标签：rod tcl 运维操作 运维 return mapper cli struct default
问题：下单操作需要多次调用数据库，查询商品信息，用户信息，修改库存数据，造成性能瓶颈。
优化方向：读取数据改为从缓存读取，修改库存数据改为修改缓存数据在用消息队列异步修改数据库。可以用rocketmq的异步事务型消息来保证redis和数据库数据同步，在缓存异常情况可以用数据库数据来恢复。
1.交易验证优化
（1）校验商品是否存在，改为缓存
之前是直接从数据库根据itemId获取商品数据
现在改为从redis缓存中获取，没有在从数据库获取
@Override
public ItemModel getItemByIdInCache(Integer id) {
ItemModel itemModel = (ItemModel)redisTemplate.opsForValue().get("item_validate_"+id);
if(itemModel == null) {
itemModel = this.getItemById(id);
redisTemplate.opsForValue().set("item_validate_"+id, itemModel);
}
return itemModel;
}
（2）校验用户信息是否存在，改为缓存
之前是直接从数据库根据userId获取用户数据
现在改为从redis缓存中获取，没有在从数据库获取
@Override
public UserModel getUserByIdInCache(Integer id) {
UserModel userModel = (UserModel)redisTemplate.opsForValue().get("user_"+id);
if(userModel == null) {
userModel = this.getUserById(id);
redisTemplate.opsForValue().getAndSet("user_"+id, userModel);
}
return userModel;
}
2. 修改库存数据改为修改缓存
库存修改表数据因为itemId为库存表主键，所以会有行锁，防止并发，但会影响效率
所以在抢购活动发布时，将库存存入redis中，下单时先修改redis缓存数据，再用rocketmq同步修改库存来保证可靠性
a.发布活动，缓存库存
//发布活动，这个应该是运维操作
@RequestMapping(value = "/publishpromo",method = {RequestMethod.GET}) @ResponseBody public CommonReturnType publisPromo(Integer id) { promoService.publishPromo(id); return CommonReturnType.create(null); }
@Override
public void publishPromo(Integer promoId) {
// 通过活动ID获取活动信息
PromoDO promoDO = promoDOMapper.selectByPrimaryKey(promoId);
if(promoDO == null || promoDO.getItemId().intValue() == 0) {
return;
}
ItemModel itemModel = itemService.getItemById(promoDO.getItemId());
//将库存同步到redis中
redisTemplate.opsForValue().getAndSet("promo_item_stock_"+promoDO.getItemId(), itemModel.getStock());
}
b.下单减库存
@Override
@Transactional
public boolean decreaseStock(Integer itemId, Integer amount) throws BusinessException {
/*int affectedRow = itemStockDOMapper.decreaseStock(itemId,amount);
if(affectedRow > 0){
//更新库存成功
return true;
}else{
//更新库存失败
return false;
}*/
Long result = redisTemplate.opsForValue().increment("promo_item_stock_"+itemId, amount.intValue()*-1);
// 发送消息队列修改数据库库存
boolean sendResult = producer.asyncReduceStock(itemId, amount);
// 还剩的数据
if(result > 0 && sendResult){
//更新库存成功
return true;
}else {
//更新库存失败
redisTemplate.opsForValue().increment("promo_item_stock_"+itemId, amount.intValue());
return false;
}
}
消息队列生产者发送
@Component
public class MQProducer {
Log log = LogFactory.getLog(getClass());
@Value("${mq.nameserver.addr}")
private String nameServer;
@Value("${mq.topicname}")
private String topicName;
DefaultMQProducer producer;
@PostConstruct
public void init() throws MQClientException {
producer = new DefaultMQProducer("producer");
producer.setNamesrvAddr(nameServer);
producer.start();
}
// 同步扣减库存消息
public boolean asyncReduceStock(Integer itemId, Integer amount) {
Map<String,Object> bodyMap = new HashMap<String,Object>();
bodyMap.put("itemId", itemId);
bodyMap.put("amount", amount);
Message msg = new Message(topicName,"increase",
JSON.toJSON(bodyMap).toString().getBytes(Charset.forName("UTF-8")));
try {
log.error("begin to send msg");
producer.send(msg);
log.error("finish send msg");
} catch (MQClientException e) {
e.printStackTrace();
return false;
} catch (RemotingException e) {
// TODO Auto-generated catch block
e.printStackTrace();
return false;
} catch (MQBrokerException e) {
// TODO Auto-generated catch block
e.printStackTrace();
return false;
} catch (InterruptedException e) {
// TODO Auto-generated catch block
e.printStackTrace();
return false;
}
return true;
}
}
消息队列消费者减库存数据库
@Component
public class MQConsumer {
@Value("${mq.nameserver.addr}")
private String nameServer;
@Value("${mq.topicname}")
private String topicName;
DefaultMQPushConsumer consumer;
@Autowired
private ItemStockDOMapper itemStockDOMapper;
private Log log = LogFactory.getLog(getClass());
@PostConstruct
public void init() throws MQClientException {
consumer = new DefaultMQPushConsumer("stock_consumer_group");
consumer.setNamesrvAddr(nameServer);
consumer.subscribe(topicName, "*");
consumer.registerMessageListener(new MessageListenerConcurrently() {
@Override
public ConsumeConcurrentlyStatus consumeMessage(List<MessageExt> msgs, ConsumeConcurrentlyContext context) {
log.error("begin to cosume msg");
Message msg = msgs.get(0);
String jsonStr = new String(msg.getBody());
Map<String,Object> map = JSON.parseObject(jsonStr, Map.class);
Integer itemId= (Integer)map.get("itemId");
Integer amount= (Integer)map.get("amount");
log.error("itemId:"+itemId+",amount:"+amount);
int affectedRow = itemStockDOMapper.decreaseStock(itemId,amount);
if(affectedRow > 0){
//更新库存成功
return ConsumeConcurrentlyStatus.CONSUME_SUCCESS;
}else{
//更新库存失败
return ConsumeConcurrentlyStatus.RECONSUME_LATER;
}
}
});
consumer.start();
}
}
标签：rod tcl 运维操作 运维 return mapper cli struct default
原文地址：https://www.cnblogs.com/t96fxi/p/12082884.html
