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fastapi-traffic/README.md

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# FastAPI Traffic
A rate limiting library for FastAPI that actually works in production. Pick your algorithm, pick your backend, and you're good to go.
## Why this library?
Most rate limiting solutions are either too simple (fixed window only) or too complex (requires a PhD to configure). This one tries to hit the sweet spot:
- **Five algorithms** to choose from, depending on your use case
- **Three storage backends**: memory for development, SQLite for single-node, Redis for distributed
- **Works how you'd expect**: decorator for endpoints, middleware for global limits
- **Fully async** and type-checked with pyright
- **Sensible defaults** but configurable when you need it
## Installation
### Using pip
```bash
# Basic installation (memory backend only)
pip install fastapi-traffic
# With Redis support
pip install fastapi-traffic[redis]
# With all extras
pip install fastapi-traffic[all]
```
### Using uv
```bash
# Basic installation
uv add fastapi-traffic
# With Redis support
uv add fastapi-traffic[redis]
# With all extras
uv add fastapi-traffic[all]
```
## Quick Start
### Basic Usage with Decorator
```python
from fastapi import FastAPI, Request
from fastapi_traffic import rate_limit
app = FastAPI()
@app.get("/api/resource")
@rate_limit(100, 60) # 100 requests per 60 seconds
async def get_resource(request: Request):
return {"message": "Hello, World!"}
```
### Using Different Algorithms
```python
from fastapi_traffic import rate_limit, Algorithm
# Token Bucket - allows bursts
@app.get("/api/burst")
@rate_limit(100, 60, algorithm=Algorithm.TOKEN_BUCKET, burst_size=20)
async def burst_endpoint(request: Request):
return {"message": "Burst allowed"}
# Sliding Window - precise rate limiting
@app.get("/api/precise")
@rate_limit(100, 60, algorithm=Algorithm.SLIDING_WINDOW)
async def precise_endpoint(request: Request):
return {"message": "Precise limiting"}
# Fixed Window - simple and efficient
@app.get("/api/simple")
@rate_limit(100, 60, algorithm=Algorithm.FIXED_WINDOW)
async def simple_endpoint(request: Request):
return {"message": "Fixed window"}
```
### Custom Key Extraction
```python
def api_key_extractor(request: Request) -> str:
"""Rate limit by API key instead of IP."""
return request.headers.get("X-API-Key", "anonymous")
@app.get("/api/by-key")
@rate_limit(1000, 3600, key_extractor=api_key_extractor)
async def api_key_endpoint(request: Request):
return {"message": "Rate limited by API key"}
```
### Using SQLite Backend (Persistent)
```python
from fastapi_traffic import RateLimiter, SQLiteBackend
from fastapi_traffic.core.limiter import set_limiter
# Configure persistent storage
backend = SQLiteBackend("rate_limits.db")
limiter = RateLimiter(backend)
set_limiter(limiter)
@app.on_event("startup")
async def startup():
await limiter.initialize()
@app.on_event("shutdown")
async def shutdown():
await limiter.close()
```
### Using Redis Backend (Distributed)
```python
from fastapi_traffic import RateLimiter
from fastapi_traffic.backends.redis import RedisBackend
# Create Redis backend
backend = await RedisBackend.from_url("redis://localhost:6379/0")
limiter = RateLimiter(backend)
set_limiter(limiter)
```
### Global Middleware
```python
from fastapi_traffic.middleware import RateLimitMiddleware
app.add_middleware(
RateLimitMiddleware,
limit=1000,
window_size=60,
exempt_paths={"/health", "/docs"},
exempt_ips={"127.0.0.1"},
)
```
### Dependency Injection
```python
from fastapi import Depends
from fastapi_traffic.core.decorator import RateLimitDependency
rate_dep = RateLimitDependency(limit=100, window_size=60)
@app.get("/api/with-info")
async def endpoint_with_info(
request: Request,
rate_info = Depends(rate_dep)
):
return {
"remaining": rate_info.remaining,
"reset_at": rate_info.reset_at,
}
```
### Exception Handling
```python
from fastapi_traffic import RateLimitExceeded
@app.exception_handler(RateLimitExceeded)
async def rate_limit_handler(request: Request, exc: RateLimitExceeded):
return JSONResponse(
status_code=429,
content={
"error": "rate_limit_exceeded",
"retry_after": exc.retry_after,
},
headers=exc.limit_info.to_headers() if exc.limit_info else {},
)
```
## Algorithms
| Algorithm | Description | Use Case |
|-----------|-------------|----------|
| `TOKEN_BUCKET` | Allows bursts up to bucket capacity | APIs that need burst handling |
| `SLIDING_WINDOW` | Precise request counting | High-accuracy rate limiting |
| `FIXED_WINDOW` | Simple time-based windows | Simple, low-overhead limiting |
| `LEAKY_BUCKET` | Smooths out request rate | Consistent throughput |
| `SLIDING_WINDOW_COUNTER` | Balance of precision and efficiency | General purpose (default) |
## Backends
### MemoryBackend (Default)
- In-memory storage with LRU eviction
- Best for single-process applications
- No persistence across restarts
### SQLiteBackend
- Persistent storage using SQLite
- WAL mode for better performance
- Suitable for single-node deployments
### RedisBackend
- Distributed storage using Redis
- Required for multi-node deployments
- Supports atomic operations via Lua scripts
## Configuration Options
```python
@rate_limit(
limit=100, # Max requests in window
window_size=60.0, # Window size in seconds
algorithm=Algorithm.SLIDING_WINDOW_COUNTER,
key_prefix="api", # Prefix for rate limit keys
key_extractor=func, # Custom key extraction function
burst_size=None, # Burst size (token/leaky bucket)
include_headers=True, # Add rate limit headers to response
error_message="...", # Custom error message
status_code=429, # HTTP status when limited
skip_on_error=False, # Skip limiting on backend errors
cost=1, # Cost per request
exempt_when=func, # Function to check exemption
on_blocked=func, # Callback when request is blocked
)
```
## Response Headers
When `include_headers=True`, responses include:
- `X-RateLimit-Limit`: Maximum requests allowed
- `X-RateLimit-Remaining`: Remaining requests in window
- `X-RateLimit-Reset`: Unix timestamp when limit resets
- `Retry-After`: Seconds until retry (when rate limited)
## Development
See [DEVELOPMENT.md](DEVELOPMENT.md) for setting up a development environment and contributing.
## License
Apache License 2.0