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Posted to issues@spark.apache.org by "belvey (JIRA)" <ji...@apache.org> on 2019/04/03 15:08:00 UTC
[jira] [Comment Edited] (SPARK-13510) Shuffle may throw
FetchFailedException: Direct buffer memory
[ https://issues.apache.org/jira/browse/SPARK-13510?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16806307#comment-16806307 ]
belvey edited comment on SPARK-13510 at 4/3/19 3:07 PM:
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[~shenhong] , hello hongsheng, I am using spark2.0 facing the same issue, I am not sure if it's merged into spark2. it's very kind for you to post your pr.
edit:
I found that solution had already been added to spark2.3 and later. i am not sure if is hong shen's pr , but the solution is similar to what hong shen's said. And for spark2.3 and later we can use "spark.maxRemoteBlockSizeFetchToMem" to control the max block size allowed for shuffle fetching data that catched in memory, it's default value is (Interger.max-512) bytes.
was (Author: belvey):
[~shenhong] , hello hongsheng, I am using spark2.0 facing the same issue, I am not sure if it's merged into spark2. it's very kind for you to post your pr.
> Shuffle may throw FetchFailedException: Direct buffer memory
> ------------------------------------------------------------
>
> Key: SPARK-13510
> URL: https://issues.apache.org/jira/browse/SPARK-13510
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.6.0
> Reporter: Hong Shen
> Priority: Major
> Attachments: spark-13510.diff
>
>
> In our cluster, when I test spark-1.6.0 with a sql, it throw exception and failed.
> {code}
> 16/02/17 15:36:03 INFO storage.ShuffleBlockFetcherIterator: Sending request for 1 blocks (915.4 MB) from 10.196.134.220:7337
> 16/02/17 15:36:03 INFO shuffle.ExternalShuffleClient: External shuffle fetch from 10.196.134.220:7337 (executor id 122)
> 16/02/17 15:36:03 INFO client.TransportClient: Sending fetch chunk request 0 to /10.196.134.220:7337
> 16/02/17 15:36:36 WARN server.TransportChannelHandler: Exception in connection from /10.196.134.220:7337
> java.lang.OutOfMemoryError: Direct buffer memory
> at java.nio.Bits.reserveMemory(Bits.java:658)
> at java.nio.DirectByteBuffer.<init>(DirectByteBuffer.java:123)
> at java.nio.ByteBuffer.allocateDirect(ByteBuffer.java:306)
> at io.netty.buffer.PoolArena$DirectArena.newChunk(PoolArena.java:645)
> at io.netty.buffer.PoolArena.allocateNormal(PoolArena.java:228)
> at io.netty.buffer.PoolArena.allocate(PoolArena.java:212)
> at io.netty.buffer.PoolArena.allocate(PoolArena.java:132)
> at io.netty.buffer.PooledByteBufAllocator.newDirectBuffer(PooledByteBufAllocator.java:271)
> at io.netty.buffer.AbstractByteBufAllocator.directBuffer(AbstractByteBufAllocator.java:155)
> at io.netty.buffer.AbstractByteBufAllocator.directBuffer(AbstractByteBufAllocator.java:146)
> at io.netty.buffer.AbstractByteBufAllocator.ioBuffer(AbstractByteBufAllocator.java:107)
> at io.netty.channel.AdaptiveRecvByteBufAllocator$HandleImpl.allocate(AdaptiveRecvByteBufAllocator.java:104)
> at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:117)
> at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:511)
> at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:468)
> at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:382)
> at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:354)
> at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:111)
> at java.lang.Thread.run(Thread.java:744)
> 16/02/17 15:36:36 ERROR client.TransportResponseHandler: Still have 1 requests outstanding when connection from /10.196.134.220:7337 is closed
> 16/02/17 15:36:36 ERROR shuffle.RetryingBlockFetcher: Failed to fetch block shuffle_3_81_2, and will not retry (0 retries)
> {code}
> The reason is that when shuffle a big block(like 1G), task will allocate the same memory, it will easily throw "FetchFailedException: Direct buffer memory".
> If I add -Dio.netty.noUnsafe=true spark.executor.extraJavaOptions, it will throw
> {code}
> java.lang.OutOfMemoryError: Java heap space
> at io.netty.buffer.PoolArena$HeapArena.newUnpooledChunk(PoolArena.java:607)
> at io.netty.buffer.PoolArena.allocateHuge(PoolArena.java:237)
> at io.netty.buffer.PoolArena.allocate(PoolArena.java:215)
> at io.netty.buffer.PoolArena.allocate(PoolArena.java:132)
> {code}
>
> In mapreduce shuffle, it will firstly judge whether the block can cache in memery, but spark doesn't.
> If the block is more than we can cache in memory, we should write to disk.
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