大数据-SparkStreaming（二）

                           大数据-SparkStreaming（二）

数据源

• socket数据源

需求：sparkStreaming实时接收socket数据，实现单词计数

业务处理流程图

安装socket服务

首先在linux服务器node01上用yum 安装nc工具，nc命令是netcat命令的简称,它是用来设置路由器。我们可以利用它向某个端口发送数据。  

yum -y install nc#执行命令向指定的端口发送数据nc -lk 9999 

代码开发

pom.xml配置

<properties><scala.version>2.11.8</scala.version><spark.version>2.3.3</spark.version>
</properties><dependencies><dependency><groupId>org.scala-lang</groupId><artifactId>scala-library</artifactId><version>${scala.version}</version></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-streaming_2.11</artifactId><version>${spark.version}</version></dependency></dependencies><build><sourceDirectory>src/main/scala</sourceDirectory><testSourceDirectory>src/test/scala</testSourceDirectory><plugins><plugin><groupId>net.alchim31.maven</groupId><artifactId>scala-maven-plugin</artifactId><version>3.2.2</version><executions><execution><goals><goal>compile</goal><goal>testCompile</goal></goals><configuration><args><arg>-dependencyfile</arg><arg>${project.build.directory}/.scala_dependencies</arg></args></configuration></execution></executions></plugin><plugin><groupId>org.apache.maven.plugins</groupId><artifactId>maven-shade-plugin</artifactId><version>2.4.3</version><executions><execution><phase>package</phase><goals><goal>shade</goal></goals><configuration><filters><filter><artifact>*:*</artifact><excludes><exclude>META-INF/*.SF</exclude><exclude>META-INF/*.DSA</exclude><exclude>META-INF/*.RSA</exclude></excludes></filter></filters><transformers><transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer"><mainClass></mainClass></transformer></transformers></configuration></execution></executions></plugin></plugins></build>

开发sparkStreaming程序

package com.kaikeba.streamingimport org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}
import org.apache.spark.streaming.{Seconds, StreamingContext}/*** sparkStreaming接受socket数据实现单词计数程序*/
object SocketWordCount {def main(args: Array[String]): Unit = {Logger.getLogger("org").setLevel(Level.ERROR)// todo: 1、创建SparkConf对象val sparkConf: SparkConf = new SparkConf().setAppName("TcpWordCount").setMaster("local[2]")// todo: 2、创建StreamingContext对象val ssc = new StreamingContext(sparkConf,Seconds(2))//todo: 3、接受socket数据val socketTextStream: ReceiverInputDStream[String] = ssc.socketTextStream("node01",9999)//todo: 4、对数据进行处理val result: DStream[(String, Int)] = socketTextStream.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_)//todo: 5、打印结果result.print()//todo: 6、开启流式计算ssc.start()ssc.awaitTermination()}
}

• HDFS数据源

需求：通过sparkStreaming监控hdfs上的目录，有新的文件产生，就把数据拉取过来进行处理。

业务处理流程图

代码开发

package com.kaikeba.streamingimport org.apache.log4j.{Level, Logger}
import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}/*** HDFS数据源*/
object HdfsWordCount {def main(args: Array[String]): Unit = {Logger.getLogger("org").setLevel(Level.ERROR)// todo: 1、创建SparkConf对象val sparkConf: SparkConf = new SparkConf().setAppName("HdfsWordCount").setMaster("local[2]")// todo: 2、创建StreamingContext对象val ssc = new StreamingContext(sparkConf,Seconds(2))//todo: 3、监控hdfs目录数据val textFileStream: DStream[String] = ssc.textFileStream("hdfs://node01:8020/data")//todo: 4、对数据进行处理val result: DStream[(String, Int)] = textFileStream.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_)//todo: 5、打印结果result.print()//todo: 6、开启流式计算ssc.start()ssc.awaitTermination()}
}

• 自定义数据源

代码开发

/*** 自定义一个Receiver，这个Receiver从socket中接收数据* 使用方式：nc -lk 8888*/
package com.kaikeba.streamingimport java.io.{BufferedReader, InputStreamReader}
import java.net.Socket
import java.nio.charset.StandardCharsetsimport org.apache.spark.SparkConf
import org.apache.spark.internal.Logging
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}
import org.apache.spark.streaming.receiver.Receiver/*** 自定义数据源*/
object CustomReceiver {def main(args: Array[String]): Unit = {Logger.getLogger("org").setLevel(Level.ERROR)// todo: 1、创建SparkConf对象val sparkConf: SparkConf = new SparkConf().setAppName("CustomReceiver").setMaster("local[2]")// todo: 2、创建StreamingContext对象val ssc = new StreamingContext(sparkConf,Seconds(2))//todo: 3、调用 receiverStream api，将自定义的Receiver传进去val receiverStream = ssc.receiverStream(new CustomReceiver("node01",8888))//todo: 4、对数据进行处理val result: DStream[(String, Int)] = receiverStream.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_)//todo: 5、打印结果result.print()//todo: 6、开启流式计算ssc.start()ssc.awaitTermination()}}/*** 自定义source数据源* @param host* @param port*/
class CustomReceiver(host:String,port:Int) extends Receiver[String](StorageLevel.MEMORY_AND_DISK_SER) with Logging{override def onStart(): Unit ={//启动一个线程，开始接受数据new Thread("socket receiver"){override def run(): Unit = {receive()}}.start()}/** Create a socket connection and receive data until receiver is stopped */private def receive() {var socket: Socket = nullvar userInput: String = nulltry {logInfo("Connecting to " + host + ":" + port)socket = new Socket(host, port)logInfo("Connected to " + host + ":" + port)val reader = new BufferedReader(new InputStreamReader(socket.getInputStream(), StandardCharsets.UTF_8))userInput = reader.readLine()while(!isStopped && userInput != null) {store(userInput)userInput = reader.readLine()}reader.close()socket.close()logInfo("Stopped receiving")restart("Trying to connect again")} catch {case e: java.net.ConnectException =>restart("Error connecting to " + host + ":" + port, e)case t: Throwable =>restart("Error receiving data", t)}
}override def onStop(): Unit ={}
}