Developing a MapReduce Application - Hadoop: The Definitive Guide - page 181

public int getAirTemperature () {
return airTemperature ;
}
}
The resulting mapper (version 2) is much simpler (see Example 6-8 ) . It just calls the pars-
er's parse() method, which parses the fields of interest from a line of input, checks
whether a valid temperature was found using the isValidTemperature() query
method, and, if it was, retrieves the year and the temperature using the getter methods on
the parser. Notice that we check the quality status field as well as checking for missing
temperatures in isValidTemperature() , to filter out poor temperature readings.
NOTE
Another benefit of creating a parser class is that it makes it easy to write related mappers for similar jobs
without duplicating code. It also gives us the opportunity to write unit tests directly against the parser, for
more targeted testing.
Example 6-8. A Mapper that uses a utility class to parse records
public class MaxTemperatureMapper
extends Mapper < LongWritable , Text , Text , IntWritable > {
private NcdcRecordParser parser = new NcdcRecordParser ();
@Override
public void map ( LongWritable key , Text value , Context context )
throws IOException , InterruptedException {
parser . parse ( value );
if ( parser . isValidTemperature () ) {
context . write ( new Text ( parser . getYear () ),
new IntWritable ( parser . getAirTemperature () ));
}
}
}
With the tests for the mapper now passing, we move on to writing the reducer.
Reducer
The reducer has to find the maximum value for a given key. Here's a simple test for this
feature, which uses a ReduceDriver :