场景设计：设计一个分布式限流器，采用令牌桶算法，漏桶算法、滑动窗口算法实现

目录

1. 令牌桶算法实现

设计思路

代码实现

2. 漏桶算法实现

设计思路

代码实现

3. 滑动窗口算法实现

设计思路

代码实现

测试代码示例

依赖引入

总结

---

以下将分别使用令牌桶算法、漏桶算法和滑动窗口算法来设计并实现一个分布式限流器的 Java 版本，同时借助 Redis 来实现分布式状态的存储与同步。

1. 令牌桶算法实现

设计思路

令牌桶算法中，系统以固定速率向令牌桶中添加令牌，每个请求需要从令牌桶中获取一个或多个令牌才能被处理。若令牌桶中没有足够的令牌，请求将被拒绝。

代码实现

java

import redis.clients.jedis.Jedis;public class TokenBucketRateLimiter {private static final String REDIS_KEY = "token_bucket_rate_limiter";private final Jedis jedis;private final int capacity;private final double rate;private static final String LUA_SCRIPT ="local key = KEYS[1] " +"local tokensNeeded = tonumber(ARGV[1]) " +"local capacity = tonumber(ARGV[2]) " +"local rate = tonumber(ARGV[3]) " +"local now = tonumber(ARGV[4]) " +"local lastUpdate = tonumber(redis.call('hget', key, 'last_update')) " +"local tokens = tonumber(redis.call('hget', key, 'tokens')) " +"local elapsedTime = now - lastUpdate " +"local newTokens = math.min(capacity, tokens + elapsedTime * rate) " +"if newTokens >= tokensNeeded then " +"    redis.call('hset', key, 'tokens', newTokens - tokensNeeded) " +"    redis.call('hset', key, 'last_update', now) " +"    return 1 " +"else " +"    redis.call('hset', key, 'tokens', newTokens) " +"    redis.call('hset', key, 'last_update', now) " +"    return 0 " +"end";public TokenBucketRateLimiter(String host, int port, int capacity, double rate) {this.jedis = new Jedis(host, port);this.capacity = capacity;this.rate = rate;initialize();}private void initialize() {jedis.hset(REDIS_KEY, "tokens", String.valueOf(capacity));jedis.hset(REDIS_KEY, "last_update", String.valueOf(System.currentTimeMillis() / 1000));}public boolean allowRequest(int tokensNeeded) {Object result = jedis.eval(LUA_SCRIPT, 1, REDIS_KEY, String.valueOf(tokensNeeded),String.valueOf(capacity), String.valueOf(rate), String.valueOf(System.currentTimeMillis() / 1000));return (Long) result == 1;}public void close() {jedis.close();}
}

2. 漏桶算法实现

设计思路

漏桶算法中，请求像水一样流入漏桶，漏桶以固定的速率处理请求。如果桶满了，多余的请求将被丢弃。

代码实现

java

import redis.clients.jedis.Jedis;public class LeakyBucketRateLimiter {private static final String REDIS_KEY = "leaky_bucket_rate_limiter";private final Jedis jedis;private final int capacity;private final double rate;private static final String LUA_SCRIPT ="local key = KEYS[1] " +"local now = tonumber(ARGV[1]) " +"local capacity = tonumber(ARGV[2]) " +"local rate = tonumber(ARGV[3]) " +"local lastUpdate = tonumber(redis.call('hget', key, 'last_update')) " +"local water = tonumber(redis.call('hget', key, 'water')) " +"local elapsedTime = now - lastUpdate " +"local leakedWater = math.min(water, elapsedTime * rate) " +"local newWater = math.max(0, water - leakedWater) " +"if newWater + 1 <= capacity then " +"    redis.call('hset', key, 'water', newWater + 1) " +"    redis.call('hset', key, 'last_update', now) " +"    return 1 " +"else " +"    redis.call('hset', key, 'water', newWater) " +"    redis.call('hset', key, 'last_update', now) " +"    return 0 " +"end";public LeakyBucketRateLimiter(String host, int port, int capacity, double rate) {this.jedis = new Jedis(host, port);this.capacity = capacity;this.rate = rate;initialize();}private void initialize() {jedis.hset(REDIS_KEY, "water", "0");jedis.hset(REDIS_KEY, "last_update", String.valueOf(System.currentTimeMillis() / 1000));}public boolean allowRequest() {Object result = jedis.eval(LUA_SCRIPT, 1, REDIS_KEY, String.valueOf(System.currentTimeMillis() / 1000),String.valueOf(capacity), String.valueOf(rate));return (Long) result == 1;}public void close() {jedis.close();}
}

3. 滑动窗口算法实现

设计思路

滑动窗口算法将时间划分为固定大小的窗口，统计每个窗口内的请求数量。如果请求数量超过了阈值，则拒绝新的请求。

代码实现

java

import redis.clients.jedis.Jedis;public class SlidingWindowRateLimiter {private static final String REDIS_KEY_PREFIX = "sliding_window_rate_limiter:";private final Jedis jedis;private final int windowSize;private final int limit;public SlidingWindowRateLimiter(String host, int port, int windowSize, int limit) {this.jedis = new Jedis(host, port);this.windowSize = windowSize;this.limit = limit;}public boolean allowRequest() {long currentTime = System.currentTimeMillis();String key = REDIS_KEY_PREFIX + (currentTime / (windowSize * 1000));// 移除窗口外的请求记录jedis.zremrangeByScore(key, 0, currentTime - windowSize * 1000);// 添加当前请求记录jedis.zadd(key, currentTime, String.valueOf(currentTime));// 设置过期时间jedis.expire(key, windowSize);// 统计窗口内的请求数量long count = jedis.zcard(key);return count <= limit;}public void close() {jedis.close();}
}

测试代码示例

java

public class RateLimiterTest {public static void main(String[] args) {// 令牌桶算法测试TokenBucketRateLimiter tokenBucketRateLimiter = new TokenBucketRateLimiter("localhost", 6379, 100, 10);for (int i = 0; i < 20; i++) {if (tokenBucketRateLimiter.allowRequest(1)) {System.out.println("Token Bucket: Request " + i + " is allowed.");} else {System.out.println("Token Bucket: Request " + i + " is denied.");}}tokenBucketRateLimiter.close();// 漏桶算法测试LeakyBucketRateLimiter leakyBucketRateLimiter = new LeakyBucketRateLimiter("localhost", 6379, 100, 10);for (int i = 0; i < 20; i++) {if (leakyBucketRateLimiter.allowRequest()) {System.out.println("Leaky Bucket: Request " + i + " is allowed.");} else {System.out.println("Leaky Bucket: Request " + i + " is denied.");}}leakyBucketRateLimiter.close();// 滑动窗口算法测试SlidingWindowRateLimiter slidingWindowRateLimiter = new SlidingWindowRateLimiter("localhost", 6379, 10, 5);for (int i = 0; i < 20; i++) {if (slidingWindowRateLimiter.allowRequest()) {System.out.println("Sliding Window: Request " + i + " is allowed.");} else {System.out.println("Sliding Window: Request " + i + " is denied.");}}slidingWindowRateLimiter.close();}
}

依赖引入

如果你使用 Maven 项目，需要在 pom.xml 中添加 Jedis 依赖：

xml

<dependency><groupId>redis.clients</groupId><artifactId>jedis</artifactId><version>3.7.0</version>
</dependency>

总结

通过上述代码，我们分别实现了基于令牌桶算法、漏桶算法和滑动窗口算法的分布式限流器。不同的算法适用于不同的场景，你可以根据实际需求选择合适的算法。同时，借助 Redis 实现了分布式状态的存储和同步，确保多个服务实例能够共享限流状态。