在idea上运行spark的wordcount

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1.环境hadoop-2.6.0 spak2.1.1 scala-sdk-2.11.12

2.maven项目创建

3.pom

<?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>org.example</groupId>
    <artifactId>spark</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <spark.version>2.1.1</spark.version>
        <scala.version>2.11</scala.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-mllib_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
    </dependencies>
    <build>
        <plugins>
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <version>2.15.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.6.0</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-surefire-plugin</artifactId>
                <version>2.19</version>
                <configuration>
                    <skip>true</skip>
                </configuration>
            </plugin>
        </plugins>
    </build>
</project>

4.阿里云settings配置 （本地仓库要与settings和reposirty路径要如下）

<settings xsi:schemaLocation="http://maven.apache.org/SETTINGS/1.0.0 https://maven.apache.org/xsd/settings-1.0.0.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://maven.apache.org/SETTINGS/1.0.0"> <mirrors> <mirror> <id>alimaven</id> <name>aliyun maven</name> <url>http://maven.aliyun.com/nexus/content/groups/public/</url> <mirrorOf>central</mirrorOf> </mirror> <mirror> <id>mirrorId</id> <mirrorOf>central</mirrorOf> <name>Human Readable Name </name> <url>http://repo1.maven.org/maven2/</url> </mirror>

<mirror> <id>alimaven</id> <name>aliyun maven</name> <url>http://central.maven.org/maven2</url> <mirrorOf>central</mirrorOf> </mirror>

<mirror> <id>alimaven</id> <name>aliyun maven</name> <url>http://maven.aliyun.com/nexus/content/repositories/central/</url> <mirrorOf>central</mirrorOf> </mirror>

<mirror> <id>junit</id> <name>junit Address/</name> <url>http://jcenter.bintray.com/</url> <mirrorOf>central</mirrorOf> </mirror> </mirrors> </settings>

5.代码

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

object WordCount {
  def main(args:Array[String]):Unit = {
    val sparkconf = new SparkConf().setAppName("wordcount").setMaster("local[*]")

    // 2、创建一个spark context 用于连接spark 集群
    val sparkContext = new SparkContext(sparkconf)

    // 3、加载需要处理的数据文件
    val textfile = sparkContext.textFile("hdfs://h1s1:9000/bigdata/5.txt")

    // 4、处理数据
    val result = textfile.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_)

    // 5、输出数据文件的结果
    result.saveAsTextFile("hdfs://h1s1:9000/bigdata/out321")
    //result.collect().foreach(println _)

    // 6、关闭spark集群的连接
    sparkContext.stop()




  }
}



6报错误Permission denied: user=administrator, access=WRITE, inode="/":root:supergroup:drwxr-xr-x

 更改你hdfs上文件权限

7.报错：HDFS的java操作异常Exception in thread "main" java.lang.IllegalArgumentException ：找不到hdfs上主机

 修改C:\Windows\System32\drivers\etc\hosts  加入IP地址和主机名

 8.检查

在idea上运行spark的wordcount

标签：read   技术   结果   project   setting   iss   dea   apach   exce   

原文地址：https://www.cnblogs.com/zbf-1998/p/12622386.html