jayzhan211 commented on code in PR #10098:
URL: https://github.com/apache/datafusion/pull/10098#discussion_r1575897841
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datafusion/physical-expr/src/scalar_function.rs:
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@@ -144,24 +144,11 @@ impl PhysicalExpr for ScalarFunctionExpr {
fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> {
// evaluate the arguments, if there are no arguments we'll instead
pass in a null array
// indicating the batch size (as a convention)
- let inputs = match (
- self.args.is_empty(),
- self.name.parse::<BuiltinScalarFunction>(),
- ) {
- // MakeArray support zero argument but has the different behavior
from the array with one null.
- (true, Ok(scalar_fun))
- if scalar_fun
- .signature()
- .type_signature
- .supports_zero_argument() =>
- {
- vec![ColumnarValue::create_null_array(batch.num_rows())]
- }
+ let inputs = match self.args.is_empty() {
// If the function supports zero argument, we pass in a null array
indicating the batch size.
// This is for user-defined functions.
- (true, Err(_))
- if self.supports_zero_argument && self.name != "make_array" =>
- {
+ // MakeArray support zero argument but has the different behavior
from the array with one null.
Review Comment:
I think we should even return empty vec instead of null array here. Those
logic should be handled in each function case by case to avoid adding specific
assumptions to all the function.
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