What I was trying to get at was that we might be creating more chunks
because the per-thread caches for all the different threads haven't yet
been filled. Hence my questions about the degree of control we have over
the thread pool Netty is using for RPC threads. But if the asymptotic
observation isn't bearing out, then that probably isn't the cause either.
On Mon, Jul 27, 2015 at 12:47 PM, Abdel Hakim Deneche <adene...@maprtech.com
> wrote:
> @Jacques, my understanding is that chunks are not owned by specific a
> thread but they are part of a specific memory arena which is in turn only
> accessed by specific threads. Do you want me to find which threads are
> associated with the same arena where we have hanging chunks ?
>
>
> On Mon, Jul 27, 2015 at 11:04 AM, Jacques Nadeau <jacq...@dremio.com>
> wrote:
>
> > It sounds like your statement is that we're cacheing too many unused
> > chunks. Hanifi and I previously discussed implementing a separate
> flushing
> > mechanism to release unallocated chunks that are hanging around. The
> main
> > question is, why are so many chunks hanging around and what threads are
> > they associated with. A Jmap dump and analysis should allow you to do
> > determine which thread owns the excess chunks. My guess would be the RPC
> > pool since those are long lasting (as opposed to the WorkManager pool,
> > which is contracting).
> >
> > --
> > Jacques Nadeau
> > CTO and Co-Founder, Dremio
> >
> > On Mon, Jul 27, 2015 at 9:53 AM, Abdel Hakim Deneche <
> > adene...@maprtech.com>
> > wrote:
> >
> > > When running a set of, mostly window function, queries concurrently on
> a
> > > single drillbit with a 8GB max direct memory. We are seeing a
> continuous
> > > increase of direct memory allocation.
> > >
> > > We repeat the following steps multiple times:
> > > - we launch in "iteration" of tests that will run all queries in a
> random
> > > order, 10 queries at a time
> > > - after the iteration finishes, we wait for a couple of minute to give
> > > Drill time to release the memory being held by the finishing fragments
> > >
> > > Using Drill's memory logger ("drill.allocator") we were able to get
> > > snapshots of how memory was internally used by Netty, we only focused
> on
> > > the number of allocated chunks, if we take this number and multiply it
> by
> > > 16MB (netty's chunk size) we get approximately the same value reported
> by
> > > Drill's direct memory allocation.
> > > Here is a graph that shows the evolution of the number of allocated
> > chunks
> > > on a 500 iterations run (I'm working on improving the plots) :
> > >
> > > http://bit.ly/1JL6Kp3
> > >
> > > In this specific case, after the first iteration Drill was allocating
> > ~2GB
> > > of direct memory, this number kept rising after each iteration to ~6GB.
> > We
> > > suspect this caused one of our previous runs to crash the JVM.
> > >
> > > If we only focus on the log lines between iterations (when Drill's
> memory
> > > usage is below 10MB) then all allocated chunks are at most 2% usage. At
> > > some point we end up with 288 nearly empty chunks, yet the next
> iteration
> > > will cause more chunks to be allocated!!!
> > >
> > > is this expected ?
> > >
> > > PS: I am running more tests and will update this thread with more
> > > informations.
> > >
> > > --
> > >
> > > Abdelhakim Deneche
> > >
> > > Software Engineer
> > >
> > > <http://www.mapr.com/>
> > >
> > >
> > > Now Available - Free Hadoop On-Demand Training
> > > <
> > >
> >
> http://www.mapr.com/training?utm_source=Email&utm_medium=Signature&utm_campaign=Free%20available
> > > >
> > >
> >
>
>
>
> --
>
> Abdelhakim Deneche
>
> Software Engineer
>
> <http://www.mapr.com/>
>
>
> Now Available - Free Hadoop On-Demand Training
> <
> http://www.mapr.com/training?utm_source=Email&utm_medium=Signature&utm_campaign=Free%20available
> >
>
