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  "path": "/en/blog/2026/ecosistema-rust-en-python/",
  "publishedAt": "2026-04-18T05:00:00.000Z",
  "site": "https://www.cosmoscalibur.com",
  "tags": [
    "Rust for CPython: Discussion and Roadmap",
    "Py2RS: Python to Rust guide",
    "Rusty Python: How we use Rust to speed up Python",
    "Nine rules for writing Python extensions in Rust",
    "PyO3: Rust bindings for Python",
    "Maturin: Build and publish crates with pyo3",
    "Pydantic-core: The engine of Pydantic V2",
    "Polars: Blazingly fast DataFrames with Apache Arrow",
    "ConnectorX: Fast SQL to DataFrame library",
    "Fastexcel: Fast Excel reader based on Calamine",
    "Rensa: High-performance MinHash library",
    "Orjson: Fast JSON library for Python",
    "Tiktoken: OpenAI’s fast tokenizer",
    "Robyn: A high-performance web framework",
    "Granian: A Rust HTTP server for Python",
    "Astral: The future of Python tooling"
  ],
  "textContent": "For years, the Python ecosystem has grappled with the “two-language problem”: we write code in Python for its ergonomics, but when performance becomes critical, we drop down to C or C++. This transition has always been painful, introducing memory safety risks and considerable maintenance complexity. However, we are witnessing a paradigm shift. **Rust has become the new standard for building Python infrastructure.**\n\nThis phenomenon is no accident. Rust offers what C++ never could: guaranteed memory safety at compile time and a concurrency model that allows us to safely bypass the dreaded _Global Interpreter Lock_ (GIL).\n\n## Critical Infrastructure: The Invisible Engine\n\nWe often don’t realize it, but many of the libraries we use daily have been silently rebuilt in Rust to optimize critical processes.\n\n  * ****pydantic-core**** : With the arrival of Pydantic V2, the validation engine was completely rewritten in Rust. The result is a performance improvement of between 5x and 50x. Since Pydantic is the foundation for frameworks like **FastAPI** , this change has accelerated thousands of applications without developers having to change a single line of their models.\n\n  * ****cryptography**** : One of the most downloaded packages on PyPI now uses Rust for critical parts of its logic. This not only improves speed but drastically reduces the attack surface for memory errors in an area where security is paramount.\n\n\n\n\n## Development and Management Tools: The Astral Era\n\nIf one company exemplifies this revolution, it is _Astral_. Their approach hasn’t just been “make it faster,” but to consolidate fragmented tools.\n\n  * ****ruff**** : It has replaced _flake8_ , _isort_ , _black_ , and others in a single binary. The difference isn’t just marginal; it’s instantaneous even in monorepos with millions of lines.\n\n  * ****uv**** : It’s not just a package installer. It’s a complete management system for Python versions, virtual environments, and dependencies. As I shared in my article on uv, its speed is due to intensive use of parallelism and extremely aggressive cache management.\n\n  * ****ty**** : The new type checker aiming to succeed _Mypy_ and _Pyright_. By performing static analysis in Rust, it offers real-time feedback in the editor—something that was previously prohibitive in large projects.\n\n\n\n\n## Libraries for Processing and Services\n\nBeyond tooling, data processing has seen a quantitative leap thanks to modern standards and native optimization.\n\n  * ****polars**** : Unlike _Pandas_ , **polars** uses a fully vectorized and lazy execution engine written in Rust, based internally on the **Apache Arrow** memory standard. This allows for efficient memory layout and _zero-copy_ operations that maximize modern hardware performance.\n\n  * ****connectorx**** : This is the fastest way to load data from SQL databases into DataFrames. Its native integration with **polars** (via the `pl.read_database` command with `engine=\"connectorx\"`) allows for moving millions of rows into a DataFrame using parallel connections without the usual Python bottlenecks.\n\n  * ****tiktoken**** : OpenAI’s tokenization library. In the world of LLMs, converting text into tokens is a constant and heavy task; doing it in Rust allows processing thousands of documents per second without blocking Python’s main loop.\n\n\n\n\nRegarding servers, while **robyn** attempts to be a complete alternative to **FastAPI** , projects like ****granian**** are proving that a high-performance HTTP server can be built in Rust that is more stable and faster than traditional _Uvicorn_ -based ones.\n\n## Specific Use Cases and Utilities\n\nEven in more everyday tasks, Rust is gaining ground as a direct optimization tool:\n\n  * ****fastexcel**** : Based on the **Calamine** Rust library, it is incredibly fast at reading heavy Excel files. In fact, it serves as the default and most efficient Excel reading engine for **polars**.\n\n  * ****rensa**** : Positioned as a modern alternative to `datasketch` or `pyminhash` for text similarity tasks using _MinHash_. In my experience, it has been fundamental for **automatic conversation template detection** , allowing for almost instant deduplication of large datasets.\n\n  * ****orjson**** : It remains the gold standard for JSON serialization. It can be integrated as a serializer in **Django** (for instance, through the _Django REST Framework_) to drastically improve API response speeds.\n\n\n\n\n## Native Extension Development: PyO3 and Maturin\n\nThis surge would not be possible without the ecosystem for creating native extensions. ****PyO3**** is the framework that allows exposing Rust code to Python with minimal friction. It has displaced _Cython_ and direct C API usage from _CPython_ due to its ability to prevent segfaults and allow the use of modern abstractions like generics and traits.\n\nIts greatest advantage is the **automatic mapping of standard data types** (such as lists, dictionaries, and strings), which greatly reduces the manual work at the interface between both languages. However, for an extension to be truly effective and leverage Rust’s full potential, it is vital to follow recommended design practices:\n\n  1. **GIL Management** : The true power of Rust is unleashed by using `py.allow_threads` to release the _Global Interpreter Lock_ during intensive computations, enabling true parallelism.\n\n  2. **Error Mapping** : It is essential to transform Rust `Result` types into appropriate Python exceptions (`PyValueError`, `PyTypeError`) so that the end-user experience is consistent.\n\n  3. **Decoupling** : A key practice is to keep the pure Rust logic in a crate separate from the Python bindings, which facilitates unit testing and code reuse.\n\n\n\n\nFinally, with ****maturin**** , distributing binaries (wheels) for multiple platforms is now a trivial process compared to the traditional complexities of _setuptools_.\n\n## Rust in the Core: The Future of CPython\n\nPerhaps the most impactful news is that the relationship between both languages is no longer just “neighborly,” but one of deep integration. There is an official initiative, known as **“Rust for CPython,”** which seeks to introduce Rust into the interpreter’s own source tree.\n\nThe goal is to allow critical parts of CPython—especially those that are difficult to maintain or error-prone in C—to be rewritten in Rust. It is expected that **Python 3.16** will be the first version to officially include Rust components, marking the beginning of an era where the language core will be safer and easier to evolve.\n\n## Critical Review: Is It Always the Solution?\n\nDespite the benefits, this “Rustification” comes at a cost. The contribution barrier is raised: a Python developer wanting to fix a bug in the core of **ruff** must now know Rust. Additionally, binary compilation introduces a layer of complexity in CI/CD systems that isn’t always justified for small tools.\n\nHowever, for core infrastructure, the verdict is clear: Python is the language of the interface, but Rust is, increasingly, the language of the engine.\n\n## References\n\n  * Rust for CPython: Discussion and Roadmap. Python Discourse.\n\n  * Py2RS: Python to Rust guide. Bruno Rocha.\n\n  * Rusty Python: How we use Rust to speed up Python. Ohad Ravid.\n\n  * Nine rules for writing Python extensions in Rust. Medium.\n\n  * PyO3: Rust bindings for Python.\n\n  * Maturin: Build and publish crates with pyo3.\n\n  * Pydantic-core: The engine of Pydantic V2.\n\n  * Polars: Blazingly fast DataFrames with Apache Arrow.\n\n  * ConnectorX: Fast SQL to DataFrame library.\n\n  * Fastexcel: Fast Excel reader based on Calamine.\n\n  * Rensa: High-performance MinHash library.\n\n  * Orjson: Fast JSON library for Python.\n\n  * Tiktoken: OpenAI’s fast tokenizer.\n\n  * Robyn: A high-performance web framework.\n\n  * Granian: A Rust HTTP server for Python.\n\n  * Astral: The future of Python tooling.\n\n\n",
  "title": "Rust Ecosystem in Python",
  "updatedAt": "2026-04-18T05:00:00.000Z"
}