zanewalker
f3453cb0fc
- Refactor Redis backend connection handling and pool management - Update algorithm implementations with improved type annotations - Enhance config loader validation with stricter Pydantic schemas - Improve decorator and middleware error handling - Expand example scripts with better docstrings and usage patterns - Add new 00_basic_usage.py example for quick start - Reorganize examples directory structure - Fix type annotation inconsistencies across core modules - Update dependencies in pyproject.toml
149 lines
3.7 KiB
ReStructuredText
149 lines
3.7 KiB
ReStructuredText
FastAPI Traffic
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===============
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**Production-grade rate limiting for FastAPI that just works.**
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.. image:: https://img.shields.io/badge/python-3.10+-blue.svg
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:target: https://www.python.org/downloads/
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.. image:: https://img.shields.io/badge/license-Apache%202.0-green.svg
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:target: https://www.apache.org/licenses/LICENSE-2.0
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----
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FastAPI Traffic is a rate limiting library designed for real-world FastAPI applications.
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It gives you five battle-tested algorithms, three storage backends, and a clean API that
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stays out of your way.
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Whether you're building a public API that needs to handle thousands of requests per second
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or a small internal service that just needs basic protection, this library has you covered.
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Quick Example
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-------------
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Here's how simple it is to add rate limiting to your FastAPI app:
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.. code-block:: python
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from fastapi import FastAPI, Request
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from fastapi_traffic import rate_limit
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app = FastAPI()
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@app.get("/api/users")
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@rate_limit(100, 60) # 100 requests per minute
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async def get_users(request: Request):
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return {"users": ["alice", "bob"]}
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That's it. Your endpoint is now rate limited. Clients get helpful headers telling them
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how many requests they have left, and when they can try again if they hit the limit.
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Why FastAPI Traffic?
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--------------------
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Most rate limiting libraries fall into one of two camps: either they're too simple
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(fixed window only, no persistence) or they're way too complicated (requires reading
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a 50-page manual just to get started).
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We tried to hit the sweet spot:
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- **Five algorithms** — Pick the one that fits your use case. Token bucket for burst
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handling, sliding window for precision, fixed window for simplicity.
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- **Three backends** — Memory for development, SQLite for single-node production,
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Redis for distributed systems.
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- **Works how you'd expect** — Decorator for endpoints, middleware for global limits,
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dependency injection if that's your style.
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- **Fully async** — Built from the ground up for async Python. No blocking calls,
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no thread pool hacks.
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- **Type-checked** — Full type hints throughout. Works great with pyright and mypy.
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What's in the Box
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-----------------
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.. grid:: 2
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:gutter: 3
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.. grid-item-card:: 🚦 Rate Limiting
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:link: getting-started/quickstart
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:link-type: doc
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Decorator-based rate limiting with sensible defaults.
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.. grid-item-card:: 🔧 Algorithms
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:link: user-guide/algorithms
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:link-type: doc
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Token bucket, sliding window, fixed window, leaky bucket, and more.
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.. grid-item-card:: 💾 Backends
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:link: user-guide/backends
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:link-type: doc
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Memory, SQLite, and Redis storage options.
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.. grid-item-card:: ⚙️ Configuration
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:link: user-guide/configuration
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:link-type: doc
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Load settings from environment variables or config files.
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.. toctree::
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:maxdepth: 2
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:caption: Getting Started
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:hidden:
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getting-started/installation
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getting-started/quickstart
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.. toctree::
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:maxdepth: 2
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:caption: User Guide
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:hidden:
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user-guide/algorithms
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user-guide/backends
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user-guide/middleware
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user-guide/configuration
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user-guide/key-extractors
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user-guide/exception-handling
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.. toctree::
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:maxdepth: 2
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:caption: Advanced Topics
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:hidden:
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advanced/distributed-systems
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advanced/performance
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advanced/testing
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.. toctree::
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:maxdepth: 2
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:caption: API Reference
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:hidden:
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api/decorator
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api/middleware
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api/algorithms
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api/backends
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api/config
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api/exceptions
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.. toctree::
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:maxdepth: 1
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:caption: Project
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:hidden:
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changelog
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contributing
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Indices and tables
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------------------
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* :ref:`genindex`
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* :ref:`modindex`
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* :ref:`search`
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