[
https://issues.apache.org/jira/browse/SPARK-8718?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ankur Dave resolved SPARK-8718.
-------------------------------
Resolution: Fixed
Fix Version/s: 1.5.0
Issue resolved by pull request 7104
[https://github.com/apache/spark/pull/7104]
> Improve EdgePartition2D for non perfect square number of partitions
> -------------------------------------------------------------------
>
> Key: SPARK-8718
> URL: https://issues.apache.org/jira/browse/SPARK-8718
> Project: Spark
> Issue Type: Improvement
> Components: GraphX
> Reporter: Andrew Ray
> Priority: Minor
> Fix For: 1.5.0
>
>
> The current implementation of EdgePartition2D has a major limitation:
> bq. One of the limitations of this approach is that the number of machines
> must either be a perfect square. We partially address this limitation by
> computing the machine assignment to the next largest perfect square and then
> mapping back down to the actual number of machines. Unfortunately, this can
> also lead to work imbalance and so it is suggested that a perfect square is
> used.
> To remove this limitation I'm proposing the following code change. It allows
> us to partition into any number of evenly sized bins while maintaining the
> property that any vertex will only need to be replicated at most 2 *
> sqrt(numParts) times. To maintain current behavior for perfect squares we use
> the old algorithm in that case, although this could be removed if we dont
> care about producing the exact same result.
> See this IPython notebook for a visualization of what is being proposed
> [https://github.com/aray/e2d/blob/master/EdgePartition2D.ipynb] and download
> it to interactively change the number of partitions.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
