Dandandan commented on a change in pull request #309:
URL: https://github.com/apache/arrow-datafusion/pull/309#discussion_r629241895
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File path: datafusion/src/physical_plan/math_expressions.rs
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@@ -17,37 +17,21 @@
//! Math expressions
-use arrow::array::{make_array, Array, ArrayData, Float32Array, Float64Array};
-use arrow::buffer::Buffer;
-use arrow::datatypes::{DataType, ToByteSlice};
-
use super::{ColumnarValue, ScalarValue};
use crate::error::{DataFusionError, Result};
-
-macro_rules! compute_op {
- ($ARRAY:expr, $FUNC:ident, $TYPE:ident) => {{
- let len = $ARRAY.len();
- let result = (0..len)
- .map(|i| $ARRAY.value(i).$FUNC() as f64)
- .collect::<Vec<f64>>();
- let data = ArrayData::new(
- DataType::Float64,
- len,
- Some($ARRAY.null_count()),
- $ARRAY.data().null_buffer().cloned(),
- 0,
- vec![Buffer::from(result.to_byte_slice())],
- vec![],
- );
- Ok(make_array(data))
- }};
-}
+use arrow::array::{Float32Array, Float64Array};
+use arrow::datatypes::DataType;
+use std::sync::Arc;
macro_rules! downcast_compute_op {
($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),
+ Some(array) => {
+ let res: $TYPE =
+ arrow::compute::kernels::arity::unary(array, |x|
x.$FUNC());
Review comment:
This avoids creating intermediate `Vec` and uses the (efficient) unary
kernel
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