// CSC 369: Distributed Computing
// Alex Dekhtyar

// Java Hadoop Template

// Section 1: Imports


                  // Data containers for Map() and Reduce() functions

                  // You would import the data types needed for your keys and values
import org.apache.hadoop.io.IntWritable; // Hadoop's serialized int wrapper class
import org.apache.hadoop.io.LongWritable; // Hadoop's serialized int wrapper class
import org.apache.hadoop.io.Text;        // Hadoop's serialized String wrapper class


                 // For Map and Reduce jobs

import org.apache.hadoop.mapreduce.Mapper; // Mapper class to be extended by our Map function
import org.apache.hadoop.mapreduce.Reducer; // Reducer class to be extended by our Reduce function

                 // To start the MapReduce process

import org.apache.hadoop.mapreduce.Job; // the MapReduce job class that is used a the driver


                // For File "I/O"

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; // class for "pointing" at input file(s)
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; // class for "pointing" at output file
import org.apache.hadoop.fs.Path;                // Hadoop's implementation of directory path/filename


// Exception handling

import java.io.IOException;


public class switchMR {


// Mapper  Class Template


public static class SwitchMapper     // Need to replace the four type labels there with actual Java class names
     extends Mapper< LongWritable, Text, LongWritable, Text > {

// @Override   // we are overriding Mapper's map() method

// map methods takes three input parameters
// first parameter: input key 
// second parameter: input value
// third parameter: container for emitting output key-value pairs

public void map(LongWritable key, Text value, Context context)
      throws IOException, InterruptedException {


   String name =  "";

   String text[] =  value.toString().split(",");
   if (text.length==2) {
      name = text[1];  // get the value
      String newKey = text[0];
      long myKey = Long.parseLong(newKey);

      if (name.contains("Alex")) {
        name = "Nick Cage";
      }
      Text out = new Text(name);

      LongWritable outKey = new LongWritable(myKey);
      context.write(outKey, out);
   }


 } // map


} // MyMapperClass


//  Reducer Class Template

public static class SwitchReducer   // needs to replace the four type labels with actual Java class names
      extends  Reducer< LongWritable, Text, LongWritable, Text> {

 // note: InValueType is a type of a single value Reducer will work with
 //       the parameter to reduce() method will be Iterable<InValueType> - i.e. a list of these values

@Override  // we are overriding the Reducer's reduce() method

// reduce takes three input parameters
// first parameter: input key
// second parameter: a list of values associated with the key
// third parameter: container  for emitting output key-value pairs

public void reduce( LongWritable key, Iterable<Text> values, Context context)
     throws IOException, InterruptedException {

  String name = "";

  for (Text val : values) {
    name = val.toString();
  } // for

  // emit final output
  context.write(key, new Text(name));   


 } // reduce


} // reducer


//  MapReduce Driver


  // we do everything here in main()
  public static void main(String[] args) throws Exception {

     // step 1: get a new MapReduce Job object
     Job  job = Job.getInstance();  //  job = new Job() is now deprecated
     
    // step 2: register the MapReduce class
      job.setJarByClass(switchMR.class);  

   //  step 3:  Set Input and Output files
       FileInputFormat.addInputPath(job, new Path("./test/", "data")); // put what you need as input file
       FileOutputFormat.setOutputPath(job, new Path("./test/","output")); // put what you need as output file

   // step 4:  Register mapper and reducer
      job.setMapperClass(SwitchMapper.class);
      job.setReducerClass(SwitchReducer.class);
  
   //  step 5: Set up output information
       job.setOutputKeyClass(LongWritable.class); // specify the output class (what reduce() emits) for key
       job.setOutputValueClass(Text.class); // specify the output class (what reduce() emits) for value

   // step 6: Set up other job parameters at will
      job.setJobName("Nick Cage!");

   // step 7:  ?

   // step 8: profit
      System.exit(job.waitForCompletion(true) ? 0:1);


  } // main()


} // MyMapReduceDriver