========================================== 仿雪花算法工具类(单例模式)
package com.taoxw.plugins.serial;
import com.taoxw.utils.date.DateFormatUtil;
import com.taoxw.utils.net.IpUtil;
import com.taoxw.utils.string.StringFormatUtil;
/**
* Twitter_Snowflake<br>
* SnowFlake的结构如下(每部分用-分开):<br>
* 0 - 0000000000 0000000000 0000000000 0000000000 0 - 00000 - 00000 - 000000000000 <br>
* 1位标识,由于long基本类型在Java中是带符号的,最高位是符号位,正数是0,负数是1,所以id一般是正数,最高位是0<br>
* 41位时间截(毫秒级),注意,41位时间截不是存储当前时间的时间截,而是存储时间截的差值(当前时间截 - 开始时间截)
* 得到的值),这里的的开始时间截,一般是我们的id生成器开始使用的时间,由我们程序来指定的(如下下面程序IdWorker类的startTime属性)。41位的时间截,可以使用69年,年T = (1L << 41) / (1000L * 60 * 60 * 24 * 365) = 69<br>
* 10位的数据机器位,可以部署在1024个节点,包括5位datacenterId和5位workerId<br>
* 12位序列,毫秒内的计数,12位的计数顺序号支持每个节点每毫秒(同一机器,同一时间截)产生4096个ID序号<br>
* 加起来刚好64位,为一个Long型。<br>
* SnowFlake的优点是,整体上按照时间自增排序,并且整个分布式系统内不会产生ID碰撞(由数据中心ID和机器ID作区分),并且效率较高,经测试,SnowFlake每秒能够产生26万ID左右。
*/
public class SnowGlobalIdWorker {
// ==============================Fields===========================================
/** 机器id所占的位数 */
private final long workerIdBits = 5L;
/** 数据标识id所占的位数 */
private final long datacenterIdBits = 5L;
/** 支持的最大机器id,结果是31 (这个移位算法可以很快的计算出几位二进制数所能表示的最大十进制数) */
private final long maxWorkerId = -1L ^ (-1L << workerIdBits);
/** 支持的最大数据标识id,结果是31 */
private final long maxDatacenterId = -1L ^ (-1L << datacenterIdBits);
/** 序列在id中占的位数 */
private final long sequenceBits = 12L;
/** 生成序列的掩码,这里为4095 (0b111111111111=0xfff=4095) */
private final long sequenceMask = -1L ^ (-1L << sequenceBits);
/** 工作机器ID(0~31) */
private long workerId;
/** 数据中心ID(0~31) */
private long datacenterId;
/** 毫秒内序列(0~4095) */
private long sequence = 0L;
/** 上次生成ID的时间截 */
private long lastTimestamp = -1L;
//==============================Constructors=====================================
private static SnowGlobalIdWorker idWorker;
/**
* 构造函数
* @param workerId 工作ID (0~31)
* @param datacenterId 数据中心ID (0~31)
*/
private SnowGlobalIdWorker() {
long workerIdInput = computWorkerId();
long datacenterIdInput = computDatacenterId();
if (workerIdInput > maxWorkerId || workerIdInput < 0) {
throw new IllegalArgumentException(String.format("worker Id can't be greater than %d or less than 0", maxWorkerId));
}
if (datacenterIdInput > maxDatacenterId || datacenterIdInput < 0) {
throw new IllegalArgumentException(String.format("datacenter Id can't be greater than %d or less than 0", maxDatacenterId));
}
this.workerId = workerIdInput;
this.datacenterId = datacenterIdInput;
}
public static SnowGlobalIdWorker getInstance() {
if(idWorker == null) {
idWorker = new SnowGlobalIdWorker();
}
return idWorker;
}
/**
* @工作ID (0~31), 可以自定义本机服务器部署APP序号(功能模块)
* @return
*/
private long computWorkerId() {
long workerIdLong = 0;
return (long)workerIdLong%32;
}
/**
* @数据中心ID (0~31), 可以通过IP计算获取
* @return
*/
private long computDatacenterId() {
long localIp4Long = IpUtil.getLocalIp4Long();
return (long)localIp4Long%32;
}
// ==============================Methods==========================================
/**
* @获得下一个ID (该方法是线程安全的)
* @return SnowflakeId
*/
public synchronized String nextId() {
long timestamp = timeGen();
//如果当前时间小于上一次ID生成的时间戳,说明系统时钟回退过这个时候应当抛出异常
if (timestamp < lastTimestamp) {
throw new RuntimeException(
String.format("Clock moved backwards. Refusing to generate id for %d milliseconds", lastTimestamp - timestamp));
}
//如果是同一时间生成的,则进行毫秒内序列
if (lastTimestamp == timestamp) {
sequence = (sequence + 1) & sequenceMask;
//毫秒内序列溢出
if (sequence == 0) {
//阻塞到下一个毫秒,获得新的时间戳
timestamp = tilNextMillis(lastTimestamp);
}
}
//时间戳改变,毫秒内序列重置
else {
sequence = 0L;
}
//上次生成ID的时间截
lastTimestamp = timestamp;
// 流水号 24 = 17 + 4 + 3
String workIdDatePice = DateFormatUtil.formatDate(timestamp, DateFormatUtil.DATE_PATTERN_yyyyMMddHHmmssSSS);
String workIdMachPice = StringFormatUtil.leftPadForZero(datacenterId+"", 2) + StringFormatUtil.leftPadForZero(workerId+"", 2);
String workIdSeqPice = StringFormatUtil.leftPadForZero(sequence+"", 3);
StringBuilder workIdBuilder = new StringBuilder();
return workIdBuilder.append(workIdDatePice).append(workIdMachPice).append(workIdSeqPice).toString();
// return workIdBuilder.append(workIdDatePice).append("-").append(workIdMachPice).append("-").append(workIdSeqPice).toString();
}
/**
* @阻塞到下一个毫秒,直到获得新的时间戳
* @param lastTimestamp 上次生成ID的时间截
* @return 当前时间戳
*/
protected long tilNextMillis(long lastTimestamp) {
long timestamp = timeGen();
while (timestamp <= lastTimestamp) {
timestamp = timeGen();
}
return timestamp;
}
/**
* 返回以毫秒为单位的当前时间
* @return 当前时间(毫秒)
*/
protected long timeGen() {
return System.currentTimeMillis();
}
}
========================================== 仿雪花算法实现类(注解模式,可通过@Service直接引用)
package com.taoxw.plugins.serial;
import org.springframework.stereotype.Service;
@Service
public class SnowGlobalIdImpl {
private static SnowGlobalIdImpl snowGlobalIdImpl;
private SnowGlobalIdWorker idWorker;
public SnowGlobalIdImpl() {
if(idWorker == null) {
idWorker = SnowGlobalIdWorker.getInstance();
}
}
public static SnowGlobalIdImpl getInstance() {
if(snowGlobalIdImpl == null) {
snowGlobalIdImpl = new SnowGlobalIdImpl();
}
return snowGlobalIdImpl;
}
/**
*
* @return
*/
public String nextId() {
return String.valueOf(idWorker.nextId());
}
}
========================================== 仿雪花算法单元测试类
package com.taoxw.plugins.serial;
import org.junit.Test;
import org.springframework.beans.factory.annotation.Autowired;
import com.taoxw._StarterTest;
public class SnowImplTest extends _StarterTest{
@Autowired
private SnowGlobalIdImpl snowGlobalIdImpl;/**
*仿 雪花算法实现序列号自增
*/
@Test
public void test_snowGlobalToSerial() {
System.out.println(snowGlobalIdImpl.nextId()); //strLen == 19
}
/**
* 仿雪花算法实现序列号自增
*/
@Test
public void test_SnowGlobalIdWorker() {
for (int i = 0; i < 100; i++) {
System.out.println(snowGlobalIdImpl.nextId());
}
}
}
========================================== 仿雪花算法压力测试线程类
package com.taoxw.plugins.serial;
import java.util.HashSet;
import java.util.Set;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class SnowGlobalImplThread extends Thread {
protected static final Logger logger = LoggerFactory.getLogger(SnowGlobalImplThread.class);
public static int ID = 0;
public static Set<String> tries = new HashSet<String>();
static Object lock = new Object();
//引用对象
private SnowGlobalIdImpl snowGlobalIdImpl;
public SnowGlobalImplThread(SnowGlobalIdImpl snowGlobalIdImpl) {
this.snowGlobalIdImpl = snowGlobalIdImpl;
}
public void run(){
String str = snowGlobalIdImpl.nextId();
// String str = SnowflakeIdImpl.getInstance().snowLongSerial();
synchronized (lock) {
tries.add(str);
ID++;
logger.info("流水号生成个数序号={}, 流水号={}", ID, str);
}
}
}
========================================== 仿雪花算法压力测试类
package com.taoxw.plugins.serial;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import org.junit.Test;
import org.springframework.beans.factory.annotation.Autowired;
import com.taoxw._StarterTest;
public class SnowGlobalImplThreadTest extends _StarterTest{
@Autowired
private SnowGlobalIdImpl snowGlobalIdImpl;
@SuppressWarnings("static-access")
@Test
public void test_serialMod() {
int n =200;
ExecutorService execupool = Executors.newFixedThreadPool(n);
for(int i=0;i<10000;i++){
Thread serial = new SnowGlobalImplThread(snowGlobalIdImpl);
execupool.execute(serial);
}
execupool.shutdown();
while(!execupool.isTerminated()){ }
SnowGlobalImplThread serialImplThread = new SnowGlobalImplThread(snowGlobalIdImpl);
System.out.println("总个数:" + (serialImplThread.ID));
System.out.println("去重总个数:" + (serialImplThread.tries.size()));
System.out.println("冲突数:" + (serialImplThread.ID - serialImplThread.tries.size()));
}
}