fluxrate 0.4.0

fluxrate
Lightweight, thread-safe rate limiting for Python

Zero dependencies. Four algorithms. One API.

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Why fluxrate?

Rate limiting is a fundamental building block of distributed systems — yet the Python ecosystem forces developers to choose between bloated frameworks and single-algorithm libraries.

• slowapi bundles Flask/Starlette dependencies and only implements fixed-window counters.
• ratelimit provides a single decorator with one algorithm and no per-key support.
• limits (the library behind flask-limiter) is well-designed but pulls in storage backend dependencies and is tightly coupled to web frameworks.
• flask-limiter only works with Flask and delegates to limits.

fluxrate fills a gap that no existing package addresses: a single, dependency-free library that provides four production-proven rate-limiting algorithms through a unified API — usable as a decorator, context manager, or manual guard, without any framework lock-in.

Quick Start

pip install fluxrate

from fluxrate import RateLimiter

# Decorator
@RateLimiter(calls=10, period=60)
def api_call():
    return do_work()

# Manual check
limiter = RateLimiter(calls=100, period=60)
if limiter.allow("user_123"):
    process_request()

# Context manager
with limiter.acquire("user_123", timeout=5):
    call_external_api()

Use Cases

Use Case	Recommended Algorithm	Why
API Rate Limiting	Token Bucket	Allows legitimate bursts while enforcing sustained rate
DDoS Protection	Sliding Window	Precise control with no boundary-exploitable windows
Resource Throttling	Leaky Bucket	Smooths output to a fixed rate, prevents resource spikes
Queue Management	Leaky Bucket	Models queue drain rate naturally
Per-User Limits	Any (with key_func)	key_func routes each user to an independent bucket
Billing Enforcement	Sliding Window	Accurate accounting across window boundaries
Simple Capping	Fixed Window	Minimal overhead when precision isn't critical

Algorithm Comparison

Choosing the right algorithm depends on your traffic pattern and precision requirements:

Token Bucket

The workhorse of rate limiting. Tokens are added at a fixed rate and consumed per request. Accumulated tokens allow bursts up to the bucket capacity, then requests are rejected until tokens refill.

When to use: APIs with variable traffic, user-facing services that need burst tolerance, any scenario where you want to be "generous but bounded."

Sliding Window

Tracks request timestamps in a rolling window. Unlike fixed windows, there are no boundary artifacts — a window starting at 12:59 and one at 13:00 are part of the same continuous time span.

When to use: Billing or quota enforcement where precision matters, compliance-sensitive rate limits, preventing window-boundary attacks.

Fixed Window

Divides time into discrete buckets (e.g., 60-second windows starting at :00 seconds). Counts requests per bucket. Simple, fast, but susceptible to boundary bursts — two requests at 12:59:59 and 13:00:01 each land in different windows.

When to use: Prototyping, internal services where approximate limits suffice, high-throughput scenarios where minimal overhead is critical.

Leaky Bucket

Models a queue with a fixed drain rate. Requests enter a queue; the queue drains at a constant rate regardless of inflow. This smooths traffic rather than rejecting bursts.

When to use: Output to external APIs with strict rate limits, database write throttling, any downstream system that can't handle bursty input.

           Token Bucket              Leaky Bucket
          ┌──────────┐              ┌──────────┐
 Tokens   │ ▓▓▓▓░░░░ │  Requests   │ ▓▓▓░░░░░ │  Queue
 refill → │          │  ──────────→ │          │ ──→ drain
 at rate  │ capacity │             │ capacity │  at rate
          └──────────┘              └──────────┘
          Allows bursts             Smooths output

Features

• 🧵 Thread-safe by default — all algorithms use internal locking
• 🔑 Per-key limiting — per user, IP, API key, or any custom key function
• 🎯 Three usage modes — decorator, context manager, manual guard
• 📊 Stats & monitoring — built-in usage statistics per key
• ⏱️ Retry-After — standard HTTP Retry-After header values out of the box
• 🚫 Zero dependencies — pure Python, no framework coupling
• 📦 Four algorithms — token bucket, sliding window, fixed window, leaky bucket

Comparison with Existing Solutions

Feature	fluxrate	slowapi	ratelimit	limits	flask-limiter
Token Bucket	✅	❌	❌	✅	✅
Sliding Window	✅	❌	❌	✅	✅
Fixed Window	✅	✅	✅	✅	✅
Leaky Bucket	✅	❌	❌	✅	❌
Zero Dependencies	✅	❌	✅	❌	❌
Per-Key Limiting	✅	✅	❌	✅	✅
Context Manager	✅	❌	❌	❌	❌
Built-in Stats	✅	❌	❌	❌	✅
Framework Agnostic	✅	❌	✅	✅	❌
Thread-Safe	✅	✅	❌	✅	✅

Design Philosophy

1. Unified API, Multiple Strategies — one RateLimiter class, four algorithms. Switch strategies by changing a string argument — no rewrites, no adapter pattern ceremony.

2. Zero-Dependency by Default — no Redis, no Flask, no Celery. Import and go. Add storage backends only when you need distributed coordination.

3. Framework Agnostic — fluxrate doesn't know or care whether you're building a Flask API, a FastAPI service, a CLI tool, or a background worker. Rate limiting is orthogonal to your framework choice.

4. Safety First — every public method is thread-safe. No footguns. RateLimitExceeded exceptions carry retry_after values so you can build compliant HTTP responses without extra math.

5. Progressive Complexity — start with @RateLimiter(calls=100, period=60). Add per-key limiting. Switch algorithms. Plug in custom key functions. Each layer of complexity is opt-in.

API Reference

RateLimiter(calls, period, algorithm="token_bucket", ...)

High-level API supporting all four algorithms.

RateLimiter(
    calls=100,           # max calls per period
    period=60,           # time window in seconds
    algorithm="token_bucket",  # "token_bucket", "sliding_window", "fixed_window", "leaky_bucket"
    key_func=lambda user: user,  # per-key rate limiting
    burst=150,           # burst capacity (token bucket)
    raise_on_limit=True, # raise exception or return None
)

Low-Level APIs

from fluxrate import TokenBucket, SlidingWindow, FixedWindow, LeakyBucket

# Token Bucket — allows bursts up to capacity
tb = TokenBucket(rate=10, capacity=50)
tb.allow()          # consume 1 token
tb.consume(5)       # consume 5 tokens
tb.retry_after()    # seconds until next available
tb.available        # current token count
tb.stats()          # usage statistics

# Sliding Window — precise, no boundary burst
sw = SlidingWindow(max_calls=100, window_seconds=60)
sw.allow("user_id")
sw.count            # calls in current window
sw.retry_after("user_id")

# Fixed Window — simple, fast
fw = FixedWindow(max_calls=100, window_seconds=60)
fw.allow("user_id")

# Leaky Bucket — smooth output rate
lb = LeakyBucket(rate=10, capacity=50)
lb.allow("user_id")
lb.level            # current queue level

Exceptions

from fluxrate.exceptions import RateLimitExceeded, TimeoutExceeded

try:
    rate_limited_func()
except RateLimitExceeded as e:
    retry_after = e.retry_after  # seconds to wait

License

MIT © Ravi Teja Prabhala Venkata