Ngone51 commented on a change in pull request #29028:
URL: https://github.com/apache/spark/pull/29028#discussion_r451528174
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File path: core/src/main/scala/org/apache/spark/rdd/RDD.scala
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@@ -1509,22 +1509,26 @@ abstract class RDD[T: ClassTag](
* @return an array of top elements
*/
def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = withScope {
- if (num == 0) {
+ if (num == 0 || partitions.length == 0) {
Array.empty
} else {
- val mapRDDs = mapPartitions { items =>
- // Priority keeps the largest elements, so let's reverse the ordering.
- val queue = new BoundedPriorityQueue[T](num)(ord.reverse)
- queue ++= collectionUtils.takeOrdered(items, num)(ord)
- Iterator.single(queue)
- }
- if (mapRDDs.partitions.length == 0) {
- Array.empty
- } else {
+ if (conf.get(RDD_TAKE_ORDERED_MERGE_IN_DRIVER)) {
+ val mapRDDs = mapPartitions { items =>
+ // Priority keeps the largest elements, so let's reverse the
ordering.
+ val queue = new BoundedPriorityQueue[T](num)(ord.reverse)
+ queue ++= collectionUtils.takeOrdered(items, num)(ord)
+ Iterator.single(queue)
+ }
mapRDDs.reduce { (queue1, queue2) =>
queue1 ++= queue2
queue1
}.toArray.sorted(ord)
+ } else {
+ mapPartitions { items =>
+ collectionUtils.takeOrdered(items, num)(ord)
+ }.repartition(1).mapPartitions { items =>
Review comment:
Then, the executor could easily be under excessive memory pressure by
`repartition(1)`?
Actually, I think it's probably a good idea to use `treeReduce` instead as
mentioned in SPARK-32212.
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