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From Imran Rashid <im...@quantifind.com>
Subject executor failures w/ scala 2.10
Date Tue, 29 Oct 2013 19:59:15 GMT
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://
spark@ddd0.quantifind.com:43068] -> [akka.tcp://
sparkExecutor@dhd2.quantifind.com:45794]: Error [Association failed with
[akka.tcp://sparkExecutor@dhd2.quantifind.com:45794]] [
akka.remote.EndpointAssociationException: Association failed with
[akka.tcp://sparkExecutor@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

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