I had ran your scripts in 5 nodes ( 2 CPUs, 8G mem) cluster, can not reproduce your failure. Should I test it with big memory node?
On Mon, Jan 5, 2015 at 4:00 PM, Sven Krasser <kras...@gmail.com> wrote: > 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 <rosenvi...@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 (kras...@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 >> sparkexecu...@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 >> sparkexecu...@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://sparkyar...@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 <rosenvi...@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 <kras...@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 --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org