Implementing Rate Limiting with Redis

Feb 13, 2026

Overview

Implement per-user rate limiting using Redis and the token bucket algorithm.

Prerequisites

Redis server running
Redis client library for your language

Implementation

1. Token Bucket Algorithm

import redis
import time

class RateLimiter:
    def __init__(self, redis_client, capacity=10, refill_rate=1):
        self.redis = redis_client
        self.capacity = capacity
        self.refill_rate = refill_rate
    
    def allow_request(self, user_id):
        key = f"rate_limit:{user_id}"
        now = time.time()
        
        pipe = self.redis.pipeline()
        pipe.hgetall(key)
        result = pipe.execute()[0]
        
        if not result:
            # First request
            self.redis.hset(key, mapping={
                'tokens': self.capacity - 1,
                'last_refill': now
            })
            self.redis.expire(key, 3600)
            return True
        
        tokens = float(result[b'tokens'])
        last_refill = float(result[b'last_refill'])
        
        # Refill tokens
        elapsed = now - last_refill
        tokens = min(self.capacity, tokens + elapsed * self.refill_rate)
        
        if tokens >= 1:
            self.redis.hset(key, mapping={
                'tokens': tokens - 1,
                'last_refill': now
            })
            return True
        
        return False

2. Usage

redis_client = redis.Redis(host='localhost', port=6379)
limiter = RateLimiter(redis_client, capacity=100, refill_rate=10)

if limiter.allow_request(user_id):
    # Process request
    pass
else:
    # Return 429 Too Many Requests
    pass

Testing

# Check Redis keys
redis-cli KEYS "rate_limit:*"

# Inspect specific user
redis-cli HGETALL "rate_limit:user123"