We should integrate the partitioning of the graph into the input
superstep to get locality as well. We can use MapReduce to try and
schedule the map tasks (workers) closest to its data and then make the
workers smart enough to only try to load their partitions.
On 7/22/12 4:30 PM, Claudio Martella wrote:
I see your reasoning. In general I'm being open to use MR when
necessary (e.g. i used to propose it instead of the automatic vertex
creation), here it could get tricky. I can see additional HDFS usage
as down (you have to be able to store 2x the graph). However, once the
graph is pre-filtered, this additional stage would not be necessary
again for the successive jobs (only when a different number of workers
is used). Though, it does add a not so small passage to the process.
On Sun, Jul 22, 2012 at 10:49 PM, Alessandro Presta <[email protected]> wrote:
Exactly. On paper, the amount of data around should be the same as during
the computation, but in practice we do use a lot more memory.
You can play with the settings and just push the problem a little farther
away, by caching less and flushing requests more frequently, so then the
bottleneck is on the servers.
We're basically sending (k-1)/k of the graph through the network, where k
is the number of workers.
What I'm thinking is that in INPUT_SUPERSTEP we're doing what MapReduce is
really good at (sorting and aggregating) in a probably inefficient (or at
least non-scalable) way.
We could try implementing it with a MapReduce job instead, where the
mappers take input splits and emit (partition_id, vertex) (they would have
access to the partitioner) and reducers just output the built partitions
to HDFS.
The computation stage would then be the usual Giraph job, where each
worker knows where to get its partitions from HDFS.
I can try making this change and see how it goes. It would just be one MR
job, so we're not selling our souls to iterative MR.
I can also see many cases where one might not want to shuffle vertices
around at all: each worker reads a roughly equal part of the input (forget
about bigger vertices for now) and simply communicates its own vertex ids
to the master. Partition "a posteriori" instead of "a priori".
What do you think?
On 7/20/12 9:42 PM, "Eli Reisman" <[email protected]> wrote:
What we are seeing in the metrics is the three-way load of
1. reading InputSplits from HDFS (mostly over the wire as there is no
locality right now)
2. creating temporary collections of vertices, sending them on netty
3. simultaneously receiving collections of vertices on netty from remote
nodes that will be place in the local workers' partitions for processing
stages
