欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识!
您现在的位置是: 首页

PiEstimator代码注释  

程序员文章站 2022-06-06 19:13:25
...
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<LongWritable, LongWritable, BooleanWritable, LongWritable> {

    /** 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<BooleanWritable, LongWritable> 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<BooleanWritable, LongWritable, WritableComparable<?>, 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<LongWritable> values,
                       OutputCollector<WritableComparable<?>, 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()+" <nMaps> <nSamples>");
      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));
  }
}