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" >
