Spark Streaming整合Flume的两种方式

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构建Maven项目，在pom.xml文件中加入如下依赖：

<!-- Spark Streaming 依赖-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>


      <!-- Spark Streaming整合Flume 依赖-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-flume_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-flume-sink_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.commons</groupId>
            <artifactId>commons-lang3</artifactId>
            <version>3.5</version>
        </dependency>

Push方式整合

FlumePushWordCount.scala

package spark

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

/**
  * Spark Streaming整合Flume的第一种方式
  */
object FlumePushWordCount {

  def main(args: Array[String]): Unit = {

    if(args.length != 2) {
      System.err.println("Usage: FlumePushWordCount <hostname> <port>")
      System.exit(1)
    }

    val Array(hostname, port) = args

    val sparkConf = new SparkConf() //.setMaster("local[2]").setAppName("FlumePushWordCount")
    val ssc = new StreamingContext(sparkConf, Seconds(5))

    //TODO... 如何使用SparkStreaming整合Flume
    val flumeStream = FlumeUtils.createStream(ssc, hostname, port.toInt)

    flumeStream.map(x=> new String(x.event.getBody.array()).trim)
      .flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).print()

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

flume的启动方式参考我的前面的几篇关于flume介绍的博客
顺序是先启动flume再启动spark streaming应用程序。

Pull方式整合

package spark

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

/**
  * Spark Streaming整合Flume的第二种方式
  */
object FlumePullWordCount {

  def main(args: Array[String]): Unit = {

    if(args.length != 2) {
      System.err.println("Usage: FlumePullWordCount <hostname> <port>")
      System.exit(1)
    }

    val Array(hostname, port) = args

    val sparkConf = new SparkConf() //.setMaster("local[2]").setAppName("FlumePullWordCount")
    val ssc = new StreamingContext(sparkConf, Seconds(5))

    //TODO... 如何使用SparkStreaming整合Flume
    val flumeStream = FlumeUtils.createPollingStream(ssc, hostname, port.toInt)

    flumeStream.map(x=> new String(x.event.getBody.array()).trim)
      .flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).print()

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