universalmind303 commented on code in PR #8985:
URL: https://github.com/apache/arrow-datafusion/pull/8985#discussion_r1484906473
##########
datafusion/expr/src/udf.rs:
##########
@@ -249,6 +261,22 @@ pub trait ScalarUDFImpl: Debug + Send + Sync {
/// the arguments
fn return_type(&self, arg_types: &[DataType]) -> Result<DataType>;
+ /// What [`DataType`] will be returned by this function, given the types of
+ /// the expr arguments
+ fn return_type_from_exprs(
Review Comment:
Hmm yeah @yyy1000 you still run into the same error then.
I'm wondering if it'd be easiest to just change the type signature on
`ExprSchemable` to be generic over the trait instead of the functions.
```rs
pub trait ExprSchemable<S: ExprSchema> {
/// given a schema, return the type of the expr
fn get_type(&self, schema: &S) -> Result<DataType>;
/// given a schema, return the nullability of the expr
fn nullable(&self, input_schema: &S) -> Result<bool>;
/// given a schema, return the expr's optional metadata
fn metadata(&self, schema: &S) -> Result<HashMap<String, String>>;
/// convert to a field with respect to a schema
fn to_field(&self, input_schema: &DFSchema) -> Result<DFField>;
/// cast to a type with respect to a schema
fn cast_to(self, cast_to_type: &DataType, schema: &S) -> Result<Expr>;
}
impl ExprSchemable<DFSchema> for Expr {
//...
}
```
then the trait can just go back to the original implementation you had using
`&DFSchema`
```rs
fn return_type_from_exprs(
&self,
arg_exprs: &[Expr],
schema: &DFSchema,
) -> Result<DataType> {
let arg_types = arg_exprs
.iter()
.map(|e| e.get_type(schema))
.collect::<Result<Vec<_>>>()?;
self.return_type(&arg_types)
}
```
I tried this locally and was able to get things to compile locally, and was
able to implement a udf using the trait.
It does make it a little less flexible as it's expecting a `DFSchema`, but i
_think_ thats ok?
I think the only other approach would be to make `ScalarUDFImpl` dynamic
over `<S: ExprSchema>`, but I feel like that's much less ideal than just using
a concrete type.
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