MapReduceFunPost-Driver.java

/**
 * main method
 * @param args
 */
public static void main(String[] args) throws Exception {
    Logger logger = LoggerFactory.getLogger(PlayerBatting.class);
    logger.info("PlayerBatting starting up...");

    /*
     * The expected command-line arguments are the paths containing
     * input and output data. Terminate the job if the number of
     * command-line arguments is not exactly 2.
     */
    if (args.length != 2) {
      System.out.println("Usage: PlayerBatting  n");
      System.exit(-1);
    }

    /*
     * Instantiate a Job object for your job's configuration.  
     */
    Job job = new Job();

    /*
     * Specify the jar file that contains your driver (PlayerBatting class), mapper, and reducer.
     * Hadoop will transfer this jar file to nodes in your cluster running mapper and reducer
     * tasks.
     */
    job.setJarByClass(PlayerBatting.class);

    /*
     * Specify an easily-decipherable name for the job.
     * This job name will appear in reports and logs.
     */
    job.setJobName("Career Batting Stats");

    /*
     * Specify the paths to the input and output data based on the
     * command-line arguments.
     */
    FileInputFormat.setInputPaths(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

    /*
     * Specify the mapper and reducer classes.
     */
    job.setMapperClass(PlayerBattingMapper.class);
    job.setReducerClass(PlayerBattingReducer.class);

    /*
     * Specify the job's output key and value classes (output from reducer)
     */
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(Text.class);

    /*
     * Start the MapReduce job and wait for it to finish.
     * If it finishes successfully, return 0. If not, return 1.
     */
    boolean success = job.waitForCompletion(true);
    System.exit(success ? 0 : 1);
}