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https://issues.apache.org/jira/browse/SPARK-9850?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16561326#comment-16561326
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Carson Wang commented on SPARK-9850:
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We have a new proposal and implementation for Spark SQL adaptive execution
discussed in SPARK-23128. Optimizing join strategy at run time and handling
skewed join are also supported. The full code is also available at
[https://github.com/Intel-bigdata/spark-adaptive|https://github.com/Intel-bigdata/spark-adaptive/].
> Adaptive execution in Spark
> ---------------------------
>
> Key: SPARK-9850
> URL: https://issues.apache.org/jira/browse/SPARK-9850
> Project: Spark
> Issue Type: Epic
> Components: Spark Core, SQL
> Reporter: Matei Zaharia
> Assignee: Yin Huai
> Priority: Major
> Attachments: AdaptiveExecutionInSpark.pdf
>
>
> Query planning is one of the main factors in high performance, but the
> current Spark engine requires the execution DAG for a job to be set in
> advance. Even with cost-based optimization, it is hard to know the behavior
> of data and user-defined functions well enough to always get great execution
> plans. This JIRA proposes to add adaptive query execution, so that the engine
> can change the plan for each query as it sees what data earlier stages
> produced.
> We propose adding this to Spark SQL / DataFrames first, using a new API in
> the Spark engine that lets libraries run DAGs adaptively. In future JIRAs,
> the functionality could be extended to other libraries or the RDD API, but
> that is more difficult than adding it in SQL.
> I've attached a design doc by Yin Huai and myself explaining how it would
> work in more detail.
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