EmilyMatt commented on code in PR #18446:
URL: https://github.com/apache/datafusion/pull/18446#discussion_r2499666263
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datafusion/physical-expr-common/src/utils.rs:
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@@ -91,6 +92,26 @@ pub fn scatter(mask: &BooleanArray, truthy: &dyn Array) ->
Result<ArrayRef> {
Ok(make_array(data))
}
+/// Evaluates expressions against a record batch.
+/// This will convert the resulting ColumnarValues to ArrayRefs,
+/// duplicating any ScalarValues that may have been returned.
+/// This function does not perform any checks that the resulting arrays
+/// have the same length as the record batch.
+// NOTE: This means that this should not be called on expressions that may
return
+// arrays of different lengths (e.g. filtering/flattening etc).
+// Also, consider avoiding this if your code can use optimizations for
ScalarValues.
+#[inline]
+pub fn evaluate_expressions_to_arrays(
+ exprs: &[Arc<dyn PhysicalExpr>],
+ batch: &RecordBatch,
+) -> Result<Vec<ArrayRef>> {
+ let num_rows = batch.num_rows();
+ exprs
+ .iter()
+ .map(|e| e.evaluate(batch).and_then(|col| col.into_array(num_rows)))
+ .collect::<Result<Vec<ArrayRef>>>()
+}
Review Comment:
I have decided to just add the required functions in ColumnarValue, as well
as make this function more generic, so it can be used directly with iterators
such as the group_by and nulls exprs case
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