Another option is merge partfiles after your app ends.
On 5 Jun 2015 20:37, "Akhil Das" <ak...@sigmoidanalytics.com> wrote:
> you can simply do rdd.repartition(1).saveAsTextFile(...), it might not be
> efficient if your output data is huge since one task will be doing the
> whole writing.
>
> Thanks
> Best Regards
>
> On Fri, Jun 5, 2015 at 3:46 PM, marcos rebelo <ole...@gmail.com> wrote:
>
>> 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
>>
>>
>>
>
