在前面的例子中，输出文件名是默认的：
_logs part-r-00001 part-r-00003 part-r-00005 part-r-00007 part-r-00009 part-r-00011 part-r-00013 _SUCCESS part-r-00000 part-r-00002 part-r-00004 part-r-00006 part-r-00008 part-r-00010 part-r-00012 part-r-00014
还有一个_SUCCESS文件标志job运行成功。
还有一个目录_logs。
但是实际情况中，我们有时候需要根据情况定制我的输出文件名。
比如我要根据did的值分组，产生不同的输出文件。所有did出现次数在[0, 2)的都输出到a文件中，在[2, 4)的输出大b文件，其他输出到c文件。
这里涉及到的输出类是MultipleOutputs类。下面是介绍如何实现。
首先有一个小优化，为了避免每次执行时输入一长串命令，利用maven exec plugin，参考pom.xml配置如下：
<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/maven-v4_0_0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>org.freebird</groupId>
<artifactId>mr1_example2</artifactId>
<packaging>jar</packaging>
<version>1.0-SNAPSHOT</version>
<name>mr1_example2</name>
<url>http://maven.apache.org</url>
<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-core</artifactId>
<version>1.2.1</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.codehaus.mojo</groupId>
<artifactId>exec-maven-plugin</artifactId>
<version>1.3.2</version>
<executions>
<execution>
<goals>
<goal>exec</goal>
</goals>
</execution>
</executions>
<configuration>
<executable>hadoop</executable>
<arguments>
<argument>jar</argument>
<argument>target/mr1_example2-1.0-SNAPSHOT.jar</argument>
<argument>org.freebird.LogJob</argument>
<argument>/user/chenshu/share/logs</argument>
<argument>/user/chenshu/share/output12</argument>
</arguments>
</configuration>
</plugin>
</plugins>
</build>
</project>
然后在LogJob.java文件添加几行代码：
package org.freebird;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.freebird.reducer.LogReducer;
import org.freebird.mapper.LogMapper;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class LogJob {
public static void main(String[] args) throws Exception {
System.out.println("args[0]:" + args[0]);
System.out.println("args[1]:" + args[1]);
Configuration conf = new Configuration();
Job job = new Job(conf, "sum_did_from_log_file");
job.setJarByClass(LogJob.class);
job.setMapperClass(org.freebird.mapper.LogMapper.class);
job.setReducerClass(org.freebird.reducer.LogReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
MultipleOutputs.addNamedOutput(job, "a", TextOutputFormat.class, Text.class, IntWritable.class);
MultipleOutputs.addNamedOutput(job, "b", TextOutputFormat.class, Text.class, Text.class);
MultipleOutputs.addNamedOutput(job, "c", TextOutputFormat.class, Text.class, Text.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
最后修改reducer类的代码：
public class LogReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
private MultipleOutputs outputs;
@Override
public void setup(Context context) throws IOException, InterruptedException {
System.out.println("enter LogReducer:::setup method");
outputs = new MultipleOutputs(context);
}
@Override
public void cleanup(Context context) throws IOException, InterruptedException {
System.out.println("enter LogReducer:::cleanup method");
outputs.close();
}
public void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
System.out.println("enter LogReducer::reduce method");
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
System.out.println("key: " + key.toString() + " sum: " + sum);
if ((sum < 2) && (sum >= 0)) {
outputs.write("a", key, sum);
} else if (sum < 4) {
outputs.write("b", key, sum);
} else {
outputs.write("c", key, sum);
}
}
}
[chenshu@hadoopMaster output12]$ ls a-r-00000 a-r-00004 a-r-00008 a-r-00012 b-r-00001 b-r-00005 b-r-00009 b-r-00013 c-r-00002 c-r-00006 c-r-00010 c-r-00014 part-r-00002 part-r-00006 part-r-00010 part-r-00014 a-r-00001 a-r-00005 a-r-00009 a-r-00013 b-r-00002 b-r-00006 b-r-00010 b-r-00014 c-r-00003 c-r-00007 c-r-00011 _logs part-r-00003 part-r-00007 part-r-00011 _SUCCESS a-r-00002 a-r-00006 a-r-00010 a-r-00014 b-r-00003 b-r-00007 b-r-00011 c-r-00000 c-r-00004 c-r-00008 c-r-00012 part-r-00000 part-r-00004 part-r-00008 part-r-00012 a-r-00003 a-r-00007 a-r-00011 b-r-00000 b-r-00004 b-r-00008 b-r-00012 c-r-00001 c-r-00005 c-r-00009 c-r-00013 part-r-00001 part-r-00005 part-r-00009 part-r-00013
5371700bc7b2231db03afeb0 6 5371700cc7b2231db03afec0 7 5371701cc7b2231db03aff8d 6 5371709dc7b2231db03b0136 6 537170a0c7b2231db03b01ac 6 537170a6c7b2231db03b01fc 6 537170a8c7b2231db03b0217 6 537170b3c7b2231db03b0268 6 53719aa9c7b2231db03b0721 6 53719ad0c7b2231db03b0731 4
MapReduce仍然会默认生成part....文件，不用理会，都是空文件。
MapReduce 编程 系列六 MultipleOutputs使用
原文地址：http://blog.csdn.net/csfreebird/article/details/39718659
