Github user squito commented on a diff in the pull request:
https://github.com/apache/spark/pull/5636#discussion_r35800587
--- Diff:
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala ---
@@ -473,6 +473,326 @@ 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
+ }
+ }
+
+ /**
+ * 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 stage abort to avoid endless retries.
+ */
+ test("Multiple consecutive stage failures should lead to task 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
+ val stage0Attempt = taskSets.last
+ checkStageId(0, attempt, stage0Attempt)
+
+ // Run stage 0
+ complete(stage0Attempt, makeCompletions(stage0Attempt, 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
+ val stage1Attempt = taskSets.last
+ checkStageId(1, attempt, stage1Attempt)
+
+ val stage1Successes = stage1Attempt.tasks.tail.map { _ => (Success,
42)}
+
+ // Run Stage 1, this time with a task failure
+ complete(stage1Attempt,
+ Seq((FetchFailed(makeBlockManagerId("hostA"), shuffleId, 0, 0,
"ignored"), null))
+ ++ stage1Successes
+ )
+
+ // 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)
+ assert(jobResult.isInstanceOf[JobFailed])
+ }
+ }
+ }
+
+ /**
+ * In this test we simulate a job failure where there are two failures
in two different stages.
+ * Specifically, stage0 fails twice, and then stage1 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
+ val stage0Attempt = taskSets.last
+ checkStageId(0, attempt, stage0Attempt)
+ // Run stage 0
+ complete(stage0Attempt, makeCompletions(stage0Attempt, 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
+ val stage1Attempt = taskSets.last
+ checkStageId(1, attempt, stage1Attempt)
+
+ val stage1Successes =
+ stage1Attempt.tasks.tail.map { _ => (Success,
makeMapStatus("hostB", 1))}
+
+ // Run Stage 1, this time with a task failure
+ complete(stage1Attempt,
+ Seq((FetchFailed(makeBlockManagerId("hostA"),
+ shuffleDepOne.shuffleId, 0, 0, "ignored"), null)
+ ) ++ stage1Successes
+ )
+ } else {
+ // Run stage 1
+ val stage1Attempt = taskSets.last
+ checkStageId(1, attempt, stage1Attempt)
+ complete(stage1Attempt, makeCompletions(stage1Attempt, 1))
+
+ // Fail stage 2
+ val stage2Attempt = taskSets.last
+ checkStageId(2, attempt-Stage.MAX_STAGE_FAILURES/2, stage2Attempt)
+ complete(stage2Attempt, Seq(
+ (FetchFailed(makeBlockManagerId("hostA"),
+ shuffleDepTwo.shuffleId, 0, 0, "ignored"), null)))
+ }
+
+ // 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()
+ }
+
+ // Complete all three stages succesfully
+ val stage0Attempt4 = taskSets.last
+ checkStageId(0, 4, stage0Attempt4)
+ complete(stage0Attempt4, makeCompletions(stage0Attempt4, 2))
+
+ val stage1Attempt4 = taskSets.last
+ checkStageId(1, 4, stage1Attempt4)
+ complete(stage1Attempt4, makeCompletions(stage1Attempt4, 1))
+
+ println(taskSets.mkString(","))
+
+ val stage2Attempt = taskSets.last
+ checkStageId(2, Stage.MAX_STAGE_FAILURES/2, stage2Attempt)
+ complete(stage2Attempt, Seq((Success, 42)))
+
+ // The first success is from the success we append in stage 1, the
second is the one we add here
+ assert(results === Map(0 -> 42))
+ }
+
+ /**
+ * 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 do not
trigger an abort on
+ * their own, but only when the stage fails enough times.
--- End diff --
can you reword slightly to make the attempts more clear : "... fetch
failures inside a single stage attempt do not trigger an abort on their own,
but only when there are enough failing stage attempts."
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