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https://issues.apache.org/jira/browse/SPARK-21591?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16110169#comment-16110169
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Liang-Chi Hsieh edited comment on SPARK-21591 at 8/2/17 2:41 AM:
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The most straightforward way is similar to Dataset.foreach, i.e., do
treeAggregate on the rdd of Dataset.
Of course it cannot enjoy Tungsten execution engine.
was (Author: viirya):
The most straightforward way is similar to Dataset.foreach, i.e., do
treeAggregate on the rdd of Dataset.
Of course it can enjoy Tungsten execution engine.
> 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
>
> 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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