雪花算法 Java 版

雪花算法根据时间戳生成有序的 64 bit 的 Long 类型的唯一 ID

各 bit 含义：

• 1 bit: 符号位，0 是正数 1 是负数， ID 为正数，所以恒取 0
• 41 bit: 时间差，我们可以选择一个参考点，用它来计算与当前时间的时间差 (毫秒数)，41 bit 存储时间差，足够使用 69 年
• 10 bit: 机器码，能编码 1024 台机器；可以手动指定含义，比如前5 bit 作为机器编号、后 5 bit 作为进程编号
• 12 bit: 序列号，同一机器同一毫秒内产生不同的序列号，12 bit 可以支持 4096 个序列号

优点：

• 灵活配置：机器码可以根据需求灵活配置含义
• 无需持久化：如果序号自增往往需要持久化，本算法不需要持久化
• ID 有含义/可逆性：ID 可以反解出来，对 ID 进行统计分析，可以很简单的分析出整个系统的繁忙曲线，还可以定位到每个机器，在某段时间承担了多少工作，分析出负载均衡情况
• 高性能：生成速度很快

public class Snowflake { /**
* 每一部分所占位数
*/
private final long unusedBits = 1L;
private final long timestampBits = 41L;
private final long datacenterIdBits = 5L;
private final long workerIdBits = 5L;
private final long sequenceBits = 12L; /**
* 向左的位移
*/
private final long timestampShift = sequenceBits + datacenterIdBits + workerIdBits;
private final long datacenterIdShift = sequenceBits + workerIdBits;
private final long workerIdShift = sequenceBits; /**
* 起始时间戳，初始化后不可修改
*/
private final long epoch = 1451606400000L; // 2016-01-01 /**
* 数据中心编码，初始化后不可修改
* 最大值: 2^5-1 取值范围: [0,31]
*/
private final long datacenterId; /**
* 机器或进程编码，初始化后不可修改
* 最大值: 2^5-1 取值范围: [0,31]
*/
private final long workerId; /**
* 序列号
* 最大值: 2^12-1 取值范围: [0,4095]
*/
private long sequence = 0L; /** 上次执行生成 ID 方法的时间戳 */
private long lastTimestamp = -1L; /*
* 每一部分最大值
*/
private final long maxDatacenterId = -1L ^ (-1L << datacenterIdBits); // 2^5-1
private final long maxWorkerId = -1L ^ (-1L << workerIdBits); // 2^5-1
private final long maxSequence = -1L ^ (-1L << sequenceBits); // 2^12-1 /**
* 生成序列号
*/
public synchronized long nextId() {
long currTimestamp = timestampGen(); if (currTimestamp < lastTimestamp) {
throw new IllegalStateException(
String.format("Clock moved backwards. Refusing to generate id for %d milliseconds",
lastTimestamp - currTimestamp));
} if (currTimestamp == lastTimestamp) {
sequence = (sequence + 1) & maxSequence;
if (sequence == 0) { // overflow: greater than max sequence
currTimestamp = waitNextMillis(currTimestamp);
} } else { // reset to 0 for next period/millisecond
sequence = 0L;
} // track and memo the time stamp last snowflake ID generated
lastTimestamp = currTimestamp; return ((currTimestamp - epoch) << timestampShift) | //
(datacenterId << datacenterIdShift) | //
(workerId << workerIdShift) | // new line for nice looking
sequence;
} public Snowflake(long datacenterId, long workerId) {
if (datacenterId > maxDatacenterId || datacenterId < 0) {
throw new IllegalArgumentException(
String.format("datacenter Id can't be greater than %d or less than 0", maxDatacenterId));
}
if (workerId > maxWorkerId || workerId < 0) {
throw new IllegalArgumentException(
String.format("worker Id can't be greater than %d or less than 0", maxWorkerId));
} this.datacenterId = datacenterId;
this.workerId = workerId;
} /**
* 追踪调用 waitNextMillis 方法的次数
*/
private final AtomicLong waitCount = new AtomicLong(0); public long getWaitCount() {
return waitCount.get();
} /**
* 循环阻塞直到下一秒
*/
protected long waitNextMillis(long currTimestamp) {
waitCount.incrementAndGet();
while (currTimestamp <= lastTimestamp) {
currTimestamp = timestampGen();
}
return currTimestamp;
} /**
* 获取当前时间戳
*/
public long timestampGen() {
return System.currentTimeMillis();
}
}


参考：snowflake ID 生成算法

完整代码：GitHub