[SPARK-26572][SQL] fix aggregate codegen result evaluation

## What changes were proposed in this pull request? This PR is a correctness fix in `HashAggregateExec` code generation. It forces evaluation of result expressions before calling `consume()` to avoid multiple executions. This PR fixes a use case where an aggregate is nested into a broadcast join and appears on the "stream" side. The issue is that Broadcast join generates it's own loop. And without forcing evaluation of `resultExpressions` of `HashAggregateExec` before the join's loop these expressions can be executed multiple times giving incorrect results. ## How was this patch tested? New UT was added. Closes apache#23731 from peter-toth/SPARK-26572. Authored-by: Peter Toth <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
Ngone51 · Feb 14, 2019 · 2228ee5 · 2228ee5
1 parent ac9c053
commit 2228ee5
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Showing 3 changed files with 51 additions and 3 deletions.
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala
@@ -290,6 +290,18 @@ trait CodegenSupport extends SparkPlan {
     evaluateVars.toString()
   }
 
+  /**
+   * Returns source code to evaluate the variables for non-deterministic expressions, and clear the
+   * code of evaluated variables, to prevent them to be evaluated twice.
+   */
+  protected def evaluateNondeterministicVariables(
+      attributes: Seq[Attribute],
+      variables: Seq[ExprCode],
+      expressions: Seq[NamedExpression]): String = {
+    val nondeterministicAttrs = expressions.filterNot(_.deterministic).map(_.toAttribute)
+    evaluateRequiredVariables(attributes, variables, AttributeSet(nondeterministicAttrs))
+  }
+
   /**
    * The subset of inputSet those should be evaluated before this plan.
    *

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala
@@ -466,10 +466,13 @@ case class HashAggregateExec(
       val resultVars = bindReferences[Expression](
         resultExpressions,
         inputAttrs).map(_.genCode(ctx))
+      val evaluateNondeterministicResults =
+        evaluateNondeterministicVariables(output, resultVars, resultExpressions)
       s"""
        $evaluateKeyVars
        $evaluateBufferVars
        $evaluateAggResults
+       $evaluateNondeterministicResults
        ${consume(ctx, resultVars)}
        """
     } else if (modes.contains(Partial) || modes.contains(PartialMerge)) {
@@ -506,10 +509,15 @@ case class HashAggregateExec(
       // generate result based on grouping key
       ctx.INPUT_ROW = keyTerm
       ctx.currentVars = null
-      val eval = bindReferences[Expression](
+      val resultVars = bindReferences[Expression](
         resultExpressions,
         groupingAttributes).map(_.genCode(ctx))
-      consume(ctx, eval)
+      val evaluateNondeterministicResults =
+        evaluateNondeterministicVariables(output, resultVars, resultExpressions)
+      s"""
+       $evaluateNondeterministicResults
+       ${consume(ctx, resultVars)}
+       """
     }
     ctx.addNewFunction(funcName,
       s"""

diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/WholeStageCodegenSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/WholeStageCodegenSuite.scala
@@ -25,7 +25,7 @@ import org.apache.spark.sql.execution.columnar.InMemoryTableScanExec
 import org.apache.spark.sql.execution.joins.BroadcastHashJoinExec
 import org.apache.spark.sql.execution.joins.SortMergeJoinExec
 import org.apache.spark.sql.expressions.scalalang.typed
-import org.apache.spark.sql.functions.{avg, broadcast, col, lit, max}
+import org.apache.spark.sql.functions._
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types.{IntegerType, StringType, StructType}
@@ -339,4 +339,32 @@ class WholeStageCodegenSuite extends QueryTest with SharedSQLContext {
 
     checkAnswer(df, Seq(Row(1, 3), Row(2, 3)))
   }
+
+  test("SPARK-26572: evaluate non-deterministic expressions for aggregate results") {
+    withSQLConf(
+      SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> Long.MaxValue.toString,
+      SQLConf.SHUFFLE_PARTITIONS.key -> "1") {
+      val baseTable = Seq(1, 1).toDF("idx")
+
+      // BroadcastHashJoinExec with a HashAggregateExec child containing no aggregate expressions
+      val distinctWithId = baseTable.distinct().withColumn("id", monotonically_increasing_id())
+        .join(baseTable, "idx")
+      assert(distinctWithId.queryExecution.executedPlan.collectFirst {
+        case WholeStageCodegenExec(
+          ProjectExec(_, BroadcastHashJoinExec(_, _, _, _, _, _: HashAggregateExec, _))) => true
+      }.isDefined)
+      checkAnswer(distinctWithId, Seq(Row(1, 0), Row(1, 0)))
+
+      // BroadcastHashJoinExec with a HashAggregateExec child containing a Final mode aggregate
+      // expression
+      val groupByWithId =
+        baseTable.groupBy("idx").sum().withColumn("id", monotonically_increasing_id())
+        .join(baseTable, "idx")
+      assert(groupByWithId.queryExecution.executedPlan.collectFirst {
+        case WholeStageCodegenExec(
+          ProjectExec(_, BroadcastHashJoinExec(_, _, _, _, _, _: HashAggregateExec, _))) => true
+      }.isDefined)
+      checkAnswer(groupByWithId, Seq(Row(1, 2, 0), Row(1, 2, 0)))
+    }
+  }
 }