findepi commented on code in PR #13717:
URL: https://github.com/apache/datafusion/pull/13717#discussion_r1901954669
##########
datafusion/core/src/catalog_common/information_schema.rs:
##########
@@ -406,6 +406,7 @@ fn get_udf_args_and_return_types(
.into_iter()
.map(|arg_types| {
// only handle the function which implemented
[`ScalarUDFImpl::return_type`] method
+ #[allow(deprecated)]
Review Comment:
> For example, if we have column `c`, we need `schema` to know it's data
type is `Int32`. With another `schema`, it may becomes `Int64`.
That's a good point.
The other way to view this is -- what does an Expr represent?
is it a syntactical expression (doesn't know types, can be applied to
multiple different inputs), or a semantical expression (anchored in the
evaluation context, knows types).
The SQL handling process goes from syntax to semantics, and expressions
(Expr) built in dataframe layer are definitely syntactical, not semantical.
This may be a challenge for
https://github.com/apache/datafusion/issues/13825, but less so for
https://github.com/apache/datafusion/issues/12604. If we have new IR with
separate Expr types, it won't be used in contexts where we need syntactical
expressions.
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