we build our own adjacency lists as well. the main motivation for us was
that graphx has some assumptions about everything fitting in memory (it has
.cache statements all over place). however if my understanding is wrong and
graphx can handle graphs that do not fit in memory i would be interested
Hello
We are building an adjacency list to represent a graph. Vertexes, Edges and
Weights for the same has been extracted from hdfs files by a Spark job.
Further we expect size of the adjacency list(Hash Map) could grow over
20Gigs.
How can we represent this in RDD, so that it will distributed in
in nature?
Basically we are trying to fit HashMap(Adjacency List) into Spark RDD. Is
there any other way other than GraphX?
Thanks and Regards,
Harsha
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Hi Harsha,
You could look through the GraphX source to see the approach taken there
for ideas in your own. I'd recommend starting at
https://github.com/apache/spark/blob/master/graphx/src/main/scala/org/apache/spark/graphx/Graph.scala#L385
to see the storage technique.
Why do you want to avoid
