kumarUjjawal commented on code in PR #19065:
URL: https://github.com/apache/datafusion/pull/19065#discussion_r2585322649
##########
datafusion/spark/src/function/math/width_bucket.rs:
##########
@@ -77,7 +123,16 @@ impl ScalarUDFImpl for SparkWidthBucket {
}
fn invoke_with_args(&self, args: ScalarFunctionArgs) ->
Result<ColumnarValue> {
- make_scalar_function(width_bucket_kern, vec![])(&args.args)
+ let [value, minv, maxv, buckets] = take_function_args(self.name(),
&args.args)?;
+
+ let arrays = vec![
+ value.to_array(args.number_rows)?,
+ minv.to_array(args.number_rows)?,
+ maxv.to_array(args.number_rows)?,
+ buckets.to_array(args.number_rows)?,
+ ];
+
+ width_bucket_kern(&arrays).map(ColumnarValue::Array)
Review Comment:
Correct me if my understanding is wrong, I only swapped out
`make_scalar_function` so I could call `take_function_args` up front, once the
UDF no longer uses Signature::user_defined, we need the shared
`take_function_args` guard to produce the standard “failed to match any
signature” planner error. If we want to keep the helper, I can move the
`take_function_args` call into the closure, but the behavior change was just to
hook into the common arity handling.
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