c21 opened a new pull request #32528: URL: https://github.com/apache/spark/pull/32528
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If you want to add or modify an error message, please read the guideline first: https://spark.apache.org/error-message-guidelines.html --> ### What changes were proposed in this pull request? <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> As title. This PR is to add code-gen support for LEFT SEMI sort merge join. The main change is to add `semiJoin` code path in `SortMergeJoinExec.doProduce()` and introduce `onlyBufferFirstMatchedRow` in `SortMergeJoinExec.genScanner()`. The latter is for left semi sort merge join without condition. For this kind of query, we don't need to buffer all matched rows, but only the first one (this is same as non-code-gen code path). Example query: ``` val df1 = spark.range(10).select($"id".as("k1")) val df2 = spark.range(4).select($"id".as("k2")) val oneJoinDF = df1.join(df2.hint("SHUFFLE_MERGE"), $"k1" === $"k2", "left_semi") ``` Example of generated code for the query: ``` == Subtree 5 / 5 (maxMethodCodeSize:302; maxConstantPoolSize:156(0.24% used); numInnerClasses:0) == *(5) Project [id#0L AS k1#2L] +- *(5) SortMergeJoin [id#0L], [k2#6L], LeftSemi :- *(2) Sort [id#0L ASC NULLS FIRST], false, 0 : +- Exchange hashpartitioning(id#0L, 5), ENSURE_REQUIREMENTS, [id=#27] : +- *(1) Range (0, 10, step=1, splits=2) +- *(4) Sort [k2#6L ASC NULLS FIRST], false, 0 +- Exchange hashpartitioning(k2#6L, 5), ENSURE_REQUIREMENTS, [id=#33] +- *(3) Project [id#4L AS k2#6L] +- *(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[2]; /* 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(1, 2147483647); /* 029 */ smj_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 030 */ smj_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 031 */ /* 032 */ } /* 033 */ /* 034 */ private boolean findNextJoinRows( /* 035 */ scala.collection.Iterator streamedIter, /* 036 */ scala.collection.Iterator bufferedIter) { /* 037 */ smj_streamedRow_0 = null; /* 038 */ int comp = 0; /* 039 */ while (smj_streamedRow_0 == null) { /* 040 */ if (!streamedIter.hasNext()) return false; /* 041 */ smj_streamedRow_0 = (InternalRow) streamedIter.next(); /* 042 */ long smj_value_0 = smj_streamedRow_0.getLong(0); /* 043 */ if (false) { /* 044 */ smj_streamedRow_0 = null; /* 045 */ continue; /* 046 */ /* 047 */ } /* 048 */ if (!smj_matches_0.isEmpty()) { /* 049 */ comp = 0; /* 050 */ if (comp == 0) { /* 051 */ comp = (smj_value_0 > smj_value_3 ? 1 : smj_value_0 < smj_value_3 ? -1 : 0); /* 052 */ } /* 053 */ /* 054 */ if (comp == 0) { /* 055 */ return true; /* 056 */ } /* 057 */ smj_matches_0.clear(); /* 058 */ } /* 059 */ /* 060 */ do { /* 061 */ if (smj_bufferedRow_0 == null) { /* 062 */ if (!bufferedIter.hasNext()) { /* 063 */ smj_value_3 = smj_value_0; /* 064 */ return !smj_matches_0.isEmpty(); /* 065 */ } /* 066 */ smj_bufferedRow_0 = (InternalRow) bufferedIter.next(); /* 067 */ long smj_value_1 = smj_bufferedRow_0.getLong(0); /* 068 */ if (false) { /* 069 */ smj_bufferedRow_0 = null; /* 070 */ continue; /* 071 */ } /* 072 */ smj_value_2 = smj_value_1; /* 073 */ } /* 074 */ /* 075 */ comp = 0; /* 076 */ if (comp == 0) { /* 077 */ comp = (smj_value_0 > smj_value_2 ? 1 : smj_value_0 < smj_value_2 ? -1 : 0); /* 078 */ } /* 079 */ /* 080 */ if (comp > 0) { /* 081 */ smj_bufferedRow_0 = null; /* 082 */ } else if (comp < 0) { /* 083 */ if (!smj_matches_0.isEmpty()) { /* 084 */ smj_value_3 = smj_value_0; /* 085 */ return true; /* 086 */ } else { /* 087 */ smj_streamedRow_0 = null; /* 088 */ } /* 089 */ } else { /* 090 */ if (smj_matches_0.isEmpty()) { /* 091 */ smj_matches_0.add((UnsafeRow) smj_bufferedRow_0); /* 092 */ } /* 093 */ /* 094 */ smj_bufferedRow_0 = null; /* 095 */ } /* 096 */ } while (smj_streamedRow_0 != null); /* 097 */ } /* 098 */ return false; // unreachable /* 099 */ } /* 100 */ /* 101 */ protected void processNext() throws java.io.IOException { /* 102 */ while (findNextJoinRows(smj_streamedInput_0, smj_bufferedInput_0)) { /* 103 */ long smj_value_4 = -1L; /* 104 */ smj_value_4 = smj_streamedRow_0.getLong(0); /* 105 */ scala.collection.Iterator<UnsafeRow> smj_iterator_0 = smj_matches_0.generateIterator(); /* 106 */ boolean smj_hasOutputRow_0 = false; /* 107 */ /* 108 */ while (!smj_hasOutputRow_0 && smj_iterator_0.hasNext()) { /* 109 */ InternalRow smj_bufferedRow_1 = (InternalRow) smj_iterator_0.next(); /* 110 */ /* 111 */ smj_hasOutputRow_0 = true; /* 112 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1); /* 113 */ /* 114 */ // common sub-expressions /* 115 */ /* 116 */ smj_mutableStateArray_0[1].reset(); /* 117 */ /* 118 */ smj_mutableStateArray_0[1].write(0, smj_value_4); /* 119 */ append((smj_mutableStateArray_0[1].getRow()).copy()); /* 120 */ /* 121 */ } /* 122 */ if (shouldStop()) return; /* 123 */ } /* 124 */ ((org.apache.spark.sql.execution.joins.SortMergeJoinExec) references[1] /* plan */).cleanupResources(); /* 125 */ } /* 126 */ /* 127 */ } ``` ### 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. Test with one query: ``` def sortMergeJoin(): Unit = { val N = 2 << 20 codegenBenchmark("left semi sort merge join", N) { val df1 = spark.range(N).selectExpr(s"id * 2 as k1") val df2 = spark.range(N).selectExpr(s"id * 3 as k2") val df = df1.join(df2, col("k1") === col("k2"), "left_semi") assert(df.queryExecution.sparkPlan.find(_.isInstanceOf[SortMergeJoinExec]).isDefined) df.noop() } } ``` Seeing 30% of run-time improvement: ``` Running benchmark: left semi sort merge join Running case: left semi sort merge join code-gen off Stopped after 2 iterations, 1369 ms Running case: left semi sort merge join code-gen on Stopped after 5 iterations, 2743 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 left semi sort merge join: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------------------------------ left semi sort merge join code-gen off 676 685 13 3.1 322.2 1.0X left semi sort merge join code-gen on 524 549 32 4.0 249.7 1.3X ``` ### Does this PR introduce _any_ user-facing change? <!-- Note that it means *any* user-facing change including all aspects such as the documentation fix. If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If possible, please also clarify if this is a user-facing change compared to the released Spark versions or within the unreleased branches such as master. If no, write 'No'. --> No. ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> Added unit test in `WholeStageCodegenSuite.scala` and `ExistenceJoinSuite.scala`. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org