theirix commented on code in PR #17023:
URL: https://github.com/apache/datafusion/pull/17023#discussion_r2331486576
##########
datafusion/functions/src/math/log.rs:
##########
@@ -121,55 +198,68 @@ impl ScalarUDFImpl for LogFunc {
fn invoke_with_args(&self, args: ScalarFunctionArgs) ->
Result<ColumnarValue> {
let args = ColumnarValue::values_to_arrays(&args.args)?;
- let mut base = ColumnarValue::Scalar(ScalarValue::Float32(Some(10.0)));
-
- let mut x = &args[0];
- if args.len() == 2 {
- x = &args[1];
- base = ColumnarValue::Array(Arc::clone(&args[0]));
- }
- // note in f64::log params order is different than in sql. e.g in sql
log(base, x) == f64::log(x, base)
- let arr: ArrayRef = match args[0].data_type() {
- DataType::Float64 => match base {
- ColumnarValue::Scalar(ScalarValue::Float32(Some(base))) => {
- Arc::new(x.as_primitive::<Float64Type>().unary::<_,
Float64Type>(
- |value: f64| f64::log(value, base as f64),
- ))
- }
- ColumnarValue::Array(base) => {
- let x = x.as_primitive::<Float64Type>();
- let base = base.as_primitive::<Float64Type>();
- let result = arrow::compute::binary::<_, _, _,
Float64Type>(
- x,
- base,
- f64::log,
- )?;
- Arc::new(result) as _
- }
- _ => {
- return exec_err!("log function requires a scalar or array
for base")
- }
- },
-
- DataType::Float32 => match base {
- ColumnarValue::Scalar(ScalarValue::Float32(Some(base))) =>
Arc::new(
- x.as_primitive::<Float32Type>()
- .unary::<_, Float32Type>(|value: f32| f32::log(value,
base)),
+ let (base, value) = if args.len() == 2 {
+ // note in f64::log params order is different than in sql. e.g in
sql log(base, x) == f64::log(x, base)
+ (ColumnarValue::Array(Arc::clone(&args[0])), &args[1])
+ } else {
+ // log(num) - assume base is 10
+ let ret_type = if args[0].data_type().is_null() {
+ &DataType::Float64
+ } else {
+ args[0].data_type()
+ };
Review Comment:
The handling of an additional optional argument makes it complicated and
different from `return_type`. I don't see a lot of UDFs with variable-number
arguments.
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