Btw, I've got System.setProperty("spark.shuffle.consolidate.files",
"true") and use ext3 (CentOS...)On Thu, Apr 17, 2014 at 3:20 PM, Ryan Compton <compton.r...@gmail.com> wrote: > Does this continue in newer versions? (I'm on 0.8.0 now) > > When I use .distinct() on moderately large datasets (224GB, 8.5B rows, > I'm guessing about 500M are distinct) my jobs fail with: > > 14/04/17 15:04:02 INFO cluster.ClusterTaskSetManager: Loss was due to > java.io.FileNotFoundException > java.io.FileNotFoundException: > /tmp/spark-local-20140417145643-a055/3c/shuffle_1_218_1157 (Too many > open files) > > ulimit -n tells me I can open 32000 files. Here's a plot of lsof on a > worker node during a failed .distinct(): > http://i.imgur.com/wyBHmzz.png , you can see tasks fail when Spark > tries to open 32000 files. > > I never ran into this in 0.7.3. Is there a parameter I can set to tell > Spark to use less than 32000 files? > > On Mon, Mar 24, 2014 at 10:23 AM, Aaron Davidson <ilike...@gmail.com> wrote: >> 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. >> >>
