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
实现
雪花算法是由scala算法编写的，有人使用java实现，github地址
https://github.com/beyondfengyu/SnowFlake/blob/master/SnowFlake.java
/** * twitter的snowflake算法 -- java实现 * * @author beyond */ public class SnowFlake { /** * 起始的时间戳 */ private final static long START_STMP = 1480166465631L; /** * 每一部分占用的位数 */ private final static long SEQUENCE_BIT = 12; //序列号占用的位数 private final static long MACHINE_BIT = 5; //机器标识占用的位数 private final static long DATACENTER_BIT = 5;//数据中心占用的位数 /** * 每一部分的最大值 */ private final static long MAX_DATACENTER_NUM = -1L ^ (-1L << DATACENTER_BIT); private final static long MAX_MACHINE_NUM = -1L ^ (-1L << MACHINE_BIT); private final static long MAX_SEQUENCE = -1L ^ (-1L << SEQUENCE_BIT); /** * 每一部分向左的位移 */ private final static long MACHINE_LEFT = SEQUENCE_BIT; private final static long DATACENTER_LEFT = SEQUENCE_BIT + MACHINE_BIT; private final static long TIMESTMP_LEFT = DATACENTER_LEFT + DATACENTER_BIT; private long datacenterId; //数据中心 private long machineId; //机器标识 private long sequence = 0L; //序列号 private long lastStmp = -1L;//上一次时间戳 public SnowFlake(long datacenterId, long machineId) { if (datacenterId > MAX_DATACENTER_NUM || datacenterId < 0) { throw new IllegalArgumentException("datacenterId can't be greater than MAX_DATACENTER_NUM or less than 0"); } if (machineId > MAX_MACHINE_NUM || machineId < 0) { throw new IllegalArgumentException("machineId can't be greater than MAX_MACHINE_NUM or less than 0"); } this.datacenterId = datacenterId; this.machineId = machineId; } /** * 产生下一个ID * * @return */ public synchronized long nextId() { long currStmp = getNewstmp(); if (currStmp < lastStmp) { throw new RuntimeException("Clock moved backwards. Refusing to generate id"); } if (currStmp == lastStmp) { //相同毫秒内，序列号自增 sequence = (sequence + 1) & MAX_SEQUENCE; //同一毫秒的序列数已经达到最大 if (sequence == 0L) { currStmp = getNextMill(); } } else { //不同毫秒内，序列号置为0 sequence = 0L; } lastStmp = currStmp; return (currStmp - START_STMP) << TIMESTMP_LEFT //时间戳部分 | datacenterId << DATACENTER_LEFT //数据中心部分 | machineId << MACHINE_LEFT //机器标识部分 | sequence; //序列号部分 } private long getNextMill() { long mill = getNewstmp(); while (mill <= lastStmp) { mill = getNewstmp(); } return mill; } private long getNewstmp() { return System.currentTimeMillis(); } public static void main(String[] args) { SnowFlake snowFlake = new SnowFlake(2, 3); for (int i = 0; i < (1 << 12); i++) { System.out.println(snowFlake.nextId()); } } }
工程落地经验
hutools工具包
SpringBoot整合雪花算法
引入hutool工具类
<dependency> <groupId>cn.hutool</groupId> <artifactId>hutool-all</artifactId> <version>5.3.1</version> </dependency>
整合
/** * 雪花算法 * * @author: 陌溪 */ public class SnowFlakeDemo { private long workerId = 0; private long datacenterId = 1; private Snowflake snowFlake = IdUtil.createSnowflake(workerId, datacenterId); @PostConstruct public void init() { try { // 将网络ip转换成long workerId = NetUtil.ipv4ToLong(NetUtil.getLocalhostStr()); } catch (Exception e) { e.printStackTrace(); } } /** * 获取雪花ID * @return */ public synchronized long snowflakeId() { return this.snowFlake.nextId(); } public synchronized long snowflakeId(long workerId, long datacenterId) { Snowflake snowflake = IdUtil.createSnowflake(workerId, datacenterId); return snowflake.nextId(); } public static void main(String[] args) { SnowFlakeDemo snowFlakeDemo = new SnowFlakeDemo(); for (int i = 0; i < 20; i++) { new Thread(() -> { System.out.println(snowFlakeDemo.snowflakeId()); }, String.valueOf(i)).start(); } } }
得到结果
1251350711346790400 1251350711346790402 1251350711346790401 1251350711346790403 1251350711346790405 1251350711346790404 1251350711346790406 1251350711346790407 1251350711350984704 1251350711350984706 1251350711350984705 1251350711350984707 1251350711350984708 1251350711350984709 1251350711350984710 1251350711350984711 1251350711350984712 1251350711355179008 1251350711355179009 1251350711355179010
优缺点
优点
- 毫秒数在高维，自增序列在低位，整个ID都是趋势递增的
- 不依赖数据库等第三方系统，以服务的方式部署，稳定性更高，生成ID的性能也是非常高的
- 可以根据自身业务特性分配bit位，非常灵活
缺点
- 依赖机器时钟，如果机器时钟回拨，会导致重复ID生成
- 在单机上是递增的，但由于涉及到分布式环境，每台机器上的时钟不可能完全同步，有时候会出现不是全局递增的情况，此缺点可以认为无所谓，一般分布式ID只要求趋势递增，并不会严格要求递增，90%的需求只要求趋势递增。
其它补充
- 为了解决时钟回拨问题，导致ID重复，后面有人专门提出了解决的方案
- 百度开源的分布式唯一ID生成器 UidGenerator
- Leaf - 美团点评分布式ID生成系统
