Hi
I wasted few days on similar problem.
I guess the problem was that during loading - if you have got W workers
and W^2 partitions there are W^2 partition caches in each worker.
Each cache can hold 10 000 vertexes by default.
I had 26 000 000 vertexes, 60 workers -> 3600 partitions. It means that
there can be up to 36 000 000 vertexes in caches in each worker if input
files are random.
Workers were assigned 450 000 vertexes but failed when they had 900 000
vertexes in memory.
Btw: Why default number of partitions is W^2 ?
(I can be wrong)
Lukas
On 08/31/13 01:54, Avery Ching wrote:
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,
Avery
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 <[email protected]
<mailto:[email protected]>> 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?
