[GitHub] [arrow] andygrove commented on a change in pull request #7967: ARROW-9751: [Rust] [DataFusion] Allow UDFs to accept multiple data types per argument

[GitBox <gi...@apache.org>]

andygrove commented on a change in pull request #7967:
URL: https://github.com/apache/arrow/pull/7967#discussion_r471146144



##########
File path: rust/datafusion/src/execution/physical_plan/math_expressions.rs
##########
@@ -20,36 +20,62 @@
 use crate::error::ExecutionError;
 use crate::execution::physical_plan::udf::ScalarFunction;
 
-use arrow::array::{Array, ArrayRef, Float64Array, Float64Builder};
-use arrow::datatypes::{DataType, Field};
+use arrow::array::{Array, ArrayRef};
+use arrow::array::{Float32Array, Float64Array};
+use arrow::datatypes::DataType;
 
 use std::sync::Arc;
 
+macro_rules! compute_op {
+    ($ARRAY:expr, $FUNC:ident, $TYPE:ident) => {{
+        let mut builder = <$TYPE>::builder($ARRAY.len());
+        for i in 0..$ARRAY.len() {
+            if $ARRAY.is_null(i) {
+                builder.append_null()?;
+            } else {
+                builder.append_value($ARRAY.value(i).$FUNC())?;
+            }
+        }
+        Ok(Arc::new(builder.finish()))
+    }};
+}
+
+macro_rules! downcast_compute_op {
+    ($ARRAY:expr, $NAME:expr, $FUNC:ident, $TYPE:ident) => {{
+        let n = $ARRAY.as_any().downcast_ref::<$TYPE>();
+        match n {
+            Some(array) => compute_op!(array, $FUNC, $TYPE),
+            _ => Err(ExecutionError::General(format!(
+                "Invalid data type for {}",
+                $NAME
+            ))),
+        }
+    }};
+}
+
+macro_rules! unary_primitive_array_op {
+    ($ARRAY:expr, $NAME:expr, $FUNC:ident) => {{
+        match ($ARRAY).data_type() {
+            DataType::Float32 => downcast_compute_op!($ARRAY, $NAME, $FUNC, Float32Array),
+            DataType::Float64 => downcast_compute_op!($ARRAY, $NAME, $FUNC, Float64Array),
+            other => Err(ExecutionError::General(format!(
+                "Unsupported data type {:?} for function {}",
+                other, $NAME,
+            ))),
+        }
+    }};
+}
+
 macro_rules! math_unary_function {
     ($NAME:expr, $FUNC:ident) => {
         ScalarFunction::new(
             $NAME,
-            vec![Field::new("n", DataType::Float64, true)],
+            // order: from faster to slower
+            vec![vec![DataType::Float32], vec![DataType::Float64]],
             DataType::Float64,

Review comment:
       Should there be a relationship between argument type and return type? should `sqrt(f32)` return `f32`, and `sqrt(f64)` return `f64` for example?




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