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六十七、Spark-两种运行方式（本地运行，提交集群运行）

本地运行：在IDEA中直接运行控制台输出结果即可

集群运行：在本地将程序打包为 jar，提交至集群运行其程序（将结果上传至hdfs）

一、本地运行spark程序

    1、pom依赖

    注：依赖配置项及其版本一定要与集群环境相适配

<?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>cn.itcast</groupId>
    <artifactId>SparkDemo</artifactId>
    <version>1.0-SNAPSHOT</version>

    <repositories>
        <repository>
            <id>aliyun</id>
            <url>http://maven.aliyun.com/nexus/content/groups/public/</url>
        </repository>
        <repository>
            <id>apache</id>
            <url>https://repository.apache.org/content/repositories/snapshots/</url>
        </repository>
        <repository>
            <id>cloudera</id>
            <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
        </repository>
    </repositories>
    <properties>
        <encoding>UTF-8</encoding>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <scala.version>2.12.11</scala.version>
        <spark.version>3.0.1</spark.version>
        <hadoop.version>2.7.5</hadoop.version>
    </properties>
    <dependencies>
        <!--依赖Scala语言-->
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>

        <!--SparkCore依赖-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!-- spark-streaming-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.12</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!--spark-streaming+Kafka依赖-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_2.12</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!--SparkSQL依赖-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.12</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!--SparkSQL+ Hive依赖-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.12</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive-thriftserver_2.12</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!--StructuredStreaming+Kafka依赖-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql-kafka-0-10_2.12</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!-- SparkMlLib机器学习模块,里面有ALS推荐算法-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-mllib_2.12</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.5</version>
        </dependency>

        <dependency>
            <groupId>com.hankcs</groupId>
            <artifactId>hanlp</artifactId>
            <version>portable-1.7.7</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>8.0.23</version>
        </dependency>

        <dependency>
            <groupId>redis.clients</groupId>
            <artifactId>jedis</artifactId>
            <version>2.9.0</version>
        </dependency>

        <dependency>
            <groupId>com.alibaba</groupId>
            <artifactId>fastjson</artifactId>
            <version>1.2.47</version>
        </dependency>

        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
            <version>1.18.2</version>
            <scope>provided</scope>
        </dependency>
    </dependencies>

    <build>
        <sourceDirectory>src/main/scala</sourceDirectory>
        <plugins>
            <!-- 指定编译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>
                <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-surefire-plugin</artifactId>
                <version>2.18.1</version>
                <configuration>
                    <useFile>false</useFile>
                    <disableXmlReport>true</disableXmlReport>
                    <includes>
                        <include>**/*Test.*</include>
                        <include>**/*Suite.*</include>
                    </includes>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>2.3</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.ManifestResourceTransformer">
                                    <mainClass></mainClass>
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
</project>

    2、数据展示


    3、代码编写

package org.example.spark

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

object word {
  def main(args: Array[String]): Unit = {
    //准备环境
    val conf = new SparkConf().setMaster("local[*]").setAppName("wordcount")
    val sc = new SparkContext(conf)

    //加载文件
    val rdd1: RDD[String] = sc.textFile("data/input/words.txt")

    // 处理数据
    val rdd2: RDD[String] = rdd1.flatMap(lp => {
      lp.split(" ")
    })

    val rdd3: RDD[(String, Int)] = rdd2.map(it => (it, 1))

    val rdd4: RDD[(String, Int)] = rdd3.reduceByKey((curr, agg) => curr + agg)

    val result: Array[(String, Int)] = rdd4.collect()

    result.foreach(i => println(i))

  }
}

    4、本地运行


    注：单词统计案例本地效果如图所示

二、集群运行spark程序

    1、修改代码

val rdd1: RDD[String] = sc.textFile("hdfs:///input/wordcount.txt")

rdd4.saveAsTextFile("hdfs://192.168.231.247:8020/output/output1")

    注：集群运行文件加载路径设置为hdfs，即每次集群运行从hdfs拿取数据，并将实时数据上传至hdfs

    2、打包jar


    注：双击maven中的package，maven会自动进行清除缓存，测试并打包为jar 

    3、找到项目路径中的jar包


    注：jar包大小最小的为不是带全部依赖的jar包，在集群运行不需要全部的依赖，即上传最小依赖的jar包即可 

    4、上传至linux


    注：此处使用xftp进行传输 jar包

    5、启动 hadoop 以及 spark 集群 


    6、进入spark安装目录下执行

bin/spark-submit --class org.example.spark.word --master spark://master:8020 /input/original-SparkDemo-1.0-SNAPSHOT.jar 


     注：单词统计集群运行如图所示

    7、进入hdfs  web端目录进行查看

Spark-两种运行方式（本地运行，提交集群运行）完成

标签： spark hdfs hadoop
本文转载自: https://blog.csdn.net/m0_54925305/article/details/122919106
版权归原作者 托马斯-酷涛 所有， 如有侵权，请联系我们删除。
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