kafka实践-热点数据展示

1 实时流式计算

1.1 概念

流式计算一般对实时性要求较高，同时一般是先定义目标计算，然后数据到来之后将计算逻辑应用于数据。同时为了提高计算效率，往往尽可能采用增量计算代替全量计算。也就是将数据先聚集在集中全量处理。

2.2 应用场景

日志分析
大屏看板统计
公交实时数据
实时文章分值计算

2 Kafka Stream

2.1 概述

Kafka Stream是Apache Kafka从0.10版本引入的一个新Feature。它是提供了对存储于Kafka内的数据进行流式处理和分析的功能。

Kafka Stream的特点如下：

Kafka Stream提供了一个非常简单而轻量的Library，它可以非常方便地嵌入任意Java应用中，也可以任意方式打包和部署
除了Kafka外，无任何外部依赖

3 实践-app端热点文章计算

3.1 引入依赖

1）在之前的kafka-demo工程的pom文件中引入

<dependency>
    <groupId>org.apache.kafka</groupId>
    <artifactId>kafka-streams</artifactId>
    <exclusions>
        <exclusion>
            <artifactId>connect-json</artifactId>
            <groupId>org.apache.kafka</groupId>
        </exclusion>
        <exclusion>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
        </exclusion>
    </exclusions>
</dependency>

2）发送者的微服务的nacos配置中加入kafka配置

```yaml
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

主要有发送的key和value的序列化器以及服务地址的重试次数

3.2 点赞行为发送给kafka流处理

在这里插入图片描述
1）调用kafkaTemplate.send发送数据

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


//观看的消息，设置类型为viesw，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));

2）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;
    }
}

这里的enum是迭代的意思，与后面聚合操作条件判断有关系

3.3 使用kafkaStream实时接收消息，聚合内容

1）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;
}

2）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";
}

两个常量的意思是需要修改redis缓存中当前项和默认项的热点缓存数据
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);

    }
}

主要通过new Aggregator<String, String, String>(){两个apply方法}定义转换的规则。这里是将相同操作的值进行相加操作，输出的JSON通过通过ArticleVisitStreamMess(包含文章id字段)添加文章id到json字符串中，进而交给消费者

3.4 消费端监听消息，完成对缓存的修改

@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);

        }
    }
}

2）updateScore更新分值，跟新mysql和redis
更新mysql

 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;

更新redis中的当前页热点数据和默认页数据

```java
/**
     * 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));

    }
}