sparkStreaming自定义数据源

要求
自定义数据源，实现监控某个端口号，获取该端口号内容
需要继承Receiver，并实现onStart、onStop方法来自定义数据源采集

代码实现

package com.atguigu

import java.io.{BufferedReader, InputStreamReader}
import java.net.Socket
import java.nio.charset.StandardCharsets

import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.receiver.Receiver

class CustomerReceiver(host: String, port: Int) extends Receiver[String](StorageLevel.MEMORY_ONLY) {

  //最初启动的时候，调用该方法，作用为：读数据并将数据发送给Spark
  override def onStart(): Unit = {
    new Thread("Socket Receiver") {
      override def run() {
        receive()
      }
    }.start()
  }

  //读数据并将数据发送给Spark
  def receive(): Unit = {

    //创建一个Socket
    var socket: Socket = new Socket(host, port)

    //定义一个变量，用来接收端口传过来的数据
    var input: String = null

    //创建一个BufferedReader用于读取端口传来的数据
    val reader = new BufferedReader(new InputStreamReader(socket.getInputStream, StandardCharsets.UTF_8))

 
    input = reader.readLine()

    //当receiver没有关闭并且输入数据不为空，则循环发送数据给Spark
    while (!isStopped() && input != null) {
      store(input)
      input = reader.readLine()
    }

    //跳出循环则关闭资源
    reader.close()
    socket.close()

    //重启任务
    restart("restart")
  }

  override def onStop(): Unit = {}
}

使用自定义的数据源采集数据

object FileStream {

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

    //1.初始化Spark配置信息
Val sparkConf = new SparkConf().setMaster("local[*]")
.setAppName("StreamWordCount")

    //2.初始化SparkStreamingContext
    val ssc = new StreamingContext(sparkConf, Seconds(5))

//3.创建自定义receiver的Streaming
val lineStream = ssc.receiverStream(new CustomerReceiver("hadoop102", 9999))

    //4.将每一行数据做切分，形成一个个单词
    val wordStreams = lineStream.flatMap(_.split("\t"))

    //5.将单词映射成元组（word,1）
    val wordAndOneStreams = wordStreams.map((_, 1))

    //6.将相同的单词次数做统计
    val wordAndCountStreams] = wordAndOneStreams.reduceByKey(_ + _)

    //7.打印
    wordAndCountStreams.print()

    //8.启动SparkStreamingContext
    ssc.start()
    ssc.awaitTermination()
  }
}