[SPARK-671] Spark runs out of memory on fork/exec (affects both pipes and python)

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Affects Version/s: 0.5.0, 0.5.1, 0.5.2, 0.6.0, 0.6.1
• Fix Version/s: 0.7.3, 0.8.0
• Component/s: PySpark, Spark Core
• Assignee: Jey Kottalam Assign to me
• Reporter: Patrick McFadin
• Created: 31/Jan/13 12:52
• Updated: 02/Nov/15 12:11
• Resolved: 28/Jun/13 20:30

Description

Because the JVM uses fork/exec to launch child processes, any child process initially has the memory footprint of its parent. In the case of a large Spark JVM that spawns many child processes (for Pipe or Python support), this quickly leads to kernel memory exhaustion.

This problem is discussed here:
https://gist.github.com/1970815

It results in errors like this:

13/01/31 20:18:48 INFO cluster.TaskSetManager: Loss was due to java.io.IOException: Cannot run program "cat": java.io.IOException: error=12, Cannot allocate memory
       at java.lang.ProcessBuilder.start(ProcessBuilder.java:475)
       at spark.rdd.PipedRDD.compute(PipedRDD.scala:38)
       at spark.RDD.computeOrReadCheckpoint(RDD.scala:203)
       at spark.RDD.iterator(RDD.scala:192)
       at spark.scheduler.ResultTask.run(ResultTask.scala:76)

I was able to workaround by allowing for memory over-commitment by the kernel on all slaves,

echo 1 > /proc/sys/vm/overcommit_memory

but we should try to include a more robust solution, such as the one here:
https://github.com/axiak/java_posix_spawn

Issue Links

• is duplicated by: SPARK-620 Default SPARK_MEM on AMI too high