Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/11242#discussion_r56882466
--- Diff: core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala ---
@@ -62,7 +64,23 @@ class UnionRDD[T: ClassTag](
var rdds: Seq[RDD[T]])
extends RDD[T](sc, Nil) { // Nil since we implement getDependencies
+ // Evaluate partitions in parallel. Partitions of each rdd will be
cached by the `partitions`
+ // val in `RDD`.
+ private[spark] lazy val parallelPartitionEval: Boolean = {
--- End diff --
One `HadoopRDD` has one `InputFormat` to generate its splits/partitions,
and one `UnionRDD` has many `HadoopRDD`s here. If each child `HadoopRDD`'s
partitions are generated in parallel, individually they still compute their
partitions serially as before. I think its access to its single instance of
`InputFormat` doesn't become (more) concurrent. And in `getPartitions` it's
only being used to compute splits rather than read data.
Either that's good news and there's no problem of this form or I have
missed a point here?
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