alamb commented on code in PR #2810:
URL: https://github.com/apache/arrow-datafusion/pull/2810#discussion_r910507550
##########
datafusion/physical-expr/src/expressions/case.rs:
##########
@@ -228,7 +238,23 @@ impl PhysicalExpr for CaseExpr {
}
fn data_type(&self, input_schema: &Schema) -> Result<DataType> {
- self.when_then_expr[0].1.data_type(input_schema)
+ // since all then results have the same data type, we can choose any
one as the
+ // return data type except for the null.
+ let mut data_type = DataType::Null;
+ for i in 0..self.when_then_expr.len() {
+ data_type = self.when_then_expr[i].1.data_type(input_schema)?;
+ if !data_type.equals_datatype(&DataType::Null) {
+ break;
+ }
+ }
+ // if all then results are null, we use data type of else expr instead
if possible.
Review Comment:
if they are all null, the output type is also null I would think.
##########
datafusion/physical-expr/src/expressions/case.rs:
##########
@@ -138,7 +138,12 @@ impl CaseExpr {
let then_value = self.when_then_expr[i]
.1
.evaluate_selection(batch, &when_match)?;
- let then_value = then_value.into_array(batch.num_rows());
+ let then_value = match then_value {
+ ColumnarValue::Scalar(value) if value.is_null() => {
+ new_null_array(&return_type, batch.num_rows())
+ }
Review Comment:
I don't understand why this code is needed -- I would have expected that
`then_value.into_array()` would have worked
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