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Posted to issues@spark.apache.org by "Rafael RENAUDIN-AVINO (JIRA)" <ji...@apache.org> on 2019/02/18 14:50:00 UTC
[jira] [Comment Edited] (SPARK-26881) Scaling issue with Gramian
computation for RowMatrix: too many results sent to driver
[ https://issues.apache.org/jira/browse/SPARK-26881?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16771138#comment-16771138 ]
Rafael RENAUDIN-AVINO edited comment on SPARK-26881 at 2/18/19 2:49 PM:
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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...
As for testing, It worked well for my use case (heuristic yields depth 4 in my case). I'll try testing at scale with spark 2.3.3 but I can't guarantee I'll be able to, as the cluster I'm working on has limited spark profiles supported. Will try though :)
was (Author: gagafunctor):
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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