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From "Rafael RENAUDIN-AVINO (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-26881) Scaling issue with Gramian computation for RowMatrix: too many results sent to driver
Date Thu, 14 Feb 2019 18:09:00 GMT
Rafael RENAUDIN-AVINO created SPARK-26881:
---------------------------------------------

             Summary: 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


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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