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https://issues.apache.org/jira/browse/SPARK-9983?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Reynold Xin closed SPARK-9983.
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Resolution: Later
I'm going to close this as later, and we will come back to this after Spark 2.0.
> Local physical operators for query execution
> --------------------------------------------
>
> Key: SPARK-9983
> URL: https://issues.apache.org/jira/browse/SPARK-9983
> Project: Spark
> Issue Type: Story
> Components: SQL
> Reporter: Reynold Xin
> Assignee: Reynold Xin
>
> In distributed query execution, there are two kinds of operators:
> (1) operators that exchange data between different executors or threads:
> examples include broadcast, shuffle.
> (2) operators that process data in a single thread: examples include project,
> filter, group by, etc.
> This ticket proposes clearly differentiating them and creating local
> operators in Spark. This leads to a lot of benefits: easier to test, easier
> to optimize data exchange, better design (single responsibility), and
> potentially even having a hyper-optimized single-node version of DataFrame.
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