[jira] [Created] (SPARK-36558) Stage has all tasks finished but with ongoing finalization can cause job hang

["wuyi (Jira)" <ji...@apache.org>]

wuyi created SPARK-36558:
----------------------------

             Summary: Stage has all tasks finished but with ongoing finalization can cause job hang
                 Key: SPARK-36558
                 URL: https://issues.apache.org/jira/browse/SPARK-36558
             Project: Spark
          Issue Type: Sub-task
          Components: Spark Core
    Affects Versions: 3.2.0, 3.3.0
            Reporter: wuyi


 

For a stage that all tasks are finished but with ongoing finalization can lead to job hang. The problem is that such stage is considered as a "missing" stage (see [https://github.com/apache/spark/blob/a47ceaf5492040063e31e17570678dc06846c36c/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L719-L721).] And it breaks the original assumption that a "missing" stage must have tasks to run. 

Normally, if stage A is the parent of (result) stage B and all tasks have finished in stage A, stage A will be skipped directly when submitting stage B. However, with this bug, stage A will be submitted, which leads to the job hang in the end.

 

The example to reproduce:
{code:java}
test("Job hang") {
  initPushBasedShuffleConfs(conf)
  conf.set(config.SHUFFLE_MERGER_LOCATIONS_MIN_STATIC_THRESHOLD, 5)
  DAGSchedulerSuite.clearMergerLocs
  DAGSchedulerSuite.addMergerLocs(Seq("host1", "host2", "host3", "host4", "host5"))
  val latch = new CountDownLatch(1)
  val myDAGScheduler = new MyDAGScheduler(
    sc,
    sc.dagScheduler.taskScheduler,
    sc.listenerBus,
    sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster],
    sc.env.blockManager.master,
    sc.env) {
    override def scheduleShuffleMergeFinalize(stage: ShuffleMapStage): Unit = {
      // By this, we can mimic a stage with all tasks finished
      // but finalization is incomplete.
      latch.countDown()
    }
  }
  sc.dagScheduler = myDAGScheduler
  sc.taskScheduler.setDAGScheduler(myDAGScheduler)
  val parts = 20
  val shuffleMapRdd = new MyRDD(sc, parts, Nil)
  val shuffleDep = new ShuffleDependency(shuffleMapRdd, new HashPartitioner(parts))
  val reduceRdd1 = new MyRDD(sc, parts, List(shuffleDep), tracker = mapOutputTracker)
  reduceRdd1.countAsync()
  latch.await()
  // set _shuffleMergedFinalized to true can avoid the hang.
  // shuffleDep._shuffleMergedFinalized = true
  val reduceRdd2 = new MyRDD(sc, parts, List(shuffleDep))
  reduceRdd2.count()
}
{code}
 



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