spark-streaming與flume整合 push

package cn.my.sparkStream

import org.apache.spark.SparkConf
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming._
import org.apache.spark.streaming.flume._


/**
 
  */
object SparkFlumePush {
  def main(args: Array[String]) {
    if (args.length < 2
 
) {
      System.err.println(
        "Usage: FlumeEventCount <host> <port>")
      System.exit(1)
    }
    LogLevel.setStreamingLogLevels()
    val Array(host, port) = args
    val batchInterval = Milliseconds(2000)
    // Create the context and set the batch size
    val sparkConf = new SparkConf().setAppName("
 
FlumeEventCount").setMaster("local[2]")
    val ssc = new StreamingContext(sparkConf, batchInterval)
    // Create a flume stream
    val stream = FlumeUtils.createStream(ssc, host, port.toInt, StorageLevel.MEMORY_ONLY_SER_2)
    // Print out the count of events received from this server in each batch
    stream.count().map(cnt => "
 
Received " + cnt + " flume events.").print()
    //拿到消息中的event,從event中拿出body,body是真正的消息體
    stream.flatMap(t=>{new String(t.event.getBody.array()).split(" ")}).map((_,1)).reduceByKey(_+_).print

    ssc.start()
    ssc.awaitTermination()
  }
}

package cn.my.sparkStream

import java.net.InetSocketAddress

import org.apache.spark.SparkConf
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming._
import org.apache.spark.streaming.flume._


/**
  *
  */
object SparkFlumePull {
  def main(args: Array[String]) {
    if (args.length < 2) {
      System.err.println(
        "Usage: FlumeEventCount <host> <port>")
      System.exit(1)
    }
    LogLevel.setStreamingLogLevels()
    val Array(host, port) = args
    val batchInterval = Milliseconds(2000)
    // Create the context and set the batch size
    val sparkConf = new SparkConf().setAppName("FlumeEventCount").setMaster("local[2]")
    val ssc = new StreamingContext(sparkConf, batchInterval)
    // Create a flume stream

    //val stream = FlumeUtils.createStream(ssc, host, port.toInt, StorageLevel.MEMORY_ONLY_SER_2)
    // val flumeStream = FlumeUtils.createPollingStream(ssc, host, port.toInt)
    /*
      def createPollingStream(
      jssc: JavaStreamingContext,
      addresses: Array[InetSocketAddress],
      storageLevel: StorageLevel
    ):
     */
    //當sink有多個的時候
    val flumesinklist = Array[InetSocketAddress](new InetSocketAddress("mini1", 8888))
    val flumeStream = FlumeUtils.createPollingStream(ssc, flumesinklist, StorageLevel.MEMORY_ONLY_2)

    flumeStream.count().map(cnt => "Received " + cnt + " flume events.").print()
    flumeStream.flatMap(t => {
      new String(t.event.getBody.array()).split(" ")
    }).map((_, 1)).reduceByKey(_ + _).print()


    // Print out the count of events received from this server in each batch
    //stream.count().map(cnt => "Received " + cnt + " flume events.").print()
    //拿到消息中的event,從event中拿出body,body是真正的消息體
    //stream.flatMap(t=>{new String(t.event.getBody.array()).split(" ")}).map((_,1)).reduceByKey(_+_).print

    ssc.start()
    ssc.awaitTermination()
  }
}

http://spark.apache.org/docs/1.6.3/streaming-flume-integration.html

spark與flume整合