大数据技术使用java实现MapReduce对文件进行切分，分类汇总

Posted 2022-02-07 liangzai2048

Java使用MapReduce切分文件

比如有海量的文本文件，如订单，页面点击事件的记录，量特别大，很难搞定。
那么我们该怎样解决海量数据的计算？

1、获取总行数
2、计算每个文件中存多少数据
3、split切分文件
4、reduce将文件进行汇总

例如这里有百万条数据，单个文件操作太麻烦，所以我们需要进行切分
在切分文件的过程中会出现文件不能整个切分的情况，可能有剩下的数据并没有被读取到，所以我们每个切分128条数据，不足128条再保留到一个文件中

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创建MapTask

import java.io.*;
import java.util.HashMap;
import java.util.Map;
import java.util.Set;

public class MapTask extends Thread 
    //用来接收具体的哪一个文件
    private File file;
    private int flag;

    public MapTask(File file, int flag) 
        this.file = file;
        this.flag = flag;
    

    @Override
    public void run() 
        try 
            BufferedReader br = new BufferedReader(new FileReader(file));
            String line;
            HashMap<String, Integer> map = new HashMap<String, Integer>();
            while ((line = br.readLine()) != null) 
                /**
                 * 统计班级人数HashMap存储
                 */
                String clazz = line.split(",")[4];
                if (!map.containsKey(clazz)) 
                    map.put(clazz, 1);
                 else 
                    map.put(clazz, map.get(clazz) + 1);
                
            
            br.close();
            BufferedWriter bw = new BufferedWriter(
                    new FileWriter("F:\\\\IDEADEMO\\\\shujiabigdata\\\\part\\\\part---" + flag));
            Set<Map.Entry<String, Integer>> entries = map.entrySet();
            for (Map.Entry<String, Integer> entry : entries) 
                String key = entry.getKey();
                Integer value = entry.getValue();
                bw.write(key + ":" + value);
                bw.newLine();
            
            bw.flush();
            bw.close();
         catch (Exception e) 
            e.printStackTrace();
        
    

创建Map

import java.io.File;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;

public class Map 
    public static void main(String[] args) 
        long start = System.currentTimeMillis();
        // 多线程连接池(线程池)
        ExecutorService executorService = Executors.newFixedThreadPool(8);
        // 获取文件列表
        File file = new File("F:\\\\IDEADEMO\\\\shujiabigdata\\\\split");
        File[] files = file.listFiles();
        //创建多线程对象
        int flag = 0;
        for (File f : files) 
            //为每一个文件启动一个线程
            MapTask mapTask = new MapTask(f, flag);
            executorService.submit(mapTask);
            flag++;
        
        executorService.shutdown();
        long end = System.currentTimeMillis();
        System.out.println(end-start);
    

创建ClazzSum

import java.io.BufferedReader;
import java.io.FileReader;
import java.util.HashMap;

public class ClazzSum 
    public static void main(String[] args) throws Exception 
        long start = System.currentTimeMillis();
        BufferedReader br = new BufferedReader(
                new FileReader("F:\\\\IDEADEMO\\\\shujiabigdata\\\\data\\\\bigstudents.txt"));
        String line;
        HashMap<String, Integer> map = new HashMap<String, Integer>();
        while ((line = br.readLine()) != null) 
            String clazz = line.split(",")[4];
            if (!map.containsKey(clazz)) 
                map.put(clazz, 1);
             else 
                map.put(clazz, map.get(clazz) + 1);
            
        
        System.out.println(map);
        long end = System.currentTimeMillis();
        System.out.println(end-start);
    

创建split128

import java.io.BufferedReader;
import java.io.BufferedWriter;
import java.io.FileReader;
import java.io.FileWriter;
import java.util.ArrayList;

public class Split128 
    public static void main(String[] args) throws Exception 
        BufferedReader br = new BufferedReader(
                new FileReader("F:\\\\IDEADEMO\\\\shujiabigdata\\\\data\\\\students.txt"));

        //用作标记文件，也作为文件名称
        int index = 0;
        BufferedWriter bw = new BufferedWriter(
                new FileWriter("F:\\\\IDEADEMO\\\\shujiabigdata\\\\split01\\\\split---" + index));

        ArrayList<String> list = new ArrayList<String>();
        String line;
        //用作累计读取了多少行数据
        int flag = 0;
        int row = 0;
        while ((line = br.readLine()) != null) 
            list.add(line);
            flag++;
            // flag = 140
            if (flag == 140) // 一个文件读写完成，生成新的文件
                row = 0 + 128 * index;
                for (int i = row; i <= row + 127; i++) 
                    bw.write(list.get(i));
                    bw.newLine();
                
                bw.flush();
                bw.close();
                /**
                 * 生成新的文件
                 * 计数清零
                 */
                index++;
                flag = 12;
                bw = new BufferedWriter(
                        new FileWriter("F:\\\\IDEADEMO\\\\shujiabigdata\\\\split01\\\\split---" + index));
            
        
        //文件读取剩余128*1.1范围之内
        for (int i = list.size() - flag; i < list.size(); i++) 
            bw.write(list.get(i));
            bw.newLine();
        
        bw.flush();
        bw.close();
    

创建Reduce

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.util.HashMap;

public class Reduce 
    public static void main(String[] args) throws Exception 
        long start = System.currentTimeMillis();
        HashMap<String, Integer> map = new HashMap<String, Integer>();
        File file = new File("F:\\\\IDEADEMO\\\\shujiabigdata\\\\part");
        File[] files = file.listFiles();
        for (File f : files) 
            BufferedReader br = new BufferedReader(new FileReader(f));
            String line;
            while ((line = br.readLine()) != null) 
                String clazz = line.split(":")[0];
                int sum = Integer.valueOf(line.split(":")[1]);
                if (!map.containsKey(clazz)) 
                    map.put(clazz, sum);
                 else 
                    map.put(clazz, map.get(clazz) + sum);
                
            
        
        long end = System.currentTimeMillis();
        System.out.println(end-start);
        System.out.println(map);
    

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最后将文件切分了8份，这里采用了线程池，建立线程连接，多个线程同时启动，比单一文件采用多线程效率更高更好使。