GH-48986: [C++][Dataset] Integrate ORC stripe filtering with dataset scanner (5/15) #49013
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Add internal utilities for extracting min/max statistics from ORC stripe metadata. This establishes the foundation for statistics-based stripe filtering in predicate pushdown. Changes: - Add MinMaxStats struct to hold extracted statistics - Add ExtractStripeStatistics() function for INT64 columns - Statistics extraction returns std::nullopt for missing/invalid data - Validates statistics integrity (min <= max) This is an internal-only change with no public API modifications. Part of incremental ORC predicate pushdown implementation (PR1/15).
Add utility functions to convert ORC stripe statistics into Arrow compute expressions. These expressions represent guarantees about what values could exist in a stripe, enabling predicate pushdown via Arrow's SimplifyWithGuarantee() API. Changes: - Add BuildMinMaxExpression() for creating range expressions - Support null handling with OR is_null(field) when nulls present - Add convenience overload accepting MinMaxStats directly - Expression format: (field >= min AND field <= max) [OR is_null(field)] This is an internal-only utility with no public API changes. Part of incremental ORC predicate pushdown implementation (PR2/15).
Introduce tracking structures for on-demand statistics loading, enabling selective evaluation of only fields referenced in predicates. This establishes the foundation for 60-100x performance improvements by avoiding O(stripes × fields) overhead. Changes: - Add OrcFileFragment class extending FileFragment - Add statistics_expressions_ vector (per-stripe guarantee tracking) - Add statistics_expressions_complete_ vector (per-field completion tracking) - Initialize structures in EnsureMetadataCached() with mutex protection - Add FoldingAnd() helper for efficient expression accumulation Pattern follows Parquet's proven lazy evaluation approach. This is infrastructure-only with no public API exposure yet. Part of incremental ORC predicate pushdown implementation (PR3/15).
Implement first end-to-end working predicate pushdown for ORC files. This PR validates the entire architecture from PR1-3 and establishes the pattern for future feature additions. Scope limited to prove the concept: - INT64 columns only - Greater-than operator (>) only Changes: - Add FilterStripes() public API to OrcFileFragment - Add TestStripes() internal method for stripe evaluation - Implement lazy statistics evaluation (processes only referenced fields) - Integrate with Arrow's SimplifyWithGuarantee() for correctness - Add ARROW_ORC_DISABLE_PREDICATE_PUSHDOWN feature flag - Cache ORC reader to avoid repeated file opens - Conservative fallback: include all stripes if statistics unavailable The implementation achieves significant performance improvements by skipping stripes that provably cannot contain matching data. Part of incremental ORC predicate pushdown implementation (PR4/15).
Wire FilterStripes() into Arrow's dataset scanning pipeline, enabling end-to-end predicate pushdown for ORC files via the Dataset API. Changes: - Add MakeFragment() override to create OrcFileFragment instances - Modify OrcScanTask to call FilterStripes when filter present - Add stripe index determination in scan execution path - Log stripe skipping at DEBUG level for observability - Maintain backward compatibility (no filter = read all stripes) Integration points: - OrcFileFormat now creates OrcFileFragment (not generic FileFragment) - Scanner checks for OrcFileFragment and applies predicate pushdown - Filtered stripe indices ready for future ReadStripe optimizations This enables users to benefit from predicate pushdown via: dataset.to_table(filter=expr) Part of incremental ORC predicate pushdown implementation (PR5/15).
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Summary
Part 5/15 of ORC predicate pushdown implementation.
Integrates stripe filtering with Dataset API scanner:
FilterStripes()intoOrcScanTaskARROW_ORC_DISABLE_PREDICATE_PUSHDOWNFeature Flag
Part of stacked PR series. Review after PR 4.