SparkStreaming入门完整案例

package com.zx.dao
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.StreamingContext._

//调研SparkingStreaming实时流计算
object SparkStreamingTest {
  def main(args:Array[String]):Unit={
    //配置spark任务参数
    val sparkConf = new SparkConf();
    sparkConf.setMaster("local[*]")
    .setAppName("sparkStreamingTest")
    .set("spark.executor.memory","8g")
    .set("spark.driver.memory","4g")
    .set("spark.executor.cores","4")
    .set("spark.cores.max","4")
    .set("spark.default.parallelism","12")
    .set("spark.num.executors","4")

    //创建sparkStreaming对象
    val sparkStreaming= new StreamingContext(sparkConf,Seconds(1))

    //设置监听服务器端口
    //val client = sparkStreaming.socketTextStream("localhost",9999)
    //设置监听指定文件目录
    val client = sparkStreaming.textFileStream("C:/Users/zixiang/Desktop/ss")
    //查看Dstream执行过程
    client.print(100)
    //对每个RDD进行操作
    client.foreachRDD {rdd => {
      //对RDD进行转换,技术统计
      var resultRDD = rdd.map(word=>(word,1)).reduceByKey(_+_)
      //若当前RDD有数据,则存盘
      if(rdd.count()>0){
        //获取当前RDD from的文件名
        var fileName  = GetFileNameFromStream.getFileName(rdd)
        resultRDD.repartition(1).saveAsTextFile("C:/Users/zixiang/Desktop/result/"+fileName)
        }
      }
    }
    //client.repartition(1).saveAsTextFiles("hdfs://master:9000/test")
    //开启流处理任务
    sparkStreaming.start()
    //等待任务终止
    sparkStreaming.awaitTermination()
    //sparkStreaming.awaitTerminationOrTimeout()
    //sparkStreaming.stop()
  }

  object GetFileNameFromStream extends java.io.Serializable {
    //获取当前RDD from 的文件路径(文件名)
    def getFileName(file: RDD[String]) :String ={
      var fileName =  file.toDebugString.split('|')(2).split(' ')(2)
      return fileName.split('/')(fileName.split('/').length-1)
    }
  }
}