Spark Streaming對接Flume有兩種方式

• Poll：Spark Streaming從flume 中拉取資料
• Push：Flume將訊息Push推給Spark Streaming

1、安裝flume1.6以上

2、下載依賴包

spark-streaming-flume-sink_2.11-2.0.2.jar放入到flume的lib目錄下

3、生成資料

伺服器上的 /root/data目錄下準備資料檔案data.txt

vi data.txt

hadoop spark hive spark
hadoop sqoop flume redis flume hadoop
solr kafka solr hadoop
 

4、配置採集方案

vi flume-poll.conf

a1.sources = r1
a1.sinks = k1
a1.channels = c1
#source
a1.sources.r1.channels = c1
a1.sources.r1.type = spooldir
a1.sources.r1.spoolDir = /root/data
a1.sources.r1.fileHeader = true
#channel
a1.channels.c1.type =memory
a1.channels.c1.capacity = 20000
a1.channels.c1.transactionCapacity=5000
#sinks
a1.sinks.k1.channel = c1
a1.sinks.k1.type = org.apache.spark.streaming.flume.sink.SparkSink
a1.sinks.k1.hostname=hdp-node-01
a1.sinks.k1.port = 8888
a1.sinks.k1.batchSize= 2000   
 

5、新增依賴

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-streaming-flume_2.10</artifactId>
    <version>2.0.2</version>
</dependency>

6、程式碼實現

package cn.cheng.spark
import java.net.InetSocketAddress
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}
import org.apache.spark.streaming.flume.{FlumeUtils, SparkFlumeEvent}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}

/**
  * sparkStreaming整合flume 拉模式Poll

  */
object SparkStreaming_Flume_Poll {
  //newValues 表示當前批次彙總成的(word,1)中相同單詞的所有的1
  //runningCount 歷史的所有相同key的value總和
  def updateFunction(newValues: Seq[Int], runningCount: Option[Int]): Option[Int] = {
    val newCount =runningCount.getOrElse(0)+newValues.sum
    Some(newCount)
  }


  def main(args: Array[String]): Unit = {
    //配置sparkConf引數
    val sparkConf: SparkConf = new SparkConf().setAppName("SparkStreaming_Flume_Poll").setMaster("local[2]")
    //構建sparkContext物件
    val sc: SparkContext = new SparkContext(sparkConf)
    //構建StreamingContext物件，每個批處理的時間間隔
    val scc: StreamingContext = new StreamingContext(sc, Seconds(5))
    //設定checkpoint
      scc.checkpoint("./")
    //設定flume的地址，可以設定多臺
    val address=Seq(new InetSocketAddress("192.168.200.160",8888))
    // 從flume中拉取資料
    val flumeStream: ReceiverInputDStream[SparkFlumeEvent] = FlumeUtils.createPollingStream(scc,address,StorageLevel.MEMORY_AND_DISK)

    //獲取flume中資料，資料存在event的body中，轉化為String
    val lineStream: DStream[String] = flumeStream.map(x=>new String(x.event.getBody.array()))
    //實現單詞彙總
   val result: DStream[(String, Int)] = lineStream.flatMap(_.split(" ")).map((_,1)).updateStateByKey(updateFunction)

    result.print()
    scc.start()
    scc.awaitTermination()
  }

}
 

7、啟動flume

flume-ng agent -n a1 -c /opt/bigdata/flume/conf -f /opt/bigdata/flume/conf/flume-poll.conf -Dflume.root.logger=INFO,console

8、啟動spark-streaming應用程式

9、檢視結果

flume將訊息Push推給Spark Streaming

1、配置採集方案

vi flume-push.conf

#push mode
a1.sources = r1
a1.sinks = k1
a1.channels = c1
#source
a1.sources.r1.channels = c1
a1.sources.r1.type = spooldir
a1.sources.r1.spoolDir = /root/data
a1.sources.r1.fileHeader = true
#channel
a1.channels.c1.type =memory
a1.channels.c1.capacity = 20000
a1.channels.c1.transactionCapacity=5000
#sinks
a1.sinks.k1.channel = c1
a1.sinks.k1.type = avro
a1.sinks.k1.hostname=172.16.43.63
a1.sinks.k1.port = 8888
a1.sinks.k1.batchSize= 2000     

注意配置檔案中指明的hostname和port是spark應用程式所在伺服器的ip地址和埠。

2、程式碼實現

package cn.cheng.spark

import java.net.InetSocketAddress

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

/**
  * sparkStreaming整合flume  推模式Push
  */
object SparkStreaming_Flume_Push {
  //newValues 表示當前批次彙總成的(word,1)中相同單詞的所有的1
  //runningCount 歷史的所有相同key的value總和
  def updateFunction(newValues: Seq[Int], runningCount: Option[Int]): Option[Int] = {
    val newCount =runningCount.getOrElse(0)+newValues.sum
    Some(newCount)
  }


  def main(args: Array[String]): Unit = {
    //配置sparkConf引數
    val sparkConf: SparkConf = new SparkConf().setAppName("SparkStreaming_Flume_Push").setMaster("local[2]")
    //構建sparkContext物件
    val sc: SparkContext = new SparkContext(sparkConf)
    //構建StreamingContext物件，每個批處理的時間間隔
    val scc: StreamingContext = new StreamingContext(sc, Seconds(5))
    //設定日誌輸出級別
    sc.setLogLevel("WARN")
    //設定檢查點目錄
    scc.checkpoint("./")
    //flume推資料過來
    // 當前應用程式部署的伺服器ip地址，跟flume配置檔案保持一致
    val flumeStream: ReceiverInputDStream[SparkFlumeEvent] = FlumeUtils.createStream(scc,"172.16.43.63",8888,StorageLevel.MEMORY_AND_DISK)

    //獲取flume中資料，資料存在event的body中，轉化為String
    val lineStream: DStream[String] = flumeStream.map(x=>new String(x.event.getBody.array()))
    //實現單詞彙總
   val result: DStream[(String, Int)] = lineStream.flatMap(_.split(" ")).map((_,1)).updateStateByKey(updateFunction)

    result.print()
    scc.start()
    scc.awaitTermination()
  }

}
}

3、啟動spark-streaming應用程式

4、生成資料

cp data.txt data2.txt

5、啟動flume

flume-ng agent -n a1 -c /opt/bigdata/flume/conf -f /opt/bigdata/flume/conf/flume-push.conf -Dflume.root.logger=INFO,console

6、檢視結果