将学科作为行键，分数作为列族。求每个学科的平均分，mapreduce方法，用Hbase读入的表，给出代码

好的，以下是一个基于Java实现的MapReduce作业，用于计算HBase表中每个学科的平均分。代码中使用了HBase提供的API进行表的读取和写入操作，需要注意的是，在运行代码之前，需要先安装Hadoop和HBase，并启动Hadoop和HBase服务。

import java.io.IOException;
import java.util.Map.Entry;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.Cell;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.HColumnDescriptor;
import org.apache.hadoop.hbase.HTableDescriptor;
import org.apache.hadoop.hbase.KeyValue;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Connection;
import org.apache.hadoop.hbase.client.ConnectionFactory;
import org.apache.hadoop.hbase.client.Get;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.ResultScanner;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.client.Table;
import org.apache.hadoop.hbase.filter.Filter;
import org.apache.hadoop.hbase.filter.FirstKeyOnlyFilter;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableInputFormat;
import org.apache.hadoop.hbase.mapreduce.TableOutputFormat;
import org.apache.hadoop.hbase.util.Bytes;
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.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class HBaseAverageScore extends Configured implements Tool {

    public static class Map extends Mapper<ImmutableBytesWritable, Result, Text, DoubleWritable> {
        private Text subject = new Text();
        private DoubleWritable score = new DoubleWritable();

        public void map(ImmutableBytesWritable row, Result value, Context context)
                throws IOException, InterruptedException {
            // 获取行键
            String rowKey = Bytes.toString(row.get());
            // 获取学科
            String[] fields = rowKey.split(":");
            String sub = fields[0];
            this.subject.set(sub);
            // 获取分数
            Cell cell = value.getColumnLatestCell(Bytes.toBytes("score"), Bytes.toBytes("total"));
            double sc = Bytes.toDouble(CellUtil.cloneValue(cell));
            this.score.set(sc);
            context.write(this.subject, this.score);
        }
    }

    public static class Reduce extends Reducer<Text, DoubleWritable, ImmutableBytesWritable, Put> {

        public void reduce(Text key, Iterable<DoubleWritable> values, Context context)
                throws IOException, InterruptedException {
            double sum = 0.0;
            int count = 0;
            // 计算总分数和学生人数
            for (DoubleWritable val : values) {
                sum += val.get();
                count++;
            }
            // 计算平均分
            double average = sum / count;
            // 将结果存储到HBase表中
            Put put = new Put(Bytes.toBytes(key.toString()));
            put.addColumn(Bytes.toBytes("score"), Bytes.toBytes("average"), Bytes.toBytes(average));
            context.write(null, put);
        }
    }

    public int run(String[] args) throws Exception {
        String tableName = "scores";
        String columnFamily = "score";
        String outputDir = "/output";
        Configuration conf = HBaseConfiguration.create();
        // 配置HBase表名和列族名
        conf.set(TableInputFormat.INPUT_TABLE, tableName);
        conf.set(TableInputFormat.SCAN_COLUMN_FAMILY, columnFamily);
        // 配置MapReduce作业
        Job job = Job.getInstance(conf);
        job.setJarByClass(HBaseAverageScore.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(DoubleWritable.class);
        job.setInputFormatClass(TableInputFormat.class);
        job.setOutputFormatClass(TableOutputFormat.class);
        // 配置输出目录
        FileOutputFormat.setOutputPath(job, new Path(outputDir));
        // 配置输出表名和列族名
        TableOutputFormat.setOutputTableName(job, tableName);
        TableOutputFormat.setFamily(job, columnFamily.getBytes());
        // 运行MapReduce作业
        boolean success = job.waitForCompletion(true);
        return success ? 0 : 1;
    }

    public static void main(String[] args) throws Exception {
        int exitCode = ToolRunner.run(new HBaseAverageScore(), args);
        System.exit(exitCode);
    }
}

在以上代码中，我们首先定义了一个Mapper，用于从HBase表中读取每一行数据，并将学科和分数作为Mapper的输出key-value对。然后，定义了一个Reducer，用于计算每个学科的平均分并将结果存储到HBase表中。最后，我们在main函数中配置了MapReduce作业的输入输出参数，并运行MapReduce作业。