Hadoop入门案列，初学者Coder

1、WordCount

　　Job类：

package com.simope.mr.wcFor;

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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;


/**
 * @deprecated 统计文本单词个数
 * @author JimLy
 * @see 20150127
 * */
public class WcForJob {

    public static void main(String[] args) {

        Configuration conf = new Configuration();
        
        try {
            Job job = new Job(conf);
            
            job.setJobName("myWC");
            job.setJarByClass(WcForJob.class);
            job.setMapperClass(WcForMapper.class);
            job.setReducerClass(WcForReducer.class);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(IntWritable.class);
            
            FileInputFormat.addInputPath(job, new Path("/usr/input/myWc"));
            FileOutputFormat.setOutputPath(job, new Path("/usr/output/myWc"));
            
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        } catch (Exception e) {
            System.out.println("错误信息:" + e);
        }
        
    }
    
}

　　Mapper类：

package com.simope.mr.wcFor;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class WcForMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

    @Override
    protected void map(LongWritable key, Text value,
                        Context context)
            throws IOException, InterruptedException {

        String line = value.toString();
        StringTokenizer st = new StringTokenizer(line);
        
        while (st.hasMoreElements()) {
            context.write(new Text(st.nextToken()), new IntWritable(1));
        }
    
    }
    
}

　　Reducer类：

package com.simope.mr.wcFor;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class WcForReducer extends Reducer<Text, IntWritable, Text, IntWritable>{

    @Override
    protected void reduce(Text key, Iterable<IntWritable> value,
                            Context context)
            throws IOException, InterruptedException {

        int sum = 0;
        
        for (IntWritable i : value) {
            sum += i.get();
        }
        context.write(key, new IntWritable(sum));
    }
    
}

　　文本输入：

　　

　　统计输出：

　　　　

　　

---

2、单列排序

　　Job类:

package com.simope.mr.sort;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**
 * @deprecated 对数字进行排序
 * @author JimLy
 * @see 20160127
 * */
public class SortJob {

    public static void main(String[] args) {
        
        Configuration conf = new Configuration();
        
        try {
            Job job = new Job(conf);
            
            job.setJobName("sortJob");
            job.setJarByClass(SortJob.class);
            job.setMapperClass(SortMapper.class);
            job.setReducerClass(SortReducer.class);
            job.setMapOutputKeyClass(IntWritable.class);
            job.setMapOutputValueClass(IntWritable.class);
            
            FileInputFormat.addInputPath(job, new Path("/usr/input/sort"));
            FileOutputFormat.setOutputPath(job, new Path("/usr/output/sort"));
            
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        } catch (Exception e) {
            System.out.println("错误信息:" + e);
        }
        
    }
    
}

　　Mapper类：

package com.simope.mr.sort;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class SortMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable>{

    @Override
    protected void map(LongWritable key, Text value,
                        Context context)
            throws IOException, InterruptedException {
        
        String line = value.toString();

        context.write(new IntWritable(Integer.parseInt(line)), new IntWritable(1));
    
    }
    
}

　　Reducer类：

package com.simope.mr.sort;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Reducer;

public class SortReducer extends Reducer<IntWritable, IntWritable, IntWritable, IntWritable> {

    private static IntWritable lineNum = new IntWritable(1); 
    
    @SuppressWarnings("unused")
    @Override
    protected void reduce(IntWritable key, Iterable<IntWritable> value,
                            Context context)
            throws IOException, InterruptedException {
        
        //考虑到有相同的值
        for (IntWritable val : value) {
            context.write(lineNum, key);
            lineNum = new IntWritable(lineNum.get() + 1);
        }
        
    }
    
}

　　输入文本：

　file1:　file2:file3:

　　统计输出：

　　

---

3、计算学科平均成绩

　　Job类：

package com.simope.mr.average;

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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;


/**
 * @deprecated 计算学生的平均成绩
 * @author JimLy
 * @see 20160127
 * */
public class AveJob {

    public static void main(String[] args) {
        
        Configuration conf = new Configuration();
        
        try {
            Job job = new Job(conf);
            
            job.setJobName("AveJob");
            job.setJarByClass(AveJob.class);
            job.setMapperClass(AveMapper.class);
            job.setReducerClass(AveReducer.class);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(IntWritable.class);
            
            FileInputFormat.addInputPath(job, new Path("/usr/input/average"));
            FileOutputFormat.setOutputPath(job, new Path("/usr/output/average"));
            
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        } catch (Exception e) {
            System.out.println("错误信息:" + e);
        }
        
    }
    
}

　　Mapper类：

package com.simope.mr.average;

import java.io.IOException;
import java.io.UnsupportedEncodingException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class AveMapper extends Mapper<LongWritable, Text, Text, IntWritable>{

    String line;
    
    @Override
    protected void map(LongWritable key, Text value,
                        Context context)
            throws IOException, InterruptedException {

        line = changeTextToUTF8(value, "GBK").toString();
        
        String[] stuArr = line.split("	");
        
        context.write(new Text(stuArr[0]), new IntWritable(Integer.parseInt(stuArr[1])));
    
    }
    
    public static Text changeTextToUTF8(Text text, String encoding) {
        String value = null;
        try {
            value = new String(text.getBytes(), 0, text.getLength(), encoding);
        } catch (UnsupportedEncodingException e) {
            e.printStackTrace();
        }
        return new Text(value);
    }
}

　　Reducer类：

package com.simope.mr.average;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class AveReducer extends Reducer<Text, IntWritable, Text, IntWritable>{

    int count, sum;
    
    @Override
    protected void reduce(Text key, Iterable<IntWritable> value,
                            Context context)
            throws IOException, InterruptedException {

        sum = 0;
        count = 0;
        
        for (IntWritable i : value) {
            count++;
            sum += i.get();
        }
        context.write(key, new IntWritable(sum/count));
    }
    
}

　　文本输入：

china:english:math:

　　统计输出：

　　附：乱码问题由于hadoop中强制以UTF-8编码格式，而我用的是GBK，未进行转码。

---

4、族谱：

　　Job类：

package com.simope.mr.grand;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**
 * @description 家族族谱
 * @author JimLy
 * @see 20160128
 * */
public class GrandJob {

    public static void main(String[] args) {
        Configuration conf = new Configuration();
        
        try {
            Job job = new Job(conf);
            
            job.setJobName("GrandJob");
            job.setJarByClass(GrandJob.class);
            job.setMapperClass(GrandMapper.class);
            job.setReducerClass(GrandReducer.class);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(Text.class);
            
            FileInputFormat.addInputPath(job, new Path("/usr/input/grand"));
            FileOutputFormat.setOutputPath(job, new Path("/usr/output/grand"));
            
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        } catch (Exception e) {
            System.out.println("错误信息:" + e);
        }
        
        
    }
    
}

　　Mapper类：

package com.simope.mr.grand;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class GrandMapper extends Mapper<LongWritable, Text, Text, Text>{

    @Override
    protected void map(LongWritable key, Text value,
                        Context context)
            throws IOException, InterruptedException {

        String line = value.toString();
        String[] faArr = line.split("	");
        
        if (faArr.length == 2) {
            if (!faArr[0].equals("parent")) {
                context.write(new Text(faArr[0]), new Text(faArr[0] + "_" + faArr[1]));
                context.write(new Text(faArr[1]), new Text(faArr[0] + "_" + faArr[1]));    
            }
            
        }
    
    }
    
}

　　Reducer类：

package com.simope.mr.grand;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class GrandReducer extends Reducer<Text, Text, Text, Text>{

    private static int time = 0; 
    
    @Override
    protected void reduce(Text key, Iterable<Text> value,
                            Context context) throws IOException,
            InterruptedException {

        List<String> paList = new ArrayList<String>();
        List<String> chList = new ArrayList<String>();
        
        String info;
        String[] arr;
        for (Text i : value) {
            info = i.toString();
            arr = info.split("_");
            if (arr.length == 2) {
                paList.add(arr[0]);
                chList.add(arr[1]);
            }
        }
        
        if (time == 0) {
            context.write(new Text("grandParent"), new Text("grandChild"));
            time++;    
        }
        
        for (int i = 0; i < paList.size(); i++) {
            for (int j = 0; j < chList.size(); j++) {
                if (paList.get(i).equals(chList.get(j))) {
                    context.write(new Text(paList.get(j)), new Text(chList.get(i)));
                    time++;
                }
            }
        }
        
    }
    
}

　　输入文本：

　　　　file1：file2：

　　统计输出：

　　、

---

5、二次排序：

　　Job类：

package com.simope.mr.secOrder;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

/**
 * @deprecated 二次排序
 * @author JimLy
 * @see 20160129
 * */
public class SecOrderJob {

    public static void main(String[] args) {
        Configuration conf = new Configuration();
        
        try {
            Job job = new Job(conf);
            
            job.setJobName("SecOrderJob");
            job.setJarByClass(SecOrderJob.class);
            job.setMapperClass(SecOrderMapper.class);
            job.setReducerClass(SecOrderReducer.class);
            job.setMapOutputKeyClass(IntWritable.class);
            job.setMapOutputValueClass(IntWritable.class);
            
            FileInputFormat.addInputPath(job, new Path("/usr/input/secOrder"));
            FileOutputFormat.setOutputPath(job, new Path("/usr/output/secOrder"));
            
            System.exit(job.waitForCompletion(true) ? 0 : 1);
        } catch (Exception e) {
            System.out.println("错误信息:" + e);
        }
    }
    
}

　　Mapper类：

package com.simope.mr.secOrder;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

public class SecOrderMapper extends Mapper<LongWritable, Text, IntWritable, IntWritable>{

    @Override
    protected void map(LongWritable key, Text value,
                        Context context)
            throws IOException, InterruptedException {

        String line = value.toString();
        
        String[] numArr = line.split("	");
        
        if (numArr.length == 2) {
            context.write(new IntWritable(Integer.parseInt(numArr[0])), new IntWritable(Integer.parseInt(numArr[1])));
        }
    
    }
    
}

　　Reducer类：

package com.simope.mr.secOrder;

import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

public class SecOrderReducer extends Reducer<IntWritable, IntWritable, IntWritable, Text>{

    @Override
    protected void reduce(IntWritable key, Iterable<IntWritable> value,
                Context context)
            throws IOException, InterruptedException {
    
        String str = "";
        
        for (IntWritable i : value) {
            str = str + "#" + i.get();
        }
        
        str = str.substring(1, str.length());
        
        String[] numArr = str.split("#");
        
        String temp;
        
        for (int i = 0; i < numArr.length; i++) {
            for (int j = 0; j < numArr.length; j++) {
                if (Integer.parseInt(numArr[j]) > Integer.parseInt(numArr[i])) {
                    temp = numArr[i];
                    numArr[i] = numArr[j];
                    numArr[j] = temp;
                }
            }
        }
        
        for (int i = 0; i < numArr.length; i++) {
            context.write(key, new Text(numArr[i]));
        }
    }
}

　　输入文本：

　　

　　统计输出：

---

6、计算1949-1955年中每年温度最高前10天

　　RunJob类:

package com.simope.mr;

import java.io.IOException;
import java.text.SimpleDateFormat;
import java.util.Calendar;
import java.util.Date;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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;

public class RunJob {

    public static SimpleDateFormat SDF = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
    
    static class HotMapper extends Mapper<LongWritable, Text, KeyPari, Text> {
        
        @Override
        protected void map(LongWritable key, Text value,
                Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            
            String[] ss = line.split("	");
            
            if (ss.length == 2) {
                try {
                    Date date = SDF.parse(ss[0]);
                    Calendar c = Calendar.getInstance();
                    c.setTime(date);
                    int year = c.get(1);
                    String hot = ss[1].substring(0, ss[1].indexOf("C"));
                    KeyPari kp = new KeyPari();
                    kp.setYear(year);
                    kp.setHot(Integer.parseInt(hot));
                    context.write(kp, value);
                } catch (Exception e) {
                    e.printStackTrace();
                }
            }
        }
    }

    
    static class HotReduce extends Reducer<KeyPari, Text, KeyPari, Text> {
        @Override
        protected void reduce(KeyPari kp, Iterable<Text> value,
            Context context)
                throws IOException, InterruptedException {

            for (Text v :value) {
                context.write(kp, v);
            }
        }
    }
    
    public static void main(String[] args) {
        
        Configuration conf = new Configuration();
        try {
            Job job = new Job(conf);
            job.setJobName("hot");
            job.setJarByClass(RunJob.class);
            job.setMapperClass(HotMapper.class);
            job.setReducerClass(HotReduce.class);
            job.setMapOutputKeyClass(KeyPari.class);
            job.setMapOutputValueClass(Text.class);
            
            job.setNumReduceTasks(2);
            job.setPartitionerClass(FirstPartition.class);
            job.setSortComparatorClass(SortHot.class);
            job.setGroupingComparatorClass(GroupHot.class);

            //mapreduce输入数据所在的目录或者文件
            FileInputFormat.addInputPath(job, new Path("/usr/input/hot"));
            //mr执行之后的输出数据的目录
            FileOutputFormat.setOutputPath(job, new Path("/usr/output/hot"));
            System.exit(job.waitForCompletion(true) ? 0 : 1);
            
            
        } catch (Exception e) {
            e.printStackTrace();
        }
        
        
    }
    
}

　　FirstPartition类：

package com.simope.mr;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;

/**
 * 实现分区
 * */
public class FirstPartition extends Partitioner<KeyPari, Text> {

    
    /** 
     * num:reduce数量
     * getPartition()方法的 
     * 输入参数：键/值对<key,value>与reducer数量num 
     * 输出参数：分配的Reducer编号，这里是result 
     * */ 
    public int getPartition(KeyPari key, Text value, int num) {
        
        return (key.getYear()) * 127 % num;    //按照年份分区
    }
    
}

　　SortHot类：

package com.simope.mr;

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

/**
 * 排序
 * 因为hadoop对数据分组后默认是按照key升序排序的，所以需要自定义排序函数将分组数据降序排序
 * 对于一般的键，只需要key值相同，则对应的value就会分配至同一个 reduce中；
 * 对于复合键，形式为TextPair<key1,key2>，通过控制 key1来进行分区，则具有相同的 key1的值会被划分至同一个分区中，但此时如果 key2不相同，则不同的key2会被划分至不同的分组
 * */
public class SortHot extends WritableComparator {

    public SortHot() {
        super(KeyPari.class, true);
    }

    @SuppressWarnings("rawtypes")
    public int compare(WritableComparable a, WritableComparable b) {
        KeyPari o1 = (KeyPari)a;
        KeyPari o2 = (KeyPari)b;
        int res = Integer.compare(o1.getYear(), o2.getYear());    //升序排序
        if (res != 0) {
            return res;
        }
        return -Integer.compare(o1.getHot(), o2.getHot());    //降序排序
    }
    
}

　　KeyPari类：

package com.simope.mr;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.WritableComparable;

/**
 * 封装key对象
 * */
public class KeyPari implements WritableComparable<KeyPari>{

    private int year;
    private int hot;
    public int getYear() {
        return year;
    }
    public void setYear(int year) {
        this.year = year;
    }
    public int getHot() {
        return hot;
    }
    public void setHot(int hot) {
        this.hot = hot;
    }

    public void readFields(DataInput in) throws IOException {
        this.year = in.readInt();
        this.hot = in.readInt();
    }

    public void write(DataOutput out) throws IOException {
        out.writeInt(year);
        out.writeInt(hot);
    }

    public int compareTo(KeyPari keyPari) {
        int res = Integer.compare(year, keyPari.getYear());
        if (res != 0) {
            return res;
        }
        return Integer.compare(hot, keyPari.getHot());
    }
    
    public String toString() {
        return year + "	" + hot;
    }
    
    public int hashCode() {
        return new Integer(year + hot).hashCode();
    }
    
}

　　GroupHot类：

package com.simope.mr;

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

/**
 * 排序
 * */
public class GroupHot extends WritableComparator {

    public GroupHot() {
        super(KeyPari.class, true);
    }

    @SuppressWarnings("rawtypes")
    public int compare(WritableComparable a, WritableComparable b) {
        KeyPari o1 = (KeyPari)a;
        KeyPari o2 = (KeyPari)b;
        return Integer.compare(o1.getYear(), o2.getYear());    //升序排序
    }
    
}

初次接触Hadoop,可能代码不是最简,存在可优化的地方还请大家指教。经过第一周无聊的环境部署,终于在这周可以写代码了。。。

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