Spark Streaming（一）

微批处理
伪实时处理

数据源

1.非自定义数据源

 val conf: SparkConf = new SparkConf().setAppName("111").setMaster("local[*]")

    val context = new StreamingContext(conf, Seconds(3))

    //从指定端口监听数据
    val line = context.socketTextStream("xxx.xxx.xxx.xxx", 9999)

    //从指定文件夹采集数据,比较鸡肋，没有flume好用
//    val file = context.textFileStream("path")
    //数据处理
    val value = line.flatMap(line => line.split(" ")).map((_, 1)).reduceByKey((_ + _))
    value.print()

    //打开监听器
    context.start()
    //Driver等待监听器停止
    context.awaitTermination()

2.自定义数据源

• 自定义收集器，集成Receiver

class MyReceiver(hostname:String,port:Int) extends Receiver[String](StorageLevel.MEMORY_ONLY){
    var socket :java.net.Socket= null

    def receive:Unit={
        socket =new java.net.Socket(hostname, port)
      val reader = new BufferedReader(new InputStreamReader(socket.getInputStream, "UTF-8"))

      var line:String = null
      while((line = reader.readLine())!=null){
        if("END".equals(line)){
          return
        }else{
          this.store(line)
        }


      }
    }
    override def onStart(): Unit = {
      new Thread(()=>receive).start()
    }

    override def onStop(): Unit = {
      if(socket != null){
        socket.close()
        socket = null
      }
    }
  }

• 使用自定义收集器

val line = context.receiverStream(new MyReceiver("xxx.xxx.xxx.xxx", 9999))

Spark Streaming（一）

原文：https://www.cnblogs.com/zqzhen/p/12871853.html