[
https://issues.apache.org/jira/browse/BEAM-4091?focusedWorklogId=370496&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-370496
]
ASF GitHub Bot logged work on BEAM-4091:
----------------------------------------
Author: ASF GitHub Bot
Created on: 12/Jan/20 20:46
Start Date: 12/Jan/20 20:46
Worklog Time Spent: 10m
Work Description: stale[bot] commented on issue #9907: [BEAM-4091] Pass
type hints in ptransform_fn
URL: https://github.com/apache/beam/pull/9907#issuecomment-573455466
This pull request has been marked as stale due to 60 days of inactivity. It
will be closed in 1 week if no further activity occurs. If you think that’s
incorrect or this pull request requires a review, please simply write any
comment. If closed, you can revive the PR at any time and @mention a reviewer
or discuss it on the [email protected] list. Thank you for your
contributions.
----------------------------------------------------------------
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:
[email protected]
Issue Time Tracking
-------------------
Worklog Id: (was: 370496)
Time Spent: 20m (was: 10m)
> 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: Major
> Time Spent: 20m
> 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}
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
