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https://issues.apache.org/jira/browse/SPARK-14560?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15666005#comment-15666005
]
KaiXu commented on SPARK-14560:
-------------------------------
spark1.6.2 has the same issue:
16/11/15 10:11:15 INFO scheduler.TaskSetManager: Finished task 16761.0 in stage
1.0 (TID 17639) in 11657 ms on eurus-node4 (16634/22488)
16/11/15 10:11:15 INFO scheduler.DAGScheduler: Executor lost: 13 (epoch 1)
16/11/15 10:11:15 WARN scheduler.TaskSetManager: Lost task 10002.0 in stage 1.0
(TID 10880, eurus-node3): java.lang.OutOfMemoryError: Unable to acquire 51
bytes of memory, got 0
at
org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
at
org.apache.spark.shuffle.sort.ShuffleExternalSorter.acquireNewPageIfNecessary(ShuffleExternalSorter.java:359)
at
org.apache.spark.shuffle.sort.ShuffleExternalSorter.insertRecord(ShuffleExternalSorter.java:380)
at
org.apache.spark.shuffle.sort.UnsafeShuffleWriter.insertRecordIntoSorter(UnsafeShuffleWriter.java:237)
at
org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:164)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
16/11/15 10:11:15 INFO scheduler.TaskSetManager: Finished task 16766.0 in stage
1.0 (TID 17644) in 11508 ms on eurus-node4 (16635/22488)
16/11/15 10:11:15 INFO scheduler.TaskSetManager: Finished task 16574.0 in stage
1.0 (TID 17452) in 21354 ms on eurus-node1 (16636/22488)
16/11/15 10:11:15 INFO scheduler.TaskSetManager: Finished task 16733.0 in stage
1.0 (TID 17611) in 13122 ms on eurus-node2 (16637/22488)
16/11/15 10:11:15 INFO storage.BlockManagerMasterEndpoint: Trying to remove
executor 13 from BlockManagerMaster.
16/11/15 10:11:15 INFO scheduler.TaskSetManager: Finished task 16735.0 in stage
1.0 (TID 17613) in 12888 ms on eurus-node2 (16638/22488)
16/11/15 10:11:15 INFO scheduler.TaskSetManager: Finished task 16723.0 in stage
1.0 (TID 17601) in 13572 ms on eurus-node2 (16639/22488)
16/11/15 10:11:15 INFO scheduler.TaskSetManager: Finished task 16521.0 in stage
1.0 (TID 17399) in 24803 ms on eurus-node4 (16640/22488)
16/11/15 10:11:15 INFO scheduler.TaskSetManager: Finished task 16722.0 in stage
1.0 (TID 17600) in 13621 ms on eurus-node2 (16641/22488)
16/11/15 10:11:15 INFO storage.BlockManagerMasterEndpoint: Removing block
manager BlockManagerId(13, eurus-node3, 42404)
16/11/15 10:11:15 WARN scheduler.TaskSetManager: Lost task 16962.0 in stage 1.0
(TID 17840, eurus-node4): FetchFailed(BlockManagerId(13, eurus-node3, 42404),
shuffleId=4, mapId=35, reduceId=16962, message=
org.apache.spark.shuffle.FetchFailedException: java.io.FileNotFoundException:
/mnt/disk1/yarn/nm/usercache/root/appcache/application_1479174444013_0002/blockmgr-0161eaa3-ea02-40b2-81f7-082606db1271/1b/shuffle_4_35_0.index
(No such file or directory)
at java.io.FileInputStream.open(Native Method)
at java.io.FileInputStream.<init>(FileInputStream.java:146)
at
org.apache.spark.shuffle.IndexShuffleBlockResolver.getBlockData(IndexShuffleBlockResolver.scala:191)
at
org.apache.spark.storage.BlockManager.getBlockData(BlockManager.scala:298)
at
org.apache.spark.network.netty.NettyBlockRpcServer$$anonfun$2.apply(NettyBlockRpcServer.scala:58)
at
org.apache.spark.network.netty.NettyBlockRpcServer$$anonfun$2.apply(NettyBlockRpcServer.scala:58)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
at
org.apache.spark.network.netty.NettyBlockRpcServer.receive(NettyBlockRpcServer.scala:58)
at
org.apache.spark.network.server.TransportRequestHandler.processRpcRequest(TransportRequestHandler.java:149)
at
org.apache.spark.network.server.TransportRequestHandler.handle(TransportRequestHandler.java:102)
at
org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:104)
at
org.apache.spark.network.server.TransportChannelHandler.channelRead0(TransportChannelHandler.java:51)
at
io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
at
io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at
io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at
io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:266)
at
io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at
io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at
io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:103)
at
io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at
io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at
org.apache.spark.network.util.TransportFrameDecoder.channelRead(TransportFrameDecoder.java:86)
at
io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:308)
at
io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:294)
at
io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:846)
at
io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
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:745)
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$11.hasNext(Iterator.scala:327)
at
org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
at
org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:197)
at
org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:103)
at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:98)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
at
org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Caused by: java.lang.RuntimeException: java.io.FileNotFoundException:
/mnt/disk1/yarn/nm/usercache/root/appcache/application_1479174444013_0002/blockmgr-0161eaa3-ea02-40b2-81f7-082606db1271/1b/shuffle_4_35_0.index
(No such file or directory)
at java.io.FileInputStream.open(Native Method)
> Cooperative Memory Management for Spillables
> --------------------------------------------
>
> Key: SPARK-14560
> URL: https://issues.apache.org/jira/browse/SPARK-14560
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 1.6.1
> Reporter: Imran Rashid
> Assignee: Lianhui Wang
> Fix For: 2.0.0
>
>
> SPARK-10432 introduced cooperative memory management for SQL operators that
> can spill; however, {{Spillable}} s used by the old RDD api still do not
> cooperate. This can lead to memory starvation, in particular on a
> shuffle-to-shuffle stage, eventually resulting in errors like:
> {noformat}
> 16/03/28 08:59:54 INFO memory.TaskMemoryManager: Memory used in task 3081
> 16/03/28 08:59:54 INFO memory.TaskMemoryManager: Acquired by
> org.apache.spark.shuffle.sort.ShuffleExternalSorter@69ab0291: 32.0 KB
> 16/03/28 08:59:54 INFO memory.TaskMemoryManager: 1317230346 bytes of memory
> were used by task 3081 but are not associated with specific consumers
> 16/03/28 08:59:54 INFO memory.TaskMemoryManager: 1317263114 bytes of memory
> are used for execution and 1710484 bytes of memory are used for storage
> 16/03/28 08:59:54 ERROR executor.Executor: Managed memory leak detected; size
> = 1317230346 bytes, TID = 3081
> 16/03/28 08:59:54 ERROR executor.Executor: Exception in task 533.0 in stage
> 3.0 (TID 3081)
> java.lang.OutOfMemoryError: Unable to acquire 75 bytes of memory, got 0
> at
> org.apache.spark.memory.MemoryConsumer.allocatePage(MemoryConsumer.java:120)
> at
> org.apache.spark.shuffle.sort.ShuffleExternalSorter.acquireNewPageIfNecessary(ShuffleExternalSorter.java:346)
> at
> org.apache.spark.shuffle.sort.ShuffleExternalSorter.insertRecord(ShuffleExternalSorter.java:367)
> at
> org.apache.spark.shuffle.sort.UnsafeShuffleWriter.insertRecordIntoSorter(UnsafeShuffleWriter.java:237)
> at
> org.apache.spark.shuffle.sort.UnsafeShuffleWriter.write(UnsafeShuffleWriter.java:164)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)
> {noformat}
> This can happen anytime the shuffle read side requires more memory than what
> is available for the task. Since the shuffle-read side doubles its memory
> request each time, it can easily end up acquiring all of the available
> memory, even if it does not use it. Eg., say that after the final spill, the
> shuffle-read side requires 10 MB more memory, and there is 15 MB of memory
> available. But if it starts at 2 MB, it will double to 4, 8, and then
> request 16 MB of memory, and in fact get all available 15 MB. Since the 15
> MB of memory is sufficient, it will not spill, and will continue holding on
> to all available memory. But this leaves *no* memory available for the
> shuffle-write side. Since the shuffle-write side cannot request the
> shuffle-read side to free up memory, this leads to an OOM.
> The simple solution is to make {{Spillable}} implement {{MemoryConsumer}} as
> well, so RDDs can benefit from the cooperative memory management introduced
> by SPARK-10342.
> Note that an additional improvement would be for the shuffle-read side to
> simple release unused memory, without spilling, in case that would leave
> enough memory, and only spill if that was inadequate. However that can come
> as a later improvement.
> *Workaround*: You can set
> {{spark.shuffle.spill.numElementsForceSpillThreshold=N}} to force spilling to
> occur every {{N}} elements, thus preventing the shuffle-read side from ever
> grabbing all of the available memory. However, this requires careful tuning
> of {{N}} to specific workloads: too big, and you will still get an OOM; too
> small, and there will be so much spilling that performance will suffer
> drastically. Furthermore, this workaround uses an *undocumented*
> configuration with *no compatibility guarantees* for future versions of spark.
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