spark编程入门-idea环境搭建

原文引自：http://blog.csdn.net/huanbia/article/details/69084895

1、环境准备

idea采用2017.3.1版本。

创建一个文件a.txt

2、构建maven工程

点击File->New->Project…

点击Next，其中GroupId和ArtifactId可随意命名

点击Next

点击Finish，出现如下界面：

3、书写wordCount代码

请在pom.xml中的version标签后追加如下配置

 <properties>
     <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
 </properties>
 <dependencies>
     <dependency>
         <groupId>junit</groupId>
         <artifactId>junit</artifactId>
         <version>3.8.1</version>
         <scope>test</scope>
     </dependency>
     <dependency>
         <groupId>org.apache.spark</groupId>
         <artifactId>spark-core_2.10</artifactId>
         <version>1.6.1</version>
     </dependency>
     <dependency>
         <groupId>org.apache.spark</groupId>
         <artifactId>spark-sql_2.10</artifactId>
         <version>1.6.1</version>
     </dependency>
     <dependency>
         <groupId>org.apache.spark</groupId>
         <artifactId>spark-hive_2.10</artifactId>
         <version>1.6.1</version>
     </dependency>
     <dependency>
         <groupId>org.apache.spark</groupId>
         <artifactId>spark-streaming_2.10</artifactId>
         <version>1.6.1</version>
     </dependency>
     <dependency>
         <groupId>org.apache.hadoop</groupId>
         <artifactId>hadoop-client</artifactId>
         <version>2.7.1</version>
     </dependency>
     <dependency>
         <groupId>org.apache.spark</groupId>
         <artifactId>spark-streaming-kafka_2.10</artifactId>
         <version>1.6.1</version>
     </dependency>
     <dependency>
         <groupId>org.apache.spark</groupId>
         <artifactId>spark-graphx_2.10</artifactId>
         <version>1.6.1</version>
     </dependency>
     <dependency>
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-assembly-plugin</artifactId>
         <version>2.2-beta-5</version>
     </dependency>
     <dependency>
         <groupId>commons-lang</groupId>
         <artifactId>commons-lang</artifactId>
         <version>2.3</version>
     </dependency>
 </dependencies>
 <build>
     <sourceDirectory>src/main/java</sourceDirectory>
     <testSourceDirectory>src/test/java</testSourceDirectory>
     <plugins>
         <plugin>
             <artifactId>maven-assembly-plugin</artifactId>
             <configuration>
                 <descriptorRefs>
                     <descriptorRef>jar-with-dependencies</descriptorRef>
                 </descriptorRefs>
                 <archive>
                     <manifest>
                         <maniClass></maniClass>
                     </manifest>
                 </archive>
             </configuration>
             <executions>
                 <execution>
                     <id>make-assembly</id>
                     <phase>package</phase>
                     <goals>
                         <goal>single</goal>
                     </goals>
                 </execution>
             </executions>
         </plugin>
         <plugin>
             <groupId>org.codehaus.mojo</groupId>
             <artifactId>exec-maven-plugin</artifactId>
             <version>1.3.1</version>
             <executions>
                 <execution>
                     <goals>
                         <goal>exec</goal>
                     </goals>
                 </execution>
             </executions>
             <configuration>
                 <executable>java</executable>
                 <includeProjectDependencies>false</includeProjectDependencies>
                 <classpathScope>compile</classpathScope>
                 <mainClass>com.dt.spark.SparkApps.App</mainClass>
             </configuration>
         </plugin>
         <plugin>
             <groupId>org.apache.maven.plugins</groupId>
             <artifactId>maven-compiler-plugin</artifactId>

             <configuration>
                 <source>1.6</source>
                 <target>1.6</target>
             </configuration>
         </plugin>
     </plugins>
 </build>

点击右下角的Import Changes导入相应的包

点击File->Project Structure…->Moudules，将src和main都选为Sources文件

在java文件夹下创建SparkWordCount java文件

该文件代码为：

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.api.java.function.VoidFunction;
import scala.Tuple2;

import java.util.Arrays;

/**
 * Created by hadoop on 17-4-4.
 */
public class SparkWordCount {
    public static void main(String[] args){
        SparkConf conf = new SparkConf()
                .setAppName("WordCountCluster");
        //第二步
        JavaSparkContext sc = new JavaSparkContext(conf);
        JavaRDD<String> lines = sc.textFile("hdfs://hadoop01:9000/a.txt");
        JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>(){
            private static final long serialVersionUID = 1L;

            @Override
            public Iterable<String> call(String line) throws Exception{
                return Arrays.asList(line.split(" "));
            }
        });

        JavaPairRDD<String,Integer> pairs = words.mapToPair(
                new PairFunction<String, String, Integer>() {

                    private  static final long serialVersionUID = 1L;

                    public Tuple2<String, Integer> call(String word) throws Exception {
                        return new Tuple2<String, Integer>(word,1);
                    }
                }
        );

        JavaPairRDD<String,Integer> wordCounts = pairs.reduceByKey(
                new Function2<Integer, Integer, Integer>() {
                    @Override
                    public Integer call(Integer v1, Integer v2) throws Exception {
                        return v1+v2;
                    }
                }
        );

        wordCounts.foreach(new VoidFunction<Tuple2<String, Integer>>() {
            @Override
            public void call(Tuple2<String, Integer> wordCount) throws Exception {
                System.out.println(wordCount._1+" : "+ wordCount._2 );
            }
        });

        sc.close();

    }
}

　　

打包：

执行

会在output目录下 生成可执行jar包 sparkStudy

4、jar包上传到集群并执行

从spark官方网站 下载spark-1.6.1-bin-hadoop2.6.tgz

Spark目录：

　　　　bin包含用来和Spark交互的可执行文件，如Spark shell。

　　　　examples包含一些单机Spark job,可以研究和运行这些例子。

　　Spark的Shell:

　　　　Spark的shell能够处理分布在集群上的数据。

　　　　Spark把数据加载到节点的内存中，因此分布式处理可在秒级完成。

　　　　快速使用迭代式计算，实时查询、分析一般能够在shells中完成。

　　　　Spark提供了Python shells和Scala shells。

解压

这里需要先启动集群：

　　启动master:  ./sbin/start-master.sh

　　启动worker:  ./bin/spark-class org.apache.spark.deploy.worker.Worker spark://localhost:7077

　　　　　　　　这里的地址为：启动master后,在浏览器输入localhost:8080,查看到的master地址

启动成功后，jps查看进程：

接下来执行提交命令，将打好的jar包上传到linux目录，jar包在项目目录下的out\artifacts下。

　　提交作业： ./bin/spark-submit --master spark://localhost:7077 --class WordCount /home/lucy/learnspark.jar

　　可以在4040端口查看job进度：

将执行的包上传到服务器上，封装执行的脚本。

然后执行脚本，执行结果如下：

任务执行结束。