springboot雪花算法的生成

为什么用snowflake

数据库自增有自增ID，但是使用起来有以下几个问题：

• 会依赖于数据库的具体实现，比如，mysql有自增，oracle没有，得用序列，mongo似乎也没有。
• 自增ID是连续的，它就依赖于数据库自身的锁，所以数据库就有瓶颈。

雪花算法不依赖于数据库本身，是分布式id生成算法中比较经典的一种。整个ID的构成大概分为这么几个部分，时间戳差值，机器编码，进程编码，序列号。
java的long是64位的从左向右依次介绍是：时间戳差值，在我们这里占了42位；机器编码5位；进程编码5位；序列号12位。所有的拼接用位运算拼接起来，于是就基本做到了每个进程中不会重复了。

1,代码

/**
 * id自增器（雪花算法）
 */
public class SnowFlake {
    private final static long twepoch = 12888349746579L;
    // 机器标识位数
    private final static long workerIdBits = 5L;
    // 数据中心标识位数
    private final static long datacenterIdBits = 5L;
 
    // 毫秒内自增位数
    private final static long sequenceBits = 12L;
    // 机器ID偏左移12位
    private final static long workerIdShift = sequenceBits;
    // 数据中心ID左移17位
    private final static long datacenterIdShift = sequenceBits + workerIdBits;
    // 时间毫秒左移22位
    private final static long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits;
    //sequence掩码，确保sequnce不会超出上限
    private final static long sequenceMask = -1L ^ (-1L << sequenceBits);
    //上次时间戳
    private static long lastTimestamp = -1L;
    //序列
    private long sequence = 0L;
    //服务器ID
    private long workerId = 1L;
    private static long workerMask = -1L ^ (-1L << workerIdBits);
    //进程编码
    private long processId = 1L;
    private static long processMask = -1L ^ (-1L << datacenterIdBits);
 
    private static SnowFlake snowFlake = null;
 
    static{
        snowFlake = new SnowFlake();
    }
    public static synchronized long nextId(){
        return snowFlake.getNextId();
    }
 
    private SnowFlake() {
 
        //获取机器编码
        this.workerId=this.getMachineNum();
        //获取进程编码
        RuntimeMXBean runtimeMXBean = ManagementFactory.getRuntimeMXBean();
        this.processId=Long.valueOf(runtimeMXBean.getName().split("@")[0]).longValue();
 
        //避免编码超出最大值
        this.workerId=workerId & workerMask;
        this.processId=processId & processMask;
    }
 
    public synchronized long getNextId() {
        //获取时间戳
        long timestamp = timeGen();
        //如果时间戳小于上次时间戳则报错
        if (timestamp < lastTimestamp) {
            try {
                throw new Exception("Clock moved backwards.  Refusing to generate id for " + (lastTimestamp - timestamp) + " milliseconds");
            } catch (Exception e) {
                e.printStackTrace();
            }
        }
        //如果时间戳与上次时间戳相同
        if (lastTimestamp == timestamp) {
            // 当前毫秒内，则+1，与sequenceMask确保sequence不会超出上限
            sequence = (sequence + 1) & sequenceMask;
            if (sequence == 0) {
                // 当前毫秒内计数满了，则等待下一秒
                timestamp = tilNextMillis(lastTimestamp);
            }
        } else {
            sequence = 0;
        }
        lastTimestamp = timestamp;
        // ID偏移组合生成最终的ID，并返回ID
        long nextId = ((timestamp - twepoch) << timestampLeftShift) | (processId << datacenterIdShift) | (workerId << workerIdShift) | sequence;
        return nextId;
    }
 
    /**
     * 再次获取时间戳直到获取的时间戳与现有的不同
     * @param lastTimestamp
     * @return 下一个时间戳
     */
    private long tilNextMillis(final long lastTimestamp) {
        long timestamp = this.timeGen();
        while (timestamp <= lastTimestamp) {
            timestamp = this.timeGen();
        }
        return timestamp;
    }
 
    private long timeGen() {
        return System.currentTimeMillis();
    }
 
    /**
     * 获取机器编码
     * @return
     */
    private long getMachineNum(){
        long machinePiece;
        StringBuilder sb = new StringBuilder();
        Enumeration<NetworkInterface> e = null;
        try {
            e = NetworkInterface.getNetworkInterfaces();
        } catch (SocketException e1) {
            e1.printStackTrace();
        }
        while (e.hasMoreElements()) {
            NetworkInterface ni = e.nextElement();
            sb.append(ni.toString());
        }
        machinePiece = sb.toString().hashCode();
        return machinePiece;
    }
}

2, 使用

long id = SnowFlake.nextId();

转载自https://blog.csdn.net/nsxqf/article/details/85850232