Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/3794#discussion_r23507001
--- Diff: core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
---
@@ -483,6 +483,20 @@ class DAGScheduler(
assert(partitions.size > 0)
val func2 = func.asInstanceOf[(TaskContext, Iterator[_]) => _]
val waiter = new JobWaiter(this, jobId, partitions.size, resultHandler)
+
+ // The reason for performing this call here is that computing the
partitions
+ // may be very expensive for certain types of RDDs (e.g. HadoopRDDs),
so therefore
+ // we'd like that computation to take place outside of the
DAGScheduler to avoid
+ // blocking its event processing loop. See SPARK-4961 for details.
+ try {
+ getParentStages(rdd, jobId).foreach(_.rdd.partitions)
--- End diff --
I just realized that this could be a thread-safety issue: `getParentStages`
could call `getShuffleMapStage`, which mutates a non-thread-safe
`shuffleToMapStage` map. Even if that map was synchronized, we could still
have race-conditions between calls from the event processing loop and external
calls.
Do you think we could just call `rdd.partitions` on the final RDD (e.g. the
`rdd` local variable here) instead of calling `getParentStages`?
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