Could you try it on AWS using EMR? That'd give you an exact replica of the environment that causes the error.
Sent from my iPhone > On Jan 7, 2015, at 10:53 AM, Davies Liu <dav...@databricks.com> wrote: > > 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
