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
