Github user yhuai commented on a diff in the pull request:
https://github.com/apache/spark/pull/8038#discussion_r36682953
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/TungstenAggregate.scala
---
@@ -61,32 +62,54 @@ case class TungstenAggregate(
}
protected override def doExecute(): RDD[InternalRow] = attachTree(this,
"execute") {
- child.execute().mapPartitions { iter =>
- val hasInput = iter.hasNext
- if (!hasInput && groupingExpressions.nonEmpty) {
- // This is a grouped aggregate and the input iterator is empty,
- // so return an empty iterator.
- Iterator.empty.asInstanceOf[Iterator[UnsafeRow]]
- } else {
- val aggregationIterator =
- new TungstenAggregationIterator(
- groupingExpressions,
- nonCompleteAggregateExpressions,
- completeAggregateExpressions,
- initialInputBufferOffset,
- resultExpressions,
- newMutableProjection,
- child.output,
- iter,
- testFallbackStartsAt)
- if (!hasInput && groupingExpressions.isEmpty) {
+ /**
+ * Set up the underlying unsafe data structures used before computing
the parent partition.
+ * This makes sure our iterator is not starved by other operators in
the same task.
+ */
+ def preparePartition(): TungstenAggregationIterator = {
+ new TungstenAggregationIterator(
+ groupingExpressions,
+ nonCompleteAggregateExpressions,
+ completeAggregateExpressions,
+ initialInputBufferOffset,
+ resultExpressions,
+ newMutableProjection,
+ child.output,
+ testFallbackStartsAt)
+ }
+
+ /** Compute a partition using the iterator already set up previously.
*/
+ def executePartition(
+ context: TaskContext,
+ partitionIndex: Int,
+ aggregationIterator: TungstenAggregationIterator,
+ parentIterator: Iterator[InternalRow]): Iterator[UnsafeRow] = {
+ val hasInput = parentIterator.hasNext
+ if (!hasInput) {
+ // We're not using the underlying map, so we just can free it here
+ aggregationIterator.free()
+ if (groupingExpressions.isEmpty) {
+ // This is a grouped aggregate and the input iterator is empty,
+ // so return an empty iterator.
Iterator.single[UnsafeRow](aggregationIterator.outputForEmptyGroupingKeyWithoutInput())
} else {
- aggregationIterator
+ Iterator[UnsafeRow]()
}
+ } else {
+ aggregationIterator.start(parentIterator)
+ aggregationIterator
}
}
+
+ // Note: we need to set up the iterator in each partition before
computing the
+ // parent partition, so we cannot simply use `mapPartitions` here
(SPARK-9747).
+ val parentPartition = child.execute()
+ val resultRdd = {
+ new MapPartitionsWithPreparationRDD[UnsafeRow, InternalRow,
TungstenAggregationIterator](
+ parentPartition, preparePartition, executePartition,
preservesPartitioning = true)
+ }
+ resultRdd.asInstanceOf[RDD[InternalRow]]
--- End diff --
Should we just return `resultRdd`? Seems we do not need to cast?
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