Redis（二）延迟队列

1.目录　

• 延迟队列
• 进一步优化

2.延迟队列

package com.redis;

import java.lang.reflect.Type;
import java.util.Set;
import java.util.UUID;

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.TypeReference;
import redis.clients.jedis.Jedis;

public class RedisDelayingQueue<T> {
    static class TaskItem<T> {
        public String id;
        public T msg;
    }

    // fastjson 序列化对象中存在 generic 类型时，需要使用 TypeReference
    private Type TaskType = new TypeReference<TaskItem<T>>() {
    }.getType();
    private Jedis jedis;
    private String queueKey;

    public RedisDelayingQueue(Jedis jedis, String queueKey) {
        this.jedis = jedis;
        this.queueKey = queueKey;
    }

    public void delay(T msg) {
        TaskItem<T> task = new TaskItem<T>();
        task.id = UUID.randomUUID().toString(); // 分配唯一的 uuid
        task.msg = msg;
        String s = JSON.toJSONString(task); // fastjson 序列化
        jedis.zadd(queueKey, System.currentTimeMillis() + , s); // 塞入延时队列 ,5s 后再试
    }

    public void loop() {
        while (!Thread.interrupted()) {

            // 只取一条
            Set<String> values = jedis.zrangeByScore(queueKey, , System.currentTimeMillis());
            if (values.isEmpty()) {
                try {
                    Thread.sleep(); // 歇会继续
                } catch (InterruptedException e) {
                    break;
                }
                continue;
            }
            String s = values.iterator().next();
            if (jedis.zrem(queueKey, s) > ) { // 抢到了
                TaskItem<T> task = JSON.parseObject(s, TaskType); // fastjson 反序列化
                this.handleMsg(task.msg);
            }
        }
    }

    public void handleMsg(T msg) {
        System.out.println(msg);
    }

    public static void main(String[] args) {
        Jedis jedis = new Jedis();
        RedisDelayingQueue<String> queue = new RedisDelayingQueue<>(jedis, "q-demo");

        Thread producer = new Thread(() -> {
            for (int i = ; i < ; i++) {
                queue.delay("codehole" + i);
            }
        });

        Thread consumer = new Thread(() -> queue.loop());
        producer.start();
        consumer.start();
        try {
            producer.join();
            Thread.sleep();
            consumer.interrupt();
            consumer.join();
        } catch (InterruptedException e) {
        }
    }
}

3.进一步优化

上面的算法中同一个任务可能会被多个进程取到之后再使用zrem进行争抢，那 些没抢到的进程都是白取了一次任务，这是浪费。可以考虑使用lua scripting来 优化一下这个逻辑，将zrangebyscore和zrem一同挪到服务器端进行原子化操 作，这样多个进程之间争抢任务时就不会出现这种浪费了