对于两个输入文件，即文件A和文件B，请编写MapReduce程序，对两个文件进行合并，并剔除其中重复的内容，得到一个新的输出文件C。下面是输入文件和输出文件的一个样例供参考。
输入文件A的样例如下：
20150101 x
20150102 y
20150103 x
20150104 y
20150105 z
20150106 x
输入文件B的样例如下：
20150101 y
20150102 y
20150103 x
20150104 z
20150105 y
根据输入文件A和B合并得到的输出文件C的样例如下：
20150101 x
20150101 y
20150102 y
20150103 x
20150104 y
20150104 z
20150105 y
20150105 z
20150106 x
import java.io.IOException;
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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class MergeSort {
//重载 map 函数，直接将输入中的 value 复制到输出数据的 key 上
public static class Map extends Mapper{
private static Text text = new Text();
public void map(Object key, Text value, Context context) throws
IOException,InterruptedException{
text = value;
context.write(text, new Text(""));
} }
//重载 reduce 函数，直接将输入中的 key 复制到输出数据的 key 上
public static class Reduce extends Reducer{
public void reduce(Text key, Iterable values, Context context ) throws IOException,InterruptedException{
context.write(key, new Text(""));
} }
public static void main(String[] args) throws Exception{
// TODO Auto-generated method stub
Configuration conf = new Configuration();
conf.set("fs.default.name","hdfs://localhost:9000");
String[] otherArgs = new String[]{"input","output"};
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount ");
System.exit(2);
}
Job job = Job.getInstance(conf,"Merge and duplicate removal");
job.setJarByClass(MergeSort.class);
job.setMapperClass(Map.class);
job.setCombinerClass(Reduce.class);
job.setReducerClass(Reduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
2.编写程序实现对输入文件的排序
现在有多个输入文件，每个文件中的每行内容均为一个整数。要求读取所有文件中的整数，进行升序排序后，输出到一个新的文件中，输出的数据格式为每行两个整数，第一个数字为第二个整数的排序位次，第二个整数为原待排列的整数。下面是输入文件和输出文件的一个样例供参考。
输入文件1的样例如下：
33
37
12
40
输入文件2的样例如下：
4
16
39
5
输入文件3的样例如下：
1
45
25
根据输入文件1、2和3得到的输出文件如下：
1 1
2 4
3 5
4 12
5 16
6 25
7 33
8 37
9 39
10 40
11 45
package Mersort;
import java.io.IOException;
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.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class MergeSort {
//map 函数读取输入中的 value，将其转化成 IntWritable 类型，最后作为输出 key
public static class Map extends Mapper{
private static IntWritable data = new IntWritable();
public void map(Object key, Text value, Context context) throws
IOException,InterruptedException{
String text = value.toString();
data.set(Integer.parseInt(text));
context.write(data, new IntWritable(1));
} }
//reduce 函数将 map 输入的 key 复制到输出的 value 上，然后根据输入的 value-list
//中元素的个数决定 key 的输出次数,定义一个全局变量 line_num 来代表 key 的位次
public static class Reduce extends Reducer{
private static IntWritable line_num = new IntWritable(1);
public void reduce(IntWritable key, Iterable values, Context context) throws IOException,InterruptedException{
for(IntWritable val : values){
context.write(line_num, key);
line_num = new IntWritable(line_num.get() + 1);
} } }
//自定义 Partition 函数，此函数根据输入数据的最大值和 MapReduce 框架中Partition 的数量获取将输入数据按照大小分块的边界，然后根据输入数值和边界的关系返回对应的 Partiton ID
public static class Partition extends Partitioner{
public int getPartition(IntWritable key, IntWritable value, int num_Partition){
int Maxnumber = 65223;//int 型的最大数值
int bound = Maxnumber/num_Partition+1;
int keynumber = key.get();
for (int i = 0; i=bound * i){
return i;
}
}
return -1;
} }
public static void main(String[] args) throws Exception{
// TODO Auto-generated method stub
Configuration conf = new Configuration();
conf.set("fs.default.name","hdfs://localhost:9000");
String[] otherArgs = new String[]{"input","output"};
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount ");
System.exit(2);
}
Job job = Job.getInstance(conf,"Merge and sort");
job.setJarByClass(MergeSort.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setPartitionerClass(Partition.class);
job.setOutputKeyClass(IntWritable.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);
} }
3.对给定的表格进行信息挖掘
下面给出一个child-parent的表格，要求挖掘其中的父子辈关系，给出祖孙辈关系的表格。
输入文件内容如下：
child parent
Steven Lucy
Steven Jack
Jone Lucy
Jone Jack
Lucy Mary
Lucy Frank
Jack Alice
Jack Jesse
David Alice
David Jesse
Philip David
Philip Alma
Mark David
Mark Alma
输出文件内容如下：
grandchild grandparent
Steven Alice
Steven Jesse
Jone Alice
Jone Jesse
Steven Mary
Steven Frank
Jone Mary
Jone Frank
Philip Alice
Philip Jesse
Mark Alice
Mark Jesse
import java.io.IOException;
import java.util.*;
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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class MergeSort{
public static int time = 0;
//Map将输入文件按照空格分割成child和parent，然后正序输出一次作为右表，反序输出一次作为左表，需要注意的是在输出的value中必须加上左右表区别标志
public static class Map extends Mapper{
public void map(Object key, Text value, Context context) throws IOException,InterruptedException{
String child_name = new String();
String parent_name = new String();
String relation_type = new String();
String line = value.toString();
int i = 0;
while(line.charAt(i) != ' '){
i++;
}
String[] values = {line.substring(0,i),line.substring(i+1)};
if(values[0].compareTo("child") != 0){
child_name = values[0];
parent_name = values[1];
relation_type = "1";//左右表区分标志
context.write(new Text(values[1]), new Text(relation_type+"+"+child_name+"+"+parent_name));
//左表
relation_type = "2";
context.write(new Text(values[0]), new Text(relation_type+"+"+child_name+"+"+parent_name));
//右表
}
}
}
public static class Reduce extends Reducer{
public void reduce(Text key, Iterable values,Context context) throws IOException,InterruptedException{
if(time == 0){ //输出表头
context.write(new Text("grand_child"), new Text("grand_parent"));
time++;
}
int grand_child_num = 0;
String grand_child[] = new String[10];
int grand_parent_num = 0;
String grand_parent[]= new String[10];
Iterator ite = values.iterator();
while(ite.hasNext()){
String record = ite.next().toString();
int len = record.length();
int i = 2;
if(len == 0) continue;
char relation_type = record.charAt(0);
String child_name = new String();
String parent_name = new String();
//获取value-list中value的child
while(record.charAt(i) != '+'){
child_name = child_name + record.charAt(i);
i++;
}
i=i+1;
//获取value-list中value的parent
while(i");
System.exit(2);
}
Job job = Job.getInstance(conf,"Single table join");
job.setJarByClass(MergeSort.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
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