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https://issues.apache.org/jira/browse/SPARK-6056?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14341260#comment-14341260
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Lianhui Wang commented on SPARK-6056:
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[~adav] from your given information, when preferDirectBufs is set to false, i
think we need to set io.netty.allocator.numDirectArenas=0 that avoid direct
allocation. right?
[~carlmartin] i think you can try the method that [~adav] said before.
> Unlimit offHeap memory use cause RM killing the container
> ---------------------------------------------------------
>
> Key: SPARK-6056
> URL: https://issues.apache.org/jira/browse/SPARK-6056
> Project: Spark
> Issue Type: Bug
> Components: Shuffle, Spark Core
> Affects Versions: 1.2.1
> Reporter: SaintBacchus
>
> No matter set the `preferDirectBufs` or limit the number of thread or not
> ,spark can not limit the use of offheap memory.
> At line 269 of the class 'AbstractNioByteChannel' in netty-4.0.23.Final,
> Netty had allocated a offheap memory buffer with the same size in heap.
> So how many buffer you want to transfor, the same size offheap memory will be
> allocated.
> But once the allocated memory size reach the capacity of the overhead momery
> set in yarn, this executor will be killed.
> I wrote a simple code to test it:
> ```scala
> val bufferRdd = sc.makeRDD(0 to 10, 10).map(x=>new
> Array[Byte](10*1024*1024)).persist
> bufferRdd.count
> val part = bufferRdd.partitions(0)
> val sparkEnv = SparkEnv.get
> val blockMgr = sparkEnv.blockManager
> val blockOption = blockMgr.get(RDDBlockId(bufferRdd.id, part.index))
> val resultIt = blockOption.get.data.asInstanceOf[Iterator[Array[Byte]]]
> val len = resultIt.map(_.length).sum
> ```
> If use multi-thread to get len, the physical memery will soon exceed the
> limit set by spark.yarn.executor.memoryOverhead
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