name := "FlumeEventCount"

version := "1.0"

scalaVersion := "2.10.4"

libraryDependencies += "org.apache.spark" %% "spark-core" % "1.4.0"

libraryDependencies += "org.apache.spark" % "spark-streaming_2.10" % "1.4.0"

libraryDependencies += "org.apache.spark" % "spark-streaming-flume_2.10" % "1.4.0"

package com.huawei.test

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

/**
 *  Produces a count of events received from Flume.
 *
 *  This should be used in conjunction with an AvroSink in Flume. It will start
 *  an Avro server on at the request host:port address and listen for requests.
 *  Your Flume AvroSink should be pointed to this address.
 *
 *  Usage: FlumeEventCount <host> <port>
 *    <host> is the host the Flume receiver will be started on - a receiver
 *           creates a server and listens for flume events.
 *    <port> is the port the Flume receiver will listen on.
 *
 *  To run this example:
 *    `$ bin/run-example org.apache.spark.examples.streaming.FlumeEventCount <host> <port> `
 */
object FlumeEventCount{
  def main(args: Array[String]) {
    if (args.length < 2) {
      System.err.println(
        "Usage: FlumeEventCount <host> <port>")
      System.exit(1)
    }

    val host = args(0)
    val port = args(1).toInt

    val batchInterval = Milliseconds(2000)

    // Create the context and set the batch size
    val sparkConf = new SparkConf().setAppName("FlumeEventCount")
    val ssc = new StreamingContext(sparkConf, batchInterval)

    // Create a flume stream
    val stream = FlumeUtils.createStream(ssc, host, port, 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()

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