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From "Eli Reisman (JIRA)" <>
Subject [jira] [Updated] (GIRAPH-247) Introduce edge based partitioning for InputSplits
Date Thu, 12 Jul 2012 17:52:34 GMT


Eli Reisman updated GIRAPH-247:

    Attachment: GIRAPH-247-2.patch

New patch uses edge count accumulated locally rather than iterate through all edges in Partition
to decide if its full and should be sent to its owner. Thanks to Allesandro, this should be
MUCH faster than patch 1.

The patch itself has proven in metered load tests on cluster to be very useful in de-lumpifying
large social graph data.

> Introduce edge based partitioning for InputSplits
> -------------------------------------------------
>                 Key: GIRAPH-247
>                 URL:
>             Project: Giraph
>          Issue Type: Improvement
>          Components: graph
>    Affects Versions: 0.2.0
>            Reporter: Eli Reisman
>            Assignee: Eli Reisman
>            Priority: Minor
>              Labels: patch
>             Fix For: 0.2.0
>         Attachments: GIRAPH-247-1.patch, GIRAPH-247-2.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
> 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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