雪花算法根據時間戳生成有序的 64 bit 的 Long 類型的唯一 ID

各 bit 含義：

• 1 bit: 符號位，0 是正數 1 是負數， ID 為正數，所以恒取 0
• 41 bit: 時間差，我們可以選擇一個參考點，用它來計算與當前時間的時間差 (毫秒數)，41 bit 存儲時間差，足夠使用 69 年
• 10 bit: 機器碼，能編碼 1024 臺機器；可以手動指定含義，比如前5 bit 作為機器編號、后 5 bit 作為進程編號
• 12 bit: 序列號，同一機器同一毫秒內產生不同的序列號，12 bit 可以支持 4096 個序列號

優點：

• 靈活配置：機器碼可以根據需求靈活配置含義
• 無需持久化：如果序號自增往往需要持久化，本算法不需要持久化
• ID 有含義/可逆性：ID 可以反解出來，對 ID 進行統計分析，可以很簡單的分析出整個系統的繁忙曲線，還可以定位到每個機器，在某段時間承擔了多少工作，分析出負載均衡情況
• 高性能：生成速度很快

public class Snowflake {/*** 每一部分所占位數*/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;/*** 起始時間戳，初始化后不可修改*/private final long epoch = 1451606400000L; // 2016-01-01/*** 數據中心編碼，初始化后不可修改* 最大值: 2^5-1 取值范圍: [0,31]*/private final long datacenterId;/*** 機器或進程編碼，初始化后不可修改* 最大值: 2^5-1 取值范圍: [0,31]*/private final long workerId;/*** 序列號* 最大值: 2^12-1 取值范圍: [0,4095]*/private long sequence = 0L;/** 上次執行生成 ID 方法的時間戳 */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/*** 生成序列號*/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;}/*** 追蹤調用 waitNextMillis 方法的次數*/private final AtomicLong waitCount = new AtomicLong(0);public long getWaitCount() {return waitCount.get();}/*** 循環阻塞直到下一秒*/protected long waitNextMillis(long currTimestamp) {waitCount.incrementAndGet();while (currTimestamp <= lastTimestamp) {currTimestamp = timestampGen();}return currTimestamp;}/*** 獲取當前時間戳*/public long timestampGen() {return System.currentTimeMillis();}
}

參考：snowflake ID 生成算法

完整代碼：GitHub

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轉載于:https://www.cnblogs.com/victorbu/p/11101850.html

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