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From Sven Krasser <kras...@gmail.com>
Subject Re: Shuffle Problems in 1.2.0
Date Tue, 06 Jan 2015 00:00:05 GMT
Thanks for the input! I've managed to come up with a repro of the error
with test data only (and without any of the custom code in the original
script), please see here:
https://gist.github.com/skrasser/4bd7b41550988c8f6071#file-gistfile1-md

The Gist contains a data generator and the script reproducing the error
(plus driver and executor logs). If I run using full cluster capacity (32
executors with 28GB), there are no issues. If I run on only two, the error
appears again and the job fails:

org.apache.spark.SparkException: PairwiseRDD: unexpected value:
List([B@294b55b7)


Any thoughts or any obvious problems you can spot by any chance?

Thank you!
-Sven

On Sun, Jan 4, 2015 at 1:11 PM, Josh Rosen <rosenville@gmail.com> wrote:

> It doesn’t seem like there’s a whole lot of clues to go on here without
> seeing the job code.  The original "org.apache.spark.SparkException:
> PairwiseRDD: unexpected value: List([B@130dc7ad)” error suggests that
> maybe there’s an issue with PySpark’s serialization / tracking of types,
> but it’s hard to say from this error trace alone.
>
> On December 30, 2014 at 5:17:08 PM, Sven Krasser (krasser@gmail.com)
> wrote:
>
>   Hey Josh,
>
> I am still trying to prune this to a minimal example, but it has been
> tricky since scale seems to be a factor. The job runs over ~720GB of data
> (the cluster's total RAM is around ~900GB, split across 32 executors). I've
> managed to run it over a vastly smaller data set without issues. Curiously,
> when I run it over slightly smaller data set of ~230GB (using sort-based
> shuffle), my job also fails, but I see no shuffle errors in the executor
> logs. All I see is the error below from the driver (this is also what the
> driver prints when erroring out on the large data set, but I assumed the
> executor errors to be the root cause).
>
> Any idea on where to look in the interim for more hints? I'll continue to
> try to get to a minimal repro.
>
> 2014-12-30 21:35:34,539 INFO
> [sparkDriver-akka.actor.default-dispatcher-14]
> spark.MapOutputTrackerMasterActor (Logging.scala:logInfo(59)) - Asked to
> send map output locations for shuffle 0 to
> sparkExecutor@ip-10-20-80-60.us-west-1.compute.internal:39739
> 2014-12-30 21:35:39,512 INFO
> [sparkDriver-akka.actor.default-dispatcher-17]
> spark.MapOutputTrackerMasterActor (Logging.scala:logInfo(59)) - Asked to
> send map output locations for shuffle 0 to
> sparkExecutor@ip-10-20-80-62.us-west-1.compute.internal:42277
> 2014-12-30 21:35:58,893 WARN
> [sparkDriver-akka.actor.default-dispatcher-16]
> remote.ReliableDeliverySupervisor (Slf4jLogger.scala:apply$mcV$sp(71)) -
> Association with remote system
> [akka.tcp://sparkYarnAM@ip-10-20-80-64.us-west-1.compute.internal:49584]
> has failed, address is now gated for [5000] ms. Reason is: [Disassociated].
> 2014-12-30 21:35:59,044 ERROR [Yarn application state monitor]
> cluster.YarnClientSchedulerBackend (Logging.scala:logError(75)) - Yarn
> application has already exited with state FINISHED!
> 2014-12-30 21:35:59,056 INFO  [Yarn application state monitor]
> handler.ContextHandler (ContextHandler.java:doStop(788)) - stopped
> o.e.j.s.ServletContextHandler{/stages/stage/kill,null}
>
> [...]
>
> 2014-12-30 21:35:59,111 INFO  [Yarn application state monitor] ui.SparkUI
> (Logging.scala:logInfo(59)) - Stopped Spark web UI at
> http://ip-10-20-80-37.us-west-1.compute.internal:4040
> 2014-12-30 21:35:59,130 INFO  [Yarn application state monitor]
> scheduler.DAGScheduler (Logging.scala:logInfo(59)) - Stopping DAGScheduler
> 2014-12-30 21:35:59,131 INFO  [Yarn application state monitor]
> cluster.YarnClientSchedulerBackend (Logging.scala:logInfo(59)) - Shutting
> down all executors
> 2014-12-30 21:35:59,132 INFO
> [sparkDriver-akka.actor.default-dispatcher-14]
> cluster.YarnClientSchedulerBackend (Logging.scala:logInfo(59)) - Asking
> each executor to shut down
> 2014-12-30 21:35:59,132 INFO  [Thread-2] scheduler.DAGScheduler
> (Logging.scala:logInfo(59)) - Job 1 failed: collect at
> /home/hadoop/test_scripts/test.py:63, took 980.751936 s
> Traceback (most recent call last):
>   File "/home/hadoop/test_scripts/test.py", line 63, in <module>
>     result = j.collect()
>   File "/home/hadoop/spark/python/pyspark/rdd.py", line 676, in collect
>     bytesInJava = self._jrdd.collect().iterator()
>   File
> "/home/hadoop/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py",
> line 538, in __call__
>   File
> "/home/hadoop/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line
> 300, in get_return_value
> py4j.protocol.Py4JJavaError2014-12-30 21:35:59,140 INFO  [Yarn application
> state monitor] cluster.YarnClientSchedulerBackend
> (Logging.scala:logInfo(59)) - Stopped
> : An error occurred while calling o117.collect.
> : org.apache.spark.SparkException: Job cancelled because SparkContext was
> shut down
>         at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:702)
>         at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:701)
>         at scala.collection.mutable.HashSet.foreach(HashSet.scala:79)
>         at
> org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:701)
>         at
> org.apache.spark.scheduler.DAGSchedulerEventProcessActor.postStop(DAGScheduler.scala:1428)
>         at akka.actor.Actor$class.aroundPostStop(Actor.scala:475)
>         at
> org.apache.spark.scheduler.DAGSchedulerEventProcessActor.aroundPostStop(DAGScheduler.scala:1375)
>         at
> akka.actor.dungeon.FaultHandling$class.akka$actor$dungeon$FaultHandling$$finishTerminate(FaultHandling.scala:210)
>         at
> akka.actor.dungeon.FaultHandling$class.terminate(FaultHandling.scala:172)
>         at akka.actor.ActorCell.terminate(ActorCell.scala:369)
>         at akka.actor.ActorCell.invokeAll$1(ActorCell.scala:462)
>         at akka.actor.ActorCell.systemInvoke(ActorCell.scala:478)
>         at
> akka.dispatch.Mailbox.processAllSystemMessages(Mailbox.scala:263)
>         at akka.dispatch.Mailbox.run(Mailbox.scala:219)
>         at
> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393)
>         at
> scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
>         at
> scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339)
>         at
> scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979)
>         at
> scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
>
>
> Thank you!
> -Sven
>
>
> On Tue, Dec 30, 2014 at 12:15 PM, Josh Rosen <rosenville@gmail.com> wrote:
>
>> Hi Sven,
>>
>> Do you have a small example program that you can share which will allow
>> me to reproduce this issue?  If you have a workload that runs into this,
>> you should be able to keep iteratively simplifying the job and reducing the
>> data set size until you hit a fairly minimal reproduction (assuming the
>> issue is deterministic, which it sounds like it is).
>>
>> On Tue, Dec 30, 2014 at 9:49 AM, Sven Krasser <krasser@gmail.com> wrote:
>>
>>> Hey all,
>>>
>>> Since upgrading to 1.2.0 a pyspark job that worked fine in 1.1.1 fails
>>> during shuffle. I've tried reverting from the sort-based shuffle back to
>>> the hash one, and that fails as well. Does anyone see similar problems or
>>> has an idea on where to look next?
>>>
>>> For the sort-based shuffle I get a bunch of exception like this in the
>>> executor logs:
>>>
>>> 2014-12-30 03:13:04,061 ERROR [Executor task launch worker-2] executor.Executor
(Logging.scala:logError(96)) - Exception in task 4523.0 in stage 1.0 (TID 4524)
>>> org.apache.spark.SparkException: PairwiseRDD: unexpected value: List([B@130dc7ad)
>>>         at org.apache.spark.api.python.PairwiseRDD$$anonfun$compute$2.apply(PythonRDD.scala:307)
>>>         at org.apache.spark.api.python.PairwiseRDD$$anonfun$compute$2.apply(PythonRDD.scala:305)
>>>         at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>         at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:219)
>>>         at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:65)
>>>         at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
>>>         at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>>>         at org.apache.spark.scheduler.Task.run(Task.scala:56)
>>>         at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)
>>>         at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>>>         at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>>>         at java.lang.Thread.run(Thread.java:745)
>>>
>>>  For the hash-based shuffle, there are now a bunch of these exceptions in the
logs:
>>>
>>>
>>> 2014-12-30 04:14:01,688 ERROR [Executor task launch worker-0] executor.Executor
(Logging.scala:logError(96)) - Exception in task 4479.0 in stage 1.0 (TID 4480)
>>> java.io.FileNotFoundException: /mnt/var/lib/hadoop/tmp/nm-local-dir/usercache/hadoop/appcache/application_1419905501183_0004/spark-local-20141230035728-8fc0/23/merged_shuffle_1_68_0
(No such file or directory)
>>>         at java.io.FileOutputStream.open(Native Method)
>>>         at java.io.FileOutputStream.<init>(FileOutputStream.java:221)
>>>         at org.apache.spark.storage.DiskBlockObjectWriter.open(BlockObjectWriter.scala:123)
>>>         at org.apache.spark.storage.DiskBlockObjectWriter.write(BlockObjectWriter.scala:192)
>>>         at org.apache.spark.shuffle.hash.HashShuffleWriter$$anonfun$write$1.apply(HashShuffleWriter.scala:67)
>>>         at org.apache.spark.shuffle.hash.HashShuffleWriter$$anonfun$write$1.apply(HashShuffleWriter.scala:65)
>>>         at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>>>         at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
>>>         at org.apache.spark.shuffle.hash.HashShuffleWriter.write(HashShuffleWriter.scala:65)
>>>         at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
>>>         at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>>>         at org.apache.spark.scheduler.Task.run(Task.scala:56)
>>>         at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196)
>>>         at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>>>         at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>>>         at java.lang.Thread.run(Thread.java:745)
>>>
>>>  Thank you!
>>> -Sven
>>>
>>>
>>>
>>> --
>>> http://sites.google.com/site/krasser/?utm_source=sig
>>>
>>
>>
>
>
> --
> http://sites.google.com/site/krasser/?utm_source=sig
>
>


-- 
http://sites.google.com/site/krasser/?utm_source=sig

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