Sharing data between master and reduce

Question

I need to perform aggregation using the results form all the reduce tasks. Basically the reduce task finds the sum and count and a value. I need to add all the sums and counts a

Answer 1

Found the solution. I used counters

http://diveintodata.org/2011/03/15/an-example-of-hadoop-mapreduce-counter/

public class FlightData {

//enum for counters used by reducers
public static enum FlightCounters {
    FLIGHT_COUNT,
    FLIGHT_DELAY;
}
public static class MyReducer 
extends Reducer<Text, Text,Text,IntWritable> {

    public void reduce(Text key, Iterable<Text> values, 
            Context context
            ) throws IOException, InterruptedException {


        delay1 = Float.parseFloat(origin[5]);
        delay2 = Float.parseFloat(dest[5]);
        context.getCounter(FlightCounters.FLIGHT_COUNT).increment(1);
        context.getCounter(FlightCounters.FLIGHT_DELAY)
        .increment((long) (delay1 + delay2));

    }
}
public static void main(String[] args) throws Exception{
    float flightCount, flightDelay;
    job.waitForCompletion(true);
    //get the final results updated in counter by all map and reduce tasks
    flightCount = job.getCounters()
            .findCounter(FlightCounters.FLIGHT_COUNT).getValue();
    flightDelay = job.getCounters()
            .findCounter(FlightCounters.FLIGHT_DELAY).getValue();
}

}

Answer 2

Override the run() of the mapper and reducer using the new org.apache.hadoop.mapreduce API. In these methods you can emit the accumulated sum/count from each mapper or reducer.

Also you would need to limit the reducer count by 1 so as to get a global sum of all the sums generated by multiple mappers.

See the below code for more clarity:

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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.input.TextInputFormat;
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;

public class AggregationExample extends Configured implements Tool {

    /**
     * This is Mapper.
     * 
     */
    public static class MapJob extends Mapper<LongWritable, Text, Text, Text> {

        private Text outputKey = new Text();
        private Text outputValue = new Text();
        private double sum;

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

            try {
                // say that you need to sum up the value part
                sum+= Double.valueOf(value);
        }

        @Override
        public void run(Context context) throws IOException, InterruptedException {

            setup(context);
            while (context.nextKeyValue()) {
                map(context.getCurrentKey(), context.getCurrentValue(), context);
            }

            // emit out the sum per mapper
            outputKey.set(sum);
            context.write(outputKey, outputValue);// Notice that the outputValue is empty
            cleanup(context);

        }
    }

    /**
     * This is Reducer.
     * 
     */
    public static class ReduceJob extends Reducer<Text, Text, Text, Text> {

        private Text outputKey = new Text();
        private Text outputValue = new Text();
        private double sum;

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


            // summation of values from each mapper
            sum += Double.valueOf(key.toString());

        }

        @Override
        public void run(Context context) throws IOException, InterruptedException {

            setup(context);
            while (context.nextKey()) {
                reduce(context.getCurrentKey(), context.getValues(), context);
            }

            // emit out the global sums
            outputKey.set(sum);
            context.write(outputKey, outputValue);
            cleanup(context);
        }
    }

    @Override
    public int run(String[] args) throws Exception {

        try {
            Configuration conf = getConf();

            // output key and value separator is empty as in final output only
            // key is emitted and value is empty
            conf.set("mapred.textoutputformat.separator", "");

            // Configuring mapred to have just one reducer as we need to find
            // single sum values from all the inputs
            conf.setInt("mapred.tasktracker.reduce.tasks.maximum", 1);
            conf.setInt("mapred.reduce.tasks", 1);

            Job job = new Job(conf);

            job.setJarByClass(AggregationExample.class);
            job.setJobName("Aggregation Example");

            job.setMapperClass(MapJob.class);
            job.setReducerClass(ReduceJob.class);
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(Text.class);

            job.setInputFormatClass(TextInputFormat.class);
            job.setOutputFormatClass(TextOutputFormat.class);
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(Text.class);
            FileInputFormat.setInputPaths(job, args[0]);
            FileOutputFormat.setOutputPath(job, new Path(args[1]));

            boolean success = job.waitForCompletion(true);
            return success ? 0 : 1;
        } catch (Exception e) {
            e.printStackTrace();
            return 1;
        }

    }

    public static void main(String[] args) throws Exception {

        if (args.length < 2) {
            System.out
                    .println("Usage: AggregationExample <comma sparated list of input directories> <output dir>");
            System.exit(-1);
        }

        int result = ToolRunner.run(new AggregationExample(), args);
        System.exit(result);
    }

}

You may very well map this approach to your problem.