在分布式系统中经常会使用到生成全局唯一不重复ID的情况。本篇主要介绍生成的一些方法。
常见的一些方式:
1、通过DB做全局自增操作
优点:简单、高效
缺点:大并发、分布式情况下性能比较低
有些同学可能会说分库、分表的策略去降低DB的瓶颈,单要做到全局不重复需要提前按照一定的区域进行划分。例如:120000 等等。但这个灵活度比较低。
针对一些并发比较低的情况也可以使用类似这种方式。但大并发时不建议使用,DB很容易成为瓶颈。
2、获取当前时间纳秒或毫秒数
这种方式需要考虑的是在分布式集群中如果保证唯一性。
3、类似UUID的生成方式
生成的串比较大
综合上述情况我们需要一种在高并发、分布式系统中提供高效生成不重复唯一的一个ID,但要求生成的结果要小
方法1:
方法1:private static long INFOID_FLAG = 1260000000000L;protected static int SERVER_ID = 1;public synchronized long nextId() throws Exception {if(SERVER_ID <= 0)throw new Exception("server id is error,please check config file!");long infoid = System.currentTimeMillis() - INFOID_FLAG;infoid=(infoid<<7)| SERVER_ID;Thread.sleep(1);return infoid;}
说明:
SERVER_ID为不同的服务器使用的不同server ID,如果不同的机器使用相同的server ID有可能会生成重复的全局ID
简单的应用在一定的并发情况下使用这种方式已经足够了,简单、高效。但是每秒生成的ID是有限的,因为Thread.sleep(1)会无形中带来一些时间的消耗。
方法2:
/*** 64位ID (42(毫秒)+5(机器ID)+5(业务编码)+12(重复累加))* @author Polim*/class IdWorker {private final static long twepoch = 1288834974657L;// 机器标识位数private final static long workerIdBits = 5L;// 数据中心标识位数private final static long datacenterIdBits = 5L;// 机器ID最大值private final static long maxWorkerId = -1L ^ (-1L << workerIdBits);// 数据中心ID最大值private final static long maxDatacenterId = -1L ^ (-1L << datacenterIdBits);// 毫秒内自增位private final static long sequenceBits = 12L;// 机器ID偏左移12位private final static long workerIdShift = sequenceBits;// 数据中心ID左移17位private final static long datacenterIdShift = sequenceBits + workerIdBits;// 时间毫秒左移22位private final static long timestampLeftShift = sequenceBits + workerIdBits + datacenterIdBits;private final static long sequenceMask = -1L ^ (-1L << sequenceBits);private static long lastTimestamp = -1L;private long sequence = 0L;private final long workerId;private final long datacenterId;public IdWorker(long workerId, long datacenterId) {if (workerId > maxWorkerId || workerId < 0) {throw new IllegalArgumentException("worker Id can't be greater than %d or less than 0");}if (datacenterId > maxDatacenterId || datacenterId < 0) {throw new IllegalArgumentException("datacenter Id can't be greater than %d or less than 0");}this.workerId = workerId;this.datacenterId = datacenterId;}public synchronized long nextId() {long timestamp = timeGen();if (timestamp < lastTimestamp) {try {throw new Exception("Clock moved backwards. Refusing to generate id for "+ (lastTimestamp - timestamp) + " milliseconds");} catch (Exception e) {e.printStackTrace();}}if (lastTimestamp == timestamp) {// 当前毫秒内,则+1sequence = (sequence + 1) & sequenceMask;if (sequence == 0) {// 当前毫秒内计数满了,则等待下一秒timestamp = tilNextMillis(lastTimestamp);}} else {sequence = 0;}lastTimestamp = timestamp;// ID偏移组合生成最终的ID,并返回IDlong nextId = ((timestamp - twepoch) << timestampLeftShift)| (datacenterId << datacenterIdShift)| (workerId << workerIdShift) | sequence;return nextId;}private long tilNextMillis(final long lastTimestamp) {long timestamp = this.timeGen();while (timestamp <= lastTimestamp) {timestamp = this.timeGen();}return timestamp;}private long timeGen() {return System.currentTimeMillis();}}
这种方式是一种比较高效的方式。也是twitter使用的一种方式。
测试类:
import java.util.concurrent.BrokenBarrierException;import java.util.concurrent.CountDownLatch;import java.util.concurrent.CyclicBarrier;import java.util.concurrent.TimeUnit;public class IdWorkerTest {public static void main(String []args){IdWorkerTest test = new IdWorkerTest();test.test2();}public void test2(){final IdWorker w = new IdWorker(1,2);final CyclicBarrier cdl = new CyclicBarrier(100);for(int i = 0; i < 100; i++){new Thread(new Runnable() {@Overridepublic void run() {try {cdl.await();} catch (InterruptedException e) {e.printStackTrace();} catch (BrokenBarrierException e) {e.printStackTrace();}System.out.println(w.nextId());}}).start();}try {TimeUnit.SECONDS.sleep(5);} catch (InterruptedException e) {e.printStackTrace();}}}
