Github user hvanhovell commented on a diff in the pull request: https://github.com/apache/spark/pull/16677#discussion_r212805327 --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/limit.scala --- @@ -93,25 +96,93 @@ trait BaseLimitExec extends UnaryExecNode with CodegenSupport { } /** - * Take the first `limit` elements of each child partition, but do not collect or shuffle them. + * Take the `limit` elements of the child output. */ -case class LocalLimitExec(limit: Int, child: SparkPlan) extends BaseLimitExec { +case class GlobalLimitExec(limit: Int, child: SparkPlan) extends UnaryExecNode { - override def outputOrdering: Seq[SortOrder] = child.outputOrdering + override def output: Seq[Attribute] = child.output override def outputPartitioning: Partitioning = child.outputPartitioning -} -/** - * Take the first `limit` elements of the child's single output partition. - */ -case class GlobalLimitExec(limit: Int, child: SparkPlan) extends BaseLimitExec { + override def outputOrdering: Seq[SortOrder] = child.outputOrdering - override def requiredChildDistribution: List[Distribution] = AllTuples :: Nil + private val serializer: Serializer = new UnsafeRowSerializer(child.output.size) - override def outputPartitioning: Partitioning = child.outputPartitioning + protected override def doExecute(): RDD[InternalRow] = { + val childRDD = child.execute() + val partitioner = LocalPartitioning(childRDD) + val shuffleDependency = ShuffleExchangeExec.prepareShuffleDependency( + childRDD, child.output, partitioner, serializer) + val numberOfOutput: Seq[Long] = if (shuffleDependency.rdd.getNumPartitions != 0) { + // submitMapStage does not accept RDD with 0 partition. + // So, we will not submit this dependency. + val submittedStageFuture = sparkContext.submitMapStage(shuffleDependency) + submittedStageFuture.get().recordsByPartitionId.toSeq + } else { + Nil + } - override def outputOrdering: Seq[SortOrder] = child.outputOrdering + // During global limit, try to evenly distribute limited rows across data + // partitions. If disabled, scanning data partitions sequentially until reaching limit number. + // Besides, if child output has certain ordering, we can't evenly pick up rows from + // each parititon. + val flatGlobalLimit = sqlContext.conf.limitFlatGlobalLimit && child.outputOrdering == Nil --- End diff -- @viirya dumb question, what is `child.outputOrdering` doing here? I am not entirely sure that we should guarantee that you should get the lowest elements of a dataset if you perform a limit in the middle of a query (a top level sort-limit does have this guarantee). I also don't think the SQL standard supports/mandates this. Moreover checking `child.outputOrdering` only checks the order of the partition and not the order of the frame as a whole. You should also add the `child.outputPartitioning`. I would be slightly in favor of removing the `child.outputOrdering` check.
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