Apache Spark - ERROR RetryingBlockFetcher: Exception while beginning fetch of 1 outstanding blocks

Question

I'm running a Spark Job in a cluster with the following configuration:

--master yarn --deploy-mode client
--executor-memory 4g 
--executor-cores 2 
--driver-memory 6g 
--num-executors 12 

The problem occurs in the job when I am taking a sample of data in the driver. The command that is run is the following:

rddTuplesA.sample(false, 0.03, 261).collect().forEach((tuple) ->
                    //build histogram...
            ); 

The rddTuplesA object is of JavaRDD<Tuple3<String, Double, Double>> type.

The Job throws the following error:

22/04/14 23:19:22 ERROR RetryingBlockFetcher: Exception while beginning fetch of 1 outstanding blocks java.io.IOException: Failed to connect to snf-8802/192.168.0.6:35615 at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:287) at org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:218) at org.apache.spark.network.netty.NettyBlockTransferService$$anon$2.createAndStart(NettyBlockTransferService.scala:123) at org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:153) at org.apache.spark.network.shuffle.RetryingBlockFetcher.start(RetryingBlockFetcher.java:133) at org.apache.spark.network.netty.NettyBlockTransferService.fetchBlocks(NettyBlockTransferService.scala:143) at org.apache.spark.network.BlockTransferService.fetchBlockSync(BlockTransferService.scala:102) at org.apache.spark.storage.BlockManager.fetchRemoteManagedBuffer(BlockManager.scala:1061) at org.apache.spark.storage.BlockManager.$anonfun$getRemoteBlock$8(BlockManager.scala:1005) at scala.Option.orElse(Option.scala:447) at org.apache.spark.storage.BlockManager.getRemoteBlock(BlockManager.scala:1005) at org.apache.spark.storage.BlockManager.getRemoteBytes(BlockManager.scala:1143) at org.apache.spark.scheduler.TaskResultGetter$$anon$3.$anonfun$run$1(TaskResultGetter.scala:88) at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23) at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1996) at org.apache.spark.scheduler.TaskResultGetter$$anon$3.run(TaskResultGetter.scala:63) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Caused by: io.netty.channel.AbstractChannel$AnnotatedConnectException: Connection refused: snf-8802/192.168.0.6:35615 Caused by: java.net.ConnectException: Connection refused at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method) at sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:714) at io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(NioSocketChannel.java:330) at io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.finishConnect(AbstractNioChannel.java:334) at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:702) at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:650) at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:576) at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:493) at io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:989) at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74) at io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30) at java.lang.Thread.run(Thread.java:748)

However, when I get a smaller sample, the job works perfectly e.g.

rddTuplesA.sample(false, 0.01, 261).collect().forEach((tuple) ->
                        //build histogram...
                ); 

Is there any config parameter to change so as to make the job run? It seems that the issue is related to the network. Also, if this occurs due to memory issues, wouldn't there be a memory related error on the Driver? Something like:

java.lang.OutOfMemoryError: GC overhead limit exceeded