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idea里搭建spark、mapreduce开发环境_idea2022社区版配置mapreduce

作者：繁依Fanyi0 | 2024-04-15 10:03:27

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idea2022社区版配置mapreduce

1、安装scala的idea插件：

file —— settings —— plugins ，输入scala，搜索插件下载安装，注意版本：

2、配置scala的SDK：

先下载解压scala，直接从linux服务器端解压一个就行

file —— project structure —— library，配置之后，new就可以看到scala class了：

配置spark和scala的环境变量：

分别下载hadoop，spark和scala解压，增加环境变量：

3、新建maven项目：

file —— new project —— maven ，

有2个xml配置文件如下：

（1）pom.xml

  1. <?xml version="1.0" encoding="UTF-8"?>
  2. <project xmlns="http://maven.apache.org/POM/4.0.0"
  3.          xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  4.          xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  5.     <modelVersion>4.0.0</modelVersion>
  6.  
  7.     <groupId>Learn-BigData</groupId>
  8.     <artifactId>bigdata</artifactId>
  9.     <version>1.0-SNAPSHOT</version>
  10.  
  11.     <properties>
  12.         <maven.compiler.source>1.8</maven.compiler.source>
  13.         <maven.compiler.target>1.8</maven.compiler.target>
  14.         <scala.version>2.11.8</scala.version>
  15.         <spark.version>2.4.0</spark.version>
  16.         <hadoop.version>2.8.5</hadoop.version>
  17.         <encoding>UTF-8</encoding>
  18.     </properties>
  19.  
  20.     <repositories>
  21.         <repository>
  22.             <id>nexus-aliyun</id>
  23.             <name>Nexus aliyun</name>
  24.             <url>http://maven.aliyun.com/nexus/content/groups/public</url>
  25.         </repository>
  26.     </repositories>
  27.  
  28.     <dependencies>
  29.         <!-- 导入scala的依赖 -->
  30.         <dependency>
  31.             <groupId>org.scala-lang</groupId>
  32.             <artifactId>scala-library</artifactId>
  33.             <version>${scala.version}</version>
  34.             <scope>compile</scope>
  35.         </dependency>
  36.  
  37.         <!-- 导入spark的依赖 -->
  38.         <dependency>
  39.             <groupId>org.apache.spark</groupId>
  40.             <artifactId>spark-core_2.11</artifactId>
  41.             <version>${spark.version}</version>
  42.             <scope>compile</scope>
  43.         </dependency>
  44.  
  45.         <dependency>
  46.             <groupId>org.apache.spark</groupId>
  47.             <artifactId>spark-sql_2.11</artifactId>
  48.             <version>${spark.version}</version>
  49.             <scope>compile</scope>
  50.         </dependency>
  51.  
  52.         <dependency>
  53.             <groupId>org.apache.spark</groupId>
  54.             <artifactId>spark-hive_2.11</artifactId>
  55.             <version>${spark.version}</version>
  56.             <scope>compile</scope>
  57.         </dependency>
  58.  
  59.         <!-- 指定hadoop-client API的版本 -->
  60.         <dependency>
  61.             <groupId>org.apache.hadoop</groupId>
  62.             <artifactId>hadoop-client</artifactId>
  63.             <version>${hadoop.version}</version>
  64.             <scope>compile</scope>
  65.         </dependency>
  66.  
  67.         <dependency>
  68.             <groupId>org.apache.hadoop</groupId>
  69.             <artifactId>hadoop-common</artifactId>
  70.             <version>${hadoop.version}</version>
  71.             <scope>compile</scope>
  72.         </dependency>
  73.  
  74.         <dependency>
  75.             <groupId>org.apache.hadoop</groupId>
  76.             <artifactId>hadoop-hdfs</artifactId>
  77.             <version>${hadoop.version}</version>
  78.             <scope>compile</scope>
  79.         </dependency>
  80.         <dependency>
  81.             <groupId>org.apache.hadoop</groupId>
  82.             <artifactId>hadoop-mapreduce-client-common</artifactId>
  83.             <version>${hadoop.version}</version>
  84.             <scope>compile</scope>
  85.         </dependency>
  86.         <dependency>
  87.             <groupId>org.apache.hadoop</groupId>
  88.             <artifactId>hadoop-mapreduce-client-core</artifactId>
  89.             <version>${hadoop.version}</version>
  90.             <scope>compile</scope>
  91.         </dependency>
  92.  
  93.         <dependency>
  94.             <groupId>org.apache.hadoop</groupId>
  95.             <artifactId>hadoop-mapreduce-client-jobclient</artifactId>
  96.             <version>${hadoop.version}</version>
  97.             <scope>compile</scope>
  98.         </dependency>
  99.  
  100.         <dependency>
  101.             <groupId>commons-cli</groupId>
  102.             <artifactId>commons-cli</artifactId>
  103.             <version>1.3.1</version>
  104.             <scope>compile</scope>
  105.         </dependency>
  106.  
  107.         <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming -->
  108.         <dependency>
  109.             <groupId>org.apache.spark</groupId>
  110.             <artifactId>spark-streaming_2.11</artifactId>
  111.             <version>${spark.version}</version>
  112.             <scope>compile</scope>
  113.         </dependency>
  114.  
  115.         <dependency>
  116.             <groupId>org.apache.spark</groupId>
  117.             <artifactId>spark-mllib_2.11</artifactId>
  118.             <version>${spark.version}</version>
  119.             <scope>compile</scope>
  120.         </dependency>
  121.  
  122.         <dependency>
  123.             <groupId>commons-configuration</groupId>
  124.             <artifactId>commons-configuration</artifactId>
  125.             <version>1.6</version>
  126.             <scope>compile</scope>
  127.         </dependency>
  128.  
  129.     </dependencies>
  130.  
  131.     <build>
  132.         <pluginManagement>
  133.             <plugins>
  134.                 <!-- 编译scala的插件 -->
  135.                 <plugin>
  136.                     <groupId>net.alchim31.maven</groupId>
  137.                     <artifactId>scala-maven-plugin</artifactId>
  138.                     <version>3.2.2</version>
  139.                 </plugin>
  140.                 <!-- 编译java的插件 -->
  141.                 <plugin>
  142.                     <groupId>org.apache.maven.plugins</groupId>
  143.                     <artifactId>maven-compiler-plugin</artifactId>
  144.                     <version>3.5.1</version>
  145.                 </plugin>
  146.             </plugins>
  147.         </pluginManagement>
  148.         <plugins>
  149.             <plugin>
  150.                 <groupId>net.alchim31.maven</groupId>
  151.                 <artifactId>scala-maven-plugin</artifactId>
  152.                 <executions>
  153.                     <execution>
  154.                         <id>scala-compile-first</id>
  155.                         <phase>process-resources</phase>
  156.                         <goals>
  157.                             <goal>add-source</goal>
  158.                             <goal>compile</goal>
  159.                         </goals>
  160.                     </execution>
  161.                     <execution>
  162.                         <id>scala-test-compile</id>
  163.                         <phase>process-test-resources</phase>
  164.                         <goals>
  165.                             <goal>testCompile</goal>
  166.                         </goals>
  167.                     </execution>
  168.                 </executions>
  169.             </plugin>
  170.  
  171.             <plugin>
  172.                 <groupId>org.apache.maven.plugins</groupId>
  173.                 <artifactId>maven-compiler-plugin</artifactId>
  174.                 <executions>
  175.                     <execution>
  176.                         <phase>compile</phase>
  177.                         <goals>
  178.                             <goal>compile</goal>
  179.                         </goals>
  180.                     </execution>
  181.                 </executions>
  182.             </plugin>
  183.  
  184.  
  185.             <!-- 打jar插件 -->
  186.             <plugin>
  187.                 <groupId>org.apache.maven.plugins</groupId>
  188.                 <artifactId>maven-shade-plugin</artifactId>
  189.                 <version>2.4.3</version>
  190.  
  191.                 <configuration>
  192.                     <createDependencyReducedPom>false</createDependencyReducedPom>
  193.                 </configuration>
  194.  
  195.                 <executions>
  196.                     <execution>
  197.                         <phase>package</phase>
  198.                         <goals>
  199.                             <goal>shade</goal>
  200.                         </goals>
  201.                         <configuration>
  202.                             <filters>
  203.                                 <filter>
  204.                                     <artifact>*:*</artifact>
  205.                                     <excludes>
  206.                                         <exclude>META-INF/*.SF</exclude>
  207.                                         <exclude>META-INF/*.DSA</exclude>
  208.                                         <exclude>META-INF/*.RSA</exclude>
  209.                                     </excludes>
  210.                                 </filter>
  211.                             </filters>
  212.                         </configuration>
  213.                     </execution>
  214.                 </executions>
  215.             </plugin>
  216.         </plugins>
  217.     </build>
  218.  
  219.  
  220. </project>

（2）dependency-reduced-pom.xml，这个文件是打包时生成的，没啥用。

4、编写JavaWordCount

  1. package cn.edu360.spark;
  2.  
  3. import org.apache.spark.SparkConf;
  4. import org.apache.spark.api.java.JavaPairRDD;
  5. import org.apache.spark.api.java.JavaRDD;
  6. import org.apache.spark.api.java.JavaSparkContext;
  7. import org.apache.spark.api.java.function.FlatMapFunction;
  8. import org.apache.spark.api.java.function.Function2;
  9. import org.apache.spark.api.java.function.PairFunction;
  10. import scala.Tuple2;
  11.  
  12. import java.util.Arrays;
  13. import java.util.Iterator;
  14.  
  15.  
  16. public class JavaWordCount {
  17.  
  18.     public static void main(String[] args) {
  19.  
  20.         SparkConf conf = new SparkConf().setAppName("JavaWordCount");
  21.         //创建sparkContext
  22.         JavaSparkContext jsc = new JavaSparkContext(conf);
  23.         //指定以后从哪里读取数据
  24.         JavaRDD<String> lines = jsc.textFile(args[0]);
  25.         //切分压平
  26.         JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
  27.             @Override
  28.             public Iterator<String> call(String line) throws Exception {
  29.                 return Arrays.asList(line.split(" ")).iterator();
  30.             }
  31.         });
  32.  
  33.         //将单词和一组合在一起
  34.         JavaPairRDD<String, Integer> wordAndOne = words.mapToPair(new PairFunction<String, String, Integer>() {
  35.             @Override
  36.             public Tuple2<String, Integer> call(String word) throws Exception {
  37.                 return new Tuple2<>(word, 1);
  38.             }
  39.         });
  40.  
  41.         //聚合
  42.         JavaPairRDD<String, Integer> reduced = wordAndOne.reduceByKey(new Function2<Integer, Integer, Integer>() {
  43.             @Override
  44.             public Integer call(Integer v1, Integer v2) throws Exception {
  45.                 return v1 + v2;
  46.             }
  47.         });
  48.  
  49.         //调换顺序
  50.         JavaPairRDD<Integer, String> swaped = reduced.mapToPair(new PairFunction<Tuple2<String, Integer>, Integer, String>() {
  51.             @Override
  52.             public Tuple2<Integer, String> call(Tuple2<String, Integer> tp) throws Exception {
  53.                 //return new Tuple2<>(tp._2, tp._1);
  54.                 return tp.swap();
  55.             }
  56.         });
  57.  
  58.         //排序
  59.         JavaPairRDD<Integer, String> sorted = swaped.sortByKey(false);
  60.  
  61.         //调整顺序
  62.         JavaPairRDD<String, Integer> result = sorted.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
  63.             @Override
  64.             public Tuple2<String, Integer> call(Tuple2<Integer, String> tp) throws Exception {
  65.                 return tp.swap();
  66.             }
  67.         });
  68.  
  69.         //将数据保存到hdfs
  70.         result.saveAsTextFile(args[1]);
  71.  
  72.         //释放资源
  73.         jsc.stop();
  74.         
  75.     }
  76. }

5、打包：双击package打包：

view  --  tool windows  --   maven project，没有出现如下目录时，点击+号，去选中pom.xml文件：

6、打包成功后，选择这个：

7、进入上传到linux服务器上：

  1. 进入spark的安装目录的bin目录，执行以下代码：
  2. spark-submit --master spark://hdp-01:7077 --class cn.edu360.spark.JavaWordCount /root/learn_dh/original-SparkTest-1.0-SNAPSHOT.jar hdfs://hdp-01:9000/spark/input/test.txt hdfs://hdp-01:9000/spark/output/wc1005

命令解释：

1、--master spark://hdp-01:7077 ，指定spark集群的master

2、--class cn.edu360.spark.JavaWordCount，指定java类名全路径

3、/root/learn_dh/original-SparkTest-1.0-SNAPSHOT.jar，指定这个jar包在linux服务器上jar的绝对路径

4、hdfs://hdp-01:9000/spark/input/test.txt ，HDFS上输入文件的路径

5、hdfs://hdp-01:9000/spark/output/wc1005，HDFS上输出文件路径。（这路径不能是已经存在的，否则会报错）

可以在http://hdp-01:8080/这里查看执行情况。

8、本地运行spark程序，则：

setMaster为local，

本地运行时，要配置输入输出文件的路径：

9、idea打开项目时，需要选中到src这一级目录，否则，打开后看不见项目结构图，这个坑的很啊：

例如，直接选中项目根目录打开是这样的，初学者注意下，有点莫名其妙的。

10、本地运行mapreduce和spark程序：

配置好上面的pom文件之后，不用再按照网上说的添加spark和hadoop的jar包，spark设置setMaster("local")就可以运行。

二步设置：

1、设置运行环境，edit configuration：

2、

新建maven项目，配置好pom后，不用再添加其他spark和hadoop的jar包，否则容易报莫名其妙的错误，估计是依赖冲突造成的。

在windows下配置好scala、hadoop、spark的环境变量之后：

在cmd下，输入，scala、spark-shell，可直接在本地编写scala、spark程序

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