Github user rdblue commented on a diff in the pull request:
https://github.com/apache/spark/pull/11242#discussion_r59059521
--- Diff: core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala ---
@@ -62,8 +62,14 @@ class UnionRDD[T: ClassTag](
var rdds: Seq[RDD[T]])
extends RDD[T](sc, Nil) { // Nil since we implement getDependencies
+ // visible for testing
+ private[spark] val isPartitionEvalParallel: Boolean =
+ rdds.length > conf.getInt("spark.rdd.parallelListingThreshold", 10)
+
override def getPartitions: Array[Partition] = {
- val array = new Array[Partition](rdds.map(_.partitions.length).sum)
+ val parRDDs = if (isPartitionEvalParallel) rdds.par else rdds
--- End diff --
@rxin, does Spark have a default thread-pool for situations like this? I
tend to agree with Sean's reasoning that we should use an existing thread-pool
over the risk of contention with user code. Both concerns could be solved by
having a pool for these one-off internal tasks.
Wouldn't user or third-party code blocking like you suggest slow down or
stop the application anyway?
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