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https://issues.apache.org/jira/browse/SPARK-13510?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15168871#comment-15168871
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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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