sunchao opened a new pull request #29565:
URL: https://github.com/apache/spark/pull/29565
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### What changes were proposed in this pull request?
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Currently, in cases like the following:
```sql
SELECT * FROM t WHERE age < 40
```
where `age` is of short type, Spark won't be able to simplify this and can
only generate filter `cast(age, int) < 40`. This won't get pushed down to
datasources and therefore is not optimized.
This PR proposes a optimizer rule to improve this when the following
constraints are satisfied:
- input expression is binary comparisons when one side is a cast operation
and another is a literal.
- both the cast child expression and literal are of integral type (i.e.,
byte, short, int or long)
When this is true, it tries to do several optimizations to either simplify
the expression or move the cast to the literal side, so
result filter for the above case becomes `age < cast(40 as smallint)`. This
is better since the cast can be optimized away later and the filter can be
pushed down to data sources.
The approach this PR uses references a similar effort in Presto
(https://prestosql.io/blog/2019/05/21/optimizing-the-casts-away.html). Here we
only handles integral types but plan to extend to other types as follow-ups.
### Why are the changes needed?
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As mentioned in the previous section, when cast is not optimized, it cannot
be pushed down to data sources which can lead
to unnecessary IO and therefore longer job time and waste of resources. This
helps to improve that.
### Does this PR introduce _any_ user-facing change?
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No.
### How was this patch tested?
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Added unit tests for both the optimizer rule and filter pushdown on
datasource level for both Orc and Parquet.
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