Hadoop Mapreduce: Reduce Method not called

Question

I'm writing a mapreduce alogirthm.

In my code the reduce(Text key, Iterable<String> values, Context context) method is not called. above it I have the @Override giving an error: Method does not override method from its superclass .

Here is my code:

package WordCountP;

import java.io.FileReader;
import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.json.simple.JSONArray;
import org.json.simple.JSONObject;
import org.json.simple.parser.JSONParser;
import org.json.simple.parser.ParseException;


public class popularity extends Configured implements Tool{

    public class PopularityMapper extends Mapper<Text, Text, Text, Text> {

        @Override
        protected void map(Text key, Text value,
                           Context context)
                throws IOException, InterruptedException {

                JSONParser jsonParser = new JSONParser();
                try {
                    JSONObject jsonobject = (JSONObject) jsonParser.parse(new FileReader("src\\testinput.json"));
                    JSONArray jsonArray = (JSONArray) jsonobject.get("votes");

                    Iterator<JSONObject> iterator = jsonArray.iterator();
                    while(iterator.hasNext()) {
                        JSONObject obj = iterator.next();
                        String song_id_rave_id = (String) obj.get("song_ID") + "|" + (String) obj.get("rave_ID");
                        String preference = (String) obj.get("preference");
                        System.out.println(song_id_rave_id + "||" + preference);
                        context.write(new Text(song_id_rave_id), new Text(preference));
                    }
                }catch(ParseException e) {
                    e.printStackTrace();
                }
        }

    }

    public class PopularityReducer extends Reducer<Text, Iterable<String>, Text, Text> {

        @Override
        protected void reduce(Text key, Iterable<String> values, Context context)
                throws IOException, InterruptedException {

            int sum = 0;
            for ( String val: values){
                if (val == "true"){
                    sum +=1;
                }
                else if (val == "false"){
                    sum -=1;
                }

            }
            String result = Integer.toString(sum);
            context.write(new Text(key), new Text(result));
        }
    }



    public static void main(String[] args) throws Exception{
        int exitCode = ToolRunner.run(new popularity(), args);
        System.exit(exitCode);
    }



    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 org.apache.hadoop.mapreduce.Job();
        job.setJarByClass(popularity.class);
        job.setJobName("PopularityCounter");

        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        job.setMapperClass(PopularityMapper.class);
        job.setReducerClass(PopularityReducer.class);

        int returnValue = job.waitForCompletion(true) ? 0:1;
        System.out.println("job.isSuccessful " + job.isSuccessful());
        return returnValue;
    }
}

I've tried naming it with R caps, ( Reduce() ) but it didn,t work either. I assume there is a mistake in the arguments given to the method, but i don't see any problems there... Any ideas?

Secondly, is there any way that I can set the output format te be a .txt file?

FYI, my input JSON code is

{"votes":[{
    "song_ID": "Piece of your heart",
    "mbr_ID": "001",
    "preference": "true",
    "timestamp": "11:22:33",
    "rave_ID": "rave001",
    },
    {
    "song_ID": "Piece of your heart",
    "mbr_ID": "002",
    "preference": "true",
    "timestamp": "11:22:33",
    "rave_ID": "rave001",
    },
    {
    "song_ID": "Atje voor de sfeer",
    "mbr_ID": "001",
    "preference": "false",
    "timestamp": "11:44:33",
    "rave_ID": "rave001",
    },
    {
    "song_ID": "Atje voor de sfeer",
    "mbr_ID": "002",
    "preference": "false",
    "timestamp": "11:44:33",
    "rave_ID": "rave001",
    },
    {
    "song_ID": "Atje voor de sfeer",
    "mbr_ID": "003",
    "preference": "true",
    "timestamp": "11:44:33",
    "rave_ID": "rave001",
    }]
}

Thanks in advance!

Answer 1

Both IOException and InterruptedException are checked hence the method reduce isn't overridden.

The method reduce in class Reducer does not throw any exception and hence you cannot declare that the method reduce in your subclass throws any checked exceptions but it can throw unchecked exceptions.

You may want to handle the exception inside the reduce method.

Answer 2

In the end we went for MongoBD mapreduce.

Hadoop is too much of a struggle.

Thanks!