本关任务：根据公司薪资数据，编写 MapReduce 程序来统计出各部门每月的平均薪资。

根据提供的引用内容，您需要编写一个MapReduce程序来统计公司每个部门每月的平均薪资。首先，您需要上传并运行一个名为"salary.jar"的jar包，使用以下命令：hadoop jar salary.jar first.mainz /input/salary.txt /output/wc2。然后，您可以通过MapReduce的序列化方法来实现该功能。在处理过程中，请确保Reducer的逻辑正确，以避免统计结果错误。根据需求描述，您的任务是编写一个MapReduce程序来计算每个部门每月的平均薪资。

相关问题

本关任务：根据手机流量数据，编写 MapReduce 程序来统计出每个手机号码的一年总流量。

思路：

- Mapper 阶段：将每行数据按照手机号码为 key，流量为 value 发射出去。
- Reducer 阶段：将同一个手机号码的所有流量累加起来，输出结果。

Mapper 代码：

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

    private Text phoneNum = new Text();
    private LongWritable flow = new LongWritable();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] fields = value.toString().split("\t");
        if (fields.length >= 3) {
            phoneNum.set(fields[0]);
            flow.set(Long.parseLong(fields[2]));
            context.write(phoneNum, flow);
        }
    }
}

Reducer 代码：

public class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> {

    private LongWritable totalFlow = new LongWritable();

    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        long sum = 0;
        for (LongWritable value : values) {
            sum += value.get();
        }
        totalFlow.set(sum);
        context.write(key, totalFlow);
    }
}

完整代码：

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;

import java.io.IOException;

public class TotalFlow {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "total_flow");

        job.setJarByClass(TotalFlow.class);
        job.setMapperClass(MyMapper.class);
        job.setReducerClass(MyReducer.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

    public static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> {

        private Text phoneNum = new Text();
        private LongWritable flow = new LongWritable();

        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String[] fields = value.toString().split("\t");
            if (fields.length >= 3) {
                phoneNum.set(fields[0]);
                flow.set(Long.parseLong(fields[2]));
                context.write(phoneNum, flow);
            }
        }
    }

    public static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> {

        private LongWritable totalFlow = new LongWritable();

        @Override
        protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
            long sum = 0;
            for (LongWritable value : values) {
                sum += value.get();
            }
            totalFlow.set(sum);
            context.write(key, totalFlow);
        }
    }
}

运行命令：

hadoop jar total_flow.jar TotalFlow input output

其中，`input` 为输入文件路径，`output` 为输出文件路径。

根据手机流量数据，编写 MapReduce 程序来统计出每个手机号码的一年总流量。

Mapper阶段：
输入：每行数据包括手机号码、日期、上行流量、下行流量，以制表符分隔。
输出：键为手机号码，值为一年内的总流量，以制表符分隔。

public class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
    private Text phoneNum = new Text();
    private LongWritable totalFlow = new LongWritable();

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] words = value.toString().split("\t");
        String phoneNumStr = words[0];
        long upFlow = Long.parseLong(words[2]);
        long downFlow = Long.parseLong(words[3]);
        phoneNum.set(phoneNumStr);
        totalFlow.set(upFlow + downFlow);
        context.write(phoneNum, totalFlow);
    }
}

Reducer阶段：
输入：每个手机号码的流量总和，以键值对的形式传递。
输出：每个手机号码的一年总流量，以制表符分隔。

public class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
    private LongWritable totalFlow = new LongWritable();

    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        long sum = 0;
        for (LongWritable value : values) {
            sum += value.get();
        }
        totalFlow.set(sum);
        context.write(key, totalFlow);
    }
}