Github user squito commented on a diff in the pull request:
https://github.com/apache/spark/pull/7770#discussion_r36135556
--- Diff: core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala
---
@@ -773,16 +773,26 @@ class DAGScheduler(
stage.pendingTasks.clear()
// First figure out the indexes of partition ids to compute.
- val partitionsToCompute: Seq[Int] = {
+ val (allPartitions: Seq[Int], partitionsToCompute: Seq[Int]) = {
stage match {
case stage: ShuffleMapStage =>
- (0 until stage.numPartitions).filter(id =>
stage.outputLocs(id).isEmpty)
+ val allPartitions = 0 until stage.numPartitions
+ val filteredPartitions = allPartitions.filter(id =>
stage.outputLocs(id).isEmpty)
+ (allPartitions, filteredPartitions)
case stage: ResultStage =>
val job = stage.resultOfJob.get
- (0 until job.numPartitions).filter(id => !job.finished(id))
+ val allPartitions = 0 until job.numPartitions
+ val filteredPartitions = allPartitions.filter(id =>
!job.finished(id))
+ (allPartitions, filteredPartitions)
}
}
+ // Reset internal accumulators only if this stage is not partially
submitted
+ // Otherwise, we may override existing accumulator values from some
tasks
--- End diff --
You can compute all the tasks for a stage again, even if they've all been
computed before, if you lose all its map output.
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