jin xing created SPARK-19263:
--------------------------------
Summary: DAGScheduler should handle stage's pendingPartitions
properly in handleTaskCompletion.
Key: SPARK-19263
URL: https://issues.apache.org/jira/browse/SPARK-19263
Project: Spark
Issue Type: Bug
Components: Scheduler
Affects Versions: 2.1.0
Reporter: jin xing
In current *DAGScheduler handleTaskCompletion* code, when *event.reason* is
*Success*, it will first do *stage.pendingPartitions -= task.partitionId*,
which maybe a bug when *FetchFailed* happens. Think about below:
1. There are 2 executors A and B, executorA got assigned with ShuffleMapTask1
and ShuffleMapTask2;
2. ShuffleMapTask1 want's to fetch blocks from local but failed;
3. Driver receive the *FetchFailed* caused by ShuffleMapTask1 on executorA and
mark executorA as lost and update *failedEpoch*;
4. Driver resubmit stages, containing ShuffleMapTask1x and ShuffleMapTask2x;
5. ShuffleMapTask2 is successfully finished on executorA and send *Success*
back to driver;
6. Driver receives *Success* and do *stage.pendingPartitions -=
task.partitionId*, but then driver finds task's epoch is not big enough *<=
failedEpoch(execId)* and just take it as bogus, do not add the *MapStatus* to
stage;
7. ShuffleMapTask1x is successfully finished on executorB;
8. Driver receives *Success* from ShuffleMapTask1x on executorB and do
*stage.pendingPartitions -= task.partitionId*, thus no pending partitions, but
then finds not all partitions are available because of step 6;
9. Driver resubmit stage; but at this moment ShuffleMapTask2x is still running;
in *TaskSchedulerImpl submitTasks*, it finds *conflictingTaskSet*, then throw
*IllegalStateException*
10. Failed.
To reproduce the bug:
1. We need to do some modification in *ShuffleBlockFetcherIterator*: check
whether the task's index in *TaskSetManager* and stage attempt equal to 0 at
the same time, if so, throw FetchFailedException;
2. Rebuild spark then submit following job:
{code}
val rdd = sc.parallelize(List((0, 1), (1, 1), (2, 1), (3, 1), (1, 2), (0,
3), (2, 1), (3, 1)), 2)
rdd.reduceByKey {
(v1, v2) => {
Thread.sleep(10000)
v1 + v2
}
}.map {
keyAndValue => {
(keyAndValue._1 % 2, keyAndValue._2)
}
}.reduceByKey {
(v1, v2) => {
Thread.sleep(10000)
v1 + v2
}
}.collect
{code}
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