unikdahal commented on code in PR #5414:
URL: https://github.com/apache/datafusion-comet/pull/5414#discussion_r3836434349
##########
native/spark-expr/src/json_funcs/from_json.rs:
##########
@@ -180,11 +180,15 @@ fn json_string_to_struct(arr: &Arc<dyn Array>, schema:
&DataType) -> Result<Arra
.map(finish_builder)
.collect::<Result<Vec<_>>>()?;
let null_buffer = NullBuffer::from(struct_nulls);
- Ok(Arc::new(StructArray::new(
- fields.clone(),
- arrays,
- Some(null_buffer),
- )))
+ // `StructArray::new` derives its length from the first child array, so it
panics when
+ // `fields` is empty (a legitimate zero-field target schema, e.g.
`from_json(_, 'struct<>')`).
+ // `new_empty_fields` takes the length explicitly instead.
+ let struct_array: ArrayRef = if fields.is_empty() {
+ Arc::new(StructArray::new_empty_fields(num_rows, Some(null_buffer)))
Review Comment:
Blank/whitespace input now short-circuits to NULL before parsing, matching
SPARK-19543, separate from non-blank malformed input (still PERMISSIVE
null-fields). New regression rows cover blank, whitespace, non-blank-malformed,
and SQL NULL, checked against real Spark output.
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