spark学习进度04（独立应用的两种形式）

1、独立应用的形式

   （上线、放在集群中运行）

1.1本地运行的方式

 idea创建web项目添加scala和maven依赖。

配置pom文件：

<?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>groupId</groupId>
    <artifactId>sparkCESHI</artifactId>
    <version>1.0-SNAPSHOT</version>
    <properties>
        <scala.version>2.11.8</scala.version>
        <spark.version>2.2.0</spark.version>
        <slf4j.version>1.7.16</slf4j.version>
        <log4j.version>1.2.17</log4j.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.0</version>
        </dependency>

        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>jcl-over-slf4j</artifactId>
            <version>${slf4j.version}</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-api</artifactId>
            <version>${slf4j.version}</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>${slf4j.version}</version>
        </dependency>
        <dependency>
            <groupId>log4j</groupId>
            <artifactId>log4j</artifactId>
            <version>${log4j.version}</version>
        </dependency>

        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.10</version>
            <scope>provided</scope>
        </dependency>
    </dependencies>

    <build>
        <sourceDirectory>src/main/scala</sourceDirectory>
        <testSourceDirectory>src/test/scala</testSourceDirectory>

        <plugins>

            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.0</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                    <encoding>UTF-8</encoding>
                </configuration>
            </plugin>

            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.2.0</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>3.1.1</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>
    
</project>

编写代码：

package cn.itcast.spark.rdd
import java.util;
import org.apache.spark.{SparkConf,SparkContext}

/**
  * @Author 带上我快跑
  * @Data 2021/1/8 13:33
  * @菩-萨-说-我-写-的-都-对@
  */
object WordCount {

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

    //1、创建spark comtext
    val conf = new SparkConf().setMaster("local[6]").setAppName("word_count")
    val sc = new SparkContext(conf)
    //2、加载文件
      //准备文件
       //读取文件
    val rdd1 = sc.textFile("dataset/wordcount.txt")
    //3、处理
    //把整句话拆分为单个单词
    val rdd2 = rdd1.flatMap(item => item.split(" "))
    //吧每个单词指定一个词频
    val rdd3 = rdd2.map( item => (item,1) )
    //聚合
    val rdd4=rdd3.reduceByKey((curr,agg) => curr + agg)
    //4、得到结果
    val result = rdd4.collect()
    //打印结果
    result.foreach(item => println(item))
  }
}

运行结果如下：

1.2提交运行的方式

修改源程序代码：

package cn.itcast.spark.rdd

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

/**
  * @Author 带上我快跑
  * @Data 2021/1/8 14:27
  * @菩-萨-说-我-写-的-都-对@
  */
object WordCount2 {

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

    //1、创建spark comtext
    val conf = new SparkConf().setAppName("word_count")
    val sc = new SparkContext(conf)
    //2、加载文件
    //准备文件
    //读取文件
    val rdd1 = sc.textFile("hdfs://hadooplinux01:9000/data/wordcount.txt")
    //3、处理
    //把整句话拆分为单个单词
    val rdd2 = rdd1.flatMap(item => item.split(" "))
    //吧每个单词指定一个词频
    val rdd3 = rdd2.map( item => (item,1) )
    //聚合
    val rdd4=rdd3.reduceByKey((curr,agg) => curr + agg)
    //4、得到结果
    val result = rdd4.collect()
    //打印结果
    result.foreach(item => println(item))
  }
}

运行package：

之后点击target：

 我们就操作这个最小的jar就ok 了：

 将他上传到虚拟机上。开启spark

 获取运行class的全路径。

 执行此命令。进行spark-submi　　t