Details
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New Feature
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Status: Resolved
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P2
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Resolution: Fixed
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None
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None
Description
If a type hint is specified for an input to beam.Filter, it attempts to infer the output type (as Iterable[input_type], consistent with FlatMap), but that inference appears to have a bug in it.
With the code:
@beam.typehints.with_input_types(int) def OddFilter(data): return data % 2 == 0 def pipeline(root): base = root | beam.Create(xrange(100)) next = base | beam.Filter(OddFilter)
The following error is returned:
File "/google3/experimental/testproj/test_beam.py", line 26, in pipeline next = base | beam.Filter(OddFilter) File "/google3/third_party/py/apache_beam/transforms/core.py", line 1147, in Filter get_type_hints(wrapper).set_output_types(typehints.Iterable[output_hint]) File "/google3/third_party/py/apache_beam/typehints/typehints.py", line 951, in __getitem__ type_param, error_msg_prefix='Parameter to an Iterable hint' File "/google3/third_party/py/apache_beam/typehints/typehints.py", line 359, in validate_composite_type_param type_param.__class__.__name__)) TypeError: Parameter to an Iterable hint must be a non-sequence, a type, or a TypeConstraint. (<type 'int'>,) is an instance of tuple.
Explicitly specifying the output type (as beam.typehints.Iterable[int]) works fine. The code in core.py seems to be correct, but I'm guessing it needs a derefence of the tuple to actually extract the type: http://google3/third_party/py/apache_beam/transforms/core.py?l=1145&rcl=228573657
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