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https://issues.apache.org/jira/browse/SPARK-12957?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15866239#comment-15866239
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Nick Dimiduk commented on SPARK-12957:
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[~sameerag] thanks for the comment. From a naive scan of the tickets, I believe
I am seeing the benefits of SPARK-13871 in that a {{IsNotNull}} constraint is
applied from the names of the join columns. However, I don't see the boon of
SPARK-13789, specifically the {{a = 5, a = b}} mentioned in the description. My
query is a join between a very small relation (100's of rows) and a very large
one (10's of billions). I've hinted the planner to broadcast the smaller table,
which it honors. After SPARK-13789, I expected to see the join column values
pushed down as well. This is not the case.
Any tips on debugging this further? I've set breakpoints in the
{{RelationProvider}} implementation and see that it's only receiving the
{{IsNotNull}} filters, nothing further from the planner.
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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