基于mysql唯一单号java实现_全局唯一订单号生成方法(参考snowflake)

backgroud

Snowflake is a network service for generating unique ID numbers at high scale with some simple guarantees.

简介

对于一个较大的订购业务场景,我们往往需要能够生成一个全局的唯一的订单号,如何在多个集群,多个节点高效生成唯一订单号?我们参考了Twitter的snowflake算法。

snowflake最初由Twitter开发,用的scala,对于Twitter而言,必须满足每秒上万条消息的请求,并且每条消息能够分配一个全局唯一的ID,因此,ID生成服务要求必须满足高性能(>10K ids/s)、低延迟(<2ms)、高可用的特性,同时生成的ID还可以进行大致的排序,以方便客户端的排序。

Snowflake满足了以上的需求。Snowflake生成的每一个ID都是64位的整型数,它的核心算法也比较简单高效,结构如下:

41位的时间序列,精确到毫秒级,41位的长度可以使用69年。时间位还有一个很重要的作用是可以根据时间进行排序。

10位的机器标识,10位的长度最多支持部署1024个节点。

12位的计数序列号,序列号即一系列的自增id,可以支持同一节点同一毫秒生成多个ID序号,12位的计数序列号支持每个节点每毫秒产生4096个ID序号。

最高位是符号位,始终为0,不可用。

原生算法java实现

/**

* 摘自网上某blog,记不得地址了。。

* @Project concurrency

* Created by wgy on 16/7/19.

*/

public class IdGen {

private long workerId;

private long datacenterId;

private long sequence = 0L;

private long twepoch = 1288834974657L; //Thu, 04 Nov 2010 01:42:54 GMT

private long workerIdBits = 5L; //节点ID长度

private long datacenterIdBits = 5L; //数据中心ID长度

private long maxWorkerId = -1L ^ (-1L << workerIdBits); //最大支持机器节点数0~31,一共32个

private long maxDatacenterId = -1L ^ (-1L << datacenterIdBits); //最大支持数据中心节点数0~31,一共32个

private long sequenceBits = 12L; //序列号12位

private long workerIdShift = sequenceBits; //机器节点左移12位

private long datacenterIdShift = sequenceBits + workerIdBits; //数据中心节点左移17位

private long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits; //时间毫秒数左移22位

private long sequenceMask = -1L ^ (-1L << sequenceBits); //4095

private long lastTimestamp = -1L;

private static class IdGenHolder {

private static final IdGen instance = new IdGen();

}

public static IdGen get(){

return IdGenHolder.instance;

}

public IdGen() {

this(0L, 0L);

}

public IdGen(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;

}

public synchronized long nextId() {

long timestamp = timeGen(); //获取当前毫秒数

//如果服务器时间有问题(时钟后退) 报错。

if (timestamp < lastTimestamp) {

throw new RuntimeException(String.format(

"Clock moved backwards. Refusing to generate id for %d milliseconds", lastTimestamp - timestamp));

}

//如果上次生成时间和当前时间相同,在同一毫秒内

if (lastTimestamp == timestamp) {

//sequence自增,因为sequence只有12bit,所以和sequenceMask相与一下,去掉高位

sequence = (sequence + 1) & sequenceMask;

//判断是否溢出,也就是每毫秒内超过4095,当为4096时,与sequenceMask相与,sequence就等于0

if (sequence == 0) {

timestamp = tilNextMillis(lastTimestamp); //自旋等待到下一毫秒

}

} else {

sequence = 0L; //如果和上次生成时间不同,重置sequence,就是下一毫秒开始,sequence计数重新从0开始累加

}

lastTimestamp = timestamp;

// 最后按照规则拼出ID。

// 000000000000000000000000000000000000000000 00000 00000 000000000000

// time datacenterId workerId sequence

return ((timestamp - twepoch) << timestampLeftShift) | (datacenterId << datacenterIdShift)

| (workerId << workerIdShift) | sequence;

}

protected long tilNextMillis(long lastTimestamp) {

long timestamp = timeGen();

while (timestamp <= lastTimestamp) {

timestamp = timeGen();

}

return timestamp;

}

protected long timeGen() {

return System.currentTimeMillis();

}

}

注释已经写的比较详细了,不做特别的说明。

订购业务唯一订单号实现

对于订购业务而言,虽然可以记录订单的创建时间,但是一般都需要带有显示的时间戳属性。因此,一个long型已无法满足实际的需求,将输出修改为String类型,前17位用于存储yyyyMMddHHMMssSSS格式的时间,后面用于记录所在集群,节点,以及自增量。

import org.apache.commons.lang.time.DateFormatUtils;

import java.net.InetAddress;

import java.net.UnknownHostException;

import java.util.Date;

/**

* 与snowflake算法区别,返回字符串id,占用更多字节,但直观从id中看出生成时间

*

* @Project concurrency

* Created by wgy on 16/7/19.

*/

public enum IdGenerator {

INSTANCE;

private long workerId; //用ip地址最后几个字节标示

private long datacenterId = 0L; //可配置在properties中,启动时加载,此处默认先写成0

private long sequence = 0L;

private long workerIdBits = 8L; //节点ID长度

private long datacenterIdBits = 2L; //数据中心ID长度,可根据时间情况设定位数

private long sequenceBits = 12L; //序列号12位

private long workerIdShift = sequenceBits; //机器节点左移12位

private long datacenterIdShift = sequenceBits + workerIdBits; //数据中心节点左移14位

private long sequenceMask = -1L ^ (-1L << sequenceBits); //4095

private long lastTimestamp = -1L;

IdGenerator(){

workerId = 0x000000FF & getLastIP();

}

public synchronized String nextId() {

long timestamp = timeGen(); //获取当前毫秒数

//如果服务器时间有问题(时钟后退) 报错。

if (timestamp < lastTimestamp) {

throw new RuntimeException(String.format(

"Clock moved backwards. Refusing to generate id for %d milliseconds", lastTimestamp - timestamp));

}

//如果上次生成时间和当前时间相同,在同一毫秒内

if (lastTimestamp == timestamp) {

//sequence自增,因为sequence只有12bit,所以和sequenceMask相与一下,去掉高位

sequence = (sequence + 1) & sequenceMask;

//判断是否溢出,也就是每毫秒内超过4095,当为4096时,与sequenceMask相与,sequence就等于0

if (sequence == 0) {

timestamp = tilNextMillis(lastTimestamp); //自旋等待到下一毫秒

}

} else {

sequence = 0L; //如果和上次生成时间不同,重置sequence,就是下一毫秒开始,sequence计数重新从0开始累加

}

lastTimestamp = timestamp;

long suffix = (datacenterId << datacenterIdShift) | (workerId << workerIdShift) | sequence;

String datePrefix = DateFormatUtils.format(timestamp, "yyyyMMddHHMMssSSS");

return datePrefix + suffix;

}

protected long tilNextMillis(long lastTimestamp) {

long timestamp = timeGen();

while (timestamp <= lastTimestamp) {

timestamp = timeGen();

}

return timestamp;

}

protected long timeGen() {

return System.currentTimeMillis();

}

private byte getLastIP(){

byte lastip = 0;

try{

InetAddress ip = InetAddress.getLocalHost();

byte[] ipByte = ip.getAddress();

lastip = ipByte[ipByte.length - 1];

} catch (UnknownHostException e) {

e.printStackTrace();

}

return lastip;

}

}

测试

测试环境

macbook Pro 2.4 GHz Intel Core i5 4 GB 1600 MHz DDR3

10个线程,每个线程生成5w个

需2000ms左右,测试代码如下:

测试代码

@Test

public void testNextId() throws Exception {

final IdGenerator idg = IdGenerator.INSTANCE;

ExecutorService es = Executors.newFixedThreadPool(10);

final HashSet idSet = new HashSet();

Collections.synchronizedCollection(idSet);

long start = System.currentTimeMillis();

System.out.println(" start generate id *");

for (int i = 0; i < 10; i++)

es.execute(new Runnable() {

public void run() {

for (int j = 0; j < 50000; j++) {

String id= idg.nextId();

synchronized (idSet){

idSet.add(id);

}

}

}

});

es.shutdown();

es.awaitTermination(10, TimeUnit.SECONDS);

long end = System.currentTimeMillis();

System.out.println(" end generate id ");

System.out.println("* cost " + (end-start) + " ms!");

Assert.assertEquals(10 * 50000, idSet.size());

}

测试结果

start generate id *

end generate id *

* cost 2091 ms!

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