BindReferences.bindReference(e, input, allowFailures = true)
+        case e: Expression => cleanExpression(e)
         case other => other
       }
       case other => other
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index 3a3541a8429b6..84e66b50cf1d7 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.sql
 
+import org.apache.spark.sql.execution.PhysicalRDD
+
 import scala.concurrent.duration._
 import scala.language.postfixOps
 
@@ -338,4 +340,18 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
       assert((accsSize - 2) == Accumulators.originals.size)
     }
   }
+
+  test("SPARK-10327 Cache Table is not working while subquery has alias in its project list") {
+    ctx.sparkContext.parallelize((1, 1) :: (2, 2) :: Nil)
+      .toDF("key", "value").selectExpr("key", "value", "key+1").registerTempTable("abc")
+    ctx.cacheTable("abc")
+
+    val sparkPlan = sql(
+      """select a.key, b.key, c.key from
+        |abc a join abc b on a.key=b.key
+        |join abc c on a.key=c.key""".stripMargin).queryExecution.sparkPlan
+
+    assert(sparkPlan.collect { case e: InMemoryColumnarTableScan => e }.size === 3)
+    assert(sparkPlan.collect { case e: PhysicalRDD => e }.size === 0)
+  }
 }

From 2143d592c802ec8f83a1eb5ce9b33ad8e48d7196 Mon Sep 17 00:00:00 2001
From: Michael Armbrust <michael@databricks.com>
Date: Tue, 8 Sep 2015 16:51:45 -0700
Subject: [PATCH 553/633] [HOTFIX] Fix build break caused by #8494

Author: Michael Armbrust <michael@databricks.com>

Closes #8659 from marmbrus/testBuildBreak.
---
 .../test/scala/org/apache/spark/sql/CachedTableSuite.scala    | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
index 84e66b50cf1d7..356d4ff3fa837 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/CachedTableSuite.scala
@@ -342,9 +342,9 @@ class CachedTableSuite extends QueryTest with SharedSQLContext {
   }
 
   test("SPARK-10327 Cache Table is not working while subquery has alias in its project list") {
-    ctx.sparkContext.parallelize((1, 1) :: (2, 2) :: Nil)
+    sparkContext.parallelize((1, 1) :: (2, 2) :: Nil)
       .toDF("key", "value").selectExpr("key", "value", "key+1").registerTempTable("abc")
-    ctx.cacheTable("abc")
+    sqlContext.cacheTable("abc")
 
     val sparkPlan = sql(
       """select a.key, b.key, c.key from

From ae74c3fa84885dac18f39368e6dda48a2f7a25a5 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 8 Sep 2015 17:36:00 -0700
Subject: [PATCH 554/633] [RELEASE] Add more contributors & only show names in
 release notes.

Author: Reynold Xin <rxin@databricks.com>

Closes #8660 from rxin/contrib.
---
 dev/create-release/generate-contributors.py | 20 +++++++++------
 dev/create-release/known_translations       | 27 +++++++++++++++++++++
 2 files changed, 39 insertions(+), 8 deletions(-)

diff --git a/dev/create-release/generate-contributors.py b/dev/create-release/generate-contributors.py
index 8aaa250bd7e29..db9c680a4bad3 100755
--- a/dev/create-release/generate-contributors.py
+++ b/dev/create-release/generate-contributors.py
@@ -178,13 +178,16 @@ def populate(issue_type, components):
             author_info[author][issue_type].add(component)
     # Find issues and components associated with this commit
     for issue in issues:
-        jira_issue = jira_client.issue(issue)
-        jira_type = jira_issue.fields.issuetype.name
-        jira_type = translate_issue_type(jira_type, issue, warnings)
-        jira_components = [translate_component(c.name, _hash, warnings)\
-          for c in jira_issue.fields.components]
-        all_components = set(jira_components + commit_components)
-        populate(jira_type, all_components)
+        try:
+            jira_issue = jira_client.issue(issue)
+            jira_type = jira_issue.fields.issuetype.name
+            jira_type = translate_issue_type(jira_type, issue, warnings)
+            jira_components = [translate_component(c.name, _hash, warnings)\
+              for c in jira_issue.fields.components]
+            all_components = set(jira_components + commit_components)
+            populate(jira_type, all_components)
+        except Exception as e:
+            print "Unexpected error:", e
     # For docs without an associated JIRA, manually add it ourselves
     if is_docs(title) and not issues:
         populate("documentation", commit_components)
@@ -223,7 +226,8 @@ def populate(issue_type, components):
     # E.g. andrewor14/SPARK-3425/SPARK-1157/SPARK-6672
     if author in invalid_authors and invalid_authors[author]:
         author = author + "/" + "/".join(invalid_authors[author])
-    line = " * %s -- %s" % (author, contribution)
+    #line = " * %s -- %s" % (author, contribution)
+    line = author
     contributors_file.write(line + "\n")
 contributors_file.close()
 print "Contributors list is successfully written to %s!" % contributors_file_name
diff --git a/dev/create-release/known_translations b/dev/create-release/known_translations
index e462302f28423..3563fe3cc3c03 100644
--- a/dev/create-release/known_translations
+++ b/dev/create-release/known_translations
@@ -138,3 +138,30 @@ lee19 - Lee
 lockwobr - Brian Lockwood
 navis - Navis Ryu
 pparkkin - Paavo Parkkinen
+HyukjinKwon - Hyukjin Kwon
+JDrit - Joseph Batchik
+JuhongPark - Juhong Park
+KaiXinXiaoLei - KaiXinXIaoLei
+NamelessAnalyst - NamelessAnalyst
+alyaxey - Alex Slusarenko
+baishuo - Shuo Bai
+fe2s - Oleksiy Dyagilev
+felixcheung - Felix Cheung
+feynmanliang - Feynman Liang
+josepablocam - Jose Cambronero
+kai-zeng - Kai Zeng
+mosessky - mosessky
+msannell - Michael Sannella
+nishkamravi2 - Nishkam Ravi
+noel-smith - Noel Smith
+petz2000 - Patrick Baier
+qiansl127 - Shilei Qian
+rahulpalamuttam - Rahul Palamuttam
+rowan000 - Rowan Chattaway
+sarutak - Kousuke Saruta
+sethah - Seth Hendrickson
+small-wang - Wang Wei
+stanzhai - Stan Zhai
+tien-dungle - Tien-Dung Le
+xuchenCN - Xu Chen
+zhangjiajin - Zhang JiaJin

From 820913f554bef610d07ca2dadaead657f916ae63 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Tue, 8 Sep 2015 20:39:15 -0700
Subject: [PATCH 555/633] [SPARK-10071] [STREAMING] Output a warning when
 writing QueueInputDStream and throw a better exception when reading
 QueueInputDStream

Output a warning when serializing QueueInputDStream rather than throwing an exception to allow unit tests use it. Moreover, this PR also throws an better exception when deserializing QueueInputDStream to make the user find out the problem easily. The previous exception is hard to understand: https://issues.apache.org/jira/browse/SPARK-8553

Author: zsxwing <zsxwing@gmail.com>

Closes #8624 from zsxwing/SPARK-10071 and squashes the following commits:

847cfa8 [zsxwing] Output a warning when writing QueueInputDStream and throw a better exception when reading QueueInputDStream
---
 .../apache/spark/streaming/Checkpoint.scala   |  6 ++--
 .../streaming/dstream/QueueInputDStream.scala |  9 ++++--
 .../streaming/StreamingContextSuite.scala     | 28 +++++++++++++------
 3 files changed, 30 insertions(+), 13 deletions(-)

diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index 3985e1a3d9dfa..27024ecfd9101 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -321,7 +321,7 @@ object CheckpointReader extends Logging {
 
     // Try to read the checkpoint files in the order
     logInfo("Checkpoint files found: " + checkpointFiles.mkString(","))
-    val compressionCodec = CompressionCodec.createCodec(conf)
+    var readError: Exception = null
     checkpointFiles.foreach(file => {
       logInfo("Attempting to load checkpoint from file " + file)
       try {
@@ -332,13 +332,15 @@ object CheckpointReader extends Logging {
         return Some(cp)
       } catch {
         case e: Exception =>
+          readError = e
           logWarning("Error reading checkpoint from file " + file, e)
       }
     })
 
     // If none of checkpoint files could be read, then throw exception
     if (!ignoreReadError) {
-      throw new SparkException(s"Failed to read checkpoint from directory $checkpointPath")
+      throw new SparkException(
+        s"Failed to read checkpoint from directory $checkpointPath", readError)
     }
     None
   }
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
index a2f5d82a79bd3..bab78a3536b47 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.streaming.dstream
 
-import java.io.{NotSerializableException, ObjectOutputStream}
+import java.io.{NotSerializableException, ObjectInputStream, ObjectOutputStream}
 
 import scala.collection.mutable.{ArrayBuffer, Queue}
 import scala.reflect.ClassTag
@@ -37,8 +37,13 @@ class QueueInputDStream[T: ClassTag](
 
   override def stop() { }
 
+  private def readObject(in: ObjectInputStream): Unit = {
+    throw new NotSerializableException("queueStream doesn't support checkpointing. " +
+      "Please don't use queueStream when checkpointing is enabled.")
+  }
+
   private def writeObject(oos: ObjectOutputStream): Unit = {
-    throw new NotSerializableException("queueStream doesn't support checkpointing")
+    logWarning("queueStream doesn't support checkpointing")
   }
 
   override def compute(validTime: Time): Option[RDD[T]] = {
diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
index 7423ef6bcb6ea..d26894e88fc26 100644
--- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
+++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala
@@ -30,7 +30,7 @@ import org.scalatest.concurrent.Timeouts
 import org.scalatest.exceptions.TestFailedDueToTimeoutException
 import org.scalatest.time.SpanSugar._
 
-import org.apache.spark.{Logging, SparkConf, SparkContext, SparkFunSuite}
+import org.apache.spark._
 import org.apache.spark.metrics.MetricsSystem
 import org.apache.spark.metrics.source.Source
 import org.apache.spark.storage.StorageLevel
@@ -726,16 +726,26 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo
   }
 
   test("queueStream doesn't support checkpointing") {
-    val checkpointDir = Utils.createTempDir()
-    ssc = new StreamingContext(master, appName, batchDuration)
-    val rdd = ssc.sparkContext.parallelize(1 to 10)
-    ssc.queueStream[Int](Queue(rdd)).print()
-    ssc.checkpoint(checkpointDir.getAbsolutePath)
-    val e = intercept[NotSerializableException] {
-      ssc.start()
+    val checkpointDirectory = Utils.createTempDir().getAbsolutePath()
+    def creatingFunction(): StreamingContext = {
+      val _ssc = new StreamingContext(conf, batchDuration)
+      val rdd = _ssc.sparkContext.parallelize(1 to 10)
+      _ssc.checkpoint(checkpointDirectory)
+      _ssc.queueStream[Int](Queue(rdd)).register()
+      _ssc
+    }
+    ssc = StreamingContext.getOrCreate(checkpointDirectory, creatingFunction _)
+    ssc.start()
+    eventually(timeout(10000 millis)) {
+      assert(Checkpoint.getCheckpointFiles(checkpointDirectory).size > 1)
+    }
+    ssc.stop()
+    val e = intercept[SparkException] {
+      ssc = StreamingContext.getOrCreate(checkpointDirectory, creatingFunction _)
     }
     // StreamingContext.validate changes the message, so use "contains" here
-    assert(e.getMessage.contains("queueStream doesn't support checkpointing"))
+    assert(e.getCause.getMessage.contains("queueStream doesn't support checkpointing. " +
+      "Please don't use queueStream when checkpointing is enabled."))
   }
 
   def addInputStream(s: StreamingContext): DStream[Int] = {

From 52fe32f6ac7a04fa9b4478fda1307c5b0c61c4a2 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 8 Sep 2015 20:51:20 -0700
Subject: [PATCH 556/633] [SPARK-9834] [MLLIB] implement weighted least squares
 via normal equation

The goal of this PR is to have a weighted least squares implementation that takes the normal equation approach, and hence to be able to provide R-like summary statistics and support IRLS (used by GLMs). The tests match R's lm and glmnet.

There are couple TODOs that can be addressed in future PRs:
* consolidate summary statistics aggregators
* move `dspr` to `BLAS`
* etc

It would be nice to have this merged first because it blocks couple other features.

dbtsai

Author: Xiangrui Meng <meng@databricks.com>

Closes #8588 from mengxr/SPARK-9834.
---
 .../spark/ml/optim/WeightedLeastSquares.scala | 296 ++++++++++++++++++
 .../org/apache/spark/mllib/linalg/BLAS.scala  |   7 +
 .../mllib/linalg/distributed/RowMatrix.scala  |   3 +-
 .../ml/optim/WeightedLeastSquaresSuite.scala  | 133 ++++++++
 4 files changed, 438 insertions(+), 1 deletion(-)
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala

diff --git a/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala b/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
new file mode 100644
index 0000000000000..a99e2ac4c6913
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
@@ -0,0 +1,296 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.optim
+
+import com.github.fommil.netlib.LAPACK.{getInstance => lapack}
+import org.netlib.util.intW
+
+import org.apache.spark.Logging
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.mllib.linalg.distributed.RowMatrix
+import org.apache.spark.rdd.RDD
+
+/**
+ * Model fitted by [[WeightedLeastSquares]].
+ * @param coefficients model coefficients
+ * @param intercept model intercept
+ */
+private[ml] class WeightedLeastSquaresModel(
+    val coefficients: DenseVector,
+    val intercept: Double) extends Serializable
+
+/**
+ * Weighted least squares solver via normal equation.
+ * Given weighted observations (w,,i,,, a,,i,,, b,,i,,), we use the following weighted least squares
+ * formulation:
+ *
+ * min,,x,z,, 1/2 sum,,i,, w,,i,, (a,,i,,^T^ x + z - b,,i,,)^2^ / sum,,i,, w_i
+ *   + 1/2 lambda / delta sum,,j,, (sigma,,j,, x,,j,,)^2^,
+ *
+ * where lambda is the regularization parameter, and delta and sigma,,j,, are controlled by
+ * [[standardizeLabel]] and [[standardizeFeatures]], respectively.
+ *
+ * Set [[regParam]] to 0.0 and turn off both [[standardizeFeatures]] and [[standardizeLabel]] to
+ * match R's `lm`.
+ * Turn on [[standardizeLabel]] to match R's `glmnet`.
+ *
+ * @param fitIntercept whether to fit intercept. If false, z is 0.0.
+ * @param regParam L2 regularization parameter (lambda)
+ * @param standardizeFeatures whether to standardize features. If true, sigma_,,j,, is the
+ *                            population standard deviation of the j-th column of A. Otherwise,
+ *                            sigma,,j,, is 1.0.
+ * @param standardizeLabel whether to standardize label. If true, delta is the population standard
+ *                         deviation of the label column b. Otherwise, delta is 1.0.
+ */
+private[ml] class WeightedLeastSquares(
+    val fitIntercept: Boolean,
+    val regParam: Double,
+    val standardizeFeatures: Boolean,
+    val standardizeLabel: Boolean) extends Logging with Serializable {
+  import WeightedLeastSquares._
+
+  require(regParam >= 0.0, s"regParam cannot be negative: $regParam")
+  if (regParam == 0.0) {
+    logWarning("regParam is zero, which might cause numerical instability and overfitting.")
+  }
+
+  /**
+   * Creates a [[WeightedLeastSquaresModel]] from an RDD of [[Instance]]s.
+   */
+  def fit(instances: RDD[Instance]): WeightedLeastSquaresModel = {
+    val summary = instances.treeAggregate(new Aggregator)(_.add(_), _.merge(_))
+    summary.validate()
+    logInfo(s"Number of instances: ${summary.count}.")
+    val triK = summary.triK
+    val bBar = summary.bBar
+    val bStd = summary.bStd
+    val aBar = summary.aBar
+    val aVar = summary.aVar
+    val abBar = summary.abBar
+    val aaBar = summary.aaBar
+    val aaValues = aaBar.values
+
+    if (fitIntercept) {
+      // shift centers
+      // A^T A - aBar aBar^T
+      RowMatrix.dspr(-1.0, aBar, aaValues)
+      // A^T b - bBar aBar
+      BLAS.axpy(-bBar, aBar, abBar)
+    }
+
+    // add regularization to diagonals
+    var i = 0
+    var j = 2
+    while (i < triK) {
+      var lambda = regParam
+      if (standardizeFeatures) {
+        lambda *= aVar(j - 2)
+      }
+      if (standardizeLabel) {
+        // TODO: handle the case when bStd = 0
+        lambda /= bStd
+      }
+      aaValues(i) += lambda
+      i += j
+      j += 1
+    }
+
+    val x = choleskySolve(aaBar.values, abBar)
+
+    // compute intercept
+    val intercept = if (fitIntercept) {
+      bBar - BLAS.dot(aBar, x)
+    } else {
+      0.0
+    }
+
+    new WeightedLeastSquaresModel(x, intercept)
+  }
+
+  /**
+   * Solves a symmetric positive definite linear system via Cholesky factorization.
+   * The input arguments are modified in-place to store the factorization and the solution.
+   * @param A the upper triangular part of A
+   * @param bx right-hand side
+   * @return the solution vector
+   */
+  // TODO: SPARK-10490 - consolidate this and the Cholesky solver in ALS
+  private def choleskySolve(A: Array[Double], bx: DenseVector): DenseVector = {
+    val k = bx.size
+    val info = new intW(0)
+    lapack.dppsv("U", k, 1, A, bx.values, k, info)
+    val code = info.`val`
+    assert(code == 0, s"lapack.dpotrs returned $code.")
+    bx
+  }
+}
+
+private[ml] object WeightedLeastSquares {
+
+  /**
+   * Case class for weighted observations.
+   * @param w weight, must be positive
+   * @param a features
+   * @param b label
+   */
+  case class Instance(w: Double, a: Vector, b: Double) {
+    require(w >= 0.0, s"Weight cannot be negative: $w.")
+  }
+
+  /**
+   * Aggregator to provide necessary summary statistics for solving [[WeightedLeastSquares]].
+   */
+  // TODO: consolidate aggregates for summary statistics
+  private class Aggregator extends Serializable {
+    var initialized: Boolean = false
+    var k: Int = _
+    var count: Long = _
+    var triK: Int = _
+    private var wSum: Double = _
+    private var wwSum: Double = _
+    private var bSum: Double = _
+    private var bbSum: Double = _
+    private var aSum: DenseVector = _
+    private var abSum: DenseVector = _
+    private var aaSum: DenseVector = _
+
+    private def init(k: Int): Unit = {
+      require(k <= 4096, "In order to take the normal equation approach efficiently, " +
+        s"we set the max number of features to 4096 but got $k.")
+      this.k = k
+      triK = k * (k + 1) / 2
+      count = 0L
+      wSum = 0.0
+      wwSum = 0.0
+      bSum = 0.0
+      bbSum = 0.0
+      aSum = new DenseVector(Array.ofDim(k))
+      abSum = new DenseVector(Array.ofDim(k))
+      aaSum = new DenseVector(Array.ofDim(triK))
+      initialized = true
+    }
+
+    /**
+     * Adds an instance.
+     */
+    def add(instance: Instance): this.type = {
+      val Instance(w, a, b) = instance
+      val ak = a.size
+      if (!initialized) {
+        init(ak)
+        initialized = true
+      }
+      assert(ak == k, s"Dimension mismatch. Expect vectors of size $k but got $ak.")
+      count += 1L
+      wSum += w
+      wwSum += w * w
+      bSum += w * b
+      bbSum += w * b * b
+      BLAS.axpy(w, a, aSum)
+      BLAS.axpy(w * b, a, abSum)
+      RowMatrix.dspr(w, a, aaSum.values)
+      this
+    }
+
+    /**
+     * Merges another [[Aggregator]].
+     */
+    def merge(other: Aggregator): this.type = {
+      if (!other.initialized) {
+        this
+      } else {
+        if (!initialized) {
+          init(other.k)
+        }
+        assert(k == other.k, s"dimension mismatch: this.k = $k but other.k = ${other.k}")
+        count += other.count
+        wSum += other.wSum
+        wwSum += other.wwSum
+        bSum += other.bSum
+        bbSum += other.bbSum
+        BLAS.axpy(1.0, other.aSum, aSum)
+        BLAS.axpy(1.0, other.abSum, abSum)
+        BLAS.axpy(1.0, other.aaSum, aaSum)
+        this
+      }
+    }
+
+    /**
+     * Validates that we have seen observations.
+     */
+    def validate(): Unit = {
+      assert(initialized, "Training dataset is empty.")
+      assert(wSum > 0.0, "Sum of weights cannot be zero.")
+    }
+
+    /**
+     * Weighted mean of features.
+     */
+    def aBar: DenseVector = {
+      val output = aSum.copy
+      BLAS.scal(1.0 / wSum, output)
+      output
+    }
+
+    /**
+     * Weighted mean of labels.
+     */
+    def bBar: Double = bSum / wSum
+
+    /**
+     * Weighted population standard deviation of labels.
+     */
+    def bStd: Double = math.sqrt(bbSum / wSum - bBar * bBar)
+
+    /**
+     * Weighted mean of (label * features).
+     */
+    def abBar: DenseVector = {
+      val output = abSum.copy
+      BLAS.scal(1.0 / wSum, output)
+      output
+    }
+
+    /**
+     * Weighted mean of (features * features^T^).
+     */
+    def aaBar: DenseVector = {
+      val output = aaSum.copy
+      BLAS.scal(1.0 / wSum, output)
+      output
+    }
+
+    /**
+     * Weighted population variance of features.
+     */
+    def aVar: DenseVector = {
+      val variance = Array.ofDim[Double](k)
+      var i = 0
+      var j = 2
+      val aaValues = aaSum.values
+      while (i < triK) {
+        val l = j - 2
+        val aw = aSum(l) / wSum
+        variance(l) = aaValues(i) / wSum - aw * aw
+        i += j
+        j += 1
+      }
+      new DenseVector(variance)
+    }
+  }
+}
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
index ab475af264dd3..9ee81eda8a8c0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
@@ -92,6 +92,13 @@ private[spark] object BLAS extends Serializable with Logging {
     }
   }
 
+  /** Y += a * x */
+  private[spark] def axpy(a: Double, X: DenseMatrix, Y: DenseMatrix): Unit = {
+    require(X.numRows == Y.numRows && X.numCols == Y.numCols, "Dimension mismatch: " +
+      s"size(X) = ${(X.numRows, X.numCols)} but size(Y) = ${(Y.numRows, Y.numCols)}.")
+    f2jBLAS.daxpy(X.numRows * X.numCols, a, X.values, 1, Y.values, 1)
+  }
+
   /**
    * dot(x, y)
    */
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 9a423ddafdc09..83779ac88989b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -678,7 +678,8 @@ object RowMatrix {
    *
    * @param U the upper triangular part of the matrix packed in an array (column major)
    */
-  private def dspr(alpha: Double, v: Vector, U: Array[Double]): Unit = {
+  // TODO: SPARK-10491 - move this method to linalg.BLAS
+  private[spark] def dspr(alpha: Double, v: Vector, U: Array[Double]): Unit = {
     // TODO: Find a better home (breeze?) for this method.
     val n = v.size
     v match {
diff --git a/mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala
new file mode 100644
index 0000000000000..652f3adb984d3
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/optim/WeightedLeastSquaresSuite.scala
@@ -0,0 +1,133 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.optim
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.optim.WeightedLeastSquares.Instance
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.rdd.RDD
+
+class WeightedLeastSquaresSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+  private var instances: RDD[Instance] = _
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    /*
+       R code:
+
+       A <- matrix(c(0, 1, 2, 3, 5, 7, 11, 13), 4, 2)
+       b <- c(17, 19, 23, 29)
+       w <- c(1, 2, 3, 4)
+     */
+    instances = sc.parallelize(Seq(
+      Instance(1.0, Vectors.dense(0.0, 5.0).toSparse, 17.0),
+      Instance(2.0, Vectors.dense(1.0, 7.0), 19.0),
+      Instance(3.0, Vectors.dense(2.0, 11.0), 23.0),
+      Instance(4.0, Vectors.dense(3.0, 13.0), 29.0)
+    ), 2)
+  }
+
+  test("WLS against lm") {
+    /*
+       R code:
+
+       df <- as.data.frame(cbind(A, b))
+       for (formula in c(b ~ . -1, b ~ .)) {
+         model <- lm(formula, data=df, weights=w)
+         print(as.vector(coef(model)))
+       }
+
+       [1] -3.727121  3.009983
+       [1] 18.08  6.08 -0.60
+     */
+
+    val expected = Seq(
+      Vectors.dense(0.0, -3.727121, 3.009983),
+      Vectors.dense(18.08, 6.08, -0.60))
+
+    var idx = 0
+    for (fitIntercept <- Seq(false, true)) {
+      val wls = new WeightedLeastSquares(
+        fitIntercept, regParam = 0.0, standardizeFeatures = false, standardizeLabel = false)
+        .fit(instances)
+      val actual = Vectors.dense(wls.intercept, wls.coefficients(0), wls.coefficients(1))
+      assert(actual ~== expected(idx) absTol 1e-4)
+      idx += 1
+    }
+  }
+
+  test("WLS against glmnet") {
+    /*
+       R code:
+
+       library(glmnet)
+
+       for (intercept in c(FALSE, TRUE)) {
+         for (lambda in c(0.0, 0.1, 1.0)) {
+           for (standardize in c(FALSE, TRUE)) {
+             model <- glmnet(A, b, weights=w, intercept=intercept, lambda=lambda,
+                             standardize=standardize, alpha=0, thresh=1E-14)
+             print(as.vector(coef(model)))
+           }
+         }
+       }
+
+       [1]  0.000000 -3.727117  3.009982
+       [1]  0.000000 -3.727117  3.009982
+       [1]  0.000000 -3.307532  2.924206
+       [1]  0.000000 -2.914790  2.840627
+       [1]  0.000000 -1.526575  2.558158
+       [1] 0.00000000 0.06984238 2.20488344
+       [1] 18.0799727  6.0799832 -0.5999941
+       [1] 18.0799727  6.0799832 -0.5999941
+       [1] 13.5356178  3.2714044  0.3770744
+       [1] 14.064629  3.565802  0.269593
+       [1] 10.1238013  0.9708569  1.1475466
+       [1] 13.1860638  2.1761382  0.6213134
+     */
+
+    val expected = Seq(
+      Vectors.dense(0.0, -3.727117, 3.009982),
+      Vectors.dense(0.0, -3.727117, 3.009982),
+      Vectors.dense(0.0, -3.307532, 2.924206),
+      Vectors.dense(0.0, -2.914790, 2.840627),
+      Vectors.dense(0.0, -1.526575, 2.558158),
+      Vectors.dense(0.0, 0.06984238, 2.20488344),
+      Vectors.dense(18.0799727, 6.0799832, -0.5999941),
+      Vectors.dense(18.0799727, 6.0799832, -0.5999941),
+      Vectors.dense(13.5356178, 3.2714044, 0.3770744),
+      Vectors.dense(14.064629, 3.565802, 0.269593),
+      Vectors.dense(10.1238013, 0.9708569, 1.1475466),
+      Vectors.dense(13.1860638, 2.1761382, 0.6213134))
+
+    var idx = 0
+    for (fitIntercept <- Seq(false, true);
+         regParam <- Seq(0.0, 0.1, 1.0);
+         standardizeFeatures <- Seq(false, true)) {
+      val wls = new WeightedLeastSquares(
+        fitIntercept, regParam, standardizeFeatures, standardizeLabel = true)
+        .fit(instances)
+      val actual = Vectors.dense(wls.intercept, wls.coefficients(0), wls.coefficients(1))
+      assert(actual ~== expected(idx) absTol 1e-4)
+      idx += 1
+    }
+  }
+}

From a1573489a37def97b7c26b798898ffbbdc4defa8 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Tue, 8 Sep 2015 20:54:02 -0700
Subject: [PATCH 557/633] [SPARK-10464] [MLLIB] Add WeibullGenerator for
 RandomDataGenerator

Add WeibullGenerator for RandomDataGenerator.
#8611 need use WeibullGenerator to generate random data based on Weibull distribution.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8622 from yanboliang/spark-10464.
---
 .../mllib/random/RandomDataGenerator.scala    | 27 +++++++++++++++++--
 .../random/RandomDataGeneratorSuite.scala     | 16 ++++++++++-
 2 files changed, 40 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
index a2d85a68cd327..9eab7efc160da 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/random/RandomDataGenerator.scala
@@ -17,8 +17,7 @@
 
 package org.apache.spark.mllib.random
 
-import org.apache.commons.math3.distribution.{ExponentialDistribution,
-  GammaDistribution, LogNormalDistribution, PoissonDistribution}
+import org.apache.commons.math3.distribution._
 
 import org.apache.spark.annotation.{Since, DeveloperApi}
 import org.apache.spark.util.random.{XORShiftRandom, Pseudorandom}
@@ -195,3 +194,27 @@ class LogNormalGenerator @Since("1.3.0") (
   @Since("1.3.0")
   override def copy(): LogNormalGenerator = new LogNormalGenerator(mean, std)
 }
+
+/**
+ * :: DeveloperApi ::
+ * Generates i.i.d. samples from the Weibull distribution with the
+ * given shape and scale parameter.
+ *
+ * @param alpha shape parameter for the Weibull distribution.
+ * @param beta scale parameter for the Weibull distribution.
+ */
+@DeveloperApi
+class WeibullGenerator(
+    val alpha: Double,
+    val beta: Double) extends RandomDataGenerator[Double] {
+
+  private val rng = new WeibullDistribution(alpha, beta)
+
+  override def nextValue(): Double = rng.sample()
+
+  override def setSeed(seed: Long): Unit = {
+    rng.reseedRandomGenerator(seed)
+  }
+
+  override def copy(): WeibullGenerator = new WeibullGenerator(alpha, beta)
+}
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
index a5ca1518f82f5..8416771552fd3 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/random/RandomDataGeneratorSuite.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.mllib.random
 
-import scala.math
+import org.apache.commons.math3.special.Gamma
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.util.StatCounter
@@ -136,4 +136,18 @@ class RandomDataGeneratorSuite extends SparkFunSuite {
         distributionChecks(gamma, expectedMean, expectedStd, 0.1)
     }
   }
+
+  test("WeibullGenerator") {
+    List((1.0, 2.0), (2.0, 3.0), (2.5, 3.5), (10.4, 2.222)).map {
+      case (alpha: Double, beta: Double) =>
+        val weibull = new WeibullGenerator(alpha, beta)
+        apiChecks(weibull)
+
+        val expectedMean = math.exp(Gamma.logGamma(1 + (1 / alpha))) * beta
+        val expectedVariance = math.exp(
+          Gamma.logGamma(1 + (2 / alpha))) * beta * beta - expectedMean * expectedMean
+        val expectedStd = math.sqrt(expectedVariance)
+        distributionChecks(weibull, expectedMean, expectedStd, 0.1)
+    }
+  }
 }

From 3a11e50e21ececbec9708eb487b08196f195cd87 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Tue, 8 Sep 2015 20:56:22 -0700
Subject: [PATCH 558/633] [SPARK-10373] [PYSPARK] move @since into pyspark from
 sql

cc mengxr

Author: Davies Liu <davies@databricks.com>

Closes #8657 from davies/move_since.
---
 python/pyspark/__init__.py       | 16 ++++++++++++++++
 python/pyspark/sql/__init__.py   | 15 ---------------
 python/pyspark/sql/column.py     |  2 +-
 python/pyspark/sql/context.py    |  2 +-
 python/pyspark/sql/dataframe.py  |  2 +-
 python/pyspark/sql/functions.py  |  3 +--
 python/pyspark/sql/group.py      |  2 +-
 python/pyspark/sql/readwriter.py |  3 +--
 python/pyspark/sql/window.py     |  3 +--
 9 files changed, 23 insertions(+), 25 deletions(-)

diff --git a/python/pyspark/__init__.py b/python/pyspark/__init__.py
index 5f70ac6ed8fe6..8475dfb1c6ad0 100644
--- a/python/pyspark/__init__.py
+++ b/python/pyspark/__init__.py
@@ -48,6 +48,22 @@
 from pyspark.status import *
 from pyspark.profiler import Profiler, BasicProfiler
 
+
+def since(version):
+    """
+    A decorator that annotates a function to append the version of Spark the function was added.
+    """
+    import re
+    indent_p = re.compile(r'\n( +)')
+
+    def deco(f):
+        indents = indent_p.findall(f.__doc__)
+        indent = ' ' * (min(len(m) for m in indents) if indents else 0)
+        f.__doc__ = f.__doc__.rstrip() + "\n\n%s.. versionadded:: %s" % (indent, version)
+        return f
+    return deco
+
+
 # for back compatibility
 from pyspark.sql import SQLContext, HiveContext, SchemaRDD, Row
 
diff --git a/python/pyspark/sql/__init__.py b/python/pyspark/sql/__init__.py
index ad9c891ba1c04..98eaf52866d23 100644
--- a/python/pyspark/sql/__init__.py
+++ b/python/pyspark/sql/__init__.py
@@ -44,21 +44,6 @@
 from __future__ import absolute_import
 
 
-def since(version):
-    """
-    A decorator that annotates a function to append the version of Spark the function was added.
-    """
-    import re
-    indent_p = re.compile(r'\n( +)')
-
-    def deco(f):
-        indents = indent_p.findall(f.__doc__)
-        indent = ' ' * (min(len(m) for m in indents) if indents else 0)
-        f.__doc__ = f.__doc__.rstrip() + "\n\n%s.. versionadded:: %s" % (indent, version)
-        return f
-    return deco
-
-
 from pyspark.sql.types import Row
 from pyspark.sql.context import SQLContext, HiveContext
 from pyspark.sql.column import Column
diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 56e75e8caee88..573f65f5bf096 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -22,9 +22,9 @@
     basestring = str
     long = int
 
+from pyspark import since
 from pyspark.context import SparkContext
 from pyspark.rdd import ignore_unicode_prefix
-from pyspark.sql import since
 from pyspark.sql.types import *
 
 __all__ = ["DataFrame", "Column", "SchemaRDD", "DataFrameNaFunctions",
diff --git a/python/pyspark/sql/context.py b/python/pyspark/sql/context.py
index 0ef46c44644ab..89c8c6e0d94f1 100644
--- a/python/pyspark/sql/context.py
+++ b/python/pyspark/sql/context.py
@@ -26,9 +26,9 @@
 
 from py4j.protocol import Py4JError
 
+from pyspark import since
 from pyspark.rdd import RDD, _prepare_for_python_RDD, ignore_unicode_prefix
 from pyspark.serializers import AutoBatchedSerializer, PickleSerializer
-from pyspark.sql import since
 from pyspark.sql.types import Row, StringType, StructType, _verify_type, \
     _infer_schema, _has_nulltype, _merge_type, _create_converter
 from pyspark.sql.dataframe import DataFrame
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index e269ef4304f3f..c5bf55791240b 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -26,11 +26,11 @@
 else:
     from itertools import imap as map
 
+from pyspark import since
 from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix
 from pyspark.serializers import BatchedSerializer, PickleSerializer, UTF8Deserializer
 from pyspark.storagelevel import StorageLevel
 from pyspark.traceback_utils import SCCallSiteSync
-from pyspark.sql import since
 from pyspark.sql.types import _parse_datatype_json_string
 from pyspark.sql.column import Column, _to_seq, _to_list, _to_java_column
 from pyspark.sql.readwriter import DataFrameWriter
diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 4b74a501521a5..26b8662718a60 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -24,10 +24,9 @@
 if sys.version < "3":
     from itertools import imap as map
 
-from pyspark import SparkContext
+from pyspark import since, SparkContext
 from pyspark.rdd import _prepare_for_python_RDD, ignore_unicode_prefix
 from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
-from pyspark.sql import since
 from pyspark.sql.types import StringType
 from pyspark.sql.column import Column, _to_java_column, _to_seq
 
diff --git a/python/pyspark/sql/group.py b/python/pyspark/sql/group.py
index 04594d5a836ce..71c0bccc5eeff 100644
--- a/python/pyspark/sql/group.py
+++ b/python/pyspark/sql/group.py
@@ -15,8 +15,8 @@
 # limitations under the License.
 #
 
+from pyspark import since
 from pyspark.rdd import ignore_unicode_prefix
-from pyspark.sql import since
 from pyspark.sql.column import Column, _to_seq
 from pyspark.sql.dataframe import DataFrame
 from pyspark.sql.types import *
diff --git a/python/pyspark/sql/readwriter.py b/python/pyspark/sql/readwriter.py
index 3fa6895880a97..f43d8bf646a9e 100644
--- a/python/pyspark/sql/readwriter.py
+++ b/python/pyspark/sql/readwriter.py
@@ -22,8 +22,7 @@
 
 from py4j.java_gateway import JavaClass
 
-from pyspark import RDD
-from pyspark.sql import since
+from pyspark import RDD, since
 from pyspark.sql.column import _to_seq
 from pyspark.sql.types import *
 
diff --git a/python/pyspark/sql/window.py b/python/pyspark/sql/window.py
index eaf4d7e98620a..57bbe340bbd4d 100644
--- a/python/pyspark/sql/window.py
+++ b/python/pyspark/sql/window.py
@@ -17,8 +17,7 @@
 
 import sys
 
-from pyspark import SparkContext
-from pyspark.sql import since
+from pyspark import since, SparkContext
 from pyspark.sql.column import _to_seq, _to_java_column
 
 __all__ = ["Window", "WindowSpec"]

From 0e2f2163314972f6be18e3453c64314d1bee7bb9 Mon Sep 17 00:00:00 2001
From: noelsmith <mail@noelsmith.com>
Date: Tue, 8 Sep 2015 21:26:20 -0700
Subject: [PATCH 559/633] [SPARK-10094] Pyspark ML Feature transformers marked
 as experimental

Modified class-level docstrings to mark all feature transformers in pyspark.ml as experimental.

Author: noelsmith <mail@noelsmith.com>

Closes #8623 from noel-smith/SPARK-10094-mark-pyspark-ml-trans-exp.
---
 python/pyspark/ml/feature.py | 52 ++++++++++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index d955307e27efd..a7c5b2b62e764 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -36,6 +36,8 @@
 @inherit_doc
 class Binarizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Binarize a column of continuous features given a threshold.
 
     >>> df = sqlContext.createDataFrame([(0.5,)], ["values"])
@@ -92,6 +94,8 @@ def getThreshold(self):
 @inherit_doc
 class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Maps a column of continuous features to a column of feature buckets.
 
     >>> df = sqlContext.createDataFrame([(0.1,), (0.4,), (1.2,), (1.5,)], ["values"])
@@ -169,6 +173,8 @@ def getSplits(self):
 @inherit_doc
 class DCT(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A feature transformer that takes the 1D discrete cosine transform
     of a real vector. No zero padding is performed on the input vector.
     It returns a real vector of the same length representing the DCT.
@@ -232,6 +238,8 @@ def getInverse(self):
 @inherit_doc
 class ElementwiseProduct(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Outputs the Hadamard product (i.e., the element-wise product) of each input vector
     with a provided "weight" vector. In other words, it scales each column of the dataset
     by a scalar multiplier.
@@ -289,6 +297,8 @@ def getScalingVec(self):
 @inherit_doc
 class HashingTF(JavaTransformer, HasInputCol, HasOutputCol, HasNumFeatures):
     """
+    .. note:: Experimental
+
     Maps a sequence of terms to their term frequencies using the
     hashing trick.
 
@@ -327,6 +337,8 @@ def setParams(self, numFeatures=1 << 18, inputCol=None, outputCol=None):
 @inherit_doc
 class IDF(JavaEstimator, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Compute the Inverse Document Frequency (IDF) given a collection of documents.
 
     >>> from pyspark.mllib.linalg import DenseVector
@@ -387,6 +399,8 @@ def _create_model(self, java_model):
 
 class IDFModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by IDF.
     """
 
@@ -395,6 +409,8 @@ class IDFModel(JavaModel):
 @ignore_unicode_prefix
 class NGram(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A feature transformer that converts the input array of strings into an array of n-grams. Null
     values in the input array are ignored.
     It returns an array of n-grams where each n-gram is represented by a space-separated string of
@@ -463,6 +479,8 @@ def getN(self):
 @inherit_doc
 class Normalizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
      Normalize a vector to have unit norm using the given p-norm.
 
     >>> from pyspark.mllib.linalg import Vectors
@@ -519,6 +537,8 @@ def getP(self):
 @inherit_doc
 class OneHotEncoder(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A one-hot encoder that maps a column of category indices to a
     column of binary vectors, with at most a single one-value per row
     that indicates the input category index.
@@ -591,6 +611,8 @@ def getDropLast(self):
 @inherit_doc
 class PolynomialExpansion(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Perform feature expansion in a polynomial space. As said in wikipedia of Polynomial Expansion,
     which is available at `http://en.wikipedia.org/wiki/Polynomial_expansion`, "In mathematics, an
     expansion of a product of sums expresses it as a sum of products by using the fact that
@@ -649,6 +671,8 @@ def getDegree(self):
 @ignore_unicode_prefix
 class RegexTokenizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A regex based tokenizer that extracts tokens either by using the
     provided regex pattern (in Java dialect) to split the text
     (default) or repeatedly matching the regex (if gaps is false).
@@ -746,6 +770,8 @@ def getPattern(self):
 @inherit_doc
 class SQLTransformer(JavaTransformer):
     """
+    .. note:: Experimental
+
     Implements the transforms which are defined by SQL statement.
     Currently we only support SQL syntax like 'SELECT ... FROM __THIS__'
     where '__THIS__' represents the underlying table of the input dataset.
@@ -797,6 +823,8 @@ def getStatement(self):
 @inherit_doc
 class StandardScaler(JavaEstimator, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Standardizes features by removing the mean and scaling to unit variance using column summary
     statistics on the samples in the training set.
 
@@ -870,6 +898,8 @@ def _create_model(self, java_model):
 
 class StandardScalerModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by StandardScaler.
     """
 
@@ -891,6 +921,8 @@ def mean(self):
 @inherit_doc
 class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A label indexer that maps a string column of labels to an ML column of label indices.
     If the input column is numeric, we cast it to string and index the string values.
     The indices are in [0, numLabels), ordered by label frequencies.
@@ -929,6 +961,8 @@ def _create_model(self, java_model):
 
 class StringIndexerModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by StringIndexer.
     """
 
@@ -1006,6 +1040,8 @@ def getCaseSensitive(self):
 @ignore_unicode_prefix
 class Tokenizer(JavaTransformer, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     A tokenizer that converts the input string to lowercase and then
     splits it by white spaces.
 
@@ -1051,6 +1087,8 @@ def setParams(self, inputCol=None, outputCol=None):
 @inherit_doc
 class VectorAssembler(JavaTransformer, HasInputCols, HasOutputCol):
     """
+    .. note:: Experimental
+
     A feature transformer that merges multiple columns into a vector column.
 
     >>> df = sqlContext.createDataFrame([(1, 0, 3)], ["a", "b", "c"])
@@ -1087,6 +1125,8 @@ def setParams(self, inputCols=None, outputCol=None):
 @inherit_doc
 class VectorIndexer(JavaEstimator, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Class for indexing categorical feature columns in a dataset of [[Vector]].
 
     This has 2 usage modes:
@@ -1186,6 +1226,8 @@ def _create_model(self, java_model):
 
 class VectorIndexerModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by VectorIndexer.
     """
 
@@ -1194,6 +1236,8 @@ class VectorIndexerModel(JavaModel):
 @ignore_unicode_prefix
 class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     Word2Vec trains a model of `Map(String, Vector)`, i.e. transforms a word into a code for further
     natural language processing or machine learning process.
 
@@ -1307,6 +1351,8 @@ def _create_model(self, java_model):
 
 class Word2VecModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by Word2Vec.
     """
 
@@ -1332,6 +1378,8 @@ def findSynonyms(self, word, num):
 @inherit_doc
 class PCA(JavaEstimator, HasInputCol, HasOutputCol):
     """
+    .. note:: Experimental
+
     PCA trains a model to project vectors to a low-dimensional space using PCA.
 
     >>> from pyspark.mllib.linalg import Vectors
@@ -1387,6 +1435,8 @@ def _create_model(self, java_model):
 
 class PCAModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by PCA.
     """
 
@@ -1470,6 +1520,8 @@ def _create_model(self, java_model):
 
 class RFormulaModel(JavaModel):
     """
+    .. note:: Experimental
+
     Model fitted by :py:class:`RFormula`.
     """
 

From 2f6fd5256c6650868916a3eefaa0beb091187cbb Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Tue, 8 Sep 2015 22:13:05 -0700
Subject: [PATCH 560/633] [SPARK-9654] [ML] [PYSPARK] Add IndexToString to
 PySpark

Adds IndexToString to PySpark.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7976 from holdenk/SPARK-9654-add-string-indexer-inverse-in-pyspark.
---
 .../spark/ml/feature/StringIndexer.scala      |  2 +-
 python/pyspark/ml/feature.py                  | 74 ++++++++++++++++++-
 python/pyspark/ml/wrapper.py                  |  3 +-
 3 files changed, 73 insertions(+), 6 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 77aeed0ab0370..b6482ffe0b2ee 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -102,7 +102,7 @@ class StringIndexer(override val uid: String) extends Estimator[StringIndexerMod
  * [[StringIndexerModel.transform]] would return the input dataset unmodified.
  * This is a temporary fix for the case when target labels do not exist during prediction.
  *
- * @param labels  Ordered list of labels, corresponding to indices to be assigned
+ * @param labels  Ordered list of labels, corresponding to indices to be assigned.
  */
 @Experimental
 class StringIndexerModel (
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index a7c5b2b62e764..8c26cfbd5a47d 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -27,10 +27,11 @@
 from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
-           'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion', 'RegexTokenizer',
-           'SQLTransformer', 'StandardScaler', 'StandardScalerModel', 'StringIndexer',
-           'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'Word2Vec',
-           'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel', 'StopWordsRemover']
+           'IndexToString', 'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion',
+           'RegexTokenizer', 'SQLTransformer', 'StandardScaler', 'StandardScalerModel',
+           'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer',
+           'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel',
+           'StopWordsRemover']
 
 
 @inherit_doc
@@ -934,6 +935,11 @@ class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol):
     >>> sorted(set([(i[0], i[1]) for i in td.select(td.id, td.indexed).collect()]),
     ...     key=lambda x: x[0])
     [(0, 0.0), (1, 2.0), (2, 1.0), (3, 0.0), (4, 0.0), (5, 1.0)]
+    >>> inverter = IndexToString(inputCol="indexed", outputCol="label2", labels=model.labels())
+    >>> itd = inverter.transform(td)
+    >>> sorted(set([(i[0], str(i[1])) for i in itd.select(itd.id, itd.label2).collect()]),
+    ...     key=lambda x: x[0])
+    [(0, 'a'), (1, 'b'), (2, 'c'), (3, 'a'), (4, 'a'), (5, 'c')]
     """
 
     @keyword_only
@@ -965,6 +971,66 @@ class StringIndexerModel(JavaModel):
 
     Model fitted by StringIndexer.
     """
+    @property
+    def labels(self):
+        """
+        Ordered list of labels, corresponding to indices to be assigned.
+        """
+        return self._java_obj.labels
+
+
+@inherit_doc
+class IndexToString(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    .. note:: Experimental
+
+    A :py:class:`Transformer` that maps a column of string indices back to a new column of
+    corresponding string values using either the ML attributes of the input column, or if
+    provided using the labels supplied by the user.
+    All original columns are kept during transformation.
+    See L{StringIndexer} for converting strings into indices.
+    """
+
+    # a placeholder to make the labels show up in generated doc
+    labels = Param(Params._dummy(), "labels",
+                   "Optional array of labels to be provided by the user, if not supplied or " +
+                   "empty, column metadata is read for labels")
+
+    @keyword_only
+    def __init__(self, inputCol=None, outputCol=None, labels=None):
+        """
+        __init__(self, inputCol=None, outputCol=None, labels=None)
+        """
+        super(IndexToString, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.IndexToString",
+                                            self.uid)
+        self.labels = Param(self, "labels",
+                            "Optional array of labels to be provided by the user, if not " +
+                            "supplied or empty, column metadata is read for labels")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inputCol=None, outputCol=None, labels=None):
+        """
+        setParams(self, inputCol=None, outputCol=None, labels=None)
+        Sets params for this IndexToString.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setLabels(self, value):
+        """
+        Sets the value of :py:attr:`labels`.
+        """
+        self._paramMap[self.labels] = value
+        return self
+
+    def getLabels(self):
+        """
+        Gets the value of :py:attr:`labels` or its default value.
+        """
+        return self.getOrDefault(self.labels)
 
 
 class StopWordsRemover(JavaTransformer, HasInputCol, HasOutputCol):
diff --git a/python/pyspark/ml/wrapper.py b/python/pyspark/ml/wrapper.py
index 253705bde913e..8218c7c5f801c 100644
--- a/python/pyspark/ml/wrapper.py
+++ b/python/pyspark/ml/wrapper.py
@@ -136,7 +136,8 @@ def _fit(self, dataset):
 class JavaTransformer(Transformer, JavaWrapper):
     """
     Base class for :py:class:`Transformer`s that wrap Java/Scala
-    implementations.
+    implementations. Subclasses should ensure they have the transformer Java object
+    available as _java_obj.
     """
 
     __metaclass__ = ABCMeta

From 91a577d2778ab5946f0c40cb80c89de24e3d10e8 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Tue, 8 Sep 2015 22:33:23 -0700
Subject: [PATCH 561/633] [SPARK-10249] [ML] [DOC] Add Python Code Example to
 StopWordsRemover User Guide

jira: https://issues.apache.org/jira/browse/SPARK-10249

update user guide since python support added.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #8620 from hhbyyh/swPyDocExample.
---
 docs/ml-features.md | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 90654d1e5a248..58b31a5a5cc47 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -512,6 +512,25 @@ DataFrame dataset = jsql.createDataFrame(rdd, schema);
 remover.transform(dataset).show();
 {% endhighlight %}
 </div>
+
+<div data-lang="python" markdown="1">
+[`StopWordsRemover`](api/python/pyspark.ml.html#pyspark.ml.feature.StopWordsRemover)
+takes an input column name, an output column name, a list of stop words,
+and a boolean indicating if the matches should be case sensitive (false
+by default).
+
+{% highlight python %}
+from pyspark.ml.feature import StopWordsRemover
+
+sentenceData = sqlContext.createDataFrame([
+  (0, ["I", "saw", "the", "red", "baloon"]),
+  (1, ["Mary", "had", "a", "little", "lamb"])
+], ["label", "raw"])
+
+remover = StopWordsRemover(inputCol="raw", outputCol="filtered")
+remover.transform(sentenceData).show(truncate=False)
+{% endhighlight %}
+</div>
 </div>
 
 ## $n$-gram

From c1bc4f439f54625c01a585691e5293cd9961eb0c Mon Sep 17 00:00:00 2001
From: Luc Bourlier <luc.bourlier@typesafe.com>
Date: Wed, 9 Sep 2015 09:57:58 +0100
Subject: [PATCH 562/633] [SPARK-10227] fatal warnings with sbt on Scala 2.11

The bulk of the changes are on `transient` annotation on class parameter. Often the compiler doesn't generate a field for this parameters, so the the transient annotation would be unnecessary.
But if the class parameter are used in methods, then fields are created. So it is safer to keep the annotations.

The remainder are some potential bugs, and deprecated syntax.

Author: Luc Bourlier <luc.bourlier@typesafe.com>

Closes #8433 from skyluc/issue/sbt-2.11.
---
 .../scala/org/apache/spark/Accumulators.scala |  2 +-
 .../scala/org/apache/spark/Dependency.scala   |  2 +-
 .../scala/org/apache/spark/Partitioner.scala  |  4 +-
 .../org/apache/spark/SparkHadoopWriter.scala  |  2 +-
 .../apache/spark/api/python/PythonRDD.scala   |  4 +-
 .../spark/input/PortableDataStream.scala      |  4 +-
 .../netty/NettyBlockTransferService.scala     |  2 +-
 .../org/apache/spark/rdd/BinaryFileRDD.scala  |  6 +--
 .../scala/org/apache/spark/rdd/BlockRDD.scala |  4 +-
 .../org/apache/spark/rdd/CartesianRDD.scala   |  4 +-
 .../org/apache/spark/rdd/CheckpointRDD.scala  |  2 +-
 .../org/apache/spark/rdd/HadoopRDD.scala      |  8 ++--
 .../apache/spark/rdd/LocalCheckpointRDD.scala |  2 +-
 .../spark/rdd/LocalRDDCheckpointData.scala    |  2 +-
 .../org/apache/spark/rdd/NewHadoopRDD.scala   | 18 ++++----
 .../spark/rdd/ParallelCollectionRDD.scala     |  4 +-
 .../spark/rdd/PartitionPruningRDD.scala       |  6 +--
 .../spark/rdd/PartitionwiseSampledRDD.scala   |  4 +-
 .../apache/spark/rdd/RDDCheckpointData.scala  |  2 +-
 .../spark/rdd/ReliableCheckpointRDD.scala     |  2 +-
 .../spark/rdd/ReliableRDDCheckpointData.scala |  2 +-
 .../apache/spark/rdd/SqlNewHadoopRDD.scala    |  6 +--
 .../scala/org/apache/spark/rdd/UnionRDD.scala |  4 +-
 .../spark/rdd/ZippedPartitionsRDD.scala       |  2 +-
 .../apache/spark/rdd/ZippedWithIndexRDD.scala |  2 +-
 .../org/apache/spark/rpc/RpcEndpointRef.scala |  2 +-
 .../apache/spark/rpc/akka/AkkaRpcEnv.scala    | 21 ++++++---
 .../apache/spark/scheduler/ResultTask.scala   |  2 +-
 .../shuffle/unsafe/UnsafeShuffleManager.scala |  2 +-
 .../apache/spark/util/ClosureCleaner.scala    |  2 +-
 .../spark/util/ShutdownHookManager.scala      |  4 +-
 .../util/logging/RollingFileAppender.scala    | 46 +++++++++----------
 .../streaming/flume/FlumeInputDStream.scala   |  2 +-
 .../flume/FlumePollingInputDStream.scala      |  2 +-
 .../kafka/DirectKafkaInputDStream.scala       |  4 +-
 .../streaming/kafka/KafkaInputDStream.scala   |  2 +-
 .../streaming/mqtt/MQTTInputDStream.scala     |  2 +-
 .../twitter/TwitterInputDStream.scala         |  2 +-
 .../org/apache/spark/graphx/EdgeRDD.scala     |  4 +-
 .../scala/org/apache/spark/graphx/Graph.scala |  6 +--
 .../org/apache/spark/graphx/VertexRDD.scala   |  4 +-
 .../apache/spark/mllib/rdd/RandomRDD.scala    | 12 ++---
 .../org/apache/spark/repl/SparkILoop.scala    |  2 +-
 .../expressions/stringExpressions.scala       |  2 +-
 .../spark/sql/types/AbstractDataType.scala    |  2 +-
 .../datasources/WriterContainer.scala         | 10 ++--
 .../spark/sql/hive/HiveMetastoreCatalog.scala |  2 +-
 .../apache/spark/sql/hive/TableReader.scala   |  8 ++--
 .../hive/execution/ScriptTransformation.scala |  2 +-
 .../spark/sql/hive/hiveWriterContainers.scala | 10 ++--
 .../apache/spark/streaming/Checkpoint.scala   |  2 +-
 .../streaming/api/python/PythonDStream.scala  | 12 ++---
 .../streaming/dstream/FileInputDStream.scala  |  2 +-
 .../streaming/dstream/InputDStream.scala      |  2 +-
 .../dstream/PluggableInputDStream.scala       |  2 +-
 .../streaming/dstream/QueueInputDStream.scala |  4 +-
 .../streaming/dstream/RawInputDStream.scala   |  2 +-
 .../dstream/ReceiverInputDStream.scala        |  2 +-
 .../dstream/SocketInputDStream.scala          |  2 +-
 .../rdd/WriteAheadLogBackedBlockRDD.scala     | 22 ++++-----
 60 files changed, 158 insertions(+), 151 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index c39c8667d013e..5592b75afb75b 100644
--- a/core/src/main/scala/org/apache/spark/Accumulators.scala
+++ b/core/src/main/scala/org/apache/spark/Accumulators.scala
@@ -47,7 +47,7 @@ import org.apache.spark.util.Utils
  * @tparam T partial data that can be added in
  */
 class Accumulable[R, T] private[spark] (
-    @transient initialValue: R,
+    initialValue: R,
     param: AccumulableParam[R, T],
     val name: Option[String],
     internal: Boolean)
diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala
index fc8cdde9348ee..cfeeb3902c033 100644
--- a/core/src/main/scala/org/apache/spark/Dependency.scala
+++ b/core/src/main/scala/org/apache/spark/Dependency.scala
@@ -66,7 +66,7 @@ abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {
  */
 @DeveloperApi
 class ShuffleDependency[K, V, C](
-    @transient _rdd: RDD[_ <: Product2[K, V]],
+    @transient private val _rdd: RDD[_ <: Product2[K, V]],
     val partitioner: Partitioner,
     val serializer: Option[Serializer] = None,
     val keyOrdering: Option[Ordering[K]] = None,
diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index 29e581bb57cbc..e4df7af81a6d2 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -104,8 +104,8 @@ class HashPartitioner(partitions: Int) extends Partitioner {
  * the value of `partitions`.
  */
 class RangePartitioner[K : Ordering : ClassTag, V](
-    @transient partitions: Int,
-    @transient rdd: RDD[_ <: Product2[K, V]],
+    partitions: Int,
+    rdd: RDD[_ <: Product2[K, V]],
     private var ascending: Boolean = true)
   extends Partitioner {
 
diff --git a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala
index f5dd36cbcfe6d..ae5926dd534a6 100644
--- a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala
+++ b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala
@@ -37,7 +37,7 @@ import org.apache.spark.util.SerializableJobConf
  * a filename to write to, etc, exactly like in a Hadoop MapReduce job.
  */
 private[spark]
-class SparkHadoopWriter(@transient jobConf: JobConf)
+class SparkHadoopWriter(jobConf: JobConf)
   extends Logging
   with SparkHadoopMapRedUtil
   with Serializable {
diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
index b4d152b336602..69da180593bb5 100644
--- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala
@@ -41,7 +41,7 @@ import org.apache.spark.util.{SerializableConfiguration, Utils}
 import scala.util.control.NonFatal
 
 private[spark] class PythonRDD(
-    @transient parent: RDD[_],
+    parent: RDD[_],
     command: Array[Byte],
     envVars: JMap[String, String],
     pythonIncludes: JList[String],
@@ -785,7 +785,7 @@ class BytesToString extends org.apache.spark.api.java.function.Function[Array[By
  * Internal class that acts as an `AccumulatorParam` for Python accumulators. Inside, it
  * collects a list of pickled strings that we pass to Python through a socket.
  */
-private class PythonAccumulatorParam(@transient serverHost: String, serverPort: Int)
+private class PythonAccumulatorParam(@transient private val serverHost: String, serverPort: Int)
   extends AccumulatorParam[JList[Array[Byte]]] {
 
   Utils.checkHost(serverHost, "Expected hostname")
diff --git a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
index a5ad47293f1c2..e2ffc3b64e5db 100644
--- a/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
+++ b/core/src/main/scala/org/apache/spark/input/PortableDataStream.scala
@@ -131,8 +131,8 @@ private[spark] class StreamInputFormat extends StreamFileInputFormat[PortableDat
  */
 @Experimental
 class PortableDataStream(
-    @transient isplit: CombineFileSplit,
-    @transient context: TaskAttemptContext,
+    isplit: CombineFileSplit,
+    context: TaskAttemptContext,
     index: Integer)
   extends Serializable {
 
diff --git a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
index 4b851bcb36597..70a42f9045e6b 100644
--- a/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
+++ b/core/src/main/scala/org/apache/spark/network/netty/NettyBlockTransferService.scala
@@ -137,7 +137,7 @@ class NettyBlockTransferService(conf: SparkConf, securityManager: SecurityManage
       new RpcResponseCallback {
         override def onSuccess(response: Array[Byte]): Unit = {
           logTrace(s"Successfully uploaded block $blockId")
-          result.success()
+          result.success((): Unit)
         }
         override def onFailure(e: Throwable): Unit = {
           logError(s"Error while uploading block $blockId", e)
diff --git a/core/src/main/scala/org/apache/spark/rdd/BinaryFileRDD.scala b/core/src/main/scala/org/apache/spark/rdd/BinaryFileRDD.scala
index 1f755db485812..6fec00dcd0d85 100644
--- a/core/src/main/scala/org/apache/spark/rdd/BinaryFileRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/BinaryFileRDD.scala
@@ -28,7 +28,7 @@ private[spark] class BinaryFileRDD[T](
     inputFormatClass: Class[_ <: StreamFileInputFormat[T]],
     keyClass: Class[String],
     valueClass: Class[T],
-    @transient conf: Configuration,
+    conf: Configuration,
     minPartitions: Int)
   extends NewHadoopRDD[String, T](sc, inputFormatClass, keyClass, valueClass, conf) {
 
@@ -36,10 +36,10 @@ private[spark] class BinaryFileRDD[T](
     val inputFormat = inputFormatClass.newInstance
     inputFormat match {
       case configurable: Configurable =>
-        configurable.setConf(conf)
+        configurable.setConf(getConf)
       case _ =>
     }
-    val jobContext = newJobContext(conf, jobId)
+    val jobContext = newJobContext(getConf, jobId)
     inputFormat.setMinPartitions(jobContext, minPartitions)
     val rawSplits = inputFormat.getSplits(jobContext).toArray
     val result = new Array[Partition](rawSplits.size)
diff --git a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
index 922030263756b..fc1710fbad0a3 100644
--- a/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/BlockRDD.scala
@@ -28,7 +28,7 @@ private[spark] class BlockRDDPartition(val blockId: BlockId, idx: Int) extends P
 }
 
 private[spark]
-class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds: Array[BlockId])
+class BlockRDD[T: ClassTag](sc: SparkContext, @transient val blockIds: Array[BlockId])
   extends RDD[T](sc, Nil) {
 
   @transient lazy val _locations = BlockManager.blockIdsToHosts(blockIds, SparkEnv.get)
@@ -64,7 +64,7 @@ class BlockRDD[T: ClassTag](@transient sc: SparkContext, @transient val blockIds
    */
   private[spark] def removeBlocks() {
     blockIds.foreach { blockId =>
-      sc.env.blockManager.master.removeBlock(blockId)
+      sparkContext.env.blockManager.master.removeBlock(blockId)
     }
     _isValid = false
   }
diff --git a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala
index c1d6971787572..18e8cddbc40db 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CartesianRDD.scala
@@ -27,8 +27,8 @@ import org.apache.spark.util.Utils
 private[spark]
 class CartesianPartition(
     idx: Int,
-    @transient rdd1: RDD[_],
-    @transient rdd2: RDD[_],
+    @transient private val rdd1: RDD[_],
+    @transient private val rdd2: RDD[_],
     s1Index: Int,
     s2Index: Int
   ) extends Partition {
diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
index 72fe215dae73e..b0364623af4cf 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala
@@ -29,7 +29,7 @@ private[spark] class CheckpointRDDPartition(val index: Int) extends Partition
 /**
  * An RDD that recovers checkpointed data from storage.
  */
-private[spark] abstract class CheckpointRDD[T: ClassTag](@transient sc: SparkContext)
+private[spark] abstract class CheckpointRDD[T: ClassTag](sc: SparkContext)
   extends RDD[T](sc, Nil) {
 
   // CheckpointRDD should not be checkpointed again
diff --git a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
index e1f8719eead02..8f2655d63b797 100644
--- a/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala
@@ -51,7 +51,7 @@ import org.apache.spark.storage.StorageLevel
 /**
  * A Spark split class that wraps around a Hadoop InputSplit.
  */
-private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSplit)
+private[spark] class HadoopPartition(rddId: Int, idx: Int, s: InputSplit)
   extends Partition {
 
   val inputSplit = new SerializableWritable[InputSplit](s)
@@ -99,7 +99,7 @@ private[spark] class HadoopPartition(rddId: Int, idx: Int, @transient s: InputSp
  */
 @DeveloperApi
 class HadoopRDD[K, V](
-    @transient sc: SparkContext,
+    sc: SparkContext,
     broadcastedConf: Broadcast[SerializableConfiguration],
     initLocalJobConfFuncOpt: Option[JobConf => Unit],
     inputFormatClass: Class[_ <: InputFormat[K, V]],
@@ -109,7 +109,7 @@ class HadoopRDD[K, V](
   extends RDD[(K, V)](sc, Nil) with Logging {
 
   if (initLocalJobConfFuncOpt.isDefined) {
-    sc.clean(initLocalJobConfFuncOpt.get)
+    sparkContext.clean(initLocalJobConfFuncOpt.get)
   }
 
   def this(
@@ -137,7 +137,7 @@ class HadoopRDD[K, V](
   // used to build JobTracker ID
   private val createTime = new Date()
 
-  private val shouldCloneJobConf = sc.conf.getBoolean("spark.hadoop.cloneConf", false)
+  private val shouldCloneJobConf = sparkContext.conf.getBoolean("spark.hadoop.cloneConf", false)
 
   // Returns a JobConf that will be used on slaves to obtain input splits for Hadoop reads.
   protected def getJobConf(): JobConf = {
diff --git a/core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala
index daa5779d688cc..bfe19195fcd37 100644
--- a/core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/LocalCheckpointRDD.scala
@@ -35,7 +35,7 @@ import org.apache.spark.storage.RDDBlockId
  * @param numPartitions the number of partitions in the checkpointed RDD
  */
 private[spark] class LocalCheckpointRDD[T: ClassTag](
-    @transient sc: SparkContext,
+    sc: SparkContext,
     rddId: Int,
     numPartitions: Int)
   extends CheckpointRDD[T](sc) {
diff --git a/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala
index d6fad896845f6..c115e0ff74d3c 100644
--- a/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/LocalRDDCheckpointData.scala
@@ -31,7 +31,7 @@ import org.apache.spark.util.Utils
  * is written to the local, ephemeral block storage that lives in each executor. This is useful
  * for use cases where RDDs build up long lineages that need to be truncated often (e.g. GraphX).
  */
-private[spark] class LocalRDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
+private[spark] class LocalRDDCheckpointData[T: ClassTag](@transient private val rdd: RDD[T])
   extends RDDCheckpointData[T](rdd) with Logging {
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
index 6a9c004d65cff..174979aaeb231 100644
--- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala
@@ -40,7 +40,7 @@ import org.apache.spark.storage.StorageLevel
 private[spark] class NewHadoopPartition(
     rddId: Int,
     val index: Int,
-    @transient rawSplit: InputSplit with Writable)
+    rawSplit: InputSplit with Writable)
   extends Partition {
 
   val serializableHadoopSplit = new SerializableWritable(rawSplit)
@@ -68,14 +68,14 @@ class NewHadoopRDD[K, V](
     inputFormatClass: Class[_ <: InputFormat[K, V]],
     keyClass: Class[K],
     valueClass: Class[V],
-    @transient conf: Configuration)
+    @transient private val _conf: Configuration)
   extends RDD[(K, V)](sc, Nil)
   with SparkHadoopMapReduceUtil
   with Logging {
 
   // A Hadoop Configuration can be about 10 KB, which is pretty big, so broadcast it
-  private val confBroadcast = sc.broadcast(new SerializableConfiguration(conf))
-  // private val serializableConf = new SerializableWritable(conf)
+  private val confBroadcast = sc.broadcast(new SerializableConfiguration(_conf))
+  // private val serializableConf = new SerializableWritable(_conf)
 
   private val jobTrackerId: String = {
     val formatter = new SimpleDateFormat("yyyyMMddHHmm")
@@ -88,10 +88,10 @@ class NewHadoopRDD[K, V](
     val inputFormat = inputFormatClass.newInstance
     inputFormat match {
       case configurable: Configurable =>
-        configurable.setConf(conf)
+        configurable.setConf(_conf)
       case _ =>
     }
-    val jobContext = newJobContext(conf, jobId)
+    val jobContext = newJobContext(_conf, jobId)
     val rawSplits = inputFormat.getSplits(jobContext).toArray
     val result = new Array[Partition](rawSplits.size)
     for (i <- 0 until rawSplits.size) {
@@ -262,7 +262,7 @@ private[spark] class WholeTextFileRDD(
     inputFormatClass: Class[_ <: WholeTextFileInputFormat],
     keyClass: Class[String],
     valueClass: Class[String],
-    @transient conf: Configuration,
+    conf: Configuration,
     minPartitions: Int)
   extends NewHadoopRDD[String, String](sc, inputFormatClass, keyClass, valueClass, conf) {
 
@@ -270,10 +270,10 @@ private[spark] class WholeTextFileRDD(
     val inputFormat = inputFormatClass.newInstance
     inputFormat match {
       case configurable: Configurable =>
-        configurable.setConf(conf)
+        configurable.setConf(getConf)
       case _ =>
     }
-    val jobContext = newJobContext(conf, jobId)
+    val jobContext = newJobContext(getConf, jobId)
     inputFormat.setMinPartitions(jobContext, minPartitions)
     val rawSplits = inputFormat.getSplits(jobContext).toArray
     val result = new Array[Partition](rawSplits.size)
diff --git a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
index e2394e28f8d26..582fa93afe34e 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ParallelCollectionRDD.scala
@@ -83,8 +83,8 @@ private[spark] class ParallelCollectionPartition[T: ClassTag](
 }
 
 private[spark] class ParallelCollectionRDD[T: ClassTag](
-    @transient sc: SparkContext,
-    @transient data: Seq[T],
+    sc: SparkContext,
+    @transient private val data: Seq[T],
     numSlices: Int,
     locationPrefs: Map[Int, Seq[String]])
     extends RDD[T](sc, Nil) {
diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala
index a00f4c1cdff91..d6a37e8cc5dac 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PartitionPruningRDD.scala
@@ -32,7 +32,7 @@ private[spark] class PartitionPruningRDDPartition(idx: Int, val parentSplit: Par
  * Represents a dependency between the PartitionPruningRDD and its parent. In this
  * case, the child RDD contains a subset of partitions of the parents'.
  */
-private[spark] class PruneDependency[T](rdd: RDD[T], @transient partitionFilterFunc: Int => Boolean)
+private[spark] class PruneDependency[T](rdd: RDD[T], partitionFilterFunc: Int => Boolean)
   extends NarrowDependency[T](rdd) {
 
   @transient
@@ -55,8 +55,8 @@ private[spark] class PruneDependency[T](rdd: RDD[T], @transient partitionFilterF
  */
 @DeveloperApi
 class PartitionPruningRDD[T: ClassTag](
-    @transient prev: RDD[T],
-    @transient partitionFilterFunc: Int => Boolean)
+    prev: RDD[T],
+    partitionFilterFunc: Int => Boolean)
   extends RDD[T](prev.context, List(new PruneDependency(prev, partitionFilterFunc))) {
 
   override def compute(split: Partition, context: TaskContext): Iterator[T] = {
diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
index a637d6f15b7e5..3b1acacf409b9 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PartitionwiseSampledRDD.scala
@@ -47,8 +47,8 @@ class PartitionwiseSampledRDDPartition(val prev: Partition, val seed: Long)
 private[spark] class PartitionwiseSampledRDD[T: ClassTag, U: ClassTag](
     prev: RDD[T],
     sampler: RandomSampler[T, U],
-    @transient preservesPartitioning: Boolean,
-    @transient seed: Long = Utils.random.nextLong)
+    preservesPartitioning: Boolean,
+    @transient private val seed: Long = Utils.random.nextLong)
   extends RDD[U](prev) {
 
   @transient override val partitioner = if (preservesPartitioning) prev.partitioner else None
diff --git a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
index 0e43520870c0a..429514b4f6bee 100644
--- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala
@@ -36,7 +36,7 @@ private[spark] object CheckpointState extends Enumeration {
  * as well as, manages the post-checkpoint state by providing the updated partitions,
  * iterator and preferred locations of the checkpointed RDD.
  */
-private[spark] abstract class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
+private[spark] abstract class RDDCheckpointData[T: ClassTag](@transient private val rdd: RDD[T])
   extends Serializable {
 
   import CheckpointState._
diff --git a/core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala
index 35d8b0bfd18c5..1c3b5da19ceba 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ReliableCheckpointRDD.scala
@@ -32,7 +32,7 @@ import org.apache.spark.util.{SerializableConfiguration, Utils}
  * An RDD that reads from checkpoint files previously written to reliable storage.
  */
 private[spark] class ReliableCheckpointRDD[T: ClassTag](
-    @transient sc: SparkContext,
+    sc: SparkContext,
     val checkpointPath: String)
   extends CheckpointRDD[T](sc) {
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala
index 1df8eef5ff2b9..e9f6060301ba3 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ReliableRDDCheckpointData.scala
@@ -28,7 +28,7 @@ import org.apache.spark.util.SerializableConfiguration
  * An implementation of checkpointing that writes the RDD data to reliable storage.
  * This allows drivers to be restarted on failure with previously computed state.
  */
-private[spark] class ReliableRDDCheckpointData[T: ClassTag](@transient rdd: RDD[T])
+private[spark] class ReliableRDDCheckpointData[T: ClassTag](@transient private val rdd: RDD[T])
   extends RDDCheckpointData[T](rdd) with Logging {
 
   // The directory to which the associated RDD has been checkpointed to
diff --git a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
index fa3fecc80cb63..9babe56267e08 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
@@ -39,7 +39,7 @@ import org.apache.spark.util.{SerializableConfiguration, ShutdownHookManager, Ut
 private[spark] class SqlNewHadoopPartition(
     rddId: Int,
     val index: Int,
-    @transient rawSplit: InputSplit with Writable)
+    rawSplit: InputSplit with Writable)
   extends SparkPartition {
 
   val serializableHadoopSplit = new SerializableWritable(rawSplit)
@@ -61,9 +61,9 @@ private[spark] class SqlNewHadoopPartition(
  * changes based on [[org.apache.spark.rdd.HadoopRDD]].
  */
 private[spark] class SqlNewHadoopRDD[V: ClassTag](
-    @transient sc : SparkContext,
+    sc : SparkContext,
     broadcastedConf: Broadcast[SerializableConfiguration],
-    @transient initDriverSideJobFuncOpt: Option[Job => Unit],
+    @transient private val initDriverSideJobFuncOpt: Option[Job => Unit],
     initLocalJobFuncOpt: Option[Job => Unit],
     inputFormatClass: Class[_ <: InputFormat[Void, V]],
     valueClass: Class[V])
diff --git a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala
index 3986645350a82..66cf4369da2ef 100644
--- a/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala
@@ -37,9 +37,9 @@ import org.apache.spark.util.Utils
  */
 private[spark] class UnionPartition[T: ClassTag](
     idx: Int,
-    @transient rdd: RDD[T],
+    @transient private val rdd: RDD[T],
     val parentRddIndex: Int,
-    @transient parentRddPartitionIndex: Int)
+    @transient private val parentRddPartitionIndex: Int)
   extends Partition {
 
   var parentPartition: Partition = rdd.partitions(parentRddPartitionIndex)
diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
index b3c64394abc76..70bf04de6400d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ZippedPartitionsRDD.scala
@@ -26,7 +26,7 @@ import org.apache.spark.util.Utils
 
 private[spark] class ZippedPartitionsPartition(
     idx: Int,
-    @transient rdds: Seq[RDD[_]],
+    @transient private val rdds: Seq[RDD[_]],
     @transient val preferredLocations: Seq[String])
   extends Partition {
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala
index e277ae28d588f..32931d59acb18 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ZippedWithIndexRDD.scala
@@ -37,7 +37,7 @@ class ZippedWithIndexRDDPartition(val prev: Partition, val startIndex: Long)
  * @tparam T parent RDD item type
  */
 private[spark]
-class ZippedWithIndexRDD[T: ClassTag](@transient prev: RDD[T]) extends RDD[(T, Long)](prev) {
+class ZippedWithIndexRDD[T: ClassTag](prev: RDD[T]) extends RDD[(T, Long)](prev) {
 
   /** The start index of each partition. */
   @transient private val startIndices: Array[Long] = {
diff --git a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
index 7409ac8859991..f25710bb5bd6e 100644
--- a/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/RpcEndpointRef.scala
@@ -26,7 +26,7 @@ import org.apache.spark.{SparkException, Logging, SparkConf}
 /**
  * A reference for a remote [[RpcEndpoint]]. [[RpcEndpointRef]] is thread-safe.
  */
-private[spark] abstract class RpcEndpointRef(@transient conf: SparkConf)
+private[spark] abstract class RpcEndpointRef(conf: SparkConf)
   extends Serializable with Logging {
 
   private[this] val maxRetries = RpcUtils.numRetries(conf)
diff --git a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
index fc17542abf81d..ad67e1c5ad4d5 100644
--- a/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
+++ b/core/src/main/scala/org/apache/spark/rpc/akka/AkkaRpcEnv.scala
@@ -87,9 +87,9 @@ private[spark] class AkkaRpcEnv private[akka] (
 
   override def setupEndpoint(name: String, endpoint: RpcEndpoint): RpcEndpointRef = {
     @volatile var endpointRef: AkkaRpcEndpointRef = null
-    // Use lazy because the Actor needs to use `endpointRef`.
+    // Use defered function because the Actor needs to use `endpointRef`.
     // So `actorRef` should be created after assigning `endpointRef`.
-    lazy val actorRef = actorSystem.actorOf(Props(new Actor with ActorLogReceive with Logging {
+    val actorRef = () => actorSystem.actorOf(Props(new Actor with ActorLogReceive with Logging {
 
       assert(endpointRef != null)
 
@@ -272,13 +272,20 @@ private[akka] class ErrorMonitor extends Actor with ActorLogReceive with Logging
 }
 
 private[akka] class AkkaRpcEndpointRef(
-    @transient defaultAddress: RpcAddress,
-    @transient _actorRef: => ActorRef,
-    @transient conf: SparkConf,
-    @transient initInConstructor: Boolean = true)
+    @transient private val defaultAddress: RpcAddress,
+    @transient private val _actorRef: () => ActorRef,
+    conf: SparkConf,
+    initInConstructor: Boolean)
   extends RpcEndpointRef(conf) with Logging {
 
-  lazy val actorRef = _actorRef
+  def this(
+      defaultAddress: RpcAddress,
+      _actorRef: ActorRef,
+      conf: SparkConf) = {
+    this(defaultAddress, () => _actorRef, conf, true)
+  }
+
+  lazy val actorRef = _actorRef()
 
   override lazy val address: RpcAddress = {
     val akkaAddress = actorRef.path.address
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
index c4dc080e2b22b..fb693721a9cb6 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
@@ -44,7 +44,7 @@ private[spark] class ResultTask[T, U](
     stageAttemptId: Int,
     taskBinary: Broadcast[Array[Byte]],
     partition: Partition,
-    @transient locs: Seq[TaskLocation],
+    locs: Seq[TaskLocation],
     val outputId: Int,
     internalAccumulators: Seq[Accumulator[Long]])
   extends Task[U](stageId, stageAttemptId, partition.index, internalAccumulators)
diff --git a/core/src/main/scala/org/apache/spark/shuffle/unsafe/UnsafeShuffleManager.scala b/core/src/main/scala/org/apache/spark/shuffle/unsafe/UnsafeShuffleManager.scala
index df7bbd64247dd..75f22f642b9d1 100644
--- a/core/src/main/scala/org/apache/spark/shuffle/unsafe/UnsafeShuffleManager.scala
+++ b/core/src/main/scala/org/apache/spark/shuffle/unsafe/UnsafeShuffleManager.scala
@@ -159,7 +159,7 @@ private[spark] class UnsafeShuffleManager(conf: SparkConf) extends ShuffleManage
       mapId: Int,
       context: TaskContext): ShuffleWriter[K, V] = {
     handle match {
-      case unsafeShuffleHandle: UnsafeShuffleHandle[K, V] =>
+      case unsafeShuffleHandle: UnsafeShuffleHandle[K @unchecked, V @unchecked] =>
         numMapsForShufflesThatUsedNewPath.putIfAbsent(handle.shuffleId, unsafeShuffleHandle.numMaps)
         val env = SparkEnv.get
         new UnsafeShuffleWriter(
diff --git a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
index 150d82b3930ef..1b49dca9dc78b 100644
--- a/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
+++ b/core/src/main/scala/org/apache/spark/util/ClosureCleaner.scala
@@ -94,7 +94,7 @@ private[spark] object ClosureCleaner extends Logging {
     if (cls.isPrimitive) {
       cls match {
         case java.lang.Boolean.TYPE => new java.lang.Boolean(false)
-        case java.lang.Character.TYPE => new java.lang.Character('\0')
+        case java.lang.Character.TYPE => new java.lang.Character('\u0000')
         case java.lang.Void.TYPE =>
           // This should not happen because `Foo(void x) {}` does not compile.
           throw new IllegalStateException("Unexpected void parameter in constructor")
diff --git a/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala b/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala
index 61ff9b89ec1c1..db4a8b304ec3e 100644
--- a/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala
+++ b/core/src/main/scala/org/apache/spark/util/ShutdownHookManager.scala
@@ -217,7 +217,9 @@ private [util] class SparkShutdownHookManager {
     }
     Try(Utils.classForName("org.apache.hadoop.util.ShutdownHookManager")) match {
       case Success(shmClass) =>
-        val fsPriority = classOf[FileSystem].getField("SHUTDOWN_HOOK_PRIORITY").get()
+        val fsPriority = classOf[FileSystem]
+          .getField("SHUTDOWN_HOOK_PRIORITY")
+          .get(null) // static field, the value is not used
           .asInstanceOf[Int]
         val shm = shmClass.getMethod("get").invoke(null)
         shm.getClass().getMethod("addShutdownHook", classOf[Runnable], classOf[Int])
diff --git a/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala b/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
index 7138b4b8e4533..1e8476c4a047e 100644
--- a/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
+++ b/core/src/main/scala/org/apache/spark/util/logging/RollingFileAppender.scala
@@ -79,32 +79,30 @@ private[spark] class RollingFileAppender(
     val rolloverSuffix = rollingPolicy.generateRolledOverFileSuffix()
     val rolloverFile = new File(
       activeFile.getParentFile, activeFile.getName + rolloverSuffix).getAbsoluteFile
-    try {
-      logDebug(s"Attempting to rollover file $activeFile to file $rolloverFile")
-      if (activeFile.exists) {
-        if (!rolloverFile.exists) {
-          Files.move(activeFile, rolloverFile)
-          logInfo(s"Rolled over $activeFile to $rolloverFile")
-        } else {
-          // In case the rollover file name clashes, make a unique file name.
-          // The resultant file names are long and ugly, so this is used only
-          // if there is a name collision. This can be avoided by the using
-          // the right pattern such that name collisions do not occur.
-          var i = 0
-          var altRolloverFile: File = null
-          do {
-            altRolloverFile = new File(activeFile.getParent,
-              s"${activeFile.getName}$rolloverSuffix--$i").getAbsoluteFile
-            i += 1
-          } while (i < 10000 && altRolloverFile.exists)
-
-          logWarning(s"Rollover file $rolloverFile already exists, " +
-            s"rolled over $activeFile to file $altRolloverFile")
-          Files.move(activeFile, altRolloverFile)
-        }
+    logDebug(s"Attempting to rollover file $activeFile to file $rolloverFile")
+    if (activeFile.exists) {
+      if (!rolloverFile.exists) {
+        Files.move(activeFile, rolloverFile)
+        logInfo(s"Rolled over $activeFile to $rolloverFile")
       } else {
-        logWarning(s"File $activeFile does not exist")
+        // In case the rollover file name clashes, make a unique file name.
+        // The resultant file names are long and ugly, so this is used only
+        // if there is a name collision. This can be avoided by the using
+        // the right pattern such that name collisions do not occur.
+        var i = 0
+        var altRolloverFile: File = null
+        do {
+          altRolloverFile = new File(activeFile.getParent,
+            s"${activeFile.getName}$rolloverSuffix--$i").getAbsoluteFile
+          i += 1
+        } while (i < 10000 && altRolloverFile.exists)
+
+        logWarning(s"Rollover file $rolloverFile already exists, " +
+          s"rolled over $activeFile to file $altRolloverFile")
+        Files.move(activeFile, altRolloverFile)
       }
+    } else {
+      logWarning(s"File $activeFile does not exist")
     }
   }
 
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
index 2bf99cb3cba1f..c8780aa83bdbd 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala
@@ -43,7 +43,7 @@ import org.jboss.netty.handler.codec.compression._
 
 private[streaming]
 class FlumeInputDStream[T: ClassTag](
-  @transient ssc_ : StreamingContext,
+  ssc_ : StreamingContext,
   host: String,
   port: Int,
   storageLevel: StorageLevel,
diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
index 0bc46209b8369..3b936d88abd3e 100644
--- a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
+++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumePollingInputDStream.scala
@@ -46,7 +46,7 @@ import org.apache.spark.streaming.flume.sink._
  * @tparam T Class type of the object of this stream
  */
 private[streaming] class FlumePollingInputDStream[T: ClassTag](
-    @transient _ssc: StreamingContext,
+    _ssc: StreamingContext,
     val addresses: Seq[InetSocketAddress],
     val maxBatchSize: Int,
     val parallelism: Int,
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
index 1000094e93cb3..8a087474d3169 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/DirectKafkaInputDStream.scala
@@ -58,7 +58,7 @@ class DirectKafkaInputDStream[
   U <: Decoder[K]: ClassTag,
   T <: Decoder[V]: ClassTag,
   R: ClassTag](
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     val kafkaParams: Map[String, String],
     val fromOffsets: Map[TopicAndPartition, Long],
     messageHandler: MessageAndMetadata[K, V] => R
@@ -79,7 +79,7 @@ class DirectKafkaInputDStream[
   override protected[streaming] val rateController: Option[RateController] = {
     if (RateController.isBackPressureEnabled(ssc.conf)) {
       Some(new DirectKafkaRateController(id,
-        RateEstimator.create(ssc.conf, ssc_.graph.batchDuration)))
+        RateEstimator.create(ssc.conf, context.graph.batchDuration)))
     } else {
       None
     }
diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala
index 04b2dc10d39ea..38730fecf332a 100644
--- a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala
+++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala
@@ -48,7 +48,7 @@ class KafkaInputDStream[
   V: ClassTag,
   U <: Decoder[_]: ClassTag,
   T <: Decoder[_]: ClassTag](
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     kafkaParams: Map[String, String],
     topics: Map[String, Int],
     useReliableReceiver: Boolean,
diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala
index 7c2f18cb35bda..116c170489e96 100644
--- a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala
+++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala
@@ -38,7 +38,7 @@ import org.apache.spark.streaming.receiver.Receiver
 
 private[streaming]
 class MQTTInputDStream(
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     brokerUrl: String,
     topic: String,
     storageLevel: StorageLevel
diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
index 7cf02d85d73d3..d7de74b350543 100644
--- a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
+++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala
@@ -39,7 +39,7 @@ import org.apache.spark.streaming.receiver.Receiver
 */
 private[streaming]
 class TwitterInputDStream(
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     twitterAuth: Option[Authorization],
     filters: Seq[String],
     storageLevel: StorageLevel
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
index 4611a3ace219b..ee7302a1edbf6 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/EdgeRDD.scala
@@ -38,8 +38,8 @@ import org.apache.spark.graphx.impl.EdgeRDDImpl
  * `impl.ReplicatedVertexView`.
  */
 abstract class EdgeRDD[ED](
-    @transient sc: SparkContext,
-    @transient deps: Seq[Dependency[_]]) extends RDD[Edge[ED]](sc, deps) {
+    sc: SparkContext,
+    deps: Seq[Dependency[_]]) extends RDD[Edge[ED]](sc, deps) {
 
   // scalastyle:off structural.type
   private[graphx] def partitionsRDD: RDD[(PartitionID, EdgePartition[ED, VD])] forSome { type VD }
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
index db73a8abc5733..869caa340f52b 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala
@@ -46,7 +46,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
    * @note vertex ids are unique.
    * @return an RDD containing the vertices in this graph
    */
-  @transient val vertices: VertexRDD[VD]
+  val vertices: VertexRDD[VD]
 
   /**
    * An RDD containing the edges and their associated attributes.  The entries in the RDD contain
@@ -59,7 +59,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
    * along with their vertex data.
    *
    */
-  @transient val edges: EdgeRDD[ED]
+  val edges: EdgeRDD[ED]
 
   /**
    * An RDD containing the edge triplets, which are edges along with the vertex data associated with
@@ -77,7 +77,7 @@ abstract class Graph[VD: ClassTag, ED: ClassTag] protected () extends Serializab
    * val numInvalid = graph.triplets.map(e => if (e.src.data == e.dst.data) 1 else 0).sum
    * }}}
    */
-  @transient val triplets: RDD[EdgeTriplet[VD, ED]]
+  val triplets: RDD[EdgeTriplet[VD, ED]]
 
   /**
    * Caches the vertices and edges associated with this graph at the specified storage level,
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
index a9f04b559c3d1..1ef7a78fbcd00 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/VertexRDD.scala
@@ -55,8 +55,8 @@ import org.apache.spark.graphx.impl.VertexRDDImpl
  * @tparam VD the vertex attribute associated with each vertex in the set.
  */
 abstract class VertexRDD[VD](
-    @transient sc: SparkContext,
-    @transient deps: Seq[Dependency[_]]) extends RDD[(VertexId, VD)](sc, deps) {
+    sc: SparkContext,
+    deps: Seq[Dependency[_]]) extends RDD[(VertexId, VD)](sc, deps) {
 
   implicit protected def vdTag: ClassTag[VD]
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala
index 910eff9540a47..f8cea7ecea6bf 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/rdd/RandomRDD.scala
@@ -35,11 +35,11 @@ private[mllib] class RandomRDDPartition[T](override val index: Int,
 }
 
 // These two classes are necessary since Range objects in Scala cannot have size > Int.MaxValue
-private[mllib] class RandomRDD[T: ClassTag](@transient sc: SparkContext,
+private[mllib] class RandomRDD[T: ClassTag](sc: SparkContext,
     size: Long,
     numPartitions: Int,
-    @transient rng: RandomDataGenerator[T],
-    @transient seed: Long = Utils.random.nextLong) extends RDD[T](sc, Nil) {
+    @transient private val rng: RandomDataGenerator[T],
+    @transient private val seed: Long = Utils.random.nextLong) extends RDD[T](sc, Nil) {
 
   require(size > 0, "Positive RDD size required.")
   require(numPartitions > 0, "Positive number of partitions required")
@@ -56,12 +56,12 @@ private[mllib] class RandomRDD[T: ClassTag](@transient sc: SparkContext,
   }
 }
 
-private[mllib] class RandomVectorRDD(@transient sc: SparkContext,
+private[mllib] class RandomVectorRDD(sc: SparkContext,
     size: Long,
     vectorSize: Int,
     numPartitions: Int,
-    @transient rng: RandomDataGenerator[Double],
-    @transient seed: Long = Utils.random.nextLong) extends RDD[Vector](sc, Nil) {
+    @transient private val rng: RandomDataGenerator[Double],
+    @transient private val seed: Long = Utils.random.nextLong) extends RDD[Vector](sc, Nil) {
 
   require(size > 0, "Positive RDD size required.")
   require(numPartitions > 0, "Positive number of partitions required")
diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index bf609ff0f65fc..33d262558b1fc 100644
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -118,5 +118,5 @@ object SparkILoop {
       }
     }
   }
-  def run(lines: List[String]): String = run(lines map (_ + "\n") mkString)
+  def run(lines: List[String]): String = run(lines.map(_ + "\n").mkString)
 }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
index 48d02bb534501..a09d5b6e3ad14 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringExpressions.scala
@@ -255,7 +255,7 @@ object StringTranslate {
     val dict = new HashMap[Character, Character]()
     var i = 0
     while (i < matching.length()) {
-      val rep = if (i < replace.length()) replace.charAt(i) else '\0'
+      val rep = if (i < replace.length()) replace.charAt(i) else '\u0000'
       if (null == dict.get(matching.charAt(i))) {
         dict.put(matching.charAt(i), rep)
       }
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
index e0667c629486d..1d2d007c2b4d2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/AbstractDataType.scala
@@ -126,7 +126,7 @@ protected[sql] object AnyDataType extends AbstractDataType {
  */
 protected[sql] abstract class AtomicType extends DataType {
   private[sql] type InternalType
-  @transient private[sql] val tag: TypeTag[InternalType]
+  private[sql] val tag: TypeTag[InternalType]
   private[sql] val ordering: Ordering[InternalType]
 
   @transient private[sql] val classTag = ScalaReflectionLock.synchronized {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index 879fd69863211..9a573db0c023a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -39,7 +39,7 @@ import org.apache.spark.util.SerializableConfiguration
 
 private[sql] abstract class BaseWriterContainer(
     @transient val relation: HadoopFsRelation,
-    @transient job: Job,
+    @transient private val job: Job,
     isAppend: Boolean)
   extends SparkHadoopMapReduceUtil
   with Logging
@@ -222,8 +222,8 @@ private[sql] abstract class BaseWriterContainer(
  * A writer that writes all of the rows in a partition to a single file.
  */
 private[sql] class DefaultWriterContainer(
-    @transient relation: HadoopFsRelation,
-    @transient job: Job,
+    relation: HadoopFsRelation,
+    job: Job,
     isAppend: Boolean)
   extends BaseWriterContainer(relation, job, isAppend) {
 
@@ -286,8 +286,8 @@ private[sql] class DefaultWriterContainer(
  * writer externally sorts the remaining rows and then writes out them out one file at a time.
  */
 private[sql] class DynamicPartitionWriterContainer(
-    @transient relation: HadoopFsRelation,
-    @transient job: Job,
+    relation: HadoopFsRelation,
+    job: Job,
     partitionColumns: Seq[Attribute],
     dataColumns: Seq[Attribute],
     inputSchema: Seq[Attribute],
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
index b8da0840ae569..0a5569b0a4446 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala
@@ -767,7 +767,7 @@ private[hive] case class InsertIntoHiveTable(
 private[hive] case class MetastoreRelation
     (databaseName: String, tableName: String, alias: Option[String])
     (val table: HiveTable)
-    (@transient sqlContext: SQLContext)
+    (@transient private val sqlContext: SQLContext)
   extends LeafNode with MultiInstanceRelation with FileRelation {
 
   override def equals(other: Any): Boolean = other match {
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
index dc355690852bd..e35468a624c3e 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/TableReader.scala
@@ -54,10 +54,10 @@ private[hive] sealed trait TableReader {
  */
 private[hive]
 class HadoopTableReader(
-    @transient attributes: Seq[Attribute],
-    @transient relation: MetastoreRelation,
-    @transient sc: HiveContext,
-    @transient hiveExtraConf: HiveConf)
+    @transient private val attributes: Seq[Attribute],
+    @transient private val relation: MetastoreRelation,
+    @transient private val sc: HiveContext,
+    hiveExtraConf: HiveConf)
   extends TableReader with Logging {
 
   // Hadoop honors "mapred.map.tasks" as hint, but will ignore when mapred.job.tracker is "local".
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
index c7651daffe36e..32bddbaeaeaf9 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/ScriptTransformation.scala
@@ -53,7 +53,7 @@ case class ScriptTransformation(
     script: String,
     output: Seq[Attribute],
     child: SparkPlan,
-    ioschema: HiveScriptIOSchema)(@transient sc: HiveContext)
+    ioschema: HiveScriptIOSchema)(@transient private val sc: HiveContext)
   extends UnaryNode {
 
   override def otherCopyArgs: Seq[HiveContext] = sc :: Nil
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 8dc796b056a72..29a6f08f40728 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -45,7 +45,7 @@ import org.apache.spark.util.SerializableJobConf
  * It is based on [[SparkHadoopWriter]].
  */
 private[hive] class SparkHiveWriterContainer(
-    @transient jobConf: JobConf,
+    jobConf: JobConf,
     fileSinkConf: FileSinkDesc)
   extends Logging
   with SparkHadoopMapRedUtil
@@ -163,7 +163,7 @@ private[spark] object SparkHiveDynamicPartitionWriterContainer {
 }
 
 private[spark] class SparkHiveDynamicPartitionWriterContainer(
-    @transient jobConf: JobConf,
+    jobConf: JobConf,
     fileSinkConf: FileSinkDesc,
     dynamicPartColNames: Array[String])
   extends SparkHiveWriterContainer(jobConf, fileSinkConf) {
@@ -194,10 +194,10 @@ private[spark] class SparkHiveDynamicPartitionWriterContainer(
     // Better solution is to add a step similar to what Hive FileSinkOperator.jobCloseOp does:
     // calling something like Utilities.mvFileToFinalPath to cleanup the output directory and then
     // load it with loadDynamicPartitions/loadPartition/loadTable.
-    val oldMarker = jobConf.getBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, true)
-    jobConf.setBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, false)
+    val oldMarker = conf.value.getBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, true)
+    conf.value.setBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, false)
     super.commitJob()
-    jobConf.setBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, oldMarker)
+    conf.value.setBoolean(SUCCESSFUL_JOB_OUTPUT_DIR_MARKER, oldMarker)
   }
 
   override def getLocalFileWriter(row: InternalRow, schema: StructType)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
index 27024ecfd9101..8a6050f5227bf 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala
@@ -32,7 +32,7 @@ import org.apache.spark.streaming.scheduler.JobGenerator
 
 
 private[streaming]
-class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time)
+class Checkpoint(ssc: StreamingContext, val checkpointTime: Time)
   extends Logging with Serializable {
   val master = ssc.sc.master
   val framework = ssc.sc.appName
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
index 2c373640d2fd9..dfc569451df86 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/api/python/PythonDStream.scala
@@ -170,7 +170,7 @@ private[python] object PythonDStream {
  */
 private[python] abstract class PythonDStream(
     parent: DStream[_],
-    @transient pfunc: PythonTransformFunction)
+    pfunc: PythonTransformFunction)
   extends DStream[Array[Byte]] (parent.ssc) {
 
   val func = new TransformFunction(pfunc)
@@ -187,7 +187,7 @@ private[python] abstract class PythonDStream(
  */
 private[python] class PythonTransformedDStream (
     parent: DStream[_],
-    @transient pfunc: PythonTransformFunction)
+    pfunc: PythonTransformFunction)
   extends PythonDStream(parent, pfunc) {
 
   override def compute(validTime: Time): Option[RDD[Array[Byte]]] = {
@@ -206,7 +206,7 @@ private[python] class PythonTransformedDStream (
 private[python] class PythonTransformed2DStream(
     parent: DStream[_],
     parent2: DStream[_],
-    @transient pfunc: PythonTransformFunction)
+    pfunc: PythonTransformFunction)
   extends DStream[Array[Byte]] (parent.ssc) {
 
   val func = new TransformFunction(pfunc)
@@ -230,7 +230,7 @@ private[python] class PythonTransformed2DStream(
  */
 private[python] class PythonStateDStream(
     parent: DStream[Array[Byte]],
-    @transient reduceFunc: PythonTransformFunction)
+    reduceFunc: PythonTransformFunction)
   extends PythonDStream(parent, reduceFunc) {
 
   super.persist(StorageLevel.MEMORY_ONLY)
@@ -252,8 +252,8 @@ private[python] class PythonStateDStream(
  */
 private[python] class PythonReducedWindowedDStream(
     parent: DStream[Array[Byte]],
-    @transient preduceFunc: PythonTransformFunction,
-    @transient pinvReduceFunc: PythonTransformFunction,
+    preduceFunc: PythonTransformFunction,
+    @transient private val pinvReduceFunc: PythonTransformFunction,
     _windowDuration: Duration,
     _slideDuration: Duration)
   extends PythonDStream(parent, preduceFunc) {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
index c358f5b5bd70b..40208a64861fb 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/FileInputDStream.scala
@@ -70,7 +70,7 @@ import org.apache.spark.util.{SerializableConfiguration, TimeStampedHashMap, Uti
  */
 private[streaming]
 class FileInputDStream[K, V, F <: NewInputFormat[K, V]](
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     directory: String,
     filter: Path => Boolean = FileInputDStream.defaultFilter,
     newFilesOnly: Boolean = true,
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
index a6c4cd220e42f..95994c983c0cc 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala
@@ -39,7 +39,7 @@ import org.apache.spark.util.Utils
  *
  * @param ssc_ Streaming context that will execute this input stream
  */
-abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext)
+abstract class InputDStream[T: ClassTag] (ssc_ : StreamingContext)
   extends DStream[T](ssc_) {
 
   private[streaming] var lastValidTime: Time = null
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala
index 186e1bf03a944..002aac9f43617 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/PluggableInputDStream.scala
@@ -23,7 +23,7 @@ import org.apache.spark.streaming.receiver.Receiver
 
 private[streaming]
 class PluggableInputDStream[T: ClassTag](
-  @transient ssc_ : StreamingContext,
+  ssc_ : StreamingContext,
   receiver: Receiver[T]) extends ReceiverInputDStream[T](ssc_) {
 
   def getReceiver(): Receiver[T] = {
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
index bab78a3536b47..a2685046e03d4 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/QueueInputDStream.scala
@@ -27,7 +27,7 @@ import org.apache.spark.streaming.{Time, StreamingContext}
 
 private[streaming]
 class QueueInputDStream[T: ClassTag](
-    @transient ssc: StreamingContext,
+    ssc: StreamingContext,
     val queue: Queue[RDD[T]],
     oneAtATime: Boolean,
     defaultRDD: RDD[T]
@@ -57,7 +57,7 @@ class QueueInputDStream[T: ClassTag](
       if (oneAtATime) {
         Some(buffer.head)
       } else {
-        Some(new UnionRDD(ssc.sc, buffer.toSeq))
+        Some(new UnionRDD(context.sc, buffer.toSeq))
       }
     } else if (defaultRDD != null) {
       Some(defaultRDD)
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala
index e2925b9e03ec3..5a9eda7c12776 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/RawInputDStream.scala
@@ -39,7 +39,7 @@ import org.apache.spark.streaming.receiver.Receiver
  */
 private[streaming]
 class RawInputDStream[T: ClassTag](
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     host: String,
     port: Int,
     storageLevel: StorageLevel
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
index 6c139f32da31d..87c20afd5c13c 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala
@@ -38,7 +38,7 @@ import org.apache.spark.streaming.{StreamingContext, Time}
  * @param ssc_ Streaming context that will execute this input stream
  * @tparam T Class type of the object of this stream
  */
-abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingContext)
+abstract class ReceiverInputDStream[T: ClassTag](ssc_ : StreamingContext)
   extends InputDStream[T](ssc_) {
 
   /**
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala
index 5ce5b7aae6e69..de84e0c9a498d 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/SocketInputDStream.scala
@@ -32,7 +32,7 @@ import org.apache.spark.streaming.receiver.Receiver
 
 private[streaming]
 class SocketInputDStream[T: ClassTag](
-    @transient ssc_ : StreamingContext,
+    ssc_ : StreamingContext,
     host: String,
     port: Int,
     bytesToObjects: InputStream => Iterator[T],
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
index e081ffe46f502..f811784b25c82 100644
--- a/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
+++ b/streaming/src/main/scala/org/apache/spark/streaming/rdd/WriteAheadLogBackedBlockRDD.scala
@@ -61,7 +61,7 @@ class WriteAheadLogBackedBlockRDDPartition(
  *
  *
  * @param sc SparkContext
- * @param blockIds Ids of the blocks that contains this RDD's data
+ * @param _blockIds Ids of the blocks that contains this RDD's data
  * @param walRecordHandles Record handles in write ahead logs that contain this RDD's data
  * @param isBlockIdValid Whether the block Ids are valid (i.e., the blocks are present in the Spark
  *                         executors). If not, then block lookups by the block ids will be skipped.
@@ -73,23 +73,23 @@ class WriteAheadLogBackedBlockRDDPartition(
  */
 private[streaming]
 class WriteAheadLogBackedBlockRDD[T: ClassTag](
-    @transient sc: SparkContext,
-    @transient blockIds: Array[BlockId],
+    sc: SparkContext,
+    @transient private val _blockIds: Array[BlockId],
     @transient val walRecordHandles: Array[WriteAheadLogRecordHandle],
-    @transient isBlockIdValid: Array[Boolean] = Array.empty,
+    @transient private val isBlockIdValid: Array[Boolean] = Array.empty,
     storeInBlockManager: Boolean = false,
     storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER)
-  extends BlockRDD[T](sc, blockIds) {
+  extends BlockRDD[T](sc, _blockIds) {
 
   require(
-    blockIds.length == walRecordHandles.length,
-    s"Number of block Ids (${blockIds.length}) must be " +
+    _blockIds.length == walRecordHandles.length,
+    s"Number of block Ids (${_blockIds.length}) must be " +
       s" same as number of WAL record handles (${walRecordHandles.length})")
 
   require(
-    isBlockIdValid.isEmpty || isBlockIdValid.length == blockIds.length,
+    isBlockIdValid.isEmpty || isBlockIdValid.length == _blockIds.length,
     s"Number of elements in isBlockIdValid (${isBlockIdValid.length}) must be " +
-      s" same as number of block Ids (${blockIds.length})")
+      s" same as number of block Ids (${_blockIds.length})")
 
   // Hadoop configuration is not serializable, so broadcast it as a serializable.
   @transient private val hadoopConfig = sc.hadoopConfiguration
@@ -99,9 +99,9 @@ class WriteAheadLogBackedBlockRDD[T: ClassTag](
 
   override def getPartitions: Array[Partition] = {
     assertValid()
-    Array.tabulate(blockIds.length) { i =>
+    Array.tabulate(_blockIds.length) { i =>
       val isValid = if (isBlockIdValid.length == 0) true else isBlockIdValid(i)
-      new WriteAheadLogBackedBlockRDDPartition(i, blockIds(i), isValid, walRecordHandles(i))
+      new WriteAheadLogBackedBlockRDDPartition(i, _blockIds(i), isValid, walRecordHandles(i))
     }
   }
 

From 2ddeb63126d26149eda197e85b7b26ef16a6e97c Mon Sep 17 00:00:00 2001
From: lewuathe <lewuathe@me.com>
Date: Wed, 9 Sep 2015 09:29:10 -0700
Subject: [PATCH 563/633] [SPARK-10117] [MLLIB] Implement SQL data source API
 for reading LIBSVM data

It is convenient to implement data source API for LIBSVM format to have a better integration with DataFrames and ML pipeline API.

Two option is implemented.
* `numFeatures`: Specify the dimension of features vector
* `featuresType`: Specify the type of output vector. `sparse` is default.

Author: lewuathe <lewuathe@me.com>

Closes #8537 from Lewuathe/SPARK-10117 and squashes the following commits:

986999d [lewuathe] Change unit test phrase
11d513f [lewuathe] Fix some reviews
21600a4 [lewuathe] Merge branch 'master' into SPARK-10117
9ce63c7 [lewuathe] Rewrite service loader file
1fdd2df [lewuathe] Merge branch 'SPARK-10117' of github.com:Lewuathe/spark into SPARK-10117
ba3657c [lewuathe] Merge branch 'master' into SPARK-10117
0ea1c1c [lewuathe] LibSVMRelation is registered into META-INF
4f40891 [lewuathe] Improve test suites
5ab62ab [lewuathe] Merge branch 'master' into SPARK-10117
8660d0e [lewuathe] Fix Java unit test
b56a948 [lewuathe] Merge branch 'master' into SPARK-10117
2c12894 [lewuathe] Remove unnecessary tag
7d693c2 [lewuathe] Resolv conflict
62010af [lewuathe] Merge branch 'master' into SPARK-10117
a97ee97 [lewuathe] Fix some points
aef9564 [lewuathe] Fix
70ee4dd [lewuathe] Add Java test
3fd8dce [lewuathe] [SPARK-10117] Implement SQL data source API for reading LIBSVM data
40d3027 [lewuathe] Add Java test
7056d4a [lewuathe] Merge branch 'master' into SPARK-10117
99accaa [lewuathe] [SPARK-10117] Implement SQL data source API for reading LIBSVM data
---
 ...pache.spark.sql.sources.DataSourceRegister |  1 +
 .../ml/source/libsvm/LibSVMRelation.scala     | 99 +++++++++++++++++++
 .../ml/source/JavaLibSVMRelationSuite.java    | 80 +++++++++++++++
 .../spark/ml/source/LibSVMRelationSuite.scala | 76 ++++++++++++++
 4 files changed, 256 insertions(+)
 create mode 100644 mllib/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
 create mode 100644 mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
 create mode 100644 mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java
 create mode 100644 mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala

diff --git a/mllib/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister b/mllib/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
new file mode 100644
index 0000000000000..f632dd603c449
--- /dev/null
+++ b/mllib/src/main/resources/META-INF/services/org.apache.spark.sql.sources.DataSourceRegister
@@ -0,0 +1 @@
+org.apache.spark.ml.source.libsvm.DefaultSource
diff --git a/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
new file mode 100644
index 0000000000000..b12cb62a4ef15
--- /dev/null
+++ b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
@@ -0,0 +1,99 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.source.libsvm
+
+import com.google.common.base.Objects
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.Since
+import org.apache.spark.mllib.linalg.VectorUDT
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.types.{StructType, StructField, DoubleType}
+import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.sources._
+
+/**
+ * LibSVMRelation provides the DataFrame constructed from LibSVM format data.
+ * @param path File path of LibSVM format
+ * @param numFeatures The number of features
+ * @param vectorType The type of vector. It can be 'sparse' or 'dense'
+ * @param sqlContext The Spark SQLContext
+ */
+private[ml] class LibSVMRelation(val path: String, val numFeatures: Int, val vectorType: String)
+    (@transient val sqlContext: SQLContext)
+  extends BaseRelation with TableScan with Logging with Serializable {
+
+  override def schema: StructType = StructType(
+    StructField("label", DoubleType, nullable = false) ::
+      StructField("features", new VectorUDT(), nullable = false) :: Nil
+  )
+
+  override def buildScan(): RDD[Row] = {
+    val sc = sqlContext.sparkContext
+    val baseRdd = MLUtils.loadLibSVMFile(sc, path, numFeatures)
+
+    baseRdd.map { pt =>
+      val features = if (vectorType == "dense") pt.features.toDense else pt.features.toSparse
+      Row(pt.label, features)
+    }
+  }
+
+  override def hashCode(): Int = {
+    Objects.hashCode(path, schema)
+  }
+
+  override def equals(other: Any): Boolean = other match {
+    case that: LibSVMRelation => (this.path == that.path) && this.schema.equals(that.schema)
+    case _ => false
+  }
+
+}
+
+/**
+ * This is used for creating DataFrame from LibSVM format file.
+ * The LibSVM file path must be specified to DefaultSource.
+ */
+@Since("1.6.0")
+class DefaultSource extends RelationProvider with DataSourceRegister {
+
+  @Since("1.6.0")
+  override def shortName(): String = "libsvm"
+
+  private def checkPath(parameters: Map[String, String]): String = {
+    require(parameters.contains("path"), "'path' must be specified")
+    parameters.get("path").get
+  }
+
+  /**
+   * Returns a new base relation with the given parameters.
+   * Note: the parameters' keywords are case insensitive and this insensitivity is enforced
+   * by the Map that is passed to the function.
+   */
+  override def createRelation(sqlContext: SQLContext, parameters: Map[String, String])
+    : BaseRelation = {
+    val path = checkPath(parameters)
+    val numFeatures = parameters.getOrElse("numFeatures", "-1").toInt
+    /**
+     * featuresType can be selected "dense" or "sparse".
+     * This parameter decides the type of returned feature vector.
+     */
+    val vectorType = parameters.getOrElse("vectorType", "sparse")
+    new LibSVMRelation(path, numFeatures, vectorType)(sqlContext)
+  }
+}
diff --git a/mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java b/mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java
new file mode 100644
index 0000000000000..11fa4eec0ccf0
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java
@@ -0,0 +1,80 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.source;
+
+import java.io.File;
+import java.io.IOException;
+
+import com.google.common.base.Charsets;
+import com.google.common.io.Files;
+
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.linalg.DenseVector;
+import org.apache.spark.mllib.linalg.Vectors;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.SQLContext;
+import org.apache.spark.util.Utils;
+
+
+/**
+ * Test LibSVMRelation in Java.
+ */
+public class JavaLibSVMRelationSuite {
+  private transient JavaSparkContext jsc;
+  private transient SQLContext jsql;
+  private transient DataFrame dataset;
+
+  private File tmpDir;
+  private File path;
+
+  @Before
+  public void setUp() throws IOException {
+    jsc = new JavaSparkContext("local", "JavaLibSVMRelationSuite");
+    jsql = new SQLContext(jsc);
+
+    tmpDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "datasource");
+    path = new File(tmpDir.getPath(), "part-00000");
+
+    String s = "1 1:1.0 3:2.0 5:3.0\n0\n0 2:4.0 4:5.0 6:6.0";
+    Files.write(s, path, Charsets.US_ASCII);
+  }
+
+  @After
+  public void tearDown() {
+    jsc.stop();
+    jsc = null;
+    Utils.deleteRecursively(tmpDir);
+  }
+
+  @Test
+  public void verifyLibSVMDF() {
+    dataset = jsql.read().format("libsvm").option("vectorType", "dense").load(path.getPath());
+    Assert.assertEquals("label", dataset.columns()[0]);
+    Assert.assertEquals("features", dataset.columns()[1]);
+    Row r = dataset.first();
+    Assert.assertEquals(1.0, r.getDouble(0), 1e-15);
+    DenseVector v = r.getAs(1);
+    Assert.assertEquals(Vectors.dense(1.0, 0.0, 2.0, 0.0, 3.0, 0.0), v);
+  }
+}
diff --git a/mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala
new file mode 100644
index 0000000000000..8ed134128c8d2
--- /dev/null
+++ b/mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala
@@ -0,0 +1,76 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.source
+
+import java.io.File
+
+import com.google.common.base.Charsets
+import com.google.common.io.Files
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.mllib.linalg.{SparseVector, Vectors, DenseVector}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.util.Utils
+
+class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
+  var path: String = _
+
+  override def beforeAll(): Unit = {
+    super.beforeAll()
+    val lines =
+      """
+        |1 1:1.0 3:2.0 5:3.0
+        |0
+        |0 2:4.0 4:5.0 6:6.0
+      """.stripMargin
+    val tempDir = Utils.createTempDir()
+    val file = new File(tempDir.getPath, "part-00000")
+    Files.write(lines, file, Charsets.US_ASCII)
+    path = tempDir.toURI.toString
+  }
+
+  test("select as sparse vector") {
+    val df = sqlContext.read.format("libsvm").load(path)
+    assert(df.columns(0) == "label")
+    assert(df.columns(1) == "features")
+    val row1 = df.first()
+    assert(row1.getDouble(0) == 1.0)
+    val v = row1.getAs[SparseVector](1)
+    assert(v == Vectors.sparse(6, Seq((0, 1.0), (2, 2.0), (4, 3.0))))
+  }
+
+  test("select as dense vector") {
+    val df = sqlContext.read.format("libsvm").options(Map("vectorType" -> "dense"))
+      .load(path)
+    assert(df.columns(0) == "label")
+    assert(df.columns(1) == "features")
+    assert(df.count() == 3)
+    val row1 = df.first()
+    assert(row1.getDouble(0) == 1.0)
+    val v = row1.getAs[DenseVector](1)
+    assert(v == Vectors.dense(1.0, 0.0, 2.0, 0.0, 3.0, 0.0))
+  }
+
+  test("select a vector with specifying the longer dimension") {
+    val df = sqlContext.read.option("numFeatures", "100").format("libsvm")
+      .load(path)
+    val row1 = df.first()
+    val v = row1.getAs[SparseVector](1)
+    assert(v == Vectors.sparse(100, Seq((0, 1.0), (2, 2.0), (4, 3.0))))
+  }
+}

From c0052d8d09eebadadb5ed35ac512caaf73919551 Mon Sep 17 00:00:00 2001
From: Jeff Zhang <zjffdu@apache.org>
Date: Wed, 9 Sep 2015 10:26:53 -0700
Subject: [PATCH 564/633] =?UTF-8?q?[SPARK-10481]=20[YARN]=20SPARK=5FPREPEN?=
 =?UTF-8?q?D=5FCLASSES=20make=20spark-yarn=20related=20jar=20could=20n?=
 =?UTF-8?q?=E2=80=A6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Throw a more readable exception. Please help review. Thanks

Author: Jeff Zhang <zjffdu@apache.org>

Closes #8649 from zjffdu/SPARK-10481.
---
 .../src/main/scala/org/apache/spark/deploy/yarn/Client.scala | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
index e9a02baafd28e..a2c4bc2f5480b 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala
@@ -1045,7 +1045,10 @@ object Client extends Logging {
           s"in favor of the $CONF_SPARK_JAR configuration variable.")
       System.getenv(ENV_SPARK_JAR)
     } else {
-      SparkContext.jarOfClass(this.getClass).head
+      SparkContext.jarOfClass(this.getClass).getOrElse(throw new SparkException("Could not "
+        + "find jar containing Spark classes. The jar can be defined using the "
+        + "spark.yarn.jar configuration option. If testing Spark, either set that option or "
+        + "make sure SPARK_PREPEND_CLASSES is not set."))
     }
   }
 

From 71da1633c4dcc4e748fbe3b3236af90032b695ae Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Wed, 9 Sep 2015 10:57:29 -0700
Subject: [PATCH 565/633] [SPARK-10461] [SQL] make sure `input.primitive` is
 always variable name not code at `GenerateUnsafeProjection`

When we generate unsafe code inside `createCodeForXXX`, we always assign the `input.primitive` to a temp variable in case `input.primitive` is expression code.

This PR did some refactor to make sure `input.primitive` is always variable name, and some other typo and style fixes.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8613 from cloud-fan/minor.
---
 .../expressions/codegen/CodeGenerator.scala   | 10 +--
 .../codegen/GenerateMutableProjection.scala   |  2 -
 .../codegen/GenerateProjection.scala          | 12 +--
 .../codegen/GenerateUnsafeProjection.scala    | 85 ++++++++++---------
 .../expressions/complexTypeCreator.scala      | 33 ++++---
 5 files changed, 75 insertions(+), 67 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
index bf96248feaef7..da3103b4ebb6b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala
@@ -84,11 +84,11 @@ class CodeGenContext {
   /**
    * Holding all the functions those will be added into generated class.
    */
-  val addedFuntions: mutable.Map[String, String] =
+  val addedFunctions: mutable.Map[String, String] =
     mutable.Map.empty[String, String]
 
   def addNewFunction(funcName: String, funcCode: String): Unit = {
-    addedFuntions += ((funcName, funcCode))
+    addedFunctions += ((funcName, funcCode))
   }
 
   final val JAVA_BOOLEAN = "boolean"
@@ -298,8 +298,8 @@ class CodeGenContext {
            |  $body
            |}
          """.stripMargin
-         addNewFunction(name, code)
-         name
+        addNewFunction(name, code)
+        name
       }
 
       functions.map(name => s"$name($row);").mkString("\n")
@@ -337,7 +337,7 @@ abstract class CodeGenerator[InType <: AnyRef, OutType <: AnyRef] extends Loggin
   }
 
   protected def declareAddedFunctions(ctx: CodeGenContext): String = {
-    ctx.addedFuntions.map { case (funcName, funcCode) => funcCode }.mkString("\n")
+    ctx.addedFunctions.map { case (funcName, funcCode) => funcCode }.mkString("\n")
   }
 
   /**
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
index b4d4df8934bd4..793023b9fbed3 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateMutableProjection.scala
@@ -17,8 +17,6 @@
 
 package org.apache.spark.sql.catalyst.expressions.codegen
 
-import scala.collection.mutable.ArrayBuffer
-
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.expressions.aggregate.NoOp
 import org.apache.spark.sql.types.DecimalType
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
index c744e84d822e8..2164ddf03d1b2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateProjection.scala
@@ -48,7 +48,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
     val columns = expressions.zipWithIndex.map {
       case (e, i) =>
         s"private ${ctx.javaType(e.dataType)} c$i = ${ctx.defaultValue(e.dataType)};\n"
-    }.mkString("\n      ")
+    }.mkString("\n")
 
     val initColumns = expressions.zipWithIndex.map {
       case (e, i) =>
@@ -67,18 +67,18 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
 
     val getCases = (0 until expressions.size).map { i =>
       s"case $i: return c$i;"
-    }.mkString("\n        ")
+    }.mkString("\n")
 
     val updateCases = expressions.zipWithIndex.map { case (e, i) =>
       s"case $i: { c$i = (${ctx.boxedType(e.dataType)})value; return;}"
-    }.mkString("\n        ")
+    }.mkString("\n")
 
     val specificAccessorFunctions = ctx.primitiveTypes.map { jt =>
       val cases = expressions.zipWithIndex.flatMap {
         case (e, i) if ctx.javaType(e.dataType) == jt =>
           Some(s"case $i: return c$i;")
         case _ => None
-      }.mkString("\n        ")
+      }.mkString("\n")
       if (cases.length > 0) {
         val getter = "get" + ctx.primitiveTypeName(jt)
         s"""
@@ -103,7 +103,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
         case (e, i) if ctx.javaType(e.dataType) == jt =>
           Some(s"case $i: { c$i = value; return; }")
         case _ => None
-      }.mkString("\n        ")
+      }.mkString("\n")
       if (cases.length > 0) {
         val setter = "set" + ctx.primitiveTypeName(jt)
         s"""
@@ -152,7 +152,7 @@ object GenerateProjection extends CodeGenerator[Seq[Expression], Projection] {
 
     val copyColumns = expressions.zipWithIndex.map { case (e, i) =>
         s"""if (!nullBits[$i]) arr[$i] = c$i;"""
-    }.mkString("\n      ")
+    }.mkString("\n")
 
     val code = s"""
     public SpecificProjection generate($exprType[] expr) {
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index b570fe86db1aa..03c5f449bf9ac 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -134,7 +134,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     ctx.addMutableState("byte[]", buffer, s"this.$buffer = new byte[$fixedSize];")
     val cursor = ctx.freshName("cursor")
     ctx.addMutableState("int", cursor, s"this.$cursor = 0;")
-    val tmp = ctx.freshName("tmpBuffer")
+    val tmpBuffer = ctx.freshName("tmpBuffer")
 
     val convertedFields = inputTypes.zip(inputs).zipWithIndex.map { case ((dt, input), i) =>
       val ev = createConvertCode(ctx, input, dt)
@@ -144,10 +144,10 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           int $numBytes = $cursor + (${genAdditionalSize(dt, ev)});
           if ($buffer.length < $numBytes) {
             // This will not happen frequently, because the buffer is re-used.
-            byte[] $tmp = new byte[$numBytes * 2];
+            byte[] $tmpBuffer = new byte[$numBytes * 2];
             Platform.copyMemory($buffer, Platform.BYTE_ARRAY_OFFSET,
-              $tmp, Platform.BYTE_ARRAY_OFFSET, $buffer.length);
-            $buffer = $tmp;
+              $tmpBuffer, Platform.BYTE_ARRAY_OFFSET, $buffer.length);
+            $buffer = $tmpBuffer;
           }
           $output.pointTo($buffer, Platform.BYTE_ARRAY_OFFSET, ${inputTypes.length}, $numBytes);
          """
@@ -207,20 +207,22 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     val buffer = ctx.freshName("buffer")
     ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
     val outputIsNull = ctx.freshName("isNull")
-    val tmp = ctx.freshName("tmp")
     val numElements = ctx.freshName("numElements")
     val fixedSize = ctx.freshName("fixedSize")
     val numBytes = ctx.freshName("numBytes")
     val elements = ctx.freshName("elements")
     val cursor = ctx.freshName("cursor")
     val index = ctx.freshName("index")
+    val elementName = ctx.freshName("elementName")
 
-    val element = GeneratedExpressionCode(
-      code = "",
-      isNull = s"$tmp.isNullAt($index)",
-      primitive = s"${ctx.getValue(tmp, elementType, index)}"
-    )
-    val convertedElement: GeneratedExpressionCode = createConvertCode(ctx, element, elementType)
+    val element = {
+      val code = s"${ctx.javaType(elementType)} $elementName = " +
+        s"${ctx.getValue(input.primitive, elementType, index)};"
+      val isNull = s"${input.primitive}.isNullAt($index)"
+      GeneratedExpressionCode(code, isNull, elementName)
+    }
+
+    val convertedElement = createConvertCode(ctx, element, elementType)
 
     // go through the input array to calculate how many bytes we need.
     val calculateNumBytes = elementType match {
@@ -272,6 +274,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
+          ${convertedElement.code}
           Platform.put${ctx.primitiveTypeName(elementType)}(
             $buffer,
             Platform.BYTE_ARRAY_OFFSET + $cursor,
@@ -280,6 +283,7 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         """
       case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
         s"""
+          ${convertedElement.code}
           Platform.putLong(
             $buffer,
             Platform.BYTE_ARRAY_OFFSET + $cursor,
@@ -307,11 +311,10 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       ${input.code}
       final boolean $outputIsNull = ${input.isNull};
       if (!$outputIsNull) {
-        final ArrayData $tmp = ${input.primitive};
-        if ($tmp instanceof UnsafeArrayData) {
-          $output = (UnsafeArrayData) $tmp;
+        if (${input.primitive} instanceof UnsafeArrayData) {
+          $output = (UnsafeArrayData) ${input.primitive};
         } else {
-          final int $numElements = $tmp.numElements();
+          final int $numElements = ${input.primitive}.numElements();
           final int $fixedSize = 4 * $numElements;
           int $numBytes = $fixedSize;
 
@@ -350,29 +353,31 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
       valueType: DataType): GeneratedExpressionCode = {
     val output = ctx.freshName("convertedMap")
     val outputIsNull = ctx.freshName("isNull")
-    val tmp = ctx.freshName("tmp")
-
-    val keyArray = GeneratedExpressionCode(
-      code = "",
-      isNull = "false",
-      primitive = s"$tmp.keyArray()"
-    )
-    val valueArray = GeneratedExpressionCode(
-      code = "",
-      isNull = "false",
-      primitive = s"$tmp.valueArray()"
-    )
-    val convertedKeys: GeneratedExpressionCode = createCodeForArray(ctx, keyArray, keyType)
-    val convertedValues: GeneratedExpressionCode = createCodeForArray(ctx, valueArray, valueType)
+    val keyArrayName = ctx.freshName("keyArrayName")
+    val valueArrayName = ctx.freshName("valueArrayName")
+
+    val keyArray = {
+      val code = s"ArrayData $keyArrayName = ${input.primitive}.keyArray();"
+      val isNull = "false"
+      GeneratedExpressionCode(code, isNull, keyArrayName)
+    }
+
+    val valueArray = {
+      val code = s"ArrayData $valueArrayName = ${input.primitive}.valueArray();"
+      val isNull = "false"
+      GeneratedExpressionCode(code, isNull, valueArrayName)
+    }
+
+    val convertedKeys = createCodeForArray(ctx, keyArray, keyType)
+    val convertedValues = createCodeForArray(ctx, valueArray, valueType)
 
     val code = s"""
       ${input.code}
       final boolean $outputIsNull = ${input.isNull};
       UnsafeMapData $output = null;
       if (!$outputIsNull) {
-        final MapData $tmp = ${input.primitive};
-        if ($tmp instanceof UnsafeMapData) {
-          $output = (UnsafeMapData) $tmp;
+        if (${input.primitive} instanceof UnsafeMapData) {
+          $output = (UnsafeMapData) ${input.primitive};
         } else {
           ${convertedKeys.code}
           ${convertedValues.code}
@@ -393,22 +398,22 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     case t: StructType =>
       val output = ctx.freshName("convertedStruct")
       val outputIsNull = ctx.freshName("isNull")
-      val tmp = ctx.freshName("tmp")
       val fieldTypes = t.fields.map(_.dataType)
       val fieldEvals = fieldTypes.zipWithIndex.map { case (dt, i) =>
-        val getFieldCode = ctx.getValue(tmp, dt, i.toString)
-        val fieldIsNull = s"$tmp.isNullAt($i)"
-        GeneratedExpressionCode("", fieldIsNull, getFieldCode)
+        val fieldName = ctx.freshName("fieldName")
+        val code = s"${ctx.javaType(dt)} $fieldName = " +
+          s"${ctx.getValue(input.primitive, dt, i.toString)};"
+        val isNull = s"${input.primitive}.isNullAt($i)"
+        GeneratedExpressionCode(code, isNull, fieldName)
       }
-      val converter = createCodeForStruct(ctx, tmp, fieldEvals, fieldTypes)
+      val converter = createCodeForStruct(ctx, input.primitive, fieldEvals, fieldTypes)
       val code = s"""
         ${input.code}
          UnsafeRow $output = null;
          final boolean $outputIsNull = ${input.isNull};
          if (!$outputIsNull) {
-           final InternalRow $tmp = ${input.primitive};
-           if ($tmp instanceof UnsafeRow) {
-             $output = (UnsafeRow) $tmp;
+           if (${input.primitive} instanceof UnsafeRow) {
+             $output = (UnsafeRow) ${input.primitive};
            } else {
              ${converter.code}
              $output = ${converter.primitive};
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
index 1c546719730b7..82eab5fb3d03a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypeCreator.scala
@@ -48,21 +48,22 @@ case class CreateArray(children: Seq[Expression]) extends Expression {
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
     val arrayClass = classOf[GenericArrayData].getName
+    val values = ctx.freshName("values")
     s"""
       final boolean ${ev.isNull} = false;
-      final Object[] values = new Object[${children.size}];
+      final Object[] $values = new Object[${children.size}];
     """ +
       children.zipWithIndex.map { case (e, i) =>
         val eval = e.gen(ctx)
         eval.code + s"""
           if (${eval.isNull}) {
-            values[$i] = null;
+            $values[$i] = null;
           } else {
-            values[$i] = ${eval.primitive};
+            $values[$i] = ${eval.primitive};
           }
          """
       }.mkString("\n") +
-      s"final ${ctx.javaType(dataType)} ${ev.primitive} = new $arrayClass(values);"
+      s"final ArrayData ${ev.primitive} = new $arrayClass($values);"
   }
 
   override def prettyName: String = "array"
@@ -94,21 +95,23 @@ case class CreateStruct(children: Seq[Expression]) extends Expression {
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val rowClass = classOf[GenericMutableRow].getName
+    val rowClass = classOf[GenericInternalRow].getName
+    val values = ctx.freshName("values")
     s"""
       boolean ${ev.isNull} = false;
-      final $rowClass ${ev.primitive} = new $rowClass(${children.size});
+      final Object[] $values = new Object[${children.size}];
     """ +
       children.zipWithIndex.map { case (e, i) =>
         val eval = e.gen(ctx)
         eval.code + s"""
           if (${eval.isNull}) {
-            ${ev.primitive}.update($i, null);
+            $values[$i] = null;
           } else {
-            ${ev.primitive}.update($i, ${eval.primitive});
+            $values[$i] = ${eval.primitive};
           }
          """
-      }.mkString("\n")
+      }.mkString("\n") +
+      s"final InternalRow ${ev.primitive} = new $rowClass($values);"
   }
 
   override def prettyName: String = "struct"
@@ -161,21 +164,23 @@ case class CreateNamedStruct(children: Seq[Expression]) extends Expression {
   }
 
   override def genCode(ctx: CodeGenContext, ev: GeneratedExpressionCode): String = {
-    val rowClass = classOf[GenericMutableRow].getName
+    val rowClass = classOf[GenericInternalRow].getName
+    val values = ctx.freshName("values")
     s"""
       boolean ${ev.isNull} = false;
-      final $rowClass ${ev.primitive} = new $rowClass(${valExprs.size});
+      final Object[] $values = new Object[${valExprs.size}];
     """ +
       valExprs.zipWithIndex.map { case (e, i) =>
         val eval = e.gen(ctx)
         eval.code + s"""
           if (${eval.isNull}) {
-            ${ev.primitive}.update($i, null);
+            $values[$i] = null;
           } else {
-            ${ev.primitive}.update($i, ${eval.primitive});
+            $values[$i] = ${eval.primitive};
           }
          """
-      }.mkString("\n")
+      }.mkString("\n") +
+      s"final InternalRow ${ev.primitive} = new $rowClass($values);"
   }
 
   override def prettyName: String = "named_struct"

From 45de518742446ddfbd4816c9d0f8501139f9bc2d Mon Sep 17 00:00:00 2001
From: Liang-Chi Hsieh <viirya@appier.com>
Date: Wed, 9 Sep 2015 16:02:27 -0700
Subject: [PATCH 566/633] [SPARK-9730] [SQL] Add Full Outer Join support for
 SortMergeJoin

This PR is based on #8383 , thanks to viirya

JIRA: https://issues.apache.org/jira/browse/SPARK-9730

This patch adds the Full Outer Join support for SortMergeJoin. A new class SortMergeFullJoinScanner is added to scan rows from left and right iterators. FullOuterIterator is simply a wrapper of type RowIterator to consume joined rows from SortMergeFullJoinScanner.

Closes #8383

Author: Liang-Chi Hsieh <viirya@appier.com>
Author: Davies Liu <davies@databricks.com>

Closes #8579 from davies/smj_fullouter.
---
 .../apache/spark/util/collection/BitSet.scala |  11 +
 .../spark/sql/execution/SparkStrategies.scala |   9 +-
 .../execution/joins/SortMergeOuterJoin.scala  | 227 +++++++++++++++++-
 .../org/apache/spark/sql/JoinSuite.scala      |   2 +-
 .../sql/execution/joins/OuterJoinSuite.scala  |  44 ++--
 5 files changed, 259 insertions(+), 34 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala
index 9c15b1188d91c..7ab67fc3a2de9 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/BitSet.scala
@@ -32,6 +32,17 @@ class BitSet(numBits: Int) extends Serializable {
    */
   def capacity: Int = numWords * 64
 
+  /**
+   * Clear all set bits.
+   */
+  def clear(): Unit = {
+    var i = 0
+    while (i < numWords) {
+      words(i) = 0L
+      i += 1
+    }
+  }
+
   /**
    * Set all the bits up to a given index
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 2170bc73a0fd6..4572d5efc92bb 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -132,15 +132,10 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
         joins.BroadcastHashOuterJoin(
           leftKeys, rightKeys, RightOuter, condition, planLater(left), planLater(right)) :: Nil
 
-      case ExtractEquiJoinKeys(LeftOuter, leftKeys, rightKeys, condition, left, right)
+      case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right)
         if sqlContext.conf.sortMergeJoinEnabled && RowOrdering.isOrderable(leftKeys) =>
         joins.SortMergeOuterJoin(
-          leftKeys, rightKeys, LeftOuter, condition, planLater(left), planLater(right)) :: Nil
-
-      case ExtractEquiJoinKeys(RightOuter, leftKeys, rightKeys, condition, left, right)
-        if sqlContext.conf.sortMergeJoinEnabled && RowOrdering.isOrderable(leftKeys) =>
-        joins.SortMergeOuterJoin(
-          leftKeys, rightKeys, RightOuter, condition, planLater(left), planLater(right)) :: Nil
+          leftKeys, rightKeys, joinType, condition, planLater(left), planLater(right)) :: Nil
 
       case ExtractEquiJoinKeys(joinType, leftKeys, rightKeys, condition, left, right) =>
         joins.ShuffledHashOuterJoin(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
index dea9e5e580a1e..ab20ee573ab5d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
@@ -17,20 +17,21 @@
 
 package org.apache.spark.sql.execution.joins
 
+import scala.collection.mutable.ArrayBuffer
+
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.plans.{JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.catalyst.plans.physical._
-import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
+import org.apache.spark.sql.catalyst.plans.{FullOuter, JoinType, LeftOuter, RightOuter}
 import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetrics}
+import org.apache.spark.sql.execution.{BinaryNode, RowIterator, SparkPlan}
+import org.apache.spark.util.collection.BitSet
 
 /**
  * :: DeveloperApi ::
  * Performs an sort merge outer join of two child relations.
- *
- * Note: this does not support full outer join yet; see SPARK-9730 for progress on this.
  */
 @DeveloperApi
 case class SortMergeOuterJoin(
@@ -52,6 +53,8 @@ case class SortMergeOuterJoin(
         left.output ++ right.output.map(_.withNullability(true))
       case RightOuter =>
         left.output.map(_.withNullability(true)) ++ right.output
+      case FullOuter =>
+        (left.output ++ right.output).map(_.withNullability(true))
       case x =>
         throw new IllegalArgumentException(
           s"${getClass.getSimpleName} should not take $x as the JoinType")
@@ -62,6 +65,7 @@ case class SortMergeOuterJoin(
     // For left and right outer joins, the output is partitioned by the streamed input's join keys.
     case LeftOuter => left.outputPartitioning
     case RightOuter => right.outputPartitioning
+    case FullOuter => UnknownPartitioning(left.outputPartitioning.numPartitions)
     case x =>
       throw new IllegalArgumentException(
         s"${getClass.getSimpleName} should not take $x as the JoinType")
@@ -71,6 +75,8 @@ case class SortMergeOuterJoin(
     // For left and right outer joins, the output is ordered by the streamed input's join keys.
     case LeftOuter => requiredOrders(leftKeys)
     case RightOuter => requiredOrders(rightKeys)
+    // there are null rows in both streams, so there is no order
+    case FullOuter => Nil
     case x => throw new IllegalArgumentException(
       s"SortMergeOuterJoin should not take $x as the JoinType")
   }
@@ -165,6 +171,26 @@ case class SortMergeOuterJoin(
           new RightOuterIterator(
             smjScanner, leftNullRow, boundCondition, resultProj, numOutputRows).toScala
 
+        case FullOuter =>
+          val leftNullRow = new GenericInternalRow(left.output.length)
+          val rightNullRow = new GenericInternalRow(right.output.length)
+          val smjScanner = new SortMergeFullOuterJoinScanner(
+            leftKeyGenerator = createLeftKeyGenerator(),
+            rightKeyGenerator = createRightKeyGenerator(),
+            keyOrdering,
+            leftIter = RowIterator.fromScala(leftIter),
+            numLeftRows,
+            rightIter = RowIterator.fromScala(rightIter),
+            numRightRows,
+            boundCondition,
+            leftNullRow,
+            rightNullRow)
+
+          new FullOuterIterator(
+            smjScanner,
+            resultProj,
+            numOutputRows).toScala
+
         case x =>
           throw new IllegalArgumentException(
             s"SortMergeOuterJoin should not take $x as the JoinType")
@@ -271,3 +297,196 @@ private class RightOuterIterator(
 
   override def getRow: InternalRow = resultProj(joinedRow)
 }
+
+private class SortMergeFullOuterJoinScanner(
+    leftKeyGenerator: Projection,
+    rightKeyGenerator: Projection,
+    keyOrdering: Ordering[InternalRow],
+    leftIter: RowIterator,
+    numLeftRows: LongSQLMetric,
+    rightIter: RowIterator,
+    numRightRows: LongSQLMetric,
+    boundCondition: InternalRow => Boolean,
+    leftNullRow: InternalRow,
+    rightNullRow: InternalRow)  {
+  private[this] val joinedRow: JoinedRow = new JoinedRow()
+  private[this] var leftRow: InternalRow = _
+  private[this] var leftRowKey: InternalRow = _
+  private[this] var rightRow: InternalRow = _
+  private[this] var rightRowKey: InternalRow = _
+
+  private[this] var leftIndex: Int = 0
+  private[this] var rightIndex: Int = 0
+  private[this] val leftMatches: ArrayBuffer[InternalRow] = new ArrayBuffer[InternalRow]
+  private[this] val rightMatches: ArrayBuffer[InternalRow] = new ArrayBuffer[InternalRow]
+  private[this] var leftMatched: BitSet = new BitSet(1)
+  private[this] var rightMatched: BitSet = new BitSet(1)
+
+  advancedLeft()
+  advancedRight()
+
+  // --- Private methods --------------------------------------------------------------------------
+
+  /**
+   * Advance the left iterator and compute the new row's join key.
+   * @return true if the left iterator returned a row and false otherwise.
+   */
+  private def advancedLeft(): Boolean = {
+    if (leftIter.advanceNext()) {
+      leftRow = leftIter.getRow
+      leftRowKey = leftKeyGenerator(leftRow)
+      numLeftRows += 1
+      true
+    } else {
+      leftRow = null
+      leftRowKey = null
+      false
+    }
+  }
+
+  /**
+   * Advance the right iterator and compute the new row's join key.
+   * @return true if the right iterator returned a row and false otherwise.
+   */
+  private def advancedRight(): Boolean = {
+    if (rightIter.advanceNext()) {
+      rightRow = rightIter.getRow
+      rightRowKey = rightKeyGenerator(rightRow)
+      numRightRows += 1
+      true
+    } else {
+      rightRow = null
+      rightRowKey = null
+      false
+    }
+  }
+
+  /**
+   * Populate the left and right buffers with rows matching the provided key.
+   * This consumes rows from both iterators until their keys are different from the matching key.
+   */
+  private def findMatchingRows(matchingKey: InternalRow): Unit = {
+    leftMatches.clear()
+    rightMatches.clear()
+    leftIndex = 0
+    rightIndex = 0
+
+    while (leftRowKey != null && keyOrdering.compare(leftRowKey, matchingKey) == 0) {
+      leftMatches += leftRow.copy()
+      advancedLeft()
+    }
+    while (rightRowKey != null && keyOrdering.compare(rightRowKey, matchingKey) == 0) {
+      rightMatches += rightRow.copy()
+      advancedRight()
+    }
+
+    if (leftMatches.size <= leftMatched.capacity) {
+      leftMatched.clear()
+    } else {
+      leftMatched = new BitSet(leftMatches.size)
+    }
+    if (rightMatches.size <= rightMatched.capacity) {
+      rightMatched.clear()
+    } else {
+      rightMatched = new BitSet(rightMatches.size)
+    }
+  }
+
+  /**
+   * Scan the left and right buffers for the next valid match.
+   *
+   * Note: this method mutates `joinedRow` to point to the latest matching rows in the buffers.
+   * If a left row has no valid matches on the right, or a right row has no valid matches on the
+   * left, then the row is joined with the null row and the result is considered a valid match.
+   *
+   * @return true if a valid match is found, false otherwise.
+   */
+  private def scanNextInBuffered(): Boolean = {
+    while (leftIndex < leftMatches.size) {
+      while (rightIndex < rightMatches.size) {
+        joinedRow(leftMatches(leftIndex), rightMatches(rightIndex))
+        if (boundCondition(joinedRow)) {
+          leftMatched.set(leftIndex)
+          rightMatched.set(rightIndex)
+          rightIndex += 1
+          return true
+        }
+        rightIndex += 1
+      }
+      rightIndex = 0
+      if (!leftMatched.get(leftIndex)) {
+        // the left row has never matched any right row, join it with null row
+        joinedRow(leftMatches(leftIndex), rightNullRow)
+        leftIndex += 1
+        return true
+      }
+      leftIndex += 1
+    }
+
+    while (rightIndex < rightMatches.size) {
+      if (!rightMatched.get(rightIndex)) {
+        // the right row has never matched any left row, join it with null row
+        joinedRow(leftNullRow, rightMatches(rightIndex))
+        rightIndex += 1
+        return true
+      }
+      rightIndex += 1
+    }
+
+    // There are no more valid matches in the left and right buffers
+    false
+  }
+
+  // --- Public methods --------------------------------------------------------------------------
+
+  def getJoinedRow(): JoinedRow = joinedRow
+
+  def advanceNext(): Boolean = {
+    // If we already buffered some matching rows, use them directly
+    if (leftIndex <= leftMatches.size || rightIndex <= rightMatches.size) {
+      if (scanNextInBuffered()) {
+        return true
+      }
+    }
+
+    if (leftRow != null && (leftRowKey.anyNull || rightRow == null)) {
+      joinedRow(leftRow.copy(), rightNullRow)
+      advancedLeft()
+      true
+    } else if (rightRow != null && (rightRowKey.anyNull || leftRow == null)) {
+      joinedRow(leftNullRow, rightRow.copy())
+      advancedRight()
+      true
+    } else if (leftRow != null && rightRow != null) {
+      // Both rows are present and neither have null values,
+      // so we populate the buffers with rows matching the next key
+      val comp = keyOrdering.compare(leftRowKey, rightRowKey)
+      if (comp <= 0) {
+        findMatchingRows(leftRowKey.copy())
+      } else {
+        findMatchingRows(rightRowKey.copy())
+      }
+      scanNextInBuffered()
+      true
+    } else {
+      // Both iterators have been consumed
+      false
+    }
+  }
+}
+
+private class FullOuterIterator(
+    smjScanner: SortMergeFullOuterJoinScanner,
+    resultProj: InternalRow => InternalRow,
+    numRows: LongSQLMetric
+  ) extends RowIterator {
+  private[this] val joinedRow: JoinedRow = smjScanner.getJoinedRow()
+
+  override def advanceNext(): Boolean = {
+    val r = smjScanner.advanceNext()
+    if (r) numRows += 1
+    r
+  }
+
+  override def getRow: InternalRow = resultProj(joinedRow)
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
index b05435bad5c5a..7a027e13089e3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala
@@ -83,7 +83,7 @@ class JoinSuite extends QueryTest with SharedSQLContext {
       ("SELECT * FROM testData right join testData2 ON key = a and key = 2",
         classOf[SortMergeOuterJoin]),
       ("SELECT * FROM testData full outer join testData2 ON key = a",
-        classOf[ShuffledHashOuterJoin]),
+        classOf[SortMergeOuterJoin]),
       ("SELECT * FROM testData left JOIN testData2 ON (key * a != key + a)",
         classOf[BroadcastNestedLoopJoin]),
       ("SELECT * FROM testData right JOIN testData2 ON (key * a != key + a)",
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
index c2e0bdac17968..09e0237a7cc50 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/joins/OuterJoinSuite.scala
@@ -76,37 +76,37 @@ class OuterJoinSuite extends SparkPlanTest with SharedSQLContext {
 
     test(s"$testName using ShuffledHashOuterJoin") {
       extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
-          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-              EnsureRequirements(sqlContext).apply(
-                ShuffledHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
-              expectedAnswer.map(Row.fromTuple),
-              sortAnswers = true)
-          }
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+            EnsureRequirements(sqlContext).apply(
+              ShuffledHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
+        }
       }
     }
 
     if (joinType != FullOuter) {
       test(s"$testName using BroadcastHashOuterJoin") {
         extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
-            withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-              checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-                BroadcastHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right),
-                expectedAnswer.map(Row.fromTuple),
-                sortAnswers = true)
-            }
+          withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+            checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+              BroadcastHashOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right),
+              expectedAnswer.map(Row.fromTuple),
+              sortAnswers = true)
+          }
         }
       }
+    }
 
-      test(s"$testName using SortMergeOuterJoin") {
-        extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
-            withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
-              checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
-                EnsureRequirements(sqlContext).apply(
-                  SortMergeOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
-                expectedAnswer.map(Row.fromTuple),
-                sortAnswers = false)
-            }
+    test(s"$testName using SortMergeOuterJoin") {
+      extractJoinParts().foreach { case (_, leftKeys, rightKeys, boundCondition, _, _) =>
+        withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+          checkAnswer2(leftRows, rightRows, (left: SparkPlan, right: SparkPlan) =>
+            EnsureRequirements(sqlContext).apply(
+              SortMergeOuterJoin(leftKeys, rightKeys, joinType, boundCondition, left, right)),
+            expectedAnswer.map(Row.fromTuple),
+            sortAnswers = true)
         }
       }
     }

From 56a0fe5c6e4ae2929c48fae2d6225558d020e5f9 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Wed, 9 Sep 2015 18:02:33 -0700
Subject: [PATCH 567/633] [SPARK-9772] [PYSPARK] [ML] Add Python API for
 ml.feature.VectorSlicer

Add Python API for ml.feature.VectorSlicer.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8102 from yanboliang/SPARK-9772.
---
 python/pyspark/ml/feature.py | 95 ++++++++++++++++++++++++++++++++++--
 1 file changed, 90 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 8c26cfbd5a47d..1c423486be8d9 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -27,11 +27,11 @@
 from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
-           'IndexToString', 'NGram', 'Normalizer', 'OneHotEncoder', 'PolynomialExpansion',
-           'RegexTokenizer', 'SQLTransformer', 'StandardScaler', 'StandardScalerModel',
-           'StringIndexer', 'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer',
-           'Word2Vec', 'Word2VecModel', 'PCA', 'PCAModel', 'RFormula', 'RFormulaModel',
-           'StopWordsRemover']
+           'IndexToString', 'NGram', 'Normalizer', 'OneHotEncoder', 'PCA', 'PCAModel',
+           'PolynomialExpansion', 'RegexTokenizer', 'RFormula', 'RFormulaModel', 'SQLTransformer',
+           'StandardScaler', 'StandardScalerModel', 'StopWordsRemover', 'StringIndexer',
+           'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'VectorSlicer',
+           'Word2Vec', 'Word2VecModel']
 
 
 @inherit_doc
@@ -1298,6 +1298,91 @@ class VectorIndexerModel(JavaModel):
     """
 
 
+@inherit_doc
+class VectorSlicer(JavaTransformer, HasInputCol, HasOutputCol):
+    """
+    .. note:: Experimental
+
+    This class takes a feature vector and outputs a new feature vector with a subarray
+    of the original features.
+
+    The subset of features can be specified with either indices (`setIndices()`)
+    or names (`setNames()`).  At least one feature must be selected. Duplicate features
+    are not allowed, so there can be no overlap between selected indices and names.
+
+    The output vector will order features with the selected indices first (in the order given),
+    followed by the selected names (in the order given).
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([
+    ...     (Vectors.dense([-2.0, 2.3, 0.0, 0.0, 1.0]),),
+    ...     (Vectors.dense([0.0, 0.0, 0.0, 0.0, 0.0]),),
+    ...     (Vectors.dense([0.6, -1.1, -3.0, 4.5, 3.3]),)], ["features"])
+    >>> vs = VectorSlicer(inputCol="features", outputCol="sliced", indices=[1, 4])
+    >>> vs.transform(df).head().sliced
+    DenseVector([2.3, 1.0])
+    """
+
+    # a placeholder to make it appear in the generated doc
+    indices = Param(Params._dummy(), "indices", "An array of indices to select features from " +
+                    "a vector column. There can be no overlap with names.")
+    names = Param(Params._dummy(), "names", "An array of feature names to select features from " +
+                  "a vector column. These names must be specified by ML " +
+                  "org.apache.spark.ml.attribute.Attribute. There can be no overlap with " +
+                  "indices.")
+
+    @keyword_only
+    def __init__(self, inputCol=None, outputCol=None, indices=None, names=None):
+        """
+        __init__(self, inputCol=None, outputCol=None, indices=None, names=None)
+        """
+        super(VectorSlicer, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.VectorSlicer", self.uid)
+        self.indices = Param(self, "indices", "An array of indices to select features from " +
+                             "a vector column. There can be no overlap with names.")
+        self.names = Param(self, "names", "An array of feature names to select features from " +
+                           "a vector column. These names must be specified by ML " +
+                           "org.apache.spark.ml.attribute.Attribute. There can be no overlap " +
+                           "with indices.")
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, inputCol=None, outputCol=None, indices=None, names=None):
+        """
+        setParams(self, inputCol=None, outputCol=None, indices=None, names=None):
+        Sets params for this VectorSlicer.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setIndices(self, value):
+        """
+        Sets the value of :py:attr:`indices`.
+        """
+        self._paramMap[self.indices] = value
+        return self
+
+    def getIndices(self):
+        """
+        Gets the value of indices or its default value.
+        """
+        return self.getOrDefault(self.indices)
+
+    def setNames(self, value):
+        """
+        Sets the value of :py:attr:`names`.
+        """
+        self._paramMap[self.names] = value
+        return self
+
+    def getNames(self):
+        """
+        Gets the value of names or its default value.
+        """
+        return self.getOrDefault(self.names)
+
+
 @inherit_doc
 @ignore_unicode_prefix
 class Word2Vec(JavaEstimator, HasStepSize, HasMaxIter, HasSeed, HasInputCol, HasOutputCol):

From 1dc7548c598c4eb4ecc7d5bb8962a735bbd2c0f7 Mon Sep 17 00:00:00 2001
From: Sean Paradiso <seanparadiso@gmail.com>
Date: Wed, 9 Sep 2015 22:09:33 -0700
Subject: [PATCH 568/633] [MINOR] [MLLIB] [ML] [DOC] fixed typo: label for
 negative result should be 0.0 (original: 1.0)

Small typo in the example for `LabelledPoint` in the MLLib docs.

Author: Sean Paradiso <seanparadiso@gmail.com>

Closes #8680 from sparadiso/docs_mllib_smalltypo.
---
 docs/mllib-data-types.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md
index 065bf4727624f..d8c7bdc63c70e 100644
--- a/docs/mllib-data-types.md
+++ b/docs/mllib-data-types.md
@@ -144,7 +144,7 @@ import org.apache.spark.mllib.regression.LabeledPoint;
 LabeledPoint pos = new LabeledPoint(1.0, Vectors.dense(1.0, 0.0, 3.0));
 
 // Create a labeled point with a negative label and a sparse feature vector.
-LabeledPoint neg = new LabeledPoint(1.0, Vectors.sparse(3, new int[] {0, 2}, new double[] {1.0, 3.0}));
+LabeledPoint neg = new LabeledPoint(0.0, Vectors.sparse(3, new int[] {0, 2}, new double[] {1.0, 3.0}));
 {% endhighlight %}
 </div>
 

From 48817cc111a9705f40b7c842315eee24291c2198 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Thu, 10 Sep 2015 16:42:12 +0200
Subject: [PATCH 569/633] [SPARK-10497] [BUILD] [TRIVIAL] Handle both locations
 for JIRAError with python-jira

Location of JIRAError has moved between old and new versions of python-jira package.
Longer term it probably makes sense to pin to specific versions (as mentioned in https://issues.apache.org/jira/browse/SPARK-10498 ) but for now, making release tools works with both new and old versions of python-jira.

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8661 from holdenk/SPARK-10497-release-utils-does-not-work-with-new-jira-python.
---
 dev/create-release/releaseutils.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/dev/create-release/releaseutils.py b/dev/create-release/releaseutils.py
index 51ab25a6a5bd8..7f152b7f53559 100755
--- a/dev/create-release/releaseutils.py
+++ b/dev/create-release/releaseutils.py
@@ -24,7 +24,11 @@
 
 try:
     from jira.client import JIRA
-    from jira.exceptions import JIRAError
+    # Old versions have JIRAError in exceptions package, new (0.5+) in utils.
+    try:
+        from jira.exceptions import JIRAError
+    except ImportError:
+        from jira.utils import JIRAError
 except ImportError:
     print "This tool requires the jira-python library"
     print "Install using 'sudo pip install jira'"

From 4f1daa1ef6b36440962f3c8faea3928599e33784 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Thu, 10 Sep 2015 10:04:38 -0700
Subject: [PATCH 570/633] [SPARK-10065] [SQL] avoid the extra copy when
 generate unsafe array

The reason for this extra copy is that we iterate the array twice: calculate elements data size and copy elements to array buffer.

A simple solution is to follow `createCodeForStruct`, we can dynamically grow the buffer when needed and thus don't need to know the data size ahead.

This PR also include some typo and style fixes, and did some minor refactor to make sure `input.primitive` is always variable name not code when generate unsafe code.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8496 from cloud-fan/avoid-copy.
---
 .../codegen/GenerateUnsafeProjection.scala    | 84 ++++++-------------
 1 file changed, 24 insertions(+), 60 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
index 03c5f449bf9ac..55562facf9652 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/GenerateUnsafeProjection.scala
@@ -206,11 +206,11 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
     ctx.addMutableState("UnsafeArrayData", output, s"$output = new UnsafeArrayData();")
     val buffer = ctx.freshName("buffer")
     ctx.addMutableState("byte[]", buffer, s"$buffer = new byte[64];")
+    val tmpBuffer = ctx.freshName("tmpBuffer")
     val outputIsNull = ctx.freshName("isNull")
     val numElements = ctx.freshName("numElements")
     val fixedSize = ctx.freshName("fixedSize")
     val numBytes = ctx.freshName("numBytes")
-    val elements = ctx.freshName("elements")
     val cursor = ctx.freshName("cursor")
     val index = ctx.freshName("index")
     val elementName = ctx.freshName("elementName")
@@ -224,57 +224,11 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
 
     val convertedElement = createConvertCode(ctx, element, elementType)
 
-    // go through the input array to calculate how many bytes we need.
-    val calculateNumBytes = elementType match {
-      case _ if ctx.isPrimitiveType(elementType) =>
-        // Should we do word align?
-        val elementSize = elementType.defaultSize
-        s"""
-          $numBytes += $elementSize * $numElements;
-        """
-      case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
-        s"""
-          $numBytes += 8 * $numElements;
-        """
-      case _ =>
-        val writer = getWriter(elementType)
-        val elementSize = s"$writer.getSize($elements[$index])"
-        // TODO(davies): avoid the copy
-        val unsafeType = elementType match {
-          case _: StructType => "UnsafeRow"
-          case _: ArrayType => "UnsafeArrayData"
-          case _: MapType => "UnsafeMapData"
-          case _ => ctx.javaType(elementType)
-        }
-        val copy = elementType match {
-          // We reuse the buffer during conversion, need copy it before process next element.
-          case _: StructType | _: ArrayType | _: MapType => ".copy()"
-          case _ => ""
-        }
-
-        val newElements = if (elementType == BinaryType) {
-          s"new byte[$numElements][]"
-        } else {
-          s"new $unsafeType[$numElements]"
-        }
-        s"""
-          final $unsafeType[] $elements = $newElements;
-          for (int $index = 0; $index < $numElements; $index++) {
-            ${convertedElement.code}
-            if (!${convertedElement.isNull}) {
-              $elements[$index] = ${convertedElement.primitive}$copy;
-              $numBytes += $elementSize;
-            }
-          }
-        """
-    }
-
     val writeElement = elementType match {
       case _ if ctx.isPrimitiveType(elementType) =>
         // Should we do word align?
         val elementSize = elementType.defaultSize
         s"""
-          ${convertedElement.code}
           Platform.put${ctx.primitiveTypeName(elementType)}(
             $buffer,
             Platform.BYTE_ARRAY_OFFSET + $cursor,
@@ -283,7 +237,6 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
         """
       case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS =>
         s"""
-          ${convertedElement.code}
           Platform.putLong(
             $buffer,
             Platform.BYTE_ARRAY_OFFSET + $cursor,
@@ -296,15 +249,23 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           $cursor += $writer.write(
             $buffer,
             Platform.BYTE_ARRAY_OFFSET + $cursor,
-            $elements[$index]);
+            ${convertedElement.primitive});
         """
     }
 
-    val checkNull = elementType match {
-      case _ if ctx.isPrimitiveType(elementType) => s"${convertedElement.isNull}"
-      case t: DecimalType => s"$elements[$index] == null" +
-        s" || !$elements[$index].changePrecision(${t.precision}, ${t.scale})"
-      case _ => s"$elements[$index] == null"
+    val checkNull = convertedElement.isNull + (elementType match {
+      case t: DecimalType =>
+        s" || !${convertedElement.primitive}.changePrecision(${t.precision}, ${t.scale})"
+      case _ => ""
+    })
+
+    val elementSize = elementType match {
+      // Should we do word align for primitive types?
+      case _ if ctx.isPrimitiveType(elementType) => elementType.defaultSize.toString
+      case t: DecimalType if t.precision <= Decimal.MAX_LONG_DIGITS => "8"
+      case _ =>
+        val writer = getWriter(elementType)
+        s"$writer.getSize(${convertedElement.primitive})"
     }
 
     val code = s"""
@@ -318,18 +279,21 @@ object GenerateUnsafeProjection extends CodeGenerator[Seq[Expression], UnsafePro
           final int $fixedSize = 4 * $numElements;
           int $numBytes = $fixedSize;
 
-          $calculateNumBytes
-
-          if ($numBytes > $buffer.length) {
-            $buffer = new byte[$numBytes];
-          }
-
           int $cursor = $fixedSize;
           for (int $index = 0; $index < $numElements; $index++) {
+            ${convertedElement.code}
             if ($checkNull) {
               // If element is null, write the negative value address into offset region.
               Platform.putInt($buffer, Platform.BYTE_ARRAY_OFFSET + 4 * $index, -$cursor);
             } else {
+              $numBytes += $elementSize;
+              if ($buffer.length < $numBytes) {
+                // This will not happen frequently, because the buffer is re-used.
+                byte[] $tmpBuffer = new byte[$numBytes * 2];
+                Platform.copyMemory($buffer, Platform.BYTE_ARRAY_OFFSET,
+                  $tmpBuffer, Platform.BYTE_ARRAY_OFFSET, $buffer.length);
+                $buffer = $tmpBuffer;
+              }
               Platform.putInt($buffer, Platform.BYTE_ARRAY_OFFSET + 4 * $index, $cursor);
               $writeElement
             }

From f892d927d7246856dd3ea617b2942873359454bc Mon Sep 17 00:00:00 2001
From: Yash Datta <Yash.Datta@guavus.com>
Date: Thu, 10 Sep 2015 10:34:00 -0700
Subject: [PATCH 571/633] [SPARK-7142] [SQL] Minor enhancement to
 BooleanSimplification Optimizer rule

Use these in the optimizer as well:

            A and (not(A) or B) => A and B
            not(A and B) => not(A) or not(B)
            not(A or B) => not(A) and not(B)

Author: Yash Datta <Yash.Datta@guavus.com>

Closes #5700 from saucam/bool_simp.
---
 .../spark/sql/catalyst/optimizer/Optimizer.scala |  9 +++++++++
 .../optimizer/BooleanSimplificationSuite.scala   | 16 ++++++++++++++++
 2 files changed, 25 insertions(+)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index a430000bef653..d9b50f3c97da0 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -434,6 +434,11 @@ object BooleanSimplification extends Rule[LogicalPlan] with PredicateHelper {
         case (_, Literal(false, BooleanType)) => Literal(false)
         // a && a  =>  a
         case (l, r) if l fastEquals r => l
+        // a && (not(a) || b) => a && b
+        case (l, Or(l1, r)) if (Not(l) fastEquals l1) => And(l, r)
+        case (l, Or(r, l1)) if (Not(l) fastEquals l1) => And(l, r)
+        case (Or(l, l1), r) if (l1 fastEquals Not(r)) => And(l, r)
+        case (Or(l1, l), r) if (l1 fastEquals Not(r)) => And(l, r)
         // (a || b) && (a || c)  =>  a || (b && c)
         case _ =>
           // 1. Split left and right to get the disjunctive predicates,
@@ -512,6 +517,10 @@ object BooleanSimplification extends Rule[LogicalPlan] with PredicateHelper {
         case LessThan(l, r) => GreaterThanOrEqual(l, r)
         // not(l <= r)  =>  l > r
         case LessThanOrEqual(l, r) => GreaterThan(l, r)
+        // not(l || r) => not(l) && not(r)
+        case Or(l, r) => And(Not(l), Not(r))
+        // not(l && r) => not(l) or not(r)
+        case And(l, r) => Or(Not(l), Not(r))
         // not(not(e))  =>  e
         case Not(e) => e
         case _ => not
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
index 1877cff1334bd..cde346e99eb17 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/BooleanSimplificationSuite.scala
@@ -89,6 +89,22 @@ class BooleanSimplificationSuite extends PlanTest with PredicateHelper {
       ('a === 'b || 'b > 3 && 'a > 3 && 'a < 5))
   }
 
+  test("a && (!a || b)") {
+    checkCondition(('a && (!('a) || 'b )), ('a && 'b))
+
+    checkCondition(('a && ('b || !('a) )), ('a && 'b))
+
+    checkCondition(((!('a) || 'b ) && 'a), ('b && 'a))
+
+    checkCondition((('b || !('a) ) && 'a), ('b && 'a))
+  }
+
+  test("!(a && b) , !(a || b)") {
+    checkCondition((!('a && 'b)), (!('a) || !('b)))
+
+    checkCondition(!('a || 'b), (!('a) && !('b)))
+  }
+
   private val caseInsensitiveAnalyzer =
     new Analyzer(EmptyCatalog, EmptyFunctionRegistry, new SimpleCatalystConf(false))
 

From 49da38e5f728e05e8e929c4dcd37145ba060151d Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Thu, 10 Sep 2015 11:01:08 -0700
Subject: [PATCH 572/633] [SPARK-10301] [SPARK-10428] [SQL] Addresses comments
 of PR #8583 and #8509 for master

Author: Cheng Lian <lian@databricks.com>

Closes #8670 from liancheng/spark-10301/address-pr-comments.
---
 .../parquet/CatalystReadSupport.scala         |  20 +-
 .../parquet/CatalystRowConverter.scala        |  10 +
 .../parquet/ParquetQuerySuite.scala           | 291 ++++++++++++++++--
 .../parquet/ParquetSchemaSuite.scala          | 246 ++++++++++++++-
 4 files changed, 522 insertions(+), 45 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index dc4ff06df6f22..5a8166fac5418 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -117,14 +117,18 @@ private[parquet] object CatalystReadSupport {
         // Only clips array types with nested type as element type.
         clipParquetListType(parquetType.asGroupType(), t.elementType)
 
-      case t: MapType if !isPrimitiveCatalystType(t.valueType) =>
-        // Only clips map types with nested type as value type.
+      case t: MapType
+        if !isPrimitiveCatalystType(t.keyType) ||
+           !isPrimitiveCatalystType(t.valueType) =>
+        // Only clips map types with nested key type or value type
         clipParquetMapType(parquetType.asGroupType(), t.keyType, t.valueType)
 
       case t: StructType =>
         clipParquetGroup(parquetType.asGroupType(), t)
 
       case _ =>
+        // UDTs and primitive types are not clipped.  For UDTs, a clipped version might not be able
+        // to be mapped to desired user-space types.  So UDTs shouldn't participate schema merging.
         parquetType
     }
   }
@@ -204,14 +208,14 @@ private[parquet] object CatalystReadSupport {
   }
 
   /**
-   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[MapType]].  The value type
-   * of the [[MapType]] should also be a nested type, namely an [[ArrayType]], a [[MapType]], or a
-   * [[StructType]].  Note that key type of any [[MapType]] is always a primitive type.
+   * Clips a Parquet [[GroupType]] which corresponds to a Catalyst [[MapType]].  Either key type or
+   * value type of the [[MapType]] must be a nested type, namely an [[ArrayType]], a [[MapType]], or
+   * a [[StructType]].
    */
   private def clipParquetMapType(
       parquetMap: GroupType, keyType: DataType, valueType: DataType): GroupType = {
-    // Precondition of this method, should only be called for maps with nested value types.
-    assert(!isPrimitiveCatalystType(valueType))
+    // Precondition of this method, only handles maps with nested key types or value types.
+    assert(!isPrimitiveCatalystType(keyType) || !isPrimitiveCatalystType(valueType))
 
     val repeatedGroup = parquetMap.getType(0).asGroupType()
     val parquetKeyType = repeatedGroup.getType(0)
@@ -221,7 +225,7 @@ private[parquet] object CatalystReadSupport {
       Types
         .repeatedGroup()
         .as(repeatedGroup.getOriginalType)
-        .addField(parquetKeyType)
+        .addField(clipParquetType(parquetKeyType, keyType))
         .addField(clipParquetType(parquetValueType, valueType))
         .named(repeatedGroup.getName)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
index f17e794b76650..2ff2fda3610b8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystRowConverter.scala
@@ -123,6 +123,16 @@ private[parquet] class CatalystRowConverter(
     updater: ParentContainerUpdater)
   extends CatalystGroupConverter(updater) with Logging {
 
+  assert(
+    parquetType.getFieldCount == catalystType.length,
+    s"""Field counts of the Parquet schema and the Catalyst schema don't match:
+       |
+       |Parquet schema:
+       |$parquetType
+       |Catalyst schema:
+       |${catalystType.prettyJson}
+     """.stripMargin)
+
   logDebug(
     s"""Building row converter for the following schema:
        |
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
index 9edbb522684eb..1c1cfa34ad04b 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetQuerySuite.scala
@@ -22,6 +22,9 @@ import java.io.File
 import org.apache.hadoop.fs.Path
 
 import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.SpecificMutableRow
+import org.apache.spark.sql.execution.datasources.parquet.TestingUDT.{NestedStruct, NestedStructUDT}
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
 import org.apache.spark.util.Utils
@@ -228,54 +231,168 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
     }
   }
 
-  test("SPARK-10301 Clipping nested structs in requested schema") {
+  test("SPARK-10301 requested schema clipping - same schema") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext.range(1).selectExpr("NAMED_STRUCT('a', id, 'b', id + 1) AS s").coalesce(1)
+      df.write.parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("a", LongType, nullable = true)
+              .add("b", LongType, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(0L, 1L)))
+    }
+  }
+
+  // This test case is ignored because of parquet-mr bug PARQUET-370
+  ignore("SPARK-10301 requested schema clipping - schemas with disjoint sets of fields") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext.range(1).selectExpr("NAMED_STRUCT('a', id, 'b', id + 1) AS s").coalesce(1)
+      df.write.parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("c", LongType, nullable = true)
+              .add("d", LongType, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(null, null)))
+    }
+  }
+
+  test("SPARK-10301 requested schema clipping - requested schema contains physical schema") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext.range(1).selectExpr("NAMED_STRUCT('a', id, 'b', id + 1) AS s").coalesce(1)
+      df.write.parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("a", LongType, nullable = true)
+              .add("b", LongType, nullable = true)
+              .add("c", LongType, nullable = true)
+              .add("d", LongType, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(0L, 1L, null, null)))
+    }
+
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext.range(1).selectExpr("NAMED_STRUCT('a', id, 'd', id + 3) AS s").coalesce(1)
+      df.write.parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("a", LongType, nullable = true)
+              .add("b", LongType, nullable = true)
+              .add("c", LongType, nullable = true)
+              .add("d", LongType, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(0L, null, null, 3L)))
+    }
+  }
+
+  test("SPARK-10301 requested schema clipping - physical schema contains requested schema") {
     withTempPath { dir =>
       val path = dir.getCanonicalPath
       val df = sqlContext
         .range(1)
-        .selectExpr("NAMED_STRUCT('a', id, 'b', id) AS s")
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id + 1, 'c', id + 2, 'd', id + 3) AS s")
         .coalesce(1)
 
-      df.write.mode("append").parquet(path)
+      df.write.parquet(path)
 
-      val userDefinedSchema = new StructType()
-        .add("s", new StructType().add("a", LongType, nullable = true), nullable = true)
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("a", LongType, nullable = true)
+              .add("b", LongType, nullable = true),
+            nullable = true)
 
       checkAnswer(
         sqlContext.read.schema(userDefinedSchema).parquet(path),
-        Row(Row(0)))
+        Row(Row(0L, 1L)))
     }
 
     withTempPath { dir =>
       val path = dir.getCanonicalPath
-
-      val df1 = sqlContext
+      val df = sqlContext
         .range(1)
-        .selectExpr("NAMED_STRUCT('a', id, 'b', id) AS s")
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id + 1, 'c', id + 2, 'd', id + 3) AS s")
         .coalesce(1)
 
-      val df2 = sqlContext
-        .range(1, 2)
-        .selectExpr("NAMED_STRUCT('b', id, 'c', id) AS s")
+      df.write.parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("a", LongType, nullable = true)
+              .add("d", LongType, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(0L, 3L)))
+    }
+  }
+
+  test("SPARK-10301 requested schema clipping - schemas overlap but don't contain each other") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+      val df = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id + 1, 'c', id + 2) AS s")
         .coalesce(1)
 
-      df1.write.parquet(path)
-      df2.write.mode(SaveMode.Append).parquet(path)
+      df.write.parquet(path)
 
-      val userDefinedSchema = new StructType()
-        .add("s",
-          new StructType()
-            .add("a", LongType, nullable = true)
-            .add("c", LongType, nullable = true),
-          nullable = true)
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("b", LongType, nullable = true)
+              .add("c", LongType, nullable = true)
+              .add("d", LongType, nullable = true),
+            nullable = true)
 
       checkAnswer(
         sqlContext.read.schema(userDefinedSchema).parquet(path),
-        Seq(
-          Row(Row(0, null)),
-          Row(Row(null, 1))))
+        Row(Row(1L, 2L, null)))
     }
+  }
 
+  test("SPARK-10301 requested schema clipping - deeply nested struct") {
     withTempPath { dir =>
       val path = dir.getCanonicalPath
 
@@ -304,4 +421,132 @@ class ParquetQuerySuite extends QueryTest with ParquetTest with SharedSQLContext
         Row(Row(Seq(Row(0, null)))))
     }
   }
+
+  test("SPARK-10301 requested schema clipping - out of order") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      val df1 = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id + 1, 'c', id + 2) AS s")
+        .coalesce(1)
+
+      val df2 = sqlContext
+        .range(1, 2)
+        .selectExpr("NAMED_STRUCT('c', id + 2, 'b', id + 1, 'd', id + 3) AS s")
+        .coalesce(1)
+
+      df1.write.parquet(path)
+      df2.write.mode(SaveMode.Append).parquet(path)
+
+      val userDefinedSchema = new StructType()
+        .add("s",
+          new StructType()
+            .add("a", LongType, nullable = true)
+            .add("b", LongType, nullable = true)
+            .add("d", LongType, nullable = true),
+          nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Seq(
+          Row(Row(0, 1, null)),
+          Row(Row(null, 2, 4))))
+    }
+  }
+
+  test("SPARK-10301 requested schema clipping - schema merging") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      val df1 = sqlContext
+        .range(1)
+        .selectExpr("NAMED_STRUCT('a', id, 'c', id + 2) AS s")
+        .coalesce(1)
+
+      val df2 = sqlContext
+        .range(1, 2)
+        .selectExpr("NAMED_STRUCT('a', id, 'b', id + 1, 'c', id + 2) AS s")
+        .coalesce(1)
+
+      df1.write.mode(SaveMode.Append).parquet(path)
+      df2.write.mode(SaveMode.Append).parquet(path)
+
+      checkAnswer(
+        sqlContext
+          .read
+          .option("mergeSchema", "true")
+          .parquet(path)
+          .selectExpr("s.a", "s.b", "s.c"),
+        Seq(
+          Row(0, null, 2),
+          Row(1, 2, 3)))
+    }
+  }
+
+  test("SPARK-10301 requested schema clipping - UDT") {
+    withTempPath { dir =>
+      val path = dir.getCanonicalPath
+
+      val df = sqlContext
+        .range(1)
+        .selectExpr(
+          """NAMED_STRUCT(
+            |  'f0', CAST(id AS STRING),
+            |  'f1', NAMED_STRUCT(
+            |    'a', CAST(id + 1 AS INT),
+            |    'b', CAST(id + 2 AS LONG),
+            |    'c', CAST(id + 3.5 AS DOUBLE)
+            |  )
+            |) AS s
+          """.stripMargin)
+        .coalesce(1)
+
+      df.write.mode(SaveMode.Append).parquet(path)
+
+      val userDefinedSchema =
+        new StructType()
+          .add(
+            "s",
+            new StructType()
+              .add("f1", new NestedStructUDT, nullable = true),
+            nullable = true)
+
+      checkAnswer(
+        sqlContext.read.schema(userDefinedSchema).parquet(path),
+        Row(Row(NestedStruct(1, 2L, 3.5D))))
+    }
+  }
+}
+
+object TestingUDT {
+  @SQLUserDefinedType(udt = classOf[NestedStructUDT])
+  case class NestedStruct(a: Integer, b: Long, c: Double)
+
+  class NestedStructUDT extends UserDefinedType[NestedStruct] {
+    override def sqlType: DataType =
+      new StructType()
+        .add("a", IntegerType, nullable = true)
+        .add("b", LongType, nullable = false)
+        .add("c", DoubleType, nullable = false)
+
+    override def serialize(obj: Any): Any = {
+      val row = new SpecificMutableRow(sqlType.asInstanceOf[StructType].map(_.dataType))
+      obj match {
+        case n: NestedStruct =>
+          row.setInt(0, n.a)
+          row.setLong(1, n.b)
+          row.setDouble(2, n.c)
+      }
+    }
+
+    override def userClass: Class[NestedStruct] = classOf[NestedStruct]
+
+    override def deserialize(datum: Any): NestedStruct = {
+      datum match {
+        case row: InternalRow =>
+          NestedStruct(row.getInt(0), row.getLong(1), row.getDouble(2))
+      }
+    }
+  }
 }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
index 5331d7c035236..5a8f772c32289 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaSuite.scala
@@ -1012,12 +1012,17 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
       """.stripMargin,
 
     catalystSchema = {
-      val f11Type = new StructType().add("f011", DoubleType, nullable = true)
-      val f01Type = ArrayType(StringType, containsNull = false)
+      val f00Type = ArrayType(StringType, containsNull = false)
+      val f01Type = ArrayType(
+        new StructType()
+          .add("f011", DoubleType, nullable = true),
+        containsNull = false)
+
       val f0Type = new StructType()
-        .add("f00", f01Type, nullable = false)
-        .add("f01", f11Type, nullable = false)
+        .add("f00", f00Type, nullable = false)
+        .add("f01", f01Type, nullable = false)
       val f1Type = ArrayType(IntegerType, containsNull = true)
+
       new StructType()
         .add("f0", f0Type, nullable = false)
         .add("f1", f1Type, nullable = true)
@@ -1046,7 +1051,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     parquetSchema =
       """message root {
         |  required group f0 {
-        |    optional group f00 {
+        |    optional group f00 (LIST) {
         |      repeated binary f00_tuple (UTF8);
         |    }
         |
@@ -1061,13 +1066,13 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
       """.stripMargin,
 
     catalystSchema = {
-      val f11ElementType = new StructType()
+      val f01ElementType = new StructType()
         .add("f011", DoubleType, nullable = true)
         .add("f012", LongType, nullable = true)
 
       val f0Type = new StructType()
-        .add("f00", ArrayType(StringType, containsNull = false), nullable = false)
-        .add("f01", ArrayType(f11ElementType, containsNull = false), nullable = false)
+        .add("f00", ArrayType(StringType, containsNull = false), nullable = true)
+        .add("f01", ArrayType(f01ElementType, containsNull = false), nullable = true)
 
       new StructType().add("f0", f0Type, nullable = false)
     },
@@ -1075,7 +1080,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     expectedSchema =
       """message root {
         |  required group f0 {
-        |    optional group f00 {
+        |    optional group f00 (LIST) {
         |      repeated binary f00_tuple (UTF8);
         |    }
         |
@@ -1095,7 +1100,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     parquetSchema =
       """message root {
         |  required group f0 {
-        |    optional group f00 {
+        |    optional group f00 (LIST) {
         |      repeated binary array (UTF8);
         |    }
         |
@@ -1110,13 +1115,13 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
       """.stripMargin,
 
     catalystSchema = {
-      val f11ElementType = new StructType()
+      val f01ElementType = new StructType()
         .add("f011", DoubleType, nullable = true)
         .add("f012", LongType, nullable = true)
 
       val f0Type = new StructType()
-        .add("f00", ArrayType(StringType, containsNull = false), nullable = false)
-        .add("f01", ArrayType(f11ElementType, containsNull = false), nullable = false)
+        .add("f00", ArrayType(StringType, containsNull = false), nullable = true)
+        .add("f01", ArrayType(f01ElementType, containsNull = false), nullable = true)
 
       new StructType().add("f0", f0Type, nullable = false)
     },
@@ -1124,7 +1129,7 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     expectedSchema =
       """message root {
         |  required group f0 {
-        |    optional group f00 {
+        |    optional group f00 (LIST) {
         |      repeated binary array (UTF8);
         |    }
         |
@@ -1236,6 +1241,63 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
          |}
        """.stripMargin)
 
+  testSchemaClipping(
+    "standard array",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 (LIST) {
+        |      repeated group list {
+        |        required binary element (UTF8);
+        |      }
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group list {
+        |        required group element {
+        |          optional int32 f010;
+        |          optional double f011;
+        |        }
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val f01ElementType = new StructType()
+        .add("f011", DoubleType, nullable = true)
+        .add("f012", LongType, nullable = true)
+
+      val f0Type = new StructType()
+        .add("f00", ArrayType(StringType, containsNull = false), nullable = true)
+        .add("f01", ArrayType(f01ElementType, containsNull = false), nullable = true)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    optional group f00 (LIST) {
+        |      repeated group list {
+        |        required binary element (UTF8);
+        |      }
+        |    }
+        |
+        |    optional group f01 (LIST) {
+        |      repeated group list {
+        |        required group element {
+        |          optional double f011;
+        |          optional int64 f012;
+        |        }
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
   testSchemaClipping(
     "empty requested schema",
 
@@ -1251,4 +1313,160 @@ class ParquetSchemaSuite extends ParquetSchemaTest {
     catalystSchema = new StructType(),
 
     expectedSchema = "message root {}")
+
+  testSchemaClipping(
+    "disjoint field sets",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 {
+        |    required int32 f00;
+        |    required int64 f01;
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema =
+      new StructType()
+        .add(
+          "f0",
+          new StructType()
+            .add("f02", FloatType, nullable = true)
+            .add("f03", DoubleType, nullable = true),
+          nullable = true),
+
+    expectedSchema =
+      """message root {
+        |  required group f0 {
+        |    optional float f02;
+        |    optional double f03;
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "parquet-avro style map",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group map (MAP_KEY_VALUE) {
+        |      required int32 key;
+        |      required group value {
+        |        required int32 value_f0;
+        |        required int64 value_f1;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val valueType =
+        new StructType()
+          .add("value_f1", LongType, nullable = false)
+          .add("value_f2", DoubleType, nullable = false)
+
+      val f0Type = MapType(IntegerType, valueType, valueContainsNull = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group map (MAP_KEY_VALUE) {
+        |      required int32 key;
+        |      required group value {
+        |        required int64 value_f1;
+        |        required double value_f2;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "standard map",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group key_value {
+        |      required int32 key;
+        |      required group value {
+        |        required int32 value_f0;
+        |        required int64 value_f1;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val valueType =
+        new StructType()
+          .add("value_f1", LongType, nullable = false)
+          .add("value_f2", DoubleType, nullable = false)
+
+      val f0Type = MapType(IntegerType, valueType, valueContainsNull = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group key_value {
+        |      required int32 key;
+        |      required group value {
+        |        required int64 value_f1;
+        |        required double value_f2;
+        |      }
+        |    }
+        |  }
+        |}
+      """.stripMargin)
+
+  testSchemaClipping(
+    "standard map with complex key",
+
+    parquetSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group key_value {
+        |      required group key {
+        |        required int32 value_f0;
+        |        required int64 value_f1;
+        |      }
+        |      required int32 value;
+        |    }
+        |  }
+        |}
+      """.stripMargin,
+
+    catalystSchema = {
+      val keyType =
+        new StructType()
+          .add("value_f1", LongType, nullable = false)
+          .add("value_f2", DoubleType, nullable = false)
+
+      val f0Type = MapType(keyType, IntegerType, valueContainsNull = false)
+
+      new StructType().add("f0", f0Type, nullable = false)
+    },
+
+    expectedSchema =
+      """message root {
+        |  required group f0 (MAP) {
+        |    repeated group key_value {
+        |      required group key {
+        |        required int64 value_f1;
+        |        required double value_f2;
+        |      }
+        |      required int32 value;
+        |    }
+        |  }
+        |}
+      """.stripMargin)
 }

From e04811137680f937669cdcc78771227aeb7cd849 Mon Sep 17 00:00:00 2001
From: Cheng Hao <hao.cheng@intel.com>
Date: Thu, 10 Sep 2015 11:48:43 -0700
Subject: [PATCH 573/633] [SPARK-10466] [SQL] UnsafeRow SerDe exception with
 data spill

Data Spill with UnsafeRow causes assert failure.

```
java.lang.AssertionError: assertion failed
	at scala.Predef$.assert(Predef.scala:165)
	at org.apache.spark.sql.execution.UnsafeRowSerializerInstance$$anon$2.writeKey(UnsafeRowSerializer.scala:75)
	at org.apache.spark.storage.DiskBlockObjectWriter.write(DiskBlockObjectWriter.scala:180)
	at org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$2$$anonfun$apply$1.apply(ExternalSorter.scala:688)
	at org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$2$$anonfun$apply$1.apply(ExternalSorter.scala:687)
	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
	at org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$2.apply(ExternalSorter.scala:687)
	at org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$2.apply(ExternalSorter.scala:683)
	at scala.collection.Iterator$class.foreach(Iterator.scala:727)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
	at org.apache.spark.util.collection.ExternalSorter.writePartitionedFile(ExternalSorter.scala:683)
	at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:80)
	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
	at org.apache.spark.scheduler.Task.run(Task.scala:88)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
```

To reproduce that with code (thanks andrewor14):
```scala
bin/spark-shell --master local
  --conf spark.shuffle.memoryFraction=0.005
  --conf spark.shuffle.sort.bypassMergeThreshold=0

sc.parallelize(1 to 2 * 1000 * 1000, 10)
  .map { i => (i, i) }.toDF("a", "b").groupBy("b").avg().count()
```

Author: Cheng Hao <hao.cheng@intel.com>

Closes #8635 from chenghao-intel/unsafe_spill.
---
 .../util/collection/ExternalSorter.scala      |  6 ++
 .../sql/execution/UnsafeRowSerializer.scala   |  2 +-
 .../execution/UnsafeRowSerializerSuite.scala  | 64 +++++++++++++++++--
 3 files changed, 67 insertions(+), 5 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
index 19287edbaf166..138c05dff19e4 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
@@ -188,6 +188,12 @@ private[spark] class ExternalSorter[K, V, C](
 
   private val spills = new ArrayBuffer[SpilledFile]
 
+  /**
+   * Number of files this sorter has spilled so far.
+   * Exposed for testing.
+   */
+  private[spark] def numSpills: Int = spills.size
+
   override def insertAll(records: Iterator[Product2[K, V]]): Unit = {
     // TODO: stop combining if we find that the reduction factor isn't high
     val shouldCombine = aggregator.isDefined
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
index 5c18558f9bde7..e060c06d9e2a2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/UnsafeRowSerializer.scala
@@ -72,7 +72,7 @@ private class UnsafeRowSerializerInstance(numFields: Int) extends SerializerInst
     override def writeKey[T: ClassTag](key: T): SerializationStream = {
       // The key is only needed on the map side when computing partition ids. It does not need to
       // be shuffled.
-      assert(key.isInstanceOf[Int])
+      assert(null == key || key.isInstanceOf[Int])
       this
     }
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
index bd02c73a26ace..0113d052e338d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/UnsafeRowSerializerSuite.scala
@@ -17,13 +17,17 @@
 
 package org.apache.spark.sql.execution
 
-import java.io.{DataOutputStream, ByteArrayInputStream, ByteArrayOutputStream}
+import java.io.{File, DataOutputStream, ByteArrayInputStream, ByteArrayOutputStream}
 
-import org.apache.spark.SparkFunSuite
+import org.apache.spark.executor.ShuffleWriteMetrics
+import org.apache.spark.storage.ShuffleBlockId
+import org.apache.spark.util.collection.ExternalSorter
+import org.apache.spark.util.Utils
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, UnsafeRow}
 import org.apache.spark.sql.types._
+import org.apache.spark._
 
 
 /**
@@ -40,9 +44,15 @@ class ClosableByteArrayInputStream(buf: Array[Byte]) extends ByteArrayInputStrea
 class UnsafeRowSerializerSuite extends SparkFunSuite {
 
   private def toUnsafeRow(row: Row, schema: Array[DataType]): UnsafeRow = {
-    val internalRow = CatalystTypeConverters.convertToCatalyst(row).asInstanceOf[InternalRow]
+    val converter = unsafeRowConverter(schema)
+    converter(row)
+  }
+
+  private def unsafeRowConverter(schema: Array[DataType]): Row => UnsafeRow = {
     val converter = UnsafeProjection.create(schema)
-    converter.apply(internalRow)
+    (row: Row) => {
+      converter(CatalystTypeConverters.convertToCatalyst(row).asInstanceOf[InternalRow])
+    }
   }
 
   test("toUnsafeRow() test helper method") {
@@ -87,4 +97,50 @@ class UnsafeRowSerializerSuite extends SparkFunSuite {
     assert(!deserializerIter.hasNext)
     assert(input.closed)
   }
+
+  test("SPARK-10466: external sorter spilling with unsafe row serializer") {
+    var sc: SparkContext = null
+    var outputFile: File = null
+    val oldEnv = SparkEnv.get // save the old SparkEnv, as it will be overwritten
+    Utils.tryWithSafeFinally {
+      val conf = new SparkConf()
+        .set("spark.shuffle.spill.initialMemoryThreshold", "1024")
+        .set("spark.shuffle.sort.bypassMergeThreshold", "0")
+        .set("spark.shuffle.memoryFraction", "0.0001")
+
+      sc = new SparkContext("local", "test", conf)
+      outputFile = File.createTempFile("test-unsafe-row-serializer-spill", "")
+      // prepare data
+      val converter = unsafeRowConverter(Array(IntegerType))
+      val data = (1 to 1000).iterator.map { i =>
+        (i, converter(Row(i)))
+      }
+      val sorter = new ExternalSorter[Int, UnsafeRow, UnsafeRow](
+        partitioner = Some(new HashPartitioner(10)),
+        serializer = Some(new UnsafeRowSerializer(numFields = 1)))
+
+      // Ensure we spilled something and have to merge them later
+      assert(sorter.numSpills === 0)
+      sorter.insertAll(data)
+      assert(sorter.numSpills > 0)
+
+      // Merging spilled files should not throw assertion error
+      val taskContext =
+        new TaskContextImpl(0, 0, 0, 0, null, null, InternalAccumulator.create(sc))
+      taskContext.taskMetrics.shuffleWriteMetrics = Some(new ShuffleWriteMetrics)
+      sorter.writePartitionedFile(ShuffleBlockId(0, 0, 0), taskContext, outputFile)
+    } {
+      // Clean up
+      if (sc != null) {
+        sc.stop()
+      }
+
+      // restore the spark env
+      SparkEnv.set(oldEnv)
+
+      if (outputFile != null) {
+        outputFile.delete()
+      }
+    }
+  }
 }

From a76bde9dae54c4641e21f3c1ceb4870e3dc91881 Mon Sep 17 00:00:00 2001
From: Holden Karau <holden@pigscanfly.ca>
Date: Thu, 10 Sep 2015 11:49:53 -0700
Subject: [PATCH 574/633] [SPARK-10469] [DOC] Try and document the three
 options

From JIRA:
Add documentation for tungsten-sort.
From the mailing list "I saw a new "spark.shuffle.manager=tungsten-sort" implemented in
https://issues.apache.org/jira/browse/SPARK-7081, but it can't be found its
corresponding description in
http://people.apache.org/~pwendell/spark-releases/spark-1.5.0-rc3-docs/configuration.html(Currenlty
there are only 'sort' and 'hash' two options)."

Author: Holden Karau <holden@pigscanfly.ca>

Closes #8638 from holdenk/SPARK-10469-document-tungsten-sort.
---
 docs/configuration.md | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index e287591f3fda1..0b1a273916314 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -447,9 +447,12 @@ Apart from these, the following properties are also available, and may be useful
   <td><code>spark.shuffle.manager</code></td>
   <td>sort</td>
   <td>
-    Implementation to use for shuffling data. There are two implementations available:
-    <code>sort</code> and <code>hash</code>. Sort-based shuffle is more memory-efficient and is
-    the default option starting in 1.2.
+    Implementation to use for shuffling data. There are three implementations available:
+    <code>sort</code>, <code>hash</code> and the new (1.5+) <code>tungsten-sort</code>.
+    Sort-based shuffle is more memory-efficient and is the default option starting in 1.2.
+    Tungsten-sort is similar to the sort based shuffle, with a direct binary cache-friendly
+    implementation with a fall back to regular sort based shuffle if its requirements are not
+    met.
   </td>
 </tr>
 <tr>

From af3bc59d1f5d9d952c2d7ad1af599c49f1dbdaf0 Mon Sep 17 00:00:00 2001
From: mcheah <mcheah@palantir.com>
Date: Thu, 10 Sep 2015 11:58:54 -0700
Subject: [PATCH 575/633] [SPARK-8167] Make tasks that fail from YARN
 preemption not fail job

The architecture is that, in YARN mode, if the driver detects that an executor has disconnected, it asks the ApplicationMaster why the executor died. If the ApplicationMaster is aware that the executor died because of preemption, all tasks associated with that executor are not marked as failed. The executor
is still removed from the driver's list of available executors, however.

There's a few open questions:
1. Should standalone mode have a similar "get executor loss reason" as well? I localized this change as much as possible to affect only YARN, but there could be a valid case to differentiate executor losses in standalone mode as well.
2. I make a pretty strong assumption in YarnAllocator that getExecutorLossReason(executorId) will only be called once per executor id; I do this so that I can remove the metadata from the in-memory map to avoid object accumulation. It's not clear if I'm being overly zealous to save space, however.

cc vanzin specifically for review because it collided with some earlier YARN scheduling work.
cc JoshRosen because it's similar to output commit coordination we did in the past
cc andrewor14 for our discussion on how to get executor exit codes and loss reasons

Author: mcheah <mcheah@palantir.com>

Closes #8007 from mccheah/feature/preemption-handling.
---
 .../org/apache/spark/TaskEndReason.scala      | 18 +++-
 .../spark/scheduler/ExecutorLossReason.scala  | 14 ++-
 .../org/apache/spark/scheduler/Pool.scala     |  4 +-
 .../apache/spark/scheduler/Schedulable.scala  |  2 +-
 .../spark/scheduler/TaskSchedulerImpl.scala   |  9 +-
 .../spark/scheduler/TaskSetManager.scala      | 21 +++--
 .../cluster/CoarseGrainedClusterMessage.scala |  8 +-
 .../CoarseGrainedSchedulerBackend.scala       | 24 +++--
 .../cluster/SparkDeploySchedulerBackend.scala |  6 +-
 .../cluster/YarnSchedulerBackend.scala        | 77 ++++++++++++++--
 .../mesos/CoarseMesosSchedulerBackend.scala   |  4 +-
 .../cluster/mesos/MesosSchedulerBackend.scala |  2 +-
 .../org/apache/spark/util/JsonProtocol.scala  |  9 +-
 .../spark/scheduler/TaskSetManagerSuite.scala | 33 ++++++-
 .../apache/spark/util/JsonProtocolSuite.scala | 10 +-
 .../spark/deploy/yarn/ApplicationMaster.scala |  7 ++
 .../spark/deploy/yarn/YarnAllocator.scala     | 92 ++++++++++++++-----
 17 files changed, 261 insertions(+), 79 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/TaskEndReason.scala b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
index 934d00dc708b9..2ae878b3e6087 100644
--- a/core/src/main/scala/org/apache/spark/TaskEndReason.scala
+++ b/core/src/main/scala/org/apache/spark/TaskEndReason.scala
@@ -48,6 +48,8 @@ case object Success extends TaskEndReason
 sealed trait TaskFailedReason extends TaskEndReason {
   /** Error message displayed in the web UI. */
   def toErrorString: String
+
+  def shouldEventuallyFailJob: Boolean = true
 }
 
 /**
@@ -194,6 +196,12 @@ case object TaskKilled extends TaskFailedReason {
 case class TaskCommitDenied(jobID: Int, partitionID: Int, attemptID: Int) extends TaskFailedReason {
   override def toErrorString: String = s"TaskCommitDenied (Driver denied task commit)" +
     s" for job: $jobID, partition: $partitionID, attempt: $attemptID"
+  /**
+   * If a task failed because its attempt to commit was denied, do not count this failure
+   * towards failing the stage. This is intended to prevent spurious stage failures in cases
+   * where many speculative tasks are launched and denied to commit.
+   */
+  override def shouldEventuallyFailJob: Boolean = false
 }
 
 /**
@@ -202,8 +210,14 @@ case class TaskCommitDenied(jobID: Int, partitionID: Int, attemptID: Int) extend
  * the task crashed the JVM.
  */
 @DeveloperApi
-case class ExecutorLostFailure(execId: String) extends TaskFailedReason {
-  override def toErrorString: String = s"ExecutorLostFailure (executor ${execId} lost)"
+case class ExecutorLostFailure(execId: String, isNormalExit: Boolean = false)
+  extends TaskFailedReason {
+  override def toErrorString: String = {
+    val exitBehavior = if (isNormalExit) "normally" else "abnormally"
+    s"ExecutorLostFailure (executor ${execId} exited ${exitBehavior})"
+  }
+
+  override def shouldEventuallyFailJob: Boolean = !isNormalExit
 }
 
 /**
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ExecutorLossReason.scala b/core/src/main/scala/org/apache/spark/scheduler/ExecutorLossReason.scala
index 2bc43a9186449..0a98c69b89ea5 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ExecutorLossReason.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ExecutorLossReason.scala
@@ -23,16 +23,20 @@ import org.apache.spark.executor.ExecutorExitCode
  * Represents an explanation for a executor or whole slave failing or exiting.
  */
 private[spark]
-class ExecutorLossReason(val message: String) {
+class ExecutorLossReason(val message: String) extends Serializable {
   override def toString: String = message
 }
 
 private[spark]
-case class ExecutorExited(val exitCode: Int)
-  extends ExecutorLossReason(ExecutorExitCode.explainExitCode(exitCode)) {
+case class ExecutorExited(exitCode: Int, isNormalExit: Boolean, reason: String)
+  extends ExecutorLossReason(reason)
+
+private[spark] object ExecutorExited {
+  def apply(exitCode: Int, isNormalExit: Boolean): ExecutorExited = {
+    ExecutorExited(exitCode, isNormalExit, ExecutorExitCode.explainExitCode(exitCode))
+  }
 }
 
 private[spark]
 case class SlaveLost(_message: String = "Slave lost")
-  extends ExecutorLossReason(_message) {
-}
+  extends ExecutorLossReason(_message)
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
index 5821afea98982..551e39a81b695 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Pool.scala
@@ -83,8 +83,8 @@ private[spark] class Pool(
     null
   }
 
-  override def executorLost(executorId: String, host: String) {
-    schedulableQueue.asScala.foreach(_.executorLost(executorId, host))
+  override def executorLost(executorId: String, host: String, reason: ExecutorLossReason) {
+    schedulableQueue.asScala.foreach(_.executorLost(executorId, host, reason))
   }
 
   override def checkSpeculatableTasks(): Boolean = {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala b/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala
index a87ef030e69c2..ab00bc8f0bf4e 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Schedulable.scala
@@ -42,7 +42,7 @@ private[spark] trait Schedulable {
   def addSchedulable(schedulable: Schedulable): Unit
   def removeSchedulable(schedulable: Schedulable): Unit
   def getSchedulableByName(name: String): Schedulable
-  def executorLost(executorId: String, host: String): Unit
+  def executorLost(executorId: String, host: String, reason: ExecutorLossReason): Unit
   def checkSpeculatableTasks(): Boolean
   def getSortedTaskSetQueue: ArrayBuffer[TaskSetManager]
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
index 1705e7f962de2..1c7bfe89c02ac 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
@@ -332,7 +332,8 @@ private[spark] class TaskSchedulerImpl(
           // We lost this entire executor, so remember that it's gone
           val execId = taskIdToExecutorId(tid)
           if (activeExecutorIds.contains(execId)) {
-            removeExecutor(execId)
+            removeExecutor(execId,
+              SlaveLost(s"Task $tid was lost, so marking the executor as lost as well."))
             failedExecutor = Some(execId)
           }
         }
@@ -464,7 +465,7 @@ private[spark] class TaskSchedulerImpl(
       if (activeExecutorIds.contains(executorId)) {
         val hostPort = executorIdToHost(executorId)
         logError("Lost executor %s on %s: %s".format(executorId, hostPort, reason))
-        removeExecutor(executorId)
+        removeExecutor(executorId, reason)
         failedExecutor = Some(executorId)
       } else {
          // We may get multiple executorLost() calls with different loss reasons. For example, one
@@ -482,7 +483,7 @@ private[spark] class TaskSchedulerImpl(
   }
 
   /** Remove an executor from all our data structures and mark it as lost */
-  private def removeExecutor(executorId: String) {
+  private def removeExecutor(executorId: String, reason: ExecutorLossReason) {
     activeExecutorIds -= executorId
     val host = executorIdToHost(executorId)
     val execs = executorsByHost.getOrElse(host, new HashSet)
@@ -497,7 +498,7 @@ private[spark] class TaskSchedulerImpl(
       }
     }
     executorIdToHost -= executorId
-    rootPool.executorLost(executorId, host)
+    rootPool.executorLost(executorId, host, reason)
   }
 
   def executorAdded(execId: String, host: String) {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
index 818b95d67f6be..62af9031b9f8b 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala
@@ -709,6 +709,11 @@ private[spark] class TaskSetManager(
         }
         ef.exception
 
+      case e: ExecutorLostFailure if e.isNormalExit =>
+        logInfo(s"Task $tid failed because while it was being computed, its executor" +
+          s" exited normally. Not marking the task as failed.")
+        None
+
       case e: TaskFailedReason =>  // TaskResultLost, TaskKilled, and others
         logWarning(failureReason)
         None
@@ -722,10 +727,9 @@ private[spark] class TaskSetManager(
       put(info.executorId, clock.getTimeMillis())
     sched.dagScheduler.taskEnded(tasks(index), reason, null, null, info, taskMetrics)
     addPendingTask(index)
-    if (!isZombie && state != TaskState.KILLED && !reason.isInstanceOf[TaskCommitDenied]) {
-      // If a task failed because its attempt to commit was denied, do not count this failure
-      // towards failing the stage. This is intended to prevent spurious stage failures in cases
-      // where many speculative tasks are launched and denied to commit.
+    if (!isZombie && state != TaskState.KILLED
+        && reason.isInstanceOf[TaskFailedReason]
+        && reason.asInstanceOf[TaskFailedReason].shouldEventuallyFailJob) {
       assert (null != failureReason)
       numFailures(index) += 1
       if (numFailures(index) >= maxTaskFailures) {
@@ -778,7 +782,7 @@ private[spark] class TaskSetManager(
   }
 
   /** Called by TaskScheduler when an executor is lost so we can re-enqueue our tasks */
-  override def executorLost(execId: String, host: String) {
+  override def executorLost(execId: String, host: String, reason: ExecutorLossReason) {
     logInfo("Re-queueing tasks for " + execId + " from TaskSet " + taskSet.id)
 
     // Re-enqueue pending tasks for this host based on the status of the cluster. Note
@@ -809,9 +813,12 @@ private[spark] class TaskSetManager(
         }
       }
     }
-    // Also re-enqueue any tasks that were running on the node
     for ((tid, info) <- taskInfos if info.running && info.executorId == execId) {
-      handleFailedTask(tid, TaskState.FAILED, ExecutorLostFailure(execId))
+      val isNormalExit: Boolean = reason match {
+        case exited: ExecutorExited => exited.isNormalExit
+        case _ => false
+      }
+      handleFailedTask(tid, TaskState.FAILED, ExecutorLostFailure(info.executorId, isNormalExit))
     }
     // recalculate valid locality levels and waits when executor is lost
     recomputeLocality()
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
index 06f5438433b6e..d94743677783f 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedClusterMessage.scala
@@ -21,6 +21,7 @@ import java.nio.ByteBuffer
 
 import org.apache.spark.TaskState.TaskState
 import org.apache.spark.rpc.RpcEndpointRef
+import org.apache.spark.scheduler.ExecutorLossReason
 import org.apache.spark.util.{SerializableBuffer, Utils}
 
 private[spark] sealed trait CoarseGrainedClusterMessage extends Serializable
@@ -70,7 +71,8 @@ private[spark] object CoarseGrainedClusterMessages {
 
   case object StopExecutors extends CoarseGrainedClusterMessage
 
-  case class RemoveExecutor(executorId: String, reason: String) extends CoarseGrainedClusterMessage
+  case class RemoveExecutor(executorId: String, reason: ExecutorLossReason)
+    extends CoarseGrainedClusterMessage
 
   case class SetupDriver(driver: RpcEndpointRef) extends CoarseGrainedClusterMessage
 
@@ -92,6 +94,10 @@ private[spark] object CoarseGrainedClusterMessages {
       hostToLocalTaskCount: Map[String, Int])
     extends CoarseGrainedClusterMessage
 
+  // Check if an executor was force-killed but for a normal reason.
+  // This could be the case if the executor is preempted, for instance.
+  case class GetExecutorLossReason(executorId: String) extends CoarseGrainedClusterMessage
+
   case class KillExecutors(executorIds: Seq[String]) extends CoarseGrainedClusterMessage
 
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
index 5730a87f960a0..18771f79b44bb 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
@@ -26,6 +26,7 @@ import org.apache.spark.rpc._
 import org.apache.spark.{ExecutorAllocationClient, Logging, SparkEnv, SparkException, TaskState}
 import org.apache.spark.scheduler._
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
+import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.ENDPOINT_NAME
 import org.apache.spark.util.{ThreadUtils, SerializableBuffer, AkkaUtils, Utils}
 
 /**
@@ -82,7 +83,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
 
     override protected def log = CoarseGrainedSchedulerBackend.this.log
 
-    private val addressToExecutorId = new HashMap[RpcAddress, String]
+    protected val addressToExecutorId = new HashMap[RpcAddress, String]
 
     private val reviveThread =
       ThreadUtils.newDaemonSingleThreadScheduledExecutor("driver-revive-thread")
@@ -128,6 +129,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
     }
 
     override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
+
       case RegisterExecutor(executorId, executorRef, hostPort, cores, logUrls) =>
         Utils.checkHostPort(hostPort, "Host port expected " + hostPort)
         if (executorDataMap.contains(executorId)) {
@@ -185,8 +187,9 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
     }
 
     override def onDisconnected(remoteAddress: RpcAddress): Unit = {
-      addressToExecutorId.get(remoteAddress).foreach(removeExecutor(_,
-        "remote Rpc client disassociated"))
+      addressToExecutorId
+        .get(remoteAddress)
+        .foreach(removeExecutor(_, SlaveLost("remote Rpc client disassociated")))
     }
 
     // Make fake resource offers on just one executor
@@ -227,7 +230,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
     }
 
     // Remove a disconnected slave from the cluster
-    def removeExecutor(executorId: String, reason: String): Unit = {
+    def removeExecutor(executorId: String, reason: ExecutorLossReason): Unit = {
       executorDataMap.get(executorId) match {
         case Some(executorInfo) =>
           // This must be synchronized because variables mutated
@@ -239,9 +242,9 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
           }
           totalCoreCount.addAndGet(-executorInfo.totalCores)
           totalRegisteredExecutors.addAndGet(-1)
-          scheduler.executorLost(executorId, SlaveLost(reason))
+          scheduler.executorLost(executorId, reason)
           listenerBus.post(
-            SparkListenerExecutorRemoved(System.currentTimeMillis(), executorId, reason))
+            SparkListenerExecutorRemoved(System.currentTimeMillis(), executorId, reason.toString))
         case None => logInfo(s"Asked to remove non-existent executor $executorId")
       }
     }
@@ -263,8 +266,11 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
     }
 
     // TODO (prashant) send conf instead of properties
-    driverEndpoint = rpcEnv.setupEndpoint(
-      CoarseGrainedSchedulerBackend.ENDPOINT_NAME, new DriverEndpoint(rpcEnv, properties))
+    driverEndpoint = rpcEnv.setupEndpoint(ENDPOINT_NAME, createDriverEndpoint(properties))
+  }
+
+  protected def createDriverEndpoint(properties: Seq[(String, String)]): DriverEndpoint = {
+    new DriverEndpoint(rpcEnv, properties)
   }
 
   def stopExecutors() {
@@ -304,7 +310,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, val rpcEnv: Rp
   }
 
   // Called by subclasses when notified of a lost worker
-  def removeExecutor(executorId: String, reason: String) {
+  def removeExecutor(executorId: String, reason: ExecutorLossReason) {
     try {
       driverEndpoint.askWithRetry[Boolean](RemoveExecutor(executorId, reason))
     } catch {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
index bbe51b4a09a22..27491ecf8b97d 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala
@@ -23,7 +23,7 @@ import org.apache.spark.rpc.RpcAddress
 import org.apache.spark.{Logging, SparkConf, SparkContext, SparkEnv}
 import org.apache.spark.deploy.{ApplicationDescription, Command}
 import org.apache.spark.deploy.client.{AppClient, AppClientListener}
-import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason, SlaveLost, TaskSchedulerImpl}
+import org.apache.spark.scheduler._
 import org.apache.spark.util.Utils
 
 private[spark] class SparkDeploySchedulerBackend(
@@ -135,11 +135,11 @@ private[spark] class SparkDeploySchedulerBackend(
 
   override def executorRemoved(fullId: String, message: String, exitStatus: Option[Int]) {
     val reason: ExecutorLossReason = exitStatus match {
-      case Some(code) => ExecutorExited(code)
+      case Some(code) => ExecutorExited(code, isNormalExit = true, message)
       case None => SlaveLost(message)
     }
     logInfo("Executor %s removed: %s".format(fullId, message))
-    removeExecutor(fullId.split("/")(1), reason.toString)
+    removeExecutor(fullId.split("/")(1), reason)
   }
 
   override def sufficientResourcesRegistered(): Boolean = {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
index 044f6288fabdd..6a4b536dee191 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/YarnSchedulerBackend.scala
@@ -17,12 +17,13 @@
 
 package org.apache.spark.scheduler.cluster
 
+import scala.collection.mutable.ArrayBuffer
 import scala.concurrent.{Future, ExecutionContext}
 
 import org.apache.spark.{Logging, SparkContext}
 import org.apache.spark.rpc._
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
-import org.apache.spark.scheduler.TaskSchedulerImpl
+import org.apache.spark.scheduler._
 import org.apache.spark.ui.JettyUtils
 import org.apache.spark.util.{ThreadUtils, RpcUtils}
 
@@ -43,8 +44,10 @@ private[spark] abstract class YarnSchedulerBackend(
 
   protected var totalExpectedExecutors = 0
 
-  private val yarnSchedulerEndpoint = rpcEnv.setupEndpoint(
-    YarnSchedulerBackend.ENDPOINT_NAME, new YarnSchedulerEndpoint(rpcEnv))
+  private val yarnSchedulerEndpoint = new YarnSchedulerEndpoint(rpcEnv)
+
+  private val yarnSchedulerEndpointRef = rpcEnv.setupEndpoint(
+    YarnSchedulerBackend.ENDPOINT_NAME, yarnSchedulerEndpoint)
 
   private implicit val askTimeout = RpcUtils.askRpcTimeout(sc.conf)
 
@@ -53,7 +56,7 @@ private[spark] abstract class YarnSchedulerBackend(
    * This includes executors already pending or running.
    */
   override def doRequestTotalExecutors(requestedTotal: Int): Boolean = {
-    yarnSchedulerEndpoint.askWithRetry[Boolean](
+    yarnSchedulerEndpointRef.askWithRetry[Boolean](
       RequestExecutors(requestedTotal, localityAwareTasks, hostToLocalTaskCount))
   }
 
@@ -61,7 +64,7 @@ private[spark] abstract class YarnSchedulerBackend(
    * Request that the ApplicationMaster kill the specified executors.
    */
   override def doKillExecutors(executorIds: Seq[String]): Boolean = {
-    yarnSchedulerEndpoint.askWithRetry[Boolean](KillExecutors(executorIds))
+    yarnSchedulerEndpointRef.askWithRetry[Boolean](KillExecutors(executorIds))
   }
 
   override def sufficientResourcesRegistered(): Boolean = {
@@ -90,6 +93,41 @@ private[spark] abstract class YarnSchedulerBackend(
     }
   }
 
+  override def createDriverEndpoint(properties: Seq[(String, String)]): DriverEndpoint = {
+    new YarnDriverEndpoint(rpcEnv, properties)
+  }
+
+  /**
+   * Override the DriverEndpoint to add extra logic for the case when an executor is disconnected.
+   * This endpoint communicates with the executors and queries the AM for an executor's exit
+   * status when the executor is disconnected.
+   */
+  private class YarnDriverEndpoint(rpcEnv: RpcEnv, sparkProperties: Seq[(String, String)])
+      extends DriverEndpoint(rpcEnv, sparkProperties) {
+
+    /**
+     * When onDisconnected is received at the driver endpoint, the superclass DriverEndpoint
+     * handles it by assuming the Executor was lost for a bad reason and removes the executor
+     * immediately.
+     *
+     * In YARN's case however it is crucial to talk to the application master and ask why the
+     * executor had exited. In particular, the executor may have exited due to the executor
+     * having been preempted. If the executor "exited normally" according to the application
+     * master then we pass that information down to the TaskSetManager to inform the
+     * TaskSetManager that tasks on that lost executor should not count towards a job failure.
+     *
+     * TODO there's a race condition where while we are querying the ApplicationMaster for
+     * the executor loss reason, there is the potential that tasks will be scheduled on
+     * the executor that failed. We should fix this by having this onDisconnected event
+     * also "blacklist" executors so that tasks are not assigned to them.
+     */
+    override def onDisconnected(rpcAddress: RpcAddress): Unit = {
+      addressToExecutorId.get(rpcAddress).foreach { executorId =>
+        yarnSchedulerEndpoint.handleExecutorDisconnectedFromDriver(executorId, rpcAddress)
+      }
+    }
+  }
+
   /**
    * An [[RpcEndpoint]] that communicates with the ApplicationMaster.
    */
@@ -101,6 +139,33 @@ private[spark] abstract class YarnSchedulerBackend(
       ThreadUtils.newDaemonCachedThreadPool("yarn-scheduler-ask-am-thread-pool")
     implicit val askAmExecutor = ExecutionContext.fromExecutor(askAmThreadPool)
 
+    private[YarnSchedulerBackend] def handleExecutorDisconnectedFromDriver(
+        executorId: String,
+        executorRpcAddress: RpcAddress): Unit = {
+      amEndpoint match {
+        case Some(am) =>
+          val lossReasonRequest = GetExecutorLossReason(executorId)
+          val future = am.ask[ExecutorLossReason](lossReasonRequest, askTimeout)
+          future onSuccess {
+            case reason: ExecutorLossReason => {
+              driverEndpoint.askWithRetry[Boolean](RemoveExecutor(executorId, reason))
+            }
+          }
+          future onFailure {
+            case NonFatal(e) => {
+              logWarning(s"Attempted to get executor loss reason" +
+                s" for executor id ${executorId} at RPC address ${executorRpcAddress}," +
+                s" but got no response. Marking as slave lost.", e)
+              driverEndpoint.askWithRetry[Boolean](RemoveExecutor(executorId, SlaveLost()))
+            }
+            case t => throw t
+          }
+        case None =>
+          logWarning("Attempted to check for an executor loss reason" +
+            " before the AM has registered!")
+      }
+    }
+
     override def receive: PartialFunction[Any, Unit] = {
       case RegisterClusterManager(am) =>
         logInfo(s"ApplicationMaster registered as $am")
@@ -113,6 +178,7 @@ private[spark] abstract class YarnSchedulerBackend(
         removeExecutor(executorId, reason)
     }
 
+
     override def receiveAndReply(context: RpcCallContext): PartialFunction[Any, Unit] = {
       case r: RequestExecutors =>
         amEndpoint match {
@@ -143,7 +209,6 @@ private[spark] abstract class YarnSchedulerBackend(
             logWarning("Attempted to kill executors before the AM has registered!")
             context.reply(false)
         }
-
     }
 
     override def onDisconnected(remoteAddress: RpcAddress): Unit = {
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index 452c32d5411cd..65df8874774ca 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -32,7 +32,7 @@ import org.apache.spark.{SecurityManager, SparkContext, SparkEnv, SparkException
 import org.apache.spark.network.netty.SparkTransportConf
 import org.apache.spark.network.shuffle.mesos.MesosExternalShuffleClient
 import org.apache.spark.rpc.RpcAddress
-import org.apache.spark.scheduler.TaskSchedulerImpl
+import org.apache.spark.scheduler.{SlaveLost, TaskSchedulerImpl}
 import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend
 import org.apache.spark.util.Utils
 
@@ -364,7 +364,7 @@ private[spark] class CoarseMesosSchedulerBackend(
         if (slaveIdToTaskId.containsKey(slaveId)) {
           val taskId: Int = slaveIdToTaskId.get(slaveId)
           taskIdToSlaveId.remove(taskId)
-          removeExecutor(sparkExecutorId(slaveId, taskId.toString), reason)
+          removeExecutor(sparkExecutorId(slaveId, taskId.toString), SlaveLost(reason))
         }
         // TODO: This assumes one Spark executor per Mesos slave,
         // which may no longer be true after SPARK-5095
diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index 2e424054be785..18da6d2491280 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -390,7 +390,7 @@ private[spark] class MesosSchedulerBackend(
                             slaveId: SlaveID, status: Int) {
     logInfo("Executor lost: %s, marking slave %s as lost".format(executorId.getValue,
                                                                  slaveId.getValue))
-    recordSlaveLost(d, slaveId, ExecutorExited(status))
+    recordSlaveLost(d, slaveId, ExecutorExited(status, isNormalExit = false))
   }
 
   override def killTask(taskId: Long, executorId: String, interruptThread: Boolean): Unit = {
diff --git a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
index cbc94fd6d54d9..24f78744ad74c 100644
--- a/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
+++ b/core/src/main/scala/org/apache/spark/util/JsonProtocol.scala
@@ -362,8 +362,9 @@ private[spark] object JsonProtocol {
         ("Stack Trace" -> stackTrace) ~
         ("Full Stack Trace" -> exceptionFailure.fullStackTrace) ~
         ("Metrics" -> metrics)
-      case ExecutorLostFailure(executorId) =>
-        ("Executor ID" -> executorId)
+      case ExecutorLostFailure(executorId, isNormalExit) =>
+        ("Executor ID" -> executorId) ~
+        ("Normal Exit" -> isNormalExit)
       case _ => Utils.emptyJson
     }
     ("Reason" -> reason) ~ json
@@ -794,8 +795,10 @@ private[spark] object JsonProtocol {
       case `taskResultLost` => TaskResultLost
       case `taskKilled` => TaskKilled
       case `executorLostFailure` =>
+        val isNormalExit = Utils.jsonOption(json \ "Normal Exit").
+          map(_.extract[Boolean])
         val executorId = Utils.jsonOption(json \ "Executor ID").map(_.extract[String])
-        ExecutorLostFailure(executorId.getOrElse("Unknown"))
+        ExecutorLostFailure(executorId.getOrElse("Unknown"), isNormalExit.getOrElse(false))
       case `unknownReason` => UnknownReason
     }
   }
diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
index edbdb485c5ea4..f0eadf240943e 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala
@@ -334,7 +334,7 @@ class TaskSetManagerSuite extends SparkFunSuite with LocalSparkContext with Logg
 
     // Now mark host2 as dead
     sched.removeExecutor("exec2")
-    manager.executorLost("exec2", "host2")
+    manager.executorLost("exec2", "host2", SlaveLost())
 
     // nothing should be chosen
     assert(manager.resourceOffer("exec1", "host1", ANY) === None)
@@ -504,13 +504,36 @@ class TaskSetManagerSuite extends SparkFunSuite with LocalSparkContext with Logg
       Array(PROCESS_LOCAL, NODE_LOCAL, NO_PREF, RACK_LOCAL, ANY)))
     // test if the valid locality is recomputed when the executor is lost
     sched.removeExecutor("execC")
-    manager.executorLost("execC", "host2")
+    manager.executorLost("execC", "host2", SlaveLost())
     assert(manager.myLocalityLevels.sameElements(Array(NODE_LOCAL, NO_PREF, ANY)))
     sched.removeExecutor("execD")
-    manager.executorLost("execD", "host1")
+    manager.executorLost("execD", "host1", SlaveLost())
     assert(manager.myLocalityLevels.sameElements(Array(NO_PREF, ANY)))
   }
 
+  test("Executors are added but exit normally while running tasks") {
+    sc = new SparkContext("local", "test")
+    val sched = new FakeTaskScheduler(sc)
+    val taskSet = FakeTask.createTaskSet(4,
+      Seq(TaskLocation("host1", "execA")),
+      Seq(TaskLocation("host1", "execB")),
+      Seq(TaskLocation("host2", "execC")),
+      Seq())
+    val manager = new TaskSetManager(sched, taskSet, 1, new ManualClock)
+    sched.addExecutor("execA", "host1")
+    manager.executorAdded()
+    sched.addExecutor("execC", "host2")
+    manager.executorAdded()
+    assert(manager.resourceOffer("exec1", "host1", ANY).isDefined)
+    sched.removeExecutor("execA")
+    manager.executorLost("execA", "host1", ExecutorExited(143, true, "Normal termination"))
+    assert(!sched.taskSetsFailed.contains(taskSet.id))
+    assert(manager.resourceOffer("execC", "host2", ANY).isDefined)
+    sched.removeExecutor("execC")
+    manager.executorLost("execC", "host2", ExecutorExited(1, false, "Abnormal termination"))
+    assert(sched.taskSetsFailed.contains(taskSet.id))
+  }
+
   test("test RACK_LOCAL tasks") {
     // Assign host1 to rack1
     FakeRackUtil.assignHostToRack("host1", "rack1")
@@ -721,8 +744,8 @@ class TaskSetManagerSuite extends SparkFunSuite with LocalSparkContext with Logg
     assert(manager.resourceOffer("execB.2", "host2", ANY) !== None)
     sched.removeExecutor("execA")
     sched.removeExecutor("execB.2")
-    manager.executorLost("execA", "host1")
-    manager.executorLost("execB.2", "host2")
+    manager.executorLost("execA", "host1", SlaveLost())
+    manager.executorLost("execB.2", "host2", SlaveLost())
     clock.advance(LOCALITY_WAIT_MS * 4)
     sched.addExecutor("execC", "host3")
     manager.executorAdded()
diff --git a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
index 343a4139b0ca8..47e548ef0d442 100644
--- a/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
+++ b/core/src/test/scala/org/apache/spark/util/JsonProtocolSuite.scala
@@ -151,7 +151,7 @@ class JsonProtocolSuite extends SparkFunSuite {
     testTaskEndReason(exceptionFailure)
     testTaskEndReason(TaskResultLost)
     testTaskEndReason(TaskKilled)
-    testTaskEndReason(ExecutorLostFailure("100"))
+    testTaskEndReason(ExecutorLostFailure("100", true))
     testTaskEndReason(UnknownReason)
 
     // BlockId
@@ -295,10 +295,10 @@ class JsonProtocolSuite extends SparkFunSuite {
 
   test("ExecutorLostFailure backward compatibility") {
     // ExecutorLostFailure in Spark 1.1.0 does not have an "Executor ID" property.
-    val executorLostFailure = ExecutorLostFailure("100")
+    val executorLostFailure = ExecutorLostFailure("100", true)
     val oldEvent = JsonProtocol.taskEndReasonToJson(executorLostFailure)
       .removeField({ _._1 == "Executor ID" })
-    val expectedExecutorLostFailure = ExecutorLostFailure("Unknown")
+    val expectedExecutorLostFailure = ExecutorLostFailure("Unknown", true)
     assert(expectedExecutorLostFailure === JsonProtocol.taskEndReasonFromJson(oldEvent))
   }
 
@@ -577,8 +577,10 @@ class JsonProtocolSuite extends SparkFunSuite {
         assertOptionEquals(r1.metrics, r2.metrics, assertTaskMetricsEquals)
       case (TaskResultLost, TaskResultLost) =>
       case (TaskKilled, TaskKilled) =>
-      case (ExecutorLostFailure(execId1), ExecutorLostFailure(execId2)) =>
+      case (ExecutorLostFailure(execId1, isNormalExit1),
+          ExecutorLostFailure(execId2, isNormalExit2)) =>
         assert(execId1 === execId2)
+        assert(isNormalExit1 === isNormalExit2)
       case (UnknownReason, UnknownReason) =>
       case _ => fail("Task end reasons don't match in types!")
     }
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
index 991b5cec00bd8..93621b44c9183 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala
@@ -590,6 +590,13 @@ private[spark] class ApplicationMaster(
           case None => logWarning("Container allocator is not ready to kill executors yet.")
         }
         context.reply(true)
+
+      case GetExecutorLossReason(eid) =>
+        Option(allocator) match {
+          case Some(a) => a.enqueueGetLossReasonRequest(eid, context)
+          case None => logWarning(s"Container allocator is not ready to find" +
+            s" executor loss reasons yet.")
+        }
     }
 
     override def onDisconnected(remoteAddress: RpcAddress): Unit = {
diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
index 5f897cbcb4e9f..fd88b8b7fe3b9 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocator.scala
@@ -21,8 +21,9 @@ import java.util.Collections
 import java.util.concurrent._
 import java.util.regex.Pattern
 
-import scala.collection.JavaConverters._
+import scala.collection.mutable
 import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet}
+import scala.collection.JavaConverters._
 
 import com.google.common.util.concurrent.ThreadFactoryBuilder
 
@@ -36,8 +37,9 @@ import org.apache.log4j.{Level, Logger}
 
 import org.apache.spark.{Logging, SecurityManager, SparkConf}
 import org.apache.spark.deploy.yarn.YarnSparkHadoopUtil._
-import org.apache.spark.rpc.RpcEndpointRef
-import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
+import org.apache.spark.rpc.{RpcCallContext, RpcEndpointRef}
+import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason}
+import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages.RemoveExecutor
 import org.apache.spark.util.Utils
 
 /**
@@ -93,6 +95,11 @@ private[yarn] class YarnAllocator(
       sparkConf.getInt("spark.executor.instances", YarnSparkHadoopUtil.DEFAULT_NUMBER_EXECUTORS)
     }
 
+  // Executor loss reason requests that are pending - maps from executor ID for inquiry to a
+  // list of requesters that should be responded to once we find out why the given executor
+  // was lost.
+  private val pendingLossReasonRequests = new HashMap[String, mutable.Buffer[RpcCallContext]]
+
   // Keep track of which container is running which executor to remove the executors later
   // Visible for testing.
   private[yarn] val executorIdToContainer = new HashMap[String, Container]
@@ -235,9 +242,7 @@ private[yarn] class YarnAllocator(
     val completedContainers = allocateResponse.getCompletedContainersStatuses()
     if (completedContainers.size > 0) {
       logDebug("Completed %d containers".format(completedContainers.size))
-
       processCompletedContainers(completedContainers.asScala)
-
       logDebug("Finished processing %d completed containers. Current running executor count: %d."
         .format(completedContainers.size, numExecutorsRunning))
     }
@@ -429,7 +434,7 @@ private[yarn] class YarnAllocator(
     for (completedContainer <- completedContainers) {
       val containerId = completedContainer.getContainerId
       val alreadyReleased = releasedContainers.remove(containerId)
-      if (!alreadyReleased) {
+      val exitReason = if (!alreadyReleased) {
         // Decrement the number of executors running. The next iteration of
         // the ApplicationMaster's reporting thread will take care of allocating.
         numExecutorsRunning -= 1
@@ -440,22 +445,42 @@ private[yarn] class YarnAllocator(
         // Hadoop 2.2.X added a ContainerExitStatus we should switch to use
         // there are some exit status' we shouldn't necessarily count against us, but for
         // now I think its ok as none of the containers are expected to exit
-        if (completedContainer.getExitStatus == ContainerExitStatus.PREEMPTED) {
-          logInfo("Container preempted: " + containerId)
-        } else if (completedContainer.getExitStatus == -103) { // vmem limit exceeded
-          logWarning(memLimitExceededLogMessage(
-            completedContainer.getDiagnostics,
-            VMEM_EXCEEDED_PATTERN))
-        } else if (completedContainer.getExitStatus == -104) { // pmem limit exceeded
-          logWarning(memLimitExceededLogMessage(
-            completedContainer.getDiagnostics,
-            PMEM_EXCEEDED_PATTERN))
-        } else if (completedContainer.getExitStatus != 0) {
-          logInfo("Container marked as failed: " + containerId +
-            ". Exit status: " + completedContainer.getExitStatus +
-            ". Diagnostics: " + completedContainer.getDiagnostics)
-          numExecutorsFailed += 1
+        val exitStatus = completedContainer.getExitStatus
+        val (isNormalExit, containerExitReason) = exitStatus match {
+          case ContainerExitStatus.SUCCESS =>
+            (true, s"Executor for container $containerId exited normally.")
+          case ContainerExitStatus.PREEMPTED =>
+            // Preemption should count as a normal exit, since YARN preempts containers merely
+            // to do resource sharing, and tasks that fail due to preempted executors could
+            // just as easily finish on any other executor. See SPARK-8167.
+            (true, s"Container $containerId was preempted.")
+          // Should probably still count memory exceeded exit codes towards task failures
+          case VMEM_EXCEEDED_EXIT_CODE =>
+            (false, memLimitExceededLogMessage(
+              completedContainer.getDiagnostics,
+              VMEM_EXCEEDED_PATTERN))
+          case PMEM_EXCEEDED_EXIT_CODE =>
+            (false, memLimitExceededLogMessage(
+              completedContainer.getDiagnostics,
+              PMEM_EXCEEDED_PATTERN))
+          case unknown =>
+            numExecutorsFailed += 1
+            (false, "Container marked as failed: " + containerId +
+              ". Exit status: " + completedContainer.getExitStatus +
+              ". Diagnostics: " + completedContainer.getDiagnostics)
+
+        }
+        if (isNormalExit) {
+          logInfo(containerExitReason)
+        } else {
+          logWarning(containerExitReason)
         }
+        ExecutorExited(0, isNormalExit, containerExitReason)
+      } else {
+        // If we have already released this container, then it must mean
+        // that the driver has explicitly requested it to be killed
+        ExecutorExited(completedContainer.getExitStatus, isNormalExit = true,
+          s"Container $containerId exited from explicit termination request.")
       }
 
       if (allocatedContainerToHostMap.contains(containerId)) {
@@ -474,18 +499,35 @@ private[yarn] class YarnAllocator(
 
       containerIdToExecutorId.remove(containerId).foreach { eid =>
         executorIdToContainer.remove(eid)
-
+        pendingLossReasonRequests.remove(eid).foreach { pendingRequests =>
+          // Notify application of executor loss reasons so it can decide whether it should abort
+          pendingRequests.foreach(_.reply(exitReason))
+        }
         if (!alreadyReleased) {
           // The executor could have gone away (like no route to host, node failure, etc)
           // Notify backend about the failure of the executor
           numUnexpectedContainerRelease += 1
-          driverRef.send(RemoveExecutor(eid,
-            s"Yarn deallocated the executor $eid (container $containerId)"))
+          driverRef.send(RemoveExecutor(eid, exitReason))
         }
       }
     }
   }
 
+  /**
+   * Register that some RpcCallContext has asked the AM why the executor was lost. Note that
+   * we can only find the loss reason to send back in the next call to allocateResources().
+   */
+  private[yarn] def enqueueGetLossReasonRequest(
+      eid: String,
+      context: RpcCallContext): Unit = synchronized {
+    if (executorIdToContainer.contains(eid)) {
+      pendingLossReasonRequests
+        .getOrElseUpdate(eid, new ArrayBuffer[RpcCallContext]) += context
+    } else {
+      logWarning(s"Tried to get the loss reason for non-existent executor $eid")
+    }
+  }
+
   private def internalReleaseContainer(container: Container): Unit = {
     releasedContainers.add(container.getId())
     amClient.releaseAssignedContainer(container.getId())
@@ -501,6 +543,8 @@ private object YarnAllocator {
     Pattern.compile(s"$MEM_REGEX of $MEM_REGEX physical memory used")
   val VMEM_EXCEEDED_PATTERN =
     Pattern.compile(s"$MEM_REGEX of $MEM_REGEX virtual memory used")
+  val VMEM_EXCEEDED_EXIT_CODE = -103
+  val PMEM_EXCEEDED_EXIT_CODE = -104
 
   def memLimitExceededLogMessage(diagnostics: String, pattern: Pattern): String = {
     val matcher = pattern.matcher(diagnostics)

From f0562e8cdbab7ce40f3186da98595312252f8b5c Mon Sep 17 00:00:00 2001
From: Iulian Dragos <jaguarul@gmail.com>
Date: Thu, 10 Sep 2015 12:00:21 -0700
Subject: [PATCH 576/633] [SPARK-6350] [MESOS] Fine-grained mode scheduler
 respects mesosExecutor.cores

This is a regression introduced in #4960, this commit fixes it and adds a test.

tnachen andrewor14 please review, this should be an easy one.

Author: Iulian Dragos <jaguarul@gmail.com>

Closes #8653 from dragos/issue/mesos/fine-grained-maxExecutorCores.
---
 .../cluster/mesos/MesosSchedulerBackend.scala |  3 +-
 .../mesos/MesosSchedulerBackendSuite.scala    | 33 ++++++++++++++++++-
 2 files changed, 33 insertions(+), 3 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
index 18da6d2491280..8edf7007a5daf 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala
@@ -32,7 +32,6 @@ import org.apache.spark.scheduler._
 import org.apache.spark.scheduler.cluster.ExecutorInfo
 import org.apache.spark.util.Utils
 
-
 /**
  * A SchedulerBackend for running fine-grained tasks on Mesos. Each Spark task is mapped to a
  * separate Mesos task, allowing multiple applications to share cluster nodes both in space (tasks
@@ -127,7 +126,7 @@ private[spark] class MesosSchedulerBackend(
     }
     val builder = MesosExecutorInfo.newBuilder()
     val (resourcesAfterCpu, usedCpuResources) =
-      partitionResources(availableResources, "cpus", scheduler.CPUS_PER_TASK)
+      partitionResources(availableResources, "cpus", mesosExecutorCores)
     val (resourcesAfterMem, usedMemResources) =
       partitionResources(resourcesAfterCpu.asJava, "mem", calculateTotalMemory(sc))
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
index 319b3173e7a6e..c4dc560031207 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendSuite.scala
@@ -42,6 +42,38 @@ import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SparkFunSui
 
 class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext with MockitoSugar {
 
+  test("Use configured mesosExecutor.cores for ExecutorInfo") {
+    val mesosExecutorCores = 3
+    val conf = new SparkConf
+    conf.set("spark.mesos.mesosExecutor.cores", mesosExecutorCores.toString)
+
+    val listenerBus = mock[LiveListenerBus]
+    listenerBus.post(
+      SparkListenerExecutorAdded(anyLong, "s1", new ExecutorInfo("host1", 2, Map.empty)))
+
+    val sc = mock[SparkContext]
+    when(sc.getSparkHome()).thenReturn(Option("/spark-home"))
+
+    when(sc.conf).thenReturn(conf)
+    when(sc.executorEnvs).thenReturn(new mutable.HashMap[String, String])
+    when(sc.executorMemory).thenReturn(100)
+    when(sc.listenerBus).thenReturn(listenerBus)
+    val taskScheduler = mock[TaskSchedulerImpl]
+    when(taskScheduler.CPUS_PER_TASK).thenReturn(2)
+
+    val mesosSchedulerBackend = new MesosSchedulerBackend(taskScheduler, sc, "master")
+
+    val resources = Arrays.asList(
+      mesosSchedulerBackend.createResource("cpus", 4),
+      mesosSchedulerBackend.createResource("mem", 1024))
+    // uri is null.
+    val (executorInfo, _) = mesosSchedulerBackend.createExecutorInfo(resources, "test-id")
+    val executorResources = executorInfo.getResourcesList
+    val cpus = executorResources.asScala.find(_.getName.equals("cpus")).get.getScalar.getValue
+
+    assert(cpus === mesosExecutorCores)
+  }
+
   test("check spark-class location correctly") {
     val conf = new SparkConf
     conf.set("spark.mesos.executor.home" , "/mesos-home")
@@ -263,7 +295,6 @@ class MesosSchedulerBackendSuite extends SparkFunSuite with LocalSparkContext wi
       .setSlaveId(SlaveID.newBuilder().setValue(s"s${id.toString}"))
       .setHostname(s"host${id.toString}").build()
 
-
     val mesosOffers = new java.util.ArrayList[Offer]
     mesosOffers.add(offer)
 

From a5ef2d0600d5e23ca05fabc1005bb81e5ada0727 Mon Sep 17 00:00:00 2001
From: Akash Mishra <akash.mishra20@gmail.com>
Date: Thu, 10 Sep 2015 12:03:11 -0700
Subject: [PATCH 577/633] [SPARK-10514] [MESOS] waiting for min no of total
 cores acquired by Spark by implementing the sufficientResourcesRegistered
 method

spark.scheduler.minRegisteredResourcesRatio configuration parameter works for YARN mode but not for Mesos Coarse grained mode.

If the parameter specified default value of 0 will be set for spark.scheduler.minRegisteredResourcesRatio in base class and this method will always return true.

There are no existing test for YARN mode too. Hence not added test for the same.

Author: Akash Mishra <akash.mishra20@gmail.com>

Closes #8672 from SleepyThread/master.
---
 .../cluster/mesos/CoarseMesosSchedulerBackend.scala          | 4 ++++
 docs/configuration.md                                        | 5 +++--
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
index 65df8874774ca..65cb5016cfcc9 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala
@@ -222,6 +222,10 @@ private[spark] class CoarseMesosSchedulerBackend(
     markRegistered()
   }
 
+  override def sufficientResourcesRegistered(): Boolean = {
+    totalCoresAcquired >= maxCores * minRegisteredRatio
+  }
+
   override def disconnected(d: SchedulerDriver) {}
 
   override def reregistered(d: SchedulerDriver, masterInfo: MasterInfo) {}
diff --git a/docs/configuration.md b/docs/configuration.md
index 0b1a273916314..1a701f18881fe 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -1112,10 +1112,11 @@ Apart from these, the following properties are also available, and may be useful
 </tr>
 <tr>
   <td><code>spark.scheduler.minRegisteredResourcesRatio</code></td>
-  <td>0.8 for YARN mode; 0.0 otherwise</td>
+  <td>0.8 for YARN mode; 0.0 for standalone mode and Mesos coarse-grained mode</td>
   <td>
     The minimum ratio of registered resources (registered resources / total expected resources)
-    (resources are executors in yarn mode, CPU cores in standalone mode)
+    (resources are executors in yarn mode, CPU cores in standalone mode and Mesos coarsed-grained
+     mode ['spark.cores.max' value is total expected resources for Mesos coarse-grained mode] )
     to wait for before scheduling begins. Specified as a double between 0.0 and 1.0.
     Regardless of whether the minimum ratio of resources has been reached,
     the maximum amount of time it will wait before scheduling begins is controlled by config

From d88abb7e212fb55f9b0398a0f76a753c86b85cf1 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Thu, 10 Sep 2015 12:06:49 -0700
Subject: [PATCH 578/633] [SPARK-9990] [SQL] Create local hash join operator

This PR includes the following changes:
- Add SQLConf to LocalNode
- Add HashJoinNode
- Add ConvertToUnsafeNode and ConvertToSafeNode.scala to test unsafe hash join.

Author: zsxwing <zsxwing@gmail.com>

Closes #8535 from zsxwing/SPARK-9990.
---
 .../sql/execution/joins/HashedRelation.scala  |   4 +-
 .../execution/local/ConvertToSafeNode.scala   |  40 +++++
 .../execution/local/ConvertToUnsafeNode.scala |  40 +++++
 .../sql/execution/local/FilterNode.scala      |   4 +-
 .../sql/execution/local/HashJoinNode.scala    | 137 ++++++++++++++++++
 .../spark/sql/execution/local/LimitNode.scala |   3 +-
 .../spark/sql/execution/local/LocalNode.scala |  83 ++++++++++-
 .../sql/execution/local/ProjectNode.scala     |   4 +-
 .../sql/execution/local/SeqScanNode.scala     |   4 +-
 .../spark/sql/execution/local/UnionNode.scala |   3 +-
 .../sql/execution/local/FilterNodeSuite.scala |   4 +-
 .../execution/local/HashJoinNodeSuite.scala   | 130 +++++++++++++++++
 .../sql/execution/local/LimitNodeSuite.scala  |   4 +-
 .../sql/execution/local/LocalNodeTest.scala   |   9 +-
 .../execution/local/ProjectNodeSuite.scala    |   4 +-
 .../sql/execution/local/UnionNodeSuite.scala  |   6 +-
 16 files changed, 455 insertions(+), 24 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToSafeNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToUnsafeNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 6c0196c21a0d1..0cff21ca618b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -38,7 +38,7 @@ import org.apache.spark.{SparkConf, SparkEnv}
  * Interface for a hashed relation by some key. Use [[HashedRelation.apply]] to create a concrete
  * object.
  */
-private[joins] sealed trait HashedRelation {
+private[execution] sealed trait HashedRelation {
   def get(key: InternalRow): Seq[InternalRow]
 
   // This is a helper method to implement Externalizable, and is used by
@@ -111,7 +111,7 @@ final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[InternalR
 // TODO(rxin): a version of [[HashedRelation]] backed by arrays for consecutive integer keys.
 
 
-private[joins] object HashedRelation {
+private[execution] object HashedRelation {
 
   def apply(
       input: Iterator[InternalRow],
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToSafeNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToSafeNode.scala
new file mode 100644
index 0000000000000..b31c5a863832e
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToSafeNode.scala
@@ -0,0 +1,40 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, FromUnsafeProjection, Projection}
+
+case class ConvertToSafeNode(conf: SQLConf, child: LocalNode) extends UnaryLocalNode(conf) {
+
+  override def output: Seq[Attribute] = child.output
+
+  private[this] var convertToSafe: Projection = _
+
+  override def open(): Unit = {
+    child.open()
+    convertToSafe = FromUnsafeProjection(child.schema)
+  }
+
+  override def next(): Boolean = child.next()
+
+  override def fetch(): InternalRow = convertToSafe(child.fetch())
+
+  override def close(): Unit = child.close()
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToUnsafeNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToUnsafeNode.scala
new file mode 100644
index 0000000000000..de2f4e661ab44
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ConvertToUnsafeNode.scala
@@ -0,0 +1,40 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Projection, UnsafeProjection}
+
+case class ConvertToUnsafeNode(conf: SQLConf, child: LocalNode) extends UnaryLocalNode(conf) {
+
+  override def output: Seq[Attribute] = child.output
+
+  private[this] var convertToUnsafe: Projection = _
+
+  override def open(): Unit = {
+    child.open()
+    convertToUnsafe = UnsafeProjection.create(child.schema)
+  }
+
+  override def next(): Boolean = child.next()
+
+  override def fetch(): InternalRow = convertToUnsafe(child.fetch())
+
+  override def close(): Unit = child.close()
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
index 81dd37c7da733..dd1113b6726cf 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/FilterNode.scala
@@ -17,12 +17,14 @@
 
 package org.apache.spark.sql.execution.local
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression}
 import org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate
 
 
-case class FilterNode(condition: Expression, child: LocalNode) extends UnaryLocalNode {
+case class FilterNode(conf: SQLConf, condition: Expression, child: LocalNode)
+  extends UnaryLocalNode(conf) {
 
   private[this] var predicate: (InternalRow) => Boolean = _
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
new file mode 100644
index 0000000000000..a3e68d6a7c341
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
@@ -0,0 +1,137 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.joins._
+import org.apache.spark.sql.execution.metric.SQLMetrics
+
+/**
+ * Much of this code is similar to [[org.apache.spark.sql.execution.joins.HashJoin]].
+ */
+case class HashJoinNode(
+    conf: SQLConf,
+    leftKeys: Seq[Expression],
+    rightKeys: Seq[Expression],
+    buildSide: BuildSide,
+    left: LocalNode,
+    right: LocalNode) extends BinaryLocalNode(conf) {
+
+  private[this] lazy val (buildNode, buildKeys, streamedNode, streamedKeys) = buildSide match {
+    case BuildLeft => (left, leftKeys, right, rightKeys)
+    case BuildRight => (right, rightKeys, left, leftKeys)
+  }
+
+  private[this] var currentStreamedRow: InternalRow = _
+  private[this] var currentHashMatches: Seq[InternalRow] = _
+  private[this] var currentMatchPosition: Int = -1
+
+  private[this] var joinRow: JoinedRow = _
+  private[this] var resultProjection: (InternalRow) => InternalRow = _
+
+  private[this] var hashed: HashedRelation = _
+  private[this] var joinKeys: Projection = _
+
+  override def output: Seq[Attribute] = left.output ++ right.output
+
+  private[this] def isUnsafeMode: Boolean = {
+    (codegenEnabled && unsafeEnabled
+      && UnsafeProjection.canSupport(buildKeys)
+      && UnsafeProjection.canSupport(schema))
+  }
+
+  private[this] def buildSideKeyGenerator: Projection = {
+    if (isUnsafeMode) {
+      UnsafeProjection.create(buildKeys, buildNode.output)
+    } else {
+      newMutableProjection(buildKeys, buildNode.output)()
+    }
+  }
+
+  private[this] def streamSideKeyGenerator: Projection = {
+    if (isUnsafeMode) {
+      UnsafeProjection.create(streamedKeys, streamedNode.output)
+    } else {
+      newMutableProjection(streamedKeys, streamedNode.output)()
+    }
+  }
+
+  override def open(): Unit = {
+    buildNode.open()
+    hashed = HashedRelation.apply(
+      new LocalNodeIterator(buildNode), SQLMetrics.nullLongMetric, buildSideKeyGenerator)
+    streamedNode.open()
+    joinRow = new JoinedRow
+    resultProjection = {
+      if (isUnsafeMode) {
+        UnsafeProjection.create(schema)
+      } else {
+        identity[InternalRow]
+      }
+    }
+    joinKeys = streamSideKeyGenerator
+  }
+
+  override def next(): Boolean = {
+    currentMatchPosition += 1
+    if (currentHashMatches == null || currentMatchPosition >= currentHashMatches.size) {
+      fetchNextMatch()
+    } else {
+      true
+    }
+  }
+
+  /**
+   * Populate `currentHashMatches` with build-side rows matching the next streamed row.
+   * @return whether matches are found such that subsequent calls to `fetch` are valid.
+   */
+  private def fetchNextMatch(): Boolean = {
+    currentHashMatches = null
+    currentMatchPosition = -1
+
+    while (currentHashMatches == null && streamedNode.next()) {
+      currentStreamedRow = streamedNode.fetch()
+      val key = joinKeys(currentStreamedRow)
+      if (!key.anyNull) {
+        currentHashMatches = hashed.get(key)
+      }
+    }
+
+    if (currentHashMatches == null) {
+      false
+    } else {
+      currentMatchPosition = 0
+      true
+    }
+  }
+
+  override def fetch(): InternalRow = {
+    val ret = buildSide match {
+      case BuildRight => joinRow(currentStreamedRow, currentHashMatches(currentMatchPosition))
+      case BuildLeft => joinRow(currentHashMatches(currentMatchPosition), currentStreamedRow)
+    }
+    resultProjection(ret)
+  }
+
+  override def close(): Unit = {
+    left.close()
+    right.close()
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
index fffc52abf6dd5..401b10a5ed307 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LimitNode.scala
@@ -17,11 +17,12 @@
 
 package org.apache.spark.sql.execution.local
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Attribute
 
 
-case class LimitNode(limit: Int, child: LocalNode) extends UnaryLocalNode {
+case class LimitNode(conf: SQLConf, limit: Int, child: LocalNode) extends UnaryLocalNode(conf) {
 
   private[this] var count = 0
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index 1c4469acbf264..c4f8ae304db39 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -17,9 +17,13 @@
 
 package org.apache.spark.sql.execution.local
 
-import org.apache.spark.sql.Row
+import scala.util.control.NonFatal
+
+import org.apache.spark.Logging
+import org.apache.spark.sql.{SQLConf, Row}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
-import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.types.StructType
 
@@ -29,7 +33,15 @@ import org.apache.spark.sql.types.StructType
  * Before consuming the iterator, open function must be called.
  * After consuming the iterator, close function must be called.
  */
-abstract class LocalNode extends TreeNode[LocalNode] {
+abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging {
+
+  protected val codegenEnabled: Boolean = conf.codegenEnabled
+
+  protected val unsafeEnabled: Boolean = conf.unsafeEnabled
+
+  lazy val schema: StructType = StructType.fromAttributes(output)
+
+  private[this] lazy val isTesting: Boolean = sys.props.contains("spark.testing")
 
   def output: Seq[Attribute]
 
@@ -73,17 +85,78 @@ abstract class LocalNode extends TreeNode[LocalNode] {
     }
     result
   }
+
+  protected def newMutableProjection(
+      expressions: Seq[Expression],
+      inputSchema: Seq[Attribute]): () => MutableProjection = {
+    log.debug(
+      s"Creating MutableProj: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
+    if (codegenEnabled) {
+      try {
+        GenerateMutableProjection.generate(expressions, inputSchema)
+      } catch {
+        case NonFatal(e) =>
+          if (isTesting) {
+            throw e
+          } else {
+            log.error("Failed to generate mutable projection, fallback to interpreted", e)
+            () => new InterpretedMutableProjection(expressions, inputSchema)
+          }
+      }
+    } else {
+      () => new InterpretedMutableProjection(expressions, inputSchema)
+    }
+  }
+
 }
 
 
-abstract class LeafLocalNode extends LocalNode {
+abstract class LeafLocalNode(conf: SQLConf) extends LocalNode(conf) {
   override def children: Seq[LocalNode] = Seq.empty
 }
 
 
-abstract class UnaryLocalNode extends LocalNode {
+abstract class UnaryLocalNode(conf: SQLConf) extends LocalNode(conf) {
 
   def child: LocalNode
 
   override def children: Seq[LocalNode] = Seq(child)
 }
+
+abstract class BinaryLocalNode(conf: SQLConf) extends LocalNode(conf) {
+
+  def left: LocalNode
+
+  def right: LocalNode
+
+  override def children: Seq[LocalNode] = Seq(left, right)
+}
+
+/**
+ * An thin wrapper around a [[LocalNode]] that provides an `Iterator` interface.
+ */
+private[local] class LocalNodeIterator(localNode: LocalNode) extends Iterator[InternalRow] {
+  private var nextRow: InternalRow = _
+
+  override def hasNext: Boolean = {
+    if (nextRow == null) {
+      val res = localNode.next()
+      if (res) {
+        nextRow = localNode.fetch()
+      }
+      res
+    } else {
+      true
+    }
+  }
+
+  override def next(): InternalRow = {
+    if (hasNext) {
+      val res = nextRow
+      nextRow = null
+      res
+    } else {
+      throw new NoSuchElementException
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
index 9b8a4fe493026..11529d6dd9b83 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ProjectNode.scala
@@ -17,11 +17,13 @@
 
 package org.apache.spark.sql.execution.local
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.{UnsafeProjection, Attribute, NamedExpression}
 
 
-case class ProjectNode(projectList: Seq[NamedExpression], child: LocalNode) extends UnaryLocalNode {
+case class ProjectNode(conf: SQLConf, projectList: Seq[NamedExpression], child: LocalNode)
+  extends UnaryLocalNode(conf) {
 
   private[this] var project: UnsafeProjection = _
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
index 242cb66e07b7f..b8467f6ae58e0 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SeqScanNode.scala
@@ -17,13 +17,15 @@
 
 package org.apache.spark.sql.execution.local
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Attribute
 
 /**
  * An operator that scans some local data collection in the form of Scala Seq.
  */
-case class SeqScanNode(output: Seq[Attribute], data: Seq[InternalRow]) extends LeafLocalNode {
+case class SeqScanNode(conf: SQLConf, output: Seq[Attribute], data: Seq[InternalRow])
+  extends LeafLocalNode(conf) {
 
   private[this] var iterator: Iterator[InternalRow] = _
   private[this] var currentRow: InternalRow = _
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
index ba4aa7671aebd..0f2b8303e7372 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/UnionNode.scala
@@ -17,10 +17,11 @@
 
 package org.apache.spark.sql.execution.local
 
+import org.apache.spark.sql.SQLConf
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions.Attribute
 
-case class UnionNode(children: Seq[LocalNode]) extends LocalNode {
+case class UnionNode(conf: SQLConf, children: Seq[LocalNode]) extends LocalNode(conf) {
 
   override def output: Seq[Attribute] = children.head.output
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
index 07209f3779248..a12670e347c25 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/FilterNodeSuite.scala
@@ -25,7 +25,7 @@ class FilterNodeSuite extends LocalNodeTest with SharedSQLContext {
     val condition = (testData.col("key") % 2) === 0
     checkAnswer(
       testData,
-      node => FilterNode(condition.expr, node),
+      node => FilterNode(conf, condition.expr, node),
       testData.filter(condition).collect()
     )
   }
@@ -34,7 +34,7 @@ class FilterNodeSuite extends LocalNodeTest with SharedSQLContext {
     val condition = (emptyTestData.col("key") % 2) === 0
     checkAnswer(
       emptyTestData,
-      node => FilterNode(condition.expr, node),
+      node => FilterNode(conf, condition.expr, node),
       emptyTestData.filter(condition).collect()
     )
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
new file mode 100644
index 0000000000000..43b6f06aead88
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
@@ -0,0 +1,130 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.execution.joins
+
+class HashJoinNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  private def wrapForUnsafe(
+      f: (LocalNode, LocalNode) => LocalNode): (LocalNode, LocalNode) => LocalNode = {
+    if (conf.unsafeEnabled) {
+      (left: LocalNode, right: LocalNode) => {
+        val _left = ConvertToUnsafeNode(conf, left)
+        val _right = ConvertToUnsafeNode(conf, right)
+        val r = f(_left, _right)
+        ConvertToSafeNode(conf, r)
+      }
+    } else {
+      f
+    }
+  }
+
+  def joinSuite(suiteName: String, confPairs: (String, String)*): Unit = {
+    test(s"$suiteName: inner join with one match per row") {
+      withSQLConf(confPairs: _*) {
+        checkAnswer2(
+          upperCaseData,
+          lowerCaseData,
+          wrapForUnsafe(
+            (node1, node2) => HashJoinNode(
+              conf,
+              Seq(upperCaseData.col("N").expr),
+              Seq(lowerCaseData.col("n").expr),
+              joins.BuildLeft,
+              node1,
+              node2)
+          ),
+          upperCaseData.join(lowerCaseData, $"n" === $"N").collect()
+        )
+      }
+    }
+
+    test(s"$suiteName: inner join with multiple matches") {
+      withSQLConf(confPairs: _*) {
+        val x = testData2.where($"a" === 1).as("x")
+        val y = testData2.where($"a" === 1).as("y")
+        checkAnswer2(
+          x,
+          y,
+          wrapForUnsafe(
+            (node1, node2) => HashJoinNode(
+              conf,
+              Seq(x.col("a").expr),
+              Seq(y.col("a").expr),
+              joins.BuildLeft,
+              node1,
+              node2)
+          ),
+          x.join(y).where($"x.a" === $"y.a").collect()
+        )
+      }
+    }
+
+    test(s"$suiteName: inner join, no matches") {
+      withSQLConf(confPairs: _*) {
+        val x = testData2.where($"a" === 1).as("x")
+        val y = testData2.where($"a" === 2).as("y")
+        checkAnswer2(
+          x,
+          y,
+          wrapForUnsafe(
+            (node1, node2) => HashJoinNode(
+              conf,
+              Seq(x.col("a").expr),
+              Seq(y.col("a").expr),
+              joins.BuildLeft,
+              node1,
+              node2)
+          ),
+          Nil
+        )
+      }
+    }
+
+    test(s"$suiteName: big inner join, 4 matches per row") {
+      withSQLConf(confPairs: _*) {
+        val bigData = testData.unionAll(testData).unionAll(testData).unionAll(testData)
+        val bigDataX = bigData.as("x")
+        val bigDataY = bigData.as("y")
+
+        checkAnswer2(
+          bigDataX,
+          bigDataY,
+          wrapForUnsafe(
+            (node1, node2) =>
+              HashJoinNode(
+                conf,
+                Seq(bigDataX.col("key").expr),
+                Seq(bigDataY.col("key").expr),
+                joins.BuildLeft,
+                node1,
+                node2)
+          ),
+          bigDataX.join(bigDataY).where($"x.key" === $"y.key").collect())
+      }
+    }
+  }
+
+  joinSuite(
+    "general", SQLConf.CODEGEN_ENABLED.key -> "false", SQLConf.UNSAFE_ENABLED.key -> "false")
+  joinSuite("tungsten", SQLConf.CODEGEN_ENABLED.key -> "true", SQLConf.UNSAFE_ENABLED.key -> "true")
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
index 523c02f4a6014..3b183902007e4 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LimitNodeSuite.scala
@@ -24,7 +24,7 @@ class LimitNodeSuite extends LocalNodeTest with SharedSQLContext {
   test("basic") {
     checkAnswer(
       testData,
-      node => LimitNode(10, node),
+      node => LimitNode(conf, 10, node),
       testData.limit(10).collect()
     )
   }
@@ -32,7 +32,7 @@ class LimitNodeSuite extends LocalNodeTest with SharedSQLContext {
   test("empty") {
     checkAnswer(
       emptyTestData,
-      node => LimitNode(10, node),
+      node => LimitNode(conf, 10, node),
       emptyTestData.limit(10).collect()
     )
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
index 95f06081bd0a8..b95d4ea7f8f2a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
@@ -20,10 +20,12 @@ package org.apache.spark.sql.execution.local
 import scala.util.control.NonFatal
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.sql.{DataFrame, Row}
-import org.apache.spark.sql.test.SQLTestUtils
+import org.apache.spark.sql.{DataFrame, Row, SQLConf}
+import org.apache.spark.sql.test.{SharedSQLContext, SQLTestUtils}
 
-class LocalNodeTest extends SparkFunSuite {
+class LocalNodeTest extends SparkFunSuite with SharedSQLContext {
+
+  def conf: SQLConf = sqlContext.conf
 
   /**
    * Runs the LocalNode and makes sure the answer matches the expected result.
@@ -92,6 +94,7 @@ class LocalNodeTest extends SparkFunSuite {
 
   protected def dataFrameToSeqScanNode(df: DataFrame): SeqScanNode = {
     new SeqScanNode(
+      conf,
       df.queryExecution.sparkPlan.output,
       df.queryExecution.toRdd.map(_.copy()).collect())
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
index ffcf092e2c66a..38e0a230c46d8 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ProjectNodeSuite.scala
@@ -26,7 +26,7 @@ class ProjectNodeSuite extends LocalNodeTest with SharedSQLContext {
     val columns = Seq(output(1), output(0))
     checkAnswer(
       testData,
-      node => ProjectNode(columns, node),
+      node => ProjectNode(conf, columns, node),
       testData.select("value", "key").collect()
     )
   }
@@ -36,7 +36,7 @@ class ProjectNodeSuite extends LocalNodeTest with SharedSQLContext {
     val columns = Seq(output(1), output(0))
     checkAnswer(
       emptyTestData,
-      node => ProjectNode(columns, node),
+      node => ProjectNode(conf, columns, node),
       emptyTestData.select("value", "key").collect()
     )
   }
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
index 34670287c3e1d..eedd7320900f9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/UnionNodeSuite.scala
@@ -25,7 +25,7 @@ class UnionNodeSuite extends LocalNodeTest with SharedSQLContext {
     checkAnswer2(
       testData,
       testData,
-      (node1, node2) => UnionNode(Seq(node1, node2)),
+      (node1, node2) => UnionNode(conf, Seq(node1, node2)),
       testData.unionAll(testData).collect()
     )
   }
@@ -34,7 +34,7 @@ class UnionNodeSuite extends LocalNodeTest with SharedSQLContext {
     checkAnswer2(
       emptyTestData,
       emptyTestData,
-      (node1, node2) => UnionNode(Seq(node1, node2)),
+      (node1, node2) => UnionNode(conf, Seq(node1, node2)),
       emptyTestData.unionAll(emptyTestData).collect()
     )
   }
@@ -44,7 +44,7 @@ class UnionNodeSuite extends LocalNodeTest with SharedSQLContext {
       emptyTestData, emptyTestData, testData, emptyTestData)
     doCheckAnswer(
       dfs,
-      nodes => UnionNode(nodes),
+      nodes => UnionNode(conf, nodes),
       dfs.reduce(_.unionAll(_)).collect()
     )
   }

From 45e3be5c138d983f40f619735d60bf7eb78c9bf0 Mon Sep 17 00:00:00 2001
From: Sun Rui <rui.sun@intel.com>
Date: Thu, 10 Sep 2015 12:21:13 -0700
Subject: [PATCH 579/633] [SPARK-10049] [SPARKR] Support collecting data of
 ArraryType in DataFrame.

this PR :
1.  Enhance reflection in RBackend. Automatically matching a Java array to Scala Seq when finding methods. Util functions like seq(), listToSeq() in R side can be removed, as they will conflict with the Serde logic that transferrs a Scala seq to R side.

2.  Enhance the SerDe to support transferring  a Scala seq to R side. Data of ArrayType in DataFrame
after collection is observed to be of Scala Seq type.

3.  Support ArrayType in createDataFrame().

Author: Sun Rui <rui.sun@intel.com>

Closes #8458 from sun-rui/SPARK-10049.
---
 R/pkg/R/DataFrame.R                           |  26 ++--
 R/pkg/R/SQLContext.R                          |   4 +-
 R/pkg/R/column.R                              |   3 +-
 R/pkg/R/functions.R                           |  12 +-
 R/pkg/R/group.R                               |   4 +-
 R/pkg/R/schema.R                              |  54 +++++---
 R/pkg/R/utils.R                               |  10 --
 R/pkg/inst/tests/test_sparkSQL.R              |  44 ++++++-
 .../apache/spark/api/r/RBackendHandler.scala  | 121 +++++++++++++-----
 .../scala/org/apache/spark/api/r/SerDe.scala  | 109 ++++++++--------
 .../org/apache/spark/sql/api/r/SQLUtils.scala |  14 +-
 11 files changed, 250 insertions(+), 151 deletions(-)

diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 8a00238b41d60..c3c1893487334 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -271,7 +271,7 @@ setMethod("names<-",
           signature(x = "DataFrame"),
           function(x, value) {
             if (!is.null(value)) {
-              sdf <- callJMethod(x@sdf, "toDF", listToSeq(as.list(value)))
+              sdf <- callJMethod(x@sdf, "toDF", as.list(value))
               dataFrame(sdf)
             }
           })
@@ -843,10 +843,10 @@ setMethod("groupBy",
            function(x, ...) {
              cols <- list(...)
              if (length(cols) >= 1 && class(cols[[1]]) == "character") {
-               sgd <- callJMethod(x@sdf, "groupBy", cols[[1]], listToSeq(cols[-1]))
+               sgd <- callJMethod(x@sdf, "groupBy", cols[[1]], cols[-1])
              } else {
                jcol <- lapply(cols, function(c) { c@jc })
-               sgd <- callJMethod(x@sdf, "groupBy", listToSeq(jcol))
+               sgd <- callJMethod(x@sdf, "groupBy", jcol)
              }
              groupedData(sgd)
            })
@@ -1079,7 +1079,7 @@ setMethod("subset", signature(x = "DataFrame"),
 #' }
 setMethod("select", signature(x = "DataFrame", col = "character"),
           function(x, col, ...) {
-            sdf <- callJMethod(x@sdf, "select", col, toSeq(...))
+            sdf <- callJMethod(x@sdf, "select", col, list(...))
             dataFrame(sdf)
           })
 
@@ -1090,7 +1090,7 @@ setMethod("select", signature(x = "DataFrame", col = "Column"),
             jcols <- lapply(list(col, ...), function(c) {
               c@jc
             })
-            sdf <- callJMethod(x@sdf, "select", listToSeq(jcols))
+            sdf <- callJMethod(x@sdf, "select", jcols)
             dataFrame(sdf)
           })
 
@@ -1106,7 +1106,7 @@ setMethod("select",
                 col(c)@jc
               }
             })
-            sdf <- callJMethod(x@sdf, "select", listToSeq(cols))
+            sdf <- callJMethod(x@sdf, "select", cols)
             dataFrame(sdf)
           })
 
@@ -1133,7 +1133,7 @@ setMethod("selectExpr",
           signature(x = "DataFrame", expr = "character"),
           function(x, expr, ...) {
             exprList <- list(expr, ...)
-            sdf <- callJMethod(x@sdf, "selectExpr", listToSeq(exprList))
+            sdf <- callJMethod(x@sdf, "selectExpr", exprList)
             dataFrame(sdf)
           })
 
@@ -1311,12 +1311,12 @@ setMethod("arrange",
           signature(x = "DataFrame", col = "characterOrColumn"),
           function(x, col, ...) {
             if (class(col) == "character") {
-              sdf <- callJMethod(x@sdf, "sort", col, toSeq(...))
+              sdf <- callJMethod(x@sdf, "sort", col, list(...))
             } else if (class(col) == "Column") {
               jcols <- lapply(list(col, ...), function(c) {
                 c@jc
               })
-              sdf <- callJMethod(x@sdf, "sort", listToSeq(jcols))
+              sdf <- callJMethod(x@sdf, "sort", jcols)
             }
             dataFrame(sdf)
           })
@@ -1664,7 +1664,7 @@ setMethod("describe",
           signature(x = "DataFrame", col = "character"),
           function(x, col, ...) {
             colList <- list(col, ...)
-            sdf <- callJMethod(x@sdf, "describe", listToSeq(colList))
+            sdf <- callJMethod(x@sdf, "describe", colList)
             dataFrame(sdf)
           })
 
@@ -1674,7 +1674,7 @@ setMethod("describe",
           signature(x = "DataFrame"),
           function(x) {
             colList <- as.list(c(columns(x)))
-            sdf <- callJMethod(x@sdf, "describe", listToSeq(colList))
+            sdf <- callJMethod(x@sdf, "describe", colList)
             dataFrame(sdf)
           })
 
@@ -1731,7 +1731,7 @@ setMethod("dropna",
 
             naFunctions <- callJMethod(x@sdf, "na")
             sdf <- callJMethod(naFunctions, "drop",
-                               as.integer(minNonNulls), listToSeq(as.list(cols)))
+                               as.integer(minNonNulls), as.list(cols))
             dataFrame(sdf)
           })
 
@@ -1815,7 +1815,7 @@ setMethod("fillna",
             sdf <- if (length(cols) == 0) {
               callJMethod(naFunctions, "fill", value)
             } else {
-              callJMethod(naFunctions, "fill", value, listToSeq(as.list(cols)))
+              callJMethod(naFunctions, "fill", value, as.list(cols))
             }
             dataFrame(sdf)
           })
diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R
index 1bc6445311473..4ac057d0f2d83 100644
--- a/R/pkg/R/SQLContext.R
+++ b/R/pkg/R/SQLContext.R
@@ -49,7 +49,7 @@ infer_type <- function(x) {
     stopifnot(length(x) > 0)
     names <- names(x)
     if (is.null(names)) {
-      list(type = "array", elementType = infer_type(x[[1]]), containsNull = TRUE)
+      paste0("array<", infer_type(x[[1]]), ">")
     } else {
       # StructType
       types <- lapply(x, infer_type)
@@ -59,7 +59,7 @@ infer_type <- function(x) {
       do.call(structType, fields)
     }
   } else if (length(x) > 1) {
-    list(type = "array", elementType = type, containsNull = TRUE)
+    paste0("array<", infer_type(x[[1]]), ">")
   } else {
     type
   }
diff --git a/R/pkg/R/column.R b/R/pkg/R/column.R
index 4805096f3f9c5..42e9d12179db7 100644
--- a/R/pkg/R/column.R
+++ b/R/pkg/R/column.R
@@ -211,8 +211,7 @@ setMethod("cast",
 setMethod("%in%",
           signature(x = "Column"),
           function(x, table) {
-            table <- listToSeq(as.list(table))
-            jc <- callJMethod(x@jc, "in", table)
+            jc <- callJMethod(x@jc, "in", as.list(table))
             return(column(jc))
           })
 
diff --git a/R/pkg/R/functions.R b/R/pkg/R/functions.R
index d848730e70433..94687edb05442 100644
--- a/R/pkg/R/functions.R
+++ b/R/pkg/R/functions.R
@@ -1331,7 +1331,7 @@ setMethod("countDistinct",
               x@jc
             })
             jc <- callJStatic("org.apache.spark.sql.functions", "countDistinct", x@jc,
-                              listToSeq(jcol))
+                              jcol)
             column(jc)
           })
 
@@ -1348,7 +1348,7 @@ setMethod("concat",
           signature(x = "Column"),
           function(x, ...) {
             jcols <- lapply(list(x, ...), function(x) { x@jc })
-            jc <- callJStatic("org.apache.spark.sql.functions", "concat", listToSeq(jcols))
+            jc <- callJStatic("org.apache.spark.sql.functions", "concat", jcols)
             column(jc)
           })
 
@@ -1366,7 +1366,7 @@ setMethod("greatest",
           function(x, ...) {
             stopifnot(length(list(...)) > 0)
             jcols <- lapply(list(x, ...), function(x) { x@jc })
-            jc <- callJStatic("org.apache.spark.sql.functions", "greatest", listToSeq(jcols))
+            jc <- callJStatic("org.apache.spark.sql.functions", "greatest", jcols)
             column(jc)
           })
 
@@ -1384,7 +1384,7 @@ setMethod("least",
           function(x, ...) {
             stopifnot(length(list(...)) > 0)
             jcols <- lapply(list(x, ...), function(x) { x@jc })
-            jc <- callJStatic("org.apache.spark.sql.functions", "least", listToSeq(jcols))
+            jc <- callJStatic("org.apache.spark.sql.functions", "least", jcols)
             column(jc)
           })
 
@@ -1675,7 +1675,7 @@ setMethod("shiftRightUnsigned", signature(y = "Column", x = "numeric"),
 #' @export
 setMethod("concat_ws", signature(sep = "character", x = "Column"),
           function(sep, x, ...) {
-            jcols <- listToSeq(lapply(list(x, ...), function(x) { x@jc }))
+            jcols <- lapply(list(x, ...), function(x) { x@jc })
             jc <- callJStatic("org.apache.spark.sql.functions", "concat_ws", sep, jcols)
             column(jc)
           })
@@ -1723,7 +1723,7 @@ setMethod("expr", signature(x = "character"),
 #' @export
 setMethod("format_string", signature(format = "character", x = "Column"),
           function(format, x, ...) {
-            jcols <- listToSeq(lapply(list(x, ...), function(arg) { arg@jc }))
+            jcols <- lapply(list(x, ...), function(arg) { arg@jc })
             jc <- callJStatic("org.apache.spark.sql.functions",
                               "format_string",
                               format, jcols)
diff --git a/R/pkg/R/group.R b/R/pkg/R/group.R
index 576ac72f40fc0..4cab1a69f601a 100644
--- a/R/pkg/R/group.R
+++ b/R/pkg/R/group.R
@@ -102,7 +102,7 @@ setMethod("agg",
                 }
               }
               jcols <- lapply(cols, function(c) { c@jc })
-              sdf <- callJMethod(x@sgd, "agg", jcols[[1]], listToSeq(jcols[-1]))
+              sdf <- callJMethod(x@sgd, "agg", jcols[[1]], jcols[-1])
             } else {
               stop("agg can only support Column or character")
             }
@@ -124,7 +124,7 @@ createMethod <- function(name) {
   setMethod(name,
             signature(x = "GroupedData"),
             function(x, ...) {
-              sdf <- callJMethod(x@sgd, name, toSeq(...))
+              sdf <- callJMethod(x@sgd, name, list(...))
               dataFrame(sdf)
             })
 }
diff --git a/R/pkg/R/schema.R b/R/pkg/R/schema.R
index 79c744ef29c23..62d4f73878d29 100644
--- a/R/pkg/R/schema.R
+++ b/R/pkg/R/schema.R
@@ -56,7 +56,7 @@ structType.structField <- function(x, ...) {
   })
   stObj <- callJStatic("org.apache.spark.sql.api.r.SQLUtils",
                        "createStructType",
-                       listToSeq(sfObjList))
+                       sfObjList)
   structType(stObj)
 }
 
@@ -114,6 +114,35 @@ structField.jobj <- function(x) {
   obj
 }
 
+checkType <- function(type) {
+  primtiveTypes <- c("byte",
+                     "integer",
+                     "float",
+                     "double",
+                     "numeric",
+                     "character",
+                     "string",
+                     "binary",
+                     "raw",
+                     "logical",
+                     "boolean",
+                     "timestamp",
+                     "date")
+  if (type %in% primtiveTypes) {
+    return()
+  } else {
+    m <- regexec("^array<(.*)>$", type)
+    matchedStrings <- regmatches(type, m)
+    if (length(matchedStrings[[1]]) >= 2) {
+      elemType <- matchedStrings[[1]][2]
+      checkType(elemType)
+      return()
+    }
+  }
+
+  stop(paste("Unsupported type for Dataframe:", type))
+}
+
 structField.character <- function(x, type, nullable = TRUE) {
   if (class(x) != "character") {
     stop("Field name must be a string.")
@@ -124,28 +153,13 @@ structField.character <- function(x, type, nullable = TRUE) {
   if (class(nullable) != "logical") {
     stop("nullable must be either TRUE or FALSE")
   }
-  options <- c("byte",
-               "integer",
-               "float",
-               "double",
-               "numeric",
-               "character",
-               "string",
-               "binary",
-               "raw",
-               "logical",
-               "boolean",
-               "timestamp",
-               "date")
-  dataType <- if (type %in% options) {
-    type
-  } else {
-    stop(paste("Unsupported type for Dataframe:", type))
-  }
+
+  checkType(type)
+
   sfObj <- callJStatic("org.apache.spark.sql.api.r.SQLUtils",
                        "createStructField",
                        x,
-                       dataType,
+                       type,
                        nullable)
   structField(sfObj)
 }
diff --git a/R/pkg/R/utils.R b/R/pkg/R/utils.R
index 3babcb519378e..69a2bc728f842 100644
--- a/R/pkg/R/utils.R
+++ b/R/pkg/R/utils.R
@@ -361,16 +361,6 @@ numToInt <- function(num) {
   as.integer(num)
 }
 
-# create a Seq in JVM
-toSeq <- function(...) {
-  callJStatic("org.apache.spark.sql.api.r.SQLUtils", "toSeq", list(...))
-}
-
-# create a Seq in JVM from a list
-listToSeq <- function(l) {
-  callJStatic("org.apache.spark.sql.api.r.SQLUtils", "toSeq", l)
-}
-
 # Utility function to recursively traverse the Abstract Syntax Tree (AST) of a
 # user defined function (UDF), and to examine variables in the UDF to decide
 # if their values should be included in the new function environment.
diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 6d331f9883d55..1ccfde59176f5 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -49,6 +49,14 @@ mockLinesNa <- c("{\"name\":\"Bob\",\"age\":16,\"height\":176.5}",
 jsonPathNa <- tempfile(pattern="sparkr-test", fileext=".tmp")
 writeLines(mockLinesNa, jsonPathNa)
 
+# For test complex types in DataFrame
+mockLinesComplexType <-
+  c("{\"c1\":[1, 2, 3], \"c2\":[\"a\", \"b\", \"c\"], \"c3\":[1.0, 2.0, 3.0]}",
+    "{\"c1\":[4, 5, 6], \"c2\":[\"d\", \"e\", \"f\"], \"c3\":[4.0, 5.0, 6.0]}",
+    "{\"c1\":[7, 8, 9], \"c2\":[\"g\", \"h\", \"i\"], \"c3\":[7.0, 8.0, 9.0]}")
+complexTypeJsonPath <- tempfile(pattern="sparkr-test", fileext=".tmp")
+writeLines(mockLinesComplexType, complexTypeJsonPath)
+
 test_that("infer types", {
   expect_equal(infer_type(1L), "integer")
   expect_equal(infer_type(1.0), "double")
@@ -56,10 +64,8 @@ test_that("infer types", {
   expect_equal(infer_type(TRUE), "boolean")
   expect_equal(infer_type(as.Date("2015-03-11")), "date")
   expect_equal(infer_type(as.POSIXlt("2015-03-11 12:13:04.043")), "timestamp")
-  expect_equal(infer_type(c(1L, 2L)),
-               list(type = "array", elementType = "integer", containsNull = TRUE))
-  expect_equal(infer_type(list(1L, 2L)),
-               list(type = "array", elementType = "integer", containsNull = TRUE))
+  expect_equal(infer_type(c(1L, 2L)), "array<integer>")
+  expect_equal(infer_type(list(1L, 2L)), "array<integer>")
   testStruct <- infer_type(list(a = 1L, b = "2"))
   expect_equal(class(testStruct), "structType")
   checkStructField(testStruct$fields()[[1]], "a", "IntegerType", TRUE)
@@ -236,8 +242,7 @@ test_that("create DataFrame with different data types", {
   expect_equal(collect(df), data.frame(l, stringsAsFactors = FALSE))
 })
 
-# TODO: enable this test after fix serialization for nested object
-#test_that("create DataFrame with nested array and struct", {
+test_that("create DataFrame with nested array and struct", {
 #  e <- new.env()
 #  assign("n", 3L, envir = e)
 #  l <- list(1:10, list("a", "b"), e, list(a="aa", b=3L))
@@ -247,7 +252,32 @@ test_that("create DataFrame with different data types", {
 #  expect_equal(count(df), 1)
 #  ldf <- collect(df)
 #  expect_equal(ldf[1,], l[[1]])
-#})
+
+
+  #  ArrayType only for now
+  l <- list(as.list(1:10), list("a", "b"))
+  df <- createDataFrame(sqlContext, list(l), c("a", "b"))
+  expect_equal(dtypes(df), list(c("a", "array<int>"), c("b", "array<string>")))
+  expect_equal(count(df), 1)
+  ldf <- collect(df)
+  expect_equal(names(ldf), c("a", "b"))
+  expect_equal(ldf[1, 1][[1]], l[[1]])
+  expect_equal(ldf[1, 2][[1]], l[[2]])
+})
+
+test_that("Collect DataFrame with complex types", {
+  # only ArrayType now
+  # TODO: tests for StructType and MapType after they are supported
+  df <- jsonFile(sqlContext, complexTypeJsonPath)
+
+  ldf <- collect(df)
+  expect_equal(nrow(ldf), 3)
+  expect_equal(ncol(ldf), 3)
+  expect_equal(names(ldf), c("c1", "c2", "c3"))
+  expect_equal(ldf$c1, list(list(1, 2, 3), list(4, 5, 6), list (7, 8, 9)))
+  expect_equal(ldf$c2, list(list("a", "b", "c"), list("d", "e", "f"), list ("g", "h", "i")))
+  expect_equal(ldf$c3, list(list(1.0, 2.0, 3.0), list(4.0, 5.0, 6.0), list (7.0, 8.0, 9.0)))
+})
 
 test_that("jsonFile() on a local file returns a DataFrame", {
   df <- jsonFile(sqlContext, jsonPath)
diff --git a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
index bb82f3285f1d9..2a792d81994fd 100644
--- a/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/RBackendHandler.scala
@@ -125,10 +125,11 @@ private[r] class RBackendHandler(server: RBackend)
       val methods = cls.getMethods
       val selectedMethods = methods.filter(m => m.getName == methodName)
       if (selectedMethods.length > 0) {
-        val methods = selectedMethods.filter { x =>
-          matchMethod(numArgs, args, x.getParameterTypes)
-        }
-        if (methods.isEmpty) {
+        val index = findMatchedSignature(
+          selectedMethods.map(_.getParameterTypes),
+          args)
+
+        if (index.isEmpty) {
           logWarning(s"cannot find matching method ${cls}.$methodName. "
             + s"Candidates are:")
           selectedMethods.foreach { method =>
@@ -136,18 +137,29 @@ private[r] class RBackendHandler(server: RBackend)
           }
           throw new Exception(s"No matched method found for $cls.$methodName")
         }
-        val ret = methods.head.invoke(obj, args : _*)
+
+        val ret = selectedMethods(index.get).invoke(obj, args : _*)
 
         // Write status bit
         writeInt(dos, 0)
         writeObject(dos, ret.asInstanceOf[AnyRef])
       } else if (methodName == "<init>") {
         // methodName should be "<init>" for constructor
-        val ctor = cls.getConstructors.filter { x =>
-          matchMethod(numArgs, args, x.getParameterTypes)
-        }.head
+        val ctors = cls.getConstructors
+        val index = findMatchedSignature(
+          ctors.map(_.getParameterTypes),
+          args)
 
-        val obj = ctor.newInstance(args : _*)
+        if (index.isEmpty) {
+          logWarning(s"cannot find matching constructor for ${cls}. "
+            + s"Candidates are:")
+          ctors.foreach { ctor =>
+            logWarning(s"$cls(${ctor.getParameterTypes.mkString(",")})")
+          }
+          throw new Exception(s"No matched constructor found for $cls")
+        }
+
+        val obj = ctors(index.get).newInstance(args : _*)
 
         writeInt(dos, 0)
         writeObject(dos, obj.asInstanceOf[AnyRef])
@@ -166,40 +178,79 @@ private[r] class RBackendHandler(server: RBackend)
 
   // Read a number of arguments from the data input stream
   def readArgs(numArgs: Int, dis: DataInputStream): Array[java.lang.Object] = {
-    (0 until numArgs).map { arg =>
+    (0 until numArgs).map { _ =>
       readObject(dis)
     }.toArray
   }
 
-  // Checks if the arguments passed in args matches the parameter types.
-  // NOTE: Currently we do exact match. We may add type conversions later.
-  def matchMethod(
-      numArgs: Int,
-      args: Array[java.lang.Object],
-      parameterTypes: Array[Class[_]]): Boolean = {
-    if (parameterTypes.length != numArgs) {
-      return false
-    }
+  // Find a matching method signature in an array of signatures of constructors
+  // or methods of the same name according to the passed arguments. Arguments
+  // may be converted in order to match a signature.
+  //
+  // Note that in Java reflection, constructors and normal methods are of different
+  // classes, and share no parent class that provides methods for reflection uses.
+  // There is no unified way to handle them in this function. So an array of signatures
+  // is passed in instead of an array of candidate constructors or methods.
+  //
+  // Returns an Option[Int] which is the index of the matched signature in the array.
+  def findMatchedSignature(
+      parameterTypesOfMethods: Array[Array[Class[_]]],
+      args: Array[Object]): Option[Int] = {
+    val numArgs = args.length
+
+    for (index <- 0 until parameterTypesOfMethods.length) {
+      val parameterTypes = parameterTypesOfMethods(index)
+
+      if (parameterTypes.length == numArgs) {
+        var argMatched = true
+        var i = 0
+        while (i < numArgs && argMatched) {
+          val parameterType = parameterTypes(i)
+
+          if (parameterType == classOf[Seq[Any]] && args(i).getClass.isArray) {
+            // The case that the parameter type is a Scala Seq and the argument
+            // is a Java array is considered matching. The array will be converted
+            // to a Seq later if this method is matched.
+          } else {
+            var parameterWrapperType = parameterType
+
+            // Convert native parameters to Object types as args is Array[Object] here
+            if (parameterType.isPrimitive) {
+              parameterWrapperType = parameterType match {
+                case java.lang.Integer.TYPE => classOf[java.lang.Integer]
+                case java.lang.Long.TYPE => classOf[java.lang.Integer]
+                case java.lang.Double.TYPE => classOf[java.lang.Double]
+                case java.lang.Boolean.TYPE => classOf[java.lang.Boolean]
+                case _ => parameterType
+              }
+            }
+            if (!parameterWrapperType.isInstance(args(i))) {
+              argMatched = false
+            }
+          }
 
-    for (i <- 0 to numArgs - 1) {
-      val parameterType = parameterTypes(i)
-      var parameterWrapperType = parameterType
-
-      // Convert native parameters to Object types as args is Array[Object] here
-      if (parameterType.isPrimitive) {
-        parameterWrapperType = parameterType match {
-          case java.lang.Integer.TYPE => classOf[java.lang.Integer]
-          case java.lang.Long.TYPE => classOf[java.lang.Integer]
-          case java.lang.Double.TYPE => classOf[java.lang.Double]
-          case java.lang.Boolean.TYPE => classOf[java.lang.Boolean]
-          case _ => parameterType
+          i = i + 1
+        }
+
+        if (argMatched) {
+          // For now, we return the first matching method.
+          // TODO: find best method in matching methods.
+
+          // Convert args if needed
+          val parameterTypes = parameterTypesOfMethods(index)
+
+          (0 until numArgs).map { i =>
+            if (parameterTypes(i) == classOf[Seq[Any]] && args(i).getClass.isArray) {
+              // Convert a Java array to scala Seq
+              args(i) = args(i).asInstanceOf[Array[_]].toSeq
+            }
+          }
+
+          return Some(index)
         }
-      }
-      if (!parameterWrapperType.isInstance(args(i))) {
-        return false
       }
     }
-    true
+    None
   }
 }
 
diff --git a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
index 190e193427af8..3c92bb7a1c73c 100644
--- a/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
+++ b/core/src/main/scala/org/apache/spark/api/r/SerDe.scala
@@ -21,6 +21,7 @@ import java.io.{DataInputStream, DataOutputStream}
 import java.sql.{Timestamp, Date, Time}
 
 import scala.collection.JavaConverters._
+import scala.collection.mutable.WrappedArray
 
 /**
  * Utility functions to serialize, deserialize objects to / from R
@@ -213,89 +214,97 @@ private[spark] object SerDe {
     }
   }
 
-  def writeObject(dos: DataOutputStream, value: Object): Unit = {
-    if (value == null) {
+  def writeObject(dos: DataOutputStream, obj: Object): Unit = {
+    if (obj == null) {
       writeType(dos, "void")
     } else {
-      value.getClass.getName match {
-        case "java.lang.Character" =>
+      // Convert ArrayType collected from DataFrame to Java array
+      // Collected data of ArrayType from a DataFrame is observed to be of
+      // type "scala.collection.mutable.WrappedArray"
+      val value =
+        if (obj.isInstanceOf[WrappedArray[_]]) {
+          obj.asInstanceOf[WrappedArray[_]].toArray
+        } else {
+          obj
+        }
+
+      value match {
+        case v: java.lang.Character =>
           writeType(dos, "character")
-          writeString(dos, value.asInstanceOf[Character].toString)
-        case "java.lang.String" =>
+          writeString(dos, v.toString)
+        case v: java.lang.String =>
           writeType(dos, "character")
-          writeString(dos, value.asInstanceOf[String])
-        case "java.lang.Long" =>
+          writeString(dos, v)
+        case v: java.lang.Long =>
           writeType(dos, "double")
-          writeDouble(dos, value.asInstanceOf[Long].toDouble)
-        case "java.lang.Float" =>
+          writeDouble(dos, v.toDouble)
+        case v: java.lang.Float =>
           writeType(dos, "double")
-          writeDouble(dos, value.asInstanceOf[Float].toDouble)
-        case "java.math.BigDecimal" =>
+          writeDouble(dos, v.toDouble)
+        case v: java.math.BigDecimal =>
           writeType(dos, "double")
-          val javaDecimal = value.asInstanceOf[java.math.BigDecimal]
-          writeDouble(dos, scala.math.BigDecimal(javaDecimal).toDouble)
-        case "java.lang.Double" =>
+          writeDouble(dos, scala.math.BigDecimal(v).toDouble)
+        case v: java.lang.Double =>
           writeType(dos, "double")
-          writeDouble(dos, value.asInstanceOf[Double])
-        case "java.lang.Byte" =>
+          writeDouble(dos, v)
+        case v: java.lang.Byte =>
           writeType(dos, "integer")
-          writeInt(dos, value.asInstanceOf[Byte].toInt)
-        case "java.lang.Short" =>
+          writeInt(dos, v.toInt)
+        case v: java.lang.Short =>
           writeType(dos, "integer")
-          writeInt(dos, value.asInstanceOf[Short].toInt)
-        case "java.lang.Integer" =>
+          writeInt(dos, v.toInt)
+        case v: java.lang.Integer =>
           writeType(dos, "integer")
-          writeInt(dos, value.asInstanceOf[Int])
-        case "java.lang.Boolean" =>
+          writeInt(dos, v)
+        case v: java.lang.Boolean =>
           writeType(dos, "logical")
-          writeBoolean(dos, value.asInstanceOf[Boolean])
-        case "java.sql.Date" =>
+          writeBoolean(dos, v)
+        case v: java.sql.Date =>
           writeType(dos, "date")
-          writeDate(dos, value.asInstanceOf[Date])
-        case "java.sql.Time" =>
+          writeDate(dos, v)
+        case v: java.sql.Time =>
           writeType(dos, "time")
-          writeTime(dos, value.asInstanceOf[Time])
-        case "java.sql.Timestamp" =>
+          writeTime(dos, v)
+        case v: java.sql.Timestamp =>
           writeType(dos, "time")
-          writeTime(dos, value.asInstanceOf[Timestamp])
+          writeTime(dos, v)
 
         // Handle arrays
 
         // Array of primitive types
 
         // Special handling for byte array
-        case "[B" =>
+        case v: Array[Byte] =>
           writeType(dos, "raw")
-          writeBytes(dos, value.asInstanceOf[Array[Byte]])
+          writeBytes(dos, v)
 
-        case "[C" =>
+        case v: Array[Char] =>
           writeType(dos, "array")
-          writeStringArr(dos, value.asInstanceOf[Array[Char]].map(_.toString))
-        case "[S" =>
+          writeStringArr(dos, v.map(_.toString))
+        case v: Array[Short] =>
           writeType(dos, "array")
-          writeIntArr(dos, value.asInstanceOf[Array[Short]].map(_.toInt))
-        case "[I" =>
+          writeIntArr(dos, v.map(_.toInt))
+        case v: Array[Int] =>
           writeType(dos, "array")
-          writeIntArr(dos, value.asInstanceOf[Array[Int]])
-        case "[J" =>
+          writeIntArr(dos, v)
+        case v: Array[Long] =>
           writeType(dos, "array")
-          writeDoubleArr(dos, value.asInstanceOf[Array[Long]].map(_.toDouble))
-        case "[F" =>
+          writeDoubleArr(dos, v.map(_.toDouble))
+        case v: Array[Float] =>
           writeType(dos, "array")
-          writeDoubleArr(dos, value.asInstanceOf[Array[Float]].map(_.toDouble))
-        case "[D" =>
+          writeDoubleArr(dos, v.map(_.toDouble))
+        case v: Array[Double] =>
           writeType(dos, "array")
-          writeDoubleArr(dos, value.asInstanceOf[Array[Double]])
-        case "[Z" =>
+          writeDoubleArr(dos, v)
+        case v: Array[Boolean] =>
           writeType(dos, "array")
-          writeBooleanArr(dos, value.asInstanceOf[Array[Boolean]])
+          writeBooleanArr(dos, v)
 
         // Array of objects, null objects use "void" type
-        case c if c.startsWith("[") =>
+        case v: Array[Object] =>
           writeType(dos, "list")
-          val array = value.asInstanceOf[Array[Object]]
-          writeInt(dos, array.length)
-          array.foreach(elem => writeObject(dos, elem))
+          writeInt(dos, v.length)
+          v.foreach(elem => writeObject(dos, elem))
 
         case _ =>
           writeType(dos, "jobj")
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
index 7f3defec3d42e..d4b834adb6e39 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala
@@ -26,6 +26,8 @@ import org.apache.spark.sql.catalyst.expressions.{Alias, Expression, NamedExpres
 import org.apache.spark.sql.types._
 import org.apache.spark.sql.{Column, DataFrame, GroupedData, Row, SQLContext, SaveMode}
 
+import scala.util.matching.Regex
+
 private[r] object SQLUtils {
   def createSQLContext(jsc: JavaSparkContext): SQLContext = {
     new SQLContext(jsc)
@@ -35,14 +37,15 @@ private[r] object SQLUtils {
     new JavaSparkContext(sqlCtx.sparkContext)
   }
 
-  def toSeq[T](arr: Array[T]): Seq[T] = {
-    arr.toSeq
-  }
-
   def createStructType(fields : Seq[StructField]): StructType = {
     StructType(fields)
   }
 
+  // Support using regex in string interpolation
+  private[this] implicit class RegexContext(sc: StringContext) {
+    def r: Regex = new Regex(sc.parts.mkString, sc.parts.tail.map(_ => "x"): _*)
+  }
+
   def getSQLDataType(dataType: String): DataType = {
     dataType match {
       case "byte" => org.apache.spark.sql.types.ByteType
@@ -58,6 +61,9 @@ private[r] object SQLUtils {
       case "boolean" => org.apache.spark.sql.types.BooleanType
       case "timestamp" => org.apache.spark.sql.types.TimestampType
       case "date" => org.apache.spark.sql.types.DateType
+      case r"\Aarray<(.*)${elemType}>\Z" => {
+        org.apache.spark.sql.types.ArrayType(getSQLDataType(elemType))
+      }
       case _ => throw new IllegalArgumentException(s"Invaid type $dataType")
     }
   }

From 3db72554be3f13478ccd5915e746491982163298 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Thu, 10 Sep 2015 13:22:35 -0700
Subject: [PATCH 580/633] [SPARK-10443] [SQL] Refactor SortMergeOuterJoin to
 reduce duplication

`LeftOutputIterator` and `RightOutputIterator` are symmetrically identical and can share a lot of code. If someone makes a change in one but forgets to do the same thing in the other we'll end up with inconsistent behavior. This patch also adds inline comments to clarify the intention of the code.

Author: Andrew Or <andrew@databricks.com>

Closes #8596 from andrewor14/smoj-cleanup.
---
 .../execution/joins/SortMergeOuterJoin.scala  | 138 ++++++++++--------
 1 file changed, 77 insertions(+), 61 deletions(-)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
index ab20ee573ab5d..c117dff9c8b1d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeOuterJoin.scala
@@ -199,99 +199,115 @@ case class SortMergeOuterJoin(
   }
 }
 
-
+/**
+ * An iterator for outputting rows in left outer join.
+ */
 private class LeftOuterIterator(
     smjScanner: SortMergeJoinScanner,
     rightNullRow: InternalRow,
     boundCondition: InternalRow => Boolean,
     resultProj: InternalRow => InternalRow,
-    numRows: LongSQLMetric
-  ) extends RowIterator {
-  private[this] val joinedRow: JoinedRow = new JoinedRow()
-  private[this] var rightIdx: Int = 0
-  assert(smjScanner.getBufferedMatches.length == 0)
-
-  private def advanceLeft(): Boolean = {
-    rightIdx = 0
-    if (smjScanner.findNextOuterJoinRows()) {
-      joinedRow.withLeft(smjScanner.getStreamedRow)
-      if (smjScanner.getBufferedMatches.isEmpty) {
-        // There are no matching right rows, so return nulls for the right row
-        joinedRow.withRight(rightNullRow)
-      } else {
-        // Find the next row from the right input that satisfied the bound condition
-        if (!advanceRightUntilBoundConditionSatisfied()) {
-          joinedRow.withRight(rightNullRow)
-        }
-      }
-      true
-    } else {
-      // Left input has been exhausted
-      false
-    }
-  }
-
-  private def advanceRightUntilBoundConditionSatisfied(): Boolean = {
-    var foundMatch: Boolean = false
-    while (!foundMatch && rightIdx < smjScanner.getBufferedMatches.length) {
-      foundMatch = boundCondition(joinedRow.withRight(smjScanner.getBufferedMatches(rightIdx)))
-      rightIdx += 1
-    }
-    foundMatch
-  }
-
-  override def advanceNext(): Boolean = {
-    val r = advanceRightUntilBoundConditionSatisfied() || advanceLeft()
-    if (r) numRows += 1
-    r
-  }
+    numOutputRows: LongSQLMetric)
+  extends OneSideOuterIterator(
+    smjScanner, rightNullRow, boundCondition, resultProj, numOutputRows) {
 
-  override def getRow: InternalRow = resultProj(joinedRow)
+  protected override def setStreamSideOutput(row: InternalRow): Unit = joinedRow.withLeft(row)
+  protected override def setBufferedSideOutput(row: InternalRow): Unit = joinedRow.withRight(row)
 }
 
+/**
+ * An iterator for outputting rows in right outer join.
+ */
 private class RightOuterIterator(
     smjScanner: SortMergeJoinScanner,
     leftNullRow: InternalRow,
     boundCondition: InternalRow => Boolean,
     resultProj: InternalRow => InternalRow,
-    numRows: LongSQLMetric
-  ) extends RowIterator {
-  private[this] val joinedRow: JoinedRow = new JoinedRow()
-  private[this] var leftIdx: Int = 0
+    numOutputRows: LongSQLMetric)
+  extends OneSideOuterIterator(
+    smjScanner, leftNullRow, boundCondition, resultProj, numOutputRows) {
+
+  protected override def setStreamSideOutput(row: InternalRow): Unit = joinedRow.withRight(row)
+  protected override def setBufferedSideOutput(row: InternalRow): Unit = joinedRow.withLeft(row)
+}
+
+/**
+ * An abstract iterator for sharing code between [[LeftOuterIterator]] and [[RightOuterIterator]].
+ *
+ * Each [[OneSideOuterIterator]] has a streamed side and a buffered side. Each row on the
+ * streamed side will output 0 or many rows, one for each matching row on the buffered side.
+ * If there are no matches, then the buffered side of the joined output will be a null row.
+ *
+ * In left outer join, the left is the streamed side and the right is the buffered side.
+ * In right outer join, the right is the streamed side and the left is the buffered side.
+ *
+ * @param smjScanner a scanner that streams rows and buffers any matching rows
+ * @param bufferedSideNullRow the default row to return when a streamed row has no matches
+ * @param boundCondition an additional filter condition for buffered rows
+ * @param resultProj how the output should be projected
+ * @param numOutputRows an accumulator metric for the number of rows output
+ */
+private abstract class OneSideOuterIterator(
+    smjScanner: SortMergeJoinScanner,
+    bufferedSideNullRow: InternalRow,
+    boundCondition: InternalRow => Boolean,
+    resultProj: InternalRow => InternalRow,
+    numOutputRows: LongSQLMetric) extends RowIterator {
+
+  // A row to store the joined result, reused many times
+  protected[this] val joinedRow: JoinedRow = new JoinedRow()
+
+  // Index of the buffered rows, reset to 0 whenever we advance to a new streamed row
+  private[this] var bufferIndex: Int = 0
+
+  // This iterator is initialized lazily so there should be no matches initially
   assert(smjScanner.getBufferedMatches.length == 0)
 
-  private def advanceRight(): Boolean = {
-    leftIdx = 0
+  // Set output methods to be overridden by subclasses
+  protected def setStreamSideOutput(row: InternalRow): Unit
+  protected def setBufferedSideOutput(row: InternalRow): Unit
+
+  /**
+   * Advance to the next row on the stream side and populate the buffer with matches.
+   * @return whether there are more rows in the stream to consume.
+   */
+  private def advanceStream(): Boolean = {
+    bufferIndex = 0
     if (smjScanner.findNextOuterJoinRows()) {
-      joinedRow.withRight(smjScanner.getStreamedRow)
+      setStreamSideOutput(smjScanner.getStreamedRow)
       if (smjScanner.getBufferedMatches.isEmpty) {
-        // There are no matching left rows, so return nulls for the left row
-        joinedRow.withLeft(leftNullRow)
+        // There are no matching rows in the buffer, so return the null row
+        setBufferedSideOutput(bufferedSideNullRow)
       } else {
-        // Find the next row from the left input that satisfied the bound condition
-        if (!advanceLeftUntilBoundConditionSatisfied()) {
-          joinedRow.withLeft(leftNullRow)
+        // Find the next row in the buffer that satisfied the bound condition
+        if (!advanceBufferUntilBoundConditionSatisfied()) {
+          setBufferedSideOutput(bufferedSideNullRow)
         }
       }
       true
     } else {
-      // Right input has been exhausted
+      // Stream has been exhausted
       false
     }
   }
 
-  private def advanceLeftUntilBoundConditionSatisfied(): Boolean = {
+  /**
+   * Advance to the next row in the buffer that satisfies the bound condition.
+   * @return whether there is such a row in the current buffer.
+   */
+  private def advanceBufferUntilBoundConditionSatisfied(): Boolean = {
     var foundMatch: Boolean = false
-    while (!foundMatch && leftIdx < smjScanner.getBufferedMatches.length) {
-      foundMatch = boundCondition(joinedRow.withLeft(smjScanner.getBufferedMatches(leftIdx)))
-      leftIdx += 1
+    while (!foundMatch && bufferIndex < smjScanner.getBufferedMatches.length) {
+      setBufferedSideOutput(smjScanner.getBufferedMatches(bufferIndex))
+      foundMatch = boundCondition(joinedRow)
+      bufferIndex += 1
     }
     foundMatch
   }
 
   override def advanceNext(): Boolean = {
-    val r = advanceLeftUntilBoundConditionSatisfied() || advanceRight()
-    if (r) numRows += 1
+    val r = advanceBufferUntilBoundConditionSatisfied() || advanceStream()
+    if (r) numOutputRows += 1
     r
   }
 

From 4204757714b36364611fb63bc008cf90fc53d8df Mon Sep 17 00:00:00 2001
From: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Date: Thu, 10 Sep 2015 13:43:13 -0700
Subject: [PATCH 581/633] Add 1.5 to master branch EC2 scripts

This change brings it to par with `branch-1.5` (and 1.5.0 release)

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #8704 from shivaram/ec2-1.5-update.
---
 ec2/spark_ec2.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/ec2/spark_ec2.py b/ec2/spark_ec2.py
index 11fd7ee0ec8df..3a2361c6d6d2b 100755
--- a/ec2/spark_ec2.py
+++ b/ec2/spark_ec2.py
@@ -51,7 +51,7 @@
     raw_input = input
     xrange = range
 
-SPARK_EC2_VERSION = "1.4.0"
+SPARK_EC2_VERSION = "1.5.0"
 SPARK_EC2_DIR = os.path.dirname(os.path.realpath(__file__))
 
 VALID_SPARK_VERSIONS = set([
@@ -71,6 +71,8 @@
     "1.3.0",
     "1.3.1",
     "1.4.0",
+    "1.4.1",
+    "1.5.0"
 ])
 
 SPARK_TACHYON_MAP = {
@@ -84,6 +86,8 @@
     "1.3.0": "0.5.0",
     "1.3.1": "0.5.0",
     "1.4.0": "0.6.4",
+    "1.4.1": "0.6.4",
+    "1.5.0": "0.7.1"
 }
 
 DEFAULT_SPARK_VERSION = SPARK_EC2_VERSION
@@ -91,7 +95,7 @@
 
 # Default location to get the spark-ec2 scripts (and ami-list) from
 DEFAULT_SPARK_EC2_GITHUB_REPO = "https://github.com/amplab/spark-ec2"
-DEFAULT_SPARK_EC2_BRANCH = "branch-1.4"
+DEFAULT_SPARK_EC2_BRANCH = "branch-1.5"
 
 
 def setup_external_libs(libs):

From 89562a172fd3efa032f60714d600407c6cfe2c2f Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 10 Sep 2015 13:54:20 -0700
Subject: [PATCH 582/633] [SPARK-7544] [SQL] [PySpark] pyspark.sql.types.Row
 implements __getitem__

pyspark.sql.types.Row implements ```__getitem__```

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8333 from yanboliang/spark-7544.
---
 python/pyspark/sql/types.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/python/pyspark/sql/types.py b/python/pyspark/sql/types.py
index 8bd58d69eeecd..1f86894855cbe 100644
--- a/python/pyspark/sql/types.py
+++ b/python/pyspark/sql/types.py
@@ -1176,6 +1176,8 @@ class Row(tuple):
     >>> row = Row(name="Alice", age=11)
     >>> row
     Row(age=11, name='Alice')
+    >>> row['name'], row['age']
+    ('Alice', 11)
     >>> row.name, row.age
     ('Alice', 11)
 
@@ -1243,6 +1245,19 @@ def __call__(self, *args):
         """create new Row object"""
         return _create_row(self, args)
 
+    def __getitem__(self, item):
+        if isinstance(item, (int, slice)):
+            return super(Row, self).__getitem__(item)
+        try:
+            # it will be slow when it has many fields,
+            # but this will not be used in normal cases
+            idx = self.__fields__.index(item)
+            return super(Row, self).__getitem__(idx)
+        except IndexError:
+            raise KeyError(item)
+        except ValueError:
+            raise ValueError(item)
+
     def __getattr__(self, item):
         if item.startswith("__"):
             raise AttributeError(item)

From 0eabea8a058ad60411c1384930ba12c1c638f5f1 Mon Sep 17 00:00:00 2001
From: Matt Massie <massie@cs.berkeley.edu>
Date: Thu, 10 Sep 2015 17:24:33 -0700
Subject: [PATCH 583/633] [SPARK-9043] Serialize key, value and combiner
 classes in ShuffleDependency

ShuffleManager implementations are currently not given type information for
the key, value and combiner classes. Serialization of shuffle objects relies
on objects being JavaSerializable, with methods defined for reading/writing
the object or, alternatively, serialization via Kryo which uses reflection.

Serialization systems like Avro, Thrift and Protobuf generate classes with
zero argument constructors and explicit schema information
(e.g. IndexedRecords in Avro have get, put and getSchema methods).

By serializing the key, value and combiner class names in ShuffleDependency,
shuffle implementations will have access to schema information when
registerShuffle() is called.

Author: Matt Massie <massie@cs.berkeley.edu>

Closes #7403 from massie/shuffle-classtags.
---
 .../scala/org/apache/spark/bagel/Bagel.scala  |  2 +-
 .../scala/org/apache/spark/Dependency.scala   | 11 ++-
 .../apache/spark/api/java/JavaPairRDD.scala   |  2 +-
 .../org/apache/spark/rdd/CoGroupedRDD.scala   |  5 +-
 .../apache/spark/rdd/PairRDDFunctions.scala   | 90 ++++++++++++++++---
 .../org/apache/spark/rdd/ShuffledRDD.scala    |  4 +-
 .../org/apache/spark/rdd/SubtractedRDD.scala  |  8 +-
 .../org/apache/spark/CheckpointSuite.scala    |  2 +-
 .../shuffle/ShuffleDependencySuite.scala      | 67 ++++++++++++++
 9 files changed, 168 insertions(+), 23 deletions(-)
 create mode 100644 core/src/test/scala/org/apache/spark/shuffle/ShuffleDependencySuite.scala

diff --git a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
index ef0bb2ac13f08..4e6b7686f771d 100644
--- a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
+++ b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
@@ -78,7 +78,7 @@ object Bagel extends Logging {
       val startTime = System.currentTimeMillis
 
       val aggregated = agg(verts, aggregator)
-      val combinedMsgs = msgs.combineByKey(
+      val combinedMsgs = msgs.combineByKeyWithClassTag(
         combiner.createCombiner _, combiner.mergeMsg _, combiner.mergeCombiners _, partitioner)
       val grouped = combinedMsgs.groupWith(verts)
       val superstep_ = superstep  // Create a read-only copy of superstep for capture in closure
diff --git a/core/src/main/scala/org/apache/spark/Dependency.scala b/core/src/main/scala/org/apache/spark/Dependency.scala
index cfeeb3902c033..9aafc9eb1cde7 100644
--- a/core/src/main/scala/org/apache/spark/Dependency.scala
+++ b/core/src/main/scala/org/apache/spark/Dependency.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark
 
+import scala.reflect.ClassTag
+
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.rdd.RDD
 import org.apache.spark.serializer.Serializer
@@ -65,7 +67,7 @@ abstract class NarrowDependency[T](_rdd: RDD[T]) extends Dependency[T] {
  * @param mapSideCombine whether to perform partial aggregation (also known as map-side combine)
  */
 @DeveloperApi
-class ShuffleDependency[K, V, C](
+class ShuffleDependency[K: ClassTag, V: ClassTag, C: ClassTag](
     @transient private val _rdd: RDD[_ <: Product2[K, V]],
     val partitioner: Partitioner,
     val serializer: Option[Serializer] = None,
@@ -76,6 +78,13 @@ class ShuffleDependency[K, V, C](
 
   override def rdd: RDD[Product2[K, V]] = _rdd.asInstanceOf[RDD[Product2[K, V]]]
 
+  private[spark] val keyClassName: String = reflect.classTag[K].runtimeClass.getName
+  private[spark] val valueClassName: String = reflect.classTag[V].runtimeClass.getName
+  // Note: It's possible that the combiner class tag is null, if the combineByKey
+  // methods in PairRDDFunctions are used instead of combineByKeyWithClassTag.
+  private[spark] val combinerClassName: Option[String] =
+    Option(reflect.classTag[C]).map(_.runtimeClass.getName)
+
   val shuffleId: Int = _rdd.context.newShuffleId()
 
   val shuffleHandle: ShuffleHandle = _rdd.context.env.shuffleManager.registerShuffle(
diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
index fb787979c1820..8344f6368ac48 100644
--- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
+++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala
@@ -239,7 +239,7 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])
       mapSideCombine: Boolean,
       serializer: Serializer): JavaPairRDD[K, C] = {
       implicit val ctag: ClassTag[C] = fakeClassTag
-    fromRDD(rdd.combineByKey(
+    fromRDD(rdd.combineByKeyWithClassTag(
       createCombiner,
       mergeValue,
       mergeCombiners,
diff --git a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
index 9c617fc719cb5..7bad749d58327 100644
--- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala
@@ -22,6 +22,7 @@ import scala.language.existentials
 import java.io.{IOException, ObjectOutputStream}
 
 import scala.collection.mutable.ArrayBuffer
+import scala.reflect.ClassTag
 
 import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
@@ -74,7 +75,9 @@ private[spark] class CoGroupPartition(
  * @param part partitioner used to partition the shuffle output
  */
 @DeveloperApi
-class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: Partitioner)
+class CoGroupedRDD[K: ClassTag](
+    @transient var rdds: Seq[RDD[_ <: Product2[K, _]]],
+    part: Partitioner)
   extends RDD[(K, Array[Iterable[_]])](rdds.head.context, Nil) {
 
   // For example, `(k, a) cogroup (k, b)` produces k -> Array(ArrayBuffer as, ArrayBuffer bs).
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 4e5f2e8a5d467..c59f0d4aa75a0 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -57,7 +57,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   with SparkHadoopMapReduceUtil
   with Serializable
 {
+
   /**
+   * :: Experimental ::
    * Generic function to combine the elements for each key using a custom set of aggregation
    * functions. Turns an RDD[(K, V)] into a result of type RDD[(K, C)], for a "combined type" C
    * Note that V and C can be different -- for example, one might group an RDD of type
@@ -70,12 +72,14 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * In addition, users can control the partitioning of the output RDD, and whether to perform
    * map-side aggregation (if a mapper can produce multiple items with the same key).
    */
-  def combineByKey[C](createCombiner: V => C,
+  @Experimental
+  def combineByKeyWithClassTag[C](
+      createCombiner: V => C,
       mergeValue: (C, V) => C,
       mergeCombiners: (C, C) => C,
       partitioner: Partitioner,
       mapSideCombine: Boolean = true,
-      serializer: Serializer = null): RDD[(K, C)] = self.withScope {
+      serializer: Serializer = null)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
     require(mergeCombiners != null, "mergeCombiners must be defined") // required as of Spark 0.9.0
     if (keyClass.isArray) {
       if (mapSideCombine) {
@@ -103,13 +107,50 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   }
 
   /**
-   * Simplified version of combineByKey that hash-partitions the output RDD.
+   * Generic function to combine the elements for each key using a custom set of aggregation
+   * functions. This method is here for backward compatibility. It does not provide combiner
+   * classtag information to the shuffle.
+   *
+   * @see [[combineByKeyWithClassTag]]
    */
-  def combineByKey[C](createCombiner: V => C,
+  def combineByKey[C](
+      createCombiner: V => C,
+      mergeValue: (C, V) => C,
+      mergeCombiners: (C, C) => C,
+      partitioner: Partitioner,
+      mapSideCombine: Boolean = true,
+      serializer: Serializer = null): RDD[(K, C)] = self.withScope {
+    combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners,
+      partitioner, mapSideCombine, serializer)(null)
+  }
+
+  /**
+   * Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
+   * This method is here for backward compatibility. It does not provide combiner
+   * classtag information to the shuffle.
+   *
+   * @see [[combineByKeyWithClassTag]]
+   */
+  def combineByKey[C](
+      createCombiner: V => C,
       mergeValue: (C, V) => C,
       mergeCombiners: (C, C) => C,
       numPartitions: Int): RDD[(K, C)] = self.withScope {
-    combineByKey(createCombiner, mergeValue, mergeCombiners, new HashPartitioner(numPartitions))
+    combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners, numPartitions)(null)
+  }
+
+  /**
+   * :: Experimental ::
+   * Simplified version of combineByKeyWithClassTag that hash-partitions the output RDD.
+   */
+  @Experimental
+  def combineByKeyWithClassTag[C](
+      createCombiner: V => C,
+      mergeValue: (C, V) => C,
+      mergeCombiners: (C, C) => C,
+      numPartitions: Int)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
+    combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners,
+      new HashPartitioner(numPartitions))
   }
 
   /**
@@ -133,7 +174,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
 
     // We will clean the combiner closure later in `combineByKey`
     val cleanedSeqOp = self.context.clean(seqOp)
-    combineByKey[U]((v: V) => cleanedSeqOp(createZero(), v), cleanedSeqOp, combOp, partitioner)
+    combineByKeyWithClassTag[U]((v: V) => cleanedSeqOp(createZero(), v),
+      cleanedSeqOp, combOp, partitioner)
   }
 
   /**
@@ -182,7 +224,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val createZero = () => cachedSerializer.deserialize[V](ByteBuffer.wrap(zeroArray))
 
     val cleanedFunc = self.context.clean(func)
-    combineByKey[V]((v: V) => cleanedFunc(createZero(), v), cleanedFunc, cleanedFunc, partitioner)
+    combineByKeyWithClassTag[V]((v: V) => cleanedFunc(createZero(), v),
+      cleanedFunc, cleanedFunc, partitioner)
   }
 
   /**
@@ -268,7 +311,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * "combiner" in MapReduce.
    */
   def reduceByKey(partitioner: Partitioner, func: (V, V) => V): RDD[(K, V)] = self.withScope {
-    combineByKey[V]((v: V) => v, func, func, partitioner)
+    combineByKeyWithClassTag[V]((v: V) => v, func, func, partitioner)
   }
 
   /**
@@ -392,7 +435,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       h1
     }
 
-    combineByKey(createHLL, mergeValueHLL, mergeHLL, partitioner).mapValues(_.cardinality())
+    combineByKeyWithClassTag(createHLL, mergeValueHLL, mergeHLL, partitioner)
+      .mapValues(_.cardinality())
   }
 
   /**
@@ -466,7 +510,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val createCombiner = (v: V) => CompactBuffer(v)
     val mergeValue = (buf: CompactBuffer[V], v: V) => buf += v
     val mergeCombiners = (c1: CompactBuffer[V], c2: CompactBuffer[V]) => c1 ++= c2
-    val bufs = combineByKey[CompactBuffer[V]](
+    val bufs = combineByKeyWithClassTag[CompactBuffer[V]](
       createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine = false)
     bufs.asInstanceOf[RDD[(K, Iterable[V])]]
   }
@@ -565,12 +609,30 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   }
 
   /**
-   * Simplified version of combineByKey that hash-partitions the resulting RDD using the
+   * Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the
+   * existing partitioner/parallelism level. This method is here for backward compatibility. It
+   * does not provide combiner classtag information to the shuffle.
+   *
+   * @see [[combineByKeyWithClassTag]]
+   */
+  def combineByKey[C](
+      createCombiner: V => C,
+      mergeValue: (C, V) => C,
+      mergeCombiners: (C, C) => C): RDD[(K, C)] = self.withScope {
+    combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners)(null)
+  }
+
+  /**
+   * :: Experimental ::
+   * Simplified version of combineByKeyWithClassTag that hash-partitions the resulting RDD using the
    * existing partitioner/parallelism level.
    */
-  def combineByKey[C](createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiners: (C, C) => C)
-    : RDD[(K, C)] = self.withScope {
-    combineByKey(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self))
+  @Experimental
+  def combineByKeyWithClassTag[C](
+      createCombiner: V => C,
+      mergeValue: (C, V) => C,
+      mergeCombiners: (C, C) => C)(implicit ct: ClassTag[C]): RDD[(K, C)] = self.withScope {
+    combineByKeyWithClassTag(createCombiner, mergeValue, mergeCombiners, defaultPartitioner(self))
   }
 
   /**
diff --git a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
index 2dc47f95937cb..cb15d912bbfb5 100644
--- a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.rdd
 
+import scala.reflect.ClassTag
+
 import org.apache.spark._
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.serializer.Serializer
@@ -37,7 +39,7 @@ private[spark] class ShuffledRDDPartition(val idx: Int) extends Partition {
  */
 // TODO: Make this return RDD[Product2[K, C]] or have some way to configure mutable pairs
 @DeveloperApi
-class ShuffledRDD[K, V, C](
+class ShuffledRDD[K: ClassTag, V: ClassTag, C: ClassTag](
     @transient var prev: RDD[_ <: Product2[K, V]],
     part: Partitioner)
   extends RDD[(K, C)](prev.context, Nil) {
diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
index 9a4fa301b06e3..25ec685eff5ab 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala
@@ -63,15 +63,17 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag](
   }
 
   override def getDependencies: Seq[Dependency[_]] = {
-    Seq(rdd1, rdd2).map { rdd =>
+    def rddDependency[T1: ClassTag, T2: ClassTag](rdd: RDD[_ <: Product2[T1, T2]])
+      : Dependency[_] = {
       if (rdd.partitioner == Some(part)) {
         logDebug("Adding one-to-one dependency with " + rdd)
         new OneToOneDependency(rdd)
       } else {
         logDebug("Adding shuffle dependency with " + rdd)
-        new ShuffleDependency(rdd, part, serializer)
+        new ShuffleDependency[T1, T2, Any](rdd, part, serializer)
       }
     }
+    Seq(rddDependency[K, V](rdd1), rddDependency[K, W](rdd2))
   }
 
   override def getPartitions: Array[Partition] = {
@@ -105,7 +107,7 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag](
         seq
       }
     }
-    def integrate(depNum: Int, op: Product2[K, V] => Unit) = {
+    def integrate(depNum: Int, op: Product2[K, V] => Unit): Unit = {
       dependencies(depNum) match {
         case oneToOneDependency: OneToOneDependency[_] =>
           val dependencyPartition = partition.narrowDeps(depNum).get.split
diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
index d343bb95cb68c..4d70bfed909b6 100644
--- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
+++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala
@@ -483,7 +483,7 @@ class FatPairRDD(parent: RDD[Int], _partitioner: Partitioner) extends RDD[(Int,
 object CheckpointSuite {
   // This is a custom cogroup function that does not use mapValues like
   // the PairRDDFunctions.cogroup()
-  def cogroup[K, V](first: RDD[(K, V)], second: RDD[(K, V)], part: Partitioner)
+  def cogroup[K: ClassTag, V: ClassTag](first: RDD[(K, V)], second: RDD[(K, V)], part: Partitioner)
     : RDD[(K, Array[Iterable[V]])] = {
     new CoGroupedRDD[K](
       Seq(first.asInstanceOf[RDD[(K, _)]], second.asInstanceOf[RDD[(K, _)]]),
diff --git a/core/src/test/scala/org/apache/spark/shuffle/ShuffleDependencySuite.scala b/core/src/test/scala/org/apache/spark/shuffle/ShuffleDependencySuite.scala
new file mode 100644
index 0000000000000..4d5f599fb12ab
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/shuffle/ShuffleDependencySuite.scala
@@ -0,0 +1,67 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.shuffle
+
+import org.apache.spark._
+
+case class KeyClass()
+
+case class ValueClass()
+
+case class CombinerClass()
+
+class ShuffleDependencySuite extends SparkFunSuite with LocalSparkContext {
+
+  val conf = new SparkConf(loadDefaults = false)
+
+  test("key, value, and combiner classes correct in shuffle dependency without aggregation") {
+    sc = new SparkContext("local", "test", conf.clone())
+    val rdd = sc.parallelize(1 to 5, 4)
+      .map(key => (KeyClass(), ValueClass()))
+      .groupByKey()
+    val dep = rdd.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]]
+    assert(!dep.mapSideCombine, "Test requires that no map-side aggregator is defined")
+    assert(dep.keyClassName == classOf[KeyClass].getName)
+    assert(dep.valueClassName == classOf[ValueClass].getName)
+  }
+
+  test("key, value, and combiner classes available in shuffle dependency with aggregation") {
+    sc = new SparkContext("local", "test", conf.clone())
+    val rdd = sc.parallelize(1 to 5, 4)
+      .map(key => (KeyClass(), ValueClass()))
+      .aggregateByKey(CombinerClass())({ case (a, b) => a }, { case (a, b) => a })
+    val dep = rdd.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]]
+    assert(dep.mapSideCombine && dep.aggregator.isDefined, "Test requires map-side aggregation")
+    assert(dep.keyClassName == classOf[KeyClass].getName)
+    assert(dep.valueClassName == classOf[ValueClass].getName)
+    assert(dep.combinerClassName == Some(classOf[CombinerClass].getName))
+  }
+
+  test("combineByKey null combiner class tag handled correctly") {
+    sc = new SparkContext("local", "test", conf.clone())
+    val rdd = sc.parallelize(1 to 5, 4)
+      .map(key => (KeyClass(), ValueClass()))
+      .combineByKey((v: ValueClass) => v,
+        (c: AnyRef, v: ValueClass) => c,
+        (c1: AnyRef, c2: AnyRef) => c1)
+    val dep = rdd.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]]
+    assert(dep.keyClassName == classOf[KeyClass].getName)
+    assert(dep.valueClassName == classOf[ValueClass].getName)
+    assert(dep.combinerClassName == None)
+  }
+
+}

From 339a527141984bfb182862b0987d3c4690c9ede1 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 10 Sep 2015 20:34:00 -0700
Subject: [PATCH 584/633] [SPARK-10023] [ML] [PySpark] Unified
 DecisionTreeParams checkpointInterval between Scala and Python API.

"checkpointInterval" is member of DecisionTreeParams in Scala API which is inconsistency with Python API, we should unified them.
```
member of DecisionTreeParams <-> Scala API
shared param for all ML Transformer/Estimator <-> Python API
```
Proposal:
"checkpointInterval" is also used by ALS, so we make it shared params at Scala.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8528 from yanboliang/spark-10023.
---
 .../DecisionTreeClassifier.scala              |  1 +
 .../ml/param/shared/SharedParamsCodeGen.scala |  3 +-
 .../spark/ml/param/shared/sharedParams.scala  |  4 +--
 .../org/apache/spark/ml/tree/treeParams.scala | 32 +++++++------------
 4 files changed, 16 insertions(+), 24 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 6f70b96b17ec6..0a75d5d22280f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -19,6 +19,7 @@ package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.param.ParamMap
+import org.apache.spark.ml.param.shared.HasCheckpointInterval
 import org.apache.spark.ml.tree.{DecisionTreeModel, DecisionTreeParams, Node, TreeClassifierParams}
 import org.apache.spark.ml.tree.impl.RandomForest
 import org.apache.spark.ml.util.{Identifiable, MetadataUtils}
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
index 8c16c6149b40d..e9e99ed1db40e 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/SharedParamsCodeGen.scala
@@ -56,7 +56,8 @@ private[shared] object SharedParamsCodeGen {
       ParamDesc[String]("inputCol", "input column name"),
       ParamDesc[Array[String]]("inputCols", "input column names"),
       ParamDesc[String]("outputCol", "output column name", Some("uid + \"__output\"")),
-      ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1)",
+      ParamDesc[Int]("checkpointInterval", "checkpoint interval (>= 1). E.g. 10 means that " +
+        "the cache will get checkpointed every 10 iterations.",
         isValid = "ParamValidators.gtEq(1)"),
       ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")),
       ParamDesc[String]("handleInvalid", "how to handle invalid entries. Options are skip (which " +
diff --git a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
index c26768953e3db..30092170863ad 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala
@@ -223,10 +223,10 @@ private[ml] trait HasOutputCol extends Params {
 private[ml] trait HasCheckpointInterval extends Params {
 
   /**
-   * Param for checkpoint interval (>= 1).
+   * Param for checkpoint interval (>= 1). E.g. 10 means that the cache will get checkpointed every 10 iterations..
    * @group param
    */
-  final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval (>= 1)", ParamValidators.gtEq(1))
+  final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "checkpoint interval (>= 1). E.g. 10 means that the cache will get checkpointed every 10 iterations.", ParamValidators.gtEq(1))
 
   /** @group getParam */
   final def getCheckpointInterval: Int = $(checkpointInterval)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
index dbd8d31571d2e..d29f5253c9c3f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/treeParams.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.tree
 import org.apache.spark.ml.classification.ClassifierParams
 import org.apache.spark.ml.PredictorParams
 import org.apache.spark.ml.param._
-import org.apache.spark.ml.param.shared.{HasMaxIter, HasSeed, HasThresholds}
+import org.apache.spark.ml.param.shared.{HasCheckpointInterval, HasMaxIter, HasSeed, HasThresholds}
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, BoostingStrategy => OldBoostingStrategy, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.impurity.{Entropy => OldEntropy, Gini => OldGini, Impurity => OldImpurity, Variance => OldVariance}
 import org.apache.spark.mllib.tree.loss.{Loss => OldLoss}
@@ -30,7 +30,7 @@ import org.apache.spark.mllib.tree.loss.{Loss => OldLoss}
  *
  * Note: Marked as private and DeveloperApi since this may be made public in the future.
  */
-private[ml] trait DecisionTreeParams extends PredictorParams {
+private[ml] trait DecisionTreeParams extends PredictorParams with HasCheckpointInterval {
 
   /**
    * Maximum depth of the tree (>= 0).
@@ -96,21 +96,6 @@ private[ml] trait DecisionTreeParams extends PredictorParams {
     " algorithm will cache node IDs for each instance. Caching can speed up training of deeper" +
     " trees.")
 
-  /**
-   * Specifies how often to checkpoint the cached node IDs.
-   * E.g. 10 means that the cache will get checkpointed every 10 iterations.
-   * This is only used if cacheNodeIds is true and if the checkpoint directory is set in
-   * [[org.apache.spark.SparkContext]].
-   * Must be >= 1.
-   * (default = 10)
-   * @group expertParam
-   */
-  final val checkpointInterval: IntParam = new IntParam(this, "checkpointInterval", "Specifies" +
-    " how often to checkpoint the cached node IDs.  E.g. 10 means that the cache will get" +
-    " checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the" +
-    " checkpoint directory is set in the SparkContext. Must be >= 1.",
-    ParamValidators.gtEq(1))
-
   setDefault(maxDepth -> 5, maxBins -> 32, minInstancesPerNode -> 1, minInfoGain -> 0.0,
     maxMemoryInMB -> 256, cacheNodeIds -> false, checkpointInterval -> 10)
 
@@ -150,12 +135,17 @@ private[ml] trait DecisionTreeParams extends PredictorParams {
   /** @group expertGetParam */
   final def getCacheNodeIds: Boolean = $(cacheNodeIds)
 
-  /** @group expertSetParam */
+  /**
+   * Specifies how often to checkpoint the cached node IDs.
+   * E.g. 10 means that the cache will get checkpointed every 10 iterations.
+   * This is only used if cacheNodeIds is true and if the checkpoint directory is set in
+   * [[org.apache.spark.SparkContext]].
+   * Must be >= 1.
+   * (default = 10)
+   * @group expertSetParam
+   */
   def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)
 
-  /** @group expertGetParam */
-  final def getCheckpointInterval: Int = $(checkpointInterval)
-
   /** (private[ml]) Create a Strategy instance to use with the old API. */
   private[ml] def getOldStrategy(
       categoricalFeatures: Map[Int, Int],

From a140dd77c62255d6f7f6817a2517d47feb8540d4 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Thu, 10 Sep 2015 20:43:38 -0700
Subject: [PATCH 585/633] [SPARK-10027] [ML] [PySpark] Add Python API missing
 methods for ml.feature

Missing method of ml.feature are listed here:
```StringIndexer``` lacks of parameter ```handleInvalid```.
```StringIndexerModel``` lacks of method ```labels```.
```VectorIndexerModel``` lacks of methods ```numFeatures``` and ```categoryMaps```.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8313 from yanboliang/spark-10027.
---
 python/pyspark/ml/feature.py                  | 31 ++++++++++++++++---
 .../ml/param/_shared_params_code_gen.py       |  5 ++-
 python/pyspark/ml/param/shared.py             | 31 +++++++++++++++++--
 3 files changed, 59 insertions(+), 8 deletions(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 1c423486be8d9..71dc636b83eac 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -920,7 +920,7 @@ def mean(self):
 
 
 @inherit_doc
-class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol):
+class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol, HasHandleInvalid):
     """
     .. note:: Experimental
 
@@ -943,19 +943,20 @@ class StringIndexer(JavaEstimator, HasInputCol, HasOutputCol):
     """
 
     @keyword_only
-    def __init__(self, inputCol=None, outputCol=None):
+    def __init__(self, inputCol=None, outputCol=None, handleInvalid="error"):
         """
-        __init__(self, inputCol=None, outputCol=None)
+        __init__(self, inputCol=None, outputCol=None, handleInvalid="error")
         """
         super(StringIndexer, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.StringIndexer", self.uid)
+        self._setDefault(handleInvalid="error")
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
     @keyword_only
-    def setParams(self, inputCol=None, outputCol=None):
+    def setParams(self, inputCol=None, outputCol=None, handleInvalid="error"):
         """
-        setParams(self, inputCol=None, outputCol=None)
+        setParams(self, inputCol=None, outputCol=None, handleInvalid="error")
         Sets params for this StringIndexer.
         """
         kwargs = self.setParams._input_kwargs
@@ -1235,6 +1236,10 @@ class VectorIndexer(JavaEstimator, HasInputCol, HasOutputCol):
     >>> model = indexer.fit(df)
     >>> model.transform(df).head().indexed
     DenseVector([1.0, 0.0])
+    >>> model.numFeatures
+    2
+    >>> model.categoryMaps
+    {0: {0.0: 0, -1.0: 1}}
     >>> indexer.setParams(outputCol="test").fit(df).transform(df).collect()[1].test
     DenseVector([0.0, 1.0])
     >>> params = {indexer.maxCategories: 3, indexer.outputCol: "vector"}
@@ -1297,6 +1302,22 @@ class VectorIndexerModel(JavaModel):
     Model fitted by VectorIndexer.
     """
 
+    @property
+    def numFeatures(self):
+        """
+        Number of features, i.e., length of Vectors which this transforms.
+        """
+        return self._call_java("numFeatures")
+
+    @property
+    def categoryMaps(self):
+        """
+        Feature value index.  Keys are categorical feature indices (column indices).
+        Values are maps from original features values to 0-based category indices.
+        If a feature is not in this map, it is treated as continuous.
+        """
+        return self._call_java("javaCategoryMaps")
+
 
 @inherit_doc
 class VectorSlicer(JavaTransformer, HasInputCol, HasOutputCol):
diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py
index 69efc424ec4ef..926375e44871d 100644
--- a/python/pyspark/ml/param/_shared_params_code_gen.py
+++ b/python/pyspark/ml/param/_shared_params_code_gen.py
@@ -121,7 +121,10 @@ def get$Name(self):
         ("checkpointInterval", "checkpoint interval (>= 1)", None),
         ("seed", "random seed", "hash(type(self).__name__)"),
         ("tol", "the convergence tolerance for iterative algorithms", None),
-        ("stepSize", "Step size to be used for each iteration of optimization.", None)]
+        ("stepSize", "Step size to be used for each iteration of optimization.", None),
+        ("handleInvalid", "how to handle invalid entries. Options are skip (which will filter " +
+         "out rows with bad values), or error (which will throw an errror). More options may be " +
+         "added later.", None)]
     code = []
     for name, doc, defaultValueStr in shared:
         param_code = _gen_param_header(name, doc, defaultValueStr)
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index 595124726366d..682170aee85fb 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -432,6 +432,33 @@ def getStepSize(self):
         return self.getOrDefault(self.stepSize)
 
 
+class HasHandleInvalid(Params):
+    """
+    Mixin for param handleInvalid: how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later..
+    """
+
+    # a placeholder to make it appear in the generated doc
+    handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.")
+
+    def __init__(self):
+        super(HasHandleInvalid, self).__init__()
+        #: param for how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.
+        self.handleInvalid = Param(self, "handleInvalid", "how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an errror). More options may be added later.")
+
+    def setHandleInvalid(self, value):
+        """
+        Sets the value of :py:attr:`handleInvalid`.
+        """
+        self._paramMap[self.handleInvalid] = value
+        return self
+
+    def getHandleInvalid(self):
+        """
+        Gets the value of handleInvalid or its default value.
+        """
+        return self.getOrDefault(self.handleInvalid)
+
+
 class DecisionTreeParams(Params):
     """
     Mixin for Decision Tree parameters.
@@ -444,7 +471,7 @@ class DecisionTreeParams(Params):
     minInfoGain = Param(Params._dummy(), "minInfoGain", "Minimum information gain for a split to be considered at a tree node.")
     maxMemoryInMB = Param(Params._dummy(), "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.")
     cacheNodeIds = Param(Params._dummy(), "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.")
-
+    
 
     def __init__(self):
         super(DecisionTreeParams, self).__init__()
@@ -460,7 +487,7 @@ def __init__(self):
         self.maxMemoryInMB = Param(self, "maxMemoryInMB", "Maximum memory in MB allocated to histogram aggregation.")
         #: param for If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.
         self.cacheNodeIds = Param(self, "cacheNodeIds", "If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees.")
-
+        
     def setMaxDepth(self, value):
         """
         Sets the value of :py:attr:`maxDepth`.

From e1d7f64296d8164cc4c935aa524af664f63bf9c1 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Fri, 11 Sep 2015 18:26:56 +0800
Subject: [PATCH 586/633] [SPARK-10472] [SQL] Fixes DataType.typeName for UDT

Before this fix, `MyDenseVectorUDT.typeName` gives `mydensevecto`, which is not desirable.

Author: Cheng Lian <lian@databricks.com>

Closes #8640 from liancheng/spark-10472/udt-type-name.
---
 .../main/scala/org/apache/spark/sql/types/DataType.scala    | 4 +++-
 .../scala/org/apache/spark/sql/UserDefinedTypeSuite.scala   | 6 ++++++
 2 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
index 7bcd623b3f33e..4b54c31dcc27a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/types/DataType.scala
@@ -51,7 +51,9 @@ abstract class DataType extends AbstractDataType {
   def defaultSize: Int
 
   /** Name of the type used in JSON serialization. */
-  def typeName: String = this.getClass.getSimpleName.stripSuffix("$").dropRight(4).toLowerCase
+  def typeName: String = {
+    this.getClass.getSimpleName.stripSuffix("$").stripSuffix("Type").stripSuffix("UDT").toLowerCase
+  }
 
   private[sql] def jsonValue: JValue = typeName
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
index fa8f9c8e00089..46d87843dfa4d 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/UserDefinedTypeSuite.scala
@@ -157,4 +157,10 @@ class UserDefinedTypeSuite extends QueryTest with SharedSQLContext {
         Nil
     )
   }
+
+  test("SPARK-10472 UserDefinedType.typeName") {
+    assert(IntegerType.typeName === "integer")
+    assert(new MyDenseVectorUDT().typeName === "mydensevector")
+    assert(new OpenHashSetUDT(IntegerType).typeName === "openhashset")
+  }
 }

From 9bbe33f318c866c0b13088917542715062f0787f Mon Sep 17 00:00:00 2001
From: Ahir Reddy <ahirreddy@gmail.com>
Date: Fri, 11 Sep 2015 13:06:14 +0100
Subject: [PATCH 587/633] [SPARK-10556] Remove explicit Scala version for sbt
 project build files

Previously, project/plugins.sbt explicitly set scalaVersion to 2.10.4. This can cause issues when using a version of sbt that is compiled against a different version of Scala (for example sbt 0.13.9 uses 2.10.5). Removing this explicit setting will cause build files to be compiled and run against the same version of Scala that sbt is compiled against.

Note that this only applies to the project build files (items in project/), it is distinct from the version of Scala we target for the actual spark compilation.

Author: Ahir Reddy <ahirreddy@gmail.com>

Closes #8709 from ahirreddy/sbt-scala-version-fix.
---
 project/plugins.sbt | 2 --
 1 file changed, 2 deletions(-)

diff --git a/project/plugins.sbt b/project/plugins.sbt
index 51820460ca1a0..c06687d8f197b 100644
--- a/project/plugins.sbt
+++ b/project/plugins.sbt
@@ -1,5 +1,3 @@
-scalaVersion := "2.10.4"
-
 resolvers += Resolver.url("artifactory", url("http://scalasbt.artifactoryonline.com/scalasbt/sbt-plugin-releases"))(Resolver.ivyStylePatterns)
 
 resolvers += "Typesafe Repository" at "http://repo.typesafe.com/typesafe/releases/"

From c268ca4ddde2f5213b2e3985dcaaac5900aea71c Mon Sep 17 00:00:00 2001
From: y-shimizu <y.shimizu0429@gmail.com>
Date: Fri, 11 Sep 2015 08:27:30 -0700
Subject: [PATCH 588/633] [SPARK-10518] [DOCS] Update code examples in spark.ml
 user guide to use LIBSVM data source instead of MLUtils

I fixed to use LIBSVM data source in the example code in spark.ml instead of MLUtils

Author: y-shimizu <y.shimizu0429@gmail.com>

Closes #8697 from y-shimizu/SPARK-10518.
---
 docs/ml-ensembles.md      | 65 ++++++++++++---------------------------
 docs/ml-features.md       | 64 +++++++++++++-------------------------
 docs/ml-linear-methods.md | 22 ++++---------
 3 files changed, 47 insertions(+), 104 deletions(-)

diff --git a/docs/ml-ensembles.md b/docs/ml-ensembles.md
index 62749909e01dc..58f566c9b4b55 100644
--- a/docs/ml-ensembles.md
+++ b/docs/ml-ensembles.md
@@ -121,10 +121,9 @@ import org.apache.spark.ml.classification.RandomForestClassifier
 import org.apache.spark.ml.classification.RandomForestClassificationModel
 import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -193,14 +192,11 @@ import org.apache.spark.ml.classification.RandomForestClassifier;
 import org.apache.spark.ml.classification.RandomForestClassificationModel;
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
 import org.apache.spark.ml.feature.*;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -268,10 +264,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.classification import RandomForestClassifier
 from pyspark.ml.feature import StringIndexer, VectorIndexer
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Index labels, adding metadata to the label column.
 # Fit on whole dataset to include all labels in index.
@@ -327,10 +322,9 @@ import org.apache.spark.ml.regression.RandomForestRegressor
 import org.apache.spark.ml.regression.RandomForestRegressionModel
 import org.apache.spark.ml.feature.VectorIndexer
 import org.apache.spark.ml.evaluation.RegressionEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -387,14 +381,11 @@ import org.apache.spark.ml.feature.VectorIndexer;
 import org.apache.spark.ml.feature.VectorIndexerModel;
 import org.apache.spark.ml.regression.RandomForestRegressionModel;
 import org.apache.spark.ml.regression.RandomForestRegressor;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -450,10 +441,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.regression import RandomForestRegressor
 from pyspark.ml.feature import VectorIndexer
 from pyspark.ml.evaluation import RegressionEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Automatically identify categorical features, and index them.
 # Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -576,10 +566,9 @@ import org.apache.spark.ml.classification.GBTClassifier
 import org.apache.spark.ml.classification.GBTClassificationModel
 import org.apache.spark.ml.feature.{StringIndexer, IndexToString, VectorIndexer}
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -648,14 +637,10 @@ import org.apache.spark.ml.classification.GBTClassifier;
 import org.apache.spark.ml.classification.GBTClassificationModel;
 import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
 import org.apache.spark.ml.feature.*;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt");
 
 // Index labels, adding metadata to the label column.
 // Fit on whole dataset to include all labels in index.
@@ -724,10 +709,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.classification import GBTClassifier
 from pyspark.ml.feature import StringIndexer, VectorIndexer
 from pyspark.ml.evaluation import MulticlassClassificationEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Index labels, adding metadata to the label column.
 # Fit on whole dataset to include all labels in index.
@@ -783,10 +767,9 @@ import org.apache.spark.ml.regression.GBTRegressor
 import org.apache.spark.ml.regression.GBTRegressionModel
 import org.apache.spark.ml.feature.VectorIndexer
 import org.apache.spark.ml.evaluation.RegressionEvaluator
-import org.apache.spark.mllib.util.MLUtils
 
 // Load and parse the data file, converting it to a DataFrame.
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -844,14 +827,10 @@ import org.apache.spark.ml.feature.VectorIndexer;
 import org.apache.spark.ml.feature.VectorIndexerModel;
 import org.apache.spark.ml.regression.GBTRegressionModel;
 import org.apache.spark.ml.regression.GBTRegressor;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 
 // Load and parse the data file, converting it to a DataFrame.
-RDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt");
-DataFrame data = jsql.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt");
 
 // Automatically identify categorical features, and index them.
 // Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -908,10 +887,9 @@ from pyspark.ml import Pipeline
 from pyspark.ml.regression import GBTRegressor
 from pyspark.ml.feature import VectorIndexer
 from pyspark.ml.evaluation import RegressionEvaluator
-from pyspark.mllib.util import MLUtils
 
 # Load and parse the data file, converting it to a DataFrame.
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 # Automatically identify categorical features, and index them.
 # Set maxCategories so features with > 4 distinct values are treated as continuous.
@@ -970,15 +948,14 @@ Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classifie
 {% highlight scala %}
 import org.apache.spark.ml.classification.{LogisticRegression, OneVsRest}
 import org.apache.spark.mllib.evaluation.MulticlassMetrics
-import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.sql.{Row, SQLContext}
 
 val sqlContext = new SQLContext(sc)
 
 // parse data into dataframe
-val data = MLUtils.loadLibSVMFile(sc, 
-  "data/mllib/sample_multiclass_classification_data.txt")
-val Array(train, test) = data.toDF().randomSplit(Array(0.7, 0.3))
+val data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_multiclass_classification_data.txt")
+val Array(train, test) = data.randomSplit(Array(0.7, 0.3))
 
 // instantiate multiclass learner and train
 val ovr = new OneVsRest().setClassifier(new LogisticRegression)
@@ -1016,9 +993,6 @@ import org.apache.spark.ml.classification.OneVsRest;
 import org.apache.spark.ml.classification.OneVsRestModel;
 import org.apache.spark.mllib.evaluation.MulticlassMetrics;
 import org.apache.spark.mllib.linalg.Matrix;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
-import org.apache.spark.rdd.RDD;
 import org.apache.spark.sql.DataFrame;
 import org.apache.spark.sql.SQLContext;
 
@@ -1026,10 +1000,9 @@ SparkConf conf = new SparkConf().setAppName("JavaOneVsRestExample");
 JavaSparkContext jsc = new JavaSparkContext(conf);
 SQLContext jsql = new SQLContext(jsc);
 
-RDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(),
-  "data/mllib/sample_multiclass_classification_data.txt");
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_multiclass_classification_data.txt");
 
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
 DataFrame[] splits = dataFrame.randomSplit(new double[] {0.7, 0.3}, 12345);
 DataFrame train = splits[0];
 DataFrame test = splits[1];
diff --git a/docs/ml-features.md b/docs/ml-features.md
index 58b31a5a5cc47..a414c21b5c280 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1179,9 +1179,9 @@ In the example below, we read in a dataset of labeled points and then use `Vecto
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.ml.feature.VectorIndexer
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 val indexer = new VectorIndexer()
   .setInputCol("features")
   .setOutputCol("indexed")
@@ -1200,16 +1200,12 @@ val indexedData = indexerModel.transform(data)
 {% highlight java %}
 import java.util.Map;
 
-import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.VectorIndexer;
 import org.apache.spark.ml.feature.VectorIndexerModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> rdd = MLUtils.loadLibSVMFile(sc.sc(),
-  "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame data = sqlContext.createDataFrame(rdd, LabeledPoint.class);
+DataFrame data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 VectorIndexer indexer = new VectorIndexer()
   .setInputCol("features")
   .setOutputCol("indexed")
@@ -1230,9 +1226,9 @@ DataFrame indexedData = indexerModel.transform(data);
 <div data-lang="python" markdown="1">
 {% highlight python %}
 from pyspark.ml.feature import VectorIndexer
-from pyspark.mllib.util import MLUtils
 
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+data = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 indexer = VectorIndexer(inputCol="features", outputCol="indexed", maxCategories=10)
 indexerModel = indexer.fit(data)
 
@@ -1253,10 +1249,9 @@ The following example demonstrates how to load a dataset in libsvm format and th
 <div data-lang="scala">
 {% highlight scala %}
 import org.apache.spark.ml.feature.Normalizer
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-val dataFrame = sqlContext.createDataFrame(data)
+val dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 
 // Normalize each Vector using $L^1$ norm.
 val normalizer = new Normalizer()
@@ -1272,15 +1267,11 @@ val lInfNormData = normalizer.transform(dataFrame, normalizer.p -> Double.Positi
 
 <div data-lang="java">
 {% highlight java %}
-import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.Normalizer;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> data =
-  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 
 // Normalize each Vector using $L^1$ norm.
 Normalizer normalizer = new Normalizer()
@@ -1297,11 +1288,10 @@ DataFrame lInfNormData =
 
 <div data-lang="python">
 {% highlight python %}
-from pyspark.mllib.util import MLUtils
 from pyspark.ml.feature import Normalizer
 
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-dataFrame = sqlContext.createDataFrame(data)
+dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 
 # Normalize each Vector using $L^1$ norm.
 normalizer = Normalizer(inputCol="features", outputCol="normFeatures", p=1.0)
@@ -1335,10 +1325,9 @@ The following example demonstrates how to load a dataset in libsvm format and th
 <div data-lang="scala">
 {% highlight scala %}
 import org.apache.spark.ml.feature.StandardScaler
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-val dataFrame = sqlContext.createDataFrame(data)
+val dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 val scaler = new StandardScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures")
@@ -1355,16 +1344,12 @@ val scaledData = scalerModel.transform(dataFrame)
 
 <div data-lang="java">
 {% highlight java %}
-import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.StandardScaler;
 import org.apache.spark.ml.feature.StandardScalerModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> data =
-  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 StandardScaler scaler = new StandardScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures")
@@ -1381,11 +1366,10 @@ DataFrame scaledData = scalerModel.transform(dataFrame);
 
 <div data-lang="python">
 {% highlight python %}
-from pyspark.mllib.util import MLUtils
 from pyspark.ml.feature import StandardScaler
 
-data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-dataFrame = sqlContext.createDataFrame(data)
+dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures",
                         withStd=True, withMean=False)
 
@@ -1424,10 +1408,9 @@ More details can be found in the API docs for
 [MinMaxScalerModel](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScalerModel).
 {% highlight scala %}
 import org.apache.spark.ml.feature.MinMaxScaler
-import org.apache.spark.mllib.util.MLUtils
 
-val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
-val dataFrame = sqlContext.createDataFrame(data)
+val dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt")
 val scaler = new MinMaxScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures")
@@ -1448,13 +1431,10 @@ More details can be found in the API docs for
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.ml.feature.MinMaxScaler;
 import org.apache.spark.ml.feature.MinMaxScalerModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.sql.DataFrame;
 
-JavaRDD<LabeledPoint> data =
-  MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD();
-DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
+DataFrame dataFrame = sqlContext.read.format("libsvm")
+  .load("data/mllib/sample_libsvm_data.txt");
 MinMaxScaler scaler = new MinMaxScaler()
   .setInputCol("features")
   .setOutputCol("scaledFeatures");
diff --git a/docs/ml-linear-methods.md b/docs/ml-linear-methods.md
index cdd9d4999fa1b..4e94e2f9c708d 100644
--- a/docs/ml-linear-methods.md
+++ b/docs/ml-linear-methods.md
@@ -59,10 +59,9 @@ $\alpha$ and `regParam` corresponds to $\lambda$.
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.ml.classification.LogisticRegression
-import org.apache.spark.mllib.util.MLUtils
 
 // Load training data
-val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 val lr = new LogisticRegression()
   .setMaxIter(10)
@@ -81,8 +80,6 @@ println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}")
 {% highlight java %}
 import org.apache.spark.ml.classification.LogisticRegression;
 import org.apache.spark.ml.classification.LogisticRegressionModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.SparkConf;
 import org.apache.spark.SparkContext;
 import org.apache.spark.sql.DataFrame;
@@ -98,7 +95,7 @@ public class LogisticRegressionWithElasticNetExample {
     String path = "data/mllib/sample_libsvm_data.txt";
 
     // Load training data
-    DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
+    DataFrame training = sqlContext.read.format("libsvm").load(path);
 
     LogisticRegression lr = new LogisticRegression()
       .setMaxIter(10)
@@ -118,11 +115,9 @@ public class LogisticRegressionWithElasticNetExample {
 <div data-lang="python" markdown="1">
 {% highlight python %}
 from pyspark.ml.classification import LogisticRegression
-from pyspark.mllib.regression import LabeledPoint
-from pyspark.mllib.util import MLUtils
 
 # Load training data
-training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
 
@@ -251,10 +246,9 @@ regression model and extracting model summary statistics.
 <div data-lang="scala" markdown="1">
 {% highlight scala %}
 import org.apache.spark.ml.regression.LinearRegression
-import org.apache.spark.mllib.util.MLUtils
 
 // Load training data
-val training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+val training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 val lr = new LinearRegression()
   .setMaxIter(10)
@@ -283,8 +277,6 @@ import org.apache.spark.ml.regression.LinearRegression;
 import org.apache.spark.ml.regression.LinearRegressionModel;
 import org.apache.spark.ml.regression.LinearRegressionTrainingSummary;
 import org.apache.spark.mllib.linalg.Vectors;
-import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.util.MLUtils;
 import org.apache.spark.SparkConf;
 import org.apache.spark.SparkContext;
 import org.apache.spark.sql.DataFrame;
@@ -300,7 +292,7 @@ public class LinearRegressionWithElasticNetExample {
     String path = "data/mllib/sample_libsvm_data.txt";
 
     // Load training data
-    DataFrame training = sql.createDataFrame(MLUtils.loadLibSVMFile(sc, path).toJavaRDD(), LabeledPoint.class);
+    DataFrame training = sqlContext.read.format("libsvm").load(path);
 
     LinearRegression lr = new LinearRegression()
       .setMaxIter(10)
@@ -329,11 +321,9 @@ public class LinearRegressionWithElasticNetExample {
 <!--- TODO: Add python model summaries once implemented -->
 {% highlight python %}
 from pyspark.ml.regression import LinearRegression
-from pyspark.mllib.regression import LabeledPoint
-from pyspark.mllib.util import MLUtils
 
 # Load training data
-training = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF()
+training = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
 
 lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
 

From b656e6134fc5cd27e1fe6b6ab30fd7633cab0b14 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Fri, 11 Sep 2015 08:50:35 -0700
Subject: [PATCH 589/633] [SPARK-10026] [ML] [PySpark] Implement some common
 Params for regression in PySpark

LinearRegression and LogisticRegression lack of some Params for Python, and some Params are not shared classes which lead we need to write them for each class. These kinds of Params are list here:
```scala
HasElasticNetParam
HasFitIntercept
HasStandardization
HasThresholds
```
Here we implement them in shared params at Python side and make LinearRegression/LogisticRegression parameters peer with Scala one.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8508 from yanboliang/spark-10026.
---
 python/pyspark/ml/classification.py           |  75 ++----------
 .../ml/param/_shared_params_code_gen.py       |  11 +-
 python/pyspark/ml/param/shared.py             | 111 ++++++++++++++++++
 python/pyspark/ml/regression.py               |  42 ++-----
 4 files changed, 143 insertions(+), 96 deletions(-)

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 83f808efc3bf0..22bdd1b322aca 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -31,7 +31,8 @@
 
 @inherit_doc
 class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
-                         HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol):
+                         HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol,
+                         HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds):
     """
     Logistic regression.
     Currently, this class only supports binary classification.
@@ -65,17 +66,6 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
     """
 
     # a placeholder to make it appear in the generated doc
-    elasticNetParam = \
-        Param(Params._dummy(), "elasticNetParam",
-              "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
-              "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
-    fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.")
-    thresholds = Param(Params._dummy(), "thresholds",
-                       "Thresholds in multi-class classification" +
-                       " to adjust the probability of predicting each class." +
-                       " Array must have length equal to the number of classes, with values >= 0." +
-                       " The class with largest value p/t is predicted, where p is the original" +
-                       " probability of that class and t is the class' threshold.")
     threshold = Param(Params._dummy(), "threshold",
                       "Threshold in binary classification prediction, in range [0, 1]." +
                       " If threshold and thresholds are both set, they must match.")
@@ -83,40 +73,23 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                 threshold=0.5, thresholds=None,
-                 probabilityCol="probability", rawPredictionCol="rawPrediction"):
+                 threshold=0.5, thresholds=None, probabilityCol="probability",
+                 rawPredictionCol="rawPrediction", standardization=True):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 threshold=0.5, thresholds=None, \
-                 probabilityCol="probability", rawPredictionCol="rawPrediction")
+                 threshold=0.5, thresholds=None, probabilityCol="probability", \
+                 rawPredictionCol="rawPrediction", standardization=True)
         If the threshold and thresholds Params are both set, they must be equivalent.
         """
         super(LogisticRegression, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.classification.LogisticRegression", self.uid)
-        #: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty
-        #  is an L2 penalty. For alpha = 1, it is an L1 penalty.
-        self.elasticNetParam = \
-            Param(self, "elasticNetParam",
-                  "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
-                  "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
-        #: param for whether to fit an intercept term.
-        self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
         #: param for threshold in binary classification, in range [0, 1].
         self.threshold = Param(self, "threshold",
                                "Threshold in binary classification prediction, in range [0, 1]." +
                                " If threshold and thresholds are both set, they must match.")
-        #: param for thresholds or cutoffs in binary or multiclass classification
-        self.thresholds = \
-            Param(self, "thresholds",
-                  "Thresholds in multi-class classification" +
-                  " to adjust the probability of predicting each class." +
-                  " Array must have length equal to the number of classes, with values >= 0." +
-                  " The class with largest value p/t is predicted, where p is the original" +
-                  " probability of that class and t is the class' threshold.")
-        self._setDefault(maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1E-6,
-                         fitIntercept=True, threshold=0.5)
+        self._setDefault(maxIter=100, regParam=0.1, tol=1E-6, threshold=0.5)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
         self._checkThresholdConsistency()
@@ -124,13 +97,13 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                  threshold=0.5, thresholds=None,
-                  probabilityCol="probability", rawPredictionCol="rawPrediction"):
+                  threshold=0.5, thresholds=None, probabilityCol="probability",
+                  rawPredictionCol="rawPrediction", standardization=True):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                  threshold=0.5, thresholds=None, \
-                  probabilityCol="probability", rawPredictionCol="rawPrediction")
+                  threshold=0.5, thresholds=None, probabilityCol="probability", \
+                  rawPredictionCol="rawPrediction", standardization=True)
         Sets params for logistic regression.
         If the threshold and thresholds Params are both set, they must be equivalent.
         """
@@ -142,32 +115,6 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
     def _create_model(self, java_model):
         return LogisticRegressionModel(java_model)
 
-    def setElasticNetParam(self, value):
-        """
-        Sets the value of :py:attr:`elasticNetParam`.
-        """
-        self._paramMap[self.elasticNetParam] = value
-        return self
-
-    def getElasticNetParam(self):
-        """
-        Gets the value of elasticNetParam or its default value.
-        """
-        return self.getOrDefault(self.elasticNetParam)
-
-    def setFitIntercept(self, value):
-        """
-        Sets the value of :py:attr:`fitIntercept`.
-        """
-        self._paramMap[self.fitIntercept] = value
-        return self
-
-    def getFitIntercept(self):
-        """
-        Gets the value of fitIntercept or its default value.
-        """
-        return self.getOrDefault(self.fitIntercept)
-
     def setThreshold(self, value):
         """
         Sets the value of :py:attr:`threshold`.
diff --git a/python/pyspark/ml/param/_shared_params_code_gen.py b/python/pyspark/ml/param/_shared_params_code_gen.py
index 926375e44871d..5b39e5dd4e25b 100644
--- a/python/pyspark/ml/param/_shared_params_code_gen.py
+++ b/python/pyspark/ml/param/_shared_params_code_gen.py
@@ -124,7 +124,16 @@ def get$Name(self):
         ("stepSize", "Step size to be used for each iteration of optimization.", None),
         ("handleInvalid", "how to handle invalid entries. Options are skip (which will filter " +
          "out rows with bad values), or error (which will throw an errror). More options may be " +
-         "added later.", None)]
+         "added later.", None),
+        ("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
+         "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.", "0.0"),
+        ("fitIntercept", "whether to fit an intercept term.", "True"),
+        ("standardization", "whether to standardize the training features before fitting the " +
+         "model.", "True"),
+        ("thresholds", "Thresholds in multi-class classification to adjust the probability of " +
+         "predicting each class. Array must have length equal to the number of classes, with " +
+         "values >= 0. The class with largest value p/t is predicted, where p is the original " +
+         "probability of that class and t is the class' threshold.", None)]
     code = []
     for name, doc, defaultValueStr in shared:
         param_code = _gen_param_header(name, doc, defaultValueStr)
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
index 682170aee85fb..af1218128602b 100644
--- a/python/pyspark/ml/param/shared.py
+++ b/python/pyspark/ml/param/shared.py
@@ -459,6 +459,117 @@ def getHandleInvalid(self):
         return self.getOrDefault(self.handleInvalid)
 
 
+class HasElasticNetParam(Params):
+    """
+    Mixin for param elasticNetParam: the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty..
+    """
+
+    # a placeholder to make it appear in the generated doc
+    elasticNetParam = Param(Params._dummy(), "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
+
+    def __init__(self):
+        super(HasElasticNetParam, self).__init__()
+        #: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
+        self.elasticNetParam = Param(self, "elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
+        self._setDefault(elasticNetParam=0.0)
+
+    def setElasticNetParam(self, value):
+        """
+        Sets the value of :py:attr:`elasticNetParam`.
+        """
+        self._paramMap[self.elasticNetParam] = value
+        return self
+
+    def getElasticNetParam(self):
+        """
+        Gets the value of elasticNetParam or its default value.
+        """
+        return self.getOrDefault(self.elasticNetParam)
+
+
+class HasFitIntercept(Params):
+    """
+    Mixin for param fitIntercept: whether to fit an intercept term..
+    """
+
+    # a placeholder to make it appear in the generated doc
+    fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.")
+
+    def __init__(self):
+        super(HasFitIntercept, self).__init__()
+        #: param for whether to fit an intercept term.
+        self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.")
+        self._setDefault(fitIntercept=True)
+
+    def setFitIntercept(self, value):
+        """
+        Sets the value of :py:attr:`fitIntercept`.
+        """
+        self._paramMap[self.fitIntercept] = value
+        return self
+
+    def getFitIntercept(self):
+        """
+        Gets the value of fitIntercept or its default value.
+        """
+        return self.getOrDefault(self.fitIntercept)
+
+
+class HasStandardization(Params):
+    """
+    Mixin for param standardization: whether to standardize the training features before fitting the model..
+    """
+
+    # a placeholder to make it appear in the generated doc
+    standardization = Param(Params._dummy(), "standardization", "whether to standardize the training features before fitting the model.")
+
+    def __init__(self):
+        super(HasStandardization, self).__init__()
+        #: param for whether to standardize the training features before fitting the model.
+        self.standardization = Param(self, "standardization", "whether to standardize the training features before fitting the model.")
+        self._setDefault(standardization=True)
+
+    def setStandardization(self, value):
+        """
+        Sets the value of :py:attr:`standardization`.
+        """
+        self._paramMap[self.standardization] = value
+        return self
+
+    def getStandardization(self):
+        """
+        Gets the value of standardization or its default value.
+        """
+        return self.getOrDefault(self.standardization)
+
+
+class HasThresholds(Params):
+    """
+    Mixin for param thresholds: Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold..
+    """
+
+    # a placeholder to make it appear in the generated doc
+    thresholds = Param(Params._dummy(), "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")
+
+    def __init__(self):
+        super(HasThresholds, self).__init__()
+        #: param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.
+        self.thresholds = Param(self, "thresholds", "Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values >= 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.")
+
+    def setThresholds(self, value):
+        """
+        Sets the value of :py:attr:`thresholds`.
+        """
+        self._paramMap[self.thresholds] = value
+        return self
+
+    def getThresholds(self):
+        """
+        Gets the value of thresholds or its default value.
+        """
+        return self.getOrDefault(self.thresholds)
+
+
 class DecisionTreeParams(Params):
     """
     Mixin for Decision Tree parameters.
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index 44f60a769566d..a9503608b7f25 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -28,7 +28,8 @@
 
 @inherit_doc
 class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
-                       HasRegParam, HasTol):
+                       HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept,
+                       HasStandardization):
     """
     Linear regression.
 
@@ -63,38 +64,30 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
     TypeError: Method setParams forces keyword arguments.
     """
 
-    # a placeholder to make it appear in the generated doc
-    elasticNetParam = \
-        Param(Params._dummy(), "elasticNetParam",
-              "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, " +
-              "the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.")
-
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6):
+                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
+                 standardization=True):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
-                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
+                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
+                 standardization=True)
         """
         super(LinearRegression, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.regression.LinearRegression", self.uid)
-        #: param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty
-        #  is an L2 penalty. For alpha = 1, it is an L1 penalty.
-        self.elasticNetParam = \
-            Param(self, "elasticNetParam",
-                  "the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty " +
-                  "is an L2 penalty. For alpha = 1, it is an L1 penalty.")
-        self._setDefault(maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
+        self._setDefault(maxIter=100, regParam=0.0, tol=1e-6)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
     @keyword_only
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6):
+                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
+                  standardization=True):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
-                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6)
+                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
+                  standardization=True)
         Sets params for linear regression.
         """
         kwargs = self.setParams._input_kwargs
@@ -103,19 +96,6 @@ def setParams(self, featuresCol="features", labelCol="label", predictionCol="pre
     def _create_model(self, java_model):
         return LinearRegressionModel(java_model)
 
-    def setElasticNetParam(self, value):
-        """
-        Sets the value of :py:attr:`elasticNetParam`.
-        """
-        self._paramMap[self.elasticNetParam] = value
-        return self
-
-    def getElasticNetParam(self):
-        """
-        Gets the value of elasticNetParam or its default value.
-        """
-        return self.getOrDefault(self.elasticNetParam)
-
 
 class LinearRegressionModel(JavaModel):
     """

From b01b26260625f0ba14e5f3010207666d62d93864 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Fri, 11 Sep 2015 08:52:28 -0700
Subject: [PATCH 590/633] [SPARK-9773] [ML] [PySpark] Add Python API for
 MultilayerPerceptronClassifier

Add Python API for ```MultilayerPerceptronClassifier```.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8067 from yanboliang/SPARK-9773.
---
 .../MultilayerPerceptronClassifier.scala      |   9 ++
 python/pyspark/ml/classification.py           | 132 +++++++++++++++++-
 2 files changed, 140 insertions(+), 1 deletion(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
index 82fc80c58054f..5f60dea91fcfa 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -17,6 +17,8 @@
 
 package org.apache.spark.ml.classification
 
+import scala.collection.JavaConverters._
+
 import org.apache.spark.annotation.Experimental
 import org.apache.spark.ml.param.shared.{HasTol, HasMaxIter, HasSeed}
 import org.apache.spark.ml.{PredictorParams, PredictionModel, Predictor}
@@ -181,6 +183,13 @@ class MultilayerPerceptronClassificationModel private[ml] (
 
   private val mlpModel = FeedForwardTopology.multiLayerPerceptron(layers, true).getInstance(weights)
 
+  /**
+   * Returns layers in a Java List.
+   */
+  private[ml] def javaLayers: java.util.List[Int] = {
+    layers.toList.asJava
+  }
+
   /**
    * Predict label for the given features.
    * This internal method is used to implement [[transform()]] and output [[predictionCol]].
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 22bdd1b322aca..88815e561f572 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -26,7 +26,8 @@
 __all__ = ['LogisticRegression', 'LogisticRegressionModel', 'DecisionTreeClassifier',
            'DecisionTreeClassificationModel', 'GBTClassifier', 'GBTClassificationModel',
            'RandomForestClassifier', 'RandomForestClassificationModel', 'NaiveBayes',
-           'NaiveBayesModel']
+           'NaiveBayesModel', 'MultilayerPerceptronClassifier',
+           'MultilayerPerceptronClassificationModel']
 
 
 @inherit_doc
@@ -755,6 +756,135 @@ def theta(self):
         return self._call_java("theta")
 
 
+@inherit_doc
+class MultilayerPerceptronClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
+                                     HasMaxIter, HasTol, HasSeed):
+    """
+    Classifier trainer based on the Multilayer Perceptron.
+    Each layer has sigmoid activation function, output layer has softmax.
+    Number of inputs has to be equal to the size of feature vectors.
+    Number of outputs has to be equal to the total number of labels.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([
+    ...     (0.0, Vectors.dense([0.0, 0.0])),
+    ...     (1.0, Vectors.dense([0.0, 1.0])),
+    ...     (1.0, Vectors.dense([1.0, 0.0])),
+    ...     (0.0, Vectors.dense([1.0, 1.0]))], ["label", "features"])
+    >>> mlp = MultilayerPerceptronClassifier(maxIter=100, layers=[2, 5, 2], blockSize=1, seed=11)
+    >>> model = mlp.fit(df)
+    >>> model.layers
+    [2, 5, 2]
+    >>> model.weights.size
+    27
+    >>> testDF = sqlContext.createDataFrame([
+    ...     (Vectors.dense([1.0, 0.0]),),
+    ...     (Vectors.dense([0.0, 0.0]),)], ["features"])
+    >>> model.transform(testDF).show()
+    +---------+----------+
+    | features|prediction|
+    +---------+----------+
+    |[1.0,0.0]|       1.0|
+    |[0.0,0.0]|       0.0|
+    +---------+----------+
+    ...
+    """
+
+    # a placeholder to make it appear in the generated doc
+    layers = Param(Params._dummy(), "layers", "Sizes of layers from input layer to output layer " +
+                   "E.g., Array(780, 100, 10) means 780 inputs, one hidden layer with 100 " +
+                   "neurons and output layer of 10 neurons, default is [1, 1].")
+    blockSize = Param(Params._dummy(), "blockSize", "Block size for stacking input data in " +
+                      "matrices. Data is stacked within partitions. If block size is more than " +
+                      "remaining data in a partition then it is adjusted to the size of this " +
+                      "data. Recommended size is between 10 and 1000, default is 128.")
+
+    @keyword_only
+    def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                 maxIter=100, tol=1e-4, seed=None, layers=None, blockSize=128):
+        """
+        __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                 maxIter=100, tol=1e-4, seed=None, layers=[1, 1], blockSize=128)
+        """
+        super(MultilayerPerceptronClassifier, self).__init__()
+        self._java_obj = self._new_java_obj(
+            "org.apache.spark.ml.classification.MultilayerPerceptronClassifier", self.uid)
+        self.layers = Param(self, "layers", "Sizes of layers from input layer to output layer " +
+                            "E.g., Array(780, 100, 10) means 780 inputs, one hidden layer with " +
+                            "100 neurons and output layer of 10 neurons, default is [1, 1].")
+        self.blockSize = Param(self, "blockSize", "Block size for stacking input data in " +
+                               "matrices. Data is stacked within partitions. If block size is " +
+                               "more than remaining data in a partition then it is adjusted to " +
+                               "the size of this data. Recommended size is between 10 and 1000, " +
+                               "default is 128.")
+        self._setDefault(maxIter=100, tol=1E-4, layers=[1, 1], blockSize=128)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
+                  maxIter=100, tol=1e-4, seed=None, layers=None, blockSize=128):
+        """
+        setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
+                  maxIter=100, tol=1e-4, seed=None, layers=[1, 1], blockSize=128)
+        Sets params for MultilayerPerceptronClassifier.
+        """
+        kwargs = self.setParams._input_kwargs
+        if layers is None:
+            return self._set(**kwargs).setLayers([1, 1])
+        else:
+            return self._set(**kwargs)
+
+    def _create_model(self, java_model):
+        return MultilayerPerceptronClassificationModel(java_model)
+
+    def setLayers(self, value):
+        """
+        Sets the value of :py:attr:`layers`.
+        """
+        self._paramMap[self.layers] = value
+        return self
+
+    def getLayers(self):
+        """
+        Gets the value of layers or its default value.
+        """
+        return self.getOrDefault(self.layers)
+
+    def setBlockSize(self, value):
+        """
+        Sets the value of :py:attr:`blockSize`.
+        """
+        self._paramMap[self.blockSize] = value
+        return self
+
+    def getBlockSize(self):
+        """
+        Gets the value of blockSize or its default value.
+        """
+        return self.getOrDefault(self.blockSize)
+
+
+class MultilayerPerceptronClassificationModel(JavaModel):
+    """
+    Model fitted by MultilayerPerceptronClassifier.
+    """
+
+    @property
+    def layers(self):
+        """
+        array of layer sizes including input and output layers.
+        """
+        return self._call_java("javaLayers")
+
+    @property
+    def weights(self):
+        """
+        vector of initial weights for the model that consists of the weights of layers.
+        """
+        return self._call_java("weights")
+
+
 if __name__ == "__main__":
     import doctest
     from pyspark.context import SparkContext

From 960d2d0ac6b5a22242a922f87f745f7d1f736181 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Fri, 11 Sep 2015 08:53:40 -0700
Subject: [PATCH 591/633] [SPARK-10537] [ML] document LIBSVM source options in
 public API doc and some minor improvements

We should document options in public API doc. Otherwise, it is hard to find out the options without looking at the code. I tried to make `DefaultSource` private and put the documentation to package doc. However, since then there exists no public class under `source.libsvm`, the Java package doc doesn't show up in the generated html file (http://bugs.java.com/bugdatabase/view_bug.do?bug_id=4492654). So I put the doc to `DefaultSource` instead. There are several minor updates in this PR:

1. Do `vectorType == "sparse"` only once.
2. Update `hashCode` and `equals`.
3. Remove inherited doc.
4. Delete temp dir in `afterAll`.

Lewuathe

Author: Xiangrui Meng <meng@databricks.com>

Closes #8699 from mengxr/SPARK-10537.
---
 .../ml/source/libsvm/LibSVMRelation.scala     | 71 ++++++++++++-------
 .../{ => libsvm}/JavaLibSVMRelationSuite.java | 24 +++----
 .../{ => libsvm}/LibSVMRelationSuite.scala    | 14 ++--
 3 files changed, 66 insertions(+), 43 deletions(-)
 rename mllib/src/test/java/org/apache/spark/ml/source/{ => libsvm}/JavaLibSVMRelationSuite.java (79%)
 rename mllib/src/test/scala/org/apache/spark/ml/source/{ => libsvm}/LibSVMRelationSuite.scala (88%)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
index b12cb62a4ef15..1f627777fc68d 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
@@ -21,12 +21,12 @@ import com.google.common.base.Objects
 
 import org.apache.spark.Logging
 import org.apache.spark.annotation.Since
-import org.apache.spark.mllib.linalg.VectorUDT
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.types.{StructType, StructField, DoubleType}
-import org.apache.spark.sql.{Row, SQLContext}
+import org.apache.spark.sql.{DataFrameReader, DataFrame, Row, SQLContext}
 import org.apache.spark.sql.sources._
+import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
 
 /**
  * LibSVMRelation provides the DataFrame constructed from LibSVM format data.
@@ -35,7 +35,7 @@ import org.apache.spark.sql.sources._
  * @param vectorType The type of vector. It can be 'sparse' or 'dense'
  * @param sqlContext The Spark SQLContext
  */
-private[ml] class LibSVMRelation(val path: String, val numFeatures: Int, val vectorType: String)
+private[libsvm] class LibSVMRelation(val path: String, val numFeatures: Int, val vectorType: String)
     (@transient val sqlContext: SQLContext)
   extends BaseRelation with TableScan with Logging with Serializable {
 
@@ -47,27 +47,56 @@ private[ml] class LibSVMRelation(val path: String, val numFeatures: Int, val vec
   override def buildScan(): RDD[Row] = {
     val sc = sqlContext.sparkContext
     val baseRdd = MLUtils.loadLibSVMFile(sc, path, numFeatures)
-
+    val sparse = vectorType == "sparse"
     baseRdd.map { pt =>
-      val features = if (vectorType == "dense") pt.features.toDense else pt.features.toSparse
+      val features = if (sparse) pt.features.toSparse else pt.features.toDense
       Row(pt.label, features)
     }
   }
 
   override def hashCode(): Int = {
-    Objects.hashCode(path, schema)
+    Objects.hashCode(path, Double.box(numFeatures), vectorType)
   }
 
   override def equals(other: Any): Boolean = other match {
-    case that: LibSVMRelation => (this.path == that.path) && this.schema.equals(that.schema)
-    case _ => false
+    case that: LibSVMRelation =>
+      path == that.path &&
+        numFeatures == that.numFeatures &&
+        vectorType == that.vectorType
+    case _ =>
+      false
   }
-
 }
 
 /**
- * This is used for creating DataFrame from LibSVM format file.
- * The LibSVM file path must be specified to DefaultSource.
+ * `libsvm` package implements Spark SQL data source API for loading LIBSVM data as [[DataFrame]].
+ * The loaded [[DataFrame]] has two columns: `label` containing labels stored as doubles and
+ * `features` containing feature vectors stored as [[Vector]]s.
+ *
+ * To use LIBSVM data source, you need to set "libsvm" as the format in [[DataFrameReader]] and
+ * optionally specify options, for example:
+ * {{{
+ *   // Scala
+ *   val df = sqlContext.read.format("libsvm")
+ *     .option("numFeatures", "780")
+ *     .load("data/mllib/sample_libsvm_data.txt")
+ *
+ *   // Java
+ *   DataFrame df = sqlContext.read.format("libsvm")
+ *     .option("numFeatures, "780")
+ *     .load("data/mllib/sample_libsvm_data.txt");
+ * }}}
+ *
+ * LIBSVM data source supports the following options:
+ *  - "numFeatures": number of features.
+ *    If unspecified or nonpositive, the number of features will be determined automatically at the
+ *    cost of one additional pass.
+ *    This is also useful when the dataset is already split into multiple files and you want to load
+ *    them separately, because some features may not present in certain files, which leads to
+ *    inconsistent feature dimensions.
+ *  - "vectorType": feature vector type, "sparse" (default) or "dense".
+ *
+ *  @see [[https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ LIBSVM datasets]]
  */
 @Since("1.6.0")
 class DefaultSource extends RelationProvider with DataSourceRegister {
@@ -75,24 +104,12 @@ class DefaultSource extends RelationProvider with DataSourceRegister {
   @Since("1.6.0")
   override def shortName(): String = "libsvm"
 
-  private def checkPath(parameters: Map[String, String]): String = {
-    require(parameters.contains("path"), "'path' must be specified")
-    parameters.get("path").get
-  }
-
-  /**
-   * Returns a new base relation with the given parameters.
-   * Note: the parameters' keywords are case insensitive and this insensitivity is enforced
-   * by the Map that is passed to the function.
-   */
+  @Since("1.6.0")
   override def createRelation(sqlContext: SQLContext, parameters: Map[String, String])
     : BaseRelation = {
-    val path = checkPath(parameters)
+    val path = parameters.getOrElse("path",
+      throw new IllegalArgumentException("'path' must be specified"))
     val numFeatures = parameters.getOrElse("numFeatures", "-1").toInt
-    /**
-     * featuresType can be selected "dense" or "sparse".
-     * This parameter decides the type of returned feature vector.
-     */
     val vectorType = parameters.getOrElse("vectorType", "sparse")
     new LibSVMRelation(path, numFeatures, vectorType)(sqlContext)
   }
diff --git a/mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java b/mllib/src/test/java/org/apache/spark/ml/source/libsvm/JavaLibSVMRelationSuite.java
similarity index 79%
rename from mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java
rename to mllib/src/test/java/org/apache/spark/ml/source/libsvm/JavaLibSVMRelationSuite.java
index 11fa4eec0ccf0..2976b38e45031 100644
--- a/mllib/src/test/java/org/apache/spark/ml/source/JavaLibSVMRelationSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/source/libsvm/JavaLibSVMRelationSuite.java
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.source;
+package org.apache.spark.ml.source.libsvm;
 
 import java.io.File;
 import java.io.IOException;
@@ -42,34 +42,34 @@
  */
 public class JavaLibSVMRelationSuite {
   private transient JavaSparkContext jsc;
-  private transient SQLContext jsql;
-  private transient DataFrame dataset;
+  private transient SQLContext sqlContext;
 
-  private File tmpDir;
-  private File path;
+  private File tempDir;
+  private String path;
 
   @Before
   public void setUp() throws IOException {
     jsc = new JavaSparkContext("local", "JavaLibSVMRelationSuite");
-    jsql = new SQLContext(jsc);
-
-    tmpDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "datasource");
-    path = new File(tmpDir.getPath(), "part-00000");
+    sqlContext = new SQLContext(jsc);
 
+    tempDir = Utils.createTempDir(System.getProperty("java.io.tmpdir"), "datasource");
+    File file = new File(tempDir, "part-00000");
     String s = "1 1:1.0 3:2.0 5:3.0\n0\n0 2:4.0 4:5.0 6:6.0";
-    Files.write(s, path, Charsets.US_ASCII);
+    Files.write(s, file, Charsets.US_ASCII);
+    path = tempDir.toURI().toString();
   }
 
   @After
   public void tearDown() {
     jsc.stop();
     jsc = null;
-    Utils.deleteRecursively(tmpDir);
+    Utils.deleteRecursively(tempDir);
   }
 
   @Test
   public void verifyLibSVMDF() {
-    dataset = jsql.read().format("libsvm").option("vectorType", "dense").load(path.getPath());
+    DataFrame dataset = sqlContext.read().format("libsvm").option("vectorType", "dense")
+      .load(path);
     Assert.assertEquals("label", dataset.columns()[0]);
     Assert.assertEquals("features", dataset.columns()[1]);
     Row r = dataset.first();
diff --git a/mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
similarity index 88%
rename from mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala
rename to mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
index 8ed134128c8d2..997f574e51f6a 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/source/libsvm/LibSVMRelationSuite.scala
@@ -15,7 +15,7 @@
  * limitations under the License.
  */
 
-package org.apache.spark.ml.source
+package org.apache.spark.ml.source.libsvm
 
 import java.io.File
 
@@ -23,11 +23,12 @@ import com.google.common.base.Charsets
 import com.google.common.io.Files
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.mllib.linalg.{SparseVector, Vectors, DenseVector}
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors}
 import org.apache.spark.mllib.util.MLlibTestSparkContext
 import org.apache.spark.util.Utils
 
 class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
+  var tempDir: File = _
   var path: String = _
 
   override def beforeAll(): Unit = {
@@ -38,12 +39,17 @@ class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext {
         |0
         |0 2:4.0 4:5.0 6:6.0
       """.stripMargin
-    val tempDir = Utils.createTempDir()
-    val file = new File(tempDir.getPath, "part-00000")
+    tempDir = Utils.createTempDir()
+    val file = new File(tempDir, "part-00000")
     Files.write(lines, file, Charsets.US_ASCII)
     path = tempDir.toURI.toString
   }
 
+  override def afterAll(): Unit = {
+    Utils.deleteRecursively(tempDir)
+    super.afterAll()
+  }
+
   test("select as sparse vector") {
     val df = sqlContext.read.format("libsvm").load(path)
     assert(df.columns(0) == "label")

From 2e3a280754a28dc36a71b9ff988e34cbf457f6c3 Mon Sep 17 00:00:00 2001
From: "Joseph K. Bradley" <joseph@databricks.com>
Date: Fri, 11 Sep 2015 08:55:35 -0700
Subject: [PATCH 592/633] [MINOR] [MLLIB] [ML] [DOC] Minor doc fixes for
 StringIndexer and MetadataUtils
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Changes:
* Make Scala doc for StringIndexerInverse clearer.  Also remove Scala doc from transformSchema, so that the doc is inherited.
* MetadataUtils.scala: “ Helper utilities for tree-based algorithms” —> not just trees anymore

CC: holdenk mengxr

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #8679 from jkbradley/doc-fixes-1.5.
---
 .../spark/ml/feature/StringIndexer.scala      | 31 +++++++------------
 .../apache/spark/ml/util/MetadataUtils.scala  |  2 +-
 python/pyspark/ml/feature.py                  | 16 +++++-----
 3 files changed, 20 insertions(+), 29 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index b6482ffe0b2ee..3a4ab9a857648 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -181,10 +181,10 @@ class StringIndexerModel (
 
 /**
  * :: Experimental ::
- * A [[Transformer]] that maps a column of string indices back to a new column of corresponding
- * string values using either the ML attributes of the input column, or if provided using the labels
- * supplied by the user.
- * All original columns are kept during transformation.
+ * A [[Transformer]] that maps a column of indices back to a new column of corresponding
+ * string values.
+ * The index-string mapping is either from the ML attributes of the input column,
+ * or from user-supplied labels (which take precedence over ML attributes).
  *
  * @see [[StringIndexer]] for converting strings into indices
  */
@@ -202,32 +202,23 @@ class IndexToString private[ml] (
   /** @group setParam */
   def setOutputCol(value: String): this.type = set(outputCol, value)
 
-  /**
-   * Optional labels to be provided by the user, if not supplied column
-   * metadata is read for labels. The default value is an empty array,
-   * but the empty array is ignored and column metadata used instead.
-   * @group setParam
-   */
+  /** @group setParam */
   def setLabels(value: Array[String]): this.type = set(labels, value)
 
   /**
-   * Param for array of labels.
-   * Optional labels to be provided by the user.
-   * Default: Empty array, in which case column metadata is used for labels.
+   * Optional param for array of labels specifying index-string mapping.
+   *
+   * Default: Empty array, in which case [[inputCol]] metadata is used for labels.
    * @group param
    */
   final val labels: StringArrayParam = new StringArrayParam(this, "labels",
-    "array of labels, if not provided metadata from inputCol is used instead.")
+    "Optional array of labels specifying index-string mapping." +
+      " If not provided or if empty, then metadata from inputCol is used instead.")
   setDefault(labels, Array.empty[String])
 
-  /**
-   * Optional labels to be provided by the user, if not supplied column
-   * metadata is read for labels.
-   * @group getParam
-   */
+  /** @group getParam */
   final def getLabels: Array[String] = $(labels)
 
-  /** Transform the schema for the inverse transformation */
   override def transformSchema(schema: StructType): StructType = {
     val inputColName = $(inputCol)
     val inputDataType = schema(inputColName).dataType
diff --git a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
index fcb517b5f735e..96a38a3bde960 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/util/MetadataUtils.scala
@@ -25,7 +25,7 @@ import org.apache.spark.sql.types.StructField
 
 
 /**
- * Helper utilities for tree-based algorithms
+ * Helper utilities for algorithms using ML metadata
  */
 private[spark] object MetadataUtils {
 
diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 71dc636b83eac..97cbee73a05ed 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -985,17 +985,17 @@ class IndexToString(JavaTransformer, HasInputCol, HasOutputCol):
     """
     .. note:: Experimental
 
-    A :py:class:`Transformer` that maps a column of string indices back to a new column of
-    corresponding string values using either the ML attributes of the input column, or if
-    provided using the labels supplied by the user.
-    All original columns are kept during transformation.
+    A :py:class:`Transformer` that maps a column of indices back to a new column of
+    corresponding string values.
+    The index-string mapping is either from the ML attributes of the input column,
+    or from user-supplied labels (which take precedence over ML attributes).
     See L{StringIndexer} for converting strings into indices.
     """
 
     # a placeholder to make the labels show up in generated doc
     labels = Param(Params._dummy(), "labels",
-                   "Optional array of labels to be provided by the user, if not supplied or " +
-                   "empty, column metadata is read for labels")
+                   "Optional array of labels specifying index-string mapping." +
+                   " If not provided or if empty, then metadata from inputCol is used instead.")
 
     @keyword_only
     def __init__(self, inputCol=None, outputCol=None, labels=None):
@@ -1006,8 +1006,8 @@ def __init__(self, inputCol=None, outputCol=None, labels=None):
         self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.IndexToString",
                                             self.uid)
         self.labels = Param(self, "labels",
-                            "Optional array of labels to be provided by the user, if not " +
-                            "supplied or empty, column metadata is read for labels")
+                            "Optional array of labels specifying index-string mapping. If not" +
+                            " provided or if empty, then metadata from inputCol is used instead.")
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 

From 6ce0886eb0916a985db142c0b6d2c2b14db5063d Mon Sep 17 00:00:00 2001
From: Yin Huai <yhuai@databricks.com>
Date: Fri, 11 Sep 2015 09:42:53 -0700
Subject: [PATCH 593/633] [SPARK-10540] [SQL] Ignore HadoopFsRelationTest's
 "test all data types" if it is too flaky

If hadoopFsRelationSuites's "test all data types" is too flaky we can disable it for now.

https://issues.apache.org/jira/browse/SPARK-10540

Author: Yin Huai <yhuai@databricks.com>

Closes #8705 from yhuai/SPARK-10540-ignore.
---
 .../org/apache/spark/sql/sources/hadoopFsRelationSuites.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 24f43cf7c15ca..13223c61584b2 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -100,7 +100,7 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with Tes
     }
   }
 
-  test("test all data types") {
+  ignore("test all data types") {
     withTempPath { file =>
       // Create the schema.
       val struct =

From 5f46444765a377696af76af6e2c77ab14bfdab8e Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Fri, 11 Sep 2015 10:32:35 -0700
Subject: [PATCH 594/633] [SPARK-8530] [ML] add python API for MinMaxScaler

jira: https://issues.apache.org/jira/browse/SPARK-8530

add python API for MinMaxScaler
jira for MinMaxScaler: https://issues.apache.org/jira/browse/SPARK-7514

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #7150 from hhbyyh/pythonMinMax.
---
 python/pyspark/ml/feature.py | 104 +++++++++++++++++++++++++++++++++--
 1 file changed, 99 insertions(+), 5 deletions(-)

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 97cbee73a05ed..92db8df80280b 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -27,11 +27,11 @@
 from pyspark.mllib.linalg import _convert_to_vector
 
 __all__ = ['Binarizer', 'Bucketizer', 'DCT', 'ElementwiseProduct', 'HashingTF', 'IDF', 'IDFModel',
-           'IndexToString', 'NGram', 'Normalizer', 'OneHotEncoder', 'PCA', 'PCAModel',
-           'PolynomialExpansion', 'RegexTokenizer', 'RFormula', 'RFormulaModel', 'SQLTransformer',
-           'StandardScaler', 'StandardScalerModel', 'StopWordsRemover', 'StringIndexer',
-           'StringIndexerModel', 'Tokenizer', 'VectorAssembler', 'VectorIndexer', 'VectorSlicer',
-           'Word2Vec', 'Word2VecModel']
+           'IndexToString', 'MinMaxScaler', 'MinMaxScalerModel', 'NGram', 'Normalizer',
+           'OneHotEncoder', 'PCA', 'PCAModel', 'PolynomialExpansion', 'RegexTokenizer',
+           'RFormula', 'RFormulaModel', 'SQLTransformer', 'StandardScaler', 'StandardScalerModel',
+           'StopWordsRemover', 'StringIndexer', 'StringIndexerModel', 'Tokenizer',
+           'VectorAssembler', 'VectorIndexer', 'VectorSlicer', 'Word2Vec', 'Word2VecModel']
 
 
 @inherit_doc
@@ -406,6 +406,100 @@ class IDFModel(JavaModel):
     """
 
 
+@inherit_doc
+class MinMaxScaler(JavaEstimator, HasInputCol, HasOutputCol):
+    """
+    .. note:: Experimental
+
+    Rescale each feature individually to a common range [min, max] linearly using column summary
+    statistics, which is also known as min-max normalization or Rescaling. The rescaled value for
+    feature E is calculated as,
+
+    Rescaled(e_i) = (e_i - E_min) / (E_max - E_min) * (max - min) + min
+
+    For the case E_max == E_min, Rescaled(e_i) = 0.5 * (max + min)
+
+    Note that since zero values will probably be transformed to non-zero values, output of the
+    transformer will be DenseVector even for sparse input.
+
+    >>> from pyspark.mllib.linalg import Vectors
+    >>> df = sqlContext.createDataFrame([(Vectors.dense([0.0]),), (Vectors.dense([2.0]),)], ["a"])
+    >>> mmScaler = MinMaxScaler(inputCol="a", outputCol="scaled")
+    >>> model = mmScaler.fit(df)
+    >>> model.transform(df).show()
+    +-----+------+
+    |    a|scaled|
+    +-----+------+
+    |[0.0]| [0.0]|
+    |[2.0]| [1.0]|
+    +-----+------+
+    ...
+    """
+
+    # a placeholder to make it appear in the generated doc
+    min = Param(Params._dummy(), "min", "Lower bound of the output feature range")
+    max = Param(Params._dummy(), "max", "Upper bound of the output feature range")
+
+    @keyword_only
+    def __init__(self, min=0.0, max=1.0, inputCol=None, outputCol=None):
+        """
+        __init__(self, min=0.0, max=1.0, inputCol=None, outputCol=None)
+        """
+        super(MinMaxScaler, self).__init__()
+        self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.MinMaxScaler", self.uid)
+        self.min = Param(self, "min", "Lower bound of the output feature range")
+        self.max = Param(self, "max", "Upper bound of the output feature range")
+        self._setDefault(min=0.0, max=1.0)
+        kwargs = self.__init__._input_kwargs
+        self.setParams(**kwargs)
+
+    @keyword_only
+    def setParams(self, min=0.0, max=1.0, inputCol=None, outputCol=None):
+        """
+        setParams(self, min=0.0, max=1.0, inputCol=None, outputCol=None)
+        Sets params for this MinMaxScaler.
+        """
+        kwargs = self.setParams._input_kwargs
+        return self._set(**kwargs)
+
+    def setMin(self, value):
+        """
+        Sets the value of :py:attr:`min`.
+        """
+        self._paramMap[self.min] = value
+        return self
+
+    def getMin(self):
+        """
+        Gets the value of min or its default value.
+        """
+        return self.getOrDefault(self.min)
+
+    def setMax(self, value):
+        """
+        Sets the value of :py:attr:`max`.
+        """
+        self._paramMap[self.max] = value
+        return self
+
+    def getMax(self):
+        """
+        Gets the value of max or its default value.
+        """
+        return self.getOrDefault(self.max)
+
+    def _create_model(self, java_model):
+        return MinMaxScalerModel(java_model)
+
+
+class MinMaxScalerModel(JavaModel):
+    """
+    .. note:: Experimental
+
+    Model fitted by :py:class:`MinMaxScaler`.
+    """
+
+
 @inherit_doc
 @ignore_unicode_prefix
 class NGram(JavaTransformer, HasInputCol, HasOutputCol):

From b231ab8938ae3c4fc2089cfc69c0d8164807d533 Mon Sep 17 00:00:00 2001
From: tedyu <yuzhihong@gmail.com>
Date: Fri, 11 Sep 2015 21:45:45 +0100
Subject: [PATCH 595/633] [SPARK-10546] Check partitionId's range in
 ExternalSorter#spill()

See this thread for background:
http://search-hadoop.com/m/q3RTt0rWvIkHAE81

We should check the range of partition Id and provide meaningful message through exception.

Alternatively, we can use abs() and modulo to force the partition Id into legitimate range. However, expectation is that user should correct the logic error in his / her code.

Author: tedyu <yuzhihong@gmail.com>

Closes #8703 from tedyu/master.
---
 .../scala/org/apache/spark/util/collection/ExternalSorter.scala | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
index 138c05dff19e4..31230d5978b2a 100644
--- a/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
+++ b/core/src/main/scala/org/apache/spark/util/collection/ExternalSorter.scala
@@ -297,6 +297,8 @@ private[spark] class ExternalSorter[K, V, C](
       val it = collection.destructiveSortedWritablePartitionedIterator(comparator)
       while (it.hasNext) {
         val partitionId = it.nextPartition()
+        require(partitionId >= 0 && partitionId < numPartitions,
+          s"partition Id: ${partitionId} should be in the range [0, ${numPartitions})")
         it.writeNext(writer)
         elementsPerPartition(partitionId) += 1
         objectsWritten += 1

From c373866774c082885a50daaf7c83f3a14b0cd714 Mon Sep 17 00:00:00 2001
From: Icaro Medeiros <icaro.medeiros@gmail.com>
Date: Fri, 11 Sep 2015 21:46:52 +0100
Subject: [PATCH 596/633] [PYTHON] Fixed typo in exception message

Just fixing a typo in exception message, raised when attempting to pickle SparkContext.

Author: Icaro Medeiros <icaro.medeiros@gmail.com>

Closes #8724 from icaromedeiros/master.
---
 python/pyspark/context.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/context.py b/python/pyspark/context.py
index 1b2a52ad64114..a0a1ccbeefb09 100644
--- a/python/pyspark/context.py
+++ b/python/pyspark/context.py
@@ -255,7 +255,7 @@ def __getnewargs__(self):
         # This method is called when attempting to pickle SparkContext, which is always an error:
         raise Exception(
             "It appears that you are attempting to reference SparkContext from a broadcast "
-            "variable, action, or transforamtion. SparkContext can only be used on the driver, "
+            "variable, action, or transformation. SparkContext can only be used on the driver, "
             "not in code that it run on workers. For more information, see SPARK-5063."
         )
 

From d5d647380f93f4773f9cb85ea6544892d409b5a1 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Fri, 11 Sep 2015 14:15:16 -0700
Subject: [PATCH 597/633] [SPARK-10442] [SQL] fix string to boolean cast

When we cast string to boolean in hive, it returns `true` if the length of string is > 0, and spark SQL follows this behavior.

However, this behavior is very different from other SQL systems:

1. [presto](https://github.com/facebook/presto/blob/master/presto-main/src/main/java/com/facebook/presto/type/VarcharOperators.java#L89-L118) will return `true` for 't' 'true' '1', `false` for 'f' 'false' '0', throw exception for others.
2. [redshift](http://docs.aws.amazon.com/redshift/latest/dg/r_Boolean_type.html) will return `true` for 't' 'true' 'y' 'yes' '1', `false` for 'f' 'false' 'n' 'no' '0', null for others.
3. [postgresql](http://www.postgresql.org/docs/devel/static/datatype-boolean.html) will return `true` for 't' 'true' 'y' 'yes' 'on' '1', `false` for 'f' 'false' 'n' 'no' 'off' '0', throw exception for others.
4. [vertica](https://my.vertica.com/docs/5.0/HTML/Master/2983.htm) will return `true` for 't' 'true' 'y' 'yes' '1', `false` for 'f' 'false' 'n' 'no' '0', null for others.
5. [impala](http://www.cloudera.com/content/cloudera/en/documentation/cloudera-impala/latest/topics/impala_boolean.html) throw exception when try to cast string to boolean.
6. mysql, oracle, sqlserver don't have boolean type

Whether we should change the cast behavior according to other SQL system or not is not decided yet, this PR is a test to see if we changed, how many compatibility tests will fail.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8698 from cloud-fan/string2boolean.
---
 .../spark/sql/catalyst/expressions/Cast.scala | 24 +++++++-
 .../spark/sql/catalyst/util/StringUtils.scala |  8 +++
 .../sql/catalyst/expressions/CastSuite.scala  | 61 ++++++++++++-------
 .../sql/sources/hadoopFsRelationSuites.scala  | 13 ++++
 4 files changed, 82 insertions(+), 24 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
index 2db954257be35..f0bce388d959a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Cast.scala
@@ -22,7 +22,7 @@ import java.math.{BigDecimal => JavaBigDecimal}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.analysis.TypeCheckResult
 import org.apache.spark.sql.catalyst.expressions.codegen._
-import org.apache.spark.sql.catalyst.util.DateTimeUtils
+import org.apache.spark.sql.catalyst.util.{StringUtils, DateTimeUtils}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.{CalendarInterval, UTF8String}
 
@@ -140,7 +140,15 @@ case class Cast(child: Expression, dataType: DataType)
   // UDFToBoolean
   private[this] def castToBoolean(from: DataType): Any => Any = from match {
     case StringType =>
-      buildCast[UTF8String](_, _.numBytes() != 0)
+      buildCast[UTF8String](_, s => {
+        if (StringUtils.isTrueString(s)) {
+          true
+        } else if (StringUtils.isFalseString(s)) {
+          false
+        } else {
+          null
+        }
+      })
     case TimestampType =>
       buildCast[Long](_, t => t != 0)
     case DateType =>
@@ -646,7 +654,17 @@ case class Cast(child: Expression, dataType: DataType)
 
   private[this] def castToBooleanCode(from: DataType): CastFunction = from match {
     case StringType =>
-      (c, evPrim, evNull) => s"$evPrim = $c.numBytes() != 0;"
+      val stringUtils = StringUtils.getClass.getName.stripSuffix("$")
+      (c, evPrim, evNull) =>
+        s"""
+          if ($stringUtils.isTrueString($c)) {
+            $evPrim = true;
+          } else if ($stringUtils.isFalseString($c)) {
+            $evPrim = false;
+          } else {
+            $evNull = true;
+          }
+        """
     case TimestampType =>
       (c, evPrim, evNull) => s"$evPrim = $c != 0;"
     case DateType =>
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala
index 9ddfb3a0d3759..c2eeb3c5650ab 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/StringUtils.scala
@@ -19,6 +19,8 @@ package org.apache.spark.sql.catalyst.util
 
 import java.util.regex.Pattern
 
+import org.apache.spark.unsafe.types.UTF8String
+
 object StringUtils {
 
   // replace the _ with .{1} exactly match 1 time of any character
@@ -44,4 +46,10 @@ object StringUtils {
       v
     }
   }
+
+  private[this] val trueStrings = Set("t", "true", "y", "yes", "1").map(UTF8String.fromString)
+  private[this] val falseStrings = Set("f", "false", "n", "no", "0").map(UTF8String.fromString)
+
+  def isTrueString(s: UTF8String): Boolean = trueStrings.contains(s.toLowerCase)
+  def isFalseString(s: UTF8String): Boolean = falseStrings.contains(s.toLowerCase)
 }
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
index 1ad70733eae03..f4db4da7646f8 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/CastSuite.scala
@@ -503,9 +503,9 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
   }
 
   test("cast from array") {
-    val array = Literal.create(Seq("123", "abc", "", null),
+    val array = Literal.create(Seq("123", "true", "f", null),
       ArrayType(StringType, containsNull = true))
-    val array_notNull = Literal.create(Seq("123", "abc", ""),
+    val array_notNull = Literal.create(Seq("123", "true", "f"),
       ArrayType(StringType, containsNull = false))
 
     checkNullCast(ArrayType(StringType), ArrayType(IntegerType))
@@ -522,7 +522,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     {
       val ret = cast(array, ArrayType(BooleanType, containsNull = true))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Seq(true, true, false, null))
+      checkEvaluation(ret, Seq(null, true, false, null))
     }
     {
       val ret = cast(array, ArrayType(BooleanType, containsNull = false))
@@ -541,12 +541,12 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     {
       val ret = cast(array_notNull, ArrayType(BooleanType, containsNull = true))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Seq(true, true, false))
+      checkEvaluation(ret, Seq(null, true, false))
     }
     {
       val ret = cast(array_notNull, ArrayType(BooleanType, containsNull = false))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Seq(true, true, false))
+      checkEvaluation(ret, Seq(null, true, false))
     }
 
     {
@@ -557,10 +557,10 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
 
   test("cast from map") {
     val map = Literal.create(
-      Map("a" -> "123", "b" -> "abc", "c" -> "", "d" -> null),
+      Map("a" -> "123", "b" -> "true", "c" -> "f", "d" -> null),
       MapType(StringType, StringType, valueContainsNull = true))
     val map_notNull = Literal.create(
-      Map("a" -> "123", "b" -> "abc", "c" -> ""),
+      Map("a" -> "123", "b" -> "true", "c" -> "f"),
       MapType(StringType, StringType, valueContainsNull = false))
 
     checkNullCast(MapType(StringType, IntegerType), MapType(StringType, StringType))
@@ -577,7 +577,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     {
       val ret = cast(map, MapType(StringType, BooleanType, valueContainsNull = true))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Map("a" -> true, "b" -> true, "c" -> false, "d" -> null))
+      checkEvaluation(ret, Map("a" -> null, "b" -> true, "c" -> false, "d" -> null))
     }
     {
       val ret = cast(map, MapType(StringType, BooleanType, valueContainsNull = false))
@@ -600,12 +600,12 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     {
       val ret = cast(map_notNull, MapType(StringType, BooleanType, valueContainsNull = true))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Map("a" -> true, "b" -> true, "c" -> false))
+      checkEvaluation(ret, Map("a" -> null, "b" -> true, "c" -> false))
     }
     {
       val ret = cast(map_notNull, MapType(StringType, BooleanType, valueContainsNull = false))
       assert(ret.resolved === true)
-      checkEvaluation(ret, Map("a" -> true, "b" -> true, "c" -> false))
+      checkEvaluation(ret, Map("a" -> null, "b" -> true, "c" -> false))
     }
     {
       val ret = cast(map_notNull, MapType(IntegerType, StringType, valueContainsNull = true))
@@ -630,8 +630,8 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     val struct = Literal.create(
       InternalRow(
         UTF8String.fromString("123"),
-        UTF8String.fromString("abc"),
-        UTF8String.fromString(""),
+        UTF8String.fromString("true"),
+        UTF8String.fromString("f"),
         null),
       StructType(Seq(
         StructField("a", StringType, nullable = true),
@@ -641,8 +641,8 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     val struct_notNull = Literal.create(
       InternalRow(
         UTF8String.fromString("123"),
-        UTF8String.fromString("abc"),
-        UTF8String.fromString("")),
+        UTF8String.fromString("true"),
+        UTF8String.fromString("f")),
       StructType(Seq(
         StructField("a", StringType, nullable = false),
         StructField("b", StringType, nullable = false),
@@ -672,7 +672,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
         StructField("c", BooleanType, nullable = true),
         StructField("d", BooleanType, nullable = true))))
       assert(ret.resolved === true)
-      checkEvaluation(ret, InternalRow(true, true, false, null))
+      checkEvaluation(ret, InternalRow(null, true, false, null))
     }
     {
       val ret = cast(struct, StructType(Seq(
@@ -704,7 +704,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
         StructField("b", BooleanType, nullable = true),
         StructField("c", BooleanType, nullable = true))))
       assert(ret.resolved === true)
-      checkEvaluation(ret, InternalRow(true, true, false))
+      checkEvaluation(ret, InternalRow(null, true, false))
     }
     {
       val ret = cast(struct_notNull, StructType(Seq(
@@ -712,7 +712,7 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
         StructField("b", BooleanType, nullable = true),
         StructField("c", BooleanType, nullable = false))))
       assert(ret.resolved === true)
-      checkEvaluation(ret, InternalRow(true, true, false))
+      checkEvaluation(ret, InternalRow(null, true, false))
     }
 
     {
@@ -731,8 +731,8 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
   test("complex casting") {
     val complex = Literal.create(
       Row(
-        Seq("123", "abc", ""),
-        Map("a" ->"123", "b" -> "abc", "c" -> ""),
+        Seq("123", "true", "f"),
+        Map("a" ->"123", "b" -> "true", "c" -> "f"),
         Row(0)),
       StructType(Seq(
         StructField("a",
@@ -755,11 +755,11 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
     assert(ret.resolved === true)
     checkEvaluation(ret, Row(
       Seq(123, null, null),
-      Map("a" -> true, "b" -> true, "c" -> false),
+      Map("a" -> null, "b" -> true, "c" -> false),
       Row(0L)))
   }
 
-  test("case between string and interval") {
+  test("cast between string and interval") {
     import org.apache.spark.unsafe.types.CalendarInterval
 
     checkEvaluation(Cast(Literal("interval -3 month 7 hours"), CalendarIntervalType),
@@ -769,4 +769,23 @@ class CastSuite extends SparkFunSuite with ExpressionEvalHelper {
       StringType),
       "interval 1 years 3 months -3 days")
   }
+
+  test("cast string to boolean") {
+    checkCast("t", true)
+    checkCast("true", true)
+    checkCast("tRUe", true)
+    checkCast("y", true)
+    checkCast("yes", true)
+    checkCast("1", true)
+
+    checkCast("f", false)
+    checkCast("false", false)
+    checkCast("FAlsE", false)
+    checkCast("n", false)
+    checkCast("no", false)
+    checkCast("0", false)
+
+    checkEvaluation(cast("abc", BooleanType), null)
+    checkEvaluation(cast("", BooleanType), null)
+  }
 }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
index 13223c61584b2..8ffcef85668d6 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/sources/hadoopFsRelationSuites.scala
@@ -375,6 +375,19 @@ abstract class HadoopFsRelationTest extends QueryTest with SQLTestUtils with Tes
     }
   }
 
+  test("saveAsTable()/load() - partitioned table - boolean type") {
+    sqlContext.range(2)
+      .select('id, ('id % 2 === 0).as("b"))
+      .write.partitionBy("b").saveAsTable("t")
+
+    withTable("t") {
+      checkAnswer(
+        sqlContext.table("t").sort('id),
+        Row(0, true) :: Row(1, false) :: Nil
+      )
+    }
+  }
+
   test("saveAsTable()/load() - partitioned table - Overwrite") {
     partitionedTestDF.write
       .format(dataSourceName)

From 1eede3b254ee3793841c92971707094ac8afee35 Mon Sep 17 00:00:00 2001
From: Yash Datta <Yash.Datta@guavus.com>
Date: Fri, 11 Sep 2015 14:55:15 -0700
Subject: [PATCH 598/633] [SPARK-7142] [SQL] Minor enhancement to
 BooleanSimplification Optimizer rule. Incorporate review comments

Adding changes suggested by cloud-fan  in #5700

cc marmbrus

Author: Yash Datta <Yash.Datta@guavus.com>

Closes #8716 from saucam/bool_simp.
---
 .../apache/spark/sql/catalyst/optimizer/Optimizer.scala   | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
index d9b50f3c97da0..0f4caec7451a2 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
@@ -435,10 +435,10 @@ object BooleanSimplification extends Rule[LogicalPlan] with PredicateHelper {
         // a && a  =>  a
         case (l, r) if l fastEquals r => l
         // a && (not(a) || b) => a && b
-        case (l, Or(l1, r)) if (Not(l) fastEquals l1) => And(l, r)
-        case (l, Or(r, l1)) if (Not(l) fastEquals l1) => And(l, r)
-        case (Or(l, l1), r) if (l1 fastEquals Not(r)) => And(l, r)
-        case (Or(l1, l), r) if (l1 fastEquals Not(r)) => And(l, r)
+        case (l, Or(l1, r)) if (Not(l) == l1) => And(l, r)
+        case (l, Or(r, l1)) if (Not(l) == l1) => And(l, r)
+        case (Or(l, l1), r) if (l1 == Not(r)) => And(l, r)
+        case (Or(l1, l), r) if (l1 == Not(r)) => And(l, r)
         // (a || b) && (a || c)  =>  a || (b && c)
         case _ =>
           // 1. Split left and right to get the disjunctive predicates,

From e626ac5f5c27dcc74113070f2fec03682bcd12bd Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Fri, 11 Sep 2015 15:00:13 -0700
Subject: [PATCH 599/633] [SPARK-9992] [SPARK-9994] [SPARK-9998] [SQL]
 Implement the local TopK, sample and intersect operators

This PR is in conflict with #8535. I will update this one when #8535 gets merged.

Author: zsxwing <zsxwing@gmail.com>

Closes #8573 from zsxwing/more-local-operators.
---
 .../spark/sql/execution/basicOperators.scala  |  2 +-
 .../sql/execution/local/IntersectNode.scala   | 63 ++++++++++++++
 .../spark/sql/execution/local/LocalNode.scala |  5 ++
 .../sql/execution/local/SampleNode.scala      | 82 +++++++++++++++++++
 .../local/TakeOrderedAndProjectNode.scala     | 73 +++++++++++++++++
 .../execution/local/IntersectNodeSuite.scala  | 35 ++++++++
 .../sql/execution/local/SampleNodeSuite.scala | 40 +++++++++
 .../TakeOrderedAndProjectNodeSuite.scala      | 54 ++++++++++++
 8 files changed, 353 insertions(+), 1 deletion(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/IntersectNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
index 3f68b05a24f44..bf6d44c098ee3 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala
@@ -138,7 +138,7 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
  *                   will be ub - lb.
  * @param withReplacement Whether to sample with replacement.
  * @param seed the random seed
- * @param child the QueryPlan
+ * @param child the SparkPlan
  */
 @DeveloperApi
 case class Sample(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/IntersectNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/IntersectNode.scala
new file mode 100644
index 0000000000000..740d485f8d9e6
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/IntersectNode.scala
@@ -0,0 +1,63 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import scala.collection.mutable
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+
+case class IntersectNode(conf: SQLConf, left: LocalNode, right: LocalNode)
+  extends BinaryLocalNode(conf) {
+
+  override def output: Seq[Attribute] = left.output
+
+  private[this] var leftRows: mutable.HashSet[InternalRow] = _
+
+  private[this] var currentRow: InternalRow = _
+
+  override def open(): Unit = {
+    left.open()
+    leftRows = mutable.HashSet[InternalRow]()
+    while (left.next()) {
+      leftRows += left.fetch().copy()
+    }
+    left.close()
+    right.open()
+  }
+
+  override def next(): Boolean = {
+    currentRow = null
+    while (currentRow == null && right.next()) {
+      currentRow = right.fetch()
+      if (!leftRows.contains(currentRow)) {
+        currentRow = null
+      }
+    }
+    currentRow != null
+  }
+
+  override def fetch(): InternalRow = currentRow
+
+  override def close(): Unit = {
+    left.close()
+    right.close()
+  }
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index c4f8ae304db39..a2c275db9b35d 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -69,6 +69,11 @@ abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging
    */
   def close(): Unit
 
+  /**
+   * Returns the content through the [[Iterator]] interface.
+   */
+  final def asIterator: Iterator[InternalRow] = new LocalNodeIterator(this)
+
   /**
    * Returns the content of the iterator from the beginning to the end in the form of a Scala Seq.
    */
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala
new file mode 100644
index 0000000000000..abf3df1c0c2af
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/SampleNode.scala
@@ -0,0 +1,82 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.local
+
+import java.util.Random
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.Attribute
+import org.apache.spark.util.random.{BernoulliCellSampler, PoissonSampler}
+
+/**
+ * Sample the dataset.
+ *
+ * @param conf the SQLConf
+ * @param lowerBound Lower-bound of the sampling probability (usually 0.0)
+ * @param upperBound Upper-bound of the sampling probability. The expected fraction sampled
+ *                   will be ub - lb.
+ * @param withReplacement Whether to sample with replacement.
+ * @param seed the random seed
+ * @param child the LocalNode
+ */
+case class SampleNode(
+    conf: SQLConf,
+    lowerBound: Double,
+    upperBound: Double,
+    withReplacement: Boolean,
+    seed: Long,
+    child: LocalNode) extends UnaryLocalNode(conf) {
+
+  override def output: Seq[Attribute] = child.output
+
+  private[this] var iterator: Iterator[InternalRow] = _
+
+  private[this] var currentRow: InternalRow = _
+
+  override def open(): Unit = {
+    child.open()
+    val (sampler, _seed) = if (withReplacement) {
+        val random = new Random(seed)
+        // Disable gap sampling since the gap sampling method buffers two rows internally,
+        // requiring us to copy the row, which is more expensive than the random number generator.
+        (new PoissonSampler[InternalRow](upperBound - lowerBound, useGapSamplingIfPossible = false),
+          // Use the seed for partition 0 like PartitionwiseSampledRDD to generate the same result
+          // of DataFrame
+          random.nextLong())
+      } else {
+        (new BernoulliCellSampler[InternalRow](lowerBound, upperBound), seed)
+      }
+    sampler.setSeed(_seed)
+    iterator = sampler.sample(child.asIterator)
+  }
+
+  override def next(): Boolean = {
+    if (iterator.hasNext) {
+      currentRow = iterator.next()
+      true
+    } else {
+      false
+    }
+  }
+
+  override def fetch(): InternalRow = currentRow
+
+  override def close(): Unit = child.close()
+
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala
new file mode 100644
index 0000000000000..53f1dcc65d8cf
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNode.scala
@@ -0,0 +1,73 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.util.BoundedPriorityQueue
+
+case class TakeOrderedAndProjectNode(
+    conf: SQLConf,
+    limit: Int,
+    sortOrder: Seq[SortOrder],
+    projectList: Option[Seq[NamedExpression]],
+    child: LocalNode) extends UnaryLocalNode(conf) {
+
+  private[this] var projection: Option[Projection] = _
+  private[this] var ord: InterpretedOrdering = _
+  private[this] var iterator: Iterator[InternalRow] = _
+  private[this] var currentRow: InternalRow = _
+
+  override def output: Seq[Attribute] = {
+    val projectOutput = projectList.map(_.map(_.toAttribute))
+    projectOutput.getOrElse(child.output)
+  }
+
+  override def open(): Unit = {
+    child.open()
+    projection = projectList.map(new InterpretedProjection(_, child.output))
+    ord = new InterpretedOrdering(sortOrder, child.output)
+    // Priority keeps the largest elements, so let's reverse the ordering.
+    val queue = new BoundedPriorityQueue[InternalRow](limit)(ord.reverse)
+    while (child.next()) {
+      queue += child.fetch()
+    }
+    // Close it eagerly since we don't need it.
+    child.close()
+    iterator = queue.iterator
+  }
+
+  override def next(): Boolean = {
+    if (iterator.hasNext) {
+      val _currentRow = iterator.next()
+      currentRow = projection match {
+        case Some(p) => p(_currentRow)
+        case None => _currentRow
+      }
+      true
+    } else {
+      false
+    }
+  }
+
+  override def fetch(): InternalRow = currentRow
+
+  override def close(): Unit = child.close()
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala
new file mode 100644
index 0000000000000..7deaa375fcfc2
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/IntersectNodeSuite.scala
@@ -0,0 +1,35 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+class IntersectNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  test("basic") {
+    val input1 = (1 to 10).map(i => (i, i.toString)).toDF("key", "value")
+    val input2 = (1 to 10).filter(_ % 2 == 0).map(i => (i, i.toString)).toDF("key", "value")
+
+    checkAnswer2(
+      input1,
+      input2,
+      (node1, node2) => IntersectNode(conf, node1, node2),
+      input1.intersect(input2).collect()
+    )
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala
new file mode 100644
index 0000000000000..87a7da453999c
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/SampleNodeSuite.scala
@@ -0,0 +1,40 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.local
+
+class SampleNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  private def testSample(withReplacement: Boolean): Unit = {
+    test(s"withReplacement: $withReplacement") {
+      val seed = 0L
+      val input = sqlContext.sparkContext.
+        parallelize((1 to 10).map(i => (i, i.toString)), 1). // Should be only 1 partition
+        toDF("key", "value")
+      checkAnswer(
+        input,
+        node => SampleNode(conf, 0.0, 0.3, withReplacement, seed, node),
+        input.sample(withReplacement, 0.3, seed).collect()
+      )
+    }
+  }
+
+  testSample(withReplacement = true)
+  testSample(withReplacement = false)
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala
new file mode 100644
index 0000000000000..ff28b24eeff14
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/TakeOrderedAndProjectNodeSuite.scala
@@ -0,0 +1,54 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.Column
+import org.apache.spark.sql.catalyst.expressions.{Ascending, Expression, SortOrder}
+
+class TakeOrderedAndProjectNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  private def columnToSortOrder(sortExprs: Column*): Seq[SortOrder] = {
+    val sortOrder: Seq[SortOrder] = sortExprs.map { col =>
+      col.expr match {
+        case expr: SortOrder =>
+          expr
+        case expr: Expression =>
+          SortOrder(expr, Ascending)
+      }
+    }
+    sortOrder
+  }
+
+  private def testTakeOrderedAndProjectNode(desc: Boolean): Unit = {
+    val testCaseName = if (desc) "desc" else "asc"
+    test(testCaseName) {
+      val input = (1 to 10).map(i => (i, i.toString)).toDF("key", "value")
+      val sortColumn = if (desc) input.col("key").desc else input.col("key")
+      checkAnswer(
+        input,
+        node => TakeOrderedAndProjectNode(conf, 5, columnToSortOrder(sortColumn), None, node),
+        input.sort(sortColumn).limit(5).collect()
+      )
+    }
+  }
+
+  testTakeOrderedAndProjectNode(desc = false)
+  testTakeOrderedAndProjectNode(desc = true)
+}

From c2af42b5f32287ff595ad027a8191d4b75702d8d Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 11 Sep 2015 15:01:37 -0700
Subject: [PATCH 600/633] [SPARK-9990] [SQL] Local hash join follow-ups

1. Hide `LocalNodeIterator` behind the `LocalNode#asIterator` method
2. Add tests for this

Author: Andrew Or <andrew@databricks.com>

Closes #8708 from andrewor14/local-hash-join-follow-up.
---
 .../sql/execution/joins/HashedRelation.scala  |   7 +-
 .../sql/execution/local/HashJoinNode.scala    |   3 +-
 .../spark/sql/execution/local/LocalNode.scala |   4 +-
 .../sql/execution/local/LocalNodeSuite.scala  | 116 ++++++++++++++++++
 4 files changed, 125 insertions(+), 5 deletions(-)
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
index 0cff21ca618b4..bc255b27502b2 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala
@@ -25,7 +25,8 @@ import org.apache.spark.shuffle.ShuffleMemoryManager
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.execution.SparkSqlSerializer
-import org.apache.spark.sql.execution.metric.LongSQLMetric
+import org.apache.spark.sql.execution.local.LocalNode
+import org.apache.spark.sql.execution.metric.{LongSQLMetric, SQLMetrics}
 import org.apache.spark.unsafe.Platform
 import org.apache.spark.unsafe.map.BytesToBytesMap
 import org.apache.spark.unsafe.memory.{MemoryLocation, ExecutorMemoryManager, MemoryAllocator, TaskMemoryManager}
@@ -113,6 +114,10 @@ final class UniqueKeyHashedRelation(private var hashTable: JavaHashMap[InternalR
 
 private[execution] object HashedRelation {
 
+  def apply(localNode: LocalNode, keyGenerator: Projection): HashedRelation = {
+    apply(localNode.asIterator, SQLMetrics.nullLongMetric, keyGenerator)
+  }
+
   def apply(
       input: Iterator[InternalRow],
       numInputRows: LongSQLMetric,
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
index a3e68d6a7c341..e7b24e3fca2b4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/HashJoinNode.scala
@@ -75,8 +75,7 @@ case class HashJoinNode(
 
   override def open(): Unit = {
     buildNode.open()
-    hashed = HashedRelation.apply(
-      new LocalNodeIterator(buildNode), SQLMetrics.nullLongMetric, buildSideKeyGenerator)
+    hashed = HashedRelation(buildNode, buildSideKeyGenerator)
     streamedNode.open()
     joinRow = new JoinedRow
     resultProjection = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index a2c275db9b35d..e540ef8555eb6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -77,7 +77,7 @@ abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging
   /**
    * Returns the content of the iterator from the beginning to the end in the form of a Scala Seq.
    */
-  def collect(): Seq[Row] = {
+  final def collect(): Seq[Row] = {
     val converter = CatalystTypeConverters.createToScalaConverter(StructType.fromAttributes(output))
     val result = new scala.collection.mutable.ArrayBuffer[Row]
     open()
@@ -140,7 +140,7 @@ abstract class BinaryLocalNode(conf: SQLConf) extends LocalNode(conf) {
 /**
  * An thin wrapper around a [[LocalNode]] that provides an `Iterator` interface.
  */
-private[local] class LocalNodeIterator(localNode: LocalNode) extends Iterator[InternalRow] {
+private class LocalNodeIterator(localNode: LocalNode) extends Iterator[InternalRow] {
   private var nextRow: InternalRow = _
 
   override def hasNext: Boolean = {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala
new file mode 100644
index 0000000000000..b89fa46f8b3b4
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeSuite.scala
@@ -0,0 +1,116 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types.IntegerType
+
+class LocalNodeSuite extends SparkFunSuite {
+  private val data = (1 to 100).toArray
+
+  test("basic open, next, fetch, close") {
+    val node = new DummyLocalNode(data)
+    assert(!node.isOpen)
+    node.open()
+    assert(node.isOpen)
+    data.foreach { i =>
+      assert(node.next())
+      // fetch should be idempotent
+      val fetched = node.fetch()
+      assert(node.fetch() === fetched)
+      assert(node.fetch() === fetched)
+      assert(node.fetch().numFields === 1)
+      assert(node.fetch().getInt(0) === i)
+    }
+    assert(!node.next())
+    node.close()
+    assert(!node.isOpen)
+  }
+
+  test("asIterator") {
+    val node = new DummyLocalNode(data)
+    val iter = node.asIterator
+    node.open()
+    data.foreach { i =>
+      // hasNext should be idempotent
+      assert(iter.hasNext)
+      assert(iter.hasNext)
+      val item = iter.next()
+      assert(item.numFields === 1)
+      assert(item.getInt(0) === i)
+    }
+    intercept[NoSuchElementException] {
+      iter.next()
+    }
+    node.close()
+  }
+
+  test("collect") {
+    val node = new DummyLocalNode(data)
+    node.open()
+    val collected = node.collect()
+    assert(collected.size === data.size)
+    assert(collected.forall(_.size === 1))
+    assert(collected.map(_.getInt(0)) === data)
+    node.close()
+  }
+
+}
+
+/**
+ * A dummy [[LocalNode]] that just returns one row per integer in the input.
+ */
+private case class DummyLocalNode(conf: SQLConf, input: Array[Int]) extends LocalNode(conf) {
+  private var index = Int.MinValue
+
+  def this(input: Array[Int]) {
+    this(new SQLConf, input)
+  }
+
+  def isOpen: Boolean = {
+    index != Int.MinValue
+  }
+
+  override def output: Seq[Attribute] = {
+    Seq(AttributeReference("something", IntegerType)())
+  }
+
+  override def children: Seq[LocalNode] = Seq.empty
+
+  override def open(): Unit = {
+    index = -1
+  }
+
+  override def next(): Boolean = {
+    index += 1
+    index < input.size
+  }
+
+  override def fetch(): InternalRow = {
+    assert(index >= 0 && index < input.size)
+    val values = Array(input(index).asInstanceOf[Any])
+    new GenericInternalRow(values)
+  }
+
+  override def close(): Unit = {
+    index = Int.MinValue
+  }
+}

From d74c6a143cbd060c25bf14a8d306841b3ec55d03 Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Fri, 11 Sep 2015 15:02:59 -0700
Subject: [PATCH 601/633] [SPARK-10564] ThreadingSuite: assertion failures in
 threads don't fail the test

This commit ensures if an assertion fails within a thread, it will ultimately fail the test. Otherwise we end up potentially masking real bugs by not propagating assertion failures properly.

Author: Andrew Or <andrew@databricks.com>

Closes #8723 from andrewor14/fix-threading-suite.
---
 .../org/apache/spark/ThreadingSuite.scala     | 68 ++++++++++++-------
 1 file changed, 45 insertions(+), 23 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
index 48509f0759a3b..cda2b245526f7 100644
--- a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
@@ -119,23 +119,30 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     val nums = sc.parallelize(1 to 2, 2)
     val sem = new Semaphore(0)
     ThreadingSuiteState.clear()
+    var throwable: Option[Throwable] = None
     for (i <- 0 until 2) {
       new Thread {
         override def run() {
-          val ans = nums.map(number => {
-            val running = ThreadingSuiteState.runningThreads
-            running.getAndIncrement()
-            val time = System.currentTimeMillis()
-            while (running.get() != 4 && System.currentTimeMillis() < time + 1000) {
-              Thread.sleep(100)
-            }
-            if (running.get() != 4) {
-              ThreadingSuiteState.failed.set(true)
-            }
-            number
-          }).collect()
-          assert(ans.toList === List(1, 2))
-          sem.release()
+          try {
+            val ans = nums.map(number => {
+              val running = ThreadingSuiteState.runningThreads
+              running.getAndIncrement()
+              val time = System.currentTimeMillis()
+              while (running.get() != 4 && System.currentTimeMillis() < time + 1000) {
+                Thread.sleep(100)
+              }
+              if (running.get() != 4) {
+                ThreadingSuiteState.failed.set(true)
+              }
+              number
+            }).collect()
+            assert(ans.toList === List(1, 2))
+          } catch {
+            case t: Throwable =>
+              throwable = Some(t)
+          } finally {
+            sem.release()
+          }
         }
       }.start()
     }
@@ -145,18 +152,25 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
                 ThreadingSuiteState.runningThreads.get() + "); failing test")
       fail("One or more threads didn't see runningThreads = 4")
     }
+    throwable.foreach { t => throw t }
   }
 
   test("set local properties in different thread") {
     sc = new SparkContext("local", "test")
     val sem = new Semaphore(0)
-
+    var throwable: Option[Throwable] = None
     val threads = (1 to 5).map { i =>
       new Thread() {
         override def run() {
-          sc.setLocalProperty("test", i.toString)
-          assert(sc.getLocalProperty("test") === i.toString)
-          sem.release()
+          try {
+            sc.setLocalProperty("test", i.toString)
+            assert(sc.getLocalProperty("test") === i.toString)
+          } catch {
+            case t: Throwable =>
+              throwable = Some(t)
+          } finally {
+            sem.release()
+          }
         }
       }
     }
@@ -165,20 +179,27 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
 
     sem.acquire(5)
     assert(sc.getLocalProperty("test") === null)
+    throwable.foreach { t => throw t }
   }
 
   test("set and get local properties in parent-children thread") {
     sc = new SparkContext("local", "test")
     sc.setLocalProperty("test", "parent")
     val sem = new Semaphore(0)
-
+    var throwable: Option[Throwable] = None
     val threads = (1 to 5).map { i =>
       new Thread() {
         override def run() {
-          assert(sc.getLocalProperty("test") === "parent")
-          sc.setLocalProperty("test", i.toString)
-          assert(sc.getLocalProperty("test") === i.toString)
-          sem.release()
+          try {
+            assert(sc.getLocalProperty("test") === "parent")
+            sc.setLocalProperty("test", i.toString)
+            assert(sc.getLocalProperty("test") === i.toString)
+          } catch {
+            case t: Throwable =>
+              throwable = Some(t)
+          } finally {
+            sem.release()
+          }
         }
       }
     }
@@ -188,6 +209,7 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     sem.acquire(5)
     assert(sc.getLocalProperty("test") === "parent")
     assert(sc.getLocalProperty("Foo") === null)
+    throwable.foreach { t => throw t }
   }
 
   test("mutations to local properties should not affect submitted jobs (SPARK-6629)") {

From c34fc19765bdf55365cdce78d9ba11b220b73bb6 Mon Sep 17 00:00:00 2001
From: 0x0FFF <programmerag@gmail.com>
Date: Fri, 11 Sep 2015 15:19:04 -0700
Subject: [PATCH 602/633] [SPARK-9014] [SQL] Allow Python spark API to use
 built-in exponential operator

This PR addresses (SPARK-9014)[https://issues.apache.org/jira/browse/SPARK-9014]
Added functionality: `Column` object in Python now supports exponential operator `**`
Example:
```
from pyspark.sql import *
df = sqlContext.createDataFrame([Row(a=2)])
df.select(3**df.a,df.a**3,df.a**df.a).collect()
```
Outputs:
```
[Row(POWER(3.0, a)=9.0, POWER(a, 3.0)=8.0, POWER(a, a)=4.0)]
```

Author: 0x0FFF <programmerag@gmail.com>

Closes #8658 from 0x0FFF/SPARK-9014.
---
 python/pyspark/sql/column.py | 13 +++++++++++++
 python/pyspark/sql/tests.py  |  2 +-
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/sql/column.py b/python/pyspark/sql/column.py
index 573f65f5bf096..9ca8e1f264cfa 100644
--- a/python/pyspark/sql/column.py
+++ b/python/pyspark/sql/column.py
@@ -91,6 +91,17 @@ def _(self):
     return _
 
 
+def _bin_func_op(name, reverse=False, doc="binary function"):
+    def _(self, other):
+        sc = SparkContext._active_spark_context
+        fn = getattr(sc._jvm.functions, name)
+        jc = other._jc if isinstance(other, Column) else _create_column_from_literal(other)
+        njc = fn(self._jc, jc) if not reverse else fn(jc, self._jc)
+        return Column(njc)
+    _.__doc__ = doc
+    return _
+
+
 def _bin_op(name, doc="binary operator"):
     """ Create a method for given binary operator
     """
@@ -151,6 +162,8 @@ def __init__(self, jc):
     __rdiv__ = _reverse_op("divide")
     __rtruediv__ = _reverse_op("divide")
     __rmod__ = _reverse_op("mod")
+    __pow__ = _bin_func_op("pow")
+    __rpow__ = _bin_func_op("pow", reverse=True)
 
     # logistic operators
     __eq__ = _bin_op("equalTo")
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index eb449e8679fa0..f2172b7a27d88 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -568,7 +568,7 @@ def test_column_operators(self):
         cs = self.df.value
         c = ci == cs
         self.assertTrue(isinstance((- ci - 1 - 2) % 3 * 2.5 / 3.5, Column))
-        rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci)
+        rcc = (1 + ci), (1 - ci), (1 * ci), (1 / ci), (1 % ci), (1 ** ci), (ci ** 1)
         self.assertTrue(all(isinstance(c, Column) for c in rcc))
         cb = [ci == 5, ci != 0, ci > 3, ci < 4, ci >= 0, ci <= 7]
         self.assertTrue(all(isinstance(c, Column) for c in cb))

From 6d8367807cb62c2cb139cee1d039dc8b12c63385 Mon Sep 17 00:00:00 2001
From: Daniel Imfeld <daniel@danielimfeld.com>
Date: Sat, 12 Sep 2015 09:19:59 +0100
Subject: [PATCH 603/633] [SPARK-10566] [CORE] SnappyCompressionCodec init
 exception handling masks important error information

When throwing an IllegalArgumentException in SnappyCompressionCodec.init, chain the existing exception. This allows potentially important debugging info to be passed to the user.

Manual testing shows the exception chained properly, and the test suite still looks fine as well.

This contribution is my original work and I license the work to the project under the project's open source license.

Author: Daniel Imfeld <daniel@danielimfeld.com>

Closes #8725 from dimfeld/dimfeld-patch-1.
---
 core/src/main/scala/org/apache/spark/io/CompressionCodec.scala | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
index 607d5a321efca..9dc36704a676d 100644
--- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
+++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala
@@ -148,7 +148,7 @@ class SnappyCompressionCodec(conf: SparkConf) extends CompressionCodec {
   try {
     Snappy.getNativeLibraryVersion
   } catch {
-    case e: Error => throw new IllegalArgumentException
+    case e: Error => throw new IllegalArgumentException(e)
   }
 
   override def compressedOutputStream(s: OutputStream): OutputStream = {

From 8285e3b0d3dc0eff669eba993742dfe0401116f9 Mon Sep 17 00:00:00 2001
From: Nithin Asokan <Nithin.Asokan@Cerner.com>
Date: Sat, 12 Sep 2015 09:50:49 +0100
Subject: [PATCH 604/633] [SPARK-10554] [CORE] Fix NPE with ShutdownHook

https://issues.apache.org/jira/browse/SPARK-10554

Fixes NPE when ShutdownHook tries to cleanup temporary folders

Author: Nithin Asokan <Nithin.Asokan@Cerner.com>

Closes #8720 from nasokan/SPARK-10554.
---
 .../scala/org/apache/spark/storage/DiskBlockManager.scala     | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
index 3f8d26e1d4cab..f7e84a2c2e14c 100644
--- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
+++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala
@@ -164,7 +164,9 @@ private[spark] class DiskBlockManager(blockManager: BlockManager, conf: SparkCon
 
   private def doStop(): Unit = {
     // Only perform cleanup if an external service is not serving our shuffle files.
-    if (!blockManager.externalShuffleServiceEnabled || blockManager.blockManagerId.isDriver) {
+    // Also blockManagerId could be null if block manager is not initialized properly.
+    if (!blockManager.externalShuffleServiceEnabled ||
+      (blockManager.blockManagerId != null && blockManager.blockManagerId.isDriver)) {
       localDirs.foreach { localDir =>
         if (localDir.isDirectory() && localDir.exists()) {
           try {

From 22730ad54d681ad30e63fe910e8d89360853177d Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sat, 12 Sep 2015 10:40:10 +0100
Subject: [PATCH 605/633] [SPARK-10547] [TEST] Streamline / improve style of
 Java API tests

Fix a few Java API test style issues: unused generic types, exceptions, wrong assert argument order

Author: Sean Owen <sowen@cloudera.com>

Closes #8706 from srowen/SPARK-10547.
---
 .../java/org/apache/spark/JavaAPISuite.java   | 451 ++++++-----
 .../kafka/JavaDirectKafkaStreamSuite.java     |  24 +-
 .../streaming/kafka/JavaKafkaRDDSuite.java    |  17 +-
 .../streaming/kafka/JavaKafkaStreamSuite.java |  14 +-
 .../twitter/JavaTwitterStreamSuite.java       |   4 +-
 .../java/org/apache/spark/Java8APISuite.java  |  46 +-
 .../spark/sql/JavaApplySchemaSuite.java       |  39 +-
 .../apache/spark/sql/JavaDataFrameSuite.java  |  29 +-
 .../org/apache/spark/sql/JavaRowSuite.java    |  15 +-
 .../org/apache/spark/sql/JavaUDFSuite.java    |   9 +-
 .../spark/sql/sources/JavaSaveLoadSuite.java  |  10 +-
 .../spark/sql/hive/JavaDataFrameSuite.java    |   8 +-
 .../hive/JavaMetastoreDataSourcesSuite.java   |  12 +-
 .../apache/spark/streaming/JavaAPISuite.java  | 752 +++++++++---------
 .../spark/streaming/JavaReceiverAPISuite.java |  86 +-
 15 files changed, 755 insertions(+), 761 deletions(-)

diff --git a/core/src/test/java/org/apache/spark/JavaAPISuite.java b/core/src/test/java/org/apache/spark/JavaAPISuite.java
index ebd3d61ae7324..fd8f7f39b7cc8 100644
--- a/core/src/test/java/org/apache/spark/JavaAPISuite.java
+++ b/core/src/test/java/org/apache/spark/JavaAPISuite.java
@@ -90,7 +90,7 @@ public void sparkContextUnion() {
     JavaRDD<String> sUnion = sc.union(s1, s2);
     Assert.assertEquals(4, sUnion.count());
     // List
-    List<JavaRDD<String>> list = new ArrayList<JavaRDD<String>>();
+    List<JavaRDD<String>> list = new ArrayList<>();
     list.add(s2);
     sUnion = sc.union(s1, list);
     Assert.assertEquals(4, sUnion.count());
@@ -103,9 +103,9 @@ public void sparkContextUnion() {
     Assert.assertEquals(4, dUnion.count());
 
     // Union of JavaPairRDDs
-    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
-    pairs.add(new Tuple2<Integer, Integer>(1, 2));
-    pairs.add(new Tuple2<Integer, Integer>(3, 4));
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<>();
+    pairs.add(new Tuple2<>(1, 2));
+    pairs.add(new Tuple2<>(3, 4));
     JavaPairRDD<Integer, Integer> p1 = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> p2 = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> pUnion = sc.union(p1, p2);
@@ -133,9 +133,9 @@ public void intersection() {
     JavaDoubleRDD dIntersection = d1.intersection(d2);
     Assert.assertEquals(2, dIntersection.count());
 
-    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
-    pairs.add(new Tuple2<Integer, Integer>(1, 2));
-    pairs.add(new Tuple2<Integer, Integer>(3, 4));
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<>();
+    pairs.add(new Tuple2<>(1, 2));
+    pairs.add(new Tuple2<>(3, 4));
     JavaPairRDD<Integer, Integer> p1 = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> p2 = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> pIntersection = p1.intersection(p2);
@@ -165,47 +165,49 @@ public void randomSplit() {
 
   @Test
   public void sortByKey() {
-    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
-    pairs.add(new Tuple2<Integer, Integer>(0, 4));
-    pairs.add(new Tuple2<Integer, Integer>(3, 2));
-    pairs.add(new Tuple2<Integer, Integer>(-1, 1));
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<>();
+    pairs.add(new Tuple2<>(0, 4));
+    pairs.add(new Tuple2<>(3, 2));
+    pairs.add(new Tuple2<>(-1, 1));
 
     JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
 
     // Default comparator
     JavaPairRDD<Integer, Integer> sortedRDD = rdd.sortByKey();
-    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    Assert.assertEquals(new Tuple2<>(-1, 1), sortedRDD.first());
     List<Tuple2<Integer, Integer>> sortedPairs = sortedRDD.collect();
-    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1));
-    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
+    Assert.assertEquals(new Tuple2<>(0, 4), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<>(3, 2), sortedPairs.get(2));
 
     // Custom comparator
     sortedRDD = rdd.sortByKey(Collections.<Integer>reverseOrder(), false);
-    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    Assert.assertEquals(new Tuple2<>(-1, 1), sortedRDD.first());
     sortedPairs = sortedRDD.collect();
-    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1));
-    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
+    Assert.assertEquals(new Tuple2<>(0, 4), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<>(3, 2), sortedPairs.get(2));
   }
 
   @SuppressWarnings("unchecked")
   @Test
   public void repartitionAndSortWithinPartitions() {
-    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
-    pairs.add(new Tuple2<Integer, Integer>(0, 5));
-    pairs.add(new Tuple2<Integer, Integer>(3, 8));
-    pairs.add(new Tuple2<Integer, Integer>(2, 6));
-    pairs.add(new Tuple2<Integer, Integer>(0, 8));
-    pairs.add(new Tuple2<Integer, Integer>(3, 8));
-    pairs.add(new Tuple2<Integer, Integer>(1, 3));
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<>();
+    pairs.add(new Tuple2<>(0, 5));
+    pairs.add(new Tuple2<>(3, 8));
+    pairs.add(new Tuple2<>(2, 6));
+    pairs.add(new Tuple2<>(0, 8));
+    pairs.add(new Tuple2<>(3, 8));
+    pairs.add(new Tuple2<>(1, 3));
 
     JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
 
     Partitioner partitioner = new Partitioner() {
+      @Override
       public int numPartitions() {
         return 2;
       }
+      @Override
       public int getPartition(Object key) {
-        return ((Integer)key).intValue() % 2;
+        return (Integer) key % 2;
       }
     };
 
@@ -214,10 +216,10 @@ public int getPartition(Object key) {
     Assert.assertTrue(repartitioned.partitioner().isPresent());
     Assert.assertEquals(repartitioned.partitioner().get(), partitioner);
     List<List<Tuple2<Integer, Integer>>> partitions = repartitioned.glom().collect();
-    Assert.assertEquals(partitions.get(0), Arrays.asList(new Tuple2<Integer, Integer>(0, 5),
-        new Tuple2<Integer, Integer>(0, 8), new Tuple2<Integer, Integer>(2, 6)));
-    Assert.assertEquals(partitions.get(1), Arrays.asList(new Tuple2<Integer, Integer>(1, 3),
-        new Tuple2<Integer, Integer>(3, 8), new Tuple2<Integer, Integer>(3, 8)));
+    Assert.assertEquals(partitions.get(0),
+        Arrays.asList(new Tuple2<>(0, 5), new Tuple2<>(0, 8), new Tuple2<>(2, 6)));
+    Assert.assertEquals(partitions.get(1),
+        Arrays.asList(new Tuple2<>(1, 3), new Tuple2<>(3, 8), new Tuple2<>(3, 8)));
   }
 
   @Test
@@ -228,35 +230,37 @@ public void emptyRDD() {
 
   @Test
   public void sortBy() {
-    List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>();
-    pairs.add(new Tuple2<Integer, Integer>(0, 4));
-    pairs.add(new Tuple2<Integer, Integer>(3, 2));
-    pairs.add(new Tuple2<Integer, Integer>(-1, 1));
+    List<Tuple2<Integer, Integer>> pairs = new ArrayList<>();
+    pairs.add(new Tuple2<>(0, 4));
+    pairs.add(new Tuple2<>(3, 2));
+    pairs.add(new Tuple2<>(-1, 1));
 
     JavaRDD<Tuple2<Integer, Integer>> rdd = sc.parallelize(pairs);
 
     // compare on first value
     JavaRDD<Tuple2<Integer, Integer>> sortedRDD = rdd.sortBy(new Function<Tuple2<Integer, Integer>, Integer>() {
-      public Integer call(Tuple2<Integer, Integer> t) throws Exception {
+      @Override
+      public Integer call(Tuple2<Integer, Integer> t) {
         return t._1();
       }
     }, true, 2);
 
-    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    Assert.assertEquals(new Tuple2<>(-1, 1), sortedRDD.first());
     List<Tuple2<Integer, Integer>> sortedPairs = sortedRDD.collect();
-    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1));
-    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2));
+    Assert.assertEquals(new Tuple2<>(0, 4), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<>(3, 2), sortedPairs.get(2));
 
     // compare on second value
     sortedRDD = rdd.sortBy(new Function<Tuple2<Integer, Integer>, Integer>() {
-      public Integer call(Tuple2<Integer, Integer> t) throws Exception {
+      @Override
+      public Integer call(Tuple2<Integer, Integer> t) {
         return t._2();
       }
     }, true, 2);
-    Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first());
+    Assert.assertEquals(new Tuple2<>(-1, 1), sortedRDD.first());
     sortedPairs = sortedRDD.collect();
-    Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(1));
-    Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(2));
+    Assert.assertEquals(new Tuple2<>(3, 2), sortedPairs.get(1));
+    Assert.assertEquals(new Tuple2<>(0, 4), sortedPairs.get(2));
   }
 
   @Test
@@ -265,7 +269,7 @@ public void foreach() {
     JavaRDD<String> rdd = sc.parallelize(Arrays.asList("Hello", "World"));
     rdd.foreach(new VoidFunction<String>() {
       @Override
-      public void call(String s) throws IOException {
+      public void call(String s) {
         accum.add(1);
       }
     });
@@ -278,7 +282,7 @@ public void foreachPartition() {
     JavaRDD<String> rdd = sc.parallelize(Arrays.asList("Hello", "World"));
     rdd.foreachPartition(new VoidFunction<Iterator<String>>() {
       @Override
-      public void call(Iterator<String> iter) throws IOException {
+      public void call(Iterator<String> iter) {
         while (iter.hasNext()) {
           iter.next();
           accum.add(1);
@@ -301,7 +305,7 @@ public void zipWithUniqueId() {
     List<Integer> dataArray = Arrays.asList(1, 2, 3, 4);
     JavaPairRDD<Integer, Long> zip = sc.parallelize(dataArray).zipWithUniqueId();
     JavaRDD<Long> indexes = zip.values();
-    Assert.assertEquals(4, new HashSet<Long>(indexes.collect()).size());
+    Assert.assertEquals(4, new HashSet<>(indexes.collect()).size());
   }
 
   @Test
@@ -317,10 +321,10 @@ public void zipWithIndex() {
   @Test
   public void lookup() {
     JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, String>("Apples", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Citrus")
-      ));
+      new Tuple2<>("Apples", "Fruit"),
+      new Tuple2<>("Oranges", "Fruit"),
+      new Tuple2<>("Oranges", "Citrus")
+    ));
     Assert.assertEquals(2, categories.lookup("Oranges").size());
     Assert.assertEquals(2, Iterables.size(categories.groupByKey().lookup("Oranges").get(0)));
   }
@@ -390,18 +394,17 @@ public String call(Tuple2<Integer, Integer> x) {
   @Test
   public void cogroup() {
     JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, String>("Apples", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Citrus")
+      new Tuple2<>("Apples", "Fruit"),
+      new Tuple2<>("Oranges", "Fruit"),
+      new Tuple2<>("Oranges", "Citrus")
       ));
     JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, Integer>("Oranges", 2),
-      new Tuple2<String, Integer>("Apples", 3)
+      new Tuple2<>("Oranges", 2),
+      new Tuple2<>("Apples", 3)
     ));
     JavaPairRDD<String, Tuple2<Iterable<String>, Iterable<Integer>>> cogrouped =
         categories.cogroup(prices);
-    Assert.assertEquals("[Fruit, Citrus]",
-                        Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
+    Assert.assertEquals("[Fruit, Citrus]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
     Assert.assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2()));
 
     cogrouped.collect();
@@ -411,23 +414,22 @@ public void cogroup() {
   @Test
   public void cogroup3() {
     JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, String>("Apples", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Citrus")
+      new Tuple2<>("Apples", "Fruit"),
+      new Tuple2<>("Oranges", "Fruit"),
+      new Tuple2<>("Oranges", "Citrus")
       ));
     JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, Integer>("Oranges", 2),
-      new Tuple2<String, Integer>("Apples", 3)
+      new Tuple2<>("Oranges", 2),
+      new Tuple2<>("Apples", 3)
     ));
     JavaPairRDD<String, Integer> quantities = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, Integer>("Oranges", 21),
-      new Tuple2<String, Integer>("Apples", 42)
+      new Tuple2<>("Oranges", 21),
+      new Tuple2<>("Apples", 42)
     ));
 
     JavaPairRDD<String, Tuple3<Iterable<String>, Iterable<Integer>, Iterable<Integer>>> cogrouped =
         categories.cogroup(prices, quantities);
-    Assert.assertEquals("[Fruit, Citrus]",
-                        Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
+    Assert.assertEquals("[Fruit, Citrus]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
     Assert.assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2()));
     Assert.assertEquals("[42]", Iterables.toString(cogrouped.lookup("Apples").get(0)._3()));
 
@@ -439,27 +441,26 @@ public void cogroup3() {
   @Test
   public void cogroup4() {
     JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, String>("Apples", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Fruit"),
-      new Tuple2<String, String>("Oranges", "Citrus")
+      new Tuple2<>("Apples", "Fruit"),
+      new Tuple2<>("Oranges", "Fruit"),
+      new Tuple2<>("Oranges", "Citrus")
       ));
     JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, Integer>("Oranges", 2),
-      new Tuple2<String, Integer>("Apples", 3)
+      new Tuple2<>("Oranges", 2),
+      new Tuple2<>("Apples", 3)
     ));
     JavaPairRDD<String, Integer> quantities = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, Integer>("Oranges", 21),
-      new Tuple2<String, Integer>("Apples", 42)
+      new Tuple2<>("Oranges", 21),
+      new Tuple2<>("Apples", 42)
     ));
     JavaPairRDD<String, String> countries = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<String, String>("Oranges", "BR"),
-      new Tuple2<String, String>("Apples", "US")
+      new Tuple2<>("Oranges", "BR"),
+      new Tuple2<>("Apples", "US")
     ));
 
     JavaPairRDD<String, Tuple4<Iterable<String>, Iterable<Integer>, Iterable<Integer>, Iterable<String>>> cogrouped =
         categories.cogroup(prices, quantities, countries);
-    Assert.assertEquals("[Fruit, Citrus]",
-                        Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
+    Assert.assertEquals("[Fruit, Citrus]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._1()));
     Assert.assertEquals("[2]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._2()));
     Assert.assertEquals("[42]", Iterables.toString(cogrouped.lookup("Apples").get(0)._3()));
     Assert.assertEquals("[BR]", Iterables.toString(cogrouped.lookup("Oranges").get(0)._4()));
@@ -471,16 +472,16 @@ public void cogroup4() {
   @Test
   public void leftOuterJoin() {
     JavaPairRDD<Integer, Integer> rdd1 = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<Integer, Integer>(1, 1),
-      new Tuple2<Integer, Integer>(1, 2),
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(3, 1)
+      new Tuple2<>(1, 1),
+      new Tuple2<>(1, 2),
+      new Tuple2<>(2, 1),
+      new Tuple2<>(3, 1)
       ));
     JavaPairRDD<Integer, Character> rdd2 = sc.parallelizePairs(Arrays.asList(
-      new Tuple2<Integer, Character>(1, 'x'),
-      new Tuple2<Integer, Character>(2, 'y'),
-      new Tuple2<Integer, Character>(2, 'z'),
-      new Tuple2<Integer, Character>(4, 'w')
+      new Tuple2<>(1, 'x'),
+      new Tuple2<>(2, 'y'),
+      new Tuple2<>(2, 'z'),
+      new Tuple2<>(4, 'w')
     ));
     List<Tuple2<Integer,Tuple2<Integer,Optional<Character>>>> joined =
       rdd1.leftOuterJoin(rdd2).collect();
@@ -548,11 +549,11 @@ public Integer call(Integer a, Integer b) {
   public void aggregateByKey() {
     JavaPairRDD<Integer, Integer> pairs = sc.parallelizePairs(
       Arrays.asList(
-        new Tuple2<Integer, Integer>(1, 1),
-        new Tuple2<Integer, Integer>(1, 1),
-        new Tuple2<Integer, Integer>(3, 2),
-        new Tuple2<Integer, Integer>(5, 1),
-        new Tuple2<Integer, Integer>(5, 3)), 2);
+        new Tuple2<>(1, 1),
+        new Tuple2<>(1, 1),
+        new Tuple2<>(3, 2),
+        new Tuple2<>(5, 1),
+        new Tuple2<>(5, 3)), 2);
 
     Map<Integer, Set<Integer>> sets = pairs.aggregateByKey(new HashSet<Integer>(),
       new Function2<Set<Integer>, Integer, Set<Integer>>() {
@@ -570,20 +571,20 @@ public Set<Integer> call(Set<Integer> a, Set<Integer> b) {
         }
       }).collectAsMap();
     Assert.assertEquals(3, sets.size());
-    Assert.assertEquals(new HashSet<Integer>(Arrays.asList(1)), sets.get(1));
-    Assert.assertEquals(new HashSet<Integer>(Arrays.asList(2)), sets.get(3));
-    Assert.assertEquals(new HashSet<Integer>(Arrays.asList(1, 3)), sets.get(5));
+    Assert.assertEquals(new HashSet<>(Arrays.asList(1)), sets.get(1));
+    Assert.assertEquals(new HashSet<>(Arrays.asList(2)), sets.get(3));
+    Assert.assertEquals(new HashSet<>(Arrays.asList(1, 3)), sets.get(5));
   }
 
   @SuppressWarnings("unchecked")
   @Test
   public void foldByKey() {
     List<Tuple2<Integer, Integer>> pairs = Arrays.asList(
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(1, 1),
-      new Tuple2<Integer, Integer>(3, 2),
-      new Tuple2<Integer, Integer>(3, 1)
+      new Tuple2<>(2, 1),
+      new Tuple2<>(2, 1),
+      new Tuple2<>(1, 1),
+      new Tuple2<>(3, 2),
+      new Tuple2<>(3, 1)
     );
     JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> sums = rdd.foldByKey(0,
@@ -602,11 +603,11 @@ public Integer call(Integer a, Integer b) {
   @Test
   public void reduceByKey() {
     List<Tuple2<Integer, Integer>> pairs = Arrays.asList(
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(2, 1),
-      new Tuple2<Integer, Integer>(1, 1),
-      new Tuple2<Integer, Integer>(3, 2),
-      new Tuple2<Integer, Integer>(3, 1)
+      new Tuple2<>(2, 1),
+      new Tuple2<>(2, 1),
+      new Tuple2<>(1, 1),
+      new Tuple2<>(3, 2),
+      new Tuple2<>(3, 1)
     );
     JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs);
     JavaPairRDD<Integer, Integer> counts = rdd.reduceByKey(
@@ -690,7 +691,7 @@ public void cartesian() {
     JavaDoubleRDD doubleRDD = sc.parallelizeDoubles(Arrays.asList(1.0, 1.0, 2.0, 3.0, 5.0, 8.0));
     JavaRDD<String> stringRDD = sc.parallelize(Arrays.asList("Hello", "World"));
     JavaPairRDD<String, Double> cartesian = stringRDD.cartesian(doubleRDD);
-    Assert.assertEquals(new Tuple2<String, Double>("Hello", 1.0), cartesian.first());
+    Assert.assertEquals(new Tuple2<>("Hello", 1.0), cartesian.first());
   }
 
   @Test
@@ -743,6 +744,7 @@ public void javaDoubleRDDHistoGram() {
   }
 
   private static class DoubleComparator implements Comparator<Double>, Serializable {
+    @Override
     public int compare(Double o1, Double o2) {
       return o1.compareTo(o2);
     }
@@ -766,14 +768,14 @@ public void min() {
   public void naturalMax() {
     JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0));
     double max = rdd.max();
-    Assert.assertTrue(4.0 == max);
+    Assert.assertEquals(4.0, max, 0.0);
   }
 
   @Test
   public void naturalMin() {
     JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0));
     double max = rdd.min();
-    Assert.assertTrue(1.0 == max);
+    Assert.assertEquals(1.0, max, 0.0);
   }
 
   @Test
@@ -809,7 +811,7 @@ public void reduceOnJavaDoubleRDD() {
     JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 2.0, 3.0, 4.0));
     double sum = rdd.reduce(new Function2<Double, Double, Double>() {
       @Override
-      public Double call(Double v1, Double v2) throws Exception {
+      public Double call(Double v1, Double v2) {
         return v1 + v2;
       }
     });
@@ -844,7 +846,7 @@ public double call(Integer x) {
         new PairFunction<Integer, Integer, Integer>() {
           @Override
           public Tuple2<Integer, Integer> call(Integer x) {
-            return new Tuple2<Integer, Integer>(x, x);
+            return new Tuple2<>(x, x);
           }
         }).cache();
     pairs.collect();
@@ -870,26 +872,25 @@ public Iterable<String> call(String x) {
     Assert.assertEquals("Hello", words.first());
     Assert.assertEquals(11, words.count());
 
-    JavaPairRDD<String, String> pairs = rdd.flatMapToPair(
+    JavaPairRDD<String, String> pairsRDD = rdd.flatMapToPair(
       new PairFlatMapFunction<String, String, String>() {
-
         @Override
         public Iterable<Tuple2<String, String>> call(String s) {
-          List<Tuple2<String, String>> pairs = new LinkedList<Tuple2<String, String>>();
+          List<Tuple2<String, String>> pairs = new LinkedList<>();
           for (String word : s.split(" ")) {
-            pairs.add(new Tuple2<String, String>(word, word));
+            pairs.add(new Tuple2<>(word, word));
           }
           return pairs;
         }
       }
     );
-    Assert.assertEquals(new Tuple2<String, String>("Hello", "Hello"), pairs.first());
-    Assert.assertEquals(11, pairs.count());
+    Assert.assertEquals(new Tuple2<>("Hello", "Hello"), pairsRDD.first());
+    Assert.assertEquals(11, pairsRDD.count());
 
     JavaDoubleRDD doubles = rdd.flatMapToDouble(new DoubleFlatMapFunction<String>() {
       @Override
       public Iterable<Double> call(String s) {
-        List<Double> lengths = new LinkedList<Double>();
+        List<Double> lengths = new LinkedList<>();
         for (String word : s.split(" ")) {
           lengths.add((double) word.length());
         }
@@ -897,36 +898,36 @@ public Iterable<Double> call(String s) {
       }
     });
     Assert.assertEquals(5.0, doubles.first(), 0.01);
-    Assert.assertEquals(11, pairs.count());
+    Assert.assertEquals(11, pairsRDD.count());
   }
 
   @SuppressWarnings("unchecked")
   @Test
   public void mapsFromPairsToPairs() {
-      List<Tuple2<Integer, String>> pairs = Arrays.asList(
-              new Tuple2<Integer, String>(1, "a"),
-              new Tuple2<Integer, String>(2, "aa"),
-              new Tuple2<Integer, String>(3, "aaa")
-      );
-      JavaPairRDD<Integer, String> pairRDD = sc.parallelizePairs(pairs);
-
-      // Regression test for SPARK-668:
-      JavaPairRDD<String, Integer> swapped = pairRDD.flatMapToPair(
-          new PairFlatMapFunction<Tuple2<Integer, String>, String, Integer>() {
-          @Override
-          public Iterable<Tuple2<String, Integer>> call(Tuple2<Integer, String> item) {
-              return Collections.singletonList(item.swap());
-          }
+    List<Tuple2<Integer, String>> pairs = Arrays.asList(
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
+    );
+    JavaPairRDD<Integer, String> pairRDD = sc.parallelizePairs(pairs);
+
+    // Regression test for SPARK-668:
+    JavaPairRDD<String, Integer> swapped = pairRDD.flatMapToPair(
+      new PairFlatMapFunction<Tuple2<Integer, String>, String, Integer>() {
+        @Override
+        public Iterable<Tuple2<String, Integer>> call(Tuple2<Integer, String> item) {
+          return Collections.singletonList(item.swap());
+        }
       });
-      swapped.collect();
+    swapped.collect();
 
-      // There was never a bug here, but it's worth testing:
-      pairRDD.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
-          @Override
-          public Tuple2<String, Integer> call(Tuple2<Integer, String> item) {
-              return item.swap();
-          }
-      }).collect();
+    // There was never a bug here, but it's worth testing:
+    pairRDD.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
+      @Override
+      public Tuple2<String, Integer> call(Tuple2<Integer, String> item) {
+        return item.swap();
+      }
+    }).collect();
   }
 
   @Test
@@ -953,7 +954,7 @@ public void mapPartitionsWithIndex() {
     JavaRDD<Integer> partitionSums = rdd.mapPartitionsWithIndex(
       new Function2<Integer, Iterator<Integer>, Iterator<Integer>>() {
         @Override
-        public Iterator<Integer> call(Integer index, Iterator<Integer> iter) throws Exception {
+        public Iterator<Integer> call(Integer index, Iterator<Integer> iter) {
           int sum = 0;
           while (iter.hasNext()) {
             sum += iter.next();
@@ -972,8 +973,8 @@ public void repartition() {
     JavaRDD<Integer> repartitioned1 = in1.repartition(4);
     List<List<Integer>> result1 = repartitioned1.glom().collect();
     Assert.assertEquals(4, result1.size());
-    for (List<Integer> l: result1) {
-      Assert.assertTrue(l.size() > 0);
+    for (List<Integer> l : result1) {
+      Assert.assertFalse(l.isEmpty());
     }
 
     // Growing number of partitions
@@ -982,7 +983,7 @@ public void repartition() {
     List<List<Integer>> result2 = repartitioned2.glom().collect();
     Assert.assertEquals(2, result2.size());
     for (List<Integer> l: result2) {
-      Assert.assertTrue(l.size() > 0);
+      Assert.assertFalse(l.isEmpty());
     }
   }
 
@@ -994,9 +995,9 @@ public void persist() {
     Assert.assertEquals(20, doubleRDD.sum(), 0.1);
 
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> pairRDD = sc.parallelizePairs(pairs);
     pairRDD = pairRDD.persist(StorageLevel.DISK_ONLY());
@@ -1046,7 +1047,7 @@ public void wholeTextFiles() throws Exception {
     Files.write(content1, new File(tempDirName + "/part-00000"));
     Files.write(content2, new File(tempDirName + "/part-00001"));
 
-    Map<String, String> container = new HashMap<String, String>();
+    Map<String, String> container = new HashMap<>();
     container.put(tempDirName+"/part-00000", new Text(content1).toString());
     container.put(tempDirName+"/part-00001", new Text(content2).toString());
 
@@ -1075,16 +1076,16 @@ public void textFilesCompressed() throws IOException {
   public void sequenceFile() {
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
 
     rdd.mapToPair(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
       @Override
       public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
-        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+        return new Tuple2<>(new IntWritable(pair._1()), new Text(pair._2()));
       }
     }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class);
 
@@ -1093,7 +1094,7 @@ public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
       Text.class).mapToPair(new PairFunction<Tuple2<IntWritable, Text>, Integer, String>() {
       @Override
       public Tuple2<Integer, String> call(Tuple2<IntWritable, Text> pair) {
-        return new Tuple2<Integer, String>(pair._1().get(), pair._2().toString());
+        return new Tuple2<>(pair._1().get(), pair._2().toString());
       }
     });
     Assert.assertEquals(pairs, readRDD.collect());
@@ -1110,7 +1111,7 @@ public void binaryFiles() throws Exception {
     FileOutputStream fos1 = new FileOutputStream(file1);
 
     FileChannel channel1 = fos1.getChannel();
-    ByteBuffer bbuf = java.nio.ByteBuffer.wrap(content1);
+    ByteBuffer bbuf = ByteBuffer.wrap(content1);
     channel1.write(bbuf);
     channel1.close();
     JavaPairRDD<String, PortableDataStream> readRDD = sc.binaryFiles(tempDirName, 3);
@@ -1131,14 +1132,14 @@ public void binaryFilesCaching() throws Exception {
     FileOutputStream fos1 = new FileOutputStream(file1);
 
     FileChannel channel1 = fos1.getChannel();
-    ByteBuffer bbuf = java.nio.ByteBuffer.wrap(content1);
+    ByteBuffer bbuf = ByteBuffer.wrap(content1);
     channel1.write(bbuf);
     channel1.close();
 
     JavaPairRDD<String, PortableDataStream> readRDD = sc.binaryFiles(tempDirName).cache();
     readRDD.foreach(new VoidFunction<Tuple2<String,PortableDataStream>>() {
       @Override
-      public void call(Tuple2<String, PortableDataStream> pair) throws Exception {
+      public void call(Tuple2<String, PortableDataStream> pair) {
         pair._2().toArray(); // force the file to read
       }
     });
@@ -1162,7 +1163,7 @@ public void binaryRecords() throws Exception {
     FileChannel channel1 = fos1.getChannel();
 
     for (int i = 0; i < numOfCopies; i++) {
-      ByteBuffer bbuf = java.nio.ByteBuffer.wrap(content1);
+      ByteBuffer bbuf = ByteBuffer.wrap(content1);
       channel1.write(bbuf);
     }
     channel1.close();
@@ -1180,24 +1181,23 @@ public void binaryRecords() throws Exception {
   public void writeWithNewAPIHadoopFile() {
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
 
     rdd.mapToPair(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
       @Override
       public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
-        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+        return new Tuple2<>(new IntWritable(pair._1()), new Text(pair._2()));
       }
-    }).saveAsNewAPIHadoopFile(outputDir, IntWritable.class, Text.class,
-      org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat.class);
+    }).saveAsNewAPIHadoopFile(
+        outputDir, IntWritable.class, Text.class,
+        org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat.class);
 
-    JavaPairRDD<IntWritable, Text> output = sc.sequenceFile(outputDir, IntWritable.class,
-      Text.class);
-    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>,
-      String>() {
+    JavaPairRDD<IntWritable, Text> output = sc.sequenceFile(outputDir, IntWritable.class, Text.class);
+    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, String>() {
       @Override
       public String call(Tuple2<IntWritable, Text> x) {
         return x.toString();
@@ -1210,24 +1210,23 @@ public String call(Tuple2<IntWritable, Text> x) {
   public void readWithNewAPIHadoopFile() throws IOException {
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
 
     rdd.mapToPair(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
       @Override
       public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
-        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+        return new Tuple2<>(new IntWritable(pair._1()), new Text(pair._2()));
       }
     }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class);
 
     JavaPairRDD<IntWritable, Text> output = sc.newAPIHadoopFile(outputDir,
-      org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat.class, IntWritable.class,
-      Text.class, new Job().getConfiguration());
-    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>,
-      String>() {
+        org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat.class,
+        IntWritable.class, Text.class, new Job().getConfiguration());
+    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, String>() {
       @Override
       public String call(Tuple2<IntWritable, Text> x) {
         return x.toString();
@@ -1251,9 +1250,9 @@ public void objectFilesOfInts() {
   public void objectFilesOfComplexTypes() {
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
     rdd.saveAsObjectFile(outputDir);
@@ -1267,23 +1266,22 @@ public void objectFilesOfComplexTypes() {
   public void hadoopFile() {
     String outputDir = new File(tempDir, "output").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-      new Tuple2<Integer, String>(1, "a"),
-      new Tuple2<Integer, String>(2, "aa"),
-      new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
 
     rdd.mapToPair(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
       @Override
       public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
-        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+        return new Tuple2<>(new IntWritable(pair._1()), new Text(pair._2()));
       }
     }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class);
 
     JavaPairRDD<IntWritable, Text> output = sc.hadoopFile(outputDir,
-      SequenceFileInputFormat.class, IntWritable.class, Text.class);
-    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>,
-      String>() {
+        SequenceFileInputFormat.class, IntWritable.class, Text.class);
+    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, String>() {
       @Override
       public String call(Tuple2<IntWritable, Text> x) {
         return x.toString();
@@ -1296,16 +1294,16 @@ public String call(Tuple2<IntWritable, Text> x) {
   public void hadoopFileCompressed() {
     String outputDir = new File(tempDir, "output_compressed").getAbsolutePath();
     List<Tuple2<Integer, String>> pairs = Arrays.asList(
-        new Tuple2<Integer, String>(1, "a"),
-        new Tuple2<Integer, String>(2, "aa"),
-        new Tuple2<Integer, String>(3, "aaa")
+      new Tuple2<>(1, "a"),
+      new Tuple2<>(2, "aa"),
+      new Tuple2<>(3, "aaa")
     );
     JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs);
 
     rdd.mapToPair(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() {
       @Override
       public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
-        return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2()));
+        return new Tuple2<>(new IntWritable(pair._1()), new Text(pair._2()));
       }
     }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class,
         DefaultCodec.class);
@@ -1313,8 +1311,7 @@ public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) {
     JavaPairRDD<IntWritable, Text> output = sc.hadoopFile(outputDir,
         SequenceFileInputFormat.class, IntWritable.class, Text.class);
 
-    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>,
-        String>() {
+    Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, String>() {
       @Override
       public String call(Tuple2<IntWritable, Text> x) {
         return x.toString();
@@ -1414,8 +1411,8 @@ public String call(Integer t) {
         return t.toString();
       }
     }).collect();
-    Assert.assertEquals(new Tuple2<String, Integer>("1", 1), s.get(0));
-    Assert.assertEquals(new Tuple2<String, Integer>("2", 2), s.get(1));
+    Assert.assertEquals(new Tuple2<>("1", 1), s.get(0));
+    Assert.assertEquals(new Tuple2<>("2", 2), s.get(1));
   }
 
   @Test
@@ -1448,20 +1445,20 @@ public void combineByKey() {
     JavaRDD<Integer> originalRDD = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5, 6));
     Function<Integer, Integer> keyFunction = new Function<Integer, Integer>() {
       @Override
-      public Integer call(Integer v1) throws Exception {
+      public Integer call(Integer v1) {
         return v1 % 3;
       }
     };
     Function<Integer, Integer> createCombinerFunction = new Function<Integer, Integer>() {
       @Override
-      public Integer call(Integer v1) throws Exception {
+      public Integer call(Integer v1) {
         return v1;
       }
     };
 
     Function2<Integer, Integer, Integer> mergeValueFunction = new Function2<Integer, Integer, Integer>() {
       @Override
-      public Integer call(Integer v1, Integer v2) throws Exception {
+      public Integer call(Integer v1, Integer v2) {
         return v1 + v2;
       }
     };
@@ -1496,21 +1493,21 @@ public void mapOnPairRDD() {
         new PairFunction<Integer, Integer, Integer>() {
           @Override
           public Tuple2<Integer, Integer> call(Integer i) {
-            return new Tuple2<Integer, Integer>(i, i % 2);
+            return new Tuple2<>(i, i % 2);
           }
         });
     JavaPairRDD<Integer, Integer> rdd3 = rdd2.mapToPair(
         new PairFunction<Tuple2<Integer, Integer>, Integer, Integer>() {
-      @Override
-      public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> in) {
-        return new Tuple2<Integer, Integer>(in._2(), in._1());
-      }
-    });
+          @Override
+          public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> in) {
+            return new Tuple2<>(in._2(), in._1());
+          }
+        });
     Assert.assertEquals(Arrays.asList(
-        new Tuple2<Integer, Integer>(1, 1),
-        new Tuple2<Integer, Integer>(0, 2),
-        new Tuple2<Integer, Integer>(1, 3),
-        new Tuple2<Integer, Integer>(0, 4)), rdd3.collect());
+        new Tuple2<>(1, 1),
+        new Tuple2<>(0, 2),
+        new Tuple2<>(1, 3),
+        new Tuple2<>(0, 4)), rdd3.collect());
 
   }
 
@@ -1523,7 +1520,7 @@ public void collectPartitions() {
         new PairFunction<Integer, Integer, Integer>() {
           @Override
           public Tuple2<Integer, Integer> call(Integer i) {
-            return new Tuple2<Integer, Integer>(i, i % 2);
+            return new Tuple2<>(i, i % 2);
           }
         });
 
@@ -1534,23 +1531,23 @@ public Tuple2<Integer, Integer> call(Integer i) {
     Assert.assertEquals(Arrays.asList(3, 4), parts[0]);
     Assert.assertEquals(Arrays.asList(5, 6, 7), parts[1]);
 
-    Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(1, 1),
-                                      new Tuple2<Integer, Integer>(2, 0)),
+    Assert.assertEquals(Arrays.asList(new Tuple2<>(1, 1),
+                                      new Tuple2<>(2, 0)),
                         rdd2.collectPartitions(new int[] {0})[0]);
 
     List<Tuple2<Integer,Integer>>[] parts2 = rdd2.collectPartitions(new int[] {1, 2});
-    Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(3, 1),
-                                      new Tuple2<Integer, Integer>(4, 0)),
+    Assert.assertEquals(Arrays.asList(new Tuple2<>(3, 1),
+                                      new Tuple2<>(4, 0)),
                         parts2[0]);
-    Assert.assertEquals(Arrays.asList(new Tuple2<Integer, Integer>(5, 1),
-                                      new Tuple2<Integer, Integer>(6, 0),
-                                      new Tuple2<Integer, Integer>(7, 1)),
+    Assert.assertEquals(Arrays.asList(new Tuple2<>(5, 1),
+                                      new Tuple2<>(6, 0),
+                                      new Tuple2<>(7, 1)),
                         parts2[1]);
   }
 
   @Test
   public void countApproxDistinct() {
-    List<Integer> arrayData = new ArrayList<Integer>();
+    List<Integer> arrayData = new ArrayList<>();
     int size = 100;
     for (int i = 0; i < 100000; i++) {
       arrayData.add(i % size);
@@ -1561,15 +1558,15 @@ public void countApproxDistinct() {
 
   @Test
   public void countApproxDistinctByKey() {
-    List<Tuple2<Integer, Integer>> arrayData = new ArrayList<Tuple2<Integer, Integer>>();
+    List<Tuple2<Integer, Integer>> arrayData = new ArrayList<>();
     for (int i = 10; i < 100; i++) {
       for (int j = 0; j < i; j++) {
-        arrayData.add(new Tuple2<Integer, Integer>(i, j));
+        arrayData.add(new Tuple2<>(i, j));
       }
     }
     double relativeSD = 0.001;
     JavaPairRDD<Integer, Integer> pairRdd = sc.parallelizePairs(arrayData);
-    List<Tuple2<Integer, Object>> res =  pairRdd.countApproxDistinctByKey(8, 0).collect();
+    List<Tuple2<Integer, Object>> res =  pairRdd.countApproxDistinctByKey(relativeSD, 8).collect();
     for (Tuple2<Integer, Object> resItem : res) {
       double count = (double)resItem._1();
       Long resCount = (Long)resItem._2();
@@ -1587,7 +1584,7 @@ public void collectAsMapWithIntArrayValues() {
         new PairFunction<Integer, Integer, int[]>() {
           @Override
           public Tuple2<Integer, int[]> call(Integer x) {
-            return new Tuple2<Integer, int[]>(x, new int[] { x });
+            return new Tuple2<>(x, new int[]{x});
           }
         });
     pairRDD.collect();  // Works fine
@@ -1598,7 +1595,7 @@ public Tuple2<Integer, int[]> call(Integer x) {
   @Test
   public void collectAsMapAndSerialize() throws Exception {
     JavaPairRDD<String,Integer> rdd =
-        sc.parallelizePairs(Arrays.asList(new Tuple2<String,Integer>("foo", 1)));
+        sc.parallelizePairs(Arrays.asList(new Tuple2<>("foo", 1)));
     Map<String,Integer> map = rdd.collectAsMap();
     ByteArrayOutputStream bytes = new ByteArrayOutputStream();
     new ObjectOutputStream(bytes).writeObject(map);
@@ -1615,7 +1612,7 @@ public void sampleByKey() {
       new PairFunction<Integer, Integer, Integer>() {
         @Override
         public Tuple2<Integer, Integer> call(Integer i) {
-          return new Tuple2<Integer, Integer>(i % 2, 1);
+          return new Tuple2<>(i % 2, 1);
         }
       });
     Map<Integer, Object> fractions = Maps.newHashMap();
@@ -1623,12 +1620,12 @@ public Tuple2<Integer, Integer> call(Integer i) {
     fractions.put(1, 1.0);
     JavaPairRDD<Integer, Integer> wr = rdd2.sampleByKey(true, fractions, 1L);
     Map<Integer, Long> wrCounts = (Map<Integer, Long>) (Object) wr.countByKey();
-    Assert.assertTrue(wrCounts.size() == 2);
+    Assert.assertEquals(2, wrCounts.size());
     Assert.assertTrue(wrCounts.get(0) > 0);
     Assert.assertTrue(wrCounts.get(1) > 0);
     JavaPairRDD<Integer, Integer> wor = rdd2.sampleByKey(false, fractions, 1L);
     Map<Integer, Long> worCounts = (Map<Integer, Long>) (Object) wor.countByKey();
-    Assert.assertTrue(worCounts.size() == 2);
+    Assert.assertEquals(2, worCounts.size());
     Assert.assertTrue(worCounts.get(0) > 0);
     Assert.assertTrue(worCounts.get(1) > 0);
   }
@@ -1641,7 +1638,7 @@ public void sampleByKeyExact() {
       new PairFunction<Integer, Integer, Integer>() {
           @Override
           public Tuple2<Integer, Integer> call(Integer i) {
-              return new Tuple2<Integer, Integer>(i % 2, 1);
+              return new Tuple2<>(i % 2, 1);
           }
       });
     Map<Integer, Object> fractions = Maps.newHashMap();
@@ -1649,25 +1646,25 @@ public Tuple2<Integer, Integer> call(Integer i) {
     fractions.put(1, 1.0);
     JavaPairRDD<Integer, Integer> wrExact = rdd2.sampleByKeyExact(true, fractions, 1L);
     Map<Integer, Long> wrExactCounts = (Map<Integer, Long>) (Object) wrExact.countByKey();
-    Assert.assertTrue(wrExactCounts.size() == 2);
+    Assert.assertEquals(2, wrExactCounts.size());
     Assert.assertTrue(wrExactCounts.get(0) == 2);
     Assert.assertTrue(wrExactCounts.get(1) == 4);
     JavaPairRDD<Integer, Integer> worExact = rdd2.sampleByKeyExact(false, fractions, 1L);
     Map<Integer, Long> worExactCounts = (Map<Integer, Long>) (Object) worExact.countByKey();
-    Assert.assertTrue(worExactCounts.size() == 2);
+    Assert.assertEquals(2, worExactCounts.size());
     Assert.assertTrue(worExactCounts.get(0) == 2);
     Assert.assertTrue(worExactCounts.get(1) == 4);
   }
 
   private static class SomeCustomClass implements Serializable {
-    public SomeCustomClass() {
+    SomeCustomClass() {
       // Intentionally left blank
     }
   }
 
   @Test
   public void collectUnderlyingScalaRDD() {
-    List<SomeCustomClass> data = new ArrayList<SomeCustomClass>();
+    List<SomeCustomClass> data = new ArrayList<>();
     for (int i = 0; i < 100; i++) {
       data.add(new SomeCustomClass());
     }
@@ -1679,7 +1676,7 @@ public void collectUnderlyingScalaRDD() {
   private static final class BuggyMapFunction<T> implements Function<T, T> {
 
     @Override
-    public T call(T x) throws Exception {
+    public T call(T x) {
       throw new IllegalStateException("Custom exception!");
     }
   }
@@ -1716,7 +1713,7 @@ public void foreachAsync() throws Exception {
     JavaFutureAction<Void> future = rdd.foreachAsync(
         new VoidFunction<Integer>() {
           @Override
-          public void call(Integer integer) throws Exception {
+          public void call(Integer integer) {
             // intentionally left blank.
           }
         }
@@ -1745,7 +1742,7 @@ public void testAsyncActionCancellation() throws Exception {
     JavaRDD<Integer> rdd = sc.parallelize(data, 1);
     JavaFutureAction<Void> future = rdd.foreachAsync(new VoidFunction<Integer>() {
       @Override
-      public void call(Integer integer) throws Exception {
+      public void call(Integer integer) throws InterruptedException {
         Thread.sleep(10000);  // To ensure that the job won't finish before it's cancelled.
       }
     });
diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
index 9db07d0507fea..fbdfbf7e509b3 100644
--- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
+++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaDirectKafkaStreamSuite.java
@@ -75,11 +75,11 @@ public void testKafkaStream() throws InterruptedException {
     String[] topic1data = createTopicAndSendData(topic1);
     String[] topic2data = createTopicAndSendData(topic2);
 
-    HashSet<String> sent = new HashSet<String>();
+    Set<String> sent = new HashSet<>();
     sent.addAll(Arrays.asList(topic1data));
     sent.addAll(Arrays.asList(topic2data));
 
-    HashMap<String, String> kafkaParams = new HashMap<String, String>();
+    Map<String, String> kafkaParams = new HashMap<>();
     kafkaParams.put("metadata.broker.list", kafkaTestUtils.brokerAddress());
     kafkaParams.put("auto.offset.reset", "smallest");
 
@@ -95,17 +95,17 @@ public void testKafkaStream() throws InterruptedException {
         // Make sure you can get offset ranges from the rdd
         new Function<JavaPairRDD<String, String>, JavaPairRDD<String, String>>() {
           @Override
-          public JavaPairRDD<String, String> call(JavaPairRDD<String, String> rdd) throws Exception {
+          public JavaPairRDD<String, String> call(JavaPairRDD<String, String> rdd) {
             OffsetRange[] offsets = ((HasOffsetRanges) rdd.rdd()).offsetRanges();
             offsetRanges.set(offsets);
-            Assert.assertEquals(offsets[0].topic(), topic1);
+            Assert.assertEquals(topic1, offsets[0].topic());
             return rdd;
           }
         }
     ).map(
         new Function<Tuple2<String, String>, String>() {
           @Override
-          public String call(Tuple2<String, String> kv) throws Exception {
+          public String call(Tuple2<String, String> kv) {
             return kv._2();
           }
         }
@@ -119,10 +119,10 @@ public String call(Tuple2<String, String> kv) throws Exception {
         StringDecoder.class,
         String.class,
         kafkaParams,
-        topicOffsetToMap(topic2, (long) 0),
+        topicOffsetToMap(topic2, 0L),
         new Function<MessageAndMetadata<String, String>, String>() {
           @Override
-          public String call(MessageAndMetadata<String, String> msgAndMd) throws Exception {
+          public String call(MessageAndMetadata<String, String> msgAndMd) {
             return msgAndMd.message();
           }
         }
@@ -133,7 +133,7 @@ public String call(MessageAndMetadata<String, String> msgAndMd) throws Exception
     unifiedStream.foreachRDD(
         new Function<JavaRDD<String>, Void>() {
           @Override
-          public Void call(JavaRDD<String> rdd) throws Exception {
+          public Void call(JavaRDD<String> rdd) {
             result.addAll(rdd.collect());
             for (OffsetRange o : offsetRanges.get()) {
               System.out.println(
@@ -155,14 +155,14 @@ public Void call(JavaRDD<String> rdd) throws Exception {
     ssc.stop();
   }
 
-  private HashSet<String> topicToSet(String topic) {
-    HashSet<String> topicSet = new HashSet<String>();
+  private static Set<String> topicToSet(String topic) {
+    Set<String> topicSet = new HashSet<>();
     topicSet.add(topic);
     return topicSet;
   }
 
-  private HashMap<TopicAndPartition, Long> topicOffsetToMap(String topic, Long offsetToStart) {
-    HashMap<TopicAndPartition, Long> topicMap = new HashMap<TopicAndPartition, Long>();
+  private static Map<TopicAndPartition, Long> topicOffsetToMap(String topic, Long offsetToStart) {
+    Map<TopicAndPartition, Long> topicMap = new HashMap<>();
     topicMap.put(new TopicAndPartition(topic, 0), offsetToStart);
     return topicMap;
   }
diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java
index a9dc6e50613ca..afcc6cfccd39a 100644
--- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java
+++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaRDDSuite.java
@@ -19,6 +19,7 @@
 
 import java.io.Serializable;
 import java.util.HashMap;
+import java.util.Map;
 
 import scala.Tuple2;
 
@@ -66,10 +67,10 @@ public void testKafkaRDD() throws InterruptedException {
     String topic1 = "topic1";
     String topic2 = "topic2";
 
-    String[] topic1data = createTopicAndSendData(topic1);
-    String[] topic2data = createTopicAndSendData(topic2);
+    createTopicAndSendData(topic1);
+    createTopicAndSendData(topic2);
 
-    HashMap<String, String> kafkaParams = new HashMap<String, String>();
+    Map<String, String> kafkaParams = new HashMap<>();
     kafkaParams.put("metadata.broker.list", kafkaTestUtils.brokerAddress());
 
     OffsetRange[] offsetRanges = {
@@ -77,8 +78,8 @@ public void testKafkaRDD() throws InterruptedException {
       OffsetRange.create(topic2, 0, 0, 1)
     };
 
-    HashMap<TopicAndPartition, Broker> emptyLeaders = new HashMap<TopicAndPartition, Broker>();
-    HashMap<TopicAndPartition, Broker> leaders = new HashMap<TopicAndPartition, Broker>();
+    Map<TopicAndPartition, Broker> emptyLeaders = new HashMap<>();
+    Map<TopicAndPartition, Broker> leaders = new HashMap<>();
     String[] hostAndPort = kafkaTestUtils.brokerAddress().split(":");
     Broker broker = Broker.create(hostAndPort[0], Integer.parseInt(hostAndPort[1]));
     leaders.put(new TopicAndPartition(topic1, 0), broker);
@@ -95,7 +96,7 @@ public void testKafkaRDD() throws InterruptedException {
     ).map(
         new Function<Tuple2<String, String>, String>() {
           @Override
-          public String call(Tuple2<String, String> kv) throws Exception {
+          public String call(Tuple2<String, String> kv) {
             return kv._2();
           }
         }
@@ -113,7 +114,7 @@ public String call(Tuple2<String, String> kv) throws Exception {
         emptyLeaders,
         new Function<MessageAndMetadata<String, String>, String>() {
           @Override
-          public String call(MessageAndMetadata<String, String> msgAndMd) throws Exception {
+          public String call(MessageAndMetadata<String, String> msgAndMd) {
             return msgAndMd.message();
           }
         }
@@ -131,7 +132,7 @@ public String call(MessageAndMetadata<String, String> msgAndMd) throws Exception
         leaders,
         new Function<MessageAndMetadata<String, String>, String>() {
           @Override
-          public String call(MessageAndMetadata<String, String> msgAndMd) throws Exception {
+          public String call(MessageAndMetadata<String, String> msgAndMd) {
             return msgAndMd.message();
           }
         }
diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java
index e4c659215b767..1e69de46cd35d 100644
--- a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java
+++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java
@@ -67,10 +67,10 @@ public void tearDown() {
   @Test
   public void testKafkaStream() throws InterruptedException {
     String topic = "topic1";
-    HashMap<String, Integer> topics = new HashMap<String, Integer>();
+    Map<String, Integer> topics = new HashMap<>();
     topics.put(topic, 1);
 
-    HashMap<String, Integer> sent = new HashMap<String, Integer>();
+    Map<String, Integer> sent = new HashMap<>();
     sent.put("a", 5);
     sent.put("b", 3);
     sent.put("c", 10);
@@ -78,7 +78,7 @@ public void testKafkaStream() throws InterruptedException {
     kafkaTestUtils.createTopic(topic);
     kafkaTestUtils.sendMessages(topic, sent);
 
-    HashMap<String, String> kafkaParams = new HashMap<String, String>();
+    Map<String, String> kafkaParams = new HashMap<>();
     kafkaParams.put("zookeeper.connect", kafkaTestUtils.zkAddress());
     kafkaParams.put("group.id", "test-consumer-" + random.nextInt(10000));
     kafkaParams.put("auto.offset.reset", "smallest");
@@ -97,7 +97,7 @@ public void testKafkaStream() throws InterruptedException {
     JavaDStream<String> words = stream.map(
       new Function<Tuple2<String, String>, String>() {
         @Override
-        public String call(Tuple2<String, String> tuple2) throws Exception {
+        public String call(Tuple2<String, String> tuple2) {
           return tuple2._2();
         }
       }
@@ -106,7 +106,7 @@ public String call(Tuple2<String, String> tuple2) throws Exception {
     words.countByValue().foreachRDD(
       new Function<JavaPairRDD<String, Long>, Void>() {
         @Override
-        public Void call(JavaPairRDD<String, Long> rdd) throws Exception {
+        public Void call(JavaPairRDD<String, Long> rdd) {
           List<Tuple2<String, Long>> ret = rdd.collect();
           for (Tuple2<String, Long> r : ret) {
             if (result.containsKey(r._1())) {
@@ -130,8 +130,8 @@ public Void call(JavaPairRDD<String, Long> rdd) throws Exception {
       Thread.sleep(200);
     }
     Assert.assertEquals(sent.size(), result.size());
-    for (String k : sent.keySet()) {
-      Assert.assertEquals(sent.get(k).intValue(), result.get(k).intValue());
+    for (Map.Entry<String, Integer> e : sent.entrySet()) {
+      Assert.assertEquals(e.getValue().intValue(), result.get(e.getKey()).intValue());
     }
   }
 }
diff --git a/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java b/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java
index e46b4e5c7531d..26ec8af455bcf 100644
--- a/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java
+++ b/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java
@@ -17,8 +17,6 @@
 
 package org.apache.spark.streaming.twitter;
 
-import java.util.Arrays;
-
 import org.junit.Test;
 import twitter4j.Status;
 import twitter4j.auth.Authorization;
@@ -30,7 +28,7 @@
 public class JavaTwitterStreamSuite extends LocalJavaStreamingContext {
   @Test
   public void testTwitterStream() {
-    String[] filters = (String[])Arrays.<String>asList("filter1", "filter2").toArray();
+    String[] filters = { "filter1", "filter2" };
     Authorization auth = NullAuthorization.getInstance();
 
     // tests the API, does not actually test data receiving
diff --git a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
index 729bc0459ce52..14975265ab2ce 100644
--- a/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
+++ b/extras/java8-tests/src/test/java/org/apache/spark/Java8APISuite.java
@@ -77,7 +77,7 @@ public void call(String s) {
   public void foreach() {
     foreachCalls = 0;
     JavaRDD<String> rdd = sc.parallelize(Arrays.asList("Hello", "World"));
-    rdd.foreach((x) -> foreachCalls++);
+    rdd.foreach(x -> foreachCalls++);
     Assert.assertEquals(2, foreachCalls);
   }
 
@@ -180,7 +180,7 @@ public void map() {
     JavaPairRDD<Integer, Integer> pairs = rdd.mapToPair(x -> new Tuple2<>(x, x))
       .cache();
     pairs.collect();
-    JavaRDD<String> strings = rdd.map(x -> x.toString()).cache();
+    JavaRDD<String> strings = rdd.map(Object::toString).cache();
     strings.collect();
   }
 
@@ -195,7 +195,9 @@ public void flatMap() {
 
     JavaPairRDD<String, String> pairs = rdd.flatMapToPair(s -> {
       List<Tuple2<String, String>> pairs2 = new LinkedList<>();
-      for (String word : s.split(" ")) pairs2.add(new Tuple2<>(word, word));
+      for (String word : s.split(" ")) {
+        pairs2.add(new Tuple2<>(word, word));
+      }
       return pairs2;
     });
 
@@ -204,11 +206,12 @@ public void flatMap() {
 
     JavaDoubleRDD doubles = rdd.flatMapToDouble(s -> {
       List<Double> lengths = new LinkedList<>();
-      for (String word : s.split(" ")) lengths.add(word.length() * 1.0);
+      for (String word : s.split(" ")) {
+        lengths.add((double) word.length());
+      }
       return lengths;
     });
 
-    Double x = doubles.first();
     Assert.assertEquals(5.0, doubles.first(), 0.01);
     Assert.assertEquals(11, pairs.count());
   }
@@ -228,7 +231,7 @@ public void mapsFromPairsToPairs() {
     swapped.collect();
 
     // There was never a bug here, but it's worth testing:
-    pairRDD.map(item -> item.swap()).collect();
+    pairRDD.map(Tuple2::swap).collect();
   }
 
   @Test
@@ -282,11 +285,11 @@ public void zipPartitions() {
     FlatMapFunction2<Iterator<Integer>, Iterator<String>, Integer> sizesFn =
       (Iterator<Integer> i, Iterator<String> s) -> {
         int sizeI = 0;
-        int sizeS = 0;
         while (i.hasNext()) {
           sizeI += 1;
           i.next();
         }
+        int sizeS = 0;
         while (s.hasNext()) {
           sizeS += 1;
           s.next();
@@ -301,30 +304,31 @@ public void zipPartitions() {
   public void accumulators() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5));
 
-    final Accumulator<Integer> intAccum = sc.intAccumulator(10);
-    rdd.foreach(x -> intAccum.add(x));
+    Accumulator<Integer> intAccum = sc.intAccumulator(10);
+    rdd.foreach(intAccum::add);
     Assert.assertEquals((Integer) 25, intAccum.value());
 
-    final Accumulator<Double> doubleAccum = sc.doubleAccumulator(10.0);
+    Accumulator<Double> doubleAccum = sc.doubleAccumulator(10.0);
     rdd.foreach(x -> doubleAccum.add((double) x));
     Assert.assertEquals((Double) 25.0, doubleAccum.value());
 
     // Try a custom accumulator type
     AccumulatorParam<Float> floatAccumulatorParam = new AccumulatorParam<Float>() {
+      @Override
       public Float addInPlace(Float r, Float t) {
         return r + t;
       }
-
+      @Override
       public Float addAccumulator(Float r, Float t) {
         return r + t;
       }
-
+      @Override
       public Float zero(Float initialValue) {
         return 0.0f;
       }
     };
 
-    final Accumulator<Float> floatAccum = sc.accumulator(10.0f, floatAccumulatorParam);
+    Accumulator<Float> floatAccum = sc.accumulator(10.0f, floatAccumulatorParam);
     rdd.foreach(x -> floatAccum.add((float) x));
     Assert.assertEquals((Float) 25.0f, floatAccum.value());
 
@@ -336,7 +340,7 @@ public Float zero(Float initialValue) {
   @Test
   public void keyBy() {
     JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2));
-    List<Tuple2<String, Integer>> s = rdd.keyBy(x -> x.toString()).collect();
+    List<Tuple2<String, Integer>> s = rdd.keyBy(Object::toString).collect();
     Assert.assertEquals(new Tuple2<>("1", 1), s.get(0));
     Assert.assertEquals(new Tuple2<>("2", 2), s.get(1));
   }
@@ -349,7 +353,7 @@ public void mapOnPairRDD() {
     JavaPairRDD<Integer, Integer> rdd3 =
       rdd2.mapToPair(in -> new Tuple2<>(in._2(), in._1()));
     Assert.assertEquals(Arrays.asList(
-      new Tuple2<Integer, Integer>(1, 1),
+      new Tuple2<>(1, 1),
       new Tuple2<>(0, 2),
       new Tuple2<>(1, 3),
       new Tuple2<>(0, 4)), rdd3.collect());
@@ -361,7 +365,7 @@ public void collectPartitions() {
 
     JavaPairRDD<Integer, Integer> rdd2 =
       rdd1.mapToPair(i -> new Tuple2<>(i, i % 2));
-    List[] parts = rdd1.collectPartitions(new int[]{0});
+    List<Integer>[] parts = rdd1.collectPartitions(new int[]{0});
     Assert.assertEquals(Arrays.asList(1, 2), parts[0]);
 
     parts = rdd1.collectPartitions(new int[]{1, 2});
@@ -371,19 +375,19 @@ public void collectPartitions() {
     Assert.assertEquals(Arrays.asList(new Tuple2<>(1, 1), new Tuple2<>(2, 0)),
       rdd2.collectPartitions(new int[]{0})[0]);
 
-    parts = rdd2.collectPartitions(new int[]{1, 2});
-    Assert.assertEquals(Arrays.asList(new Tuple2<>(3, 1), new Tuple2<>(4, 0)), parts[0]);
+    List<Tuple2<Integer, Integer>>[] parts2 = rdd2.collectPartitions(new int[]{1, 2});
+    Assert.assertEquals(Arrays.asList(new Tuple2<>(3, 1), new Tuple2<>(4, 0)), parts2[0]);
     Assert.assertEquals(Arrays.asList(new Tuple2<>(5, 1), new Tuple2<>(6, 0), new Tuple2<>(7, 1)),
-      parts[1]);
+      parts2[1]);
   }
 
   @Test
   public void collectAsMapWithIntArrayValues() {
     // Regression test for SPARK-1040
-    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(new Integer[]{1}));
+    JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1));
     JavaPairRDD<Integer, int[]> pairRDD =
       rdd.mapToPair(x -> new Tuple2<>(x, new int[]{x}));
     pairRDD.collect();  // Works fine
-    Map<Integer, int[]> map = pairRDD.collectAsMap();  // Used to crash with ClassCastException
+    pairRDD.collectAsMap();  // Used to crash with ClassCastException
   }
 }
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
index bf693c7c393f6..7b50aad4ad498 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaApplySchemaSuite.java
@@ -18,6 +18,7 @@
 package test.org.apache.spark.sql;
 
 import java.io.Serializable;
+import java.math.BigDecimal;
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.List;
@@ -83,7 +84,7 @@ public void setAge(int age) {
 
   @Test
   public void applySchema() {
-    List<Person> personList = new ArrayList<Person>(2);
+    List<Person> personList = new ArrayList<>(2);
     Person person1 = new Person();
     person1.setName("Michael");
     person1.setAge(29);
@@ -95,12 +96,13 @@ public void applySchema() {
 
     JavaRDD<Row> rowRDD = javaCtx.parallelize(personList).map(
       new Function<Person, Row>() {
+        @Override
         public Row call(Person person) throws Exception {
           return RowFactory.create(person.getName(), person.getAge());
         }
       });
 
-    List<StructField> fields = new ArrayList<StructField>(2);
+    List<StructField> fields = new ArrayList<>(2);
     fields.add(DataTypes.createStructField("name", DataTypes.StringType, false));
     fields.add(DataTypes.createStructField("age", DataTypes.IntegerType, false));
     StructType schema = DataTypes.createStructType(fields);
@@ -118,7 +120,7 @@ public Row call(Person person) throws Exception {
 
   @Test
   public void dataFrameRDDOperations() {
-    List<Person> personList = new ArrayList<Person>(2);
+    List<Person> personList = new ArrayList<>(2);
     Person person1 = new Person();
     person1.setName("Michael");
     person1.setAge(29);
@@ -129,27 +131,28 @@ public void dataFrameRDDOperations() {
     personList.add(person2);
 
     JavaRDD<Row> rowRDD = javaCtx.parallelize(personList).map(
-            new Function<Person, Row>() {
-              public Row call(Person person) throws Exception {
-                return RowFactory.create(person.getName(), person.getAge());
-              }
-            });
-
-    List<StructField> fields = new ArrayList<StructField>(2);
-    fields.add(DataTypes.createStructField("name", DataTypes.StringType, false));
+        new Function<Person, Row>() {
+          @Override
+          public Row call(Person person) {
+            return RowFactory.create(person.getName(), person.getAge());
+          }
+        });
+
+    List<StructField> fields = new ArrayList<>(2);
+    fields.add(DataTypes.createStructField("", DataTypes.StringType, false));
     fields.add(DataTypes.createStructField("age", DataTypes.IntegerType, false));
     StructType schema = DataTypes.createStructType(fields);
 
     DataFrame df = sqlContext.applySchema(rowRDD, schema);
     df.registerTempTable("people");
     List<String> actual = sqlContext.sql("SELECT * FROM people").toJavaRDD().map(new Function<Row, String>() {
-
+      @Override
       public String call(Row row) {
-        return row.getString(0) + "_" + row.get(1).toString();
+        return row.getString(0) + "_" + row.get(1);
       }
     }).collect();
 
-    List<String> expected = new ArrayList<String>(2);
+    List<String> expected = new ArrayList<>(2);
     expected.add("Michael_29");
     expected.add("Yin_28");
 
@@ -165,7 +168,7 @@ public void applySchemaToJSON() {
       "{\"string\":\"this is another simple string.\", \"integer\":11, \"long\":21474836469, " +
         "\"bigInteger\":92233720368547758069, \"double\":1.7976931348623157E305, " +
         "\"boolean\":false, \"null\":null}"));
-    List<StructField> fields = new ArrayList<StructField>(7);
+    List<StructField> fields = new ArrayList<>(7);
     fields.add(DataTypes.createStructField("bigInteger", DataTypes.createDecimalType(20, 0),
       true));
     fields.add(DataTypes.createStructField("boolean", DataTypes.BooleanType, true));
@@ -175,10 +178,10 @@ public void applySchemaToJSON() {
     fields.add(DataTypes.createStructField("null", DataTypes.StringType, true));
     fields.add(DataTypes.createStructField("string", DataTypes.StringType, true));
     StructType expectedSchema = DataTypes.createStructType(fields);
-    List<Row> expectedResult = new ArrayList<Row>(2);
+    List<Row> expectedResult = new ArrayList<>(2);
     expectedResult.add(
       RowFactory.create(
-        new java.math.BigDecimal("92233720368547758070"),
+        new BigDecimal("92233720368547758070"),
         true,
         1.7976931348623157E308,
         10,
@@ -187,7 +190,7 @@ public void applySchemaToJSON() {
         "this is a simple string."));
     expectedResult.add(
       RowFactory.create(
-        new java.math.BigDecimal("92233720368547758069"),
+        new BigDecimal("92233720368547758069"),
         false,
         1.7976931348623157E305,
         11,
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index 4867cebf5328c..d981ce947f435 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -61,7 +61,7 @@ public void tearDown() {
   @Test
   public void testExecution() {
     DataFrame df = context.table("testData").filter("key = 1");
-    Assert.assertEquals(df.select("key").collect()[0].get(0), 1);
+    Assert.assertEquals(1, df.select("key").collect()[0].get(0));
   }
 
   /**
@@ -119,7 +119,7 @@ public void testShow() {
 
   public static class Bean implements Serializable {
     private double a = 0.0;
-    private Integer[] b = new Integer[]{0, 1};
+    private Integer[] b = { 0, 1 };
     private Map<String, int[]> c = ImmutableMap.of("hello", new int[] { 1, 2 });
     private List<String> d = Arrays.asList("floppy", "disk");
 
@@ -161,7 +161,7 @@ public void testCreateDataFrameFromJavaBeans() {
       schema.apply("d"));
     Row first = df.select("a", "b", "c", "d").first();
     Assert.assertEquals(bean.getA(), first.getDouble(0), 0.0);
-    // Now Java lists and maps are converetd to Scala Seq's and Map's. Once we get a Seq below,
+    // Now Java lists and maps are converted to Scala Seq's and Map's. Once we get a Seq below,
     // verify that it has the expected length, and contains expected elements.
     Seq<Integer> result = first.getAs(1);
     Assert.assertEquals(bean.getB().length, result.length());
@@ -180,7 +180,8 @@ public void testCreateDataFrameFromJavaBeans() {
     }
   }
 
-  private static Comparator<Row> CrosstabRowComparator = new Comparator<Row>() {
+  private static final Comparator<Row> crosstabRowComparator = new Comparator<Row>() {
+    @Override
     public int compare(Row row1, Row row2) {
       String item1 = row1.getString(0);
       String item2 = row2.getString(0);
@@ -193,16 +194,16 @@ public void testCrosstab() {
     DataFrame df = context.table("testData2");
     DataFrame crosstab = df.stat().crosstab("a", "b");
     String[] columnNames = crosstab.schema().fieldNames();
-    Assert.assertEquals(columnNames[0], "a_b");
-    Assert.assertEquals(columnNames[1], "1");
-    Assert.assertEquals(columnNames[2], "2");
+    Assert.assertEquals("a_b", columnNames[0]);
+    Assert.assertEquals("1", columnNames[1]);
+    Assert.assertEquals("2", columnNames[2]);
     Row[] rows = crosstab.collect();
-    Arrays.sort(rows, CrosstabRowComparator);
+    Arrays.sort(rows, crosstabRowComparator);
     Integer count = 1;
     for (Row row : rows) {
       Assert.assertEquals(row.get(0).toString(), count.toString());
-      Assert.assertEquals(row.getLong(1), 1L);
-      Assert.assertEquals(row.getLong(2), 1L);
+      Assert.assertEquals(1L, row.getLong(1));
+      Assert.assertEquals(1L, row.getLong(2));
       count++;
     }
   }
@@ -210,7 +211,7 @@ public void testCrosstab() {
   @Test
   public void testFrequentItems() {
     DataFrame df = context.table("testData2");
-    String[] cols = new String[]{"a"};
+    String[] cols = {"a"};
     DataFrame results = df.stat().freqItems(cols, 0.2);
     Assert.assertTrue(results.collect()[0].getSeq(0).contains(1));
   }
@@ -219,14 +220,14 @@ public void testFrequentItems() {
   public void testCorrelation() {
     DataFrame df = context.table("testData2");
     Double pearsonCorr = df.stat().corr("a", "b", "pearson");
-    Assert.assertTrue(Math.abs(pearsonCorr) < 1e-6);
+    Assert.assertTrue(Math.abs(pearsonCorr) < 1.0e-6);
   }
 
   @Test
   public void testCovariance() {
     DataFrame df = context.table("testData2");
     Double result = df.stat().cov("a", "b");
-    Assert.assertTrue(Math.abs(result) < 1e-6);
+    Assert.assertTrue(Math.abs(result) < 1.0e-6);
   }
 
   @Test
@@ -234,7 +235,7 @@ public void testSampleBy() {
     DataFrame df = context.range(0, 100, 1, 2).select(col("id").mod(3).as("key"));
     DataFrame sampled = df.stat().<Integer>sampleBy("key", ImmutableMap.of(0, 0.1, 1, 0.2), 0L);
     Row[] actual = sampled.groupBy("key").count().orderBy("key").collect();
-    Row[] expected = new Row[] {RowFactory.create(0, 5), RowFactory.create(1, 8)};
+    Row[] expected = {RowFactory.create(0, 5), RowFactory.create(1, 8)};
     Assert.assertArrayEquals(expected, actual);
   }
 }
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaRowSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaRowSuite.java
index 4ce1d1dddb26a..3ab4db2a035d3 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaRowSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaRowSuite.java
@@ -18,6 +18,7 @@
 package test.org.apache.spark.sql;
 
 import java.math.BigDecimal;
+import java.nio.charset.StandardCharsets;
 import java.sql.Date;
 import java.sql.Timestamp;
 import java.util.Arrays;
@@ -52,12 +53,12 @@ public void setUp() {
     shortValue = (short)32767;
     intValue = 2147483647;
     longValue = 9223372036854775807L;
-    floatValue = (float)3.4028235E38;
+    floatValue = 3.4028235E38f;
     doubleValue = 1.7976931348623157E308;
     decimalValue = new BigDecimal("1.7976931348623157E328");
     booleanValue = true;
     stringValue = "this is a string";
-    binaryValue = stringValue.getBytes();
+    binaryValue = stringValue.getBytes(StandardCharsets.UTF_8);
     dateValue = Date.valueOf("2014-06-30");
     timestampValue = Timestamp.valueOf("2014-06-30 09:20:00.0");
   }
@@ -123,8 +124,8 @@ public void constructSimpleRow() {
     Assert.assertEquals(binaryValue, simpleRow.get(16));
     Assert.assertEquals(dateValue, simpleRow.get(17));
     Assert.assertEquals(timestampValue, simpleRow.get(18));
-    Assert.assertEquals(true, simpleRow.isNullAt(19));
-    Assert.assertEquals(null, simpleRow.get(19));
+    Assert.assertTrue(simpleRow.isNullAt(19));
+    Assert.assertNull(simpleRow.get(19));
   }
 
   @Test
@@ -134,7 +135,7 @@ public void constructComplexRow() {
       stringValue + " (1)", stringValue + " (2)", stringValue + "(3)");
 
     // Simple map
-    Map<String, Long> simpleMap = new HashMap<String, Long>();
+    Map<String, Long> simpleMap = new HashMap<>();
     simpleMap.put(stringValue + " (1)", longValue);
     simpleMap.put(stringValue + " (2)", longValue - 1);
     simpleMap.put(stringValue + " (3)", longValue - 2);
@@ -149,7 +150,7 @@ public void constructComplexRow() {
     List<Row> arrayOfRows = Arrays.asList(simpleStruct);
 
     // Complex map
-    Map<List<Row>, Row> complexMap = new HashMap<List<Row>, Row>();
+    Map<List<Row>, Row> complexMap = new HashMap<>();
     complexMap.put(arrayOfRows, simpleStruct);
 
     // Complex struct
@@ -167,7 +168,7 @@ public void constructComplexRow() {
     Assert.assertEquals(arrayOfMaps, complexStruct.get(3));
     Assert.assertEquals(arrayOfRows, complexStruct.get(4));
     Assert.assertEquals(complexMap, complexStruct.get(5));
-    Assert.assertEquals(null, complexStruct.get(6));
+    Assert.assertNull(complexStruct.get(6));
 
     // A very complex row
     Row complexRow = RowFactory.create(arrayOfMaps, arrayOfRows, complexMap, complexStruct);
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
index bb02b58cca9be..4a78dca7fea66 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaUDFSuite.java
@@ -20,6 +20,7 @@
 import java.io.Serializable;
 
 import org.junit.After;
+import org.junit.Assert;
 import org.junit.Before;
 import org.junit.Test;
 
@@ -61,13 +62,13 @@ public void udf1Test() {
 
     sqlContext.udf().register("stringLengthTest", new UDF1<String, Integer>() {
       @Override
-      public Integer call(String str) throws Exception {
+      public Integer call(String str) {
         return str.length();
       }
     }, DataTypes.IntegerType);
 
     Row result = sqlContext.sql("SELECT stringLengthTest('test')").head();
-    assert(result.getInt(0) == 4);
+    Assert.assertEquals(4, result.getInt(0));
   }
 
   @SuppressWarnings("unchecked")
@@ -81,12 +82,12 @@ public void udf2Test() {
 
     sqlContext.udf().register("stringLengthTest", new UDF2<String, String, Integer>() {
       @Override
-      public Integer call(String str1, String str2) throws Exception {
+      public Integer call(String str1, String str2) {
         return str1.length() + str2.length();
       }
     }, DataTypes.IntegerType);
 
     Row result = sqlContext.sql("SELECT stringLengthTest('test', 'test2')").head();
-    assert(result.getInt(0) == 9);
+    Assert.assertEquals(9, result.getInt(0));
   }
 }
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
index 6f9e7f68dc39c..9e241f20987c0 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/sources/JavaSaveLoadSuite.java
@@ -44,7 +44,7 @@ public class JavaSaveLoadSuite {
   File path;
   DataFrame df;
 
-  private void checkAnswer(DataFrame actual, List<Row> expected) {
+  private static void checkAnswer(DataFrame actual, List<Row> expected) {
     String errorMessage = QueryTest$.MODULE$.checkAnswer(actual, expected);
     if (errorMessage != null) {
       Assert.fail(errorMessage);
@@ -64,7 +64,7 @@ public void setUp() throws IOException {
       path.delete();
     }
 
-    List<String> jsonObjects = new ArrayList<String>(10);
+    List<String> jsonObjects = new ArrayList<>(10);
     for (int i = 0; i < 10; i++) {
       jsonObjects.add("{\"a\":" + i + ", \"b\":\"str" + i + "\"}");
     }
@@ -82,7 +82,7 @@ public void tearDown() {
 
   @Test
   public void saveAndLoad() {
-    Map<String, String> options = new HashMap<String, String>();
+    Map<String, String> options = new HashMap<>();
     options.put("path", path.toString());
     df.write().mode(SaveMode.ErrorIfExists).format("json").options(options).save();
     DataFrame loadedDF = sqlContext.read().format("json").options(options).load();
@@ -91,11 +91,11 @@ public void saveAndLoad() {
 
   @Test
   public void saveAndLoadWithSchema() {
-    Map<String, String> options = new HashMap<String, String>();
+    Map<String, String> options = new HashMap<>();
     options.put("path", path.toString());
     df.write().format("json").mode(SaveMode.ErrorIfExists).options(options).save();
 
-    List<StructField> fields = new ArrayList<StructField>();
+    List<StructField> fields = new ArrayList<>();
     fields.add(DataTypes.createStructField("b", DataTypes.StringType, true));
     StructType schema = DataTypes.createStructType(fields);
     DataFrame loadedDF = sqlContext.read().format("json").schema(schema).options(options).load();
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
index 019d8a30266e2..b4bf9eef8fca5 100644
--- a/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaDataFrameSuite.java
@@ -40,7 +40,7 @@ public class JavaDataFrameSuite {
 
   DataFrame df;
 
-  private void checkAnswer(DataFrame actual, List<Row> expected) {
+  private static void checkAnswer(DataFrame actual, List<Row> expected) {
     String errorMessage = QueryTest$.MODULE$.checkAnswer(actual, expected);
     if (errorMessage != null) {
       Assert.fail(errorMessage);
@@ -52,7 +52,7 @@ public void setUp() throws IOException {
     hc = TestHive$.MODULE$;
     sc = new JavaSparkContext(hc.sparkContext());
 
-    List<String> jsonObjects = new ArrayList<String>(10);
+    List<String> jsonObjects = new ArrayList<>(10);
     for (int i = 0; i < 10; i++) {
       jsonObjects.add("{\"key\":" + i + ", \"value\":\"str" + i + "\"}");
     }
@@ -71,7 +71,7 @@ public void tearDown() throws IOException {
   @Test
   public void saveTableAndQueryIt() {
     checkAnswer(
-      df.select(functions.avg("key").over(
+      df.select(avg("key").over(
         Window.partitionBy("value").orderBy("key").rowsBetween(-1, 1))),
       hc.sql("SELECT avg(key) " +
         "OVER (PARTITION BY value " +
@@ -95,7 +95,7 @@ public void testUDAF() {
           registeredUDAF.apply(col("value")),
           callUDF("mydoublesum", col("value")));
 
-    List<Row> expectedResult = new ArrayList<Row>();
+    List<Row> expectedResult = new ArrayList<>();
     expectedResult.add(RowFactory.create(4950.0, 9900.0, 9900.0, 9900.0));
     checkAnswer(
       aggregatedDF,
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
index 4192155975c47..c8d272794d10b 100644
--- a/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/JavaMetastoreDataSourcesSuite.java
@@ -53,7 +53,7 @@ public class JavaMetastoreDataSourcesSuite {
   FileSystem fs;
   DataFrame df;
 
-  private void checkAnswer(DataFrame actual, List<Row> expected) {
+  private static void checkAnswer(DataFrame actual, List<Row> expected) {
     String errorMessage = QueryTest$.MODULE$.checkAnswer(actual, expected);
     if (errorMessage != null) {
       Assert.fail(errorMessage);
@@ -77,7 +77,7 @@ public void setUp() throws IOException {
       fs.delete(hiveManagedPath, true);
     }
 
-    List<String> jsonObjects = new ArrayList<String>(10);
+    List<String> jsonObjects = new ArrayList<>(10);
     for (int i = 0; i < 10; i++) {
       jsonObjects.add("{\"a\":" + i + ", \"b\":\"str" + i + "\"}");
     }
@@ -97,7 +97,7 @@ public void tearDown() throws IOException {
 
   @Test
   public void saveExternalTableAndQueryIt() {
-    Map<String, String> options = new HashMap<String, String>();
+    Map<String, String> options = new HashMap<>();
     options.put("path", path.toString());
     df.write()
       .format("org.apache.spark.sql.json")
@@ -120,7 +120,7 @@ public void saveExternalTableAndQueryIt() {
 
   @Test
   public void saveExternalTableWithSchemaAndQueryIt() {
-    Map<String, String> options = new HashMap<String, String>();
+    Map<String, String> options = new HashMap<>();
     options.put("path", path.toString());
     df.write()
       .format("org.apache.spark.sql.json")
@@ -132,7 +132,7 @@ public void saveExternalTableWithSchemaAndQueryIt() {
       sqlContext.sql("SELECT * FROM javaSavedTable"),
       df.collectAsList());
 
-    List<StructField> fields = new ArrayList<StructField>();
+    List<StructField> fields = new ArrayList<>();
     fields.add(DataTypes.createStructField("b", DataTypes.StringType, true));
     StructType schema = DataTypes.createStructType(fields);
     DataFrame loadedDF =
@@ -148,7 +148,7 @@ public void saveExternalTableWithSchemaAndQueryIt() {
 
   @Test
   public void saveTableAndQueryIt() {
-    Map<String, String> options = new HashMap<String, String>();
+    Map<String, String> options = new HashMap<>();
     df.write()
       .format("org.apache.spark.sql.json")
       .mode(SaveMode.Append)
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
index e0718f73aa13f..c5217149224e4 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java
@@ -18,24 +18,22 @@
 package org.apache.spark.streaming;
 
 import java.io.*;
-import java.lang.Iterable;
 import java.nio.charset.Charset;
 import java.util.*;
 import java.util.concurrent.atomic.AtomicBoolean;
 
+import scala.Tuple2;
+
+import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 import org.apache.hadoop.io.LongWritable;
 import org.apache.hadoop.io.Text;
 import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
 
-import scala.Tuple2;
-
 import org.junit.Assert;
-import static org.junit.Assert.*;
 import org.junit.Test;
 
 import com.google.common.base.Optional;
-import com.google.common.collect.Lists;
 import com.google.common.io.Files;
 import com.google.common.collect.Sets;
 
@@ -54,14 +52,14 @@
 // see http://stackoverflow.com/questions/758570/.
 public class JavaAPISuite extends LocalJavaStreamingContext implements Serializable {
 
-  public void equalIterator(Iterator<?> a, Iterator<?> b) {
+  public static void equalIterator(Iterator<?> a, Iterator<?> b) {
     while (a.hasNext() && b.hasNext()) {
       Assert.assertEquals(a.next(), b.next());
     }
     Assert.assertEquals(a.hasNext(), b.hasNext());
   }
 
-  public void equalIterable(Iterable<?> a, Iterable<?> b) {
+  public static void equalIterable(Iterable<?> a, Iterable<?> b) {
       equalIterator(a.iterator(), b.iterator());
   }
 
@@ -74,14 +72,14 @@ public void testInitialization() {
   @Test
   public void testContextState() {
     List<List<Integer>> inputData = Arrays.asList(Arrays.asList(1, 2, 3, 4));
-    Assert.assertTrue(ssc.getState() == StreamingContextState.INITIALIZED);
+    Assert.assertEquals(StreamingContextState.INITIALIZED, ssc.getState());
     JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaTestUtils.attachTestOutputStream(stream);
-    Assert.assertTrue(ssc.getState() == StreamingContextState.INITIALIZED);
+    Assert.assertEquals(StreamingContextState.INITIALIZED, ssc.getState());
     ssc.start();
-    Assert.assertTrue(ssc.getState() == StreamingContextState.ACTIVE);
+    Assert.assertEquals(StreamingContextState.ACTIVE, ssc.getState());
     ssc.stop();
-    Assert.assertTrue(ssc.getState() == StreamingContextState.STOPPED);
+    Assert.assertEquals(StreamingContextState.STOPPED, ssc.getState());
   }
 
   @SuppressWarnings("unchecked")
@@ -118,7 +116,7 @@ public void testMap() {
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream<Integer> letterCount = stream.map(new Function<String, Integer>() {
         @Override
-        public Integer call(String s) throws Exception {
+        public Integer call(String s) {
           return s.length();
         }
     });
@@ -180,7 +178,7 @@ public void testWindowWithSlideDuration() {
   public void testFilter() {
     List<List<String>> inputData = Arrays.asList(
         Arrays.asList("giants", "dodgers"),
-        Arrays.asList("yankees", "red socks"));
+        Arrays.asList("yankees", "red sox"));
 
     List<List<String>> expected = Arrays.asList(
         Arrays.asList("giants"),
@@ -189,7 +187,7 @@ public void testFilter() {
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream<String> filtered = stream.filter(new Function<String, Boolean>() {
       @Override
-      public Boolean call(String s) throws Exception {
+      public Boolean call(String s) {
         return s.contains("a");
       }
     });
@@ -243,11 +241,11 @@ public void testRepartitionFewerPartitions() {
   public void testGlom() {
     List<List<String>> inputData = Arrays.asList(
         Arrays.asList("giants", "dodgers"),
-        Arrays.asList("yankees", "red socks"));
+        Arrays.asList("yankees", "red sox"));
 
     List<List<List<String>>> expected = Arrays.asList(
         Arrays.asList(Arrays.asList("giants", "dodgers")),
-        Arrays.asList(Arrays.asList("yankees", "red socks")));
+        Arrays.asList(Arrays.asList("yankees", "red sox")));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream<List<String>> glommed = stream.glom();
@@ -262,22 +260,22 @@ public void testGlom() {
   public void testMapPartitions() {
     List<List<String>> inputData = Arrays.asList(
         Arrays.asList("giants", "dodgers"),
-        Arrays.asList("yankees", "red socks"));
+        Arrays.asList("yankees", "red sox"));
 
     List<List<String>> expected = Arrays.asList(
         Arrays.asList("GIANTSDODGERS"),
-        Arrays.asList("YANKEESRED SOCKS"));
+        Arrays.asList("YANKEESRED SOX"));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream<String> mapped = stream.mapPartitions(
         new FlatMapFunction<Iterator<String>, String>() {
           @Override
           public Iterable<String> call(Iterator<String> in) {
-            String out = "";
+            StringBuilder out = new StringBuilder();
             while (in.hasNext()) {
-              out = out + in.next().toUpperCase();
+              out.append(in.next().toUpperCase(Locale.ENGLISH));
             }
-            return Lists.newArrayList(out);
+            return Arrays.asList(out.toString());
           }
         });
     JavaTestUtils.attachTestOutputStream(mapped);
@@ -286,16 +284,16 @@ public Iterable<String> call(Iterator<String> in) {
     Assert.assertEquals(expected, result);
   }
 
-  private class IntegerSum implements Function2<Integer, Integer, Integer> {
+  private static class IntegerSum implements Function2<Integer, Integer, Integer> {
     @Override
-    public Integer call(Integer i1, Integer i2) throws Exception {
+    public Integer call(Integer i1, Integer i2) {
       return i1 + i2;
     }
   }
 
-  private class IntegerDifference implements Function2<Integer, Integer, Integer> {
+  private static class IntegerDifference implements Function2<Integer, Integer, Integer> {
     @Override
-    public Integer call(Integer i1, Integer i2) throws Exception {
+    public Integer call(Integer i1, Integer i2) {
       return i1 - i2;
     }
   }
@@ -347,13 +345,13 @@ private void testReduceByWindow(boolean withInverse) {
         Arrays.asList(24));
 
     JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
-    JavaDStream<Integer> reducedWindowed = null;
+    JavaDStream<Integer> reducedWindowed;
     if (withInverse) {
       reducedWindowed = stream.reduceByWindow(new IntegerSum(),
-        new IntegerDifference(), new Duration(2000), new Duration(1000));
+                                              new IntegerDifference(), new Duration(2000), new Duration(1000));
     } else {
       reducedWindowed = stream.reduceByWindow(new IntegerSum(),
-        new Duration(2000), new Duration(1000));
+                                              new Duration(2000), new Duration(1000));
     }
     JavaTestUtils.attachTestOutputStream(reducedWindowed);
     List<List<Integer>> result = JavaTestUtils.runStreams(ssc, 4, 4);
@@ -378,11 +376,11 @@ public void testQueueStream() {
         Arrays.asList(7,8,9));
 
     JavaSparkContext jsc = new JavaSparkContext(ssc.ssc().sc());
-    JavaRDD<Integer> rdd1 = ssc.sparkContext().parallelize(Arrays.asList(1, 2, 3));
-    JavaRDD<Integer> rdd2 = ssc.sparkContext().parallelize(Arrays.asList(4, 5, 6));
-    JavaRDD<Integer> rdd3 = ssc.sparkContext().parallelize(Arrays.asList(7,8,9));
+    JavaRDD<Integer> rdd1 = jsc.parallelize(Arrays.asList(1, 2, 3));
+    JavaRDD<Integer> rdd2 = jsc.parallelize(Arrays.asList(4, 5, 6));
+    JavaRDD<Integer> rdd3 = jsc.parallelize(Arrays.asList(7,8,9));
 
-    LinkedList<JavaRDD<Integer>> rdds = Lists.newLinkedList();
+    Queue<JavaRDD<Integer>> rdds = new LinkedList<>();
     rdds.add(rdd1);
     rdds.add(rdd2);
     rdds.add(rdd3);
@@ -410,10 +408,10 @@ public void testTransform() {
     JavaDStream<Integer> transformed = stream.transform(
       new Function<JavaRDD<Integer>, JavaRDD<Integer>>() {
         @Override
-        public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception {
+        public JavaRDD<Integer> call(JavaRDD<Integer> in) {
           return in.map(new Function<Integer, Integer>() {
             @Override
-            public Integer call(Integer i) throws Exception {
+            public Integer call(Integer i) {
               return i + 2;
             }
           });
@@ -435,70 +433,70 @@ public void testVariousTransform() {
     JavaDStream<Integer> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
 
     List<List<Tuple2<String, Integer>>> pairInputData =
-        Arrays.asList(Arrays.asList(new Tuple2<String, Integer>("x", 1)));
+        Arrays.asList(Arrays.asList(new Tuple2<>("x", 1)));
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(
         JavaTestUtils.attachTestInputStream(ssc, pairInputData, 1));
 
-    JavaDStream<Integer> transformed1 = stream.transform(
+    stream.transform(
         new Function<JavaRDD<Integer>, JavaRDD<Integer>>() {
           @Override
-          public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception {
+          public JavaRDD<Integer> call(JavaRDD<Integer> in) {
             return null;
           }
         }
     );
 
-    JavaDStream<Integer> transformed2 = stream.transform(
+    stream.transform(
       new Function2<JavaRDD<Integer>, Time, JavaRDD<Integer>>() {
-        @Override public JavaRDD<Integer> call(JavaRDD<Integer> in, Time time) throws Exception {
+        @Override public JavaRDD<Integer> call(JavaRDD<Integer> in, Time time) {
           return null;
         }
       }
     );
 
-    JavaPairDStream<String, Integer> transformed3 = stream.transformToPair(
+    stream.transformToPair(
         new Function<JavaRDD<Integer>, JavaPairRDD<String, Integer>>() {
-          @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in) throws Exception {
+          @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<String, Integer> transformed4 = stream.transformToPair(
+    stream.transformToPair(
         new Function2<JavaRDD<Integer>, Time, JavaPairRDD<String, Integer>>() {
-          @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in, Time time) throws Exception {
+          @Override public JavaPairRDD<String, Integer> call(JavaRDD<Integer> in, Time time) {
             return null;
           }
         }
     );
 
-    JavaDStream<Integer> pairTransformed1 = pairStream.transform(
+    pairStream.transform(
         new Function<JavaPairRDD<String, Integer>, JavaRDD<Integer>>() {
-          @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in) throws Exception {
+          @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in) {
             return null;
           }
         }
     );
 
-    JavaDStream<Integer> pairTransformed2 = pairStream.transform(
+    pairStream.transform(
         new Function2<JavaPairRDD<String, Integer>, Time, JavaRDD<Integer>>() {
-          @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in, Time time) throws Exception {
+          @Override public JavaRDD<Integer> call(JavaPairRDD<String, Integer> in, Time time) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<String, String> pairTransformed3 = pairStream.transformToPair(
+    pairStream.transformToPair(
         new Function<JavaPairRDD<String, Integer>, JavaPairRDD<String, String>>() {
-          @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in) throws Exception {
+          @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<String, String> pairTransformed4 = pairStream.transformToPair(
+    pairStream.transformToPair(
         new Function2<JavaPairRDD<String, Integer>, Time, JavaPairRDD<String, String>>() {
-          @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in, Time time) throws Exception {
+          @Override public JavaPairRDD<String, String> call(JavaPairRDD<String, Integer> in, Time time) {
             return null;
           }
         }
@@ -511,32 +509,32 @@ public JavaRDD<Integer> call(JavaRDD<Integer> in) throws Exception {
   public void testTransformWith() {
     List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, String>("california", "dodgers"),
-            new Tuple2<String, String>("new york", "yankees")),
+            new Tuple2<>("california", "dodgers"),
+            new Tuple2<>("new york", "yankees")),
         Arrays.asList(
-            new Tuple2<String, String>("california", "sharks"),
-            new Tuple2<String, String>("new york", "rangers")));
+            new Tuple2<>("california", "sharks"),
+            new Tuple2<>("new york", "rangers")));
 
     List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, String>("california", "giants"),
-            new Tuple2<String, String>("new york", "mets")),
+            new Tuple2<>("california", "giants"),
+            new Tuple2<>("new york", "mets")),
         Arrays.asList(
-            new Tuple2<String, String>("california", "ducks"),
-            new Tuple2<String, String>("new york", "islanders")));
+            new Tuple2<>("california", "ducks"),
+            new Tuple2<>("new york", "islanders")));
 
 
     List<HashSet<Tuple2<String, Tuple2<String, String>>>> expected = Arrays.asList(
         Sets.newHashSet(
-            new Tuple2<String, Tuple2<String, String>>("california",
-                new Tuple2<String, String>("dodgers", "giants")),
-            new Tuple2<String, Tuple2<String, String>>("new york",
-                new Tuple2<String, String>("yankees", "mets"))),
+            new Tuple2<>("california",
+                         new Tuple2<>("dodgers", "giants")),
+            new Tuple2<>("new york",
+                         new Tuple2<>("yankees", "mets"))),
         Sets.newHashSet(
-            new Tuple2<String, Tuple2<String, String>>("california",
-                new Tuple2<String, String>("sharks", "ducks")),
-            new Tuple2<String, Tuple2<String, String>>("new york",
-                new Tuple2<String, String>("rangers", "islanders"))));
+            new Tuple2<>("california",
+                         new Tuple2<>("sharks", "ducks")),
+            new Tuple2<>("new york",
+                         new Tuple2<>("rangers", "islanders"))));
 
     JavaDStream<Tuple2<String, String>> stream1 = JavaTestUtils.attachTestInputStream(
         ssc, stringStringKVStream1, 1);
@@ -552,14 +550,12 @@ public void testTransformWith() {
             JavaPairRDD<String, String>,
             JavaPairRDD<String, String>,
             Time,
-            JavaPairRDD<String, Tuple2<String, String>>
-          >() {
+            JavaPairRDD<String, Tuple2<String, String>>>() {
           @Override
           public JavaPairRDD<String, Tuple2<String, String>> call(
               JavaPairRDD<String, String> rdd1,
               JavaPairRDD<String, String> rdd2,
-              Time time
-          ) throws Exception {
+              Time time) {
             return rdd1.join(rdd2);
           }
         }
@@ -567,9 +563,9 @@ public JavaPairRDD<String, Tuple2<String, String>> call(
 
     JavaTestUtils.attachTestOutputStream(joined);
     List<List<Tuple2<String, Tuple2<String, String>>>> result = JavaTestUtils.runStreams(ssc, 2, 2);
-    List<HashSet<Tuple2<String, Tuple2<String, String>>>> unorderedResult = Lists.newArrayList();
+    List<HashSet<Tuple2<String, Tuple2<String, String>>>> unorderedResult = new ArrayList<>();
     for (List<Tuple2<String, Tuple2<String, String>>> res: result) {
-        unorderedResult.add(Sets.newHashSet(res));
+      unorderedResult.add(Sets.newHashSet(res));
     }
 
     Assert.assertEquals(expected, unorderedResult);
@@ -587,89 +583,89 @@ public void testVariousTransformWith() {
     JavaDStream<String> stream2 = JavaTestUtils.attachTestInputStream(ssc, inputData2, 1);
 
     List<List<Tuple2<String, Integer>>> pairInputData1 =
-        Arrays.asList(Arrays.asList(new Tuple2<String, Integer>("x", 1)));
+        Arrays.asList(Arrays.asList(new Tuple2<>("x", 1)));
     List<List<Tuple2<Double, Character>>> pairInputData2 =
-        Arrays.asList(Arrays.asList(new Tuple2<Double, Character>(1.0, 'x')));
+        Arrays.asList(Arrays.asList(new Tuple2<>(1.0, 'x')));
     JavaPairDStream<String, Integer> pairStream1 = JavaPairDStream.fromJavaDStream(
         JavaTestUtils.attachTestInputStream(ssc, pairInputData1, 1));
     JavaPairDStream<Double, Character> pairStream2 = JavaPairDStream.fromJavaDStream(
         JavaTestUtils.attachTestInputStream(ssc, pairInputData2, 1));
 
-    JavaDStream<Double> transformed1 = stream1.transformWith(
+    stream1.transformWith(
         stream2,
         new Function3<JavaRDD<Integer>, JavaRDD<String>, Time, JavaRDD<Double>>() {
           @Override
-          public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception {
+          public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaDStream<Double> transformed2 = stream1.transformWith(
+    stream1.transformWith(
         pairStream1,
         new Function3<JavaRDD<Integer>, JavaPairRDD<String, Integer>, Time, JavaRDD<Double>>() {
           @Override
-          public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception {
+          public JavaRDD<Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<Double, Double> transformed3 = stream1.transformWithToPair(
+    stream1.transformWithToPair(
         stream2,
         new Function3<JavaRDD<Integer>, JavaRDD<String>, Time, JavaPairRDD<Double, Double>>() {
           @Override
-          public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception {
+          public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaRDD<String> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<Double, Double> transformed4 = stream1.transformWithToPair(
+    stream1.transformWithToPair(
         pairStream1,
         new Function3<JavaRDD<Integer>, JavaPairRDD<String, Integer>, Time, JavaPairRDD<Double, Double>>() {
           @Override
-          public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception {
+          public JavaPairRDD<Double, Double> call(JavaRDD<Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaDStream<Double> pairTransformed1 = pairStream1.transformWith(
+    pairStream1.transformWith(
         stream2,
         new Function3<JavaPairRDD<String, Integer>, JavaRDD<String>, Time, JavaRDD<Double>>() {
           @Override
-          public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception {
+          public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaDStream<Double> pairTransformed2_ = pairStream1.transformWith(
+    pairStream1.transformWith(
         pairStream1,
         new Function3<JavaPairRDD<String, Integer>, JavaPairRDD<String, Integer>, Time, JavaRDD<Double>>() {
           @Override
-          public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) throws Exception {
+          public JavaRDD<Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<String, Integer> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<Double, Double> pairTransformed3 = pairStream1.transformWithToPair(
+    pairStream1.transformWithToPair(
         stream2,
         new Function3<JavaPairRDD<String, Integer>, JavaRDD<String>, Time, JavaPairRDD<Double, Double>>() {
           @Override
-          public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) throws Exception {
+          public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaRDD<String> rdd2, Time time) {
             return null;
           }
         }
     );
 
-    JavaPairDStream<Double, Double> pairTransformed4 = pairStream1.transformWithToPair(
+    pairStream1.transformWithToPair(
         pairStream2,
         new Function3<JavaPairRDD<String, Integer>, JavaPairRDD<Double, Character>, Time, JavaPairRDD<Double, Double>>() {
           @Override
-          public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<Double, Character> rdd2, Time time) throws Exception {
+          public JavaPairRDD<Double, Double> call(JavaPairRDD<String, Integer> rdd1, JavaPairRDD<Double, Character> rdd2, Time time) {
             return null;
           }
         }
@@ -690,13 +686,13 @@ public void testStreamingContextTransform(){
     );
 
     List<List<Tuple2<Integer, String>>> pairStream1input = Arrays.asList(
-        Arrays.asList(new Tuple2<Integer, String>(1, "x")),
-        Arrays.asList(new Tuple2<Integer, String>(2, "y"))
+        Arrays.asList(new Tuple2<>(1, "x")),
+        Arrays.asList(new Tuple2<>(2, "y"))
     );
 
     List<List<Tuple2<Integer, Tuple2<Integer, String>>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<Integer, Tuple2<Integer, String>>(1, new Tuple2<Integer, String>(1, "x"))),
-        Arrays.asList(new Tuple2<Integer, Tuple2<Integer, String>>(2, new Tuple2<Integer, String>(2, "y")))
+        Arrays.asList(new Tuple2<>(1, new Tuple2<>(1, "x"))),
+        Arrays.asList(new Tuple2<>(2, new Tuple2<>(2, "y")))
     );
 
     JavaDStream<Integer> stream1 = JavaTestUtils.attachTestInputStream(ssc, stream1input, 1);
@@ -707,7 +703,7 @@ public void testStreamingContextTransform(){
     List<JavaDStream<?>> listOfDStreams1 = Arrays.<JavaDStream<?>>asList(stream1, stream2);
 
     // This is just to test whether this transform to JavaStream compiles
-    JavaDStream<Long> transformed1 = ssc.transform(
+    ssc.transform(
       listOfDStreams1,
       new Function2<List<JavaRDD<?>>, Time, JavaRDD<Long>>() {
         @Override
@@ -733,8 +729,8 @@ public JavaPairRDD<Integer, Tuple2<Integer, String>> call(List<JavaRDD<?>> listO
           JavaPairRDD<Integer, String> prdd3 = JavaPairRDD.fromJavaRDD(rdd3);
           PairFunction<Integer, Integer, Integer> mapToTuple = new PairFunction<Integer, Integer, Integer>() {
             @Override
-            public Tuple2<Integer, Integer> call(Integer i) throws Exception {
-              return new Tuple2<Integer, Integer>(i, i);
+            public Tuple2<Integer, Integer> call(Integer i) {
+              return new Tuple2<>(i, i);
             }
           };
           return rdd1.union(rdd2).mapToPair(mapToTuple).join(prdd3);
@@ -763,7 +759,7 @@ public void testFlatMap() {
     JavaDStream<String> flatMapped = stream.flatMap(new FlatMapFunction<String, String>() {
       @Override
       public Iterable<String> call(String x) {
-        return Lists.newArrayList(x.split("(?!^)"));
+        return Arrays.asList(x.split("(?!^)"));
       }
     });
     JavaTestUtils.attachTestOutputStream(flatMapped);
@@ -782,39 +778,39 @@ public void testPairFlatMap() {
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, String>(6, "g"),
-            new Tuple2<Integer, String>(6, "i"),
-            new Tuple2<Integer, String>(6, "a"),
-            new Tuple2<Integer, String>(6, "n"),
-            new Tuple2<Integer, String>(6, "t"),
-            new Tuple2<Integer, String>(6, "s")),
+            new Tuple2<>(6, "g"),
+            new Tuple2<>(6, "i"),
+            new Tuple2<>(6, "a"),
+            new Tuple2<>(6, "n"),
+            new Tuple2<>(6, "t"),
+            new Tuple2<>(6, "s")),
         Arrays.asList(
-            new Tuple2<Integer, String>(7, "d"),
-            new Tuple2<Integer, String>(7, "o"),
-            new Tuple2<Integer, String>(7, "d"),
-            new Tuple2<Integer, String>(7, "g"),
-            new Tuple2<Integer, String>(7, "e"),
-            new Tuple2<Integer, String>(7, "r"),
-            new Tuple2<Integer, String>(7, "s")),
+            new Tuple2<>(7, "d"),
+            new Tuple2<>(7, "o"),
+            new Tuple2<>(7, "d"),
+            new Tuple2<>(7, "g"),
+            new Tuple2<>(7, "e"),
+            new Tuple2<>(7, "r"),
+            new Tuple2<>(7, "s")),
         Arrays.asList(
-            new Tuple2<Integer, String>(9, "a"),
-            new Tuple2<Integer, String>(9, "t"),
-            new Tuple2<Integer, String>(9, "h"),
-            new Tuple2<Integer, String>(9, "l"),
-            new Tuple2<Integer, String>(9, "e"),
-            new Tuple2<Integer, String>(9, "t"),
-            new Tuple2<Integer, String>(9, "i"),
-            new Tuple2<Integer, String>(9, "c"),
-            new Tuple2<Integer, String>(9, "s")));
+            new Tuple2<>(9, "a"),
+            new Tuple2<>(9, "t"),
+            new Tuple2<>(9, "h"),
+            new Tuple2<>(9, "l"),
+            new Tuple2<>(9, "e"),
+            new Tuple2<>(9, "t"),
+            new Tuple2<>(9, "i"),
+            new Tuple2<>(9, "c"),
+            new Tuple2<>(9, "s")));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<Integer, String> flatMapped = stream.flatMapToPair(
       new PairFlatMapFunction<String, Integer, String>() {
         @Override
-        public Iterable<Tuple2<Integer, String>> call(String in) throws Exception {
-          List<Tuple2<Integer, String>> out = Lists.newArrayList();
+        public Iterable<Tuple2<Integer, String>> call(String in) {
+          List<Tuple2<Integer, String>> out = new ArrayList<>();
           for (String letter: in.split("(?!^)")) {
-            out.add(new Tuple2<Integer, String>(in.length(), letter));
+            out.add(new Tuple2<>(in.length(), letter));
           }
           return out;
         }
@@ -859,13 +855,13 @@ public void testUnion() {
    */
   public static <T> void assertOrderInvariantEquals(
       List<List<T>> expected, List<List<T>> actual) {
-    List<Set<T>> expectedSets = new ArrayList<Set<T>>();
+    List<Set<T>> expectedSets = new ArrayList<>();
     for (List<T> list: expected) {
-      expectedSets.add(Collections.unmodifiableSet(new HashSet<T>(list)));
+      expectedSets.add(Collections.unmodifiableSet(new HashSet<>(list)));
     }
-    List<Set<T>> actualSets = new ArrayList<Set<T>>();
+    List<Set<T>> actualSets = new ArrayList<>();
     for (List<T> list: actual) {
-      actualSets.add(Collections.unmodifiableSet(new HashSet<T>(list)));
+      actualSets.add(Collections.unmodifiableSet(new HashSet<>(list)));
     }
     Assert.assertEquals(expectedSets, actualSets);
   }
@@ -877,25 +873,25 @@ public static <T> void assertOrderInvariantEquals(
   public void testPairFilter() {
     List<List<String>> inputData = Arrays.asList(
         Arrays.asList("giants", "dodgers"),
-        Arrays.asList("yankees", "red socks"));
+        Arrays.asList("yankees", "red sox"));
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, Integer>("giants", 6)),
-        Arrays.asList(new Tuple2<String, Integer>("yankees", 7)));
+        Arrays.asList(new Tuple2<>("giants", 6)),
+        Arrays.asList(new Tuple2<>("yankees", 7)));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = stream.mapToPair(
         new PairFunction<String, String, Integer>() {
           @Override
-          public Tuple2<String, Integer> call(String in) throws Exception {
-            return new Tuple2<String, Integer>(in, in.length());
+          public Tuple2<String, Integer> call(String in) {
+            return new Tuple2<>(in, in.length());
           }
         });
 
     JavaPairDStream<String, Integer> filtered = pairStream.filter(
         new Function<Tuple2<String, Integer>, Boolean>() {
       @Override
-      public Boolean call(Tuple2<String, Integer> in) throws Exception {
+      public Boolean call(Tuple2<String, Integer> in) {
         return in._1().contains("a");
       }
     });
@@ -906,28 +902,28 @@ public Boolean call(Tuple2<String, Integer> in) throws Exception {
   }
 
   @SuppressWarnings("unchecked")
-  private List<List<Tuple2<String, String>>> stringStringKVStream = Arrays.asList(
-      Arrays.asList(new Tuple2<String, String>("california", "dodgers"),
-          new Tuple2<String, String>("california", "giants"),
-          new Tuple2<String, String>("new york", "yankees"),
-          new Tuple2<String, String>("new york", "mets")),
-      Arrays.asList(new Tuple2<String, String>("california", "sharks"),
-          new Tuple2<String, String>("california", "ducks"),
-          new Tuple2<String, String>("new york", "rangers"),
-          new Tuple2<String, String>("new york", "islanders")));
+  private final List<List<Tuple2<String, String>>> stringStringKVStream = Arrays.asList(
+      Arrays.asList(new Tuple2<>("california", "dodgers"),
+                    new Tuple2<>("california", "giants"),
+                    new Tuple2<>("new york", "yankees"),
+                    new Tuple2<>("new york", "mets")),
+      Arrays.asList(new Tuple2<>("california", "sharks"),
+                    new Tuple2<>("california", "ducks"),
+                    new Tuple2<>("new york", "rangers"),
+                    new Tuple2<>("new york", "islanders")));
 
   @SuppressWarnings("unchecked")
-  private List<List<Tuple2<String, Integer>>> stringIntKVStream = Arrays.asList(
+  private final List<List<Tuple2<String, Integer>>> stringIntKVStream = Arrays.asList(
       Arrays.asList(
-          new Tuple2<String, Integer>("california", 1),
-          new Tuple2<String, Integer>("california", 3),
-          new Tuple2<String, Integer>("new york", 4),
-          new Tuple2<String, Integer>("new york", 1)),
+          new Tuple2<>("california", 1),
+          new Tuple2<>("california", 3),
+          new Tuple2<>("new york", 4),
+          new Tuple2<>("new york", 1)),
       Arrays.asList(
-          new Tuple2<String, Integer>("california", 5),
-          new Tuple2<String, Integer>("california", 5),
-          new Tuple2<String, Integer>("new york", 3),
-          new Tuple2<String, Integer>("new york", 1)));
+          new Tuple2<>("california", 5),
+          new Tuple2<>("california", 5),
+          new Tuple2<>("new york", 3),
+          new Tuple2<>("new york", 1)));
 
   @SuppressWarnings("unchecked")
   @Test
@@ -936,22 +932,22 @@ public void testPairMap() { // Maps pair -> pair of different type
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
         Arrays.asList(
-                new Tuple2<Integer, String>(1, "california"),
-                new Tuple2<Integer, String>(3, "california"),
-                new Tuple2<Integer, String>(4, "new york"),
-                new Tuple2<Integer, String>(1, "new york")),
+            new Tuple2<>(1, "california"),
+            new Tuple2<>(3, "california"),
+            new Tuple2<>(4, "new york"),
+            new Tuple2<>(1, "new york")),
         Arrays.asList(
-                new Tuple2<Integer, String>(5, "california"),
-                new Tuple2<Integer, String>(5, "california"),
-                new Tuple2<Integer, String>(3, "new york"),
-                new Tuple2<Integer, String>(1, "new york")));
+            new Tuple2<>(5, "california"),
+            new Tuple2<>(5, "california"),
+            new Tuple2<>(3, "new york"),
+            new Tuple2<>(1, "new york")));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
     JavaPairDStream<Integer, String> reversed = pairStream.mapToPair(
         new PairFunction<Tuple2<String, Integer>, Integer, String>() {
           @Override
-          public Tuple2<Integer, String> call(Tuple2<String, Integer> in) throws Exception {
+          public Tuple2<Integer, String> call(Tuple2<String, Integer> in) {
             return in.swap();
           }
         });
@@ -969,23 +965,23 @@ public void testPairMapPartitions() { // Maps pair -> pair of different type
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, String>(1, "california"),
-            new Tuple2<Integer, String>(3, "california"),
-            new Tuple2<Integer, String>(4, "new york"),
-            new Tuple2<Integer, String>(1, "new york")),
+            new Tuple2<>(1, "california"),
+            new Tuple2<>(3, "california"),
+            new Tuple2<>(4, "new york"),
+            new Tuple2<>(1, "new york")),
         Arrays.asList(
-            new Tuple2<Integer, String>(5, "california"),
-            new Tuple2<Integer, String>(5, "california"),
-            new Tuple2<Integer, String>(3, "new york"),
-            new Tuple2<Integer, String>(1, "new york")));
+            new Tuple2<>(5, "california"),
+            new Tuple2<>(5, "california"),
+            new Tuple2<>(3, "new york"),
+            new Tuple2<>(1, "new york")));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
     JavaPairDStream<Integer, String> reversed = pairStream.mapPartitionsToPair(
         new PairFlatMapFunction<Iterator<Tuple2<String, Integer>>, Integer, String>() {
           @Override
-          public Iterable<Tuple2<Integer, String>> call(Iterator<Tuple2<String, Integer>> in) throws Exception {
-            LinkedList<Tuple2<Integer, String>> out = new LinkedList<Tuple2<Integer, String>>();
+          public Iterable<Tuple2<Integer, String>> call(Iterator<Tuple2<String, Integer>> in) {
+            List<Tuple2<Integer, String>> out = new LinkedList<>();
             while (in.hasNext()) {
               Tuple2<String, Integer> next = in.next();
               out.add(next.swap());
@@ -1014,7 +1010,7 @@ public void testPairMap2() { // Maps pair -> single
     JavaDStream<Integer> reversed = pairStream.map(
         new Function<Tuple2<String, Integer>, Integer>() {
           @Override
-          public Integer call(Tuple2<String, Integer> in) throws Exception {
+          public Integer call(Tuple2<String, Integer> in) {
             return in._2();
           }
         });
@@ -1030,23 +1026,23 @@ public Integer call(Tuple2<String, Integer> in) throws Exception {
   public void testPairToPairFlatMapWithChangingTypes() { // Maps pair -> pair
     List<List<Tuple2<String, Integer>>> inputData = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Integer>("hi", 1),
-            new Tuple2<String, Integer>("ho", 2)),
+            new Tuple2<>("hi", 1),
+            new Tuple2<>("ho", 2)),
         Arrays.asList(
-            new Tuple2<String, Integer>("hi", 1),
-            new Tuple2<String, Integer>("ho", 2)));
+            new Tuple2<>("hi", 1),
+            new Tuple2<>("ho", 2)));
 
     List<List<Tuple2<Integer, String>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, String>(1, "h"),
-            new Tuple2<Integer, String>(1, "i"),
-            new Tuple2<Integer, String>(2, "h"),
-            new Tuple2<Integer, String>(2, "o")),
+            new Tuple2<>(1, "h"),
+            new Tuple2<>(1, "i"),
+            new Tuple2<>(2, "h"),
+            new Tuple2<>(2, "o")),
         Arrays.asList(
-            new Tuple2<Integer, String>(1, "h"),
-            new Tuple2<Integer, String>(1, "i"),
-            new Tuple2<Integer, String>(2, "h"),
-            new Tuple2<Integer, String>(2, "o")));
+            new Tuple2<>(1, "h"),
+            new Tuple2<>(1, "i"),
+            new Tuple2<>(2, "h"),
+            new Tuple2<>(2, "o")));
 
     JavaDStream<Tuple2<String, Integer>> stream =
         JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
@@ -1054,10 +1050,10 @@ public void testPairToPairFlatMapWithChangingTypes() { // Maps pair -> pair
     JavaPairDStream<Integer, String> flatMapped = pairStream.flatMapToPair(
         new PairFlatMapFunction<Tuple2<String, Integer>, Integer, String>() {
           @Override
-          public Iterable<Tuple2<Integer, String>> call(Tuple2<String, Integer> in) throws Exception {
-            List<Tuple2<Integer, String>> out = new LinkedList<Tuple2<Integer, String>>();
+          public Iterable<Tuple2<Integer, String>> call(Tuple2<String, Integer> in) {
+            List<Tuple2<Integer, String>> out = new LinkedList<>();
             for (Character s : in._1().toCharArray()) {
-              out.add(new Tuple2<Integer, String>(in._2(), s.toString()));
+              out.add(new Tuple2<>(in._2(), s.toString()));
             }
             return out;
           }
@@ -1075,11 +1071,11 @@ public void testPairGroupByKey() {
 
     List<List<Tuple2<String, List<String>>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, List<String>>("california", Arrays.asList("dodgers", "giants")),
-            new Tuple2<String, List<String>>("new york", Arrays.asList("yankees", "mets"))),
+            new Tuple2<>("california", Arrays.asList("dodgers", "giants")),
+            new Tuple2<>("new york", Arrays.asList("yankees", "mets"))),
         Arrays.asList(
-            new Tuple2<String, List<String>>("california", Arrays.asList("sharks", "ducks")),
-            new Tuple2<String, List<String>>("new york", Arrays.asList("rangers", "islanders"))));
+            new Tuple2<>("california", Arrays.asList("sharks", "ducks")),
+            new Tuple2<>("new york", Arrays.asList("rangers", "islanders"))));
 
     JavaDStream<Tuple2<String, String>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, String> pairStream = JavaPairDStream.fromJavaDStream(stream);
@@ -1111,11 +1107,11 @@ public void testPairReduceByKey() {
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Integer>("california", 4),
-            new Tuple2<String, Integer>("new york", 5)),
+            new Tuple2<>("california", 4),
+            new Tuple2<>("new york", 5)),
         Arrays.asList(
-            new Tuple2<String, Integer>("california", 10),
-            new Tuple2<String, Integer>("new york", 4)));
+            new Tuple2<>("california", 10),
+            new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
@@ -1136,20 +1132,20 @@ public void testCombineByKey() {
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Integer>("california", 4),
-            new Tuple2<String, Integer>("new york", 5)),
+            new Tuple2<>("california", 4),
+            new Tuple2<>("new york", 5)),
         Arrays.asList(
-            new Tuple2<String, Integer>("california", 10),
-            new Tuple2<String, Integer>("new york", 4)));
+            new Tuple2<>("california", 10),
+            new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
 
-    JavaPairDStream<String, Integer> combined = pairStream.<Integer>combineByKey(
+    JavaPairDStream<String, Integer> combined = pairStream.combineByKey(
         new Function<Integer, Integer>() {
           @Override
-          public Integer call(Integer i) throws Exception {
+          public Integer call(Integer i) {
             return i;
           }
         }, new IntegerSum(), new IntegerSum(), new HashPartitioner(2));
@@ -1170,13 +1166,13 @@ public void testCountByValue() {
 
     List<List<Tuple2<String, Long>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Long>("hello", 1L),
-            new Tuple2<String, Long>("world", 1L)),
+            new Tuple2<>("hello", 1L),
+            new Tuple2<>("world", 1L)),
         Arrays.asList(
-            new Tuple2<String, Long>("hello", 1L),
-            new Tuple2<String, Long>("moon", 1L)),
+            new Tuple2<>("hello", 1L),
+            new Tuple2<>("moon", 1L)),
         Arrays.asList(
-            new Tuple2<String, Long>("hello", 1L)));
+            new Tuple2<>("hello", 1L)));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Long> counted = stream.countByValue();
@@ -1193,16 +1189,16 @@ public void testGroupByKeyAndWindow() {
 
     List<List<Tuple2<String, List<Integer>>>> expected = Arrays.asList(
       Arrays.asList(
-        new Tuple2<String, List<Integer>>("california", Arrays.asList(1, 3)),
-        new Tuple2<String, List<Integer>>("new york", Arrays.asList(1, 4))
+        new Tuple2<>("california", Arrays.asList(1, 3)),
+        new Tuple2<>("new york", Arrays.asList(1, 4))
       ),
       Arrays.asList(
-        new Tuple2<String, List<Integer>>("california", Arrays.asList(1, 3, 5, 5)),
-        new Tuple2<String, List<Integer>>("new york", Arrays.asList(1, 1, 3, 4))
+        new Tuple2<>("california", Arrays.asList(1, 3, 5, 5)),
+        new Tuple2<>("new york", Arrays.asList(1, 1, 3, 4))
       ),
       Arrays.asList(
-        new Tuple2<String, List<Integer>>("california", Arrays.asList(5, 5)),
-        new Tuple2<String, List<Integer>>("new york", Arrays.asList(1, 3))
+        new Tuple2<>("california", Arrays.asList(5, 5)),
+        new Tuple2<>("new york", Arrays.asList(1, 3))
       )
     );
 
@@ -1220,16 +1216,16 @@ public void testGroupByKeyAndWindow() {
     }
   }
 
-  private HashSet<Tuple2<String, HashSet<Integer>>> convert(List<Tuple2<String, List<Integer>>> listOfTuples) {
-    List<Tuple2<String, HashSet<Integer>>> newListOfTuples = new ArrayList<Tuple2<String, HashSet<Integer>>>();
+  private static Set<Tuple2<String, HashSet<Integer>>> convert(List<Tuple2<String, List<Integer>>> listOfTuples) {
+    List<Tuple2<String, HashSet<Integer>>> newListOfTuples = new ArrayList<>();
     for (Tuple2<String, List<Integer>> tuple: listOfTuples) {
       newListOfTuples.add(convert(tuple));
     }
-    return new HashSet<Tuple2<String, HashSet<Integer>>>(newListOfTuples);
+    return new HashSet<>(newListOfTuples);
   }
 
-  private Tuple2<String, HashSet<Integer>> convert(Tuple2<String, List<Integer>> tuple) {
-    return new Tuple2<String, HashSet<Integer>>(tuple._1(), new HashSet<Integer>(tuple._2()));
+  private static Tuple2<String, HashSet<Integer>> convert(Tuple2<String, List<Integer>> tuple) {
+    return new Tuple2<>(tuple._1(), new HashSet<>(tuple._2()));
   }
 
   @SuppressWarnings("unchecked")
@@ -1238,12 +1234,12 @@ public void testReduceByKeyAndWindow() {
     List<List<Tuple2<String, Integer>>> inputData = stringIntKVStream;
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, Integer>("california", 4),
-            new Tuple2<String, Integer>("new york", 5)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 14),
-            new Tuple2<String, Integer>("new york", 9)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 10),
-            new Tuple2<String, Integer>("new york", 4)));
+        Arrays.asList(new Tuple2<>("california", 4),
+                      new Tuple2<>("new york", 5)),
+        Arrays.asList(new Tuple2<>("california", 14),
+                      new Tuple2<>("new york", 9)),
+        Arrays.asList(new Tuple2<>("california", 10),
+                      new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
@@ -1262,12 +1258,12 @@ public void testUpdateStateByKey() {
     List<List<Tuple2<String, Integer>>> inputData = stringIntKVStream;
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, Integer>("california", 4),
-            new Tuple2<String, Integer>("new york", 5)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 14),
-            new Tuple2<String, Integer>("new york", 9)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 14),
-            new Tuple2<String, Integer>("new york", 9)));
+        Arrays.asList(new Tuple2<>("california", 4),
+                      new Tuple2<>("new york", 5)),
+        Arrays.asList(new Tuple2<>("california", 14),
+                      new Tuple2<>("new york", 9)),
+        Arrays.asList(new Tuple2<>("california", 14),
+                      new Tuple2<>("new york", 9)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
@@ -1278,10 +1274,10 @@ public void testUpdateStateByKey() {
           public Optional<Integer> call(List<Integer> values, Optional<Integer> state) {
             int out = 0;
             if (state.isPresent()) {
-              out = out + state.get();
+              out += state.get();
             }
             for (Integer v : values) {
-              out = out + v;
+              out += v;
             }
             return Optional.of(out);
           }
@@ -1298,19 +1294,19 @@ public void testUpdateStateByKeyWithInitial() {
     List<List<Tuple2<String, Integer>>> inputData = stringIntKVStream;
 
     List<Tuple2<String, Integer>> initial = Arrays.asList (
-            new Tuple2<String, Integer> ("california", 1),
-            new Tuple2<String, Integer> ("new york", 2));
+        new Tuple2<>("california", 1),
+            new Tuple2<>("new york", 2));
 
     JavaRDD<Tuple2<String, Integer>> tmpRDD = ssc.sparkContext().parallelize(initial);
     JavaPairRDD<String, Integer> initialRDD = JavaPairRDD.fromJavaRDD (tmpRDD);
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, Integer>("california", 5),
-            new Tuple2<String, Integer>("new york", 7)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 15),
-            new Tuple2<String, Integer>("new york", 11)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 15),
-            new Tuple2<String, Integer>("new york", 11)));
+        Arrays.asList(new Tuple2<>("california", 5),
+                      new Tuple2<>("new york", 7)),
+        Arrays.asList(new Tuple2<>("california", 15),
+                      new Tuple2<>("new york", 11)),
+        Arrays.asList(new Tuple2<>("california", 15),
+                      new Tuple2<>("new york", 11)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
@@ -1321,10 +1317,10 @@ public void testUpdateStateByKeyWithInitial() {
           public Optional<Integer> call(List<Integer> values, Optional<Integer> state) {
             int out = 0;
             if (state.isPresent()) {
-              out = out + state.get();
+              out += state.get();
             }
             for (Integer v : values) {
-              out = out + v;
+              out += v;
             }
             return Optional.of(out);
           }
@@ -1341,19 +1337,19 @@ public void testReduceByKeyAndWindowWithInverse() {
     List<List<Tuple2<String, Integer>>> inputData = stringIntKVStream;
 
     List<List<Tuple2<String, Integer>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, Integer>("california", 4),
-            new Tuple2<String, Integer>("new york", 5)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 14),
-            new Tuple2<String, Integer>("new york", 9)),
-        Arrays.asList(new Tuple2<String, Integer>("california", 10),
-            new Tuple2<String, Integer>("new york", 4)));
+        Arrays.asList(new Tuple2<>("california", 4),
+                      new Tuple2<>("new york", 5)),
+        Arrays.asList(new Tuple2<>("california", 14),
+                      new Tuple2<>("new york", 9)),
+        Arrays.asList(new Tuple2<>("california", 10),
+                      new Tuple2<>("new york", 4)));
 
     JavaDStream<Tuple2<String, Integer>> stream = JavaTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaPairDStream<String, Integer> pairStream = JavaPairDStream.fromJavaDStream(stream);
 
     JavaPairDStream<String, Integer> reduceWindowed =
         pairStream.reduceByKeyAndWindow(new IntegerSum(), new IntegerDifference(),
-            new Duration(2000), new Duration(1000));
+                                        new Duration(2000), new Duration(1000));
     JavaTestUtils.attachTestOutputStream(reduceWindowed);
     List<List<Tuple2<String, Integer>>> result = JavaTestUtils.runStreams(ssc, 3, 3);
 
@@ -1370,15 +1366,15 @@ public void testCountByValueAndWindow() {
 
     List<HashSet<Tuple2<String, Long>>> expected = Arrays.asList(
         Sets.newHashSet(
-            new Tuple2<String, Long>("hello", 1L),
-            new Tuple2<String, Long>("world", 1L)),
+            new Tuple2<>("hello", 1L),
+            new Tuple2<>("world", 1L)),
         Sets.newHashSet(
-            new Tuple2<String, Long>("hello", 2L),
-            new Tuple2<String, Long>("world", 1L),
-            new Tuple2<String, Long>("moon", 1L)),
+            new Tuple2<>("hello", 2L),
+            new Tuple2<>("world", 1L),
+            new Tuple2<>("moon", 1L)),
         Sets.newHashSet(
-            new Tuple2<String, Long>("hello", 2L),
-            new Tuple2<String, Long>("moon", 1L)));
+            new Tuple2<>("hello", 2L),
+            new Tuple2<>("moon", 1L)));
 
     JavaDStream<String> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
@@ -1386,7 +1382,7 @@ public void testCountByValueAndWindow() {
       stream.countByValueAndWindow(new Duration(2000), new Duration(1000));
     JavaTestUtils.attachTestOutputStream(counted);
     List<List<Tuple2<String, Long>>> result = JavaTestUtils.runStreams(ssc, 3, 3);
-    List<HashSet<Tuple2<String, Long>>> unorderedResult = Lists.newArrayList();
+    List<Set<Tuple2<String, Long>>> unorderedResult = new ArrayList<>();
     for (List<Tuple2<String, Long>> res: result) {
       unorderedResult.add(Sets.newHashSet(res));
     }
@@ -1399,27 +1395,27 @@ public void testCountByValueAndWindow() {
   public void testPairTransform() {
     List<List<Tuple2<Integer, Integer>>> inputData = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(1, 5),
-            new Tuple2<Integer, Integer>(4, 5),
-            new Tuple2<Integer, Integer>(2, 5)),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(1, 5),
+            new Tuple2<>(4, 5),
+            new Tuple2<>(2, 5)),
         Arrays.asList(
-            new Tuple2<Integer, Integer>(2, 5),
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(4, 5),
-            new Tuple2<Integer, Integer>(1, 5)));
+            new Tuple2<>(2, 5),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(4, 5),
+            new Tuple2<>(1, 5)));
 
     List<List<Tuple2<Integer, Integer>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, Integer>(1, 5),
-            new Tuple2<Integer, Integer>(2, 5),
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(4, 5)),
+            new Tuple2<>(1, 5),
+            new Tuple2<>(2, 5),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(4, 5)),
         Arrays.asList(
-            new Tuple2<Integer, Integer>(1, 5),
-            new Tuple2<Integer, Integer>(2, 5),
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(4, 5)));
+            new Tuple2<>(1, 5),
+            new Tuple2<>(2, 5),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(4, 5)));
 
     JavaDStream<Tuple2<Integer, Integer>> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
@@ -1428,7 +1424,7 @@ public void testPairTransform() {
     JavaPairDStream<Integer, Integer> sorted = pairStream.transformToPair(
         new Function<JavaPairRDD<Integer, Integer>, JavaPairRDD<Integer, Integer>>() {
           @Override
-          public JavaPairRDD<Integer, Integer> call(JavaPairRDD<Integer, Integer> in) throws Exception {
+          public JavaPairRDD<Integer, Integer> call(JavaPairRDD<Integer, Integer> in) {
             return in.sortByKey();
           }
         });
@@ -1444,15 +1440,15 @@ public JavaPairRDD<Integer, Integer> call(JavaPairRDD<Integer, Integer> in) thro
   public void testPairToNormalRDDTransform() {
     List<List<Tuple2<Integer, Integer>>> inputData = Arrays.asList(
         Arrays.asList(
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(1, 5),
-            new Tuple2<Integer, Integer>(4, 5),
-            new Tuple2<Integer, Integer>(2, 5)),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(1, 5),
+            new Tuple2<>(4, 5),
+            new Tuple2<>(2, 5)),
         Arrays.asList(
-            new Tuple2<Integer, Integer>(2, 5),
-            new Tuple2<Integer, Integer>(3, 5),
-            new Tuple2<Integer, Integer>(4, 5),
-            new Tuple2<Integer, Integer>(1, 5)));
+            new Tuple2<>(2, 5),
+            new Tuple2<>(3, 5),
+            new Tuple2<>(4, 5),
+            new Tuple2<>(1, 5)));
 
     List<List<Integer>> expected = Arrays.asList(
         Arrays.asList(3,1,4,2),
@@ -1465,11 +1461,11 @@ public void testPairToNormalRDDTransform() {
     JavaDStream<Integer> firstParts = pairStream.transform(
         new Function<JavaPairRDD<Integer, Integer>, JavaRDD<Integer>>() {
           @Override
-          public JavaRDD<Integer> call(JavaPairRDD<Integer, Integer> in) throws Exception {
+          public JavaRDD<Integer> call(JavaPairRDD<Integer, Integer> in) {
             return in.map(new Function<Tuple2<Integer, Integer>, Integer>() {
               @Override
-              public Integer call(Tuple2<Integer, Integer> in) {
-                return in._1();
+              public Integer call(Tuple2<Integer, Integer> in2) {
+                return in2._1();
               }
             });
           }
@@ -1487,14 +1483,14 @@ public void testMapValues() {
     List<List<Tuple2<String, String>>> inputData = stringStringKVStream;
 
     List<List<Tuple2<String, String>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "DODGERS"),
-            new Tuple2<String, String>("california", "GIANTS"),
-            new Tuple2<String, String>("new york", "YANKEES"),
-            new Tuple2<String, String>("new york", "METS")),
-        Arrays.asList(new Tuple2<String, String>("california", "SHARKS"),
-            new Tuple2<String, String>("california", "DUCKS"),
-            new Tuple2<String, String>("new york", "RANGERS"),
-            new Tuple2<String, String>("new york", "ISLANDERS")));
+        Arrays.asList(new Tuple2<>("california", "DODGERS"),
+                      new Tuple2<>("california", "GIANTS"),
+                      new Tuple2<>("new york", "YANKEES"),
+                      new Tuple2<>("new york", "METS")),
+        Arrays.asList(new Tuple2<>("california", "SHARKS"),
+                      new Tuple2<>("california", "DUCKS"),
+                      new Tuple2<>("new york", "RANGERS"),
+                      new Tuple2<>("new york", "ISLANDERS")));
 
     JavaDStream<Tuple2<String, String>> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
@@ -1502,8 +1498,8 @@ public void testMapValues() {
 
     JavaPairDStream<String, String> mapped = pairStream.mapValues(new Function<String, String>() {
       @Override
-      public String call(String s) throws Exception {
-        return s.toUpperCase();
+      public String call(String s) {
+        return s.toUpperCase(Locale.ENGLISH);
       }
     });
 
@@ -1519,22 +1515,22 @@ public void testFlatMapValues() {
     List<List<Tuple2<String, String>>> inputData = stringStringKVStream;
 
     List<List<Tuple2<String, String>>> expected = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "dodgers1"),
-            new Tuple2<String, String>("california", "dodgers2"),
-            new Tuple2<String, String>("california", "giants1"),
-            new Tuple2<String, String>("california", "giants2"),
-            new Tuple2<String, String>("new york", "yankees1"),
-            new Tuple2<String, String>("new york", "yankees2"),
-            new Tuple2<String, String>("new york", "mets1"),
-            new Tuple2<String, String>("new york", "mets2")),
-        Arrays.asList(new Tuple2<String, String>("california", "sharks1"),
-            new Tuple2<String, String>("california", "sharks2"),
-            new Tuple2<String, String>("california", "ducks1"),
-            new Tuple2<String, String>("california", "ducks2"),
-            new Tuple2<String, String>("new york", "rangers1"),
-            new Tuple2<String, String>("new york", "rangers2"),
-            new Tuple2<String, String>("new york", "islanders1"),
-            new Tuple2<String, String>("new york", "islanders2")));
+        Arrays.asList(new Tuple2<>("california", "dodgers1"),
+                      new Tuple2<>("california", "dodgers2"),
+                      new Tuple2<>("california", "giants1"),
+                      new Tuple2<>("california", "giants2"),
+                      new Tuple2<>("new york", "yankees1"),
+                      new Tuple2<>("new york", "yankees2"),
+                      new Tuple2<>("new york", "mets1"),
+                      new Tuple2<>("new york", "mets2")),
+        Arrays.asList(new Tuple2<>("california", "sharks1"),
+                      new Tuple2<>("california", "sharks2"),
+                      new Tuple2<>("california", "ducks1"),
+                      new Tuple2<>("california", "ducks2"),
+                      new Tuple2<>("new york", "rangers1"),
+                      new Tuple2<>("new york", "rangers2"),
+                      new Tuple2<>("new york", "islanders1"),
+                      new Tuple2<>("new york", "islanders2")));
 
     JavaDStream<Tuple2<String, String>> stream = JavaTestUtils.attachTestInputStream(
         ssc, inputData, 1);
@@ -1545,7 +1541,7 @@ public void testFlatMapValues() {
         new Function<String, Iterable<String>>() {
           @Override
           public Iterable<String> call(String in) {
-            List<String> out = new ArrayList<String>();
+            List<String> out = new ArrayList<>();
             out.add(in + "1");
             out.add(in + "2");
             return out;
@@ -1562,29 +1558,29 @@ public Iterable<String> call(String in) {
   @Test
   public void testCoGroup() {
     List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "dodgers"),
-            new Tuple2<String, String>("new york", "yankees")),
-        Arrays.asList(new Tuple2<String, String>("california", "sharks"),
-            new Tuple2<String, String>("new york", "rangers")));
+        Arrays.asList(new Tuple2<>("california", "dodgers"),
+                      new Tuple2<>("new york", "yankees")),
+        Arrays.asList(new Tuple2<>("california", "sharks"),
+                      new Tuple2<>("new york", "rangers")));
 
     List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "giants"),
-            new Tuple2<String, String>("new york", "mets")),
-        Arrays.asList(new Tuple2<String, String>("california", "ducks"),
-            new Tuple2<String, String>("new york", "islanders")));
+        Arrays.asList(new Tuple2<>("california", "giants"),
+                      new Tuple2<>("new york", "mets")),
+        Arrays.asList(new Tuple2<>("california", "ducks"),
+                      new Tuple2<>("new york", "islanders")));
 
 
     List<List<Tuple2<String, Tuple2<List<String>, List<String>>>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Tuple2<List<String>, List<String>>>("california",
-                new Tuple2<List<String>, List<String>>(Arrays.asList("dodgers"), Arrays.asList("giants"))),
-            new Tuple2<String, Tuple2<List<String>, List<String>>>("new york",
-                new Tuple2<List<String>, List<String>>(Arrays.asList("yankees"), Arrays.asList("mets")))),
+            new Tuple2<>("california",
+                         new Tuple2<>(Arrays.asList("dodgers"), Arrays.asList("giants"))),
+            new Tuple2<>("new york",
+                         new Tuple2<>(Arrays.asList("yankees"), Arrays.asList("mets")))),
         Arrays.asList(
-            new Tuple2<String, Tuple2<List<String>, List<String>>>("california",
-                new Tuple2<List<String>, List<String>>(Arrays.asList("sharks"), Arrays.asList("ducks"))),
-            new Tuple2<String, Tuple2<List<String>, List<String>>>("new york",
-                new Tuple2<List<String>, List<String>>(Arrays.asList("rangers"), Arrays.asList("islanders")))));
+            new Tuple2<>("california",
+                         new Tuple2<>(Arrays.asList("sharks"), Arrays.asList("ducks"))),
+            new Tuple2<>("new york",
+                         new Tuple2<>(Arrays.asList("rangers"), Arrays.asList("islanders")))));
 
 
     JavaDStream<Tuple2<String, String>> stream1 = JavaTestUtils.attachTestInputStream(
@@ -1620,29 +1616,29 @@ public void testCoGroup() {
   @Test
   public void testJoin() {
     List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "dodgers"),
-            new Tuple2<String, String>("new york", "yankees")),
-        Arrays.asList(new Tuple2<String, String>("california", "sharks"),
-            new Tuple2<String, String>("new york", "rangers")));
+        Arrays.asList(new Tuple2<>("california", "dodgers"),
+                      new Tuple2<>("new york", "yankees")),
+        Arrays.asList(new Tuple2<>("california", "sharks"),
+                      new Tuple2<>("new york", "rangers")));
 
     List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "giants"),
-            new Tuple2<String, String>("new york", "mets")),
-        Arrays.asList(new Tuple2<String, String>("california", "ducks"),
-            new Tuple2<String, String>("new york", "islanders")));
+        Arrays.asList(new Tuple2<>("california", "giants"),
+                      new Tuple2<>("new york", "mets")),
+        Arrays.asList(new Tuple2<>("california", "ducks"),
+                      new Tuple2<>("new york", "islanders")));
 
 
     List<List<Tuple2<String, Tuple2<String, String>>>> expected = Arrays.asList(
         Arrays.asList(
-            new Tuple2<String, Tuple2<String, String>>("california",
-                new Tuple2<String, String>("dodgers", "giants")),
-            new Tuple2<String, Tuple2<String, String>>("new york",
-                new Tuple2<String, String>("yankees", "mets"))),
+            new Tuple2<>("california",
+                         new Tuple2<>("dodgers", "giants")),
+            new Tuple2<>("new york",
+                         new Tuple2<>("yankees", "mets"))),
         Arrays.asList(
-            new Tuple2<String, Tuple2<String, String>>("california",
-                new Tuple2<String, String>("sharks", "ducks")),
-            new Tuple2<String, Tuple2<String, String>>("new york",
-                new Tuple2<String, String>("rangers", "islanders"))));
+            new Tuple2<>("california",
+                         new Tuple2<>("sharks", "ducks")),
+            new Tuple2<>("new york",
+                         new Tuple2<>("rangers", "islanders"))));
 
 
     JavaDStream<Tuple2<String, String>> stream1 = JavaTestUtils.attachTestInputStream(
@@ -1664,13 +1660,13 @@ public void testJoin() {
   @Test
   public void testLeftOuterJoin() {
     List<List<Tuple2<String, String>>> stringStringKVStream1 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "dodgers"),
-            new Tuple2<String, String>("new york", "yankees")),
-        Arrays.asList(new Tuple2<String, String>("california", "sharks") ));
+        Arrays.asList(new Tuple2<>("california", "dodgers"),
+                      new Tuple2<>("new york", "yankees")),
+        Arrays.asList(new Tuple2<>("california", "sharks") ));
 
     List<List<Tuple2<String, String>>> stringStringKVStream2 = Arrays.asList(
-        Arrays.asList(new Tuple2<String, String>("california", "giants") ),
-        Arrays.asList(new Tuple2<String, String>("new york", "islanders") )
+        Arrays.asList(new Tuple2<>("california", "giants") ),
+        Arrays.asList(new Tuple2<>("new york", "islanders") )
 
     );
 
@@ -1713,7 +1709,7 @@ public void testCheckpointMasterRecovery() throws InterruptedException {
     JavaDStream<String> stream = JavaCheckpointTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream<Integer> letterCount = stream.map(new Function<String, Integer>() {
       @Override
-      public Integer call(String s) throws Exception {
+      public Integer call(String s) {
         return s.length();
       }
     });
@@ -1752,6 +1748,7 @@ public void testContextGetOrCreate() throws InterruptedException {
     // (used to detect the new context)
     final AtomicBoolean newContextCreated = new AtomicBoolean(false);
     Function0<JavaStreamingContext> creatingFunc = new Function0<JavaStreamingContext>() {
+      @Override
       public JavaStreamingContext call() {
         newContextCreated.set(true);
         return new JavaStreamingContext(conf, Seconds.apply(1));
@@ -1765,20 +1762,20 @@ public JavaStreamingContext call() {
 
     newContextCreated.set(false);
     ssc = JavaStreamingContext.getOrCreate(corruptedCheckpointDir, creatingFunc,
-        new org.apache.hadoop.conf.Configuration(), true);
+        new Configuration(), true);
     Assert.assertTrue("new context not created", newContextCreated.get());
     ssc.stop();
 
     newContextCreated.set(false);
     ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc,
-        new org.apache.hadoop.conf.Configuration());
+        new Configuration());
     Assert.assertTrue("old context not recovered", !newContextCreated.get());
     ssc.stop();
 
     newContextCreated.set(false);
     JavaSparkContext sc = new JavaSparkContext(conf);
     ssc = JavaStreamingContext.getOrCreate(checkpointDir, creatingFunc,
-        new org.apache.hadoop.conf.Configuration());
+        new Configuration());
     Assert.assertTrue("old context not recovered", !newContextCreated.get());
     ssc.stop();
   }
@@ -1800,7 +1797,7 @@ public void testCheckpointofIndividualStream() throws InterruptedException {
     JavaDStream stream = JavaCheckpointTestUtils.attachTestInputStream(ssc, inputData, 1);
     JavaDStream letterCount = stream.map(new Function<String, Integer>() {
       @Override
-      public Integer call(String s) throws Exception {
+      public Integer call(String s) {
         return s.length();
       }
     });
@@ -1818,29 +1815,26 @@ public Integer call(String s) throws Exception {
   // InputStream functionality is deferred to the existing Scala tests.
   @Test
   public void testSocketTextStream() {
-      JavaReceiverInputDStream<String> test = ssc.socketTextStream("localhost", 12345);
+    ssc.socketTextStream("localhost", 12345);
   }
 
   @Test
   public void testSocketString() {
-
-    class Converter implements Function<InputStream, Iterable<String>> {
-      public Iterable<String> call(InputStream in) throws IOException {
-        BufferedReader reader = new BufferedReader(new InputStreamReader(in));
-        List<String> out = new ArrayList<String>();
-        while (true) {
-          String line = reader.readLine();
-          if (line == null) { break; }
-          out.add(line);
-        }
-        return out;
-      }
-    }
-
-    JavaDStream<String> test = ssc.socketStream(
+    ssc.socketStream(
       "localhost",
       12345,
-      new Converter(),
+      new Function<InputStream, Iterable<String>>() {
+        @Override
+        public Iterable<String> call(InputStream in) throws IOException {
+          List<String> out = new ArrayList<>();
+          try (BufferedReader reader = new BufferedReader(new InputStreamReader(in))) {
+            for (String line; (line = reader.readLine()) != null;) {
+              out.add(line);
+            }
+          }
+          return out;
+        }
+      },
       StorageLevel.MEMORY_ONLY());
   }
 
@@ -1870,7 +1864,7 @@ public void testFileStream() throws IOException {
       TextInputFormat.class,
       new Function<Path, Boolean>() {
         @Override
-        public Boolean call(Path v1) throws Exception {
+        public Boolean call(Path v1) {
           return Boolean.TRUE;
         }
       },
@@ -1879,7 +1873,7 @@ public Boolean call(Path v1) throws Exception {
     JavaDStream<String> test = inputStream.map(
       new Function<Tuple2<LongWritable, Text>, String>() {
         @Override
-        public String call(Tuple2<LongWritable, Text> v1) throws Exception {
+        public String call(Tuple2<LongWritable, Text> v1) {
           return v1._2().toString();
         }
     });
@@ -1892,19 +1886,15 @@ public String call(Tuple2<LongWritable, Text> v1) throws Exception {
 
   @Test
   public void testRawSocketStream() {
-    JavaReceiverInputDStream<String> test = ssc.rawSocketStream("localhost", 12345);
+    ssc.rawSocketStream("localhost", 12345);
   }
 
-  private List<List<String>> fileTestPrepare(File testDir) throws IOException {
+  private static List<List<String>> fileTestPrepare(File testDir) throws IOException {
     File existingFile = new File(testDir, "0");
     Files.write("0\n", existingFile, Charset.forName("UTF-8"));
-    assertTrue(existingFile.setLastModified(1000) && existingFile.lastModified() == 1000);
-
-    List<List<String>> expected = Arrays.asList(
-      Arrays.asList("0")
-    );
-
-    return expected;
+    Assert.assertTrue(existingFile.setLastModified(1000));
+    Assert.assertEquals(1000, existingFile.lastModified());
+    return Arrays.asList(Arrays.asList("0"));
   }
 
   @SuppressWarnings("unchecked")
diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java
index 1b0787fe69dec..ec2bffd6a5b97 100644
--- a/streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java
+++ b/streaming/src/test/java/org/apache/spark/streaming/JavaReceiverAPISuite.java
@@ -36,7 +36,6 @@
 import java.io.Serializable;
 import java.net.ConnectException;
 import java.net.Socket;
-import java.util.concurrent.atomic.AtomicInteger;
 import java.util.concurrent.atomic.AtomicLong;
 
 public class JavaReceiverAPISuite implements Serializable {
@@ -64,16 +63,16 @@ public void testReceiver() throws InterruptedException {
         ssc.receiverStream(new JavaSocketReceiver("localhost", server.port()));
       JavaDStream<String> mapped = input.map(new Function<String, String>() {
         @Override
-        public String call(String v1) throws Exception {
+        public String call(String v1) {
           return v1 + ".";
         }
       });
       mapped.foreachRDD(new Function<JavaRDD<String>, Void>() {
         @Override
-        public Void call(JavaRDD<String> rdd) throws Exception {
-        long count = rdd.count();
-        dataCounter.addAndGet(count);
-        return null;
+        public Void call(JavaRDD<String> rdd) {
+          long count = rdd.count();
+          dataCounter.addAndGet(count);
+          return null;
         }
       });
 
@@ -83,7 +82,7 @@ public Void call(JavaRDD<String> rdd) throws Exception {
 
       Thread.sleep(200);
       for (int i = 0; i < 6; i++) {
-        server.send("" + i + "\n"); // \n to make sure these are separate lines
+        server.send(i + "\n"); // \n to make sure these are separate lines
         Thread.sleep(100);
       }
       while (dataCounter.get() == 0 && System.currentTimeMillis() - startTime < timeout) {
@@ -95,50 +94,49 @@ public Void call(JavaRDD<String> rdd) throws Exception {
       server.stop();
     }
   }
-}
 
-class JavaSocketReceiver extends Receiver<String> {
+  private static class JavaSocketReceiver extends Receiver<String> {
 
-  String host = null;
-  int port = -1;
+    String host = null;
+    int port = -1;
 
-  public JavaSocketReceiver(String host_ , int port_) {
-    super(StorageLevel.MEMORY_AND_DISK());
-    host = host_;
-    port = port_;
-  }
+    JavaSocketReceiver(String host_ , int port_) {
+      super(StorageLevel.MEMORY_AND_DISK());
+      host = host_;
+      port = port_;
+    }
 
-  @Override
-  public void onStart() {
-    new Thread()  {
-      @Override public void run() {
-        receive();
-      }
-    }.start();
-  }
+    @Override
+    public void onStart() {
+      new Thread()  {
+        @Override public void run() {
+          receive();
+        }
+      }.start();
+    }
 
-  @Override
-  public void onStop() {
-  }
+    @Override
+    public void onStop() {
+    }
 
-  private void receive() {
-    Socket socket = null;
-    try {
-      socket = new Socket(host, port);
-      BufferedReader in = new BufferedReader(new InputStreamReader(socket.getInputStream()));
-      String userInput;
-      while ((userInput = in.readLine()) != null) {
-        store(userInput);
+    private void receive() {
+      try {
+        Socket socket = new Socket(host, port);
+        BufferedReader in = new BufferedReader(new InputStreamReader(socket.getInputStream()));
+        String userInput;
+        while ((userInput = in.readLine()) != null) {
+          store(userInput);
+        }
+        in.close();
+        socket.close();
+      } catch(ConnectException ce) {
+        ce.printStackTrace();
+        restart("Could not connect", ce);
+      } catch(Throwable t) {
+        t.printStackTrace();
+        restart("Error receiving data", t);
       }
-      in.close();
-      socket.close();
-    } catch(ConnectException ce) {
-      ce.printStackTrace();
-      restart("Could not connect", ce);
-    } catch(Throwable t) {
-      t.printStackTrace();
-      restart("Error receiving data", t);
     }
   }
-}
 
+}

From f4a22808e03fa12bfe1bfc82cf713cfda7e063a9 Mon Sep 17 00:00:00 2001
From: JihongMa <linlin200605@gmail.com>
Date: Sat, 12 Sep 2015 10:17:15 -0700
Subject: [PATCH 606/633] [SPARK-6548] Adding stddev to DataFrame functions

Adding STDDEV support for DataFrame using 1-pass online /parallel algorithm to compute variance. Please review the code change.

Author: JihongMa <linlin200605@gmail.com>
Author: Jihong MA <linlin200605@gmail.com>
Author: Jihong MA <jihongma@jihongs-mbp.usca.ibm.com>
Author: Jihong MA <jihongma@Jihongs-MacBook-Pro.local>

Closes #6297 from JihongMA/SPARK-SQL.
---
 R/pkg/inst/tests/test_sparkSQL.R              |   2 +-
 python/pyspark/sql/dataframe.py               |  36 +--
 .../catalyst/analysis/FunctionRegistry.scala  |   3 +
 .../catalyst/analysis/HiveTypeCoercion.scala  |   3 +
 .../spark/sql/catalyst/dsl/package.scala      |   3 +
 .../expressions/aggregate/functions.scala     | 143 ++++++++++
 .../expressions/aggregate/utils.scala         |  18 ++
 .../sql/catalyst/expressions/aggregates.scala | 245 ++++++++++++++++++
 .../org/apache/spark/sql/DataFrame.scala      |   6 +-
 .../org/apache/spark/sql/GroupedData.scala    |  39 +++
 .../org/apache/spark/sql/functions.scala      |  27 ++
 .../apache/spark/sql/JavaDataFrameSuite.java  |   1 +
 .../spark/sql/DataFrameAggregateSuite.scala   |  33 +++
 .../org/apache/spark/sql/DataFrameSuite.scala |   2 +-
 .../org/apache/spark/sql/SQLQuerySuite.scala  |  42 ++-
 .../execution/AggregationQuerySuite.scala     |  35 ---
 16 files changed, 574 insertions(+), 64 deletions(-)

diff --git a/R/pkg/inst/tests/test_sparkSQL.R b/R/pkg/inst/tests/test_sparkSQL.R
index 1ccfde59176f5..98d4402d368e1 100644
--- a/R/pkg/inst/tests/test_sparkSQL.R
+++ b/R/pkg/inst/tests/test_sparkSQL.R
@@ -1147,7 +1147,7 @@ test_that("describe() and summarize() on a DataFrame", {
   stats <- describe(df, "age")
   expect_equal(collect(stats)[1, "summary"], "count")
   expect_equal(collect(stats)[2, "age"], "24.5")
-  expect_equal(collect(stats)[3, "age"], "5.5")
+  expect_equal(collect(stats)[3, "age"], "7.7781745930520225")
   stats <- describe(df)
   expect_equal(collect(stats)[4, "name"], "Andy")
   expect_equal(collect(stats)[5, "age"], "30")
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index c5bf55791240b..fb995fa3a76b5 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -653,25 +653,25 @@ def describe(self, *cols):
         guarantee about the backward compatibility of the schema of the resulting DataFrame.
 
         >>> df.describe().show()
-        +-------+---+
-        |summary|age|
-        +-------+---+
-        |  count|  2|
-        |   mean|3.5|
-        | stddev|1.5|
-        |    min|  2|
-        |    max|  5|
-        +-------+---+
+        +-------+------------------+
+        |summary|               age|
+        +-------+------------------+
+        |  count|                 2|
+        |   mean|               3.5|
+        | stddev|2.1213203435596424|
+        |    min|                 2|
+        |    max|                 5|
+        +-------+------------------+
         >>> df.describe(['age', 'name']).show()
-        +-------+---+-----+
-        |summary|age| name|
-        +-------+---+-----+
-        |  count|  2|    2|
-        |   mean|3.5| null|
-        | stddev|1.5| null|
-        |    min|  2|Alice|
-        |    max|  5|  Bob|
-        +-------+---+-----+
+        +-------+------------------+-----+
+        |summary|               age| name|
+        +-------+------------------+-----+
+        |  count|                 2|    2|
+        |   mean|               3.5| null|
+        | stddev|2.1213203435596424| null|
+        |    min|                 2|Alice|
+        |    max|                 5|  Bob|
+        +-------+------------------+-----+
         """
         if len(cols) == 1 and isinstance(cols[0], list):
             cols = cols[0]
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
index cd5a90d788151..11b4866bf264b 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala
@@ -168,6 +168,9 @@ object FunctionRegistry {
     expression[Last]("last"),
     expression[Max]("max"),
     expression[Min]("min"),
+    expression[Stddev]("stddev"),
+    expression[StddevPop]("stddev_pop"),
+    expression[StddevSamp]("stddev_samp"),
     expression[Sum]("sum"),
 
     // string functions
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
index 87c11abbad490..87a3845b2d9e5 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala
@@ -297,6 +297,9 @@ object HiveTypeCoercion {
       case Sum(e @ StringType()) => Sum(Cast(e, DoubleType))
       case SumDistinct(e @ StringType()) => Sum(Cast(e, DoubleType))
       case Average(e @ StringType()) => Average(Cast(e, DoubleType))
+      case Stddev(e @ StringType()) => Stddev(Cast(e, DoubleType))
+      case StddevPop(e @ StringType()) => StddevPop(Cast(e, DoubleType))
+      case StddevSamp(e @ StringType()) => StddevSamp(Cast(e, DoubleType))
     }
   }
 
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
index a7e3a49327655..699c4cc63d09a 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/dsl/package.scala
@@ -159,6 +159,9 @@ package object dsl {
     def lower(e: Expression): Expression = Lower(e)
     def sqrt(e: Expression): Expression = Sqrt(e)
     def abs(e: Expression): Expression = Abs(e)
+    def stddev(e: Expression): Expression = Stddev(e)
+    def stddev_pop(e: Expression): Expression = StddevPop(e)
+    def stddev_samp(e: Expression): Expression = StddevSamp(e)
 
     implicit class DslSymbol(sym: Symbol) extends ImplicitAttribute { def s: String = sym.name }
     // TODO more implicit class for literal?
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
index a73024d6adba1..02cd0ac0db118 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/functions.scala
@@ -249,6 +249,149 @@ case class Min(child: Expression) extends AlgebraicAggregate {
   override val evaluateExpression = min
 }
 
+// Compute the sample standard deviation of a column
+case class Stddev(child: Expression) extends StddevAgg(child) {
+
+  override def isSample: Boolean = true
+  override def prettyName: String = "stddev"
+}
+
+// Compute the population standard deviation of a column
+case class StddevPop(child: Expression) extends StddevAgg(child) {
+
+  override def isSample: Boolean = false
+  override def prettyName: String = "stddev_pop"
+}
+
+// Compute the sample standard deviation of a column
+case class StddevSamp(child: Expression) extends StddevAgg(child) {
+
+  override def isSample: Boolean = true
+  override def prettyName: String = "stddev_samp"
+}
+
+// Compute standard deviation based on online algorithm specified here:
+// http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+abstract class StddevAgg(child: Expression) extends AlgebraicAggregate {
+
+  override def children: Seq[Expression] = child :: Nil
+
+  override def nullable: Boolean = true
+
+  def isSample: Boolean
+
+  // Return data type.
+  override def dataType: DataType = resultType
+
+  // Expected input data type.
+  // TODO: Right now, we replace old aggregate functions (based on AggregateExpression1) to the
+  // new version at planning time (after analysis phase). For now, NullType is added at here
+  // to make it resolved when we have cases like `select stddev(null)`.
+  // We can use our analyzer to cast NullType to the default data type of the NumericType once
+  // we remove the old aggregate functions. Then, we will not need NullType at here.
+  override def inputTypes: Seq[AbstractDataType] = Seq(TypeCollection(NumericType, NullType))
+
+  private val resultType = DoubleType
+
+  private val preCount = AttributeReference("preCount", resultType)()
+  private val currentCount = AttributeReference("currentCount", resultType)()
+  private val preAvg = AttributeReference("preAvg", resultType)()
+  private val currentAvg = AttributeReference("currentAvg", resultType)()
+  private val currentMk = AttributeReference("currentMk", resultType)()
+
+  override val bufferAttributes = preCount :: currentCount :: preAvg ::
+                                  currentAvg :: currentMk :: Nil
+
+  override val initialValues = Seq(
+    /* preCount = */ Cast(Literal(0), resultType),
+    /* currentCount = */ Cast(Literal(0), resultType),
+    /* preAvg = */ Cast(Literal(0), resultType),
+    /* currentAvg = */ Cast(Literal(0), resultType),
+    /* currentMk = */ Cast(Literal(0), resultType)
+  )
+
+  override val updateExpressions = {
+
+    // update average
+    // avg = avg + (value - avg)/count
+    def avgAdd: Expression = {
+      currentAvg + ((Cast(child, resultType) - currentAvg) / currentCount)
+    }
+
+    // update sum of square of difference from mean
+    // Mk = Mk + (value - preAvg) * (value - updatedAvg)
+    def mkAdd: Expression = {
+      val delta1 = Cast(child, resultType) - preAvg
+      val delta2 = Cast(child, resultType) - currentAvg
+      currentMk + (delta1 * delta2)
+    }
+
+    Seq(
+      /* preCount = */ If(IsNull(child), preCount, currentCount),
+      /* currentCount = */ If(IsNull(child), currentCount,
+                           Add(currentCount, Cast(Literal(1), resultType))),
+      /* preAvg = */ If(IsNull(child), preAvg, currentAvg),
+      /* currentAvg = */ If(IsNull(child), currentAvg, avgAdd),
+      /* currentMk = */ If(IsNull(child), currentMk, mkAdd)
+    )
+  }
+
+  override val mergeExpressions = {
+
+    // count merge
+    def countMerge: Expression = {
+      currentCount.left + currentCount.right
+    }
+
+    // average merge
+    def avgMerge: Expression = {
+      ((currentAvg.left * preCount) + (currentAvg.right * currentCount.right)) /
+      (preCount + currentCount.right)
+    }
+
+    // update sum of square differences
+    def mkMerge: Expression = {
+      val avgDelta = currentAvg.right - preAvg
+      val mkDelta = (avgDelta * avgDelta) * (preCount * currentCount.right) /
+        (preCount + currentCount.right)
+
+      currentMk.left + currentMk.right + mkDelta
+    }
+
+    Seq(
+      /* preCount = */ If(IsNull(currentCount.left),
+                         Cast(Literal(0), resultType), currentCount.left),
+      /* currentCount = */ If(IsNull(currentCount.left), currentCount.right,
+                             If(IsNull(currentCount.right), currentCount.left, countMerge)),
+      /* preAvg = */ If(IsNull(currentAvg.left), Cast(Literal(0), resultType), currentAvg.left),
+      /* currentAvg = */ If(IsNull(currentAvg.left), currentAvg.right,
+                           If(IsNull(currentAvg.right), currentAvg.left, avgMerge)),
+      /* currentMk = */ If(IsNull(currentMk.left), currentMk.right,
+                          If(IsNull(currentMk.right), currentMk.left, mkMerge))
+    )
+  }
+
+  override val evaluateExpression = {
+    // when currentCount == 0, return null
+    // when currentCount == 1, return 0
+    // when currentCount >1
+    // stddev_samp = sqrt (currentMk/(currentCount -1))
+    // stddev_pop = sqrt (currentMk/currentCount)
+    val varCol = {
+      if (isSample) {
+        currentMk / Cast((currentCount - Cast(Literal(1), resultType)), resultType)
+      }
+      else {
+        currentMk / currentCount
+      }
+    }
+
+    If(EqualTo(currentCount, Cast(Literal(0), resultType)), Cast(Literal(null), resultType),
+      If(EqualTo(currentCount, Cast(Literal(1), resultType)), Cast(Literal(0), resultType),
+        Cast(Sqrt(varCol), resultType)))
+  }
+}
+
 case class Sum(child: Expression) extends AlgebraicAggregate {
 
   override def children: Seq[Expression] = child :: Nil
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala
index 4a43318a95490..ce3dddad87f55 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregate/utils.scala
@@ -85,6 +85,24 @@ object Utils {
             mode = aggregate.Complete,
             isDistinct = false)
 
+        case expressions.Stddev(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.Stddev(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.StddevPop(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.StddevPop(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
+        case expressions.StddevSamp(child) =>
+          aggregate.AggregateExpression2(
+            aggregateFunction = aggregate.StddevSamp(child),
+            mode = aggregate.Complete,
+            isDistinct = false)
+
         case expressions.Sum(child) =>
           aggregate.AggregateExpression2(
             aggregateFunction = aggregate.Sum(child),
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
index 5e8298aaaa9cb..f1c47f39043c8 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala
@@ -691,3 +691,248 @@ case class LastFunction(expr: Expression, base: AggregateExpression1) extends Ag
     result
   }
 }
+
+// Compute standard deviation based on online algorithm specified here:
+// http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+abstract class StddevAgg1(child: Expression) extends UnaryExpression with PartialAggregate1 {
+  override def nullable: Boolean = true
+  override def dataType: DataType = DoubleType
+
+  def isSample: Boolean
+
+  override def asPartial: SplitEvaluation = {
+    val partialStd = Alias(ComputePartialStd(child), "PartialStddev")()
+    SplitEvaluation(MergePartialStd(partialStd.toAttribute, isSample), partialStd :: Nil)
+  }
+
+  override def newInstance(): StddevFunction = new StddevFunction(child, this, isSample)
+
+  override def checkInputDataTypes(): TypeCheckResult =
+    TypeUtils.checkForNumericExpr(child.dataType, "function stddev")
+
+}
+
+// Compute the sample standard deviation of a column
+case class Stddev(child: Expression) extends StddevAgg1(child) {
+
+  override def toString: String = s"STDDEV($child)"
+  override def isSample: Boolean = true
+}
+
+// Compute the population standard deviation of a column
+case class StddevPop(child: Expression) extends StddevAgg1(child) {
+
+  override def toString: String = s"STDDEV_POP($child)"
+  override def isSample: Boolean = false
+}
+
+// Compute the sample standard deviation of a column
+case class StddevSamp(child: Expression) extends StddevAgg1(child) {
+
+  override def toString: String = s"STDDEV_SAMP($child)"
+  override def isSample: Boolean = true
+}
+
+case class ComputePartialStd(child: Expression) extends UnaryExpression with AggregateExpression1 {
+    def this() = this(null)
+
+    override def children: Seq[Expression] = child :: Nil
+    override def nullable: Boolean = false
+    override def dataType: DataType = ArrayType(DoubleType)
+    override def toString: String = s"computePartialStddev($child)"
+    override def newInstance(): ComputePartialStdFunction =
+      new ComputePartialStdFunction(child, this)
+}
+
+case class ComputePartialStdFunction (
+    expr: Expression,
+    base: AggregateExpression1
+) extends AggregateFunction1 {
+  def this() = this(null, null)  // Required for serialization
+
+  private val computeType = DoubleType
+  private val zero = Cast(Literal(0), computeType)
+  private var partialCount: Long = 0L
+
+  // the mean of data processed so far
+  private val partialAvg: MutableLiteral = MutableLiteral(zero.eval(null), computeType)
+
+  // update average based on this formula:
+  // avg = avg + (value - avg)/count
+  private def avgAddFunction (value: Literal): Expression = {
+    val delta = Subtract(Cast(value, computeType), partialAvg)
+    Add(partialAvg, Divide(delta, Cast(Literal(partialCount), computeType)))
+  }
+
+  // the sum of squares of difference from mean
+  private val partialMk: MutableLiteral = MutableLiteral(zero.eval(null), computeType)
+
+  // update sum of square of difference from mean based on following formula:
+  // Mk = Mk + (value - preAvg) * (value - updatedAvg)
+  private def mkAddFunction(value: Literal, prePartialAvg: MutableLiteral): Expression = {
+    val delta1 = Subtract(Cast(value, computeType), prePartialAvg)
+    val delta2 = Subtract(Cast(value, computeType), partialAvg)
+    Add(partialMk, Multiply(delta1, delta2))
+  }
+
+  override def update(input: InternalRow): Unit = {
+    val evaluatedExpr = expr.eval(input)
+    if (evaluatedExpr != null) {
+      val exprValue = Literal.create(evaluatedExpr, expr.dataType)
+      val prePartialAvg = partialAvg.copy()
+      partialCount += 1
+      partialAvg.update(avgAddFunction(exprValue), input)
+      partialMk.update(mkAddFunction(exprValue, prePartialAvg), input)
+    }
+  }
+
+  override def eval(input: InternalRow): Any = {
+    new GenericArrayData(Array(Cast(Literal(partialCount), computeType).eval(null),
+        partialAvg.eval(null),
+        partialMk.eval(null)))
+  }
+}
+
+case class MergePartialStd(
+    child: Expression,
+    isSample: Boolean
+) extends UnaryExpression with AggregateExpression1 {
+  def this() = this(null, false) // required for serialization
+
+  override def children: Seq[Expression] = child:: Nil
+  override def nullable: Boolean = false
+  override def dataType: DataType = DoubleType
+  override def toString: String = s"MergePartialStd($child)"
+  override def newInstance(): MergePartialStdFunction = {
+    new MergePartialStdFunction(child, this, isSample)
+  }
+}
+
+case class MergePartialStdFunction(
+    expr: Expression,
+    base: AggregateExpression1,
+    isSample: Boolean
+) extends AggregateFunction1 {
+  def this() = this (null, null, false) // Required for serialization
+
+  private val computeType = DoubleType
+  private val zero = Cast(Literal(0), computeType)
+  private val combineCount = MutableLiteral(zero.eval(null), computeType)
+  private val combineAvg = MutableLiteral(zero.eval(null), computeType)
+  private val combineMk = MutableLiteral(zero.eval(null), computeType)
+
+  private def avgUpdateFunction(preCount: Expression,
+                                partialCount: Expression,
+                                partialAvg: Expression): Expression = {
+    Divide(Add(Multiply(combineAvg, preCount),
+               Multiply(partialAvg, partialCount)),
+           Add(preCount, partialCount))
+  }
+
+  override def update(input: InternalRow): Unit = {
+    val evaluatedExpr = expr.eval(input).asInstanceOf[ArrayData]
+
+    if (evaluatedExpr != null) {
+      val exprValue = evaluatedExpr.toArray(computeType)
+      val (partialCount, partialAvg, partialMk) =
+        (Literal.create(exprValue(0), computeType),
+         Literal.create(exprValue(1), computeType),
+         Literal.create(exprValue(2), computeType))
+
+      if (Cast(partialCount, LongType).eval(null).asInstanceOf[Long] > 0) {
+        val preCount = combineCount.copy()
+        combineCount.update(Add(combineCount, partialCount), input)
+
+        val preAvg = combineAvg.copy()
+        val avgDelta = Subtract(partialAvg, preAvg)
+        val mkDelta = Multiply(Multiply(avgDelta, avgDelta),
+                               Divide(Multiply(preCount, partialCount),
+                                      combineCount))
+
+        // update average based on following formula
+        // (combineAvg * preCount + partialAvg * partialCount) / (preCount + partialCount)
+        combineAvg.update(avgUpdateFunction(preCount, partialCount, partialAvg), input)
+
+        // update sum of square differences from mean based on following formula
+        // (combineMk + partialMk + (avgDelta * avgDelta) * (preCount * partialCount/combineCount)
+        combineMk.update(Add(combineMk, Add(partialMk, mkDelta)), input)
+      }
+    }
+  }
+
+  override def eval(input: InternalRow): Any = {
+    val count: Long = Cast(combineCount, LongType).eval(null).asInstanceOf[Long]
+
+    if (count == 0) null
+    else if (count < 2) zero.eval(null)
+    else {
+      // when total count > 2
+      // stddev_samp = sqrt (combineMk/(combineCount -1))
+      // stddev_pop = sqrt (combineMk/combineCount)
+      val varCol = {
+        if (isSample) {
+          Divide(combineMk, Cast(Literal(count - 1), computeType))
+        }
+        else {
+          Divide(combineMk, Cast(Literal(count), computeType))
+        }
+      }
+      Sqrt(varCol).eval(null)
+    }
+  }
+}
+
+case class StddevFunction(
+    expr: Expression,
+    base: AggregateExpression1,
+    isSample: Boolean
+) extends AggregateFunction1 {
+
+  def this() = this(null, null, false) // Required for serialization
+
+  private val computeType = DoubleType
+  private var curCount: Long = 0L
+  private val zero = Cast(Literal(0), computeType)
+  private val curAvg = MutableLiteral(zero.eval(null), computeType)
+  private val curMk = MutableLiteral(zero.eval(null), computeType)
+
+  private def curAvgAddFunction(value: Literal): Expression = {
+    val delta = Subtract(Cast(value, computeType), curAvg)
+    Add(curAvg, Divide(delta, Cast(Literal(curCount), computeType)))
+  }
+  private def curMkAddFunction(value: Literal, preAvg: MutableLiteral): Expression = {
+    val delta1 = Subtract(Cast(value, computeType), preAvg)
+    val delta2 = Subtract(Cast(value, computeType), curAvg)
+    Add(curMk, Multiply(delta1, delta2))
+  }
+
+  override def update(input: InternalRow): Unit = {
+    val evaluatedExpr = expr.eval(input)
+    if (evaluatedExpr != null) {
+      val preAvg: MutableLiteral = curAvg.copy()
+      val exprValue = Literal.create(evaluatedExpr, expr.dataType)
+      curCount += 1L
+      curAvg.update(curAvgAddFunction(exprValue), input)
+      curMk.update(curMkAddFunction(exprValue, preAvg), input)
+    }
+  }
+
+  override def eval(input: InternalRow): Any = {
+    if (curCount == 0) null
+    else if (curCount < 2) zero.eval(null)
+    else {
+      // when total count > 2,
+      // stddev_samp = sqrt(curMk/(curCount - 1))
+      // stddev_pop = sqrt(curMk/curCount)
+      val varCol = {
+        if (isSample) {
+          Divide(curMk, Cast(Literal(curCount - 1), computeType))
+        }
+        else {
+          Divide(curMk, Cast(Literal(curCount), computeType))
+        }
+      }
+      Sqrt(varCol).eval(null)
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 791c10c3d7ce7..1a687b2374f14 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1288,15 +1288,11 @@ class DataFrame private[sql](
   @scala.annotation.varargs
   def describe(cols: String*): DataFrame = {
 
-    // TODO: Add stddev as an expression, and remove it from here.
-    def stddevExpr(expr: Expression): Expression =
-      Sqrt(Subtract(Average(Multiply(expr, expr)), Multiply(Average(expr), Average(expr))))
-
     // The list of summary statistics to compute, in the form of expressions.
     val statistics = List[(String, Expression => Expression)](
       "count" -> Count,
       "mean" -> Average,
-      "stddev" -> stddevExpr,
+      "stddev" -> Stddev,
       "min" -> Min,
       "max" -> Max)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
index ee31d83cce42c..102b802ad0a0a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
@@ -124,6 +124,9 @@ class GroupedData protected[sql](
       case "avg" | "average" | "mean" => Average
       case "max" => Max
       case "min" => Min
+      case "stddev" => Stddev
+      case "stddev_pop" => StddevPop
+      case "stddev_samp" => StddevSamp
       case "sum" => Sum
       case "count" | "size" =>
         // Turn count(*) into count(1)
@@ -283,6 +286,42 @@ class GroupedData protected[sql](
     aggregateNumericColumns(colNames : _*)(Min)
   }
 
+  /**
+   * Compute the sample standard deviation for each numeric columns for each group.
+   * The resulting [[DataFrame]] will also contain the grouping columns.
+   * When specified columns are given, only compute the stddev for them.
+   *
+   * @since 1.6.0
+   */
+  @scala.annotation.varargs
+  def stddev(colNames: String*): DataFrame = {
+    aggregateNumericColumns(colNames : _*)(Stddev)
+  }
+
+  /**
+   * Compute the population standard deviation for each numeric columns for each group.
+   * The resulting [[DataFrame]] will also contain the grouping columns.
+   * When specified columns are given, only compute the stddev for them.
+   *
+   * @since 1.6.0
+   */
+  @scala.annotation.varargs
+  def stddev_pop(colNames: String*): DataFrame = {
+    aggregateNumericColumns(colNames : _*)(StddevPop)
+  }
+
+  /**
+   * Compute the sample standard deviation for each numeric columns for each group.
+   * The resulting [[DataFrame]] will also contain the grouping columns.
+   * When specified columns are given, only compute the stddev for them.
+   *
+   * @since 1.6.0
+   */
+  @scala.annotation.varargs
+  def stddev_samp(colNames: String*): DataFrame = {
+    aggregateNumericColumns(colNames : _*)(StddevSamp)
+  }
+
   /**
    * Compute the sum for each numeric columns for each group.
    * The resulting [[DataFrame]] will also contain the grouping columns.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
index 435e6319a64c4..60d9c509104d5 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/functions.scala
@@ -294,6 +294,33 @@ object functions {
    */
   def min(columnName: String): Column = min(Column(columnName))
 
+  /**
+   * Aggregate function: returns the unbiased sample standard deviation
+   * of the expression in a group.
+   *
+   * @group agg_funcs
+   * @since 1.6.0
+   */
+  def stddev(e: Column): Column = Stddev(e.expr)
+
+  /**
+   * Aggregate function: returns the population standard deviation of
+   * the expression in a group.
+   *
+   * @group agg_funcs
+   * @since 1.6.0
+   */
+  def stddev_pop(e: Column): Column = StddevPop(e.expr)
+
+  /**
+   * Aggregate function: returns the unbiased sample standard deviation of
+   * the expression in a group.
+   *
+   * @group agg_funcs
+   * @since 1.6.0
+   */
+  def stddev_samp(e: Column): Column = StddevSamp(e.expr)
+
   /**
    * Aggregate function: returns the sum of all values in the expression.
    *
diff --git a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
index d981ce947f435..5f9abd4999ce0 100644
--- a/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
+++ b/sql/core/src/test/java/test/org/apache/spark/sql/JavaDataFrameSuite.java
@@ -90,6 +90,7 @@ public void testVarargMethods() {
     df.groupBy().mean("key");
     df.groupBy().max("key");
     df.groupBy().min("key");
+    df.groupBy().stddev("key");
     df.groupBy().sum("key");
 
     // Varargs in column expressions
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
index c0950b09b14ad..f5ef9ffd7f4f2 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala
@@ -175,6 +175,39 @@ class DataFrameAggregateSuite extends QueryTest with SharedSQLContext {
       Row(0, null))
   }
 
+  test("stddev") {
+    val testData2ADev = math.sqrt(4/5.0)
+
+    checkAnswer(
+      testData2.agg(stddev('a)),
+      Row(testData2ADev))
+
+    checkAnswer(
+      testData2.agg(stddev_pop('a)),
+      Row(math.sqrt(4/6.0)))
+
+    checkAnswer(
+      testData2.agg(stddev_samp('a)),
+      Row(testData2ADev))
+  }
+
+  test("zero stddev") {
+    val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
+    assert(emptyTableData.count() == 0)
+
+    checkAnswer(
+    emptyTableData.agg(stddev('a)),
+    Row(null))
+
+    checkAnswer(
+    emptyTableData.agg(stddev_pop('a)),
+    Row(null))
+
+    checkAnswer(
+    emptyTableData.agg(stddev_samp('a)),
+    Row(null))
+  }
+
   test("zero sum") {
     val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b")
     checkAnswer(
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
index dbed4fc247140..c167999af580e 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameSuite.scala
@@ -436,7 +436,7 @@ class DataFrameSuite extends QueryTest with SharedSQLContext {
     val describeResult = Seq(
       Row("count", "4", "4"),
       Row("mean", "33.0", "178.0"),
-      Row("stddev", "16.583123951777", "10.0"),
+      Row("stddev", "19.148542155126762", "11.547005383792516"),
       Row("min", "16", "164"),
       Row("max", "60", "192"))
 
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
index 664b7a1512faf..962b100b532c9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/SQLQuerySuite.scala
@@ -328,6 +328,13 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
       testCodeGen(
         "SELECT min(key) FROM testData3x",
         Row(1) :: Nil)
+      // STDDEV
+      testCodeGen(
+        "SELECT a, stddev(b), stddev_pop(b) FROM testData2 GROUP BY a",
+        (1 to 3).map(i => Row(i, math.sqrt(0.5), math.sqrt(0.25))))
+      testCodeGen(
+        "SELECT stddev(b), stddev_pop(b), stddev_samp(b) FROM testData2",
+        Row(math.sqrt(1.5 / 5), math.sqrt(1.5 / 6), math.sqrt(1.5 / 5)) :: Nil)
       // Some combinations.
       testCodeGen(
         """
@@ -348,8 +355,8 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
         Row(100, 1, 50.5, 300, 100) :: Nil)
       // Aggregate with Code generation handling all null values
       testCodeGen(
-        "SELECT  sum('a'), avg('a'), count(null) FROM testData",
-        Row(null, null, 0) :: Nil)
+        "SELECT  sum('a'), avg('a'), stddev('a'), count(null) FROM testData",
+        Row(null, null, null, 0) :: Nil)
     } finally {
       sqlContext.dropTempTable("testData3x")
       sqlContext.setConf(SQLConf.CODEGEN_ENABLED, originalValue)
@@ -515,8 +522,8 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
 
   test("aggregates with nulls") {
     checkAnswer(
-      sql("SELECT MIN(a), MAX(a), AVG(a), SUM(a), COUNT(a) FROM nullInts"),
-      Row(1, 3, 2, 6, 3)
+      sql("SELECT MIN(a), MAX(a), AVG(a), STDDEV(a), SUM(a), COUNT(a) FROM nullInts"),
+      Row(1, 3, 2, 1, 6, 3)
     )
   }
 
@@ -722,6 +729,33 @@ class SQLQuerySuite extends QueryTest with SharedSQLContext {
     }
   }
 
+  test("stddev") {
+    checkAnswer(
+      sql("SELECT STDDEV(a) FROM testData2"),
+      Row(math.sqrt(4/5.0))
+    )
+  }
+
+  test("stddev_pop") {
+    checkAnswer(
+      sql("SELECT STDDEV_POP(a) FROM testData2"),
+      Row(math.sqrt(4/6.0))
+    )
+  }
+
+  test("stddev_samp") {
+    checkAnswer(
+      sql("SELECT STDDEV_SAMP(a) FROM testData2"),
+      Row(math.sqrt(4/5.0))
+    )
+  }
+
+  test("stddev agg") {
+    checkAnswer(
+      sql("SELECT a, stddev(b), stddev_pop(b), stddev_samp(b) FROM testData2 GROUP BY a"),
+      (1 to 3).map(i => Row(i, math.sqrt(1/2.0), math.sqrt(1/4.0), math.sqrt(1/2.0))))
+  }
+
   test("inner join where, one match per row") {
     checkAnswer(
       sql("SELECT * FROM upperCaseData JOIN lowerCaseData WHERE n = N"),
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
index b126ec455fc69..a73b1bd52c09f 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/AggregationQuerySuite.scala
@@ -507,41 +507,6 @@ abstract class AggregationQuerySuite extends QueryTest with SQLTestUtils with Te
       }.getMessage
       assert(errorMessage.contains("implemented based on the new Aggregate Function interface"))
     }
-
-    // TODO: once we support Hive UDAF in the new interface,
-    // we can remove the following two tests.
-    withSQLConf("spark.sql.useAggregate2" -> "true") {
-      val errorMessage = intercept[AnalysisException] {
-        sqlContext.sql(
-          """
-            |SELECT
-            |  key,
-            |  mydoublesum(value + 1.5 * key),
-            |  stddev_samp(value)
-            |FROM agg1
-            |GROUP BY key
-          """.stripMargin).collect()
-      }.getMessage
-      assert(errorMessage.contains("implemented based on the new Aggregate Function interface"))
-
-      // This will fall back to the old aggregate
-      val newAggregateOperators = sqlContext.sql(
-        """
-          |SELECT
-          |  key,
-          |  sum(value + 1.5 * key),
-          |  stddev_samp(value)
-          |FROM agg1
-          |GROUP BY key
-        """.stripMargin).queryExecution.executedPlan.collect {
-        case agg: aggregate.SortBasedAggregate => agg
-        case agg: aggregate.TungstenAggregate => agg
-      }
-      val message =
-        "We should fallback to the old aggregation code path if " +
-          "there is any aggregate function that cannot be converted to the new interface."
-      assert(newAggregateOperators.isEmpty, message)
-    }
   }
 }
 

From b3a7480ab0821ab38f710de96e3ac4a13f62dbca Mon Sep 17 00:00:00 2001
From: Josh Rosen <joshrosen@databricks.com>
Date: Sat, 12 Sep 2015 16:23:55 -0700
Subject: [PATCH 607/633] [SPARK-10330] Add Scalastyle rule to require use of
 SparkHadoopUtil JobContext methods

This is a followup to #8499 which adds a Scalastyle rule to mandate the use of SparkHadoopUtil's JobContext accessor methods and fixes the existing violations.

Author: Josh Rosen <joshrosen@databricks.com>

Closes #8521 from JoshRosen/SPARK-10330-part2.
---
 .../src/main/scala/org/apache/spark/SparkContext.scala |  6 +++---
 .../org/apache/spark/deploy/SparkHadoopUtil.scala      |  4 ++++
 .../scala/org/apache/spark/rdd/PairRDDFunctions.scala  |  8 +++++---
 .../scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala   |  2 +-
 core/src/test/scala/org/apache/spark/FileSuite.scala   |  6 ++++--
 .../org/apache/spark/examples/CassandraCQLTest.scala   |  3 +++
 .../org/apache/spark/examples/CassandraTest.scala      |  2 ++
 scalastyle-config.xml                                  |  8 ++++++++
 .../sql/execution/datasources/WriterContainer.scala    |  8 ++++++--
 .../sql/execution/datasources/json/JSONRelation.scala  |  2 +-
 .../datasources/parquet/CatalystReadSupport.scala      |  6 +++++-
 .../parquet/DirectParquetOutputCommitter.scala         |  6 +++++-
 .../datasources/parquet/ParquetRelation.scala          | 10 +++++++---
 .../datasources/parquet/ParquetTypesConverter.scala    |  6 +++++-
 .../org/apache/spark/sql/hive/orc/OrcRelation.scala    |  4 ++--
 15 files changed, 61 insertions(+), 20 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index cbfe8bf31c3d6..e27b3c4962221 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -858,7 +858,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     // Use setInputPaths so that wholeTextFiles aligns with hadoopFile/textFile in taking
     // comma separated files as input. (see SPARK-7155)
     NewFileInputFormat.setInputPaths(job, path)
-    val updateConf = job.getConfiguration
+    val updateConf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
     new WholeTextFileRDD(
       this,
       classOf[WholeTextFileInputFormat],
@@ -910,7 +910,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     // Use setInputPaths so that binaryFiles aligns with hadoopFile/textFile in taking
     // comma separated files as input. (see SPARK-7155)
     NewFileInputFormat.setInputPaths(job, path)
-    val updateConf = job.getConfiguration
+    val updateConf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
     new BinaryFileRDD(
       this,
       classOf[StreamInputFormat],
@@ -1092,7 +1092,7 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     // Use setInputPaths so that newAPIHadoopFile aligns with hadoopFile/textFile in taking
     // comma separated files as input. (see SPARK-7155)
     NewFileInputFormat.setInputPaths(job, path)
-    val updatedConf = job.getConfiguration
+    val updatedConf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
     new NewHadoopRDD(this, fClass, kClass, vClass, updatedConf).setName(path)
   }
 
diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
index f7723ef5bde4c..a0b7365df900a 100644
--- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
+++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala
@@ -192,7 +192,9 @@ class SparkHadoopUtil extends Logging {
    * while it's interface in Hadoop 2.+.
    */
   def getConfigurationFromJobContext(context: JobContext): Configuration = {
+    // scalastyle:off jobconfig
     val method = context.getClass.getMethod("getConfiguration")
+    // scalastyle:on jobconfig
     method.invoke(context).asInstanceOf[Configuration]
   }
 
@@ -204,7 +206,9 @@ class SparkHadoopUtil extends Logging {
    */
   def getTaskAttemptIDFromTaskAttemptContext(
       context: MapReduceTaskAttemptContext): MapReduceTaskAttemptID = {
+    // scalastyle:off jobconfig
     val method = context.getClass.getMethod("getTaskAttemptID")
+    // scalastyle:on jobconfig
     method.invoke(context).asInstanceOf[MapReduceTaskAttemptID]
   }
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index c59f0d4aa75a0..199d79b811d65 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -996,8 +996,9 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     job.setOutputKeyClass(keyClass)
     job.setOutputValueClass(valueClass)
     job.setOutputFormatClass(outputFormatClass)
-    job.getConfiguration.set("mapred.output.dir", path)
-    saveAsNewAPIHadoopDataset(job.getConfiguration)
+    val jobConfiguration = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
+    jobConfiguration.set("mapred.output.dir", path)
+    saveAsNewAPIHadoopDataset(jobConfiguration)
   }
 
   /**
@@ -1064,7 +1065,8 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val formatter = new SimpleDateFormat("yyyyMMddHHmm")
     val jobtrackerID = formatter.format(new Date())
     val stageId = self.id
-    val wrappedConf = new SerializableConfiguration(job.getConfiguration)
+    val jobConfiguration = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
+    val wrappedConf = new SerializableConfiguration(jobConfiguration)
     val outfmt = job.getOutputFormatClass
     val jobFormat = outfmt.newInstance
 
diff --git a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
index 9babe56267e08..0228c54e0511c 100644
--- a/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/SqlNewHadoopRDD.scala
@@ -86,7 +86,7 @@ private[spark] class SqlNewHadoopRDD[V: ClassTag](
     if (isDriverSide) {
       initDriverSideJobFuncOpt.map(f => f(job))
     }
-    job.getConfiguration
+    SparkHadoopUtil.get.getConfigurationFromJobContext(job)
   }
 
   private val jobTrackerId: String = {
diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala
index 418763f4e5ffa..fdb00aafc4a48 100644
--- a/core/src/test/scala/org/apache/spark/FileSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FileSuite.scala
@@ -19,6 +19,7 @@ package org.apache.spark
 
 import java.io.{File, FileWriter}
 
+import org.apache.spark.deploy.SparkHadoopUtil
 import org.apache.spark.input.PortableDataStream
 import org.apache.spark.storage.StorageLevel
 
@@ -506,8 +507,9 @@ class FileSuite extends SparkFunSuite with LocalSparkContext {
     job.setOutputKeyClass(classOf[String])
     job.setOutputValueClass(classOf[String])
     job.setOutputFormatClass(classOf[NewTextOutputFormat[String, String]])
-    job.getConfiguration.set("mapred.output.dir", tempDir.getPath + "/outputDataset_new")
-    randomRDD.saveAsNewAPIHadoopDataset(job.getConfiguration)
+    val jobConfig = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
+    jobConfig.set("mapred.output.dir", tempDir.getPath + "/outputDataset_new")
+    randomRDD.saveAsNewAPIHadoopDataset(jobConfig)
     assert(new File(tempDir.getPath + "/outputDataset_new/part-r-00000").exists() === true)
   }
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
index fa07c1e5017cd..d1b9b8d398dd8 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
@@ -16,6 +16,7 @@
  */
 
  // scalastyle:off println
+ // scalastyle:off jobcontext
 package org.apache.spark.examples
 
 import java.nio.ByteBuffer
@@ -81,6 +82,7 @@ object CassandraCQLTest {
 
     val job = new Job()
     job.setInputFormatClass(classOf[CqlPagingInputFormat])
+    val configuration = job.getConfiguration
     ConfigHelper.setInputInitialAddress(job.getConfiguration(), cHost)
     ConfigHelper.setInputRpcPort(job.getConfiguration(), cPort)
     ConfigHelper.setInputColumnFamily(job.getConfiguration(), KeySpace, InputColumnFamily)
@@ -135,3 +137,4 @@ object CassandraCQLTest {
   }
 }
 // scalastyle:on println
+// scalastyle:on jobcontext
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
index 2e56d24c60c33..1e679bfb55343 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
@@ -16,6 +16,7 @@
  */
 
 // scalastyle:off println
+// scalastyle:off jobcontext
 package org.apache.spark.examples
 
 import java.nio.ByteBuffer
@@ -130,6 +131,7 @@ object CassandraTest {
   }
 }
 // scalastyle:on println
+// scalastyle:on jobcontext
 
 /*
 create keyspace casDemo;
diff --git a/scalastyle-config.xml b/scalastyle-config.xml
index 68fdb4141cf27..64a0c71bbef2a 100644
--- a/scalastyle-config.xml
+++ b/scalastyle-config.xml
@@ -168,6 +168,14 @@ This file is divided into 3 sections:
     scala.collection.JavaConverters._ and use .asScala / .asJava methods</customMessage>
   </check>
 
+  <!-- As of SPARK-10330 JobContext methods should not be called directly -->
+  <check customId="jobcontext" level="error" class="org.scalastyle.scalariform.TokenChecker" enabled="true">
+    <parameters><parameter name="regex">^getConfiguration$|^getTaskAttemptID$</parameter></parameters>
+    <customMessage>Instead of calling .getConfiguration() or .getTaskAttemptID() directly,
+    use SparkHadoopUtil's getConfigurationFromJobContext() and getTaskAttemptIDFromTaskAttemptContext() methods.
+    </customMessage>
+  </check>
+
   <!-- ================================================================================ -->
   <!--       rules we'd like to enforce, but haven't cleaned up the codebase yet        -->
   <!-- ================================================================================ -->
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
index 9a573db0c023a..f8ef674ed29c1 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/WriterContainer.scala
@@ -47,7 +47,8 @@ private[sql] abstract class BaseWriterContainer(
 
   protected val dataSchema = relation.dataSchema
 
-  protected val serializableConf = new SerializableConfiguration(job.getConfiguration)
+  protected val serializableConf =
+    new SerializableConfiguration(SparkHadoopUtil.get.getConfigurationFromJobContext(job))
 
   // This UUID is used to avoid output file name collision between different appending write jobs.
   // These jobs may belong to different SparkContext instances. Concrete data source implementations
@@ -89,7 +90,8 @@ private[sql] abstract class BaseWriterContainer(
     // This UUID is sent to executor side together with the serialized `Configuration` object within
     // the `Job` instance.  `OutputWriters` on the executor side should use this UUID to generate
     // unique task output files.
-    job.getConfiguration.set("spark.sql.sources.writeJobUUID", uniqueWriteJobId.toString)
+    SparkHadoopUtil.get.getConfigurationFromJobContext(job).
+      set("spark.sql.sources.writeJobUUID", uniqueWriteJobId.toString)
 
     // Order of the following two lines is important.  For Hadoop 1, TaskAttemptContext constructor
     // clones the Configuration object passed in.  If we initialize the TaskAttemptContext first,
@@ -182,7 +184,9 @@ private[sql] abstract class BaseWriterContainer(
   private def setupIDs(jobId: Int, splitId: Int, attemptId: Int): Unit = {
     this.jobId = SparkHadoopWriter.createJobID(new Date, jobId)
     this.taskId = new TaskID(this.jobId, true, splitId)
+    // scalastyle:off jobcontext
     this.taskAttemptId = new TaskAttemptID(taskId, attemptId)
+    // scalastyle:on jobcontext
   }
 
   private def setupConf(): Unit = {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
index 7a49157d9e72c..8ee0127c3bde8 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/json/JSONRelation.scala
@@ -81,7 +81,7 @@ private[sql] class JSONRelation(
 
   private def createBaseRdd(inputPaths: Array[FileStatus]): RDD[String] = {
     val job = new Job(sqlContext.sparkContext.hadoopConfiguration)
-    val conf = job.getConfiguration
+    val conf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
 
     val paths = inputPaths.map(_.getPath)
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
index 5a8166fac5418..8c819f1a48cd6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/CatalystReadSupport.scala
@@ -72,7 +72,11 @@ private[parquet] class CatalystReadSupport extends ReadSupport[InternalRow] with
 
   // Called before `prepareForRead()` when initializing Parquet record reader.
   override def init(context: InitContext): ReadContext = {
-    val conf = context.getConfiguration
+    val conf = {
+      // scalastyle:off jobcontext
+      context.getConfiguration
+      // scalastyle:on jobcontext
+    }
 
     // If the target file was written by Spark SQL, we should be able to find a serialized Catalyst
     // schema of this file from its metadata.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
index 2c6b914328b60..de1fd0166ac5a 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/DirectParquetOutputCommitter.scala
@@ -53,7 +53,11 @@ private[parquet] class DirectParquetOutputCommitter(outputPath: Path, context: T
   override def setupTask(taskContext: TaskAttemptContext): Unit = {}
 
   override def commitJob(jobContext: JobContext) {
-    val configuration = ContextUtil.getConfiguration(jobContext)
+    val configuration = {
+      // scalastyle:off jobcontext
+      ContextUtil.getConfiguration(jobContext)
+      // scalastyle:on jobcontext
+    }
     val fileSystem = outputPath.getFileSystem(configuration)
 
     if (configuration.getBoolean(ParquetOutputFormat.ENABLE_JOB_SUMMARY, true)) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
index c6bbc392cad4c..953fcab126970 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetRelation.scala
@@ -211,7 +211,11 @@ private[sql] class ParquetRelation(
   override def sizeInBytes: Long = metadataCache.dataStatuses.map(_.getLen).sum
 
   override def prepareJobForWrite(job: Job): OutputWriterFactory = {
-    val conf = ContextUtil.getConfiguration(job)
+    val conf = {
+      // scalastyle:off jobcontext
+      ContextUtil.getConfiguration(job)
+      // scalastyle:on jobcontext
+    }
 
     // SPARK-9849 DirectParquetOutputCommitter qualified name should be backward compatible
     val committerClassname = conf.get(SQLConf.PARQUET_OUTPUT_COMMITTER_CLASS.key)
@@ -528,7 +532,7 @@ private[sql] object ParquetRelation extends Logging {
       assumeBinaryIsString: Boolean,
       assumeInt96IsTimestamp: Boolean,
       followParquetFormatSpec: Boolean)(job: Job): Unit = {
-    val conf = job.getConfiguration
+    val conf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
     conf.set(ParquetInputFormat.READ_SUPPORT_CLASS, classOf[CatalystReadSupport].getName)
 
     // Try to push down filters when filter push-down is enabled.
@@ -572,7 +576,7 @@ private[sql] object ParquetRelation extends Logging {
       FileInputFormat.setInputPaths(job, inputFiles.map(_.getPath): _*)
     }
 
-    overrideMinSplitSize(parquetBlockSize, job.getConfiguration)
+    overrideMinSplitSize(parquetBlockSize, SparkHadoopUtil.get.getConfigurationFromJobContext(job))
   }
 
   private[parquet] def readSchema(
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
index 142301fe87cb6..b647bb6116afa 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTypesConverter.scala
@@ -123,7 +123,11 @@ private[parquet] object ParquetTypesConverter extends Logging {
       throw new IllegalArgumentException("Unable to read Parquet metadata: path is null")
     }
     val job = new Job()
-    val conf = configuration.getOrElse(ContextUtil.getConfiguration(job))
+    val conf = {
+      // scalastyle:off jobcontext
+      configuration.getOrElse(ContextUtil.getConfiguration(job))
+      // scalastyle:on jobcontext
+    }
     val fs: FileSystem = origPath.getFileSystem(conf)
     if (fs == null) {
       throw new IllegalArgumentException(s"Incorrectly formatted Parquet metadata path $origPath")
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
index 7e89109259955..d1f30e188eafb 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/orc/OrcRelation.scala
@@ -208,7 +208,7 @@ private[sql] class OrcRelation(
   }
 
   override def prepareJobForWrite(job: Job): OutputWriterFactory = {
-    job.getConfiguration match {
+    SparkHadoopUtil.get.getConfigurationFromJobContext(job) match {
       case conf: JobConf =>
         conf.setOutputFormat(classOf[OrcOutputFormat])
       case conf =>
@@ -289,7 +289,7 @@ private[orc] case class OrcTableScan(
 
   def execute(): RDD[InternalRow] = {
     val job = new Job(sqlContext.sparkContext.hadoopConfiguration)
-    val conf = job.getConfiguration
+    val conf = SparkHadoopUtil.get.getConfigurationFromJobContext(job)
 
     // Tries to push down filters if ORC filter push-down is enabled
     if (sqlContext.conf.orcFilterPushDown) {

From 1dc614b874badde0eee60def46fb47f608bc4759 Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Sun, 13 Sep 2015 08:36:46 +0100
Subject: [PATCH 608/633] [SPARK-10222] [GRAPHX] [DOCS] More thoroughly
 deprecate Bagel in favor of GraphX

Finish deprecating Bagel; remove reference to nonexistent example

Author: Sean Owen <sowen@cloudera.com>

Closes #8731 from srowen/SPARK-10222.
---
 .../src/main/scala/org/apache/spark/bagel/Bagel.scala  |  6 ++++++
 docs/bagel-programming-guide.md                        | 10 +---------
 docs/index.md                                          |  1 -
 pom.xml                                                |  2 +-
 4 files changed, 8 insertions(+), 11 deletions(-)

diff --git a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
index 4e6b7686f771d..8399033ac61ec 100644
--- a/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
+++ b/bagel/src/main/scala/org/apache/spark/bagel/Bagel.scala
@@ -22,6 +22,7 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
 
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 object Bagel extends Logging {
   val DEFAULT_STORAGE_LEVEL = StorageLevel.MEMORY_AND_DISK
 
@@ -270,18 +271,21 @@ object Bagel extends Logging {
   }
 }
 
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 trait Combiner[M, C] {
   def createCombiner(msg: M): C
   def mergeMsg(combiner: C, msg: M): C
   def mergeCombiners(a: C, b: C): C
 }
 
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 trait Aggregator[V, A] {
   def createAggregator(vert: V): A
   def mergeAggregators(a: A, b: A): A
 }
 
 /** Default combiner that simply appends messages together (i.e. performs no aggregation) */
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 class DefaultCombiner[M: Manifest] extends Combiner[M, Array[M]] with Serializable {
   def createCombiner(msg: M): Array[M] =
     Array(msg)
@@ -297,6 +301,7 @@ class DefaultCombiner[M: Manifest] extends Combiner[M, Array[M]] with Serializab
  * Subclasses may store state along with each vertex and must
  * inherit from java.io.Serializable or scala.Serializable.
  */
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 trait Vertex {
   def active: Boolean
 }
@@ -307,6 +312,7 @@ trait Vertex {
  * Subclasses may contain a payload to deliver to the target vertex
  * and must inherit from java.io.Serializable or scala.Serializable.
  */
+@deprecated("Uses of Bagel should migrate to GraphX", "1.6.0")
 trait Message[K] {
   def targetId: K
 }
diff --git a/docs/bagel-programming-guide.md b/docs/bagel-programming-guide.md
index c2fe6b0e286ce..347ca4a7af989 100644
--- a/docs/bagel-programming-guide.md
+++ b/docs/bagel-programming-guide.md
@@ -4,7 +4,7 @@ displayTitle: Bagel Programming Guide
 title: Bagel
 ---
 
-**Bagel will soon be superseded by [GraphX](graphx-programming-guide.html); we recommend that new users try GraphX instead.**
+**Bagel is deprecated, and superseded by [GraphX](graphx-programming-guide.html).**
 
 Bagel is a Spark implementation of Google's [Pregel](http://portal.acm.org/citation.cfm?id=1807184) graph processing framework. Bagel currently supports basic graph computation, combiners, and aggregators.
 
@@ -157,11 +157,3 @@ trait Message[K] {
   def targetId: K
 }
 {% endhighlight %}
-
-# Where to Go from Here
-
-Two example jobs, PageRank and shortest path, are included in `examples/src/main/scala/org/apache/spark/examples/bagel`. You can run them by passing the class name to the `bin/run-example` script included in Spark; e.g.:
-
-    ./bin/run-example org.apache.spark.examples.bagel.WikipediaPageRank
-
-Each example program prints usage help when run without any arguments.
diff --git a/docs/index.md b/docs/index.md
index d85cf12defefd..c0dc2b8d7412a 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -90,7 +90,6 @@ options for deployment:
   * [Spark SQL and DataFrames](sql-programming-guide.html): support for structured data and relational queries
   * [MLlib](mllib-guide.html): built-in machine learning library
   * [GraphX](graphx-programming-guide.html): Spark's new API for graph processing
-  * [Bagel (Pregel on Spark)](bagel-programming-guide.html): older, simple graph processing model
 
 **API Docs:**
 
diff --git a/pom.xml b/pom.xml
index 88ebceca769e9..421357e141572 100644
--- a/pom.xml
+++ b/pom.xml
@@ -87,7 +87,7 @@
 
   <modules>
     <module>core</module>
-    <module>bagel</module>
+    <module>bagel</module> <!-- Deprecated -->
     <module>graphx</module>
     <module>mllib</module>
     <module>tools</module>

From d81565465cc6d4f38b4ed78036cded630c700388 Mon Sep 17 00:00:00 2001
From: Bertrand Dechoux <BertrandDechoux@users.noreply.github.com>
Date: Mon, 14 Sep 2015 09:18:46 +0100
Subject: [PATCH 609/633] [SPARK-9720] [ML] Identifiable types need UID in
 toString methods

A few Identifiable types did override their toString method but without using the parent implementation. As a consequence, the uid was not present anymore in the toString result. It is the default behaviour.

This patch is a quick fix. The question of enforcement is still up.

No tests have been written to verify the toString method behaviour. That would be long to do because all types should be tested and not only those which have a regression now.

It is possible to enforce the condition using the compiler by making the toString method final but that would introduce unwanted potential API breaking changes (see jira).

Author: Bertrand Dechoux <BertrandDechoux@users.noreply.github.com>

Closes #8062 from BertrandDechoux/SPARK-9720.
---
 .../spark/ml/classification/DecisionTreeClassifier.scala      | 2 +-
 .../org/apache/spark/ml/classification/GBTClassifier.scala    | 2 +-
 .../scala/org/apache/spark/ml/classification/NaiveBayes.scala | 2 +-
 .../spark/ml/classification/RandomForestClassifier.scala      | 2 +-
 .../src/main/scala/org/apache/spark/ml/feature/RFormula.scala | 4 ++--
 .../apache/spark/ml/regression/DecisionTreeRegressor.scala    | 2 +-
 .../scala/org/apache/spark/ml/regression/GBTRegressor.scala   | 2 +-
 .../apache/spark/ml/regression/RandomForestRegressor.scala    | 2 +-
 8 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index 0a75d5d22280f..b8eb49f9bdb48 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -146,7 +146,7 @@ final class DecisionTreeClassificationModel private[ml] (
   }
 
   override def toString: String = {
-    s"DecisionTreeClassificationModel of depth $depth with $numNodes nodes"
+    s"DecisionTreeClassificationModel (uid=$uid) of depth $depth with $numNodes nodes"
   }
 
   /** (private[ml]) Convert to a model in the old API */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index 3073a2a61ce83..ad8683648b975 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -200,7 +200,7 @@ final class GBTClassificationModel(
   }
 
   override def toString: String = {
-    s"GBTClassificationModel with $numTrees trees"
+    s"GBTClassificationModel (uid=$uid) with $numTrees trees"
   }
 
   /** (private[ml]) Convert to a model in the old API */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
index 69cb88a7e6718..082ea1ffad58f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -198,7 +198,7 @@ class NaiveBayesModel private[ml] (
   }
 
   override def toString: String = {
-    s"NaiveBayesModel with ${pi.size} classes"
+    s"NaiveBayesModel (uid=$uid) with ${pi.size} classes"
   }
 
 }
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 11a6d72468333..a6ebee1bb10af 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -193,7 +193,7 @@ final class RandomForestClassificationModel private[ml] (
   }
 
   override def toString: String = {
-    s"RandomForestClassificationModel with $numTrees trees"
+    s"RandomForestClassificationModel (uid=$uid) with $numTrees trees"
   }
 
   /**
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
index a7fa50444209b..dcd6fe3c406a4 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala
@@ -129,7 +129,7 @@ class RFormula(override val uid: String) extends Estimator[RFormulaModel] with R
 
   override def copy(extra: ParamMap): RFormula = defaultCopy(extra)
 
-  override def toString: String = s"RFormula(${get(formula)})"
+  override def toString: String = s"RFormula(${get(formula)}) (uid=$uid)"
 }
 
 /**
@@ -171,7 +171,7 @@ class RFormulaModel private[feature](
   override def copy(extra: ParamMap): RFormulaModel = copyValues(
     new RFormulaModel(uid, resolvedFormula, pipelineModel))
 
-  override def toString: String = s"RFormulaModel(${resolvedFormula})"
+  override def toString: String = s"RFormulaModel(${resolvedFormula}) (uid=$uid)"
 
   private def transformLabel(dataset: DataFrame): DataFrame = {
     val labelName = resolvedFormula.label
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index a2bcd67401d08..d9a244bea28d2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -118,7 +118,7 @@ final class DecisionTreeRegressionModel private[ml] (
   }
 
   override def toString: String = {
-    s"DecisionTreeRegressionModel of depth $depth with $numNodes nodes"
+    s"DecisionTreeRegressionModel (uid=$uid) of depth $depth with $numNodes nodes"
   }
 
   /** Convert to a model in the old API */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index b66e61f37dd5e..d841ecb9e58d6 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -189,7 +189,7 @@ final class GBTRegressionModel(
   }
 
   override def toString: String = {
-    s"GBTRegressionModel with $numTrees trees"
+    s"GBTRegressionModel (uid=$uid) with $numTrees trees"
   }
 
   /** (private[ml]) Convert to a model in the old API */
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 2f36da371f577..ddb7214416a69 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -155,7 +155,7 @@ final class RandomForestRegressionModel private[ml] (
   }
 
   override def toString: String = {
-    s"RandomForestRegressionModel with $numTrees trees"
+    s"RandomForestRegressionModel (uid=$uid) with $numTrees trees"
   }
 
   /**

From 32407bfd2bdbf84d65cacfa7554dae6a2332bc37 Mon Sep 17 00:00:00 2001
From: Wenchen Fan <cloud0fan@outlook.com>
Date: Mon, 14 Sep 2015 11:51:39 -0700
Subject: [PATCH 610/633] [SPARK-9899] [SQL] log warning for direct output
 committer with speculation enabled

This is a follow-up of https://github.com/apache/spark/pull/8317.

When speculation is enabled, there may be multiply tasks writing to the same path. Generally it's OK as we will write to a temporary directory first and only one task can commit the temporary directory to target path.

However, when we use direct output committer, tasks will write data to target path directly without temporary directory. This causes problems like corrupted data. Please see [PR comment](https://github.com/apache/spark/pull/8191#issuecomment-131598385) for more details.

Unfortunately, we don't have a simple flag to tell if a output committer will write to temporary directory or not, so for safety, we have to disable any customized output committer when `speculation` is true.

Author: Wenchen Fan <cloud0fan@outlook.com>

Closes #8687 from cloud-fan/direct-committer.
---
 .../apache/spark/rdd/PairRDDFunctions.scala   | 44 ++++++++++++++++---
 .../hive/execution/InsertIntoHiveTable.scala  | 17 ++++++-
 .../spark/sql/hive/hiveWriterContainers.scala |  1 -
 3 files changed, 53 insertions(+), 9 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
index 199d79b811d65..a981b63942e6d 100644
--- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
+++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala
@@ -1018,6 +1018,11 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   /**
    * Output the RDD to any Hadoop-supported file system, using a Hadoop `OutputFormat` class
    * supporting the key and value types K and V in this RDD.
+   *
+   * Note that, we should make sure our tasks are idempotent when speculation is enabled, i.e. do
+   * not use output committer that writes data directly.
+   * There is an example in https://issues.apache.org/jira/browse/SPARK-10063 to show the bad
+   * result of using direct output committer with speculation enabled.
    */
   def saveAsHadoopFile(
       path: String,
@@ -1030,10 +1035,7 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val hadoopConf = conf
     hadoopConf.setOutputKeyClass(keyClass)
     hadoopConf.setOutputValueClass(valueClass)
-    // Doesn't work in Scala 2.9 due to what may be a generics bug
-    // TODO: Should we uncomment this for Scala 2.10?
-    // conf.setOutputFormat(outputFormatClass)
-    hadoopConf.set("mapred.output.format.class", outputFormatClass.getName)
+    conf.setOutputFormat(outputFormatClass)
     for (c <- codec) {
       hadoopConf.setCompressMapOutput(true)
       hadoopConf.set("mapred.output.compress", "true")
@@ -1047,6 +1049,19 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
       hadoopConf.setOutputCommitter(classOf[FileOutputCommitter])
     }
 
+    // When speculation is on and output committer class name contains "Direct", we should warn
+    // users that they may loss data if they are using a direct output committer.
+    val speculationEnabled = self.conf.getBoolean("spark.speculation", false)
+    val outputCommitterClass = hadoopConf.get("mapred.output.committer.class", "")
+    if (speculationEnabled && outputCommitterClass.contains("Direct")) {
+      val warningMessage =
+        s"$outputCommitterClass may be an output committer that writes data directly to " +
+          "the final location. Because speculation is enabled, this output committer may " +
+          "cause data loss (see the case in SPARK-10063). If possible, please use a output " +
+          "committer that does not have this behavior (e.g. FileOutputCommitter)."
+      logWarning(warningMessage)
+    }
+
     FileOutputFormat.setOutputPath(hadoopConf,
       SparkHadoopWriter.createPathFromString(path, hadoopConf))
     saveAsHadoopDataset(hadoopConf)
@@ -1057,6 +1072,11 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
    * Configuration object for that storage system. The Conf should set an OutputFormat and any
    * output paths required (e.g. a table name to write to) in the same way as it would be
    * configured for a Hadoop MapReduce job.
+   *
+   * Note that, we should make sure our tasks are idempotent when speculation is enabled, i.e. do
+   * not use output committer that writes data directly.
+   * There is an example in https://issues.apache.org/jira/browse/SPARK-10063 to show the bad
+   * result of using direct output committer with speculation enabled.
    */
   def saveAsNewAPIHadoopDataset(conf: Configuration): Unit = self.withScope {
     // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
@@ -1115,6 +1135,20 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     val jobAttemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = true, 0, 0)
     val jobTaskContext = newTaskAttemptContext(wrappedConf.value, jobAttemptId)
     val jobCommitter = jobFormat.getOutputCommitter(jobTaskContext)
+
+    // When speculation is on and output committer class name contains "Direct", we should warn
+    // users that they may loss data if they are using a direct output committer.
+    val speculationEnabled = self.conf.getBoolean("spark.speculation", false)
+    val outputCommitterClass = jobCommitter.getClass.getSimpleName
+    if (speculationEnabled && outputCommitterClass.contains("Direct")) {
+      val warningMessage =
+        s"$outputCommitterClass may be an output committer that writes data directly to " +
+          "the final location. Because speculation is enabled, this output committer may " +
+          "cause data loss (see the case in SPARK-10063). If possible, please use a output " +
+          "committer that does not have this behavior (e.g. FileOutputCommitter)."
+      logWarning(warningMessage)
+    }
+
     jobCommitter.setupJob(jobTaskContext)
     self.context.runJob(self, writeShard)
     jobCommitter.commitJob(jobTaskContext)
@@ -1129,7 +1163,6 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
   def saveAsHadoopDataset(conf: JobConf): Unit = self.withScope {
     // Rename this as hadoopConf internally to avoid shadowing (see SPARK-2038).
     val hadoopConf = conf
-    val wrappedConf = new SerializableConfiguration(hadoopConf)
     val outputFormatInstance = hadoopConf.getOutputFormat
     val keyClass = hadoopConf.getOutputKeyClass
     val valueClass = hadoopConf.getOutputValueClass
@@ -1157,7 +1190,6 @@ class PairRDDFunctions[K, V](self: RDD[(K, V)])
     writer.preSetup()
 
     val writeToFile = (context: TaskContext, iter: Iterator[(K, V)]) => {
-      val config = wrappedConf.value
       // Hadoop wants a 32-bit task attempt ID, so if ours is bigger than Int.MaxValue, roll it
       // around by taking a mod. We expect that no task will be attempted 2 billion times.
       val taskAttemptId = (context.taskAttemptId % Int.MaxValue).toInt
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
index 58f7fa640e8a9..0c700bdb370ac 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/InsertIntoHiveTable.scala
@@ -28,7 +28,7 @@ import org.apache.hadoop.hive.ql.{Context, ErrorMsg}
 import org.apache.hadoop.hive.serde2.Serializer
 import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils.ObjectInspectorCopyOption
 import org.apache.hadoop.hive.serde2.objectinspector._
-import org.apache.hadoop.mapred.{FileOutputFormat, JobConf}
+import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf}
 
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.Row
@@ -62,7 +62,7 @@ case class InsertIntoHiveTable(
 
   def output: Seq[Attribute] = Seq.empty
 
-  def saveAsHiveFile(
+  private def saveAsHiveFile(
       rdd: RDD[InternalRow],
       valueClass: Class[_],
       fileSinkConf: FileSinkDesc,
@@ -178,6 +178,19 @@ case class InsertIntoHiveTable(
     val jobConf = new JobConf(sc.hiveconf)
     val jobConfSer = new SerializableJobConf(jobConf)
 
+    // When speculation is on and output committer class name contains "Direct", we should warn
+    // users that they may loss data if they are using a direct output committer.
+    val speculationEnabled = sqlContext.sparkContext.conf.getBoolean("spark.speculation", false)
+    val outputCommitterClass = jobConf.get("mapred.output.committer.class", "")
+    if (speculationEnabled && outputCommitterClass.contains("Direct")) {
+      val warningMessage =
+        s"$outputCommitterClass may be an output committer that writes data directly to " +
+          "the final location. Because speculation is enabled, this output committer may " +
+          "cause data loss (see the case in SPARK-10063). If possible, please use a output " +
+          "committer that does not have this behavior (e.g. FileOutputCommitter)."
+      logWarning(warningMessage)
+    }
+
     val writerContainer = if (numDynamicPartitions > 0) {
       val dynamicPartColNames = partitionColumnNames.takeRight(numDynamicPartitions)
       new SparkHiveDynamicPartitionWriterContainer(jobConf, fileSinkConf, dynamicPartColNames)
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
index 29a6f08f40728..4ca8042d22367 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/hiveWriterContainers.scala
@@ -32,7 +32,6 @@ import org.apache.hadoop.mapred._
 import org.apache.hadoop.hive.common.FileUtils
 
 import org.apache.spark.mapred.SparkHadoopMapRedUtil
-import org.apache.spark.sql.Row
 import org.apache.spark.{Logging, SerializableWritable, SparkHadoopWriter}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.util.DateTimeUtils

From cf2821ef5fd9965eb6256e8e8b3f1e00c0788098 Mon Sep 17 00:00:00 2001
From: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Date: Mon, 14 Sep 2015 12:06:23 -0700
Subject: [PATCH 611/633] [SPARK-10584] [DOC] [SQL] Documentation about
 spark.sql.hive.metastore.version is wrong.

The default value of hive metastore version is 1.2.1 but the documentation says the value of `spark.sql.hive.metastore.version` is 0.13.1.
Also, we cannot get the default value by `sqlContext.getConf("spark.sql.hive.metastore.version")`.

Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>

Closes #8739 from sarutak/SPARK-10584.
---
 docs/sql-programming-guide.md                         |  2 +-
 .../scala/org/apache/spark/sql/hive/HiveContext.scala | 11 +++++++----
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 6a1b0fbfa1eb3..a0b911d207243 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -1687,7 +1687,7 @@ The following options can be used to configure the version of Hive that is used
   <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
   <tr>
     <td><code>spark.sql.hive.metastore.version</code></td>
-    <td><code>0.13.1</code></td>
+    <td><code>1.2.1</code></td>
     <td>
       Version of the Hive metastore. Available
       options are <code>0.12.0</code> through <code>1.2.1</code>.
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
index 2e791cea96b41..d37ba5ddc2d80 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala
@@ -111,8 +111,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
    * this does not necessarily need to be the same version of Hive that is used internally by
    * Spark SQL for execution.
    */
-  protected[hive] def hiveMetastoreVersion: String =
-    getConf(HIVE_METASTORE_VERSION, hiveExecutionVersion)
+  protected[hive] def hiveMetastoreVersion: String = getConf(HIVE_METASTORE_VERSION)
 
   /**
    * The location of the jars that should be used to instantiate the HiveMetastoreClient.  This
@@ -202,7 +201,7 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) with Logging {
           "Builtin jars can only be used when hive execution version == hive metastore version. " +
           s"Execution: ${hiveExecutionVersion} != Metastore: ${hiveMetastoreVersion}. " +
           "Specify a vaild path to the correct hive jars using $HIVE_METASTORE_JARS " +
-          s"or change $HIVE_METASTORE_VERSION to $hiveExecutionVersion.")
+          s"or change ${HIVE_METASTORE_VERSION.key} to $hiveExecutionVersion.")
       }
 
       // We recursively find all jars in the class loader chain,
@@ -606,7 +605,11 @@ private[hive] object HiveContext {
   /** The version of hive used internally by Spark SQL. */
   val hiveExecutionVersion: String = "1.2.1"
 
-  val HIVE_METASTORE_VERSION: String = "spark.sql.hive.metastore.version"
+  val HIVE_METASTORE_VERSION = stringConf("spark.sql.hive.metastore.version",
+    defaultValue = Some(hiveExecutionVersion),
+    doc = "Version of the Hive metastore. Available options are " +
+        s"<code>0.12.0</code> through <code>$hiveExecutionVersion</code>.")
+
   val HIVE_METASTORE_JARS = stringConf("spark.sql.hive.metastore.jars",
     defaultValue = Some("builtin"),
     doc = s"""

From ce6f3f163bc667cb5da9ab4331c8bad10cc0d701 Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 14 Sep 2015 12:08:52 -0700
Subject: [PATCH 612/633] [SPARK-10194] [MLLIB] [PYSPARK] SGD algorithms need
 convergenceTol parameter in Python

[SPARK-3382](https://issues.apache.org/jira/browse/SPARK-3382) added a ```convergenceTol``` parameter for GradientDescent-based methods in Scala. We need that parameter in Python; otherwise, Python users will not be able to adjust that behavior (or even reproduce behavior from previous releases since the default changed).

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8457 from yanboliang/spark-10194.
---
 .../mllib/api/python/PythonMLLibAPI.scala     | 20 +++++++++---
 python/pyspark/mllib/classification.py        | 17 +++++++---
 python/pyspark/mllib/regression.py            | 32 ++++++++++++-------
 3 files changed, 48 insertions(+), 21 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index f585aacd452e0..69ce7f50709a1 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -132,7 +132,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       regParam: Double,
       regType: String,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val lrAlg = new LinearRegressionWithSGD()
     lrAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -141,6 +142,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     lrAlg.optimizer.setUpdater(getUpdaterFromString(regType))
     trainRegressionModel(
       lrAlg,
@@ -159,7 +161,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       miniBatchFraction: Double,
       initialWeights: Vector,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val lassoAlg = new LassoWithSGD()
     lassoAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -168,6 +171,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     trainRegressionModel(
       lassoAlg,
       data,
@@ -185,7 +189,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       miniBatchFraction: Double,
       initialWeights: Vector,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val ridgeAlg = new RidgeRegressionWithSGD()
     ridgeAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -194,6 +199,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     trainRegressionModel(
       ridgeAlg,
       data,
@@ -212,7 +218,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       initialWeights: Vector,
       regType: String,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val SVMAlg = new SVMWithSGD()
     SVMAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -221,6 +228,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     SVMAlg.optimizer.setUpdater(getUpdaterFromString(regType))
     trainRegressionModel(
       SVMAlg,
@@ -240,7 +248,8 @@ private[python] class PythonMLLibAPI extends Serializable {
       regParam: Double,
       regType: String,
       intercept: Boolean,
-      validateData: Boolean): JList[Object] = {
+      validateData: Boolean,
+      convergenceTol: Double): JList[Object] = {
     val LogRegAlg = new LogisticRegressionWithSGD()
     LogRegAlg.setIntercept(intercept)
       .setValidateData(validateData)
@@ -249,6 +258,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       .setRegParam(regParam)
       .setStepSize(stepSize)
       .setMiniBatchFraction(miniBatchFraction)
+      .setConvergenceTol(convergenceTol)
     LogRegAlg.optimizer.setUpdater(getUpdaterFromString(regType))
     trainRegressionModel(
       LogRegAlg,
diff --git a/python/pyspark/mllib/classification.py b/python/pyspark/mllib/classification.py
index 8f27c446a66e8..cb4ee83678081 100644
--- a/python/pyspark/mllib/classification.py
+++ b/python/pyspark/mllib/classification.py
@@ -241,7 +241,7 @@ class LogisticRegressionWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
               initialWeights=None, regParam=0.01, regType="l2", intercept=False,
-              validateData=True):
+              validateData=True, convergenceTol=0.001):
         """
         Train a logistic regression model on the given data.
 
@@ -274,11 +274,13 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
         :param validateData:      Boolean parameter which indicates if
                                   the algorithm should validate data
                                   before training. (default: True)
+        :param convergenceTol:    A condition which decides iteration termination.
+                                  (default: 0.001)
         """
         def train(rdd, i):
             return callMLlibFunc("trainLogisticRegressionModelWithSGD", rdd, int(iterations),
                                  float(step), float(miniBatchFraction), i, float(regParam), regType,
-                                 bool(intercept), bool(validateData))
+                                 bool(intercept), bool(validateData), float(convergenceTol))
 
         return _regression_train_wrapper(train, LogisticRegressionModel, data, initialWeights)
 
@@ -439,7 +441,7 @@ class SVMWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, regParam=0.01,
               miniBatchFraction=1.0, initialWeights=None, regType="l2",
-              intercept=False, validateData=True):
+              intercept=False, validateData=True, convergenceTol=0.001):
         """
         Train a support vector machine on the given data.
 
@@ -472,11 +474,13 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01,
         :param validateData:      Boolean parameter which indicates if
                                   the algorithm should validate data
                                   before training. (default: True)
+        :param convergenceTol:    A condition which decides iteration termination.
+                                  (default: 0.001)
         """
         def train(rdd, i):
             return callMLlibFunc("trainSVMModelWithSGD", rdd, int(iterations), float(step),
                                  float(regParam), float(miniBatchFraction), i, regType,
-                                 bool(intercept), bool(validateData))
+                                 bool(intercept), bool(validateData), float(convergenceTol))
 
         return _regression_train_wrapper(train, SVMModel, data, initialWeights)
 
@@ -600,12 +604,15 @@ class StreamingLogisticRegressionWithSGD(StreamingLinearAlgorithm):
     :param miniBatchFraction: Fraction of data on which SGD is run for each
                               iteration.
     :param regParam: L2 Regularization parameter.
+    :param convergenceTol: A condition which decides iteration termination.
     """
-    def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.01):
+    def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, regParam=0.01,
+                 convergenceTol=0.001):
         self.stepSize = stepSize
         self.numIterations = numIterations
         self.regParam = regParam
         self.miniBatchFraction = miniBatchFraction
+        self.convergenceTol = convergenceTol
         self._model = None
         super(StreamingLogisticRegressionWithSGD, self).__init__(
             model=self._model)
diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py
index 41946e3674fbe..256b7537fef6b 100644
--- a/python/pyspark/mllib/regression.py
+++ b/python/pyspark/mllib/regression.py
@@ -28,7 +28,8 @@
            'LinearRegressionModel', 'LinearRegressionWithSGD',
            'RidgeRegressionModel', 'RidgeRegressionWithSGD',
            'LassoModel', 'LassoWithSGD', 'IsotonicRegressionModel',
-           'IsotonicRegression']
+           'IsotonicRegression', 'StreamingLinearAlgorithm',
+           'StreamingLinearRegressionWithSGD']
 
 
 class LabeledPoint(object):
@@ -202,7 +203,7 @@ class LinearRegressionWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
               initialWeights=None, regParam=0.0, regType=None, intercept=False,
-              validateData=True):
+              validateData=True, convergenceTol=0.001):
         """
         Train a linear regression model using Stochastic Gradient
         Descent (SGD).
@@ -244,11 +245,14 @@ def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
         :param validateData:      Boolean parameter which indicates if
                                   the algorithm should validate data
                                   before training. (default: True)
+        :param convergenceTol:    A condition which decides iteration termination.
+                                  (default: 0.001)
         """
         def train(rdd, i):
             return callMLlibFunc("trainLinearRegressionModelWithSGD", rdd, int(iterations),
                                  float(step), float(miniBatchFraction), i, float(regParam),
-                                 regType, bool(intercept), bool(validateData))
+                                 regType, bool(intercept), bool(validateData),
+                                 float(convergenceTol))
 
         return _regression_train_wrapper(train, LinearRegressionModel, data, initialWeights)
 
@@ -330,7 +334,7 @@ class LassoWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, regParam=0.01,
               miniBatchFraction=1.0, initialWeights=None, intercept=False,
-              validateData=True):
+              validateData=True, convergenceTol=0.001):
         """
         Train a regression model with L1-regularization using
         Stochastic Gradient Descent.
@@ -362,11 +366,13 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01,
         :param validateData:      Boolean parameter which indicates if
                                   the algorithm should validate data
                                   before training. (default: True)
+        :param convergenceTol:    A condition which decides iteration termination.
+                                  (default: 0.001)
         """
         def train(rdd, i):
             return callMLlibFunc("trainLassoModelWithSGD", rdd, int(iterations), float(step),
                                  float(regParam), float(miniBatchFraction), i, bool(intercept),
-                                 bool(validateData))
+                                 bool(validateData), float(convergenceTol))
 
         return _regression_train_wrapper(train, LassoModel, data, initialWeights)
 
@@ -449,7 +455,7 @@ class RidgeRegressionWithSGD(object):
     @classmethod
     def train(cls, data, iterations=100, step=1.0, regParam=0.01,
               miniBatchFraction=1.0, initialWeights=None, intercept=False,
-              validateData=True):
+              validateData=True, convergenceTol=0.001):
         """
         Train a regression model with L2-regularization using
         Stochastic Gradient Descent.
@@ -481,11 +487,13 @@ def train(cls, data, iterations=100, step=1.0, regParam=0.01,
         :param validateData:      Boolean parameter which indicates if
                                   the algorithm should validate data
                                   before training. (default: True)
+        :param convergenceTol:    A condition which decides iteration termination.
+                                  (default: 0.001)
         """
         def train(rdd, i):
             return callMLlibFunc("trainRidgeModelWithSGD", rdd, int(iterations), float(step),
                                  float(regParam), float(miniBatchFraction), i, bool(intercept),
-                                 bool(validateData))
+                                 bool(validateData), float(convergenceTol))
 
         return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights)
 
@@ -636,15 +644,17 @@ class StreamingLinearRegressionWithSGD(StreamingLinearAlgorithm):
     After training on a batch of data, the weights obtained at the end of
     training are used as initial weights for the next batch.
 
-    :param: stepSize Step size for each iteration of gradient descent.
-    :param: numIterations Total number of iterations run.
-    :param: miniBatchFraction Fraction of data on which SGD is run for each
+    :param stepSize: Step size for each iteration of gradient descent.
+    :param numIterations: Total number of iterations run.
+    :param miniBatchFraction: Fraction of data on which SGD is run for each
                               iteration.
+    :param convergenceTol: A condition which decides iteration termination.
     """
-    def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0):
+    def __init__(self, stepSize=0.1, numIterations=50, miniBatchFraction=1.0, convergenceTol=0.001):
         self.stepSize = stepSize
         self.numIterations = numIterations
         self.miniBatchFraction = miniBatchFraction
+        self.convergenceTol = convergenceTol
         self._model = None
         super(StreamingLinearRegressionWithSGD, self).__init__(
             model=self._model)

From 8a634e9bcc671167613fb575c6c0c054fb4b3479 Mon Sep 17 00:00:00 2001
From: Nick Pritchard <nicholas.pritchard@falkonry.com>
Date: Mon, 14 Sep 2015 13:27:45 -0700
Subject: [PATCH 613/633] [SPARK-10573] [ML] IndexToString output schema should
 be StringType

Fixes bug where IndexToString output schema was DoubleType. Correct me if I'm wrong, but it doesn't seem like the output needs to have any "ML Attribute" metadata.

Author: Nick Pritchard <nicholas.pritchard@falkonry.com>

Closes #8751 from pnpritchard/SPARK-10573.
---
 .../scala/org/apache/spark/ml/feature/StringIndexer.scala | 5 ++---
 .../org/apache/spark/ml/feature/StringIndexerSuite.scala  | 8 ++++++++
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
index 3a4ab9a857648..2b1592930e77b 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/StringIndexer.scala
@@ -27,7 +27,7 @@ import org.apache.spark.ml.Transformer
 import org.apache.spark.ml.util.Identifiable
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.{DoubleType, NumericType, StringType, StructType}
+import org.apache.spark.sql.types._
 import org.apache.spark.util.collection.OpenHashMap
 
 /**
@@ -229,8 +229,7 @@ class IndexToString private[ml] (
     val outputColName = $(outputCol)
     require(inputFields.forall(_.name != outputColName),
       s"Output column $outputColName already exists.")
-    val attr = NominalAttribute.defaultAttr.withName($(outputCol))
-    val outputFields = inputFields :+ attr.toStructField()
+    val outputFields = inputFields :+ StructField($(outputCol), StringType)
     StructType(outputFields)
   }
 
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
index 05e05bdc64bb1..ddcdb5f4212be 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/StringIndexerSuite.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.ml.feature
 
+import org.apache.spark.sql.types.{StringType, StructType, StructField, DoubleType}
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.ml.attribute.{Attribute, NominalAttribute}
 import org.apache.spark.ml.param.ParamsSuite
@@ -165,4 +166,11 @@ class StringIndexerSuite extends SparkFunSuite with MLlibTestSparkContext {
         assert(a === b)
     }
   }
+
+  test("IndexToString.transformSchema (SPARK-10573)") {
+    val idxToStr = new IndexToString().setInputCol("input").setOutputCol("output")
+    val inSchema = StructType(Seq(StructField("input", DoubleType)))
+    val outSchema = idxToStr.transformSchema(inSchema)
+    assert(outSchema("output").dataType === StringType)
+  }
 }

From 7e32387ae6303fd1cd32389d47df87170b841c67 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 14 Sep 2015 14:10:54 -0700
Subject: [PATCH 614/633] [SPARK-10522] [SQL] Nanoseconds of Timestamp in
 Parquet should be positive

Or Hive can't read it back correctly.

Thanks vanzin for report this.

Author: Davies Liu <davies@databricks.com>

Closes #8674 from davies/positive_nano.
---
 .../spark/sql/catalyst/util/DateTimeUtils.scala | 12 +++++++-----
 .../sql/catalyst/util/DateTimeUtilsSuite.scala  | 17 ++++++++---------
 2 files changed, 15 insertions(+), 14 deletions(-)

diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
index d652fce3fd9b6..687ca000d12bb 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
@@ -42,6 +42,7 @@ object DateTimeUtils {
   final val SECONDS_PER_DAY = 60 * 60 * 24L
   final val MICROS_PER_SECOND = 1000L * 1000L
   final val NANOS_PER_SECOND = MICROS_PER_SECOND * 1000L
+  final val MICROS_PER_DAY = MICROS_PER_SECOND * SECONDS_PER_DAY
 
   final val MILLIS_PER_DAY = SECONDS_PER_DAY * 1000L
 
@@ -190,13 +191,14 @@ object DateTimeUtils {
 
   /**
    * Returns Julian day and nanoseconds in a day from the number of microseconds
+   *
+   * Note: support timestamp since 4717 BC (without negative nanoseconds, compatible with Hive).
    */
   def toJulianDay(us: SQLTimestamp): (Int, Long) = {
-    val seconds = us / MICROS_PER_SECOND
-    val day = seconds / SECONDS_PER_DAY + JULIAN_DAY_OF_EPOCH
-    val secondsInDay = seconds % SECONDS_PER_DAY
-    val nanos = (us % MICROS_PER_SECOND) * 1000L
-    (day.toInt, secondsInDay * NANOS_PER_SECOND + nanos)
+    val julian_us = us + JULIAN_DAY_OF_EPOCH * MICROS_PER_DAY
+    val day = julian_us / MICROS_PER_DAY
+    val micros = julian_us % MICROS_PER_DAY
+    (day.toInt, micros * 1000L)
   }
 
   /**
diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
index 1596bb79fa94b..6b9a11f0ff743 100644
--- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
+++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/util/DateTimeUtilsSuite.scala
@@ -52,15 +52,14 @@ class DateTimeUtilsSuite extends SparkFunSuite {
     assert(ns === 0)
     assert(fromJulianDay(d, ns) == 0L)
 
-    val t = Timestamp.valueOf("2015-06-11 10:10:10.100")
-    val (d1, ns1) = toJulianDay(fromJavaTimestamp(t))
-    val t1 = toJavaTimestamp(fromJulianDay(d1, ns1))
-    assert(t.equals(t1))
-
-    val t2 = Timestamp.valueOf("2015-06-11 20:10:10.100")
-    val (d2, ns2) = toJulianDay(fromJavaTimestamp(t2))
-    val t22 = toJavaTimestamp(fromJulianDay(d2, ns2))
-    assert(t2.equals(t22))
+    Seq(Timestamp.valueOf("2015-06-11 10:10:10.100"),
+      Timestamp.valueOf("2015-06-11 20:10:10.100"),
+      Timestamp.valueOf("1900-06-11 20:10:10.100")).foreach { t =>
+      val (d, ns) = toJulianDay(fromJavaTimestamp(t))
+      assert(ns > 0)
+      val t1 = toJavaTimestamp(fromJulianDay(d, ns))
+      assert(t.equals(t1))
+    }
   }
 
   test("SPARK-6785: java date conversion before and after epoch") {

From 64f04154e3078ec7340da97e3c2b07cf24e89098 Mon Sep 17 00:00:00 2001
From: Edoardo Vacchi <uncommonnonsense@gmail.com>
Date: Mon, 14 Sep 2015 14:56:04 -0700
Subject: [PATCH 615/633] [SPARK-6981] [SQL] Factor out SparkPlanner and
 QueryExecution from SQLContext

Alternative to PR #6122; in this case the refactored out classes are replaced by inner classes with the same name for backwards binary compatibility

   * process in a lighter-weight, backwards-compatible way

Author: Edoardo Vacchi <uncommonnonsense@gmail.com>

Closes #6356 from evacchi/sqlctx-refactoring-lite.
---
 .../org/apache/spark/sql/DataFrame.scala      |   4 +-
 .../org/apache/spark/sql/SQLContext.scala     | 138 ++----------------
 .../spark/sql/execution/QueryExecution.scala  |  85 +++++++++++
 .../spark/sql/execution/SQLExecution.scala    |   2 +-
 .../spark/sql/execution/SparkPlanner.scala    |  92 ++++++++++++
 .../spark/sql/execution/SparkStrategies.scala |   2 +-
 6 files changed, 195 insertions(+), 128 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanner.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 1a687b2374f14..3e61123c145cd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -37,7 +37,7 @@ import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.plans.{Inner, JoinType}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, ScalaReflection, SqlParser}
-import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, FileRelation, LogicalRDD, SQLExecution}
+import org.apache.spark.sql.execution.{EvaluatePython, ExplainCommand, FileRelation, LogicalRDD, QueryExecution, SQLExecution}
 import org.apache.spark.sql.execution.datasources.{CreateTableUsingAsSelect, LogicalRelation}
 import org.apache.spark.sql.execution.datasources.json.JacksonGenerator
 import org.apache.spark.sql.sources.HadoopFsRelation
@@ -114,7 +114,7 @@ private[sql] object DataFrame {
 @Experimental
 class DataFrame private[sql](
     @transient val sqlContext: SQLContext,
-    @DeveloperApi @transient val queryExecution: SQLContext#QueryExecution) extends Serializable {
+    @DeveloperApi @transient val queryExecution: QueryExecution) extends Serializable {
 
   // Note for Spark contributors: if adding or updating any action in `DataFrame`, please make sure
   // you wrap it with `withNewExecutionId` if this actions doesn't call other action.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
index 4e8414af50b44..e3fdd782e6ff6 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala
@@ -38,6 +38,10 @@ import org.apache.spark.sql.catalyst.optimizer.{DefaultOptimizer, Optimizer}
 import org.apache.spark.sql.catalyst.plans.logical.{LocalRelation, LogicalPlan}
 import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.apache.spark.sql.catalyst.{InternalRow, ParserDialect, _}
+import org.apache.spark.sql.execution.{Filter, _}
+import org.apache.spark.sql.{execution => sparkexecution}
+import org.apache.spark.sql.execution._
+import org.apache.spark.sql.sources._
 import org.apache.spark.sql.execution._
 import org.apache.spark.sql.execution.datasources._
 import org.apache.spark.sql.execution.ui.{SQLListener, SQLTab}
@@ -188,9 +192,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
 
   protected[sql] def parseSql(sql: String): LogicalPlan = ddlParser.parse(sql, false)
 
-  protected[sql] def executeSql(sql: String): this.QueryExecution = executePlan(parseSql(sql))
+  protected[sql] def executeSql(sql: String):
+    org.apache.spark.sql.execution.QueryExecution = executePlan(parseSql(sql))
 
-  protected[sql] def executePlan(plan: LogicalPlan) = new this.QueryExecution(plan)
+  protected[sql] def executePlan(plan: LogicalPlan) =
+    new sparkexecution.QueryExecution(this, plan)
 
   @transient
   protected[sql] val tlSession = new ThreadLocal[SQLSession]() {
@@ -781,77 +787,11 @@ class SQLContext(@transient val sparkContext: SparkContext)
     }.toArray
   }
 
-  protected[sql] class SparkPlanner extends SparkStrategies {
-    val sparkContext: SparkContext = self.sparkContext
-
-    val sqlContext: SQLContext = self
-
-    def codegenEnabled: Boolean = self.conf.codegenEnabled
-
-    def unsafeEnabled: Boolean = self.conf.unsafeEnabled
-
-    def numPartitions: Int = self.conf.numShufflePartitions
-
-    def strategies: Seq[Strategy] =
-      experimental.extraStrategies ++ (
-      DataSourceStrategy ::
-      DDLStrategy ::
-      TakeOrderedAndProject ::
-      HashAggregation ::
-      Aggregation ::
-      LeftSemiJoin ::
-      EquiJoinSelection ::
-      InMemoryScans ::
-      BasicOperators ::
-      CartesianProduct ::
-      BroadcastNestedLoopJoin :: Nil)
-
-    /**
-     * Used to build table scan operators where complex projection and filtering are done using
-     * separate physical operators.  This function returns the given scan operator with Project and
-     * Filter nodes added only when needed.  For example, a Project operator is only used when the
-     * final desired output requires complex expressions to be evaluated or when columns can be
-     * further eliminated out after filtering has been done.
-     *
-     * The `prunePushedDownFilters` parameter is used to remove those filters that can be optimized
-     * away by the filter pushdown optimization.
-     *
-     * The required attributes for both filtering and expression evaluation are passed to the
-     * provided `scanBuilder` function so that it can avoid unnecessary column materialization.
-     */
-    def pruneFilterProject(
-        projectList: Seq[NamedExpression],
-        filterPredicates: Seq[Expression],
-        prunePushedDownFilters: Seq[Expression] => Seq[Expression],
-        scanBuilder: Seq[Attribute] => SparkPlan): SparkPlan = {
-
-      val projectSet = AttributeSet(projectList.flatMap(_.references))
-      val filterSet = AttributeSet(filterPredicates.flatMap(_.references))
-      val filterCondition =
-        prunePushedDownFilters(filterPredicates).reduceLeftOption(catalyst.expressions.And)
-
-      // Right now we still use a projection even if the only evaluation is applying an alias
-      // to a column.  Since this is a no-op, it could be avoided. However, using this
-      // optimization with the current implementation would change the output schema.
-      // TODO: Decouple final output schema from expression evaluation so this copy can be
-      // avoided safely.
-
-      if (AttributeSet(projectList.map(_.toAttribute)) == projectSet &&
-          filterSet.subsetOf(projectSet)) {
-        // When it is possible to just use column pruning to get the right projection and
-        // when the columns of this projection are enough to evaluate all filter conditions,
-        // just do a scan followed by a filter, with no extra project.
-        val scan = scanBuilder(projectList.asInstanceOf[Seq[Attribute]])
-        filterCondition.map(Filter(_, scan)).getOrElse(scan)
-      } else {
-        val scan = scanBuilder((projectSet ++ filterSet).toSeq)
-        Project(projectList, filterCondition.map(Filter(_, scan)).getOrElse(scan))
-      }
-    }
-  }
+  @deprecated("use org.apache.spark.sql.SparkPlanner", "1.6.0")
+  protected[sql] class SparkPlanner extends sparkexecution.SparkPlanner(this)
 
   @transient
-  protected[sql] val planner = new SparkPlanner
+  protected[sql] val planner: sparkexecution.SparkPlanner = new sparkexecution.SparkPlanner(this)
 
   @transient
   protected[sql] lazy val emptyResult = sparkContext.parallelize(Seq.empty[InternalRow], 1)
@@ -898,59 +838,9 @@ class SQLContext(@transient val sparkContext: SparkContext)
     protected[sql] lazy val conf: SQLConf = new SQLConf
   }
 
-  /**
-   * :: DeveloperApi ::
-   * The primary workflow for executing relational queries using Spark.  Designed to allow easy
-   * access to the intermediate phases of query execution for developers.
-   */
-  @DeveloperApi
-  protected[sql] class QueryExecution(val logical: LogicalPlan) {
-    def assertAnalyzed(): Unit = analyzer.checkAnalysis(analyzed)
-
-    lazy val analyzed: LogicalPlan = analyzer.execute(logical)
-    lazy val withCachedData: LogicalPlan = {
-      assertAnalyzed()
-      cacheManager.useCachedData(analyzed)
-    }
-    lazy val optimizedPlan: LogicalPlan = optimizer.execute(withCachedData)
-
-    // TODO: Don't just pick the first one...
-    lazy val sparkPlan: SparkPlan = {
-      SparkPlan.currentContext.set(self)
-      planner.plan(optimizedPlan).next()
-    }
-    // executedPlan should not be used to initialize any SparkPlan. It should be
-    // only used for execution.
-    lazy val executedPlan: SparkPlan = prepareForExecution.execute(sparkPlan)
-
-    /** Internal version of the RDD. Avoids copies and has no schema */
-    lazy val toRdd: RDD[InternalRow] = executedPlan.execute()
-
-    protected def stringOrError[A](f: => A): String =
-      try f.toString catch { case e: Throwable => e.toString }
-
-    def simpleString: String =
-      s"""== Physical Plan ==
-         |${stringOrError(executedPlan)}
-      """.stripMargin.trim
-
-    override def toString: String = {
-      def output =
-        analyzed.output.map(o => s"${o.name}: ${o.dataType.simpleString}").mkString(", ")
-
-      s"""== Parsed Logical Plan ==
-         |${stringOrError(logical)}
-         |== Analyzed Logical Plan ==
-         |${stringOrError(output)}
-         |${stringOrError(analyzed)}
-         |== Optimized Logical Plan ==
-         |${stringOrError(optimizedPlan)}
-         |== Physical Plan ==
-         |${stringOrError(executedPlan)}
-         |Code Generation: ${stringOrError(executedPlan.codegenEnabled)}
-      """.stripMargin.trim
-    }
-  }
+  @deprecated("use org.apache.spark.sql.QueryExecution", "1.6.0")
+  protected[sql] class QueryExecution(logical: LogicalPlan)
+    extends sparkexecution.QueryExecution(this, logical)
 
   /**
    * Parses the data type in our internal string representation. The data type string should
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala
new file mode 100644
index 0000000000000..7bb4133a29059
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/QueryExecution.scala
@@ -0,0 +1,85 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.annotation.{Experimental, DeveloperApi}
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.catalyst.{InternalRow, optimizer}
+import org.apache.spark.sql.{SQLContext, Row}
+import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan
+
+/**
+ * :: DeveloperApi ::
+ * The primary workflow for executing relational queries using Spark.  Designed to allow easy
+ * access to the intermediate phases of query execution for developers.
+ */
+@DeveloperApi
+class QueryExecution(val sqlContext: SQLContext, val logical: LogicalPlan) {
+  val analyzer = sqlContext.analyzer
+  val optimizer = sqlContext.optimizer
+  val planner = sqlContext.planner
+  val cacheManager = sqlContext.cacheManager
+  val prepareForExecution = sqlContext.prepareForExecution
+
+  def assertAnalyzed(): Unit = analyzer.checkAnalysis(analyzed)
+
+  lazy val analyzed: LogicalPlan = analyzer.execute(logical)
+  lazy val withCachedData: LogicalPlan = {
+    assertAnalyzed()
+    cacheManager.useCachedData(analyzed)
+  }
+  lazy val optimizedPlan: LogicalPlan = optimizer.execute(withCachedData)
+
+  // TODO: Don't just pick the first one...
+  lazy val sparkPlan: SparkPlan = {
+    SparkPlan.currentContext.set(sqlContext)
+    planner.plan(optimizedPlan).next()
+  }
+  // executedPlan should not be used to initialize any SparkPlan. It should be
+  // only used for execution.
+  lazy val executedPlan: SparkPlan = prepareForExecution.execute(sparkPlan)
+
+  /** Internal version of the RDD. Avoids copies and has no schema */
+  lazy val toRdd: RDD[InternalRow] = executedPlan.execute()
+
+  protected def stringOrError[A](f: => A): String =
+    try f.toString catch { case e: Throwable => e.toString }
+
+  def simpleString: String =
+    s"""== Physical Plan ==
+       |${stringOrError(executedPlan)}
+      """.stripMargin.trim
+
+
+  override def toString: String = {
+    def output =
+      analyzed.output.map(o => s"${o.name}: ${o.dataType.simpleString}").mkString(", ")
+
+    s"""== Parsed Logical Plan ==
+       |${stringOrError(logical)}
+       |== Analyzed Logical Plan ==
+       |${stringOrError(output)}
+       |${stringOrError(analyzed)}
+       |== Optimized Logical Plan ==
+       |${stringOrError(optimizedPlan)}
+       |== Physical Plan ==
+       |${stringOrError(executedPlan)}
+       |Code Generation: ${stringOrError(executedPlan.codegenEnabled)}
+    """.stripMargin.trim
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
index cee58218a885b..1422e15549c94 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SQLExecution.scala
@@ -37,7 +37,7 @@ private[sql] object SQLExecution {
    * we can connect them with an execution.
    */
   def withNewExecutionId[T](
-      sqlContext: SQLContext, queryExecution: SQLContext#QueryExecution)(body: => T): T = {
+      sqlContext: SQLContext, queryExecution: QueryExecution)(body: => T): T = {
     val sc = sqlContext.sparkContext
     val oldExecutionId = sc.getLocalProperty(EXECUTION_ID_KEY)
     if (oldExecutionId == null) {
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanner.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanner.scala
new file mode 100644
index 0000000000000..b346f43faebe2
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkPlanner.scala
@@ -0,0 +1,92 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution
+
+import org.apache.spark.SparkContext
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.sql._
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.execution.datasources.DataSourceStrategy
+
+@Experimental
+class SparkPlanner(val sqlContext: SQLContext) extends SparkStrategies {
+  val sparkContext: SparkContext = sqlContext.sparkContext
+
+  def codegenEnabled: Boolean = sqlContext.conf.codegenEnabled
+
+  def unsafeEnabled: Boolean = sqlContext.conf.unsafeEnabled
+
+  def numPartitions: Int = sqlContext.conf.numShufflePartitions
+
+  def strategies: Seq[Strategy] =
+    sqlContext.experimental.extraStrategies ++ (
+      DataSourceStrategy ::
+      DDLStrategy ::
+      TakeOrderedAndProject ::
+      HashAggregation ::
+      Aggregation ::
+      LeftSemiJoin ::
+      EquiJoinSelection ::
+      InMemoryScans ::
+      BasicOperators ::
+      CartesianProduct ::
+      BroadcastNestedLoopJoin :: Nil)
+
+  /**
+   * Used to build table scan operators where complex projection and filtering are done using
+   * separate physical operators.  This function returns the given scan operator with Project and
+   * Filter nodes added only when needed.  For example, a Project operator is only used when the
+   * final desired output requires complex expressions to be evaluated or when columns can be
+   * further eliminated out after filtering has been done.
+   *
+   * The `prunePushedDownFilters` parameter is used to remove those filters that can be optimized
+   * away by the filter pushdown optimization.
+   *
+   * The required attributes for both filtering and expression evaluation are passed to the
+   * provided `scanBuilder` function so that it can avoid unnecessary column materialization.
+   */
+  def pruneFilterProject(
+      projectList: Seq[NamedExpression],
+      filterPredicates: Seq[Expression],
+      prunePushedDownFilters: Seq[Expression] => Seq[Expression],
+      scanBuilder: Seq[Attribute] => SparkPlan): SparkPlan = {
+
+    val projectSet = AttributeSet(projectList.flatMap(_.references))
+    val filterSet = AttributeSet(filterPredicates.flatMap(_.references))
+    val filterCondition =
+      prunePushedDownFilters(filterPredicates).reduceLeftOption(catalyst.expressions.And)
+
+    // Right now we still use a projection even if the only evaluation is applying an alias
+    // to a column.  Since this is a no-op, it could be avoided. However, using this
+    // optimization with the current implementation would change the output schema.
+    // TODO: Decouple final output schema from expression evaluation so this copy can be
+    // avoided safely.
+
+    if (AttributeSet(projectList.map(_.toAttribute)) == projectSet &&
+        filterSet.subsetOf(projectSet)) {
+      // When it is possible to just use column pruning to get the right projection and
+      // when the columns of this projection are enough to evaluate all filter conditions,
+      // just do a scan followed by a filter, with no extra project.
+      val scan = scanBuilder(projectList.asInstanceOf[Seq[Attribute]])
+      filterCondition.map(Filter(_, scan)).getOrElse(scan)
+    } else {
+      val scan = scanBuilder((projectSet ++ filterSet).toSeq)
+      Project(projectList, filterCondition.map(Filter(_, scan)).getOrElse(scan))
+    }
+  }
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
index 4572d5efc92bb..5e40d77689045 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala
@@ -31,7 +31,7 @@ import org.apache.spark.sql.types._
 import org.apache.spark.sql.{SQLContext, Strategy, execution}
 
 private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
-  self: SQLContext#SparkPlanner =>
+  self: SparkPlanner =>
 
   object LeftSemiJoin extends Strategy with PredicateHelper {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {

From 217e4964444f4e07b894b1bca768a0cbbe799ea0 Mon Sep 17 00:00:00 2001
From: zsxwing <zsxwing@gmail.com>
Date: Mon, 14 Sep 2015 15:00:27 -0700
Subject: [PATCH 616/633] [SPARK-9996] [SPARK-9997] [SQL] Add local expand and
 NestedLoopJoin operators

This PR is in conflict with #8535 and #8573. Will update this one when they are merged.

Author: zsxwing <zsxwing@gmail.com>

Closes #8642 from zsxwing/expand-nest-join.
---
 .../sql/execution/local/ExpandNode.scala      |  60 +++++
 .../spark/sql/execution/local/LocalNode.scala |  55 +++-
 .../execution/local/NestedLoopJoinNode.scala  | 156 ++++++++++++
 .../sql/execution/local/ExpandNodeSuite.scala |  51 ++++
 .../execution/local/HashJoinNodeSuite.scala   |  14 -
 .../sql/execution/local/LocalNodeTest.scala   |  14 +
 .../local/NestedLoopJoinNodeSuite.scala       | 239 ++++++++++++++++++
 7 files changed, 574 insertions(+), 15 deletions(-)
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/ExpandNode.scala
 create mode 100644 sql/core/src/main/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNode.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala
 create mode 100644 sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ExpandNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ExpandNode.scala
new file mode 100644
index 0000000000000..2aff156d18b54
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/ExpandNode.scala
@@ -0,0 +1,60 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.{Attribute, Expression, Projection}
+
+case class ExpandNode(
+    conf: SQLConf,
+    projections: Seq[Seq[Expression]],
+    output: Seq[Attribute],
+    child: LocalNode) extends UnaryLocalNode(conf) {
+
+  assert(projections.size > 0)
+
+  private[this] var result: InternalRow = _
+  private[this] var idx: Int = _
+  private[this] var input: InternalRow = _
+  private[this] var groups: Array[Projection] = _
+
+  override def open(): Unit = {
+    child.open()
+    groups = projections.map(ee => newProjection(ee, child.output)).toArray
+    idx = groups.length
+  }
+
+  override def next(): Boolean = {
+    if (idx >= groups.length) {
+      if (child.next()) {
+        input = child.fetch()
+        idx = 0
+      } else {
+        return false
+      }
+    }
+    result = groups(idx)(input)
+    idx += 1
+    true
+  }
+
+  override def fetch(): InternalRow = result
+
+  override def close(): Unit = child.close()
+}
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
index e540ef8555eb6..9840080e16953 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/LocalNode.scala
@@ -23,7 +23,7 @@ import org.apache.spark.Logging
 import org.apache.spark.sql.{SQLConf, Row}
 import org.apache.spark.sql.catalyst.{CatalystTypeConverters, InternalRow}
 import org.apache.spark.sql.catalyst.expressions._
-import org.apache.spark.sql.catalyst.expressions.codegen.GenerateMutableProjection
+import org.apache.spark.sql.catalyst.expressions.codegen._
 import org.apache.spark.sql.catalyst.trees.TreeNode
 import org.apache.spark.sql.types.StructType
 
@@ -69,6 +69,18 @@ abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging
    */
   def close(): Unit
 
+  /** Specifies whether this operator outputs UnsafeRows */
+  def outputsUnsafeRows: Boolean = false
+
+  /** Specifies whether this operator is capable of processing UnsafeRows */
+  def canProcessUnsafeRows: Boolean = false
+
+  /**
+   * Specifies whether this operator is capable of processing Java-object-based Rows (i.e. rows
+   * that are not UnsafeRows).
+   */
+  def canProcessSafeRows: Boolean = true
+
   /**
    * Returns the content through the [[Iterator]] interface.
    */
@@ -91,6 +103,28 @@ abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging
     result
   }
 
+  protected def newProjection(
+      expressions: Seq[Expression],
+      inputSchema: Seq[Attribute]): Projection = {
+    log.debug(
+      s"Creating Projection: $expressions, inputSchema: $inputSchema, codegen:$codegenEnabled")
+    if (codegenEnabled) {
+      try {
+        GenerateProjection.generate(expressions, inputSchema)
+      } catch {
+        case NonFatal(e) =>
+          if (isTesting) {
+            throw e
+          } else {
+            log.error("Failed to generate projection, fallback to interpret", e)
+            new InterpretedProjection(expressions, inputSchema)
+          }
+      }
+    } else {
+      new InterpretedProjection(expressions, inputSchema)
+    }
+  }
+
   protected def newMutableProjection(
       expressions: Seq[Expression],
       inputSchema: Seq[Attribute]): () => MutableProjection = {
@@ -113,6 +147,25 @@ abstract class LocalNode(conf: SQLConf) extends TreeNode[LocalNode] with Logging
     }
   }
 
+  protected def newPredicate(
+      expression: Expression,
+      inputSchema: Seq[Attribute]): (InternalRow) => Boolean = {
+    if (codegenEnabled) {
+      try {
+        GeneratePredicate.generate(expression, inputSchema)
+      } catch {
+        case NonFatal(e) =>
+          if (isTesting) {
+            throw e
+          } else {
+            log.error("Failed to generate predicate, fallback to interpreted", e)
+            InterpretedPredicate.create(expression, inputSchema)
+          }
+      }
+    } else {
+      InterpretedPredicate.create(expression, inputSchema)
+    }
+  }
 }
 
 
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNode.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNode.scala
new file mode 100644
index 0000000000000..7321fc66b4dde
--- /dev/null
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNode.scala
@@ -0,0 +1,156 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.catalyst.plans.{FullOuter, RightOuter, LeftOuter, JoinType}
+import org.apache.spark.sql.execution.joins.{BuildLeft, BuildRight, BuildSide}
+import org.apache.spark.util.collection.{BitSet, CompactBuffer}
+
+case class NestedLoopJoinNode(
+    conf: SQLConf,
+    left: LocalNode,
+    right: LocalNode,
+    buildSide: BuildSide,
+    joinType: JoinType,
+    condition: Option[Expression]) extends BinaryLocalNode(conf) {
+
+  override def output: Seq[Attribute] = {
+    joinType match {
+      case LeftOuter =>
+        left.output ++ right.output.map(_.withNullability(true))
+      case RightOuter =>
+        left.output.map(_.withNullability(true)) ++ right.output
+      case FullOuter =>
+        left.output.map(_.withNullability(true)) ++ right.output.map(_.withNullability(true))
+      case x =>
+        throw new IllegalArgumentException(
+          s"NestedLoopJoin should not take $x as the JoinType")
+    }
+  }
+
+  private[this] def genResultProjection: InternalRow => InternalRow = {
+    if (outputsUnsafeRows) {
+      UnsafeProjection.create(schema)
+    } else {
+      identity[InternalRow]
+    }
+  }
+
+  private[this] var currentRow: InternalRow = _
+
+  private[this] var iterator: Iterator[InternalRow] = _
+
+  override def open(): Unit = {
+    val (streamed, build) = buildSide match {
+      case BuildRight => (left, right)
+      case BuildLeft => (right, left)
+    }
+    build.open()
+    val buildRelation = new CompactBuffer[InternalRow]
+    while (build.next()) {
+      buildRelation += build.fetch().copy()
+    }
+    build.close()
+
+    val boundCondition =
+      newPredicate(condition.getOrElse(Literal(true)), left.output ++ right.output)
+
+    val leftNulls = new GenericMutableRow(left.output.size)
+    val rightNulls = new GenericMutableRow(right.output.size)
+    val joinedRow = new JoinedRow
+    val matchedBuildTuples = new BitSet(buildRelation.size)
+    val resultProj = genResultProjection
+    streamed.open()
+
+    // streamedRowMatches also contains null rows if using outer join
+    val streamedRowMatches: Iterator[InternalRow] = streamed.asIterator.flatMap { streamedRow =>
+      val matchedRows = new CompactBuffer[InternalRow]
+
+      var i = 0
+      var streamRowMatched = false
+
+      // Scan the build relation to look for matches for each streamed row
+      while (i < buildRelation.size) {
+        val buildRow = buildRelation(i)
+        buildSide match {
+          case BuildRight => joinedRow(streamedRow, buildRow)
+          case BuildLeft => joinedRow(buildRow, streamedRow)
+        }
+        if (boundCondition(joinedRow)) {
+          matchedRows += resultProj(joinedRow).copy()
+          streamRowMatched = true
+          matchedBuildTuples.set(i)
+        }
+        i += 1
+      }
+
+      // If this row had no matches and we're using outer join, join it with the null rows
+      if (!streamRowMatched) {
+        (joinType, buildSide) match {
+          case (LeftOuter | FullOuter, BuildRight) =>
+            matchedRows += resultProj(joinedRow(streamedRow, rightNulls)).copy()
+          case (RightOuter | FullOuter, BuildLeft) =>
+            matchedRows += resultProj(joinedRow(leftNulls, streamedRow)).copy()
+          case _ =>
+        }
+      }
+
+      matchedRows.iterator
+    }
+
+    // If we're using outer join, find rows on the build side that didn't match anything
+    // and join them with the null row
+    lazy val unmatchedBuildRows: Iterator[InternalRow] = {
+      var i = 0
+      buildRelation.filter { row =>
+        val r = !matchedBuildTuples.get(i)
+        i += 1
+        r
+      }.iterator
+    }
+    iterator = (joinType, buildSide) match {
+      case (RightOuter | FullOuter, BuildRight) =>
+        streamedRowMatches ++
+          unmatchedBuildRows.map { buildRow => resultProj(joinedRow(leftNulls, buildRow)) }
+      case (LeftOuter | FullOuter, BuildLeft) =>
+        streamedRowMatches ++
+          unmatchedBuildRows.map { buildRow => resultProj(joinedRow(buildRow, rightNulls)) }
+      case _ => streamedRowMatches
+    }
+  }
+
+  override def next(): Boolean = {
+    if (iterator.hasNext) {
+      currentRow = iterator.next()
+      true
+    } else {
+      false
+    }
+  }
+
+  override def fetch(): InternalRow = currentRow
+
+  override def close(): Unit = {
+    left.close()
+    right.close()
+  }
+
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala
new file mode 100644
index 0000000000000..cfa7f3f6dcb97
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/ExpandNodeSuite.scala
@@ -0,0 +1,51 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+class ExpandNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  test("expand") {
+    val input = Seq((1, 1), (2, 2), (3, 3), (4, 4), (5, 5)).toDF("key", "value")
+    checkAnswer(
+      input,
+      node =>
+        ExpandNode(conf, Seq(
+          Seq(
+            input.col("key") + input.col("value"), input.col("key") - input.col("value")
+          ).map(_.expr),
+          Seq(
+            input.col("key") * input.col("value"), input.col("key") / input.col("value")
+          ).map(_.expr)
+        ), node.output, node),
+      Seq(
+        (2, 0),
+        (1, 1),
+        (4, 0),
+        (4, 1),
+        (6, 0),
+        (9, 1),
+        (8, 0),
+        (16, 1),
+        (10, 0),
+        (25, 1)
+      ).toDF().collect()
+    )
+  }
+}
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
index 43b6f06aead88..78d891351f4a9 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/HashJoinNodeSuite.scala
@@ -24,20 +24,6 @@ class HashJoinNodeSuite extends LocalNodeTest {
 
   import testImplicits._
 
-  private def wrapForUnsafe(
-      f: (LocalNode, LocalNode) => LocalNode): (LocalNode, LocalNode) => LocalNode = {
-    if (conf.unsafeEnabled) {
-      (left: LocalNode, right: LocalNode) => {
-        val _left = ConvertToUnsafeNode(conf, left)
-        val _right = ConvertToUnsafeNode(conf, right)
-        val r = f(_left, _right)
-        ConvertToSafeNode(conf, r)
-      }
-    } else {
-      f
-    }
-  }
-
   def joinSuite(suiteName: String, confPairs: (String, String)*): Unit = {
     test(s"$suiteName: inner join with one match per row") {
       withSQLConf(confPairs: _*) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
index b95d4ea7f8f2a..86dd28064cc6a 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/LocalNodeTest.scala
@@ -27,6 +27,20 @@ class LocalNodeTest extends SparkFunSuite with SharedSQLContext {
 
   def conf: SQLConf = sqlContext.conf
 
+  protected def wrapForUnsafe(
+      f: (LocalNode, LocalNode) => LocalNode): (LocalNode, LocalNode) => LocalNode = {
+    if (conf.unsafeEnabled) {
+      (left: LocalNode, right: LocalNode) => {
+        val _left = ConvertToUnsafeNode(conf, left)
+        val _right = ConvertToUnsafeNode(conf, right)
+        val r = f(_left, _right)
+        ConvertToSafeNode(conf, r)
+      }
+    } else {
+      f
+    }
+  }
+
   /**
    * Runs the LocalNode and makes sure the answer matches the expected result.
    * @param input the input data to be used.
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala
new file mode 100644
index 0000000000000..b1ef26ba82f16
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/local/NestedLoopJoinNodeSuite.scala
@@ -0,0 +1,239 @@
+/*
+* Licensed to the Apache Software Foundation (ASF) under one or more
+* contributor license agreements.  See the NOTICE file distributed with
+* this work for additional information regarding copyright ownership.
+* The ASF licenses this file to You under the Apache License, Version 2.0
+* (the "License"); you may not use this file except in compliance with
+* the License.  You may obtain a copy of the License at
+*
+*    http://www.apache.org/licenses/LICENSE-2.0
+*
+* Unless required by applicable law or agreed to in writing, software
+* distributed under the License is distributed on an "AS IS" BASIS,
+* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+* See the License for the specific language governing permissions and
+* limitations under the License.
+*/
+
+package org.apache.spark.sql.execution.local
+
+import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.catalyst.plans.{FullOuter, LeftOuter, RightOuter}
+import org.apache.spark.sql.execution.joins.{BuildLeft, BuildRight, BuildSide}
+
+class NestedLoopJoinNodeSuite extends LocalNodeTest {
+
+  import testImplicits._
+
+  private def joinSuite(
+      suiteName: String, buildSide: BuildSide, confPairs: (String, String)*): Unit = {
+    test(s"$suiteName: left outer join") {
+      withSQLConf(confPairs: _*) {
+        checkAnswer2(
+          upperCaseData,
+          lowerCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              LeftOuter,
+              Some((upperCaseData.col("N") === lowerCaseData.col("n")).expr))
+          ),
+          upperCaseData.join(lowerCaseData, $"n" === $"N", "left").collect())
+
+        checkAnswer2(
+          upperCaseData,
+          lowerCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              LeftOuter,
+              Some(
+                (upperCaseData.col("N") === lowerCaseData.col("n") &&
+                  lowerCaseData.col("n") > 1).expr))
+          ),
+          upperCaseData.join(lowerCaseData, $"n" === $"N" && $"n" > 1, "left").collect())
+
+        checkAnswer2(
+          upperCaseData,
+          lowerCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              LeftOuter,
+              Some(
+                (upperCaseData.col("N") === lowerCaseData.col("n") &&
+                  upperCaseData.col("N") > 1).expr))
+          ),
+          upperCaseData.join(lowerCaseData, $"n" === $"N" && $"N" > 1, "left").collect())
+
+        checkAnswer2(
+          upperCaseData,
+          lowerCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              LeftOuter,
+              Some(
+                (upperCaseData.col("N") === lowerCaseData.col("n") &&
+                  lowerCaseData.col("l") > upperCaseData.col("L")).expr))
+          ),
+          upperCaseData.join(lowerCaseData, $"n" === $"N" && $"l" > $"L", "left").collect())
+      }
+    }
+
+    test(s"$suiteName: right outer join") {
+      withSQLConf(confPairs: _*) {
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              RightOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N")).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N", "right").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              RightOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                lowerCaseData.col("n") > 1).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"n" > 1, "right").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              RightOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                upperCaseData.col("N") > 1).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"N" > 1, "right").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              RightOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                lowerCaseData.col("l") > upperCaseData.col("L")).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"l" > $"L", "right").collect())
+      }
+    }
+
+    test(s"$suiteName: full outer join") {
+      withSQLConf(confPairs: _*) {
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              FullOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N")).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N", "full").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              FullOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                lowerCaseData.col("n") > 1).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"n" > 1, "full").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              FullOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                upperCaseData.col("N") > 1).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"N" > 1, "full").collect())
+
+        checkAnswer2(
+          lowerCaseData,
+          upperCaseData,
+          wrapForUnsafe(
+            (node1, node2) => NestedLoopJoinNode(
+              conf,
+              node1,
+              node2,
+              buildSide,
+              FullOuter,
+              Some((lowerCaseData.col("n") === upperCaseData.col("N") &&
+                lowerCaseData.col("l") > upperCaseData.col("L")).expr))
+          ),
+          lowerCaseData.join(upperCaseData, $"n" === $"N" && $"l" > $"L", "full").collect())
+      }
+    }
+  }
+
+  joinSuite(
+    "general-build-left",
+    BuildLeft,
+    SQLConf.CODEGEN_ENABLED.key -> "false", SQLConf.UNSAFE_ENABLED.key -> "false")
+  joinSuite(
+    "general-build-right",
+    BuildRight,
+    SQLConf.CODEGEN_ENABLED.key -> "false", SQLConf.UNSAFE_ENABLED.key -> "false")
+  joinSuite(
+    "tungsten-build-left",
+    BuildLeft,
+    SQLConf.CODEGEN_ENABLED.key -> "true", SQLConf.UNSAFE_ENABLED.key -> "true")
+  joinSuite(
+    "tungsten-build-right",
+    BuildRight,
+    SQLConf.CODEGEN_ENABLED.key -> "true", SQLConf.UNSAFE_ENABLED.key -> "true")
+}

From 16b6d18613e150c7038c613992d80a7828413e66 Mon Sep 17 00:00:00 2001
From: Erick Tryzelaar <erick.tryzelaar@gmail.com>
Date: Mon, 14 Sep 2015 15:02:38 -0700
Subject: [PATCH 617/633] [SPARK-10594] [YARN] Remove reference to
 --num-executors, add --properties-file

`ApplicationMaster` no longer has the `--num-executors` flag, and had an undocumented `--properties-file` configuration option.

cc srowen

Author: Erick Tryzelaar <erick.tryzelaar@gmail.com>

Closes #8754 from erickt/master.
---
 .../apache/spark/deploy/yarn/ApplicationMasterArguments.scala   | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
index b08412414aa1c..17d9943c795e3 100644
--- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
+++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMasterArguments.scala
@@ -105,9 +105,9 @@ class ApplicationMasterArguments(val args: Array[String]) {
       |                       place on the PYTHONPATH for Python apps.
       |  --args ARGS          Arguments to be passed to your application's main class.
       |                       Multiple invocations are possible, each will be passed in order.
-      |  --num-executors NUM    Number of executors to start (Default: 2)
       |  --executor-cores NUM   Number of cores for the executors (Default: 1)
       |  --executor-memory MEM  Memory per executor (e.g. 1000M, 2G) (Default: 1G)
+      |  --properties-file FILE Path to a custom Spark properties file.
       """.stripMargin)
     // scalastyle:on println
     System.exit(exitCode)

From 4e2242bb41dda922573046c00c5142745632f95f Mon Sep 17 00:00:00 2001
From: Sean Owen <sowen@cloudera.com>
Date: Mon, 14 Sep 2015 15:03:51 -0700
Subject: [PATCH 618/633] [SPARK-10576] [BUILD] Move .java files out of
 src/main/scala

Move .java files in `src/main/scala` to `src/main/java` root, except for `package-info.java` (to stay next to package.scala)

Author: Sean Owen <sowen@cloudera.com>

Closes #8736 from srowen/SPARK-10576.
---
 .../org/apache/spark/annotation/AlphaComponent.java               | 0
 .../{scala => java}/org/apache/spark/annotation/DeveloperApi.java | 0
 .../{scala => java}/org/apache/spark/annotation/Experimental.java | 0
 .../main/{scala => java}/org/apache/spark/annotation/Private.java | 0
 .../{scala => java}/org/apache/spark/graphx/TripletFields.java    | 0
 .../org/apache/spark/graphx/impl/EdgeActiveness.java              | 0
 .../org/apache/spark/sql/types/SQLUserDefinedType.java            | 0
 .../org/apache/spark/streaming/StreamingContextState.java         | 0
 8 files changed, 0 insertions(+), 0 deletions(-)
 rename core/src/main/{scala => java}/org/apache/spark/annotation/AlphaComponent.java (100%)
 rename core/src/main/{scala => java}/org/apache/spark/annotation/DeveloperApi.java (100%)
 rename core/src/main/{scala => java}/org/apache/spark/annotation/Experimental.java (100%)
 rename core/src/main/{scala => java}/org/apache/spark/annotation/Private.java (100%)
 rename graphx/src/main/{scala => java}/org/apache/spark/graphx/TripletFields.java (100%)
 rename graphx/src/main/{scala => java}/org/apache/spark/graphx/impl/EdgeActiveness.java (100%)
 rename sql/catalyst/src/main/{scala => java}/org/apache/spark/sql/types/SQLUserDefinedType.java (100%)
 rename streaming/src/main/{scala => java}/org/apache/spark/streaming/StreamingContextState.java (100%)

diff --git a/core/src/main/scala/org/apache/spark/annotation/AlphaComponent.java b/core/src/main/java/org/apache/spark/annotation/AlphaComponent.java
similarity index 100%
rename from core/src/main/scala/org/apache/spark/annotation/AlphaComponent.java
rename to core/src/main/java/org/apache/spark/annotation/AlphaComponent.java
diff --git a/core/src/main/scala/org/apache/spark/annotation/DeveloperApi.java b/core/src/main/java/org/apache/spark/annotation/DeveloperApi.java
similarity index 100%
rename from core/src/main/scala/org/apache/spark/annotation/DeveloperApi.java
rename to core/src/main/java/org/apache/spark/annotation/DeveloperApi.java
diff --git a/core/src/main/scala/org/apache/spark/annotation/Experimental.java b/core/src/main/java/org/apache/spark/annotation/Experimental.java
similarity index 100%
rename from core/src/main/scala/org/apache/spark/annotation/Experimental.java
rename to core/src/main/java/org/apache/spark/annotation/Experimental.java
diff --git a/core/src/main/scala/org/apache/spark/annotation/Private.java b/core/src/main/java/org/apache/spark/annotation/Private.java
similarity index 100%
rename from core/src/main/scala/org/apache/spark/annotation/Private.java
rename to core/src/main/java/org/apache/spark/annotation/Private.java
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/TripletFields.java b/graphx/src/main/java/org/apache/spark/graphx/TripletFields.java
similarity index 100%
rename from graphx/src/main/scala/org/apache/spark/graphx/TripletFields.java
rename to graphx/src/main/java/org/apache/spark/graphx/TripletFields.java
diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeActiveness.java b/graphx/src/main/java/org/apache/spark/graphx/impl/EdgeActiveness.java
similarity index 100%
rename from graphx/src/main/scala/org/apache/spark/graphx/impl/EdgeActiveness.java
rename to graphx/src/main/java/org/apache/spark/graphx/impl/EdgeActiveness.java
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/types/SQLUserDefinedType.java b/sql/catalyst/src/main/java/org/apache/spark/sql/types/SQLUserDefinedType.java
similarity index 100%
rename from sql/catalyst/src/main/scala/org/apache/spark/sql/types/SQLUserDefinedType.java
rename to sql/catalyst/src/main/java/org/apache/spark/sql/types/SQLUserDefinedType.java
diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContextState.java b/streaming/src/main/java/org/apache/spark/streaming/StreamingContextState.java
similarity index 100%
rename from streaming/src/main/scala/org/apache/spark/streaming/StreamingContextState.java
rename to streaming/src/main/java/org/apache/spark/streaming/StreamingContextState.java

From ffbbc2c58b9bf1e2abc2ea797feada6821ab4de8 Mon Sep 17 00:00:00 2001
From: Tom Graves <tgraves@yahoo-inc.com>
Date: Mon, 14 Sep 2015 15:05:19 -0700
Subject: [PATCH 619/633] [SPARK-10549] scala 2.11 spark on yarn with security
 - Repl doesn't work

Make this lazy so that it can set the yarn mode before creating the securityManager.

Author: Tom Graves <tgraves@yahoo-inc.com>
Author: Thomas Graves <tgraves@staydecay.corp.gq1.yahoo.com>

Closes #8719 from tgravescs/SPARK-10549.
---
 .../scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
index be31eb2eda546..627148df80c11 100644
--- a/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
+++ b/repl/scala-2.11/src/main/scala/org/apache/spark/repl/Main.scala
@@ -35,7 +35,8 @@ object Main extends Logging {
   s.processArguments(List("-Yrepl-class-based",
     "-Yrepl-outdir", s"${outputDir.getAbsolutePath}",
     "-classpath", getAddedJars.mkString(File.pathSeparator)), true)
-  val classServer = new HttpServer(conf, outputDir, new SecurityManager(conf))
+  // the creation of SecurityManager has to be lazy so SPARK_YARN_MODE is set if needed
+  lazy val classServer = new HttpServer(conf, outputDir, new SecurityManager(conf))
   var sparkContext: SparkContext = _
   var sqlContext: SQLContext = _
   var interp = new SparkILoop // this is a public var because tests reset it.

From fd1e8cddf2635c55fec2ac6e1f1c221c9685af0f Mon Sep 17 00:00:00 2001
From: Forest Fang <forest.fang@outlook.com>
Date: Mon, 14 Sep 2015 15:07:13 -0700
Subject: [PATCH 620/633] [SPARK-10543] [CORE] Peak Execution Memory Quantile
 should be Per-task Basis

Read `PEAK_EXECUTION_MEMORY` using `update` to get per task partial value instead of cumulative value.

I tested with this workload:

```scala
val size = 1000
val repetitions = 10
val data = sc.parallelize(1 to size, 5).map(x => (util.Random.nextInt(size / repetitions),util.Random.nextDouble)).toDF("key", "value")
val res = data.toDF.groupBy("key").agg(sum("value")).count
```

Before:
![image](https://cloud.githubusercontent.com/assets/4317392/9828197/07dd6874-58b8-11e5-9bd9-6ba927c38b26.png)

After:
![image](https://cloud.githubusercontent.com/assets/4317392/9828151/a5ddff30-58b7-11e5-8d31-eda5dc4eae79.png)

Tasks view:
![image](https://cloud.githubusercontent.com/assets/4317392/9828199/17dc2b84-58b8-11e5-92a8-be89ce4d29d1.png)

cc andrewor14 I appreciate if you can give feedback on this since I think you introduced display of this metric.

Author: Forest Fang <forest.fang@outlook.com>

Closes #8726 from saurfang/stagepage.
---
 .../org/apache/spark/ui/jobs/StagePage.scala  |  2 +-
 .../org/apache/spark/ui/StagePageSuite.scala  | 29 ++++++++++++++-----
 2 files changed, 23 insertions(+), 8 deletions(-)

diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
index 4adc6596ba21c..2b71f55b7bb4f 100644
--- a/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
+++ b/core/src/main/scala/org/apache/spark/ui/jobs/StagePage.scala
@@ -368,7 +368,7 @@ private[ui] class StagePage(parent: StagesTab) extends WebUIPage("stage") {
           val peakExecutionMemory = validTasks.map { case TaskUIData(info, _, _) =>
             info.accumulables
               .find { acc => acc.name == InternalAccumulator.PEAK_EXECUTION_MEMORY }
-              .map { acc => acc.value.toLong }
+              .map { acc => acc.update.getOrElse("0").toLong }
               .getOrElse(0L)
               .toDouble
           }
diff --git a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
index 3388c6dca81f1..86699e7f56953 100644
--- a/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ui/StagePageSuite.scala
@@ -23,7 +23,7 @@ import scala.xml.Node
 
 import org.mockito.Mockito.{mock, when, RETURNS_SMART_NULLS}
 
-import org.apache.spark.{LocalSparkContext, SparkConf, SparkFunSuite, Success}
+import org.apache.spark._
 import org.apache.spark.executor.TaskMetrics
 import org.apache.spark.scheduler._
 import org.apache.spark.ui.jobs.{JobProgressListener, StagePage, StagesTab}
@@ -47,6 +47,14 @@ class StagePageSuite extends SparkFunSuite with LocalSparkContext {
     assert(html3.contains(targetString))
   }
 
+  test("SPARK-10543: peak execution memory should be per-task rather than cumulative") {
+    val unsafeConf = "spark.sql.unsafe.enabled"
+    val conf = new SparkConf(false).set(unsafeConf, "true")
+    val html = renderStagePage(conf).toString().toLowerCase
+    // verify min/25/50/75/max show task value not cumulative values
+    assert(html.contains("<td>10.0 b</td>" * 5))
+  }
+
   /**
    * Render a stage page started with the given conf and return the HTML.
    * This also runs a dummy stage to populate the page with useful content.
@@ -67,12 +75,19 @@ class StagePageSuite extends SparkFunSuite with LocalSparkContext {
 
     // Simulate a stage in job progress listener
     val stageInfo = new StageInfo(0, 0, "dummy", 1, Seq.empty, Seq.empty, "details")
-    val taskInfo = new TaskInfo(0, 0, 0, 0, "0", "localhost", TaskLocality.ANY, false)
-    jobListener.onStageSubmitted(SparkListenerStageSubmitted(stageInfo))
-    jobListener.onTaskStart(SparkListenerTaskStart(0, 0, taskInfo))
-    taskInfo.markSuccessful()
-    jobListener.onTaskEnd(
-      SparkListenerTaskEnd(0, 0, "result", Success, taskInfo, TaskMetrics.empty))
+    // Simulate two tasks to test PEAK_EXECUTION_MEMORY correctness
+    (1 to 2).foreach {
+      taskId =>
+        val taskInfo = new TaskInfo(taskId, taskId, 0, 0, "0", "localhost", TaskLocality.ANY, false)
+        val peakExecutionMemory = 10
+        taskInfo.accumulables += new AccumulableInfo(0, InternalAccumulator.PEAK_EXECUTION_MEMORY,
+          Some(peakExecutionMemory.toString), (peakExecutionMemory * taskId).toString, true)
+        jobListener.onStageSubmitted(SparkListenerStageSubmitted(stageInfo))
+        jobListener.onTaskStart(SparkListenerTaskStart(0, 0, taskInfo))
+        taskInfo.markSuccessful()
+        jobListener.onTaskEnd(
+          SparkListenerTaskEnd(0, 0, "result", Success, taskInfo, TaskMetrics.empty))
+    }
     jobListener.onStageCompleted(SparkListenerStageCompleted(stageInfo))
     page.render(request)
   }

From 7b6c856367b9c36348e80e83959150da9656c4dd Mon Sep 17 00:00:00 2001
From: Andrew Or <andrew@databricks.com>
Date: Mon, 14 Sep 2015 15:09:43 -0700
Subject: [PATCH 621/633] [SPARK-10564] ThreadingSuite: assertion failures in
 threads don't fail the test (round 2)

This is a follow-up patch to #8723. I missed one case there.

Author: Andrew Or <andrew@databricks.com>

Closes #8727 from andrewor14/fix-threading-suite.
---
 .../org/apache/spark/ThreadingSuite.scala     | 23 ++++++++++++-------
 1 file changed, 15 insertions(+), 8 deletions(-)

diff --git a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
index cda2b245526f7..a96a4ce201c21 100644
--- a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
+++ b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala
@@ -147,12 +147,12 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
       }.start()
     }
     sem.acquire(2)
+    throwable.foreach { t => throw t }
     if (ThreadingSuiteState.failed.get()) {
       logError("Waited 1 second without seeing runningThreads = 4 (it was " +
                 ThreadingSuiteState.runningThreads.get() + "); failing test")
       fail("One or more threads didn't see runningThreads = 4")
     }
-    throwable.foreach { t => throw t }
   }
 
   test("set local properties in different thread") {
@@ -178,8 +178,8 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     threads.foreach(_.start())
 
     sem.acquire(5)
-    assert(sc.getLocalProperty("test") === null)
     throwable.foreach { t => throw t }
+    assert(sc.getLocalProperty("test") === null)
   }
 
   test("set and get local properties in parent-children thread") {
@@ -207,15 +207,16 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     threads.foreach(_.start())
 
     sem.acquire(5)
+    throwable.foreach { t => throw t }
     assert(sc.getLocalProperty("test") === "parent")
     assert(sc.getLocalProperty("Foo") === null)
-    throwable.foreach { t => throw t }
   }
 
   test("mutations to local properties should not affect submitted jobs (SPARK-6629)") {
     val jobStarted = new Semaphore(0)
     val jobEnded = new Semaphore(0)
     @volatile var jobResult: JobResult = null
+    var throwable: Option[Throwable] = None
 
     sc = new SparkContext("local", "test")
     sc.setJobGroup("originalJobGroupId", "description")
@@ -232,14 +233,19 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     // Create a new thread which will inherit the current thread's properties
     val thread = new Thread() {
       override def run(): Unit = {
-        assert(sc.getLocalProperty(SparkContext.SPARK_JOB_GROUP_ID) === "originalJobGroupId")
-        // Sleeps for a total of 10 seconds, but allows cancellation to interrupt the task
         try {
-          sc.parallelize(1 to 100).foreach { x =>
-            Thread.sleep(100)
+          assert(sc.getLocalProperty(SparkContext.SPARK_JOB_GROUP_ID) === "originalJobGroupId")
+          // Sleeps for a total of 10 seconds, but allows cancellation to interrupt the task
+          try {
+            sc.parallelize(1 to 100).foreach { x =>
+              Thread.sleep(100)
+            }
+          } catch {
+            case s: SparkException => // ignored so that we don't print noise in test logs
           }
         } catch {
-          case s: SparkException => // ignored so that we don't print noise in test logs
+          case t: Throwable =>
+            throwable = Some(t)
         }
       }
     }
@@ -252,6 +258,7 @@ class ThreadingSuite extends SparkFunSuite with LocalSparkContext with Logging {
     // modification of the properties object should not affect the properties of running jobs
     sc.cancelJobGroup("originalJobGroupId")
     jobEnded.tryAcquire(10, TimeUnit.SECONDS)
+    throwable.foreach { t => throw t }
     assert(jobResult.isInstanceOf[JobFailed])
   }
 }

From 1a0955250bb65cd6f5818ad60efb62ea4b45d18e Mon Sep 17 00:00:00 2001
From: Matei Zaharia <matei@databricks.com>
Date: Mon, 14 Sep 2015 21:47:40 -0400
Subject: [PATCH 622/633] [SPARK-9851] Support submitting map stages
 individually in DAGScheduler

This patch adds support for submitting map stages in a DAG individually so that we can make downstream decisions after seeing statistics about their output, as part of SPARK-9850. I also added more comments to many of the key classes in DAGScheduler. By itself, the patch is not super useful except maybe to switch between a shuffle and broadcast join, but with the other subtasks of SPARK-9850 we'll be able to do more interesting decisions.

The main entry point is SparkContext.submitMapStage, which lets you run a map stage and see stats about the map output sizes. Other stats could also be collected through accumulators. See AdaptiveSchedulingSuite for a short example.

Author: Matei Zaharia <matei@databricks.com>

Closes #8180 from mateiz/spark-9851.
---
 .../apache/spark/MapOutputStatistics.scala    |  27 ++
 .../org/apache/spark/MapOutputTracker.scala   |  49 +++-
 .../scala/org/apache/spark/SparkContext.scala |  17 ++
 .../apache/spark/scheduler/ActiveJob.scala    |  34 ++-
 .../apache/spark/scheduler/DAGScheduler.scala | 251 +++++++++++++++---
 .../spark/scheduler/DAGSchedulerEvent.scala   |  10 +
 .../apache/spark/scheduler/ResultStage.scala  |  17 +-
 .../spark/scheduler/ShuffleMapStage.scala     |  13 +-
 .../org/apache/spark/scheduler/Stage.scala    |  26 +-
 .../scala/org/apache/spark/FailureSuite.scala |  21 ++
 .../scheduler/AdaptiveSchedulingSuite.scala   |  65 +++++
 .../spark/scheduler/DAGSchedulerSuite.scala   | 243 ++++++++++++++++-
 12 files changed, 710 insertions(+), 63 deletions(-)
 create mode 100644 core/src/main/scala/org/apache/spark/MapOutputStatistics.scala
 create mode 100644 core/src/test/scala/org/apache/spark/scheduler/AdaptiveSchedulingSuite.scala

diff --git a/core/src/main/scala/org/apache/spark/MapOutputStatistics.scala b/core/src/main/scala/org/apache/spark/MapOutputStatistics.scala
new file mode 100644
index 0000000000000..f8a6f1d0d8cbb
--- /dev/null
+++ b/core/src/main/scala/org/apache/spark/MapOutputStatistics.scala
@@ -0,0 +1,27 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark
+
+/**
+ * Holds statistics about the output sizes in a map stage. May become a DeveloperApi in the future.
+ *
+ * @param shuffleId ID of the shuffle
+ * @param bytesByPartitionId approximate number of output bytes for each map output partition
+ *   (may be inexact due to use of compressed map statuses)
+ */
+private[spark] class MapOutputStatistics(val shuffleId: Int, val bytesByPartitionId: Array[Long])
diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
index a387592783850..94eb8daa85c53 100644
--- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
+++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala
@@ -18,6 +18,7 @@
 package org.apache.spark
 
 import java.io._
+import java.util.Arrays
 import java.util.concurrent.ConcurrentHashMap
 import java.util.zip.{GZIPInputStream, GZIPOutputStream}
 
@@ -132,13 +133,43 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging
    *         describing the shuffle blocks that are stored at that block manager.
    */
   def getMapSizesByExecutorId(shuffleId: Int, reduceId: Int)
-  : Seq[(BlockManagerId, Seq[(BlockId, Long)])] = {
+      : Seq[(BlockManagerId, Seq[(BlockId, Long)])] = {
     logDebug(s"Fetching outputs for shuffle $shuffleId, reduce $reduceId")
-    val startTime = System.currentTimeMillis
+    val statuses = getStatuses(shuffleId)
+    // Synchronize on the returned array because, on the driver, it gets mutated in place
+    statuses.synchronized {
+      return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, statuses)
+    }
+  }
 
+  /**
+   * Return statistics about all of the outputs for a given shuffle.
+   */
+  def getStatistics(dep: ShuffleDependency[_, _, _]): MapOutputStatistics = {
+    val statuses = getStatuses(dep.shuffleId)
+    // Synchronize on the returned array because, on the driver, it gets mutated in place
+    statuses.synchronized {
+      val totalSizes = new Array[Long](dep.partitioner.numPartitions)
+      for (s <- statuses) {
+        for (i <- 0 until totalSizes.length) {
+          totalSizes(i) += s.getSizeForBlock(i)
+        }
+      }
+      new MapOutputStatistics(dep.shuffleId, totalSizes)
+    }
+  }
+
+  /**
+   * Get or fetch the array of MapStatuses for a given shuffle ID. NOTE: clients MUST synchronize
+   * on this array when reading it, because on the driver, we may be changing it in place.
+   *
+   * (It would be nice to remove this restriction in the future.)
+   */
+  private def getStatuses(shuffleId: Int): Array[MapStatus] = {
     val statuses = mapStatuses.get(shuffleId).orNull
     if (statuses == null) {
       logInfo("Don't have map outputs for shuffle " + shuffleId + ", fetching them")
+      val startTime = System.currentTimeMillis
       var fetchedStatuses: Array[MapStatus] = null
       fetching.synchronized {
         // Someone else is fetching it; wait for them to be done
@@ -160,7 +191,7 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging
       }
 
       if (fetchedStatuses == null) {
-        // We won the race to fetch the output locs; do so
+        // We won the race to fetch the statuses; do so
         logInfo("Doing the fetch; tracker endpoint = " + trackerEndpoint)
         // This try-finally prevents hangs due to timeouts:
         try {
@@ -175,22 +206,18 @@ private[spark] abstract class MapOutputTracker(conf: SparkConf) extends Logging
           }
         }
       }
-      logDebug(s"Fetching map output location for shuffle $shuffleId, reduce $reduceId took " +
+      logDebug(s"Fetching map output statuses for shuffle $shuffleId took " +
         s"${System.currentTimeMillis - startTime} ms")
 
       if (fetchedStatuses != null) {
-        fetchedStatuses.synchronized {
-          return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, fetchedStatuses)
-        }
+        return fetchedStatuses
       } else {
         logError("Missing all output locations for shuffle " + shuffleId)
         throw new MetadataFetchFailedException(
-          shuffleId, reduceId, "Missing all output locations for shuffle " + shuffleId)
+          shuffleId, -1, "Missing all output locations for shuffle " + shuffleId)
       }
     } else {
-      statuses.synchronized {
-        return MapOutputTracker.convertMapStatuses(shuffleId, reduceId, statuses)
-      }
+      return statuses
     }
   }
 
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index e27b3c4962221..dee6091ce3caf 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1984,6 +1984,23 @@ class SparkContext(config: SparkConf) extends Logging with ExecutorAllocationCli
     new SimpleFutureAction(waiter, resultFunc)
   }
 
+  /**
+   * Submit a map stage for execution. This is currently an internal API only, but might be
+   * promoted to DeveloperApi in the future.
+   */
+  private[spark] def submitMapStage[K, V, C](dependency: ShuffleDependency[K, V, C])
+      : SimpleFutureAction[MapOutputStatistics] = {
+    assertNotStopped()
+    val callSite = getCallSite()
+    var result: MapOutputStatistics = null
+    val waiter = dagScheduler.submitMapStage(
+      dependency,
+      (r: MapOutputStatistics) => { result = r },
+      callSite,
+      localProperties.get)
+    new SimpleFutureAction[MapOutputStatistics](waiter, result)
+  }
+
   /**
    * Cancel active jobs for the specified group. See [[org.apache.spark.SparkContext.setJobGroup]]
    * for more information.
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala b/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala
index 50a69379412d2..a3d2db31301b3 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ActiveJob.scala
@@ -23,18 +23,42 @@ import org.apache.spark.TaskContext
 import org.apache.spark.util.CallSite
 
 /**
- * Tracks information about an active job in the DAGScheduler.
+ * A running job in the DAGScheduler. Jobs can be of two types: a result job, which computes a
+ * ResultStage to execute an action, or a map-stage job, which computes the map outputs for a
+ * ShuffleMapStage before any downstream stages are submitted. The latter is used for adaptive
+ * query planning, to look at map output statistics before submitting later stages. We distinguish
+ * between these two types of jobs using the finalStage field of this class.
+ *
+ * Jobs are only tracked for "leaf" stages that clients directly submitted, through DAGScheduler's
+ * submitJob or submitMapStage methods. However, either type of job may cause the execution of
+ * other earlier stages (for RDDs in the DAG it depends on), and multiple jobs may share some of
+ * these previous stages. These dependencies are managed inside DAGScheduler.
+ *
+ * @param jobId A unique ID for this job.
+ * @param finalStage The stage that this job computes (either a ResultStage for an action or a
+ *   ShuffleMapStage for submitMapStage).
+ * @param callSite Where this job was initiated in the user's program (shown on UI).
+ * @param listener A listener to notify if tasks in this job finish or the job fails.
+ * @param properties Scheduling properties attached to the job, such as fair scheduler pool name.
  */
 private[spark] class ActiveJob(
     val jobId: Int,
-    val finalStage: ResultStage,
-    val func: (TaskContext, Iterator[_]) => _,
-    val partitions: Array[Int],
+    val finalStage: Stage,
     val callSite: CallSite,
     val listener: JobListener,
     val properties: Properties) {
 
-  val numPartitions = partitions.length
+  /**
+   * Number of partitions we need to compute for this job. Note that result stages may not need
+   * to compute all partitions in their target RDD, for actions like first() and lookup().
+   */
+  val numPartitions = finalStage match {
+    case r: ResultStage => r.partitions.length
+    case m: ShuffleMapStage => m.rdd.partitions.length
+  }
+
+  /** Which partitions of the stage have finished */
   val finished = Array.fill[Boolean](numPartitions)(false)
+
   var numFinished = 0
 }
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
index 09e963f5cdf60..b4f90e8347894 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
@@ -45,17 +45,65 @@ import org.apache.spark.storage.BlockManagerMessages.BlockManagerHeartbeat
  * The high-level scheduling layer that implements stage-oriented scheduling. It computes a DAG of
  * stages for each job, keeps track of which RDDs and stage outputs are materialized, and finds a
  * minimal schedule to run the job. It then submits stages as TaskSets to an underlying
- * TaskScheduler implementation that runs them on the cluster.
+ * TaskScheduler implementation that runs them on the cluster. A TaskSet contains fully independent
+ * tasks that can run right away based on the data that's already on the cluster (e.g. map output
+ * files from previous stages), though it may fail if this data becomes unavailable.
  *
- * In addition to coming up with a DAG of stages, this class also determines the preferred
+ * Spark stages are created by breaking the RDD graph at shuffle boundaries. RDD operations with
+ * "narrow" dependencies, like map() and filter(), are pipelined together into one set of tasks
+ * in each stage, but operations with shuffle dependencies require multiple stages (one to write a
+ * set of map output files, and another to read those files after a barrier). In the end, every
+ * stage will have only shuffle dependencies on other stages, and may compute multiple operations
+ * inside it. The actual pipelining of these operations happens in the RDD.compute() functions of
+ * various RDDs (MappedRDD, FilteredRDD, etc).
+ *
+ * In addition to coming up with a DAG of stages, the DAGScheduler also determines the preferred
  * locations to run each task on, based on the current cache status, and passes these to the
  * low-level TaskScheduler. Furthermore, it handles failures due to shuffle output files being
  * lost, in which case old stages may need to be resubmitted. Failures *within* a stage that are
  * not caused by shuffle file loss are handled by the TaskScheduler, which will retry each task
  * a small number of times before cancelling the whole stage.
  *
+ * When looking through this code, there are several key concepts:
+ *
+ *  - Jobs (represented by [[ActiveJob]]) are the top-level work items submitted to the scheduler.
+ *    For example, when the user calls an action, like count(), a job will be submitted through
+ *    submitJob. Each Job may require the execution of multiple stages to build intermediate data.
+ *
+ *  - Stages ([[Stage]]) are sets of tasks that compute intermediate results in jobs, where each
+ *    task computes the same function on partitions of the same RDD. Stages are separated at shuffle
+ *    boundaries, which introduce a barrier (where we must wait for the previous stage to finish to
+ *    fetch outputs). There are two types of stages: [[ResultStage]], for the final stage that
+ *    executes an action, and [[ShuffleMapStage]], which writes map output files for a shuffle.
+ *    Stages are often shared across multiple jobs, if these jobs reuse the same RDDs.
+ *
+ *  - Tasks are individual units of work, each sent to one machine.
+ *
+ *  - Cache tracking: the DAGScheduler figures out which RDDs are cached to avoid recomputing them
+ *    and likewise remembers which shuffle map stages have already produced output files to avoid
+ *    redoing the map side of a shuffle.
+ *
+ *  - Preferred locations: the DAGScheduler also computes where to run each task in a stage based
+ *    on the preferred locations of its underlying RDDs, or the location of cached or shuffle data.
+ *
+ *  - Cleanup: all data structures are cleared when the running jobs that depend on them finish,
+ *    to prevent memory leaks in a long-running application.
+ *
+ * To recover from failures, the same stage might need to run multiple times, which are called
+ * "attempts". If the TaskScheduler reports that a task failed because a map output file from a
+ * previous stage was lost, the DAGScheduler resubmits that lost stage. This is detected through a
+ * CompletionEvent with FetchFailed, or an ExecutorLost event. The DAGScheduler will wait a small
+ * amount of time to see whether other nodes or tasks fail, then resubmit TaskSets for any lost
+ * stage(s) that compute the missing tasks. As part of this process, we might also have to create
+ * Stage objects for old (finished) stages where we previously cleaned up the Stage object. Since
+ * tasks from the old attempt of a stage could still be running, care must be taken to map any
+ * events received in the correct Stage object.
+ *
  * Here's a checklist to use when making or reviewing changes to this class:
  *
+ *  - All data structures should be cleared when the jobs involving them end to avoid indefinite
+ *    accumulation of state in long-running programs.
+ *
  *  - When adding a new data structure, update `DAGSchedulerSuite.assertDataStructuresEmpty` to
  *    include the new structure. This will help to catch memory leaks.
  */
@@ -295,12 +343,12 @@ class DAGScheduler(
    */
   private def newResultStage(
       rdd: RDD[_],
-      numTasks: Int,
+      func: (TaskContext, Iterator[_]) => _,
+      partitions: Array[Int],
       jobId: Int,
       callSite: CallSite): ResultStage = {
     val (parentStages: List[Stage], id: Int) = getParentStagesAndId(rdd, jobId)
-    val stage: ResultStage = new ResultStage(id, rdd, numTasks, parentStages, jobId, callSite)
-
+    val stage = new ResultStage(id, rdd, func, partitions, parentStages, jobId, callSite)
     stageIdToStage(id) = stage
     updateJobIdStageIdMaps(jobId, stage)
     stage
@@ -500,12 +548,25 @@ class DAGScheduler(
     jobIdToStageIds -= job.jobId
     jobIdToActiveJob -= job.jobId
     activeJobs -= job
-    job.finalStage.resultOfJob = None
+    job.finalStage match {
+      case r: ResultStage =>
+        r.resultOfJob = None
+      case m: ShuffleMapStage =>
+        m.mapStageJobs = m.mapStageJobs.filter(_ != job)
+    }
   }
 
   /**
-   * Submit a job to the job scheduler and get a JobWaiter object back. The JobWaiter object
+   * Submit an action job to the scheduler and get a JobWaiter object back. The JobWaiter object
    * can be used to block until the the job finishes executing or can be used to cancel the job.
+   *
+   * @param rdd target RDD to run tasks on
+   * @param func a function to run on each partition of the RDD
+   * @param partitions set of partitions to run on; some jobs may not want to compute on all
+   *   partitions of the target RDD, e.g. for operations like first()
+   * @param callSite where in the user program this job was called
+   * @param resultHandler callback to pass each result to
+   * @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
    */
   def submitJob[T, U](
       rdd: RDD[T],
@@ -524,6 +585,7 @@ class DAGScheduler(
 
     val jobId = nextJobId.getAndIncrement()
     if (partitions.size == 0) {
+      // Return immediately if the job is running 0 tasks
       return new JobWaiter[U](this, jobId, 0, resultHandler)
     }
 
@@ -536,6 +598,18 @@ class DAGScheduler(
     waiter
   }
 
+  /**
+   * Run an action job on the given RDD and pass all the results to the resultHandler function as
+   * they arrive. Throws an exception if the job fials, or returns normally if successful.
+   *
+   * @param rdd target RDD to run tasks on
+   * @param func a function to run on each partition of the RDD
+   * @param partitions set of partitions to run on; some jobs may not want to compute on all
+   *   partitions of the target RDD, e.g. for operations like first()
+   * @param callSite where in the user program this job was called
+   * @param resultHandler callback to pass each result to
+   * @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
+   */
   def runJob[T, U](
       rdd: RDD[T],
       func: (TaskContext, Iterator[T]) => U,
@@ -559,6 +633,17 @@ class DAGScheduler(
     }
   }
 
+  /**
+   * Run an approximate job on the given RDD and pass all the results to an ApproximateEvaluator
+   * as they arrive. Returns a partial result object from the evaluator.
+   *
+   * @param rdd target RDD to run tasks on
+   * @param func a function to run on each partition of the RDD
+   * @param evaluator [[ApproximateEvaluator]] to receive the partial results
+   * @param callSite where in the user program this job was called
+   * @param timeout maximum time to wait for the job, in milliseconds
+   * @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
+   */
   def runApproximateJob[T, U, R](
       rdd: RDD[T],
       func: (TaskContext, Iterator[T]) => U,
@@ -575,6 +660,41 @@ class DAGScheduler(
     listener.awaitResult()    // Will throw an exception if the job fails
   }
 
+  /**
+   * Submit a shuffle map stage to run independently and get a JobWaiter object back. The waiter
+   * can be used to block until the the job finishes executing or can be used to cancel the job.
+   * This method is used for adaptive query planning, to run map stages and look at statistics
+   * about their outputs before submitting downstream stages.
+   *
+   * @param dependency the ShuffleDependency to run a map stage for
+   * @param callback function called with the result of the job, which in this case will be a
+   *   single MapOutputStatistics object showing how much data was produced for each partition
+   * @param callSite where in the user program this job was submitted
+   * @param properties scheduler properties to attach to this job, e.g. fair scheduler pool name
+   */
+  def submitMapStage[K, V, C](
+      dependency: ShuffleDependency[K, V, C],
+      callback: MapOutputStatistics => Unit,
+      callSite: CallSite,
+      properties: Properties): JobWaiter[MapOutputStatistics] = {
+
+    val rdd = dependency.rdd
+    val jobId = nextJobId.getAndIncrement()
+    if (rdd.partitions.length == 0) {
+      throw new SparkException("Can't run submitMapStage on RDD with 0 partitions")
+    }
+
+    // We create a JobWaiter with only one "task", which will be marked as complete when the whole
+    // map stage has completed, and will be passed the MapOutputStatistics for that stage.
+    // This makes it easier to avoid race conditions between the user code and the map output
+    // tracker that might result if we told the user the stage had finished, but then they queries
+    // the map output tracker and some node failures had caused the output statistics to be lost.
+    val waiter = new JobWaiter(this, jobId, 1, (i: Int, r: MapOutputStatistics) => callback(r))
+    eventProcessLoop.post(MapStageSubmitted(
+      jobId, dependency, callSite, waiter, SerializationUtils.clone(properties)))
+    waiter
+  }
+
   /**
    * Cancel a job that is running or waiting in the queue.
    */
@@ -583,6 +703,9 @@ class DAGScheduler(
     eventProcessLoop.post(JobCancelled(jobId))
   }
 
+  /**
+   * Cancel all jobs in the given job group ID.
+   */
   def cancelJobGroup(groupId: String): Unit = {
     logInfo("Asked to cancel job group " + groupId)
     eventProcessLoop.post(JobGroupCancelled(groupId))
@@ -720,31 +843,77 @@ class DAGScheduler(
     try {
       // New stage creation may throw an exception if, for example, jobs are run on a
       // HadoopRDD whose underlying HDFS files have been deleted.
-      finalStage = newResultStage(finalRDD, partitions.length, jobId, callSite)
+      finalStage = newResultStage(finalRDD, func, partitions, jobId, callSite)
     } catch {
       case e: Exception =>
         logWarning("Creating new stage failed due to exception - job: " + jobId, e)
         listener.jobFailed(e)
         return
     }
-    if (finalStage != null) {
-      val job = new ActiveJob(jobId, finalStage, func, partitions, callSite, listener, properties)
-      clearCacheLocs()
-      logInfo("Got job %s (%s) with %d output partitions".format(
-        job.jobId, callSite.shortForm, partitions.length))
-      logInfo("Final stage: " + finalStage + "(" + finalStage.name + ")")
-      logInfo("Parents of final stage: " + finalStage.parents)
-      logInfo("Missing parents: " + getMissingParentStages(finalStage))
-      val jobSubmissionTime = clock.getTimeMillis()
-      jobIdToActiveJob(jobId) = job
-      activeJobs += job
-      finalStage.resultOfJob = Some(job)
-      val stageIds = jobIdToStageIds(jobId).toArray
-      val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
-      listenerBus.post(
-        SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
-      submitStage(finalStage)
+
+    val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
+    clearCacheLocs()
+    logInfo("Got job %s (%s) with %d output partitions".format(
+      job.jobId, callSite.shortForm, partitions.length))
+    logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")")
+    logInfo("Parents of final stage: " + finalStage.parents)
+    logInfo("Missing parents: " + getMissingParentStages(finalStage))
+
+    val jobSubmissionTime = clock.getTimeMillis()
+    jobIdToActiveJob(jobId) = job
+    activeJobs += job
+    finalStage.resultOfJob = Some(job)
+    val stageIds = jobIdToStageIds(jobId).toArray
+    val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
+    listenerBus.post(
+      SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
+    submitStage(finalStage)
+
+    submitWaitingStages()
+  }
+
+  private[scheduler] def handleMapStageSubmitted(jobId: Int,
+      dependency: ShuffleDependency[_, _, _],
+      callSite: CallSite,
+      listener: JobListener,
+      properties: Properties) {
+    // Submitting this map stage might still require the creation of some parent stages, so make
+    // sure that happens.
+    var finalStage: ShuffleMapStage = null
+    try {
+      // New stage creation may throw an exception if, for example, jobs are run on a
+      // HadoopRDD whose underlying HDFS files have been deleted.
+      finalStage = getShuffleMapStage(dependency, jobId)
+    } catch {
+      case e: Exception =>
+        logWarning("Creating new stage failed due to exception - job: " + jobId, e)
+        listener.jobFailed(e)
+        return
+    }
+
+    val job = new ActiveJob(jobId, finalStage, callSite, listener, properties)
+    clearCacheLocs()
+    logInfo("Got map stage job %s (%s) with %d output partitions".format(
+      jobId, callSite.shortForm, dependency.rdd.partitions.size))
+    logInfo("Final stage: " + finalStage + " (" + finalStage.name + ")")
+    logInfo("Parents of final stage: " + finalStage.parents)
+    logInfo("Missing parents: " + getMissingParentStages(finalStage))
+
+    val jobSubmissionTime = clock.getTimeMillis()
+    jobIdToActiveJob(jobId) = job
+    activeJobs += job
+    finalStage.mapStageJobs = job :: finalStage.mapStageJobs
+    val stageIds = jobIdToStageIds(jobId).toArray
+    val stageInfos = stageIds.flatMap(id => stageIdToStage.get(id).map(_.latestInfo))
+    listenerBus.post(
+      SparkListenerJobStart(job.jobId, jobSubmissionTime, stageInfos, properties))
+    submitStage(finalStage)
+
+    // If the whole stage has already finished, tell the listener and remove it
+    if (!finalStage.outputLocs.contains(Nil)) {
+      markMapStageJobAsFinished(job, mapOutputTracker.getStatistics(dependency))
     }
+
     submitWaitingStages()
   }
 
@@ -814,7 +983,7 @@ class DAGScheduler(
         case s: ResultStage =>
           val job = s.resultOfJob.get
           partitionsToCompute.map { id =>
-            val p = job.partitions(id)
+            val p = s.partitions(id)
             (id, getPreferredLocs(stage.rdd, p))
           }.toMap
       }
@@ -844,7 +1013,7 @@ class DAGScheduler(
         case stage: ShuffleMapStage =>
           closureSerializer.serialize((stage.rdd, stage.shuffleDep): AnyRef).array()
         case stage: ResultStage =>
-          closureSerializer.serialize((stage.rdd, stage.resultOfJob.get.func): AnyRef).array()
+          closureSerializer.serialize((stage.rdd, stage.func): AnyRef).array()
       }
 
       taskBinary = sc.broadcast(taskBinaryBytes)
@@ -875,7 +1044,7 @@ class DAGScheduler(
         case stage: ResultStage =>
           val job = stage.resultOfJob.get
           partitionsToCompute.map { id =>
-            val p: Int = job.partitions(id)
+            val p: Int = stage.partitions(id)
             val part = stage.rdd.partitions(p)
             val locs = taskIdToLocations(id)
             new ResultTask(stage.id, stage.latestInfo.attemptId,
@@ -1052,13 +1221,21 @@ class DAGScheduler(
                 logInfo("Resubmitting " + shuffleStage + " (" + shuffleStage.name +
                   ") because some of its tasks had failed: " +
                   shuffleStage.outputLocs.zipWithIndex.filter(_._1.isEmpty)
-                      .map(_._2).mkString(", "))
+                    .map(_._2).mkString(", "))
                 submitStage(shuffleStage)
+              } else {
+                // Mark any map-stage jobs waiting on this stage as finished
+                if (shuffleStage.mapStageJobs.nonEmpty) {
+                  val stats = mapOutputTracker.getStatistics(shuffleStage.shuffleDep)
+                  for (job <- shuffleStage.mapStageJobs) {
+                    markMapStageJobAsFinished(job, stats)
+                  }
+                }
               }
 
               // Note: newly runnable stages will be submitted below when we submit waiting stages
             }
-          }
+        }
 
       case Resubmitted =>
         logInfo("Resubmitted " + task + ", so marking it as still running")
@@ -1412,6 +1589,17 @@ class DAGScheduler(
     Nil
   }
 
+  /** Mark a map stage job as finished with the given output stats, and report to its listener. */
+  def markMapStageJobAsFinished(job: ActiveJob, stats: MapOutputStatistics): Unit = {
+    // In map stage jobs, we only create a single "task", which is to finish all of the stage
+    // (including reusing any previous map outputs, etc); so we just mark task 0 as done
+    job.finished(0) = true
+    job.numFinished += 1
+    job.listener.taskSucceeded(0, stats)
+    cleanupStateForJobAndIndependentStages(job)
+    listenerBus.post(SparkListenerJobEnd(job.jobId, clock.getTimeMillis(), JobSucceeded))
+  }
+
   def stop() {
     logInfo("Stopping DAGScheduler")
     messageScheduler.shutdownNow()
@@ -1445,6 +1633,9 @@ private[scheduler] class DAGSchedulerEventProcessLoop(dagScheduler: DAGScheduler
     case JobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties) =>
       dagScheduler.handleJobSubmitted(jobId, rdd, func, partitions, callSite, listener, properties)
 
+    case MapStageSubmitted(jobId, dependency, callSite, listener, properties) =>
+      dagScheduler.handleMapStageSubmitted(jobId, dependency, callSite, listener, properties)
+
     case StageCancelled(stageId) =>
       dagScheduler.handleStageCancellation(stageId)
 
diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
index f72a52e85dc15..dda3b6cc7f960 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/DAGSchedulerEvent.scala
@@ -35,6 +35,7 @@ import org.apache.spark.util.CallSite
  */
 private[scheduler] sealed trait DAGSchedulerEvent
 
+/** A result-yielding job was submitted on a target RDD */
 private[scheduler] case class JobSubmitted(
     jobId: Int,
     finalRDD: RDD[_],
@@ -45,6 +46,15 @@ private[scheduler] case class JobSubmitted(
     properties: Properties = null)
   extends DAGSchedulerEvent
 
+/** A map stage as submitted to run as a separate job */
+private[scheduler] case class MapStageSubmitted(
+  jobId: Int,
+  dependency: ShuffleDependency[_, _, _],
+  callSite: CallSite,
+  listener: JobListener,
+  properties: Properties = null)
+  extends DAGSchedulerEvent
+
 private[scheduler] case class StageCancelled(stageId: Int) extends DAGSchedulerEvent
 
 private[scheduler] case class JobCancelled(jobId: Int) extends DAGSchedulerEvent
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala
index bf81b9aca4810..c0451da1f0247 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ResultStage.scala
@@ -17,23 +17,30 @@
 
 package org.apache.spark.scheduler
 
+import org.apache.spark.TaskContext
 import org.apache.spark.rdd.RDD
 import org.apache.spark.util.CallSite
 
 /**
- * The ResultStage represents the final stage in a job.
+ * ResultStages apply a function on some partitions of an RDD to compute the result of an action.
+ * The ResultStage object captures the function to execute, `func`, which will be applied to each
+ * partition, and the set of partition IDs, `partitions`. Some stages may not run on all partitions
+ * of the RDD, for actions like first() and lookup().
  */
 private[spark] class ResultStage(
     id: Int,
     rdd: RDD[_],
-    numTasks: Int,
+    val func: (TaskContext, Iterator[_]) => _,
+    val partitions: Array[Int],
     parents: List[Stage],
     firstJobId: Int,
     callSite: CallSite)
-  extends Stage(id, rdd, numTasks, parents, firstJobId, callSite) {
+  extends Stage(id, rdd, partitions.length, parents, firstJobId, callSite) {
 
-  // The active job for this result stage. Will be empty if the job has already finished
-  // (e.g., because the job was cancelled).
+  /**
+   * The active job for this result stage. Will be empty if the job has already finished
+   * (e.g., because the job was cancelled).
+   */
   var resultOfJob: Option[ActiveJob] = None
 
   override def toString: String = "ResultStage " + id
diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
index 48d8d8e9c4b78..7d92960876403 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapStage.scala
@@ -23,7 +23,15 @@ import org.apache.spark.storage.BlockManagerId
 import org.apache.spark.util.CallSite
 
 /**
- * The ShuffleMapStage represents the intermediate stages in a job.
+ * ShuffleMapStages are intermediate stages in the execution DAG that produce data for a shuffle.
+ * They occur right before each shuffle operation, and might contain multiple pipelined operations
+ * before that (e.g. map and filter). When executed, they save map output files that can later be
+ * fetched by reduce tasks. The `shuffleDep` field describes the shuffle each stage is part of,
+ * and variables like `outputLocs` and `numAvailableOutputs` track how many map outputs are ready.
+ *
+ * ShuffleMapStages can also be submitted independently as jobs with DAGScheduler.submitMapStage.
+ * For such stages, the ActiveJobs that submitted them are tracked in `mapStageJobs`. Note that
+ * there can be multiple ActiveJobs trying to compute the same shuffle map stage.
  */
 private[spark] class ShuffleMapStage(
     id: Int,
@@ -37,6 +45,9 @@ private[spark] class ShuffleMapStage(
 
   override def toString: String = "ShuffleMapStage " + id
 
+  /** Running map-stage jobs that were submitted to execute this stage independently (if any) */
+  var mapStageJobs: List[ActiveJob] = Nil
+
   var numAvailableOutputs: Int = 0
 
   def isAvailable: Boolean = numAvailableOutputs == numPartitions
diff --git a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
index c086535782c23..b37eccbd0f7b8 100644
--- a/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
+++ b/core/src/main/scala/org/apache/spark/scheduler/Stage.scala
@@ -24,27 +24,33 @@ import org.apache.spark.rdd.RDD
 import org.apache.spark.util.CallSite
 
 /**
- * A stage is a set of independent tasks all computing the same function that need to run as part
+ * A stage is a set of parallel tasks all computing the same function that need to run as part
  * of a Spark job, where all the tasks have the same shuffle dependencies. Each DAG of tasks run
  * by the scheduler is split up into stages at the boundaries where shuffle occurs, and then the
  * DAGScheduler runs these stages in topological order.
  *
  * Each Stage can either be a shuffle map stage, in which case its tasks' results are input for
- * another stage, or a result stage, in which case its tasks directly compute the action that
- * initiated a job (e.g. count(), save(), etc). For shuffle map stages, we also track the nodes
- * that each output partition is on.
+ * other stage(s), or a result stage, in which case its tasks directly compute a Spark action
+ * (e.g. count(), save(), etc) by running a function on an RDD. For shuffle map stages, we also
+ * track the nodes that each output partition is on.
  *
  * Each Stage also has a firstJobId, identifying the job that first submitted the stage.  When FIFO
  * scheduling is used, this allows Stages from earlier jobs to be computed first or recovered
  * faster on failure.
  *
- * The callSite provides a location in user code which relates to the stage. For a shuffle map
- * stage, the callSite gives the user code that created the RDD being shuffled. For a result
- * stage, the callSite gives the user code that executes the associated action (e.g. count()).
- *
- * A single stage can consist of multiple attempts. In that case, the latestInfo field will
- * be updated for each attempt.
+ * Finally, a single stage can be re-executed in multiple attempts due to fault recovery. In that
+ * case, the Stage object will track multiple StageInfo objects to pass to listeners or the web UI.
+ * The latest one will be accessible through latestInfo.
  *
+ * @param id Unique stage ID
+ * @param rdd RDD that this stage runs on: for a shuffle map stage, it's the RDD we run map tasks
+ *   on, while for a result stage, it's the target RDD that we ran an action on
+ * @param numTasks Total number of tasks in stage; result stages in particular may not need to
+ *   compute all partitions, e.g. for first(), lookup(), and take().
+ * @param parents List of stages that this stage depends on (through shuffle dependencies).
+ * @param firstJobId ID of the first job this stage was part of, for FIFO scheduling.
+ * @param callSite Location in the user program associated with this stage: either where the target
+ *   RDD was created, for a shuffle map stage, or where the action for a result stage was called.
  */
 private[scheduler] abstract class Stage(
     val id: Int,
diff --git a/core/src/test/scala/org/apache/spark/FailureSuite.scala b/core/src/test/scala/org/apache/spark/FailureSuite.scala
index aa50a49c50232..f58756e6f6179 100644
--- a/core/src/test/scala/org/apache/spark/FailureSuite.scala
+++ b/core/src/test/scala/org/apache/spark/FailureSuite.scala
@@ -217,6 +217,27 @@ class FailureSuite extends SparkFunSuite with LocalSparkContext {
     FailureSuiteState.clear()
   }
 
+  // Run a 3-task map stage where one task fails once.
+  test("failure in tasks in a submitMapStage") {
+    sc = new SparkContext("local[1,2]", "test")
+    val rdd = sc.makeRDD(1 to 3, 3).map { x =>
+      FailureSuiteState.synchronized {
+        FailureSuiteState.tasksRun += 1
+        if (x == 1 && FailureSuiteState.tasksFailed == 0) {
+          FailureSuiteState.tasksFailed += 1
+          throw new Exception("Intentional task failure")
+        }
+      }
+      (x, x)
+    }
+    val dep = new ShuffleDependency[Int, Int, Int](rdd, new HashPartitioner(2))
+    sc.submitMapStage(dep).get()
+    FailureSuiteState.synchronized {
+      assert(FailureSuiteState.tasksRun === 4)
+    }
+    FailureSuiteState.clear()
+  }
+
   // TODO: Need to add tests with shuffle fetch failures.
 }
 
diff --git a/core/src/test/scala/org/apache/spark/scheduler/AdaptiveSchedulingSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/AdaptiveSchedulingSuite.scala
new file mode 100644
index 0000000000000..3fe28027c3c21
--- /dev/null
+++ b/core/src/test/scala/org/apache/spark/scheduler/AdaptiveSchedulingSuite.scala
@@ -0,0 +1,65 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.scheduler
+
+import org.apache.spark.rdd.{ShuffledRDDPartition, RDD, ShuffledRDD}
+import org.apache.spark._
+
+object AdaptiveSchedulingSuiteState {
+  var tasksRun = 0
+
+  def clear(): Unit = {
+    tasksRun = 0
+  }
+}
+
+/** A special ShuffledRDD where we can pass a ShuffleDependency object to use */
+class CustomShuffledRDD[K, V, C](@transient dep: ShuffleDependency[K, V, C])
+  extends RDD[(K, C)](dep.rdd.context, Seq(dep)) {
+
+  override def compute(split: Partition, context: TaskContext): Iterator[(K, C)] = {
+    val dep = dependencies.head.asInstanceOf[ShuffleDependency[K, V, C]]
+    SparkEnv.get.shuffleManager.getReader(dep.shuffleHandle, split.index, split.index + 1, context)
+      .read()
+      .asInstanceOf[Iterator[(K, C)]]
+  }
+
+  override def getPartitions: Array[Partition] = {
+    Array.tabulate[Partition](dep.partitioner.numPartitions)(i => new ShuffledRDDPartition(i))
+  }
+}
+
+class AdaptiveSchedulingSuite extends SparkFunSuite with LocalSparkContext {
+  test("simple use of submitMapStage") {
+    try {
+      sc = new SparkContext("local[1,2]", "test")
+      val rdd = sc.parallelize(1 to 3, 3).map { x =>
+        AdaptiveSchedulingSuiteState.tasksRun += 1
+        (x, x)
+      }
+      val dep = new ShuffleDependency[Int, Int, Int](rdd, new HashPartitioner(2))
+      val shuffled = new CustomShuffledRDD[Int, Int, Int](dep)
+      sc.submitMapStage(dep).get()
+      assert(AdaptiveSchedulingSuiteState.tasksRun == 3)
+      assert(shuffled.collect().toSet == Set((1, 1), (2, 2), (3, 3)))
+      assert(AdaptiveSchedulingSuiteState.tasksRun == 3)
+    } finally {
+      AdaptiveSchedulingSuiteState.clear()
+    }
+  }
+}
diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
index 1b9ff740ff530..1c55f90ad9b44 100644
--- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
+++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
@@ -152,6 +152,14 @@ class DAGSchedulerSuite
     override def jobFailed(exception: Exception) = { failure = exception }
   }
 
+  /** A simple helper class for creating custom JobListeners */
+  class SimpleListener extends JobListener {
+    val results = new HashMap[Int, Any]
+    var failure: Exception = null
+    override def taskSucceeded(index: Int, result: Any): Unit = results.put(index, result)
+    override def jobFailed(exception: Exception): Unit = { failure = exception }
+  }
+
   before {
     sc = new SparkContext("local", "DAGSchedulerSuite")
     sparkListener.submittedStageInfos.clear()
@@ -229,7 +237,7 @@ class DAGSchedulerSuite
     }
   }
 
-  /** Sends the rdd to the scheduler for scheduling and returns the job id. */
+  /** Submits a job to the scheduler and returns the job id. */
   private def submit(
       rdd: RDD[_],
       partitions: Array[Int],
@@ -240,6 +248,15 @@ class DAGSchedulerSuite
     jobId
   }
 
+  /** Submits a map stage to the scheduler and returns the job id. */
+  private def submitMapStage(
+      shuffleDep: ShuffleDependency[_, _, _],
+      listener: JobListener = jobListener): Int = {
+    val jobId = scheduler.nextJobId.getAndIncrement()
+    runEvent(MapStageSubmitted(jobId, shuffleDep, CallSite("", ""), listener))
+    jobId
+  }
+
   /** Sends TaskSetFailed to the scheduler. */
   private def failed(taskSet: TaskSet, message: String) {
     runEvent(TaskSetFailed(taskSet, message, None))
@@ -1313,6 +1330,230 @@ class DAGSchedulerSuite
     assert(stackTraceString.contains("org.scalatest.FunSuite"))
   }
 
+  test("simple map stage submission") {
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, new HashPartitioner(1))
+    val reduceRdd = new MyRDD(sc, 1, List(shuffleDep))
+
+    // Submit a map stage by itself
+    submitMapStage(shuffleDep)
+    assert(results.size === 0)  // No results yet
+    completeShuffleMapStageSuccessfully(0, 0, 1)
+    assert(results.size === 1)
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Submit a reduce job that depends on this map stage; it should directly do the reduce
+    submit(reduceRdd, Array(0))
+    completeNextResultStageWithSuccess(2, 0)
+    assert(results === Map(0 -> 42))
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Check that if we submit the map stage again, no tasks run
+    submitMapStage(shuffleDep)
+    assert(results.size === 1)
+    assertDataStructuresEmpty()
+  }
+
+  test("map stage submission with reduce stage also depending on the data") {
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, new HashPartitioner(1))
+    val reduceRdd = new MyRDD(sc, 1, List(shuffleDep))
+
+    // Submit the map stage by itself
+    submitMapStage(shuffleDep)
+
+    // Submit a reduce job that depends on this map stage
+    submit(reduceRdd, Array(0))
+
+    // Complete tasks for the map stage
+    completeShuffleMapStageSuccessfully(0, 0, 1)
+    assert(results.size === 1)
+    results.clear()
+
+    // Complete tasks for the reduce stage
+    completeNextResultStageWithSuccess(1, 0)
+    assert(results === Map(0 -> 42))
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Check that if we submit the map stage again, no tasks run
+    submitMapStage(shuffleDep)
+    assert(results.size === 1)
+    assertDataStructuresEmpty()
+  }
+
+  test("map stage submission with fetch failure") {
+    val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, new HashPartitioner(2))
+    val shuffleId = shuffleDep.shuffleId
+    val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
+
+    // Submit a map stage by itself
+    submitMapStage(shuffleDep)
+    complete(taskSets(0), Seq(
+      (Success, makeMapStatus("hostA", reduceRdd.partitions.size)),
+      (Success, makeMapStatus("hostB", reduceRdd.partitions.size))))
+    assert(results.size === 1)
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Submit a reduce job that depends on this map stage, but where one reduce will fail a fetch
+    submit(reduceRdd, Array(0, 1))
+    complete(taskSets(1), Seq(
+      (Success, 42),
+      (FetchFailed(makeBlockManagerId("hostA"), shuffleId, 0, 0, "ignored"), null)))
+    // Ask the scheduler to try it again; TaskSet 2 will rerun the map task that we couldn't fetch
+    // from, then TaskSet 3 will run the reduce stage
+    scheduler.resubmitFailedStages()
+    complete(taskSets(2), Seq((Success, makeMapStatus("hostA", reduceRdd.partitions.size))))
+    complete(taskSets(3), Seq((Success, 43)))
+    assert(results === Map(0 -> 42, 1 -> 43))
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Run another reduce job without a failure; this should just work
+    submit(reduceRdd, Array(0, 1))
+    complete(taskSets(4), Seq(
+      (Success, 44),
+      (Success, 45)))
+    assert(results === Map(0 -> 44, 1 -> 45))
+    results.clear()
+    assertDataStructuresEmpty()
+
+    // Resubmit the map stage; this should also just work
+    submitMapStage(shuffleDep)
+    assert(results.size === 1)
+    results.clear()
+    assertDataStructuresEmpty()
+  }
+
+  /**
+   * In this test, we have three RDDs with shuffle dependencies, and we submit map stage jobs
+   * that are waiting on each one, as well as a reduce job on the last one. We test that all of
+   * these jobs complete even if there are some fetch failures in both shuffles.
+   */
+  test("map stage submission with multiple shared stages and failures") {
+    val rdd1 = new MyRDD(sc, 2, Nil)
+    val dep1 = new ShuffleDependency(rdd1, new HashPartitioner(2))
+    val rdd2 = new MyRDD(sc, 2, List(dep1))
+    val dep2 = new ShuffleDependency(rdd2, new HashPartitioner(2))
+    val rdd3 = new MyRDD(sc, 2, List(dep2))
+
+    val listener1 = new SimpleListener
+    val listener2 = new SimpleListener
+    val listener3 = new SimpleListener
+
+    submitMapStage(dep1, listener1)
+    submitMapStage(dep2, listener2)
+    submit(rdd3, Array(0, 1), listener = listener3)
+
+    // Complete the first stage
+    assert(taskSets(0).stageId === 0)
+    complete(taskSets(0), Seq(
+      (Success, makeMapStatus("hostA", rdd1.partitions.size)),
+      (Success, makeMapStatus("hostB", rdd1.partitions.size))))
+    assert(mapOutputTracker.getMapSizesByExecutorId(dep1.shuffleId, 0).map(_._1).toSet ===
+      HashSet(makeBlockManagerId("hostA"), makeBlockManagerId("hostB")))
+    assert(listener1.results.size === 1)
+
+    // When attempting the second stage, show a fetch failure
+    assert(taskSets(1).stageId === 1)
+    complete(taskSets(1), Seq(
+      (Success, makeMapStatus("hostA", rdd2.partitions.size)),
+      (FetchFailed(makeBlockManagerId("hostA"), dep1.shuffleId, 0, 0, "ignored"), null)))
+    scheduler.resubmitFailedStages()
+    assert(listener2.results.size === 0)    // Second stage listener should not have a result yet
+
+    // Stage 0 should now be running as task set 2; make its task succeed
+    assert(taskSets(2).stageId === 0)
+    complete(taskSets(2), Seq(
+      (Success, makeMapStatus("hostC", rdd2.partitions.size))))
+    assert(mapOutputTracker.getMapSizesByExecutorId(dep1.shuffleId, 0).map(_._1).toSet ===
+      HashSet(makeBlockManagerId("hostC"), makeBlockManagerId("hostB")))
+    assert(listener2.results.size === 0)    // Second stage listener should still not have a result
+
+    // Stage 1 should now be running as task set 3; make its first task succeed
+    assert(taskSets(3).stageId === 1)
+    complete(taskSets(3), Seq(
+      (Success, makeMapStatus("hostB", rdd2.partitions.size)),
+      (Success, makeMapStatus("hostD", rdd2.partitions.size))))
+    assert(mapOutputTracker.getMapSizesByExecutorId(dep2.shuffleId, 0).map(_._1).toSet ===
+      HashSet(makeBlockManagerId("hostB"), makeBlockManagerId("hostD")))
+    assert(listener2.results.size === 1)
+
+    // Finally, the reduce job should be running as task set 4; make it see a fetch failure,
+    // then make it run again and succeed
+    assert(taskSets(4).stageId === 2)
+    complete(taskSets(4), Seq(
+      (Success, 52),
+      (FetchFailed(makeBlockManagerId("hostD"), dep2.shuffleId, 0, 0, "ignored"), null)))
+    scheduler.resubmitFailedStages()
+
+    // TaskSet 5 will rerun stage 1's lost task, then TaskSet 6 will rerun stage 2
+    assert(taskSets(5).stageId === 1)
+    complete(taskSets(5), Seq(
+      (Success, makeMapStatus("hostE", rdd2.partitions.size))))
+    complete(taskSets(6), Seq(
+      (Success, 53)))
+    assert(listener3.results === Map(0 -> 52, 1 -> 53))
+    assertDataStructuresEmpty()
+  }
+
+  /**
+   * In this test, we run a map stage where one of the executors fails but we still receive a
+   * "zombie" complete message from that executor. We want to make sure the stage is not reported
+   * as done until all tasks have completed.
+   */
+  test("map stage submission with executor failure late map task completions") {
+    val shuffleMapRdd = new MyRDD(sc, 3, Nil)
+    val shuffleDep = new ShuffleDependency(shuffleMapRdd, new HashPartitioner(2))
+
+    submitMapStage(shuffleDep)
+
+    val oldTaskSet = taskSets(0)
+    runEvent(CompletionEvent(oldTaskSet.tasks(0), Success, makeMapStatus("hostA", 2),
+      null, createFakeTaskInfo(), null))
+    assert(results.size === 0)    // Map stage job should not be complete yet
+
+    // Pretend host A was lost
+    val oldEpoch = mapOutputTracker.getEpoch
+    runEvent(ExecutorLost("exec-hostA"))
+    val newEpoch = mapOutputTracker.getEpoch
+    assert(newEpoch > oldEpoch)
+
+    // Suppose we also get a completed event from task 1 on the same host; this should be ignored
+    runEvent(CompletionEvent(oldTaskSet.tasks(1), Success, makeMapStatus("hostA", 2),
+      null, createFakeTaskInfo(), null))
+    assert(results.size === 0)    // Map stage job should not be complete yet
+
+    // A completion from another task should work because it's a non-failed host
+    runEvent(CompletionEvent(oldTaskSet.tasks(2), Success, makeMapStatus("hostB", 2),
+      null, createFakeTaskInfo(), null))
+    assert(results.size === 0)    // Map stage job should not be complete yet
+
+    // Now complete tasks in the second task set
+    val newTaskSet = taskSets(1)
+    assert(newTaskSet.tasks.size === 2)     // Both tasks 0 and 1 were on on hostA
+    runEvent(CompletionEvent(newTaskSet.tasks(0), Success, makeMapStatus("hostB", 2),
+      null, createFakeTaskInfo(), null))
+    assert(results.size === 0)    // Map stage job should not be complete yet
+    runEvent(CompletionEvent(newTaskSet.tasks(1), Success, makeMapStatus("hostB", 2),
+      null, createFakeTaskInfo(), null))
+    assert(results.size === 1)    // Map stage job should now finally be complete
+    assertDataStructuresEmpty()
+
+    // Also test that a reduce stage using this shuffled data can immediately run
+    val reduceRDD = new MyRDD(sc, 2, List(shuffleDep))
+    results.clear()
+    submit(reduceRDD, Array(0, 1))
+    complete(taskSets(2), Seq((Success, 42), (Success, 43)))
+    assert(results === Map(0 -> 42, 1 -> 43))
+    results.clear()
+    assertDataStructuresEmpty()
+  }
+
   /**
    * Assert that the supplied TaskSet has exactly the given hosts as its preferred locations.
    * Note that this checks only the host and not the executor ID.

From 55204181004c105c7a3e8c31a099b37e48bfd953 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Mon, 14 Sep 2015 19:46:34 -0700
Subject: [PATCH 623/633] [SPARK-10542] [PYSPARK] fix serialize namedtuple

Author: Davies Liu <davies@databricks.com>

Closes #8707 from davies/fix_namedtuple.
---
 python/pyspark/cloudpickle.py | 15 ++++++++++++++-
 python/pyspark/serializers.py |  1 +
 python/pyspark/tests.py       |  5 +++++
 3 files changed, 20 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/cloudpickle.py b/python/pyspark/cloudpickle.py
index 3b647985801b7..95b3abc74244b 100644
--- a/python/pyspark/cloudpickle.py
+++ b/python/pyspark/cloudpickle.py
@@ -350,6 +350,11 @@ def save_global(self, obj, name=None, pack=struct.pack):
             if new_override:
                 d['__new__'] = obj.__new__
 
+            # workaround for namedtuple (hijacked by PySpark)
+            if getattr(obj, '_is_namedtuple_', False):
+                self.save_reduce(_load_namedtuple, (obj.__name__, obj._fields))
+                return
+
             self.save(_load_class)
             self.save_reduce(typ, (obj.__name__, obj.__bases__, {"__doc__": obj.__doc__}), obj=obj)
             d.pop('__doc__', None)
@@ -382,7 +387,7 @@ def save_instancemethod(self, obj):
             self.save_reduce(types.MethodType, (obj.__func__, obj.__self__), obj=obj)
         else:
             self.save_reduce(types.MethodType, (obj.__func__, obj.__self__, obj.__self__.__class__),
-                         obj=obj)
+                             obj=obj)
     dispatch[types.MethodType] = save_instancemethod
 
     def save_inst(self, obj):
@@ -744,6 +749,14 @@ def _load_class(cls, d):
     return cls
 
 
+def _load_namedtuple(name, fields):
+    """
+    Loads a class generated by namedtuple
+    """
+    from collections import namedtuple
+    return namedtuple(name, fields)
+
+
 """Constructors for 3rd party libraries
 Note: These can never be renamed due to client compatibility issues"""
 
diff --git a/python/pyspark/serializers.py b/python/pyspark/serializers.py
index 411b4dbf481f1..2a1326947f4f5 100644
--- a/python/pyspark/serializers.py
+++ b/python/pyspark/serializers.py
@@ -359,6 +359,7 @@ def _hack_namedtuple(cls):
     def __reduce__(self):
         return (_restore, (name, fields, tuple(self)))
     cls.__reduce__ = __reduce__
+    cls._is_namedtuple_ = True
     return cls
 
 
diff --git a/python/pyspark/tests.py b/python/pyspark/tests.py
index 8bfed074c9052..647504c32f156 100644
--- a/python/pyspark/tests.py
+++ b/python/pyspark/tests.py
@@ -218,6 +218,11 @@ def test_namedtuple(self):
         p2 = loads(dumps(p1, 2))
         self.assertEqual(p1, p2)
 
+        from pyspark.cloudpickle import dumps
+        P2 = loads(dumps(P))
+        p3 = P2(1, 3)
+        self.assertEqual(p1, p3)
+
     def test_itemgetter(self):
         from operator import itemgetter
         ser = CloudPickleSerializer()

From 4ae4d54794778042b2cc983e52757edac02412ab Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 14 Sep 2015 21:37:43 -0700
Subject: [PATCH 624/633] [SPARK-9793] [MLLIB] [PYSPARK] PySpark DenseVector,
 SparseVector implement __eq__ and __hash__ correctly

PySpark DenseVector, SparseVector ```__eq__``` method should use semantics equality, and DenseVector can compared with SparseVector.
Implement PySpark DenseVector, SparseVector ```__hash__``` method based on the first 16 entries. That will make PySpark Vector objects can be used in collections.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8166 from yanboliang/spark-9793.
---
 python/pyspark/mllib/linalg/__init__.py | 90 ++++++++++++++++++++-----
 python/pyspark/mllib/tests.py           | 32 +++++++++
 2 files changed, 107 insertions(+), 15 deletions(-)

diff --git a/python/pyspark/mllib/linalg/__init__.py b/python/pyspark/mllib/linalg/__init__.py
index 334dc8e38bb8f..380f86e9b44f8 100644
--- a/python/pyspark/mllib/linalg/__init__.py
+++ b/python/pyspark/mllib/linalg/__init__.py
@@ -25,6 +25,7 @@
 
 import sys
 import array
+import struct
 
 if sys.version >= '3':
     basestring = str
@@ -122,6 +123,13 @@ def _format_float_list(l):
     return [_format_float(x) for x in l]
 
 
+def _double_to_long_bits(value):
+    if np.isnan(value):
+        value = float('nan')
+    # pack double into 64 bits, then unpack as long int
+    return struct.unpack('Q', struct.pack('d', value))[0]
+
+
 class VectorUDT(UserDefinedType):
     """
     SQL user-defined type (UDT) for Vector.
@@ -404,11 +412,31 @@ def __repr__(self):
         return "DenseVector([%s])" % (', '.join(_format_float(i) for i in self.array))
 
     def __eq__(self, other):
-        return isinstance(other, DenseVector) and np.array_equal(self.array, other.array)
+        if isinstance(other, DenseVector):
+            return np.array_equal(self.array, other.array)
+        elif isinstance(other, SparseVector):
+            if len(self) != other.size:
+                return False
+            return Vectors._equals(list(xrange(len(self))), self.array, other.indices, other.values)
+        return False
 
     def __ne__(self, other):
         return not self == other
 
+    def __hash__(self):
+        size = len(self)
+        result = 31 + size
+        nnz = 0
+        i = 0
+        while i < size and nnz < 128:
+            if self.array[i] != 0:
+                result = 31 * result + i
+                bits = _double_to_long_bits(self.array[i])
+                result = 31 * result + (bits ^ (bits >> 32))
+                nnz += 1
+            i += 1
+        return result
+
     def __getattr__(self, item):
         return getattr(self.array, item)
 
@@ -704,20 +732,14 @@ def __repr__(self):
         return "SparseVector({0}, {{{1}}})".format(self.size, entries)
 
     def __eq__(self, other):
-        """
-        Test SparseVectors for equality.
-
-        >>> v1 = SparseVector(4, [(1, 1.0), (3, 5.5)])
-        >>> v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
-        >>> v1 == v2
-        True
-        >>> v1 != v2
-        False
-        """
-        return (isinstance(other, self.__class__)
-                and other.size == self.size
-                and np.array_equal(other.indices, self.indices)
-                and np.array_equal(other.values, self.values))
+        if isinstance(other, SparseVector):
+            return other.size == self.size and np.array_equal(other.indices, self.indices) \
+                and np.array_equal(other.values, self.values)
+        elif isinstance(other, DenseVector):
+            if self.size != len(other):
+                return False
+            return Vectors._equals(self.indices, self.values, list(xrange(len(other))), other.array)
+        return False
 
     def __getitem__(self, index):
         inds = self.indices
@@ -739,6 +761,19 @@ def __getitem__(self, index):
     def __ne__(self, other):
         return not self.__eq__(other)
 
+    def __hash__(self):
+        result = 31 + self.size
+        nnz = 0
+        i = 0
+        while i < len(self.values) and nnz < 128:
+            if self.values[i] != 0:
+                result = 31 * result + int(self.indices[i])
+                bits = _double_to_long_bits(self.values[i])
+                result = 31 * result + (bits ^ (bits >> 32))
+                nnz += 1
+            i += 1
+        return result
+
 
 class Vectors(object):
 
@@ -841,6 +876,31 @@ def parse(s):
     def zeros(size):
         return DenseVector(np.zeros(size))
 
+    @staticmethod
+    def _equals(v1_indices, v1_values, v2_indices, v2_values):
+        """
+        Check equality between sparse/dense vectors,
+        v1_indices and v2_indices assume to be strictly increasing.
+        """
+        v1_size = len(v1_values)
+        v2_size = len(v2_values)
+        k1 = 0
+        k2 = 0
+        all_equal = True
+        while all_equal:
+            while k1 < v1_size and v1_values[k1] == 0:
+                k1 += 1
+            while k2 < v2_size and v2_values[k2] == 0:
+                k2 += 1
+
+            if k1 >= v1_size or k2 >= v2_size:
+                return k1 >= v1_size and k2 >= v2_size
+
+            all_equal = v1_indices[k1] == v2_indices[k2] and v1_values[k1] == v2_values[k2]
+            k1 += 1
+            k2 += 1
+        return all_equal
+
 
 class Matrix(object):
 
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py
index 5097c5e8ba4cd..636f9a06cab7b 100644
--- a/python/pyspark/mllib/tests.py
+++ b/python/pyspark/mllib/tests.py
@@ -194,6 +194,38 @@ def test_squared_distance(self):
         self.assertEquals(3.0, _squared_distance(sv, arr))
         self.assertEquals(3.0, _squared_distance(sv, narr))
 
+    def test_hash(self):
+        v1 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
+        v3 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v4 = SparseVector(4, [(1, 1.0), (3, 2.5)])
+        self.assertEquals(hash(v1), hash(v2))
+        self.assertEquals(hash(v1), hash(v3))
+        self.assertEquals(hash(v2), hash(v3))
+        self.assertFalse(hash(v1) == hash(v4))
+        self.assertFalse(hash(v2) == hash(v4))
+
+    def test_eq(self):
+        v1 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
+        v3 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v4 = SparseVector(6, [(1, 1.0), (3, 5.5)])
+        v5 = DenseVector([0.0, 1.0, 0.0, 2.5])
+        v6 = SparseVector(4, [(1, 1.0), (3, 2.5)])
+        self.assertEquals(v1, v2)
+        self.assertEquals(v1, v3)
+        self.assertFalse(v2 == v4)
+        self.assertFalse(v1 == v5)
+        self.assertFalse(v1 == v6)
+
+    def test_equals(self):
+        indices = [1, 2, 4]
+        values = [1., 3., 2.]
+        self.assertTrue(Vectors._equals(indices, values, list(range(5)), [0., 1., 3., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 3., 1., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 3., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 1., 3., 2., 2.]))
+
     def test_conversion(self):
         # numpy arrays should be automatically upcast to float64
         # tests for fix of [SPARK-5089]

From 610971ecfe858b1a48ce69b25614afe52bcbe77f Mon Sep 17 00:00:00 2001
From: noelsmith <mail@noelsmith.com>
Date: Mon, 14 Sep 2015 21:58:52 -0700
Subject: [PATCH 625/633] [SPARK-10273] Add @since annotation to
 pyspark.mllib.feature

Duplicated the since decorator from pyspark.sql into pyspark (also tweaked to handle functions without docstrings).

Added since to methods + "versionadded::" to classes (derived from the git file history in pyspark).

Author: noelsmith <mail@noelsmith.com>

Closes #8633 from noel-smith/SPARK-10273-since-mllib-feature.
---
 python/pyspark/mllib/feature.py | 58 ++++++++++++++++++++++++++++++++-
 1 file changed, 57 insertions(+), 1 deletion(-)

diff --git a/python/pyspark/mllib/feature.py b/python/pyspark/mllib/feature.py
index f921e3ad1a314..7b077b058c3fd 100644
--- a/python/pyspark/mllib/feature.py
+++ b/python/pyspark/mllib/feature.py
@@ -30,7 +30,7 @@
 
 from py4j.protocol import Py4JJavaError
 
-from pyspark import SparkContext
+from pyspark import SparkContext, since
 from pyspark.rdd import RDD, ignore_unicode_prefix
 from pyspark.mllib.common import callMLlibFunc, JavaModelWrapper
 from pyspark.mllib.linalg import (
@@ -84,11 +84,14 @@ class Normalizer(VectorTransformer):
     >>> nor2 = Normalizer(float("inf"))
     >>> nor2.transform(v)
     DenseVector([0.0, 0.5, 1.0])
+
+    .. versionadded:: 1.2.0
     """
     def __init__(self, p=2.0):
         assert p >= 1.0, "p should be greater than 1.0"
         self.p = float(p)
 
+    @since('1.2.0')
     def transform(self, vector):
         """
         Applies unit length normalization on a vector.
@@ -133,7 +136,11 @@ class StandardScalerModel(JavaVectorTransformer):
     .. note:: Experimental
 
     Represents a StandardScaler model that can transform vectors.
+
+    .. versionadded:: 1.2.0
     """
+
+    @since('1.2.0')
     def transform(self, vector):
         """
         Applies standardization transformation on a vector.
@@ -149,6 +156,7 @@ def transform(self, vector):
         """
         return JavaVectorTransformer.transform(self, vector)
 
+    @since('1.4.0')
     def setWithMean(self, withMean):
         """
         Setter of the boolean which decides
@@ -157,6 +165,7 @@ def setWithMean(self, withMean):
         self.call("setWithMean", withMean)
         return self
 
+    @since('1.4.0')
     def setWithStd(self, withStd):
         """
         Setter of the boolean which decides
@@ -189,6 +198,8 @@ class StandardScaler(object):
     >>> for r in result.collect(): r
     DenseVector([-0.7071, 0.7071, -0.7071])
     DenseVector([0.7071, -0.7071, 0.7071])
+
+    .. versionadded:: 1.2.0
     """
     def __init__(self, withMean=False, withStd=True):
         if not (withMean or withStd):
@@ -196,6 +207,7 @@ def __init__(self, withMean=False, withStd=True):
         self.withMean = withMean
         self.withStd = withStd
 
+    @since('1.2.0')
     def fit(self, dataset):
         """
         Computes the mean and variance and stores as a model to be used
@@ -215,7 +227,11 @@ class ChiSqSelectorModel(JavaVectorTransformer):
     .. note:: Experimental
 
     Represents a Chi Squared selector model.
+
+    .. versionadded:: 1.4.0
     """
+
+    @since('1.4.0')
     def transform(self, vector):
         """
         Applies transformation on a vector.
@@ -245,10 +261,13 @@ class ChiSqSelector(object):
     SparseVector(1, {0: 6.0})
     >>> model.transform(DenseVector([8.0, 9.0, 5.0]))
     DenseVector([5.0])
+
+    .. versionadded:: 1.4.0
     """
     def __init__(self, numTopFeatures):
         self.numTopFeatures = int(numTopFeatures)
 
+    @since('1.4.0')
     def fit(self, data):
         """
         Returns a ChiSquared feature selector.
@@ -265,6 +284,8 @@ def fit(self, data):
 class PCAModel(JavaVectorTransformer):
     """
     Model fitted by [[PCA]] that can project vectors to a low-dimensional space using PCA.
+
+    .. versionadded:: 1.5.0
     """
 
 
@@ -281,6 +302,8 @@ class PCA(object):
     1.648...
     >>> pcArray[1]
     -4.013...
+
+    .. versionadded:: 1.5.0
     """
     def __init__(self, k):
         """
@@ -288,6 +311,7 @@ def __init__(self, k):
         """
         self.k = int(k)
 
+    @since('1.5.0')
     def fit(self, data):
         """
         Computes a [[PCAModel]] that contains the principal components of the input vectors.
@@ -312,14 +336,18 @@ class HashingTF(object):
     >>> doc = "a a b b c d".split(" ")
     >>> htf.transform(doc)
     SparseVector(100, {...})
+
+    .. versionadded:: 1.2.0
     """
     def __init__(self, numFeatures=1 << 20):
         self.numFeatures = numFeatures
 
+    @since('1.2.0')
     def indexOf(self, term):
         """ Returns the index of the input term. """
         return hash(term) % self.numFeatures
 
+    @since('1.2.0')
     def transform(self, document):
         """
         Transforms the input document (list of terms) to term frequency
@@ -339,7 +367,10 @@ def transform(self, document):
 class IDFModel(JavaVectorTransformer):
     """
     Represents an IDF model that can transform term frequency vectors.
+
+    .. versionadded:: 1.2.0
     """
+    @since('1.2.0')
     def transform(self, x):
         """
         Transforms term frequency (TF) vectors to TF-IDF vectors.
@@ -358,6 +389,7 @@ def transform(self, x):
         """
         return JavaVectorTransformer.transform(self, x)
 
+    @since('1.4.0')
     def idf(self):
         """
         Returns the current IDF vector.
@@ -401,10 +433,13 @@ class IDF(object):
     DenseVector([0.0, 0.0, 1.3863, 0.863])
     >>> model.transform(Vectors.sparse(n, (1, 3), (1.0, 2.0)))
     SparseVector(4, {1: 0.0, 3: 0.5754})
+
+    .. versionadded:: 1.2.0
     """
     def __init__(self, minDocFreq=0):
         self.minDocFreq = minDocFreq
 
+    @since('1.2.0')
     def fit(self, dataset):
         """
         Computes the inverse document frequency.
@@ -420,7 +455,10 @@ def fit(self, dataset):
 class Word2VecModel(JavaVectorTransformer, JavaSaveable, JavaLoader):
     """
     class for Word2Vec model
+
+    .. versionadded:: 1.2.0
     """
+    @since('1.2.0')
     def transform(self, word):
         """
         Transforms a word to its vector representation
@@ -435,6 +473,7 @@ def transform(self, word):
         except Py4JJavaError:
             raise ValueError("%s not found" % word)
 
+    @since('1.2.0')
     def findSynonyms(self, word, num):
         """
         Find synonyms of a word
@@ -450,6 +489,7 @@ def findSynonyms(self, word, num):
         words, similarity = self.call("findSynonyms", word, num)
         return zip(words, similarity)
 
+    @since('1.4.0')
     def getVectors(self):
         """
         Returns a map of words to their vector representations.
@@ -457,7 +497,11 @@ def getVectors(self):
         return self.call("getVectors")
 
     @classmethod
+    @since('1.5.0')
     def load(cls, sc, path):
+        """
+        Load a model from the given path.
+        """
         jmodel = sc._jvm.org.apache.spark.mllib.feature \
             .Word2VecModel.load(sc._jsc.sc(), path)
         return Word2VecModel(jmodel)
@@ -507,6 +551,8 @@ class Word2Vec(object):
     ...     rmtree(path)
     ... except OSError:
     ...     pass
+
+    .. versionadded:: 1.2.0
     """
     def __init__(self):
         """
@@ -519,6 +565,7 @@ def __init__(self):
         self.seed = random.randint(0, sys.maxsize)
         self.minCount = 5
 
+    @since('1.2.0')
     def setVectorSize(self, vectorSize):
         """
         Sets vector size (default: 100).
@@ -526,6 +573,7 @@ def setVectorSize(self, vectorSize):
         self.vectorSize = vectorSize
         return self
 
+    @since('1.2.0')
     def setLearningRate(self, learningRate):
         """
         Sets initial learning rate (default: 0.025).
@@ -533,6 +581,7 @@ def setLearningRate(self, learningRate):
         self.learningRate = learningRate
         return self
 
+    @since('1.2.0')
     def setNumPartitions(self, numPartitions):
         """
         Sets number of partitions (default: 1). Use a small number for
@@ -541,6 +590,7 @@ def setNumPartitions(self, numPartitions):
         self.numPartitions = numPartitions
         return self
 
+    @since('1.2.0')
     def setNumIterations(self, numIterations):
         """
         Sets number of iterations (default: 1), which should be smaller
@@ -549,6 +599,7 @@ def setNumIterations(self, numIterations):
         self.numIterations = numIterations
         return self
 
+    @since('1.2.0')
     def setSeed(self, seed):
         """
         Sets random seed.
@@ -556,6 +607,7 @@ def setSeed(self, seed):
         self.seed = seed
         return self
 
+    @since('1.4.0')
     def setMinCount(self, minCount):
         """
         Sets minCount, the minimum number of times a token must appear
@@ -564,6 +616,7 @@ def setMinCount(self, minCount):
         self.minCount = minCount
         return self
 
+    @since('1.2.0')
     def fit(self, data):
         """
         Computes the vector representation of each word in vocabulary.
@@ -596,10 +649,13 @@ class ElementwiseProduct(VectorTransformer):
     >>> rdd = sc.parallelize([a, b])
     >>> eprod.transform(rdd).collect()
     [DenseVector([2.0, 2.0, 9.0]), DenseVector([9.0, 6.0, 12.0])]
+
+    .. versionadded:: 1.5.0
     """
     def __init__(self, scalingVector):
         self.scalingVector = _convert_to_vector(scalingVector)
 
+    @since('1.5.0')
     def transform(self, vector):
         """
         Computes the Hadamard product of the vector.

From a2249359d5b0368318a714b292bb1d0dc70c0e27 Mon Sep 17 00:00:00 2001
From: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Date: Mon, 14 Sep 2015 21:59:40 -0700
Subject: [PATCH 626/633] [SPARK-10275] [MLLIB] Add @since annotation to
 pyspark.mllib.random

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8666 from yu-iskw/SPARK-10275.
---
 python/pyspark/mllib/random.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
index 06fbc0eb6aef0..9c733b1332bc0 100644
--- a/python/pyspark/mllib/random.py
+++ b/python/pyspark/mllib/random.py
@@ -21,6 +21,7 @@
 
 from functools import wraps
 
+from pyspark import since
 from pyspark.mllib.common import callMLlibFunc
 
 
@@ -39,9 +40,12 @@ class RandomRDDs(object):
     """
     Generator methods for creating RDDs comprised of i.i.d samples from
     some distribution.
+
+    .. addedversion:: 1.1.0
     """
 
     @staticmethod
+    @since("1.1.0")
     def uniformRDD(sc, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the
@@ -72,6 +76,7 @@ def uniformRDD(sc, size, numPartitions=None, seed=None):
         return callMLlibFunc("uniformRDD", sc._jsc, size, numPartitions, seed)
 
     @staticmethod
+    @since("1.1.0")
     def normalRDD(sc, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the standard normal
@@ -100,6 +105,7 @@ def normalRDD(sc, size, numPartitions=None, seed=None):
         return callMLlibFunc("normalRDD", sc._jsc, size, numPartitions, seed)
 
     @staticmethod
+    @since("1.3.0")
     def logNormalRDD(sc, mean, std, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the log normal
@@ -132,6 +138,7 @@ def logNormalRDD(sc, mean, std, size, numPartitions=None, seed=None):
                              size, numPartitions, seed)
 
     @staticmethod
+    @since("1.1.0")
     def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the Poisson
@@ -158,6 +165,7 @@ def poissonRDD(sc, mean, size, numPartitions=None, seed=None):
         return callMLlibFunc("poissonRDD", sc._jsc, float(mean), size, numPartitions, seed)
 
     @staticmethod
+    @since("1.3.0")
     def exponentialRDD(sc, mean, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the Exponential
@@ -184,6 +192,7 @@ def exponentialRDD(sc, mean, size, numPartitions=None, seed=None):
         return callMLlibFunc("exponentialRDD", sc._jsc, float(mean), size, numPartitions, seed)
 
     @staticmethod
+    @since("1.3.0")
     def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of i.i.d. samples from the Gamma
@@ -216,6 +225,7 @@ def gammaRDD(sc, shape, scale, size, numPartitions=None, seed=None):
 
     @staticmethod
     @toArray
+    @since("1.1.0")
     def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -241,6 +251,7 @@ def uniformVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
 
     @staticmethod
     @toArray
+    @since("1.1.0")
     def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -266,6 +277,7 @@ def normalVectorRDD(sc, numRows, numCols, numPartitions=None, seed=None):
 
     @staticmethod
     @toArray
+    @since("1.3.0")
     def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -300,6 +312,7 @@ def logNormalVectorRDD(sc, mean, std, numRows, numCols, numPartitions=None, seed
 
     @staticmethod
     @toArray
+    @since("1.1.0")
     def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -330,6 +343,7 @@ def poissonVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
 
     @staticmethod
     @toArray
+    @since("1.3.0")
     def exponentialVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn
@@ -360,6 +374,7 @@ def exponentialVectorRDD(sc, mean, numRows, numCols, numPartitions=None, seed=No
 
     @staticmethod
     @toArray
+    @since("1.3.0")
     def gammaVectorRDD(sc, shape, scale, numRows, numCols, numPartitions=None, seed=None):
         """
         Generates an RDD comprised of vectors containing i.i.d. samples drawn

From 833be73314b85b390a9007ed6ed63dc47bbd9e4f Mon Sep 17 00:00:00 2001
From: Jacek Laskowski <jacek.laskowski@deepsense.io>
Date: Mon, 14 Sep 2015 23:40:29 -0700
Subject: [PATCH 627/633] Small fixes to docs

Links work now properly + consistent use of *Spark standalone cluster* (Spark uppercase + lowercase the rest -- seems agreed in the other places in the docs).

Author: Jacek Laskowski <jacek.laskowski@deepsense.io>

Closes #8759 from jaceklaskowski/docs-submitting-apps.
---
 docs/submitting-applications.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/submitting-applications.md b/docs/submitting-applications.md
index e58645274e525..7ea4d6f1a3f8f 100644
--- a/docs/submitting-applications.md
+++ b/docs/submitting-applications.md
@@ -65,8 +65,8 @@ For Python applications, simply pass a `.py` file in the place of `<application-
 and add Python `.zip`, `.egg` or `.py` files to the search path with `--py-files`.
 
 There are a few options available that are specific to the
-[cluster manager](#cluster-overview.html#cluster-manager-types) that is being used.
-For example, with a [Spark Standalone](#spark-standalone) cluster with `cluster` deploy mode,
+[cluster manager](cluster-overview.html#cluster-manager-types) that is being used.
+For example, with a [Spark standalone cluster](spark-standalone.html) with `cluster` deploy mode,
 you can also specify `--supervise` to make sure that the driver is automatically restarted if it
 fails with non-zero exit code. To enumerate all such options available to `spark-submit`,
 run it with `--help`. Here are a few examples of common options:
@@ -79,7 +79,7 @@ run it with `--help`. Here are a few examples of common options:
   /path/to/examples.jar \
   100
 
-# Run on a Spark Standalone cluster in client deploy mode
+# Run on a Spark standalone cluster in client deploy mode
 ./bin/spark-submit \
   --class org.apache.spark.examples.SparkPi \
   --master spark://207.184.161.138:7077 \
@@ -88,7 +88,7 @@ run it with `--help`. Here are a few examples of common options:
   /path/to/examples.jar \
   1000
 
-# Run on a Spark Standalone cluster in cluster deploy mode with supervise
+# Run on a Spark standalone cluster in cluster deploy mode with supervise
 ./bin/spark-submit \
   --class org.apache.spark.examples.SparkPi \
   --master spark://207.184.161.138:7077 \
@@ -109,7 +109,7 @@ export HADOOP_CONF_DIR=XXX
   /path/to/examples.jar \
   1000
 
-# Run a Python application on a Spark Standalone cluster
+# Run a Python application on a Spark standalone cluster
 ./bin/spark-submit \
   --master spark://207.184.161.138:7077 \
   examples/src/main/python/pi.py \

From 6503c4b5f3cf3ba9aefc850e7874a62a218b9b0a Mon Sep 17 00:00:00 2001
From: Robin East <robin.east@xense.co.uk>
Date: Mon, 14 Sep 2015 23:41:06 -0700
Subject: [PATCH 628/633] [SPARK-10598] [DOCS]

Comments preceding toMessage method state: "The edge partition is encoded in the lower
   * 30 bytes of the Int, and the position is encoded in the upper 2 bytes of the Int.". References to bytes should be changed to bits.

This contribution is my original work and I license the work to the Spark project under it's open source license.

Author: Robin East <robin.east@xense.co.uk>

Closes #8756 from insidedctm/master.
---
 .../org/apache/spark/graphx/impl/RoutingTablePartition.scala    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
index eb3c997e0f3c0..4f1260a5a67b2 100644
--- a/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
+++ b/graphx/src/main/scala/org/apache/spark/graphx/impl/RoutingTablePartition.scala
@@ -34,7 +34,7 @@ object RoutingTablePartition {
   /**
    * A message from an edge partition to a vertex specifying the position in which the edge
    * partition references the vertex (src, dst, or both). The edge partition is encoded in the lower
-   * 30 bytes of the Int, and the position is encoded in the upper 2 bytes of the Int.
+   * 30 bits of the Int, and the position is encoded in the upper 2 bits of the Int.
    */
   type RoutingTableMessage = (VertexId, Int)
 

From 09b7e7c19897549a8622aec095f27b8b38a1a4d3 Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Tue, 15 Sep 2015 00:54:20 -0700
Subject: [PATCH 629/633] Update version to 1.6.0-SNAPSHOT.

Author: Reynold Xin <rxin@databricks.com>

Closes #8350 from rxin/1.6.
---
 R/pkg/DESCRIPTION                                  |  2 +-
 assembly/pom.xml                                   |  2 +-
 bagel/pom.xml                                      |  2 +-
 core/pom.xml                                       |  2 +-
 core/src/main/scala/org/apache/spark/package.scala |  2 +-
 docs/_config.yml                                   |  4 ++--
 examples/pom.xml                                   |  2 +-
 external/flume-assembly/pom.xml                    |  2 +-
 external/flume-sink/pom.xml                        |  2 +-
 external/flume/pom.xml                             |  2 +-
 external/kafka-assembly/pom.xml                    |  2 +-
 external/kafka/pom.xml                             |  2 +-
 external/mqtt-assembly/pom.xml                     |  2 +-
 external/mqtt/pom.xml                              |  2 +-
 external/twitter/pom.xml                           |  2 +-
 external/zeromq/pom.xml                            |  2 +-
 extras/java8-tests/pom.xml                         |  2 +-
 extras/kinesis-asl-assembly/pom.xml                |  2 +-
 extras/kinesis-asl/pom.xml                         |  2 +-
 extras/spark-ganglia-lgpl/pom.xml                  |  2 +-
 graphx/pom.xml                                     |  2 +-
 launcher/pom.xml                                   |  2 +-
 mllib/pom.xml                                      |  2 +-
 network/common/pom.xml                             |  2 +-
 network/shuffle/pom.xml                            |  2 +-
 network/yarn/pom.xml                               |  2 +-
 pom.xml                                            |  2 +-
 project/MimaBuild.scala                            |  2 +-
 project/MimaExcludes.scala                         | 13 +++++++++++--
 repl/pom.xml                                       |  2 +-
 sql/catalyst/pom.xml                               |  2 +-
 sql/core/pom.xml                                   |  2 +-
 sql/hive-thriftserver/pom.xml                      |  2 +-
 sql/hive/pom.xml                                   |  2 +-
 streaming/pom.xml                                  |  2 +-
 tools/pom.xml                                      |  2 +-
 unsafe/pom.xml                                     |  2 +-
 yarn/pom.xml                                       |  2 +-
 38 files changed, 49 insertions(+), 40 deletions(-)

diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index d0d7201f004a2..a3a16c42a6214 100644
--- a/R/pkg/DESCRIPTION
+++ b/R/pkg/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: SparkR
 Type: Package
 Title: R frontend for Spark
-Version: 1.5.0
+Version: 1.6.0
 Date: 2013-09-09
 Author: The Apache Software Foundation
 Maintainer: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
diff --git a/assembly/pom.xml b/assembly/pom.xml
index e9c6d26ccddc7..4b60ee00ffbe5 100644
--- a/assembly/pom.xml
+++ b/assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/bagel/pom.xml b/bagel/pom.xml
index ed5c37e595a96..3baf8d47b4dc7 100644
--- a/bagel/pom.xml
+++ b/bagel/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/core/pom.xml b/core/pom.xml
index a46292c13bcc0..e31d90f608892 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/core/src/main/scala/org/apache/spark/package.scala b/core/src/main/scala/org/apache/spark/package.scala
index 8ae76c5f72f2e..7515aad09db73 100644
--- a/core/src/main/scala/org/apache/spark/package.scala
+++ b/core/src/main/scala/org/apache/spark/package.scala
@@ -43,5 +43,5 @@ package org.apache
 
 package object spark {
   // For package docs only
-  val SPARK_VERSION = "1.5.0-SNAPSHOT"
+  val SPARK_VERSION = "1.6.0-SNAPSHOT"
 }
diff --git a/docs/_config.yml b/docs/_config.yml
index c0e031a83ba9c..c59cc465ef89d 100644
--- a/docs/_config.yml
+++ b/docs/_config.yml
@@ -14,8 +14,8 @@ include:
 
 # These allow the documentation to be updated with newer releases
 # of Spark, Scala, and Mesos.
-SPARK_VERSION: 1.5.0-SNAPSHOT
-SPARK_VERSION_SHORT: 1.5.0
+SPARK_VERSION: 1.6.0-SNAPSHOT
+SPARK_VERSION_SHORT: 1.6.0
 SCALA_BINARY_VERSION: "2.10"
 SCALA_VERSION: "2.10.4"
 MESOS_VERSION: 0.21.0
diff --git a/examples/pom.xml b/examples/pom.xml
index e6884b09dca94..f5ab2a7fdc098 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/external/flume-assembly/pom.xml b/external/flume-assembly/pom.xml
index 561ed4babe5d0..dceedcf23ed5b 100644
--- a/external/flume-assembly/pom.xml
+++ b/external/flume-assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/flume-sink/pom.xml b/external/flume-sink/pom.xml
index 0664cfb2021e1..d7c2ac474a18d 100644
--- a/external/flume-sink/pom.xml
+++ b/external/flume-sink/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 14f7daaf417e0..132062f94fb45 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/kafka-assembly/pom.xml b/external/kafka-assembly/pom.xml
index 6f4e2a89e9af7..a9ed39ef8c9a0 100644
--- a/external/kafka-assembly/pom.xml
+++ b/external/kafka-assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index ded863bd985e8..05abd9e2e6810 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/mqtt-assembly/pom.xml b/external/mqtt-assembly/pom.xml
index 8412600633734..89713a28ca6a8 100644
--- a/external/mqtt-assembly/pom.xml
+++ b/external/mqtt-assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 69b309876a0db..05e6338a08b0a 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 178ae8de13b57..244ad58ae9593 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index 37bfd10d43663..171df8682c848 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml
index 3636a9037d43f..81794a8536318 100644
--- a/extras/java8-tests/pom.xml
+++ b/extras/java8-tests/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/kinesis-asl-assembly/pom.xml b/extras/kinesis-asl-assembly/pom.xml
index 51af3e6f2225f..61ba4787fbf90 100644
--- a/extras/kinesis-asl-assembly/pom.xml
+++ b/extras/kinesis-asl-assembly/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml
index 521b53e230c4a..6dd8ff69c2943 100644
--- a/extras/kinesis-asl/pom.xml
+++ b/extras/kinesis-asl/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/extras/spark-ganglia-lgpl/pom.xml b/extras/spark-ganglia-lgpl/pom.xml
index 478d0019a25f0..87a4f05a05961 100644
--- a/extras/spark-ganglia-lgpl/pom.xml
+++ b/extras/spark-ganglia-lgpl/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/graphx/pom.xml b/graphx/pom.xml
index 853dea9a7795e..202fc19002d12 100644
--- a/graphx/pom.xml
+++ b/graphx/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/launcher/pom.xml b/launcher/pom.xml
index 2fd768d8119c4..ed38e66aa2467 100644
--- a/launcher/pom.xml
+++ b/launcher/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/mllib/pom.xml b/mllib/pom.xml
index a5db14407b4fc..22c0c6008ba37 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/network/common/pom.xml b/network/common/pom.xml
index 4141fcb8267a5..1cc054a8936c5 100644
--- a/network/common/pom.xml
+++ b/network/common/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/network/shuffle/pom.xml b/network/shuffle/pom.xml
index 3d2edf9d94515..7a66c968041ce 100644
--- a/network/shuffle/pom.xml
+++ b/network/shuffle/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/network/yarn/pom.xml b/network/yarn/pom.xml
index a99f7c4392d3d..e745180eace78 100644
--- a/network/yarn/pom.xml
+++ b/network/yarn/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/pom.xml b/pom.xml
index 421357e141572..6535994641145 100644
--- a/pom.xml
+++ b/pom.xml
@@ -26,7 +26,7 @@
   </parent>
   <groupId>org.apache.spark</groupId>
   <artifactId>spark-parent_2.10</artifactId>
-  <version>1.5.0-SNAPSHOT</version>
+  <version>1.6.0-SNAPSHOT</version>
   <packaging>pom</packaging>
   <name>Spark Project Parent POM</name>
   <url>http://spark.apache.org/</url>
diff --git a/project/MimaBuild.scala b/project/MimaBuild.scala
index f16bf989f200b..519052620246f 100644
--- a/project/MimaBuild.scala
+++ b/project/MimaBuild.scala
@@ -91,7 +91,7 @@ object MimaBuild {
 
   def mimaSettings(sparkHome: File, projectRef: ProjectRef) = {
     val organization = "org.apache.spark"
-    val previousSparkVersion = "1.4.0"
+    val previousSparkVersion = "1.5.0"
     val fullId = "spark-" + projectRef.project + "_2.10"
     mimaDefaultSettings ++
     Seq(previousArtifact := Some(organization % fullId % previousSparkVersion),
diff --git a/project/MimaExcludes.scala b/project/MimaExcludes.scala
index 3b8b6c8ffa375..87b141cd3b058 100644
--- a/project/MimaExcludes.scala
+++ b/project/MimaExcludes.scala
@@ -35,8 +35,17 @@ object MimaExcludes {
   def excludes(version: String) = version match {
     case v if v.startsWith("1.6") =>
       Seq(
-        MimaBuild.excludeSparkPackage("network")
-        )
+        MimaBuild.excludeSparkPackage("deploy"),
+        MimaBuild.excludeSparkPackage("network"),
+        // These are needed if checking against the sbt build, since they are part of
+        // the maven-generated artifacts in 1.3.
+        excludePackage("org.spark-project.jetty"),
+        MimaBuild.excludeSparkPackage("unused"),
+        // SQL execution is considered private.
+        excludePackage("org.apache.spark.sql.execution")
+      ) ++
+      MimaBuild.excludeSparkClass("streaming.flume.FlumeTestUtils") ++
+      MimaBuild.excludeSparkClass("streaming.flume.PollingFlumeTestUtils")
     case v if v.startsWith("1.5") =>
       Seq(
         MimaBuild.excludeSparkPackage("network"),
diff --git a/repl/pom.xml b/repl/pom.xml
index a5a0f1fc2c857..5cf416a4a5448 100644
--- a/repl/pom.xml
+++ b/repl/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/catalyst/pom.xml b/sql/catalyst/pom.xml
index 75ab575dfde83..6cfd53e868f83 100644
--- a/sql/catalyst/pom.xml
+++ b/sql/catalyst/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index 349007789f634..465aa3a3888c2 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/hive-thriftserver/pom.xml b/sql/hive-thriftserver/pom.xml
index 3566c87dd248c..f7fe085f34d84 100644
--- a/sql/hive-thriftserver/pom.xml
+++ b/sql/hive-thriftserver/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index be1607476e254..ac67fe5f47be9 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../../pom.xml</relativePath>
   </parent>
 
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 697895e72fe5b..5cc9001b0e9ab 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -21,7 +21,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/tools/pom.xml b/tools/pom.xml
index 298ee2348b58e..1e64f280e5bed 100644
--- a/tools/pom.xml
+++ b/tools/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/unsafe/pom.xml b/unsafe/pom.xml
index 89475ee3cf5a1..066abe92e51c0 100644
--- a/unsafe/pom.xml
+++ b/unsafe/pom.xml
@@ -22,7 +22,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
diff --git a/yarn/pom.xml b/yarn/pom.xml
index f6737695307a2..d8e4a4bbead81 100644
--- a/yarn/pom.xml
+++ b/yarn/pom.xml
@@ -20,7 +20,7 @@
   <parent>
     <groupId>org.apache.spark</groupId>
     <artifactId>spark-parent_2.10</artifactId>
-    <version>1.5.0-SNAPSHOT</version>
+    <version>1.6.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 

From c35fdcb7e9c01271ce560dba4e0bd37569c8f5d1 Mon Sep 17 00:00:00 2001
From: Yuhao Yang <hhbyyh@gmail.com>
Date: Tue, 15 Sep 2015 09:58:49 -0700
Subject: [PATCH 630/633] [SPARK-10491] [MLLIB] move RowMatrix.dspr to BLAS

jira: https://issues.apache.org/jira/browse/SPARK-10491

We implemented dspr with sparse vector support in `RowMatrix`. This method is also used in WeightedLeastSquares and other places. It would be useful to move it to `linalg.BLAS`.

Let me know if new UT needed.

Author: Yuhao Yang <hhbyyh@gmail.com>

Closes #8663 from hhbyyh/movedspr.
---
 .../spark/ml/optim/WeightedLeastSquares.scala |  4 +-
 .../org/apache/spark/mllib/linalg/BLAS.scala  | 44 +++++++++++++++++++
 .../mllib/linalg/distributed/RowMatrix.scala  | 40 +----------------
 .../apache/spark/mllib/linalg/BLASSuite.scala | 25 +++++++++++
 4 files changed, 72 insertions(+), 41 deletions(-)

diff --git a/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala b/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
index a99e2ac4c6913..0ff8931b0bab4 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala
@@ -88,7 +88,7 @@ private[ml] class WeightedLeastSquares(
     if (fitIntercept) {
       // shift centers
       // A^T A - aBar aBar^T
-      RowMatrix.dspr(-1.0, aBar, aaValues)
+      BLAS.spr(-1.0, aBar, aaValues)
       // A^T b - bBar aBar
       BLAS.axpy(-bBar, aBar, abBar)
     }
@@ -203,7 +203,7 @@ private[ml] object WeightedLeastSquares {
       bbSum += w * b * b
       BLAS.axpy(w, a, aSum)
       BLAS.axpy(w * b, a, abSum)
-      RowMatrix.dspr(w, a, aaSum.values)
+      BLAS.spr(w, a, aaSum)
       this
     }
 
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
index 9ee81eda8a8c0..df9f4ae145b88 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/BLAS.scala
@@ -236,6 +236,50 @@ private[spark] object BLAS extends Serializable with Logging {
     _nativeBLAS
   }
 
+  /**
+   * Adds alpha * x * x.t to a matrix in-place. This is the same as BLAS's ?SPR.
+   *
+   * @param U the upper triangular part of the matrix in a [[DenseVector]](column major)
+   */
+  def spr(alpha: Double, v: Vector, U: DenseVector): Unit = {
+    spr(alpha, v, U.values)
+  }
+
+  /**
+   * Adds alpha * x * x.t to a matrix in-place. This is the same as BLAS's ?SPR.
+   *
+   * @param U the upper triangular part of the matrix packed in an array (column major)
+   */
+  def spr(alpha: Double, v: Vector, U: Array[Double]): Unit = {
+    val n = v.size
+    v match {
+      case DenseVector(values) =>
+        NativeBLAS.dspr("U", n, alpha, values, 1, U)
+      case SparseVector(size, indices, values) =>
+        val nnz = indices.length
+        var colStartIdx = 0
+        var prevCol = 0
+        var col = 0
+        var j = 0
+        var i = 0
+        var av = 0.0
+        while (j < nnz) {
+          col = indices(j)
+          // Skip empty columns.
+          colStartIdx += (col - prevCol) * (col + prevCol + 1) / 2
+          col = indices(j)
+          av = alpha * values(j)
+          i = 0
+          while (i <= j) {
+            U(colStartIdx + indices(i)) += av * values(i)
+            i += 1
+          }
+          j += 1
+          prevCol = col
+        }
+    }
+  }
+
   /**
    * A := alpha * x * x^T^ + A
    * @param alpha a real scalar that will be multiplied to x * x^T^.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index 83779ac88989b..e55ef26858adb 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -24,7 +24,6 @@ import scala.collection.mutable.ListBuffer
 import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, SparseVector => BSV, axpy => brzAxpy,
   svd => brzSvd, MatrixSingularException, inv}
 import breeze.numerics.{sqrt => brzSqrt}
-import com.github.fommil.netlib.BLAS.{getInstance => blas}
 
 import org.apache.spark.Logging
 import org.apache.spark.SparkContext._
@@ -123,7 +122,7 @@ class RowMatrix @Since("1.0.0") (
     // Compute the upper triangular part of the gram matrix.
     val GU = rows.treeAggregate(new BDV[Double](new Array[Double](nt)))(
       seqOp = (U, v) => {
-        RowMatrix.dspr(1.0, v, U.data)
+        BLAS.spr(1.0, v, U.data)
         U
       }, combOp = (U1, U2) => U1 += U2)
 
@@ -673,43 +672,6 @@ class RowMatrix @Since("1.0.0") (
 @Experimental
 object RowMatrix {
 
-  /**
-   * Adds alpha * x * x.t to a matrix in-place. This is the same as BLAS's DSPR.
-   *
-   * @param U the upper triangular part of the matrix packed in an array (column major)
-   */
-  // TODO: SPARK-10491 - move this method to linalg.BLAS
-  private[spark] def dspr(alpha: Double, v: Vector, U: Array[Double]): Unit = {
-    // TODO: Find a better home (breeze?) for this method.
-    val n = v.size
-    v match {
-      case DenseVector(values) =>
-        blas.dspr("U", n, alpha, values, 1, U)
-      case SparseVector(size, indices, values) =>
-        val nnz = indices.length
-        var colStartIdx = 0
-        var prevCol = 0
-        var col = 0
-        var j = 0
-        var i = 0
-        var av = 0.0
-        while (j < nnz) {
-          col = indices(j)
-          // Skip empty columns.
-          colStartIdx += (col - prevCol) * (col + prevCol + 1) / 2
-          col = indices(j)
-          av = alpha * values(j)
-          i = 0
-          while (i <= j) {
-            U(colStartIdx + indices(i)) += av * values(i)
-            i += 1
-          }
-          j += 1
-          prevCol = col
-        }
-    }
-  }
-
   /**
    * Fills a full square matrix from its upper triangular part.
    */
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
index 8db5c8424abe9..96e5ffef7a131 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/linalg/BLASSuite.scala
@@ -126,6 +126,31 @@ class BLASSuite extends SparkFunSuite {
     }
   }
 
+  test("spr") {
+    // test dense vector
+    val alpha = 0.1
+    val x = new DenseVector(Array(1.0, 2, 2.1, 4))
+    val U = new DenseVector(Array(1.0, 2, 2, 3, 3, 3, 4, 4, 4, 4))
+    val expected = new DenseVector(Array(1.1, 2.2, 2.4, 3.21, 3.42, 3.441, 4.4, 4.8, 4.84, 5.6))
+
+    spr(alpha, x, U)
+    assert(U ~== expected absTol 1e-9)
+
+    val matrix33 = new DenseVector(Array(1.0, 2, 3, 4, 5))
+    withClue("Size of vector must match the rank of matrix") {
+      intercept[Exception] {
+        spr(alpha, x, matrix33)
+      }
+    }
+
+    // test sparse vector
+    val sv = new SparseVector(4, Array(0, 3), Array(1.0, 2))
+    val U2 = new DenseVector(Array(1.0, 2, 2, 3, 3, 3, 4, 4, 4, 4))
+    spr(0.1, sv, U2)
+    val expectedSparse = new DenseVector(Array(1.1, 2.0, 2.0, 3.0, 3.0, 3.0, 4.2, 4.0, 4.0, 4.4))
+    assert(U2 ~== expectedSparse absTol 1e-15)
+  }
+
   test("syr") {
     val dA = new DenseMatrix(4, 4,
       Array(0.0, 1.2, 2.2, 3.1, 1.2, 3.2, 5.3, 4.6, 2.2, 5.3, 1.8, 3.0, 3.1, 4.6, 3.0, 0.8))

From 8abef21dac1a6538c4e4e0140323b83d804d602b Mon Sep 17 00:00:00 2001
From: Marcelo Vanzin <vanzin@cloudera.com>
Date: Tue, 15 Sep 2015 10:45:02 -0700
Subject: [PATCH 631/633] [SPARK-10300] [BUILD] [TESTS] Add support for test
 tags in run-tests.py.

This change does two things:

- tag a few tests and adds the mechanism in the build to be able to disable those tags,
  both in maven and sbt, for both junit and scalatest suites.
- add some logic to run-tests.py to disable some tags depending on what files have
  changed; that's used to disable expensive tests when a module hasn't explicitly
  been changed, to speed up testing for changes that don't directly affect those
  modules.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #8437 from vanzin/test-tags.
---
 core/pom.xml                                  | 10 -------
 dev/run-tests.py                              | 19 ++++++++++++--
 dev/sparktestsupport/modules.py               | 24 ++++++++++++++++-
 external/flume/pom.xml                        | 10 -------
 external/kafka/pom.xml                        | 10 -------
 external/mqtt/pom.xml                         | 10 -------
 external/twitter/pom.xml                      | 10 -------
 external/zeromq/pom.xml                       | 10 -------
 extras/java8-tests/pom.xml                    | 10 -------
 extras/kinesis-asl/pom.xml                    |  5 ----
 launcher/pom.xml                              |  5 ----
 mllib/pom.xml                                 | 10 -------
 network/common/pom.xml                        | 10 -------
 network/shuffle/pom.xml                       | 10 -------
 pom.xml                                       | 17 ++++++++++--
 project/SparkBuild.scala                      | 13 ++++++++--
 sql/core/pom.xml                              |  5 ----
 .../execution/HiveCompatibilitySuite.scala    |  2 ++
 sql/hive/pom.xml                              |  5 ----
 .../spark/sql/hive/ExtendedHiveTest.java      | 26 +++++++++++++++++++
 .../spark/sql/hive/client/VersionsSuite.scala |  2 ++
 streaming/pom.xml                             | 10 -------
 unsafe/pom.xml                                | 10 -------
 .../spark/deploy/yarn/ExtendedYarnTest.java   | 26 +++++++++++++++++++
 .../spark/deploy/yarn/YarnClusterSuite.scala  |  1 +
 .../yarn/YarnShuffleIntegrationSuite.scala    |  1 +
 26 files changed, 124 insertions(+), 147 deletions(-)
 create mode 100644 sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java
 create mode 100644 yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java

diff --git a/core/pom.xml b/core/pom.xml
index e31d90f608892..8a20181096223 100644
--- a/core/pom.xml
+++ b/core/pom.xml
@@ -331,16 +331,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.apache.curator</groupId>
       <artifactId>curator-test</artifactId>
diff --git a/dev/run-tests.py b/dev/run-tests.py
index d8b22e1665e7b..1a816585187d9 100755
--- a/dev/run-tests.py
+++ b/dev/run-tests.py
@@ -118,6 +118,14 @@ def determine_modules_to_test(changed_modules):
     return modules_to_test.union(set(changed_modules))
 
 
+def determine_tags_to_exclude(changed_modules):
+    tags = []
+    for m in modules.all_modules:
+        if m not in changed_modules:
+            tags += m.test_tags
+    return tags
+
+
 # -------------------------------------------------------------------------------------------------
 # Functions for working with subprocesses and shell tools
 # -------------------------------------------------------------------------------------------------
@@ -369,6 +377,7 @@ def detect_binary_inop_with_mima():
 
 def run_scala_tests_maven(test_profiles):
     mvn_test_goals = ["test", "--fail-at-end"]
+
     profiles_and_goals = test_profiles + mvn_test_goals
 
     print("[info] Running Spark tests using Maven with these arguments: ",
@@ -392,7 +401,7 @@ def run_scala_tests_sbt(test_modules, test_profiles):
     exec_sbt(profiles_and_goals)
 
 
-def run_scala_tests(build_tool, hadoop_version, test_modules):
+def run_scala_tests(build_tool, hadoop_version, test_modules, excluded_tags):
     """Function to properly execute all tests passed in as a set from the
     `determine_test_suites` function"""
     set_title_and_block("Running Spark unit tests", "BLOCK_SPARK_UNIT_TESTS")
@@ -401,6 +410,10 @@ def run_scala_tests(build_tool, hadoop_version, test_modules):
 
     test_profiles = get_hadoop_profiles(hadoop_version) + \
         list(set(itertools.chain.from_iterable(m.build_profile_flags for m in test_modules)))
+
+    if excluded_tags:
+        test_profiles += ['-Dtest.exclude.tags=' + ",".join(excluded_tags)]
+
     if build_tool == "maven":
         run_scala_tests_maven(test_profiles)
     else:
@@ -500,8 +513,10 @@ def main():
         target_branch = os.environ["ghprbTargetBranch"]
         changed_files = identify_changed_files_from_git_commits("HEAD", target_branch=target_branch)
         changed_modules = determine_modules_for_files(changed_files)
+        excluded_tags = determine_tags_to_exclude(changed_modules)
     if not changed_modules:
         changed_modules = [modules.root]
+        excluded_tags = []
     print("[info] Found the following changed modules:",
           ", ".join(x.name for x in changed_modules))
 
@@ -541,7 +556,7 @@ def main():
         detect_binary_inop_with_mima()
 
     # run the test suites
-    run_scala_tests(build_tool, hadoop_version, test_modules)
+    run_scala_tests(build_tool, hadoop_version, test_modules, excluded_tags)
 
     modules_with_python_tests = [m for m in test_modules if m.python_test_goals]
     if modules_with_python_tests:
diff --git a/dev/sparktestsupport/modules.py b/dev/sparktestsupport/modules.py
index 346452f3174e4..65397f1f3e0bc 100644
--- a/dev/sparktestsupport/modules.py
+++ b/dev/sparktestsupport/modules.py
@@ -31,7 +31,7 @@ class Module(object):
 
     def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=(), environ={},
                  sbt_test_goals=(), python_test_goals=(), blacklisted_python_implementations=(),
-                 should_run_r_tests=False):
+                 test_tags=(), should_run_r_tests=False):
         """
         Define a new module.
 
@@ -50,6 +50,8 @@ def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=
         :param blacklisted_python_implementations: A set of Python implementations that are not
             supported by this module's Python components. The values in this set should match
             strings returned by Python's `platform.python_implementation()`.
+        :param test_tags A set of tags that will be excluded when running unit tests if the module
+            is not explicitly changed.
         :param should_run_r_tests: If true, changes in this module will trigger all R tests.
         """
         self.name = name
@@ -60,6 +62,7 @@ def __init__(self, name, dependencies, source_file_regexes, build_profile_flags=
         self.environ = environ
         self.python_test_goals = python_test_goals
         self.blacklisted_python_implementations = blacklisted_python_implementations
+        self.test_tags = test_tags
         self.should_run_r_tests = should_run_r_tests
 
         self.dependent_modules = set()
@@ -85,6 +88,9 @@ def contains_file(self, filename):
         "catalyst/test",
         "sql/test",
         "hive/test",
+    ],
+    test_tags=[
+        "org.apache.spark.sql.hive.ExtendedHiveTest"
     ]
 )
 
@@ -398,6 +404,22 @@ def contains_file(self, filename):
 )
 
 
+yarn = Module(
+    name="yarn",
+    dependencies=[],
+    source_file_regexes=[
+        "yarn/",
+        "network/yarn/",
+    ],
+    sbt_test_goals=[
+        "yarn/test",
+        "network-yarn/test",
+    ],
+    test_tags=[
+        "org.apache.spark.deploy.yarn.ExtendedYarnTest"
+    ]
+)
+
 # The root module is a dummy module which is used to run all of the tests.
 # No other modules should directly depend on this module.
 root = Module(
diff --git a/external/flume/pom.xml b/external/flume/pom.xml
index 132062f94fb45..3154e36c21ef5 100644
--- a/external/flume/pom.xml
+++ b/external/flume/pom.xml
@@ -66,16 +66,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml
index 05abd9e2e6810..7d0d46dadc727 100644
--- a/external/kafka/pom.xml
+++ b/external/kafka/pom.xml
@@ -86,16 +86,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml
index 05e6338a08b0a..913c47d33f488 100644
--- a/external/mqtt/pom.xml
+++ b/external/mqtt/pom.xml
@@ -58,16 +58,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.apache.activemq</groupId>
       <artifactId>activemq-core</artifactId>
diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml
index 244ad58ae9593..9137bf25ee8ae 100644
--- a/external/twitter/pom.xml
+++ b/external/twitter/pom.xml
@@ -58,16 +58,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml
index 171df8682c848..6fec4f0e8a0f9 100644
--- a/external/zeromq/pom.xml
+++ b/external/zeromq/pom.xml
@@ -57,16 +57,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/extras/java8-tests/pom.xml b/extras/java8-tests/pom.xml
index 81794a8536318..dba3dda8a9562 100644
--- a/extras/java8-tests/pom.xml
+++ b/extras/java8-tests/pom.xml
@@ -58,16 +58,6 @@
       <type>test-jar</type>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
 
   <profiles>
diff --git a/extras/kinesis-asl/pom.xml b/extras/kinesis-asl/pom.xml
index 6dd8ff69c2943..760f183a2ef37 100644
--- a/extras/kinesis-asl/pom.xml
+++ b/extras/kinesis-asl/pom.xml
@@ -74,11 +74,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/launcher/pom.xml b/launcher/pom.xml
index ed38e66aa2467..80696280a1d18 100644
--- a/launcher/pom.xml
+++ b/launcher/pom.xml
@@ -42,11 +42,6 @@
       <artifactId>log4j</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
       <artifactId>mockito-core</artifactId>
diff --git a/mllib/pom.xml b/mllib/pom.xml
index 22c0c6008ba37..5dedacb38874e 100644
--- a/mllib/pom.xml
+++ b/mllib/pom.xml
@@ -94,16 +94,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
       <artifactId>mockito-core</artifactId>
diff --git a/network/common/pom.xml b/network/common/pom.xml
index 1cc054a8936c5..9c12cca0df609 100644
--- a/network/common/pom.xml
+++ b/network/common/pom.xml
@@ -64,16 +64,6 @@
     </dependency>
 
     <!-- Test dependencies -->
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>log4j</groupId>
       <artifactId>log4j</artifactId>
diff --git a/network/shuffle/pom.xml b/network/shuffle/pom.xml
index 7a66c968041ce..e4f4c57b683c8 100644
--- a/network/shuffle/pom.xml
+++ b/network/shuffle/pom.xml
@@ -78,16 +78,6 @@
       <type>test-jar</type>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>log4j</groupId>
       <artifactId>log4j</artifactId>
diff --git a/pom.xml b/pom.xml
index 6535994641145..2927d3e107563 100644
--- a/pom.xml
+++ b/pom.xml
@@ -181,6 +181,7 @@
     <libthrift.version>0.9.2</libthrift.version>
 
     <test.java.home>${java.home}</test.java.home>
+    <test.exclude.tags></test.exclude.tags>
 
     <!--
       Dependency scopes that can be overridden by enabling certain profiles. These profiles are
@@ -339,6 +340,16 @@
       <artifactId>scalatest_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <scope>test</scope>
+    </dependency>
+    <dependency>
+      <groupId>com.novocode</groupId>
+      <artifactId>junit-interface</artifactId>
+      <scope>test</scope>
+    </dependency>
   </dependencies>
   <dependencyManagement>
     <dependencies>
@@ -742,7 +753,7 @@
       <dependency>
         <groupId>junit</groupId>
         <artifactId>junit</artifactId>
-        <version>4.10</version>
+        <version>4.11</version>
         <scope>test</scope>
       </dependency>
       <dependency>
@@ -760,7 +771,7 @@
       <dependency>
         <groupId>com.novocode</groupId>
         <artifactId>junit-interface</artifactId>
-        <version>0.10</version>
+        <version>0.11</version>
         <scope>test</scope>
       </dependency>
       <dependency>
@@ -1915,6 +1926,7 @@
               <test.src.tables>src</test.src.tables>
             </systemProperties>
             <failIfNoTests>false</failIfNoTests>
+            <excludedGroups>${test.exclude.tags}</excludedGroups>
           </configuration>
         </plugin>
         <!-- Scalatest runs all Scala tests -->
@@ -1952,6 +1964,7 @@
               <!-- Needed by sql/hive tests. -->
               <test.src.tables>__not_used__</test.src.tables>
             </systemProperties>
+            <tagsToExclude>${test.exclude.tags}</tagsToExclude>
           </configuration>
           <executions>
             <execution>
diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala
index 901cfa538d23e..d80d300f1c3b2 100644
--- a/project/SparkBuild.scala
+++ b/project/SparkBuild.scala
@@ -567,11 +567,20 @@ object TestSettings {
     javaOptions in Test ++= "-Xmx3g -Xss4096k -XX:PermSize=128M -XX:MaxNewSize=256m -XX:MaxPermSize=1g"
       .split(" ").toSeq,
     javaOptions += "-Xmx3g",
+    // Exclude tags defined in a system property
+    testOptions in Test += Tests.Argument(TestFrameworks.ScalaTest,
+      sys.props.get("test.exclude.tags").map { tags =>
+        tags.split(",").flatMap { tag => Seq("-l", tag) }.toSeq
+      }.getOrElse(Nil): _*),
+    testOptions in Test += Tests.Argument(TestFrameworks.JUnit,
+      sys.props.get("test.exclude.tags").map { tags =>
+        Seq("--exclude-categories=" + tags)
+      }.getOrElse(Nil): _*),
     // Show full stack trace and duration in test cases.
     testOptions in Test += Tests.Argument("-oDF"),
-    testOptions += Tests.Argument(TestFrameworks.JUnit, "-v", "-a"),
+    testOptions in Test += Tests.Argument(TestFrameworks.JUnit, "-v", "-a"),
     // Enable Junit testing.
-    libraryDependencies += "com.novocode" % "junit-interface" % "0.9" % "test",
+    libraryDependencies += "com.novocode" % "junit-interface" % "0.11" % "test",
     // Only allow one test at a time, even across projects, since they run in the same JVM
     parallelExecution in Test := false,
     // Make sure the test temp directory exists.
diff --git a/sql/core/pom.xml b/sql/core/pom.xml
index 465aa3a3888c2..fa6732db183d8 100644
--- a/sql/core/pom.xml
+++ b/sql/core/pom.xml
@@ -73,11 +73,6 @@
       <artifactId>jackson-databind</artifactId>
       <version>${fasterxml.jackson.version}</version>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.scalacheck</groupId>
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
diff --git a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
index ab309e0a1d36b..ffc4c32794ca4 100644
--- a/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
+++ b/sql/hive/compatibility/src/test/scala/org/apache/spark/sql/hive/execution/HiveCompatibilitySuite.scala
@@ -24,11 +24,13 @@ import org.apache.spark.sql.catalyst.rules.RuleExecutor
 import org.scalatest.BeforeAndAfter
 
 import org.apache.spark.sql.SQLConf
+import org.apache.spark.sql.hive.ExtendedHiveTest
 import org.apache.spark.sql.hive.test.TestHive
 
 /**
  * Runs the test cases that are included in the hive distribution.
  */
+@ExtendedHiveTest
 class HiveCompatibilitySuite extends HiveQueryFileTest with BeforeAndAfter {
   // TODO: bundle in jar files... get from classpath
   private lazy val hiveQueryDir = TestHive.getHiveFile(
diff --git a/sql/hive/pom.xml b/sql/hive/pom.xml
index ac67fe5f47be9..82cfeb2bb95d3 100644
--- a/sql/hive/pom.xml
+++ b/sql/hive/pom.xml
@@ -160,11 +160,6 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.apache.spark</groupId>
       <artifactId>spark-sql_${scala.binary.version}</artifactId>
diff --git a/sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java b/sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java
new file mode 100644
index 0000000000000..e2183183fb559
--- /dev/null
+++ b/sql/hive/src/test/java/org/apache/spark/sql/hive/ExtendedHiveTest.java
@@ -0,0 +1,26 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.hive;
+
+import java.lang.annotation.*;
+import org.scalatest.TagAnnotation;
+
+@TagAnnotation
+@Retention(RetentionPolicy.RUNTIME)
+@Target({ElementType.METHOD, ElementType.TYPE})
+public @interface ExtendedHiveTest { }
diff --git a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
index f0bb77092c0cf..888d1b7b45532 100644
--- a/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
+++ b/sql/hive/src/test/scala/org/apache/spark/sql/hive/client/VersionsSuite.scala
@@ -23,6 +23,7 @@ import org.apache.spark.sql.hive.HiveContext
 import org.apache.spark.{Logging, SparkFunSuite}
 import org.apache.spark.sql.catalyst.expressions.{NamedExpression, Literal, AttributeReference, EqualTo}
 import org.apache.spark.sql.catalyst.util.quietly
+import org.apache.spark.sql.hive.ExtendedHiveTest
 import org.apache.spark.sql.types.IntegerType
 import org.apache.spark.util.Utils
 
@@ -32,6 +33,7 @@ import org.apache.spark.util.Utils
  * sure that reflective calls are not throwing NoSuchMethod error, but the actually functionality
  * is not fully tested.
  */
+@ExtendedHiveTest
 class VersionsSuite extends SparkFunSuite with Logging {
 
   // Do not use a temp path here to speed up subsequent executions of the unit test during
diff --git a/streaming/pom.xml b/streaming/pom.xml
index 5cc9001b0e9ab..1e6ee009ca6d5 100644
--- a/streaming/pom.xml
+++ b/streaming/pom.xml
@@ -84,21 +84,11 @@
       <artifactId>scalacheck_${scala.binary.version}</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.seleniumhq.selenium</groupId>
       <artifactId>selenium-java</artifactId>
       <scope>test</scope>
     </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory>
diff --git a/unsafe/pom.xml b/unsafe/pom.xml
index 066abe92e51c0..4e8b9a84bb67f 100644
--- a/unsafe/pom.xml
+++ b/unsafe/pom.xml
@@ -55,16 +55,6 @@
     </dependency>
 
     <!-- Test dependencies -->
-    <dependency>
-      <groupId>junit</groupId>
-      <artifactId>junit</artifactId>
-      <scope>test</scope>
-    </dependency>
-    <dependency>
-      <groupId>com.novocode</groupId>
-      <artifactId>junit-interface</artifactId>
-      <scope>test</scope>
-    </dependency>
     <dependency>
       <groupId>org.mockito</groupId>
       <artifactId>mockito-core</artifactId>
diff --git a/yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java b/yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java
new file mode 100644
index 0000000000000..7a8f2fe979c1f
--- /dev/null
+++ b/yarn/src/test/java/org/apache/spark/deploy/yarn/ExtendedYarnTest.java
@@ -0,0 +1,26 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.deploy.yarn;
+
+import java.lang.annotation.*;
+import org.scalatest.TagAnnotation;
+
+@TagAnnotation
+@Retention(RetentionPolicy.RUNTIME)
+@Target({ElementType.METHOD, ElementType.TYPE})
+public @interface ExtendedYarnTest { }
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
index b5a42fd6afd98..105c3090d489d 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala
@@ -39,6 +39,7 @@ import org.apache.spark.util.Utils
  * applications, and require the Spark assembly to be built before they can be successfully
  * run.
  */
+@ExtendedYarnTest
 class YarnClusterSuite extends BaseYarnClusterSuite {
 
   override def newYarnConfig(): YarnConfiguration = new YarnConfiguration()
diff --git a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
index 8d9c9b3004eda..4700e2428df08 100644
--- a/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
+++ b/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnShuffleIntegrationSuite.scala
@@ -32,6 +32,7 @@ import org.apache.spark.network.yarn.{YarnShuffleService, YarnTestAccessor}
 /**
  * Integration test for the external shuffle service with a yarn mini-cluster
  */
+@ExtendedYarnTest
 class YarnShuffleIntegrationSuite extends BaseYarnClusterSuite {
 
   override def newYarnConfig(): YarnConfiguration = {

From 7ca30b505c3561dc2832b463be4c6301a90380e4 Mon Sep 17 00:00:00 2001
From: noelsmith <mail@noelsmith.com>
Date: Tue, 15 Sep 2015 12:23:20 -0700
Subject: [PATCH 632/633] [PYSPARK] [MLLIB] [DOCS] Replaced addversion with
 versionadded in mllib.random

Missed this when reviewing `pyspark.mllib.random` for SPARK-10275.

Author: noelsmith <mail@noelsmith.com>

Closes #8773 from noel-smith/mllib-random-versionadded-fix.
---
 python/pyspark/mllib/random.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/python/pyspark/mllib/random.py b/python/pyspark/mllib/random.py
index 9c733b1332bc0..6a3c643b66417 100644
--- a/python/pyspark/mllib/random.py
+++ b/python/pyspark/mllib/random.py
@@ -41,7 +41,7 @@ class RandomRDDs(object):
     Generator methods for creating RDDs comprised of i.i.d samples from
     some distribution.
 
-    .. addedversion:: 1.1.0
+    .. versionadded:: 1.1.0
     """
 
     @staticmethod

From 0d9ab016755d5b56ce4043f229602169fd752e88 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Tue, 15 Sep 2015 12:25:31 -0700
Subject: [PATCH 633/633] Closes #8738 Closes #8767 Closes #2491 Closes #6795
 Closes #2096 Closes #7722