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https://blog.csdn.net/jdh99/article/details/37565825
hadoop编程：分析CSDN注冊邮箱分布情况
本文博客链接:http://blog.csdn.net/jdh99,作者:jdh,转载请注明.
环境：
主机:Ubuntu10.04
hadoop版本号:1.2.1
开发工具:eclipse4.4.0
说明:
要求：原始数据共6428632条。分析不同邮箱的注冊情况，并按使用人数从大到小排序。分析：hadoop自带一个排序，是按key值来进行排序的。要按值（value）进行排序，须要二次排序。
步骤：
1.job1：统计不同注冊邮箱的使用人数，用默认的key值排序，保存在HDFS系统中
2.job2：对job1的输出进行二次排序，按值从大到小排序
结果输出:
使用人数在1W以上的邮箱共同拥有24个：
qq.com 1976196
163.com 1766927
126.com 807895
sina.com 351596
yahoo.com.cn 205491
hotmail.com 202948
gmail.com 186843
sohu.com 104736
yahoo.cn 87048
tom.com 72365
yeah.net 53295
21cn.com 50710
vip.qq.com 35119
139.com 29207
263.net 24779
sina.com.cn 19156
live.cn 18920
sina.cn 18601
yahoo.com 18454
foxmail.com 16432
163.net 15176
msn.com 14211
eyou.com 13372
yahoo.com.tw 10810
源码:
JOB1：统计不同注冊邮箱的人数
CsdnData.java
package com.bazhangkeji.hadoop;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class CsdnData
{
public static void main(String[] args) throws Exception
{
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2)
{
System.err.println("Usage: csdndata <in> <out>");
System.exit(2);
}
Job job = new Job(conf, "csdndata");
job.setJarByClass(CsdnData.class);
job.setMapperClass(MapData.class);
job.setReducerClass(ReducerData.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
MapData.java
package com.bazhangkeji.hadoop;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Mapper.Context;
public class MapData extends Mapper<Object, Text, Text, IntWritable>
{
IntWritable one = new IntWritable(1);
Text word = new Text();
public void map(Object key, Text value, Context context) throws IOException, InterruptedException
{
StringBuffer str_in = new StringBuffer();
StringBuffer str_out = new StringBuffer();
int index = 0;
//初始化字符串
str_in.setLength(0);
str_out.setLength(0);
str_in.append(value.toString());
//获得邮箱的起始位置
index = str_in.toString().lastIndexOf('@');
if (index != -1)
{
word.set(str_in.toString().substring(index + 1).trim().toLowerCase());
context.write(word, one);
}
}
}
ReducerData.java
package com.bazhangkeji.hadoop;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Reducer.Context;
public class ReducerData extends Reducer<Text,IntWritable,Text,IntWritable>
{
IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException
{
int sum = 0;
for (IntWritable val : values)
{
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
JOB2：对job1的输出进行二次排序。按值从大到小排序
SortSecond.java
package com.bazhangkeji.hadoop2;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class SortSecond
{
public static void main(String[] args) throws Exception
{
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2)
{
System.err.println("Usage: csdndata <in> <out>");
System.exit(2);
}
Job job = new Job(conf, "sortsecond");
job.setJarByClass(SortSecond.class);
job.setMapperClass(MapSecond.class);
job.setReducerClass(ReduceSecond.class);
job.setSortComparatorClass(SortMy.class); //设置自己定义二次排序策略
job.setOutputKeyClass(KeyMy.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
MapSecond.java
package com.bazhangkeji.hadoop2;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Mapper.Context;
public class MapSecond extends Mapper<LongWritable, Text, KeyMy, IntWritable>
{
IntWritable one = new IntWritable(1);
Text word = new Text();
KeyMy keymy = new KeyMy();
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException
{
String str_in = value.toString();
int index = 0;
index = str_in.indexOf(' ');
if (value.toString().length() > 3 && index != -1)
{
String str1 = str_in.substring(0, index);
String str2 = str_in.substring(index + 1);
if (str1.length() != 0 && str2.length() != 0)
{
one.set(Integer.parseInt(str2));
word.set(str1);
keymy.setFirstKey(word);
keymy.setSecondKey(one);
context.write(keymy, one);
}
}
}
}
ReduceSecond.java
package com.bazhangkeji.hadoop2;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Reducer.Context;
public class ReduceSecond extends Reducer<KeyMy,IntWritable,Text,IntWritable>
{
IntWritable result = new IntWritable();
public void reduce(KeyMy key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException
{
context.write(key.getFirstKey(), key.getSecondKey());
}
}
KeyMy.java
package com.bazhangkeji.hadoop2;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* 自己定义组合键
*/
public class KeyMy implements WritableComparable<KeyMy>{
private static final Logger logger = LoggerFactory.getLogger(KeyMy.class);
private Text firstKey;
private IntWritable secondKey;
public KeyMy() {
this.firstKey = new Text();
this.secondKey = new IntWritable();
}
public Text getFirstKey() {
return this.firstKey;
}
public void setFirstKey(Text firstKey) {
this.firstKey = firstKey;
}
public IntWritable getSecondKey() {
return this.secondKey;
}
public void setSecondKey(IntWritable secondKey) {
this.secondKey = secondKey;
}
@Override
public void readFields(DataInput dateInput) throws IOException {
// TODO Auto-generated method stub
this.firstKey.readFields(dateInput);
this.secondKey.readFields(dateInput);
}
@Override
public void write(DataOutput outPut) throws IOException {
this.firstKey.write(outPut);
this.secondKey.write(outPut);
}
/**
* 自己定义比較策略
* 注意：该比較策略用于 mapreduce的第一次默认排序，也就是发生在map阶段的sort小阶段，
* 发生地点为环形缓冲区(能够通过io.sort.mb进行大小调整)
*/
@Override
public int compareTo(KeyMy KeyMy) {
logger.info("-------KeyMy flag-------");
return this.firstKey.compareTo(KeyMy.getFirstKey());
}
}
SortMy.java
package com.bazhangkeji.hadoop2;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* 自己定义二次排序策略
*/
public class SortMy extends WritableComparator {
private static final Logger logger = LoggerFactory.getLogger(SortMy.class);
public SortMy() {
super(KeyMy.class,true);
}
@Override
public int compare(WritableComparable KeyMyOne,
WritableComparable KeyMyOther)
{
logger.info("---------enter SortMy flag---------");
KeyMy c1 = (KeyMy) KeyMyOne;
KeyMy c2 = (KeyMy) KeyMyOther;
return c2.getSecondKey().get()-c1.getSecondKey().get();//0,负数,正数
}
}
參考资料:
1.《hadoop权威指南》
2. http://zengzhaozheng.blog.51cto.com/8219051/1379271