The issue is related to this
https://issues.apache.org/jira/browse/SPARK-13906
.set("spark.rpc.netty.dispatcher.numThreads","2")
seem to fix the problem
On Tue, Mar 15, 2016 at 6:45 AM, David Gomez Saavedra <[email protected]>
wrote:
> I have updated the config since I realized the actor system was listening
> on driver port + 1. So changed the ports in my program + the docker images
>
> val conf = new SparkConf()
> .setMaster(sparkMaster)
> //.setMaster("local[2]")
> .setAppName(sparkApp)
> .set("spark.cassandra.connection.host", CassandraConfig.host)
> .set("spark.logConf", "true")
> .set("spark.driver.port","7001")
> .set("spark.driver.host","192.168.33.10")
> .set("spark.fileserver.port","6002")
> .set("spark.broadcast.port","6003")
> .set("spark.replClassServer.port","6004")
> .set("spark.blockManager.port","6005")
> .set("spark.executor.port","6006")
>
> .set("spark.broadcast.factory","org.apache.spark.broadcast.HttpBroadcastFactory")
> .setJars(sparkJars)
>
> Netstat of my stream app
>
> tcp6 0 0 :::6002 :::* LISTEN
> 9314/java
> tcp6 0 0 :::6003 :::* LISTEN
> 9314/java
> tcp6 0 0 :::6005 :::* LISTEN
> 9314/java
> tcp6 0 0 192.168.33.10:7001 :::*
> LISTEN 9314/java
> tcp6 0 0 192.168.33.10:7002 :::*
> LISTEN 9314/java
> tcp6 0 0 :::4040 :::* LISTEN
> 9314/java
>
> netstat of the master running on docker
>
> Proto Recv-Q Send-Q Local Address Foreign Address State
> PID/Program name
> tcp6 0 0 172.18.0.3:7077 :::*
> LISTEN -
> tcp6 0 0 :::8080 :::* LISTEN
> -
> tcp6 0 0 172.18.0.3:6066 :::*
> LISTEN -
>
> netstat of worker running on docker
>
> Proto Recv-Q Send-Q Local Address Foreign Address State
> PID/Program name
> tcp6 0 0 :::8081 :::* LISTEN
> -
> tcp6 0 0 :::6005 :::* LISTEN
> -
> tcp6 0 0 172.18.0.2:6006 :::*
> LISTEN -
> tcp6 0 0 172.18.0.2:8888 :::*
> LISTEN -
>
>
> so far still no success
>
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> On Mon, Mar 14, 2016 at 11:14 PM, Shixiong(Ryan) Zhu <
> [email protected]> wrote:
>
>> 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
>>>
>>>
>>
>
