jayzhan211 commented on code in PR #13756:
URL: https://github.com/apache/datafusion/pull/13756#discussion_r1887765061
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datafusion/functions-nested/src/extract.rs:
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@@ -993,3 +993,84 @@ where
let data = mutable.freeze();
Ok(arrow::array::make_array(data))
}
+
+#[cfg(test)]
+mod tests {
+ use super::array_element_udf;
+ use arrow_schema::{DataType, Field};
+ use datafusion_common::{Column, DFSchema, ScalarValue};
+ use datafusion_expr::expr::ScalarFunction;
+ use datafusion_expr::{cast, Expr, ExprSchemable};
+ use std::collections::HashMap;
+
+ #[test]
+ fn test_array_element_return_type() {
+ let complex_type = DataType::FixedSizeList(
Review Comment:
> the function arguments should already be of the right coerced type
> I don't know the context of why we needed to apply coercion rules in the
first place
The reason is because we can't guarantee the input is already coerced.
To determine the return type of a function for a given set of inputs, we
follow these steps:
1. Input Validation: Check if the number of inputs is correct and whether
their types match the expected types.
2. Type Coercion: If the input types don't match exactly, attempt to coerce
them into compatible types.
3. Return Type Decision: Once coercion is complete (if applicable), decide
the return type based on the resulting input types.
That is why we have coercion in `get_type` for `return_type`. We can move
out the coercion in `get_type` to `ScalarFunction::new_udf`
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