热文章计算

一、热点文章-定时计算

1、 需求分析

需求：为每个频道缓存热度较高的30条文章优先展示

判断文章热度较高的标准是什么？

文章：阅读，点赞，评论，收藏

2、 实现思路

3、 实现步骤

分值计算不涉及到前端工程，也无需提供api接口，是一个纯后台的功能的开发。

1） 频道列表远程接口准备

计算完成新热数据后，需要给每个频道缓存一份数据，所以需要查询所有频道信息

① 在heima-leadnews-feign-api定义远程接口

package com.heima.apis.wemedia;

import com.heima.model.common.dtos.ResponseResult;
import org.springframework.cloud.openfeign.FeignClient;
import org.springframework.web.bind.annotation.GetMapping;

@FeignClient("leadnews-wemedia")
public interface IWemediaClient {

    @GetMapping("/api/v1/channel/list")
    public ResponseResult getChannels();
}

② heima-leadnews-wemedia端提供接口

package com.heima.wemedia.feign;

import com.heima.apis.wemedia.IWemediaClient;
import com.heima.model.common.dtos.ResponseResult;
import com.heima.wemedia.service.WmChannelService;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.GetMapping;
import org.springframework.web.bind.annotation.RestController;

@RestController
public class WemediaClient implements IWemediaClient {

    @Autowired
    private WmChannelService wmChannelService;

    @GetMapping("/api/v1/channel/list")
    @Override
    public ResponseResult getChannels() {
        return wmChannelService.findAll();
    }
}

在ApArticleMapper.xml新增方法

<select id="findArticleListByLast5days" resultMap="resultMap">
    SELECT
    aa.*
    FROM
    `ap_article` aa
    LEFT JOIN ap_article_config aac ON aa.id = aac.article_id
    <where>
        and aac.is_delete != 1
        and aac.is_down != 1
        <if test="dayParam != null">
            and aa.publish_time <![CDATA[>=]]> #{dayParam}
        </if>
    </where>
</select>

修改ApArticleMapper类

package com.heima.article.mapper;

import com.baomidou.mybatisplus.core.mapper.BaseMapper;
import com.heima.model.article.dtos.ArticleHomeDto;
import com.heima.model.article.pojos.ApArticle;
import org.apache.ibatis.annotations.Mapper;
import org.apache.ibatis.annotations.Param;

import java.util.Date;
import java.util.List;

@Mapper
public interface ApArticleMapper extends BaseMapper<ApArticle> {

    /**
     * 加载文章列表
     * @param dto
     * @param type  1  加载更多   2记载最新
     * @return
     */
    public List<ApArticle> loadArticleList(ArticleHomeDto dto,Short type);

    public List<ApArticle> findArticleListByLast5days(@Param("dayParam") Date dayParam);
}

2） 热文章业务层

定义业务层接口

package com.heima.article.service;

public interface HotArticleService {

    /**
     * 计算热点文章
     */
    public void computeHotArticle();
}

修改ArticleConstans

package com.heima.common.constants;

public class ArticleConstants {
    public static final Short LOADTYPE_LOAD_MORE = 1;
    public static final Short LOADTYPE_LOAD_NEW = 2;
    public static final String DEFAULT_TAG = "__all__";

    public static final String ARTICLE_ES_SYNC_TOPIC = "article.es.sync.topic";

    public static final Integer HOT_ARTICLE_LIKE_WEIGHT = 3;
    public static final Integer HOT_ARTICLE_COMMENT_WEIGHT = 5;
    public static final Integer HOT_ARTICLE_COLLECTION_WEIGHT = 8;

    public static final String HOT_ARTICLE_FIRST_PAGE = "hot_article_first_page_";
}

创建一个vo接收计算分值后的对象

package com.heima.model.article.vos;

import com.heima.model.article.pojos.ApArticle;
import lombok.Data;

@Data
public class HotArticleVo extends ApArticle {
    /**
     * 文章分值
     */
    private Integer score;
}

业务层实现类

package com.heima.article.service.impl;

import com.alibaba.fastjson.JSON;
import com.heima.apis.wemedia.IWemediaClient;
import com.heima.article.mapper.ApArticleMapper;
import com.heima.article.service.HotArticleService;
import com.heima.common.constants.ArticleConstants;
import com.heima.common.redis.CacheService;
import com.heima.model.article.pojos.ApArticle;
import com.heima.model.article.vos.HotArticleVo;
import com.heima.model.common.dtos.ResponseResult;
import com.heima.model.wemedia.pojos.WmChannel;
import lombok.extern.slf4j.Slf4j;
import org.joda.time.DateTime;
import org.springframework.beans.BeanUtils;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import org.springframework.transaction.annotation.Transactional;

import java.util.ArrayList;
import java.util.Comparator;
import java.util.Date;
import java.util.List;
import java.util.stream.Collectors;

@Service
@Slf4j
@Transactional
public class HotArticleServiceImpl implements HotArticleService {

    @Autowired
    private ApArticleMapper apArticleMapper;

    /**
     * 计算热点文章
     */
    @Override
    public void computeHotArticle() {
        //1.查询前5天的文章数据
        Date dateParam = DateTime.now().minusDays(50).toDate();
        List<ApArticle> apArticleList = apArticleMapper.findArticleListByLast5days(dateParam);

        //2.计算文章的分值
        List<HotArticleVo> hotArticleVoList = computeHotArticle(apArticleList);

        //3.为每个频道缓存30条分值较高的文章
        cacheTagToRedis(hotArticleVoList);

    }

    @Autowired
    private IWemediaClient wemediaClient;

    @Autowired
    private CacheService cacheService;

    /**
     * 为每个频道缓存30条分值较高的文章
     * @param hotArticleVoList
     */
    private void cacheTagToRedis(List<HotArticleVo> hotArticleVoList) {
        //每个频道缓存30条分值较高的文章
        ResponseResult responseResult = wemediaClient.getChannels();
        if(responseResult.getCode().equals(200)){
            String channelJson = JSON.toJSONString(responseResult.getData());
            List<WmChannel> wmChannels = JSON.parseArray(channelJson, WmChannel.class);
            //检索出每个频道的文章
            if(wmChannels != null && wmChannels.size() > 0){
                for (WmChannel wmChannel : wmChannels) {
                    List<HotArticleVo> hotArticleVos = hotArticleVoList.stream().filter(x -> x.getChannelId().equals(wmChannel.getId())).collect(Collectors.toList());
                    //给文章进行排序，取30条分值较高的文章存入redis  key：频道id   value：30条分值较高的文章
                    sortAndCache(hotArticleVos, ArticleConstants.HOT_ARTICLE_FIRST_PAGE + wmChannel.getId());
                }
            }
        }


        //设置推荐数据
        //给文章进行排序，取30条分值较高的文章存入redis  key：频道id   value：30条分值较高的文章
        sortAndCache(hotArticleVoList, ArticleConstants.HOT_ARTICLE_FIRST_PAGE+ArticleConstants.DEFAULT_TAG);


    }

    /**
     * 排序并且缓存数据
     * @param hotArticleVos
     * @param key
     */
    private void sortAndCache(List<HotArticleVo> hotArticleVos, String key) {
        hotArticleVos = hotArticleVos.stream().sorted(Comparator.comparing(HotArticleVo::getScore).reversed()).collect(Collectors.toList());
        if (hotArticleVos.size() > 30) {
            hotArticleVos = hotArticleVos.subList(0, 30);
        }
        cacheService.set(key, JSON.toJSONString(hotArticleVos));
    }

    /**
     * 计算文章分值
     * @param apArticleList
     * @return
     */
    private List<HotArticleVo> computeHotArticle(List<ApArticle> apArticleList) {

        List<HotArticleVo> hotArticleVoList = new ArrayList<>();

        if(apArticleList != null && apArticleList.size() > 0){
            for (ApArticle apArticle : apArticleList) {
                HotArticleVo hot = new HotArticleVo();
                BeanUtils.copyProperties(apArticle,hot);
                Integer score = computeScore(apArticle);
                hot.setScore(score);
                hotArticleVoList.add(hot);
            }
        }
        return hotArticleVoList;
    }

    /**
     * 计算文章的具体分值
     * @param apArticle
     * @return
     */
    private Integer computeScore(ApArticle apArticle) {
        Integer scere = 0;
        if(apArticle.getLikes() != null){
            scere += apArticle.getLikes() * ArticleConstants.HOT_ARTICLE_LIKE_WEIGHT;
        }
        if(apArticle.getViews() != null){
            scere += apArticle.getViews();
        }
        if(apArticle.getComment() != null){
            scere += apArticle.getComment() * ArticleConstants.HOT_ARTICLE_COMMENT_WEIGHT;
        }
        if(apArticle.getCollection() != null){
            scere += apArticle.getCollection() * ArticleConstants.HOT_ARTICLE_COLLECTION_WEIGHT;
        }

        return scere;
    }
}

在ArticleApplication的引导类中添加以下注解

@EnableFeignClients(basePackages = "com.heima.apis")

现在数据库中准备点数据

package com.heima.article.service.impl;

import com.heima.article.ArticleApplication;
import com.heima.article.service.HotArticleService;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.test.context.junit4.SpringRunner;

@SpringBootTest(classes = ArticleApplication.class)
@RunWith(SpringRunner.class)
public class HotArticleServiceImplTest {

    @Autowired
    private HotArticleService hotArticleService;

    @Test
    public void computeHotArticle() {
        hotArticleService.computeHotArticle();
    }
}

3） xxl-job定时计算-步骤

①：在heima-leadnews-article中的pom文件中新增依赖

<!--xxl-job-->
<dependency>
    <groupId>com.xuxueli</groupId>
    <artifactId>xxl-job-core</artifactId>
    <version>2.3.0</version>
</dependency>

② 在xxl-job-admin中新建执行器和任务

新建执行器：leadnews-hot-article-executor

新建任务：路由策略为轮询，Cron表达式：0 0 2 * * ?

③ leadnews-article中集成xxl-job

XxlJobConfig

package com.heima.article.config;

import com.xxl.job.core.executor.impl.XxlJobSpringExecutor;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

/**
 * xxl-job config
 *
 * @author xuxueli 2017-04-28
 */
@Configuration
public class XxlJobConfig {
    private Logger logger = LoggerFactory.getLogger(XxlJobConfig.class);

    @Value("${xxl.job.admin.addresses}")
    private String adminAddresses;

    @Value("${xxl.job.executor.appname}")
    private String appname;

    @Value("${xxl.job.executor.port}")
    private int port;


    @Bean
    public XxlJobSpringExecutor xxlJobExecutor() {
        logger.info(">>>>>>>>>>> xxl-job config init.");
        XxlJobSpringExecutor xxlJobSpringExecutor = new XxlJobSpringExecutor();
        xxlJobSpringExecutor.setAdminAddresses(adminAddresses);
        xxlJobSpringExecutor.setAppname(appname);
        xxlJobSpringExecutor.setPort(port);
        return xxlJobSpringExecutor;
    }


}

在nacos配置新增配置

xxl:
  job:
    admin:
      addresses: http://192.168.200.130:8888/xxl-job-admin
    executor:
      appname: leadnews-hot-article-executor
      port: 9999

④：在article微服务中新建任务类

package com.heima.article.job;

import com.heima.article.service.HotArticleService;
import com.xxl.job.core.handler.annotation.XxlJob;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Component;

@Component
@Slf4j
public class ComputeHotArticleJob {

    @Autowired
    private HotArticleService hotArticleService;

    @XxlJob("computeHotArticleJob")
    public void handle(){
        log.info("热文章分值计算调度任务开始执行...");
        hotArticleService.computeHotArticle();
        log.info("热文章分值计算调度任务结束...");

    }
}

4、查询文章接口改造

1） 思路分析

2) 功能实现

(1) 在ApArticleService中新增方法

/**
     * 加载文章列表
     * @param dto
     * @param type  1 加载更多   2 加载最新
     * @param firstPage  true  是首页  flase 非首页
     * @return
     */
public ResponseResult load2(ArticleHomeDto dto,Short type,boolean firstPage);

实现方法

/**
     * 加载文章列表
     * @param dto
     * @param type      1 加载更多   2 加载最新
     * @param firstPage true  是首页  flase 非首页
     * @return
     */
@Override
public ResponseResult load2(ArticleHomeDto dto, Short type, boolean firstPage) {
    if(firstPage){
        String jsonStr = cacheService.get(ArticleConstants.HOT_ARTICLE_FIRST_PAGE + dto.getTag());
        if(StringUtils.isNotBlank(jsonStr)){
            List<HotArticleVo> hotArticleVoList = JSON.parseArray(jsonStr, HotArticleVo.class);
            ResponseResult responseResult = ResponseResult.okResult(hotArticleVoList);
            return responseResult;
        }
    }
    return load(type,dto);
}

(2)修改控制器

/**
     * 加载首页
     * @param dto
     * @return
     */
@PostMapping("/load")
public ResponseResult load(@RequestBody ArticleHomeDto dto){
    //        return apArticleService.load(dto, ArticleConstants.LOADTYPE_LOAD_MORE);
    return apArticleService.load2(dto, ArticleConstants.LOADTYPE_LOAD_MORE,true);
}

二、热点文章实时计算

1、 思路说明

2、 功能实现

1） 用户行为（阅读量，评论，点赞，收藏）发送消息，以阅读和点赞为例

①在heima-leadnews-behavior微服务中集成kafka生产者配置

修改nacos，新增内容

spring:
  application:
    name: leadnews-behavior
  kafka:
    bootstrap-servers: 192.168.200.130:9092
    producer:
      retries: 10
      key-serializer: org.apache.kafka.common.serialization.StringSerializer
      value-serializer: org.apache.kafka.common.serialization.StringSerializer

②修改ApLikesBehaviorServiceImpl新增发送消息

定义消息发送封装类：UpdateArticleMess

package com.heima.model.mess;

import lombok.Data;

@Data
public class UpdateArticleMess {

    /**
     * 修改文章的字段类型
      */
    private UpdateArticleType type;
    /**
     * 文章ID
     */
    private Long articleId;
    /**
     * 修改数据的增量，可为正负
     */
    private Integer add;

    public enum UpdateArticleType{
        COLLECTION,COMMENT,LIKES,VIEWS;
    }
}

topic常量类：

package com.heima.common.constants;

public class HotArticleConstants {

    public static final String HOT_ARTICLE_SCORE_TOPIC="hot.article.score.topic";
   
}

完整代码如下：

package com.heima.behavior.service.impl;

import com.alibaba.fastjson.JSON;
import com.heima.behavior.service.ApLikesBehaviorService;
import com.heima.common.constants.BehaviorConstants;
import com.heima.common.constants.HotArticleConstants;
import com.heima.common.redis.CacheService;
import com.heima.model.behavior.dtos.LikesBehaviorDto;
import com.heima.model.common.dtos.ResponseResult;
import com.heima.model.common.enums.AppHttpCodeEnum;
import com.heima.model.mess.UpdateArticleMess;
import com.heima.model.user.pojos.ApUser;
import com.heima.utils.thread.AppThreadLocalUtil;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.stereotype.Service;
import org.springframework.transaction.annotation.Transactional;


@Service
@Transactional
@Slf4j
public class ApLikesBehaviorServiceImpl implements ApLikesBehaviorService {

    @Autowired
    private CacheService cacheService;

    @Autowired
    private KafkaTemplate<String,String> kafkaTemplate;

    @Override
    public ResponseResult like(LikesBehaviorDto dto) {

        //1.检查参数
        if (dto == null || dto.getArticleId() == null || checkParam(dto)) {
            return ResponseResult.errorResult(AppHttpCodeEnum.PARAM_INVALID);
        }

        //2.是否登录
        ApUser user = AppThreadLocalUtil.getUser();
        if (user == null) {
            return ResponseResult.errorResult(AppHttpCodeEnum.NEED_LOGIN);
        }

        UpdateArticleMess mess = new UpdateArticleMess();
        mess.setArticleId(dto.getArticleId());
        mess.setType(UpdateArticleMess.UpdateArticleType.LIKES);

        //3.点赞  保存数据
        if (dto.getOperation() == 0) {
            Object obj = cacheService.hGet(BehaviorConstants.LIKE_BEHAVIOR + dto.getArticleId().toString(), user.getId().toString());
            if (obj != null) {
                return ResponseResult.errorResult(AppHttpCodeEnum.PARAM_INVALID, "已点赞");
            }
            // 保存当前key
            log.info("保存当前key:{} ,{}, {}", dto.getArticleId(), user.getId(), dto);
            cacheService.hPut(BehaviorConstants.LIKE_BEHAVIOR + dto.getArticleId().toString(), user.getId().toString(), JSON.toJSONString(dto));
            mess.setAdd(1);
        } else {
            // 删除当前key
            log.info("删除当前key:{}, {}", dto.getArticleId(), user.getId());
            cacheService.hDelete(BehaviorConstants.LIKE_BEHAVIOR + dto.getArticleId().toString(), user.getId().toString());
            mess.setAdd(-1);
        }

        //发送消息，数据聚合
        kafkaTemplate.send(HotArticleConstants.HOT_ARTICLE_SCORE_TOPIC,JSON.toJSONString(mess));


        return ResponseResult.okResult(AppHttpCodeEnum.SUCCESS);

    }

    /**
     * 检查参数
     *
     * @return
     */
    private boolean checkParam(LikesBehaviorDto dto) {
        if (dto.getType() > 2 || dto.getType() < 0 || dto.getOperation() > 1 || dto.getOperation() < 0) {
            return true;
        }
        return false;
    }
}

③修改阅读行为的类ApReadBehaviorServiceImpl发送消息

完整代码：

package com.heima.behavior.service.impl;

import com.alibaba.fastjson.JSON;
import com.heima.behavior.service.ApReadBehaviorService;
import com.heima.common.constants.BehaviorConstants;
import com.heima.common.constants.HotArticleConstants;
import com.heima.common.redis.CacheService;
import com.heima.model.behavior.dtos.ReadBehaviorDto;
import com.heima.model.common.dtos.ResponseResult;
import com.heima.model.common.enums.AppHttpCodeEnum;
import com.heima.model.mess.UpdateArticleMess;
import com.heima.model.user.pojos.ApUser;
import com.heima.utils.thread.AppThreadLocalUtil;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang3.StringUtils;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.kafka.core.KafkaTemplate;
import org.springframework.stereotype.Service;
import org.springframework.transaction.annotation.Transactional;

@Service
@Transactional
@Slf4j
public class ApReadBehaviorServiceImpl implements ApReadBehaviorService {

    @Autowired
    private CacheService cacheService;

    @Autowired
    private KafkaTemplate<String,String> kafkaTemplate;

    @Override
    public ResponseResult readBehavior(ReadBehaviorDto dto) {
        //1.检查参数
        if (dto == null || dto.getArticleId() == null) {
            return ResponseResult.errorResult(AppHttpCodeEnum.PARAM_INVALID);
        }

        //2.是否登录
        ApUser user = AppThreadLocalUtil.getUser();
        if (user == null) {
            return ResponseResult.errorResult(AppHttpCodeEnum.NEED_LOGIN);
        }
        //更新阅读次数
        String readBehaviorJson = (String) cacheService.hGet(BehaviorConstants.READ_BEHAVIOR + dto.getArticleId().toString(), user.getId().toString());
        if (StringUtils.isNotBlank(readBehaviorJson)) {
            ReadBehaviorDto readBehaviorDto = JSON.parseObject(readBehaviorJson, ReadBehaviorDto.class);
            dto.setCount((short) (readBehaviorDto.getCount() + dto.getCount()));
        }
        // 保存当前key
        log.info("保存当前key:{} {} {}", dto.getArticleId(), user.getId(), dto);
        cacheService.hPut(BehaviorConstants.READ_BEHAVIOR + dto.getArticleId().toString(), user.getId().toString(), JSON.toJSONString(dto));

        //发送消息，数据聚合
        UpdateArticleMess mess = new UpdateArticleMess();
        mess.setArticleId(dto.getArticleId());
        mess.setType(UpdateArticleMess.UpdateArticleType.VIEWS);
        mess.setAdd(1);
        kafkaTemplate.send(HotArticleConstants.HOT_ARTICLE_SCORE_TOPIC,JSON.toJSONString(mess));
        
        
        return ResponseResult.okResult(AppHttpCodeEnum.SUCCESS);

    }
}

2） 使用kafkaStream实时接收消息，聚合内容

①在leadnews-article微服务中集成kafkaStream (参考kafka-demo)

②定义实体类，用于聚合之后的分值封装

package com.heima.model.article.mess;

import lombok.Data;

@Data
public class ArticleVisitStreamMess {
    /**
     * 文章id
     */
    private Long articleId;
    /**
     * 阅读
     */
    private int view;
    /**
     * 收藏
     */
    private int collect;
    /**
     * 评论
     */
    private int comment;
    /**
     * 点赞
     */
    private int like;
}

修改常量类：增加常量

package com.heima.common.constans;

public class HotArticleConstants {

    public static final String HOT_ARTICLE_SCORE_TOPIC="hot.article.score.topic";
    public static final String HOT_ARTICLE_INCR_HANDLE_TOPIC="hot.article.incr.handle.topic";
}

③ 定义stream,接收消息并聚合

package com.heima.article.stream;

import com.alibaba.fastjson.JSON;
import com.heima.common.constants.HotArticleConstants;
import com.heima.model.mess.ArticleVisitStreamMess;
import com.heima.model.mess.UpdateArticleMess;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang3.StringUtils;
import org.apache.kafka.streams.KeyValue;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.kstream.*;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;

import java.time.Duration;

@Configuration
@Slf4j
public class HotArticleStreamHandler {

    @Bean
    public KStream<String,String> kStream(StreamsBuilder streamsBuilder){
        //接收消息
        KStream<String,String> stream = streamsBuilder.stream(HotArticleConstants.HOT_ARTICLE_SCORE_TOPIC);
        //聚合流式处理
        stream.map((key,value)->{
            UpdateArticleMess mess = JSON.parseObject(value, UpdateArticleMess.class);
            //重置消息的key:1234343434   和  value: likes:1
            return new KeyValue<>(mess.getArticleId().toString(),mess.getType().name()+":"+mess.getAdd());
        })
                //按照文章id进行聚合
                .groupBy((key,value)->key)
                //时间窗口
                .windowedBy(TimeWindows.of(Duration.ofSeconds(10)))
                /**
                 * 自行的完成聚合的计算
                 */
                .aggregate(new Initializer<String>() {
                    /**
                     * 初始方法，返回值是消息的value
                     * @return
                     */
                    @Override
                    public String apply() {
                        return "COLLECTION:0,COMMENT:0,LIKES:0,VIEWS:0";
                    }
                    /**
                     * 真正的聚合操作，返回值是消息的value
                     */
                }, new Aggregator<String, String, String>() {
                    @Override
                    public String apply(String key, String value, String aggValue) {
                        if(StringUtils.isBlank(value)){
                            return aggValue;
                        }
                        String[] aggAry = aggValue.split(",");
                        int col = 0,com=0,lik=0,vie=0;
                        for (String agg : aggAry) {
                            String[] split = agg.split(":");
                            /**
                             * 获得初始值，也是时间窗口内计算之后的值
                             */
                            switch (UpdateArticleMess.UpdateArticleType.valueOf(split[0])){
                                case COLLECTION:
                                    col = Integer.parseInt(split[1]);
                                    break;
                                case COMMENT:
                                    com = Integer.parseInt(split[1]);
                                    break;
                                case LIKES:
                                    lik = Integer.parseInt(split[1]);
                                    break;
                                case VIEWS:
                                    vie = Integer.parseInt(split[1]);
                                    break;
                            }
                        }
                        /**
                         * 累加操作
                         */
                        String[] valAry = value.split(":");
                        switch (UpdateArticleMess.UpdateArticleType.valueOf(valAry[0])){
                            case COLLECTION:
                                col += Integer.parseInt(valAry[1]);
                                break;
                            case COMMENT:
                                com += Integer.parseInt(valAry[1]);
                                break;
                            case LIKES:
                                lik += Integer.parseInt(valAry[1]);
                                break;
                            case VIEWS:
                                vie += Integer.parseInt(valAry[1]);
                                break;
                        }

                        String formatStr = String.format("COLLECTION:%d,COMMENT:%d,LIKES:%d,VIEWS:%d", col, com, lik, vie);
                        System.out.println("文章的id:"+key);
                        System.out.println("当前时间窗口内的消息处理结果："+formatStr);
                        return formatStr;
                    }
                }, Materialized.as("hot-atricle-stream-count-001"))
                .toStream()
                .map((key,value)->{
                    return new KeyValue<>(key.key().toString(),formatObj(key.key().toString(),value));
                })
                //发送消息
                .to(HotArticleConstants.HOT_ARTICLE_INCR_HANDLE_TOPIC);

        return stream;


    }

    /**
     * 格式化消息的value数据
     * @param articleId
     * @param value
     * @return
     */
    public String formatObj(String articleId,String value){
        ArticleVisitStreamMess mess = new ArticleVisitStreamMess();
        mess.setArticleId(Long.valueOf(articleId));
        //COLLECTION:0,COMMENT:0,LIKES:0,VIEWS:0
        String[] valAry = value.split(",");
        for (String val : valAry) {
            String[] split = val.split(":");
            switch (UpdateArticleMess.UpdateArticleType.valueOf(split[0])){
                case COLLECTION:
                    mess.setCollect(Integer.parseInt(split[1]));
                    break;
                case COMMENT:
                    mess.setComment(Integer.parseInt(split[1]));
                    break;
                case LIKES:
                    mess.setLike(Integer.parseInt(split[1]));
                    break;
                case VIEWS:
                    mess.setView(Integer.parseInt(split[1]));
                    break;
            }
        }
        log.info("聚合消息处理之后的结果为:{}",JSON.toJSONString(mess));
        return JSON.toJSONString(mess);

    }
}

3） 重新计算文章的分值，更新到数据库和缓存中

①在ApArticleService添加方法，用于更新数据库中的文章分值

/**
     * 更新文章的分值  同时更新缓存中的热点文章数据
     * @param mess
     */
public void updateScore(ArticleVisitStreamMess mess);

实现类方法

/**
     * 更新文章的分值  同时更新缓存中的热点文章数据
     * @param mess
     */
@Override
public void updateScore(ArticleVisitStreamMess mess) {
    //1.更新文章的阅读、点赞、收藏、评论的数量
    ApArticle apArticle = updateArticle(mess);
    //2.计算文章的分值
    Integer score = computeScore(apArticle);
    score = score * 3;

    //3.替换当前文章对应频道的热点数据
    replaceDataToRedis(apArticle, score, ArticleConstants.HOT_ARTICLE_FIRST_PAGE + apArticle.getChannelId());

    //4.替换推荐对应的热点数据
    replaceDataToRedis(apArticle, score, ArticleConstants.HOT_ARTICLE_FIRST_PAGE + ArticleConstants.DEFAULT_TAG);

}

/**
     * 替换数据并且存入到redis
     * @param apArticle
     * @param score
     * @param s
     */
private void replaceDataToRedis(ApArticle apArticle, Integer score, String s) {
    String articleListStr = cacheService.get(s);
    if (StringUtils.isNotBlank(articleListStr)) {
        List<HotArticleVo> hotArticleVoList = JSON.parseArray(articleListStr, HotArticleVo.class);

        boolean flag = true;

        //如果缓存中存在该文章，只更新分值
        for (HotArticleVo hotArticleVo : hotArticleVoList) {
            if (hotArticleVo.getId().equals(apArticle.getId())) {
                hotArticleVo.setScore(score);
                flag = false;
                break;
            }
        }

        //如果缓存中不存在，查询缓存中分值最小的一条数据，进行分值的比较，如果当前文章的分值大于缓存中的数据，就替换
        if (flag) {
            if (hotArticleVoList.size() >= 30) {
                hotArticleVoList = hotArticleVoList.stream().sorted(Comparator.comparing(HotArticleVo::getScore).reversed()).collect(Collectors.toList());
                HotArticleVo lastHot = hotArticleVoList.get(hotArticleVoList.size() - 1);
                if (lastHot.getScore() < score) {
                    hotArticleVoList.remove(lastHot);
                    HotArticleVo hot = new HotArticleVo();
                    BeanUtils.copyProperties(apArticle, hot);
                    hot.setScore(score);
                    hotArticleVoList.add(hot);
                }


            } else {
                HotArticleVo hot = new HotArticleVo();
                BeanUtils.copyProperties(apArticle, hot);
                hot.setScore(score);
                hotArticleVoList.add(hot);
            }
        }
        //缓存到redis
        hotArticleVoList = hotArticleVoList.stream().sorted(Comparator.comparing(HotArticleVo::getScore).reversed()).collect(Collectors.toList());
        cacheService.set(s, JSON.toJSONString(hotArticleVoList));

    }
}

/**
     * 更新文章行为数量
     * @param mess
     */
private ApArticle updateArticle(ArticleVisitStreamMess mess) {
    ApArticle apArticle = getById(mess.getArticleId());
    apArticle.setCollection(apArticle.getCollection()==null?0:apArticle.getCollection()+mess.getCollect());
    apArticle.setComment(apArticle.getComment()==null?0:apArticle.getComment()+mess.getComment());
    apArticle.setLikes(apArticle.getLikes()==null?0:apArticle.getLikes()+mess.getLike());
    apArticle.setViews(apArticle.getViews()==null?0:apArticle.getViews()+mess.getView());
    updateById(apArticle);
    return apArticle;

}

/**
     * 计算文章的具体分值
     * @param apArticle
     * @return
     */
private Integer computeScore(ApArticle apArticle) {
    Integer score = 0;
    if(apArticle.getLikes() != null){
        score += apArticle.getLikes() * ArticleConstants.HOT_ARTICLE_LIKE_WEIGHT;
    }
    if(apArticle.getViews() != null){
        score += apArticle.getViews();
    }
    if(apArticle.getComment() != null){
        score += apArticle.getComment() * ArticleConstants.HOT_ARTICLE_COMMENT_WEIGHT;
    }
    if(apArticle.getCollection() != null){
        score += apArticle.getCollection() * ArticleConstants.HOT_ARTICLE_COLLECTION_WEIGHT;
    }

    return score;
}

②定义监听，接收聚合之后的数据，文章的分值重新进行计算

package com.heima.article.listener;

import com.alibaba.fastjson.JSON;
import com.heima.article.service.ApArticleService;
import com.heima.common.constants.HotArticleConstants;
import com.heima.model.mess.ArticleVisitStreamMess;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.lang3.StringUtils;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.kafka.annotation.KafkaListener;
import org.springframework.stereotype.Component;

@Component
@Slf4j
public class ArticleIncrHandleListener {

    @Autowired
    private ApArticleService apArticleService;

    @KafkaListener(topics = HotArticleConstants.HOT_ARTICLE_INCR_HANDLE_TOPIC)
    public void onMessage(String mess){
        if(StringUtils.isNotBlank(mess)){
            ArticleVisitStreamMess articleVisitStreamMess = JSON.parseObject(mess, ArticleVisitStreamMess.class);
            apArticleService.updateScore(articleVisitStreamMess);

        }
    }
}