Support inferring schemas from Python dataclasses

[Abacn]

Fix #22085

Tested with JdbcIO write using dataclass as input type and worked. Previously would get

  File "/Users/../beam/sdks/python/apache_beam/coders/typecoders.py", line 175, in get_coder
    return coder.from_type_hint(typehint, self)
           ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
  File "/Users/../beam/sdks/python/apache_beam/coders/row_coder.py", line 117, in from_type_hint
    schema = schema_from_element_type(type_hint)
  File "/Users/../beam/sdks/python/apache_beam/typehints/schemas.py", line 642, in schema_from_element_type
    raise TypeError(
    ...<2 lines>...
        "https://s.apache.org/beam-python-schemas")
TypeError: Could not determine schema for type hint <class '__main__.JdbcWriteTestRow'>. Did you mean to create a schema-aware PCollection? See https://s.apache.org/beam-python-schemas

Please add a meaningful description for your change here

---

Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily:

• Mention the appropriate issue in your description (for example: addresses #123), if applicable. This will automatically add a link to the pull request in the issue. If you would like the issue to automatically close on merging the pull request, comment fixes #<ISSUE NUMBER> instead.
• Update CHANGES.md with noteworthy changes.
• If this contribution is large, please file an Apache Individual Contributor License Agreement.

See the Contributor Guide for more tips on how to make review process smoother.

To check the build health, please visit https://github.com/apache/beam/blob/master/.test-infra/BUILD_STATUS.md

GitHub Actions Tests Status (on master branch)

See CI.md for more information about GitHub Actions CI or the workflows README to see a list of phrases to trigger workflows.

[gemini-code-assist]

Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request enhances Apache Beam's type hinting capabilities by introducing comprehensive support for Python dataclasses. It enables the system to recognize, convert, and infer schemas from dataclasses, integrating them seamlessly into Beam's data processing model, similar to how NamedTuples are handled.

Highlights

• Dataclass Recognition: A new utility function, match_is_dataclass, was added to accurately identify Python dataclass types within the type hinting system.
• Type Conversion Integration: The convert_to_beam_type mechanism was updated to include dataclass recognition, allowing Beam to properly process and convert dataclass types.
• RowType Constraint Generation: The RowTypeConstraint.from_user_type method was extended to correctly extract fields and their types from dataclasses, facilitating the generation of Beam schemas.
• Schema Inference Update: The schema_from_element_type function was modified to infer schemas directly from dataclasses, providing consistent behavior with existing NamedTuple support.
• Comprehensive Testing: New unit tests were introduced to validate the roundtrip conversion of dataclasses to Beam schemas and to ensure their correct schema inference.

🧠 New Feature in Public Preview: You can now enable Memory to help Gemini Code Assist learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. Click here to enable Memory in your admin console.

Changelog

• sdks/python/apache_beam/typehints/native_type_compatibility.py
  • Imported the dataclasses module.
  • Added a new function match_is_dataclass to check if a type is a dataclass.
  • Updated convert_to_beam_type to include a _TypeMapEntry for dataclasses.
• sdks/python/apache_beam/typehints/row_type.py
  • Imported match_is_dataclass from native_type_compatibility.
  • Extended the from_user_type static method to handle dataclass types, extracting their fields and types to construct RowTypeConstraint instances.
• sdks/python/apache_beam/typehints/schemas.py
  • Imported match_is_dataclass from native_type_compatibility.
  • Modified schema_from_element_type to infer schemas from dataclasses, alongside named tuples.
• sdks/python/apache_beam/typehints/schemas_test.py
  • Imported the dataclasses module.
  • Added test_dataclass_roundtrip to verify the conversion of dataclasses to RowTypeConstraint and schema generation.
  • Added test_trivial_example_dataclass to test the schema inference for a basic dataclass structure.

Activity

• Addressed issue [Feature Request]: Support for inferring schemas from Python dataclasses #22085.
• Included a standard contribution checklist in the pull request description.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[github-actions]

Checks are failing. Will not request review until checks are succeeding. If you'd like to override that behavior, comment assign set of reviewers

[Abacn]

assign set of reviewers

[github-actions]

Assigning reviewers:

R: @claudevdm for label python.

Note: If you would like to opt out of this review, comment assign to next reviewer.

Available commands:

• stop reviewer notifications - opt out of the automated review tooling
• remind me after tests pass - tag the comment author after tests pass
• waiting on author - shift the attention set back to the author (any comment or push by the author will return the attention set to the reviewers)

The PR bot will only process comments in the main thread (not review comments).

[claudevdm]

This only applies when the typehint is beam.Row right? Just want to be sure we arent changing default coders in update incompatible way.

[claudevdm]

/gemini review

[gemini-code-assist]

Code Review

This pull request adds support for inferring schemas from Python dataclasses, which is a great enhancement. The changes are logical and include good test coverage for the new functionality. I've found one area for improvement regarding code duplication, which could enhance maintainability.

[Abacn]

This only applies when the typehint is beam.Row right? Just want to be sure we arent changing default coders in update incompatible way.

Yes, the entrypoint of the change being effective is

https://github.com/apache/beam/pull/37728/changes#diff-d31b9184f7423473c4e6deda80b237aa474228143a0bab2faedd9afe2e944982R633

Previously, same config results in TypeError (shown in PR description), therefore no upgrade compatibility concern.

There was a change in native_type_compatibility.py added dataclass into type_map that might change typehint of existing dataclasses

0aa835d#diff-3f6570dbd8f6850deb466f36b7b7983e9b5c1adaec3810dfe672e108188a35ebR426

Further testing shows this change was actually dead code. Dataclass will hit this branch:

https://github.com/Abacn/beam/blob/0aa835dcb2d2b037121cb75ee6dc439f1d364ad0/sdks/python/apache_beam/typehints/native_type_compatibility.py#L393

and return before iterating over type_map. Reverted in the latest commit.

[claudevdm]

What do you think about adding a test that does beam.Create([MyCuteDataclass()]).wit_output_types(beam.Row) -> Reshuffle (the results should be named tuples)?

And also a test that beam.Create([MyCuteDataclass()]).wit_output_types(beam.Row) -> Reshuffle the resulting type is still the original dataclass?

[Abacn]

Thanks, added unit tests for both named tuple and dataclasses (they are expected to resulting in same generated user type passing through GBK)

Noticing current schemas_test unit tests does not involve pipeline tests. Created a row_type_test to settle these tests