环境为Hadoop2.5.2（如何搭建环境教程），在pom.xml中加入以下配置文件。

<dependency><groupId>org.apache.hadoop</groupId><artifactId>hadoop-common</artifactId><version>2.5.2</version>
</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-client</artifactId><version>2.5.2</version>
</dependency><dependency><groupId>junit</groupId><artifactId>junit</artifactId><version>3.8.1</version><scope>test</scope>
</dependency>

测试HDFS

public class HdfsTest
{public static void main( String[] args ) throws IOException {String uri = "hdfs://192.168.1.112:9000/";Configuration config = new Configuration();FileSystem fs = FileSystem.get(URI.create(uri), config);//列出目录所有文件FileStatus[] statuses = fs.listStatus(new Path("/data"));for (FileStatus status : statuses) {System.out.println(status);}//创建新文件FSDataOutputStream os = fs.create(new Path("/data/hdfs_test.txt"));os.write("测试HDFS第一条\r\n".getBytes());os.write("测试HDFS第二条\r\n".getBytes());os.flush();os.close();//读取文件InputStream is = fs.open(new Path("/data/hdfs_test.txt"));IOUtils.copyBytes(is, System.out, 1024, true);}
}

测试Map/Reduce

实例：将多个文件里面的内容去掉重复行。

思路：把数据行当做map/reduce的key来处理即可。value可以为空。

代码实现如下：

package com.zhm;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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 org.apache.hadoop.util.GenericOptionsParser;import java.io.IOException;/*** Created by zhm on 2015/7/8.*/
public class MapReduceTest {public static class MyMapper extends Mapper<Object, Text, Text, Text> {public void map(Object key, Text value, Context context) throws IOException, InterruptedException {//将文本行放入keycontext.write(value,new Text(""));}}public static class MyReducer extends Reducer<Text,Text,Text,Text> {public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {//输出keycontext.write(key, new Text(""));}}public static void main(String[] args) throws Exception {Configuration conf = new Configuration();String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();if (otherArgs.length < 2) {System.err.println("Usage: MapReduceTest <in> <out>");System.exit(2);}Job job = Job.getInstance(conf, "MapReduceTest");job.setJarByClass(MapReduceTest.class);job.setMapperClass(MyMapper.class);job.setCombinerClass(MyReducer.class);job.setReducerClass(MyReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(Text.class);FileInputFormat.addInputPath(job, new Path(otherArgs[0]));FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));System.exit(job.waitForCompletion(true) ? 0 : 1);}
}

运行：mvn clean package 打好jar包。并上传至服务器的hadoop安装目录中。

在服务器上创建需要统计去重的文件。

mkdir /tmp/mapredcd /tmp/mapred
vi file1.txt
#输入以下内容
192.168.1.1
192.168.1.2
192.168.1.4vi file2.txt
#输入以下内容
192.168.1.3
192.168.1.2
192.168.1.5vi file3.txt
#输入以下内容
192.168.1.1
192.168.1.3
192.168.1.4#清空hdfs目录，tmp目录不要删除。主要是测试方便，也可以不删除目录，只要将文件指定一个新的测试目录就行。
hdfs dfs -rm -r -f -skipTrash /目录名#将创建好的文件上传至HDFShdfs dfs -put /tmp/mapred /input#进入hadoop安装主目录
hadoop jar maven_hadoop-1.0-SNAPSHOT.jar com.zhm.MapReduceTest /input /output#查看结果
hdfs dfs -cat /output/part-r-00000
#结果如下：
192.168.1.1
192.168.1.2
192.168.1.3
192.168.1.4
192.168.1.5