某马头条——day11+day12

实时计算和定时计算 

流式计算

 

kafkaStream

入门案例

导入依赖

<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>

创建原生的kafka staream入门案例  

/**
 * 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");
    }
}

 SpringBoot集成kafka Stream

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);
    }
}

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

@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;
    }
}

热点文章—实时计算

实现思路

实现步骤

用户行为收集

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.constants;

public class HotArticleConstants {

    public static final String HOT_ARTICLE_SCORE_TOPIC="hot.article.score.topic";
   
}

完整代码如下：  

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);

    }
}

 流式聚合处理

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;
}

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";
}

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);

    }
}

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);

        }
    }
}

下面是day12

 持续集成

软件开发模式

 

Jenkins

 艹，好麻烦，不做了。以后用到再去看吧。不搞了。 还特么要用百度网盘下一个10g的镜像....