标签：io ar os sp for java on bs ad
javabean必须实现WritableComparable接口，并实现该接口的序列化，反序列话和比较方法
package com.my.hadoop.mapreduce.sort;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.WritableComparable;
public class InfoBean implements WritableComparable<InfoBean> {
private String account;
private double income;
private double expences;
private double surplus;
public void set(String account, double income, double expences){
this.account = account;
this.income = income;
this.expences = expences;
this.surplus = income - expences;
}
@Override
public String toString() {
return income+"\t"+expences+"\t"+surplus;
}
@Override
public void readFields(DataInput in) throws IOException {
this.account = in.readUTF();
this.income = in.readDouble();
this.expences = in.readDouble();
this.surplus = in.readDouble();
}
@Override
public void write(DataOutput out) throws IOException {
out.writeUTF(this.account);
out.writeDouble(this.income);
out.writeDouble(this.expences);
out.writeDouble(this.surplus);
}
@Override
public int compareTo(InfoBean o) {
if (this.income == o.getIncome()) {
return this.expences > o.getExpences() ? 1 : -1;
} else {
return this.income > o.getIncome() ? -1 : 1;
}
}
public String getAccount() {
return account;
}
public void setAccount(String account) {
this.account = account;
}
public double getIncome() {
return income;
}
public void setIncome(double income) {
this.income = income;
}
public double getExpences() {
return expences;
}
public void setExpences(double expences) {
this.expences = expences;
}
public double getSurplus() {
return surplus;
}
public void setSurplus(double surplus) {
this.surplus = surplus;
}
}
先求和
package com.my.hadoop.mapreduce.sort;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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;
public class SumStep {
public static class SumMap extends Mapper<LongWritable, Text, Text, InfoBean>{
private Text k = new Text();
private InfoBean v = new InfoBean();
@Override
public void map(LongWritable key, Text value, Context context) throws java.io.IOException ,InterruptedException {
String[] fields = value.toString().split("\t");
String account = fields[0];
double in = Double.parseDouble(fields[1]);
double out = Double.parseDouble(fields[2]);
k.set(account);
v.set(account, in, out);
context.write(k, v);
}
}
public static class SumReduce extends Reducer<Text, InfoBean, Text, InfoBean>{
private InfoBean v = new InfoBean();
@Override
public void reduce(Text key, Iterable<InfoBean> value, Context context) throws java.io.IOException ,InterruptedException {
double in_sum = 0;
double out_sum = 0;
for (InfoBean bean : value) {
in_sum += bean.getIncome();
out_sum += bean.getExpences();
}
v.set("", in_sum, out_sum);
context.write(key, v);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, SumStep.class.getSimpleName());
job.setJarByClass(SumStep.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
job.setMapperClass(SumMap.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(InfoBean.class);
job.setReducerClass(SumReduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(InfoBean.class);
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 :1);
}
}
后排序
package com.my.hadoop.mapreduce.sort;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class SortStep {
public static class SortMap extends Mapper<LongWritable, Text, InfoBean, NullWritable>{
private InfoBean k = new InfoBean();
@Override
public void map(LongWritable key, Text value, Context context) throws java.io.IOException ,InterruptedException {
System.out.println("===="+value.toString()+"====");
String[] fields = value.toString().split("\t");
String account = fields[0];
double in = Double.parseDouble(fields[1]);
double out = Double.parseDouble(fields[2]);
k.set(account, in, out);
context.write(k, NullWritable.get());
}
}
public static class SortReduce extends Reducer<InfoBean, NullWritable, Text, InfoBean>{
private Text k = new Text();
@Override
public void reduce(InfoBean bean, Iterable<NullWritable> value, Context context) throws java.io.IOException ,InterruptedException {
k.set(bean.getAccount());
context.write(k, bean);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, SortStep.class.getSimpleName());
job.setJarByClass(SortStep.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
job.setMapperClass(SortMap.class);
job.setMapOutputKeyClass(InfoBean.class);
job.setMapOutputValueClass(NullWritable.class);
job.setReducerClass(SortReduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(InfoBean.class);
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 :1);
}
}
标签：io ar os sp for java on bs ad
原文地址：http://www.cnblogs.com/mengyao/p/4151509.html
