项目如何生成全局唯一的id主键

一。新建自增序列表方式

使用于mysql，新建一个只有id为自动递增的表，每次获取id，先往自增序列表写入数据，然后获取id

CREATE TABLE `org_dept`  (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  PRIMARY KEY (`id`) USING BTREE
); 

缺点：

1.不适用于分库场景

2.不适用于特高并发场景

优点：

1.利用mysql数据库本身，实现简单

二。新建序列方式

适用于Oracle，SQLserver

-- 对于编号为1的数据库：
create sequence seq_id increment 1 start 1000000000000 ;

-- 对于编号为2的数据库：
create sequence seq_id increment 1 start 2000000000000 ;

-- 各库建表语句
create table t_table_name(
  n_id bigint not null default nextval('seq_id'), -- 内部编号 自动生成
-- 其他字段
primary key(n_id)
);

优点：

1.适用分库分表

三。UUID方式

通过UUID产生

优点：

1.不依赖于第三方

缺点：

1.不连续

四。雪花算法

import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.concurrent.CountDownLatch;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import lombok.ToString;

/**   
* Copyright: Copyright (c) 2019 
* 
* @ClassName: IdWorker.java
* @Description: 
SnowFlake 算法，是 Twitter 开源的分布式 id 生成算法。
* 					其核心思想就是：使用一个 64 bit 的 long 型的数字作为全局唯一 id。
* 					这 64 个 bit 中，其中 1 个 bit 是不用的，然后用其中的 41 bit 作为毫秒数，
* 					用 10 bit 作为工作机器 id，12 bit 作为序列号
*				
* @version: v1.0.0
* @author: BianPeng
* @date: 2019年4月11日 下午3:13:41 
*
* Modification History:
* Date         		Author          Version          Description
*---------------------------------------------------------------*
* 2019年4月11日      		BianPeng        v1.0.0           initialize
*/
@ToString
public class SnowflakeIdFactory {
 
	static Logger log = LoggerFactory.getLogger(SnowflakeIdFactory.class);
	
    private final long twepoch = 1288834974657L;
    private final long workerIdBits = 5L;
    private final long datacenterIdBits = 5L;
    private final long maxWorkerId = -1L ^ (-1L << workerIdBits);
    private final long maxDatacenterId = -1L ^ (-1L << datacenterIdBits);
    private final long sequenceBits = 12L;
    private final long workerIdShift = sequenceBits;
    private final long datacenterIdShift = sequenceBits + workerIdBits;
    private final long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits;
    private final long sequenceMask = -1L ^ (-1L << sequenceBits);
 
    private long workerId;
    private long datacenterId;
    private long sequence = 0L;
    private long lastTimestamp = -1L;
 
 
 
    public SnowflakeIdFactory(long workerId, long datacenterId) {
        if (workerId > maxWorkerId || workerId < 0) {
            throw new IllegalArgumentException(String.format("worker Id can't be greater than %d or less than 0", maxWorkerId));
        }
        if (datacenterId > maxDatacenterId || datacenterId < 0) {
            throw new IllegalArgumentException(String.format("datacenter Id can't be greater than %d or less than 0", maxDatacenterId));
        }
        this.workerId = workerId;
        this.datacenterId = datacenterId;
    }
 
    public synchronized long nextId() {
        long timestamp = timeGen();
        if (timestamp < lastTimestamp) {
            //服务器时钟被调整了,ID生成器停止服务.
            throw new RuntimeException(String.format("Clock moved backwards.  Refusing to generate id for %d milliseconds", lastTimestamp - timestamp));
        }
        if (lastTimestamp == timestamp) {
            sequence = (sequence + 1) & sequenceMask;
            if (sequence == 0) {
                timestamp = tilNextMillis(lastTimestamp);
            }
        } else {
            sequence = 0L;
        }
 
        lastTimestamp = timestamp;
        return ((timestamp - twepoch) << timestampLeftShift) | (datacenterId << datacenterIdShift) | (workerId << workerIdShift) | sequence;
    }
 
    protected long tilNextMillis(long lastTimestamp) {
        long timestamp = timeGen();
        while (timestamp <= lastTimestamp) {
            timestamp = timeGen();
        }
        return timestamp;
    }
 
    protected long timeGen() {
        return System.currentTimeMillis();
    }
 
    public static void testProductIdByMoreThread(int dataCenterId, int workerId, int n) throws InterruptedException {
        List tlist = new ArrayList<>();
        Set setAll = new HashSet<>();
        CountDownLatch cdLatch = new CountDownLatch(10);
        long start = System.currentTimeMillis();
        int threadNo = dataCenterId;
        Map idFactories = new HashMap<>();
        for(int i=0;i<10;i++){
            //用线程名称做map key.
            idFactories.put("snowflake"+i,new SnowflakeIdFactory(workerId, threadNo++));
        }
        for(int i=0;i<10;i++){
            Thread temp =new Thread(new Runnable() {
                @Override
                public void run() {
                    Set setId = new HashSet<>();
                    SnowflakeIdFactory idWorker = idFactories.get(Thread.currentThread().getName());
                    for(int j=0;j setOne = new HashSet<>();
        Set setTow = new HashSet<>();
        long start = System.currentTimeMillis();
        for (int i = 0; i < n; i++) {
            setOne.add(idWorker.nextId());//加入set
        }
        long end1 = System.currentTimeMillis() - start;
        log.info("第一批ID预计生成{}个,实际生成{}个<<<<*>>>>共耗时:{}",n,setOne.size(),end1);
 
        for (int i = 0; i < n; i++) {
            setTow.add(idWorker2.nextId());//加入set
        }
        long end2 = System.currentTimeMillis() - start;
        log.info("第二批ID预计生成{}个,实际生成{}个<<<<*>>>>共耗时:{}",n,setTow.size(),end2);
 
        setOne.addAll(setTow);
        log.info("合并总计生成ID个数:{}",setOne.size());
 
    }
 
    public static void testPerSecondProductIdNums(){
        SnowflakeIdFactory idWorker = new SnowflakeIdFactory(1, 2);
        long start = System.currentTimeMillis();
        int count = 0;
        for (int i = 0; System.currentTimeMillis()-start<1000; i++,count=i) {
            /**  测试方法一: 此用法纯粹的生产ID,每秒生产ID个数为400w+ */
        	//idWorker.nextId();
            /**  测试方法二: 在log中打印,同时获取ID,此用法生产ID的能力受限于log.error()的吞吐能力.
             * 每秒徘徊在10万左右. */
        	log.info(""+idWorker.nextId());
        }
        long end = System.currentTimeMillis()-start;
        System.out.println(end);
        System.out.println(count);
    }
 
    public static void main(String[] args) {
        /** case1: 测试每秒生产id个数?
         *   结论: 每秒生产id个数400w+ 
         */
        //testPerSecondProductIdNums();
 
        /** case2: 单线程-测试多个生产者同时生产N个id,验证id是否有重复?
         *   结论: 验证通过,没有重复. 
         */
        //testProductId(1,2,10000);//验证通过!
        //testProductId(1,2,20000);//验证通过!
 
        /** case3: 多线程-测试多个生产者同时生产N个id, 全部id在全局范围内是否会重复?
         *   结论: 验证通过,没有重复.
         */
        try {
            testProductIdByMoreThread(1,2,100000);//单机测试此场景,性能损失至少折半!
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
 
    }
}

优点：

1.不依赖第三方

2.实现简单

3.适用分库分表