David Seddon, a Django expert at Octopus Energy, and Samuel Colvin, the creator of Pydantic, dive into the integration of Rust into Python for enhanced performance. They discuss the massive shifts in the Python ecosystem and the benefits of using Rust, particularly through the Pydantic library. Topics include managing a large-scale Django monolith, the stricter parsing behavior of Pydantic, and the introduction of Logfire for observability. The conversation also highlights tools like Jitter and the architectural strategies for building Python packages.
The migration of performance-sensitive Python components to Rust significantly enhances efficiency and safety, exemplified by tools like UV and Ruff.
Pydantic's transition to Rust for its core functionalities has driven immense growth, culminating in Pydantic V2 and hundreds of millions of downloads.
Integrating Rust into Python projects, while initially challenging, offers substantial performance benefits and can be facilitated by tools like Maturin.
Deep dives
The Shift to Rust in Python Projects
There is a growing trend in the Python community, with many projects moving critical components to Rust for performance improvements. The decision to rewrite performance-sensitive sections in Rust is driven by its ability to enhance efficiency and safety compared to traditional Python code. This transition is exemplified by tools like UV and Ruff, which are adopting Rust instead of Python for core functionalities. Such ventures not only promise faster execution times but also improve the reliability of software by leveraging Rust's memory safety features.
Pydantic's Transformation and Impact
Pydantic, a popular data validation library in Python, underwent a significant overhaul by migrating its core functionalities to Rust. Initially a side project, Pydantic saw explosive growth, prompting its creator to transition to full-time development and establish a company around it. The new version, Pydantic V2, released after substantial investment, has further expanded its user base, achieving hundreds of millions of downloads. This shift not only enhances performance but also positions Pydantic favorably within the Python ecosystem as it continues to evolve.
Innovative Tools for Efficient Workflows
Developers are increasingly relying on tools like Jitter and Kraken to streamline their Python applications by integrating Rust components effectively. Jitter is a high-performance JSON parser that utilizes Rust’s capabilities to enable faster parsing, addressing common bottlenecks in Python development. On the other hand, Kraken, a large Django monolith used in energy management, showcases how Rust can be used to refactor complex systems for improved performance. These tools illustrate the synergy between Python and Rust, promoting efficient workflows and reducing development time.
Architectural Discipline with Import Linter
The Import Linter tool serves as a vital resource for maintaining architectural integrity in Python projects. By analyzing import statements, it builds a dependency graph that helps enforce architectural rules among modules and packages, ensuring clean and maintainable code. Users can define contracts in simple configuration files, enabling continuous integration systems to catch architectural violations early in the development process. This approach not only simplifies the codebase but also facilitates collaboration among large teams by preventing tightly coupled dependencies.
Adopting Rust: Benefits and Challenges
Integrating Rust into existing Python projects comes with both advantages and challenges, particularly in terms of learning curve and performance. While Rust offers memory safety and performance improvements, developers may initially feel overwhelmed by its complexities compared to Python’s dynamic nature. However, familiarizing oneself with Rust is seen as a valuable investment that can significantly enhance programming skill sets. Moreover, tools like Maturin help bridge the gap between Rust and Python, allowing developers to manage dependencies and streamline their workflow.
There has been a lot of changes in the low-level Python space these days. The biggest has to be how many projects have rewritten core performance-intensive sections in Rust. Or even the wholesale adoption of Rust for newer projects such as uv and ruff. On this episode, we dive into the tools and workflow needed to build these portions of Python apps in Rust with David Seddon and Samuel Colvin.