MapReduce Job: How do I take in <Text, IntWritable> during Map phase and output <Text, Text> in Reduce phase?

Question

I am trying to make my output look like the following: Model output

But I am stuck with this: My output

How do I convert the value (IntWritable) from the output to Text and concatenate the string " words" into the output? I also need to format the numbers from the output to start at the same spot as shown in the model answer. The input is <Text, IntWritable> and I am guessing the output has to be <Text, Text>.

My codes for mapper:

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

private final static IntWritable one = new IntWritable(1);
private final static IntWritable zero = new IntWritable(0);


private Text word1 = new Text("1.X short:");
private Text word2 = new Text("2.short:");
private Text word3 = new Text("3.medium:");
private Text word4 = new Text("4.long:");
private Text word5 = new Text("5.X long:");
private Text word6 = new Text("6.XX long:");

public void map(Object key, Text value, Context context
                ) throws IOException, InterruptedException {
  StringTokenizer itr = new StringTokenizer(value.toString());
  while (itr.hasMoreTokens()) {
      
      String word = itr.nextToken();
      int length = word.length();
      
      if ((length >= 1) && (length <= 3)){
          context.write(word1, one);
      }
      else
          context.write(word1, zero);

      if ((length >= 4) && (length <= 5)){
          context.write(word2, one);
      }
      else
          context.write(word2, zero);

      if ((length >= 6) && (length <= 8)){
          context.write(word3, one);
      }
      else
          context.write(word3, zero);

      if ((length >= 9) && (length <= 12)){
          context.write(word4, one);
      }
      else
          context.write(word4, zero);

      if ((length >= 13) && (length <= 15)){
          context.write(word5, one);
      }
      else
          context.write(word5, zero);

      if (length >= 16){
          context.write(word6, one);
      }
      else
          context.write(word6, zero);
     
      
  }
}

My codes for Reducer:

 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);
key.set(key.toString().substring(1));
  context.write(key, result);
}

Answer 1

So, first, you don't need to write zeros at all in the mapper. Just focus on the ones if you are summing data.

Then, it's a simple change - Change your output type

// in the driver
job.setOutputValueClass(Text.class);

And

extends Reducer<Text,IntWritable, Text, Text> 

And just return the correct information

context.write(key, new Text(String.format("%d words", result)))

format the numbers from the output to start at the same spot

Is that really necessary? You can do this with string padding in the String.format method, but I wouldn't really worry about it.