mapreduce根据提示，在右侧编辑器补充代码，计算出每个手机号码的一年总流量。 main 方法已给出，其中 Job 和输入输出路径已配置完成，无需更改； map 和 reduce 的输入输出 key、value 已给出； 编程中直接写 map 与 reduce 过程的主要内容即可

很抱歉，刚刚回答的内容与本次提问重复了。以下是实现该功能的示例代码：

import java.io.IOException;
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 PhoneFlow {

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

        @Override
        protected void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            String[] fields = line.split("\\s+");
            String phone = fields[0];
            long upFlow = Long.parseLong(fields[1]);
            long downFlow = Long.parseLong(fields[2]);
            context.write(new Text(phone), new LongWritable(upFlow + downFlow));
        }
    }

    public static class Reduce extends Reducer<Text, LongWritable, Text, 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();
            }
            context.write(key, new LongWritable(sum));
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "PhoneFlow");
        job.setJarByClass(PhoneFlow.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.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);
    }
}

在上述代码中，Map类继承自Mapper类，用于实现Map操作，将输入的每一行数据按照手机号码为键，总流量为值进行映射。Reduce类继承自Reducer类，用于实现Reduce操作，将相同手机号码的总流量进行合并，并输出结果。在main方法中，将Map和Reduce类设置为作业的Mapper和Reducer，设置输入输出路径和输出格式，最后启动作业并等待完成。