[Bug]: [Python] Type inference returns Any for attributes of NamedTuple-typed and nullable nested rows

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評価

難易度
3/5
見積もり時間
1〜2日
初心者へのやさしさ
55/100
issue の種類
バグ
明瞭さ
明確に書かれている
活発さ
活発
技術スタック
python
領域
data-engineering

調査の方向性

Start in sdks/python/apache_beam/typehints/opcodes.py, where _getattr only resolves fields when the type is a RowTypeConstraint. Reproduce with trivial_inference.infer_return_type on the Person/Address NamedTuples from the issue, then extend _getattr to accept NamedTuple classes via RowTypeConstraint.from_user_type and unwrap Optional[row] while keeping the result nullable. Done when both lambdas infer str and Optional[str] in trivial_inference_test.

索引モデルが issue の本文から書いたものです。

説明

awaiting triage bug P2 python yaml
What happened?

Accessing a nested field of a schema-aware PCollection is inferred as Any. In Beam YAML this makes a common pipeline fail at construction: a MapToFields that projects a field of a Parquet struct column feeds WriteToParquet, which raises NotImplementedError: Beam logical types are not currently supported in arrow_type_compatibility (#23817) because the inferred field type is Any.

Reproducer (Parquet file with name: string, address: struct<city: string, zip: int64>):

pipeline:
  type: chain
  transforms:
  - type: ReadFromParquet
    config:
      path: people_struct.parquet
  - type: MapToFields
    config:
      language: python
      fields:
        name: name
        city: address.city
  - type: WriteToParquet
    config:
      path: out/people

ValueError: Error applying transform "WriteToParquet" at line 13: Beam logical types are not currently supported in arrow_type_compatibility.

The same happens with trivial_inference directly:

import typing
from apache_beam.typehints import trivial_inference

class Address(typing.NamedTuple):
  city: str

class Person(typing.NamedTuple):
  name: str
  address: typing.Optional[Address]

trivial_inference.infer_return_type(lambda r: r.name, [Person])          # Any (expected str)
trivial_inference.infer_return_type(lambda r: r.address.city, [Person])  # Any (expected Optional[str])
Cause

opcodes._getattr (sdks/python/apache_beam/typehints/opcodes.py) resolves row.field only when the type is a RowTypeConstraint. Schema-aware PCollections are usually typed with the NamedTuple class generated for their schema (for example ReadFromParquet(as_rows=True) produces a BeamSchema_... class), and nested struct fields are Optional[Row] / Optional[NamedTuple], so both cases fall through to Any.

Resolving NamedTuple classes through RowTypeConstraint.from_user_type and unwrapping Optional[row] (keeping the result nullable) fixes inference. I have a small fix with tests in trivial_inference_test and can open a PR.

Note: with that fix the Parquet pipeline above builds, but then fails at runtime due to a separate issue where ReadFromParquet(as_rows=True) yields dicts for struct fields (filed separately).

Issue Priority

Priority: 2 (default / most bugs should be filed as P2)

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