[
https://issues.apache.org/jira/browse/SPARK-13510?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hong Shen updated SPARK-13510:
------------------------------
Description:
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.
was:
In our cluster, when I test spark-1.6.0 with a sql, it throw exception and
failed.
{code}
org.apache.spark.shuffle.FetchFailedException: Direct buffer memory
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.throwFetchFailedException(ShuffleBlockFetcherIterator.scala:323)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:300)
at
org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:51)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
at
org.apache.spark.sql.execution.UnsafeExternalRowSorter.sort(UnsafeExternalRowSorter.java:167)
at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:90)
at org.apache.spark.sql.execution.Sort$$anonfun$1.apply(Sort.scala:64)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:759)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$21.apply(RDD.scala:759)
{code}
The reason is that when shuffle a big block(like 1G), task will allocate the
same memory, it will easily throw "FetchFailedException: Direct buffer memory".
If I add -Dio.netty.noUnsafe=true spark.executor.extraJavaOptions, it will
throw
{code}
java.lang.OutOfMemoryError: Java heap space
at
io.netty.buffer.PoolArena$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.
> 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
>
> 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.
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