Dandandan commented on code in PR #23458:
URL: https://github.com/apache/datafusion/pull/23458#discussion_r3563460572
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datafusion/functions/src/math/nanvl.rs:
##########
@@ -148,46 +152,81 @@ fn scalar_is_nan(scalar: &ScalarValue) -> bool {
/// - otherwise -> output is x (which may itself be NULL)
fn nanvl(args: &[ArrayRef]) -> Result<ArrayRef> {
match args[0].data_type() {
- Float64 => {
- let x = args[0].as_primitive::<Float64Type>();
- let y = args[1].as_primitive::<Float64Type>();
- let result: Float64Array = x
- .iter()
- .zip(y.iter())
- .map(|(x_value, y_value)| match x_value {
- Some(x_value) if x_value.is_nan() => y_value,
- _ => x_value,
- })
- .collect();
- Ok(Arc::new(result) as ArrayRef)
- }
- Float32 => {
- let x = args[0].as_primitive::<Float32Type>();
- let y = args[1].as_primitive::<Float32Type>();
- let result: Float32Array = x
+ Float64 => Ok(Arc::new(nanvl_impl::<Float64Type>(
+ args[0].as_primitive(),
+ args[1].as_primitive(),
+ ))),
+ Float32 => Ok(Arc::new(nanvl_impl::<Float32Type>(
+ args[0].as_primitive(),
+ args[1].as_primitive(),
+ ))),
+ Float16 => Ok(Arc::new(nanvl_impl::<Float16Type>(
+ args[0].as_primitive(),
+ args[1].as_primitive(),
+ ))),
+ other => exec_err!("Unsupported data type {other:?} for function
nanvl"),
+ }
+}
+
+/// Element-wise `nanvl`: selects `y[i]` where `x[i]` is `NaN`, otherwise
`x[i]`
+/// (a null `x` selects `x`, i.e. propagates null).
+///
+/// This produces output identical to collecting an iterator of `Option`s but
+/// splits out a null-free fast path that iterates the raw value slices,
+/// skipping per-element validity checks and `Option` handling. The null-aware
+/// path builds its null buffer lazily via [`NullBufferBuilder`] exactly as the
+/// `FromIterator` implementation does, so the result is byte-for-byte the
same.
Review Comment:
```suggestion
/// path builds its null buffer lazily via [`NullBufferBuilder`].
```
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