DStream实战之Spark Streaming整合fulme实战, Flume向Spark Streaming中push推数据 36

Myth丶恋晨 2023-05-29 03:35 27阅读 0赞

# 前言 #

**本文所需要的安装包&Flume配置文件,博主都已上传,链接为[本文涉及安装包&Flume配置文件][Flume]本文涉及的安装包&Flume配置文件,请自行下载~**

*  flume作为日志实时采集的框架, 可以与Spark Streaming实时处理框架进行对接.
 *  flume实时产生数据, Spark Streaming做实时处理
 *  **Spark Streaming对接fluem有两种方式,一种是Flume将消息Push推给Spark Streaming;还有一种是Spark Streaming从flume中Poll拉取数据.**

# 1. Flume向Spark Streaming中push推数据 #

## 1.1 Flume前期准备 ##

*  安装flume1.6以上
 *  下载依赖包  
    **spark-streaming-flume-sink\_2.11-2.0.2.jar**放入到**flume的lib目录下.**
 *  修改**flume/lib**下的**scala依赖包版本**  
    从spark安装目录的**jars**文件夹下找到**scala-library-2.11.8.jar 包, 替换掉flume/lib**目录下自带的scala-library-2.11.8.jar包.
 *  写**flume的agent**, 注意既然是拉取的方式,**那么flume向自己所在的机器上产数据就行.**
 *  编写**flume-push.conf配置文件**  
    **注意: 因为是Flume主动向Spark Streaming推送数据,所以sink要指定Spark Streaming程序启动的IP地址和port端口号.**
    
    **注意配置文件中指明的hostname和port是spark应用程序所在服务器的ip地址和端口。**  
    ![在这里插入图片描述][2019110314294868.png]

#push mode
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1
    
    #source
    a1.sources.r1.channels = c1
    a1.sources.r1.type = exec
    a1.sources.r1.command = tail -f /root/test.txt
    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
    # Spark Streaming程序启动的IP地址和端口号
    a1.sinks.k1.hostname=172.16.43.63
    a1.sinks.k1.port = 9999
    a1.sinks.k1.batchSize= 2000

## 1.2 Spark Streaming前期准备,编写Spark Streaming程序 ##

*  导入pom依赖

<dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-streaming-flume_2.11</artifactId>
        <version>2.0.2</version>
    </dependency>

**注意: 程序中需要指定本程序运行机器的IP地址和Port端口号,要和Flume配置文件flume-push.conf中sink指导的一样**  
![在这里插入图片描述][2019110314294868.png]  
![在这里插入图片描述][20191103143434480.png]

*  使用scala编写程序

package cn.acece.sparkStreamingtest
    
    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 表示当前批次汇总成的(word,1)中相同单词的所有的1
      //runningCount 历史的所有相同key的value总和
      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参数
        val sparkConf: SparkConf = new SparkConf().setAppName("SparkStreaming_Flume_Push").setMaster("local[2]")
        //构建sparkContext对象
        val sc: SparkContext = new SparkContext(sparkConf)
        //构建StreamingContext对象，每个批处理的时间间隔
        val scc: StreamingContext = new StreamingContext(sc, Seconds(5))
        //设置日志输出级别
        sc.setLogLevel("WARN")
        //设置检查点目录
        scc.checkpoint("./")
        //flume推数据过来
        // 当前应用程序部署的服务器ip地址，跟flume配置文件保持一致
        val flumeStream: ReceiverInputDStream[SparkFlumeEvent] = FlumeUtils.createStream(scc,"172.16.43.63",9999,StorageLevel.MEMORY_AND_DISK)
    
        //获取flume中数据，数据存在event的body中，转化为String
        val lineStream: DStream[String] = flumeStream.map(x=>new String(x.event.getBody.array()))
        //实现单词汇总
       val result: DStream[(String, Int)] = lineStream.flatMap(_.split(" ")).map((_,1)).updateStateByKey(updateFunction)
    
        result.print()
        scc.start()
        scc.awaitTermination()
      }
    
    }
    }

## 1.3 Flume向Spark Streaming中push推数据, 要先启动Spark Streaming程序 ##

*  先启动Spark Streaming程序,在IDEA中启动程序  
    ![在这里插入图片描述][20191103143601687.png]
 *  后启动Flume程序, 先把\*\*/root/data/ata.txt.COMPLETED 重命名为data.txt\*\*,然后执行以下shell命令

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

![在这里插入图片描述][20191103143809130.png]

## 1.4 观察IDEA控制台输出 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjY0MTkwOQ_size_16_color_FFFFFF_t_70]  
**Flume向Spark Streaming中push推数据成功, 完美运行~**

[Flume]: https://download.csdn.net/download/weixin_42641909/11954162
[2019110314294868.png]: https://img-blog.csdnimg.cn/2019110314294868.png
[20191103143434480.png]: https://img-blog.csdnimg.cn/20191103143434480.png
[20191103143601687.png]: https://img-blog.csdnimg.cn/20191103143601687.png
[20191103143809130.png]: https://img-blog.csdnimg.cn/20191103143809130.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjY0MTkwOQ_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20191103143817499.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjY0MTkwOQ==,size_16,color_FFFFFF,t_70