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.
