分布式主键解决方案之--Snowflake雪花算法

0--前言

　　对于分布式系统环境，主键ID的设计很关键，什么自增intID那些是绝对不用的，比较早的时候，大部分系统都用UUID/GUID来作为主键，优点是方便又能解决问题，缺点是插入时因为UUID/GUID的不规则导致每插入一条数据就需要重新排列一次，性能低下；也有人提出用UUID/GUID转long的方式，可以很明确的告诉你，这种方式long不能保证唯一，大并发下会有重复long出现，所以也不可取，这个主键设计问题曾经是很多公司系统设计的一个头疼点，所以大部分公司愿意牺牲一部分性能而直接采用简单粗暴的UUID/GUID来作为分布式系统的主键；

　　twitter开源了一个snowflake算法，俗称雪花算法；就是为了解决分布式环境下生成不同ID的问题；该算法会生成19位的long型有序数字，MySQL中用bigint来存储（bigint长度为20位）；该算法应该是目前分布式环境中主键ID最好的解决方案之一了；

1--snowflake雪花算法实现

　　好，废话不多说，直接上算法实现

package com.anson;

import java.lang.management.ManagementFactory;
import java.net.InetAddress;
import java.net.NetworkInterface;

//雪花算法代码实现
public class IdWorker {
    // 时间起始标记点，作为基准，一般取系统的最近时间（一旦确定不能变动）
    private final static long twepoch = 1288834974657L;
    // 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;
    // 机器ID最大值
    private final static long maxWorkerId = -1L ^ (-1L << workerIdBits);
    // 数据中心ID最大值
    private final static long maxDatacenterId = -1L ^ (-1L << datacenterIdBits);
    // 毫秒内自增位
    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;

    private final static long sequenceMask = -1L ^ (-1L << sequenceBits);
    /* 上次生产id时间戳 */
    private static long lastTimestamp = -1L;
    // 0，并发控制
    private long sequence = 0L;

    private final long workerId;
    // 数据标识id部分
    private final long datacenterId;

    public IdWorker(){
        this.datacenterId = getDatacenterId(maxDatacenterId);
        this.workerId = getMaxWorkerId(datacenterId, maxWorkerId);
    }
    /**
     * @param workerId
     *            工作机器ID
     * @param datacenterId
     *            序列号
     */
    public IdWorker(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;
    }
    /**
     * 获取下一个ID
     *
     * @return
     */
    public synchronized long nextId() {
        long timestamp = timeGen();
        if (timestamp < lastTimestamp) {
            throw new RuntimeException(String.format("Clock moved backwards.  Refusing to generate id for %d milliseconds", lastTimestamp - timestamp));
        }

        if (lastTimestamp == timestamp) {
            // 当前毫秒内，则+1
            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 = 0L;
        }
        lastTimestamp = timestamp;
        // ID偏移组合生成最终的ID，并返回ID
        long nextId = ((timestamp - twepoch) << timestampLeftShift)
                | (datacenterId << datacenterIdShift)
                | (workerId << workerIdShift) | sequence;

        return nextId;
    }

    private long tilNextMillis(final long lastTimestamp) {
        long timestamp = this.timeGen();
        while (timestamp <= lastTimestamp) {
            timestamp = this.timeGen();
        }
        return timestamp;
    }

    private long timeGen() {
        return System.currentTimeMillis();
    }

    /**
     * <p>
     * 获取 maxWorkerId
     * </p>
     */
    protected static long getMaxWorkerId(long datacenterId, long maxWorkerId) {
        StringBuffer mpid = new StringBuffer();
        mpid.append(datacenterId);
        String name = ManagementFactory.getRuntimeMXBean().getName();
        if (!name.isEmpty()) {
            /*
             * GET jvmPid
             */
            mpid.append(name.split("@")[0]);
        }
        /*
         * MAC + PID 的 hashcode 获取16个低位
         */
        return (mpid.toString().hashCode() & 0xffff) % (maxWorkerId + 1);
    }

    /**
     * <p>
     * 数据标识id部分
     * </p>
     */
    protected static long getDatacenterId(long maxDatacenterId) {
        long id = 0L;
        try {
            InetAddress ip = InetAddress.getLocalHost();
            NetworkInterface network = NetworkInterface.getByInetAddress(ip);
            if (network == null) {
                id = 1L;
            } else {
                byte[] mac = network.getHardwareAddress();
                id = ((0x000000FF & (long) mac[mac.length - 1])
                        | (0x0000FF00 & (((long) mac[mac.length - 2]) << 8))) >> 6;
                id = id % (maxDatacenterId + 1);
            }
        } catch (Exception e) {
            System.out.println(" getDatacenterId: " + e.getMessage());
        }
        return id;
    }
}

3--测试

package com.anson;

/**
 * @description: TODO
 * @author: anson
 * @Date: 2019/10/7 22:16
 * @version: 1.0
 */
public class snow
{
    public static  void main(String[] args) throws Exception
    {
        try
        {


        IdWorker idw = new IdWorker(1,1);
        long ids = idw.nextId();


        for(int i=0;i<10000;i++)
        {
            ids = idw.nextId();
            System.out.println(ids);
        }

        }
        catch (Exception ex)
        {

        }

    }
}

结果如下图：

 程序生成了19位的有序数字，这个既解决了分布式ID生成唯一性问题，也解决了性能问题，建议系统ID设计都采用该算法生成。