Github user holdenk commented on a diff in the pull request:
https://github.com/apache/spark/pull/11105#discussion_r83689480
--- Diff: core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala ---
@@ -104,10 +105,26 @@ class ShuffledRDD[K: ClassTag, V: ClassTag, C:
ClassTag](
}
override def compute(split: Partition, context: TaskContext):
Iterator[(K, C)] = {
+ // Use -1 for our Shuffle ID since we are on the read side of the
shuffle.
+ val shuffleWriteId = -1
+ // If our task has data property accumulators we need to keep track of
which partitions
+ // we are processing.
+ if (context.taskMetrics.hasDataPropertyAccumulators()) {
+ context.setRDDPartitionInfo(id, shuffleWriteId, split.index)
+ }
val dep = dependencies.head.asInstanceOf[ShuffleDependency[K, V, C]]
- SparkEnv.get.shuffleManager.getReader(dep.shuffleHandle, split.index,
split.index + 1, context)
+ val itr = SparkEnv.get.shuffleManager.getReader(dep.shuffleHandle,
split.index, split.index + 1,
+ context)
--- End diff --
So the short version is no, because the only user controlled code which
happens underneath this is eagerly evaluated on the entire shuffle input -
namely `combineValuesByKey` and `combineCombinersByKey` both consume the entire
input before passing back control. I'll add this in a comment explaining this
behaviour.
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