在雪花算法自定义解决时钟回拨问题一文中，对雪花算法的时钟回拨解决思路进行了说明，由于顺序号保存在内存中，每次启动都是从初始值开始，在特定场景下，比如停止服务后进行了时钟回拨，在理论上，还是可能出现序列号重复的情况。
这里将序列号持久化到本地磁盘文件中，这样下次启动时，首先会读取之前保存的持久化文件，获取序列号，而不是直接从固定值（比如1）开始，这样就算回拨了时间，只要顺序号还是持续增加的，就不会出现序列号相同的情况。
package com.demo.server.config;import cn.hutool.core.net.NetUtil;import org.slf4j.Logger;import org.slf4j.LoggerFactory;import org.springframework.core.io.ClassPathResource;import java.io.File;import java.io.FileInputStream;import java.io.FileOutputStream;import java.io.IOException;import java.io.InputStream;import java.nio.charset.StandardCharsets;public class CustomSnowFlake {private static Logger log = LoggerFactory.getLogger(CustomSnowFlake.class);// 2020-01-01 00:00:00 对应的秒private final static long beginTs = 1577808000L;// 顺序号最大值（大约419万）private final static long maxSequence = 4194300L;// 最大时钟回拨(秒)，4194秒，大约69分钟private final static long maxTimeback = 4194L;private long lastTs = 0L;private long processId;private int processIdBits = 10;private long sequence = 1L;private int sequenceBits = 22;public CustomSnowFlake() throws IOException {String ipAddr = NetUtil.getLocalhostStr();log.info("当前机器的ipAddr:" + ipAddr);Long workerId = NetUtil.ipv4ToLong(ipAddr);workerId = workerId % 1024;log.info("当前机器的workId:" + workerId);this.processId = workerId;}public CustomSnowFlake(Long workerId, Long sequence) {this.processId = workerId;this.sequence = sequence;}public CustomSnowFlake(long processId) {if (processId > ((1 << processIdBits) - 1)) {throw new RuntimeException("进程ID超出范围，设置位数" + processIdBits + "，最大" + ((1 << processIdBits) - 1));}this.processId = processId;}public void restoreCheckPoint() {String fileName = "/snowflake.properties";ClassPathResource resource = new ClassPathResource(fileName);try {String confPath = resource.getURI().getPath();//File confFile = new File(confPath);//InputStream inputStream = new FileInputStream(confFile);InputStream inputStream = resource.getInputStream();// inputStream = this.getClass().getResourceAsStream(fileName);// 设置1KB的缓冲区byte buf[] = new byte[1024];int read = inputStream.read(buf);inputStream.close();String buffInfo = new String(buf);String confInfo = buffInfo.substring(0, read);if (confInfo.startsWith("sequence=")) {String splits[] = confInfo.split("=");String sequence = splits[1];this.sequence = Long.parseLong(sequence);}} catch (Exception e) {e.printStackTrace();}}public void saveCheckPoint() {String fileName = "/snowflake.properties";ClassPathResource resource = new ClassPathResource(fileName);try {String confPath = resource.getURI().getPath();System.out.println("confPath = " + confPath);File confFile = new File(confPath);FileOutputStream outputStream = new FileOutputStream(confFile);String configInfo = "sequence=" + sequence;outputStream.write(configInfo.getBytes(StandardCharsets.UTF_8));outputStream.close();} catch (Exception e) {e.printStackTrace();}}protected long timeGen() {return System.currentTimeMillis() / 1000;}public synchronized long nextId() {// 获取当前时间(秒为单位)long ts = timeGen();// 刚刚生成的时间戳比上次的时间戳还小，出错long tempDiff = lastTs - ts;if (tempDiff >= maxTimeback) {log.warn("时钟回拨超过4194秒，存在Id重复风险");}sequence = sequence + 1;if(sequence >= maxSequence){sequence = 1;}// 更新lastTs时间戳lastTs = ts;long timeDiff = ts - beginTs;return (timeDiff << (processIdBits + sequenceBits)) | (processId << sequenceBits) | sequence;} public static void main(String[] args) throws Exception { // TODO Auto-generated method stub CustomSnowFlake ig = new CustomSnowFlake();ig.restoreCheckPoint(); for (int i = 0; i < 10; i++) { System.out.println(ig.nextId());ig.saveCheckPoint(); Thread.sleep(1000); } }}
根据情况，还可以进行一定的优化，比如不一定每次生成id都进行持久化，而是当生成id次数达到一定数量或者间隔一定时间（比如3秒或5秒）以后，再进行持久化，这样就可以提高效率。当然这样做的结果，就可能有部分序列号没有成功保存的情况。因为序列号中的时间因子是每秒编号，所有程序重新启动后，往往时间就和之前的不相同了。
对于并发量小的情况，每次生成id都进行持久化，对性能和效率的影响也不大，对并发量大的情况，就需要考虑采用间隔一定数量或时间的方式来进行优化了。