Java--Spring项目生成雪花算法数字（Twitter SnowFlake）

前言

• 分布式系统常需要全局唯一的数字作为id，且该id要求有序，twitter的SnowFlake解决了这种需求，生成了符合条件的这种数字，本文将提供一个接口获取雪花算法数字。以下为代码。

步骤

1. SnowFlakeUtils 雪花算法工具类。

   @Slf4j
   public class SnowFlakeUtils {private static final RedisOperation REDIS_OPERATION = ApplicationContextHelper.getBean(RedisOperation.class);private static final String LOCAL_IP = getLocalIp();private static volatile SnowFlakeUtils instance;/*** 该任务开始时间,必须手动设置(差值的唯一性)* 建议在生产部署时选择某一日的0时0分0秒0毫秒的时间戳,方便计算*/private static final long START_TIME = 1588733692671L;/*** 各个位的位数,Timestamp为41L(无需定义)*/private static final long DATA_CENTER_ID_BITS = 5L;private static final long WORKER_ID_BITS = 1L;private static final long SEQUENCE_BITS = 16L;/*** 各位的最大值*/private static final long DATA_CENTER_ID_MAX = ~(-1 << DATA_CENTER_ID_BITS);private static final long WORKER_ID_MAX = ~(-1 << WORKER_ID_BITS);private static final long SEQUENCE_MAX = ~(-1 << SEQUENCE_BITS);/*** 各位应该向左移动位数*/private static final long TIMESTAMP_SHIFT = SEQUENCE_BITS + WORKER_ID_BITS + DATA_CENTER_ID_BITS;private static final long DATA_CENTER_ID_SHIFT = SEQUENCE_BITS + WORKER_ID_BITS;private static final long WORKER_ID_SHIFT = SEQUENCE_BITS;/*** 数据中心ID*/private final long dataCenterId;private static final String DATA_CENTER_ID = "DATACENTERID";/*** 工作线程ID*/private final long workerId;private static final String WORKER_ID = "WORKERID";/*** 序列号*/private long sequence = 0L;/*** 上次时间(保证不回退)*/private long lastTimestamp = -1L;/**** 是否在高并发下*/private boolean isClock = false;public static SnowFlakeUtils getInstance() {if (instance == null) {synchronized (SnowFlakeUtils.class) {if (instance == null) {int dataCenterId = 0;int workerId = 0;while (true) {// tryCatch保证即使redis等出现问题也可以保证当前线程阻塞,重启redis即可处理继续处理try {String replace = RedisKeyConstant.SNOW_FLAKE_KEY.replace(DATA_CENTER_ID, String.valueOf(dataCenterId)).replace(WORKER_ID, String.valueOf(workerId));if (REDIS_OPERATION.setnx(replace, LOCAL_IP, 1, TimeUnit.MINUTES)) {instance = new SnowFlakeUtils(dataCenterId, workerId);break;}// 进行重新set直至成功,目前只运用dataCenterIdif (dataCenterId++ == DATA_CENTER_ID_MAX) {log.error("SnowFlake is getting CacheLock, please checkDATACENTERID_MAX={}", DATA_CENTER_ID_MAX);dataCenterId = 0;}} catch (Exception e) {log.error("SnowFlakeUtils get CacheLock Error, errorMsg:", e);try {Thread.sleep(MagicNum.THOUSAND);} catch (InterruptedException ex) {log.error(ex.getMessage(), ex);}}}}}}return instance;}public SnowFlakeUtils(long dataCenterId, long workerId) {if (dataCenterId > DATA_CENTER_ID_MAX || dataCenterId < 0) {throw new IllegalArgumentException(String.format("data center id can't be greater than %d or less than 0", DATA_CENTER_ID_MAX));}if (workerId > WORKER_ID_MAX || workerId < 0) {throw new IllegalArgumentException(String.format("worker id can't be greater than %d or less than 0", WORKER_ID_MAX));}this.dataCenterId = dataCenterId;this.workerId = workerId;String key = RedisKeyConstant.SNOW_FLAKE_KEY.replace(DATA_CENTER_ID, String.valueOf(dataCenterId)).replace(WORKER_ID, String.valueOf(workerId));log.info("SnowFlakeUtils Cache Key={}", key);// 起线程保证workerId和dataCenter组合不重复Thread thread = new Thread(new Runnable() {@Overridepublic void run() {while (true) {try {log.debug("SnowFlakeUtils is keep geting CacheLock-{}", key);String localIp = REDIS_OPERATION.get(key);if (LOCAL_IP.equals(localIp)) {REDIS_OPERATION.setex(key, LOCAL_IP, 1, TimeUnit.MINUTES);} else if (!REDIS_OPERATION.setnx(key, LOCAL_IP, 1, TimeUnit.MINUTES)) {throw new ProcessException(CommonConstants.ENUM_PROCESSING_EXCEPTION,"SnowFlakeUtils losed CacheLock-" + key + "." +"CacheLockKeeperThread broken!" +"Reday to retrieve CacheLock and Single Instance!");}Thread.sleep(MagicNum.FIFTY * MagicNum.THOUSAND);} catch (Exception e) {// 发生异常 将单例清除 并退出循环结束子线程synchronized (SnowFlakeUtils.class) {instance = null;}log.error(e.getMessage(),e);break;}}}});thread.setName("SnowFlake-CacheLockKeeper-" + dataCenterId + "-" + workerId);thread.start();}public void setClock(boolean clock) {this.isClock = clock;}public synchronized long nextId() {long timestamp = this.getTime();if (timestamp < lastTimestamp) {long offset = lastTimestamp - timestamp;if (offset <= MagicNum.FIVE) {try {this.wait(offset << 1);timestamp = this.getTime();if (timestamp < lastTimestamp) {throw new RuntimeException(String.format("Clock moved backwards, Refusing to generate id for %d milliseconds", offset));}} catch (InterruptedException e) {log.error(e.getMessage(), e);}} else {throw new RuntimeException(String.format("Clock moved backwards, Refusing to generate id for %d milliseconds", offset));}}if (lastTimestamp == timestamp) {sequence = sequence + 1;if (sequence > SEQUENCE_MAX) {timestamp = tilNextMillis(timestamp);sequence = 0;}} else {sequence = 0;}lastTimestamp = timestamp;return ((timestamp - START_TIME) << TIMESTAMP_SHIFT) |(dataCenterId << DATA_CENTER_ID_SHIFT) |(workerId << WORKER_ID_SHIFT) |sequence;}/*** 该毫秒达到上限,等待到下1毫秒*/private long tilNextMillis(long timestamp) {while (getTime() <= timestamp) {log.debug("单毫秒主键生成达到上限");}return this.getTime();}private long getTime() {if (isClock) {return SystemClock.currentTimeMillis();} else {return System.currentTimeMillis();}}private static String getLocalIp() {String ip = "";try {InetAddress addr = InetAddress.getLocalHost();ip += addr.getHostAddress();} catch (Exception e) {ip += "127.0.0.1";}ip += "_" + System.currentTimeMillis() + "_" + Math.random();log.info("SnowFlakeUtils Cache Value={}", ip);return ip;}
   }
   

2. SystemClock工具类。

   /*** 由于高并发,在同一毫秒中会多次获取currentTimeMillis,而每次使用System.currentTimeMillis都会占用CPU(native方法).* 于是自定义类(single)来获取currentTimeMillis,实现方法是在此类中定义时间并设置一个周期任务(定时线程)1毫秒更新类中的时间*/
   public final class SystemClock {private static final SystemClock INSTANCE = new SystemClock(1);public static SystemClock getInstance() {return INSTANCE;}/*** 更新时间的时间间隔,默认为1毫秒*/private final long period;/*** 当前时间*/private final AtomicLong now;private SystemClock(long period) {this.period = period;this.now = new AtomicLong(System.currentTimeMillis());scheduleClockUpdate();}/*** 定时任务(设置为守护线程,1毫秒后开始更新)* scheduleAtFixedRate: 每次开始间隔为1毫秒* scheduleWithFixedDelay: 每次结束与开始为1毫秒*/private void scheduleClockUpdate() {ScheduledExecutorService executorService = Executors.newSingleThreadScheduledExecutor(new ThreadFactory() {@Overridepublic Thread newThread(Runnable r) {Thread thread = new Thread(r, "System Clock");thread.setDaemon(true);return thread;}});executorService.scheduleAtFixedRate(new Runnable() {@Overridepublic void run() {now.set(System.currentTimeMillis());}}, period, period, TimeUnit.MILLISECONDS);}public static long currentTimeMillis() {return getInstance().now.get();}
   }
   

3. ApplicationContextHelper Spring上下文工具类。

   @Slf4j
   @Component
   public class ApplicationContextHelper implements ApplicationContextAware {
   /**
   * Spring上下文
   */
   private static ApplicationContext applicationContext;/*** @return ApplicationContext*/public static ApplicationContext getApplicationContext() {return applicationContext;}/*** 获取ApplicationContextAware**/@Overridepublic void setApplicationContext(ApplicationContext applicationContext) {ApplicationContextHelper.applicationContext = applicationContext;}/*** 根据Class获取对应实例**/public static <T> T getBean(Class<T> clz) {return applicationContext.getBean(clz);}/*** 根据beanName获取对应实例*/public static <T> T getBean(String name, Class<T> requiredType) {return applicationContext.getBean(name, requiredType);}public static Object getBean(String name) {return applicationContext.getBean(name);}
   }
   

4. RedisOperation获取 RedisOperation，Redis操作工具类。

5. 在Controller里编写接口，测试结果。

   @RestController
   @RequestMapping("/part/util")
   public class UtilController {@ApiOperation("获取雪花数字")@GetMapping("/getSnowFlakeNo")public Result getSnowFlakeNo() {return Result.ok().data(String.valueOf(SnowFlakeUtils.getInstance().nextId()));}
   }
   

查看结果

• 启动项目，有postman访问接口，查看结果如下，返回结果中data的值即为雪花算法数字。