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From Avery Ching <>
Subject Re: Out of memory with giraph-release-1.0.0-RC3, used to work on old Giraph
Date Fri, 30 Aug 2013 23:54:29 GMT
Ah, the new caches. =)  These make things a lot faster (bulk data 
sending), but do take up some additional memory.  if you look at 
GiraphConstants, you can find ways to change the cache sizes (this will 
reduce that memory usage).
For example, MAX_EDGE_REQUEST_SIZE will affect the size of the edge 
cache.  MAX_MSG_REQUEST_SIZE will affect the size of the message cache.  
The caches are per worker, so 100 workers would require 50 MB  per 
worker by default.  Feel free to trim it if you like.

The byte arrays for the edges are the most efficient storage possible 
(although not as performance as the native edge stores).

Hope that helps,


On 8/29/13 4:53 PM, Jeff Peters wrote:
> Avery, it would seem that optimizations to Giraph have, unfortunately, 
> turned the majority of the heap into "dark matter". The two snapshots 
> are at unknown points in a superstep but I waited for several 
> supersteps so that the activity had more or less stabilized. About the 
> only thing comparable between the two snapshots are the vertexes, 
> 192561 X "RecsVertex" in the new version and 191995 X "Coloring" in 
> the old system. But with the new Giraph 672710176 out of 824886184 
> bytes are stored as primitive byte arrays. That's probably indicative 
> of some very fine performance optimization work, but it makes it 
> extremely difficult to know what's really out there, and why. I did 
> notice that a number of caches have appeared that did not exist 
> before, namely SendEdgeCache, SendPartitionCache, SendMessageCache 
> and SendMutationsCache.
> Could any of those account for a larger per-worker footprint in a 
> modern Giraph? Should I simply assume that I need to force AWS to 
> configure its EMR Hadoop so that each instance has fewer map tasks but 
> with a somewhat larger VM max, say 3GB instead of 2GB?
> On Wed, Aug 28, 2013 at 4:57 PM, Avery Ching < 
> <>> wrote:
>     Try dumping a histogram of memory usage from a running JVM and see
>     where the memory is going.  I can't think of anything in
>     particular that changed...
>     On 8/28/13 4:39 PM, Jeff Peters wrote:
>         I am tasked with updating our ancient (circa 7/10/2012) Giraph
>         to giraph-release-1.0.0-RC3. Most jobs run fine but our
>         largest job now runs out of memory using the same AWS
>         elastic-mapreduce configuration we have always used. I have
>         never tried to configure either Giraph or the AWS Hadoop. We
>         build for Hadoop 1.0.2 because that's closest to the 1.0.3 AWS
>         provides us. The 8 X m2.4xlarge cluster we use seems to
>         provide 8*14=112 map tasks fitted out with 2GB heap each. Our
>         code is completely unchanged except as required to adapt to
>         the new Giraph APIs. Our vertex, edge, and message data are
>         completely unchanged. On smaller jobs, that work, the
>         aggregate heap usage high-water mark seems about the same as
>         before, but the "committed heap" seems to run higher. I can't
>         even make it work on a cluster of 12. In that case I get one
>         map task that seems to end up with nearly twice as many
>         messages as most of the others so it runs out of memory
>         anyway. It only takes one to fail the job. Am I missing
>         something here? Should I be configuring my new Giraph in some
>         way I didn't used to need to with the old one?

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