rohitrastogi commented on code in PR #399:
URL: https://github.com/apache/datafusion-comet/pull/399#discussion_r1593107084
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core/src/execution/datafusion/expressions/cast.rs:
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@@ -232,6 +232,189 @@ macro_rules! cast_int_to_int_macro {
}};
}
+// When Spark casts to Byte/Short Types, it does not cast directly to
Byte/Short.
Review Comment:
The float and decimal macros have a similar shape, so I considered
consolidating them. One way to consolidate them is to generalize the macros to
accept closures to handle the differences between the decimal and float casts.
I think the generalized macro would have to accept closures for the following
functional differences:
1) determining whether there is an overflow
2) generating the appropriate overflow error
3) transforming the downcasted value (truncating, casting) before entering
the match statement
I tried this, but the code seemed more opaque and verbose in
spark_cast_nonintegral_numeric_to_integral() than the current approach.
Please let me know if you see other ways to refactor to make this more
concise.
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