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.
-Sandy On Fri, Jan 23, 2015 at 9:36 AM, Sven Krasser <kras...@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 : > |- 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 > > [...] > > > Full output here: https://gist.github.com/skrasser/e3e2ee8dede5ef6b082c > > Thank you! > -Sven > > -- > http://sites.google.com/site/krasser/?utm_source=sig >
