用法及说明
需要继承Receiver，并实现onStart、onStop方法来自定义数据源采集。
案例实操
1）需求：自定义数据源，实现监控某个端口号，获取该端口号内容。
2）自定义数据源
package com.test
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))
//读取数据
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 = {}
}
3）使用自定义的数据源采集数据
package com.test
import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.dstream.DStream
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 wordStream = lineStream.flatMap(_.split("\t"))
//5.将单词映射成元组（word,1）
val wordAndOneStream = wordStream.map((_, 1))
//6.将相同的单词次数做统计
val wordAndCountStream = wordAndOneStream.reduceByKey(_ + _)
//7.打印
wordAndCountStream.print()
//8.启动SparkStreamingContext
ssc.start()
ssc.awaitTermination()
}
}