【转】ChainMapper 实例理解一

通过ChainMapper可以将多个map类合并成一个map任务。

下面个这个例子没什么实际意思，但是很好的演示了ChainMapper的作用。

源文件
100 tom 90
101 mary 85
102 kate 60

map00的结果，过滤掉100的记录
101 mary 85
102 kate 60

map01的结果，过滤掉101的记录
102 kate 60

reduce结果
102 kate 60

import java.io.IOException;
import java.util.*;
import java.lang.String;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.util.*;
import org.apache.hadoop.mapred.lib.*;

public class WordCount
{

public static class Map00 extends MapReduceBase implements Mapper
{

public void map(Text key, Text value, OutputCollector output, Reporter reporter) throws IOException
{

Text ft = new Text(“100″);

if(!key.equals(ft))
{
output.collect(key, value);
}
}
}

public static class Map01 extends MapReduceBase implements Mapper
{

public void map(Text key, Text value, OutputCollector output, Reporter reporter) throws IOException
{

Text ft = new Text(“101″);

if(!key.equals(ft))
{
output.collect(key, value);
}
}
}

public static class Reduce extends MapReduceBase implements Reducer
{
public void reduce(Text key, Iterator values, OutputCollector output, Reporter reporter) throws IOException
{

while(values.hasNext())
{
output.collect(key, values.next());
}

}
}

public static void main(String[] args) throws Exception
{

JobConf conf = new JobConf(WordCount.class);
conf.setJobName(“wordcount00″);

conf.setInputFormat(KeyValueTextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);

ChainMapper cm = new ChainMapper();

JobConf mapAConf = new JobConf(false);
cm.addMapper(conf, Map00.class, Text.class, Text.class, Text.class, Text.class, true, mapAConf);

JobConf mapBConf = new JobConf(false);
cm.addMapper(conf, Map01.class, Text.class, Text.class, Text.class, Text.class, true, mapBConf);

conf.setReducerClass(Reduce.class);

conf00.setOutputKeyClass(Text.class);
conf00.setOutputValueClass(Text.class);

FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));

JobClient.runJob(conf);

}
}

总结：

　　1.一句话：ChainMapper即在Reduce之前进行多次Mapper

　　2.ChainMapper必须保证所有的子mapper输入输出是一致的！

　　3.ChainMapper中的子mapper是线性执行的