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From Mayur Rustagi <mayur.rust...@gmail.com>
Subject Re:
Date Thu, 27 Mar 2014 20:10:56 GMT
You have to raise the global limit as root. Also you have to do that on the
whole cluster.
Regards
Mayur

Mayur Rustagi
Ph: +1 (760) 203 3257
http://www.sigmoidanalytics.com
@mayur_rustagi <https://twitter.com/mayur_rustagi>



On Thu, Mar 27, 2014 at 4:07 AM, Hahn Jiang <hahn.jiang.pro@gmail.com>wrote:

> I set "ulimit -n 100000" in conf/spark-env.sh, is it too small?
>
>
> On Thu, Mar 27, 2014 at 3:36 PM, Sonal Goyal <sonalgoyal4@gmail.com>wrote:
>
>> Hi Hahn,
>>
>> What's the ulimit on your systems? Please check the following link for a
>> discussion on the too many files open.
>>
>>
>> http://mail-archives.apache.org/mod_mbox/spark-user/201402.mbox/%3CCANGvG8qpn_WLLsRcJEGDB7HMza2uX7mYxZhfvTZ+b-sDxdKRUg@mail.gmail.com%3E
>>
>>
>> Sent from my iPad
>>
>> > On Mar 27, 2014, at 12:15 PM, Hahn Jiang <hahn.jiang.pro@gmail.com>
>> wrote:
>> >
>> > Hi, all
>> >
>> > I write a spark program on yarn. When I use small size input file, my
>> program can run well. But my job will failed if input size is more than 40G.
>> >
>> > the error log:
>> > java.io.FileNotFoundException (java.io.FileNotFoundException:
>> /home/work/data12/yarn/nodemanager/usercache/appcache/application_1392894597330_86813/spark-local-20140327144433-716b/24/shuffle_0_22_890
>> (Too many open files))
>> > java.io.FileOutputStream.openAppend(Native Method)
>> > java.io.FileOutputStream.<init>(FileOutputStream.java:192)
>> >
>> org.apache.spark.storage.DiskBlockObjectWriter.open(BlockObjectWriter.scala:113)
>> >
>> org.apache.spark.storage.DiskBlockObjectWriter.write(BlockObjectWriter.scala:174)
>> >
>> org.apache.spark.scheduler.ShuffleMapTask$$anonfun$runTask$1.apply(ShuffleMapTask.scala:164)
>> >
>> org.apache.spark.scheduler.ShuffleMapTask$$anonfun$runTask$1.apply(ShuffleMapTask.scala:161)
>> > scala.collection.Iterator$class.foreach(Iterator.scala:727)
>> >
>> org.apache.spark.util.collection.ExternalAppendOnlyMap$ExternalIterator.foreach(ExternalAppendOnlyMap.scala:239)
>> >
>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:161)
>> >
>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:102)
>> > org.apache.spark.scheduler.Task.run(Task.scala:53)
>> >
>> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:213)
>> >
>> org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:49)
>> > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178)
>> >
>> java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886)
>> >
>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908)
>> > java.lang.Thread.run(Thread.java:662)
>> >
>> >
>> > my object:
>> > object Test {
>> >
>> >   def main(args: Array[String]) {
>> >     val sc = new SparkContext(args(0), "Test",
>> >       System.getenv("SPARK_HOME"),
>> SparkContext.jarOfClass(this.getClass))
>> >
>> >     val mg = sc.textFile("/user/.../part-*")
>> >     val mct = sc.textFile("/user/.../part-*")
>> >
>> >     val pair1 = mg.map {
>> >       s =>
>> >         val cols = s.split("\t")
>> >         (cols(0), cols(1))
>> >     }
>> >     val pair2 = mct.map {
>> >       s =>
>> >         val cols = s.split("\t")
>> >         (cols(0), cols(1))
>> >     }
>> >     val merge = pair1.union(pair2)
>> >     val result = merge.reduceByKey(_ + _)
>> >     val outputPath = new Path("/user/xxx/temp/spark-output")
>> >     outputPath.getFileSystem(new Configuration()).delete(outputPath,
>> true)
>> >     result.saveAsTextFile(outputPath.toString)
>> >
>> >     System.exit(0)
>> >   }
>> >
>> > }
>> >
>> > My spark version is 0.9 and I run my job use this command
>> "/opt/soft/spark/bin/spark-class org.apache.spark.deploy.yarn.Client --jar
>> ./spark-example_2.10-0.1-SNAPSHOT.jar --class Test --queue default --args
>> yarn-standalone --num-workers 500 --master-memory 7g --worker-memory 7g
>> --worker-cores 2"
>> >
>>
>
>

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