[SPARK-5395] Large number of Python workers causing resource depletion

• Type: Bug
• Status: Resolved
• Priority: Major
• Resolution: Fixed
• Affects Version/s: 1.2.0, 1.3.0
• Fix Version/s: 1.2.2, 1.3.0
• Component/s: PySpark

Environment

AWS ElasticMapReduce

• Target Version/s: 1.2.2, 1.3.0
• Assignee: Davies Liu
• Reporter: Sven Krasser
• Created: 24/Jan/15 08:19
• Updated: 17/Feb/15 04:36
• Resolved: 17/Feb/15 04:35

Description

During job execution a large number of Python worker accumulates eventually causing YARN to kill containers for being over their memory allocation (in the case below that is about 8G for executors plus 6G for overhead per container).

In this instance, at the time of killing the container 97 pyspark.daemon processes had accumulated.

2015-01-23 15:36:53,654 INFO [Reporter] yarn.YarnAllocationHandler (Logging.scala:logInfo(59)) - Container marked as failed: container_1421692415636_0052_01_000030. Exit status: 143. Diagnostics: Container [pid=35211,containerID=container_1421692415636_0052_01_000030] is running beyond physical memory limits. Current usage: 14.9 GB of 14.5 GB physical memory used; 41.3 GB of 72.5 GB virtual memory used. Killing container.
Dump of the process-tree for container_1421692415636_0052_01_000030 :
|- PID PPID PGRPID SESSID CMD_NAME USER_MODE_TIME(MILLIS) SYSTEM_TIME(MILLIS) VMEM_USAGE(BYTES) RSSMEM_USAGE(PAGES) FULL_CMD_LINE
|- 54101 36625 36625 35211 (python) 78 1 332730368 16834 python -m pyspark.daemon
|- 52140 36625 36625 35211 (python) 58 1 332730368 16837 python -m pyspark.daemon
|- 36625 35228 36625 35211 (python) 65 604 331685888 17694 python -m pyspark.daemon
	[...]

The configuration used uses 64 containers with 2 cores each.

Full output here: https://gist.github.com/skrasser/e3e2ee8dede5ef6b082c

Mailinglist discussion: https://www.mail-archive.com/[e-mail]/msg20102.html

Issue Links

• links to: [Github] Pull Request #4238 (davies)