Spring Boot实战解决高并发数据入库之 Redis 缓存+MySQL 批量入库问题

前言

最近在做阅读类的业务，需要记录用户的PV，UV；

项目状况：前期尝试业务阶段；

特点：
快速实现（不需要做太重，满足初期推广运营即可）快速投入市场去运营
收集用户的原始数据，三要素：
谁在什么时间阅读哪篇文章
提到PV，UV脑海中首先浮现特点：
需要考虑性能（每个客户每打开一篇文章进行记录）允许数据有较小误差（少部分数据丢失）

架构设计

架构图：

时序图

记录基础数据MySQL表结构

CREATE TABLE `zh_article_count` (
  `id` bigint(20) NOT NULL AUTO_INCREMENT,
  `bu_no` varchar(32) DEFAULT NULL COMMENT '业务编码',
  `customer_id` varchar(32) DEFAULT NULL COMMENT '用户编码',
  `type` int(2) DEFAULT '0' COMMENT '统计类型:0APP内文章阅读',
  `article_no` varchar(32) DEFAULT NULL COMMENT '文章编码',
  `read_time` datetime DEFAULT NULL COMMENT '阅读时间',
  `create_time` datetime DEFAULT CURRENT_TIMESTAMP COMMENT '创建时间',
  `update_time` datetime DEFAULT CURRENT_TIMESTAMP COMMENT '更新时间',
  `param1` int(2) DEFAULT NULL COMMENT '预留字段1',
  `param2` int(4) DEFAULT NULL COMMENT '预留字段2',
  `param3` int(11) DEFAULT NULL COMMENT '预留字段3',
  `param4` varchar(20) DEFAULT NULL COMMENT '预留字段4',
  `param5` varchar(32) DEFAULT NULL COMMENT '预留字段5',
  `param6` varchar(64) DEFAULT NULL COMMENT '预留字段6',
  PRIMARY KEY (`id`) USING BTREE,
  UNIQUE KEY `uk_zh_article_count_buno` (`bu_no`),
  KEY `key_zh_article_count_csign` (`customer_id`),
  KEY `key_zh_article_count_ano` (`article_no`),
  KEY `key_zh_article_count_rtime` (`read_time`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COMMENT='文章阅读统计表';

技术实现方案

SpringBoot

Redis

MySQL

代码实现

完整代码（GitHub，欢迎大家Star,Fork,Watch)

https://github.com/dangnianchuntian/springboot

主要代码展示

Controller

/*
 * Copyright (c) 2020. zhanghan_java@163.com All Rights Reserved.
 * 项目名称：Spring Boot实战解决高并发数据入库: Redis 缓存+MySQL 批量入库
 * 类名称：ArticleCountController.java
 * 创建人：张晗
 * 联系方式：zhanghan_java@163.com
 * 开源地址: https://github.com/dangnianchuntian/springboot
 * 博客地址: https://zhanghan.blog.csdn.net
 */

package com.zhanghan.zhredistodb.controller;
import org.springframework.beans.factory.annotation.Autowired;
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.RestController;
import com.zhanghan.zhredistodb.controller.request.PostArticleViewsRequest;
import com.zhanghan.zhredistodb.service.ArticleCountService;
@RestController
public class ArticleCountController {
    @Autowired
    private ArticleCountService articleCountService;
   /**
    * 记录用户访问记录
    */
    @RequestMapping(value = "/post/article/views", method = RequestMethod.POST)
    public Object postArticleViews(@RequestBody @Validated PostArticleViewsRequest postArticleViewsRequest) {
        return articleCountService.postArticleViews(postArticleViewsRequest);
    }
    /**
     *  批量将缓存中的数据同步到MySQL（模拟定时任务操作）
     */
    @RequestMapping(value = "/post/batch", method = RequestMethod.POST)
    public Object postBatch() {
        return articleCountService.postBatchRedisToDb();
}

Service

/*
 * Copyright (c) 2020. zhanghan_java@163.com All Rights Reserved.
 * 项目名称：Spring Boot实战解决高并发数据入库: Redis 缓存+MySQL 批量入库
 * 类名称：ArticleCountServiceImpl.java
 * 创建人：张晗
 * 联系方式：zhanghan_java@163.com
 * 开源地址: https://github.com/dangnianchuntian/springboot
 * 博客地址: https://zhanghan.blog.csdn.net
 */

package com.zhanghan.zhredistodb.service.impl;
import java.util.ArrayList;
import java.util.Date;
import java.util.List;
import java.util.stream.Collectors;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.stereotype.Service;
import org.springframework.util.CollectionUtils;
import com.alibaba.fastjson.JSON;
import com.zhanghan.zhredistodb.controller.request.PostArticleViewsRequest;
import com.zhanghan.zhredistodb.dto.ArticleCountDto;
import com.zhanghan.zhredistodb.mybatis.mapper.XArticleCountMapper;
import com.zhanghan.zhredistodb.service.ArticleCountService;
import com.zhanghan.zhredistodb.util.wrapper.WrapMapper;
import cn.hutool.core.util.IdUtil;
@Service
public class ArticleCountServiceImpl implements ArticleCountService {
    private static Logger logger = LoggerFactory.getLogger(ArticleCountServiceImpl.class);
    @Autowired
    private RedisTemplate<String, String> strRedisTemplate;
    private XArticleCountMapper xArticleCountMapper;
    @Value("${zh.article.count.redis.key:zh}")
    private String zhArticleCountRedisKey;
    @Value("#{T(java.lang.Integer).parseInt('${zh..article.read.num:3}')}")
    private Integer articleReadNum;
    /**
     * 记录用户访问记录
     */
    @Override
    public Object postArticleViews(PostArticleViewsRequest postArticleViewsRequest) {
        ArticleCountDto articleCountDto = new ArticleCountDto();
        articleCountDto.setBuNo(IdUtil.simpleUUID());
        articleCountDto.setCustomerId(postArticleViewsRequest.getCustomerId());
        articleCountDto.setArticleNo(postArticleViewsRequest.getArticleNo());
        articleCountDto.setReadTime(new Date());
        String strArticleCountDto = JSON.toJSONString(articleCountDto);
        strRedisTemplate.opsForList().rightPush(zhArticleCountRedisKey, strArticleCountDto);
        return WrapMapper.ok();
    }
     * 批量将缓存中的数据同步到MySQL
    public Object postBatchRedisToDb() {
        Date now = new Date();
        while (true) {
            List<String> strArticleCountList =
                    strRedisTemplate.opsForList().range(zhArticleCountRedisKey, 0, articleReadNum);
            if (CollectionUtils.isEmpty(strArticleCountList)) {
                return WrapMapper.ok();
            }
            List<ArticleCountDto> articleCountDtoList = new ArrayList<>();
            strArticleCountList.stream().forEach(x -> {
                ArticleCountDto articleCountDto = JSON.parseObject(x, ArticleCountDto.class);
                articleCountDtoList.add(articleCountDto);
            });
            //过滤出本次定时任务之前的缓存中数据,防止死循环
            List<ArticleCountDto> beforeArticleCountDtoList = articleCountDtoList.stream().filter(x -> x.getReadTime()
                    .before(now)).collect(Collectors.toList());
            if (CollectionUtils.isEmpty(beforeArticleCountDtoList)) {
            xArticleCountMapper.batchAdd(beforeArticleCountDtoList);
            Integer delSize = beforeArticleCountDtoList.size();
            strRedisTemplate.opsForList().trim(zhArticleCountRedisKey, delSize, -1L);
        }
}