使用Maven工程来管理MR作业

当您的工程规模越来越大时，会变得非常复杂，不易管理。我们采用类似Maven这样的软件项目管理工具来进行管理。其操作步骤如下。

1.确保本地安装好Maven。

2.在IDE打开,并编辑pom.xml文件，在dependencies内添加如下内容。

<dependency>
     <groupId>org.apache.hadoop</groupId>
     <artifactId>hadoop-mapreduce-client-common</artifactId>
     <version>2.6.0</version>
 </dependency>
 <dependency>
     <groupId>org.apache.hadoop</groupId>
     <artifactId>hadoop-common</artifactId>
     <version>2.6.0</version>
 </dependency>

3.Wrodcount实例代码(以下代码来源于Hadoop官网)。

import java.io.IOException;
import java.util.StringTokenizer;
 
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;
 
public class WordCount2 {
 
    public static class TokenizerMapper
            extends Mapper<Object, Text, Text, IntWritable>{
 
        private final static 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()) {
                word.set(itr.nextToken());
                context.write(word, one);
            }
        }
    }
 
    public static class IntSumReducer
            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;
            for (IntWritable val : values) {
                sum += val.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }
 
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(WordCount2.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

4.编译并打包上传。
在工程目录下执行以下命令，您在工程目录的target目录下看到一个wordcountv2-1.0-SNAPSHOT.jar，将jar包上传到服务器上。

mvn clean package -DskipTests

5.作业输入输出。
hadoop作业的输入和输出文件，可以放在HDFS上，也可以选择放在KS3上。

（1）使用HDFS

将输入文件放到HDFS上，假设输入文件为TWILIGHT.txt。首先su hdfs用户下。

hadoop dfs -mkdir -p /user/hadoop/examples/input 
hadoop dfs -put TWILIGHT.txt /user/hadoop/examples/input

（2）使用KS3

hadoop dfs -mkdir -p ks3://kmrtest9/wordcount/lib 
hadoop dfs -mkdir -p ks3://kmrtest9/wordcount/input 
hadoop dfs -put wordcountv2-1.0-SNAPSHOT.jar ks3://kmrtest9/wordcount/lib 
hadoop dfs -put TWILIGHT.txt ks3://kmrtest9/wordcount/input

作业提交方法

（1）输入输出在HDFS上：

hadoop jar wordcountv2-1.0-SNAPSHOT.jar WordCount2 /user/hadoop/examples/input/ /user/hadoop/examples/output   

（2）输入输出在KS3上：

hadoop jar wordcountv2-1.0-SNAPSHOT.jar WordCount2 ks3://kmrtest9/wordcount/input/ ks3://kmrtest9/wordcount/output