[GitHub] [beam] saavannanavati commented on a change in pull request #12657: [BEAM-10777] Add two blog posts detailing changes to the type hints module of the Python SDK

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saavannanavati commented on a change in pull request #12657:
URL: https://github.com/apache/beam/pull/12657#discussion_r475122546



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File path: website/www/site/content/en/blog/python-improved-annotations.md
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+---
+layout: post
+title:  "Improved Annotation Support for the Python SDK"
+date:   2020-08-21 00:00:01 -0800
+categories:
+  - blog 
+  - python 
+  - typing
+authors:
+  - saavan
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+The importance of static type checking in a dynamically 
+typed language like Python is not up for debate. Type hints 
+allow developers to leverage a strong typing system to:
+ - write better code, 
+ - self-document ambiguous programming logic, and 
+ - inform intelligent code completion in IDEs like PyCharm.
+
+This is why we're excited to announce upcoming improvements to 
+the `typehints` module of Beam's Python SDK, including support 
+for typed PCollections and Python 3 style annotations on PTransforms.
+
+# Improved Annotations
+Today, you have the option to declare type hints on PTransforms using either
+class decorators or inline functions.
+
+For instance, a PTransform with decorated type hints might look like this:
+```
+@beam.typehints.with_input_types(int)
+@beam.typehints.with_output_types(str)
+class IntToStr(beam.PTransform):
+    def expand(self, pcoll):
+        return pcoll | beam.Map(lambda num: str(num))
+
+strings = numbers | beam.ParDo(IntToStr())
+```
+
+Using inline functions instead, the same transform would look like this:
+```
+class IntToStr(beam.PTransform):
+    def expand(self, pcoll):
+        return pcoll | beam.Map(lambda num: str(num))
+
+strings = numbers | beam.ParDo(IntToStr()).with_input_types(int).with_output_types(str)
+```
+
+Both methods have problems. Class decorators are syntax-heavy, 
+requiring two additional lines of code, whereas inline functions provide type hints 
+that aren't reusable across other instances of the same transform. Additionally, both 
+methods are incompatible with static type checkers like MyPy.
+
+With Python 3 annotations however, we can subvert these problems to provide a 
+clean and reusable type hint experience. Our previous transform now looks like this:
+```
+class IntToStr(beam.PTransform):
+    def expand(self, pcoll: PCollection[int]) -> PCollection[str]:
+        return pcoll | beam.Map(lambda num: str(num))
+
+strings = numbers | beam.ParDo(IntToStr())
+```
+
+These type hints will actively hook into the internal Beam typing system to
+play a role in pipeline type checking, and runtime type checking. 
+
+So how does this work?
+
+## Typed PCollections
+You guessed it! The PCollection class inherits from `typing.Generic`, allowing it to be 
+parameterized with either zero types (denoted `PCollection`) or one type (denoted `PCollection[T]`). 
+- A PCollection with zero types is implicitly converted to `PCollection[any]`.
+- A PCollection with one type can have any nested type (e.g. `Union[int, str]`).
+
+Internally, Beam's typing system makes these annotations compatible with other 
+type hints by removing the outer PCollection container.
+
+## PBegin, PDone, None
+Finally, besides PCollection, a valid annotation on the `expand(...)` method of a PTransform is
+`PBegin`, `PDone`, and `None`. These are generally used for I/O operations.
+
+For instance, when saving data, your transform's output type should be `None`.
+```
+class SaveResults(beam.PTransform):
+    def expand(self, pcoll: PCollection[str]) -> None:
+        return pcoll | beam.io.WriteToBigQuery(...)
+```
+
+# Next Steps
+What are you waiting for.. start using annotations on your transforms!
+
+For more background on type hints in Python, see:
+[Ensuring Python Type Safety](https://beam.apache.org/documentation/sdks/python-type-safety/). 
+
+Finally, please 

Review comment:
       Thanks! :)




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