简介：

        kafkaStream：提供了对存储在kafka中的数据进行流式处理和分析的功能

特点：

        KafkasSream提供了一个非常简单轻量的Library，它可以非常方便的嵌入到java程序中，也可以任何方式打包部署

入门案例：

  1、新建工程kafka-demo

           引入kafkaStream依赖

    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <!-- kafkfa -->
        <dependency>
            <groupId>org.springframework.kafka</groupId>
            <artifactId>spring-kafka</artifactId>
            <exclusions>
                <exclusion>
                    <groupId>org.apache.kafka</groupId>
                    <artifactId>kafka-clients</artifactId>
                </exclusion>
            </exclusions>
        </dependency>
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
        </dependency>
        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
        </dependency>

        <!--kafkaStream-->
        <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>
    </dependencies>

   2、新建流式处理类

          代码如下

package com.heima.kafkademo.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) {
        /*创建kafka配置中心并配置参数*/
        Properties prop = new Properties();
        //连接地址
        prop.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG,"192.168.200.130:9092");
        //key序列化
        prop.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        //value序列化
        prop.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
        //创建id名称
        prop.put(StreamsConfig.APPLICATION_ID_CONFIG,"streams-quickstart");

        //stream构造器
        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();
    }

    //流式计算方法
    private static void streamProcessor(StreamsBuilder streamsBuilder) {
        //创建kafka对象，同时指定从哪个topic获取消息
        KStream<String, String> stream = streamsBuilder.stream("itcast-topic-input");
        //处理消息的value
        stream.flatMapValues(new ValueMapper<String, Iterable<?>>() {
            @Override
            public Iterable<String> apply(String value) {
                return Arrays.asList(value.split(" "));
            }
        })      //按照value进行聚合
                .groupBy((key,value)->value)
                //时间窗口,每隔10秒更新一次
                .windowedBy(TimeWindows.of(Duration.ofSeconds(10)))
                //统计单词个数
                .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");
    }
}

3、启动消费者类和流式处理类监听消息

        使用生产者类发送消息

       消费者和生产者类代码参考Kafka：安装和配置_Success___的博客-CSDN博客

4、测试

        成功接收到消息