At first glance it looks like a huge shuffle. What operations are you invoking? Also, is it actually filling up all available space or just filling up the disk you configured to hold spark.local.dirs? if that's /tmp, it could be you're just filling up the tiny tmp partition.
On Wed, Feb 11, 2015 at 3:39 AM, Peng Cheng <rhw...@gmail.com> wrote: > I'm running a small job on a cluster with 15G of mem and 8G of disk per > machine. > > The job always get into a deadlock where the last error message is: > > 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$$anonfun$write$3.apply$mcV$sp(BlockObjectWriter.scala:86) > at > org.apache.spark.storage.DiskBlockObjectWriter.org$apache$spark$storage$DiskBlockObjectWriter$$callWithTiming(BlockObjectWriter.scala:221) > at > org.apache.spark.storage.DiskBlockObjectWriter$TimeTrackingOutputStream.write(BlockObjectWriter.scala:86) > at java.io.BufferedOutputStream.write(BufferedOutputStream.java:122) > at > org.xerial.snappy.SnappyOutputStream.dumpOutput(SnappyOutputStream.java:300) > at > org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:247) > at > org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:107) > at > java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876) > at > java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1914) > at > java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1575) > at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:350) > at > org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42) > at > org.apache.spark.storage.DiskBlockObjectWriter.write(BlockObjectWriter.scala:195) > at > org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$4$$anonfun$apply$2.apply(ExternalSorter.scala:751) > at > org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$4$$anonfun$apply$2.apply(ExternalSorter.scala:750) > at scala.collection.Iterator$class.foreach(Iterator.scala:727) > at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) > at > org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$4.apply(ExternalSorter.scala:750) > at > org.apache.spark.util.collection.ExternalSorter$$anonfun$writePartitionedFile$4.apply(ExternalSorter.scala:746) > at scala.collection.Iterator$class.foreach(Iterator.scala:727) > at scala.collection.AbstractIterator.foreach(Iterator.scala:1157) > at > org.apache.spark.util.collection.ExternalSorter.writePartitionedFile(ExternalSorter.scala:746) > at > org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:68) > 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:56) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > at java.lang.Thread.run(Thread.java:745) > > By the time it happens the shuffle write size is 0.0B and input size is > 3.4MB. I wonder what operation could quickly eat up the entire 5G free disk > space. > > In addition, The storage level of the entire job is confined to > MEMORY_ONLY_SERIALIZED and checkpointing is completely disabled. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
