Can you apply this patch too and check the logs of Driver and worker. diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/StandaloneSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/StandaloneSchedulerBackend.scala index b6f0ec9..ad0ebf7 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/StandaloneSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/StandaloneSchedulerBackend.scala @@ -132,7 +132,7 @@ class StandaloneSchedulerBackend(scheduler: ClusterScheduler, actorSystem: Actor // Remove a disconnected slave from the cluster def removeExecutor(executorId: String, reason: String) { if (executorActor.contains(executorId)) { - logInfo("Executor " + executorId + " disconnected, so removing it") + logInfo("Executor " + executorId + " disconnected, so removing it, reason:" + reason) val numCores = freeCores(executorId) actorToExecutorId -= executorActor(executorId) addressToExecutorId -= executorAddress(executorId)
On Wed, Oct 30, 2013 at 8:18 PM, Imran Rashid <im...@quantifind.com> wrote: > I just realized something about the failing stages -- they generally occur > in steps like this: > > rdd.mapPartitions{itr => > val myCounters = initializeSomeDataStructure() > itr.foreach{ > //update myCounter in here > ... > } > > myCounters.iterator.map{ > //some other transformation here ... > } > } > > that is, as a partition is processed, nothing gets output, we just > accumulate some values. Only at the end of the partition do we output some > accumulated values. > > These stages don't always fail, and generally they do succeed after the > executor has died and a new one has started -- so I'm pretty confident its > not a problem w/ the code. But maybe we need to add something like a > periodic heartbeat in this kind of operation? > > > > On Wed, Oct 30, 2013 at 8:56 AM, Imran Rashid <im...@quantifind.com>wrote: > >> I'm gonna try turning on more akka debugging msgs as described at >> http://akka.io/faq/ >> and >> >> http://doc.akka.io/docs/akka/current/scala/testing.html#Tracing_Actor_Invocations >> >> unfortunately that will require a patch to spark, but hopefully that will >> give us more info to go on ... >> >> >> On Wed, Oct 30, 2013 at 8:10 AM, Prashant Sharma <scrapco...@gmail.com>wrote: >> >>> I have things running (from scala 2.10 branch) for over 3-4 hours now >>> without a problem and my jobs write data about the same as you suggested. >>> My cluster size is 7 nodes and not *congested* for memory. I going to leave >>> jobs running all night long. Meanwhile I had encourage you to try to spot >>> the problem such that it is reproducible that can help a ton in fixing the >>> issue. >>> >>> Thanks for testing and reporting your experience. I still feel there is >>> something else wrong !. About tolerance for network connection timeouts, >>> setting those properties should work, but I am afraid about Disassociation >>> Event though. I will have to check this is indeed hard to reproduce bug if >>> it is, I mean how do I simulate network delays ? >>> >>> >>> On Wed, Oct 30, 2013 at 6:05 PM, Imran Rashid <im...@quantifind.com>wrote: >>> >>>> This is a spark-standalone setup (not mesos), on our own cluster. >>>> >>>> At first I thought it must be some temporary network problem too -- but >>>> the times between receiving task completion events from an executor and >>>> declaring it failed are really small, so I didn't think that could possibly >>>> be it. Plus we tried increasing various akka timeouts, but that didn't >>>> help. Or maybe there are some other spark / akka properities we should be >>>> setting? It certainly should be resilient to such a temporary network >>>> issue, if that is the problem. >>>> >>>> btw, I think I've noticed this happens most often during >>>> ShuffleMapTasks. The tasks write out very small amounts of data (64 MB >>>> total for the entire stage). >>>> >>>> thanks >>>> >>>> On Wed, Oct 30, 2013 at 6:47 AM, Prashant Sharma >>>> <scrapco...@gmail.com>wrote: >>>> >>>>> Are you using mesos ? I admit to have not properly tested things on >>>>> mesos though. >>>>> >>>>> >>>>> On Wed, Oct 30, 2013 at 11:31 AM, Prashant Sharma < >>>>> scrapco...@gmail.com> wrote: >>>>> >>>>>> Those log messages are new to the Akka 2.2 and are usually seen when >>>>>> a node is disassociated with other by either a network failure or even >>>>>> clean shutdown. This suggests some network issue to me, are you running >>>>>> on >>>>>> EC2 ? It might be a temporary thing in that case. >>>>>> >>>>>> I had like to have more details on the long jobs though, how long ? >>>>>> >>>>>> >>>>>> On Wed, Oct 30, 2013 at 1:29 AM, Imran Rashid >>>>>> <im...@quantifind.com>wrote: >>>>>> >>>>>>> We've been testing out the 2.10 branch of spark, and we're running >>>>>>> into some issues were akka disconnects from the executors after a while. >>>>>>> We ran some simple tests first, and all was well, so we started >>>>>>> upgrading >>>>>>> our whole codebase to 2.10. Everything seemed to be working, but then >>>>>>> we >>>>>>> noticed that when we run long jobs, and then things start failing. >>>>>>> >>>>>>> >>>>>>> The first suspicious thing is that we get akka warnings about >>>>>>> undeliverable messages sent to deadLetters: >>>>>>> >>>>>>> 22013-10-29 11:03:54,577 [spark-akka.actor.default-dispatcher-17] >>>>>>> INFO akka.actor.LocalActorRef - Message >>>>>>> [akka.remote.transport.ActorTransportAdapter$DisassociateUnderlying] >>>>>>> from >>>>>>> Actor[akka://spark/deadLetters] to >>>>>>> Actor[akka://spark/system/transports/akkaprotocolmanager.tcp0/akkaProtocol-tcp%3A%2F%2Fspark%4010.10.5.81%3A46572-3#656094700] >>>>>>> was not delivered. [4] dead letters encountered. This logging can be >>>>>>> turned >>>>>>> off or adjusted with configuration settings 'akka.log-dead-letters' and >>>>>>> 'akka.log-dead-letters-during-shutdown'. >>>>>>> >>>>>>> 2013-10-29 11:03:54,579 [spark-akka.actor.default-dispatcher-19] >>>>>>> INFO akka.actor.LocalActorRef - Message >>>>>>> [akka.remote.transport.AssociationHandle$Disassociated] from >>>>>>> Actor[akka://spark/deadLetters] to >>>>>>> Actor[akka://spark/system/transports/akkaprotocolmanager.tcp0/akkaProtocol-tcp%3A%2F%2Fspark%4010.10.5.81%3A46572-3#656094700] >>>>>>> was not delivered. [5] dead letters encountered. This logging can be >>>>>>> turned >>>>>>> off or adjusted with configuration settings 'akka.log-dead-letters' and >>>>>>> 'akka.log-dead-letters-during-shutdown'. >>>>>>> >>>>>>> >>>>>>> >>>>>>> Generally within a few seconds after the first such message, there >>>>>>> are a bunch more, and then the executor is marked as failed, and a new >>>>>>> one >>>>>>> is started: >>>>>>> >>>>>>> 2013-10-29 11:03:58,775 [spark-akka.actor.default-dispatcher-3] >>>>>>> INFO akka.actor.LocalActorRef - Message >>>>>>> [akka.remote.transport.ActorTransportAdapter$DisassociateUnderlying] >>>>>>> from >>>>>>> Actor[akka://spark/deadLetters] to >>>>>>> Actor[akka://spark/system/transports/akkaprotocolmanager.tcp0/akkaProtocol-tcp%3A%2F%2FsparkExecutor% >>>>>>> 40dhd2.quantifind.com%3A45794-6#-890135716] was not delivered. [10] >>>>>>> dead letters encountered, no more dead letters will be logged. This >>>>>>> logging >>>>>>> can be turned off or adjusted with configuration settings >>>>>>> 'akka.log-dead-letters' and 'akka.log-dead-letters-during-shutdown'. >>>>>>> >>>>>>> 2013-10-29 11:03:58,778 [spark-akka.actor.default-dispatcher-17] >>>>>>> INFO org.apache.spark.deploy.client.Client$ClientActor - Executor >>>>>>> updated: >>>>>>> app-20131029110000-0000/1 is now FAILED (Command exited with code 1) >>>>>>> >>>>>>> 2013-10-29 11:03:58,784 [spark-akka.actor.default-dispatcher-17] >>>>>>> INFO org.apache.spark.deploy.client.Client$ClientActor - Executor >>>>>>> added: >>>>>>> app-20131029110000-0000/2 on >>>>>>> worker-20131029105824-dhd2.quantifind.com-51544 ( >>>>>>> dhd2.quantifind.com:51544) with 24 cores >>>>>>> >>>>>>> 2013-10-29 11:03:58,784 [spark-akka.actor.default-dispatcher-18] >>>>>>> ERROR akka.remote.EndpointWriter - AssociationError [akka.tcp:// >>>>>>> sp...@ddd0.quantifind.com:43068] -> [akka.tcp:// >>>>>>> sparkexecu...@dhd2.quantifind.com:45794]: Error [Association failed >>>>>>> with [akka.tcp://sparkexecu...@dhd2.quantifind.com:45794]] [ >>>>>>> akka.remote.EndpointAssociationException: Association failed with >>>>>>> [akka.tcp://sparkexecu...@dhd2.quantifind.com:45794] >>>>>>> Caused by: >>>>>>> akka.remote.transport.netty.NettyTransport$$anonfun$associate$1$$anon$2: >>>>>>> Connection refused: dhd2.quantifind.com/10.10.5.64:45794] >>>>>>> >>>>>>> >>>>>>> >>>>>>> Looking in the logs of the failed executor, there are some similar >>>>>>> messages about undeliverable messages, but I don't see any reason: >>>>>>> >>>>>>> 13/10/29 11:03:52 INFO executor.Executor: Finished task ID 943 >>>>>>> >>>>>>> 13/10/29 11:03:53 INFO actor.LocalActorRef: Message >>>>>>> [akka.actor.FSM$Timer] from Actor[akka://sparkExecutor/deadLetters] to >>>>>>> Actor[akka://sparkExecutor/system/transports/akkaprotocolmanager.tcp0/akkaProtocol-tcp%3A%2F%2Fspark% >>>>>>> 40ddd0.quantifind.com%3A43068-1#772172548] was not delivered. [1] >>>>>>> dead letters encountered. This logging can be turned off or adjusted >>>>>>> with >>>>>>> configuration settings 'akka.log-dead-letters' and >>>>>>> 'akka.log-dead-letters-during-shutdown'. >>>>>>> >>>>>>> 13/10/29 11:03:53 INFO actor.LocalActorRef: Message >>>>>>> [akka.remote.transport.AssociationHandle$Disassociated] from >>>>>>> Actor[akka://sparkExecutor/deadLetters] to >>>>>>> Actor[akka://sparkExecutor/system/transports/akkaprotocolmanager.tcp0/akkaProtocol-tcp%3A%2F%2Fspark% >>>>>>> 40ddd0.quantifind.com%3A43068-1#772172548] was not delivered. [2] >>>>>>> dead letters encountered. This logging can be turned off or adjusted >>>>>>> with >>>>>>> configuration settings 'akka.log-dead-letters' and >>>>>>> 'akka.log-dead-letters-during-shutdown'. >>>>>>> >>>>>>> 13/10/29 11:03:53 INFO actor.LocalActorRef: Message >>>>>>> [akka.remote.transport.AssociationHandle$Disassociated] from >>>>>>> Actor[akka://sparkExecutor/deadLetters] to >>>>>>> Actor[akka://sparkExecutor/system/transports/akkaprotocolmanager.tcp0/akkaProtocol-tcp%3A%2F%2Fspark% >>>>>>> 40ddd0.quantifind.com%3A43068-1#772172548] was not delivered. [3] >>>>>>> dead letters encountered. This logging can be turned off or adjusted >>>>>>> with >>>>>>> configuration settings 'akka.log-dead-letters' and >>>>>>> 'akka.log-dead-letters-during-shutdown'. >>>>>>> >>>>>>> 13/10/29 11:03:53 ERROR executor.StandaloneExecutorBackend: Driver >>>>>>> terminated or disconnected! Shutting down. >>>>>>> >>>>>>> 13/10/29 11:03:53 INFO actor.LocalActorRef: Message >>>>>>> [akka.remote.transport.ActorTransportAdapter$DisassociateUnderlying] >>>>>>> from >>>>>>> Actor[akka://sparkExecutor/deadLetters] to >>>>>>> Actor[akka://sparkExecutor/system/transports/akkaprotocolmanager.tcp0/akkaProtocol-tcp%3A%2F%2Fspark% >>>>>>> 40ddd0.quantifind.com%3A43068-1#772172548] was not delivered. [4] >>>>>>> dead letters encountered. This logging can be turned off or adjusted >>>>>>> with >>>>>>> configuration settings 'akka.log-dead-letters' and >>>>>>> 'akka.log-dead-letters-during-shutdown'. >>>>>>> >>>>>>> >>>>>>> After this happens, spark does launch a new executor successfully, >>>>>>> and continue the job. Sometimes, the job just continues happily and >>>>>>> there >>>>>>> aren't any other problems. However, that executor may have to run a >>>>>>> bunch >>>>>>> of steps to re-compute some cached RDDs -- and during that time, another >>>>>>> executor may crash similarly, and then we end up in a never ending >>>>>>> loop, of >>>>>>> one executor crashing, then trying to reload data, while the others sit >>>>>>> around. >>>>>>> >>>>>>> I have no idea what is triggering this behavior -- there isn't any >>>>>>> particular point in the job that it regularly occurs at. Certain steps >>>>>>> seem more prone to this, but there isn't any step which regularly causes >>>>>>> the problem. In a long pipeline of steps, though, that loop becomes >>>>>>> very >>>>>>> likely. I don't think its a timeout issue -- the initial failing >>>>>>> executors >>>>>>> can be actively completing stages just seconds before this failure >>>>>>> happens. We did try adjusting some of the spark / akka timeouts: >>>>>>> >>>>>>> -Dspark.storage.blockManagerHeartBeatMs=300000 >>>>>>> -Dspark.akka.frameSize=150 >>>>>>> -Dspark.akka.timeout=120 >>>>>>> -Dspark.akka.askTimeout=30 >>>>>>> -Dspark.akka.logLifecycleEvents=true >>>>>>> >>>>>>> but those settings didn't seem to help the problem at all. I figure >>>>>>> it must be some configuration with the new version of akka that we're >>>>>>> missing, but we haven't found anything. Any ideas? >>>>>>> >>>>>>> our code works fine w/ the 0.8.0 release on scala 2.9.3. The >>>>>>> failures occur on the tip of the scala-2.10 branch (5429d62d) >>>>>>> >>>>>>> thanks, >>>>>>> Imran >>>>>>> >>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> s >>>>>> >>>>> >>>>> >>>>> >>>>> -- >>>>> s >>>>> >>>> >>>> >>> >>> >>> -- >>> s >>> >> >> > -- s