hadoop - 如何使用Hadoop分布式缓存将文件放入内存？

Question

据我所知，分布式缓存将文件复制到每个节点，然后映射或减少从本地文件系统读取文件。

我的问题是:有没有一种方法可以使用 Hadoop 分布式缓存将文件放入内存，以便每个 map 或 reduce 都可以直接从内存中读取文件？

我的MapReduce程序向每个节点分发一张1M左右的png图片，然后每个map任务从分布式缓存中读取图片，并用来自map输入的另一张图片做一些图像处理。

Answer 1

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.net.URI;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.filecache.DistributedCache;
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 org.apache.hadoop.util.GenericOptionsParser;

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 {

          Path[] uris = DistributedCache.getLocalCacheFiles(context
                    .getConfiguration());





                    try{
                        BufferedReader readBuffer1 = new BufferedReader(new FileReader(uris[0].toString()));
                        String line;
                        while ((line=readBuffer1.readLine())!=null){
                            System.out.println(line);

                        }
                        readBuffer1.close(); 
                    }       
                    catch (Exception e){
                        System.out.println(e.toString());
                    }

                  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();
      }
      int length=key.getLength();
      System.out.println("length"+length);
      result.set(sum);
/*      key.set("lenght"+lenght);*/
      context.write(key, result);


    }
  }

  public static void main(String[] args) throws Exception {

      final String NAME_NODE = "hdfs://localhost:9000";
    Configuration conf = new Configuration();

    String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
    if (otherArgs.length != 2) {
      System.err.println("Usage: wordcount <in> <out>");
      System.exit(2);
    }
    Job job = new Job(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);


    DistributedCache.addCacheFile(new URI(NAME_NODE
      + "/dataset1.txt"),
      job.getConfiguration());



    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }

}