Developing a MapReduce Application - Hadoop: The Definitive Guide - page 179

(an IntWritable representing the temperature, −1.1°C), before finally calling the
runTest() method to execute the test. If the expected output values are not emitted by
the mapper, MRUnit will fail the test. Notice that the input key could be set to any value
because our mapper ignores it.
Proceeding in a test-driven fashion, we create a Mapper implementation that passes the
test (see Example 6-6 ) . Because we will be evolving the classes in this chapter, each is put
in a different package indicating its version for ease of exposition. For example,
v1.MaxTemperatureMapper is version 1 of MaxTemperatureMapper . In real-
ity, of course, you would evolve classes without repackaging them.
Example 6-6. First version of a Mapper that passes MaxTemperatureMapperTest
public class MaxTemperatureMapper
extends Mapper < LongWritable , Text , Text , IntWritable > {
@Override
public void map ( LongWritable key , Text value , Context context )
throws IOException , InterruptedException {
String line = value . toString ();
String year = line . substring ( 15 , 19 );
int airTemperature = Integer . parseInt ( line . substring ( 87 , 92 ));
context . write ( new Text ( year ), new IntWritable ( airTemperature ));
}
}
This is a very simple implementation that pulls the year and temperature fields from the
line and writes them to the Context . Let's add a test for missing values, which in the
raw data are represented by a temperature of +9999 :
@Test
public void ignoresMissingTemperatureRecord () throws IOException ,
InterruptedException {
Text value = new
Text ( "0043011990999991950051518004+68750+023550FM-12+0382" +
// Year ^^^^
"99999V0203201N00261220001CN9999999N9+99991+99999999999" );
// Temperature ^^^^^
new MapDriver < LongWritable , Text , Text , IntWritable >()
. withMapper ( new MaxTemperatureMapper ())
. withInput ( new LongWritable ( 0 ), value )
. runTest ();
}