[python][io] Add native GenerateSequence bounded PTransform (#18088)

[mtauha]

Description

This PR adds a native Python GenerateSequence bounded PTransform to the
Python SDK, equivalent to the Java SDK's GenerateSequence (formerly known
as CountingInput).

Addresses #18088

Motivation

The Python SDK previously had no native equivalent of Java's GenerateSequence
/ CountingInput transform. The only existing Python implementation
(apache_beam/io/external/generate_sequence.py) requires a Java expansion
service and only works with the Flink runner, making it inaccessible to most
Python users.

This PR introduces a pure Python implementation that works on all runners
(DirectRunner, Dataflow, etc.) without any Java dependency.

Changes

• Added sdks/python/apache_beam/io/generate_sequence.py:
  • GenerateSequence — a PTransform that produces a bounded sequence
    of integers from start (inclusive) to stop (exclusive)
  • _BoundedCountingSource — a BoundedSource backed by
    OffsetRangeTracker, supporting efficient splitting and dynamic
    work rebalancing across workers
• Added sdks/python/apache_beam/io/generate_sequence_test.py with unit
  tests covering basic usage, edge cases, splitting behaviour, and
  size estimation

Notes

• This is Phase 1 (bounded only). Unbounded streaming support with
  rate limiting will follow in a separate PR.
• The existing external Flink-only version at
  apache_beam/io/external/generate_sequence.py is untouched.
• Implementation is modelled after the Java CountingSource.java and
  follows the same BoundedSource + OffsetRangeTracker pattern used
  by other Python SDK IO sources.

Testing

cd sdks/python
python -m pytest apache_beam/io/generate_sequence_test.py -v

[gemini-code-assist]

Summary of Changes

Hello @mtauha, 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 significantly enhances the Python SDK by introducing a native GenerateSequence PTransform. This new capability allows Python users to generate bounded sequences of integers directly within their pipelines, ensuring compatibility across all Beam runners without external Java dependencies. The implementation leverages Beam's BoundedSource and OffsetRangeTracker for robust and scalable data generation, filling a critical gap in the Python SDK's feature set.

Highlights

• Native Python GenerateSequence PTransform: Introduced a pure Python implementation of the GenerateSequence PTransform, providing a native equivalent to the Java SDK's GenerateSequence (formerly CountingInput).
• Runner Compatibility: The new GenerateSequence works across all Apache Beam runners (DirectRunner, Dataflow, etc.) without requiring any Java dependencies, unlike the previous Flink-only external version.
• Bounded Source Implementation: Implemented _BoundedCountingSource, a BoundedSource backed by OffsetRangeTracker, which supports efficient splitting and dynamic work rebalancing for generating integer sequences.
• Comprehensive Testing: Added extensive unit tests covering basic usage, edge cases, splitting behavior, and size estimation for both GenerateSequence and _BoundedCountingSource.
• Future Unbounded Support: This initial release focuses on bounded sequence generation, with plans for unbounded streaming support and rate limiting to be addressed in a subsequent pull request.

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Changelog

• CHANGES.md
  • Added an entry for the new native Python GenerateSequence transform.
• sdks/python/apache_beam/io/generate_sequence.py
  • Added GenerateSequence PTransform for generating bounded integer sequences.
  • Implemented _BoundedCountingSource as a BoundedSource for sequence generation, including estimate_size, split, get_range_tracker, and read methods.
  • Included example usage and documentation for the new transform.
• sdks/python/apache_beam/io/generate_sequence_test.py
  • Added unit tests for GenerateSequence covering basic functionality, start/stop values, empty ranges, single elements, and invalid inputs.
  • Added unit tests for _BoundedCountingSource to verify size estimation, splitting logic, range tracking, reading behavior, and default output coder.

Activity

• No human activity has been recorded on this pull request yet.

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[github-actions]

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[mtauha]

assign set of reviewers

[github-actions]

Assigning reviewers:

R: @shunping for label python.

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[mtauha]

remind me after tests pass

[github-actions]

Ok - I'll remind @mtauha after tests pass

[mtauha]

Hey @damccorm @robertwb @claudevdm @kennknowles @shunping!

I'm a GSoC 2026 applicant and this is one of my first contributions to Apache Beam as part of my prep. I've implemented the bounded GenerateSequence PTransform in Python (Phase 1 of #18088), modelled after Java's CountingSource.

Looks like some checks might be failing and I'm honestly not sure why. Would really appreciate it if you could take a look and guide me in the right direction! Any review feedback is super welcome too.

Thanks a lot for your time!

R: @damccorm @robertwb @claudevdm @kennknowles @shunping

[github-actions]

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[codecov]

Codecov Report

❌ Patch coverage is 97.95918% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 57.03%. Comparing base (87c50c6) to head (b07aa51).
⚠️ Report is 2 commits behind head on master.

Files with missing lines	Patch %	Lines
sdks/python/apache_beam/io/generate_sequence.py	97.95%	1 Missing ⚠️

Additional details and impacted files

@@              Coverage Diff              @@
##             master   #37675       +/-   ##
=============================================
+ Coverage     40.08%   57.03%   +16.94%     
  Complexity     3416     3416               
=============================================
  Files          1178     1179        +1     
  Lines        187411   187580      +169     
  Branches       3588     3588               
=============================================
+ Hits          75120   106981    +31861     
+ Misses       108901    77209    -31692     
  Partials       3390     3390               

Flag	Coverage Δ
python	80.43% <97.95%> (+40.71%)	⬆️

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