Hadoop对文本文件的快速全局排序实现方法及分析

public K[] getSample(InputFormat<K,V> inf, JobConf job) throws IOException {
   InputSplit[] splits = inf.getSplits(job, job.getNumMapTasks());
   ArrayList<K> samples = new ArrayList<K>(numSamples);
   Random r = new Random();
   long seed = r.nextLong();
   r.setSeed(seed);
   LOG.debug("seed: " + seed);   
   // 对splits【0】抽样
   for (int i = 0; i < 1; i++) {
    System.out.println("PartialSampler will getSample splits["+i+"]");
    RecordReader<K,V> reader = inf.getRecordReader(splits[i], job,
      Reporter.NULL);
    K key = reader.createKey();
    V value = reader.createValue();
    while (reader.next(key, value)) {
     if (r.nextDouble() <= freq) {
      if (samples.size() < numSamples) {
        // 选择value中的第一列抽样
        Text value0 = new Text(value.toString().split("\t")[0]);     
        samples.add((K) value0);        
      } else {
       // When exceeding the maximum number of samples, replace a
       // random element with this one, then adjust the frequency
       // to reflect the possibility of existing elements being
       // pushed out
       int ind = r.nextInt(numSamples);
       if (ind != numSamples) {
        Text value0 = new Text(value.toString().split("\t")[0]); 
        samples.set(ind, (K) value0);
       }
       freq *= (numSamples - 1) / (double) numSamples;
      }
      key = reader.createKey();
     }
    }    
    reader.close();
   }
   return (K[])samples.toArray();
  }

//Class<K> keyClass = (Class<K>)job.getMapOutputKeyClass();
Class<K> keyClass = (Class<K>)Text.class;

public int getPartition(K key, V value, int numPartitions) {
  Text value0 = new Text(value.toString().split("\t")[0]); 
  return partitions.findPartition((K) value0);
 }

 Class<? extends InputFormat> inputFormatClass = TextInputFormat.class;
  Class<? extends OutputFormat> outputFormatClass = TextOutputFormat.class;
  Class<? extends WritableComparable> outputKeyClass = Text.class;
  Class<? extends Writable> outputValueClass = Text.class;
  jobConf.setMapOutputKeyClass(LongWritable.class);
  // Set user-supplied (possibly default) job configs
  jobConf.setNumReduceTasks(num_reduces);
  jobConf.setInputFormat(inputFormatClass);
  jobConf.setOutputFormat(outputFormatClass);
  jobConf.setOutputKeyClass(outputKeyClass);
  jobConf.setOutputValueClass(outputValueClass);
  if (sampler != null) {
   System.out.println("Sampling input to effect total-order sort...");
   jobConf.setPartitionerClass(TotalOrderPartitioner.class);
   Path inputDir = FileInputFormat.getInputPaths(jobConf)[0];
   inputDir = inputDir.makeQualified(inputDir.getFileSystem(jobConf));
   //Path partitionFile = new Path(inputDir, "_sortPartitioning");
   TotalOrderPartitioner.setPartitionFile(jobConf, partitionFile);
   InputSampler.<K,V>writePartitionFile(jobConf, sampler);
   URI partitionUri = new URI(partitionFile.toString() + "#" + "_sortPartitioning");
   DistributedCache.addCacheFile(partitionUri, jobConf);
   DistributedCache.createSymlink(jobConf);
  }
  FileSystem hdfs = FileSystem.get(jobConf);
  hdfs.delete(outputpath);
  hdfs.close();
  System.out.println("Running on " +
    cluster.getTaskTrackers() +
    " nodes to sort from " + 
    FileInputFormat.getInputPaths(jobConf)[0] + " into " +
    FileOutputFormat.getOutputPath(jobConf) +
    " with " + num_reduces + " reduces.");
  Date startTime = new Date();
  System.out.println("Job started: " + startTime);
  jobResult = JobClient.runJob(jobConf);