viirya commented on a change in pull request #31468:
URL: https://github.com/apache/spark/pull/31468#discussion_r578951088
##########
File path: sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala
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@@ -52,16 +53,25 @@ case class CollectLimitExec(limit: Int, child: SparkPlan)
extends LimitExec {
SQLShuffleReadMetricsReporter.createShuffleReadMetrics(sparkContext)
override lazy val metrics = readMetrics ++ writeMetrics
protected override def doExecute(): RDD[InternalRow] = {
- val locallyLimited = child.execute().mapPartitionsInternal(_.take(limit))
- val shuffled = new ShuffledRowRDD(
- ShuffleExchangeExec.prepareShuffleDependency(
- locallyLimited,
- child.output,
- SinglePartition,
- serializer,
- writeMetrics),
- readMetrics)
- shuffled.mapPartitionsInternal(_.take(limit))
+ val childRDD = child.execute()
+ if (childRDD.getNumPartitions == 0) {
+ new ParallelCollectionRDD(sparkContext, Seq.empty[InternalRow], 1,
Map.empty)
Review comment:
> @viirya the `outputPartitioning` of `CollectLimitExec` is
`SinglePartition`, so the output rdd should have single partition.
Oh, my previous comment was confusing. I mean I am not sure if its
`outputPartitioning` must be ` SinglePartition`.
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