Hadoop mapreduce 数据去重 数据排序小例子

cmfr

数据去重：
　　数据去重，只是让出现的数据仅一次，所以在reduce阶段key作为输入，而对于values-in没有要求，即输入的key直接作为输出的key，并将value置空。具体步骤类似于wordcount：
　　Tip：输入输出路径配置。
　　

import java.io.IOException;
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;
public class Dedup {
/**
* @param XD
*/
public static class Map extends Mapper<Object,Text,Text,Text>{
private static Text line = new Text();
//map function
public void map(Object key,Text value,Context context) throws IOException, InterruptedException{
line  =  value;
context.write(line, new Text(&quot;&quot;));
}
}
public static class Reduce extends Reducer<Text,Text,Text,Text>{
public void reduce(Text key,Iterable<Text>values,Context context) throws IOException, InterruptedException{
context.write(key, new Text(&quot;&quot;));
}
}
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
// TODO Auto-generated method stub
//初始化配置
Configuration conf = new Configuration();
/*类比与之前默认的args，只是在程序中实现配置，这样不必去eclipse的arguments属性添加参数，
**但是认为作用一样根据个人喜好设置,如下图所示：
*/
//设置输入输出路径
String[] ioArgs = new String[]{&quot;hdfs://localhost:9000/home/xd/hadoop_tmp/DedupIn&quot;,
&quot;hdfs://localhost:9000/home/xd/hadoop_tmp/DedupOut&quot;};
String[] otherArgs = new GenericOptionsParser(conf,ioArgs).getRemainingArgs();
if(otherArgs.length!=2){
System.err.println(&quot;Usage:Data Deduplication <in> <out>&quot;);
System.exit(2);
}
//设置作业
Job job = new Job(conf,&quot;Dedup Job&quot;);
job.setJarByClass(Dedup.class);
//设置处理map,combine,reduce的类
job.setMapperClass(Map.class);
job.setCombinerClass(Reduce.class);
job.setReducerClass(Reduce.class);
//设置输入输出格式的处理
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
//设定路径
FileInputFormat.addInputPath(job,new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job,new Path(otherArgs[1]));
/*
* 对应于自动的寻找路径
* FileInputFormat.addInputPath(job,new Path(args[0]));
* FileOutputFormat.setOutputPath(job,new Path(args[1]));
* */
job.waitForCompletion(true);
//打印相关信息
System.out.println(&quot;任务名称: &quot;+job.getJobName());
System.out.println(&quot;任务成功: &quot;+(job.isSuccessful()?&quot;Yes&quot;:&quot;No&quot;));
}
}



　　

数据排序：

数据排序的时候，在map的阶段已经处理好了， 只是reduce在输出的时候用行号去标记一下，样例如下：
import java.io.IOException;
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;
import org.apache.hadoop.util.GenericOptionsParser;
public class DataSort {
/**
* @param XD
*/
public static class Map extends Mapper<Object,Text,IntWritable,IntWritable>{
private static IntWritable data = new IntWritable();
public void map(Object key,Text value,Context context) throws IOException, InterruptedException{
String line = value.toString();
data.set(Integer.parseInt(line));
context.write(data, new IntWritable(1));
}
}
public static class Reduce extends Reducer<IntWritable,IntWritable,IntWritable,IntWritable>{
private static IntWritable linenum = new IntWritable(1);
public void reduce(IntWritable key,Iterable<IntWritable> values,Context context) throws IOException, InterruptedException{
for(IntWritable val:values){
context.write(linenum,key);
linenum = new IntWritable(linenum.get()+1);
}
}
}
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
// TODO Auto-generated method stub
//初始化配置
Configuration conf = new Configuration();
/*类比与之前默认的args，只是在程序中实现配置，这样不必去eclipse的arguments属性添加参数，
**但是认为作用一样根据个人喜好设置,如下图所示：
*/
//设置输入输出路径
String[] ioArgs = new String[]{&quot;hdfs://localhost:9000/home/xd/hadoop_tmp/Sort_in&quot;,
&quot;hdfs://localhost:9000/home/xd/hadoop_tmp/Sort_out&quot;};
String[] otherArgs = new GenericOptionsParser(conf,ioArgs).getRemainingArgs();
if(otherArgs.length!=2){
System.err.println(&quot;Usage:Data Deduplication <in> <out>&quot;);
System.exit(2);
}
//设置作业
Job job = new Job(conf,&quot;Datasort Job&quot;);
job.setJarByClass(DataSort.class);
//设置处理map,reduce的类
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
//设置输入输出格式的处理
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(IntWritable.class);
//设定路径
FileInputFormat.addInputPath(job,new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job,new Path(otherArgs[1]));
/*
* 对应于自动的寻找路径
* FileInputFormat.addInputPath(job,new Path(args[0]));
* FileOutputFormat.setOutputPath(job,new Path(args[1]));
* */
job.waitForCompletion(true);
//打印相关信息
System.out.println(&quot;任务名称: &quot;+job.getJobName());
System.out.println(&quot;任务成功: &quot;+(job.isSuccessful()?&quot;Yes&quot;:&quot;No&quot;));
}
}







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