Virtual Memory Usage

Eric Wong normalperson at
Thu Nov 10 18:50:35 EST 2011

Ben Somers <somers.ben at> wrote:
> m1.xlarge running Ubuntu 9.10 (yes, we know it's out od date), with
> 15GB RAM, 4x2GHz CPU.
> The server is behaving fine, with most of the unicorn workers sitting
> at about 280MB resident memory, comparable to passenger's workers. But
> I've been testing how many workers to run with, and I've noticed that
> once I get above three workers, a couple of them get ballooning
> virtual memory, jumping up to a little over 2GB. I initially thought
> that the server was overloaded and swapping, but swap usage is still
> 0, and I get this behavior at over 50% free memory. It doesn't seem to
> be causing performance problems, but I was wondering if anyone else
> has observed this or similar behavior? I'm trying to decide if it's
> normal or an item for concern.

Since you're on Linux, you can run "pmap $PID" to see how chunks of
virtual memory are used by any process.

If a chunk is backed by a regular file, it hardly matters, the kernel
will share that with other processes and won't swap that memory (since
it's already backed by a regular file).

Big "[ anon ]" chunks you see in pmap output are usually not shared, so
potentially more problematic.  It depends on your application and the
libraries it uses, of course.  Some libraries will share anonymous
memory between processes (raindrops does this for example, but only for
small integer counters).

I've seen Unicorn processes using TokyoCabinet and TDB[1] with database
files many gigabytes large with large VM sizes to match.  I also know
both of those database libraries cooperate with the VM subsystem so I
had nothing to worry about as far as memory usage goes :)

In case you (or somebody else) does notice high RSS and you're using the
stock glibc malloc, try setting MALLOC_MMAP_THRESHOLD_=131072 or
similar.  The author of jemalloc once blogged about this behavior with
glibc malloc, the title was "Mr.  Malloc gets schooled" or something
along those lines.  Making the malloc implementation use mmap() more
to reduce memory usage may hurt performance, though.

I always do incremental processing on large datasets so that helps
me avoid issues with unchecked/unpredictable memory growth due to
malloc behavior.


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