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理解分布式id生成算法SnowFlake

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理解分布式id生成算法SnowFlake

https://segmentfault.com/a/1190000011282426#articleHeader2

分布式id生成算法的有很多种,Twitter的SnowFlake就是其中经典的一种。
概述
SnowFlake算法生成id的结果是一个64bit大小的整数,它的结构如下图:

图片描述

1位,不用。二进制中最高位为1的都是负数,但是我们生成的id一般都使用整数,所以这个最高位固定是0
41位,用来记录时间戳(毫秒)。

41位可以表示241?1个数字,
如果只用来表示正整数(计算机中正数包含0),可以表示的数值范围是:0 至 241?1,减1是因为可表示的数值范围是从0开始算的,而不是1。
也就是说41位可以表示241?1个毫秒的值,转化成单位年则是(241?1)/(1000?60?60?24?365)=69年
10位,用来记录工作机器id。

可以部署在210=1024个节点,包括5位datacenterId和5位workerId
5位(bit)可以表示的最大正整数是25?1=31,即可以用0、1、2、3、....31这32个数字,来表示不同的datecenterId或workerId
12位,序列号,用来记录同毫秒内产生的不同id。

12位(bit)可以表示的最大正整数是212?1=4095,即可以用0、1、2、3、....4094这4095个数字,来表示同一机器同一时间截(毫秒)内产生的4095个ID序号
由于在Java中64bit的整数是long类型,所以在Java中SnowFlake算法生成的id就是long来存储的。

SnowFlake可以保证:

所有生成的id按时间趋势递增
整个分布式系统内不会产生重复id(因为有datacenterId和workerId来做区分)
Talk is cheap, show you the code
以下是Twitter官方原版的,用Scala写的,(我也不懂Scala,当成Java看即可):

/** Copyright 2010-2012 Twitter, Inc.*/
package com.twitter.service.snowflake

import com.twitter.ostrich.stats.Stats
import com.twitter.service.snowflake.gen._
import java.util.Random
import com.twitter.logging.Logger

/**

  • An object that generates IDs.
  • This is broken into a separate class in case
  • we ever want to support multiple worker threads
  • per process
    */
    class IdWorker(
    val workerId: Long,
    val datacenterId: Long,
    private val reporter: Reporter,
    var sequence: Long = 0L) extends Snowflake.Iface {

private[this] def genCounter(agent: String) = {
Stats.incr("ids_generated")
Stats.incr("ids_generated_%s".format(agent))
}
private[this] val exceptionCounter = Stats.getCounter("exceptions")
private[this] val log = Logger.get
private[this] val rand = new Random

val twepoch = 1288834974657L

private[this] val workerIdBits = 5L
private[this] val datacenterIdBits = 5L
private[this] val maxWorkerId = -1L ^ (-1L << workerIdBits)
private[this] val maxDatacenterId = -1L ^ (-1L << datacenterIdBits)
private[this] val sequenceBits = 12L

private[this] val workerIdShift = sequenceBits
private[this] val datacenterIdShift = sequenceBits + workerIdBits
private[this] val timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits
private[this] val sequenceMask = -1L ^ (-1L << sequenceBits)

private[this] var lastTimestamp = -1L

// sanity check for workerId
if (workerId > maxWorkerId || workerId < 0) {
exceptionCounter.incr(1)
throw new IllegalArgumentException("worker Id can‘t be greater than %d or less than 0".format(maxWorkerId))
}

if (datacenterId > maxDatacenterId || datacenterId < 0) {
exceptionCounter.incr(1)
throw new IllegalArgumentException("datacenter Id can‘t be greater than %d or less than 0".format(maxDatacenterId))
}

log.info("worker starting. timestamp left shift %d, datacenter id bits %d, worker id bits %d, sequence bits %d, workerid %d",
timestampLeftShift, datacenterIdBits, workerIdBits, sequenceBits, workerId)

def get_id(useragent: String): Long = {
if (!validUseragent(useragent)) {
exceptionCounter.incr(1)
throw new InvalidUserAgentError
}

val id = nextId()
genCounter(useragent)

reporter.report(new AuditLogEntry(id, useragent, rand.nextLong))
id

}

def get_worker_id(): Long = workerId
def get_datacenter_id(): Long = datacenterId
def get_timestamp() = System.currentTimeMillis

protected[snowflake] def nextId(): Long = synchronized {
var timestamp = timeGen()

if (timestamp < lastTimestamp) {
  exceptionCounter.incr(1)
  log.error("clock is moving backwards.  Rejecting requests until %d.", lastTimestamp);
  throw new InvalidSystemClock("Clock moved backwards.  Refusing to generate id for %d milliseconds".format(
    lastTimestamp - timestamp))
}

if (lastTimestamp == timestamp) {
  sequence = (sequence + 1) & sequenceMask
  if (sequence == 0) {
    timestamp = tilNextMillis(lastTimestamp)
  }
} else {
  sequence = 0
}

lastTimestamp = timestamp
((timestamp - twepoch) << timestampLeftShift) |
  (datacenterId << datacenterIdShift) |
  (workerId << workerIdShift) | 
  sequence

}

protected def tilNextMillis(lastTimestamp: Long): Long = {
var timestamp = timeGen()
while (timestamp <= lastTimestamp) {
timestamp = timeGen()
}
timestamp
}

protected def timeGen(): Long = System.currentTimeMillis()

val AgentParser = """([a-zA-Z][a-zA-Z-0-9]*)""".r

def validUseragent(useragent: String): Boolean = useragent match {
case AgentParser() => true
case
=> false
}
}
Scala是一门可以编译成字节码的语言,简单理解是在Java语法基础上加上了很多语法糖,例如不用每条语句后写分号,可以使用动态类型等等。抱着试一试的心态,我把Scala版的代码“翻译”成Java版本的,对scala代码改动的地方如下:

/** Copyright 2010-2012 Twitter, Inc.*/
package com.twitter.service.snowflake

import com.twitter.ostrich.stats.Stats
import com.twitter.service.snowflake.gen._
import java.util.Random
import com.twitter.logging.Logger

/**

  • An object that generates IDs.
  • This is broken into a separate class in case
  • we ever want to support multiple worker threads
  • per process
    */
    class IdWorker( // |
    val workerId: Long, // |
    val datacenterId: Long, // |<--这部分改成Java的构造函数形式
    private val reporter: Reporter,//日志相关,删 // |
    var sequence: Long = 0L) // |
    extends Snowflake.Iface { //接口找不到,删 // |

private[this] def genCounter(agent: String) = { // |
Stats.incr("ids_generated") // |
Stats.incr("ids_generated_%s".format(agent)) // |<--错误、日志处理相关,删
} // |
private[this] val exceptionCounter = Stats.getCounter("exceptions") // |
private[this] val log = Logger.get // |
private[this] val rand = new Random // |

val twepoch = 1288834974657L

private[this] val workerIdBits = 5L
private[this] val datacenterIdBits = 5L
private[this] val maxWorkerId = -1L ^ (-1L << workerIdBits)
private[this] val maxDatacenterId = -1L ^ (-1L << datacenterIdBits)
private[this] val sequenceBits = 12L

private[this] val workerIdShift = sequenceBits
private[this] val datacenterIdShift = sequenceBits + workerIdBits
private[this] val timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits
private[this] val sequenceMask = -1L ^ (-1L << sequenceBits)

private[this] var lastTimestamp = -1L

//----------------------------------------------------------------------------------------------------------------------------//
// sanity check for workerId //
if (workerId > maxWorkerId || workerId < 0) { //
exceptionCounter.incr(1) //<--错误处理相关,删 //
throw new IllegalArgumentException("worker Id can‘t be greater than %d or less than 0".format(maxWorkerId)) //这
// |-->改成:throw new IllegalArgumentException //部
// (String.format("worker Id can‘t be greater than %d or less than 0",maxWorkerId)) //分
} //放
//到
if (datacenterId > maxDatacenterId || datacenterId < 0) { //构
exceptionCounter.incr(1) //<--错误处理相关,删 //造
throw new IllegalArgumentException("datacenter Id can‘t be greater than %d or less than 0".format(maxDatacenterId)) //函
// |-->改成:throw new IllegalArgumentException //数
// (String.format("datacenter Id can‘t be greater than %d or less than 0",maxDatacenterId)) //中
} //
//
log.info("worker starting. timestamp left shift %d, datacenter id bits %d, worker id bits %d, sequence bits %d, workerid %d", //
timestampLeftShift, datacenterIdBits, workerIdBits, sequenceBits, workerId) //
// |-->改成:System.out.printf("worker...%d...",timestampLeftShift,...); //
//----------------------------------------------------------------------------------------------------------------------------//

//-------------------------------------------------------------------//
//这个函数删除错误处理相关的代码后,剩下一行代码:val id = nextId() //
//所以我们直接调用nextId()函数可以了,所以在“翻译”时可以删除这个函数 //
def get_id(useragent: String): Long = { //
if (!validUseragent(useragent)) { //
exceptionCounter.incr(1) //
throw new InvalidUserAgentError //删
} //除
//
val id = nextId() //
genCounter(useragent) //
//
reporter.report(new AuditLogEntry(id, useragent, rand.nextLong)) //
id //
} //
//-------------------------------------------------------------------//

def get_worker_id(): Long = workerId // |
def get_datacenter_id(): Long = datacenterId // |<--改成Java函数
def get_timestamp() = System.currentTimeMillis // |

protected[snowflake] def nextId(): Long = synchronized { // 改成Java函数
var timestamp = timeGen()

if (timestamp < lastTimestamp) {
  exceptionCounter.incr(1) // 错误处理相关,删
  log.error("clock is moving backwards.  Rejecting requests until %d.", lastTimestamp); // 改成System.err.printf(...)
  throw new InvalidSystemClock("Clock moved backwards.  Refusing to generate id for %d milliseconds".format(
    lastTimestamp - timestamp)) // 改成RumTimeException
}

if (lastTimestamp == timestamp) {
  sequence = (sequence + 1) & sequenceMask
  if (sequence == 0) {
    timestamp = tilNextMillis(lastTimestamp)
  }
} else {
  sequence = 0
}

lastTimestamp = timestamp
((timestamp - twepoch) << timestampLeftShift) | // |<--加上关键字return
  (datacenterId << datacenterIdShift) |         // |
  (workerId << workerIdShift) |                 // |
  sequence                                      // |

}

protected def tilNextMillis(lastTimestamp: Long): Long = { // 改成Java函数
var timestamp = timeGen()
while (timestamp <= lastTimestamp) {
timestamp = timeGen()
}
timestamp // 加上关键字return
}

protected def timeGen(): Long = System.currentTimeMillis() // 改成Java函数

val AgentParser = """([a-zA-Z][a-zA-Z-0-9]*)""".r // |
// |
def validUseragent(useragent: String): Boolean = useragent match { // |<--日志相关,删
case AgentParser() => true // |
case
=> false // |
} // |
}
改出来的Java版:

public class IdWorker{

private long workerId;
private long datacenterId;
private long sequence;

public IdWorker(long workerId, long datacenterId, long sequence){
    // sanity check for workerId
    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));
    }
    System.out.printf("worker starting. timestamp left shift %d, datacenter id bits %d, worker id bits %d, sequence bits %d, workerid %d",
            timestampLeftShift, datacenterIdBits, workerIdBits, sequenceBits, workerId);

    this.workerId = workerId;
    this.datacenterId = datacenterId;
    this.sequence = sequence;
}

private long twepoch = 1288834974657L;

private long workerIdBits = 5L;
private long datacenterIdBits = 5L;
private long maxWorkerId = -1L ^ (-1L << workerIdBits);
private long maxDatacenterId = -1L ^ (-1L << datacenterIdBits);
private long sequenceBits = 12L;

private long workerIdShift = sequenceBits;
private long datacenterIdShift = sequenceBits + workerIdBits;
private long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits;
private long sequenceMask = -1L ^ (-1L << sequenceBits);

private long lastTimestamp = -1L;

public long getWorkerId(){
    return workerId;
}

public long getDatacenterId(){
    return datacenterId;
}

public long getTimestamp(){
    return System.currentTimeMillis();
}

public synchronized long nextId() {
    long timestamp = timeGen();

    if (timestamp < lastTimestamp) {
        System.err.printf("clock is moving backwards.  Rejecting requests until %d.", lastTimestamp);
        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 = 0;
    }

    lastTimestamp = timestamp;
    return ((timestamp - twepoch) << timestampLeftShift) |
            (datacenterId << datacenterIdShift) |
            (workerId << workerIdShift) |
            sequence;
}

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

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

//---------------测试---------------
public static void main(String[] args) {
    IdWorker worker = new IdWorker(1,1,1);
    for (int i = 0; i < 30; i++) {
        System.out.println(worker.nextId());
    }
}

}
代码理解
上面的代码中,有部分位运算的代码,如:

sequence = (sequence + 1) & sequenceMask;

private long maxWorkerId = -1L ^ (-1L << workerIdBits);

return ((timestamp - twepoch) << timestampLeftShift) |
(datacenterId << datacenterIdShift) |
(workerId << workerIdShift) |
sequence;
为了能更好理解,我对相关知识研究了一下。

负数的二进制表示
在计算机中,负数的二进制是用补码来表示的。
假设我是用Java中的int类型来存储数字的,
int类型的大小是32个二进制位(bit),即4个字节(byte)。(1 byte = 8 bit)
那么十进制数字3在二进制中的表示应该是这样的:

00000000 00000000 00000000 00000011
// 3的二进制表示,就是原码
那数字-3在二进制中应该如何表示?
我们可以反过来想想,因为-3+3=0,
在二进制运算中把-3的二进制看成未知数x来求解,
求解算式的二进制表示如下:

00000000 00000000 00000000 00000011 //3,原码

  • xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx //-3,补码

    00000000 00000000 00000000 00000000
    反推x的值,3的二进制加上什么值才使结果变成00000000 00000000 00000000 00000000?:

    00000000 00000000 00000000 00000011 //3,原码
  • 11111111 11111111 11111111 11111101 //-3,补码

    1 00000000 00000000 00000000 00000000
    反推的思路是3的二进制数从最低位开始逐位加1,使溢出的1不断向高位溢出,直到溢出到第33位。然后由于int类型最多只能保存32个二进制位,所以最高位的1溢出了,剩下的32位就成了(十进制的)0。

补码的意义就是可以拿补码和原码(3的二进制)相加,最终加出一个“溢出的0”

以上是理解的过程,实际中记住公式就很容易算出来:

补码 = 反码 + 1
补码 = (原码 - 1)再取反码
因此-1的二进制应该这样算:

00000000 00000000 00000000 00000001 //原码:1的二进制
11111111 11111111 11111111 11111110 //取反码:1的二进制的反码
11111111 11111111 11111111 11111111 //加1:-1的二进制表示(补码)
用位运算计算n个bit能表示的最大数值
比如这样一行代码:

private long workerIdBits = 5L;
private long maxWorkerId = -1L ^ (-1L << workerIdBits);       

上面代码换成这样看方便一点:
long maxWorkerId = -1L ^ (-1L << 5L)

咋一看真的看不准哪个部分先计算,于是查了一下Java运算符的优先级表:
图片描述

所以上面那行代码中,运行顺序是:

-1 左移 5,得结果a
-1 异或 a
long maxWorkerId = -1L ^ (-1L << 5L)的二进制运算过程如下:

-1 左移 5,得结果a :

    11111111 11111111 11111111 11111111 //-1的二进制表示(补码)

11111 11111111 11111111 11111111 11100000 //高位溢出的不要,低位补0
11111111 11111111 11111111 11100000 //结果a
-1 异或 a :

    11111111 11111111 11111111 11111111 //-1的二进制表示(补码)
^   11111111 11111111 11111111 11100000 //两个操作数的位中,相同则为0,不同则为1

    00000000 00000000 00000000 00011111 //最终结果31

最终结果是31,二进制00000000 00000000 00000000 00011111转十进制可以这么算:
24+23+22+21+20=16+8+4+2+1=31
那既然现在知道算出来long maxWorkerId = -1L ^ (-1L << 5L)中的maxWorkerId = 31,有什么含义?为什么要用左移5来算?如果你看过概述部分,请找到这段内容看看:

5位(bit)可以表示的最大正整数是25?1=31,即可以用0、1、2、3、....31这32个数字,来表示不同的datecenterId或workerId
-1L ^ (-1L << 5L)结果是31,25?1的结果也是31,所以在代码中,-1L ^ (-1L << 5L)的写法是利用位运算计算出5位能表示的最大正整数是多少

用mask防止溢出
有一段有趣的代码:

sequence = (sequence + 1) & sequenceMask;
分别用不同的值测试一下,你就知道它怎么有趣了:

    long seqMask = -1L ^ (-1L << 12L); //计算12位能耐存储的最大正整数,相当于:2^12-1 = 4095
    System.out.println("seqMask: "+seqMask);
    System.out.println(1L & seqMask);
    System.out.println(2L & seqMask);
    System.out.println(3L & seqMask);
    System.out.println(4L & seqMask);
    System.out.println(4095L & seqMask);
    System.out.println(4096L & seqMask);
    System.out.println(4097L & seqMask);
    System.out.println(4098L & seqMask);


    /**
    seqMask: 4095
    1
    2
    3
    4
    4095
    0
    1
    2
    */

这段代码通过位与运算保证计算的结果范围始终是 0-4095 !

用位运算汇总结果
还有另外一段诡异的代码:

return ((timestamp - twepoch) << timestampLeftShift) |
(datacenterId << datacenterIdShift) |
(workerId << workerIdShift) |
sequence;
为了弄清楚这段代码,

首先 需要计算一下相关的值:

private long twepoch = 1288834974657L; //起始时间戳,用于用当前时间戳减去这个时间戳,算出偏移量

private long workerIdBits = 5L; //workerId占用的位数:5
private long datacenterIdBits = 5L; //datacenterId占用的位数:5
private long maxWorkerId = -1L ^ (-1L << workerIdBits);  // workerId可以使用的最大数值:31
private long maxDatacenterId = -1L ^ (-1L << datacenterIdBits); // datacenterId可以使用的最大数值:31
private long sequenceBits = 12L;//序列号占用的位数:12

private long workerIdShift = sequenceBits; // 12
private long datacenterIdShift = sequenceBits + workerIdBits; // 12+5 = 17
private long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits; // 12+5+5 = 22
private long sequenceMask = -1L ^ (-1L << sequenceBits);//4095

private long lastTimestamp = -1L;

其次 写个测试,把参数都写死,并运行打印信息,方便后面来核对计算结果:

//---------------测试---------------
public static void main(String[] args) {
    long timestamp = 1505914988849L;
    long twepoch = 1288834974657L;
    long datacenterId = 17L;
    long workerId = 25L;
    long sequence = 0L;

    System.out.printf("\ntimestamp: %d \n",timestamp);
    System.out.printf("twepoch: %d \n",twepoch);
    System.out.printf("datacenterId: %d \n",datacenterId);
    System.out.printf("workerId: %d \n",workerId);
    System.out.printf("sequence: %d \n",sequence);
    System.out.println();
    System.out.printf("(timestamp - twepoch): %d \n",(timestamp - twepoch));
    System.out.printf("((timestamp - twepoch) << 22L): %d \n",((timestamp - twepoch) << 22L));
    System.out.printf("(datacenterId << 17L): %d \n" ,(datacenterId << 17L));
    System.out.printf("(workerId << 12L): %d \n",(workerId << 12L));
    System.out.printf("sequence: %d \n",sequence);

    long result = ((timestamp - twepoch) << 22L) |
            (datacenterId << 17L) |
            (workerId << 12L) |
            sequence;
    System.out.println(result);

}

/** 打印信息:
    timestamp: 1505914988849 
    twepoch: 1288834974657 
    datacenterId: 17 
    workerId: 25 
    sequence: 0 
    
    (timestamp - twepoch): 217080014192 
    ((timestamp - twepoch) << 22L): 910499571845562368 
    (datacenterId << 17L): 2228224 
    (workerId << 12L): 102400 
    sequence: 0 
    910499571847892992
*/

代入位移的值得之后,就是这样:

return ((timestamp - 1288834974657) << 22) |
(datacenterId << 17) |
(workerId << 12) |
sequence;
对于尚未知道的值,我们可以先看看概述 中对SnowFlake结构的解释,再代入在合法范围的值(windows系统可以用计算器方便计算这些值的二进制),来了解计算的过程。
当然,由于我的测试代码已经把这些值写死了,那直接用这些值来手工验证计算结果即可:

    long timestamp = 1505914988849L;
    long twepoch = 1288834974657L;
    long datacenterId = 17L;
    long workerId = 25L;
    long sequence = 0L;

设:timestamp = 1505914988849,twepoch = 1288834974657
1505914988849 - 1288834974657 = 217080014192 (timestamp相对于起始时间的毫秒偏移量),其(a)二进制左移22位计算过程如下:

                    |<--这里开始左右22位                            ?

00000000 00000000 000000|00 00110010 10001010 11111010 00100101 01110000 // a = 217080014192
00001100 10100010 10111110 10001001 01011100 00|000000 00000000 00000000 // a左移22位后的值(la)
|<--这里后面的位补0
设:datacenterId = 17,其(b)二进制左移17位计算过程如下:

               |<--这里开始左移17位    

00000000 00000000 0|0000000 ?00000000 00000000 00000000 00000000 00010001 // b = 17
0000000?0 00000000 00000000 00000000 00000000 0010001|0 00000000 00000000 // b左移17位后的值(lb)
|<--这里后面的位补0
设:workerId = 25,其(c)二进制左移12位计算过程如下:

         |<--这里开始左移12位    

?00000000 0000|0000 00000000 00000000 00000000 00000000 00000000 00011001? // c = 25
00000000 00000000 00000000 00000000 00000000 00000001 1001|0000 00000000? // c左移12位后的值(lc)
|<--这里后面的位补0
设:sequence = 0,其二进制如下:

00000000 00000000 00000000 00000000 00000000 00000000 0000?0000 00000000? // sequence = 0
现在知道了每个部分左移后的值(la,lb,lc),代码可以简化成下面这样去理解:

return ((timestamp - 1288834974657) << 22) |
(datacenterId << 17) |
(workerId << 12) |
sequence;
-----------------------------
|
|简化
|/
-----------------------------
return (la) |
(lb) |
(lc) |
sequence;
上面的管道符号|在Java中也是一个位运算符。其含义是:
x的第n位和y的第n位 只要有一个是1,则结果的第n位也为1,否则为0,因此,我们对四个数的位或运算如下:

1 | 41 | 5 | 5 | 12

0|0001100 10100010 10111110 10001001 01011100 00|00000|0 0000|0000 00000000 //la
0|000000?0 00000000 00000000 00000000 00000000 00|10001|0 0000|0000 00000000 //lb
0|0000000 00000000 00000000 00000000 00000000 00|00000|1 1001|0000 00000000 //lc
or 0|0000000 00000000 00000000 00000000 00000000 00|00000|0 0000|?0000 00000000? //sequence
------------------------------------------------------------------------------------------
0|0001100 10100010 10111110 10001001 01011100 00|10001|1 1001|?0000 00000000? //结果:910499571847892992
结果计算过程:
1) 从至左列出1出现的下标(从0开始算):

0000 1 1 00 1 0 1 000 1 0 1 0 1 1 1 1 1 0 1 000 1 00 1 0 1 0 1 1 1 0000 1 000 1 1 1 00 1? 0000 0000 0000
59 58 55 53 49 47 45 44 43 42 41 39 35 32 30 28 27 26 21 17 16 15 12
2) 各个下标作为2的幂数来计算,并相加:

259+258+255+253+249+247+245+244+243+242+241+239+235+232+230+228+227+226+221+217+216+215+22
2^59} : 576460752303423488
2^58} : 288230376151711744
2^55} : 36028797018963968
2^53} : 9007199254740992
2^49} : 562949953421312
2^47} : 140737488355328
2^45} : 35184372088832
2^44} : 17592186044416
2^43} : 8796093022208
2^42} : 4398046511104
2^41} : 2199023255552
2^39} : 549755813888
2^35} : 34359738368
2^32} : 4294967296
2^30} : 1073741824
2^28} : 268435456
2^27} : 134217728
2^26} : 67108864
2^21} : 2097152
2^17} : 131072
2^16} : 65536
2^15} : 32768

  • 2^12} : 4096

         910499571847892992
    计算截图:
    图片描述

跟测试程序打印出来的结果一样,手工验证完毕!

观察
1 | 41 | 5 | 5 | 12

0|0001100 10100010 10111110 10001001 01011100 00| | | //la
0| |10001| | //lb
0| | |1 1001| //lc
or 0| | | |?0000 00000000? //sequence
------------------------------------------------------------------------------------------
0|0001100 10100010 10111110 10001001 01011100 00|10001|1 1001|?0000 00000000? //结果:910499571847892992
上面的64位我按1、41、5、5、12的位数截开了,方便观察。

纵向观察发现:

在41位那一段,除了la一行有值,其它行(lb、lc、sequence)都是0,(我爸其它)
在左起第一个5位那一段,除了lb一行有值,其它行都是0
在左起第二个5位那一段,除了lc一行有值,其它行都是0
按照这规律,如果sequence是0以外的其它值,12位那段也会有值的,其它行都是0
横向观察发现:

在la行,由于左移了5+5+12位,5、5、12这三段都补0了,所以la行除了41那段外,其它肯定都是0
同理,lb、lc、sequnece行也以此类推
正因为左移的操作,使四个不同的值移到了SnowFlake理论上相应的位置,然后四行做位或运算(只要有1结果就是1),就把4段的二进制数合并成一个二进制数。
结论:
所以,在这段代码中

return ((timestamp - 1288834974657) << 22) |
(datacenterId << 17) |
(workerId << 12) |
sequence;
左移运算是为了将数值移动到对应的段(41、5、5,12那段因为本来就在最右,因此不用左移)。

然后对每个左移后的值(la、lb、lc、sequence)做位或运算,是为了把各个短的数据合并起来,合并成一个二进制数。

最后转换成10进制,就是最终生成的id

扩展
在理解了这个算法之后,其实还有一些扩展的事情可以做:

根据自己业务修改每个位段存储的信息。算法是通用的,可以根据自己需求适当调整每段的大小以及存储的信息。
解密id,由于id的每段都保存了特定的信息,所以拿到一个id,应该可以尝试反推出原始的每个段的信息。反推出的信息可以帮助我们分析。比如作为订单,可以知道该订单的生成日期,负责处理的数据中心等等。

理解分布式id生成算法SnowFlake

标签:main   EAP   删除   process   mil   protected   分布式   数值范围   audit   

原文地址:https://www.cnblogs.com/Leo_wl/p/9565560.html

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