[jira] [Updated] (SPARK-21591) Implement treeAggregate on Dataset API

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-21591?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon updated SPARK-21591:
---------------------------------
    Labels: bulk-closed  (was: )

> Implement treeAggregate on Dataset API
> --------------------------------------
>
>                 Key: SPARK-21591
>                 URL: https://issues.apache.org/jira/browse/SPARK-21591
>             Project: Spark
>          Issue Type: Brainstorming
>          Components: SQL
>    Affects Versions: 2.2.0
>            Reporter: Yanbo Liang
>            Priority: Major
>              Labels: bulk-closed
>
> The Tungsten execution engine substantially improved the efficiency of memory and CPU for Spark application. However, in MLlib we still not migrate the internal computing workload from {{RDD}} to {{DataFrame}}.
> There are lots of blocking issues for the migration, lack of {{treeAggregate}} on {{DataFrame}} is one of them. {{treeAggregate}} is very important for MLlib algorithms, since they do aggregate on {{Vector}} which may has millions of elements. As we all know, {{RDD}} based {{treeAggregate}} reduces the aggregation time by an order of magnitude for  lots of MLlib algorithms(https://databricks.com/blog/2014/09/22/spark-1-1-mllib-performance-improvements.html).
> I open this JIRA to discuss to implement {{treeAggregate}} on {{DataFrame}} API and do the performance benchmark related issues. And I think other scenarios except for MLlib will also benefit from this improvement if we get it done.



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