【转载】MapReduce编程 Intellij Idea配置MapReduce编程环境

目录(?)[-]

1. 一软件环境
2. 二创建maven工程
3. 三添加maven依赖
4. 四配置log4j
5. 五启动Hadoop
6. 六运行WordCount从本地读取文件
7. 七运行WordCount从HDFS读取文件
8. 八代码下载

 

介绍如何在Intellij Idea中通过创建maven工程配置MapReduce的编程环境。

一、软件环境

我使用的软件版本如下:

1. Intellij Idea 2017.1
2. Maven 3.3.9
3. Hadoop伪分布式环境( 安装教程可参考这里)

二、创建maven工程

打开Idea,file->new->Project,左侧面板选择maven工程。(如果只跑MapReduce创建Java工程即可，不用勾选Creat from archetype，如果想创建web工程或者使用骨架可以勾选) 
 
设置GroupId和ArtifactId，下一步。 
 
设置工程存储路径，下一步。 
 
Finish之后，空白工程的路径如下图所示。

完整的工程路径如下图所示： 

三、添加maven依赖

在pom.xml添加依赖，对于Hadoop 2.7.3版本的hadoop，需要的jar包有以下几个：

• hadoop-common
• hadoop-hdfs
• hadoop-mapreduce-client-core
• hadoop-mapreduce-client-jobclient

• log4j( 打印日志)

  pom.xml中的依赖如下:

    <dependencies>
        <dependency>
            <groupId>junit</groupId> <artifactId>junit</artifactId> <version>4.12</version> <scope>test</scope> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-common</artifactId> <version>2.7.3</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-hdfs</artifactId> <version>2.7.3</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-mapreduce-client-core</artifactId> <version>2.7.3</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-mapreduce-client-jobclient</artifactId> <version>2.7.3</version> </dependency> <dependency> <groupId>log4j</groupId> <artifactId>log4j</artifactId> <version>1.2.17</version> </dependency> </dependencies>

四、配置log4j

在src/main/resources目录下新增log4j的配置文件log4j.properties，内容如下：

log4j.rootLogger = debug,stdout

### 输出信息到控制抬 ###
log4j.appender.stdout = org.apache.log4j.ConsoleAppender log4j.appender.stdout.Target = System.out log4j.appender.stdout.layout = org.apache.log4j.PatternLayout log4j.appender.stdout.layout.ConversionPattern = [%-5p] %d{yyyy-MM-dd HH:mm:ss,SSS} method:%l%n%m%n 

五、启动Hadoop

启动Hadoop，运行命令：

cd hadoop-2.7.3/
./sbin/start-all.sh

访问http://localhost:50070/查看hadoop是否正常启动。

六、运行WordCount(从本地读取文件)

在工程根目录下新建input文件夹，input文件夹下新增dream.txt，随便写入一些单词:

I have a  dream
a dream

在src/main/java目录下新建包，新增FileUtil.java，创建一个删除output文件的函数，以后就不用手动删除了。内容如下:

package com.mrtest.hadoop;

import java.io.File;

/**
 * Created by bee on 3/25/17.
 */
public class FileUtil { public static boolean deleteDir(String path) { File dir = new File(path); if (dir.exists()) { for (File f : dir.listFiles()) { if (f.isDirectory()) { deleteDir(f.getName()); } else { f.delete(); } } dir.delete(); return true; } else { System.out.println("文件(夹)不存在!"); return false; } } }

编写WordCount的MapReduce程序WordCount.java，内容如下:

package com.mrtest.hadoop;

import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import java.io.IOException; import java.util.Iterator; import java.util.StringTokenizer; /** * Created by bee on 3/25/17. */ public class WordCount { public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> { public static final IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(Object key, Text value, Context context) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { this.word.set(itr.nextToken()); context.write(this.word, one); } } } public static class IntSumReduce extends Reducer<Text, IntWritable, Text, IntWritable> { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; IntWritable val; for (Iterator i = values.iterator(); i.hasNext(); sum += val.get()) { val = (IntWritable) i.next(); } this.result.set(sum); context.write(key, this.result); } } public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { FileUtil.deleteDir("output"); Configuration conf = new Configuration(); String[] otherArgs = new String[]{"input/dream.txt","output"}; if (otherArgs.length != 2) { System.err.println("Usage:Merge and duplicate removal <in> <out>"); System.exit(2); } Job job = Job.getInstance(conf, "WordCount"); job.setJarByClass(WordCount.class); job.setMapperClass(WordCount.TokenizerMapper.class); job.setReducerClass(WordCount.IntSumReduce.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } } 

运行完毕以后，会在工程根目录下增加一个output文件夹，打开output/part-r-00000，内容如下:

I   1
a   2
dream   2
have    1

这里在main函数中新增了一个String类型的数组，如果想用main函数的args数组接受参数，在运行时指定输入和输出路径也是可以的。运行WordCount之前，配置Configuration并指定Program arguments即可。 

---

七、运行WordCount(从HDFS读取文件)

在HDFS上新建文件夹:

hadoop fs -mkdir /worddir

如果出现Namenode安全模式导致的不能创建文件夹提示：

mkdir: Cannot create directory /worddir. Name node is in safe mode.

运行以下命令关闭safe mode:

hadoop dfsadmin -safemode leave

上传本地文件:

hadoop fs -put dream.txt /worddir

修改otherArgs参数，指定输入为文件在HDFS上的路径:

String[] otherArgs = new String[]{"hdfs://localhost:9000/wo

验证过程：

 

转载于:https://www.cnblogs.com/licheng/p/6833342.html

介绍如何在Intellij Idea中通过创建maven工程配置MapReduce的编程环境。

一、软件环境

我使用的软件版本如下:

1. Intellij Idea 2017.1
2. Maven 3.3.9
3. Hadoop伪分布式环境( 安装教程可参考这里)

二、创建maven工程

打开Idea,file->new->Project,左侧面板选择maven工程。(如果只跑MapReduce创建Java工程即可，不用勾选Creat from archetype，如果想创建web工程或者使用骨架可以勾选) 
 
设置GroupId和ArtifactId，下一步。 
 
设置工程存储路径，下一步。 
 
Finish之后，空白工程的路径如下图所示。

完整的工程路径如下图所示： 

三、添加maven依赖

在pom.xml添加依赖，对于Hadoop 2.7.3版本的hadoop，需要的jar包有以下几个：

• hadoop-common
• hadoop-hdfs
• hadoop-mapreduce-client-core
• hadoop-mapreduce-client-jobclient

• log4j( 打印日志)

  pom.xml中的依赖如下:

    <dependencies>
        <dependency>
            <groupId>junit</groupId> <artifactId>junit</artifactId> <version>4.12</version> <scope>test</scope> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-common</artifactId> <version>2.7.3</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-hdfs</artifactId> <version>2.7.3</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-mapreduce-client-core</artifactId> <version>2.7.3</version> </dependency> <dependency> <groupId>org.apache.hadoop</groupId> <artifactId>hadoop-mapreduce-client-jobclient</artifactId> <version>2.7.3</version> </dependency> <dependency> <groupId>log4j</groupId> <artifactId>log4j</artifactId> <version>1.2.17</version> </dependency> </dependencies>

四、配置log4j

在src/main/resources目录下新增log4j的配置文件log4j.properties，内容如下：

log4j.rootLogger = debug,stdout

### 输出信息到控制抬 ###
log4j.appender.stdout = org.apache.log4j.ConsoleAppender log4j.appender.stdout.Target = System.out log4j.appender.stdout.layout = org.apache.log4j.PatternLayout log4j.appender.stdout.layout.ConversionPattern = [%-5p] %d{yyyy-MM-dd HH:mm:ss,SSS} method:%l%n%m%n 

五、启动Hadoop

启动Hadoop，运行命令：

cd hadoop-2.7.3/
./sbin/start-all.sh

访问http://localhost:50070/查看hadoop是否正常启动。

六、运行WordCount(从本地读取文件)

在工程根目录下新建input文件夹，input文件夹下新增dream.txt，随便写入一些单词:

I have a  dream
a dream

在src/main/java目录下新建包，新增FileUtil.java，创建一个删除output文件的函数，以后就不用手动删除了。内容如下:

package com.mrtest.hadoop;

import java.io.File;

/**
 * Created by bee on 3/25/17.
 */
public class FileUtil { public static boolean deleteDir(String path) { File dir = new File(path); if (dir.exists()) { for (File f : dir.listFiles()) { if (f.isDirectory()) { deleteDir(f.getName()); } else { f.delete(); } } dir.delete(); return true; } else { System.out.println("文件(夹)不存在!"); return false; } } }

编写WordCount的MapReduce程序WordCount.java，内容如下:

package com.mrtest.hadoop;

import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import java.io.IOException; import java.util.Iterator; import java.util.StringTokenizer; /** * Created by bee on 3/25/17. */ public class WordCount { public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> { public static final IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(Object key, Text value, Context context) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { this.word.set(itr.nextToken()); context.write(this.word, one); } } } public static class IntSumReduce extends Reducer<Text, IntWritable, Text, IntWritable> { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; IntWritable val; for (Iterator i = values.iterator(); i.hasNext(); sum += val.get()) { val = (IntWritable) i.next(); } this.result.set(sum); context.write(key, this.result); } } public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { FileUtil.deleteDir("output"); Configuration conf = new Configuration(); String[] otherArgs = new String[]{"input/dream.txt","output"}; if (otherArgs.length != 2) { System.err.println("Usage:Merge and duplicate removal <in> <out>"); System.exit(2); } Job job = Job.getInstance(conf, "WordCount"); job.setJarByClass(WordCount.class); job.setMapperClass(WordCount.TokenizerMapper.class); job.setReducerClass(WordCount.IntSumReduce.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } } 

运行完毕以后，会在工程根目录下增加一个output文件夹，打开output/part-r-00000，内容如下:

I   1
a   2
dream   2
have    1

这里在main函数中新增了一个String类型的数组，如果想用main函数的args数组接受参数，在运行时指定输入和输出路径也是可以的。运行WordCount之前，配置Configuration并指定Program arguments即可。 

---

七、运行WordCount(从HDFS读取文件)

在HDFS上新建文件夹:

hadoop fs -mkdir /worddir

如果出现Namenode安全模式导致的不能创建文件夹提示：

mkdir: Cannot create directory /worddir. Name node is in safe mode.

运行以下命令关闭safe mode:

hadoop dfsadmin -safemode leave

上传本地文件:

hadoop fs -put dream.txt /worddir

修改otherArgs参数，指定输入为文件在HDFS上的路径:

String[] otherArgs = new String[]{"hdfs://localhost:9000/wo

验证过程：