比如，能够无需改动类的实现而在虚类中加入一个方法(即用默认的实现)。
在新的API中。mapper和reducer如今都是虚类。
新的API 放在org.apache.hadoop.mapreduce 包(和子包)中。之前版本号的API 依然放在org.apache.hadoop.mapred中。
新的API充分使用上下文对象(Context)，使用户代码能与MapReduce系统通信。比如，MapContext 基本具备了JobConf、OutputCollector和Reporter的功能。
新的API 同一时候支持"推"(push)和"拉"(pull)式的迭代。
这两类API。均能够将键/值对记录推给mapper。但除此之外，新的API 也同意把记录从map()方法中拉出。
对reducer来说是一样的。"拉"式处理数据的优点是能够实现数据的批量处理，而非逐条记录地处理。
新增的API实现了配置的统一。旧API 通过一个特殊的JobConf 对象配置作业。该对象是Hadoop配置对象的一个扩展。
在新的API 中，我们丢弃这样的区分，全部作业的配置均通过Configuration 来完毕。
新API中作业控制由Job类实现。而非JobClient类，新API中删除了JobClient类。
输出文件的命名方式稍有不同。map的输出文件名称为part-m-nnnnn。而reduce的输出为part-r-nnnnn(当中nnnnn表示分块序号。为整数，且从0開始算。
这里我设置的是1天（60*24）
删除数据rm后，会将数据move到当前文件夹下的.Trash文件夹(一般在HDFS的/user/root文件夹下)
(b)測试
1）新建文件夹input
hadoop/bin/hadoop fs -mkdir input
2）上传文件
root@master:/data/soft# hadoop/bin/hadoop fs -copyFromLocal /data/soft/file0* input
3）删除文件夹input
[root@master data]# hadoop fs -rmr input
Moved to trash: hdfs://master:9000/user/root/input
4）參看当前文件夹(回收文件夹在HDFS的/user/root文件夹下)
[root@master data]# hadoop fs -ls
Found 2 items
drwxr-xr-x - root supergroup 0 2014-08-12 13:21 /user/root/.Trash
发现input删除，多了一个文件夹.Trash
5）恢复刚刚删除的文件夹(注意设置源 和 目的地址)
[root@master data]# hadoop fs -mv /user/root/.Trash/Current/user/root/input /user/root/input
6）检查恢复的数据
[root@master data]# hadoop fs -ls input
Found 2 items
-rw-r--r-- 3 root supergroup 22 2014-08-12 13:21 /user/root/input/file01
-rw-r--r-- 3 root supergroup 28 2014-08-12 13:21 /user/root/input/file02
7）删除.Trash文件夹（清理垃圾）
[root@master data]# hadoop fs -rmr .Trash
Deleted hdfs://master:9000/user/root/.Trash
执行MR作业但在web页面(http://hadoop:50070和http://hadoop:50030)看不到作业执行记录，建议把程序打成jar包。用命令行提交。
为了简化命令行方式执行作业。Hadoop自带了一些辅助类。
//比如例如以下的程序
public class WordCount {
// 略...
public static void main(String[] args) throws Exception {
//新API就是通过Configuration对象进行作业的配置
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf,
args).getRemainingArgs();
// 略...
Job job = new Job(conf, "word count");
// 略...
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}这段程序中使用到了GenericOptionsParser这个类，它的作用是将命令行中參数自己主动设置到变量conf中。
通常不直接使用GenericOptionsParser，更方便的方式是：实现Tool接口。通过ToolRunner来执行应用程序。ToolRunner内部调用GenericOptionsParser
改动后的代码变成了这样：
public class WordCount extends Configured implements Tool {
@Override
public int run(String[] arg0) throws Exception {
//在run方法中通过getConf()方法获得Configuration对象
//run()方法隐藏了通过辅助类获取输入參数以设置Configuration对象的代码
Job job = new Job(getConf(), "word count");
// 略...
System.exit(job.waitForCompletion(true) ? 0 : 1);
return 0;
}
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new Configuration(), new WordCount(), args);
System.exit(res);
}
}(5)在用命令行执行MR作业时。假设出现ClassNotFoundException可能是由于缺少第三方jar包。能够把第三方jar包copy到hadoop安装文件夹下放置jar的那个文件夹。
程序：新API版
package inAction;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
//基于新API的WordCount样例(用extends Configured implements Tool的方式便于管理作业)
public class MyWordCount extends Configured implements Tool {
public static class MyMapper extends
Mapper<Object, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
@Override
protected void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
StringTokenizer st = new StringTokenizer(value.toString());
while (st.hasMoreTokens()) {
word.set(st.nextToken());
context.write(word, one);
}
}
}
public static class MyReducer extends
Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
@Override
protected void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
int count = 0;
for (IntWritable value : values) {
count += value.get();
}
result.set(count);
context.write(key, result);
}
}
@Override
public int run(String[] args) throws Exception {
Configuration conf=getConf();//新APIConfiguration对象负责作业配置
//ToolRunner工具会自己主动调用隐藏的GenericOptionsParser将命令行參数设置到conf中
Job job=new Job(conf,"MyWordCount");
job.setJarByClass(MyWordCount.class);
job.setMapperClass(MyMapper.class);
job.setCombinerClass(MyReducer.class);
job.setReducerClass(MyReducer.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
//输入输出參数能够在命令行执行时设置。也能够在程序中直接设置，右击run on hadoop
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true)?0:1);
return 0;
}
public static void main(String[] args) throws Exception {
int res=ToolRunner.run(new Configuration(), new MyWordCount(), args);
System.exit(res);
}
}执行：hadoop jar /root/wordCount.jar /usr/input /usr/output程序：旧API版
package inAction;
import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
//基于旧API的WordCount实现
public class WordCount2 {
public static class MyMapper extends MapReduceBase implements
Mapper<LongWritable, Text, Text, IntWritable> {
private IntWritable one = new IntWritable(1);
private Text word = new Text();
@Override
public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
StringTokenizer st = new StringTokenizer(value.toString());
while (st.hasMoreTokens()) {
word.set(st.nextToken());
output.collect(word, one);
}
}
}
public static class MyReduce extends MapReduceBase implements
Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable res = new IntWritable();
@Override
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
int sum = 0;
while (values.hasNext()) {
sum += values.next().get();
}
res.set(sum);
output.collect(key, res);
}
}
public static void main(String[] args) throws IOException {
//旧API使用JobConf配置作业
JobConf conf=new JobConf(WordCount2.class);
conf.setJobName("OldAPIWordCount");
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);
conf.setMapperClass(MyMapper.class);
conf.setCombinerClass(MyReduce.class);
conf.setReducerClass(MyReduce.class);
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path("hdfs://hadoop:9000/usr/wordsIn"));
FileOutputFormat.setOutputPath(conf, new Path("hdfs://hadoop:9000/usr/wordsOut2"));
JobClient.runJob(conf);//新API中JobClient已删除
}
}关于wordCount的详解能够參考这篇文章：http://www.cnblogs.com/xia520pi/archive/2012/05/16/2504205.htmlpackage inAction;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
//找出每一年的最高气温(本例仅仅用了1901年和1902年的数据,网上下的)
public class MaxTemperature extends Configured implements Tool{
//Mapper的功能是提取每一行原始数据中的年份和温度值
public static class MyMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
private static final int MISSING=9999;//假设一行的气温值是9999即表明该年气温缺失
@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line=value.toString();
String year=line.substring(15,19);//年份是15-19个字符
int temperature;
//气温有正负要差别对待,Integer.parseInt不能处理负数
if(line.charAt(87)=='+'){
temperature=Integer.parseInt(line.substring(88,92));
}else{
temperature=Integer.parseInt(line.substring(87,92));
}
String quantity=line.substring(92,93);//quantity.matches("[01459]")表明数量仅仅有是01459时才是有效气温值
//仅仅有有效气温值才输出
if(quantity.matches("[01459]")&&temperature!=MISSING){
context.write(new Text(year), new IntWritable(temperature));
}
}
}
public static class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
@Override
protected void reduce(Text key, Iterable<IntWritable> values,
Context context)
throws IOException, InterruptedException {
int maxValue=Integer.MIN_VALUE;
for(IntWritable temp:values){
maxValue=Math.max(temp.get(), maxValue);
}
context.write(key, new IntWritable(maxValue));
}
}
@Override
public int run(String[] args) throws Exception {
Configuration conf=getConf();
Job job=new Job(conf,"MaxTemperature");
job.setJarByClass(MaxTemperature.class);
job.setMapperClass(MyMapper.class);
job.setCombinerClass(MyReducer.class);//设置Combiner降低传递给Reducer的数据量,提高性能
job.setReducerClass(MyReducer.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
//输入输出參数能够在命令行执行时设置。也能够在程序中直接设置，右击run on hadoop
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true)?0:1);
return 0;
}
public static void main(String[] args) throws Exception {
int res=ToolRunner.run(new Configuration(), new MaxTemperature(), args);
System.exit(res);
}
}关于MaxTemperature的详解能够參考这篇文章：http://www.linuxidc.com/Linux/2012-05/61196.htm即输出形如：专利号1 引用专利1的专利号，引用专利1的专利号...(本例来自《Hadoop in action》)
package inAction;
import java.io.IOException;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.*;
public class MyJob2 extends Configured implements Tool {
public static class MapClass extends Mapper<LongWritable, Text, Text, Text> {
@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String[] citation = value.toString().split(",");
context.write(new Text(citation[1]), new Text(citation[0]));
}
}
public static class Reduce extends Reducer<Text, Text, Text, Text> {
protected void reduce(Text key, Iterable<Text> value, Context context)
throws IOException, InterruptedException {
String csv = "";
for (Text val : value) {
if (csv.length() > 0)
csv += ",";
csv += val.toString();
}
context.write(key, new Text(csv));
}
}
@Override
public int run(String[] arg0) throws Exception {
Configuration conf=getConf();
Job job=new Job(conf,"MyJob2");
job.setJarByClass(MyJob2.class);
Path in=new Path("/root/cite75_99.txt");
Path out=new Path("/root/inAction3");
FileInputFormat.setInputPaths(job, in);
FileOutputFormat.setOutputPath(job, out);
job.setMapperClass(MapClass.class);
job.setReducerClass(Reduce.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
System.exit(job.waitForCompletion(true)?0:1);
return 0;
}
public static void main(String[] args) throws Exception {
int res=ToolRunner.run(new Configuration(), new MyJob2(), args);
System.exit(res);
}
}
//旧API程序MyJob.java
package inAction;
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.fs.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.util.*;
public class MyJob extends Configured implements Tool{
public MyJob() {
// TODO Auto-generated constructor stub
}
public static class MapClass extends MapReduceBase implements Mapper<Text, Text, Text, Text>{
@Override
public void map(Text key, Text value, OutputCollector<Text, Text> output,
Reporter reporter) throws IOException {
// TODO Auto-generated method stub
//把专利号和被引用专利号倒过来
output.collect(value, key);
}
}
//把每一个专利的引用专利列在其后面的Reduce
public static class Reduce extends MapReduceBase implements Reducer<Text, Text, Text, Text>{
@Override
public void reduce(Text key, Iterator<Text> values,
OutputCollector<Text, Text> output, Reporter reporter)
throws IOException {
// TODO Auto-generated method stub
String csv="";
while(values.hasNext()){
if(csv.length()>0) csv+=",";
csv+=values.next().toString();
}
output.collect(key, new Text(csv));
}
}
//计数功能的Reduce
// public static class Reduce extends MapReduceBase implements Reducer<Text, Text, Text, IntWritable>{
//
// @Override
// public void reduce(Text key, Iterator<Text> values,
// OutputCollector<Text, IntWritable> output, Reporter reporter)
// throws IOException {
// // TODO Auto-generated method stub
// int count=0;
// while(values.hasNext()){
// values.next();
// count++;
// }
// output.collect(key, new IntWritable(count));
// }
// }
@Override
public int run(String[] arg0) throws Exception {
// TODO Auto-generated method stub
Configuration conf=getConf();
JobConf job=new JobConf(conf,MyJob.class);
Path in=new Path("hdfs://hadoop:9000/zpc/cite75_99.txt");
Path out=new Path("hdfs://hadoop:9000/zpc/output1");
FileInputFormat.setInputPaths(job, in);
FileOutputFormat.setOutputPath(job, out);
job.setJobName("zpcJob1");
job.setMapperClass(MapClass.class);
job.setReducerClass(Reduce.class);
job.setInputFormat(KeyValueTextInputFormat.class);
job.setOutputFormat(TextOutputFormat.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.set("key.value.separator.in.input.line",",");
JobClient.runJob(job);
return 0;
}
public static void main(String[] args) throws Exception{
int res=ToolRunner.run(new Configuration(), new MyJob(), args);
System.exit(res);
}
}
//统计不同引用次数的专利数目,比方被引用一次的专利有几个、被引用n次的专利有几个
程序CitationHistogram.java
package inAction;
import inAction.MyJob.MapClass;
import inAction.MyJob.Reduce;
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.fs.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;
import org.apache.hadoop.util.*;
import org.apache.hadoop.conf.Configured;
//统计不同引用次数的专利数目。比方被引用一次的专利有几个，引用两次的专利有几个
public class CitationHistogram extends Configured implements Tool{
public static class MapClass extends MapReduceBase implements Mapper<Text, Text, IntWritable, IntWritable>{
private final static IntWritable uno=new IntWritable(1);
private IntWritable citationCount=new IntWritable();
public void map(Text key, Text value,
OutputCollector<IntWritable, IntWritable> output, Reporter reporter)
throws IOException {
// TODO Auto-generated method stub
citationCount.set(Integer.parseInt(value.toString()));
output.collect(citationCount, uno);
}
}
public static class Reduce extends MapReduceBase implements Reducer<IntWritable, IntWritable, IntWritable, IntWritable>{
@Override
public void reduce(IntWritable key, Iterator<IntWritable> value,
OutputCollector<IntWritable, IntWritable> output, Reporter reporter)
throws IOException {
// TODO Auto-generated method stub
int count=0;
while(value.hasNext()){
count+=value.next().get();
}
output.collect(key, new IntWritable(count));
}
}
@Override
public int run(String[] arg0) throws Exception {
// TODO Auto-generated method stub
Configuration conf=getConf();
JobConf job=new JobConf(conf,CitationHistogram.class);
Path in=new Path("/root/inAction1-1/part-00000");
Path out=new Path("/root/inAction2");
FileInputFormat.setInputPaths(job, in);
FileOutputFormat.setOutputPath(job, out);
job.setJobName("zpcJob2");
job.setMapperClass(MapClass.class);
job.setReducerClass(Reduce.class);
job.setInputFormat(KeyValueTextInputFormat.class);
job.setOutputFormat(TextOutputFormat.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(IntWritable.class);
JobClient.runJob(job);
return 0;
}
public static void main(String[] args) throws Exception{
int res=ToolRunner.run(new Configuration(), new CitationHistogram(), args);
System.exit(res);
}
}注：以上程序的专利数据集apat63_99.txt和cite75_99.txt能够从网上下载。
要求在输出中每行有两个间隔的数字，当中，第一个代表原始数据在原始数据集中的位次。第二个代表原始数据。
例子输入：
1）file1：
2
32
654
32
15
756
65223
2）file2：
5956
22
650
92
3）file3：
26
54
6
例子输出：
1 2
2 6
3 15
4 22
5 26
6 32
7 32
8 54
9 92
10 650
11 654
12 756
13 5956
14 65223
程序：Sort.java
对输入文件里数据进行就算学生平均成绩。
输入文件里的每行内容均为一个学生的姓名和他对应的成绩，假设有多门学科，则每门学科为一个文件。
要求在输出中每行有两个间隔的数据。当中，第一个代表学生的姓名，第二个代表其平均成绩。
样本输入：
1）math：
张三 88
李四 99
王五 66
赵六 77
2）china：
张三 78
李四 89
王五 96
赵六 67
3）english：
张三 80
李四 82
王五 84
赵六 86
样本输出：
张三 82
李四 90
王五 82
赵六 76
程序：AverageScore.java
例子输入例如以下所看到的。
1）factory：
factoryname addressed
Beijing Red Star 1
Shenzhen Thunder 3
Guangzhou Honda 2
Beijing Rising 1
Guangzhou Development Bank 2
Tencent 3
Bank of Beijing 1
2）address：
addressID addressname
1 Beijing
2 Guangzhou
3 Shenzhen
4 Xian
例子输出例如以下所看到的
factoryname addressname
Bank of Beijing Beijing
Beijing Red Star Beijing
Beijing Rising Beijing
Guangzhou Development Bank Guangzhou
Guangzhou Honda Guangzhou
Shenzhen Thunder Shenzhen
Tencent Shenzhen
多表关联和单表关联相似，都类似于数据库中的自然连接。
相比单表关联，多表关联的左右表和连接列更加清楚。
所以能够採用和单表关联的同样的处理方式，map识别出输入的行属于哪个表之后，对其进行切割，将连接的列值保存在key中。
还有一列和左右表标识保存在value中。然后输出。reduce拿到连接结果之后，解析value内容，依据标志将左右表内容分开存放，然后求笛卡尔积，最后直接输出。
程序：MTjoin.java
版权声明：本文博客原创文章，博客，未经同意，不得转载。
原文：http://www.cnblogs.com/gcczhongduan/p/4640855.html