jorgecarleitao commented on a change in pull request #303:
URL: https://github.com/apache/arrow-datafusion/pull/303#discussion_r630285020
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File path: datafusion/src/physical_plan/functions.rs
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@@ -1373,20 +1370,26 @@ impl PhysicalExpr for ScalarFunctionExpr {
}
fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> {
- // evaluate the arguments
- let inputs = self
- .args
- .iter()
- .map(|e| e.evaluate(batch))
- .collect::<Result<Vec<_>>>()?;
+ // evaluate the arguments, if there are no arguments we'll instead
pass in a null array of
+ // batch size (as a convention)
+ let inputs = match self.args.len() {
+ 0 => vec![ColumnarValue::Array(Arc::new(NullArray::new(
Review comment:
Note that `NullArray` is composed by zero buffers, zero childs, no
validity and one datatype, so the cost to instantiate it is really small. The
advantage over a `ScalarValue` is that the semantics of getting a length are
preserved: use `array.len()` as any other array.
I am not married with any; was just trying to think about this from a
documentations' perspective:
> We support zero-argument UDFs. They MUST be declared as accepting zero
arguments and the function signature MUST be a single argument. DataFusion will
pass an `Array` to it, from which you can retrieve its length via
`Array::len()`. The function MUST return an array whose number of rows equals
the length of the array.
If we pass a scalar of any type, if the evaluation is distributed, I believe
that we will have to serialize `Scalar -> Array` in Ballista.
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