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https://issues.apache.org/jira/browse/SPARK-26881?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16771138#comment-16771138
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Rafael RENAUDIN-AVINO commented on SPARK-26881:
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Basically I see two improvements that could be made:
1- Allow the depth of the aggregation to be configurable by the user
2- If not specified by the user, the depth of the treeAggregate can be computed
with the heuristic in the ticket description.
I'm not sure about the value of 2- without integrating 1-: if a framework uses
a heuristic to determine a parameter without giving the user the option of
setting the parameter himself, it might be annoying...
> Scaling issue with Gramian computation for RowMatrix: too many results sent
> to driver
> -------------------------------------------------------------------------------------
>
> Key: SPARK-26881
> URL: https://issues.apache.org/jira/browse/SPARK-26881
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 2.2.0
> Reporter: Rafael RENAUDIN-AVINO
> Priority: Minor
>
> This issue hit me when running PCA on large dataset (~1Billion rows, ~30k
> columns).
> Computing Gramian of a big RowMatrix allows to reproduce the issue.
>
> The problem arises in the treeAggregate phase of the gramian matrix
> computation: results sent to driver are enormous.
> A potential solution to this could be to replace the hard coded depth (2) of
> the tree aggregation by a heuristic computed based on the number of
> partitions, driver max result size, and memory size of the dense vectors that
> are being aggregated, cf below for more detail:
> (nb_partitions)^(1/depth) * dense_vector_size <= driver_max_result_size
> I have a potential fix ready (currently testing it at scale), but I'd like to
> hear the community opinion about such a fix to know if it's worth investing
> my time into a clean pull request.
>
> Note that I only faced this issue with spark 2.2 but I suspect it affects
> later versions aswell.
>
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