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[SPARK-33853][SQL] EXPLAIN CODEGEN and BenchmarkQueryTest don't show …
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…subquery code

### What changes were proposed in this pull request?

This PR fixes an issue that `EXPLAIN CODEGEN` and `BenchmarkQueryTest` don't show the corresponding code for subqueries.

The following example is about `EXPLAIN CODEGEN`.
```
spark.conf.set("spark.sql.adaptive.enabled", "false")
val df = spark.range(1, 100)
df.createTempView("df")
spark.sql("SELECT (SELECT min(id) AS v FROM df)").explain("CODEGEN")

scala> spark.sql("SELECT (SELECT min(id) AS v FROM df)").explain("CODEGEN")
Found 1 WholeStageCodegen subtrees.
== Subtree 1 / 1 (maxMethodCodeSize:55; maxConstantPoolSize:97(0.15% used); numInnerClasses:0) ==
*(1) Project [Subquery scalar-subquery#3, [id=#24] AS scalarsubquery()#5L]
:  +- Subquery scalar-subquery#3, [id=#24]
:     +- *(2) HashAggregate(keys=[], functions=[min(id#0L)], output=[v#2L])
:        +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#20]
:           +- *(1) HashAggregate(keys=[], functions=[partial_min(id#0L)], output=[min#8L])
:              +- *(1) Range (1, 100, step=1, splits=12)
+- *(1) Scan OneRowRelation[]

Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */   return new GeneratedIteratorForCodegenStage1(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=1
/* 006 */ final class GeneratedIteratorForCodegenStage1 extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */   private Object[] references;
/* 008 */   private scala.collection.Iterator[] inputs;
/* 009 */   private scala.collection.Iterator rdd_input_0;
/* 010 */   private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] project_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[1];
/* 011 */
/* 012 */   public GeneratedIteratorForCodegenStage1(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 */     rdd_input_0 = inputs[0];
/* 020 */     project_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 021 */
/* 022 */   }
/* 023 */
/* 024 */   private void project_doConsume_0() throws java.io.IOException {
/* 025 */     // common sub-expressions
/* 026 */
/* 027 */     project_mutableStateArray_0[0].reset();
/* 028 */
/* 029 */     if (false) {
/* 030 */       project_mutableStateArray_0[0].setNullAt(0);
/* 031 */     } else {
/* 032 */       project_mutableStateArray_0[0].write(0, 1L);
/* 033 */     }
/* 034 */     append((project_mutableStateArray_0[0].getRow()));
/* 035 */
/* 036 */   }
/* 037 */
/* 038 */   protected void processNext() throws java.io.IOException {
/* 039 */     while ( rdd_input_0.hasNext()) {
/* 040 */       InternalRow rdd_row_0 = (InternalRow) rdd_input_0.next();
/* 041 */       ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1);
/* 042 */       project_doConsume_0();
/* 043 */       if (shouldStop()) return;
/* 044 */     }
/* 045 */   }
/* 046 */
/* 047 */ }
```

After this change, the corresponding code for subqueries are shown.
```
Found 3 WholeStageCodegen subtrees.
== Subtree 1 / 3 (maxMethodCodeSize:282; maxConstantPoolSize:206(0.31% used); numInnerClasses:0) ==
*(1) HashAggregate(keys=[], functions=[partial_min(id#0L)], output=[min#8L])
+- *(1) Range (1, 100, step=1, splits=12)

Generated code:
/* 001 */ public Object generate(Object[] references) {
/* 002 */   return new GeneratedIteratorForCodegenStage1(references);
/* 003 */ }
/* 004 */
/* 005 */ // codegenStageId=1
/* 006 */ final class GeneratedIteratorForCodegenStage1 extends org.apache.spark.sql.execution.BufferedRowIterator {
/* 007 */   private Object[] references;
/* 008 */   private scala.collection.Iterator[] inputs;
/* 009 */   private boolean agg_initAgg_0;
/* 010 */   private boolean agg_bufIsNull_0;
/* 011 */   private long agg_bufValue_0;
/* 012 */   private boolean range_initRange_0;
/* 013 */   private long range_nextIndex_0;
/* 014 */   private TaskContext range_taskContext_0;
/* 015 */   private InputMetrics range_inputMetrics_0;
/* 016 */   private long range_batchEnd_0;
/* 017 */   private long range_numElementsTodo_0;
/* 018 */   private boolean agg_agg_isNull_2_0;
/* 019 */   private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] range_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[3];
/* 020 */
/* 021 */   public GeneratedIteratorForCodegenStage1(Object[] references) {
/* 022 */     this.references = references;
/* 023 */   }
/* 024 */
/* 025 */   public void init(int index, scala.collection.Iterator[] inputs) {
/* 026 */     partitionIndex = index;
/* 027 */     this.inputs = inputs;
/* 028 */
/* 029 */     range_taskContext_0 = TaskContext.get();
/* 030 */     range_inputMetrics_0 = range_taskContext_0.taskMetrics().inputMetrics();
/* 031 */     range_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 032 */     range_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 033 */     range_mutableStateArray_0[2] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0);
/* 034 */
/* 035 */   }
/* 036 */
/* 037 */   private void agg_doAggregateWithoutKey_0() throws java.io.IOException {
/* 038 */     // initialize aggregation buffer
/* 039 */     agg_bufIsNull_0 = true;
/* 040 */     agg_bufValue_0 = -1L;
/* 041 */
/* 042 */     // initialize Range
/* 043 */     if (!range_initRange_0) {
/* 044 */       range_initRange_0 = true;
/* 045 */       initRange(partitionIndex);
/* 046 */     }
/* 047 */
/* 048 */     while (true) {
/* 049 */       if (range_nextIndex_0 == range_batchEnd_0) {
/* 050 */         long range_nextBatchTodo_0;
/* 051 */         if (range_numElementsTodo_0 > 1000L) {
/* 052 */           range_nextBatchTodo_0 = 1000L;
/* 053 */           range_numElementsTodo_0 -= 1000L;
/* 054 */         } else {
/* 055 */           range_nextBatchTodo_0 = range_numElementsTodo_0;
/* 056 */           range_numElementsTodo_0 = 0;
/* 057 */           if (range_nextBatchTodo_0 == 0) break;
/* 058 */         }
/* 059 */         range_batchEnd_0 += range_nextBatchTodo_0 * 1L;
/* 060 */       }
/* 061 */
/* 062 */       int range_localEnd_0 = (int)((range_batchEnd_0 - range_nextIndex_0) / 1L);
/* 063 */       for (int range_localIdx_0 = 0; range_localIdx_0 < range_localEnd_0; range_localIdx_0++) {
/* 064 */         long range_value_0 = ((long)range_localIdx_0 * 1L) + range_nextIndex_0;
/* 065 */
/* 066 */         agg_doConsume_0(range_value_0);
/* 067 */
/* 068 */         // shouldStop check is eliminated
/* 069 */       }
/* 070 */       range_nextIndex_0 = range_batchEnd_0;
/* 071 */       ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(range_localEnd_0);
/* 072 */       range_inputMetrics_0.incRecordsRead(range_localEnd_0);
/* 073 */       range_taskContext_0.killTaskIfInterrupted();
/* 074 */     }
/* 075 */
/* 076 */   }
/* 077 */
/* 078 */   private void initRange(int idx) {
/* 079 */     java.math.BigInteger index = java.math.BigInteger.valueOf(idx);
/* 080 */     java.math.BigInteger numSlice = java.math.BigInteger.valueOf(12L);
/* 081 */     java.math.BigInteger numElement = java.math.BigInteger.valueOf(99L);
/* 082 */     java.math.BigInteger step = java.math.BigInteger.valueOf(1L);
/* 083 */     java.math.BigInteger start = java.math.BigInteger.valueOf(1L);
/* 084 */     long partitionEnd;
/* 085 */
/* 086 */     java.math.BigInteger st = index.multiply(numElement).divide(numSlice).multiply(step).add(start);
/* 087 */     if (st.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 088 */       range_nextIndex_0 = Long.MAX_VALUE;
/* 089 */     } else if (st.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 090 */       range_nextIndex_0 = Long.MIN_VALUE;
/* 091 */     } else {
/* 092 */       range_nextIndex_0 = st.longValue();
/* 093 */     }
/* 094 */     range_batchEnd_0 = range_nextIndex_0;
/* 095 */
/* 096 */     java.math.BigInteger end = index.add(java.math.BigInteger.ONE).multiply(numElement).divide(numSlice)
/* 097 */     .multiply(step).add(start);
/* 098 */     if (end.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) {
/* 099 */       partitionEnd = Long.MAX_VALUE;
/* 100 */     } else if (end.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) {
/* 101 */       partitionEnd = Long.MIN_VALUE;
/* 102 */     } else {
/* 103 */       partitionEnd = end.longValue();
/* 104 */     }
/* 105 */
/* 106 */     java.math.BigInteger startToEnd = java.math.BigInteger.valueOf(partitionEnd).subtract(
/* 107 */       java.math.BigInteger.valueOf(range_nextIndex_0));
/* 108 */     range_numElementsTodo_0  = startToEnd.divide(step).longValue();
/* 109 */     if (range_numElementsTodo_0 < 0) {
/* 110 */       range_numElementsTodo_0 = 0;
/* 111 */     } else if (startToEnd.remainder(step).compareTo(java.math.BigInteger.valueOf(0L)) != 0) {
/* 112 */       range_numElementsTodo_0++;
/* 113 */     }
/* 114 */   }
/* 115 */
/* 116 */   private void agg_doConsume_0(long agg_expr_0_0) throws java.io.IOException {
/* 117 */     // do aggregate
/* 118 */     // common sub-expressions
/* 119 */
/* 120 */     // evaluate aggregate functions and update aggregation buffers
/* 121 */
/* 122 */     agg_agg_isNull_2_0 = true;
/* 123 */     long agg_value_2 = -1L;
/* 124 */
/* 125 */     if (!agg_bufIsNull_0 && (agg_agg_isNull_2_0 ||
/* 126 */         agg_value_2 > agg_bufValue_0)) {
/* 127 */       agg_agg_isNull_2_0 = false;
/* 128 */       agg_value_2 = agg_bufValue_0;
/* 129 */     }
/* 130 */
/* 131 */     if (!false && (agg_agg_isNull_2_0 ||
/* 132 */         agg_value_2 > agg_expr_0_0)) {
/* 133 */       agg_agg_isNull_2_0 = false;
/* 134 */       agg_value_2 = agg_expr_0_0;
/* 135 */     }
/* 136 */
/* 137 */     agg_bufIsNull_0 = agg_agg_isNull_2_0;
/* 138 */     agg_bufValue_0 = agg_value_2;
/* 139 */
/* 140 */   }
/* 141 */
/* 142 */   protected void processNext() throws java.io.IOException {
/* 143 */     while (!agg_initAgg_0) {
/* 144 */       agg_initAgg_0 = true;
/* 145 */       long agg_beforeAgg_0 = System.nanoTime();
/* 146 */       agg_doAggregateWithoutKey_0();
/* 147 */       ((org.apache.spark.sql.execution.metric.SQLMetric) references[2] /* aggTime */).add((System.nanoTime() - agg_beforeAgg_0) / 1000000);
/* 148 */
/* 149 */       // output the result
/* 150 */
/* 151 */       ((org.apache.spark.sql.execution.metric.SQLMetric) references[1] /* numOutputRows */).add(1);
/* 152 */       range_mutableStateArray_0[2].reset();
/* 153 */
/* 154 */       range_mutableStateArray_0[2].zeroOutNullBytes();
/* 155 */
/* 156 */       if (agg_bufIsNull_0) {
/* 157 */         range_mutableStateArray_0[2].setNullAt(0);
/* 158 */       } else {
/* 159 */         range_mutableStateArray_0[2].write(0, agg_bufValue_0);
/* 160 */       }
/* 161 */       append((range_mutableStateArray_0[2].getRow()));
/* 162 */     }
/* 163 */   }
/* 164 */
/* 165 */ }
```

### Why are the changes needed?

For better debuggability.

### Does this PR introduce _any_ user-facing change?

Yes. After this change, users can see subquery code by `EXPLAIN CODEGEN`.

### How was this patch tested?

New test.

Closes apache#30859 from sarutak/explain-codegen-subqueries.

Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
(cherry picked from commit f4e1069)
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
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@sarutak @dongjoon-hyun
sarutak authored and dongjoon-hyun committed Dec 21, 2020
1 parent ce75ce0 commit 9c946c3
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Showing 3 changed files with 36 additions and 9 deletions.
15 changes: 10 additions & 5 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/debug/package.scala
View file
Original file line number Diff line number Diff line change
Expand Up @@ -107,12 +107,17 @@ package object debug {
*/
def codegenStringSeq(plan: SparkPlan): Seq[(String, String, ByteCodeStats)] = {
val codegenSubtrees = new collection.mutable.HashSet[WholeStageCodegenExec]()
plan transform {
case s: WholeStageCodegenExec =>
codegenSubtrees += s
s
case s => s

def findSubtrees(plan: SparkPlan): Unit = {
plan foreach {
case s: WholeStageCodegenExec =>
codegenSubtrees += s
case s =>
s.subqueries.foreach(findSubtrees)
}
}

findSubtrees(plan)
codegenSubtrees.toSeq.sortBy(_.codegenStageId).map { subtree =>
val (_, source) = subtree.doCodeGen()
val codeStats = try {
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14 changes: 10 additions & 4 deletions sql/core/src/test/scala/org/apache/spark/sql/BenchmarkQueryTest.scala
View file
Original file line number Diff line number Diff line change
Expand Up @@ -63,11 +63,17 @@ abstract class BenchmarkQueryTest extends QueryTest with SharedSparkSession {

protected def checkGeneratedCode(plan: SparkPlan, checkMethodCodeSize: Boolean = true): Unit = {
val codegenSubtrees = new collection.mutable.HashSet[WholeStageCodegenExec]()
plan foreach {
case s: WholeStageCodegenExec =>
codegenSubtrees += s
case _ =>

def findSubtrees(plan: SparkPlan): Unit = {
plan foreach {
case s: WholeStageCodegenExec =>
codegenSubtrees += s
case s =>
s.subqueries.foreach(findSubtrees)
}
}

findSubtrees(plan)
codegenSubtrees.toSeq.foreach { subtree =>
val code = subtree.doCodeGen()._2
val (_, ByteCodeStats(maxMethodCodeSize, _, _)) = try {
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16 changes: 16 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/ExplainSuite.scala
View file
Original file line number Diff line number Diff line change
Expand Up @@ -228,6 +228,22 @@ class ExplainSuite extends ExplainSuiteHelper with DisableAdaptiveExecutionSuite
}
}

test("SPARK-33853: explain codegen - check presence of subquery") {
withSQLConf(SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key -> "true") {
withTempView("df") {
val df1 = spark.range(1, 100)
df1.createTempView("df")

val sqlText = "EXPLAIN CODEGEN SELECT (SELECT min(id) FROM df)"
val expectedText = "Found 3 WholeStageCodegen subtrees."

withNormalizedExplain(sqlText) { normalizedOutput =>
assert(normalizedOutput.contains(expectedText))
}
}
}
}

test("explain formatted - check presence of subquery in case of DPP") {
withTable("df1", "df2") {
withSQLConf(SQLConf.DYNAMIC_PARTITION_PRUNING_ENABLED.key -> "true",
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