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 >
