Metadata-Version: 2.4
Name: fast-collections
Version: 0.0.0
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Rust
Classifier: Operating System :: OS Independent
Summary: Blazing-fast collections you've been missing
Author-email: khabib73 <kh73bb@yahoo.com>
License: MIT
Requires-Python: >=3.8
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM

# fast-collections

[![Docs](https://img.shields.io/badge/docs-mkdocs-blue)](https://github.com/Fast-Python/fast-collections/)
[![License](https://img.shields.io/badge/license-MIT-green.svg)](LICENSE)
[![test](https://github.com/Fast-Python/fast-collections/actions/workflows/ci.yaml/badge.svg)](https://github.com/Fast-Python/fast-collections//actions/workflows/test.yaml)
[![GitHub stars](https://img.shields.io/github/stars/Fast-Python/fast-collections?style=social)](https://github.com/Fast-Python/fast-collections/)

**Blazing-fast data structures and algorithms you've been missing** — written in Rust, usable from Python.

---

## Why this exists

Python's standard library is missing many fundamental data structures and algorithms. Every time you need a trie, an LRU cache, a B-tree, a suffix array, or anything beyond what's in the standard library, you either copy a slow pure-Python implementation from GitHub or write your own and debug edge cases for hours.

**fast-collections** fills those gaps with Rust implementations. You get production-ready data structures and algorithms that are orders of magnitude faster than anything you could write in pure Python — with compact memory layout and full type annotations.


## Why not write it yourself in Python?

Pure Python is slow for data structure internals. Every node allocation creates a Python object, every lookup goes through the dict machinery. A Rust implementation avoids all of that — no object overhead, just raw performance.


## Who is this for?

- Anyone who has ever needed a data structure or algorithm that isn't in the standard library.
- Anyone who wants Rust performance without leaving Python.


## Quickstart

```bash
pip install fast-collections
```

```python
from fast_collections import Trie

t = Trie()
t.insert("hello")
t.insert("world")

assert "hello" in t
assert t.starts_with("he")
```

See the [full documentation](https://github.com/Fast-Python/fast-collections/) for all available data structures, algorithms, and their APIs.
