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From "Aaron Davidson (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-6056) Unlimit offHeap memory use cause RM killing the container
Date Sun, 01 Mar 2015 06:24:05 GMT

    [ https://issues.apache.org/jira/browse/SPARK-6056?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14341970#comment-14341970
] 

Aaron Davidson commented on SPARK-6056:
---------------------------------------

It's possible that it's actually the shuffle-read that's actually doing memory-mapping --
please try setting spark.storage.memoryMapThreshold to around 1073741824 (1 GB) to disable
this form of memory mapping for the test.

> 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:
> {code:title=test.scala|borderStyle=solid}
> import org.apache.spark.storage._
> import org.apache.spark._
> 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
> def test = {
>         val blockOption = blockMgr.get(RDDBlockId(bufferRdd.id, part.index))
>         val resultIt = blockOption.get.data.asInstanceOf[Iterator[Array[Byte]]]
>         val len = resultIt.map(_.length).sum
>         println(s"[${Thread.currentThread.getId}] get block length = $len")
> }
> def test_driver(count:Int, parallel:Int)(f: => Unit) = {
>     val tpool = new scala.concurrent.forkjoin.ForkJoinPool(parallel)
>     val taskSupport  = new scala.collection.parallel.ForkJoinTaskSupport(tpool)
>     val parseq = (1 to count).par
>     parseq.tasksupport = taskSupport
>     parseq.foreach(x=>f)
>     tpool.shutdown
>     tpool.awaitTermination(100, java.util.concurrent.TimeUnit.SECONDS)
> }
> {code}
> progress:
> 1. bin/spark-shell --master yarn-cilent --executor-cores 40 --num-executors 1
> 2. :load test.scala in spark-shell
> 3. use such comman to catch executor on slave node
> {code}
> pid=$(jps|grep CoarseGrainedExecutorBackend |awk '{print $1}');top -b -p $pid|grep $pid
> {code}
> 4. test_driver(20,100)(test) in spark-shell
> 5. watch the output of the command on slave node
> 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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