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)
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