Done! I updated the output in the same place:
https://gist.github.com/hakman/44afddaf4eb67cda28c6

Thanks for looking into this so fast!

Ciprian

--
Performance Monitoring * Log Analytics * Search Analytics
Solr & Elasticsearch Support * http://sematext.com/

On Wed, Dec 16, 2015 at 2:53 PM, Rainer Gerhards <[email protected]>
wrote:

> 2015-12-16 13:48 GMT+01:00 Ciprian Hacman <[email protected]>:
> > Hi,
> >
> > I upgraded a server to Rsyslog 8.15 last night and today the process was
> > using almost 200MB of RAM (raising steadily).
> > Tried running the process in Valgrind to see if I get an idea about what
> > happens, but wasn't that much help for me.
>
> That's because debug symbols are unloaded at module unload time. This
> makes valgrind stacktraces unusable. Nevertheless, the information
> looks very promising.
>
> Can you build rsyslog yourself for that box? All we need is the
> --enable-valgrind option, which will essentially remove the module
> unloads and make the stacktrace usable.
>
> Rainer
> >
> > If someone has better debugging skills, I pasted the output here. Not
> sure
> > if I let it run enough or leave it running longer.
> > https://gist.github.com/hakman/44afddaf4eb67cda28c6
> >
> > Thanks,
> > Ciprian
> >
> >
> > --
> > Performance Monitoring * Log Analytics * Search Analytics
> > Solr & Elasticsearch Support * http://sematext.com/
> >
> > On Tue, Dec 15, 2015 at 2:41 PM, David Lang <[email protected]> wrote:
> >
> >> On Tue, 15 Dec 2015, Ciprian Hacman wrote:
> >>
> >> Hi David,
> >>>
> >>> maxMessageSize="10000"
> >>> queue.size="10000" - main queue
> >>> queue.size="10000" - elasticsearch queue
> >>>
> >>> Based on my calculations this brings me to a max of 200MB of memory,
> maybe
> >>> a little more depending on how maxMessageSize is calculated.
> >>>
> >>> I read logs from a file and push them to Elasticsearch (on the same
> >>> network), so TCP is the only possibility. This server has a very simple
> >>> setup.
> >>>
> >>> If I don't find the reason for this issue, I might have to go implement
> >>> the
> >>> forwarding to a central location and push to Elasticsearch from there.
> >>>
> >>
> >> There are advantages to sending things to a central server.
> >>
> >> it's one place to queue data, so you can either allocate more ram, or go
> >> to disk as needed without impacting other workloads.
> >>
> >> it's more efficient, the central server is more likely to have larger
> >> batches of data to feed to ES, and ES only needs to be running one
> thread
> >> processing inbound data
> >>
> >> while it is one more point to have to look at, I think it simplifies
> >> troubleshooting as all the communication to ES (and therefor all the
> errors
> >> for such communication) happen in one place instead of distributed.
> >>
> >> anyway, let's see how things look with 8.15
> >>
> >>
> >> David Lang
> >> _______________________________________________
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