流處理平臺：

這裡是第四步的實現：
Spark Streaming整合Kafka採用的是Receiver-based，另一種方式Direct Approach，稍作修改就行。

package spark

import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
  * Spark Streaming對接Kafka
  */
object KafkaStreamingApp {
 


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

    if(args.length != 4) {
      System.err.println("Usage: KafkaStreamingApp <zkQuorum> <group> <topics> <numThreads>")
    }

    val Array(zkQuorum, group, topics, numThreads) = args

    val sparkConf = new SparkConf().setAppName
 
("KafkaReceiverWordCount")
      .setMaster("local[2]")

    val ssc = new StreamingContext(sparkConf, Seconds(5))

    val topicMap = topics.split(",").map((_, numThreads.toInt)).toMap

    // Spark Streaming如何對接Kafka
    val messages = KafkaUtils.createStream(ssc, zkQuorum, group,topicMap)

    messages.
 
map(_._2).count().print()

    ssc.start()
    ssc.awaitTermination()
  }
}