常用限流算法的Java实现

深碍√TFBOYSˉ_ 2023-09-23 17:07 120阅读 0赞

主要内容为**滑动日志，令牌桶，漏桶**三种限流算法的Java实现

获取连接许可的接口

public interface Limiter {
        //获取许可
        boolean tryAcquire();
    }
    复制代码

1.滑动日志

用一个有序集合来存储所有请求的时间戳，以空间换时间的方式来简化计算

public class CountLimiter implements Limiter{
        //维护一个优先队列记录请求的时间戳
        //将PriorityQueue替换为Redis的ZSet的话可以实现分布式的限流
        private final PriorityQueue<Long> queue = new PriorityQueue<>();
        //限流大小
        private final int limitCount;
        //限流时间范围
        private final Long limitTime;
    
        public CountLimiter(int limitCount, Long limitTime) {
            this.limitCount = limitCount;
            this.limitTime = limitTime;
        }
    
        @Override
        public synchronized boolean tryAcquire() {
            long nowTime = System.currentTimeMillis();
            if(queue.size() < limitCount){
                queue.add(nowTime);
                return true;
            }else{
                long preTime = nowTime - limitTime;
                //淘汰已经超过时间限制的请求
                while(!queue.isEmpty() && preTime > queue.peek()){
                    queue.poll();
                }
                if(queue.size() < limitCount){
                    queue.add(nowTime);
                    return true;
                }else{
                    return false;
                }
            }
        }
    }
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2.令牌桶

利用延迟计算来维护令牌数量

public class TokenBucketLimiter implements Limiter{
        //最大容量
        private final long capacity;
        //令牌的生成速率 每rate毫秒生成一个令牌
        private final long rate;
        //下一个令牌的发放时间
        long nextTokenTime = System.currentTimeMillis();
        //当前持有的令牌总数
        private long currentTokens;
    
        public TokenBucketLimiter(long rate, int capacity,int currentTokens) {
            this.rate = rate;
            this.capacity = capacity;
            //根据需求设置令牌桶中令牌的初始数量
            this.currentTokens = currentTokens;
        }
    
        @Override
        public boolean tryAcquire() {
            long nowTime = System.currentTimeMillis();
            if(nowTime - nextTokenTime > rate){
                //计算新产生的令牌数
                long newTokens = (nowTime - nextTokenTime) / rate;
                //更新当前持有的令牌数量
                currentTokens = Math.min(currentTokens + newTokens,capacity);
                //更新可以获取令牌的时间
                nextTokenTime = nowTime;
            }
            if(currentTokens > 0){
                currentTokens--;
                return true;
            }
            return false;
        }
    }
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3.漏桶

漏桶算法原理类似于线程池，请求到来先放入等待队列，然后消费端从请求队列中拉取请求，当请求队列超过最大容量后，执行拒绝策略

public class LeakyBucketLimiter<T> implements Limiter{
    
        private final long capacity;
        //漏桶流出的速率 每rate毫秒放出一个请求
        private final long rate;
        //当前水量
        private AtomicInteger currentWater = new AtomicInteger(0);
        //存放请求的请求队列
        private final BlockingDeque<T> queue;
        //定时任务线程池
        private final ScheduledExecutorService executorService = new ScheduledThreadPoolExecutor(1);
    
        public LeakyBucketLimiter(long rate, int capacity,BlockingDeque<T> queue) {
            this.rate = rate;
            this.capacity = capacity;
            this.queue = new LinkedBlockingDeque<>();
    
            executorService.schedule(new Runnable() {
                @Override
                public void run() {
                    //队列中没有请求的时候阻塞等待，防止空转消费资源
                    try {
                        //如果是比较耗时间的任务可以定义额外的线程池来处理，将分发任务和执行任务分隔开
                        System.out.println("处理请求：" + queue.take());
                    } catch (InterruptedException e) {
                        throw new RuntimeException(e);
                    }
                    currentWater.addAndGet(-1);
                    executorService.schedule(this,rate, TimeUnit.MILLISECONDS);
                }
            },rate,TimeUnit.MILLISECONDS);
        }
    
        @Override
        public synchronized boolean tryAcquire() {
            //成功增加水池水量，失败则回滚
            if(currentWater.addAndGet(1) <= capacity){
                return true;
            }else{
                currentWater.addAndGet(-1);
                return false;
            }
        }
    }