SparkStreaming两种方式连接Flume

a1.sources = r1
a1.sinks = k1
a1.channels = c1
# source
a1.sources.r1.type=spooldir
a1.sources.r1.spoolDir=/home/zhuzhu/apps/flumeSpooding
a1.sources.r1.fileHeader=true

# Describe the sink
a1.sinks.k1.type = org.apache.spark.streaming.flume.sink.SparkSink
# 当前主机端口
a1.sinks.k1.hostname = 192.168.137.88
a1.sinks.k1.port = 9999

# Use a channel which buffers events in memory
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# Bind the source and sink to the channel
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

package day02

import java.net.InetSocketAddress

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

/**
 * @ClassName: StreamingFlume
 * @Description TODO 实时监控flume，统计flume数据产生,是Spark
 * @Author: Charon 
 * @Date: 2021/4/7 13:19
 * @Version 1.0
 **/
object StreamingFlume {

  def main(args: Array[String]): Unit = {
    //1.创建SparkConf对象
    val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("StreamingFlume")
    //2.创建SparkContext对象
    val sc = new SparkContext(conf)
    //设置日志输出格式，只打印异常日志，在这里设置没有用
    //sc.setLogLevel("WARN")
    //3.创建StreamingContext,Seconds(5)：轮询机制，多久执行一次
    val ssc = new StreamingContext(sc, Seconds(5))
    //4.定义一个flume集合，可以接受多个flume数据,多个用，隔开需要new
    val addresses = Seq(new InetSocketAddress("127.0.0.1", 5555))
    //5.获取flume中的数据，
    val stream: ReceiverInputDStream[SparkFlumeEvent] = FlumeUtils.createPollingStream(ssc, addresses, StorageLevel.MEMORY_AND_DISK_2)
    // 6.截取flume数据：{"header":xxxxx   "body":xxxxxx}
    val lineDstream: DStream[String] = stream.map(x => new String(x.event.getBody.array()))
    lineDstream.flatMap(x=>x.split(" ")).map(x=>(x,1)).reduceByKey(_+_).print()
    ssc.start()
    ssc.awaitTermination()
  }
}