SparkStreaming整合Flume-Pull方式（核心）

------------------------------------------SparkStreaming第二种方式整合Flume-----------------------------------
详细学习地址：https://spark.apache.org/docs/latest/streaming-flume-integration.html
注意这种方式相比第一种方式更可靠，支持容错，工作环境中更常用（事务机制保障），条件是接收到，而且有副本才会去处理
Pull-based Approach using a Custom Sink 
Pull方式整合
步骤一：编写flume配置文件（建议参考官方文档）

Flume Agent的编写： flume_pull_streaming.conf


simple-agent.sources = netcat-source
simple-agent.sinks = spark-sink
simple-agent.channels = memory-channel


simple-agent.sources.netcat-source.type = netcat
simple-agent.sources.netcat-source.bind = hadoop000
simple-agent.sources.netcat-source.port = 44444


simple-agent.sinks.spark-sink.type = org.apache.spark.streaming.flume.sink.SparkSink
simple-agent.sinks.spark-sink.hostname = hadoop000
simple-agent.sinks.spark-sink.port = 41414


simple-agent.channels.memory-channel.type = memory


simple-agent.sources.netcat-source.channels = memory-channel
simple-agent.sinks.spark-sink.channel = memory-channel步骤二：编写测试类（wordcount）
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测试类：
package com.imooc.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()
  }
}pom.xml依赖：

        <!-- 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.spark</groupId>

            <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>

            <version>${spark.version}</version>

        </dependency>



        <dependency>

            <groupId>org.apache.commons</groupId>

            <artifactId>commons-lang3</artifactId>

            <version>3.5</version>

        </dependency>

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本地联调：
注意点：先启动flume 后启动Spark Streaming应用程序
注意在idea配置参数，edit configurations ----->programe arguments hadoop000 41414 
flume-ng agent  \

--name simple-agent   \

--conf $FLUME_HOME/conf    \

--conf-file $FLUME_HOME/conf/flume_pull_streaming.conf  \

-Dflume.root.logger=INFO,console


spark-submit \

--class com.imooc.spark.FlumePullWordCount \

--master local[2] \

--packages org.apache.spark:spark-streaming-flume_2.11:2.2.0 \

/home/hadoop/lib/sparktrain-1.0.jar \

hadoop000 41414