Github user squito commented on a diff in the pull request:
https://github.com/apache/spark/pull/5636#discussion_r35694369
--- Diff:
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala ---
@@ -473,6 +473,319 @@ 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-1)
+ }
+
+ /**
+ * 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.") {
+ // 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
+ }
+ }
+
+ sc.listenerBus.addListener(new EndListener())
+
+ 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 <- 1 to Stage.MAX_STAGE_FAILURES) {
+ // Complete all the tasks for the current attempt of stage 0
successfully
+ val stage0Attempt = taskSets.last
+
+ // Confirm that this is the first attempt for stage 0
+ checkStageId(0, attempt, stage0Attempt)
+
+ // Make each task in stage 0 success
+ val completions = stage0Attempt.tasks.zipWithIndex.map{ case (task,
idx) =>
+ (Success, makeMapStatus("host" + ('A' + idx).toChar, 2))
+ }.toSeq
+
+ // Run stage 0
+ complete(stage0Attempt, completions)
+
+ // 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) {
+ 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")
{
+ 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))
+
+ // In the first two iterations, Stage 0 succeeds and stage 1 fails. In
the next two iterations,
+ // stage 0 fails.
+ for (attempt <- 1 to Stage.MAX_STAGE_FAILURES) {
+ // Complete all the tasks for the current attempt of stage 0
successfully
+ val stage0Attempt = taskSets.last
+
+ // Confirm that this is the first attempt for stage 0
+ checkStageId(0, attempt, stage0Attempt)
+
+ if (attempt < Stage.MAX_STAGE_FAILURES/2) {
+ // Make each task in stage 0 success
+ val completions = stage0Attempt.tasks.zipWithIndex.map{ case
(task, idx) =>
+ (Success, makeMapStatus("host" + ('A' + idx).toChar, 2))
+ }.toSeq
+
+ // Run stage 0
+ complete(stage0Attempt, completions)
+
+ // 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
+ )
+ } else {
+ val stage0Successes = stage0Attempt.tasks.tail.map { _ =>
(Success, 42)}
+
+ // Run stage 0 and fail
+ complete(stage0Attempt,
+ Seq((FetchFailed(makeBlockManagerId("hostA"), shuffleId, 0, 0,
"ignored"), null))
+ ++ stage0Successes
+ )
+ }
+
+ // 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()
+ }
+
+ val stage0Attempt = taskSets.last
+ val completions = stage0Attempt.tasks.zipWithIndex.map{ case (task,
idx) =>
+ (Success, makeMapStatus("host" + ('A' + idx).toChar, 2))
+ }.toSeq
+
+ // Complete first task
+ complete(taskSets.last, completions)
+
+ // Complete second task
+ complete(taskSets.last, 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(1 -> 42, 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 .
+ */
+ test("Multiple task failures in same stage should not abort the stage.")
{
+ // 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
+ }
+ }
+
+ sc.listenerBus.addListener(new EndListener())
+
+ val parts = 8;
+ val shuffleMapRdd = new MyRDD(sc, parts, Nil)
+ val shuffleDep = new ShuffleDependency(shuffleMapRdd, null)
+ val shuffleId = shuffleDep.shuffleId
+ val reduceRdd = new MyRDD(sc, parts, List(shuffleDep))
+ submit(reduceRdd, (0 until parts).toArray)
+
+ val stage0Attempt = taskSets.last
+
+ // Make each task in stage 0 success, then fail all of stage 1
+ val completions = stage0Attempt.tasks.zipWithIndex.map{ case (task,
idx) =>
+ (Success, makeMapStatus("host" + ('A' + idx).toChar, parts))
+ }.toSeq
+
+ complete(stage0Attempt, completions)
--- End diff --
this is used so much we should refactor into a helper method
`completeWithMapStatusPerHost(taskSet)` or something.
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