Spring Boot高并发数据入库
当年的春天 人气:0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); } }
测试
模拟用户请求访问后台(多次请求)
查看缓存中访问数据
模拟定时任务将缓存中数据同步到DB中
这时查看缓存中的数据已经没了
查看数据库表结构
总结
- 项目中定时任务
- 问演示方便用http代替定时任务调度;实际项目中用XXL-job,参考:定时任务的选型及改造
- 定时任务项目中用redis锁防止并发(定时任务调度端多次调度等),参考:Redis实现计数器—接口防刷—升级版(Redis+Lua)
- 后期运营数据可以从阅读记录表中拉数据进行相关分析
- 访问量大:可以将MySQL中的阅读记录表定时迁移走(MySQL建历史表,MongoDB等)
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