Decompose io/pyarrow.py into focused modules to enable pluggable compute engines

Bắt đầu với pyiceberg/io/pyarrow.py và phần tách FileIO được mô tả trong PR #3738; xem xét mã PyArrowFile và PyArrowFileIO hiện có cùng các test liên quan. Chỉ di chuyển phần trách nhiệm của FileIO, giữ nguyên các re-export từ đường dẫn module ban đầu và chạy bộ test hiện có để xác minh hành vi và các import không thay đổi.

Summary

pyiceberg/io/pyarrow.py is a 3,100+ line monolith that handles six unrelated concerns: filesystem I/O, schema conversion, expression translation, scan/read orchestration, write logic, and Parquet statistics. This makes it difficult to test individual components, extend behavior, or substitute alternative engines for specific operations.

This issue proposes an incremental decomposition - a series of small, independently-reviewable refactoring PRs that split the file by concern while maintaining full backward compatibility via re-exports. The end goal is clean seam points where bounded-memory compute engines (DataFusion, etc.) can be introduced for operations that currently OOM on large data.

Motivation

Several open issues depend on bounded-memory compute that PyArrow's kernel library cannot provide:

• Equality delete resolution (#1210, #3270) - requires anti-join with spill
• Sort-on-write (#271) - requires external merge sort
• Data compaction (#1092) - requires sort + join + rewrite pipeline
• CoW deletes on large files - full file materialization causes OOM

A previous attempt to deliver all of this at once (#3715, PR #3716) was rejected for being too large to review. This issue takes the opposite approach: decompose first, add capabilities later.

Approach

Phase 1: Split the monolith (pure refactoring)

Extract each concern into its own module under pyiceberg/io/. The original pyarrow.py becomes a thin re-export shim so all existing imports continue to work.

PR	Extraction	Approximate scope
A	FileIO (PyArrowFile, PyArrowFileIO) - #3738	~700 lines
B	Schema conversion (schema_to_pyarrow, pyarrow_to_schema, visitors)	~900 lines
C	Expression translation (expression_to_pyarrow, _ConvertToArrowExpression)	~300 lines
D	Statistics (StatsAggregator, PyArrowStatisticsCollector, ParquetFormatWriter)	~500 lines
E	Write path (write_file, _dataframe_to_data_files, partitioning, bin packing)	~1200 lines
F	Scan/Read (ArrowScan, _task_to_record_batches, delete resolution)	~300 lines

Each PR:

• Moves code, does not change behavior
• Re-exports from the original module path
• All existing tests pass unchanged
• No new dependencies

These PRs are largely independent of each other (no strict ordering required).

Phase 2: Introduce a compute protocol

Once concerns are separated, introduce a thin ComputeEngine protocol for the operations that benefit from bounded-memory execution:

class ComputeEngine(Protocol):
    def filter_batches(self, batches, expr, schema) -> Iterator[RecordBatch]: ...
    def sort_batches(self, batches, sort_order, schema) -> Iterator[RecordBatch]: ...
    def anti_join(self, left, right, keys) -> Iterator[RecordBatch]: ...

The default implementation delegates to the existing PyArrow code. No behavior change, just an indirection point.

Phase 3: DataFusion as optional compute engine

With the protocol in place, a DataFusionComputeEngine implementation slots in as an optional extra. Each capability (equality delete resolution, sort-on-write, etc.) is its own PR wiring the protocol into the specific code path.

What this is NOT

• Not a rewrite. Phase 1 is purely moving existing code into new files.
• Not adding DataFusion as a hard dependency. It remains an optional extra.
• Not changing the public API. All existing imports and behaviors are preserved.

Prior art / references

• #3715 / PR #3716: Previous pluggable backend attempt (rejected as too large)
• Community sync discussion (June 30, 2026): established that read/write/compute should be separable
• #3554: Original DataFusion integration proposal
• #271: Sort-on-write (requires external merge sort)
• #1210, #3270: Equality delete resolution
• #1092: Data compaction

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I plan to start with PR A (FileIO extraction, #3738) as a proof of concept for the approach. Feedback on the overall direction is welcome before I proceed further.