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 --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
