mridulm commented on code in PR #37728:
URL: https://github.com/apache/spark/pull/37728#discussion_r959208095
##########
core/src/main/scala/org/apache/spark/rdd/RDD.scala:
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@@ -1523,22 +1523,21 @@ 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 || this.getNumPartitions == 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 {
- mapRDDs.reduce { (queue1, queue2) =>
- queue1 ++= queue2
- queue1
- }.toArray.sorted(ord)
+ this.mapPartitionsWithIndex { case (pid, iter) =>
+ if (iter.nonEmpty) {
+ // Priority keeps the largest elements, so let's reverse the
ordering.
+ Iterator.single(collectionUtils.takeOrdered(iter, num)(ord).toArray)
+ } else if (pid == 0) {
+ // make sure partition 0 always returns an array to avoid reduce on
empty RDD
+ Iterator.single(Array.empty[T])
+ } else {
+ Iterator.empty
+ }
+ }.reduce { (array1, array2) =>
+ collectionUtils.mergeOrdered[T](Seq(array1,
array2))(ord).take(num).toArray
Review Comment:
Wondering if there was any impact on the perf numbers, or it was essentially
noise :-) (might affect gc more I guess - with large enough buffer and higher
number of tasks per executors probably)
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