Stream整合Flume

package com.bawei.stream

import java.net.InetSocketAddress

import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}
import org.apache.spark.streaming.flume.{FlumeUtils, SparkFlumeEvent}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}


object StreamFlume {
  def updateFunction(newValues: Seq[Int], runningCount: Option[Int]): Option[Int] = {
    val newCount =runningCount.getOrElse(0)+newValues.sum
    Some(newCount)
  }


  def main(args: Array[String]): Unit = {
    //配置sparkConf参数
    val sparkConf: SparkConf = new SparkConf().setAppName("SparkStreaming_Flume_Poll").setMaster("local[2]")
    //构建sparkContext对象
    val sc: SparkContext = new SparkContext(sparkConf)
    sc.setLogLevel("WARN")
    //构建StreamingContext对象，每个批处理的时间间隔
    val scc: StreamingContext = new StreamingContext(sc, Seconds(5))
    //设置checkpoint
    scc.checkpoint("C:\\Users\\Desktop\\checkpoint2")
    //设置flume的地址，可以设置多台
    val address=Seq(new InetSocketAddress("192.168.182.147",8888))
    // 从flume中拉取数据
    val flumeStream: ReceiverInputDStream[SparkFlumeEvent] = FlumeUtils.createPollingStream(scc,address,StorageLevel.MEMORY_AND_DISK)

    //获取flume中数据，数据存在event的body中，转化为String
    val lineStream: DStream[String] = flumeStream.map(x=>new String(x.event.getBody.array()))
    //实现单词汇总
    val result: DStream[(String, Int)] = lineStream.flatMap(_.split(" ")).map((_,1)).updateStateByKey(updateFunction)

    result.print()
    scc.start()
    scc.awaitTermination()
  }
}