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....@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 <sonalgoy...@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-sdxdk...@mail.gmail.com%3E >> >> >> Sent from my iPad >> >> > On Mar 27, 2014, at 12:15 PM, Hahn Jiang <hahn.jiang....@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" >> > >> > >
