Duplicate “values” for some key in map-reduce java program

I am new in mapreduce and hadoop (hadoop 3.2.3 and java 8). I am trying to separate some lines based on a symbol in a line. Example: “q1,a,q0,” should be return (‘a’,”q1,a,q0,”) as (key, value). My dataset contains ten(10) lines , five(5) for key ‘a’ and five for key ‘b’.

I expect to get 5 line for each key but i always get five for ‘a’ and 10 for ‘b’

Data

A,q0,a,q1;A,q0,b,q0;A,q1,a,q1;A,q1,b,q2;A,q2,a,q1;A,q2,b,q0;B,s0,a,s0;B,s0,b,s1;B,s1,a,s1;B,s1,b,s0

Mapper class:

import java.io.IOException;

import org.apache.hadoop.io.ByteWritable;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;






public class MyMapper extends Mapper<LongWritable, Text, ByteWritable ,Text>{
    private  ByteWritable key1 = new ByteWritable();
    //private int n ;
    private int count =0 ;
    private Text wordObject = new Text();
    @Override
    public void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {
        String ftext = value.toString();
        for (String line: ftext.split(";")) {
            
            wordObject = new Text();
            if (line.split(",")[2].equals("b")) {
                
                key1.set((byte) 'b');
                wordObject.set(line) ;
                context.write(key1,wordObject);
                continue ;
            }
                key1.set((byte) 'a');
                wordObject.set(line) ;
                context.write(key1,wordObject);
             
            
        
            
            
        }
        
        
        




        
    }
}

Reducer class:

import java.io.IOException;


import org.apache.hadoop.io.ByteWritable;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.Reducer.Context;



public class MyReducer extends Reducer<ByteWritable, Text, ByteWritable ,Text>{
    
    private Integer count=0 ;

    




    @Override
    public void reduce(ByteWritable key, Iterable<Text>  values, Context context) throws IOException, InterruptedException {
        
        
        
        
        for(Text val : values ) {
            count++ ;
            
            
        }
        Text symb = new Text(count.toString()) ;
        context.write(key , symb);

    
    
}
}

Driver class:

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.ByteWritable;

import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;




public class MyDriver extends Configured implements Tool {
    public int run(String[] args) throws Exception {
        if (args.length != 2) {
            System.out.printf("Usage: %s [generic options] <inputdir> <outputdir>n", getClass().getSimpleName());
            return -1;
        }
        @SuppressWarnings("deprecation")
        Job job = new Job(getConf());
        job.setJarByClass(MyDriver.class);
        job.setJobName("separation ");
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        job.setMapperClass(MyMapper.class);
        job.setReducerClass(MyReducer.class);
        job.setMapOutputKeyClass(ByteWritable.class);
        job.setMapOutputValueClass(Text.class);
        job.setOutputKeyClass(ByteWritable.class);
        job.setOutputValueClass(Text.class);
        boolean success = job.waitForCompletion(true);
        return success ? 0 : 1;
        }
        public static void main(String[] args) throws Exception {
            int exitCode = ToolRunner.run(new Configuration(), new MyDriver(), args);
            System.exit(exitCode);
        }
}

Answer

The problem was solved by putting the variable “count” inside the function “Reduce()”.

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Answer