用MapReduce统计Hbase表单科排名的代码

### 回答1：
这是一个使用 MapReduce 统计 HBase 表中科目排名的示例代码：

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.mapreduce.TableMapper;
import org.apache.hadoop.hbase.mapreduce.TableReducer;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;

public class SubjectRank {

  static class SubjectMapper extends TableMapper<Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);
    private Text subject = new Text();

    public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
      String subjectStr = new String(value.getValue(Bytes.toBytes("grades"), Bytes.toBytes("subject")));
      subject.set(subjectStr);
      context.write(subject, one);
    }
  }

  static class SubjectReducer extends TableReducer<Text, IntWritable, ImmutableBytesWritable> {
    public void reduce(Text subject, Iterable<IntWritable> counts, Context context) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable count : counts) {
        sum += count.get();
      }
      Put put = new Put(Bytes.toBytes(subject.toString()));
      put.addColumn(Bytes.toBytes("rank"), Bytes.toBytes("count"), Bytes.toBytes(sum));
      context.write(null, put);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = HBaseConfiguration.create();
    Job job = Job.getInstance(conf, "subject rank");
    job.setJarByClass(SubjectRank.class);

    Scan scan = new Scan();
    scan.addColumn(Bytes.toBytes("grades"), Bytes.toBytes("subject"));

    TableMapReduceUtil.initTableMapperJob("grades", scan, SubjectMapper.class, Text.class, IntWritable.class, job);
    TableMapReduceUtil.initTableReducerJob("subject_rank", SubjectReducer.class, job);

    job.waitForCompletion(true);
  }
}

这段代码

### 回答2：
使用MapReduce统计HBase表单科排名的代码通常包括以下几个步骤：

1. 创建HBase表单科数据的Mapper类。

public class SpecialtyMapper extends TableMapper<Text, IntWritable> {
    private Text outputKey = new Text();
    private IntWritable outputValue = new IntWritable();

    @Override
    protected void map(ImmutableBytesWritable key, Result value, Context context) throws IOException, InterruptedException {
        // 从HBase表中获取学生的单科成绩信息
        String subject = Bytes.toString(key.get());
        int score = Bytes.toInt(value.getValue(Bytes.toBytes("cf"), Bytes.toBytes("score")));

        // 设置Mapper输出的Key为学科名称，Value为学生成绩
        outputKey.set(subject);
        outputValue.set(score);
        context.write(outputKey, outputValue);
    }
}

2. 创建HBase表单科排名的Reducer类。

public class SpecialtyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    private List<Pair<Integer, Text>> scores = new ArrayList<>();

    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum = 0;
        int count = 0;

        // 统计每个学科的总成绩和人数
        for (IntWritable value : values) {
            sum += value.get();
            count++;
        }

        // 将学科总成绩与学科名称保存到List中
        scores.add(new Pair<>(sum, key));
    }

    @Override
    protected void cleanup(Context context) throws IOException, InterruptedException {
        // 对学科总成绩进行排序
        scores.sort(Comparator.reverseOrder());

        // 输出学科排名结果
        int rank = 1;
        for (Pair<Integer, Text> score : scores) {
            context.write(score.getValue(), new IntWritable(rank++));
        }
    }
}

3. 创建Main函数来配置和运行MapReduce任务。

public class SpecialtyRanking {
    public static void main(String[] args) throws Exception {
        Configuration conf = HBaseConfiguration.create();
        Job job = Job.getInstance(conf, "Specialty Ranking");
        job.setJarByClass(SpecialtyRanking.class);

        // 配置Mapper和Reducer类
        job.setMapperClass(SpecialtyMapper.class);
        job.setReducerClass(SpecialtyReducer.class);

        // 配置输入和输出的HBase表
        TableMapReduceUtil.initTableMapperJob("YourHBaseTableName", new Scan(), SpecialtyMapper.class, Text.class, IntWritable.class, job);
        TableMapReduceUtil.initTableReducerJob("YourOutputTableName", SpecialtyReducer.class, job);

        // 设置最终输出的Key和Value类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

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

以上是一个简单的使用MapReduce统计HBase表单科排名的代码示例，可以根据具体需求进行适当的修改和扩展。

### 回答3：
使用MapReduce统计Hbase表单科排名的代码大致可以分为以下几个步骤：

1. 创建一个Hadoop MapReduce Job并设置相应的配置信息。
2. 实现Mapper类，用于读取Hbase表并提取所需的数据进行初步处理。
3. 实现Reducer类，用于对Mapper输出的数据进行二次处理并得出结果。
4. 在主程序中组织Mapper和Reducer，并指定输入输出路径。
5. 运行MapReduce Job并等待结果。

下面是一个示例代码的伪代码版本，具体细节需要根据实际情况做相应调整：

// 导入所需的包
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.Result;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;

public class HBaseMapReduce {
  
  // Mapper类
  public static class HBaseMapper extends Mapper<ImmutableBytesWritable, Result, Text, IntWritable> {
    
    private final static IntWritable one = new IntWritable(1);
    private Text subject = new Text();
    
    public void map(ImmutableBytesWritable key, Result value, Context context) throws IOException, InterruptedException {
      // 从Hbase表中获取所需的数据，例如学科信息和成绩
      String subjectStr = Bytes.toString(value.getValue(Bytes.toBytes("cf"), Bytes.toBytes("subject")));
      int score = Bytes.toInt(value.getValue(Bytes.toBytes("cf"), Bytes.toBytes("score")));
      
      // 输出学科和成绩，作为Reducer的输入
      subject.set(subjectStr);
      context.write(subject, one);
    }
  }
  
  // Reducer类
  public static class HBaseReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
      int sum = 0;
      
      // 对统计结果进行累加求和
      for (IntWritable value : values) {
        sum += value.get();
      }
      
      // 输出学科和总分，作为最终结果
      context.write(key, new IntWritable(sum));
    }
  }
  
  public static void main(String[] args) throws Exception {
    // 创建Hadoop MapReduce Job并设置配置信息
    Configuration conf = HBaseConfiguration.create();
    Job job = Job.getInstance(conf, "HBase MapReduce");
    job.setJarByClass(HBaseMapReduce.class);
    
    // 从Hbase表中读取数据
    Scan scan = new Scan();
    scan.setCaching(500);
    scan.setCacheBlocks(false);
    TableMapReduceUtil.initTableMapperJob("tablename", scan, HBaseMapper.class, Text.class, IntWritable.class, job);
    
    // 指定Reducer类，并设置最终的输出路径
    job.setReducerClass(HBaseReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileOutputFormat.setOutputPath(job, new Path("output"));
    
    // 运行MapReduce Job并等待结果
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

这个示例代码中，假设Hbase表中包含"tablename"表，具有"subject"和"score"两列。代码通过Mapper将学科和成绩作为键值对输出，在Reducer中对学科的成绩进行累加求和，并将结果输出到指定的输出路径中。请根据实际情况对代码进行相应调整，并确保相关依赖包的正确导入。