如何使用IntWritable的值作为条件对数据进行分区？

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;

public class PartitionExample {
  
  public static class MapClass extends Mapper<LongWritable, Text, IntWritable, Text> {
    
    private IntWritable outputKey = new IntWritable();
    
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
      // 解析数据，并获取需要作为条件的整数值
      int intValue = parseValue(value);
      
      // 设置IntWritable为条件的整数值
      outputKey.set(intValue);
      
      // 输出键值对
      context.write(outputKey, value);
    }
    
    private int parseValue(Text value) {
      // 解析数据，获取整数值
      // ...
    }
  }
  
  public static class ReduceClass extends Reducer<IntWritable, Text, IntWritable, Text> {
    
    protected void reduce(IntWritable key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
      // 根据条件进行分区处理
      int conditionValue = key.get();
      
      for (Text value : values) {
        // 处理数据
        // ...
        
        // 输出分区后的结果
        context.write(key, value);
      }
    }
  }
  
  public static void main(String[] args) throws Exception {
    // 创建配置
    Configuration conf = new Configuration();
    
    // 创建Job
    Job job = Job.getInstance(conf, "PartitionExample");
    job.setJarByClass(PartitionExample.class);
    
    // 设置Mapper和Reducer
    job.setMapperClass(MapClass.class);
    job.setReducerClass(ReduceClass.class);
    
    // 设置输出键值对的类型
    job.setOutputKeyClass(IntWritable.class);
    job.setOutputValueClass(Text.class);
    
    // 设置输入输出路径
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    
    // 提交Job并等待完成
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}