We start by going through a simple Hadoop job with Mapper only.

A mapper which processes (key, value) pairs of types (Kin, Vin) and produces (key, value) pairs of types (Kout, Vout) must be a subclass of Mapper<Kin, Vin, Kout, Vout>. In our case, the mapper is subclass of Mapper<Text, Text, Text, Text>.

The mapper must define a map method and may provide setup and context method:

  public static class TheMapper extends Mapper<Text, Text, Text, Text>{
    public void setup(Context context) throws IOException, InterruptedException {}
 
    public void map(Text key, Text value, Context context) throws IOException, InterruptedException {}
 
    public void cleanup(Context context) throws IOException, InterruptedException {}
  }

Outputting (key, value) pairs is performed using the MapContext<Kin, Vin, Kout, Vout> object (the Context is an abbreviation for this type), with the method context.write(Kout key, Vout value).

Here is the source of the whole Hadoop job:

MapperOnlyHadoopJob.java

import java.io.IOException;
 
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 MapperOnlyHadoopJob extends Configured implements Tool {
  // Mapper
  public static class TheMapper extends Mapper<Text, Text, Text, Text>{
    public void setup(Context context) {
    }
 
    public void map(Text key, Text value, Context context) throws IOException, InterruptedException {
      if (key.getLength() > 0 && Character.toUpperCase(key.charAt(0)) == 'A') {
        context.write(key, value);
      }
    }
 
    public void cleanup(Context context) {
    }
  }
 
  // Job configuration
  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());    // Create class representing Hadoop job.
                                                                // Name of the job is the name of current class.
 
    job.setJarByClass(this.getClass());                         // Use jar containing current class.
    job.setMapperClass(TheMapper.class);                        // The mapper of the job.
    job.setOutputKeyClass(Text.class);                          // Type of the output keys.
    job.setOutputValueClass(Text.class);                        // Type of the output values.
 
    job.setInputFormatClass(KeyValueTextInputFormat.class);     // Input format.
                                                                // Output format is the default -- TextOutputFormat
 
    FileInputFormat.addInputPath(job, new Path(args[0]));       // Input path is on command line.
    FileOutputFormat.setOutputPath(job, new Path(args[1]));     // Output path is on command line too.
 
    return job.waitForCompletion(true) ? 0 : 1;
  }
 
  // Main method
  public static void main(String[] args) throws Exception {
    int res = ToolRunner.run(new MapperOnlyHadoopJob(), args);
 
    System.exit(res);
  }
}

Remarks:

• The filename must be the same as the name of the class – this is enforced by Java compiler.
• In one class multiple jobs can be submitted, either in sequence or in parallel.

Download the source and compile it.

The official way of running Hadoop jobs is to use the /SGE/HADOOP/active/bin/hadoop script. Jobs submitted through this script can be configured using Hadoop properties only. Therefore a wrapper script is provided, with similar options as the Perl API runner:

• net/projects/hadoop/bin/hadoop [-r number_of_reducers] job.jar [generic Hadoop properties] input_path output_path – executes the given job locally in a single thread. It is useful for debugging.
• net/projects/hadoop/bin/hadoop -jt cluster_master [-r number_of_reducers] job.jar [generic Hadoop properties] input_path output_path – submits the job to given cluster_master.
• net/projects/hadoop/bin/hadoop -c number_of_machines [-w secs_to_wait_after_job_finishes] [-r number_of_reducers] job.jar [generic Hadoop properties] input_path output_path – creates a new cluster with specified number of machines, which executes given job, and then waits for specified number of seconds before it stops.