website/www/site/content/en/blog/python-improved-annotations.md

---
layout: post
title:  "Improved Annotation Support for the Python SDK"
date:   2020-08-21 00:00:01 -0800
categories:
  - blog
  - python
  - typing
authors:
  - saavannanavati
---
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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` or `None`. These are generally used for PTransforms that begin or end with an I/O operation.

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](/documentation/sdks/python-type-safety/).

Finally, please
[let us know](/community/contact-us/)
if you encounter any issues.