/**
 * 
 */
package org.detronizator;

import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
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.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;

/**
 * This Class implements a very basical Map-Reduce for Apache Hadoop.
 * It's an example derived from the WordCount (1.0) available at
 * <a href="http://hadoop.apache.org/core/docs/current/mapred_tutorial.html">Hadoop MapReduce Tutorial</a>:
 * this class counts the Characters' occurrence instead of the Words'.
 * 
 * @class CharacterCount
 * @author Ivan De Marino
 * @version 0.1
 */
public class CharacterCount {

	/**
	 * Map Implementation.
	 * This mapping will collect all the Characters in pairs
	 * [Key, Value] where Key = "The Character" and Value = "1".
	 */
	public static class Map extends MapReduceBase implements
			Mapper<LongWritable, Text, Text, IntWritable> {
		
		private final static IntWritable KINTWRITABLE_ONE = new IntWritable(1);
		private Text iCurrWord = new Text();
		private Text iCurrChar = new Text(); // No Char field
		private char [] iCurrCharArray = new char[1];
		private String iCurrString;
		
		/**
		 * Map phase implementation.
		 * 
		 * @param aKey The Key associated with the current InputLine
		 * @param aValueTextLine The Text Line to produce the Map from
		 * @param aOutputCollector The OutputCollector: 
		 * 	is where this method will store the different
		 * 	[Key = Character, Value = "1"] pairs
		 * @param aReporter Monitoring facility (not used in this example)
		 */
		public void map(LongWritable aKey, Text aValueTextLine,
				OutputCollector<Text, IntWritable> aOutputCollector,
				Reporter aReporter)
				throws IOException {
			// Converting the Input Text Line in a String
			String currentLine = aValueTextLine.toString();
			// Tokenizer
			StringTokenizer tokenizer = new StringTokenizer(currentLine);
			
			while ( tokenizer.hasMoreTokens() ) {
				// Collect the Tokens as Strings
				iCurrWord.set( tokenizer.nextToken() );
				// For every character in the current Token-String
				for ( int i = 0; i < iCurrWord.getLength(); ++i ) {
					
					// TODO-Rubish code. Rewrite.
					iCurrCharArray[0] = (char)iCurrWord.charAt(i);
					iCurrString = new String(iCurrCharArray);
					iCurrChar.set(iCurrString);
					
					// Emitting the <key (character), value ("1")>
					aOutputCollector.collect(iCurrChar, KINTWRITABLE_ONE);
				}
				
			}
		}
	}
	
	/**
	 * Reduce Implementation.
	 * This reducing will receive the pairs [Key, Value] produced by the
	 * Mapping and will count the occurrence of every character (just
	 * making a sum of every "1").
	 * 
	 * In this example it will be used ALSO as a "Local Combiner" so that
	 * the Input for the Reduce Phase will have an already reduced input.
	 * This will make the TaskTracker that does a Mapping, responsible for
	 * a "minimal Reduce" too, so the load on the "final Reduce" TaskTracker
	 * will be "reduced" ;-).
	 */
	public static class Reduce extends MapReduceBase implements
			Reducer<Text, IntWritable, Text, IntWritable> {
		
		/**
		 * Reduce phase implementation.
		 * 
		 * @param key The Key (the Character in this case)
		 * @param values An Iterator ready to be used over the Pairs having
		 * 	the same Key.
		 * @param aOutputCollector The OutputCollector: 
		 * 	is where this method will store the different
		 * 	[Key = Character, Value = CharacterOccurrence] pairs
		 * @param aReporter Monitoring facility (not used in this example)
		 */
		public void reduce(Text key, Iterator<IntWritable> values,
				OutputCollector<Text, IntWritable> output, Reporter reporter)
				throws IOException {
			// Because of the abstraction level of this framework, there is
			// no need to change the WordCount example Reduce.
			int sum = 0;
			while (values.hasNext()) {
				sum += values.next().get();
			}
			output.collect(key, new IntWritable(sum));
		}
	}

	/**
	 * This is the Main method that will be executed on the
	 * JobTracker to Initialize and Run the Job over the Nodes.
	 * 
	 * @param args Command Line arguments
	 */
	public static void main(String[] args) throws Exception {
		JobConf conf = new JobConf(CharacterCount.class);
		conf.setJobName("CharacterCount");

		conf.setOutputKeyClass(Text.class);
		conf.setOutputValueClass(IntWritable.class);

		conf.setMapperClass(Map.class);
		conf.setCombinerClass(Reduce.class); // "Local Reduce"
		conf.setReducerClass(Reduce.class);

		conf.setInputFormat(TextInputFormat.class);
		conf.setOutputFormat(TextOutputFormat.class);

		conf.setInputPath(new Path(args[0]));
		conf.setOutputPath(new Path(args[1]));

		JobClient.runJob(conf);
	}
}