pom.xml

    <dependencies>
        <!-- 导入scala的依赖 -->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>

        <!-- kafka依赖jar包-->
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka-clients</artifactId>
            <version>${kafka.version}</version>
        </dependency>

    </dependencies>

生产者：Producer

package com.yege.kafkademo

import java.util.Properties

import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord}
import org.apache.kafka.common.serialization.StringSerializer

object ProducerDemo {

  def main(args: Array[String]): Unit = {
    // 1 配置参数
    val props = new Properties()
    // 连接kafka节点
    props.setProperty("bootstrap.servers", "linux01:9092,linux02:9092,linux03:9092")
    //指定key序列化方式
    props.setProperty("key.serializer", "org.apache.kafka.common.serialization.StringSerializer")
    //指定value序列化方式
    props.setProperty("value.serializer", classOf[StringSerializer].getName) // 两种写法都行

    val topic = "wordcount"

    // 2 kafka的生产者
    val producer: KafkaProducer[String, String] = new KafkaProducer[String, String](props)

    for (i <- 1 to 10) {
      // 3 封装的对象

      //将数据发送到指定的分区编号
      val record = new ProducerRecord[String, String](topic, 2 , null,"myvalue:"+i)

      //val partitionNum = i % 3  // 指定数据均匀写入3个分区中
      //val record = new ProducerRecord[String, String](topic, partitionNum, null,"myvalue:"+i)


      //不指定分区编号，指定key，   分区编号 = key.hasacode % 3
      //相同key的数据一定会到kafka的同一个分区，但是同一个分区中可以有多个key的数据
      //val record = new ProducerRecord[String, String](topic , "test1","myvalue:"+i)


      //根据key的hashcode值模除以topic分区的数量，返回一个分区编号
      //val record = new ProducerRecord[String, String](topic , UUID.randomUUID().toString ,"myvalue:"+i)

      //没有指定Key和分区，默认的策略就是轮询，将数据均匀写入多个分区中
      //val record = new ProducerRecord[String, String](topic,"value-" + i)

      // 4 发送消息
      producer.send(record)

    }

    println("message send success")


    // 释放资源
    producer.close()
  }


}

消费者：Consumer

package com.yege.kafkademo

import java.time.Duration
import java.util.Properties

import org.apache.kafka.clients.consumer.{ConsumerRecords, KafkaConsumer}
import org.apache.kafka.common.serialization.StringDeserializer

object ConsumerDemo {

  def main(args: Array[String]): Unit = {

    // 1 配置参数
    val props = new Properties()
    //从哪些broker消费数据
    props.setProperty("bootstrap.servers", "linux01:9092,linux01:9092,linux01:9092")
    // 反序列化的参数
    props.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer")
    props.setProperty("value.deserializer",classOf[StringDeserializer].getName)
    // 指定group.id
    props.setProperty("group.id","g106")

    // 指定消费的offset从哪里开始
    //earliest：从头开始 --from-beginning
    //latest:从消费者启动之后
    props.setProperty("auto.offset.reset","earliest") //[latest, earliest, none]

    // 是否自动提交偏移量  offset
    // enable.auto.commit 默认值就是true【5秒钟更新一次】，消费者定期会更新偏移量 groupid,topic,parition -> offset
    //props.setProperty("enable.auto.commit", "false") // 不让kafka自动维护偏移量     手动维护偏移量
    //enable.auto.commit   5000

    // 2 消费者的实例对象
    val consumer: KafkaConsumer[String, String] = new KafkaConsumer[String, String](props)

    // 订阅   参数类型  java的集合
    val topic: java.util.List[String] = java.util.Arrays.asList("wordcount")

    // 3 订阅主题
    consumer.subscribe(topic)

    while (true){
      // 4  拉取数据
      val msgs: ConsumerRecords[String, String] = consumer.poll(Duration.ofMillis(2000))

      //导入隐式转换
      import scala.collection.JavaConverters._
      //将Java的集合或迭代器转成Scala的集合或迭代器
      for(cr <- msgs.asScala){
        //ConsumerRecord[String, String]
        println(cr)
      }
    }

    //consumer.close()

  }


}

输出：