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From "Eli Reisman (JIRA)" <>
Subject [jira] [Created] (GIRAPH-247) Introduce edge based partitioning for InputSplits
Date Wed, 11 Jul 2012 21:20:35 GMT
Eli Reisman created GIRAPH-247:

             Summary: Introduce edge based partitioning for InputSplits
                 Key: GIRAPH-247
             Project: Giraph
          Issue Type: Improvement
          Components: graph
    Affects Versions: 0.2.0
            Reporter: Eli Reisman
            Assignee: Eli Reisman
            Priority: Minor
             Fix For: 0.2.0
         Attachments: GIRAPH-247-1.patch

Experiments on larger data input sets while maintaining low memory profile has revealed that
typical social graph data is very lumpy and partitioning by vertices can easily overload some
unlucky worker nodes who end up with partitions containing highly-connected vertices while
other nodes process partitions with the same number of vertices but far fewer out-edges per
vertex. This often results in cascading failures during data load-in even on tiny data sets.

By partitioning using edges (the default I set in GiraphJob.MAX_EDGES_PER_PARTITION_DEFAULT
is 200,000 per partition, or the old default # of vertices, whichever the user's input format
reaches first when reading InputSplits) I have seen dramatic "de-lumpification" of data, allow
the processing of 8x larger data sets before memory problems occur at a given configuration

This needs more tuning, but comes with a -Dgiraph.maxEdgesPerPartition that can be set to
more edges/partition as your data sets grow or memory limitations shrink. This might be considered
a first attempt, perhaps simply allowing us to default to this type of partitioning or the
old version would be more compatible with existing users' needs? That would not be a hard
feature to add to this. But I think this method of partition production has merit for typical
large-scale graph data that Giraph is designed to process.

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