MapReduce练习

转载：https://www.cnblogs.com/shishanyuan/p/4190403.html

Q:

1) 求各个部门的总工资 2) 求各个部门的人数和平均工资 3) 求每个部门最早进入公司的员工姓名 4) 求各个城市的员工的总工资 5) 列出工资比上司高的员工姓名及其工资 6) 列出工资比公司平均工资要高的员工姓名及其工资 7) 列出名字以J开头的员工姓名及其所属部门名称 8) 列出工资最高的头三名员工姓名及其工资 9) 将全体员工按照总收入（工资+提成）从高到低排列，要求列出姓名及其总收入 10) 如果每位员工只能和他的直接上司，直接下属，同一部门的同事交流，求任何两名员工之间若要进行信息传递所需要经过的中间节点数。请评价一下这个问题是否适合使用map-reduce解决

 数据:

/**
dept表=====================
id,部门名称,地点
=====================
10,ACCOUNTING,NEW YORK
20,RESEARCH,DALLAS
30,SALES,CHICAGO
40,OPERATIONS,BOSTON

emp表=====================
id,姓名,岗位名称,员工上司编号,入职日期,工资,提成,部门id
=====================
7369,SMITH,CLERK,7902,17-12月-80,800,,20
7499,ALLEN,SALESMAN,7698,20-2月-81,1600,300,30
7521,WARD,SALESMAN,7698,22-2月-81,1250,500,30
7566,JONES,MANAGER,7839,02-4月-81,2975,,20
7654,MARTIN,SALESMAN,7698,28-9月-81,1250,1400,30
7698,BLAKE,MANAGER,7839,01-5月-81,2850,,30
7782,CLARK,MANAGER,7839,09-6月-81,2450,,10
7839,KING,PRESIDENT,,17-11月-81,5000,,10
7844,TURNER,SALESMAN,7698,08-9月-81,1500,0,30
7900,JAMES,CLERK,7698,03-12月-81,950,,30
7902,FORD,ANALYST,7566,03-12月-81,3000,,20
7934,MILLER,CLERK,7782,23-1月-82,1300,,10
 */

原文代码如下:

1) 求各个部门的总工资

package cn.xjw.mapreduce;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.filecache.DistributedCache;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

/**
 * @Author: w
 * @Description: 1) 求各个部门的总工资
 * @Date: 2019/3/1
 */
public class Q1SumDeptSalary extends Configured implements Tool {



    public static class MapClass extends Mapper {
        /**用于缓存 dept文件中的数据*/
        private Map deptMap = new HashMap();
        private String[] kv;

        /**此方法会在Map方法执行之前执行且执行一次*/
        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            BufferedReader in = null;
            try {
                // 从当前作业中获取要缓存的文件
                Path[] paths = DistributedCache.getLocalCacheFiles(context.getConfiguration());
                String deptIdName = null;
                for (Path path : paths) {
                    // 对部门文件字段进行拆分并缓存到deptMap中
                    if (path.toString().contains("dept")) {
                        in = new BufferedReader(new FileReader(path.toString()));
                        while (null != (deptIdName = in.readLine())) {
                            // 对部门文件字段进行拆分并缓存到deptMap中
                            // 其中Map中key为部门编号，value为所在部门名称
                            deptMap.put(deptIdName.split(",")[0], deptIdName.split(",")[1]);
                        }
                    }
                }
            } catch (IOException e) {
                e.printStackTrace();
            } finally {
                try {
                    if (in != null) {
                        in.close();
                    }
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }

        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 对员工文件字段进行拆分
            kv = value.toString().split(",");
            // map join: 在map阶段过滤掉不需要的数据，输出key为部门名称和value为员工工资
            if (deptMap.containsKey(kv[7])) {
                if (null != kv[5] && !"".equals(kv[5].toString())) {
                    context.write(new Text(deptMap.get(kv[7].trim())), new Text(kv[5].trim()));
                }
            }
        }
    }



    public static class Reduce extends Reducer {
        @Override
        public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
            // 对同一部门的员工工资进行求和
            long sumSalary = 0;
            for (Text val : values) {
                sumSalary += Long.parseLong(val.toString());
            }
            // 输出key为部门名称和value为该部门员工工资总和
            context.write(key, new LongWritable(sumSalary));
        }
    }


//    @Override
    public int run(String[] args) throws Exception {
        // 实例化作业对象，设置作业名称、Mapper和Reduce类
        Job job = new Job(getConf(), "Q1SumDeptSalary");
        job.setJobName("Q1SumDeptSalary");
        job.setJarByClass(Q1SumDeptSalary.class);
        job.setMapperClass(MapClass.class);
        job.setReducerClass(Reduce.class);
        // 设置输入格式类
        job.setInputFormatClass(TextInputFormat.class);
        // 设置输出格式
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        // 第1个参数为缓存的部门数据路径、第2个参数为员工数据路径和第3个参数为输出路径
        String[] otherArgs = new GenericOptionsParser(job.getConfiguration(), args).getRemainingArgs();
        DistributedCache.addCacheFile(new Path(otherArgs[0]).toUri(), job.getConfiguration());
        FileInputFormat.addInputPath(job, new Path(otherArgs[1]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[2]));
        job.waitForCompletion(true);
        return job.isSuccessful() ? 0 : 1;
    }

    /**
     * 主方法，执行入口
     * @param args 输入参数
     */
    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new Q1SumDeptSalary(), args);
        System.exit(res);
    }

}

2) 求各个部门的人数和平均工资

package cn.xjw.mapreduce;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.filecache.DistributedCache;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

/**
 * @Author: w
 * @Description: 2) 求各个部门的人数和平均工资
 * @Date: 2019/3/1
 */
public class Q2DeptNumberAveSalary extends Configured implements Tool {

    public static class MapClass extends Mapper {
        /**用于缓存 dept文件中的数据*/
        private Map deptMap = new HashMap();
        private String[] kv;
        /**此方法会在Map方法执行之前执行且执行一次*/
        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            BufferedReader in = null;
            try {
                // 从当前作业中获取要缓存的文件
                Path[] paths = DistributedCache.getLocalCacheFiles(context.getConfiguration());
                String deptIdName = null;
                for (Path path : paths) {
                    // 对部门文件字段进行拆分并缓存到deptMap中
                    if (path.toString().contains("dept")) {
                        in = new BufferedReader(new FileReader(path.toString()));
                        while (null != (deptIdName = in.readLine())) {
                            // 对部门文件字段进行拆分并缓存到deptMap中
                            // 其中Map中key为部门编号，value为所在部门名称
                            deptMap.put(deptIdName.split(",")[0], deptIdName.split(",")[1]);
                        }
                    }
                }
            } catch (IOException e) {
                e.printStackTrace();
            } finally {
                try {
                    if (in != null) {
                        in.close();
                    }
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }

        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 对员工文件字段进行拆分
            kv = value.toString().split(",");
            // map join: 在map阶段过滤掉不需要的数据，输出key为部门名称和value为员工工资
            if (deptMap.containsKey(kv[7])) {
                if (null != kv[5] && !"".equals(kv[5].toString())) {
                    context.write(new Text(deptMap.get(kv[7].trim())), new Text(kv[5].trim()));
                }
            }
        }
    }


    public static class Reduce extends Reducer {
        @Override
        public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
            long sumSalary = 0;
            int deptNumber = 0;
            // 对同一部门的员工工资进行求和
            for (Text val : values) {
                sumSalary += Long.parseLong(val.toString());
                deptNumber++;
            }
            // 输出key为部门名称和value为该部门员工工资平均值
            context.write(key, new Text("Dept Number:" + deptNumber + ", Ave Salary:" + sumSalary / deptNumber));
        }
    }



//    @Override
    public int run(String[] args) throws Exception {
        // 实例化作业对象，设置作业名称、Mapper和Reduce类
        Job job = new Job(getConf(), "Q2DeptNumberAveSalary");
        job.setJobName("Q2DeptNumberAveSalary");
        job.setJarByClass(Q2DeptNumberAveSalary.class);
        job.setMapperClass(MapClass.class);
        job.setReducerClass(Reduce.class);
        // 设置输入格式类
        job.setInputFormatClass(TextInputFormat.class);
        // 设置输出格式类
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        // 第1个参数为缓存的部门数据路径、第2个参数为员工数据路径和第3个参数为输出路径
        String[] otherArgs = new GenericOptionsParser(job.getConfiguration(), args).getRemainingArgs();
        DistributedCache.addCacheFile(new Path(otherArgs[0]).toUri(), job.getConfiguration());
        FileInputFormat.addInputPath(job, new Path(otherArgs[1]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[2]));
        job.waitForCompletion(true);
        return job.isSuccessful() ? 0 : 1;
    }

    /**
     * 主方法，执行入口
     * @param args 输入参数
     */
    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new Q2DeptNumberAveSalary(), args);
        System.exit(res);
    }

}

3) 求每个部门最早进入公司的员工姓名

package cn.xjw.mapreduce;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.text.DateFormat;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.HashMap;
import java.util.Map;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.filecache.DistributedCache;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;



/**
 * @Author: w
 * @Description: 3) 求每个部门最早进入公司的员工姓名
 * @Date: 2019/3/1
 */
public class Q3DeptEarliestEmp extends Configured implements Tool {

    public static class MapClass extends Mapper {
        /**用于缓存 dept文件中的数据*/
        private Map deptMap = new HashMap();
        private String[] kv;
        /**此方法会在Map方法执行之前执行且执行一次*/
        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            BufferedReader in = null;
            try {
                // 从当前作业中获取要缓存的文件
                Path[] paths = DistributedCache.getLocalCacheFiles(context.getConfiguration());
                String deptIdName = null;
                for (Path path : paths) {
                    if (path.toString().contains("dept")) {
                        in = new BufferedReader(new FileReader(path.toString()));
                        while (null != (deptIdName = in.readLine())) {
                            // 对部门文件字段进行拆分并缓存到deptMap中
                            // 其中Map中key为部门编号，value为所在部门名称
                            deptMap.put(deptIdName.split(",")[0], deptIdName.split(",")[1]);
                        }
                    }
                }
            } catch (IOException e) {
                e.printStackTrace();
            } finally {
                try {
                    if (in != null) {
                        in.close();
                    }
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }


        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 对员工文件字段进行拆分
            kv = value.toString().split(",");
            // map join: 在map阶段过滤掉不需要的数据
            // 输出key为部门名称和value为员工姓名+","+员工进入公司日期
            if (deptMap.containsKey(kv[7])) {
                if (null != kv[4] && !"".equals(kv[4].toString())) {
                    context.write(new Text(deptMap.get(kv[7].trim())), new Text(kv[1].trim() + "," + kv[4].trim()));
                }
            }
        }
    }


    public static class Reduce extends Reducer {
        @Override
        public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
            // 员工姓名和进入公司日期
            String empName = null;
            String empEnterDate = null;
            // 设置日期转换格式和最早进入公司的员工、日期
            DateFormat df = new SimpleDateFormat("dd-MM月-yy");
            Date earliestDate = new Date();
            String earliestEmp = null;
            // 遍历该部门下所有员工，得到最早进入公司的员工信息
            for (Text val : values) {
                empName = val.toString().split(",")[0];
                empEnterDate = val.toString().split(",")[1].toString().trim();
                try {
                    System.out.println(df.parse(empEnterDate));
                    if (df.parse(empEnterDate).compareTo(earliestDate) < 0) {
                        earliestDate = df.parse(empEnterDate);
                        earliestEmp = empName;
                    }
                } catch (ParseException e) {
                    e.printStackTrace();
                }
            }
            // 输出key为部门名称和value为该部门最早进入公司员工
            context.write(key, new Text("The earliest emp of dept:" + earliestEmp + ", Enter            date:" + new SimpleDateFormat("yyyy-MM-dd").format(earliestDate)));
        }
    }


    //@Override
    public int run(String[] args) throws Exception {
        // 实例化作业对象，设置作业名称
        Job job = new Job(getConf(), "Q3DeptEarliestEmp");
        job.setJobName("Q3DeptEarliestEmp");
        // 设置Mapper和Reduce类
        job.setJarByClass(Q3DeptEarliestEmp.class);
        job.setMapperClass(MapClass.class);
        job.setReducerClass(Reduce.class);
        // 设置输入格式类
        job.setInputFormatClass(TextInputFormat.class);
        // 设置输出格式类
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        // 第1个参数为缓存的部门数据路径、第2个参数为员工数据路径和第三个参数为输出路径
        String[] otherArgs = new GenericOptionsParser(job.getConfiguration(), args).getRemainingArgs();
        DistributedCache.addCacheFile(new Path(otherArgs[0]).toUri(), job.getConfiguration());
        FileInputFormat.addInputPath(job, new Path(otherArgs[1]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[2]));
        job.waitForCompletion(true);
        return job.isSuccessful() ? 0 : 1;
    }


    /**
     * 主方法，执行入口
     * @param args 输入参数
     */
    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new Q3DeptEarliestEmp(), args);
        System.exit(res);
    }

}

4) 求各个城市的员工的总工资

package cn.xjw.mapreduce;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.filecache.DistributedCache;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;


/**
 * @Author: w
 * @Description: 4) 求各个城市的员工的总工资
 * @Date: 2019/3/1
 */
public class Q4SumCitySalary extends Configured implements Tool {


    public static class MapClass extends Mapper {
// 用于缓存 dept文件中的数据
        private Map deptMap = new HashMap();
        private String[] kv;
// 此方法会在Map方法执行之前执行且执行一次
        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            BufferedReader in = null;
            try {
                // 从当前作业中获取要缓存的文件
                Path[] paths = DistributedCache.getLocalCacheFiles(context.getConfiguration());
                String deptIdName = null;
                for (Path path : paths) {
                    if (path.toString().contains("dept")) {
                        in = new BufferedReader(new FileReader(path.toString()));
                        while (null != (deptIdName = in.readLine())) {
                            // 对部门文件字段进行拆分并缓存到deptMap中
                            // 其中Map中key为部门编号，value为所在城市名称
                            deptMap.put(deptIdName.split(",")[0], deptIdName.split(",")[2]);
                        }
                    }
                }
            } catch (IOException e) {
                e.printStackTrace();
            } finally {
                try {
                    if (in != null) {
                        in.close();
                    }
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }


        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 对员工文件字段进行拆分
            kv = value.toString().split(",");
            // map join: 在map阶段过滤掉不需要的数据，输出key为城市名称和value为员工工资
            if (deptMap.containsKey(kv[7])) {
                if (null != kv[5] && !"".equals(kv[5].toString())) {
                    context.write(new Text(deptMap.get(kv[7].trim())), new Text(kv[5].trim()));
                }
            }
        }
    }


    public static class Reduce extends Reducer {
        @Override
        public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
            // 对同一城市的员工工资进行求和
            long sumSalary = 0;
            for (Text val : values) {
                sumSalary += Long.parseLong(val.toString());
            }
            // 输出key为城市名称和value为该城市工资总和
            context.write(key, new LongWritable(sumSalary));
        }
    }


    //@Override
    public int run(String[] args) throws Exception {
        // 实例化作业对象，设置作业名称
        Job job = new Job(getConf(), "Q4SumCitySalary");
        job.setJobName("Q4SumCitySalary");
        // 设置Mapper和Reduce类
        job.setJarByClass(Q4SumCitySalary.class);
        job.setMapperClass(MapClass.class);
        job.setReducerClass(Reduce.class);
        // 设置输入格式类
        job.setInputFormatClass(TextInputFormat.class);
        // 设置输出格式类
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        // 第1个参数为缓存的部门数据路径、第2个参数为员工数据路径和第3个参数为输出路径
        String[] otherArgs = new GenericOptionsParser(job.getConfiguration(), args).getRemainingArgs();
        DistributedCache.addCacheFile(new Path(otherArgs[0]).toUri(), job.getConfiguration());
        FileInputFormat.addInputPath(job, new Path(otherArgs[1]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[2]));
        job.waitForCompletion(true);
        return job.isSuccessful() ? 0 : 1;
    }


    /**
     * 主方法，执行入口
     * @param args 输入参数
     */
    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new Q4SumCitySalary(), args);
        System.exit(res);
    }

}

5) 列出工资比上司高的员工姓名及其工资

package cn.xjw.mapreduce;

import java.io.IOException;
import java.util.HashMap;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;


/**
 * @Author: w
 * @Description: 5) 列出工资比上司高的员工姓名及其工资
 * @Date: 2019/3/1
 */
public class Q5EarnMoreThanManager extends Configured implements Tool {


    public static class MapClass extends Mapper {
        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 对员工文件字段进行拆分
            String[] kv = value.toString().split(",");
            // 输出经理表数据，其中key为员工编号和value为M+该员工工资
            context.write(new Text(kv[0].toString()), new Text("M," + kv[5]));
            // 输出员工对应经理表数据，其中key为经理编号和value为(E，该员工姓名，该员工工资)
            if (null != kv[3] && !"".equals(kv[3].toString())) {
                context.write(new Text(kv[3].toString()), new Text("E," + kv[1] + "," + kv[5]));
            }
        }
    }


    public static class Reduce extends Reducer {
        @Override
        public void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
            // 定义员工姓名、工资和存放部门员工Map
            String empName;
            long empSalary = 0;
            HashMap empMap = new HashMap();
            // 定义经理工资变量
            long mgrSalary = 0;
            for (Text val : values) {
                if (val.toString().startsWith("E")) {
                    // 当是员工标示时，获取该员工对应的姓名和工资并放入Map中
                    empName = val.toString().split(",")[1];
                    empSalary = Long.parseLong(val.toString().split(",")[2]);
                    empMap.put(empName, empSalary);
                } else {
                    // 当时经理标志时，获取该经理工资
                    mgrSalary = Long.parseLong(val.toString().split(",")[1]);
                }
            }
            // 遍历该经理下属，比较员工与经理工资高低，输出工资高于经理的员工
            for (java.util.Map.Entry entry : empMap.entrySet()) {
                if (entry.getValue() > mgrSalary) {
                    context.write(new Text(entry.getKey()), new Text("" + entry.getValue()));
                }
            }
        }
    }


    //    @Override
    public int run(String[] args) throws Exception {
        // 实例化作业对象，设置作业名称
        Job job = new Job(getConf(), "Q5EarnMoreThanManager");
        job.setJobName("Q5EarnMoreThanManager");
        // 设置Mapper和Reduce类
        job.setJarByClass(Q5EarnMoreThanManager.class);
        job.setMapperClass(MapClass.class);
        job.setReducerClass(Reduce.class);
        // 设置输入格式类
        job.setInputFormatClass(TextInputFormat.class);
        // 设置输出格式类
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        // 第1个参数为员工数据路径和第2个参数为输出路径
        String[] otherArgs = new GenericOptionsParser(job.getConfiguration(), args).getRemainingArgs();
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        job.waitForCompletion(true);
        return job.isSuccessful() ? 0 : 1;
    }


    /**
     * 主方法，执行入口
     * @param args 输入参数
     */
    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new Q5EarnMoreThanManager(), args);
        System.exit(res);
    }

}

6) 列出工资比公司平均工资要高的员工姓名及其工资

package cn.xjw.mapreduce;

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;


/**
 * @Author: w
 * @Description: 6) 列出工资比公司平均工资要高的员工姓名及其工资
 * @Date: 2019/3/1
 */
public class Q6HigherThanAveSalary extends Configured implements Tool {


    public static class MapClass extends Mapper {
        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 对员工文件字段进行拆分
            String[] kv = value.toString().split(",");
            // 获取所有员工数据，其中key为0和value为该员工工资
            context.write(new IntWritable(0), new Text(kv[5]));
            // 获取所有员工数据，其中key为0和value为(该员工姓名 ,员工工资)
            context.write(new IntWritable(1), new Text(kv[1] + "," + kv[5]));
        }
    }


    public static class Reduce extends Reducer {
        // 定义员工工资、员工数和平均工资
        private long allSalary = 0;
        private int allEmpCount = 0;
        private long aveSalary = 0;
        // 定义员工工资变量
        private long empSalary = 0;

        @Override
        public void reduce(IntWritable key, Iterable values, Context context) throws IOException, InterruptedException {
            for (Text val : values) {
                if (0 == key.get()) {
                    // 获取所有员工工资和员工数
                    allSalary += Long.parseLong(val.toString());
                    allEmpCount++;
                    System.out.println("allEmpCount = " + allEmpCount);
                } else if (1 == key.get()) {
                    if (aveSalary == 0) {
                        aveSalary = allSalary / allEmpCount;
                        context.write(new Text("Average Salary = "), new Text("" + aveSalary));
                        context.write(new Text("Following employees have salarys higher than                        Average:"), new Text(""));
                    }
                    // 获取员工的平均工资
                    System.out.println("Employee salary = " + val.toString());
                    aveSalary = allSalary / allEmpCount;
                    // 比较员工与平均工资的大小，输出比平均工资高的员工和对应的工资
                    empSalary = Long.parseLong(val.toString().split(",")[1]);
                    if (empSalary > aveSalary) {
                        context.write(new Text(val.toString().split(",")[0]), new Text("" + empSalary));
                    }
                }
            }
        }
    }


    //@Override
    public int run(String[] args) throws Exception {
        // 实例化作业对象，设置作业名称
        Job job = new Job(getConf(), "Q6HigherThanAveSalary");
        job.setJobName("Q6HigherThanAveSalary");
        // 设置Mapper和Reduce类
        job.setJarByClass(Q6HigherThanAveSalary.class);
        job.setMapperClass(MapClass.class);
        job.setReducerClass(Reduce.class);
        // 必须设置Reduce任务数为1 # -D mapred.reduce.tasks = 1
        // 这是该作业设置的核心，这样才能够保证各reduce是串行的
        job.setNumReduceTasks(1);
        // 设置输出格式类
        job.setMapOutputKeyClass(IntWritable.class);
        job.setMapOutputValueClass(Text.class);
        // 设置输出键和值类型
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        // 第1个参数为员工数据路径和第2个参数为输出路径
        String[] otherArgs = new GenericOptionsParser(job.getConfiguration(), args).getRemainingArgs();
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        job.waitForCompletion(true);
        return job.isSuccessful() ? 0 : 1;
    }


    /**
     * 主方法，执行入口
     * @param args 输入参数
     */
    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new Q6HigherThanAveSalary(), args);
        System.exit(res);
    }

}

7) 列出名字以J开头的员工姓名及其所属部门名称

package cn.xjw.mapreduce;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.filecache.DistributedCache;
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.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;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;


/**
 * @Author: w
 * @Description: 7) 列出名字以J开头的员工姓名及其所属部门名称
 * @Date: 2019/3/1
 */
public class Q7NameDeptOfStartJ extends Configured implements Tool {


    public static class MapClass extends Mapper {
// 用于缓存 dept文件中的数据
        private Map deptMap = new HashMap();
        private String[] kv;
// 此方法会在Map方法执行之前执行且执行一次
        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            BufferedReader in = null;
            try {
                // 从当前作业中获取要缓存的文件
                Path[] paths = DistributedCache.getLocalCacheFiles(context.getConfiguration());
                String deptIdName = null;
                for (Path path : paths) {
                    // 对部门文件字段进行拆分并缓存到deptMap中
                    if (path.toString().contains("dept")) {
                        in = new BufferedReader(new FileReader(path.toString()));
                        while (null != (deptIdName = in.readLine())) {
                            // 对部门文件字段进行拆分并缓存到deptMap中
                            // 其中Map中key为部门编号，value为所在部门名称
                            deptMap.put(deptIdName.split(",")[0], deptIdName.split(",")[1]);
                        }
                    }
                }
            } catch (IOException e) {
                e.printStackTrace();
            } finally {
                try {
                    if (in != null) {
                        in.close();
                    }
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }


        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 对员工文件字段进行拆分
            kv = value.toString().split(",");
            // 输出员工姓名为J开头的员工信息，key为员工姓名和value为员工所在部门名称
            if (kv[1].toString().trim().startsWith("J")) {
                context.write(new Text(kv[1].trim()), new Text(deptMap.get(kv[7].trim())));
            }
        }
    }


    //@Override
    public int run(String[] args) throws Exception {
        // 实例化作业对象，设置作业名称
        Job job = new Job(getConf(), "Q7NameDeptOfStartJ");
        job.setJobName("Q7NameDeptOfStartJ");
        // 设置Mapper和Reduce类
        job.setJarByClass(Q7NameDeptOfStartJ.class);
        job.setMapperClass(MapClass.class);
        // 设置输入格式类
        job.setInputFormatClass(TextInputFormat.class);
        // 设置输出格式类
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);
        // 第1个参数为缓存的部门数据路径、第2个参数为员工数据路径和第3个参数为输出路径
        String[] otherArgs = new GenericOptionsParser(job.getConfiguration(), args).getRemainingArgs();
        DistributedCache.addCacheFile(new Path(otherArgs[0]).toUri(), job.getConfiguration());
        FileInputFormat.addInputPath(job, new Path(otherArgs[1]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[2]));
        job.waitForCompletion(true);
        return job.isSuccessful() ? 0 : 1;
    }


    /**
     * 主方法，执行入口
     * @param args 输入参数
     */
    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new Q7NameDeptOfStartJ(), args);
        System.exit(res);
    }

}

8) 列出工资最高的头三名员工姓名及其工资

package cn.xjw.mapreduce;

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;


/**
 * @Author: w
 * @Description: 8) 列出工资最高的头三名员工姓名及其工资
 * @Date: 2019/3/1
 */
public class Q8SalaryTop3Salary extends Configured implements Tool {


    public static class MapClass extends Mapper {
        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 对员工文件字段进行拆分
            String[] kv = value.toString().split(",");
            // 输出key为0和value为员工姓名+","+员工工资
            context.write(new IntWritable(0), new Text(kv[1].trim() + "," + kv[5].trim()));
        }
    }


    public static class Reduce extends Reducer {


        @Override
        public void reduce(IntWritable key, Iterable values, Context context) throws IOException, InterruptedException {
            // 定义工资前三员工姓名
            String empName;
            String firstEmpName = "";
            String secondEmpName = "";
            String thirdEmpName = "";
            // 定义工资前三工资
            long empSalary = 0;
            long firstEmpSalary = 0;
            long secondEmpSalary = 0;
            long thirdEmpSalary = 0;
            // 通过冒泡法遍历所有员工，比较员工工资多少，求出前三名
            for (Text val : values) {
                empName = val.toString().split(",")[0];
                empSalary = Long.parseLong(val.toString().split(",")[1]);
                if (empSalary > firstEmpSalary) {
                    thirdEmpName = secondEmpName;
                    thirdEmpSalary = secondEmpSalary;
                    secondEmpName = firstEmpName;
                    secondEmpSalary = firstEmpSalary;
                    firstEmpName = empName;
                    firstEmpSalary = empSalary;
                } else if (empSalary > secondEmpSalary) {
                    thirdEmpName = secondEmpName;
                    thirdEmpSalary = secondEmpSalary;
                    secondEmpName = empName;
                    secondEmpSalary = empSalary;
                } else if (empSalary > thirdEmpSalary) {
                    thirdEmpName = empName;
                    thirdEmpSalary = empSalary;
                }
            }
            // 输出工资前三名信息
            context.write(new Text("First employee name:" + firstEmpName), new Text("Salary:" + firstEmpSalary));
            context.write(new Text("Second employee name:" + secondEmpName), new Text("Salary:" + secondEmpSalary));
            context.write(new Text("Third employee name:" + thirdEmpName), new Text("Salary:" + thirdEmpSalary));
        }
    }


    //@Override
    public int run(String[] args) throws Exception {
        // 实例化作业对象，设置作业名称
        Job job = new Job(getConf(), "Q8SalaryTop3Salary");
        job.setJobName("Q8SalaryTop3Salary");
        // 设置Mapper和Reduce类
        job.setJarByClass(Q8SalaryTop3Salary.class);
        job.setMapperClass(MapClass.class);
        job.setReducerClass(Reduce.class);
        job.setMapOutputKeyClass(IntWritable.class);
        job.setMapOutputValueClass(Text.class);
        // 设置输入格式类
        job.setInputFormatClass(TextInputFormat.class);
        // 设置输出格式类
        job.setOutputKeyClass(Text.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setOutputValueClass(Text.class);
        // 第1个参数为员工数据路径和第2个参数为输出路径
        String[] otherArgs = new GenericOptionsParser(job.getConfiguration(), args).getRemainingArgs();
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        job.waitForCompletion(true);
        return job.isSuccessful() ? 0 : 1;
    }


    /**
     * 主方法，执行入口
     * @param args 输入参数
     */
    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new Q8SalaryTop3Salary(), args);
        System.exit(res);
    }

}

9) 将全体员工按照总收入（工资+提成）从高到低排列，要求列出姓名及其总收入

package cn.xjw.mapreduce;

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

/**
 * @Author: w
 * @Description: 9) 将全体员工按照总收入（工资+提成）从高到低排列，要求列出姓名及其总收入
 * @Date: 2019/3/1
 */
public class Q9EmpSalarySort extends Configured implements Tool {


    public static class MapClass extends Mapper {
        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 对员工文件字段进行拆分
            String[] kv = value.toString().split(",");
            // 输出key为员工所有工资和value为员工姓名
            int empAllSalary = "".equals(kv[6]) ? Integer.parseInt(kv[5]) : Integer.parseInt(kv[5]) + Integer.parseInt(kv[6]);
            context.write(new IntWritable(empAllSalary), new Text(kv[1]));
        }
    }


    /**
     * 递减排序算法
     */
    public static class DecreaseComparator extends IntWritable.Comparator {
        @Override
        public int compare(WritableComparable a, WritableComparable b) {
            return -super.compare(a, b);
        }
        @Override
        public int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {
            return -super.compare(b1, s1, l1, b2, s2, l2);
        }
    }


    //@Override
    public int run(String[] args) throws Exception {
        // 实例化作业对象，设置作业名称
        Job job = new Job(getConf(), "Q9EmpSalarySort");
        job.setJobName("Q9EmpSalarySort");
        // 设置Mapper和Reduce类
        job.setJarByClass(Q9EmpSalarySort.class);
        job.setMapperClass(MapClass.class);
        // 设置输出格式类
        job.setMapOutputKeyClass(IntWritable.class);
        job.setMapOutputValueClass(Text.class);
        job.setSortComparatorClass(DecreaseComparator.class);
        // 第1个参数为员工数据路径和第2个参数为输出路径
        String[] otherArgs = new GenericOptionsParser(job.getConfiguration(), args).getRemainingArgs();
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        job.waitForCompletion(true);
        return job.isSuccessful() ? 0 : 1;
    }


    /**
     * 主方法，执行入口
     * @param args 输入参数
     */
    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new Q9EmpSalarySort(), args);
        System.exit(res);
    }

}

10) 如果每位员工只能和他的直接上司，直接下属，同一部门的同事交流，求任何两名员工之间若要进行信息传递所需要经过的中间节点数。请评价一下这个问题是否适合使用map-reduce解决

package cn.xjw.mapreduce;

import java.io.IOException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;


/**
 * @Author: w
 * @Description: 10) 如果每位员工只能和他的直接上司，直接下属，同一部门的同事交流，求任何两名员工之间若要进行信息传递所需要经过的中间节点数。请评价一下这个问题是否适合使用map-reduce解决
 * @Date: 2019/3/1
 */
public class Q10MiddlePersonsCountForComm extends Configured implements Tool {


    public static class MapClass extends Mapper {
        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 对员工文件字段进行拆分
            String[] kv = value.toString().split(",");
            // 输出key为0和value为员工编号+","+员工经理编号
            context.write(new IntWritable(0), new Text(kv[0] + "," + ("".equals(kv[3]) ? " " : kv[3])));
        }
    }


    public static class Reduce extends Reducer {
        // 定义员工列表和员工对应经理Map
        List employeeList = new ArrayList();
        Map employeeToManagerMap = new HashMap();

        @Override
        public void reduce(IntWritable key, Iterable values, Context context) throws IOException, InterruptedException {
            // 在reduce阶段把所有员工放到员工列表和员工对应经理Map中
            for (Text value : values) {
                employeeList.add(value.toString().split(",")[0].trim());
                employeeToManagerMap.put(value.toString().split(",")[0].trim(), value.toString().split(",")[1].trim());
            }
        }


        @Override

        protected void cleanup(Context context) throws IOException, InterruptedException {
            int totalEmployee = employeeList.size();
            int i, j;
            int distance;
            System.out.println(employeeList);
            System.out.println(employeeToManagerMap);
            // 对任意两个员工计算出沟通的路径长度并输出
            for (i = 0; i < (totalEmployee - 1); i++) {
                for (j = (i + 1); j < totalEmployee; j++) {
                    distance = calculateDistance(i, j);
                    String value = employeeList.get(i) + " and " + employeeList.get(j) + " =                    " + distance;
                    context.write(NullWritable.get(), new Text(value));
                }
            }
        }


        /**
         * 该公司可以由所有员工形成树形结构，求两个员工的沟通的中间节点数，可以转换在员工树中两员工之间的距离
         * 由于在树中任意两点都会在某上级节点汇合，根据该情况设计了如下算法
         */
        private int calculateDistance(int i, int j) {
            String employeeA = employeeList.get(i);
            String employeeB = employeeList.get(j);
            int distance = 0;
            // 如果A是B的经理，反之亦然
            if (employeeToManagerMap.get(employeeA).equals(employeeB) || employeeToManagerMap.get(employeeB).equals(employeeA)) {
                distance = 0;
            }
            // A和B在同一经理下
            else if (employeeToManagerMap.get(employeeA).equals(
                    employeeToManagerMap.get(employeeB))) {
                distance = 0;
            } else {
                // 定义A和B对应经理链表
                List employeeA_ManagerList = new ArrayList();
                List employeeB_ManagerList = new ArrayList();
                // 获取从A开始经理链表
                employeeA_ManagerList.add(employeeA);
                String current = employeeA;
                while (false == employeeToManagerMap.get(current).isEmpty()) {
                    current = employeeToManagerMap.get(current);
                    employeeA_ManagerList.add(current);
                }
                // 获取从B开始经理链表
                employeeB_ManagerList.add(employeeB);
                current = employeeB;
                while (false == employeeToManagerMap.get(current).isEmpty()) {
                    current = employeeToManagerMap.get(current);
                    employeeB_ManagerList.add(current);
                }
                int ii = 0, jj = 0;
                String currentA_manager, currentB_manager;
                boolean found = false;
                // 遍历A与B开始经理链表，找出汇合点计算
                for (ii = 0; ii < employeeA_ManagerList.size(); ii++) {
                    currentA_manager = employeeA_ManagerList.get(ii);
                    for (jj = 0; jj < employeeB_ManagerList.size(); jj++) {
                        currentB_manager = employeeB_ManagerList.get(jj);
                        if (currentA_manager.equals(currentB_manager)) {
                            found = true;
                            break;
                        }
                    }
                    if (found) {
                        break;
                    }
                }
                // 最后获取两只之前的路径
                distance = ii + jj - 1;
            }
            return distance;
        }
    }


    //@Override
    public int run(String[] args) throws Exception {
        // 实例化作业对象，设置作业名称
        Job job = new Job(getConf(), "Q10MiddlePersonsCountForComm");
        job.setJobName("Q10MiddlePersonsCountForComm");
        // 设置Mapper和Reduce类
        job.setJarByClass(Q10MiddlePersonsCountForComm.class);
        job.setMapperClass(MapClass.class);
        job.setReducerClass(Reduce.class);
        // 设置Mapper输出格式类
        job.setMapOutputKeyClass(IntWritable.class);
        job.setMapOutputValueClass(Text.class);
        // 设置Reduce输出键和值类型
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setOutputKeyClass(NullWritable.class);
        job.setOutputValueClass(Text.class);
        // 第1个参数为员工数据路径和第2个参数为输出路径
        String[] otherArgs = new GenericOptionsParser(job.getConfiguration(), args).getRemainingArgs();
        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        job.waitForCompletion(true);
        return job.isSuccessful() ? 0 : 1;
    }


    /**
     * 主方法，执行入口
     * @param args 输入参数
     */
    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new Q10MiddlePersonsCountForComm(), args);
        System.exit(res);
    }

}