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  • Apache Spark User List - distinct on huge dataset
    via by Unknown author,
    • cluster.ClusterTaskSetManager: Loss was due to > > > /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(): > , 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 < > 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 < > 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?: /tmp/spark-local-20140324074221-b8f1/01/temp_1ab674f9-4556-4239-9f21-688dfc9f17d2 (Too many open files) at Method) at<init>( at at 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$3.apply(RDD.scala:471) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:34) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241) at org.apache.spark.rdd.RDD.iterator(RDD.scala:232) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:161) at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:102) at at org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:213) at org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:49) at org.apache.spark.executor.Executor$ at java.util.concurrent.ThreadPoolExecutor$Worker.runTask( at java.util.concurrent.ThreadPoolExecutor$ at
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