import redis
import time
import hashlib
from functools import wraps
from flask import Flask, request, jsonify, g
app = Flask(__name__)
redis_client = redis.Redis(host='localhost', port=6379, db=0, decode_responses=True)
class SlidingWindowRateLimiter:
"""Sliding window rate limiter using Redis sorted sets."""
def __init__(self, redis_conn):
self.redis = redis_conn
def is_allowed(self, key, max_requests, window_seconds):
"""Check if request is allowed and record it."""
now = time.time()
window_start = now - window_seconds
pipe = self.redis.pipeline()
# Remove expired entries
pipe.zremrangebyscore(key, 0, window_start)
# Count requests in current window
pipe.zcard(key)
# Add current request
pipe.zadd(key, {f"{now}:{hashlib.md5(str(now).encode()).hexdigest()[:8]}": now})
# Set TTL on the key
pipe.expire(key, window_seconds + 1)
results = pipe.execute()
current_count = results[1]
if current_count >= max_requests:
# Calculate retry-after
oldest = self.redis.zrange(key, 0, 0, withscores=True)
if oldest:
retry_after = int(oldest[0][1] + window_seconds - now) + 1
else:
retry_after = window_seconds
return False, current_count, max_requests, retry_after
return True, current_count + 1, max_requests, 0
rate_limiter = SlidingWindowRateLimiter(redis_client)
def rate_limit(max_requests, window_seconds, key_func=None):
"""Decorator for rate limiting API endpoints."""
def decorator(f):
@wraps(f)
def wrapped(*args, **kwargs):
# Determine the rate limit key
if key_func:
identifier = key_func()
elif hasattr(g, 'user_id'):
identifier = f"user:{g.user_id}"
else:
identifier = f"ip:{request.remote_addr}"
key = f"ratelimit:{request.endpoint}:{identifier}"
allowed, current, limit, retry_after = rate_limiter.is_allowed(
key, max_requests, window_seconds)
# Always set rate limit headers
headers = {
"X-RateLimit-Limit": str(limit),
"X-RateLimit-Remaining": str(max(0, limit - current)),
"X-RateLimit-Reset": str(int(time.time()) + window_seconds),
}
if not allowed:
headers["Retry-After"] = str(retry_after)
response = jsonify({
"error": "rate_limit_exceeded",
"message": "Too many requests. Please try again later.",
"retry_after": retry_after
})
response.status_code = 429
for h, v in headers.items():
response.headers[h] = v
return response
response = f(*args, **kwargs)
for h, v in headers.items():
response.headers[h] = v
return response
return wrapped
return decorator
# Apply rate limiting to endpoints
@app.route('/api/v1/auth/login', methods=['POST'])
@rate_limit(max_requests=5, window_seconds=60,
key_func=lambda: f"ip:{request.remote_addr}")
def login():
# Login logic
return jsonify({"message": "Login successful"})
@app.route('/api/v1/users/me', methods=['GET'])
@rate_limit(max_requests=60, window_seconds=60)
def get_profile():
# Profile logic
return jsonify({"user": "data"})
@app.route('/api/v1/search', methods=['GET'])
@rate_limit(max_requests=10, window_seconds=60)
def search():
# Search logic
return jsonify({"results": []})
Step 3: Token Bucket Rate Limiter
import redis
import time
class TokenBucketRateLimiter:
"""Token bucket rate limiter allowing burst traffic within limits."""
def __init__(self, redis_conn):
self.redis = redis_conn
def is_allowed(self, key, max_tokens, refill_rate, refill_interval=1):
"""
Token bucket algorithm:
- max_tokens: Maximum burst capacity
- refill_rate: Tokens added per refill_interval
- refill_interval: Seconds between refills
"""
now = time.time()
bucket_key = f"tb:{key}"
# Lua script for atomic token bucket operation
lua_script = """
local key = KEYS[1]
local max_tokens = tonumber(ARGV[1])
local refill_rate = tonumber(ARGV[2])
local refill_interval = tonumber(ARGV[3])
local now = tonumber(ARGV[4])
local bucket = redis.call('hmget', key, 'tokens', 'last_refill')
local tokens = tonumber(bucket[1])
local last_refill = tonumber(bucket[2])
if tokens == nil then
tokens = max_tokens
last_refill = now
end
-- Refill tokens
local elapsed = now - last_refill
local refills = math.floor(elapsed / refill_interval)
if refills > 0 then
tokens = math.min(max_tokens, tokens + (refills * refill_rate))
last_refill = last_refill + (refills * refill_interval)
end
local allowed = 0
if tokens >= 1 then
tokens = tokens - 1
allowed = 1
end
redis.call('hmset', key, 'tokens', tokens, 'last_refill', last_refill)
redis.call('expire', key, math.ceil(max_tokens / refill_rate * refill_interval) + 10)
return {allowed, tokens, max_tokens}
"""
result = self.redis.eval(lua_script, 1, bucket_key,
max_tokens, refill_rate, refill_interval, now)
allowed = bool(result[0])
remaining = int(result[1])
limit = int(result[2])
return allowed, remaining, limit
Step 4: Tiered Rate Limiting with User Plans
from enum import Enum
class UserTier(Enum):
FREE = "free"
PREMIUM = "premium"
ENTERPRISE = "enterprise"
TIER_LIMITS = {
UserTier.FREE: {
"default": (60, 60), # 60 req/min
"search": (10, 60), # 10 req/min
"export": (5, 3600), # 5 req/hour
"daily_total": (1000, 86400), # 1000 req/day
},
UserTier.PREMIUM: {
"default": (300, 60),
"search": (50, 60),
"export": (20, 3600),
"daily_total": (10000, 86400),
},
UserTier.ENTERPRISE: {
"default": (1000, 60),
"search": (200, 60),
"export": (100, 3600),
"daily_total": (100000, 86400),
},
}
def get_rate_limit_for_request(user_tier, endpoint_category="default"):
"""Get rate limit configuration based on user tier and endpoint."""
tier_config = TIER_LIMITS.get(user_tier, TIER_LIMITS[UserTier.FREE])
limit_config = tier_config.get(endpoint_category, tier_config["default"])
return limit_config # (max_requests, window_seconds)
class TieredRateLimitMiddleware:
"""Middleware that applies rate limits based on user subscription tier."""
def __init__(self, app, redis_conn):
self.app = app
self.limiter = SlidingWindowRateLimiter(redis_conn)
def __call__(self, environ, start_response):
# Extract user info from request
user_id = environ.get("HTTP_X_USER_ID")
user_tier = UserTier(environ.get("HTTP_X_USER_TIER", "free"))
endpoint = environ.get("PATH_INFO", "/")
# Determine endpoint category
category = "default"
if "/search" in endpoint:
category = "search"
elif "/export" in endpoint:
category = "export"
max_requests, window = get_rate_limit_for_request(user_tier, category)
key = f"tiered:{user_id or environ.get('REMOTE_ADDR')}:{category}"
allowed, current, limit, retry_after = self.limiter.is_allowed(
key, max_requests, window)
if not allowed:
status = "429 Too Many Requests"
headers = [
("Content-Type", "application/json"),
("Retry-After", str(retry_after)),
("X-RateLimit-Limit", str(limit)),
("X-RateLimit-Remaining", "0"),
]
start_response(status, headers)
body = f'{{"error":"rate_limit_exceeded","retry_after":{retry_after},"tier":"{user_tier.value}"}}'
return [body.encode()]
return self.app(environ, start_response)
Step 5: Distributed Rate Limiting for Microservices
# Centralized rate limiting service using Redis Cluster
import redis
from redis.cluster import RedisCluster
class DistributedRateLimiter:
"""Rate limiter for microservice architectures using Redis Cluster."""
def __init__(self):
self.redis = RedisCluster(
startup_nodes=[
{"host": "redis-node-1", "port": 6379},
{"host": "redis-node-2", "port": 6379},
{"host": "redis-node-3", "port": 6379},
],
decode_responses=True
)
def check_and_increment(self, service_name, user_id, endpoint,
max_requests, window_seconds):
"""Atomic check-and-increment using Redis Lua script."""
key = f"rl:{{{service_name}}}:{user_id}:{endpoint}"
# Lua script ensures atomicity across the check and increment
lua_script = """
local key = KEYS[1]
local max_requests = tonumber(ARGV[1])
local window = tonumber(ARGV[2])
local now = tonumber(ARGV[3])
local window_start = now - window
-- Remove old entries
redis.call('zremrangebyscore', key, '-inf', window_start)
-- Count current entries
local count = redis.call('zcard', key)
if count >= max_requests then
-- Get oldest entry for retry-after calculation
local oldest = redis.call('zrange', key, 0, 0, 'WITHSCORES')
local retry_after = 0
if #oldest > 0 then
retry_after = math.ceil(tonumber(oldest[2]) + window - now)
end
return {0, count, retry_after}
end
-- Add new entry
redis.call('zadd', key, now, now .. ':' .. math.random(100000))
redis.call('expire', key, window + 1)
return {1, count + 1, 0}
"""
result = self.redis.eval(lua_script, 1, key,
max_requests, window_seconds, time.time())
return {
"allowed": bool(result[0]),
"current": int(result[1]),
"retry_after": int(result[2]),
}
Key Concepts
Term
Definition
Sliding Window
Rate limiting algorithm that tracks requests in a rolling time window, providing smoother rate enforcement than fixed windows
Token Bucket
Algorithm where tokens are added at a fixed rate and consumed per request, allowing controlled bursts up to the bucket capacity
Fixed Window
Simplest rate limiting where requests are counted per fixed time window (e.g., per minute), susceptible to burst at window boundaries
429 Too Many Requests
HTTP status code indicating the client has exceeded the rate limit, accompanied by Retry-After header
Retry-After Header
HTTP response header telling the client how many seconds to wait before retrying, essential for well-behaved API clients
Distributed Rate Limiting
Rate limiting across multiple server instances using shared state (Redis, Memcached) to maintain accurate global counters
Tools & Systems
Redis: In-memory data store used for distributed rate limit counters with atomic operations via Lua scripts
Kong Rate Limiting Plugin: API gateway plugin supporting fixed-window and sliding-window rate limiting with Redis backend
express-rate-limit: Express.js middleware for simple rate limiting with Redis, Memcached, or in-memory stores
Flask-Limiter: Flask extension for rate limiting with support for multiple backends and configurable limits per endpoint
Envoy Rate Limit Service: Centralized rate limiting service for Envoy-based service mesh architectures
Common Scenarios
Scenario: Implementing Rate Limiting for a Public API
Context: A company launches a public API with free, premium, and enterprise tiers. The API must protect against abuse while providing fair access to paying customers. The API runs on 6 instances behind an AWS ALB.
Approach:
Deploy Redis Cluster (3 nodes) for distributed rate limit state
Implement sliding window rate limiter using Redis sorted sets with Lua scripts for atomicity
