Using a custom Combiner...it may be ignored? - hadoop

Question

I have this in Main...
job.setMapperClass(AverageIntMapper.class);
job.setCombinerClass(AverageIntCombiner.class);
job.setReducerClass(AverageIntReducer.class);
And the Combiner has different code but the Combiner is being completely ignored as the output the Reducer is using is the output from the Mapper.
I understand that a Combiner may not be used but I thought that was the case when the Combiner is the same as the Reducer. I don't really understand the point of being able to create a custom Combiner but the system can still skip its usage.
If that's not supposed to happen, what could be a reason that the Combiner is not being used?
Code...
import java.io.IOException;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class AverageInt {
public static class AverageIntMapper extends Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String n_string = value.toString();
context.write(new Text("Value"), new Text(n_string));
}
}
public static class AverageIntCombiner extends Reducer<Text, Text, Text, Text> {
public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int sum = 0;
int count = 0;
for(IntWritable value : values) {
int temp = Integer.parseInt(value.toString());
sum += value.get();
count += 1;
}
String sum_count = Integer.toString(sum) + "," + Integer.toString(count);
context.write(key, new Text(sum_count));
}
}
public static class AverageIntReducer extends Reducer<Text, Text, Text, Text> {
public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
int total = 0;
int count = 0;
for(Text value : values) {
String temp = value.toString();
String[] split = temp.split(",");
total += Integer.parseInt(split[0]);
count += Integer.parseInt(split[1]);
}
Double average = (double)total/count;
context.write(key, new Text(average.toString()));
}
}
public static void main(String[] args) throws Exception {
if(args.length != 2) {
System.err.println("Usage: AverageInt <input path> <output path>");
System.exit(-1);
}
Job job = new Job();
job.setJarByClass(AverageInt.class);
job.setJobName("Average");
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(AverageIntMapper.class);
job.setCombinerClass(AverageIntCombiner.class);
job.setReducerClass(AverageIntReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

Answer 1

If you look at what your mapper is emitting:
public void map(LongWritable key, Text value, Context context)
Its sending two Text objects, but whilst you've declared the combiner class itself correctly, the reduce method has:
public void reduce(Text key, Iterable<IntWritable> values, Context context)
It should be:
public void reduce(Text key, Iterable<Text> values, Context context)