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https://issues.apache.org/jira/browse/BEAM-4091?focusedWorklogId=489291&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-489291
]
ASF GitHub Bot logged work on BEAM-4091:
----------------------------------------
Author: ASF GitHub Bot
Created on: 23/Sep/20 04:50
Start Date: 23/Sep/20 04:50
Worklog Time Spent: 10m
Work Description: udim commented on a change in pull request #9907:
URL: https://github.com/apache/beam/pull/9907#discussion_r492965241
##########
File path: sdks/python/apache_beam/options/pipeline_options.py
##########
@@ -476,6 +499,26 @@ def _add_argparse_args(cls, parser):
'time. NOTE: only supported with portable runners '
'(including the DirectRunner)')
+ def validate(self, unused_validator):
+ errors = []
+ if beam.version.__version__ >= '3':
Review comment:
This error is like a long-term TODO so that we don't forget to update
defaults in v3.
It could mean that we enable everything and retire this flag. It depends on
how stable type hints are.
##########
File path: CHANGES.md
##########
@@ -73,6 +73,10 @@
* In Interactive Beam, ib.show() and ib.collect() now have "n" and "duration"
as parameters. These mean read only up to "n" elements and up to "duration"
seconds of data read from the recording
([BEAM-10603](https://issues.apache.org/jira/browse/BEAM-10603)).
* Initial preview of
[Dataframes](https://s.apache.org/simpler-python-pipelines-2020#slide=id.g905ac9257b_1_21)
support.
See also example at apache_beam/examples/wordcount_dataframe.py
+* Fixed support for type hints on `@ptransform_fn` decorators in the Python
SDK.
Review comment:
I can add that
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Issue Time Tracking
-------------------
Worklog Id: (was: 489291)
Time Spent: 6h 50m (was: 6h 40m)
> 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
> Priority: P2
> Time Spent: 6h 50m
> 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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