Helo everyone: I have a problem when setting the number of partitions inside Graphx with the ConnectedComponents function. When I launch the application with the default number of partition everything runs smoothly. However when I increase the number of partitions to 150 for example ( it happens with bigger values as well) it gets stuck in stage 5 in the last task.
<http://apache-spark-user-list.1001560.n3.nabble.com/file/n27629/Screen_Shot_2016-08-31_at_13.png> with the following error [Stage 5:=====================================================================> (190 + 10) / 200]241.445: [GC [PSYoungGen: 118560K->800K(233472K)] 418401K->301406K(932864K), 0.0029430 secs] [Times: user=0.02 sys=0.00, real=0.01 secs] [Stage 5:=========================================================================>(199 + 1) / 200]16/08/31 11:09:23 ERROR spark.ContextCleaner: Error cleaning broadcast 4 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$$anon$1.run(NettyRpcEnv.scala:241) at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471) at java.util.concurrent.FutureTask.run(FutureTask.java:262) at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178) at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) Caused by: java.util.concurrent.TimeoutException: Cannot receive any reply in 120 seconds at org.apache.spark.rpc.netty.NettyRpcEnv$$anon$1.run(NettyRpcEnv.scala:242) ... 7 more The way I set the number of partitions is when reading the graph through: val graph = GraphLoader.edgeListFile(sc, input, true, minEdge, StorageLevel.MEMORY_AND_DISK, StorageLevel.MEMORY_AND_DISK) val res = graph.connectedComponents().vertices The version of Spark I'm using is 1.6 and the size of the file is 5GB generated through the Graphx utility to generate data. Thanks in advance. -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Problem-with-Graphx-and-number-of-partitions-tp27629.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
