SparkStreaming读取Kafka数据源

一、前提工作

---

• 安装了zookeeper
• 安装了Kafka
• 实验环境：kafka_2.11-2.3.1.tgz + zookeeper-3.4.5.tar.gz + spark2.4.8
• 实验流程
  

二、实验内容

---

• 实验要求：本实验实现的是wordcount程序
• 启动zookeeper，可参考：zookeeper单机安装与配置
• 启动Kafka
• 创建Kafka主题，如test，可参考：Kafka的安装与基本操作
• 启动Kafka控制台生产者，可参考：Kafka的安装与基本操作
• 创建maven项目
• 添加kafka依赖

  <dependency>
          <groupId>org.apache.spark</groupId>
          <artifactId>spark-streaming-kafka-0-10_2.12</artifactId>
          <version>2.4.8</version>
      </dependency>
  

• 编写程序，如下所示：

  package com.spark.streaming.kafka
  
  import org.apache.kafka.common.serialization.StringDeserializer
  import org.apache.spark.SparkConf
  import org.apache.spark.streaming.dstream.DStream
  import org.apache.spark.streaming.{Seconds, StreamingContext}
  import org.apache.spark.streaming.kafka010._
  import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
  import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
  
  object KafkaDemo {
    def main(args: Array[String]): Unit = {
      val sparkConf = new SparkConf().setMaster("local[2]").setAppName("KafkaTest")
      val streamingContext = new StreamingContext(sparkConf, Seconds(2))
  
      val kafkaParams = Map[String, Object](
        "bootstrap.servers" -> "niit01:9092",
        "key.deserializer" -> classOf[StringDeserializer],
        "value.deserializer" -> classOf[StringDeserializer],
        "group.id" -> "use_a_separate_group_id_for_each_stream",
        "auto.offset.reset" -> "latest",
        "enable.auto.commit" -> (false: java.lang.Boolean)
      )
  
      val topics = Array("test", "t100")
      val stream = KafkaUtils.createDirectStream[String, String](
        streamingContext,
        PreferConsistent,
        Subscribe[String, String](topics, kafkaParams)
      )
  
      val mapDStream: DStream[(String, String)] = stream.map(record => (record.key, record.value))
  
      val resultRDD: DStream[(String, Int)] = mapDStream.flatMap(_._2.split(" ")).map((_, 1)).reduceByKey(_ + _)
  
      // 打印
      resultRDD.print()
  
      // 启动
      streamingContext.start()
  
      // 等待计算结束
      streamingContext.awaitTermination()
  
  
    }
  }
  
  

• 运行程序
• 往Kafka控制台生产者中写入数据，观察程序控制台输出