PiEstimator代码注释

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package org.apache.hadoop.examples;

import java.io.IOException;
import java.math.BigDecimal;
import java.util.Iterator;

import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BooleanWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.io.SequenceFile.CompressionType;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.SequenceFileInputFormat;
import org.apache.hadoop.mapred.SequenceFileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

/**hadoop的map/reduce程序例子程序，演示用准蒙特-卡洛方法估算PI
的值。这是欧洲最早计算PI的方法。
在一个单位矩形中，内切一个圆。
往给矩形内投任意次针，记下针在圆内的次数，和投的总次数。
当数据足够多的时候，圆内的次数约等于圆的面积，总次数
约等于单位矩形的面积，在园内次数/总次数=园面积/单位矩形面积=(PI/4)/1
所以PI大概等于4*（园内次数/总次数）
 * A Map-reduce program to estimate the value of Pi
 * using quasi-Monte Carlo method.
 *
 * Mapper:
 *   Generate points in a unit square
 *   and then count points inside/outside of the inscribed circle of the square.
 *
 * Reducer:
 *   Accumulate points inside/outside results from the mappers.
 *
 * Let numTotal = numInside + numOutside.
 * The fraction numInside/numTotal is a rational approximation of
 * the value (Area of the circle)/(Area of the square),
 * where the area of the inscribed circle is Pi/4
 * and the area of unit square is 1.
 * Then, Pi is estimated value to be 4(numInside/numTotal).  
 */
public class PiEstimator extends Configured implements Tool {
  /** tmp directory for input/output */
  static private final Path TMP_DIR = new Path(
      PiEstimator.class.getSimpleName() + "_TMP_3_141592654");
  
  /** 二维哈尔顿序列的类，哈尔顿序列常常用来产生空间点，因为这个序列的数看上去想随机的。可以用任意一个素数做基数，来生成一系列的的序列。比如说以2的基数，产生的哈尔顿序列是：1/2, 1/4, 3/4, 1/8, 5/8, 3/8, 7/8, 1/16, 9/16。
  实现的伪代码如下：
FUNCTION (index, base)
   BEGIN
       result = 0;
       f = 1 / base;
       i = index;
       WHILE (i > 0) 
       BEGIN
           result = result + f * (i % base);
           i = FLOOR(i / base);
           f = f / base;
       END
       RETURN result;
   END


 2-dimensional Halton sequence {H(i)},
   * where H(i) is a 2-dimensional point and i >= 1 is the index.
   * Halton sequence is used to generate sample points for Pi estimation. 
   */
  private static class HaltonSequence {
    /** Bases */
    static final int[] P = {2, 3}; 
    /** Maximum number of digits allowed */
    static final int[] K = {63, 40}; 

    private long index;
    private double[] x;
    private double[][] q;
    private int[][] d;

    /** Initialize to H(startindex),
     * so the sequence begins with H(startindex+1).
     */
    HaltonSequence(long startindex) {
      index = startindex;
      x = new double[K.length];
      q = new double[K.length][];
      d = new int[K.length][];
      for(int i = 0; i < K.length; i++) {
        q[i] = new double[K[i]];
        d[i] = new int[K[i]];
      }

      for(int i = 0; i < K.length; i++) {
        long k = index;
        x[i] = 0;
        
        for(int j = 0; j < K[i]; j++) {
          q[i][j] = (j == 0? 1.0: q[i][j-1])/P[i];
          d[i][j] = (int)(k % P[i]);
          k = (k - d[i][j])/P[i];
          x[i] += d[i][j] * q[i][j];
        }
      }
    }

    /**
   生成下一个随机点 Compute next point.
     * Assume the current point is H(index).
     * Compute H(index+1).
     * 
     * @return a 2-dimensional point with coordinates in [0,1)^2
     */
    double[] nextPoint() {
      index++;
      for(int i = 0; i < K.length; i++) {
        for(int j = 0; j < K[i]; j++) {
          d[i][j]++;
          x[i] += q[i][j];
          if (d[i][j] < P[i]) {
            break;
          }
          d[i][j] = 0;
          x[i] -= (j == 0? 1.0: q[i][j-1]);
        }
      }
      return x;
    }
  }

  /**mapper类
输入是offset从0开始的序列的序号，size 是每个map处理的点的大小
输出 true（圆内），数目；false（圆外），数目
   * Mapper class for Pi estimation.
   * Generate points in a unit square
   * and then count points inside/outside of the inscribed circle of the square.
   */
  public static class PiMapper extends MapReduceBase
    implements Mapper {

    /** Map method.
     * @param offset samples starting from the (offset+1)th sample.
     * @param size the number of samples for this map
     * @param out output {ture->numInside, false->numOutside}
     * @param reporter
     */
    public void map(LongWritable offset,
                    LongWritable size,
                    OutputCollector out,
                    Reporter reporter) throws IOException {

      final HaltonSequence haltonsequence = new HaltonSequence(offset.get());
      long numInside = 0L;
      long numOutside = 0L;

      for(long i = 0; i < size.get(); ) {
        //generate points in a unit square
        final double[] point = haltonsequence.nextPoint();

        //判断点是否在圆内，并且对在圆内情况和圆外情况计数count points inside/outside of the inscribed circle of the square
        final double x = point[0] - 0.5;
        final double y = point[1] - 0.5;
        if (x*x + y*y > 0.25) {
          numOutside++;
        } else {
          numInside++;
        }

        //report status
        i++;
        if (i % 1000 == 0) {
          reporter.setStatus("Generated " + i + " samples.");
        }
      }

      //output map results
      out.collect(new BooleanWritable(true), new LongWritable(numInside));
      out.collect(new BooleanWritable(false), new LongWritable(numOutside));
    }
  }

  /**reducer类
   * Reducer class for Pi estimation.
   * Accumulate points inside/outside results from the mappers.
   */
  public static class PiReducer extends MapReduceBase
    implements Reducer, Writable> {
    
    private long numInside = 0; //公共变量
    private long numOutside = 0;//公共变量
    private JobConf conf; //configuration for accessing the file system
      
    /**保存job做公共变量，为了方便close方法调用。 
     Store job configuration. */
    @Override
    public void configure(JobConf job) {
      conf = job;
    }

    /**统计map的总的圆内数目和园外数目
     * Accumulate number of points inside/outside results from the mappers.
     * @param isInside Is the points inside? 
     * @param values An iterator to a list of point counts
     * @param output dummy, not used here.
     * @param reporter
     */
    public void reduce(BooleanWritable isInside,
                       Iterator values,
                       OutputCollector, Writable> output,
                       Reporter reporter) throws IOException {
      if (isInside.get()) {
        for(; values.hasNext(); numInside += values.next().get());
      } else {
        for(; values.hasNext(); numOutside += values.next().get());
      }
    }

    /**job结束，把圆内数目和圆外数目写到一个文件里
     * Reduce task done, write output to a file.
     */
    @Override
    public void close() throws IOException {
      //write output to a file
      Path outDir = new Path(TMP_DIR, "out");
      Path outFile = new Path(outDir, "reduce-out");
      FileSystem fileSys = FileSystem.get(conf);
      SequenceFile.Writer writer = SequenceFile.createWriter(fileSys, conf,
          outFile, LongWritable.class, LongWritable.class, 
          CompressionType.NONE);
      writer.append(new LongWritable(numInside), new LongWritable(numOutside));
      writer.close();
    }
  }

  /**
   * Run a map/reduce job for estimating Pi.
   *
   * @return the estimated value of Pi
   */
  public static BigDecimal estimate(int numMaps, long numPoints, JobConf jobConf
      ) throws IOException {
    //setup job conf
    jobConf.setJobName(PiEstimator.class.getSimpleName());
 //设置job的名字
    jobConf.setInputFormat(SequenceFileInputFormat.class);
 //设置输入格式二进制格式SequenceFileInputFormat
    jobConf.setOutputKeyClass(BooleanWritable.class);//设置map输出key类型
    jobConf.setOutputValueClass(LongWritable.class);//设置map输出value类型
    jobConf.setOutputFormat(SequenceFileOutputFormat.class);
 //设置输出文件是二进制类型SequenceFileOutputFormat
    jobConf.setMapperClass(PiMapper.class);//设置map类
    jobConf.setNumMapTasks(numMaps);//设置map的数目

    jobConf.setReducerClass(PiReducer.class);//设置reduce的类
    jobConf.setNumReduceTasks(1);//设置只有一个reduce，不然没法做总的数据统计

    // turn off speculative execution, because DFS doesn't handle
    // multiple writers to the same file.
    jobConf.setSpeculativeExecution(false);
   //关闭speculative execution属性，因为DFS不能处理多个writers操作同一一个文件
    //setup input/output directories建立输入输出目录
    final Path inDir = new Path(TMP_DIR, "in");
    final Path outDir = new Path(TMP_DIR, "out");
    FileInputFormat.setInputPaths(jobConf, inDir);
    FileOutputFormat.setOutputPath(jobConf, outDir);

    final FileSystem fs = FileSystem.get(jobConf);
    if (fs.exists(TMP_DIR)) {
      throw new IOException("Tmp directory " + fs.makeQualified(TMP_DIR)
          + " already exists.  Please remove it first.");
    }
    if (!fs.mkdirs(inDir)) {
      throw new IOException("Cannot create input directory " + inDir);
    }
  /*创建numMaps个文件，文件名是part+ i ,内容之有一个（key，value）对分别是（offset ，size）*/
    try {
      //generate an input file for each map task
      for(int i=0; i < numMaps; ++i) {
        final Path file = new Path(inDir, "part"+i);
        final LongWritable offset = new LongWritable(i * numPoints);
        final LongWritable size = new LongWritable(numPoints);
        final SequenceFile.Writer writer = SequenceFile.createWriter(
            fs, jobConf, file,
            LongWritable.class, LongWritable.class, CompressionType.NONE);
        try {
          writer.append(offset, size);
        } finally {
          writer.close();
        }
        System.out.println("Wrote input for Map #"+i);
      }
  
      //start a map/reduce job
      System.out.println("Starting Job");
      final long startTime = System.currentTimeMillis();
      JobClient.runJob(jobConf);
      final double duration = (System.currentTimeMillis() - startTime)/1000.0;
      System.out.println("Job Finished in " + duration + " seconds");

/*从输出结果文件reduce-out中读取结果圆内数目和圆外数目*/     
 //read outputs
      Path inFile = new Path(outDir, "reduce-out");
      LongWritable numInside = new LongWritable();
      LongWritable numOutside = new LongWritable();
      SequenceFile.Reader reader = new SequenceFile.Reader(fs, inFile, jobConf);
      try {
        reader.next(numInside, numOutside);
      } finally {
        reader.close();
      }
 
      //算出PI的值：于4*（园内次数/总次数） compute estimated value
      return BigDecimal.valueOf(4).setScale(20)
          .multiply(BigDecimal.valueOf(numInside.get()))
          .divide(BigDecimal.valueOf(numMaps))
          .divide(BigDecimal.valueOf(numPoints));
    } finally {
      fs.delete(TMP_DIR, true);//删除临时目录
    }
  }

  /**
   * Parse arguments and then runs a map/reduce job.
   * Print output in standard out.
   * 
   * @return a non-zero if there is an error.  Otherwise, return 0.  
   */
  public int run(String[] args) throws Exception {
    if (args.length != 2) {
      System.err.println("Usage: "+getClass().getName()+"  ");
      ToolRunner.printGenericCommandUsage(System.err);
      return -1;
    }
    
    final int nMaps = Integer.parseInt(args[0]);
    final long nSamples = Long.parseLong(args[1]);
        
    System.out.println("Number of Maps  = " + nMaps);
    System.out.println("Samples per Map = " + nSamples);
        
    final JobConf jobConf = new JobConf(getConf(), getClass());
    System.out.println("Estimated value of Pi is "
        + estimate(nMaps, nSamples, jobConf));
    return 0;
  }

  /**
   * main method for running it as a stand alone command. 
   */
  public static void main(String[] argv) throws Exception {
    System.exit(ToolRunner.run(null, new PiEstimator(), argv));
  }
}