从零搭建基于SpringBoot的秒杀系统(四)：雪花算法生成订单号以及抢购功能实现
抢购功能是整个系统的核心，接下来的很多优化都是在优化抢购功能，在写抢购功能模块之前，先封装几个公共的类。
先想一下抢购逻辑，点击购买按钮后，通过post请求将数据传递给接口，接口返回成功或失败信息。因此我们需要先封装一个类描述返回信息，在response文件夹下新建BaseResponse，包含一个状态码，成功失败信息以及数据
package com.sdxb.secondkill.response;
import com.sdxb.secondkill.enums.StatusCode;
public class BaseResponse<T> {
private Integer code;
private String msg;
private T data;
public BaseResponse(Integer code, String msg, T data) {
this.code = code;
this.msg = msg;
this.data = data;
}
public BaseResponse(StatusCode code) {
this.code = code.getCode();
this.msg = code.getMsg();
}
public BaseResponse(Integer code, String msg) {
this.code=code;
this.msg=msg;
}
public Integer getCode() {
return code;
}
public void setCode(Integer code) {
this.code = code;
}
public String getMsg() {
return msg;
}
public void setMsg(String msg) {
this.msg = msg;
}
public T getData() {
return data;
}
public void setData(T data) {
this.data = data;
}
@Override
public String toString() {
return "BaseResponse{" +
"code=" + code +
", msg='" + msg + '\'' +
", data=" + data +
'}';
}
}BaseResponse中的状态码和成功失败信息我们通过枚举类来总结，在enums中新建一个StatusCode
package com.sdxb.secondkill.enums;
public enum StatusCode {
//表示成功
Success(0,"成功"),
//表示失败
Fail(-1,"失败"),
//表示参数非法
InvalidParam(201,"非法的参数"),
//表示用户未登录
UserNotLog(202,"用户未登录"),
;
private Integer code;
private String msg;
StatusCode(Integer code,String msg){
this.code=code;
this.msg=msg;
}
public Integer getCode() {
return code;
}
public void setCode(Integer code) {
this.code = code;
}
public String getMsg() {
return msg;
}
public void setMsg(String msg) {
this.msg = msg;
}
}接下来就可以通过BaseResponse来返回接口的成功或失败信息。
再写一个枚举类用于记录订单支付的状态，在enums下新建SysConstant
public class SysConstant {
public enum OrderStatus{
//订单无效
Invalid(-1,"无效"),
//订单成功未付款
SuccessNotPayed(0,"成功-未付款"),
//订单已付款
HasPayed(1,"已付款"),
//订单已取消
Cancel(2,"已取消"),
;
private Integer code;
private String msg;
OrderStatus(Integer code, String msg) {
this.code = code;
this.msg = msg;
}
public Integer getCode() {
return code;
}
public void setCode(Integer code) {
this.code = code;
}
public String getMsg() {
return msg;
}
public void setMsg(String msg) {
this.msg = msg;
}
}
}高并发环境下需要快速生成唯一且递增的订单编号，这个ID需要全局唯一，为了防止ID冲突可以使用36位的UUID，但是UUID有以下缺点：
这里就可以用雪花算法来解决订单编号的问题：雪花算法是推特开源的分布式id生成算法，雪花算法的具体原理我们不做介绍，只要知道它可以在硬件级别上快速生成递增id就可以了，直接放代码：
package com.sdxb.secondkill.utils;
public class SnowFlake {
/**
* 起始的时间戳
*/
private final static long START_STAMP = 1480166465631L;
/**
* 每一部分占用的位数
*/
private final static long SEQUENCE_BIT = 12; //序列号占用的位数
private final static long MACHINE_BIT = 5; //机器标识占用的位数
private final static long DATA_CENTER_BIT = 5;//数据中心占用的位数
/**
* 每一部分的最大值
*/
private final static long MAX_DATA_CENTER_NUM = -1L ^ (-1L << DATA_CENTER_BIT);
private final static long MAX_MACHINE_NUM = -1L ^ (-1L << MACHINE_BIT);
private final static long MAX_SEQUENCE = -1L ^ (-1L << SEQUENCE_BIT);
/**
* 每一部分向左的位移
*/
private final static long MACHINE_LEFT = SEQUENCE_BIT;
private final static long DATA_CENTER_LEFT = SEQUENCE_BIT + MACHINE_BIT;
private final static long TIMESTAMP_LEFT = DATA_CENTER_LEFT + DATA_CENTER_BIT;
private long dataCenterId; //数据中心
private long machineId; //机器标识
private long sequence = 0L; //序列号
private long lastStamp = -1L;//上一次时间戳
public SnowFlake(long dataCenterId, long machineId) {
if (dataCenterId > MAX_DATA_CENTER_NUM || dataCenterId < 0) {
throw new IllegalArgumentException("dataCenterId can't be greater than MAX_DATA_CENTER_NUM or less than 0");
}
if (machineId > MAX_MACHINE_NUM || machineId < 0) {
throw new IllegalArgumentException("machineId can't be greater than MAX_MACHINE_NUM or less than 0");
}
this.dataCenterId = dataCenterId;
this.machineId = machineId;
}
/**
* 产生下一个ID
*
* @return
*/
public synchronized long nextId() {
long currStamp = getNewStamp();
if (currStamp < lastStamp) {
throw new RuntimeException("Clock moved backwards. Refusing to generate id");
}
if (currStamp == lastStamp) {
//相同毫秒内，序列号自增
sequence = (sequence + 1) & MAX_SEQUENCE;
//同一毫秒的序列数已经达到最大
if (sequence == 0L) {
currStamp = getNextMill();
}
} else {
//不同毫秒内，序列号置为0
sequence = 0L;
}
lastStamp = currStamp;
return (currStamp - START_STAMP) << TIMESTAMP_LEFT //时间戳部分
| dataCenterId << DATA_CENTER_LEFT //数据中心部分
| machineId << MACHINE_LEFT //机器标识部分
| sequence; //序列号部分
}
private long getNextMill() {
long mill = getNewStamp();
while (mill <= lastStamp) {
mill = getNewStamp();
}
return mill;
}
private long getNewStamp() {
return System.currentTimeMillis();
}
public static void main(String[] args) {
SnowFlake snowFlake = new SnowFlake(2, 3);
long start = System.currentTimeMillis();
for (int i = 0; i < 1000000; i++) {
System.out.println("当前生成的有序数字串："+snowFlake.nextId());
}
System.out.println("总共耗时："+(System.currentTimeMillis() - start));
}
}我在类中写了一个main方法测试生成100万个id的速度，生成100万个id一共花费4.4秒
三、抢购处理逻辑编写
抢购逻辑中需要编写两项DTO类，KillDto中保存订单id和用户id，抢购时就通过这两个数据来发起抢购，dto下新建KillDto：
@Data
@ToString
public class KillDto implements Serializable {
private Integer killid;
private Integer userid;
public KillDto() {
}
public KillDto(Integer killid, Integer userid) {
this.killid = killid;
this.userid = userid;
}
}在Controller文件下新建一个KillController，用来接受抢购请求
package com.sdxb.secondkill.controller;
import com.sdxb.secondkill.enums.StatusCode;
import com.sdxb.secondkill.response.BaseResponse;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.http.MediaType;
import org.springframework.stereotype.Controller;
import org.springframework.validation.BindingResult;
import org.springframework.validation.annotation.Validated;
import org.springframework.web.bind.annotation.RequestBody;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestMethod;
import org.springframework.web.bind.annotation.ResponseBody;
import javax.servlet.http.HttpSession;
@Controller
public class KillController {
private static final String prefix="kill";
@Autowired
private KillService killService;
@RequestMapping(value = prefix+"/execute",method = RequestMethod.POST,consumes = MediaType.APPLICATION_JSON_UTF8_VALUE)
@ResponseBody
public BaseResponse execute(@RequestBody @Validated KillDto killDto, BindingResult result, HttpSession httpSession){
if (result.hasErrors()||killDto.getKillid()<0){
return new BaseResponse(StatusCode.InvalidParam);
}
//未创建登陆模块前先默认为10
Integer userid=10;
try {
Boolean res=killService.KillItem(killDto.getKillid(),userid);
if (!res){
return new BaseResponse(StatusCode.Fail.getCode(),"商品已经抢购完或您已抢购过该商品");
}
} catch (Exception e) {
e.printStackTrace();
}
BaseResponse baseResponse=new BaseResponse(StatusCode.Success);
return baseResponse;
}
@RequestMapping(value = prefix+"/execute/success",method = RequestMethod.GET)
public String killsuccess(){
return "killsuccess";
}
@RequestMapping(value = prefix+"/execute/fail",method = RequestMethod.GET)
public String killfail(){
return "killfail";
}
}所有的业务处理都放到Service中去处理，在Service文件下创建KillService接口
public interface KillService {
Boolean KillItem(Integer killId,Integer userId) throws Exception;
}在Service下的Impl文件夹中创建KillServiceImpl类，用于处理详细的业务：
@Service
public class KillServiceImpl implements KillService {
private SnowFlake snowFlake=new SnowFlake(2,3);
@Autowired
private ItemKillMapper itemKillMapper;
@Autowired
private ItemKillSuccessMapper itemKillSuccessMapper;
public Boolean KillItem(Integer killId, Integer userId) throws Exception {
Boolean result=false;
//判断当前用户是否抢购过该商品
if (itemKillSuccessMapper.countByKillUserId(killId,userId)<=0){
//获取商品详情
ItemKill itemKill=itemKillMapper.selectByid(killId);
if (itemKill!=null&&itemKill.getCanKill()==1){
int res=itemKillMapper.updateKillItem(killId);
if (res>0){
commonRecordKillSuccessInfo(itemKill,userId);
result=true;
}
}
}else {
System.out.println("您已经抢购过该商品");
}
return result;
}
private void commonRecordKillSuccessInfo(ItemKill itemKill, Integer userId) {
ItemKillSuccess entity=new ItemKillSuccess();
String orderNo=String.valueOf(snowFlake.nextId());
entity.setCode(orderNo);
entity.setItemId(itemKill.getItemId());
entity.setKillId(itemKill.getId());
entity.setUserId(userId.toString());
entity.setStatus(SysConstant.OrderStatus.SuccessNotPayed.getCode().byteValue());
entity.setCreateTime(DateTime.now().toDate());
if (itemKillSuccessMapper.countByKillUserId(itemKill.getId(),userId) <= 0){
int res=itemKillSuccessMapper.insertSelective(entity);
if(res>0){
//处理抢购成功后的流程
//这里的业务可以自己加
}
}
}对于订单抢购的数据库操作在ItemKillSuccessMapper 中进行，在mapper文件下编写ItemKillSuccessMapper :主要的操作有根据用户ID查询订单以及订单抢购成功后插入item_kill_success表
@Mapper
public interface ItemKillSuccessMapper {
@Select("select count(1) from item_kill_success where user_id=#{userId} and kill_id=#{killId} and status in (0)")
int countByKillUserId(@Param("killId") Integer killId, @Param("userId") Integer userId);
@Insert("insert into item_kill_success(code,item_id,kill_id,user_id,status,create_time) values(#{code},#{itemId},#{killId},#{userId},#{status},#{createTime})")
int insertSelective(ItemKillSuccess entity);
}在ItemKillMapper中增加一条更新数据的代码，用来处理抢购成功后更新余量
@Update("update item_kill set total=total-1 where id=#{killId}")
int updateKillItem(Integer killId);运行项目，进入首页http://localhost:8080/item
点击详情：
输出购买成功，数据库中生成一条信息
当再次购买时，显示已经抢购：
到当前功能的代码都放到https://github.com/OliverLiy/SecondKill/tree/version3.0中
我搭建了一个微信公众号《Java鱼仔》，如果你对本项目有任何疑问，欢迎在公众号中联系我，我会尽自己所能为大家解答。