Hi all I'm running spark in a single local machine, no hadoop, just reading and writing in local disk.
I need to have a single file as output of my calculation. if I do "rdd.saveAsTextFile(...)" all runs ok but I get allot of files. Since I need a single file I was considering to do something like: Try {new FileWriter(outputPath)} match { case Success(writer) => try { rdd.toLocalIterator.foreach({line => val str = line.toString writer.write(str) } } } ... } I get: [error] o.a.s.e.Executor - Exception in task 0.0 in stage 41.0 (TID 32) java.lang.OutOfMemoryError: Java heap space at java.util.Arrays.copyOf(Arrays.java:3236) ~[na:1.8.0_45] at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:118) ~[na:1.8.0_45] at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93) ~[na:1.8.0_45] at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153) ~[na:1.8.0_45] at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877) ~[na:1.8.0_45] [error] o.a.s.u.SparkUncaughtExceptionHandler - Uncaught exception in thread Thread[Executor task launch worker-1,5,main] java.lang.OutOfMemoryError: Java heap space at java.util.Arrays.copyOf(Arrays.java:3236) ~[na:1.8.0_45] at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:118) ~[na:1.8.0_45] at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93) ~[na:1.8.0_45] at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153) ~[na:1.8.0_45] at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877) ~[na:1.8.0_45] [error] o.a.s.s.TaskSetManager - Task 0 in stage 41.0 failed 1 times; aborting job [warn] application - Can't write to /tmp/err1433498283479.csv: {} org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 41.0 failed 1 times, most recent failure: Lost task 0.0 in stage 41.0 (TID 32, localhost): java.lang.OutOfMemoryError: Java heap space at java.util.Arrays.copyOf(Arrays.java:3236) at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:118) at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93) at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153) at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1877) at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1786) at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1189) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44) at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:80) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1204) ~[spark-core_2.10-1.3.1.jar:1.3.1] at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1193) ~[spark-core_2.10-1.3.1.jar:1.3.1] at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1192) ~[spark-core_2.10-1.3.1.jar:1.3.1] at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) ~[scala-library-2.10.5.jar:na] at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) ~[scala-library-2.10.5.jar:na] if this rdd.toLocalIterator.foreach(...) doesn't work, what is the better solution? Best Regards Marcos