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 <[email protected]> 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 <[email protected]>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 >> <[email protected]>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 <[email protected]>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:// >>>> [email protected]:43068] -> [akka.tcp:// >>>> [email protected]:45794]: Error [Association failed >>>> with [akka.tcp://[email protected]:45794]] [ >>>> akka.remote.EndpointAssociationException: Association failed with >>>> [akka.tcp://[email protected]: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
