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
