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>>() {@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");}
}

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

@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;}
}

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-behaviorkafka:bootstrap-servers: 192.168.200.130:9092producer:retries: 10key-serializer: org.apache.kafka.common.serialization.StringSerializervalue-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 {@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);}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));mess.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());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 {@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);}//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));//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 {@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>() {@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().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);}
}

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;
}

 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);}}
}

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....