feat(parquet): support pushing bitmaps down to page-level filtering (except for nested columns)

[zhf999]

Purpose

This PR follows PR #371.

• Support pushing selection bitmaps down to Parquet page-level filtering so only matching pages are read when both predicate and bitmap pruning are available.
• Refactor target row-group filtering metadata into a dedicated TargetRowGroup type to carry row-group index, partial-match status, row ranges, and read-range exclusion flags consistently across planning and reading phases.

Changes

• src/paimon/format/parquet/target_row_group.h

  • Introduced TargetRowGroup class and TargetRowGroups alias.
  • Replaced ad-hoc struct usage from row_ranges.h.

• src/paimon/format/parquet/parquet_file_batch_reader.h

  • Updated row-group filtering APIs to operate on TargetRowGroups instead of raw std::vector<int32_t>.

• src/paimon/format/parquet/parquet_file_batch_reader.cpp

  • In SetReadSchema, switched row-group candidate list to TargetRowGroups and applied bitmap filter before page-index filtering.
  • Added bitmap-driven row-group/page pruning:
    • If any nested columns are included in read schema, bitmap filtering fallbacks to row-group-level.
    • Translate bitmap to row_ranges to filter page with bitmap.
    • The translation strategy follows Paimon, which prunes unneeded rows at the start and end of each page.

Tests

• Added bitmap + page-level integration tests:
  • BitmapAllPagesSomeRowGroups
  • BitmapPartialPagesSingleRowGroup
  • BitmapAllAndPartialPagesMixed
  • BitmapAllPagesWithPredicate
  • BitmapPartialPagesWithPredicate
  • BitmapMixedWithPredicate
  • NestedMapBitmapFallback
  • NestedListBitmapFallback
  • NestedStructBitmapFallback
• Coverage includes bitmap-only pruning, bitmap+predicate pruning, and mixed partial/full page hit cases.

Datasets

Multiple performance testing were conducted on 2 dataset.

Dataset	File size	num_rows	num_columns	num_rowgroups	page length (in rows)
Dataset#1	6.7GB	2M	1,176	39	1024
Dataset#2	3GB	11M	4	23	1024

Test Methodology

To validate bitmap behavior, we organize tests into six groups based on bitmap distribution patterns. Each group contains five distinct datasets, and the final result reported is the average of these five runs.

Test Cases

1. Matching records are contiguous and only one row is queried.
2. Matching records are contiguous and 100 rows are queried.
3. Matching records are contiguous and 10,000 rows are queried.
4. Matching records are scattered in 5 segments, each segment containing 1 row.
5. Matching records are scattered in 5 segments, each segment containing 100 rows.
6. Matching records are scattered in 100 segments, each segment containing 1 row.
7. Scattered bitmap, skip 1 row, read 1 row, skip 1 row, read 1 row until EOF (i.e. 1010101010...).
8. Scattered bitmap, skip 99 rows , read 1 row, skip 99 rows, skip 1 row until EOF.
9. Scattered bitmap, skip 9999 rows , read 1 row, skip 9999 rows, skip 1 row until EOF.

Test Results

On Dataset#1

Branch	Case 1	Case 2	Case3	Case 4	Case 5	Case 6	Case 7	Case 8	Case 9
Main	2.27s	2.28s	2.19s	8.74ss	8.75s	105.58s	126.08s	125.96s	126.00s
This PR	0.33s	0.32s	0.90s	1.38s	1.35s	15.82s	126.81s	116.24s	20.77s

On Dataset#2

Branch	Case 1	Case 2	Case3	Case 4	Case 5	Case 6	Case 7	Case 8	Case 9
Main	1.30s	1.26s	1.26s	2.77s	2.76s	30.48s	31.27s	31.28s	31.28s
This PR	8ms	7ms	39ms	25ms	26ms	0.46s	35.18s	32.31s	1.99s

API and Format

• No public API changes.
• No storage format/protocol changes.

Documentation

• No user-facing docs required for this change.

Generative AI tooling

• gpt-5.3-codex

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[zjw1111]

Thanks for the follow-up work on page-level bitmap pushdown, and for the solid test coverage. A few follow-up nits on stale comments and naming, on top of the points already raised in the earlier review.

[zjw1111]

Thanks for the quick updates — most of the earlier comments are addressed. Two small follow-ups below.

[lxy-9602]

+1

[zjw1111]

Thanks for the thorough follow-ups and the added test coverage. All review comments are addressed. LGTM.