hadoop 学习自定义排序

自定义排序，是基于k2的排序，设现有以下一组数据，分别表示矩形的长和宽，先按照面积的升序进行排序。

9    9
6    6
7    8
1    1
5    4

　　

现在需要重新定义数据类型，MR的key值必须继承WritableComparable接口，因此定义RectangleWritable数据类型如下：

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.WritableComparable;
public class RectangleWritable implements WritableComparable {
int length,width;

public RectangleWritable() {
super();
// TODO Auto-generated constructor stub
}
public RectangleWritable(int length, int width) {
super();
this.length = length;
this.width = width;
}

public int getLength() {
return length;
}
public void setLength(int length) {
this.length = length;
}
public int getWidth() {
return width;
}
public void setWidth(int width) {
this.width = width;
}
@Override
public void write(DataOutput out) throws IOException {
// TODO Auto-generated method stub
out.writeInt(length);
out.writeInt(width);
}
@Override
public void readFields(DataInput in) throws IOException {
// TODO Auto-generated method stub
this.length=in.readInt();
this.width=in.readInt();
}
@Override
public int compareTo(Object arg0) {
// TODO Auto-generated method stub
RectangleWritable other = (RectangleWritable)arg0;
if (this.getLength() * this.getWidth() > other.length * other.width ) {
return 1;
}
if (this.getLength() * this.getWidth() < other.length * other.width ) {
return -1;
}
return 0;
}
@Override
public String toString() {
return   this.getLength() + "\t" + this.getWidth();
}

}

　　

其中，compareTo方法自定义排序规则，然后由框架进行排序。

map函数和Reduce函数并无大变化，还是按照WrodCount的思路进行，具体代码如下：

import java.io.IOException;
import java.net.URI;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.Reducer;
public class SelfDefineSort {
/**
* @param args
* @author nwpulisz
* @date 2016.4.1
*/
static final String INPUT_PATH="hdfs://192.168.255.132:9000/input";
static final String OUTPUT_PATH="hdfs://192.168.255.132:9000/output";

public static void main(String[] args) throws Exception {
// TODO Auto-generated method stub
Configuration conf = new Configuration();
Path outPut_path= new Path(OUTPUT_PATH);
Job job = new Job(conf, "SelfDefineSort");

//如果输出路径是存在的，则提前删除输出路径
FileSystem fileSystem = FileSystem.get(new URI(OUTPUT_PATH), conf);
if(fileSystem.exists(outPut_path))
{
fileSystem.delete(outPut_path,true);
}
job.setJarByClass(RectangleWritable.class);
FileInputFormat.setInputPaths(job, INPUT_PATH);
FileOutputFormat.setOutputPath(job, outPut_path);

job.setMapperClass(MyMapper.class);
job.setReducerClass(MyReducer.class);

job.setMapOutputKeyClass(RectangleWritable.class);
job.setMapOutputValueClass(NullWritable.class);

job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(IntWritable.class);
job.waitForCompletion(true);
}

static class MyMapper extends Mapper<LongWritable, Text, RectangleWritable, NullWritable>{
protected void map(LongWritable k1, Text v1,
Context context) throws IOException, InterruptedException {
String[] splits = v1.toString().split("\t");
RectangleWritable k2 = new RectangleWritable(Integer.parseInt(splits[0]),
Integer.parseInt(splits[1]));

context.write(k2,NullWritable.get());
}
}

static class MyReducer extends Reducer<RectangleWritable, NullWritable,
IntWritable, IntWritable>{
protected void reduce(RectangleWritable k2,
Iterable<NullWritable> v2s,
Context context)
throws IOException, InterruptedException {
// TODO Auto-generated method stub
context.write(new IntWritable(k2.getLength()), new IntWritable(k2.getWidth()));
}

}
}

　　

根据自定义结果，输出结果如下：



来自为知笔记(Wiz)