前一篇中數據源采用的是從一個socket中拿數據，有點屬於“旁門左道”，正經的是從kafka等消息隊列中拿數據！

主要支持的source，由官網得知如下：

　　獲取數據的形式包括推送push和拉取pull

一、spark streaming整合flume

　　1.push的方式

　　　　更推薦的是pull的拉取方式

　　　　引入依賴：

     <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId
 
>spark-streaming-flume_2.10</artifactId>
            <version>${spark.version}</version>
        </dependency>

　　　　編寫代碼：

package com.streaming

import org.apache.spark.SparkConf
import org.apache.spark.streaming.flume.FlumeUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

 
/**
  * Created by ZX on 2015/6/22.
  */
object FlumePushWordCount {

  def main(args: Array[String]) {
    val host = args(0)
    val port = args(1).toInt
    val conf = new SparkConf().setAppName("FlumeWordCount")//.setMaster("local[2]")
    // 使用此構造器將可以省略sc，由構造器構建
    val ssc = new StreamingContext(conf, Seconds(5))
    
 
// 推送方式: flume向spark發送數據（註意這裏的host和Port是streaming的地址和端口，讓別人發送到這個地址）
    val flumeStream = FlumeUtils.createStream(ssc, host, port)
    // flume中的數據通過event.getBody()才能拿到真正的內容
    val words = flumeStream.flatMap(x => new String(x.event.getBody().array()).split(" ")).map((_, 1))

    val results = words.reduceByKey(_ + _)
    results.print()
    ssc.start()
    ssc.awaitTermination()
  }
}

　　　　flume-push.conf——flume端配置文件：

# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1

# source
a1.sources.r1.type = spooldir
a1.sources.r1.spoolDir = /export/data/flume
a1.sources.r1.fileHeader = true

# Describe the sink
a1.sinks.k1.type = avro
#這是接收方
a1.sinks.k1.hostname = 192.168.31.172
a1.sinks.k1.port = 8888

# 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

　　2.pull的方式

　　　　屬於推薦的方式，通過streaming來主動拉取flume產生的數據

　　　　編寫代碼：（依賴同上）

package com.streaming

import java.net.InetSocketAddress

import org.apache.spark.SparkConf
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.flume.FlumeUtils
import org.apache.spark.streaming.{Seconds, StreamingContext}

object FlumePollWordCount {
  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("FlumePollWordCount").setMaster("local[2]")
    val ssc = new StreamingContext(conf, Seconds(5))
    //從flume中拉取數據(flume的地址)，通過Seq序列，裏面可以new多個地址，從多個flume地址拉取
    val address = Seq(new InetSocketAddress("172.16.0.11", 8888))
    val flumeStream = FlumeUtils.createPollingStream(ssc, address, StorageLevel.MEMORY_AND_DISK)
    val words = flumeStream.flatMap(x => new String(x.event.getBody().array()).split(" ")).map((_,1))
    val results = words.reduceByKey(_+_)
    results.print()
    ssc.start()
    ssc.awaitTermination()
  }
}

　　　　　　配置flume

　　通過拉取的方式需要flume的lib目錄中有相關的JAR（要通過spark程序來調flume拉取），通過官網可以得知具體的JAR信息：

　　　　配置flume：

# Name the components on this agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1

# source
a1.sources.r1.type = spooldir
a1.sources.r1.spoolDir = /export/data/flume
a1.sources.r1.fileHeader = true

# Describe the sink（配置的是flume的地址，等待拉取）
a1.sinks.k1.type = org.apache.spark.streaming.flume.sink.SparkSink
a1.sinks.k1.hostname = mini1
a1.sinks.k1.port = 8888

# 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

　　　　啟動flume，然後啟動IDEA中的spark streaming：

bin/flume-ng agent -c conf -f conf/netcat-logger.conf -n a1  -Dflume.root.logger=INFO,console
// -D後參數可選

大數據入門第二十四天——SparkStreaming（2）與flume、kafka整合