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https://issues.apache.org/jira/browse/SPARK-23128?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Li Yuanjian updated SPARK-23128:
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Attachment: AdaptiveExecutioninBaidu.pdf
> A new approach to do adaptive execution in Spark SQL
> ----------------------------------------------------
>
> Key: SPARK-23128
> URL: https://issues.apache.org/jira/browse/SPARK-23128
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Carson Wang
> Priority: Major
> Attachments: AdaptiveExecutioninBaidu.pdf
>
>
> SPARK-9850 proposed the basic idea of adaptive execution in Spark. In
> DAGScheduler, a new API is added to support submitting a single map stage.
> The current implementation of adaptive execution in Spark SQL supports
> changing the reducer number at runtime. An Exchange coordinator is used to
> determine the number of post-shuffle partitions for a stage that needs to
> fetch shuffle data from one or multiple stages. The current implementation
> adds ExchangeCoordinator while we are adding Exchanges. However there are
> some limitations. First, it may cause additional shuffles that may decrease
> the performance. We can see this from EnsureRequirements rule when it adds
> ExchangeCoordinator. Secondly, it is not a good idea to add
> ExchangeCoordinators while we are adding Exchanges because we don’t have a
> global picture of all shuffle dependencies of a post-shuffle stage. I.e. for
> 3 tables’ join in a single stage, the same ExchangeCoordinator should be used
> in three Exchanges but currently two separated ExchangeCoordinator will be
> added. Thirdly, with the current framework it is not easy to implement other
> features in adaptive execution flexibly like changing the execution plan and
> handling skewed join at runtime.
> We'd like to introduce a new way to do adaptive execution in Spark SQL and
> address the limitations. The idea is described at
> [https://docs.google.com/document/d/1mpVjvQZRAkD-Ggy6-hcjXtBPiQoVbZGe3dLnAKgtJ4k/edit?usp=sharing]
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