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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2018/08/01 16:26:00 UTC
[jira] [Assigned] (SPARK-24989) BlockFetcher should retry while
getting OutOfDirectMemoryError
[ https://issues.apache.org/jira/browse/SPARK-24989?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-24989:
------------------------------------
Assignee: Apache Spark
> BlockFetcher should retry while getting OutOfDirectMemoryError
> --------------------------------------------------------------
>
> Key: SPARK-24989
> URL: https://issues.apache.org/jira/browse/SPARK-24989
> Project: Spark
> Issue Type: Improvement
> Components: Shuffle
> Affects Versions: 2.2.0
> Reporter: Li Yuanjian
> Assignee: Apache Spark
> Priority: Major
> Attachments: FailedStage.png
>
>
> h3. Description
> This problem can be reproduced stably by a large parallelism job migrate from map reduce to Spark in our practice, some metrics list below:
> ||Item||Value||
> |spark.executor.instances|1000|
> |spark.executor.cores|5|
> |task number of shuffle writer stage|18038|
> |task number of shuffle reader stage|80000|
> While the shuffle writer stage successful ended, the shuffle reader stage starting and keep failing by FetchFail. Each fetch request need the netty sever allocate a buffer in 16MB(detailed stack attached below), the huge amount of fetch request will use up default maxDirectMemory rapidly, even though we bump up io.netty.maxDirectMemory to 50GB!
> {code:java}
> org.apache.spark.shuffle.FetchFailedException: failed to allocate 16777216 byte(s) of direct memory (used: 21474836480, max: 21474836480)
> at org.apache.spark.storage.ShuffleBlockFetcherIterator.throwFetchFailedException(ShuffleBlockFetcherIterator.scala:514)
> at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:445)
> at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:61)
> at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
> at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
> at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
> at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:199)
> at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:119)
> at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:105)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:108)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> Caused by: io.netty.util.internal.OutOfDirectMemoryError: failed to allocate 16777216 byte(s) of direct memory (used: 21474836480, max: 21474836480)
> at io.netty.util.internal.PlatformDependent.incrementMemoryCounter(PlatformDependent.java:530)
> at io.netty.util.internal.PlatformDependent.allocateDirectNoCleaner(PlatformDependent.java:484)
> at io.netty.buffer.PoolArena$DirectArena.allocateDirect(PoolArena.java:711)
> at io.netty.buffer.PoolArena$DirectArena.newChunk(PoolArena.java:700)
> at io.netty.buffer.PoolArena.allocateNormal(PoolArena.java:237)
> at io.netty.buffer.PoolArena.allocate(PoolArena.java:221)
> at io.netty.buffer.PoolArena.allocate(PoolArena.java:141)
> at io.netty.buffer.PooledByteBufAllocator.newDirectBuffer(PooledByteBufAllocator.java:296)
> at io.netty.buffer.AbstractByteBufAllocator.directBuffer(AbstractByteBufAllocator.java:177)
> at io.netty.buffer.AbstractByteBufAllocator.directBuffer(AbstractByteBufAllocator.java:168)
> at io.netty.buffer.AbstractByteBufAllocator.ioBuffer(AbstractByteBufAllocator.java:129)
> at io.netty.channel.AdaptiveRecvByteBufAllocator$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:643)
> at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:566)
> at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:480)
> at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:442)
> at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:131)
> at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
> ... 1 more
> {code}
> h3. Solution
> Add retry support and bump up java option io.netty.maxDirectMemory and try lager spark.shuffle.io.retryWait can help the job passing and , I think we need more discussion about load balance of fetch requests, but maybe the retry support is necessary first.
>
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