Hi,
I have switch 'spark.shuffle.blockTransferService' to 'nio'. But the
problem still exists. However the stack trace is a little bit different:
PART one:
15/09/16 06:20:32 ERROR executor.Executor: Exception in task 1.2 in stage
15.0 (TID 5341)
java.io.IOException: Failed without being ACK'd
at
org.apache.spark.network.nio.ConnectionManager$MessageStatus.failWithoutAck(ConnectionManager.scala:72)
at
org.apache.spark.network.nio.ConnectionManager$$anonfun$removeConnection$3.apply(ConnectionManager.scala:533)
at
org.apache.spark.network.nio.ConnectionManager$$anonfun$removeConnection$3.apply(ConnectionManager.scala:531)
at scala.collection.immutable.List.foreach(List.scala:318)
at
org.apache.spark.network.nio.ConnectionManager.removeConnection(ConnectionManager.scala:531)
at
org.apache.spark.network.nio.ConnectionManager$$anonfun$addListeners$3.apply(ConnectionManager.scala:510)
at
org.apache.spark.network.nio.ConnectionManager$$anonfun$addListeners$3.apply(ConnectionManager.scala:510)
at
org.apache.spark.network.nio.Connection.callOnCloseCallback(Connection.scala:162)
at
org.apache.spark.network.nio.Connection.close(Connection.scala:130)
at
org.apache.spark.network.nio.ConnectionManager$$anonfun$stop$1.apply(ConnectionManager.scala:1000)
at
org.apache.spark.network.nio.ConnectionManager$$anonfun$stop$1.apply(ConnectionManager.scala:1000)
at
scala.collection.mutable.HashMap$$anon$2$$anonfun$foreach$3.apply(HashMap.scala:107)
at
scala.collection.mutable.HashMap$$anon$2$$anonfun$foreach$3.apply(HashMap.scala:107)
at
scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:226)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:39)
at
scala.collection.mutable.HashMap$$anon$2.foreach(HashMap.scala:107)
at
org.apache.spark.network.nio.ConnectionManager.stop(ConnectionManager.scala:1000)
at
org.apache.spark.network.nio.NioBlockTransferService.close(NioBlockTransferService.scala:78)
at
org.apache.spark.storage.BlockManager.stop(BlockManager.scala:1228)
at org.apache.spark.SparkEnv.stop(SparkEnv.scala:100)
at org.apache.spark.executor.Executor.stop(Executor.scala:144)
at
org.apache.spark.executor.CoarseGrainedExecutorBackend$$anonfun$receive$1.applyOrElse(CoarseGrainedExecutorBackend.scala:113)
at org.apache.spark.rpc.akka.AkkaRpcEnv.org
$apache$spark$rpc$akka$AkkaRpcEnv$$processMessage(AkkaRpcEnv.scala:177)
at
org.apache.spark.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1$$anonfun$receiveWithLogging$1$$anonfun$applyOrElse$4.apply$mcV$sp(AkkaRpcEnv.scala:126)
at org.apache.spark.rpc.akka.AkkaRpcEnv.org
$apache$spark$rpc$akka$AkkaRpcEnv$$safelyCall(AkkaRpcEnv.scala:197)
at
org.apache.spark.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1$$anonfun$receiveWithLogging$1.applyOrElse(AkkaRpcEnv.scala:125)
at
scala.runtime.AbstractPartialFunction$mcVL$sp.apply$mcVL$sp(AbstractPartialFunction.scala:33)
at
scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:33)
at
scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:25)
at
org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:59)
at
org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:42)
at
scala.PartialFunction$class.applyOrElse(PartialFunction.scala:118)
at
org.apache.spark.util.ActorLogReceive$$anon$1.applyOrElse(ActorLogReceive.scala:42)
at akka.actor.Actor$class.aroundReceive(Actor.scala:467)
at
org.apache.spark.rpc.akka.AkkaRpcEnv$$anonfun$actorRef$lzycompute$1$1$$anon$1.aroundReceive(AkkaRpcEnv.scala:92)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
at akka.actor.ActorCell.invoke(ActorCell.scala:487)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
at akka.dispatch.Mailbox.run(Mailbox.scala:220)
at
akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:397)
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)
PART two:
15/09/16 06:14:36 INFO nio.ConnectionManager: Removing SendingConnection to
ConnectionManagerId()
15/09/16 06:14:36 INFO nio.ConnectionManager: Removing ReceivingConnection
to ConnectionManagerId()
15/09/16 06:14:36 ERROR nio.ConnectionManager: Corresponding
SendingConnection to ConnectionManagerId() not found
15/09/16 06:14:36 INFO nio.ConnectionManager: Key not valid ?
sun.nio.ch.SelectionKeyImpl@3011c7c9
15/09/16 06:14:36 INFO nio.ConnectionManager: key already cancelled ?
sun.nio.ch.SelectionKeyImpl@3011c7c9
java.nio.channels.CancelledKeyException
at
org.apache.spark.network.nio.ConnectionManager.run(ConnectionManager.scala:461)
at
org.apache.spark.network.nio.ConnectionManager$$anon$7.run(ConnectionManager.scala:193)
java8964 <java8...@hotmail.com>于2015年9月16日周三 下午8:17写道：
> Can you try for "nio", instead of "netty".
>
> set "spark.shuffle.blockTransferService", to "nio" and give it a try.
>
> Yong
>
> ------------------------------
> From: z.qian...@gmail.com
> Date: Wed, 16 Sep 2015 03:21:02 +0000
>
> Subject: Re: application failed on large dataset
> To: java8...@hotmail.com; user@spark.apache.org
>
>
> Hi,
> after check with the yarn logs, all the error stack looks like below:
>
> 15/09/15 19:58:23 ERROR shuffle.OneForOneBlockFetcher: Failed while
> starting block fetches
> java.io.IOException: Connection reset by peer
> at sun.nio.ch.FileDispatcherImpl.read0(Native Method)
> at sun.nio.ch.SocketDispatcher.read(SocketDispatcher.java:39)
> at sun.nio.ch.IOUtil.readIntoNativeBuffer(IOUtil.java:223)
> at sun.nio.ch.IOUtil.read(IOUtil.java:192)
> at sun.nio.ch.SocketChannelImpl.read(SocketChannelImpl.java:379)
> at
> io.netty.buffer.PooledUnsafeDirectByteBuf.setBytes(PooledUnsafeDirectByteBuf.java:313)
> at
> io.netty.buffer.AbstractByteBuf.writeBytes(AbstractByteBuf.java:881)
> at
> io.netty.channel.socket.nio.NioSocketChannel.doReadBytes(NioSocketChannel.java:242)
> at
> io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:119)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
> at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
> at
> io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
> at java.lang.Thread.run(Thread.java:745)
>
> It seems that some error occurs when try to fetch the block, and
> after several retries, the executor just dies with such error.
> And for your question, I did not see any executor restart during
> the job.
> PS: the operator I am using during that stage if
> rdd.glom().mapPartitions()
>
>
> java8964 <java8...@hotmail.com>于2015年9月15日周二 下午11:44写道：
>
> When you saw this error, does any executor die due to whatever error?
>
> Do you check to see if any executor restarts during your job?
>
> It is hard to help you just with the stack trace. You need to tell us the
> whole picture when your jobs are running.
>
> Yong
>
> ------------------------------
> From: qhz...@apache.org
> Date: Tue, 15 Sep 2015 15:02:28 +0000
> Subject: Re: application failed on large dataset
> To: user@spark.apache.org
>
>
> has anyone met the same problems?
> 周千昊 <qhz...@apache.org>于2015年9月14日周一 下午9:07写道：
>
> Hi, community
> I am facing a strange problem:
> all executors does not respond, and then all of them failed with the
> ExecutorLostFailure.
> when I look into yarn logs, there are full of such exception
>
> 15/09/14 04:35:33 ERROR shuffle.RetryingBlockFetcher: Exception while
> beginning fetch of 1 outstanding blocks (after 3 retries)
> java.io.IOException: Failed to connect to host/ip:port
> at
> org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:193)
> at
> org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:156)
> at
> org.apache.spark.network.netty.NettyBlockTransferService$$anon$1.createAndStart(NettyBlockTransferService.scala:88)
> at
> org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:140)
> at
> org.apache.spark.network.shuffle.RetryingBlockFetcher.access$200(RetryingBlockFetcher.java:43)
> at
> org.apache.spark.network.shuffle.RetryingBlockFetcher$1.run(RetryingBlockFetcher.java:170)
> at
> java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
> at java.util.concurrent.FutureTask.run(FutureTask.java:262)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: java.net.ConnectException: Connection refused: host/ip:port
> at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
> at
> sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:739)
> at
> io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(NioSocketChannel.java:208)
> at
> io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.finishConnect(AbstractNioChannel.java:287)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:528)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
> at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
> at
> io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:116)
> ... 1 more
>
>
> The strange thing is that, if I reduce the input size, the problems
> just disappeared. I have found a similar issue in the mail-archive(
> http://mail-archives.us.apache.org/mod_mbox/spark-user/201502.mbox/%3CCAOHP_tHRtuxDfWF0qmYDauPDhZ1=MAm5thdTfgAhXDN=7kq...@mail.gmail.com%3E
> <http://mail-archives.us.apache.org/mod_mbox/spark-user/201502.mbox/%3cCAOHP_tHRtuxDfWF0qmYDauPDhZ1=MAm5thdTfgAhXDN=7KQM8A%40mail.gmail.com%3e>),
> however I didn't see the solution. So I am wondering if anyone could help
> with that?
>
> My env is:
> hdp 2.2.6
> spark(1.4.1)
> mode: yarn-client
> spark-conf:
> spark.driver.extraJavaOptions -Dhdp.version=2.2.6.0-2800
> spark.yarn.am.extraJavaOptions -Dhdp.version=2.2.6.0-2800
> spark.executor.memory 6g
> spark.storage.memoryFraction 0.3
> spark.dynamicAllocation.enabled true
> spark.shuffle.service.enabled true
>
> --
> Best Regard
> ZhouQianhao
>
