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 (krasser@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 sparkExecutor@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 sparkExecutor@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://sparkYarnAM@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 <rosenville@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 <krasser@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





--