一、安装Scala插件
1、File->Settings
2、Plugins->Msrketplace->搜索Scala并安装
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
5、查看结果
http://node01:50070/explorer.html#/wordcount/output47_3