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GH-36010: [GLib][Ruby][Parquet] Add buffered reader properties - #51277
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emecii wants to merge 2 commits into
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GH-36010: [GLib][Ruby][Parquet] Add buffered reader properties #51277emecii wants to merge 2 commits into
emecii wants to merge 2 commits into
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Expose buffered stream controls and additive properties-aware reader constructors. Copy native properties and retain the input stream; disable whole-column read-ahead when buffered streams are selected. Generated-by: OpenAI Codex
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Component: Ruby
Component: GLib
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Sep 10, 2026
Free the reader after the .open block so Windows can remove the memory-mapped temporary file without waiting for garbage collection. Generated-by: OpenAI Codex
emecii
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Sep 10, 2026
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Fixed the MinGW temporary-file cleanup failure in b39e1a5. The new path test passed its assertions but retained the memory-mapped reader after the .open block, causing Windows to reject Tempfile deletion. The test now explicitly releases the reader in ensure.
Validated with a native mapping check that fails before the change and passes afterward, the full local Red Parquet suite (18 tests, 24 assertions), and the GLib suite (2,247 tests, zero failures/errors; 173 unavailable-feature omissions). Ruby syntax, RuboCop, and RAT checks pass. The hosted Windows rerun is pending.
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Rationale for this change
GH-36010 requests Ruby bindings for
parquet::ReaderProperties, following the buffered-read discussion in GH-36001. GLib currently opens readers with defaults, so Ruby cannot select buffered streams or their buffer size.What changes are included in this PR?
GParquetReaderPropertieswith buffered-stream enable/disable/state and buffer-size get/set.Parquet::ArrowFileReader.new(source_or_path, properties)without a Ruby adapter.Are these changes tested?
Fresh Debug C++/Parquet/GLib/introspection and native Ruby builds on macOS arm64, Ruby 4.0.6:
Are there any user-facing changes?
Ruby/GLib callers can opt into buffered Parquet reading and configure the buffer size. The API documentation explains the read-ahead interaction and that decoded data and individual page reads can exceed that size.
AI assistance
OpenAI Codex generated the implementation, regression tests and local validation harnesses, and ran the checks described above.