用java写一个雪花算法
时间: 2023-06-08 16:08:17 浏览: 52
好的,以下是Java代码实现雪花算法:
```java
public class SnowFlake {
//开始时间截 (2019-01-01)
private final long twepoch = 1546272000000L;
//机器id所占的位数
private final long workerIdBits = 5L;
//数据标识id所占的位数
private final long datacenterIdBits = 5L;
//支持的最大机器id,结果是31
private final long maxWorkerId = -1L ^ (-1L << workerIdBits);
//支持的最大数据标识id,结果是31
private final long maxDatacenterId = -1L ^ (-1L << datacenterIdBits);
//序列在id中占的位数
private final long sequenceBits = 12L;
//机器ID向左移12位
private final long workerIdShift = sequenceBits;
//数据标识id向左移17位(12+5)
private final long datacenterIdShift = sequenceBits + workerIdBits;
//时间截向左移22位(5+5+12)
private final long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits;
//生成序列的掩码,这里为4095 (0b111111111111=0xfff=4095)
private final long sequenceMask = -1L ^ (-1L << sequenceBits);
//工作机器ID(0~31)
private long workerId;
//数据中心ID(0~31)
private long datacenterId;
//毫秒内序列(0~4095)
private long sequence = 0L;
//上次生成ID的时间截
private long lastTimestamp = -1L;
/**
* 构造函数
*
* @param workerId 工作ID (0~31)
* @param datacenterId 数据中心ID (0~31)
*/
public SnowFlake(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 SnowflakeId
*/
public synchronized long nextId() {
long timestamp = timeGen();
//如果当前时间小于上一次ID生成的时间戳,说明系统时钟回退过这个时候应当抛出异常
if (timestamp < lastTimestamp) {
throw new RuntimeException(
String.format("Clock moved backwards. Refusing to generate id for %d milliseconds", lastTimestamp - timestamp));
}
//如果是同一时间生成的,则进行毫秒内序列递增
if (lastTimestamp == timestamp) {
sequence = (sequence + 1) & sequenceMask;
//毫秒内序列溢出
if (sequence == 0) {
//阻塞到下一个毫秒,获得新的时间戳
timestamp = tilNextMillis(lastTimestamp);
}
}
//时间戳改变,毫秒内序列重置
else {
sequence = 0L;
}
//上次生成ID的时间截
lastTimestamp = timestamp;
//移位并通过或运算拼到一起组成64位的ID
return ((timestamp - twepoch) << timestampLeftShift) //
| (datacenterId << datacenterIdShift) //
| (workerId << workerIdShift) //
| sequence;
}
/**
* 阻塞到下一个毫秒,直到获得新的时间戳
*
* @param lastTimestamp 上次生成ID的时间戳
* @return 当前时间戳
*/
protected long tilNextMillis(long lastTimestamp) {
long timestamp = timeGen();
while (timestamp <= lastTimestamp) {
timestamp = timeGen();
}
return timestamp;
}
/**
* 返回以毫秒为单位的当前时间
*
* @return 当前时间(毫秒)
*/
protected long timeGen() {
return System.currentTimeMillis();
}
}
```
需要注意的是,SnowFlake算法依赖于系统时间,如果系统时间不稳定会导致生成的ID不唯一。因此,可以使用NTP服务器来同步系统时间。
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