@ToString
@Slf4j
public class SnowflakeIdFactory {private final long twepoch = 1288834974657L;private final long workerIdBits = 5L;private final long datacenterIdBits = 5L;private final long maxWorkerId = -1L ^ (-1L << workerIdBits);private final long maxDatacenterId = -1L ^ (-1L << datacenterIdBits);private final long sequenceBits = 12L;private final long workerIdShift = sequenceBits;private final long datacenterIdShift = sequenceBits + workerIdBits;private final long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits;private final long sequenceMask = -1L ^ (-1L << sequenceBits);private long workerId;private long datacenterId;private long sequence = 0L;private long lastTimestamp = -1L;public SnowflakeIdFactory(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;}public synchronized long nextId() {long timestamp = timeGen();if (timestamp < lastTimestamp) {//服务器时钟被调整了,ID生成器停止服务.throw new RuntimeException(String.format("Clock moved backwards.  Refusing to generate id for %d milliseconds", lastTimestamp - timestamp));}if (lastTimestamp == timestamp) {sequence = (sequence + 1) & sequenceMask;if (sequence == 0) {timestamp = tilNextMillis(lastTimestamp);}} else {sequence = 0L;}lastTimestamp = timestamp;return ((timestamp - twepoch) << timestampLeftShift) | (datacenterId << datacenterIdShift) | (workerId << workerIdShift) | sequence;}protected long tilNextMillis(long lastTimestamp) {long timestamp = timeGen();while (timestamp <= lastTimestamp) {timestamp = timeGen();}return timestamp;}protected long timeGen() {return System.currentTimeMillis();}public static void testProductIdByMoreThread(int dataCenterId, int workerId, int n) throws InterruptedException {List<Thread> tlist = new ArrayList<>();Set<Long> setAll = new HashSet<>();CountDownLatch cdLatch = new CountDownLatch(10);long start = System.currentTimeMillis();int threadNo = dataCenterId;Map<String,SnowflakeIdFactory> idFactories = new HashMap<>();for(int i=0;i<10;i++){//用线程名称做map key.idFactories.put("snowflake"+i,new SnowflakeIdFactory(workerId, threadNo++));}for(int i=0;i<10;i++){Thread temp =new Thread(new Runnable() {@Overridepublic void run() {Set<Long> setId = new HashSet<>();SnowflakeIdFactory idWorker = idFactories.get(Thread.currentThread().getName());for(int j=0;j<n;j++){setId.add(idWorker.nextId());}synchronized (setAll){setAll.addAll(setId);log.info("{}生产了{}个id,并成功加入到setAll中.",Thread.currentThread().getName(),n);}cdLatch.countDown();}},"snowflake"+i);tlist.add(temp);}for(int j=0;j<10;j++){tlist.get(j).start();}cdLatch.await();long end1 = System.currentTimeMillis() - start;log.info("共耗时:{}毫秒,预期应该生产{}个id, 实际合并总计生成ID个数:{}",end1,10*n,setAll.size());}public static void testProductId(int dataCenterId, int workerId, int n){SnowflakeIdFactory idWorker = new SnowflakeIdFactory(workerId, dataCenterId);SnowflakeIdFactory idWorker2 = new SnowflakeIdFactory(workerId+1, dataCenterId);Set<Long> setOne = new HashSet<>();Set<Long> setTow = new HashSet<>();long start = System.currentTimeMillis();for (int i = 0; i < n; i++) {setOne.add(idWorker.nextId());//加入set}long end1 = System.currentTimeMillis() - start;log.info("第一批ID预计生成{}个,实际生成{}个<<<<*>>>>共耗时:{}",n,setOne.size(),end1);for (int i = 0; i < n; i++) {setTow.add(idWorker2.nextId());//加入set}long end2 = System.currentTimeMillis() - start;log.info("第二批ID预计生成{}个,实际生成{}个<<<<*>>>>共耗时:{}",n,setTow.size(),end2);setOne.addAll(setTow);log.info("合并总计生成ID个数:{}",setOne.size());}public static void testPerSecondProductIdNums(){SnowflakeIdFactory idWorker = new SnowflakeIdFactory(1, 2);long start = System.currentTimeMillis();int count = 0;for (int i = 0; System.currentTimeMillis()-start<1000; i++,count=i) {/**  测试方法一: 此用法纯粹的生产ID,每秒生产ID个数为300w+ */idWorker.nextId();/**  测试方法二: 在log中打印,同时获取ID,此用法生产ID的能力受限于log.error()的吞吐能力.* 每秒徘徊在10万左右. *///log.error("{}",idWorker.nextId());}long end = System.currentTimeMillis()-start;System.out.println(end);System.out.println(count);}public static void main(String[] args) {/** case1: 测试每秒生产id个数?*   结论: 每秒生产id个数300w+ *///testPerSecondProductIdNums();/** case2: 单线程-测试多个生产者同时生产N个id,验证id是否有重复?*   结论: 验证通过,没有重复. *///testProductId(1,2,10000);//验证通过!//testProductId(1,2,20000);//验证通过!/** case3: 多线程-测试多个生产者同时生产N个id, 全部id在全局范围内是否会重复?*   结论: 验证通过,没有重复. */try {testProductIdByMoreThread(1,2,100000);//单机测试此场景,性能损失至少折半!} catch (InterruptedException e) {e.printStackTrace();}}
}

测试用例：

/** case1: 测试每秒生产id个数?*   结论: 每秒生产id个数300w+ */
//testPerSecondProductIdNums();/** case2: 单线程-测试多个生产者同时生产N个id,验证id是否有重复?*   结论: 验证通过,没有重复. */
//testProductId(1,2,10000);//验证通过!
//testProductId(1,2,20000);//验证通过!/** case3: 多线程-测试多个生产者同时生产N个id, 全部id在全局范围内是否会重复?*   结论: 验证通过,没有重复. */
try {testProductIdByMoreThread(1,2,100000);//单机测试此场景,性能损失至少折半!
} catch (InterruptedException e) {e.printStackTrace();
}