kosiew opened a new pull request, #22770:
URL: https://github.com/apache/datafusion/pull/22770
## Which issue does this PR close?
* Part of #22669
## Rationale for this change
`regexp_count_inner` currently uses an 8-arm match over combinations of
scalar and array inputs for regex, start position, and flags. Many of these
branches duplicate the same logic for length validation, null handling, regex
compilation/cache lookup, and match counting.
This refactor reduces duplication and centralizes row processing while
preserving existing SQL-visible behavior, error messages, error ordering, and
regex cache usage.
## What changes are included in this PR?
* Replaced the large 8-arm scalar/array match in `regexp_count_inner` with a
unified row-processing implementation.
* Added private helper abstractions:
* `StringValueSource` for scalar-or-array string arguments.
* `StartValueSource` for scalar-or-array start arguments.
* `string_value_opt` for null-aware string access.
* `validate_array_len` for centralized length validation.
* `compile_scalar_pattern` for compiling reusable scalar regex/flags
combinations once.
* Preserved existing scalar `NULL` regex short-circuit behavior by returning
a zero-filled result array before validating other arguments.
* Preserved regex cache reuse through `compile_and_cache_regex`.
* Removed the dependency on `itertools::izip` by consolidating processing
into a single row loop.
* Kept outer type dispatch and public interfaces unchanged.
## Are these changes tested?
Yes.
Added tests covering behavior that is sensitive to validation and error
ordering:
* `test_regexp_count_error_order_invalid_scalar_regex_before_start_len`
* `test_regexp_count_error_order_flags_len_before_start_len`
Existing `regexp_count` tests continue to run unchanged.
## Are there any user-facing changes?
No.
This change is an internal refactor intended to preserve existing behavior,
error messages, validation ordering, and SQL-visible results.
## LLM-generated code disclosure
This PR includes LLM-generated code and comments. All LLM-generated content
has been manually reviewed and tested.
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