andygrove opened a new pull request, #3224:
URL: https://github.com/apache/datafusion-comet/pull/3224
## Summary
Optimizes struct field processing in native shuffle by using field-major
instead of row-major order. This moves type dispatch from O(rows × fields) to
O(fields), eliminating per-row type matching overhead.
**The problem:**
Previously, for each row we iterated over all fields and called
`append_field()` which did a type match for EVERY field in EVERY row. For a
struct with N fields and M rows, that's N×M type matches where the types never
change.
```rust
// Old approach - row-major order
for row in rows { // M rows
for (idx, field) in fields.iter() { // N fields
append_field(field.data_type(), ...); // Type match happens here
}
}
// Total: M × N type matches
```
**The solution:**
Field-major processing with two passes:
1. First pass: Loop over rows, build struct validity
2. Second pass: For each field, get typed builder once, then process all
rows for that field
```rust
// New approach - field-major order
// Pass 1: Build struct validity
for row in rows {
struct_builder.append(is_valid);
}
// Pass 2: Process fields
for (field_idx, field) in fields.iter() { // N fields
match field.data_type() { // Type match ONCE per field
DataType::Int32 => {
let builder =
struct_builder.field_builder::<Int32Builder>(field_idx);
for row in rows { // M rows
builder.append_value(...); // No type match
}
}
// ... other types
}
}
// Total: N type matches
```
This reduces type dispatch from O(rows × fields) to O(fields).
For complex nested types (struct, list, map), falls back to existing
`append_field` since they have their own recursive processing logic.
## Test plan
- [x] All Rust tests pass (115 tests)
- [x] Native shuffle tests pass (16 tests)
- [x] Fuzz tests pass (120 tests)
- [x] Clippy clean
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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