【黑马头条】-day11热点文章实时计算-kafka-kafkaStream-Redis

文章目录

今日内容
1 实时流式计算
- 1.1 应用场景
- 1.2 技术方案选型
2 Kafka Stream
- 2.1 概述
- 2.2 KafkaStream
- 2.3 入门demo
- - 2.3.1 需求分析
  - 2.3.2 实现
  - - 2.3.2.1 添加依赖
    - 2.3.2.2 创建快速启动，生成kafka流
    - 2.3.2.3 修改生产者
    - 2.3.2.4 修改消费者
    - 2.3.2.5 测试
- 2.4 SpringBoot集合KafkaStream
- - 2.4.1 创建自定配置参数类
  - 2.4.2 修改配置文件
  - 2.4.3 创建配置类创建KStream对象
  - 2.4.4 测试
3 热点文章实时计算
- 3.1 思路说明
- 3.2 实现步骤
- 3.3 具体实现
- - 3.3.1 为行为微服务添加kafka配置
  - 3.3.2 行为微服务中发送消息的消息体实体类
  - 3.3.3 定义kafka流接收的topic
  - 3.3.4 修改用户行为后的逻辑（相当于生产者）
  - 3.3.5 Stream聚合
  - - 3.3.5.1 创建自定配置参数类
    - 3.3.5.2 添加kafkaStream的配置
    - 3.3.5.3 定义kafka流转发的topic
    - 3.3.5.4 文章微服务中的发送消息的消息体实体类
    - 3.3.5.5 创建配置类创建KStream对象
  - 3.3.6 创建消费者，用于监听聚合后的消息
  - 3.3.7 在文章微服务中更新当前分值
  - 3.3.8 Service添加updateScore方法
- 3.4 测试

今日内容

在这里插入图片描述

1 实时流式计算

在这里插入图片描述

1.1 应用场景

在这里插入图片描述

1.2 技术方案选型

在这里插入图片描述

2 Kafka Stream

2.1 概述

在这里插入图片描述

2.2 KafkaStream

在这里插入图片描述

2.3 入门demo

2.3.1 需求分析

在这里插入图片描述

2.3.2 实现

在这里插入图片描述

还是在kafka-demo的模块里实现

2.3.2.1 添加依赖

<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>

2.3.2.2 创建快速启动，生成kafka流

public class KafkaStreamQuickStart {public static void main(String[] args) {//kafka的配置信息Properties prop = new Properties();prop.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.204.129:9092");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>>() {@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+",vlaue:"+value);return new KeyValue<>(key.key().toString(),value.toString());})//发送消息.to("itcast-topic-out");}
}

2.3.2.3 修改生产者

修改com.heima.kafka.sample.ProducerQuickStart的方法

public class ProducerQuickStart {public static void main(String[] args) throws ExecutionException, InterruptedException {//1.kafka的配置信息Properties properties = new Properties();//kafka的连接地址properties.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.204.129:9092");//发送失败，失败的重试次数properties.put(ProducerConfig.RETRIES_CONFIG,5);//消息key的序列化器properties.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringSerializer");//消息value的序列化器properties.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,"org.apache.kafka.common.serialization.StringSerializer");//2.生产者对象KafkaProducer<String,String> producer = new KafkaProducer<String, String>(properties);/*** 第一个参数：topic 第二个参数：key 第三个参数：value*///封装发送的消息//ProducerRecord<String,String> record = new ProducerRecord<String, String>("topic-first","key-001","hello kafka");for(int i=0;i<5;i++){ProducerRecord<String,String> record = new ProducerRecord<String, String>("itcast-topic-input","hello kafka"+" "+i);//3.发送消息producer.send(record);}producer.close();

2.3.2.4 修改消费者

修改com.heima.kafka.sample.ConsumerQuickStart的方法

public class ConsumerQuickStart {public static void main(String[] args) {//1.添加kafka的配置信息Properties properties = new Properties();//kafka的连接地址properties.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.204.129:9092");//消费者组properties.put(ConsumerConfig.GROUP_ID_CONFIG, "group1");//消息的反序列化器properties.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");properties.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringDeserializer");//手动提交偏移量//properties.put(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG, "false");//2.消费者对象KafkaConsumer<String, String> consumer = new KafkaConsumer<String, String>(properties);//3.订阅主题consumer.subscribe(Collections.singletonList("itcast-topic-out"));//当前线程一直处于监听状态while (true) {//4.获取消息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());}}}}

2.3.2.5 测试

先启动消费者，再启动kafkaStream，再启动生产者

发送消息为"hello kafka"+" "+i，一共五次

在这里插入图片描述

符合我们发的

2.4 SpringBoot集合KafkaStream

在这里插入图片描述

2.4.1 创建自定配置参数类

在kafka-demo中创建com.heima.kafka.config.KafkaStreamConfig类

/*** 通过重新注册KafkaStreamsConfiguration对象，设置自定配置参数*/
@Getter
@Setter
@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);//连接信息props.put(StreamsConfig.APPLICATION_ID_CONFIG, this.getGroup()+"_stream_aid");//组props.put(StreamsConfig.CLIENT_ID_CONFIG, this.getGroup()+"_stream_cid");//应用名称props.put(StreamsConfig.RETRIES_CONFIG, 10);//重试次数props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());//key序列化器props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());return new KafkaStreamsConfiguration(props);}
}

2.4.2 修改配置文件

修改heima-leadnews-test/kafka-demo/src/main/resources/application.yaml

将其放到最底下

kafka:hosts: 192.168.204.129:9092group: ${spring.application.name}

server:port: 9991
spring:application:name: kafka-demokafka:bootstrap-servers: 192.168.204.129:9092producer:retries: 10key-serializer: org.apache.kafka.common.serialization.StringSerializervalue-serializer: org.apache.kafka.common.serialization.StringSerializerconsumer:group-id: ${spring.application.name}-testkey-deserializer: org.apache.kafka.common.serialization.StringDeserializervalue-deserializer: org.apache.kafka.common.serialization.StringDeserializer
kafka:hosts: 192.168.204.129:9092group: ${spring.application.name}

2.4.3 创建配置类创建KStream对象

创建com.heima.kafka.stream.KafkaStreamHelloListener

等于KStream放入spring容器中进行直接监听

@Configuration
@Slf4j
public class KafkaStreamHelloListener {@Beanpublic 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>>() {@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;}
}

2.4.4 测试

启动kafka启动类，启动消费者和生产者

发送消息是"hello kafka"，一共五次

在这里插入图片描述

3 热点文章实时计算

3.1 思路说明

在这里插入图片描述

3.2 实现步骤

在这里插入图片描述

3.3 具体实现

3.3.1 为行为微服务添加kafka配置

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

spring:application:name: leadnews-behaviorkafka:bootstrap-servers: 192.168.204.129:9092producer:retries: 10key-serializer: org.apache.kafka.common.serialization.StringSerializervalue-serializer: org.apache.kafka.common.serialization.StringSerializer

3.3.2 行为微服务中发送消息的消息体实体类

定义消息发送封装类：UpdateArticleMess

在heima-leadnews-model中创建com.heima.model.message.UpdateArticleMess实体类

package com.heima.model.message;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;}
}

3.3.3 定义kafka流接收的topic

在heima-leadnews-common中创建com.heima.common.constants.HotArticleConstants常量类

package com.heima.common.constants;
public class HotArticleConstants {public static final String HOT_ARTICLE_SCORE_TOPIC="hot.article.score.topic";
}

3.3.4 修改用户行为后的逻辑（相当于生产者）

点赞之后就要发送消息了，所以去修改用户点赞的实现类com.heima.behavior.service.impl.ApLikesBehaviorServiceImpl

@Service
@Transactional
@Slf4j
public class ApLikesBehaviorServiceImpl implements ApLikesBehaviorService {@Autowiredprivate CacheService cacheService;@Autowiredprivate KafkaTemplate<String, String> kafkaTemplate;@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 user = AppThreadLocalUtil.getUser();if (user == null) {return ResponseResult.errorResult(AppHttpCodeEnum.NEED_LOGIN);}//组装发送给kafka的消息类UpdateArticleMess message =new UpdateArticleMess();message.setArticleId(dto.getArticleId());message.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keylog.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));//添加行为的正负message.setAdd(1);} else {// 删除当前keylog.info("删除当前key:{}, {}", dto.getArticleId(), user.getId());cacheService.hDelete(BehaviorConstants.LIKE_BEHAVIOR + dto.getArticleId().toString(), user.getId().toString());//添加行为的正负message.setAdd(-1);}//4.给kafka发送消息kafkaTemplate.send(HotArticleConstants.HOT_ARTICLE_SCORE_TOPIC, JSON.toJSONString(message));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;}
}

点赞有，阅读都有，一样需要改

@Service
@Transactional
@Slf4j
public class ApReadBehaviorServiceImpl implements ApReadBehaviorService {@Autowiredprivate CacheService cacheService;@Autowiredprivate KafkaTemplate<String, String> kafkaTemplate;@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);}//组装发送给kafka的消息类UpdateArticleMess message =new UpdateArticleMess();message.setArticleId(dto.getArticleId());message.setType(UpdateArticleMess.UpdateArticleType.VIEWS);//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));//添加行为的正负message.setAdd(1);//发送消息kafkaTemplate.send(HotArticleConstants.HOT_ARTICLE_SCORE_TOPIC, JSON.toJSONString(message));return ResponseResult.okResult(AppHttpCodeEnum.SUCCESS);}
}

3.3.5 Stream聚合

因为用户行为最后都体现在文章上面，所以kafkaStream的数据聚合应该在文章微服务中。

3.3.5.1 创建自定配置参数类

在heima-leadnews-article中创建com.heima.article.config.KafkaStreamConfig配置类

@Getter
@Setter
@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);//连接信息props.put(StreamsConfig.APPLICATION_ID_CONFIG, this.getGroup()+"_stream_aid");//组props.put(StreamsConfig.CLIENT_ID_CONFIG, this.getGroup()+"_stream_cid");//应用名称props.put(StreamsConfig.RETRIES_CONFIG, 10);//重试次数props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());//key序列化器props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());return new KafkaStreamsConfiguration(props);}
}

3.3.5.2 添加kafkaStream的配置

在nacos中为文章微服务添加kafkaStream的配置

kafka:hosts: 192.168.204.129:9092group: ${spring.application.name}

3.3.5.3 定义kafka流转发的topic

在com.heima.common.constants.HotArticleConstants中添加HOT_ARTICLE_INCR_HANDLE_TOPIC

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";
}

3.3.5.4 文章微服务中的发送消息的消息体实体类

因为聚合后的数据是COLLECTION:0,COMMENT:0,LIKES:0,VIEWS:0，不包含文章id，所以需要一个类把他们封装起来

在heima-leadnews-model中创建com.heima.model.message.ArticleVisitStreamMess实体类

@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;
}

3.3.5.5 创建配置类创建KStream对象

定义stream,接收消息并聚合，创建com.heima.article.stream.HotArticleStreamHandler类

@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 初始值，也就是aggValue* @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>() {/*** key:文章id value:消息的value  aggValue:初始值* @param key key:1234343434* @param value value: likes:1* @param aggValue 初始值 COLLECTION:0,COMMENT:0,LIKES:0,VIEWS:0* @return*/@Overridepublic 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()/*** 格式化消息的key和value*/.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);}
}

3.3.6 创建消费者，用于监听聚合后的消息

创建com.heima.article.listener.ArticleIncrHandleListener用于监听和处理聚合后的消息

@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);}}
}

3.3.7 在文章微服务中更新当前分值

在这里插入图片描述

3.3.8 Service添加updateScore方法

在文章微服务的service中完善功能

接口

void updateScore(ArticleVisitStreamMess articleVisitStreamMess);

实现：

/*** 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()* ArticleConstants.HOT_ARTICLE_VIEW_WEIGHT;}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;
}