Hi Sven,

What version of Spark are you running?  Recent versions have a change that allows PySpark to share a pool of processes instead of starting a new one for each task.


On Fri, Jan 23, 2015 at 9:36 AM, Sven Krasser <krasser@gmail.com> wrote:
Hey all,

I am running into a problem where YARN kills containers for being over their memory allocation (which is about 8G for executors plus 6G for overhead), and I noticed that in those containers there are tons of pyspark.daemon processes hogging memory. Here's a snippet from a container with 97 pyspark.daemon processes. The total sum of RSS usage across all of these is 1,764,956 pages (i.e. 6.7GB on the system).

Any ideas what's happening here and how I can get the number of pyspark.daemon processes back to a more reasonable count?

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 :
|- 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

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

Thank you!