Spark Streaming接入Socket的数据模拟一个wordcount的功能，结果打印到控制台，使用Local模式，使用Scala语言。
项目目录
pom.xml
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.sid.spark</groupId>
<artifactId>spark-train</artifactId>
<version>1.0</version>
<inceptionYear>2008</inceptionYear>
<properties>
<scala.version>2.11.8</scala.version>
<kafka.version>0.9.0.0</kafka.version>
<spark.version>2.2.0</spark.version>
<hadoop.version>2.9.0</hadoop.version>
<hbase.version>1.4.4</hbase.version>
</properties>
<repositories>
<repository>
<id>scala-tools.org</id>
<name>Scala-Tools Maven2 Repository</name>
<url>http://scala-tools.org/repo-releases</url>
</repository>
</repositories>
<pluginRepositories>
<pluginRepository>
<id>scala-tools.org</id>
<name>Scala-Tools Maven2 Repository</name>
<url>http://scala-tools.org/repo-releases</url>
</pluginRepository>
</pluginRepositories>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.11</artifactId>
<version>${kafka.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
</dependency>
<!--<dependency>-->
<!--<groupId>org.apache.hbase</groupId>-->
<!--<artifactId>hbase-clinet</artifactId>-->
<!--<version>${hbase.version}</version>-->
<!--</dependency>-->
<!--<dependency>-->
<!--<groupId>org.apache.hbase</groupId>-->
<!--<artifactId>hbase-server</artifactId>-->
<!--<version>${hbase.version}</version>-->
<!--</dependency>-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>net.jpountz.lz4</groupId>
<artifactId>lz4</artifactId>
<version>1.3.0</version>
</dependency>
</dependencies>
<build>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<plugin>
<groupId>org.scala-tools</groupId>
<artifactId>maven-scala-plugin</artifactId>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
<configuration>
<scalaVersion>${scala.version}</scalaVersion>
<args>
<arg>-target:jvm-1.5</arg>
</args>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-eclipse-plugin</artifactId>
<configuration>
<downloadSources>true</downloadSources>
<buildcommands>
<buildcommand>ch.epfl.lamp.sdt.core.scalabuilder</buildcommand>
</buildcommands>
<additionalProjectnatures>
<projectnature>ch.epfl.lamp.sdt.core.scalanature</projectnature>
</additionalProjectnatures>
<classpathContainers>
<classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINER</classpathContainer>
<classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINER</classpathContainer>
</classpathContainers>
</configuration>
</plugin>
</plugins>
</build>
<reporting>
<plugins>
<plugin>
<groupId>org.scala-tools</groupId>
<artifactId>maven-scala-plugin</artifactId>
<configuration>
<scalaVersion>${scala.version}</scalaVersion>
</configuration>
</plugin>
</plugins>
</reporting>
</project>
代码
package com.sid.spark
import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
/**
* Created by jy02268879 on 2018/7/16.
*
* Spark Streaming 处理 Socket数据
*
* 测试用nc -lk 6789
*/
object SocketWordCount {
def main(args: Array[String]): Unit = {
/**
* 当运行Spark Streaming应用程序的时候如果使用的Local模式，
* 不要使用local或者local[1]作为master的URL。
* 因为这种写法意味着仅仅只有一个线程能被使用，
* 如果使用基于Receiver的input DStream（如果用的HDFS上面的文件就可以用local[1]或local），
* Receiver就已经占用了线程了，
* 主流程就处理不了数据了。
* 所以要用local[n]，n>Receiver的数量。
* */
val sparkConf = new SparkConf().setMaster("local[2]").setAppName("SocketWordCount")
//构建StreamingContext
/**
* 创建StreamingContext需要两个参数：sparkConf和batch interval
* */
val ssc = new StreamingContext(sparkConf,Seconds(5))
val lines = ssc.socketTextStream("node1",6789)
val result = lines.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_)
result.print()
ssc.start()
ssc.awaitTermination()
}
}
在node1的控制台启动
nc -lk 6789
IDEA点击run启动程序
在控制台nc监控下输入a a a b b c d d
查看控制台输出
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