feat(yaml): add schema validation for flatten transform #35675
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Check schema compatibility when flattening PCollections to ensure all inputs have matching schemas. This prevents runtime errors from schema mismatches.
- Split long comment line for better readability in yaml_provider.py - Rename test method create_has_schema to test_create_has_schema - Change expected exception type from ValueError to Exception in schema validation test - Add new test cases for flattening compatible schemas and null values - Fix circular reference test to actually test successful execution
| pickle_library='cloudpickle')) as p: | ||
| # This should raise an error because null values create different schema types | ||
| # (nullable logical type vs INT64) | ||
| with self.assertRaisesRegex( |
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@damccorm I do not like this but Beam treat this case with different schemas since passenger_count should be INT64 not None. So we report the error now with this validation.
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Oh interesting - one option would be to define our own deep equality function here (basically a consistency check), or we could try to move forward with #35672
I don't think we can move forward with this change if it regresses these use cases
| pickle_library='cloudpickle')) as p: | ||
| # This should raise an error because null values create different schema types | ||
| # (nullable logical type vs INT64) | ||
| with self.assertRaisesRegex( |
There was a problem hiding this comment.
Oh interesting - one option would be to define our own deep equality function here (basically a consistency check), or we could try to move forward with #35672
I don't think we can move forward with this change if it regresses these use cases
|
decided to do the forward fix #35672 later |
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Check schema compatibility when flattening PCollections to ensure all inputs have matching schemas. This prevents runtime errors from schema mismatches.
Addresses #35666
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