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); // 2^5-1private final long maxWorkerId = -1L ^ (-1L << workerIdBits); // 2^5-1private 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 sequencecurrTimestamp = waitNextMillis(currTimestamp);}} else { // reset to 0 for next period/millisecondsequence = 0L;}// track and memo the time stamp last snowflake ID generatedlastTimestamp = currTimestamp;return ((currTimestamp - epoch) << timestampShift) | //(datacenterId << datacenterIdShift) | //(workerId << workerIdShift) | // new line for nice lookingsequence;}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

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