c21 opened a new pull request #32476:
URL: https://github.com/apache/spark/pull/32476
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### What changes were proposed in this pull request?
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This PR is to add code-gen support for LEFT OUTER / RIGHT OUTER sort merge
join. Currently sort merge join only supports inner join type
(https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/SortMergeJoinExec.scala#L374
). There's no fundamental reason why we cannot support code-gen for other join
types. Here we add code-gen for LEFT OUTER / RIGHT OUTER join. Will submit
followup PRs to add LEFT SEMI, LEFT ANTI and FULL OUTER code-gen separately.
The change is to extend current sort merge join logic to work with LEFT
OUTER and RIGHT OUTER (should work with LEFT SEMI/ANTI as well, but FULL OUTER
join needs some other more code change). Replace left/right with
streamed/buffered to make code extendable to other join types besides inner
join.
Example query:
```
val df1 = spark.range(10).select($"id".as("k1"), $"id".as("k3"))
val df2 = spark.range(4).select($"id".as("k2"), $"id".as("k4"))
df1.join(df2.hint("SHUFFLE_MERGE"), $"k1" === $"k2" && $"k3" + 1 < $"k4",
"left_outer").explain("codegen")
```
Example generated code:
```
== Subtree 5 / 5 (maxMethodCodeSize:396; maxConstantPoolSize:159(0.24%
used); numInnerClasses:0) ==
*(5) SortMergeJoin [k1#2L], [k2#8L], LeftOuter, ((k3#3L + 1) < k4#9L)
:- *(2) Sort [k1#2L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(k1#2L, 5), ENSURE_REQUIREMENTS, [id=#26]
: +- *(1) Project [id#0L AS k1#2L, id#0L AS k3#3L]
: +- *(1) Range (0, 10, step=1, splits=2)
+- *(4) Sort [k2#8L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(k2#8L, 5), ENSURE_REQUIREMENTS, [id=#32]
+- *(3) Project [id#6L AS k2#8L, id#6L AS k4#9L]
+- *(3) Range (0, 4, step=1, splits=2)
Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */ return new GeneratedIteratorForCodegenStage5(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=5
/* 006 */ final class GeneratedIteratorForCodegenStage5 extends
org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */ private Object[] references;
/* 008 */ private scala.collection.Iterator[] inputs;
/* 009 */ private scala.collection.Iterator smj_streamedInput_0;
/* 010 */ private scala.collection.Iterator smj_bufferedInput_0;
/* 011 */ private InternalRow smj_streamedRow_0;
/* 012 */ private InternalRow smj_bufferedRow_0;
/* 013 */ private long smj_value_2;
/* 014 */ private
org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray smj_matches_0;
/* 015 */ private long smj_value_3;
/* 016 */ private
org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[]
smj_mutableStateArray_0 = new
org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[1];
/* 017 */
/* 018 */ public GeneratedIteratorForCodegenStage5(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 */ smj_streamedInput_0 = inputs[0];
/* 026 */ smj_bufferedInput_0 = inputs[1];
/* 027 */
/* 028 */ smj_matches_0 = new
org.apache.spark.sql.execution.ExternalAppendOnlyUnsafeRowArray(2147483632,
2147483647);
/* 029 */ smj_mutableStateArray_0[0] = new
org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(4, 0);
/* 030 */
/* 031 */ }
/* 032 */
/* 033 */ private boolean findNextJoinRows(
/* 034 */ scala.collection.Iterator streamedIter,
/* 035 */ scala.collection.Iterator bufferedIter) {
/* 036 */ smj_streamedRow_0 = null;
/* 037 */ int comp = 0;
/* 038 */ while (smj_streamedRow_0 == null) {
/* 039 */ if (!streamedIter.hasNext()) return false;
/* 040 */ smj_streamedRow_0 = (InternalRow) streamedIter.next();
/* 041 */ long smj_value_0 = smj_streamedRow_0.getLong(0);
/* 042 */ if (false) {
/* 043 */ if (!smj_matches_0.isEmpty()) {
/* 044 */ smj_matches_0.clear();
/* 045 */ }
/* 046 */ return false;
/* 047 */
/* 048 */ }
/* 049 */ if (!smj_matches_0.isEmpty()) {
/* 050 */ comp = 0;
/* 051 */ if (comp == 0) {
/* 052 */ comp = (smj_value_0 > smj_value_3 ? 1 : smj_value_0 <
smj_value_3 ? -1 : 0);
/* 053 */ }
/* 054 */
/* 055 */ if (comp == 0) {
/* 056 */ return true;
/* 057 */ }
/* 058 */ smj_matches_0.clear();
/* 059 */ }
/* 060 */
/* 061 */ do {
/* 062 */ if (smj_bufferedRow_0 == null) {
/* 063 */ if (!bufferedIter.hasNext()) {
/* 064 */ smj_value_3 = smj_value_0;
/* 065 */ return !smj_matches_0.isEmpty();
/* 066 */ }
/* 067 */ smj_bufferedRow_0 = (InternalRow) bufferedIter.next();
/* 068 */ long smj_value_1 = smj_bufferedRow_0.getLong(0);
/* 069 */ if (false) {
/* 070 */ smj_bufferedRow_0 = null;
/* 071 */ continue;
/* 072 */ }
/* 073 */ smj_value_2 = smj_value_1;
/* 074 */ }
/* 075 */
/* 076 */ comp = 0;
/* 077 */ if (comp == 0) {
/* 078 */ comp = (smj_value_0 > smj_value_2 ? 1 : smj_value_0 <
smj_value_2 ? -1 : 0);
/* 079 */ }
/* 080 */
/* 081 */ if (comp > 0) {
/* 082 */ smj_bufferedRow_0 = null;
/* 083 */ } else if (comp < 0) {
/* 084 */ if (!smj_matches_0.isEmpty()) {
/* 085 */ smj_value_3 = smj_value_0;
/* 086 */ return true;
/* 087 */ } else {
/* 088 */ return false;
/* 089 */ }
/* 090 */ } else {
/* 091 */ smj_matches_0.add((UnsafeRow) smj_bufferedRow_0);
/* 092 */ smj_bufferedRow_0 = null;
/* 093 */ }
/* 094 */ } while (smj_streamedRow_0 != null);
/* 095 */ }
/* 096 */ return false; // unreachable
/* 097 */ }
/* 098 */
/* 099 */ protected void processNext() throws java.io.IOException {
/* 100 */ while (smj_streamedInput_0.hasNext()) {
/* 101 */ findNextJoinRows(smj_streamedInput_0, smj_bufferedInput_0);
/* 102 */ long smj_value_4 = -1L;
/* 103 */ long smj_value_5 = -1L;
/* 104 */ boolean smj_loaded_0 = false;
/* 105 */ smj_value_5 = smj_streamedRow_0.getLong(1);
/* 106 */ scala.collection.Iterator<UnsafeRow> smj_iterator_0 =
smj_matches_0.generateIterator();
/* 107 */ boolean smj_foundMatch_0 = false;
/* 108 */
/* 109 */ // the last iteration of this loop is to emit an empty row
if there is no matched rows.
/* 110 */ while (smj_iterator_0.hasNext() || !smj_foundMatch_0) {
/* 111 */ InternalRow smj_bufferedRow_1 = smj_iterator_0.hasNext() ?
/* 112 */ (InternalRow) smj_iterator_0.next() : null;
/* 113 */ boolean smj_isNull_5 = true;
/* 114 */ long smj_value_9 = -1L;
/* 115 */ if (smj_bufferedRow_1 != null) {
/* 116 */ long smj_value_8 = smj_bufferedRow_1.getLong(1);
/* 117 */ smj_isNull_5 = false;
/* 118 */ smj_value_9 = smj_value_8;
/* 119 */ }
/* 120 */ if (smj_bufferedRow_1 != null) {
/* 121 */ boolean smj_isNull_6 = true;
/* 122 */ boolean smj_value_10 = false;
/* 123 */ long smj_value_11 = -1L;
/* 124 */
/* 125 */ smj_value_11 = smj_value_5 + 1L;
/* 126 */
/* 127 */ if (!smj_isNull_5) {
/* 128 */ smj_isNull_6 = false; // resultCode could change
nullability.
/* 129 */ smj_value_10 = smj_value_11 < smj_value_9;
/* 130 */
/* 131 */ }
/* 132 */ if (smj_isNull_6 || !smj_value_10) {
/* 133 */ continue;
/* 134 */ }
/* 135 */ }
/* 136 */ if (!smj_loaded_0) {
/* 137 */ smj_loaded_0 = true;
/* 138 */ smj_value_4 = smj_streamedRow_0.getLong(0);
/* 139 */ }
/* 140 */ boolean smj_isNull_3 = true;
/* 141 */ long smj_value_7 = -1L;
/* 142 */ if (smj_bufferedRow_1 != null) {
/* 143 */ long smj_value_6 = smj_bufferedRow_1.getLong(0);
/* 144 */ smj_isNull_3 = false;
/* 145 */ smj_value_7 = smj_value_6;
/* 146 */ }
/* 147 */ smj_foundMatch_0 = true;
/* 148 */ ((org.apache.spark.sql.execution.metric.SQLMetric)
references[0] /* numOutputRows */).add(1);
/* 149 */
/* 150 */ smj_mutableStateArray_0[0].reset();
/* 151 */
/* 152 */ smj_mutableStateArray_0[0].zeroOutNullBytes();
/* 153 */
/* 154 */ smj_mutableStateArray_0[0].write(0, smj_value_4);
/* 155 */
/* 156 */ smj_mutableStateArray_0[0].write(1, smj_value_5);
/* 157 */
/* 158 */ if (smj_isNull_3) {
/* 159 */ smj_mutableStateArray_0[0].setNullAt(2);
/* 160 */ } else {
/* 161 */ smj_mutableStateArray_0[0].write(2, smj_value_7);
/* 162 */ }
/* 163 */
/* 164 */ if (smj_isNull_5) {
/* 165 */ smj_mutableStateArray_0[0].setNullAt(3);
/* 166 */ } else {
/* 167 */ smj_mutableStateArray_0[0].write(3, smj_value_9);
/* 168 */ }
/* 169 */ append((smj_mutableStateArray_0[0].getRow()).copy());
/* 170 */
/* 171 */ }
/* 172 */ if (shouldStop()) return;
/* 173 */ }
/* 174 */ ((org.apache.spark.sql.execution.joins.SortMergeJoinExec)
references[1] /* plan */).cleanupResources();
/* 175 */ }
/* 176 */
/* 177 */ }
```
### Why are the changes needed?
<!--
Please clarify why the changes are needed. For instance,
1. If you propose a new API, clarify the use case for a new API.
2. If you fix a bug, you can clarify why it is a bug.
-->
Improve query CPU performance. Example micro benchmark below showed 10%
run-time improvement.
```
def sortMergeJoinWithDuplicates(): Unit = {
val N = 2 << 20
codegenBenchmark("sort merge join with duplicates", N) {
val df1 = spark.range(N)
.selectExpr(s"(id * 15485863) % ${N*10} as k1", "id as k3")
val df2 = spark.range(N)
.selectExpr(s"(id * 15485867) % ${N*10} as k2", "id as k4")
val df = df1.join(df2, col("k1") === col("k2") && col("k3") * 3 <
col("k4"), "left_outer")
assert(df.queryExecution.sparkPlan.find(_.isInstanceOf[SortMergeJoinExec]).isDefined)
df.noop()
}
}
```
```
Running benchmark: sort merge join with duplicates
Running case: sort merge join with duplicates outer-smj-codegen off
Stopped after 2 iterations, 2696 ms
Running case: sort merge join with duplicates outer-smj-codegen on
Stopped after 5 iterations, 6058 ms
Java HotSpot(TM) 64-Bit Server VM 1.8.0_181-b13 on Mac OS X 10.16
Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz
sort merge join with duplicates: Best Time(ms) Avg
Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative
-------------------------------------------------------------------------------------------------------------------------------------
sort merge join with duplicates outer-smj-codegen off 1333
1348 21 1.6 635.7 1.0X
sort merge join with duplicates outer-smj-codegen on 1169
1212 47 1.8 557.4 1.1X
```
### Does this PR introduce _any_ user-facing change?
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the documentation fix.
If yes, please clarify the previous behavior and the change this PR proposes
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No.
### How was this patch tested?
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Added unit test in `WholeStageCodegenSuite.scala` and
`WholeStageCodegenSuite.scala`.
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