主键id,Snowflake雪花算法，优点：生成有顺序的id，提高数据库的性能

布满荆棘的人生 2022-05-14 10:09 220阅读 0赞

## 主键id,Snowflake雪花算法，优点：生成有顺序的id，提高数据库的性能,现在大部分都不用uuid作为主键，因为不规则，每向数据库插入一条数据就要重新排列，使数据库性能降低。 ##

1，导入依赖

<dependency>
    			<groupId>xyz.downgoon</groupId>
    			<artifactId>snowflake</artifactId>
    			<version>1.0.0</version>
    		</dependency>

2，写个工具类

public class SnowFlakeIdGenerator{
    
      private static final  Snowflake snowFlake  = new Snowflake(1,2);
    
        
      public static String getNextId() {
        return String.valueOf(snowFlake.nextId());
      }
    }

3,原理：感兴趣者可以看下

package com.example.demo.test;
    
    
    import java.util.*;
    import java.util.concurrent.CountDownLatch;
    
    public class SnowflakeIdFactory {
     
        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<Thread> tlist = new ArrayList<>();
            Set<Long> setAll = new HashSet<>();
            CountDownLatch cdLatch = new CountDownLatch(10);
            long start = System.currentTimeMillis();
            int threadNo = dataCenterId;
            Map<String,SnowflakeIdFactory> 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<Long> setId = new HashSet<>();
                        SnowflakeIdFactory idWorker = idFactories.get(Thread.currentThread().getName());
                        for(int j=0;j<n;j++){
                            setId.add(idWorker.nextId());
                        }
                        synchronized (setAll){
                            setAll.addAll(setId);
                            //log.info("{}生产了{}个id,并成功加入到setAll中.",Thread.currentThread().getName(),n);
                        }
                        cdLatch.countDown();
                    }
                },"snowflake"+i);
                tlist.add(temp);
            }
            for(int j=0;j<10;j++){
                tlist.get(j).start();
            }
            cdLatch.await();
     
            long end1 = System.currentTimeMillis() - start;
     
            //log.info("共耗时:{}毫秒,预期应该生产{}个id, 实际合并总计生成ID个数:{}",end1,10*n,setAll.size());
     
        }
     
        public static void testProductId(int dataCenterId, int workerId, int n){
            SnowflakeIdFactory idWorker = new SnowflakeIdFactory(workerId, dataCenterId);
            SnowflakeIdFactory idWorker2 = new SnowflakeIdFactory(workerId+1, dataCenterId);
            Set<Long> setOne = new HashSet<>();
            Set<Long> 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300w+ */
                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.error("{}",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300w+ */
            //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();
            }
     */
            SnowflakeIdFactory factory = new SnowflakeIdFactory(1,2);
            for(int i =0;i<10;i++){
                System.out.println(factory.nextId());
            }
    
    
        }
    
    
    
    }