Github user ilganeli commented on a diff in the pull request:
https://github.com/apache/spark/pull/5636#discussion_r38566621
--- Diff:
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala ---
@@ -473,6 +473,280 @@ class DAGSchedulerSuite
assertDataStructuresEmpty()
}
+ // Helper function to validate state when creating tests for task
failures
+ def checkStageId(stageId: Int, attempt: Int, stageAttempt: TaskSet) {
+ assert(stageAttempt.stageId === stageId)
+ assert(stageAttempt.stageAttemptId == attempt)
+ }
+
+ def makeCompletions(stageAttempt: TaskSet, reduceParts: Int):
Seq[(Success.type, MapStatus)] = {
+ stageAttempt.tasks.zipWithIndex.map { case (task, idx) =>
+ (Success, makeMapStatus("host" + ('A' + idx).toChar, reduceParts))
+ }.toSeq
+ }
+
+ def setupStageAbortTest(sc: SparkContext) {
+ sc.listenerBus.addListener(new EndListener())
+ ended = false
+ jobResult = null
+ }
+
+ // Create a new Listener to confirm that the listenerBus sees the JobEnd
message
+ // when we abort the stage. This message will also be consumed by the
EventLoggingListener
+ // so this will propagate up to the user.
+ var ended = false
+ var jobResult : JobResult = null
+
+ class EndListener extends SparkListener {
+ override def onJobEnd(jobEnd: SparkListenerJobEnd): Unit = {
+ jobResult = jobEnd.jobResult
+ ended = true
+ }
+ }
+
+ // Helper functions to extract commonly used code in Fetch Failure test
cases
+ /**
+ * Common code to get the next stage attempt, confirm it's the one we
expect, and complete it
+ * succesfullly.
+ *
+ * @param stageId - The current stageId
+ * @param attemptIdx - The current attempt count
+ * @param numShufflePartitions - The number of partitions in the next
stage
+ */
+ def completeNextShuffleMapSuccesfully(stageId: Int, attemptIdx: Int,
+ numShufflePartitions: Int): Unit = {
+ val stageAttempt = taskSets.last
+ checkStageId(stageId, attemptIdx, stageAttempt)
+ complete(stageAttempt, makeCompletions(stageAttempt,
numShufflePartitions))
+ }
+
+ /**
+ * Common code to get the next stage attempt, confirm it's the one we
expect, and complete it
+ * with all FetchFailure.
--- End diff --
Imran â I donât have cycles to do a significant refactor at the moment.
I would suggest we merge and follow up later.
From: Imran Rashid
<[email protected]<mailto:[email protected]>>
Reply-To: apache/spark
<[email protected]<mailto:[email protected]>>
Date: Wednesday, September 2, 2015 at 11:24 AM
To: apache/spark <[email protected]<mailto:[email protected]>>
Cc: "Ganelin, Ilya"
<[email protected]<mailto:[email protected]>>
Subject: Re: [spark] [SPARK-5945] Spark should not retry a stage infinitely
on a FetchFailedException (#5636)
In
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala<https://github.com/apache/spark/pull/5636#discussion_r38566341>:
> + * succesfullly.
> + *
> + * @param stageId - The current stageId
> + * @param attemptIdx - The current attempt count
> + * @param numShufflePartitions - The number of partitions in the next
stage
> + */
> + def completeNextShuffleMapSuccesfully(stageId: Int, attemptIdx: Int,
> + numShufflePartitions: Int): Unit = {
> + val stageAttempt = taskSets.last
> + checkStageId(stageId, attemptIdx, stageAttempt)
> + complete(stageAttempt, makeCompletions(stageAttempt,
numShufflePartitions))
> + }
> +
> + /**
> + * Common code to get the next stage attempt, confirm it's the one we
expect, and complete it
> + * with all FetchFailure.
yeah, agree that as is, that test isn't really adding anything over the
other tests as you've noted. I certainly don't think I'd say "too hard to fix"
-- I suppose its just my antsy-ness to get this in, but objectively, it
probably makes sense to fix. all you are really asking is to change
completeNextStageWithFetchFailure to oneFetchFailureInNextStage and change
"Multiple tasks w/ fetch failures..." to just directly do what this method is
doing now, pretty minor change.
How about this: wait a day for @ilganeli<https://github.com/ilganeli> to
update, and if he doesn't get to it we merge as-is and I do a simple follow-up
pr?
â
Reply to this email directly or view it on
GitHub<https://github.com/apache/spark/pull/5636/files#r38566341>.
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