mapreduce数据处理——统计排序

接上篇https://www.cnblogs.com/sengzhao666/p/11850849.html

2、数据处理：

·统计最受欢迎的视频/文章的Top10访问次数（id）

·按照地市统计最受欢迎的Top10课程（ip）

·按照流量统计最受欢迎的Top10课程（traffic）

分两步：

统计；排序

初始文件部分样例：

1.192.25.84    2016-11-10-00:01:14    10    54    video    5551    
1.194.144.222    2016-11-10-00:01:20    10    54    video    3589    
1.194.187.2    2016-11-10-00:01:05    10    54    video    2212    
1.203.177.243    2016-11-10-00:01:18    10    6050    video    7361    
1.203.177.243    2016-11-10-00:01:19    10    72    video    7361    
1.203.177.243    2016-11-10-00:01:22    10    6050    video    7361    
1.30.162.63    2016-11-10-00:01:46    10    54    video    3639    
1.84.205.195    2016-11-10-00:01:12    10    54    video    1412

统计：

package priv.tzk.mapreduce.dataProcess.visits;

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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class DataVisits {
    public static String INPUT_PATH="/home/hadoop/out";  
    public static String OUTPUT_PATH="hdfs://localhost:9000/mapReduce/mymapreduce1/out";    

    public static class Map extends Mapper<Object,Text,Text,IntWritable>{    //将输入输出作为string类型，对应Text类型
            private static Text newKey=new Text();    //每一行作为一个数据  
            public void map(Object key, Text value, Context context) throws IOException, InterruptedException{   
                String line=value.toString();//转为字符串类型
                //System.out.println(line);
                if(!("".equals(line)))//增加控制语句，使得line为”“时能够停止。否则不符合reduce接受的数据不会执行reduce
                {
                    String arr[]=line.split("\t");//splite是按照输入的值拆分成数组
                    newKey.set(arr[5]);
                    int click=1;
                    context.write(newKey,new IntWritable(click)); 
                    //System.out.println(newKey+"  "+new IntWritable(click));
                }
             } 
         }   
         
    public static class Reduce extends Reducer<Text,IntWritable,Text,IntWritable>{   
        public void reduce(Text key,Iterable<IntWritable> values,Context context) throws IOException,InterruptedException{      
                 int count=0;
                 for(IntWritable val:values) {
                     //Iterable迭代器
                     count++;
                 }         
                 context.write(key,new IntWritable(count));
                 //System.out.println("reduceStart");
             }   
        }    
        
        public static void main(String[] args) throws IOException,ClassNotFoundException,InterruptedException{              
            Configuration conf=new Configuration();   
            System.out.println("start");
            Job job=Job.getInstance(conf); 
            job.setJobName("MyAverage");
            //Job job =new Job(conf,"MyAverage");
            job.setJarByClass(DataVisits.class);
            job.setMapperClass(Map.class);  
            job.setReducerClass(Reduce.class);
            job.setOutputKeyClass(Text.class);  
            job.setOutputValueClass(IntWritable.class);//设置map的输出格式
            job.setInputFormatClass(TextInputFormat.class);
            job.setOutputFormatClass(TextOutputFormat.class);
            Path outputpath=new Path(OUTPUT_PATH); 
            Path inputpath=new Path(INPUT_PATH); 
            FileInputFormat.addInputPath(job,inputpath );  
            FileOutputFormat.setOutputPath(job,outputpath);  
            boolean flag = job.waitForCompletion(true);
            System.out.println(flag);
            System.exit(flag? 0 : 1);
         }
        
}

统计部分结果样例：

针对统计结果排序：

package priv.tzk.mapreduce.dataProcess.visits;

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.io.WritableComparable;
import org.apache.hadoop.io.WritableComparator;
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;

public class visitsSort {
    public static String INPUT_PATH="/home/hadoop/visits_out";  
    public static String OUTPUT_PATH="hdfs://localhost:9000/mapReduce/mymapreduce1/out1";    
        
    public static class Sort extends WritableComparator {
        public Sort(){
        //这里就是看你map中填的输出key是什么数据类型，就给什么类型
        super(IntWritable.class,true);
        }
        @Override
        public int compare(WritableComparable a, WritableComparable b) {
        return -a.compareTo(b);//加个负号就是倒序，把负号去掉就是正序。
        }
    }
    
    public static class Map extends Mapper<Object,Text,IntWritable,Text>{    //将输入输出作为string类型，对应Text类型
            private static Text mid=new Text(); 
            private static IntWritable num=new IntWritable();
            public void map(Object key, Text value, Context context) throws IOException, InterruptedException{   
                String line=value.toString();//转为字符串类型
                if(!("".equals(line)))//增加控制语句，使得line为”“时能够停止。否则不符合reduce接受的数据不会执行reduce
                {
                    String arr[]=line.split("\t");//splite是按照输入的值拆分成数组
                    mid.set(arr[0]);
                    num.set(Integer.parseInt(arr[1]));
                    context.write(num,mid); 
                }
             } 
         }   
         //MapReduce框架默认排序规则。它是按照key值进行排序的
    public static class Reduce extends Reducer<IntWritable,Text,IntWritable,Text>{ 
        private static int i=0;
        public void reduce(IntWritable key,Iterable<Text> values,Context context) throws IOException,InterruptedException{      

                 for(Text val:values) {
                     //Iterable迭代器
                     if(i<10) {
                         i++;
                         context.write(key, val);
                     }
                 }
                 //System.out.println("reduceStart");
             }   
        }    
        
        public static void main(String[] args) throws IOException,ClassNotFoundException,InterruptedException{              
            Configuration conf=new Configuration();   
            System.out.println("start");
            Job job=Job.getInstance(conf); 
            //Job job =new Job(conf,"");
            job.setJarByClass(visitsSort.class);
            job.setMapperClass(Map.class);  
            job.setReducerClass(Reduce.class);
            job.setSortComparatorClass(Sort.class);
            //设置map的输出格式
            job.setOutputKeyClass(IntWritable.class);  
            job.setOutputValueClass(Text.class);
            job.setInputFormatClass(TextInputFormat.class);
            job.setOutputFormatClass(TextOutputFormat.class);
            Path outputpath=new Path(OUTPUT_PATH); 
            Path inputpath=new Path(INPUT_PATH); 
            FileInputFormat.addInputPath(job,inputpath );  
            FileOutputFormat.setOutputPath(job,outputpath);  
            boolean flag = job.waitForCompletion(true);
            System.out.println(flag);
            System.exit(flag? 0 : 1);
         }
        
}

排序结果：

31    2402
19    1309
18    3078
18    2801
16    5683
16    3369
16    1336
16    4018
15    11239
15    13098

posted @ 2019-11-14 21:40 田智凯阅读(619) 评论(0) 编辑收藏举报

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mapreduce数据处理——统计排序

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