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External Shuffle service connection idle for more than 120seconds while there are outstanding requests.

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External Shuffle service connection idle for more than 120seconds while there are outstanding requests.

New Contributor

I am running a spark job on yarn. The job runs properly on the Amazon EMR. (1 Master and 2 slaves with m4.xlarge)

I have set up similar infra using HDP 2.6 distribution on AWS ec2 machines. While running a spark job it gets stuck in between

and the following error is thrown by the container.

18/06/25 07:15:31 INFO spark.MapOutputTrackerWorker: Doing the fetch; tracker endpoint = NettyRpcEndpointRef(spark://MapOutputTracker@10.210.150.150:44343)
18/06/25 07:15:31 INFO spark.MapOutputTrackerWorker: Don't have map outputs for shuffle 9, fetching them
18/06/25 07:15:31 INFO spark.MapOutputTrackerWorker: Don't have map outputs for shuffle 9, fetching them
18/06/25 07:15:31 INFO spark.MapOutputTrackerWorker: Got the output locations
18/06/25 07:15:31 INFO storage.ShuffleBlockFetcherIterator: Getting 5 non-empty blocks out of 1000 blocks
18/06/25 07:15:31 INFO storage.ShuffleBlockFetcherIterator: Started 1 remote fetches in 0 ms
18/06/25 07:15:31 INFO storage.ShuffleBlockFetcherIterator: Getting 5 non-empty blocks out of 1000 blocks
18/06/25 07:15:31 INFO storage.ShuffleBlockFetcherIterator: Started 0 remote fetches in 0 ms
18/06/25 07:15:31 INFO storage.ShuffleBlockFetcherIterator: Getting 5 non-empty blocks out of 1000 blocks
18/06/25 07:15:31 INFO storage.ShuffleBlockFetcherIterator: Started 1 remote fetches in 0 ms
18/06/25 07:15:31 INFO storage.ShuffleBlockFetcherIterator: Getting 5 non-empty blocks out of 1000 blocks
18/06/25 07:15:31 INFO storage.ShuffleBlockFetcherIterator: Started 1 remote fetches in 1 ms
18/06/25 07:15:31 INFO codegen.CodeGenerator: Code generated in 4.822611 ms
18/06/25 07:15:31 INFO codegen.CodeGenerator: Code generated in 8.430244 ms
18/06/25 07:17:31 ERROR server.TransportChannelHandler: Connection to ip-10-210-150-180.********/10.210.150.180:7447 has been quiet for 120000 ms while there are outstanding requests. Assuming connection is dead; please adjust spark.network.timeout if this is wrong.
18/06/25 07:17:31 ERROR client.TransportResponseHandler: Still have 307 requests outstanding when connection from ip-10-210-150-180.********/10.210.150.180:7447 is closed
18/06/25 07:17:31 INFO shuffle.RetryingBlockFetcher: Retrying fetch (1/3) for 197 outstanding blocks after 5000 ms
18/06/25 07:17:31 ERROR shuffle.OneForOneBlockFetcher: Failed while starting block fetches
java.io.IOException: Connection from ip-10-210-150-180.********/10.210.150.180:7447 closed
at org.apache.spark.network.client.TransportResponseHandler.channelInactive(TransportResponseHandler.java:146)
at org.apache.spark.network.server.TransportChannelHandler.channelInactive(TransportChannelHandler.java:108)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:241)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:227)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:220)
at io.netty.channel.ChannelInboundHandlerAdapter.channelInactive(ChannelInboundHandlerAdapter.java:75)
at io.netty.handler.timeout.IdleStateHandler.channelInactive(IdleStateHandler.java:278)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:241)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:227)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:220)
at io.netty.channel.ChannelInboundHandlerAdapter.channelInactive(ChannelInboundHandlerAdapter.java:75)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:241)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:227)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:220)
at io.netty.channel.ChannelInboundHandlerAdapter.channelInactive(ChannelInboundHandlerAdapter.java:75)
at org.apache.spark.network.util.TransportFrameDecoder.channelInactive(TransportFrameDecoder.java:182)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:241)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:227)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:220)
at io.netty.channel.DefaultChannelPipeline$HeadContext.channelInactive(DefaultChannelPipeline.java:1289)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:241)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:227)
at io.netty.channel.DefaultChannelPipeline.fireChannelInactive(DefaultChannelPipeline.java:893)
at io.netty.channel.AbstractChannel$AbstractUnsafe$7.run(AbstractChannel.java:691)
at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:399)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:446)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:131)
at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
at java.lang.Thread.run(Thread.java:748)
18/06/25 07:17:31 INFO shuffle.RetryingBlockFetcher: Retrying fetch (1/3) for 166 outstanding blocks after 5000 ms
18/06/25 07:17:31 ERROR shuffle.OneForOneBlockFetcher: Failed while starting block fetches
java.io.IOException: Connection from ip-10-210-150-180.********/10.210.150.180:7447 closed
at org.apache.spark.network.client.TransportResponseHandler.channelInactive(TransportResponseHandler.java:146)
at org.apache.spark.network.server.TransportChannelHandler.channelInactive(TransportChannelHandler.java:108)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:241)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:227)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:220)
at io.netty.channel.ChannelInboundHandlerAdapter.channelInactive(ChannelInboundHandlerAdapter.java:75)
at io.netty.handler.timeout.IdleStateHandler.channelInactive(IdleStateHandler.java:278)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:241)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:227)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:220)
at io.netty.channel.ChannelInboundHandlerAdapter.channelInactive(ChannelInboundHandlerAdapter.java:75)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:241)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:227)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:220)
at io.netty.channel.ChannelInboundHandlerAdapter.channelInactive(ChannelInboundHandlerAdapter.java:75)
at org.apache.spark.network.util.TransportFrameDecoder.channelInactive(TransportFrameDecoder.java:182)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:241)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:227)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:220)
at io.netty.channel.DefaultChannelPipeline$HeadContext.channelInactive(DefaultChannelPipeline.java:1289)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:241)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:227)
at io.netty.channel.DefaultChannelPipeline.fireChannelInactive(DefaultChannelPipeline.java:893)
at io.netty.channel.AbstractChannel$AbstractUnsafe$7.run(AbstractChannel.java:691)
at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:399)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:446)
at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:131)
at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
at java.lang.Thread.run(Thread.java:748)

As per the error, the connection b/n shuffle service and the container is idle for more than 120s. This happens mainly during shuffle and I have tried increasing the timeout to larger value but with no luck.

I am currently running spark on yarn cluster with the following configurations.

spark-defaults.conf on the master machine.

spark.eventLog.dir=hdfs:///user/spark/applicationHistory
spark.eventLog.enabled=true
spark.yarn.historyServer.address=ppv-qa12-tenant8-spark-cluster-master.periscope-solutions.local:18080
spark.shuffle.service.enabled=true
spark.dynamicAllocation.enabled=true
spark.driver.extraLibraryPath=/usr/hdp/current/hadoop-client/lib/native:/usr/hdp/current/hadoop-client/lib/native/Linux-amd64-64
spark.executor.extraLibraryPath=/usr/hdp/current/hadoop-client/lib/native:/usr/hdp/current/hadoop-client/lib/native/Linux-amd64-64
spark.driver.maxResultSize=0
spark.driver.extraJavaOptions=-XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled -XX:OnOutOfMemoryError='kill -9 %p'
spark.executor.extraJavaOptions=-verbose:gc -XX:+PrintGCDetails -XX:+PrintGCDateStamps -XX:+UseConcMarkSweepGC -XX:CMSInitiatingOccupancyFraction=70 -XX:MaxHeapFreeRatio=70 -XX:+CMSClassUnloadingEnabled -XX:OnOutOfMemoryError='kill -9 %p'
spark.executor.memory=5g
spark.driver.memory=1g
spark.executor.cores=4

yarn-site.xml of slave machines

<configuration>
  <property>
    <name>yarn.application.classpath</name>
    <value>/usr/hdp/current/spark2-client/aux/*,/etc/hadoop/conf,/usr/hdp/current/hadoop-client/*,/usr/hdp/current/hadoop-client/lib/*,/usr/hdp/current/hadoop-hdfs-client/*,/usr/hdp/current/hadoop-hdfs-client/lib/*,/usr/hdp/current/hadoop-yarn-client/*,/usr/hdp/current/hadoop-yarn-client/lib/*</value>
  </property>
  <property>
    <name>yarn.nodemanager.aux-services</name>
    <value>spark2_shuffle</value>
  </property>
  <property>
    <name>yarn.nodemanager.aux-services.mapreduce_shuffle.class</name>
    <value>org.apache.hadoop.mapred.ShuffleHandler</value>
  </property>
  <property>
    <name>yarn.nodemanager.aux-services.spark2_shuffle.class</name>
    <value>org.apache.spark.network.yarn.YarnShuffleService</value>
  </property>
  <property>
    <name>yarn.nodemanager.container-manager.thread-count</name>
    <value>64</value>
  </property>
  <property>
    <name>yarn.nodemanager.localizer.client.thread-count</name>
    <value>20</value>
  </property>
  <property>
    <name>yarn.nodemanager.vmem-pmem-ratio</name>
    <value>5</value>
  </property>
  <property>
    <name>yarn.resourcemanager.hostname</name>
    <value>************</value>
  </property>
  <property>
    <name>yarn.resourcemanager.resource-tracker.client.thread-count</name>
    <value>64</value>
  </property>
  <property>
    <name>yarn.resourcemanager.scheduler.client.thread-count</name>
    <value>64</value>
  </property>
  <property>
    <name>yarn.scheduler.increment-allocation-mb</name>
    <value>32</value>
  </property>
  <property>
    <name>yarn.scheduler.increment-allocation-vcores</name>
    <value>1</value>
  </property>
  <property>
    <name>yarn.scheduler.maximum-allocation-vcores</name>
    <value>128</value>
  </property>
  <property>
    <name>yarn.scheduler.minimum-allocation-mb</name>
    <value>32</value>
  </property>
  <property>
    <name>yarn.timeline-service.enabled</name>
    <value>true</value>
  </property>
  <property>
  <name>yarn.nodemanager.resource.cpu-vcores</name>
    <value>8</value>
  </property>
  <property>
  <name>yarn.nodemanager.resource.memory-mb</name>
    <value>11520</value>
  </property>
  <property>
  <name>yarn.scheduler.maximum-allocation-mb</name>
    <value>11520</value>
  </property>
  <property>
  <name>yarn.nodemanager.hostname</name>
    <value>*************</value>
  </property>
</configuration>








1 ACCEPTED SOLUTION

Accepted Solutions

Re: External Shuffle service connection idle for more than 120seconds while there are outstanding requests.

New Contributor

Short Answer:

Turn off scatter gather

Long Version:

The data transfer b/n container and shuffle service happens through RPC Calls(ChunkFetchRequest, ChunkFetchSuccess and ChunkFetchFailure)

On further debugging with trace level logs, we found that RPC calls were indeed happening b/n the container and the shuffle service and after some time the RPC call's were abruptly suppressed(meaning no more RPC calls were logged) from both shuffle service and container.

On looking into kernel and system activity logs we found the following

xen_netfront: xennet: skb rides the rocket: 19 slots

That means that our ec2 machines were having network packet loss.

More info on this log can be found in the following thread

http://www.brendangregg.com/blog/2014-09-11/perf-kernel-line-tracing.html

So we tried turning off the scatter-gather using the following command.

sudo ethtool -K eth0 sg off

The error was gone after that.

1 REPLY 1

Re: External Shuffle service connection idle for more than 120seconds while there are outstanding requests.

New Contributor

Short Answer:

Turn off scatter gather

Long Version:

The data transfer b/n container and shuffle service happens through RPC Calls(ChunkFetchRequest, ChunkFetchSuccess and ChunkFetchFailure)

On further debugging with trace level logs, we found that RPC calls were indeed happening b/n the container and the shuffle service and after some time the RPC call's were abruptly suppressed(meaning no more RPC calls were logged) from both shuffle service and container.

On looking into kernel and system activity logs we found the following

xen_netfront: xennet: skb rides the rocket: 19 slots

That means that our ec2 machines were having network packet loss.

More info on this log can be found in the following thread

http://www.brendangregg.com/blog/2014-09-11/perf-kernel-line-tracing.html

So we tried turning off the scatter-gather using the following command.

sudo ethtool -K eth0 sg off

The error was gone after that.

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