黑马头条Day11- 实时计算热点文章、KafkaStream

一、今日内容

1. 定时计算与实时计算

2. 今日内容

KafkaStream

• 什么是流式计算
• KafkaStream概述
• KafkaStream入门案例
• SpringBoot集成KafkaStream

实时计算

• 用户行为发送消息
• KafkaStream聚合处理消息
• 更新文章行为数量
• 替换热点文章数据

二、实时流式计算

1. 概念

一般流式计算会与批量计算相比较。在流式计算模型这种，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替代全量计算。

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：

对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，实时进行数据分析，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

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计算文章的分值，分值越高就越被推荐。

3. 技术方案选项

Hadoop

Apache 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1.0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
• 提高记录级的处理能力，从而实现毫秒级的低延迟
• 支持基于事件时间的窗口操作，并且可以处理晚到的数据（late arrival of records）
• 同时提供底层的处理原理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 Stream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。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

（2）KStream

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自身的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，它将返回了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

（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文件中引入依赖

<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 stream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的配置中心Properties properties = new Properties();properties.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.200.130:9092");properties.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());properties.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());properties.put(StreamsConfig.APPLICATION_ID_CONFIG, "streams-quickstart");// stream构建器StreamsBuilder streamsBuilder = new StreamsBuilder();// 流式处理StreamProcessor(streamsBuilder);// 创建KafkaStream对象KafkaStreams kafkaStreams = new KafkaStreams(streamsBuilder.build(), properties);// 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) {// 1. 创建kstream对象，同时指定从哪个topic中接收消息KStream<String, String> stream = streamsBuilder.stream("itcast-topic-input");// 2. 处理消息的valuestream.flatMapValues(new ValueMapper<String, Iterable<String>>() {@Overridepublic 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 + ", value: " + 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

package com.heima.kafka.sample;import org.apache.kafka.clients.producer.*;import java.util.Properties;
import java.util.concurrent.ExecutionException;/*** 生产者*/
public class ProducerQuickStart {public static void main(String[] args) throws ExecutionException, InterruptedException {//1.kafka链接配置信息Properties prop = new Properties();//kafka链接地址prop.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.200.130:9092");//key和value的序列化prop.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringSerializer");prop.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringSerializer");//ack配置  消息确认机制prop.put(ProducerConfig.ACKS_CONFIG,"all");//重试次数prop.put(ProducerConfig.RETRIES_CONFIG,10);//数据压缩prop.put(ProducerConfig.COMPRESSION_TYPE_CONFIG,"lz4");//2.创建kafka生产者对象KafkaProducer<String,String> producer = new KafkaProducer<String,String>(prop);//3.发送消息/*** 第一个参数 ：topic* 第二个参数：消息的key* 第三个参数：消息的value*/for (int i = 0; i < 5; i++) {ProducerRecord<String,String> kvProducerRecord = new ProducerRecord<String,String>("itcast-topic-input","hello kafka");producer.send(kvProducerRecord);}//同步发送消息/*RecordMetadata recordMetadata = producer.send(kvProducerRecord).get();System.out.println(recordMetadata.offset());*///异步消息发送/* producer.send(kvProducerRecord, new Callback() {@Overridepublic void onCompletion(RecordMetadata recordMetadata, Exception e) {if(e != null){System.out.println("记录异常信息到日志表中");}System.out.println(recordMetadata.offset());}});*///4.关闭消息通道  必须要关闭，否则消息发送不成功producer.close();}}

使用消费者接收topic为：itcast_topic_input

package com.heima.kafka.sample;import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;import java.time.Duration;
import java.util.Collections;
import java.util.Properties;/*** 消费者*/
public class ConsumerQuickStart {public static void main(String[] args) {//1.kafka的配置信息Properties prop = new Properties();//链接地址prop.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.200.130:9092");//key和value的反序列化器prop.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");prop.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");//设置消费者组prop.put(ConsumerConfig.GROUP_ID_CONFIG, "group2");//手动提交偏移量prop.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, false);//2.创建消费者对象KafkaConsumer<String, String> consumer = new KafkaConsumer<String, String>(prop);//3.订阅主题consumer.subscribe(Collections.singletonList("itcast-topic-out"));//4.拉取消息//同步提交和异步提交偏移量try {while (true) {ConsumerRecords<String, String> consumerRecords = consumer.poll(Duration.ofMillis(1000));for (ConsumerRecord<String, String> consumerRecord : consumerRecords) {System.out.println(consumerRecord.key());System.out.println(consumerRecord.value());/* System.out.println(consumerRecord.offset());System.out.println(consumerRecord.partition());*/}//异步提交偏移量consumer.commitAsync();}}catch (Exception e){e.printStackTrace();System.out.println("记录错误的信息："+e);}finally {//同步consumer.commitSync();}/*while (true){ConsumerRecords<String, String> consumerRecords = consumer.poll(Duration.ofMillis(1000));for (ConsumerRecord<String, String> consumerRecord : consumerRecords) {System.out.println(consumerRecord.key());System.out.println(consumerRecord.value());System.out.println(consumerRecord.offset());System.out.println(consumerRecord.partition());*//* try {//同步提交偏移量consumer.commitSync();}catch (CommitFailedException e){System.out.println("记录提交失败的异常："+e);}*//*}//异步的方式提交偏移量*//*consumer.commitAsync(new OffsetCommitCallback() {@Overridepublic void onComplete(Map<TopicPartition, OffsetAndMetadata> map, Exception e) {if(e != null){System.out.println("记录错误的提交偏移量："+map+",异常信息为："+e);}}});*//*}*/}}

先启动Consumer，再启动KafkaStream，最后启动Producer发送消息

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

（1）自定义配置参数

package com.heima.kafka.config;import lombok.Getter;
import lombok.Setter;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.StreamsConfig;
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: 192.168.200.130:9092group: ${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 {@Beanpublic KStream<String, String> kStream(StreamsBuilder streamsBuilder) {// 创建KStream对象，同时指定从哪个topic中接收消息KStream<String, String> stream = streamsBuilder.stream("itcast-topic-input");stream.flatMapValues(new ValueMapper<String, Iterable<String>>() {@Overridepublic 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;}
}

（3）测试：先启动ConsumerQuickStart -> KafkaDemoApplication -> ProducerQuickStart

启动微服务，正常发送消息，可以正常接收到消息

四、App端热点文章计算

1. 思路说明

2. 功能实现

2.1 用户行为（阅读、评论、点赞、收藏）发送消息，以阅读和点赞为例

（在第9天的实战中，我已经把这部分做了）

步骤①：在heima-leadnews-behavior微服务中集成Kafka生产者配置

在nacos修改leadnews-behavior.yml

spring:kafka:bootstrap-servers: 192.168.200.130:9092producer:retries: 10key-serializer: org.apache.kafka.common.serialization.StringSerializervalue-serializer: org.apache.kafka.common.serialization.StringSerializerautoconfigure:exclude: org.springframework.boot.autoconfigure.jdbc.DataSourceAutoConfigurationredis:host: 192.168.200.130password: leadnewsport: 6379

步骤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：HotArticleConstants

package com.heima.common.constants;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";
}

ApLikesBehaviorServiceImpl:

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 {@Autowiredprivate CacheService cacheService;@Autowiredprivate KafkaTemplate<String, String> kafkaTemplate;/*** 点赞或取消点赞* @param dto 0:点赞 1:取消点赞* @return*/@Overridepublic 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 apUser = AppThreadLocalUtil.getUser();if(apUser == 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(), apUser.getId().toString());if(obj != null) {return ResponseResult.errorResult(AppHttpCodeEnum.PARAM_INVALID, "已点赞");}// 保存当前keylog.info("保存当前key：{}, {}, {}", dto.getArticleId(), apUser.getId(), dto);cacheService.hPut(BehaviorConstants.LIKE_BEHAVIOR + dto.getArticleId().toString(), apUser.getId().toString(), JSON.toJSONString(dto));mess.setAdd(1);} else {// 取消点赞，删除当前keylog.info("删除当前key:{}, {}", dto.getArticleId(), apUser.getId());cacheService.hDelete(BehaviorConstants.LIKE_BEHAVIOR + dto.getArticleId().toString(), apUser.getId().toString());mess.setAdd(-1);}// 4. 发送消息，数据聚合kafkaTemplate.send(HotArticleConstants.HOT_ARTICLE_SCORE_TOPIC,JSON.toJSONString(mess));// 5. 结果返回return ResponseResult.okResult(AppHttpCodeEnum.SUCCESS);}/*** 检查参数* @param dto* @return*/private boolean checkParam(LikesBehaviorDto dto) {// 参数有误if(dto.getType() > 2 || dto.getType() < 0 || dto.getOperation() > 1 || dto.getOperation() < 0) {return true;}return false;}
}

步骤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 {@Autowiredprivate CacheService cacheService;@Autowiredprivate KafkaTemplate<String, String> kafkaTemplate;/*** 用户行为 - 阅读* @param dto* @return*/@Overridepublic 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);}// 3. 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()));}// 4. 保存当前keylog.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));// 5. 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));// 6. 结果返回return ResponseResult.okResult(AppHttpCodeEnum.SUCCESS);}
}

2.2 使用KafkaStream实时接收消息，聚合内容

步骤①：在heima-leadnews-article微服务中集成kafkaStream

在pom.xml(heima-leadnews-article)添加Kafka Stream的依赖

<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>

KafkaStreamConfig:

package com.heima.article.config;import lombok.Getter;
import lombok.Setter;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.StreamsConfig;
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);}
}

在nacos配置正常的leadnews-article添加如下

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

步骤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;
}

步骤③：定义stream，接收消息聚合

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 {@Beanpublic 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:1return 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*/@Overridepublic 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>() {/**** @param key* @param value  likes:1* @param aggValue   COLLECTION:0,COMMENT:0,LIKES:0,VIEWS:0* @return*/@Overridepublic String apply(String key, String value, String aggValue) {System.out.println(value);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   likes:1*/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:0String[] 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);}
}

2.3 重新计算文章的分值，更新到数据库和缓存中

步骤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redishotArticleVoList = 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;
}

步骤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 {@Autowiredprivate 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);}}
}