neilconway commented on code in PR #20336:
URL: https://github.com/apache/datafusion/pull/20336#discussion_r2806788520
##########
datafusion/functions/src/macros.rs:
##########
@@ -393,37 +394,76 @@ macro_rules! make_math_binary_udf {
&self,
args: ScalarFunctionArgs,
) -> Result<ColumnarValue> {
- let args = ColumnarValue::values_to_arrays(&args.args)?;
- let arr: ArrayRef = match args[0].data_type() {
- DataType::Float64 => {
- let y = args[0].as_primitive::<Float64Type>();
- let x = args[1].as_primitive::<Float64Type>();
- let result = arrow::compute::binary::<_, _, _,
Float64Type>(
- y,
- x,
- |y, x| f64::$BINARY_FUNC(y, x),
- )?;
- Arc::new(result) as _
+ let return_type = args.return_type().clone();
Review Comment:
Can we refactor this to avoid doing the `clone()` unconditionally, even for
non-NULL inputs?
##########
datafusion/functions/src/macros.rs:
##########
@@ -393,37 +394,76 @@ macro_rules! make_math_binary_udf {
&self,
args: ScalarFunctionArgs,
) -> Result<ColumnarValue> {
- let args = ColumnarValue::values_to_arrays(&args.args)?;
- let arr: ArrayRef = match args[0].data_type() {
- DataType::Float64 => {
- let y = args[0].as_primitive::<Float64Type>();
- let x = args[1].as_primitive::<Float64Type>();
- let result = arrow::compute::binary::<_, _, _,
Float64Type>(
- y,
- x,
- |y, x| f64::$BINARY_FUNC(y, x),
- )?;
- Arc::new(result) as _
+ let return_type = args.return_type().clone();
+ let [y, x] = take_function_args(self.name(), args.args)?;
+
+ match (y, x) {
+ (
+ ColumnarValue::Scalar(y_scalar),
+ ColumnarValue::Scalar(x_scalar),
+ ) => {
+ if y_scalar.is_null() || x_scalar.is_null() {
Review Comment:
Might be a bit cleaner to move this into the `match` below, since we're
already matching on (y_scalar, x_scalar).
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