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Posted to issues@beam.apache.org by "ASF GitHub Bot (Jira)" <ji...@apache.org> on 2020/08/27 21:25:00 UTC
[jira] [Work logged] (BEAM-4091) Typehint annotations don't work
with @ptransform_fn annotation
[ https://issues.apache.org/jira/browse/BEAM-4091?focusedWorklogId=475552&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-475552 ]
ASF GitHub Bot logged work on BEAM-4091:
----------------------------------------
Author: ASF GitHub Bot
Created on: 27/Aug/20 21:24
Start Date: 27/Aug/20 21:24
Worklog Time Spent: 10m
Work Description: udim opened a new pull request #9907:
URL: https://github.com/apache/beam/pull/9907
------------------------
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Issue Time Tracking
-------------------
Worklog Id: (was: 475552)
Time Spent: 2h 40m (was: 2.5h)
> Typehint annotations don't work with @ptransform_fn annotation
> --------------------------------------------------------------
>
> Key: BEAM-4091
> URL: https://issues.apache.org/jira/browse/BEAM-4091
> Project: Beam
> Issue Type: Bug
> Components: sdk-py-core
> Affects Versions: 2.4.0
> Reporter: Chuan Yu Foo
> Assignee: Udi Meiri
> Priority: P2
> Labels: stale-assigned
> Time Spent: 2h 40m
> Remaining Estimate: 0h
>
> Typehint annotations don't work with functions annotated with {{@ptransform_fn}}, but they do work with the equivalent classes.
> The following is a minimal example illustrating this:
> {code:python}
> @beam.typehints.with_input_types(float)
> @beam.typehints.with_output_types(bytes)
> @beam.ptransform_fn
> def _DoStuffFn(pcoll):
> return pcoll | 'TimesTwo' >> beam.Map(lambda x: x * 2)
> @beam.typehints.with_input_types(float)
> @beam.typehints.with_output_types(bytes)
> class _DoStuffClass(beam.PTransform):
> def expand(self, pcoll):
> return pcoll | 'TimesTwo' >> beam.Map(lambda x: x * 2)
> {code}
> With definitions as above, the class correctly fails the typecheck:
> {code:python}
> def class_correctly_fails():
> p = beam.Pipeline(options=PipelineOptions(runtime_type_check=True))
> _ = (p
> | 'Create' >> beam.Create([1, 2, 3, 4, 5])
> | 'DoStuff1' >> _DoStuffClass()
> | 'DoStuff2' >> _DoStuffClass()
> | 'Write' >> beam.io.WriteToText('/tmp/output'))
> p.run().wait_until_finish()
> # apache_beam.typehints.decorators.TypeCheckError: Input type hint violation at DoStuff1: expected <type 'float'>, got <type 'int'>
> {code}
> But the {{ptransform_fn}} incorrectly passes the typecheck:
> {code:python}
> def ptransform_incorrectly_passes():
> p = beam.Pipeline(options=PipelineOptions(runtime_type_check=True))
> _ = (p
> | 'Create' >> beam.Create([1, 2, 3, 4, 5])
> | 'DoStuff1' >> _DoStuffFn()
> | 'DoStuff2' >> _DoStuffFn()
> | 'Write' >> beam.io.WriteToText('/tmp/output'))
> p.run().wait_until_finish()
> # No error
> {code}
> Note that changing the order of the {{@ptransform_fn}} and type hint annotations doesn't change the result, i.e. changing {{_DoStuffFn}} to the following still results in it incorrectly passing the typecheck:
> {code:python}
> @beam.ptransform_fn
> @beam.typehints.with_input_types(float)
> @beam.typehints.with_output_types(bytes)
> def _DoStuffFn(pcoll):
> return pcoll | 'TimesTwo' >> beam.Map(lambda x: x * 2)
> {code}
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