linzebing commented on a change in pull request #27223:
[SPARK-30511][SPARK-28403][CORE] Don't treat failed/killed speculative tasks as
pending in ExecutorAllocationManager
URL: https://github.com/apache/spark/pull/27223#discussion_r372187103
##########
File path: core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala
##########
@@ -614,18 +614,24 @@ private[spark] class ExecutorAllocationManager(
stageAttemptToNumRunningTask -= stageAttempt
}
}
- // If the task failed, we expect it to be resubmitted later. To ensure
we have
- // enough resources to run the resubmitted task, we need to mark the
scheduler
- // as backlogged again if it's not already marked as such (SPARK-8366)
- if (taskEnd.reason != Success) {
- if (totalPendingTasks() == 0) {
- allocationManager.onSchedulerBacklogged()
- }
- if (taskEnd.taskInfo.speculative) {
- stageAttemptToSpeculativeTaskIndices.get(stageAttempt).foreach
{_.remove(taskIndex)}
- } else {
- stageAttemptToTaskIndices.get(stageAttempt).foreach
{_.remove(taskIndex)}
- }
+
+ if (taskEnd.taskInfo.speculative) {
+ stageAttemptToSpeculativeTaskIndices.get(stageAttempt).foreach
{_.remove{taskIndex}}
+ stageAttemptToNumSpeculativeTasks(stageAttempt) -= 1
Review comment:
> sorry I don't follow the logic. According to `TSM.dequeueTask()`:
>
> > ```
> > // Tries to schedule a regular task first; if it returns None, then
schedules
> > // a speculative task
> > ```
>
> It would first try to schedule a regular task (which would return None in
this case), then it would try to schedule a speculative task. Since we have
only the original task running, it shall schedule one speculative task in this
case. Am I missing something? I also searched the test case but didn't locate
the place describing the behavior.
@jiangxb1987 Let's make sure we are on the same page. I assume you are
talking about the case on `speculation task failure`. As I have explained
above, when a speculative task fails, `handleFailedTask` will be invoked, and
we will have `addPendingTask(index)` at L894. This will add a pending task for
the failed task index (with `speculatable` default to false).
Then from `TSM.dequeueTask()`, we have a call chain of `dequeueTaskHelper`
-> `dequeueTaskFromList`, see
https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala#L289-L293:
as we already have one copy running (the original task), the enqueued pending
task will not be resubmitted here. Instead, in a future speculation cycle, we
will see the original task running too slow and will try to speculate again on
the original task from there.
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