Apache Spark简单构建一个应用程序

Spark

简单的构建一个Apache Spark应用程序

开发环境的准备：

Spark 可以运行在 Linux, Max OS X, 或者是 Windows 上。这里我是在Windows上运行的。在本地机器上需要有Java8.x和maven环境，另外我们推荐使用 ItelliJ IDEA 作为 Flink 应用程序的开发 IDE。
首先在我们的pom.xml文件中添加Spark相关的依赖。
pom如下：

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<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>SparkDemo</groupId>
  <artifactId>SparkDemo</artifactId>
  <version>1.0-SNAPSHOT</version>
  <inceptionYear>2008</inceptionYear>
  <properties>
    <scala.version>2.11.1</scala.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>

    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.11</artifactId>
      <version>2.2.0</version>
    </dependency>


    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.4</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.specs</groupId>
      <artifactId>specs</artifactId>
      <version>1.2.5</version>
      <scope>test</scope>
    </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>

工作目录：
图片说明
编写Spark程序：
创建WordCount程序：
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package SparkDemo

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.rdd.RDD
/**
  * @Author: luomo
  * @CreateTime: 2019/10/24
  * @Description:WordCount
  */
object WordCount {
  def main(args: Array[String]): Unit = {
    //配置文件
    val conf = new SparkConf().
      setAppName("wordcount") // 运行时候的作业名称
      .setMaster("local")
    //上下文 拿着Conf信息创建出来 写spark应用程序的对象  通往集群的入口
    val sc = new SparkContext(conf)
    //传入文件对象 返回RDD集合
    val input = sc.textFile("E:///test.txt")
    //对文件行数据 按照空格切割 返回RDD集合 得到每个单词
    val lines = input.flatMap(line => line.split(" "))
    //统计单词数量 计数 得到RDD集合 按照相同的Key先分组，之后再对组内的Value进行操作
    val count = lines.map(word => (word, 1)).reduceByKey(_ + _)
    //将结果遍历打印到控制台
    count.foreach(x =>{
      println(x)
    })
    //将结果输出到文件中
    val output = count.saveAsTextFile("E:///wordCount")
    //关闭流 在内存中释放这个spark对象
    //sc.stop()
  }
}

运行程序如图：
图片说明

文章目录
  1. 1. 简单的构建一个Apache Spark应用程序
    1. 1.1. 开发环境的准备：
,