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https://issues.apache.org/jira/browse/SPARK-23128?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16553700#comment-16553700
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Carson Wang commented on SPARK-23128:
-------------------------------------
[~tgraves], We have split the code into 7 PRs based on Spark 2.3 and they are
all available at [https://github.com/Intel-bigdata/spark-adaptive/pulls]
(AE2.3-01 to AE2.3-07). Many companies have evaluated this feature including
Baidu, Alibaba, JD.com, eBay etc and have very good feedback. Many of them also
have merged the feature into their own Spark version. However there is little
progress for contributing this to upstream. Any suggestion for moving this
forward?
> 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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