Github user GraceH commented on a diff in the pull request:
https://github.com/apache/spark/pull/8128#discussion_r37045748
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/basicOperators.scala ---
@@ -224,6 +225,56 @@ case class Limit(limit: Int, child: SparkPlan)
/**
* :: DeveloperApi ::
+ * Take the first limit elements. and the limit can be any number less
than Integer.MAX_VALUE.
+ * If it is terminal and is invoked using executeCollect, it probably
cause OOM if the
+ * records number is large enough. Not like the Limit clause, this
operator will not change
+ * any partitions of its child operator.
+ */
+@DeveloperApi
+case class LargeLimit(limit: Int, child: SparkPlan)
+ extends UnaryNode {
+ /** We must copy rows when sort based shuffle is on */
+ private def sortBasedShuffleOn =
SparkEnv.get.shuffleManager.isInstanceOf[SortShuffleManager]
+
+ override def output: Seq[Attribute] = child.output
+
+ override def executeCollect(): Array[Row] = child.executeTake(limit)
+
+ protected override def doExecute(): RDD[InternalRow] = {
+ val rdd = if (sortBasedShuffleOn) {
+ child.execute().map(_.copy()).persist(StorageLevel.MEMORY_AND_DISK)
+ } else {
+ child.execute().persist(StorageLevel.MEMORY_AND_DISK)
--- End diff --
Beside it is very hard to tell what kind of storage level to pick up.
Another option may be to add document as the first step and mark this as TODO
item. If your limit size is larger than LIMIT, you should run with
LargeLimit. However, it may bring certain performance loss. But at least, you
can finish the query without any OOM exception.
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