Spark入门之idea编写Scala脚本

一、安装Scala插件

1、File->Settings

2、Plugins->Msrketplace->搜索Scala并安装

（或者自己下载合适的scala版本，教程：自己给idea下载Scala插件 - 我试试这个昵称好使不 - 博客园 (cnblogs.com)）

3、重启idea

二、新建Scala项目

1、新建Maven项目File->new->Project

2、pom.xml

<?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>hello_spark</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>5.1.38</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>

3、src like this（data可以忽视）

4、新建WordCound.scala

package org.example.hello

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

/**
 * Author itcast
 * Desc 演示Spark入门案例-WordCount
 */
object WordCount {
  def main(args: Array[String]): Unit = {
    if(args.length < 2){
      println("请指定input和output")
      System.exit(1)//非0表示非正常退出程序
    }
    //TODO 1.env/准备sc/SparkContext/Spark上下文执行环境
    val conf: SparkConf = new SparkConf().setAppName("wc")//.setMaster("local[*]")
    val sc: SparkContext = new SparkContext(conf)
    sc.setLogLevel("WARN")

    //TODO 2.source/读取数据
    //RDD:A Resilient Distributed Dataset (RDD):弹性分布式数据集,简单理解为分布式集合!使用起来和普通集合一样简单!
    //RDD[就是一行行的数据]
    val lines: RDD[String] = sc.textFile(args(0))//注意提交任务时需要指定input参数

    //TODO 3.transformation/数据操作/转换
    //切割:RDD[一个个的单词]
    val words: RDD[String] = lines.flatMap(_.split(" "))
    //记为1:RDD[(单词, 1)]
    val wordAndOnes: RDD[(String, Int)] = words.map((_,1))
    //分组聚合:groupBy + mapValues(_.map(_._2).reduce(_+_)) ===>在Spark里面分组+聚合一步搞定:reduceByKey
    val result: RDD[(String, Int)] = wordAndOnes.reduceByKey(_+_)

    //TODO 4.sink/输出
    //直接输出
    //result.foreach(println)
    //收集为本地集合再输出
    //println(result.collect().toBuffer)
    //输出到指定path(可以是文件/夹)
    //如果涉及到HDFS权限问题不能写入,需要执行:
    //hadoop fs -chmod -R 777  /
    //并添加如下代码
    System.setProperty("HADOOP_USER_NAME", "hadoop")
    result.repartition(1).saveAsTextFile(args(1))//注意提交任务时需要指定output参数

    //为了便于查看Web-UI可以让程序睡一会
    //Thread.sleep(1000 * 60)

    //TODO 5.关闭资源
    sc.stop()
  }
}

三、打包并上传

在下面找到jar包输出路径

将jar包上传至虚拟机

四、虚拟机

1、新建words.txt

vim /data/words.txt

hello me you her
hello me you
hello me
hello

2、新建hdfs目录并上传words.txt

hadoop fs -mkdir -p /wordcount/input

hadoop fs -put /data/words.txt /wordcount/input/words.txt

3、提交任务

SPARK_HOME=/export/server/spark
${SPARK_HOME}/bin/spark-submit \
--master yarn \
--deploy-mode cluster \
--driver-memory 512m \
--executor-memory 512m \
--num-executors 1 \
--class cn.itcast.hello.WordCount \
/data/wc.jar \
hdfs://node01:8020/wordcount/input/words.txt \
hdfs://node01:8020/wordcount/output47_3

4、查看任务进程

http://node01:8088

5、查看结果

http://node01:50070/explorer.html#/wordcount/output47_3