今天主要来说说怎么在Hadoop2.2.0分布式上面运行写好的 Mapreduce 程序。
可以在eclipse写好程序，export或用fatjar打包成jar文件。
先给出这个程序所依赖的Maven包：
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>Temperature</groupId>
<artifactId>Temperature</artifactId>
<version>0.0.1-SNAPSHOT</version>
<build>
<sourceDirectory>src</sourceDirectory>
<plugins>
<plugin>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.1</version>
<configuration>
<source>1.7</source>
<target>1.7</target>
</configuration>
</plugin>
</plugins>
</build>
<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.2.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.2.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-common</artifactId>
<version>2.2.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-jobclient</artifactId>
<version>2.2.0</version>
</dependency>
</dependencies>
</project>
好了，现在给出程序，代码如下：
Mapper
package org.ccnt.mr;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
public class Map extends MapReduceBase implements
Mapper<LongWritable, Text, Text, IntWritable> {
private static final int MISSING = 9999;
@Override
public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
String line = value.toString();
String year = line.substring(15, 19);
int airTemperature;
if (line.charAt(87) == ‘+‘)
airTemperature = Integer.parseInt(line.substring(88, 92));
else
airTemperature = Integer.parseInt(line.substring(87, 92));
String quality = line.substring(92, 93);
if (airTemperature != MISSING && quality.matches("[01459]")) {
output.collect(new Text(year), new IntWritable(airTemperature));
}
}
}
Reducer：
package org.ccnt.mr;
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
public class Reduce extends MapReduceBase implements
Reducer<Text, IntWritable, Text, IntWritable> {
@Override
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
int maxValue = Integer.MIN_VALUE;
while (values.hasNext()) {
maxValue = Math.max(maxValue, values.next().get());
}
output.collect(key, new IntWritable(maxValue));
}
}
Main
package org.ccnt.mr;
import java.io.IOException;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
public class MaxTemperature {
public static void main(String[] args) throws IOException {
System.out.println(args.length);
for (String string : args) {
System.out.println(string);
}
if (args.length != 2) {
System.err.println("Error");
System.exit(1);
}
JobConf conf = new JobConf(MaxTemperature.class);
conf.setJobName("Max Temperature");
FileInputFormat.addInputPath(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
conf.setMapperClass(Map.class);
conf.setReducerClass(Reduce.class);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
JobClient.runJob(conf);
}
}
将上面的程序编译和打包成jar文件，然后开始在Hadoop2.2.0（本文假定用户都部署好了Hadoop2.2.0）上面部署了。下面主要讲讲如何去部署：
首先，启动Hadoop2.2.0，命令如下：
sbin/start-dfs.sh
sbin/start-yarn.sh
打包编译jar文件有两种方式：
1）直接用export导出jar包，生成默认的MANIFEST.MF文件，不需要写main方法所在的类
使用的命令：
bin/hadoop jar ~/Downlowd/MaxTemperature.jar org.ccnt.mr.MaxTemperature input/data.txt result
2）用Fat jar工具导出jar包，不需要导出依赖的（hadoop环境有），其实也就是MANIFEST.MF文件有了main方法所在的类。
bin/hadoop jar ~/Download/Temperature input/data.txt result2
结果是一样的。
附程序测试的数据的下载地址：http://pan.baidu.com/s/1iSacM
Reference：
编写简单的Mapreduce程序并部署在Hadoop2.2.0上运行,布布扣,bubuko.com
编写简单的Mapreduce程序并部署在Hadoop2.2.0上运行
原文：http://www.cnblogs.com/549294286/p/3593573.html