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.StringSerializer②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....
