Github user squito commented on a diff in the pull request:
https://github.com/apache/spark/pull/11105#discussion_r55446400
--- Diff: core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala
---
@@ -66,7 +67,10 @@ private[spark] class ResultTask[T, U](
_executorDeserializeTime = System.currentTimeMillis() -
deserializeStartTime
metrics = Some(context.taskMetrics)
- func(context, rdd.iterator(partition, context))
+ val itr = rdd.iterator(partition, context)
+ val result = func(context, itr)
+ val computedFullPartition = itr.isEmpty || rdd.storageLevel !=
StorageLevel.NONE
--- End diff --
hmm, doesn't this need to know whether we've computed the full partition
for *all* the rdds involved in this task, not just the last one? Eg., what if
an earlier rdd is cached, but the final one is not. Or what if there is a
coalesce, so the coalesced partition is not computed fully, but the partitions
of the original rdds are computed fully?
:( kinda worried this is a critical issue ...
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