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24.Redis实现全局唯一ID

article 2024/11/14 14:40:27

是一种分布式系统下用来生成全局唯一ID的工具。

特点

1.唯一性

2.高可用

3.高性能

4.递增性，数据也要保持一种递增，有利于数据库进行查询。

5.安全性

全局唯一ID的生成策略

1.UUID(没有顺序，字符串类型，效率不高)

2.Redis自增

3.snowflake算法

4.数据库自增（单独设计一张表存放多张表的主键id）

Redis自增ID策略

每天一个key,方便统计订单量。

ID构造是时间戳+计数器

package com.xkj.org.utils;

import org.springframework.data.redis.core.StringRedisTemplate;
import org.springframework.stereotype.Component;

import java.time.LocalDateTime;
import java.time.ZoneOffset;
import java.time.format.DateTimeFormatter;

@Component
public class RedisIdWorker {
    /**
     * 开始时间戳
     */
    private static final long BEGIN_TIMESTAMP = 1640995200L;
    /**
     * 序列号的位数
     */
    private static final int COUNT_BITS = 32;

    private StringRedisTemplate stringRedisTemplate;

    public RedisIdWorker(StringRedisTemplate stringRedisTemplate) {
        this.stringRedisTemplate = stringRedisTemplate;
    }

    public long nextId(String keyPrefix) {
        // 1.生成时间戳
        LocalDateTime now = LocalDateTime.now();
        long nowSecond = now.toEpochSecond(ZoneOffset.UTC);
        long timestamp = nowSecond - BEGIN_TIMESTAMP;

        // 2.生成序列号
        // 2.1.获取当前日期，精确到天
        String date = now.format(DateTimeFormatter.ofPattern("yyyy:MM:dd"));
        // 2.2.自增长
        long count = stringRedisTemplate.opsForValue().increment("icr:" + keyPrefix + ":" + date);

        // 3.拼接并返回
        // timestamp的值向左移动32位，然后加上count的值
        return timestamp << COUNT_BITS | count;
    }
}

package com.xkj.org;

import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.xkj.org.entity.Shop;
import com.xkj.org.entity.Student;
import com.xkj.org.service.impl.ShopServiceImpl;
import com.xkj.org.utils.CacheClient;
import com.xkj.org.utils.RedisConstants;
import com.xkj.org.utils.RedisIdWorker;
import org.junit.jupiter.api.Test;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.data.redis.core.StringRedisTemplate;

import javax.annotation.Resource;
import java.util.Map;
import java.util.concurrent.CountDownLatch;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;

@SpringBootTest
public class SpringBootDataRedisTest {

    @Resource
    private RedisIdWorker redisIdWorker;

    private ExecutorService executorService = Executors.newFixedThreadPool(500);

    @Test
    void testIDWorker() throws InterruptedException {
        long begin = System.currentTimeMillis();
        CountDownLatch countDownLatch = new CountDownLatch(300);
        Runnable task = () -> {
            for (int i = 0; i < 100; i++) {
                long orderId = redisIdWorker.nextId("order");
                System.out.println("orderId=="+orderId);
            }
            countDownLatch.countDown();
        };
        for (int i = 0; i < 300; i++) {
            executorService.submit(task);
        }
        countDownLatch.await();
        long end = System.currentTimeMillis();
        System.out.println("耗费时间："+(end - begin));

    }
}