sunchao commented on code in PR #7381:
URL: https://github.com/apache/datafusion-comet/pull/7381#discussion_r4437121055
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native/spark-expr/src/struct_funcs/get_struct_field.rs:
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@@ -123,6 +125,20 @@ impl PhysicalExpr for GetStructField {
}
}
+/// The scalar that a one-row array holds. A list wraps the array as is:
`try_from_array` rebuilds
+/// a list's element field from its name and nullability, which drops metadata
such as a Variant
+/// element's extension type, so the scalar's type would no longer match
`data_type`.
+fn single_row_scalar(array: ArrayRef) -> DataFusionResult<ScalarValue> {
+ Ok(match array.data_type() {
+ DataType::List(_) =>
ScalarValue::List(Arc::new(array.as_list::<i32>().clone())),
+ DataType::LargeList(_) =>
ScalarValue::LargeList(Arc::new(array.as_list::<i64>().clone())),
+ DataType::FixedSizeList(_, _) => {
+
ScalarValue::FixedSizeList(Arc::new(array.as_fixed_size_list().clone()))
+ }
+ _ => ScalarValue::try_from_array(&array, 0)?,
Review Comment:
**Preserve the element field for `ListView` and `LargeListView` too.** I
evaluated `GetStructField` on a one-row `Literal(Struct)` whose list-view
element had `ARROW:extension:name` metadata. For both offset widths,
`expr.data_type()` retained the metadata but `expr.evaluate()` returned a
scalar without it. The base expression preserved it on the same input.
DataFusion 55.2 rebuilds these two views in `try_from_array` just as it does
the other list types. Wrapping their one-row arrays directly and extending the
existing metadata test to both widths would keep the declared and evaluated
types consistent. I reproduced this through the native `PhysicalExpr` API.
Spark’s standard `ArrayType` maps to `List`, and I found no Spark SQL producer
for this case.
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