Look up setting ulimit, though note the distinction between soft and hard limits, and that updating your hard limit may require changing /etc/security/limits.confand restarting each worker.
On Mon, Mar 24, 2014 at 1:39 AM, Kane <kane.ist...@gmail.com> wrote: > Got a bit further, i think out of memory error was caused by setting > spark.spill to false. Now i have this error, is there an easy way to > increase file limit for spark, cluster-wide?: > > java.io.FileNotFoundException: > > /tmp/spark-local-20140324074221-b8f1/01/temp_1ab674f9-4556-4239-9f21-688dfc9f17d2 > (Too many open files) > at java.io.FileOutputStream.openAppend(Native Method) > at java.io.FileOutputStream.<init>(FileOutputStream.java:192) > at > > org.apache.spark.storage.DiskBlockObjectWriter.open(BlockObjectWriter.scala:113) > at > > org.apache.spark.storage.DiskBlockObjectWriter.write(BlockObjectWriter.scala:174) > at > > org.apache.spark.util.collection.ExternalAppendOnlyMap.spill(ExternalAppendOnlyMap.scala:191) > at > > org.apache.spark.util.collection.ExternalAppendOnlyMap.insert(ExternalAppendOnlyMap.scala:141) > at > org.apache.spark.Aggregator.combineValuesByKey(Aggregator.scala:59) > at > > org.apache.spark.rdd.PairRDDFunctions$$anonfun$1.apply(PairRDDFunctions.scala:95) > at > > org.apache.spark.rdd.PairRDDFunctions$$anonfun$1.apply(PairRDDFunctions.scala:94) > at org.apache.spark.rdd.RDD$$anonfun$3.apply(RDD.scala:471) > at org.apache.spark.rdd.RDD$$anonfun$3.apply(RDD.scala:471) > at > org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:34) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:232) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:161) > at > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:102) > at org.apache.spark.scheduler.Task.run(Task.scala:53) > at > > org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:213) > at > org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:49) > at > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178) > at > > java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886) > at > > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) > at java.lang.Thread.run(Thread.java:662) > > > > -- > View this message in context: > http://apache-spark-user-list.1001560.n3.nabble.com/distinct-on-huge-dataset-tp3025p3084.html > Sent from the Apache Spark User List mailing list archive at Nabble.com. >
