Java에서 분산 컴퓨팅 및 양식 데이터의 분산 처리를 구현하는 방법은 무엇입니까?

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

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

public class WordCount {

  public static class TokenizerMapper
       extends Mapper<Object, Text, Text, IntWritable>{

    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException {
      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);
      }
    }
  }

  public static class IntSumReducer
       extends Reducer<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values,
                       Context context
                       ) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.Job;
import org.springframework.boot.CommandLineRunner;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

import java.io.IOException;

@SpringBootApplication
public class Application implements CommandLineRunner {

  // Hadoop配置文件路径
  private static final String HADOOP_CONF_PATH = "/path/to/hadoop/conf";

  // 输入文件路径
  private static final String INPUT_PATH = "/path/to/input/file";

  // 输出文件路径
  private static final String OUTPUT_PATH = "/path/to/output/file";

  public static void main(String[] args) {
    SpringApplication.run(Application.class, args);
  }

  @Override
  public void run(String... args) throws Exception {
    // 创建Hadoop配置对象
    Configuration configuration = new Configuration();
    configuration.addResource(new Path(HADOOP_CONF_PATH + "/core-site.xml"));
    configuration.addResource(new Path(HADOOP_CONF_PATH + "/hdfs-site.xml"));
    configuration.addResource(new Path(HADOOP_CONF_PATH + "/mapred-site.xml"));

    // 创建HDFS文件系统对象
    FileSystem fs = FileSystem.get(configuration);

    // 创建Job对象
    Job job = Job.getInstance(configuration, "WordCount");

    // 设置任务的类路径
    job.setJarByClass(Application.class);

    // 设置输入和输出文件路径
    FileInputFormat.addInputPath(job, new Path(INPUT_PATH));
    FileOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH));

    // 提交任务
    job.waitForCompletion(true);

    // 处理任务的结果
    if (job.isSuccessful()) {
      // 输出处理结果
      System.out.println("Job completed successfully.");
      // 读取输出文件内容
      // ...
    } else {
      // 输出处理失败信息
      System.out.println("Job failed.");
    }
  }
}