使用 MapReduce 统计 HBase 成绩表中单科最高单科排名和总分排名的完整代码

### 回答1：
使用 MapReduce 统计 HBase 成绩表中单科最高单科排名和总分排名的完整代码如下：

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 HBaseRankCalculator {
  static class HBaseMapper extends TableMapper<Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

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

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

  public static void main(String[] args) throws Exception {
    Configuration conf = HBaseConfiguration.create();
    Job job = Job.getInstance(conf, "HBase Rank Calculator");
    job.setJarByClass(HBaseRankCalculator.class);

    Scan scan = new Scan();
    scan.addColumn(Bytes.toBytes("grade"), Bytes.toBytes("math"));

    TableMapReduceUtil.initTableMapperJob("scores", scan, HBaseMapper.class, Text.class, IntWritable.class, job);
    TableMapReduceUtil.initTableReducerJob("r  

### 回答2：
使用MapReduce统计HBase成绩表中单科最高分和总分排名的代码如下：

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
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.mapreduce.TableMapper;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Reducer;

public class ScoreRanking {

public static class ScoreMapper extends TableMapper<NullWritable, Text> {
@Override
public void map(ImmutableBytesWritable row, Result columns, Context context)
throws IOException, InterruptedException {

String subject = Bytes.toString(row.get());
int score = Bytes.toInt(columns.getValue(Bytes.toBytes("cf"), Bytes.toBytes("score")));

context.write(NullWritable.get(), new Text(subject + "," + score));
}
}

public static class ScoreReducer extends Reducer<NullWritable, Text, NullWritable, Text> {
private int maxScore = Integer.MIN_VALUE;
private String topSubject = "";

@Override
public void reduce(NullWritable key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {

for (Text value : values) {
String subject = value.toString().split(",")[0];
int score = Integer.parseInt(value.toString().split(",")[1]);

// 统计单科最高分和对应科目
if (score > maxScore) {
maxScore = score;
topSubject = subject;
}
}

context.write(NullWritable.get(), new Text("最高分科目：" + topSubject + "，分数：" + maxScore));
}
}

public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Configuration config = HBaseConfiguration.create();
// 设置HBase配置信息

Job job = Job.getInstance(config, "Score Ranking");
job.setJarByClass(ScoreRanking.class);

Scan scan = new Scan();
// 设置HBase表扫描配置

TableMapReduceUtil.initTableMapperJob("score_table", scan, ScoreMapper.class,
NullWritable.class, Text.class, job);

job.setReducerClass(ScoreReducer.class);
job.setOutputKeyClass(NullWritable.class);
job.setOutputValueClass(Text.class);

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

注意，以上代码是一个基本的MapReduce程序，还需要根据实际情况进行适当调整和优化。另外，需要在代码中设置正确的HBase表名称、列簇和列名。  

### 回答3：
给定一个 HBase 成绩表，包含学生的姓名、科目和成绩，我们需要使用 MapReduce 统计单科最高成绩的排名和总分的排名。

首先，我们需要准备一个 Mapper 类用于将 HBase 成绩表中的数据映射为键值对。Mapper 类的输出键是学生姓名，值是科目和成绩的组合。实现过程如下：

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
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.lib.output.NullOutputFormat;

public class ScoreMapper extends Mapper<ImmutableBytesWritable, Result, Text, Text> {

private Text outputKey = new Text();
private Text outputValue = new Text();

@Override
protected void map(ImmutableBytesWritable key, Result value, Context context) throws IOException, InterruptedException {
String rowKey = Bytes.toString(key.get());
String[] parts = rowKey.split("_");
String studentName = parts[0];
String subject = parts[1];
String score = Bytes.toString(value.getValue(Bytes.toBytes("cf"), Bytes.toBytes("score")));
outputKey.set(studentName);
outputValue.set(subject + "_" + score);
context.write(outputKey, outputValue);
}

}

接下来，我们需要准备一个 Reducer 类用于对 Mapper 类输出的键值对进行汇总。Reducer 类将学生姓名作为键，将科目和成绩的组合作为值。在 Reducer 类中，我们可以按照科目计算单科最高成绩的排名，并在最后计算总分排名。实现过程如下：

import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

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

private Text outputValue = new Text();

@Override
protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
Map<String, Integer> subjectScoreMap = new HashMap<>();
int totalScore = 0;

for (Text value : values) {
String[] parts = value.toString().split("_");
String subject = parts[0];
int score = Integer.parseInt(parts[1]);

subjectScoreMap.put(subject, Math.max(subjectScoreMap.getOrDefault(subject, 0), score));
totalScore += score;
}

StringBuilder sb = new StringBuilder();
for (Map.Entry<String, Integer> entry : subjectScoreMap.entrySet()) {
sb.append(entry.getKey()).append("_").append(entry.getValue()).append(",");
}
String subjectMaxScore = sb.substring(0, sb.length() - 1);

outputValue.set(subjectMaxScore + ";" + totalScore);
context.write(key, outputValue);
}

}

最后，我们需要完成主函数，用于配置和运行 MapReduce 作业。在主函数中，我们需要设置 HBase 的配置信息、表名、Mapper 和 Reducer 类等。实现过程如下：

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.TableName;
import org.apache.hadoop.hbase.client.Scan;
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;

public class ScoreJob {

public static void main(String[] args) throws Exception {
Configuration conf = HBaseConfiguration.create();

Job job = Job.getInstance(conf, "HBase Score Analysis");
job.setJarByClass(ScoreJob.class);

Scan scan = new Scan();
scan.addColumn(Bytes.toBytes("cf"), Bytes.toBytes("score"));

TableMapReduceUtil.initTableMapperJob(TableName.valueOf("your_hbase_table_name"), scan, ScoreMapper.class, Text.class, Text.class, job);
job.setReducerClass(ScoreReducer.class);

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

job.setOutputFormatClass(NullOutputFormat.class);

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

}

以上就是使用 MapReduce 统计 HBase 成绩表中单科最高单科排名和总分排名的完整代码。请注意将代码中的 "your_hbase_table_name" 替换为实际的 HBase 表名。另外，你需要根据自己的环境配置 HBase 的相关信息。