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 {@Resourceprivate RedisIdWorker redisIdWorker;private ExecutorService executorService = Executors.newFixedThreadPool(500);@Testvoid 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));}
}

因为是多个线程交叉运行，所以输出的结果没有顺序性。其实它是有序的。

产生的id也是long型的。

 可以统计出这一天产生的订单数量。