Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/16998
> Another issue is we actually don't need the additional constraints at
most of time. For example, if there is a constraint "a > b", and "a" is aliased
to "c" and "d". When we use this constraint in filtering, we don't need all
constraints "a > b", "c > b", "d > b". We only need "a > b" because if it is
false, it is guaranteed that all other constraints are false too.
I do not get your points. What does this mean? Constraint propagation is a
bottom up mechanism for inferring the constraints. Can you elaborate your idea
in the more formal way.
I did not read the code. Just wondering if we could miss the chance of plan
optimization after this PR? What is the negative impact, if exists?
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