yinli-systems commented on code in PR #25300:
URL: https://github.com/apache/datafusion/pull/25300#discussion_r4039647809
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datafusion/functions-nested/src/set_ops.rs:
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@@ -354,26 +366,18 @@ fn generic_set_lists<OffsetSize: OffsetSizeTrait>(
// Normalize -0.0 → +0.0 so RowConverter (which uses IEEE 754 totalOrder
// and treats ±0 as distinct) groups them together. Use the normalized
// arrays for both row conversion and the final output values.
- let l_values_norm = normalize_float_zero(l.values());
- let r_values_norm = normalize_float_zero(r.values());
-
- // Only convert the visible portion of the values array. For sliced
- // ListArrays, values() returns the full underlying array but only
- // elements between the first and last offset are referenced.
let l_first = l.offsets()[0].as_usize();
let l_len = l.offsets()[l.len()].as_usize() - l_first;
- let l_values = l_values_norm.slice(l_first, l_len);
- let rows_l = converter.convert_columns(&[Arc::clone(&l_values)])?;
+ let l_values_norm = normalize_visible_values(l);
+ let rows_l = converter.convert_columns(&[Arc::clone(&l_values_norm)])?;
- let r_first = r.offsets()[0].as_usize();
- let r_len = r.offsets()[r.len()].as_usize() - r_first;
- let r_values = r_values_norm.slice(r_first, r_len);
- let rows_r = converter.convert_columns(&[Arc::clone(&r_values)])?;
+ let r_values_norm = normalize_visible_values(r);
+ let rows_r = converter.convert_columns(&[Arc::clone(&r_values_norm)])?;
// Indices from the row converter are 0-based in the per-side slice;
// concatenating those same slices lets indices map directly into the
// combined values array.
- let combined_values = concat(&[l_values.as_ref(), r_values.as_ref()])?;
+ let combined_values = concat(&[l_values_norm.as_ref(),
r_values_norm.as_ref()])?;
let r_offset = l_len;
Review Comment:
Applied as suggested in 800a042cd — the offset now comes directly from
`l_values_norm.len()`, and the redundant offset calculations are gone. All 128
nested-function tests and full all-target/all-feature Clippy pass.
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