cloud-fan commented on a change in pull request #24892: [SPARK-25341][Core]
Support rolling back a shuffle map stage and re-generate the shuffle files
URL: https://github.com/apache/spark/pull/24892#discussion_r312920338
##########
File path:
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala
##########
@@ -2753,15 +2753,140 @@ class DAGSchedulerSuite extends SparkFunSuite with
LocalSparkContext with TimeLi
}.head.findMissingPartitions() == Seq(0, 1))
scheduler.resubmitFailedStages()
+ (shuffleId1, shuffleId2)
+ }
+ test("SPARK-25341: abort stage while using old fetch protocol") {
+ // reset the test context with using old fetch protocol
+ afterEach()
+ val conf = new SparkConf()
+ conf.set(config.SHUFFLE_USE_OLD_FETCH_PROTOCOL.key, "true")
+ init(conf)
+
+ val (shuffleId1, _) = constructIndeterminateStageRetryScenario()
// The first task of the `shuffleMapRdd2` failed with fetch failure
+ runEvent(makeCompletionEvent(
+ taskSets(3).tasks(1),
+ Success,
+ makeMapStatus("hostC", 2)))
runEvent(makeCompletionEvent(
taskSets(3).tasks(0),
FetchFailed(makeBlockManagerId("hostA"), shuffleId1, 0, 0, "ignored"),
null))
- // The job should fail because Spark can't rollback the shuffle map stage.
- assert(failure != null && failure.getMessage.contains("Spark cannot
rollback"))
+ // The job should fail because Spark can't rollback the shuffle map stage
while
+ // using old protocol.
+ assert(failure != null && failure.getMessage.contains(
+ "Spark can only do this while using the new shuffle block fetching
protocol"))
+ }
+
+ test("SPARK-25341: retry all the succeeding stages when the map stage is
indeterminate") {
+ val (shuffleId1, shuffleId2) = constructIndeterminateStageRetryScenario()
+
+ // The first task of the `shuffleMapRdd2` failed with fetch failure
+ runEvent(makeCompletionEvent(
+ taskSets(3).tasks(0),
+ FetchFailed(makeBlockManagerId("hostA"), shuffleId1, 0, 0, "ignored"),
+ null))
+
+ val newFailedStages = scheduler.failedStages.toSeq
+ assert(newFailedStages.map(_.id) == Seq(0, 1))
+
+ scheduler.resubmitFailedStages()
+
+ // First shuffle map stage resubmitted and reran all tasks.
+ assert(taskSets(4).stageId == 0)
+ assert(taskSets(4).stageAttemptId == 1)
+ assert(taskSets(4).tasks.length == 2)
+
+ // Finish all stage.
+ complete(taskSets(4), Seq(
+ (Success, makeMapStatus("hostA", 2)),
+ (Success, makeMapStatus("hostB", 2))))
+ assert(mapOutputTracker.findMissingPartitions(shuffleId1) ===
Some(Seq.empty))
+ assert(taskSets(4).tasks.head.localProperties.getProperty(
+ SparkContext.SHUFFLE_GENERATION_ID_PREFIX + shuffleId1.toString) == "1")
+
+ complete(taskSets(5), Seq(
+ (Success, makeMapStatus("hostC", 2)),
+ (Success, makeMapStatus("hostD", 2))))
+ assert(mapOutputTracker.findMissingPartitions(shuffleId2) ===
Some(Seq.empty))
+ assert(taskSets(5).tasks.head.localProperties.getProperty(
+ SparkContext.SHUFFLE_GENERATION_ID_PREFIX + shuffleId2.toString) == "2")
+
+ complete(taskSets(6), Seq((Success, 11), (Success, 12)))
+
+ // Job successful ended.
+ assert(results === Map(0 -> 11, 1 -> 12))
+ results.clear()
+ assertDataStructuresEmpty()
+ }
+
+ test("SPARK-25341: continuous indeterminate stage roll back") {
+ // shuffleMapRdd1/2/3 are all indeterminate.
Review comment:
how about we make the `shuffleMapRdd3` deterministic? To prove that all
parent stages need to rerun even if they are deterministic.
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