Hey Sven, I tried with all of your configurations, 2 node with 2 executors each, but in standalone mode, it worked fine.
Could you try to narrow down the possible change of configurations? Davies On Tue, Jan 6, 2015 at 8:03 PM, Sven Krasser <kras...@gmail.com> wrote: > Hey Davies, > > Here are some more details on a configuration that causes this error for me. > Launch an AWS Spark EMR cluster as follows: > > aws emr create-cluster --region us-west-1 --no-auto-terminate \ > > --ec2-attributes KeyName=your-key-here,SubnetId=your-subnet-here \ > > --bootstrap-actions > Path=s3://support.elasticmapreduce/spark/install-spark,Args='["-g"]' \ > > --ami-version 3.3 --instance-groups > InstanceGroupType=MASTER,InstanceCount=1,InstanceType=m3.xlarge \ > > InstanceGroupType=CORE,InstanceCount=10,InstanceType=r3.xlarge --name > "Spark Issue Repro" \ > > --visible-to-all-users --applications Name=Ganglia > > This is a 10 node cluster (not sure if this makes a difference outside of > HDFS block locality). Then use this Gist here as your spark-defaults file > (it'll configure 2 executors per job as well): > https://gist.github.com/skrasser/9b978d3d572735298d16 > > With that, I am seeing this again: > > 2015-01-07 03:43:51,751 ERROR [Executor task launch worker-1] > executor.Executor (Logging.scala:logError(96)) - Exception in task 13.0 in > stage 0.0 (TID 27) > org.apache.spark.SparkException: PairwiseRDD: unexpected value: > List([B@4cfae71c) > > Thanks for the performance pointers -- the repro script is fairly unpolished > (just enough to cause the aforementioned exception). > > Hope this sheds some light on the error. From what I can tell so far, > something in the spark-defaults file triggers it (with other settings it > completes just fine). > > Thanks for your help! > -Sven > > > On Tue, Jan 6, 2015 at 12:29 PM, Davies Liu <dav...@databricks.com> wrote: >> >> I still can not reproduce it with 2 nodes (4 CPUs). >> >> Your repro.py could be faster (10 min) than before (22 min): >> >> inpdata.map(lambda (pc, x): (x, pc=='p' and 2 or >> 1)).reduceByKey(lambda x, y: x|y).filter(lambda (x, pc): >> pc==3).collect() >> >> (also, no cache needed anymore) >> >> Davies >> >> >> >> On Tue, Jan 6, 2015 at 9:02 AM, Sven Krasser <kras...@gmail.com> wrote: >> > The issue has been sensitive to the number of executors and input data >> > size. >> > I'm using 2 executors with 4 cores each, 25GB of memory, 3800MB of >> > memory >> > overhead for YARN. This will fit onto Amazon r3 instance types. >> > -Sven >> > >> > On Tue, Jan 6, 2015 at 12:46 AM, Davies Liu <dav...@databricks.com> >> > wrote: >> >> >> >> 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 >> > >> > >> > >> > >> > -- >> > 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
