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From David Gomez Saavedra <mikr...@gmail.com>
Subject Re: Docker configuration for akka spark streaming
Date Tue, 15 Mar 2016 20:27:54 GMT
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 <mikrodj@gmail.com>
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 <
> shixiong@databricks.com> 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 <mikrodj@gmail.com>
>> 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
>>>
>>>
>>
>

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