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ASF GitHub Bot logged work on BEAM-4132: ---------------------------------------- Author: ASF GitHub Bot Created on: 18/Oct/19 04:43 Start Date: 18/Oct/19 04:43 Worklog Time Spent: 10m Work Description: robertwb commented on pull request #9810: [BEAM-4132] Support multi-output type inference URL: https://github.com/apache/beam/pull/9810#discussion_r336318475 ########## File path: sdks/python/apache_beam/pipeline.py ########## @@ -571,6 +573,10 @@ def _infer_result_type(self, transform, inputs, result_pcollection): else: result_pcollection.element_type = transform.infer_output_type( input_element_type) + elif isinstance(result_pcollection, pvalue.DoOutputsTuple): + # Single-input, multi-output inference. + for pcoll in result_pcollection: + self._infer_result_type(transform, inputs, pcoll) Review comment: How does this infer a different type to the different collections? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org Issue Time Tracking ------------------- Worklog Id: (was: 330278) Time Spent: 0.5h (was: 20m) > Element type inference doesn't work for multi-output DoFns > ---------------------------------------------------------- > > Key: BEAM-4132 > URL: https://issues.apache.org/jira/browse/BEAM-4132 > Project: Beam > Issue Type: Bug > Components: sdk-py-core > Affects Versions: 2.4.0 > Reporter: Chuan Yu Foo > Assignee: Udi Meiri > Priority: Major > Time Spent: 0.5h > Remaining Estimate: 0h > > TLDR: if you have a multi-output DoFn, then the non-main PCollections with > incorrectly have their element types set to None. This affects type checking > for pipelines involving these PCollections. > Minimal example: > {code} > import apache_beam as beam > class TripleDoFn(beam.DoFn): > def process(self, elem): > yield_elem > if elem % 2 == 0: > yield beam.pvalue.TaggedOutput('ten_times', elem * 10) > if elem % 3 == 0: > yield beam.pvalue.TaggedOutput('hundred_times', elem * 100) > > @beam.typehints.with_input_types(int) > @beam.typehints.with_output_types(int) > class MultiplyBy(beam.DoFn): > def __init__(self, multiplier): > self._multiplier = multiplier > def process(self, elem): > return elem * self._multiplier > > def main(): > with beam.Pipeline() as p: > x, a, b = ( > p > | 'Create' >> beam.Create([1, 2, 3]) > | 'TripleDo' >> beam.ParDo(TripleDoFn()).with_outputs( > 'ten_times', 'hundred_times', main='main_output')) > _ = a | 'MultiplyBy2' >> beam.ParDo(MultiplyBy(2)) > if __name__ == '__main__': > main() > {code} > Running this yields the following error: > {noformat} > apache_beam.typehints.decorators.TypeCheckError: Type hint violation for > 'MultiplyBy2': requires <type 'int'> but got None for elem > {noformat} > Replacing {{a}} with {{b}}Â yields the same error. Replacing {{a}} with {{x}} > instead yields the following error: > {noformat} > apache_beam.typehints.decorators.TypeCheckError: Type hint violation for > 'MultiplyBy2': requires <type 'int'> but got Union[TaggedOutput, int] for elem > {noformat} > I would expect Beam to correctly infer that {{a}} and {{b}} have element > types of {{int}} rather than {{None}}, and I would also expect Beam to > correctly figure out that the element types of {{x}} are compatible with > {{int}}. -- This message was sent by Atlassian Jira (v8.3.4#803005)