com.benchmark.mapred.PiEstimator.java Source code

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/**
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.benchmark.mapred;

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;

/**
 * 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");

    /** 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];
                }
            }
        }

        /** 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 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();

                //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 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;
        private long numOutside = 0;
        private JobConf conf; //configuration for accessing the file system

        /** Store job configuration. */
        @Override
        public void configure(JobConf job) {
            conf = job;
        }

        /**
         * 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())
                    ;
            }
        }

        /**
         * 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());

        jobConf.setInputFormat(SequenceFileInputFormat.class);

        jobConf.setOutputKeyClass(BooleanWritable.class);
        jobConf.setOutputValueClass(LongWritable.class);
        jobConf.setOutputFormat(SequenceFileOutputFormat.class);

        jobConf.setMapperClass(PiMapper.class);
        jobConf.setNumMapTasks(numMaps);

        jobConf.setReducerClass(PiReducer.class);
        jobConf.setNumReduceTasks(1);

        // turn off speculative execution, because DFS doesn't handle
        // multiple writers to the same file.
        jobConf.setSpeculativeExecution(false);

        //setup input/output directories
        //final Path inDir = new Path(TMP_DIR, "in");
        final Path inDir = new Path("/home/hadoop1/tmp_dir", "in");
        System.out.println("inDir =" + inDir.toString());
        //final Path outDir = new Path(TMP_DIR, "out");
        final Path outDir = new Path("/home/hadoop1/tmp_dir", "out");
        System.out.println("outDir =" + outDir.toString());
        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);
        }

        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");

            //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();
            }

            //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);
        }
    }

    /**
     * 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));
    }
}