source: proiecte/HadoopJUnit/hadoop-0.20.1/src/test/org/apache/hadoop/record/TestRecordMR.java @ 120

/**
 * 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 org.apache.hadoop.record;

import org.apache.hadoop.mapred.*;
import org.apache.hadoop.fs.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.io.SequenceFile.CompressionType;
import org.apache.hadoop.conf.*;
import junit.framework.TestCase;
import java.io.*;
import java.util.*;


/**********************************************************
 * MapredLoadTest generates a bunch of work that exercises
 * a Hadoop Map-Reduce system (and DFS, too).  It goes through
 * the following steps:
 *
 * 1) Take inputs 'range' and 'counts'.
 * 2) Generate 'counts' random integers between 0 and range-1.
 * 3) Create a file that lists each integer between 0 and range-1,
 *    and lists the number of times that integer was generated.
 * 4) Emit a (very large) file that contains all the integers
 *    in the order generated.
 * 5) After the file has been generated, read it back and count
 *    how many times each int was generated.
 * 6) Compare this big count-map against the original one.  If
 *    they match, then SUCCESS!  Otherwise, FAILURE!
 *
 * OK, that's how we can think about it.  What are the map-reduce
 * steps that get the job done?
 *
 * 1) In a non-mapred thread, take the inputs 'range' and 'counts'.
 * 2) In a non-mapread thread, generate the answer-key and write to disk.
 * 3) In a mapred job, divide the answer key into K jobs.
 * 4) A mapred 'generator' task consists of K map jobs.  Each reads
 *    an individual "sub-key", and generates integers according to
 *    to it (though with a random ordering).
 * 5) The generator's reduce task agglomerates all of those files
 *    into a single one.
 * 6) A mapred 'reader' task consists of M map jobs.  The output
 *    file is cut into M pieces. Each of the M jobs counts the 
 *    individual ints in its chunk and creates a map of all seen ints.
 * 7) A mapred job integrates all the count files into a single one.
 *
 **********************************************************/
public class TestRecordMR extends TestCase {
  /**
   * Modified to make it a junit test.
   * The RandomGen Job does the actual work of creating
   * a huge file of assorted numbers.  It receives instructions
   * as to how many times each number should be counted.  Then
   * it emits those numbers in a crazy order.
   *
   * The map() function takes a key/val pair that describes
   * a value-to-be-emitted (the key) and how many times it 
   * should be emitted (the value), aka "numtimes".  map() then
   * emits a series of intermediate key/val pairs.  It emits
   * 'numtimes' of these.  The key is a random number and the
   * value is the 'value-to-be-emitted'.
   *
   * The system collates and merges these pairs according to
   * the random number.  reduce() function takes in a key/value
   * pair that consists of a crazy random number and a series
   * of values that should be emitted.  The random number key
   * is now dropped, and reduce() emits a pair for every intermediate value.
   * The emitted key is an intermediate value.  The emitted value
   * is just a blank string.  Thus, we've created a huge file
   * of numbers in random order, but where each number appears
   * as many times as we were instructed.
   */
  static public class RandomGenMapper implements Mapper<RecInt, RecInt,
                                                        RecInt, RecString> {
    Random r = new Random();
    public void configure(JobConf job) {
    }

    public void map(RecInt key,
                    RecInt val,
                    OutputCollector<RecInt, RecString> out,
                    Reporter reporter) throws IOException {
      int randomVal = key.getData();
      int randomCount = val.getData();

      for (int i = 0; i < randomCount; i++) {
        out.collect(new RecInt(Math.abs(r.nextInt())),
                    new RecString(Integer.toString(randomVal)));
      }
    }
    public void close() {
    }
  }
  /**
   */
  static public class RandomGenReducer implements Reducer<RecInt, RecString,
                                                          RecInt, RecString> {
    public void configure(JobConf job) {
    }

    public void reduce(RecInt key,
                       Iterator<RecString> it,
                       OutputCollector<RecInt, RecString> out,
                       Reporter reporter) throws IOException {
      int keyint = key.getData();
      while (it.hasNext()) {
        String val = it.next().getData();
        out.collect(new RecInt(Integer.parseInt(val)),
                    new RecString(""));
      }
    }
    public void close() {
    }
  }

  /**
   * The RandomCheck Job does a lot of our work.  It takes
   * in a num/string keyspace, and transforms it into a
   * key/count(int) keyspace.
   *
   * The map() function just emits a num/1 pair for every
   * num/string input pair.
   *
   * The reduce() function sums up all the 1s that were
   * emitted for a single key.  It then emits the key/total
   * pair.
   *
   * This is used to regenerate the random number "answer key".
   * Each key here is a random number, and the count is the
   * number of times the number was emitted.
   */
  static public class RandomCheckMapper implements Mapper<RecInt, RecString,
                                                          RecInt, RecString> {
    public void configure(JobConf job) {
    }

    public void map(RecInt key,
                    RecString val,
                    OutputCollector<RecInt, RecString> out,
                    Reporter reporter) throws IOException {
      int pos = key.getData();
      String str = val.getData();
      out.collect(new RecInt(pos), new RecString("1"));
    }
    public void close() {
    }
  }
  /**
   */
  static public class RandomCheckReducer implements Reducer<RecInt, RecString,
                                                            RecInt, RecString> {
    public void configure(JobConf job) {
    }
        
    public void reduce(RecInt key,
                       Iterator<RecString> it,
                       OutputCollector<RecInt, RecString> out,
                       Reporter reporter) throws IOException {
      int keyint = key.getData();
      int count = 0;
      while (it.hasNext()) {
        it.next();
        count++;
      }
      out.collect(new RecInt(keyint), new RecString(Integer.toString(count)));
    }
    public void close() {
    }
  }

  /**
   * The Merge Job is a really simple one.  It takes in
   * an int/int key-value set, and emits the same set.
   * But it merges identical keys by adding their values.
   *
   * Thus, the map() function is just the identity function
   * and reduce() just sums.  Nothing to see here!
   */
  static public class MergeMapper implements Mapper<RecInt, RecString,
                                                    RecInt, RecInt> {
    public void configure(JobConf job) {
    }

    public void map(RecInt key,
                    RecString val,
                    OutputCollector<RecInt, RecInt> out,
                    Reporter reporter) throws IOException {
      int keyint = key.getData();
      String valstr = val.getData();
      out.collect(new RecInt(keyint), new RecInt(Integer.parseInt(valstr)));
    }
    public void close() {
    }
  }
  static public class MergeReducer implements Reducer<RecInt, RecInt,
                                                      RecInt, RecInt> {
    public void configure(JobConf job) {
    }
        
    public void reduce(RecInt key,
                       Iterator<RecInt> it,
                       OutputCollector<RecInt, RecInt> out,
                       Reporter reporter) throws IOException {
      int keyint = key.getData();
      int total = 0;
      while (it.hasNext()) {
        total += it.next().getData();
      }
      out.collect(new RecInt(keyint), new RecInt(total));
    }
    public void close() {
    }
  }

  private static int range = 10;
  private static int counts = 100;
  private static Random r = new Random();
  private static Configuration conf = new Configuration();

  public void testMapred() throws Exception {
    launch();
  }

  /**
   * 
   */
  public static void launch() throws Exception {
    //
    // Generate distribution of ints.  This is the answer key.
    //
    int countsToGo = counts;
    int dist[] = new int[range];
    for (int i = 0; i < range; i++) {
      double avgInts = (1.0 * countsToGo) / (range - i);
      dist[i] = (int) Math.max(0, Math.round(avgInts + (Math.sqrt(avgInts) * r.nextGaussian())));
      countsToGo -= dist[i];
    }
    if (countsToGo > 0) {
      dist[dist.length-1] += countsToGo;
    }

    //
    // Write the answer key to a file.  
    //
    FileSystem fs = FileSystem.get(conf);
    Path testdir = new Path("mapred.loadtest");
    if (!fs.mkdirs(testdir)) {
      throw new IOException("Mkdirs failed to create directory " + testdir.toString());
    }

    Path randomIns = new Path(testdir, "genins");
    if (!fs.mkdirs(randomIns)) {
      throw new IOException("Mkdirs failed to create directory " + randomIns.toString());
    }

    Path answerkey = new Path(randomIns, "answer.key");
    SequenceFile.Writer out = SequenceFile.createWriter(fs, conf, 
                                                        answerkey, RecInt.class, RecInt.class, 
                                                        CompressionType.NONE);
    try {
      for (int i = 0; i < range; i++) {
        RecInt k = new RecInt();
        RecInt v = new RecInt();
        k.setData(i);
        v.setData(dist[i]);
        out.append(k, v);
      }
    } finally {
      out.close();
    }

    //
    // Now we need to generate the random numbers according to
    // the above distribution.
    //
    // We create a lot of map tasks, each of which takes at least
    // one "line" of the distribution.  (That is, a certain number
    // X is to be generated Y number of times.)
    //
    // A map task emits Y key/val pairs.  The val is X.  The key
    // is a randomly-generated number.
    //
    // The reduce task gets its input sorted by key.  That is, sorted
    // in random order.  It then emits a single line of text that
    // for the given values.  It does not emit the key.
    //
    // Because there's just one reduce task, we emit a single big
    // file of random numbers.
    //
    Path randomOuts = new Path(testdir, "genouts");
    fs.delete(randomOuts, true);


    JobConf genJob = new JobConf(conf, TestRecordMR.class);
    FileInputFormat.setInputPaths(genJob, randomIns);
    genJob.setInputFormat(SequenceFileInputFormat.class);
    genJob.setMapperClass(RandomGenMapper.class);

    FileOutputFormat.setOutputPath(genJob, randomOuts);
    genJob.setOutputKeyClass(RecInt.class);
    genJob.setOutputValueClass(RecString.class);
    genJob.setOutputFormat(SequenceFileOutputFormat.class);
    genJob.setReducerClass(RandomGenReducer.class);
    genJob.setNumReduceTasks(1);

    JobClient.runJob(genJob);

    //
    // Next, we read the big file in and regenerate the 
    // original map.  It's split into a number of parts.
    // (That number is 'intermediateReduces'.)
    //
    // We have many map tasks, each of which read at least one
    // of the output numbers.  For each number read in, the
    // map task emits a key/value pair where the key is the
    // number and the value is "1".
    //
    // We have a single reduce task, which receives its input
    // sorted by the key emitted above.  For each key, there will
    // be a certain number of "1" values.  The reduce task sums
    // these values to compute how many times the given key was
    // emitted.
    //
    // The reduce task then emits a key/val pair where the key
    // is the number in question, and the value is the number of
    // times the key was emitted.  This is the same format as the
    // original answer key (except that numbers emitted zero times
    // will not appear in the regenerated key.)  The answer set
    // is split into a number of pieces.  A final MapReduce job
    // will merge them.
    //
    // There's not really a need to go to 10 reduces here 
    // instead of 1.  But we want to test what happens when
    // you have multiple reduces at once.
    //
    int intermediateReduces = 10;
    Path intermediateOuts = new Path(testdir, "intermediateouts");
    fs.delete(intermediateOuts, true);
    JobConf checkJob = new JobConf(conf, TestRecordMR.class);
    FileInputFormat.setInputPaths(checkJob, randomOuts);
    checkJob.setInputFormat(SequenceFileInputFormat.class);
    checkJob.setMapperClass(RandomCheckMapper.class);

    FileOutputFormat.setOutputPath(checkJob, intermediateOuts);
    checkJob.setOutputKeyClass(RecInt.class);
    checkJob.setOutputValueClass(RecString.class);
    checkJob.setOutputFormat(SequenceFileOutputFormat.class);
    checkJob.setReducerClass(RandomCheckReducer.class);
    checkJob.setNumReduceTasks(intermediateReduces);

    JobClient.runJob(checkJob);

    //
    // OK, now we take the output from the last job and
    // merge it down to a single file.  The map() and reduce()
    // functions don't really do anything except reemit tuples.
    // But by having a single reduce task here, we end up merging
    // all the files.
    //
    Path finalOuts = new Path(testdir, "finalouts");        
    fs.delete(finalOuts, true);
    JobConf mergeJob = new JobConf(conf, TestRecordMR.class);
    FileInputFormat.setInputPaths(mergeJob, intermediateOuts);
    mergeJob.setInputFormat(SequenceFileInputFormat.class);
    mergeJob.setMapperClass(MergeMapper.class);
        
    FileOutputFormat.setOutputPath(mergeJob, finalOuts);
    mergeJob.setOutputKeyClass(RecInt.class);
    mergeJob.setOutputValueClass(RecInt.class);
    mergeJob.setOutputFormat(SequenceFileOutputFormat.class);
    mergeJob.setReducerClass(MergeReducer.class);
    mergeJob.setNumReduceTasks(1);
        
    JobClient.runJob(mergeJob);
        
 
    //
    // Finally, we compare the reconstructed answer key with the
    // original one.  Remember, we need to ignore zero-count items
    // in the original key.
    //
    boolean success = true;
    Path recomputedkey = new Path(finalOuts, "part-00000");
    SequenceFile.Reader in = new SequenceFile.Reader(fs, recomputedkey, conf);
    int totalseen = 0;
    try {
      RecInt key = new RecInt();
      RecInt val = new RecInt();            
      for (int i = 0; i < range; i++) {
        if (dist[i] == 0) {
          continue;
        }
        if (!in.next(key, val)) {
          System.err.println("Cannot read entry " + i);
          success = false;
          break;
        } else {
          if (!((key.getData() == i) && (val.getData() == dist[i]))) {
            System.err.println("Mismatch!  Pos=" + key.getData() + ", i=" + i + ", val=" + val.getData() + ", dist[i]=" + dist[i]);
            success = false;
          }
          totalseen += val.getData();
        }
      }
      if (success) {
        if (in.next(key, val)) {
          System.err.println("Unnecessary lines in recomputed key!");
          success = false;
        }
      }
    } finally {
      in.close();
    }
    int originalTotal = 0;
    for (int i = 0; i < dist.length; i++) {
      originalTotal += dist[i];
    }
    System.out.println("Original sum: " + originalTotal);
    System.out.println("Recomputed sum: " + totalseen);

    //
    // Write to "results" whether the test succeeded or not.
    //
    Path resultFile = new Path(testdir, "results");
    BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(fs.create(resultFile)));
    try {
      bw.write("Success=" + success + "\n");
      System.out.println("Success=" + success);            
    } finally {
      bw.close();
    }
    fs.delete(testdir, true);
  }

  /**
   * Launches all the tasks in order.
   */
  public static void main(String[] argv) throws Exception {
    if (argv.length < 2) {
      System.err.println("Usage: TestRecordMR <range> <counts>");
      System.err.println();
      System.err.println("Note: a good test will have a <counts> value that is substantially larger than the <range>");
      return;
    }

    int i = 0;
    int range = Integer.parseInt(argv[i++]);
    int counts = Integer.parseInt(argv[i++]);
    launch();
  }
}