radmirnovii opened a new pull request, #24586:
URL: https://github.com/apache/datafusion/pull/24586
## Which issue does this PR close?
Refs #20935 and #19458.
## Rationale for this change
Scalar functions over dictionary-encoded columns have no benchmark coverage:
neither the hand-written dictionary arms (e.g. `reverse`'s, from #23930) nor
the cast away from the encoding that every function without preservation pays
(e.g. `encode`). This lands the baseline first, so follow-up work
generalizing
the arms shows its delta on stable benchmark ids.
## What changes are included in this PR?
One criterion benchmark:
- `reverse` over a dictionary of its own per batch (`cold`) and one shared
across batches the way a Parquet column chunk delivers them (`warm`) —
today
the two cost the same, since an arm cannot reuse anything across batches;
- `cast_away`: the dictionary cast to its value type and the function called
once per row — what any function without encoding preservation pays;
- `flat`: the same rows with no encoding anywhere, as a bound.
`encode` cannot be called over a dictionary today, so it has no
dictionary-typed groups yet.
## Are these changes tested?
`cargo bench -p datafusion-physical-expr --bench scalar_function_dictionary`
runs clean.
## Are there any user-facing changes?
No.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
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