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From "Nick Dimiduk (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-12957) Derive and propagate data constrains in logical plan
Date Tue, 14 Feb 2017 01:01:38 GMT

    [ https://issues.apache.org/jira/browse/SPARK-12957?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15864796#comment-15864796
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Nick Dimiduk commented on SPARK-12957:
--------------------------------------

I'm trying to understand the current state of SPARK-13219/SPARK-12532, which it seems are
deferring to this issue. I see all subtasks here have been fixedFor in 2.0; is there further
work to be done on this ticket? How does the sum of this work relate back to the predicate
pushdown join optimization described in SPARK-13219. Basically, I'm trying to determine if
I get this very useful enhancement by upgrading to 2.x. Thanks a lot!

> Derive and propagate data constrains in logical plan 
> -----------------------------------------------------
>
>                 Key: SPARK-12957
>                 URL: https://issues.apache.org/jira/browse/SPARK-12957
>             Project: Spark
>          Issue Type: New Feature
>          Components: SQL
>            Reporter: Yin Huai
>            Assignee: Sameer Agarwal
>         Attachments: ConstraintPropagationinSparkSQL.pdf
>
>
> Based on the semantic of a query plan, we can derive data constrains (e.g. if a filter
defines {{a > 10}}, we know that the output data of this filter satisfy the constrain of
{{a > 10}} and {{a is not null}}). We should build a framework to derive and propagate
constrains in the logical plan, which can help us to build more advanced optimizations.



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