[SPARK-29213][SQL] Generate extra IsNotNull predicate in FilterExec by wangshuo128 · Pull Request #25902 · apache/spark

wangshuo128 · 2019-09-23T09:48:34Z

What changes were proposed in this pull request?

Currently the behavior of getting output and generating null checks in FilterExec is different. Thus some nullable attribute could be treated as not nullable by mistake.

In FilterExec.ouput, an attribute is marked as nullable or not by finding its exprId in notNullAttributes:

a.nullable && notNullAttributes.contains(a.exprId)

But in FilterExec.doConsume, a nullCheck is generated or not for a predicate is decided by whether there is semantic equal not null predicate:

 val nullChecks = c.references.map { r =>
val idx = notNullPreds.indexWhere { n => n.asInstanceOf[IsNotNull].child.semanticEquals(r)}
if (idx != -1 && !generatedIsNotNullChecks(idx)) {
generatedIsNotNullChecks(idx) = true
// Use the child's output. The nullability is what the child produced.
genPredicate(notNullPreds(idx), input, child.output)
} else {
""
}
}.mkString("\n").trim

NPE will happen when run the SQL below:

sql("create table table1(x string)")
sql("create table table2(x bigint)")
sql("create table table3(x string)")
sql("insert into table2 select null as x")
sql(
"""
|select t1.x
|from (
| select x from table1) t1
|left join (
| select x from (
| select x from table2
| union all
| select substr(x,5) x from table3
| ) a
| where length(x)>0
|) t3
|on t1.x=t3.x
""".stripMargin).collect()

NPE Exception:

java.lang.NullPointerException
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(generated.java:40)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:726)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458)
at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:135)
at org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:94)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:449)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:452)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)

the generated code:

== Subtree 4 / 5 ==
*(2) Project [cast(x#7L as string) AS x#9]
+- *(2) Filter ((length(cast(x#7L as string)) > 0) AND isnotnull(cast(x#7L as string)))
+- Scan hive default.table2 [x#7L], HiveTableRelation `default`.`table2`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [x#7L]
Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIteratorForCodegenStage2(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=2
/* 006 */ final class GeneratedIteratorForCodegenStage2 extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */ private Object[] references;
/* 008 */ private scala.collection.Iterator[] inputs;
/* 009 */ private scala.collection.Iterator inputadapter_input_0;
/* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] filter_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[2];
/* 011 */
/* 012 */ public GeneratedIteratorForCodegenStage2(Object[] references) {
/* 013 */ this.references = references;
/* 014 */ }
/* 015 */
/* 016 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 017 */ partitionIndex = index;
/* 018 */ this.inputs = inputs;
/* 019 */ inputadapter_input_0 = inputs[0];
/* 020 */ filter_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 021 */ filter_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 32);
/* 022 */
/* 023 */ }
/* 024 */
/* 025 */ protected void processNext() throws java.io.IOException {
/* 026 */ while ( inputadapter_input_0.hasNext()) {
/* 027 */ InternalRow inputadapter_row_0 = (InternalRow) inputadapter_input_0.next();
/* 028 */
/* 029 */ do {
/* 030 */ boolean inputadapter_isNull_0 = inputadapter_row_0.isNullAt(0);
/* 031 */ long inputadapter_value_0 = inputadapter_isNull_0 ?
/* 032 */ -1L : (inputadapter_row_0.getLong(0));
/* 033 */
/* 034 */ boolean filter_isNull_2 = inputadapter_isNull_0;
/* 035 */ UTF8String filter_value_2 = null;
/* 036 */ if (!inputadapter_isNull_0) {
/* 037 */ filter_value_2 = UTF8String.fromString(String.valueOf(inputadapter_value_0));
/* 038 */ }
/* 039 */ int filter_value_1 = -1;
/* 040 */ filter_value_1 = (filter_value_2).numChars();
/* 041 */
/* 042 */ boolean filter_value_0 = false;
/* 043 */ filter_value_0 = filter_value_1 > 0;
/* 044 */ if (!filter_value_0) continue;
/* 045 */
/* 046 */ boolean filter_isNull_6 = inputadapter_isNull_0;
/* 047 */ UTF8String filter_value_6 = null;
/* 048 */ if (!inputadapter_isNull_0) {
/* 049 */ filter_value_6 = UTF8String.fromString(String.valueOf(inputadapter_value_0));
/* 050 */ }
/* 051 */ if (!(!filter_isNull_6)) continue;
/* 052 */
/* 053 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1);
/* 054 */
/* 055 */ boolean project_isNull_0 = false;
/* 056 */ UTF8String project_value_0 = null;
/* 057 */ if (!false) {
/* 058 */ project_value_0 = UTF8String.fromString(String.valueOf(inputadapter_value_0));
/* 059 */ }
/* 060 */ filter_mutableStateArray_0[1].reset();
/* 061 */
/* 062 */ filter_mutableStateArray_0[1].zeroOutNullBytes();
/* 063 */
/* 064 */ if (project_isNull_0) {
/* 065 */ filter_mutableStateArray_0[1].setNullAt(0);
/* 066 */ } else {
/* 067 */ filter_mutableStateArray_0[1].write(0, project_value_0);
/* 068 */ }
/* 069 */ append((filter_mutableStateArray_0[1].getRow()));
/* 070 */
/* 071 */ } while(false);
/* 072 */ if (shouldStop()) return;
/* 073 */ }
/* 074 */ }
/* 075 */
/* 076 */ }

This PR proposes to use semantic comparison both in FilterExec.output and FilterExec.doConsume for nullable attribute.

With this PR, the generated code snippet is below:

== Subtree 2 / 5 ==
*(3) Project [substring(x#8, 5, 2147483647) AS x#5]
+- *(3) Filter ((length(substring(x#8, 5, 2147483647)) > 0) AND isnotnull(substring(x#8, 5, 2147483647)))
+- Scan hive default.table3 [x#8], HiveTableRelation `default`.`table3`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [x#8]
Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIteratorForCodegenStage3(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=3
/* 006 */ final class GeneratedIteratorForCodegenStage3 extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */ private Object[] references;
/* 008 */ private scala.collection.Iterator[] inputs;
/* 009 */ private scala.collection.Iterator inputadapter_input_0;
/* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] filter_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[2];
/* 011 */
/* 012 */ public GeneratedIteratorForCodegenStage3(Object[] references) {
/* 013 */ this.references = references;
/* 014 */ }
/* 015 */
/* 016 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 017 */ partitionIndex = index;
/* 018 */ this.inputs = inputs;
/* 019 */ inputadapter_input_0 = inputs[0];
/* 020 */ filter_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 32);
/* 021 */ filter_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 32);
/* 022 */
/* 023 */ }
/* 024 */
/* 025 */ protected void processNext() throws java.io.IOException {
/* 026 */ while ( inputadapter_input_0.hasNext()) {
/* 027 */ InternalRow inputadapter_row_0 = (InternalRow) inputadapter_input_0.next();
/* 028 */
/* 029 */ do {
/* 030 */ boolean inputadapter_isNull_0 = inputadapter_row_0.isNullAt(0);
/* 031 */ UTF8String inputadapter_value_0 = inputadapter_isNull_0 ?
/* 032 */ null : (inputadapter_row_0.getUTF8String(0));
/* 033 */
/* 034 */ boolean filter_isNull_0 = true;
/* 035 */ boolean filter_value_0 = false;
/* 036 */ boolean filter_isNull_2 = true;
/* 037 */ UTF8String filter_value_2 = null;
/* 038 */
/* 039 */ if (!inputadapter_isNull_0) {
/* 040 */ filter_isNull_2 = false; // resultCode could change nullability.
/* 041 */ filter_value_2 = inputadapter_value_0.substringSQL(5, 2147483647);
/* 042 */
/* 043 */ }
/* 044 */ boolean filter_isNull_1 = filter_isNull_2;
/* 045 */ int filter_value_1 = -1;
/* 046 */
/* 047 */ if (!filter_isNull_2) {
/* 048 */ filter_value_1 = (filter_value_2).numChars();
/* 049 */ }
/* 050 */ if (!filter_isNull_1) {
/* 051 */ filter_isNull_0 = false; // resultCode could change nullability.
/* 052 */ filter_value_0 = filter_value_1 > 0;
/* 053 */
/* 054 */ }
/* 055 */ if (filter_isNull_0 || !filter_value_0) continue;
/* 056 */ boolean filter_isNull_8 = true;
/* 057 */ UTF8String filter_value_8 = null;
/* 058 */
/* 059 */ if (!inputadapter_isNull_0) {
/* 060 */ filter_isNull_8 = false; // resultCode could change nullability.
/* 061 */ filter_value_8 = inputadapter_value_0.substringSQL(5, 2147483647);
/* 062 */
/* 063 */ }
/* 064 */ if (!(!filter_isNull_8)) continue;
/* 065 */
/* 066 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1);
/* 067 */
/* 068 */ boolean project_isNull_0 = true;
/* 069 */ UTF8String project_value_0 = null;
/* 070 */
/* 071 */ if (!inputadapter_isNull_0) {
/* 072 */ project_isNull_0 = false; // resultCode could change nullability.
/* 073 */ project_value_0 = inputadapter_value_0.substringSQL(5, 2147483647);
/* 074 */
/* 075 */ }
/* 076 */ filter_mutableStateArray_0[1].reset();
/* 077 */
/* 078 */ filter_mutableStateArray_0[1].zeroOutNullBytes();
/* 079 */
/* 080 */ if (project_isNull_0) {
/* 081 */ filter_mutableStateArray_0[1].setNullAt(0);
/* 082 */ } else {
/* 083 */ filter_mutableStateArray_0[1].write(0, project_value_0);
/* 084 */ }
/* 085 */ append((filter_mutableStateArray_0[1].getRow()));
/* 086 */
/* 087 */ } while(false);
/* 088 */ if (shouldStop()) return;
/* 089 */ }
/* 090 */ }
/* 091 */
/* 092 */ }

Why are the changes needed?

Fix NPE bug in FilterExec.

Does this PR introduce any user-facing change?

no

How was this patch tested?

new UT

wangshuo128 · 2019-09-23T09:50:45Z

@viirya @cloud-fan gentle ping, would you like to review this, thank you. :)

cloud-fan · 2019-09-23T15:41:30Z

ok to test

cloud-fan · 2019-09-23T15:43:45Z

 override def output: Seq[Attribute] = {
 child.output.map { a =>
- if (a.nullable && notNullAttributes.contains(a.exprId)) {
+ if (a.nullable && notNullPreds.exists(_.semanticEquals(a))) {


What's the real difference? AFAIK Attribute#sementicEquals just compare the expr id.

SparkQA · 2019-09-23T17:55:49Z

Test build #111232 has finished for PR 25902 at commit 7e54ccb.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

viirya · 2019-09-23T21:55:49Z

 override def output: Seq[Attribute] = {
 child.output.map { a =>
- if (a.nullable && notNullAttributes.contains(a.exprId)) {
+ if (a.nullable && notNullPreds.exists(_.semanticEquals(a))) {


notNullPreds is a list of IsNotNull expressions. I think this semanticEquals comparison will fail at all?

yeah, this will fail all. This change is not good enough. I'll try another way.

wangshuo128 · 2019-09-24T04:16:05Z

@cloud-fan @viirya
Sorry, my earlier description and fix are not good enough.

The real problem is here:
nullcheck will not be generated for (length(cast(x#7L as string)) > 0) because
(length(cast(x#7L as string)) > 0) and cast(x#7L as string)(child of IsNotNull predicate isnotnull(cast(x#7L as string))) are not semantic equal when generating code for Filter ((length(cast(x#7L as string)) > 0) AND isnotnull(cast(x#7L as string))).

 val generated = otherPreds.map { c =>
val nullChecks = c.references.map { r =>
val idx = notNullPreds.indexWhere { n => n.asInstanceOf[IsNotNull].child.semanticEquals(r)}
if (idx != -1 && !generatedIsNotNullChecks(idx)) {
generatedIsNotNullChecks(idx) = true
// Use the child's output. The nullability is what the child produced.
genPredicate(notNullPreds(idx), input, child.output)
} else {
""
}
}.mkString("\n").trim
// Here we use *this* operator's output with this output's nullability since we already
// enforced them with the IsNotNull checks above.
s"""
|$nullChecks
|${genPredicate(c, input, output)}
""".stripMargin.trim
}.mkString("\n")

At the same time, filter output attribute x#7L is marked as not nullable. Nullability will not be checked when generating code for x#7L.
So NPE is thrown when reading null data in generated code of (length(cast(x#7L as string)) > 0) .

To fix this, I think we can filter attributes both in notNullPreds and otherPreds when get notNullAttributes:

 private val notNullAttributes = notNullPreds.flatMap(_.references).distinct.map(_.exprId)
.diff(otherPreds.flatMap(_.references).distinct.map(_.exprId))

SparkQA · 2019-09-24T05:52:12Z

Test build #111255 has finished for PR 25902 at commit af0fb3f.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2019-09-24T06:47:47Z

Test build #111256 has finished for PR 25902 at commit bc0a688.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

Some slight modifications so we no longer depend on Hive.

cloud-fan · 2019-09-24T09:21:45Z


 // The columns that will filtered out by `IsNotNull` could be considered as not nullable.
 private val notNullAttributes = notNullPreds.flatMap(_.references).distinct.map(_.exprId)
+ .diff(otherPreds.flatMap(_.references).distinct.map(_.exprId))


This fix seems sub-optimal to me. If we have a IsNotNull(a), then a should be a notNullAttribute even if a appears in otherPreds like EqualTo(a, b).

I think the bug is about how we codegen null checks, the current logic is:
split the predicates into notNullPreds (IsNotNull expressions) and otherPreds
try codegen otherPreds first, then notNullPreds.
if an other-predicate can leverage a specific not-null-predicate, then codegen that not-null-predicate first, so that we can codegen the other-predicate with non-nullable attributes.
There is a problem in step 3. Image that we have a IsNotNull(SubString(a)) and a SomeFunc(a). Then a is a not-null attribute for sure, even if there is no IsNullNull(a) predicate.
When we codegen SomeFunc(a), we can't find a IsNotNull(a) expression, so we skip the null check. However, we assume a is not nullable when codegen SomeFunc(a), which is wrong as IsNotNull(SubString(a)) has not been codegened yet.

viirya · 2019-09-24T21:05:36Z

I think the problem is because the child of IsNotNull is not a reference. So when we look at the IsNotNull that can filter null values for a non-IsNotNull predicate, we fail to look at it up:

validx= notNullPreds.indexWhere { n => n.asInstanceOf[IsNotNull].child.semanticEquals(r)}

Doesn't the easiest fix look:

validx= notNullPreds.indexWhere { n =>
n.asInstanceOf[IsNotNull].child.references.contains(r)
}

cloud-fan · 2019-09-25T03:02:10Z

@viirya your proposal works, but is a little against the original design goal. Think about IsNotNull(a + b + c + d ..) and Substring(a).

When codegen Substring(a), with your proposal, we will codegen IsNotNull(a + b + c + d ..) first, which loads more columns and may cause perf regression.

I have a slightly different proposal: when codegen Substring(a), check if there is a IsNotNull(a) predicate. If there is, codegen it first. If not, and a is a not-nullable attribute, then generate a IsNotNull(a) and codegen it first.

viirya · 2019-09-25T03:38:06Z

Ah, I see. You are correct. Your proposal sounds good.

wangshuo128 · 2019-09-25T04:00:39Z

@cloud-fan

I have a slightly different proposal: when codegen Substring(a), check if there is a IsNotNull(a) predicate. If there is, codegen it first. If not, and a is a not-nullable attribute, then generate a IsNotNull(a) and codegen it first.

Substring(a)a could be an expression. If we have predicates like Substring(Substring(a)) and IsNotNulll(Substring(a)), we could codegen IsNotNulll(Substring(a)) for null check first?

To be more comprehensive, should we check if there is IsNotNull predicate for a predicate and all its children before generate IsNotNull for its references? But this is a little complicated.

cloud-fan · 2019-09-25T07:24:54Z

@wangshuo128 you are right that we can improve the algorithm further, and we can do it once the bug is fixed. For now let's fix the bug without introducing a regression.

That said, for any predicate (not IsNotNull), find all its references (e.g. a and b), and check if there are corresponding IsNotNull predicates (e.g. IsNotNull(a) and IsNotNull(b)). If a reference has a corresponding IsNotNull predicate, codegen it first. Otherwise, generate a new IsNotNull predicate for it and codegen it, if the reference is a not-nullable attribute.

wangshuo128 · 2019-09-25T07:36:58Z

For now let's fix the bug without introducing a regression.

Ok, I'll try to fix this bug first.

SparkQA · 2019-09-25T09:46:49Z

Test build #111341 has finished for PR 25902 at commit 5555731.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds no public classes.

wangshuo128 · 2019-09-25T09:59:50Z

Sorry, @JoshRosen , do you mind I use your code to move test from sql/hive to sql/core ?

JoshRosen · 2019-09-25T10:13:23Z

Sorry, @JoshRosen , do you mind I use your code to move test from sql/hive to sql/core ?

Yes, please feel free to use that! This bug is somewhat hard to reproduce in unit tests because filter pushdown to data source scans will prevent the FilterExec code from processing the null-containing input rows. In d1658bb I worked round that by using typed Dataset operations to create black-box operators through which the optimizer cannot push filters.

(I'm following this PR because I'm interested in other aspects of IsNotNull optimizations; I have some notes on this particular code in #24765, something of a backburner project as I learn more about this code)

cloud-fan · 2019-09-25T14:32:30Z

 // TODO: revisit this. We can consider reordering predicates as well.
 val generatedIsNotNullChecks = new Array[Boolean](notNullPreds.length)
+
+ val extraIsNotNullReferences = mutable.Set[Attribute]()


nit: extraIsNotNullAttrs

cloud-fan · 2019-09-25T14:34:16Z

+
+ val extraIsNotNullReferences = mutable.Set[Attribute]()
+
+ def outputContainsNotNull(ref: Attribute): Boolean = {


This method always return true, so we add IsNotNull for all attributes which is sub-optimal. We should check notNullAttributes.contains(ref.exprId)

Ah, my mistake.

cloud-fan · 2019-09-25T14:37:12Z

 }
 }
+
+ test("SPARK-29213 Make it consistent when get notnull output and generate null " +


We can simply describe the bug in the test name: SPARK-29213: FilterExec should not throw NPE

wangshuo128 · 2019-09-26T02:02:21Z

Yes, please feel free to use that!

@JoshRosen Thanks a lot!

maropu · 2019-09-26T05:21:25Z

 }
+
+ test("SPARK-29213: FilterExec should not throw NPE") {
+ withView("t1", "t2", "t3") {


nit: withView -> withTempView

maropu · 2019-09-26T05:26:53Z

+ sql("select ''").as[String].map(identity).toDF("x").createOrReplaceTempView("t3")
+ sql(
+ """
+ |select t1.x


nit: No strict rule though, I like capitalized words for SQL keywords: e.g., select -> SELECT, ....

SparkQA · 2019-09-26T05:31:19Z

Test build #111377 has finished for PR 25902 at commit 5272097.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2019-09-26T05:44:15Z

Test build #111378 has finished for PR 25902 at commit fcada1c.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

SparkQA · 2019-09-26T07:05:02Z

Test build #111393 has finished for PR 25902 at commit f33d34c.

This patch fails due to an unknown error code, -9.
This patch does not merge cleanly.
This patch adds no public classes.

maropu · 2019-09-27T02:21:53Z

@wangshuo128 Can you resolve the conflict by git-rebase?

wangshuo128 · 2019-09-27T02:22:31Z

@wangshuo128 Can you resolve the conflict first?

Ok

… null checks in FilteExec

…ributes

This is from JoshRosen@d1658bb

SparkQA · 2019-09-27T06:18:25Z

Test build #111460 has finished for PR 25902 at commit 124ad87.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

cloud-fan · 2019-09-27T07:14:32Z

thanks, merging to master/2.4!

wangshuo128 · 2019-09-27T07:27:41Z

Thanks!

Currently the behavior of getting output and generating null checks in `FilterExec` is different. Thus some nullable attribute could be treated as not nullable by mistake. In `FilterExec.ouput`, an attribute is marked as nullable or not by finding its `exprId` in notNullAttributes: ``` a.nullable && notNullAttributes.contains(a.exprId) ``` But in `FilterExec.doConsume`, a `nullCheck` is generated or not for a predicate is decided by whether there is semantic equal not null predicate: ``` val nullChecks = c.references.map { r => val idx = notNullPreds.indexWhere { n => n.asInstanceOf[IsNotNull].child.semanticEquals(r)} if (idx != -1 && !generatedIsNotNullChecks(idx)) { generatedIsNotNullChecks(idx) = true // Use the child's output. The nullability is what the child produced. genPredicate(notNullPreds(idx), input, child.output) } else { "" } }.mkString("\n").trim ``` NPE will happen when run the SQL below: ``` sql("create table table1(x string)") sql("create table table2(x bigint)") sql("create table table3(x string)") sql("insert into table2 select null as x") sql( """ |select t1.x |from ( | select x from table1) t1 |left join ( | select x from ( | select x from table2 | union all | select substr(x,5) x from table3 | ) a | where length(x)>0 |) t3 |on t1.x=t3.x """.stripMargin).collect() ``` NPE Exception: ``` java.lang.NullPointerException at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(generated.java:40) at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:726) at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:458) at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:135) at org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:94) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52) at org.apache.spark.scheduler.Task.run(Task.scala:127) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:449) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:452) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) ``` the generated code: ``` == Subtree 4 / 5 == *(2) Project [cast(x#7L as string) AS x#9] +- *(2) Filter ((length(cast(x#7L as string)) > 0) AND isnotnull(cast(x#7L as string))) +- Scan hive default.table2 [x#7L], HiveTableRelation `default`.`table2`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [x#7L] Generated code: /* 001 */ public Object generate(Object[] references) { /* 002 */ return new GeneratedIteratorForCodegenStage2(references); /* 003 */ } /* 004 */ /* 005 */ // codegenStageId=2 /* 006 */ final class GeneratedIteratorForCodegenStage2 extends org.apache.spark.sql.execution.BufferedRowIterator { /* 007 */ private Object[] references; /* 008 */ private scala.collection.Iterator[] inputs; /* 009 */ private scala.collection.Iterator inputadapter_input_0; /* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] filter_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[2]; /* 011 */ /* 012 */ public GeneratedIteratorForCodegenStage2(Object[] references) { /* 013 */ this.references = references; /* 014 */ } /* 015 */ /* 016 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 017 */ partitionIndex = index; /* 018 */ this.inputs = inputs; /* 019 */ inputadapter_input_0 = inputs[0]; /* 020 */ filter_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 021 */ filter_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 32); /* 022 */ /* 023 */ } /* 024 */ /* 025 */ protected void processNext() throws java.io.IOException { /* 026 */ while ( inputadapter_input_0.hasNext()) { /* 027 */ InternalRow inputadapter_row_0 = (InternalRow) inputadapter_input_0.next(); /* 028 */ /* 029 */ do { /* 030 */ boolean inputadapter_isNull_0 = inputadapter_row_0.isNullAt(0); /* 031 */ long inputadapter_value_0 = inputadapter_isNull_0 ? /* 032 */ -1L : (inputadapter_row_0.getLong(0)); /* 033 */ /* 034 */ boolean filter_isNull_2 = inputadapter_isNull_0; /* 035 */ UTF8String filter_value_2 = null; /* 036 */ if (!inputadapter_isNull_0) { /* 037 */ filter_value_2 = UTF8String.fromString(String.valueOf(inputadapter_value_0)); /* 038 */ } /* 039 */ int filter_value_1 = -1; /* 040 */ filter_value_1 = (filter_value_2).numChars(); /* 041 */ /* 042 */ boolean filter_value_0 = false; /* 043 */ filter_value_0 = filter_value_1 > 0; /* 044 */ if (!filter_value_0) continue; /* 045 */ /* 046 */ boolean filter_isNull_6 = inputadapter_isNull_0; /* 047 */ UTF8String filter_value_6 = null; /* 048 */ if (!inputadapter_isNull_0) { /* 049 */ filter_value_6 = UTF8String.fromString(String.valueOf(inputadapter_value_0)); /* 050 */ } /* 051 */ if (!(!filter_isNull_6)) continue; /* 052 */ /* 053 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1); /* 054 */ /* 055 */ boolean project_isNull_0 = false; /* 056 */ UTF8String project_value_0 = null; /* 057 */ if (!false) { /* 058 */ project_value_0 = UTF8String.fromString(String.valueOf(inputadapter_value_0)); /* 059 */ } /* 060 */ filter_mutableStateArray_0[1].reset(); /* 061 */ /* 062 */ filter_mutableStateArray_0[1].zeroOutNullBytes(); /* 063 */ /* 064 */ if (project_isNull_0) { /* 065 */ filter_mutableStateArray_0[1].setNullAt(0); /* 066 */ } else { /* 067 */ filter_mutableStateArray_0[1].write(0, project_value_0); /* 068 */ } /* 069 */ append((filter_mutableStateArray_0[1].getRow())); /* 070 */ /* 071 */ } while(false); /* 072 */ if (shouldStop()) return; /* 073 */ } /* 074 */ } /* 075 */ /* 076 */ } ``` This PR proposes to use semantic comparison both in `FilterExec.output` and `FilterExec.doConsume` for nullable attribute. With this PR, the generated code snippet is below: ``` == Subtree 2 / 5 == *(3) Project [substring(x#8, 5, 2147483647) AS x#5] +- *(3) Filter ((length(substring(x#8, 5, 2147483647)) > 0) AND isnotnull(substring(x#8, 5, 2147483647))) +- Scan hive default.table3 [x#8], HiveTableRelation `default`.`table3`, org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, [x#8] Generated code: /* 001 */ public Object generate(Object[] references) { /* 002 */ return new GeneratedIteratorForCodegenStage3(references); /* 003 */ } /* 004 */ /* 005 */ // codegenStageId=3 /* 006 */ final class GeneratedIteratorForCodegenStage3 extends org.apache.spark.sql.execution.BufferedRowIterator { /* 007 */ private Object[] references; /* 008 */ private scala.collection.Iterator[] inputs; /* 009 */ private scala.collection.Iterator inputadapter_input_0; /* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] filter_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[2]; /* 011 */ /* 012 */ public GeneratedIteratorForCodegenStage3(Object[] references) { /* 013 */ this.references = references; /* 014 */ } /* 015 */ /* 016 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 017 */ partitionIndex = index; /* 018 */ this.inputs = inputs; /* 019 */ inputadapter_input_0 = inputs[0]; /* 020 */ filter_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 32); /* 021 */ filter_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 32); /* 022 */ /* 023 */ } /* 024 */ /* 025 */ protected void processNext() throws java.io.IOException { /* 026 */ while ( inputadapter_input_0.hasNext()) { /* 027 */ InternalRow inputadapter_row_0 = (InternalRow) inputadapter_input_0.next(); /* 028 */ /* 029 */ do { /* 030 */ boolean inputadapter_isNull_0 = inputadapter_row_0.isNullAt(0); /* 031 */ UTF8String inputadapter_value_0 = inputadapter_isNull_0 ? /* 032 */ null : (inputadapter_row_0.getUTF8String(0)); /* 033 */ /* 034 */ boolean filter_isNull_0 = true; /* 035 */ boolean filter_value_0 = false; /* 036 */ boolean filter_isNull_2 = true; /* 037 */ UTF8String filter_value_2 = null; /* 038 */ /* 039 */ if (!inputadapter_isNull_0) { /* 040 */ filter_isNull_2 = false; // resultCode could change nullability. /* 041 */ filter_value_2 = inputadapter_value_0.substringSQL(5, 2147483647); /* 042 */ /* 043 */ } /* 044 */ boolean filter_isNull_1 = filter_isNull_2; /* 045 */ int filter_value_1 = -1; /* 046 */ /* 047 */ if (!filter_isNull_2) { /* 048 */ filter_value_1 = (filter_value_2).numChars(); /* 049 */ } /* 050 */ if (!filter_isNull_1) { /* 051 */ filter_isNull_0 = false; // resultCode could change nullability. /* 052 */ filter_value_0 = filter_value_1 > 0; /* 053 */ /* 054 */ } /* 055 */ if (filter_isNull_0 || !filter_value_0) continue; /* 056 */ boolean filter_isNull_8 = true; /* 057 */ UTF8String filter_value_8 = null; /* 058 */ /* 059 */ if (!inputadapter_isNull_0) { /* 060 */ filter_isNull_8 = false; // resultCode could change nullability. /* 061 */ filter_value_8 = inputadapter_value_0.substringSQL(5, 2147483647); /* 062 */ /* 063 */ } /* 064 */ if (!(!filter_isNull_8)) continue; /* 065 */ /* 066 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1); /* 067 */ /* 068 */ boolean project_isNull_0 = true; /* 069 */ UTF8String project_value_0 = null; /* 070 */ /* 071 */ if (!inputadapter_isNull_0) { /* 072 */ project_isNull_0 = false; // resultCode could change nullability. /* 073 */ project_value_0 = inputadapter_value_0.substringSQL(5, 2147483647); /* 074 */ /* 075 */ } /* 076 */ filter_mutableStateArray_0[1].reset(); /* 077 */ /* 078 */ filter_mutableStateArray_0[1].zeroOutNullBytes(); /* 079 */ /* 080 */ if (project_isNull_0) { /* 081 */ filter_mutableStateArray_0[1].setNullAt(0); /* 082 */ } else { /* 083 */ filter_mutableStateArray_0[1].write(0, project_value_0); /* 084 */ } /* 085 */ append((filter_mutableStateArray_0[1].getRow())); /* 086 */ /* 087 */ } while(false); /* 088 */ if (shouldStop()) return; /* 089 */ } /* 090 */ } /* 091 */ /* 092 */ } ``` Fix NPE bug in FilterExec. no new UT Closes#25902 from wangshuo128/filter-codegen-npe. Authored-by: Wang Shuo <wangshuo128@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> (cherry picked from commit bd28e8e) Signed-off-by: Wenchen Fan <wenchen@databricks.com>

wangshuo128 changed the title ~~[SPARK-29213][SQL] Make it consistent when get notnull output and generate…~~[SPARK-29213][SQL] Make it consistent when get notnull output and generate null checks in FilterExecSep 23, 2019

wangshuo128 force-pushed the filter-codegen-npe branch from 045df47 to 7e54ccbCompare September 23, 2019 09:55

cloud-fan reviewed Sep 23, 2019
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dongjoon-hyun added the SQL label Sep 23, 2019

JoshRosen added a commit to JoshRosen/spark that referenced this pull request Sep 24, 2019
Adapt regression test fromapache#25902
d1658bb
Some slight modifications so we no longer depend on Hive.

cloud-fan reviewed Sep 24, 2019
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cloud-fan reviewed Sep 25, 2019
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maropu reviewed Sep 26, 2019
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wangshuo128 changed the title ~~[SPARK-29213][SQL] Make it consistent when get notnull output and generate null checks in FilterExec~~[SPARK-29213][SQL] Generate extra IsNotNull predicate in FilterExecSep 26, 2019

wangshuo128 requested a review from cloud-fan September 27, 2019 02:19

wangshuo128 added 8 commits September 27, 2019 10:36

[SPARK-29213] Make it consistent when get notnull output and generate…
ad6df5c
… null checks in FilteExec

remove exprId both in notNullPreds and otherPreds when get notNullAtt…
270172b
…ributes

modify test table names
9ad9723

Some slight modifications so we no longer depend on Hive.
c03dd95
This is from JoshRosen@d1658bb

Generate extra IsNotNull predicate for null check
30e2ac7

Address some comments
8480de2

Code sytle modification
a8cb828

Refine test
124ad87

wangshuo128 force-pushed the filter-codegen-npe branch from f33d34c to 124ad87Compare September 27, 2019 02:39

cloud-fan closed this in bd28e8eSep 27, 2019

wangshuo128 deleted the filter-codegen-npe branch September 27, 2019 08:08

wangshuo128 restored the filter-codegen-npe branch September 27, 2019 08:15


		val extraIsNotNullReferences = mutable.Set[Attribute]()

		def outputContainsNotNull(ref: Attribute): Boolean = {

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