用IDEA编写spark的WordCount

我习惯用Maven项目 所以用IDEA新建一个Maven项目

　　　　　　　　　　　　

下面是pom文件 我粘上来吧

<?xml version="1.0" encoding="UTF-8"?>

<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/xsd/maven-4.0.0.xsd">

    <modelVersion>4.0.0</modelVersion>

    <groupId>com</groupId>

    <artifactId>ScalaMavenTest2</artifactId>

    <version>1.0-SNAPSHOT</version>

    <properties>

        <maven.compiler.source>1.7</maven.compiler.source>

        <maven.compiler.target>1.7</maven.compiler.target>

        <encoding>UTF-8</encoding>

        <scala.version>2.11.8</scala.version>

        <scala.compat.version>2.11</scala.compat.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-core_2.11</artifactId>

            <version>2.2.2</version>

        </dependency>

        <dependency>

            <groupId>org.apache.spark</groupId>

            <artifactId>spark-streaming_2.11</artifactId>

            <version>2.2.2</version>

        </dependency>

        <dependency>

            <groupId>org.apache.hadoop</groupId>

            <artifactId>hadoop-client</artifactId>

            <version>2.6.5</version>

        </dependency>

    </dependencies>

    <!-- 指定插件-->

    <build>

        <plugins>

            <!--编译java的插件-->

            <plugin>

                <groupId>org.apache.maven.plugins</groupId>

                <artifactId>maven-compiler-plugin</artifactId>

                <version>3.5.1</version>

            </plugin>

            <!-- 指定编译scala的插件 -->

            <plugin>

                <groupId>net.alchim31.maven</groupId>

                <artifactId>scala-maven-plugin</artifactId>

                <version>3.2.2</version>

            </plugin>

            <!-- maven打包的插件 -->

            <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.AppendingTransformer">

                                    <resource>reference.conf</resource>

                                </transformer>

                                <!-- 指定main方法 -->

                                <transformer

                                        implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">

                                    <mainClass></mainClass>

                                </transformer>

                            </transformers>

                        </configuration>

                    </execution>

                </executions>

            </plugin>

        </plugins>

    </build>

</project>

　　代码如下

import org.apache.spark.{SparkConf, SparkContext}

object WordCount {

  def main(args: Array[String]): Unit = {

    //设置配置文件和名字

    val conf = new SparkConf().setAppName("wordConut")

    //生成Spark的上下文

    val sc = new SparkContext(conf)

    //传入文件

    val rdd = sc.textFile(args(0))

    //先用split()按照空格进行分词 在通过flatMap对分割的单词进行展评,展评完毕后  使用map(x=>(x,1))对每个单词

    //计数1最后使用ReduceByKey(_+_) 根据Key也就是单词进行计数 这是一个Shuffer过程

    val wordcount = rdd.flatMap(_.split(" ")).map(x=>(x,1)).reduceByKey(_+_)

    //先使用map(x=>(x._2,x._1))对单词结果的k V 进行转换然后通过sortByKey(false)根据K V也就是词频进行排序 false要求

    //按照倒序进行排列,最后再次通过map(x=>(x._2,x._1))让 K V 再次进行互换 形成最后结果

    val wordsort = wordcount.map(x=>(x._2,x._1)).sortByKey(false).map(x=>(x._2,x._1))

    //写出路径

    wordsort.saveAsTextFile(args(1))

    sc.stop()

  }

}

之后设置参数 指定master 和文件所在目录  和输出目录

成功之后的结果