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Jan 8 in Big Data | Hadoop

Q: WordCount v2.0 MapReducer

1 Answer

Jan 8

Here is a more complete WordCount which uses many of the features provided by the MapReduce framework we discussed so far.

This needs the HDFS to be up and running, especially for the DistributedCache-related features. Hence it only works with a pseudo-distributed or fully-distributed Hadoop installation.

Source Code

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.net.URI;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.Counter;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.StringUtils;

public class WordCount2 {

  public static class TokenizerMapper
       extends Mapper<Object, Text, Text, IntWritable>{

    static enum CountersEnum { INPUT_WORDS }

    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    private boolean caseSensitive;
    private Set<String> patternsToSkip = new HashSet<String>();

    private Configuration conf;
    private BufferedReader fis;

    @Override
    public void setup(Context context) throws IOException,
        InterruptedException {
      conf = context.getConfiguration();
      caseSensitive = conf.getBoolean("wordcount.case.sensitive", true);
      if (conf.getBoolean("wordcount.skip.patterns", false)) {
        URI[] patternsURIs = Job.getInstance(conf).getCacheFiles();
        for (URI patternsURI : patternsURIs) {
          Path patternsPath = new Path(patternsURI.getPath());
          String patternsFileName = patternsPath.getName().toString();
          parseSkipFile(patternsFileName);
        }
      }
    }

    private void parseSkipFile(String fileName) {
      try {
        fis = new BufferedReader(new FileReader(fileName));
        String pattern = null;
        while ((pattern = fis.readLine()) != null) {
          patternsToSkip.add(pattern);
        }
      } catch (IOException ioe) {
        System.err.println("Caught exception while parsing the cached file '"
            + StringUtils.stringifyException(ioe));
      }
    }

    @Override
    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException {
      String line = (caseSensitive) ?
          value.toString() : value.toString().toLowerCase();
      for (String pattern : patternsToSkip) {
        line = line.replaceAll(pattern, "");
      }
      StringTokenizer itr = new StringTokenizer(line);
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);
        Counter counter = context.getCounter(CountersEnum.class.getName(),
            CountersEnum.INPUT_WORDS.toString());
        counter.increment(1);
      }
    }
  }

  public static class IntSumReducer
       extends Reducer<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();

    public void reduce(Text key, Iterable<IntWritable> values,
                       Context context
                       ) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    GenericOptionsParser optionParser = new GenericOptionsParser(conf, args);
    String[] remainingArgs = optionParser.getRemainingArgs();
    if ((remainingArgs.length != 2) && (remainingArgs.length != 4)) {
      System.err.println("Usage: wordcount <in> <out> [-skip skipPatternFile]");
      System.exit(2);
    }
    Job job = Job.getInstance(conf, "word count");
    job.setJarByClass(WordCount2.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);

    List<String> otherArgs = new ArrayList<String>();
    for (int i=0; i < remainingArgs.length; ++i) {
      if ("-skip".equals(remainingArgs[i])) {
        job.addCacheFile(new Path(remainingArgs[++i]).toUri());
        job.getConfiguration().setBoolean("wordcount.skip.patterns", true);
      } else {
        otherArgs.add(remainingArgs[i]);
      }
    }
    FileInputFormat.addInputPath(job, new Path(otherArgs.get(0)));
    FileOutputFormat.setOutputPath(job, new Path(otherArgs.get(1)));

    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

Sample Runs

Sample text-files as input:

$ bin/hadoop fs -ls /user/joe/wordcount/input/
/user/joe/wordcount/input/file01
/user/joe/wordcount/input/file02

$ bin/hadoop fs -cat /user/joe/wordcount/input/file01
Hello World, Bye World!

$ bin/hadoop fs -cat /user/joe/wordcount/input/file02
Hello Hadoop, Goodbye to hadoop.

Run the application:

$ bin/hadoop jar wc.jar WordCount2 /user/joe/wordcount/input /user/joe/wordcount/output

Output:

$ bin/hadoop fs -cat /user/joe/wordcount/output/part-r-00000
Bye 1
Goodbye 1
Hadoop, 1
Hello 2
World! 1
World, 1
hadoop. 1
to 1

Notice that the inputs differ from the first version we looked at, and how they affect the outputs.

Now, lets plug-in a pattern-file which lists the word-patterns to be ignored, via the DistributedCache.

$ bin/hadoop fs -cat /user/joe/wordcount/patterns.txt
\.
\,
\!
to

Run it again, this time with more options:

$ bin/hadoop jar wc.jar WordCount2 -Dwordcount.case.sensitive=true /user/joe/wordcount/input /user/joe/wordcount/output -skip /user/joe/wordcount/patterns.txt

As expected, the output:

$ bin/hadoop fs -cat /user/joe/wordcount/output/part-r-00000
Bye 1
Goodbye 1
Hadoop 1
Hello 2
World 2
hadoop 1

Run it once more, this time switch-off case-sensitivity:

$ bin/hadoop jar wc.jar WordCount2 -Dwordcount.case.sensitive=false /user/joe/wordcount/input /user/joe/wordcount/output -skip /user/joe/wordcount/patterns.txt

Sure enough, the output:

$ bin/hadoop fs -cat /user/joe/wordcount/output/part-r-00000
bye 1
goodbye 1
hadoop 2
hello 2
horld 2
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