雪花算法-Java实现-解决时钟回拨的一种方法

背景: 

前不久发生了一次严重的生产事件, 与雪花算法有关,但不是雪花算法的问题

具体问题参考代码main中的注释, 结论如下

序列可以使用69年, 序列的长度变化是这样的, 假设以当前时间为初始化值
* 12 13 14 15 16 17 18(约7年) 19(约58年)
* 长度随时间增加, 长度越长, 保持相同长度的时间越长

---

在分析讨论问题的过程中, 发现了一种可能是解决时钟回拨的方法, 仅改动代码, 不需要其他辅助.

首先给出我参考的java标准实现,

Java实现雪花算法(附源码)

在此基础上修改, 修改点都加了标记, 如果有问题, 可以留言讨论.

下面做一些简要说明

主要思路:

当发生时钟回拨后, 将当前时间设置为lastTimestamp表示的未来时间, 当毫秒内sequence表示的序列用完后, 将 lastTimestamp表示的未来时间加1.

给出一张简图, 展示 当前时间 和 未来时间 表示的意思, 可能对理解思路有帮助.

第一步: 发生时钟回拨时, 不抛出异常, 直接将 timestamp 设置为 上一次时间 lastTimestamp

// 判断是否小于上次时间戳，如果小于的话，就抛出异常
if (timestamp < lastTimestamp) {

            //--20220813--1---------------------------------------
            //这里不再抛出异常, 改为记录时钟回拨发生时间点

            //发生时钟回拨后, 当前时间 timestamp 就变成了 过去的时间
            //此时将 timestamp 设置为 上一次时间, 即相对于当前时间的未来时间
            timestamp = lastTimestamp;
            isBackwordsFlag = true;

            //记录时钟回拨发生的时间点, 后续需要跳过已经使用的未来时间段
            if (isFirstBackwordsFlag) {
                backBaseTimestamp = timestamp;
                isFirstBackwordsFlag = false;
                System.out.println("时钟回拨已发生-->" + backBaseTimestamp);
            }
            //--20220813--1---------------------------------------

第二步: 当时钟回拨发生后, 不再重置 lastTimestamp, 

// 这儿记录一下最近一次生成id的时间戳，单位是毫秒
//lastTimestamp = timestamp;

//--20220813--2---------------------------------------
if(isBackwordsFlag) {
    //什么都不做
} else {
    lastTimestamp = timestamp;
}
//--20220813--2---------------------------------------

第三步:  方法开头需要检测 时钟回拨发生后, 随着时间流逝, 当前时间是否到达了回拨点时间

// 这个是核心方法，通过调用nextId()方法，
// 让当前这台机器上的snowflake算法程序生成一个全局唯一的id
public synchronized long nextId() {
    // 这儿就是获取当前时间戳，单位是毫秒
    long timestamp = timeGen();

        //--20220813--3---------------------------------------
        if (isBackwordsFlag) {
            //当回拨时间再次叨叨回拨时间点时, 跳过回拨这段时间里已经使用了的未来时间
            if (timestamp >= backBaseTimestamp && timestamp < lastTimestamp) {
                //直接将当前时间设置为最新的未来时间
                timestamp = lastTimestamp;
            } else if(timestamp > lastTimestamp) {
                //当前时间已经大于上次时间, 重置时钟回拨标志
                isBackwordsFlag = false;
                isFirstBackwordsFlag = true;
                System.out.println("时间已恢复正常-->" + timestamp);
            } else {
                // timestamp == lastTimestamp 等于的情况在后面
            }
        }
        //--20220813--3----------------------------------------

第四步: 当毫秒内sequence表示的序列用完后, 将 lastTimestamp表示的未来时间加1

// 下面是说假设在同一个毫秒内，又发送了一个请求生成一个id
// 这个时候就得把seqence序号给递增1，最多就是4096
if (timestamp == lastTimestamp) {

    // 这个意思是说一个毫秒内最多只能有4096个数字，无论你传递多少进来，
    //这个位运算保证始终就是在4096这个范围内，避免你自己传递个sequence超过了4096这个范围
    sequence = (sequence + 1) & sequenceMask;
    //当某一毫秒的时间，产生的id数 超过4095，系统会进入等待，直到下一毫秒，系统继续产生ID
    if (sequence == 0) {
        //timestamp = tilNextMillis(lastTimestamp);

                //--20220813--4---------------------------------------
                //这里也不能阻塞了, 因为阻塞方法中需要用到当前时间, 改为将此时代表未来的时间 加 1
                if (isBackwordsFlag) {
                    lastTimestamp++;
                } else {
                    timestamp = tilNextMillis(lastTimestamp);
                }
                //--20220813--4---------------------------------------

修改后的完整代码

package com.yu.fierys.snowflow;

import java.io.IOException;
import java.time.LocalDateTime;
import java.time.ZoneOffset;
import java.time.format.DateTimeFormatter;

/**相较于标准算法,加入了时钟回拨解决方法,仅单机研究,仅个人思考,仅供参考
 */
public class SnowFlow {
    //因为二进制里第一个 bit 为如果是 1，那么都是负数，但是我们生成的 id 都是正数，所以第一个 bit 统一都是 0。

    //机器ID  2进制5位  32位减掉1位 31个
    private long workerId;
    //机房ID 2进制5位  32位减掉1位 31个
    private long datacenterId;
    //代表一毫秒内生成的多个id的最新序号  12位 4096 -1 = 4095 个
    private long sequence;
    //设置一个时间初始值    2^41 - 1   差不多可以用69年
    private long twepoch = 1420041600000L;
    //5位的机器id
    private long workerIdBits = 5L;
    //5位的机房id；。‘
    private long datacenterIdBits = 5L;
    //每毫秒内产生的id数 2 的 12次方
    private long sequenceBits = 12L;
    // 这个是二进制运算，就是5 bit最多只能有31个数字，也就是说机器id最多只能是32以内
    private long maxWorkerId = -1L ^ (-1L << workerIdBits);
    // 这个是一个意思，就是5 bit最多只能有31个数字，机房id最多只能是32以内
    private long maxDatacenterId = -1L ^ (-1L << datacenterIdBits);

    private long workerIdShift = sequenceBits;
    private long datacenterIdShift = sequenceBits + workerIdBits;
    private long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits;

    // -1L 二进制就是1111 1111  为什么？
    // -1 左移12位就是 1111  1111 0000 0000 0000 0000
    // 异或  相同为0 ，不同为1
    // 1111  1111  0000  0000  0000  0000
    // ^
    // 1111  1111  1111  1111  1111  1111
    // 0000 0000 1111 1111 1111 1111 换算成10进制就是4095
    private long sequenceMask = -1L ^ (-1L << sequenceBits);
    //记录产生时间毫秒数，判断是否是同1毫秒
    private long lastTimestamp = -1L;
    public long getWorkerId(){
        return workerId;
    }
    public long getDatacenterId() {
        return datacenterId;
    }
    public long getTimestamp() {
        return System.currentTimeMillis();
    }

    //是否发生了时钟回拨
    private boolean isBackwordsFlag = false;
    //是否是第一次发生时钟回拨, 用于设置时钟回拨时间点
    private boolean isFirstBackwordsFlag = true;
    //记录时钟回拨发生时间点, 用于判断回拨后的时间达到回拨时间点时, 跳过 已经用过的 时钟回拨发生时间点 之后的时间 到 未来时间的当前时间点
    private long backBaseTimestamp = -1L;

    public SnowFlow() {
    }

    public SnowFlow(long workerId, long datacenterId, long sequence) {

        // 检查机房id和机器id是否超过31 不能小于0
        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;
        this.sequence = sequence;
    }

    // 这个是核心方法，通过调用nextId()方法，
    // 让当前这台机器上的snowflake算法程序生成一个全局唯一的id
    public synchronized long nextId() {
        // 这儿就是获取当前时间戳，单位是毫秒
        long timestamp = timeGen();

        //--20220813--1---------------------------------------
        if (isBackwordsFlag) {
            //当回拨时间再次叨叨回拨时间点时, 跳过回拨这段时间里已经使用了的未来时间
            if (timestamp >= backBaseTimestamp && timestamp < lastTimestamp) {
                //直接将当前时间设置为最新的未来时间
                timestamp = lastTimestamp;
            } else if(timestamp > lastTimestamp) {
                //当前时间已经大于上次时间, 重置时钟回拨标志
                isBackwordsFlag = false;
                isFirstBackwordsFlag = true;
                System.out.println("时间已恢复正常-->" + timestamp);
            } else {
                // timestamp == lastTimestamp 等于的情况在后面
            }
        }
        //--20220813--1----------------------------------------

        // 判断是否小于上次时间戳，如果小于的话，就抛出异常
        if (timestamp < lastTimestamp) {

            System.err.printf("lastTimestamp=%d, timestamp=%d, l-t=%d \n", lastTimestamp, timestamp, lastTimestamp - timestamp);
//            throw new RuntimeException(
//                    String.format("Clock moved backwards. Refusing to generate id for %d milliseconds",
//                            lastTimestamp - timestamp));

            //--20220813--2---------------------------------------
            //这里不再抛出异常, 改为记录时钟回拨发生时间点

            //发生时钟回拨后, 当前时间 timestamp 就变成了 过去的时间
            //此时将 timestamp 设置为 上一次时间, 即相对于当前时间的未来时间
            timestamp = lastTimestamp;
            isBackwordsFlag = true;

            //记录时钟回拨发生的时间点, 后续需要跳过已经使用的未来时间段
            if (isFirstBackwordsFlag) {
                backBaseTimestamp = timestamp;
                isFirstBackwordsFlag = false;
                System.out.println("时钟回拨已发生-->" + backBaseTimestamp);
            }
            //--20220813--2---------------------------------------
        }

        // 下面是说假设在同一个毫秒内，又发送了一个请求生成一个id
        // 这个时候就得把seqence序号给递增1，最多就是4096
        if (timestamp == lastTimestamp) {

            // 这个意思是说一个毫秒内最多只能有4096个数字，无论你传递多少进来，
            //这个位运算保证始终就是在4096这个范围内，避免你自己传递个sequence超过了4096这个范围
            sequence = (sequence + 1) & sequenceMask;
            //当某一毫秒的时间，产生的id数 超过4095，系统会进入等待，直到下一毫秒，系统继续产生ID
            if (sequence == 0) {
                //timestamp = tilNextMillis(lastTimestamp);

                //--20220813--3---------------------------------------
                //这里也不能阻塞了, 因为阻塞方法中需要用到当前时间, 改为将此时代表未来的时间 加 1
                if (isBackwordsFlag) {
                    lastTimestamp++;
                } else {
                    timestamp = tilNextMillis(lastTimestamp);
                }
                //--20220813--3---------------------------------------
            }

        } else {
            //sequence = 0;
            //每毫秒的序列号都从0开始的话，会导致没有竞争情况返回的都是偶数。解决方法是用时间戳&1，这样就会随机得到1或者0。
            sequence = timestamp & 1;
        }
        // 这儿记录一下最近一次生成id的时间戳，单位是毫秒
        //lastTimestamp = timestamp;

        //--20220813--4---------------------------------------
        if(isBackwordsFlag) {
            //什么都不做
        } else {
            lastTimestamp = timestamp;
        }
        //--20220813--4---------------------------------------

        // 这儿就是最核心的二进制位运算操作，生成一个64bit的id
        // 先将当前时间戳左移，放到41 bit那儿；将机房id左移放到5 bit那儿；将机器id左移放到5 bit那儿；将序号放最后12 bit
        // 最后拼接起来成一个64 bit的二进制数字，转换成10进制就是个long型
        long sn = ((timestamp - twepoch) << timestampLeftShift) |
                (datacenterId << datacenterIdShift) |
                (workerId << workerIdShift) | sequence;

        if (isBackwordsFlag) {
            System.out.printf("sn=%d\n", sn);
        }
        return sn;
    }

    /**
     * 当某一毫秒的时间，产生的id数 超过4095，系统会进入等待，直到下一毫秒，系统继续产生ID
     * @param lastTimestamp
     * @return
     */
    private long tilNextMillis(long lastTimestamp) {

        long timestamp = timeGen();

        while (timestamp <= lastTimestamp) {
            timestamp = timeGen();
        }
        return timestamp;
    }
    //获取当前时间戳
    private long timeGen(){
        return System.currentTimeMillis();
    }

    /**
     *  main 测试类
     * @param args
     */
    public static void main(String[] args) throws IOException, InterruptedException {

        SnowFlow snowFlow = new SnowFlow(1, 1, 1);
        int count = 10000000;
        for (int i = 0; i < count; i++) {
            //实际测试发现遍历太快输出日志过多导致卡顿, 增加睡眠时间, 或输出到文件
            snowFlow.nextId();

            Thread.sleep(100);

//            System.out.println(snowFlow.nextId());

//            if (i == 1000) {
                //不具有管理员权限的用户, 修改不成功
                //testClockMvoedBackwords(30);
//            }
            //改为 手动修改,  右键cmd,以管理员权限打开后,使用time命令即可, time 16:15:00
        }


        System.out.println(System.currentTimeMillis());
        /**
         * 这里为什么特意输出一个开始时间呢, 其实就是一个运行了两年的程序突然有一天出bug了,导致了严重的生产事件!
         * 那么时间初始化影响什么呢, 答案是 序列的长度
         * 有人就说了, 这个一般是作为 主键用的, 长度貌似影响不大, 确实是这样的
         * 这次的bug不是雪花算法本身的问题, 而是程序里面有个功能是严格长度截取的, 并且只考虑了长度不够的情况, 没有考虑到变长的情况
         * 最根本的原因是 本人截取的时候 序列的长度一直是18位, 然后截取9位的代码是这么写的 substring(9);
         * 当未来的某一天序列长度增加到了19位,那么这个截取就会返回10位长度, 最终导致一个大范围的交易失败......
         * 锅当然是本人背, 这里提出这种情况, 供大家参考.
         * 经过仔细研究所谓的序列可以使用69年, 序列的长度变化是这样的, 假设以当前时间为初始化值
         * 12 13 14 15 16 17 18(约7年) 19(约58年)
         * 长度随时间增加, 长度越长, 保持相同长度的时间越长
         */
        DateTimeFormatter dtf2 = DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss");
        String dateString = "2015-01-01 00:00:00";
        LocalDateTime localDateTime = LocalDateTime.parse(dateString,dtf2);
        System.out.println(localDateTime.toInstant(ZoneOffset.ofHours(8)).toEpochMilli());

    }

    //windows os 模拟时钟回拨, 将当前时间减去几秒
    private static void testClockMvoedBackwords(long seconds) throws IOException {
        System.out.println(LocalDateTime.now().format(DateTimeFormatter.ofPattern("HH:mm:ss")));

        LocalDateTime localDateTime = LocalDateTime.now();
        String backTime = localDateTime.minusSeconds(seconds).format(DateTimeFormatter.ofPattern("HH:mm:ss"));
        System.out.println(backTime);

        if (System.getProperty("os.name").contains("Windows")) {
            String cmd = "cmd /c start time 15:41:56";// + backTime;
            //不具有管理员权限的用户, 修改不生效, 提示 客户端无所需特权
            Runtime.getRuntime().exec(cmd);
//            Runtime.getRuntime().exec("cmd /c notepad");
            System.out.println(LocalDateTime.now().format(DateTimeFormatter.ofPattern("HH:mm:ss")));
        }
    }
}

测试与预期

附带一份测试输出的日志, 也可以随意设置 过去的时间或未来的时间, 观察输出

预期结果是 最终生成的序列可以保持时间向前的阶段性有序

lastTimestamp=1660380067549, timestamp=1660380000074, l-t=67475 
时钟回拨已发生-->1660380067549
sn=1008052595794776066

......

lastTimestamp=1660380129308, timestamp=1660380129227, l-t=81 
sn=1008052854830797071
时间已恢复正常-->1660380129336

lastTimestamp=1660380067549, timestamp=1660380000074, l-t=67475 
时钟回拨已发生-->1660380067549
sn=1008052595794776066
lastTimestamp=1660380067549, timestamp=1660380000182, l-t=67367 
sn=1008052595794776067
lastTimestamp=1660380067549, timestamp=1660380000291, l-t=67258 
sn=1008052595794776068
lastTimestamp=1660380067549, timestamp=1660380000400, l-t=67149 
sn=1008052595794776069
lastTimestamp=1660380067549, timestamp=1660380000508, l-t=67041 
sn=1008052595794776070
lastTimestamp=1660380067549, timestamp=1660380000616, l-t=66933 
sn=1008052595794776071
lastTimestamp=1660380067549, timestamp=1660380000724, l-t=66825 
sn=1008052595794776072
lastTimestamp=1660380067549, timestamp=1660380000832, l-t=66717 
sn=1008052595794776073
lastTimestamp=1660380067549, timestamp=1660380000942, l-t=66607 
sn=1008052595794776074
lastTimestamp=1660380067549, timestamp=1660380001054, l-t=66495 
sn=1008052595794776075
lastTimestamp=1660380067549, timestamp=1660380001164, l-t=66385 
sn=1008052595794776076
lastTimestamp=1660380067549, timestamp=1660380001272, l-t=66277 
sn=1008052595794776077
lastTimestamp=1660380067549, timestamp=1660380001380, l-t=66169 
sn=1008052595794776078
lastTimestamp=1660380067549, timestamp=1660380001489, l-t=66060 
sn=1008052595794776079
lastTimestamp=1660380067549, timestamp=1660380001596, l-t=65953 
sn=1008052595794776080
lastTimestamp=1660380067549, timestamp=1660380001705, l-t=65844 
sn=1008052595794776081
lastTimestamp=1660380067549, timestamp=1660380001813, l-t=65736 
sn=1008052595794776082
lastTimestamp=1660380067549, timestamp=1660380001921, l-t=65628 
sn=1008052595794776083
lastTimestamp=1660380067549, timestamp=1660380002029, l-t=65520 
sn=1008052595794776084
lastTimestamp=1660380067549, timestamp=1660380002138, l-t=65411 
sn=1008052595794776085
lastTimestamp=1660380067549, timestamp=1660380002247, l-t=65302 
sn=1008052595794776086
lastTimestamp=1660380067549, timestamp=1660380002358, l-t=65191 
sn=1008052595794776087
lastTimestamp=1660380067549, timestamp=1660380002470, l-t=65079 
sn=1008052595794776088
lastTimestamp=1660380067549, timestamp=1660380002579, l-t=64970 
sn=1008052595794776089
lastTimestamp=1660380067549, timestamp=1660380002687, l-t=64862 
sn=1008052595794776090
lastTimestamp=1660380067549, timestamp=1660380002796, l-t=64753 
sn=1008052595794776091
lastTimestamp=1660380067549, timestamp=1660380002905, l-t=64644 
sn=1008052595794776092
lastTimestamp=1660380067549, timestamp=1660380003013, l-t=64536 
sn=1008052595794776093
lastTimestamp=1660380067549, timestamp=1660380003122, l-t=64427 
sn=1008052595794776094
lastTimestamp=1660380067549, timestamp=1660380003230, l-t=64319 
sn=1008052595794776095
lastTimestamp=1660380067549, timestamp=1660380003339, l-t=64210 
sn=1008052595794776096
lastTimestamp=1660380067549, timestamp=1660380003446, l-t=64103 
sn=1008052595794776097
lastTimestamp=1660380067549, timestamp=1660380003554, l-t=63995 
sn=1008052595794776098
lastTimestamp=1660380067549, timestamp=1660380003665, l-t=63884 
sn=1008052595794776099
lastTimestamp=1660380067549, timestamp=1660380003775, l-t=63774 
sn=1008052595794776100
lastTimestamp=1660380067549, timestamp=1660380003887, l-t=63662 
sn=1008052595794776101
lastTimestamp=1660380067549, timestamp=1660380003994, l-t=63555 
sn=1008052595794776102
lastTimestamp=1660380067549, timestamp=1660380004103, l-t=63446 
sn=1008052595794776103
lastTimestamp=1660380067549, timestamp=1660380004212, l-t=63337 
sn=1008052595794776104
lastTimestamp=1660380067549, timestamp=1660380004321, l-t=63228 
sn=1008052595794776105
lastTimestamp=1660380067549, timestamp=1660380004429, l-t=63120 
sn=1008052595794776106
lastTimestamp=1660380067549, timestamp=1660380004538, l-t=63011 
sn=1008052595794776107
lastTimestamp=1660380067549, timestamp=1660380004645, l-t=62904 
sn=1008052595794776108
lastTimestamp=1660380067549, timestamp=1660380004753, l-t=62796 
sn=1008052595794776109
lastTimestamp=1660380067549, timestamp=1660380004862, l-t=62687 
sn=1008052595794776110
lastTimestamp=1660380067549, timestamp=1660380004971, l-t=62578 
sn=1008052595794776111
lastTimestamp=1660380067549, timestamp=1660380005078, l-t=62471 
sn=1008052595794776112
lastTimestamp=1660380067549, timestamp=1660380005190, l-t=62359 
sn=1008052595794776113
lastTimestamp=1660380067549, timestamp=1660380005300, l-t=62249 
sn=1008052595794776114
lastTimestamp=1660380067549, timestamp=1660380005408, l-t=62141 
sn=1008052595794776115
lastTimestamp=1660380067549, timestamp=1660380005517, l-t=62032 
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时间已恢复正常-->1660380120050
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时钟回拨已发生-->1660380129308
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时间已恢复正常-->1660380129336