When distributing Python code, the two most common formats are wheel and source distributions. However, choosing between the two and understanding the best practices for each can be tricky. In this post, we’ll dive deep into wheel and source distributions, providing essential guidelines for developers working on large-scale Python projects. We’ll explore the benefits of each format, the common tools used in high-level engineering teams (including those at MAANG companies), and how they fit into CI/CD pipelines, private repositories, and deployment strategies.
Why Wheel and Source Distributions Matter
In the Python ecosystem, packaging and distributing code are crucial tasks for ensuring software is reusable, installable, and easily deployable. Whether you’re building a library for public consumption or managing private internal tools, understanding the difference between wheel and source distributions—and when to use each—is essential for both efficiency and stability.
Before we dive into the best practices, it’s worth clarifying what wheel and source distributions are:
- Wheel: The wheel format (.whl) is a built package that includes compiled code, making it faster and more efficient to install.
- Source Distribution: The source distribution (.tar.gz, .zip) contains the raw Python files and needs to be compiled during installation, which makes it slower compared to wheels.
Best Practices for Wheel Distribution
Wheels are the preferred distribution format for many reasons, primarily their efficiency and ease of use. By following these best practices, you can ensure smooth packaging, distribution, and installation:
1. Use Standardized Naming Conventions
To ensure maximum compatibility, adhere to the Python Wheel Archive naming convention. This means that the wheel file name should follow this format:
library_name-version-py3-none-any.whl
Where:
- library_name: The name of your library
- version: The version number
- py3: The supported Python version (e.g., ‘py3’ for Python 3)
- none: The ABI (Application Binary Interface) tag
- any: The architecture tag (usually ‘any’ for pure Python packages)
2. Build Wheels for Multiple Platforms
If your library contains compiled extensions, it’s crucial to build and distribute platform-specific wheels. This can be automated using tools like cibuildwheel, which can cross-compile wheels for various platforms (Linux, macOS, Windows) and architectures (e.g., x86_64, arm64).
This allows users to install the correct wheel for their platform without the need to manually compile the code, reducing installation time and complexity.
3. Maintain Consistency with CI/CD
High-level teams—especially in MAANG companies—often rely on continuous integration (CI) pipelines to automate the process of packaging and distributing Python code. Popular CI tools include Jenkins, GitHub Actions, GitLab CI, and CircleCI. These pipelines should:
- Run automated tests before generating the wheel distribution to ensure quality.
- Build wheels on every commit or release to ensure that the distribution is always up-to-date.
- Distribute the generated wheels to internal or public package indexes (e.g., PyPI, Artifactory).
Integrating these steps into your CI/CD pipeline not only ensures reliability but also streamlines your deployment process.
4. Leverage Private Repositories
In large companies, developers often need to work with internal libraries that are not shared with the public. For this purpose, setting up a private Python package repository (e.g., using Nexus, Artifactory, or GitHub Packages) is a great practice. This ensures that your team can safely distribute internal packages while keeping control over versions and access.
Private repositories can also host wheel distributions for faster installations across teams and services.
5. Optimize for Dependency Management
In modern Python development, managing dependencies effectively is essential. Use a tool like poetry or pipenv to lock down your dependencies and create deterministic environments. When distributing your package, ensure that your wheel includes the necessary metadata to enable dependency resolution. Tools like pip or conda will respect these constraints during installation, ensuring that your package works seamlessly with others.
Best Practices for Source Distributions
While wheels are the preferred format for most use cases, source distributions are still valuable—especially when you’re targeting users who need to modify the code, or when compiling native code is necessary during installation. The following best practices will help you maintain a robust source distribution workflow:
1. Ensure Complete Source Distribution
When preparing a source distribution, ensure that all necessary files are included. This typically involves:
- The source code itself (Python files)
- Any required configuration files (e.g.,
setup.py,MANIFEST.in) - Test files (if relevant) for users who wish to contribute or test locally
- Documentation (e.g., README, LICENSE)
Make use of a MANIFEST.in file to explicitly define which files should be included in the source distribution. This will ensure that no critical files are omitted during packaging.
2. Keep the Setup Process Simple
Source distributions require a build step before installation. To keep the process simple, make sure that your setup.py is well-structured and easy to use. Avoid unnecessary complexity in the setup process, and document any required steps clearly. Also, include a comprehensive README to guide users who may be unfamiliar with building from source.
3. Provide Clear Instructions for Users
Not everyone is familiar with building Python packages from source. Therefore, always provide clear instructions on how to install from a source distribution. This could include:
- Running
python setup.py installfor basic installations - Using
pip install .for local installations - Any platform-specific notes for building native extensions (e.g., using
makeon Linux, or Xcode on macOS)
Code Importing in Notebooks, Lambdas, and Other Environments
Once your wheel or source distribution is created, importing your code into environments like Jupyter notebooks, AWS Lambda, or any cloud service is the next step. Here’s how to approach each environment:
1. Jupyter Notebooks
In a Jupyter notebook, the easiest way to import a package is by ensuring it’s installed via pip. For example, if you’ve published your package to PyPI, you can use:
!pip install your-package-name
If you’re working with a private repository or a local wheel file, you can install directly from the file system:
!pip install /path/to/your-package.whl
2. AWS Lambda
In AWS Lambda, you can package your wheel file or source distribution as part of the deployment process. If using a wheel file, ensure it is compatible with the Lambda execution environment (Python 3.8, 3.9, etc.). Lambda functions can then import libraries from the deployment package.
3. Cloud-Based Services
Cloud services like Google Cloud Functions and Azure Functions also rely on packaged distributions. The deployment process typically involves uploading a ZIP or wheel file containing the code and dependencies. Ensure that all dependencies are resolved by including them in your distribution.
Common Tools for Wheel and Source Distributions
Several tools are commonly used in the Python ecosystem to streamline the process of creating, managing, and distributing wheels and source distributions:
- Setuptools: The de facto standard for packaging Python projects.
- Wheel: A tool to create wheel distributions.
- Poetry: A modern dependency management and packaging tool that simplifies creating both wheels and source distributions.
- Cibuildwheel: An excellent tool for automating the build of wheels across platforms.
- Twine: A tool for securely uploading Python packages to PyPI or private repositories.
Conclusion
Distributing Python code efficiently requires understanding the nuances of wheel and source distributions. By following best practices such as building for multiple platforms, integrating CI/CD, and managing dependencies, you can ensure your code is packaged and distributed reliably. Whether you’re working with private repositories or deploying to cloud services like AWS Lambda, using the right tools and formats will streamline the process, improve installation times, and maintain a high level of consistency across environments.
Adhering to these best practices will help you produce better Python libraries and applications—something that is critical when scaling code in large companies like those at MAANG level. Ultimately, the goal is to make it as easy as possible for your users to get the software up and running with minimal friction.