Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/15986#discussion_r89421559
--- Diff:
core/src/test/scala/org/apache/spark/scheduler/TaskSchedulerImplSuite.scala ---
@@ -274,4 +274,30 @@ class TaskSchedulerImplSuite extends SparkFunSuite
with LocalSparkContext with L
assert("executor1" === taskDescriptions3(0).executorId)
}
+ test("if an executor is lost then state for tasks running on that
executor is cleaned up") {
+ sc = new SparkContext("local", "TaskSchedulerImplSuite")
+ val taskScheduler = new TaskSchedulerImpl(sc)
+ taskScheduler.initialize(new FakeSchedulerBackend)
+ // Need to initialize a DAGScheduler for the taskScheduler to use for
callbacks.
+ new DAGScheduler(sc, taskScheduler) {
+ override def taskStarted(task: Task[_], taskInfo: TaskInfo) {}
+ override def executorAdded(execId: String, host: String) {}
+ }
+
+ val e0Offers = Seq(new WorkerOffer("executor0", "host0", 1))
+ val attempt1 = FakeTask.createTaskSet(1)
+
+ // submit attempt 1, offer resources, task gets scheduled
+ taskScheduler.submitTasks(attempt1)
+ val taskDescriptions = taskScheduler.resourceOffers(e0Offers).flatten
+ assert(1 === taskDescriptions.length)
+
+ // mark executor0 as dead
+ taskScheduler.executorLost("executor0", SlaveLost())
+
+ // Check that state associated with the lost task attempt is cleaned
up:
+ assert(taskScheduler.taskIdToExecutorId.isEmpty)
--- End diff --
I don't think that case can occur because `TaskState.LOST` should only be
sent in Mesos fine-grained mode where we'll only ever have a single task per
executor (in other words, executorLoss can only happen via statusUpdate in
situations where there would never be concurrent tasks on an executor).
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