一般流式计算会与批量计算相比较。在流式计算模型中，输入是持续的，可以认为在时间上是无界的，也就意味着，永远拿不到全量数据去做计算。同时，计算结果是持续输出的，也即计算结果在时间上也是无界的。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。

流式计算就相当于上图的右侧扶梯，是可以源源不断的产生数据，源源不断的接收数据，没有边界。

2. 应用场景

日志分析

网站的用户访问日志进行实时的分析，计算访问量，用户画像，留存率等等，实时的进行数据分析，帮助企业进行决策
大屏看板统计

可以实时的查看网站注册数量，订单数量，购买数量，金额等。
公交实时数据

可以随时更新公交车方位，计算多久到达站牌等
实时文章分值计算

头条类文章的分值计算，通过用户的行为实时文章的分值，分值越高就越被推荐。

3.技术方案选型

Hadoop

Apche Storm

Storm 是一个分布式实时大数据处理系统，可以帮助我们方便地处理海量数据，具有高可靠、高容错、高扩展的特点。是流式框架，有很高的数据吞吐能力。
Kafka Stream

可以轻松地将其嵌入任何Java应用程序中，并与用户为其流应用程序所拥有的任何现有打包，部署和操作工具集成

三. Kafka Stream

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
充分利用Kafka分区机制实现水平扩展和顺序性保证
通过可容错的state store实现高效的状态操作（如windowed join和aggregation）
支持正好一次处理语义
提供记录级的处理能力，从而实现毫秒级的低延迟
支持基于事件时间的窗口操作，并且可处理晚到的数据（late arrival of records）
同时提供底层的处理原语Processor（类似于Storm的spout和bolt），以及高层抽象的DSL（类似于Spark的map/group/reduce）

2.Kafka Streams的关键概念

源处理器（Source Processor）：源处理器是一个没有任何上游处理器的特殊类型的流处理器。它从一个或多个kafka主题生成输入流。通过消费这些主题的消息并将它们转发到下游处理器。
Sink处理器：sink处理器是一个没有下游流处理器的特殊类型的流处理器。它接收上游流处理器的消息发送到一个指定的Kafka主题。

3. KStream

1）数据结构类似于map,如下图，key-value键值对

KStream数据流（data stream），即是一段顺序的，可以无限长，不断更新的数据集。数据流中比较常记录的是事件，这些事件可以是一次鼠标点击（click），一次交易，或是传感器记录的位置数据。

KStream负责抽象的，就是数据流。与Kafka自身topic中的数据一样，类似日志，每一次操作都是向其中插入（insert）新数据。

为了说明这一点，让我们想象一下以下两个数据记录正在发送到流中：

（“ alice”，1）->（“” alice“，3）

如果您的流处理应用是要总结每个用户的价值，它将返回4了alice。为什么？因为第二条数据记录将不被视为先前记录的更新。（insert）新数据

4. Kafka Stream入门案例编写

（1）需求分析，求单词个数（word count）

（2）引入依赖

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>

(3)创建原生的kafka staream入门案例


package com.heima.kafka.sample;
 
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.KeyValue;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.TimeWindows;
import org.apache.kafka.streams.kstream.ValueMapper;
 
import java.time.Duration;
import java.util.Arrays;
import java.util.Properties;
 
/**
 * 流式处理
 */
public class KafkaStreamQuickStart {
 
    public static void main(String[] args) {
 
        //kafka的配置信心
        Properties prop = new Properties();
        prop.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.200.130:9092");
        prop.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        prop.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        prop.put(StreamsConfig.APPLICATION_ID_CONFIG,"streams-quickstart");
 
        //stream 构建器
        StreamsBuilder streamsBuilder = new StreamsBuilder();
 
        //流式计算
        streamProcessor(streamsBuilder);
 
 
        //创建kafkaStream对象
        KafkaStreams kafkaStreams = new KafkaStreams(streamsBuilder.build(),prop);
        //开启流式计算
        kafkaStreams.start();
    }
 
    /**
     * 流式计算
     * 消息的内容：hello kafka  hello itcast
     * @param streamsBuilder
     */
    private static void streamProcessor(StreamsBuilder streamsBuilder) {
        //创建kstream对象，同时指定从那个topic中接收消息
        KStream<String, String> stream = streamsBuilder.stream("itcast-topic-input");
        /**
         * 处理消息的value
         */
        stream.flatMapValues(new ValueMapper<String, Iterable<String>>() {
            @Override
            public Iterable<String> apply(String value) {
                return Arrays.asList(value.split(" "));
            }
        })
                //按照value进行聚合处理
                .groupBy((key,value)->value)
                //时间窗口
                .windowedBy(TimeWindows.of(Duration.ofSeconds(10)))
                //统计单词的个数
                .count()
                //转换为kStream
                .toStream()
                .map((key,value)->{
                    System.out.println("key:"+key+",vlaue:"+value);
                    return new KeyValue<>(key.key().toString(),value.toString());
                })
                //发送消息
                .to("itcast-topic-out");
    }
}

(4)测试准备

使用生产者在topic为：itcast_topic_input中发送多条消息
使用消费者接收topic为：itcast_topic_out

结果：

通过流式计算，会把生产者的多条消息汇总成一条发送到消费者中输出

5.SpringBoot集成Kafka Stream


package com.heima.kafka.config;
 
import lombok.Getter;
import lombok.Setter;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.Topology;
import org.springframework.boot.context.properties.ConfigurationProperties;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.kafka.annotation.EnableKafkaStreams;
import org.springframework.kafka.annotation.KafkaStreamsDefaultConfiguration;
import org.springframework.kafka.config.KafkaStreamsConfiguration;
 
import java.util.HashMap;
import java.util.Map;
 
/**
 * 通过重新注册KafkaStreamsConfiguration对象，设置自定配置参数
 */
 
@Setter
@Getter
@Configuration
@EnableKafkaStreams
@ConfigurationProperties(prefix="kafka")
public class KafkaStreamConfig {
    private static final int MAX_MESSAGE_SIZE = 16* 1024 * 1024;
    private String hosts;
    private String group;
    @Bean(name = KafkaStreamsDefaultConfiguration.DEFAULT_STREAMS_CONFIG_BEAN_NAME)
    public KafkaStreamsConfiguration defaultKafkaStreamsConfig() {
        Map<String, Object> props = new HashMap<>();
        props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, hosts);
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, this.getGroup()+"_stream_aid");
        props.put(StreamsConfig.CLIENT_ID_CONFIG, this.getGroup()+"_stream_cid");
        props.put(StreamsConfig.RETRIES_CONFIG, 10);
        props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        return new KafkaStreamsConfiguration(props);
    }
}

修改application.yml文件，在最下方添加自定义配置


kafka:
  hosts: 192.168.200.130:9092
  group: ${spring.application.name}

(2)新增配置类，创建KStream对象，进行聚合


package com.heima.kafka.stream;
 
import lombok.extern.slf4j.Slf4j;
import org.apache.kafka.streams.KeyValue;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.TimeWindows;
import org.apache.kafka.streams.kstream.ValueMapper;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
 
import java.time.Duration;
import java.util.Arrays;
 
@Configuration
@Slf4j
public class KafkaStreamHelloListener {
 
    @Bean
    public KStream<String,String> kStream(StreamsBuilder streamsBuilder){
        //创建kstream对象，同时指定从那个topic中接收消息
        KStream<String, String> stream = streamsBuilder.stream("itcast-topic-input");
        stream.flatMapValues(new ValueMapper<String, Iterable<String>>() {
            @Override
            public Iterable<String> apply(String value) {
                return Arrays.asList(value.split(" "));
            }
        })
                //根据value进行聚合分组
                .groupBy((key,value)->value)
                //聚合计算时间间隔
                .windowedBy(TimeWindows.of(Duration.ofSeconds(10)))
                //求单词的个数
                .count()
                .toStream()
                //处理后的结果转换为string字符串
                .map((key,value)->{
                    System.out.println("key:"+key+",value:"+value);
                    return new KeyValue<>(key.key().toString(),value.toString());
                })
                //发送消息
                .to("itcast-topic-out");
        return stream;
    }
}

启动测试类，向itcast-topic-input中发送十次消息


@SpringBootTest
public class KafKaStreamTest {
 
    @Test
    public void testSendMsg(){
 
        Properties properties = new Properties();
 
        properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.81.128:9092");
        properties.put(ProducerConfig.RETRIES_CONFIG,10);
        properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringSerializer");
        properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringSerializer");
 
        //生产者对象
        KafkaProducer<String,String> kafkaProducer = new KafkaProducer<String, String>(properties);
 
        ProducerRecord<String,String> record = new ProducerRecord<>("itcast-topic-input","hello kafka stream");
 
        for (int i = 0; i < 10; i++) {
            //发送消息
            kafkaProducer.send(record);
        }
        
    }
 
 
}

配置监听器


@Component
public class KafkaListener {
 
    /**
     * 消费者监听out主题
     * @param msg
     */
    @org.springframework.kafka.annotation.KafkaListener(topics = "itcast-topic-out")
    public void listen(String msg){
 
        System.out.println(msg);
 
 
    }
 
}

测试:


 
2023-12-27 15:37:09.537  INFO 30248 --- [-StreamThread-1] o.a.k.c.c.internals.ConsumerCoordinator  : [Consumer clientId=kafka-demo-test_stream_cid-StreamThread-1-consumer, groupId=kafka-demo-test_stream_aid] Revoke previously assigned partitions kafka-demo-test_stream_aid-KSTREAM-AGGREGATE-STATE-STORE-0000000003-repartition-0
2023-12-27 15:37:09.537  INFO 30248 --- [-StreamThread-1] o.a.k.s.p.internals.StreamThread         : stream-thread [kafka-demo-test_stream_cid-StreamThread-1] State transition from RUNNING to PARTITIONS_REVOKED
2023-12-27 15:37:09.537  INFO 30248 --- [-StreamThread-1] org.apache.kafka.streams.KafkaStreams    : stream-client [kafka-demo-test_stream_cid] State transition from RUNNING to REBALANCING
key:[hello@1703662620000/1703662630000],value:10
key:[kafka@1703662620000/1703662630000],value:10
key:[stream@1703662620000/1703662630000],value:10
10
10
10

可以看到成功的将单词个数统计了出来

四.app端热点文章计算

功能实现

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.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";
}

用户点赞行为修改


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


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);
 
    }
}

3,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;
}

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);
 
    }
}

4.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);
 
        }
    }
}

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