[jira] [Commented] (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=16825798#comment-16825798
]
Mike Chan commented on SPARK-13510:
---
Thanks man
> 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.(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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[jira] [Commented] (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=16825756#comment-16825756
]
belvey commented on SPARK-13510:
@Mike in my case I set it to '536870912' (512m) ，and it can be set to '512m'
as spark will treat it equally.
> 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.(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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[jira] [Commented] (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=16825721#comment-16825721
]
Mike Chan commented on SPARK-13510:
---
Hi [~belvey] I'm having similar issue and our
"spark.maxRemoteBlockSizeFetchToMem" is at 188. From the forums I can tell
this parameter should be set below 2GB. How do you set your parameter? Should
it be "2g" or 2 * 1024 * 1024 * 1024 = 2147483648? I'm at Spark 2.3 on Azure
> 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.(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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[jira] [Commented] (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=16806307#comment-16806307
]
belvey commented on SPARK-13510:
[~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.(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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[jira] [Commented] (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=15860220#comment-15860220
]
Srikanth Daggumalli commented on SPARK-13510:
-
OOM is not an issue. But would like to know about the following ERRORs
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)
why does they written in logs as ERROR?
> 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
> 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.(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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[jira] [Commented] (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=15671415#comment-15671415
]
Sital Kedia commented on SPARK-13510:
-
[~shenhong] - We are seeing the same issue on our side. Do you have a PR for
this yet?
> 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
> 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.(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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[jira] [Commented] (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=15445571#comment-15445571
]
song fengfei commented on SPARK-13510:
--
Hi, Hong Shen
You mean that the shuffle data fetched based on Netty were stored in
off-memory?and, didn't write them to file even though the block data too big?
> 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
> 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.(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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[jira] [Commented] (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=15173675#comment-15173675
]
Hong Shen commented on SPARK-13510:
---
I have resolve in our own edition, I will add a pull request this weekend.
> 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.(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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[jira] [Commented] (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=15173658#comment-15173658
]
Hong Shen commented on SPARK-13510:
---
We can't resolve all the shuffle OOM by allocate more memory, If reduce shuffle
a block more than 5GB, it shouldn't restore in memory.
> 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.(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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[jira] [Commented] (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=15173648#comment-15173648
]
Hong Shen commented on SPARK-13510:
---
In our cluster, we have lot of sql run on hive, I want to use spark sql to
replace hive.
But there is a lot of sql's input are more the 10TB, shuffe block could be
more than 5GB,
When I run some sql on spark sql, some sql failed because shuffle OOM, I can't
allocate such more memory to resolve all the failed sql.
> 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.(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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[jira] [Commented] (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=15173647#comment-15173647
]
Sean Owen commented on SPARK-13510:
---
[~shenhong] don't reopen JIRAs unless the discussion has meaningfully changed.
I'm going to close this.
> 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.(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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[jira] [Commented] (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=15171330#comment-15171330
]
Hong Shen commented on SPARK-13510:
---
Add more log, fetch a block of 915.4 MB.
16/02/17 15:36:03 INFO storage.ShuffleBlockFetcherIterator: Sending request for
1 blocks (915.4 MB) from 10.196.134.220:7337
> 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.(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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[jira] [Commented] (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=15168871#comment-15168871
]
Sean Owen commented on SPARK-13510:
---
Without any detail about what you're running, it's hard to help. It sounds
like oyu're just out of memory, which is not a bug. I don't see reason to
believe there's an unreasonable amount of memory being allocated
> 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}
> 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.
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[jira] [Commented] (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=15168693#comment-15168693
]
Hong Shen commented on SPARK-13510:
---
I will add the logic in my edition.
> 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}
> 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.
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