MapReduce简单案例

MapReduce简单案例

案例一文件合并和去重操作

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：

数据
20150101 x
20150103 x
20150104 y
20150102 y
20150105 z
20150106 x

输入文件B的样例如下：

数据
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：

数据
20150101 x
20150101 y
20150102 y
20150103 x
20150104 y
20150104 z
20150105 y
20150105 z
20150106 x

代码：

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 hebing {
    public static class Mymapper extends Mapper<Object, Text, Text, Text> {  
        public void map(Object key, Text value, Context content) throws IOException, InterruptedException {  
            content.write(value, new Text(""));  
        }  
    }  
        public static class Myreducer extends Reducer<Text, Text, Text, Text> {  
        public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {  
            context.write(key, new Text(""));  
        }  
    }
        public static void main(String[] args) throws Exception{

        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf,"hebing");
        job.setJarByClass(hebing.class);
        job.setMapperClass(hebing.Mymapper.class);
        job.setCombinerClass(hebing.Myreducer.class);
        job.setReducerClass(hebing.Myreducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        FileInputFormat.addInputPath(job, new Path("hdfs://localhost:9000/input"));
        FileOutputFormat.setOutputPath(job, new Path("hdfs://localhost:9000/output"));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

案例二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：

数据
33
37
12
40

输入文件2的样例如下：

数据
4
16
39
5

输入文件3的样例如下：

数据
1
45
25

根据输入文件1、2和3得到的输出文件如下：

序号	数据
1	1
2	4
3	5
4	12
5	16
6	25
7	33
8	37
9	39
10	40
11	45

代码：

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 Sort {
    public static class Mymapper extends Mapper<Object, Text, IntWritable, IntWritable>{
        private static IntWritable v = new IntWritable();
        public void map(Object key, Text value, Context context) throws IOException,InterruptedException{
        v.set(Integer.parseInt(value.toString()));
        context.write(v, new IntWritable(1));
        }
    }
    public static class Myreducer extends Reducer<IntWritable, IntWritable, IntWritable, IntWritable>{
        private static IntWritable line_num = new IntWritable(1);
        public void reduce(IntWritable key, Iterable<IntWritable> values, Context context) throws IOException,InterruptedException{
           for(IntWritable num : values) {
             context.write(line_num, key);
             line_num = new IntWritable(line_num.get() + 1);
    }
  }
}
    public static void main(String[] args) throws Exception{
    	/**Designed by 王立同**/
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf,"Sort");
        job.setJarByClass(Sort.class);
        job.setMapperClass(Sort.Mymapper.class);
        job.setReducerClass(Sort.Myreducer.class);
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(IntWritable.class);
        FileInputFormat.addInputPath(job, new Path("hdfs://localhost:9000/input"));
        FileOutputFormat.setOutputPath(job, new Path("hdfs://localhost:9000/output"));
        System.exit(job.waitForCompletion(true) ? 0 : 1);

    }
}

案例三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

代码：

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 Child2Parent {
    public static class Mymapper extends Mapper<Object, Text, Text, Text>{
        public void map(Object key, Text value, Context context) throws IOException,InterruptedException{
             String[] cap=value.toString().split("[\\s|\\t]+");//分割数据
              if (!"child".equals(cap[0])) {
                  String cName = cap[0];
                  String pName = cap[1];
                  context.write(new Text(pName), new Text("r#"+cName));//打标签
                  context.write(new Text(cName), new Text("l#"+pName));
              }
        }
    }
    public static class Myreduce extends Reducer<Text, Text, Text, Text>{
    	public static int runtime = 0;
        public void reduce(Text key, Iterable<Text> values,Context context) throws IOException,InterruptedException{
            if (runtime == 0) {
                context.write(new Text("grandchild"), new Text("grandparent"));
                runtime++;
            }
            List<String> grandChild = new ArrayList<>();
            List<String> grandParent = new ArrayList<>();
            for (Text text : values) {
                String[] relation = text.toString().split("#");
                if ("l".equals(relation[0])) {
                    grandChild.add(relation[1]);
                } else {
                    grandParent.add(relation[1]);
                }
            }
            for (String l:grandChild) {
                for (String r:grandParent) {
                	context.write(new Text(r), new Text(l));
                }
            }
        }
    }
    public static void main(String[] args) throws Exception{
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf,"TableJoin");
        job.setJarByClass(Child2Parent.class);
        job.setMapperClass(Child2Parent.Mymapper.class);
        job.setReducerClass(Child2Parent.Myreduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        FileInputFormat.addInputPath(job, new Path("hdfs://localhost:9000/input"));
        FileOutputFormat.setOutputPath(job, new Path("hdfs://localhost:9000/output"));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

MapReduce简单案例

MapReduce简单案例

案例一 文件合并和去重操作

案例二 实现对输入文件的排序

案例三 对给定的表格进行信息挖掘

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案例一文件合并和去重操作

案例二实现对输入文件的排序

案例三对给定的表格进行信息挖掘