Developing a MapReduce Application - Hadoop: The Definitive Guide - page 183

Running Locally on Test Data
Now that we have the mapper and reducer working on controlled inputs, the next step is to
write a job driver and run it on some test data on a development machine.
Running a Job in a Local Job Runner
Using the Tool interface introduced earlier in the chapter, it's easy to write a driver to run
our MapReduce job for finding the maximum temperature by year (see MaxTemperat-
ureDriver in Example 6-10 ) .
Example 6-10. Application to find the maximum temperature
public class MaxTemperatureDriver extends Configured implements Tool {
@Override
public int run ( String [] args ) throws Exception {
if ( args . length != 2 ) {
System . err . printf ( "Usage: %s [generic options] <input> <output>\n" ,
getClass (). getSimpleName ());
ToolRunner . printGenericCommandUsage ( System . err );
return - 1 ;
}
Job job = new Job ( getConf (), "Max temperature" );
job . setJarByClass ( getClass ());
FileInputFormat . addInputPath ( job , new Path ( args [ 0 ]));
FileOutputFormat . setOutputPath ( job , new Path ( args [ 1 ]));
job . setMapperClass ( MaxTemperatureMapper . class );
job . setCombinerClass ( MaxTemperatureReducer . class );
job . setReducerClass ( MaxTemperatureReducer . class );
job . setOutputKeyClass ( Text . class );
job . setOutputValueClass ( IntWritable . class );
return job . waitForCompletion ( true ) ? 0 : 1 ;
}
public static void main ( String [] args ) throws Exception {
int exitCode = ToolRunner . run ( new MaxTemperatureDriver (), args );
System . exit ( exitCode );
}
}