sparkStreaming整合flume 推模式Push

package com.spark.streaming

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}

/**
  * sparkStreaming整合flume  推模式Push
  */
object SparkStreamingFlumePush {
     

  System.setProperty("hadoop.home.dir", "d://soft//hadoop//hadoop-2.7.3")

  //newValues 表示当前批次汇总成的(word,1)中相同单词的所有的1
  //runningCount 历史的所有相同key的value总和
  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 = new SparkConf().setAppName("SparkStreaming_Flume_Push").setMaster("local[2]")
    //构建sparkContext对象
    val sc = new SparkContext(sparkConf)
    //构建StreamingContext对象，每个批处理的时间间隔
    val scc = new StreamingContext(sc, Seconds(5))
    //设置日志输出级别
    sc.setLogLevel("WARN")
    //设置检查点目录
    scc.checkpoint("./")
    //flume推数据过来
    // 当前应用程序部署的服务器ip地址，跟flume配置文件保持一致
    val flumeStream = FlumeUtils.createStream(scc,"192.168.1.159",8888,StorageLevel.MEMORY_AND_DISK)

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

    result.print(30)
    scc.start()
    scc.awaitTermination()
  }

}

测试和配置信息https://blog.csdn.net/star5610/article/details/106522455