[SPARK-16844][SQL] Support codegen for sort-based aggreagate by maropu · Pull Request #17164 · apache/spark

maropu · 2017-03-04T16:46:40Z

What changes were proposed in this pull request?

This pr supported codegen for SortAggregate.
This is the rework of #14481.

Close#14481

How was this patch tested?

Checked tests in DataFrameAggregateSuite.

SparkQA · 2017-03-04T16:49:36Z

Test build #73907 has finished for PR 17164 at commit f2ccc65.

This patch fails Scala style tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
abstract class AggregateExec extends UnaryExecNode
trait CodegenAggregateSupport extends CodegenSupport

maropu · 2017-03-04T16:49:57Z

A benchmark result:
https://github.com/apache/spark/pull/17164/files#diff-b7bf86a20a79d572f81093300568db6eR44

/*
range/limit/sum: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
----------------------------------------------------------------------------------------------
range/limit/sum wholestage off 617 / 617 13.6 73.5 1.0X
range/limit/sum wholestage on 70 / 92 120.2 8.3 8.8X
*/
/*
aggregate non-sorted data: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
----------------------------------------------------------------------------------------------
non-sorted data wholestage off 2540 / 2735 3.3 302.8 1.0X
non-sorted data wholestage on 1226 / 1528 6.8 146.1 2.1X
*/
/*
aggregate cached and sorted data: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
----------------------------------------------------------------------------------------------
cached and sorted data wholestage off 1455 / 1586 5.8 173.4 1.0X
cached and sorted data wholestage on 663 / 767 12.7 79.0 2.2X
*/

maropu · 2017-03-04T17:01:57Z

import org.apache.spark.sql.execution.debug._
spark.conf.set("spark.sql.aggregate.preferSortAggregate", "true")
val df = spark.range(10).selectExpr("id % 2 AS key", "rand() AS value")
df.groupBy($"key").sum("value").debugCodegen
Found 2 WholeStageCodegen subtrees.
== Subtree 1 / 2 ==
*SortAggregate(key=[key#3L], functions=[sum(value#4)], output=[key#3L, sum(value)#12])
+- *Sort [key#3L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(key#3L, 200)
+- *SortAggregate(key=[key#3L], functions=[partial_sum(value#4)], output=[key#3L, sum#17])
+- *Sort [key#3L ASC NULLS FIRST], false, 0
+- *Project [(id#0L % 2) AS key#3L, rand(-2342342825239413884) AS value#4]
+- *Range (0, 10, step=1, splits=Some(4))
Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIterator(references);
/* 003 */ }
/* 004 */
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private scala.collection.Iterator[] inputs;
/* 008 */ private org.apache.spark.sql.execution.metric.SQLMetric sagg_numOutputRows;
/* 009 */ private boolean sort_needToSort;
/* 010 */ private org.apache.spark.sql.execution.SortExec sort_plan;
/* 011 */ private org.apache.spark.sql.execution.UnsafeExternalRowSorter sort_sorter;
/* 012 */ private org.apache.spark.executor.TaskMetrics sort_metrics;
/* 013 */ private scala.collection.Iterator<UnsafeRow> sort_sortedIter;
/* 014 */ private scala.collection.Iterator inputadapter_input;
/* 015 */ private org.apache.spark.sql.execution.metric.SQLMetric sort_peakMemory;
/* 016 */ private org.apache.spark.sql.execution.metric.SQLMetric sort_spillSize;
/* 017 */ private org.apache.spark.sql.execution.metric.SQLMetric sort_sortTime;
/* 018 */ private UnsafeRow sagg_currentGroupingKey;
/* 019 */ private boolean sagg_bufIsNull;
/* 020 */ private double sagg_bufValue;
/* 021 */ private UnsafeRow sagg_result;
/* 022 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder sagg_holder;
/* 023 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter sagg_rowWriter;
/* 024 */ private UnsafeRow sagg_result1;
/* 025 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder sagg_holder1;
/* 026 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter sagg_rowWriter1;
/* 027 */ private org.apache.spark.sql.execution.aggregate.SortAggregateExec sagg_sortAggregate;
/* 028 */ private UnsafeRow sagg_result2;
/* 029 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder sagg_holder2;
/* 030 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter sagg_rowWriter2;
/* 031 */ private org.apache.spark.sql.execution.metric.SQLMetric wholestagecodegen_numOutputRows;
/* 032 */
/* 033 */ public GeneratedIterator(Object[] references) {
/* 034 */ this.references = references;
/* 035 */ }
/* 036 */
/* 037 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 038 */ partitionIndex = index;
/* 039 */ this.inputs = inputs;
/* 040 */ wholestagecodegen_init_0();
/* 041 */ wholestagecodegen_init_1();
/* 042 */ /* 043 */ }
/* 044 */ /* 045 */ private void wholestagecodegen_init_0() {
/* 046 */ this.sagg_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[0];
/* 047 */ sort_needToSort = true;
/* 048 */ this.sort_plan = (org.apache.spark.sql.execution.SortExec) references[1];
/* 049 */ sort_sorter = sort_plan.createSorter();
/* 050 */ sort_metrics = org.apache.spark.TaskContext.get().taskMetrics();
/* 051 */ /* 052 */ inputadapter_input = inputs[0];
/* 053 */ this.sort_peakMemory = (org.apache.spark.sql.execution.metric.SQLMetric) references[2];
/* 054 */ this.sort_spillSize = (org.apache.spark.sql.execution.metric.SQLMetric) references[3];
/* 055 */ this.sort_sortTime = (org.apache.spark.sql.execution.metric.SQLMetric) references[4];
/* 056 */ sagg_currentGroupingKey = null;
/* 057 */ /* 058 */ sagg_result = new UnsafeRow(1);
/* 059 */ this.sagg_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(sagg_result, 0);
/* 060 */ this.sagg_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(sagg_holder, 1);
/* 061 */ sagg_result1 = new UnsafeRow(1);
/* 062 */ this.sagg_holder1 = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(sagg_result1, 0);
/* 063 */ this.sagg_rowWriter1 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(sagg_holder1, 1);
/* 064 */ /* 065 */ }
/* 066 */ /* 067 */ private void wholestagecodegen_init_1() {
/* 068 */ this.sagg_sortAggregate = (org.apache.spark.sql.execution.aggregate.SortAggregateExec) references[5];
/* 069 */ sagg_result2 = new UnsafeRow(2);
/* 070 */ this.sagg_holder2 = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(sagg_result2, 0);
/* 071 */ this.sagg_rowWriter2 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(sagg_holder2, 2);
/* 072 */ this.wholestagecodegen_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[6];
/* 073 */ /* 074 */ }
/* 075 */ /* 076 */ private void sort_addToSorter() throws java.io.IOException {
/* 077 */ while (inputadapter_input.hasNext() && !stopEarly()) {
/* 078 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 079 */ sort_sorter.insertRow((UnsafeRow)inputadapter_row);
/* 080 */ if (shouldStop()) return;
/* 081 */ }
/* 082 */ /* 083 */ }
/* 084 */ /* 085 */ protected void processNext() throws java.io.IOException {
/* 086 */ if (sort_needToSort) {
/* 087 */ long sort_spillSizeBefore = sort_metrics.memoryBytesSpilled();
/* 088 */ sort_addToSorter();
/* 089 */ sort_sortedIter = sort_sorter.sort();
/* 090 */ sort_sortTime.add(sort_sorter.getSortTimeNanos() / 1000000);
/* 091 */ sort_peakMemory.add(sort_sorter.getPeakMemoryUsage());
/* 092 */ sort_spillSize.add(sort_metrics.memoryBytesSpilled() - sort_spillSizeBefore);
/* 093 */ sort_metrics.incPeakExecutionMemory(sort_sorter.getPeakMemoryUsage());
/* 094 */ sort_needToSort = false;
/* 095 */ }
/* 096 */ /* 097 */ while (sort_sortedIter.hasNext()) {
/* 098 */ UnsafeRow sort_outputRow = (UnsafeRow)sort_sortedIter.next();
/* 099 */ /* 100 */ boolean sort_isNull = sort_outputRow.isNullAt(0);
/* 101 */ long sort_value = sort_isNull ? -1L : (sort_outputRow.getLong(0));
/* 102 */ /* 103 */ // generate grouping keys
/* 104 */ sagg_rowWriter.zeroOutNullBytes();
/* 105 */ /* 106 */ if (sort_isNull) {
/* 107 */ sagg_rowWriter.setNullAt(0);
/* 108 */ } else {
/* 109 */ sagg_rowWriter.write(0, sort_value);
/* 110 */ }
/* 111 */ /* 112 */ if (sagg_currentGroupingKey == null) {
/* 113 */ sagg_currentGroupingKey = sagg_result.copy();
/* 114 */ // init aggregation buffer vars
/* 115 */ final double sagg_value = -1.0;
/* 116 */ sagg_bufIsNull = true;
/* 117 */ sagg_bufValue = sagg_value;
/* 118 */ // do aggregation
/* 119 */ /* 120 */ // do aggregate
/* 121 */ // common sub-expressions
/* 122 */ /* 123 */ // evaluate aggregate function
/* 124 */ boolean sagg_isNull2 = true;
/* 125 */ double sagg_value2 = -1.0;
/* 126 */ /* 127 */ boolean sagg_isNull3 = sagg_bufIsNull;
/* 128 */ double sagg_value3 = sagg_bufValue;
/* 129 */ if (sagg_isNull3) {
/* 130 */ boolean sagg_isNull5 = false;
/* 131 */ double sagg_value5 = -1.0;
/* 132 */ if (!false) {
/* 133 */ sagg_value5 = (double) 0;
/* 134 */ }
/* 135 */ if (!sagg_isNull5) {
/* 136 */ sagg_isNull3 = false;
/* 137 */ sagg_value3 = sagg_value5;
/* 138 */ }
/* 139 */ }
/* 140 */
/* 141 */ boolean sort_isNull1 = sort_outputRow.isNullAt(1);
/* 142 */ double sort_value1 = sort_isNull1 ? -1.0 : (sort_outputRow.getDouble(1));
/* 143 */ if (!sort_isNull1) {
/* 144 */ sagg_isNull2 = false; // resultCode could change nullability.
/* 145 */ sagg_value2 = sagg_value3 + sort_value1;
/* 146 */
/* 147 */ }
/* 148 */ boolean sagg_isNull1 = sagg_isNull2;
/* 149 */ double sagg_value1 = sagg_value2;
/* 150 */ if (sagg_isNull1) {
/* 151 */ if (!sagg_bufIsNull) {
/* 152 */ sagg_isNull1 = false;
/* 153 */ sagg_value1 = sagg_bufValue;
/* 154 */ }
/* 155 */ }
/* 156 */ // update aggregation buffer
/* 157 */ sagg_bufIsNull = sagg_isNull1;
/* 158 */ sagg_bufValue = sagg_value1;
/* 159 */ /* 160 */ // continue;
/* 161 */ } else {
/* 162 */ if (sagg_currentGroupingKey.equals(sagg_result)) {
/* 163 */ // do aggregate
/* 164 */ // common sub-expressions
/* 165 */
/* 166 */ // evaluate aggregate function
/* 167 */ boolean sagg_isNull2 = true;
/* 168 */ double sagg_value2 = -1.0;
/* 169 */
/* 170 */ boolean sagg_isNull3 = sagg_bufIsNull;
/* 171 */ double sagg_value3 = sagg_bufValue;
/* 172 */ if (sagg_isNull3) {
/* 173 */ boolean sagg_isNull5 = false;
/* 174 */ double sagg_value5 = -1.0;
/* 175 */ if (!false) {
/* 176 */ sagg_value5 = (double) 0;
/* 177 */ }
/* 178 */ if (!sagg_isNull5) {
/* 179 */ sagg_isNull3 = false;
/* 180 */ sagg_value3 = sagg_value5;
/* 181 */ }
/* 182 */ }
/* 183 */ /* 184 */ boolean sort_isNull1 = sort_outputRow.isNullAt(1);
/* 185 */ double sort_value1 = sort_isNull1 ? -1.0 : (sort_outputRow.getDouble(1));
/* 186 */ if (!sort_isNull1) {
/* 187 */ sagg_isNull2 = false; // resultCode could change nullability.
/* 188 */ sagg_value2 = sagg_value3 + sort_value1;
/* 189 */ /* 190 */ }
/* 191 */ boolean sagg_isNull1 = sagg_isNull2;
/* 192 */ double sagg_value1 = sagg_value2;
/* 193 */ if (sagg_isNull1) {
/* 194 */ if (!sagg_bufIsNull) {
/* 195 */ sagg_isNull1 = false;
/* 196 */ sagg_value1 = sagg_bufValue;
/* 197 */ }
/* 198 */ }
/* 199 */ // update aggregation buffer
/* 200 */ sagg_bufIsNull = sagg_isNull1;
/* 201 */ sagg_bufValue = sagg_value1;
/* 202 */
/* 203 */ // continue;
/* 204 */ } else {
/* 205 */ wholestagecodegen_numOutputRows.add(1);
/* 206 */
/* 207 */ sagg_rowWriter1.zeroOutNullBytes();
/* 208 */
/* 209 */ if (sagg_bufIsNull) {
/* 210 */ sagg_rowWriter1.setNullAt(0);
/* 211 */ } else {
/* 212 */ sagg_rowWriter1.write(0, sagg_bufValue);
/* 213 */ }
/* 214 */
/* 215 */ boolean sagg_isNull11 = sagg_currentGroupingKey.isNullAt(0);
/* 216 */ long sagg_value11 = sagg_isNull11 ? -1L : (sagg_currentGroupingKey.getLong(0));
/* 217 */ boolean sagg_isNull12 = sagg_result1.isNullAt(0);
/* 218 */ double sagg_value12 = sagg_isNull12 ? -1.0 : (sagg_result1.getDouble(0));
/* 219 */ /* 220 */ sagg_rowWriter2.zeroOutNullBytes();
/* 221 */ /* 222 */ if (sagg_isNull11) {
/* 223 */ sagg_rowWriter2.setNullAt(0);
/* 224 */ } else {
/* 225 */ sagg_rowWriter2.write(0, sagg_value11);
/* 226 */ }
/* 227 */ /* 228 */ if (sagg_isNull12) {
/* 229 */ sagg_rowWriter2.setNullAt(1);
/* 230 */ } else {
/* 231 */ sagg_rowWriter2.write(1, sagg_value12);
/* 232 */ }
/* 233 */ append(sagg_result2);
/* 234 */
/* 235 */ // init buffer vars for a next partition
/* 236 */ sagg_currentGroupingKey = sagg_result.copy();
/* 237 */ final double sagg_value = -1.0;
/* 238 */ sagg_bufIsNull = true;
/* 239 */ sagg_bufValue = sagg_value;
/* 240 */
/* 241 */ // do aggregate
/* 242 */ // common sub-expressions
/* 243 */
/* 244 */ // evaluate aggregate function
/* 245 */ boolean sagg_isNull2 = true;
/* 246 */ double sagg_value2 = -1.0;
/* 247 */ /* 248 */ boolean sagg_isNull3 = sagg_bufIsNull;
/* 249 */ double sagg_value3 = sagg_bufValue;
/* 250 */ if (sagg_isNull3) {
/* 251 */ boolean sagg_isNull5 = false;
/* 252 */ double sagg_value5 = -1.0;
/* 253 */ if (!false) {
/* 254 */ sagg_value5 = (double) 0;
/* 255 */ }
/* 256 */ if (!sagg_isNull5) {
/* 257 */ sagg_isNull3 = false;
/* 258 */ sagg_value3 = sagg_value5;
/* 259 */ }
/* 260 */ }
/* 261 */
/* 262 */ boolean sort_isNull1 = sort_outputRow.isNullAt(1);
/* 263 */ double sort_value1 = sort_isNull1 ? -1.0 : (sort_outputRow.getDouble(1));
/* 264 */ if (!sort_isNull1) {
/* 265 */ sagg_isNull2 = false; // resultCode could change nullability.
/* 266 */ sagg_value2 = sagg_value3 + sort_value1;
/* 267 */
/* 268 */ }
/* 269 */ boolean sagg_isNull1 = sagg_isNull2;
/* 270 */ double sagg_value1 = sagg_value2;
/* 271 */ if (sagg_isNull1) {
/* 272 */ if (!sagg_bufIsNull) {
/* 273 */ sagg_isNull1 = false;
/* 274 */ sagg_value1 = sagg_bufValue;
/* 275 */ }
/* 276 */ }
/* 277 */ // update aggregation buffer
/* 278 */ sagg_bufIsNull = sagg_isNull1;
/* 279 */ sagg_bufValue = sagg_value1;
/* 280 */ /* 281 */ }
/* 282 */ }
/* 283 */ /* 284 */ if (shouldStop()) return;
/* 285 */ }
/* 286 */ /* 287 */ if (sagg_currentGroupingKey != null) {
/* 288 */ // for the last aggregation
/* 289 */ sagg_numOutputRows.add(1);
/* 290 */ /* 291 */ sagg_rowWriter1.zeroOutNullBytes();
/* 292 */ /* 293 */ if (sagg_bufIsNull) {
/* 294 */ sagg_rowWriter1.setNullAt(0);
/* 295 */ } else {
/* 296 */ sagg_rowWriter1.write(0, sagg_bufValue);
/* 297 */ }
/* 298 */
/* 299 */ boolean sagg_isNull11 = sagg_currentGroupingKey.isNullAt(0);
/* 300 */ long sagg_value11 = sagg_isNull11 ? -1L : (sagg_currentGroupingKey.getLong(0));
/* 301 */ boolean sagg_isNull12 = sagg_result1.isNullAt(0);
/* 302 */ double sagg_value12 = sagg_isNull12 ? -1.0 : (sagg_result1.getDouble(0));
/* 303 */
/* 304 */ sagg_rowWriter2.zeroOutNullBytes();
/* 305 */
/* 306 */ if (sagg_isNull11) {
/* 307 */ sagg_rowWriter2.setNullAt(0);
/* 308 */ } else {
/* 309 */ sagg_rowWriter2.write(0, sagg_value11);
/* 310 */ }
/* 311 */
/* 312 */ if (sagg_isNull12) {
/* 313 */ sagg_rowWriter2.setNullAt(1);
/* 314 */ } else {
/* 315 */ sagg_rowWriter2.write(1, sagg_value12);
/* 316 */ }
/* 317 */ append(sagg_result2);
/* 318 */ /* 319 */ sagg_currentGroupingKey = null;
/* 320 */ }
/* 321 */ }
/* 322 */ }
== Subtree 2 / 2 ==
*SortAggregate(key=[key#3L], functions=[partial_sum(value#4)], output=[key#3L, sum#17])
+- *Sort [key#3L ASC NULLS FIRST], false, 0
+- *Project [(id#0L % 2) AS key#3L, rand(-2342342825239413884) AS value#4]
+- *Range (0, 10, step=1, splits=Some(4))
Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIterator(references);
/* 003 */ }
/* 004 */
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private scala.collection.Iterator[] inputs;
/* 008 */ private org.apache.spark.sql.execution.metric.SQLMetric sagg_numOutputRows;
/* 009 */ private boolean sort_needToSort;
/* 010 */ private org.apache.spark.sql.execution.SortExec sort_plan;
/* 011 */ private org.apache.spark.sql.execution.UnsafeExternalRowSorter sort_sorter;
/* 012 */ private org.apache.spark.executor.TaskMetrics sort_metrics;
/* 013 */ private scala.collection.Iterator<UnsafeRow> sort_sortedIter;
/* 014 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numOutputRows;
/* 015 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numGeneratedRows;
/* 016 */ private boolean range_initRange;
/* 017 */ private long range_number;
/* 018 */ private TaskContext range_taskContext;
/* 019 */ private InputMetrics range_inputMetrics;
/* 020 */ private long range_batchEnd;
/* 021 */ private long range_numElementsTodo;
/* 022 */ private scala.collection.Iterator range_input;
/* 023 */ private UnsafeRow range_result;
/* 024 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder range_holder;
/* 025 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter range_rowWriter;
/* 026 */ private org.apache.spark.util.random.XORShiftRandom project_rng;
/* 027 */ private UnsafeRow project_result;
/* 028 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder project_holder;
/* 029 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter project_rowWriter;
/* 030 */ private org.apache.spark.sql.execution.metric.SQLMetric sort_peakMemory;
/* 031 */ private org.apache.spark.sql.execution.metric.SQLMetric sort_spillSize;
/* 032 */ private org.apache.spark.sql.execution.metric.SQLMetric sort_sortTime;
/* 033 */ private UnsafeRow sagg_currentGroupingKey;
/* 034 */ private boolean sagg_bufIsNull;
/* 035 */ private double sagg_bufValue;
/* 036 */ private UnsafeRow sagg_result;
/* 037 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder sagg_holder;
/* 038 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter sagg_rowWriter;
/* 039 */ private UnsafeRow sagg_result1;
/* 040 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder sagg_holder1;
/* 041 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter sagg_rowWriter1;
/* 042 */ private org.apache.spark.sql.execution.aggregate.SortAggregateExec sagg_sortAggregate;
/* 043 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowJoiner sagg_unsafeRowJoiner;
/* 044 */ private org.apache.spark.sql.execution.metric.SQLMetric wholestagecodegen_numOutputRows;
/* 045 */ /* 046 */ public GeneratedIterator(Object[] references) {
/* 047 */ this.references = references;
/* 048 */ }
/* 049 */ /* 050 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 051 */ partitionIndex = index;
/* 052 */ this.inputs = inputs;
/* 053 */ wholestagecodegen_init_0();
/* 054 */ wholestagecodegen_init_1();
/* 055 */ wholestagecodegen_init_2();
/* 056 */ project_rng = new org.apache.spark.util.random.XORShiftRandom(-2342342825239413884L + partitionIndex);
/* 057 */ }
/* 058 */
/* 059 */ private void wholestagecodegen_init_0() {
/* 060 */ this.sagg_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[0];
/* 061 */ sort_needToSort = true;
/* 062 */ this.sort_plan = (org.apache.spark.sql.execution.SortExec) references[1];
/* 063 */ sort_sorter = sort_plan.createSorter();
/* 064 */ sort_metrics = org.apache.spark.TaskContext.get().taskMetrics();
/* 065 */
/* 066 */ this.range_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[2];
/* 067 */ this.range_numGeneratedRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[3];
/* 068 */ range_initRange = false;
/* 069 */ range_number = 0L;
/* 070 */ range_taskContext = TaskContext.get();
/* 071 */ range_inputMetrics = range_taskContext.taskMetrics().inputMetrics();
/* 072 */ range_batchEnd = 0;
/* 073 */ range_numElementsTodo = 0L;
/* 074 */ range_input = inputs[0];
/* 075 */ range_result = new UnsafeRow(1);
/* 076 */ this.range_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(range_result, 0);
/* 077 */ this.range_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(range_holder, 1);
/* 078 */
/* 079 */ project_result = new UnsafeRow(2);
/* 080 */ this.project_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(project_result, 0);
/* 081 */
/* 082 */ }
/* 083 */
/* 084 */ private void wholestagecodegen_init_2() {
/* 085 */ this.wholestagecodegen_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[8];
/* 086 */
/* 087 */ }
/* 088 */
/* 089 */ private void wholestagecodegen_init_1() {
/* 090 */ this.project_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(project_holder, 2);
/* 091 */ this.sort_peakMemory = (org.apache.spark.sql.execution.metric.SQLMetric) references[4];
/* 092 */ this.sort_spillSize = (org.apache.spark.sql.execution.metric.SQLMetric) references[5];
/* 093 */ this.sort_sortTime = (org.apache.spark.sql.execution.metric.SQLMetric) references[6];
/* 094 */ sagg_currentGroupingKey = null;
/* 095 */
/* 096 */ sagg_result = new UnsafeRow(1);
/* 097 */ this.sagg_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(sagg_result, 0);
/* 098 */ this.sagg_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(sagg_holder, 1);
/* 099 */ sagg_result1 = new UnsafeRow(1);
/* 100 */ this.sagg_holder1 = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(sagg_result1, 0);
/* 101 */ this.sagg_rowWriter1 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(sagg_holder1, 1);
/* 102 */ this.sagg_sortAggregate = (org.apache.spark.sql.execution.aggregate.SortAggregateExec) references[7];
/* 103 */ sagg_unsafeRowJoiner = sagg_sortAggregate.createUnsafeJoiner();
/* 104 */
/* 105 */ }
/* 106 */
/* 107 */ private void initRange(int idx) {
/* 108 */ java.math.BigInteger index = java.math.BigInteger.valueOf(idx);
/* 109 */ java.math.BigInteger numSlice = java.math.BigInteger.valueOf(4L);
/* 110 */ java.math.BigInteger numElement = java.math.BigInteger.valueOf(10L);
/* 111 */ java.math.BigInteger step = java.math.BigInteger.valueOf(1L);
/* 112 */ java.math.BigInteger start = java.math.BigInteger.valueOf(0L);
/* 113 */ long partitionEnd;
/* 114 */
/* 115 */ java.math.BigInteger st = index.multiply(numElement).divide(numSlice).multiply(step).add(start);
/* 116 */ if (st.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 117 */ range_number = Long.MAX_VALUE;
/* 118 */ } else if (st.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 119 */ range_number = Long.MIN_VALUE;
/* 120 */ } else {
/* 121 */ range_number = st.longValue();
/* 122 */ }
/* 123 */ range_batchEnd = range_number;
/* 124 */
/* 125 */ java.math.BigInteger end = index.add(java.math.BigInteger.ONE).multiply(numElement).divide(numSlice)
/* 126 */ .multiply(step).add(start);
/* 127 */ if (end.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 128 */ partitionEnd = Long.MAX_VALUE;
/* 129 */ } else if (end.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 130 */ partitionEnd = Long.MIN_VALUE;
/* 131 */ } else {
/* 132 */ partitionEnd = end.longValue();
/* 133 */ }
/* 134 */
/* 135 */ java.math.BigInteger startToEnd = java.math.BigInteger.valueOf(partitionEnd).subtract(
/* 136 */ java.math.BigInteger.valueOf(range_number));
/* 137 */ range_numElementsTodo = startToEnd.divide(step).longValue();
/* 138 */ if (range_numElementsTodo < 0) {
/* 139 */ range_numElementsTodo = 0;
/* 140 */ } else if (startToEnd.remainder(step).compareTo(java.math.BigInteger.valueOf(0L)) != 0) {
/* 141 */ range_numElementsTodo++;
/* 142 */ }
/* 143 */ }
/* 144 */
/* 145 */ private void sort_addToSorter() throws java.io.IOException {
/* 146 */ // initialize Range
/* 147 */ if (!range_initRange) {
/* 148 */ range_initRange = true;
/* 149 */ initRange(partitionIndex);
/* 150 */ }
/* 151 */
/* 152 */ while (true) {
/* 153 */ while (range_number != range_batchEnd) {
/* 154 */ long range_value = range_number;
/* 155 */ range_number += 1L;
/* 156 */
/* 157 */ final double project_value3 = project_rng.nextDouble();
/* 158 */
/* 159 */ boolean project_isNull = false;
/* 160 */ long project_value = -1L;
/* 161 */ if (2L == 0) {
/* 162 */ project_isNull = true;
/* 163 */ } else {
/* 164 */ project_value = (long)(range_value % 2L);
/* 165 */ }
/* 166 */ project_rowWriter.zeroOutNullBytes();
/* 167 */ /* 168 */ if (project_isNull) {
/* 169 */ project_rowWriter.setNullAt(0);
/* 170 */ } else {
/* 171 */ project_rowWriter.write(0, project_value);
/* 172 */ }
/* 173 */
/* 174 */ project_rowWriter.write(1, project_value3);
/* 175 */ sort_sorter.insertRow((UnsafeRow)project_result);
/* 176 */
/* 177 */ if (shouldStop()) return;
/* 178 */ }
/* 179 */
/* 180 */ if (range_taskContext.isInterrupted()) {
/* 181 */ throw new TaskKilledException();
/* 182 */ }
/* 183 */
/* 184 */ long range_nextBatchTodo;
/* 185 */ if (range_numElementsTodo > 1000L) {
/* 186 */ range_nextBatchTodo = 1000L;
/* 187 */ range_numElementsTodo -= 1000L;
/* 188 */ } else {
/* 189 */ range_nextBatchTodo = range_numElementsTodo;
/* 190 */ range_numElementsTodo = 0;
/* 191 */ if (range_nextBatchTodo == 0) break;
/* 192 */ }
/* 193 */ range_numOutputRows.add(range_nextBatchTodo);
/* 194 */ range_inputMetrics.incRecordsRead(range_nextBatchTodo);
/* 195 */
/* 196 */ range_batchEnd += range_nextBatchTodo * 1L;
/* 197 */ }
/* 198 */
/* 199 */ }
/* 200 */
/* 201 */ protected void processNext() throws java.io.IOException {
/* 202 */ if (sort_needToSort) {
/* 203 */ long sort_spillSizeBefore = sort_metrics.memoryBytesSpilled();
/* 204 */ sort_addToSorter();
/* 205 */ sort_sortedIter = sort_sorter.sort();
/* 206 */ sort_sortTime.add(sort_sorter.getSortTimeNanos() / 1000000);
/* 207 */ sort_peakMemory.add(sort_sorter.getPeakMemoryUsage());
/* 208 */ sort_spillSize.add(sort_metrics.memoryBytesSpilled() - sort_spillSizeBefore);
/* 209 */ sort_metrics.incPeakExecutionMemory(sort_sorter.getPeakMemoryUsage());
/* 210 */ sort_needToSort = false;
/* 211 */ }
/* 212 */
/* 213 */ while (sort_sortedIter.hasNext()) {
/* 214 */ UnsafeRow sort_outputRow = (UnsafeRow)sort_sortedIter.next();
/* 215 */
/* 216 */ boolean sort_isNull = sort_outputRow.isNullAt(0);
/* 217 */ long sort_value = sort_isNull ? -1L : (sort_outputRow.getLong(0));
/* 218 */ double sort_value1 = sort_outputRow.getDouble(1);
/* 219 */
/* 220 */ // generate grouping keys
/* 221 */ sagg_rowWriter.zeroOutNullBytes();
/* 222 */
/* 223 */ if (sort_isNull) {
/* 224 */ sagg_rowWriter.setNullAt(0);
/* 225 */ } else {
/* 226 */ sagg_rowWriter.write(0, sort_value);
/* 227 */ }
/* 228 */
/* 229 */ if (sagg_currentGroupingKey == null) {
/* 230 */ sagg_currentGroupingKey = sagg_result.copy();
/* 231 */ // init aggregation buffer vars
/* 232 */ final double sagg_value = -1.0;
/* 233 */ sagg_bufIsNull = true;
/* 234 */ sagg_bufValue = sagg_value;
/* 235 */ // do aggregation
/* 236 */
/* 237 */ // do aggregate
/* 238 */ // common sub-expressions
/* 239 */
/* 240 */ // evaluate aggregate function
/* 241 */ boolean sagg_isNull1 = false;
/* 242 */
/* 243 */ boolean sagg_isNull2 = sagg_bufIsNull;
/* 244 */ double sagg_value2 = sagg_bufValue;
/* 245 */ if (sagg_isNull2) {
/* 246 */ boolean sagg_isNull4 = false;
/* 247 */ double sagg_value4 = -1.0;
/* 248 */ if (!false) {
/* 249 */ sagg_value4 = (double) 0;
/* 250 */ }
/* 251 */ if (!sagg_isNull4) {
/* 252 */ sagg_isNull2 = false;
/* 253 */ sagg_value2 = sagg_value4;
/* 254 */ }
/* 255 */ }
/* 256 */
/* 257 */ boolean sagg_isNull6 = false;
/* 258 */ double sagg_value6 = -1.0;
/* 259 */ if (!false) {
/* 260 */ sagg_value6 = sort_value1;
/* 261 */ }
/* 262 */ double sagg_value1 = -1.0;
/* 263 */ sagg_value1 = sagg_value2 + sagg_value6;
/* 264 */ // update aggregation buffer
/* 265 */ sagg_bufIsNull = false;
/* 266 */ sagg_bufValue = sagg_value1;
/* 267 */
/* 268 */ // continue;
/* 269 */ } else {
/* 270 */ if (sagg_currentGroupingKey.equals(sagg_result)) {
/* 271 */ // do aggregate
/* 272 */ // common sub-expressions
/* 273 */
/* 274 */ // evaluate aggregate function
/* 275 */ boolean sagg_isNull1 = false;
/* 276 */
/* 277 */ boolean sagg_isNull2 = sagg_bufIsNull;
/* 278 */ double sagg_value2 = sagg_bufValue;
/* 279 */ if (sagg_isNull2) {
/* 280 */ boolean sagg_isNull4 = false;
/* 281 */ double sagg_value4 = -1.0;
/* 282 */ if (!false) {
/* 283 */ sagg_value4 = (double) 0;
/* 284 */ }
/* 285 */ if (!sagg_isNull4) {
/* 286 */ sagg_isNull2 = false;
/* 287 */ sagg_value2 = sagg_value4;
/* 288 */ }
/* 289 */ }
/* 290 */
/* 291 */ boolean sagg_isNull6 = false;
/* 292 */ double sagg_value6 = -1.0;
/* 293 */ if (!false) {
/* 294 */ sagg_value6 = sort_value1;
/* 295 */ }
/* 296 */ double sagg_value1 = -1.0;
/* 297 */ sagg_value1 = sagg_value2 + sagg_value6;
/* 298 */ // update aggregation buffer
/* 299 */ sagg_bufIsNull = false;
/* 300 */ sagg_bufValue = sagg_value1;
/* 301 */
/* 302 */ // continue;
/* 303 */ } else {
/* 304 */ wholestagecodegen_numOutputRows.add(1);
/* 305 */
/* 306 */ sagg_rowWriter1.zeroOutNullBytes();
/* 307 */
/* 308 */ if (sagg_bufIsNull) {
/* 309 */ sagg_rowWriter1.setNullAt(0);
/* 310 */ } else {
/* 311 */ sagg_rowWriter1.write(0, sagg_bufValue);
/* 312 */ }
/* 313 */
/* 314 */ UnsafeRow sagg_resultRow = sagg_unsafeRowJoiner.join(sagg_currentGroupingKey, sagg_result1);
/* 315 */
/* 316 */ append(sagg_resultRow);
/* 317 */
/* 318 */ // init buffer vars for a next partition
/* 319 */ sagg_currentGroupingKey = sagg_result.copy();
/* 320 */ final double sagg_value = -1.0;
/* 321 */ sagg_bufIsNull = true;
/* 322 */ sagg_bufValue = sagg_value;
/* 323 */
/* 324 */ // do aggregate
/* 325 */ // common sub-expressions
/* 326 */
/* 327 */ // evaluate aggregate function
/* 328 */ boolean sagg_isNull1 = false;
/* 329 */
/* 330 */ boolean sagg_isNull2 = sagg_bufIsNull;
/* 331 */ double sagg_value2 = sagg_bufValue;
/* 332 */ if (sagg_isNull2) {
/* 333 */ boolean sagg_isNull4 = false;
/* 334 */ double sagg_value4 = -1.0;
/* 335 */ if (!false) {
/* 336 */ sagg_value4 = (double) 0;
/* 337 */ }
/* 338 */ if (!sagg_isNull4) {
/* 339 */ sagg_isNull2 = false;
/* 340 */ sagg_value2 = sagg_value4;
/* 341 */ }
/* 342 */ }
/* 343 */
/* 344 */ boolean sagg_isNull6 = false;
/* 345 */ double sagg_value6 = -1.0;
/* 346 */ if (!false) {
/* 347 */ sagg_value6 = sort_value1;
/* 348 */ }
/* 349 */ double sagg_value1 = -1.0;
/* 350 */ sagg_value1 = sagg_value2 + sagg_value6;
/* 351 */ // update aggregation buffer
/* 352 */ sagg_bufIsNull = false;
/* 353 */ sagg_bufValue = sagg_value1;
/* 354 */ /* 353 */ sagg_bufValue = sagg_value1;
/* 354 */ /* 355 */ }
/* 356 */ }
/* 357 */
/* 358 */ if (shouldStop()) return;
/* 359 */ }
/* 360 */
/* 361 */ if (sagg_currentGroupingKey != null) {
/* 362 */ // for the last aggregation
/* 363 */ sagg_numOutputRows.add(1);
/* 364 */
/* 365 */ sagg_rowWriter1.zeroOutNullBytes();
/* 366 */
/* 367 */ if (sagg_bufIsNull) {
/* 368 */ sagg_rowWriter1.setNullAt(0);
/* 369 */ } else {
/* 370 */ sagg_rowWriter1.write(0, sagg_bufValue);
/* 371 */ }
/* 372 */
/* 373 */ UnsafeRow sagg_resultRow = sagg_unsafeRowJoiner.join(sagg_currentGroupingKey, sagg_result1);
/* 374 */
/* 375 */ append(sagg_resultRow);
/* 376 */
/* 377 */ sagg_currentGroupingKey = null;
/* 378 */ }
/* 379 */ }
/* 380 */ }

maropu · 2017-03-04T17:07:00Z

import org.apache.spark.sql.execution.debug._
spark.conf.set("spark.sql.aggregate.preferSortAggregate", "true")
val df = spark.range(10).selectExpr("id % 2 AS key", "rand() AS value")
df.groupBy().count.debugCodegen
Found 2 WholeStageCodegen subtrees.
== Subtree 1 / 2 ==
*SortAggregate(key=[], functions=[partial_count(1)], output=[count#51L])
+- *Project
+- *Range (0, 10, step=1, splits=Some(4))
Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIterator(references);
/* 003 */ }
/* 004 */
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private scala.collection.Iterator[] inputs;
/* 008 */ private boolean sagg_initAgg;
/* 009 */ private boolean sagg_bufIsNull;
/* 010 */ private long sagg_bufValue;
/* 011 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numOutputRows;
/* 012 */ private org.apache.spark.sql.execution.metric.SQLMetric range_numGeneratedRows;
/* 013 */ private boolean range_initRange;
/* 014 */ private long range_number;
/* 015 */ private TaskContext range_taskContext;
/* 016 */ private InputMetrics range_inputMetrics;
/* 017 */ private long range_batchEnd;
/* 018 */ private long range_numElementsTodo;
/* 019 */ private scala.collection.Iterator range_input;
/* 020 */ private UnsafeRow range_result;
/* 021 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder range_holder;
/* 022 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter range_rowWriter;
/* 023 */ private org.apache.spark.sql.execution.metric.SQLMetric sagg_numOutputRows;
/* 024 */ private org.apache.spark.sql.execution.metric.SQLMetric sagg_aggTime;
/* 025 */ private UnsafeRow sagg_result;
/* 026 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder sagg_holder;
/* 027 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter sagg_rowWriter;
/* 028 */
/* 029 */ public GeneratedIterator(Object[] references) {
/* 030 */ this.references = references;
/* 031 */ }
/* 032 */
/* 033 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 034 */ partitionIndex = index;
/* 035 */ this.inputs = inputs;
/* 036 */ sagg_initAgg = false;
/* 037 */
/* 038 */ this.range_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[0];
/* 039 */ this.range_numGeneratedRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[1];
/* 040 */ range_initRange = false;
/* 041 */ range_number = 0L;
/* 042 */ range_taskContext = TaskContext.get();
/* 043 */ range_inputMetrics = range_taskContext.taskMetrics().inputMetrics();
/* 044 */ range_batchEnd = 0;
/* 045 */ range_numElementsTodo = 0L;
/* 046 */ range_input = inputs[0];
/* 047 */ range_result = new UnsafeRow(1);
/* 048 */ this.range_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(range_result, 0);
/* 049 */ this.range_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(range_holder, 1);
/* 050 */ this.sagg_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[2];
/* 051 */ this.sagg_aggTime = (org.apache.spark.sql.execution.metric.SQLMetric) references[3];
/* 052 */ sagg_result = new UnsafeRow(1);
/* 053 */ this.sagg_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(sagg_result, 0);
/* 054 */ this.sagg_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(sagg_holder, 1);
/* 055 */
/* 056 */ }
/* 057 */
/* 058 */ private void sagg_doAggregateWithoutKey() throws java.io.IOException {
/* 059 */ // initialize aggregation buffer
/* 060 */ sagg_bufIsNull = false;
/* 061 */ sagg_bufValue = 0L;
/* 062 */
/* 063 */ // initialize Range
/* 064 */ if (!range_initRange) {
/* 065 */ range_initRange = true;
/* 066 */ initRange(partitionIndex);
/* 067 */ }
/* 068 */
/* 069 */ while (true) {
/* 070 */ while (range_number != range_batchEnd) {
/* 071 */ long range_value = range_number;
/* 072 */ range_number += 1L;
/* 073 */
/* 074 */ // do aggregate
/* 075 */ // common sub-expressions
/* 076 */
/* 077 */ // evaluate aggregate function
/* 078 */ boolean sagg_isNull1 = false;
/* 079 */
/* 080 */ long sagg_value1 = -1L;
/* 081 */ sagg_value1 = sagg_bufValue + 1L;
/* 082 */ // update aggregation buffer
/* 083 */ sagg_bufIsNull = false;
/* 084 */ sagg_bufValue = sagg_value1;
/* 085 */
/* 086 */ if (shouldStop()) return;
/* 087 */ }
/* 088 */
/* 089 */ if (range_taskContext.isInterrupted()) {
/* 090 */ throw new TaskKilledException();
/* 091 */ }
/* 092 */
/* 093 */ long range_nextBatchTodo;
/* 094 */ if (range_numElementsTodo > 1000L) {
/* 095 */ range_nextBatchTodo = 1000L;
/* 096 */ range_numElementsTodo -= 1000L;
/* 097 */ } else {
/* 098 */ range_nextBatchTodo = range_numElementsTodo;
/* 099 */ range_numElementsTodo = 0;
/* 100 */ if (range_nextBatchTodo == 0) break;
/* 101 */ }
/* 102 */ range_numOutputRows.add(range_nextBatchTodo);
/* 103 */ range_inputMetrics.incRecordsRead(range_nextBatchTodo);
/* 104 */
/* 105 */ range_batchEnd += range_nextBatchTodo * 1L;
/* 106 */ }
/* 107 */
/* 108 */ }
/* 109 */
/* 110 */ private void initRange(int idx) {
/* 111 */ java.math.BigInteger index = java.math.BigInteger.valueOf(idx);
/* 112 */ java.math.BigInteger numSlice = java.math.BigInteger.valueOf(4L);
/* 113 */ java.math.BigInteger numElement = java.math.BigInteger.valueOf(10L);
/* 114 */ java.math.BigInteger step = java.math.BigInteger.valueOf(1L);
/* 115 */ java.math.BigInteger start = java.math.BigInteger.valueOf(0L);
/* 116 */ long partitionEnd;
/* 117 */
/* 118 */ java.math.BigInteger st = index.multiply(numElement).divide(numSlice).multiply(step).add(start);
/* 119 */ if (st.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 120 */ range_number = Long.MAX_VALUE;
/* 121 */ } else if (st.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 122 */ range_number = Long.MIN_VALUE;
/* 123 */ } else {
/* 124 */ range_number = st.longValue();
/* 125 */ }
/* 126 */ range_batchEnd = range_number;
/* 127 */
/* 128 */ java.math.BigInteger end = index.add(java.math.BigInteger.ONE).multiply(numElement).divide(numSlice)
/* 129 */ .multiply(step).add(start);
/* 130 */ if (end.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 131 */ partitionEnd = Long.MAX_VALUE;
/* 132 */ } else if (end.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 133 */ partitionEnd = Long.MIN_VALUE;
/* 134 */ } else {
/* 135 */ partitionEnd = end.longValue();
/* 136 */ }
/* 137 */
/* 138 */ java.math.BigInteger startToEnd = java.math.BigInteger.valueOf(partitionEnd).subtract(
/* 139 */ java.math.BigInteger.valueOf(range_number));
/* 140 */ range_numElementsTodo = startToEnd.divide(step).longValue();
/* 141 */ if (range_numElementsTodo < 0) {
/* 142 */ range_numElementsTodo = 0;
/* 143 */ } else if (startToEnd.remainder(step).compareTo(java.math.BigInteger.valueOf(0L)) != 0) {
/* 144 */ range_numElementsTodo++;
/* 145 */ }
/* 146 */ }
/* 147 */
/* 148 */ protected void processNext() throws java.io.IOException {
/* 149 */ while (!sagg_initAgg) {
/* 150 */ sagg_initAgg = true;
/* 151 */ long sagg_beforeAgg = System.nanoTime();
/* 152 */ sagg_doAggregateWithoutKey();
/* 153 */ sagg_aggTime.add((System.nanoTime() - sagg_beforeAgg) / 1000000);
/* 154 */
/* 155 */ // output the result
/* 156 */
/* 157 */ sagg_numOutputRows.add(1);
/* 158 */ sagg_rowWriter.zeroOutNullBytes();
/* 159 */
/* 160 */ if (sagg_bufIsNull) {
/* 161 */ sagg_rowWriter.setNullAt(0);
/* 162 */ } else {
/* 163 */ sagg_rowWriter.write(0, sagg_bufValue);
/* 164 */ }
/* 165 */ append(sagg_result);
/* 166 */ }
/* 167 */ }
/* 168 */ }
== Subtree 2 / 2 ==
*SortAggregate(key=[], functions=[count(1)], output=[count#47L])
+- Exchange SinglePartition
+- *SortAggregate(key=[], functions=[partial_count(1)], output=[count#51L])
+- *Project
+- *Range (0, 10, step=1, splits=Some(4))
Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIterator(references);
/* 003 */ }
/* 004 */
/* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 006 */ private Object[] references;
/* 007 */ private scala.collection.Iterator[] inputs;
/* 008 */ private boolean sagg_initAgg;
/* 009 */ private boolean sagg_bufIsNull;
/* 010 */ private long sagg_bufValue;
/* 011 */ private scala.collection.Iterator inputadapter_input;
/* 012 */ private org.apache.spark.sql.execution.metric.SQLMetric sagg_numOutputRows;
/* 013 */ private org.apache.spark.sql.execution.metric.SQLMetric sagg_aggTime;
/* 014 */ private UnsafeRow sagg_result;
/* 015 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder sagg_holder;
/* 016 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter sagg_rowWriter;
/* 017 */
/* 018 */ public GeneratedIterator(Object[] references) {
/* 019 */ this.references = references;
/* 020 */ }
/* 021 */
/* 022 */ public void init(int index, scala.collection.Iterator[] inputs) {
/* 023 */ partitionIndex = index;
/* 024 */ this.inputs = inputs;
/* 025 */ sagg_initAgg = false;
/* 026 */
/* 027 */ inputadapter_input = inputs[0];
/* 028 */ this.sagg_numOutputRows = (org.apache.spark.sql.execution.metric.SQLMetric) references[0];
/* 029 */ this.sagg_aggTime = (org.apache.spark.sql.execution.metric.SQLMetric) references[1];
/* 030 */ sagg_result = new UnsafeRow(1);
/* 031 */ this.sagg_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(sagg_result, 0);
/* 032 */ this.sagg_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(sagg_holder, 1);
/* 033 */
/* 034 */ }
/* 035 */
/* 036 */ private void sagg_doAggregateWithoutKey() throws java.io.IOException {
/* 037 */ // initialize aggregation buffer
/* 038 */ sagg_bufIsNull = false;
/* 039 */ sagg_bufValue = 0L;
/* 040 */
/* 041 */ while (inputadapter_input.hasNext() && !stopEarly()) {
/* 042 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next();
/* 043 */ // do aggregate
/* 044 */ // common sub-expressions
/* 045 */
/* 046 */ // evaluate aggregate function
/* 047 */ boolean sagg_isNull3 = false;
/* 048 */
/* 049 */ long inputadapter_value = inputadapter_row.getLong(0);
/* 050 */ long sagg_value3 = -1L;
/* 051 */ sagg_value3 = sagg_bufValue + inputadapter_value;
/* 052 */ // update aggregation buffer
/* 053 */ sagg_bufIsNull = false;
/* 054 */ sagg_bufValue = sagg_value3;
/* 055 */ if (shouldStop()) return;
/* 056 */ }
/* 057 */
/* 058 */ }
/* 059 */
/* 060 */ protected void processNext() throws java.io.IOException {
/* 061 */ while (!sagg_initAgg) {
/* 062 */ sagg_initAgg = true;
/* 063 */ long sagg_beforeAgg = System.nanoTime();
/* 064 */ sagg_doAggregateWithoutKey();
/* 065 */ sagg_aggTime.add((System.nanoTime() - sagg_beforeAgg) / 1000000);
/* 066 */
/* 067 */ // output the result
/* 068 */
/* 069 */ sagg_numOutputRows.add(1);
/* 070 */ sagg_rowWriter.zeroOutNullBytes();
/* 071 */
/* 072 */ if (sagg_bufIsNull) {
/* 073 */ sagg_rowWriter.setNullAt(0);
/* 074 */ } else {
/* 075 */ sagg_rowWriter.write(0, sagg_bufValue);
/* 076 */ }
/* 077 */ append(sagg_result);
/* 078 */ }
/* 079 */ }
/* 080 */ }

maropu · 2017-03-04T17:08:35Z

@hvanhovell I reworked #14481 though, I'm not sure it is still worth trying this codegen. Could you give me insight first? Thanks!

SparkQA · 2017-03-04T17:53:40Z

Test build #73908 has finished for PR 17164 at commit 9a26a0a.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
abstract class AggregateExec extends UnaryExecNode
trait CodegenAggregateSupport extends CodegenSupport

hvanhovell · 2017-03-04T22:15:28Z

@maropu I think this is pretty exciting. This is very useful in situations where we have a lot of groups, in that case I will happily take a 2x performance improvement any day. This is still pretty decent if you consider that this aggregate is dominate by sorting.

maropu · 2017-03-04T23:13:45Z

@hvanhovell okay! I'll brush up code, then if I finished, I'll let you know for code review. Thanks.

SparkQA · 2017-03-05T10:51:06Z

Test build #73924 has finished for PR 17164 at commit f63e663.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
abstract class AggregateExec extends UnaryExecNode
trait CodegenAggregateSupport extends CodegenSupport

SparkQA · 2017-03-05T10:56:31Z

Test build #73925 has finished for PR 17164 at commit a45048a.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
trait AggregateCodegenHelper
abstract class AggregateExec extends UnaryExecNode

SparkQA · 2017-03-05T11:02:09Z

Test build #73926 has finished for PR 17164 at commit b29c22d.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
trait AggregateCodegenHelper
abstract class AggregateExec extends UnaryExecNode

SparkQA · 2017-03-05T16:10:45Z

Test build #73935 has finished for PR 17164 at commit 5b138f7.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
trait AggregateCodegenHelper
abstract class AggregateExec extends UnaryExecNode

SparkQA · 2017-03-06T00:03:55Z

Test build #73948 has finished for PR 17164 at commit 8413dd7.

This patch fails to build.
This patch merges cleanly.
This patch adds the following public classes (experimental):
trait AggregateCodegenHelper
abstract class AggregateExec extends UnaryExecNode

SparkQA · 2017-03-06T00:09:09Z

Test build #73949 has finished for PR 17164 at commit 29c713b.

This patch fails to build.
This patch merges cleanly.
This patch adds the following public classes (experimental):
trait AggregateCodegenHelper
abstract class AggregateExec extends UnaryExecNode

SparkQA · 2017-03-06T01:43:39Z

Test build #73952 has finished for PR 17164 at commit 6af8064.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
trait AggregateCodegenHelper
abstract class AggregateExec extends UnaryExecNode

SparkQA · 2017-03-06T07:12:31Z

Test build #73974 has started for PR 17164 at commit d5cc0f0.

maropu · 2017-03-06T08:47:35Z

Jenkins, retest this please.

SparkQA · 2017-03-06T10:08:07Z

Test build #73986 has finished for PR 17164 at commit d5cc0f0.

This patch fails Spark unit tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
trait AggregateCodegenHelper
abstract class AggregateExec extends UnaryExecNode

maropu · 2017-03-06T10:22:01Z

Jenkins, retest this please.

SparkQA · 2017-03-06T13:01:33Z

Test build #73993 has finished for PR 17164 at commit 2a018cb.

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

SparkQA · 2017-03-07T05:52:53Z

Test build #74066 has finished for PR 17164 at commit fc01d07.

This patch passes all tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
trait AggregateCodegenHelper
abstract class AggregateExec extends UnaryExecNode

maropu · 2017-03-07T06:09:04Z

This pr added an new SQL option spark.sql.aggregate.preferSortAggregate to preferably select SortAggregate for easy-to-test in DataFrameAggregateSuite.scala. In some cases (e.g., input data is already sorted in cache), sort aggregate is faster than hash one (See: https://issues.apache.org/jira/browse/SPARK-18591). But, you know, the current spark does not adaptively select sort aggregate in these cases. So, I probably think this option is some useful to control aggregate strategies by user. What do u think? cc: @hvanhovell If yes, I'd like to make another pr to add this option before this pr reviewed. master...maropu:SPARK-16844-3

maropu · 2017-03-10T06:18:55Z

@hvanhovell ping

kiszk · 2017-03-10T14:12:04Z

Do we need to use while? Can we use if instead of while?

IIUC we can't because continue may exist in ${consume(ctx, resultVars).trim}.

I see, thanks

kiszk · 2017-03-10T14:15:41Z

Do we need this do { } while (false);?

maropu · 2017-03-14T23:56:53Z

@hvanhovell ping

SparkQA · 2017-03-15T02:05:47Z

Test build #74569 has finished for PR 17164 at commit 5baa928.

This patch passes all tests.
This patch merges cleanly.
This patch adds the following public classes (experimental):
trait AggregateCodegenHelper
abstract class AggregateExec extends UnaryExecNode

maropu · 2017-03-18T02:37:33Z

@hvanhovell ping

hvanhovell · 2017-03-26T16:44:36Z

@maropu I do think this is useful. However we really need to refactor the planner, if we want to get the most value from this.

maropu · 2017-03-27T00:15:34Z

okay, it'd be better to close this?

hvanhovell · 2017-03-27T13:13:16Z

@maropu I am not sure. I like to keep interesting PRs open. Would you be interested in doing some work on the planner?

maropu · 2017-03-27T14:02:36Z

okay, I keep this open. Yea, sure and I'm interested in. If there are sub-tasks for that, I'd be grad if you ping me. Thanks!

gatorsmile · 2017-06-13T06:54:10Z

@maropu Maybe we can close this PR at first?

maropu · 2017-06-13T07:22:51Z

ok

maropu force-pushed the SPARK-16844 branch from f2ccc65 to 9a26a0aCompare March 4, 2017 16:51

maropu changed the title ~~[SPARK-16844][SQL] Support codegen for sort-based aggreagate~~[SPARK-16844][SQL][WIP] Support codegen for sort-based aggreagateMar 4, 2017

maropu force-pushed the SPARK-16844 branch 3 times, most recently from a45048a to b29c22dCompare March 5, 2017 09:53

maropu force-pushed the SPARK-16844 branch from b29c22d to 5b138f7Compare March 5, 2017 14:49

maropu force-pushed the SPARK-16844 branch 2 times, most recently from 8413dd7 to 29c713bCompare March 5, 2017 23:58

maropu force-pushed the SPARK-16844 branch from 29c713b to 6af8064Compare March 6, 2017 00:24

maropu force-pushed the SPARK-16844 branch from 6af8064 to d5cc0f0Compare March 6, 2017 07:07

maropu force-pushed the SPARK-16844 branch from 2a018cb to fc01d07Compare March 7, 2017 03:55

maropu changed the title ~~[SPARK-16844][SQL][WIP] Support codegen for sort-based aggreagate~~[SPARK-16844][SQL] Support codegen for sort-based aggreagateMar 7, 2017

kiszk reviewed Mar 10, 2017
View reviewed changes

Yucai Yuand others added 3 commits March 15, 2017 08:58

[SPARK-16844][SQL] Generate code for sort based aggregation
5f2460e

Create setValue to update a variable for a given DataType
fb574ec

Rework from a stale pr
5baa928

maropu force-pushed the SPARK-16844 branch from fc01d07 to 5baa928Compare March 14, 2017 23:58

maropu closed this Jun 13, 2017

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