jayzhan211 commented on code in PR #10193:
URL: https://github.com/apache/datafusion/pull/10193#discussion_r1580746996
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datafusion/physical-expr/src/scalar_function.rs:
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@@ -142,25 +137,21 @@ 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() {
- // If the function supports zero argument, we pass in a null array
indicating the batch size.
- // This is for user-defined functions.
- // MakeArray support zero argument but has the different behavior
from the array with one null.
- true if self.supports_zero_argument && self.name != "make_array"
=> {
- vec![ColumnarValue::create_null_array(batch.num_rows())]
- }
- _ => self
- .args
- .iter()
- .map(|e| e.evaluate(batch))
- .collect::<Result<Vec<_>>>()?,
- };
+ let inputs = self
+ .args
+ .iter()
+ .map(|e| e.evaluate(batch))
+ .collect::<Result<Vec<_>>>()?;
// evaluate the function
match self.fun {
- ScalarFunctionDefinition::UDF(ref fun) => fun.invoke(&inputs),
+ ScalarFunctionDefinition::UDF(ref fun) => {
+ if fun.support_randomness() {
+ fun.invoke_no_args(batch.num_rows())
Review Comment:
I forgot that `make_array` accept any number of args including 0, so we need
to somehow specialize the function
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