A reducer in a Hadoop job must be a subclass of Reducer<Kin, Vin, Kout, Vout>.

As in the Perl API, any reducer can be used as a combiner.

Here is a Hadoop job computing the number of occurrences of all words:

WordCount.java

import java.io.IOException;
import java.util.StringTokenizer;
 
import org.apache.hadoop.conf.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.*;
import org.apache.hadoop.mapreduce.lib.output.*;
import org.apache.hadoop.util.*;
 
public class WordCount extends Configured implements Tool {
  public static class TheMapper extends Mapper<Text, Text, Text, IntWritable>{
 
    private Text word = new Text();
    private IntWritable one = new IntWritable(1);
 
    public void map(Text key, Text value, Context context) throws IOException, InterruptedException {
      for (String token : value.toString().split("\\W+")) {
        word.set(token);
        context.write(word, one);
      }
    }
  }
 
  public static class TheReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    private IntWritable sumWritable = new IntWritable();
 
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable value : values) {
        sum += value.get();
      }
      sumWritable.set(sum);
      context.write(key, sumWritable);
    }
  }
 
  public int run(String[] args) throws Exception {
    if (args.length < 2) {
      System.err.printf("Usage: %s.jar in-path out-path", this.getClass().getName());
      return 1;
    }
 
    Job job = new Job(getConf(), this.getClass().getName());
 
    job.setJarByClass(this.getClass());
    job.setMapperClass(TheMapper.class);
    job.setCombinerClass(TheReducer.class);
    job.setReducerClass(TheReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
 
    job.setInputFormatClass(KeyValueTextInputFormat.class);
 
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
 
    return job.waitForCompletion(true) ? 0 : 1;
  }
 
  public static void main(String[] args) throws Exception {
    int res = ToolRunner.run(new WordCount(), args);
 
    System.exit(res);
  }
}

wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_export/code/courses:mapreduce-tutorial:step-25?codeblock=0' -O 'WordCount.java'
make -f /net/projects/hadoop/java/Makefile WordCount.jar
rm -rf step-25-out-ex1; /net/projects/hadoop/bin/hadoop -r 0 WordCount.jar /home/straka/wiki/cs-text-small step-25-out-ex1
less step-25-out-ex1/part-*

• The reducer is used also as combiner.
• The reducer could have defined setup and cleanup methods, just like in Perl API.
• Mind the way how the code uses Text and IntWritable. By design, all the Writable subclasses should be reused as often as possible to avoid allocation. Therefore a Writable instance is usually allocated only once per mapper / reducer and methods get and set should be used to work with its content. That is why the last two lines in the reducer are

  sumWritable.set(sum);
  context.write(key, sumWritable)

  instead of

  context.write(key, new IntWritable(sum));

• VIM users: As in the mapper, code completion is puzzled by the 'Context' type. This time it can be replaced by ReduceContext<Text, IntWritable, Text, IntWritable> for the code completion to work.

A partitioner is a subclass of Partitioner<K, V>, which has the only method getPartition(KEY key, VALUE value, int numPartitions).

A slightly complicated example follows. We want a Hadoop job which will parse (wiki_article_name, wiki_article_content) pairs and should create two outputs – a sorted list of article names and a sorted list of words present int he article content. We use:

• Mapper<Text, Text, Text, IntWritable> which outputs (wiki_article_name, 0) and (word, 1) pairs.
• Partitioner<Text, IntWritable> which for pair (text, number) outputs the value of number.
• Combiner<Text, IntWritable, Text, IntWritable> which for a key and many values outputs one pair (key, value). Ideally we would like to output (key, null) pair, but that is not possible – a Combiner must not change the type of keys and values.
• Reducer<Text, IntWritable, Text, NullWritable> which discards the value and outputs key only.

The solution is a bit clumsy. If the mapper could output (key, value, partition) instead of just (key, value), we would not have to use the value as a partition number and the types would be simplified.

ArticlesAndWords.java

import java.io.IOException;
import java.util.StringTokenizer;
 
import org.apache.hadoop.conf.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.*;
import org.apache.hadoop.mapreduce.lib.output.*;
import org.apache.hadoop.util.*;
 
public class ArticlesAndWords extends Configured implements Tool {
  public static class TheMapper extends Mapper<Text, Text, Text, IntWritable>{
    private Text word = new Text();
    private IntWritable zero = new IntWritable(0);
    private IntWritable one = new IntWritable(1);
 
    public void map(Text key, Text value, Context context) throws IOException, InterruptedException {
      context.write(key, zero);
      for (String token : value.toString().split("\\W+")) {
        word.set(token);
        context.write(word, one);
      }
    }
  }
 
  public static class ThePartitioner extends Partitioner<Text, IntWritable> {
    public int getPartition(Text key, IntWritable value, int numPartitions) {
      return value.get();
    }
  }
 
  public static class TheCombiner extends Reducer<Text, IntWritable, Text, IntWritable> {
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
      for (IntWritable value : values) {
        context.write(key, value);
        return;
      }
    }
  }
 
  public static class TheReducer extends Reducer<Text, IntWritable, Text, NullWritable> {
    public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
      context.write(key, NullWritable.get());
    }
  }
 
  public int run(String[] args) throws Exception {
    if (args.length < 2) {
      System.err.printf("Usage: %s.jar in-path out-path", this.getClass().getName());
      return 1;
    }
 
    Job job = new Job(getConf(), this.getClass().getName());
 
    job.setJarByClass(this.getClass());
    job.setMapperClass(TheMapper.class);
    job.setPartitionerClass(ThePartitioner.class);
    job.setCombinerClass(TheCombiner.class);
    job.setReducerClass(TheReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(NullWritable.class);
 
    job.setInputFormatClass(KeyValueTextInputFormat.class);
 
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
 
    return job.waitForCompletion(true) ? 0 : 1;
  }
 
  public static void main(String[] args) throws Exception {
    int res = ToolRunner.run(new ArticlesAndWords(), args);
 
    System.exit(res);
  }
}

wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_export/code/courses:mapreduce-tutorial:step-25?codeblock=1' -O 'ArticlesAndWords.java'
make -f /net/projects/hadoop/java/Makefile ArticlesAndWords.jar
rm -rf step-25-out-ex2; /net/projects/hadoop/bin/hadoop -r 0 ArticlesAndWords.jar /home/straka/wiki/cs-text-small step-25-out-ex2
less step-25-out-ex2/part-*