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From "Eli Reisman (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (GIRAPH-247) Introduce edge based partitioning for InputSplits
Date Sat, 14 Jul 2012 18:57:34 GMT

    [ https://issues.apache.org/jira/browse/GIRAPH-247?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13414474#comment-13414474
] 

Eli Reisman commented on GIRAPH-247:
------------------------------------

UPDATE: I'm working on another issue (to be posted soon) that has given me some insight into
this issue. This fix cannot delumpify the graph entirely, as it turns out that each of these
Partition objects are sent to their new home when flushed as a collection of vertices to be
simple combined at the other end (including locally) with an existing partition object of
the same owner Id.

This means, even if you set the maxEdgesPerPartition or maxVerticesPerPartition very low,
you are merely altering the granularity of the network messages (groups of vertices) being
sent to their new owners after being read from InputSplits at a given local worker.

It turns out this is still very helpful, but has given me insight into why Netty fails when
I ratchet up the maximum edges to what seems like a more reasonable default, and why it seems
to thrive when I set it to my original lower setting (100k edges per set of vertices sent
out over the wire)

In conclusion: this patch is still a very valuable way to regulate the size of outgoing groups
of vertices on their way across the wire to a new home during INPUT_SUPERSTEP, but the default
should be lower (perhaps even 50k total edges per message) and it further validates why checking
for # of edges and then # of vertices in a collection that is to be sent out will catch oversizes
messages will catch oversized messages before they get too big much more effectively than
the old "10k vertices or more" check in the existing codebase.

I will upload an updated patch with a lower default that is more in keeping with the cluster
metrics I'm getting in terms of optimal time and memory use during INPUT_SUPERSTEP given this
new insight. I think the patch (which not doing what the title claims) is still very useful
and effective for the reasons I've outlined here.

                
> Introduce edge based partitioning for InputSplits
> -------------------------------------------------
>
>                 Key: GIRAPH-247
>                 URL: https://issues.apache.org/jira/browse/GIRAPH-247
>             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, GIRAPH-247-3.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
setting.
> 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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