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Posted to github@arrow.apache.org by GitBox <gi...@apache.org> on 2021/09/01 09:36:29 UTC

[GitHub] [arrow-datafusion] rdettai edited a comment on issue #962: Moving cost based optimizations to physical planning

rdettai edited a comment on issue #962:
URL: https://github.com/apache/arrow-datafusion/issues/962#issuecomment-909268730


   As suggested by @Dandandan, we might also want to consider the fact that the statistics can be updated at runtime (like Spark AQE). In Ballista, an execution plan for a stage that takes a shuffle as input might be re-optimized according to the statistics of the shuffle boundary. For instance, this might change the optimal build/probe order of the tables for a join that has the shuffle boundary as one of its inputs.


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