CODEWITHSUNDEEP
google-cloud-dataproc
sthomps
sthomps

Reputation: 4840

How to investigate failing dataproc worker processes?

I'm running a PySpark job and Im having trouble determining the cause of failure on worker processes.

While my job is running I started noticing stack traces in the job output such as:

16/04/10 03:24:21 WARN org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Container marked as failed: container_1460240417530_0021_01_000003 on host: cluster-2-w-0.c.my-project.internal. Exit status: -100. Diagnostics: Container released on a *lost* node
[Stage 0:=================================>                      (19 + 13) / 32]16/04/10 03:26:21 WARN org.apache.spark.rpc.netty.NettyRpcEndpointRef: Error sending message [message = RemoveExecutor(2,Container marked as failed: container_1460240417530_0021_01_000003 on host: cluster-2-w-0.c.my-project.internal. Exit status: -100. Diagnostics: Container released on a *lost* node)] in 1 attempts
org.apache.spark.rpc.RpcTimeoutException: Futures timed out after [120 seconds]. This timeout is controlled by spark.rpc.askTimeout
    at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
    at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
    at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
    at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
    at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:76)
    at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:101)
    at org.apache.spark.rpc.RpcEndpointRef.askWithRetry(RpcEndpointRef.scala:77)
    at org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend.removeExecutor(CoarseGrainedSchedulerBackend.scala:359)
    at org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnSchedulerEndpoint$$anonfun$receive$1.applyOrElse(YarnSchedulerBackend.scala:176)
    at org.apache.spark.rpc.netty.Inbox$$anonfun$process$1.apply$mcV$sp(Inbox.scala:116)
    at org.apache.spark.rpc.netty.Inbox.safelyCall(Inbox.scala:204)
    at org.apache.spark.rpc.netty.Inbox.process(Inbox.scala:100)
    at org.apache.spark.rpc.netty.Dispatcher$MessageLoop.run(Dispatcher.scala:215)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Caused by: java.util.concurrent.TimeoutException: Futures timed out after [120 seconds]
    at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
    at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
    at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
    at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
    at scala.concurrent.Await$.result(package.scala:107)
    at org.apache.spark.rpc.RpcTimeout.awaitResult(RpcTimeout.scala:75)
    ... 11 more
16/04/10 03:26:40 WARN org.apache.spark.rpc.netty.NettyRpcEndpointRef: Error sending message [message = RequestExecutors(23,0,Map())] in 1 attempts
org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 120 seconds. This timeout is controlled by spark.rpc.askTimeout
    at org.apache.spark.rpc.RpcTimeout.org$apache$spark$rpc$RpcTimeout$$createRpcTimeoutException(RpcTimeout.scala:48)
    at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:63)
    at org.apache.spark.rpc.RpcTimeout$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
    at scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:33)
    at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:185)
    at scala.util.Try$.apply(Try.scala:161)
    at scala.util.Failure.recover(Try.scala:185)
    at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
    at scala.concurrent.Future$$anonfun$recover$1.apply(Future.scala:324)
    at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
    at org.spark-project.guava.util.concurrent.MoreExecutors$SameThreadExecutorService.execute(MoreExecutors.java:293)
    at scala.concurrent.impl.ExecutionContextImpl$$anon$1.execute(ExecutionContextImpl.scala:133)
    at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
    at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
    at scala.concurrent.Promise$class.complete(Promise.scala:55)
    at scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
    at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
    at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
    at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.processBatch$1(Future.scala:643)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply$mcV$sp(Future.scala:658)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch$$anonfun$run$1.apply(Future.scala:635)
    at scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
    at scala.concurrent.Future$InternalCallbackExecutor$Batch.run(Future.scala:634)
    at scala.concurrent.Future$InternalCallbackExecutor$.scala$concurrent$Future$InternalCallbackExecutor$$unbatchedExecute(Future.scala:694)
    at scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:685)
    at scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
    at scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
    at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
    at scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
    at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:241)
    at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
    at java.util.concurrent.FutureTask.run(FutureTask.java:266)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
    at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:745)
Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds
    at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
    ... 7 more
[Stage 0:=================================>                      (19 + 13) / 32]

Ill also notice the overall CPU usage of the cluster slowly drop as worker nodes fail. These nodes seem to permanently fail and do not re-join the cluster:

enter image description here

I'm using preemtible machines but when I check the status of these machines they are still running and have not been preempted. So I'm guessing its something wrong on the worker:

enter image description here

Upvotes: 3

Views: 783

Answers (1)

alunarbeach
alunarbeach

Reputation: 51

It could be because of the heavy workload in the workers. Try to increase spark.network.timeout (default 120) to a bigger number.

If that is not resolving the error, most likely causes are garbage collection. Try to run a memory profile with the following options. -verbose:gc -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/tmp/ -XX:+CMSClassUnloadingEnabled

Upvotes: 1

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