热点文章定时计算

需求：为每个频道缓存热度较高的30条文章优先展示

断文章热度较高的标准是什么？文章：阅读，点赞，评论，收藏

                                             

 

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.time.DateTimeException;
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;

    @Autowired
    private IWemediaClient wemediaClient;
    
    @Autowired
    private CacheService cacheService;
    

    /**
     * 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(5).toDate();
        List<ApArticle> apArticleList = apArticleMapper.findArticleListByLast5days(dateParam);
        //2、计算文章的分值
        List<HotArticleVo> hotArticleVoList = ocomputeHotArticle(apArticleList);
        //3、为每个频道缓存30条分值较高的文章
        cacheTagToRedis(hotArticleVoList);
    }



    /**
     * 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);
            //检索每个频道文章
            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条较高的分值存入Reid是， key：频道id， value:30条分值较高的文章
                    sortArticleCache(hotArticleVos, ArticleConstants.HOT_ARTICLE_FIRST_PAGE + wmChannel.getId());
                }
            }
        }
        //设置推荐数据
        sortArticleCache(hotArticleVoList, ArticleConstants.HOT_ARTICLE_FIRST_PAGE + ArticleConstants.DEFAULT_TAG);

    }

    /**
     * 给文章进行排序，取30条较高的分值存入Reid是， key：频道id， value:30条分值较高的文章
     * @param hotArticleVos
     * @param key
     */
    private void sortArticleCache(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> ocomputeHotArticle(List<ApArticle> apArticleList) {

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

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

    /**
     * 计算某个文章具体分数
     * @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;
    }
}