Hey Davies, Sure thing, it's filed here now: https://issues.apache.org/jira/browse/SPARK-5395
As far as a repro goes, what is a normal number of workers I should expect? Even shortly after kicking the job off, I see workers in the double-digits per container. Here's an example using pstree on a worker node (the worker node runs 16 containers, i.e. the java--bash branches below). The initial Python process per container is forked between 7 and 33 times. ├─java─┬─bash───java─┬─python───22*[python] │ │ └─89*[{java}] │ ├─bash───java─┬─python───13*[python] │ │ └─80*[{java}] │ ├─bash───java─┬─python───9*[python] │ │ └─78*[{java}] │ ├─bash───java─┬─python───24*[python] │ │ └─91*[{java}] │ ├─3*[bash───java─┬─python───19*[python]] │ │ └─86*[{java}]] │ ├─bash───java─┬─python───25*[python] │ │ └─90*[{java}] │ ├─bash───java─┬─python───33*[python] │ │ └─98*[{java}] │ ├─bash───java─┬─python───7*[python] │ │ └─75*[{java}] │ ├─bash───java─┬─python───15*[python] │ │ └─80*[{java}] │ ├─bash───java─┬─python───18*[python] │ │ └─84*[{java}] │ ├─bash───java─┬─python───10*[python] │ │ └─85*[{java}] │ ├─bash───java─┬─python───26*[python] │ │ └─90*[{java}] │ ├─bash───java─┬─python───17*[python] │ │ └─85*[{java}] │ ├─bash───java─┬─python───24*[python] │ │ └─92*[{java}] │ └─265*[{java}] Each container has 2 cores in case that makes a difference. Thank you! -Sven On Fri, Jan 23, 2015 at 11:52 PM, Davies Liu <dav...@databricks.com> wrote: > It should be a bug, the Python worker did not exit normally, could you > file a JIRA for this? > > Also, could you show how to reproduce this behavior? > > On Fri, Jan 23, 2015 at 11:45 PM, Sven Krasser <kras...@gmail.com> wrote: > > Hey Adam, > > > > I'm not sure I understand just yet what you have in mind. My takeaway > from > > the logs is that the container actually was above its allotment of about > > 14G. Since 6G of that are for overhead, I assumed there to be plenty of > > space for Python workers, but there seem to be more of those than I'd > > expect. > > > > Does anyone know if that is actually the intended behavior, i.e. in this > > case over 90 Python processes on a 2 core executor? > > > > Best, > > -Sven > > > > > > On Fri, Jan 23, 2015 at 10:04 PM, Adam Diaz <adam.h.d...@gmail.com> > wrote: > >> > >> Yarn only has the ability to kill not checkpoint or sig suspend. If you > >> use too much memory it will simply kill tasks based upon the yarn > config. > >> https://issues.apache.org/jira/browse/YARN-2172 > >> > >> > >> On Friday, January 23, 2015, Sandy Ryza <sandy.r...@cloudera.com> > wrote: > >>> > >>> 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 > >>>> > >>>> -- > >>>> krasser > >>> > >>> > > > > > > > > -- > > http://sites.google.com/site/krasser/?utm_source=sig > -- http://sites.google.com/site/krasser/?utm_source=sig