mvn+eclipse构建hadoop项目并运行(超简单hadoop开发入门指南)

本文详述如何在windows开发环境下通过mvn+eclipse构建hadoop项目并运行

必备环境

• windows7操作系统
• eclipse-4.4.2
• mvn-3.0.3及用mvn生成项目架构(参阅mvn入门指南)
• hadoop-2.5.2（直接上hadoop官网下载hadoop-2.5.2.tar.gz并解压到某个目录）

windows7下环境配置

1、本地hadoop环境配置
添加环境变量HADOOP_HOME=E:\doc_api\ebook\hadoop-2.5.2
追加环境变量path内容：%HADOOP_HOME%\bin

2、bin下增加hadoop.dll,winutils.exe文件
从github或从我的csdn资源页下载hadoop.dll,winutils.exe，放置到${HADOOP_HOME}\bin目录下

构建hadoop项目

下面以经典的WordCount为例，构建我们第一个hadoop项目。

• 引包

pom文件中加入依赖包

<dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-mapreduce-client-core</artifactId>
    <version>2.5.2</version>
</dependency>
<dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-common</artifactId>
    <version>2.5.2</version>
    <exclusions>
                <exclusion>
                    <groupId>tomcat</groupId>
                    <artifactId>jasper-compiler</artifactId>
                </exclusion>
    </exclusions>
</dependency>
<dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-hdfs</artifactId>
    <version>2.5.2</version>
</dependency>
<dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-mapreduce-client-common</artifactId>
    <version>2.5.2</version>
</dependency>

注意：hadoop-common引入项排除了jasper-compiler.jar包，否则可能与tomcat自带的jsp编译器冲突，报如下错误
org.eclipse.jdt.internal.compiler.CompilationResult.getProblems()[Lorg/eclipse/jdt/core/compiler/IProblem

• 编写WordCount类如下

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

/**
 * @version 1.0
 * @author tangqian
 */
public class WordCount extends Configured implements Tool {

    public static void main(String[] args) throws Exception {
        int result = ToolRunner.run(new Configuration(),new WordCount(), args);
        System.exit(result);
    }

    @Override
    public int run(String[] args) throws Exception {
        Path inputPath, outputPath;
        if(args.length == 2){
            inputPath = new Path(args[0]);
            outputPath = new Path(args[1]);
        }else{
            System.out.println("usage <input> <output>");
            return 1;
        }
        Configuration conf = getConf();
        Job job = Job.getInstance(conf, "word count");

        job.setJarByClass(WordCount.class);
        job.setMapperClass(WordCountMapper.class);
        job.setReducerClass(WordCountReducer.class);

        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        FileInputFormat.addInputPath(job, inputPath);
        FileOutputFormat.setOutputPath(job, outputPath);

        return job.waitForCompletion(true) ? 0 : 1;
    }

    public static class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());
                context.write(word, one);
            }

        }
    }

    public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        @Override
        public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable value : values) {
                sum += value.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

}

然后在该类上右键Run As->Run Configurations->Arguments标签的Program arguments中指定输入路径和输出路径如下：

file:///e:/word.txt file:///e:/hadoop/result2

点Run即可运行该类，此时可在Console看到输出信息。等完成后，可到e:/hadoop/result2看到结果文件part-r-00000内容如下

is  1
test    2
this    1
two 1

说明：由于是在本地hadoop单机模式下运行，故采用本地文件系统（以file://开头指定输入输出路径）。

附
hadoop-2.5.2集群安装指南(参阅http://blog.csdn.net/tang9140/article/details/42869531)

如何修改Windows7下的hosts文件？
hosts文件一般在C:\Windows\System32\drivers\etc目录下，在windows7下如果不是管理员身份登录，可能无权限修改，此时可右键hosts文件->属性->安全->编辑，选择当前登录用户，开放修改权限即可，具体操作如下图。

<script type="text/javascript"> $(function () { $('pre.prettyprint code').each(function () { var lines = $(this).text().split('\n').length; var $numbering = $('<ul/>').addClass('pre-numbering').hide(); $(this).addClass('has-numbering').parent().append($numbering); for (i = 1; i <= lines; i++) { $numbering.append($('<li/>').text(i)); }; $numbering.fadeIn(1700); }); }); </script>

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