雪花算法(分布式自增长ID)

分布式ID生成
雪花算法(分布式自增长ID)


一、简介

snowflake是Twitter开源的分布式ID⽣成算法,结果是⼀个long型的ID。其核⼼思想是:使⽤41bit作为毫秒数,10bit作为机器的ID(5个bit是数据中⼼,5个bit的机器ID),12bit作为毫秒内的流⽔号(意味着每个节点在每毫秒可以产⽣ 4096 个 ID),最后还有⼀个符号位,永远是0。

请添加图片描述

二、文件

CSDN

https://download.csdn.net/download/weixin_44624117/85339346

三、代码

package com.changgou.util;

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

/**
 * <p>名称:IdWorker.java</p>
 * <p>描述:分布式自增长ID</p>
 * <pre>
 *     Twitter的 Snowflake JAVA实现方案
 * </pre>
 * 核心代码为其IdWorker这个类实现,其原理结构如下,我分别用一个0表示一位,用—分割开部分的作用:
 * 1||0---0000000000 0000000000 0000000000 0000000000 0 --- 00000 ---00000 ---000000000000
 * 在上面的字符串中,第一位为未使用(实际上也可作为long的符号位),接下来的41位为毫秒级时间,
 * 然后5位datacenter标识位,5位机器ID(并不算标识符,实际是为线程标识),
 * 然后12位该毫秒内的当前毫秒内的计数,加起来刚好64位,为一个Long型。
 * 这样的好处是,整体上按照时间自增排序,并且整个分布式系统内不会产生ID碰撞(由datacenter和机器ID作区分),
 * 并且效率较高,经测试,snowflake每秒能够产生26万ID左右,完全满足需要。
 * <p>
 * 64位ID (42(毫秒)+5(机器ID)+5(业务编码)+12(重复累加))
 *
 * @author Polim
 */
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;
    }


    public static void main(String[] args) {
        //推特  26万个不重复的ID
        IdWorker idWorker = new IdWorker(0,0);
        for (int i = 0; i <2600 ; i++) {
            System.out.println(idWorker.nextId());
        }
    }

}
posted @ 2022-08-03 23:54  ah_lydms  阅读(121)  评论(0编辑  收藏  举报