Manage episode 341537692 series 1297742
Static typing versus dynamic typing is one of the oldest debates in software development. In recent years a number of dynamic languages have worked toward a middle ground by adding support for type hints. Python’s type annotations have given rise to an ecosystem of tools that use that type information to validate the correctness of programs and help identify potential bugs. At Instagram they created the Pyre project with a focus on speed to allow for scaling to huge Python projects. In this episode Shannon Zhu discusses how it is implemented, how to use it in your development process, and how it compares to other type checkers in the Python ecosystem.
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- Your host as usual is Tobias Macey and today I’m interviewing Shannon Zhu about Pyre, a type checker for Python 3 built from the ground up to support gradual typing and deliver responsive incremental checks
- How did you get introduced to Python?
- Can you describe what Pyre is and the story behind it?
- There have been a number of tools created to support various aspects of typing for Python. How would you describe the various goals that they support and how Pyre fits in that ecosystem?
- What are the core goals and notable features of Pyre?
- Can you describe how Pyre is implemented?
- How have the design and goals of the project changed/evolved since you started working on it?
- What are the different ways that Pyre is used in the development workflow for a team or individual?
- What are some of the challenges/roadblocks that people run into when adopting type definitions in their Python projects?
- How has the evolution of type annotations and overall support for them affected your work on Pyre?
- As someone who is working closely with type systems, what are the strongest aspects of Python’s implementation and opportunities for improvement?
- What are the most interesting, innovative, or unexpected ways that you have seen Pyre used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Pyre?
- When is Pyre the wrong choice?
- What do you have planned for the future of Pyre?
Keep In Touch
- shannonzhu on GitHub
- Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.
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- PEP 484
- Continuous Integration
- PEP 675 – Arbitrary literal strings
- Gradual Typing
- AST == Abstract Syntax Tree
- Language Server Protocol
- Type Arithmetic
- PyCon: Securing Code With The Python Type System
- PyCon: Type Checked Python In The Real World
- PyCon: Łukasz Lange 2022 Keynote