Github user kayousterhout commented on a diff in the pull request:
https://github.com/apache/spark/pull/13603#discussion_r67775034
--- Diff:
core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala ---
@@ -272,6 +261,55 @@ class TaskSchedulerImplSuite extends SparkFunSuite
with LocalSparkContext with L
val taskDescriptions3 = taskScheduler.resourceOffers(e1Offers).flatten
assert(1 === taskDescriptions3.length)
assert("executor1" === taskDescriptions3(0).executorId)
+ assert(!failedTaskSet)
+ }
+
+ test("abort stage if executor loss results in unschedulability from
previously failed tasks") {
+ // Make sure we can detect when a taskset becomes unschedulability
from a blacklisting. This
+ // test explores a particular corner case -- you may have one task
fail, but still be
+ // schedulable on another executor. However, that executor may fail
later on, leaving the
+ // first task with no place to run.
+ val taskScheduler = setupScheduler(
+ // set this to something much longer than the test duration
+ "spark.scheduler.executorTaskBlacklistTime" -> "10000000"
+ )
+
+ val taskSet = FakeTask.createTaskSet(2)
+ taskScheduler.submitTasks(taskSet)
+ val tsm = taskScheduler.taskSetManagerForAttempt(taskSet.stageId,
taskSet.stageAttemptId).get
+
+ val firstTasks = taskScheduler.resourceOffers(Seq(
+ new WorkerOffer("executor0", "host0", 1),
+ new WorkerOffer("executor1", "host1", 1)
+ )).flatten
+ assert(Set("executor0", "executor1") ===
firstTasks.map{_.executorId}.toSet)
+
+ // fail one of the tasks, but leave the other running
+ val failedTask = firstTasks.find(_.executorId == "executor0").get
+ taskScheduler.handleFailedTask(tsm, failedTask.taskId,
TaskState.FAILED, TaskResultLost)
+ // at this point, our failed task could run on the other executor, so
don't give up the task
+ // set yet.
--- End diff --
does it make sense to verify !failedTaskSet here?
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