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Posted to github@beam.apache.org by "claudevdm (via GitHub)" <gi...@apache.org> on 2025/11/10 17:22:23 UTC
[PR] Type inference tests [beam]
claudevdm opened a new pull request, #36776:
URL: https://github.com/apache/beam/pull/36776
Documents how typevars, typehint decorators and typehint inference works.
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Re: [PR] Type inference tests [beam]
Posted by "gemini-code-assist[bot] (via GitHub)" <gi...@apache.org>.
gemini-code-assist[bot] commented on PR #36776:
URL: https://github.com/apache/beam/pull/36776#issuecomment-3512997892
## Summary of Changes
Hello @claudevdm, I'm Gemini Code Assist[^1]! 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 improves the clarity and robustness of type inference within Apache Beam's Python SDK. It introduces detailed documentation for the `_infer_result_type` function, explaining the mechanics of type variable binding and substitution. Additionally, new test cases have been added to thoroughly verify the behavior of type hints in various `PTransform` configurations, ensuring predictable type inference outcomes and highlighting current limitations.
### Highlights
* **Enhanced Type Inference Documentation**: A comprehensive docstring has been added to the `_infer_result_type` function in `pipeline.py`, detailing how type variables, typehint decorators, and type inference work, including binding algorithms and examples.
* **New Type Inference Test Cases**: Three new test cases were introduced in `ptransform_test.py` to validate type variable binding behavior in `PTransform`s, covering scenarios with complete hints, missing input hints, and missing output hints on child transforms.
* **Dependency Cleanup**: Unused `FrozenSet` and `Iterable` imports were removed from `pipeline.py` to streamline dependencies.
<details>
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Re: [PR] Type inference tests [beam]
Posted by "gemini-code-assist[bot] (via GitHub)" <gi...@apache.org>.
gemini-code-assist[bot] commented on code in PR #36776:
URL: https://github.com/apache/beam/pull/36776#discussion_r2511385855
##########
sdks/python/apache_beam/transforms/ptransform_test.py:
##########
@@ -1402,6 +1402,106 @@ def process(self, element, five):
assert_that(d, equal_to([6, 7, 8]))
self.p.run()
+ def test_child_with_both_input_and_output_hints_binds_typevars_correctly(
+ self):
+ """
+ When a child transform has both input and output type hints with type
+ variables, those variables bind correctly from the actual input data.
+
+ Example: Child with .with_input_types(Tuple[K, V])
+ .with_output_types(Tuple[K, V]) receiving Tuple['a', 'hello'] will bind
+ K=str, V=str correctly.
+ """
+ K = typehints.TypeVariable('K')
+ V = typehints.TypeVariable('V')
+
+ @typehints.with_input_types(typehints.Tuple[K, V])
+ @typehints.with_output_types(typehints.Tuple[K, V])
+ class TransformWithoutChildHints(beam.PTransform):
+ class MyDoFn(beam.DoFn):
+ def process(self, element):
+ k, v = element
+ yield (k, v.upper())
+
+ def expand(self, pcoll):
+ return (
+ pcoll
+ | beam.ParDo(self.MyDoFn()).with_input_types(
+ tuple[K, V]).with_output_types(tuple[K, V]))
+
+ with TestPipeline() as p:
+ result = (
+ p
+ | beam.Create([('a', 'hello'), ('b', 'world')])
+ | TransformWithoutChildHints())
+
+ self.assertEqual(result.element_type, typehints.Tuple[str, str])
+
+ def test_child_without_input_hints_fails_to_bind_typevars(self):
+ """
+ When a child transform lacks input type hints, type variables in its output
+ hints cannot bind and default to Any, even when parent composite has
+ decorated type hints.
+
+ This test demonstrates the current limitation: without explicit input hints
+ on the child, the type variable K in .with_output_types(Tuple[K, str])
+ remains unbound, resulting in Tuple[Any, str] instead of the expected
+ Tuple[str, str].
+ """
+ K = typehints.TypeVariable('K')
+
+ @typehints.with_input_types(typehints.Tuple[K, str])
+ @typehints.with_output_types(typehints.Tuple[K, str])
+ class TransformWithoutChildHints(beam.PTransform):
+ class MyDoFn(beam.DoFn):
+ def process(self, element):
+ k, v = element
+ yield (k, v.upper())
+
+ def expand(self, pcoll):
+ return (
+ pcoll
+ | beam.ParDo(self.MyDoFn()).with_output_types(tuple[K, str]))
+
+ with TestPipeline() as p:
+ result = (
+ p
+ | beam.Create([('a', 'hello'), ('b', 'world')])
+ | TransformWithoutChildHints())
+
+ self.assertEqual(result.element_type, typehints.Tuple[typehints.Any, str])
+
+ def test_child_without_output_hints_infers_partial_types_from_dofn(self):
+ """
+ When a child transform has input hints but no output hints, type inference
+ from the DoFn's process method produces partially inferred types.
+
+ With .with_input_types(Tuple[K, V]) and actual input Tuple['a', 'hello'], K
+ binds to str from the input. However, without output hints, the DoFn's
+ yield statement inference produces Tuple[str, Any].
+ """
+ K = typehints.TypeVariable('K')
+ V = typehints.TypeVariable('K')
Review Comment:
The `TypeVariable` `V` is created with the name 'K', which is the same as the `TypeVariable` `K`. While `TypeVariable` identity is based on the object instance and not the name, this is very confusing and could lead to future errors. It should be named 'V' for clarity.
```python
V = typehints.TypeVariable('V')
```
##########
sdks/python/apache_beam/transforms/ptransform_test.py:
##########
@@ -1402,6 +1402,106 @@ def process(self, element, five):
assert_that(d, equal_to([6, 7, 8]))
self.p.run()
+ def test_child_with_both_input_and_output_hints_binds_typevars_correctly(
+ self):
+ """
+ When a child transform has both input and output type hints with type
+ variables, those variables bind correctly from the actual input data.
+
+ Example: Child with .with_input_types(Tuple[K, V])
+ .with_output_types(Tuple[K, V]) receiving Tuple['a', 'hello'] will bind
+ K=str, V=str correctly.
+ """
+ K = typehints.TypeVariable('K')
+ V = typehints.TypeVariable('V')
+
+ @typehints.with_input_types(typehints.Tuple[K, V])
+ @typehints.with_output_types(typehints.Tuple[K, V])
+ class TransformWithoutChildHints(beam.PTransform):
+ class MyDoFn(beam.DoFn):
+ def process(self, element):
+ k, v = element
+ yield (k, v.upper())
+
+ def expand(self, pcoll):
+ return (
+ pcoll
+ | beam.ParDo(self.MyDoFn()).with_input_types(
+ tuple[K, V]).with_output_types(tuple[K, V]))
+
+ with TestPipeline() as p:
+ result = (
+ p
+ | beam.Create([('a', 'hello'), ('b', 'world')])
+ | TransformWithoutChildHints())
+
+ self.assertEqual(result.element_type, typehints.Tuple[str, str])
+
+ def test_child_without_input_hints_fails_to_bind_typevars(self):
+ """
+ When a child transform lacks input type hints, type variables in its output
+ hints cannot bind and default to Any, even when parent composite has
+ decorated type hints.
+
+ This test demonstrates the current limitation: without explicit input hints
+ on the child, the type variable K in .with_output_types(Tuple[K, str])
+ remains unbound, resulting in Tuple[Any, str] instead of the expected
+ Tuple[str, str].
+ """
+ K = typehints.TypeVariable('K')
+
+ @typehints.with_input_types(typehints.Tuple[K, str])
+ @typehints.with_output_types(typehints.Tuple[K, str])
+ class TransformWithoutChildHints(beam.PTransform):
+ class MyDoFn(beam.DoFn):
+ def process(self, element):
+ k, v = element
+ yield (k, v.upper())
+
+ def expand(self, pcoll):
+ return (
+ pcoll
+ | beam.ParDo(self.MyDoFn()).with_output_types(tuple[K, str]))
+
+ with TestPipeline() as p:
+ result = (
+ p
+ | beam.Create([('a', 'hello'), ('b', 'world')])
+ | TransformWithoutChildHints())
+
+ self.assertEqual(result.element_type, typehints.Tuple[typehints.Any, str])
+
+ def test_child_without_output_hints_infers_partial_types_from_dofn(self):
+ """
+ When a child transform has input hints but no output hints, type inference
+ from the DoFn's process method produces partially inferred types.
+
+ With .with_input_types(Tuple[K, V]) and actual input Tuple['a', 'hello'], K
+ binds to str from the input. However, without output hints, the DoFn's
+ yield statement inference produces Tuple[str, Any].
Review Comment:
The docstring is slightly incomplete. It says "K binds to str from the input", but given the input `Tuple['a', 'hello']` and the hint `Tuple[K, V]`, `V` also binds to `str`. The docstring should reflect this for clarity.
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Re: [PR] Type inference tests [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #36776:
URL: https://github.com/apache/beam/pull/36776#issuecomment-3513047386
Stopping reviewer notifications for this pull request: review requested by someone other than the bot, ceding control. If you'd like to restart, comment `assign set of reviewers`
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Re: [PR] Type inference tests [beam]
Posted by "claudevdm (via GitHub)" <gi...@apache.org>.
claudevdm commented on PR #36776:
URL: https://github.com/apache/beam/pull/36776#issuecomment-3513043664
R: @jrmccluskey can you please check my understanding?
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Re: [PR] Type inference tests [beam]
Posted by "github-actions[bot] (via GitHub)" <gi...@apache.org>.
github-actions[bot] commented on PR #36776:
URL: https://github.com/apache/beam/pull/36776#issuecomment-3513051610
Stopping reviewer notifications for this pull request: review requested by someone other than the bot, ceding control. If you'd like to restart, comment `assign set of reviewers`
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Re: [PR] Type inference tests [beam]
Posted by "claudevdm (via GitHub)" <gi...@apache.org>.
claudevdm commented on PR #36776:
URL: https://github.com/apache/beam/pull/36776#issuecomment-3513001675
/gemini review
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Re: [PR] Type inference tests [beam]
Posted by "claudevdm (via GitHub)" <gi...@apache.org>.
claudevdm commented on PR #36776:
URL: https://github.com/apache/beam/pull/36776#issuecomment-3513038074
R: @damccorm
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Re: [PR] Type inference tests [beam]
Posted by "jrmccluskey (via GitHub)" <gi...@apache.org>.
jrmccluskey merged PR #36776:
URL: https://github.com/apache/beam/pull/36776
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