Hi group,
I got a question concerning the graph partitioning step. If I understood
the code correctly, the graph is distributed to n partitions by using
vertexID.hashCode() & n. I got two questions concerning that step.
1) Is the whole graph loaded and partitioned only by the Master? This
would mean, the whole data has to be moved to that Master map job and
then moved to the physical node the specific worker for the partition
runs on. As this sounds like a huge overhead, I further inspected the code:
I saw that there is also a WorkerGraphPartitioner and I assume he calls
the partitioning method on his local data (lets say his local HDFS
blocks) and if the resulting partition for a vertex is not himself, the
data gets moved to that worker, which reduces the overhead. Is this
assumption correct?
2) Let's say the graph is already partitioned in the file system, e.g.
blocks on physical nodes contain logical connected graph nodes. Is it
possible to just read the data as it is and skip the partitioning step?
In that case I currently assume, that the vertexID should contain the
partitionID and the custom partitioning would be an identity function in
that case (instead of hashing or range).
Thanks for your time and help!
Cheers,
Martin
