1. 创建数据表user_order
create table user_order( id int(11) not null auto_increment, order_no varchar(100) not null comment "订单编号", user_id int(11) not null comment "用户id", is_active smallint(4) default 1 comment "状态(1=有效;0=无效)", create_time datetime default current_timestamp comment "下单时间", update_time timestamp null default null on update current_timestamp, primary key(id), unique key idx_order_no(order_no) using btree ) engine=InnoDB auto_increment=5 default charset=utf8 comment="用户下单记录表"; insert into user_order(order_no, user_id, is_active) values ("Book20191222001", 10010, 1); insert into user_order(order_no, user_id, is_active) values ("Book20191223001", 10011, 1); insert into user_order(order_no, user_id, is_active) values ("Book20191223003", 10012, 0);2. 通过逆向工程生成对应的entity, mapper接口和映射文件
3. UserOrderService.java
@EnableScheduling @Service public class UserOrderService { private static final Logger log= LoggerFactory.getLogger(UserOrderService.class); //雪花算法工具-用于分布式环境（高并发）生成全局唯一ID的工具 private static final Snowflake SNOWFLAKE=new Snowflake(3,2); //存储至缓存的用户订单编号的前缀 private static final String RedisUserOrderPrefix="SpringBootRedis:UserOrder:"; //用户订单失效的时间配置 - 30s private static final Long UserOrderTimeOut=30L; @Autowired private UserOrderMapper userOrderMapper; @Autowired private RedisTemplate redisTemplate; //下单服务 @Transactional(rollbackFor = Exception.class) public String putOrder(UserOrder entity) throws Exception{ //用户下单-入库 String orderNo=SNOWFLAKE.nextIdStr();//利用雪花算法生成全局唯一的id entity.setOrderNo(orderNo);//设置编号 entity.setOrderTime(new Date());//设置时间 int res=userOrderMapper.insertSelective(entity); if (res>0){ //TODO:插入db之后，将订单编号塞入缓存中，同时设置好ttl redisTemplate.opsForValue().set(RedisUserOrderPrefix+orderNo,entity.getId(),UserOrderTimeOut,TimeUnit.SECONDS); } return orderNo; } }其中利用雪花算法来生成全局唯一ID(订单编号)
4. UserOrderController.java
@RestController @RequestMapping("user/order") public class UserOrderController { private static final Logger log= LoggerFactory.getLogger(UserOrderController.class); @Autowired private UserOrderService userOrderService; //下单 @RequestMapping(value = "put",method = RequestMethod.POST,consumes = MediaType.APPLICATION_JSON_UTF8_VALUE) public BaseResponse put(@RequestBody @Validated UserOrder userOrder, BindingResult result){ String checkRes= ValidatorUtil.checkResult(result); if (StrUtil.isNotBlank(checkRes)){ return new BaseResponse(StatusCode.InvalidParams.getCode(),checkRes); } BaseResponse response=new BaseResponse(StatusCode.Success); try { log.info("--用户下单：{}",userOrder); String res=userOrderService.putOrder(userOrder); response.setData(res); }catch (Exception e){ log.error("--用户下单-发生异常：",e.fillInStackTrace()); response=new BaseResponse(StatusCode.Fail.getCode(),e.getMessage()); } return response; } }借助定时任务调度将已失效的订单在数据中修改，其实就是定时任务定时拉取DB中未失效的订单(按照下单时间升序),和缓存一一作对比，判断缓存中hasKey是否为True
@Async表示开启多个线程去异步执行
@Scheduled：服务只要一启动，就会自动地去调用。
//TODO：定时任务调度-拉取出 有效 + 未支付 的订单列表，前往缓存查询订单是否已失效 @Scheduled(cron = "0/1 * * * * ?") @Async("threadPoolTaskExecutor") public void schedulerCheckOrder(){ try { List<UserOrder> orders=userOrderMapper.selectUnPayOrders(); if (orders!=null && !orders.isEmpty()){ orders.forEach(entity -> { String key=RedisUserOrderPrefix+entity.getOrderNo(); if (!redisTemplate.hasKey(key)){ //TODO:表示缓存中该订单编号已经过期失效了，我们需要前往数据库失效该订单... userOrderMapper.unActiveOrder(entity.getId()); log.info("----缓存中当前订单超过了TTL未支付，故而失效该表中的对应记录：orderNo={}",entity.getOrderNo()); } }); } }catch (Exception e){ log.error("定时任务调度-拉取出 有效 + 未支付 的订单列表，前往缓存查询订单是否已失效-发生异常：",e); } }如果有定时任务调度的话，需要加上一个线程池的配置，多个调度会被分配至多个线程去执行。
@Configuration public class ThreadConfig { @Bean("threadPoolTaskExecutor") public Executor threadPoolTaskExecutor(){ ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor(); executor.setCorePoolSize(4); executor.setMaxPoolSize(8); executor.setKeepAliveSeconds(10); executor.setQueueCapacity(8); executor.setRejectedExecutionHandler(new ThreadPoolExecutor.CallerRunsPolicy()); return executor; } }缺陷
如果成功下单但是却迟迟未支付的订单量过大时，将很占内存~可能会演变为悲伤的故事。
改进:采用RabbitMQ的死信队列;采用Redisson的延迟队列...
