分布式全局不重复ID生成算法
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。本篇博客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:1~10000、10001~20000 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:
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
*/
public 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+1
sequence = (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ID
long 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() {
@Override
public 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();
}
}
}
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:1~10000、10001~20000 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:
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
*/
public 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+1
sequence = (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ID
long 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() {
@Override
public 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();
}
}
}