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https://issues.apache.org/jira/browse/SPARK-15828?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15322865#comment-15322865
]
Saisai Shao commented on SPARK-15828:
-------------------------------------
I see, but I don't clearly understand your scenario, are the NMs decommissioned
automatically when there's no container running on it? From my understanding it
should be triggered manually and occasionally.
This may not be a Spark-only problem, I think there should be some changes in
the YARN side. Basically I think your usage of YARN may have some conflicts
with dynamic allocation and it requires mutual operation between two sides.
> YARN is not aware of Spark's External Shuffle Service
> -----------------------------------------------------
>
> Key: SPARK-15828
> URL: https://issues.apache.org/jira/browse/SPARK-15828
> Project: Spark
> Issue Type: Bug
> Affects Versions: 1.6.1
> Environment: EMR
> Reporter: Miles Crawford
>
> When using Spark with dynamic allocation, it is common for all containers on a
> particular YARN node to be released. This is generally okay because of the
> external shuffle service.
> The problem arises when YARN is attempting to downsize the cluster - once all
> containers on the node are gone, YARN will decommission the node, regardless
> of
> whether the external shuffle service is still required!
> The once the node is shut down, jobs begin failing with messages such as:
> {code}
> 2016-06-07 18:56:40,016 ERROR o.a.s.n.shuffle.RetryingBlockFetcher: Exception
> while beginning fetch of 13 outstanding blocks
> java.io.IOException: Failed to connect to
> ip-10-12-32-67.us-west-2.compute.internal/10.12.32.67:7337
> at
> org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:216)
>
> ~[d58092b50d2880a1c259cb51c6ed83955f97e34a4b75cedaa8ab00f89a09df50-spark-network-common_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.network.client.TransportClientFactory.createClient(TransportClientFactory.java:167)
>
> ~[d58092b50d2880a1c259cb51c6ed83955f97e34a4b75cedaa8ab00f89a09df50-spark-network-common_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.network.shuffle.ExternalShuffleClient$1.createAndStart(ExternalShuffleClient.java:105)
>
> ~[2d5c6a1b64d0070faea2e852616885c0110121f4f5c3206cbde88946abce11c3-spark-network-shuffle_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.network.shuffle.RetryingBlockFetcher.fetchAllOutstanding(RetryingBlockFetcher.java:140)
>
> [2d5c6a1b64d0070faea2e852616885c0110121f4f5c3206cbde88946abce11c3-spark-network-shuffle_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.network.shuffle.RetryingBlockFetcher.start(RetryingBlockFetcher.java:120)
>
> [2d5c6a1b64d0070faea2e852616885c0110121f4f5c3206cbde88946abce11c3-spark-network-shuffle_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.network.shuffle.ExternalShuffleClient.fetchBlocks(ExternalShuffleClient.java:114)
>
> [2d5c6a1b64d0070faea2e852616885c0110121f4f5c3206cbde88946abce11c3-spark-network-shuffle_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.sendRequest(ShuffleBlockFetcherIterator.scala:152)
>
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.fetchUpToMaxBytes(ShuffleBlockFetcherIterator.scala:316)
>
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.initialize(ShuffleBlockFetcherIterator.scala:263)
>
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.storage.ShuffleBlockFetcherIterator.<init>(ShuffleBlockFetcherIterator.scala:112)
>
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:43)
>
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:98)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> [d56f3336b4a0fcc71fe8beb90052dbafd0e88a749bdb4bbb15d37894cf443364-spark-core_2.11-1.6.1.jar:1.6.1]
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> [na:1.8.0_91]
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> [na:1.8.0_91]
> at java.lang.Thread.run(Thread.java:745) [na:1.8.0_91]
> Caused by: java.net.ConnectException: Connection refused:
> ip-10-12-32-67.us-west-2.compute.internal/10.12.32.67:7337
> at sun.nio.ch.SocketChannelImpl.checkConnect(Native Method)
> ~[na:1.8.0_91]
> at
> sun.nio.ch.SocketChannelImpl.finishConnect(SocketChannelImpl.java:717)
> ~[na:1.8.0_91]
> at
> io.netty.channel.socket.nio.NioSocketChannel.doFinishConnect(NioSocketChannel.java:224)
>
> ~[9cb53ee0c581954e1f791bedcaea3a54e8e31b2f093da96e943faa342baad529-netty-all-4.0.29.Final.jar:4.0.29.Final]
> at
> io.netty.channel.nio.AbstractNioChannel$AbstractNioUnsafe.finishConnect(AbstractNioChannel.java:289)
>
> ~[9cb53ee0c581954e1f791bedcaea3a54e8e31b2f093da96e943faa342baad529-netty-all-4.0.29.Final.jar:4.0.29.Final]
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:528)
> ~[9cb53ee0c581954e1f791bedcaea3a54e8e31b2f093da96e943faa342baad529-netty-all-4.0.29.Final.jar:4.0.29.Final]
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
>
> ~[9cb53ee0c581954e1f791bedcaea3a54e8e31b2f093da96e943faa342baad529-netty-all-4.0.29.Final.jar:4.0.29.Final]
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
> ~[9cb53ee0c581954e1f791bedcaea3a54e8e31b2f093da96e943faa342baad529-netty-all-4.0.29.Final.jar:4.0.29.Final]
> at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
> ~[9cb53ee0c581954e1f791bedcaea3a54e8e31b2f093da96e943faa342baad529-netty-all-4.0.29.Final.jar:4.0.29.Final]
> at
> io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
>
> ~[9cb53ee0c581954e1f791bedcaea3a54e8e31b2f093da96e943faa342baad529-netty-all-4.0.29.Final.jar:4.0.29.Final]
> ... 1 common frames omitted
> {code}
> Because of this issue dynamic allocation and auto-scaling cannot be used
> together.
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