While the job is running, just look in the directory and see whats the root
cause of it (is it the logs? is it the shuffle? etc). Here's a few
configuration options which you can try:
- Disable shuffle : spark.shuffle.spill=false (It might end up in OOM)
- Enable log rotation:
sparkConf.set("spark.executor.logs.rolling.strategy", "size")
.set("spark.executor.logs.rolling.size.maxBytes", "1024")
.set("spark.executor.logs.rolling.maxRetainedFiles", "3")
Thanks
Best Regards
On Mon, Jul 6, 2015 at 10:44 AM, Devarajan Srinivasan <
devathecool1...@gmail.com> wrote:
> Hi ,
>
> I am trying to run an ETL on spark which involves expensive shuffle
> operation. Basically I require a self-join to be performed on a
> sparkDataFrame RDD . The job runs fine for around 15 hours and when the
> stage(which performs the sef-join) is about to complete, I get a
> *"java.io.IOException:
> No space left on device"*. I initially thought this could be due to
> *spark.local.dir* pointing to */tmp* directory which was configured with
> *2GB* of space, since this job requires expensive shuffles,spark
> requires more space to write the shuffle files. Hence I configured
> *spark.local.dir* to point to a different directory which has *1TB* of
> space. But still I get the same *no space left exception*. What could be
> the root cause of this issue?
>
>
> Thanks in advance.
>
> *Exception stacktrace:*
>
> *java.io.IOException: No space left on device
> at java.io.FileOutputStream.writeBytes(Native Method)
> at java.io.FileOutputStream.write(FileOutputStream.java:345)
> at
> org.apache.spark.storage.DiskBlockObjectWriter$TimeTrackingOutputStream$$anonfun$write$3.apply$mcV$sp(BlockObjectWriter.scala:87)
> at org.apache.spark.storage.DiskBlockObjectWriter.org
> <http://org.apache.spark.storage.DiskBlockObjectWriter.org>$apache$spark$storage$DiskBlockObjectWriter$$callWithTiming(BlockObjectWriter.scala:229)
> at
> org.apache.spark.storage.DiskBlockObjectWriter$TimeTrackingOutputStream.write(BlockObjectWriter.scala:87)
> at
> java.io.BufferedOutputStream.flushBuffer(BufferedOutputStream.java:82)
> at java.io.BufferedOutputStream.write(BufferedOutputStream.java:126)
> at
> org.xerial.snappy.SnappyOutputStream.dump(SnappyOutputStream.java:297)
> at
> org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:244)
> at
> org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:99)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1785)
> at
> java.io.ObjectOutputStream.writeNonProxyDesc(ObjectOutputStream.java:1285)
> at
> java.io.ObjectOutputStream.writeClassDesc(ObjectOutputStream.java:1230)
> at
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1426)
> at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
> at
> java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1576)
> at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:350)
> at
> org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44)
> at
> org.apache.spark.storage.DiskBlockObjectWriter.write(BlockObjectWriter.scala:204)
> at
> org.apache.spark.util.collection.ExternalSorter.spillToPartitionFiles(ExternalSorter.scala:370)
> at
> org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:211)
> at
> org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:63)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:68)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:64)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> at java.lang.Thread.run(Thread.java:745)*
>
>
>
