Could you use netstat to show the ports that the driver is listening?
On Mon, Mar 14, 2016 at 1:45 PM, David Gomez Saavedra <[email protected]>
wrote:
> hi everyone,
>
> I'm trying to set up spark streaming using akka with a similar example of
> the word count provided. When using spark master in local mode everything
> works but when I try to run it the driver and executors using docker I get
> the following exception
>
>
> 16/03/14 20:32:03 WARN NettyRpcEndpointRef: Error sending message [message =
> Heartbeat(0,[Lscala.Tuple2;@5ad3f40c,BlockManagerId(0, 172.18.0.4, 7005))] in
> 1 attempts
> org.apache.spark.rpc.RpcTimeoutException: Cannot receive any reply in 10
> seconds. This timeout is controlled by spark.executor.heartbeatInterval
> 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$$anonfun$addMessageIfTimeout$1.applyOrElse(RpcTimeout.scala:59)
> at
> scala.runtime.AbstractPartialFunction.apply(AbstractPartialFunction.scala:36)
> at scala.util.Failure$$anonfun$recover$1.apply(Try.scala:216)
> at scala.util.Try$.apply(Try.scala:192)
> at scala.util.Failure.recover(Try.scala:216)
> 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$$anon$1.execute(ExecutionContextImpl.scala:136)
> at
> scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
> at
> scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
> at scala.concurrent.Promise$class.complete(Promise.scala:55)
> at
> scala.concurrent.impl.Promise$DefaultPromise.complete(Promise.scala:153)
> at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
> at scala.concurrent.Future$$anonfun$map$1.apply(Future.scala:235)
> at scala.concurrent.impl.CallbackRunnable.run(Promise.scala:32)
> at
> scala.concurrent.BatchingExecutor$Batch$$anonfun$run$1.processBatch$1(BatchingExecutor.scala:63)
> at
> scala.concurrent.BatchingExecutor$Batch$$anonfun$run$1.apply$mcV$sp(BatchingExecutor.scala:78)
> at
> scala.concurrent.BatchingExecutor$Batch$$anonfun$run$1.apply(BatchingExecutor.scala:55)
> at
> scala.concurrent.BatchingExecutor$Batch$$anonfun$run$1.apply(BatchingExecutor.scala:55)
> at
> scala.concurrent.BlockContext$.withBlockContext(BlockContext.scala:72)
> at
> scala.concurrent.BatchingExecutor$Batch.run(BatchingExecutor.scala:54)
> at
> scala.concurrent.Future$InternalCallbackExecutor$.unbatchedExecute(Future.scala:599)
> at
> scala.concurrent.BatchingExecutor$class.execute(BatchingExecutor.scala:106)
> at
> scala.concurrent.Future$InternalCallbackExecutor$.execute(Future.scala:597)
> at
> scala.concurrent.impl.CallbackRunnable.executeWithValue(Promise.scala:40)
> at
> scala.concurrent.impl.Promise$DefaultPromise.tryComplete(Promise.scala:248)
> at scala.concurrent.Promise$class.tryFailure(Promise.scala:112)
> at
> scala.concurrent.impl.Promise$DefaultPromise.tryFailure(Promise.scala:153)
> at
> org.apache.spark.rpc.netty.NettyRpcEnv$$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
> 10 seconds
> at
> org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242)
> ... 7 more
>
>
>
> Here is the config of the spark streaming app
>
> val conf = new SparkConf()
> .setMaster(sparkMaster)
> .setAppName(sparkApp)
> .set("spark.cassandra.connection.host", CassandraConfig.host)
> .set("spark.logConf", "true")
> .set("spark.fileserver.port","7002")
> .set("spark.broadcast.port","7003")
> .set("spark.replClassServer.port","7004")
> .set("spark.blockManager.port","7005")
> .set("spark.executor.port","7006")
>
> .set("spark.broadcast.factory","org.apache.spark.broadcast.HttpBroadcastFactory")
> .setJars(sparkJars)
>
> val sc = new SparkContext(conf)
>
> val ssc = new StreamingContext(sc, Seconds(5))
>
> val tags = ssc.actorStream[String](Props(new
> GifteeTagStreamingActor("akka.tcp://spark-engine@spark-engine:9083/user/integrationActor")),
> "TagsReceiver")
>
>
> the docker images for master and worker expose those ports.
>
> master ---> EXPOSE 8080 7077 4040 7001 7002 7003 7004 7005 7006
> worker ---> EXPOSE 8888 8081 4040 7001 7002 7003 7004 7005 7006
>
> I'm using those images docker images to run spark jobs without a problem.
> I only get errors on the streaming app.
>
> any pointers on what can be wrong?
>
> Thank you very much in advanced.
>
> David
>
>
