table: add opt-in pyiceberg-core arrow reader#2
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Fan scan tasks out over a thread pool of native ArrowReaders so decode uses multiple cores instead of one, streaming batches as they complete with at most one decoded batch per shard in flight. A default batch size amortizes the per-batch GIL handoff that otherwise dominates the fan-in. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A limited scan previously always fell back to PyArrow. Push the limit through the native reader instead: truncate the streamed result at the limit (slicing the crossing batch and closing the shards so they stop decoding early) and cap the batch size to the limit so a small limit does not decode a full batch per shard. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The native reader emits arrow-rs's own types (string rather than large_string, and run-end-encoded identity-partition columns), so its output diverged from the PyArrow scan path. Cast every batch to schema_to_pyarrow(projected_schema), decoding run-end-encoded columns first since there is no direct cast kernel for them, so the native path is a faithful drop-in. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
PYICEBERG_RUST_SCAN_PLANNING plans the scan in pyiceberg-core (Table.plan_files) instead of PyIceberg's Python manifest planning, then streams the planned tasks through the same sharded, casted reader as the read path. Falls back to PyArrow on any scan pyiceberg-core cannot handle. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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Stack position: PyIceberg PR after #1 (ABA-158).
Adds an env-gated
DataScan.to_arrow_batch_reader()path using pyiceberg_core ArrowReader whenPYICEBERG_RUST_ARROW_SCANis enabled. Existing PyArrow behavior remains the default. The native path is skipped for limits and for projections that would require reading filter-only fields; unsupported native shapes fall back with a warning.Validation:
Challenger loop: red-team found limit handling and missing-core fallback issues; both were fixed. Final review green.