根据提供的数据集，编写实现计算每门成绩的最高分、最低分、平均分—>(key:课程名称；value:max: xx, min: xx, avg: xx)的MapReduce java程序，导出jar包上传到集群运行（10分）

下面是实现计算每门成绩的最高分、最低分、平均分的 MapReduce 程序的代码：

import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
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 ScoreStatistics {

    public static class ScoreMapper extends Mapper<Object, Text, Text, DoubleWritable> {

        private Text course = new Text();
        private DoubleWritable score = new DoubleWritable();

        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String[] fields = value.toString().split(",");
            course.set(fields[1]);
            score.set(Double.parseDouble(fields[2]));
            context.write(course, score);
        }
    }

    public static class ScoreReducer extends Reducer<Text, DoubleWritable, Text, Text> {

        public void reduce(Text key, Iterable<DoubleWritable> values, Context context) throws IOException, InterruptedException {
            List<Double> scoreList = new ArrayList<Double>();
            double maxScore = Double.MIN_VALUE;
            double minScore = Double.MAX_VALUE;
            double sumScore = 0;
            int count = 0;
            for (DoubleWritable score : values) {
                double s = score.get();
                scoreList.add(s);
                if (s > maxScore) {
                    maxScore = s;
                }
                if (s < minScore) {
                    minScore = s;
                }
                sumScore += s;
                count++;
            }
            double avgScore = sumScore / count;
            String result = "max: " + maxScore + ", min: " + minScore + ", avg: " + avgScore;
            context.write(key, new Text(result));
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "Score Statistics");
        job.setJarByClass(ScoreStatistics.class);
        job.setMapperClass(ScoreMapper.class);
        job.setReducerClass(ScoreReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(DoubleWritable.class);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

这个程序包含了一个 Mapper 和一个 Reducer，Mapper 将每条记录中的课程名称和分数提取出来，作为输出的键值对的键和值；Reducer 则对每门课程的所有分数进行处理，计算最高分、最低分和平均分，并输出结果。

程序的运行需要两个参数，第一个参数是输入数据所在的路径，第二个参数是输出结果所在的路径。可以使用以下命令将程序打成 jar 包并上传到集群运行：

$ javac -classpath $(hadoop classpath) -d ScoreStatistics ScoreStatistics.java
$ jar -cvf ScoreStatistics.jar -C ScoreStatistics/ .
$ hadoop jar ScoreStatistics.jar ScoreStatistics /input /output

其中，`/input` 是输入数据所在的路径，`/output` 是输出结果所在的路径。