---

    ____           __              _ 
   / __/___ ______/ /_____ _____  (_)
  / /_/ __ `/ ___/ __/ __ `/ __ \/ / 
 / __/ /_/ (__  ) /_/ /_/ / /_/ / /  
/_/  \__,_/____/\__/\__,_/ .___/_/   
                        /_/          
         ___     ___                         _           __             
   _____/ (_)___/ (_)___  ____ _   _      __(_)___  ____/ /___ _      __
  / ___/ / / __  / / __ \/ __ `/  | | /| / / / __ \/ __  / __ \ | /| / /
 (__  ) / / /_/ / / / / / /_/ /   | |/ |/ / / / / / /_/ / /_/ / |/ |/ / 
/____/_/_/\__,_/_/_/ /_/\__, /    |__/|__/_/_/ /_/\__,_/\____/|__/|__/  
                       /____/                                           

📦 installation

pip install fastapi-sliding-window
pip install fastapi-sliding-window[redis]  # with Redis support
pip install "fastapi-sliding-window[redis]" uvicorn  # to run examples/

Full working examples are in the examples/ directory.

---

📑 quick start

Decorator API (recommended)

from fastapi import FastAPI
from fastapi_sliding_window import Limiter, InMemoryBackend

app = FastAPI()
limiter = Limiter(backend=InMemoryBackend())

@app.get("/login")
@limiter.limit("5/minute")
async def login():
    return {"status": "ok"}

Dependency API

from fastapi import Depends
from fastapi_sliding_window import RateLimit

@app.get("/login", dependencies=[Depends(RateLimit(requests=5, window_seconds=60))])
async def login():
    return {"status": "ok"}

---

🧩 features

• 🎯 5 algorithms — Sliding Window Log, Sliding Window Counter, Fixed Window, Token Bucket, GCRA
• 💾 in-memory & Redis — no external deps for single-node, Redis for distributed
• 🔧 three usage styles — @limiter.limit() decorator, Depends(RateLimit(...)), RateLimitMiddleware
• 📝 standard & IETF headers — X-RateLimit-* or RateLimit-* — configurable
• 🚧 circuit breaker — RedisWithFallbackBackend falls back to in-memory on Redis failure
• ✅ fully typed — py.typed marker included
• 🚀 async-native — thread-safe with asyncio.Lock

---

⚖️ comparison with alternatives

Feature	fastapi-sliding-window	slowapi	fastapi-limiter
Algorithms	5 (Fixed, Sliding Log, Sliding Counter, Token Bucket, GCRA)	2 (Fixed + Sliding)	1 (Fixed)
In-memory backend	✅ zero deps	✅ (via limits lib)	❌ Redis only
Redis backend	✅	✅	✅
Circuit Breaker	✅	❌	❌
Middleware	✅	✅	❌
Dependency	✅	✅	✅
Decorator API	✅	✅	❌
IETF Headers (RateLimit-*)	✅	❌	❌
Cost per request	✅	❌	❌
Burst support	✅	❌	❌
Thread-safe	✅	❌	❌
WebSocket	❌	❌	✅
memcached	❌	✅	❌
mypy strict	✅	❌	❌
Python versions	3.10+	3.7+	3.9+
External deps (basic mode)	zero	flask-limiter + limits	pyrate-limiter

Each library has its own strengths — choose what fits your use case.

---

📖 usage

Limiter (decorator API)

from fastapi_sliding_window import Limiter, InMemoryBackend

limiter = Limiter(
    backend=InMemoryBackend(),
    default_limits=["1000/hour"],       # global defaults
    include_headers=True,
    use_ietf_headers=False,              # set True for RateLimit-* headers
    on_breach=lambda req, exc: ...,      # custom 429 handler
)

@app.get("/api")
@limiter.limit("10/minute")              # 10/min + 1000/hour inherited
async def api():
    return {"ok": True}

@app.get("/expensive")
@limiter.limit("5/minute", cost=5)       # each request costs 5 tokens
async def expensive():
    return {"ok": True}

@app.get("/health")
@limiter.exempt                          # no rate limit
async def health():
    return {"ok": True}

@app.get("/internal")
@limiter.limit("10/minute", exempt_when=lambda r: r.headers.get("X-Internal") == "true")
async def internal():
    return {"ok": True}

Depends (per-route, standalone)

from fastapi import Depends
from fastapi_sliding_window import RateLimit, Algorithm

@app.get("/api/data", dependencies=[
    Depends(RateLimit(requests=100, window_seconds=60))
])
async def get_data():
    return {"data": "..."}

@app.post("/api/upload", dependencies=[
    Depends(RateLimit(requests=10, window_seconds=60, algorithm=Algorithm.FIXED_WINDOW))
])
async def upload():
    return {"status": "uploaded"}

async def user_key(request):
    return request.headers.get("X-User-ID", "anonymous")

@app.get("/api/profile", dependencies=[
    Depends(RateLimit(requests=50, window_seconds=60, key_func=user_key))
])
async def profile():
    return {"user": "..."}

Depends (with Limiter delegation)

limiter = Limiter(backend=InMemoryBackend(), default_limits=["1000/hour"])

@app.get("/api", dependencies=[Depends(RateLimit(limiter=limiter))])
@limiter.limit("10/minute")              # endpoint rules + limiter defaults
async def api():
    return {"ok": True}

Middleware (global)

from fastapi_sliding_window import RateLimitMiddleware, Algorithm

app.add_middleware(
    RateLimitMiddleware,
    requests=100,
    window_seconds=60.0,
    algorithm=Algorithm.SLIDING_WINDOW_LOG,
    exclude_paths=["/health"],
)

Middleware (with Limiter delegation)

from fastapi_sliding_window import Limiter, InMemoryBackend, RateLimitMiddleware

limiter = Limiter(backend=InMemoryBackend(), default_limits=["1000/hour"])
app.add_middleware(RateLimitMiddleware, limiter=limiter)

Redis backend

from fastapi_sliding_window import Limiter
from fastapi_sliding_window._utils import from_url

limiter = Limiter(backend=from_url("redis://localhost:6379/0"))

Redis with circuit breaker fallback

from fastapi_sliding_window._backends.fallback import RedisWithFallbackBackend

backend = RedisWithFallbackBackend(
    redis_url="redis://localhost:6379/0",
    failure_threshold=5,       # open circuit after 5 failures
    recovery_timeout=30.0,     # try again after 30s
)
limiter = Limiter(backend=backend)

Burst (Token Bucket / GCRA)

@limiter.limit("5/s burst 10")    # 5 req/s sustained, burst up to 10
async def bursty():
    return {"ok": True}

String-based limits

parse("100/hour")          # RateLimitItem(limit=100, window=3600, unit='hour')
parse("5/s")               # RateLimitItem(limit=5, window=1, unit='s')
parse_many("100/hour;10/minute")  # list[RateLimitItem, RateLimitItem]

Custom cost

@limiter.limit("100/hour", cost=5)                     # fixed cost
@limiter.limit("100/hour", cost=lambda r: 5 if r.method == "POST" else 1)

---

📊 algorithms

Algorithm	Accuracy	Memory	Speed	Best for
Sliding Window Log	100%	O(requests/window)	O(1) amortized	accuracy-critical
Sliding Window Counter	~99%	O(1) per key	O(1)	high-traffic APIs
Fixed Window	~50% (boundary burst)	O(1) per key	O(1)	simple quotas
Token Bucket	exact	O(1) per key	O(1)	bursty workloads
GCRA	exact	O(1) per key	O(1)	burst + smooth

Sliding Window Log

Stores a timestamp for every request. Prunes entries outside the window on each check. Perfectly accurate, memory grows linearly with request volume within the window.

Sliding Window Counter

Maintains two counters (current and previous window) and computes a weighted average. Near-accurate with constant memory.

Fixed Window

Simple counter per time bucket. Suffers from boundary burst (clients can send 2x the limit across a window boundary).

Token Bucket

Maintains a token count that refills at a fixed rate. Allows bursting up to the bucket capacity. When tokens are exhausted, requests are denied until tokens refill.

GCRA (Generic Cell Rate Algorithm)

Tracks the next allowed timestamp (TAT). Supports smooth traffic shaping with burst tolerance. O(1) memory per key.

---

📊 benchmark results

Pure backend speed (100k direct calls)

Algorithm	checks/s	avg µs
Fixed Window	200,700	5.0
Sliding Window Log	199,000	5.0
Sliding Window Counter	142,800	7.0
Token Bucket	189,100	5.3
GCRA	207,300	4.8

All algorithms complete a check in 5–7 µs — the overhead is negligible for any real application.

Full-stack throughput (50 concurrent clients)

Through RateLimitMiddleware (pure ASGI, no serialization) + FastAPI.

Algorithm	RPS	vs baseline
Baseline (no limit)	72,850	—
Fixed Window	67,660	93%
Sliding Window Log	67,320	92%
Sliding Window Counter	67,040	92%
Token Bucket	67,320	92%
GCRA	67,370	92%

Overhead of the rate-limit middleware is ~7–8% at scale.

Accuracy (limit 10/s, 50 requests at 10ms intervals)

Algorithm	Allowed	Blocked	Notes
Fixed Window	10	40
Sliding Window Log	10	40
Sliding Window Counter	10	40
Token Bucket	15	35	5 extra allowed (burst by design)
GCRA	15	35	5 extra allowed (burst by design)

Full benchmark script and methodology: benchmarks/.

---

📝 HTTP headers

Standard (default)

Header	Description
X-RateLimit-Limit	maximum requests allowed
X-RateLimit-Remaining	requests remaining in window
X-RateLimit-Reset	unix timestamp when window resets
Retry-After	seconds until next request is allowed (429 only)

IETF (set use_ietf_headers=True)

Header	Description
RateLimit-Limit	maximum requests allowed
RateLimit-Remaining	requests remaining in window
RateLimit-Reset	unix timestamp when window resets
Retry-After	seconds until next request is allowed (429 only)

---

🔗 API Reference

Limiter(backend, key_func, default_limits, on_breach, include_headers, use_ietf_headers)

Main class — decorator API.

Parameter	Type	Default	Description
backend	RateLimitBackend	required	storage backend
key_func	KeyFunc	IP-based	client key function
default_limits	list[str]	None	global rate limits
on_breach	Callable	None	custom 429 handler
include_headers	bool	True	add rate limit headers
use_ietf_headers	bool	False	use RateLimit-* instead of X-RateLimit-*

Methods:

• @limiter.limit("100/hour", cost=1, key_func=None, exempt_when=None) — decorator
• @limiter.exempt — skip rate limiting
• await limiter.check(request, response, items) — programmatic check

RateLimit(requests, window_seconds, algorithm, key_func, include_headers, detail, backend, cost, limiter, use_ietf_headers)

FastAPI dependency for per-route rate limiting.

Parameter	Type	Default	Description
requests	`int	str`	100
window_seconds	float	60.0	window duration
algorithm	Algorithm	SLIDING_WINDOW_LOG	algorithm
key_func	KeyFunc	IP-based	client key function
include_headers	bool	True	add headers
detail	str	"Rate limit exceeded"	429 detail message
backend	RateLimitBackend	None	custom backend
cost	int	1	token cost per request
limiter	Limiter	None	delegate to Limiter
use_ietf_headers	bool	False	IETF headers

RateLimitMiddleware(app, requests, window_seconds, algorithm, key_func, include_headers, exclude_paths, detail, backend, cost, limiter, use_ietf_headers)

Starlette middleware for global rate limiting.

Parameter	Type	Default	Description
requests	`int	str`	100
window_seconds	float	60.0	window duration
algorithm	Algorithm	SLIDING_WINDOW_LOG	algorithm
key_func	KeyFunc	IP-based	client key function
include_headers	bool	True	add headers
exclude_paths	list[str]	None	paths to skip
detail	str	"Rate limit exceeded"	429 detail
backend	RateLimitBackend	None	custom backend
cost	int	1	token cost per request
limiter	Limiter	None	delegate to Limiter
use_ietf_headers	bool	False	IETF headers

Algorithm enum

SLIDING_WINDOW_LOG, SLIDING_WINDOW_COUNTER, FIXED_WINDOW, TOKEN_BUCKET, GCRA

from_url(url)

Factory: "memory://" → InMemoryBackend, "redis://..." → RedisBackend

Utility functions

• parse("100/hour") → RateLimitItem
• parse_many("100/hour;10/minute") → list[RateLimitItem]
• rate_limit_headers(result, use_ietf=False) → dict[str, str]

---

📜 license

MIT