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https://issues.apache.org/jira/browse/BEAM-4132?focusedWorklogId=330637&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-330637
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ASF GitHub Bot logged work on BEAM-4132:
----------------------------------------
Author: ASF GitHub Bot
Created on: 18/Oct/19 16:46
Start Date: 18/Oct/19 16:46
Worklog Time Spent: 10m
Work Description: aaltay commented on pull request #9810: [BEAM-4132]
Support multi-output type inference
URL: https://github.com/apache/beam/pull/9810#discussion_r336580986
##########
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:
I do not believe it does, I missed this while reviewing.
I have a more fundamental question. Is it possible to infer types for
multiple outputs without explicit typehints from the user? DoFn will choose
their taggedoutput dynamically based on the element. And I am not sure how we
can infer different output types only by knowing the input type and all list of
possible return types from a function. In other words, is it possible to infer
output types (without explicit hints) to be anything other than union of all
possible output types from that transform?
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Issue Time Tracking
-------------------
Worklog Id: (was: 330637)
Time Spent: 50m (was: 40m)
> 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: 50m
> 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}}.
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