Fix #37736: Allow composite transforms to use implicit input chaining

[liferoad]

Issue

#37736

When using type: composite in Beam YAML, each sub-transform requires an explicit input, unlike type: chain which automatically passes the output of one transform to the next.

Fix

Modified expand_composite_transform() in sdks/python/apache_beam/yaml/yaml_transform.py to automatically chain sub-transforms when no explicit inputs/outputs are specified, similar to how chain type transforms work.

Testing

Added test_composite_implicit_input_chaining test case in yaml_transform_test.py to verify the fix.

[gemini-code-assist]

Summary of Changes

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This pull request enhances the usability of composite transforms within Beam YAML by introducing implicit input chaining. Previously, users had to explicitly define inputs and outputs for each sub-transform within a composite block. This change aligns composite transforms with the more streamlined chain type, automatically connecting the output of one sub-transform to the input of the next when no explicit I/O is provided. This simplifies the definition of sequential data processing pipelines using composite transforms, making them more intuitive and reducing boilerplate.

Highlights

• Implicit Input Chaining for Composite Transforms: Enabled composite transforms in Beam YAML to automatically chain inputs and outputs between sub-transforms when no explicit input or output is specified, mirroring the behavior of chain type transforms.
• Core Logic Update: Modified the expand_composite_transform function to incorporate the new logic for detecting and applying implicit input/output chaining.
• New Test Case: Added a dedicated test case, test_composite_implicit_input_chaining, to ensure the correct functionality of this new implicit chaining behavior.

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Changelog

• sdks/python/apache_beam/yaml/yaml_transform.py
  • Implemented logic within expand_composite_transform to detect if a composite transform's sub-transforms lack explicit input/output definitions.
  • Added code to automatically assign the output of a preceding sub-transform as the input for the next sub-transform in the sequence.
  • Modified the composite's overall output to implicitly refer to the last sub-transform's output if not explicitly defined.
• sdks/python/apache_beam/yaml/yaml_transform_test.py
  • Added test_composite_implicit_input_chaining to verify that a composite transform with multiple sub-transforms (Square, Increment) correctly processes data with implicit input chaining, producing the expected output.

Activity

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