13518219792
本篇文章为大家展示了Hadoop中怎么自定义输出排序，内容简明扼要并且容易理解，绝对能使你眼前一亮，通过这篇文章的详细介绍希望你能有所收获。
package com.hgs;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
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;
public class AvgValue {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
if(args.length!=2) {
System.err.println("Usage: MaxTemperature ");
System.exit(1);
}
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "avg of grades");
job.setJarByClass(AvgValue.class);
job.setMapperClass(InputClass.class);
job.setReducerClass(OutputClass.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(DoubleWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true)?0:1);
}
}
class InputClass extends Mapper{
@Override
protected void map(LongWritable key, Text value, Mapper.Context context)
throws IOException, InterruptedException {
String line = value.toString();
if(line.length()>0){
String[] array = line.split("\t");
if(array.length==2){
String name=array[0];
int grade = Integer.parseInt(array[1]);
context.write(new Text(name), new DoubleWritable(grade));
}
}
}
}
class OutputClass extends Reducer{
@Override
protected void reduce(Text text, Iterable iterable,
Reducer.Context context) throws IOException, InterruptedException {
int sum = 0;
int cnt= 0 ;
for(DoubleWritable iw : iterable) {
sum+=iw.get();
cnt++;
}
NameKey nk = new NameKey(text,new DoubleWritable(sum/cnt));
context.write(nk, new DoubleWritable(sum/cnt));
}
}
//该处通过将输出记过封装为一个bean并且实现WritableComparable类，重写compareTo，来实现对自定义排序
class NameKey implements WritableComparable{
private Text name ;
private DoubleWritable grade ;
public NameKey(Text name,DoubleWritable grade) {
this.name = name;
this.grade = grade;
}
public Text getName() {
return name;
}
public void setName(Text name) {
this.name = name;
}
public DoubleWritable getGrade() {
return grade;
}
public void setGrade(DoubleWritable grade) {
this.grade = grade;
}
@Override
public void write(DataOutput out) throws IOException {
name.write(out);
grade.write(out);
}
@Override
public void readFields(DataInput in) throws IOException {
name.readFields(in);
grade.readFields(in);
}
@Override
public String toString() {
return name.toString();
}
@Override
public int compareTo(NameKey o) {
double me = grade.get();
double other = o.getGrade().get();
int slid = (int)(me-other);
return slid;
}
}
//class Maxreducer extends Reducer 上述内容就是Hadoop中怎么自定义输出排序，你们学到知识或技能了吗？如果还想学到更多技能或者丰富自己的知识储备，欢迎关注创新互联-成都网站建设公司行业资讯频道。
