Github user squito commented on a diff in the pull request:
https://github.com/apache/spark/pull/5636#discussion_r36405856
--- 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.
+ *
+ * @param stageId - The current stageId
+ * @param attemptIdx - The current attempt count
+ * @param shuffleDep - The shuffle dependency of the stage with a fetch
failure
+ */
+ def completeNextStageWithFetchFailure(stageId: Int,
+ attemptIdx: Int,
+ shuffleDep: ShuffleDependency[_, _, _]): Unit = {
+ val stageAttempt = taskSets.last
+ checkStageId(stageId, attemptIdx, stageAttempt)
+
+ complete(stageAttempt, stageAttempt.tasks.zipWithIndex.map{ case
(task, idx) =>
+ (FetchFailed(makeBlockManagerId("hostA"), shuffleDep.shuffleId, 0,
idx, "ignored"), null)
+ }.toSeq)
+ }
+
+ /**
+ * Common code to get the next result stage attempt, confirm it's the
one we expect, and
+ * complete it with a success where we return 42.
+ *
+ * @param stageId - The current stageId
+ * @param attemptIdx - The current attempt count
+ */
+ def completeNextResultStageWithSuccess (stageId: Int, attemptIdx: Int):
Unit = {
+ val stageAttempt = taskSets.last
+ checkStageId(stageId, attemptIdx, stageAttempt)
+ assert(scheduler.stageIdToStage(stageId).isInstanceOf[ResultStage])
+ complete(stageAttempt, stageAttempt.tasks.zipWithIndex.map(_ =>
(Success, 42)).toSeq)
+ }
+
+ /**
+ * In this test we simulate a job failure where the first stage
completes successfully and
+ * the second stage fails due to a fetch failure. Multiple successive
fetch failures of a stage
+ * trigger an overall job abort to avoid endless retries.
+ */
+ test("Multiple consecutive stage failures should lead to job being
aborted.") {
+ setupStageAbortTest(sc)
+
+ val shuffleMapRdd = new MyRDD(sc, 2, Nil)
+ val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
+ val shuffleId = shuffleDep.shuffleId
+ val reduceRdd = new MyRDD(sc, 2, List(shuffleDep))
+ submit(reduceRdd, Array(0, 1))
+
+ for (attempt <- 0 until Stage.MAX_STAGE_FAILURES) {
+ // Complete all the tasks for the current attempt of stage 0
successfully
+ completeNextShuffleMapSuccesfully(0, attempt, numShufflePartitions =
2)
+
+ // Now we should have a new taskSet, for a new attempt of stage 1.
+ // We will have one fetch failure for this task set
+ completeNextStageWithFetchFailure(1, attempt, shuffleDep)
+
+ // this will (potentially) trigger a resubmission of stage 0, since
we've lost some of its
+ // map output, for the next iteration through the loop
+ scheduler.resubmitFailedStages()
+
+ if (attempt < Stage.MAX_STAGE_FAILURES-1) {
+ assert(scheduler.runningStages.nonEmpty)
+ assert(!ended)
+ } else {
+ // Stage has been aborted and removed from running stages
+ assertDataStructuresEmpty()
+ sc.listenerBus.waitUntilEmpty(1000)
+ assert(ended)
+ jobResult match {
+ case JobFailed(reason) =>
+ assert(reason.getMessage.contains("ResultStage 1 () has failed
the maximum"))
+ case other => fail(s"expected JobFailed, not $other")
+ }
+ }
+ }
+ }
+
+ /**
+ * In this test we simulate a job failure where there are two failures
in two different stages.
+ * Specifically, stage1 fails twice, and then stage2 twice. In total,
the job has had four
+ * failures overall but not four failures for a particular stage, and as
such should not be
+ * aborted.
+ */
+ test("Failures in different stages should not trigger an overall abort")
{
+ setupStageAbortTest(sc)
+
+ val shuffleOneRdd = new MyRDD(sc, 2, Nil).cache()
+ val shuffleDepOne = new ShuffleDependency(shuffleOneRdd, null)
+ val shuffleTwoRdd = new MyRDD(sc, 2, List(shuffleDepOne)).cache()
+ val shuffleDepTwo = new ShuffleDependency(shuffleTwoRdd, null)
+ val finalRdd = new MyRDD(sc, 1, List(shuffleDepTwo))
+ submit(finalRdd, Array(0))
+
+ // In the first two iterations, Stage 0 succeeds and stage 1 fails. In
the next two iterations,
+ // stage 2 fails.
+ for (attempt <- 0 until Stage.MAX_STAGE_FAILURES) {
+ // Complete all the tasks for the current attempt of stage 0
successfully
+ completeNextShuffleMapSuccesfully(0, attempt, numShufflePartitions =
2)
+
+ if (attempt < Stage.MAX_STAGE_FAILURES/2) {
+ // Now we should have a new taskSet, for a new attempt of stage 1.
+ // We will have one fetch failure for this task set
+ completeNextStageWithFetchFailure(1, attempt, shuffleDepOne)
+ } else {
+ completeNextShuffleMapSuccesfully(1, attempt, numShufflePartitions
= 1)
+
+ // Fail stage 2
+ completeNextStageWithFetchFailure(2, attempt -
Stage.MAX_STAGE_FAILURES / 2,
+ shuffleDepTwo)
+ }
+
+ // this will (potentially) trigger a resubmission of stage 0, since
we've lost some of its
+ // map output, for the next iteration through the loop
+ scheduler.resubmitFailedStages()
+ }
+
+ completeNextShuffleMapSuccesfully(0, 4, numShufflePartitions = 2)
+ completeNextShuffleMapSuccesfully(1, 4, numShufflePartitions = 1)
+
+ // Succeed stage2 with a "42"
+ completeNextResultStageWithSuccess(2, Stage.MAX_STAGE_FAILURES/2)
+
+ assert(results === Map(0 -> 42))
+ assertDataStructuresEmpty()
+ }
+
+ /**
+ * In this test we simulate a job failure where a stage may have many
tasks, many of which fail.
+ * We want to show that many fetch failures inside a single stage
attempt do not trigger an abort
+ * on their own, but only when there are enough failing stage attempts.
+ */
+ test("Multiple tasks w/ fetch failures in same stage should not abort
the stage.") {
--- End diff --
... in same stage *attempt* should not ...
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